f23/Cole_Lecture_Notes/27_Pandas1/Lec27_Pandas1_Solution_Nelson.ipynb

+%% Cell type:code id: tags:
+
+``` python
+# Warmup 0: Pandas!
+import pandas as pd
+```
+
+%% Cell type:code id: tags:
+
+``` python
+# Warmup 1: What are your 2 favorite data structures that we have learned so far?
+```
+
+%% Cell type:code id: tags:
+
+``` python
+# Warmup 2: Find the mean, median, mode, and standard deviation
+#           of the following list of scores.
+my_scores = [44, 32, 19, 67, 23, 23, 92, 47, 47, 78, 84]
+```
+
+%% Cell type:markdown id: tags:
+
+## Learning Objectives
+- Create a pandas Series from a list or from a dict
+- Use Series methods max, min, mean, median, mode, quantile, value counts
+- Extract elements from a Series using Boolean indexing
+- Access Series members using .loc, .iloc, .items, and slicing
+- Perform Series element-wise operations
+
+%% Cell type:markdown id: tags:
+
+# Pandas
+
+%% Cell type:markdown id: tags:
+
+**What is Pandas?**
+ - Pandas is a package of tools for doing Data Science
+ - Pandas was installed with Anaconda, so its on your computers
+ - [Learn More](https://en.wikipedia.org/wiki/Pandas_(software))
+
+If for some reason, you don't have pandas installed, run the following command in terminal or powershell...
+<pre>pip install pandas</pre>
+
+%% Cell type:markdown id: tags:
+
+A Pandas Series is like a combination of a list and a dictionary. The word 'index' is used to describe position.
+
+%% Cell type:markdown id: tags:
+
+## Series from a `list`
+
+%% Cell type:code id: tags:
+
+``` python
+scores = pd.Series([44, 32, 19, 67, 23, 23, 92, 47, 47, 78, 84])
+scores
+```
+
+%% Output
+
+    0     44
+    1     32
+    2     19
+    3     67
+    4     23
+    5     23
+    6     92
+    7     47
+    8     47
+    9     78
+    10    84
+    dtype: int64
+
+%% Cell type:markdown id: tags:
+
+A Pandas series acts a lot like a list; you can index and slice.
+
+%% Cell type:code id: tags:
+
+``` python
+scores[3]
+```
+
+%% Output
+
+    67
+
+%% Cell type:code id: tags:
+
+``` python
+scores[3:6]
+```
+
+%% Output
+
+    3    67
+    4    23
+    5    23
+    dtype: int64
+
+%% Cell type:code id: tags:
+
+``` python
+## Series calculations
+## mean, median, mode, quartiles, sd, count
+print(scores.mean())
+print(scores.median())
+print(scores.std())
+```
+
+%% Output
+
+    50.54545454545455
+    47.0
+    26.051347897426098
+
+%% Cell type:code id: tags:
+
+``` python
+# There could be multiple modes, so mode returns a Series
+print(scores.mode())
+```
+
+%% Output
+
+    0    23
+    1    47
+    dtype: int64
+
+%% Cell type:code id: tags:
+
+``` python
+# 5-Number summary
+print(scores.quantile([1.0, 0.75, 0.5, 0.25, 0]))
+```
+
+%% Output
+
+    1.00    92.0
+    0.75    72.5
+    0.50    47.0
+    0.25    27.5
+    0.00    19.0
+    dtype: float64
+
+%% Cell type:code id: tags:
+
+``` python
+print(scores.quantile([0.9, 0.1]))
+```
+
+%% Output
+
+    0.9    84.0
+    0.1    23.0
+    dtype: float64
+
+%% Cell type:markdown id: tags:
+
+`value_counts` creates a series where the index is the data, and the value is its count in the series
+
+%% Cell type:code id: tags:
+
+``` python
+ages = pd.Series([18, 19, 20, 20, 20, 17, 18, 24, 25, 35, 22, 20, 21, 21, 20, 23, 23, 19, 19, 19, 20, 21])
+ages.value_counts()
+```
+
+%% Output
+
+    20    6
+    19    4
+    21    3
+    18    2
+    23    2
+    17    1
+    24    1
+    25    1
+    35    1
+    22    1
+    dtype: int64
+
+%% Cell type:markdown id: tags:
+
+A series can be sorted by index or by values
+
+%% Cell type:code id: tags:
+
+``` python
+ages.value_counts().sort_index()
+```
+
+%% Output
+
+    17    1
+    18    2
+    19    4
+    20    6
+    21    3
+    22    1
+    23    2
+    24    1
+    25    1
+    35    1
+    dtype: int64
+
+%% Cell type:code id: tags:
+
+``` python
+ages.value_counts().sort_values(ascending=False)
+```
+
+%% Output
+
+    20    6
+    19    4
+    21    3
+    18    2
+    23    2
+    17    1
+    24    1
+    25    1
+    35    1
+    22    1
+    dtype: int64
+
+%% Cell type:markdown id: tags:
+
+### Plotting
+
+%% Cell type:code id: tags:
+
+``` python
+## Series bar chart
+age_plot = ages.value_counts().sort_index().plot.bar(color='lightsalmon')
+age_plot.set(xlabel = "age", ylabel = "count")
+```
+
+%% Output
+
+    [Text(0.5, 0, 'age'), Text(0, 0.5, 'count')]
+
+
+
+%% Cell type:markdown id: tags:
+
+### Filtering
+ - & means 'and'
+ - | means 'or'
+ - ~ means 'not'
+ - we must use () for compound boolean expressions
+
+%% Cell type:code id: tags:
+
+``` python
+# What ages are in the range 18 to 20, inclusive?
+certain_students = ages[(ages >= 18) & (ages <= 20)]
+certain_students
+```
+
+%% Output
+
+    0     18
+    1     19
+    2     20
+    3     20
+    4     20
+    6     18
+    11    20
+    14    20
+    17    19
+    18    19
+    19    19
+    20    20
+    dtype: int64
+
+%% Cell type:code id: tags:
+
+``` python
+# What percentage of students are in this age range?
+len(certain_students) / len(ages)
+```
+
+%% Output
+
+    0.5454545454545454
+
+%% Cell type:code id: tags:
+
+``` python
+# What percentage of students are ages 18 OR 21?
+stu_18_or_21 = ages[(ages == 18) | (ages == 20)]
+len(stu_18_or_21) / len(ages)
+```
+
+%% Output
+
+    0.36363636363636365
+
+%% Cell type:code id: tags:
+
+``` python
+# what percentage of students are NOT 19?
+stu_not_19 = ages [~(ages==19)]
+len(stu_not_19) / len(ages)
+```
+
+%% Output
+
+    0.8181818181818182
+
+%% Cell type:code id: tags:
+
+``` python
+# One more thing....we can perform an operation on all values in a Series
+# Let's add 1 to everyone's age
+print(ages.value_counts())
+ages = ages + 1
+print(ages.value_counts())
+```
+
+%% Output
+
+    20    6
+    19    4
+    21    3
+    18    2
+    23    2
+    17    1
+    24    1
+    25    1
+    35    1
+    22    1
+    dtype: int64
+    21    6
+    20    4
+    22    3
+    19    2
+    24    2
+    18    1
+    25    1
+    26    1
+    36    1
+    23    1
+    dtype: int64
+
+%% Cell type:code id: tags:
+
+``` python
+# Modified from https://automatetheboringstuff.com/chapter14/
+import csv
+def process_csv(filename):
+    example_file = open(filename, encoding="utf-8")
+    example_reader = csv.reader(example_file)
+    example_data = list(example_reader)
+    example_file.close()
+    return example_data
+
+data = process_csv("pokemon_stats.csv")
+header = data[0]
+print(len(data))
+data = data[1:]
+data[15:18]
+```
+
+%% Output
+
+    886
+
+    [['15',
+      'Pidgey',
+      '45',
+      '40',
+      '40',
+      'Kanto',
+      '35',
+      '35',
+      '56',
+      'Normal',
+      'Flying'],
+     ['16',
+      'Pidgeotto',
+      '60',
+      '55',
+      '63',
+      'Kanto',
+      '50',
+      '50',
+      '71',
+      'Normal',
+      'Flying'],
+     ['17',
+      'Pidgeot',
+      '80',
+      '75',
+      '83',
+      'Kanto',
+      '70',
+      '70',
+      '101',
+      'Normal',
+      'Flying']]
+
+%% Cell type:code id: tags:
+
+``` python
+# Create a Series of all the Pokemon names.
+pokemon_list = [row[1] for row in data]
+pokemons = pd.Series(pokemon_list)
+pokemons
+```
+
+%% Output
+
+    0       Bulbasaur
+    1         Ivysaur
+    2        Venusaur
+    3      Charmander
+    4      Charmeleon
+              ...
+    880     Regieleki
+    881     Regidrago
+    882     Glastrier
+    883     Spectrier
+    884       Calyrex
+    Length: 885, dtype: object
+
+%% Cell type:code id: tags:
+
+``` python
+# Create a Series of all the Pokemon HPs.
+hp_list = [int(row[4]) for row in data]
+hps = pd.Series(hp_list)
+hps
+```
+
+%% Output
+
+    0       45
+    1       60
+    2       80
+    3       39
+    4       58
+          ...
+    880     80
+    881    200
+    882    100
+    883    100
+    884    100
+    Length: 885, dtype: int64
+
+%% Cell type:code id: tags:
+
+``` python
+# Find the most common HP
+hps.mode()
+```
+
+%% Output
+
+    0    60
+    dtype: int64
+
+%% Cell type:code id: tags:
+
+``` python
+# Find how many pokemon have that most common hp
+len(hps[hps == hps.mode()[0]])
+```
+
+%% Output
+
+    74
+
+%% Cell type:code id: tags:
+
+``` python
+# How many Pokemon have HP between 50 and 75 (inclusive)?
+len(hps[(hps >= 50) & (hps <= 75)])
+```
+
+%% Output
+
+    427
+
+%% Cell type:code id: tags:
+
+``` python
+# What are the names of weak pokemon (<30 HP)?
+weak_hps_idx = hps[hps < 30].index
+pokemons[weak_hps_idx]
+```
+
+%% Output
+
+    47       Diglett
+    60          Abra
+    78     Magnemite
+    124     Magikarp
+    167        Pichu
+    208      Shuckle
+    275        Ralts
+    287     Shedinja
+    344       Feebas
+    350      Duskull
+    760       Wimpod
+    812      Blipbug
+    871       Dreepy
+    dtype: object
+
+%% Cell type:code id: tags:
+
+``` python
+# What are the names of the pokemon from strongest to weakest (using health)?
+all_hps_desc = hps.sort_values(ascending=False)
+pokemons[all_hps_desc.index]
+```
+
+%% Output
+
+    237      Blissey
+    109      Chansey
+    787     Guzzlord
+    881    Regidrago
+    197    Wobbuffet
+             ...
+    350      Duskull
+    124     Magikarp
+    208      Shuckle
+    47       Diglett
+    287     Shedinja
+    Length: 885, dtype: object
+
+%% Cell type:markdown id: tags:
+
+## Series from a `dict`
+A Series is a cross between a list and a dict, so we can make a series from a dict as well
+
+%% Cell type:code id: tags:
+
+``` python
+## Series from a dict
+game1points = pd.Series({"Chris": 10, "Kiara": 3, "Mikayla": 7, "Ann": 8, "Trish": 6})
+print(game1points)
+```
+
+%% Output
+
+    Chris      10
+    Kiara       3
+    Mikayla     7
+    Ann         8
+    Trish       6
+    dtype: int64
+
+%% Cell type:code id: tags:
+
+``` python
+game2points = pd.Series({"Kiara": 7, "Chris": 3,  "Trish": 11, "Mikayla": 2, "Ann": 5})
+print(game2points)
+```
+
+%% Output
+
+    Kiara       7
+    Chris       3
+    Trish      11
+    Mikayla     2
+    Ann         5
+    dtype: int64
+
+%% Cell type:code id: tags:
+
+``` python
+# Pandas can perform operations on two series by matching up their indices
+total = game1points  + game2points
+total
+```
+
+%% Output
+
+    Ann        13
+    Chris      13
+    Kiara      10
+    Mikayla     9
+    Trish      17
+    dtype: int64
+
+%% Cell type:code id: tags:
+
+``` python
+# Who has the most points?
+print(total.max())
+print(total.idxmax())
+```
+
+%% Output
+
+    17
+    Trish
+
+%% Cell type:code id: tags:
+
+``` python
+# We can use [] to name the index or by its sequence number
+print(total['Kiara'], total[2])
+```
+
+%% Output
+
+    10 10
+
+%% Cell type:code id: tags:
+
+``` python
+# We can have multi-indexing....slightly different from slicing
+total[["Chris", "Trish"]]
+```
+
+%% Output
+
+    Chris    13
+    Trish    17
+    dtype: int64
+
+%% Cell type:code id: tags:
+
+``` python
+total_sorted = total.sort_values(ascending=False)
+total_sorted
+```
+
+%% Output
+
+    Trish      17
+    Ann        13
+    Chris      13
+    Kiara      10
+    Mikayla     9
+    dtype: int64
+
+%% Cell type:code id: tags:
+
+``` python
+ax = total_sorted.plot.bar(color="green", fontsize=16)
+ax.set_ylabel("total points", fontsize=16)
+```
+
+%% Output
+
+    Text(0, 0.5, 'total points')
+
+
+
+%% Cell type:markdown id: tags:
+
+## More things to know about Series
+Next time, we'll get into more ways to access data using `loc` and `iloc`.
+
+We'll also talk about `DataFrame`, which is a series of series (like a spreadsheet!)
+
+%% Cell type:code id: tags:
+
+``` python
+game1points
+```
+
+%% Output
+
+    Chris      10
+    Kiara       3
+    Mikayla     7
+    Ann         8
+    Trish       6
+    dtype: int64
+
+%% Cell type:code id: tags:
+
+``` python
+game1points.iloc[2] # looks up by integer position
+```
+
+%% Output
+
+    7
+
+%% Cell type:code id: tags:
+
+``` python
+game1points.loc["Mikayla"] # looks up by pandas index
+```
+
+%% Output
+
+    7
+
+%% Cell type:code id: tags:
+
+``` python
+my_new_series = pd.Series({1: 89, 2: 104, 3: 681}) # this can be tricky!
+my_new_series
+```
+
+%% Output
+
+    1     89
+    2    104
+    3    681
+    dtype: int64
+
+%% Cell type:code id: tags:
+
+``` python
+my_new_series.iloc[1] # by integer position
+```
+
+%% Output
+
+    104
+
+%% Cell type:code id: tags:
+
+``` python
+my_new_series.loc[1] # by index
+```
+
+%% Output
+
+    89
+
+%% Cell type:code id: tags:
+
+``` python
+my_new_series[1] # by index!
+```
+
+%% Output
+
+    89
+
+%% Cell type:code id: tags:
+
+``` python
+my_new_series[my_new_series > 100] # ... and also boolean masking!
+```
+
+%% Output
+
+    2    104
+    3    681
+    dtype: int64
+
+%% Cell type:markdown id: tags:
+
+Feel overwhelmed? Do the required reading. Pandas operations are good to put on your notesheet.

f23/Cole_Lecture_Notes/27_Pandas1/Lec27_Pandas1_Template_Nelson.ipynb

+%% Cell type:code id: tags:
+
+``` python
+# Warmup 0: Pandas!
+import pandas as pd
+```
+
+%% Cell type:code id: tags:
+
+``` python
+# Warmup 1: What are your 2 favorite data structures that we have learned so far?
+```
+
+%% Cell type:code id: tags:
+
+``` python
+# Warmup 2: Find the mean, median, mode, and standard deviation
+#           of the following list of scores.
+my_scores = [44, 32, 19, 67, 23, 23, 92, 47, 47, 78, 84]
+```
+
+%% Cell type:markdown id: tags:
+
+## Learning Objectives
+- Create a pandas Series from a list or from a dict
+- Use Series methods max, min, mean, median, mode, quantile, value counts
+- Extract elements from a Series using Boolean indexing
+- Access Series members using .loc, .iloc, .items, and slicing
+- Perform Series element-wise operations
+
+%% Cell type:markdown id: tags:
+
+# Pandas
+
+%% Cell type:markdown id: tags:
+
+**What is Pandas?**
+ - Pandas is a package of tools for doing Data Science
+ - Pandas was installed with Anaconda, so its on your computers
+ - [Learn More](https://en.wikipedia.org/wiki/Pandas_(software))
+
+If for some reason, you don't have pandas installed, run the following command in terminal or powershell...
+<pre>pip install pandas</pre>
+
+%% Cell type:markdown id: tags:
+
+A Pandas Series is like a combination of a list and a dictionary. The word 'index' is used to describe position.
+
+%% Cell type:markdown id: tags:
+
+## Series from a `list`
+
+%% Cell type:code id: tags:
+
+``` python
+scores = pd.Series([44, 32, 19, 67, 23, 23, 92, 47, 47, 78, 84])
+scores
+```
+
+%% Cell type:markdown id: tags:
+
+A Pandas series acts a lot like a list; you can index and slice.
+
+%% Cell type:code id: tags:
+
+``` python
+scores[3]
+```
+
+%% Cell type:code id: tags:
+
+``` python
+scores[3:6]
+```
+
+%% Cell type:code id: tags:
+
+``` python
+## Series calculations
+## mean, median, mode, quartiles, sd, count
+```
+
+%% Cell type:code id: tags:
+
+``` python
+# There could be multiple modes, so mode returns a Series
+print(scores.mode())
+```
+
+%% Cell type:code id: tags:
+
+``` python
+# 5-Number summary
+print(scores.quantile([1.0, 0.75, 0.5, 0.25, 0]))
+```
+
+%% Cell type:code id: tags:
+
+``` python
+print(scores.quantile([0.9, 0.1]))
+```
+
+%% Cell type:markdown id: tags:
+
+`value_counts` creates a series where the index is the data, and the value is its count in the series
+
+%% Cell type:code id: tags:
+
+``` python
+ages = pd.Series([18, 19, 20, 20, 20, 17, 18, 24, 25, 35, 22, 20, 21, 21, 20, 23, 23, 19, 19, 19, 20, 21])
+ages.value_counts()
+```
+
+%% Cell type:markdown id: tags:
+
+A series can be sorted by index or by values
+
+%% Cell type:code id: tags:
+
+``` python
+ages.value_counts().sort_index()
+```
+
+%% Cell type:code id: tags:
+
+``` python
+ages.value_counts().sort_values(ascending=False)
+```
+
+%% Cell type:markdown id: tags:
+
+### Plotting
+
+%% Cell type:code id: tags:
+
+``` python
+## Series bar chart
+age_plot = ages.value_counts().sort_index().plot.bar(color='lightsalmon')
+age_plot.set(xlabel = "age", ylabel = "count")
+```
+
+%% Cell type:markdown id: tags:
+
+### Filtering
+ - & means 'and'
+ - | means 'or'
+ - ~ means 'not'
+ - we must use () for compound boolean expressions
+
+%% Cell type:code id: tags:
+
+``` python
+# What ages are in the range 18 to 20, inclusive?
+```
+
+%% Cell type:code id: tags:
+
+``` python
+# What percentage of students are in this age range?
+```
+
+%% Cell type:code id: tags:
+
+``` python
+# What percentage of students are ages 18 OR 21?
+```
+
+%% Cell type:code id: tags:
+
+``` python
+# what percentage of students are NOT 19?
+```
+
+%% Cell type:code id: tags:
+
+``` python
+# One more thing....we can perform an operation on all values in a Series
+# Let's add 1 to everyone's age
+print(ages.value_counts())
+ages = ages + 1
+print(ages.value_counts())
+```
+
+%% Cell type:code id: tags:
+
+``` python
+# Modified from https://automatetheboringstuff.com/chapter14/
+import csv
+def process_csv(filename):
+    example_file = open(filename, encoding="utf-8")
+    example_reader = csv.reader(example_file)
+    example_data = list(example_reader)
+    example_file.close()
+    return example_data
+
+data = process_csv("pokemon_stats.csv")
+header = data[0]
+print(len(data))
+data = data[1:]
+data[15:18]
+```
+
+%% Cell type:code id: tags:
+
+``` python
+# Create a Series of all the Pokemon names.
+```
+
+%% Cell type:code id: tags:
+
+``` python
+# Create a Series of all the Pokemon HPs.
+```
+
+%% Cell type:code id: tags:
+
+``` python
+# Find the most common HP
+```
+
+%% Cell type:code id: tags:
+
+``` python
+# Find how many pokemon have that most common hp
+```
+
+%% Cell type:code id: tags:
+
+``` python
+# How many Pokemon have HP between 50 and 75 (inclusive)?
+```
+
+%% Cell type:code id: tags:
+
+``` python
+# What are the names of weak pokemon (<30 HP)?
+```
+
+%% Cell type:code id: tags:
+
+``` python
+# What are the names of the pokemon from strongest to weakest (using health)?
+```
+
+%% Cell type:markdown id: tags:
+
+## Series from a `dict`
+A Series is a cross between a list and a dict, so we can make a series from a dict as well
+
+%% Cell type:code id: tags:
+
+``` python
+## Series from a dict
+game1points = pd.Series({"Chris": 10, "Kiara": 3, "Mikayla": 7, "Ann": 8, "Trish": 6})
+print(game1points)
+```
+
+%% Cell type:code id: tags:
+
+``` python
+game2points = pd.Series({"Kiara": 7, "Chris": 3,  "Trish": 11, "Mikayla": 2, "Ann": 5})
+print(game2points)
+```
+
+%% Cell type:code id: tags:
+
+``` python
+# Pandas can perform operations on two series by matching up their indices
+total = game1points  + game2points
+total
+```
+
+%% Cell type:code id: tags:
+
+``` python
+# Who has the most points?
+print(total.max())
+print(total.idxmax())
+```
+
+%% Cell type:code id: tags:
+
+``` python
+# We can use [] to name the index or by its sequence number
+print(total['Kiara'], total[2])
+```
+
+%% Cell type:code id: tags:
+
+``` python
+# We can have multi-indexing....slightly different from slicing
+total[["Chris", "Trish"]]
+```
+
+%% Cell type:code id: tags:
+
+``` python
+total_sorted = total.sort_values(ascending=False)
+total_sorted
+```
+
+%% Cell type:code id: tags:
+
+``` python
+ax = total_sorted.plot.bar(color="green", fontsize=16)
+ax.set_ylabel("total points", fontsize=16)
+```
+
+%% Cell type:markdown id: tags:
+
+## More things to know about Series
+Next time, we'll get into more ways to access data using `loc` and `iloc`.
+
+We'll also talk about `DataFrame`, which is a series of series (like a spreadsheet!)
+
+Feel overwhelmed? Do the required reading & worksheet. Pandas operations are good to put on your notesheet.
+
+%% Cell type:code id: tags:
+
+``` python
+game1points
+```
+
+%% Cell type:code id: tags:
+
+``` python
+game1points.iloc[2] # looks up by integer position
+```
+
+%% Cell type:code id: tags:
+
+``` python
+game1points.loc["Mikayla"] # looks up by pandas index
+```
+
+%% Cell type:code id: tags:
+
+``` python
+my_new_series = pd.Series({1: 89, 2: 104, 3: 681}) # this can be tricky!
+my_new_series
+```
+
+%% Cell type:code id: tags:
+
+``` python
+my_new_series.iloc[1] # by integer position
+```
+
+%% Cell type:code id: tags:
+
+``` python
+my_new_series.loc[1] # by index
+```
+
+%% Cell type:code id: tags:
+
+``` python
+my_new_series[1] # by index!
+```
+
+%% Cell type:code id: tags:
+
+``` python
+my_new_series[my_new_series > 100] # ... and also boolean masking!
+```
+
+%% Cell type:markdown id: tags:
+
+Feel overwhelmed? Do the required reading. Pandas operations are good to put on your notesheet.

f23/Cole_Lecture_Notes/27_Pandas1/pokemon_stats.csv

+,Name,Attack,Defense,HP,Region,Sp. Atk,Sp. Def,Speed,Type 1,Type 2
+0,Bulbasaur,49,49,45,Kanto,65,65,45,Grass,Poison
+1,Ivysaur,62,63,60,Kanto,80,80,60,Grass,Poison
+2,Venusaur,82,83,80,Kanto,100,100,80,Grass,Poison
+3,Charmander,52,43,39,Kanto,60,50,65,Fire,None
+4,Charmeleon,64,58,58,Kanto,80,65,80,Fire,None
+5,Charizard,84,78,78,Kanto,109,85,100,Fire,Flying
+6,Squirtle,48,65,44,Kanto,50,64,43,Water,None
+7,Wartortle,63,80,59,Kanto,65,80,58,Water,None
+8,Blastoise,83,100,79,Kanto,85,105,78,Water,None
+9,Caterpie,30,35,45,Kanto,20,20,45,Bug,None
+10,Metapod,20,55,50,Kanto,25,25,30,Bug,None
+11,Butterfree,45,50,60,Kanto,90,80,70,Bug,Flying
+12,Weedle,35,30,40,Kanto,20,20,50,Bug,Poison
+13,Kakuna,25,50,45,Kanto,25,25,35,Bug,Poison
+14,Beedrill,90,40,65,Kanto,45,80,75,Bug,Poison
+15,Pidgey,45,40,40,Kanto,35,35,56,Normal,Flying
+16,Pidgeotto,60,55,63,Kanto,50,50,71,Normal,Flying
+17,Pidgeot,80,75,83,Kanto,70,70,101,Normal,Flying
+18,Rattata,56,35,30,Kanto,25,35,72,Normal,None
+19,Raticate,81,60,55,Kanto,50,70,97,Normal,None
+20,Spearow,60,30,40,Kanto,31,31,70,Normal,Flying
+21,Fearow,90,65,65,Kanto,61,61,100,Normal,Flying
+22,Ekans,60,44,35,Kanto,40,54,55,Poison,None
+23,Arbok,95,69,60,Kanto,65,79,80,Poison,None
+24,Pikachu,55,40,35,Kanto,50,50,90,Electric,None
+25,Raichu,90,55,60,Kanto,90,80,110,Electric,None
+26,Sandshrew,75,85,50,Kanto,20,30,40,Ground,None
+27,Sandslash,100,110,75,Kanto,45,55,65,Ground,None
+28,Nidorina,62,67,70,Kanto,55,55,56,Poison,None
+29,Nidoqueen,92,87,90,Kanto,75,85,76,Poison,Ground
+30,Nidorino,72,57,61,Kanto,55,55,65,Poison,None
+31,Nidoking,102,77,81,Kanto,85,75,85,Poison,Ground
+32,Clefairy,45,48,70,Kanto,60,65,35,Fairy,None
+33,Clefable,70,73,95,Kanto,95,90,60,Fairy,None
+34,Vulpix,41,40,38,Kanto,50,65,65,Fire,None
+35,Ninetales,76,75,73,Kanto,81,100,100,Fire,None
+36,Jigglypuff,45,20,115,Kanto,45,25,20,Normal,Fairy
+37,Wigglytuff,70,45,140,Kanto,85,50,45,Normal,Fairy
+38,Zubat,45,35,40,Kanto,30,40,55,Poison,Flying
+39,Golbat,80,70,75,Kanto,65,75,90,Poison,Flying
+40,Oddish,50,55,45,Kanto,75,65,30,Grass,Poison
+41,Gloom,65,70,60,Kanto,85,75,40,Grass,Poison
+42,Vileplume,80,85,75,Kanto,110,90,50,Grass,Poison
+43,Paras,70,55,35,Kanto,45,55,25,Bug,Grass
+44,Parasect,95,80,60,Kanto,60,80,30,Bug,Grass
+45,Venonat,55,50,60,Kanto,40,55,45,Bug,Poison
+46,Venomoth,65,60,70,Kanto,90,75,90,Bug,Poison
+47,Diglett,55,25,10,Kanto,35,45,95,Ground,None
+48,Dugtrio,100,50,35,Kanto,50,70,120,Ground,None
+49,Meowth,45,35,40,Kanto,40,40,90,Normal,None
+50,Persian,70,60,65,Kanto,65,65,115,Normal,None
+51,Psyduck,52,48,50,Kanto,65,50,55,Water,None
+52,Golduck,82,78,80,Kanto,95,80,85,Water,None
+53,Mankey,80,35,40,Kanto,35,45,70,Fighting,None
+54,Primeape,105,60,65,Kanto,60,70,95,Fighting,None
+55,Growlithe,70,45,55,Kanto,70,50,60,Fire,None
+56,Arcanine,110,80,90,Kanto,100,80,95,Fire,None
+57,Poliwag,50,40,40,Kanto,40,40,90,Water,None
+58,Poliwhirl,65,65,65,Kanto,50,50,90,Water,None
+59,Poliwrath,95,95,90,Kanto,70,90,70,Water,Fighting
+60,Abra,20,15,25,Kanto,105,55,90,Psychic,None
+61,Kadabra,35,30,40,Kanto,120,70,105,Psychic,None
+62,Alakazam,50,45,55,Kanto,135,95,120,Psychic,None
+63,Machop,80,50,70,Kanto,35,35,35,Fighting,None
+64,Machoke,100,70,80,Kanto,50,60,45,Fighting,None
+65,Machamp,130,80,90,Kanto,65,85,55,Fighting,None
+66,Bellsprout,75,35,50,Kanto,70,30,40,Grass,Poison
+67,Weepinbell,90,50,65,Kanto,85,45,55,Grass,Poison
+68,Victreebel,105,65,80,Kanto,100,70,70,Grass,Poison
+69,Tentacool,40,35,40,Kanto,50,100,70,Water,Poison
+70,Tentacruel,70,65,80,Kanto,80,120,100,Water,Poison
+71,Geodude,80,100,40,Kanto,30,30,20,Rock,Ground
+72,Graveler,95,115,55,Kanto,45,45,35,Rock,Ground
+73,Golem,120,130,80,Kanto,55,65,45,Rock,Ground
+74,Ponyta,85,55,50,Kanto,65,65,90,Fire,None
+75,Rapidash,100,70,65,Kanto,80,80,105,Fire,None
+76,Slowpoke,65,65,90,Kanto,40,40,15,Water,Psychic
+77,Slowbro,75,110,95,Kanto,100,80,30,Water,Psychic
+78,Magnemite,35,70,25,Kanto,95,55,45,Electric,Steel
+79,Magneton,60,95,50,Kanto,120,70,70,Electric,Steel
+80,Doduo,85,45,35,Kanto,35,35,75,Normal,Flying
+81,Dodrio,110,70,60,Kanto,60,60,110,Normal,Flying
+82,Seel,45,55,65,Kanto,45,70,45,Water,None
+83,Dewgong,70,80,90,Kanto,70,95,70,Water,Ice
+84,Grimer,80,50,80,Kanto,40,50,25,Poison,None
+85,Muk,105,75,105,Kanto,65,100,50,Poison,None
+86,Shellder,65,100,30,Kanto,45,25,40,Water,None
+87,Cloyster,95,180,50,Kanto,85,45,70,Water,Ice
+88,Gastly,35,30,30,Kanto,100,35,80,Ghost,Poison
+89,Haunter,50,45,45,Kanto,115,55,95,Ghost,Poison
+90,Gengar,65,60,60,Kanto,130,75,110,Ghost,Poison
+91,Onix,45,160,35,Kanto,30,45,70,Rock,Ground
+92,Drowzee,48,45,60,Kanto,43,90,42,Psychic,None
+93,Hypno,73,70,85,Kanto,73,115,67,Psychic,None
+94,Krabby,105,90,30,Kanto,25,25,50,Water,None
+95,Kingler,130,115,55,Kanto,50,50,75,Water,None
+96,Voltorb,30,50,40,Kanto,55,55,100,Electric,None
+97,Electrode,50,70,60,Kanto,80,80,150,Electric,None
+98,Exeggcute,40,80,60,Kanto,60,45,40,Grass,Psychic
+99,Exeggutor,95,85,95,Kanto,125,75,55,Grass,Psychic
+100,Cubone,50,95,50,Kanto,40,50,35,Ground,None
+101,Marowak,80,110,60,Kanto,50,80,45,Ground,None
+102,Hitmonlee,120,53,50,Kanto,35,110,87,Fighting,None
+103,Hitmonchan,105,79,50,Kanto,35,110,76,Fighting,None
+104,Lickitung,55,75,90,Kanto,60,75,30,Normal,None
+105,Koffing,65,95,40,Kanto,60,45,35,Poison,None
+106,Weezing,90,120,65,Kanto,85,70,60,Poison,None
+107,Rhyhorn,85,95,80,Kanto,30,30,25,Ground,Rock
+108,Rhydon,130,120,105,Kanto,45,45,40,Ground,Rock
+109,Chansey,5,5,250,Kanto,35,105,50,Normal,None
+110,Tangela,55,115,65,Kanto,100,40,60,Grass,None
+111,Kangaskhan,95,80,105,Kanto,40,80,90,Normal,None
+112,Horsea,40,70,30,Kanto,70,25,60,Water,None
+113,Seadra,65,95,55,Kanto,95,45,85,Water,None
+114,Goldeen,67,60,45,Kanto,35,50,63,Water,None
+115,Seaking,92,65,80,Kanto,65,80,68,Water,None
+116,Staryu,45,55,30,Kanto,70,55,85,Water,None
+117,Starmie,75,85,60,Kanto,100,85,115,Water,Psychic
+118,Scyther,110,80,70,Kanto,55,80,105,Bug,Flying
+119,Jynx,50,35,65,Kanto,115,95,95,Ice,Psychic
+120,Electabuzz,83,57,65,Kanto,95,85,105,Electric,None
+121,Magmar,95,57,65,Kanto,100,85,93,Fire,None
+122,Pinsir,125,100,65,Kanto,55,70,85,Bug,None
+123,Tauros,100,95,75,Kanto,40,70,110,Normal,None
+124,Magikarp,10,55,20,Kanto,15,20,80,Water,None
+125,Gyarados,125,79,95,Kanto,60,100,81,Water,Flying
+126,Lapras,85,80,130,Kanto,85,95,60,Water,Ice
+127,Ditto,48,48,48,Kanto,48,48,48,Normal,None
+128,Eevee,55,50,55,Kanto,45,65,55,Normal,None
+129,Vaporeon,65,60,130,Kanto,110,95,65,Water,None
+130,Jolteon,65,60,65,Kanto,110,95,130,Electric,None
+131,Flareon,130,60,65,Kanto,95,110,65,Fire,None
+132,Porygon,60,70,65,Kanto,85,75,40,Normal,None
+133,Omanyte,40,100,35,Kanto,90,55,35,Rock,Water
+134,Omastar,60,125,70,Kanto,115,70,55,Rock,Water
+135,Kabuto,80,90,30,Kanto,55,45,55,Rock,Water
+136,Kabutops,115,105,60,Kanto,65,70,80,Rock,Water
+137,Aerodactyl,105,65,80,Kanto,60,75,130,Rock,Flying
+138,Snorlax,110,65,160,Kanto,65,110,30,Normal,None
+139,Articuno,85,100,90,Kanto,95,125,85,Ice,Flying
+140,Zapdos,90,85,90,Kanto,125,90,100,Electric,Flying
+141,Moltres,100,90,90,Kanto,125,85,90,Fire,Flying
+142,Dratini,64,45,41,Kanto,50,50,50,Dragon,None
+143,Dragonair,84,65,61,Kanto,70,70,70,Dragon,None
+144,Dragonite,134,95,91,Kanto,100,100,80,Dragon,Flying
+145,Mewtwo,110,90,106,Kanto,154,90,130,Psychic,None
+146,Mew,100,100,100,Kanto,100,100,100,Psychic,None
+147,Chikorita,49,65,45,Johto,49,65,45,Grass,None
+148,Bayleef,62,80,60,Johto,63,80,60,Grass,None
+149,Meganium,82,100,80,Johto,83,100,80,Grass,None
+150,Cyndaquil,52,43,39,Johto,60,50,65,Fire,None
+151,Quilava,64,58,58,Johto,80,65,80,Fire,None
+152,Typhlosion,84,78,78,Johto,109,85,100,Fire,None
+153,Totodile,65,64,50,Johto,44,48,43,Water,None
+154,Croconaw,80,80,65,Johto,59,63,58,Water,None
+155,Feraligatr,105,100,85,Johto,79,83,78,Water,None
+156,Sentret,46,34,35,Johto,35,45,20,Normal,None
+157,Furret,76,64,85,Johto,45,55,90,Normal,None
+158,Hoothoot,30,30,60,Johto,36,56,50,Normal,Flying
+159,Noctowl,50,50,100,Johto,86,96,70,Normal,Flying
+160,Ledyba,20,30,40,Johto,40,80,55,Bug,Flying
+161,Ledian,35,50,55,Johto,55,110,85,Bug,Flying
+162,Spinarak,60,40,40,Johto,40,40,30,Bug,Poison
+163,Ariados,90,70,70,Johto,60,70,40,Bug,Poison
+164,Crobat,90,80,85,Johto,70,80,130,Poison,Flying
+165,Chinchou,38,38,75,Johto,56,56,67,Water,Electric
+166,Lanturn,58,58,125,Johto,76,76,67,Water,Electric
+167,Pichu,40,15,20,Johto,35,35,60,Electric,None
+168,Cleffa,25,28,50,Johto,45,55,15,Fairy,None
+169,Igglybuff,30,15,90,Johto,40,20,15,Normal,Fairy
+170,Togepi,20,65,35,Johto,40,65,20,Fairy,None
+171,Togetic,40,85,55,Johto,80,105,40,Fairy,Flying
+172,Natu,50,45,40,Johto,70,45,70,Psychic,Flying
+173,Xatu,75,70,65,Johto,95,70,95,Psychic,Flying
+174,Mareep,40,40,55,Johto,65,45,35,Electric,None
+175,Flaaffy,55,55,70,Johto,80,60,45,Electric,None
+176,Ampharos,75,85,90,Johto,115,90,55,Electric,None
+177,Bellossom,80,95,75,Johto,90,100,50,Grass,None
+178,Marill,20,50,70,Johto,20,50,40,Water,Fairy
+179,Azumarill,50,80,100,Johto,60,80,50,Water,Fairy
+180,Sudowoodo,100,115,70,Johto,30,65,30,Rock,None
+181,Politoed,75,75,90,Johto,90,100,70,Water,None
+182,Hoppip,35,40,35,Johto,35,55,50,Grass,Flying
+183,Skiploom,45,50,55,Johto,45,65,80,Grass,Flying
+184,Jumpluff,55,70,75,Johto,55,95,110,Grass,Flying
+185,Aipom,70,55,55,Johto,40,55,85,Normal,None
+186,Sunkern,30,30,30,Johto,30,30,30,Grass,None
+187,Sunflora,75,55,75,Johto,105,85,30,Grass,None
+188,Yanma,65,45,65,Johto,75,45,95,Bug,Flying
+189,Wooper,45,45,55,Johto,25,25,15,Water,Ground
+190,Quagsire,85,85,95,Johto,65,65,35,Water,Ground
+191,Espeon,65,60,65,Johto,130,95,110,Psychic,None
+192,Umbreon,65,110,95,Johto,60,130,65,Dark,None
+193,Murkrow,85,42,60,Johto,85,42,91,Dark,Flying
+194,Slowking,75,80,95,Johto,100,110,30,Water,Psychic
+195,Misdreavus,60,60,60,Johto,85,85,85,Ghost,None
+196,Unown,72,48,48,Johto,72,48,48,Psychic,None
+197,Wobbuffet,33,58,190,Johto,33,58,33,Psychic,None
+198,Girafarig,80,65,70,Johto,90,65,85,Normal,Psychic
+199,Pineco,65,90,50,Johto,35,35,15,Bug,None
+200,Forretress,90,140,75,Johto,60,60,40,Bug,Steel
+201,Dunsparce,70,70,100,Johto,65,65,45,Normal,None
+202,Gligar,75,105,65,Johto,35,65,85,Ground,Flying
+203,Steelix,85,200,75,Johto,55,65,30,Steel,Ground
+204,Snubbull,80,50,60,Johto,40,40,30,Fairy,None
+205,Granbull,120,75,90,Johto,60,60,45,Fairy,None
+206,Qwilfish,95,85,65,Johto,55,55,85,Water,Poison
+207,Scizor,130,100,70,Johto,55,80,65,Bug,Steel
+208,Shuckle,10,230,20,Johto,10,230,5,Bug,Rock
+209,Heracross,125,75,80,Johto,40,95,85,Bug,Fighting
+210,Sneasel,95,55,55,Johto,35,75,115,Dark,Ice
+211,Teddiursa,80,50,60,Johto,50,50,40,Normal,None
+212,Ursaring,130,75,90,Johto,75,75,55,Normal,None
+213,Slugma,40,40,40,Johto,70,40,20,Fire,None
+214,Magcargo,50,120,60,Johto,90,80,30,Fire,Rock
+215,Swinub,50,40,50,Johto,30,30,50,Ice,Ground
+216,Piloswine,100,80,100,Johto,60,60,50,Ice,Ground
+217,Corsola,55,95,65,Johto,65,95,35,Water,Rock
+218,Remoraid,65,35,35,Johto,65,35,65,Water,None
+219,Octillery,105,75,75,Johto,105,75,45,Water,None
+220,Delibird,55,45,45,Johto,65,45,75,Ice,Flying
+221,Mantine,40,70,85,Johto,80,140,70,Water,Flying
+222,Skarmory,80,140,65,Johto,40,70,70,Steel,Flying
+223,Houndour,60,30,45,Johto,80,50,65,Dark,Fire
+224,Houndoom,90,50,75,Johto,110,80,95,Dark,Fire
+225,Kingdra,95,95,75,Johto,95,95,85,Water,Dragon
+226,Phanpy,60,60,90,Johto,40,40,40,Ground,None
+227,Donphan,120,120,90,Johto,60,60,50,Ground,None
+228,Porygon2,80,90,85,Johto,105,95,60,Normal,None
+229,Stantler,95,62,73,Johto,85,65,85,Normal,None
+230,Smeargle,20,35,55,Johto,20,45,75,Normal,None
+231,Tyrogue,35,35,35,Johto,35,35,35,Fighting,None
+232,Hitmontop,95,95,50,Johto,35,110,70,Fighting,None
+233,Smoochum,30,15,45,Johto,85,65,65,Ice,Psychic
+234,Elekid,63,37,45,Johto,65,55,95,Electric,None
+235,Magby,75,37,45,Johto,70,55,83,Fire,None
+236,Miltank,80,105,95,Johto,40,70,100,Normal,None
+237,Blissey,10,10,255,Johto,75,135,55,Normal,None
+238,Raikou,85,75,90,Johto,115,100,115,Electric,None
+239,Entei,115,85,115,Johto,90,75,100,Fire,None
+240,Suicune,75,115,100,Johto,90,115,85,Water,None
+241,Larvitar,64,50,50,Johto,45,50,41,Rock,Ground
+242,Pupitar,84,70,70,Johto,65,70,51,Rock,Ground
+243,Tyranitar,134,110,100,Johto,95,100,61,Rock,Dark
+244,Lugia,90,130,106,Johto,90,154,110,Psychic,Flying
+245,Ho-oh,130,90,106,Johto,110,154,90,Fire,Flying
+246,Celebi,100,100,100,Johto,100,100,100,Psychic,Grass
+247,Treecko,45,35,40,Hoenn,65,55,70,Grass,None
+248,Grovyle,65,45,50,Hoenn,85,65,95,Grass,None
+249,Sceptile,85,65,70,Hoenn,105,85,120,Grass,None
+250,Torchic,60,40,45,Hoenn,70,50,45,Fire,None
+251,Combusken,85,60,60,Hoenn,85,60,55,Fire,Fighting
+252,Blaziken,120,70,80,Hoenn,110,70,80,Fire,Fighting
+253,Mudkip,70,50,50,Hoenn,50,50,40,Water,None
+254,Marshtomp,85,70,70,Hoenn,60,70,50,Water,Ground
+255,Swampert,110,90,100,Hoenn,85,90,60,Water,Ground
+256,Poochyena,55,35,35,Hoenn,30,30,35,Dark,None
+257,Mightyena,90,70,70,Hoenn,60,60,70,Dark,None
+258,Zigzagoon,30,41,38,Hoenn,30,41,60,Normal,None
+259,Linoone,70,61,78,Hoenn,50,61,100,Normal,None
+260,Wurmple,45,35,45,Hoenn,20,30,20,Bug,None
+261,Silcoon,35,55,50,Hoenn,25,25,15,Bug,None
+262,Beautifly,70,50,60,Hoenn,100,50,65,Bug,Flying
+263,Cascoon,35,55,50,Hoenn,25,25,15,Bug,None
+264,Dustox,50,70,60,Hoenn,50,90,65,Bug,Poison
+265,Lotad,30,30,40,Hoenn,40,50,30,Water,Grass
+266,Lombre,50,50,60,Hoenn,60,70,50,Water,Grass
+267,Ludicolo,70,70,80,Hoenn,90,100,70,Water,Grass
+268,Seedot,40,50,40,Hoenn,30,30,30,Grass,None
+269,Nuzleaf,70,40,70,Hoenn,60,40,60,Grass,Dark
+270,Shiftry,100,60,90,Hoenn,90,60,80,Grass,Dark
+271,Taillow,55,30,40,Hoenn,30,30,85,Normal,Flying
+272,Swellow,85,60,60,Hoenn,75,50,125,Normal,Flying
+273,Wingull,30,30,40,Hoenn,55,30,85,Water,Flying
+274,Pelipper,50,100,60,Hoenn,95,70,65,Water,Flying
+275,Ralts,25,25,28,Hoenn,45,35,40,Psychic,Fairy
+276,Kirlia,35,35,38,Hoenn,65,55,50,Psychic,Fairy
+277,Gardevoir,65,65,68,Hoenn,125,115,80,Psychic,Fairy
+278,Surskit,30,32,40,Hoenn,50,52,65,Bug,Water
+279,Masquerain,60,62,70,Hoenn,100,82,80,Bug,Flying
+280,Shroomish,40,60,60,Hoenn,40,60,35,Grass,None
+281,Breloom,130,80,60,Hoenn,60,60,70,Grass,Fighting
+282,Slakoth,60,60,60,Hoenn,35,35,30,Normal,None
+283,Vigoroth,80,80,80,Hoenn,55,55,90,Normal,None
+284,Slaking,160,100,150,Hoenn,95,65,100,Normal,None
+285,Nincada,45,90,31,Hoenn,30,30,40,Bug,Ground
+286,Ninjask,90,45,61,Hoenn,50,50,160,Bug,Flying
+287,Shedinja,90,45,1,Hoenn,30,30,40,Bug,Ghost
+288,Whismur,51,23,64,Hoenn,51,23,28,Normal,None
+289,Loudred,71,43,84,Hoenn,71,43,48,Normal,None
+290,Exploud,91,63,104,Hoenn,91,73,68,Normal,None
+291,Makuhita,60,30,72,Hoenn,20,30,25,Fighting,None
+292,Hariyama,120,60,144,Hoenn,40,60,50,Fighting,None
+293,Azurill,20,40,50,Hoenn,20,40,20,Normal,Fairy
+294,Nosepass,45,135,30,Hoenn,45,90,30,Rock,None
+295,Skitty,45,45,50,Hoenn,35,35,50,Normal,None
+296,Delcatty,65,65,70,Hoenn,55,55,90,Normal,None
+297,Sableye,75,75,50,Hoenn,65,65,50,Dark,Ghost
+298,Mawile,85,85,50,Hoenn,55,55,50,Steel,Fairy
+299,Aron,70,100,50,Hoenn,40,40,30,Steel,Rock
+300,Lairon,90,140,60,Hoenn,50,50,40,Steel,Rock
+301,Aggron,110,180,70,Hoenn,60,60,50,Steel,Rock
+302,Meditite,40,55,30,Hoenn,40,55,60,Fighting,Psychic
+303,Medicham,60,75,60,Hoenn,60,75,80,Fighting,Psychic
+304,Electrike,45,40,40,Hoenn,65,40,65,Electric,None
+305,Manectric,75,60,70,Hoenn,105,60,105,Electric,None
+306,Plusle,50,40,60,Hoenn,85,75,95,Electric,None
+307,Minun,40,50,60,Hoenn,75,85,95,Electric,None
+308,Volbeat,73,75,65,Hoenn,47,85,85,Bug,None
+309,Illumise,47,75,65,Hoenn,73,85,85,Bug,None
+310,Roselia,60,45,50,Hoenn,100,80,65,Grass,Poison
+311,Gulpin,43,53,70,Hoenn,43,53,40,Poison,None
+312,Swalot,73,83,100,Hoenn,73,83,55,Poison,None
+313,Carvanha,90,20,45,Hoenn,65,20,65,Water,Dark
+314,Sharpedo,120,40,70,Hoenn,95,40,95,Water,Dark
+315,Wailmer,70,35,130,Hoenn,70,35,60,Water,None
+316,Wailord,90,45,170,Hoenn,90,45,60,Water,None
+317,Numel,60,40,60,Hoenn,65,45,35,Fire,Ground
+318,Camerupt,100,70,70,Hoenn,105,75,40,Fire,Ground
+319,Torkoal,85,140,70,Hoenn,85,70,20,Fire,None
+320,Spoink,25,35,60,Hoenn,70,80,60,Psychic,None
+321,Grumpig,45,65,80,Hoenn,90,110,80,Psychic,None
+322,Spinda,60,60,60,Hoenn,60,60,60,Normal,None
+323,Trapinch,100,45,45,Hoenn,45,45,10,Ground,None
+324,Vibrava,70,50,50,Hoenn,50,50,70,Ground,Dragon
+325,Flygon,100,80,80,Hoenn,80,80,100,Ground,Dragon
+326,Cacnea,85,40,50,Hoenn,85,40,35,Grass,None
+327,Cacturne,115,60,70,Hoenn,115,60,55,Grass,Dark
+328,Swablu,40,60,45,Hoenn,40,75,50,Normal,Flying
+329,Altaria,70,90,75,Hoenn,70,105,80,Dragon,Flying
+330,Zangoose,115,60,73,Hoenn,60,60,90,Normal,None
+331,Seviper,100,60,73,Hoenn,100,60,65,Poison,None
+332,Lunatone,55,65,90,Hoenn,95,85,70,Rock,Psychic
+333,Solrock,95,85,90,Hoenn,55,65,70,Rock,Psychic
+334,Barboach,48,43,50,Hoenn,46,41,60,Water,Ground
+335,Whiscash,78,73,110,Hoenn,76,71,60,Water,Ground
+336,Corphish,80,65,43,Hoenn,50,35,35,Water,None
+337,Crawdaunt,120,85,63,Hoenn,90,55,55,Water,Dark
+338,Baltoy,40,55,40,Hoenn,40,70,55,Ground,Psychic
+339,Claydol,70,105,60,Hoenn,70,120,75,Ground,Psychic
+340,Lileep,41,77,66,Hoenn,61,87,23,Rock,Grass
+341,Cradily,81,97,86,Hoenn,81,107,43,Rock,Grass
+342,Anorith,95,50,45,Hoenn,40,50,75,Rock,Bug
+343,Armaldo,125,100,75,Hoenn,70,80,45,Rock,Bug
+344,Feebas,15,20,20,Hoenn,10,55,80,Water,None
+345,Milotic,60,79,95,Hoenn,100,125,81,Water,None
+346,Castform,70,70,70,Hoenn,70,70,70,Normal,None
+347,Kecleon,90,70,60,Hoenn,60,120,40,Normal,None
+348,Shuppet,75,35,44,Hoenn,63,33,45,Ghost,None
+349,Banette,115,65,64,Hoenn,83,63,65,Ghost,None
+350,Duskull,40,90,20,Hoenn,30,90,25,Ghost,None
+351,Dusclops,70,130,40,Hoenn,60,130,25,Ghost,None
+352,Tropius,68,83,99,Hoenn,72,87,51,Grass,Flying
+353,Chimecho,50,80,75,Hoenn,95,90,65,Psychic,None
+354,Absol,130,60,65,Hoenn,75,60,75,Dark,None
+355,Wynaut,23,48,95,Hoenn,23,48,23,Psychic,None
+356,Snorunt,50,50,50,Hoenn,50,50,50,Ice,None
+357,Glalie,80,80,80,Hoenn,80,80,80,Ice,None
+358,Spheal,40,50,70,Hoenn,55,50,25,Ice,Water
+359,Sealeo,60,70,90,Hoenn,75,70,45,Ice,Water
+360,Walrein,80,90,110,Hoenn,95,90,65,Ice,Water
+361,Clamperl,64,85,35,Hoenn,74,55,32,Water,None
+362,Huntail,104,105,55,Hoenn,94,75,52,Water,None
+363,Gorebyss,84,105,55,Hoenn,114,75,52,Water,None
+364,Relicanth,90,130,100,Hoenn,45,65,55,Water,Rock
+365,Luvdisc,30,55,43,Hoenn,40,65,97,Water,None
+366,Bagon,75,60,45,Hoenn,40,30,50,Dragon,None
+367,Shelgon,95,100,65,Hoenn,60,50,50,Dragon,None
+368,Salamence,135,80,95,Hoenn,110,80,100,Dragon,Flying
+369,Beldum,55,80,40,Hoenn,35,60,30,Steel,Psychic
+370,Metang,75,100,60,Hoenn,55,80,50,Steel,Psychic
+371,Metagross,135,130,80,Hoenn,95,90,70,Steel,Psychic
+372,Regirock,100,200,80,Hoenn,50,100,50,Rock,None
+373,Regice,50,100,80,Hoenn,100,200,50,Ice,None
+374,Registeel,75,150,80,Hoenn,75,150,50,Steel,None
+375,Latias,80,90,80,Hoenn,110,130,110,Dragon,Psychic
+376,Latios,90,80,80,Hoenn,130,110,110,Dragon,Psychic
+377,Kyogre,100,90,100,Hoenn,150,140,90,Water,None
+378,Groudon,150,140,100,Hoenn,100,90,90,Ground,None
+379,Rayquaza,150,90,105,Hoenn,150,90,95,Dragon,Flying
+380,Jirachi,100,100,100,Hoenn,100,100,100,Steel,Psychic
+381,Deoxys,150,50,50,Hoenn,150,50,150,Psychic,None
+382,Turtwig,68,64,55,Sinnoh,45,55,31,Grass,None
+383,Grotle,89,85,75,Sinnoh,55,65,36,Grass,None
+384,Torterra,109,105,95,Sinnoh,75,85,56,Grass,Ground
+385,Chimchar,58,44,44,Sinnoh,58,44,61,Fire,None
+386,Monferno,78,52,64,Sinnoh,78,52,81,Fire,Fighting
+387,Infernape,104,71,76,Sinnoh,104,71,108,Fire,Fighting
+388,Piplup,51,53,53,Sinnoh,61,56,40,Water,None
+389,Prinplup,66,68,64,Sinnoh,81,76,50,Water,None
+390,Empoleon,86,88,84,Sinnoh,111,101,60,Water,Steel
+391,Starly,55,30,40,Sinnoh,30,30,60,Normal,Flying
+392,Staravia,75,50,55,Sinnoh,40,40,80,Normal,Flying
+393,Staraptor,120,70,85,Sinnoh,50,60,100,Normal,Flying
+394,Bidoof,45,40,59,Sinnoh,35,40,31,Normal,None
+395,Bibarel,85,60,79,Sinnoh,55,60,71,Normal,Water
+396,Kricketot,25,41,37,Sinnoh,25,41,25,Bug,None
+397,Kricketune,85,51,77,Sinnoh,55,51,65,Bug,None
+398,Shinx,65,34,45,Sinnoh,40,34,45,Electric,None
+399,Luxio,85,49,60,Sinnoh,60,49,60,Electric,None
+400,Luxray,120,79,80,Sinnoh,95,79,70,Electric,None
+401,Budew,30,35,40,Sinnoh,50,70,55,Grass,Poison
+402,Roserade,70,65,60,Sinnoh,125,105,90,Grass,Poison
+403,Cranidos,125,40,67,Sinnoh,30,30,58,Rock,None
+404,Rampardos,165,60,97,Sinnoh,65,50,58,Rock,None
+405,Shieldon,42,118,30,Sinnoh,42,88,30,Rock,Steel
+406,Bastiodon,52,168,60,Sinnoh,47,138,30,Rock,Steel
+407,Burmy,29,45,40,Sinnoh,29,45,36,Bug,None
+408,Wormadam,59,85,60,Sinnoh,79,105,36,Bug,Grass
+409,Mothim,94,50,70,Sinnoh,94,50,66,Bug,Flying
+410,Combee,30,42,30,Sinnoh,30,42,70,Bug,Flying
+411,Vespiquen,80,102,70,Sinnoh,80,102,40,Bug,Flying
+412,Pachirisu,45,70,60,Sinnoh,45,90,95,Electric,None
+413,Buizel,65,35,55,Sinnoh,60,30,85,Water,None
+414,Floatzel,105,55,85,Sinnoh,85,50,115,Water,None
+415,Cherubi,35,45,45,Sinnoh,62,53,35,Grass,None
+416,Cherrim,60,70,70,Sinnoh,87,78,85,Grass,None
+417,Shellos,48,48,76,Sinnoh,57,62,34,Water,None
+418,Gastrodon,83,68,111,Sinnoh,92,82,39,Water,Ground
+419,Ambipom,100,66,75,Sinnoh,60,66,115,Normal,None
+420,Drifloon,50,34,90,Sinnoh,60,44,70,Ghost,Flying
+421,Drifblim,80,44,150,Sinnoh,90,54,80,Ghost,Flying
+422,Buneary,66,44,55,Sinnoh,44,56,85,Normal,None
+423,Lopunny,76,84,65,Sinnoh,54,96,105,Normal,None
+424,Mismagius,60,60,60,Sinnoh,105,105,105,Ghost,None
+425,Honchkrow,125,52,100,Sinnoh,105,52,71,Dark,Flying
+426,Glameow,55,42,49,Sinnoh,42,37,85,Normal,None
+427,Purugly,82,64,71,Sinnoh,64,59,112,Normal,None
+428,Chingling,30,50,45,Sinnoh,65,50,45,Psychic,None
+429,Stunky,63,47,63,Sinnoh,41,41,74,Poison,Dark
+430,Skuntank,93,67,103,Sinnoh,71,61,84,Poison,Dark
+431,Bronzor,24,86,57,Sinnoh,24,86,23,Steel,Psychic
+432,Bronzong,89,116,67,Sinnoh,79,116,33,Steel,Psychic
+433,Bonsly,80,95,50,Sinnoh,10,45,10,Rock,None
+434,Happiny,5,5,100,Sinnoh,15,65,30,Normal,None
+435,Chatot,65,45,76,Sinnoh,92,42,91,Normal,Flying
+436,Spiritomb,92,108,50,Sinnoh,92,108,35,Ghost,Dark
+437,Gible,70,45,58,Sinnoh,40,45,42,Dragon,Ground
+438,Gabite,90,65,68,Sinnoh,50,55,82,Dragon,Ground
+439,Garchomp,130,95,108,Sinnoh,80,85,102,Dragon,Ground
+440,Munchlax,85,40,135,Sinnoh,40,85,5,Normal,None
+441,Riolu,70,40,40,Sinnoh,35,40,60,Fighting,None
+442,Lucario,110,70,70,Sinnoh,115,70,90,Fighting,Steel
+443,Hippopotas,72,78,68,Sinnoh,38,42,32,Ground,None
+444,Hippowdon,112,118,108,Sinnoh,68,72,47,Ground,None
+445,Skorupi,50,90,40,Sinnoh,30,55,65,Poison,Bug
+446,Drapion,90,110,70,Sinnoh,60,75,95,Poison,Dark
+447,Croagunk,61,40,48,Sinnoh,61,40,50,Poison,Fighting
+448,Toxicroak,106,65,83,Sinnoh,86,65,85,Poison,Fighting
+449,Carnivine,100,72,74,Sinnoh,90,72,46,Grass,None
+450,Finneon,49,56,49,Sinnoh,49,61,66,Water,None
+451,Lumineon,69,76,69,Sinnoh,69,86,91,Water,None
+452,Mantyke,20,50,45,Sinnoh,60,120,50,Water,Flying
+453,Snover,62,50,60,Sinnoh,62,60,40,Grass,Ice
+454,Abomasnow,92,75,90,Sinnoh,92,85,60,Grass,Ice
+455,Weavile,120,65,70,Sinnoh,45,85,125,Dark,Ice
+456,Magnezone,70,115,70,Sinnoh,130,90,60,Electric,Steel
+457,Lickilicky,85,95,110,Sinnoh,80,95,50,Normal,None
+458,Rhyperior,140,130,115,Sinnoh,55,55,40,Ground,Rock
+459,Tangrowth,100,125,100,Sinnoh,110,50,50,Grass,None
+460,Electivire,123,67,75,Sinnoh,95,85,95,Electric,None
+461,Magmortar,95,67,75,Sinnoh,125,95,83,Fire,None
+462,Togekiss,50,95,85,Sinnoh,120,115,80,Fairy,Flying
+463,Yanmega,76,86,86,Sinnoh,116,56,95,Bug,Flying
+464,Leafeon,110,130,65,Sinnoh,60,65,95,Grass,None
+465,Glaceon,60,110,65,Sinnoh,130,95,65,Ice,None
+466,Gliscor,95,125,75,Sinnoh,45,75,95,Ground,Flying
+467,Mamoswine,130,80,110,Sinnoh,70,60,80,Ice,Ground
+468,Porygon-Z,80,70,85,Sinnoh,135,75,90,Normal,None
+469,Gallade,125,65,68,Sinnoh,65,115,80,Psychic,Fighting
+470,Probopass,55,145,60,Sinnoh,75,150,40,Rock,Steel
+471,Dusknoir,100,135,45,Sinnoh,65,135,45,Ghost,None
+472,Froslass,80,70,70,Sinnoh,80,70,110,Ice,Ghost
+473,Rotom,50,77,50,Sinnoh,95,77,91,Electric,Ghost
+474,Uxie,75,130,75,Sinnoh,75,130,95,Psychic,None
+475,Mesprit,105,105,80,Sinnoh,105,105,80,Psychic,None
+476,Azelf,125,70,75,Sinnoh,125,70,115,Psychic,None
+477,Dialga,120,120,100,Sinnoh,150,100,90,Steel,Dragon
+478,Palkia,120,100,90,Sinnoh,150,120,100,Water,Dragon
+479,Heatran,90,106,91,Sinnoh,130,106,77,Fire,Steel
+480,Regigigas,160,110,110,Sinnoh,80,110,100,Normal,None
+481,Giratina,100,120,150,Sinnoh,100,120,90,Ghost,Dragon
+482,Cresselia,70,120,120,Sinnoh,75,130,85,Psychic,None
+483,Phione,80,80,80,Sinnoh,80,80,80,Water,None
+484,Manaphy,100,100,100,Sinnoh,100,100,100,Water,None
+485,Darkrai,90,90,70,Sinnoh,135,90,125,Dark,None
+486,Shaymin,100,100,100,Sinnoh,100,100,100,Grass,None
+487,Arceus,120,120,120,Sinnoh,120,120,120,Normal,None
+488,Victini,100,100,100,Unova,100,100,100,Psychic,Fire
+489,Snivy,45,55,45,Unova,45,55,63,Grass,None
+490,Servine,60,75,60,Unova,60,75,83,Grass,None
+491,Serperior,75,95,75,Unova,75,95,113,Grass,None
+492,Tepig,63,45,65,Unova,45,45,45,Fire,None
+493,Pignite,93,55,90,Unova,70,55,55,Fire,Fighting
+494,Emboar,123,65,110,Unova,100,65,65,Fire,Fighting
+495,Oshawott,55,45,55,Unova,63,45,45,Water,None
+496,Dewott,75,60,75,Unova,83,60,60,Water,None
+497,Samurott,100,85,95,Unova,108,70,70,Water,None
+498,Patrat,55,39,45,Unova,35,39,42,Normal,None
+499,Watchog,85,69,60,Unova,60,69,77,Normal,None
+500,Lillipup,60,45,45,Unova,25,45,55,Normal,None
+501,Herdier,80,65,65,Unova,35,65,60,Normal,None
+502,Stoutland,110,90,85,Unova,45,90,80,Normal,None
+503,Purrloin,50,37,41,Unova,50,37,66,Dark,None
+504,Liepard,88,50,64,Unova,88,50,106,Dark,None
+505,Pansage,53,48,50,Unova,53,48,64,Grass,None
+506,Simisage,98,63,75,Unova,98,63,101,Grass,None
+507,Pansear,53,48,50,Unova,53,48,64,Fire,None
+508,Simisear,98,63,75,Unova,98,63,101,Fire,None
+509,Panpour,53,48,50,Unova,53,48,64,Water,None
+510,Simipour,98,63,75,Unova,98,63,101,Water,None
+511,Munna,25,45,76,Unova,67,55,24,Psychic,None
+512,Musharna,55,85,116,Unova,107,95,29,Psychic,None
+513,Pidove,55,50,50,Unova,36,30,43,Normal,Flying
+514,Tranquill,77,62,62,Unova,50,42,65,Normal,Flying
+515,Unfezant,115,80,80,Unova,65,55,93,Normal,Flying
+516,Blitzle,60,32,45,Unova,50,32,76,Electric,None
+517,Zebstrika,100,63,75,Unova,80,63,116,Electric,None
+518,Roggenrola,75,85,55,Unova,25,25,15,Rock,None
+519,Boldore,105,105,70,Unova,50,40,20,Rock,None
+520,Gigalith,135,130,85,Unova,60,80,25,Rock,None
+521,Woobat,45,43,65,Unova,55,43,72,Psychic,Flying
+522,Swoobat,57,55,67,Unova,77,55,114,Psychic,Flying
+523,Drilbur,85,40,60,Unova,30,45,68,Ground,None
+524,Excadrill,135,60,110,Unova,50,65,88,Ground,Steel
+525,Audino,60,86,103,Unova,60,86,50,Normal,None
+526,Timburr,80,55,75,Unova,25,35,35,Fighting,None
+527,Gurdurr,105,85,85,Unova,40,50,40,Fighting,None
+528,Conkeldurr,140,95,105,Unova,55,65,45,Fighting,None
+529,Tympole,50,40,50,Unova,50,40,64,Water,None
+530,Palpitoad,65,55,75,Unova,65,55,69,Water,Ground
+531,Seismitoad,95,75,105,Unova,85,75,74,Water,Ground
+532,Throh,100,85,120,Unova,30,85,45,Fighting,None
+533,Sawk,125,75,75,Unova,30,75,85,Fighting,None
+534,Sewaddle,53,70,45,Unova,40,60,42,Bug,Grass
+535,Swadloon,63,90,55,Unova,50,80,42,Bug,Grass
+536,Leavanny,103,80,75,Unova,70,80,92,Bug,Grass
+537,Venipede,45,59,30,Unova,30,39,57,Bug,Poison
+538,Whirlipede,55,99,40,Unova,40,79,47,Bug,Poison
+539,Scolipede,100,89,60,Unova,55,69,112,Bug,Poison
+540,Cottonee,27,60,40,Unova,37,50,66,Grass,Fairy
+541,Whimsicott,67,85,60,Unova,77,75,116,Grass,Fairy
+542,Petilil,35,50,45,Unova,70,50,30,Grass,None
+543,Lilligant,60,75,70,Unova,110,75,90,Grass,None
+544,Basculin,92,65,70,Unova,80,55,98,Water,None
+545,Sandile,72,35,50,Unova,35,35,65,Ground,Dark
+546,Krokorok,82,45,60,Unova,45,45,74,Ground,Dark
+547,Krookodile,117,80,95,Unova,65,70,92,Ground,Dark
+548,Darumaka,90,45,70,Unova,15,45,50,Fire,None
+549,Darmanitan,140,55,105,Unova,30,55,95,Fire,None
+550,Maractus,86,67,75,Unova,106,67,60,Grass,None
+551,Dwebble,65,85,50,Unova,35,35,55,Bug,Rock
+552,Crustle,105,125,70,Unova,65,75,45,Bug,Rock
+553,Scraggy,75,70,50,Unova,35,70,48,Dark,Fighting
+554,Scrafty,90,115,65,Unova,45,115,58,Dark,Fighting
+555,Sigilyph,58,80,72,Unova,103,80,97,Psychic,Flying
+556,Yamask,30,85,38,Unova,55,65,30,Ghost,None
+557,Cofagrigus,50,145,58,Unova,95,105,30,Ghost,None
+558,Tirtouga,78,103,54,Unova,53,45,22,Water,Rock
+559,Carracosta,108,133,74,Unova,83,65,32,Water,Rock
+560,Archen,112,45,55,Unova,74,45,70,Rock,Flying
+561,Archeops,140,65,75,Unova,112,65,110,Rock,Flying
+562,Trubbish,50,62,50,Unova,40,62,65,Poison,None
+563,Garbodor,95,82,80,Unova,60,82,75,Poison,None
+564,Zorua,65,40,40,Unova,80,40,65,Dark,None
+565,Zoroark,105,60,60,Unova,120,60,105,Dark,None
+566,Minccino,50,40,55,Unova,40,40,75,Normal,None
+567,Cinccino,95,60,75,Unova,65,60,115,Normal,None
+568,Gothita,30,50,45,Unova,55,65,45,Psychic,None
+569,Gothorita,45,70,60,Unova,75,85,55,Psychic,None
+570,Gothitelle,55,95,70,Unova,95,110,65,Psychic,None
+571,Solosis,30,40,45,Unova,105,50,20,Psychic,None
+572,Duosion,40,50,65,Unova,125,60,30,Psychic,None
+573,Reuniclus,65,75,110,Unova,125,85,30,Psychic,None
+574,Ducklett,44,50,62,Unova,44,50,55,Water,Flying
+575,Swanna,87,63,75,Unova,87,63,98,Water,Flying
+576,Vanillite,50,50,36,Unova,65,60,44,Ice,None
+577,Vanillish,65,65,51,Unova,80,75,59,Ice,None
+578,Vanilluxe,95,85,71,Unova,110,95,79,Ice,None
+579,Deerling,60,50,60,Unova,40,50,75,Normal,Grass
+580,Sawsbuck,100,70,80,Unova,60,70,95,Normal,Grass
+581,Emolga,75,60,55,Unova,75,60,103,Electric,Flying
+582,Karrablast,75,45,50,Unova,40,45,60,Bug,None
+583,Escavalier,135,105,70,Unova,60,105,20,Bug,Steel
+584,Foongus,55,45,69,Unova,55,55,15,Grass,Poison
+585,Amoonguss,85,70,114,Unova,85,80,30,Grass,Poison
+586,Frillish,40,50,55,Unova,65,85,40,Water,Ghost
+587,Jellicent,60,70,100,Unova,85,105,60,Water,Ghost
+588,Alomomola,75,80,165,Unova,40,45,65,Water,None
+589,Joltik,47,50,50,Unova,57,50,65,Bug,Electric
+590,Galvantula,77,60,70,Unova,97,60,108,Bug,Electric
+591,Ferroseed,50,91,44,Unova,24,86,10,Grass,Steel
+592,Ferrothorn,94,131,74,Unova,54,116,20,Grass,Steel
+593,Klink,55,70,40,Unova,45,60,30,Steel,None
+594,Klang,80,95,60,Unova,70,85,50,Steel,None
+595,Klinklang,100,115,60,Unova,70,85,90,Steel,None
+596,Tynamo,55,40,35,Unova,45,40,60,Electric,None
+597,Eelektrik,85,70,65,Unova,75,70,40,Electric,None
+598,Eelektross,115,80,85,Unova,105,80,50,Electric,None
+599,Elgyem,55,55,55,Unova,85,55,30,Psychic,None
+600,Beheeyem,75,75,75,Unova,125,95,40,Psychic,None
+601,Litwick,30,55,50,Unova,65,55,20,Ghost,Fire
+602,Lampent,40,60,60,Unova,95,60,55,Ghost,Fire
+603,Chandelure,55,90,60,Unova,145,90,80,Ghost,Fire
+604,Axew,87,60,46,Unova,30,40,57,Dragon,None
+605,Fraxure,117,70,66,Unova,40,50,67,Dragon,None
+606,Haxorus,147,90,76,Unova,60,70,97,Dragon,None
+607,Cubchoo,70,40,55,Unova,60,40,40,Ice,None
+608,Beartic,130,80,95,Unova,70,80,50,Ice,None
+609,Cryogonal,50,50,80,Unova,95,135,105,Ice,None
+610,Shelmet,40,85,50,Unova,40,65,25,Bug,None
+611,Accelgor,70,40,80,Unova,100,60,145,Bug,None
+612,Stunfisk,66,84,109,Unova,81,99,32,Ground,Electric
+613,Mienfoo,85,50,45,Unova,55,50,65,Fighting,None
+614,Mienshao,125,60,65,Unova,95,60,105,Fighting,None
+615,Druddigon,120,90,77,Unova,60,90,48,Dragon,None
+616,Golett,74,50,59,Unova,35,50,35,Ground,Ghost
+617,Golurk,124,80,89,Unova,55,80,55,Ground,Ghost
+618,Pawniard,85,70,45,Unova,40,40,60,Dark,Steel
+619,Bisharp,125,100,65,Unova,60,70,70,Dark,Steel
+620,Bouffalant,110,95,95,Unova,40,95,55,Normal,None
+621,Rufflet,83,50,70,Unova,37,50,60,Normal,Flying
+622,Braviary,123,75,100,Unova,57,75,80,Normal,Flying
+623,Vullaby,55,75,70,Unova,45,65,60,Dark,Flying
+624,Mandibuzz,65,105,110,Unova,55,95,80,Dark,Flying
+625,Heatmor,97,66,85,Unova,105,66,65,Fire,None
+626,Durant,109,112,58,Unova,48,48,109,Bug,Steel
+627,Deino,65,50,52,Unova,45,50,38,Dark,Dragon
+628,Zweilous,85,70,72,Unova,65,70,58,Dark,Dragon
+629,Hydreigon,105,90,92,Unova,125,90,98,Dark,Dragon
+630,Larvesta,85,55,55,Unova,50,55,60,Bug,Fire
+631,Volcarona,60,65,85,Unova,135,105,100,Bug,Fire
+632,Cobalion,90,129,91,Unova,90,72,108,Steel,Fighting
+633,Terrakion,129,90,91,Unova,72,90,108,Rock,Fighting
+634,Virizion,90,72,91,Unova,90,129,108,Grass,Fighting
+635,Tornadus,115,70,79,Unova,125,80,111,Flying,None
+636,Thundurus,115,70,79,Unova,125,80,111,Electric,Flying
+637,Reshiram,120,100,100,Unova,150,120,90,Dragon,Fire
+638,Zekrom,150,120,100,Unova,120,100,90,Dragon,Electric
+639,Landorus,125,90,89,Unova,115,80,101,Ground,Flying
+640,Kyurem,130,90,125,Unova,130,90,95,Dragon,Ice
+641,Keldeo,72,90,91,Unova,129,90,108,Water,Fighting
+642,Meloetta,77,77,100,Unova,128,128,90,Normal,Psychic
+643,Genesect,120,95,71,Unova,120,95,99,Bug,Steel
+644,Chespin,61,65,56,Kalos,48,45,38,Grass,None
+645,Quilladin,78,95,61,Kalos,56,58,57,Grass,None
+646,Chesnaught,107,122,88,Kalos,74,75,64,Grass,Fighting
+647,Fennekin,45,40,40,Kalos,62,60,60,Fire,None
+648,Braixen,59,58,59,Kalos,90,70,73,Fire,None
+649,Delphox,69,72,75,Kalos,114,100,104,Fire,Psychic
+650,Froakie,56,40,41,Kalos,62,44,71,Water,None
+651,Frogadier,63,52,54,Kalos,83,56,97,Water,None
+652,Greninja,95,67,72,Kalos,103,71,122,Water,Dark
+653,Bunnelby,36,38,38,Kalos,32,36,57,Normal,None
+654,Diggersby,56,77,85,Kalos,50,77,78,Normal,Ground
+655,Fletchling,50,43,45,Kalos,40,38,62,Normal,Flying
+656,Fletchinder,73,55,62,Kalos,56,52,84,Fire,Flying
+657,Talonflame,81,71,78,Kalos,74,69,126,Fire,Flying
+658,Scatterbug,35,40,38,Kalos,27,25,35,Bug,None
+659,Spewpa,22,60,45,Kalos,27,30,29,Bug,None
+660,Vivillon,52,50,80,Kalos,90,50,89,Bug,Flying
+661,Litleo,50,58,62,Kalos,73,54,72,Fire,Normal
+662,Pyroar,68,72,86,Kalos,109,66,106,Fire,Normal
+663,Floette,45,47,54,Kalos,75,98,52,Fairy,None
+664,Florges,65,68,78,Kalos,112,154,75,Fairy,None
+665,Skiddo,65,48,66,Kalos,62,57,52,Grass,None
+666,Gogoat,100,62,123,Kalos,97,81,68,Grass,None
+667,Pancham,82,62,67,Kalos,46,48,43,Fighting,None
+668,Pangoro,124,78,95,Kalos,69,71,58,Fighting,Dark
+669,Furfrou,80,60,75,Kalos,65,90,102,Normal,None
+670,Espurr,48,54,62,Kalos,63,60,68,Psychic,None
+671,Meowstic,48,76,74,Kalos,83,81,104,Psychic,None
+672,Honedge,80,100,45,Kalos,35,37,28,Steel,Ghost
+673,Doublade,110,150,59,Kalos,45,49,35,Steel,Ghost
+674,Aegislash,50,140,60,Kalos,50,140,60,Steel,Ghost
+675,Spritzee,52,60,78,Kalos,63,65,23,Fairy,None
+676,Aromatisse,72,72,101,Kalos,99,89,29,Fairy,None
+677,Swirlix,48,66,62,Kalos,59,57,49,Fairy,None
+678,Slurpuff,80,86,82,Kalos,85,75,72,Fairy,None
+679,Inkay,54,53,53,Kalos,37,46,45,Dark,Psychic
+680,Malamar,92,88,86,Kalos,68,75,73,Dark,Psychic
+681,Binacle,52,67,42,Kalos,39,56,50,Rock,Water
+682,Barbaracle,105,115,72,Kalos,54,86,68,Rock,Water
+683,Skrelp,60,60,50,Kalos,60,60,30,Poison,Water
+684,Dragalge,75,90,65,Kalos,97,123,44,Poison,Dragon
+685,Clauncher,53,62,50,Kalos,58,63,44,Water,None
+686,Clawitzer,73,88,71,Kalos,120,89,59,Water,None
+687,Helioptile,38,33,44,Kalos,61,43,70,Electric,Normal
+688,Heliolisk,55,52,62,Kalos,109,94,109,Electric,Normal
+689,Tyrunt,89,77,58,Kalos,45,45,48,Rock,Dragon
+690,Tyrantrum,121,119,82,Kalos,69,59,71,Rock,Dragon
+691,Amaura,59,50,77,Kalos,67,63,46,Rock,Ice
+692,Aurorus,77,72,123,Kalos,99,92,58,Rock,Ice
+693,Sylveon,65,65,95,Kalos,110,130,60,Fairy,None
+694,Hawlucha,92,75,78,Kalos,74,63,118,Fighting,Flying
+695,Dedenne,58,57,67,Kalos,81,67,101,Electric,Fairy
+696,Carbink,50,150,50,Kalos,50,150,50,Rock,Fairy
+697,Goomy,50,35,45,Kalos,55,75,40,Dragon,None
+698,Sliggoo,75,53,68,Kalos,83,113,60,Dragon,None
+699,Goodra,100,70,90,Kalos,110,150,80,Dragon,None
+700,Klefki,80,91,57,Kalos,80,87,75,Steel,Fairy
+701,Phantump,70,48,43,Kalos,50,60,38,Ghost,Grass
+702,Trevenant,110,76,85,Kalos,65,82,56,Ghost,Grass
+703,Pumpkaboo,66,70,49,Kalos,44,55,51,Ghost,Grass
+704,Gourgeist,90,122,65,Kalos,58,75,84,Ghost,Grass
+705,Bergmite,69,85,55,Kalos,32,35,28,Ice,None
+706,Avalugg,117,184,95,Kalos,44,46,28,Ice,None
+707,Noibat,30,35,40,Kalos,45,40,55,Flying,Dragon
+708,Noivern,70,80,85,Kalos,97,80,123,Flying,Dragon
+709,Xerneas,131,95,126,Kalos,131,98,99,Fairy,None
+710,Yveltal,131,95,126,Kalos,131,98,99,Dark,Flying
+711,Zygarde,100,121,108,Kalos,81,95,95,Dragon,Ground
+712,Diancie,100,150,50,Kalos,100,150,50,Rock,Fairy
+713,Hoopa,110,60,80,Kalos,150,130,70,Psychic,Ghost
+714,Volcanion,110,120,80,Kalos,130,90,70,Fire,Water
+715,Rowlet,55,55,68,Alola,50,50,42,Grass,Flying
+716,Dartrix,75,75,78,Alola,70,70,52,Grass,Flying
+717,Decidueye,107,75,78,Alola,100,100,70,Grass,Ghost
+718,Litten,65,40,45,Alola,60,40,70,Fire,None
+719,Torracat,85,50,65,Alola,80,50,90,Fire,None
+720,Incineroar,115,90,95,Alola,80,90,60,Fire,Dark
+721,Popplio,54,54,50,Alola,66,56,40,Water,None
+722,Brionne,69,69,60,Alola,91,81,50,Water,None
+723,Primarina,74,74,80,Alola,126,116,60,Water,Fairy
+724,Pikipek,75,30,35,Alola,30,30,65,Normal,Flying
+725,Trumbeak,85,50,55,Alola,40,50,75,Normal,Flying
+726,Toucannon,120,75,80,Alola,75,75,60,Normal,Flying
+727,Yungoos,70,30,48,Alola,30,30,45,Normal,None
+728,Gumshoos,110,60,88,Alola,55,60,45,Normal,None
+729,Grubbin,62,45,47,Alola,55,45,46,Bug,None
+730,Charjabug,82,95,57,Alola,55,75,36,Bug,Electric
+731,Vikavolt,70,90,77,Alola,145,75,43,Bug,Electric
+732,Crabrawler,82,57,47,Alola,42,47,63,Fighting,None
+733,Crabominable,132,77,97,Alola,62,67,43,Fighting,Ice
+734,Oricorio,70,70,75,Alola,98,70,93,Fire,Flying
+735,Cutiefly,45,40,40,Alola,55,40,84,Bug,Fairy
+736,Ribombee,55,60,60,Alola,95,70,124,Bug,Fairy
+737,Rockruff,65,40,45,Alola,30,40,60,Rock,None
+738,Lycanroc,115,65,75,Alola,55,65,112,Rock,None
+739,Wishiwashi,20,20,45,Alola,25,25,40,Water,None
+740,Mareanie,53,62,50,Alola,43,52,45,Poison,Water
+741,Toxapex,63,152,50,Alola,53,142,35,Poison,Water
+742,Mudbray,100,70,70,Alola,45,55,45,Ground,None
+743,Mudsdale,125,100,100,Alola,55,85,35,Ground,None
+744,Dewpider,40,52,38,Alola,40,72,27,Water,Bug
+745,Araquanid,70,92,68,Alola,50,132,42,Water,Bug
+746,Fomantis,55,35,40,Alola,50,35,35,Grass,None
+747,Lurantis,105,90,70,Alola,80,90,45,Grass,None
+748,Morelull,35,55,40,Alola,65,75,15,Grass,Fairy
+749,Shiinotic,45,80,60,Alola,90,100,30,Grass,Fairy
+750,Salandit,44,40,48,Alola,71,40,77,Poison,Fire
+751,Salazzle,64,60,68,Alola,111,60,117,Poison,Fire
+752,Stufful,75,50,70,Alola,45,50,50,Normal,Fighting
+753,Bewear,125,80,120,Alola,55,60,60,Normal,Fighting
+754,Bounsweet,30,38,42,Alola,30,38,32,Grass,None
+755,Steenee,40,48,52,Alola,40,48,62,Grass,None
+756,Tsareena,120,98,72,Alola,50,98,72,Grass,None
+757,Comfey,52,90,51,Alola,82,110,100,Fairy,None
+758,Oranguru,60,80,90,Alola,90,110,60,Normal,Psychic
+759,Passimian,120,90,100,Alola,40,60,80,Fighting,None
+760,Wimpod,35,40,25,Alola,20,30,80,Bug,Water
+761,Golisopod,125,140,75,Alola,60,90,40,Bug,Water
+762,Sandygast,55,80,55,Alola,70,45,15,Ghost,Ground
+763,Palossand,75,110,85,Alola,100,75,35,Ghost,Ground
+764,Pyukumuku,60,130,55,Alola,30,130,5,Water,None
+765,Silvally,95,95,95,Alola,95,95,95,Normal,None
+766,Minior,60,100,60,Alola,60,100,60,Rock,Flying
+767,Komala,115,65,65,Alola,75,95,65,Normal,None
+768,Turtonator,78,135,60,Alola,91,85,36,Fire,Dragon
+769,Togedemaru,98,63,65,Alola,40,73,96,Electric,Steel
+770,Mimikyu,90,80,55,Alola,50,105,96,Ghost,Fairy
+771,Bruxish,105,70,68,Alola,70,70,92,Water,Psychic
+772,Drampa,60,85,78,Alola,135,91,36,Normal,Dragon
+773,Dhelmise,131,100,70,Alola,86,90,40,Ghost,Grass
+774,Jangmo-o,55,65,45,Alola,45,45,45,Dragon,None
+775,Hakamo-o,75,90,55,Alola,65,70,65,Dragon,Fighting
+776,Kommo-o,110,125,75,Alola,100,105,85,Dragon,Fighting
+777,Cosmog,29,31,43,Alola,29,31,37,Psychic,None
+778,Cosmoem,29,131,43,Alola,29,131,37,Psychic,None
+779,Solgaleo,137,107,137,Alola,113,89,97,Psychic,Steel
+780,Lunala,113,89,137,Alola,137,107,97,Psychic,Ghost
+781,Nihilego,53,47,109,Alola,127,131,103,Rock,Poison
+782,Buzzwole,139,139,107,Alola,53,53,79,Bug,Fighting
+783,Pheromosa,137,37,71,Alola,137,37,151,Bug,Fighting
+784,Xurkitree,89,71,83,Alola,173,71,83,Electric,None
+785,Celesteela,101,103,97,Alola,107,101,61,Steel,Flying
+786,Kartana,181,131,59,Alola,59,31,109,Grass,Steel
+787,Guzzlord,101,53,223,Alola,97,53,43,Dark,Dragon
+788,Necrozma,107,101,97,Alola,127,89,79,Psychic,None
+789,Magearna,95,115,80,Alola,130,115,65,Steel,Fairy
+790,Marshadow,125,80,90,Alola,90,90,125,Fighting,Ghost
+791,Poipole,73,67,67,Alola,73,67,73,Poison,None
+792,Naganadel,73,73,73,Alola,127,73,121,Poison,Dragon
+793,Stakataka,131,211,61,Alola,53,101,13,Rock,Steel
+794,Blacephalon,127,53,53,Alola,151,79,107,Fire,Ghost
+795,Zeraora,112,75,88,Alola,102,80,143,Electric,None
+796,Meltan,65,65,46,Alola,55,35,34,Steel,None
+797,Melmetal,143,143,135,Alola,80,65,34,Steel,None
+798,Grookey,65,50,50,Galar,40,40,65,Grass,None
+799,Thwackey,85,70,70,Galar,55,60,80,Grass,None
+800,Rillaboom,125,90,100,Galar,60,70,85,Grass,None
+801,Scorbunny,71,40,50,Galar,40,40,69,Fire,None
+802,Raboot,86,60,65,Galar,55,60,94,Fire,None
+803,Cinderace,116,75,80,Galar,65,75,119,Fire,None
+804,Sobble,40,40,50,Galar,70,40,70,Water,None
+805,Drizzile,60,55,65,Galar,95,55,90,Water,None
+806,Inteleon,85,65,70,Galar,125,65,120,Water,None
+807,Skwovet,55,55,70,Galar,35,35,25,Normal,None
+808,Greedent,95,95,120,Galar,55,75,20,Normal,None
+809,Rookidee,47,35,38,Galar,33,35,57,Flying,None
+810,Corvisquire,67,55,68,Galar,43,55,77,Flying,None
+811,Corviknight,87,105,98,Galar,53,85,67,Flying,Steel
+812,Blipbug,20,20,25,Galar,25,45,45,Bug,None
+813,Dottler,35,80,50,Galar,50,90,30,Bug,Psychic
+814,Orbeetle,45,110,60,Galar,80,120,90,Bug,Psychic
+815,Nickit,28,28,40,Galar,47,52,50,Dark,None
+816,Thievul,58,58,70,Galar,87,92,90,Dark,None
+817,Gossifleur,40,60,40,Galar,40,60,10,Grass,None
+818,Eldegoss,50,90,60,Galar,80,120,60,Grass,None
+819,Wooloo,40,55,42,Galar,40,45,48,Normal,None
+820,Dubwool,80,100,72,Galar,60,90,88,Normal,None
+821,Chewtle,64,50,50,Galar,38,38,44,Water,None
+822,Drednaw,115,90,90,Galar,48,68,74,Water,Rock
+823,Yamper,45,50,59,Galar,40,50,26,Electric,None
+824,Boltund,90,60,69,Galar,90,60,121,Electric,None
+825,Rolycoly,40,50,30,Galar,40,50,30,Rock,None
+826,Carkol,60,90,80,Galar,60,70,50,Rock,Fire
+827,Coalossal,80,120,110,Galar,80,90,30,Rock,Fire
+828,Applin,40,80,40,Galar,40,40,20,Grass,Dragon
+829,Flapple,110,80,70,Galar,95,60,70,Grass,Dragon
+830,Appletun,85,80,110,Galar,100,80,30,Grass,Dragon
+831,Silicobra,57,75,52,Galar,35,50,46,Ground,None
+832,Sandaconda,107,125,72,Galar,65,70,71,Ground,None
+833,Cramorant,85,55,70,Galar,85,95,85,Flying,Water
+834,Arrokuda,63,40,41,Galar,40,30,66,Water,None
+835,Barraskewda,123,60,61,Galar,60,50,136,Water,None
+836,Toxel,38,35,40,Galar,54,35,40,Electric,Poison
+837,Toxtricity,98,70,75,Galar,114,70,75,Electric,Poison
+838,Sizzlipede,65,45,50,Galar,50,50,45,Fire,Bug
+839,Centiskorch,115,65,100,Galar,90,90,65,Fire,Bug
+840,Clobbopus,68,60,50,Galar,50,50,32,Fighting,None
+841,Grapploct,118,90,80,Galar,70,80,42,Fighting,None
+842,Sinistea,45,45,40,Galar,74,54,50,Ghost,None
+843,Polteageist,65,65,60,Galar,134,114,70,Ghost,None
+844,Hatenna,30,45,42,Galar,56,53,39,Psychic,None
+845,Hattrem,40,65,57,Galar,86,73,49,Psychic,None
+846,Hatterene,90,95,57,Galar,136,103,29,Psychic,Fairy
+847,Impidimp,45,30,45,Galar,55,40,50,Dark,Fairy
+848,Morgrem,60,45,65,Galar,75,55,70,Dark,Fairy
+849,Grimmsnarl,120,65,95,Galar,95,75,60,Dark,Fairy
+850,Obstagoon,90,101,93,Galar,60,81,95,Dark,Normal
+851,Perrserker,110,100,70,Galar,50,60,50,Steel,None
+852,Cursola,95,50,60,Galar,145,130,30,Ghost,None
+853,Runerigus,95,145,58,Galar,50,105,30,Ground,Ghost
+854,Milcery,40,40,45,Galar,50,61,34,Fairy,None
+855,Alcremie,60,75,65,Galar,110,121,64,Fairy,None
+856,Falinks,100,100,65,Galar,70,60,75,Fighting,None
+857,Pincurchin,101,95,48,Galar,91,85,15,Electric,None
+858,Snom,25,35,30,Galar,45,30,20,Ice,Bug
+859,Frosmoth,65,60,70,Galar,125,90,65,Ice,Bug
+860,Stonjourner,125,135,100,Galar,20,20,70,Rock,None
+861,Eiscue,80,110,75,Galar,65,90,50,Ice,None
+862,Indeedee,65,55,60,Galar,105,95,95,Psychic,Normal
+863,Morpeko,95,58,58,Galar,70,58,97,Electric,Dark
+864,Cufant,80,49,72,Galar,40,49,40,Steel,None
+865,Copperajah,130,69,122,Galar,80,69,30,Steel,None
+866,Dracozolt,100,90,90,Galar,80,70,75,Electric,Dragon
+867,Arctozolt,100,90,90,Galar,90,80,55,Electric,Ice
+868,Dracovish,90,100,90,Galar,70,80,75,Water,Dragon
+869,Arctovish,90,100,90,Galar,80,90,55,Water,Ice
+870,Duraludon,95,115,70,Galar,120,50,85,Steel,Dragon
+871,Dreepy,60,30,28,Galar,40,30,82,Dragon,Ghost
+872,Drakloak,80,50,68,Galar,60,50,102,Dragon,Ghost
+873,Dragapult,120,75,88,Galar,100,75,142,Dragon,Ghost
+874,Zacian,170,115,92,Galar,80,115,148,Fairy,Steel
+875,Zamazenta,130,145,92,Galar,80,145,128,Fighting,Steel
+876,Eternatus,85,95,140,Galar,145,95,130,Poison,Dragon
+877,Kubfu,90,60,60,Galar,53,50,72,Fighting,None
+878,Urshifu,130,100,100,Galar,63,60,97,Fighting,Dark
+879,Zarude,120,105,105,Galar,70,95,105,Dark,Grass
+880,Regieleki,100,50,80,Galar,100,50,200,Electric,None
+881,Regidrago,100,50,200,Galar,100,50,80,Dragon,None
+882,Glastrier,145,130,100,Galar,65,110,30,Ice,None
+883,Spectrier,65,60,100,Galar,145,80,130,Ghost,None
+884,Calyrex,80,80,100,Galar,80,80,80,Psychic,Grass

f23/Cole_Lecture_Notes/28_Pandas2/IMDB-Movie-Data.csv

+Index,Title,Genre,Director,Cast,Year,Runtime,Rating,Revenue
+0,Guardians of the Galaxy,"Action,Adventure,Sci-Fi",James Gunn,"Chris Pratt, Vin Diesel, Bradley Cooper, Zoe Saldana",2014,121,8.1,333.13
+1,Prometheus,"Adventure,Mystery,Sci-Fi",Ridley Scott,"Noomi Rapace, Logan Marshall-Green, Michael         fassbender, Charlize Theron",2012,124,7,126.46M
+2,Split,"Horror,Thriller",M. Night Shyamalan,"James McAvoy, Anya Taylor-Joy, Haley Lu Richardson, Jessica Sula",2016,117,7.3,138.12M
+3,Sing,"Animation,Comedy,Family",Christophe Lourdelet,"Matthew McConaughey,Reese Witherspoon, Seth MacFarlane, Scarlett Johansson",2016,108,7.2,270.32
+4,Suicide Squad,"Action,Adventure,Fantasy",David Ayer,"Will Smith, Jared Leto, Margot Robbie, Viola Davis",2016,123,6.2,325.02
+5,The Great Wall,"Action,Adventure,Fantasy",Yimou Zhang,"Matt Damon, Tian Jing, Willem Dafoe, Andy Lau",2016,103,6.1,45.13
+6,La La Land,"Comedy,Drama,Music",Damien Chazelle,"Ryan Gosling, Emma Stone, Rosemarie DeWitt, J.K. Simmons",2016,128,8.3,151.06M
+7,Mindhorn,Comedy,Sean Foley,"Essie Davis, Andrea Riseborough, Julian Barratt,Kenneth Branagh",2016,89,6.4,0
+8,The Lost City of Z,"Action,Adventure,Biography",James Gray,"Charlie Hunnam, Robert Pattinson, Sienna Miller, Tom Holland",2016,141,7.1,8.01
+9,Passengers,"Adventure,Drama,Romance",Morten Tyldum,"Jennifer Lawrence, Chris Pratt, Michael Sheen,Laurence Fishburne",2016,116,7,100.01M
+10,Fantastic Beasts and Where to Find Them,"Adventure,Family,Fantasy",David Yates,"Eddie Redmayne, Katherine Waterston, Alison Sudol,Dan Fogler",2016,133,7.5,234.02
+11,Hidden Figures,"Biography,Drama,History",Theodore Melfi,"Taraji P. Henson, Octavia Spencer, Janelle Monáe,Kevin Costner",2016,127,7.8,169.27M
+12,Rogue One,"Action,Adventure,Sci-Fi",Gareth Edwards,"Felicity Jones, Diego Luna, Alan Tudyk, Donnie Yen",2016,133,7.9,532.17
+13,Moana,"Animation,Adventure,Comedy",Ron Clements,"Auli'i Cravalho, Dwayne Johnson, Rachel House, Temuera Morrison",2016,107,7.7,248.75
+14,Colossal,"Action,Comedy,Drama",Nacho Vigalondo,"Anne Hathaway, Jason Sudeikis, Austin Stowell,Tim Blake Nelson",2016,109,6.4,2.87
+15,The Secret Life of Pets,"Animation,Adventure,Comedy",Chris Renaud,"Louis C.K., Eric Stonestreet, Kevin Hart, Lake Bell",2016,87,6.6,368.31
+16,Hacksaw Ridge,"Biography,Drama,History",Mel Gibson,"Andrew Garfield, Sam Worthington, Luke Bracey,Teresa Palmer",2016,139,8.2,67.12
+17,Jason Bourne,"Action,Thriller",Paul Greengrass,"Matt Damon, Tommy Lee Jones, Alicia Vikander,Vincent Cassel",2016,123,6.7,162.16M
+18,Lion,"Biography,Drama",Garth Davis,"Dev Patel, Nicole Kidman, Rooney Mara, Sunny Pawar",2016,118,8.1,51.69
+19,Arrival,"Drama,Mystery,Sci-Fi",Denis Villeneuve,"Amy Adams, Jeremy Renner, Forest Whitaker,Michael Stuhlbarg",2016,116,8,100.5M
+20,Gold,"Adventure,Drama,Thriller",Stephen Gaghan,"Matthew McConaughey, Edgar Ramírez, Bryce Dallas Howard, Corey Stoll",2016,120,6.7,7.22
+21,Manchester by the Sea,Drama,Kenneth Lonergan,"Casey Affleck, Michelle Williams, Kyle Chandler,Lucas Hedges",2016,137,7.9,47.7
+22,Hounds of Love,"Crime,Drama,Horror",Ben Young,"Emma Booth, Ashleigh Cummings, Stephen Curry,Susie Porter",2016,108,6.7,0
+23,Trolls,"Animation,Adventure,Comedy",Walt Dohrn,"Anna Kendrick, Justin Timberlake,Zooey Deschanel, Christopher Mintz-Plasse",2016,92,6.5,153.69M
+24,Independence Day: Resurgence,"Action,Adventure,Sci-Fi",Roland Emmerich,"Liam Hemsworth, Jeff Goldblum, Bill Pullman,Maika Monroe",2016,120,5.3,103.14M
+25,Paris pieds nus,Comedy,Dominique Abel,"Fiona Gordon, Dominique Abel,Emmanuelle Riva, Pierre Richard",2016,83,6.8,0
+26,Bahubali: The Beginning,"Action,Adventure,Drama",S.S. Rajamouli,"Prabhas, Rana Daggubati, Anushka Shetty,Tamannaah Bhatia",2015,159,8.3,6.5
+27,Dead Awake,"Horror,Thriller",Phillip Guzman,"Jocelin Donahue, Jesse Bradford, Jesse Borrego,Lori Petty",2016,99,4.7,0.01
+28,Bad Moms,Comedy,Jon Lucas,"Mila Kunis, Kathryn Hahn, Kristen Bell,Christina Applegate",2016,100,6.2,113.08M
+29,Assassin's Creed,"Action,Adventure,Drama",Justin Kurzel,"Michael      Fassbender, Marion Cotillard, Jeremy Irons,Brendan Gleeson",2016,115,5.9,54.65
+30,Why Him?,Comedy,John Hamburg,"Zoey Deutch, James Franco, Tangie Ambrose,Cedric the Entertainer",2016,111,6.3,60.31
+31,Nocturnal Animals,"Drama,Thriller",Tom Ford,"Amy Adams, Jake Gyllenhaal, Michael Shannon, Aaron Taylor-Johnson",2016,116,7.5,10.64
+32,X-Men: Apocalypse,"Action,Adventure,Sci-Fi",Bryan Singer,"James McAvoy, Michael Fassbender, Jennifer Lawrence, Nicholas Hoult",2016,144,7.1,155.33M
+33,Deadpool,"Action,Adventure,Comedy",Tim Miller,"Ryan Reynolds, Morena Baccarin, T.J. Miller, Ed Skrein",2016,108,8,363.02
+34,Resident Evil: The Final Chapter,"Action,Horror,Sci-Fi",Paul W.S. Anderson,"Milla Jovovich, Iain Glen, Ali Larter, Shawn Roberts",2016,107,5.6,26.84
+35,Captain America: Civil War,"Action,Adventure,Sci-Fi",Anthony Russo,"Chris Evans, Robert Downey Jr.,Scarlett Johansson, Sebastian Stan",2016,147,7.9,408.08
+36,Interstellar,"Adventure,Drama,Sci-Fi",Christopher Nolan,"Matthew McConaughey, Anne Hathaway, Jessica Chastain, Mackenzie Foy",2014,169,8.6,187.99M
+37,Doctor Strange,"Action,Adventure,Fantasy",Scott Derrickson,"Benedict Cumberbatch, Chiwetel Ejiofor, Rachel McAdams, Benedict Wong",2016,115,7.6,232.6
+38,The Magnificent Seven,"Action,Adventure,Western",Antoine Fuqua,"Denzel      washington, Chris Pratt, Ethan Hawke,Vincent D'Onofrio",2016,132,6.9,93.38
+39,5/25/1977,"Comedy,Drama",Patrick Read Johnson,"John Francis Daley, Austin Pendleton, Colleen Camp, Neil Flynn",2007,113,7.1,0
+40,Sausage Party,"Animation,Adventure,Comedy",Greg Tiernan,"Seth Rogen, Kristen Wiig, Jonah Hill, Alistair Abell",2016,89,6.3,97.66
+41,Moonlight,Drama,Barry Jenkins,"Mahershala Ali, Shariff Earp, Duan Sanderson, Alex R. Hibbert",2016,111,7.5,27.85
+42,Don't Fuck in the Woods,Horror,Shawn Burkett,"Brittany Blanton, Ayse Howard, Roman Jossart,Nadia White",2016,73,2.7,0
+43,The Founder,"Biography,Drama,History",John Lee Hancock,"Michael Keaton, Nick Offerman, John Carroll Lynch, Linda Cardellini",2016,115,7.2,12.79
+44,Lowriders,Drama,Ricardo de Montreuil,"Gabriel Chavarria, Demián Bichir, Theo Rossi,Tony Revolori",2016,99,6.3,4.21
+45,Pirates of the Caribbean: On Stranger Tides,"Action,Adventure,Fantasy",Rob Marshall,"Johnny Depp, Penélope Cruz, Ian McShane, Geoffrey Rush",2011,136,6.7,241.06
+46,Miss Sloane,"Drama,Thriller",John Madden,"Jessica Chastain, Mark Strong, Gugu Mbatha-Raw,Michael Stuhlbarg",2016,132,7.3,3.44
+47,Fallen,"Adventure,Drama,Fantasy",Scott Hicks,"Hermione Corfield, Addison Timlin, Joely Richardson,Jeremy Irvine",2016,91,5.6,0
+48,Star Trek Beyond,"Action,Adventure,Sci-Fi",Justin Lin,"Chris Pine, Zachary Quinto, Karl Urban, Zoe Saldana",2016,122,7.1,158.8M
+49,The Last Face,Drama,Sean Penn,"Charlize Theron, Javier Bardem, Adèle Exarchopoulos,Jared Harris",2016,130,3.7,0
+50,Star Wars: Episode VII - The Force Awakens,"Action,Adventure,Fantasy",J.J. Abrams,"Daisy Ridley, John Boyega, Oscar Isaac, Domhnall Gleeson",2015,136,8.1,936.63
+51,Underworld: Blood Wars,"Action,Adventure,Fantasy",Anna Foerster,"Kate Beckinsale, Theo James, Tobias Menzies, Lara Pulver",2016,91,5.8,30.35
+52,Mother's Day,"Comedy,Drama",Garry Marshall,"Jennifer Aniston, Kate Hudson, Julia Roberts, Jason Sudeikis",2016,118,5.6,32.46
+53,John Wick,"Action,Crime,Thriller",Chad Stahelski,"Keanu Reeves, Michael Nyqvist, Alfie Allen, Willem Dafoe",2014,101,7.2,43
+54,The Dark Knight,"Action,Crime,Drama",Christopher Nolan,"Christian Bale, Heath Ledger, Aaron Eckhart,Michael Caine",2008,152,9,533.32
+55,Silence,"Adventure,Drama,History",Martin Scorsese,"Andrew Garfield, Adam Driver, Liam Neeson,Tadanobu Asano",2016,161,7.3,7.08
+56,Don't Breathe,"Crime,Horror,Thriller",Fede Alvarez,"Stephen Lang, Jane Levy, Dylan Minnette, Daniel Zovatto",2016,88,7.2,89.21
+57,Me Before You,"Drama,Romance",Thea Sharrock,"Emilia Clarke, Sam Claflin, Janet McTeer, Charles Dance",2016,106,7.4,56.23
+58,Their Finest,"Comedy,Drama,Romance",Lone Scherfig,"Gemma Arterton, Sam Claflin, Bill Nighy, Jack Huston",2016,117,7,3.18
+59,Sully,"Biography,Drama",Clint Eastwood,"Tom Hanks, Aaron Eckhart, Laura Linney, Valerie Mahaffey",2016,96,7.5,125.07M
+60,Batman v Superman: Dawn of Justice,"Action,Adventure,Sci-Fi",Zack Snyder,"Ben Affleck, Henry Cavill, Amy Adams, Jesse Eisenberg",2016,151,6.7,330.25
+61,The Autopsy of Jane Doe,"Horror,Mystery,Thriller",André Øvredal,"Brian Cox, Emile Hirsch, Ophelia Lovibond, Michael McElhatton",2016,86,6.8,0
+62,The Girl on the Train,"Crime,Drama,Mystery",Tate Taylor,"Emily Blunt, Haley Bennett, Rebecca Ferguson, Justin Theroux",2016,112,6.5,75.31
+63,Fifty Shades of Grey,"Drama,Romance,Thriller",Sam Taylor-Johnson,"Dakota Johnson, Jamie Dornan, Jennifer Ehle,Eloise Mumford",2015,125,4.1,166.15M
+64,The Prestige,"Drama,Mystery,Sci-Fi",Christopher Nolan,"Christian Bale, Hugh Jackman, Scarlett Johansson, Michael Caine",2006,130,8.5,53.08
+65,Kingsman: The Secret Service,"Action,Adventure,Comedy",Matthew Vaughn,"Colin Firth, Taron Egerton, Samuel L. Jackson,Michael Caine",2014,129,7.7,128.25M
+66,Patriots Day,"Drama,History,Thriller",Peter Berg,"Mark Wahlberg, Michelle Monaghan, J.K. Simmons, John Goodman",2016,133,7.4,31.86
+67,Mad Max: Fury Road,"Action,Adventure,Sci-Fi",George Miller,"Tom Hardy, Charlize Theron, Nicholas Hoult, Zoë Kravitz",2015,120,8.1,153.63M
+68,Wakefield,Drama,Robin Swicord,"Bryan Cranston, Jennifer Garner, Beverly D'Angelo,Jason O'Mara",2016,106,7.5,0.01
+69,Deepwater Horizon,"Action,Drama,Thriller",Peter Berg,"Mark Wahlberg, Kurt Russell, Douglas M. Griffin, James DuMont",2016,107,7.2,61.28
+70,The Promise,"Drama,History",Terry George,"Oscar Isaac, Charlotte Le Bon, Christian Bale, Daniel Giménez Cacho",2016,133,5.9,0
+71,Allied,"Action,Drama,Romance",Robert Zemeckis,"Brad Pitt, Marion Cotillard, Jared Harris, Vincent Ebrahim",2016,124,7.1,40.07
+72,A Monster Calls,"Drama,Fantasy",J.A. Bayona,"Lewis MacDougall, Sigourney Weaver, Felicity Jones,Toby Kebbell",2016,108,7.5,3.73
+73,Collateral Beauty,"Drama,Romance",David Frankel,"Will Smith, Edward Norton, Kate Winslet, Michael Peña",2016,97,6.8,30.98
+74,Zootopia,"Animation,Adventure,Comedy",Byron Howard,"Ginnifer Goodwin, Jason Bateman, Idris Elba, Jenny Slate",2016,108,8.1,341.26
+75,Pirates of the Caribbean: At World's End,"Action,Adventure,Fantasy",Gore Verbinski,"Johnny Depp, Orlando Bloom, Keira Knightley,Geoffrey Rush",2007,169,7.1,309.4
+76,The Avengers,"Action,Sci-Fi",Joss Whedon,"Robert Downey Jr., Chris Evans, Scarlett Johansson,Jeremy Renner",2012,143,8.1,623.28
+77,Inglourious Basterds,"Adventure,Drama,War",Quentin Tarantino,"Brad Pitt, Diane Kruger, Eli Roth,Mélanie Laurent",2009,153,8.3,120.52M
+78,Pirates of the Caribbean: Dead Man's Chest,"Action,Adventure,Fantasy",Gore Verbinski,"Johnny Depp, Orlando Bloom, Keira Knightley, Jack Davenport",2006,151,7.3,423.03
+79,Ghostbusters,"Action,Comedy,Fantasy",Paul Feig,"Melissa McCarthy, Kristen Wiig, Kate McKinnon, Leslie Jones",2016,116,5.3,128.34M
+80,Inception,"Action,Adventure,Sci-Fi",Christopher Nolan,"Leonardo DiCaprio, Joseph Gordon-Levitt, Ellen Page, Ken Watanabe",2010,148,8.8,292.57
+81,Captain Fantastic,"Comedy,Drama",Matt Ross,"Viggo Mortensen, George MacKay, Samantha Isler,Annalise Basso",2016,118,7.9,5.88
+82,The Wolf of Wall Street,"Biography,Comedy,Crime",Martin Scorsese,"Leonardo DiCaprio, Jonah Hill, Margot Robbie,Matthew McConaughey",2013,180,8.2,116.87M
+83,Gone Girl,"Crime,Drama,Mystery",David Fincher,"Ben Affleck, Rosamund Pike, Neil Patrick Harris,Tyler Perry",2014,149,8.1,167.74M
+84,Furious Seven,"Action,Crime,Thriller",James Wan,"Vin Diesel, Paul Walker, Dwayne Johnson, Jason Statham",2015,137,7.2,350.03
+85,Jurassic World,"Action,Adventure,Sci-Fi",Colin Trevorrow,"Chris Pratt, Bryce Dallas Howard, Ty Simpkins,Judy Greer",2015,124,7,652.18
+86,Live by Night,"Crime,Drama",Ben Affleck,"Ben Affleck, Elle Fanning, Brendan Gleeson, Chris Messina",2016,129,6.4,10.38
+87,Avatar,"Action,Adventure,Fantasy",James Cameron,"Sam Worthington, Zoe Saldana, Sigourney Weaver, Michelle Rodriguez",2009,162,7.8,760.51
+88,The Hateful Eight,"Crime,Drama,Mystery",Quentin Tarantino,"Samuel L. Jackson, Kurt Russell, Jennifer Jason Leigh, Walton Goggins",2015,187,7.8,54.12
+89,The Accountant,"Action,Crime,Drama",Gavin O'Connor,"Ben Affleck, Anna Kendrick, J.K. Simmons, Jon Bernthal",2016,128,7.4,86.2
+90,Prisoners,"Crime,Drama,Mystery",Denis Villeneuve,"Hugh Jackman, Jake Gyllenhaal, Viola Davis,Melissa Leo",2013,153,8.1,60.96
+91,Warcraft,"Action,Adventure,Fantasy",Duncan Jones,"Travis Fimmel, Paula Patton, Ben Foster, Dominic Cooper",2016,123,7,47.17
+92,The Help,Drama,Tate Taylor,"Emma Stone, Viola Davis, Octavia Spencer, Bryce Dallas Howard",2011,146,8.1,169.71M
+93,War Dogs,"Comedy,Crime,Drama",Todd Phillips,"Jonah Hill, Miles Teller, Steve Lantz, Gregg Weiner",2016,114,7.1,43.02
+94,Avengers: Age of Ultron,"Action,Adventure,Sci-Fi",Joss Whedon,"Robert Downey Jr., Chris Evans, Mark Ruffalo, Chris Hemsworth",2015,141,7.4,458.99
+95,The Nice Guys,"Action,Comedy,Crime",Shane Black,"Russell Crowe, Ryan Gosling, Angourie Rice, Matt Bomer",2016,116,7.4,36.25
+96,Kimi no na wa,"Animation,Drama,Fantasy",Makoto Shinkai,"Ryûnosuke Kamiki, Mone Kamishiraishi, Ryô Narita, Aoi Yuki",2016,106,8.6,4.68
+97,The Void,"Horror,Mystery,Sci-Fi",Jeremy Gillespie,"Aaron Poole, Kenneth Welsh,Daniel Fathers, Kathleen Munroe",2016,90,5.8,0.15
+98,Personal Shopper,"Drama,Mystery,Thriller",Olivier Assayas,"Kristen Stewart, Lars Eidinger, Sigrid Bouaziz,Anders Danielsen Lie",2016,105,6.3,1.29
+99,The Departed,"Crime,Drama,Thriller",Martin Scorsese,"Leonardo DiCaprio, Matt Damon, Jack Nicholson, Mark Wahlberg",2006,151,8.5,132.37M
+100,Legend,"Biography,Crime,Drama",Brian Helgeland,"Tom Hardy, Emily Browning, Taron Egerton, Paul Anderson",2015,132,7,1.87
+101,Thor,"Action,Adventure,Fantasy",Kenneth Branagh,"Chris Hemsworth, Anthony Hopkins, Natalie Portman, Tom Hiddleston",2011,115,7,181.02M
+102,The Martian,"Adventure,Drama,Sci-Fi",Ridley Scott,"Matt Damon, Jessica Chastain, Kristen Wiig, Kate Mara",2015,144,8,228.43
+103,Contratiempo,"Crime,Mystery,Thriller",Oriol Paulo,"Mario Casas, Ana Wagener, José Coronado, Bárbara Lennie",2016,106,7.9,0
+104,The Man from U.N.C.L.E.,"Action,Adventure,Comedy",Guy Ritchie,"Henry Cavill, Armie Hammer, Alicia Vikander, Elizabeth Debicki",2015,116,7.3,45.43
+105,Hell or High Water,"Crime,Drama,Thriller",David Mackenzie,"Chris Pine, Ben Foster, Jeff Bridges, Gil Birmingham",2016,102,7.7,26.86
+106,The Comedian,Comedy,Taylor Hackford,"Robert De Niro, Leslie Mann, Danny DeVito, Edie Falco",2016,120,5.4,1.66
+107,The Legend of Tarzan,"Action,Adventure,Drama",David Yates,"Alexander Skarsgård, Rory J. Saper, Christian Stevens, Christoph Waltz",2016,110,6.3,126.59M
+108,All We Had,Drama,Katie Holmes,"Eve Lindley, Richard Kind, Mark Consuelos, Katherine Reis",2016,105,5.8,0
+109,Ex Machina,"Drama,Mystery,Sci-Fi",Alex Garland,"Alicia Vikander, Domhnall Gleeson, Oscar Isaac,Sonoya Mizuno",2014,108,7.7,25.44
+110,The Belko Experiment,"Action,Horror,Thriller",Greg McLean,"John Gallagher Jr., Tony Goldwyn, Adria Arjona, John C. McGinley",2016,89,6.3,10.16
+111,12 Years a Slave,"Biography,Drama,History",Steve McQueen,"Chiwetel Ejiofor, Michael Kenneth Williams, Michael Fassbender, Brad Pitt",2013,134,8.1,56.67
+112,The Bad Batch,"Romance,Sci-Fi",Ana Lily Amirpour,"Keanu Reeves, Jason Momoa, Jim Carrey, Diego Luna",2016,118,6.1,0
+113,300,"Action,Fantasy,War",Zack Snyder,"Gerard Butler, Lena Headey, David Wenham, Dominic West",2006,117,7.7,210.59
+114,Harry Potter and the Deathly Hallows: Part 2,"Adventure,Drama,Fantasy",David Yates,"Daniel Radcliffe, Emma Watson, Rupert Grint, Michael Gambon",2011,130,8.1,380.96
+115,Office Christmas Party,Comedy,Josh Gordon,"Jason Bateman, Olivia Munn, T.J. Miller,Jennifer Aniston",2016,105,5.8,54.73
+116,The Neon Demon,"Horror,Thriller",Nicolas Winding Refn,"Elle Fanning, Christina Hendricks, Keanu Reeves, Karl Glusman",2016,118,6.2,1.33
+117,Dangal,"Action,Biography,Drama",Nitesh Tiwari,"Aamir Khan, Sakshi Tanwar, Fatima Sana Shaikh,Sanya Malhotra",2016,161,8.8,11.15
+118,10 Cloverfield Lane,"Drama,Horror,Mystery",Dan Trachtenberg,"John Goodman, Mary Elizabeth Winstead, John Gallagher Jr., Douglas M. Griffin",2016,104,7.2,71.9
+119,Finding Dory,"Animation,Adventure,Comedy",Andrew Stanton,"Ellen DeGeneres, Albert Brooks,Ed O'Neill, Kaitlin Olson",2016,97,7.4,486.29
+120,Miss Peregrine's Home for Peculiar Children,"Adventure,Drama,Family",Tim Burton,"Eva Green, Asa Butterfield, Samuel L. Jackson, Judi Dench",2016,127,6.7,87.24
+121,Divergent,"Adventure,Mystery,Sci-Fi",Neil Burger,"Shailene Woodley, Theo James, Kate Winslet, Jai Courtney",2014,139,6.7,150.83M
+122,Mike and Dave Need Wedding Dates,"Adventure,Comedy,Romance",Jake Szymanski,"Zac Efron, Adam Devine, Anna Kendrick, Aubrey Plaza",2016,98,6,46.01
+123,Boyka: Undisputed IV,Action,Todor Chapkanov,"Scott Adkins, Teodora Duhovnikova, Alon Aboutboul, Julian Vergov",2016,86,7.4,0
+124,The Dark Knight Rises,"Action,Thriller",Christopher Nolan,"Christian Bale, Tom Hardy, Anne Hathaway,Gary Oldman",2012,164,8.5,448.13
+125,The Jungle Book,"Adventure,Drama,Family",Jon Favreau,"Neel Sethi, Bill Murray, Ben Kingsley, Idris Elba",2016,106,7.5,364
+126,Transformers: Age of Extinction,"Action,Adventure,Sci-Fi",Michael Bay,"Mark Wahlberg, Nicola Peltz, Jack Reynor, Stanley Tucci",2014,165,5.7,245.43
+127,Nerve,"Adventure,Crime,Mystery",Henry Joost,"Emma Roberts, Dave Franco, Emily Meade, Miles Heizer",2016,96,6.6,38.56
+128,Mamma Mia!,"Comedy,Family,Musical",Phyllida Lloyd,"Meryl Streep, Pierce Brosnan, Amanda Seyfried,Stellan Skarsgård",2008,108,6.4,143.7M
+129,The Revenant,"Adventure,Drama,Thriller",Alejandro González Iñárritu,"Leonardo DiCaprio, Tom Hardy, Will Poulter, Domhnall Gleeson",2015,156,8,183.64M
+130,Fences,Drama,Denzel Washington,"Denzel  Washington, Viola Davis, Stephen Henderson, Jovan Adepo",2016,139,7.3,57.64
+131,Into the Woods,"Adventure,Comedy,Drama",Rob Marshall,"Anna Kendrick, Meryl Streep, Chris Pine, Emily Blunt",2014,125,6,128M
+132,The Shallows,"Drama,Horror,Thriller",Jaume Collet-Serra,"Blake Lively, Óscar Jaenada, Angelo Josue Lozano Corzo, Brett Cullen",2016,86,6.4,55.12
+133,Whiplash,"Drama,Music",Damien Chazelle,"Miles Teller, J.K. Simmons, Melissa Benoist, Paul Reiser",2014,107,8.5,13.09
+134,Furious 6,"Action,Crime,Thriller",Justin Lin,"Vin Diesel, Paul Walker, Dwayne Johnson, Michelle Rodriguez",2013,130,7.1,238.67
+135,The Place Beyond the Pines,"Crime,Drama,Thriller",Derek Cianfrance,"Ryan Gosling, Bradley Cooper, Eva Mendes,Craig Van Hook",2012,140,7.3,21.38
+136,No Country for Old Men,"Crime,Drama,Thriller",Ethan Coen,"Tommy Lee Jones, Javier Bardem, Josh Brolin, Woody Harrelson",2007,122,8.1,74.27
+137,The Great Gatsby,"Drama,Romance",Baz Luhrmann,"Leonardo DiCaprio, Carey Mulligan, Joel Edgerton,Tobey Maguire",2013,143,7.3,144.81M
+138,Shutter Island,"Mystery,Thriller",Martin Scorsese,"Leonardo DiCaprio, Emily Mortimer, Mark Ruffalo,Ben Kingsley",2010,138,8.1,127.97M
+139,Brimstone,"Mystery,Thriller,Western",Martin Koolhoven,"Dakota Fanning, Guy Pearce, Kit Harington,Carice van Houten",2016,148,7.1,0
+140,Star Trek,"Action,Adventure,Sci-Fi",J.J. Abrams,"Chris Pine, Zachary Quinto, Simon Pegg, Leonard Nimoy",2009,127,8,257.7
+141,Diary of a Wimpy Kid,"Comedy,Family",Thor Freudenthal,"Zachary Gordon, Robert Capron, Rachael Harris,Steve Zahn",2010,94,6.2,64
+142,The Big Short,"Biography,Comedy,Drama",Adam McKay,"Christian Bale, Steve Carell, Ryan Gosling, Brad Pitt",2015,130,7.8,70.24
+143,Room,Drama,Lenny Abrahamson,"Brie Larson, Jacob Tremblay, Sean Bridgers,Wendy Crewson",2015,118,8.2,14.68
+144,Django Unchained,"Drama,Western",Quentin Tarantino,"Jamie Foxx, Christoph Waltz, Leonardo DiCaprio,Kerry Washington",2012,165,8.4,162.8M
+145,Ah-ga-ssi,"Drama,Mystery,Romance",Chan-wook Park,"Min-hee Kim, Jung-woo Ha, Jin-woong Jo, So-ri Moon",2016,144,8.1,2.01
+146,The Edge of Seventeen,"Comedy,Drama",Kelly Fremon Craig,"Hailee Steinfeld, Haley Lu Richardson, Blake Jenner, Kyra Sedgwick",2016,104,7.4,14.26
+147,Watchmen,"Action,Drama,Mystery",Zack Snyder,"Jackie Earle Haley, Patrick Wilson, Carla Gugino,Malin Akerman",2009,162,7.6,107.5M
+148,Superbad,Comedy,Greg Mottola,"Michael Cera, Jonah Hill, Christopher Mintz-Plasse, Bill Hader",2007,113,7.6,121.46M
+149,Inferno,"Action,Adventure,Crime",Ron Howard,"Tom Hanks, Felicity Jones, Irrfan Khan, Ben Foster",2016,121,6.2,34.26
+150,The BFG,"Adventure,Family,Fantasy",Steven Spielberg,"Mark Rylance, Ruby Barnhill, Penelope Wilton,Jemaine Clement",2016,117,6.4,55.47
+151,The Hunger Games,"Adventure,Sci-Fi,Thriller",Gary Ross,"Jennifer Lawrence, Josh Hutcherson, Liam Hemsworth,Stanley Tucci",2012,142,7.2,408
+152,White Girl,Drama,Elizabeth Wood,"Morgan Saylor, Brian Marc, Justin Bartha, Adrian Martinez",2016,88,5.8,0.2
+153,Sicario,"Action,Crime,Drama",Denis Villeneuve,"Emily Blunt, Josh Brolin, Benicio Del Toro, Jon Bernthal",2015,121,7.6,46.88
+154,Twin Peaks: The Missing Pieces,"Drama,Horror,Mystery",David Lynch,"Chris Isaak, Kiefer Sutherland, C.H. Evans, Sandra Kinder",2014,91,8.1,0
+155,Aliens vs Predator - Requiem,"Action,Horror,Sci-Fi",Colin Strause,"Reiko Aylesworth, Steven Pasquale,Shareeka Epps, John Ortiz",2007,94,4.7,41.8
+156,Pacific Rim,"Action,Adventure,Sci-Fi",Guillermo del Toro,"Idris Elba, Charlie Hunnam, Rinko Kikuchi,Charlie Day",2013,131,7,101.79M
+157,"Crazy, Stupid, Love.","Comedy,Drama,Romance",Glenn Ficarra,"Steve Carell, Ryan Gosling, Julianne Moore, Emma Stone",2011,118,7.4,84.24
+158,Scott Pilgrim vs. the World,"Action,Comedy,Fantasy",Edgar Wright,"Michael Cera, Mary Elizabeth Winstead, Kieran Culkin, Alison Pill",2010,112,7.5,31.49
+159,Hot Fuzz,"Action,Comedy,Mystery",Edgar Wright,"Simon Pegg, Nick Frost, Martin Freeman, Bill Nighy",2007,121,7.9,23.62
+160,Mine,"Thriller,War",Fabio Guaglione,"Armie Hammer, Annabelle Wallis,Tom Cullen, Clint Dyer",2016,106,6,0
+161,Free Fire,"Action,Comedy,Crime",Ben Wheatley,"Sharlto Copley, Brie Larson, Armie Hammer, Cillian Murphy",2016,90,7,1.8
+162,X-Men: Days of Future Past,"Action,Adventure,Sci-Fi",Bryan Singer,"Patrick Stewart, Ian McKellen, Hugh Jackman, James McAvoy",2014,132,8,233.91
+163,Jack Reacher: Never Go Back,"Action,Adventure,Crime",Edward Zwick,"Tom Cruise, Cobie Smulders, Aldis Hodge, Robert Knepper",2016,118,6.1,58.4
+164,Casino Royale,"Action,Adventure,Thriller",Martin Campbell,"Daniel Craig, Eva Green, Judi Dench, Jeffrey Wright",2006,144,8,167.01M
+165,Twilight,"Drama,Fantasy,Romance",Catherine Hardwicke,"Kristen Stewart, Robert Pattinson, Billy Burke,Sarah Clarke",2008,122,5.2,191.45M
+166,Now You See Me 2,"Action,Adventure,Comedy",Jon M. Chu,"Jesse Eisenberg, Mark Ruffalo, Woody Harrelson, Dave Franco",2016,129,6.5,65.03
+167,Woman in Gold,"Biography,Drama,History",Simon Curtis,"Helen Mirren, Ryan Reynolds, Daniel Brühl, Katie Holmes",2015,109,7.3,33.31
+168,13 Hours,"Action,Drama,History",Michael Bay,"John Krasinski, Pablo Schreiber, James Badge Dale,David Denman",2016,144,7.3,52.82
+169,Spectre,"Action,Adventure,Thriller",Sam Mendes,"Daniel Craig, Christoph Waltz, Léa Seydoux, Ralph Fiennes",2015,148,6.8,200.07
+170,Nightcrawler,"Crime,Drama,Thriller",Dan Gilroy,"Jake Gyllenhaal, Rene Russo, Bill Paxton, Riz Ahmed",2014,118,7.9,32.28
+171,Kubo and the Two Strings,"Animation,Adventure,Family",Travis Knight,"Charlize Theron, Art Parkinson, Matthew McConaughey, Ralph Fiennes",2016,101,7.9,48.02
+172,Beyond the Gates,"Adventure,Horror",Jackson Stewart,"Graham Skipper, Chase Williamson, Brea Grant,Barbara Crampton",2016,84,5.2,0
+173,Her,"Drama,Romance,Sci-Fi",Spike Jonze,"Joaquin Phoenix, Amy Adams, Scarlett Johansson,Rooney Mara",2013,126,8,25.56
+174,Frozen,"Animation,Adventure,Comedy",Chris Buck,"Kristen Bell, Idina Menzel, Jonathan Groff, Josh Gad",2013,102,7.5,400.74
+175,Tomorrowland,"Action,Adventure,Family",Brad Bird,"George Clooney, Britt Robertson, Hugh Laurie, Raffey Cassidy",2015,130,6.5,93.42
+176,Dawn of the Planet of the Apes,"Action,Adventure,Drama",Matt Reeves,"Gary Oldman, Keri Russell, Andy Serkis, Kodi Smit-McPhee",2014,130,7.6,208.54
+177,Tropic Thunder,"Action,Comedy",Ben Stiller,"Ben Stiller, Jack Black, Robert Downey Jr., Jeff Kahn",2008,107,7,110.42M
+178,The Conjuring 2,"Horror,Mystery,Thriller",James Wan,"Vera Farmiga, Patrick Wilson, Madison Wolfe, Frances O'Connor",2016,134,7.4,102.46M
+179,Ant-Man,"Action,Adventure,Comedy",Peyton Reed,"Paul Rudd, Michael Douglas, Corey Stoll, Evangeline Lilly",2015,117,7.3,180.19M
+180,Bridget Jones's Baby,"Comedy,Romance",Sharon Maguire,"Renée Zellweger, Gemma Jones, Jim Broadbent,Sally Phillips",2016,118,6.7,24.09
+181,The VVitch: A New-England Folktale,"Horror,Mystery",Robert Eggers,"Anya Taylor-Joy, Ralph Ineson, Kate Dickie, Julian Richings",2015,92,6.8,25.14
+182,Cinderella,"Drama,Family,Fantasy",Kenneth Branagh,"Lily James, Cate Blanchett, Richard Madden,Helena Bonham Carter",2015,105,7,201.15
+183,Realive,Sci-Fi,Mateo Gil,"Tom Hughes, Charlotte Le Bon, Oona Chaplin, Barry Ward",2016,112,5.9,0
+184,Forushande,"Drama,Thriller",Asghar Farhadi,"Taraneh Alidoosti, Shahab Hosseini, Babak Karimi,Farid Sajjadi Hosseini",2016,124,8,3.4
+185,Love,"Drama,Romance",Gaspar Noé,"Aomi Muyock, Karl Glusman, Klara Kristin, Juan Saavedra",2015,135,6,0
+186,Billy Lynn's Long Halftime Walk,"Drama,War",Ang Lee,"Joe Alwyn, Garrett Hedlund, Arturo Castro, Mason Lee",2016,113,6.3,1.72
+187,Crimson Peak,"Drama,Fantasy,Horror",Guillermo del Toro,"Mia Wasikowska, Jessica Chastain, Tom Hiddleston, Charlie Hunnam",2015,119,6.6,31.06
+188,Drive,"Crime,Drama",Nicolas Winding Refn,"Ryan Gosling, Carey Mulligan, Bryan Cranston, Albert Brooks",2011,100,7.8,35.05
+189,Trainwreck,"Comedy,Drama,Romance",Judd Apatow,"Amy Schumer, Bill Hader, Brie Larson, Colin Quinn",2015,125,6.3,110.01M
+190,The Light Between Oceans,"Drama,Romance",Derek Cianfrance,"Michael       Fassbender, Alicia Vikander, Rachel Weisz, Florence Clery",2016,133,7.2,12.53
+191,Below Her Mouth,Drama,April Mullen,"Erika Linder, Natalie Krill, Sebastian Pigott, Mayko Nguyen",2016,94,5.6,0
+192,Spotlight,"Crime,Drama,History",Tom McCarthy,"Mark Ruffalo, Michael Keaton, Rachel McAdams, Liev Schreiber",2015,128,8.1,44.99
+193,Morgan,"Horror,Sci-Fi,Thriller",Luke Scott,"Kate Mara, Anya Taylor-Joy, Rose Leslie, Michael Yare",2016,92,5.8,3.91
+194,Warrior,"Action,Drama,Sport",Gavin O'Connor,"Tom Hardy, Nick Nolte, Joel Edgerton, Jennifer Morrison",2011,140,8.2,13.65
+195,Captain America: The First Avenger,"Action,Adventure,Sci-Fi",Joe Johnston,"Chris Evans, Hugo Weaving, Samuel L. Jackson,Hayley Atwell",2011,124,6.9,176.64M
+196,Hacker,"Crime,Drama,Thriller",Akan Satayev,"Callan McAuliffe, Lorraine Nicholson, Daniel Eric Gold, Clifton Collins Jr.",2016,95,6.3,0
+197,Into the Wild,"Adventure,Biography,Drama",Sean Penn,"Emile Hirsch, Vince Vaughn, Catherine Keener, Marcia Gay Harden",2007,148,8.1,18.35
+198,The Imitation Game,"Biography,Drama,Thriller",Morten Tyldum,"Benedict Cumberbatch, Keira Knightley, Matthew Goode, Allen Leech",2014,114,8.1,91.12
+199,Central Intelligence,"Action,Comedy,Crime",Rawson Marshall Thurber,"Dwayne Johnson, Kevin Hart, Danielle Nicolet, Amy Ryan",2016,107,6.3,127.38M
+200,Edge of Tomorrow,"Action,Adventure,Sci-Fi",Doug Liman,"Tom Cruise, Emily Blunt, Bill Paxton, Brendan Gleeson",2014,113,7.9,100.19M
+201,A Cure for Wellness,"Drama,Fantasy,Horror",Gore Verbinski,"Dane DeHaan, Jason Isaacs, Mia Goth, Ivo Nandi",2016,146,6.5,8.1
+202,Snowden,"Biography,Drama,Thriller",Oliver Stone,"Joseph Gordon-Levitt, Shailene Woodley, Melissa Leo,Zachary Quinto",2016,134,7.3,21.48
+203,Iron Man,"Action,Adventure,Sci-Fi",Jon Favreau,"Robert Downey Jr., Gwyneth Paltrow, Terrence Howard, Jeff Bridges",2008,126,7.9,318.3
+204,Allegiant,"Action,Adventure,Mystery",Robert Schwentke,"Shailene Woodley, Theo James, Jeff Daniels,Naomi Watts",2016,120,5.7,66
+205,X: First Class,"Action,Adventure,Sci-Fi",Matthew Vaughn,"James McAvoy, Michael          Fassbender, Jennifer Lawrence, Kevin Bacon",2011,132,7.8,146.41M
+206,Raw (II),"Drama,Horror",Julia Ducournau,"Garance Marillier, Ella Rumpf, Rabah Nait Oufella,Laurent Lucas",2016,99,7.5,0.51
+207,Paterson,"Comedy,Drama,Romance",Jim Jarmusch,"Adam Driver, Golshifteh Farahani, Nellie, Rizwan Manji",2016,118,7.5,2.14
+208,Bridesmaids,"Comedy,Romance",Paul Feig,"Kristen Wiig, Maya Rudolph, Rose Byrne, Terry Crews",2011,125,6.8,169.08M
+209,The Girl with All the Gifts,"Drama,Horror,Thriller",Colm McCarthy,"Gemma Arterton, Glenn Close, Dominique Tipper,Paddy Considine",2016,111,6.7,0
+210,San Andreas,"Action,Adventure,Drama",Brad Peyton,"Dwayne Johnson, Carla Gugino, Alexandra Daddario,Colton Haynes",2015,114,6.1,155.18M
+211,Spring Breakers,Drama,Harmony Korine,"Vanessa Hudgens, Selena Gomez, Ashley Benson,Rachel Korine",2012,94,5.3,14.12
+212,Transformers,"Action,Adventure,Sci-Fi",Michael Bay,"Shia LaBeouf, Megan Fox, Josh Duhamel, Tyrese Gibson",2007,144,7.1,318.76
+213,Old Boy,"Action,Drama,Mystery",Spike Lee,"Josh Brolin, Elizabeth Olsen, Samuel L. Jackson, Sharlto Copley",2013,104,5.8,0
+214,Thor: The Dark World,"Action,Adventure,Fantasy",Alan Taylor,"Chris Hemsworth, Natalie Portman, Tom Hiddleston,Stellan Skarsgård",2013,112,7,206.36
+215,Gods of Egypt,"Action,Adventure,Fantasy",Alex Proyas,"Brenton Thwaites, Nikolaj Coster-Waldau, Gerard Butler, Chadwick Boseman",2016,126,5.5,31.14
+216,Captain America: The Winter Soldier,"Action,Adventure,Sci-Fi",Anthony Russo,"Chris Evans, Samuel L. Jackson,Scarlett Johansson, Robert Redford",2014,136,7.8,259.75
+217,Monster Trucks,"Action,Adventure,Comedy",Chris Wedge,"Lucas Till, Jane Levy, Thomas Lennon, Barry Pepper",2016,104,5.7,33.04
+218,A Dark Song,"Drama,Horror",Liam Gavin,"Mark Huberman, Susan Loughnane, Steve Oram,Catherine Walker",2016,100,6.1,0
+219,Kick-Ass,"Action,Comedy",Matthew Vaughn,"Aaron Taylor-Johnson, Nicolas Cage, Chloë Grace Moretz, Garrett M. Brown",2010,117,7.7,48.04
+220,Hardcore Henry,"Action,Adventure,Sci-Fi",Ilya Naishuller,"Sharlto Copley, Tim Roth, Haley Bennett, Danila Kozlovsky",2015,96,6.7,9.24
+221,Cars,"Animation,Adventure,Comedy",John Lasseter,"Owen Wilson, Bonnie Hunt, Paul Newman, Larry the Cable Guy",2006,117,7.1,244.05
+222,It Follows,"Horror,Mystery",David Robert Mitchell,"Maika Monroe, Keir Gilchrist, Olivia Luccardi,Lili Sepe",2014,100,6.9,14.67
+223,The Girl with the Dragon Tattoo,"Crime,Drama,Mystery",David Fincher,"Daniel Craig, Rooney Mara, Christopher Plummer,Stellan Skarsgård",2011,158,7.8,102.52M
+224,We're the Millers,"Comedy,Crime",Rawson Marshall Thurber,"Jason Sudeikis, Jennifer Aniston, Emma Roberts, Ed Helms",2013,110,7,150.37M
+225,American Honey,Drama,Andrea Arnold,"Sasha Lane, Shia LaBeouf, Riley Keough, McCaul Lombardi",2016,163,7,0.66
+226,The Lobster,"Comedy,Drama,Romance",Yorgos Lanthimos,"Colin Farrell, Rachel Weisz, Jessica Barden,Olivia Colman",2015,119,7.1,8.7
+227,Predators,"Action,Adventure,Sci-Fi",Nimród Antal,"Adrien Brody, Laurence Fishburne, Topher Grace,Alice Braga",2010,107,6.4,52
+228,Maleficent,"Action,Adventure,Family",Robert Stromberg,"Angelina Jolie, Elle Fanning, Sharlto Copley,Lesley Manville",2014,97,7,241.41
+229,Rupture,"Horror,Sci-Fi,Thriller",Steven Shainberg,"Noomi Rapace, Michael Chiklis, Kerry Bishé,Peter Stormare",2016,102,4.8,0
+230,Pan's Labyrinth,"Drama,Fantasy,War",Guillermo del Toro,"Ivana Baquero, Ariadna Gil, Sergi López,Maribel Verdú",2006,118,8.2,37.62
+231,A Kind of Murder,"Crime,Drama,Thriller",Andy Goddard,"Patrick Wilson, Jessica Biel, Haley Bennett, Vincent Kartheiser",2016,95,5.2,0
+232,Apocalypto,"Action,Adventure,Drama",Mel Gibson,"Gerardo Taracena, Raoul Max Trujillo, Dalia Hernández,Rudy Youngblood",2006,139,7.8,50.86
+233,Mission: Impossible - Rogue Nation,"Action,Adventure,Thriller",Christopher McQuarrie,"Tom Cruise, Rebecca Ferguson, Jeremy Renner, Simon Pegg",2015,131,7.4,195M
+234,The Huntsman: Winter's War,"Action,Adventure,Drama",Cedric Nicolas-Troyan,"Chris Hemsworth, Jessica Chastain, Charlize Theron, Emily Blunt",2016,114,6.1,47.95
+235,The Perks of Being a Wallflower,"Drama,Romance",Stephen Chbosky,"Logan Lerman, Emma Watson, Ezra Miller, Paul Rudd",2012,102,8,17.74
+236,Jackie,"Biography,Drama,History",Pablo Larraín,"Natalie Portman, Peter Sarsgaard, Greta Gerwig,Billy Crudup",2016,100,6.8,13.96
+237,The Disappointments Room,"Drama,Horror,Thriller",D.J. Caruso,"Kate Beckinsale, Mel Raido, Duncan Joiner, Lucas Till",2016,85,3.9,2.41
+238,The Grand Budapest Hotel,"Adventure,Comedy,Drama",Wes Anderson,"Ralph Fiennes, F. Murray Abraham, Mathieu Amalric,Adrien Brody",2014,99,8.1,59.07
+239,The Host ,"Action,Adventure,Romance",Andrew Niccol,"Saoirse Ronan, Max Irons, Jake Abel, Diane Kruger",2013,125,5.9,26.62
+240,Fury,"Action,Drama,War",David Ayer,"Brad Pitt, Shia LaBeouf, Logan Lerman, Michael Peña",2014,134,7.6,85.71
+241,Inside Out,"Animation,Adventure,Comedy",Pete Docter,"Amy Poehler, Bill Hader, Lewis Black, Mindy Kaling",2015,95,8.2,356.45
+242,Rock Dog,"Animation,Adventure,Comedy",Ash Brannon,"Luke Wilson, Eddie Izzard, J.K. Simmons, Lewis Black",2016,90,5.8,9.4
+243,Terminator Genisys,"Action,Adventure,Sci-Fi",Alan Taylor,"Arnold Schwarzenegger, Jason Clarke, Emilia Clarke,Jai Courtney",2015,126,6.5,89.73
+244,Percy Jackson & the Olympians: The Lightning Thief,"Adventure,Family,Fantasy",Chris Columbus,"Logan Lerman, Kevin McKidd, Steve Coogan,Brandon T. Jackson",2010,118,5.9,88.76
+245,Les Misérables,"Drama,Musical,Romance",Tom Hooper,"Hugh Jackman, Russell Crowe, Anne Hathaway,Amanda Seyfried",2012,158,7.6,148.78M
+246,Children of Men,"Drama,Sci-Fi,Thriller",Alfonso Cuarón,"Julianne Moore, Clive Owen, Chiwetel Ejiofor,Michael Caine",2006,109,7.9,35.29
+247,20th Century Women,"Comedy,Drama",Mike Mills,"Annette Bening, Elle Fanning, Greta Gerwig, Billy Crudup",2016,119,7.4,5.66
+248,Spy,"Action,Comedy,Crime",Paul Feig,"Melissa McCarthy, Rose Byrne, Jude Law, Jason Statham",2015,119,7.1,110.82M
+249,The Intouchables,"Biography,Comedy,Drama",Olivier Nakache,"François Cluzet, Omar Sy, Anne Le Ny, Audrey Fleurot",2011,112,8.6,13.18
+250,Bonjour Anne,"Comedy,Drama,Romance",Eleanor Coppola,"Diane Lane, Alec Baldwin, Arnaud Viard, Linda Gegusch",2016,92,4.9,0.32
+251,Kynodontas,"Drama,Thriller",Yorgos Lanthimos,"Christos Stergioglou, Michele Valley, Angeliki Papoulia, Hristos Passalis",2009,94,7.3,0.11
+252,Straight Outta Compton,"Biography,Drama,History",F. Gary Gray,"O'Shea Jackson Jr., Corey Hawkins, Jason Mitchell,Neil Brown Jr.",2015,147,7.9,161.03M
+253,The Amazing Spider-Man 2,"Action,Adventure,Sci-Fi",Marc Webb,"Andrew Garfield, Emma Stone, Jamie Foxx, Paul Giamatti",2014,142,6.7,202.85
+254,The Conjuring,"Horror,Mystery,Thriller",James Wan,"Patrick Wilson, Vera Farmiga, Ron Livingston, Lili Taylor",2013,112,7.5,137.39M
+255,The Hangover,Comedy,Todd Phillips,"Zach Galifianakis, Bradley Cooper, Justin Bartha, Ed Helms",2009,100,7.8,277.31
+256,Battleship,"Action,Adventure,Sci-Fi",Peter Berg,"Alexander Skarsgård, Brooklyn Decker, Liam Neeson,Rihanna",2012,131,5.8,65.17
+257,Rise of the Planet of the Apes,"Action,Drama,Sci-Fi",Rupert Wyatt,"James Franco, Andy Serkis, Freida Pinto, Karin Konoval",2011,105,7.6,176.74M
+258,Lights Out,Horror,David F. Sandberg,"Teresa Palmer, Gabriel Bateman, Maria Bello,Billy Burke",2016,81,6.4,67.24
+259,Norman: The Moderate Rise and Tragic Fall of a New York Fixer,"Drama,Thriller",Joseph Cedar,"Richard Gere, Lior Ashkenazi, Michael Sheen,Charlotte Gainsbourg",2016,118,7.1,2.27
+260,Birdman or (The Unexpected Virtue of Ignorance),"Comedy,Drama,Romance",Alejandro González Iñárritu,"Michael Keaton, Zach Galifianakis,Edward Norton, Andrea Riseborough",2014,119,7.8,42.34
+261,Black Swan,"Drama,Thriller",Darren Aronofsky,"Natalie Portman, Mila Kunis, Vincent Cassel,Winona Ryder",2010,108,8,106.95M
+262,Dear White People,"Comedy,Drama",Justin Simien,"Tyler James Williams, Tessa Thompson, Kyle Gallner,Teyonah Parris",2014,108,6.2,4.4
+263,Nymphomaniac: Vol. I,Drama,Lars von Trier,"Charlotte Gainsbourg, Stellan Skarsgård, Stacy Martin, Shia LaBeouf",2013,117,7,0.79
+264,Teenage Mutant Ninja Turtles: Out of the Shadows,"Action,Adventure,Comedy",Dave Green,"Megan Fox, Will Arnett, Tyler Perry, Laura Linney",2016,112,6,0.54
+265,Knock Knock,"Drama,Horror,Thriller",Eli Roth,"Keanu Reeves, Lorenza Izzo, Ana de Armas, Aaron Burns",2015,99,4.9,0.03
+266,Dirty Grandpa,Comedy,Dan Mazer,"Robert De Niro, Zac Efron, Zoey Deutch, Aubrey Plaza",2016,102,6,35.54
+267,Cloud Atlas,"Drama,Sci-Fi",Tom Tykwer,"Tom Hanks, Halle Berry, Hugh Grant, Hugo Weaving",2012,172,7.5,27.1
+268,X-Men Origins: Wolverine,"Action,Adventure,Sci-Fi",Gavin Hood,"Hugh Jackman, Liev Schreiber, Ryan Reynolds, Danny Huston",2009,107,6.7,179.88M
+269,Satanic,Horror,Jeffrey G. Hunt,"Sarah Hyland, Steven Krueger, Justin Chon, Clara Mamet",2016,85,3.7,0
+270,Skyfall,"Action,Adventure,Thriller",Sam Mendes,"Daniel Craig, Javier Bardem, Naomie Harris, Judi Dench",2012,143,7.8,304.36
+271,The Hobbit: An Unexpected Journey,"Adventure,Fantasy",Peter Jackson,"Martin Freeman, Ian McKellen, Richard Armitage,Andy Serkis",2012,169,7.9,303
+272,21 Jump Street,"Action,Comedy,Crime",Phil Lord,"Jonah Hill, Channing Tatum, Ice Cube,Brie Larson",2012,110,7.2,138.45M
+273,Sing Street,"Comedy,Drama,Music",John Carney,"Ferdia Walsh-Peelo, Aidan Gillen, Maria Doyle Kennedy, Jack Reynor",2016,106,8,3.23
+274,Ballerina,"Animation,Adventure,Comedy",Eric Summer,"Elle Fanning, Dane DeHaan, Carly Rae Jepsen, Maddie Ziegler",2016,89,6.8,0
+275,Oblivion,"Action,Adventure,Mystery",Joseph Kosinski,"Tom Cruise, Morgan Freeman, Andrea Riseborough, Olga Kurylenko",2013,124,7,89.02
+276,22 Jump Street,"Action,Comedy,Crime",Phil Lord,"Channing Tatum, Jonah Hill, Ice Cube,Nick Offerman",2014,112,7.1,191.62M
+277,Zodiac,"Crime,Drama,History",David Fincher,"Jake Gyllenhaal, Robert Downey Jr., Mark Ruffalo,Anthony Edwards",2007,157,7.7,33.05
+278,Everybody Wants Some!!,Comedy,Richard Linklater,"Blake Jenner, Tyler Hoechlin, Ryan Guzman,Zoey Deutch",2016,117,7,3.37
+279,Iron Man Three,"Action,Adventure,Sci-Fi",Shane Black,"Robert Downey Jr., Guy Pearce, Gwyneth Paltrow,Don Cheadle",2013,130,7.2,408.99
+280,Now You See Me,"Crime,Mystery,Thriller",Louis Leterrier,"Jesse Eisenberg, Common, Mark Ruffalo, Woody Harrelson",2013,115,7.3,117.7M
+281,Sherlock Holmes,"Action,Adventure,Crime",Guy Ritchie,"Robert Downey Jr., Jude Law, Rachel McAdams, Mark Strong",2009,128,7.6,209.02
+282,Death Proof,Thriller,Quentin Tarantino,"Kurt Russell, Zoë Bell, Rosario Dawson, Vanessa Ferlito",2007,113,7.1,0
+283,The Danish Girl,"Biography,Drama,Romance",Tom Hooper,"Eddie Redmayne, Alicia Vikander, Amber Heard, Ben Whishaw",2015,119,7,12.71
+284,Hercules,"Action,Adventure",Brett Ratner,"Dwayne Johnson, John Hurt, Ian McShane, Joseph Fiennes",2014,98,6,72.66
+285,Sucker Punch,"Action,Fantasy",Zack Snyder,"Emily Browning, Vanessa Hudgens, Abbie Cornish,Jena Malone",2011,110,6.1,36.38
+286,Keeping Up with the Joneses,"Action,Comedy",Greg Mottola,"Zach Galifianakis, Isla Fisher, Jon Hamm, Gal Gadot",2016,105,5.8,14.9
+287,Jupiter Ascending,"Action,Adventure,Sci-Fi",Lana Wachowski,"Channing Tatum, Mila Kunis,Eddie Redmayne, Sean Bean",2015,127,5.3,47.38
+288,Masterminds,"Action,Comedy,Crime",Jared Hess,"Zach Galifianakis, Kristen Wiig, Owen Wilson, Ross Kimball",2016,95,5.8,17.36
+289,Iris,Thriller,Jalil Lespert,"Romain Duris, Charlotte Le Bon, Jalil Lespert, Camille Cottin",2016,99,6.1,0
+290,Busanhaeng,"Action,Drama,Horror",Sang-ho Yeon,"Yoo Gong, Soo-an Kim, Yu-mi Jung, Dong-seok Ma",2016,118,7.5,2.13
+291,Pitch Perfect,"Comedy,Music,Romance",Jason Moore,"Anna Kendrick, Brittany Snow, Rebel Wilson, Anna Camp",2012,112,7.2,65
+292,Neighbors 2: Sorority Rising,Comedy,Nicholas Stoller,"Seth Rogen, Rose Byrne, Zac Efron, Chloë Grace Moretz",2016,92,5.7,55.29
+293,The Exception,Drama,David Leveaux,"Lily James, Jai Courtney, Christopher Plummer, Loïs van Wijk",2016,107,7.7,0
+294,Man of Steel,"Action,Adventure,Fantasy",Zack Snyder,"Henry Cavill, Amy Adams, Michael Shannon, Diane Lane",2013,143,7.1,291.02
+295,The Choice,"Drama,Romance",Ross Katz,"Benjamin Walker, Teresa Palmer, Alexandra Daddario,Maggie Grace",2016,111,6.6,18.71
+296,Ice Age: Collision Course,"Animation,Adventure,Comedy",Mike Thurmeier,"Ray Romano, Denis Leary, John Leguizamo, Chris Wedge",2016,94,5.7,64.06
+297,The Devil Wears Prada,"Comedy,Drama",David Frankel,"Anne Hathaway, Meryl Streep, Adrian Grenier, Emily Blunt",2006,109,6.8,124.73M
+298,The Infiltrator,"Biography,Crime,Drama",Brad Furman,"Bryan Cranston, John Leguizamo, Diane Kruger, Amy Ryan",2016,127,7.1,15.43
+299,There Will Be Blood,"Drama,History",Paul Thomas Anderson,"Daniel Day-Lewis, Paul Dano, Ciarán Hinds,Martin Stringer",2007,158,8.1,40.22
+300,The Equalizer,"Action,Crime,Thriller",Antoine Fuqua,"Denzel                      Washington, Marton Csokas, Chloë Grace Moretz, David Harbour",2014,132,7.2,101.53M
+301,Lone Survivor,"Action,Biography,Drama",Peter Berg,"Mark Wahlberg, Taylor Kitsch, Emile Hirsch, Ben Foster",2013,121,7.5,125.07M
+302,The Cabin in the Woods,Horror,Drew Goddard,"Kristen Connolly, Chris Hemsworth, Anna Hutchison,Fran Kranz",2012,95,7,42.04
+303,The House Bunny,"Comedy,Romance",Fred Wolf,"Anna Faris, Colin Hanks, Emma Stone, Kat Dennings",2008,97,5.5,48.24
+304,She's Out of My League,"Comedy,Romance",Jim Field Smith,"Jay Baruchel, Alice Eve, T.J. Miller, Mike Vogel",2010,104,6.4,31.58
+305,Inherent Vice,"Comedy,Crime,Drama",Paul Thomas Anderson,"Joaquin Phoenix, Josh Brolin, Owen Wilson,Katherine Waterston",2014,148,6.7,8.09
+306,Alice Through the Looking Glass,"Adventure,Family,Fantasy",James Bobin,"Mia Wasikowska, Johnny Depp, Helena Bonham Carter, Anne Hathaway",2016,113,6.2,77.04
+307,Vincent N Roxxy,"Crime,Drama,Thriller",Gary Michael Schultz,"Emile Hirsch, Zoë Kravitz, Zoey Deutch,Emory Cohen",2016,110,5.5,0
+308,The Fast and the Furious: Tokyo Drift,"Action,Crime,Thriller",Justin Lin,"Lucas Black, Zachery Ty Bryan, Shad Moss, Damien Marzette",2006,104,6,62.49
+309,How to Be Single,"Comedy,Romance",Christian Ditter,"Dakota Johnson, Rebel Wilson, Leslie Mann, Alison Brie",2016,110,6.1,46.81
+310,The Blind Side,"Biography,Drama,Sport",John Lee Hancock,"Quinton Aaron, Sandra Bullock, Tim McGraw,Jae Head",2009,129,7.7,255.95
+311,La vie d'Adèle,"Drama,Romance",Abdellatif Kechiche,"Léa Seydoux, Adèle Exarchopoulos, Salim Kechiouche, Aurélien Recoing",2013,180,7.8,2.2
+312,The Babadook,"Drama,Horror",Jennifer Kent,"Essie Davis, Noah Wiseman, Daniel Henshall, Hayley McElhinney",2014,93,6.8,0.92
+313,The Hobbit: The Battle of the Five Armies,"Adventure,Fantasy",Peter Jackson,"Ian McKellen, Martin Freeman, Richard Armitage,Cate Blanchett",2014,144,7.4,255.11
+314,Harry Potter and the Order of the Phoenix,"Adventure,Family,Fantasy",David Yates,"Daniel Radcliffe, Emma Watson, Rupert Grint, Brendan Gleeson",2007,138,7.5,292
+315,Snowpiercer,"Action,Drama,Sci-Fi",Bong Joon Ho,"Chris Evans, Jamie Bell, Tilda Swinton, Ed Harris",2013,126,7,4.56
+316,The 5th Wave,"Action,Adventure,Sci-Fi",J Blakeson,"Chloë Grace Moretz, Matthew Zuk, Gabriela Lopez,Bailey Anne Borders",2016,112,5.2,34.91
+317,The Stakelander,"Action,Horror",Dan Berk,"Connor Paolo, Nick Damici, Laura Abramsen, A.C. Peterson",2016,81,5.3,0
+318,The Visit,"Comedy,Horror,Thriller",M. Night Shyamalan,"Olivia DeJonge, Ed Oxenbould, Deanna Dunagan, Peter McRobbie",2015,94,6.2,65.07
+319,Fast Five,"Action,Crime,Thriller",Justin Lin,"Vin Diesel, Paul Walker, Dwayne Johnson, Jordana Brewster",2011,131,7.3,209.81
+320,Step Up,"Crime,Drama,Music",Anne Fletcher,"Channing Tatum, Jenna Dewan Tatum, Damaine Radcliff, De'Shawn Washington",2006,104,6.5,65.27
+321,Lovesong,Drama,So Yong Kim,"Riley Keough, Jena Malone, Jessie Ok Gray, Cary Joji Fukunaga",2016,84,6.4,0.01
+322,RocknRolla,"Action,Crime,Thriller",Guy Ritchie,"Gerard Butler, Tom Wilkinson, Idris Elba, Thandie Newton",2008,114,7.3,5.69
+323,In Time,"Action,Sci-Fi,Thriller",Andrew Niccol,"Justin Timberlake, Amanda Seyfried, Cillian Murphy,Olivia Wilde",2011,109,6.7,37.55
+324,The Social Network,"Biography,Drama",David Fincher,"Jesse Eisenberg, Andrew Garfield, Justin Timberlake,Rooney Mara",2010,120,7.7,96.92
+325,The Last Witch Hunter,"Action,Adventure,Fantasy",Breck Eisner,"Vin Diesel, Rose Leslie, Elijah Wood, Ólafur Darri Ólafsson",2015,106,6,27.36
+326,Victor Frankenstein,"Drama,Horror,Sci-Fi",Paul McGuigan,"Daniel Radcliffe, James McAvoy, Jessica Brown Findlay, Andrew Scott",2015,110,6,5.77
+327,A Street Cat Named Bob,"Biography,Comedy,Drama",Roger Spottiswoode,"Luke Treadaway, Bob the Cat, Ruta Gedmintas, Joanne Froggatt",2016,103,7.4,0.04
+328,Green Room,"Crime,Horror,Thriller",Jeremy Saulnier,"Anton Yelchin, Imogen Poots, Alia Shawkat,Patrick Stewart",2015,95,7,3.22
+329,Blackhat,"Crime,Drama,Mystery",Michael Mann,"Chris Hemsworth, Viola Davis, Wei Tang, Leehom Wang",2015,133,5.4,7.1
+330,Storks,"Animation,Adventure,Comedy",Nicholas Stoller,"Andy Samberg, Katie Crown,Kelsey Grammer, Jennifer Aniston",2016,87,6.9,72.66
+331,American Sniper,"Action,Biography,Drama",Clint Eastwood,"Bradley Cooper, Sienna Miller, Kyle Gallner, Cole Konis",2014,133,7.3,350.12
+332,Dallas Buyers Club,"Biography,Drama",Jean-Marc Vallée,"Matthew McConaughey, Jennifer Garner, Jared Leto, Steve Zahn",2013,117,8,27.3
+333,Lincoln,"Biography,Drama,History",Steven Spielberg,"Daniel Day-Lewis, Sally Field, David Strathairn,Joseph Gordon-Levitt",2012,150,7.4,182.2M
+334,Rush,"Action,Biography,Drama",Ron Howard,"Daniel Brühl, Chris Hemsworth, Olivia Wilde,Alexandra Maria Lara",2013,123,8.1,26.9
+335,Before I Wake,"Drama,Fantasy,Horror",Mike Flanagan,"Kate Bosworth, Thomas Jane, Jacob Tremblay,Annabeth Gish",2016,97,6.1,0
+336,Silver Linings Playbook,"Comedy,Drama,Romance",David O. Russell,"Bradley Cooper, Jennifer Lawrence, Robert De Niro, Jacki Weaver",2012,122,7.8,132.09M
+337,Tracktown,"Drama,Sport",Alexi Pappas,"Alexi Pappas, Chase Offerle, Rachel Dratch, Andy Buckley",2016,88,5.9,0
+338,The Fault in Our Stars,"Drama,Romance",Josh Boone,"Shailene Woodley, Ansel Elgort, Nat Wolff, Laura Dern",2014,126,7.8,124.87M
+339,Blended,"Comedy,Romance",Frank Coraci,"Adam Sandler, Drew Barrymore, Wendi McLendon-Covey, Kevin Nealon",2014,117,6.5,46.28
+340,Fast & Furious,"Action,Crime,Thriller",Justin Lin,"Vin Diesel, Paul Walker, Michelle Rodriguez, Jordana Brewster",2009,107,6.6,155.02M
+341,Looper,"Action,Crime,Drama",Rian Johnson,"Joseph Gordon-Levitt, Bruce Willis, Emily Blunt, Paul Dano",2012,119,7.4,66.47
+342,White House Down,"Action,Drama,Thriller",Roland Emmerich,"Channing Tatum, Jamie Foxx, Maggie Gyllenhaal,Jason Clarke",2013,131,6.4,73.1
+343,Pete's Dragon,"Adventure,Family,Fantasy",David Lowery,"Bryce Dallas Howard, Robert Redford, Oakes Fegley,Oona Laurence",2016,102,6.8,76.2
+344,Spider-Man 3,"Action,Adventure",Sam Raimi,"Tobey Maguire, Kirsten Dunst, Topher Grace, Thomas Haden Church",2007,139,6.2,336.53
+345,The Three Musketeers,"Action,Adventure,Romance",Paul W.S. Anderson,"Logan Lerman, Matthew Macfadyen, Ray Stevenson, Milla Jovovich",2011,110,5.8,20.32
+346,Stardust,"Adventure,Family,Fantasy",Matthew Vaughn,"Charlie Cox, Claire Danes, Sienna Miller, Ian McKellen",2007,127,7.7,38.35
+347,American Hustle,"Crime,Drama",David O. Russell,"Christian Bale, Amy Adams, Bradley Cooper,Jennifer Lawrence",2013,138,7.3,150.12M
+348,Jennifer's Body,"Comedy,Horror",Karyn Kusama,"Megan Fox, Amanda Seyfried, Adam Brody, Johnny Simmons",2009,102,5.1,16.2
+349,Midnight in Paris,"Comedy,Fantasy,Romance",Woody Allen,"Owen Wilson, Rachel McAdams, Kathy Bates, Kurt Fuller",2011,94,7.7,56.82
+350,Lady Macbeth,Drama,William Oldroyd,"Florence Pugh, Christopher Fairbank, Cosmo Jarvis, Naomi Ackie",2016,89,7.3,0
+351,Joy,Drama,David O. Russell,"Jennifer Lawrence, Robert De Niro, Bradley Cooper, Edgar Ramírez",2015,124,6.6,56.44
+352,The Dressmaker,"Comedy,Drama",Jocelyn Moorhouse,"Kate Winslet, Judy Davis, Liam Hemsworth,Hugo Weaving",2015,119,7.1,2.02
+353,Café Society,"Comedy,Drama,Romance",Woody Allen,"Jesse Eisenberg, Kristen Stewart, Steve Carell, Blake Lively",2016,96,6.7,11.08
+354,Insurgent,"Adventure,Sci-Fi,Thriller",Robert Schwentke,"Shailene Woodley, Ansel Elgort, Theo James,Kate Winslet",2015,119,6.3,130M
+355,Seventh Son,"Action,Adventure,Fantasy",Sergei Bodrov,"Ben Barnes, Julianne Moore, Jeff Bridges, Alicia Vikander",2014,102,5.5,17.18
+356,The Theory of Everything,"Biography,Drama,Romance",James Marsh,"Eddie Redmayne, Felicity Jones, Tom Prior, Sophie Perry",2014,123,7.7,35.89
+357,This Is the End,"Comedy,Fantasy",Evan Goldberg,"James Franco, Jonah Hill, Seth Rogen,Jay Baruchel",2013,107,6.6,101.47M
+358,About Time,"Comedy,Drama,Fantasy",Richard Curtis,"Domhnall Gleeson, Rachel McAdams, Bill Nighy,Lydia Wilson",2013,123,7.8,15.29
+359,Step Brothers,Comedy,Adam McKay,"Will Ferrell, John C. Reilly, Mary Steenburgen,Richard Jenkins",2008,98,6.9,100.47M
+360,Clown,"Horror,Thriller",Jon Watts,"Andy Powers, Laura Allen, Peter Stormare, Christian Distefano",2014,100,5.7,0.05
+361,Star Trek Into Darkness,"Action,Adventure,Sci-Fi",J.J. Abrams,"Chris Pine, Zachary Quinto, Zoe Saldana, Benedict Cumberbatch",2013,132,7.8,228.76
+362,Zombieland,"Adventure,Comedy,Horror",Ruben Fleischer,"Jesse Eisenberg, Emma Stone, Woody Harrelson,Abigail Breslin",2009,88,7.7,75.59
+363,"Hail, Caesar!","Comedy,Mystery",Ethan Coen,"Josh Brolin, George Clooney, Alden Ehrenreich, Ralph Fiennes",2016,106,6.3,30
+364,Slumdog Millionaire,Drama,Danny Boyle,"Dev Patel, Freida Pinto, Saurabh Shukla, Anil Kapoor",2008,120,8,141.32M
+365,The Twilight Saga: Breaking Dawn - Part 2,"Adventure,Drama,Fantasy",Bill Condon,"Kristen Stewart, Robert Pattinson, Taylor Lautner, Peter Facinelli",2012,115,5.5,292.3
+366,American Wrestler: The Wizard,"Drama,Sport",Alex Ranarivelo,"William Fichtner, Jon Voight, Lia Marie Johnson,Gabriel Basso",2016,117,6.9,0
+367,The Amazing Spider-Man,"Action,Adventure",Marc Webb,"Andrew Garfield, Emma Stone, Rhys Ifans, Irrfan Khan",2012,136,7,262.03
+368,Ben-Hur,"Action,Adventure,Drama",Timur Bekmambetov,"Jack Huston, Toby Kebbell, Rodrigo Santoro,Nazanin Boniadi",2016,123,5.7,26.38
+369,Sleight,"Action,Drama,Sci-Fi",J.D. Dillard,"Jacob Latimore, Seychelle Gabriel, Dulé Hill, Storm Reid",2016,89,6,3.85
+370,The Maze Runner,"Action,Mystery,Sci-Fi",Wes Ball,"Dylan O'Brien, Kaya Scodelario, Will Poulter, Thomas Brodie-Sangster",2014,113,6.8,102.41M
+371,Criminal,"Action,Crime,Drama",Ariel Vromen,"Kevin Costner, Ryan Reynolds, Gal Gadot, Gary Oldman",2016,113,6.3,14.27
+372,Wanted,"Action,Crime,Fantasy",Timur Bekmambetov,"Angelina Jolie, James McAvoy, Morgan Freeman, Terence Stamp",2008,110,6.7,134.57M
+373,Florence Foster Jenkins,"Biography,Comedy,Drama",Stephen Frears,"Meryl Streep, Hugh Grant, Simon Helberg, Rebecca Ferguson",2016,111,6.9,27.37
+374,Collide,"Action,Crime,Thriller",Eran Creevy,"Nicholas Hoult, Felicity Jones, Anthony Hopkins, Ben Kingsley",2016,99,5.7,2.2
+375,Black Mass,"Biography,Crime,Drama",Scott Cooper,"Johnny Depp, Benedict Cumberbatch, Dakota Johnson, Joel Edgerton",2015,123,6.9,62.56
+376,Creed,"Drama,Sport",Ryan Coogler,"Michael B. Jordan, Sylvester Stallone, Tessa Thompson, Phylicia Rashad",2015,133,7.6,109.71M
+377,Swiss Army Man,"Adventure,Comedy,Drama",Dan Kwan,"Paul Dano, Daniel Radcliffe, Mary Elizabeth Winstead, Antonia Ribero",2016,97,7.1,4.21
+378,The Expendables 3,"Action,Adventure,Thriller",Patrick Hughes,"Sylvester Stallone, Jason Statham, Jet Li, Antonio Banderas",2014,126,6.1,39.29
+379,What We Do in the Shadows,"Comedy,Fantasy,Horror",Jemaine Clement,"Jemaine Clement, Taika Waititi,Cori Gonzalez-Macuer, Jonny Brugh",2014,86,7.6,3.33
+380,Southpaw,"Drama,Sport",Antoine Fuqua,"Jake Gyllenhaal, Rachel McAdams, Oona Laurence,Forest Whitaker",2015,124,7.4,52.42
+381,Hush,"Horror,Thriller",Mike Flanagan,"John Gallagher Jr., Kate Siegel, Michael Trucco,Samantha Sloyan",2016,81,6.6,0
+382,Bridge of Spies,"Drama,History,Thriller",Steven Spielberg,"Tom Hanks, Mark Rylance, Alan Alda, Amy Ryan",2015,142,7.6,72.31
+383,The Lego Movie,"Animation,Action,Adventure",Phil Lord,"Chris Pratt, Will Ferrell, Elizabeth Banks, Will Arnett",2014,100,7.8,257.76
+384,Everest,"Action,Adventure,Drama",Baltasar Kormákur,"Jason Clarke, Ang Phula Sherpa, Thomas M. Wright, Martin Henderson",2015,121,7.1,43.25
+385,Pixels,"Action,Comedy,Family",Chris Columbus,"Adam Sandler, Kevin James, Michelle Monaghan,Peter Dinklage",2015,105,5.6,78.75
+386,Robin Hood,"Action,Adventure,Drama",Ridley Scott,"Russell Crowe, Cate Blanchett, Matthew Macfadyen,Max von Sydow",2010,140,6.7,105.22M
+387,The Wolverine,"Action,Adventure,Sci-Fi",James Mangold,"Hugh Jackman, Will Yun Lee, Tao Okamoto, Rila Fukushima",2013,126,6.7,132.55M
+388,John Carter,"Action,Adventure,Sci-Fi",Andrew Stanton,"Taylor Kitsch, Lynn Collins, Willem Dafoe,Samantha Morton",2012,132,6.6,73.06
+389,Keanu,"Action,Comedy",Peter Atencio,"Keegan-Michael Key, Jordan Peele, Tiffany Haddish,Method Man",2016,100,6.3,20.57
+390,The Gunman,"Action,Crime,Drama",Pierre Morel,"Sean Penn, Idris Elba, Jasmine Trinca, Javier Bardem",2015,115,5.8,10.64
+391,Steve Jobs,"Biography,Drama",Danny Boyle,"Michael   Fassbender, Kate Winslet, Seth Rogen, Jeff Daniels",2015,122,7.2,17.75
+392,Whisky Galore,"Comedy,Romance",Gillies MacKinnon,"Tim Pigott-Smith, Naomi Battrick, Ellie Kendrick,James Cosmo",2016,98,5,0
+393,Grown Ups 2,Comedy,Dennis Dugan,"Adam Sandler, Kevin James, Chris Rock, David Spade",2013,101,5.4,133.67M
+394,The Age of Adaline,"Drama,Fantasy,Romance",Lee Toland Krieger,"Blake Lively, Michiel Huisman, Harrison Ford,Kathy Baker",2015,112,7.2,42.48
+395,The Incredible Hulk,"Action,Adventure,Sci-Fi",Louis Leterrier,"Edward Norton, Liv Tyler, Tim Roth, William Hurt",2008,112,6.8,134.52M
+396,Couples Retreat,Comedy,Peter Billingsley,"Vince Vaughn, Malin Akerman, Jon Favreau, Jason Bateman",2009,113,5.5,109.18M
+397,Absolutely Anything,"Comedy,Sci-Fi",Terry Jones,"Simon Pegg, Kate Beckinsale, Sanjeev Bhaskar, Rob Riggle",2015,85,6,0
+398,Magic Mike,"Comedy,Drama",Steven Soderbergh,"Channing Tatum, Alex Pettyfer, Olivia Munn,Matthew McConaughey",2012,110,6.1,113.71M
+399,Minions,"Animation,Action,Adventure",Kyle Balda,"Sandra Bullock, Jon Hamm, Michael Keaton, Pierre Coffin",2015,91,6.4,336.03
+400,The Black Room,Horror,Rolfe Kanefsky,"Natasha Henstridge, Lukas Hassel, Lin Shaye,Dominique Swain",2016,91,3.9,0
+401,Bronson,"Action,Biography,Crime",Nicolas Winding Refn,"Tom Hardy, Kelly Adams, Luing Andrews,Katy Barker",2008,92,7.1,0.1
+402,Despicable Me,"Animation,Adventure,Comedy",Pierre Coffin,"Steve Carell, Jason Segel, Russell Brand, Julie Andrews",2010,95,7.7,251.5
+403,The Best of Me,"Drama,Romance",Michael Hoffman,"James Marsden, Michelle Monaghan, Luke Bracey,Liana Liberato",2014,118,6.7,26.76
+404,The Invitation,"Drama,Mystery,Thriller",Karyn Kusama,"Logan Marshall-Green, Emayatzy Corinealdi, Michiel Huisman, Tammy Blanchard",2015,100,6.7,0.23
+405,Zero Dark Thirty,"Drama,History,Thriller",Kathryn Bigelow,"Jessica Chastain, Joel Edgerton, Chris Pratt, Mark Strong",2012,157,7.4,95.72
+406,Tangled,"Animation,Adventure,Comedy",Nathan Greno,"Mandy Moore, Zachary Levi, Donna Murphy, Ron Perlman",2010,100,7.8,200.81
+407,The Hunger Games: Mockingjay - Part 2,"Action,Adventure,Sci-Fi",Francis Lawrence,"Jennifer Lawrence, Josh Hutcherson, Liam Hemsworth, Woody Harrelson",2015,137,6.6,281.67
+408,Vacation,"Adventure,Comedy",John Francis Daley,"Ed Helms, Christina Applegate, Skyler Gisondo, Steele Stebbins",2015,99,6.1,58.88
+409,Taken,"Action,Thriller",Pierre Morel,"Liam Neeson, Maggie Grace, Famke Janssen, Leland Orser",2008,93,7.8,145M
+410,Pitch Perfect 2,"Comedy,Music",Elizabeth Banks,"Anna Kendrick, Rebel Wilson, Hailee Steinfeld,Brittany Snow",2015,115,6.5,183.44M
+411,Monsters University,"Animation,Adventure,Comedy",Dan Scanlon,"Billy Crystal, John Goodman, Steve Buscemi, Helen Mirren",2013,104,7.3,268.49
+412,Elle,"Crime,Drama,Thriller",Paul Verhoeven,"Isabelle Huppert, Laurent Lafitte, Anne Consigny,Charles Berling",2016,130,7.2,0
+413,Mechanic: Resurrection,"Action,Adventure,Crime",Dennis Gansel,"Jason Statham, Jessica Alba, Tommy Lee Jones,Michelle Yeoh",2016,98,5.6,21.2
+414,Tusk,"Comedy,Drama,Horror",Kevin Smith,"Justin Long, Michael Parks, Haley Joel Osment,Genesis Rodriguez",2014,102,5.4,1.82
+415,The Headhunter's Calling,Drama,Mark Williams,"Alison Brie, Gerard Butler, Willem Dafoe, Gretchen Mol",2016,108,6.9,0
+416,Atonement,"Drama,Mystery,Romance",Joe Wright,"Keira Knightley, James McAvoy, Brenda Blethyn,Saoirse Ronan",2007,123,7.8,50.92
+417,Harry Potter and the Deathly Hallows: Part 1,"Adventure,Family,Fantasy",David Yates,"Daniel Radcliffe, Emma Watson, Rupert Grint, Bill Nighy",2010,146,7.7,294.98
+418,Shame,Drama,Steve McQueen,"Michael        Fassbender, Carey Mulligan, James Badge Dale, Lucy Walters",2011,101,7.2,4
+419,Hanna,"Action,Drama,Thriller",Joe Wright,"Saoirse Ronan, Cate Blanchett, Eric Bana, Vicky Krieps",2011,111,6.8,40.25
+420,The Babysitters,Drama,David Ross,"Lauren Birkell, Paul Borghese, Chira Cassel, Anthony Cirillo",2007,88,5.7,0.04
+421,Pride and Prejudice and Zombies,"Action,Horror,Romance",Burr Steers,"Lily James, Sam Riley, Jack Huston, Bella Heathcote",2016,108,5.8,10.91
+422,300: Rise of an Empire,"Action,Drama,Fantasy",Noam Murro,"Sullivan Stapleton, Eva Green, Lena Headey, Hans Matheson",2014,102,6.2,106.37M
+423,London Has Fallen,"Action,Crime,Drama",Babak Najafi,"Gerard Butler, Aaron Eckhart, Morgan Freeman,Angela Bassett",2016,99,5.9,62.4
+424,The Curious Case of Benjamin Button,"Drama,Fantasy,Romance",David Fincher,"Brad Pitt, Cate Blanchett, Tilda Swinton, Julia Ormond",2008,166,7.8,127.49M
+425,Sin City: A Dame to Kill For,"Action,Crime,Thriller",Frank Miller,"Mickey Rourke, Jessica Alba, Josh Brolin, Joseph Gordon-Levitt",2014,102,6.5,13.75
+426,The Bourne Ultimatum,"Action,Mystery,Thriller",Paul Greengrass,"Matt Damon, Edgar Ramírez, Joan Allen, Julia Stiles",2007,115,8.1,227.14
+427,Srpski film,"Horror,Mystery,Thriller",Srdjan Spasojevic,"Srdjan 'Zika' Todorovic, Sergej Trifunovic,Jelena Gavrilovic, Slobodan Bestic",2010,104,5.2,0
+428,The Purge: Election Year,"Action,Horror,Sci-Fi",James DeMonaco,"Frank Grillo, Elizabeth Mitchell, Mykelti Williamson, Joseph Julian Soria",2016,109,6,79
+429,3 Idiots,"Comedy,Drama",Rajkumar Hirani,"Aamir Khan, Madhavan, Mona Singh, Sharman Joshi",2009,170,8.4,6.52
+430,Zoolander 2,Comedy,Ben Stiller,"Ben Stiller, Owen Wilson, Penélope Cruz, Will Ferrell",2016,102,4.7,28.84
+431,World War Z,"Action,Adventure,Horror",Marc Forster,"Brad Pitt, Mireille Enos, Daniella Kertesz, James Badge Dale",2013,116,7,202.35
+432,Mission: Impossible - Ghost Protocol,"Action,Adventure,Thriller",Brad Bird,"Tom Cruise, Jeremy Renner, Simon Pegg, Paula Patton",2011,132,7.4,209.36
+433,Let Me Make You a Martyr,"Action,Crime,Drama",Corey Asraf,"Marilyn Manson, Mark Boone Junior, Sam Quartin, Niko Nicotera",2016,102,6.4,0
+434,Filth,"Comedy,Crime,Drama",Jon S. Baird,"James McAvoy, Jamie Bell, Eddie Marsan, Imogen Poots",2013,97,7.1,0.03
+435,The Longest Ride,"Drama,Romance",George Tillman Jr.,"Scott Eastwood, Britt Robertson, Alan Alda, Jack Huston",2015,123,7.1,37.43
+436,The imposible,"Drama,Thriller",J.A. Bayona,"Naomi Watts, Ewan McGregor, Tom Holland, Oaklee Pendergast",2012,114,7.6,19
+437,Kick-Ass 2,"Action,Comedy,Crime",Jeff Wadlow,"Aaron Taylor-Johnson, Chloë Grace Moretz,Christopher Mintz-Plasse, Jim Carrey",2013,103,6.6,28.75
+438,Folk Hero & Funny Guy,Comedy,Jeff Grace,"Alex Karpovsky, Wyatt Russell, Meredith Hagner,Melanie Lynskey",2016,88,5.6,0
+439,Oz the Great and Powerful,"Adventure,Family,Fantasy",Sam Raimi,"James Franco, Michelle Williams, Rachel Weisz, Mila Kunis",2013,130,6.3,234.9
+440,Brooklyn,"Drama,Romance",John Crowley,"Saoirse Ronan, Emory Cohen, Domhnall Gleeson,Jim Broadbent",2015,117,7.5,38.32
+441,Coraline,"Animation,Family,Fantasy",Henry Selick,"Dakota Fanning, Teri Hatcher, John Hodgman, Jennifer Saunders",2009,100,7.7,75.28
+442,Blue Valentine,"Drama,Romance",Derek Cianfrance,"Ryan Gosling, Michelle Williams, John Doman,Faith Wladyka",2010,112,7.4,9.7
+443,The Thinning,Thriller,Michael J. Gallagher,"Logan Paul, Peyton List, Lia Marie Johnson,Calum Worthy",2016,81,6,0
+444,Silent Hill,"Adventure,Horror,Mystery",Christophe Gans,"Radha Mitchell, Laurie Holden, Sean Bean,Deborah Kara Unger",2006,125,6.6,46.98
+445,Dredd,"Action,Sci-Fi",Pete Travis,"Karl Urban, Olivia Thirlby, Lena Headey, Rachel Wood",2012,95,7.1,13.4
+446,Hunt for the Wilderpeople,"Adventure,Comedy,Drama",Taika Waititi,"Sam Neill, Julian Dennison, Rima Te Wiata, Rachel House",2016,101,7.9,5.2
+447,Big Hero 6,"Animation,Action,Adventure",Don Hall,"Ryan Potter, Scott Adsit, Jamie Chung,T.J. Miller",2014,102,7.8,222.49
+448,Carrie,"Drama,Horror",Kimberly Peirce,"Chloë Grace Moretz, Julianne Moore, Gabriella Wilde, Portia Doubleday",2013,100,5.9,35.27
+449,Iron Man 2,"Action,Adventure,Sci-Fi",Jon Favreau,"Robert Downey Jr., Mickey Rourke, Gwyneth Paltrow,Don Cheadle",2010,124,7,312.06
+450,Demolition,"Comedy,Drama",Jean-Marc Vallée,"Jake Gyllenhaal, Naomi Watts, Chris Cooper,Judah Lewis",2015,101,7,1.82
+451,Pandorum,"Action,Horror,Mystery",Christian Alvart,"Dennis Quaid, Ben Foster, Cam Gigandet, Antje Traue",2009,108,6.8,10.33
+452,Olympus Has Fallen,"Action,Thriller",Antoine Fuqua,"Gerard Butler, Aaron Eckhart, Morgan Freeman,Angela Bassett",2013,119,6.5,98.9
+453,I Am Number Four,"Action,Adventure,Sci-Fi",D.J. Caruso,"Alex Pettyfer, Timothy Olyphant, Dianna Agron, Teresa Palmer",2011,109,6.1,55.09
+454,Jagten,Drama,Thomas Vinterberg,"Mads Mikkelsen, Thomas Bo Larsen, Annika Wedderkopp, Lasse Fogelstrøm",2012,115,8.3,0.61
+455,The Proposal,"Comedy,Drama,Romance",Anne Fletcher,"Sandra Bullock, Ryan Reynolds, Mary Steenburgen,Craig T. Nelson",2009,108,6.7,163.95M
+456,Get Hard,"Comedy,Crime",Etan Cohen,"Will Ferrell, Kevin Hart, Alison Brie, T.I.",2015,100,6,90.35
+457,Just Go with It,"Comedy,Romance",Dennis Dugan,"Adam Sandler, Jennifer Aniston, Brooklyn Decker,Nicole Kidman",2011,117,6.4,103.03M
+458,Revolutionary Road,"Drama,Romance",Sam Mendes,"Leonardo DiCaprio, Kate Winslet, Christopher Fitzgerald, Jonathan Roumie",2008,119,7.3,22.88
+459,The Town,"Crime,Drama,Thriller",Ben Affleck,"Ben Affleck, Rebecca Hall, Jon Hamm, Jeremy Renner",2010,125,7.6,92.17
+460,The Boy,"Horror,Mystery,Thriller",William Brent Bell,"Lauren Cohan, Rupert Evans, James Russell, Jim Norton",2016,97,6,35.79
+461,Denial,"Biography,Drama",Mick Jackson,"Rachel Weisz, Tom Wilkinson, Timothy Spall, Andrew Scott",2016,109,6.6,4.07
+462,Predestination,"Drama,Mystery,Sci-Fi",Michael Spierig,"Ethan Hawke, Sarah Snook, Noah Taylor, Madeleine West",2014,97,7.5,0
+463,Goosebumps,"Adventure,Comedy,Family",Rob Letterman,"Jack Black, Dylan Minnette, Odeya Rush, Ryan Lee",2015,103,6.3,80.02
+464,Sherlock Holmes: A Game of Shadows,"Action,Adventure,Crime",Guy Ritchie,"Robert Downey Jr., Jude Law, Jared Harris, Rachel McAdams",2011,129,7.5,186.83M
+465,Salt,"Action,Crime,Mystery",Phillip Noyce,"Angelina Jolie, Liev Schreiber, Chiwetel Ejiofor, Daniel Olbrychski",2010,100,6.4,118.31M
+466,Enemy,"Mystery,Thriller",Denis Villeneuve,"Jake Gyllenhaal, Mélanie Laurent, Sarah Gadon,Isabella Rossellini",2013,91,6.9,1.01
+467,District 9,"Action,Sci-Fi,Thriller",Neill Blomkamp,"Sharlto Copley, David James, Jason Cope, Nathalie Boltt",2009,112,8,115.65M
+468,The Other Guys,"Action,Comedy,Crime",Adam McKay,"Will Ferrell, Mark Wahlberg, Derek Jeter, Eva Mendes",2010,107,6.7,119.22M
+469,American Gangster,"Biography,Crime,Drama",Ridley Scott,"denzel Washington, Russell Crowe, Chiwetel Ejiofor,Josh Brolin",2007,157,7.8,130.13M
+470,Marie Antoinette,"Biography,Drama,History",Sofia Coppola,"Kirsten Dunst, Jason Schwartzman, Rip Torn, Judy Davis",2006,123,6.4,15.96
+471,2012,"Action,Adventure,Sci-Fi",Roland Emmerich,"John Cusack, Thandie Newton, Chiwetel Ejiofor,Amanda Peet",2009,158,5.8,166.11M
+472,Harry Potter and the Half-Blood Prince,"Adventure,Family,Fantasy",David Yates,"Daniel Radcliffe, Emma Watson, Rupert Grint, Michael Gambon",2009,153,7.5,301.96
+473,Argo,"Biography,Drama,History",Ben Affleck,"Ben Affleck, Bryan Cranston, John Goodman, Alan Arkin",2012,120,7.7,136.02M
+474,Eddie the Eagle,"Biography,Comedy,Drama",Dexter Fletcher,"Taron Egerton, Hugh Jackman, Tom Costello, Jo Hartley",2016,106,7.4,15.79
+475,The Lives of Others,"Drama,Thriller",Florian Henckel von Donnersmarck,"Ulrich Mühe, Martina Gedeck,Sebastian Koch, Ulrich Tukur",2006,137,8.5,11.28
+476,Pet,"Horror,Thriller",Carles Torrens,"Dominic Monaghan, Ksenia Solo, Jennette McCurdy,Da'Vone McDonald",2016,94,5.7,0
+477,Paint It Black,Drama,Amber Tamblyn,"Alia Shawkat, Nancy Kwan, Annabelle Attanasio,Alfred Molina",2016,96,8.3,0
+478,Macbeth,"Drama,War",Justin Kurzel,"Michael        Fassbender, Marion Cotillard, Jack Madigan,Frank Madigan",2015,113,6.7,0
+479,Forgetting Sarah Marshall,"Comedy,Drama,Romance",Nicholas Stoller,"Kristen Bell, Jason Segel, Paul Rudd, Mila Kunis",2008,111,7.2,62.88
+480,The Giver,"Drama,Romance,Sci-Fi",Phillip Noyce,"Brenton Thwaites, Jeff Bridges, Meryl Streep, Taylor Swift",2014,97,6.5,45.09
+481,Triple 9,"Action,Crime,Drama",John Hillcoat,"Casey Affleck, Chiwetel Ejiofor, Anthony Mackie,Aaron Paul",2016,115,6.3,12.63
+482,Perfetti sconosciuti,"Comedy,Drama",Paolo Genovese,"Giuseppe Battiston, Anna Foglietta, Marco Giallini,Edoardo Leo",2016,97,7.7,0
+483,Angry Birds,"Animation,Action,Adventure",Clay Kaytis,"Jason Sudeikis, Josh Gad, Danny McBride, Maya Rudolph",2016,97,6.3,107.51M
+484,Moonrise Kingdom,"Adventure,Comedy,Drama",Wes Anderson,"Jared Gilman, Kara Hayward, Bruce Willis, Bill Murray",2012,94,7.8,45.51
+485,Hairspray,"Comedy,Drama,Family",Adam Shankman,"John Travolta, Queen Latifah, Nikki Blonsky,Michelle Pfeiffer",2007,117,6.7,118.82M
+486,Safe Haven,"Drama,Romance,Thriller",Lasse Hallström,"Julianne Hough, Josh Duhamel, Cobie Smulders,David Lyons",2013,115,6.7,71.35
+487,Focus,"Comedy,Crime,Drama",Glenn Ficarra,"Will Smith, Margot Robbie, Rodrigo Santoro, Adrian Martinez",2015,105,6.6,53.85
+488,Ratatouille,"Animation,Comedy,Family",Brad Bird,"Brad Garrett, Lou Romano, Patton Oswalt,Ian Holm",2007,111,8,206.44
+489,Stake Land,"Drama,Horror,Sci-Fi",Jim Mickle,"Connor Paolo, Nick Damici, Kelly McGillis, Gregory Jones",2010,98,6.5,0.02
+490,The Book of Eli,"Action,Adventure,Drama",Albert Hughes,"Denzel       Washington, Mila Kunis, Ray Stevenson, Gary Oldman",2010,118,6.9,94.82
+491,Cloverfield,"Action,Horror,Sci-Fi",Matt Reeves,"Mike Vogel, Jessica Lucas, Lizzy Caplan, T.J. Miller",2008,85,7,80.03
+492,Point Break,"Action,Crime,Sport",Ericson Core,"Edgar Ramírez, Luke Bracey, Ray Winstone, Teresa Palmer",2015,114,5.3,28.77
+493,Under the Skin,"Drama,Horror,Sci-Fi",Jonathan Glazer,"Scarlett Johansson, Jeremy McWilliams, Lynsey Taylor Mackay, Dougie McConnell",2013,108,6.3,2.61
+494,I Am Legend,"Drama,Horror,Sci-Fi",Francis Lawrence,"Will Smith, Alice Braga, Charlie Tahan, Salli Richardson-Whitfield",2007,101,7.2,256.39
+495,Men in Black 3,"Action,Adventure,Comedy",Barry Sonnenfeld,"Will Smith, Tommy Lee Jones, Josh Brolin,Jemaine Clement",2012,106,6.8,179.02M
+496,Super 8,"Mystery,Sci-Fi,Thriller",J.J. Abrams,"Elle Fanning, AJ Michalka, Kyle Chandler, Joel Courtney",2011,112,7.1,126.98M
+497,Law Abiding Citizen,"Crime,Drama,Thriller",F. Gary Gray,"Gerard Butler, Jamie Foxx, Leslie Bibb, Colm Meaney",2009,109,7.4,73.34
+498,Up,"Animation,Adventure,Comedy",Pete Docter,"Edward Asner, Jordan Nagai, John Ratzenberger, Christopher Plummer",2009,96,8.3,292.98
+499,Maze Runner: The Scorch Trials,"Action,Sci-Fi,Thriller",Wes Ball,"Dylan O'Brien, Kaya Scodelario, Thomas Brodie-Sangster,Giancarlo Esposito",2015,131,6.3,81.69
+500,Carol,"Drama,Romance",Todd Haynes,"Cate Blanchett, Rooney Mara, Sarah Paulson, Kyle Chandler",2015,118,7.2,0.25
+501,Imperium,"Crime,Drama,Thriller",Daniel Ragussis,"Daniel Radcliffe, Toni Collette, Tracy Letts, Sam Trammell",2016,109,6.5,0
+502,Youth,"Comedy,Drama,Music",Paolo Sorrentino,"Michael Caine, Harvey Keitel, Rachel Weisz, Jane Fonda",2015,124,7.3,2.7
+503,Mr. Nobody,"Drama,Fantasy,Romance",Jaco Van Dormael,"Jared Leto, Sarah Polley, Diane Kruger, Linh Dan Pham",2009,141,7.9,0
+504,City of Tiny Lights,"Crime,Drama,Thriller",Pete Travis,"Riz Ahmed, Billie Piper, James Floyd, Cush Jumbo",2016,110,5.7,0
+505,Savages,"Crime,Drama,Thriller",Oliver Stone,"Aaron Taylor-Johnson, Taylor Kitsch, Blake Lively,Benicio Del Toro",2012,131,6.5,47.31
+506,(500) Days of Summer,"Comedy,Drama,Romance",Marc Webb,"Zooey Deschanel, Joseph Gordon-Levitt, Geoffrey Arend, Chloë Grace Moretz",2009,95,7.7,32.39
+507,Movie 43,"Comedy,Romance",Elizabeth Banks,"Emma Stone, Stephen Merchant, Richard Gere, Liev Schreiber",2013,94,4.3,8.83
+508,Gravity,"Drama,Sci-Fi,Thriller",Alfonso Cuarón,"Sandra Bullock, George Clooney, Ed Harris, Orto Ignatiussen",2013,91,7.8,274.08
+509,The Boy in the Striped Pyjamas,"Drama,War",Mark Herman,"Asa Butterfield, David Thewlis, Rupert Friend, Zac Mattoon O'Brien",2008,94,7.8,9.03
+510,Shooter,"Action,Crime,Drama",Antoine Fuqua,"Mark Wahlberg, Michael Peña, Rhona Mitra, Danny Glover",2007,124,7.2,46.98
+511,The Happening,"Sci-Fi,Thriller",M. Night Shyamalan,"Mark Wahlberg, Zooey Deschanel, John Leguizamo, Ashlyn Sanchez",2008,91,5,64.51
+512,Bone Tomahawk,"Adventure,Drama,Horror",S. Craig Zahler,"Kurt Russell, Patrick Wilson, Matthew Fox, Richard Jenkins",2015,132,7.1,66.01
+513,Magic Mike XXL,"Comedy,Drama,Music",Gregory Jacobs,"Channing Tatum, Joe Manganiello, Matt Bomer,Adam Rodriguez",2015,115,5.7,0
+514,Easy A,"Comedy,Drama,Romance",Will Gluck,"Emma Stone, Amanda Bynes, Penn Badgley, Dan Byrd",2010,92,7.1,58.4
+515,Exodus: Gods and Kings,"Action,Adventure,Drama",Ridley Scott,"Christian Bale, Joel Edgerton, Ben Kingsley, Sigourney Weaver",2014,150,6,65.01
+516,Chappie,"Action,Crime,Drama",Neill Blomkamp,"Sharlto Copley, Dev Patel, Hugh Jackman,Sigourney Weaver",2015,120,6.9,31.57
+517,The Hobbit: The Desolation of Smaug,"Adventure,Fantasy",Peter Jackson,"Ian McKellen, Martin Freeman, Richard Armitage,Ken Stott",2013,161,7.9,258.36
+518,Half of a Yellow Sun,"Drama,Romance",Biyi Bandele,"Chiwetel Ejiofor, Thandie Newton, Anika Noni Rose,Joseph Mawle",2013,111,6.2,0.05
+519,Anthropoid,"Biography,History,Thriller",Sean Ellis,"Jamie Dornan, Cillian Murphy, Brian Caspe, Karel Hermánek Jr.",2016,120,7.2,2.96
+520,The Counselor,"Crime,Drama,Thriller",Ridley Scott,"Michael   fassbender, Penélope Cruz, Cameron Diaz,Javier Bardem",2013,117,5.3,16.97
+521,Viking,"Action,Drama,History",Andrey Kravchuk,"Anton Adasinsky, Aleksandr Armer, Vilen Babichev, Rostislav Bershauer",2016,133,4.7,23.05
+522,Whiskey Tango Foxtrot,"Biography,Comedy,Drama",Glenn Ficarra,"Tina Fey, Margot Robbie, Martin Freeman, Alfred Molina",2016,112,6.6,0
+523,Trust,"Crime,Drama,Thriller",David Schwimmer,"Clive Owen, Catherine Keener, Liana Liberato,Jason Clarke",2010,106,7,0.06
+524,Birth of the Dragon,"Action,Biography,Drama",George Nolfi,"Billy Magnussen, Terry Chen, Teresa Navarro,Vanessa Ross",2016,103,3.9,93.05
+525,Elysium,"Action,Drama,Sci-Fi",Neill Blomkamp,"Matt Damon, Jodie Foster, Sharlto Copley, Alice Braga",2013,109,6.6,0
+526,The Green Inferno,"Adventure,Horror",Eli Roth,"Lorenza Izzo, Ariel Levy, Aaron Burns, Kirby Bliss Blanton",2013,100,5.4,7.19
+527,Godzilla,"Action,Adventure,Sci-Fi",Gareth Edwards,"Aaron Taylor-Johnson, Elizabeth Olsen, Bryan Cranston, Ken Watanabe",2014,123,6.4,200.66
+528,The Bourne Legacy,"Action,Adventure,Mystery",Tony Gilroy,"Jeremy Renner, Rachel Weisz, Edward Norton, Scott Glenn",2012,135,6.7,113.17M
+529,A Good Year,"Comedy,Drama,Romance",Ridley Scott,"Russell Crowe, Abbie Cornish, Albert Finney, Marion Cotillard",2006,117,6.9,7.46
+530,Friend Request,"Horror,Thriller",Simon Verhoeven,"Alycia Debnam-Carey, William Moseley, Connor Paolo, Brit Morgan",2016,92,5.4,64.03
+531,Deja Vu,"Action,Sci-Fi,Thriller",Tony Scott,"Denzel Washington, Paula Patton, Jim Caviezel, Val Kilmer",2006,126,7,0
+532,Lucy,"Action,Sci-Fi,Thriller",Luc Besson,"Scarlett Johansson, Morgan Freeman, Min-sik Choi,Amr Waked",2014,89,6.4,126.55M
+533,A Quiet Passion,"Biography,Drama",Terence Davies,"Cynthia Nixon, Jennifer Ehle, Duncan Duff, Keith Carradine",2016,125,7.2,1.08
+534,Need for Speed,"Action,Crime,Drama",Scott Waugh,"Aaron Paul, Dominic Cooper, Imogen Poots, Scott Mescudi",2014,132,6.5,43.57
+535,Jack Reacher,"Action,Crime,Mystery",Christopher McQuarrie,"Tom Cruise, Rosamund Pike, Richard Jenkins, Werner Herzog",2012,130,7,58.68
+536,The Do-Over,"Action,Adventure,Comedy",Steven Brill,"Adam Sandler, David Spade, Paula Patton, Kathryn Hahn",2016,108,5.7,0.54
+537,True Crimes,"Crime,Drama,Thriller",Alexandros Avranas,"Jim Carrey, Charlotte Gainsbourg, Marton Csokas, Kati Outinen",2016,92,7.3,0
+538,American Pastoral,"Crime,Drama",Ewan McGregor,"Ewan McGregor, Jennifer Connelly, Dakota Fanning, Peter Riegert",2016,108,6.1,0
+539,The Ghost Writer,"Mystery,Thriller",Roman Polanski,"Ewan McGregor, Pierce Brosnan, Olivia Williams,Jon Bernthal",2010,128,7.2,15.52
+540,Limitless,"Mystery,Sci-Fi,Thriller",Neil Burger,"Bradley Cooper, Anna Friel, Abbie Cornish, Robert De Niro",2011,105,7.4,79.24
+541,Spectral,"Action,Mystery,Sci-Fi",Nic Mathieu,"James Badge Dale, Emily Mortimer, Bruce Greenwood,Max Martini",2016,107,6.3,0
+542,P.S. I Love You,"Drama,Romance",Richard LaGravenese,"Hilary Swank, Gerard Butler, Harry Connick Jr., Lisa Kudrow",2007,126,7.1,53.68
+543,Zipper,"Drama,Thriller",Mora Stephens,"Patrick Wilson, Lena Headey, Ray Winstone,Richard Dreyfuss",2015,103,5.7,0
+544,Midnight Special,"Drama,Mystery,Sci-Fi",Jeff Nichols,"Michael Shannon, Joel Edgerton, Kirsten Dunst, Adam Driver",2016,112,6.7,3.71
+545,Don't Think Twice,"Comedy,Drama",Mike Birbiglia,"Keegan-Michael Key, Gillian Jacobs, Mike Birbiglia,Chris Gethard",2016,92,6.8,4.42
+546,Alice in Wonderland,"Adventure,Family,Fantasy",Tim Burton,"Mia Wasikowska, Johnny Depp, Helena Bonham Carter,Anne Hathaway",2010,108,6.5,334.19
+547,Chuck,"Biography,Drama,Sport",Philippe Falardeau,"Elisabeth Moss, Naomi Watts, Ron Perlman, Liev Schreiber",2016,98,6.8,0.11
+548,"I, Daniel Blake",Drama,Ken Loach,"Dave Johns, Hayley Squires, Sharon Percy, Briana Shann",2016,100,7.9,0
+549,The Break-Up,"Comedy,Drama,Romance",Peyton Reed,"Jennifer Aniston, Vince Vaughn, Jon Favreau, Joey Lauren Adams",2006,106,5.8,118.68M
+550,Loving,"Biography,Drama,Romance",Jeff Nichols,"Ruth Negga, Joel Edgerton, Will Dalton, Dean Mumford",2016,123,7.1,7.7
+551,Fantastic Four,"Action,Adventure,Sci-Fi",Josh Trank,"Miles Teller, Kate Mara, Michael B. Jordan, Jamie Bell",2015,100,4.3,56.11
+552,The Survivalist,"Drama,Sci-Fi,Thriller",Stephen Fingleton,"Mia Goth, Martin McCann, Barry Ward, Andrew Simpson",2015,104,6.3,0
+553,Colonia,"Drama,Romance,Thriller",Florian Gallenberger,"Emma Watson, Daniel Brühl, Michael Nyqvist,Richenda Carey",2015,106,7.1,0
+554,The Boy Next Door,"Mystery,Thriller",Rob Cohen,"Jennifer Lopez, Ryan Guzman, Kristin Chenoweth, John Corbett",2015,91,4.6,35.39
+555,The Gift,"Mystery,Thriller",Joel Edgerton,"Jason Bateman, Rebecca Hall, Joel Edgerton, Allison Tolman",2015,108,7.1,43.77
+556,Dracula Untold,"Action,Drama,Fantasy",Gary Shore,"Luke Evans, Dominic Cooper, Sarah Gadon, Art Parkinson",2014,92,6.3,55.94
+557,In the Heart of the Sea,"Action,Adventure,Biography",Ron Howard,"Chris Hemsworth, Cillian Murphy, Brendan Gleeson,Ben Whishaw",2015,122,6.9,24.99
+558,Idiocracy,"Adventure,Comedy,Sci-Fi",Mike Judge,"Luke Wilson, Maya Rudolph, Dax Shepard, Terry Crews",2006,84,6.6,0.44
+559,The Expendables,"Action,Adventure,Thriller",Sylvester Stallone,"Sylvester Stallone, Jason Statham, Jet Li, Dolph Lundgren",2010,103,6.5,102.98M
+560,Evil Dead,"Fantasy,Horror",Fede Alvarez,"Jane Levy, Shiloh Fernandez, Jessica Lucas, Lou Taylor Pucci",2013,91,6.5,54.24
+561,Sinister,"Horror,Mystery",Scott Derrickson,"Ethan Hawke, Juliet Rylance, James Ransone,Fred Dalton Thompson",2012,110,6.8,48.06
+562,Wreck-It Ralph,"Animation,Adventure,Comedy",Rich Moore,"John C. Reilly, Jack McBrayer, Jane Lynch, Sarah Silverman",2012,101,7.8,189.41M
+563,Snow White and the Huntsman,"Action,Adventure,Drama",Rupert Sanders,"Kristen Stewart, Chris Hemsworth, Charlize Theron, Sam Claflin",2012,127,6.1,155.11M
+564,Pan,"Adventure,Family,Fantasy",Joe Wright,"Levi Miller, Hugh Jackman, Garrett Hedlund, Rooney Mara",2015,111,5.8,34.96
+565,Transformers: Dark of the Moon,"Action,Adventure,Sci-Fi",Michael Bay,"Shia LaBeouf, Rosie Huntington-Whiteley, Tyrese Gibson, Josh Duhamel",2011,154,6.3,352.36
+566,Juno,"Comedy,Drama",Jason Reitman,"Ellen Page, Michael Cera, Jennifer Garner, Jason Bateman",2007,96,7.5,143.49M
+567,A Hologram for the King,"Comedy,Drama",Tom Tykwer,"Tom Hanks, Sarita Choudhury, Ben Whishaw,Alexander Black",2016,98,6.1,4.2
+568,Money Monster,"Crime,Drama,Thriller",Jodie Foster,"George Clooney, Julia Roberts, Jack O'Connell,Dominic West",2016,98,6.5,41.01
+569,The Other Woman,"Comedy,Romance",Nick Cassavetes,"Cameron Diaz, Leslie Mann, Kate Upton, Nikolaj Coster-Waldau",2014,109,6,83.91
+570,Enchanted,"Animation,Comedy,Family",Kevin Lima,"Amy Adams, Susan Sarandon, James Marsden, Patrick Dempsey",2007,107,7.1,127.71M
+571,The Intern,"Comedy,Drama",Nancy Meyers,"Robert De Niro, Anne Hathaway, Rene Russo,Anders Holm",2015,121,7.1,75.27
+572,Little Miss Sunshine,"Comedy,Drama",Jonathan Dayton,"Steve Carell, Toni Collette, Greg Kinnear, Abigail Breslin",2006,101,7.8,59.89
+573,Bleed for This,"Biography,Drama,Sport",Ben Younger,"Miles Teller, Aaron Eckhart, Katey Sagal, Ciarán Hinds",2016,117,6.8,4.85
+574,Clash of the Titans,"Action,Adventure,Fantasy",Louis Leterrier,"Sam Worthington, Liam Neeson, Ralph Fiennes,Jason Flemyng",2010,106,5.8,163.19M
+575,The Finest Hours,"Action,Drama,History",Craig Gillespie,"Chris Pine, Casey Affleck, Ben Foster, Eric Bana",2016,117,6.8,27.55
+576,Tron,"Action,Adventure,Sci-Fi",Joseph Kosinski,"Jeff Bridges, Garrett Hedlund, Olivia Wilde, Bruce Boxleitner",2010,125,6.8,172.05M
+577,The Hunger Games: Catching Fire,"Action,Adventure,Mystery",Francis Lawrence,"Jennifer Lawrence, Josh Hutcherson, Liam Hemsworth, Philip Seymour Hoffman",2013,146,7.6,424.65
+578,All Good Things,"Crime,Drama,Mystery",Andrew Jarecki,"Ryan Gosling, Kirsten Dunst, Frank Langella, Lily Rabe",2010,101,6.3,0.58
+579,Kickboxer: Vengeance,Action,John Stockwell,"Dave Bautista, Alain Moussi, Gina Carano, Jean-Claude Van Damme",2016,90,4.9,131.56M
+580,The Last Airbender,"Action,Adventure,Family",M. Night Shyamalan,"Noah Ringer, Nicola Peltz, Jackson Rathbone,Dev Patel",2010,103,4.2,0
+581,Sex Tape,"Comedy,Romance",Jake Kasdan,"Jason Segel, Cameron Diaz, Rob Corddry, Ellie Kemper",2014,94,5.1,38.54
+582,What to Expect When You're Expecting,"Comedy,Drama,Romance",Kirk Jones,"Cameron Diaz, Matthew Morrison, J. Todd Smith, Dennis Quaid",2012,110,5.7,41.1
+583,Moneyball,"Biography,Drama,Sport",Bennett Miller,"Brad Pitt, Robin Wright, Jonah Hill, Philip Seymour Hoffman",2011,133,7.6,75.61
+584,Ghost Rider,"Action,Fantasy,Thriller",Mark Steven Johnson,"Nicolas Cage, Eva Mendes, Sam Elliott, Matt Long",2007,114,5.2,115.8M
+585,Unbroken,"Biography,Drama,Sport",Angelina Jolie,"Jack O'Connell, Miyavi, Domhnall Gleeson, Garrett Hedlund",2014,137,7.2,115.6M
+586,Immortals,"Action,Drama,Fantasy",Tarsem Singh,"Henry Cavill, Mickey Rourke, John Hurt, Stephen Dorff",2011,110,6,83.5
+587,Sunshine,"Adventure,Sci-Fi,Thriller",Danny Boyle,"Cillian Murphy, Rose Byrne, Chris Evans, Michelle Yeoh",2007,107,7.3,3.68
+588,Brave,"Animation,Adventure,Comedy",Mark Andrews,"Kelly Macdonald,Billy Connolly, Emma Thompson, Julie Walters",2012,93,7.2,237.28
+589,Män som hatar kvinnor,"Drama,Mystery,Thriller",Niels Arden Oplev,"Michael Nyqvist, Noomi Rapace, Ewa Fröling,Lena Endre",2009,152,7.8,10.1
+590,Adoration,"Drama,Romance",Anne Fontaine,"Naomi Watts, Robin Wright, Xavier Samuel, James Frecheville",2013,112,6.2,0.32
+591,The Drop,"Crime,Drama,Mystery",Michaël R. Roskam,"Tom Hardy, Noomi Rapace, James Gandolfini,Matthias Schoenaerts",2014,106,7.1,10.72
+592,She's the Man,"Comedy,Romance,Sport",Andy Fickman,"Amanda Bynes, Laura Ramsey, Channing Tatum,Vinnie Jones",2006,105,6.4,2.34
+593,Daddy's Home,"Comedy,Family",Sean Anders,"Will Ferrell, Mark Wahlberg, Linda Cardellini, Thomas Haden Church",2015,96,6.1,150.32M
+594,Let Me In,"Drama,Horror,Mystery",Matt Reeves,"Kodi Smit-McPhee, Chloë Grace Moretz, Richard Jenkins, Cara Buono",2010,116,7.2,12.13
+595,Never Back Down,"Action,Drama,Sport",Jeff Wadlow,"Sean Faris, Djimon Hounsou, Amber Heard, Cam Gigandet",2008,110,6.6,24.85
+596,Grimsby,"Action,Adventure,Comedy",Louis Leterrier,"Sacha Baron Cohen, Mark Strong, Rebel Wilson,Freddie Crowder",2016,83,6.2,6.86
+597,Moon,"Drama,Mystery,Sci-Fi",Duncan Jones,"Sam Rockwell, Kevin Spacey, Dominique McElligott,Rosie Shaw",2009,97,7.9,5.01
+598,Megamind,"Animation,Action,Comedy",Tom McGrath,"Will Ferrell, Jonah Hill, Brad Pitt, Tina Fey",2010,95,7.3,148.34M
+599,Gangster Squad,"Action,Crime,Drama",Ruben Fleischer,"Sean Penn, Ryan Gosling, Emma Stone, Giovanni Ribisi",2013,113,6.7,46
+600,Blood Father,"Action,Crime,Drama",Jean-François Richet,"Mel Gibson, Erin Moriarty, Diego Luna, Michael Parks",2016,88,6.4,93.95
+601,He's Just Not That Into You,"Comedy,Drama,Romance",Ken Kwapis,"Jennifer Aniston, Jennifer Connelly, Morgan Lily,Trenton Rogers",2009,129,6.4,0
+602,Kung Fu Panda 3,"Animation,Action,Adventure",Alessandro Carloni,"Jack Black, Bryan Cranston, Dustin Hoffman, Angelina Jolie",2016,95,7.2,143.52M
+603,The Rise of the Krays,"Crime,Drama",Zackary Adler,"Matt Vael, Simon Cotton, Kevin Leslie, Olivia Moyles",2015,110,5.1,6.53
+604,Handsome Devil,Drama,John Butler,"Fionn O'Shea, Nicholas Galitzine, Andrew Scott, Moe Dunford",2016,95,7.4,0
+605,Winter's Bone,Drama,Debra Granik,"Jennifer Lawrence, John Hawkes, Garret Dillahunt,Isaiah Stone",2010,100,7.2,0
+606,Horrible Bosses,"Comedy,Crime",Seth Gordon,"Jason Bateman, Charlie Day, Jason Sudeikis, Steve Wiebe",2011,98,6.9,117.53M
+607,Mommy,Drama,Xavier Dolan,"Anne Dorval, Antoine-Olivier Pilon, Suzanne Clément,Patrick Huard",2014,139,8.1,3.49
+608,Hellboy II: The Golden Army,"Action,Adventure,Fantasy",Guillermo del Toro,"Ron Perlman, Selma Blair, Doug Jones, John Alexander",2008,120,7,75.75
+609,Beautiful Creatures,"Drama,Fantasy,Romance",Richard LaGravenese,"Alice Englert, Viola Davis, Emma Thompson,Alden Ehrenreich",2013,124,6.2,19.45
+610,Toni Erdmann,"Comedy,Drama",Maren Ade,"Sandra Hüller, Peter Simonischek, Michael Wittenborn,Thomas Loibl",2016,162,7.6,1.48
+611,The Lovely Bones,"Drama,Fantasy,Thriller",Peter Jackson,"Rachel Weisz, Mark Wahlberg, Saoirse Ronan, Susan Sarandon",2009,135,6.7,43.98
+612,The Assassination of Jesse James by the Coward Robert Ford,"Biography,Crime,Drama",Andrew Dominik,"Brad Pitt, Casey Affleck, Sam Shepard, Mary-Louise Parker",2007,160,7.5,3.9
+613,Don Jon,"Comedy,Drama,Romance",Joseph Gordon-Levitt,"Joseph Gordon-Levitt, Scarlett Johansson,Julianne Moore, Tony Danza",2013,90,6.6,24.48
+614,Bastille Day,"Action,Crime,Drama",James Watkins,"Idris Elba, Richard Madden, Charlotte Le Bon, Kelly Reilly",2016,92,6.3,0.04
+615,2307: Winter's Dream,Sci-Fi,Joey Curtis,"Paul Sidhu, Branden Coles, Arielle Holmes, Kelcey Watson",2016,101,4,20.76
+616,Free State of Jones,"Action,Biography,Drama",Gary Ross,"Matthew McConaughey, Gugu Mbatha-Raw, Mahershala Ali, Keri Russell",2016,139,6.9,0
+617,Mr. Right,"Action,Comedy,Romance",Paco Cabezas,"Anna Kendrick, Sam Rockwell, Tim Roth, James Ransone",2015,95,6.3,0.03
+618,The Secret Life of Walter Mitty,"Adventure,Comedy,Drama",Ben Stiller,"Ben Stiller, Kristen Wiig, Jon Daly, Kathryn Hahn",2013,114,7.3,58.23
+619,Dope,"Comedy,Crime,Drama",Rick Famuyiwa,"Shameik Moore, Tony Revolori, Kiersey Clemons,Kimberly Elise",2015,103,7.3,17.47
+620,Underworld Awakening,"Action,Fantasy,Horror",Måns Mårlind,"Kate Beckinsale, Michael Ealy, India Eisley, Stephen Rea",2012,88,6.4,62.32
+621,Antichrist,"Drama,Horror",Lars von Trier,"Willem Dafoe, Charlotte Gainsbourg, Storm Acheche Sahlstrøm",2009,108,6.6,0.4
+622,Friday the 13th,Horror,Marcus Nispel,"Jared Padalecki, Amanda Righetti, Derek Mears,Danielle Panabaker",2009,97,5.6,65
+623,Taken 3,"Action,Thriller",Olivier Megaton,"Liam Neeson, Forest Whitaker, Maggie Grace,Famke Janssen",2014,109,6,89.25
+624,Total Recall,"Action,Adventure,Mystery",Len Wiseman,"Colin Farrell, Bokeem Woodbine, Bryan Cranston,Kate Beckinsale",2012,118,6.3,58.88
+625,X-Men: The Last Stand,"Action,Adventure,Fantasy",Brett Ratner,"Patrick Stewart, Hugh Jackman, Halle Berry, Famke Janssen",2006,104,6.7,234.36
+626,The Escort,"Comedy,Drama,Romance",Will Slocombe,"Lyndsy Fonseca, Michael Doneger, Tommy Dewey,Bruce Campbell",2016,88,6,0
+627,The Whole Truth,"Crime,Drama,Mystery",Courtney Hunt,"Keanu Reeves, Renée Zellweger, Gugu Mbatha-Raw, Gabriel Basso",2016,93,6.1,0
+628,Night at the Museum: Secret of the Tomb,"Adventure,Comedy,Family",Shawn Levy,"Ben Stiller, Robin Williams, Owen Wilson, Dick Van Dyke",2014,98,6.2,113.73M
+629,Love & Other Drugs,"Comedy,Drama,Romance",Edward Zwick,"Jake Gyllenhaal, Anne Hathaway, Judy Greer, Oliver Platt",2010,112,6.7,32.36
+630,The Interview,Comedy,Evan Goldberg,"James Franco, Seth Rogen, Randall Park, Lizzy Caplan",2014,112,6.6,6.11
+631,The Host,"Comedy,Drama,Horror",Bong Joon Ho,"Kang-ho Song, Hee-Bong Byun, Hae-il Park, Doona Bae",2006,120,7,2.2
+632,Megan Is Missing,"Drama,Horror,Thriller",Michael Goi,"Amber Perkins, Rachel Quinn, Dean Waite, Jael Elizabeth Steinmeyer",2011,85,4.9,0
+633,WALL·E,"Animation,Adventure,Family",Andrew Stanton,"Ben Burtt, Elissa Knight, Jeff Garlin, Fred Willard",2008,98,8.4,223.81
+634,Knocked Up,"Comedy,Romance",Judd Apatow,"Seth Rogen, Katherine Heigl, Paul Rudd, Leslie Mann",2007,129,7,148.73M
+635,Source Code,"Mystery,Romance,Sci-Fi",Duncan Jones,"Jake Gyllenhaal, Michelle Monaghan, Vera Farmiga,Jeffrey Wright",2011,93,7.5,54.7
+636,Lawless,"Crime,Drama",John Hillcoat,"Tom Hardy, Shia LaBeouf, Guy Pearce, Jason Clarke",2012,116,7.3,37.4
+637,Unfriended,"Drama,Horror,Mystery",Levan Gabriadze,"Heather Sossaman, Matthew Bohrer, Courtney Halverson, Shelley Hennig",2014,83,5.6,31.54
+638,American Reunion,Comedy,Jon Hurwitz,"Jason Biggs, Alyson Hannigan,Seann William Scott, Chris Klein",2012,113,6.7,56.72
+639,The Pursuit of Happyness,"Biography,Drama",Gabriele Muccino,"Will Smith, Thandie Newton, Jaden Smith, Brian Howe",2006,117,8,162.59M
+640,Relatos salvajes,"Comedy,Drama,Thriller",Damián Szifron,"Darío Grandinetti, María Marull, Mónica Villa, Rita Cortese",2014,122,8.1,3.08
+641,The Ridiculous 6,"Comedy,Western",Frank Coraci,"Adam Sandler, Terry Crews, Jorge Garcia, Taylor Lautner",2015,119,4.8,0
+642,Frantz,"Drama,History,War",François Ozon,"Pierre Niney, Paula Beer, Ernst Stötzner, Marie Gruber",2016,113,7.5,0.86
+643,Viral,"Drama,Horror,Sci-Fi",Henry Joost,"Sofia Black-D'Elia, Analeigh Tipton,Travis Tope, Michael Kelly",2016,85,5.5,0
+644,Gran Torino,Drama,Clint Eastwood,"Clint Eastwood, Bee Vang, Christopher Carley,Ahney Her",2008,116,8.2,148.09M
+645,Burnt,"Comedy,Drama",John Wells,"Bradley Cooper, Sienna Miller, Daniel Brühl, Riccardo Scamarcio",2015,101,6.6,13.65
+646,Tall Men,"Fantasy,Horror,Thriller",Jonathan Holbrook,"Dan Crisafulli, Kay Whitney, Richard Garcia, Pat Cashman",2016,133,3.2,0
+647,Sleeping Beauty,"Drama,Romance",Julia Leigh,"Emily Browning, Rachael Blake, Ewen Leslie, Bridgette Barrett",2011,101,5.3,0.03
+648,Vampire Academy,"Action,Comedy,Fantasy",Mark Waters,"Zoey Deutch, Lucy Fry, Danila Kozlovsky, Gabriel Byrne",2014,104,5.6,7.79
+649,Sweeney Todd: The Demon Barber of Fleet Street,"Drama,Horror,Musical",Tim Burton,"Johnny Depp, Helena Bonham Carter, Alan Rickman,Timothy Spall",2007,116,7.4,52.88
+650,Solace,"Crime,Drama,Mystery",Afonso Poyart,"Anthony Hopkins, Jeffrey Dean Morgan, Abbie Cornish, Colin Farrell",2015,101,6.4,0
+651,Insidious,"Horror,Mystery,Thriller",James Wan,"Patrick Wilson, Rose Byrne, Ty Simpkins, Lin Shaye",2010,103,6.8,53.99
+652,Popstar: Never Stop Never Stopping,"Comedy,Music",Akiva Schaffer,"Andy Samberg, Jorma Taccone,Akiva Schaffer, Sarah Silverman",2016,87,6.7,9.39
+653,The Levelling,Drama,Hope Dickson Leach,"Ellie Kendrick, David Troughton, Jack Holden,Joe Blakemore",2016,83,6.4,0
+654,Public Enemies,"Biography,Crime,Drama",Michael Mann,"Christian Bale, Johnny Depp, Christian Stolte, Jason Clarke",2009,140,7,97.03
+655,Boyhood,Drama,Richard Linklater,"Ellar Coltrane, Patricia Arquette, Ethan Hawke,Elijah Smith",2014,165,7.9,25.36
+656,Teenage Mutant Ninja Turtles,"Action,Adventure,Comedy",Jonathan Liebesman,"Megan Fox, Will Arnett, William Fichtner, Noel Fisher",2014,101,5.9,190.87M
+657,Eastern Promises,"Crime,Drama,Mystery",David Cronenberg,"Naomi Watts, Viggo Mortensen, Armin Mueller-Stahl, Josef Altin",2007,100,7.7,17.11
+658,The Daughter,Drama,Simon Stone,"Geoffrey Rush, Nicholas Hope, Sam Neill, Ewen Leslie",2015,96,6.7,0.03
+659,Pineapple Express,"Action,Comedy,Crime",David Gordon Green,"Seth Rogen, James Franco, Gary Cole, Danny McBride",2008,111,7,87.34
+660,The First Time,"Comedy,Drama,Romance",Jon Kasdan,"Dylan O'Brien, Britt Robertson, Victoria Justice, James Frecheville",2012,95,6.9,0.02
+661,Gone Baby Gone,"Crime,Drama,Mystery",Ben Affleck,"Morgan Freeman, Ed Harris, Casey Affleck, Michelle Monaghan",2007,114,7.7,20.3
+662,The Heat,"Action,Comedy,Crime",Paul Feig,"Sandra Bullock, Michael McDonald, Melissa McCarthy,Demián Bichir",2013,117,6.6,159.58M
+663,L'avenir,Drama,Mia Hansen-Løve,"Isabelle Huppert, André Marcon, Roman Kolinka,Edith Scob",2016,102,7.1,0.28
+664,Anna Karenina,"Drama,Romance",Joe Wright,"Keira Knightley, Jude Law, Aaron Taylor-Johnson,Matthew Macfadyen",2012,129,6.6,12.8
+665,Regression,"Crime,Drama,Mystery",Alejandro Amenábar,"Ethan Hawke, David Thewlis, Emma Watson,Dale Dickey",2015,106,5.7,0.05
+666,Ted 2,"Adventure,Comedy,Romance",Seth MacFarlane,"Mark Wahlberg, Seth MacFarlane, Amanda Seyfried, Jessica Barth",2015,115,6.3,81.26
+667,Pain & Gain,"Comedy,Crime,Drama",Michael Bay,"Mark Wahlberg, Dwayne Johnson, Anthony Mackie,Tony Shalhoub",2013,129,6.5,49.87
+668,Blood Diamond,"Adventure,Drama,Thriller",Edward Zwick,"Leonardo DiCaprio, Djimon Hounsou, Jennifer Connelly, Kagiso Kuypers",2006,143,8,57.37
+669,Devil's Knot,"Biography,Crime,Drama",Atom Egoyan,"Colin Firth, Reese Witherspoon, Alessandro Nivola,James Hamrick",2013,114,6.1,0
+670,Child 44,"Crime,Drama,Thriller",Daniel Espinosa,"Tom Hardy, Gary Oldman, Noomi Rapace, Joel Kinnaman",2015,137,6.5,1.21
+671,The Hurt Locker,"Drama,History,Thriller",Kathryn Bigelow,"Jeremy Renner, Anthony Mackie, Brian Geraghty,Guy Pearce",2008,131,7.6,15.7
+672,Green Lantern,"Action,Adventure,Sci-Fi",Martin Campbell,"Ryan Reynolds, Blake Lively, Peter Sarsgaard,Mark Strong",2011,114,5.6,116.59M
+673,War on Everyone,"Action,Comedy",John Michael McDonagh,"Alexander Skarsgård, Michael Peña, Theo James, Tessa Thompson",2016,98,5.9,0
+674,The Mist,Horror,Frank Darabont,"Thomas Jane, Marcia Gay Harden, Laurie Holden,Andre Braugher",2007,126,7.2,25.59
+675,Escape Plan,"Action,Crime,Mystery",Mikael Håfström,"Sylvester Stallone, Arnold Schwarzenegger, 50 Cent, Vincent D'Onofrio",2013,115,6.7,25.12
+676,"Love, Rosie","Comedy,Romance",Christian Ditter,"Lily Collins, Sam Claflin, Christian Cooke, Jaime Winstone",2014,102,7.2,0.01
+677,The DUFF,Comedy,Ari Sandel,"Mae Whitman, Bella Thorne, Robbie Amell, Allison Janney",2015,101,6.5,34.02
+678,The Age of Shadows,"Action,Drama,Thriller",Jee-woon Kim,"Byung-hun Lee, Yoo Gong, Kang-ho Song, Ji-min Han",2016,140,7.2,0.54
+679,The Hunger Games: Mockingjay - Part 1,"Action,Adventure,Sci-Fi",Francis Lawrence,"Jennifer Lawrence, Josh Hutcherson, Liam Hemsworth, Woody Harrelson",2014,123,6.7,337.1
+680,We Need to Talk About Kevin,"Drama,Mystery,Thriller",Lynne Ramsay,"Tilda Swinton, John C. Reilly, Ezra Miller, Jasper Newell",2011,112,7.5,1.74
+681,Love & Friendship,"Comedy,Drama,Romance",Whit Stillman,"Kate Beckinsale, Chloë Sevigny, Xavier Samuel,Emma Greenwell",2016,90,6.5,14.01
+682,The Mortal Instruments: City of Bones,"Action,Fantasy,Horror",Harald Zwart,"Lily Collins, Jamie Campbell Bower, Robert Sheehan,Jemima West",2013,130,5.9,31.17
+683,Seven Pounds,"Drama,Romance",Gabriele Muccino,"Will Smith, Rosario Dawson, Woody Harrelson,Michael Ealy",2008,123,7.7,69.95
+684,The King's Speech,"Biography,Drama",Tom Hooper,"Colin Firth, Geoffrey Rush, Helena Bonham Carter,Derek Jacobi",2010,118,8,138.8M
+685,Hunger,"Biography,Drama",Steve McQueen,"Stuart Graham, Laine Megaw, Brian Milligan, Liam McMahon",2008,96,7.6,0.15
+686,Jumper,"Action,Adventure,Sci-Fi",Doug Liman,"Hayden Christensen, Samuel L. Jackson, Jamie Bell,Rachel Bilson",2008,88,6.1,80.17
+687,Toy Story 3,"Animation,Adventure,Comedy",Lee Unkrich,"Tom Hanks, Tim Allen, Joan Cusack, Ned Beatty",2010,103,8.3,414.98
+688,Tinker Tailor Soldier Spy,"Drama,Mystery,Thriller",Tomas Alfredson,"Gary Oldman, Colin Firth, Tom Hardy, Mark Strong",2011,122,7.1,24.1
+689,Resident Evil: Retribution,"Action,Horror,Sci-Fi",Paul W.S. Anderson,"Milla Jovovich, Sienna Guillory, Michelle Rodriguez, Aryana Engineer",2012,96,5.4,42.35
+690,Dear Zindagi,"Drama,Romance",Gauri Shinde,"Alia Bhatt, Shah Rukh Khan, Kunal Kapoor, Priyanka Moodley",2016,151,7.8,1.4
+691,Genius,"Biography,Drama",Michael Grandage,"Colin Firth, Jude Law, Nicole Kidman, Laura Linney",2016,104,6.5,1.36
+692,Pompeii,"Action,Adventure,Drama",Paul W.S. Anderson,"Kit Harington, Emily Browning, Kiefer Sutherland, Adewale Akinnuoye-Agbaje",2014,105,5.5,23.22
+693,Life of Pi,"Adventure,Drama,Fantasy",Ang Lee,"Suraj Sharma, Irrfan Khan, Adil Hussain, Tabu",2012,127,7.9,124.98M
+694,Hachi: A Dog's Tale,"Drama,Family",Lasse Hallström,"Richard Gere, Joan Allen, Cary-Hiroyuki Tagawa,Sarah Roemer",2009,93,8.1,0
+695,10 Years,"Comedy,Drama,Romance",Jamie Linden,"Channing Tatum, Rosario Dawson, Chris Pratt, Jenna Dewan Tatum",2011,100,6.1,0.2
+696,I Origins,"Drama,Romance,Sci-Fi",Mike Cahill,"Michael Pitt, Steven Yeun, Astrid Bergès-Frisbey, Brit Marling",2014,106,7.3,0.33
+697,Live Free or Die Hard,"Action,Adventure,Thriller",Len Wiseman,"Bruce Willis, Justin Long, Timothy Olyphant, Maggie Q",2007,128,7.2,134.52M
+698,The Matchbreaker,"Comedy,Romance",Caleb Vetter,"Wesley Elder, Christina Grimmie, Osric Chau, Olan Rogers",2016,94,5.5,0
+699,Funny Games,"Crime,Drama,Horror",Michael Haneke,"Naomi Watts, Tim Roth, Michael Pitt, Brady Corbet",2007,111,6.5,1.29
+700,Ted,"Comedy,Fantasy",Seth MacFarlane,"Mark Wahlberg, Mila Kunis, Seth MacFarlane, Joel McHale",2012,106,7,218.63
+701,RED,"Action,Comedy,Crime",Robert Schwentke,"Bruce Willis, Helen Mirren, Morgan Freeman,Mary-Louise Parker",2010,111,7.1,90.36
+702,Australia,"Adventure,Drama,Romance",Baz Luhrmann,"Nicole Kidman, Hugh Jackman, Shea Adams, Eddie Baroo",2008,165,6.6,49.55
+703,Faster,"Action,Crime,Drama",George Tillman Jr.,"Dwayne Johnson, Billy Bob Thornton, Maggie Grace, Mauricio Lopez",2010,98,6.5,23.23
+704,The Neighbor,"Crime,Horror,Thriller",Marcus Dunstan,"Josh Stewart, Bill Engvall, Alex Essoe, Ronnie Gene Blevins",2016,87,5.8,0
+705,The Adjustment Bureau,"Romance,Sci-Fi,Thriller",George Nolfi,"Matt Damon, Emily Blunt, Lisa Thoreson, Florence Kastriner",2011,106,7.1,62.45
+706,The Hollars,"Comedy,Drama,Romance",John Krasinski,"Sharlto Copley, Charlie Day, Richard Jenkins, Anna Kendrick",2016,88,6.5,1.02
+707,The Judge,"Crime,Drama",David Dobkin,"Robert Downey Jr., Robert Duvall, Vera Farmiga, Billy Bob Thornton",2014,141,7.4,47.11
+708,Closed Circuit,"Crime,Drama,Mystery",John Crowley,"Eric Bana, Rebecca Hall, Jim Broadbent, Ciarán Hinds",2013,96,6.2,5.73
+709,Transformers: Revenge of the Fallen,"Action,Adventure,Sci-Fi",Michael Bay,"Shia LaBeouf, Megan Fox, Josh Duhamel, Tyrese Gibson",2009,150,6,402.08
+710,La tortue rouge,"Animation,Fantasy",Michael Dudok de Wit,"Emmanuel Garijo, Tom Hudson, Baptiste Goy, Axel Devillers",2016,80,7.6,0.92
+711,The Book of Life,"Animation,Adventure,Comedy",Jorge R. Gutiérrez,"Diego Luna, Zoe Saldana, Channing Tatum, Ron Perlman",2014,95,7.3,50.15
+712,Incendies,"Drama,Mystery,War",Denis Villeneuve,"Lubna Azabal, Mélissa Désormeaux-Poulin, Maxim Gaudette, Mustafa Kamel",2010,131,8.2,6.86
+713,The Heartbreak Kid,"Comedy,Romance",Bobby Farrelly,"Ben Stiller, Michelle Monaghan,Malin Akerman, Jerry Stiller",2007,116,5.8,36.77
+714,Happy Feet,"Animation,Comedy,Family",George Miller,"Elijah Wood, Brittany Murphy, Hugh Jackman, Robin Williams",2006,108,6.5,197.99M
+715,Entourage,Comedy,Doug Ellin,"Adrian Grenier, Kevin Connolly, Jerry Ferrara, Kevin Dillon",2015,104,6.6,32.36
+716,The Strangers,"Horror,Mystery,Thriller",Bryan Bertino,"Scott Speedman, Liv Tyler, Gemma Ward, Alex Fisher",2008,86,6.2,52.53
+717,Noah,"Action,Adventure,Drama",Darren Aronofsky,"Russell Crowe, Jennifer Connelly, Anthony Hopkins, Emma Watson",2014,138,5.8,101.16M
+718,Neighbors,Comedy,Nicholas Stoller,"Seth Rogen, Rose Byrne, Zac Efron, Lisa Kudrow",2014,97,6.4,150.06M
+719,Nymphomaniac: Vol. II,Drama,Lars von Trier,"Charlotte Gainsbourg, Stellan Skarsgård, Willem Dafoe, Jamie Bell",2013,123,6.7,0.33
+720,Wild,"Adventure,Biography,Drama",Jean-Marc Vallée,"Reese Witherspoon, Laura Dern, Gaby Hoffmann,Michiel Huisman",2014,115,7.1,37.88
+721,Grown Ups,Comedy,Dennis Dugan,"Adam Sandler, Salma Hayek, Kevin James, Chris Rock",2010,102,6,162M
+722,Blair Witch,"Horror,Thriller",Adam Wingard,"James Allen McCune, Callie Hernandez, Corbin Reid, Brandon Scott",2016,89,5.1,20.75
+723,The Karate Kid,"Action,Drama,Family",Harald Zwart,"Jackie Chan, Jaden Smith, Taraji P. Henson, Wenwen Han",2010,140,6.2,176.59M
+724,Dark Shadows,"Comedy,Fantasy,Horror",Tim Burton,"Johnny Depp, Michelle Pfeiffer, Eva Green, Helena Bonham Carter",2012,113,6.2,79.71
+725,Friends with Benefits,"Comedy,Romance",Will Gluck,"Mila Kunis, Justin Timberlake, Patricia Clarkson, Jenna Elfman",2011,109,6.6,55.8
+726,The Illusionist,"Drama,Mystery,Romance",Neil Burger,"Edward Norton, Jessica Biel, Paul Giamatti, Rufus Sewell",2006,110,7.6,39.83
+727,The A-Team,"Action,Adventure,Comedy",Joe Carnahan,"Liam Neeson, Bradley Cooper, Sharlto Copley,Jessica Biel",2010,117,6.8,77.21
+728,The Guest,Thriller,Adam Wingard,"Dan Stevens, Sheila Kelley, Maika Monroe, Joel David Moore",2014,100,6.7,0.32
+729,The Internship,Comedy,Shawn Levy,"Vince Vaughn, Owen Wilson, Rose Byrne, Aasif Mandvi",2013,119,6.3,44.67
+730,Paul,"Adventure,Comedy,Sci-Fi",Greg Mottola,"Simon Pegg, Nick Frost, Seth Rogen, Mia Stallard",2011,104,7,37.37
+731,This Beautiful Fantastic,"Comedy,Drama,Fantasy",Simon Aboud,"Jessica Brown Findlay, Andrew Scott, Jeremy Irvine,Tom Wilkinson",2016,100,6.9,0
+732,The Da Vinci Code,"Mystery,Thriller",Ron Howard,"Tom Hanks, Audrey Tautou, Jean Reno, Ian McKellen",2006,149,6.6,217.54
+733,Mr. Church,"Comedy,Drama",Bruce Beresford,"Eddie Murphy, Britt Robertson, Natascha McElhone, Xavier Samuel",2016,104,7.7,0.69
+734,Hugo,"Adventure,Drama,Family",Martin Scorsese,"Asa Butterfield, Chloë Grace Moretz, Christopher Lee, Ben Kingsley",2011,126,7.5,73.82
+735,The Blackcoat's Daughter,"Horror,Thriller",Oz Perkins,"Emma Roberts, Kiernan Shipka, Lauren Holly, Lucy Boynton",2015,93,5.6,0.02
+736,Body of Lies,"Action,Drama,Romance",Ridley Scott,"Leonardo DiCaprio, Russell Crowe, Mark Strong,Golshifteh Farahani",2008,128,7.1,39.38
+737,Knight of Cups,"Drama,Romance",Terrence Malick,"Christian Bale, Cate Blanchett, Natalie Portman,Brian Dennehy",2015,118,5.7,0.56
+738,The Mummy: Tomb of the Dragon Emperor,"Action,Adventure,Fantasy",Rob Cohen,"Brendan Fraser, Jet Li, Maria Bello, Michelle Yeoh",2008,112,5.2,102.18M
+739,The Boss,Comedy,Ben Falcone,"Melissa McCarthy, Kristen Bell, Peter Dinklage, Ella Anderson",2016,99,5.4,63.03
+740,Hands of Stone,"Action,Biography,Drama",Jonathan Jakubowicz,"Edgar Ramírez, Usher Raymond, Robert De Niro, Rubén Blades",2016,111,6.6,4.71
+741,El secreto de sus ojos,"Drama,Mystery,Romance",Juan José Campanella,"Ricardo Darín, Soledad Villamil, Pablo Rago,Carla Quevedo",2009,129,8.2,20.17
+742,True Grit,"Adventure,Drama,Western",Ethan Coen,"Jeff Bridges, Matt Damon, Hailee Steinfeld,Josh Brolin",2010,110,7.6,171.03M
+743,We Are Your Friends,"Drama,Music,Romance",Max Joseph,"Zac Efron, Wes Bentley, Emily Ratajkowski, Jonny Weston",2015,96,6.2,3.59
+744,A Million Ways to Die in the West,"Comedy,Romance,Western",Seth MacFarlane,"Seth MacFarlane, Charlize Theron, Liam Neeson,Amanda Seyfried",2014,116,6.1,42.62
+745,Only for One Night,Thriller,Chris Stokes,"Brian White, Karrueche Tran, Angelique Pereira,Jessica Vanessa DeLeon",2016,86,4.6,0
+746,Rules Don't Apply,"Comedy,Drama,Romance",Warren Beatty,"Lily Collins, Haley Bennett, Taissa Farmiga, Steve Tom",2016,127,5.7,3.65
+747,Ouija: Origin of Evil,"Horror,Thriller",Mike Flanagan,"Elizabeth Reaser, Lulu Wilson, Annalise Basso,Henry Thomas",2016,99,6.1,34.9
+748,Percy Jackson: Sea of Monsters,"Adventure,Family,Fantasy",Thor Freudenthal,"Logan Lerman, Alexandra Daddario, Brandon T. Jackson, Nathan Fillion",2013,106,5.9,68.56
+749,Fracture,"Crime,Drama,Mystery",Gregory Hoblit,"Anthony Hopkins, Ryan Gosling, David Strathairn,Rosamund Pike",2007,113,7.2,39
+750,Oculus,"Horror,Mystery",Mike Flanagan,"Karen Gillan, Brenton Thwaites, Katee Sackhoff,Rory Cochrane",2013,104,6.5,27.69
+751,In Bruges,"Comedy,Crime,Drama",Martin McDonagh,"Colin Farrell, Brendan Gleeson, Ciarán Hinds,Elizabeth Berrington",2008,107,7.9,7.76
+752,This Means War,"Action,Comedy,Romance",McG,"Reese Witherspoon, Chris Pine, Tom Hardy, Til Schweiger",2012,103,6.3,54.76
+753,Lída Baarová,"Biography,Drama,History",Filip Renc,"Tatiana Pauhofová, Karl Markovics, Gedeon Burkhard,Simona Stasová",2016,106,5,0
+754,The Road,"Adventure,Drama",John Hillcoat,"Viggo Mortensen, Charlize Theron, Kodi Smit-McPhee,Robert Duvall",2009,111,7.3,0.06
+755,Lavender,"Drama,Thriller",Ed Gass-Donnelly,"Abbie Cornish, Dermot Mulroney, Justin Long,Diego Klattenhoff",2016,92,5.2,0
+756,Deuces,Drama,Jamal Hill,"Larenz Tate, Meagan Good, Rotimi, Rick Gonzalez",2016,87,6.6,0
+757,Conan the Barbarian,"Action,Adventure,Fantasy",Marcus Nispel,"Jason Momoa, Ron Perlman, Rose McGowan,Stephen Lang",2011,113,5.2,21.27
+758,The Fighter,"Action,Biography,Drama",David O. Russell,"Mark Wahlberg, Christian Bale, Amy Adams,Melissa Leo",2010,116,7.8,93.57
+759,August Rush,"Drama,Music",Kirsten Sheridan,"Freddie Highmore, Keri Russell, Jonathan Rhys Meyers, Terrence Howard",2007,114,7.5,31.66
+760,Chef,"Comedy,Drama",Jon Favreau,"Jon Favreau, Robert Downey Jr., Scarlett Johansson,Dustin Hoffman",2014,114,7.3,31.24
+761,Eye in the Sky,"Drama,Thriller,War",Gavin Hood,"Helen Mirren, Aaron Paul, Alan Rickman, Barkhad Abdi",2015,102,7.3,18.7
+762,Eagle Eye,"Action,Mystery,Thriller",D.J. Caruso,"Shia LaBeouf, Michelle Monaghan, Rosario Dawson,Michael Chiklis",2008,118,6.6,101.11M
+763,The Purge,"Horror,Sci-Fi,Thriller",James DeMonaco,"Ethan Hawke, Lena Headey, Max Burkholder,Adelaide Kane",2013,85,5.7,64.42
+764,PK,"Comedy,Drama,Romance",Rajkumar Hirani,"Aamir Khan, Anushka Sharma, Sanjay Dutt,Boman Irani",2014,153,8.2,10.57
+765,Ender's Game,"Action,Sci-Fi",Gavin Hood,"Harrison Ford, Asa Butterfield, Hailee Steinfeld, Abigail Breslin",2013,114,6.7,61.66
+766,Indiana Jones and the Kingdom of the Crystal Skull,"Action,Adventure,Fantasy",Steven Spielberg,"Harrison Ford, Cate Blanchett, Shia LaBeouf,Karen Allen",2008,122,6.2,317.01
+767,Paper Towns,"Drama,Mystery,Romance",Jake Schreier,"Nat Wolff, Cara Delevingne, Austin Abrams, Justice Smith",2015,109,6.3,31.99
+768,High-Rise,Drama,Ben Wheatley,"Tom Hiddleston, Jeremy Irons, Sienna Miller, Luke Evans",2015,119,5.7,0.34
+769,Quantum of Solace,"Action,Adventure,Thriller",Marc Forster,"Daniel Craig, Olga Kurylenko, Mathieu Amalric, Judi Dench",2008,106,6.6,168.37M
+770,The Assignment,"Action,Crime,Thriller",Walter Hill,"Sigourney Weaver, Michelle Rodriguez, Tony Shalhoub,Anthony LaPaglia",2016,95,4.5,0
+771,How to Train Your Dragon,"Animation,Action,Adventure",Dean DeBlois,"Jay Baruchel, Gerard Butler,Christopher Mintz-Plasse, Craig Ferguson",2010,98,8.1,217.39
+772,Lady in the Water,"Drama,Fantasy,Mystery",M. Night Shyamalan,"Paul Giamatti, Bryce Dallas Howard, Jeffrey Wright, Bob Balaban",2006,110,5.6,42.27
+773,The Fountain,"Drama,Sci-Fi",Darren Aronofsky,"Hugh Jackman, Rachel Weisz, Sean Patrick Thomas, Ellen Burstyn",2006,96,7.3,10.14
+774,Cars 2,"Animation,Adventure,Comedy",John Lasseter,"Owen Wilson, Larry the Cable Guy,Michael Caine, Emily Mortimer",2011,106,6.2,191.45M
+775,31,"Horror,Thriller",Rob Zombie,"Malcolm McDowell, Richard Brake, Jeff Daniel Phillips,Sheri Moon Zombie",2016,102,5.1,0.78
+776,Final Girl,"Action,Thriller",Tyler Shields,"Abigail Breslin, Wes Bentley, Logan Huffman,Alexander Ludwig",2015,90,4.7,0
+777,Chalk It Up,Comedy,Hisonni Johnson,"Maddy Curley, John DeLuca, Nikki SooHoo, Drew Seeley",2016,90,4.8,0
+778,The Man Who Knew Infinity,"Biography,Drama",Matt Brown,"Dev Patel, Jeremy Irons, Malcolm Sinclair, Raghuvir Joshi",2015,108,7.2,3.86
+779,Unknown,"Action,Mystery,Thriller",Jaume Collet-Serra,"Liam Neeson, Diane Kruger, January Jones,Aidan Quinn",2011,113,6.9,61.09
+780,Self/less,"Action,Mystery,Sci-Fi",Tarsem Singh,"Ryan Reynolds, Natalie Martinez, Matthew Goode,Ben Kingsley",2015,117,6.5,12.28
+781,Mr. Brooks,"Crime,Drama,Thriller",Bruce A. Evans,"Kevin Costner, Demi Moore, William Hurt, Dane Cook",2007,120,7.3,28.48
+782,Tramps,"Comedy,Romance",Adam Leon,"Callum Turner, Grace Van Patten, Michal Vondel, Mike Birbiglia",2016,82,6.5,0
+783,Before We Go,"Comedy,Drama,Romance",Chris Evans,"Chris Evans, Alice Eve, Emma Fitzpatrick, John Cullum",2014,95,6.9,0.04
+784,Captain Phillips,"Biography,Drama,Thriller",Paul Greengrass,"Tom Hanks, Barkhad Abdi, Barkhad Abdirahman,Catherine Keener",2013,134,7.8,107.1M
+785,The Secret Scripture,Drama,Jim Sheridan,"Rooney Mara, Eric Bana, Theo James, Aidan Turner",2016,108,6.8,0
+786,Max Steel,"Action,Adventure,Family",Stewart Hendler,"Ben Winchell, Josh Brener, Maria Bello, Andy Garcia",2016,92,4.6,3.77
+787,Hotel Transylvania 2,"Animation,Comedy,Family",Genndy Tartakovsky,"Adam Sandler, Andy Samberg, Selena Gomez, Kevin James",2015,89,6.7,169.69M
+788,Hancock,"Action,Crime,Drama",Peter Berg,"Will Smith, Charlize Theron, Jason Bateman, Jae Head",2008,92,6.4,227.95
+789,Sisters,Comedy,Jason Moore,"Amy Poehler, Tina Fey, Maya Rudolph, Ike Barinholtz",2015,118,6,87.03
+790,The Family,"Comedy,Crime,Thriller",Luc Besson,"Robert De Niro, Michelle Pfeiffer, Dianna Agron, John D'Leo",2013,111,6.3,36.92
+791,Zack and Miri Make a Porno,"Comedy,Romance",Kevin Smith,"Seth Rogen, Elizabeth Banks, Craig Robinson, Gerry Bednob",2008,101,6.6,31.45
+792,Ma vie de Courgette,"Animation,Comedy,Drama",Claude Barras,"Gaspard Schlatter, Sixtine Murat, Paulin Jaccoud,Michel Vuillermoz",2016,66,7.8,0.29
+793,Man on a Ledge,"Action,Crime,Thriller",Asger Leth,"Sam Worthington, Elizabeth Banks, Jamie Bell, Mandy Gonzalez",2012,102,6.6,18.6
+794,No Strings Attached,"Comedy,Romance",Ivan Reitman,"Natalie Portman, Ashton Kutcher, Kevin Kline, Cary Elwes",2011,108,6.2,70.63
+795,Rescue Dawn,"Adventure,Biography,Drama",Werner Herzog,"Christian Bale, Steve Zahn, Jeremy Davies, Zach Grenier",2006,120,7.3,5.48
+796,Despicable Me 2,"Animation,Adventure,Comedy",Pierre Coffin,"Steve Carell, Kristen Wiig, Benjamin Bratt, Miranda Cosgrove",2013,98,7.4,368.05
+797,A Walk Among the Tombstones,"Crime,Drama,Mystery",Scott Frank,"Liam Neeson, Dan Stevens, David Harbour, Boyd Holbrook",2014,114,6.5,25.98
+798,The World's End,"Action,Comedy,Sci-Fi",Edgar Wright,"Simon Pegg, Nick Frost, Martin Freeman, Rosamund Pike",2013,109,7,26
+799,Yoga Hosers,"Comedy,Fantasy,Horror",Kevin Smith,"Lily-Rose Depp, Harley Quinn Smith, Johnny Depp,Adam Brody",2016,88,4.3,0
+800,Seven Psychopaths,"Comedy,Crime",Martin McDonagh,"Colin Farrell, Woody Harrelson, Sam Rockwell,Christopher Walken",2012,110,7.2,14.99
+801,Beowulf,"Animation,Action,Adventure",Robert Zemeckis,"Ray Winstone, Crispin Glover, Angelina Jolie,Robin Wright",2007,115,6.2,82.16
+802,Jack Ryan: Shadow Recruit,"Action,Drama,Thriller",Kenneth Branagh,"Chris Pine, Kevin Costner, Keira Knightley,Kenneth Branagh",2014,105,6.2,50.55
+803,1408,"Fantasy,Horror",Mikael Håfström,"John Cusack, Samuel L. Jackson, Mary McCormack, Paul Birchard",2007,104,6.8,71.98
+804,The Gambler,"Crime,Drama,Thriller",Rupert Wyatt,"Mark Wahlberg, Jessica Lange, John Goodman, Brie Larson",2014,111,6,33.63
+805,Prince of Persia: The Sands of Time,"Action,Adventure,Fantasy",Mike Newell,"Jake Gyllenhaal, Gemma Arterton, Ben Kingsley,Alfred Molina",2010,116,6.6,90.76
+806,The Spectacular Now,"Comedy,Drama,Romance",James Ponsoldt,"Miles Teller, Shailene Woodley, Kyle Chandler,Jennifer Jason Leigh",2013,95,7.1,6.85
+807,A United Kingdom,"Biography,Drama,Romance",Amma Asante,"David Oyelowo, Rosamund Pike, Tom Felton, Jack Davenport",2016,111,6.8,3.9
+808,USS Indianapolis: Men of Courage,"Action,Drama,History",Mario Van Peebles,"Nicolas Cage, Tom Sizemore, Thomas Jane,Matt Lanter",2016,128,5.2,0
+809,Turbo Kid,"Action,Adventure,Comedy",François Simard,"Munro Chambers, Laurence Leboeuf, Michael Ironside, Edwin Wright",2015,93,6.7,0.05
+810,Mama,"Horror,Thriller",Andrés Muschietti,"Jessica Chastain, Nikolaj Coster-Waldau, Megan Charpentier, Isabelle Nélisse",2013,100,6.2,71.59
+811,Orphan,"Horror,Mystery,Thriller",Jaume Collet-Serra,"Vera Farmiga, Peter Sarsgaard, Isabelle Fuhrman, CCH Pounder",2009,123,7,41.57
+812,To Rome with Love,"Comedy,Romance",Woody Allen,"Woody Allen, Penélope Cruz, Jesse Eisenberg, Ellen Page",2012,112,6.3,16.68
+813,Fantastic Mr. Fox,"Animation,Adventure,Comedy",Wes Anderson,"George Clooney, Meryl Streep, Bill Murray, Jason Schwartzman",2009,87,7.8,21
+814,Inside Man,"Crime,Drama,Mystery",Spike Lee,"Denzel Washington, Clive Owen, Jodie Foster,Christopher Plummer",2006,129,7.6,88.5
+815,I.T.,"Crime,Drama,Mystery",John Moore,"Pierce Brosnan, Jason Barry, Karen Moskow, Kai Ryssdal",2016,95,5.4,0
+816,127 Hours,"Adventure,Biography,Drama",Danny Boyle,"James Franco, Amber Tamblyn, Kate Mara, Sean Bott",2010,94,7.6,18.33
+817,Annabelle,"Horror,Mystery,Thriller",John R. Leonetti,"Ward Horton, Annabelle Wallis, Alfre Woodard,Tony Amendola",2014,99,5.4,84.26
+818,Wolves at the Door,"Horror,Thriller",John R. Leonetti,"Katie Cassidy, Elizabeth Henstridge, Adam Campbell, Miles Fisher",2016,73,4.6,0
+819,Suite Française,"Drama,Romance,War",Saul Dibb,"Michelle Williams, Kristin Scott Thomas, Margot Robbie,Eric Godon",2014,107,6.9,0
+820,The Imaginarium of Doctor Parnassus,"Adventure,Fantasy,Mystery",Terry Gilliam,"Christopher Plummer, Lily Cole, Heath Ledger,Andrew Garfield",2009,123,6.8,7.69
+821,G.I. Joe: The Rise of Cobra,"Action,Adventure,Sci-Fi",Stephen Sommers,"Dennis Quaid, Channing Tatum, Marlon Wayans,Adewale Akinnuoye-Agbaje",2009,118,5.8,150.17M
+822,Christine,"Biography,Drama",Antonio Campos,"Rebecca Hall, Michael C. Hall, Tracy Letts, Maria Dizzia",2016,119,7,0.3
+823,Man Down,"Drama,Thriller",Dito Montiel,"Shia LaBeouf, Jai Courtney, Gary Oldman, Kate Mara",2015,90,5.8,0
+824,Crawlspace,"Horror,Thriller",Phil Claydon,"Michael Vartan, Erin Moriarty, Nadine Velazquez,Ronnie Gene Blevins",2016,88,5.3,0
+825,Shut In,"Drama,Horror,Thriller",Farren Blackburn,"Naomi Watts, Charlie Heaton, Jacob Tremblay,Oliver Platt",2016,91,4.6,6.88
+826,The Warriors Gate,"Action,Adventure,Fantasy",Matthias Hoene,"Mark Chao, Ni Ni, Dave Bautista, Sienna Guillory",2016,108,5.3,0
+827,Grindhouse,"Action,Horror,Thriller",Robert Rodriguez,"Kurt Russell, Rose McGowan, Danny Trejo, Zoë Bell",2007,191,7.6,25.03
+828,Disaster Movie,Comedy,Jason Friedberg,"Carmen Electra, Vanessa Lachey,Nicole Parker, Matt Lanter",2008,87,1.9,14.17
+829,Rocky Balboa,"Drama,Sport",Sylvester Stallone,"Sylvester Stallone, Antonio Tarver, Milo Ventimiglia, Burt Young",2006,102,7.2,70.27
+830,Diary of a Wimpy Kid: Dog Days,"Comedy,Family",David Bowers,"Zachary Gordon, Robert Capron, Devon Bostick,Steve Zahn",2012,94,6.4,49
+831,Jane Eyre,"Drama,Romance",Cary Joji Fukunaga,"Mia Wasikowska, Michael Fassbender, Jamie Bell, Su Elliot",2011,120,7.4,11.23
+832,Fool's Gold,"Action,Adventure,Comedy",Andy Tennant,"Matthew McConaughey, Kate Hudson, Donald Sutherland, Alexis Dziena",2008,112,5.7,70.22
+833,The Dictator,Comedy,Larry Charles,"Sacha Baron Cohen, Anna Faris, John C. Reilly, Ben Kingsley",2012,83,6.4,59.62
+834,The Loft,"Mystery,Romance,Thriller",Erik Van Looy,"Karl Urban, James Marsden, Wentworth Miller, Eric Stonestreet",2014,108,6.3,5.98
+835,Bacalaureat,"Crime,Drama",Cristian Mungiu,"Adrian Titieni, Maria-Victoria Dragus, Lia Bugnar,Malina Manovici",2016,128,7.5,0.13
+836,You Don't Mess with the Zohan,"Action,Comedy",Dennis Dugan,"Adam Sandler, John Turturro, Emmanuelle Chriqui,Nick Swardson",2008,113,5.5,100.02M
+837,Exposed,"Crime,Drama,Mystery",Gee Malik Linton,"Ana de Armas, Keanu Reeves, Christopher McDonald, Mira Sorvino",2016,102,4.2,0
+838,Maudie,"Biography,Drama,Romance",Aisling Walsh,"Ethan Hawke, Sally Hawkins, Kari Matchett, Zachary Bennett",2016,115,7.8,0
+839,Horrible Bosses 2,"Comedy,Crime",Sean Anders,"Jason Bateman, Jason Sudeikis, Charlie Day, Jennifer Aniston",2014,108,6.3,54.41
+840,A Bigger Splash,"Drama,Thriller",Luca Guadagnino,"Tilda Swinton, Matthias Schoenaerts, Ralph Fiennes, Dakota Johnson",2015,125,6.4,1.98
+841,Melancholia,Drama,Lars von Trier,"Kirsten Dunst, Charlotte Gainsbourg, Kiefer Sutherland, Alexander Skarsgård",2011,135,7.1,3.03
+842,The Princess and the Frog,"Animation,Adventure,Comedy",Ron Clements,"Anika Noni Rose, Keith David, Oprah Winfrey, Bruno Campos",2009,97,7.1,104.37M
+843,Unstoppable,"Action,Thriller",Tony Scott,"Denzel Washington, Chris Pine, Rosario Dawson, Ethan Suplee",2010,98,6.8,81.56
+844,Flight,"Drama,Thriller",Robert Zemeckis,"Denzel           Washington, Nadine Velazquez, Don Cheadle, John Goodman",2012,138,7.3,93.75
+845,Home,"Animation,Adventure,Comedy",Tim Johnson,"Jim Parsons, Rihanna, Steve Martin, Jennifer Lopez",2015,94,6.7,177.34M
+846,La migliore offerta,"Crime,Drama,Mystery",Giuseppe Tornatore,"Geoffrey Rush, Jim Sturgess, Sylvia Hoeks,Donald Sutherland",2013,131,7.8,0.09
+847,Mean Dreams,Thriller,Nathan Morlando,"Sophie Nélisse, Josh Wiggins, Joe Cobden, Bill Paxton",2016,108,6.3,0
+848,42,"Biography,Drama,Sport",Brian Helgeland,"Chadwick Boseman, T.R. Knight, Harrison Ford,Nicole Beharie",2013,128,7.5,95
+849,21,"Crime,Drama,Thriller",Robert Luketic,"Jim Sturgess, Kate Bosworth, Kevin Spacey, Aaron Yoo",2008,123,6.8,81.16
+850,Begin Again,"Drama,Music",John Carney,"Keira Knightley, Mark Ruffalo, Adam Levine, Hailee Steinfeld",2013,104,7.4,16.17
+851,Out of the Furnace,"Crime,Drama,Thriller",Scott Cooper,"Christian Bale, Casey Affleck, Zoe Saldana, Woody Harrelson",2013,116,6.8,11.33
+852,Vicky Cristina Barcelona,"Drama,Romance",Woody Allen,"Rebecca Hall, Scarlett Johansson, Javier Bardem,Christopher Evan Welch",2008,96,7.1,23.21
+853,Kung Fu Panda,"Animation,Action,Adventure",Mark Osborne,"Jack Black, Ian McShane,Angelina Jolie, Dustin Hoffman",2008,92,7.6,215.4
+854,Barbershop: The Next Cut,"Comedy,Drama",Malcolm D. Lee,"Ice Cube, Regina Hall, Anthony Anderson, Eve",2016,111,5.9,54.01
+855,Terminator Salvation,"Action,Adventure,Drama",McG,"Christian Bale, Sam Worthington, Anton Yelchin, Moon Bloodgood",2009,115,6.6,125.32M
+856,Freedom Writers,"Biography,Crime,Drama",Richard LaGravenese,"Hilary Swank, Imelda Staunton, Patrick Dempsey, Scott Glenn",2007,123,7.5,36.58
+857,The Hills Have Eyes,Horror,Alexandre Aja,"Ted Levine, Kathleen Quinlan, Dan Byrd, Emilie de Ravin",2006,107,6.4,41.78
+858,Changeling,"Biography,Drama,Mystery",Clint Eastwood,"Angelina Jolie, Colm Feore, Amy Ryan, Gattlin Griffith",2008,141,7.8,35.71
+859,Remember Me,"Drama,Romance",Allen Coulter,"Robert Pattinson, Emilie de Ravin, Caitlyn Rund,Moisés Acevedo",2010,113,7.2,19.06
+860,Koe no katachi,"Animation,Drama,Romance",Naoko Yamada,"Miyu Irino, Saori Hayami, Aoi Yuki, Kenshô Ono",2016,129,8.4,0
+861,"Alexander and the Terrible, Horrible, No Good, Very Bad Day","Comedy,Family",Miguel Arteta,"Steve Carell, Jennifer Garner, Ed Oxenbould, Dylan Minnette",2014,81,6.2,66.95
+862,Locke,Drama,Steven Knight,"Tom Hardy, Olivia Colman, Ruth Wilson, Andrew Scott",2013,85,7.1,1.36
+863,The 9th Life of Louis Drax,"Mystery,Thriller",Alexandre Aja,"Jamie Dornan, Aiden Longworth, Sarah Gadon,Aaron Paul",2016,108,6.3,0
+864,Horns,"Drama,Fantasy,Horror",Alexandre Aja,"Daniel Radcliffe, Juno Temple, Max Minghella, Joe Anderson",2013,120,6.5,0.16
+865,Indignation,"Drama,Romance",James Schamus,"Logan Lerman, Sarah Gadon, Tijuana Ricks, Sue Dahlman",2016,110,6.9,3.4
+866,The Stanford Prison Experiment,"Biography,Drama,History",Kyle Patrick Alvarez,"Ezra Miller, Tye Sheridan, Billy Crudup, Olivia Thirlby",2015,122,6.9,0.64
+867,Diary of a Wimpy Kid: Rodrick Rules,"Comedy,Family",David Bowers,"Zachary Gordon, Devon Bostick, Robert Capron,Rachael Harris",2011,99,6.6,52.69
+868,Mission: Impossible III,"Action,Adventure,Thriller",J.J. Abrams,"Tom Cruise, Michelle Monaghan, Ving Rhames, Philip Seymour Hoffman",2006,126,6.9,133.38M
+869,En man som heter Ove,"Comedy,Drama",Hannes Holm,"Rolf Lassgård, Bahar Pars, Filip Berg, Ida Engvoll",2015,116,7.7,3.36
+870,Dragonball Evolution,"Action,Adventure,Fantasy",James Wong,"Justin Chatwin, James Marsters, Yun-Fat Chow, Emmy Rossum",2009,85,2.7,9.35
+871,Red Dawn,"Action,Thriller",Dan Bradley,"Chris Hemsworth, Isabel Lucas, Josh Hutcherson, Josh Peck",2012,93,5.4,44.8
+872,One Day,"Drama,Romance",Lone Scherfig,"Anne Hathaway, Jim Sturgess, Patricia Clarkson,Tom Mison",2011,107,7,13.77
+873,Life as We Know It,"Comedy,Drama,Romance",Greg Berlanti,"Katherine Heigl, Josh Duhamel, Josh Lucas, Alexis Clagett",2010,114,6.6,53.36
+874,28 Weeks Later,"Drama,Horror,Sci-Fi",Juan Carlos Fresnadillo,"Jeremy Renner, Rose Byrne, Robert Carlyle, Harold Perrineau",2007,100,7,28.64
+875,Warm Bodies,"Comedy,Horror,Romance",Jonathan Levine,"Nicholas Hoult, Teresa Palmer, John Malkovich,Analeigh Tipton",2013,98,6.9,66.36
+876,Blue Jasmine,Drama,Woody Allen,"Cate Blanchett, Alec Baldwin, Peter Sarsgaard, Sally Hawkins",2013,98,7.3,33.4
+877,Wrath of the Titans,"Action,Adventure,Fantasy",Jonathan Liebesman,"Sam Worthington, Liam Neeson, Rosamund Pike, Ralph Fiennes",2012,99,5.8,83.64
+878,Shin Gojira,"Action,Adventure,Drama",Hideaki Anno,"Hiroki Hasegawa, Yutaka Takenouchi,Satomi Ishihara, Ren Ôsugi",2016,120,6.9,1.91
+879,Saving Mr. Banks,"Biography,Comedy,Drama",John Lee Hancock,"Emma Thompson, Tom Hanks, Annie Rose Buckley, Colin Farrell",2013,125,7.5,83.3
+880,Transcendence,"Drama,Mystery,Romance",Wally Pfister,"Johnny Depp, Rebecca Hall, Morgan Freeman, Cillian Murphy",2014,119,6.3,23.01
+881,Rio,"Animation,Adventure,Comedy",Carlos Saldanha,"Jesse Eisenberg, Anne Hathaway, George Lopez,Karen Disher",2011,96,6.9,143.62M
+882,Equals,"Drama,Romance,Sci-Fi",Drake Doremus,"Nicholas Hoult, Kristen Stewart, Vernetta Lopez,Scott Lawrence",2015,101,6.1,0.03
+883,Babel,Drama,Alejandro González Iñárritu,"Brad Pitt, Cate Blanchett, Gael García Bernal, Mohamed Akhzam",2006,143,7.5,34.3
+884,The Tree of Life,"Drama,Fantasy",Terrence Malick,"Brad Pitt, Sean Penn, Jessica Chastain, Hunter McCracken",2011,139,6.8,13.3
+885,The Lucky One,"Drama,Romance",Scott Hicks,"Zac Efron, Taylor Schilling, Blythe Danner, Riley Thomas Stewart",2012,101,6.5,60.44
+886,Piranha 3D,"Comedy,Horror,Thriller",Alexandre Aja,"Elisabeth Shue, Jerry O'Connell, Richard Dreyfuss,Ving Rhames",2010,88,5.5,25
+887,50/50,"Comedy,Drama,Romance",Jonathan Levine,"Joseph Gordon-Levitt, Seth Rogen, Anna Kendrick, Bryce Dallas Howard",2011,100,7.7,34.96
+888,The Intent,"Crime,Drama",Femi Oyeniran,"Dylan Duffus, Scorcher,Shone Romulus, Jade Asha",2016,104,3.5,0
+889,This Is 40,"Comedy,Romance",Judd Apatow,"Paul Rudd, Leslie Mann, Maude Apatow, Iris Apatow",2012,134,6.2,67.52
+890,Real Steel,"Action,Drama,Family",Shawn Levy,"Hugh Jackman, Evangeline Lilly, Dakota Goyo,Anthony Mackie",2011,127,7.1,85.46
+891,Sex and the City,"Comedy,Drama,Romance",Michael Patrick King,"Sarah Jessica Parker, Kim Cattrall, Cynthia Nixon, Kristin Davis",2008,145,5.5,152.64M
+892,Rambo,"Action,Thriller,War",Sylvester Stallone,"Sylvester Stallone, Julie Benz, Matthew Marsden, Graham McTavish",2008,92,7.1,42.72
+893,Planet Terror,"Action,Comedy,Horror",Robert Rodriguez,"Rose McGowan, Freddy Rodríguez, Josh Brolin,Marley Shelton",2007,105,7.1,0
+894,Concussion,"Biography,Drama,Sport",Peter Landesman,"Will Smith, Alec Baldwin, Albert Brooks, David Morse",2015,123,7.1,34.53
+895,The Fall,"Adventure,Comedy,Drama",Tarsem Singh,"Lee Pace, Catinca Untaru, Justine Waddell, Kim Uylenbroek",2006,117,7.9,2.28
+896,The Ugly Truth,"Comedy,Romance",Robert Luketic,"Katherine Heigl, Gerard Butler, Bree Turner, Eric Winter",2009,96,6.5,88.92
+897,Bride Wars,"Comedy,Romance",Gary Winick,"Kate Hudson, Anne Hathaway, Candice Bergen, Bryan Greenberg",2009,89,5.5,58.72
+898,Sleeping with Other People,"Comedy,Drama,Romance",Leslye Headland,"Jason Sudeikis, Alison Brie, Jordan Carlos,Margarita Levieva",2015,101,6.5,0.81
+899,Snakes on a Plane,"Action,Adventure,Crime",David R. Ellis,"Samuel L. Jackson, Julianna Margulies, Nathan Phillips, Rachel Blanchard",2006,105,5.6,34.01
+900,What If,"Comedy,Romance",Michael Dowse,"Daniel Radcliffe, Zoe Kazan, Megan Park, Adam Driver",2013,98,6.8,3.45
+901,How to Train Your Dragon 2,"Animation,Action,Adventure",Dean DeBlois,"Jay Baruchel, Cate Blanchett, Gerard Butler, Craig Ferguson",2014,102,7.9,177M
+902,RoboCop,"Action,Crime,Sci-Fi",José Padilha,"Joel Kinnaman, Gary Oldman, Michael Keaton, Abbie Cornish",2014,117,6.2,58.61
+903,In Dubious Battle,Drama,James Franco,"Nat Wolff, James Franco, Vincent D'Onofrio, Selena Gomez",2016,110,6.2,0
+904,"Hello, My Name Is Doris","Comedy,Drama,Romance",Michael Showalter,"Sally Field, Max Greenfield, Tyne Daly, Wendi McLendon-Covey",2015,95,6.7,14.44
+905,Ocean's Thirteen,"Crime,Thriller",Steven Soderbergh,"George Clooney, Brad Pitt, Matt Damon,Michael Mantell",2007,122,6.9,117.14M
+906,Slither,"Comedy,Horror,Sci-Fi",James Gunn,"Nathan Fillion, Elizabeth Banks, Michael Rooker, Don Thompson",2006,95,6.5,7.77
+907,Contagion,"Drama,Thriller",Steven Soderbergh,"Matt Damon, Kate Winslet, Jude Law, Gwyneth Paltrow",2011,106,6.6,75.64
+908,Il racconto dei racconti - Tale of Tales,"Drama,Fantasy,Horror",Matteo Garrone,"Salma Hayek, Vincent Cassel, Toby Jones, John C. Reilly",2015,133,6.4,0.08
+909,I Am the Pretty Thing That Lives in the House,Thriller,Oz Perkins,"Ruth Wilson, Paula Prentiss, Lucy Boynton, Bob Balaban",2016,87,4.7,0
+910,Bridge to Terabithia,"Adventure,Drama,Family",Gabor Csupo,"Josh Hutcherson, AnnaSophia Robb, Zooey Deschanel, Robert Patrick",2007,96,7.2,82.23
+911,Coherence,"Mystery,Sci-Fi,Thriller",James Ward Byrkit,"Emily Baldoni, Maury Sterling, Nicholas Brendon, Elizabeth Gracen",2013,89,7.2,0.07
+912,Notorious,"Biography,Crime,Drama",George Tillman Jr.,"Jamal Woolard, Anthony Mackie, Derek Luke,Momo Dione",2009,122,6.7,36.84
+913,Goksung,"Drama,Fantasy,Horror",Hong-jin Na,"Jun Kunimura, Jung-min Hwang, Do-won Kwak, Woo-hee Chun",2016,156,7.5,0.79
+914,The Expendables 2,"Action,Adventure,Thriller",Simon West,"Sylvester Stallone, Liam Hemsworth, Randy Couture,Jean-Claude Van Damme",2012,103,6.6,85.02
+915,The Girl Next Door,"Crime,Drama,Horror",Gregory Wilson,"William Atherton, Blythe Auffarth, Blanche Baker,Kevin Chamberlin",2007,91,6.7,0
+916,Perfume: The Story of a Murderer,"Crime,Drama,Fantasy",Tom Tykwer,"Ben Whishaw, Dustin Hoffman, Alan Rickman,Francesc Albiol",2006,147,7.5,2.21
+917,The Golden Compass,"Adventure,Family,Fantasy",Chris Weitz,"Nicole Kidman, Daniel Craig, Dakota Blue Richards, Ben Walker",2007,113,6.1,70.08
+918,Centurion,"Action,Adventure,Drama",Neil Marshall,"Michael      fassbender, Dominic West, Olga Kurylenko,Andreas Wisniewski",2010,97,6.4,0.12
+919,Scouts Guide to the Zombie Apocalypse,"Action,Comedy,Horror",Christopher Landon,"Tye Sheridan, Logan Miller, Joey Morgan,Sarah Dumont",2015,93,6.3,3.64
+920,17 Again,"Comedy,Drama,Family",Burr Steers,"Zac Efron, Matthew Perry, Leslie Mann, Thomas Lennon",2009,102,6.4,64.15
+921,No Escape,"Action,Thriller",John Erick Dowdle,"Lake Bell, Pierce Brosnan, Owen Wilson,Chatchawai Kamonsakpitak",2015,103,6.8,27.29
+922,Superman Returns,"Action,Adventure,Sci-Fi",Bryan Singer,"Brandon Routh, Kevin Spacey, Kate Bosworth, James Marsden",2006,154,6.1,200.07
+923,The Twilight Saga: Breaking Dawn - Part 1,"Adventure,Drama,Fantasy",Bill Condon,"Kristen Stewart, Robert Pattinson, Taylor Lautner, Gil Birmingham",2011,117,4.9,281.28
+924,Precious,Drama,Lee Daniels,"Gabourey Sidibe, Mo'Nique, Paula Patton, Mariah Carey",2009,110,7.3,47.54
+925,The Sea of Trees,Drama,Gus Van Sant,"Matthew McConaughey, Naomi Watts, Ken Watanabe,Ryoko Seta",2015,110,5.9,0.02
+926,Good Kids,Comedy,Chris McCoy,"Zoey Deutch, Nicholas Braun, Mateo Arias, Israel Broussard",2016,86,6.1,0
+927,The Master,Drama,Paul Thomas Anderson,"Philip Seymour Hoffman, Joaquin Phoenix,Amy Adams, Jesse Plemons",2012,144,7.1,16.38
+928,Footloose,"Comedy,Drama,Music",Craig Brewer,"Kenny Wormald, Julianne Hough, Dennis Quaid,Andie MacDowell",2011,113,5.9,51.78
+929,If I Stay,"Drama,Fantasy,Music",R.J. Cutler,"Chloë Grace Moretz, Mireille Enos, Jamie Blackley,Joshua Leonard",2014,107,6.8,50.46
+930,The Ticket,Drama,Ido Fluk,"Dan Stevens, Malin Akerman, Oliver Platt, Kerry Bishé",2016,97,5.4,0
+931,Detour,Thriller,Christopher Smith,"Tye Sheridan, Emory Cohen, Bel Powley,Stephen Moyer",2016,97,6.3,0
+932,The Love Witch,"Comedy,Horror",Anna Biller,"Samantha Robinson, Jeffrey Vincent Parise, Laura Waddell, Gian Keys",2016,120,6.2,0.22
+933,Talladega Nights: The Ballad of Ricky Bobby,"Action,Comedy,Sport",Adam McKay,"Will Ferrell, John C. Reilly, Sacha Baron Cohen, Gary Cole",2006,108,6.6,148.21M
+934,The Human Centipede (First Sequence),Horror,Tom Six,"Dieter Laser, Ashley C. Williams, Ashlynn Yennie, Akihiro Kitamura",2009,92,4.4,0.18
+935,Super,"Comedy,Drama",James Gunn,"Rainn Wilson, Ellen Page, Liv Tyler, Kevin Bacon",2010,96,6.8,0.32
+936,The Siege of Jadotville,"Action,Drama,Thriller",Richie Smyth,"Jamie Dornan, Mark Strong, Jason O'Mara, Michael McElhatton",2016,108,7.3,0
+937,Up in the Air,"Drama,Romance",Jason Reitman,"George Clooney, Vera Farmiga, Anna Kendrick,Jason Bateman",2009,109,7.4,83.81
+938,The Midnight Meat Train,"Horror,Mystery",Ryûhei Kitamura,"Vinnie Jones, Bradley Cooper, Leslie Bibb, Brooke Shields",2008,98,6.1,0.07
+939,The Twilight Saga: Eclipse,"Adventure,Drama,Fantasy",David Slade,"Kristen Stewart, Robert Pattinson, Taylor Lautner,Xavier Samuel",2010,124,4.9,300.52
+940,Transpecos,Thriller,Greg Kwedar,"Johnny Simmons, Gabriel Luna, Clifton Collins Jr.,David Acord",2016,86,5.8,0
+941,What's Your Number?,"Comedy,Romance",Mark Mylod,"Anna Faris, Chris Evans, Ari Graynor, Blythe Danner",2011,106,6.1,13.99
+942,Riddick,"Action,Sci-Fi,Thriller",David Twohy,"Vin Diesel, Karl Urban, Katee Sackhoff, Jordi Mollà",2013,119,6.4,42
+943,Triangle,"Fantasy,Mystery,Thriller",Christopher Smith,"Melissa George, Joshua McIvor, Jack Taylor,Michael Dorman",2009,99,6.9,0
+944,The Butler,"Biography,Drama",Lee Daniels,"Forest Whitaker, Oprah Winfrey, John Cusack, Jane Fonda",2013,132,7.2,116.63M
+945,King Cobra,"Crime,Drama",Justin Kelly,"Garrett Clayton, Christian Slater, Molly Ringwald,James Kelley",2016,91,5.6,0.03
+946,After Earth,"Action,Adventure,Sci-Fi",M. Night Shyamalan,"Jaden Smith, David Denman, Will Smith,Sophie Okonedo",2013,100,4.9,60.52
+947,Kicks,Adventure,Justin Tipping,"Jahking Guillory, Christopher Jordan Wallace,Christopher Meyer, Kofi Siriboe",2016,80,6.1,0.15
+948,Me and Earl and the Dying Girl,"Comedy,Drama",Alfonso Gomez-Rejon,"Thomas Mann, RJ Cyler, Olivia Cooke, Nick Offerman",2015,105,7.8,6.74
+949,The Descendants,"Comedy,Drama",Alexander Payne,"George Clooney, Shailene Woodley, Amara Miller, Nick Krause",2011,115,7.3,82.62
+950,Sex and the City 2,"Comedy,Drama,Romance",Michael Patrick King,"Sarah Jessica Parker, Kim Cattrall, Kristin Davis, Cynthia Nixon",2010,146,4.3,95.33
+951,The Kings of Summer,"Adventure,Comedy,Drama",Jordan Vogt-Roberts,"Nick Robinson, Gabriel Basso, Moises Arias,Nick Offerman",2013,95,7.2,1.29
+952,Death Race,"Action,Sci-Fi,Thriller",Paul W.S. Anderson,"Jason Statham, Joan Allen, Tyrese Gibson, Ian McShane",2008,105,6.4,36.06
+953,That Awkward Moment,"Comedy,Romance",Tom Gormican,"Zac Efron, Michael B. Jordan, Miles Teller, Imogen Poots",2014,94,6.2,26.05
+954,Legion,"Action,Fantasy,Horror",Scott Stewart,"Paul Bettany, Dennis Quaid, Charles S. Dutton, Lucas Black",2010,100,5.2,40.17
+955,End of Watch,"Crime,Drama,Thriller",David Ayer,"Jake Gyllenhaal, Michael Peña, Anna Kendrick, America Ferrera",2012,109,7.7,40.98
+956,3 Days to Kill,"Action,Drama,Thriller",McG,"Kevin Costner, Hailee Steinfeld, Connie Nielsen, Amber Heard",2014,117,6.2,30.69
+957,Lucky Number Slevin,"Crime,Drama,Mystery",Paul McGuigan,"Josh Hartnett, Ben Kingsley, Morgan Freeman, Lucy Liu",2006,110,7.8,22.49
+958,Trance,"Crime,Drama,Mystery",Danny Boyle,"James McAvoy, Rosario Dawson, Vincent Cassel,Danny Sapani",2013,101,7,2.32
+959,Into the Forest,"Drama,Sci-Fi,Thriller",Patricia Rozema,"Ellen Page, Evan Rachel Wood, Max Minghella,Callum Keith Rennie",2015,101,5.9,0.01
+960,The Other Boleyn Girl,"Biography,Drama,History",Justin Chadwick,"Natalie Portman, Scarlett Johansson, Eric Bana,Jim Sturgess",2008,115,6.7,26.81
+961,I Spit on Your Grave,"Crime,Horror,Thriller",Steven R. Monroe,"Sarah Butler, Jeff Branson, Andrew Howard,Daniel Franzese",2010,108,6.3,0.09
+962,Custody,Drama,James Lapine,"Viola Davis, Hayden Panettiere, Catalina Sandino Moreno, Ellen Burstyn",2016,104,6.9,0
+963,Inland Empire,"Drama,Mystery,Thriller",David Lynch,"Laura Dern, Jeremy Irons, Justin Theroux, Karolina Gruszka",2006,180,7,0
+964,L'odyssée,"Adventure,Biography",Jérôme Salle,"Lambert Wilson, Pierre Niney, Audrey Tautou,Laurent Lucas",2016,122,6.7,0
+965,The Walk,"Adventure,Biography,Crime",Robert Zemeckis,"Joseph Gordon-Levitt, Charlotte Le Bon,Guillaume Baillargeon, Émilie Leclerc",2015,123,7.3,10.14
+966,Wrecker,"Action,Horror,Thriller",Micheal Bafaro,"Anna Hutchison, Andrea Whitburn, Jennifer Koenig,Michael Dickson",2015,83,3.5,0
+967,The Lone Ranger,"Action,Adventure,Western",Gore Verbinski,"Johnny Depp, Armie Hammer, William Fichtner,Tom Wilkinson",2013,150,6.5,89.29
+968,Texas Chainsaw 3D,"Horror,Thriller",John Luessenhop,"Alexandra Daddario, Tania Raymonde, Scott Eastwood, Trey Songz",2013,92,4.8,34.33M
+969,Disturbia,"Drama,Mystery,Thriller",D.J. Caruso,"Shia LaBeouf, David Morse, Carrie-Anne Moss, Sarah Roemer",2007,105,6.9,80.05
+970,Rock of Ages,"Comedy,Drama,Musical",Adam Shankman,"Julianne Hough, Diego Boneta, Tom Cruise, Alec Baldwin",2012,123,5.9,38.51
+971,Scream 4,"Horror,Mystery",Wes Craven,"Neve Campbell, Courteney Cox, David Arquette, Lucy Hale",2011,111,6.2,38.18
+972,Queen of Katwe,"Biography,Drama,Sport",Mira Nair,"Madina Nalwanga, David Oyelowo, Lupita Nyong'o, Martin Kabanza",2016,124,7.4,8.81
+973,My Big Fat Greek Wedding 2,"Comedy,Family,Romance",Kirk Jones,"Nia Vardalos, John Corbett, Michael Constantine, Lainie Kazan",2016,94,6,59.57
+974,Dark Places,"Drama,Mystery,Thriller",Gilles Paquet-Brenner,"Charlize Theron, Nicholas Hoult, Christina Hendricks, Chloë Grace Moretz",2015,113,6.2,0
+975,Amateur Night,Comedy,Lisa Addario,"Jason Biggs, Janet Montgomery,Ashley Tisdale, Bria L. Murphy",2016,92,5,0
+976,It's Only the End of the World,Drama,Xavier Dolan,"Nathalie Baye, Vincent Cassel, Marion Cotillard, Léa Seydoux",2016,97,7,0
+977,The Skin I Live In,"Drama,Thriller",Pedro Almodóvar,"Antonio Banderas, Elena Anaya, Jan Cornet,Marisa Paredes",2011,120,7.6,3.19
+978,Miracles from Heaven,"Biography,Drama,Family",Patricia Riggen,"Jennifer Garner, Kylie Rogers, Martin Henderson,Brighton Sharbino",2016,109,7,61.69
+979,Annie,"Comedy,Drama,Family",Will Gluck,"Quvenzhané Wallis, Cameron Diaz, Jamie Foxx, Rose Byrne",2014,118,5.3,85.91
+980,Across the Universe,"Drama,Fantasy,Musical",Julie Taymor,"Evan Rachel Wood, Jim Sturgess, Joe Anderson, Dana Fuchs",2007,133,7.4,24.34
+981,Let's Be Cops,Comedy,Luke Greenfield,"Jake Johnson, Damon Wayans Jr., Rob Riggle, Nina Dobrev",2014,104,6.5,82.39
+982,Max,"Adventure,Family",Boaz Yakin,"Thomas Haden Church, Josh Wiggins, Luke Kleintank,Lauren Graham",2015,111,6.8,42.65
+983,Your Highness,"Adventure,Comedy,Fantasy",David Gordon Green,"Danny McBride, Natalie Portman, James Franco, Rasmus Hardiker",2011,102,5.6,21.56
+984,Final Destination 5,"Horror,Thriller",Steven Quale,"Nicholas D'Agosto, Emma Bell, Arlen Escarpeta, Miles Fisher",2011,92,5.9,42.58
+985,Endless Love,"Drama,Romance",Shana Feste,"Gabriella Wilde, Alex Pettyfer, Bruce Greenwood,Robert Patrick",2014,104,6.3,23.39
+986,Martyrs,Horror,Pascal Laugier,"Morjana Alaoui, Mylène Jampanoï, Catherine Bégin,Robert Toupin",2008,99,7.1,0
+987,Selma,"Biography,Drama,History",Ava DuVernay,"David Oyelowo, Carmen Ejogo, Tim Roth, Lorraine Toussaint",2014,128,7.5,52.07
+988,Underworld: Rise of the Lycans,"Action,Adventure,Fantasy",Patrick Tatopoulos,"Rhona Mitra, Michael Sheen, Bill Nighy, Steven Mackintosh",2009,92,6.6,45.8
+989,Taare Zameen Par,"Drama,Family,Music",Aamir Khan,"Darsheel Safary, Aamir Khan, Tanay Chheda, Sachet Engineer",2007,165,8.5,1.2
+990,Take Me Home Tonight,"Comedy,Drama,Romance",Michael Dowse,"Topher Grace, Anna Faris, Dan Fogler, Teresa Palmer",2011,97,6.3,6.92
+991,Resident Evil: Afterlife,"Action,Adventure,Horror",Paul W.S. Anderson,"Milla Jovovich, Ali Larter, Wentworth Miller,Kim Coates",2010,97,5.9,60.13
+992,Project X,Comedy,Nima Nourizadeh,"Thomas Mann, Oliver Cooper, Jonathan Daniel Brown, Dax Flame",2012,88,6.7,54.72
+993,Secret in Their Eyes,"Crime,Drama,Mystery",Billy Ray,"Chiwetel Ejiofor, Nicole Kidman, Julia Roberts, Dean Norris",2015,111,6.2,0
+994,Hostel: Part II,Horror,Eli Roth,"Lauren German, Heather Matarazzo, Bijou Phillips, Roger Bart",2007,94,5.5,17.54
+995,Step Up 2: The Streets,"Drama,Music,Romance",Jon M. Chu,"Robert Hoffman, Briana Evigan, Cassie Ventura, Adam G. Sevani",2008,98,6.2,58.01
+996,Search Party,"Adventure,Comedy",Scot Armstrong,"Adam Pally, T.J. Miller, Thomas Middleditch,Shannon Woodward",2014,93,5.6,0
+997,Nine Lives,"Comedy,Family,Fantasy",Barry Sonnenfeld,"Kevin Spacey, Jennifer Garner, Robbie Amell,Cheryl Hines",2016,87,5.3,19.64

f23/Cole_Lecture_Notes/28_Pandas2/Lec28_Pandas2_Solution_Nelson.ipynb

+%% Cell type:code id: tags:
+
+``` python
+import pandas as pd
+from pandas import Series, DataFrame
+# We can explictly import Series and DataFrame, why might we do this?
+```
+
+%% Cell type:markdown id: tags:
+
+###  Series Review
+
+%% Cell type:markdown id: tags:
+
+#### Series from `list`
+
+%% Cell type:code id: tags:
+
+``` python
+scores_list = [54, 22, 19, 73, 80]
+scores_series = Series(scores_list)
+scores_series
+
+# what name do we call the  0, 1, 2, ... ??       A:  index
+# what name do we call the  54, 22, 19, .... ??   A:  value
+# what name do we call the numbers we can't see?  A:  integer position
+```
+
+%% Output
+
+    0    54
+    1    22
+    2    19
+    3    73
+    4    80
+    dtype: int64
+
+%% Cell type:markdown id: tags:
+
+#### Selecting certain scores.
+What are all the scores `> 50`?
+
+%% Cell type:code id: tags:
+
+``` python
+scores_series[scores_series > 50]
+```
+
+%% Output
+
+    0    54
+    3    73
+    4    80
+    dtype: int64
+
+%% Cell type:markdown id: tags:
+
+**Answer:** Boolean indexing. Try the following...
+
+%% Cell type:code id: tags:
+
+``` python
+scores_series[[True, True, False, False, True]] # often called a "mask"
+```
+
+%% Output
+
+    0    54
+    1    22
+    4    80
+    dtype: int64
+
+%% Cell type:markdown id: tags:
+
+We are really writing a "mask" for our data.
+
+%% Cell type:code id: tags:
+
+``` python
+scores_series > 50
+```
+
+%% Output
+
+    0     True
+    1    False
+    2    False
+    3     True
+    4     True
+    dtype: bool
+
+%% Cell type:code id: tags:
+
+``` python
+scores_series[scores_series > 50]
+```
+
+%% Output
+
+    0    54
+    3    73
+    4    80
+    dtype: int64
+
+%% Cell type:markdown id: tags:
+
+#### Series from `dict`
+
+%% Cell type:code id: tags:
+
+``` python
+# Imagine we hire students and track their weekly hours
+week1 = Series({"Rita":5, "Therese":3, "Janice": 6})
+week2 = Series({"Rita":3, "Therese":7, "Janice": 4})
+week3 = Series({"Therese":5, "Janice":5, "Rita": 8}) # Wrong order! Will this matter?
+print(week1)
+print(week2)
+print(week3)
+```
+
+%% Output
+
+    Rita       5
+    Therese    3
+    Janice     6
+    dtype: int64
+    Rita       3
+    Therese    7
+    Janice     4
+    dtype: int64
+    Therese    5
+    Janice     5
+    Rita       8
+    dtype: int64
+
+%% Cell type:markdown id: tags:
+
+####  For everyone in Week 1, add 3 to their hours
+
+%% Cell type:code id: tags:
+
+``` python
+week1 = week1 + 3
+week1
+```
+
+%% Output
+
+    Rita       8
+    Therese    6
+    Janice     9
+    dtype: int64
+
+%% Cell type:markdown id: tags:
+
+#### Total up everyone's hours
+
+%% Cell type:code id: tags:
+
+``` python
+total_hours = week1 + week2 + week3
+total_hours
+```
+
+%% Output
+
+    Janice     18
+    Rita       19
+    Therese    18
+    dtype: int64
+
+%% Cell type:markdown id: tags:
+
+#### What is week1 / week3 ?
+
+%% Cell type:code id: tags:
+
+``` python
+week1 / week3
+# Notice that we didn't have to worry about the order of indices
+```
+
+%% Output
+
+    Janice     1.8
+    Rita       1.0
+    Therese    1.2
+    dtype: float64
+
+%% Cell type:markdown id: tags:
+
+#### What type of values are stored in  week1 > week2?
+
+%% Cell type:code id: tags:
+
+``` python
+print(week1)
+print(week2)
+week1 > week2 # indices are ordered the same
+```
+
+%% Output
+
+    Rita       8
+    Therese    6
+    Janice     9
+    dtype: int64
+    Rita       3
+    Therese    7
+    Janice     4
+    dtype: int64
+
+    Rita        True
+    Therese    False
+    Janice      True
+    dtype: bool
+
+%% Cell type:markdown id: tags:
+
+####  What is week1 > week3?
+
+%% Cell type:code id: tags:
+
+``` python
+print(week1)
+print(week3)
+# week1 > week3 # indices not in same order
+# week1.sort_index() > week3.sort_index() #proper way
+```
+
+%% Output
+
+    Rita       8
+    Therese    6
+    Janice     9
+    dtype: int64
+    Therese    5
+    Janice     5
+    Rita       8
+    dtype: int64
+
+%% Cell type:markdown id: tags:
+
+
+# Lecture 28:  Pandas 2 - DataFrames
+
+
+Learning Objectives:
+- Create a DataFrame from
+ - a dictionary of Series, lists, or dicts
+ - a list of Series, lists, dicts
+- Select a column, row, cell, or rectangular region of a DataFrame
+- Convert CSV files into DataFrames and DataFrames into CSV Files
+- Access the head or tail of a DataFrame
+
+%% Cell type:markdown id: tags:
+
+**Big Idea**: Data Frames store 2-dimensional data in tables! It is a collection of Series.
+
+%% Cell type:markdown id: tags:
+
+## You can create a DataFrame in a variety of ways!
+### From a dictionary of Series
+
+%% Cell type:code id: tags:
+
+``` python
+names = Series(["Alice", "Bob", "Cindy", "Dan"])
+scores = Series([6, 7, 8, 9])
+
+# to make a dictionary of Series, need to write column names for the keys
+DataFrame({
+    "Player name": names,
+    "Score": scores
+})
+```
+
+%% Output
+
+      Player name  Score
+    0       Alice      6
+    1         Bob      7
+    2       Cindy      8
+    3         Dan      9
+
+%% Cell type:markdown id: tags:
+
+### From a dictionary of lists
+
+%% Cell type:code id: tags:
+
+``` python
+name_list = ["Alice", "Bob", "Cindy", "Dan"]
+score_list = [6, 7, 8, 9]
+# this is the same as above, reminding us that Series act like lists
+DataFrame({
+    "Player name": name_list,
+    "Score": score_list
+})
+```
+
+%% Output
+
+      Player name  Score
+    0       Alice      6
+    1         Bob      7
+    2       Cindy      8
+    3         Dan      9
+
+%% Cell type:markdown id: tags:
+
+### From a dictionary of dictionaries
+We need to make up keys to match the things in each column
+
+%% Cell type:code id: tags:
+
+``` python
+data = {
+    "Player name": {0: "Alice", 1: "Bob", 2: "Cindy", 3: "Dan"},
+    "Score": {0: 6, 1: 7, 2: 8, 3: 9}
+}
+DataFrame(data)
+```
+
+%% Output
+
+      Player name  Score
+    0       Alice      6
+    1         Bob      7
+    2       Cindy      8
+    3         Dan      9
+
+%% Cell type:markdown id: tags:
+
+### From a list of lists
+We have to add the column names, we do this with `columns = [name1, name2, ....]`
+
+%% Cell type:code id: tags:
+
+``` python
+data = [
+    ["Alice", 6],
+    ["Bob", 7],
+    ["Cindy", 8],
+    ["Dan", 9]
+]
+data
+DataFrame(data, columns = ["Player name", "Score"])
+```
+
+%% Output
+
+      Player name  Score
+    0       Alice      6
+    1         Bob      7
+    2       Cindy      8
+    3         Dan      9
+
+%% Cell type:markdown id: tags:
+
+### From a list of dicts
+
+%% Cell type:code id: tags:
+
+``` python
+data = [
+    {"Player name": "Alice", "Score": 6},
+    {"Player name": "Bob", "Score": 7},
+    {"Player name": "Cindy", "Score": 8},
+    {"Player name": "Dan", "Score": 9}
+]
+data
+DataFrame(data)
+```
+
+%% Output
+
+      Player name  Score
+    0       Alice      6
+    1         Bob      7
+    2       Cindy      8
+    3         Dan      9
+
+%% Cell type:markdown id: tags:
+
+### Explicitly naming the indices
+We can use `index = [name1, name2, ...]` to rename the index of each row
+
+%% Cell type:code id: tags:
+
+``` python
+#
+data = [
+    {"Player name": "Alice", "Score": 6},
+    {"Player name": "Bob", "Score": 7},
+    {"Player name": "Cindy", "Score": 8},
+    {"Player name": "Dan", "Score": 9}
+]
+data
+DataFrame(data, index=["A", "B", "C", "D"]) # must have a name for each row
+```
+
+%% Output
+
+      Player name  Score
+    A       Alice      6
+    B         Bob      7
+    C       Cindy      8
+    D         Dan      9
+
+%% Cell type:code id: tags:
+
+``` python
+# You try:
+# Make a DataFrame of 4 people you know with different ages
+# Give names to both the columns and rows
+ages = [
+    ["Alice", 6],
+    ["Bob", 7],
+    ["Cindy", 8],
+    ["Dan", 9]
+]
+DataFrame(ages, index=["A", "B", "C", "D"], columns=["Name", "Age"])
+
+# Share how you did with this with your neighbor
+# If you both did it the same way, try it a different way.
+```
+
+%% Output
+
+        Name  Age
+    A  Alice    6
+    B    Bob    7
+    C  Cindy    8
+    D    Dan    9
+
+%% Cell type:markdown id: tags:
+
+## Select a column, row, cell, or rectangular region of a DataFrame
+### Data lookup: Series
+- `s.loc[X]`   <- lookup by pandas index
+- `s.iloc[X]`  <- lookup by integer position
+
+%% Cell type:code id: tags:
+
+``` python
+hours = Series({"Alice":6, "Bob":7, "Cindy":8, "Dan":9})
+hours
+```
+
+%% Output
+
+    Alice    6
+    Bob      7
+    Cindy    8
+    Dan      9
+    dtype: int64
+
+%% Cell type:code id: tags:
+
+``` python
+# Lookup Bob's hours by pandas index.
+hours.loc["Bob"]
+```
+
+%% Output
+
+    7
+
+%% Cell type:code id: tags:
+
+``` python
+# Lookup Bob's hours by integer position.
+hours.iloc[1]
+```
+
+%% Output
+
+    7
+
+%% Cell type:code id: tags:
+
+``` python
+# Lookup Cindy's hours by pandas index.
+hours.loc["Cindy"]
+```
+
+%% Output
+
+    8
+
+%% Cell type:markdown id: tags:
+
+###  Data lookup: DataFrame
+
+
+- `d.loc[r]`     lookup ROW by pandas ROW index
+- `d.iloc[r]`    lookup ROW by ROW integer position
+- `d[c]`         lookup COL by pandas COL index
+- `d.loc[r, c]`  lookup by pandas ROW index and pandas COL index
+- `d.iloc[r, c]`  lookup by ROW integer position and COL integer position
+
+%% Cell type:code id: tags:
+
+``` python
+# We often call the object that we make df
+data = [
+    ["Alice", 6],
+    ["Bob", 7],
+    ["Cindy", 8],
+    ["Dan", 9]
+]
+df = DataFrame(data, index=["A", "B", "C", "D"], columns = ["Player name", "Score"])
+df
+```
+
+%% Output
+
+      Player name  Score
+    A       Alice      6
+    B         Bob      7
+    C       Cindy      8
+    D         Dan      9
+
+%% Cell type:markdown id: tags:
+
+### What are 3 different ways of accessing row D?
+
+%% Cell type:code id: tags:
+
+``` python
+#df["D"] # Nope!
+print(df.loc["D"])
+print(df.iloc[3])
+print(df.iloc[-1])
+```
+
+%% Output
+
+    Player name    Dan
+    Score            9
+    Name: D, dtype: object
+    Player name    Dan
+    Score            9
+    Name: D, dtype: object
+    Player name    Dan
+    Score            9
+    Name: D, dtype: object
+
+%% Cell type:markdown id: tags:
+
+### How about accessing a column?
+
+%% Cell type:code id: tags:
+
+``` python
+df
+```
+
+%% Output
+
+      Player name  Score
+    A       Alice      6
+    B         Bob      7
+    C       Cindy      8
+    D         Dan      9
+
+%% Cell type:code id: tags:
+
+``` python
+#df[0] # Nope!
+print(df["Player name"])
+```
+
+%% Output
+
+    A    Alice
+    B      Bob
+    C    Cindy
+    D      Dan
+    Name: Player name, dtype: object
+
+%% Cell type:markdown id: tags:
+
+### What are 3 different ways to access a single cell?
+
+%% Cell type:code id: tags:
+
+``` python
+df
+```
+
+%% Output
+
+      Player name  Score
+    A       Alice      6
+    B         Bob      7
+    C       Cindy      8
+    D         Dan      9
+
+%% Cell type:code id: tags:
+
+``` python
+# How to access Cindy?
+#print(df["C", "Player name"]) # Nope!
+print(df.loc["C", "Player name"])
+print(df["Player name"].loc["C"])
+print(df.iloc[2, 0])
+```
+
+%% Output
+
+    Cindy
+    Cindy
+    Cindy
+
+%% Cell type:markdown id: tags:
+
+## How to set values for a specific entry?
+
+- `d.loc[r, c] = new_val`
+- `d.iloc[r, c] = new_val`
+
+%% Cell type:code id: tags:
+
+``` python
+#change player D's name
+df.loc["D", "Player name"] = "Bianca"
+df
+```
+
+%% Output
+
+      Player name  Score
+    A       Alice      6
+    B         Bob      7
+    C       Cindy      8
+    D      Bianca      9
+
+%% Cell type:code id: tags:
+
+``` python
+# then add 3 to that player's score using .loc
+df.loc["D","Score"] += 3
+df
+```
+
+%% Output
+
+      Player name  Score
+    A       Alice      6
+    B         Bob      7
+    C       Cindy      8
+    D      Bianca     12
+
+%% Cell type:code id: tags:
+
+``` python
+# add 7 to a different player's score using .iloc
+df.iloc[0, 1] += 7
+df
+```
+
+%% Output
+
+      Player name  Score
+    A       Alice     13
+    B         Bob      7
+    C       Cindy      8
+    D      Bianca     12
+
+%% Cell type:markdown id: tags:
+
+### Find the max score and the mean score
+
+%% Cell type:code id: tags:
+
+``` python
+# find the max and mean of the "Score" column
+print(df["Score"].max(), df["Score"].mean())
+```
+
+%% Output
+
+    13 10.0
+
+%% Cell type:markdown id: tags:
+
+### Find the highest scoring player
+
+%% Cell type:code id: tags:
+
+``` python
+df
+```
+
+%% Output
+
+      Player name  Score
+    A       Alice     13
+    B         Bob      7
+    C       Cindy      8
+    D      Bianca     12
+
+%% Cell type:code id: tags:
+
+``` python
+highest_scorer = df["Score"].idxmax()
+df["Player name"].loc[highest_scorer]
+```
+
+%% Output
+
+    'Alice'
+
+%% Cell type:markdown id: tags:
+
+##  Slicing a DataFrame
+
+- `df.iloc[ROW_SLICE, COL_SLICE]` <- make a rectangular slice from the DataFrame using integer positions
+- `df.loc[ROW_SLICE, COL_SLICE]` <- make a rectangular slice from the DataFrame using index
+
+%% Cell type:code id: tags:
+
+``` python
+df.iloc[1:3, 0:2]
+```
+
+%% Output
+
+      Player name  Score
+    B         Bob      7
+    C       Cindy      8
+
+%% Cell type:code id: tags:
+
+``` python
+df.loc["B":"C", "Player name":"Score"] # notice that this way is inclusive of endpoints
+```
+
+%% Output
+
+      Player name  Score
+    B         Bob      7
+    C       Cindy      8
+
+%% Cell type:markdown id: tags:
+
+## Set values for sliced DataFrame
+
+- `d.loc[ROW_SLICE, COL_SLICE] = new_val` <- set value by ROW INDEX and COL INDEX
+- `d.iloc[ROW_SLICE, COL_SLICE] = new_val` <- set value by ROW Integer position and COL Integer position
+
+%% Cell type:code id: tags:
+
+``` python
+df
+```
+
+%% Output
+
+      Player name  Score
+    A       Alice     13
+    B         Bob      7
+    C       Cindy      8
+    D      Bianca     12
+
+%% Cell type:code id: tags:
+
+``` python
+df.loc["B":"C", "Score"] += 5
+df
+```
+
+%% Output
+
+      Player name  Score
+    A       Alice     13
+    B         Bob     12
+    C       Cindy     13
+    D      Bianca     12
+
+%% Cell type:markdown id: tags:
+
+### Pandas allows slicing of non-contiguous columns
+
+%% Cell type:code id: tags:
+
+``` python
+# just get Player name for Index B and D
+df.loc[["B", "D"],"Player name"]
+```
+
+%% Output
+
+    B       Bob
+    D    Bianca
+    Name: Player name, dtype: object
+
+%% Cell type:code id: tags:
+
+``` python
+# add 2 to the people in rows B and D
+df.loc[["B", "D"],"Score"] += 2
+df
+```
+
+%% Output
+
+      Player name  Score
+    A       Alice     13
+    B         Bob     14
+    C       Cindy     13
+    D      Bianca     14
+
+%% Cell type:markdown id: tags:
+
+## Boolean indexing on a DataFrame
+
+- `d[BOOL SERIES]`  <- makes a new DF of all rows that lined up were True
+
+%% Cell type:code id: tags:
+
+``` python
+df
+```
+
+%% Output
+
+      Player name  Score
+    A       Alice     13
+    B         Bob     14
+    C       Cindy     13
+    D      Bianca     14
+
+%% Cell type:markdown id: tags:
+
+### Make a Series of Booleans based on Score >= 15
+
+%% Cell type:code id: tags:
+
+``` python
+b = df["Score"] >= 15
+b
+```
+
+%% Output
+
+    A    False
+    B    False
+    C    False
+    D    False
+    Name: Score, dtype: bool
+
+%% Cell type:markdown id: tags:
+
+### use b to slice the DataFrame
+if b is true, include this row in the new df
+
+%% Cell type:code id: tags:
+
+``` python
+df[b]
+```
+
+%% Output
+
+    Empty DataFrame
+    Columns: [Player name, Score]
+    Index: []
+
+%% Cell type:markdown id: tags:
+
+### do the last two things in a single step
+
+%% Cell type:code id: tags:
+
+``` python
+df[df["Score"] >= 15]
+```
+
+%% Output
+
+    Empty DataFrame
+    Columns: [Player name, Score]
+    Index: []
+
+%% Cell type:markdown id: tags:
+
+## Creating DataFrame from csv
+
+%% Cell type:code id: tags:
+
+``` python
+# it's that easy!
+df = pd.read_csv("IMDB-Movie-Data.csv")
+df
+```
+
+%% Output
+
+         Index                    Title                     Genre  \
+    0        0  Guardians of the Galaxy   Action,Adventure,Sci-Fi
+    1        1               Prometheus  Adventure,Mystery,Sci-Fi
+    2        2                    Split           Horror,Thriller
+    3        3                     Sing   Animation,Comedy,Family
+    4        4            Suicide Squad  Action,Adventure,Fantasy
+    ..     ...                      ...                       ...
+    993    993     Secret in Their Eyes       Crime,Drama,Mystery
+    994    994          Hostel: Part II                    Horror
+    995    995   Step Up 2: The Streets       Drama,Music,Romance
+    996    996             Search Party          Adventure,Comedy
+    997    997               Nine Lives     Comedy,Family,Fantasy
+    
+                     Director                                               Cast  \
+    0              James Gunn  Chris Pratt, Vin Diesel, Bradley Cooper, Zoe S...
+    1            Ridley Scott  Noomi Rapace, Logan Marshall-Green, Michael   ...
+    2      M. Night Shyamalan  James McAvoy, Anya Taylor-Joy, Haley Lu Richar...
+    3    Christophe Lourdelet  Matthew McConaughey,Reese Witherspoon, Seth Ma...
+    4              David Ayer  Will Smith, Jared Leto, Margot Robbie, Viola D...
+    ..                    ...                                                ...
+    993             Billy Ray  Chiwetel Ejiofor, Nicole Kidman, Julia Roberts...
+    994              Eli Roth  Lauren German, Heather Matarazzo, Bijou Philli...
+    995            Jon M. Chu  Robert Hoffman, Briana Evigan, Cassie Ventura,...
+    996        Scot Armstrong  Adam Pally, T.J. Miller, Thomas Middleditch,Sh...
+    997      Barry Sonnenfeld  Kevin Spacey, Jennifer Garner, Robbie Amell,Ch...
+    
+         Year  Runtime  Rating  Revenue
+    0    2014      121     8.1   333.13
+    1    2012      124     7.0  126.46M
+    2    2016      117     7.3  138.12M
+    3    2016      108     7.2   270.32
+    4    2016      123     6.2   325.02
+    ..    ...      ...     ...      ...
+    993  2015      111     6.2        0
+    994  2007       94     5.5    17.54
+    995  2008       98     6.2    58.01
+    996  2014       93     5.6        0
+    997  2016       87     5.3    19.64
+    
+    [998 rows x 9 columns]
+
+%% Cell type:markdown id: tags:
+
+###   View the first few lines of the DataFrame
+- `.head(n)` gets the first n lines, 5 is the default
+
+%% Cell type:code id: tags:
+
+``` python
+df.head()
+```
+
+%% Output
+
+       Index                    Title                     Genre  \
+    0      0  Guardians of the Galaxy   Action,Adventure,Sci-Fi
+    1      1               Prometheus  Adventure,Mystery,Sci-Fi
+    2      2                    Split           Horror,Thriller
+    3      3                     Sing   Animation,Comedy,Family
+    4      4            Suicide Squad  Action,Adventure,Fantasy
+    
+                   Director                                               Cast  \
+    0            James Gunn  Chris Pratt, Vin Diesel, Bradley Cooper, Zoe S...
+    1          Ridley Scott  Noomi Rapace, Logan Marshall-Green, Michael   ...
+    2    M. Night Shyamalan  James McAvoy, Anya Taylor-Joy, Haley Lu Richar...
+    3  Christophe Lourdelet  Matthew McConaughey,Reese Witherspoon, Seth Ma...
+    4            David Ayer  Will Smith, Jared Leto, Margot Robbie, Viola D...
+    
+       Year  Runtime  Rating  Revenue
+    0  2014      121     8.1   333.13
+    1  2012      124     7.0  126.46M
+    2  2016      117     7.3  138.12M
+    3  2016      108     7.2   270.32
+    4  2016      123     6.2   325.02
+
+%% Cell type:markdown id: tags:
+
+### get the first 2 rows
+
+%% Cell type:code id: tags:
+
+``` python
+df.head(2)
+```
+
+%% Output
+
+       Index                    Title                     Genre      Director  \
+    0      0  Guardians of the Galaxy   Action,Adventure,Sci-Fi    James Gunn
+    1      1               Prometheus  Adventure,Mystery,Sci-Fi  Ridley Scott
+    
+                                                    Cast  Year  Runtime  Rating  \
+    0  Chris Pratt, Vin Diesel, Bradley Cooper, Zoe S...  2014      121     8.1
+    1  Noomi Rapace, Logan Marshall-Green, Michael   ...  2012      124     7.0
+    
+       Revenue
+    0   333.13
+    1  126.46M
+
+%% Cell type:markdown id: tags:
+
+###   View the first few lines of the DataFrame
+- `.tail(n)` gets the last n lines, 5 is the default
+
+%% Cell type:code id: tags:
+
+``` python
+df.tail()
+```
+
+%% Output
+
+         Index                   Title                  Genre          Director  \
+    993    993    Secret in Their Eyes    Crime,Drama,Mystery         Billy Ray
+    994    994         Hostel: Part II                 Horror          Eli Roth
+    995    995  Step Up 2: The Streets    Drama,Music,Romance        Jon M. Chu
+    996    996            Search Party       Adventure,Comedy    Scot Armstrong
+    997    997              Nine Lives  Comedy,Family,Fantasy  Barry Sonnenfeld
+    
+                                                      Cast  Year  Runtime  Rating  \
+    993  Chiwetel Ejiofor, Nicole Kidman, Julia Roberts...  2015      111     6.2
+    994  Lauren German, Heather Matarazzo, Bijou Philli...  2007       94     5.5
+    995  Robert Hoffman, Briana Evigan, Cassie Ventura,...  2008       98     6.2
+    996  Adam Pally, T.J. Miller, Thomas Middleditch,Sh...  2014       93     5.6
+    997  Kevin Spacey, Jennifer Garner, Robbie Amell,Ch...  2016       87     5.3
+    
+        Revenue
+    993       0
+    994   17.54
+    995   58.01
+    996       0
+    997   19.64
+
+%% Cell type:code id: tags:
+
+``` python
+df.tail(3)
+```
+
+%% Output
+
+         Index                   Title                  Genre          Director  \
+    995    995  Step Up 2: The Streets    Drama,Music,Romance        Jon M. Chu
+    996    996            Search Party       Adventure,Comedy    Scot Armstrong
+    997    997              Nine Lives  Comedy,Family,Fantasy  Barry Sonnenfeld
+    
+                                                      Cast  Year  Runtime  Rating  \
+    995  Robert Hoffman, Briana Evigan, Cassie Ventura,...  2008       98     6.2
+    996  Adam Pally, T.J. Miller, Thomas Middleditch,Sh...  2014       93     5.6
+    997  Kevin Spacey, Jennifer Garner, Robbie Amell,Ch...  2016       87     5.3
+    
+        Revenue
+    995   58.01
+    996       0
+    997   19.64
+
+%% Cell type:markdown id: tags:
+
+### What is the last year in our DataFrame?
+
+%% Cell type:code id: tags:
+
+``` python
+df["Year"].max()
+```
+
+%% Output
+
+    2016
+
+%% Cell type:code id: tags:
+
+``` python
+### What are the rows that correspond to movies whose title contains "Harry" ?
+df[df["Title"].str.contains("Harry")]
+```
+
+%% Output
+
+         Index                                         Title  \
+    114    114  Harry Potter and the Deathly Hallows: Part 2
+    314    314     Harry Potter and the Order of the Phoenix
+    417    417  Harry Potter and the Deathly Hallows: Part 1
+    472    472        Harry Potter and the Half-Blood Prince
+    
+                            Genre     Director  \
+    114   Adventure,Drama,Fantasy  David Yates
+    314  Adventure,Family,Fantasy  David Yates
+    417  Adventure,Family,Fantasy  David Yates
+    472  Adventure,Family,Fantasy  David Yates
+    
+                                                      Cast  Year  Runtime  Rating  \
+    114  Daniel Radcliffe, Emma Watson, Rupert Grint, M...  2011      130     8.1
+    314  Daniel Radcliffe, Emma Watson, Rupert Grint, B...  2007      138     7.5
+    417  Daniel Radcliffe, Emma Watson, Rupert Grint, B...  2010      146     7.7
+    472  Daniel Radcliffe, Emma Watson, Rupert Grint, M...  2009      153     7.5
+    
+        Revenue
+    114  380.96
+    314     292
+    417  294.98
+    472  301.96
+
+%% Cell type:markdown id: tags:
+
+### What is the movie at index 6 ?
+
+%% Cell type:code id: tags:
+
+``` python
+df.iloc[6]
+```
+
+%% Output
+
+    Index                                                       6
+    Title                                              La La Land
+    Genre                                      Comedy,Drama,Music
+    Director                                      Damien Chazelle
+    Cast        Ryan Gosling, Emma Stone, Rosemarie DeWitt, J....
+    Year                                                     2016
+    Runtime                                                   128
+    Rating                                                    8.3
+    Revenue                                               151.06M
+    Name: 6, dtype: object
+
+%% Cell type:code id: tags:
+
+``` python
+df
+```
+
+%% Output
+
+         Index                    Title                     Genre  \
+    0        0  Guardians of the Galaxy   Action,Adventure,Sci-Fi
+    1        1               Prometheus  Adventure,Mystery,Sci-Fi
+    2        2                    Split           Horror,Thriller
+    3        3                     Sing   Animation,Comedy,Family
+    4        4            Suicide Squad  Action,Adventure,Fantasy
+    ..     ...                      ...                       ...
+    993    993     Secret in Their Eyes       Crime,Drama,Mystery
+    994    994          Hostel: Part II                    Horror
+    995    995   Step Up 2: The Streets       Drama,Music,Romance
+    996    996             Search Party          Adventure,Comedy
+    997    997               Nine Lives     Comedy,Family,Fantasy
+    
+                     Director                                               Cast  \
+    0              James Gunn  Chris Pratt, Vin Diesel, Bradley Cooper, Zoe S...
+    1            Ridley Scott  Noomi Rapace, Logan Marshall-Green, Michael   ...
+    2      M. Night Shyamalan  James McAvoy, Anya Taylor-Joy, Haley Lu Richar...
+    3    Christophe Lourdelet  Matthew McConaughey,Reese Witherspoon, Seth Ma...
+    4              David Ayer  Will Smith, Jared Leto, Margot Robbie, Viola D...
+    ..                    ...                                                ...
+    993             Billy Ray  Chiwetel Ejiofor, Nicole Kidman, Julia Roberts...
+    994              Eli Roth  Lauren German, Heather Matarazzo, Bijou Philli...
+    995            Jon M. Chu  Robert Hoffman, Briana Evigan, Cassie Ventura,...
+    996        Scot Armstrong  Adam Pally, T.J. Miller, Thomas Middleditch,Sh...
+    997      Barry Sonnenfeld  Kevin Spacey, Jennifer Garner, Robbie Amell,Ch...
+    
+         Year  Runtime  Rating  Revenue
+    0    2014      121     8.1   333.13
+    1    2012      124     7.0  126.46M
+    2    2016      117     7.3  138.12M
+    3    2016      108     7.2   270.32
+    4    2016      123     6.2   325.02
+    ..    ...      ...     ...      ...
+    993  2015      111     6.2        0
+    994  2007       94     5.5    17.54
+    995  2008       98     6.2    58.01
+    996  2014       93     5.6        0
+    997  2016       87     5.3    19.64
+    
+    [998 rows x 9 columns]
+
+%% Cell type:markdown id: tags:
+
+## Notice that there are two index columns
+- That happened because when you write a csv from pandas to a file, it writes a new index column
+- So if the dataFrame already contains an index, you are going to get two index columns
+- Let's fix that problem
+
+%% Cell type:markdown id: tags:
+
+### How can you use slicing to get just columns with Title and Year?
+
+%% Cell type:code id: tags:
+
+``` python
+df2 = df[["Title", "Year"]]
+df2
+# notice that this does not have the 'index' column
+```
+
+%% Output
+
+                           Title  Year
+    0    Guardians of the Galaxy  2014
+    1                 Prometheus  2012
+    2                      Split  2016
+    3                       Sing  2016
+    4              Suicide Squad  2016
+    ..                       ...   ...
+    993     Secret in Their Eyes  2015
+    994          Hostel: Part II  2007
+    995   Step Up 2: The Streets  2008
+    996             Search Party  2014
+    997               Nine Lives  2016
+    
+    [998 rows x 2 columns]
+
+%% Cell type:markdown id: tags:
+
+### How can you use slicing to get rid of the first column?
+
+%% Cell type:code id: tags:
+
+``` python
+df = df.iloc[:, 1:] #all the rows, not column 0
+df
+```
+
+%% Output
+
+                           Title                     Genre              Director  \
+    0    Guardians of the Galaxy   Action,Adventure,Sci-Fi            James Gunn
+    1                 Prometheus  Adventure,Mystery,Sci-Fi          Ridley Scott
+    2                      Split           Horror,Thriller    M. Night Shyamalan
+    3                       Sing   Animation,Comedy,Family  Christophe Lourdelet
+    4              Suicide Squad  Action,Adventure,Fantasy            David Ayer
+    ..                       ...                       ...                   ...
+    993     Secret in Their Eyes       Crime,Drama,Mystery             Billy Ray
+    994          Hostel: Part II                    Horror              Eli Roth
+    995   Step Up 2: The Streets       Drama,Music,Romance            Jon M. Chu
+    996             Search Party          Adventure,Comedy        Scot Armstrong
+    997               Nine Lives     Comedy,Family,Fantasy      Barry Sonnenfeld
+    
+                                                      Cast  Year  Runtime  Rating  \
+    0    Chris Pratt, Vin Diesel, Bradley Cooper, Zoe S...  2014      121     8.1
+    1    Noomi Rapace, Logan Marshall-Green, Michael   ...  2012      124     7.0
+    2    James McAvoy, Anya Taylor-Joy, Haley Lu Richar...  2016      117     7.3
+    3    Matthew McConaughey,Reese Witherspoon, Seth Ma...  2016      108     7.2
+    4    Will Smith, Jared Leto, Margot Robbie, Viola D...  2016      123     6.2
+    ..                                                 ...   ...      ...     ...
+    993  Chiwetel Ejiofor, Nicole Kidman, Julia Roberts...  2015      111     6.2
+    994  Lauren German, Heather Matarazzo, Bijou Philli...  2007       94     5.5
+    995  Robert Hoffman, Briana Evigan, Cassie Ventura,...  2008       98     6.2
+    996  Adam Pally, T.J. Miller, Thomas Middleditch,Sh...  2014       93     5.6
+    997  Kevin Spacey, Jennifer Garner, Robbie Amell,Ch...  2016       87     5.3
+    
+         Revenue
+    0     333.13
+    1    126.46M
+    2    138.12M
+    3     270.32
+    4     325.02
+    ..       ...
+    993        0
+    994    17.54
+    995    58.01
+    996        0
+    997    19.64
+    
+    [998 rows x 8 columns]
+
+%% Cell type:markdown id: tags:
+
+### Write a df to a csv file
+
+%% Cell type:code id: tags:
+
+``` python
+df.to_csv("better_movies.csv", index = False)
+```
+
+%% Cell type:markdown id: tags:
+
+## Practice on your own.....Data Analysis with Data Frames
+
+%% Cell type:code id: tags:
+
+``` python
+# What are all the movies that have above average run time?
+long_movies = df [df["Runtime"] > df["Runtime"].mean()]
+long_movies
+```
+
+%% Output
+
+                           Title                     Genre            Director  \
+    0    Guardians of the Galaxy   Action,Adventure,Sci-Fi          James Gunn
+    1                 Prometheus  Adventure,Mystery,Sci-Fi        Ridley Scott
+    2                      Split           Horror,Thriller  M. Night Shyamalan
+    4              Suicide Squad  Action,Adventure,Fantasy          David Ayer
+    6                 La La Land        Comedy,Drama,Music     Damien Chazelle
+    ..                       ...                       ...                 ...
+    977       The Skin I Live In            Drama,Thriller     Pedro Almodóvar
+    979                    Annie       Comedy,Drama,Family          Will Gluck
+    980      Across the Universe     Drama,Fantasy,Musical        Julie Taymor
+    987                    Selma   Biography,Drama,History        Ava DuVernay
+    989         Taare Zameen Par        Drama,Family,Music          Aamir Khan
+    
+                                                      Cast  Year  Runtime  Rating  \
+    0    Chris Pratt, Vin Diesel, Bradley Cooper, Zoe S...  2014      121     8.1
+    1    Noomi Rapace, Logan Marshall-Green, Michael   ...  2012      124     7.0
+    2    James McAvoy, Anya Taylor-Joy, Haley Lu Richar...  2016      117     7.3
+    4    Will Smith, Jared Leto, Margot Robbie, Viola D...  2016      123     6.2
+    6    Ryan Gosling, Emma Stone, Rosemarie DeWitt, J....  2016      128     8.3
+    ..                                                 ...   ...      ...     ...
+    977  Antonio Banderas, Elena Anaya, Jan Cornet,Mari...  2011      120     7.6
+    979  Quvenzhané Wallis, Cameron Diaz, Jamie Foxx, R...  2014      118     5.3
+    980  Evan Rachel Wood, Jim Sturgess, Joe Anderson, ...  2007      133     7.4
+    987  David Oyelowo, Carmen Ejogo, Tim Roth, Lorrain...  2014      128     7.5
+    989  Darsheel Safary, Aamir Khan, Tanay Chheda, Sac...  2007      165     8.5
+    
+         Revenue
+    0     333.13
+    1    126.46M
+    2    138.12M
+    4     325.02
+    6    151.06M
+    ..       ...
+    977     3.19
+    979    85.91
+    980    24.34
+    987    52.07
+    989      1.2
+    
+    [432 rows x 8 columns]
+
+%% Cell type:code id: tags:
+
+``` python
+# of these movies, what was the min rating?
+min_rating = long_movies["Rating"].min()
+min_rating
+```
+
+%% Output
+
+    3.2
+
+%% Cell type:code id: tags:
+
+``` python
+# Which movies had this min rating?
+long_movies[long_movies["Rating"] == min_rating]
+```
+
+%% Output
+
+            Title                    Genre           Director  \
+    646  Tall Men  Fantasy,Horror,Thriller  Jonathan Holbrook
+    
+                                                      Cast  Year  Runtime  Rating  \
+    646  Dan Crisafulli, Kay Whitney, Richard Garcia, P...  2016      133     3.2
+    
+        Revenue
+    646       0
+
+%% Cell type:markdown id: tags:
+
+### What are all long_movies with someone in the cast named "Emma" ?
+
+%% Cell type:code id: tags:
+
+``` python
+long_movies[long_movies["Cast"].str.contains("Emma")]
+```
+
+%% Output
+
+                                                Title                     Genre  \
+    6                                      La La Land        Comedy,Drama,Music
+    92                                       The Help                     Drama
+    114  Harry Potter and the Deathly Hallows: Part 2   Adventure,Drama,Fantasy
+    157                          Crazy, Stupid, Love.      Comedy,Drama,Romance
+    253                      The Amazing Spider-Man 2   Action,Adventure,Sci-Fi
+    314     Harry Potter and the Order of the Phoenix  Adventure,Family,Fantasy
+    367                        The Amazing Spider-Man          Action,Adventure
+    417  Harry Potter and the Deathly Hallows: Part 1  Adventure,Family,Fantasy
+    472        Harry Potter and the Half-Blood Prince  Adventure,Family,Fantasy
+    609                           Beautiful Creatures     Drama,Fantasy,Romance
+    717                                          Noah    Action,Adventure,Drama
+    879                              Saving Mr. Banks    Biography,Comedy,Drama
+    
+                    Director                                               Cast  \
+    6        Damien Chazelle  Ryan Gosling, Emma Stone, Rosemarie DeWitt, J....
+    92           Tate Taylor  Emma Stone, Viola Davis, Octavia Spencer, Bryc...
+    114          David Yates  Daniel Radcliffe, Emma Watson, Rupert Grint, M...
+    157        Glenn Ficarra  Steve Carell, Ryan Gosling, Julianne Moore, Em...
+    253            Marc Webb  Andrew Garfield, Emma Stone, Jamie Foxx, Paul ...
+    314          David Yates  Daniel Radcliffe, Emma Watson, Rupert Grint, B...
+    367            Marc Webb  Andrew Garfield, Emma Stone, Rhys Ifans, Irrfa...
+    417          David Yates  Daniel Radcliffe, Emma Watson, Rupert Grint, B...
+    472          David Yates  Daniel Radcliffe, Emma Watson, Rupert Grint, M...
+    609  Richard LaGravenese  Alice Englert, Viola Davis, Emma Thompson,Alde...
+    717     Darren Aronofsky  Russell Crowe, Jennifer Connelly, Anthony Hopk...
+    879     John Lee Hancock  Emma Thompson, Tom Hanks, Annie Rose Buckley, ...
+    
+         Year  Runtime  Rating  Revenue
+    6    2016      128     8.3  151.06M
+    92   2011      146     8.1  169.71M
+    114  2011      130     8.1   380.96
+    157  2011      118     7.4    84.24
+    253  2014      142     6.7   202.85
+    314  2007      138     7.5      292
+    367  2012      136     7.0   262.03
+    417  2010      146     7.7   294.98
+    472  2009      153     7.5   301.96
+    609  2013      124     6.2    19.45
+    717  2014      138     5.8  101.16M
+    879  2013      125     7.5     83.3
+
+%% Cell type:code id: tags:
+
+``` python
+# What is the title of the shortest movie?
+df[df["Runtime"] == df["Runtime"].min()]["Title"]
+```
+
+%% Output
+
+    792    Ma vie de Courgette
+    Name: Title, dtype: object
+
+%% Cell type:code id: tags:
+
+``` python
+# What movie had the highest revenue?
+# df["Revnue"].max() did not work
+# we need to clean our data
+
+def format_revenue(revenue):
+    #TODO: Check the last character of the string
+    if type(revenue) == float: # need this in here if we run code multiple times
+        return revenue
+    elif revenue[-1] == 'M': # some have an "M" at the end
+        return float(revenue[:-1]) * 1e6
+    else:
+        return float(revenue) * 1e6
+```
+
+%% Cell type:code id: tags:
+
+``` python
+# What movie had the highest revenue?
+revenue = df["Revenue"].apply(format_revenue) # apply a function to a column
+print(revenue.head())
+max_revenue = revenue.max()
+
+# make a copy of our df
+rev_df = df.copy()
+rev_df["Rev as fl"] = revenue
+rev_df
+```
+
+%% Output
+
+    0    333130000.0
+    1    126460000.0
+    2    138120000.0
+    3    270320000.0
+    4    325020000.0
+    Name: Revenue, dtype: float64
+
+                           Title                     Genre              Director  \
+    0    Guardians of the Galaxy   Action,Adventure,Sci-Fi            James Gunn
+    1                 Prometheus  Adventure,Mystery,Sci-Fi          Ridley Scott
+    2                      Split           Horror,Thriller    M. Night Shyamalan
+    3                       Sing   Animation,Comedy,Family  Christophe Lourdelet
+    4              Suicide Squad  Action,Adventure,Fantasy            David Ayer
+    ..                       ...                       ...                   ...
+    993     Secret in Their Eyes       Crime,Drama,Mystery             Billy Ray
+    994          Hostel: Part II                    Horror              Eli Roth
+    995   Step Up 2: The Streets       Drama,Music,Romance            Jon M. Chu
+    996             Search Party          Adventure,Comedy        Scot Armstrong
+    997               Nine Lives     Comedy,Family,Fantasy      Barry Sonnenfeld
+    
+                                                      Cast  Year  Runtime  Rating  \
+    0    Chris Pratt, Vin Diesel, Bradley Cooper, Zoe S...  2014      121     8.1
+    1    Noomi Rapace, Logan Marshall-Green, Michael   ...  2012      124     7.0
+    2    James McAvoy, Anya Taylor-Joy, Haley Lu Richar...  2016      117     7.3
+    3    Matthew McConaughey,Reese Witherspoon, Seth Ma...  2016      108     7.2
+    4    Will Smith, Jared Leto, Margot Robbie, Viola D...  2016      123     6.2
+    ..                                                 ...   ...      ...     ...
+    993  Chiwetel Ejiofor, Nicole Kidman, Julia Roberts...  2015      111     6.2
+    994  Lauren German, Heather Matarazzo, Bijou Philli...  2007       94     5.5
+    995  Robert Hoffman, Briana Evigan, Cassie Ventura,...  2008       98     6.2
+    996  Adam Pally, T.J. Miller, Thomas Middleditch,Sh...  2014       93     5.6
+    997  Kevin Spacey, Jennifer Garner, Robbie Amell,Ch...  2016       87     5.3
+    
+         Revenue    Rev as fl
+    0     333.13  333130000.0
+    1    126.46M  126460000.0
+    2    138.12M  138120000.0
+    3     270.32  270320000.0
+    4     325.02  325020000.0
+    ..       ...          ...
+    993        0          0.0
+    994    17.54   17540000.0
+    995    58.01   58010000.0
+    996        0          0.0
+    997    19.64   19640000.0
+    
+    [998 rows x 9 columns]
+
+%% Cell type:code id: tags:
+
+``` python
+# Now we can answer the question!
+rev_df[rev_df["Rev as fl"] == max_revenue]
+```
+
+%% Output
+
+                                             Title                     Genre  \
+    50  Star Wars: Episode VII - The Force Awakens  Action,Adventure,Fantasy
+    
+           Director                                               Cast  Year  \
+    50  J.J. Abrams  Daisy Ridley, John Boyega, Oscar Isaac, Domhna...  2015
+    
+        Runtime  Rating Revenue    Rev as fl
+    50      136     8.1  936.63  936630000.0
+
+%% Cell type:code id: tags:
+
+``` python
+# Or more generally...
+rev_df.sort_values(by="Rev as fl", ascending=False)
+```
+
+%% Output
+
+                                              Title                     Genre  \
+    50   Star Wars: Episode VII - The Force Awakens  Action,Adventure,Fantasy
+    87                                       Avatar  Action,Adventure,Fantasy
+    85                               Jurassic World   Action,Adventure,Sci-Fi
+    76                                 The Avengers             Action,Sci-Fi
+    54                              The Dark Knight        Action,Crime,Drama
+    ..                                          ...                       ...
+    888                                  The Intent               Crime,Drama
+    392                               Whisky Galore            Comedy,Romance
+    478                                     Macbeth                 Drama,War
+    823                                    Man Down            Drama,Thriller
+    476                                         Pet           Horror,Thriller
+    
+                  Director                                               Cast  \
+    50         J.J. Abrams  Daisy Ridley, John Boyega, Oscar Isaac, Domhna...
+    87       James Cameron  Sam Worthington, Zoe Saldana, Sigourney Weaver...
+    85     Colin Trevorrow  Chris Pratt, Bryce Dallas Howard, Ty Simpkins,...
+    76         Joss Whedon  Robert Downey Jr., Chris Evans, Scarlett Johan...
+    54   Christopher Nolan  Christian Bale, Heath Ledger, Aaron Eckhart,Mi...
+    ..                 ...                                                ...
+    888      Femi Oyeniran    Dylan Duffus, Scorcher,Shone Romulus, Jade Asha
+    392  Gillies MacKinnon  Tim Pigott-Smith, Naomi Battrick, Ellie Kendri...
+    478      Justin Kurzel  Michael        Fassbender, Marion Cotillard, J...
+    823       Dito Montiel  Shia LaBeouf, Jai Courtney, Gary Oldman, Kate ...
+    476     Carles Torrens  Dominic Monaghan, Ksenia Solo, Jennette McCurd...
+    
+         Year  Runtime  Rating Revenue    Rev as fl
+    50   2015      136     8.1  936.63  936630000.0
+    87   2009      162     7.8  760.51  760510000.0
+    85   2015      124     7.0  652.18  652180000.0
+    76   2012      143     8.1  623.28  623280000.0
+    54   2008      152     9.0  533.32  533320000.0
+    ..    ...      ...     ...     ...          ...
+    888  2016      104     3.5       0          0.0
+    392  2016       98     5.0       0          0.0
+    478  2015      113     6.7       0          0.0
+    823  2015       90     5.8       0          0.0
+    476  2016       94     5.7       0          0.0
+    
+    [998 rows x 9 columns]
+
+%% Cell type:code id: tags:
+
+``` python
+df
+```
+
+%% Output
+
+                           Title                     Genre              Director  \
+    0    Guardians of the Galaxy   Action,Adventure,Sci-Fi            James Gunn
+    1                 Prometheus  Adventure,Mystery,Sci-Fi          Ridley Scott
+    2                      Split           Horror,Thriller    M. Night Shyamalan
+    3                       Sing   Animation,Comedy,Family  Christophe Lourdelet
+    4              Suicide Squad  Action,Adventure,Fantasy            David Ayer
+    ..                       ...                       ...                   ...
+    993     Secret in Their Eyes       Crime,Drama,Mystery             Billy Ray
+    994          Hostel: Part II                    Horror              Eli Roth
+    995   Step Up 2: The Streets       Drama,Music,Romance            Jon M. Chu
+    996             Search Party          Adventure,Comedy        Scot Armstrong
+    997               Nine Lives     Comedy,Family,Fantasy      Barry Sonnenfeld
+    
+                                                      Cast  Year  Runtime  Rating  \
+    0    Chris Pratt, Vin Diesel, Bradley Cooper, Zoe S...  2014      121     8.1
+    1    Noomi Rapace, Logan Marshall-Green, Michael   ...  2012      124     7.0
+    2    James McAvoy, Anya Taylor-Joy, Haley Lu Richar...  2016      117     7.3
+    3    Matthew McConaughey,Reese Witherspoon, Seth Ma...  2016      108     7.2
+    4    Will Smith, Jared Leto, Margot Robbie, Viola D...  2016      123     6.2
+    ..                                                 ...   ...      ...     ...
+    993  Chiwetel Ejiofor, Nicole Kidman, Julia Roberts...  2015      111     6.2
+    994  Lauren German, Heather Matarazzo, Bijou Philli...  2007       94     5.5
+    995  Robert Hoffman, Briana Evigan, Cassie Ventura,...  2008       98     6.2
+    996  Adam Pally, T.J. Miller, Thomas Middleditch,Sh...  2014       93     5.6
+    997  Kevin Spacey, Jennifer Garner, Robbie Amell,Ch...  2016       87     5.3
+    
+         Revenue
+    0     333.13
+    1    126.46M
+    2    138.12M
+    3     270.32
+    4     325.02
+    ..       ...
+    993        0
+    994    17.54
+    995    58.01
+    996        0
+    997    19.64
+    
+    [998 rows x 8 columns]
+
+%% Cell type:code id: tags:
+
+``` python
+# What is the average runtime for movies by "Francis Lawrence"?
+fl_movies = df[df["Director"] == "Francis Lawrence"]
+fl_movies["Runtime"].mean()
+```
+
+%% Output
+
+    126.75
+
+%% Cell type:markdown id: tags:
+
+### More complicated questions...
+
+%% Cell type:code id: tags:
+
+``` python
+# which director had the highest average rating?
+
+# one way is to make a python dict of director, list of ratings
+director_dict = dict()
+
+# make the dictionary: key is director, value is list of ratings
+for i in range(len(df)):
+    director = df.loc[i, "Director"]
+    rating = df.loc[i, "Rating"]
+    #print(i, director, rating)
+    if director not in director_dict:
+        director_dict[director] = []
+    director_dict[director].append(rating)
+
+# make a ratings dict key is directory, value is average
+# only include directors with > 4 movies
+ratings_dict = {k:sum(v)/len(v) for (k,v) in director_dict.items() if len(v) > 4}
+
+#sort a dict by values
+dict(sorted(ratings_dict.items(), key=lambda t:t[-1], reverse=True))
+
+```
+
+%% Output
+
+    {'Christopher Nolan': 8.680000000000001,
+     'Martin Scorsese': 7.92,
+     'David Fincher': 7.8199999999999985,
+     'Denis Villeneuve': 7.76,
+     'J.J. Abrams': 7.58,
+     'David Yates': 7.433333333333334,
+     'Danny Boyle': 7.42,
+     'Antoine Fuqua': 7.040000000000001,
+     'Zack Snyder': 7.040000000000001,
+     'Woody Allen': 7.019999999999999,
+     'Peter Berg': 6.860000000000001,
+     'Ridley Scott': 6.85,
+     'Justin Lin': 6.82,
+     'Michael Bay': 6.483333333333334,
+     'Paul W.S. Anderson': 5.766666666666666,
+     'M. Night Shyamalan': 5.533333333333332}
+
+%% Cell type:code id: tags:
+
+``` python
+# FOR DEMONSTRATION PURPOSES ONLY
+# We haven't (and will not) learn about "groupby"
+# Pandas has many operations which will be helpful!
+
+# Consider what you already know, and what Pandas can solve
+# when formulating your solutions.
+rating_groups = df.groupby("Director")["Rating"]
+rating_groups.mean()[rating_groups.count() > 4].sort_values(ascending=False)
+```
+
+%% Output
+
+    Director
+    Christopher Nolan     8.680000
+    Martin Scorsese       7.920000
+    David Fincher         7.820000
+    Denis Villeneuve      7.760000
+    J.J. Abrams           7.580000
+    David Yates           7.433333
+    Danny Boyle           7.420000
+    Antoine Fuqua         7.040000
+    Zack Snyder           7.040000
+    Woody Allen           7.020000
+    Peter Berg            6.860000
+    Ridley Scott          6.850000
+    Justin Lin            6.820000
+    Michael Bay           6.483333
+    Paul W.S. Anderson    5.766667
+    M. Night Shyamalan    5.533333
+    Name: Rating, dtype: float64

f23/Cole_Lecture_Notes/28_Pandas2/Lec28_Pandas2_Template_Nelson.ipynb

+%% Cell type:code id: tags:
+
+``` python
+import pandas as pd
+from pandas import Series, DataFrame
+# We can explictly import Series and DataFrame, why might we do this?
+```
+
+%% Cell type:markdown id: tags:
+
+###  Series Review
+
+%% Cell type:markdown id: tags:
+
+#### Series from `list`
+
+%% Cell type:code id: tags:
+
+``` python
+scores_list = [54, 22, 19, 73, 80]
+scores_series = Series(scores_list)
+scores_series
+
+# what name do we call the  0, 1, 2, ... ??       A:
+# what name do we call the  54, 22, 19, .... ??   A:
+# what name do we call the numbers we can't see?  A:
+```
+
+%% Cell type:markdown id: tags:
+
+#### Selecting certain scores.
+What are all the scores `> 50`?
+
+%% Cell type:code id: tags:
+
+``` python
+```
+
+%% Cell type:markdown id: tags:
+
+**Answer:** Boolean indexing. Try the following...
+
+%% Cell type:code id: tags:
+
+``` python
+scores_series[[True, True, False, False, True]] # often called a "mask"
+```
+
+%% Cell type:markdown id: tags:
+
+We are really writing a "mask" for our data.
+
+%% Cell type:code id: tags:
+
+``` python
+scores_series > 50
+```
+
+%% Cell type:markdown id: tags:
+
+#### Series from `dict`
+
+%% Cell type:code id: tags:
+
+``` python
+# Imagine we hire students and track their weekly hours
+week1 = Series({"Rita":5, "Therese":3, "Janice": 6})
+week2 = Series({"Rita":3, "Therese":7, "Janice": 4})
+week3 = Series({"Therese":5, "Janice":5, "Rita": 8}) # Wrong order! Will this matter?
+print(week1)
+print(week2)
+print(week3)
+```
+
+%% Cell type:markdown id: tags:
+
+####  For everyone in Week 1, add 3 to their hours
+
+%% Cell type:code id: tags:
+
+``` python
+week1 = week1 + 3
+week1
+```
+
+%% Cell type:markdown id: tags:
+
+#### Total up everyone's hours
+
+%% Cell type:code id: tags:
+
+``` python
+total_hours = week1 + week2 + week3
+total_hours
+```
+
+%% Cell type:markdown id: tags:
+
+#### What is week1 / week3 ?
+
+%% Cell type:code id: tags:
+
+``` python
+week1 / week3
+# Notice that we didn't have to worry about the order of indices
+```
+
+%% Cell type:markdown id: tags:
+
+#### What type of values are stored in  week1 > week2?
+
+%% Cell type:code id: tags:
+
+``` python
+print(week1)
+print(week2)
+week1 > week2 # indices are ordered the same
+```
+
+%% Cell type:markdown id: tags:
+
+####  What is week1 > week3?
+
+%% Cell type:code id: tags:
+
+``` python
+print(week1)
+print(week3)
+# week1 > week3 # indices not in same order
+# week1.sort_index() > week3.sort_index() #proper way
+```
+
+%% Cell type:markdown id: tags:
+
+
+# Lecture 28:  Pandas 2 - DataFrames
+
+
+Learning Objectives:
+- Create a DataFrame from
+ - a dictionary of Series, lists, or dicts
+ - a list of Series, lists, dicts
+- Select a column, row, cell, or rectangular region of a DataFrame
+- Convert CSV files into DataFrames and DataFrames into CSV Files
+- Access the head or tail of a DataFrame
+
+%% Cell type:markdown id: tags:
+
+**Big Idea**: Data Frames store 2-dimensional data in tables! It is a collection of Series.
+
+%% Cell type:markdown id: tags:
+
+## You can create a DataFrame in a variety of ways!
+### From a dictionary of Series
+
+%% Cell type:code id: tags:
+
+``` python
+names = Series(["Alice", "Bob", "Cindy", "Dan"])
+scores = Series([6, 7, 8, 9])
+
+# to make a dictionary of Series, need to write column names for the keys
+DataFrame({
+    "Player name": names,
+    "Score": scores
+})
+```
+
+%% Cell type:markdown id: tags:
+
+### From a dictionary of lists
+
+%% Cell type:code id: tags:
+
+``` python
+name_list = ["Alice", "Bob", "Cindy", "Dan"]
+score_list = [6, 7, 8, 9]
+# this is the same as above, reminding us that Series act like lists
+DataFrame({
+    "Player name": name_list,
+    "Score": score_list
+})
+```
+
+%% Cell type:markdown id: tags:
+
+### From a dictionary of dictionaries
+We need to make up keys to match the things in each column
+
+%% Cell type:code id: tags:
+
+``` python
+data = {
+    "Player name": {0: "Alice", 1: "Bob", 2: "Cindy", 3: "Dan"},
+    "Score": {0: 6, 1: 7, 2: 8, 3: 9}
+}
+DataFrame(data)
+```
+
+%% Cell type:markdown id: tags:
+
+### From a list of lists
+We have to add the column names, we do this with `columns = [name1, name2, ....]`
+
+%% Cell type:code id: tags:
+
+``` python
+data = [
+    ["Alice", 6],
+    ["Bob", 7],
+    ["Cindy", 8],
+    ["Dan", 9]
+]
+data
+DataFrame(data, columns = ["Player name", "Score"])
+```
+
+%% Cell type:markdown id: tags:
+
+### From a list of dicts
+
+%% Cell type:code id: tags:
+
+``` python
+data = [
+    {"Player name": "Alice", "Score": 6},
+    {"Player name": "Bob", "Score": 7},
+    {"Player name": "Cindy", "Score": 8},
+    {"Player name": "Dan", "Score": 9}
+]
+data
+DataFrame(data)
+```
+
+%% Cell type:markdown id: tags:
+
+### Explicitly naming the indices
+We can use `index = [name1, name2, ...]` to rename the index of each row
+
+%% Cell type:code id: tags:
+
+``` python
+#
+data = [
+    {"Player name": "Alice", "Score": 6},
+    {"Player name": "Bob", "Score": 7},
+    {"Player name": "Cindy", "Score": 8},
+    {"Player name": "Dan", "Score": 9}
+]
+data
+DataFrame(data, index=["A", "B", "C", "D"]) # must have a name for each row
+```
+
+%% Cell type:code id: tags:
+
+``` python
+# You try:
+# Make a DataFrame of 4 people you know with different ages
+# Give names to both the columns and rows
+
+# Share how you did with this with your neighbor
+# If you both did it the same way, try it a different way.
+```
+
+%% Cell type:markdown id: tags:
+
+## Select a column, row, cell, or rectangular region of a DataFrame
+### Data lookup: Series
+- `s.loc[X]`   <- lookup by pandas index
+- `s.iloc[X]`  <- lookup by integer position
+
+%% Cell type:code id: tags:
+
+``` python
+hours = Series({"Alice":6, "Bob":7, "Cindy":8, "Dan":9})
+hours
+```
+
+%% Cell type:code id: tags:
+
+``` python
+# Lookup Bob's hours by pandas index.
+hours.loc["Bob"]
+```
+
+%% Cell type:code id: tags:
+
+``` python
+# Lookup Bob's hours by integer position.
+hours.iloc[2]
+```
+
+%% Cell type:code id: tags:
+
+``` python
+# Lookup Cindy's hours by pandas index.
+```
+
+%% Cell type:markdown id: tags:
+
+###  Data lookup: DataFrame
+
+
+- `d.loc[r]`     lookup ROW by pandas ROW index
+- `d.iloc[r]`    lookup ROW by ROW integer position
+- `d[c]`         lookup COL by pandas COL index
+- `d.loc[r, c]`  lookup by pandas ROW index and pandas COL index
+- `d.iloc[r, c]`  lookup by ROW integer position and COL integer position
+
+%% Cell type:code id: tags:
+
+``` python
+# We often call the object that we make df
+data = [
+    ["Alice", 6],
+    ["Bob", 7],
+    ["Cindy", 8],
+    ["Dan", 9]
+]
+df = DataFrame(data, index=["A", "B", "C", "D"], columns = ["Player name", "Score"])
+df
+```
+
+%% Cell type:markdown id: tags:
+
+### What are 3 different ways of accessing row D?
+
+%% Cell type:code id: tags:
+
+``` python
+```
+
+%% Cell type:markdown id: tags:
+
+### How about accessing a column?
+
+%% Cell type:code id: tags:
+
+``` python
+```
+
+%% Cell type:markdown id: tags:
+
+### What are 3 different ways to access a single cell?
+
+%% Cell type:code id: tags:
+
+``` python
+```
+
+%% Cell type:markdown id: tags:
+
+## How to set values for a specific entry?
+
+- `d.loc[r, c] = new_val`
+- `d.iloc[r, c] = new_val`
+
+%% Cell type:code id: tags:
+
+``` python
+#change player D's name
+df.loc["D", "Player name"] = "Bianca"
+df
+```
+
+%% Cell type:code id: tags:
+
+``` python
+# then add 3 to that player's score using .loc
+```
+
+%% Cell type:code id: tags:
+
+``` python
+# add 7 to a different player's score using .iloc
+```
+
+%% Cell type:markdown id: tags:
+
+### Find the max score and the mean score
+
+%% Cell type:code id: tags:
+
+``` python
+# find the max and mean of the "Score" column
+print(df["Score"].max(), df["Score"].mean())
+```
+
+%% Cell type:markdown id: tags:
+
+### Find the highest scoring player
+
+%% Cell type:code id: tags:
+
+``` python
+```
+
+%% Cell type:markdown id: tags:
+
+##  Slicing a DataFrame
+
+- `df.iloc[ROW_SLICE, COL_SLICE]` <- make a rectangular slice from the DataFrame using integer positions
+- `df.loc[ROW_SLICE, COL_SLICE]` <- make a rectangular slice from the DataFrame using index
+
+%% Cell type:code id: tags:
+
+``` python
+df.iloc[1:3, 0:2]
+```
+
+%% Cell type:code id: tags:
+
+``` python
+df.loc["B":"C", "Player name":"Score"] # notice that this way is inclusive of endpoints
+```
+
+%% Cell type:markdown id: tags:
+
+## Set values for sliced DataFrame
+
+- `d.loc[ROW_SLICE, COL_SLICE] = new_val` <- set value by ROW INDEX and COL INDEX
+- `d.iloc[ROW_SLICE, COL_SLICE] = new_val` <- set value by ROW Integer position and COL Integer position
+
+%% Cell type:code id: tags:
+
+``` python
+df
+```
+
+%% Cell type:code id: tags:
+
+``` python
+df.loc["B":"C", "Score"] += 5
+df
+```
+
+%% Cell type:markdown id: tags:
+
+### Pandas allows slicing of non-contiguous columns
+
+%% Cell type:code id: tags:
+
+``` python
+# just get Player name for Index B and D
+df.loc[["B", "D"],"Player name"]
+```
+
+%% Cell type:code id: tags:
+
+``` python
+# add 2 to the people in rows B and D
+df.loc[["B", "D"],"Score"] += 2
+df
+```
+
+%% Cell type:markdown id: tags:
+
+## Boolean indexing on a DataFrame
+
+- `d[BOOL SERIES]`  <- makes a new DF of all rows that lined up were True
+
+%% Cell type:code id: tags:
+
+``` python
+df
+```
+
+%% Cell type:markdown id: tags:
+
+### Make a Series of Booleans based on Score >= 15
+
+%% Cell type:code id: tags:
+
+``` python
+b = df["Score"] >= 15
+b
+```
+
+%% Cell type:markdown id: tags:
+
+### use b to slice the DataFrame
+if b is true, include this row in the new df
+
+%% Cell type:code id: tags:
+
+``` python
+df[b]
+```
+
+%% Cell type:markdown id: tags:
+
+### do the last two things in a single step
+
+%% Cell type:code id: tags:
+
+``` python
+df[df["Score"] >= 15]
+```
+
+%% Cell type:markdown id: tags:
+
+## Creating DataFrame from csv
+
+%% Cell type:code id: tags:
+
+``` python
+# it's that easy!
+df = pd.read_csv("IMDB-Movie-Data.csv")
+df
+```
+
+%% Cell type:markdown id: tags:
+
+###   View the first few lines of the DataFrame
+- `.head(n)` gets the first n lines, 5 is the default
+
+%% Cell type:code id: tags:
+
+``` python
+df.head()
+```
+
+%% Cell type:markdown id: tags:
+
+### get the first 2 rows
+
+%% Cell type:code id: tags:
+
+``` python
+df.head(2)
+```
+
+%% Cell type:markdown id: tags:
+
+###   View the first few lines of the DataFrame
+- `.tail(n)` gets the last n lines, 5 is the default
+
+%% Cell type:code id: tags:
+
+``` python
+df.tail()
+```
+
+%% Cell type:code id: tags:
+
+``` python
+df.tail(3)
+```
+
+%% Cell type:markdown id: tags:
+
+### What is the last year in our DataFrame?
+
+%% Cell type:code id: tags:
+
+``` python
+df["Year"].max()
+```
+
+%% Cell type:markdown id: tags:
+
+### What are the rows that correspond to movies whose title contains "Harry" ?
+
+%% Cell type:code id: tags:
+
+``` python
+```
+
+%% Cell type:markdown id: tags:
+
+### What is the movie at index 6 ?
+
+%% Cell type:code id: tags:
+
+``` python
+```
+
+%% Cell type:markdown id: tags:
+
+## Notice that there are two index columns
+- That happened because when you write a csv from pandas to a file, it writes a new index column
+- So if the dataFrame already contains an index, you are going to get two index columns
+- Let's fix that problem
+
+%% Cell type:markdown id: tags:
+
+### How can you use slicing to get just columns with Title and Year?
+
+%% Cell type:code id: tags:
+
+``` python
+df2 = ???
+df2
+# notice that this does not have the 'index' column
+```
+
+%% Cell type:markdown id: tags:
+
+### How can you use slicing to get rid of the first column?
+
+%% Cell type:code id: tags:
+
+``` python
+df = df.iloc[:, 1:] #all the rows, not column 0
+df
+```
+
+%% Cell type:markdown id: tags:
+
+### Write a df to a csv file
+
+%% Cell type:code id: tags:
+
+``` python
+df.to_csv("better_movies.csv", index = False)
+```
+
+%% Cell type:markdown id: tags:
+
+## Practice on your own.....Data Analysis with Data Frames
+
+%% Cell type:code id: tags:
+
+``` python
+# What are all the movies that have above average run time?
+long_movies = ???
+long_movies
+```
+
+%% Cell type:code id: tags:
+
+``` python
+# of these movies, what was the min rating?
+min_rating = ???
+min_rating
+```
+
+%% Cell type:code id: tags:
+
+``` python
+# Which movies had this min rating?
+???
+```
+
+%% Cell type:markdown id: tags:
+
+### What are all long_movies with someone in the cast named "Emma" ?
+
+%% Cell type:code id: tags:
+
+``` python
+???
+```
+
+%% Cell type:markdown id: tags:
+
+### What is the title of the shortest movie?
+
+%% Cell type:code id: tags:
+
+``` python
+???
+```
+
+%% Cell type:markdown id: tags:
+
+### What movie had the highest revenue?
+
+%% Cell type:code id: tags:
+
+``` python
+# df["Revnue"].max() did not work
+# we need to clean our data
+
+def format_revenue(revenue):
+    #TODO: Check the last character of the string
+    if type(revenue) == float: # need this in here if we run code multiple times
+        return revenue
+    elif revenue[-1] == 'M': # some have an "M" at the end
+        return float(revenue[:-1]) * 1e6
+    else:
+        return float(revenue) * 1e6
+```
+
+%% Cell type:code id: tags:
+
+``` python
+# What movie had the highest revenue?
+revenue = df["Revenue"].apply(format_revenue) # apply a function to a column
+print(revenue.head())
+max_revenue = revenue.max()
+
+# make a copy of our df
+rev_df = df.copy()
+rev_df["Rev as fl"] = revenue
+rev_df
+```
+
+%% Cell type:code id: tags:
+
+``` python
+# Now we can answer the question!
+???
+```
+
+%% Cell type:code id: tags:
+
+``` python
+# Or more generally...
+rev_df.sort_values(by="Rev as fl", ascending=False)
+```
+
+%% Cell type:code id: tags:
+
+``` python
+df
+```
+
+%% Cell type:code id: tags:
+
+``` python
+# What is the average runtime for movies by "Francis Lawrence"?
+???
+```
+
+%% Cell type:markdown id: tags:
+
+### More complicated questions...
+
+%% Cell type:code id: tags:
+
+``` python
+# Which director had the highest average rating?
+
+# one way is to make a python dict of director, list of ratings
+director_dict = dict()
+
+# make the dictionary: key is director, value is list of ratings
+for i in range(len(df)):
+    director = df.loc[i, "Director"]
+    rating = df.loc[i, "Rating"]
+    #print(i, director, rating)
+    if director not in director_dict:
+        director_dict[director] = []
+    director_dict[director].append(rating)
+
+# make a ratings dict key is directory, value is average
+# only include directors with > 4 movies
+ratings_dict = {k:sum(v)/len(v) for (k,v) in director_dict.items() if len(v) > 4}
+
+#sort a dict by values
+dict(sorted(ratings_dict.items(), key=lambda t:t[-1], reverse=True))
+
+```
+
+%% Cell type:code id: tags:
+
+``` python
+# FOR DEMONSTRATION PURPOSES ONLY
+# We haven't (and will not) learn about "groupby"
+# Pandas has many operations which will be helpful!
+
+# Consider what you already know, and what Pandas can solve
+# when formulating your solutions.
+rating_groups = df.groupby("Director")["Rating"]
+rating_groups.mean()[rating_groups.count() > 4].sort_values(ascending=False)
+```

f23/Cole_Lecture_Notes/29_Web1/Lec29_Web1_Solution_Nelson.ipynb

+%% Cell type:code id: tags:
+
+``` python
+# We'll need these modules!
+import os
+import requests
+import json
+import pandas as pd
+from pandas import Series, DataFrame
+```
+
+%% Cell type:code id: tags:
+
+``` python
+# Warmup 0
+# Did you hardcode the slashes in P10 rather than using os.path.join?
+# It likely won't run on the grader. Check your code and check the autograder ASAP.
+os.path.join("data", "cole.csv")
+```
+
+%% Output
+
+    'data\\cole.csv'
+
+%% Cell type:code id: tags:
+
+``` python
+# Warmup 1: Read the data from "new_movie_data.csv" into a pandas DataFrame called "movies"
+movies = pd.read_csv("new_movie_data.csv")
+movies
+```
+
+%% Output
+
+                                   Title                         Genre  \
+    0            Guardians of the Galaxy       Action,Adventure,Sci-Fi
+    1                         Prometheus      Adventure,Mystery,Sci-Fi
+    2                              Split               Horror,Thriller
+    3                               Sing       Animation,Comedy,Family
+    4                      Suicide Squad      Action,Adventure,Fantasy
+    ...                              ...                           ...
+    1063  Guardians of the Galaxy Vol. 2     Action, Adventure, Comedy
+    1064                     Baby Driver          Action, Crime, Drama
+    1065                  Only the Brave      Action, Biography, Drama
+    1066                   Incredibles 2  Animation, Action, Adventure
+    1067                  A Star Is Born         Drama, Music, Romance
+    
+                      Director                                               Cast  \
+    0               James Gunn  Chris Pratt, Vin Diesel, Bradley Cooper, Zoe S...
+    1             Ridley Scott  Noomi Rapace, Logan Marshall-Green, Michael   ...
+    2       M. Night Shyamalan  James McAvoy, Anya Taylor-Joy, Haley Lu Richar...
+    3     Christophe Lourdelet  Matthew McConaughey,Reese Witherspoon, Seth Ma...
+    4               David Ayer  Will Smith, Jared Leto, Margot Robbie, Viola D...
+    ...                    ...                                                ...
+    1063            James Gunn  Chris Pratt, Zoe Saldana, Dave Bautista, Vin D...
+    1064          Edgar Wright  Ansel Elgort, Jon Bernthal, Jon Hamm, Eiza Gon...
+    1065       Joseph Kosinski  Josh Brolin, Miles Teller, Jeff Bridges, Jenni...
+    1066             Brad Bird  Craig T. Nelson, Holly Hunter, Sarah Vowell, H...
+    1067        Bradley Cooper  Lady Gaga, Bradley Cooper, Sam Elliott, Greg G...
+    
+          Year  Runtime  Rating  Revenue
+    0     2014      121     8.1   333.13
+    1     2012      124     7.0  126.46M
+    2     2016      117     7.3  138.12M
+    3     2016      108     7.2   270.32
+    4     2016      123     6.2   325.02
+    ...    ...      ...     ...      ...
+    1063  2017      136     7.6   389.81
+    1064  2017      113     7.6   107.83
+    1065  2017      134     7.6    18.34
+    1066  2018      118     7.6   608.58
+    1067  2018      136     7.6   215.29
+    
+    [1068 rows x 8 columns]
+
+%% Cell type:code id: tags:
+
+``` python
+# Warmup 2: What years does this new movie dataset cover?
+print(movies['Year'].min(), 'to', movies['Year'].max())
+```
+
+%% Output
+
+    2006 to 2020
+
+%% Cell type:code id: tags:
+
+``` python
+# Warmup 3a: What are the movies with the median rating?
+median_movies = movies[movies['Rating'] == movies['Rating'].median()]
+median_movies
+```
+
+%% Output
+
+                                      Title                       Genre  \
+    38                The Magnificent Seven    Action,Adventure,Western
+    195  Captain America: The First Avenger     Action,Adventure,Sci-Fi
+    222                          It Follows              Horror,Mystery
+    330                              Storks  Animation,Adventure,Comedy
+    359                       Step Brothers                      Comedy
+    366       American Wrestler: The Wizard                 Drama,Sport
+    373             Florence Foster Jenkins      Biography,Comedy,Drama
+    375                          Black Mass       Biography,Crime,Drama
+    415            The Headhunter's Calling                       Drama
+    466                               Enemy            Mystery,Thriller
+    490                     The Book of Eli      Action,Adventure,Drama
+    516                             Chappie          Action,Crime,Drama
+    529                         A Good Year        Comedy,Drama,Romance
+    557             In the Heart of the Sea  Action,Adventure,Biography
+    606                     Horrible Bosses                Comedy,Crime
+    616                 Free State of Jones      Action,Biography,Drama
+    660                      The First Time        Comedy,Drama,Romance
+    731            This Beautiful Fantastic        Comedy,Drama,Fantasy
+    779                             Unknown     Action,Mystery,Thriller
+    783                        Before We Go        Comedy,Drama,Romance
+    819                     Suite Française           Drama,Romance,War
+    865                         Indignation               Drama,Romance
+    866      The Stanford Prison Experiment     Biography,Drama,History
+    868             Mission: Impossible III   Action,Adventure,Thriller
+    875                         Warm Bodies       Comedy,Horror,Romance
+    878                         Shin Gojira      Action,Adventure,Drama
+    881                                 Rio  Animation,Adventure,Comedy
+    905                    Ocean's Thirteen              Crime,Thriller
+    943                            Triangle    Fantasy,Mystery,Thriller
+    962                             Custody                       Drama
+    969                           Disturbia      Drama,Mystery,Thriller
+    
+                      Director                                               Cast  \
+    38           Antoine Fuqua  Denzel      washington, Chris Pratt, Ethan Haw...
+    195           Joe Johnston  Chris Evans, Hugo Weaving, Samuel L. Jackson,H...
+    222  David Robert Mitchell  Maika Monroe, Keir Gilchrist, Olivia Luccardi,...
+    330       Nicholas Stoller  Andy Samberg, Katie Crown,Kelsey Grammer, Jenn...
+    359             Adam McKay  Will Ferrell, John C. Reilly, Mary Steenburgen...
+    366        Alex Ranarivelo  William Fichtner, Jon Voight, Lia Marie Johnso...
+    373         Stephen Frears  Meryl Streep, Hugh Grant, Simon Helberg, Rebec...
+    375           Scott Cooper  Johnny Depp, Benedict Cumberbatch, Dakota John...
+    415          Mark Williams  Alison Brie, Gerard Butler, Willem Dafoe, Gret...
+    466       Denis Villeneuve  Jake Gyllenhaal, Mélanie Laurent, Sarah Gadon,...
+    490          Albert Hughes  Denzel       Washington, Mila Kunis, Ray Steve...
+    516         Neill Blomkamp  Sharlto Copley, Dev Patel, Hugh Jackman,Sigour...
+    529           Ridley Scott  Russell Crowe, Abbie Cornish, Albert Finney, M...
+    557             Ron Howard  Chris Hemsworth, Cillian Murphy, Brendan Glees...
+    606            Seth Gordon  Jason Bateman, Charlie Day, Jason Sudeikis, St...
+    616              Gary Ross  Matthew McConaughey, Gugu Mbatha-Raw, Mahersha...
+    660             Jon Kasdan  Dylan O'Brien, Britt Robertson, Victoria Justi...
+    731            Simon Aboud  Jessica Brown Findlay, Andrew Scott, Jeremy Ir...
+    779     Jaume Collet-Serra  Liam Neeson, Diane Kruger, January Jones,Aidan...
+    783            Chris Evans  Chris Evans, Alice Eve, Emma Fitzpatrick, John...
+    819              Saul Dibb  Michelle Williams, Kristin Scott Thomas, Margo...
+    865          James Schamus  Logan Lerman, Sarah Gadon, Tijuana Ricks, Sue ...
+    866   Kyle Patrick Alvarez  Ezra Miller, Tye Sheridan, Billy Crudup, Olivi...
+    868            J.J. Abrams  Tom Cruise, Michelle Monaghan, Ving Rhames, Ph...
+    875        Jonathan Levine  Nicholas Hoult, Teresa Palmer, John Malkovich,...
+    878           Hideaki Anno  Hiroki Hasegawa, Yutaka Takenouchi,Satomi Ishi...
+    881        Carlos Saldanha  Jesse Eisenberg, Anne Hathaway, George Lopez,K...
+    905      Steven Soderbergh  George Clooney, Brad Pitt, Matt Damon,Michael ...
+    943      Christopher Smith  Melissa George, Joshua McIvor, Jack Taylor,Mic...
+    962           James Lapine  Viola Davis, Hayden Panettiere, Catalina Sandi...
+    969            D.J. Caruso  Shia LaBeouf, David Morse, Carrie-Anne Moss, S...
+    
+         Year  Runtime  Rating  Revenue
+    38   2016      132     6.9    93.38
+    195  2011      124     6.9  176.64M
+    222  2014      100     6.9    14.67
+    330  2016       87     6.9    72.66
+    359  2008       98     6.9  100.47M
+    366  2016      117     6.9        0
+    373  2016      111     6.9    27.37
+    375  2015      123     6.9    62.56
+    415  2016      108     6.9        0
+    466  2013       91     6.9     1.01
+    490  2010      118     6.9    94.82
+    516  2015      120     6.9    31.57
+    529  2006      117     6.9     7.46
+    557  2015      122     6.9    24.99
+    606  2011       98     6.9  117.53M
+    616  2016      139     6.9        0
+    660  2012       95     6.9     0.02
+    731  2016      100     6.9        0
+    779  2011      113     6.9    61.09
+    783  2014       95     6.9     0.04
+    819  2014      107     6.9        0
+    865  2016      110     6.9      3.4
+    866  2015      122     6.9     0.64
+    868  2006      126     6.9  133.38M
+    875  2013       98     6.9    66.36
+    878  2016      120     6.9     1.91
+    881  2011       96     6.9  143.62M
+    905  2007      122     6.9  117.14M
+    943  2009       99     6.9        0
+    962  2016      104     6.9        0
+    969  2007      105     6.9    80.05
+
+%% Cell type:code id: tags:
+
+``` python
+# Warmup 3b: Of these, what was the average runtime?
+median_movies["Runtime"].mean()
+```
+
+%% Output
+
+    110.2258064516129
+
+%% Cell type:code id: tags:
+
+``` python
+# Warmup 3c: ... and how does that compare to the average runtime of all movies?
+movies["Runtime"].mean()
+```
+
+%% Output
+
+    114.09363295880149
+
+%% Cell type:code id: tags:
+
+``` python
+# Warmup 3d: ... and how does that compare to the average runtime of all Sport movies?
+movies[movies["Genre"].str.contains("Sport")]["Runtime"].mean()
+```
+
+%% Output
+
+    118.33333333333333
+
+%% Cell type:code id: tags:
+
+``` python
+# Warmup 4a: What does this function do?
+def format_revenue(revenue):
+    if revenue[-1] == 'M': # some have an "M" at the end
+        return float(revenue[:-1]) * 1e6
+    else:                    # otherwise, assume millions.
+        return float(revenue) * 1e6
+```
+
+%% Cell type:code id: tags:
+
+``` python
+# Warmup 4b: Using the above function, create a new column called
+#            "CountableRevenue" with the revenue as a float.
+movies["CountableRevenue"] = movies["Revenue"].apply(format_revenue)
+movies
+```
+
+%% Output
+
+                                   Title                         Genre  \
+    0            Guardians of the Galaxy       Action,Adventure,Sci-Fi
+    1                         Prometheus      Adventure,Mystery,Sci-Fi
+    2                              Split               Horror,Thriller
+    3                               Sing       Animation,Comedy,Family
+    4                      Suicide Squad      Action,Adventure,Fantasy
+    ...                              ...                           ...
+    1063  Guardians of the Galaxy Vol. 2     Action, Adventure, Comedy
+    1064                     Baby Driver          Action, Crime, Drama
+    1065                  Only the Brave      Action, Biography, Drama
+    1066                   Incredibles 2  Animation, Action, Adventure
+    1067                  A Star Is Born         Drama, Music, Romance
+    
+                      Director                                               Cast  \
+    0               James Gunn  Chris Pratt, Vin Diesel, Bradley Cooper, Zoe S...
+    1             Ridley Scott  Noomi Rapace, Logan Marshall-Green, Michael   ...
+    2       M. Night Shyamalan  James McAvoy, Anya Taylor-Joy, Haley Lu Richar...
+    3     Christophe Lourdelet  Matthew McConaughey,Reese Witherspoon, Seth Ma...
+    4               David Ayer  Will Smith, Jared Leto, Margot Robbie, Viola D...
+    ...                    ...                                                ...
+    1063            James Gunn  Chris Pratt, Zoe Saldana, Dave Bautista, Vin D...
+    1064          Edgar Wright  Ansel Elgort, Jon Bernthal, Jon Hamm, Eiza Gon...
+    1065       Joseph Kosinski  Josh Brolin, Miles Teller, Jeff Bridges, Jenni...
+    1066             Brad Bird  Craig T. Nelson, Holly Hunter, Sarah Vowell, H...
+    1067        Bradley Cooper  Lady Gaga, Bradley Cooper, Sam Elliott, Greg G...
+    
+          Year  Runtime  Rating  Revenue  CountableRevenue
+    0     2014      121     8.1   333.13       333130000.0
+    1     2012      124     7.0  126.46M       126460000.0
+    2     2016      117     7.3  138.12M       138120000.0
+    3     2016      108     7.2   270.32       270320000.0
+    4     2016      123     6.2   325.02       325020000.0
+    ...    ...      ...     ...      ...               ...
+    1063  2017      136     7.6   389.81       389810000.0
+    1064  2017      113     7.6   107.83       107830000.0
+    1065  2017      134     7.6    18.34        18340000.0
+    1066  2018      118     7.6   608.58       608580000.0
+    1067  2018      136     7.6   215.29       215290000.0
+    
+    [1068 rows x 9 columns]
+
+%% Cell type:code id: tags:
+
+``` python
+# Warmup 5: What are the top 10 highest-revenue movies?
+movies.sort_values(by="CountableRevenue", ascending=False).head(10)
+```
+
+%% Output
+
+                                               Title  \
+    50    Star Wars: Episode VII - The Force Awakens
+    1006                           Avengers: Endgame
+    87                                        Avatar
+    1007                      Avengers: Infinity War
+    85                                Jurassic World
+    76                                  The Avengers
+    998                                     Hamilton
+    1066                               Incredibles 2
+    54                               The Dark Knight
+    12                                     Rogue One
+    
+                                 Genre           Director  \
+    50        Action,Adventure,Fantasy        J.J. Abrams
+    1006      Action, Adventure, Drama      Anthony Russo
+    87        Action,Adventure,Fantasy      James Cameron
+    1007     Action, Adventure, Sci-Fi      Anthony Russo
+    85         Action,Adventure,Sci-Fi    Colin Trevorrow
+    76                   Action,Sci-Fi        Joss Whedon
+    998      Biography, Drama, History        Thomas Kail
+    1066  Animation, Action, Adventure          Brad Bird
+    54              Action,Crime,Drama  Christopher Nolan
+    12         Action,Adventure,Sci-Fi     Gareth Edwards
+    
+                                                       Cast  Year  Runtime  \
+    50    Daisy Ridley, John Boyega, Oscar Isaac, Domhna...  2015      136
+    1006  Joe Russo, Robert Downey Jr., Chris Evans, Mar...  2019      181
+    87    Sam Worthington, Zoe Saldana, Sigourney Weaver...  2009      162
+    1007  Joe Russo, Robert Downey Jr., Chris Hemsworth,...  2018      149
+    85    Chris Pratt, Bryce Dallas Howard, Ty Simpkins,...  2015      124
+    76    Robert Downey Jr., Chris Evans, Scarlett Johan...  2012      143
+    998   Lin-Manuel Miranda, Phillipa Soo, Leslie Odom ...  2020      160
+    1066  Craig T. Nelson, Holly Hunter, Sarah Vowell, H...  2018      118
+    54    Christian Bale, Heath Ledger, Aaron Eckhart,Mi...  2008      152
+    12    Felicity Jones, Diego Luna, Alan Tudyk, Donnie...  2016      133
+    
+          Rating Revenue  CountableRevenue
+    50       8.1  936.63       936630000.0
+    1006     8.4  858.37       858370000.0
+    87       7.8  760.51       760510000.0
+    1007     8.4  678.82       678820000.0
+    85       7.0  652.18       652180000.0
+    76       8.1  623.28       623280000.0
+    998      8.6  612.82       612820000.0
+    1066     7.6  608.58       608580000.0
+    54       9.0  533.32       533320000.0
+    12       7.9  532.17       532170000.0
+
+%% Cell type:markdown id: tags:
+
+# Web 1 - How to get data from the 'Net
+
+Core Ideas:
+ - Network structure
+     - Client/Server
+     - Request/Response
+ - HTTP protocol
+     - URL
+     - Headers
+     - Status Codes
+ - The requests module
+
+%% Cell type:markdown id: tags:
+
+## HTTP Status Codes overview
+- 1XX : Informational
+- 2XX : Successful
+- 3XX : Redirection
+- 4XX : Client Error
+- 5XX : Server Error
+
+https://en.wikipedia.org/wiki/List_of_HTTP_status_codes
+
+%% Cell type:markdown id: tags:
+
+## requests.get : Simple string example
+- URL: https://cs220.cs.wisc.edu/hello.txt
+
+%% Cell type:code id: tags:
+
+``` python
+url = "https://cs220.cs.wisc.edu/hello.txt"
+r = requests.get(url) # r is the response
+print(r.status_code)
+print(r.text)
+```
+
+%% Output
+
+    200
+    Hello CS220 / CS319 students! Welcome to our website. Hope you are staying safe and healthy!
+    
+
+%% Cell type:code id: tags:
+
+``` python
+# Q: What if the web site does not exist?
+typo_url = "https://cs220.cs.wisc.edu/hello.txttttt"
+r = requests.get(typo_url)
+print(r.status_code)
+print(r.text)
+
+# A: We get a 403 (client error)
+```
+
+%% Output
+
+    403
+    <html>
+    <head><title>403 Forbidden</title></head>
+    <body>
+    <h1>403 Forbidden</h1>
+    <ul>
+    <li>Code: AccessDenied</li>
+    <li>Message: Access Denied</li>
+    <li>RequestId: 07YJ296B9R21R3Z1</li>
+    <li>HostId: ukRbAHURwMp537u4fprHN/lvhUM04PVNZlyQhTUWDbgcR0tyQEXrFzfQcQkVycivBcW1d3QblVg=</li>
+    </ul>
+    <h3>An Error Occurred While Attempting to Retrieve a Custom Error Document</h3>
+    <ul>
+    <li>Code: AccessDenied</li>
+    <li>Message: Access Denied</li>
+    </ul>
+    <hr/>
+    </body>
+    </html>
+    
+
+%% Cell type:code id: tags:
+
+``` python
+# We can check for a status_code error by using an assert
+typo_url = "https://cs220.cs.wisc.edu/hello.txttttt"
+r = requests.get(typo_url)
+assert r.status_code == 200
+print(r.status_code)
+print(r.text)
+```
+
+%% Output
+
+    ---------------------------------------------------------------------------
+    AssertionError                            Traceback (most recent call last)
+Cell     In[14], line 4
+          2 typo_url = "https://cs220.cs.wisc.edu/hello.txttttt"
+          3 r = requests.get(typo_url)
+    ----> 4 assert r.status_code == 200
+          5 print(r.status_code)
+          6 print(r.text)
+    AssertionError:
+
+%% Cell type:code id: tags:
+
+``` python
+# Instead of using an assert, we often use raise_for_status()
+r = requests.get(typo_url)
+r.raise_for_status() #similar to asserting r.status_code == 200
+r.text
+
+# Note the error you get.... We will use this in the next cell
+```
+
+%% Output
+
+    ---------------------------------------------------------------------------
+    HTTPError                                 Traceback (most recent call last)
+Cell     In[15], line 3
+          1 # Instead of using an assert, we often use raise_for_status()
+          2 r = requests.get(typo_url)
+    ----> 3 r.raise_for_status() #similar to asserting r.status_code == 200
+          4 r.text
+File     ~\anaconda3\Lib\site-packages\requests\models.py:1021, in Response.raise_for_status(self)
+       1016     http_error_msg = (
+       1017         f"{self.status_code} Server Error: {reason} for url: {self.url}"
+       1018     )
+       1020 if http_error_msg:
+    -> 1021     raise HTTPError(http_error_msg, response=self)
+    HTTPError: 403 Client Error: Forbidden for url: https://cs220.cs.wisc.edu/hello.txttttt
+
+%% Cell type:code id: tags:
+
+``` python
+# Let's try to catch that error
+
+try:
+    r = requests.get(typo_url)
+    r.raise_for_status() #similar to asserting r.status_code == 200
+    r.text
+except requests.HTTPError as e: # What's still wrong here?
+    print("oops!!", e)
+
+```
+
+%% Output
+
+    oops!! 403 Client Error: Forbidden for url: https://cs220.cs.wisc.edu/hello.txttttt
+
+%% Cell type:markdown id: tags:
+
+## requests.get : JSON file example
+- URL: https://www.msyamkumar.com/scores.json
+- `json.load` (FILE_OBJECT)
+- `json.loads` (STRING)
+
+%% Cell type:code id: tags:
+
+``` python
+# GETting a JSON file, the long way
+url = "https://www.msyamkumar.com/scores.json"
+r = requests.get(url)
+r.raise_for_status()
+urltext = r.text
+print(urltext)
+d = json.loads(urltext)
+print(type(d), d)
+```
+
+%% Output
+
+    {
+      "alice": 100,
+      "bob": 200,
+      "cindy": 300
+    }
+    
+    <class 'dict'> {'alice': 100, 'bob': 200, 'cindy': 300}
+
+%% Cell type:code id: tags:
+
+``` python
+# GETting a JSON file, the shortcut way
+url = "https://www.msyamkumar.com/scores.json"
+#Shortcut to bypass using json.loads()
+r = requests.get(url)
+r.raise_for_status()
+d2 = r.json()
+print(type(d2), d2)
+```
+
+%% Output
+
+    <class 'dict'> {'alice': 100, 'bob': 200, 'cindy': 300}
+
+%% Cell type:markdown id: tags:
+
+## Good GET Etiquette
+
+Don't make a lot of requests to the same server all at once.
+ - Requests use up the server's time
+ - Major websites will often ban users who make too many requests
+ - You can break a server....similar to DDoS attacks (DON'T DO THIS)
+
+In CS220 we will usually give you a link to a copied file to avoid overloading the site.
+
+%% Cell type:markdown id: tags:
+
+### Explore real-world JSON
+
+How to explore an unknown JSON?
+- If you run into a `dict`, try `.keys()` method to look at the keys of the dictionary, then use lookup process to explore further
+- If you run into a `list`, iterate over the list and print each item
+
+### Weather for UW-Madison campus
+- URL: https://api.weather.gov/gridpoints/MKX/37,63/forecast
+
+%% Cell type:code id: tags:
+
+``` python
+# TODO: GET the forecast from the URL below. Then, explore the data we are working with!
+url = "https://api.weather.gov/gridpoints/MKX/37,63/forecast"
+r = requests.get(url)
+r.raise_for_status()
+weather_data = r.json()
+print(type(weather_data))
+# print(weather_data) #uncomment to see all the json
+```
+
+%% Output
+
+    <class 'dict'>
+
+%% Cell type:code id: tags:
+
+``` python
+# TODO: display the keys of the weather_data dict
+print(list(weather_data.keys()))
+
+# TODO: lookup the value corresponding to the 'properties'
+weather_data["properties"]
+
+# TODO: you know what to do next ... explore type again
+print(type(weather_data["properties"]))
+```
+
+%% Output
+
+    ['@context', 'type', 'geometry', 'properties']
+    <class 'dict'>
+
+%% Cell type:code id: tags:
+
+``` python
+# TODO: display the keys of the properties dict
+print(list(weather_data["properties"].keys()))
+
+# TODO: lookup the value corresponding to the 'periods'
+# weather_data["properties"]["periods"] # uncomment to see the output
+
+# TODO: you know what to do next ... explore type again
+print(type(weather_data["properties"]["periods"]))
+```
+
+%% Output
+
+    ['updated', 'units', 'forecastGenerator', 'generatedAt', 'updateTime', 'validTimes', 'elevation', 'periods']
+    <class 'list'>
+
+%% Cell type:code id: tags:
+
+``` python
+# TODO: extract periods list into a variable
+periods_list = weather_data["properties"]["periods"]
+
+# TODO: create a DataFrame using periods_list
+# TODO: What does each inner data structure represent in your DataFrame?
+#       Keep in mind that outer data structure is a list.
+#       A. rows (because outer data structure is a list)
+periods_df = DataFrame(periods_list)
+periods_df
+```
+
+%% Output
+
+        number            name                  startTime  \
+    0        1           Today  2023-11-15T06:00:00-06:00
+    1        2         Tonight  2023-11-15T18:00:00-06:00
+    2        3        Thursday  2023-11-16T06:00:00-06:00
+    3        4  Thursday Night  2023-11-16T18:00:00-06:00
+    4        5          Friday  2023-11-17T06:00:00-06:00
+    5        6    Friday Night  2023-11-17T18:00:00-06:00
+    6        7        Saturday  2023-11-18T06:00:00-06:00
+    7        8  Saturday Night  2023-11-18T18:00:00-06:00
+    8        9          Sunday  2023-11-19T06:00:00-06:00
+    9       10    Sunday Night  2023-11-19T18:00:00-06:00
+    10      11          Monday  2023-11-20T06:00:00-06:00
+    11      12    Monday Night  2023-11-20T18:00:00-06:00
+    12      13         Tuesday  2023-11-21T06:00:00-06:00
+    13      14   Tuesday Night  2023-11-21T18:00:00-06:00
+    
+                          endTime  isDaytime  temperature temperatureUnit  \
+    0   2023-11-15T18:00:00-06:00       True           63               F
+    1   2023-11-16T06:00:00-06:00      False           42               F
+    2   2023-11-16T18:00:00-06:00       True           65               F
+    3   2023-11-17T06:00:00-06:00      False           37               F
+    4   2023-11-17T18:00:00-06:00       True           44               F
+    5   2023-11-18T06:00:00-06:00      False           29               F
+    6   2023-11-18T18:00:00-06:00       True           49               F
+    7   2023-11-19T06:00:00-06:00      False           29               F
+    8   2023-11-19T18:00:00-06:00       True           48               F
+    9   2023-11-20T06:00:00-06:00      False           34               F
+    10  2023-11-20T18:00:00-06:00       True           46               F
+    11  2023-11-21T06:00:00-06:00      False           36               F
+    12  2023-11-21T18:00:00-06:00       True           43               F
+    13  2023-11-22T06:00:00-06:00      False           28               F
+    
+       temperatureTrend                      probabilityOfPrecipitation  \
+    0           falling  {'unitCode': 'wmoUnit:percent', 'value': None}
+    1              None  {'unitCode': 'wmoUnit:percent', 'value': None}
+    2              None  {'unitCode': 'wmoUnit:percent', 'value': None}
+    3              None    {'unitCode': 'wmoUnit:percent', 'value': 30}
+    4           falling  {'unitCode': 'wmoUnit:percent', 'value': None}
+    5              None  {'unitCode': 'wmoUnit:percent', 'value': None}
+    6           falling  {'unitCode': 'wmoUnit:percent', 'value': None}
+    7              None  {'unitCode': 'wmoUnit:percent', 'value': None}
+    8           falling  {'unitCode': 'wmoUnit:percent', 'value': None}
+    9              None  {'unitCode': 'wmoUnit:percent', 'value': None}
+    10             None    {'unitCode': 'wmoUnit:percent', 'value': 30}
+    11             None    {'unitCode': 'wmoUnit:percent', 'value': 50}
+    12             None    {'unitCode': 'wmoUnit:percent', 'value': 30}
+    13             None    {'unitCode': 'wmoUnit:percent', 'value': 20}
+    
+                                                 dewpoint  \
+    0   {'unitCode': 'wmoUnit:degC', 'value': 4.444444...
+    1   {'unitCode': 'wmoUnit:degC', 'value': 4.444444...
+    2   {'unitCode': 'wmoUnit:degC', 'value': 7.777777...
+    3   {'unitCode': 'wmoUnit:degC', 'value': 7.777777...
+    4   {'unitCode': 'wmoUnit:degC', 'value': -1.66666...
+    5   {'unitCode': 'wmoUnit:degC', 'value': -5.55555...
+    6   {'unitCode': 'wmoUnit:degC', 'value': -1.11111...
+    7   {'unitCode': 'wmoUnit:degC', 'value': -0.55555...
+    8   {'unitCode': 'wmoUnit:degC', 'value': 0.555555...
+    9            {'unitCode': 'wmoUnit:degC', 'value': 0}
+    10  {'unitCode': 'wmoUnit:degC', 'value': 3.333333...
+    11  {'unitCode': 'wmoUnit:degC', 'value': 2.777777...
+    12  {'unitCode': 'wmoUnit:degC', 'value': 2.222222...
+    13           {'unitCode': 'wmoUnit:degC', 'value': 0}
+    
+                                    relativeHumidity     windSpeed windDirection  \
+    0   {'unitCode': 'wmoUnit:percent', 'value': 61}   5 to 10 mph             W
+    1   {'unitCode': 'wmoUnit:percent', 'value': 89}   5 to 10 mph             S
+    2   {'unitCode': 'wmoUnit:percent', 'value': 86}  10 to 20 mph             S
+    3   {'unitCode': 'wmoUnit:percent', 'value': 70}  15 to 25 mph             W
+    4   {'unitCode': 'wmoUnit:percent', 'value': 70}  10 to 15 mph            NW
+    5   {'unitCode': 'wmoUnit:percent', 'value': 69}   5 to 10 mph             W
+    6   {'unitCode': 'wmoUnit:percent', 'value': 63}  10 to 15 mph             W
+    7   {'unitCode': 'wmoUnit:percent', 'value': 92}   5 to 10 mph            NW
+    8   {'unitCode': 'wmoUnit:percent', 'value': 92}         5 mph             E
+    9   {'unitCode': 'wmoUnit:percent', 'value': 85}   5 to 10 mph            SE
+    10  {'unitCode': 'wmoUnit:percent', 'value': 89}        10 mph            SE
+    11  {'unitCode': 'wmoUnit:percent', 'value': 92}  10 to 15 mph            NE
+    12  {'unitCode': 'wmoUnit:percent', 'value': 92}        15 mph            NW
+    13  {'unitCode': 'wmoUnit:percent', 'value': 82}        15 mph            NW
+    
+                                                     icon  \
+    0   https://api.weather.gov/icons/land/day/few?siz...
+    1   https://api.weather.gov/icons/land/night/few?s...
+    2   https://api.weather.gov/icons/land/day/sct?siz...
+    3   https://api.weather.gov/icons/land/night/rain_...
+    4   https://api.weather.gov/icons/land/day/few?siz...
+    5   https://api.weather.gov/icons/land/night/few?s...
+    6   https://api.weather.gov/icons/land/day/few?siz...
+    7   https://api.weather.gov/icons/land/night/few?s...
+    8   https://api.weather.gov/icons/land/day/few?siz...
+    9   https://api.weather.gov/icons/land/night/bkn?s...
+    10  https://api.weather.gov/icons/land/day/rain_sh...
+    11  https://api.weather.gov/icons/land/night/rain_...
+    12  https://api.weather.gov/icons/land/day/rain_sh...
+    13  https://api.weather.gov/icons/land/night/snow,...
+    
+                              shortForecast  \
+    0                                 Sunny
+    1                          Mostly Clear
+    2                          Mostly Sunny
+    3                   Chance Rain Showers
+    4                                 Sunny
+    5                          Mostly Clear
+    6                                 Sunny
+    7                          Mostly Clear
+    8                                 Sunny
+    9                         Mostly Cloudy
+    10                  Chance Rain Showers
+    11                  Chance Rain Showers
+    12                  Chance Rain Showers
+    13  Slight Chance Rain And Snow Showers
+    
+                                         detailedForecast
+    0   Sunny. High near 63, with temperatures falling...
+    1   Mostly clear, with a low around 42. South wind...
+    2   Mostly sunny, with a high near 65. South wind ...
+    3   A chance of rain showers. Mostly cloudy, with ...
+    4   Sunny. High near 44, with temperatures falling...
+    5   Mostly clear, with a low around 29. West wind ...
+    6   Sunny. High near 49, with temperatures falling...
+    7   Mostly clear, with a low around 29. Northwest ...
+    8   Sunny. High near 48, with temperatures falling...
+    9                Mostly cloudy, with a low around 34.
+    10  A chance of rain showers. Mostly cloudy, with ...
+    11  A chance of rain showers. Mostly cloudy, with ...
+    12  A chance of rain showers. Mostly cloudy, with ...
+    13  A slight chance of rain and snow showers. Most...
+
+%% Cell type:markdown id: tags:
+
+#### What are the maximum and minimum observed temperatures?
+
+%% Cell type:code id: tags:
+
+``` python
+min_temp = periods_df["temperature"].min()
+max_temp = periods_df["temperature"].max()
+
+print("Min of {} degrees, max of {} degrees".format(min_temp, max_temp))
+```
+
+%% Output
+
+    Min of 28 degrees, max of 65 degrees
+
+%% Cell type:markdown id: tags:
+
+#### Which days `detailedForecast` contains `rain`?
+
+%% Cell type:code id: tags:
+
+``` python
+snow_days_df = periods_df[periods_df["detailedForecast"].str.contains("rain")]
+snow_days_df
+```
+
+%% Output
+
+        number            name                  startTime  \
+    3        4  Thursday Night  2023-11-16T18:00:00-06:00
+    10      11          Monday  2023-11-20T06:00:00-06:00
+    11      12    Monday Night  2023-11-20T18:00:00-06:00
+    12      13         Tuesday  2023-11-21T06:00:00-06:00
+    13      14   Tuesday Night  2023-11-21T18:00:00-06:00
+    
+                          endTime  isDaytime  temperature temperatureUnit  \
+    3   2023-11-17T06:00:00-06:00      False           37               F
+    10  2023-11-20T18:00:00-06:00       True           46               F
+    11  2023-11-21T06:00:00-06:00      False           36               F
+    12  2023-11-21T18:00:00-06:00       True           43               F
+    13  2023-11-22T06:00:00-06:00      False           28               F
+    
+       temperatureTrend                    probabilityOfPrecipitation  \
+    3              None  {'unitCode': 'wmoUnit:percent', 'value': 30}
+    10             None  {'unitCode': 'wmoUnit:percent', 'value': 30}
+    11             None  {'unitCode': 'wmoUnit:percent', 'value': 50}
+    12             None  {'unitCode': 'wmoUnit:percent', 'value': 30}
+    13             None  {'unitCode': 'wmoUnit:percent', 'value': 20}
+    
+                                                 dewpoint  \
+    3   {'unitCode': 'wmoUnit:degC', 'value': 7.777777...
+    10  {'unitCode': 'wmoUnit:degC', 'value': 3.333333...
+    11  {'unitCode': 'wmoUnit:degC', 'value': 2.777777...
+    12  {'unitCode': 'wmoUnit:degC', 'value': 2.222222...
+    13           {'unitCode': 'wmoUnit:degC', 'value': 0}
+    
+                                    relativeHumidity     windSpeed windDirection  \
+    3   {'unitCode': 'wmoUnit:percent', 'value': 70}  15 to 25 mph             W
+    10  {'unitCode': 'wmoUnit:percent', 'value': 89}        10 mph            SE
+    11  {'unitCode': 'wmoUnit:percent', 'value': 92}  10 to 15 mph            NE
+    12  {'unitCode': 'wmoUnit:percent', 'value': 92}        15 mph            NW
+    13  {'unitCode': 'wmoUnit:percent', 'value': 82}        15 mph            NW
+    
+                                                     icon  \
+    3   https://api.weather.gov/icons/land/night/rain_...
+    10  https://api.weather.gov/icons/land/day/rain_sh...
+    11  https://api.weather.gov/icons/land/night/rain_...
+    12  https://api.weather.gov/icons/land/day/rain_sh...
+    13  https://api.weather.gov/icons/land/night/snow,...
+    
+                              shortForecast  \
+    3                   Chance Rain Showers
+    10                  Chance Rain Showers
+    11                  Chance Rain Showers
+    12                  Chance Rain Showers
+    13  Slight Chance Rain And Snow Showers
+    
+                                         detailedForecast
+    3   A chance of rain showers. Mostly cloudy, with ...
+    10  A chance of rain showers. Mostly cloudy, with ...
+    11  A chance of rain showers. Mostly cloudy, with ...
+    12  A chance of rain showers. Mostly cloudy, with ...
+    13  A slight chance of rain and snow showers. Most...
+
+%% Cell type:markdown id: tags:
+
+#### Which day's `detailedForecast` has the most lengthy description?
+
+%% Cell type:code id: tags:
+
+``` python
+idx_max_desc = periods_df["detailedForecast"].str.len().idxmax()
+periods_df.loc[idx_max_desc]['name']
+```
+
+%% Output
+
+    'Thursday Night'
+
+%% Cell type:markdown id: tags:
+
+#### ... and what was the forecast for that day?
+
+%% Cell type:code id: tags:
+
+``` python
+periods_df.loc[idx_max_desc]['detailedForecast']
+```
+
+%% Output
+
+    'A chance of rain showers. Mostly cloudy, with a low around 37. West wind 15 to 25 mph, with gusts as high as 35 mph. Chance of precipitation is 30%. New rainfall amounts less than a tenth of an inch possible.'
+
+%% Cell type:markdown id: tags:
+
+#### Of the days where the temperature is above freezing, what is the wind speed forecast?
+
+%% Cell type:code id: tags:
+
+``` python
+winds = periods_df[periods_df["temperature"] > 32]["windSpeed"]
+winds
+```
+
+%% Output
+
+    0      5 to 10 mph
+    1      5 to 10 mph
+    2     10 to 20 mph
+    3     15 to 25 mph
+    4     10 to 15 mph
+    6     10 to 15 mph
+    8            5 mph
+    9      5 to 10 mph
+    10          10 mph
+    11    10 to 15 mph
+    12          15 mph
+    Name: windSpeed, dtype: object
+
+%% Cell type:markdown id: tags:
+
+#### ... and what was the maximum predicted wind speed?
+
+%% Cell type:code id: tags:
+
+``` python
+better_winds = winds.str[:-4]
+better_winds
+```
+
+%% Output
+
+    0      5 to 10
+    1      5 to 10
+    2     10 to 20
+    3     15 to 25
+    4     10 to 15
+    6     10 to 15
+    8            5
+    9      5 to 10
+    10          10
+    11    10 to 15
+    12          15
+    Name: windSpeed, dtype: object
+
+%% Cell type:code id: tags:
+
+``` python
+best_winds = better_winds.apply(lambda wind : int(wind) if " to " not in wind else int(wind.split(" to ")[1]))
+best_winds.max()
+```
+
+%% Output
+
+    25
+
+%% Cell type:markdown id: tags:
+
+### Write it out to a CSV file on your drive
+You now have your own copy!
+
+%% Cell type:code id: tags:
+
+``` python
+# Write it all out to a single CSV file
+periods_df.to_csv("campus_weather.csv", index=False)
+```
+
+%% Cell type:markdown id: tags:
+
+### Other Cool APIs
+
+%% Cell type:markdown id: tags:
+
+- City of Madison Transit: http://transitdata.cityofmadison.com/
+- Reddit: https://reddit.com/r/UWMadison.json
+- Lord of the Rings: https://the-one-api.dev/
+- Pokemon: https://pokeapi.co/
+- CS571: https://www.cs571.org/ (you have to be enrolled!)
+
+Remember: Be judicious when making requests; don't overwhelm the server! :)
+
+%% Cell type:markdown id: tags:
+
+## Next Time
+What other documents can we get via the Web? HTML is very popular! We'll explore this.

f23/Cole_Lecture_Notes/29_Web1/Lec29_Web1_Template_Nelson.ipynb

+%% Cell type:code id: tags:
+
+``` python
+# We'll need these modules!
+import os
+import requests
+import json
+import pandas as pd
+from pandas import Series, DataFrame
+```
+
+%% Cell type:code id: tags:
+
+``` python
+# Warmup 0
+# Did you hardcode the slashes in P10 rather than using os.path.join?
+# It likely won't run on the grader. Check your code and check the autograder ASAP.
+os.path.join("data", "cole.csv")
+```
+
+%% Cell type:code id: tags:
+
+``` python
+# Warmup 1: Read the data from "new_movie_data.csv" into a pandas DataFrame called "movies"
+```
+
+%% Cell type:code id: tags:
+
+``` python
+# Warmup 2: What years does this new movie dataset cover?
+```
+
+%% Cell type:code id: tags:
+
+``` python
+# Warmup 3a: What are the movies with the median rating?
+```
+
+%% Cell type:code id: tags:
+
+``` python
+# Warmup 3b: Of these, what was the average runtime?
+```
+
+%% Cell type:code id: tags:
+
+``` python
+# Warmup 3c: ... and how does that compare to the average runtime of all movies?
+```
+
+%% Cell type:code id: tags:
+
+``` python
+# Warmup 3d: ... and how does that compare to the average runtime of all Sport movies?
+```
+
+%% Cell type:code id: tags:
+
+``` python
+# Warmup 4a: What does this function do?
+def format_revenue(revenue):
+    if revenue[-1] == 'M': # some have an "M" at the end
+        return float(revenue[:-1]) * 1e6
+    else:                    # otherwise, assume millions.
+        return float(revenue) * 1e6
+```
+
+%% Cell type:code id: tags:
+
+``` python
+# Warmup 4b: Using the above function, create a new column called
+#            "CountableRevenue" with the revenue as a float.
+```
+
+%% Output
+
+    ---------------------------------------------------------------------------
+    NameError                                 Traceback (most recent call last)
+Cell     In[1], line 3
+          1 # Warmup 4b: Using the above function, create a new column called
+          2 #            "CountableRevenue" with the revenue as a float.
+    ----> 3 movies.index()
+    NameError: name 'movies' is not defined
+
+%% Cell type:code id: tags:
+
+``` python
+# Warmup 5: What are the top 10 highest-revenue movies?
+```
+
+%% Cell type:markdown id: tags:
+
+# Web 1 - How to get data from the 'Net
+
+Core Ideas:
+ - Network structure
+     - Client/Server
+     - Request/Response
+ - HTTP protocol
+     - URL
+     - Headers
+     - Status Codes
+ - The requests module
+
+%% Cell type:markdown id: tags:
+
+## HTTP Status Codes overview
+- 1XX : Informational
+- 2XX : Successful
+- 3XX : Redirection
+- 4XX : Client Error
+- 5XX : Server Error
+
+https://en.wikipedia.org/wiki/List_of_HTTP_status_codes
+
+%% Cell type:markdown id: tags:
+
+## requests.get : Simple string example
+- URL: https://cs220.cs.wisc.edu/hello.txt
+
+%% Cell type:code id: tags:
+
+``` python
+url = "https://cs220.cs.wisc.edu/hello.txt"
+r = requests.get(url) # r is the response
+print(r.status_code)
+print(r.text)
+```
+
+%% Cell type:code id: tags:
+
+``` python
+# Q: What if the web site does not exist?
+typo_url = "https://cs220.cs.wisc.edu/hello.txttttt"
+r = requests.get(typo_url)
+print(r.status_code)
+print(r.text)
+
+# A:
+```
+
+%% Cell type:code id: tags:
+
+``` python
+# We can check for a status_code error by using an assert
+typo_url = "https://cs220.cs.wisc.edu/hello.txttttt"
+r = requests.get(typo_url)
+assert r.status_code == 200
+print(r.status_code)
+print(r.text)
+```
+
+%% Cell type:code id: tags:
+
+``` python
+# Instead of using an assert, we often use raise_for_status()
+r = requests.get(typo_url)
+r.raise_for_status() #similar to asserting r.status_code == 200
+r.text
+
+# Note the error you get.... We will use this in the next cell
+```
+
+%% Cell type:code id: tags:
+
+``` python
+# Let's try to catch that error
+
+try:
+
+except:
+    print("oops!!", e)
+
+```
+
+%% Cell type:markdown id: tags:
+
+## requests.get : JSON file example
+- URL: https://www.msyamkumar.com/scores.json
+- `json.load` (FILE_OBJECT)
+- `json.loads` (STRING)
+
+%% Cell type:code id: tags:
+
+``` python
+# GETting a JSON file, the long way
+url = "https://cs220.cs.wisc.edu/scores.json"
+r = requests.get(url)
+r.raise_for_status()
+urltext = r.text
+print(urltext)
+d = json.loads(urltext)
+print(type(d), d)
+```
+
+%% Cell type:code id: tags:
+
+``` python
+# GETting a JSON file, the shortcut way
+url = "https://cs220.cs.wisc.edu/scores.json"
+#Shortcut to bypass using json.loads()
+r = requests.get(url)
+r.raise_for_status()
+d2 = r.json()
+print(type(d2), d2)
+```
+
+%% Cell type:markdown id: tags:
+
+## Good GET Etiquette
+
+Don't make a lot of requests to the same server all at once.
+ - Requests use up the server's time
+ - Major websites will often ban users who make too many requests
+ - You can break a server....similar to DDoS attacks (DON'T DO THIS)
+
+In CS220 we will usually give you a link to a copied file to avoid overloading the site.
+
+%% Cell type:markdown id: tags:
+
+### Explore real-world JSON
+
+How to explore an unknown JSON?
+- If you run into a `dict`, try `.keys()` method to look at the keys of the dictionary, then use lookup process to explore further
+- If you run into a `list`, iterate over the list and print each item
+
+### Weather for UW-Madison campus
+- URL: https://api.weather.gov/gridpoints/MKX/37,63/forecast
+
+%% Cell type:code id: tags:
+
+``` python
+# TODO: GET the forecast from the URL below. Then, explore the data we are working with!
+url = "https://api.weather.gov/gridpoints/MKX/37,63/forecast"
+...
+```
+
+%% Cell type:code id: tags:
+
+``` python
+# TODO: extract periods list into a variable, and then make a data frame out of it
+```
+
+%% Cell type:markdown id: tags:
+
+#### What are the maximum and minimum observed temperatures?
+
+%% Cell type:code id: tags:
+
+``` python
+```
+
+%% Cell type:markdown id: tags:
+
+#### Which days `detailedForecast` contains `rain`?
+
+%% Cell type:code id: tags:
+
+``` python
+```
+
+%% Cell type:markdown id: tags:
+
+#### Which day's `detailedForecast` has the most lengthy description?
+
+%% Cell type:code id: tags:
+
+``` python
+```
+
+%% Cell type:markdown id: tags:
+
+#### ... and what was the forecast for that day?
+
+%% Cell type:code id: tags:
+
+``` python
+```
+
+%% Cell type:markdown id: tags:
+
+#### Of the days where the temperature is above freezing, what is the wind speed forecast?
+
+%% Cell type:code id: tags:
+
+``` python
+```
+
+%% Cell type:markdown id: tags:
+
+#### ... and what was the maximum predicted wind speed?
+
+%% Cell type:code id: tags:
+
+``` python
+```
+
+%% Cell type:markdown id: tags:
+
+### Write it out to a CSV file on your drive
+You now have your own copy!
+
+%% Cell type:code id: tags:
+
+``` python
+# Write it all out to a single CSV file
+periods_df.to_csv("campus_weather.csv", index=False)
+```
+
+%% Cell type:markdown id: tags:
+
+### Other Cool APIs
+
+%% Cell type:markdown id: tags:
+
+- City of Madison Transit: http://transitdata.cityofmadison.com/
+- Reddit: https://reddit.com/r/UWMadison.json
+- Lord of the Rings: https://the-one-api.dev/
+- Pokemon: https://pokeapi.co/
+- CS571: https://www.cs571.org/ (you have to be enrolled!)
+
+Remember: Be judicious when making requests; don't overwhelm the server! :)
+
+%% Cell type:markdown id: tags:
+
+## Next Time
+What other documents can we get via the Web? HTML is very popular! We'll explore this.