f23/Cole_Lecture_Notes/34_Plotting1/Lec34_Plotting1_Template_Nelson.ipynb

+%% Cell type:code id: tags:
+
+``` python
+# Warmup 0
+import pandas as pd
+from pandas import DataFrame, Series
+import sqlite3
+import os
+import copy
+```
+
+%% Cell type:code id: tags:
+
+``` python
+# Warmup 1: Open a connection, define the qry function, and save all of the movies
+#           data to a dataframe called "movies_df"
+movies_path = "movies.db"
+assert os.path.exists(movies_path)
+c = sqlite3.connect(movies_path)
+
+def qry(sql, conn = c):
+    return pd.read_sql(sql, conn)
+
+movies_df = qry("""
+SELECT *
+FROM movies
+""")
+# movies_df
+```
+
+%% Cell type:code id: tags:
+
+``` python
+# Warmup 2: What are the names and revenues of the top 2 movies by James Gunn?
+qry("""
+
+""")
+```
+
+%% Cell type:code id: tags:
+
+``` python
+# Warmup 3: What is the average revenue of movies by Brad Bird?
+qry("""
+
+""")
+```
+
+%% Cell type:code id: tags:
+
+``` python
+# Warmup 4: What is the average revenue of movies for each director? Sort your answer from highest to lowest.
+qry("""
+
+""")
+```
+
+%% Cell type:code id: tags:
+
+``` python
+# Warmup 5: Of those directors who had more than 3 movies, what is the average revenue
+#           of movies for each director? Sort your answer from highest to lowest.
+qry("""
+
+""")
+```
+
+%% Cell type:code id: tags:
+
+``` python
+# Warmup 6: Of those directors who had more than 3 movies with ratings above 7.0,
+#           what is the average revenue of those movies for each director?
+#           Sort your answer from highest to lowest.
+qry("""
+
+""")
+```
+
+%% Cell type:markdown id: tags:
+
+![image.png](attachment:image.png)
+
+%% Cell type:code id: tags:
+
+``` python
+# Warmup 7: Make a scatter plot where the rating is on the x-axis
+#           and the revenue is on the y-axis
+```
+
+%% Cell type:code id: tags:
+
+``` python
+# Warmup 8: What is the correlation between the rating and the revenue?
+```
+
+%% Cell type:markdown id: tags:
+
+# Lecture 34: Bar Plots
+
+Learning Objectives:
+- Make a bar plot from a Pandas Series
+- Add features: x-label, y-label, title, gridlines, color to plot
+- Set the index of a DataFrame certain column
+- Create an 'other' column in a DataFrame
+
+%% Cell type:code id: tags:
+
+``` python
+# Without this Jupyter notebook cannot display the "first" plot in older versions of Python / mathplotlib / jupyter
+%matplotlib inline
+```
+
+%% Cell type:markdown id: tags:
+
+### Helpful documentation and an overview of how Matplotlib works
+https://matplotlib.org/stable/tutorials/introductory/usage.html
+
+%% Cell type:code id: tags:
+
+``` python
+# matplotlib is a plotting module similar to MATLAB
+import matplotlib
+from matplotlib import pyplot as plt
+
+# matplotlib is highly configurable, acts like a style sheet for Pandas Plots
+# rc stands for runtime config, syntax is like a dictionary
+
+#matplotlib.rcParams                        # show all parameters
+#matplotlib.rcParams["font.size"]          # show current font size setting
+matplotlib.rcParams["font.size"] = 18   # change current font size setting
+```
+
+%% Cell type:markdown id: tags:
+
+## Bar plots: From a Series
+The index is the x-label
+
+The values are the height of each bar
+
+https://pandas.pydata.org/docs/reference/api/pandas.Series.plot.bar.html
+
+%% Cell type:code id: tags:
+
+``` python
+s = Series({"Police": 5000000, "Fire": 3000000, "Schools": 2000000})
+
+# make a bar plot...notice the type
+```
+
+%% Cell type:code id: tags:
+
+``` python
+# if we store the returned object in a variable, we can configure the Axes
+# typically the variable name used is 'ax'
+ax = s.plot.bar()
+type(ax)
+```
+
+%% Cell type:markdown id: tags:
+
+### How can we set the x-axis, y-axis labels, and title?
+- use the Axes object
+
+%% Cell type:code id: tags:
+
+``` python
+# better plot:
+# instead of 1e6, divide all values in s by 1 million
+ax = (s / 1000000).plot.bar()
+
+# give the x ticklabels a rotation of 45 degrees
+ax.set_xticklabels(list(s.index), rotation = 45)
+
+# set the y_label to "Dollars (Millions)"
+ax.set_ylabel("Dollars (Millions)")
+
+# set the title to "Annual City Spending"
+ax.set_title("Annual City Spending")
+```
+
+%% Cell type:markdown id: tags:
+
+### How can we change the figure size?
+- figsize keyword argument
+- should be a tuple with two values: width and height (in inches)
+
+
+%% Cell type:code id: tags:
+
+``` python
+ax = (s / 1000000).plot.bar(figsize = (1.5, 4))
+ax.set_ylabel("Dollars (Millions)")
+ax.set_title("Annual City Spending")
+```
+
+%% Cell type:markdown id: tags:
+
+### How can we make the bars horizontal?
+https://pandas.pydata.org/docs/reference/api/pandas.Series.plot.barh.html
+- switch figsize arguments
+- change y-label to x-label
+
+%% Cell type:code id: tags:
+
+``` python
+# paste the previous code cell here and modify
+```
+
+%% Cell type:markdown id: tags:
+
+### Change bar color by using the argument color= ' '
+<pre>
+- plot.bar(figsize = (width,height ) , color = ??? )
+- 8 standard colors: r, g, b, c, m, y, k, w    color = 'c'  cyan
+- can use value of grey between 0 and 1        color = '0.6'
+- can use a tuple (r,g,b) between 0 and 1      color = (0, .3, .4)
+</pre>
+
+%% Cell type:code id: tags:
+
+``` python
+# color as a single char
+ax = (s / 1000000).plot.barh(figsize = (4, 1.5), color='c')
+ax.set_xlabel("Dollars (Millions)")
+ax.set_title("Annual City Spending")
+```
+
+%% Cell type:code id: tags:
+
+``` python
+# color as a value of grey
+ax = (s / 1000000).plot.barh(figsize = (4, 1.5), color='0.5')
+ax.set_xlabel("Dollars (Millions)")
+ax.set_title("Annual City Spending")
+```
+
+%% Cell type:code id: tags:
+
+``` python
+# color as tuple of (r,g,b)
+ax = (s / 1000000).plot.barh(figsize = (4, 1.5), color=(.2, .5, 0))
+ax.set_xlabel("Dollars (Millions)")
+ax.set_title("Annual City Spending")
+```
+
+%% Cell type:markdown id: tags:
+
+### How can we mark gridlines?
+- use ax.grid()
+
+%% Cell type:code id: tags:
+
+``` python
+# copy the previous code and add grid lines
+ax = (s / 1000000).plot.barh(figsize = (4, 1.5), color='y')
+ax.set_xlabel("Dollars (Millions)")
+ax.set_title("Annual City Spending")
+ax.grid()
+```
+
+%% Cell type:markdown id: tags:
+
+## Examples with the Movies Database
+
+%% Cell type:code id: tags:
+
+``` python
+# What happens if we just plot the entire data frame?
+movies_df.plot.bar() # Answer:
+```
+
+%% Cell type:code id: tags:
+
+``` python
+# Let's see that more clearly, plot the first 3 movies
+```
+
+%% Cell type:code id: tags:
+
+``` python
+# How about just the revenue?
+```
+
+%% Cell type:code id: tags:
+
+``` python
+# What is movie 0??? Let's make the title our index (hint: it's unique!)
+```
+
+%% Cell type:code id: tags:
+
+``` python
+# What are revenues of the top 10 revenue movies?
+```
+
+%% Cell type:code id: tags:
+
+``` python
+# Now plot them!
+```
+
+%% Cell type:code id: tags:
+
+``` python
+# Wouldn't it be nice to have an "other" bar to represent other revenue?
+```
+
+%% Cell type:code id: tags:
+
+``` python
+# Add the argument logy=True to show on a logarithmic scale
+```

f23/Cole_Lecture_Notes/35_Plotting2/Lec35_Plotting2_Template_Nelson.ipynb

+%% Cell type:code id: tags:
+
+``` python
+import pandas as pd
+from pandas import DataFrame, Series
+
+import sqlite3
+import os
+
+import matplotlib
+from matplotlib import pyplot as plt
+
+import requests
+matplotlib.rcParams["font.size"] = 12
+```
+
+%% Cell type:markdown id: tags:
+
+![image.png](attachment:image.png)
+
+%% Cell type:markdown id: tags:
+
+### IRIS dataset: http://archive.ics.uci.edu/ml/datasets/iris
+- This set of data is used in beginning Machine Learning Courses
+- You can train a ML algorithm to use the values to predict the class of iris
+- Dataset link: https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data
+
+%% Cell type:code id: tags:
+
+``` python
+# Warmup 1:  Requests and file writing
+
+# use requests to get this file  "https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data"
+response = requests.get("https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data")
+
+# check that the request was successful
+
+
+# open a file called "iris.csv" for writing the data locally to avoid spamming their server
+
+# write the text of response to the file object
+
+# close the file object
+
+# Look at the file you downloaded. What's wrong with it?
+```
+
+%% Cell type:code id: tags:
+
+``` python
+# Warmup 2:  Making a DataFrame
+
+# read the "iris.csv" file into a Pandas dataframe
+
+# display the head of the data frame
+```
+
+%% Cell type:code id: tags:
+
+``` python
+# Warmup 3: Our CSV file has no header....let's add column names.
+#           Refer to the documentation: https://pandas.pydata.org/docs/reference/api/pandas.read_csv.html
+
+# Attribute Information:
+# 1. sepal length in cm
+# 2. sepal width in cm
+# 3. petal length in cm
+# 4. petal width in cm
+# 5. class: Iris Setosa, Iris Versicolour, Iris Virginica
+
+# These should be our headers ["sep-length", "sep-width", "pet-length", "pet-width", "class"]
+```
+
+%% Cell type:code id: tags:
+
+``` python
+# Warmup 4: Connect to our database version of this data!
+iris_conn = sqlite3.connect("iris-flowers.db")
+pd.read_sql("SELECT * FROM sqlite_master WHERE type='table'", iris_conn)
+```
+
+%% Cell type:code id: tags:
+
+``` python
+# Warmup 5: Using SQL, get the 10 'Iris-setosa' flowers with the longest sepal length.
+#           Break any ties by ordering by the shortest sepal width.
+
+pd.read_sql("""
+
+""", iris_conn)
+```
+
+%% Cell type:code id: tags:
+
+``` python
+# Warmup 6: Using SQL, get the average petal length and width for each class of flower.
+
+pd.read_sql("""
+
+""", iris_conn)
+```
+
+%% Cell type:markdown id: tags:
+
+# Lecture 35:  Scatter Plots
+**Learning Objectives**
+- Set the marker, color, and size of scatter plot data
+- Calculate correlation between DataFrame columns
+- Use subplots to group scatterplot data
+
+%% Cell type:markdown id: tags:
+
+## Set the marker, color, and size of scatter plot data
+
+To start, let's look at some made-up data about Trees.
+The city of Madison maintains a database of all the trees they care for.
+
+%% Cell type:code id: tags:
+
+``` python
+trees = [
+    {"age": 1, "height": 1.5, "diameter": 0.8},
+    {"age": 1, "height": 1.9, "diameter": 1.2},
+    {"age": 1, "height": 1.8, "diameter": 1.4},
+    {"age": 2, "height": 1.8, "diameter": 0.9},
+    {"age": 2, "height": 2.5, "diameter": 1.5},
+    {"age": 2, "height": 3, "diameter": 1.8},
+    {"age": 2, "height": 2.9, "diameter": 1.7},
+    {"age": 3, "height": 3.2, "diameter": 2.1},
+    {"age": 3, "height": 3, "diameter": 2},
+    {"age": 3, "height": 2.4, "diameter": 2.2},
+    {"age": 2, "height": 3.1, "diameter": 2.9},
+    {"age": 4, "height": 2.5, "diameter": 3.1},
+    {"age": 4, "height": 3.9, "diameter": 3.1},
+    {"age": 4, "height": 4.9, "diameter": 2.8},
+    {"age": 4, "height": 5.2, "diameter": 3.5},
+    {"age": 4, "height": 4.8, "diameter": 4},
+]
+trees_df = DataFrame(trees)
+trees_df.head()
+```
+
+%% Cell type:markdown id: tags:
+
+### Scatter Plots
+We can make a scatter plot of a DataFrame using the following function...
+
+`df_name.plot.scatter(x="x_col_name", y="y_col_name", color="peachpuff")`
+
+Plot the trees data comparing a tree's age to its height...
+ - What is `df_name`?
+ - What is `x_col_name`?
+ - What is `y_col_name`?
+
+%% Cell type:code id: tags:
+
+``` python
+```
+
+%% Cell type:markdown id: tags:
+
+Now plot with a little more beautification...
+ - Use a new [color](https://matplotlib.org/3.5.0/_images/sphx_glr_named_colors_003.png)
+ - Use a type of [marker](https://matplotlib.org/stable/api/markers_api.html)
+ - Change the size (any int)
+
+%% Cell type:code id: tags:
+
+``` python
+# Plot with some more beautification options.
+```
+
+%% Cell type:code id: tags:
+
+``` python
+# Add a title to your plot.
+```
+
+%% Cell type:markdown id: tags:
+
+#### Correlation
+
+%% Cell type:code id: tags:
+
+``` python
+# What is the correlation between our DataFrame columns?
+```
+
+%% Cell type:code id: tags:
+
+``` python
+# What is the correlation between age and height (don't use .iloc)
+```
+
+%% Cell type:markdown id: tags:
+
+### Variating Stylistic Parameters
+
+%% Cell type:code id: tags:
+
+``` python
+# Option 1:
+trees_df.plot.scatter(x="age", y="height",  marker="H", s="diameter")
+```
+
+%% Cell type:code id: tags:
+
+``` python
+# Option 2:
+trees_df.plot.scatter(x="age", y="height", marker = "H", s=trees_df["diameter"] * 50) # this way allows you to make it bigger
+```
+
+%% Cell type:markdown id: tags:
+
+## Use subplots to group scatterplot data
+
+%% Cell type:markdown id: tags:
+
+### Re-visit the Iris Data
+
+%% Cell type:code id: tags:
+
+``` python
+iris_df
+```
+
+%% Cell type:markdown id: tags:
+
+### How do we create a *scatter plot* for various *class types*?
+First, gather all the class types.
+
+%% Cell type:code id: tags:
+
+``` python
+# In Pandas
+varietes = ???
+varietes
+```
+
+%% Cell type:code id: tags:
+
+``` python
+# In SQL
+varietes = pd.read_sql("""
+
+""", iris_conn)
+varietes
+```
+
+%% Cell type:markdown id: tags:
+
+In reality, you can choose to write Pandas or SQL queries (or a mix of both!). For the rest of this lecture, we'll use Pandas.
+
+%% Cell type:code id: tags:
+
+``` python
+# If you want to continue using SQL instead, don't close the connection!
+iris_conn.close()
+```
+
+%% Cell type:code id: tags:
+
+``` python
+# Change this scatter plot so that the data is only for class ='Iris-setosa'
+iris_df.plot.scatter(x = "pet-width", y = "pet-length")
+```
+
+%% Cell type:code id: tags:
+
+``` python
+# Write a for loop that iterates through each variety in classes
+# and makes a plot for only that class
+
+for i in range(len(varietes)):
+    variety = varietes[i]
+    pass
+```
+
+%% Cell type:code id: tags:
+
+``` python
+# copy/paste the code above, but this time make each plot a different color
+colors = ["blue", "green", "red"]
+```
+
+%% Cell type:code id: tags:
+
+``` python
+# copy/paste the code above, but this time make each plot a different color AND marker
+colors = ["blue", "green", "red"]
+markers = ["o", "^", "v"]
+```
+
+%% Cell type:code id: tags:
+
+``` python
+# Did you notice that it made 3 plots?!?! What's decieving about this?
+```
+
+%% Cell type:code id: tags:
+
+``` python
+colors = ["blue", "green", "red"]
+markers = ["o", "^", "v"]
+```
+
+%% Cell type:code id: tags:
+
+``` python
+# Have to be VERY careful to not crop out data.
+# We'll talk about this next lecture.
+```
+
+%% Cell type:code id: tags:
+
+``` python
+# Better yet, we could combine these.
+```
+
+%% Cell type:markdown id: tags:
+
+### We can make Subplots in plots, called an AxesSubplot, keyword ax
+1. if AxesSuplot ax passed, then plot in that subplot
+2. if ax is None, create a new AxesSubplot
+3. return AxesSubplot that was used
+
+%% Cell type:code id: tags:
+
+``` python
+# complete this code to make 3 plots in one
+
+plot_area = None   # don't change this...look at this variable in line 12
+colors = ["blue", "green", "red"]
+markers = ["o", "^", "v"]
+```
+
+%% Cell type:markdown id: tags:
+
+### Time-Permitting
+Plot this data in an interesting/meaningful way & identify any correlations.
+
+%% Cell type:code id: tags:
+
+``` python
+students = pd.DataFrame({
+    "name": [
+        "Cole",
+        "Cynthia",
+        "Alice",
+        "Seth"
+    ],
+    "grade": [
+        "C",
+        "AB",
+        "B",
+        "BC"
+    ],
+    "gpa": [
+        2.0,
+        3.5,
+        3.0,
+        2.5
+    ],
+    "attendance": [
+        4,
+        11,
+        10,
+        6
+    ],
+    "height": [
+        68,
+        66,
+        60,
+        72
+    ]
+})
+students
+```
+
+%% Cell type:code id: tags:
+
+``` python
+# Min, Max, and Overall Difference in Student Height
+min_height = students["height"].min()
+max_height = students["height"].max()
+diff_height = max_height - min_height
+
+# Normalize students heights on a scale of [0, 1] (black to white)
+height_colors = (students["height"] - min_height) / diff_height
+
+# Normalize students heights on a scale of [0, 0.5] (black to gray)
+height_colors = height_colors / 2
+
+# Color must be a string (e.g. c='0.34')
+height_colors = height_colors.astype("string")
+
+height_colors
+```
+
+%% Cell type:code id: tags:
+
+``` python
+# Plot!
+```
+
+%% Cell type:code id: tags:
+
+``` python
+# What are the correlations?
+```
+
+%% Cell type:markdown id: tags:
+
+![image.png](attachment:image.png)
+
+%% Cell type:markdown id: tags:
+
+https://www.researchgate.net/publication/247907373_Stupid_Data_Miner_Tricks_Overfitting_the_SP_500

f23/Cole_Lecture_Notes/35_Plotting2/iris.csv

+5.1,3.5,1.4,0.2,Iris-setosa
+4.9,3.0,1.4,0.2,Iris-setosa
+4.7,3.2,1.3,0.2,Iris-setosa
+4.6,3.1,1.5,0.2,Iris-setosa
+5.0,3.6,1.4,0.2,Iris-setosa
+5.4,3.9,1.7,0.4,Iris-setosa
+4.6,3.4,1.4,0.3,Iris-setosa
+5.0,3.4,1.5,0.2,Iris-setosa
+4.4,2.9,1.4,0.2,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+5.4,3.7,1.5,0.2,Iris-setosa
+4.8,3.4,1.6,0.2,Iris-setosa
+4.8,3.0,1.4,0.1,Iris-setosa
+4.3,3.0,1.1,0.1,Iris-setosa
+5.8,4.0,1.2,0.2,Iris-setosa
+5.7,4.4,1.5,0.4,Iris-setosa
+5.4,3.9,1.3,0.4,Iris-setosa
+5.1,3.5,1.4,0.3,Iris-setosa
+5.7,3.8,1.7,0.3,Iris-setosa
+5.1,3.8,1.5,0.3,Iris-setosa
+5.4,3.4,1.7,0.2,Iris-setosa
+5.1,3.7,1.5,0.4,Iris-setosa
+4.6,3.6,1.0,0.2,Iris-setosa
+5.1,3.3,1.7,0.5,Iris-setosa
+4.8,3.4,1.9,0.2,Iris-setosa
+5.0,3.0,1.6,0.2,Iris-setosa
+5.0,3.4,1.6,0.4,Iris-setosa
+5.2,3.5,1.5,0.2,Iris-setosa
+5.2,3.4,1.4,0.2,Iris-setosa
+4.7,3.2,1.6,0.2,Iris-setosa
+4.8,3.1,1.6,0.2,Iris-setosa
+5.4,3.4,1.5,0.4,Iris-setosa
+5.2,4.1,1.5,0.1,Iris-setosa
+5.5,4.2,1.4,0.2,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+5.0,3.2,1.2,0.2,Iris-setosa
+5.5,3.5,1.3,0.2,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+4.4,3.0,1.3,0.2,Iris-setosa
+5.1,3.4,1.5,0.2,Iris-setosa
+5.0,3.5,1.3,0.3,Iris-setosa
+4.5,2.3,1.3,0.3,Iris-setosa
+4.4,3.2,1.3,0.2,Iris-setosa
+5.0,3.5,1.6,0.6,Iris-setosa
+5.1,3.8,1.9,0.4,Iris-setosa
+4.8,3.0,1.4,0.3,Iris-setosa
+5.1,3.8,1.6,0.2,Iris-setosa
+4.6,3.2,1.4,0.2,Iris-setosa
+5.3,3.7,1.5,0.2,Iris-setosa
+5.0,3.3,1.4,0.2,Iris-setosa
+7.0,3.2,4.7,1.4,Iris-versicolor
+6.4,3.2,4.5,1.5,Iris-versicolor
+6.9,3.1,4.9,1.5,Iris-versicolor
+5.5,2.3,4.0,1.3,Iris-versicolor
+6.5,2.8,4.6,1.5,Iris-versicolor
+5.7,2.8,4.5,1.3,Iris-versicolor
+6.3,3.3,4.7,1.6,Iris-versicolor
+4.9,2.4,3.3,1.0,Iris-versicolor
+6.6,2.9,4.6,1.3,Iris-versicolor
+5.2,2.7,3.9,1.4,Iris-versicolor
+5.0,2.0,3.5,1.0,Iris-versicolor
+5.9,3.0,4.2,1.5,Iris-versicolor
+6.0,2.2,4.0,1.0,Iris-versicolor
+6.1,2.9,4.7,1.4,Iris-versicolor
+5.6,2.9,3.6,1.3,Iris-versicolor
+6.7,3.1,4.4,1.4,Iris-versicolor
+5.6,3.0,4.5,1.5,Iris-versicolor
+5.8,2.7,4.1,1.0,Iris-versicolor
+6.2,2.2,4.5,1.5,Iris-versicolor
+5.6,2.5,3.9,1.1,Iris-versicolor
+5.9,3.2,4.8,1.8,Iris-versicolor
+6.1,2.8,4.0,1.3,Iris-versicolor
+6.3,2.5,4.9,1.5,Iris-versicolor
+6.1,2.8,4.7,1.2,Iris-versicolor
+6.4,2.9,4.3,1.3,Iris-versicolor
+6.6,3.0,4.4,1.4,Iris-versicolor
+6.8,2.8,4.8,1.4,Iris-versicolor
+6.7,3.0,5.0,1.7,Iris-versicolor
+6.0,2.9,4.5,1.5,Iris-versicolor
+5.7,2.6,3.5,1.0,Iris-versicolor
+5.5,2.4,3.8,1.1,Iris-versicolor
+5.5,2.4,3.7,1.0,Iris-versicolor
+5.8,2.7,3.9,1.2,Iris-versicolor
+6.0,2.7,5.1,1.6,Iris-versicolor
+5.4,3.0,4.5,1.5,Iris-versicolor
+6.0,3.4,4.5,1.6,Iris-versicolor
+6.7,3.1,4.7,1.5,Iris-versicolor
+6.3,2.3,4.4,1.3,Iris-versicolor
+5.6,3.0,4.1,1.3,Iris-versicolor
+5.5,2.5,4.0,1.3,Iris-versicolor
+5.5,2.6,4.4,1.2,Iris-versicolor
+6.1,3.0,4.6,1.4,Iris-versicolor
+5.8,2.6,4.0,1.2,Iris-versicolor
+5.0,2.3,3.3,1.0,Iris-versicolor
+5.6,2.7,4.2,1.3,Iris-versicolor
+5.7,3.0,4.2,1.2,Iris-versicolor
+5.7,2.9,4.2,1.3,Iris-versicolor
+6.2,2.9,4.3,1.3,Iris-versicolor
+5.1,2.5,3.0,1.1,Iris-versicolor
+5.7,2.8,4.1,1.3,Iris-versicolor
+6.3,3.3,6.0,2.5,Iris-virginica
+5.8,2.7,5.1,1.9,Iris-virginica
+7.1,3.0,5.9,2.1,Iris-virginica
+6.3,2.9,5.6,1.8,Iris-virginica
+6.5,3.0,5.8,2.2,Iris-virginica
+7.6,3.0,6.6,2.1,Iris-virginica
+4.9,2.5,4.5,1.7,Iris-virginica
+7.3,2.9,6.3,1.8,Iris-virginica
+6.7,2.5,5.8,1.8,Iris-virginica
+7.2,3.6,6.1,2.5,Iris-virginica
+6.5,3.2,5.1,2.0,Iris-virginica
+6.4,2.7,5.3,1.9,Iris-virginica
+6.8,3.0,5.5,2.1,Iris-virginica
+5.7,2.5,5.0,2.0,Iris-virginica
+5.8,2.8,5.1,2.4,Iris-virginica
+6.4,3.2,5.3,2.3,Iris-virginica
+6.5,3.0,5.5,1.8,Iris-virginica
+7.7,3.8,6.7,2.2,Iris-virginica
+7.7,2.6,6.9,2.3,Iris-virginica
+6.0,2.2,5.0,1.5,Iris-virginica
+6.9,3.2,5.7,2.3,Iris-virginica
+5.6,2.8,4.9,2.0,Iris-virginica
+7.7,2.8,6.7,2.0,Iris-virginica
+6.3,2.7,4.9,1.8,Iris-virginica
+6.7,3.3,5.7,2.1,Iris-virginica
+7.2,3.2,6.0,1.8,Iris-virginica
+6.2,2.8,4.8,1.8,Iris-virginica
+6.1,3.0,4.9,1.8,Iris-virginica
+6.4,2.8,5.6,2.1,Iris-virginica
+7.2,3.0,5.8,1.6,Iris-virginica
+7.4,2.8,6.1,1.9,Iris-virginica
+7.9,3.8,6.4,2.0,Iris-virginica
+6.4,2.8,5.6,2.2,Iris-virginica
+6.3,2.8,5.1,1.5,Iris-virginica
+6.1,2.6,5.6,1.4,Iris-virginica
+7.7,3.0,6.1,2.3,Iris-virginica
+6.3,3.4,5.6,2.4,Iris-virginica
+6.4,3.1,5.5,1.8,Iris-virginica
+6.0,3.0,4.8,1.8,Iris-virginica
+6.9,3.1,5.4,2.1,Iris-virginica
+6.7,3.1,5.6,2.4,Iris-virginica
+6.9,3.1,5.1,2.3,Iris-virginica
+5.8,2.7,5.1,1.9,Iris-virginica
+6.8,3.2,5.9,2.3,Iris-virginica
+6.7,3.3,5.7,2.5,Iris-virginica
+6.7,3.0,5.2,2.3,Iris-virginica
+6.3,2.5,5.0,1.9,Iris-virginica
+6.5,3.0,5.2,2.0,Iris-virginica
+6.2,3.4,5.4,2.3,Iris-virginica
+5.9,3.0,5.1,1.8,Iris-virginica
+