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%% Cell type:code id: tags:
``` python
# ignore this cell (it's just to make certain text red later, but you don't need to understand it).
from IPython.core.display import display, HTML
display(HTML('<style>em { color: red; }</style> <style>.container { width:100% !important; }</style>'))
```
%% Cell type:code id: tags:
``` python
%matplotlib inline
```
%% Cell type:code id: tags:
``` python
# import statements
import sqlite3
import pandas as pd
from pandas import DataFrame, Series
import matplotlib
from matplotlib import pyplot as plt
matplotlib.rcParams["font.size"] = 16
```
%% Cell type:markdown id: tags:
#### Warmup 1: Write a function that converts any Fehrenheit temp to Celcius
C = (5/9) * (f-32)
%% Cell type:code id: tags:
``` python
def f_to_c(f):
return (5/9) * (f-32)
# test it by making several calls
print(f_to_c())
print(f_to_c())
print(f_to_c())
print(f_to_c(212))
print(f_to_c(32))
print(f_to_c(67))
```
%% Cell type:markdown id: tags:
#### Warmup 2a: What is the name of the only table inside of iris-flowers.db?
%% Cell type:code id: tags:
``` python
# Establish a connection to "iris-flowers.db" database
iris_conn = ???
???
iris_conn = sqlite3.connect("iris-flowers.db")
pd.read_sql("SELECT * FROM sqlite_master WHERE type='table'", iris_conn)
```
%% Cell type:markdown id: tags:
#### Warmup 2b: Save & display all the data from this table to a variable called "iris_df"
%% Cell type:code id: tags:
``` python
iris_df = ???
iris_df = pd.read_sql("SELECT * FROM iris", iris_conn)
iris_df
```
%% Cell type:markdown id: tags:
#### Warmup 3: Scatter plot to visualize relationship between `pet-width` and `pet-length`
#### Warmup 3a: What are all the class types?
%% Cell type:code id: tags:
``` python
# v1: pandas
varietes = iris_df["class"]
varietes
```
%% Cell type:code id: tags:
``` python
# v2: SQL
varietes = list(pd.read_sql("""
SELECT DISTINCT class
FROM iris
""", iris_conn)["class"])
varietes
```
%% Cell type:markdown id: tags:
#### Warmup 3b: Scatter plot to visualize relationship between `pet-width` and `pet-length`
%% Cell type:code id: tags:
``` python
# complete this code to make 3 plots in one
colors = ["blue", "green", "red"]
markers = ["o", "^", "v"]
# getting unique class column values
varietes = list(set(iris_df["class"]))
plot_area = None
# Iterate over indices of varieties list
# Q: Why are we iterating over indices instead values here?
# Discuss how it will be useful to extract information from other lists
# like colors and markers
for i in range(len(varietes)):
variety = varietes[i]
curr_color = ??? # write code to extract color
curr_marker = ??? # write code to extract marker
# make a df just of just the data for this variety
variety_df = iris_df[iris_df["class"] == variety]
# print each subset DataFrame and verify that the output is correct
#make a scatter plot for this variety
plot_area = variety_df.plot.scatter(x = "pet-width", y = "pet-length", \
label = variety, color = colors[i],
marker = markers[i], \
ax = plot_area)
#variety_df.plot.scatter(x = "pet-width", y = "pet-length")
```
%% Cell type:markdown id: tags:
#### Let's focus on "Iris-virginica" data
%% Cell type:code id: tags:
``` python
iris_virginica = ???
# assert that length of iris_virginica is exactly 50
???
iris_virginica.head()
```
%% Cell type:markdown id: tags:
#### Create scatter plot to visualize relationship between `pet-width` and `pet-length`
%% Cell type:code id: tags:
``` python
iris_virginica.plot.scatter(x = "pet-width", y = "pet-length")
```
%% Cell type:markdown id: tags:
### Let's learn about *xlim* and *ylim*
- Allows us to set x-axis and y-axis limits
- Takes either a single value (LOWER-BOUND) or a tuple containing two values (LOWER-BOUND, UPPER-BOUND)
- You need to be careful about setting the UPPER-BOUND
%% Cell type:code id: tags:
``` python
iris_virginica.plot.scatter(x = "pet-width", y = "pet-length", xlim = ???, ylim = ???)
```
%% Cell type:code id: tags:
``` python
ax = iris_virginica.plot.scatter(x = "pet-width", y = "pet-length",
xlim = ???, ylim = ???,
figsize = (3, 3))
# What is wrong with this plot?
```
%% Cell type:markdown id: tags:
What is the maximum `pet-length`?
%% Cell type:code id: tags:
``` python
# How do we extract `pet-length` column Series?
iris_virginica["pet-length"]
```
%% Cell type:markdown id: tags:
For every set method, there is a corresponding get method. Try `ax.get_ylim()`.
%% Cell type:code id: tags:
``` python
```
%% Cell type:markdown id: tags:
Let's include assert statements to make sure we don't crop the plot!
%% Cell type:code id: tags:
``` python
ax = iris_virginica.plot.scatter(x = "pet-width", y = "pet-length",
xlim = (0, 6), ylim = (0, 6),
figsize = (3, 3))
#print("Ran into AssertionError while checking axes limits")
```
%% Cell type:markdown id: tags:
### Now let's try all 4 assert statements
```
assert iris_virginica[ax.get_xlabel()].min() >= ax.get_xlim()[0]
assert iris_virginica[ax.get_xlabel()].max() <= ax.get_xlim()[1]
assert iris_virginica[ax.get_ylabel()].min() >= ax.get_ylim()[0]
assert iris_virginica[ax.get_ylabel()].max() <= ax.get_ylim()[1]
```
%% Cell type:code id: tags:
``` python
ax = iris_virginica.plot.scatter(x = "pet-width", y = "pet-length",
xlim = (0, 7), ylim = (0, 7),
figsize = (3, 3))
```
%% Cell type:code id: tags:
``` python
# Close the database connection.
iris_conn.close()
```
%% Cell type:markdown id: tags:
# Plotting Applications
**Learning Objectives**
- Make a line plot on a series or on a DataFrame
- Apply features of line plots and bar plots to visualize results of data investigations
- Clean Series data by dropping NaN values and by converting to int
- Make a stacked bar plot
%% Cell type:markdown id: tags:
## Line plots
- `SERIES.plot.line()` each value in the Series becomes y-value and each index becomes x-value
- `DATAFRAME.plot.line()` each column in the data frame becomes a line in the plot
- ***IMPORTANT***: lines in line plots shouldn't be crooked, you need to sort the values based on increasing order of indices!
https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.plot.line.html
%% Cell type:markdown id: tags:
### Plotting line from a Series
%% Cell type:code id: tags:
``` python
# when you make a series from a list, the default indices 0, 1, 2, ...
s = Series([0, 100, 300, 200, 400])
s
```
%% Cell type:code id: tags:
``` python
s = Series([0, 100, 300, 200, 400], index = [0, 20, 21, 22, 1])
s # oops this produces a crooked line plot!
```
%% Cell type:code id: tags:
``` python
# Let's fix it by sorting the Series values based on the indices
```
%% Cell type:markdown id: tags:
### Craft breweries example
%% Cell type:code id: tags:
``` python
# You can make a series from a list and add indices
s = Series([1758, 2002, 2408, 2898, 3814, 4803, 5713, 6661, 7618, 8391, 8764], \
index=[2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020])
# We can save the AxesSubplot and "beautify" it like the other plots...
# Set title to "Craft Breweries in the USA"
# Set x-axis label to "Year"
# Set y-axis label to "# Craft Breweries"
```
%% Cell type:code id: tags:
``` python
# Be careful! If the indices are out of order you get a mess
# pandas plots each (index, value) in the order given
s = Series([1758, 2408, 2898, 3814, 4803, 5713, 6661, 7618, 8391, 8764, 2002], \
index=[2010, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2011])
# TODO: fix this crooked line plot
s.plot.line()
```
%% Cell type:code id: tags:
``` python
# Fix it here
```
%% Cell type:markdown id: tags:
### Temperature example
Plotting lines from a DataFrame
- `DATAFRAME.plot.line()` each column in the data frame becomes a line in the plot
- ***IMPORTANT***: lines in line plots shouldn't be crooked, you need to sort the values based on increasing order of indices!
%% Cell type:code id: tags:
``` python
# This DataFrame is made using a dict of lists
# City of Madison normal high and low (degrees F) by month
temp_df = DataFrame( {
"high": [26, 31, 43, 57, 68, 78, 82, 79, 72, 59, 44, 30],
"low": [11, 15, 25, 36, 46, 56, 61, 59, 50, 39, 28, 16]}
)
# Q: do "high" and "low" become rows or columns within the DataFrame?
# A:
temp_df
```
%% Cell type:code id: tags:
``` python
# Let's create line plots
# not a nice plot
# Let's fix the aesthetics
```
%% Cell type:markdown id: tags:
### A Line Plot made from a DataFrame automatically plots all columns
The same is true for bar plots; we'll see this later.
`ax.xticks(...)`: takes as argument a sequence of numbers and add ticks at those locations.
%% Cell type:code id: tags:
``` python
# You can also add ticks and ticklabels to a line plot
# TODOs:
# 1. Also add figure size as (8, 4)
# 2. Add xticks - how many do we need?
# 3. Add xticklables and rotate them by 45 degrees
#["Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec"]
ax = temp_df.plot.line(???)
ax.set_title("Average Temperatures in Madison, WI")
ax.set_xlabel("Month")
ax.set_ylabel("Temp (Fahrenheit)")
ax.set_xticks(???) # makes a sequence of integers from 0 to 11
ax.set_xticklabels(???, ???)
# This gets rid of the weird output
None
```
%% Cell type:code id: tags:
``` python
# We could explicitly pass arguments to the "x" and "y" parameters
temp_df_with_month = DataFrame(
{
"month": ["Jan", "Feb", "Mar", "Apr", "May", "Jun",
"Jul", "Aug", "Sep", "Oct", "Nov", "Dec"],
"high": [26, 31, 43, 57, 68, 78, 82, 79, 72, 59, 44, 30],
"low": [11, 15, 25, 36, 46, 56, 61, 59, 50, 39, 28, 16]}
)
ax = temp_df_with_month.plot.line(x = ???, y = ???, figsize = (8, 4))
ax.set_title("Average Temperatures in Madison, WI")
ax.set_xlabel("Month")
ax.set_ylabel("Temp (Fahrenheit)")
```
%% Cell type:markdown id: tags:
### We can perform a calculation on an entire DataFrame
Let's change the entire DataFrame to Celcius
%% Cell type:code id: tags:
``` python
# call the function on the dataframe
celcius_df = ???
celcius_df
```
%% Cell type:code id: tags:
``` python
# here is one way to add a horizontal line to our line plots
celcius_df[???] = ???
celcius_df
```
%% Cell type:code id: tags:
``` python
# this plots each column as lines
# with rotation for the tick labels
ax = celcius_df.plot.line(figsize = (8, 4))
ax.set_xlabel("Month")
ax.set_ylabel("Temp (Celcius)")
ax.set_xticks(range(12))
ax.set_xticklabels(["Jan", "Feb", "Mar", "Apr", "May", "Jun",
"Jul", "Aug", "Sep", "Oct", "Nov", "Dec"], rotation = 45)
ax.grid()
None
```
%% Cell type:markdown id: tags:
## Bar plots using DataFrames
%% Cell type:markdown id: tags:
Bar Plot Example w/ Fire Hydrants
- General review of pandas
- Some new bar plot options
%% Cell type:code id: tags:
``` python
# TODO: read "Fire_Hydrants.csv" into a DataFrame
hdf = ???
hdf.tail()
```
%% Cell type:code id: tags:
``` python
# Extract just the column names
```
%% Cell type:markdown id: tags:
### Let's create a *bar plot* to visualize *colors* of fire hydrants.
%% Cell type:code id: tags:
``` python
# Make a series called counts_series which stores the value counts of the "nozzle_color"
color_counts = ???
color_counts # what is wrong with this data?
```
%% Cell type:code id: tags:
``` python
# TODO: Clean the data ......use str.upper()
color_counts = ???
color_counts
```
%% Cell type:code id: tags:
``` python
# Make a horizontal bar plot of counts of colors and have the colors match
# use color list: ["b", "g", "darkorange", "r", "c", "0.5"]
ax = ???
ax.set_xlabel("Fire hydrant count")
```
%% Cell type:markdown id: tags:
### Let's create a *bar plot* to visualize *style* of fire hydrants.
%% Cell type:code id: tags:
``` python
# Do the same thing as we did for the colors but this time for the "Style"
style_counts = ???
style_counts
```
%% Cell type:code id: tags:
``` python
```
%% Cell type:code id: tags:
``` python
# Grab the top 12
top12 = ???
# and them add an index to our Series for the sum of all the "other" for
top12[???] = ???
```
%% Cell type:code id: tags:
``` python
# Plot the results
ax = ???(color = "firebrick")
ax.set_ylabel("Hydrant Count")
ax.set_xlabel("Hydrant Type")
```
%% Cell type:markdown id: tags:
### In what *decade* were *pacers manufactured*?
### Take a peek at the *Style* column data
%% Cell type:code id: tags:
``` python
hdf["Style"]
```
%% Cell type:markdown id: tags:
### Which *column* gives *year* information?
%% Cell type:code id: tags:
``` python
hdf.columns
```
%% Cell type:markdown id: tags:
### How to get the *year_manufactured* for *pacers* and *others*?
%% Cell type:code id: tags:
``` python
# Let's get the year manufactured for all of the "Pacer" hydrants.
pacer_years = ???
# Note: We can do this either way
# pacer_years = hdf["year_manufactured"][hdf["Style"] == "Pacer"]
pacer_years
```
%% Cell type:code id: tags:
``` python
# then do the same for all the other data
other_years = ???
other_years
```
%% Cell type:markdown id: tags:
### How to get the *decade* for *pacers*?
%% Cell type:code id: tags:
``` python
# Round each year down to the start of the decade.
# e.g. 1987 --> 1980, 2003 --> 2000
pacer_decades = ???
pacer_decades
```
%% Cell type:markdown id: tags:
### How to convert the *decades* back to *int*?
- `astype(...)` method
- `dropna(...)` method
%% Cell type:code id: tags:
``` python
# Drop the NaN values, convert to int, and do value counts
pacer_decades = ???
```
%% Cell type:markdown id: tags:
### How to *count the decades* for pacers?
%% Cell type:code id: tags:
``` python
pacer_decades_count = ???
pacer_decades_count
```
%% Cell type:markdown id: tags:
### Count the *decades* for others.
%% Cell type:code id: tags:
``` python
# Do the same thing for other_years. Save to a variable called "other_decades"
other_decades = ???
other_decades_count = ???
other_decades_count
```
%% Cell type:markdown id: tags:
### Build a DataFrame from a dictionary of key, Series
%% Cell type:code id: tags:
``` python
plot_df = DataFrame(???)
plot_df # observe the NaN values
```
%% Cell type:code id: tags:
``` python
# make a bar plot
ax = ???
ax.set_xlabel("Decade")
ax.set_ylabel("Hydrant Count")
```
%% Cell type:markdown id: tags:
#### Ignore data from before 1950 using boolean indexing.
%% Cell type:code id: tags:
``` python
ax = ???
ax.set_xlabel("Decade")
ax.set_ylabel("Hydrant Count")
```
%% Cell type:markdown id: tags:
### Stacked Bar Chart
`stacked` parameter accepts boolean value as argument
%% Cell type:code id: tags:
``` python
ax = ???
ax.set_xlabel("Decade")
ax.set_ylabel("Hydrant Count")
None
```
......
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