Chapter 02: Statistical Visualizations Using Matplotlib and Seaborn

Activity 4: Line Graphs with the Object-Oriented API and Pandas DataFrames

1. Import the required libraries in the Jupyter notebook and read the dataset from the Auto-MPG dataset repository:

   %matplotlib inline
   import matplotlib as mpl
   import matplotlib.pyplot as pltimport numpy as np
   import pandas as pd
   
   url = "https://archive.ics.uci.edu/ml/machine-learning-databases/auto-mpg/auto-mpg.data"
   df = pd.read_csv(url)

2. Provide the column names to simplify the dataset, as illustrated here:

   column_names = ['mpg', 'cylinders', 'displacement', 'horsepower', 'weight', 'acceleration', 'year', 'origin', 'name']

3. Now read the new dataset with column names and display it:

   df = pd.read_csv(url, names= column_names, delim_whitespace=True)
   df.head()

   The plot is as follows:

   

   Figure 2.29: The auto-mpg DataFrame

4. Convert the horsepower and year data types to float and integer using the following command:

   df.loc[df.horsepower == '?', 'horsepower'] = np.nan
   df['horsepower'] = pd.to_numeric(df['horsepower'])
   df['full_date'] = pd.to_datetime(df.year, format='%y')
   df['year'] = df['full_date'].dt.year

5. Let's display the data types:

   df.dtypes

   The output is as follows:

   

   Figure 2.30: The data types

6. Now plot the average horsepower per year using the following command:

   df.groupby('year')['horsepower'].mean().plot()

   The output is as follows:

   

   Figure 2.31: Line plot

Activity 5: Understanding Relationships of Variables Using Scatter Plots

1. Import the required libraries into the Jupyter notebook and read the dataset from the Auto-MPG dataset repository:

   %matplotlib inline
   import pandas as pd
   import numpy as np
   import matplotlib as mpl
   import matplotlib.pyplot as plt
   import seaborn as sns
   
   url = "https://archive.ics.uci.edu/ml/machine-learning-databases/auto-mpg/auto-mpg.data"
   df = pd.read_csv(url)

2. Provide the column names to simplify the dataset as illustrated here:

   column_names = ['mpg', 'cylinders', 'displacement', 'horsepower', 'weight', 'acceleration', 'year', 'origin', 'name']

3. Now read the new dataset with column names and display it:

   df = pd.read_csv(url, names= column_names, delim_whitespace=True)
   df.head()

   The plot is as follows:

   

   Figure 2.32: Auto-mpg DataFrame

4. Now plot the scatter plot using the scatter method:

   fig, ax = plt.subplots()
   ax.scatter(x = df['horsepower'], y=df['weight'])

   The output will be as follows:

   

   Figure 2.33: Scatter plot using the scatter method

Activity 6: Exporting a Graph to a File on Disk

1. Import the required libraries in the Jupyter notebook and read the dataset from the Auto-MPG dataset repository:

   %matplotlib inline
   import pandas as pd
   import numpy as np
   import matplotlib as mpl
   import matplotlib.pyplot as plt
   import seaborn as sns
   
   url = "https://archive.ics.uci.edu/ml/machine-learning-databases/auto-mpg/auto-mpg.data"
   df = pd.read_csv(url)

2. Provide the column names to simplify the dataset, as illustrated here:

   column_names = ['mpg', 'cylinders', 'displacement', 'horsepower', 'weight', 'acceleration', 'year', 'origin', 'name']

3. Now read the new dataset with column names and display it:

   df = pd.read_csv(url, names= column_names, delim_whitespace=True)

4. Create a bar plot using the following command:

   fig, ax = plt.subplots()
   df.weight.plot(kind='hist', ax=ax)

   The output is as follows:

   

   Figure 2.34: Bar plot

5. Export it to a PNG file using the savefig function:

   fig.savefig('weight_hist.png')

Activity 7: Complete Plot Design

1. Import the required libraries in the Jupyter notebook and read the dataset from the Auto-MPG dataset repository:

   %matplotlib inline
   import pandas as pd
   import numpy as np
   import matplotlib as mpl
   import matplotlib.pyplot as plt
   import seaborn as sns
   
   url = "https://archive.ics.uci.edu/ml/machine-learning-databases/auto-mpg/auto-mpg.data"
   df = pd.read_csv(url)

2. Provide the column names to simplify the dataset, as illustrated here:

   column_names = ['mpg', 'cylinders', 'displacement', 'horsepower', 'weight', 'acceleration', 'year', 'origin', 'name']

3. Now read the new dataset with column names and display it:

   df = pd.read_csv(url, names= column_names, delim_whitespace=True)

4. Perform GroupBy on year and cylinders, and unset the option to use them as indexes:

   df_g = df.groupby(['year', 'cylinders'], as_index=False)

5. Calculate the average miles per gallon over the grouping and set year as index:

   df_g = df_g.mpg.mean()

6. Set year as the DataFrame index:

   df_g = df_g.set_index(df_g.year)

7. Create the figure and axes using the object-oriented API:

   import matplotlib.pyplot as plt
   fig, axes = plt.subplots()

8. Group the df_g dataset by cylinders and plot the miles per gallon variable using the axes created with size (10,8):

   df = df.convert_objects(convert_numeric=True)
   df_g = df.groupby(['year', 'cylinders'], as_index=False).horsepower.mean()
   df_g = df_g.set_index(df_g.year)

9. Set the title, x label, and y label on the axes:

   fig, axes = plt.subplots()
   df_g.groupby('cylinders').horsepower.plot(axes=axes, figsize=(12,10))
   _ = axes.set(
       title="Average car power per year",
       xlabel="Year",
       ylabel="Power (horsepower)"
       
   )

   The output is as follows:

   

   Figure 2.35: Line plot for average car power per year (without legends)

10. Include legends, as follows:

    axes.legend(title='Cylinders', fancybox=True)

    

    Figure 2.36: Line plot for average car power per year (with legends)

11. Save the figure to disk as a PNG file:

    fig.savefig('mpg_cylinder_year.png')