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...