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Python Data Analysis and Visualization

Solution Review: Visualizing Auto MPG Dataset

This lesson provides the solutions to the previous challenges.

We'll cover the following...

Scatter plot #

Python 3.5
import pandas as pd
import seaborn as sns
# Load data 
def read_csv():
    # Define the column names as a list
    names = ["mpg", "cylinders", "displacement", "horsepower", "weight", "acceleration", "model_year", "origin", "car_name"]
    # Read in the CSV file from the webpage using the defined column names
    df = pd.read_csv("auto-mpg.data", header=None, names=names, delim_whitespace=True)
    return df
# Create the scatter plot
def scatter_plot(df):
    sns.lmplot(x="displacement", y="acceleration", data = df)
    # Remove excess chart lines and ticks for a nicer looking plot
    sns.despine()
# calling function
scatter_plot(read_csv())

According to the problem statement, we need to find the relationship between acceleration and displacement. To plot a visualization, we import seaborn module at line 2. Before doing it, we have to read the data first. There’s no need to explain how to read the data, as we studied that in detail previously. Dataset is read from line 5 to line 10.

Moving towards the main implementation, look at the header of the scatter_plot(df) function at line 13. It takes one arguments as input:

  • df: A dataframe containing the dataset in the form of a matrix.

Line 14 is the most important line. We are using a built-in function lmplot from seaborn library which takes three main argument:

  • x: Column whose values are to plotted at the x-axis
  • y: Column whose values are to plotted at the y-axis
  • data: Dataframe containing data in the form of a matrix

We set displacement as x and ...