Introduction to Data Visualization with Seaborn

# Importing the course packages
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

# Importing the course datasets
country_data = pd.read_csv('datasets/countries-of-the-world.csv', decimal=",")
mpg = pd.read_csv('datasets/mpg.csv')
student_data = pd.read_csv('datasets/student-alcohol-consumption.csv', index_col=0)
survey = pd.read_csv('datasets/young-people-survey-responses.csv', index_col=0)

Take Notes

Add notes about the concepts you've learned and code cells with code you want to keep.

Add your notes here

# Add your code snippets here

Explore Datasets

Use the DataFrames imported in the first cell to explore the data and practice your skills!

From country_data, create a scatter plot to look at the relationship between GDP and Literacy. Use color to segment the data points by region.
Use mpg to create a line plot with model_year on the x-axis and weight on the y-axis. Create differentiating lines for each country of origin (origin).
Create a box plot from student_data to explore the relationship between the number of failures (failures) and the average final grade (G3).
Create a bar plot from survey to compare how Loneliness differs across values for Internet usage. Format it to have two subplots for gender.
Make sure to add titles and labels to your plots and adjust their format for readability!

INTRODUCTION TO SEABORN

Basic scatter plots with seaborn

Student data

student_data

sns.relplot(x="G1",
                y="G3",
                data=student_data,
                kind="scatter",
                hue="sex")

plt.show()

sns.relplot(x="G1",
            y="G3",
            data=student_data,
            kind="scatter",
            col="schoolsup",
            hue="sex", 
            palette={"F": "red", "M": "blue"})

plt.show()

sns.relplot(data=student_data,
            x="G1",
            y="G3",
            col="schoolsup",
            row="famsup",
            hue="location",
            palette={"Rural": "green", "Urban": "blue"})

plt.show()

sns.countplot(data=student_data,
              y="study_time",
              hue="sex",
              palette={"F":"red", "M":"blue"})

plt.show()

RELATIONAL PLOTS

How to visualize two quantitative variables

MPG

mpg

sns.relplot(x="horsepower", 
                y="mpg",
                data=mpg,
                kind="scatter",
                size="cylinders",
                hue="cylinders")

plt.show()

sns.relplot(x="horsepower",
           y="mpg",
           data=mpg,
           kind="scatter",
           hue="origin",
           style="origin")

plt.show()

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