Intermediate Data Visualization with Seaborn
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    Intermediate Data Visualization with Seaborn

    Run the hidden code cell below to import the data used in this course.

    # 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
bike_share = pd.read_csv('datasets/bike_share.csv')
college_data = pd.read_csv('datasets/college_datav3.csv')
daily_show = pd.read_csv('datasets/daily_show_guests_cleaned.csv')
insurance = pd.read_csv('datasets/insurance_premiums.csv')
grants = pd.read_csv('datasets/schoolimprovement2010grants.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!

    • Use lmplot() to look at the relationship between temp and total_rentals from bike_share. Plot two regression lines for working and non-working days (workingday).
    • Create a heat map from daily_show to see how the types of guests (Group) have changed yearly.
    • Explore the variables from insurance and their relationship by creating pairwise plots and experimenting with different variables and types of plots. Additionally, you can use color to segment visually for region.
    • Make sure to add titles and labels to your plots and adjust their format for readability!