7. Combining What We Have Learned

Activity 7.01: Implementing Matplotlib and Seaborn on the New York City Database

Solution:

1. Create an Activity7.01.ipynb Jupyter Notebook in the Chapter07/Activity7.01 folder to implement this activity. Import all the necessary libraries:

   # Import statements
   import pandas as pd
   import numpy as np
   import seaborn as sns
   import matplotlib
   import matplotlib.pyplot as plt
   import squarify
   sns.set()

2. Use pandas to read both CSV files located in the Datasets folder:

   p_ny = pd.read_csv('../../Datasets/acs2017/pny.csv')
   h_ny = pd.read_csv('../../Datasets/acs2017/hny.csv')

3. Use the given PUMA (public use microdata area code based on the 2010 census definition, which are areas with populations of 100,000 or more) ranges to further divide the dataset into NYC districts (Bronx, Manhattan, Staten Island, Brooklyn, and Queens):

   # PUMA ranges
   bronx = [3701, 3710]
   manhatten = [3801, 3810]
   staten_island = [3901, 3903]
   brooklyn =...