Class 3: Data visualization#
“Data visualization”, “chart”, “graph”, and will be used interchangeably.
Today’s goal: Visualizing requests per community district#
This should help us better understand trends across the city.
Start by importing necessary packages#
import pandas as pd
import plotly.express as px
Populations#
Load the data:
population = pd.read_csv("https://data.cityofnewyork.us/api/views/xi7c-iiu2/rows.csv")
population.head()
| Borough | CD Number | CD Name | 1970 Population | 1980 Population | 1990 Population | 2000 Population | 2010 Population | |
|---|---|---|---|---|---|---|---|---|
| 0 | Bronx | 1 | Melrose, Mott Haven, Port Morris | 138557 | 78441 | 77214 | 82159 | 91497 |
| 1 | Bronx | 2 | Hunts Point, Longwood | 99493 | 34399 | 39443 | 46824 | 52246 |
| 2 | Bronx | 3 | Morrisania, Crotona Park East | 150636 | 53635 | 57162 | 68574 | 79762 |
| 3 | Bronx | 4 | Highbridge, Concourse Village | 144207 | 114312 | 119962 | 139563 | 146441 |
| 4 | Bronx | 5 | University Hts., Fordham, Mt. Hope | 121807 | 107995 | 118435 | 128313 | 128200 |
Adapting the basic histogram example:
fig = px.histogram(
population,
x="Borough",
title="Number of community districts in each borough",
)
fig.show()