Lecture 30 – Perception, Case Study¶

Data 94, Spring 2021¶

from datascience import *
import numpy as np
Table.interactive_plots()
import plotly.express as px

sky = Table.read_table('data/skyscrapers.csv') \
           .where('status.current', are.contained_in(['completed', 'under construction'])) \
           .select('name', 'location.city', 'location.latitude', 'location.longitude', 
                   'statistics.floors above', 'statistics.height', 'status.completed.year') \
           .relabeled(['location.city', 'location.latitude', 'location.longitude',
                       'statistics.floors above', 'statistics.height', 'status.completed.year'],
                      ['city', 'latitude', 'longitude', 'floors', 'height', 'year']) \
           .where('height', are.above(0)) \
           .where('floors', are.above(0))

sky

Perception¶

sky.group('city') \
   .where('count', are.above_or_equal_to(40)) \
   .sort('count', descending = True) \
   .barh('city', title = 'Number of Skyscrapers Per City')

name	city	latitude	longitude	floors	height	year
One World Trade Center	New York City	40.7131	-74.0134	94	541.3	2014
Central Park Tower	New York City	40.7664	-73.9809	95	541.02	2019
Willis Tower	Chicago	41.8789	-87.6359	108	442.14	1974
111 West 57th Street	New York City	40.7648	-73.9775	80	438.3	2018
432 Park Avenue	New York City	40.7616	-73.9719	85	425.5	2015
Trump International Hotel & Tower	Chicago	41.8889	-87.6264	98	423.22	2009
30 Hudson Yards	New York City	40.754	-74.0008	73	386.61	2019
Empire State Building	New York City	40.7484	-73.9856	102	381	1931
Bank of America Tower	New York City	40.7554	-73.9844	55	365.8	2009
Aon Center	Chicago	41.8852	-87.6215	83	346.26	1973