Using Heatmaps

import transportation_tutorials as tt
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
pd.options.display.max_columns = 100

Questions

Prepare a heatmap that visualizes the joint distribution of the number of trips taken by each household and the number of automobiles owned by the household.

1. For households with 2 automobiles, what is the most frequent number of trips made by those households in the data?
2. For households making 2 trips in the data, how many automobiles do most households own?

Data

To answer the questions, use the following data files:

trip = pd.read_csv(tt.data('SERPM8-BASE2015-TRIPS'))
trip.head()

	hh_id	person_id	person_num	tour_id	stop_id	inbound	tour_purpose	orig_purpose	dest_purpose	orig_mgra	dest_mgra	parking_mgra	stop_period	trip_mode	trip_board_tap	trip_alight_tap	tour_mode	smplRate_geo	autotech	tncmemb
0	1690841	4502948	1	0	-1	0	Work	Home	Work	7736	9290	-1	8	6	0	0	6	1.0	0	0
1	1690841	4502948	1	0	-1	1	Work	Work	Home	9290	7736	0	29	3	0	0	6	1.0	0	0
2	1690841	4502948	1	1	-1	0	Work-Based	Work	Work-Based	9290	7980	-1	13	1	0	0	1	1.0	0	0
3	1690841	4502948	1	1	-1	1	Work-Based	Work-Based	Work	7980	9290	-1	14	1	0	0	1	1.0	0	0
4	1690841	4502948	1	2	-1	0	Work-Based	Work	Work-Based	9290	10608	-1	19	3	0	0	3	1.0	0	0

hh = pd.read_csv(tt.data('SERPM8-BASE2015-HOUSEHOLDS'))
hh.head()

	Unnamed: 0	hh_id	home_mgra	income	autos	transponder	cdap_pattern	jtf_choice	autotech	tncmemb
0	426629	1690841	7736	512000	2	1	MMMM0	0	0	0
1	426630	1690961	7736	27500	1	0	MNMM0	0	0	0
2	426631	1690866	7736	150000	2	0	HMM0	0	0	0
3	426632	1690895	7736	104000	2	1	MMMM0	0	0	0
4	426633	1690933	7736	95000	2	1	MNM0	0	0	0

Solution

To find the number of trips that each household makes, we can create a dummy column with value 1 and use .groupby() method to count the total number of trips for each household. Then, we merge this information with household information to get the final dataframe.

hh_trip_count = trip.groupby(['hh_id']).size().rename('trip_count')

hh_trip_count.head()

hh_id
1690841    20
1690851    12
1690853     8
1690856    23
1690858     5
Name: trip_count, dtype: int64

data = pd.merge(hh_trip_count, hh, on = 'hh_id', how = 'left')

data.head()

	hh_id	trip_count	Unnamed: 0	home_mgra	income	autos	transponder	cdap_pattern	jtf_choice	autotech	tncmemb
0	1690841	20	426629	7736	512000	2	1	MMMM0	0	0	0
1	1690851	12	426645	7736	68200	4	1	HNM0	0	0	0
2	1690853	8	426643	7736	127000	2	0	MMMH0	0	0	0
3	1690856	23	426642	7736	59500	4	1	MNMMMM0	0	0	0
4	1690858	5	426640	7736	200030	1	0	M0	0	0	0

data.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 17260 entries, 0 to 17259
Data columns (total 11 columns):
hh_id           17260 non-null int64
trip_count      17260 non-null int64
Unnamed: 0      17260 non-null int64
home_mgra       17260 non-null int64
income          17260 non-null int64
autos           17260 non-null int64
transponder     17260 non-null int64
cdap_pattern    17260 non-null object
jtf_choice      17260 non-null int64
autotech        17260 non-null int64
tncmemb         17260 non-null int64
dtypes: int64(10), object(1)
memory usage: 1.6+ MB

Then we create a pivot table using autos and trip_count columns in the dataframe as index and columns in .pivot_table() method. We use aggfunc = 'size' to get the count of households in this two-dimensional table.

data_pivot = data.pivot_table(index = 'autos', columns = 'trip_count', aggfunc = 'size')

Finally, we create a heatmap using seaborn package. We can adjust the height and width of the heatmap using plt.figure(figsize = (width, height)). Please make sure to specify the figure size before creating heatmap.

We can set annot = True in sns.heatmap() to get the numbers visible in the plot. Also, there are some other arguments that we can specify to get the heatmap in desirable format.

plt.figure(figsize = (25, 3))
ax = sns.heatmap(data_pivot.fillna(0), annot = True, fmt = ",.0f", linewidths=.5, cmap = 'OrRd')

From the heatmap that we just created, we can easily find answers looking at the numbers as well as color map.

For households with 2 automobiles, 6 is the most common number of trips per day, although 4 trips is a close runner-up.

For households making only 2 trips in a day, the vast majority of them have 1 automobile.