pivot() Method: Pivot DataFrame Without Aggregation Operation¶

Date published: 2018-11-24

Category: Data Analysis

Subcategory: Data Wrangling

Tags: pivot, python, pandas

In pandas, we can pivot our DataFrame without applying an aggregate operation. This pivot is helpful to see our data in a different way - often turning a format with many rows that would require scrolling into a new format with fewer rows but perhaps more columns.

Import Module¶

In [48]:

import pandas as pd

import pandas as pd

Example: Pivot Tesla Car Acceleration Details¶

Here is fictional acceleration tests for three popular Tesla car models. In order to verify acceleration of the cars, I figured a third-party may make three runs to test the three models alongside one another.

In [49]:

s = 'Tesla Model S P100D'
x = 'Tesla Model X P100D'
three = 'Tesla Model 3 AWD Dual Motor'

models = [s, x, three]*3
dates = ['Sept 1 9am']*3 + ['Sept 1 10am']*3 + ['Sept 1 11am']*3
acceleration_times = [2.5, 2.92, 3.33, 2.51, 2.91, 3.31, 2.51, 2.92, 3.3]

data = {'car_model': models, 
        'date': dates, 
        '0-60mph_in_seconds': acceleration_times} 
df = pd.DataFrame(data)
df

s = 'Tesla Model S P100D' x = 'Tesla Model X P100D' three = 'Tesla Model 3 AWD Dual Motor' models = [s, x, three]*3 dates = ['Sept 1 9am']*3 + ['Sept 1 10am']*3 + ['Sept 1 11am']*3 acceleration_times = [2.5, 2.92, 3.33, 2.51, 2.91, 3.31, 2.51, 2.92, 3.3] data = {'car_model': models, 'date': dates, '0-60mph_in_seconds': acceleration_times} df = pd.DataFrame(data) df

Out[49]:

	car_model	date	0-60mph_in_seconds
0	Tesla Model S P100D	Sept 1 9am	2.50
1	Tesla Model X P100D	Sept 1 9am	2.92
2	Tesla Model 3 AWD Dual Motor	Sept 1 9am	3.33
3	Tesla Model S P100D	Sept 1 10am	2.51
4	Tesla Model X P100D	Sept 1 10am	2.91
5	Tesla Model 3 AWD Dual Motor	Sept 1 10am	3.31
6	Tesla Model S P100D	Sept 1 11am	2.51
7	Tesla Model X P100D	Sept 1 11am	2.92
8	Tesla Model 3 AWD Dual Motor	Sept 1 11am	3.30

I want to pivot this data so each row is a unique car model, the columns are dates and the values in the table are the acceleration speeds.

In [50]:

df.pivot(index='car_model', columns='date', values='0-60mph_in_seconds')

df.pivot(index='car_model', columns='date', values='0-60mph_in_seconds')

Out[50]:

date	Sept 1 10am	Sept 1 11am	Sept 1 9am
car_model
Tesla Model 3 AWD Dual Motor	3.31	3.30	3.33
Tesla Model S P100D	2.51	2.51	2.50
Tesla Model X P100D	2.91	2.92	2.92

This format may be easier to read so you can easily focus your attention on just the acceleration times for the 3 models.

You can read more about pandas pivot() on the official documentation page.