Data Wrangling in Python – How to Group Data By Time

Group Data By Time


/* Import required packages */
import pandas as pd
import datetime
import numpy as np

Next, let’s create some sample data that we can group by time as an sample. In this example I am creating a dataframe with two columns with 365 rows. One column is a date, the second column is a numeric value.

Create Data

/* Create a datetime variable for today */
base =
/* Create a list variable that creates 365 days of rows of datetime values */
date_list = [base - datetime.timedelta(days=x) for x in range(0, 365)]
/* Create a list variable of 365 numeric values */
score_list = list(np.random.randint(low=1, high=1000, size=365))
/* Create an empty dataframe */
df = pd.DataFrame()

/* Create a column from the datetime variable */
df['datetime'] = date_list
/* Convert that column into a datetime datatype */
df['datetime'] = pd.to_datetime(df['datetime'])
/* Set the datetime column as the index */
df.index = df['datetime'] 
/* Create a column from the numeric score variable */
df['score'] = score_list
/* Let's take a took at the data */
datetime score
2016-06-02 09:57:54.793972 2016-06-02 09:57:54.793972 900
2016-06-01 09:57:54.793972 2016-06-01 09:57:54.793972 121
2016-05-31 09:57:54.793972 2016-05-31 09:57:54.793972 547
2016-05-30 09:57:54.793972 2016-05-30 09:57:54.793972 504
2016-05-29 09:57:54.793972 2016-05-29 09:57:54.793972 304

Group Data By Date

In pandas, the most common way to group by time is to use the .resample() function. In v0.18.0 this function is two-stage. This means that ‘df.resample(’M’)’ creates an object to which we can apply other functions (‘mean’, ‘count’, ‘sum’, etc.)

/* Group the data by month, and take the mean for each group (i.e. each month) */
2015-06-30 513.629630
2015-07-31 561.516129
2015-08-31 448.032258
2015-09-30 548.000000
2015-10-31 480.419355
2015-11-30 487.033333
2015-12-31 499.935484
2016-01-31 429.193548
2016-02-29 520.413793
2016-03-31 349.806452
2016-04-30 395.500000
2016-05-31 503.451613
2016-06-30 510.500000
/* Group the data by month, and take the sum for each group (i.e. each month) */
2015-06-30 13868
2015-07-31 17407
2015-08-31 13889
2015-09-30 16440
2015-10-31 14893
2015-11-30 14611
2015-12-31 15498
2016-01-31 13305
2016-02-29 15092
2016-03-31 10844
2016-04-30 11865
2016-05-31 15607
2016-06-30 1021

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