To **resample** time series data means to summarize or aggregate the data by a new time period.

We can use the following basic syntax to resample time series data in Python:

#find sum of values in column1 by month weekly_df['column1'] = df['column1'].resample('M').sum() #find mean of values in column1 by week weekly_df['column1'] = df['column1'].resample('W').mean()

Note that we can resample the time series data by various time periods, including:

**S**: Seconds**min**: Minutes**H**: Hours**D**: Day**W**: Week**M**: Month**Q**: Quarter**A**: Year

The following example shows how to resample time series data in practice.

**Example: Resample Time Series Data in Python**

Suppose we have the following pandas DataFrame that shows the total sales made each hour by some company during a one-year period:

import pandas as pd import numpy as np #make this example reproducible np.random.seed(0) #create DataFrame with hourly index df = pd.DataFrame(index=pd.date_range('2020-01-06', '2020-12-27', freq='h')) #add column to show sales by hour df['sales'] = np.random.randint(low=0, high=20, size=len(df.index)) #view first five rows of DataFrame df.head() sales 2020-01-06 00:00:00 12 2020-01-06 01:00:00 15 2020-01-06 02:00:00 0 2020-01-06 03:00:00 3 2020-01-06 04:00:00 3

If we create a line plot to visualize the sales data, it would look like this:

import matplotlib.pyplot as plt #plot time series data plt.plot(df.index, df.sales, linewidth=3)

This plot is difficult to interpret, so we may instead summarize the sales data by week:

#create new DataFrame weekly_df = pd.DataFrame() #create 'sales' column that summarizes total sales by week weekly_df['sales'] = df['sales'].resample('W').sum() #view first five rows of DataFrame weekly_df.head() sales 2020-01-12 1519 2020-01-19 1589 2020-01-26 1540 2020-02-02 1562 2020-02-09 1614

This new DataFrame shows the sum of sales by week.

We can then create a time series plot using this weekly data:

import matplotlib.pyplot as plt #plot weekly sales data plt.plot(weekly_df.index, weekly_df.sales, linewidth=3)

This plot is much easier to read because we only plot sales data for 51 individual weeks as opposed to sales data for 8,545 individual hours in the first example.

**Note**: In this example, we summarized the sales data by week but we could also summarize by month or quarter if we would like to plot even fewer data points.

**Additional Resources**

The following tutorials explain how to perform other common operations in Python:

How to Plot a Time Series in Matplotlib

How to Plot a Time Series in Seaborn

How to Calculate MAPE of Time Series in Python