To assign a column, you can create a rolling object based on your Series:
df['new_col'] = data['column'].rolling(5).mean()
The answer posted by ac2001 is not the most performant way of doing this. He is calculating a rolling mean on every column in the dataframe, then he is assigning the "ma" column using the "pop" column. The first method of the following is much more efficient:
%timeit df['ma'] = data['pop'].rolling(5).mean()
%timeit df['ma_2'] = data.rolling(5).mean()['pop']
1000 loops, best of 3: 497 µs per loop
100 loops, best of 3: 2.6 ms per loop
I would not recommend using the second method unless you need to store computed rolling means on all other columns.
Answer from Andrew L on Stack OverflowPandas
pandas.pydata.org › docs › reference › api › pandas.DataFrame.rolling.html
pandas.DataFrame.rolling — pandas 3.0.6 documentation
For a DataFrame, a column label or Index level on which to calculate the rolling window, rather than the DataFrame’s index.
Calmcode
calmcode.io › course › pandas-datetime › rolling-stats
Calmcode - pandas datetime: Rolling Stats
To calculate a rolling mean, you can call .rolling() on the dataframe. This returns an object that represents rolling subsets of the entire dataframe.
29:42
12 Practical Pandas Rolling Examples (You’ll Actually Use!) - ...
Pandas Windows Rolling Function Tutorial
How to Use Pandas Rolling - A Simple Illustrated Guide
09:35
Moving Average (Rolling Average) in Pandas and Python - Set Window ...
04:05
Calculation of mean with rolling lookback window in pandas DataFrame ...
Top answer 1 of 3
99
To assign a column, you can create a rolling object based on your Series:
df['new_col'] = data['column'].rolling(5).mean()
The answer posted by ac2001 is not the most performant way of doing this. He is calculating a rolling mean on every column in the dataframe, then he is assigning the "ma" column using the "pop" column. The first method of the following is much more efficient:
%timeit df['ma'] = data['pop'].rolling(5).mean()
%timeit df['ma_2'] = data.rolling(5).mean()['pop']
1000 loops, best of 3: 497 µs per loop
100 loops, best of 3: 2.6 ms per loop
I would not recommend using the second method unless you need to store computed rolling means on all other columns.
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14
Edit: pd.rolling_mean is deprecated in pandas and will be removed in future. Instead: Using pd.rolling you can do:
df['MA'] = df['pop'].rolling(window=5,center=False).mean()
for a dataframe df:
Date stock pop
0 2016-01-04 325.316 82
1 2016-01-11 320.036 83
2 2016-01-18 299.169 79
3 2016-01-25 296.579 84
4 2016-02-01 295.334 82
5 2016-02-08 309.777 81
6 2016-02-15 317.397 75
7 2016-02-22 328.005 80
8 2016-02-29 315.504 81
9 2016-03-07 328.802 81
To get:
Date stock pop MA
0 2016-01-04 325.316 82 NaN
1 2016-01-11 320.036 83 NaN
2 2016-01-18 299.169 79 NaN
3 2016-01-25 296.579 84 NaN
4 2016-02-01 295.334 82 82.0
5 2016-02-08 309.777 81 81.8
6 2016-02-15 317.397 75 80.2
7 2016-02-22 328.005 80 80.4
8 2016-02-29 315.504 81 79.8
9 2016-03-07 328.802 81 79.6
Documentation: http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.rolling.html
Old: Although it is deprecated you can use:
df['MA']=pd.rolling_mean(df['pop'], window=5)
to get:
Date stock pop MA
0 2016-01-04 325.316 82 NaN
1 2016-01-11 320.036 83 NaN
2 2016-01-18 299.169 79 NaN
3 2016-01-25 296.579 84 NaN
4 2016-02-01 295.334 82 82.0
5 2016-02-08 309.777 81 81.8
6 2016-02-15 317.397 75 80.2
7 2016-02-22 328.005 80 80.4
8 2016-02-29 315.504 81 79.8
9 2016-03-07 328.802 81 79.6
Documentation: http://pandas.pydata.org/pandas-docs/version/0.17.0/generated/pandas.rolling_mean.html
Spark By {Examples}
sparkbyexamples.com › home › pandas › pandas rolling() mean, average, sum examples
Pandas rolling() Mean, Average, Sum Examples - Spark By {Examples}
October 14, 2024 - pandas.DataFrame.rolling() function can be used to get the rolling mean, average, sum, median, max, min e.t.c for one or multiple columns. Rolling mean is
Statology
statology.org › home › how to calculate a rolling mean in pandas
How to Calculate a Rolling Mean in Pandas
October 16, 2023 - A rolling mean is simply the mean of a certain number of previous periods in a time series.
Medium
medium.com › @whyamit101 › understanding-pandas-rolling-f8f6d6796c07
Understanding Pandas Rolling. If you think you need to spend $2,000… | by why amit | Medium
February 26, 2025 - You might have heard the term “rolling” tossed around in the context of data analysis, but what does it actually mean? In Pandas, the rolling() function allows you to perform window-based calculations on your data.
GeeksforGeeks
geeksforgeeks.org › python › python-pandas-dataframe-rolling
Python | Pandas dataframe.rolling() - GeeksforGeeks
February 21, 2022 - Pandas dataframe.rolling() function provides the feature of rolling window calculations. The concept of rolling window calculation is most primarily used in signal processing and time-series data.
Pandas
pandas.pydata.org › pandas-docs › version › 2.1 › reference › api › pandas.core.window.rolling.Rolling.mean.html
pandas.core.window.rolling.Rolling.mean — pandas 2.1.4 documentation
>>> s.rolling(3).mean() 0 NaN 1 NaN 2 2.0 3 3.0 dtype: float64
datagy
datagy.io › home › pandas tutorials › data analysis in pandas › how to calculate a rolling average (mean) in pandas
How to Calculate a Rolling Average (Mean) in Pandas • datagy
April 2, 2023 - We then apply the rolling average ... a minimum of 3 period for a valid result. We use the mean() function to calculate the actual rolling average for each window within the groups....
Pandas
pandas.pydata.org › docs › › reference › api › pandas.api.typing.Rolling.mean.html
pandas.api.typing.Rolling.mean — pandas 3.0.6 documentation
>>> s = pd.Series([1, 2, 3, 4]) >>> s.rolling(2).mean() 0 NaN 1 1.5 2 2.5 3 3.5 dtype: float64
EDUCBA
educba.com › home › software development › software development tutorials › pandas tutorial › pandas rolling
Pandas rolling | How rolling() Function works in Pandas Dataframe?
June 7, 2023 - Pandas rolling() function gives the element of moving window counts. The idea of moving window figuring is most essentially utilized in signal handling and time arrangement information.
Call: +917738666252
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Programiz
programiz.com › python-programming › pandas › methods › rolling
Pandas rolling()
In this example, the rolling() method calculates the mean using a specified window size. We've set window=2, which means it calculates the mean of every 2 consecutive values. The parameter min_periods is set to 2, which means that at least 2 non-NaN values are needed to compute the mean.
GeeksforGeeks
geeksforgeeks.org › python › pandas-rolling-mean-by-time-interval
Pandas - Rolling mean by time interval - GeeksforGeeks
July 23, 2025 - The First 29 rows of the column MA30 will have a value NULL and the first non NULL value will be at row 30. Now we will be calculating the rolling mean with a window of 200.
Top answer 1 of 9
129
In the meantime, a time-window capability was added. See this link.
In [1]: df = DataFrame({'B': range(5)})
In [2]: df.index = [Timestamp('20130101 09:00:00'),
...: Timestamp('20130101 09:00:02'),
...: Timestamp('20130101 09:00:03'),
...: Timestamp('20130101 09:00:05'),
...: Timestamp('20130101 09:00:06')]
In [3]: df
Out[3]:
B
2013-01-01 09:00:00 0
2013-01-01 09:00:02 1
2013-01-01 09:00:03 2
2013-01-01 09:00:05 3
2013-01-01 09:00:06 4
In [4]: df.rolling(2, min_periods=1).sum()
Out[4]:
B
2013-01-01 09:00:00 0.0
2013-01-01 09:00:02 1.0
2013-01-01 09:00:03 3.0
2013-01-01 09:00:05 5.0
2013-01-01 09:00:06 7.0
In [5]: df.rolling('2s', min_periods=1).sum()
Out[5]:
B
2013-01-01 09:00:00 0.0
2013-01-01 09:00:02 1.0
2013-01-01 09:00:03 3.0
2013-01-01 09:00:05 3.0
2013-01-01 09:00:06 7.0
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What about something like this:
First resample the data frame into 1D intervals. This takes the mean of the values for all duplicate days. Use the fill_method option to fill in missing date values. Next, pass the resampled frame into pd.rolling_mean with a window of 3 and min_periods=1 :
pd.rolling_mean(df.resample("1D", fill_method="ffill"), window=3, min_periods=1)
favorable unfavorable other
enddate
2012-10-25 0.495000 0.485000 0.025000
2012-10-26 0.527500 0.442500 0.032500
2012-10-27 0.521667 0.451667 0.028333
2012-10-28 0.515833 0.450000 0.035833
2012-10-29 0.488333 0.476667 0.038333
2012-10-30 0.495000 0.470000 0.038333
2012-10-31 0.512500 0.460000 0.029167
2012-11-01 0.516667 0.456667 0.026667
2012-11-02 0.503333 0.463333 0.033333
2012-11-03 0.490000 0.463333 0.046667
2012-11-04 0.494000 0.456000 0.043333
2012-11-05 0.500667 0.452667 0.036667
2012-11-06 0.507333 0.456000 0.023333
2012-11-07 0.510000 0.443333 0.013333
UPDATE: As Ben points out in the comments, with pandas 0.18.0 the syntax has changed. With the new syntax this would be:
df.resample("1d").sum().fillna(0).rolling(window=3, min_periods=1).mean()
Pandas
pandas.pydata.org › pandas-docs › version › 2.2 › reference › api › pandas.core.window.rolling.Rolling.mean.html
pandas.core.window.rolling.Rolling.mean — pandas 2.2.3 documentation
>>> s.rolling(3).mean() 0 NaN 1 NaN 2 2.0 3 3.0 dtype: float64
KajoData
kajodata.com › main page › knowledge base – excel, sql, python, powerbi, tableau, statistics › knowledge base – python › how pandas rolling works in python? best example
How pandas rolling works in Python? Best example - KajoData
April 15, 2025 - The rolling() function in pandas creates a rolling view of a given dataset, meaning it applies operations over a moving window of values.
Finxter
blog.finxter.com › home › learn python blog › how to use pandas rolling – a simple illustrated guide
How to Use Pandas Rolling - A Simple Illustrated Guide - Be on the Right Side of Change
April 11, 2022 - What does the pandas.DataFrame.rolling() method do? In short, it performs rolling windows calculations. It is often used when working with time-series data or signal processing. I will shortly dive into a few practical examples to clarify what ...
Pandas
pandas.pydata.org › docs › dev › reference › window.html
Rolling window functions - Pandas - PyData |
pandas.api.typing.Rolling instances are returned by .rolling calls: pandas.DataFrame.rolling() and pandas.Series.rolling(). pandas.api.typing.Expanding instances are returned by .expanding calls: pandas.DataFrame.expanding() and pandas.Series.expanding().
Educative
educative.io › answers › how-to-compute-the-rolling-mean-of-a-time-series-in-python
How to compute the rolling mean of a time series in Python
Line 4: We load the time series data into a DataFrame using the read_csv() function from pandas. Line 7: We use the rolling() function to compute the rolling mean with a window size of 3.
HAR Data Extractor
jonathansoma.com › lede › foundations-2018 › pandas › rolling-averages-in-pandas
Rolling averages in pandas
import pandas as pd import matplotlib.pyplot as plt %matplotlib inline ... You can use df.rolling, and then ask it for the .mean().