Rolling Mean on pandas on a specific column

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Problem :

I have a data frame like this which is imported from a CSV.

              stock  pop
Date
2016-01-04  325.316   82
2016-01-11  320.036   83
2016-01-18  299.169   79
2016-01-25  296.579   84
2016-02-01  295.334   82
2016-02-08  309.777   81
2016-02-15  317.397   75
2016-02-22  328.005   80
2016-02-29  315.504   81
2016-03-07  328.802   81
2016-03-14  339.559   86
2016-03-21  352.160   82
2016-03-28  348.773   84
2016-04-04  346.482   83
2016-04-11  346.980   80
2016-04-18  357.140   75
2016-04-25  357.439   77
2016-05-02  356.443   78
2016-05-09  365.158   78
2016-05-16  352.160   72
2016-05-23  344.540   74
2016-05-30  354.998   81
2016-06-06  347.428   77
2016-06-13  341.053   78
2016-06-20  363.515   80
2016-06-27  349.669   80
2016-07-04  371.583   82
2016-07-11  358.335   81
2016-07-18  362.021   79
2016-07-25  368.844   77
...             ...  ...

I wanted to add a new column MA which calculates Rolling mean for the column pop. I tried the following

df['MA']=data.rolling(5,on='pop').mean()

I get an error

ValueError: Wrong number of items passed 2, placement implies 1

So I thought let me try if it just works without adding a column. I used

 data.rolling(5,on='pop').mean()

I got the output

               stock  pop
Date
2016-01-04       NaN   82
2016-01-11       NaN   83
2016-01-18       NaN   79
2016-01-25       NaN   84
2016-02-01  307.2868   82
2016-02-08  304.1790   81
2016-02-15  303.6512   75
2016-02-22  309.4184   80
2016-02-29  313.2034   81
2016-03-07  319.8970   81
2016-03-14  325.8534   86
2016-03-21  332.8060   82
2016-03-28  336.9596   84
2016-04-04  343.1552   83
2016-04-11  346.7908   80
2016-04-18  350.3070   75
2016-04-25  351.3628   77
2016-05-02  352.8968   78
2016-05-09  356.6320   78
2016-05-16  357.6680   72
2016-05-23  355.1480   74
2016-05-30  354.6598   81
2016-06-06  352.8568   77
2016-06-13  348.0358   78
2016-06-20  350.3068   80
2016-06-27  351.3326   80
2016-07-04  354.6496   82
2016-07-11  356.8310   81
2016-07-18  361.0246   79
2016-07-25  362.0904   77
...              ...  ...

I can’t seem to apply Rolling mean on the column pop. What am I doing wrong?

Solution :

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 ┬Ás 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.

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

This solution worked for me.

data['MA'] = data.rolling(5).mean()['pop']

I think the issue may be that the on=’pop’ is just changing the column to perform the rolling window from the index.

From the doc string: ” For a DataFrame, column on which to calculate the rolling window, rather than the index”

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