The minimum number of values in a window to compute the statistic. If the number of observations in a window is less than min_periods, then a NaN is returned for that window. By default, min_periods=1.

NOTE

A window is essentially a sequence of numbers (observations) from which a statistic is computed.

2. center | boolean | optional

If True, then the observation is set to the center of the window.
If False, then the observation is set to the right of the window.

By default, center=False. Consult examples below for clarification.

3. axis | int or string | optional

Whether to perform expansion row-wise or column-wise:

Axis	Description
`"index"` or `0`	Expand column-wise.
`"columns"` or `1`	Expand row-wise.

Return Value

A Window object that will be subsequently used to compute some cumulative statistics.

Examples

Computing the cumulative sum

Consider the following DataFrame:


        
        
            
                
                
                    df = pd.DataFrame({"A":[2,4,8],"B":[4,5,6]})
df
                
            
               A  B
0  2  4
1  4  5
2  8  6

Column-wise

To compute the cumulative sum column-wise:


        
        
            
                
                
                    df.expanding().sum()
                
            
               A     B
0  2.0   4.0
1  6.0   9.0
2  14.0  15.0

Row-wise

To compute the cumulative sum row-wise:


        
        
            
                
                
                    df.expanding(axis=1).sum()
                
            
               A    B
0  2.0  6.0
1  4.0  9.0
2  8.0  14.0

WARNING

This example is just to demonstrate how to use the method. Pandas has a method DataFrame.cumsum(~) that directly computes the cumulative sums.

Specifying min_periods

Consider the same DataFrame as above:


        
        
            
                
                
                    df = pd.DataFrame({"A":[2,4,8],"B":[4,5,6]})
df
                
            
               A  B
0  2  4
1  4  5
2  8  6

Calling expanding(~) with min_periods=2:


        
        
            
                
                
                    df.expanding(2).sum()
                
            
               A     B
0  NaN   NaN
1  6.0   9.0
2  14.0  15.0

Here, we get NaN for the first row because the very first cumulative sum is computed using only 1 value, which is lower than the specified minimum of 2.

Specifying the center parameter

Consider the following DataFrame:


        
        
            
                
                
                    df = pd.DataFrame({"A": [2,4,8,16,25]})
df
                
            
               A
0  2
1  4
2  8
3  16
4  25

Calling expanding(~) with the center parameter set to True:


        
        
            
                
                
                    df.expanding(center=True).sum()
                
            
               A
0  14.0
1  30.0
2  55.0
3  53.0
4  49.0

The size of the initial selection of values is determined as follows:


        
        
            
                
                
                    ceil(num_of_rows/2) = 3

Here's a breakdown of how the numbers are computed:


        
        
            
                
                
                    A[0]: sum(2, 4, 8) = 14
A[1]: sum(2, 4, 8, 16) = 30
A[2]: sum(2, 4, 8, 16, 25) = 55
A[3]: sum(4, 8, 16, 25) = 53
A[4]: sum(8, 16, 25) = 49

Published by Isshin Inada

Edited by 0 others

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Official Pandas Documentation

https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.expanding.html

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