Cumulative sum of a column in Pandas – Python
Cumulative sum of a column in Pandas can be easily calculated with the use of a pre-defined function cumsum().
Syntax: cumsum(axis=None, skipna=True, *args, **kwargs)
Parameters:
axis: {index (0), columns (1)}
skipna: Exclude NA/null values. If an entire row/column is NA, the result will be NA
Returns: Cumulative sum of the column
Example 1:
Python3
import pandas as pd import numpy as np # Create a dataframe df1 = pd.DataFrame({ "A" :[ 2 , 3 , 8 , 14 ], "B" :[ 1 , 2 , 4 , 3 ], "C" :[ 5 , 3 , 9 , 2 ]}) # Computing sum over Index axis print (df1.cumsum(axis = 0 )) |
Output:
A B C 0 2 1 5 1 5 3 8 2 13 7 17 3 27 10 19
Example 2:
Python3
import pandas as pd import numpy as np # Create a dataframe df1 = pd.DataFrame({ "A" :[ None , 3 , 8 , 14 ], "B" :[ 1 , None , 4 , 3 ], "C" :[ 5 , 3 , 9 , None ]}) # Computing sum over Index axis print (df1.cumsum(axis = 0 , skipna = True )) |
Output:
A B C 0 NaN 1.0 5.0 1 3.0 NaN 8.0 2 11.0 5.0 17.0 3 25.0 8.0 NaN
Example 3:
Python3
import pandas as pd import numpy as np # Create a dataframe df1 = pd.DataFrame({ "A" :[ 2 , 3 , 8 , 14 ], "B" :[ 1 , 2 , 4 , 3 ], "C" :[ 5 , 3 , 9 , 2 ]}) # Computing sum over Index axis print (df1.cumsum(axis = 1 )) |
Output:
A B C 0 2 3 8 1 3 5 8 2 8 12 21 3 14 17 19
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