Python is a great language for doing data analysis, primarily because of the fantastic ecosystem of data-centric python packages. Pandas is one of those packages and makes importing and analyzing data much easier.
Pandas dataframe.cumsum() is used to find the cumulative sum value over any axis. Each cell is populated with the cumulative sum of the values seen so far.
Syntax: DataFrame.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 NAReturns: cumsum : Series
Example #1: Use cumsum() function to find the cumulative sum of the values along the index axis.
# importing pandas as pdimport pandas as pd # Creating the dataframedf = pd.DataFrame({"A":[5, 3, 6, 4], "B":[11, 2, 4, 3], "C":[4, 3, 8, 5], "D":[5, 4, 2, 8]}) # Print the dataframedf |
Output :
Now find the cumulative sum of the values over the index axis
# To find the cumulative sumdf.cumsum(axis = 0) |
Output :
Example #2: Use cumsum() function to find the cumulative sum of the values seen so far along the column axis.
# importing pandas as pdimport pandas as pd # Creating the dataframedf = pd.DataFrame({"A":[5, 3, 6, 4], "B":[11, 2, 4, 3], "C":[4, 3, 8, 5], "D":[5, 4, 2, 8]}) # To find the cumulative sum along column axisdf.cumsum(axis = 1) |
Output :
Example #3: Use cumsum() function to find the cumulative sum of the values seen so far along the index axis in a data frame with NaN value present in dataframe.
# importing pandas as pdimport pandas as pd # Creating the dataframedf = pd.DataFrame({"A":[5, 3, None, 4], "B":[None, 2, 4, 3], "C":[4, 3, 8, 5], "D":[5, 4, 2, None]}) # To find the cumulative sumdf.cumsum(axis = 0, skipna = True) |
Output :
The output is a dataframe with cells containing the cumulative sum of the values seen so far along the index axis. Any Nan value in the dataframe is skipped.
