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 pd import pandas as pd
# Creating the dataframe df = pd.DataFrame({"A":[5, 3, 6, 4],
"B":[11, 2, 4, 3],
"C":[4, 3, 8, 5],
"D":[5, 4, 2, 8]})
# Print the dataframe df |
Output :
Now find the cumulative sum of the values over the index axis
# To find the cumulative sum df.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 pd import pandas as pd
# Creating the dataframe df = 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 axis df.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 pd import pandas as pd
# Creating the dataframe df = 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 sum df.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.