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.cumprod() is used to find the cumulative product of the values seen so far over any axis.
Each cell is populated with the cumulative product of the values seen so far.
Syntax: DataFrame.cumprod(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: cumprod : Series
Example #1: Use cumprod() function to find the cumulative product of the values seen so far 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 product of the values seen so far over the index axis
# To find the cumulative prod df.cumprod(axis = 0) |
Output :
Example #2: Use cumprod() function to find the cumulative product 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]}) # cumulative product along column axis df.cumprod(axis = 1) |
Output :
Example #3: Use cumprod() function to find the cumulative product 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 product df.cumprod(axis = 0, skipna = True) |
Output :
The output is a dataframe with cells containing the cumulative product of the values seen so far along the index axis. Any Nan value in the dataframe is skipped.
