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.cummin() is used to find the cumulative minimum value over any axis. Each cell is populated with the minimum value seen so far.
Syntax: DataFrame.cummin(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: cummin : Series
Example #1: Use cummin() function to find the cumulative minimum value 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 minimum value over the index axis
# To find the cumulative min df.cummin(axis = 0) |
Output :
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Example #2: Use cummin() function to find the cumulative minimum value 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 min along column axis df.cummin(axis = 1) |
Output :
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Example #3: Use cummin() function to find the cumulative minimum value along the index axis in a data frame with NaN value.
# 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 min df.cummin(axis = 0, skipna = True) |
Output :
