Python | Pandas dataframe.cummin()
Last Updated :
16 Nov, 2018
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 NA
Returns: cummin : Series
Example #1: Use cummin()
function to find the cumulative minimum value along the index axis.
import pandas as pd
df = pd.DataFrame({ "A" :[ 5 , 3 , 6 , 4 ],
"B" :[ 11 , 2 , 4 , 3 ],
"C" :[ 4 , 3 , 8 , 5 ],
"D" :[ 5 , 4 , 2 , 8 ]})
df
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Output :
Now find the cumulative minimum value over the index axis
Output :
Example #2: Use cummin()
function to find the cumulative minimum value along the column axis.
import pandas as pd
df = pd.DataFrame({ "A" :[ 5 , 3 , 6 , 4 ],
"B" :[ 11 , 2 , 4 , 3 ],
"C" :[ 4 , 3 , 8 , 5 ],
"D" :[ 5 , 4 , 2 , 8 ]})
df.cummin(axis = 1 )
|
Output :
Example #3: Use cummin()
function to find the cumulative minimum value along the index axis in a data frame with NaN
value.
import pandas as pd
df = pd.DataFrame({ "A" :[ 5 , 3 , None , 4 ],
"B" :[ None , 2 , 4 , 3 ],
"C" :[ 4 , 3 , 8 , 5 ],
"D" :[ 5 , 4 , 2 , None ]})
df.cummin(axis = 0 , skipna = True )
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Output :
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