Pandas DataFrame - cummin() function

The Pandas DataFrame cummin() function computes cumulative minimum over a DataFrame or Series axis and returns a DataFrame or Series of the same size containing the cumulative minimum.

Syntax

DataFrame.cummin(axis=None, skipna=True)

Parameters

`axis`	`Optional.` Specify {0 or 'index', 1 or 'columns'}. If 0 or 'index', cumulative minimums are generated for each column. If 1 or 'columns', cumulative minimums are generated for each row. Default: 0
`skipna`	`Optional.` Specify True to exclude NA/null values when computing the result. Default is True.

Return Value

Return cumulative minimum of Series or DataFrame.

Example: using cummin() column-wise on whole DataFrame

In the example below, a DataFrame info is created. The cummin() function is used to get the cumulative minimum of each column.

import pandas as pd
import numpy as np

info = pd.DataFrame({
  "Salary": [25, 24, 30, 28, 25],
  "Bonus": [10, 8, 9, np.nan, 9]},
  index= ["2015", "2016", "2017", "2018", "2019"]
)

#displaying the dataframe
print(info,"\n")

#displaying the cumulative minimum
print("info.cummin() returns:")
print(info.cummin(),"\n")

#using skipna=False
print("info.cummin(skipna=False) returns:")
print(info.cummin(skipna=False))

The output of the above code will be:

      Salary  Bonus
2015      25   10.0
2016      24    8.0
2017      30    9.0
2018      28    NaN
2019      25    9.0 

info.cummin() returns:
      Salary  Bonus
2015      25   10.0
2016      24    8.0
2017      24    8.0
2018      24    NaN
2019      24    8.0 

info.cummin(skipna=False) returns:
      Salary  Bonus
2015      25   10.0
2016      24    8.0
2017      24    8.0
2018      24    NaN
2019      24    NaN

Example: using cummin() row-wise on whole DataFrame

To get the row-wise cumulative minimum, the axis parameter can be set to 1.

import pandas as pd
import numpy as np

info = pd.DataFrame({
  "2016": [25, 24, 30, 28, 25],
  "2017": [18, 20, 25, np.nan, 28],
  "2018": [25, 24, 25, 30, 25]},
  index= ["P1", "P2", "P3", "P4", "P5"]
)

#displaying the dataframe
print(info,"\n")

#displaying the cumulative minimum
print("info.cummin(axis=1) returns:")
print(info.cummin(axis=1),"\n")

#using skipna=False
print("info.cummin(axis=1, skipna=False) returns:")
print(info.cummin(axis=1, skipna=False))

The output of the above code will be:

    2016  2017  2018
P1    25  18.0    25
P2    24  20.0    24
P3    30  25.0    25
P4    28   NaN    30
P5    25  28.0    25 

info.cummin(axis=1) returns:
    2016  2017  2018
P1  25.0  18.0  18.0
P2  24.0  20.0  20.0
P3  30.0  25.0  25.0
P4  28.0   NaN  28.0
P5  25.0  25.0  25.0 

info.cummin(axis=1, skipna=False) returns:
    2016  2017  2018
P1  25.0  18.0  18.0
P2  24.0  20.0  20.0
P3  30.0  25.0  25.0
P4  28.0   NaN   NaN
P5  25.0  25.0  25.0

Example: using cummin() on selected column

Instead of whole DataFrame, the cummin() function can be applied on selected columns. Consider the following example.

import pandas as pd
import numpy as np

info = pd.DataFrame({
  "Salary": [25, 24, 30, 28, 25],
  "Bonus": [10, 8, 9, np.nan, 9],
  "Others": [5, 4, 7, 5, 8]},
  index= ["2015", "2016", "2017", "2018", "2019"]
)

#displaying the dataframe
print(info,"\n")

#cumulative minimum on single column
print("info['Salary'].cummin() returns:")
print(info['Salary'].cummin(),"\n")

#cumulative minimum on multiple column
print("info[['Salary', 'Others']].cummin() returns:")
print(info[['Salary', 'Others']].cummin(),"\n")

The output of the above code will be:

      Salary  Bonus  Others
2015      25   10.0       5
2016      24    8.0       4
2017      30    9.0       7
2018      28    NaN       5
2019      25    9.0       8 

info['Salary'].cummin() returns:
2015    25
2016    24
2017    24
2018    24
2019    24
Name: Salary, dtype: int64 

info[['Salary', 'Others']].cummin() returns:
      Salary  Others
2015      25       5
2016      24       4
2017      24       4
2018      24       4
2019      24       4

❮ Pandas DataFrame - Functions