Pandas Series: cummin() function
Cumulative minimum over a Pandas DataFrame or Series axis
The cummin() function is used to get cumulative minimum over a DataFrame or Series axis.
Returns a DataFrame or Series of the same size containing the cumulative minimum.
Syntax:
Series.cummin(self, axis=None, skipna=True, *args, **kwargs)
Parameters:
Name | Description | Type/Default Value | Required / Optional |
---|---|---|---|
axis | The index or the name of the axis. 0 is equivalent to None or ‘index’. | {0 or ‘index’, 1 or ‘columns’} Default Value: 0 |
Required |
skipna | Exclude NA/null values. If an entire row/column is NA, the result will be NA. | boolean Default Value: True |
Required |
*args, **kwargs | Additional keywords have no effect but might be accepted for compatibility with NumPy. | Required |
Returns: scalar or Series
Example - Series:
Python-Pandas Code:
import numpy as np
import pandas as pd
s = pd.Series([2, np.nan, 7, -3, 0])
s
Output:
0 2.0 1 NaN 2 7.0 3 -3.0 4 0.0 dtype: float64
Example - By default, NA values are ignored:
Python-Pandas Code:
import numpy as np
import pandas as pd
s = pd.Series([2, np.nan, 7, -3, 0])
s.cummin()
Output:
0 2.0 1 NaN 2 2.0 3 -3.0 4 -3.0 dtype: float64
Example - To include NA values in the operation, use skipna=False:
Python-Pandas Code:
import numpy as np
import pandas as pd
s = pd.Series([2, np.nan, 7, -3, 0])
s.cummin(skipna=False)
Output:
0 2.0 1 NaN 2 NaN 3 NaN 4 NaN dtype: float64
Example - DataFrame:
Python-Pandas Code:
import numpy as np
import pandas as pd
df = pd.DataFrame([[2.0, 3.0],
[4.0, np.nan],
[2.0, 0.0]],
columns=list('XY'))
df
Output:
X Y 0 2.0 3.0 1 4.0 NaN 2 2.0 0.0
Example - By default, iterates over rows and finds the minimum in each column. This is equivalent to axis=None or axis='index':
Python-Pandas Code:
import numpy as np
import pandas as pd
df = pd.DataFrame([[2.0, 3.0],
[4.0, np.nan],
[2.0, 0.0]],
columns=list('XY'))
df.cummin()
Output:
X Y 0 2.0 3.0 1 2.0 NaN 2 2.0 0.0
Example - To iterate over columns and find the minimum in each row, use axis=1:
Python-Pandas Code:
import numpy as np
import pandas as pd
df = pd.DataFrame([[2.0, 3.0],
[4.0, np.nan],
[2.0, 0.0]],
columns=list('XY'))
df.cummin(axis=1)
Output:
X Y 0 2.0 2.0 1 4.0 NaN 2 2.0 0.0
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