Pandas Series: min() function
Minimum values in Pandas requested axis
The min() function is used to get the minimum of the values for the requested axis.
If you want the index of the minimum, use idxmin. This is the equivalent of the numpy.ndarray method argmin.
Syntax:
Series.min(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs)
Parameters:
| Name | Description | Type/Default Value | Required / Optional |
|---|---|---|---|
| axis | Axis for the function to be applied on. | {index (0)} | Required |
| skipna | Exclude NA/null values when computing the result. | bool Default Value: True |
Required |
| level | If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a scalar. | int or level name Default Value: None |
Required |
| numeric_only | Include only float, int, boolean columns. If None, will attempt to use everything, then use only numeric data. Not implemented for Series. | bool Default Value: None |
Required |
| **kwargs | Additional keyword arguments to be passed to the function. | Required |
Returns: scalar or Series (if level specified)
Example:
Python-Pandas Code:
import numpy as np
import pandas as pd
idx = pd.MultiIndex.from_arrays([
['warm', 'warm', 'cold', 'cold'],
['fox', 'lion', 'snake', 'spider']],
names=['blooded', 'animal'])
s = pd.Series([4, 4, 0, 8], name='legs', index=idx)
s
Output:
blooded animal
warm fox 4
lion 4
cold snake 0
spider 8
Name: legs, dtype: int64
Python-Pandas Code:
import numpy as np
import pandas as pd
idx = pd.MultiIndex.from_arrays([
['warm', 'warm', 'cold', 'cold'],
['fox', 'lion', 'snake', 'spider']],
names=['blooded', 'animal'])
s = pd.Series([4, 4, 0, 8], name='legs', index=idx)
s.min()
Output:
0
Example - Min using level names, as well as indices:
Python-Pandas Code:
import numpy as np
import pandas as pd
idx = pd.MultiIndex.from_arrays([
['warm', 'warm', 'cold', 'cold'],
['fox', 'lion', 'snake', 'spider']],
names=['blooded', 'animal'])
s = pd.Series([4, 4, 0, 8], name='legs', index=idx)
s.min(level='blooded')
Output:
blooded warm 4 cold 0 Name: legs, dtype: int64
Python-Pandas Code:
import numpy as np
import pandas as pd
idx = pd.MultiIndex.from_arrays([
['warm', 'warm', 'cold', 'cold'],
['fox', 'lion', 'snake', 'spider']],
names=['blooded', 'animal'])
s = pd.Series([4, 4, 0, 8], name='legs', index=idx)
s.min(level=0)
Output:
blooded warm 4 cold 0 Name: legs, dtype: int64
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