Python | Pandas Series.cummin() to find cumulative minimum of a series
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 Series.cummin()
is used to find Cumulative minimum of a series. In cumulative minimum, the length of returned series is same as input series and every element is equal to the smaller one between current element and previous element.
Syntax: Series.cummin(axis=None, skipna=True)
Parameters:
axis: 0 or ‘index’ for row wise operation and 1 or ‘columns’ for column wise operation
skipna: Skips NaN addition for elements after the very next one if True.
Return type: Series
Example #1:
In this example, a series is created from a Python list. The list also contains a Null value and the skipna
parameter is kept default, that is True.
import pandas as pd
import numpy as np
values = [ 3 , 4 , np.nan, 7 , 2 , 0 ]
series = pd.Series(values)
cummin = series.cummin()
cummin
|
Output:
0 3.0
1 3.0
2 NaN
3 3.0
4 2.0
5 0.0
dtype: float64
Explanation: Cummin is comparison of current value with previous value. The first element is always equal to first of caller series.
3
3 (3<4)
NaN (Since NaN cannot be compared to integer values)
3 (3<7)
2 (2<3)
0 (0<2)
Example #2: Keeping skipna=False
In this example, a series is created just like in the above example. But the skipna parameter is kept False. Hence NULL values won’t be ignored and it would be compared every time on it’s occurrence.
import pandas as pd
import numpy as np
values = [ 12 , 4 , 33 , np.nan, 0 , 1 , 76 , 5 ]
series = pd.Series(values)
cummin = series.cummin(skipna = False )
cummin
|
Output:
0 12.0
1 4.0
2 4.0
3 NaN
4 NaN
5 NaN
6 NaN
7 NaN
dtype: float64
Explanation: Just like in the above example, minimum of current and previous values was stored at every position until NaN occurred. Since NaN compared with anything returns NaN and skipna parameter is kept False, the cumulative minimum after its occurrence is NaN due to comparison of all the values with NaN.
Last Updated :
20 Nov, 2018
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