Pandas Series.cummin() is used to find the cumulative minimum of a series. In the cumulative minimum, the returned row is the same length as the input row, and each element is equal to the shorter one between the current element and the 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:
This example creates a series from a Python list. The list also contains Null, and the skipna parameter remains the default, which is True.
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Output:
0 3.0 1 3.0 2 NaN 3 3.0 4 2.0 5 0.0 dtype: float64
Explanation: Cummin — it is a comparison of the current value with the previous value. The first element is always the first of the caller.
3 3 (3 "4) NaN (Since NaN cannot be compared to integer values) 3 (3 "7) 2 (2 "3) 0 (0 "2)
Example # 2: saving skipna=False
In this example, a series is created in the same way as in the above example. But the skipna parameter remains False. Therefore, NULL values will not be ignored and they will be compared each time they are found.
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Exit:
0 12.0 1 4.0 2 4.0 3 NaN 4 NaN 5 NaN 6 NaN 7 NaN dtype: float64
Explanation: Ka to and in the previous example, the minimum of the current and previous values was stored at each position until NaN appeared. Since NaN compared to anything returns NaN and skipna remains False, the cumulative minimum after it appears is NaN due to the comparison of all values to NaN.
