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Python | Pandas series.cummax() to find Cumulative maximum of a series
• Difficulty Level : Basic
• Last Updated : 20 Nov, 2018

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.cummax()` is used to find Cumulative maximum of a series. In cumulative maximum, the length of returned series is same as input series and every element is equal to the greater one between current element and previous element.

Syntax: Series.cummax(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.

 `# importing pandas module ``import` `pandas as pd ``   ` `# importing numpy module ``import` `numpy as np ``   ` `# making list of values ``values ``=` `[``3``, ``4``, np.nan, ``7``, ``2``, ``0``] ``   ` `# making series from list ``series ``=` `pd.Series(values) ``   ` `# calling method ``cummax ``=` `series.cummax() ``   ` `# display ``cummax`

Output:

```0    3.0
1    4.0
2    NaN
3    7.0
4    7.0
5    7.0
dtype: float64```

Explanation: Cummax is comparison of current value with previous value. The first element is always equal to first of caller series.

```3
4  (4>3)
NaN (Since NaN cannot be compared to integer values)
7  (7>4)
7  (7>2)
7  (7>0)
```

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.

 `# importing pandas module ``import` `pandas as pd ``   ` `# importing numpy module ``import` `numpy as np ``   ` `# making list of values ``values ``=` `[``9``, ``4``, ``33``, np.nan, ``0``, ``1``, ``76``, ``5``] ``   ` `# making series from list ``series ``=` `pd.Series(values) ``   ` `# calling method ``cummax ``=` `series.cummax(skipna ``=` `False``) ``   ` `# display ``cummax `

Output:

```0     9.0
1     9.0
2    33.0
3     NaN
4     NaN
5     NaN
6     NaN
7     NaN
dtype: float64```

Explanation: Just like in the above example, maximum of current and previous values were stored at every position until NaN occurred. Since NaN compared with anything returns NaN and skipna parameter is kept False, the cumulative maximum after its occurrence is NaN due to comparison of all the values with NaN.

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