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

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.**Series.cummin()**

Syntax:Series.cummin(axis=None, skipna=True)

Parameters:axis:0 or ‘index’ for row wise operation and 1 or ‘columns’ for column wise operationskipna: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 ` `cummin ` `=` `series.cummin() ` ` ` `# display ` `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.

`# importing pandas module ` `import` `pandas as pd ` ` ` `# importing numpy module ` `import` `numpy as np ` ` ` `# making list of values ` `values ` `=` `[` `12` `, ` `4` `, ` `33` `, np.nan, ` `0` `, ` `1` `, ` `76` `, ` `5` `] ` ` ` `# making series from list ` `series ` `=` `pd.Series(values) ` ` ` `# calling method ` `cummin ` `=` `series.cummin(skipna ` `=` `False` `) ` ` ` `# display ` `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.

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