# Rolling Minimum in a Pandas Column

Window functions in Python are quite useful in estimating descriptive statistics like mean, median, min, max, etc. over a rolling window. In this tutorial, we will look at how to compute the rolling minimum in a pandas column.

You can use the pandas `rolling()` function to get a rolling window of your desired size over the series and then apply the pandas `min()` function to get the rolling minimum. The following is the syntax –

```# s is pandas series, n is the window size
s.rolling(n).min()```

Here, `n` is the size of the moving window you want to use, that is, the number of observations you want to use to compute the rolling statistic, in our case, the minimum. If you apply the above function on a pandas dataframe, it will result in a rolling min for all the numerical columns in the dataframe.

Let’s look at some examples to see the above syntax in action. First, let’s create a sample dataframe that we will be using throughout this tutorial.

```import pandas as pd

# create dataframe
df = pd.DataFrame({'PageViews': [100, 120, 180, 200, 240, 160, 130],
'Revenue': [10, 15, 12, 20, 30, 22, 14]},
index = ['2020-03-01', '2020-03-02', '2020-03-03', '2020-03-04',\
'2020-03-05', '2020-03-06', '2020-03-07'])
# print the dataframe
print(df)```

Output:

```            PageViews  Revenue
2020-03-01        100       10
2020-03-02        120       15
2020-03-03        180       12
2020-03-04        200       20
2020-03-05        240       30
2020-03-06        160       22
2020-03-07        130       14```

We now have a dataframe containing the daily “PageViews” and “Revenue” of a blogging website.

Let’s get the minimum “PageViews” over a 3-day rolling window from the above data. For this, we apply the `rolling()` function with a window size of `3` and then apply the `min()` function to get the minimum value over that window.

```# 3-day rolling minimum of PageViews
df['PageViews'].rolling(3).min()```

Output:

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```2020-03-01      NaN
2020-03-02      NaN
2020-03-03    100.0
2020-03-04    120.0
2020-03-05    180.0
2020-03-06    160.0
2020-03-07    130.0
Name: PageViews, dtype: float64```

You can see that we get `NaN` in the first two rows because we cannot calculate the rolling minimum as there are no preceding values to make the three-day window complete. The third row is 100 which is the minimum of the pageviews over the three-day window containing 100, 120, and 180.

If you apply the above function directly on a dataframe, it will compute the rolling minimum for all the numerical columns in the dataframe. For example, let’s get the 3-day rolling minimum over df

```# 3-day rolling minimum of entire dataframe df
df.rolling(3).min()```

Output:

```            PageViews  Revenue
2020-03-01        NaN      NaN
2020-03-02        NaN      NaN
2020-03-03      100.0     10.0
2020-03-04      120.0     12.0
2020-03-05      180.0     12.0
2020-03-06      160.0     20.0
2020-03-07      130.0     14.0```

You can see that we get the rolling minimum for both the columns.

With this, we come to the end of this tutorial. The code examples and results presented in this tutorial have been implemented in a Jupyter Notebook with a python (version 3.8.3) kernel having pandas version 1.0.5