**Grouping** in `pandas` involves dividing a DataFrame into groups based on the values in one or more columns. You can then apply a function to each group to compute a **summary statistic**, such as the mean, sum, or count.

To group a DataFrame in `pandas`, use the `.groupby()` method. This method accepts a **column name** or a **list of column names** and returns a `groupby` object.

Here is an example:

```python
# Grouping by 'column_name' and calculating the mean of each group
grouped_data = df.groupby('column_name').mean()
```

This example demonstrates how to calculate the mean for each group formed based on the values in '`column_name'.`



In this project, we are going to understand what Pandas is and why it is so powerful.

Unveiling the Power of Data Manipulation with Pandas

Grouping in Pandas

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