pandas groupby

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Okay, let's dive deep into the world of `pandas` `groupby`. This is one of the most powerful and versatile features `pandas` offers, allowing you to split your data into groups based on one or more criteria and then perform calculations and transformations on those groups.

**Table of Contents**

1. **What is `groupby`?**
2. **Basic `groupby` Usage**
3. **Grouping by a Single Column**
4. **Grouping by Multiple Columns**
5. **Aggregation with `groupby`**
* Built-in Aggregation Functions
* Custom Aggregation Functions
* `agg` Method for Multiple Aggregations
6. **Transformation with `groupby`**
* The `transform` Method
* Standardizing Data within Groups
7. **Filtering with `groupby`**
* The `filter` Method
8. **Applying Custom Functions with `groupby`**
* The `apply` Method
9. **Working with `groupby` on MultiIndex DataFrames**
10. **`groupby` and `Categorical` Data**
11. **Pivoting and Unstacking After `groupby`**
12. **Advanced `groupby` Techniques**
* Grouping by Index Levels
* Using `NamedAgg` for Clearer Aggregations
13. **Common `groupby` Pitfalls and Solutions**
14. **Conclusion**

**1. What is `groupby`?**

The `groupby` operation in `pandas` follows a "split-apply-combine" strategy. Imagine you have a table of data (a `DataFrame`). `groupby` enables you to:

* **Split:** Divide the `DataFrame` into groups based on values in one or more columns.
* **Apply:** Perform a function (aggregation, transformation, or filtering) to each of these groups independently.
* **Combine:** Combine the results of the function application back into a new `DataFrame` or `Series`.

**2. Basic `groupby` Usage**

Let's create a sample `DataFrame` to work with:
Output:
The basic syntax for `groupby` is:
Output:
`grouped` is now a `DataFrameGroupBy` object. It doesn't perform any computation until you explicitly ask it to. It holds the information about how the `DataFrame` should be grouped.

**3. Grou ...

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