## Introduction

One of the key tasks in data analysis is grouping data to gain insights and make informed decisions. In this tutorial, we will show you how to **group** the rows of a **Pandas DataFrame **and apply different **aggregations** on the grouped data. In order to do this, we will use the `groupby()`

method of Pandas in combination with various aggregation functions.

## Import Libraries

First, we import the following python modules**:**

`import pandas as pd`

**Create Pandas DataFrame**

Next, we create a **Pandas DataFrame** with some example data from a **dictionary**:

```
data = {
"language": ["Python", "Python", "Java", "JavaScript", "Python"],
"framework": ["Django", "FastAPI", "Spring", "ReactJS", "FastAPI"],
"users": [20000, 9000, 7000, 5000, 13000]
}
df = pd.DataFrame(data)
df
```

## Group DataFrame and Apply Aggregations

The `groupby()`

method of Pandas allows you to group data of a **Pandas DataFrame **based on one or more columns.

Once grouped, you can use various aggregation functions to summarize the grouped data. For example, you could use one of the following aggregation functions:

- Calculate
**number of rows**for each group:`count()`

- Calculate
**minimum**of values for each group:`min()`

- Calculate
**maximum**of values for each group:`max()`

- Calculate
**sum**of values for each group:`sum()`

- Calculate
**mean**of values for each group:`mean()`

**Group DataFrame by Single Column**

We want to group the rows of the **Pandas DataFrame **based on the column "language". Besides, we want to calculate the mean of the column "users" for each group.

To do this, we use the `groupby()`

method in combination with the `mean()`

method of Pandas:

```
grouped_df = df.groupby("language")["users"].mean()
grouped_df
```

**Group DataFrame by Multiple Columns**

We want to group the rows of the **Pandas DataFrame** based on the columns "language" and "framework". Besides, we want to calculate the sum of the column "users" for each group.

To do this, we use the `groupby()`

method in combination with the `sum()`

method of Pandas:

```
grouped_df = df.groupby(["language", "framework"])["users"].sum()
grouped_df
```

## Conclusion

Congratulations! Now you are one step closer to become an **AI Expert**. In this blog post, we've explored the basics of grouping data in **Pandas DataFrames**. This functionality is crucial for data analysis and gaining insights into large datasets.

You have seen that it is very easy to group data of a **Pandas DataFrame** and apply different aggregations to the grouped data. We can simply use the `groupby()`

method in combination with specific aggregation methods of Pandas like `count()`

, `sum()`

or `mean()`

. Try it yourself!

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