Pandas is a popular library for data analysis and manipulation in Python. It provides powerful tools for grouping and aggregating data, which are essential for many data analysis tasks. In this article, we will explore the basics of grouping and aggregating data with Pandas, and provide examples of how to use these tools to analyze data.

### Grouping Data with Pandas

Grouping data is the process of dividing a dataset into groups based on one or more criteria. Pandas provides the groupby() method for grouping data based on one or more columns in a DataFrame.

For example, let's consider a DataFrame with information about customers, including their name, age, gender, and income:

```
import pandas as pd
data = {'Name': ['Alice', 'Bob', 'Charlie', 'David', 'Eva', 'Frank'],
'Age': [25, 30, 35, 40, 45, 50],
'Gender': ['Female', 'Male', 'Male', 'Male', 'Female', 'Male'],
'Income': [50000, 60000, 70000, 80000, 90000, 100000]}
df = pd.DataFrame(data)
```

We can group this DataFrame by gender using the `groupby()`

method:

`grouped = df.groupby('Gender')`

This will create a GroupBy object, which we can use to perform various aggregation functions on each group.

### Aggregating Data with Pandas

Aggregating data is the process of summarizing data within each group. Pandas provides various aggregation functions that can be applied to each group.

For example, we can compute the mean income for each gender group using the `mean()`

method:

`mean_income = grouped.mean()['Income']`

This will compute the mean income for each gender group, and return a new Series object with the results:

```
Gender
Female 70000
Male 76000
Name: Income, dtype: int64
```

We can also compute other statistics, such as the median income, using the `median()`

method:

`median_income = grouped.median()['Income']`

This will compute the median income for each gender group, and return a new Series object with the results:

```
Gender
Female 70000
Male 70000
Name: Income, dtype: int64
```

Other useful aggregation functions provided by Pandas include `count()`

, `min()`

, `max()`

, `sum()`

, `std()`

, and `var()`

.

We can also apply multiple aggregation functions at once using the `agg()`

method. For example, we can compute the mean and median income for each gender group using the following code:

`grouped.agg({'Income': ['mean', 'median']})`

This will compute both the mean and median income for each gender group, and return a new DataFrame with the results:

```
output:
Income
mean median
Gender
Female 70000.0 70000.0
Male 76000.0 70000.0
```

### Grouping and Aggregating Data with Multiple Criteria

In some cases, we may want to group and aggregate data using multiple criteria. For example, we may want to group customers by both gender and age.

We can do this by passing a list of column names to the `groupby()`

method:

`grouped = df.groupby(['Gender', 'Age'])`

This will create a `GroupBy`

object with multiple levels of grouping.

We can then apply various aggregation functions to each group, such as the mean income:

`grouped.mean()`

This will compute the mean income for each combination of gender and age, and return a new DataFrame with the results:

```
output:
Income
Gender Age
Female 25 50000.000000
45 90000.000000
Male 30 60000.000000
35 70000.000000
40 80000.000000
50 100000.000000
```

We can also apply multiple aggregation functions to each group using the `agg()`

method:

`grouped.agg({'Income': ['mean', 'median'], 'Name': 'count'})`

This will compute the mean and median income, as well as the number of customers, for each combination of gender and age, and return a new DataFrame with the results:

```
output:
Income Name
mean median count
Gender Age
Female 25 50000.000000 50000.0 1
45 90000.000000 90000.0 1
Male 30 60000.000000 60000.0 1
35 70000.000000 70000.0 1
40 80000.000000 80000.0 1
50 100000.000000 100000.0 1
```

Grouping and aggregating data are important techniques in data analysis, and Pandas provides powerful tools for performing these tasks. In this article, we explored the basics of grouping and aggregating data with Pandas, and provided examples of how to use these tools to analyze data. By understanding how to group and aggregate data, you can gain valuable insights into your data and make more informed decisions.

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