To group by data in a column with pandas, you can use the `groupby()`

function along with the column you want to group by. This function allows you to split the data into groups based on a particular column, and then perform operations on these groups. You can then apply various aggregation functions to calculate statistics for each group, such as mean, count, sum, etc. Grouping data in a column with pandas is a powerful tool for analyzing and summarizing your data based on specific categories or criteria.

## How to sort grouped data in pandas?

You can sort grouped data in pandas using the `sort_values`

method on the groupby object. Here's an example:

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 |
import pandas as pd # Create a sample DataFrame data = {'category': ['A', 'A', 'B', 'B', 'A', 'B'], 'value': [1, 2, 3, 4, 5, 6]} df = pd.DataFrame(data) # Group the data by the 'category' column grouped = df.groupby('category') # Sort the grouped data by the 'value' column sorted_grouped = grouped.apply(lambda x: x.sort_values(by='value')) # Display the sorted grouped data print(sorted_grouped) |

In this example, we first group the data by the 'category' column. Then, we use the `apply`

method to sort each group by the 'value' column. Finally, we display the sorted grouped data using the `print`

function.

## How to perform group by operations in pandas?

To perform group by operations in Pandas, you can use the `groupby()`

method. Here is a step-by-step guide on how to do this:

- Import the Pandas library:

```
1
``` |
```
import pandas as pd
``` |

- Create a DataFrame:

1 2 3 4 |
data = {'Name': ['Alice', 'Bob', 'Charlie', 'Alice', 'Bob', 'Charlie'], 'Age': [25, 30, 35, 28, 32, 37], 'Salary': [50000, 60000, 70000, 55000, 65000, 75000]} df = pd.DataFrame(data) |

- Perform a group by operation on the DataFrame:

```
1
``` |
```
grouped = df.groupby('Name')
``` |

- Perform an aggregation operation on the grouped data:

```
1
``` |
```
grouped_mean = grouped.mean()
``` |

- You can also perform multiple group by operations and aggregations:

1 2 |
double_grouped = df.groupby(['Name', 'Age']) double_grouped_mean = double_grouped.mean() |

- You can also apply custom aggregation functions using the agg() method:

```
1
``` |
```
custom_aggregation = grouped.agg({'Salary': 'mean', 'Age': 'max'})
``` |

That's it! You have successfully performed group by operations in Pandas.

## How to filter data after grouping in pandas?

After grouping the data in pandas using the `groupby`

function, you can filter the data using the `filter`

function.

Here is an example of how to filter data after grouping in pandas:

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 |
import pandas as pd # Create a sample DataFrame data = {'Category': ['A', 'A', 'B', 'B', 'A', 'B'], 'Value': [10, 20, 30, 40, 50, 60]} df = pd.DataFrame(data) # Group the data by the 'Category' column grouped = df.groupby('Category') # Filter the data to only include groups where the sum of 'Value' is greater than 50 filtered_data = grouped.filter(lambda x: x['Value'].sum() > 50) print(filtered_data) |

In this example, we first group the data by the 'Category' column. Then we use the `filter`

function along with a lambda function to filter the groups based on a condition. In this case, we are filtering groups where the sum of the 'Value' column is greater than 50.

You can adjust the filter condition as needed to filter the grouped data based on different criteria.

## How to group data in a column with pandas?

To group data in a column with pandas, you can use the `groupby()`

function. Here is a step-by-step guide on how to do this:

- Import the pandas library:

```
1
``` |
```
import pandas as pd
``` |

- Create a DataFrame with your data:

1 2 3 |
data = {'Category': ['A', 'B', 'A', 'B', 'A', 'A'], 'Value': [10, 20, 15, 25, 30, 35]} df = pd.DataFrame(data) |

- Group the data by the 'Category' column:

```
1
``` |
```
grouped = df.groupby('Category')
``` |

- Perform an aggregation operation on the grouped data, such as finding the sum of the values in each group:

1 2 |
result = grouped.sum() print(result) |

This will group the data in the 'Category' column and calculate the sum of the 'Value' column for each group. You can also perform other aggregation operations, such as finding the mean, median, minimum, or maximum value for each group.

Additionally, you can also group by multiple columns by passing a list of column names to the `groupby()`

function:

```
1
``` |
```
grouped = df.groupby(['Category', 'City'])
``` |

This will group the data by both the 'Category' and 'City' columns.