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

function. This function allows you to split the data into groups based on a specified column or columns. Once the data is grouped, you can then apply aggregate functions or perform other operations on each group. Grouping data can be useful for performing analysis on subsets of data or for summarizing large datasets.

## How to group data in a pandas DataFrame and apply custom functions?

To group data in a pandas DataFrame and apply custom functions, you can use the `groupby`

method along with the `agg`

method.

Here's an example:

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import pandas as pd # Create a sample DataFrame data = {'Category': ['A', 'A', 'B', 'B', 'C'], 'Value': [10, 20, 30, 40, 50]} df = pd.DataFrame(data) # Group the data by 'Category' and apply custom functions result = df.groupby('Category').agg({'Value': ['mean', 'sum']}) # Custom function to calculate the difference between the max and min values in each group def custom_function(x): return x.max() - x.min() result['Custom'] = df.groupby('Category')['Value'].apply(custom_function) print(result) |

In this example, we first create a sample DataFrame with categories and values. We then group the data by the 'Category' column using the `groupby`

method and apply the `agg`

method to calculate the mean and sum of the 'Value' column in each group.

We also define a custom function `custom_function`

that calculates the difference between the maximum and minimum values in each group. We apply this custom function using the `apply`

method within the `agg`

function.

Finally, we print the resulting DataFrame with the mean, sum, and custom function values for each group.

## How to group data in a pandas DataFrame and fill missing values within the groups?

You can group data in a pandas DataFrame using the `groupby`

method and then fill missing values within each group using the `fillna`

method. Here's an example:

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import pandas as pd # Sample DataFrame data = {'A': [1, 2, 3, None, 5, 6], 'B': [10, None, 30, 40, 50, None], 'group': ['X', 'X', 'Y', 'Y', 'Z', 'Z']} df = pd.DataFrame(data) # Group data by the 'group' column grouped = df.groupby('group') # Fill missing values within each group with the mean of that group filled_df = grouped.apply(lambda group: group.fillna(group.mean())) print(filled_df) |

This will group the data based on the 'group' column and fill the missing values within each group with the mean value of that group. You can also fill missing values with other statistical measures such as median, mode, etc. by using the respective aggregation functions within the `apply`

method.

## How to group data in a pandas DataFrame and sort the result?

To group data in a pandas DataFrame and sort the result, you can use the `groupby()`

function along with the `sort_values()`

function. Here's an example of how to do this:

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import pandas as pd # Create a sample DataFrame data = {'Category': ['A', 'B', 'A', 'C', 'B', 'C'], 'Value': [10, 20, 15, 25, 30, 35]} df = pd.DataFrame(data) # Group the data by 'Category' and calculate the sum of 'Value' for each group grouped_data = df.groupby('Category')['Value'].sum().reset_index() # Sort the grouped data by 'Value' in descending order sorted_data = grouped_data.sort_values(by='Value', ascending=False) print(sorted_data) |

In this example, we first group the data in the DataFrame `df`

by the 'Category' column and calculate the sum of 'Value' for each group. We then reset the index to convert the grouped data back to a DataFrame.

Next, we use the `sort_values()`

function to sort the grouped data by the sum of 'Value' in descending order. Finally, we print the sorted result.