Sure! To combine groupby, rolling, and apply in pandas, you can first group the data using the groupby method, then use the rolling method to create a rolling window over the grouped data, and finally apply a custom function or calculation using the apply method. This allows you to perform a calculation on a rolling window of data within each group, taking advantage of the flexibility and power of pandas for data manipulation and analysis.

## What is the difference between group by and rolling in pandas?

In pandas, `groupby()`

is used to split the data into groups based on some criteria, such as a column value, and allows us to perform aggregate functions on each group. It is useful for performing operations on distinct subsets of the data.

On the other hand, `rolling()`

is used to create a rolling window object which can be used to calculate rolling statistics on a particular column or series of data. It allows us to compute statistics like mean, sum, standard deviation, etc. over a specified window of time.

In short, `groupby()`

is used for grouping data based on some criteria, whereas `rolling()`

is used for calculating rolling statistics on a specified window of data.

## How to group data by a specific column in pandas?

You can group data by a specific column in pandas using the `groupby()`

function. Here is an example of how to do this:

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import pandas as pd # Create a sample DataFrame data = {'Category': ['A', 'B', 'A', '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') # Calculate the sum of values in each group sum_values = grouped.sum() print(sum_values) |

This code will group the data in the DataFrame by the 'Category' column and then calculate the sum of values in each group. You can also perform other operations such as mean, count, etc. on the grouped data using the `agg()`

function.

## How to perform aggregation on grouped data in pandas?

To perform aggregation on grouped data in pandas, you can use the `groupby()`

function to group the data by a certain column or columns, and then apply an aggregation function to the grouped data.

Here is an example of how to perform aggregation on grouped data in pandas:

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import pandas as pd # Create a sample dataframe data = {'fruit': ['apple', 'banana', 'apple', 'banana', 'orange'], 'quantity': [5, 7, 3, 2, 8], 'price': [1, 2, 1.5, 1.2, 1.8]} df = pd.DataFrame(data) # Group the data by the 'fruit' column grouped = df.groupby('fruit') # Perform aggregation using the 'sum()' function aggregated_data = grouped.agg({'quantity': 'sum', 'price': 'mean'}) print(aggregated_data) |

In this example, we first create a sample dataframe with columns 'fruit', 'quantity', and 'price'. We then group the data by the 'fruit' column using the `groupby()`

function. Finally, we apply the `sum()`

function to the 'quantity' column and the `mean()`

function to the 'price' column using the `agg()`

function to aggregate the grouped data. The resulting aggregated data will show the total quantity and average price for each fruit.