Pandas Aggregation and Grouping: The Key to Making Sense of Your Data

Pandas provide several methods for aggregation and grouping of data. The most commonly used method is the groupby() function, which allows you to group data by one or more columns and then apply a aggregation function to each group. The aggregation functions in pandas include sum(), mean(), count(), min(), max(), and many others.

For example, if a DataFrame with columns ‘A’ and ‘B’ and you want to group the data by column ‘A’ and calculate the mean of column ‘B’ for each group, you would use the following code:

df.groupby('A').mean()['B']

Another method is the pivot_table() which allows you to create a spreadsheet-style pivot table as a DataFrame. It can be used to group data by one or more columns and apply an aggregation function to each group.

Additionally, the aggregate() function can be used to apply multiple aggregation functions to a single groupby operation.

df.groupby('A').agg({'B':['mean','max'], 'C':'sum'})

These are just a few of the aggregation and grouping methods available in pandas, and the library provides a plethora of other options and capabilities for working with and analysing data.

Pandas’ groupby function allows us to group a dataframe by one or more columns and then apply a function to each group. As an illustration:

import pandas as pd df = pd.DataFrame({'key1': ['K0', 'K0', 'K1', 'K1', 'K2', 'K2'], 'key2': ['K0', 'K1', 'K0', 'K1', 'K0', 'K1'], 'data': [0, 1, 2, 3, 4, 5]}) 
grouped = df.groupby(['key1', 'key2']) 
result = grouped.mean()

The groupby function returns a DataFrameGroupBy object, which has a variety of methods for performing operations on the groups. In this example, we use the mean method to compute the mean of the data column for each group.

The resulting dataframe will look like this:

data key1 key2 K0 K0 0.0 K1 1.0 K1 K0 2.0 K1 3.0 K2 K0 4.0 K1 5.0

You can also apply a custom function to the groups using the apply method. For example:

def my_func(data): return data.max() - data.min() 
result = grouped.apply(my_func)

The aggregate function is similar to apply, but it can take a list of functions and apply them to the groups. For example:

result = grouped.aggregate([min, max])

The transform function applies a function to the data in each group and returns a transformed version of the data. It can be useful for scaling or normalizing the data within each group. For example:

def scale(data): return (data - data.mean()) / data.std() result = grouped.transform(scale)

Finally, the describe method can be used to compute various summary statistics for each group. It returns a dataframe with the summary statistics for each group.