How to Group rows in a Pandas DataFrame in Python

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How to Group rows in a Pandas DataFrame in Python

Grouping rows in a Pandas DataFrame in Python is a powerful way to perform aggregations and other data manipulation tasks. The process of grouping rows is also known as “groupby” in Pandas, and it can be used to group rows based on the values in one or more columns. In this blog, we will go over the basic usage of the groupby method and the different ways to use it to group rows in a DataFrame.

The basic syntax for using the groupby method is:

df.groupby("column_name")

 

This will group the DataFrame by the values in the “column_name” column. Once the rows are grouped, you can perform various aggregations on the grouped data using built-in methods such as mean(), sum(), count(), min(), max() etc.

For example, you could group a DataFrame by the values in the “City” column, and then find the average “Age” for each city:

 

df.groupby("City")["Age"].mean()

 

This will return a new DataFrame with the average age for each city.

You can also group by multiple columns, to do so, you pass a list of columns names to the groupby method:

df.groupby(["City","Gender"])["Age"].mean()

 

This will group the DataFrame by the values in both the “City” and “Gender” columns and then calculate the mean of the “Age” column

Another common use case for grouping rows in a DataFrame is to perform more advanced aggregations. For this, you can use the agg() method. This method takes a dictionary of aggregate functions as an argument, with the keys as the column names and the values as the aggregate function to apply. For example, you could group the DataFrame by the “City” column and calculate the mean, standard deviation, and count of the “Age” column:

df.groupby("City")["Age"].agg({"mean": "mean", "std": "std", "count": "count"})

 

You can also apply custom aggregate functions, you will need to import numpy and then use it inside the agg() function. For example, you could use numpy’s np.median() function to calculate the median age for each city:

import numpy as np
df.groupby("City")["Age"].agg({"median":np.median})

 

It’s worth noting that, the groupby method returns a special DataFrameGroupBy object, this object is not a DataFrame, and most of the methods that are applied to it return a new grouped DataFrame. If you want to keep the original DataFrame format, you can use the as_index parameter and set it to False.

In conclusion, grouping rows in a Pandas DataFrame in Python is a powerful technique that allows you to perform various data manipulation tasks such as aggregations and advanced data summarization. With the groupby() method and its associated methods such as mean(), sum(), agg() etc, it’s easy to group rows in a DataFrame based on the values in one or more columns and perform the desired operations.

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