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Pandas DataFrame corr() Method

Pandas dataframe.groupby() Method

Last Updated : 03 Dec, 2024

Pandas groupby() function is a powerful tool used to split a DataFrame into groups based on one or more columns, allowing for efficient data analysis and aggregation. It follows a "split-apply-combine" strategy, where data is divided into groups, a function is applied to each group, and the results are combined into a new DataFrame. For example, if you have a dataset of sales transactions, you can use groupby() to group the data by product category and calculate the total sales for each category.

Pandas-dataframe-groupby-Method — pandas dataframe groupby

The code is providing total sales for each product category, demonstrating the core idea of grouping data and applying an aggregation function.

How to Use Pandas GroupBy Method?

The groupby() function in Pandas involves three main steps: Splitting, Applying, and Combining.

Splitting: This step involves dividing the DataFrame into groups based on some criteria. The groups are defined by unique values in one or more columns.
Applying: In this step, a function is applied to each group independently. You can apply various functions to each group, such as:
- Aggregation: Calculate summary statistics (e.g., sum, mean, count) for each group.
- Transformation: Modify the values within each group.
- Filtering: Keep or discard groups based on certain conditions.
Combining: Finally, the results of the applied function are combined into a new DataFrame or Series.

The groupby method has several parameters that can be customized:

DataFrame.groupby(by=None, axis=0, level=None, as_index=True, sort=True, group_keys=True, observed=False, dropna=True)
Parameters :
by: Required parameter to specify the column(s) to group by.
axis: Optional, specifies the axis to group by (default is 0 for rows).
level: Optional, used for grouping by a certain level in a MultiIndex.
as_index: Optional, whether to use the group labels as the index (default is True).
sort: Optional, whether to sort the group keys (default is True).
group_keys: Optional, whether to add the group keys to the index (default is True).
dropna: Optional, whether to include rows/columns with NULL values (default is True

Example 1: Grouping by a Single Column

In this example, we will demonstrate how to group data by a single column using the groupby method. We will work with NBA-dataset that contains information about NBA players, including their teams, points scored, and assists. We'll group the data by the Team column and calculate the total points scored for each team.

Python

import pandas as pd df = pd.read_csv("https://media.geeksforgeeks.org/wp-content/uploads/nba.csv")  team = df.groupby('Team') print(team.first()) # Let's print the first entries in all the groups formed.

Output:

Pandas-dataframe-groupby-Method — Pandas dataframe.groupby() Method

Note : This is just the snapshot of the output, not all rows are covered here.

Example 2: Grouping by Multiple Columns

Grouping by multiple columns allows you to break down the data into finer categories and compute statistics for each unique combination of those columns. Let's use the same NBA dataset and group the data by both Team and positionto calculate the total points scored by each position within each team.

Python

import pandas as pd df = pd.read_csv("https://media.geeksforgeeks.org/wp-content/uploads/nba.csv")  grouping = df.groupby(['Team', 'Position']) print(grouping.first())

Output:

groupbymultiplecolumns — Grouping by Multiple Columns

Note : This is just the snapshot of the output, not all rows are covered here.

Example 3 : Applying Aggregation with GroupBy

Aggregation is one of the most common operations when using groupby. After grouping the data, you can apply functions like sum(), mean(), min(), max(), and more.

sum(): Calculates the total sum of values for each group. Useful when you need to know the total amount of a numeric column grouped by specific categories.
mean(): Computes the average value of each group, helpful for understanding trends and patterns within grouped data.
count(): Counts the number of entries in each group, returns the number of non-null entries for each group.

Let's continue with the same NBA dataset and demonstrate how aggregation works in practice using the sum(), mean(), and count() functions. The example will group the data by both Team and Position, and apply all three aggregation functions to understand the total salary, average salary, and the number of players in each group.

Python

import pandas as pd df = pd.read_csv("https://media.geeksforgeeks.org/wp-content/uploads/nba.csv")  aggregated_data = df.groupby(['Team', 'Position']).agg(     total_salary=('Salary', 'sum'),     avg_salary=('Salary', 'mean'),     player_count=('Name', 'count') )  print(aggregated_data)

Output:

aggregation-with-group-by — Applying Aggregation with GroupBy

Example 4: How to Apply Transformation Methods?

Transformation functions return an object that is indexed the same as the original group. This is useful when you need to apply operations that maintain the original structure of the data, such as normalization or standardization within groups. Purpose: Apply group-specific operations while maintaining the original shape of the dataset. Unlike aggregation, which reduces data, transformations allow group-specific modifications without altering the shape of the data.

For example: Let’s understand how to: Rank players within their teams based on their salaries - Ranking players within their teams by salary can help identify the highest- and lowest-paid players in each group.

Python

import pandas as pd df = pd.read_csv("https://media.geeksforgeeks.org/wp-content/uploads/nba.csv")  # Rank players within each team by Salary df['Rank within Team'] = df.groupby('Team')['Salary'].transform(lambda x: x.rank(ascending=False)) print(df)

Output:

How-to-Apply-Transformation-Methods — Apply Transformation Methods

We grouped by Team and used rank() to assign rankings to salaries within each team, with the highest salary receiving a rank of 1. Missing values in salary column is leading to nan amounts.

Example 5 : Filtering Groups Using Filtration Methods

Filtration allows you to drop entire groups from a GroupBy object based on a condition. This method helps in cleaning data by removing groups that do not meet specific criteria, thus focusing analysis on relevant subsets. For example: Let’s demonstrate how to filter out groups where the average salary of players is below a certain threshold.

Python

import pandas as pd df = pd.read_csv("https://media.geeksforgeeks.org/wp-content/uploads/nba.csv")  # Filter groups where the average Salary is >= 5 million filtered_df = df.groupby('Team').filter(lambda x: x['Salary'].mean() >= 1000000) print(filtered_df)

Output

              Name            Team  ...            College     Salary 0    Avery Bradley  Boston Celtics  ...              Texas  7730337.0 1      Jae Crowder  Boston Celtics  ...          Marquette  ...

Note : This is just the gist of the output, not all rows are covered here.

Pandas DataFrame corr() Method

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