Skip to main content
freelanceshack.com

Back to all posts

How to Group By One Column Or Another In Pandas?

Published on
5 min read
How to Group By One Column Or Another In Pandas? image

Best Data Analysis Tools to Buy in October 2025

1 Colomira 51mm Espresso Accessories Kit, Dosing Funnel and Puck Screen Set, Magnetic Coffee Funnel, 51mm Reusable Espresso Puck Screen, Espresso Tools(Panda)

Colomira 51mm Espresso Accessories Kit, Dosing Funnel and Puck Screen Set, Magnetic Coffee Funnel, 51mm Reusable Espresso Puck Screen, Espresso Tools(Panda)

  • ENSURE PERFECT EXTRACTION: OPTIMIZE YOUR ESPRESSO WITH EVEN WATER DISTRIBUTION.

  • STRONG MAGNETIC LOCK: QUICK ATTACHMENT FOR HASSLE-FREE BREWING EVERY TIME.

  • DURABLE & EASY TO CLEAN: PREMIUM MATERIALS ENSURE LONGEVITY AND LOW MAINTENANCE.

BUY & SAVE
$12.99
Colomira 51mm Espresso Accessories Kit, Dosing Funnel and Puck Screen Set, Magnetic Coffee Funnel, 51mm Reusable Espresso Puck Screen, Espresso Tools(Panda)
2 PH PandaHall 2 Pack Wooden Ring Clamp Ring Jewelers Holder Benchwork Hand Tool for Polishing Repairing Rings Vice With Wedge Lock Wedge Leather Stone Setting Engraving Jewelry Making Tool Valentine

PH PandaHall 2 Pack Wooden Ring Clamp Ring Jewelers Holder Benchwork Hand Tool for Polishing Repairing Rings Vice With Wedge Lock Wedge Leather Stone Setting Engraving Jewelry Making Tool Valentine

  • DURABLE HARDWOOD AND STEEL FOR LONG-LASTING PERFORMANCE.
  • LIGHTWEIGHT DESIGN FOR EASY HANDLING DURING JEWELRY WORK.
  • LEATHER-LINED JAWS SECURE RINGS WITHOUT SCRATCHING OR DAMAGING.
BUY & SAVE
$15.49
PH PandaHall 2 Pack Wooden Ring Clamp Ring Jewelers Holder Benchwork Hand Tool for Polishing Repairing Rings Vice With Wedge Lock Wedge Leather Stone Setting Engraving Jewelry Making Tool Valentine
3 Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems

Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems

  • MASTER SCIKIT-LEARN: TRACK ML PROJECTS FROM START TO FINISH.
  • EXPLORE DIVERSE MODELS: SVMS, TREES, FORESTS, AND ENSEMBLE METHODS.
  • BUILD NEURAL NETS: LEVERAGE TENSORFLOW FOR ADVANCED AI APPLICATIONS.
BUY & SAVE
$49.50 $89.99
Save 45%
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
4 Princeton Review Digital SAT Premium Prep, 2025: 5 Full-Length Practice Tests (2 in Book + 3 Adaptive Tests Online) + Online Flashcards + Review & Tools (2025) (College Test Preparation)

Princeton Review Digital SAT Premium Prep, 2025: 5 Full-Length Practice Tests (2 in Book + 3 Adaptive Tests Online) + Online Flashcards + Review & Tools (2025) (College Test Preparation)

BUY & SAVE
$37.99
Princeton Review Digital SAT Premium Prep, 2025: 5 Full-Length Practice Tests (2 in Book + 3 Adaptive Tests Online) + Online Flashcards + Review & Tools (2025) (College Test Preparation)
5 Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython

Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython

BUY & SAVE
$64.65
Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython
6 NOMEDOGYIm Giant Pandas and Cubs Car Steering Wheel Cover a Group of Cute Pandas Crawling Steering Wheel Cover Car Decor Suitable for Most Vehicles Including Trucks and SUV

NOMEDOGYIm Giant Pandas and Cubs Car Steering Wheel Cover a Group of Cute Pandas Crawling Steering Wheel Cover Car Decor Suitable for Most Vehicles Including Trucks and SUV

  • UNIVERSAL FIT: FITS MOST VEHICLES FOR VERSATILE, HASSLE-FREE USE.
  • DURABLE & NON-SLIP: PREMIUM NEOPRENE ENSURES LONGEVITY AND SAFETY.
  • STYLISH & EASY CARE: EYE-CATCHING DESIGNS WITH SIMPLE CLEANING OPTIONS.
BUY & SAVE
$8.99
NOMEDOGYIm Giant Pandas and Cubs Car Steering Wheel Cover a Group of Cute Pandas Crawling Steering Wheel Cover Car Decor Suitable for Most Vehicles Including Trucks and SUV
7 OnFireGuy Plastic Capsule Tube with 20 AirTite Coin Holders, Red Lid, Coin Storage Tube & Holders for 1oz Silver Eagle, Panda, Libertad, Kangaroo

OnFireGuy Plastic Capsule Tube with 20 AirTite Coin Holders, Red Lid, Coin Storage Tube & Holders for 1oz Silver Eagle, Panda, Libertad, Kangaroo

  • UNIVERSAL FIT: PERFECTLY ACCOMMODATES VARIOUS 1OZ COINS, ENHANCING VALUE.
  • SECURE PACKAGING: FOAM LAYERS ENSURE SAFE SHIPPING FOR YOUR CAPSULES.
  • COMPLETE SET: INCLUDES 20 CAPSULES & A STYLISH TUBE FOR ORGANIZED STORAGE.
BUY & SAVE
$21.95
OnFireGuy Plastic Capsule Tube with 20 AirTite Coin Holders, Red Lid, Coin Storage Tube & Holders for 1oz Silver Eagle, Panda, Libertad, Kangaroo
+
ONE MORE?

In pandas, you can group by one column or another by using the [groupby](https://almarefa.net/blog/how-to-use-count-groupby-and-max-in-pandas)() function along with specifying the columns you want to group by. Simply pass the column name or column names as arguments to the groupby() function to group the data based on those columns. This will create groups based on the unique values in the specified column(s) and allow you to perform operations on each group separately.

How to group by a column and calculate the mean in pandas?

You can use the groupby() function in pandas along with the mean() function to group by a column and calculate the mean of another column. Here's an example:

import pandas as pd

Create a sample dataframe

data = { 'category': ['A', 'B', 'A', 'B', 'A'], 'value': [10, 20, 30, 40, 50] } df = pd.DataFrame(data)

Group by the 'category' column and calculate the mean of the 'value' column

mean_values = df.groupby('category')['value'].mean()

print(mean_values)

Output:

category A 30.0 B 30.0 Name: value, dtype: float64

This code groups the dataframe by the 'category' column and calculates the mean of the 'value' column for each group. The result is a Series with the mean values for each category.

How to group by one column and drop duplicates within each group in pandas?

You can achieve this by using the groupby and drop_duplicates methods in pandas.

Here's an example code snippet to group by one column and drop duplicates within each group:

import pandas as pd

Create a sample DataFrame

data = {'Group': ['A', 'A', 'B', 'B', 'C', 'C'], 'Value': [1, 2, 3, 3, 4, 5]} df = pd.DataFrame(data)

Group by 'Group' column and drop duplicates within each group

output_df = df.groupby('Group').apply(lambda x: x.drop_duplicates())

print(output_df)

In this code snippet, we first create a sample DataFrame with two columns 'Group' and 'Value'. We then use the groupby method to group the DataFrame by the 'Group' column. Next, we use the apply method along with a lambda function to apply the drop_duplicates method to each group within the DataFrame. Finally, we print the resulting DataFrame output_df.

How to group by a numeric column in pandas?

To group by a numeric column in pandas, you can use the groupby() function along with the column you want to group by. Here is an example:

import pandas as pd

Create a sample dataframe

data = {'Category': ['A', 'B', 'A', 'B', 'A'], 'Value': [10, 20, 30, 40, 50]} df = pd.DataFrame(data)

Group by the 'Category' column

grouped = df.groupby('Category')

Sum the values in each group

sum_values = grouped['Value'].sum()

print(sum_values)

This will group the dataframe by the 'Category' column and calculate the sum of the 'Value' column for each group. You can also perform other aggregations such as mean, count, max, min, etc. by using different aggregation functions with the agg() method.

How to group by one column and calculate the cumulative sum in pandas?

You can group by one column and calculate the cumulative sum in pandas using the groupby() and cumsum() functions. Here's an example:

import pandas as pd

Create a sample DataFrame

data = {'Category': ['A', 'A', 'B', 'B', 'A', 'B'], 'Value': [10, 20, 15, 25, 30, 35]} df = pd.DataFrame(data)

Group by 'Category' and calculate the cumulative sum

df['Cumulative Sum'] = df.groupby('Category')['Value'].cumsum()

print(df)

Output:

Category Value Cumulative Sum 0 A 10 10 1 A 20 30 2 B 15 15 3 B 25 40 4 A 30 60 5 B 35 75

In this example, we first create a DataFrame with two columns 'Category' and 'Value'. We then use the groupby() function to group the DataFrame by the 'Category' column, and calculate the cumulative sum of the 'Value' column within each group using the cumsum() function. The resulting cumulative sum values are stored in a new column 'Cumulative Sum' in the DataFrame.

How to group by one column and sort the results in pandas?

You can group by one column and sort the results in pandas using the following steps:

  1. First, import pandas library:

import pandas as pd

  1. Create a DataFrame:

data = {'A': ['foo', 'bar', 'foo', 'bar', 'foo', 'bar'], 'B': [1, 2, 3, 4, 5, 6], 'C': [7, 8, 9, 10, 11, 12]} df = pd.DataFrame(data)

  1. Group by column 'A' and apply the sort_values() method to sort the results within each group:

sorted_df = df.groupby('A').apply(lambda x: x.sort_values('B')).reset_index(drop=True)

In this code snippet, we first group the DataFrame df by column 'A'. Then, we use the apply() method to apply the sort_values() method on column 'B' within each group. Finally, we reset the index of the resulting DataFrame using the reset_index() method with drop=True to remove the original index.

Now, sorted_df will be a new DataFrame with the rows grouped by column 'A' and sorted within each group based on the values in column 'B'.

How to group by one column and aggregate multiple columns in pandas?

To group by one column and aggregate multiple columns in Pandas, you can use the groupby() function in combination with the agg() function.

Here's an example of how to do this:

import pandas as pd

Sample data

data = { 'group': ['A', 'A', 'B', 'B', 'C'], 'value1': [10, 20, 15, 25, 30], 'value2': [5, 10, 8, 12, 15] }

df = pd.DataFrame(data)

Group by 'group' column and aggregate 'value1' and 'value2' columns

agg_df = df.groupby('group').agg({ 'value1': 'sum', 'value2': 'mean' })

print(agg_df)

This will output:

   value1  value2

group
A 30 7.5 B 40 10.0 C 30 15.0

In this example, we are grouping the data by the 'group' column and aggregating the 'value1' column using the sum function and the 'value2' column using the mean function. You can also use other aggregation functions such as 'max', 'min', 'count', etc. to aggregate the values in the columns.