Pandas
pandas.pydata.org › docs › reference › api › pandas.DataFrame.groupby.html
pandas.DataFrame.groupby — pandas 3.0.1 documentation
Group DataFrame using a mapper or by a Series of columns. A groupby operation involves some combination of splitting the object, applying a function, and combining the results.
GeeksforGeeks
geeksforgeeks.org › pandas › python-pandas-dataframe-groupby
Pandas dataframe.groupby() Method - GeeksforGeeks
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, ...
Published July 11, 2025
Videos
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Group By and Aggregate Functions in Pandas | Python Pandas Tutorials ...
44:17
The Complete Guide to Python Pandas Groupby - YouTube
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How to use Pandas Groupby Like a Pro! - YouTube
19:03
How to use the Pandas GroupBy function | Pandas tutorial - YouTube
10:27
Mastering Pandas .groupby() in 10 Minutes! | Step-by-Step Python ...
Pandas
pandas.pydata.org › docs › user_guide › groupby.html
Group by: split-apply-combine — pandas 3.0.1 documentation
An aggregation is a GroupBy operation that reduces the dimension of the grouping object. The result of an aggregation is, or at least is treated as, a scalar value for each column in a group. For example, producing the sum of each column in a group of values.
Pandas
pandas.pydata.org › docs › dev › reference › api › pandas.DataFrame.groupby.html
pandas.DataFrame.groupby — pandas documentation
Group DataFrame using a mapper or by a Series of columns. A groupby operation involves some combination of splitting the object, applying a function, and combining the results.
Pandas
pandas.pydata.org › docs › reference › groupby.html
GroupBy — pandas 3.0.1 documentation - PyData |
pandas.api.typing.DataFrameGroupBy and pandas.api.typing.SeriesGroupBy instances are returned by groupby calls pandas.DataFrame.groupby() and pandas.Series.groupby() respectively.
W3Schools
w3schools.com › python › pandas › ref_df_groupby.asp
Pandas DataFrame groupby() Method
import pandas as pd data = { 'co2': [95, 90, 99, 104, 105, 94, 99, 104], 'model': ['Citigo', 'Fabia', 'Fiesta', 'Rapid', 'Focus', 'Mondeo', 'Octavia', 'B-Max'], 'car': ['Skoda', 'Skoda', 'Ford', 'Skoda', 'Ford', 'Ford', 'Skoda', 'Ford'] } df = pd.DataFrame(data) print(df.groupby(["car"]).mean()) Try it Yourself » ·
Real Python
realpython.com › pandas-groupby
pandas GroupBy: Your Guide to Grouping Data in Python – Real Python
January 19, 2025 - The pandas .groupby() method allows you to efficiently analyze and transform datasets when working with data in Python. With df.groupby(), you can split a DataFrame into groups based on column values, apply functions to each group, and combine ...
Built In
builtin.com › data-science › pandas-groupby
Pandas Groupby: 5 Methods to Know in Python | Built In
Pandas groupby splits all the records from your data set into different categories or groups so that you can analyze the data by these groups. When you use the .groupby() function on any categorical column of DataFrame, it returns a GroupBy ...
DataCamp
datacamp.com › tutorial › pandas-groupby
Pandas GroupBy Explained: Syntax, Examples, and Tips | DataCamp
September 22, 2025 - Pandas takes the original DataFrame and partitions it into smaller DataFrames based on the criteria you provide in the by parameter. Each of these smaller DataFrames contains rows that share the same value for the specified key(s). For our example df, if we group by the 'Team' column ...
TutorialsPoint
tutorialspoint.com › python_pandas › python_pandas_groupby.htm
Pandas GroupBy in Python
Pandas objects can be split into groups based on any of their column values using the groupby() method. Let us now see how the grouping objects can be applied to the Pandas DataFrame using the groupby() method.
Codecademy
codecademy.com › docs › python:pandas › dataframe › .groupby()
Python:Pandas | DataFrame | .groupby() | Codecademy
August 11, 2025 - The Pandas DataFrame .groupby() function groups a DataFrame using a mapper or a series of columns and returns a GroupBy object.
Reddit
reddit.com › r/learnpython › how does pandas .groupby() works?
r/learnpython on Reddit: How does pandas .groupby() works?
November 6, 2021 -
I'm new to pandas and I'd love to know how does the groupby() method work. To clarify I'm familiar with the GROUP BY concept from SQL. What concerns me though is that for some reason this method changes the type of the pandas object to a generator object - <class 'pandas.core.groupby.generic.DataFrameGroupBy'> which isn't printable by itself. When I then use list() on it, it appears to have messed up the columns. What should I do with that?
Top answer 1 of 3
3
Have you read the docs ? There is also a very extensive page in the user guide .
2 of 3
2
Pandas assumes most data-driven tasks are, in real use cases, pretty complex: you want to filter then group then analyze then group then filter ... etc. Plus they assume your data could be millions or even a billion rows, so a full data scan at every step of data transformation could result in hours of runtime for your program. As a result, Pandas tries to defer this processing as much as possible until you actually do something that requires seeing the whole table. That's why groupby() returns a composable method instead of the "finished product" - it's called lazy evaluation. It's trying to defer the hard work for as long as possible.
Apache
spark.apache.org › docs › latest › api › python › reference › pyspark.pandas › api › pyspark.pandas.DataFrame.groupby.html
pyspark.pandas.DataFrame.groupby — PySpark 4.1.1 documentation
Group DataFrame or Series using one or more columns. A groupby operation involves some combination of splitting the object, applying a function, and combining the results.
Pandas
pandas.pydata.org › docs › reference › api › pandas.core.groupby.DataFrameGroupBy.mean.html
pandas.core.groupby.DataFrameGroupBy.mean — pandas 2.3.3 documentation
>>> df = pd.DataFrame({'A': [1, 1, 2, 1, 2], ... 'B': [np.nan, 2, 3, 4, 5], ... 'C': [1, 2, 1, 1, 2]}, columns=['A', 'B', 'C']) Groupby one column and return the mean of the remaining columns in each group.
Spark By {Examples}
sparkbyexamples.com › home › pandas › pandas groupby() explained with examples
Pandas groupby() Explained With Examples - Spark By {Examples}
June 26, 2025 - groupby() is a powerful function in pandas that is used for grouping data based on some criteria. It enables you to split a DataFrame into groups based on one or more columns and then apply a function (such as aggregation, transformation, or ...
Pandas
pandas.pydata.org › docs › reference › api › pandas.core.groupby.DataFrameGroupBy.first.html
pandas.core.groupby.DataFrameGroupBy.first — pandas 2.3.3 documentation
Apply a function groupby to each row or column of a DataFrame. pandas.core.groupby.DataFrameGroupBy.last · Compute the last non-null entry of each column. pandas.core.groupby.DataFrameGroupBy.nth · Take the nth row from each group. Examples · >>> df = pd.DataFrame(dict(A=[1, 1, 3], B=[None, 5, 6], C=[1, 2, 3], ...
GeeksforGeeks
geeksforgeeks.org › pandas › pandas-groupby
Pandas GroupBy - GeeksforGeeks
The groupby() function in Pandas is important for data analysis as it allows us to group data by one or more categories and then apply different functions to those groups.
Published July 11, 2025
Top answer 1 of 16
794
You can do this using groupby to group on the column of interest and then apply list to every group:
In [1]: df = pd.DataFrame( {'a':['A','A','B','B','B','C'], 'b':[1,2,5,5,4,6]})
df
Out[1]:
a b
0 A 1
1 A 2
2 B 5
3 B 5
4 B 4
5 C 6
In [2]: df.groupby('a')['b'].apply(list)
Out[2]:
a
A [1, 2]
B [5, 5, 4]
C [6]
Name: b, dtype: object
In [3]: df1 = df.groupby('a')['b'].apply(list).reset_index(name='new')
df1
Out[3]:
a new
0 A [1, 2]
1 B [5, 5, 4]
2 C [6]
2 of 16
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A handy way to achieve this would be:
df.groupby('a').agg({'b':lambda x: list(x)})
Look into writing Custom Aggregations: https://www.kaggle.com/akshaysehgal/how-to-group-by-aggregate-using-py
Shane Lynn
shanelynn.ie › home › use pandas groupby to group and summarise dataframes
Group and Aggregate your Data Better using Pandas Groupby
October 16, 2021 - Aggregation and grouping of Dataframes is accomplished in Python Pandas using “groupby()” and “agg()” functions. Apply max, min, count, distinct to groups.
Medium
medium.com › @heyamit10 › understanding-groupby-and-aggregate-in-pandas-f45e524538b9
Understanding groupby() and aggregate() in Pandas | by Hey Amit | Medium
March 6, 2025 - That’s exactly what groupby() combined with basic aggregation functions like sum(), mean(), and max() can do. Here’s how you do it: import pandas as pd # Sample data data = { 'Department': ['HR', 'HR', 'IT', 'IT', 'Finance', 'Finance'], 'Employee': ['John', 'Emma', 'Steve', 'Mia', 'Tom', 'Sophia'], 'Salary': [50000, 60000, 75000, 80000, 65000, 70000] } df = pd.DataFrame(data) # Sum of salaries by department print(df.groupby('Department')['Salary'].sum()) # Mean salary by department print(df.groupby('Department')['Salary'].mean()) What you’ll get: Department Finance 135000 HR 110000 IT 155000 Name: Salary, dtype: int64 Department Finance 67500.0 HR 55000.0 IT 77500.0 Name: Salary, dtype: float64 ·