Pandas
pandas.pydata.org โบ docs โบ reference โบ api โบ pandas.DataFrame.groupby.html
pandas.DataFrame.groupby โ pandas 3.0.2 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 โบ pandas โบ 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 ย July 11, 2025
Videos
11:05
Group By and Aggregate Functions in Pandas | Python Pandas Tutorials ...
44:17
The Complete Guide to Python Pandas Groupby - YouTube
05:58
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 โบ docs โบ user_guide โบ groupby.html
Group by: split-apply-combine โ 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 โบ docs โบ dev โบ reference โบ api โบ pandas.DataFrame.groupby.html
pandas.DataFrame.groupby โ 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 โบ docs โบ reference โบ groupby.html
GroupBy โ 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 โบ python โบ pandas โบ 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 ยป ยท
Pandas
pandas.pydata.org โบ pandas-docs โบ stable โบ user_guide โบ groupby.html
Group by: split-apply-combine โ pandas 3.0.2 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.
DataCamp
datacamp.com โบ tutorial โบ 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 ...
Codecademy
codecademy.com โบ docs โบ python:pandas โบ dataframe โบ .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.
TutorialsPoint
tutorialspoint.com โบ python_pandas โบ 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.
Reddit
reddit.com โบ r/learnpython โบ 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 โบ docs โบ latest โบ api โบ python โบ reference โบ pyspark.pandas โบ api โบ pyspark.pandas.DataFrame.groupby.html
pyspark.pandas.DataFrame.groupby โ 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.
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
134
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
Pandas
pandas.pydata.org โบ docs โบ reference โบ api โบ pandas.core.groupby.DataFrameGroupBy.first.html
pandas.core.groupby.DataFrameGroupBy.first โ pandas 2.3.3 documentation
Apply a function groupby to each row or column of a DataFrame. pandas.core.groupby.DataFrameGroupBy.last ยท Compute the last non-null entry of each column. pandas.core.groupby.DataFrameGroupBy.nth ยท Take the nth row from each group. Examples ยท >>> df = pd.DataFrame(dict(A=[1, 1, 3], B=[None, 5, 6], C=[1, 2, 3], ...
Pandas
pandas.pydata.org โบ docs โบ reference โบ api โบ pandas.core.groupby.DataFrameGroupBy.mean.html
pandas.core.groupby.DataFrameGroupBy.mean โ 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.
GeeksforGeeks
geeksforgeeks.org โบ pandas โบ 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 ย July 11, 2025