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DOC: User Guide Page on user-defined functions #61195

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Merged
merged 21 commits into from
May 18, 2025
Merged

DOC: User Guide Page on user-defined functions #61195

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udf user guide introduction
arthurlw Mar 26, 2025
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@arthurlw
arthurlw committed Mar 28, 2025
commit d20bcc735bc39d07a21018527ea3f000e1d17eb0
26 changes: 13 additions & 13 deletions 26 doc/source/user_guide/user_defined_functions.rst
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Expand Up @@ -9,7 +9,7 @@ Introduction to User-Defined Functions
In pandas, User-Defined Functions (UDFs) provide a way to extend the library’s
functionality by allowing users to apply custom computations to their data. While
pandas comes with a set of built-in functions for data manipulation, UDFs offer
flexibility when built-in methods are not sufficient. These functions can be
flexibility when built-in methods are not sufficient. These functions can be
applied at different levels: element-wise, row-wise, column-wise, or group-wise,
and change the data differently, depending on the method used.
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nit: "change the data differently" sounds very close to mutating in a UDF, which we explicitly do not support. What do you think of "behave differently".

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@arthurlw arthurlw Apr 12, 2025 •
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“Behave differently” sounds clearer and avoids implying mutation. I'll update it!


Expand All @@ -19,13 +19,13 @@ Why Use User-Defined Functions?
Pandas is designed for high-performance data processing, but sometimes your specific
needs go beyond standard aggregation, transformation, or filtering. User-defined functions allow you to:

* **Customize Computations**: Implement logic tailored to your dataset, such as complex
* **Customize Computations**: Implement logic tailored to your dataset, such as complex
transformations, domain-specific calculations, or conditional modifications.
* **Improve Code Readability**: Encapsulate logic into functions rather than writing long,
complex expressions.
* **Handle Complex Grouped Operations**: Perform operations on grouped data that standard
methods do not support.
* **Extend pandas' Functionality**: Apply external libraries or advanced calculations that
* **Extend pandas' Functionality**: Apply external libraries or advanced calculations that
are not natively available.


Expand Down Expand Up @@ -58,14 +58,14 @@ The :meth:`DataFrame.apply` allows applying a user-defined functions along eithe
.. ipython:: python

import pandas as pd

# Sample DataFrame
df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})

# User-Defined Function
def add_one(x):
return x + 1

# Apply function
df_applied = df.apply(add_one)
print(df_applied)
Expand All @@ -81,14 +81,14 @@ The :meth:`DataFrame.apply` allows applying a user-defined functions along eithe

# Sample DataFrame
df = pd.DataFrame({'A': [1, 2, 3], 'B': [1, 2, 3]})

# User-Defined Function
def add_one(x):
return x + 1

def add_two(x):
return x + 2

# Apply function
df_applied = df.apply({"A": add_one, "B": add_two})
print(df_applied)
Expand All @@ -103,11 +103,11 @@ The :meth:`DataFrame.apply` allows applying a user-defined functions along eithe

# Sample Series
s = pd.Series([1, 2, 3])

# User-Defined Function
def add_one(x):
return x + 1

# Apply function
s_applied = s.apply(add_one)
print(s_applied)
Expand All @@ -128,11 +128,11 @@ The :meth:`DataFrame.agg` allows aggregation with a user-defined function along
'Category': ['A', 'A', 'B', 'B'],
'Values': [10, 20, 30, 40]
})

# Define a function for group operations
def group_mean(group):
return group.mean()

# Apply UDF to each group
grouped_result = df.groupby('Category')['Values'].agg(group_mean)
print(grouped_result)
Expand All @@ -149,7 +149,7 @@ transformations and custom row-wise or element-wise operations.
The :meth:`DataFrame.transform` allows transforms a Dataframe, Series or Grouped object
while preserving the original shape of the object.

.. ipython:: python
.. ipython:: python

# Sample DataFrame
df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})
Expand Down
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