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Pandas DataFrame | pipe method

Programming
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Python
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Pandas
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Documentation
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DataFrame
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Functions and Aggregations
schedule Mar 9, 2022
Last updated
local_offer PythonPandas
Tags

Pandas DataFrame.pipe(~) method applies a specified function on the source DataFrame. This is not done in-place, meaning the source DataFrame is left intact and a new DataFrame is returned.

Parameters

1. func | function

The function to apply on the source DataFrame.

2. args | iterable | optional

The positional arguments to pass to func.

3. kwargs | mapping | optional

The keyword arguments to pass to func.

Return Value

A new DataFrame.

Examples

Basic usage

Consider the following DataFrame:

df = pd.DataFrame({"A":[3,4],"B":[5,6]})
df
A B
0 3 5
1 4 6

Suppose we wanted to add 10 to each entry:

def add_ten(x): # x is a DataFrame (df)
return x + 10

df.pipe(add_ten)
A B
0 13 15
1 14 16

Here, a new DataFrame is created and our original df is left intact.

Method chaining

What's nice about pipe(~) is that we can make a chain:

def add_ten(x):
return x + 10

df = pd.DataFrame({"A":[3,4],"B":[5,6]})
df.pipe(add_ten).pipe(add_ten)
A B
0 23 25
1 24 26

Specifying positional arguments

We can pass in positional arguments for our func:

def add(x, y, z):
return x + y + z

df = pd.DataFrame({"A":[3,4],"B":[5,6]})
df.pipe(add, 4, 6)
A B
0 13 15
1 14 16

Here, the arguments of add are as follows:

  • x is the source DataFrame (df)

  • y is assigned a value of 4

  • z is assigned a value of 6

Specifying keyword arguments

We can pass in keyword arguments for our func like so:

def add(x, y, k):
return x + y + k

df = pd.DataFrame({"A":[3,4],"B":[5,6]})
df.pipe(add, 4, k=6)
A B
0 13 15
1 14 16
robocat
Published by Isshin Inada
Edited by 0 others
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