[Python basics] using pipe() in pandas to improve code readability

python basics using pipe pandas


1 brief introduction

We're using pandas When conducting data analysis , Try to avoid too much 「 fragmentation 」 Organization code , Especially creating too many unnecessary 「 Intermediate variable 」, It's a waste 「 Memory 」, It also brings the trouble of variable naming , It is not conducive to the readability of the whole analysis process code , Therefore, it is necessary to organize the code in a pipeline way .

chart 1

And in some of the articles I've written before , I introduced to you pandas Medium eval() and query() These two help us chain code , Build a practical data analysis workflow API, Plus the following pipe(), We can take whatever pandas The code is perfectly organized into a pipeline .

2 stay pandas Flexible use of pipe()

pipe() seeing the name of a thing one thinks of its function , It is specially used for Series and DataFrame The operation of the pipeline (pipeline) Transformed API, Its function is to transform the nested function call process into 「 The chain 」 The process , Its first parameter func Afferent acts on the corresponding Series or DataFrame Function of .

say concretely pipe() There are two ways to use it ,「 The first way 」 Next , The parameter in the first position of the input function must be the target Series or DataFrame, Other related parameters use the conventional 「 Key value pair 」 It can be passed in , Like the following example , We make our own function to 「 Titanic dataset 」 Carry out some basic engineering treatment :

import pandas as pd
train = pd.read_csv('train.csv')
def do_something(data, dummy_columns):
'''
Self compiled sample function
'''
data = (
pd
# Generate dummy variables for the specified column
.get_dummies(data, # Delete first data Column specified in
columns=dummy_columns,
drop_first=True)
)
return data
# Chain assembly line
(
train
# take Pclass Columns are converted to character type for subsequent dummy variable processing
.eval('Pclass=Pclass.astype("str")', engine='python')
# Delete the specified column
.drop(columns=['PassengerId', 'Name', 'Cabin', 'Ticket'])
# utilize pipe Call your own function in a chained way
.pipe(do_something,
dummy_columns=['Pclass', 'Sex', 'Embarked'])
# Delete rows with missing values
.dropna()
)

You can see , And then drop() The next step is pipe() in , We pass in the custom function as its first parameter , Thus, a series of operations are skillfully embedded in the chain process .

「 The second way to use it 」 Fit the target Series and DataFrame Not for the first parameter of the pass in function , For example, in the following example, we assume that the target input data is the second parameter data2, be pipe() The first parameter of should take ( Function name , ' Parameter name ') In the format of :

def do_something(data1, data2, axis):
'''
Self compiled sample function
'''
data = (
pd
.concat([data1, data2], axis=axis)
)
return data
# pipe() The second way to use it
(
train
.pipe((do_something, 'data2'), data1=train, axis=0)
)

In such a design, we can avoid many nested function calls , Optimize our code at will ~


The above is the whole content of this paper , Welcome to discuss with me in the comments section ~

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Original publication time : 2020-11-09

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本文为[Huang Bo's machine learning circle]所创,转载请带上原文链接,感谢

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