Data Wrangling in Python – How to Create A Pipeline In Pandas

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Create A Pipeline In Pandas

Pandas’ pipeline feature allows you to string together Python functions in order to build a pipeline of data processing.

Preliminaries

import pandas as pd

Create Dataframe

/* Create empty dataframe */
df = pd.DataFrame()

/* Create a column */
df['name'] = ['John', 'Steve', 'Sarah']
df['gender'] = ['Male', 'Male', 'Female']
df['age'] = [31, 32, 19]

/* View dataframe */
df
name gender age
0 John Male 31
1 Steve Male 32
2 Sarah Female 19

Create Functions To Process Data

/* Create a function that */
def mean_age_by_group(dataframe, col):
    /* groups the data by a column and returns the mean age per group */
    return dataframe.groupby(col).mean()
/* Create a function that */
def uppercase_column_name(dataframe):
    /* Capitalizes all the column headers */
    dataframe.columns = dataframe.columns.str.upper()
    /* And returns them */
    return dataframe

Create A Pipeline Of Those Functions

/* Create a pipeline that applies the mean_age_by_group function */
(df.pipe(mean_age_by_group, col='gender')
   .pipe(uppercase_column_name)    /* then applies the uppercase column name function */
)
AGE
gender
Female 19.0
Male 31.5

 

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