Pandas’ pipeline feature allows you to string together Python functions in order to build a pipeline of data processing.
import pandas as pd
/* 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
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 */ )
Python Example for Beginners
Two Machine Learning Fields
There are two sides to machine learning:
- Practical Machine Learning:This is about querying databases, cleaning data, writing scripts to transform data and gluing algorithm and libraries together and writing custom code to squeeze reliable answers from data to satisfy difficult and ill defined questions. It’s the mess of reality.
- Theoretical Machine Learning: This is about math and abstraction and idealized scenarios and limits and beauty and informing what is possible. It is a whole lot neater and cleaner and removed from the mess of reality.
Data Science Resources: Data Science Recipes and Applied Machine Learning Recipes
Introduction to Applied Machine Learning & Data Science for Beginners, Business Analysts, Students, Researchers and Freelancers with Python & R Codes @ Western Australian Center for Applied Machine Learning & Data Science (WACAMLDS) !!!
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