5 Tools for Transforming Data with Pandas.txt

 Kyle McKiou Director of Data Science at The Marketing Store 

5 Tools for Transforming Data with Pandas

If you want to become a data scientist, it's critical that you learn how to transform data efficiently.  Master the 5 techniques below (links with examples in comments), and you will become a data-shaping wizard that is ready to tackle any dataset. 


Reshaping Pandas DataFrames

1. pivot 

Creates a pivot table without aggregation.  Can handle categorical data.  Incredibly useful when you want to transform column data into rows.


2. pivot_table  

Generalization of pivot that can take multiple indices and performs aggregation. Great for when you want to pivot but have duplicate values for an index-column pair.


3-4. stack and unstack  

Stack pivots a level of the column labels to rows.
Unstack pivots a level of row labels to columns.

Extremely useful when manipulating multi-indexed DataFrames.


5. groupby

Groupby is a flexible method that creates an object that can be used to split a DataFrame into groups based on a column or row label, apply an operation to the groups, and aggregate them back together into a DataFrame.

Very useful when you want information about categorical features.



Check out these examples to learn more: 
👉 https://lnkd.in/gH7WQwg 



More Resources :


->pivot
https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.pivot.html 

->pivot_table
https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.pivot_table.html

->stack
https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.stack.html

->unstack
https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.unstack.html

->groupby
https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.groupby.html

https://pandas.pydata.org/pandas-docs/stable/groupby.html