Data Wrangling in Python – Crosstabs In pandas

Crosstabs In pandas

Import pandas

import pandas as pd
raw_data = {'regiment': ['Nighthawks', 'Nighthawks', 'Nighthawks', 'Nighthawks', 'Dragoons', 'Dragoons', 'Dragoons', 'Dragoons', 'Scouts', 'Scouts', 'Scouts', 'Scouts'], 
        'company': ['infantry', 'infantry', 'cavalry', 'cavalry', 'infantry', 'infantry', 'cavalry', 'cavalry','infantry', 'infantry', 'cavalry', 'cavalry'], 
        'experience': ['veteran', 'rookie', 'veteran', 'rookie', 'veteran', 'rookie', 'veteran', 'rookie','veteran', 'rookie', 'veteran', 'rookie'],
        'name': ['Miller', 'Jacobson', 'Ali', 'Milner', 'Cooze', 'Jacon', 'Ryaner', 'Sone', 'Sloan', 'Piger', 'Riani', 'Ali'], 
        'preTestScore': [4, 24, 31, 2, 3, 4, 24, 31, 2, 3, 2, 3],
        'postTestScore': [25, 94, 57, 62, 70, 25, 94, 57, 62, 70, 62, 70]}
df = pd.DataFrame(raw_data, columns = ['regiment', 'company', 'experience', 'name', 'preTestScore', 'postTestScore'])
regiment company experience name preTestScore postTestScore
0 Nighthawks infantry veteran Miller 4 25
1 Nighthawks infantry rookie Jacobson 24 94
2 Nighthawks cavalry veteran Ali 31 57
3 Nighthawks cavalry rookie Milner 2 62
4 Dragoons infantry veteran Cooze 3 70
5 Dragoons infantry rookie Jacon 4 25
6 Dragoons cavalry veteran Ryaner 24 94
7 Dragoons cavalry rookie Sone 31 57
8 Scouts infantry veteran Sloan 2 62
9 Scouts infantry rookie Piger 3 70
10 Scouts cavalry veteran Riani 2 62
11 Scouts cavalry rookie Ali 3 70

Create a crosstab table by company and regiment

Counting the number of observations by regiment and category

pd.crosstab(df.regiment,, margins=True)
company cavalry infantry All
Dragoons 2 2 4
Nighthawks 2 2 4
Scouts 2 2 4
All 6 6 12

Create a crosstab of the number of rookie and veteran cavalry and infantry soldiers per regiment

pd.crosstab([, df.experience], df.regiment,  margins=True)
regiment Dragoons Nighthawks Scouts All
company experience
cavalry rookie 1 1 1 3
veteran 1 1 1 3
infantry rookie 1 1 1 3
veteran 1 1 1 3
All 4 4 4 12

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) !!!

Latest end-to-end Learn by Coding Recipes in Project-Based Learning:

Applied Statistics with R for Beginners and Business Professionals

Data Science and Machine Learning Projects in Python: Tabular Data Analytics

Data Science and Machine Learning Projects in R: Tabular Data Analytics

Python Machine Learning & Data Science Recipes: Learn by Coding

R Machine Learning & Data Science Recipes: Learn by Coding

Comparing Different Machine Learning Algorithms in Python for Classification (FREE)

Disclaimer: The information and code presented within this recipe/tutorial is only for educational and coaching purposes for beginners and developers. Anyone can practice and apply the recipe/tutorial presented here, but the reader is taking full responsibility for his/her actions. The author (content curator) of this recipe (code / program) has made every effort to ensure the accuracy of the information was correct at time of publication. The author (content curator) does not assume and hereby disclaims any liability to any party for any loss, damage, or disruption caused by errors or omissions, whether such errors or omissions result from accident, negligence, or any other cause. The information presented here could also be found in public knowledge domains.  

Google –> SETScholars