Pandas Python Example for Beginners Python for Business Analyst Python for Citizen Data Scientist Python for Data Analyst

Data Wrangling in Python – How to Find Hierarchical Data In pandas

By on Tuesday, June 1, 2021

Hierarchical Data In pandas


/* import modules */
import pandas as pd
/* Create dataframe */
raw_data = {'regiment': ['Nighthawks', 'Nighthawks', 'Nighthawks', 'Nighthawks', 'Dragoons', 'Dragoons', 'Dragoons', 'Dragoons', 'Scouts', 'Scouts', 'Scouts', 'Scouts'], 
        'company': ['1st', '1st', '2nd', '2nd', '1st', '1st', '2nd', '2nd','1st', '1st', '2nd', '2nd'], 
        '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', 'name', 'preTestScore', 'postTestScore'])
df
regiment company name preTestScore postTestScore
0 Nighthawks 1st Miller 4 25
1 Nighthawks 1st Jacobson 24 94
2 Nighthawks 2nd Ali 31 57
3 Nighthawks 2nd Milner 2 62
4 Dragoons 1st Cooze 3 70
5 Dragoons 1st Jacon 4 25
6 Dragoons 2nd Ryaner 24 94
7 Dragoons 2nd Sone 31 57
8 Scouts 1st Sloan 2 62
9 Scouts 1st Piger 3 70
10 Scouts 2nd Riani 2 62
11 Scouts 2nd Ali 3 70 
/* Set the hierarchical index but leave the columns inplace */
df = df.set_index(['regiment', 'company'], drop=False)
df
regiment company name preTestScore postTestScore
regiment company
Nighthawks 1st Nighthawks 1st Miller 4 25
1st Nighthawks 1st Jacobson 24 94
2nd Nighthawks 2nd Ali 31 57
2nd Nighthawks 2nd Milner 2 62
Dragoons 1st Dragoons 1st Cooze 3 70
1st Dragoons 1st Jacon 4 25
2nd Dragoons 2nd Ryaner 24 94
2nd Dragoons 2nd Sone 31 57
Scouts 1st Scouts 1st Sloan 2 62
1st Scouts 1st Piger 3 70
2nd Scouts 2nd Riani 2 62
2nd Scouts 2nd Ali 3 70 
/* Set the hierarchical index to be by regiment, and then by company */
df = df.set_index(['regiment', 'company'])
df
name preTestScore postTestScore
regiment company
Nighthawks 1st Miller 4 25
1st Jacobson 24 94
2nd Ali 31 57
2nd Milner 2 62
Dragoons 1st Cooze 3 70
1st Jacon 4 25
2nd Ryaner 24 94
2nd Sone 31 57
Scouts 1st Sloan 2 62
1st Piger 3 70
2nd Riani 2 62
2nd Ali 3 70 

/* View the index */
df.index
MultiIndex(levels=[['Dragoons', 'Nighthawks', 'Scouts'], ['1st', '2nd']],
           labels=[[1, 1, 1, 1, 0, 0, 0, 0, 2, 2, 2, 2], [0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1]],
           names=['regiment', 'company'])

/* Swap the levels in the index */
df.swaplevel('regiment', 'company')
name preTestScore postTestScore
company regiment
1st Nighthawks Miller 4 25
Nighthawks Jacobson 24 94
2nd Nighthawks Ali 31 57
Nighthawks Milner 2 62
1st Dragoons Cooze 3 70
Dragoons Jacon 4 25
2nd Dragoons Ryaner 24 94
Dragoons Sone 31 57
1st Scouts Sloan 2 62
Scouts Piger 3 70
2nd Scouts Riani 2 62
Scouts Ali 3 70 
/* Summarize the results by regiment */
df.sum(level='regiment')
preTestScore postTestScore
regiment
Nighthawks 61 238
Dragoons 62 246
Scouts 10 264

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

0
Related Posts