Data Wrangling in Python – Dropping Rows And Columns In pandas Dataframe

Dropping Rows And Columns In pandas Dataframe

Import modules


import pandas as pd

Create a dataframe

data = {'name': ['Jason', 'Molly', 'Tina', 'Jake', 'Amy'], 
        'year': [2012, 2012, 2013, 2014, 2014], 
        'reports': [4, 24, 31, 2, 3]}
df = pd.DataFrame(data, index = ['Cochice', 'Pima', 'Santa Cruz', 'Maricopa', 'Yuma'])
df
name reports year
Cochice Jason 4 2012
Pima Molly 24 2012
Santa Cruz Tina 31 2013
Maricopa Jake 2 2014
Yuma Amy 3 2014

Drop an observation (row)

df.drop(['Cochice', 'Pima'])
name reports year
Santa Cruz Tina 31 2013
Maricopa Jake 2 2014
Yuma Amy 3 2014

Drop a variable (column)

Note: axis=1 denotes that we are referring to a column, not a row

df.drop('reports', axis=1)
name year
Cochice Jason 2012
Pima Molly 2012
Santa Cruz Tina 2013
Maricopa Jake 2014
Yuma Amy 2014

Drop a row if it contains a certain value (in this case, “Tina”)

Specifically: Create a new dataframe called df that includes all rows where the value of a cell in the name column does not equal “Tina”

df[df.name != 'Tina']
name reports year
Cochice Jason 4 2012
Pima Molly 24 2012
Maricopa Jake 2 2014
Yuma Amy 3 2014

Drop a row by row number (in this case, row 3)

Note that Pandas uses zero based numbering, so 0 is the first row, 1 is the second row, etc.

df.drop(df.index[2])
name reports year
Cochice Jason 4 2012
Pima Molly 24 2012
Maricopa Jake 2 2014
Yuma Amy 3 2014

can be extended to dropping a range

df.drop(df.index[[2,3]])
name reports year
Cochice Jason 4 2012
Pima Molly 24 2012
Yuma Amy 3 2014

or dropping relative to the end of the DF.

df.drop(df.index[-2])
name reports year
Cochice Jason 4 2012
Pima Molly 24 2012
Santa Cruz Tina 31 2013
Yuma Amy 3 2014

you can select ranges relative to the top or drop relative to the bottom of the DF as well.

df[:3] #keep top 3
name reports year
Cochice Jason 4 2012
Pima Molly 24 2012
Santa Cruz Tina 31 2013
df[:-3] #drop bottom 3 
name reports year
Cochice Jason 4 2012
Pima Molly 24 2012

 

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