Data Cleanup in Python Pandas #27: Removing Rows and Columns

Description: Dive into the 27th installment of our Python Pandas Data Cleanup series, where we tackle the essential task of removing unwanted rows and columns from your dataset. Whether you're dealing with missing values, outliers, or redundant information, this tutorial provides step-by-step guidance on how to efficiently clean up your data using Pandas. Gain insights into various techniques, such as dropping rows based on conditions, eliminating columns with irrelevant data, and optimizing your DataFrame for more meaningful analyses. Uncover the power of data manipulation in Pandas and enhance your proficiency in data cleanup with this informative tutorial.


Chapters:
1:30 - Drop specific rows from a dataframe in pandas
3:56 - Setting an index to the dataframe
4:35 - How to drop rows based on the string type labeled indexes
6:55 - Sort indexes with the help of sort_index() method
7:16 - Dropping the rows based on the integer labeled value
8:37 - Convert the datatype of the column from float to int with astype method
9:27 - Drop all NaN (Null) values from the dataframe with the help of drop method
10:48 - Use of del function to drop the dataframe columns

#python #pandas #dataanalysis #drop

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how to drop columns in pandas
Python Pandas
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Pandas Tutorial
Data Cleaning
Data Analysis


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