12 Data Wrangling Functions In Python That You Should Know

Posted on by Annie D

Data wrangling in Python refers to the process of cleaning, transforming, and organizing raw data into a more structured format for analysis. It involves tasks such as data cleaning, data integration, handling missing values, data formatting, and feature engineering.

Data analysts can best utilize data wrangling in Python to ensure the data is reliable, consistent, and suitable for analysis. In this video, Gaelim shares 12 essential Data Wrangling functions in Python to be familiar with.

*****Video Details*****
00:00 Introduction
00:38 Dataset 1
00:42 Libraries to import
01:16 Dataset 2
01:35 Merging data set
02:13 Droping columns
02:44 Fill missing values
03:15 Replacing missing values
03:44 Replacing a specific value
04:25 Replacing a specific string
05:32 Creating a column with sum of two other columns
06:02 Grouping data
06:47 Add per group back to the dataset
07:23 Creating a pivot table
07:59 Renaming a column
08:20 Comparing values

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