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12 Data Wrangling Functions In Python That You Should Know

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 **** Learning The Microsoft Stack? **** FREE Courses - https://bit.ly/45fu3tw FREE Resources - https://bit.ly/455Hw6O Enterprise DNA On-Demand - app.enterprisedna.co Enterprise DNA Subscriptions - app.enterprisedna.co/pricing Enterprise DNA Workouts - https://bit.ly/41F9weH Enterprise DNA Events - https://bit.ly/3pOWNJ2 #EnterpriseDNA #Python #PythonTutorial #DataWrangling