Cleaning Data with Pandas
Practical data cleaning, correlation analysis and data preparation with pandas.
In the real world, data is rarely organized in clean tables ready to be used directly in a machine learning model or for data analysis. Data found in practice is often messy, containing many missing values and other issues to resolve before drawing meaningful inferences.
Dataset used: Hotel Booking Demand (Kaggle) — ~120,000 rows, 32 columns
Data cleaning is the process of correcting or removing incorrect, missing, duplicate, and corrupted data from a given dataset.
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What's inside
6 sections- 1 Table of Contents
- 2 Course Overview
- 3 Introduction to Data Cleaning with Pandas
- 4 Correlation Analysis and Data Preparation
- 5 Reference Diagrams
- 6 Pandas Method Reference Tables
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