Ultimate Data Cleaning Tutorial in Python: Step-by-Step Guide with Jupyter Notebook
Learn how to clean data efficiently in this Ultimate Data Cleaning Tutorial in Python. This step-by-step guide covers handling missing values, removing duplicates, dealing with outliers, converting data types, and more—all using Jupyter Notebook. Plus, as a bonus, discover how to create simple and effective visualizations to analyze your cleaned data, including bar charts, histograms, and scatter plots. Perfect for beginners and advanced users looking to master data preprocessing for analysis and machine learning. Start your journey to cleaner, more reliable data today!
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⏳ Timestamps ⏳
00:00 Welcome, Please Subscribe, & Introduction
01:05 Open Jupyter Notebook and upload file to notebook
01:49 Review the dataset
02:50 Open new workbook to start
03:28 Step 1 – Start
04:13 Load the dataset & preview dataset (code)
06:25 Step 2: Identify Mission Values
8:22 Step 3: How to handle Mission Values
12:02 Step 4: Check and remove duplicate rows
12:28 Stripping any spacing in the dataset
12:57 Convert text in the dataset to lower case
14:21 Remove duplicates by a variable name
17:02 Step 5: Handle outliers and what are outliers
23:30 Step 6: Convert Data types (using datetime formatting)
24:51 Step 7: Encode Categorical Variables (Convert to numeric)
29:25 Save the cleaned dataset to a csv file.
31:03 Start the visualizations from the cleaned dataset
31:20 Import the cleaned dataset & review
32:56 Bar chart visualization
35:08 Histogram visualization
33:20 Line plot visualization
37:40 Scatter Plot visualization
38:41 Thank you, and please subscribe to my channel. Thank you for your support.
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