Learn essential Python Pandas data science skills in this comprehensive tutorial. Explore techniques for reading CSV and Excel files, sorting and filtering data, performing groupby operations, and handling large datasets. Master fundamental concepts including data loading, column manipulation, conditional filtering, and aggregate statistics. Practice with real-world examples and gain hands-on experience in data analysis and manipulation using the powerful Pandas library.
Complete Python Pandas Data Science Tutorial - Reading CSV-Excel Files, Sorting, Filtering, Groupby
Keith Galli via YouTube
Overview
Syllabus
- Why Pandas?
- Installing Pandas
- Getting the data used in this video
- Loading the data into Pandas CSVs, Excel, TXTs, etc.
- Reading Data Getting Rows, Columns, Cells, Headers, etc.
- Iterate through each Row
- Getting rows based on a specific condition
- High Level description of your data min, max, mean, std dev, etc.
- Sorting Values Alphabetically, Numerically
- Making Changes to the DataFrame
- Adding a column
- Deleting a column
- Summing Multiple Columns to Create new Column.
- Rearranging columns
- Saving our Data CSV, Excel, TXT, etc.
- Filtering Data based on multiple conditions
- Reset Index
- Regex Filtering filter based on textual patterns
- Conditional Changes
- Aggregate Statistics using Groupby Sum, Mean, Counting
- Working with large amounts of data setting chunksize
Taught by
Keith Galli
