This course teaches learners how to utilize the Python Pandas Data Science Library for various data manipulation tasks. By the end of the course, students will be able to read CSV and Excel files, sort and filter data, use the groupby function for aggregate statistics, and work with large datasets efficiently. The course covers skills such as installing Pandas, loading data, reading specific data elements, making conditional changes, and filtering based on multiple conditions. The teaching method involves practical demonstrations and examples, making it suitable for individuals looking to enhance their data science skills using Python Pandas.
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
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