Working with data in Python for the Aspiring Data Scientists
summary
Shows practical Python data work using Jupyter Notebooks with pandas DataFrame plus matplotlib/Seaborn, loading a zipped CSV of 2.7 million rows (~300–400MB) into memory in seconds on a 32GB laptop, warns about memory limits and selecting columns to reduce usage. Demonstrates type conversion, rename/drop, loc/iloc filtering, groupby max, updating a bad value (Rory McIlroy to 318.8), plotting histograms/lines, exporting CSV, and options for writing to SQL Server (pyodbc, Spark connector) and using Modin for speed.
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chapters (6)
- 0:00Start
- 10:23Display only selected columns
- 12:30DDL Modifications
- 15:43Rename a column
- 23:28Altering/Manipulating Data Changing Data
- 24:37ding a new column
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