From Spreadsheets to DataFrames
summary
Quick yes or no: this walks an Excel user through concrete steps not platitudes. It claims to replace tedious spreadsheets by actually downloading SEC data, parsing and transforming with Requests and Pandas, and building a small Flask app that visualizes results. It demos environment setup with Docker and an Airflow scheduling example, shows numbers (millions of rows loaded in seconds), and ends with a refactored script that works end to end so you can replicate it. hands on with Pandas
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chapters (47)
- 0:00Start
- 0:38Section One
- 2:33Real World Python Example
- 7:33Activate Spreadsheets to Data Frames
- 9:24Anaconda Navigator
- 10:02Using an Ide
- 13:36Comments
- 14:00Keywords
- 15:59Text Functions
- 16:56Input Function
- 19:27Data Types
- 20:37Lists
- 21:55Comparison Operators
- 22:20Pre-Installed Add-Ins
- 22:32Excel Add-Ins
- 24:32The Python Package Index
- 26:18File Types
- 27:33Open a File in Python
- 29:32Data Structures
- 30:16Dictionaries
- 32:38For Loop
- 33:27String Formatting
- 35:33Sneak Peek of Pandas
- 36:12Error Messages
- 36:22Fix Your Error Messages
- 37:41Install Our Third-Party Packages
- 38:19Run the Jupiter Notebook
- 40:31Types of Data Types
- 41:50Variable Names
- 43:25Built-In Functions
- 46:35Methods
- 51:12Indexing and Slicing
- 57:49Part Two of the Notebooks
- 58:57Jupiter Notebooks
- 1:00:50Lists and Dictionaries
- 1:05:52Standard Library
- 1:09:18Path To Create a File Path Dynamically
- 1:10:04Check if the File Exists
- 1:10:49Opening a File
- 1:13:59Find the Location of a Value in a String
- 1:19:17Preferred Method in Python
- 1:21:55For Loops
- 1:26:51Csv Reader
- 1:27:04The Csv Reader
- 1:27:35Enumerate
- 1:35:06Pretty Print
- 1:39:39Functions
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