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@MattMacarty ## 🐍 Python Aggregate Stock Data: Combine Multiple Price CSV Files into One Pandas DataFrame | Part 4 Welcome to Part 4 of the **Python Stock Analysis Course**! In this video, we tackle a fundamental data aggregation problem: consolidating all the individual stock price CSV files (downloaded in Part 3) into a **single, master Pandas DataFrame**. This unified DataFrame is necessary for any market-wide analysis, correlation, or comparison. You will learn how to read multiple files from a folder, select only the necessary column (Close or Adjusted Close), rename that column to the **ticker symbol**, and then use the `pd.concat` function to combine them horizontally along `axis=1`. ### 🎯 Key Learning Outcomes: 1. **Reading Multiple Files:** Write logic to efficiently read all valid CSV files from a specified local directory. 2. **Flexible Column Extraction:** Implement conditional logic to extract either the **Close** or **Adjusted Close** column based on user preference. 3. **Data Structuring:** Use Pandas to rename the extracted column to the specific **Ticker Symbol** to maintain clear data identification. 4. **Data Aggregation:** Master the use of **`pd.concat`** along `axis=1` to stitch all single-column DataFrames together into one large, cohesive DataFrame indexed by date. 5. **Output:** Save the final, aggregated closing price data to a master CSV file for reuse. ### ⏱️ Video Chapters (Jump Ahead!): 0:00 - Introduction & Review (From files to a single dataset) 0:54 - Defining the `get_closing_prices` Function 1:34 - **Reading All Valid CSV Files from the Folder Path** 2:14 - Conditional Logic: Extracting **Close vs. Adjusted Close** 3:19 - Renaming the Extracted Column to the **Ticker Symbol** 4:19 - **Concatenating DataFrames Horizontally (`pd.concat` on `axis=1`)** 5:12 - Saving the Final Aggregated Data to a Master CSV File 5:55 - Testing the Function on both Small and Large Datasets 6:30 - Preview of Part 5: Calculating Return Data ### 🔗 Course Series & Resources: * **Part 1 (Tickers List):** [https://youtu.be/bKUZrBAzqJs](https://youtu.be/bKUZrBAzqJs) * **Part 2 (Filtering):** [https://youtu.be/KKVZHKDEKFA](https://youtu.be/KKVZHKDEKFA) * **Part 3 (Download & Save):** [https://youtu.be/emHY55Svxac](https://youtu.be/emHY55Svxac) * **Get the Code:** https://github.com/mjmacarty/python-stock-analysis ***Disclaimer: This video is for educational purposes only. The information provided should not be construed as investment advice. *** \#Pandas \#PythonDataManipulation \#DataAggregation \#pd.concat \#PythonForFinance \#StockData \#Time-Series \#Part4
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