Скачать или смотреть How to Use Truncated Filenames as Column Headers in Pandas DataFrame from Multiple CSV Files

Learn how to create a Pandas DataFrame from multiple CSV files, using truncated filenames as headers for specific date and close columns.
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How to Use Truncated Filenames as Column Headers in Pandas DataFrame from Multiple CSV Files

When working with financial data, it’s common to encounter multiple CSV files for different tickers, each containing relevant information such as date, open, high, low, close, and volume. If you need to consolidate this data into a single DataFrame, you might want to use the truncated filenames as column headers and set the date as the index. This can be a bit challenging, but fear not! In this guide, we'll walk you through how to achieve it using Python's Pandas library.

The Problem: Combining Multiple CSV Files

Let’s say you have several CSV files named like this:

ZZZ.TO.csv

FOO.TO.csv

ZOMD.V.csv

Each of these files contains multiple rows of stock data, but you are specifically interested in the "Date" and "Close" columns from each file. Furthermore, you want to set the "Date" column as the index of your resulting DataFrame and have each ticker symbol displayed as column headers.

The Goal: Create a Unified DataFrame

After processing, your final DataFrame should look something like this:

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The Solution: Step-by-Step Guide

Here’s how to achieve this using Python and Pandas:

1. Import Required Libraries

First, ensure that you have Pandas installed. Then, you can start by importing the necessary libraries.

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2. Define Your Data Path

Specify the path where your CSV files are stored. Use pathlib for handling path operations elegantly.

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3. Initialize a Dictionary to Store Data

Create an empty dictionary to hold your data for each ticker.

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4. Read and Process Each CSV File

Loop through each CSV file, read the relevant columns, and store them in the dictionary using the truncated filename as the key.

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5. Concatenate the Data into a Single DataFrame

Finally, combine all your data into a single DataFrame using pd.concat(), aligning by the index.

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Output Your DataFrame

To see the results, you can print the DataFrame:

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Summary of Output

The resulting DataFrame will have "Date" as an index and each ticker as a column, with the corresponding closing prices:

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Conclusion

By following this guide, you can easily consolidate multiple CSV files into a single Pandas DataFrame, appropriately using truncated filenames as column headers. This can greatly simplify your data analysis process, allowing for more organized and efficient handling of financial datasets.

Feel free to experiment and modify the code to suit your specific needs. Happy coding!