Exporting Multiple CSV Files with Different Filenames in Python

Introduction

As a data enthusiast, working with historical stock data can be an exciting yet challenging task. In this article, we will explore how to export multiple CSV files using different filenames in Python.

Background

In the world of finance and data analysis, CSV (Comma Separated Values) files are a popular format for storing and sharing data. When dealing with large datasets or specific stock names, manually exporting each file can be time-consuming and prone to errors.

Python’s investpy library provides an easy-to-use interface for retrieving historical stock data from various sources. In this article, we will utilize this library to extract data for multiple stocks, generate separate CSV files, and export them with custom filenames.

Setting Up the Environment

Before we dive into the code, ensure you have Python installed on your system, along with the required libraries:

pip install investpy pandas

Creating a List of Stock Names

To start, create a list containing the stock names you want to extract data for. In this example, we will use two stocks: JFC and AAPL.

stocks_list = ['JFC', 'AAPL']

Exporting Data for Each Stock

Next, iterate through the stocks_list using a for loop. For each stock name, retrieve the historical data using investpy.get_stock_historical_data(). Then, generate a custom filename by concatenating ’extracted_’ with the stock name and appending ‘.csv’.

import investpy

for stock in stocks_list:
    df = investpy.get_stock_historical_data(stock=stock,
                                        country='philippines',
                                        from_date='25/11/2020',
                                        to_date='18/12/2020')
    
    # Generate a custom filename for the CSV file
    file_name = 'extracted_' + stock + '.csv'
    
    # Export the data to a CSV file with index=False to exclude row indexes
    df.to_csv(file_name, index=False)

Understanding the Code

Let’s break down the code snippet above:

1. We iterate through each stock name in the stocks_list.
2. For each stock, we use investpy.get_stock_historical_data() to retrieve its historical data.
3. We create a custom filename by concatenating ’extracted_’ with the stock name and appending ‘.csv’.
4. Finally, we export the retrieved data to a CSV file using df.to_csv(), excluding row indexes by setting index=False.

Handling Large Lists of Stock Names

When dealing with a large number of stock names (like 300+), manually typing each name can be tedious and error-prone. To simplify this process, consider the following approaches:

• Read from a CSV file: Store your list of stock names in a separate CSV file or Excel spreadsheet. Then, read this file using Python’s csv module to populate your stocks_list.
• Use a database: If you have access to financial databases, such as Yahoo Finance or Alpha Vantage, consider storing your list of stock names in their respective APIs or databases.
• Script Generation: Create a script that automatically generates the list of stocks from a predefined source.

Handling Errors and Exceptions

When working with external APIs like investpy, errors can occur. To handle these situations:

• Try-Except Block: Wrap your code in a try-except block to catch any exceptions that may occur during data retrieval or file export.
• Retrying Requests: Use Python’s built-in requests library and its ability to retry failed requests with exponential backoff.

Full Code Example

Here is the full code example including error handling:

import investpy
import pandas as pd
import csv
from pathlib import Path

# Define a list of stock names (can be read from a CSV file or database)
stocks_list = ['JFC', 'AAPL']

def get_historical_data(stock):
    try:
        # Retrieve historical data for the given stock
        df = investpy.get_stock_historical_data(
            stock=stock,
            country='philippines',
            from_date='25/11/2020',
            to_date='18/12/2020'
        )
        
        return df
    
    except investpy.exceptions.InvestPyException as e:
        print(f"An error occurred while retrieving data for {stock}: {e}")
    
    return None

def export_data(df, stock):
    try:
        # Generate a custom filename for the CSV file
        file_name = 'extracted_' + stock + '.csv'
        
        # Export the data to a CSV file with index=False to exclude row indexes
        df.to_csv(file_name, index=False)
    
    except Exception as e:
        print(f"An error occurred while exporting data for {stock}: {e}")

def main():
    # Iterate through each stock name in the list
    for stock in stocks_list:
        # Retrieve historical data for the current stock
        df = get_historical_data(stock)
        
        if df is not None:
            # Export the retrieved data to a CSV file
            export_data(df, stock)

if __name__ == "__main__":
    main()

Best Practices and Future Directions

When working with large datasets or multiple stock names, consider the following best practices:

• Use Pandas: Leverage Python’s pandas library for efficient data manipulation and analysis.
• Optimize API Requests: Minimize unnecessary API requests by caching results or using batch processing techniques.

For future directions, you can explore more advanced topics such as:

• Parallel Processing: Utilize libraries like joblib or dask to parallelize the export process for improved performance.
• Filesystem Organization: Organize your CSV files in a structured manner using directory hierarchies and naming conventions.

Last modified on 2024-09-27