Updating a DataFrame with New CSV Files

In this tutorial, we will explore how to dynamically update a Pandas DataFrame with the contents of new CSV files added to a specified folder. This approach is particularly useful when working with large datasets that are periodically updated.

Understanding the Problem

The current implementation reads all CSV files at once and stores them in a single DataFrame. However, this approach has limitations when dealing with dynamic data updates. To overcome this, we need to create a system that detects new CSV files added to a folder and appends their contents to an existing DataFrame.

Prerequisites

Before diving into the solution, make sure you have the following:

• Python 3.x installed on your system
• Pandas library installed (using pip install pandas)
• Hugo-powered website for publishing (not necessary for this tutorial)

Step 1: Setting Up the Environment

Create a new folder to store the CSV files, and add some sample data to it. You can use tools like Excel or LibreOffice Calc to create your dataset.

import os
import glob
import pandas as pd

Set the path to the CSV file folder:

path_csv = r"C:\Users\Username\CSVFILES"

Reading Existing Dataframe

To update the DataFrame with new data, we need to read an existing DataFrame first. This can be done by loading a previously saved CSV file from another folder.

# Path to the existing DataFrame
path_saved_df = r"C:/Users/Username/saved_csv"

# File name of the existing DataFrame
filename = "my_old_files.csv"

# Load the existing DataFrame
df_old = pd.read_csv(path_saved_df + '/' + filename, sep=";")

Detecting New CSV Files

To detect new CSV files added to the folder, we use glob.glob() to list all CSV files in the specified path. The max() function is used with os.path.getctime() as the key to find the latest addition.

# List of all CSV files in the folder
list_of_csv = glob.glob(path_csv + "\\\\*.csv")

# Find the latest CSV file added to the folder
latest_csv = max(list_of_csv, key=os.path.getctime)

Reading New File and Updating DataFrame

To update the existing DataFrame with the new CSV file’s contents, we use pd.concat().

# Read the new CSV file into a new DataFrame
new_file = pd.read_csv(latest_csv, sep=";", encoding="iso-8859-1")

# Concatenate the new DataFrame with the existing one
New_df = pd.concat([df_old, new_file])

Putting It All Together

Now that we’ve covered each step individually, let’s put them all together in a single function.

import os
import glob
import pandas as pd

def update_dataframe(path_csv):
    # Path to the existing DataFrame
    path_saved_df = r"C:/Users/Username/saved_csv"

    # File name of the existing DataFrame
    filename = "my_old_files.csv"

    # Load the existing DataFrame
    df_old = pd.read_csv(path_saved_df + '/' + filename, sep=";")

    # List of all CSV files in the folder
    list_of_csv = glob.glob(path_csv + "\\\\*.csv")

    # Find the latest CSV file added to the folder
    latest_csv = max(list_of_csv, key=os.path.getctime)

    # Read the new CSV file into a new DataFrame
    new_file = pd.read_csv(latest_csv, sep=";", encoding="iso-8859-1")

    # Concatenate the new DataFrame with the existing one
    New_df = pd.concat([df_old, new_file])

    return New_df

# Example usage:
New_df = update_dataframe(r"C:\Users\Username\CSVFILES")
print(New_df)

This code will create a new DataFrame by reading an existing CSV file and appending it to a new one. The new file is detected using the glob library, which lists all CSV files in the specified folder.

Conclusion

In this tutorial, we explored how to dynamically update a Pandas DataFrame with the contents of new CSV files added to a specified folder. We used various techniques such as reading existing DataFrames and detecting new files using the glob library.

Additional Tips:

• When dealing with large datasets or periodic updates, consider using more efficient data structures like NumPy arrays or Pandas Series.
• To handle missing values in CSV files, you can use the na_values parameter when reading a CSV file.
• For more advanced data analysis tasks, consider exploring other libraries such as Matplotlib or Scikit-learn.

Future Improvements:

• Implement error handling to deal with cases where new files cannot be read due to missing dependencies or other issues.
• Explore using more efficient algorithms for updating DataFrames, such as merging or joining operations.

Last modified on 2024-07-22