Adding Data to Existing CSV Files with pandas in Python

Understanding the Process:

• pandas: This library provides powerful data structures like DataFrames for handling tabular data.
• CSV (Comma-Separated Values): A common file format for storing data in plain text, where each row represents a record and columns are separated by commas (or other delimiters).

Steps to Append Data:

1. Import Libraries:

   import pandas as pd
   

2. existing_data = pd.read_csv("your_existing_csv.csv")
   

3. Prepare New Data:

   • If you have the data in another DataFrame:

     new_data = your_new_pandas_dataframe
     

   • If you need to create the new data on the fly:

     new_data = pd.DataFrame({"column1": [values], "column2": [values]})
     

4. Append Data (Key Step): Use the to_csv() method of the existing DataFrame (existing_data) with the mode='a' parameter to specify append mode.

   existing_data.to_csv("your_existing_csv.csv", mode='a', header=False, index=False)  # Append rows
   

   • header=False ensures the header isn't written again (assuming it exists already).
   • index=False prevents appending the DataFrame's index as an extra column.

Explanation of Parameters:

• mode='a': This tells to_csv() to open the file in append mode, adding new data to the end instead of overwriting it.
• header=False: Prevents the header row from being written again during the append, assuming your existing CSV already has a header.
• index=False: Excludes the DataFrame's index from being written as an extra column in the CSV.

Complete Example:

import pandas as pd

existing_data = pd.read_csv("your_existing_csv.csv")

new_data = pd.DataFrame({"column1": [10, 20, 30], "column2": ["A", "B", "C"]})

existing_data.to_csv("your_existing_csv.csv", mode='a', header=False, index=False)

new_data.to_csv("your_existing_csv.csv", mode='a', header=False, index=False)  # Append new_data

This code will:

1. Read the existing CSV data into existing_data.
2. Create a new DataFrame new_data with example data.
3. Append new rows from existing_data (avoiding duplicate headers and index) to the CSV.
4. Further append the rows from new_data to the CSV.

Important Considerations:

• Ensure the column order (and data types, if applicable) in new_data matches the existing CSV.
• If the CSV doesn't have a header row initially, include header=True in the to_csv() call when appending the first data.

import pandas as pd

# Sample existing CSV data (assuming it has a header row)
existing_data_dict = {'Name': ['Alice', 'Bob'], 'Age': [25, 30]}
existing_df = pd.DataFrame(existing_data_dict)
existing_df.to_csv("existing_data.csv", index=False)  # Save initial data

# New data to append
new_data_dict = {'Name': ['Charlie', 'David'], 'Age': [40, 50], 'City': ['New York', 'Los Angeles']}  # New column (City)
new_df = pd.DataFrame(new_data_dict)

# Append data, handling headers and column order
existing_df = pd.read_csv("existing_data.csv")  # Read existing data back
if existing_df.shape[0] == 0:  # Check if CSV is empty (no header yet)
    existing_df.to_csv("existing_data.csv", header=True, index=False)  # Write header for first append
existing_df = pd.concat([existing_df, new_df], ignore_index=True)  # Concatenate DataFrames
existing_df.to_csv("existing_data.csv", mode='a', header=False, index=False)  # Append to existing CSV

print("Data successfully appended to existing_data.csv!")

1. Creates sample existing data in a DataFrame existing_df and saves it to "existing_data.csv".
2. Creates new_df with new data, including an extra column ("City").
3. Checks if the CSV is empty (no header). If so, it writes the header once using to_csv() with header=True.
4. Concatenates existing_df and new_df using pd.concat(), ensuring proper column order.
5. Appends the combined DataFrame to the CSV using to_csv() with mode='a', header=False, and index=False.
6. Prints a success message.

This example demonstrates how to handle appending data to an existing CSV while maintaining column order and headers.

Using pd.concat() with ignore_index=True:

This method involves concatenating the existing DataFrame (existing_data) with the new data (new_data) and then writing the combined DataFrame to the CSV file.

import pandas as pd

# ... (existing_data and new_data creation as before)

combined_df = pd.concat([existing_data, new_data], ignore_index=True)  # Concatenate DataFrames
combined_df.to_csv("existing_data.csv", index=False)  # Write to CSV with new index

print("Data appended using pd.concat()!")

Explanation:

• pd.concat() combines two DataFrames (existing and new) along a specified axis (default is 0 for rows).
• ignore_index=True prevents the creation of a new index for the combined DataFrame. This ensures the resulting CSV file has a continuous index starting from 0.
• combined_df.to_csv("existing_data.csv", index=False) writes the combined DataFrame to the CSV file, excluding the index column.

Using a loop (less efficient for large datasets):

This method iterates through the rows of the new data and appends them one by one to the existing DataFrame. Finally, the entire DataFrame is written to the CSV file.

import pandas as pd

# ... (existing_data and new_data creation as before)

for index, row in new_data.iterrows():
    existing_data = existing_data.append(row, ignore_index=True)

existing_data.to_csv("existing_data.csv", index=False)

print("Data appended using loop!")

• new_data.iterrows() iterates through each row of new_data.
  • index holds the index of the current row.
  • row represents the actual data in the current row.
• Inside the loop, existing_data.append(row, ignore_index=True) appends the current row (row) to the existing DataFrame and avoids creating a new index for the appended row.

Choosing the Right Method:

• pd.concat() is generally the preferred method for efficiency and clarity, especially for larger datasets.
• The loop method can be useful for specific scenarios where you need to perform additional processing on each row before appending. However, it's less efficient for large datasets.

I hope these alternate methods provide you with additional flexibility for adding pandas data to existing CSV files!