How to append dataframes inside a for loop in Python

How to append dataframes inside a for loop in Python

To append DataFrames inside a for loop in Python, you can use the pandas.concat function to concatenate the DataFrames vertically. Here's an example:

import pandas as pd # Create an empty DataFrame to store the concatenated data result_df = pd.DataFrame() # Assuming you have a list of DataFrames, you can iterate through them # and concatenate them to the result_df for i in range(5): # Replace with your actual loop conditions # Assuming df is your current DataFrame in the loop df = pd.DataFrame({'A': [i], 'B': [i * 2]}) # Concatenate the current DataFrame to the result_df result_df = pd.concat([result_df, df], ignore_index=True) # Print the result print(result_df)

In this example:

  • We create an empty DataFrame result_df to store the concatenated data.
  • Inside the for loop, we create a sample DataFrame df with some dummy data.
  • We use pd.concat to concatenate the current DataFrame (df) to the result_df. The ignore_index=True parameter is used to reset the index of the resulting DataFrame.
  • After the loop, result_df contains the concatenated data.

Note that using pd.concat inside a loop can be less efficient for large datasets. In such cases, consider appending DataFrames to a list within the loop and then using a single pd.concat operation outside the loop for better performance.

import pandas as pd # Create an empty list to store DataFrames dfs = [] # Assuming you have a list of DataFrames, you can iterate through them # and append them to the list for i in range(5): # Replace with your actual loop conditions # Assuming df is your current DataFrame in the loop df = pd.DataFrame({'A': [i], 'B': [i * 2]}) # Append the current DataFrame to the list dfs.append(df) # Concatenate all DataFrames in the list result_df = pd.concat(dfs, ignore_index=True) # Print the result print(result_df)

This approach is generally more efficient when dealing with a larger number of DataFrames.

Examples

  1. Python pandas append dataframes in a for loop:

    # Code import pandas as pd dfs = [] for i in range(5): data = {'Column1': [i], 'Column2': [i * 2]} df = pd.DataFrame(data) dfs.append(df) result_df = pd.concat(dfs, ignore_index=True)

    Description: Appends dataframes created in a for loop to a list and then concatenates them using pd.concat().

  2. Python pandas append dataframes with different columns:

    # Code import pandas as pd dfs = [] for i in range(5): data = {'Column1': [i], 'Column2': [i * 2]} df = pd.DataFrame(data) dfs.append(df) result_df = pd.concat(dfs, ignore_index=True, sort=False)

    Description: Appends dataframes with different columns and concatenates them while ignoring the column order.

  3. Python pandas append dataframes with common columns:

    # Code import pandas as pd common_columns = ['Column1', 'Column2'] result_df = pd.DataFrame(columns=common_columns) for i in range(5): data = {'Column1': [i], 'Column2': [i * 2]} df = pd.DataFrame(data) result_df = result_df.append(df, ignore_index=True)

    Description: Appends dataframes with common columns using the DataFrame.append() method.

  4. Python pandas append dataframes with different row indexes:

    # Code import pandas as pd dfs = [] for i in range(5): data = {'Column1': [i], 'Column2': [i * 2]} df = pd.DataFrame(data) dfs.append(df) result_df = pd.concat(dfs, ignore_index=True)

    Description: Appends dataframes with different row indexes and concatenates them using pd.concat().

  5. Python pandas append dataframes with conditional check:

    # Code import pandas as pd dfs = [] for i in range(5): data = {'Column1': [i], 'Column2': [i * 2]} df = pd.DataFrame(data) if some_condition: dfs.append(df) result_df = pd.concat(dfs, ignore_index=True)

    Description: Appends dataframes to the list based on a conditional check and concatenates them using pd.concat().

  6. Python pandas append dataframes with additional columns:

    # Code import pandas as pd dfs = [] for i in range(5): data = {'Column1': [i], 'Column2': [i * 2]} df = pd.DataFrame(data) df['AdditionalColumn'] = 'Value' dfs.append(df) result_df = pd.concat(dfs, ignore_index=True)

    Description: Appends dataframes with additional columns and concatenates them using pd.concat().

  7. Python pandas append dataframes with duplicate columns:

    # Code import pandas as pd dfs = [] for i in range(5): data = {'Column1': [i], 'Column2': [i * 2]} df = pd.DataFrame(data) df['Column1'] = df['Column1'] * 2 # Create a duplicate column dfs.append(df) result_df = pd.concat(dfs, ignore_index=True)

    Description: Appends dataframes with duplicate columns and concatenates them using pd.concat().

  8. Python pandas append dataframes with renaming columns:

    # Code import pandas as pd dfs = [] for i in range(5): data = {'Column1': [i], 'Column2': [i * 2]} df = pd.DataFrame(data) df = df.rename(columns={'Column1': 'NewColumn1', 'Column2': 'NewColumn2'}) dfs.append(df) result_df = pd.concat(dfs, ignore_index=True)

    Description: Appends dataframes and renames columns before concatenating them using pd.concat().

  9. Python pandas append dataframes with column-wise aggregation:

    # Code import pandas as pd dfs = [] for i in range(5): data = {'Column1': [i], 'Column2': [i * 2]} df = pd.DataFrame(data) dfs.append(df) result_df = pd.concat(dfs, axis=1)

    Description: Appends dataframes and aggregates them column-wise using pd.concat() with axis=1.

  10. Python pandas append dataframes with row-wise aggregation:

    # Code import pandas as pd dfs = [] for i in range(5): data = {'Column1': [i], 'Column2': [i * 2]} df = pd.DataFrame(data) dfs.append(df) result_df = pd.concat(dfs, ignore_index=True, axis=1).transpose().reset_index(drop=True)

    Description: Appends dataframes and aggregates them row-wise using pd.concat() with axis=1 and then transposes and resets the index.

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