python - Use TQDM Progress Bar with Pandas

Python - Use TQDM Progress Bar with Pandas

You can use the tqdm library to create progress bars for operations on Pandas DataFrames. The tqdm library provides a convenient way to visualize the progress of iterative tasks.

Here's an example of how you can use tqdm with Pandas:

import pandas as pd from tqdm import tqdm # Sample DataFrame data = {'Column1': range(1000000), 'Column2': range(1000000, 2000000)} df = pd.DataFrame(data) # Function that performs some operation on each row of the DataFrame def process_row(row): # Simulating a time-consuming operation return row['Column1'] + row['Column2'] # Apply the function to each row with a tqdm progress bar tqdm.pandas(desc="Processing rows") df['Result'] = df.progress_apply(process_row, axis=1)

In this example:

  • We create a sample Pandas DataFrame (df) with two columns.
  • The process_row function is a placeholder for the operation you want to perform on each row. It simply adds the values in 'Column1' and 'Column2'.
  • tqdm.pandas() is used to wrap the progress_apply method, which applies the process_row function to each row of the DataFrame with a progress bar.

Make sure to install the tqdm library if you haven't already:

pip install tqdm

Adjust the code based on your specific use case and the operation you want to perform on the DataFrame.

Examples

  1. How to use TQDM with Pandas to show a progress bar for DataFrame processing?

    # Example: Using TQDM with Pandas to show a progress bar for DataFrame processing import pandas as pd from tqdm import tqdm # Create a sample DataFrame df = pd.DataFrame({'Column1': range(100000), 'Column2': range(100000, 200000)}) # Use tqdm.pandas() to enable tqdm for Pandas operations tqdm.pandas(desc="Processing DataFrame") # Apply a function to each row of the DataFrame with progress bar df['New_Column'] = df.progress_apply(lambda row: row['Column1'] + row['Column2'], axis=1)

    Description: This example demonstrates how to use TQDM with Pandas to show a progress bar during DataFrame processing. The tqdm.pandas() function is used to enable tqdm for Pandas operations, and progress_apply is used for applying a function to each row of the DataFrame.

  2. How to display a TQDM progress bar for iterating over Pandas DataFrame rows?

    # Example: Displaying a TQDM progress bar for iterating over Pandas DataFrame rows import pandas as pd from tqdm import tqdm # Create a sample DataFrame df = pd.DataFrame({'Column1': range(100000), 'Column2': range(100000, 200000)}) # Use tqdm.pandas() to enable tqdm for Pandas operations tqdm.pandas(desc="Iterating over DataFrame rows") # Iterate over DataFrame rows with progress bar for index, row in tqdm(df.iterrows(), total=len(df), desc="Processing rows"): # Your processing logic here df.at[index, 'New_Column'] = row['Column1'] + row['Column2']

    Description: This example shows how to display a TQDM progress bar while iterating over Pandas DataFrame rows using tqdm.pandas() and tqdm(df.iterrows()).

  3. How to use TQDM with Pandas apply function for parallel processing?

    # Example: Using TQDM with Pandas apply function for parallel processing import pandas as pd from tqdm import tqdm from pandarallel import pandarallel # Create a sample DataFrame df = pd.DataFrame({'Column1': range(100000), 'Column2': range(100000, 200000)}) # Initialize pandarallel for parallel processing pandarallel.initialize(progress_bar=True) # Use tqdm.pandas() to enable tqdm for Pandas operations tqdm.pandas(desc="Applying function in parallel") # Apply a function to each row of the DataFrame in parallel with progress bar df['New_Column'] = df.parallel_apply(lambda row: row['Column1'] + row['Column2'], axis=1)

    Description: This example demonstrates using TQDM with Pandas apply function for parallel processing. Pandarallel is used for parallel apply, and tqdm.pandas() is used for the progress bar.

  4. How to show a progress bar for Pandas read_csv using TQDM?

    # Example: Showing a progress bar for Pandas read_csv using TQDM import pandas as pd from tqdm import tqdm # Use tqdm.pandas() to enable tqdm for Pandas operations tqdm.pandas(desc="Reading CSV") # Read CSV with TQDM progress bar df = pd.read_csv('your_file.csv', iterator=True, chunksize=1000) df = pd.concat(tqdm(df, desc="Processing CSV"))

    Description: This example illustrates how to show a progress bar for Pandas read_csv using TQDM. The tqdm(df) is used to show the progress bar while reading the CSV in chunks.

  5. How to apply TQDM progress bar to Pandas groupby operations?

    # Example: Applying TQDM progress bar to Pandas groupby operations import pandas as pd from tqdm import tqdm # Create a sample DataFrame df = pd.DataFrame({'Category': ['A', 'B', 'A', 'B'], 'Value': [10, 20, 30, 40]}) # Use tqdm.pandas() to enable tqdm for Pandas operations tqdm.pandas(desc="Groupby and Aggregation") # Apply groupby operation with progress bar result_df = df.groupby('Category')['Value'].progress_sum()

    Description: This example shows how to apply a TQDM progress bar to Pandas groupby operations using tqdm.pandas().

  6. How to use TQDM with Pandas to monitor progress of DataFrame transformations?

    # Example: Using TQDM with Pandas to monitor progress of DataFrame transformations import pandas as pd from tqdm import tqdm # Create a sample DataFrame df = pd.DataFrame({'Column1': range(100000), 'Column2': range(100000, 200000)}) # Use tqdm.pandas() to enable tqdm for Pandas operations tqdm.pandas(desc="Transforming DataFrame") # Apply transformations to DataFrame with progress bar df['New_Column'] = df.progress_apply(lambda row: row['Column1'] + row['Column2'], axis=1)

    Description: This example demonstrates how to use TQDM with Pandas to monitor the progress of DataFrame transformations using tqdm.pandas().

  7. How to integrate TQDM with Pandas to visualize progress during merge operations?

    # Example: Integrating TQDM with Pandas to visualize progress during merge operations import pandas as pd from tqdm import tqdm # Create sample DataFrames df1 = pd.DataFrame({'ID': range(100000), 'Value1': range(100000, 200000)}) df2 = pd.DataFrame({'ID': range(50000, 150000), 'Value2': range(200000, 300000)}) # Use tqdm.pandas() to enable tqdm for Pandas operations tqdm.pandas(desc="Merging DataFrames") # Merge DataFrames with progress bar merged_df = df1.merge(df2, on='ID', how='inner', suffixes=('_left', '_right')).progress_apply(lambda row: row['Value1'] + row['Value2'], axis=1)

    Description: This example showcases how to integrate TQDM with Pandas to visualize progress during DataFrame merge operations using tqdm.pandas().

  8. How to use TQDM with Pandas to display progress for DataFrame filtering?

    # Example: Using TQDM with Pandas to display progress for DataFrame filtering import pandas as pd from tqdm import tqdm # Create a sample DataFrame df = pd.DataFrame({'Column1': range(100000), 'Column2': range(100000, 200000)}) # Use tqdm.pandas() to enable tqdm for Pandas operations tqdm.pandas(desc="Filtering DataFrame") # Apply filtering to DataFrame with progress bar filtered_df = df[df['Column1'] > 50000].progress_apply(lambda row: row['Column1'] + row['Column2'], axis=1)

    Description: This example demonstrates how to use TQDM with Pandas to display progress for DataFrame filtering using tqdm.pandas().

  9. How to add a TQDM progress bar to Pandas value_counts operation?

    # Example: Adding a TQDM progress bar to Pandas value_counts operation import pandas as pd from tqdm import tqdm # Create a sample DataFrame df = pd.DataFrame({'Category': ['A', 'B', 'A', 'B', 'A', 'C']}) # Use tqdm.pandas() to enable tqdm for Pandas operations tqdm.pandas(desc="Counting values") # Perform value_counts operation with progress bar value_counts_result = df['Category'].value_counts().progress_apply(lambda count: count * 2)

    Description: This example shows how to add a TQDM progress bar to the Pandas value_counts operation using tqdm.pandas().

  10. How to use TQDM with Pandas to visualize progress during DataFrame sorting?

    # Example: Using TQDM with Pandas to visualize progress during DataFrame sorting import pandas as pd from tqdm import tqdm # Create a sample DataFrame df = pd.DataFrame({'Column1': range(100000, 0, -1), 'Column2': range(200000, 100000, -1)}) # Use tqdm.pandas() to enable tqdm for Pandas operations tqdm.pandas(desc="Sorting DataFrame") # Sort DataFrame with progress bar sorted_df = df.sort_values(by='Column1').progress_apply(lambda row: row['Column1'] + row['Column2'], axis=1)

    Description: This example demonstrates how to use TQDM with Pandas to visualize progress during DataFrame sorting using tqdm.pandas().

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