Use TQDM Progress Bar with Pandas

Use TQDM Progress Bar with Pandas

You can use the tqdm library to add a progress bar when iterating over Pandas DataFrame rows using the apply() function or any other iteration method. Here's how to do it:

  1. Install TQDM: If you haven't already, you need to install the tqdm library:

    pip install tqdm

  2. Import Libraries: Import the necessary libraries at the beginning of your script or Jupyter Notebook:

    import pandas as pd from tqdm import tqdm tqdm.pandas() # Enables progress_apply for Pandas DataFrame

  3. Create or Load DataFrame: Create or load your Pandas DataFrame.

  4. Use tqdm with apply() or Iteration: You can use tqdm with the apply() function to iterate over rows and display a progress bar:

    def process_row(row): # Your processing logic here return row df = pd.DataFrame(...) # Create or load your DataFrame # Use progress_apply to apply a function with tqdm progress bar df_processed = df.progress_apply(process_row, axis=1)

    Replace process_row() with your custom row processing function.

Alternatively, you can use tqdm with a regular loop or any other iteration method:

df = pd.DataFrame(...) # Create or load your DataFrame # Iterate over rows with tqdm progress bar for index, row in tqdm(df.iterrows(), total=len(df)): # Your processing logic here

In both cases, the tqdm progress bar will show the progress of your operations on the DataFrame rows.

Remember that using progress bars might slow down the execution slightly due to the overhead of updating the progress display. However, they provide valuable feedback, especially for longer operations or when working with large datasets.

Examples

  1. "TQDM Progress Bar with Pandas DataFrame Apply"

    • Description: Use tqdm to monitor progress when applying a function to a Pandas DataFrame.
    • Code:
      # First, make sure tqdm is installed !pip install tqdm 
      import pandas as pd from tqdm import tqdm # Register tqdm with pandas tqdm.pandas() df = pd.DataFrame({ 'numbers': range(10), }) def square(x): return x ** 2 # Use tqdm's progress_apply df['squared'] = df['numbers'].progress_apply(square) print(df)

  2. "Using TQDM with Pandas Groupby"

    • Description: Display a progress bar when iterating over Pandas DataFrame groups.
    • Code:
      import pandas as pd from tqdm import tqdm # Register tqdm with pandas tqdm.pandas() data = { 'group': ['A', 'B', 'A', 'B', 'A'], 'value': [1, 2, 3, 4, 5], } df = pd.DataFrame(data) grouped = df.groupby('group') # Use tqdm to iterate over groups with a progress bar for name, group in tqdm(grouped, desc='Processing groups'): print(name, group)

  3. "Using TQDM Progress Bar with Pandas Iterrows"

    • Description: Apply tqdm progress bar when using iterrows to loop through a Pandas DataFrame.
    • Code:
      import pandas as pd from tqdm import tqdm df = pd.DataFrame({ 'numbers': range(100), }) results = [] # Use tqdm with iterrows to track progress for index, row in tqdm(df.iterrows(), total=len(df), desc='Processing rows'): results.append(row['numbers'] ** 2) print(results[:10]) # Display first 10 results

  4. "TQDM with Pandas DataFrame to List"

    • Description: Display a progress bar while converting a Pandas DataFrame into a list of values.
    • Code:
      import pandas as pd from tqdm import tqdm df = pd.DataFrame({ 'values': range(100), }) # Use tqdm to create a list with progress tracking result_list = [row['values'] for row in tqdm(df.itertuples(), total=len(df), desc='Converting to list')] print(result_list[:10]) # Display first 10 elements

  5. "Tracking Progress with TQDM and Pandas Read CSV"

    • Description: Monitor progress while reading a large CSV file with Pandas.
    • Code:
      import pandas as pd from tqdm import tqdm # Use tqdm with chunksize to track progress during CSV read chunk_size = 1000 file_path = 'large_dataset.csv' df = pd.concat([chunk for chunk in tqdm(pd.read_csv(file_path, chunksize=chunk_size), desc='Reading CSV')]) print(df.head()) # Display the first few rows of the DataFrame

  6. "Using TQDM with Pandas Merge Operations"

    • Description: Track progress when merging large Pandas DataFrames with tqdm.
    • Code:
      import pandas as pd from tqdm import tqdm df1 = pd.DataFrame({ 'key': range(1000), 'value1': [x * 2 for x in range(1000)], }) df2 = pd.DataFrame({ 'key': range(500, 1500), 'value2': [x * 3 for x in range(500, 1500)], }) # Monitor progress during merge operation df_merged = tqdm( pd.merge(df1, df2, on='key', how='inner'), total=1000, desc='Merging DataFrames' ) print(df_merged.head()) # Display first few rows of the merged DataFrame

  7. "Using TQDM with Pandas for Data Cleaning"

    • Description: Implement tqdm to track progress during data cleaning operations with Pandas.
    • Code:
      import pandas as pd from tqdm import tqdm df = pd.DataFrame({ 'names': ['Alice', 'Bob', 'Charlie', 'Dana', 'Eve', ''], 'ages': [25, 30, 35, 40, 45, 50], }) def clean_name(name): return name.strip().capitalize() # Use tqdm to track progress during data cleaning df['cleaned_names'] = df['names'].progress_apply(clean_name, desc='Cleaning Names') print(df) # Display cleaned DataFrame

  8. "Using TQDM with Pandas to Write CSV"

    • Description: Use tqdm to track the progress of writing a large Pandas DataFrame to a CSV file.
    • Code:
      import pandas as pd from tqdm import tqdm df = pd.DataFrame({ 'col1': range(100000), 'col2': [x * 2 for x in range(100000)], }) file_path = 'output.csv' # Track progress while writing to CSV with tqdm and custom hook with tqdm(total=len(df), desc='Writing to CSV') as pbar: df.to_csv( file_path, index=False, chunksize=1000, line_terminator='\n', callback=lambda: pbar.update(1000) )

  9. "Using TQDM with Pandas for Data Transformation"

    • Description: Use tqdm to track progress when performing large-scale data transformations with Pandas.
    • Code:
      import pandas as pd from tqdm import tqdm df = pd.DataFrame({ 'value': range(100), }) # Track progress when applying transformation df['transformed_value'] = df['value'].progress_apply(lambda x: x * 2, desc='Transforming Data') print(df.head()) # Display the first few transformed rows

  10. "Using TQDM with Pandas for Data Analysis"

    • Description: Use tqdm to track progress while performing various data analysis tasks with Pandas.
    • Code:
      import pandas as pd from tqdm import tqdm df = pd.DataFrame({ 'category': ['A', 'B', 'C', 'D', 'E'] * 20, 'value': [x for x in range(100)], }) # Track progress when performing analysis with groupby and aggregation grouped_data = df.groupby('category').progress_apply( lambda group: { 'mean_value': group['value'].mean(), 'sum_value': group['value'].sum(), }, desc='Analyzing Data' ) print(grouped_data) # Display analyzed data

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