Filter Pandas DataFrame by time index

Filter Pandas DataFrame by time index

To filter a Pandas DataFrame by a time index, you can use boolean indexing based on the time values. Here's how you can do it:

Assuming you have a DataFrame with a time-based index, first make sure that the index is of the datetime data type. You can convert it using pd.to_datetime if it's not already in the correct format:

import pandas as pd # Sample DataFrame with a datetime index data = {'Value': [10, 20, 30, 40, 50]} dates = ['2023-09-01', '2023-09-02', '2023-09-03', '2023-09-04', '2023-09-05'] df = pd.DataFrame(data, index=pd.to_datetime(dates))

Now, you can filter the DataFrame based on the time index. For example, to filter rows for a specific date range:

# Filter DataFrame for a specific date range start_date = '2023-09-02' end_date = '2023-09-04' filtered_df = df[(df.index >= start_date) & (df.index <= end_date)] print(filtered_df)

This will output:

 Value 2023-09-02 20 2023-09-03 30 2023-09-04 40

You can adjust the start_date and end_date to specify the date range you want to filter.

If you want to filter by a specific time of day (e.g., between 9:00 AM and 3:00 PM), you can extract the time component from the index using .time and apply the filter accordingly:

# Filter DataFrame for a specific time range start_time = '09:00:00' end_time = '15:00:00' filtered_df = df[(df.index.time >= pd.to_datetime(start_time).time()) & (df.index.time <= pd.to_datetime(end_time).time())] print(filtered_df)

This code will filter the DataFrame for rows with timestamps within the specified time range. Adjust start_time and end_time as needed.

Remember to ensure that your DataFrame has a datetime index for these filtering operations to work correctly.

Examples

  1. Filtering Pandas DataFrame by a specific time range

    • Description: This query focuses on filtering a Pandas DataFrame to include only rows within a specified time range.
    • Code:
      import pandas as pd # Assuming 'df' is the DataFrame and 'start_time'/'end_time' are datetime objects filtered_df = df[(df.index >= start_time) & (df.index <= end_time)]

  2. Extracting rows from a Pandas DataFrame based on a specific date

    • Description: This query aims to extract rows from a Pandas DataFrame that match a specific date.
    • Code:
      import pandas as pd # Assuming 'df' is the DataFrame and 'specific_date' is a datetime object filtered_df = df[df.index.date == specific_date.date()]

  3. Filtering DataFrame rows based on time index hour criteria

    • Description: This query focuses on filtering Pandas DataFrame rows based on specific hour criteria of the time index.
    • Code:
      import pandas as pd # Assuming 'df' is the DataFrame and 'desired_hour' is the desired hour (e.g., 9) filtered_df = df[df.index.hour == desired_hour]

  4. Selecting DataFrame rows based on time index day criteria

    • Description: This query aims to select Pandas DataFrame rows based on specific day criteria of the time index.
    • Code:
      import pandas as pd # Assuming 'df' is the DataFrame and 'desired_day' is the desired day (e.g., Monday) filtered_df = df[df.index.dayofweek == desired_day]

  5. Filtering DataFrame rows by a specific month of the time index

    • Description: This query focuses on filtering Pandas DataFrame rows based on a specific month of the time index.
    • Code:
      import pandas as pd # Assuming 'df' is the DataFrame and 'desired_month' is the desired month (e.g., 6 for June) filtered_df = df[df.index.month == desired_month]

  6. Extracting DataFrame rows based on time index year criteria

    • Description: This query aims to extract Pandas DataFrame rows based on specific year criteria of the time index.
    • Code:
      import pandas as pd # Assuming 'df' is the DataFrame and 'desired_year' is the desired year (e.g., 2023) filtered_df = df[df.index.year == desired_year]

  7. Filtering DataFrame rows by time index minute criteria

    • Description: This query focuses on filtering Pandas DataFrame rows based on specific minute criteria of the time index.
    • Code:
      import pandas as pd # Assuming 'df' is the DataFrame and 'desired_minute' is the desired minute (e.g., 30) filtered_df = df[df.index.minute == desired_minute]

  8. Selecting DataFrame rows based on time index weekday criteria

    • Description: This query aims to select Pandas DataFrame rows based on specific weekday criteria of the time index.
    • Code:
      import pandas as pd # Assuming 'df' is the DataFrame and 'desired_weekday' is the desired weekday (e.g., Tuesday) filtered_df = df[df.index.day_name() == desired_weekday]

  9. Filtering DataFrame rows by time index quarter criteria

    • Description: This query focuses on filtering Pandas DataFrame rows based on specific quarter criteria of the time index.
    • Code:
      import pandas as pd # Assuming 'df' is the DataFrame and 'desired_quarter' is the desired quarter (e.g., Q1) filtered_df = df[df.index.quarter == desired_quarter]

  10. Extracting DataFrame rows based on time index month and year criteria

    • Description: This query aims to extract Pandas DataFrame rows based on specific month and year criteria of the time index.
    • Code:
      import pandas as pd # Assuming 'df' is the DataFrame, 'desired_month' is the desired month (e.g., 6 for June), and 'desired_year' is the desired year (e.g., 2023) filtered_df = df[(df.index.month == desired_month) & (df.index.year == desired_year)]

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