Python Pandas: Calculate moving average within group

Python Pandas: Calculate moving average within group

You can calculate a moving average within groups using the groupby() function along with the rolling() function in pandas. Here's an example of how to do this:

Let's assume you have a DataFrame with columns "Date", "Group", and "Value", and you want to calculate a moving average of the "Value" column within each group:

import pandas as pd # Sample data data = {'Date': ['2023-01-01', '2023-01-02', '2023-01-03', '2023-01-01', '2023-01-02', '2023-01-03'], 'Group': ['A', 'A', 'A', 'B', 'B', 'B'], 'Value': [10, 15, 20, 5, 10, 15]} df = pd.DataFrame(data) df['Date'] = pd.to_datetime(df['Date']) # Calculate moving average within each group window_size = 2 # Adjust this according to your desired window size df['MovingAverage'] = df.groupby('Group')['Value'].rolling(window=window_size, min_periods=1).mean().reset_index(level=0, drop=True) print(df)

Output:

 Date Group Value MovingAverage 0 2023-01-01 A 10 10.000000 1 2023-01-02 A 15 12.500000 2 2023-01-03 A 20 17.500000 3 2023-01-01 B 5 5.000000 4 2023-01-02 B 10 7.500000 5 2023-01-03 B 15 12.500000

In this example, the rolling() function is used with the groupby() function to calculate a moving average of the "Value" column within each group. The window_size parameter determines the number of periods to consider for the moving average.

Make sure to adjust the window_size according to your desired moving average window size.

Examples

  1. Calculating moving average within group in Pandas DataFrame: Description: Learn how to compute a moving average within each group of a Pandas DataFrame using the groupby() function and rolling() method.

    import pandas as pd # Sample DataFrame data = {'Group': ['A', 'A', 'A', 'B', 'B', 'B'], 'Value': [10, 20, 30, 15, 25, 35]} df = pd.DataFrame(data) # Calculate moving average within each group df['Moving_Avg'] = df.groupby('Group')['Value'].rolling(window=2).mean().reset_index(drop=True) print(df)

  2. Pandas DataFrame: Group-wise moving average calculation: Description: Explore how to compute a group-wise moving average in a Pandas DataFrame by utilizing the groupby() function and rolling() method.

    # Group-wise moving average calculation df['Moving_Avg'] = df.groupby('Group')['Value'].rolling(window=2).mean().reset_index(drop=True) print(df)

  3. Calculating rolling mean within each group in Pandas DataFrame: Description: Implement the calculation of a rolling mean within each group of a Pandas DataFrame to analyze trends within specific categories.

    # Calculating rolling mean within each group df['Moving_Avg'] = df.groupby('Group')['Value'].rolling(window=2).mean().reset_index(drop=True) print(df)

  4. Pandas DataFrame: Group-wise moving average with variable window size: Description: Compute a group-wise moving average with a variable window size for flexible trend analysis within different groups.

    # Group-wise moving average with variable window size window_size = 3 df['Moving_Avg'] = df.groupby('Group')['Value'].rolling(window=window_size).mean().reset_index(drop=True) print(df)

  5. Using Pandas to calculate moving average within group efficiently: Description: Utilize Pandas efficiently to compute a moving average within each group of a DataFrame for group-specific trend analysis.

    # Calculate moving average within group efficiently df['Moving_Avg'] = df.groupby('Group')['Value'].rolling(window=2).mean().reset_index(drop=True) print(df)

  6. Python Pandas: Group-wise rolling average calculation example: Description: Illustrate an example of calculating a group-wise rolling average in Python using Pandas for insightful data analysis.

    # Group-wise rolling average calculation example df['Moving_Avg'] = df.groupby('Group')['Value'].rolling(window=2).mean().reset_index(drop=True) print(df)

  7. Applying rolling mean within group in Pandas DataFrame: Description: Apply the rolling mean function within each group of a Pandas DataFrame to analyze trends specific to each group.

    # Applying rolling mean within group df['Moving_Avg'] = df.groupby('Group')['Value'].rolling(window=2).mean().reset_index(drop=True) print(df)

  8. Pandas DataFrame: Efficient group-wise moving average calculation: Description: Compute a group-wise moving average efficiently in a Pandas DataFrame to analyze trends within different groups of data.

    # Efficient group-wise moving average calculation df['Moving_Avg'] = df.groupby('Group')['Value'].rolling(window=2).mean().reset_index(drop=True) print(df)

  9. Python Pandas: Rolling average within group with custom window size: Description: Calculate a rolling average within each group of a Pandas DataFrame with a custom window size for tailored trend analysis.

    # Rolling average within group with custom window size window_size = 3 df['Moving_Avg'] = df.groupby('Group')['Value'].rolling(window=window_size).mean().reset_index(drop=True) print(df)

  10. Group-wise moving average computation in Pandas DataFrame: Description: Perform group-wise moving average computation in a Pandas DataFrame to gain insights into trends within specific groups.

    # Group-wise moving average computation df['Moving_Avg'] = df.groupby('Group')['Value'].rolling(window=2).mean().reset_index(drop=True) print(df)

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