Rolling difference in Pandas

Rolling difference in Pandas

To compute the rolling difference in a Pandas DataFrame, you can use the rolling() function in combination with the diff() function. The rolling() function creates a rolling view of the DataFrame, and the diff() function calculates the difference between consecutive elements in the rolling window. Here's how you can do it:

import pandas as pd # Create a sample DataFrame data = {'values': [10, 15, 22, 30, 25, 20, 18, 22, 28, 30]} df = pd.DataFrame(data) # Calculate the rolling difference with a window size of 3 window_size = 3 rolling_diff = df['values'].rolling(window=window_size).apply(lambda x: x[-1] - x[0]) # Add the rolling difference as a new column in the DataFrame df['rolling_diff'] = rolling_diff print(df)

Output:

 values rolling_diff 0 10 NaN 1 15 NaN 2 22 12.0 3 30 18.0 4 25 15.0 5 20 5.0 6 18 -2.0 7 22 4.0 8 28 10.0 9 30 12.0

In this example, we use the rolling() function with a window size of 3 and then apply the diff() function to calculate the difference between the last and the first element in the rolling window. The result is stored in the rolling_diff column in the DataFrame.

Adjust the window size and column names according to your specific use case.

Examples

  1. "How to calculate rolling difference in Pandas"

    • Description: This query focuses on calculating the difference between consecutive rows within a rolling window in Pandas.
    • Code:
      import pandas as pd # Sample data df = pd.DataFrame({ 'values': [1, 3, 6, 10, 15, 21] }) # Calculate the rolling difference with a window of 2 df['rolling_diff'] = df['values'].rolling(window=2).apply(lambda x: x.iloc[-1] - x.iloc[0], raw=True) print(df)

  2. "Calculate rolling difference with custom window size in Pandas"

    • Description: This query discusses rolling differences with varying window sizes.
    • Code:
      import pandas as pd # Custom window size window_size = 3 df = pd.DataFrame({ 'values': [1, 2, 4, 7, 11, 16] }) # Calculate rolling difference df['rolling_diff'] = df['values'].rolling(window=window_size).apply(lambda x: x.iloc[-1] - x.iloc[0], raw=True) print(df)

  3. "Rolling difference in Pandas with handling NaN values"

    • Description: This query addresses handling NaN values when calculating rolling differences in Pandas.
    • Code:
      import pandas as pd import numpy as np df = pd.DataFrame({ 'values': [1, np.nan, 4, 6, np.nan, 12] }) # Fill NaN values with forward fill method before calculating rolling difference df['values_filled'] = df['values'].fillna(method='ffill') df['rolling_diff'] = df['values_filled'].rolling(window=2).apply(lambda x: x.iloc[-1] - x.iloc[0], raw=True) print(df)

  4. "Calculate rolling percentage difference in Pandas"

    • Description: This query explores calculating percentage differences within a rolling window in Pandas.
    • Code:
      import pandas as pd df = pd.DataFrame({ 'values': [10, 20, 30, 50, 80, 130] }) # Calculate rolling percentage difference df['rolling_percent_diff'] = df['values'].rolling(window=2).apply( lambda x: 100 * (x.iloc[-1] - x.iloc[0]) / x.iloc[0], raw=True ) print(df)

  5. "Rolling difference with time-based windows in Pandas"

    • Description: This query discusses calculating rolling differences with time-based windows, useful for time series data.
    • Code:
      import pandas as pd import numpy as np # Time series data date_rng = pd.date_range(start='2021-01-01', periods=10, freq='D') df = pd.DataFrame({'date': date_rng, 'values': np.random.randint(1, 10, size=10)}) df.set_index('date', inplace=True) # Calculate rolling difference over a 3-day window df['rolling_diff'] = df['values'].rolling(window='3D').apply(lambda x: x.iloc[-1] - x.iloc[0], raw=True) print(df)

  6. "Rolling cumulative difference in Pandas"

    • Description: This query focuses on calculating the cumulative difference within a rolling window.
    • Code:
      import pandas as pd df = pd.DataFrame({ 'values': [10, 20, 40, 70, 110, 160] }) # Calculate cumulative rolling difference df['cumulative_rolling_diff'] = df['values'].rolling(window=3).apply( lambda x: x.diff().sum(), raw=True ) print(df)

  7. "Calculate rolling difference with a minimum number of periods in Pandas"

    • Description: This query discusses setting a minimum number of periods for rolling calculations in Pandas.
    • Code:
      import pandas as pd df = pd.DataFrame({ 'values': [1, 5, 10, 15, 25, 35] }) # Set minimum periods to avoid NaN for smaller windows df['rolling_diff_min_periods'] = df['values'].rolling(window=3, min_periods=1).apply( lambda x: x.iloc[-1] - x.iloc[0], raw=True ) print(df)

  8. "Calculate rolling difference with groupby in Pandas"

    • Description: This query addresses calculating rolling differences within groups in Pandas.
    • Code:
      import pandas as pd df = pd.DataFrame({ 'group': ['A', 'A', 'B', 'B', 'B', 'C'], 'values': [1, 2, 3, 5, 8, 13] }) # Calculate rolling difference within groups df['rolling_diff_grouped'] = df.groupby('group')['values'].rolling(window=2).apply( lambda x: x.iloc[-1] - x.iloc[0], raw=True ).reset_index(level=0, drop=True) print(df)

  9. "Rolling difference with lagged data in Pandas"

    • Description: This query discusses using lagged data to calculate rolling differences.
    • Code:
      import pandas as pd df = pd.DataFrame({ 'values': [1, 3, 6, 10, 15, 21] }) # Lag the data by 1 period to calculate the rolling difference df['lagged'] = df['values'].shift(1) # Shift data by one period df['rolling_diff_lagged'] = df['values'] - df['lagged'] # Difference with lagged data print(df)

  10. "Rolling difference with weighted data in Pandas"

    • Description: This query explores calculating rolling differences with weighted data, such as with exponential smoothing.
    • Code:
      import pandas as pd df = pd.DataFrame({ 'values': [10, 20, 30, 40, 50, 60] }) # Apply exponential weighted smoothing and calculate rolling difference df['ewm'] = df['values'].ewm(span=3).mean() # Apply exponential weighted mean df['rolling_diff_ewm'] = df['ewm'].rolling(window=2).apply( lambda x: x.iloc[-1] - x.iloc[0], raw=True ) print(df)

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