Pandas Rolling Apply custom

Pandas Rolling Apply custom

To apply a custom function using a rolling window in a Pandas DataFrame or Series, you can use the .rolling() method in combination with the .apply() method. The .rolling() method creates a rolling view of the data, and then you can apply your custom function to this rolling view.

Here's an example of how you can achieve this:

import pandas as pd

# Sample DataFrame
data = {'value': [10, 20, 30, 40, 50, 60]}
df = pd.DataFrame(data)

# Define a custom function to apply to the rolling window
def custom_function(window):
    # Your custom logic here, for example, return the sum of the window
    return window.sum()

# Apply the custom function using rolling window of size 3
rolling_result = df['value'].rolling(window=3).apply(custom_function, raw=True)

# Add the rolling result as a new column in the DataFrame
df['rolling_sum'] = rolling_result

print(df)

In this example, we create a custom function custom_function that calculates the sum of the values within the rolling window. We then use the .rolling() method to create a rolling view of the value column with a window size of 3. The .apply() method is used to apply the custom function to each rolling window. The raw=True parameter is used to pass the raw NumPy array of values to the custom function.

Finally, we add the rolling result as a new column named rolling_sum to the original DataFrame.

Keep in mind that rolling window calculations can be memory-intensive, especially for large datasets. If you're working with a large dataset, you might need to consider more efficient ways to perform rolling window calculations.

Examples

  1. How to apply a custom function with rolling window in Pandas?

    • Description: This query seeks a method to apply a custom function over a rolling window in a Pandas DataFrame or Series.
    • Code Implementation:
      import pandas as pd

# Sample DataFrame
df = pd.DataFrame({'A': [1, 2, 3, 4, 5]})

# Define custom function
def custom_function(data):
    return data.max() - data.min()

# Apply custom function with rolling window
result = df['A'].rolling(window=3).apply(custom_function)
print(result)

  2. How to compute rolling mean with a custom window size in Pandas?

    • Description: This query focuses on computing the rolling mean using a custom-defined window size and applying it to a Pandas DataFrame or Series.
    • Code Implementation:
      import pandas as pd

# Sample DataFrame
df = pd.DataFrame({'A': [1, 2, 3, 4, 5]})

# Define custom function for rolling mean
def custom_rolling_mean(data, window_size):
    return data.rolling(window=window_size).mean()

# Apply custom rolling mean with window size 3
result = custom_rolling_mean(df['A'], window_size=3)
print(result)

  3. How to apply a custom aggregation function with rolling window in Pandas?

    • Description: This query looks for a way to apply a custom aggregation function over a rolling window in a Pandas DataFrame or Series.
    • Code Implementation:
      import pandas as pd

# Sample DataFrame
df = pd.DataFrame({'A': [1, 2, 3, 4, 5]})

# Define custom aggregation function
def custom_aggregation(data):
    return data.sum() / len(data)

# Apply custom aggregation function with rolling window
result = df['A'].rolling(window=3).apply(custom_aggregation)
print(result)

  4. How to compute rolling standard deviation with a custom window size in Pandas?

    • Description: This query involves calculating the rolling standard deviation using a custom window size in a Pandas DataFrame or Series.
    • Code Implementation:
      import pandas as pd

# Sample DataFrame
df = pd.DataFrame({'A': [1, 2, 3, 4, 5]})

# Define custom function for rolling standard deviation
def custom_rolling_std(data, window_size):
    return data.rolling(window=window_size).std()

# Apply custom rolling standard deviation with window size 3
result = custom_rolling_std(df['A'], window_size=3)
print(result)

  5. How to apply a custom transformation with rolling window in Pandas?

    • Description: This query explores applying a custom transformation function over a rolling window in a Pandas DataFrame or Series.
    • Code Implementation:
      import pandas as pd

# Sample DataFrame
df = pd.DataFrame({'A': [1, 2, 3, 4, 5]})

# Define custom transformation function
def custom_transformation(data):
    return data * 2

# Apply custom transformation with rolling window
result = df['A'].rolling(window=3).apply(custom_transformation)
print(result)

  6. How to compute rolling median with a custom window size in Pandas?

    • Description: This query focuses on computing the rolling median using a custom window size and applying it to a Pandas DataFrame or Series.
    • Code Implementation:
      import pandas as pd

# Sample DataFrame
df = pd.DataFrame({'A': [1, 2, 3, 4, 5]})

# Define custom function for rolling median
def custom_rolling_median(data, window_size):
    return data.rolling(window=window_size).median()

# Apply custom rolling median with window size 3
result = custom_rolling_median(df['A'], window_size=3)
print(result)

  7. How to apply a custom function with rolling window and handle missing values in Pandas?

    • Description: This query investigates applying a custom function over a rolling window while handling missing values (NaNs) appropriately in a Pandas DataFrame or Series.
    • Code Implementation:
      import pandas as pd

# Sample DataFrame with missing values
df = pd.DataFrame({'A': [1, 2, None, 4, 5]})

# Define custom function
def custom_function(data):
    return data.max() - data.min()

# Apply custom function with rolling window and handle missing values
result = df['A'].rolling(window=3, min_periods=1).apply(custom_function)
print(result)

  8. How to compute rolling sum with a custom window size in Pandas?

    • Description: This query involves calculating the rolling sum using a custom window size and applying it to a Pandas DataFrame or Series.
    • Code Implementation:
      import pandas as pd

# Sample DataFrame
df = pd.DataFrame({'A': [1, 2, 3, 4, 5]})

# Define custom function for rolling sum
def custom_rolling_sum(data, window_size):
    return data.rolling(window=window_size).sum()

# Apply custom rolling sum with window size 3
result = custom_rolling_sum(df['A'], window_size=3)
print(result)

  9. How to apply a custom weighted average with rolling window in Pandas?

    • Description: This query explores applying a custom weighted average function over a rolling window in a Pandas DataFrame or Series.
    • Code Implementation:
      import pandas as pd

# Sample DataFrame
df = pd.DataFrame({'A': [1, 2, 3, 4, 5]})

# Define custom function for weighted average
def custom_weighted_average(data):
    weights = [0.25, 0.5, 0.25]  # Example weights
    return sum(data * weights)

# Apply custom weighted average with rolling window
result = df['A'].rolling(window=3).apply(custom_weighted_average)
print(result)

  10. How to compute rolling maximum with a custom window size in Pandas?

    • Description: This query focuses on computing the rolling maximum using a custom window size and applying it to a Pandas DataFrame or Series.
    • Code Implementation:
      import pandas as pd

# Sample DataFrame
df = pd.DataFrame({'A': [1, 2, 3, 4, 5]})

# Define custom function for rolling maximum
def custom_rolling_max(data, window_size):
    return data.rolling(window=window_size).max()

# Apply custom rolling maximum with window size 3
result = custom_rolling_max(df['A'], window_size=3)
print(result)

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