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Esto permite un uso más flexible de la función *rolling*, tanto por poder aplicar una función custom como por poder usar más de un argumento para esta función (este ejemplo es para dos arguments pero es fácilmente modificable para otro número de argu
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| ## custom function | |
| def custom_function_example(x,y): | |
| return x + y | |
| ## apply rolling on a custom function | |
| def apply_rolling_function(df, window_size, func, column1, column2, verbose = False): | |
| # validate arguments | |
| assert column1 in df.columns.tolist() | |
| assert column2 in df.columns.tolist() | |
| # initialize | |
| result = [] | |
| # loop | |
| for i in range(len(df) - window_size + 1): | |
| # collect data | |
| window = df.iloc[i:i+window_size] | |
| # apply function for this 2 columns | |
| try: | |
| result.append(func(window[column1], window[column2])) | |
| except Exception as e: | |
| result.append(np.nan) | |
| if verbose: | |
| print(f"Error in record {i}: {str(e)}") | |
| #return pd.Series([np.nan] * (window_size - 1) + result).values | |
| return pd.Series([result[0]] * (window_size - 1) + result).values | |
| # windows size | |
| window_size = 3 | |
| # usage | |
| data[new_column] = apply_rolling_function(data, window_size, estimate_regression_r_value, column1, column2) |
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