How Can I Optimize DataFrame Iteration in Pandas?

Optimizing DataFrame Iteration in Pandas

Iterating through dataframes in a sequential manner to perform complex analysis is a common task in financial data processing. While the provided code using enumerate() with df.values provides a straightforward approach, it raises questions about its efficiency.

To address this, pandas offers a specialized solution. The iterrows() function allows direct iteration over dataframe rows, returning a tuple of index and corresponding row values. This method:

for index, row in df.iterrows():
    # perform analysis based on index and row values

For improved performance, the itertuples() function offers a memory-efficient alternative to iterrows().

Alternatively, a highly efficient approach is to leverage numpy functions directly on dataframe columns, avoiding row iteration altogether. numpy operations act on entire columns, enabling faster vectorized calculations. For example, to calculate the mean open price:

import numpy as np
mean_open = np.mean(df['Open'])

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