Creating an empty Pandas DataFrame and iteratively filling it is a common task in data manipulation. However, the ideal approach may not be immediately apparent.
The code snippet you provided is one way to create an empty DataFrame and iteratively fill it. However, this method is inefficient and may lead to memory-related issues. The reason is that you are creating a new row for each iteration, which requires reallocating memory. This process becomes increasingly cumbersome as the DataFrame grows.
The preferred approach is to accumulate data in a list and then create the DataFrame in one step using the pd.DataFrame() function. This method is significantly more efficient and memory-friendly. Here's how it works:
# Accumulate data in a list data = [] for row in some_function_that_yields_data(): data.append(row) # Create the DataFrame from the list df = pd.DataFrame(data)
When dealing with large data sets, accumulating data in a list and creating the DataFrame in one step is the recommended approach. It is computationally efficient, memory-friendly, and simplifies the data manipulation process.
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