Why is Populating a Pandas DataFrame Row-by-Row Inefficient, and What\'s a Better Approach?

Creating and Populating an Empty Pandas DataFrame

Conceptually, one may want to start by creating an empty DataFrame and then incrementally fill it with values. However, this approach is inefficient and prone to causing performance issues.

The Pitfalls of Growing a DataFrame Row-wise

Iteratively appending rows to an empty DataFrame is computationally expensive. It leads to quadratic complexity operations due to the dynamic memory allocation and reassignment required. This can severely impact performance, especially when dealing with large datasets.

An Alternative Approach: Accumulating Data in a List

Instead of growing a DataFrame row-wise, it's recommended to accumulate data in a list. This has several advantages:

• It is more efficient and significantly faster.
• Lists have a smaller memory footprint compared to DataFrames.
• Data types are automatically inferred, eliminating the need for manual adjustments.
• Lists support appending operations without altering memory allocation.

Creating a DataFrame from a List

Once data has been accumulated in a list, a DataFrame can be easily created by converting the list using pd.DataFrame(). This ensures proper data type inference and automates setting a RangeIndex for the DataFrame.

Example

Consider the scenario described in the question. The following code demonstrates how to accumulate data in a list and then create a DataFrame:

import pandas as pd

data = []
dates = [pd.to_datetime(f"2023-08-{day}") for day in range(10, 0, -1)]

valdict = {'A': [], 'B': [], 'C': []}  # Initialize symbol value lists

for date in dates:
    for symbol in valdict:
        if date == dates[0]:
            valdict[symbol].append(0)
        else:
            valdict[symbol].append(1 + valdict[symbol][-1])

# Create a DataFrame from the accumulated data
df = pd.DataFrame(valdict, index=dates)

This approach ensures efficient data accumulation and seamless DataFrame creation without any performance overhead or concerns about object columns.

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