How to Efficiently Fill a Pandas DataFrame Iteratively?

Creating an empty Pandas DataFrame, and then filling it

Iteratively filling a DataFrame with values

Using the given DataFrame documentation, you want to iteratively fill the DataFrame with values in a time series kind of calculation. The goal is to initialize the DataFrame with columns A, B, and timestamp rows, all 0 or all NaN. Then, you want to add initial values and go over this data calculating the new row from the row before, say row[A][t] = row[A][t-1] 1 or so.

While the current code using iterators, scipy's zeros function, and datetime may work, it can be improved.

Why not grow a DataFrame row-wise?

Growing a DataFrame row-wise is generally not recommended for the following reasons:

• Computational cost: Appending to a list and creating a DataFrame in one go is less computationally intensive than creating an empty DataFrame and appending to it over and over again.
• Memory usage: Lists take up less memory and are a lighter data structure to work with than DataFrames, making them more efficient for appending and removing.
• Data type inference: If you append to a DataFrame, you may end up with object columns, which can hinder pandas' performance. Lists, on the other hand, allow dtypes to be automatically inferred.
• Index management: A RangeIndex is automatically created for your data when you create a DataFrame from a list, which saves you the hassle of managing the index yourself.

The recommended approach: Accumulate data in a list

Instead of growing a DataFrame row-wise, it is better to accumulate the data in a list and then initialize a DataFrame using pd.DataFrame(data). This approach offers the following advantages:

• Efficiency: It is more computationally efficient and requires less memory.
• Flexibility: Lists can be converted to both list-of-lists and list-of-dicts formats, which are accepted by pd.DataFrame.
• Convenience: It handles index management and data type inference automatically.

Alternatives to consider

While accumulating data in a list is the preferred approach, there are two worse alternatives to avoid:

• Append or concat inside a loop: This is inefficient and error-prone because it repeatedly reallocates memory and may lead to object columns.
• Creating an empty DataFrame of NaNs: This approach also creates object columns and requires manual index management.

Conclusion

To effectively fill a DataFrame with values, it is best to accumulate the data in a list and then initialize the DataFrame using pd.DataFrame(data). This method is efficient, flexible, and convenient, making it the preferred approach for working with pandas DataFrames.

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