How Can I Efficiently Create an Empty DataFrame for Time-Series Data Before Filling It?

Conundrum: Creating an Empty Data Structure

You aspire to construct a DataFrame and subsequently fill it with time-series data. Initially, you envision an empty DataFrame furnished with specific columns and timestamps, all adorned with zeros or NaN values.

Current Approach: An Inelegant Solution

Your current code initializes a DataFrame with all-zero columns and timestamp rows before iterating through the data to calculate new values. While this approach serves its purpose, it feels cumbersome and suggests the existence of a more efficient solution.

Preferred Solution: Accumulating Data in a List

To optimize this process, it's prudent to steer clear of row-wise growth in the DataFrame. Instead, accumulate data into a list and then initialize a DataFrame once data collection is complete. Lists are more lightweight, consume less memory, and facilitate automatic dtypes inference and index assignment.

data = []
for row in some_function_that_yields_data():
    data.append(row)

df = pd.DataFrame(data)

Advantages of Accumulation in a List

• Enhanced Computational Efficiency: Appending to a list and creating a DataFrame in one go is significantly faster than iterative appends to an empty DataFrame.
• Memory Optimization: Lists occupy less memory and are easier to append and manipulate.
• Automatic Dtypes Inference: DataFrame constructor automatically infers dtypes based on the data added.
• Synchronized Index Creation: A RangeIndex is automatically generated for the resulting DataFrame.

Deprecated Methods to Avoid

Certain practices, prevalent among novice users, should be avoided due to their inefficiency and nuances:

• Iterative Append or Concat: This approach suffers from quadratic complexity and data type concerns.
• appending via loc: Appending using loc incurs the same memory reallocation issues as iterative append.
• Empty DataFrame of NaNs: Creating a DataFrame with NaN values results in object columns, which complicates data operations. It's better to allocate memory once in advance to avoid memory fragmentation.

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