What Happens When You Append Rows to a Pandas DataFrame

Understanding the internal working of Pandas DataFrame.

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Consider the following graph. It depicts the run-time of a row append operation on Pandas DataFrame.

• Why do we observe an uptrend here?

• And why are there so many spikes in between?

Let’s understand this today.

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Why append run-time increases?

To understand this, first, we must learn about Pandas DataFrame data structure and how it is stored in memory.

A DataFrame is a column-major data structure.

This means that consecutive elements in a column are stored next to each other in memory, as depicted below:

A simplified version of how columns are stored in memory

Of course, the individual columns may be spread across different locations in memory. However, the elements of each column are ALWAYS together.

Now, as new rows are added, Pandas always tries to preserve its column-major form.

But while adding new rows, at times, there’s not enough space to accommodate them while also preserving the column-major structure.

In such a case, existing data is moved to a new memory location where Pandas finds a contiguous block of memory.

Thus, as the size grows, memory reallocation gets more frequent, and the run time keeps increasing.

This explains the run-time increase we saw earlier.

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How to mitigate this?

It is clear that the increase in run-time solely arises because Pandas is trying to maintain its column-major structure.

While the run-time of append operation is in milliseconds, which may not matter too much in many situations, nonetheless, if you care about it:

• First, convert the DataFrame to another data structure — a dictionary or a NumPy array.

• Then, carry out the append operations in this data structure

• When you are done, convert it back to a Pandas DataFrame.

That said, please note that this is just about appending new rows.

Adding new columns is not a problem since it does not conflict with other columns.

👉 Over to you: What could be some other ways to mitigate this?

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