Here are a few title options, keeping in mind the question format and focus on large DataFrame handling: Option 1 (General & Direct): * How to Efficiently Process Large DataFrames in Pandas? Op

Pandas: Slicing Large DataFrames into Chunks

Memory errors can arise when working with extensive dataframes. To alleviate this issue, partitioning the dataframe into manageable portions becomes essential. This approach involves slicing the dataframe, passing it through a function for processing, and then concatenating the resulting chunks back into a single, comprehensive dataframe.

For instance, consider a large dataframe with over 3 million rows of data. To avoid memory exhaustion, we can utilize one of two methods to slice the dataframe:

• Chunked Slicing: Using list comprehension or NumPy's array_split function, we can create a list of smaller dataframes. These chunks can then be accessed individually or processed in parallel.
• Slicing by Unique Values: If the dataframe contains unique values in a specific column (e.g., AcctName), we can group the rows by that column and slice the dataframe accordingly.

After slicing, the chunks are processed individually using a designated function. Subsequently, these processed chunks are combined back into a single dataframe using Pandas' concat function.

This approach allows for efficient processing of large dataframes while mitigating memory limitations. By slicing the dataframe into smaller chunks, we can avoid overwhelming memory resources and ensure smooth execution.

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