Pandas: working with MemoryError when attempting to concatenate multiple large DataFrames
I've looked through the documentation and I'm still confused about I'm relatively new to this, so bear with me... After trying multiple solutions online, I still can't figure this out. I've been struggling with this for a few days now and could really use some help. I'm trying to concatenate several large DataFrames using pandas version 1.5.0, but I'm running into a `MemoryError`. My DataFrames are each around 2 million rows and have 10 columns, which I thought would be manageable, but it seems to exceed my system's memory limit. Here’s a snippet of the code I’m using: ```python import pandas as pd # Simulating large DataFrames dfs = [pd.DataFrame({'A': range(2000000), 'B': range(2000000, 4000000)}) for _ in range(5)] # Attempting to concatenate big_df = pd.concat(dfs, ignore_index=True) ``` When I run this, I get the following behavior message: ``` MemoryError: Unable to allocate 40.0 MiB for an array with shape (2000000, 10) and data type int64 ``` I’ve tried increasing my swap space and closing other applications to free up memory, but the question continues. Is there a more memory-efficient way to concatenate these DataFrames, or a strategy to handle large datasets more effectively with pandas? Any advice would be greatly appreciated! My development environment is Windows. Any advice would be much appreciated. This is for a application running on Linux. Is there a better approach? My team is using Python for this REST API. How would you solve this?