Can Python Pandas Handle 10 Million Columns?

Let’s start with the topic Can Python Pandas Handle 10 Million Columns? One of the most reputed and widely used data science tools for manipulating and analyzing data in Python has been Pandas. In the real world, data is inherently chaotic. And when it comes to cleaning, transforming, manipulating, and analyzing data, Pandas is truly a game changer. In plain terms, pandas assist in tidying up the mess.

Can Python Pandas Handle 10 Million Columns?

Yes, Pandas can easily handle 10 million columns. Pandas work very well with small amounts of data (usually between 100MB and 1GB), and performance is usually not a problem.

How To Use Pandas With Large Data?

The following explanation will be based on my experience with a big anonymous data collection (40–50 GB) that I had to compress for analysis to fit in local memory (even before reading the data set to a data frame).

Read CSV File Data In Chunk Size

I was perplexed when I ran into a problem and couldn’t read the data from a CSV file before realizing that the 16GB of RAM on my local PC wasn’t enough to hold the data. The good points and the beauty of pandas are here: I discovered the chunk size argument in pandas. Read CSV exists!

Essentially, the parameter specifies how many rows must be read into a data frame at once for it to fit in the local memory. Since there are more than 70 million rows in the data, I divided the enormous data set into numerous smaller chunks by specifying a chunk size of 1 million rows.

A TextFileReader object for iteration was produced by the action above. In a strict sense, the df chunk is not a data frame but an object that will be used in the following operation.

Once I prepared the object, the fundamental approach was to act on each chunk before joining them all together to create a data frame (as shown below). I used a method called chunk preprocessing to perform data filtering/preprocessing for each chunk before adding each chunk to a list. To fit the list into the local memory, I then concatenated the list into a final data frame.

Save Memory By Removing Unnecessary Columns

Great. I already had a data frame to perform any necessary analysis at this point. I additionally filtered out certain unnecessary columns to conserve more RAM to reduce the amount of time required for data manipulation and computation.

Change Types For Columns

Using astype is the simplest way to change the data type in a panda’s column (). I can attest that switching data types in Pandas is quite beneficial for memory savings, especially if you have a lot of data that needs to be processed or computed intensively (For example, feed data into your machine learning model for training).

I was able to cut down on the number of bits needed to hold the data, which resulted in a 50% reduction in overall memory consumption!

Conclusion

Did you get Can Python Pandas Handle 10 Million Columns? 100 GB The maximum amount of free storage space that pandas could use Dataframe was 100 GB. Your Mac will move resources not currently in use into a swapfile for temporary storage when it requires memory.

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