Histopathologic-Cancer-Detection | Kaggle Competition : Identify metastatic tissue | Machine Learning library

This is for a machine learning project.
I have a CSV file which I have read in as a Pandas dataframe. The CSV looks like this:

I am working with a dataset to train a Keras Deep Learning model on a Kaggle notebook with a GPU. The dataset has a csv which contains an id, for a .tif image in another directory, and a label, 1 or 0. I balanced the data and saved it using numpy.save() (See Code 1). This works fine and afterwards, I download the files and reupload them as a dataset. However, when I try to use this dataset in a different notebook using numpy.load() (See Code 2), I get the following error:

This is for a machine learning program.

I am working with a dataset that has a csv which contains an id, for a .tif image in another directory, and a label, 1 or 0. There are 220,025 rows in the csv. I have loaded this csv as a pandas dataframe. Currently in the dataframe, there are 220,025 rows, with 130,908 rows with label 0 and 89,117 rows with label 1.

There are 41,791 more rows with label 0 than label 1. I want to randomly drop the extra rows with label 1. After that, I want to decrease the sample size from 178,234 to just 50,000, with 25,000 ids for each label.

Another approach might be to randomly drop 105,908 rows with label 1 and 64,117 with label 0.

How can I do this using pandas?

I have already looked at using .groupby and then using .sample, but that drops an equal amount of rows in both labels, while I only want to drop rows in one label.

Sample of the csv: