Do you have experience processing and analyzing large volumes of data?

Yes, experience in processing and analyzing large volumes of data in Python is usually associated with libraries such as pandas, NumPy, Dask, or PySpark.

Main tasks when working with big data:

• Efficient reading and writing of data (e.g., using Parquet, HDF5 formats).
• Memory and computation optimization (e.g., processing data in chunks, using generators).
• Parallel and distributed processing (Dask, PySpark).
• Data analysis and visualization.

Example of reading a large CSV with pandas using chunking:

import pandas as pd

chunksize = 10 ** 6  # one million rows at a time
for chunk in pd.read_csv('large_data.csv', chunksize=chunksize):
    # process each chunk separately
    process(chunk)

This approach allows working with files that do not fit entirely into RAM.