If you want to analyze a CSV dataset that is larger than the space available in RAM, then you can iteratively process each observation and store/calculate only what you need. There is a way to do this in standard Python as well as the popular library Pandas.

Standard Library

import csv
with open('/path/to/data.csv', newline='') as csvfile:
   reader = csv.reader(csvfile, delimeter=',')
   for row in reader:
       for column in row:
           do_something()

Pandas

Pandas is slightly different in where you specify a chunksize which is the number of rows per chunk and you get a pandas dataframe with that many rows

import pandas as pd
chunksize = 100
for chunk in pd.read_csv('/path/to/data.csv', chunksize=chunksize):
    do_something(chunk)