在加载到pandas数据帧之前过滤掉CSV中的行

Question

I have a large csv file, that I cannot load into a DataFrame using read_csv() due to memory issues. However in the first column of the csv there is a {0,1} flag, and I only need to load the rows with a '1', which will easily be small enough to fit in a DataFrame. Is there any way to load the data with a condition, or to manipulate the csv prior to loading it (similar to grep)?

Answer 1

You can use pd.read_csv s the comment parameter and set it to '0'

import pandas as pd
from io import StringIO

txt = """col1,col2
1,a
0,b
1,c
0,d"""

pd.read_csv(StringIO(txt), comment='0')

   col1 col2
0     1    a
1     1    c

You can also use chunksize to turn pd.read_csv into an iterator and process it with query and pd.concat
NOTE: As the OP pointed out, chunk size of 1 isn't realistic. I used it for demonstration purposes only. Please increase it to suit individual needs.

pd.concat([df.query('col1 == 1') for df in pd.read_csv(StringIO(txt), chunksize=1)])
# Equivalent to and slower than... use the commented line for better performance
# pd.concat([df[df.col1 == 1] for df in pd.read_csv(StringIO(txt), chunksize=1)])

   col1 col2
0     1    a
2     1    c