Is there a built-in way to use read_csv to read only the first n lines of a file without knowing the length of the lines ahead of time? I have a large file that takes a long time to read, and occasionally only want to use the first, say, 20 lines to get a sample of it (and prefer not to load the full thing and take the head of it).
If I knew the total number of lines I could do something like footer_lines = total_lines - n and pass this to the skipfooter keyword arg. My current solution is to manually grab the first n lines with python and StringIO it to pandas:
import pandas as pd
from StringIO import StringIO
n = 20
with open('big_file.csv', 'r') as f:
head = ''.join(f.readlines(n))
df = pd.read_csv(StringIO(head))
It's not that bad, but is there a more concise, 'pandasic' (?) way to do it with keywords or something?
I think you can use the nrows parameter. From the docs:
nrows : int, default None
Number of rows of file to read. Useful for reading pieces of large files
which seems to work. Using one of the standard large test files (988504479 bytes, 5344499 lines):
In [1]: import pandas as pd
In [2]: time z = pd.read_csv("P00000001-ALL.csv", nrows=20)
CPU times: user 0.00 s, sys: 0.00 s, total: 0.00 s
Wall time: 0.00 s
In [3]: len(z)
Out[3]: 20
In [4]: time z = pd.read_csv("P00000001-ALL.csv")
CPU times: user 27.63 s, sys: 1.92 s, total: 29.55 s
Wall time: 30.23 s
Source: Stackoverflow.com