How to use a consistent random sample in Python Pandas?

Question

Below I have a code where a read a csv file and take a random sample of 700 from the file. I need to do this on multiple files, but if I iterate over the files, the sample (as it is random) will be different for each file, wheras I want to keep it the same once it's randomly generated.

df = pd.read_csv(file.csv, delim_whitespace=True)
df_s = df.sample(n=700)

My ideas are to take the row number and then pass it to the next file, however this does not seem to be very elegant.

Do you know any good solutions to this issue?

CLARIFICATION

The file lengths are different, but there is a minimum file length: 750.

desired outcome EXAMPLE

df1 = pd.read_csv(file1.csv, delim_whitespace=True)
df_s1 = df1.sample(n=700) # choose random sample

df2 = pd.read_csv(file2.csv, delim_whitespace=True)
df_s2 = df2.sample(n=700) # use same random sample as above

Answer 1

I think you can use random_state parameter in sample, but it works only if same sizes of all files, so add parameter nrows to read_csv:

df = pd.read_csv(file.csv, delim_whitespace=True, nrows=750)
df_s = df.sample(n=700, random_state=123)