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in Data Science by (17.6k points)

I have a very big csv file so that I can not read them all into the memory. I only want to read and process a few lines in it. So I am seeking a function in Pandas which could handle this task, which the basic python can handle this well:

with open('abc.csv') as f:

    line = f.readline()

    # pass until it reaches a particular line number....

However, if I do this in pandas, I always read the first line:

datainput1 = pd.read_csv('matrix.txt',sep=',', header = None, nrows = 1 )

datainput2 = pd.read_csv('matrix.txt',sep=',', header = None, nrows = 1 )

I am looking for some easier way to handle this task in pandas. For example, if I want to read rows from 1000 to 2000. How can I do this quickly?

I want to use pandas because I want to read data into the dataframe.

1 Answer

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by (41.4k points)

Use chunksize:

for df in pd.read_csv('matrix.txt',sep=',', header = None, chunksize=1):

    #do something

To answer your second part do this:

df = pd.read_csv('matrix.txt',sep=',', header = None, skiprows=1000, chunksize=1000)

This will skip the first 1000 rows and then only read the next 1000 rows giving you rows 1000-2000, unclear if you require the end points to be included or not but you can fiddle the numbers to get what you want.

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