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I have a csv file with around 130 columns and 6000 rows

what is the best way to import them into python, so that I can later use them in a classification algorithm(columns are the labels and rows are individual samples)

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Use pandas library:

import pandas as pd 
pd.read_csv('foo.csv')

Pandas identify the headers automatically and is a great tool for data wrangling.
10 Minutes intro to pandas

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For small data, I think pandas.read_csv is the way to go.

For "medium" data, I recommend dask.read_csv

And for big data, I recommend spark.read.csv

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  • $\begingroup$ Agreed. On my laptop (8G ram, i5 processor), reading in CSV's up to around 1GB isn't much of a problem using pandas.read_csv. Keep in mind also that read_csv has the handy feature of being able to read gzipped CSV's. $\endgroup$ Sep 15 '16 at 17:43
  • $\begingroup$ So do the other two! $\endgroup$
    – Emre
    Sep 15 '16 at 17:55
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You can also SFrame. First install graph lab. Then:

import graphlab as gl
data = gl.SFrame.read_csv('data.csv')

If you're 'hardcore' you can use python's basic csv reader, but then you will have to write loops to manage the data yourself, so why bother reinvent the wheel, just use pandas or Frame.

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  • $\begingroup$ what's better? pandas or SFrame? $\endgroup$
    – krishna
    Sep 20 '16 at 1:59
  • $\begingroup$ SFrame is technically better, but it's use is restricted and limited documentation. Pandas is free and has more community support. $\endgroup$ Sep 20 '16 at 2:02

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