csv file to numpy array via Python

Question

I have a csv file of the following format that I am trying to normalise. The numbers represent the counts for associated strings. The file contains close to 100K entries.

159028,CASSVDGSYEQYFGPG
86832,CASSLQLYFGEG
74720,CASSQDQDTQYFGPG
71701,CASSRVGSDYTFGSG
69360,CARNVTPPKSYAVFFGKG
52458,CAAEQFFGPG
51406,CASSSGDQDTQYFGPG
50305,CASQLYFGEG
38745,CAYFGPG
32565,CASSPDWGENTLYFGAG

I have tried to create a dictionary using the following

import csv
input = csv.DictReader(open("data.csv"))
for row in input:
    print(row)

Result

{'159028': '86832', 'CASSVDGSYEQYFGPG': 'CASSLQLYFGEG'}
{'159028': '74720', 'CASSVDGSYEQYFGPG': 'CASSQDQDTQYFGPG'}
{'159028': '71701', 'CASSVDGSYEQYFGPG': 'CASSRVGSDYTFGSG'}
{'159028': '69360', 'CASSVDGSYEQYFGPG': 'CARNVTPPKSYAVFFGKG'}
{'159028': '52458', 'CASSVDGSYEQYFGPG': 'CAAEQFFGPG'}
{'159028': '51406', 'CASSVDGSYEQYFGPG': 'CASSSGDQDTQYFGPG'}
{'159028': '50305', 'CASSVDGSYEQYFGPG': 'CASQLYFGEG'}
{'159028': '38745', 'CASSVDGSYEQYFGPG': 'CAYFGPG'}
{'159028': '32565', 'CASSVDGSYEQYFGPG': 'CASSPDWGENTLYFGAG'}
...

Instead of

        {'CASSVDGSYEQYFGPG': 159028}        
        {'CASSLQLYFGEG': '86832'}
        {'CASSQDQDTQYFGPG': '74720'}
        {'CASSRVGSDYTFGSG': '71701'}
        {'CARNVTPPKSYAVFFGKG': '69360'}
        {'CAAEQFFGPG': '52458'}
        {'CASSSGDQDTQYFGPG': '51406'}
        {'CASQLYFGEG': '50305'}
        {'CAYFGPG': '38745'}
        {'CASSPDWGENTLYFGAG': '32565'}
        ...

I also tried converting the csv file into a numpy array, but I get the following:

>>>from numpy import genfromtxt
>>>data = genfromtxt('data.csv', delimiter=',')
>>>data
array([[  1.59028000e+05,              nan],
       [  8.68320000e+04,              nan],
       [  7.47200000e+04,              nan],
       ...,
       [  1.00000000e+00,              nan],
       [  1.00000000e+00,              nan],
       [  1.00000000e+00,              nan]])

There may be other ways of normalising and other data processing this data via Python.

file = {x[1]: x[0] for x in np.loadtxt("data.csv", dtype=str, delimiter=",")} Why a dictionary? — Andrew
– Andrew, Commented Sep 7, 2016 at 18:55

Andrew · Accepted Answer · 2016-09-07 19:07:32Z

1

Use Numpy loadtxt to import, then use a dict comprehension if you need it as a dict.

import numpy as np

arr = np.loadtxt('data.csv', dtype=str, delimiter=",")

b = dict([(y, x) for (x, y) in arr])

answered Sep 7, 2016 at 19:07

Andrew

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Comments

Warren Weckesser · Accepted Answer · 2016-09-07 19:16:50Z

genfromtxt has many arguments, and it can take a while to learn the right incantation to read any given file.

Here's how you can do it with your file. The array data returned by genfromtxt is a one-dimensional structured array with two fields, called count and string:

In [11]: data = np.genfromtxt("counts_strings.csv", delimiter=',', names=['count', 'string'], dtype=None)

In [12]: data['count']
Out[12]: 
array([159028,  86832,  74720,  71701,  69360,  52458,  51406,  50305,
        38745,  32565])

In [13]: data['string']
Out[13]: 
array([b'CASSVDGSYEQYFGPG', b'CASSLQLYFGEG', b'CASSQDQDTQYFGPG',
       b'CASSRVGSDYTFGSG', b'CARNVTPPKSYAVFFGKG', b'CAAEQFFGPG',
       b'CASSSGDQDTQYFGPG', b'CASQLYFGEG', b'CAYFGPG', b'CASSPDWGENTLYFGAG'], 
      dtype='|S18')

In [14]: data[0]
Out[14]: (159028, b'CASSVDGSYEQYFGPG')

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