How do I read CSV data into a record array in NumPy?

Use pandas.read_csv:

import pandas as pd
df = pd.read_csv('myfile.csv', sep=',', header=None)
print(df.values)

array([[ 1. ,  2. ,  3. ],
       [ 4. ,  5.5,  6. ]])

This gives a pandas DataFrame which provides many useful data manipulation functions which are not directly available with numpy record arrays.

DataFrame is a 2-dimensional labeled data structure with columns of potentially different types. You can think of it like a spreadsheet or SQL table...

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I would also recommend numpy.genfromtxt. However, since the question asks for a record array, as opposed to a normal array, the dtype=None parameter needs to be added to the genfromtxt call:

import numpy as np
np.genfromtxt('myfile.csv', delimiter=',')

For the following 'myfile.csv':

1.0, 2, 3
4, 5.5, 6

the code above gives an array:

array([[ 1. ,  2. ,  3. ],
       [ 4. ,  5.5,  6. ]])

and

np.genfromtxt('myfile.csv', delimiter=',', dtype=None)

gives a record array:

array([(1.0, 2.0, 3), (4.0, 5.5, 6)], 
      dtype=[('f0', '<f8'), ('f1', '<f8'), ('f2', '<i4')])

This has the advantage that files with multiple data types (including strings) can be easily imported.

Use numpy.genfromtxt() by setting the delimiter kwarg to a comma:

from numpy import genfromtxt
my_data = genfromtxt('my_file.csv', delimiter=',')