File IO with numpy

Saving and loading numpy arrays using binary files

x = np.random.random([100,100])
x.tofile('/path/to/dir/saved_binary.npy')
y = fromfile('/path/to/dir/saved_binary.npy')
z = y.reshape(100,100)
all(x==z)
# Output:
#     True

Loading numerical data from text files with consistent structure

The function np.loadtxt can be used to read csv-like files:

# File:
#    # Col_1 Col_2
#    1, 1
#    2, 4
#    3, 9
np.loadtxt('/path/to/dir/csvlike.txt', delimiter=',', comments='#')
# Output:
# array([[ 1.,  1.],
#        [ 2.,  4.],
#        [ 3.,  9.]])

The same file could be read using a regular expression with np.fromregex:

np.fromregex('/path/to/dir/csvlike.txt', r'(\d+),\s(\d+)', np.int64)
# Output:
# array([[1, 1],
#        [2, 4],
#        [3, 9]])

Saving data as CSV style ASCII file

Analog to np.loadtxt, np.savetxt can be used to save data in an ASCII file

import numpy as np
x = np.random.random([100,100])
np.savetxt("filename.txt", x)

To control formatting:

np.savetxt("filename.txt", x, delimiter=", " , 
    newline="\n", comments="$ ", fmt="%1.2f",
    header="commented example text")

Output:

$ commented example text
0.30, 0.61, 0.34, 0.13, 0.52, 0.62, 0.35, 0.87, 0.48, [...]

Reading CSV files

Three main functions available (description from man pages):

fromfile - A highly efficient way of reading binary data with a known data-type, as well as parsing simply formatted text files. Data written using the tofile method can be read using this function.

genfromtxt - Load data from a text file, with missing values handled as specified. Each line past the first skip_header lines is split at the delimiter character, and characters following the comments character are discarded.

loadtxt - Load data from a text file. Each row in the text file must have the same number of values.

genfromtxt is a wrapper function for loadtxt. genfromtxt is the most straight-forward to use as it has many parameters for dealing with the input file.

Consistent number of columns, consistent data type (numerical or string):

Given an input file, myfile.csv with the contents:

#descriptive text line to skip
1.0, 2, 3
4, 5.5, 6

import numpy as np
np.genfromtxt('path/to/myfile.csv',delimiter=',',skiprows=1)

gives an array:

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

Consistent number of columns, mixed data type (across columns):

1   2.0000  buckle_my_shoe
3   4.0000  margery_door

import numpy as np
np.genfromtxt('filename', dtype= None)


array([(1, 2.0, 'buckle_my_shoe'), (3, 4.0, 'margery_door')], 
dtype=[('f0', '<i4'), ('f1', '<f8'), ('f2', '|S14')])

Note the use of dtype=None results in a recarray.

Inconsistent number of columns:

file: 1 2 3 4 5 6 7 8 9 10 11 22 13 14 15 16 17 18 19 20 21 22 23 24

Into single row array:

result=np.fromfile(path_to_file,dtype=float,sep="\t",count=-1)