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
genfromtxt is the most straight-forward to use as it has many parameters for dealing with the input file.
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. ]])
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