Question

I am trying to import a simple tab separated text file using genfromtxt. I need to have access to each column header name, along with the data in the column associated with that name. Currently I am accomplishing this in a way that seems kind odd. All values in the txt file, including the header, are decimal numbers.

sample input file:

1     2     3     4      # header row
1.2   5.3   2.8   9.5
3.1   4.5   1.1   6.7
1.2   5.3   2.8   9.5
3.1   4.5   1.1   6.7
1.2   5.3   2.8   9.5
3.1   4.5   1.1   6.7


table_data = np.genfromtxt(file_path)       #import file as numpy array
header_values = table_data[0,:]             # grab first row
table_values = np.delete(table_data,0,0)    # grab everything else

I know there must be a more proper way to import a text file of data. I need to make it easy to access each column’s header and the respective data pertaining to that header value. I appreciate any help you can provide.

Clarification:

I want to be able to access a column of data by using something along the lines of table_values[header_of_first_column]. How would I accomplish this?

Answer 1

Use the names parameter to use the first valid line as column names:

data = np.genfromtxt(
    fname,
    names = True, #  If `names` is True, the field names are read from the first valid line
    comments = '#', # Skip characters after #
    delimiter = '\t', # tab separated values
    dtype = None)  # guess the dtype of each column

---

For example, if I modify the data you posted to be truly tab-separated, then the following code works:

import numpy as np
import os
fname = os.path.expanduser('~/test/data')
data = np.genfromtxt(
    fname,
    names = True, #  If `names` is True, the field names are read from the first valid line
    comments = '#', # Skip characters after #
    delimiter = '\t', # tab separated values
    dtype = None)  # guess the dtype of each column
print(data)
# [(1.2, 5.3, 2.8, 9.5) (3.1, 4.5, 1.1, 6.7) (1.2, 5.3, 2.8, 9.5)
#  (3.1, 4.5, 1.1, 6.7) (1.2, 5.3, 2.8, 9.5) (3.1, 4.5, 1.1, 6.7)]

print(data['1'])
# [ 1.2  3.1  1.2  3.1  1.2  3.1]