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Customizing your NumPy import

What if there are rows, such as a header, that you don't want to import? What if your file has a delimiter other than a comma? What if you only wish to import particular columns?

There are a number of arguments that np.loadtxt() takes that you'll find useful:

  • delimiter changes the delimiter that loadtxt() is expecting.
    • You can use ',' for comma-delimited.
    • You can use '\t' for tab-delimited.
  • skiprows allows you to specify how many rows (not indices) you wish to skip.
  • usecols takes a list of the indices of the columns you wish to keep.

The file that you'll be importing, digits_header.txt, has a header and is tab-delimited.

This exercise is part of the course

Introduction to Importing Data in Python

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Exercise instructions

  • Complete the arguments of np.loadtxt(): the file you're importing is tab-delimited, you want to skip the first row and you only want to import the first and third columns.
  • Complete the argument of the print() call in order to print the entire array that you just imported.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Import numpy
import numpy as np

# Assign the filename: file
file = 'digits_header.txt'

# Load the data: data
data = np.loadtxt(____, delimiter='____', skiprows=____, usecols=[____])

# Print data
print(____)
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