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 thatloadtxt()
is expecting.- You can use
','
for comma-delimited. - You can use
'\t'
for tab-delimited.
- You can use
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
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(____)