Using NumPy to import flat files
In this exercise, you're now going to load the MNIST digit recognition dataset using the numpy function loadtxt() and see just how easy it can be:
- The first argument will be the filename.
- The second will be the delimiter which, in this case, is a comma.
You can find more information about the MNIST dataset here on the webpage of Yann LeCun, who is currently Director of AI Research at Facebook and Founding Director of the NYU Center for Data Science, among many other things.
This exercise is part of the course
Importing Data in Python
Exercise instructions
- Fill in the arguments of
np.loadtxt()by passingfileand a comma','for the delimiter. - Fill in the argument of
print()to print the type of the objectdigits. Use the functiontype(). - Execute the rest of the code to visualize one of the rows of the data.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Import package
import numpy as np
# Assign filename to variable: file
file = 'digits.csv'
# Load file as array: digits
digits = np.loadtxt(____, delimiter=____)
# Print datatype of digits
print(____)
# Select and reshape a row
im = digits[21, 1:]
im_sq = np.reshape(im, (28, 28))
# Plot reshaped data (matplotlib.pyplot already loaded as plt)
plt.imshow(im_sq, cmap='Greys', interpolation='nearest')
plt.show()