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.
The MNIST dataset is a collection of handwritten digits from 0 to 9, frequently used in the field of machine learning. It serves as a benchmark for evaluating algorithm performance in recognizing and classifying these numbers.
This exercise is part of the course
Introduction to 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 packages
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()