For more info, see here:
https://scikit-learn.org/stable/datasets/toy_dataset.html#optical-recognition-of-handwritten-digits-dataset

The optical recognition of handwritten digits dataset is loaded using load_digits(). This returns a ‘Bunch’ object with the following keys:

Key Description
DESCR Description of the dataset
images 1797 8x8 images represented as 8x8 arrays of integers from 0 to 16
data 1797 8x8 images represented as 1x64 arrays of integers from 0 to 16
target_names Names of the target data (ie the numerals from 0 to 9)
target The target data (ie the 1797 numerals that are shown in the images) 
from sklearn.datasets import load_digits

# Load the dataset
digits = load_digits()

# Show the dataset's keys
print(list(digits))
## ['data', 'target', 'frame', 'feature_names', 'target_names', 'images', 'DESCR']
# Description of the dataset
print(digits['DESCR'])
## .. _digits_dataset:
## 
## Optical recognition of handwritten digits dataset
## --------------------------------------------------
## 
## **Data Set Characteristics:**
## 
##     :Number of Instances: 1797
##     :Number of Attributes: 64
##     :Attribute Information: 8x8 image of integer pixels in the range 0..16.
##     :Missing Attribute Values: None
##     :Creator: E. Alpaydin (alpaydin '@' boun.edu.tr)
##     :Date: July; 1998
## 
## This is a copy of the test set of the UCI ML hand-written digits datasets
## https://archive.ics.uci.edu/ml/datasets/Optical+Recognition+of+Handwritten+Digits
## 
## The data set contains images of hand-written digits: 10 classes where
## each class refers to a digit.
## 
## Preprocessing programs made available by NIST were used to extract
## normalized bitmaps of handwritten digits from a preprinted form. From a
## total of 43 people, 30 contributed to the training set and different 13
## to the test set. 32x32 bitmaps are divided into nonoverlapping blocks of
## 4x4 and the number of on pixels are counted in each block. This generates
## an input matrix of 8x8 where each element is an integer in the range
## 0..16. This reduces dimensionality and gives invariance to small
## distortions.
## 
## For info on NIST preprocessing routines, see M. D. Garris, J. L. Blue, G.
## T. Candela, D. L. Dimmick, J. Geist, P. J. Grother, S. A. Janet, and C.
## L. Wilson, NIST Form-Based Handprint Recognition System, NISTIR 5469,
## 1994.
## 
## .. topic:: References
## 
##   - C. Kaynak (1995) Methods of Combining Multiple Classifiers and Their
##     Applications to Handwritten Digit Recognition, MSc Thesis, Institute of
##     Graduate Studies in Science and Engineering, Bogazici University.
##   - E. Alpaydin, C. Kaynak (1998) Cascading Classifiers, Kybernetika.
##   - Ken Tang and Ponnuthurai N. Suganthan and Xi Yao and A. Kai Qin.
##     Linear dimensionalityreduction using relevance weighted LDA. School of
##     Electrical and Electronic Engineering Nanyang Technological University.
##     2005.
##   - Claudio Gentile. A New Approximate Maximal Margin Classification
##     Algorithm. NIPS. 2000.
# Two of the 1797 8x8 images represented as 8x8 arrays of integers from 0 to 16
print(digits['images'][:2])
## [[[ 0.  0.  5. 13.  9.  1.  0.  0.]
##   [ 0.  0. 13. 15. 10. 15.  5.  0.]
##   [ 0.  3. 15.  2.  0. 11.  8.  0.]
##   [ 0.  4. 12.  0.  0.  8.  8.  0.]
##   [ 0.  5.  8.  0.  0.  9.  8.  0.]
##   [ 0.  4. 11.  0.  1. 12.  7.  0.]
##   [ 0.  2. 14.  5. 10. 12.  0.  0.]
##   [ 0.  0.  6. 13. 10.  0.  0.  0.]]
## 
##  [[ 0.  0.  0. 12. 13.  5.  0.  0.]
##   [ 0.  0.  0. 11. 16.  9.  0.  0.]
##   [ 0.  0.  3. 15. 16.  6.  0.  0.]
##   [ 0.  7. 15. 16. 16.  2.  0.  0.]
##   [ 0.  0.  1. 16. 16.  3.  0.  0.]
##   [ 0.  0.  1. 16. 16.  6.  0.  0.]
##   [ 0.  0.  1. 16. 16.  6.  0.  0.]
##   [ 0.  0.  0. 11. 16. 10.  0.  0.]]]
# Two of the 1797 8x8 images represented as 1x64 arrays of integers from 0 to 16
print(digits['data'][:2])
## [[ 0.  0.  5. 13.  9.  1.  0.  0.  0.  0. 13. 15. 10. 15.  5.  0.  0.  3.
##   15.  2.  0. 11.  8.  0.  0.  4. 12.  0.  0.  8.  8.  0.  0.  5.  8.  0.
##    0.  9.  8.  0.  0.  4. 11.  0.  1. 12.  7.  0.  0.  2. 14.  5. 10. 12.
##    0.  0.  0.  0.  6. 13. 10.  0.  0.  0.]
##  [ 0.  0.  0. 12. 13.  5.  0.  0.  0.  0.  0. 11. 16.  9.  0.  0.  0.  0.
##    3. 15. 16.  6.  0.  0.  0.  7. 15. 16. 16.  2.  0.  0.  0.  0.  1. 16.
##   16.  3.  0.  0.  0.  0.  1. 16. 16.  6.  0.  0.  0.  0.  1. 16. 16.  6.
##    0.  0.  0.  0.  0. 11. 16. 10.  0.  0.]]
# Names of the target data (ie the numerals from 0 to 9)
print(digits['target_names'])
## [0 1 2 3 4 5 6 7 8 9]
# The target data (ie the 1797 numerals that are shown in the images)
print(digits['target'])
## [0 1 2 ... 8 9 8]

The arrays of numbers representing the images of the handwritten digits can be viewed - as images - as follows:

import matplotlib.pyplot as plt

imgplot = plt.imshow(digits['images'][1])
plt.show()

# What digit is being displayed?
print(digits['target'][1])
## 1
imgplot = plt.imshow(digits['images'][150])
plt.show()

# What digit is being displayed?
print(digits['target'][150])
## 0

If the array is coming from the data column, it first needs to be re-shaped into an 8x8 configuration:

imgplot = plt.imshow(digits['data'][2].reshape((8, 8)))
plt.show()

# What digit is being displayed?
print(digits['target'][2])
## 2
imgplot = plt.imshow(digits['data'][100].reshape((8, 8)))
plt.show()

# What digit is being displayed?
print(digits['target'][100])
## 4