Digits at a Glance

Part of the Data and Plots section of Coddy's Machine Learning journey. Lesson 59 of 60.

load_digits holds 1,797 handwritten digits. Each sample is 64 pixel brightnesses, row after row of an 8 by 8 picture, from 0 (blank) to 16 (full ink).

No two writers draw a 4 alike:

Ten handwritten 4s from load_digits in two rows of five, white ink on black, each slanted and shaped a little differently

A teacher wants the typical one: the average picture, where each pixel is the mean of that pixel over every sample of the digit.

Challenge

Hard

The program reads a digit d. Load the digits without as_frame, so .data and .target are NumPy arrays.

Compute the average picture of digit d.
Print the row and column of its brightest pixel, counting from 0.
Print its average ink: the mean of its 64 pixel means, rounded to 2 decimals with np.round.
Draw it as an 8 by 8 image in gray, titled Average and the digit.

Yours should look like this, for 4:

An 8 by 8 gray picture titled Average 4: a soft, blurred white 4 on black, brightest where most writers cross the stroke

For the input 4, the whole output is:

Brightest pixel: row 5, column 4
Average ink: 4.85
Charts drawn: 1
Title: Average 4
Images: 1
Size: 8 rows, 8 columns
Color map: gray
Row 0: [0.0, 0.0, 0.5, 7.1, 11.5, 2.0, 0.2, 0.1]
Row 1: [0.0, 0.1, 3.5, 13.3, 8.5, 2.0, 1.1, 0.3]
Row 2: [0.0, 0.7, 10.5, 11.4, 4.9, 5.8, 3.8, 0.3]
Row 3: [0.0, 4.6, 14.5, 6.4, 7.4, 10.6, 6.1, 0.0]
Row 4: [0.0, 8.6, 14.6, 9.7, 13.0, 14.0, 5.3, 0.0]
Row 5: [0.1, 6.3, 11.3, 12.4, 14.6, 10.4, 1.6, 0.0]
Row 6: [0.1, 1.1, 2.9, 7.8, 13.5, 4.2, 0.0, 0.0]
Row 7: [0.0, 0.0, 0.6, 7.8, 11.8, 2.0, 0.0, 0.0]

Try it yourself

import numpy as np
import matplotlib.pyplot as plt
from sklearn.datasets import load_digits

d = int(input())

# Write your code below

Test yourself

This lesson includes a short quiz. Start the lesson to answer it and track your progress.

All lessons in Data and Plots

1Introduction

Hello, Machine Learning What Is a Model?Importing Libraries Your First Plot

4Shaping Data

Reshaping Arrays Joining Arrays Random Numbers Recap: Dice Simulation

7pandas DataFrames

Series DataFrames Reading CSV Files Inspecting Data Selecting Columns Recap: Movie Ratings

10Plotting DataFrames

Plotting a DataFrame Scatter from Columns One Color per Group Histogram of a Column Recap: Cafe Sales

2NumPy Arrays

Creating Arrays Shape and Size Ranges of Numbers Indexing and Slicing Recap: Temperature Grid

5Line and Scatter Plots

Labels and Titles Scatter Plots Several Lines and Legends Colors and Markers Figures and Axes Recap: Weekly Steps

8Filtering and Sorting

Selecting Rows Filtering Rows Sorting Values New Columns Recap: Top Products

11Datasets for ML

Built-in Datasets Features and Labels Exploring by Class Plotting the Classes Recap: Wine Dataset

3Array Math

Math on Whole Arrays Summary Statistics Statistics by Axis Broadcasting Boolean Masks Recap: Exam Scores

6Charts for Data

Bar Charts Histograms Several Charts in One Images as Arrays Recap: Rainfall Report

9Grouping and Cleaning

Counting Values Grouping Data Several Summaries Missing Values Recap: Survey Results

12Final Challenges

Sales Report Sensor ReadingsDigits at a GlancePenguin Explorer

Practice on your own: Online Python compiler