Plan for the lab

• Background discussion.

• Exercise-1: Calculate a centered data matrix.

• Exercise-2: Plot histograms.

• Exercise-3: Scatter plot.

• Discussion related to wine dataset and pandas.

The iris data-set

peek into iris.csv

• Has three classes: Setosa, Versicolor and Virginica.

Loading the Iris dataset

• The Iris dataset normally comes as a comma-separated csv file.

• one specimen per row

• Each row has values of sepal length, sepal width, petal length, petal width.

• Use built-in csv function to create numpy array that can be iterated.

• Convert (cast) class column to represent integers instead of strings.

import csv
FILE = 'iris.csv'
with open(FILE, 'r') as mycsvfile:
    dataset = csv.reader(mycsvfile, delimiter='.', quotechar='"')
    for row in dataset:
        #py 2: print ', '.join(row)
        #py 3:
        print(row, sep=',')

Interest for Data Science

• given the annotated dataset, can we measure differences in average measures?

. . .

• can difference in size become a classifier?

. . .

Please see the Kaggle notebook

Homework:

• Develop your solution for measuring

• mean of the dataset and

• variance in each dimension.

How are they defined on multi-dimensional data?

Background: Dot product

Background: Norm

Mean

Variance

with Numpy

import numpy as np 
data = np.arange(6).reshape((3,2))
print(data)
print(np.average(data, axis=1))
print(np.average(data, axis=0))

Centering the dataset

Exercise 1

• Use iris_centered_matrix.py for this exercise.

• Later, a solution will be available as file iris_centered_matrix_solution.py

Visualisation

Histogram

• A graph consisting of rectangles whose area is proportional to the frequency of a feature and whose width is equal to the class interval denoted as bins.

Scatterplots

• A 2D visualization of datapoints against Cartesian coordinates.

• Normally, the measured variable is on the x-axis.

• if time is available then it is always on the x-axis.

A plot function in python

    def histogram_sepal_width(data):
        # create an empty figure object
        fig = plt.figure()
        # create a single axis on that figure
        ax = fig.add_subplot(1,1,1)
        # todo: histogram the data and label the axes
        ax.set_xlabel("Change me please")
        ax.set_ylabel("Change me please")

Visualization exercise:

• Use iris-baseline.py for the following exercises.

Exercise 2

• Plot a histogram of the sepal width column.

Exercise 3

• Scatter-plot Sepal length Vs. Sepal width

• Later, a solution will be available as file iris-plotting.py

Try at home

• Can you reproduce these visualizations?