1.4. Exercises

These exercises are from [4], [2] and [3].

1.4.1. Python Notebook

Exercise 1.1 (Hello World!)

Please set up a Python Notebook environment and type print('Hello World!').

Exercise 1.2

Please set up a Python Notebook and start a new virtual environment and type print('Hello World!').

1.4.2. Basic Python

Exercise 1.3 (Play with lists)

Please complete the following tasks.

• Write a for loop to print values from 0 to 4.

• Combine two lists ['apple', 'orange'] and ['banana'] using +.

• Sort the list ['apple', 'orange', 'banana'] using sorted().

Solution to Exercise 1.3 (Play with lists)

for i in range(5):
    print(i)

newlist = ['apple', 'orange'] + ['banana']

sorted(['apple', 'orange', 'banana'])

Please be careful about the last line. sorted() doesn’t change the original list. It create a new list. There are some Python functions which change the inputed object in-place. Please read documents on all packages you use to get the desired results.

Exercise 1.4 (Play with list, dict and pandas.)

Please complete the following tasks.

• Create a new dictionary people with two keys name and age. The values are all empty list.

• Add Tony to the name list in people.

• Add Harry to the name list in people.

• Add number 100 to the age list in people.

• Add number 10 to the age list in people.

• Find all the keys of people and save them into a list namelist.

• Convert the dictionary people to a Pandas DataFrame df.

Solution to Exercise 1.4 (Play with list, dict and pandas.)

import pandas as pd

people = {'name': list(), 'age': list()}
people['name'].append('Tony')
people['name'].append('Harry')
people['age'].append(100)
people['age'].append(10)

namelist = people.keys()

df = pd.DataFrame(people)

Exercise 1.5 (The dataset iris)

from sklearn.datasets import load_iris
iris = load_iris()

Please explore this dataset.

• Please get the features for iris and save it into X as an numpy array.

• What is the meaning of these features?

• Please get the labels for iris and save it into y as an numpy array.

• What is the meaning of labels?

Solution to Exercise 1.5 (The dataset iris)

We first find that iris is a dictionary. Then we can look at all the keys by iris.keys(). The interesting keys are data, target, target_names and feature_names. We can also read the description of the dataset by looking at DESCR.

X = iris['data']
print(iris['feature_names'])
y = iris['target']
print(iris['target'])

Since the data is already saved as numpy arrays, we don’t need to do anything to change its type.

Exercise 1.6 (Play with Pandas)

Please download the Titanic data file from here. Then follow the instructions to perform the required tasks.

• Use pandas.read_csv to read the dataset and save it as a dataframe object df.

• Change the values of the Sex column that male is 0 and female is 1.

• Pick the columns Pclass, Sex, Age, Siblings/Spouses Aboard, Parents/Children Aboard and Fare and transform them into a 2-dimensional numpy.ndarray, and save it as X.

• Pick the column Survived and transform it into a 1-dimensional numpy.ndarray and save it as y.

Solution to Exercise 1.6 (Play with Pandas)

Not yet done!