Jupyter Guide

To get started, open the file on the right entitled "Step 1...". You'll complete each task for Step 1 in that Jupyter Notebook file. Remember, you must run the cells (ctrl/cmd + Enter) for each task before moving onto the next task in the Jupyter Notebook. Continue until you have completed all tasks in this step. Then when you are ready to move onto the next step, you'll come back and click on the file for the next step until you have completed all tasks in all steps of the lab.

Exploring NumPy and its Features

To review the concepts covered in this step, please refer to the Introduction to NumPy module of the Getting Started with NumPy course.

Understanding the features and benefits of NumPy is important because it helps to understand why and when to use NumPy in data analysis.

In this step, you will create a very simple NumPy array with integers and perform some basic operations on it. You will also compare the performance of NumPy arrays with Python lists to understand the efficiency of NumPy. You'll run some provided code to time the execution of numpy array operations vs performing those operations on lists.

Task 1.1: Creating a NumPy Array

Use the provided code to import numpy into the evironment and then create a simple NumPy array with integers from 1 to 10 using the np.array function. Print the array you create.

import numpy as np

# Create a numpy array with integers from 1 to 10
array = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
print(array)

Task 1.2: Performing Basic Operations

Perform addition, subtraction, multiplication, and division operations on the array you created. Print the results.

# Perform addition, subtraction, multiplication, and division on the array
print("addition", array + 10)
print("subtraction", array - 5)
print("multiplication", array * 3)
print("division", array / 2)

Task 1.3: Comparing Performance with Python Lists

Use the provided code to compare the performance of NumPy arrays with Python lists by timing the execution of operations on both. The provided code adds 10 to every number from 1 to 1,000,000 using a list and using a numpy array. Run the code a few times and see which method is faster.

import time

# Create a Python list with integers from 1 to 1 000 000
oneMillion = [num for num in range(1, int(1e6)+1)]

# Time the execution of addition operation on the list
start_time = time.time()
list = [i + 10 for i in oneMillion]
end_time = time.time()

# Print the execution time
print('Execution time for list (seconds): ', end_time - start_time)

# Create the array
array = np.array(oneMillion)

# Time the execution of addition operation on the array
start_time = time.time()
array = array + 10
end_time = time.time()

# Print the execution time
print('Execution time for array (seconds): ', end_time - start_time)

Understanding the N-Dimensional Array Object

To review the concepts covered in this step, please refer to the Understanding the Ndarray Object module of the Getting Started with NumPy course.

Understanding the n-dimensional array (ndarray) object is important because it is the fundamental object underlying all of NumPy. This step will cover the creation of simple ndarray objects and their attributes.

In this step, you will create an instance of ndarray using the np.array function. You will also explore the attributes of the ndarray object, such as shape, size, ndim, and dtype. Use the np.array function to create your ndarray and the 'dot' notation to access its attributes.

Task 2.1: Creating a Simple ndarray

Import numpy with the alias np, and then create a simple ndarray object using the numpy.array function. The array should contain integers and be in any shape you want. Display the array.

# Import numpy
import numpy as np

# Create an ndarray using np.array
array = np.array([1, 2, 3, 4, 5])

# Print the array
print(array)

Task 2.2: Exploring ndarray Attributes: Shape

Print the shape of the ndarray you created in the previous task.

# Print the shape of the array
print(array.shape)

Task 2.3: Exploring ndarray Attributes: Size

Print the size of the ndarray you created in the first task.

# Print the size of the array
print(array.size)

Task 2.4: Exploring ndarray Attributes: ndim

Print the number of dimensions of the ndarray you created in the first task.

# Print the number of dimensions of the array
print(array.ndim)

Task 2.5: Exploring ndarray Attributes: dtype

Print the data type of the elements in the ndarray you created in the first task.

# Print the data type of the elements in the array
print(array.dtype)

Creating Ndarrays

To review the concepts covered in this step, please refer to the Creating Ndarrays module of the Getting Started with NumPy course.

Creating ndarrays is important because it is the first step in working with NumPy. This step will cover different ways of creating ndarrays, such as from existing data or within a numerical range.

In this step, you will create ndarrays using different methods. You will use the np.empty, np.zeros, np.ones, and np.full routines to create ndarrays. You will also create an ndarray from existing data using the np.asarray routine and an ndarray within a numerical range using the np.arange and np.linspace routines. At each stage, we'll print the results.

Task 3.1: Creating an Empty Ndarray

Import numpy with the alias np, and create an empty ndarray of shape (3, 3) using the np.empty routine. Display the resulting array.

# Import numpy
import numpy as np

# Create an empty ndarray
empty_array = np.empty((3, 3))
empty_array

Task 3.2: Creating a Zeros Ndarray

Create an ndarray of zeros with shape (3, 3) using the np.zeros routine. Display the resulting array.

# Create a zeros ndarray
zeros_array = np.zeros((3, 3))
zeros_array

Task 3.3: Creating a Ones Ndarray

Create an ndarray of ones with shape (3, 3) using the np.ones routine. Display the resulting array.

# Create a ones ndarray
ones_array = np.ones((3, 3))
ones_array

Task 3.4: Creating a Full Ndarray

Create an ndarray filled with the number 7 and of shape (3, 3) using the np.full routine. Display the resulting array.

# Create a full ndarray
full_array = np.full((3, 3), 7)
full_array

Task 3.5: Creating an Ndarray from Existing Data

Create an ndarray from the list [1, 2, 3, 4, 5] using the np.asarray routine. Display the resulting array.

# Create an ndarray from existing data
list_array = np.asarray([1, 2, 3, 4, 5])
list_array

Task 3.6: Creating an Ndarray within a Numerical Range

Create an ndarray of numbers from 0 to 10 with a step of 2 using the np.arange routine. Display the resulting array.

# Create an ndarray within a numerical range
range_array = np.arange(0, 10, 2)
range_array

Task 3.7: Creating an Ndarray of Evenly Spaced Values

Create an ndarray of 5 evenly spaced numbers between 0 and 1 using the np.linspace routine. Display the resulting array.

# Create an ndarray of evenly spaced values
linspace_array = np.linspace(0, 1, 5)
linspace_array