Array creation (array, arange, linspace) in Data Analysis Python - Mini Project: Build & Apply

Array Creation with NumPy

📖 Scenario: You are working as a data analyst. You need to create different types of number arrays to analyze sales data over time.

🎯 Goal: Learn how to create arrays using array, arange, and linspace from the NumPy library.

📋 What You'll Learn

Use NumPy to create arrays

Create an array from a list

Create an array with evenly spaced values using arange

Create an array with a specific number of points using linspace

💡 Why This Matters

🌍 Real World

Creating arrays is a basic step in data analysis for organizing and manipulating numerical data like sales, time, or measurements.

💼 Career

Data analysts and scientists use array creation to prepare data for calculations, statistics, and visualizations.

1

Create a NumPy array from a list

Import the NumPy library as np. Then create a NumPy array called sales from the list [100, 200, 300, 400, 500].

Data Analysis Python

import numpy as np
# Create a NumPy array called sales from the list [100, 200, 300, 400, 500]
# Your code here

Need a hint?

Use np.array() to convert a list to a NumPy array.

2

Create an array with arange

Create a NumPy array called days using np.arange that contains integers from 1 to 7 inclusive.

Data Analysis Python

import numpy as np
sales = np.array([100, 200, 300, 400, 500])
# Create a NumPy array called days with values from 1 to 7 using np.arange
# Your code here

Need a hint?

Remember that np.arange(start, stop) includes start but excludes stop.

3

Create an array with linspace

Create a NumPy array called time_points using np.linspace that contains 5 evenly spaced numbers between 0 and 10 inclusive.

Data Analysis Python

import numpy as np
sales = np.array([100, 200, 300, 400, 500])
days = np.arange(1, 8)
# Create a NumPy array called time_points with 5 values from 0 to 10 using np.linspace
# Your code here

Need a hint?

np.linspace(start, stop, num) creates num points evenly spaced between start and stop, including both ends.

4

Print all arrays

Print the arrays sales, days, and time_points each on a separate line.

Data Analysis Python

import numpy as np
sales = np.array([100, 200, 300, 400, 500])
days = np.arange(1, 8)
time_points = np.linspace(0, 10, 5)
# Print sales, days, and time_points arrays
# Your code here

Need a hint?

Use three separate print() statements to show each array.