Array creation (array, arange, linspace) in Data Analysis Python

Introduction

We create arrays to store and work with many numbers together easily. Arrays help us do math and analyze data faster.

When you want to store a list of numbers to analyze or plot.

When you need a sequence of numbers with equal steps between them.

When you want a specific number of points evenly spaced between two values.

When you want to convert a list or tuple into an array for faster calculations.

When preparing data for machine learning or statistics.

Syntax

Data Analysis Python

import numpy as np

# Create an array from a list
array = np.array([1, 2, 3, 4])

# Create an array with numbers from start to stop (exclusive) with step size
arange_array = np.arange(start, stop, step)

# Create an array with a specific number of points between start and stop (inclusive)
linspace_array = np.linspace(start, stop, num=num_points)

np.array converts a list or tuple into an array.

np.arange creates numbers with a fixed step size but excludes the stop value.

Examples

Creates an empty array when the input list is empty.

Data Analysis Python

import numpy as np

# Empty array from empty list
empty_array = np.array([])
print(empty_array)

Array with only one number inside.

Data Analysis Python

import numpy as np

# Array with one element
single_element_array = np.array([10])
print(single_element_array)

Numbers from 0 up to but not including 5, stepping by 1.

Data Analysis Python

import numpy as np

# arange from 0 to 5 with step 1
range_array = np.arange(0, 5, 1)
print(range_array)

5 numbers evenly spaced between 0 and 1, including both ends.

Data Analysis Python

import numpy as np

# linspace from 0 to 1 with 5 points
linspace_array = np.linspace(0, 1, 5)
print(linspace_array)

Sample Program

This program shows how to create arrays using three methods: from a list, with fixed steps, and with a fixed number of points.

Data Analysis Python

import numpy as np

# Create an array from a list
numbers_list = [2, 4, 6, 8]
numbers_array = np.array(numbers_list)
print("Array from list:", numbers_array)

# Create an array using arange from 1 to 10 with step 2
arange_array = np.arange(1, 10, 2)
print("Arange array:", arange_array)

# Create an array using linspace from 0 to 5 with 6 points
linspace_array = np.linspace(0, 5, 6)
print("Linspace array:", linspace_array)

Important Notes

Time complexity: Creating arrays is fast and usually O(n), where n is the number of elements.

Space complexity: Arrays use memory proportional to the number of elements stored.

Common mistake: Using np.arange with floating point steps can cause unexpected results due to rounding errors. Use np.linspace for precise spacing.

Use np.array to convert existing data. Use np.arange when you want a range with a fixed step size. Use np.linspace when you want a fixed number of points between two values.

Summary

Arrays store many numbers together for easy math and analysis.

np.array converts lists to arrays.

np.arange creates sequences with fixed steps, excluding the stop value.

np.linspace creates sequences with a fixed number of points including start and stop.