To compare each element of a NumPy array `arr`

against the scalar `x`

using any of the greater (>), greater equal (>=), smaller (<), smaller equal (<=), or equal (==) operators, use the **broadcasting feature** with the array as one operand and the scalar as another operand. For example, the greater comparison `arr > x`

results in an array of Boolean values from the element-wise comparisons.

array > scalar

array >= scalar

array < scalar

array <= scalar

array == scalar`# yields a new Boolean array [True/False ... True/False]`

Table of Contents

## Problem Formulation

Given are:

- A NumPy array
`arr`

. - A scalar value
`x`

.

❓ How to ** compare each element** of the NumPy array

`arr`

against the scalar `x`

using any of the greater (>), greater equal (>=), smaller (<), smaller equal (<=), or equal (==) operators? The desired result is a NumPy array of ** Boolean values** respresenting the element-wise comparison results.

For example, consider the following pseudocode of what you’re trying to accomplish:

**# Given**
arr = [1 10 100]
x = 3
**# Desired**
res = [1>x 10>x 100>x] = [False True True]

## Solution: Broadcasting

You can use all comparison operators of a scalar value on a NumPy array:

**Greater**:`arr > x`

**Greater or equal**:`arr >= x`

**Smaller**:`arr < x`

**Smaller or equal**:`arr <= x`

**Equal**:`arr == x`

NumPy will automatically bring both operands into the same shape (a feature called “* broadcasting*“).

import numpy as np # Given arr = np.array([1, 10, 100]) x = 3 # Greater: print(arr > x) # [False True True] # Greater or equal: print(arr >= x) # [False True True] # Smaller: print(arr < x) # [ True False False] # Smaller or equal: print(arr <= x) # [ True False False] # Equal: print(arr == x) # [False False False]

The comparison is performed element-wise and the result of the operation is a Boolean array as desired.

## Data Science Puzzle

import numpy as np # popular instagram accounts # (millions followers) inst = [232, #"@instagram" 133, #"@selenagomez" 59, #"@victoriassecret" 120, #"@cristiano" 111, #"@beyonce" 76] #"@nike" inst = np.array(inst) superstars = inst > 100 print(superstars[0]) print(superstars[2])

**Exercise**: *What is the output of this puzzle?*

You can solve this puzzle on our interactive puzzle-based training app and track your Python skills:

NumPy is a popular Python library for data science focusing on linear algebra.

The following handy NumPy feature will prove useful throughout your career. You can use comparison operators directly on NumPy arrays. The result is an equally-sized NumPy array with Boolean values. Each Boolean indicates whether the comparison evaluates to `True`

for the respective value in the original array.

The puzzle creates a list of integers. Each integer represents the number of followers of popular Instagram accounts (in millions).

- First, we convert this list to a NumPy array.
- Second, we determine for each account whether it has more than 100 million followers.

We print the first and the third boolean value of the resulting NumPy array. The result is `True`

for `@instagram`

with 232 million followers and `False`

for `@victoriassecret`

with 59 million followers.

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