Start your data science journey with Python. Learn practical Python programming skills for basic data manipulation and analysis.

- Home
- Python Essentials for Data Analysis IToggle Dropdown
- 1.1 Getting started - Hello, World!
- 1.2 Variables
- 1.3 Data types
- 1.4 Printing
- 1.5 Lists
- 1.6 Dictionaries
- 1.7 Input function
- 1.8 Arithmetic operators
- 1.9 Comparison operators
- 1.10 Logical operators
- 1.11 Identity operators
- 1.12 Membership operators
- 1.13 Conditional statements (if-elif-else)
- 1.14 Importing modules
- 1.15 For loops
- 1.16 While loops

- Python Essentials for Data Analysis IIToggle Dropdown
- 2.1 Introduction to Functions in Python
- 2.2 Functions - Arguments
- 2.3 Functions with Return Values
- 2.4 Functions - A Fun Exercise!
- 2.5 Functions - Arbitrary Arguments (*args)
- 2.6 Functions - Arbitrary Keyword Arguments (**kwargs)
- 2.7 Recursive Functions
- 2.8 Lambda Expressions
- 2.9 Functions - More Exercises

- Data Analysis with PandasToggle Dropdown
- PD.1 Introduction to Pandas
- PD.2 Basics of Pandas
- PD.3 Finding and Describing data
- PD.4 Assigning Data
- PD.5 Manipulating Data
- PD.6 Handling Missing Data
- PD.7 Removing and adding data
- PD.8 Renaming data
- PD.9 Combining data
- PD.10 Using Pandas with other functions/mods
- PD.11 Data classification and summary
- PD.12 Data visualisation

- Data Analysis with NumPy
- NP.1 Introduction to NumPy
- NP.2 Create Arrays Using lists
- NP.3 Creating Arrays with NumPy Functions
- NP.4 Array Slicing
- NP.5 Array Reshaping
- NP.6 Math with NumPy I
- NP.7 Combining 2 arrays
- NP.8 Adding elements to arrays
- NP.9 Inserting elements into arrays
- NP.10 Deleting elements from arrays
- NP.11 Finding unique elements and sorting
- NP.12 Math with NumPy II
- NP.13 Analysing data across arrays
- NP.14 NumPy Exercises

- Learning Resources
- Contact Us

You can select elements in an array based on conditions. Let's begin with 2 arrays.

`np.where`

lets you select elements from an array based on conditions. It **returns the index** of the elements that meets the conditions.

In this example, using the 2 arrays from above, we can use `np.where`

to find which:

- elements that are the same in both arrays
- elements in
`x`

that are bigger than or equal to its corresponding element in`y`

- elements in
`x`

multiplied by 2 that are smaller than its corresponding element in`y`

- elements in
`y`

that are bigger than 5

Conditional operators like `=`

, `<`

, `>`

can be used to compare arrays. Using them will **return Boolean values**.

In addition, you can use this syntax to **return the index** of elements that meets the condition:

y [y > 3]

Similarly, you can also use `np.where`

:

Use these arrays for the following practice:

myArray_1 = np.array([2,2,3,4]) myArray_2 = np.array([3,2,3,4])

1. Find the elements that are the same in both arrays

2. Find elements in `myArray_1`

that are smaller than or equal to its corresponding element in `myArray_2`

3. Find the elements in `myArray_1`

that is bigger than 2

- Last Updated: Feb 6, 2024 10:02 AM
- URL: https://libguides.ntu.edu.sg/python
- Print Page

You are expected to comply with University policies and guidelines namely, Appropriate Use of Information Resources Policy, IT Usage Policy and Social Media Policy.
Users will be personally liable for any infringement of Copyright and Licensing laws.

Unless otherwise stated, all guide content is licensed by CC BY-NC 4.0.