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

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- 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

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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: Jun 24, 2024 9:14 AM
- URL: https://libguides.ntu.edu.sg/python
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