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 II
- 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 NumPyToggle Dropdown
- 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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Using `return`

, you can write a function that returns a value. You can assign the returned values to a variable which can be used in whatever manner you choose.

This is useful when you need to use the results your function produces for something else.

First let's create a simple multiplication function. We will see an output of 40.

Now let us add 2 to the variable `mul`

print(mul+2)

With the updated function below, we will see an output of 42.

You can also create a function that takes in multiple arguments, and add them both together.

**Results:**

4

11

15

Since we do not declare variables in Python, this means that we can use the addition function we created above to add any 2 sets of data together as long as they are both the same type (string, float, integer).

Let's add these codes into our trinket and see what happens!

print(addition("one","time")) print(addition([1,1],[1,1]))

The results shows that the function concatenated "one" and "time", and merged the 2 lists to become [1, 1, 1, 1].

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