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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Lambda expressions does what a function does without the need to properly define the function using `def`

.

The body of a lambda expression will be similar to what we will put into the `return`

of a function defined with `def`

.

Let's take a look at an example using a multiplication function.

Since this function only has `return`

we can even write it in 1 line.

To simplify that even further, we can use the lambda expression.

lambda x: x*20

You may notice that if you simply run the lambda expression you will get

```
<function __main__.<lambda>(x)>
```

This isn't very helpful. So in order to make use of a lambda expression, we assign it a variable.

Now you might be asking, why are we using this lambda expression instead of a normal function?

This is because there are times when you only need to use the function once so you do not have to define it formally.

For example, if we want to use the filter function.

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