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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More often than not, data analysis will involve some form of mathematical calculations. Having a large data set also means that you would not want to have to manually count each data point. What you can do is to perform mathematical calculations using arrays.

Basic mathematical functions on arrays are element wise operators. Let's take a look at what this means.

Let's start by creating some arrays to work with:

There are 2 ways to perform element-wise addition with arrays.

**Method 1: **Simply just add them together using `+`

operator

print(x + y)

**Method 2:** Use `np.add`

print(np.add(x,y))

Similarly, to subtract use `np.subtract`

or `-`

, to multiply use `np.multiply`

or `*`

, and to divide use `np.divide`

or `/`

.

To perform element-wise square root we can use `np.sqrt`

**Syntax**

print(np.sqrt(x+y))

To look for the inner product, or matrix multiplication of arrays, use `np.dot`

.

We have to first make sure that the rows and columns match, just like we would for any other matrix multiplication.

To sum up elements in an array, use `np.sum`

**Syntax**

(np.sum(array))

To find the sum of each column (adding up all row values for each column) we just need to specify the `axis = 0 `

**Syntax**

print(np.sum(x, axis=0))

To find the sum of each row we use `axis = 1`

**Syntax**

print(np.sum(x, axis=1))

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