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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To delete elements in your dataset, use`np.delete`

**Syntax**

np.delete(ndarray,elements,axis)

Let's start with this array in `x`

and delete the 2nd and 4th elements. To do so, we simply need to specify the positions in `np.delete`

.

Now lets say we have a 2D array. Removing elements will no longer be as easy as removing them from 1D arrays. If you remove just 1 element, the array will have improper dimensions. Hence, all that can be done is to remove entire columns or rows.

Let's start with a new array `y`

and delete the elements in the 2nd row.

Below, 0 and `axis = 1`

refers to the **elements **in the **first column**. Recall that axis defines whether you choose columns or rows.

y = np.delete(y, 0, axis = 1)

Using the same array, let's try a different combination to delete elements from the second row. Here, 0 and `axis = 0`

refers to the **elements **in the **first row**.

y = np.delete(y, 0, axis = 0)

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