Learn Python & Numpy with simple videos
- Rating: 4.3 out of 54.3 (439 ratings)
- 34,731 students
- Created by Surendra Varma Pericherla
- Last updated 07/2023
- English
This course includes:
- 2 hours on-demand video
- Access on mobile and TV
- Full lifetime access
- Certificate of completion
What you’ll learn
- Understanding Python Numpy basics in just 2 hours
- Understanding Lists are different from arrays
- Implementing array operations using Numpy module in python
- Searching and sorting operations in Arrays
Course content
3 sections • 37 lectures • 2h 3m total lengthCollapse all sections
Understanding Lists are different from Numpy arrays5 lectures • 50min
- CHECK MY OTHER UDEMY COURSESPreview01:05
- Creating & Accessing Listsin PythonPreview07:06
- Working with built-in functions in ListsPreview31:05
- Functions04:28
- Examples on Functions06:20
Using Numpy Module in Python20 lectures • 1hr 1min
- Online IDE for running Python Numpy programs01:06
- Creating & Accessing elements in 1D Array01:56
- Creating & Accessing elements in 2D Array03:55
- Finding Dimension of the Array02:46
- Using Negative Indexing to access elements in 1D array02:16
- Using Negative Indexing to access elements in 2D array03:27
- Slicing an Array10:29
- Checking Datatype of an Array01:00
- Copy Operation on an array03:32
- Iterating 1D array01:22
- Iterating 2D array01:22
- Finding Shape of the Array01:54
- Reshaping 1D Array to 2D Array02:41
- Joining Two Arrays02:28
- Splitting an Array02:47
- Sorting an Array03:46
- Searching for an Element in Array06:34
- Filtering an Array04:20
- Generate a Random Integer02:00
- Generating a Random Array01:29
Quizzes12 lectures • 12min
- Question #100:23
- Solution to Question #101:07
- Question #200:27
- Solution to Question #201:31
- Question #300:25
- Solution to Question #302:05
- Question #400:13
- Solution to Question #402:00
- Question #500:23
- Solution to Question #502:31
- Question #600:14
- Solution to Question #600:44
Requirements
- Python Programming
Description
NumPy is a basic level external library in Python used for complex mathematical operations. NumPy overcomes slower executions with the use of multi-dimensional array objects. It has built-in functions for manipulating arrays. We can convert different algorithms to can into functions for applying on arrays. NumPy has applications that are not only limited to itself. It is a very diverse library and has a wide range of applications in other sectors. Numpy can be put to use along with Data Science, Data Analysis and Machine Learning. It is also a base for other python libraries. These libraries use the functionalities in NumPy to increase their capabilities.
This course introduce with all majority of concept of NumPy – numerical python library.
You will learn following topics :
1) Creating Arrays using Numpy in Python
2) Accessing Arrays using Numpy in Python
3) Finding Dimension of the Array using Numpy in Python
4) Negative Indexing on Arrays using Numpy in Python
5) Slicing an Array using Numpy in Python
6) Checking Datatype of an Array using Numpy in Python
7) Copying an Array using Numpy in Python
8) Iterating through arrays using Numpy in Python
9) Shape of Arrays using Numpy in Python
10) Reshaping Arrays using Numpy in Python
11) Joining Arrays using Numpy in Python
12) Splitting Array using Numpy in Python
13) Sorting an Array using Numpy in Python
14) Searching in Array using Numpy in Python
15) Filtering an Array using Numpy in Python
16) Generating a Random Array using Numpy in Python
The Numpy arrays are homogenous sets of elements. The most important feature of NumPy arrays is they are homogenous in nature. This differentiates them from python arrays. It maintains uniformity for mathematical operations that would not be possible with heterogeneous elements. Another benefit of using NumPy arrays is there are a large number of functions that are applicable to these arrays. These functions could not be performed when applied to python arrays due to their heterogeneous nature.
Happy learning
Surendra Varma Pericherla
Who this course is for:
- Engineering Students, Software Developers, And Aspiring Data Scientists
