Python with NumPy For Absolute Beginners
Learn Python & Numpy with simple videos
Language: english
Note: 4.7/5 (211 notes) 16,282 students
Instructor(s): Surendra Varma Pericherla
Last update: 2022-10-18
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
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
Arrays in Numpy are equivalent to lists in python. Like lists 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
Course content
- Understanding Lists are different from Numpy arrays
- Creating & Accessing Listsin Python
- Working with built-in functions in Lists
- Functions in Python
- Functions
- Examples on Functions
- Using Numpy Module in Python
- Online IDE for running Python Numpy programs
- Creating & Accessing elements in 1D Array
- Creating & Accessing elements in 2D Array
- Finding Dimension of the Array
- Using Negative Indexing to access elements in 1D array
- Using Negative Indexing to access elements in 2D array
- Slicing an Array
- Checking Datatype of an Array
- Copy Operation on an array
- Iterating 1D array
- Iterating 2D array
- Finding Shape of the Array
- Reshaping 1D Array to 2D Array
- Joining Two Arrays
- Splitting an Array
- Sorting an Array
- Searching for an Element in Array
- Filtering an Array
- Generate a Random Integer
- Generating a Random Array
- Quizzes
- Question #1
- Solution to Question #1
- Question #2
- Solution to Question #2
- Question #3
- Solution to Question #3
- Question #4
- Solution to Question #4
- Question #5
- Solution to Question #5
- Question #6
- Solution to Question #6
Time remaining or 500 enrolls left
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