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This plan includes
- Instant access to 11,000+ online courses
- Play & Pause Course Videos
- HD Video Recorded Lectures
- Learn on Mobile/PC/Tablet
- Quizzes and Real Projects
- Lifetime Course Certificate
- Instructor Chat Support
- Cancel Plan Anytime
What you'll learn?
- Understanding Python Numpy basics in just 1.5 hours
- Implementing array operations using Numpy module in python
- Searching and sorting operations in Arrays
Course Overview
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.
Pre-requisites
- Python Programming
Target Audience
- Engineering Students
- Software Developers
- Aspiring Data Scientists
Curriculum 31 Lectures 01:12:07
-
Section 1 : Working with 1D arrays
- Lecture 2 :
- Using Negative Indexing to access elements in 1D array
- Lecture 3 :
- Iterating 1D arrays
-
Section 2 : Working with 2D arrays
- Lecture 1 :
- Creating & Accessing elements in 2D Array
- Lecture 2 :
- Using Negative Indexing to access elements in 2D array
- Lecture 3 :
- Iterating 2D arrays
-
Section 3 : Operations on arrays
- Lecture 1 :
- Finding Dimension of the Array
- Lecture 2 :
- Slicing an Array
- Lecture 3 :
- Checking Datatype of an array
- Lecture 4 :
- Copy operation on an array
- Lecture 5 :
- Getting shape of an array
- Lecture 6 :
- Reshaping 1D array to 2D array
- Lecture 7 :
- Joining two arrays
- Lecture 8 :
- Splitting an array
- Lecture 9 :
- Sorting an Array
- Lecture 10 :
- Searching for an element in array
- Lecture 11 :
- Filtering an array
- Lecture 12 :
- Generating a random integer
- Lecture 13 :
- Generating a random array
-
Section 4 : Test your knowledge on Numpy
- Lecture 1 :
- Question #1
- Lecture 2 :
- Solution to Question #1
- Lecture 3 :
- Question #2
- Lecture 4 :
- Solution to Question #2
- Lecture 5 :
- Question #3
- Lecture 6 :
- Solution to Question #3
- Lecture 7 :
- Question #4
- Lecture 8 :
- Solution to Question #4
- Lecture 9 :
- Question #5
- Lecture 10 :
- Solution to Question #5
- Lecture 11 :
- Question #6
- Lecture 12 :
- Solution to Question #6
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