# Python For NumPy For Absolute Beginners 2023

Learn Complete NumPy from Basic to Advance topic like Indexing, Slicing, Broadcasting, Joining, Splitting, Searching …

NumPy (short for “Numerical Python”) is a Python library used for scientific computing and data analysis. It provides a powerful set of tools for working with arrays and matrices of numerical data. NumPy is particularly useful for numerical calculations involving large amounts of data, as it is designed to be efficient and fast.

What you’ll learn

• Understanding NumPy Library from zero to advanced.
• Why We Use NumPy Array Over Python List.
• Implementing array operations using NumPy module in python.
• How to work in High dimension Datasets with NumPy array.

Course Content

• Introduction –> 4 lectures • 9min.
• How to Create Numpy Array –> 6 lectures • 12min.
• Numpy Array Attributes –> 4 lectures • 10min.
• Numpy Array Operation –> 3 lectures • 6min.
• Numpy Array Function –> 5 lectures • 18min.
• Indexing and Slicing in Numpy array –> 4 lectures • 19min.
• Iteration of Numpy Array –> 3 lectures • 5min.
• Reshaping of Numpy Array –> 3 lectures • 4min.
• Joining Two Array –> 2 lectures • 6min.
• Splitting Two Array –> 2 lectures • 6min.
• Advance Level –> 2 lectures • 9min. Requirements

NumPy (short for “Numerical Python”) is a Python library used for scientific computing and data analysis. It provides a powerful set of tools for working with arrays and matrices of numerical data. NumPy is particularly useful for numerical calculations involving large amounts of data, as it is designed to be efficient and fast.

One of the main features of NumPy is its ability to handle multi-dimensional arrays of data. These arrays can be used to represent vectors, matrices, or any other kind of numerical data. NumPy provides a large number of built-in functions for performing operations on these arrays, such as mathematical functions (like sin, cos, and exp), statistical functions (like mean and variance), and linear algebra functions (like matrix multiplication and eigendecomposition).

NumPy also provides a number of tools for working with structured data, such as CSV files or other tabular data. These tools allow you to easily import and manipulate data, and to perform complex calculations and analyses on it.

In addition to its core functionality, NumPy is often used as a foundation for other Python libraries that are used in scientific computing and data analysis, such as Pandas and SciPy. This makes NumPy an essential tool for anyone working in these fields.

Overall, NumPy is a powerful and versatile library that is an essential tool for anyone working in scientific computing, data analysis, or related fields.

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)Finding the Shape of the Array using NumPy

5) Checking Datatype of an Array using NumPy in Python

6) Changing Datatype of an Array using NumPy in Python

7) Reshaping of an arrays using NumPy in Python

8) Iterating through arrays using NumPy in Python

9) Indexing on (1D, 2D, 3D)  Arrays using NumPy in Python

10) Slicing an (1D, 2D, 3D) Array using NumPy in Python

11) Operation (Scalar , Relational , Vector ) of NumPy Array

12) Joining Arrays using NumPy in Python

13) Splitting Array using NumPy in Python

14) Sorting an Array using NumPy in Python

15) Searching in Array using NumPy in Python

16) Filtering an Array using NumPy in Python

17) Generating a Random Array using NumPy in Python

18) Dot Product of NumPy Array

19) Converting N D to 1 D NumPy Array

20) Plotting Graphs

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