This document provides an overview of NumPy, an open source Python library for numerical computing and scientific computing. It introduces NumPy and its key features like N-dimensional arrays for fast mathematical calculations. It then covers various NumPy concepts and functions such as initialization and creation of NumPy arrays, accessing and modifying arrays, concatenation, splitting, reshaping arrays, adding dimensions, utility functions, and broadcasting. The document aims to simplify learning of these NumPy concepts.

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2. Agenda
● Introduction to NumPy
● Initialization & Creation of NumPy
● Accessing
● Concatenation
● Splitting
● Shape,Size & ndim
● Reshaping
● Changing Dimension
● NumPy Utility Functions
● Broadcasting

3. Introduction to NumPy
● N-dim array for fast mathematical calculation
● Homogeneous data structure
● Written in C/C++
● Scikit-learn is implemented using this
● Python List is generalized & thus slow

4. Initialization
● Size is important parameter for all of them
● Zeros - initialized with zero values
● Ones - initialized with one values
● Empty - uninitialized array ( contains garbage value )
● Full - initialized with value passed
● Random - different methods to initialize random values
● Lin_space - generate equally spaced numbers between start & stop values
● Ones_like - based on passed numpy object, size is allocated with all one

5. Initialization

6. Initialization

7. Initialization

8. Access
● Very similar to that of accessing list

9. Concatenation
● Axis = 0 means vertical
● Axis = 1 means horizontal
● Concatenate - join numpy array along provided axis
● Hstack - joining numpy array horizontally
● Vstack - joining numpy array vertically
● For any of these operations to happen, the joining edge should be of shape
size. 3 X 2 matrix hstacks with 3 X 5

10. Concatenation

11. Splitting
● Splits array into subarrays
● Split - Splits the array as per axis mentioned
● Hsplit - This splits the horizontal axis
● Vsplit - This splits the vertical axis

12. Splitting

13. Shape, Size, ndim

14. Reshaping
● Learning algorithms expects data in certain shape & dimension
● Using reshaping utility we can convert data into desired shape.
● But, the desired transformation will also be of same size
● Adding 1 dimensions will not alter data size

15. Reshaping

16. Adding Dimension
● Learning algorithms consumes data in
two dimension
● Below, we are converting 1-D array to 2-D

17. Adding Dimension

18. Utility Functions
● NumPy has a huge list of mathematical functions to make implementing
machine learning algorithms very easy
● A few of them are
● Max
● Min
● Mean
● Std
● Cov
● Cumprod
● all
● any
● Mean
● dot
● multiply
● sqrt

19. Broadcasting
● Single row matrix are known as vectors
● Broadcasting is a technique using which NumPy does mathematical
computation on data of different shapes & dimension
● We might need to reshape the data sometimes to enable broadcasting

20. Broadcasting

21. Thank You !!!

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