Essential NumPy

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Essential NumPy
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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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