Lab4--Python--Numpy for basics and variables.pdf

Artificial Intelligence
Third Year Students
Department of Computer Science
T.A. Rehab Emad El-Dein Sayed
FCI-Minia University 1

Course Contents
I. PART 1
 Introduction to Python Programming
Why Python
Install Python Environment
II. PART 2
 Python Basic Syntax
 Python Statements & Comments
 Python Variables and Data Types
 Python I/O and Import
 Python Operators

Course Contents
III. PART 3
 Python Flow Control
 Python if…else
 Python for Loop
 Python while Loop
IV. PART 4
 Python Functions
 Python Recursion
 Python Anonymous Function
V. PART 5
 Python Object & Class
 Python Inheritance
VI.PART 6
Python Numpy

Agenda
 Array in Numpy
 Creating a Numpy Array
 Accessing the Numpy array Index
 Data Types in Numpy

Numpy
 Numpy
 Is a general-purpose array-processing package.
 It provides a high-performance multidimensional array object,
 It provides tools for working with these arrays.
 It is the fundamental package for scientific computing with Python.
 Numpy can also be used as an efficient multi-dimensional container of generic data.

Numpy
6
 N-Dimensional array (ndarray) in Numpy
 Array in Numpy:
 is a table of elements (usually numbers),
 all of the same type,
 indexed by a tuple of positive integers.
 Rank of the array: is the Number of dimensions of the array.
 Shape of the array: is a tuple of integers giving the size of the array along each
dimension. (no. of rows, no. of columns)
 ndarray: An array class.
 Elements in Numpy arrays are accessed by using square brackets and can be initialized by
using nested Python Lists.

7
Numpy
Example :
[ [ 1, 2, 3],
[ 4, 2, 5] ]
rank = 2 (as it is 2-dimensional or it has 2 axes)
First dimension(axis) length = 2,
second dimension has length = 3
shape can be expressed as: (2, 3)

Agenda
 Array in Numpy
Creating a Numpy Array

Creating a Numpy Array
 Arrays in Numpy can be created by multiple ways, with various number of Ranks, defining
the size of the Array.
 Arrays can also be created with the use of various data types such as lists, tuples, etc.
 The type of the resultant array is deduced from the type of the elements in the sequences.
 Numpy offers several functions to create arrays with initial placeholder content. These
minimize the necessity of growing arrays, an expensive operation.
For example: np.zeros, np.ones, np.full, np.empty, etc.

Creating Array from
List with rank=1
Creating Array from
List with rank=2
Creating Array from
Tuple with rank=1

Print type of arr
Print rank of arr =2
Print shape of arr
(2,3)
Print size of arr
no. of elements=6
Print type of arr
elements

13
Creating Array: empty
numpy.empty(shape, dtype = float, order = ‘C’)
 Return a new array of given shape and type, with random values.
 Parameters :
 shape : Number of rows,
 order : C_contiguous or F_contiguous.
 dtype : [optional, float(by Default)] Data type of returned array.

15
numpy.zeros(shape, dtype = float, order = ‘C’)
 Return a new array of given shape and type, with zero values.
Creating Array: zeros

Creating Array: zeros

Creating Array: arange
arange([start,] stop[, step,][, dtype])
 Returnsan array with evenly spaced elements as per the interval.
 The interval mentioned is half opened i.e. [Start, Stop)
 Parameters :
 start : [optional]start of interval range. By default start = 0
 stop : end of interval range
 step : [optional] step size of interval. By default step size = 1, For any output
out, this is the distance between two adjacent values, out[i+1]- out[i].
 dtype : type of outputarray

Creating Array: arange

numpy.reshape(array, shape, order = ‘C’)
 shapes an array without changing data of array.
 Parameters :
 array : [array_like] Input array
 shape : [int or tuples_of_int] e.g. if we are aranging an array with
10 elements then shaping it like numpy.reshape(4, 8) is wrong;
Creating Array: reshape

Creating Array: reshape

Creating Array: linspace
numpy.linspace(start, stop, num = 50, endpoint = True, retstep = False, dtype = None)
 Returnsnumber spaces evenly w.r.t interval.
 Similar to arange but instead of step it uses sample number.
 Parameters :
 start : [optional]start of interval range. By default start = 0
 stop : end of interval range
 restep : (representstep) If True, return (samples, step). By deflut restep = False
 num : [int, optional]No. of samples to generate
 dtype : type of outputarray

Creating Array: linspace

numpy.ndarray.flatten(order = ‘C’)
 Return a copy of the array collapsed into one dimension.
 Flatten array: We can use flatten method to get a copy of array collapsed into one
dimension.
 It accepts order argument:
 Default value is ‘C’ (for row-major order).
 Use ‘F’ for column major order.
Creating Array: flatten

Creating Array: flatten

Agenda
 Array in Numpy
Data Types in Numpy

Data Type in Numpy
 Every ndarray has an associated data type (dtype) object.
 This data type object (dtype) informs us about the layout of the array. This means it
gives us information about :
 Type of the data (integer, float, Python object etc.)
 Size of the data (number of bytes)
 Byte order of the data (little-endian or big-endian)
 If the data type is a sub-array, what is its shape and data type.
 Constructing a data type (dtype) object : Data type object is an instance of
numpy.dtype class and it can be created using numpy.dtype.

 The type specifiercan take different forms:
 b1, i1, i2, i4, i8, u1, u2, u4, u8, f2, f4, f8, c8, c16, a
(representing bytes, ints, unsigned ints, floats, complex and
fixed length strings of specified byte lengths)
 int8,…,uint8,…,float16, float32, float64, complex64, complex128
(this time with bit sizes)
Data Type in Numpy

Data Type in Numpy

Data type Objects with Structured Arrays
Data Type in Numpy

Agenda
 Array in Numpy
Accessing the Numpy array Index

Numpy Indexing: Basic
Basic Slicing and Indexing
Start from 10 to 1
with a step=-2
elem_index= 3
elem_index = 1
elem_index = 2

elem_index = 1
elem_index = 3
elem_index =-3

 Consider the syntax x[obj] where x is the array and obj is the index. Slice object
is the index in case of basic slicing.
 Basic slicing occurs when obj is :
 a slice object that is of the form [start : stop : step] inside of brackets
 an integer
 or a tupleof slice objects and integers
 All arrays generated by basic slicing are always view of the original array.

Start from -8 to 17
with a step=1
Start from 10 to
end

Start from -2 to 10
Start from -3 to 3
with a step= -1

 Ellipsis can also be used along with basic slicing.
 Ellipsis (…) is the number of : objects needed to make a selection tuple of the same
length as the dimensions of the array.
b[… , 1] = b[ : , : , 1]

Advanced Slicing and Indexing
 Advanced indexing is triggered when obj is :
 an ndarray of type integer or Boolean.
 or a tuplewith at least one sequence object.
 is a non tuplesequence object.
 Advanced indexing returnsa copy of data rather than a view of it.
 Advanced indexing is of two types integer and Boolean.
Numpy Indexing:Advanced

 Purely integer indexing : When integers are used for indexing.
 Each element of first dimension is paired with the element of the second
dimension.
 So the index of the elements in this case are (0,0),(1,0),(2,1) and the
correspondingelements are selected.

Boolean Indexing
 This indexing has some booleanexpression as the index.
 Those elements are returned which satisfy that Boolean expression.
 It is used for filtering the desired element values.

 Boolean Indexing
Select numbers that are
divible by 40, and
Square them.
Select numbers that
their sumrow divible by
10

Thanks…
For more reading about Numpy
 https://docs.scipy.org/doc/numpy/reference/index.html
 https://www.geeksforgeeks.org/python-numpy/

Lab4--Python--Numpy for basics and variables.pdf

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