Numpy _

NumPy- Numerical
Python
DAP
Lecture

Installation of
Numpy
Step1. In pycharm,go to
terminal
Step 2.Write following
command
Or
Step 1. Goto project settings in
Pycharm
Step2. Select Python interpreter
Step3. Search package to install and
add

Arrays in
python
import array
# initializing array with array values
# initializes array with signed integers
arr = array.array('i', [1, 2, 3])
# printing original array
print ("The new created array is :
",end="")
for i in range (0,3):
print (arr[i], end="
")
Array module can be imported to create arrays
in python but operations on such arrays was
very slow Due to its slow response,it lead to use
of Numpy

Datatype of Arrays in
python
TYPE CODE C TYPE
PYTHON MINIMUM
SIZE
TYPE IN BYTES
‘b’ signed char int 1
‘B’ unsigned char int 1
‘i’ signed int int 2
‘I’ unsigned int int 2
‘f’ float float 4
‘d’ double float 8
These are the datatypes which can be used while creating array using array
module

Array creation using
numpy
We can use arange function from NumPy that creates a NumPy
array for us with integers 0 to n.
import numpy
arr=numpy.arange(5)
for i in range(0,
len(arr)):
print(arr[i])
Output

Difference between code with numpy and without
numpy
Took 3000
microseconds
Took 2000
microseconds
for i in range(0, len(arr1)):
arr3.append(arr1[i]
+arr2[i]) print(arr3[i],
end=" ")
Without numpy
import datetime
def printelements(n):
arr1=range(n)
arr2=range(n)
arr3=[]
start = datetime.datetime.now()
printelements( 300)
diff = datetime.datetime.now() - start
print("time taken",
diff.microseconds)
import numpy as n
import datetime
def printelements(num):
arr1=n.arange(num)
arr2=n.arange(num)
arr3=arr1+arr2
for i in range(0, len(arr1)):
print(arr3[i], end=" ")
start = datetime.datetime.now()
printelements( 300)
diff = datetime.datetime.now() - start
print("time taken",
diff.microseconds)
Adding two arrays
With
numpy

Datatypes in
Numpy
Python has an integer type, a float type, and a complex type, however, this is not
enough for scientific computing and, for this reason, NumPy has a lot more data
types.Datatypes are associated with different precision and different memory size.
We can view the datatype of array created
import numpy as n
arr1=n.arange( 12
)
print(arr1.dtype)
The data type of array a is int32 (at least on my machine), but you may get int64 as
output if you are using 64-bit Python.

Examples of creating arrays with
dtype
arr1=numpy.arange(7,dtype="float16")
print(arr1)
arr1=numpy.arange(7,dtype="int16")
print(arr1)
arr1=numpy.arange(7,dtype=“complex")
print(arr1)
Output:
[0. 1. 2. 3. 4. 5. 6.]
[0 1 2 3 4 5 6]
[0.+0.j 1.+0.j 2.+0.j 3.+0.j 4.+0.j
5.+0.j 6.+0.j]

Character codes associated with
datatypes
Character codes are included for backward compatibility with Numeric. Numeric
is the predecessor of NumPy. Their use is not recommended, but the codes are
provided here because they pop up in several places. You should instead use
dtype objects.
Example:
Output
:

Datatype(dtype) Objects &
Constructors
Data type objects are instances of the numpy.dtype class. Once again, arrays have a data
type. Every element in a NumPy array has the same data type. Each dtype object has
itemsize attribute which tell you the size of the data in bytes.
dtype constructors
We have a variety of ways to create data types. We can use either numeric
datatypes or character codes to be passed to constructor to create object as
shown below:
D1=dtype(“float1
6) D2=dtype(“f”)
Create the record:
To create heterogenous datatype which contain multiple
datatype D3 = dtype([('name', str, 40), ('numitems', int),
('price',float)])

Dtype constructor
examples
▪ We can use numeric
datatypes: dtype(float32)
▪ We can specify a single precision float with a
character code: dtype('f')
▪ We can use a double precision float character
code: dtype('d')
▪ We can use a complex character code: dtype(‘D')
▪ We can give the data type constructor a two-
character code. The first character signifies the
type; the second character is a number specifying
the number of bytes in the type: dtype('f8')
Example
:
Outpu
t:

Dtype
Attributes
Attribute Description
dtype.char Gives a unique character code for each of the
21 different built-in types. E.g ‘D’
dtype.type corresponds to the type of object of the
array elements e.g float32
dtype.str Gives string representation of the data type.
It starts with a character representing
endianness(<or>), then a character code,
followed by a number corresponding to the
number of bytes that each array item requires.
Eg. ‘<f8’ <- Endianness, f-float,8-8 bytes
dtype.byteorder
(Endiannness)
Endianness, here, means the way bytes are ordered
within a 32 or 64-bit word. In big-endian order, the most
significant byte is stored first. In little-endian order, the
least significant byte is stored first.
dtype.itemsize Gives size of data in bytes example 64

ENDIANNESS
0x00040000 0x00040001 0x00040002 0x00040003
0x00040000 0x00040001 0x00040002 0x00040003
Consider number 0x12674592 in 32-bit representation can be stored
as −
MSB LSB

Creating Multidimensional
Array
Use array() function to create 2-D array
from numpy import *
matrix1= array([[0,2],[3,4]])
Or
row1=array([1,2])
row2=array([3,4],’I’)
matrix1 = array([row1,row2])
print(matrix1)
□[0,2]
[3,4]

Creating Multidimensional
Array
Program to create a matrix and
display it
from numpy import *
matrix1 = array([[0,2],[3,4]])
for i in range(0,len(matrix1[0])):
for jin range(0, len(matrix1)):
print(matrix1[i][j],end="
")
print("n")
#len(matrix1[0]) give number of columns in first row
#len(matrix1) gives number of rows
Outpu
t
To add two matrices simply create two matrices with data
and third zero matrix
matrix1 = array([[0,2],[3,4]])
matrix2 = array([[0,2],[3,4]])
matrix3 = array([[0,0],[0,0]])
Then Simply add in loop
matrix3[i][j]= matrix1[i][j]+matrix2[i][j]=

Numpy _

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