![Numerical Python (Num Pie)- It offers function for fast mathematical computation on arrays or
matrices.
PACKAGE:
numpy
import numpy
as np
# Create an array from a list
>>>L=[2,3,4,5]
>>>a=np.array(L)
# use iterator to create array
np.fromiter(iter(range(5)),dtype=int)
array([0, 1, 2, 3, 4])
# Find length of each element of array in bytes
>>>a.itemsize
8
#uninitialized array of specified shape and dtype
x = np.empty([3,2], dtype = int32)
# Find dimension of Numpy array, i.e no of elements
along each axis.
>>>a.shape # returns a Tuple
>>> (4,)
#a reshape function to resize an array.
>>>a.reshape(4,1)
# slicing array
s = slice(1,7,2) OR b = a[1:7:2]
[ 3 5 ] [ 3 5 ]
# ellipsis(...)
a=p.array([[1,2,3],[3,4,5],[4,5,6]])
print(a[...,1],a[1,...],a[...,1:])
[2 4 5] [3 4 5] [[2 3]
[4 5]
[5 6]]
#a new array of specified size, filled with zeros.
x = np.zeros(5)
[ 0. 0. 0. 0. 0.]
#a new array of specified size and type, filled with
ones.
x = np.ones(5)
[ 1. 1. 1. 1. 1.]
# Find data type of an array
>>>a.dtype
dtype('float64')](https://image.slidesharecdn.com/numpy-200721180722/85/Numpy-3-320.jpg)
![L=[1,2,3.4,5,6,7]
a=numpy.array(L)
#array([1. , 2. , 3.4, 5.
, 6. , 7. ])
a.shape
(6,)
a .itemsize
8
a.dtype
dtype('float64')
a=numpy.array(L,dty
pe='O')
K=[[2,3,4],[1,5],[8]]
k=numpy.array(K)
k.shape
(3,)
k.itemsize
4
k.dtype
dtype('int32')
k=numpy.array(K,dtype
='O')
S=['w','e','l','c','m','e']
s=numpy.array(S)
s.shape
(6,)
s.itemsize
4
s.dtype
dtype('<U1') # Unicode
s=numpy.array(S,dtype='
O')
l=[ [1,2,3], [4.5,6], ['s', {1:'n'} ], [ (6,7) ] ]
L=numpy.array(l,dtype=object) # dtype must
# L is array([list([1, 2, 3]), list([4.5, 6]), list(['s', {1:
'n'}]),list([(6, 7)])], dtype=object)
L.shape
(4,)
L.itemsize
4
L.dtype
dtype('O') # Object
L=numpy.array(l,dtype='O')
Creating arrays Syntax import numpy as np
Using arange() arr=numpy.arange([start],end,[,step][,dtype]) a=np.arange(5,50,5)
array([ 5, 10, 15, 20, 25, 30, 35, 40, 45])
Using linspace() arr=numpy.linspace(start,end,no. Of values) a=np.linspace(5,50,5)
array([ 5. , 16.25, 27.5 , 38.75, 50. ])](https://image.slidesharecdn.com/numpy-200721180722/85/Numpy-4-320.jpg)
Numpy
- 1. CLASS- XII PCT-2 UNIT - 1 CHAPTER -8 Data Visualization : Numpy Array Prepared By - Swati Patil BE, MCA,PGDHRM Contact - 8618134279 (Queries only through SMS) https://pythonxiisolutions.blogspot.com prippython12.blogspot.com
- 2. Difference between List and Numpy Array List Array The list can be homogeneous or heterogeneous The array are homogeneous Elements of List are not stored contiguously in memory. 2D list may have different number of rows and columns can Elements of an array are stored contiguously in memory. For example, all rows of a 2D array must have the same number of columns. Or a 3D array must have the same number of rows and columns on each card. Python list is by default 1 dimensional. But we can create an N-Dimensional list. But then too it will be 1 D list storing another 1D list Arrays are multi dimensional Element wise operation is not possible in list. Example- # adding 4 to each element of list ls = ls + 4 Element wise operation is possible in array. Example- # adding 4 to each element of Numpy array arr = arr + 4 range () is used arange() is used
- 3. Numerical Python (Num Pie)- It offers function for fast mathematical computation on arrays or matrices. PACKAGE: numpy import numpy as np # Create an array from a list >>>L=[2,3,4,5] >>>a=np.array(L) # use iterator to create array np.fromiter(iter(range(5)),dtype=int) array([0, 1, 2, 3, 4]) # Find length of each element of array in bytes >>>a.itemsize 8 #uninitialized array of specified shape and dtype x = np.empty([3,2], dtype = int32) # Find dimension of Numpy array, i.e no of elements along each axis. >>>a.shape # returns a Tuple >>> (4,) #a reshape function to resize an array. >>>a.reshape(4,1) # slicing array s = slice(1,7,2) OR b = a[1:7:2] [ 3 5 ] [ 3 5 ] # ellipsis(...) a=p.array([[1,2,3],[3,4,5],[4,5,6]]) print(a[...,1],a[1,...],a[...,1:]) [2 4 5] [3 4 5] [[2 3] [4 5] [5 6]] #a new array of specified size, filled with zeros. x = np.zeros(5) [ 0. 0. 0. 0. 0.] #a new array of specified size and type, filled with ones. x = np.ones(5) [ 1. 1. 1. 1. 1.] # Find data type of an array >>>a.dtype dtype('float64')
- 4. L=[1,2,3.4,5,6,7] a=numpy.array(L) #array([1. , 2. , 3.4, 5. , 6. , 7. ]) a.shape (6,) a .itemsize 8 a.dtype dtype('float64') a=numpy.array(L,dty pe='O') K=[[2,3,4],[1,5],[8]] k=numpy.array(K) k.shape (3,) k.itemsize 4 k.dtype dtype('int32') k=numpy.array(K,dtype ='O') S=['w','e','l','c','m','e'] s=numpy.array(S) s.shape (6,) s.itemsize 4 s.dtype dtype('<U1') # Unicode s=numpy.array(S,dtype=' O') l=[ [1,2,3], [4.5,6], ['s', {1:'n'} ], [ (6,7) ] ] L=numpy.array(l,dtype=object) # dtype must # L is array([list([1, 2, 3]), list([4.5, 6]), list(['s', {1: 'n'}]),list([(6, 7)])], dtype=object) L.shape (4,) L.itemsize 4 L.dtype dtype('O') # Object L=numpy.array(l,dtype='O') Creating arrays Syntax import numpy as np Using arange() arr=numpy.arange([start],end,[,step][,dtype]) a=np.arange(5,50,5) array([ 5, 10, 15, 20, 25, 30, 35, 40, 45]) Using linspace() arr=numpy.linspace(start,end,no. Of values) a=np.linspace(5,50,5) array([ 5. , 16.25, 27.5 , 38.75, 50. ])
- 5. Dtypes of array Data Type Size Range np.int8 1 byte -128 to 127 np.int16 2 byte -32768 to 32767 np.int32 4 byte -216 to 216 -1 np.int64 8 byte -232 to 232 -1 np.float_ 8 byte 11exp + 52manti np.float16 2byte 5exp + 10manti np.float32 4 byte 8exp+ 23manti np.float64 8 byte 11exp + 52manti Problem Statements to work out :. 1.Create a 3 x 4 two-dimensional ndarray from the range of integers 13..24 2.Write commands to perform following operations on two 4 x 4 ndarrays namely P and Q : i>Adding 10 to P ii> multiplication of P & Q iii>Divide all elements of Q by 7 iv>Calculate log of all elements of P v>Round all elements of Q to nearest integer vi>calculate remainders of all elements of P when divided by 7 vi>Calculate square root of all elements of Q