numpy
It is a Python library that provides a:
multidimensional array object,
various derived objects (such as masked arrays and matrices), and
an assortment of routines for fast operations on arrays,
including mathematical, logical, shape manipulation, sorting, selecting, I/O,
basic linear algebra, basic statistical operations, random simulation,etc.
In NumPy dimensions are called axes.
[[1., 0., 0.],
[0., 1., 2.]]
the first axis represents the number of rows.
the second axis represents the number of columns.
axis '0' means columns.
axis '1' means row
The first axis has a length of 2, the second axis has a length of 3
important attributes of numpy:
1.ndarray.ndim:returns number of dimensions
2.ndarray.shape:returns a tuple of:
in 1 d:(n cols,)
in 2d:(n rows,n columns)
in 3d:(n tables,n rows,n columns)
3.ndarray.dtype: an object describing the type of the elements in the array.
4.ndarray.size: the total number of elements of the array.
5.ndarray.itemsize:the size in bytes of each element of the array.
data types in array:
for string:
datatype='S'
for int:
datatype='i'
for unsigned integer:
datatype=''
for float:
dataype='f'
for complex:
datatype='c'
for boolean:
datatype='b'
for object:
datatype='O'
array creation
there are multiple types of array:
zero-dimensional:single element
code:
import numpy as np
a=np.array(9)
print(a.ndim)
output:
0
one dimensional:single row multiple colums
code:
import numpy as np
a=np.array([4,5,7,8])
print(a.ndim)
output:
1
two dimensional:multiple row multiple column(single table)
code:
import numpy as np
a=np.array([
[5,7,3],
[4,7,3],
[3,8,5]
])
print(a.ndim)
output:
2
three dimensional:multiple tables multiple rows multiple columns(database)
code:
import numpy as np
a=np.array([
[
[4,6,3,5],
[4,6,3,7]
],
[
[7,4,2,7],
[6,4,7,8]
]
])
print(a.ndim)
output:
3
other array creation functions
np.zeros():to create an array filled with 0’s
syntax:
np.zeros(tuple_of_rows_and_columns,dtype=data_type)
note:
*tuple_of_rows_and_columns=(2,3)#2 rows and 3 columns
*dtype='S'
*dtype='i'
*dtype='f'
*dtype='c'
example:
import numpy as np
a=np.zeros((4,3),dtype='float')
print(a)
output:
[[0. 0. 0.]
[0. 0. 0.]
[0. 0. 0.]
[0. 0. 0.]]
np.ones(): to create an array filled with 1’s
syntax:
np.ones(tuple_of_rows_and_columns,dtype=data_type)
np.empty():to create an array filled with random values
syntax:
np.empty(tuple_of_rows_and_columns,dtype=data_type)
np.arrange():to create an array filled with given range of data
syntax:
np.arrange(first_number, last_number,gap)
sorting an array
np.sort():sorts an array
Numpy provides a variety of functions for sorting and searching.
There are various sorting algorithms like quicksort, merge sort and heapsort which is implemented using the numpy.sort() function.
SYNTAX:
np.sort(arr,order=)
import numpy as np
a = np.array([
[10, 2, 3],
[4, 5, 6],
[7, 8, 9]
])
print("Sorting along the columns:")
print(np.sort(a))
Sorting along the columns:
[[ 2 3 10]
[ 4 5 6]
[ 7 8 9]]
print("Sorting along the rows:")
print(np.sort(a, 0))
Sorting along the rows:
[[ 4 2 3]
[ 7 5 6]
[10 8 9]]
sorting on the basis of data type
data_type = np.dtype([('name', 'S10'), ('marks', int)])
arr = np.array([('Bhavesh', 200), ('Aman', 251)], dtype=data_type)
print("Sorting data ordered by name")
print(np.sort(arr, order='name'))
Sorting data ordered by name
[(b'Atul', 251) (b'Bhanu', 200)]
using the indices to sort the array
a 12
d 12
b 90
e 211
c 380
numpy.lexsort() function
This function is used to sort the array using the sequence of keys indirectly. This function performs similarly to the numpy.argsort() which returns the array of indices of sorted data.
import numpy as np
a = np.array(['a', 'b', 'c', 'd', 'e'])
b = np.array([12, 90, 380, 12, 211])
ind = np.lexsort((a, b))
print("printing indices of sorted data")
print(ind)
printing indices of sorted data
[0 3 1 4 2]
print("using the indices to sort the array")
for i in ind:
print(a[i], b[i])
Finding the minimum and maximum elements from the array
The numpy.amin() and numpy.amax() functions are used to find the minimum and maximum of the array elements along the specified axis respectively.
import numpy as np
a = np.array([[2, 10, 20], [80, 43, 31], [22, 43, 10]])
print("The original array:\n")
print(a)
print("\nThe minimum element among the array:", np.amin(a))
print("The maximum element among the array:", np.amax(a))
print("\nThe minimum element among the rows of array", np.amin(a, 0))
print("The maximum element among the rows of array", np.amax(a, 0))
print("\nThe minimum element among the columns of array", np.amin(a, 1))
print("The maximum element among the columns of array", np.amax(a, 1))
random in numpy
Random number does NOT mean a different number every time. Random means something that can not be predicted logically.
randint
getting random numbers for an array:
from numpy import random
x = random.randint(100)
print(x)
The randint() takes a size parameter by which you can define the shape of an array.
from numpy import random
x=random.randint(100, size=(5))
print(x)
random.choice
from numpy import randomx = random.choice([3, 5, 7, 9], size=(10))#getting 10 element array containing only [3,5,7,9]
print(x)
example 1:
from numpy import random
x = random.choice([3, 5, 7, 9], p=[0.1, 0.3, 0.6, 0.9], size=(100))
print(x)
change the dimensions:ndmin
import numpy as np
a=np.array([4,5,34,4,5],ndmin=3)
print(a)
astype():
a method that returns an array from a collection with the
defined data type
import numpy as np
a=np.array([4,5,34,4,5])
print(a)
b=a.astype(str)
print(b)
view
import numpy as np
a=np.array([4,5,34,4,5])
b=a
b[0]=54
print(a)
print(b)
print()
c=np.array([4,5,3,54,56])
d=c.view()
print(c)
print(d)
c[0]=3
print(c)
print(d)
copy
import numpy as np
a=np.array([4,5,34,4,5])
b=a.copy()
b[0]=54
print(a)
print()
print(b)
transpose
import numpy as np
a=np.array([
[4,3,5],
[12,34,56]
])
print(a.transpose())
split:
import numpy as np
a=np.array([3,2,4,4,3,2])
b=np.array_split(a,3)
print(b)
4,3,5,3,5,3,5
4,3
5,3
5,3
error:division
array_split:
import numpy as np
a=np.array([3,2,4,4,3,2,6])
b=np.array_split(a,3)
print(b)
3,4,2,5,3,5,3
3,4,2
5,3
5,3
slicing
indexing:
positive:
1.starts from 0
2.works from left to right
negative:
1.starts from -1
2.works from right to left
import numpy as np
a=np.array([3,2,4,44,34,2,66,56,23])
print(a[0])
print(a[-1])
print(a[1:5])
print(a[-4:-2])
print(a[1:-1:3])
print(a[::-1])
print(a[3:])
print(a[:5])
import numpy as np
a=np.array([
[3,2,4,44,34,2],
[66,56,23,4,6,7]
])
print(a[1,4])
import numpy as np
a=np.array(
[
[
[3,2,4,44,34,2],
[66,56,23,4,6,7]
],
[
[3,2,4,44,344,4],
[66,56,243,4,6,7]
]
])
print(a.ndim)
print(a[1,1,2])
print(a[0,1,2])
print(a[1,0,4])
searching
where():a method of numpy that returns the indexes of the data those match with the condition.
import numpy as np
a=np.array([4,5,3,54,56,67,7,4,2,4])
indexes=np.where(a==4)
print(indexes)
random in numpy
from numpy import random
a=random.choice(["gold","diamond","platinum"],p=[0.5,0.3,0.2],size=15)
print(a.tolist().count("gold"))
print(a.tolist().count("diamond"))
print(a.tolist().count("platinum"))
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