l = [1,2,3,4]
print(l)
print(type(l))
[1,
2, 3, 4]
<class
'list'>
import numpy as np
a= np.array([12929,505,118292,161912])
print(a)
print(type(a))
a.shape
[
12929 505 118292 161912]
<class
'numpy.ndarray'>
Out[10]: (4,)
a.dtype
Out[8]: dtype('int32')
np.array(range(5))
Out[11]: array([0,
1, 2, 3, 4])
np.array(range(5),dtype=float)
Out[12]: array([0.,
1., 2., 3., 4.])
a = np.empty(4) # 1 parenthesis in case of 1 dimensional
array
print (a.dtype)
print(a)
float64
[1.
2. 3. 4.]
a = np.empty((3,4)) # 2 parenthesis in case of multi dimensional
array
print (a.dtype)
print(a)
float64
[[0.
0. 0. 0.]
[0. 0. 0. 0.]
[0. 0. 0. 0.]]
a=np.array(['ab','cd','ef'])
a.dtype
Out[13]:
dtype('<U2')
a = np.zeros((3,5),
dtype='int32')
print(a)
[[0
0 0 0 0]
[0 0 0 0 0]
[0 0 0 0 0]]
a = np.zeros((3,5), dtype='float64')
print(a)
[[0.
0. 0. 0. 0.]
[0. 0. 0. 0. 0.]
[0. 0. 0. 0. 0.]]
a = np.arange(1,30,5)
print(a)
[
1 6 11 16 21 26]
a.reshape(2,3)
Out[30]:
array([[
1, 6, 11],
[16, 21, 26]])
for x in a.ravel():
print (x)
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
a = np.arange(24).reshape(3,8)
print(a)
[[
0 1
2 3 4
5 6 7]
[ 8 9
10 11 12 13 14 15]
[16 17 18 19 20 21 22 23]]
print("---------------1---------------------")
print(a[:,:]) # All values [3x8]
print("---------------2---------------------")
print(a[0,5]) # 1st row 6th column single value
print("---------------3---------------------")
print(a[0,:5]) # 1st row upto 5 columns [1x5]
print("---------------4---------------------")
print(a[:,5]) # All rows 6th column
print("---------------5---------------------")
print(a[:,-1]) # All rows last column
print("--------------6----------------------")
print(a[:,-2]) # All rows 2nd last column
print("---------------7---------------------")
print(a[1,:]) # 2nd entire row with all columns
print("--------------8----------------------")
print(a[:,:-2]) # All rows and columns upto 2nd last column
print("-------------9-----------------------")
print(a[:,:-1]) # All rows and columns upto last but one column
print("-----------------10-------------------")
print(a[:,::-1]) # Reverse the array
print("------------------11------------------")
print(a[:,::-2]) # Reverse the array with 1 alternate columns
print("-------------------12-----------------")
print(a[:,::-3]) # Reverse the array with 2 alternate columns
---------------1---------------------
[[
0 1
2 3 4
5 6 7]
[ 8 9
10 11 12 13 14 15]
[16 17 18 19 20 21 22 23]]
---------------2---------------------
5
---------------3---------------------
[0
1 2 3 4]
---------------4---------------------
[
5 13 21]
---------------5---------------------
[
7 15 23]
--------------6----------------------
[
6 14 22]
---------------7---------------------
[
8 9 10 11 12 13 14 15]
--------------8----------------------
[[
0 1
2 3 4 5]
[ 8 9
10 11 12 13]
[16 17 18 19 20 21]]
-------------9-----------------------
[[
0 1
2 3 4
5 6]
[ 8 9
10 11 12 13 14]
[16 17 18 19 20 21 22]]
-----------------10-------------------
[[
7 6
5 4 3
2 1 0]
[15 14 13 12 11 10 9 8]
[23 22 21 20 19 18 17 16]]
------------------11------------------
[[
7 5
3 1]
[15 13 11
9]
[23 21 19 17]]
-------------------12-----------------
[[
7 4
1]
[15 12
9]
[23 20 17]]
print(a)
print(a*2)
[[
0 1
2 3 4
5 6 7]
[ 8 9
10 11 12 13 14 15]
[16 17 18 19 20 21 22 23]]
[[
0 2
4 6 8 10 12 14]
[16 18 20 22 24 26 28 30]
[32 34 36 38 40 42 44 46]]
a = np.arange(1,10).reshape(3,3)
b = np.arange(10,19).reshape(3,3)
print(a)
print(b)
[[1
2 3]
[4 5 6]
[7 8 9]]
[[10
11 12]
[13 14 15]
[16 17 18]]
a[1][1]=50 # assign a new value at particular position using
[][]
a[2,2]=90 # assign a new
value at particular position using[,]
print(a)
[[
1 2
3]
[ 4 50
6]
[ 7 8
90]]
c=a+b # Addition of 2 arrays
print(c)
[[
11 13
15]
[ 17
64 21]
[ 23 25
108]]
print("---------------a-----------")
print(a)
print("---------------b-----------")
print(b)
np.hstack((a,b)) # Horizontal stack
---------------a-----------
[[1
2 3]
[4 5 6]
[7 8 9]]
---------------b-----------
[[10
11 12]
[13 14 15]
[16 17 18]]
Out[86]:
array([[
1, 2,
3, 10, 11, 12],
[ 4,
5, 6, 13, 14, 15],
[ 7,
8, 9, 16, 17, 18]])
print("---------------a-----------")
print(a)
print("---------------b-----------")
print(b)
np.vstack((a,b)) #
Vertical stack
---------------a-----------
[[1
2 3]
[4 5 6]
[7 8 9]]
---------------b-----------
[[10
11 12]
[13 14 15]
[16 17 18]]
Out[87]:
array([[
1, 2,
3],
[ 4,
5, 6],
[ 7,
8, 9],
[10, 11, 12],
[13, 14, 15],
[16, 17, 18]])
print("---------------one way-----------")
print(a.transpose())
print("---------------other way-----------")
print(a.T) # T for
transpose
---------------one
way-----------
[[
1 4
7]
[ 2 50
8]
[ 3 6
90]]
---------------other
way-----------
[[
1 4
7]
[ 2 50
8]
[ 3 6
90]]
import numpy as np
a= np.array([[1,2,3],[4,5,6]])
print(a)
print(a.shape)
[[1
2 3]
[4 5 6]]
(2,
3)
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