/ Python And R Data science skills: 35 Numpy array cont

Sunday 4 February 2018

35 Numpy array cont

35 python array
In [1]:
import numpy as np
In [2]:
z = np.arange(1,11)
In [3]:
z
Out[3]:
array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10])
In [4]:
z>6
Out[4]:
array([False, False, False, False, False, False,  True,  True,  True,  True], dtype=bool)
In [5]:
bz=z>6
In [6]:
bz
Out[6]:
array([False, False, False, False, False, False,  True,  True,  True,  True], dtype=bool)
In [7]:
z[bz]
Out[7]:
array([ 7,  8,  9, 10])
In [9]:
z[z>7]
Out[9]:
array([ 8,  9, 10])
In [10]:
a1=np.arange(1,11)
In [11]:
a1
Out[11]:
array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10])
In [12]:
a1>6
Out[12]:
array([False, False, False, False, False, False,  True,  True,  True,  True], dtype=bool)
In [13]:
ba1=a1>6
In [14]:
ba1
Out[14]:
array([False, False, False, False, False, False,  True,  True,  True,  True], dtype=bool)
In [15]:
a1[ba1]
Out[15]:
array([ 7,  8,  9, 10])
In [16]:
a1[a1<6]
Out[16]:
array([1, 2, 3, 4, 5])
In [18]:
m1=np.arange(1,51).reshape(5,10)
In [19]:
m1
Out[19]:
array([[ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10],
       [11, 12, 13, 14, 15, 16, 17, 18, 19, 20],
       [21, 22, 23, 24, 25, 26, 27, 28, 29, 30],
       [31, 32, 33, 34, 35, 36, 37, 38, 39, 40],
       [41, 42, 43, 44, 45, 46, 47, 48, 49, 50]])
In [20]:
m1[:2,:2]
Out[20]:
array([[ 1,  2],
       [11, 12]])
In [28]:
m1[0:1]
Out[28]:
array([[ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10]])

No comments:

Post a Comment