Day 4. Numpy02

import numpy as np

In [4]:

arr = 10 * np.random.randn(2,5)
arr

Out[4]:

array([[-0.84567361,  7.28647028,  0.73088876, -1.45098102, -3.88956156],
       [ 3.07073042, -1.28789786, -1.6420603 ,  6.05817637,  6.10595292]])

In [5]:

arr.mean()

Out[5]:

1.4136044402485524

In [7]:

# apply for every rows
arr.mean(axis = 1)

Out[7]:

array([0.36622857, 2.46098031])

In [8]:

# apply for every cols
arr.mean(axis = 0)

Out[8]:

array([ 1.11252841,  2.99928621, -0.45558577,  2.30359768,  1.10819568])

In [10]:

arr.sum()

Out[10]:

14.136044402485524

In [12]:

np.median(arr, axis = 1)

Out[12]:

array([-0.84567361,  3.07073042])

In [13]:

unsorted = np.random.randn(10)
unsorted

Out[13]:

array([-0.53667066,  0.65386895, -1.42620924, -2.12688789, -0.30355003,
        0.85132587, -0.79651244, -0.84666615,  1.03276588,  0.7316291 ])

In [26]:

sorted = np.array(unsorted)
sorted.sort()

print(sorted)
print()
print(unsorted)

[-2.12688789 -1.42620924 -0.84666615 -0.79651244 -0.53667066 -0.30355003
  0.65386895  0.7316291   0.85132587  1.03276588]

[-2.12688789 -1.42620924 -0.84666615 -0.79651244 -0.53667066 -0.30355003
  0.65386895  0.7316291   0.85132587  1.03276588]
None

In [28]:

unsorted.sort()
unsorted

Out[28]:

array([-2.12688789, -1.42620924, -0.84666615, -0.79651244, -0.53667066,
       -0.30355003,  0.65386895,  0.7316291 ,  0.85132587,  1.03276588])

In [29]:

array = np.array([1,2,3,5,6,2,3,2,1])
np.unique(array)

Out[29]:

array([1, 2, 3, 5, 6])

In [30]:

s1 = np.array(['desk', 'chair','bulb'])
s2 = np.array(['lamp', 'chair','bulb'])
print(s1, s2)

['desk' 'chair' 'bulb'] ['lamp' 'chair' 'bulb']

In [31]:

np.intersect1d(s1, s2)

Out[31]:

array(['bulb', 'chair'], dtype='<U5')

In [32]:

np.union1d(s1, s2)

Out[32]:

array(['bulb', 'chair', 'desk', 'lamp'], dtype='<U5')

In [34]:

np.setdiff1d(s1, s2) #elements in s1 that are not in s2

Out[34]:

array(['desk'], dtype='<U5')

In [35]:

np.in1d(s1, s2) #which element of s1 is also in s2

Out[35]:

array([False,  True,  True])

In [36]:

#broadcasting
arr = np.zeros((4,3))

In [37]:

arr

Out[37]:

array([[0., 0., 0.],
       [0., 0., 0.],
       [0., 0., 0.],
       [0., 0., 0.]])

In [42]:

rowToAdded = np.array([1,0,2])
rowToAdded

Out[42]:

array([1, 0, 2])

In [40]:

arr + rowToAdded
res = arr + rowToAdded
res

Out[40]:

array([[1., 0., 2.],
       [1., 0., 2.],
       [1., 0., 2.],
       [1., 0., 2.]])

In [44]:

colToAdded = np.array([[0,1,2]])
colToAdded

Out[44]:

array([[0, 1, 2]])

In [45]:

res = arr + colToAdded
res

Out[45]:

array([[0., 1., 2.],
       [0., 1., 2.],
       [0., 1., 2.],
       [0., 1., 2.]])

In [ ]: