Introduction to Numpy - 🐊

import numpy as np

Whereas panda helps sort and work with data. Numpy is for calculations.

Let’s create an array.

a = np.array([1, 2, 3, 4, 5, 6])
print(a)

[1 2 3 4 5 6]

b = np.array([[1, 2, 3],[4, 5, 6]])
print(b)

[[1 2 3]
 [4 5 6]]

Two-dimensions.

c = np.array([[[1], [2], [3]],[[4], [5], [6]]])
print(c)

[[[1]
  [2]
  [3]]

 [[4]
  [5]
  [6]]]

We can actually check this with the function ndim.

print(a.ndim)
print(b.ndim)
print(c.ndim)

1
2
3

or we can check the whole shape…

print(a.shape)
print(b.shape)
print(c.shape)

(6,)
(2, 3)
(2, 3, 1)

Your turn: Create a Rubik’s cube where each color is a number.

# code

We can also work with pandas and numpy together!

b = pd.DataFrame([[1, 2, 3],[4, 5, 6]])
b.columns = ['name1','name2','name3'] 
print(b)

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
Cell In[8], line 1
----> 1 b = pd.DataFrame([[1, 2, 3],[4, 5, 6]])
      2 b.columns = ['name1','name2','name3'] 
      3 print(b)

NameError: name 'pd' is not defined

b.to_numpy()

array([[1, 2, 3],
       [4, 5, 6]])

or a specific column

b['name1'].to_numpy()

array([1, 4])

Now we can do something with the array.

d = b['name1'].to_numpy()
e = d + 10
print(e)

[11 14]

Let’s send it back to pandas.

b['name1'] = pd.array(e)
print(b)

   name1  name2  name3
0     11      2      3
1     14      5      6

Your turn: using your rubrik’s cube

• Add a constant to each value

• Send to pandas

• And add columns