Python Numpy tutorial¶
This notebook helps to understand numpy from scratch, this exercise was taken from python data science handbook.¶
1) Create a length-10 integer array filled with zeros¶
In [12]:
import numpy as np
a = np.zeros(10, dtype = int)
a
Out[12]:
array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])
2) Create a 3x5 floating-point array filled with 1s¶
In [22]:
a = np.ones((3,5), dtype = float)
a
Out[22]:
array([[1., 1., 1., 1., 1.],
[1., 1., 1., 1., 1.],
[1., 1., 1., 1., 1.]])
3) Create a 3x5 array filled with 3.14¶
In [14]:
a = np.full((3,5), 3.14)
a
Out[14]:
array([[3.14, 3.14, 3.14, 3.14, 3.14],
[3.14, 3.14, 3.14, 3.14, 3.14],
[3.14, 3.14, 3.14, 3.14, 3.14]])
4) Create an array filled with a linear sequence. Starting at 0, ending at 20, stepping by 2 (this is similar to the built-in range() function).¶
In [15]:
np.arange(0, 20, 2)
Out[15]:
array([ 0, 2, 4, 6, 8, 10, 12, 14, 16, 18])
5) Create an array of five values evenly spaced between 0 and 1¶
In [16]:
np.linspace(0, 1, 5)
Out[16]:
array([0. , 0.25, 0.5 , 0.75, 1. ])
6) Create a 3x3 array of uniformly distributed random values between 0 and 1¶
HINT when you just use np.random(3, 3), then you get this error: 'module' object is not callable
In [17]:
a = np.random.random((3, 3))
a
Out[17]:
array([[0.92882601, 0.15277742, 0.61847288],
[0.26835166, 0.1343164 , 0.13999919],
[0.43270145, 0.89307018, 0.52218327]])
7) Create a 3x3 array of normally distributed random values with mean 0 and standard deviation 1¶
In [18]:
a = np.random.normal(0, 1, (3, 3))
a
Out[18]:
array([[ 0.54278146, -0.29350707, -0.76737292],
[-0.00821164, -0.49738633, 0.13785728],
[ 1.27666228, -1.43017619, -1.2518886 ]])
8) Create a 3x3 array of random integers in the interval [0, 10)¶
In [19]:
a = np.random.randint(0, 10, (3, 3))
a
Out[19]:
array([[8, 7, 3],
[7, 7, 0],
[1, 7, 8]])
9) Create a 3x3 identity matrix¶
In [20]:
a = np.eye(3)
a
Out[20]:
array([[1., 0., 0.],
[0., 1., 0.],
[0., 0., 1.]])
10) Create an uninitialized array of three integers. The values will be whatever happens to already exist at that memory location¶
In [21]:
np.empty(3)
Out[21]:
array([1., 1., 1.])