# NumPy Exercises¶

Testing basic NumPy knowledge.

#### Import NumPy as np¶

In [1]:
import numpy as np


#### Create an array of 10 zeros¶

In [2]:
np.zeros(10)

Out[2]:
array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])

#### Create an array of 10 ones¶

In [3]:
np.ones(10)

Out[3]:
array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])

#### Create an array of 10 fives¶

In [4]:
np.array([5.0]*10)

Out[4]:
array([5., 5., 5., 5., 5., 5., 5., 5., 5., 5.])

#### Create an array of the integers from 10 to 50¶

In [5]:
np.arange(10,51)

Out[5]:
array([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])

#### Create an array of all the even integers from 10 to 50¶

In [6]:
np.arange(10,51,2)

Out[6]:
array([10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32, 34, 36, 38, 40, 42,
44, 46, 48, 50])

#### Create a 3x3 matrix with values ranging from 0 to 8¶

In [7]:
np.arange(9).reshape(3,3)

Out[7]:
array([[0, 1, 2],
[3, 4, 5],
[6, 7, 8]])

#### Create a 3x3 identity matrix¶

In [8]:
np.eye(3)

Out[8]:
array([[1., 0., 0.],
[0., 1., 0.],
[0., 0., 1.]])

#### Use NumPy to generate a random number between 0 and 1¶

In [9]:
np.random.rand(1)

Out[9]:
array([0.7903837])

#### Use NumPy to generate an array of 25 random numbers sampled from a standard normal distribution¶

In [10]:
np.random.randn(25)

Out[10]:
array([ 0.86287048,  0.34043398,  0.97092782, -0.12990277,  0.38232832,
-0.69926378, -0.31600325,  0.85971356,  1.10996902, -1.40665255,
-0.7009659 ,  0.14205376, -0.14403028, -0.62144374, -0.62569855,
-0.09088263,  1.8967801 ,  1.46763251,  0.78542142, -0.16063449,
-0.69190568, -1.50980316,  0.70223806,  0.24406165, -0.65443729])

#### Create the following matrix:¶

In [11]:
np.arange(0.01,1.01,0.01).reshape(10,10)

Out[11]:
array([[0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1 ],
[0.11, 0.12, 0.13, 0.14, 0.15, 0.16, 0.17, 0.18, 0.19, 0.2 ],
[0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3 ],
[0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4 ],
[0.41, 0.42, 0.43, 0.44, 0.45, 0.46, 0.47, 0.48, 0.49, 0.5 ],
[0.51, 0.52, 0.53, 0.54, 0.55, 0.56, 0.57, 0.58, 0.59, 0.6 ],
[0.61, 0.62, 0.63, 0.64, 0.65, 0.66, 0.67, 0.68, 0.69, 0.7 ],
[0.71, 0.72, 0.73, 0.74, 0.75, 0.76, 0.77, 0.78, 0.79, 0.8 ],
[0.81, 0.82, 0.83, 0.84, 0.85, 0.86, 0.87, 0.88, 0.89, 0.9 ],
[0.91, 0.92, 0.93, 0.94, 0.95, 0.96, 0.97, 0.98, 0.99, 1.  ]])

#### Create an array of 20 linearly spaced points between 0 and 1:¶

In [12]:
np.linspace(0,1,20)

Out[12]:
array([0.        , 0.05263158, 0.10526316, 0.15789474, 0.21052632,
0.26315789, 0.31578947, 0.36842105, 0.42105263, 0.47368421,
0.52631579, 0.57894737, 0.63157895, 0.68421053, 0.73684211,
0.78947368, 0.84210526, 0.89473684, 0.94736842, 1.        ])

## Numpy Indexing and Selection¶

#### Generate a 5 by 5 matrix:¶

In [13]:
mat = np.arange(1,26).reshape(5,5)
mat

Out[13]:
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]])

#### Slice the matrix¶

In [15]:
mat[2:,1:]

Out[15]:
array([[12, 13, 14, 15],
[17, 18, 19, 20],
[22, 23, 24, 25]])

#### Select value based on location in the matrix¶

In [17]:
mat[3,4]

Out[17]:
20

#### First three elements in the 2nd column¶

In [20]:
mat[:3,1:2]

Out[20]:
array([[ 2],
[ 7],
[12]])
In [ ]:
#### Fifth

In [22]:
mat[4]

Out[22]:
array([21, 22, 23, 24, 25])
In [23]:
# WRITE CODE HERE THAT REPRODUCES THE OUTPUT OF THE CELL BELOW
# BE CAREFUL NOT TO RUN THE CELL BELOW, OTHERWISE YOU WON'T
# BE ABLE TO SEE THE OUTPUT ANY MORE

In [24]:
mat[3:]

Out[24]:
array([[16, 17, 18, 19, 20],
[21, 22, 23, 24, 25]])

### Now do the following¶

#### Get the sum of all the values in mat¶

In [25]:
mat.sum()

Out[25]:
325

#### Get the standard deviation of the values in mat¶

In [26]:
mat.std()

Out[26]:
7.211102550927978

#### Get the sum of all the columns in mat¶

In [27]:
mat.sum(axis=0)

Out[27]:
array([55, 60, 65, 70, 75])