Tricks in numpy

Tricky actions numpy could provide

In [1]:

import numpy as np

Array concatenation¶

here we have separate RGB channels of an image, concatenate them using np.concatenate and np.stack separately

In [2]:

h, w = 224, 224
R = np.random.randint(0, 256, size=(h, w))
G = np.random.randint(0, 256, size=(h, w))
B = np.random.randint(0, 256, size=(h, w))

In [3]:

# your code here #

Array creation¶

p is a 1D array indicating probability of being dog(y=1). let's say all samples are truly dog and we need to create a ground-truth array for loss computation (don't worry :) you don't need to implement loss function here). imagine you can't explicitly use any shape information for this matter.

In [4]:

num_samples = 100
p = np.random.rand(num_samples)

In [5]:

# your code here

Data stats¶

salary is a 2D array denoting salaries of six employees during 100 months. let's say the manager asks you to analyze his/her employees by answering the following questions (np.argmax, np.max, np.argwhere, np.median and np.percentile could be helpful :) )

In [6]:

salary = np.random.randint(5, 150, size=(6, 100))

for the 2nd employee, most salary earned during a month and when

In [7]:

# your code here

find the most salary earned, by whom and when (might be multiple employees in multiple times)

In [8]:

# your code here

per employee, the median salary they earned

In [9]:

# your code here

per employee, their 25th max salary they earned

In [10]:

# your code here

Indexing (including boolean) and slicing¶

salary is salaries of six employees during a year. implement code for the following parts

In [11]:

salary = np.random.randint(5, 100, size=(6, 12))

show the salary of all employees in second month

In [12]:

# your code here

show the salary of 1st and 3rd employees in even months

In [13]:

# your code here

show salary of 1st employee in 5th month, 2nd one in 11st, 3rd in 7th

In [14]:

# your code here

one day boss comes and seems angry :) telling you the most salary someone can earn is 90 and asks you to shift the salaries more than that to it.

In [ ]:

# your code here

Other utilities¶

create an array of 1000 samples in a linear form between 0 and 1 using np.linspace and check the difference of all two subsequent elements are close to 0.001

In [15]:

# your code here

increase value of arr by one in places provided by indices

In [16]:

arr = np.zeros(5)
indices = np.random.randint(0, 5, size=(1000))

In [17]:

# your code here

for arr, select the 4th and 5th axis of last dimension using both numpy indexing and ellipsis and check their value equality using np.all

In [18]:

arr = np.random.rand(10, 10, 10, 10, 10, 8)

In [19]:

# your code here

A solution

Tricks in numpy: solution

tricky actions numpy could provide

Authors

Alireza Dizaji

Author