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Jul 5, 20214 min read

Most Commonly Asked Python Usecases - Part 1 (Python Basics)


Hi All!! Hope you have a great weekend!! Python as a programming language is widely talked about and practiced in the field of Data Science. As I was going through various interview topics, I found many tutorials that explain Python topics/concepts. But I was preferring to see some use cases where we can apply the concepts. I have collated the widely commonly asked Python interview use cases:

Most commonly asked Python topics and use cases

Lists:


1. Program to square of the list values:

#Lists
#1. Program to square of the list values
lst = [1,2,3,4]
#using simple function
def sum_list(lst):
    ret=[]
    for i in lst:
        ret.appen(1**2)
     return ret
 output = sum_array(lst)
 print(output)
 [1,4,9,16]

2. Square of the list values using map & lambda function:

output = list(map(lambda a:a**2, lst))
print(output)
[1,4,9,16]

3. Filter only the vowels:

input = ['a','b','c','d','e','f']
vowel_list = ['a','e','i','o','u']
output = list(filter(lambda a: a in vowel_list, input))
print(output)
['a','e']

4. rotation of list:

lst = [1,2,3,4,5,6]
def rot_array(lst, num):
    #using list slicing
    temp = lst[0:num]
    fin_lst = lst[num:]+temp
    return fin_lst
output = rot_array(lst,2)
print(output)
[3, 4, 5, 6, 1, 2]

5. smallest and largest of the list:

a = [5, 10, 15, 20, 25]
[a[0], a[-1]]
[5, 25]

Related concepts: List comprehension,for loop, map, lambda, filter and list indexing

Numpy Array

1. Replace colunm 2 and replace with new column:

import numpy as np
input_arr = np.array([[10,20,30],[40,50,60],[70,80,90]]) 
print("Input Array:")
print("Array after deleting column 2 on axis 1")

input_arr = np.delete(input_arr , 1, axis = 1) 

print (input_arr)

new_arr = np.array([[10,10,10]])

print("Array after inserting column 2 on axis 1")

input_arr = np.insert(input_arr , 1, new_arr, axis = 1) 

print (input_arr)
Input Array:
Array after deleting column 2 on axis 1
[[10 30]
 [40 60]
 [70 90]]
Array after inserting column 2 on axis 1
[[10 10 30]
 [40 10 60]
 [70 10 90]]

2. Print zeros with 2 rows and 3 columns:

np.zeros((2,3))
array([[0., 0., 0.],
       [0., 0., 0.]])

3. Print Range Between 1 To 15 and show 4 integers random numbers:

np.random.randint(1,15,4)
array([12,  1, 14,  9])

4. Use numpy to generate array of 25 random numbers sampled from a standard normal distribution:

np.random.rand(25)
array([0.73293502, 0.38245306, 0.52894979, 0.35896097, 0.83880269,
       0.38216059, 0.11173257, 0.38598484, 0.74322228, 0.31497052,
       0.32662347, 0.4081118 , 0.47730562, 0.3063162 , 0.44843887,
       0.96982928, 0.50541644, 0.71667506, 0.53888407, 0.37803468,
       0.74569824, 0.74611054, 0.56089105, 0.28962139, 0.5674749 ])

5. Stack horizontally the two arrays:

import numpy as np
np.arange(10).reshape(2,-1)
array([[0, 1, 2, 3, 4],
       [5, 6, 7, 8, 9]])
a = np.arange(10).reshape(2,-1)
b = np.repeat(1, 10).reshape(2,-1)

np.concatenate([a, b], axis=1)
array([[0, 1, 2, 3, 4, 1, 1, 1, 1, 1],
       [5, 6, 7, 8, 9, 1, 1, 1, 1, 1]])

6. Find unique value in array 1 that are not array 2:

a = np.array([1,2,3,4,5])
b = np.array([5,6,7,8,9])

np.setdiff1d(a,b)
array([1, 2, 3, 4])

Related concepts: import numpy library, create array, delete array values and insert array values, unique values, reshape array values,generate random number, randon numbers in standard normal distribution.

Dictionary:

1. Python program to find the sum of all items in a dictionary:

def returnSum(dict):
      
     sum = 0
     for i in dict.values():
           sum = sum + i
       
     return sum
  
# Driver Function
dict = {'a': 100, 'b':200, 'c':300}
print("Sum :", returnSum(dict))
Sum : 600

2. Delete a key in dict:

my_dict = {'case 1': 1, 'case 2': 2, 'case 3': 3}
print('Original dict is:' +str(my_dict))
my_dict.pop('case 1')
print('Updated dict is :'+ str(my_dict))
Original dict is:{'case 1': 1, 'case 2': 2, 'case 3': 3}
Updated dict is :{'case 2': 2, 'case 3': 3}

Related concepts: create dictionary, delete key-value pairs, operations in dicts

Tuple:

1. Find Maximum and Minimum K elements in Tuple:

# initializing tuple
test_tup = (5, 20, 3, 7, 6, 8)
  
# printing original tuple
print("The original tuple is : " + str(test_tup))
  
# initializing K 
K = 1
  
# Maximum and Minimum K elements in Tuple
# Using slicing + sorted()
test_tup = list(test_tup)
temp = sorted(test_tup)
res = tuple(temp[:K] + temp[-K:])
  
# printing result 
print("The extracted values : " + str(res)) 
The original tuple is : (5, 20, 3, 7, 6, 8)
The extracted values : (3, 20)

Related concepts: creating a tuple, slicing and indexing a list

Bonus:

  • Array is a reference type that means any operation on one variable will change that value for all properties that reference that object. To prevent that, use copy().

  • List can hold values of different data types whereas Array can only hold values of one data type.

  • Tuple is not iterable. it is widely used only where the definition of items is only once.

  • Map takes all objects in a list and allows you to apply a function to it.

  • Filter takes all objects in a list and runs that through a function to create a new list with all objects that return True in that function.

  • Set is initialized using {} similar to dictionary however dictionary holds key values pairs and sets do not hold duplicates.

Best Sources for practice:


Part 2 will be out next week. If you like this content, please like, share and subscribe!!


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