Devised back in 1989, Python wasn’t one of the programming languages until the onset of digitalization. Today, the world of Big Data and analytics has gained enormous popularity and consequently, Python has become the preferred programming language among data scientists. Scalability, simpler coding, and its collection of libraries and frameworks are just some of the features that make Python suitable for companies engaged in Big Data or machine learning-based initiatives.
Are you applying for a job that involves knowledge of Python? Here are some of the important interview questions that you may face in your Python-related interview. Dive into these Python interview questions and see just how well-versed you are in this programming language.
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50 Important Python Interview Questions and Answers
1. What is the Difference Between a Shallow Copy and Deep Copy?
Deepcopy creates a different object and populates it with the child objects of the original object. Therefore, changes in the original object are not reflected in the copy.
copy.deepcopy() creates a Deep Copy.
Shallow copy creates a different object and populates it with the references of the child objects within the original object. Therefore, changes in the original object are reflected in the copy.
copy.copy creates a Shallow Copy.
2. How Is Multithreading Achieved in Python?
Multithreading usually implies that multiple threads are executed concurrently. The Python Global Interpreter Lock doesn't allow more than one thread to hold the Python interpreter at that particular point of time. So multithreading in python is achieved through context switching. It is quite different from multiprocessing which actually opens up multiple processes across multiple threads.
3. Discuss Django Architecture.
Django is a web service used to build your web pages. Its architecture is as shown:
- Template: the front end of the web page
- Model: the back end where the data is stored
- View: It interacts with the model and template and maps it to the URL
- Django: serves the page to the user
4. What Advantage Does the Numpy Array Have over a Nested List?
Numpy is written in C so that all its complexities are backed into a simple to use a module. Lists, on the other hand, are dynamically typed. Therefore, Python must check the data type of each element every time it uses it. This makes Numpy arrays much faster than lists.
Numpy has a lot of additional functionality that list doesn’t offer; for instance, a lot of things can be automated in Numpy.
5. What are Pickling and Unpickling?
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Pickling |
Unpickling |
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If you just created a neural network model, you can save that model to your hard drive, pickle it, and then unpickle to bring it back into another software program or to use it at a later time.
6. How is Memory managed in Python?
Python has a private heap space that stores all the objects. The Python memory manager regulates various aspects of this heap, such as sharing, caching, segmentation, and allocation. The user has no control over the heap; only the Python interpreter has access.
7. Are Arguments in Python Passed by Value or by Reference?
Arguments are passed in python by a reference. This means that any changes made within a function are reflected in the original object.
Consider two sets of code shown below:
In the first example, we only assigned a value to one element of ‘l’, so the output is [3, 2, 3, 4].
In the second example, we have created a whole new object for ‘l’. But, the values [3, 2, 3, 4] doesn’t show up in the output as it is outside the definition of the function.
8. How Would You Generate Random Numbers in Python?
To generate random numbers in Python, you must first import the random module.
The random() function generates a random float value between 0 & 1.
> random.random()
The randrange() function generates a random number within a given range.
Syntax: randrange(beginning, end, step)
Example - > random.randrange(1,10,2)
9. What Does the // Operator Do?
In Python, the / operator performs division and returns the quotient in the float.
For example: 5 / 2 returns 2.5
The // operator, on the other hand, returns the quotient in integer.
For example: 5 // 2 returns 2
10. What Does the ‘is’ Operator Do?
The ‘is’ operator compares the id of the two objects.
list1=[1,2,3]
list2=[1,2,3]
list3=list1
list1 == list2 🡪 True
list1 is list2 🡪 False
list1 is list3 🡪 True
11. What Is the Purpose of the Pass Statement?
The pass statement is used when there's a syntactic but not an operational requirement. For example - The program below prints a string ignoring the spaces.
var="Si mplilea rn"
for i in var:
if i==" ":
pass
else:
print(i,end="")
Here, the pass statement refers to ‘no action required.’
12. How Will You Check If All the Characters in a String Are Alphanumeric?
Python has an inbuilt method isalnum() which returns true if all characters in the string are alphanumeric.
Example -
>> "abcd123".isalnum()
Output: True
>>”abcd@123#”.isalnum()
Output: False
Another way is to use regex as shown.
>>import re
>>bool(re.match(‘[A-Za-z0-9]+$','abcd123’))
Output: True
>> bool(re.match(‘[A-Za-z0-9]+$','abcd@123’))
Output: False
13. How Will You Merge Elements in a Sequence?
There are three types of sequences in Python:
- Lists
- Tuples
- Strings
Example of Lists -
>>l1=[1,2,3]
>>l2=[4,5,6]
>>l1+l2
Output: [1,2,3,4,5,6]
Example of Tuples -
>>t1=(1,2,3)
>>t2=(4,5,6)
>>t1+t2
Output: (1,2,3,4,5,6)
Example of String -
>>s1=“Simpli”
>>s2=“learn”
>>s1+s2
Output: ‘Simplilearn’
14. How Would You Remove All Leading Whitespace in a String?
Python provides the inbuilt function lstrip() to remove all leading spaces from a string.
>>“ Python”.lstrip
Output: Python
15. How Would You Replace All Occurrences of a Substring with a New String?
The replace() function can be used with strings for replacing a substring with a given string. Syntax:
str.replace(old, new, count)
replace() returns a new string without modifying the original string.
Example -
>>"Hey John. How are you, John?".replace(“john",“John",1)
Output: “Hey John. How are you, John?
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16. What Is the Difference Between Del and Remove() on Lists?
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del |
remove() |
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Here is an example to understand the two statements -
>>lis=[‘a’, ‘b’, ‘c’, ‘d’]
>>del lis[1:3]
>>lis
Output: [“a”,”d”]
>>lis=[‘a’, ‘b’, ‘b’, ‘d’]
>>lis.remove(‘b’)
>>lis
Output: [‘a’, ‘b’, ‘d’]
Note that in the range 1:3, the elements are counted up to 2 and not 3.
17. How Do You Display the Contents of a Text File in Reverse Order?
You can display the contents of a text file in reverse order using the following steps:
- Open the file using the open() function
- Store the contents of the file into a list
- Reverse the contents of the list
- Run a for loop to iterate through the list
18. Differentiate Between append() and extend().
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append() |
extend() |
>>lst=[1,2,3] >>lst.append(4) >>lst Output:[1,2,3,4] |
>>lst=[1,2,3] >>lst.extend([4,5,6]) >>lst Output:[1,2,3,4,5,6] |
19. What Is the Output of the below Code? Justify Your Answer.
>>def addToList(val, list=[]):
>> list.append(val)
>> return list
>>list1 = addToList(1)
>>list2 = addToList(123,[])
>>list3 = addToList('a’)
>>print ("list1 = %s" % list1)
>>print ("list2 = %s" % list2)
>>print ("list3 = %s" % list3)
Output:
list1 = [1,’a’]
list2 = [123]
lilst3 = [1,’a’]
Note that list1 and list3 are equal. When we passed the information to the addToList, we did it without a second value. If we don't have an empty list as the second value, it will start off with an empty list, which we then append. For list2, we appended the value to an empty list, so its value becomes [123].
For list3, we're adding ‘a’ to the list. Because we didn't designate the list, it is a shared value. It means the list doesn’t reset and we get its value as [1, ‘a’].
Remember that a default list is created only once during the function and not during its call number.
20. What Is the Difference Between a List and a Tuple?
Lists are mutable while tuples are immutable.
Example:
List
>>lst = [1,2,3]
>>lst[2] = 4
>>lst
Output:[1,2,4]
Tuple
>>tpl = (1,2,3)
>>tpl[2] = 4
>>tpl
Output:TypeError: 'tuple'
the object does not support item
assignment
There is an error because you can't change the tuple 1 2 3 into 1 2 4. You have to completely reassign tuple to a new value.
21. What Is Docstring in Python?
Docstrings are used in providing documentation to various Python modules, classes, functions, and methods.
Example -
def add(a,b):
" " "This function adds two numbers." " "
sum=a+b
return sum
sum=add(10,20)
print("Accessing doctstring method 1:",add.__doc__)
print("Accessing doctstring method 2:",end="")
help(add)
Output -
Accessing docstring method 1: This function adds two numbers.
Accessing docstring method 2: Help on function add-in module __main__:
add(a, b)
This function adds two numbers.
22. How Do You Use Print() Without the Newline?
The solution to this depends on the Python version you are using.
Python v2
>>print(“Hi. ”),
>>print(“How are you?”)
Output: Hi. How are you?
Python v3
>>print(“Hi”,end=“ ”)
>>print(“How are you?”)
Output: Hi. How are you?
23. How Do You Use the Split() Function in Python?
The split() function splits a string into a number of strings based on a specific delimiter.
Syntax -
string.split(delimiter, max)
Where:
the delimiter is the character based on which the string is split. By default it is space.
max is the maximum number of splits
Example -
>>var=“Red,Blue,Green,Orange”
>>lst=var.split(“,”,2)
>>print(lst)
Output:
[‘Red’,’Blue’,’Green, Orange’]
Here, we have a variable var whose values are to be split with commas. Note that ‘2’ indicates that only the first two values will be split.
24. Is Python Object-oriented or Functional Programming?
Python is considered a multi-paradigm language.
Python follows the object-oriented paradigm
- Python allows the creation of objects and their manipulation through specific methods
- It supports most of the features of OOPS such as inheritance and polymorphism
Python follows the functional programming paradigm
- Functions may be used as the first-class object
- Python supports Lambda functions which are characteristic of the functional paradigm
25. Write a Function Prototype That Takes a Variable Number of Arguments.
The function prototype is as follows:
def function_name(*list)
>>def fun(*var):
>> for i in var:
print(i)
>>fun(1)
>>fun(1,25,6)
In the above code, * indicates that there are multiple arguments of a variable.
26. What Are *args and *kwargs?
*args
- It is used in a function prototype to accept a varying number of arguments.
- It's an iterable object.
- Usage - def fun(*args)
*kwargs
- It is used in a function prototype to accept the varying number of keyworded arguments.
- It's an iterable object
- Usage - def fun(**kwargs):
fun(colour=”red”.units=2)
27. “in Python, Functions Are First-class Objects.” What Do You Infer from This?
It means that a function can be treated just like an object. You can assign them to variables, or pass them as arguments to other functions. You can even return them from other functions.
28. What Is the Output Of: Print(__name__)? Justify Your Answer.
__name__ is a special variable that holds the name of the current module. Program execution starts from main or code with 0 indentations. Thus, __name__ has a value __main__ in the above case. If the file is imported from another module, __name__ holds the name of this module.
29. What Is a Numpy Array?
A numpy array is a grid of values, all of the same type, and is indexed by a tuple of non-negative integers. The number of dimensions determines the rank of the array. The shape of an array is a tuple of integers giving the size of the array along each dimension.
30. What Is the Difference Between Matrices and Arrays?
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Matrices |
Arrays |
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31. How Do You Get Indices of N Maximum Values in a Numpy Array?
>>import numpy as np
>>arr=np.array([1, 3, 2, 4, 5])
>>print(arr.argsort( ) [ -N: ][: : -1])
32. How Would You Obtain the Res_set from the Train_set and the Test_set from Below?
>>train_set=np.array([1, 2, 3])
>>test_set=np.array([[0, 1, 2], [1, 2, 3])
Res_set 🡪 [[1, 2, 3], [0, 1, 2], [1, 2, 3]]
Choose the correct option:
- res_set = train_set.append(test_set)
- res_set = np.concatenate([train_set, test_set]))
- resulting_set = np.vstack([train_set, test_set])
- None of these
Here, options a and b would both do horizontal stacking, but we want vertical stacking. So, option c is the right statement.
resulting_set = np.vstack([train_set, test_set])
33. How Would You Import a Decision Tree Classifier in Sklearn? Choose the Correct Option.
- from sklearn.decision_tree import DecisionTreeClassifier
- from sklearn.ensemble import DecisionTreeClassifier
- from sklearn.tree import DecisionTreeClassifier
- None of these
Answer - 3. from sklearn.tree import DecisionTreeClassifier
34. You Have Uploaded the Dataset in Csv Format on Google Spreadsheet and Shared It Publicly. How Can You Access This in Python?
We can use the following code:
>>link = https://docs.google.com/spreadsheets/d/...
>>source = StringIO.StringIO(requests.get(link).content))
>>data = pd.read_csv(source)
35. What Is the Difference Between the Two Data Series given Below?
df[‘Name’] and df.loc[:, ‘Name’], where:
df = pd.DataFrame(['aa', 'bb', 'xx', 'uu'], [21, 16, 50, 33], columns = ['Name', 'Age'])
Choose the correct option:
- 1 is the view of original dataframe and 2 is a copy of original dataframe
- 2 is the view of original dataframe and 1 is a copy of original dataframe
- Both are copies of original dataframe
- Both are views of original dataframe
Answer - 3. Both are copies of the original dataframe.
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36. You Get the Error “temp.Csv” While Trying to Read a File Using Pandas. Which of the Following Could Correct It?
Error:
Traceback (most recent call last): File "<input>", line 1, in<module> UnicodeEncodeError:
'ascii' codec can't encode character.
Choose the correct option:
- pd.read_csv(“temp.csv”, compression=’gzip’)
- pd.read_csv(“temp.csv”, dialect=’str’)
- pd.read_csv(“temp.csv”, encoding=’utf-8′)
- None of these
The error relates to the difference between utf-8 coding and a Unicode.
So option 3. pd.read_csv(“temp.csv”, encoding=’utf-8′) can correct it.
37. How Do You Set a Line Width in the Plot given Below?
>>import matplotlib.pyplot as plt
>>plt.plot([1,2,3,4])
>>plt.show()
Choose the correct option:
- In line two, write plt.plot([1,2,3,4], width=3)
- In line two, write plt.plot([1,2,3,4], line_width=3
- In line two, write plt.plot([1,2,3,4], lw=3)
- None of these
Answer - 3. In line two, write plt.plot([1,2,3,4], lw=3)
38. How Would You Reset the Index of a Dataframe to a given List? Choose the Correct Option.
- df.reset_index(new_index,)
- df.reindex(new_index,)
- df.reindex_like(new_index,)
- None of these
Answer - 3. df.reindex_like(new_index,)
39. How Can You Copy Objects in Python?
The function used to copy objects in Python are:
copy.copy for shallow copy and
copy.deepcopy() for deep copy
40. What Is the Difference Between range() and xrange() Functions in Python?
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range() |
xrange() |
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41. How Can You Check Whether a Pandas Dataframe Is Empty or Not?
The attribute df.empty is used to check whether a pandas data frame is empty or not.
>>import pandas as pd
>>df=pd.DataFrame({A:[]})
>>df.empty
Output: True
42. Write a Code to Sort an Array in Numpy by the (N-1)Th Column.
This can be achieved by using argsort() function. Let us take an array X; the code to sort the (n-1)th column will be x[x [: n-2].argsoft()]
The code is as shown below:
>>import numpy as np
>>X=np.array([[1,2,3],[0,5,2],[2,3,4]])
>>X[X[:,1].argsort()]
Output:array([[1,2,3],[0,5,2],[2,3,4]])
43. How Do You Create a Series from a List, Numpy Array, and Dictionary?
The code is as shown:
>> #Input
>>import numpy as np
>>import pandas as pd
>>mylist = list('abcedfghijklmnopqrstuvwxyz’)
>>myarr = np.arange(26)
>>mydict = dict(zip(mylist, myarr))
>> #Solution
>>ser1 = pd.Series(mylist)
>>ser2 = pd.Series(myarr)
>>ser3 = pd.Series(mydict)
>>print(ser3.head())
44. How Do You Get the Items Not Common to Both Series a and Series B?
>> #Input
>>import pandas as pd
>>ser1 = pd.Series([1, 2, 3, 4, 5])
>>ser2 = pd.Series([4, 5, 6, 7, 8])
>> #Solution
>>ser_u = pd.Series(np.union1d(ser1, ser2)) # union
>>ser_i = pd.Series(np.intersect1d(ser1, ser2)) # intersect
>>ser_u[~ser_u.isin(ser_i)]
45. How Do You Keep Only the Top Two Most Frequent Values as It Is and Replace Everything Else as ‘other’ in a Series?
>> #Input
>>import pandas as pd
>>np.random.RandomState(100)
>>ser = pd.Series(np.random.randint(1, 5, [12]))
>> #Solution
>>print("Top 2 Freq:", ser.value_counts())
>>ser[~ser.isin(ser.value_counts().index[:2])] = 'Other’
>>ser
46. How Do You Find the Positions of Numbers That Are Multiples of Three from a Series?
>> #Input
>>import pandas as pd
>>ser = pd.Series(np.random.randint(1, 10, 7))
>>ser
>> #Solution
>>print(ser)
>>np.argwhere(ser % 3==0)
47. How Do You Compute the Euclidean Distance Between Two Series?
The code is as shown:
>> #Input
>>p = pd.Series([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
>>q = pd.Series([10, 9, 8, 7, 6, 5, 4, 3, 2, 1])
>> #Solution
>>sum((p - q)**2)**.5
>> #Solution using func
>>np.linalg.norm(p-q)
You can see that the Euclidean distance can be calculated using two ways.
48. How Do You Reverse the Rows of a Data Frame?
>> #Input
>>df = pd.DataFrame(np.arange(25).reshape(5, -1))
>> #Solution
>>df.iloc[::-1, :]
49. If You Split Your Data into Train/Test Splits, Is It Possible to over Fit Your Model?
Yes. One common beginner mistake is re-tuning a model or training new models with different parameters after seeing its performance on the test set.
50. Which Python Library Is Built on Top of Matplotlib and Pandas to Ease Data Plotting?
Seaborn is a Python library built on top of matplotlib and pandas to ease data plotting. It is a data visualization library in Python that provides a high-level interface for drawing statistical informative graphs.
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Conclusion
Cracking a job interview requires careful preparation apart from the right blend of skills and knowledge. There are a number of emerging job opportunities that demand proficiency in Python. As recruiters hunt for professionals with relevant skills, you need to ensure that you have a thorough knowledge of Python fundamentals and the ability to answer all the Python interview questions. You can enroll in Simplilearn’s Data Science with Python course to gain expertise in this language and become a potential candidate for your next job interview.
