We are sure you all have this question: How to prepare for a Python Data Science interview? This blog of 'Top Python data science interview questions' has been carefully compiled, with questions frequently appearing in all companies' interviews. Learning them thoroughly will help you understand the concepts quickly and be more confident in the interviews you're preparing for.
Data Science is one of the most sought-after and pursued fields today. With the market growing by leaps and bounds, there is a high demand for talented data scientists who can assist businesses in gaining valuable insights. Having expertise with at least one programming language is a minimum to step into this field. Python is an excellent skill needed for a data scientist career.
In this blog, we'll look at all the topics that come under Python Data Science to make you interview-ready. All these interview questions are collected after consulting with MindMajix's Python Data Science certification training experts.
For your better understanding, we have segregated the questions in the following manner.
1. What are built-in data types in Python?
2. How to find duplicate values in a dataset?
3. What is list comprehension in Python?
4. What is the difference between range, xrange, and range?
6. What are the deep learning frameworks?
7. How to add titles to subplots in Matplotlib?
8. What are the different parts of a plot in matplotlib?
9. What is the difference between remove(),del(), and pop() in python?
10. What is the bias/variance trade-off?
Python data types define the variable type. Here are a few built-in data types in Python:
Numeric (int, float, and complex)
Sequence (list, tuple, range)
Binary (bytes, byte array, memory view)
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The reason for Python's popularity is its extensive collection of libraries. These libraries include various functionalities and tools to analyze and manage data. The popular Python libraries for data science are:
Negative Indexing is used in Python to begin slicing from the end of the string i.e., the last. Slicing in Python gets a sub-string from a string. The slicing range is set as parameters, i.e., start, stop, and step.
Slicing in Python gets a substring from a string. In Python, negative Indexing begins slicing from the string's end, the last. The parameters of slicing include start, stop, and step.
Let's see the syntax.
#slicing from index start to index stop-1
In Python, dictionary comprehension allows us to create a dictionary in one way by merging two sets of data, either lists or arrays.
rollNumbers =[10, 11, 12, 13]
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The below table illustrates the differences between Python lists and Python tuples:
Lists are mutable.
Tuples are immutable.
Lists include several built-in methods.
Tuples don’t have any built-in methods because of immutability.
Memory consumption is more in lists.
Consumes less memory compared to lists.
Insertion and deletion are easier in lists.
Accessing elements is easier with the tuple data type.
Iterations are time-consuming.
Iterations are comparatively faster.
One thing to note is that Seaborn is built on top of Matplotlib. Both Seaborn and Matplotlib act as a backbone for data visualization in Python. Here are a few things to know before deciding on seaborn or Matplotlib:
Note: This question asks about your preferences. The library you choose might depend on the task or your familiarity with the tool.
Series can only contain a single list with an index, whereas a DataFrame contains more than one series.
A series is a one-dimensional array that supports any datatype, including integers, float, strings, etc. In contrast, a dataframe is a two-dimensional data structure with columns that can support different data types.
The Pandas duplicated() method is used in Python to find and remove duplicate values. It helps analyze duplicate values and returns a True Boolean series for unique elements.
Keep - Controls how to consider duplicate values.
First - Consider the first value as unique and the rest as duplicates.
Last - Consider the last value as unique and the rest as duplicates.
False - Considers all of the same values as duplicates.
Lambda functions are similar to user-defined functions but don't have any names. They are anonymous functions. They are effective only when you want to create a function with simple expressions. It means single-line statements.
They are mostly preferred while using functions at once.
You can define a lambda function like the one below:
lambda argument(s) : expression
Lambda: It's a keyword to define an anonymous function.
Argument: It's a placeholder that holds the value of the variable you want to pass into the function expression. A lambda function can have multiple variables depending on the requirement.
Expression: It's the code you want to execute.
The answer is no. The modules with references to other objects are only sometimes freed on exiting Python. Also, it's impossible to deallocate the memory portions reserved by the C library.
Python provides several compound data types to process data in groups. Some of the common are
[ Learn Top Data Science Interview Questions that help you grab high-paying jobs
Python list comprehension defines and creates new lists based on the values of existing values.
It contains brackets to have the expression executed for each element and the for loop to iterate over each element. The benefit of list comprehension is it's more time efficient and space-efficient than loops.
newList = [ expression(element) for element in oldList if condition ]
Let's see an example,
Based on the list of fruits, you want a new list containing only the fruits with the letter "a" in the name.
fruits = ["apple", "orange", "goa", "kiwi", "carrot"]
['apple', ‘orange’, ‘goa’, ‘carrot’]
Unpacking a tuple means splitting the elements of the tuple into individual variables.
fruits = ("apple", "banana", "cherry")
Python has two division operators, namely / and //.
A single-slash operator does float division and returns the value in decimal form.
A double-slash operator does the floor division and returns the value in natural number form.
11 / 2 returns 5.5
11 // 2 returns 5
Python's built-in str() function is the most popular method for converting an integer to a string. You may use numerous ways to accomplish this, but this function will convert any data type into a string.
Python modules are collections of related code packed together in a program. It's a single file containing functions, classes, or variables designed to perform specific tasks. It's a .py extension file. Popular built-in Python modules include sys, os, random, math, etc.
Python libraries are a collection of modules or packages. It allows us to do specific tasks without having to write code. It doesn't have any particular context. Popular built-in Python libraries include Pytorch, Pygame, Matplotlib, and more.
PEP stands for Python Enhancement Proposal. PEP8 is a document that provides a set of guidelines and practices on how to write Python code. Its primary focus is to improve the readability and consistency of the Python code.
In Python, every variable holds an instance of objects. There are two types of objects, i.e., Mutable and Immutable objects.
E.g., Lists, Dicts, Sets
E.g., Int, Float, Bool
A generator in Python is a special function that can control the iteration behavior of the loop. A decorator allows us to modify the functionality of existing code.
The enumerate() function returns indexes of all time in iterables. An iterable is a collection of lists, sets, and dictionaries.
Whereas the zip() function aggregates the multiple iterables.
Break: The break statement allows terminating the loop. If it is used inside the nested loop, the current loop gets terminated, and flow continues for the following code after the loop.
The below flowchart illustrates the break statement working:
This statement skips the code that comes after it, and the flow control is passed back to the beginning for the next iteration.
The below flowchart illustrates the working of the continue statement:
This statement acts as a placeholder inside the functions, classes, loops, etc., that are meant to be implemented later. The Python pass statement is a null statement.
A RegEx or regular expression is a series of characters that are used to form search patterns.
Some of the important RegEx functions in Python:
It returns a list that contain all matches.
If there is any match in the string, returns Match Object.
It replaces one or more matches with a string.
It returns a list where the string has been split at each match.
[ Visit here to know: Python Regular Expression (RegEx) Cheatsheet ]
A namespace is a naming mechanism to ensure each item has a unique name. There appears to be space assigned to every variable mapped to the object. As a result, the specified area or container and the corresponding object are looked for whenever we call out this variable. Python maintains a dictionary for this.
Types of namespaces in Python:
The built-in namespace includes the global namespace, and the global namespace consists of the local namespace.
In Python, we use special symbols for passing arguments:
*args (Non-Keyword Arguments):
This is to pass a variable number of arguments to a function.
**kwargs (Keyword Arguments):
This is to pass a keyworded, variable-length argument list. We use kwargs with double stars because it enables us to pass through keyword arguments.
In Python, a default parameter is a fallback value in the default argument. The argument gets its default value if the function is called without the argument.
We can set the default value by using the assignment(=) operator and the syntax keywordname=value.
A runtime error is a type of error that happens during the execution of the program. Some of the common examples of runtime errors in Python are
The widely used libraries for data science are
[ Related page: Data Science Tutorial ]
First, create a new array by combining the sizes of the first and second arrays. Then create a function that simultaneously checks array 1 and array 2, determines which of the two arrays contains the smaller integer, and adds that value to the new array.
A dataset can be of two types - wide and long.
A wide format contains information that does not repeat in the first column. In contrast, a long format includes the information that repeats in the first column.
For example, consider two datasets that contain the same information expressed in different formats:
Import pandas as pd
Data = pd.read_CV('sample_url')
A universal function executes mathematical operations on each element of the n-dimensional array.
Examples include np.exp() and np.sqrt(), which evaluate the exponential of each element and the square root of an array.
Deep learning frameworks act as the interface for quickly creating deep learning models without digging too deeply into the complex algorithms. Some popular deep learning frameworks are
There are three ways of reshaping the Pandas DataFrame:
Duplicates identify whether the records are duplicates or not. It results in True or False. Whereas, Drop-duplicates puts duplicates by a column name.
There are two categorical distribution plots - box plots and violin plots.
These allow us to choose a numerical variable and plot the distribution for each category in a designated categorical variable.
The Pairplot function allows us to plot pairwise relationships between variables in a dataset.
fig, axarr = plt.subplots(2, sharex=True, sharey=True)
The ability of NumPy to handle arrays of various shapes during arithmetic operations is referred to as broadcasting. Element-to-element operations are impossible if the dimensions of two arrays are different.
However, it is still possible to perform operations on arrays with various shape types because of NumPy's broadcasting functionality. NumPy's broadcasting rule removes this limitation when the arrays' shapes satisfy specific conditions. For the smaller array and the larger array to have similar shapes, they are broadcasted to the same size.
[ Visit here to know about: Top Data Science Tools ]
Both pivot_table and groupby are used to aggregate the dataframe. The only difference is the resulting shape.
A Matplotlib consists of the following:
The figure keeps track of all the child axes, canvas, and special artists (titles, figure legends, etc.).
There are two Axis objects in the Axes that manage the data limits.
These are the objects that have a number line-like design. They are in response to making the axis markings, known as ticks and ticklabels, and setting the boundaries of the graph (strings labeling the ticks). While a Formatter object produces the tick label strings, a Locator object decides where the ticks should be placed. When the appropriate Locator and Formatter are used together, you can adjust the labels and locations of the ticks precisely.
The artist produced everything you see in the figure (even the Figure, Axes, and Axis objects). This includes text objects, line2d objects, collection objects, and patch objects. All the artists are drawn to the canvas when the figure is created. Most artists are linked to one axe, unable to be shared or moved between axes.
Grid-like plots within a single figure are called subplots. The subplots() function in the matplotlib.pyplot module can be used to plot subplots.
a = [0, 2, 3, 2]
[0, 3, 2]
a = [3, 2, 2, 1]
[3, 2, 2]
a = [4, 3, 5]
A scatter plot is a two-dimensional data visualization showing how two variables relate to one another. The first is plotted against the x-axis, while the second is plotted along the y-axis.
[ Also Read: What is Data Visualization? ]
A heatmap is a two-dimensional graphic representation of data that uses a matrix to hold individual values. The values, represented by different shades of the same color, display the correlation values. Darker shades represent higher correlations between the variables, while lighter shades represent lower correlations.
You can find the median of the 'points' column from the 'reviews' dataframe
You can find the min and max of 'price' for different 'variety' column from 'reviews' dataframe
import seaborn as sns
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
through .isnull() helps in identifying the missing values.
The code below gives the total number of missing data points in the data frame
missing_values_count = sf_permits.isnull().sum()
To convert dates from String to Date
import pandas as pd
df[‘Date_parsed’] = pd.to_datetime(df[‘Date’], format=”%m/%d/%Y”)
It's a machine-learning algorithm used for classification. It estimates the probability of the possible outcomes of a single trial.
62. What is SVM?
Support vector machines represent training data as a collection of points in space segmented into groups by a clear, as wide-spaced gap as possible. New samples are then projected into that area and expected to fall into a category depending on which side of the gap they fall.
[ Grace your interview by having these Python Interview Questions ]
The Bias Variance Trade-offs are important in supervised machine learning, particularly in predictive modeling. One can assess the method's performance by analyzing an algorithm's prediction error.
Error from Bias
Error from variance:
This fairly simple Python problem consists of setting up a distribution, creating n samples, and displaying them. We can do this using the SciPy scientific computing library.
Create a normal distribution with a mean of 0 and a standard deviation of 1 initially. The rvs(n) function is then used to build samples.
The numpy.linalg.inv(array) function allows you to find the inverse of any square matrix. The 'array', in this case, would be the matrix that needs to be inverted.
This problem can be resolved in two ways. The first step is to establish exactly how each matrix entry's index changes with a 90° clockwise rotation. The second method involves visualizing a series of more effortless matrix transformations that, when applied one after the other, produce a 90-degree rotation clockwise.
You can do it in two ways. The worker "Amitah" is first filtered using the operator "==" The second one locates the letter "a" in a string by using the find() function.
bins = [0, 50, 75, 90, 100]
labels=['<50','<75','<90' , '<100']
df['test score'] = pd.cut(df['test score'], bins,labels=labels)
Following are a few preparation tips for Python Data Science Interviews:
First, prepare your resume well. Your resume should list at least 2-3 Python data science projects to show your knowledge and skill in the area.
If you are going to interview, do your research about the company first. In the case of Python data science, be aware of the libraries the company uses, the models they are building, and other information.
Your fundamentals should be solid enough to handle the interviewer's coding challenge. Attempt mock tests and quizzes, and learn every detail while coding.
They are the fundamental pillars of programming. Therefore, you must be well-versed in that as well.
There are various built-in data types in Python, such as
Data analysis is a process that provides information to make business decisions. Steps in the process include data cleansing, transformation, and modeling. Data analysis libraries like Pandas, Numpy, etc., give users the necessary functionality in Python.
Python uses negative indexing to begin slicing from the final position in the string or the end.
The major difference is tuples cannot be modified, whereas lists are modified.
Matplotlib is better for basic plots, while seaborn is better for more advanced statistical plots.
Python is one of the most preferred coding languages in data science because of its versatility and the number of data science libraries available.
It is essential to master Python before learning data science. Otherwise, you may need help implementing well-known libraries and working with scalable code that other engineers can contribute to.
Python Data Science professionals have lucrative careers and many job opportunities. These Python Data Science interview questions can help you get one step closer to your dream job.
If you have attended Python Data Science interviews or have any questions you would like to get answered, please share them in the comments section. We'll respond to you at the earliest.
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Madhuri is a Senior Content Creator at MindMajix. She has written about a range of different topics on various technologies, which include, Splunk, Tensorflow, Selenium, and CEH. She spends most of her time researching on technology, and startups. Connect with her via LinkedIn and Twitter .
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