Python Data Science Interview Coding Questions

What Are Python Modules

Files containing Python codes are referred to as Python Modules. This code can either be classes, functions, or variables and saves the programmer time by providing the predefined functionalities when needed. It is a file with .py extension containing an executable code.

Commonly used built modules are listed below:

Data Science Interview Coding Questions + Solution Code

Here are some solved data cleansing code snippets that you can use in your interviews or projects. Click on these links below to download the python code for these problems. A complete list of ready-to-use solved use-cases is available here. How to use auto encoder for unsupervised learning models?â

Sample Python Data Science Interview Questions For Practice

As you dig deeper and prepare for Python data science interview questions, do practice the following questions as well:

If you want further insights into what a Python data science interview looks like and how to prepare for it, check out Understanding Technical Interviews at FAANG and How to Crack Them.

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Python Coding Interview Questions And Answers

1) How do you debug a Python program?

Answer) By using this command we can debug a python program

$ python -m pdb python-script.py

2) What is < Yield> Keyword in Python?

A) The < yield> keyword in Python can turn any function into a generator. Yields work like a standard return keyword.

But itll always return a generator object. Also, a function can have multiple calls to the < yield> keyword.

Example:

def testgen:weekdays = yield weekdaysyield weekdaysday = testgenprint next, next

Output: sun mon

3) How to convert a list into a string?

A) When we want to convert a list into a string, we can use the < .join> method which joins all the elements into one and returns as a string.

Example:

weekdays = listAsString = ' '.joinprintP

4) How to convert a list into a tuple?

A) By using Python < tuple> function we can convert a list into a tuple. But we cant change the list after turning it into tuple, because it becomes immutable.

Example:

weekdays = listAsTuple = tupleprint

output:

5) How to convert a list into a set?

A) User can convert list into set by using < set> function.

Example:

weekdays = listAsSet = setprint

output: set

6) How to count the occurrences of a particular element in the list?

A) In Python list, we can count the occurrences of an individual element by using a < count> function.

Example # 1:

weekdays = print])

output: , , , , , ]

7) What is NumPy array?

8) How can you create Empty NumPy Array In Python?

A) We can create Empty NumPy Array in two ways in Python,

What Is The Difference Between The Long Format Data And Wide Format Data

LONG FORMAT DATA: It contains values that repeat in the first column. In this format, each row is a one-time point per subject.

WIDE FORMAT DATA: In the Wide Format Data, the datas repeated responses will be in a single row, and each response can be recorded in separate columns.

Long format Table:

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Q103 How Does An Lstm Network Work

Long-Short-Term Memory is a special kind of recurrent neural network capable of learning long-term dependencies, remembering information for long periods as its default behaviour. There are three steps in an LSTM network:

• Step 1: The network decides what to forget and what to remember.
• Step 2: It selectively updates cell state values.
• Step 3: The network decides what part of the current state makes it to the output.

Python Data Science Interview Questions And Answers

As you prepare for the Python data science interview questions, keep the following in mind and prepare accordingly. According to our observations, these questions have helped software engineers nail their tech interviews:

Q1. What is Python, and what is it used for?

An interpreted high-level, general-purpose programming language, Python is often used in building websites and software applications. Apart from this, it is also useful in automating tasks and conducting data analysis. While the programming language can create an array of programs, it hasnât been designed keeping in mind a specific problem.

Q2. List the important features of Python.

Some significant features of Python are:

• It supports structured and functional programmings
• It developed high-level dynamic data types
• It can be compiled to byte-code for creating larger applications
• It uses automated garbage collection
• It can be used along with Java, COBRA, C, C++, ActiveX, and COM

Q3. What are the different built-in data types in Python?

Python uses many built-in data types. Some of these are:

Q5. What is a negative index used for in Python?

Negative indexes in Python are used to assess and index lists and arrays from the end, counting backward. For instance, n-1 shows the last time in a list while n-2 shows the second to last.

To understand such technical concepts better, go over our Learn section. We have covered several topics in great detail to help you prepare Python data science interview questions.

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Q109 What Is Back Propagation And Explain Its Working

Backpropagation is a training algorithm used for multilayer neural network. In this method, we move the error from an end of the network to all weights inside the network and thus allowing efficient computation of the gradient.

It has the following steps:

• Forward Propagation of Training Data

• Derivatives are computed using output and target

• Back Propagate for computing derivative of error wrt output activation

• Using previously calculated derivatives for output

• Update the Weights

Explain When Bee Swarm And Violin Plots Are Used

We use a bee swarm plot when we want a good representation of distribution of the values in our dataset. It is created using the swarm function in seaborn library. One disadvantage is that it does not scale well for large number of observations.

A violin plot is similar to a box plot and it used when we want to see the distribution of a numeric data. This is useful when we need to compare multiple groups. Unlike a box plot, violin plots give us a deeper understanding of the density.

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What Are Dimensionality Reduction And Its Benefits

The Dimensionality reduction refers to the process of converting a data set with vast dimensions into data with fewer dimensions to convey similar information concisely.

This reduction helps in compressing data and reducing storage space. It also reduces computation time as fewer dimensions lead to less computing. It removes redundant features for example, there’s no point in storing a value in two different units .

Related Interview Questions and Answers

Using The Merged Data Calculate The Percentage Of Customer Who Is More Than 65 Years Old And Round The Result To The Nearest Integer

Create a loop for age more than 65 and divide with the length of the total customers. The round before bracket will round the number to the nearest integer, with no decimal points.

Or another simpler way of calculating is as below:

• This notebook was run using Google Colab and the code and data are available in GitHub.

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Q115 What Is A Boltzmann Machine

Boltzmann machines have a simple learning algorithm that allows them to discover interesting features that represent complex regularities in the training data. The Boltzmann machine is basically used to optimise the weights and the quantity for the given problem. The learning algorithm is very slow in networks with many layers of feature detectors. Restricted Boltzmann Machines algorithm has a single layer of feature detectors which makes it faster than the rest.

Q116. What Is Dropout and Batch Normalization?

Dropout is a technique of dropping out hidden and visible units of a network randomly to prevent overfitting of data . It doubles the number of iterations needed to converge the network.

Batch normalization is the technique to improve the performance and stability of neural networks by normalizing the inputs in every layer so that they have mean output activation of zero and standard deviation of one.

Q24 Name Mutable And Immutable Objects

The mutability of a data structure is the ability to change the portion of the data structure without having to recreate it. Mutable objects are lists, sets, values in a dictionary.

Immutability is the state of the data structure that cannot be changed after its creation. Immutable objects are integers, strings, float, bool, tuples, keys of a dictionary.

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What Is Docstring In Python

Python lets users include a description for their methods using documentation strings or docstrings. Docstrings are different from regular comments in Python as, rather than being completely ignored by the Python Interpreter like in the case of comments, these are defined within triple quotes.

Syntax:

"""Using docstring as a comment.This code add two numbers"""x=7y=9z=x+yprint

Q225 What Is The Use Of The With Statement

With statement helps in exception handling and also in processing the files when used with an open file. Using this way:

with open as file_name:

We can open and process the file, and we do not need to close the file explicitly. Post the with block exists., then the file object is closed. The With statement is resourceful and ensures that the file stream process is not stopped, and in case an exception is raised, it ends properly.

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Question : Calculate The Churn Rate Percentage

Calculate the churn rate, the percentage of the customer who churned, round to the nearest integer, and visualize through plotting.

From the plot above, 70% decided to churn and 29% decided to stay. Another simpler way of doing this is to display the calculated percentages and plot in a simple barplot.

Technical Concepts Tested In Python Pandas Interview Questions

The problems involving Pandas can be broadly grouped into the following categories.

• Sorting DataFrames
• Applying functions

In this article we will start with the basics and cover the first five areas. The remaining areas are covered in the second part of the series. Check out the second part here Python Pandas Questions for Data Science .

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‘people Who Bought This Also Bought’ Recommendations Seen On Amazon Are A Result Of Which Algorithm

The recommendation engine is accomplished with collaborative filtering. Collaborative filtering explains the behavior of other users and their purchase history in terms of ratings, selection, etc.

The engine makes predictions on what might interest a person based on the preferences of other users. In this algorithm, item features are unknown.

For example, a sales page shows that a certain number of people buy a new phone and also buy tempered glass at the same time. Next time, when a person buys a phone, he or she may see a recommendation to buy tempered glass as well.

How Can We Sort The Dataframe

We can efficiently perform sorting in the DataFrame through different kinds:

The DataFrame can be sorted by using the sort_index method. It can be done by passing the axis arguments and the order of sorting. The sorting is done on row labels in ascending order by default.

It is another kind through which sorting can be performed in the DataFrame. Like index sorting, sort_values is a method for sorting the values.

It also provides a feature in which we can specify the column name of the DataFrame with which values are to be sorted. It is done by passing the ” argument.

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What Is A Bias

Bias: Due to an oversimplification of a Machine Learning Algorithm, an error occurs in our model, which is known as Bias. This can lead to an issue of underfitting and might lead to oversimplified assumptions at the model training time to make target functions easier and simpler to understand.

Some of the popular machine learning algorithms which are low on the bias scale are –

Support Vector Machines , K-Nearest Neighbors , and Decision Trees.

Algorithms that are high on the bias scale –

Logistic Regression and Linear Regression.

Variance: Because of a complex machine learning algorithm, a model performs really badly on a test data set as the model learns even noise from the training data set. This error that occurs in the Machine Learning model is called Variance and can generate overfitting and hyper-sensitivity in Machine Learning models.

While trying to get over bias in our model, we try to increase the complexity of the machine learning algorithm. Though it helps in reducing the bias, after a certain point, it generates an overfitting effect on the model hence resulting in hyper-sensitivity and high variance.

Bias-Variance trade-off: To achieve the best performance, the main target of a supervised machine learning algorithm is to have low variance and bias.

The following things are observed regarding some of the popular machine learning algorithms –

How To Add An Index Row Or Column To A Pandas Dataframe

Adding an Index to a DataFrame

Pandas allow adding the inputs to the index argument if you create a DataFrame. It will make sure that you have the desired index. If you don?t specify inputs, the DataFrame contains, by default, a numerically valued index that starts with 0 and ends on the last row of the DataFrame.

Adding Rows to a DataFrame

We can use .loc, iloc, and ix to insert the rows in the DataFrame.

• The loc basically works for the labels of our index. It can be understood as if we insert in loc, which means we are looking for that values of DataFrame that have an index labeled 4.
• The iloc basically works for the positions in the index. It can be understood as if we insert in iloc, which means we are looking for the values of DataFrame that are present at index ‘4`.
• The ix is a complex case because if the index is integer-based, we pass a label to ix. The ix means that we are looking in the DataFrame for those values that have an index labeled 4. However, if the index is not only integer-based, ix will deal with the positions as iloc.

Adding Columns to a DataFrame

If we want to add the column to the DataFrame, we can easily follow the same procedure as adding an index to the DataFrame by using loc or iloc.

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Difference Between Normalisation And Standardization

Q59 What Is Supervised Learning

Supervised learning is the machine learning task of inferring a function from labeled training data. The training data consist of a set of training examples.

Algorithms: Support Vector Machines, Regression, Naive Bayes, Decision Trees, K-nearest Neighbor Algorithm and Neural Networks

E.g. If you built a fruit classifier, the labels will be this is an orange, this is an apple and this is a banana, based on showing the classifier examples of apples, oranges and bananas.

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Explain Reindexing In Pandas

Reindexing is used to conform DataFrame to a new index with optional filling logic. It places NA/NaN in that location where the values are not present in the previous index. It returns a new object unless the new index is produced as equivalent to the current one, and the value of copy becomes False. It is used to change the index of the rows and columns of the DataFrame.

Q: What Are The Differences Between Lists And Tuples

Ans: Lists and tuples both are the values of any data type but there are some differences between them.

• The basic difference between lists and tuples is that lists are mutable whereas tuples are immutable.

• Lists are slower than tuples.

• Lists are built with square brackets while tuples are enclosed in parentheses.

with Python Code Example)

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Q: Querying Data With Mongodb

Lets try to replicate the BoughtItem table first, as you did in SQL. To do this, you must append a new field to a customer. MongoDBs documentation specifies that the keyword operator set can be used to update a record without having to write all the existing fields:

# Just add "boughtitems" to the customer where the firstname is Bobbob=customers.update_many

Notice how you added additional fields to the customer without explicitly defining the schema beforehand. Nifty!

In fact, you can update another customer with a slightly altered schema:

amy=customers.update_manyprint)# pymongo.results.UpdateResult

Similar to SQL, document-based databases also allow queries and aggregations to be executed. However, the functionality can differ both syntactically and in the underlying execution. In fact, you might have noticed that MongoDB reserves the $ character to specify some command or aggregation on the records, such as $group. You can learn more about this behavior in the official docs.

You can perform queries just like you did in SQL. To start, you can create an index: