Q315 What Is A Heatmap
A heatmap is a two-dimensional graphical representation of data containing individual values in a matrix format. The values show the correlation values and are represented by various shades of the same color. The darker shades indicate a higher correlation between the variables, and lighter shades reflect lower correlation values.
Q74 What Are Recommender Systems
Recommender Systems are a subclass of information filtering systems that are meant to predict the preferences or ratings that a user would give to a product. Recommender systems are widely used in movies, news, research articles, products, social tags, music, etc.
Examples include movie recommenders in IMDB, Netflix & BookMyShow, product recommenders in e-commerce sites like Amazon, eBay & Flipkart, YouTube video recommendations and game recommendations in Xbox.
What Is Init Method In Python
The init method works similarly to the constructors in Java. The method is run as soon as an object is instantiated. It is useful for initializing any attributes or default behaviour of the object at the time of instantiation.For example:
classInterviewbitEmployee:# init method / constructordef__init__: self.emp_name = emp_name# introduce methoddefintroduce:printemp = InterviewbitEmployee # __init__ method is called here and initializes the object name with "Mr Employee"emp.introduce
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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.
Python Coding Interview Question #: Lowest Priced Orders
One of the easiest ways to join two tables in Python is by using the merge function. Well do that to solve the Amazon question:
Find the lowest order cost of each customer. Output the customer id along with the first name and the lowest order price.
Link to the question:
Youre given two tables to work with. The first table is customers:
| id |
Since you need the data from both tables, youll have to merge or inner join them:
import pandas as pdimport numpy as npmerge = pd.merge
You do that on the column id from the table customers, and the column cust_id from the table orders. The result shows two tables as one:
Once youve done that, use the groupby function to group the output by cust_id and first_name. These are the columns the question asks you to show. You need to show the lowest order cost for each customer, too. You do that using the min function.
The complete answer is thus:
import pandas as pdimport numpy as npmerge = pd.mergeresult = merge.groupby.min.reset_index
This code returns the desired output.
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What Is Slicing In Python
Slicing is a process used to select a range of elements from sequence data type like list, string and tuple. Slicing is beneficial and easy to extract out the elements. It requires a : which separates the start index and end index of the field. All the data sequence types List or tuple allows us to use slicing to get the needed elements. Although we can get elements by specifying an index, we get only a single element whereas using slicing we can get a group or appropriate range of needed elements.
Syntax:
List_name
What Are The Assumptions Required For A Linear Regression
There are four major assumptions.
1. There is a linear relationship between the dependent variables and the regressors, meaning the model you are creating actually fits the data.
2. The errors or residuals of the data are normally distributed and independent from each other. 3. There is minimal multicollinearity between explanatory variables
4. Homoscedasticitythe variance around the regression lineis the same for all values of the predictor variable.
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Q22 What Is A Python Module How Is It Different From Libraries
A module is a single file containing functions, definitions, and variables designed to do certain tasks. It is a .py extension file. It can be imported at any time during a session and needs to be imported only once. To import a python module, there are two ways: import or from module_name import.
A library is a collection of reusable functionality of codes that allows us to perform a variety of tasks without having to write the code. A Python library does not have any specific context to it. It loosely refers to a collection of modules. These codes can be used by importing the library and by calling that librarys method with a period.
Faqs On Python Data Science Interview Questions
Q1. How do I prepare for Python data science interview questions?
While there is no fixed way to prepare for Python data science interview questions, having a good grasp of the basics can never go wrong. Some important topics you should keep in mind for Python interview questions for data science are: basic control flow for loops, while loops, if-else-elif statements, different data types and data structures of Python, Pandas and its various functions, and how to use list comprehension and dictionary comprehension.
Q2. Will Python be allowed in coding interviews?
While the simple answer is yes, it can vary from company to company. Python can be allowed in coding rounds, and several companies even use platforms such as HackerRank to conduct Python data science interview questions.
Q3. Explain Arrays in Python data science interview questions.
Arrays are a data structure, just like lists. With a number of objects of different data types, Python arrays can be repeated and have several built-in functions to handle them. Such conceptual questions play a vital role in Python data science interview questions. So keep this in mind when preparing.
Q4. Which resources to use to prepare for Python data science interview questions?
Some free resources to prepare for Python data science interview questions are CodeAcademy, FreeCodeCamp, DataCamp, Udacity, and Geeks for Geeks.
Q5. How long does it take to learn Python?
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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.
Question #6 Check If String Is A Palindrome
A string or phrase is a palindrome if after converting all uppercase letters into lowercase letters and removing all non-alphanumeric characters it reads the same forward and backward. For example, the string Bob is a palindrome, as its the same no matter from which side you read it.
Problem description: Given a string s, return True if its a palindrome, or False otherwise.
To start, you should lowercase the string and remove all non-alphanumeric characters. Then, you can iterate over every letter of the string and reverse it by adding it to the front of the string:
And the results:
Image 7 Palindrome check test results
The alternative approach would be to reverse the string using Pythons slicing notation , or by using the reversed functions but these are Python-specific and likely dont apply to other programming languages.
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Question #5 Check If A String Is An Anagram
An Anagram is a word or phrase formed by rearranging the letters of a different word or phrase, typically using all the letters exactly once. For example, the words anagram and nagaram are anagrams.
Problem description: Given two strings a and b, return True if b is an anagram of a, and False otherwise.
Two strings cant be anagrams if they arent of the same length, so youll have to check for that first. If the length check passes, you can then store individual letters and their counts in two Python dictionaries one for each word. If the dictionaries are identical, the strings are anagrams:
Here are the results:
Image 6 Anagram check test results
Our logic works! Sure, there are more Pythonic ways to check for an anagram, such as using the sorted or set functions. However, the solution weve implemented should work in any programming language, as syntax isnt Python-specific. Thats exactly what most interviewers will look for.
Q318 How Is Violinplot Different From Boxplot
A box plot shows the distribution of quantitative data that helps compare between variables or across levels of a categorical variable. A box plot is the visual representation of the statistical five-number summary of a given data set.
The box shows the quartiles of the dataset while the whiskers extend to show the rest of the distribution, except for points that are determined to be outliers using a method that is a function of the inter-quartile range.
A violin plot plays a similar role as a box and whisker plot. It shows the distribution of quantitative data across several levels of one categorical variables such that those distributions can be compared.
Unlike a box plot, where all of the plot components correspond to actual data points, the violin plot features a kernel density estimation of the underlying distribution.
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Q220 What Are Namespaces In Python
A namespace is a naming system that is used to ensure that every object has a unique name. It is like space is assigned to every variable which is mapped to the object. So, when we call out this variable, this assigned space or container is searched and hence the corresponding object as well. Python maintains a dictionary for this purpose.
What Is Functional Programming Does Python Follow A Functional Programming Style If Yes List A Few Methods To Implement Functionally Oriented Programming In Python
Functional programming is a coding style where the main source of logic in a program comes from functions.
Incorporating functional programming in our codes means writing pure functions.
Pure functions are functions that cause little or no changes outside the scope of the function. These changes are referred to as side effects. To reduce side effects, pure functions are used, which makes the code easy-to-follow, test, or debug.
Python does follow a functional programming style. Following are some examples of functional programming in Python.
filter: Filter lets us filter some values based on a conditional logic.list)) map: Map applies a function to every element in an iterable.list)) reduce: Reduce repeatedly reduces a sequence pair-wise until it reaches a single value.from functools import reduce > > > reduce -13
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Python For Scientific Computing
This chapter will cover topics on scientific computing in Python. We’ll start by explaining the difference between NumPy arrays and lists. We’ll define why the former ones suit better for complex calculations. Next, we’ll cover some useful techniques to manipulate with pandas DataFrames. Finally, we’ll do some data visualization using scatterplots, histograms, and boxplots.
How Are Numpy Arrays Advantageous Over Python Lists
- The list data structure of python is very highly efficient and is capable of performing various functions. But, they have severe limitations when it comes to the computation of vectorized operations which deals with element-wise multiplication and addition. The python lists also require the information regarding the type of every element which results in overhead as type dispatching code gets executes every time any operation is performed on any element. This is where the NumPy arrays come into the picture as all the limitations of python lists are handled in NumPy arrays.
- Additionally, as the size of the NumPy arrays increases, NumPy becomes around 30x times faster than the Python List. This is because the Numpy arrays are densely packed in the memory due to their homogenous nature. This ensures the memory free up is also faster.
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What Is A Dictionary In Python
Python dictionary is one of the supported data types in Python. It is an unordered collection of elements. The elements in dictionaries are stored as key-value pairs. Dictionaries are indexed by keys.
For example, below we have a dictionary named dict. It contains two keys, Country and Capital, along with their corresponding values, India and New Delhi.
Syntax:
dict=
Output: Country: India, Capital: New Delhi
Data Manipulation And String Extraction In Python
As the engineering culture keeps growing, Data Scientists often team up with other engineers to build pipelines and perform a ton of soft engineering stuff. Job candidates are expected to face extensive coding challenges in R/Python and SQL .
From my past interview experiences, simply being able to code is far from enough. What differentiates experienced programmers from code-camp-trained beginners is the ability to dissect the big question into smaller pieces and then code it up.
Its my aha moment.
For the past few months, Ive been deliberately practiced to dissect the code and walk through my thinking processes, as many you may notice if you track my progress. I will do the same for todays content.
Data manipulation and string extraction are essential components of Data Science Interviews, both as an individual subject or combined with other topics. Ive elaborated on 6 Python questions for Data Scientists in a previous post and provide additional authentic interview questions asked by major tech companies:
There is a common misconception in the field, which is using the Brutal Force method isnt something worth recommending due to its inefficiency and memory usage.
Without further ado, lets dig in
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Python Coding Interview Question #1: Customer Revenue In March
The last question is by Meta/Facebook:
Calculate the total revenue from each customer in March 2019. Include only customers who were active in March 2019.
Output the revenue along with the customer id and sort the results based on the revenue in descending order.
Link to the question:
Youll need to_datetime on the column order_date. Then extract March and the year 2019 from the same column. Finally, group by the cust_id and sum the column total_order_cost, which will be the revenue youre looking for. Use the sort_values to sort the output according to revenue in descending order.
How Can Outlier Values Be Treated
You can drop outliers only if it is a garbage value.
Example: height of an adult = abc ft. This cannot be true, as the height cannot be a string value. In this case, outliers can be removed.
If the outliers have extreme values, they can be removed. For example, if all the data points are clustered between zero to 10, but one point lies at 100, then we can remove this point.
If you cannot drop outliers, you can try the following:
- Try a different model. Data detected as outliers by linear models can be fit by nonlinear models. Therefore, be sure you are choosing the correct model.
- Try normalizing the data. This way, the extreme data points are pulled to a similar range.
- You can use algorithms that are less affected by outliers an example would be random forests.
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What Do You Know About Pandas
- Pandas is an open-source, python-based library used in data manipulation applications requiring high performance. The name is derived from Panel Data having multidimensional data. This was developed in 2008 by Wes McKinney and was developed for data analysis.
- Pandas are useful in performing 5 major steps of data analysis – Load the data, clean/manipulate it, prepare it, model it, and analyze the data.
How Do You Treat Outliers In A Dataset
An outlier is a data point that is distant from other similar points. They may be due to variability in the measurement or may indicate experimental errors.
The graph depicted below shows there are three outliers in the dataset.
To deal with outliers, you can use the following four methods:
- Drop the outlier records
- Try a new transformation
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What Is Observational And Experimental Data In Statistics
Observational data correlates to the data that is obtained from observational studies, where variables are observed to see if there is any correlation between them.
Experimental data is derived from experimental studies, where certain variables are held constant to see if any discrepancy is raised in the working.
What Are The Essential Functions And Responsibilities Of A Data Scientist
A Data Scientist identifies the business issues that need to be answered and then develops and tests new algorithms for quicker and more accurate data analytics utilizing a range of technologies such as Tableau, Python, Hive, and others. A Data Scientist also collects, integrates, and analyses data to acquire insights and reduce data issues so that strategies and prediction models may be developed.
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