Congratulations! You've landed an interview I understand, and now it's time to prepare for it!
One of the most important guides at your disposal is this interview guide. think of it as your roadmap to success, guiding you through the twists and turns of the interview process. Here's how to decode and utilize this essential document effectively.
Start by carefully reading through this interview guide from start to end. Pay attention to any instructions, formatting, or specific questions provided.
Spend time on each topic, take notes, strive for understanding, and, most importantly, attempt to model these complex problems using Python.
While this interview guide provides a detailed framework, be prepared to adapt and think on your feet. Interviewers may ask unexpected or follow-up questions to test deeper into certain areas.
After the interview, reflect on your performance and seek feedback from trusted sources, such as mentors, career advisors, or interview coaches. Again, take notes of areas of improvement and incorporate them into your preparation for future interviews.
Python Primer: A Beginner's Guide to Interview Success
What is an IDE and a Python Code Editor? and highlight the primary differences between the two.
At a fundamental level, both Python IDEs (Integrated Development Environments) and Python Code Editors are tools that help developers write and manage Python code. However, there are some key differences between the two.
Python Code Editor:
A code editor is a lightweight tool primarily focused on facilitating the task of writing and editing code. While code editors are primarily for writing code, many modern editors, like Visual Studio Code or Atom, support plugins or extensions which can provide additional functionality. They might offer syntax highlighting, code formatting, and simple debugging tools.
Examples: Jupyter Notebook, Visual Studio Code, Atom, Sublime Text, and Notepad++ are some well-known code editors.
Python IDE (Integrated Development Environment):
An IDE is a more comprehensive toolset that provides an integrated environment for developers to write, debug, test, and deploy their code. Apart from basic code-writing capabilities, IDEs usually come packed with a wide range of features, including an integrated debugger, code suggestions/completion, a built-in terminal, project management tools, version control, and more. They are specifically designed to cater to the entire development lifecycle.
Examples: PyCharm, Eclipse with PyDev, and Spyder are popular IDEs for Python development.
Primary Differences:
Scope: IDEs generally provide a broader scope of features catering to the entire development lifecycle, while code editors are more streamlined for code writing and editing.
Performance: Given the added features, IDEs can be more resource-intensive compared to lightweight code editors.
Customization: While both can be customized, code editors, especially newer ones like VS Code, often have a vast marketplace of extensions allowing developers to tailor their environment as needed.
the choice between an IDE and a code editor largely depends on the specific needs of a developer. If they're looking for a comprehensive environment with a lot of integrated tools, they might opt for an IDE. However, if they need something lightweight and highly customizable, a code editor might be more suitable.
What are the basic data types in Python?
(1) String, (2) Integer, (3) Float, (4) Boolean
strings: strings are sequences of characters, such as words or sentences, and they are specified inside single or double quotation marks. [for example, "Hello World!" and 'Hello World!' are strings]
integers: integers are whole numbers that can be positive, negative, or zero. Integers are represented as a sequence of digits without a decimal point. [for example, 0, -1234567890, and 2345678901 are all integers]
floats: floats are numbers with a decimal point. Floats can be positive, negative, or zero. [for example, 3.75, -3.55, and 4.20 are all floats]
booleans: booleans are a special data type that can only have two values: True or False. They are often used to represent the results of logical tests or comparisons.
these data types are also called static data types and there are also some sequential data types, to be covered in our classroom.
What are the basic methods for Python String DataType?
Python provides a wide range of built-in functions to perform operations and methods to manipulate strings. Here are some commonly used string methods.
lower(): the lower() method is used to return a string in lowercase. this method converts all the characters in a string to lowercase.
upper(): the upper() method is used to return a string in uppercase. this method converts all the characters in a string to uppercase.
replace(): the replace() method is used to replace a specific substring with another string in a given string.
strip(): the strip() method is used to remove leading and trailing whitespace from a string.
split(): the split() method is a method of the str class that can be used to split a string into a list of substrings based on a specified delimiter.
join(): the join() method is a method of the str class that can be used to join a list of strings into a single string.
What are the operators in Python?
(1) Assignment, (2) Arithmetic, (3) Comparison, (4) Logical
Arithmetic Operators: (+) addition, (-) subtraction, (*) multiplication, (/) division, (//) floor division, (%) modulus, (**) exponent
Assignment Operators: (=) assignment, (+=) addition assignment, (-=) subtraction assignment, (*=) multiplication assignment, (/=) division assignment, (//=) floor division assignment, (%=) modulus assignment, (**=) exponentiation assignment
Comparison Operators: (==) equal to, (!=) not equal to, (>) greater than, (<) less than, (>=) greater than or equal to, (<=) less than or equal to
Logical Operators: (and) returns True if both operands are True, (or) returns True if at least one operand is True, (not) returns True if the operand is False, and False if the operand is True
there are two additional operators
(5) Identity and (6) Bitwise to be covered in our classroom.
What would you suggest for naming variables in Python?
While naming variables in Python, it is essential to follow proper naming conventions for code clarity and maintainability. Here are some guidelines to follow when naming variables:
Start with a Letter or Underscore: Variable names must begin with a letter (a-z, A-Z) or an underscore (_) character. for example: "stockPrice" or "_stockPrice" are acceptable variable names.
Avoid Starting with a Number: Variable names cannot begin with a number. for example: "1stockPrice" or "1_stockPrice" are invalid variable names.
Use Alphanumeric Characters and Underscores: Variable names can only contain alphanumeric characters (A-Z, a-z, 0-9) and underscores (_). for example: "stockPrice", "stock_Price", "stockPrice_1", and "stockPrice_2" are all valid variable names.
Avoid Whitespace and Special Characters: Variable names should not contain whitespace or special characters such as +, -, etc. for example: "stock price" or "stock-price" are invalid variable names.
Case Sensitive: Variable names are case-sensitive. for example: "StockPrice", "stockPrice", and "Stockprice" are considered distinct variables.
Avoid Python Keywords: Avoid using Python keywords as variable names. for example: keywords such as "str", "is", and "for" cannot be used as variable names as they are reserved keywords in Python.
In addition to these guidelines, professional programmers follow certain conventions to enhance code readability (best practices). these practices include using a name that describes the purpose "stockPrice", instead of using dummy or temporary names "temp". It's also common practice to separate words in variable names with underscores "stock_price", and start variable names with lowercase letters "stockPrice".
Differentiate between mutable and immutable objects.
In Python, mutable objects are changeable, in terms of their state or values, while immutable objects are not changeable.
for example:
mutable objects in Python include lists, sets, and dictionaries. You can modify these objects by adding, removing, or changing the elements they contain. For example, you can use the append() method to add an element to a list, or you can use the update() method to add elements to a dictionary.
immutable objects in Python include strings, integers, floats, and tuples. You cannot modify these objects once they have been created. For example, you cannot change the value of an integer or a string after it has been created. If you want to modify the value of an immutable object, you need to create a new object with the modified value.
What are the major differences between a List & Tuple in Python?
lists and tuples in Python are both ordered collections of items, but they differ in mutability, syntax, performance, memory usage, and use cases.
immutability: tuples are immutable, which means that we cannot add, remove, or modify elements once we create a tuple or from an existing tuple. Lists, on the other hand, are mutable, which means that we can modify their elements. If we need to ensure that the contents of an object are not modified by mistake (keep them secure), a tuple is a better choice.
speed: tuples are generally faster to access and iterate over than lists because they are implemented using a more efficient data structure. If you need to perform a lot of operations on a large collection of data, a tuple is a faster choice.
dictionary keys: tuples can be used as keys in dictionaries because they are immutable. Lists cannot be used as dictionary keys because they are mutable. If you need to create a dictionary where the keys are tuples of values, a tuple is a better choice.
syntax: tuples are created using parentheses (), while lists are created using square brackets []. If you are more inclined to use parentheses (), a tuple is the only choice.
What is a dictionary? & How can we create a dictionary in Python?
A dictionary is a collection of key-value pair elements in Python. The keys in a dictionary must be unique and are used to access their corresponding values. The values in a dictionary can be of any data type such as string, integer, float, boolean, list, tuple, set, or data frame, and they do not have to be unique.
There are several ways to create a dictionary in Python. One way is to use curly braces {} and colons to separate keys from values, separated by commas between each pair element. For example, test_dict = {'John': 3.75, 'Lucy': 3.55, 'Mike': 4.20, 'Harry':4.05}, and this is how a dictionary can be created.
Can you explain the difference between a list, tuple, and dictionary in Python?
Summary: Lists are ordered, changeable, and indexed collections of items, Tuples are ordered, unchangeable, and indexed collections of items, and Dictionaries are unordered, changeable, and indexed collections of items.
a list is a collection of items that are ordered and changeable. Lists are defined by square brackets [] and elements are separated by commas. Lists are mutable, which means that their elements can be modified after they are created.
a tuple is a collection of items that are ordered and unchangeable. Tuples are defined by parentheses () and elements are separated by commas. Because tuples are immutable, their elements cannot be modified after they are created. This makes them useful for situations where you need to store data that should not be changed.
a dictionary is a collection of items that are unordered, changeable, and indexed. Dictionaries are defined by curly braces {} and each item has a key-value pair. The keys of a dictionary must be unique, immutable and of any type, while the values can be of any type. Dictionaries are useful for situations where you need to store data that can be quickly retrieved by a key.
What is a use case for sets in python?
Can you give an example of a problem you solved using set operations and explain how it helped you arrive at the solution?
Sets are an important data structure in Python and are commonly used for various purposes such as:
because sets only allow unique elements, they are often used to remove duplicates from a collection of data.
you can use the in keyword to check if an element is in a set, which makes sets an efficient data structure for membership testing.
Python provides a number of set operations, such as union, intersection, and difference, which can be useful for manipulating sets.
sets are hashable, which means that they can be used as dictionary keys. This can be useful if you want to create a dictionary with a set of values as the keys.
Can you name any 5 exceptions that can be handled using a try-except statement?
Python provides a wide range of built-in exceptions that cover different types of errors.
NameError – raised when an undefined variable is used.
TypeError – raised when an operation or function is applied to an object of an inappropriate type.
ValueError – raised when a function receives an argument of the correct type, but with an inappropriate value.
ZeroDivisionError – raised when you try to divide a number by zero.
IndexError – raised when you try to access an index that does not exist in a list or tuple.
KeyError – raised when you try to access a non-existent key in a dictionary.
FileNotFoundError – raised when a file or directory is not found.
MemoryError – raised when the interpreter runs out of memory.
SyntaxError – raised when there is a syntax error in the code.
ModuleNotFoundError – raised when an import statement fails to find the specified module.
How would you ensure that you catch only the specific exceptions that you expect to occur and avoid masking bugs?
To catch specific exceptions, I can use separate except statements for each type of exception. For example, if I'm expecting a ValueError and a TypeError, I can use an except statement for different exceptions to handle each type of exception differently. This can help me to write more robust and reliable code.
It's good practice to catch only the specific exceptions that the programmer expects to occur. Catching all exceptions using a bare except block can mask bugs and make it harder to diagnose and fix problems.
Can you explain the differences between Pandas data series and Python lists?
Python lists are general-purpose data structures, Pandas Series are specialized data structures designed for data analysis and manipulation.
A Pandas data series can hold homogeneous data, ie., data of a single data type such as integers, floats, or strings. In contrast, Python lists can hold heterogeneous data, i.e., data of multiple data types.
In a Pandas data series, each element is labeled with a unique index. These labels can be used to access specific elements of the series, and the labels themselves can have a data type of their own. In contrast, a Python list does not have labeled elements, and elements can only be accessed using their position (index) in the list.
Pandas data series are optimized for data analysis and are built on top of NumPy arrays, which are faster and more memory-efficient than Python lists. In contrast, Python lists are more general-purpose and can be used for a wide range of tasks, but they may not be as efficient for data analysis.
Pandas data series comes with built-in functions for data analysis and manipulation, such as arithmetic and statistical operations, merging and joining datasets, and data filtering. Python lists, on the other hand, do not have built-in functions for these tasks and require more coding to achieve the same results.
How would you fill missing values (NaN) in a Python DataFrame or Series? Can you provide some examples?
There are several methods for filling missing values in Python, and one common way is to use the fillna() method in Pandas. This method can be used:
to fill null values with 0 or any other specified value.
to fill null values with the mean, median, or mode of the values in a column or series.
to forward-fill or backward-fill missing values, which involves filling missing values with the most recent non-null value in a column or series.
Which Python libraries have you worked with or used?
I have worked with several Python libraries, including NumPy, SciPy, Pandas, and StatsModels.
Pandas: Pandas have been a game-changer for me, offering exceptional flexibility for data manipulation and analysis. Its intuitive data structures and functions have streamlined my workflow, working with structured financial data a breeze.
NumPy: When it comes to handling large datasets and performing complex numerical computations, NumPy is my fav. Its support for multi-dimensional arrays and matrices, with a vast array of mathematical functions, has been easy to solve in my quantitative modeling.
Scikit-learn: I've found Scikit-learn to be invaluable because of its diverse range of algorithms for regression analysis, volatility clustering, and dimensionality reduction have enabled me to build predictive models and uncover hidden patterns in financial data.
StatsModels: statistical analysis lies at the heart of finance, and StatsModels has always been in my Import Libraries section. tools for econometrics, time series analysis, and hypothesis testing for understanding the dynamics of underlying factors affecting markets.
QuantLib: for complex quantitative finance tasks such as pricing derivatives and modeling interest rates. It has tools and algorithms to tackle even the most complex and challenging problems in quantitative finance and risk management with ease. However, I prefer to develop my own models, as that gives me the flexibility to tweak the model and incorporate changes in real-time.
yfinance, Pandas-Datareader: to access financial data from various sources has never been easier for free. these libraries allow retrieving data directly into Pandas data frames, facilitating seamless analysis.
Matplotlib and Seaborn: visualizing financial data is essential for gaining insights while working with large datasets and communicating findings.
these libraries are very powerful and can be used together to perform complex calculations and statistical analysis on large datasets, including automation.
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[Important Terminologies]
Object-Oriented Programming (OOP) | Mutable Objects | Immutable Objects