A quick beginning to a series of posts which will guide you through the top python interview questions asked in any data science interview.
Check out some great career options that require Python as a skill:
1. What are local and global variables in Python?
Variable declared outside a function or in global space is called global variables. Any function in the program can access these variables.
Any function declared inside a function is known as a local variable. This variable is present in the local space and not in the global space.
a=2 #Global Variable def add(); b=3 #Local Variable c=a+b print(c) add()
2. What are the built-in types in Python?
The built-in types in Python are:
• Complex numbers
• Built-in functions
3. What is a lambda function in Python?
An anonymous function is known as a lambda function. This function can have any number of parameters but, can have just one statement.
a= lambda x,y : x+y print(a(5,6))
4. What are the top Python libraries used in Data Science?
Pandas: It allows the user to manipulate the tabular data for data analysis.
Check out some Pandas tutorials to gain in-depth knowledge about the library:
Numpy : It performs many mathematical tasks on a large number of multidimensional arrays.
Check out the official documentation of NumPy to know more about the library:
Matplotlib: This library is used to ease up the visualization tasks and create simplified charts and graphs.
This notebook organizes various tips and contents of Matplotlib which we browse every day.
Scipy: Scipy is a library used for scientific computing that helps with linear algebra, optimization, and statistical tasks.
Check this notebook - Numpy, Scipy and pandas library basics with code examples and brief descriptions.
Take a look at these career options that require these libraries as a required skill:
5. What advantages do NumPy arrays offer over (nested) Python lists?
• Python lists are efficient general-purpose containers. They support (fairly) efficient insertion, deletion, appending, and concatenation, and Python list comprehensions make them easy to construct and manipulate.
• They have certain limitations: they don't support "vectorized" operations like elementwise addition and multiplication, and the fact that they can contain objects of differing types means that Python must store type information for every element, and must execute type dispatching code when operating on each element.
• NumPy is not just more efficient, it is also more convenient. You get a lot of vector and matrix operations for free, which sometimes allows one to avoid unnecessary work. And they are also efficiently implemented.
• NumPy array is faster and you get a lot built-in with NumPy, FFTs, convolutions, fast searching, basic statistics, linear algebra, histograms, etc.
I hope you found this content informative. This is the beginning of the" Top Python Interview Questions" series, stay tuned for other parts.
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Until Next Time!