Avatto>>DATA SCIENTIST>>SHORT QUESTIONS>>Python Interview Questions
There are many ways to improve the performance of a Python program. Some of these are as follows:
Data Structure: We have to select the right data structure for our purpose in a Python program.
Standard Library: Wherever possible, we should use methods from the standard library. Methods implemented in the standard libraries have much better performance than user implementation.
Abstraction: At times, a lot of abstraction and indirection can cause slow performance of a program. We should remove the redundant abstraction in code.
Algorithm: The use of the right algorithm can make a big difference in a program. We have to find and select a suitable algorithm to solve our problem with high performance.
Python is strong that even Google uses it. Some of the benefits of using Python are as follows:
Efficient: Python is very efficient in memory management. For a large data set like Big Data, it is much easier to program in Python.
Faster: Though Python code is interpreted, still Python has a very fast performance.
Wide usage: Python is widely used among different organizations for different projects. Due to this wide usage, there are thousands of add-ons available for use with Python.
Easy to learn: Python is quite easy to learn. This is the biggest benefit of using Python. Complex tasks can be very easily implemented in Python.
By default, every source code file in Python is in UTF-8 encoding. But we can also specify our own encoding for source files. This can be done by adding the following line after #! line in the source file.
# -*- coding: encoding -*-
In the above line, we can replace encoding with the encoding that we want to use.
PEP 8 is a style guide for Python code. This document provides the coding conventions for writing code in Python. Coding conventions are about indentation, formatting, tabs, maximum line length, import organization, line spacing, etc. We use PEP 8 to bring consistency to our code. We consistency it is easier for other developers to read the code.
Pickling is a process by which a Python object hierarchy can be converted into a byte stream. The reverse operation of Pickling is Unpickling.
Python has a module named pickle. This module has the implementation of a powerful algorithm for serialization and de-serialization of Python object structure.
Some people also call Pickling as Serialization or Marshalling.
With Serialization, we can transfer Python objects over the network. It is also used in persisting the state of a Python object. We can write it to a file or a database.