Created in 1991, the Python programming language has quickly evolved into the most preferred coding platform for data science professionals. Python’s object oriented programming with minimal coding lines might look like a cakewalk when you first try it, but the journey becomes tough and complex if you don’t take this general purpose programming language seriously. This becomes all the more evident when you appear for your first few python interviews for a data science role. Only a handful of professionals actually manage to satisfactorily answer 2 or more Python interview questions for data science. In the past, we have covered the various interview questions that Python professionals think are enough to get through an interview—but a lot has changed in the industry since then.
I researched the top Python stories and interview experiences from various online communities and came up with a list of top Python interview questions for data science professionals.
Can Python be used for web scripting?
Python’s climb as a ubiquitous programming language for AI and ML has been a phenomenon. Yet, it could never really challenge the dominance of Java and JavaScript in web and mobile application development.
But that has changed. Python developers can now think of replacing web and app development on JavaScript with something from Python itself.
So, the answer is “Yes, you can write a web browsing and application code using Python. It’s available through WebAssembly or WASM.
You can find the entire workflow here on Github – https://github.com/ethanhs/python-wasm
Here’s what the author of this Python development kit had to say on Twitter.
Interesting times ahead as the race heats up between Python, Java, C, and C++.
Which is better Java or Python?
Python may be gaining a lot of ground in the recent, but Java continues to be the choice of the masters. Java has a much larger database and developer ecosystem compared to Python, which naturally means you can test a lot many frameworks and tools in areas where Java still remains popular.
On the other hand, Python is great for rapid prototype assignments in AI and machine learning, in areas that can be further scaled with NumPy and SciPy libraries. Many open source DevOps platforms like Google’s Tensor Flow favor Python for deep learning.
So, it all depends on the field of the project and the expertise you seek from the ecosystem.
And yes, Java is still the number tool for android mobile app development, which doesn’t seem to change despite Python’s rapid rise.
How to delete a Python File?
This is a tricky question and assumed to be a pet project for a majority of interviewers who think a Python professional often misses the nuances involved in a Python project.
Python functions can remove a file only if the file exists in the first place.
To remove a Python file: use “os.remove()”
If you want to remove the file “Subscribers.txt”, use this os.remove(Subscribers.txt)”.
So, if you want to first test if the file exists or not, and then go ahead with the deletion, use this syntax:
#!/usr/bin/python
import os
myfile=”/tmp/foo.txt”
## If file exists, delete it ##
if os.path.isfile(myfile):
os.remove(myfile)
else: ## Show an error ##
print(“Error: %s file not found” % myfile)
How to remove a directory?
You can remove a directory using these functions;
- rmdir()
- rmtree()
You can also use Path or pathlib at different instances.
How many arguments can take Python Lambda to take?
Python Lambda functions can take an “infinite” number of arguments. But each of these arguments can have the only form of expression. These are used to justify anonymous functions explicitly used for a very short period of time.
Can you explain how geospatial data is interpreted in Python?
We can interpret geospatial data using Open source Python GeoPandas. The activity involves the use of the GeoPandas module that examines how vector GIS data are represented in vector format in Python.
Shapefiles used in Python are similar to what we attribute to Rows /Columns in an excel sheet.
We can manipulate, map and reproject map data into vector format or reference systems. The 3 key vector data structures that you should be able to examine using Python are Points, Lines, and Polygons.
