Python provides a basic and simple way to handle such requirements where we have to switch to and fro between multiple languages
Python is great. Really great.
But the field is getting/will get language agnostic with time. And a lot of great work is being done in many other languages.
While I still treat Python as a primary language, I never hesitate to move to a different language if it gets the work done.
The fact is that every language has evolved in such a way that it has built its stronghold in certain areas. For example, *Some people may find it easier to use R for regression, or Plot in R using ggplot(Though I sincerely feel Python has come a long way in the *visualization* department.)*
Sometimes it is because a particular library is written in Java/C and someone hasn’t yet ported it to Python.
But is there a better way to handle this constant nuisance?
I like Python because I understand it well now. It is easy for me to do so many things in Python as compared to doing it in R or Java or Scala.
Why do I have to code my data preparation steps in R if I just want to use the Linear Regression package in R?
Or why do I have to learn to create charts in Java if I only want to use the Stacknet package?
Now Python and R have many wrappers. How can I use R in Python or How I can use Python in R?
These packages are all well and good, and they may solve some problems.* But they don’t address the generic problem. Every time I want to switch from one language to another I need to learn a whole new package/library. Not scalable at all.*
In this series of posts named Python Shorts, I will explain some simple constructs provided by Python, some essential tips and some use cases I come up with regularly in my Data Science work.
This post is about utilizing a particular package/library from another language, while not leaving the comfort of coding in our primary language.
I will start with a problem statement to explain this. Let’s say I had to create a graph using R, but I wanted to prepare my data in Python.
It is a generic problem any data scientist can potentially face. Do something in one language and then move to another language to do some other thing.
Can I do this without leaving my Jupyter notebook? Or my Python Script?
Here is how I could accomplish this. It might seem hacky to some but I love hacks.
import pandas as pd data=pd.read_csv("data.csv") data = preprocess(data) data.to_csv("data.csv",index=None) os.system("Rscript create_visualization.R")
<strong><em>os.system</em></strong>* command provides me with a way to access my shell using Python*. And the shell is a potent tool at your disposal. You can run almost any language on the shell.
Rscript that will run in python would look something like:
data<-read.table("data.csv") ggplot(...) ggsave("plot.png")
I can then maybe load the png file and show it in my Jupyter notebook using something like a markdown hack.
![alt text](plot.png "Title")
For R users, who don’t want to leave the comfort of R, R also has a
systemcommand analogous to
os.system that you can use to run Python code in R.
<strong><em>os.system</em></strong>* in Python provides us a way to do each and everything in Python by letting us call shell commands from Python.*
I have used it in plenty of my projects where I have used this concept to send e-mails using Mutt. Or to run some Java program or to fiddle around.
It seems like a hacky way, but it works and is generalized enough that you don’t have to learn a new library anytime you want to do something with any other language and get it integrated with Python.
In the programming world, Data types play an important role. Each Variable is stored in different data types and responsible for various functions. Python had two different objects, and They are mutable and immutable objects.
Magic Methods are the special methods which gives us the ability to access built in syntactical features such as ‘<’, ‘>’, ‘==’, ‘+’ etc.. You must have worked with such methods without knowing them to be as magic methods. Magic methods can be identified with their names which start with __ and ends with __ like __init__, __call__, __str__ etc. These methods are also called Dunder Methods, because of their name starting and ending with Double Underscore (Dunder).
Python is an interpreted, high-level, powerful general-purpose programming language. You may ask, Python’s a snake right? and Why is this programming language named after it?
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Python any() function returns True if any element of an iterable is True otherwise any() function returns False. The syntax is any().