Let us begin by reading the Quote of Guido van Rossum on Python. It goes like this, “The joy of coding Python should be in seeing short, concise, readable classes that express a lot of action in a small amount of clear code — not in reams of trivial code that bores the reader to death.” So let’s see why Python is the best language for Machine Learning and Data Science.
First, let’s see why Python is best for Machine Learning and then let’s see why it is best for Data Science.
Why is Python Best for Machine Learning?
Python is the most popular programming language for Machine Learning (ML). Python for ML has the following advantages. First, Python has a great collection of in-built libraries. Some of the in-built libraries are,
- NumPy: This is used for scientific calculation.
- Scikit-learn: It has tools for data mining and analysis that optimizes Python’s brilliant ML usability.
- Pandas: It is a package that provides developers with high-performance structures and data analysis tools. Moreover, it helps developers to reduce project implementation time.
- SciPy: This is used for advanced computation.
- Pybrain: It is exclusively used in Machine Learning
- Seaborn and Matplotlib: Seaborn is an excellent visualization library aimed at statistical plotting. On the other hand, Matplotlib is the most commonly used 2D Python visualization library.
Secondly, Python enables a moderate learning curve. Python is very accessible and easy to learn and use. Moreover, it focuses on code readability and is a versatile and well-structured language.
Thirdly, Python is a general-purpose programming language which is a good choice for project requirements if they are more than just information.
Fourthly, Python is easy to integrate. Python incorporates better for business environments. Moreover, it is easy to integrate it with lower-level languages such as C, C++, and Java. Likewise, Python-based stack is easy to incorporate with the work of a data scientist.
Fifthly, less amount of code. ML has a huge amount of algorithms, and Python makes it simpler for developers in testing. It comes with the potential of implementing the same logic with as less as one-fifth of code required in other OOP (object-oriented programming) languages.
Sixthly, it is easy to create prototypes with Python. As Python requires less coding, you can create prototypes and test your concepts quickly and easily.
Seventhly, Python supports both object-oriented and procedural programming models. Significantly, classes and objects in object-oriented programming help to model the real world while functions in procedural programming enable to reuse the code.
Lastly, the advantage of portability. Code written in Python can be run on another platform. This is called Write Once Run Anywhere (WORA).
All the above supportive cases for Python makes it a part of the vital teaching curriculum in many Python training institutes. Now let’s see Python’s benefits for Data Science.
Why is Python Best for Data Science?
Let’s get straight to the tech part. Python libraries for Data Science are similar to that of ML. They are Numpy, Matplotlib, Scikit-learn, Seaborn, and Pandas.
Basically, Python is good for Data Science for the following reasons,
- Python is flexible and an open source language.
- With it’s simple and easy to read syntax Python cuts development time in half.
- Python powerfully enables data manipulation, analyzes, and visualization.
- It provides good libraries for scientific computations.
Let’s get into pointing out solid factors that makes Python a valuable choice for Data Science projects.
Less is More
Python employs fewer codes. It automatically identifies and associates data types and follows an indentation based nesting structure. With Python, there is no limit to data processing. For a good hands-on, please check Learn basics of SQL.
Moreover, Python is faster with the Anaconda platform. Hence it is fast in both development and execution.
Python is Compatible with Hadoop
Hadoop is a popular open-source big data platform and the inherent compatibility of Python is another reason to prefer it over other languages. Importantly, the PyDoop package offers access to the HDFS API for Hadoop and hence allows to write Hadoop MapReduce programs and applications.
Moreover, PyDoop also offers MapReduce API for complex problem solving with minimal programming efforts. Eventually, this API can be used seamlessly to apply advanced data science concepts like ‘Counters’ and ‘Record Readers’.
Python is Good for Data Visualization
APIs like Plotly and libraries like Matplotlib, ggplot, Pygal, NetworkX can bring about breathtaking data visualizations. Moreover, you can use Tabpy to integrate Tableau and use win32com and Pythoncom to integrate Qlikview.
Python has a lot of Deep Learning Frameworks
There are several deep learning frameworks like Caffe, TensorFlow, PyTorch, Keras, and mxnet. You can pick from any of these tools that will fit your project and allow you to build deep learning architectures with few lines of Python code.
Python is Good for Writing Scraping Software
Python has a variety of tools for scraping data and the largest community support for doing so. Moreover, you can choose many different scraping ecosystems such as Scrapy, BeautifulSoup, or requests.
Scrapy for example can handle a lot of dirty work for you, by providing a structure for your spiders. By using Scrapy you can write web spiders in minutes.
Python is Versatile
Being a general-purpose programming language, Python is a quick and powerful tool with a lot of capabilities. From building web services, data mining to data mining, Python is a programming language that helps you to solve data problems end-to-end.
Python is Good for Building Analytics Tools
When it comes to creating a web service to allow others to find outliers in their datasets, Python is a good way forward. This is even more important when self-service analytics is becoming more important.
Python is Best for Deep Learning
Plenty of packages such as Theano, Keras, and TensorFlow make it really easy to create neural networks in Python. While some of these packages are being ported to R, the support available in Python is far advanced.
For all the above reasons, Python class conducted by private professional institutes emphasize the leading role of Python over other languages for use in Data Science.
Let us put in a nutshell the advantages of Python for Machine Learning and Data Science. They are,
- A great library ecosystem
- It has a low entry barrier
- Platform independence
- Great visualization options
- Community support and
- Growing popularity
Let us end this article with another attribute to Python. Google’s Peter Norvig has this to say about Python, “Python has been an important part of Google since the beginning, and remains so as the system grows and evolves. Today dozens of Google engineers use Python, and we’re looking for more people with skills in this language.”
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