We are all aware of Python – the simple language that is currently defining the digital world. Pairing machine learning capabilities with simple coding, Python is a big hit among data scientists, along with the data science specific language, R. However, if you really wish to master Python and build your career as a data scientist, then you should know the most popular Python libraries. Python, because of its simplicity, offers a lot of libraries for different use-cases. And if you are someone who is looking to make your mark as a data scientist, not only should you familiarize yourself with Python but with these libraries:
Table of Contents
NumPy
NumPy is a great open-source library which is mostly dedicated to numerics. It has some pre-compiled functions that make working with large multidimensional matrices and arrays easy. Even when you apply basic numerical standards, Numpy makes it so simple that you don’t have to write Loops like in C++. While it may not have an integrated data analysis facility, its array computing can be paired with other data analysis tools and make it easier.
Scipy
SciPy is a module in Python which provides fast N-dimensional array manipulation. It not only makes numerical routines easier but it can also help with numerical optimization and integration. It even has modules like linear algebra, optimization and integration – all important tools in data science.
Matplotlib
If you want to add visualization in your project, the Matplotlib is the best way to go. It can be used to quickly make pie charts, line diagrams, histograms or other professional visual items. You can even customize certain aspects of any specific figure. The best part, you can export the images into graphic formats like png, jpeg, pdf, etc.
Scikit-Learn
Since machine learning is the way of the future, Scikit-Learn is the machine learning module introduces to Python. It gives you a set of common machine learning algorithms providing it to you through a consistent interface. There are a lot of algorithms available in Scikit-Learn and it also comes handy with machine learning-based tasks like regression, clustering, etc.
Pandas
For data munging, the best Python module is Pandas. It has high-level data structures and the tools present here are best suited for faster data analyses. It is based on NumPy and so, NumPy can be used easily on it.
NLTK
NLTK is one of the best programmes that can work with human language. It has a simple interface and more than 50 corpora and lexical resources like WordNet, which can be used for tokenization, tagging, parsing and many more. NLTK is so popular that it is often used to create prototypes of research systems.
Statsmodels
Statsmodels tries to estimate different statistical models by exploring data and performing statistical tests. It has a list of different plotting functions and statistics based on results for each type of data.
Statsmodels tries to estimate different statistical models by exploring data and performing statistical tests. It has a list of different plotting functions and statistics based on results for each type of data.
PyBrain
Python-Based Reinforcement Learning, Artificial Intelligence, and Neural Network or PyBrain are for neural networks. It can be used for both unsupervised learning and reinforcement learning. If you want a tool for real-time analytics, this is the best way to go.
Gensim
Built on both Scipy and Numpy, the Gensim library is for topic modelling. From fast scalability of language to optimized math routine, this open-source library will keep your delighted with its simple interface and platform independence.
Theano
Almost like Numpy, Theano is a library that focuses on numeric computation. It allows evaluation and optimization of mathematical expressions which also involves efficient treatment of multi-dimensional arrays.
So, get your mindset and start your data science journey with some must-know Python libraries. To make sure your python game is strong, you can also look at some of the courses offered by Coding Ninjas. Have a look at our course on Machine Learning and Data Science and set out on your journey to becoming a distinguished data scientist. Best of luck.
To learn more about Python frameworks and libraries that you can use to build your next project, click here.
