Why is Python Essential for Data Analysis?

For many people (myself among them), the Python language is easy to fall in love with. Since its first appearance in 1991, Python has become one of the most popular dynamic, programming languages.

It is a general-purpose programming language, meaning it can be used in the development of both building websites (using their numerous web frameworks, like TurboGears, Web2py and Django) and desktop applications. It’s also useful in the development of complex numeric and scientific applications.

Python is a simple, clear and intuitive programming language. It’s Easy to Learn Thanks to Python’s focus on simplicity and readability, it boasts a gradual and relatively low learning curve. This ease of learning makes Python an ideal tool for beginning programmers. Python offers programmers the advantage of using fewer lines of code to accomplish tasks than one needs when using older languages.

That’s why scientists choose Python for many scientific and numeric applications. Perhaps they prefer getting into the core task quickly (e.g., finding out the effect or correlation of a variable with an output) instead of spending hundreds of hours learning the nuances of a “complex” programming language.

This allows scientists, engineers, researchers and analysts to get into the project more quickly, thereby gaining valuable insights in the least amount of time and resources, in other words, you spend more time playing with it and less time dealing with code.

It’s Flexible If you want to try something creative that’s never done before; then Python is perfect for you. It’s ideal for developers who want to script applications and websites.

Python is open-source, which means it’s free and uses a community-based model for development. Python is designed to run on Windows, Mac and Linux environments. Also, it can easily be ported to multiple platforms. Its Well-Supported, there are countless resources that will tell you how to do almost anything. If you have any question, it’s very likely that someone else has already asked that and another that solved it for you (Google and Stack Overflow are your friends). This makes Python even more popular because of the availability of resources online.

There are now many packages, libraries and tools that make the use of Python in data analysis and machine learning much easier. professionals are able to focus on the more important aspects of their projects and problems. For example, they could just use Numpy, scikit-learn, and TensorFlow to quickly gain insights instead of building everything from scratch the focus should always be on the problem and the opportunities it might introduce. TensorFlow, Theano, scikit-learn, Numpy, and pandas are just some of the libraries that make data science faster and easier.
With this sort of versatility, it comes as no surprise that Python is one of the fastest-growing programming languages in the world

 

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Python Libraries for Data Science You Should Know

Python is a general-purpose programming language, meaning it can be used in the development of both web and desktop applications. It’s also useful in the development of complex numeric and scientific applications. With this sort of versatility, it comes as no surprise that Python is one of the fastest-growing programming languages in the world

For data analysis and interactive, exploratory computing and data visualization, Python will inevitably draw comparisons with the many other domain-specific open source and commercial programming languages and tools in wide use, such as R, MATLAB, SAS, Stata, and others. In recent years, Python’s improved library support has made it a strong alternative for data science tasks. Combined with Python’s strength in general purpose programming, it is an excellent choice as a single language for building data science applications.

There are many open-source Python libraries for data analysis and data science such as Data manipulation, Data Visualization, Statistics, Mathematics, Machine Learning, and Natural Language Processing.

Python Libraries

Python Libraries overview of each library

NumPy

NumPy short for Numerical Python, is the foundational package for scientific computing in Python. It provides, among other things, a fast and efficient multidimensional array object ndarray, Functions for performing element-wise computations with arrays or mathematical operations between arrays, Tools for reading and writing array-based data sets to disk, Linear algebra operations, Fourier transform, and random number generation. For numerical data, NumPy arrays are a much more efficient way of storing and manipulating data than the other built-in Python data structures. Also, libraries written in a lower-level language, such as C or Fortran, can operate on the data stored in a NumPy array without copying any data.

Pandas

pandas provide rich data structures and functions designed to make working with structured data fast, easy, and expressive. It is, as you will see, one of the critical ingredients enabling Python to be a powerful and productive data analysis environment. The primary object in pandas that is the DataFrame, a two-dimensional tabular, column-oriented data structure with both row and column label.

Pandas allows importing data from various file formats such as comma-separated values, JSON, SQL, Microsoft Excel. Pandas combine the high-performance array-computing features of NumPy with the flexible data manipulation capabilities of spreadsheets and relational databases (such as SQL). It provides sophisticated indexing functionality to allows various data manipulation operations such as merging, slicing, reshaping, aggregations, selecting, as well as data cleaning, and data wrangling features.

Matplotlib

matplotlib is the most popular Python library for producing plots, static, animated, and interactive visualizations, create high-quality graphs, charts and other 2D data visualizations. thus, providing a comfortable interactive environment for plotting and exploring data. The plots are also interactive; you can zoom in on a section of the plot and pan around the plot using the toolbar in the plot.

IPython

IPython is the component in the standard scientific Python toolset that ties everything together. It provides a robust and productive environment for interactive and exploratory computing. It is an enhanced Python shell designed to accelerate the writing, testing, and debugging of Python code. It is particularly useful for interactively working with data and visualizing data with matplotlib. IPython is usually involved with the majority of my Python work, including running, debugging, and testing code. Aside from the standard terminal-based IPython shell, the project also provides • A Mathematica-like HTML notebook for connecting to IPython through a web browser (more on this later). • A Qt framework-based GUI console with inline plotting, multiline editing, and syntax highlighting • An infrastructure for interactive parallel and distributed computing I will devote a chapter to IPython and how to get the most out of its features.

BeautifulSoup

Beautiful Soup is a Python library for pulling data (Web Scraping) out of HTML and XML files. It works with your favorite parser to provide idiomatic ways of navigating, searching, and modifying the parse tree. It commonly saves programmers hours or days of work.

Scikit-learn

is probably the most useful open-source library for machine learning in Python, that supports supervised and unsupervised learning, It also provides various tools for model fitting, data preprocessing, model selection and evaluation, and many other utilities  The Scikit-learn library contains a lot of efficient tools for statistical modeling including classification, regression, clustering and dimensionality reduction.

TensorFlow

TensorFlow is an open-source library developed by Google primarily for deep learning applications. It also supports traditional machine learning. TensorFlow was originally developed for large numerical computations without keeping deep learning in mind. However, it proved to be very useful for deep learning development as well.

Seaborn

Seaborn is a library for making statistical graphics in Python. It builds on top of matplotlib and integrates closely with pandas data structures.
Seaborn helps you explore and understand your data. Its plotting functions operate on dataframes and arrays containing whole datasets and internally perform the necessary semantic mapping and statistical aggregation to produce informative plots. Its dataset-oriented, declarative API lets you focus on what the different elements of your plots mean, rather than on the details of how to draw them.