Python is a popular high-level programming language used mainly for data science, automation, web development, and Artificial Intelligence. It is a general-purpose programming language supporting functional programming, object-oriented programming, and procedural programming. Over the years, Python is known to be the best programming language for data science, and it is commonly used by big tech companies for data science tasks.
Following are some useful features of Python language:
It uses the elegant syntax, hence the programs are easier to read.
It is a simple to access language, which makes it easy to achieve the program working.
The large standard library and community support.
The interactive mode of Python makes its simple to test codes.
In Python, it is also simple to extend the code by appending new modules that are implemented in other compiled language like C++ or C.
Python is an expressive language which is possible to embed into applications to offer a programmable interface.
Allows developer to run the code anywhere, including Windows, Mac OS X, UNIX, and Linux.
It is free software in a couple of categories. It does not cost anything to use or download Pythons or to add it to the application.
Python Offers All the Libraries
You badly need water, and you have just two cups on the table. One is a quarter filled with water while the other one is almost full. Would you carry the cup with much water or the other one, though they both have water? You’d want to carry the cup containing a lot of water because you really need water. This is relatable to Python, it offers all the libraries you’d ever need for data science, you would definitely not want to use another programming language with only a few libraries available.
You will have a great experience working with these libraries because they are really easy to use. If you need to install any library, search for the library name at PyPI.org and follow the instructions towards the end of this article to install the library.
1. Numerical Python - NumPy
NumPy is one of the most commonly used data science libraries. It allows you to work with numeric and scientific tasks in Python. Data is represented using arrays or what you may refer to as lists, which can be in any dimension: 1-dimensional (1D) array, 2-dimensional (2D) array, 3-dimensional (3D) array, and so on.
Pandas is also a popular data science library used in data preparation, data processing, data visualization. With Pandas, you can import data in different formats such as CSV (comma-separated values) or TSV (Tab-separated values). Pandas works like Matplotlib because it allows you to make different types of plots. Another cool feature Pandas offers is that it allows you to read SQL queries. So, if you have connected to your database, and you want to write and run SQL queries in Python, Pandas is a great choice.
3. Matplotlib and Seaborn
Matplotlib is another awesome library Python offers. It has been developed on top of MatLab - a programming language used mainly for scientific and visualization purposes. Matplotlib allows you to plot different kinds of graphs with just a few lines of code.
You can plot graphs to visualize any data, helping you to gain insights from your data, or giving you a better representation of the data. Other libraries like Pandas, Seaborn, and OpenCV also use Matplotlib for plotting sophisticated graphs.
Seaborn (not Seaborne) is just like Matplotlib, just that you have more options - to give different parts of your graphs different colors, or hues. You can plot nice graphs and customize the look to make the data representation better.
4. Open Computer Vision - OpenCV
Perhaps you want to build an Optical Character Recognition (OCR) system, document scanner, image filter, motion sensor, security system, or anything else related to computer vision, you should try OpenCV. This amazing and free library offered by Python allows you to build computer vision systems over just a few lines of code. You can work with images, videos, or even your webcam feed and deploy.
5. Scikit-learn - Sklearn
Scikit-learn is the most popular library used specifically for machine learning tasks in data science. Sklearn offers all the utilities you need to make use of your data and build machine learning models in just a few lines of code.
There are various machine learning tasks like linear regression (simple and multiple), logistic regression, k-nearest neighbors, naive bayes, support vector regression, random forest regression, polynomial regression, including classification and clustering tasks.
Python IDEs For Data Science
Data Science is a field that is used to study and understand data and draw various conclusions with the help of different scientific processes. Python is a popular language that is quite useful for data science because of its capacity for statistical analysis and its easy readability. Python also has various packages for machine learning, natural language processing, data visualization, data analysis, etc. that make it suited for data science. Some of the Python IDE’s that are used for Data Science are given as follows:
1. Jupyter notebook –
Jupyter notebook is an open source IDE that is used to create Jupyter documents that can be created and shared with live codes. Also, it is a web-based interactive computational environment. The Jupyter notebook can support various languages that are popular in data science such as Python, Julia, Scala, R, etc.
2. Spyder –
Spyder is an open source IDE that was originally created and developed by Pierre Raybaut in 2009. It can be integrated with many different Python packages such as NumPy, SymPy, SciPy, pandas, IPython, etc. The Spyder editor also supports code introspection, code completion, syntax highlighting, horizontal and vertical splitting, etc.
3. Sublime text –
Sublime text is a proprietary code editor and it supports a Python API. Some of the features of Sublime text are project-specific preferences, quick navigation, supportive plugins for cross-platform, etc. While the Sublime text is quite fast and has a good support group, it is not available for free.
4. Visual Studio Code –
Visual Studio Code is a code editor that was developed by Microsoft. It was developed using Electron but it does not use Atom. Some of the features of Visual Studio Code are embedded Git control, intelligent code completion, support for debugging, syntax highlighting, code refactoring, etc. It is also quite fast and lightweight as well.
5. Pycharm –
Pycharm is an IDE developed by JetBrains and created specifically for Python. It has various features such as code analysis, integrated unit tester, integrated Python debugger, support for web frameworks, etc. Pycharm is particularly useful in machine learning because it supports libraries such as Pandas, Matplotlib, Scikit-Learn, NumPy, etc.
6. Rodeo –
Rodeo is an open source IDE that was developed by Yhat for data science in Python. So Rodeo includes Python tutorials and also cheat sheets that can be used for reference if required. Some of the features of Rodeo are syntax highlighting, auto-completion, easy interaction with data frames and plots, built-in IPython support, etc.
7. Thonny –
Thonny is an IDE that was developed at the The University of Tartu for Python. It is created for beginners that are learning to programe in Python or for those that are teaching it. Some of the features of Thonny are statement stepping without breakpoints, simple pip GUI, line numbers, live variables during debugging, etc.
8. Atom –
Atom is an open source text and code editor that was developed using Electron. It has multiple features such as a sleek interface, a file system browser, various extensions, etc. Atom also has an extension that can support Python while it is running.
Geany is a free text editor that supports Python and contains IDE features as well. It was originally authored by Enrico Tröger in C and C++. Some of the features of Geany are Symbol lists, Auto-completion, Syntax highlighting, Code navigation, Multiple document support, etc.
How to Install Any Data Science Library in Python
Given you already have Python installed on your computer, this step-by-step section will guide you through how to install any data science library on your Windows computer. NumPy will be installed in this case, follow the steps below:
Press Start and type cmd. Right-click the result and choose Run as administrator.
You need PIP to install Python libraries from PyPi. If you already have, feel free to skip this step; if not, please read how to install PIP on your computer.
Type pip install numpy and press Enter to run. This process will install NumPy on your computer and you can now import and use NumPy on your computer. This process should look similar to the screenshot shown below, ignore the warning and blank spaces. (If you use Linux or macOS, simply open a terminal and enter the pip install command).
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