🐍 Python Notes
Python library:
Pandas is used for data manipulation and analysis.
NumPy is used for numerical operations on arrays.
Matplotlib is used for data visualization.
Scikit-learn is used for machine learning.
How Python works
Python tools
- Pandas • used for data analysis
- Scipy • algorithms to use with numpy
- HDFS • C/C++ wrapper for Hadoop
- pyMySQL • MYSQL connector
- Airflow • data engineering tool
- PyBrain • algorithms for ML
- Matplotlib • data visualization tool
- Python • powerful shell
- Redis • Redis'access libraries
- Dask • data engineering tool
- SymPy • symbolic math
- Keras • high-level neural networks API
- Lasagne • build and train neural networks in Theano
- Jupyter • research collaboration tool
- SQLAlchemy • Python SQL Toolkit
- Seaborn • data visualization tool
- Elasticsearch • data search engine
- Numpy • Multidimensional arrays
- Pymongo • MongoDB driver
- Bokeh • data visualization tool
- Luigi • data engineering tool
- Pattern • natural language processing
- HDF5 • used to store and manipulate data
Key differences between Python & R
| Python | R |
|---|---|
| A high-level, general-purpose programming language for development & deployment | A software environment and statistical programming language for statistical software & graphics |
| Data science and data analysis | Manipulating data |
| Web application development | Statistical analysis |
| Automation/scripting | Data visualizing |
Python ecosystem
Used in the industry: Google™, Instagram, Youtube, Spotify, Quora
Python libraries
- Machine Learning: Numpy, Keras, Theano, Pandas, PyTorch, TensorFlow, Scikit-Learn, Matplotlib, Scipy, Seaborn
- Web Development: Django, Flask, Bottle, CherryPy, Pyramid, Web2Py, TurboGears, CubicWeb, Dash, Falcon
- Automation Testing: Splinter, Robot, Behave, PyUnit, PyTest
- Game Development, PyGame, PyGlet, PyOpenGL, Arcade, Panda3D
- Image Processing, OpenCV, Mahotas, Scikit-Image, Pgmagick, SimplelTK
- Web Scraping: Requests, Beautiful Soup, Scrapy, Selenium, Ixml
Python libraries for Data Analytics
- Plotly: This graphic library can create a variety of interactive, high-quality data visualizations, such as scatter plots, heatmaps, histograms, box plots, bubble charts, and polar charts.
- NumPy: essential for numerical computing in Python, boasts -dimensional arrays, Fourier transforms, and advanced randomization. Its varied math functions are pivotal in multidimensional array operations for data analytics.
- Scipy: Short for Scientific Python, SciPy is an open-source, free Python library that is commonly used for high-level computations. SciPy is built on NumPy and contains many high-level commands that aid with manipulating and visualizing data.
- Vispy: This data visualization library, catering to 2D3D needs, facilitates rapid creation of professional, interactive visuals. With various user-friendly interfaces, it accommodates different experience levels, offering optimal customization for proficient users.
- Pandas: Python Data Analysis, or Pandas, is commonly used in data science, but also has applications for data analytics, wrangling, and cleaning. Pandas offers eloquent syntax, as well as high- level data structures and tools for manipulation.
- Matplotlib: This is Python's first data visualization library. It is still considered to be the most popular and widely used data visualization library. Matplotlib can create a variety of graphs, such as line graphs, scatter graphs, histograms, heat plots, and interactive 2D graphs.
- Taipy: is an open-source Python library for easy, end-to-end application development, featuring what-if analyses, smart pipeline execution, built- in scheduling, and deployment tools.
- Seaborn: This data visualization library is based on Matplotlib. It offers a high- level interface that can be used for depicting informative and stunning statistical graphics. At its heart, Seaborn strives to make visualization a core component of the data exploration and analysis process.
Python libraries for Data Science
- Python utilities: Web development & design: Django, Flask
- Machine Learning: Sklearn, Keras
- Build Mobile Application: Kivy
- Hacking and Pentesting: Nmap, Scapy
- Data Science: Pandas, Numpy
Python Data Structures
Python Roadmap
Learn Python
🥉 Easy: Variables, Conditions, Chained Conditionals, Operators, Control Flow (If/Else), Loops And Iterables, Basic Data Structures, Functions, Mutable VS Immutable, Common Methods, File I/0
🥈 Mid: OOPS, Data Structures, Comprehensions, Lambda Functions, Map & Filter, Collections, *Args & **Kwargs, Inheritance, Dunder Methods, PIP, Environments, Modules, Async 10
🥇 Pro: Decorators, Generators, Context Managers, Metaclasses, Parallelism, Testing, Packages, Cython
See also Python Datastructures
JavaScript / Python project ideas
🥉 Easy: Rock Paper Scissor, Memory Game, Calculator, Pong, Dice Rolling, Contact Book, Hangman, Alarm Clock, Notifier App, Snake Game, Timer, Tic-Tac-Toe, Number Guessing, Morse Generator
🥈 Mid: Youtube Downloader, URL Shortener, Password Managing, Music player, Notepad, Web Crawler, Email Automation, Quiz Application
🥇 Pro: Music Player, Face Detection, Twitter Clone, Twitter or Tinder Bot, 2D Game
Key differences between Python & R
| Python | R |
|---|---|
| A high-level, general-purpose programming language for development & deployment | A software environment and statistical programming language for statistical software & graphics |
| Data science and data analysis | Manipulating data |
| Web application development | Statistical analysis |
| Automation/scripting | Data visualizing |
Python Full Stack Web Developer Profile
Python Full Stack Web Developer Profile
