ESSENTIAL PYTHON TOOLS FOR BEGINNERS

What is Python?

Python is one of the most popular and powerful computer programming language often used to build websites and software, automate tasks, and analyze data. Python is a general-purpose language, not specialized for any specific problems, and used to create various programmers.

What is Python used for?

Python has become one of the most popular programming languages in recent years. It's been used in everything from machine learning to building websites and software testing. Developers and non-developers alike find it useful.

Python is commonly used for developing websites and software, task automation, data analysis, and data visualization. Since it’s relatively easy to learn, Python has been adopted by many non-programmers, such as accountants and scientists, for a variety of everyday tasks, like organizing finances.

Things that can be done in Python:

·       Data analysis and machine learning

·       Web development

·       Automation or scripting

·       Software testing and prototyping

·       Everyday tasks

ESSENTIAL PYTHON TOOLS

Let us learn about the Essential Python Tools like Python Development Tools, Python Web Scraping Tools, Python Data Analysis Tools and Python Data Visualization Tools.

Python Development Tools

Python Development Tools are useful to enhance fast and valid Python solutions.

It includes Integrated Development Environment (IDE), Python package manager, and productive extensions. These tools have made it easy to test the software, debug, and deploy solutions in production. 

Jupyter Notebook

Jupyter Notebook is a web-based IDE for research with code and reveal the output.  It is most popular among Data Scientists and Machine Learning Practitioners. It also provides results for small set of code by running them rather that running the entire file. The Juypter Notebook allows us to add heading and description using Markdown and provides the output in the form of PDF and  .ipynb files.

When we combine scientific computation with Python development, we get a Jupyter Notebook. These days instructors are using it for instructing data science courses, data analysts are using it to generate reports, and machine learning engineers are using trial and create high-performing model architecture. 

Pip

Pip is a tool that utilize Python Package Index to install and manage Python software. There are 393,343 projects for you to download and install with lightning speed. The Python ecosystem works on it. 

pip install<package_name>

Pip is not just an installer. We can create and manage Python environments, install dependencies, and install packages from third-party repositories using URLs.

Python-m pip install-r requirements.txt

Python Web Scraping Tools

Web scraping 

Web Scraping empowers data scientists and analysts to extract data from websites, enabling access to valuable information for analysis. However, the challenging aspect of web scraping lies in the subsequent task of cleaning and transforming the extracted data into a readable and structured format. In this segment, we will delve into the essential tools widely employed for both web scraping and data cleaning processes, facilitating efficient extraction and preparation of web data for analysis.

Requests

Requests simplifies the process of sending HTTP requests, eliminating the need for manual inclusion of authentication, arguments, and query string configurations in URLs. By utilizing the requests API and its get JSON method, data professionals can seamlessly retrieve data from multiple-page websites, making Requests a favored library for web scraping tasks. 

Flask

Flask, an open-source web framework, simplifies the development of web applications and REST APIs. Compared to Django, Flask is more approachable and easier to grasp, enabling users to quickly assemble a basic web API with just a few lines of code, facilitating local execution for rapid prototyping and testing.

Built upon the WSGI (Web Server Gateway Interface) toolkit and Jinja2 template engine, Flask is versatile, accommodating the development of both straightforward and complex web applications. Its capabilities extend to diverse domains, including blogging platforms, social media applications, portfolio websites, machine learning tools, and analytics dashboards, making it a suitable framework for a wide range of projects.

Python Data Analysis Tools:

Data Analysis tools permit the users to collect, clean, and rearrange data for statistical analysis. Every data professional must understand the core functionality of these tools to perform data analysis, machine learning, data engineering, and business intelligence tasks. 

Pandas

Pandas is a gateway into the world of data science.  As a beginner we can load a CVS file using read cvs(). Pandas is an essential tool for all data professionals. 

We can load a dataset, clean it, rearrange it, calculate statistics, create visualizations, and save the data into various file formats. The pandas API is simple and intuitive. We can load and save CSV and text files, Microsoft Excel, and SQL databases. 

Numpy

NumPy is a primary Python package for scientific computations, and most advanced tools are built upon it. As a data scientist, we will use the Numpy array for mathematical calculations and data wrangling. It provides multidimensional array objects to perform fast operations such as logical, shape manipulation, sorting, selection, basic statics operation, and random simulation.

Numpy will help you understand the fundamentals of mathematics in data science and how to convert complex equations into Python code. We can use it to create machine learning models, customized statical formulas, scientific simulations, and perform advanced data analytics tasks. 

SQLAlchemy

SQLAlchemy is a Python SQL toolkit for us to execute tasks related to the access and management of relational databases. The utilization of an Object Relational Mapper (ORM) enables the incorporation of SQL's potent features and the adaptability it offers, enhancing overall functionality

This tool is required for data scientists and analytics to adept at employing Python for data manipulation and analysis tasks, driving insights and decision-making processes. We have the flexibility to conduct data analysis using SQL scripts or opt for an object-based methodology, utilizing a user-friendly Python API to carry out analogous tasks more efficiently.

Dask

For processing huge data or files, Dask is an essential tool.  Tasks of a similar nature are performed by Dask using parallel computing techniques, with assistance from libraries such as NumPy, pandas, and scikit-learn.

Even with enhanced computational resources, processing a simple logical function on a 4GB dataset would still require a minimum of 10 minutes. Achieving processing times of just a few seconds remains unattainable. Nonetheless, Dask's utilization of dynamic task scheduling and parallel data collection allows for rapid results on the same machine.

Python Data Visualization Tools

Data visualization serves as the cornerstone of effective communication in data analysis, particularly when addressing non-technical executives. To effectively convey insights, data analysts weave a narrative through visual elements like bar charts, line plots, scatter plots, heat maps, and histograms. With the aid of visualization tools, analysts can effortlessly create interactive, vibrant, and polished visualizations with minimal coding.

Matplotlib 

 

Matplotlib serves as the entry point into the realm of data visualization and is often featured in introductory materials. With Matplotlib, users gain the capability to generate static, animated, and interactive visualizations, all fully customizable to suit specific needs. Its intuitive interface enables the creation of diverse visualizations, including 3D, multilevel, and detailed plots. The gallery showcases numerous examples of various visualizations, providing ample inspiration and guidance for users.

 Seaborn

 

Seaborn, built on Matplotlib, offers a high-level interface focused on crafting visually appealing statistical graphics. Like Matplotlib, Seaborn simplifies the creation of interactive visualizations with just a single line of code. Its adaptability makes it particularly beneficial for beginners in data visualization.

While Matplotlib remains available for customization needs, Seaborn automates much of the process, providing features like distplot, violin plot, residplot, lmplot, joint plot, and boxplot, streamlining visualization tasks.

Plotly

When aiming for the capabilities akin to Tableau or PowerBI, the Plotly Python library becomes instrumental for showcasing interactive and publication-ready graphs. With Plotly, users can seamlessly zoom into graphs, isolate specific bars, apply filters, and even incorporate animations as per their requirements. Its functionality allows for dynamic and highly customizable visualizations, resembling the features found in Tableau or PowerBI.

Equipped with customizable controls, Plotly empowers users to animate their visualizations and perform data transformations effortlessly. Additionally, Plotly offers an array of advanced features, including Jupyter widgets, 3D charts, AI charts, financial charts, and scientific charts, catering to diverse analytical needs. With Plotly's comprehensive toolkit, users can enhance their visualizations, explore complex datasets, and derive deeper insights across various domains.

Plotly emerges as the optimal choice for generating data analytics reports within Jupyter environments. Rather than generating numerous static plots, Plotly enables the creation of a single dynamic visualization, enriched with custom controls for in-depth exploration and elucidation of data insights. This approach streamlines the reporting process, fostering interactive narratives that facilitate comprehensive understanding and exploration of data findings.

Pandas-profiling

Pandas-profiling serves as an Automated Exploratory Data Analysis (AutoEDA) tool, simplifying the creation of comprehensive reports with just one line of code. These reports encompass a wide range of analytical insights, including column types, missing values, unique values, quantile and descriptive statistics, histograms, correlation analysis, text analysis, and even file and image examination.

This tool proves invaluable when time is limited for exploration, such as during technical assessments, team meeting preparations, or participation in competitions. Its efficiency allows users to swiftly gain insights into their data, facilitating quick decision-making and effective communication of findings in various time-sensitive scenarios.

 

Conclusion:

These essential Python tools are necessary for software and web development, web scraping, data analytics and visualization, and machine learning. Understanding the functionalities of these tools help you to gain experience.