{"id":20356601,"url":"https://github.com/madhurimarawat/data-visualization-using-python","last_synced_at":"2025-10-27T20:34:28.800Z","repository":{"id":186048622,"uuid":"669814830","full_name":"madhurimarawat/Data-Visualization-using-python","owner":"madhurimarawat","description":"This repository contains data visualization programs on various datasets done using 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Notebook","readme":"# Data-Visualization-using-python\n\nThis repository contains data visualization programs on various datasets done using python.\n\n\u003ch1\u003e Data Visualization\u003c/h1\u003e\n\n![What-is-Data-Visualization-Blog-Header](https://github.com/madhurimarawat/Data-Visualization-using-python/assets/105432776/ea30609d-c156-4c80-b701-05194192e6a6)\n\n\u003cbr\u003e\n--\u003e Data visualization is the graphical representation of information and data in a pictorial or graphical format(Example: charts, graphs, and maps). \u003cbr\u003e\u003cbr\u003e\n--\u003e Data visualization tools provide an accessible way to see and understand trends, patterns in data, and outliers. \u003cbr\u003e\u003cbr\u003e\n--\u003e Data visualization tools and technologies are essential to analyzing massive amounts of information and making data-driven decisions.\u003cbr\u003e\u003cbr\u003e\n--\u003e The concept of using pictures is to understand data that has been used for centuries. General types of data visualization are Charts, Tables, Graphs, Maps, Dashboards.\n\n---\n\n\u003ch1\u003e Various forms of Data Visualization\u003c/h1\u003e\n\u003cimg src=\"https://github.com/madhurimarawat/Data-Visualization-using-python/assets/105432776/65a0869d-2263-4f47-874a-26f95244e70b\" title=\"Various forms of Data Visualization\" alt=\"Various forms of Data Visualization\" width=1010\u003e\n\n---\n\n# About Python Programming\n\n--\u003e Python is a high-level, general-purpose, and very popular programming language.\u003cbr\u003e\u003cbr\u003e\n--\u003e Python programming language (latest Python 3) is being used in web development, Machine Learning applications, along with all cutting-edge technology in Software Industry.\u003cbr\u003e\u003cbr\u003e\n--\u003e Python is available across widely used platforms like Windows, Linux, and macOS.\u003cbr\u003e\u003cbr\u003e\n--\u003e The biggest strength of Python is huge collection of standard library.\u003cbr\u003e\n\n---\n\n# Mode of Execution Used \u003cimg src=\"https://colab.research.google.com/img/colab_favicon_256px.png\" title=\"Google Colab\" alt=\"Google Colab\" width=\"40\" height=\"40\"\u003e\n\n--\u003e Colaboratory, or “Colab” for short, is a product from Google Research which allows anybody to write and execute python code in Jupyter notebook through the browser.\u003cbr\u003e\u003cbr\u003e\n--\u003e Visit colab at:\u0026nbsp; \u003ca href=\"https://colab.research.google.com/\"\u003e \u003cimg src=\"https://colab.research.google.com/img/colab_favicon_256px.png\" title=\"Google Colab\" alt=\"Google Colab\" width=\"40\" height=\"40\"\u003e\u003c/a\u003e\u003cbr\u003e\u003cbr\u003e\n--\u003e Create account using google account.\u003cbr\u003e\u003cbr\u003e\n--\u003e Once account creation is done, we can directly start coding in colab.\u003cbr\u003e\u003cbr\u003e\n--\u003e It supports Python and R.\u003cbr\u003e\u003cbr\u003e\n--\u003e Files are directly saved in Google Drive.\u003cbr\u003e\n\n---\n\n## Table Of Contents 📔 🔖 📑\n\n### 1. [House Pricing Dataset - Aesthetics Mapping](https://github.com/madhurimarawat/Data-Visualization-using-python/blob/main/Experiment%201%20House%20pricing%20dataset.ipynb)\n\n**Description:** In this experiment, we download the House Pricing dataset from Kaggle and map the values to various aesthetics using visualizations such as color, shape, and size to represent the data features.\n\n### 2. [Rainfall Prediction - Different Color Scales](https://github.com/madhurimarawat/Data-Visualization-using-python/blob/main/Experiment%202%20Rainfall%20Prediction%20Color%20Scales.ipynb)\n\n**Description:** This experiment involves using different color scales to visualize the Rainfall Prediction dataset. We explore the impact of various color palettes and their readability in different visual contexts.\n\n### 3. [Bar Plots for Dataset Variables](https://github.com/madhurimarawat/Data-Visualization-using-python/blob/main/Experiment%203%20Bar%20plots%20for%20variable.ipynb)\n\n**Description:** We create different bar plots to represent categorical variables from a given dataset, providing insights into the distribution and comparison across categories.\n\n### 4. [Skewed Data - Detection and Removal](https://github.com/madhurimarawat/Data-Visualization-using-python/blob/main/Experiment%204%20Skewedness%20and%20Removal%20of%20Skewedness.ipynb)\n\n**Description:** This experiment demonstrates how to identify skewed data, visualize its distribution, and apply transformations to remove skewness for more accurate analysis.\n\n### 5. [Time Series Visualization for Sales Data](https://github.com/madhurimarawat/Data-Visualization-using-python/blob/main/Experiment%205%20Time%20Series%20Visualization.ipynb)\n\n**Description:** A time series visualization is performed on a sales dataset, showcasing trends, seasonality, and patterns in the data over time.\n\n### 6. [Scatterplot with Dimension Reduction Suggestions](https://github.com/madhurimarawat/Data-Visualization-using-python/blob/main/Experiment%206%20Dimension%20Reduction.ipynb)\n\n**Description:** A scatterplot is created for a dataset, followed by recommendations for dimension reduction techniques such as PCA or t-SNE to simplify the data while preserving key information.\n\n### 7. [Geospatial Data and Projections](https://github.com/madhurimarawat/Data-Visualization-using-python/blob/main/Experiment%207%20Geospatial%20Data%20Projections.ipynb)\n\n**Description:** This experiment covers the use of geospatial data and applying various projections to visualize geographical datasets accurately on different types of maps.\n\n### 8. [Trend Line with Confidence Band](https://github.com/madhurimarawat/Data-Visualization-using-python/blob/main/Experiment%208%20Trend%20Line%20and%20Confidence%20Band.ipynb)\n\n**Description:** A trend line is plotted with a confidence band to showcase the relationship between variables in a dataset, offering insights into trends and uncertainty around predictions.\n\n### 9. [Partial Transparency and Jittering](https://github.com/madhurimarawat/Data-Visualization-using-python/blob/main/Experiment%209%20Partial%20Transparency%20and%20Jittering.ipynb)\n\n**Description:** This experiment illustrates the use of partial transparency and jittering in scatter plots to handle overlapping points and improve clarity in dense data visualizations.\n\n### 10. [Usage of Different Color Codes](https://github.com/madhurimarawat/Data-Visualization-using-python/blob/main/Experiment%2010%20Use%20of%20Color%20Codes.ipynb)\n\n**Description:** The experiment explores how different color codes (RGB, HEX, and named colors) can be applied to enhance data visualizations, improving the visual appeal and understanding of complex datasets.\n\n---\n\n## Various Libraries in Python for Data Visualization\n\nTo install python library this command is used-\n\n```\npip install library_name\n```\n\n\u003cimg src= \"https://github.com/madhurimarawat/Data-Visualization-using-python/assets/105432776/e15f70d3-4924-4e7e-9275-57fe51f30c3b\" width=1010 title=\"Various libraries in Python for Data Visualization\" alt=\"python Library\"\u003e\n\n---\n\n# Dataset Used\n\n\u003ch2\u003eHousing Dataset\u003c/h2\u003e\n--\u003e Dataset is taken from: \u003ca href=\"https://www.kaggle.com/datasets/yasserh/housing-prices-dataset\"\u003e\u003cimg src=\"https://cdn4.iconfinder.com/data/icons/logos-and-brands/512/189_Kaggle_logo_logos-1024.png\" height =40 width=40 title=\"Housing Dataset\" alt=\"Housing Dataset\"\u003e \u003c/a\u003e\u003cbr\u003e\u003cbr\u003e\n--\u003e CSV file which contains house pricing data.\u003cbr\u003e\u003cbr\u003e\n--\u003e Price of house with respect to area and other basic amenties.\u003cbr\u003e\n\n\u003ch2\u003eRainfall Prediction Dataset\u003c/h2\u003e\n--\u003e Dataset is taken from: \u003ca href=\"https://www.kaggle.com/datasets/rajanand/rainfall-in-india\"\u003e\u003cimg src=\"https://cdn4.iconfinder.com/data/icons/logos-and-brands/512/189_Kaggle_logo_logos-1024.png\" height =40 width=40 title=\"Housing Dataset\" alt=\"Housing Dataset\"\u003e \u003c/a\u003e\u003cbr\u003e\u003cbr\u003e\n--\u003e CSV file which contains the rainfall data.\u003cbr\u003e\u003cbr\u003e\n--\u003e Sub-division wise monthly data for 115 years from 1901-2015.\u003cbr\u003e\n\n\u003ch2\u003eBuisness Dataset\u003c/h2\u003e\n--\u003e Dataset is taken from: \u003ca href=\"https://www.stats.govt.nz/assets/Uploads/Business-financial-data/Business-financial-data-September-2022-quarter/Download-data/business-financial-data-september-2022-quarter.xlsx\"\u003e\u003cimg src=\"https://www.stats.govt.nz/themes/stats/images/logo.svg\" height=40 width=40 title=\"Buisness Dataset\" alt=\"Buisness Dataset\"\u003e\u003c/a\u003e\u003cbr\u003e\u003cbr\u003e\n--\u003e Business financial data provides sales, purchases, salaries and wages, and operating profit estimates for most market industries in New Zealand, and information on stocks for selected industries.\u003cbr\u003e\u003cbr\u003e\n--\u003e This collection uses a combination of survey, tax, and other administrative data.\u003cbr\u003e\n\n\u003ch2\u003eSales Dataset\u003c/h2\u003e\n--\u003e Dataset is taken from: \u003ca href=\"https://www.kaggle.com/datasets/kyanyoga/sample-sales-data\"\u003e\u003cimg src=\"https://cdn4.iconfinder.com/data/icons/logos-and-brands/512/189_Kaggle_logo_logos-1024.png\" height =40 width=40 title=\"Sales Dataset\" alt=\"Sales Dataset\"\u003e \u003c/a\u003e\u003cbr\u003e\u003cbr\u003e\n--\u003e CSV file which contains the sales data.\u003cbr\u003e\u003cbr\u003e\n\n\u003ch2\u003eMineral ores round the world Dataset\u003c/h2\u003e\n--\u003e Dataset is taken from: \u003ca href=\"https://www.kaggle.com/datasets/ramjasmaurya/mineral-ores-around-the-world\"\u003e\u003cimg src=\"https://cdn4.iconfinder.com/data/icons/logos-and-brands/512/189_Kaggle_logo_logos-1024.png\" height =40 width=40 title=\"Minerals Dataset\" alt=\"Minerals Dataset\"\u003e \u003c/a\u003e\u003cbr\u003e\u003cbr\u003e\n--\u003e Dataset of minerals found around the world.\u003cbr\u003e\n\n\u003ch2\u003eAutomobile Dataset\u003c/h2\u003e\n--\u003e Dataset is taken from: \u003ca href=\"https://gist.github.com/lauvshree/20ee07bfaa6d97364006fc2320092526\"\u003e🔗\u003cimg src=\"https://github.com/devicons/devicon/blob/master/icons/github/github-original-wordmark.svg\" height =40 width=40 title=\"Automobile Dataset\" alt=\"Automobile Dataset\"\u003e \u003c/a\u003e\u003cbr\u003e\u003cbr\u003e\n--\u003e This contains data about various automobile in Comma Separated Value (CSV) format.\u003cbr\u003e\u003cbr\u003e\n--\u003e CSV file contains the details of automobile-mileage,length,body-style among other attributes.\u003cbr\u003e\u003cbr\u003e\n--\u003e It contains the following dimensions-[60 rows X 6 columns].\u003cbr\u003e\u003cbr\u003e\n--\u003e The csv file is already preprocessed ,thus their is no need for data cleaning.\u003cbr\u003e\n\n\u003ch2\u003eNBA Players Dataset\u003c/h2\u003e\n--\u003e Dataset is taken from: \u003ca href=\"https://gist.github.com/ganeshbabuNN/80b55569fde8eb6a81518d4c8921c7a6\" \u003e🔗\u003cimg src=\"https://github.com/devicons/devicon/blob/master/icons/github/github-original-wordmark.svg\" height =40 width=40 title=\"NBA Dataset\" alt=\"NBA Dataset\"\u003e \u003c/a\u003e\u003cbr\u003e\u003cbr\u003e\n--\u003e This contains data about various NBA Players in Comma Separated Value (CSV) format.\u003cbr\u003e\u003cbr\u003e\n--\u003e CSV file contains the details of players-height,weight,team,position among other attributes.\u003cbr\u003e\u003cbr\u003e\n--\u003e It contains the following dimensions-[457 rows X 9 columns].\u003cbr\u003e\u003cbr\u003e\n--\u003e The csv file is already preprocessed ,thus their is no need for data cleaning.\u003cbr\u003e\n\n---\n\n\u003ch1\u003eLibraries Used\u003c/h1\u003e\n\u003cp\u003eShort Description about all libraries used.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eNumPy (Numerical Python) – Enables with collection of mathematical functions\nto operate on array and matrices. \u003c/li\u003e\n  \u003cli\u003ePandas (Panel Data/ Python Data Analysis) - This library is mostly used for analyzing,\ncleaning, exploring, and manipulating data.\u003c/li\u003e\n\u003cli\u003eMatplotlib - It is a data visualization and graphical plotting library.\u003c/li\u003e\n\u003cli\u003eSeaborn - It is an extension of Matplotlib library used to create more attractive and\ninformative statistical graphics.\u003c/li\u003e\n\u003cli\u003eSciPy (Scientific Python) - used for scientific computation. SciPy contains modules for optimization, linear algebra, integration, interpolation, special\nfunctions, FFT, signal and image processing\u003c/li\u003e\n\u003cli\u003eScikit-learn - It is a machine learning library that enables tools for used for many other\nmachine learning algorithms such as classification, prediction, etc.\u003c/li\u003e\n\u003cli\u003eGeopandas-GeoPandas, as the name suggests, extends the popular data science library pandas by adding support for geospatial data.\u003c/li\u003e\n\u003c/ul\u003e\n\n---\n\n## Thanks for Visiting 😄\n\nDrop a 🌟 if you find this repository useful.\u003cbr\u003e\u003cbr\u003e\nIf you have any doubts or suggestions, feel free to reach me.\u003cbr\u003e\u003cbr\u003e\n📫 How to reach me: \u0026nbsp; [![Linkedin Badge](https://img.shields.io/badge/-madhurima-blue?style=flat\u0026logo=Linkedin\u0026logoColor=white)](https://www.linkedin.com/in/madhurima-rawat/) \u0026nbsp; \u0026nbsp;\n\u003ca href =\"mailto:rawatmadhurima@gmail.com\"\u003e\u003cimg src=\"https://github.com/madhurimarawat/Machine-Learning-Using-Python/assets/105432776/b6a0873a-e961-42c0-8fbf-ab65828c961a\" height=35 width=30 title=\"Mail Illustration\" alt=\"Mail Illustration📫\" \u003e \u003c/a\u003e\n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmadhurimarawat%2Fdata-visualization-using-python","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmadhurimarawat%2Fdata-visualization-using-python","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmadhurimarawat%2Fdata-visualization-using-python/lists"}