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https://github.com/madhurimarawat/data-visualization-using-python

This repository contains data visualization programs on various datasets done using python.
https://github.com/madhurimarawat/data-visualization-using-python

automobile-dataset bar-plots color-codes data-visualization dimensionality-reduction geopandas geospatial-data house-pricing-dataset jittering left-skwed matplotlib pandas partial-transparency principal-component-analysis python3 right-skwed seaborn skewed-data time-series-analysis trend-line-chart

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This repository contains data visualization programs on various datasets done using python.

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# Data-Visualization-using-python



This repository contains data visualization programs on various datasets done using python.



 Data Visualization



![What-is-Data-Visualization-Blog-Header](https://github.com/madhurimarawat/Data-Visualization-using-python/assets/105432776/ea30609d-c156-4c80-b701-05194192e6a6)






--> Data visualization is the graphical representation of information and data in a pictorial or graphical format(Example: charts, graphs, and maps). 



--> Data visualization tools provide an accessible way to see and understand trends, patterns in data, and outliers. 



--> Data visualization tools and technologies are essential to analyzing massive amounts of information and making data-driven decisions.



--> 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.



---



 Various forms of Data Visualization


Various forms of Data Visualization



---



# About Python Programming



--> Python is a high-level, general-purpose, and very popular programming language.



--> Python programming language (latest Python 3) is being used in web development, Machine Learning applications, along with all cutting-edge technology in Software Industry.



--> Python is available across widely used platforms like Windows, Linux, and macOS.



--> The biggest strength of Python is huge collection of standard library.



---



# Mode of Execution Used Google Colab



--> 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.



--> Visit colab at:   Google Colab



--> Create account using google account.



--> Once account creation is done, we can directly start coding in colab.



--> It supports Python and R.



--> Files are directly saved in Google Drive.



---



## Table Of Contents 📔 🔖 📑



### 1. [House Pricing Dataset - Aesthetics Mapping](https://github.com/madhurimarawat/Data-Visualization-using-python/blob/main/Experiment%201%20House%20pricing%20dataset.ipynb)



**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.



### 2. [Rainfall Prediction - Different Color Scales](https://github.com/madhurimarawat/Data-Visualization-using-python/blob/main/Experiment%202%20Rainfall%20Prediction%20Color%20Scales.ipynb)



**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.



### 3. [Bar Plots for Dataset Variables](https://github.com/madhurimarawat/Data-Visualization-using-python/blob/main/Experiment%203%20Bar%20plots%20for%20variable.ipynb)



**Description:** We create different bar plots to represent categorical variables from a given dataset, providing insights into the distribution and comparison across categories.



### 4. [Skewed Data - Detection and Removal](https://github.com/madhurimarawat/Data-Visualization-using-python/blob/main/Experiment%204%20Skewedness%20and%20Removal%20of%20Skewedness.ipynb)



**Description:** This experiment demonstrates how to identify skewed data, visualize its distribution, and apply transformations to remove skewness for more accurate analysis.



### 5. [Time Series Visualization for Sales Data](https://github.com/madhurimarawat/Data-Visualization-using-python/blob/main/Experiment%205%20Time%20Series%20Visualization.ipynb)



**Description:** A time series visualization is performed on a sales dataset, showcasing trends, seasonality, and patterns in the data over time.



### 6. [Scatterplot with Dimension Reduction Suggestions](https://github.com/madhurimarawat/Data-Visualization-using-python/blob/main/Experiment%206%20Dimension%20Reduction.ipynb)



**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.



### 7. [Geospatial Data and Projections](https://github.com/madhurimarawat/Data-Visualization-using-python/blob/main/Experiment%207%20Geospatial%20Data%20Projections.ipynb)



**Description:** This experiment covers the use of geospatial data and applying various projections to visualize geographical datasets accurately on different types of maps.



### 8. [Trend Line with Confidence Band](https://github.com/madhurimarawat/Data-Visualization-using-python/blob/main/Experiment%208%20Trend%20Line%20and%20Confidence%20Band.ipynb)



**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.



### 9. [Partial Transparency and Jittering](https://github.com/madhurimarawat/Data-Visualization-using-python/blob/main/Experiment%209%20Partial%20Transparency%20and%20Jittering.ipynb)



**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.



### 10. [Usage of Different Color Codes](https://github.com/madhurimarawat/Data-Visualization-using-python/blob/main/Experiment%2010%20Use%20of%20Color%20Codes.ipynb)



**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.



---



## Various Libraries in Python for Data Visualization



To install python library this command is used-



```

pip install library_name

```



python Library



---



# Dataset Used



Housing Dataset


--> Dataset is taken from: Housing Dataset 



--> CSV file which contains house pricing data.



--> Price of house with respect to area and other basic amenties.



Rainfall Prediction Dataset


--> Dataset is taken from: Housing Dataset 



--> CSV file which contains the rainfall data.



--> Sub-division wise monthly data for 115 years from 1901-2015.



Buisness Dataset


--> Dataset is taken from: Buisness Dataset



--> 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.



--> This collection uses a combination of survey, tax, and other administrative data.



Sales Dataset


--> Dataset is taken from: Sales Dataset 



--> CSV file which contains the sales data.




Mineral ores round the world Dataset


--> Dataset is taken from: Minerals Dataset 



--> Dataset of minerals found around the world.



Automobile Dataset


--> Dataset is taken from: 🔗Automobile Dataset 



--> This contains data about various automobile in Comma Separated Value (CSV) format.



--> CSV file contains the details of automobile-mileage,length,body-style among other attributes.



--> It contains the following dimensions-[60 rows X 6 columns].



--> The csv file is already preprocessed ,thus their is no need for data cleaning.



NBA Players Dataset


--> Dataset is taken from: 🔗NBA Dataset 



--> This contains data about various NBA Players in Comma Separated Value (CSV) format.



--> CSV file contains the details of players-height,weight,team,position among other attributes.



--> It contains the following dimensions-[457 rows X 9 columns].



--> The csv file is already preprocessed ,thus their is no need for data cleaning.



---



Libraries Used


Short Description about all libraries used.


    

  • NumPy (Numerical Python) – Enables with collection of mathematical functions

    to operate on array and matrices. 
    • 

      
  • Pandas (Panel Data/ Python Data Analysis) - This library is mostly used for analyzing,

    cleaning, exploring, and manipulating data.
    • 

  • Matplotlib - It is a data visualization and graphical plotting library.
    • 

  • Seaborn - It is an extension of Matplotlib library used to create more attractive and

    informative statistical graphics.
    • 

  • SciPy (Scientific Python) - used for scientific computation. SciPy contains modules for optimization, linear algebra, integration, interpolation, special

    functions, FFT, signal and image processing
    • 

  • Scikit-learn - It is a machine learning library that enables tools for used for many other

    machine learning algorithms such as classification, prediction, etc.
    • 

  • Geopandas-GeoPandas, as the name suggests, extends the popular data science library pandas by adding support for geospatial data.
    • 




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## Thanks for Visiting 😄



Drop a 🌟 if you find this repository useful.



If you have any doubts or suggestions, feel free to reach me.



📫 How to reach me:   [![Linkedin Badge](https://img.shields.io/badge/-madhurima-blue?style=flat&logo=Linkedin&logoColor=white)](https://www.linkedin.com/in/madhurima-rawat/)    

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