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In the final project of Data Visualisation with Python, you create impactful visualizations using popular libraries like Matplotlib, Seaborn, and Plotly. Apply skills learned throughout the course to analyze real-world data, effectively communicate insights, and enhance your data visualisation proficiency.

elfeenah/Data-Visualization-with-Python

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README.md

Data Visualization with Python

The aim of these labs is to introduce you to data visualization with Python as concrete and as consistent as possible. Speaking of consistency, because there is no best data visualization library available for Python — up to creating these labs — we have to introduce different libraries and show their benefits when we are discussing new visualization concepts. Doing so, we hope to make students well-rounded with visualization libraries and concepts so that they are able to judge and decide on the best visualization technique and tool for a given problem and audience.

Please make sure that you have completed the prerequisites for this course, namely Python Basics for Data Science and Analyzing Data with Python.

Note: The majority of the plots and visualizations will be generated using data stored in pandas dataframes. Therefore, in this lab, we provide a brief crash course on pandas. However, if you are interested in learning more about the pandas library, detailed description and explanation of how to use it and how to clean, munge, and process data stored in a pandas dataframe are provided in our course Analyzing Data with Python.

pandas is an essential data analysis toolkit for Python. From their website:

pandas is a Python package providing fast, flexible, and expressive data structures designed to make working with “relational” or “labeled” data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real world data analysis in Python.

The course heavily relies on pandas for data wrangling, analysis, and visualization. We encourage you to spend some time and familiarize yourself with the pandas API Reference: http://pandas.pydata.org/pandas-docs/stable/api.html.

The Dataset: Immigration to Canada from 1980 to 2013

The dataset contains annual data on the flows of international immigrants as recorded by the countries of destination. The data presents both inflows and outflows according to the place of birth, citizenship or place of previous / next residence both for foreigners and nationals. The current version presents data pertaining to 45 countries.

In this lab, we will focus on the Canadian immigration data.

The Canada Immigration dataset can be fetched from here.

Pie Charts, Box Plots, Scatter Plots, and Bubble Plots

• Explore Matplotlib library further
• Create pie charts, box plots, scatter plots and bubble charts

Waffle Charts, Word Clouds, and Regression Plots

Generating Maps with Python

In this lab, we will learn how to create maps for different objectives. To do that, we will part ways with Matplotlib and work with another Python visualization library, namely Folium. What is nice about Folium is that it was developed for the sole purpose of visualizing geospatial data. While other libraries are available to visualize geospatial data, such as plotly, they might have a cap on how many API calls you can make within a defined time frame. Folium, on the other hand, is completely free.

As a data analyst, the given a task to monitor and report US domestic airline flights performance. Goal is to analyze the performance of the reporting airline to improve flight reliability thereby improving customer reliability.

Below are the key report items,

• Yearly airline performance report
• Yearly average flight delay statistics NOTE: Year range is between 2005 and 2020.

Components of the report items

1. Yearly airline performance report For the chosen year provide,

• Number of flights under different cancellation categories using bar chart.
• Average flight time by reporting airline using line chart.
• Percentage of diverted airport landings per reporting airline using pie chart.
• Number of flights flying from each state using choropleth map.
• Number of flights flying to each state from each reporting airline using treemap chart.

1. Yearly average flight delay statistics For the chosen year provide,

• Monthly average carrier delay by reporting airline for the given year.
• Monthly average weather delay by reporting airline for the given year.
• Monthly average national air system delay by reporting airline for the given year.
• Monthly average security delay by reporting airline for the given year.
• Monthly average late aircraft delay by reporting airline for the given year.

Requirements to create the expected result

• Two dropdown menus: For choosing report type and year
• Each dropdown will be designed as follows:
• An outer division with two inner divisions (as shown in the expected layout)
• One of the inner divisions will have information about the dropdown and the other one is dropdown.
• Layout for adding graphs.
• Callback function to compute data, create graph and return to the layout.

About

In the final project of Data Visualisation with Python, you create impactful visualizations using popular libraries like Matplotlib, Seaborn, and Plotly. Apply skills learned throughout the course to analyze real-world data, effectively communicate insights, and enhance your data visualisation proficiency.

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Data visualization is the visual presentation of data or information. The goal of data visualization is to communicate data or information clearly and effectively to readers. Typically, data is visualized in the form of a chart, infographic, diagram or map.

License

rojaAchary/Data-Visualization-with-Python

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README.md

Data visualization is the visual presentation of data or information. The goal of data visualization is to communicate data or information clearly and effectively to readers. Typically, data is visualized in the form of a chart, infographic, diagram or map.

• Identify trends and outliers
• Tell a story within the data
• Reinforce an argument or opinion
• Highlight an important point in a set of data

Use the package manager pip to install below

pip install matplotlib pip install seaborn pip install plotnine pip install plotly pip install bokeh

• Seaborn harnesses the power of matplotlib to create beautiful charts in a few lines of code. The key difference is Seaborn’s default styles and color palettes, which are designed to be more aesthetically pleasing and modern. Since Seaborn is built on top of matplotlib, you’ll need to know matplotlib to tweak Seaborn’s defaults.

• Bokeh is an interactive visualization library for modern web browsers. It provides elegant, concise construction of versatile graphics, and affords high-performance interactivity over large or streaming datasets. Bokeh can help anyone who would like to quickly and easily make interactive plots, dashboards, and data applications.

• plotly is an interactive, open-source, and browser-based graphing library for Python Built on top of plotly.js, plotly.py is a high-level, declarative charting library. plotly.js ships with over 30 chart types, including scientific charts, 3D graphs, statistical charts, SVG maps, financial charts, and more.

✅ Get an overview of various plots.
✅ Work with different plotting libraries and get to know their strengths and weaknesses.
✅ Learn how to create insightful visualizations.
✅ Understand what makes a good visualization.
✅ Improve your Python data wrangling skills.
✅ Learn the industry standard tools.
✅ Develop your general understanding of data formats and representations.

@miscCharged Neuron, author = Roja Achary>, title = Data Visualisation with Python>, Credits = GfG,websites> month = August>, year = 2021> >

About

Data visualization is the visual presentation of data or information. The goal of data visualization is to communicate data or information clearly and effectively to readers. Typically, data is visualized in the form of a chart, infographic, diagram or map.

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This repository serve as a reference guide for creating various types of data visualizations using Python. It will feature over 25 visualizations.

License

soareseric/data-visualization-with-python

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README.md

Data Visualization with Python

The purpose of this repository is to serve as a reference guide for creating various types of data visualizations using Python. Upon completion, it will feature over 25 visualizations, utilizing more than 5 datasets, some of which will be scraped from websites.

Dataviz 01 — A Scatter Plot represents the relationship between two variables

Dataviz 02 — Scatter Plot showing the relationship between two variables with the points colored based on categories of a third variable.

About

This repository serve as a reference guide for creating various types of data visualizations using Python. It will feature over 25 visualizations.

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