Data-Visualization-with-Python ✨
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
Libraries required
Use the package manager pip to install below
pip install matplotlibpip install seabornpip install plotninepip install plotlypip install bokehBrief about Libraries:
Matplotlib:
- Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python.
| No | Topics | Code Link |
|---|---|---|
| 1 | Basic Plotting | Code |
| 2 | Line_and_color_style | [Code] |
| 3 | Plot_with_line_styles | Code |
| 4 | Scatter_Plots | Code |
| 5 | Density_and_Contour_Plots | Code |
| 6 | Histograms_and_Binnings | Code |
| 7 | Customizing_legends | Code |
Seaborn
- 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.
| No | Topics | Code Link |
|---|---|---|
| 1 | Quick_Intro | Code |
| 2 | Categorical | Code |
| 3 | Distribution_plot | Code |
| 4 | Regression_Plots | Code |
| 5 | Matrix_Plots | Code |
| 6 | Multi_Plot | Code |
Plotnine
- plotnine is an implementation of a grammar of graphics in Python, it is based on ggplot2. The grammar allows users to compose plots by explicitly mapping data to the visual objects that make up the plot.
| No | Topics | Code Link |
|---|---|---|
| 1 | Intro | Code |
| 2 | Stage | Code |
| 3 | Scale_x_Continuous | Code |
| 4 | After_Scale | Code |
| 5 | Facet_grid | Code |
| 6 | Facet_Wrap | Code |
Bokeh
- 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.
| No | Topics | Code Link |
|---|---|---|
| 1 | Basic_Plotting | Code |
| 2 | Styling_and_Theming | Code |
| 3 | Data_sources_and_transformations | Code |
| 4 | Adding_Annotations | Code |
| 5 | Presentations_Layout | Code |
| 6 | Linking_and_Interactions | Code |
Plotly
- plotly is an interactive, open-source, and browser-based graphing library for PythonBuilt 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.
| No | Topics | Code Link |
|---|---|---|
| 1 | First_Steps | Code |
| 2 | Line_Plots | Code |
| 3 | Bar_Charts | Code |
| 4 | Pie_Charts | Code |
| 5 | Sunburst | Code |
| 6 | Bubble_chart | Code |
What you will learn
Samples of Plots
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@misc{Charged Neuron, author = {Roja Achary}, title = {Data Visualisation with Python}, Credits = {GfG,websites} month = {August}, year = {2021}}
