Hello,
Ideas on better plotting of distributions (banding and how the population lies for any variable):
https://rafalab.dfci.harvard.edu/dsbook/dataviz-distributions.html
https://seaborn.pydata.org/tutorial/distributions.html - Using seaborn
https://github.com/cuttlefishh/python-for-data-analysis/blob/master/lessons/lesson10.ipynb
How to connect big query with python: https://codelabs.developers.google.com/codelabs/cloud-bigquery-python#0
The idea here is to assimilate resources from across the internet that will help me level up my visualization game using python. I am pretty much a noob at this and want to get better.
Most helpful visualization libraries in python (https://www.kaggle.com/discussions/getting-started/1087922)
1- matplotlib
matplotlib is the O.G. of Python data visualization libraries. Despite being over a decade old, it’s still the most widely used library for plotting in the Python community. It was designed to closely resemble MATLAB, a proprietary programming language developed in the 1980s.
2- 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.
3- ggplot
ggplot is based on ggplot2, an R plotting system, and concepts from The Grammar of Graphics. ggplot operates differently than matplotlib: it lets you layer components to create a complete plot. For instance, you can start with axes, then add points, then a line, a trendline, etc. Although The Grammar of Graphics has been praised as an “intuitive” method for plotting, seasoned matplotlib users might need time to adjust to this new mindset.
4- Bokeh
Like ggplot, Bokeh is based on The Grammar of Graphics, but unlike ggplot, it’s native to Python, not ported over from R. Its strength lies in the ability to create interactive, web-ready plots, which can be easily outputted as JSON objects, HTML documents, or interactive web applications. Bokeh also supports streaming and real-time data.
5- pygal
Like Bokeh and Plotly, pygal offers interactive plots that can be embedded in the web browser. Its prime differentiator is the ability to output charts as SVGs. As long as you’re working with smaller datasets, SVGs will do you just fine. But if you’re making charts with hundreds of thousands of data points, they’ll have trouble rendering and become sluggish.
6- Plotly
You might know Plotly as an online platform for data visualization, but did you also know you can access its capabilities from a Python notebook? Like Bokeh, Plotly’s forte is making interactive plots, but it offers some charts you won’t find in most libraries, like contour plots, dendograms, and 3D charts.
7- geoplotlib
geoplotlib is a toolbox for creating maps and plotting geographical data. You can use it to create a variety of map-types, like choropleths, heatmaps, and dot density maps. You must have Pyglet (an object-oriented programming interface) installed to use geoplotlib. Nonetheless, since most Python data visualization libraries don’t offer maps, it’s nice to have a library dedicated solely to them.
8- Gleam
Gleam is inspired by R’s Shiny package. It allows you to turn analyses into interactive web apps using only Python scripts, so you don’t have to know any other languages like HTML, CSS, or JavaScript. Gleam works with any Python data visualization library. Once you’ve created a plot, you can build fields on top of it so users can filter and sort data.
9- missingno
Dealing with missing data is a pain. missingno allows you to quickly gauge the completeness of a dataset with a visual summary, instead of trudging through a table. You can filter and sort data based on completion or spot correlations with a heatmap or a dendrogram.
10- Leather
Leather’s creator, Christopher Groskopf, puts it best: “Leather is the Python charting library for those who need charts now and don’t care if they’re perfect.” It’s designed to work with all data types and produces charts as SVGs, so you can scale them without losing image quality.
https://github.com/mathisonian/awesome-visualization-research
https://mode.com/blog/python-data-visualization-libraries
1. Seaborn
Seaborn is built on top of the matplotlib library. it has many built-in functions using which you can create beautiful plots with just simple lines of codes. It provides a variety of advanced visualization plots with simple syntax like box plots, violin plots, dist plots, Joint plots, pair plots, heatmap, and many more.
Key Features:It can be used to determine the relationship between two variables.
Differentiate when analyzing uni-variate or bi-variate distributions.
Plot the linear regression model for the dependent variable.
Provides multi-grid plotting
Official website: https://seaborn.pydata.org/
2. Plotly
Plotly is an advanced Python analytics library that helps in building interactive dashboards. The graphs build using Plotly are interactive plots, which means you can easily find value at any particular point or session of the graphs. Plotly makes it super easy to generate dashboards and deploying them on the server. It supports Python, R, and the Julia programming language.
You can create a wide range of graphs using Plotly:Basic Charts
Statistical charts
Scientific charts
Financial Charts
Maps
Subplots
Transforms
Jupyter Widgets Interaction
Official website: https://plotly.com/
3. Geoplotlib
Geoplotlib is an open-source Python toolbox for visualizing geographical data. It supports the development of hardware-accelerated interactive visualizations in pure Python and provides implementations of dot maps, kernel density estimation, spatial graphs, Voronoi tesselation, shapefiles, and many more common spatial visualizations.
Geoplotlib can be used to make a variety of maps, such as equivalent area maps, heat maps, and point density maps. There are also several extended modules:geoplotlib
geoplotlib.layers
geoplotlib.utils
geoplotlib.core
geoplotlib.colors
Official website: https://andrea-cuttone.github.io/geoplotlib/
4. Gleam
Gleam is inspired by R’s Shiny package. It allows you to turn analyses into interactive web apps using only Python scripts, so you don’t have to know any other languages like HTML, CSS, or JavaScript. Gleam works with any Python data visualization library. Once you’ve created a plot, you can build fields on top of it so users can filter and sort data.
Official website: https://github.com/dgrtwo/gleam
5. ggplot/ggplot2
ggplot works differently from matplotlib. It lets you add multiple components as layers to create a complete graph or plot at the end. For example, at the start you can add an axis, then points, and other components like a trend line.
They always say that you should store your data in a data frame before using ggplot to get simpler and efficient results.
Official website: https://ggplot2.tidyverse.org/reference/ggplot.html
Key code snippets:
How to connect big query with python: https://codelabs.developers.google.com/codelabs/cloud-bigquery-python#0
The idea here is to assimilate resources from across the internet that will help me level up my visualization game using python. I am pretty much a noob at this and want to get better.
Most helpful visualization libraries in python (https://www.kaggle.com/discussions/getting-started/1087922)
1- matplotlib
matplotlib is the O.G. of Python data visualization libraries. Despite being over a decade old, it’s still the most widely used library for plotting in the Python community. It was designed to closely resemble MATLAB, a proprietary programming language developed in the 1980s.
2- 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.
3- ggplot
ggplot is based on ggplot2, an R plotting system, and concepts from The Grammar of Graphics. ggplot operates differently than matplotlib: it lets you layer components to create a complete plot. For instance, you can start with axes, then add points, then a line, a trendline, etc. Although The Grammar of Graphics has been praised as an “intuitive” method for plotting, seasoned matplotlib users might need time to adjust to this new mindset.
4- Bokeh
Like ggplot, Bokeh is based on The Grammar of Graphics, but unlike ggplot, it’s native to Python, not ported over from R. Its strength lies in the ability to create interactive, web-ready plots, which can be easily outputted as JSON objects, HTML documents, or interactive web applications. Bokeh also supports streaming and real-time data.
5- pygal
Like Bokeh and Plotly, pygal offers interactive plots that can be embedded in the web browser. Its prime differentiator is the ability to output charts as SVGs. As long as you’re working with smaller datasets, SVGs will do you just fine. But if you’re making charts with hundreds of thousands of data points, they’ll have trouble rendering and become sluggish.
6- Plotly
You might know Plotly as an online platform for data visualization, but did you also know you can access its capabilities from a Python notebook? Like Bokeh, Plotly’s forte is making interactive plots, but it offers some charts you won’t find in most libraries, like contour plots, dendograms, and 3D charts.
7- geoplotlib
geoplotlib is a toolbox for creating maps and plotting geographical data. You can use it to create a variety of map-types, like choropleths, heatmaps, and dot density maps. You must have Pyglet (an object-oriented programming interface) installed to use geoplotlib. Nonetheless, since most Python data visualization libraries don’t offer maps, it’s nice to have a library dedicated solely to them.
8- Gleam
Gleam is inspired by R’s Shiny package. It allows you to turn analyses into interactive web apps using only Python scripts, so you don’t have to know any other languages like HTML, CSS, or JavaScript. Gleam works with any Python data visualization library. Once you’ve created a plot, you can build fields on top of it so users can filter and sort data.
9- missingno
Dealing with missing data is a pain. missingno allows you to quickly gauge the completeness of a dataset with a visual summary, instead of trudging through a table. You can filter and sort data based on completion or spot correlations with a heatmap or a dendrogram.
10- Leather
Leather’s creator, Christopher Groskopf, puts it best: “Leather is the Python charting library for those who need charts now and don’t care if they’re perfect.” It’s designed to work with all data types and produces charts as SVGs, so you can scale them without losing image quality.
https://github.com/mathisonian/awesome-visualization-research
https://mode.com/blog/python-data-visualization-libraries
1. Seaborn
Seaborn is built on top of the matplotlib library. it has many built-in functions using which you can create beautiful plots with just simple lines of codes. It provides a variety of advanced visualization plots with simple syntax like box plots, violin plots, dist plots, Joint plots, pair plots, heatmap, and many more.
Key Features:It can be used to determine the relationship between two variables.
Differentiate when analyzing uni-variate or bi-variate distributions.
Plot the linear regression model for the dependent variable.
Provides multi-grid plotting
Official website: https://seaborn.pydata.org/
2. Plotly
Plotly is an advanced Python analytics library that helps in building interactive dashboards. The graphs build using Plotly are interactive plots, which means you can easily find value at any particular point or session of the graphs. Plotly makes it super easy to generate dashboards and deploying them on the server. It supports Python, R, and the Julia programming language.
You can create a wide range of graphs using Plotly:Basic Charts
Statistical charts
Scientific charts
Financial Charts
Maps
Subplots
Transforms
Jupyter Widgets Interaction
Official website: https://plotly.com/
3. Geoplotlib
Geoplotlib is an open-source Python toolbox for visualizing geographical data. It supports the development of hardware-accelerated interactive visualizations in pure Python and provides implementations of dot maps, kernel density estimation, spatial graphs, Voronoi tesselation, shapefiles, and many more common spatial visualizations.
Geoplotlib can be used to make a variety of maps, such as equivalent area maps, heat maps, and point density maps. There are also several extended modules:geoplotlib
geoplotlib.layers
geoplotlib.utils
geoplotlib.core
geoplotlib.colors
Official website: https://andrea-cuttone.github.io/geoplotlib/
4. Gleam
Gleam is inspired by R’s Shiny package. It allows you to turn analyses into interactive web apps using only Python scripts, so you don’t have to know any other languages like HTML, CSS, or JavaScript. Gleam works with any Python data visualization library. Once you’ve created a plot, you can build fields on top of it so users can filter and sort data.
Official website: https://github.com/dgrtwo/gleam
5. ggplot/ggplot2
ggplot works differently from matplotlib. It lets you add multiple components as layers to create a complete graph or plot at the end. For example, at the start you can add an axis, then points, and other components like a trend line.
They always say that you should store your data in a data frame before using ggplot to get simpler and efficient results.
Official website: https://ggplot2.tidyverse.org/reference/ggplot.html
Key code snippets:
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