Top 5 Python Libraries for Data Visualization

a picture is worth a thousand words and some can be said with data and so in this video i will be giving you the five top data visualization library in python and so without further ado we're starting right now and so the first library for data visualization is of course matplotlib so matplotlib is probably one of the oldest data visualization library for python and it has a wide range of functions for you to visualize data in all sorts of ways and so in all of the websites for the five data visualization libraries all of them will have a gallery and so for example if i click on examples here i will see all of the possible plots that i could make using matplotlib and so on the right hand side you can see a table of contents which you can click and it will jump to the one that you want so here we're just gonna scroll down so it has bar charts lines even histogram and scatter plot fused into one okay so this is contours and also for images so contour plot is pretty good in allowing you to visualize three-dimensional data whereby the x and y are indicated here and then the z dimension is indicated by the data samples that you see along with its corresponding colors and then this is for customizing the various axes labels making a multi-plot figure adding text to the image plot arranging the image plot as a grid and also a lot of plots for statistics and of course there's the pi plot and the polar chart as well and they provide you some examples on how to create all sorts of annotated image plots how to add latex into the image plot here okay so all sorts of annotation examples and also how to work with colors in the plot that you're making and so as you can see there's a lot of things that you could do with matplotlib even animated histograms as well okay so i'm just gonna scroll it because there's so many plus as you will see here even 3d here 3d contour plus okay actually if you are interested in any of the plots here just click on it and then and so it will provide you the example code to get you started in creating the plot indicated here and so i would normally just shop for the image plot that i like look at the example and then replace the example data with my own data and you could also download the jupiter notebook of each of the image plus to your own computer and customize okay so this is matplotlib and it's a pretty good library for data visualization so let's hop on to the next one seaborn so seaborn in my opinion looks a bit better than matplotlib i think it's because of the color scheme in that it resembles that of the ggplot2 as you can see here the coloring scheme looks amazing so let's have a look at the gallery so the gallery is not as extensive as that of the map.lib but it does have quite a few standard plots that you would need let's click on one of them and similar to matplotlib it provides you the example code that could get you started to reproduce this if you're interested in the heat map you can make it just follow this example just copy and paste it into a jupiter notebook and replace the example data with your own data but also to compare and have a look at their own data sets and your own data set are the shape similar if not then you have to reshape your data so that it resembles the one in the example here so that it could plug and play and work with the plots for the example right so there's quite a lot of things that you could do here and the great thing is they also provide the tutorial as well so let's click on that and they did a pretty good job in categorizing the tutorials into making the basic plots making a multi-plot grid and also to take a look at some of the data that it accepts whether the data is in the long form or in the y form and also to customize the various aesthetic of the plot like for example by default you will see that it provides you a gray background like this one but you could also make it into a black and white right like with the black outline here and the white background so without the major grid minor grid as shown here just a plain gray background or a plain white background or even a white background with a grid line a horizontal grid line okay let's go back and have a look at matplotlib for a short moment and it also has a tutorial section as well and it categorizes into different levels like introductory level intermediate level advanced and also how to customize the colors okay so both matplotlib and seaborn they're quite similar in that the plots that you will create are pretty static meaning that usually they're not interactive by interactive let me show you what i mean let's have a look at bokeh so bokeh plotly and also altair they're interactive for example if i hover the mouse you see that there is a number appearing one five seven four six if i change the over to a new location the number changes right four one zero nine seven if i move it again the number updates plotly also let's have a look so it's interactive charts let's have a look here let's click on one of them so you can see here that when we hover the mouse over a data point here we get to see the numbers displaying like total bill of 18.71 with a tip of four and it updates interactively even on the line the trend line here same thing with bokeh it's also interactive as shown in the examples here let's have a look at some of the example plots and right here it's quite amazing in that it looks quite a lot like shiny and as you can see here is inspired by the shiny example of the shiny movie explorer all of this resembles shiny the one from the data visualization package in r let's have a look so if you hover on it it updates itself and the panel here is also interactive so this is awesome see update here the data gets updated okay let's have a look at the documentation so there's a lot of different plots that you could make and they're all interactive see if you hover on to an area here you get to see the name of the county updated for texas and the corresponding is the example code and they also provide you some tutorial as well and it's linking out to a vendor which will spin up a cloud notebook which will allow you to interactively use this bokeh notebook let's have a look is loading up loading okay do we need to run it let's try it okay so it doesn't seem to show the example part first to see maybe it's loading oh well so they do provide you with the tutorial which should be interactive and so you could click on a topic of your interest and follow along in their tutorial and let's have a look at the out here so this is also another data visualization library in python that is interactive and it looks quite good as well let's have a look at some of the example plots so it's quite similar to matplotlib in that it provides you a catalog of the example plots that you could generate and the color is quite vibrant as well scanner plots instagram maps maps are pretty awesome here interactive charts as well and also some case study examples so they're pretty much the use of altair in actual data sets or public data sets like london two blinds or seattle weather interactive data visualization or looking at the carbon dioxide concentration and also some other example charts that they have created here pretty much miscellaneous okay and so there you have it five top data visualization libraries for your data projects data analytics data science data visualization so in conclusion as previously mentioned matplotlib and seaborn are data visualization library that will allow you to generate static plots but then the other three consisting of bokeh plotly and also out here allows you to create interactive plots that you could zoom in zoom out or over to have a look at the data labels and as shown in the demo of plotly and also bokeh both are quite similar to shiny in that it also has a web component meaning that you could create a web application using bokeh as you will see in the prior example they have created a mimic of the shiny explorer here and also plotly is deeply integrated with dash so you could generate a dashboard that will showcase some of the plots interactively and so let me know in the comments which library is your favorite and if you're finding value out of this video please give it a like subscribe if you haven't already hit on the notification bell so that you will be notified of the next video and until next time happy coding

Channel: Coding Professor

Views: 7,523

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Keywords: data visualization, data visualisation, python data visualization, python data visualisation, data visualization python, data visualisation python, matplotlib, seaborn, altair, bokeh, plotly, data visualization python libraries, data visualisation python libraries, python data visualization libraries, python data visualisation libraries, data visualization python library, data visualisation library, python data visualization library, python data visualisation library, data viz, dataviz

Id: jNiQaErXg8s

Length: 11min 32sec (692 seconds)

Published: Fri Apr 23 2021

Reddit Comments

[u/tech_auto]

I like plotly for the interactive selection and hovering. I'm currently using it with flask it's slightly clunky because I have to pass everything as JSON to the html template that renders it in javascript

[u/-p-a-b-l-o-]

Nice! Subscribed to your channel 👍

[u/Naseer-Ahmad-Lone]

👍👍👍

[u/ML_Engineer7]

Nice list!

[u/knowledgebass]

I know it is not really in the same category as these libraries but what about Dash? Anyone used it and have any thoughts about it?