4/18/2021 0 Comments Python Plotting Tutorial
It is a cross-platform library for making 2D plots from data in arrays.It provides an object-oriented API that helps in embedding plots in applications using Python GUI toolkits such as PyQt, WxPythonotTkinter.
It can be used in Python and IPython shells, Jupyter notebook and web application servers also. We assume that the readers of this tutorial have basic knowledge of Python. We are generating average ash and average flavonoids per wine category as a bar chart. Its totally based on d3.js (data visualization javascript library) and ipywidgets (python jupyter notebook widgets library). The main aim of bqplot is to bring in benefits of d3.js functionality to python along with utilizing widgets facility of ipywidgets by keeping all plot components as widgets to infuse flexibility. The library is developed with keeping interactive widgets in mind which allows us to change widgets value to reflect changes in the plot. All of the individual components of the graph in bqplot are interactive widgets based on ipywidgets. This gives a lot of flexibility with regard to creating interactive visualization as well as easy integration with other notebook widgets. Each of these objects behaves as a widget and can be linked to other widgets. Well also give various examples explaining about individual components of graph and modification of them to create aesthetically pleasing graphs. ![]() It has information about wine ingredients and their presence in three different wine categories. It has information about attributes like happiness score, perception of corruption, healthy life expectancy, social support by govt., freedom to make life choices, generosity and GDP per capita for various countries of the earth. Well now start by plotting various plots to explain the usage of bqplots pyplot API. Well plot the alcohol vs malic acid relationship using a scatter plot. Below we have explained another way of setting axis attributes by passing them as a dictionary to the axesoptions parameter. We need to use stroke and strokewidth parameters to modify the line property of markers. We have used square markers for this scatter plot and 2 different colors to color individual markers. We are also setting the x-axis label, y-axis label and x-axis limit to further enhance the graph. We are also color-encoding points according to the wine category. We also have changed the color bar location through the axesoptions parameter. We are color-encoding points of scatter plot by using different wine categories. We need to pass graph attributes that will be used to generate tooltip contents. We are using the contents of the x-axis, y-axis and color (wine category) for displaying on the tooltip. If you have background in matplotlib then itll be helpful with learning bqplot. We have first grouped entries of wine dataframe to group entries according to wine categories and then have taken average to collect dataframe with average values of all columns per wine category. Well be further using these average values per wine category dataframe in the future with other charts as well.
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