Introduction

Matplotlib is great library which offers huge flexibility due to its object oriented programming style. However, most of the times, we the users don’t need that much flexibiliy and just want to get things done as quickly as possible. For example why should I write at least three lines to plot a simple array with legend when same can be done in one line and my purpose is just to view the array. Why I can’t simply do plot(data) or imshow(img). This motivation gave birth to this library. easy_mpl stands for easy matplotlib. The purpose of this is to ease the use of matplotlib while keeping the flexibility of object oriented programming paradigm of matplotlib intact. Using these one liners will save the time and will not hurt. Moreover, you can swap most function of this library with that of matplotlib and vice versa.

easy_mpl contains two kinds of functions, one which are just wrappers around their matplotlib alternatives. These include plot(), scatter(), bar_chart(), pie(), hist(), imshow() and boxplot(). As the name suggests, these are just alternatives to their matplotlib aliases. All of these functions take same input arguments as taken by corresponding matplotlib functions. If these functions are given same input arguments as to their matplotlib alternatives, then these functions return same output as returned by matplotlib. Therefore, we can consider them as alternative to matplotlib (for most cases). All these functions take three further input arguments. These are ax, ax_kws and show. The meanings of these three arguments are as below

• ax stands for axes, the matplotlib axes object matplotlib.axes. If ax argument is given, then the plots are drawn on this otherwise either a new matplotlib axes is created or currently available axes is used.

• ax_kws is a dictionary which includes the arguments to manipulate the x and y labels, ticklabels, title. These arguments are passed to easy_mpl.utils.process_axes() function.

• The show argument determines whether to draw the plot after the function or not. If show is set to False, then the axes is not exhausted, which means, we can manipulate it if required and call plt.show or plt.draw after manipulating the axes. Otherwise, in default case (when show is True), the plot is drawn immediately after calling the corresponding function.

Moreover these wrapper functions also take some auxiliary input arguments which can be used for further manipulation of these plots. For example the imshow() function takes the whiten_grid argument. The second kinds of functions in this library are helper functions for data visualization and analysis. These include regplot(), dumbbell_plot(), ridge(), parallel_coordinates(), taylor_plot(), lollipop_plot(), circular_bar_plot(), violin_plot() and spider_plot() . Thus easy_mpl is not a replacement to matplotlib in all the cases but it can be your go to tool for the plots given in examples and API in most of the cases.