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%load_ext watermark
%watermark -u -v -d -p matplotlib,numpy
Last updated: 07/30/2015 CPython 3.4.3IPython 3.2.0matplotlib 1.4.3numpy 1.9.2
%matplotlib inline
Matplotlib Tips and Tricks¶
Sections¶
When to use the figure object¶
Often, we see code that explicitely instantiates a newfigure object:
importmatplotlib.pyplotaspltfig=plt.figure()plt.plot([0,1],[0,1])plt.show()
If we are not planning to manipulate the figure object or add subplots to the figure, this may be redundant. Why? As nicely explained onSO, theplot function retrieves the current figure automatically viagcf ("get current figure") nested inside agca ("get current axes") call. Thus, it really doesn't matter if we create a figure prior toplot unless we are planning to modify it in some way.
importmatplotlib.pyplotaspltplt.plot([0,1],[0,1])plt.show()
Fitting the margins¶
Although a plot appears perfectly fine inline in a IPython notebook, sometimes we may notice that certain figure elements went missing if we save it to an image file viasavefig. Thetight_layout() function often helps to produce a "tighter" appearence of the plot, it is not sufficient if to fit our graph nicely into the figure boundaries if we plot outside it's margins. Consider the following example:
Typically, this issue can be easily resolved by setting thebbox_inches attribute to'tight' insidesavefig. You can find the complete code example for producing the plot below:
importmatplotlib.pyplotaspltimportnumpyasnp%matplotlib inlinedefgini(p):return(p)*(1-(p))+(1-p)*(1-(1-p))defentropy(p):return-p*np.log2(p)-(1-p)*np.log2((1-p))deferror(p):return1-np.max([p,1-p])x=np.arange(0.0,1.0,0.01)ent=[entropy(p)ifp!=0elseNoneforpinx]sc_ent=[e*0.5ifeelseNoneforeinent]err=[error(i)foriinx]fig=plt.figure()ax=plt.subplot(111)fori,lab,ls,c,inzip([ent,sc_ent,gini(x),err],['Entropy','Entropy (scaled)','Gini Impurity','Misclassification Error'],['-','-','--','-.'],['black','lightgray','red','green','cyan']):line=ax.plot(x,i,label=lab,linestyle=ls,lw=2,color=c)ax.legend(loc='upper center',bbox_to_anchor=(0.5,1.15),ncol=3,fancybox=True,shadow=False)ax.axhline(y=0.5,linewidth=1,color='k',linestyle='--')ax.axhline(y=1.0,linewidth=1,color='k',linestyle='--')plt.ylim([0,1.1])plt.xlabel('p(i=1)')plt.ylabel('Impurity Index')plt.tight_layout()# uncomment the following line to save the image to disk# plt.savefig('./impurity.png', dpi=300, bbox_inches='tight')plt.show()