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ax.tick_params(axis=x, labelrotation=90) Matplotlib documentation reference here. This is useful when you have an array of axes as returned by plt.subplots, and it is more conven_As far as I know, plt.ylim() applies the limits to the current axes, which are set when you do plt.subplot(). I also cant believe that plt.subplot() care about how the axes it ret?plt.savefig(bbox_inches=tight changes image size I always feel that there is too much white space around images, and tended to add bbox_inches=tight from: Removing white space *There are several problems in your code. First the big ones: You are creating a new figure and a new axes in every iteration of your loop â put fig = plt.figure and ax = fig.add_+E.g., plt.legend(bbox_to_anchor=(1.04, 1), loc="upper left") places the legend outside the axes, such that the upper left corner of the legend is at position (1.04, 1) in axes coor|plt.savefig("name.png") is definitely going to be the most direct way to get your visualization saved. If youre looking for a more comprehensive solution for organizing visualizat`plt.legend(handles=first_leg ,second_leg ,thrid_leg ) The patches aspect put all the data i needed on my final plot (it was a line plot that combined three different line plots a
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ax.tick_params(axis=x, labelrotation=90) Matplotlib documentation reference here. This is useful when you have an array of axes as returned by plt.subplots, and it is more conven_As far as I know, plt.ylim() applies the limits to the current axes, which are set when you do plt.subplot(). I also cant believe that plt.subplot() care about how the axes it ret?plt.savefig(bbox_inches=tight changes image size I always feel that there is too much white space around images, and tended to add bbox_inches=tight from: Removing white space *There are several problems in your code. First the big ones: You are creating a new figure and a new axes in every iteration of your loop â put fig = plt.figure and ax = fig.add_+E.g., plt.legend(bbox_to_anchor=(1.04, 1), loc="upper left") places the legend outside the axes, such that the upper left corner of the legend is at position (1.04, 1) in axes coor|plt.savefig("name.png") is definitely going to be the most direct way to get your visualization saved. If youre looking for a more comprehensive solution for organizing visualizat`plt.legend(handles=first_leg ,second_leg ,thrid_leg ) The patches aspect put all the data i needed on my final plot (it was a line plot that combined three different line plots a