4/30/2023 0 Comments Srt annotation edit![]() ![]() But if we use color to single out a bar or a line and use an annotation to describe it instead of a legend, the impact is much higher. They can also be combined with other elements to have an even higher impact.įor example, colors are commonly accompanied by a legend. # data x = y = # figure and axis fig, ax = plt.subplots(1, figsize=(16,8), facecolor='#CECECE') ax.set_facecolor('#CECECE') # bars plt.bar(x, y, zorder=1, color='#1D4DC6') # arrow and box for annotations arrowprops = dict(arrowstyle="wedge,tail_width=0.5", alpha=0.7, color='w') bbox=dict(boxstyle="round", alpha=0.7, color='w') # annotations plt.annotate('R2-D2\nSeries', xy=(2, 250), size=13, color = 'w', ha='center', va="center") plt.annotate('Faster\nProcessing', xy=(3, 13), xytext=(1, 50), textcoords='offset points', size=13, ha='center', va="center", bbox=bbox, arrowprops=arrowprops) plt.annotate('Cheaper', xy=(5, 96), xytext=(1, 50), textcoords='offset points', size=13, ha='center', va="center", bbox=bbox, arrowprops=arrowprops) # remove spines ax.t_visible(False) ax.t_visible(False) # grid ax.set_axisbelow(True) ax.id(color='white', linestyle='dashed', alpha=0.8) # title, labels, and caption plt.title('Astromech droids popularity', pad=30, loc='left', fontsize = 24, color='#4B4B4B') plt.ylabel('Appearances') plt.xlabel('Series') src = 'Source: ' plt.text(-0.75, -40, src, ha='left', fontsize = 10, alpha=0.9) plt.ylim(0,500) plt.show()Īnnotated bar chart - Image by the author ![]() Let’s try adding some annotations to a bar chart. It may be a detail relevant to the analysis, an important insight, or even a heads-up for missing data in our chart. ![]() We are pointing to a part of our chart and saying something to our audience. Now we’ll look at an excellent solution for highlighting parts of visualization with text.Īnnotations are quite intuitive. We covered some of the main text components of a chart and should be able to describe what’s in it properly. fig, ax = plt.subplots(1, figsize=(11,6)) plt.plot(date, spam, color='#C62C1D', lw=2.5, marker='o') plt.ylim(0,400) # remove spines ax.t_visible(False) ax.t_visible(False) # grid ax.set_axisbelow(True) ax.id(color='gray', linestyle='dashed', alpha=0.3) ax.id(color='gray', linestyle='dashed', alpha=0.3) # TITLE plt.suptitle('Global volume of spam e-mail', x=0.125, y=0.98, ha='left', fontsize=18) # SUBTITLE plt.title('Daily average by month', loc='left', fontsize=14) # AXIS LABELS plt.ylabel('Billions') plt.xlabel('2020') # CAPTION plt.text(-0.5, -60, 'Source: Cisco, ', ha='left', fontsize = 11, alpha=0.9) plt.show() Dates, years, or categories are usually relatively easy to understand and might not require labels. They can describe the chart, highlight some information, or add details such as where, how, or when.Ĭaptions can display information about our data source, notes, or any other relevant information.Īxis labels are required when the information on our axis is not explicit. They are placed below the title with a smaller font and are very versatile. ![]() Subtitles are not required but are very helpful. We need a title, which is the text with the largest font, usually placed at the top of the chart. The elements used to inform our audience about the content of our chart are almost the same. Commentary as the title: The Economist, The Economist.Insight as the title: Business Insider, MSNBC. ![]()
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