Shulou(Shulou.com)06/01 Report--

How to use Python to copy a data visualization diagram of R language, for this problem, this article describes the corresponding analysis and solution in detail, hoping to help more small partners who want to solve this problem find a simpler and easier way.

profile

To get started, the data visualization we're going to emulate today comes from the #TidyTuesday campaign, which launched January 28, 2020, under the "San Francisco Street Trees Dataset". Among the many entries, Philippe Massicotte created the very popular "Street trees of San Francisco"(shown in Figure 1).

And road network data we can use osmnx to get online, just pass in our San Francisco surface data bbox range, with

Osmnx can be obtained by:

Then, based on the above data, we count the number of street trees in each community surface and divide the data into boxes, with color values of preset intervals:

#Count the number of trees in each community sf_trees = \( gpd #Space Connection .sjoin(left_df=sf, right_df=trees, op='contains', how='left') #Counted by name group (not connected to any number of communities here) #is inherently wrong, but it doesn't affect us after we draw segments) .groupby('name') .agg({ 'name': 'count', 'geometry': 'first' }) .rename(columns={'name':' quantity'}) .reset_index(drop=False) #Go directly to GeoDataFrame .pipe(gpd.GeoDataFrame, crs ='EPSG: 4326'))sf_trees ['Color '] = ( pd .cut(sf_trees ['number '], bins=[0, 2500, 5000, 7500, 10000, max(sf_trees ['number '])], labels=['#e4f1e1', '#c0dfd1', '#67a9a2', '#3b8383', '#145e64']))

Finally, don't forget our buffer generation as an outline:

#Generate contour buffer sf_bounds = gpd.GeoSeries([sf.buffer(0.001).unary_union], crs='EPSG: 4326')

"Drawing of Major Visual Elements"

After doing these preparations, we can directly draw the main elements of the image:

import matplotlib.pyplot as pltfrom matplotlib import font_manager as fm#Set global default font plt.rcParams <$'font.sans-serif'] =<$'Times New Roman'] fig, ax = plt.subplots(figsize=(6, 6))#Set background color ax.set_facecolor ('#3333') fig.set_facecolor ('#33333')#Layer 1: Buffer outline ax = ( sf_bounds .plot(ax=ax, facecolor='none', edgecolor='#cc', linewidth=1)#Layer 2: Community polygon with tree statistics ax = ( sf_trees .plot(color=sf_trees ['color '], edgecolor='#33333', linewidth=0.5, ax=ax))#Layer 3: osm road network ax = ( roads .plot(linewidth=0.05, edgecolor='#3c3d3d', ax=ax))#Set x axis ax.set_xticks ([-122.5, -122.45, -122.4, -122.35])ax.set_xticklabels (['122.5° W','122.45° W','122.4° W','122.35° W'])#Set y-axis ax.set_yticks ([37.72, 37.74, 37.76, 37.78, 37.8, 37.82])ax.set_yticklabels (['37.72° N','37.74° N','37.76° N','37.78° N','37.8° N','37.82° N'])#Set axis style ax.tick_params (axis='both', labelcolor='#737373', color='none', labelsize=8)#Hide surrounding spines ax.spines <$'left'].set_color ('none')ax.spines <$'right'].set_color ('none') ax.spines <$'top'].set_color ('none')ax.spines <$'bottom'].set_color ('none')#export image fig.savefig ('fig. 4.png', dpi=600, bbox_inches ='tight ')

"Addition of auxiliary visual elements"

Next we only need to add a variety of finishing touches on the small elements, which is worth mentioning is the legend below we use inset_axes() inserted into the sub-graph flexible way to achieve.

And the use of external font files is also very colorful, we use two special fonts here in the "title" and "scale label" respectively (you can find all the font files I use in the Github repository at the beginning):

fig, ax = plt.subplots(figsize=(6, 6))#Set background color ax.set_facecolor ('#33333') fig.set_facecolor ('#3333')#Layer 1: Buffer outline ax = ( sf_bounds .plot(ax=ax, facecolor='none', edgecolor='#cc', linewidth=1)#Layer 2: Community polygon with tree statistics ax = ( sf_trees .plot(color=sf_trees ['color '], edgecolor='#33333', linewidth=0.5, ax=ax))#Layer 3: osm road network ax = ( roads .plot(linewidth=0.05, edgecolor='#3c3d3d', ax=ax))#Set x axis ax.set_xticks ([-122.5, -122.45, -122.4, -122.35])ax.set_xticklabels (['122.5° W','122.45° W','122.4° W','122.35° W'])#Set y-axis ax.set_yticks ([37.72, 37.74, 37.76, 37.78, 37.8, 37.82])ax.set_yticklabels (['37.72° N','37.74° N','37.76° N','37.78° N','37.8° N','37.82° N'])#Set axis style ax.tick_params (axis='both', labelcolor='#737373', color='none', labelsize=8)#Hide surrounding spines ax.spines <$'left'].set_color ('none')ax.spines <$'right'].set_color ('none') ax.spines <$'top'].set_color ('none')ax.spines <$'bottom'].set_color ('none')#Add the following legend ax_bar = ax.inset_axes by inserting subgraphs ((0.25, -0.12, 0.5, 0.015))ax_bar.set_facecolor ('#333333')ax_bar.spines['left'].set_color('none')ax_bar.spines['right'].set_color('none')ax_bar.spines['top'].set_color('none')ax_bar.spines['bottom'].set_color('none')ax_bar.bar(range(5),[1]*5, width=0.975, color=['#e4f1e1', '#c0dfd1', '#67a9a2', '#3b8383', '#145e64'])ax_bar.set_yticks([])ax_bar.set_xticks([i+0.5 for i in range(4)])ax_bar.set_xticklabels(['2500', '5000', '7500', '10000'], fontdict={'fontproperties': fm.FontProperties(fname="RobotoCondensed-Regular.ttf")})ax_bar.tick_params(color='none', labelcolor='#ffffff', labelsize=8, pad=0)ax.set_title('Street trees of San Francisco', fontsize=24, color='#ffffff', pad=40, fontproperties=fm.FontProperties(fname="Amaranth-Bold.ttf"))ax.text(0.5, 1.08, '''There are a total of 192987 trees in San Francisco regrouped into 571 species.The district with the most number of trees is Mission whereas the one withthe least number of trees is LincoLn Park / Ft. Miley. ''', transform=ax.transAxes, ma='center', ha='center', va='top', color='#ffffff')ax.text(0.5, -0.22, 'Visualization by CNFeffery', fontsize=8, color='#737373', ha=' center', transform=ax.transAxes)#export image fig.savefig ('Figure 5.png', dpi=600, bbox_inches ='tight ') About how to use Python to copy a R language data visualization problem to share here, I hope the above content can be of some help to you, if you still have a lot of doubts, you can pay attention to the industry information channel to learn more related knowledge.

Welcome to subscribe "Shulou Technology Information " to get latest news, interesting things and hot topics in the IT industry, and controls the hottest and latest Internet news, technology news and IT industry trends.