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

This article mainly introduces "how Python realizes data visualization to analyze the residence freedom index of 38 cities". In the daily operation, it is believed that many people have doubts about how Python can realize data visualization and analyze the residence freedom index of 38 cities. The editor consulted all kinds of materials and sorted out simple and easy-to-use operation methods. I hope it will be helpful for you to answer the question of "how to achieve data visualization by Python to analyze the living freedom index of 38 cities"! Next, please follow the editor to study!

1 introduction

Several data visualization works in this report are quite commendable, and as the beginning of a series of articles (intensive data visualization in imitation), I will simulate reproduction in a pure Python way based on the way I observed the original data visualization works and conceived it.

2 repetition process

2.1 observe the original work

In fact, the original work looks a little complicated at first, but after observation, it is not complicated to divide the main elements of the original picture into several parts to conceive the way of reproduction. I summarize it into the following parts:

"1 coordinate system part"

Anyone who knows a little bit about data visualization should be able to see that the coordinates of the original work are not the conventional Cartesian coordinate system, but the polar coordinate system. The difficulty in reproducing the polar coordinate system of the original work here is that it is not a complete polar coordinate system, that is, the area on the left slightly smaller than the semicircle hides the reference line.

Therefore, instead of trying to hide part of the reference line on the basis of the polar coordinate system in matplotlib, it is better to reverse thinking and organize and construct the reference line from the point of view of constructing the reference line, which will be more free and flexible.

"2 Color fill"

The "color filling" here refers to the color-filled area between the broken line of the housing freedom index and the broken line of the rental freedom index, but the difficulty here is that when the housing freedom index is higher than the rental freedom index, the corresponding color is light turquoise, and in turn turns gray, which echoes with the colors of the home purchase freedom index and the rental freedom index.

We only need to set the central point parameters at the South Pole or North Pole, combined with simple longitude and latitude related knowledge, we can forge any longitude and latitude, and then use the projection transformation in geopandas to convert to the set "orthophoto projection", and then draw as plane coordinates.

For example, follow this idea to create 5 latitude lines and longitude lines for 38 cities between 10 degrees east longitude and 220 degrees east longitude, and between-90 degrees and-80 degrees south latitude:

Import geopandas as gpdfrom shapely.geometry import LineString, Point Polygonimport matplotlib.pyplot as pltimport numpy as npimport warningsplt.rcParams ['font.sans-serif'] = [' SimHei'] # solve the problem of garbled code in matplotlib plt.rcParams ['axes.unicode_minus'] = False # solve the problem of matplotlib negative sign display warnings.filterwarnings (' ignore') # set the orthographic projection of the center point at the South Pole crs ='+ proj=ortho + lon_0=0 + lat_0=-90'# construct the longitude line and set the geographic location corresponding to longitude and latitude Standard system lng_lines = gpd.GeoDataFrame ({'geometry': [LineString ([[lng]) -90], [lng,-78]]) for lng in np.arange (10,220,210 / 38)}, crs='EPSG:4326') # construct the latitude line and set it to the geographic coordinate system lat_lines = gpd.GeoDataFrame corresponding to longitude and latitude ({'geometry': [LineString ([[lng, lat] for lng in range (10,220)]) for lat in range (- 90,-79,2)]}, crs='EPSG:4326')

After the data is constructed, convert the GeoDataFrame corresponding to the longitude and latitude to the set "orthographic projection" crs, and then overlay the drawing as different layers:

Forge the sample data according to the rules inferred above, and correct the unreasonable data in the process of forgery:

Def fake_index (value): fake = [] fake.append (value+np.random.uniform (5,10)) fake.append (value-np.random.uniform (5,10)) return np.random.choice (fake, size=2, replace=False). Tolist () data ['Home purchase Freedom Index'] Data ['Rental Freedom Index'] = list (zip (* data ['Residential Freedom Index']. Apply (fake_index)) # corrects the situation where data.loc [:, 'habitation Freedom Index':] = data.loc [:, 'residence Freedom Index':] .applymap (lambda v: 100 if v > 100 else v) data.loc [:, 'residence Freedom Index':] = data.loc [: 'Freedom of residence Index':] .applymap (lambda v: 0 if v

< 0 else v)data.head() 接下来我们就来为每个指标构造线与散点部分的矢量数据，并在统一转换坐标参考系到「正射投影」之后叠加到之前的图像上： # 为每个城市生成1条经线lng_lines = gpd.GeoDataFrame({ 'geometry': [LineString([[lng, -90], [lng, -78]]) for lng in np.arange(10, 220, 210 / data.shape[0])]}, crs='EPSG:4326')# 居住自由指数对应的折线line1 = gpd.GeoDataFrame({ 'geometry': [LineString([(lng, lat) for lng, lat in zip(np.arange(10, 220, 210 / data.shape[0]), data['居住自由指数_映射值'])])]}, crs='EPSG:4326')# 居住自由指数对应的折线上的散点scatter1 = gpd.GeoDataFrame({ 'geometry': [Point(lng, lat) for lng, lat in zip(np.arange(10, 220, 210 / data.shape[0]), data['居住自由指数_映射值'])]}, crs='EPSG:4326')# 购房自由指数对应的折线line2 = gpd.GeoDataFrame({ 'geometry': [LineString([(lng, lat) for lng, lat in zip(np.arange(10, 220, 210 / data.shape[0]), data['购房自由指数_映射值'])])]}, crs='EPSG:4326')# 购房自由指数对应的折线上的散点scatter2 = gpd.GeoDataFrame({ 'geometry': [Point(lng, lat) for lng, lat in zip(np.arange(10, 220, 210 / data.shape[0]), data['购房自由指数_映射值'])]}, crs='EPSG:4326')# 租房自由指数对应的折线line3 = gpd.GeoDataFrame({ 'geometry': [LineString([(lng, lat) for lng, lat in zip(np.arange(10, 220, 210 / data.shape[0]), data['租房自由指数_映射值'])])]}, crs='EPSG:4326')# 租房自由指数对应的折线上的散点scatter3 = gpd.GeoDataFrame({ 'geometry': [Point(lng, lat) for lng, lat in zip(np.arange(10, 220, 210 / data.shape[0]), data['租房自由指数_映射值'])]}, crs='EPSG:4326') fig, ax = plt.subplots(figsize=(8, 8))# 绘制经度线与纬度线ax = lng_lines.to_crs(crs).plot(ax=ax, linewidth=0.4, edgecolor='lightgrey')ax = lat_lines.to_crs(crs).plot(ax=ax, linewidth=0.75, edgecolor='grey', alpha=0.8)ax = line1.to_crs(crs).plot(ax=ax, color='black', linewidth=1)ax = scatter1.to_crs(crs).plot(ax=ax, color='black', markersize=12)ax = line2.to_crs(crs).plot(ax=ax, color='#00CED1', linewidth=0.6)ax = scatter2.to_crs(crs).plot(ax=ax, color='#00CED1', markersize=4)ax = line3.to_crs(crs).plot(ax=ax, color='lightgrey', linewidth=0.6)ax = scatter3.to_crs(crs).plot(ax=ax, color='lightgrey', markersize=4)ax.axis('off'); # 关闭坐标轴fig.savefig('图11.png', dpi=500, inches_bbox='tight', inches_pad=0)

Then the next thing we need to do is so easy. All we need to do is to get the rest of the two sides after removing the overlapping faces, and then draw them on the image in figure 11 with the corresponding fill color overlay, which can be easily realized by using difference in geopandas:

Fig, ax= plt.subplots (figsize= (8,8)) # plots longitude and latitude lines ax= lng_lines.to_crs (crs). Plot (ax=ax, linewidth=0.4, edgecolor='lightgrey') ax= lat_lines.to_crs (crs). Plot (ax=ax, linewidth=0.75, edgecolor='grey', alpha=0.8) ax= line1.to_crs (crs). Plot (ax=ax, color='black', linewidth=1) ax= scatter1.to_crs (crs) .plot (ax=ax, color='black') Markersize=12) ax= line2.to_crs (crs) .plot (ax=ax, color='#00CED1', linewidth=0.6) ax= scatter2.to_crs (crs) .plot (ax=ax, color='#00CED1', markersize=4) ax= line3.to_crs (crs) .plot (ax=ax, color='lightgrey', linewidth=0.6) ax= scatter3.to_crs (crs) .plot (ax=ax, color='lightgrey', markersize=4) ax= polygon1.difference (polygon2) .plot (ax=ax, color='#00CED1', alpha=0.2) polygon2.difference (polygon1) .plot (ax=ax) Color='lightgrey', alpha=0.6) ax.axis ('off') # close the axis fig.savefig ('figure 13.pnglines, dpi=500, inches_bbox='tight', inches_pad=0)

At this point, the study on "how to achieve data visualization by Python to analyze the residential freedom index of 38 cities" is over. I hope to be able to solve your doubts. The collocation of theory and practice can better help you learn, go and try it! If you want to continue to learn more related knowledge, please continue to follow the website, the editor will continue to work hard to bring you more practical articles!

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.