[英]matplotlib geopandas plot chloropleth with set bins for colorscheme
axes
in the same figure? axes
设置一致的颜色方案? The following should be a wholly reproducible example to run the code and get the same figure I have posted below. 以下应该是一个完全可复制的示例,用于运行代码并获得与我在下面发布的相同的图。
Get the shapefile data from the Office for National Statistics . 从国家统计局获取shapefile数据。 Run this in a terminal as a
bash
file / commands. 作为
bash
文件/命令在终端中运行它。
wget --output-document 'LA_authorities_boundaries.zip' 'https://opendata.arcgis.com/datasets/8edafbe3276d4b56aec60991cbddda50_1.zip?outSR=%7B%22latestWkid%22%3A27700%2C%22wkid%22%3A27700%7D&session=850489311.1553456889'
mkdir LA_authorities_boundaries
cd LA_authorities_boundaries
unzip ../LA_authorities_boundaries.zip
The python code that reads the shapefile and creates a dummy GeoDataFrame
for reproducing the behaviour. 读取shapefile并创建一个虚拟
GeoDataFrame
来重现行为的python代码。
import geopandas as gpd
import pandas as pd
import matplotlib.pyplot as plt
gdf = gpd.read_file(
'LA_authorities_boundaries/Local_Authority_Districts_December_2015_Full_Extent_Boundaries_in_Great_Britain.shp'
)
# 380 values
df = pd.DataFrame([])
df['AREA_CODE'] = gdf.lad15cd.values
df['central_pop'] = np.random.normal(30, 15, size=(len(gdf.lad15cd.values)))
df['low_pop'] = np.random.normal(10, 15, size=(len(gdf.lad15cd.values)))
df['high_pop'] = np.random.normal(50, 15, size=(len(gdf.lad15cd.values)))
Join the shapefile from ONS and create a geopandas.GeoDataFrame
从ONS加入shapefile并创建
geopandas.GeoDataFrame
def join_df_to_shp(pd_df, gpd_gdf):
""""""
df_ = pd.merge(pd_df, gpd_gdf[['lad15cd','geometry']], left_on='AREA_CODE', right_on='lad15cd', how='left')
# DROP the NI counties
df_ = df_.dropna(subset=['geometry'])
# convert back to a geopandas object (for ease of plotting etc.)
crs = {'init': 'epsg:4326'}
gdf_ = gpd.GeoDataFrame(df_, crs=crs, geometry='geometry')
# remove the extra area_code column joined from gdf
gdf_.drop('lad15cd',axis=1, inplace=True)
return gdf_
pop_gdf = join_df_to_shp(df, gdf)
Make the plots 绘制情节
fig,(ax1,ax2,ax3,) = plt.subplots(1,3,figsize=(15,6))
pop_gdf.plot(
column='low_pop', ax=ax1, legend=True, scheme='quantiles', cmap='OrRd',
)
pop_gdf.plot(
column='central_pop', ax=ax2, legend=True, scheme='quantiles', cmap='OrRd',
)
pop_gdf.plot(
column='high_pop', ax=ax3, legend=True, scheme='quantiles', cmap='OrRd',
)
for ax in (ax1,ax2,ax3,):
ax.axis('off')
ax
objects to share the same bins (preferable the central_pop
scenario quantiles
) so that the legend is consistent for the whole figure. ax
对象共享相同的垃圾箱(最好是central_pop
场景quantiles
),以便图例对于整个图形是一致的。 The quantiles
from ONE scenario (central) would become the levels
for all quantiles
从一个场景(中心)将成为该levels
的所有 This way I should see darker colors (more red) in the far right ax
showing the high_pop
scenario. 这样,我应该在显示
high_pop
场景的最右端的ax
看到较深的颜色(更多的红色)。
How can I set the colorscheme bins for the whole figure / each of the ax
objects? 如何为整个图形/每个
ax
对象设置colorscheme bin?
The simplest way I can see this working is either a) Provide a set of bins to the geopandas.plot()
function b) extract the colorscheme / bins from one ax
and apply it to another. 我可以看到此工作的最简单方法是:a)向
geopandas.plot()
函数提供一组垃圾箱b)从一个ax
提取geopandas.plot()
/ bins并将其应用于另一个ax
。
From geopandas 0.5 onwards you can use a custom scheme defined as scheme="User_Defined"
and supply the binning via classification_kwds
. 从geopandas 0.5起,可以使用定义为自定义方案
scheme="User_Defined"
,并且经由提供合并classification_kwds
。
import geopandas as gpd
print(gpd.__version__) ## 0.5
import numpy as np; np.random.seed(42)
import matplotlib.pyplot as plt
gdf = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))
gdf['quant']=np.random.rand(len(gdf))*100-20
fig, ax = plt.subplots()
gdf.plot(column='quant', cmap='RdBu', scheme="User_Defined",
legend=True, classification_kwds=dict(bins=[-10,20,30,50,70]),
ax=ax)
plt.show()
So the remaining task is to get a list of bins from the quantiles of one of the columns. 因此,剩下的任务是从其中一列的分位数中获得垃圾箱列表。 This should be easily done, eg via
这应该很容易做到,例如通过
import mapclassify
bins = mapclassify.Quantiles(gdf['quant'], k=5).bins
then setting classification_kwds=dict(bins=bins)
in the above code. 然后在上面的代码中设置
classification_kwds=dict(bins=bins)
。
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