matplotlib geopandas绘制带有设置容器的叶绿素的colorcheme [英] matplotlib geopandas plot chloropleth with set bins for colorscheme

问题描述

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数字.

The following should be a wholly reproducible example to run the code and get the same figure I have posted below.

从国家统计局获取shapefile数据.在终端中将其作为bash文件/命令运行.

Get the shapefile data from the Office for National Statistics. Run this in a terminal as a bash file / commands.

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

读取shapefile并创建虚拟GeoDataFrame以再现行为的python代码.

The python code that reads the shapefile and creates a dummy GeoDataFrame for reproducing the behaviour.

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)))

从ONS加入shapefile并创建一个geopandas.GeoDataFrame

Join the shapefile from ONS and create a 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)

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')

这样，我应该在最右侧的ax中看到较暗的颜色(更多的红色)，显示high_pop情况.

This way I should see darker colors (more red) in the far right ax showing the high_pop scenario.

如何为整个图形/每个ax对象设置colorcheme bin?

How can I set the colorscheme bins for the whole figure / each of the ax objects?

我能看到此工作的最简单方法是 a)为geopandas.plot()功能提供一组垃圾箱 b)从一个ax中提取颜色方案/容器，然后将其应用于另一个.

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.

推荐答案

从geopandas 0.5开始，您可以使用定义为scheme="User_Defined"的自定义方案，并通过classification_kwds提供合并.

From geopandas 0.5 onwards you can use a custom scheme defined as scheme="User_Defined" and supply the binning via 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()

所以剩下的任务是从其中一列的分位数中获得bin列表.这应该很容易做到，例如通过

So the remaining task is to get a list of bins from the quantiles of one of the columns. This should be easily done, e.g. via

import mapclassify
bins = mapclassify.Quantiles(gdf['quant'], k=5).bins

然后在上面的代码中设置classification_kwds=dict(bins=bins).

then setting classification_kwds=dict(bins=bins) in the above code.

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