如何绘制Geopandas数据框的多个地图？

如何解决如何绘制Geopandas数据框的多个地图？

我有一个几何特征列表，我想将它们并排可视化为子图。当我键入：

for i in iaq:

fig,ax = plt.subplots(figsize=(8,5))
df_g2[df_g2['aq_date'] == i].plot(column='zone_id',cmap='Greens',ax=ax,legend=True)
ax.set_title('Analysis :'+ str(i))
plt.show()

40张地图的列表在列表中一个接一个地出现。但是我想以5 * 8的行列排列它们。当我尝试给出像这样的排列大小时：

fig,ax = plt.subplots(nrows=8,ncols=5)
fig.set_size_inches(6,4)

for i in iaq:
   df_g2[df_g2['aq_date'] == i].plot(column='zone_id',legend=True)
   ax.set_title('Analysis :'+ str(i))
   plt.show()

我收到错误消息：

请帮助。

解决方法

我与官方reference解决了这个问题。如果没有trim_axs()函数，则会出现“ numpy.ndrray”。

import numpy as np
import matplotlib.pyplot as plt

figsize = (9,9)
cols = 5
rows = 8

x = np.linspace(0,10,500)
y = np.sin(x)

def trim_axs(axs,N):
    """
    Reduce *axs* to *N* Axes. All further Axes are removed from the figure.
    """
    axs = axs.flat
    for ax in axs[N:]:
        ax.remove()
    return axs[:N]

axs = plt.figure(figsize=figsize,constrained_layout=True).subplots(rows,cols)
axs = trim_axs(axs,cols*rows)
for ax,i in zip(axs,range(1,(cols*rows)+1)):
    ax.set_title('Analysis :'+ str(i))
    ax.plot(x,y,'o',ls='-',ms=4)

,

由于我无权访问您的数据框，因此我将使用内置的naturalearth_lowres绘制一系列选定国家/地区。阅读代码中的注释以阐明重要步骤。

import geopandas as gpd
import matplotlib.pyplot as plt

# for demo purposes,use the builtin data
world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))

# set number of subplots' (columns,rows) enough to use
cols,rows = 2,3  #num of subplots <= (cols x rows)

# create figure with array of axes
fig,axs = plt.subplots(nrows=rows,ncols=cols)
fig.set_size_inches(6,10)  #set it big enough for all subplots

# select some countries to plot
# number of them is intended to be less than (cols x rows)
# the remaining subplots will be discarded
iaq = ['IND','TZA','CAN','THA','BRN']

count = 0
for irow in range(axs.shape[0]):
    for icol in range(axs.shape[1]):
        #print(icol,irow)
        if count<len(iaq):
            # plot that country on current axes
            world[ world['iso_a3'] == iaq[count] ].plot(ax=axs[irow][icol])
            axs[irow][icol].set_title('world:iso_a3: '+iaq[count])
            count +=1
        else:
            # hide extra axes
            axs[irow][icol].set_visible(False)

plt.show()

结果图：