Matplotlib axvspan() 函数填充错误的日期 在 pandas.plot 中更正 xaxis dtype 以更正 vspan 位置

编程问答 2022-11-19

问题描述

当我想使用 axvspan 填充日期时，它填充了错误的区域。我认为它逆转了 axvspans。问题的原因可能是我从世界银行导入的数据框。我该如何解决？谢谢。

import matplotlib.pyplot as plt
import matplotlib
from pandas_datareader import wb
import seaborn as sns
import pandas as pd
import datetime
start = datetime.datetime (2000,1,1)
end = datetime.datetime (2021,5,1)
ind = ['FM.LBL.BMNY.ZG','FR.INR.DPST'
       ]
df = wb.download(indicator=ind,country='CHN',start=start,end=end).dropna();df.reset_index(inplace=True)
df.columns = ['Country','Year','broad money growth (annual %) - China','Deposit interest rate (%)'
              ]
df=df.sort_values(by='Year',ascending=True)
axes= df.plot(x='Year',subplots=True,figsize=(20,12),layout=(1,2),colormap='summer',legend=True)

for ax,col in zip(axes.flatten(),df.columns):
    ax.axvspan('2007-1-12','2009-6-1',color='teal',alpha=0.5,label='2008 Crisis')
    ax.axvspan('2019-12-1','2020-2-1',color='orange',label='Pandemic')
    ax.set_title(col)

axes[0,0].set_title('broad money growth (annual %) - China')
axes[0,0].invert_xaxis()
axes[0,0].legend(loc='upper left')
axes[0,0].set_ylabel('Percent(Annual)')
axes[0,0].invert_xaxis()

axes[0,1].set_title('Deposit interest rate (%)')
axes[0,1].invert_xaxis()
axes[0,1].legend(loc='upper left')
axes[0,1].set_ylabel('Percent(Annual)')
axes[0,1].invert_xaxis()



plt.suptitle("Financial Sector in China",fontweight="bold")
plt.show()

you can see it didn't fill 2019,fills another dates.

enter image description here

解决方法

在 pandas.plot 中更正 xaxis dtype 以更正 vspan 位置

df 中的 year 列是一个对象而不是 int，所以 xlim 是 (-1,21) 并且标签被格式化在这些索引位置的顶部。因此，当您放置 vspan 时，输入的 x 位置与 x 轴的位置不匹配。要解决此问题，只需将年份列设为整数，并将以年份为单位的 vspan 设为整数。

enter image description here

import matplotlib.pyplot as plt
import matplotlib
from pandas_datareader import wb
import seaborn as sns
import pandas as pd
import datetime
import matplotlib.ticker as ticker

start = datetime.datetime (2000,1,1)
end = datetime.datetime (2021,5,1)
ind = ['FM.LBL.BMNY.ZG','FR.INR.DPST'
       ]
df = wb.download(indicator=ind,country='CHN',start=start,end=end).dropna();df.reset_index(inplace=True)
df.columns = ['Country','Year','Broad money growth (annual %) - China','Deposit interest rate (%)'
              ]
df=df.sort_values(by='Year',ascending=True)
df['Year'] = df['Year'].astype(int)

axes= df.plot(x='Year',subplots=True,figsize=(15,5),layout=(1,2),colormap='summer',legend=True)

for ax,col in zip(axes.flatten(),df.columns):
    ax.axvspan(2007,2009,color='teal',alpha=0.5,label='2008 Crisis')
    ax.axvspan(2019,2020,color='orange',label='Pandemic')
    ax.set_title(col)
    ax.set_xlim(2000,2021)
    ax.xaxis.set_major_locator(ticker.MultipleLocator(2))

axes[0,0].set_title('Broad money growth (annual %) - China')
axes[0,0].legend(loc='upper left')
axes[0,0].set_ylabel('Percent(Annual)')


axes[0,1].set_title('Deposit interest rate (%)')
axes[0,1].legend(loc='upper left')
axes[0,1].set_ylabel('Percent(Annual)')

plt.suptitle("Financial Sector in China",fontweight="bold")
matplotlibpandaspandas-datareaderpython