我正在使用matplotlib绘制一个图表，如下图所示。x轴是日期。我希望以yyyy-mm-dd格式显示每日数据，以月为单位。我希望每年的第一天显示为yy.m，而不是m。一年的第一天可能不是1月。根据数据，它可能是2月或10月。
下面的例子是2022-11-1。在这种情况下，我想将其显示为22.1。
我尽了最大的努力写了下面的代码，但仍然不够。
我以前见过类似的问题和答案，但我找不到关于在随机设置的日期中按年份标记yy.m的第一次约会的答案。

import pandas as pd
import io
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
from matplotlib.ticker import FuncFormatter

temp = u"""
date,yield
2022-11-01,4.0419 \n 2022-11-02,4.1005
\n 2022-11-03,4.1469\n 2022-11-04,4.1584\n 2022-11-05,4.1584\n 2022-11-06,4.1584\n 2022-11-07,4.2135\n 2022-11-08,4.1234\n 2022-11-09,4.0923\n 2022-11-10,3.8125\n 2022-11-11,3.8125\n 2022-11-12,3.8125\n 2022-11-13,3.8125\n 2022-11-14,3.8536
\n 2022-11-15,3.7696\n 2022-11-16,3.6899\n 2022-11-17,3.7657\n 2022-11-18,3.8288\n 2022-11-19,3.8288\n 2022-11-20,3.8288\n 2022-11-21,3.8269\n 2022-11-22,3.7559\n 2022-11-23,3.6927\n 2022-11-24,3.6927\n 2022-11-25,3.6776\n 2022-11-26,3.6776
\n 2022-11-27,3.6776\n 2022-11-28,3.6812\n 2022-11-29,3.7441\n 2022-11-30,3.6054\n 2022-12-01,3.5048\n 2022-12-02,3.4862\n 2022-12-03,3.4862\n 2022-12-04,3.4862\n 2022-12-05,3.5736\n 2022-12-06,3.5314\n 2022-12-07,3.4169\n 2022-12-08,3.4819
\n 2022-12-09,3.5783\n 2022-12-10,3.5783\n 2022-12-11,3.5783\n 2022-12-12,3.6113\n 2022-12-13,3.5012\n 2022-12-14,3.4774\n 2022-12-15,3.4463\n 2022-12-16,3.4822\n 2022-12-17,3.4822\n 2022-12-18,3.4822\n 2022-12-19,3.5846\n 2022-12-20,3.6825
\n 2022-12-21,3.662 \n 2022-12-22,3.6786\n 2022-12-23,3.7472\n 2022-12-24,3.7472\n 2022-12-25,3.7472\n 2022-12-26,3.7472\n 2022-12-27,3.8411\n 2022-12-28,3.8827\n 2022-12-29,3.8145\n 2022-12-30,3.8748\n 2022-12-31,3.8748\n 2023-01-01,3.8748
\n 2023-01-02,3.8748\n 2023-01-03,3.7389\n 2023-01-04,3.6827\n 2023-01-05,3.7181\n 2023-01-06,3.558\n 2023-01-07,3.558\n 2023-01-08,3.558\n 2023-01-09,3.5321\n 2023-01-10,3.6188\n 2023-01-11,3.5392\n 2023-01-12,3.44\n 2023-01-13,3.5035\n 2023-01-14,3.5035\n 2023-01-15,3.5035
\n 2023-01-16,3.5035\n 2023-01-17,3.5476\n 2023-01-18,3.3698\n 2023-01-19,3.3915\n 2023-01-20,3.4787\n 2023-01-21,3.4787\n 2023-01-22,3.4787\n 2023-01-23,3.5098\n 2023-01-24,3.4527\n 2023-01-25,3.4416\n 2023-01-26,3.4947\n 2023-01-27,3.5035\n 2023-01-28,3.5035\n 2023-01-29,3.5035
\n 2023-01-30,3.5366\n 2023-01-31,3.5069\n 2023-02-01,3.4166\n 2023-02-02,3.3927\n 2023-02-03,3.5246\n 2023-02-04,3.5246\n 2023-02-05,3.5246\n 2023-02-06,3.6399\n 2023-02-07,3.6735\n 2023-02-08,3.6098\n 2023-02-09,3.6579\n 2023-02-10,3.732
\n 2023-02-11,3.732\n 2023-02-12,3.732\n 2023-02-13,3.7016\n 2023-02-14,3.7435\n 2023-02-15,3.8049\n 2023-02-16,3.8608\n 2023-02-17,3.8148\n 2023-02-18,3.8148\n 2023-02-19,3.8148\n 2023-02-20,3.8148\n 2023-02-21,3.9525\n 2023-02-22,3.9156\n 2023-02-23,3.8768\n 2023-02-24,3.9432\n 2023-02-25,3.9432
\n 2023-02-26,3.9432\n 2023-02-27,3.9141\n 2023-02-28,3.92\n 2023-03-01,3.9925\n 2023-03-02,4.0556\n 2023-03-03,3.9517\n 2023-03-04,3.9517\n 2023-03-05,3.9517\n 2023-03-06,3.9577\n 2023-03-07,3.9637\n 2023-03-08,3.9913\n 2023-03-09,3.9032\n 2023-03-10,3.6987\n 2023-03-11,3.6987\n 2023-03-12,3.6987\n 2023-03-13,3.5732\n 2023-03-14,3.6892\n 2023-03-15,3.4548\n 2023-03-16,3.577
\n 2023-03-17,3.4286\n 2023-03-18,3.4286\n 2023-03-19,3.4286\n 2023-03-20,3.4847\n 2023-03-21,3.6094\n 2023-03-22,3.4341\n 2023-03-23,3.4266\n 2023-03-24,3.3762\n 2023-03-25,3.3762\n 2023-03-26,3.3762\n 2023-03-27,3.5299\n 2023-03-28,3.5696\n 2023-03-29,3.5639\n 2023-03-30,3.5488
\n 2023-03-31,3.4676\n 2023-04-01,3.4676\n 2023-04-02,3.4676\n 2023-04-03,3.4114\n 2023-04-04,3.3387\n 2023-04-05,3.3108\n 2023-04-06,3.305\n 2023-04-07,3.3906\n 2023-04-08,3.3906\n 2023-04-09,3.3906\n 2023-04-10,3.4168\n 2023-04-11,3.4262\n 2023-04-12,3.3906\n 2023-04-13,3.4449
\n 2023-04-14,3.5128\n 2023-04-15,3.5128\n 2023-04-16,3.5128\n 2023-04-17,3.6004\n 2023-04-18,3.5756\n 2023-04-19,3.5908\n 2023-04-20,3.5318\n 2023-04-21,3.5718\n 2023-04-22,3.5718\n 2023-04-23,3.5718\n 2023-04-24,3.4901\n 2023-04-25,3.3996\n 2023-04-26,3.4485\n 2023-04-27,3.5204\n 2023-04-28,3.422
\n 2023-04-29,3.422\n 2023-04-30,3.422\n 2023-05-01,3.5681\n 2023-05-02,3.4239\n 2023-05-03,3.3356\n 2023-05-04,3.3787\n 2023-05-05,3.437\n 2023-05-06,3.437\n 2023-05-07,3.437\n 2023-05-08,3.5072\n 2023-05-09,3.5186\n 2023-05-10,3.4426\n 2023-05-11,3.3843\n 2023-05-12,3.4625\n 2023-05-13,3.4625
\n 2023-05-14,3.4625\n 2023-05-15,3.5019\n 2023-05-16,3.5339\n 2023-05-17,3.5641\n 2023-05-18,3.6457\n 2023-05-19,3.6726\n 2023-05-20,3.6726\n 2023-05-21,3.6726\n 2023-05-22,3.7148\n 2023-05-23,3.6919
\n 2023-05-24,3.7419\n 2023-05-25,3.8174\n 2023-05-26,3.7983\n 2023-05-27,3.7983\n 2023-05-28,3.7983\n 2023-05-29,3.7983\n 2023-05-30,3.6866\n 2023-05-31,3.6426\n 2023-06-01,3.595\n 2023-06-02,3.6907\n 2023-06-03,3.6907\n 2023-06-04,3.6907\n 2023-06-05,3.6831

"""

temp = pd.read_csv(io.StringIO(temp), sep=",", parse_dates=False)
temp.date = pd.to_datetime(temp.date)

plt.plot(temp['date'], temp['yield'])

def custom_date_format(x, pos):
    date = mdates.num2date(x)
    if date.month == 1 and date.day == 1:
        return date.strftime('%y.%m')
    else:
        return date.strftime('%m')
    

ax = plt.gca()
ax.xaxis.set_major_locator(mdates.MonthLocator())
date_form = FuncFormatter(custom_date_format)
ax.xaxis.set_major_formatter(date_form)
plt.grid(True)
plt.xticks(rotation = 45)

字符串
x1c 0d1x的数据