<p>我认为最好的方法是过滤掉Jun/Jul/Aug数据,就像R代码中所做的那样。这将有助于:</p>
<pre><code>def tworzeniedaty():
import pandas as pd
rng1 = list(pd.date_range(start='2016-01-01', end='2016-02-29', freq='D'))
rng2 = list(pd.date_range(start='2016-12-15', end='2017-02-28', freq='D'))
rng3 = list(pd.date_range(start='2017-12-15', end='2018-02-28', freq='D'))
rng4 = list(pd.date_range(start='2018-12-15', end='2019-02-28', freq='D'))
rng5 = list(pd.date_range(start='2019-12-15', end='2020-02-29', freq='D'))
return rng1 + rng2 + rng3 + rng4 + rng5
import random
import pandas as pd
import matplotlib.pyplot as plt
lista = [random.randrange(1, 10, 1) for i in range(len(tworzeniedaty()))]
df = pd.DataFrame({'Date': tworzeniedaty(), 'temperature': lista})
df['Date'] = pd.to_datetime(df['Date'], format="%Y/%m/%d")
years = list(set(df.Date.dt.year))
fig, ax = plt.subplots(1, len(years))
for i in years:
df_set = df[df.Date.dt.year == i]
df_set.set_index("Date", inplace = True)
df_set.index = df_set.index.map(str)
ax[years.index(i)].plot(df_set)
ax[years.index(i)].title.set_text(i)
plt.show()
</code></pre>