<p>下面是我该怎么做。非常简单:用你想要的形状创建一个新的df,然后用你想要的东西填充它。在</p>
<pre><code>from datetime import datetime
import numpy as np
import pandas as pd
# This is my re-creation of the data you have. (I'm calling it df1.)
# It's essential that your date-time be in datetime.datetime format, not strings
byear = 1996 # arbitrary
eyear = 2005 # arbitrary
obs_n = 50000 # arbitrary
start_time = datetime.timestamp(datetime(byear,1,1,0,0,0,0))
end_time = datetime.timestamp(datetime(eyear,12,31,23,59,59,999999))
obs_times = np.linspace(start_time,end_time,num=obs_n)
index1 = pd.Index([datetime.fromtimestamp(i) for i in obs_times])
df1 = pd.DataFrame(data=np.random.rand(obs_n)*20,index=index1,columns=['temp'])
# ^some random data
# Here is the new empty dataframe (df2) where you will put your daily averages.
index2 = pd.Index(range(byear,eyear+1))
columns2 = range(1,367) # change to 366 if you want to assume 365-day years
df2 = pd.DataFrame(index=index2,columns=columns2)
# Some quick manipulations that allow the two dfs' indexes to talk to one another.
df1['year'] = df1.index.year # a new column with the observation's year as an integer
df1['day'] = df1.index.dayofyear # a new column with the day of the year as integer
df1 = df1.reset_index().set_index(['year','day'])
# Now get the averages for each day and assign them to df2.
for year in index2:
for day in columns2[:365]: # for all but the last entry in the range
df2.loc[year,day] = df1.loc[(year,day),'temp'].mean()
if (year,366) in df1.index: # then if it's a leap year...
df2.loc[year,366] = df1.loc[(year,366),'temp'].mean()
</code></pre>
<p>如果您不希望最后的df在第366天有任何null值,那么您可以删除final If语句,重写<code>columns2 = range(1,366)</code>,然后df2将拥有所有非null值(假设在观察的时间段内每天至少有一个度量)。在</p>