<p>因为你的例子非常基础,如果你有边缘案例,你想让我处理,只要问。此解决方案意味着:</p>
<p>解决方案:</p>
<pre><code># For this solution your DataFrame needs to be sorted by date.
limit = 100
df = pd.DataFrame({
'date1': ['1/1/2014', '2/1/2014', '3/1/2014','1/1/2014',
'2/1/2014', '3/1/2014','1/1/2014', '2/1/2014', '3/1/2014'],
'Place':['A','A','A','B','B','B','C','C','C'],
'AVG': [62,14,47,25,74,60,78,27,41]})
df2 = pd.DataFrame({'date2': ['1/1/2014', '2/1/2014'], 'Place':['A','C']})
result = []
for row in df2.to_dict('records'):
# For each date, I want to select the date that comes AFTER this one.
# Then, I take the .cumsum(), because it's the agg you wish to do.
# Filter by your limit and take the first occurrence.
# Converting this to a dict, appending it to a list, makes it easy
# to rebuild a DataFrame later.
ndf = df.loc[ (df['date1'] >= row['date2']) & (df['Place'] == row['Place']) ]\
.sort_values(by='date1')
ndf['avgsum'] = ndf['AVG'].cumsum()
final_df = ndf.loc[ ndf['avgsum'] >= limit ]
# Error handling, in case there is not avgsum above the threshold.
try:
final_df = final_df.iloc[0][['date1', 'avgsum']].rename({'date1' : 'date100'})
result.append( final_df.to_dict() )
except IndexError:
continue
df3 = pd.DataFrame(result)
final_df = pd.concat([df2, df3], axis=1, sort=False)
print(final_df)
# date2 Place avgsum date100
# 0 1/1/2014 A 123.0 3/1/2014
# 1 2/1/2014 C NaN NaN
</code></pre>