擅长:python、mysql、java
<p>我不确定您的预期输出是什么,但是如果您想找到具有多个事务的日期上最接近大小的平均值,您可以这样做。如果您正在寻找其他内容,请提供预期输出:</p>
<pre><code>df = pd.read_clipboard()
# find the diff on the size column and backfill the NaN values
df['diff'] = df.groupby('date')['size'].diff().fillna(method='bfill')
# group by date and use the lambda function to find the min diff
df2 = df.groupby(['date']).apply(lambda x: x[x['diff'] == x['diff'].min()])
# find the mean of price
df2.groupby('date')['price'].mean()
date
2018-08-01 232.5
2018-08-02 220.0
Name: price, dtype: float64
</code></pre>