问题
Suppose I have a dataframe as follows:
In [1]: test_dup_df
Out[1]:
exe_price exe_vol flag
2008-03-13 14:41:07 84.5 200 yes
2008-03-13 14:41:37 85.0 10000 yes
2008-03-13 14:41:38 84.5 69700 yes
2008-03-13 14:41:39 84.5 1200 yes
2008-03-13 14:42:00 84.5 1000 yes
2008-03-13 14:42:08 84.5 300 yes
2008-03-13 14:42:10 84.5 88100 yes
2008-03-13 14:42:10 84.5 11900 yes
2008-03-13 14:42:15 84.5 5000 yes
2008-03-13 14:42:16 84.5 3200 yes
I want to group a duplicate data at time 14:42:10 and apply different functions to exe_price and exe_vol (e.g., sum the exe_vol and compute volume weighted average of exe_price). I know that I can do
In [2]: grouped = test_dup_df.groupby(level=0)
to group the duplicate indices and then use the first() or last() functions to get either the first or the last row but this is not really what I want.
Is there a way to group and then apply different (written by me) functions to values in different column?
回答1:
Apply your own function:
In [12]: def func(x):
exe_price = (x['exe_price']*x['exe_vol']).sum() / x['exe_vol'].sum()
exe_vol = x['exe_vol'].sum()
flag = True
return Series([exe_price, exe_vol, flag], index=['exe_price', 'exe_vol', 'flag'])
In [13]: test_dup_df.groupby(test_dup_df.index).apply(func)
Out[13]:
exe_price exe_vol flag
date_time
2008-03-13 14:41:07 84.5 200 True
2008-03-13 14:41:37 85 10000 True
2008-03-13 14:41:38 84.5 69700 True
2008-03-13 14:41:39 84.5 1200 True
2008-03-13 14:42:00 84.5 1000 True
2008-03-13 14:42:08 84.5 300 True
2008-03-13 14:42:10 20.71 100000 True
2008-03-13 14:42:15 84.5 5000 True
2008-03-13 14:42:16 84.5 3200 True
回答2:
I like @waitingkuo's answer because it is very clear and readable.
I'm keeping this around anyway because it does appear to be faster -- at least with Pandas version 0.10.0. The situation may (hopefully) change in the future, so be sure to rerun the benchmark especially if you are using a different version of Pandas.
import pandas as pd
import io
import timeit
data = '''\
date time exe_price exe_vol flag
2008-03-13 14:41:07 84.5 200 yes
2008-03-13 14:41:37 85.0 10000 yes
2008-03-13 14:41:38 84.5 69700 yes
2008-03-13 14:41:39 84.5 1200 yes
2008-03-13 14:42:00 84.5 1000 yes
2008-03-13 14:42:08 84.5 300 yes
2008-03-13 14:42:10 10 88100 yes
2008-03-13 14:42:10 100 11900 yes
2008-03-13 14:42:15 84.5 5000 yes
2008-03-13 14:42:16 84.5 3200 yes'''
df = pd.read_table(io.BytesIO(data), sep='\s+', parse_dates=[[0, 1]],
index_col=0)
def func(subf):
exe_vol = subf['exe_vol'].sum()
exe_price = ((subf['exe_price']*subf['exe_vol']).sum()
/ exe_vol)
flag = True
return pd.Series([exe_price, exe_vol, flag],
index=['exe_price', 'exe_vol', 'flag'])
# return exe_price
def using_apply():
return df.groupby(df.index).apply(func)
def using_helper_column():
df['weight'] = df['exe_price'] * df['exe_vol']
grouped = df.groupby(level=0, group_keys=True)
result = grouped.agg({'weight': 'sum', 'exe_vol': 'sum'})
result['exe_price'] = result['weight'] / result['exe_vol']
result['flag'] = True
result = result.drop(['weight'], axis=1)
return result
result = using_apply()
print(result)
result = using_helper_column()
print(result)
time_apply = timeit.timeit('m.using_apply()',
'import __main__ as m ',
number=1000)
time_helper = timeit.timeit('m.using_helper_column()',
'import __main__ as m ',
number=1000)
print('using_apply: {t}'.format(t = time_apply))
print('using_helper_column: {t}'.format(t = time_helper))
yields
exe_vol exe_price flag
date_time
2008-03-13 14:41:07 200 84.50 True
2008-03-13 14:41:37 10000 85.00 True
2008-03-13 14:41:38 69700 84.50 True
2008-03-13 14:41:39 1200 84.50 True
2008-03-13 14:42:00 1000 84.50 True
2008-03-13 14:42:08 300 84.50 True
2008-03-13 14:42:10 100000 20.71 True
2008-03-13 14:42:15 5000 84.50 True
2008-03-13 14:42:16 3200 84.50 True
with timeit benchmarks of:
using_apply: 3.0081038475
using_helper_column: 1.35300707817
回答3:
Not terribly familiar with pandas, but in pure numpy you could do:
tot_vol = np.sum(grouped['exe_vol'])
avg_price = np.average(grouped['exe_price'], weights=grouped['exe_vol'])
来源：https://stackoverflow.com/questions/15262134/apply-different-functions-to-different-items-in-group-object-python-pandas