我有一个非常大的pandas数据集,在某些时候我需要使用以下函数
def proc_trader(data):
data['_seq'] = np.nan
# make every ending of a roundtrip with its index
data.ix[data.cumq == 0,'tag'] = np.arange(1, (data.cumq == 0).sum() + 1)
# backfill the roundtrip index until previous roundtrip;
# then fill the rest with 0s (roundtrip incomplete for most recent trades)
data['_seq'] =data['tag'].fillna(method = 'bfill').fillna(0)
return data['_seq']
# btw, why on earth this function returns a dataframe instead of the series `data['_seq']`??
我用apply
^{pr2}$显然,我不能在这里共享数据,但是你看到我的代码有瓶颈吗?可能是arange
的事吗?数据中有许多name-productid
组合。在
最小工作示例:
import pandas as pd
import numpy as np
reshaped= pd.DataFrame({'trader' : ['a','a','a','a','a','a','a'],'stock' : ['a','a','a','a','a','a','b'], 'day' :[0,1,2,4,5,10,1],'delta':[10,-10,15,-10,-5,5,0] ,'out': [1,1,2,2,2,0,1]})
reshaped.sort_values(by=['trader', 'stock','day'], inplace=True)
reshaped['cumq']=reshaped.groupby(['trader', 'stock']).delta.transform('cumsum')
reshaped['_spell']=reshaped.groupby(['trader','stock'])[['cumq']].apply(proc_trader).reset_index()['_seq']
这里没什么特别的,只是在一些地方做了些调整。实际上不需要输入函数,所以我没有。在这个小样本数据中,它的速度大约是原始数据的两倍。在
结果:
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