我有3个维度的数据，每个组合的日期范围都相同，并带有数字标签。我的目标是添加一列带有前n天标签平均值的列。
我有一个有效的解决方案，但需要花一些时间（在2.400种可能的尺寸组合中，对于2.270.400行约20分钟）。我假设主要问题是d.loc查找作为插入方法。
您对提高性能有什么建议？对于采用相同方法得出相同结果的不同方法，我也感到非常满意。
测试设置代码：
## create data to simulate
import pandas as pd
import random
## create test dataframes
df1 = pd.DataFrame({'A':[1,2,3,4,5,6,7,8,9,10,11,12]})
df2 = pd.DataFrame({'B':["r","s","t","u","v","w","x","y","z"]})
df3 = pd.DataFrame({'C':["a","b","c","d","e","f","g","k","h"]})
numdays = 600
date_list = pd.date_range(pd.datetime.today(), periods=numdays).tolist()
df4 = pd.DataFrame({'date':pd.to_datetime(date_list)})
df4['date'] = df4['date'].dt.date
## add dummy keys
df1['key'] = 0
df2['key'] = 0
df3['key'] = 0
df4['key'] = 0
## merge all together
dfn = df1.merge(df2, how='outer',on="key")
dfn = dfn.merge(df3, how='outer',on="key")
dfn = dfn.merge(df4, how='outer',on="key")
## drop dummy key
dfn.drop(columns=['key'],inplace=True)
## add vector
dfn['dim_vector'] = dfn.apply(lambda row: str(row.A) + '_' + row.B + '_' + row.C, axis=1)
## add random labels
dfn['label'] = dfn.apply(lambda x: random.randrange(0,10, 1),axis=1)
## set date as index
dfn = dfn.set_index(dfn['date'])
我的（慢速）解决方案：
def add_last_n_days_avg_with_days_at_index(df,match_on_col='dim_vector',label_col='label',count_of_days=7,round_to=0):
vectors = df[match_on_col].unique()
new_label_col_name = label_col + '_'+str(count_of_days)+'D'
for vector in vectors:
chunk = df.loc[df[match_on_col] == vector].copy()
chunk[new_label_col_name] = chunk[label_col].rolling(count_of_days,count_of_days,axis=0).mean()
chunk[new_label_col_name] = chunk[new_label_col_name].shift()
df.loc[df[match_on_col] == vector,new_label_col_name] = round(chunk[new_label_col_name],round_to)
add_last_n_days_avg_with_days_at_index(df=dfn,match_on_col='dim_vector',label_col='label',count_of_days=7,round_to=0)
dfn.head(50)
只有9天的结果：
date A B C date dim_vector label label_7D
2018-12-14 1 r a 2018-12-14 1_r_a 1 NaN
2018-12-15 1 r a 2018-12-15 1_r_a 1 NaN
2018-12-16 1 r a 2018-12-16 1_r_a 0 NaN
2018-12-17 1 r a 2018-12-17 1_r_a 3 NaN
2018-12-18 1 r a 2018-12-18 1_r_a 0 NaN
2018-12-19 1 r a 2018-12-19 1_r_a 6 NaN
2018-12-20 1 r a 2018-12-20 1_r_a 7 NaN
2018-12-21 1 r a 2018-12-21 1_r_a 3 3.0
2018-12-22 1 r a 2018-12-22 1_r_a 0 3.0
2018-12-14 1 r b 2018-12-14 1_r_b 5 NaN
2018-12-15 1 r b 2018-12-15 1_r_b 2 NaN
2018-12-16 1 r b 2018-12-16 1_r_b 5 NaN
2018-12-17 1 r b 2018-12-17 1_r_b 2 NaN
2018-12-18 1 r b 2018-12-18 1_r_b 3 NaN
2018-12-19 1 r b 2018-12-19 1_r_b 0 NaN
2018-12-20 1 r b 2018-12-20 1_r_b 8 NaN
2018-12-21 1 r b 2018-12-21 1_r_b 2 4.0
2018-12-22 1 r b 2018-12-22 1_r_b 2 3.0
答案 0 :(得分：0)
您甚至不需要循环或制作块即可应用滚动功能。比这简单得多。
def add_last_n_days_avg_with_days_at_index(df,
label_col='label',count_of_days=7,round_to=0):
new_label_col_name = label_col + '_'+str(count_of_days)+'D'
# create a new column, apply mean and round
df[new_label_col_name] = df[label_col].rolling(count_of_days).mean().round(round_to);
# I removed match_on_col parameter
add_last_n_days_avg_with_days_at_index(df=dfn,label_col='label',count_of_days=7,round_to=0)
结果：
A B C date dim_vector label label_7D
date
2018-12-14 1 r a 2018-12-14 1_r_a 1 NaN
2018-12-15 1 r a 2018-12-15 1_r_a 7 NaN
2018-12-16 1 r a 2018-12-16 1_r_a 8 NaN
2018-12-17 1 r a 2018-12-17 1_r_a 7 NaN
2018-12-18 1 r a 2018-12-18 1_r_a 5 NaN
2018-12-19 1 r a 2018-12-19 1_r_a 7 NaN
2018-12-20 1 r a 2018-12-20 1_r_a 1 5.0
2018-12-21 1 r a 2018-12-21 1_r_a 6 6.0
2018-12-22 1 r a 2018-12-22 1_r_a 9 6.0
2018-12-23 1 r a 2018-12-23 1_r_a 1 5.0
2018-12-24 1 r a 2018-12-24 1_r_a 1 4.0
2018-12-25 1 r a 2018-12-25 1_r_a 0 4.0
2018-12-26 1 r a 2018-12-26 1_r_a 3 3.0
2018-12-27 1 r a 2018-12-27 1_r_a 0 3.0
2018-12-28 1 r a 2018-12-28 1_r_a 0 2.0
2018-12-29 1 r a 2018-12-29 1_r_a 9 2.0
2018-12-30 1 r a 2018-12-30 1_r_a 1 2.0
2018-12-31 1 r a 2018-12-31 1_r_a 2 2.0
2019-01-01 1 r a 2019-01-01 1_r_a 1 2.0
2019-01-02 1 r a 2019-01-02 1_r_a 9 3.0