在多个日期窗口上应用熊猫滚动的更快方法

Question

我有 3 维数据和每个组合的相同日期范围和一个数字标签。我的目标是添加一个具有前 n 天标签平均值的列。
我有一个可行的解决方案，但它需要很长时间（在 2.400 种可能的维度组合中，2.270.400 行大约需要 20 分钟）。我认为主要问题是 d.loc 查找作为插入方法。

您对如何提高性能有什么建议吗？我也对导致相同结果的不同方法感到非常满意。

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）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)

如果只有 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

Answer 1

您甚至不需要循环或制作块来应用滚动功能。它比这简单得多。

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