在Pandas MultiIndex中重新采样

Question

我有一些层次结构数据，这些数据触底到达时间序列数据，看起来像这样：

df = pandas.DataFrame(
    {'value_a': values_a, 'value_b': values_b},
    index=[states, cities, dates])
df.index.names = ['State', 'City', 'Date']
df

                               value_a  value_b
State   City       Date                        
Georgia Atlanta    2012-01-01        0       10
                   2012-01-02        1       11
                   2012-01-03        2       12
                   2012-01-04        3       13
        Savanna    2012-01-01        4       14
                   2012-01-02        5       15
                   2012-01-03        6       16
                   2012-01-04        7       17
Alabama Mobile     2012-01-01        8       18
                   2012-01-02        9       19
                   2012-01-03       10       20
                   2012-01-04       11       21
        Montgomery 2012-01-01       12       22
                   2012-01-02       13       23
                   2012-01-03       14       24
                   2012-01-04       15       25

我想对每个城市进行时间重采样，所以类似

df.resample("2D", how="sum")

将输出

                             value_a  value_b
State   City       Date                        
Georgia Atlanta    2012-01-01        1       21
                   2012-01-03        5       25
        Savanna    2012-01-01        9       29
                   2012-01-03       13       33
Alabama Mobile     2012-01-01       17       37
                   2012-01-03       21       41
        Montgomery 2012-01-01       25       45
                   2012-01-03       29       49

就这样，df.resample('2D', how='sum')让我

TypeError: Only valid with DatetimeIndex or PeriodIndex

足够公平，但是我有点希望这能起作用：

>>> df.swaplevel('Date', 'State').resample('2D', how='sum')
TypeError: Only valid with DatetimeIndex or PeriodIndex

在这一点上，我真的没什么主意了……是否有某种方法可以帮助我解决问题？

Answer 1

pd.Grouper
允许您指定“目标对象的groupby指令”。特别是，即使df.index不是，您也可以使用它按日期分组DatetimeIndex：

df.groupby(pd.Grouper(freq='2D', level=-1))

在level=-1讲述pd.Grouper寻找在多指标的最后一个级别的日期。此外，您可以将其与索引中的其他级别值结合使用：

level_values = df.index.get_level_values
result = (df.groupby([level_values(i) for i in [0,1]]
                      +[pd.Grouper(freq='2D', level=-1)]).sum())

它看起来有些尴尬，但using_Grouper比我最初的建议要快得多using_reset_index：

import numpy as np
import pandas as pd
import datetime as DT

def using_Grouper(df):
    level_values = df.index.get_level_values
    return (df.groupby([level_values(i) for i in [0,1]]
                       +[pd.Grouper(freq='2D', level=-1)]).sum())

def using_reset_index(df):
    df = df.reset_index(level=[0, 1])
    return df.groupby(['State','City']).resample('2D').sum()

def using_stack(df):
    # http://stackoverflow.com/a/15813787/190597
    return (df.unstack(level=[0,1])
              .resample('2D').sum()
              .stack(level=[2,1])
              .swaplevel(2,0))

def make_orig():
    values_a = range(16)
    values_b = range(10, 26)
    states = ['Georgia']*8 + ['Alabama']*8
    cities = ['Atlanta']*4 + ['Savanna']*4 + ['Mobile']*4 + ['Montgomery']*4
    dates = pd.DatetimeIndex([DT.date(2012,1,1)+DT.timedelta(days = i) for i in range(4)]*4)
    df = pd.DataFrame(
        {'value_a': values_a, 'value_b': values_b},
        index = [states, cities, dates])
    df.index.names = ['State', 'City', 'Date']
    return df

def make_df(N):
    dates = pd.date_range('2000-1-1', periods=N)
    states = np.arange(50)
    cities = np.arange(10)
    index = pd.MultiIndex.from_product([states, cities, dates], 
                                       names=['State', 'City', 'Date'])
    df = pd.DataFrame(np.random.randint(10, size=(len(index),2)), index=index,
                      columns=['value_a', 'value_b'])
    return df

df = make_orig()
print(using_Grouper(df))

产量

                               value_a  value_b
State   City       Date                        
Alabama Mobile     2012-01-01       17       37
                   2012-01-03       21       41
        Montgomery 2012-01-01       25       45
                   2012-01-03       29       49
Georgia Atlanta    2012-01-01        1       21
                   2012-01-03        5       25
        Savanna    2012-01-01        9       29
                   2012-01-03       13       33

这里是一个标杆比较using_Grouper，using_reset_index，using_stack在一个有5000行数据帧：

In [30]: df = make_df(10)

In [34]: len(df)
Out[34]: 5000

In [32]: %timeit using_Grouper(df)
100 loops, best of 3: 6.03 ms per loop

In [33]: %timeit using_stack(df)
10 loops, best of 3: 22.3 ms per loop

In [31]: %timeit using_reset_index(df)
1 loop, best of 3: 659 ms per loop