我的问题是如何对我的数据帧中的多个“分组”中的每一个进行上采样。 (就我而言,对于每个'团队'和'LeadWeek'分组)。
我看到内置函数和大量示例用于对时间序列进行上采样,但不是用于上采样整数。由于各种原因,我现在不会进入,我想用整数代替时间序列。
在我的情况下,我有'团队'和'LeadWeeks',我想为每个'团队'和'LeadWeek'组合上传'转换周'为[0,1,2,3,4]。
我认为有一种方法可以用multi-index
/ groupby
+ resample()
来做到这一点,但是我不够聪明,经过几个小时的修修补补。在这里向明智的人寻求帮助......
所以这是示例数据框:
df = pd.DataFrame([
['Team A', pd.datetime(2017, 12, 1), 0, 2]
,['Team A', pd.datetime(2017, 12, 1), 2, 1]
,['Team A', pd.datetime(2017, 12, 1), 4, 1]
,['Team A', pd.datetime(2017, 12, 8), 3, 2]
,['Team B', pd.datetime(2017, 12, 1), 0, 1]
,['Team B', pd.datetime(2017, 12, 1), 2, 3]
,['Team B', pd.datetime(2017, 12, 8), 1, 3]
,['Team B', pd.datetime(2017, 12, 8), 3, 2]
]
, columns=['Team', 'LeadWeek', 'ConversionWeek', 'Conversions']
)
我想要的输出如下,每个Team / LeadWeek分组有5个“ConversionWeek”行,编号为0到4:
Team LeadWeek ConversionWeek Conversions
0 Team A 2017-12-01 0 2.0
1 Team A 2017-12-01 1 0.0
2 Team A 2017-12-01 2 1.0
3 Team A 2017-12-01 3 0.0
4 Team A 2017-12-01 4 1.0
5 Team A 2017-12-08 0 0.0
6 Team A 2017-12-08 1 0.0
7 Team A 2017-12-08 2 0.0
8 Team A 2017-12-08 3 2.0
9 Team A 2017-12-08 4 0.0
10 Team B 2017-12-01 0 1.0
11 Team B 2017-12-01 1 0.0
12 Team B 2017-12-01 2 3.0
13 Team B 2017-12-01 3 0.0
14 Team B 2017-12-01 4 0.0
15 Team B 2017-12-08 0 0.0
16 Team B 2017-12-08 1 3.0
17 Team B 2017-12-08 2 0.0
18 Team B 2017-12-08 3 2.0
19 Team B 2017-12-08 4 0.0
我确实有一个解决方案,但它不是非常pythonic。它与我在SQL中如何解决它是一样的,即使用所有不同元素的笛卡尔积来创建一个“脚手架”,然后将我的实际转换加入到它中。在Python中,此方法使用itertools.product()
我的解决方案是:
import pandas as pd
import numpy as np
import itertools as it
df = pd.DataFrame([
['Team A', pd.datetime(2017, 12, 1), 0, 2]
,['Team A', pd.datetime(2017, 12, 1), 2, 1]
,['Team A', pd.datetime(2017, 12, 1), 4, 1]
,['Team A', pd.datetime(2017, 12, 8), 3, 2]
,['Team B', pd.datetime(2017, 12, 1), 0, 1]
,['Team B', pd.datetime(2017, 12, 1), 2, 3]
,['Team B', pd.datetime(2017, 12, 8), 1, 3]
,['Team B', pd.datetime(2017, 12, 8), 3, 2]
]
, columns=['Team', 'LeadWeek', 'ConversionWeek', 'Conversions']
)
ConversionWeek = np.linspace(0, 4, 5, dtype=int)
Team = df['Team'].unique()
LeadWeek = df['LeadWeek'].unique()
scaffold_raw = []
for i in it.product(Team, LeadWeek, ConversionWeek):
scaffold_raw.append(i)
scaffold = pd.DataFrame(scaffold_raw, columns=['Team', 'LeadWeek', 'ConversionWeek'])
new_frame = scaffold.merge(df, how='left')
new_frame = new_frame.sort_values(by=['Team', 'LeadWeek', 'ConversionWeek']).reset_index(drop=True)
new_frame['Conversions'].fillna(0, inplace=True)
在更优雅的解决方案上获得任何帮助。
通过reindex
使用pd.MultiIndex
-
idx = pd.MultiIndex.from_product(
[df.Team.unique(), df.LeadWeek.unique(), np.arange(5)]
)
v = df.set_index(['Team', 'LeadWeek', 'ConversionWeek'])\
.reindex(idx)\
.fillna(0)\
.reset_index()
v.columns = df.columns
v
Team LeadWeek ConversionWeek Conversions
0 Team A 2017-12-01 0 2.0
1 Team A 2017-12-01 1 0.0
2 Team A 2017-12-01 2 1.0
3 Team A 2017-12-01 3 0.0
4 Team A 2017-12-01 4 1.0
5 Team A 2017-12-08 0 0.0
6 Team A 2017-12-08 1 0.0
7 Team A 2017-12-08 2 0.0
8 Team A 2017-12-08 3 2.0
9 Team A 2017-12-08 4 0.0
10 Team B 2017-12-01 0 1.0
11 Team B 2017-12-01 1 0.0
12 Team B 2017-12-01 2 3.0
13 Team B 2017-12-01 3 0.0
14 Team B 2017-12-01 4 0.0
15 Team B 2017-12-08 0 0.0
16 Team B 2017-12-08 1 3.0
17 Team B 2017-12-08 2 0.0
18 Team B 2017-12-08 3 2.0
19 Team B 2017-12-08 4 0.0