我的问题是如何为数据帧中的多个“分组”中的每一个向上采样int系列。(在我的例子中,针对每个“团队”和“领导周”分组)。你知道吗
我看到了内置函数和很多例子,用于对时间序列进行上采样,但不用于对整数进行上采样。由于各种原因,我现在不想讨论,我想用整数来代替时间序列。你知道吗
在我的例子中,我有'Teams'和'LeadWeeks',我想对每个'Team'和'LeadWeek'组合的'Conversion Weeks'进行上采样为[0,1,2,3,4]。你知道吗
我认为有一种方法可以用multi-index
/groupby
+resample()
来实现这一点,但我不够聪明,在经过几个小时的修补之后就想出来了。向这里的智者寻求帮助。。。你知道吗
下面是示例数据帧:
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行'ConversionWeek',编号从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
我有一个解决办法,但它不是很Python。这与我在SQL中解决它的方法是一样的,即使用所有不同元素的笛卡尔积创建一个“scaffold”,然后将我的实际转换连接到它。在Python中,这个方法使用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)
感谢您对更优雅解决方案的帮助。你知道吗
通过传递
pd.MultiIndex
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