pandas 计算每个日期过去 7 天的值 [英] pandas count values for last 7 days from each date

问题描述

有两个数据框.首先是这样的:

打印df1id 日期 月份 is_buy0 17 2015-01-16 2015-01 11 17 2015-01-26 2015-01 12 17 2015-01-27 2015-01 13 17 2015-02-11 2015-02 14 17 2015-03-14 2015-03 15 18 2015-01-28 2015-01 16 18 2015-02-12 2015-02 17 18 2015-02-25 2015-02 18 18 2015-03-04 2015-03 1

在第二个数据框中，有一些从第一个数据框中按月汇总的数据:

df2 = df1[df1['is_buy'] == 1].groupby(['id', 'month']).agg({'is_buy': np.sum})打印df2id月购买0 17 2015-01 31 17 2015-02 12 17 2015-03 13 18 2015-01 14 18 2015-02 25 18 2015-03 1

我正在尝试获取名为last_week_buys"的新 df2 列，其中包含从每个 df1['month'] 的第一天起的最后 7 天的聚合购买.换句话说，我想得到这个:

 id 月份购买 last_week_buys0 17 2015 年 1 月 3 日1 17 2015-02 1 22 17 2015-03 1 03 18 2015-01 1 NaN4 18 2015-02 2 15 18 2015-03 1 1

有什么想法可以得到这个专栏吗?

解决方案

这可以通过一些日期操作魔法和 group-bys 来完成:

# datetimeindex 方便操作date = pd.DatetimeIndex(df1['date'])# 计算 df2:按月总计df1['月'] = date.to_period('M')df2 = df1[df1['is_buy'] == 1].groupby(['id', 'month']).sum()# 计算 df3:过去 7 天的总数从日期时间导入时间增量is_last_seven = date.to_period('M') != (date + timedelta(days=7)).to_period('M')df3 = df1[(df1['is_buy'] == 1) &is_last_seven].groupby(['id', df1.month + 1]).sum()# 加入结果结果 = df2.join(df3, rsuffix='_last_seven')

结果如下:

<预><代码>>>>打印(结果)is_buy is_buy_last_seven编号月份17 2015-01 3 NaN2015-02 1 22015 年 3 月 1 日18 2015-01 1 NaN2015-02 2 12015-03 1 1

然后您可以根据需要填充 NaN 值.

There are two Dataframes. First is like this:

print df1

        id        date    month  is_buy
     0  17  2015-01-16  2015-01       1
     1  17  2015-01-26  2015-01       1
     2  17  2015-01-27  2015-01       1
     3  17  2015-02-11  2015-02       1
     4  17  2015-03-14  2015-03       1
     5  18  2015-01-28  2015-01       1
     6  18  2015-02-12  2015-02       1
     7  18  2015-02-25  2015-02       1
     8  18  2015-03-04  2015-03       1

In second data frame there are some aggregated data by month from the first one:

df2 = df1[df1['is_buy'] == 1].groupby(['id', 'month']).agg({'is_buy': np.sum})

print df2

        id    month       buys
     0  17  2015-01          3
     1  17  2015-02          1
     2  17  2015-03          1
     3  18  2015-01          1
     4  18  2015-02          2
     5  18  2015-03          1

I'm trying to get new df2 column named 'last_week_buys' with aggregated buys by last 7 days from first day of each df1['month']. In other words, I want to get this:

        id    month       buys    last_week_buys
     0  17  2015-01          3               NaN
     1  17  2015-02          1                 2
     2  17  2015-03          1                 0
     3  18  2015-01          1               NaN
     4  18  2015-02          2                 1
     5  18  2015-03          1                 1

Are there any ideas to get this column?

解决方案

This can be done with a bit of date manipulation magic and group-bys:

# datetimeindex makes convenient manipulations
date = pd.DatetimeIndex(df1['date'])

# compute df2: totals by month
df1['month'] = date.to_period('M')
df2 = df1[df1['is_buy'] == 1].groupby(['id', 'month']).sum()

# compute df3: totals by last seven days
from datetime import timedelta
is_last_seven = date.to_period('M') != (date + timedelta(days=7)).to_period('M')
df3 = df1[(df1['is_buy'] == 1) & is_last_seven].groupby(['id', df1.month + 1]).sum()

# join the results
result = df2.join(df3, rsuffix='_last_seven')

Here is the result:

>>> print(result)

            is_buy  is_buy_last_seven
id month                             
17 2015-01       3                NaN
   2015-02       1                  2
   2015-03       1                NaN
18 2015-01       1                NaN
   2015-02       2                  1
   2015-03       1                  1

You can then fill the NaN values as you desire.

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