Pandas Date Offset - Groupby，并显示下周和下月的值答案

【问题标题】：Pandas Date Offset - Groupby, and display value for next week and monthPandas Date Offset - Groupby，并显示下周和下月的值
【发布时间】：2019-07-05 07:18:13
【问题描述】：

我正在尝试向 Pandas DataFrame 添加两列两列 - 一个代表接下来几周的值，另一个代表接下来 4 周的总和。

现有 DataFrame 的示例如下所示。下面的 DataFrame 只是整个 DataFrame 的一个简短的 sn-p，它跨越了数年。下面的 DataFrame 是使用以下函数派生的：df = df.groupby([pd.Grouper(key='date', freq='W'), pd.Grouper('company_name').agg({'returns': 'sum'})

date         company_name    returns
2014-12-07	Amazon        -0.5
2014-12-14	Amazon        -0.1
2014-12-21	Amazon        0.5
2014-12-28	Amazon        0.3
2015-01-04	Amazon        0.1
2014-12-07	Facebook      0.5
2014-12-14	Facebook      0.5
2014-12-21	Facebook      0.5
2014-12-28	Facebook      -0.5
2015-01-04	Facebook      -0.5
2014-12-07	Google        0.1
2014-12-14	Google        0.1
2014-12-21	Google        0.1
2014-12-28	Google        0.1
2015-01-04	Google        0.1
2014-12-07	Intel         0.2
2014-12-14	Intel         0.2
2014-12-21	Intel         0.2
2014-12-28	Intel         0.2
2015-01-04	Intel         0.2

所需的输出将返回下周的值，以及从“日期”列开始的接下来 4 周的总和。所需输出的示例如下所示。

date         company_name    returns  next_week_return  next_month_return
2014-12-07	Amazon        -0.5        -0.5              0.8
2014-12-14	Amazon        -0.1        0.5               0.8
2014-12-21	Amazon        0.5         0.3               0.8
2014-12-28	Amazon        0.3         0.1               0.8
2015-01-04	Amazon        0.1         0.1               ...           
2014-12-07	Facebook      0.5         0.5               0.0               
2014-12-14	Facebook      0.5         0.5               0.0
2014-12-21	Facebook      0.5         -0.5              0.0
2014-12-28	Facebook      -0.5        -0.5              0.0
2015-01-04	Facebook      -0.5        0.1               ...
2014-12-07	Google        0.1         0.1               0.4
2014-12-14	Google        0.1         0.1               0.4
2014-12-21	Google        0.1         0.1               0.4
2014-12-28	Google        0.1         0.1               0.4
2015-01-04	Google        0.1         0.1               ...
2014-12-07	Intel         0.2         0.2               0.8
2014-12-14	Intel         0.2         0.2               0.8
2014-12-21	Intel         0.2         0.2               0.8
2014-12-28	Intel         0.2         0.2               0.8
2015-01-04	Intel         0.2         0.2

原始 CSV 的 sn-p 如下所示。

date	CompanyName	return
07/12/2014	8x8 Inc	-0.0038835
14/12/2014	8x8 Inc	0.036923354
21/12/2014	8x8 Inc	0.108854405
28/12/2014	8x8 Inc	0.042793145
04/01/2015	8x8 Inc	-0.027219971
11/01/2015	8x8 Inc	-0.038249882
18/01/2015	8x8 Inc	0.045946457
25/01/2015	8x8 Inc	-0.107796707
01/02/2015	8x8 Inc	-0.056725981
08/02/2015	8x8 Inc	0.024344572
15/02/2015	8x8 Inc	0.00756624
22/02/2015	8x8 Inc	-0.04365263
01/03/2015	8x8 Inc	-0.02794593
08/03/2015	8x8 Inc	-0.039922714
15/03/2015	8x8 Inc	0.020848566
22/03/2015	8x8 Inc	0.116712617
29/03/2015	8x8 Inc	0.028952565
05/04/2015	8x8 Inc	0.053253322
12/04/2015	8x8 Inc	-0.006787356
19/04/2015	8x8 Inc	-0.00912207
26/04/2015	8x8 Inc	0.013652089
03/05/2015	8x8 Inc	-0.021702736
10/05/2015	8x8 Inc	-0.021004273
17/05/2015	8x8 Inc	0.012888286
24/05/2015	8x8 Inc	-0.021177262
31/05/2015	8x8 Inc	-0.027630051
07/12/2014	AB SA	-1.015859196
14/12/2014	AB SA	-0.01810143
21/12/2014	AB SA	-0.073869849
28/12/2014	AB SA	0.000666445
04/01/2015	AB SA	0.051293294
11/01/2015	AB SA	0.004735605
18/01/2015	AB SA	0.014073727
25/01/2015	AB SA	0.097002705
01/02/2015	AB SA	0.00337648
08/02/2015	AB SA	0.018093743
15/02/2015	AB SA	0.019667392
22/02/2015	AB SA	0.024844339
01/03/2015	AB SA	0.015707129
08/03/2015	AB SA	0.109611209
15/03/2015	AB SA	-0.039164849
22/03/2015	AB SA	-0.002909093
29/03/2015	AB SA	0.007256926
05/04/2015	AB SA	-0.025385791
12/04/2015	AB SA	0.019584469
19/04/2015	AB SA	-0.01342302
26/04/2015	AB SA	0.073405725
03/05/2015	AB SA	-0.018666287
10/05/2015	AB SA	0.019350984
17/05/2015	AB SA	-0.030814439
24/05/2015	AB SA	0.027386256
31/05/2015	AB SA	-0.033285978
07/12/2014	ACCO Brands Corp	0.432332004
14/12/2014	ACCO Brands Corp	-0.064822249
21/12/2014	ACCO Brands Corp	0.010163837
28/12/2014	ACCO Brands Corp	0.022223137
04/01/2015	ACCO Brands Corp	-0.034659702
11/01/2015	ACCO Brands Corp	-0.026514522
18/01/2015	ACCO Brands Corp	-0.018868484
25/01/2015	ACCO Brands Corp	0.013010237
01/02/2015	ACCO Brands Corp	-0.071850737
08/02/2015	ACCO Brands Corp	0.00126183
15/02/2015	ACCO Brands Corp	-0.016000601
22/02/2015	ACCO Brands Corp	-0.01420295
01/03/2015	ACCO Brands Corp	-0.010457612
08/03/2015	ACCO Brands Corp	-0.006591982
15/03/2015	ACCO Brands Corp	-0.008257798
22/03/2015	ACCO Brands Corp	0.039272062
29/03/2015	ACCO Brands Corp	0.035312622
05/04/2015	ACCO Brands Corp	0.012315427
12/04/2015	ACCO Brands Corp	0.037241541
19/04/2015	ACCO Brands Corp	-0.025075941
26/04/2015	ACCO Brands Corp	-0.010535083
03/05/2015	ACCO Brands Corp	-0.044016885
10/05/2015	ACCO Brands Corp	-0.013845407
17/05/2015	ACCO Brands Corp	0.005056901
24/05/2015	ACCO Brands Corp	-0.024251348
31/05/2015	ACCO Brands Corp	-0.051701374
07/12/2014	Acer Inc	3.829777429
07/12/2014	Acer Inc	-3.46435286
14/12/2014	Acer Inc	0.042160811
14/12/2014	Acer Inc	0.021342273
21/12/2014	Acer Inc	-0.056618894
21/12/2014	Acer Inc	-0.046304568
28/12/2014	Acer Inc	0.033415997
28/12/2014	Acer Inc	0.062759689
04/01/2015	Acer Inc	0.002344667
04/01/2015	Acer Inc	-0.004460974
11/01/2015	Acer Inc	0.082988363
11/01/2015	Acer Inc	0.093933758
18/01/2015	Acer Inc	-0.033983853
18/01/2015	Acer Inc	-0.042689409
25/01/2015	Acer Inc	0.017136282
25/01/2015	Acer Inc	-0.012539349
01/02/2015	Acer Inc	0.002424244
01/02/2015	Acer Inc	0.010980502
08/02/2015	Acer Inc	-0.014634408
08/02/2015	Acer Inc	-0.015723594
15/02/2015	Acer Inc	-0.014851758
15/02/2015	Acer Inc	0.025040432
22/02/2015	Acer Inc	0
22/02/2015	Acer Inc	0.022919261
01/03/2015	Acer Inc	0.024631787
01/03/2015	Acer Inc	-0.007581537
08/03/2015	Acer Inc	0.05445132
08/03/2015	Acer Inc	0.027028672
15/03/2015	Acer Inc	-0.023311079
15/03/2015	Acer Inc	-0.022472856
22/03/2015	Acer Inc	-0.002361276
22/03/2015	Acer Inc	0
29/03/2015	Acer Inc	-0.021506205
29/03/2015	Acer Inc	0.012048339
05/04/2015	Acer Inc	-0.021978907
05/04/2015	Acer Inc	-0.028109292
12/04/2015	Acer Inc	-0.004950505
12/04/2015	Acer Inc	0.02756683
19/04/2015	Acer Inc	-0.007472015
19/04/2015	Acer Inc	0.003016594
26/04/2015	Acer Inc	0.009950331
26/04/2015	Acer Inc	0.006006024
03/05/2015	Acer Inc	-0.004962789
03/05/2015	Acer Inc	0.002989539
10/05/2015	Acer Inc	-0.040614719
10/05/2015	Acer Inc	-0.087282784
17/05/2015	Acer Inc	-0.064193158
17/05/2015	Acer Inc	-0.072605718
24/05/2015	Acer Inc	0.008253142
24/05/2015	Acer Inc	-0.032031208
31/05/2015	Acer Inc	0.005464494
31/05/2015	Acer Inc	0.057961788

从上面我希望每行添加两列 - 一个next_week_return，其中显示了特定公司的接下来几周的回报；另一个：next_month_return，这将是接下来四个星期回报的总和。

任何人都可以提供的任何帮助将不胜感激。

【问题讨论】：

您能在聚合之前发布来自df 的一些示例行吗？
我只是使用：df = pd.read_csv 从 CSV 读取数据。然后我将date 转换为日期时间df['date'] = pd.to_datetime(df['date'])。这会产生每家公司的每日信息。然后我使用以下函数进行聚合：df = df.groupby([pd.Grouper(key='date', freq='W'), pd.Grouper('company_name').agg({'returns': 'sum'})。我希望这会有所帮助
明白了，谢谢。获取实际数据样本将非常有帮助，我们可以轻松地将其复制粘贴到 python shell 中并开始试验。
是否可以将文件添加到问题中？恐怕我看不到那个选项。
无需添加整个文件（尽管您可以在指向 Google Drive 文件夹、Dropbox 文件、GitHub gist 等的链接中进行编辑）。我们所需要的只是文件中几行的可复制粘贴样本，足以让您在输入很小的情况下指定您期望的确切输出。

标签： python pandas pandas-groupby

【解决方案1】：

从输入 CSV 的示例 sn-p 开始，一种解决方案是编写一个自定义函数以与 df.apply() 一起使用，该函数接受每个公司的子数据帧，并且对于子数据帧中的每个日期，计算return 在指定的前瞻天数内的总和。

以下代码假定 df 保存原始 CSV 中的示例数据。

# Convert string dates to pandas.Timestamp 
df['date'] = pd.to_datetime(df['date'])

# Within each CompanyName, sort by date, because we'll
# set the date column as a DatetimeIndex and will
# index-slice it with pandas date offsets, and this
# requires a sorted index.
df.sort_values(['CompanyName', 'date'], inplace=True)

# Set a MultiIndex to ensure that the calculated
# columns returned by the custom function align correctly
df.set_index(['CompanyName', 'date'], inplace=True)    

# Define a custom function to sum the values of `return` for 
# each CompanyName sub-DataFrame. The defaults of 1 and 1+28
# capture the month (defined to be 28 days) immediately following
# each date, excluding the date itself. To get just the 
# next week's values, use start=1, end=7.
def sum_return_over_next_i_to_j_days(df, first=1, last=1+28):
    day = pd.offsets.Day(1)
    df.reset_index(level=0, drop=True, inplace=True)
    rets = [df.loc[today + first*day : today + last*day, 'return'].sum(min_count=1) 
            for today in df.index]
    return pd.DataFrame(rets, 
                        index=df.index, 
                        columns=[f'sum_return_next_{first}-{last}_days'])

# Apply the above function to input CSV
df['next_week_return'] = df.groupby('CompanyName').apply(sum_return_over_next_i_to_j_days, 1, 7)
df['next_month_return'] = df.groupby('CompanyName').apply(sum_return_over_next_i_to_j_days, 1, 1+28)

df = df.reset_index()

# Print result
df.head(10)
  CompanyName       date    return  next_week_return  next_month_return
0     8x8 Inc 2014-07-12 -0.003883               NaN                NaN
1     8x8 Inc 2014-12-14  0.036923          0.108854           0.066976
2     8x8 Inc 2014-12-21  0.108854          0.042793           0.004068
3     8x8 Inc 2014-12-28  0.042793         -0.084672          -0.146522
4     8x8 Inc 2015-01-02 -0.056726         -0.027946          -0.089796
5     8x8 Inc 2015-01-03 -0.027946               NaN          -0.061850
6     8x8 Inc 2015-01-18  0.045946         -0.107797          -0.100230
7     8x8 Inc 2015-01-25 -0.107797               NaN          -0.036086
8     8x8 Inc 2015-02-15  0.007566         -0.043653          -0.044507
9     8x8 Inc 2015-02-22 -0.043653               NaN           0.115858

df.tail(10)
    CompanyName       date    return  next_week_return  next_month_return
120    Acer Inc 2015-08-02 -0.014634           0.08148           0.081480
121    Acer Inc 2015-08-02 -0.015724           0.08148           0.081480
122    Acer Inc 2015-08-03  0.054451               NaN                NaN
123    Acer Inc 2015-08-03  0.027029               NaN                NaN
124    Acer Inc 2015-10-05 -0.040615               NaN           0.176922
125    Acer Inc 2015-10-05 -0.087283               NaN           0.176922
126    Acer Inc 2015-11-01  0.082988               NaN                NaN
127    Acer Inc 2015-11-01  0.093934               NaN                NaN
128    Acer Inc 2015-12-04 -0.004951               NaN                NaN
129    Acer Inc 2015-12-04  0.027567               NaN                NaN

【讨论】：