问题描述
我正在尝试离散化我的数据框，如下所示：
| 开始日期 | 停放时间（分钟） | 充电时间（分钟） | 能源（千瓦时） | |
|---|---|---|---|---|
| 49698 | 2016-01-01 11:48:00 | 230 | 92.0 | 3.034643 |
| 49710 | 2016-01-01 13:43:00 | 225 | 225.0 | 12.427662 |
| 49732 | 2016-01-01 22:43:00 | 708 | 111.0 | 10.752058 |
| 49736 | 2016-01-02 07:09:00 | 149 | 149.0 | 11.160776 |
| 49745 | 2016-01-02 10:29:00 | 156 | 156.0 | 10.298505 |
| 49758 | 2016-01-02 13:06:00 | 84 | 84.0 | 2.904127 |
| 49768 | 2016-01-02 15:00:00 | 27 | 26.0 | 2.573858 |
| 49773 | 2016-01-02 15:31:00 | 174 | 152.0 | 14.961943 |
| 49775 | 2016-01-02 16:01:00 | 195 | 167.0 | 16.317518 |
| 49790 | 2016-01-02 19:37:00 | 108 | 108.0 | 10.829344 |
| 49791 | 2016-01-02 19:56:00 | 289 | 26.0 | 2.552439 |
| 49802 | 2016-01-03 09:23:00 | 58 | 58.0 | 5.243358 |
| 49803 | 2016-01-03 09:33:00 | 264 | 134.0 | 6.782309 |
| 49813 | 2016-01-03 11:12:00 | 240 | 0.0 | 0.008115 |
| 49825 | 2016-01-03 14:12:00 | 97 | 96.0 | 5.29069 |
| 49833 | 2016-01-03 15:52:00 | 201 | 201.0 | 16.058235 |
| 49834 | 2016-01-03 15:52:00 | 53 | 52.0 | 5.304866 |
| 49840 | 2016-01-03 17:27:00 | 890 | 219.0 | 15.878921 |
| 49857 | 2016-01-04 05:57:00 | 198 | 127.0 | 6.368932 |
| 49871 | 2016-01-04 08:48:00 | 75 | 74.0 | 5.99877 |
我想做的是在 2 小时内采样，如下所示：
| 开始日期 | 能源（千瓦时） | 充电时间（分钟） | 费用 |
|---|---|---|---|
| 2016-01-01 10:00:00 | 3.034643 | 92.0 | 0.0 |
| 2016-01-01 12:00:00 | 12.427662 | 225.0 | 0.0 |
| 2016-01-01 14:00:00 | 0.0 | 0.0 | 0.0 |
| 2016-01-01 16:00:00 | 0.0 | 0.0 | 0.0 |
| 2016-01-01 18:00:00 | 0.0 | 0.0 | 0.0 |
| 2016-01-01 20:00:00 | 0.0 | 0.0 | 0.0 |
| 2016-01-01 22:00:00 | 10.752058 | 111.0 | 0.0 |
| 2016-01-02 00:00:00 | 0.0 | 0.0 | 0.0 |
| 2016-01-02 02:00:00 | 0.0 | 0.0 | 0.0 |
| 2016-01-02 04:00:00 | 0.0 | 0.0 | 0.0 |
我做了什么
data.resample('2H',on='Start Date').agg(({'Energy (kWh)':'sum','Charge Duration (mins)':'sum'}))
但是问题是数据溢出了，正如您从第一行看到的那样，充电持续时间为 92 分钟。然而，这 92 分钟中只有 12 分钟在 10:00:00 - 12:00:00 时间段内，但是我使用 resample 的方式将所有充电持续时间分配给了该时间段。我想要的行为是根据开始日期和充电持续时间在时间段中“均匀地”分割它们，这样 12 分钟属于第一个时间段，其余 80 分钟属于下一个时间段。也有 EV 充电超过 3 个周期的情况。我希望这是有道理的。 你会怎么做？
这里是逗号分隔值的原始数据：
,开始日期,停车时间（分钟），充电时间（分钟），能量（千瓦时） 49698,2016-01-01 11:48:00,230,92.0,3.034643 49710,2016-01-01 13:43:00,225,225.0,12.427662 49732,2016-01-01 22:43:00,708,111.0,10.752058 49736,2016-01-02 07:09:00,149,149.0,11.160776 49745,2016-01-02 10:29:00,156,156.0,10.298505 49758,2016-01-02 13:06:00,84,84.0,2.904127 49768,2016-01-02 15:00:00,27,26.0,2.573858 49773,2016-01-02 15:31:00,174,152.0,14.961943 49775,2016-01-02 16:01:00,195,167.0,16.317518 49790,2016-01-02 19:37:00,108,108.0,10.829344 49791,2016-01-02 19:56:00,289,2.552439 49802,2016-01-03 09:23:00,58,58.0,5.243358 49803,2016-01-03 09:33:00,264,134.0,6.782309 49813,2016-01-03 11:12:00,240,0.0,0.008115 49825,2016-01-03 14:12:00,97,96.0,5.29069 49833,2016-01-03 15:52:00,201,201.0,16.058235 49834,53,52.0,5.304866 49840,2016-01-03 17:27:00,890,219.0,15.878921 49857,2016-01-04 05:57:00,198,127.0,6.368932 49871,2016-01-04 08:48:00,75,74.0,5.99877
解决方法
我没有看到一个完全直接的方法。有效地为每一行构建了一个数据框，并使用比率在目标行之间拆分值。
import io
df = pd.read_csv(io.StringIO(""" Start Date Park Duration (mins) Charge Duration (mins) Energy (kWh)
49698 2016-01-01 11:48:00 230 92.0 3.034643
49710 2016-01-01 13:43:00 225 225.0 12.427662
49732 2016-01-01 22:43:00 708 111.0 10.752058
49736 2016-01-02 07:09:00 149 149.0 11.160776
49745 2016-01-02 10:29:00 156 156.0 10.298505
49758 2016-01-02 13:06:00 84 84.0 2.904127
49768 2016-01-02 15:00:00 27 26.0 2.573858
49773 2016-01-02 15:31:00 174 152.0 14.961943
49775 2016-01-02 16:01:00 195 167.0 16.317518
49790 2016-01-02 19:37:00 108 108.0 10.829344
49791 2016-01-02 19:56:00 289 26.0 2.552439
49802 2016-01-03 09:23:00 58 58.0 5.243358
49803 2016-01-03 09:33:00 264 134.0 6.782309
49813 2016-01-03 11:12:00 240 0.0 0.008115
49825 2016-01-03 14:12:00 97 96.0 5.29069
49833 2016-01-03 15:52:00 201 201.0 16.058235
49834 2016-01-03 15:52:00 53 52.0 5.304866
49840 2016-01-03 17:27:00 890 219.0 15.878921
49857 2016-01-04 05:57:00 198 127.0 6.368932
49871 2016-01-04 08:48:00 75 74.0 5.99877"""),sep="\t",index_col=0)
df["Start Date"] = pd.to_datetime(df["Start Date"])
def proportionalsplit(s,freq="2H"):
st = s["Start Date"]
et = st + pd.Timedelta(minutes=s["Charge Duration (mins)"])
tr = pd.date_range(st.floor(freq),et,freq=freq)
lmin = {"2H":120}
# ratio of how numeric values should be split across new buckets
ratio = np.minimum((np.where(tr<st,tr.shift()-st,et-tr)/(10**9*60)).astype(int),np.full(len(tr),lmin[freq]))
ratio = ratio / ratio.sum()
return {"Start Date":tr,"Original Duration":np.full(len(tr),s["Charge Duration (mins)"]),"Original Start":np.full(len(tr),s["Start Date"]),"Original Index": np.full(len(tr),s.name),"Charge Duration (mins)": s["Charge Duration (mins)"] * ratio,"Energy (kWh)": s["Energy (kWh)"] * ratio,}
df2 = pd.concat([pd.DataFrame(v) for v in df.apply(proportionalsplit,axis=1).values]).reset_index(drop=True)
# everything OK?
print(df2["Energy (kWh)"].sum().round(3)==df["Energy (kWh)"].sum().round(3),df2["Charge Duration (mins)"].sum().round(3)==df["Charge Duration (mins)"].sum().round(3),)
# let's have a look at everything in 2H resample...
df3 = df2.groupby(["Start Date"]).agg({**{c:lambda s: list(s) for c in df2.columns if "Original" in c},**{c:"sum" for c in ["Charge Duration (mins)","Energy (kWh)"]}})
输出
Original Duration Original Start Original Index Charge Duration (mins) Energy (kWh)
Start Date
2016-01-01 10:00:00 [92.0] [2016-01-01 11:48:00] [49698] 12.0 0.395823
2016-01-01 12:00:00 [92.0,225.0] [2016-01-01 11:48:00,2016-01-01 13:43:00] [49698,49710] 97.0 3.577799
2016-01-01 14:00:00 [225.0] [2016-01-01 13:43:00] [49710] 120.0 6.628086
2016-01-01 16:00:00 [225.0] [2016-01-01 13:43:00] [49710] 88.0 4.860597
2016-01-01 22:00:00 [111.0] [2016-01-01 22:43:00] [49732] 77.0 7.458635
2016-01-02 00:00:00 [111.0] [2016-01-01 22:43:00] [49732] 34.0 3.293423
2016-01-02 06:00:00 [149.0] [2016-01-02 07:09:00] [49736] 51.0 3.820131
2016-01-02 08:00:00 [149.0] [2016-01-02 07:09:00] [49736] 98.0 7.340645
2016-01-02 10:00:00 [156.0] [2016-01-02 10:29:00] [49745] 91.0 6.007461
2016-01-02 12:00:00 [156.0,84.0] [2016-01-02 10:29:00,2016-01-02 13:06:00] [49745,49758] 119.0 6.157983
2016-01-02 14:00:00 [84.0,26.0,152.0] [2016-01-02 13:06:00,2016-01-02 15:00:00,2016-01-02 15:31:00] [49758,49768,49773] 85.0 6.465627
2016-01-02 16:00:00 [152.0,167.0] [2016-01-02 15:31:00,2016-01-02 16:01:00] [49773,49775] 239.0 23.439513
2016-01-02 18:00:00 [152.0,167.0,108.0,26.0] [2016-01-02 15:31:00,2016-01-02 16:01:00,2016-01-02 19:37:00,2016-01-02 19:56:00] [49773,49775,49790,49791] 78.0 7.684299
2016-01-02 20:00:00 [108.0,26.0] [2016-01-02 19:37:00,2016-01-02 19:56:00] [49790,49791] 107.0 10.682851
2016-01-03 08:00:00 [58.0,134.0] [2016-01-03 09:23:00,2016-01-03 09:33:00] [49802,49803] 64.0 4.711485
2016-01-03 10:00:00 [58.0,134.0,0.0] [2016-01-03 09:23:00,2016-01-03 09:33:00,2016-01-03 11:12:00] [49802,49803,49813] 128.0 7.322297
2016-01-03 14:00:00 [96.0,201.0,52.0] [2016-01-03 14:12:00,2016-01-03 15:52:00,2016-01-03 15:52:00] [49825,49833,49834] 112.0 6.745957
2016-01-03 16:00:00 [201.0,52.0,219.0] [2016-01-03 15:52:00,2016-01-03 17:27:00] [49833,49834,49840] 197.0 16.468453
2016-01-03 18:00:00 [201.0,219.0] [2016-01-03 15:52:00,2016-01-03 17:27:00] [49833,49840] 193.0 14.532874
2016-01-03 20:00:00 [219.0] [2016-01-03 17:27:00] [49840] 66.0 4.785428
2016-01-04 04:00:00 [127.0] [2016-01-04 05:57:00] [49857] 3.0 0.150447
2016-01-04 06:00:00 [127.0] [2016-01-04 05:57:00] [49857] 120.0 6.017889
2016-01-04 08:00:00 [127.0,74.0] [2016-01-04 05:57:00,2016-01-04 08:48:00] [49857,49871] 76.0 6.037237
2016-01-04 10:00:00 [74.0] [2016-01-04 08:48:00] [49871] 2.0 0.162129