我在一個機器學習項目中工作，當我提取特征時，我發現一些消費者 LCLid 在我按 LCLid 分組時從數據集中消失了數據集：倫敦家庭 SmartMeter 能源消耗數據這是原始數據集這是我用來提取一些特征的代碼LCLid=[]for i in range(68): LCLid.append('MAC0000'+str(228+i)) consommation=data.groupby('LCLid')['KWH/hh'].sum() consommation_min=data.groupby('LCLid')['KWH/hh'].min() consommation_max=data.groupby('LCLid')['KWH/hh'].max() consommation_mean=data.groupby('LCLid')['KWH/hh'].mean() consommation_evening=data.groupby(['LCLid','period'])['KWH/hh'].mean()#creation de dataframelist_of_tuples = list(zip (LCLid, consommation, consommation_min, consommation_max, consommation_mean)) data2= pd.DataFrame(list_of_tuples, columns = ['LCLid', 'Consumption', 'Consumption_min', 'Consumption_max', 'Consumption_mean']) 正如您在執行代碼后看到的那樣，數據集在 LCLid 282 中停止，而在原始數據集中，數據集還包含從 283 到 295 的 LCLid
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MYYA
TA貢獻1868條經驗 獲得超4個贊
使用來自倫敦家庭 SmartMeter 能源消耗數據的低碳倫敦數據
問題是LCLid不會1從MAC000228到均勻遞增MAC000295。
print(data.LCLid.unique())
array(['MAC000228', 'MAC000229', 'MAC000230', 'MAC000231', 'MAC000232',
'MAC000233', 'MAC000234', 'MAC000235', 'MAC000237', 'MAC000238',
'MAC000239', 'MAC000240', 'MAC000241', 'MAC000242', 'MAC000243',
'MAC000244', 'MAC000245', 'MAC000246', 'MAC000248', 'MAC000249',
'MAC000250', 'MAC000251', 'MAC000252', 'MAC000253', 'MAC000254',
'MAC000255', 'MAC000256', 'MAC000258', 'MAC000260', 'MAC000262',
'MAC000263', 'MAC000264', 'MAC000267', 'MAC000268', 'MAC000269',
'MAC000270', 'MAC000271', 'MAC000272', 'MAC000273', 'MAC000274',
'MAC000275', 'MAC000276', 'MAC000277', 'MAC000279', 'MAC000280',
'MAC000281', 'MAC000282', 'MAC000283', 'MAC000284', 'MAC000285',
'MAC000287', 'MAC000289', 'MAC000291', 'MAC000294', 'MAC000295'],
dtype=object)
print(len(data.LCLid.unique()))
>>> 55
解決問題
import pandas as pd
import numpy as np
df = pd.read_csv('Power-Networks-LCL-June2015(withAcornGps)v2.csv')
# determine the rows needed for the MAC000228 - MAC000295
df[df.LCLid == 'MAC000228'].iloc[0, :] # first row of 228
df[df.LCLid == 'MAC000295'].iloc[-1, :] # last row of 295
# create a dataframe with the desired data
data = df[['LCLid', 'DateTime', 'KWH/hh (per half hour) ']].iloc[6989700:9032044, :].copy()
# fix the data
data.DateTime = pd.to_datetime(data.DateTime)
data.rename(columns={'KWH/hh (per half hour) ': 'KWH/hh'}, inplace=True)
data['KWH/hh'] = data['KWH/hh'].str.replace('Null', 'NaN')
data['KWH/hh'].fillna(np.nan, inplace=True)
data['KWH/hh'] = data['KWH/hh'].astype('float')
data.reset_index(drop=True, inplace=True)
# aggregate your functions
agg_data = data.groupby('LCLid')['KWH/hh'].agg(['sum', 'min', 'max', 'mean']).reset_index()
print(agg_data)
agg_data
LCLid sum min max mean
0 MAC000228 5761.288000 0.021 1.616 0.146356
1 MAC000229 6584.866999 0.008 3.294 0.167456
2 MAC000230 8911.154000 0.029 2.750 0.226384
3 MAC000231 3174.314000 0.000 1.437 0.080663
4 MAC000232 2083.042000 0.005 0.736 0.052946
5 MAC000233 2241.591000 0.000 3.137 0.056993
6 MAC000234 9700.328001 0.029 2.793 0.246646
7 MAC000235 8473.999003 0.011 3.632 0.223194
8 MAC000237 22263.294998 0.036 4.450 0.598299
9 MAC000238 7814.889998 0.016 2.835 0.198781
10 MAC000239 6113.029000 0.015 1.346 0.155481
11 MAC000240 7280.662000 0.000 3.146 0.222399
12 MAC000241 4181.169999 0.024 1.733 0.194963
13 MAC000242 1654.336000 0.000 1.481 0.042088
14 MAC000243 11057.366999 0.009 3.588 0.281989
15 MAC000244 5894.271000 0.005 1.884 0.149939
16 MAC000245 22788.699005 0.037 4.743 0.580087
17 MAC000246 13787.060005 0.014 3.516 0.351075
18 MAC000248 10192.239001 0.000 4.351 0.259536
19 MAC000249 24401.468995 0.148 5.242 0.893042
20 MAC000250 5850.003000 0.000 2.185 0.148999
21 MAC000251 8400.234000 0.035 3.505 0.213931
22 MAC000252 21748.489004 0.135 4.171 0.554978
23 MAC000253 9739.408999 0.009 1.714 0.248201
24 MAC000254 9351.614001 0.009 2.484 0.238209
25 MAC000255 14142.974002 0.097 3.305 0.360220
26 MAC000256 20398.665001 0.049 3.019 0.520680
27 MAC000258 6646.485998 0.017 2.319 0.169666
28 MAC000260 5952.563001 0.006 2.192 0.151952
29 MAC000262 13909.603999 0.000 2.878 0.355181
30 MAC000263 3753.997000 0.015 1.060 0.095863
31 MAC000264 7022.967000 0.020 0.910 0.179432
32 MAC000267 8797.094000 0.029 2.198 0.224898
33 MAC000268 3734.252001 0.000 1.599 0.095359
34 MAC000269 2395.232000 0.000 1.029 0.061167
35 MAC000270 15569.711002 0.131 2.249 0.397501
36 MAC000271 7244.860000 0.028 1.794 0.184974
37 MAC000272 8703.658998 0.034 3.295 0.222446
38 MAC000273 3622.199002 0.005 5.832 0.092587
39 MAC000274 28724.718997 0.032 3.927 0.734422
40 MAC000275 5564.004999 0.012 1.840 0.161290
41 MAC000276 11060.774001 0.000 1.709 0.315724
42 MAC000277 8446.528999 0.027 1.938 0.241075
43 MAC000279 3444.160999 0.016 1.846 0.098354
44 MAC000280 12595.780001 0.125 1.988 0.360436
45 MAC000281 6282.568000 0.024 1.433 0.179538
46 MAC000282 4457.989001 0.030 1.830 0.127444
47 MAC000283 5024.917000 0.011 2.671 0.143627
48 MAC000284 1293.503000 0.000 0.752 0.047975
49 MAC000285 2399.018000 0.006 0.931 0.068567
50 MAC000287 1407.290000 0.000 2.372 0.045253
51 MAC000289 4767.490999 0.000 2.287 0.136436
52 MAC000291 13456.678999 0.072 3.354 0.385060
53 MAC000294 9477.966000 0.053 2.438 0.271264
54 MAC000295 7750.128000 0.010 1.839 0.221774
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