Overview

Brought to you by YData

Dataset statistics

Number of variables7
Number of observations48,489
Missing cells9,234
Missing cells (%)2.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.6 MiB
Average record size in memory56.0 B

Variable types

Text2
Categorical2
Numeric3

Alerts

type_of_the_bulb is highly imbalanced (67.1%) Imbalance
wattage_of_the_bulb has 9234 (19.0%) missing values Missing
wattage_of_the_bulb has 8576 (17.7%) zeros Zeros
no_of_hours_bulb_was_on_during_daytime_last_week has 42744 (88.2%) zeros Zeros
no_of_hours_bulb_was_on_during_night_last_week has 13926 (28.7%) zeros Zeros

Reproduction

Analysis started2024-12-06 05:54:46.469423
Analysis finished2024-12-06 05:54:48.195246
Duration1.73 second
Software versionydata-profiling vv4.11.0
Download configurationconfig.json

Variables

household_ID
Text

Distinct4054
Distinct (%)8.4%
Missing0
Missing (%)0.0%
Memory size378.9 KiB
2024-12-06T11:24:48.367656image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters290,934
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)< 0.1%

Sample

1st rowID0001
2nd rowID0001
3rd rowID0001
4th rowID0001
5th rowID0001
ValueCountFrequency (%)
id0469 228
 
0.5%
id2033 225
 
0.5%
id0278 209
 
0.4%
id0282 182
 
0.4%
id1589 171
 
0.4%
id1841 165
 
0.3%
id0399 162
 
0.3%
id0069 152
 
0.3%
id0901 150
 
0.3%
id2072 144
 
0.3%
Other values (4044) 46701
96.3%
2024-12-06T11:24:48.681341image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
I 48489
16.7%
D 48489
16.7%
0 30565
10.5%
1 27712
9.5%
2 25976
8.9%
3 22658
7.8%
4 15122
 
5.2%
6 14938
 
5.1%
7 14483
 
5.0%
8 14336
 
4.9%
Other values (2) 28166
9.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 193956
66.7%
Uppercase Letter 96978
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 30565
15.8%
1 27712
14.3%
2 25976
13.4%
3 22658
11.7%
4 15122
7.8%
6 14938
7.7%
7 14483
7.5%
8 14336
7.4%
9 14213
7.3%
5 13953
7.2%
Uppercase Letter
ValueCountFrequency (%)
I 48489
50.0%
D 48489
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 193956
66.7%
Latin 96978
33.3%

Most frequent character per script

Common
ValueCountFrequency (%)
0 30565
15.8%
1 27712
14.3%
2 25976
13.4%
3 22658
11.7%
4 15122
7.8%
6 14938
7.7%
7 14483
7.5%
8 14336
7.4%
9 14213
7.3%
5 13953
7.2%
Latin
ValueCountFrequency (%)
I 48489
50.0%
D 48489
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 290934
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
I 48489
16.7%
D 48489
16.7%
0 30565
10.5%
1 27712
9.5%
2 25976
8.9%
3 22658
7.8%
4 15122
 
5.2%
6 14938
 
5.1%
7 14483
 
5.0%
8 14336
 
4.9%
Other values (2) 28166
9.7%

room_ID
Categorical

Distinct32
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size378.9 KiB
I1
10095 
I2
5696 
I3
5097 
I4
4918 
I5
4649 
Other values (27)
18034 

Length

Max length6
Median length2
Mean length2.1524263
Min length2

Characters and Unicode

Total characters104,369
Distinct characters14
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowI1
2nd rowI1
3rd rowI1
4th rowI1
5th rowI2

Common Values

ValueCountFrequency (%)
I1 10095
20.8%
I2 5696
11.7%
I3 5097
10.5%
I4 4918
10.1%
I5 4649
9.6%
I6 4066
8.4%
I7 3443
 
7.1%
I8 2533
 
5.2%
I9 2033
 
4.2%
I10 1347
 
2.8%
Other values (22) 4612
9.5%

Length

2024-12-06T11:24:48.799980image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
i1 10095
20.8%
i2 5696
11.7%
i3 5097
10.5%
i4 4918
10.1%
i5 4649
9.6%
i6 4066
8.4%
i7 3443
 
7.1%
i8 2533
 
5.2%
i9 2033
 
4.2%
i10 1347
 
2.8%
Other values (22) 4612
9.5%

Most occurring characters

ValueCountFrequency (%)
I 48489
46.5%
1 16872
 
16.2%
2 6582
 
6.3%
3 5767
 
5.5%
4 5374
 
5.1%
5 5009
 
4.8%
6 4310
 
4.1%
7 3627
 
3.5%
8 2666
 
2.6%
9 2142
 
2.1%
Other values (4) 3531
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 53750
51.5%
Uppercase Letter 49199
47.1%
Lowercase Letter 1420
 
1.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 16872
31.4%
2 6582
 
12.2%
3 5767
 
10.7%
4 5374
 
10.0%
5 5009
 
9.3%
6 4310
 
8.0%
7 3627
 
6.7%
8 2666
 
5.0%
9 2142
 
4.0%
0 1401
 
2.6%
Uppercase Letter
ValueCountFrequency (%)
I 48489
98.6%
O 710
 
1.4%
Lowercase Letter
ValueCountFrequency (%)
t 710
50.0%
h 710
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 53750
51.5%
Latin 50619
48.5%

Most frequent character per script

Common
ValueCountFrequency (%)
1 16872
31.4%
2 6582
 
12.2%
3 5767
 
10.7%
4 5374
 
10.0%
5 5009
 
9.3%
6 4310
 
8.0%
7 3627
 
6.7%
8 2666
 
5.0%
9 2142
 
4.0%
0 1401
 
2.6%
Latin
ValueCountFrequency (%)
I 48489
95.8%
O 710
 
1.4%
t 710
 
1.4%
h 710
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 104369
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
I 48489
46.5%
1 16872
 
16.2%
2 6582
 
6.3%
3 5767
 
5.5%
4 5374
 
5.1%
5 5009
 
4.8%
6 4310
 
4.1%
7 3627
 
3.5%
8 2666
 
2.6%
9 2142
 
2.1%
Other values (4) 3531
 
3.4%

light_ID
Text

Distinct412
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size378.9 KiB
2024-12-06T11:24:49.004955image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length9
Median length5
Mean length5.1832581
Min length5

Characters and Unicode

Total characters251,331
Distinct characters16
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique82 ?
Unique (%)0.2%

Sample

1st rowI1_L1
2nd rowI1_L2
3rd rowI1_L3
4th rowI1_L4
5th rowI2_L1
ValueCountFrequency (%)
i1_l1 4017
 
8.3%
i2_l1 3928
 
8.1%
i3_l1 3797
 
7.8%
i4_l1 3683
 
7.6%
i5_l1 3395
 
7.0%
i6_l1 2877
 
5.9%
i7_l1 2200
 
4.5%
i1_l2 2160
 
4.5%
i8_l1 1567
 
3.2%
i1_l3 1237
 
2.6%
Other values (402) 19628
40.5%
2024-12-06T11:24:49.323397image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
I 48489
19.3%
_ 48489
19.3%
L 48489
19.3%
1 48408
19.3%
2 14577
 
5.8%
3 9281
 
3.7%
4 7662
 
3.0%
5 6479
 
2.6%
6 5337
 
2.1%
7 4379
 
1.7%
Other values (6) 9741
 
3.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 103734
41.3%
Uppercase Letter 97688
38.9%
Connector Punctuation 48489
19.3%
Lowercase Letter 1420
 
0.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 48408
46.7%
2 14577
 
14.1%
3 9281
 
8.9%
4 7662
 
7.4%
5 6479
 
6.2%
6 5337
 
5.1%
7 4379
 
4.2%
8 3272
 
3.2%
9 2602
 
2.5%
0 1737
 
1.7%
Uppercase Letter
ValueCountFrequency (%)
I 48489
49.6%
L 48489
49.6%
O 710
 
0.7%
Lowercase Letter
ValueCountFrequency (%)
t 710
50.0%
h 710
50.0%
Connector Punctuation
ValueCountFrequency (%)
_ 48489
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 152223
60.6%
Latin 99108
39.4%

Most frequent character per script

Common
ValueCountFrequency (%)
_ 48489
31.9%
1 48408
31.8%
2 14577
 
9.6%
3 9281
 
6.1%
4 7662
 
5.0%
5 6479
 
4.3%
6 5337
 
3.5%
7 4379
 
2.9%
8 3272
 
2.1%
9 2602
 
1.7%
Latin
ValueCountFrequency (%)
I 48489
48.9%
L 48489
48.9%
O 710
 
0.7%
t 710
 
0.7%
h 710
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 251331
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
I 48489
19.3%
_ 48489
19.3%
L 48489
19.3%
1 48408
19.3%
2 14577
 
5.8%
3 9281
 
3.7%
4 7662
 
3.0%
5 6479
 
2.6%
6 5337
 
2.1%
7 4379
 
1.7%
Other values (6) 9741
 
3.9%

type_of_the_bulb
Categorical

Imbalance 

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size378.9 KiB
LED
37862 
CFL
8139 
Incandescent
 
1193
Tube Light (conventional)
 
398
Halogen
 
296
Other values (4)
 
601

Length

Max length25
Median length3
Mean length3.5187775
Min length3

Characters and Unicode

Total characters170,622
Distinct characters28
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLED
2nd rowLED
3rd rowLED
4th rowLED
5th rowCFL

Common Values

ValueCountFrequency (%)
LED 37862
78.1%
CFL 8139
 
16.8%
Incandescent 1193
 
2.5%
Tube Light (conventional) 398
 
0.8%
Halogen 296
 
0.6%
Other 228
 
0.5%
Tube Light (LED) 212
 
0.4%
Flood light 139
 
0.3%
Flashlight 22
 
< 0.1%

Length

2024-12-06T11:24:49.429011image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-06T11:24:49.518338image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
led 38074
76.4%
cfl 8139
 
16.3%
incandescent 1193
 
2.4%
light 749
 
1.5%
tube 610
 
1.2%
conventional 398
 
0.8%
halogen 296
 
0.6%
other 228
 
0.5%
flood 139
 
0.3%
flashlight 22
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
L 46823
27.4%
E 38074
22.3%
D 38074
22.3%
F 8300
 
4.9%
C 8139
 
4.8%
n 5069
 
3.0%
e 3918
 
2.3%
c 2784
 
1.6%
t 2590
 
1.5%
a 1909
 
1.1%
Other values (18) 14942
 
8.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 141737
83.1%
Lowercase Letter 26306
 
15.4%
Space Separator 1359
 
0.8%
Open Punctuation 610
 
0.4%
Close Punctuation 610
 
0.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 5069
19.3%
e 3918
14.9%
c 2784
10.6%
t 2590
9.8%
a 1909
 
7.3%
o 1370
 
5.2%
d 1332
 
5.1%
s 1215
 
4.6%
i 1169
 
4.4%
g 1067
 
4.1%
Other values (6) 3883
14.8%
Uppercase Letter
ValueCountFrequency (%)
L 46823
33.0%
E 38074
26.9%
D 38074
26.9%
F 8300
 
5.9%
C 8139
 
5.7%
I 1193
 
0.8%
T 610
 
0.4%
H 296
 
0.2%
O 228
 
0.2%
Space Separator
ValueCountFrequency (%)
1359
100.0%
Open Punctuation
ValueCountFrequency (%)
( 610
100.0%
Close Punctuation
ValueCountFrequency (%)
) 610
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 168043
98.5%
Common 2579
 
1.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
L 46823
27.9%
E 38074
22.7%
D 38074
22.7%
F 8300
 
4.9%
C 8139
 
4.8%
n 5069
 
3.0%
e 3918
 
2.3%
c 2784
 
1.7%
t 2590
 
1.5%
a 1909
 
1.1%
Other values (15) 12363
 
7.4%
Common
ValueCountFrequency (%)
1359
52.7%
( 610
23.7%
) 610
23.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 170622
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
L 46823
27.4%
E 38074
22.3%
D 38074
22.3%
F 8300
 
4.9%
C 8139
 
4.8%
n 5069
 
3.0%
e 3918
 
2.3%
c 2784
 
1.6%
t 2590
 
1.5%
a 1909
 
1.1%
Other values (18) 14942
 
8.8%

wattage_of_the_bulb
Real number (ℝ)

Missing  Zeros 

Distinct70
Distinct (%)0.2%
Missing9234
Missing (%)19.0%
Infinite0
Infinite (%)0.0%
Mean17.386983
Minimum0
Maximum908
Zeros8576
Zeros (%)17.7%
Negative0
Negative (%)0.0%
Memory size378.9 KiB
2024-12-06T11:24:49.634906image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13.75
median7
Q312
95-th percentile60
Maximum908
Range908
Interquartile range (IQR)8.25

Descriptive statistics

Standard deviation68.652667
Coefficient of variation (CV)3.9485096
Kurtosis136.2298
Mean17.386983
Median Absolute Deviation (MAD)5
Skewness11.276204
Sum682526
Variance4713.1887
MonotonicityNot monotonic
2024-12-06T11:24:49.748700image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 8576
17.7%
5 5248
10.8%
12 5205
10.7%
7 3623
 
7.5%
9 2483
 
5.1%
15 2136
 
4.4%
10 1536
 
3.2%
8 1193
 
2.5%
6 1059
 
2.2%
18 935
 
1.9%
Other values (60) 7261
15.0%
(Missing) 9234
19.0%
ValueCountFrequency (%)
0 8576
17.7%
1 80
 
0.2%
2 378
 
0.8%
3 479
 
1.0%
3.5 301
 
0.6%
4 86
 
0.2%
5 5248
10.8%
5.5 237
 
0.5%
6 1059
 
2.2%
7 3623
7.5%
ValueCountFrequency (%)
908 1
 
< 0.1%
900 179
0.4%
675 64
 
0.1%
250 1
 
< 0.1%
168 1
 
< 0.1%
165 255
0.5%
150 4
 
< 0.1%
125 1
 
< 0.1%
123 144
0.3%
120 57
 
0.1%

no_of_hours_bulb_was_on_during_daytime_last_week
Real number (ℝ)

Zeros 

Distinct123
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6227784
Minimum0
Maximum70
Zeros42744
Zeros (%)88.2%
Negative0
Negative (%)0.0%
Memory size378.9 KiB
2024-12-06T11:24:49.859394image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile10
Maximum70
Range70
Interquartile range (IQR)0

Descriptive statistics

Standard deviation7.0569084
Coefficient of variation (CV)4.3486581
Kurtosis51.180155
Mean1.6227784
Median Absolute Deviation (MAD)0
Skewness6.5893157
Sum78686.902
Variance49.799956
MonotonicityNot monotonic
2024-12-06T11:24:49.972708image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 42744
88.2%
7 1087
 
2.2%
14 701
 
1.4%
21 539
 
1.1%
1 492
 
1.0%
2 321
 
0.7%
10 264
 
0.5%
70 218
 
0.4%
35 196
 
0.4%
3 173
 
0.4%
Other values (113) 1754
 
3.6%
ValueCountFrequency (%)
0 42744
88.2%
0.033 1
 
< 0.1%
0.05 3
 
< 0.1%
0.1 5
 
< 0.1%
0.12 1
 
< 0.1%
0.125 1
 
< 0.1%
0.175 1
 
< 0.1%
0.2 2
 
< 0.1%
0.21 2
 
< 0.1%
0.25 112
 
0.2%
ValueCountFrequency (%)
70 218
0.4%
69 1
 
< 0.1%
66.5 4
 
< 0.1%
66 1
 
< 0.1%
65 1
 
< 0.1%
63 8
 
< 0.1%
60 18
 
< 0.1%
57 1
 
< 0.1%
56 19
 
< 0.1%
54 2
 
< 0.1%

no_of_hours_bulb_was_on_during_night_last_week
Real number (ℝ)

Zeros 

Distinct294
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.515142
Minimum0
Maximum98
Zeros13926
Zeros (%)28.7%
Negative0
Negative (%)0.0%
Memory size378.9 KiB
2024-12-06T11:24:50.090758image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median7
Q328
95-th percentile59.5
Maximum98
Range98
Interquartile range (IQR)28

Descriptive statistics

Standard deviation20.584469
Coefficient of variation (CV)1.2463997
Kurtosis2.9408673
Mean16.515142
Median Absolute Deviation (MAD)7
Skewness1.678896
Sum800802.71
Variance423.72035
MonotonicityNot monotonic
2024-12-06T11:24:50.208464image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 13926
28.7%
28 6070
12.5%
7 3642
 
7.5%
14 3567
 
7.4%
21 3407
 
7.0%
35 2355
 
4.9%
1 1531
 
3.2%
2 1006
 
2.1%
3.5 923
 
1.9%
42 911
 
1.9%
Other values (284) 11151
23.0%
ValueCountFrequency (%)
0 13926
28.7%
0.00083 2
 
< 0.1%
0.0023 1
 
< 0.1%
0.025 2
 
< 0.1%
0.03 2
 
< 0.1%
0.033 1
 
< 0.1%
0.05 8
 
< 0.1%
0.066 1
 
< 0.1%
0.075 1
 
< 0.1%
0.083 1
 
< 0.1%
ValueCountFrequency (%)
98 396
0.8%
97 1
 
< 0.1%
96 6
 
< 0.1%
95 7
 
< 0.1%
94.5 1
 
< 0.1%
92 1
 
< 0.1%
91 143
 
0.3%
90 31
 
0.1%
88 1
 
< 0.1%
87.5 2
 
< 0.1%

Interactions

2024-12-06T11:24:47.504809image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-06T11:24:46.920201image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-06T11:24:47.245161image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-06T11:24:47.799779image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-06T11:24:47.079169image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-06T11:24:47.327055image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-06T11:24:47.889124image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-06T11:24:47.164270image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-06T11:24:47.419172image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-12-06T11:24:50.282874image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
no_of_hours_bulb_was_on_during_daytime_last_weekno_of_hours_bulb_was_on_during_night_last_weekroom_IDtype_of_the_bulbwattage_of_the_bulb
no_of_hours_bulb_was_on_during_daytime_last_week1.0000.1490.0280.0630.067
no_of_hours_bulb_was_on_during_night_last_week0.1491.0000.0760.1410.113
room_ID0.0280.0761.0000.0580.045
type_of_the_bulb0.0630.1410.0581.0000.087
wattage_of_the_bulb0.0670.1130.0450.0871.000

Missing values

2024-12-06T11:24:47.998744image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-12-06T11:24:48.120805image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

household_IDroom_IDlight_IDtype_of_the_bulbwattage_of_the_bulbno_of_hours_bulb_was_on_during_daytime_last_weekno_of_hours_bulb_was_on_during_night_last_week
0ID0001I1I1_L1LED5.00.002.00
1ID0001I1I1_L2LED5.00.002.00
2ID0001I1I1_L3LED5.00.002.00
3ID0001I1I1_L4LED5.00.002.00
4ID0001I2I2_L1CFL5.00.000.25
5ID0001I3I3_L1CFL5.00.000.25
6ID0001I4I4_L1CFL5.00.000.25
7ID0001I5I5_L1CFL5.00.250.00
8ID0001I6I6_L1CFL5.02.001.00
9ID0001I7I7_L1CFL5.00.002.00
household_IDroom_IDlight_IDtype_of_the_bulbwattage_of_the_bulbno_of_hours_bulb_was_on_during_daytime_last_weekno_of_hours_bulb_was_on_during_night_last_week
48479ID4062I3I3_L1LED7.00.00.0
48480ID4062I4I4_L1LED7.028.028.0
48481ID4062I6I6_L1LED7.00.07.0
48482ID4062I7I7_L1LED7.00.00.0
48483ID4062I7I7_L2LED7.00.00.0
48484ID4063I1I1_L1LED7.00.035.0
48485ID4063I2I2_L1LED7.00.02.0
48486ID4063I3I3_L1LED7.00.04.0
48487ID4063I7I7_L1LED7.00.02.0
48488ID4063I8I8_L1LED7.04.020.0