Overview

Brought to you by YData

Dataset statistics

Number of variables9
Number of observations1171
Missing cells356
Missing cells (%)3.4%
Duplicate rows87
Duplicate rows (%)7.4%
Total size in memory123.8 KiB
Average record size in memory108.2 B

Variable types

Categorical3
Text1
Numeric5

Alerts

Dataset has 87 (7.4%) duplicate rowsDuplicates
is_room_fully_sealed is highly imbalanced (64.1%) Imbalance
wattage_of_the_ac has 356 (30.4%) missing values Missing
wattage_of_the_ac has 610 (52.1%) zeros Zeros
btu_of_the_ac has 67 (5.7%) zeros Zeros
no_of_hours_ac_was_on_during_daytime_last_week has 993 (84.8%) zeros Zeros
no_of_hours_ac_was_on_during_night_last_week has 499 (42.6%) zeros Zeros

Reproduction

Analysis started2024-11-18 08:40:39.356478
Analysis finished2024-11-18 08:40:42.140671
Duration2.78 seconds
Software versionydata-profiling vv4.11.0
Download configurationconfig.json

Variables

room_ID
Categorical

Distinct23
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size50.6 KiB
I_2
282 
I_3
255 
I_4
176 
I_5
115 
I_6
76 
Other values (18)
267 

Length

Max length7
Median length3
Mean length3.1323655
Min length3

Characters and Unicode

Total characters3668
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowI_3
2nd rowI_3
3rd rowI_5
4th rowI_2
5th rowI_8

Common Values

ValueCountFrequency (%)
I_2 282
24.1%
I_3 255
21.8%
I_4 176
15.0%
I_5 115
9.8%
I_6 76
 
6.5%
I_1 58
 
5.0%
I_7 38
 
3.2%
I_8 30
 
2.6%
I_11 25
 
2.1%
I_10 22
 
1.9%
Other values (13) 94
 
8.0%

Length

2024-11-18T14:10:42.209749image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
i_2 282
24.1%
i_3 255
21.8%
i_4 176
15.0%
i_5 115
9.8%
i_6 76
 
6.5%
i_1 58
 
5.0%
i_7 38
 
3.2%
i_8 30
 
2.6%
i_11 25
 
2.1%
i_10 22
 
1.9%
Other values (13) 94
 
8.0%

Most occurring characters

ValueCountFrequency (%)
_ 1182
32.2%
I 1171
31.9%
2 299
 
8.2%
3 277
 
7.6%
1 198
 
5.4%
4 186
 
5.1%
5 124
 
3.4%
6 80
 
2.2%
7 40
 
1.1%
8 34
 
0.9%
Other values (5) 77
 
2.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3668
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
_ 1182
32.2%
I 1171
31.9%
2 299
 
8.2%
3 277
 
7.6%
1 198
 
5.4%
4 186
 
5.1%
5 124
 
3.4%
6 80
 
2.2%
7 40
 
1.1%
8 34
 
0.9%
Other values (5) 77
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3668
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
_ 1182
32.2%
I 1171
31.9%
2 299
 
8.2%
3 277
 
7.6%
1 198
 
5.4%
4 186
 
5.1%
5 124
 
3.4%
6 80
 
2.2%
7 40
 
1.1%
8 34
 
0.9%
Other values (5) 77
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3668
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
_ 1182
32.2%
I 1171
31.9%
2 299
 
8.2%
3 277
 
7.6%
1 198
 
5.4%
4 186
 
5.1%
5 124
 
3.4%
6 80
 
2.2%
7 40
 
1.1%
8 34
 
0.9%
Other values (5) 77
 
2.1%

ac_ID
Text

Distinct51
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Memory size50.6 KiB
2024-11-18T14:10:42.348785image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length12
Median length8
Mean length8.1323655
Min length8

Characters and Unicode

Total characters9523
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)0.9%

Sample

1st rowI_3_AC_1
2nd rowI_3_AC_1
3rd rowI_5_AC_1
4th rowI_2_AC_1
5th rowI_8_AC_1
ValueCountFrequency (%)
i_2_ac_1 273
23.3%
i_3_ac_1 245
20.9%
i_4_ac_1 168
14.3%
i_5_ac_1 107
 
9.1%
i_6_ac_1 68
 
5.8%
i_1_ac_1 47
 
4.0%
i_7_ac_1 32
 
2.7%
i_11_ac_1 22
 
1.9%
i_8_ac_1 22
 
1.9%
i_10_ac_1 18
 
1.5%
Other values (41) 169
14.4%
2024-11-18T14:10:42.598419image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
_ 3524
37.0%
1 1284
 
13.5%
I 1171
 
12.3%
C 1171
 
12.3%
A 1171
 
12.3%
2 356
 
3.7%
3 304
 
3.2%
4 187
 
2.0%
5 124
 
1.3%
6 80
 
0.8%
Other values (7) 151
 
1.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9523
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
_ 3524
37.0%
1 1284
 
13.5%
I 1171
 
12.3%
C 1171
 
12.3%
A 1171
 
12.3%
2 356
 
3.7%
3 304
 
3.2%
4 187
 
2.0%
5 124
 
1.3%
6 80
 
0.8%
Other values (7) 151
 
1.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9523
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
_ 3524
37.0%
1 1284
 
13.5%
I 1171
 
12.3%
C 1171
 
12.3%
A 1171
 
12.3%
2 356
 
3.7%
3 304
 
3.2%
4 187
 
2.0%
5 124
 
1.3%
6 80
 
0.8%
Other values (7) 151
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9523
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
_ 3524
37.0%
1 1284
 
13.5%
I 1171
 
12.3%
C 1171
 
12.3%
A 1171
 
12.3%
2 356
 
3.7%
3 304
 
3.2%
4 187
 
2.0%
5 124
 
1.3%
6 80
 
0.8%
Other values (7) 151
 
1.6%

type_of_the_ac
Real number (ℝ)

Distinct6
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.645602
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size50.6 KiB
2024-11-18T14:10:42.687331image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median3
Q33
95-th percentile4
Maximum6
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.1509657
Coefficient of variation (CV)0.4350487
Kurtosis-0.57487012
Mean2.645602
Median Absolute Deviation (MAD)0
Skewness-0.20159113
Sum3098
Variance1.3247221
MonotonicityNot monotonic
2024-11-18T14:10:42.773169image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3 596
50.9%
1 332
28.4%
4 207
 
17.7%
2 14
 
1.2%
6 12
 
1.0%
5 10
 
0.9%
ValueCountFrequency (%)
1 332
28.4%
2 14
 
1.2%
3 596
50.9%
4 207
 
17.7%
5 10
 
0.9%
6 12
 
1.0%
ValueCountFrequency (%)
6 12
 
1.0%
5 10
 
0.9%
4 207
 
17.7%
3 596
50.9%
2 14
 
1.2%
1 332
28.4%

is_the_ac_inverter_or_not
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size50.6 KiB
1
748 
2
423 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1171
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
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 row1
2nd row2
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 748
63.9%
2 423
36.1%

Length

2024-11-18T14:10:42.865053image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-18T14:10:42.941552image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1 748
63.9%
2 423
36.1%

Most occurring characters

ValueCountFrequency (%)
1 748
63.9%
2 423
36.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1171
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 748
63.9%
2 423
36.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1171
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 748
63.9%
2 423
36.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1171
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 748
63.9%
2 423
36.1%

is_room_fully_sealed
Categorical

Imbalance 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size50.6 KiB
1
1047 
2
 
96
3
 
28

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1171
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
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 row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 1047
89.4%
2 96
 
8.2%
3 28
 
2.4%

Length

2024-11-18T14:10:43.022612image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-18T14:10:43.101849image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1 1047
89.4%
2 96
 
8.2%
3 28
 
2.4%

Most occurring characters

ValueCountFrequency (%)
1 1047
89.4%
2 96
 
8.2%
3 28
 
2.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1171
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 1047
89.4%
2 96
 
8.2%
3 28
 
2.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1171
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 1047
89.4%
2 96
 
8.2%
3 28
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1171
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 1047
89.4%
2 96
 
8.2%
3 28
 
2.4%

wattage_of_the_ac
Real number (ℝ)

Missing  Zeros 

Distinct45
Distinct (%)5.5%
Missing356
Missing (%)30.4%
Infinite0
Infinite (%)0.0%
Mean571.87117
Minimum0
Maximum18000
Zeros610
Zeros (%)52.1%
Negative0
Negative (%)0.0%
Memory size50.6 KiB
2024-11-18T14:10:43.191927image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2000
Maximum18000
Range18000
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2337.6704
Coefficient of variation (CV)4.0877571
Kurtosis23.630431
Mean571.87117
Median Absolute Deviation (MAD)0
Skewness4.8141448
Sum466075
Variance5464703
MonotonicityNot monotonic
2024-11-18T14:10:43.306915image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
0 610
52.1%
12 31
 
2.6%
55 23
 
2.0%
12000 18
 
1.5%
18 16
 
1.4%
9000 10
 
0.9%
1000 8
 
0.7%
25 7
 
0.6%
24 7
 
0.6%
1100 7
 
0.6%
Other values (35) 78
 
6.7%
(Missing) 356
30.4%
ValueCountFrequency (%)
0 610
52.1%
1 2
 
0.2%
2 1
 
0.1%
5 2
 
0.2%
9 5
 
0.4%
10 2
 
0.2%
12 31
 
2.6%
13 3
 
0.3%
15 5
 
0.4%
18 16
 
1.4%
ValueCountFrequency (%)
18000 3
 
0.3%
12000 18
1.5%
9000 10
0.9%
8000 2
 
0.2%
6000 1
 
0.1%
5800 1
 
0.1%
5000 2
 
0.2%
2500 1
 
0.1%
2000 4
 
0.3%
1890 2
 
0.2%

btu_of_the_ac
Real number (ℝ)

Zeros 

Distinct30
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5519.2536
Minimum0
Maximum100000
Zeros67
Zeros (%)5.7%
Negative0
Negative (%)0.0%
Memory size50.6 KiB
2024-11-18T14:10:43.415130image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1999
median999
Q312000
95-th percentile18000
Maximum100000
Range100000
Interquartile range (IQR)11001

Descriptive statistics

Standard deviation8763.6977
Coefficient of variation (CV)1.5878411
Kurtosis44.761527
Mean5519.2536
Median Absolute Deviation (MAD)1
Skewness5.2703441
Sum6463046
Variance76802397
MonotonicityNot monotonic
2024-11-18T14:10:43.519588image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
999 553
47.2%
12000 233
19.9%
1000 109
 
9.3%
9000 71
 
6.1%
0 67
 
5.7%
18000 39
 
3.3%
8000 18
 
1.5%
10000 16
 
1.4%
24000 12
 
1.0%
2000 9
 
0.8%
Other values (20) 44
 
3.8%
ValueCountFrequency (%)
0 67
 
5.7%
999 553
47.2%
1000 109
 
9.3%
1200 3
 
0.3%
1600 2
 
0.2%
1800 2
 
0.2%
2000 9
 
0.8%
2200 2
 
0.2%
2400 2
 
0.2%
3000 1
 
0.1%
ValueCountFrequency (%)
100000 2
 
0.2%
90000 1
 
0.1%
80000 3
 
0.3%
60000 2
 
0.2%
41000 1
 
0.1%
40000 2
 
0.2%
24000 12
 
1.0%
20000 3
 
0.3%
18000 39
3.3%
16000 3
 
0.3%

no_of_hours_ac_was_on_during_daytime_last_week
Real number (ℝ)

Zeros 

Distinct27
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2492143
Minimum0
Maximum70
Zeros993
Zeros (%)84.8%
Negative0
Negative (%)0.0%
Memory size50.6 KiB
2024-11-18T14:10:43.613102image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile14
Maximum70
Range70
Interquartile range (IQR)0

Descriptive statistics

Standard deviation7.63311
Coefficient of variation (CV)3.3936783
Kurtosis29.528784
Mean2.2492143
Median Absolute Deviation (MAD)0
Skewness4.9085963
Sum2633.83
Variance58.264368
MonotonicityNot monotonic
2024-11-18T14:10:43.714614image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0 993
84.8%
7 37
 
3.2%
14 31
 
2.6%
28 16
 
1.4%
21 12
 
1.0%
2 10
 
0.9%
1 8
 
0.7%
4 7
 
0.6%
35 7
 
0.6%
42 7
 
0.6%
Other values (17) 43
 
3.7%
ValueCountFrequency (%)
0 993
84.8%
0.33 1
 
0.1%
0.5 2
 
0.2%
1 8
 
0.7%
2 10
 
0.9%
3 5
 
0.4%
3.5 6
 
0.5%
4 7
 
0.6%
5 2
 
0.2%
6 5
 
0.4%
ValueCountFrequency (%)
70 4
 
0.3%
49 1
 
0.1%
48 1
 
0.1%
42 7
0.6%
35 7
0.6%
28 16
1.4%
24 1
 
0.1%
21 12
1.0%
20 1
 
0.1%
18 1
 
0.1%

no_of_hours_ac_was_on_during_night_last_week
Real number (ℝ)

Zeros 

Distinct65
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.361085
Minimum0
Maximum98
Zeros499
Zeros (%)42.6%
Negative0
Negative (%)0.0%
Memory size50.6 KiB
2024-11-18T14:10:43.827430image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4
Q330
95-th percentile70
Maximum98
Range98
Interquartile range (IQR)30

Descriptive statistics

Standard deviation23.892416
Coefficient of variation (CV)1.3762053
Kurtosis1.0044618
Mean17.361085
Median Absolute Deviation (MAD)4
Skewness1.3788235
Sum20329.83
Variance570.84753
MonotonicityNot monotonic
2024-11-18T14:10:43.944655image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 499
42.6%
14 87
 
7.4%
42 63
 
5.4%
7 61
 
5.2%
56 60
 
5.1%
21 40
 
3.4%
28 39
 
3.3%
35 27
 
2.3%
70 24
 
2.0%
30 23
 
2.0%
Other values (55) 248
21.2%
ValueCountFrequency (%)
0 499
42.6%
0.25 3
 
0.3%
0.5 4
 
0.3%
0.75 1
 
0.1%
0.99 1
 
0.1%
1 19
 
1.6%
1.5 3
 
0.3%
1.66 1
 
0.1%
1.75 1
 
0.1%
2 18
 
1.5%
ValueCountFrequency (%)
98 14
1.2%
90 1
 
0.1%
86 1
 
0.1%
85 1
 
0.1%
84 10
0.9%
81 1
 
0.1%
80 3
 
0.3%
77 2
 
0.2%
72 2
 
0.2%
70.5 1
 
0.1%

Interactions

2024-11-18T14:10:41.481982image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:10:39.465752image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:10:39.897768image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:10:40.613764image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:10:41.047545image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:10:41.563516image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:10:39.547133image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:10:39.980563image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:10:40.694086image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:10:41.131289image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:10:41.656564image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:10:39.637707image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:10:40.334230image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:10:40.796569image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:10:41.222574image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:10:41.739910image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:10:39.725965image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:10:40.429572image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:10:40.879450image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:10:41.307152image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:10:41.832056image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:10:39.816555image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:10:40.523415image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:10:40.967080image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-18T14:10:41.396644image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Missing values

2024-11-18T14:10:41.945450image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-11-18T14:10:42.081200image/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

room_IDac_IDtype_of_the_acis_the_ac_inverter_or_notis_room_fully_sealedwattage_of_the_acbtu_of_the_acno_of_hours_ac_was_on_during_daytime_last_weekno_of_hours_ac_was_on_during_night_last_week
household_ID
ID0012I_3I_3_AC_14111500.09990.045.0
ID0014I_3I_3_AC_16210.09990.00.0
ID0018I_5I_5_AC_13112000.09990.056.0
ID0025I_2I_2_AC_11110.09990.00.0
ID0039I_8I_8_AC_13110.099912.012.0
ID0039I_9I_9_AC_13110.0900012.012.0
ID0041I_8I_8_AC_15110.09990.021.0
ID0043I_3I_3_AC_13210.09990.00.0
ID0043I_18I_18_AC_13210.099942.021.0
ID0043I_19I_19_AC_13210.099942.042.0
room_IDac_IDtype_of_the_acis_the_ac_inverter_or_notis_room_fully_sealedwattage_of_the_acbtu_of_the_acno_of_hours_ac_was_on_during_daytime_last_weekno_of_hours_ac_was_on_during_night_last_week
household_ID
ID3910I_3I_3_AC_1311NaN120000.056.0
ID3910I_13I_13_AC_1323NaN120000.04.0
ID3910I_14I_14_AC_1311NaN120000.021.0
ID3910I_15I_15_AC_1311NaN120000.00.0
ID3943I_2I_2_AC_1122NaN9990.00.0
ID3950I_1I_1_AC_1421NaN10000.02.0
ID4015I_2I_2_AC_1411NaN9990.00.0
ID4026I_2I_2_AC_1411NaN9990.02.0
ID4053I_2I_2_AC_1421NaN10007.07.0
ID4055I_4I_4_AC_1411NaN10000.00.0

Duplicate rows

Most frequently occurring

room_IDac_IDtype_of_the_acis_the_ac_inverter_or_notis_room_fully_sealedwattage_of_the_acbtu_of_the_acno_of_hours_ac_was_on_during_daytime_last_weekno_of_hours_ac_was_on_during_night_last_week# duplicates
12I_2I_2_AC_13110.09990.00.011
23I_2I_2_AC_14110.09990.00.08
6I_2I_2_AC_11110.09990.00.07
46I_3I_3_AC_14110.09990.00.07
32I_3I_3_AC_1111NaN9990.00.06
38I_3I_3_AC_1311NaN9990.00.06
63I_4I_4_AC_1311NaN9990.00.05
81I_6I_6_AC_13110.0120000.00.05
16I_2I_2_AC_1311NaN9990.00.04
19I_2I_2_AC_13210.09990.00.04