Overview

Dataset statistics

Number of variables7
Number of observations10000
Missing cells20000
Missing cells (%)28.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory625.3 KiB
Average record size in memory64.0 B

Variable types

Numeric2
Categorical2
DateTime1
Unsupported2

Dataset

Description파일 다운로드
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-15966/S/1/datasetView.do

Alerts

모델명 has constant value ""Constant
고유번호 is highly imbalanced (59.7%)Imbalance
악취저감장치 연속OFF시간 has 10000 (100.0%) missing valuesMissing
등록일시 has 10000 (100.0%) missing valuesMissing
기관 명 is highly skewed (γ1 = 30.50597919)Skewed
악취저감장치 연속OFF시간 is an unsupported type, check if it needs cleaning or further analysisUnsupported
등록일시 is an unsupported type, check if it needs cleaning or further analysisUnsupported
IoT기기상태값(꺼짐:0, 켜짐:1, 알수없음:2) has 8218 (82.2%) zerosZeros

Reproduction

Analysis started2024-05-11 16:40:56.990850
Analysis finished2024-05-11 16:40:58.314269
Duration1.32 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기관 명
Real number (ℝ)

SKEWED 

Distinct900
Distinct (%)9.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean730.2672
Minimum0
Maximum101475
Zeros9
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size107.7 KiB
2024-05-12T01:40:58.448287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile51
Q1273
median552
Q3876
95-th percentile2063
Maximum101475
Range101475
Interquartile range (IQR)603

Descriptive statistics

Standard deviation3224.6579
Coefficient of variation (CV)4.4157233
Kurtosis950.36555
Mean730.2672
Median Absolute Deviation (MAD)298
Skewness30.505979
Sum7302672
Variance10398419
MonotonicityNot monotonic
2024-05-12T01:40:58.697817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
276 35
 
0.4%
69 28
 
0.3%
23 25
 
0.2%
873 23
 
0.2%
438 23
 
0.2%
40 23
 
0.2%
373 21
 
0.2%
756 21
 
0.2%
950 21
 
0.2%
182 20
 
0.2%
Other values (890) 9760
97.6%
ValueCountFrequency (%)
0 9
0.1%
1 8
0.1%
2 10
0.1%
4 13
0.1%
5 8
0.1%
6 15
0.1%
7 7
0.1%
8 11
0.1%
9 11
0.1%
10 16
0.2%
ValueCountFrequency (%)
101475 10
0.1%
2117 17
0.2%
2116 14
0.1%
2114 13
0.1%
2113 13
0.1%
2112 11
0.1%
2111 13
0.1%
2110 12
0.1%
2108 10
0.1%
2107 9
0.1%

모델명
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size97.9 KiB
1
10000 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 10000
100.0%

Length

2024-05-12T01:40:58.921699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-12T01:40:59.075466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 10000
100.0%

고유번호
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size97.9 KiB
1
9197 
0
 
803

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 9197
92.0%
0 803
 
8.0%

Length

2024-05-12T01:40:59.235667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-12T01:40:59.396217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 9197
92.0%
0 803
 
8.0%
Distinct1245
Distinct (%)12.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean608.6133
Minimum0
Maximum23223
Zeros8218
Zeros (%)82.2%
Negative0
Negative (%)0.0%
Memory size107.7 KiB
2024-05-12T01:40:59.586046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile4065.3
Maximum23223
Range23223
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2172.2451
Coefficient of variation (CV)3.5691712
Kurtosis32.467068
Mean608.6133
Median Absolute Deviation (MAD)0
Skewness5.1692631
Sum6086133
Variance4718648.6
MonotonicityNot monotonic
2024-05-12T01:40:59.942907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 8218
82.2%
13 11
 
0.1%
19 11
 
0.1%
8 10
 
0.1%
11 9
 
0.1%
10 9
 
0.1%
46 9
 
0.1%
20 9
 
0.1%
9 8
 
0.1%
5 8
 
0.1%
Other values (1235) 1698
 
17.0%
ValueCountFrequency (%)
0 8218
82.2%
2 6
 
0.1%
3 7
 
0.1%
4 8
 
0.1%
5 8
 
0.1%
6 5
 
0.1%
7 5
 
0.1%
8 10
 
0.1%
9 8
 
0.1%
10 9
 
0.1%
ValueCountFrequency (%)
23223 1
< 0.1%
23215 1
< 0.1%
23209 1
< 0.1%
23196 1
< 0.1%
23195 1
< 0.1%
23194 1
< 0.1%
23173 1
< 0.1%
23163 1
< 0.1%
23162 1
< 0.1%
23143 1
< 0.1%
Distinct9909
Distinct (%)99.1%
Missing0
Missing (%)0.0%
Memory size97.9 KiB
Minimum2024-03-04 00:21:56
Maximum2024-03-07 14:38:39
2024-05-12T01:41:00.173663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:41:00.417624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

악취저감장치 연속OFF시간
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size107.7 KiB

등록일시
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size107.7 KiB

Interactions

2024-05-12T01:40:57.658967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:40:57.353633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:40:57.815292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:40:57.507477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-12T01:41:00.578669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기관 명고유번호IoT기기상태값(꺼짐:0, 켜짐:1, 알수없음:2)
기관 명1.0000.0000.000
고유번호0.0001.0000.484
IoT기기상태값(꺼짐:0, 켜짐:1, 알수없음:2)0.0000.4841.000

Missing values

2024-05-12T01:40:58.011151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-12T01:40:58.220957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

기관 명모델명고유번호IoT기기상태값(꺼짐:0, 켜짐:1, 알수없음:2)악취저감장치상태(꺼짐:0, 켜짐:1, 알수없음:2)악취저감장치 연속OFF시간등록일시
서울시NTS1008141102024-03-06 04:13:54<NA><NA>
NTS1002151194222024-03-06 13:28:33<NA><NA>
NTS1007451102024-03-05 08:42:27<NA><NA>
NTS100451102024-03-07 11:29:16<NA><NA>
NTS1003721102024-03-07 01:13:28<NA><NA>
NTS100421102024-03-04 09:08:41<NA><NA>
NTS1006551102024-03-04 15:31:02<NA><NA>
NTS1006851102024-03-05 04:00:47<NA><NA>
NTS1002931102024-03-05 22:40:00<NA><NA>
NTS1008161102024-03-07 13:12:51<NA><NA>
기관 명모델명고유번호IoT기기상태값(꺼짐:0, 켜짐:1, 알수없음:2)악취저감장치상태(꺼짐:0, 켜짐:1, 알수없음:2)악취저감장치 연속OFF시간등록일시
서울시NTS100631102024-03-04 15:23:43<NA><NA>
NTS1007131102024-03-06 16:42:00<NA><NA>
NTS1002851102024-03-05 14:04:55<NA><NA>
NTS10010531102024-03-04 23:25:37<NA><NA>
NTS1003681102024-03-07 00:25:58<NA><NA>
NTS100971102024-03-07 13:50:34<NA><NA>
NTS1003821127502024-03-07 13:12:11<NA><NA>
NTS1009811167612024-03-06 16:46:19<NA><NA>
NTS1004411102024-03-04 23:15:49<NA><NA>
NTS1004711102024-03-06 12:43:52<NA><NA>