Overview

Dataset statistics

Number of variables7
Number of observations10000
Missing cells20000
Missing cells (%)28.6%
Duplicate rows44
Duplicate rows (%)0.4%
Total size in memory615.5 KiB
Average record size in memory63.0 B

Variable types

Unsupported3
Categorical2
Numeric1
DateTime1

Dataset

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

Alerts

모델명 has constant value ""Constant
Dataset has 44 (0.4%) duplicate rowsDuplicates
고유번호 is highly imbalanced (59.3%)Imbalance
악취저감장치 연속OFF시간 has 10000 (100.0%) missing valuesMissing
등록일시 has 10000 (100.0%) missing valuesMissing
기관 명 is an unsupported type, check if it needs cleaning or further analysisUnsupported
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 8367 (83.7%) zerosZeros

Reproduction

Analysis started2024-05-11 16:40:29.587867
Analysis finished2024-05-11 16:40:30.870795
Duration1.28 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기관 명
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size97.9 KiB

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

2024-05-12T01:40:31.068199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-12T01:40:31.352748image/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
9187 
0
 
813

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 9187
91.9%
0 813
 
8.1%

Length

2024-05-12T01:40:31.655789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-12T01:40:31.950216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 9187
91.9%
0 813
 
8.1%
Distinct1176
Distinct (%)11.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean469.9746
Minimum0
Maximum22370
Zeros8367
Zeros (%)83.7%
Negative0
Negative (%)0.0%
Memory size107.7 KiB
2024-05-12T01:40:32.286043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2111.65
Maximum22370
Range22370
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1861.6428
Coefficient of variation (CV)3.9611563
Kurtosis40.075888
Mean469.9746
Median Absolute Deviation (MAD)0
Skewness5.7930015
Sum4699746
Variance3465714.1
MonotonicityNot monotonic
2024-05-12T01:40:32.697587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 8367
83.7%
1234 9
 
0.1%
60 7
 
0.1%
1244 7
 
0.1%
1298 6
 
0.1%
3 6
 
0.1%
1252 6
 
0.1%
1239 6
 
0.1%
1278 5
 
0.1%
1177 5
 
0.1%
Other values (1166) 1576
 
15.8%
ValueCountFrequency (%)
0 8367
83.7%
2 2
 
< 0.1%
3 6
 
0.1%
4 2
 
< 0.1%
5 3
 
< 0.1%
6 4
 
< 0.1%
7 4
 
< 0.1%
8 2
 
< 0.1%
9 2
 
< 0.1%
10 3
 
< 0.1%
ValueCountFrequency (%)
22370 1
< 0.1%
22369 1
< 0.1%
22367 1
< 0.1%
22355 1
< 0.1%
22341 1
< 0.1%
22323 1
< 0.1%
22319 1
< 0.1%
22301 1
< 0.1%
17074 1
< 0.1%
17064 1
< 0.1%
Distinct9913
Distinct (%)99.1%
Missing0
Missing (%)0.0%
Memory size97.9 KiB
Minimum2024-01-29 00:00:06
Maximum2024-02-01 13:56:03
2024-05-12T01:40:33.087640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:40:33.512562image/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

2024-05-12T01:40:29.959288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-12T01:40:33.771868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
고유번호IoT기기상태값(꺼짐:0, 켜짐:1, 알수없음:2)
고유번호1.0000.492
IoT기기상태값(꺼짐:0, 켜짐:1, 알수없음:2)0.4921.000

Missing values

2024-05-12T01:40:30.297031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-12T01:40:30.696968image/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
서울시NTS10000000002421112452024-01-29 14:03:52<NA><NA>
NTS1004041102024-01-31 16:05:11<NA><NA>
NTS1005841102024-01-31 19:57:37<NA><NA>
NTS10000000004211102024-01-30 21:18:00<NA><NA>
NTS10000000008681102024-01-30 23:44:49<NA><NA>
NTS1007511102024-02-01 12:25:01<NA><NA>
NTS10000000006731102024-01-31 07:29:58<NA><NA>
NTS10010721102024-02-01 08:36:18<NA><NA>
NTS10000000010091102024-01-30 03:29:44<NA><NA>
NTS10000000004191102024-01-31 01:59:15<NA><NA>
기관 명모델명고유번호IoT기기상태값(꺼짐:0, 켜짐:1, 알수없음:2)악취저감장치상태(꺼짐:0, 켜짐:1, 알수없음:2)악취저감장치 연속OFF시간등록일시
서울시NTS10000000007251102024-01-29 06:21:12<NA><NA>
NTS10000000004941102024-01-31 01:59:19<NA><NA>
NTS10000000002871102024-01-31 03:31:38<NA><NA>
NTS10020691102024-02-01 08:37:24<NA><NA>
NTS10000000004241102024-01-30 01:53:07<NA><NA>
NTS10000000002251102024-01-29 06:15:10<NA><NA>
NTS10000000003891102024-01-31 00:26:26<NA><NA>
NTS10000000006361102024-01-29 17:15:28<NA><NA>
NTS10000000005501102024-01-31 02:00:13<NA><NA>
NTS1001101102024-01-31 08:56:37<NA><NA>

Duplicate rows

Most frequently occurring

모델명고유번호IoT기기상태값(꺼짐:0, 켜짐:1, 알수없음:2)악취저감장치상태(꺼짐:0, 켜짐:1, 알수없음:2)# duplicates
261102024-01-31 02:04:565
61102024-01-29 02:24:224
131102024-01-29 02:31:034
241102024-01-30 02:38:414
291102024-01-31 02:09:304
401102024-02-01 02:15:254
21102024-01-29 02:18:533
91102024-01-29 02:27:373
121102024-01-29 02:28:253
171102024-01-29 02:34:103