Overview

Dataset statistics

Number of variables6
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory566.4 KiB
Average record size in memory58.0 B

Variable types

DateTime1
Numeric2
Categorical3

Dataset

Description김해도시개발공사 상동 하수처리시설별에 대한 시간대별 가동시간 현황을 조회하는 서비스로 기준연월일, 기준시간, 하수처리장구분명, 가동시간 등의 정보를 제공
Author김해시도시개발공사
URLhttps://www.data.go.kr/data/15096554/fileData.do

Alerts

하수처리장구분명 has constant value ""Constant
태그 is highly overall correlated with 태그설명High correlation
태그설명 is highly overall correlated with 태그High correlation
기준시간 has 432 (4.3%) zerosZeros
가동시간 has 880 (8.8%) zerosZeros

Reproduction

Analysis started2023-12-12 06:43:57.080072
Analysis finished2023-12-12 06:43:58.126244
Duration1.05 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기준연월일
Date

Distinct1657
Distinct (%)16.6%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2016-12-31 00:00:00
Maximum2021-08-25 00:00:00
2023-12-12T15:43:58.209076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:43:58.404830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

기준시간
Real number (ℝ)

ZEROS 

Distinct24
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.4763
Minimum0
Maximum23
Zeros432
Zeros (%)4.3%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T15:43:58.589842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q15
median11
Q317
95-th percentile22
Maximum23
Range23
Interquartile range (IQR)12

Descriptive statistics

Standard deviation6.9291009
Coefficient of variation (CV)0.60377482
Kurtosis-1.1937361
Mean11.4763
Median Absolute Deviation (MAD)6
Skewness0.0052111207
Sum114763
Variance48.01244
MonotonicityNot monotonic
2023-12-12T15:43:58.715315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
4 456
 
4.6%
11 444
 
4.4%
23 442
 
4.4%
3 434
 
4.3%
13 433
 
4.3%
0 432
 
4.3%
15 430
 
4.3%
12 428
 
4.3%
17 419
 
4.2%
10 419
 
4.2%
Other values (14) 5663
56.6%
ValueCountFrequency (%)
0 432
4.3%
1 416
4.2%
2 398
4.0%
3 434
4.3%
4 456
4.6%
5 383
3.8%
6 414
4.1%
7 388
3.9%
8 414
4.1%
9 410
4.1%
ValueCountFrequency (%)
23 442
4.4%
22 413
4.1%
21 402
4.0%
20 404
4.0%
19 410
4.1%
18 394
3.9%
17 419
4.2%
16 401
4.0%
15 430
4.3%
14 416
4.2%

하수처리장구분명
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
상동 공공하수처리시설
10000 

Length

Max length11
Median length11
Mean length11
Min length11

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row상동 공공하수처리시설
2nd row상동 공공하수처리시설
3rd row상동 공공하수처리시설
4th row상동 공공하수처리시설
5th row상동 공공하수처리시설

Common Values

ValueCountFrequency (%)
상동 공공하수처리시설 10000
100.0%

Length

2023-12-12T15:43:58.854521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:43:58.964643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
상동 10000
50.0%
공공하수처리시설 10000
50.0%

태그설명
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
미세목스크린
4210 
세목스크린
4131 
협잡물이송콘베어
1659 

Length

Max length8
Median length6
Mean length5.9187
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row세목스크린
2nd row세목스크린
3rd row미세목스크린
4th row세목스크린
5th row협잡물이송콘베어

Common Values

ValueCountFrequency (%)
미세목스크린 4210
42.1%
세목스크린 4131
41.3%
협잡물이송콘베어 1659
 
16.6%

Length

2023-12-12T15:43:59.100815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:43:59.220838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
미세목스크린 4210
42.1%
세목스크린 4131
41.3%
협잡물이송콘베어 1659
 
16.6%

태그
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
MS-102A
5869 
MS-103
4131 

Length

Max length7
Median length7
Mean length6.5869
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMS-103
2nd rowMS-103
3rd rowMS-102A
4th rowMS-103
5th rowMS-102A

Common Values

ValueCountFrequency (%)
MS-102A 5869
58.7%
MS-103 4131
41.3%

Length

2023-12-12T15:43:59.344790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:43:59.784851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
ms-102a 5869
58.7%
ms-103 4131
41.3%

가동시간
Real number (ℝ)

ZEROS 

Distinct56
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.7001
Minimum0
Maximum60
Zeros880
Zeros (%)8.8%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T15:43:59.927813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16
median12
Q317
95-th percentile40
Maximum60
Range60
Interquartile range (IQR)11

Descriptive statistics

Standard deviation14.119309
Coefficient of variation (CV)0.89931334
Kurtosis0.88999849
Mean15.7001
Median Absolute Deviation (MAD)5
Skewness1.2870849
Sum157001
Variance199.3549
MonotonicityNot monotonic
2023-12-12T15:44:00.142289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15 1382
13.8%
40 1258
12.6%
10 1163
11.6%
0 880
 
8.8%
12 785
 
7.8%
8 665
 
6.7%
4 640
 
6.4%
5 332
 
3.3%
2 309
 
3.1%
20 275
 
2.8%
Other values (46) 2311
23.1%
ValueCountFrequency (%)
0 880
8.8%
1 136
 
1.4%
2 309
 
3.1%
3 49
 
0.5%
4 640
6.4%
5 332
 
3.3%
6 193
 
1.9%
7 151
 
1.5%
8 665
6.7%
9 151
 
1.5%
ValueCountFrequency (%)
60 224
2.2%
59 1
 
< 0.1%
58 2
 
< 0.1%
56 1
 
< 0.1%
53 2
 
< 0.1%
51 1
 
< 0.1%
49 1
 
< 0.1%
48 1
 
< 0.1%
47 1
 
< 0.1%
46 2
 
< 0.1%

Interactions

2023-12-12T15:43:57.653940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:43:57.436951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:43:57.758439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:43:57.559518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T15:44:00.268090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준시간태그설명태그가동시간
기준시간1.0000.0000.0000.053
태그설명0.0001.0001.0000.405
태그0.0001.0001.0000.293
가동시간0.0530.4050.2931.000
2023-12-12T15:44:00.388656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
태그태그설명
태그1.0001.000
태그설명1.0001.000
2023-12-12T15:44:00.500424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준시간가동시간태그설명태그
기준시간1.0000.0110.0000.000
가동시간0.0111.0000.2700.249
태그설명0.0000.2701.0001.000
태그0.0000.2491.0001.000

Missing values

2023-12-12T15:43:57.913794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T15:43:58.061389image/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

기준연월일기준시간하수처리장구분명태그설명태그가동시간
306762020-07-204상동 공공하수처리시설세목스크린MS-10340
172192019-01-0211상동 공공하수처리시설세목스크린MS-10312
650982019-12-0310상동 공공하수처리시설미세목스크린MS-102A40
292172020-05-209상동 공공하수처리시설세목스크린MS-1030
837852017-06-181상동 공공하수처리시설협잡물이송콘베어MS-102A17
885282018-01-0816상동 공공하수처리시설협잡물이송콘베어MS-102A20
494642018-02-160상동 공공하수처리시설미세목스크린MS-102A16
391382021-07-2418상동 공공하수처리시설세목스크린MS-1036
474852017-11-1913상동 공공하수처리시설미세목스크린MS-102A15
254422019-12-122상동 공공하수처리시설세목스크린MS-10315
기준연월일기준시간하수처리장구분명태그설명태그가동시간
859352017-09-2015상동 공공하수처리시설협잡물이송콘베어MS-102A10
749632021-01-2211상동 공공하수처리시설미세목스크린MS-102A2
764562021-04-0316상동 공공하수처리시설미세목스크린MS-102A4
512402018-05-030상동 공공하수처리시설미세목스크린MS-102A8
566522018-12-1512상동 공공하수처리시설미세목스크린MS-102A15
327512020-10-1415상동 공공하수처리시설세목스크린MS-10340
945062018-09-2218상동 공공하수처리시설협잡물이송콘베어MS-102A12
885022018-01-0714상동 공공하수처리시설협잡물이송콘베어MS-102A20
655122019-12-2216상동 공공하수처리시설미세목스크린MS-102A40
40422017-06-1710상동 공공하수처리시설세목스크린MS-1035