Overview

Dataset statistics

Number of variables9
Number of observations158
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory11.7 KiB
Average record size in memory75.8 B

Variable types

Text2
Categorical5
Numeric2

Dataset

Description관리번호,구분코드,관리기관,관리부서,시설물명,집수정위치,자치구,X좌표,Y좌표
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-21117/S/1/datasetView.do

Alerts

구분코드 has constant value ""Constant
관리기관 has constant value ""Constant
시설물명 has constant value ""Constant
X좌표 is highly overall correlated with 관리부서 and 1 other fieldsHigh correlation
Y좌표 is highly overall correlated with 관리부서 and 1 other fieldsHigh correlation
관리부서 is highly overall correlated with X좌표 and 2 other fieldsHigh correlation
자치구 is highly overall correlated with X좌표 and 2 other fieldsHigh correlation
관리번호 has unique valuesUnique

Reproduction

Analysis started2023-12-11 07:48:10.885682
Analysis finished2023-12-11 07:48:12.102801
Duration1.22 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

관리번호
Text

UNIQUE 

Distinct158
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
2023-12-11T16:48:12.313434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters1738
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique158 ?
Unique (%)100.0%

Sample

1st row2017_1_0212
2nd row2017_1_0214
3rd row2017_1_0215
4th row2017_1_0216
5th row2017_1_0217
ValueCountFrequency (%)
2017_1_0212 1
 
0.6%
2017_2_0251 1
 
0.6%
2017_2_0256 1
 
0.6%
2017_2_0244 1
 
0.6%
2017_2_0245 1
 
0.6%
2017_2_0247 1
 
0.6%
2017_2_0248 1
 
0.6%
2017_2_0249 1
 
0.6%
2017_2_0250 1
 
0.6%
2017_2_0241 1
 
0.6%
Other values (148) 148
93.7%
2023-12-11T16:48:12.716194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 414
23.8%
0 345
19.9%
_ 316
18.2%
1 263
15.1%
7 192
11.0%
3 46
 
2.6%
6 35
 
2.0%
8 33
 
1.9%
9 33
 
1.9%
4 32
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1422
81.8%
Connector Punctuation 316
 
18.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 414
29.1%
0 345
24.3%
1 263
18.5%
7 192
13.5%
3 46
 
3.2%
6 35
 
2.5%
8 33
 
2.3%
9 33
 
2.3%
4 32
 
2.3%
5 29
 
2.0%
Connector Punctuation
ValueCountFrequency (%)
_ 316
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1738
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 414
23.8%
0 345
19.9%
_ 316
18.2%
1 263
15.1%
7 192
11.0%
3 46
 
2.6%
6 35
 
2.0%
8 33
 
1.9%
9 33
 
1.9%
4 32
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1738
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 414
23.8%
0 345
19.9%
_ 316
18.2%
1 263
15.1%
7 192
11.0%
3 46
 
2.6%
6 35
 
2.0%
8 33
 
1.9%
9 33
 
1.9%
4 32
 
1.8%

구분코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
2
158 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 158
100.0%

Length

2023-12-11T16:48:12.861458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T16:48:12.958949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 158
100.0%

관리기관
Categorical

CONSTANT 

Distinct1
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
㈜KT
158 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row㈜KT
2nd row㈜KT
3rd row㈜KT
4th row㈜KT
5th row㈜KT

Common Values

ValueCountFrequency (%)
㈜KT 158
100.0%

Length

2023-12-11T16:48:13.057582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T16:48:13.153594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
㈜kt 158
100.0%

관리부서
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)6.3%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
통신구팀
84 
kt 신촌지점
14 
kt 광화문지사(혜화)
12 
kt 성수지점
10 
kt 서대문지사(가좌)
10 
Other values (5)
28 

Length

Max length12
Median length4
Mean length6.1898734
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowkt 성수지점
2nd rowkt 성수지점
3rd rowkt 성수지점
4th rowkt 성수지점
5th rowkt 성수지점

Common Values

ValueCountFrequency (%)
통신구팀 84
53.2%
kt 신촌지점 14
 
8.9%
kt 광화문지사(혜화) 12
 
7.6%
kt 성수지점 10
 
6.3%
kt 서대문지사(가좌) 10
 
6.3%
kt 원효지점 10
 
6.3%
KT구로지사 9
 
5.7%
kt 도봉지점(방학) 4
 
2.5%
KT동작지사 3
 
1.9%
kt 서대문지사(홍제) 2
 
1.3%

Length

2023-12-11T16:48:13.272521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T16:48:13.400404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
통신구팀 84
38.2%
kt 62
28.2%
신촌지점 14
 
6.4%
광화문지사(혜화 12
 
5.5%
성수지점 10
 
4.5%
서대문지사(가좌 10
 
4.5%
원효지점 10
 
4.5%
kt구로지사 9
 
4.1%
도봉지점(방학 4
 
1.8%
kt동작지사 3
 
1.4%

시설물명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
통신구
158 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
통신구 158
100.0%

Length

2023-12-11T16:48:13.580096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T16:48:13.678005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
통신구 158
100.0%

집수정위치
Text

Distinct114
Distinct (%)72.2%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
2023-12-11T16:48:13.890280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length37
Median length29
Mean length23.405063
Min length14

Characters and Unicode

Total characters3698
Distinct characters200
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique70 ?
Unique (%)44.3%

Sample

1st row성동구 아차산로 13길47 (KT성수지점앞)
2nd row성동구 아차산로 13길54(신한은행앞)
3rd row광진구 동일로 190(화양사거리)
4th row성동구 아차산로 13길47 (KT 성수지점앞)
5th row성동구 아차산로 13길47 (KT 성수지점앞)
ValueCountFrequency (%)
서울시 39
 
6.5%
성북구 24
 
4.0%
집수정 24
 
4.0%
종로구 18
 
3.0%
동대문구 16
 
2.7%
앞 16
 
2.7%
서대문구 14
 
2.3%
마포구 14
 
2.3%
용산구 10
 
1.7%
연건동 8
 
1.3%
Other values (183) 416
69.4%
2023-12-11T16:48:14.284790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
453
 
12.2%
로 177
 
4.8%
( 158
 
4.3%
) 158
 
4.3%
구 147
 
4.0%
동 133
 
3.6%
1 128
 
3.5%
2 98
 
2.7%
문 74
 
2.0%
4 73
 
2.0%
Other values (190) 2099
56.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2161
58.4%
Decimal Number 653
 
17.7%
Space Separator 453
 
12.2%
Open Punctuation 158
 
4.3%
Close Punctuation 158
 
4.3%
Dash Punctuation 57
 
1.5%
Other Punctuation 26
 
0.7%
Uppercase Letter 16
 
0.4%
Lowercase Letter 16
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 177
 
8.2%
구 147
 
6.8%
동 133
 
6.2%
문 74
 
3.4%
앞 64
 
3.0%
서 61
 
2.8%
대 55
 
2.5%
시 49
 
2.3%
성 48
 
2.2%
가 43
 
2.0%
Other values (171) 1310
60.6%
Decimal Number
ValueCountFrequency (%)
1 128
19.6%
2 98
15.0%
4 73
11.2%
3 66
10.1%
7 52
8.0%
6 52
8.0%
8 51
 
7.8%
5 47
 
7.2%
9 45
 
6.9%
0 41
 
6.3%
Uppercase Letter
ValueCountFrequency (%)
T 8
50.0%
K 8
50.0%
Lowercase Letter
ValueCountFrequency (%)
t 8
50.0%
k 8
50.0%
Space Separator
ValueCountFrequency (%)
453
100.0%
Open Punctuation
ValueCountFrequency (%)
( 158
100.0%
Close Punctuation
ValueCountFrequency (%)
) 158
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 57
100.0%
Other Punctuation
ValueCountFrequency (%)
, 26
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2161
58.4%
Common 1505
40.7%
Latin 32
 
0.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 177
 
8.2%
구 147
 
6.8%
동 133
 
6.2%
문 74
 
3.4%
앞 64
 
3.0%
서 61
 
2.8%
대 55
 
2.5%
시 49
 
2.3%
성 48
 
2.2%
가 43
 
2.0%
Other values (171) 1310
60.6%
Common
ValueCountFrequency (%)
453
30.1%
( 158
 
10.5%
) 158
 
10.5%
1 128
 
8.5%
2 98
 
6.5%
4 73
 
4.9%
3 66
 
4.4%
- 57
 
3.8%
7 52
 
3.5%
6 52
 
3.5%
Other values (5) 210
14.0%
Latin
ValueCountFrequency (%)
T 8
25.0%
K 8
25.0%
t 8
25.0%
k 8
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2161
58.4%
ASCII 1537
41.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
453
29.5%
( 158
 
10.3%
) 158
 
10.3%
1 128
 
8.3%
2 98
 
6.4%
4 73
 
4.7%
3 66
 
4.3%
- 57
 
3.7%
7 52
 
3.4%
6 52
 
3.4%
Other values (9) 242
15.7%
Hangul
ValueCountFrequency (%)
로 177
 
8.2%
구 147
 
6.8%
동 133
 
6.2%
문 74
 
3.4%
앞 64
 
3.0%
서 61
 
2.8%
대 55
 
2.5%
시 49
 
2.3%
성 48
 
2.2%
가 43
 
2.0%
Other values (171) 1310
60.6%

자치구
Categorical

HIGH CORRELATION 

Distinct16
Distinct (%)10.1%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
성북구
26 
종로구
22 
동대문구
18 
마포구
16 
중 구
16 
Other values (11)
60 

Length

Max length4
Median length3
Mean length3.2911392
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row성동구
2nd row성동구
3rd row광진구
4th row성동구
5th row성동구

Common Values

ValueCountFrequency (%)
성북구 26
16.5%
종로구 22
13.9%
동대문구 18
11.4%
마포구 16
10.1%
중 구 16
10.1%
서대문구 12
7.6%
용산구 12
7.6%
성동구 8
 
5.1%
관악구 6
 
3.8%
은평구 4
 
2.5%
Other values (6) 18
11.4%

Length

2023-12-11T16:48:14.414795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
성북구 26
14.9%
종로구 22
12.6%
동대문구 18
10.3%
마포구 16
9.2%
중 16
9.2%
구 16
9.2%
서대문구 12
6.9%
용산구 12
6.9%
성동구 8
 
4.6%
관악구 6
 
3.4%
Other values (7) 22
12.6%

X좌표
Real number (ℝ)

HIGH CORRELATION 

Distinct76
Distinct (%)48.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean199168.87
Minimum189979.6
Maximum206022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-12-11T16:48:14.558920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum189979.6
5-th percentile192139.6
Q1195370.9
median200129.2
Q3202655.1
95-th percentile205470.4
Maximum206022
Range16042.4
Interquartile range (IQR)7284.2

Descriptive statistics

Standard deviation4579.6085
Coefficient of variation (CV)0.022993596
Kurtosis-1.1797124
Mean199168.87
Median Absolute Deviation (MAD)3627.6
Skewness-0.26277667
Sum31468681
Variance20972814
MonotonicityNot monotonic
2023-12-11T16:48:14.757007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
205470.4 6
 
3.8%
200129.2 4
 
2.5%
194195.2 3
 
1.9%
205190.0 2
 
1.3%
203628.4 2
 
1.3%
204030.0 2
 
1.3%
204759.2 2
 
1.3%
205464.0 2
 
1.3%
197224.8 2
 
1.3%
197292.8 2
 
1.3%
Other values (66) 131
82.9%
ValueCountFrequency (%)
189979.6 2
1.3%
190331.2 1
0.6%
191185.6 2
1.3%
191433.2 2
1.3%
192139.6 2
1.3%
192335.2 2
1.3%
192342.8 2
1.3%
192363.6 2
1.3%
192412.4 2
1.3%
193193.2 2
1.3%
ValueCountFrequency (%)
206022.0 2
 
1.3%
205551.6 2
 
1.3%
205470.4 6
3.8%
205464.0 2
 
1.3%
205348.8 2
 
1.3%
205190.0 2
 
1.3%
205045.2 2
 
1.3%
204990.8 2
 
1.3%
204938.8 2
 
1.3%
204831.6 2
 
1.3%

Y좌표
Real number (ℝ)

HIGH CORRELATION 

Distinct76
Distinct (%)48.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean452087.46
Minimum442229.2
Maximum463028
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-12-11T16:48:14.954931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum442229.2
5-th percentile442724.4
Q1450889.6
median452362.4
Q3454055
95-th percentile456722.4
Maximum463028
Range20798.8
Interquartile range (IQR)3165.4

Descriptive statistics

Standard deviation3770.6771
Coefficient of variation (CV)0.008340592
Kurtosis1.8303138
Mean452087.46
Median Absolute Deviation (MAD)1473.6
Skewness-0.31410007
Sum71429819
Variance14218006
MonotonicityNot monotonic
2023-12-11T16:48:15.108902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
449751.6 6
 
3.8%
452999.2 4
 
2.5%
445781.2 3
 
1.9%
451523.2 2
 
1.3%
455880.4 2
 
1.3%
456104.8 2
 
1.3%
456660.0 2
 
1.3%
455882.8 2
 
1.3%
451165.6 2
 
1.3%
451356.4 2
 
1.3%
Other values (66) 131
82.9%
ValueCountFrequency (%)
442229.2 1
 
0.6%
442266.4 2
1.3%
442519.6 2
1.3%
442535.2 2
1.3%
442724.4 2
1.3%
445781.2 3
1.9%
447359.6 2
1.3%
447432.0 2
1.3%
448432.0 2
1.3%
448483.2 2
1.3%
ValueCountFrequency (%)
463028.0 2
1.3%
462838.8 2
1.3%
458283.2 2
1.3%
457076.0 2
1.3%
456660.0 2
1.3%
456104.8 2
1.3%
455952.8 2
1.3%
455910.8 2
1.3%
455882.8 2
1.3%
455880.4 2
1.3%

Interactions

2023-12-11T16:48:11.716213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:48:11.189391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:48:11.808847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:48:11.328015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T16:48:15.201111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관리부서자치구X좌표Y좌표
관리부서1.0000.9720.9450.919
자치구0.9721.0000.9020.970
X좌표0.9450.9021.0000.695
Y좌표0.9190.9700.6951.000
2023-12-11T16:48:15.307417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
자치구관리부서
자치구1.0000.850
관리부서0.8501.000
2023-12-11T16:48:15.397741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
X좌표Y좌표관리부서자치구
X좌표1.0000.4070.6150.651
Y좌표0.4071.0000.7400.856
관리부서0.6150.7401.0000.850
자치구0.6510.8560.8501.000

Missing values

2023-12-11T16:48:11.919351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T16:48:12.050817image/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

관리번호구분코드관리기관관리부서시설물명집수정위치자치구X좌표Y좌표
02017_1_02122㈜KTkt 성수지점통신구성동구 아차산로 13길47 (KT성수지점앞)성동구205470.4449751.6
12017_1_02142㈜KTkt 성수지점통신구성동구 아차산로 13길54(신한은행앞)성동구205551.6449792.8
22017_1_02152㈜KTkt 성수지점통신구광진구 동일로 190(화양사거리)광진구206022.0449916.4
32017_1_02162㈜KTkt 성수지점통신구성동구 아차산로 13길47 (KT 성수지점앞)성동구205470.4449751.6
42017_1_02172㈜KTkt 성수지점통신구성동구 아차산로 13길47 (KT 성수지점앞)성동구205470.4449751.6
52017_1_02202㈜KTkt 서대문지사(가좌)통신구서대문구 응암로121 (kt가좌지사 우측편)서대문구192363.6453791.6
62017_1_02212㈜KTkt 서대문지사(가좌)통신구서대문구 응암로113 (중소기업은행앞)서대문구192335.2453693.2
72017_1_02222㈜KTkt 서대문지사(가좌)통신구서대문구 증가로30길 25 (kt가좌지사 후면)서대문구192342.8453836.0
82017_1_02232㈜KTkt 서대문지사(가좌)통신구서대문구 증가로 261(증산2교 북단 우측)은평구192139.6453812.4
92017_1_02242㈜KTkt 서대문지사(가좌)통신구은평구 증산로 213(증산빗물펌프장앞)은평구191433.2453189.2
관리번호구분코드관리기관관리부서시설물명집수정위치자치구X좌표Y좌표
1482017_2_02982㈜KT통신구팀통신구논현로872(신사동610-2), 압구정사거리 집수정강남구202546.8447432.0
1492017_2_02992㈜KT통신구팀통신구서빙고로4-12(한강3가 49-3), 용산병원 집수정용산구196919.6447359.6
1502017_2_03002㈜KT통신구팀통신구신촌로297(북아현동126-30), 아현삼거리 집수정마포구196394.4450896.8
1512017_2_03012㈜KT통신구팀통신구세종대로83(태평로2가344-3), 시청 집수정중 구197923.2451572.4
1522017_2_03022㈜KT통신구팀통신구을지로54(을지로2가199-78), 중앙국사 분기 집수정중 구198556.4451812.8
1532017_2_03032㈜KT통신구팀통신구을지로79(을지로2가50), 을지로2가 집수정중 구198812.8451885.2
1542017_2_03042㈜KT통신구팀통신구다산로248(신당동100-1), 율원파출소 집수정중 구201447.2451723.6
1552017_2_03052㈜KT통신구팀통신구종로266(종로6가262-1), 청계6가 집수정종로구200612.0452362.4
1562017_2_03062㈜KT통신구팀통신구낙산성곽길2(창신동697-3), 동대문 집수정종로구200872.4452469.2
1572017_2_03072㈜KT통신구팀통신구장충단로247(을지로6가18-21), 동대문운동장 집수장동대문구200643.2451926.4