Overview

Dataset statistics

Number of variables6
Number of observations75
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.8 KiB
Average record size in memory51.8 B

Variable types

Text2
Numeric2
Categorical2

Dataset

Description고정형CCTV지번주소,위도,경도,자치구,단속지점명,현장구분
Author금천구
URLhttps://data.seoul.go.kr/dataList/OA-20489/S/1/datasetView.do

Alerts

자치구 has constant value ""Constant
현장구분 has constant value ""Constant
위도 is highly overall correlated with 경도High correlation
경도 is highly overall correlated with 위도High correlation
위도 has unique valuesUnique
경도 has unique valuesUnique
단속지점명 has unique valuesUnique

Reproduction

Analysis started2024-04-06 13:09:59.585350
Analysis finished2024-04-06 13:10:01.246944
Duration1.66 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

고정형CCTV지번주소
Text

Distinct74
Distinct (%)98.7%
Missing0
Missing (%)0.0%
Memory size732.0 B
2024-04-06T22:10:01.555808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length16
Mean length12.226667
Min length4

Characters and Unicode

Total characters917
Distinct characters23
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique73 ?
Unique (%)97.3%

Sample

1st row독산1동 297-6
2nd row가산동 151-36
3rd row가산동 142-41
4th row독산3동163-25
5th row독산2동1032-3
ValueCountFrequency (%)
서울 27
 
14.9%
금천구 27
 
14.9%
가산동 16
 
8.8%
시흥동 9
 
5.0%
독산동 9
 
5.0%
시흥5동 5
 
2.8%
독산1동 5
 
2.8%
시흥1동 3
 
1.7%
독산2동 2
 
1.1%
시흥2동 2
 
1.1%
Other values (75) 76
42.0%
2024-04-06T22:10:02.314739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
129
 
14.1%
동 75
 
8.2%
1 70
 
7.6%
- 67
 
7.3%
2 52
 
5.7%
산 50
 
5.5%
9 39
 
4.3%
3 37
 
4.0%
4 32
 
3.5%
8 30
 
3.3%
Other values (13) 336
36.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 362
39.5%
Decimal Number 359
39.1%
Space Separator 129
 
14.1%
Dash Punctuation 67
 
7.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 75
20.7%
산 50
13.8%
흥 27
 
7.5%
천 27
 
7.5%
금 27
 
7.5%
울 27
 
7.5%
서 27
 
7.5%
독 27
 
7.5%
시 27
 
7.5%
구 27
 
7.5%
Decimal Number
ValueCountFrequency (%)
1 70
19.5%
2 52
14.5%
9 39
10.9%
3 37
10.3%
4 32
8.9%
8 30
8.4%
0 29
8.1%
5 27
 
7.5%
7 22
 
6.1%
6 21
 
5.8%
Space Separator
ValueCountFrequency (%)
129
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 67
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 555
60.5%
Hangul 362
39.5%

Most frequent character per script

Common
ValueCountFrequency (%)
129
23.2%
1 70
12.6%
- 67
12.1%
2 52
9.4%
9 39
 
7.0%
3 37
 
6.7%
4 32
 
5.8%
8 30
 
5.4%
0 29
 
5.2%
5 27
 
4.9%
Other values (2) 43
 
7.7%
Hangul
ValueCountFrequency (%)
동 75
20.7%
산 50
13.8%
흥 27
 
7.5%
천 27
 
7.5%
금 27
 
7.5%
울 27
 
7.5%
서 27
 
7.5%
독 27
 
7.5%
시 27
 
7.5%
구 27
 
7.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 555
60.5%
Hangul 362
39.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
129
23.2%
1 70
12.6%
- 67
12.1%
2 52
9.4%
9 39
 
7.0%
3 37
 
6.7%
4 32
 
5.8%
8 30
 
5.4%
0 29
 
5.2%
5 27
 
4.9%
Other values (2) 43
 
7.7%
Hangul
ValueCountFrequency (%)
동 75
20.7%
산 50
13.8%
흥 27
 
7.5%
천 27
 
7.5%
금 27
 
7.5%
울 27
 
7.5%
서 27
 
7.5%
독 27
 
7.5%
시 27
 
7.5%
구 27
 
7.5%

위도
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct75
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.465851
Minimum37.4463
Maximum37.483939
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size807.0 B
2024-04-06T22:10:02.604703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.4463
5-th percentile37.447235
Q137.454945
median37.467868
Q337.476122
95-th percentile37.480779
Maximum37.483939
Range0.037638778
Interquartile range (IQR)0.02117696

Descriptive statistics

Standard deviation0.011345187
Coefficient of variation (CV)0.00030281408
Kurtosis-1.2316282
Mean37.465851
Median Absolute Deviation (MAD)0.0089430568
Skewness-0.31527572
Sum2809.9388
Variance0.00012871327
MonotonicityNot monotonic
2024-04-06T22:10:02.894283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.473103 1
 
1.3%
37.4722768317023 1
 
1.3%
37.4732207718169 1
 
1.3%
37.47256275525576 1
 
1.3%
37.4839387775352 1
 
1.3%
37.455149606042696 1
 
1.3%
37.4824313837703 1
 
1.3%
37.47705419362634 1
 
1.3%
37.48232335316192 1
 
1.3%
37.474541 1
 
1.3%
Other values (65) 65
86.7%
ValueCountFrequency (%)
37.4463 1
1.3%
37.44666321848974 1
1.3%
37.446707 1
1.3%
37.447218 1
1.3%
37.447242 1
1.3%
37.447451 1
1.3%
37.4475978438284 1
1.3%
37.44893 1
1.3%
37.450028 1
1.3%
37.450124 1
1.3%
ValueCountFrequency (%)
37.4839387775352 1
1.3%
37.4824313837703 1
1.3%
37.48232335316192 1
1.3%
37.481246 1
1.3%
37.480579168473 1
1.3%
37.480473 1
1.3%
37.480058 1
1.3%
37.479354 1
1.3%
37.478375 1
1.3%
37.478165 1
1.3%

경도
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct75
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.89902
Minimum126.87746
Maximum126.9195
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size807.0 B
2024-04-06T22:10:03.171061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.87746
5-th percentile126.88108
Q1126.89177
median126.90201
Q3126.90591
95-th percentile126.90987
Maximum126.9195
Range0.042039107
Interquartile range (IQR)0.014142042

Descriptive statistics

Standard deviation0.0096547767
Coefficient of variation (CV)7.6082357 × 10-5
Kurtosis-0.51024507
Mean126.89902
Median Absolute Deviation (MAD)0.005784
Skewness-0.57974878
Sum9517.4266
Variance9.3214713 × 10-5
MonotonicityNot monotonic
2024-04-06T22:10:03.435858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.895142 1
 
1.3%
126.883339285603 1
 
1.3%
126.884298623646 1
 
1.3%
126.90159290653084 1
 
1.3%
126.879573943646 1
 
1.3%
126.9057636338343 1
 
1.3%
126.877463892587 1
 
1.3%
126.8962169054466 1
 
1.3%
126.88462349514552 1
 
1.3%
126.890729 1
 
1.3%
Other values (65) 65
86.7%
ValueCountFrequency (%)
126.877463892587 1
1.3%
126.878937831497 1
1.3%
126.879245 1
1.3%
126.879573943646 1
1.3%
126.88172 1
1.3%
126.883339285603 1
1.3%
126.883438 1
1.3%
126.884298623646 1
1.3%
126.88442009294 1
1.3%
126.88462349514552 1
1.3%
ValueCountFrequency (%)
126.919503 1
1.3%
126.914575166464 1
1.3%
126.912839 1
1.3%
126.910185 1
1.3%
126.909732 1
1.3%
126.90906 1
1.3%
126.90868958530604 1
1.3%
126.908595 1
1.3%
126.908533 1
1.3%
126.90850106454243 1
1.3%

자치구
Categorical

CONSTANT 

Distinct1
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size732.0 B
금천구
75 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
금천구 75
100.0%

Length

2024-04-06T22:10:03.682472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T22:10:03.863422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
금천구 75
100.0%

단속지점명
Text

UNIQUE 

Distinct75
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size732.0 B
2024-04-06T22:10:04.285691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length19
Mean length13.813333
Min length8

Characters and Unicode

Total characters1036
Distinct characters201
Distinct categories4 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique75 ?
Unique (%)100.0%

Sample

1st rowN101 기업은행 맞은편
2nd rowN102 성지병원
3rd rowN103 디지털오거리부근
4th rowN104 재성ENG
5th rowN105 말미사거리부근
ValueCountFrequency (%)
앞 20
 
9.0%
삼거리 7
 
3.1%
맞은편 6
 
2.7%
사거리 5
 
2.2%
시흥초등학교 3
 
1.3%
정문 3
 
1.3%
후문 3
 
1.3%
독산로36길 2
 
0.9%
53 2
 
0.9%
독산 1
 
0.4%
Other values (171) 171
76.7%
2024-04-06T22:10:04.978001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
149
 
14.4%
1 103
 
9.9%
N 76
 
7.3%
4 26
 
2.5%
리 25
 
2.4%
5 25
 
2.4%
2 24
 
2.3%
3 22
 
2.1%
6 21
 
2.0%
앞 20
 
1.9%
Other values (191) 545
52.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 524
50.6%
Decimal Number 276
26.6%
Space Separator 149
 
14.4%
Uppercase Letter 87
 
8.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 25
 
4.8%
앞 20
 
3.8%
산 18
 
3.4%
거 18
 
3.4%
로 17
 
3.2%
교 16
 
3.1%
은 10
 
1.9%
시 9
 
1.7%
지 9
 
1.7%
흥 8
 
1.5%
Other values (173) 374
71.4%
Decimal Number
ValueCountFrequency (%)
1 103
37.3%
4 26
 
9.4%
5 25
 
9.1%
2 24
 
8.7%
3 22
 
8.0%
6 21
 
7.6%
0 19
 
6.9%
7 17
 
6.2%
8 10
 
3.6%
9 9
 
3.3%
Uppercase Letter
ValueCountFrequency (%)
N 76
87.4%
C 3
 
3.4%
G 3
 
3.4%
E 2
 
2.3%
K 1
 
1.1%
A 1
 
1.1%
L 1
 
1.1%
Space Separator
ValueCountFrequency (%)
149
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 524
50.6%
Common 425
41.0%
Latin 87
 
8.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 25
 
4.8%
앞 20
 
3.8%
산 18
 
3.4%
거 18
 
3.4%
로 17
 
3.2%
교 16
 
3.1%
은 10
 
1.9%
시 9
 
1.7%
지 9
 
1.7%
흥 8
 
1.5%
Other values (173) 374
71.4%
Common
ValueCountFrequency (%)
149
35.1%
1 103
24.2%
4 26
 
6.1%
5 25
 
5.9%
2 24
 
5.6%
3 22
 
5.2%
6 21
 
4.9%
0 19
 
4.5%
7 17
 
4.0%
8 10
 
2.4%
Latin
ValueCountFrequency (%)
N 76
87.4%
C 3
 
3.4%
G 3
 
3.4%
E 2
 
2.3%
K 1
 
1.1%
A 1
 
1.1%
L 1
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 524
50.6%
ASCII 512
49.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
149
29.1%
1 103
20.1%
N 76
14.8%
4 26
 
5.1%
5 25
 
4.9%
2 24
 
4.7%
3 22
 
4.3%
6 21
 
4.1%
0 19
 
3.7%
7 17
 
3.3%
Other values (8) 30
 
5.9%
Hangul
ValueCountFrequency (%)
리 25
 
4.8%
앞 20
 
3.8%
산 18
 
3.4%
거 18
 
3.4%
로 17
 
3.2%
교 16
 
3.1%
은 10
 
1.9%
시 9
 
1.7%
지 9
 
1.7%
흥 8
 
1.5%
Other values (173) 374
71.4%

현장구분
Categorical

CONSTANT 

Distinct1
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size732.0 B
불법주정차구역
75 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
불법주정차구역 75
100.0%

Length

2024-04-06T22:10:05.242921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T22:10:05.441715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
불법주정차구역 75
100.0%

Interactions

2024-04-06T22:10:00.643440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T22:09:59.941486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T22:10:00.797423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T22:10:00.118428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-06T22:10:05.558598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
고정형CCTV지번주소위도경도단속지점명
고정형CCTV지번주소1.0000.9231.0001.000
위도0.9231.0000.6231.000
경도1.0000.6231.0001.000
단속지점명1.0001.0001.0001.000
2024-04-06T22:10:05.744958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위도경도
위도1.000-0.714
경도-0.7141.000

Missing values

2024-04-06T22:10:01.005936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-06T22:10:01.175269image/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

고정형CCTV지번주소위도경도자치구단속지점명현장구분
0독산1동 297-637.473103126.895142금천구N101 기업은행 맞은편불법주정차구역
1가산동 151-3637.476434126.892442금천구N102 성지병원불법주정차구역
2가산동 142-4137.478164126.890928금천구N103 디지털오거리부근불법주정차구역
3독산3동163-2537.471238126.900617금천구N104 재성ENG불법주정차구역
4독산2동1032-337.466434126.899108금천구N105 말미사거리부근불법주정차구역
5독산3동893-737.476881126.90906금천구N106 라온하우스불법주정차구역
6가산동450-337.479354126.879245금천구N107 태림모피불법주정차구역
7시흥1동856-2237.459667126.904914금천구N108 현대시장입구불법주정차구역
8독산2동 1042-937.46485126.902567금천구N109 진내과의원불법주정차구역
9독산3동990-1637.472855126.903202금천구N110 아이유하임불법주정차구역
고정형CCTV지번주소위도경도자치구단속지점명현장구분
65서울 금천구 시흥동 936-837.447598126.904182금천구N166 성석장로교회 앞불법주정차구역
66서울 금천구 시흥동 803-1937.458925126.905883금천구N165 시흥4동 성당 앞불법주정차구역
67서울 금천구 가산동 458-237.480579126.878938금천구N170 가산디지털2로 142 현대테라타워불법주정차구역
68서울 금천구 시흥동 798-4037.460434126.907359금천구N172 독산로36길 53불법주정차구역
69서울 금천구 독산동 101437.46883126.898845금천구N168 시흥대로116길 16 ACE홈센터 뒤편불법주정차구역
70서울 금천구 독산동 178-737.47094126.902861금천구N167 독산로 270 디앤에이치빌 삼거리불법주정차구역
71서울 금천구 시흥동 238-437.452121126.914575금천구N173 탑골로 49불법주정차구역
72서울 금천구 독산동 182-237.47051126.906596금천구N174 독산로78다길 53불법주정차구역
73서울 금천구 시흥동 4-12037.460882126.90869금천구N169 독산로36길 80 여민교회 삼거리불법주정차구역
74서울 금천구 가산동 345-137.476746126.88442금천구N163 디지털로 154 롯데택배불법주정차구역