Overview

Dataset statistics

Number of variables6
Number of observations216
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory10.7 KiB
Average record size in memory50.6 B

Variable types

Text2
Numeric2
Categorical2

Dataset

Description고정형CCTV지번주소,위도,경도,자치구,단속지점명,현장구분
Author관악구
URLhttps://data.seoul.go.kr/dataList/OA-20492/S/1/datasetView.do

Alerts

자치구 has constant value ""Constant
현장구분 has constant value ""Constant
단속지점명 has unique valuesUnique

Reproduction

Analysis started2024-04-29 20:33:36.009609
Analysis finished2024-04-29 20:33:37.696272
Duration1.69 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

고정형CCTV지번주소
Text

Distinct208
Distinct (%)96.3%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
2024-04-30T05:33:37.898032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length17
Mean length10.439815
Min length6

Characters and Unicode

Total characters2255
Distinct characters49
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

Unique201 ?
Unique (%)93.1%

Sample

1st row난곡동617-9
2nd row대학동1516-3
3rd row조원동518-56
4th row남현동1066-31
5th row낙성대동300-19
ValueCountFrequency (%)
서울 48
 
13.3%
관악구 48
 
13.3%
신림동 30
 
8.3%
봉천동 15
 
4.2%
청룡동920 3
 
0.8%
조원동1728 2
 
0.6%
미성동1730 2
 
0.6%
미성동589-1 2
 
0.6%
은천동942-29 2
 
0.6%
신원동1695 2
 
0.6%
Other values (204) 206
57.2%
2024-04-30T05:33:38.310107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 228
 
10.1%
동 216
 
9.6%
- 191
 
8.5%
144
 
6.4%
3 119
 
5.3%
2 102
 
4.5%
4 95
 
4.2%
5 92
 
4.1%
6 90
 
4.0%
0 78
 
3.5%
Other values (39) 900
39.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1022
45.3%
Other Letter 898
39.8%
Dash Punctuation 191
 
8.5%
Space Separator 144
 
6.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 216
24.1%
신 66
 
7.3%
서 59
 
6.6%
림 50
 
5.6%
악 48
 
5.3%
울 48
 
5.3%
구 48
 
5.3%
관 48
 
5.3%
성 40
 
4.5%
천 28
 
3.1%
Other values (27) 247
27.5%
Decimal Number
ValueCountFrequency (%)
1 228
22.3%
3 119
11.6%
2 102
10.0%
4 95
9.3%
5 92
9.0%
6 90
 
8.8%
0 78
 
7.6%
8 78
 
7.6%
7 72
 
7.0%
9 68
 
6.7%
Dash Punctuation
ValueCountFrequency (%)
- 191
100.0%
Space Separator
ValueCountFrequency (%)
144
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1357
60.2%
Hangul 898
39.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 216
24.1%
신 66
 
7.3%
서 59
 
6.6%
림 50
 
5.6%
악 48
 
5.3%
울 48
 
5.3%
구 48
 
5.3%
관 48
 
5.3%
성 40
 
4.5%
천 28
 
3.1%
Other values (27) 247
27.5%
Common
ValueCountFrequency (%)
1 228
16.8%
- 191
14.1%
144
10.6%
3 119
8.8%
2 102
7.5%
4 95
7.0%
5 92
6.8%
6 90
 
6.6%
0 78
 
5.7%
8 78
 
5.7%
Other values (2) 140
10.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1357
60.2%
Hangul 898
39.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 228
16.8%
- 191
14.1%
144
10.6%
3 119
8.8%
2 102
7.5%
4 95
7.0%
5 92
6.8%
6 90
 
6.6%
0 78
 
5.7%
8 78
 
5.7%
Other values (2) 140
10.3%
Hangul
ValueCountFrequency (%)
동 216
24.1%
신 66
 
7.3%
서 59
 
6.6%
림 50
 
5.6%
악 48
 
5.3%
울 48
 
5.3%
구 48
 
5.3%
관 48
 
5.3%
성 40
 
4.5%
천 28
 
3.1%
Other values (27) 247
27.5%

위도
Real number (ℝ)

Distinct214
Distinct (%)99.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.4801
Minimum37.45893
Maximum37.493315
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2024-04-30T05:33:38.428579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.45893
5-th percentile37.466018
Q137.47582
median37.482042
Q337.485483
95-th percentile37.48997
Maximum37.493315
Range0.03438457
Interquartile range (IQR)0.0096631967

Descriptive statistics

Standard deviation0.0074230951
Coefficient of variation (CV)0.0001980543
Kurtosis-0.12965688
Mean37.4801
Median Absolute Deviation (MAD)0.004503
Skewness-0.73536908
Sum8095.7016
Variance5.5102341 × 10-5
MonotonicityNot monotonic
2024-04-30T05:33:38.564272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.492218 2
 
0.9%
37.485202 2
 
0.9%
37.484418 1
 
0.5%
37.487054 1
 
0.5%
37.475941 1
 
0.5%
37.483793 1
 
0.5%
37.476927 1
 
0.5%
37.484358 1
 
0.5%
37.480289 1
 
0.5%
37.480979 1
 
0.5%
Other values (204) 204
94.4%
ValueCountFrequency (%)
37.45893 1
0.5%
37.46038 1
0.5%
37.4610086278593 1
0.5%
37.461753 1
0.5%
37.462727 1
0.5%
37.463015 1
0.5%
37.463067 1
0.5%
37.4639481345346 1
0.5%
37.4644762024804 1
0.5%
37.464696 1
0.5%
ValueCountFrequency (%)
37.4933145697694 1
0.5%
37.492664 1
0.5%
37.49247 1
0.5%
37.492218 2
0.9%
37.490591 1
0.5%
37.490504 1
0.5%
37.490334 1
0.5%
37.4901681154487 1
0.5%
37.490029 1
0.5%
37.4899843258209 1
0.5%

경도
Real number (ℝ)

Distinct213
Distinct (%)98.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.93328
Minimum126.90242
Maximum126.98177
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2024-04-30T05:33:38.676386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.90242
5-th percentile126.91004
Q1126.9185
median126.92964
Q3126.94636
95-th percentile126.96299
Maximum126.98177
Range0.07935
Interquartile range (IQR)0.027862549

Descriptive statistics

Standard deviation0.017894368
Coefficient of variation (CV)0.0001409746
Kurtosis-0.31171682
Mean126.93328
Median Absolute Deviation (MAD)0.01256
Skewness0.56506658
Sum27417.588
Variance0.00032020841
MonotonicityNot monotonic
2024-04-30T05:33:38.808019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.90996 2
 
0.9%
126.94077 2
 
0.9%
126.93928 2
 
0.9%
126.92626 1
 
0.5%
126.94196 1
 
0.5%
126.96139 1
 
0.5%
126.90959 1
 
0.5%
126.95156 1
 
0.5%
126.92197 1
 
0.5%
126.91996 1
 
0.5%
Other values (203) 203
94.0%
ValueCountFrequency (%)
126.90242 1
0.5%
126.9025 1
0.5%
126.90261 1
0.5%
126.902891303133 1
0.5%
126.903288305855 1
0.5%
126.90491 1
0.5%
126.90534 1
0.5%
126.90697885759202 1
0.5%
126.90959 1
0.5%
126.90996 2
0.9%
ValueCountFrequency (%)
126.98177 1
0.5%
126.98147 1
0.5%
126.97997 1
0.5%
126.97738 1
0.5%
126.97616 1
0.5%
126.974917461382 1
0.5%
126.97402 1
0.5%
126.973587158503 1
0.5%
126.96677 1
0.5%
126.96567 1
0.5%

자치구
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
관악구
216 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
관악구 216
100.0%

Length

2024-04-30T05:33:38.915053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T05:33:39.013941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
관악구 216
100.0%

단속지점명
Text

UNIQUE 

Distinct216
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
2024-04-30T05:33:39.246770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length36
Median length32
Mean length23.050926
Min length15

Characters and Unicode

Total characters4979
Distinct characters261
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique216 ?
Unique (%)100.0%

Sample

1st rowN7004 난곡로34길 1 우리들약국 부근
2nd rowN7005 신림로 105 CU편의점 부근
3rd rowN7006 난곡로 357 신대방역 난곡방향 부근
4th rowN8001 남현1길 2 남현주차장 부근
5th rowN8002 낙성대로 29 인헌초교 부근
ValueCountFrequency (%)
부근 172
 
16.2%
주변 28
 
2.6%
난곡로 13
 
1.2%
신림로 12
 
1.1%
남부순환로 10
 
0.9%
은천로 6
 
0.6%
23 6
 
0.6%
맞은편 6
 
0.6%
20 5
 
0.5%
봉천로 5
 
0.5%
Other values (660) 802
75.3%
2024-04-30T05:33:39.594251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
861
 
17.3%
0 431
 
8.7%
1 241
 
4.8%
부 212
 
4.3%
근 174
 
3.5%
로 167
 
3.4%
2 164
 
3.3%
3 129
 
2.6%
길 125
 
2.5%
5 117
 
2.3%
Other values (251) 2358
47.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2254
45.3%
Decimal Number 1511
30.3%
Space Separator 861
 
17.3%
Uppercase Letter 317
 
6.4%
Dash Punctuation 16
 
0.3%
Close Punctuation 5
 
0.1%
Open Punctuation 5
 
0.1%
Lowercase Letter 5
 
0.1%
Control 3
 
0.1%
Other Punctuation 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
부 212
 
9.4%
근 174
 
7.7%
로 167
 
7.4%
길 125
 
5.5%
신 68
 
3.0%
교 51
 
2.3%
림 45
 
2.0%
남 41
 
1.8%
초 40
 
1.8%
원 39
 
1.7%
Other values (213) 1292
57.3%
Uppercase Letter
ValueCountFrequency (%)
D 61
19.2%
N 57
18.0%
G 34
10.7%
C 30
9.5%
S 27
8.5%
P 24
 
7.6%
A 21
 
6.6%
B 17
 
5.4%
E 14
 
4.4%
J 7
 
2.2%
Other values (8) 25
7.9%
Decimal Number
ValueCountFrequency (%)
0 431
28.5%
1 241
15.9%
2 164
 
10.9%
3 129
 
8.5%
5 117
 
7.7%
6 105
 
6.9%
4 97
 
6.4%
7 85
 
5.6%
9 83
 
5.5%
8 59
 
3.9%
Lowercase Letter
ValueCountFrequency (%)
a 2
40.0%
p 1
20.0%
l 1
20.0%
z 1
20.0%
Space Separator
ValueCountFrequency (%)
861
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 16
100.0%
Close Punctuation
ValueCountFrequency (%)
) 5
100.0%
Open Punctuation
ValueCountFrequency (%)
( 5
100.0%
Control
ValueCountFrequency (%)
3
100.0%
Other Punctuation
ValueCountFrequency (%)
, 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2403
48.3%
Hangul 2254
45.3%
Latin 322
 
6.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
부 212
 
9.4%
근 174
 
7.7%
로 167
 
7.4%
길 125
 
5.5%
신 68
 
3.0%
교 51
 
2.3%
림 45
 
2.0%
남 41
 
1.8%
초 40
 
1.8%
원 39
 
1.7%
Other values (213) 1292
57.3%
Latin
ValueCountFrequency (%)
D 61
18.9%
N 57
17.7%
G 34
10.6%
C 30
9.3%
S 27
8.4%
P 24
 
7.5%
A 21
 
6.5%
B 17
 
5.3%
E 14
 
4.3%
J 7
 
2.2%
Other values (12) 30
9.3%
Common
ValueCountFrequency (%)
861
35.8%
0 431
17.9%
1 241
 
10.0%
2 164
 
6.8%
3 129
 
5.4%
5 117
 
4.9%
6 105
 
4.4%
4 97
 
4.0%
7 85
 
3.5%
9 83
 
3.5%
Other values (6) 90
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2725
54.7%
Hangul 2254
45.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
861
31.6%
0 431
15.8%
1 241
 
8.8%
2 164
 
6.0%
3 129
 
4.7%
5 117
 
4.3%
6 105
 
3.9%
4 97
 
3.6%
7 85
 
3.1%
9 83
 
3.0%
Other values (28) 412
15.1%
Hangul
ValueCountFrequency (%)
부 212
 
9.4%
근 174
 
7.7%
로 167
 
7.4%
길 125
 
5.5%
신 68
 
3.0%
교 51
 
2.3%
림 45
 
2.0%
남 41
 
1.8%
초 40
 
1.8%
원 39
 
1.7%
Other values (213) 1292
57.3%

현장구분
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
불법주정차구역
216 

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 216
100.0%

Length

2024-04-30T05:33:39.719629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T05:33:39.798475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
불법주정차구역 216
100.0%

Interactions

2024-04-30T05:33:37.372249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T05:33:37.161673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T05:33:37.454703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T05:33:37.292317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-30T05:33:39.845893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위도경도
위도1.0000.680
경도0.6801.000
2024-04-30T05:33:39.915128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위도경도
위도1.0000.030
경도0.0301.000

Missing values

2024-04-30T05:33:37.569167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-30T05:33:37.659462image/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
0난곡동617-937.472615126.91929관악구N7004 난곡로34길 1 우리들약국 부근불법주정차구역
1대학동1516-337.470692126.9377관악구N7005 신림로 105 CU편의점 부근불법주정차구역
2조원동518-5637.48609126.91364관악구N7006 난곡로 357 신대방역 난곡방향 부근불법주정차구역
3남현동1066-3137.475876126.97738관악구N8001 남현1길 2 남현주차장 부근불법주정차구역
4낙성대동300-1937.476005126.95904관악구N8002 낙성대로 29 인헌초교 부근불법주정차구역
5인헌동1639-937.47377126.96677관악구N8003 인헌길 40 매일약국 부근불법주정차구역
6인헌동1632-1037.476074126.96429관악구N8004 봉천로 604-1 CU편의점 부근불법주정차구역
7신원동1628-8937.479364126.92807관악구N8005 문성로 217 CU편의점 부근불법주정차구역
8신사동503-3537.485197126.91593관악구N8006 조원로 123 갈보리교회 부근불법주정차구역
9미성동589-137.476206126.91751관악구N8007 난우길 40 난우초교 부근불법주정차구역
고정형CCTV지번주소위도경도자치구단속지점명현장구분
206서울 관악구 봉천동 881-3637.480557126.947681관악구P9007 남부순환로214길23 관악전화국 주변불법주정차구역
207서울 관악구 신림동 1425-1237.486418126.929423관악구P6039 신림로 360-1 KTplaza 부근불법주정차구역
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