Overview

Dataset statistics

Number of variables6
Number of observations156
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.7 KiB
Average record size in memory50.8 B

Variable types

Text2
Numeric2
Categorical2

Dataset

Description고정형CCTV지번주소,위도,경도,자치구,단속지점명,현장구분
Author노원구
URLhttps://data.seoul.go.kr/dataList/OA-20482/S/1/datasetView.do

Alerts

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

Reproduction

Analysis started2024-04-06 13:46:34.949481
Analysis finished2024-04-06 13:46:35.652110
Duration0.7 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

고정형CCTV지번주소
Text

Distinct138
Distinct (%)88.5%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
2024-04-06T22:46:35.851722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length42
Median length30
Mean length17.621795
Min length7

Characters and Unicode

Total characters2749
Distinct characters195
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique128 ?
Unique (%)82.1%

Sample

1st row상계동 1274
2nd row중계동 157-5
3rd row중계동 435-2
4th row상계동 348-5
5th row상계동 348-5
ValueCountFrequency (%)
상계동 31
 
5.7%
노원구 25
 
4.6%
앞 19
 
3.5%
중계동 18
 
3.3%
서울 17
 
3.1%
공릉2동 16
 
3.0%
공릉동 15
 
2.8%
사거리 11
 
2.0%
하계1동 10
 
1.9%
삼거리 8
 
1.5%
Other values (276) 370
68.5%
2024-04-06T22:46:36.241018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
413
 
15.0%
동 167
 
6.1%
계 149
 
5.4%
1 149
 
5.4%
- 119
 
4.3%
2 118
 
4.3%
4 78
 
2.8%
3 73
 
2.7%
7 70
 
2.5%
6 65
 
2.4%
Other values (185) 1348
49.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1399
50.9%
Decimal Number 751
27.3%
Space Separator 413
 
15.0%
Dash Punctuation 119
 
4.3%
Other Punctuation 19
 
0.7%
Close Punctuation 18
 
0.7%
Open Punctuation 18
 
0.7%
Uppercase Letter 12
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 167
 
11.9%
계 149
 
10.7%
상 63
 
4.5%
중 47
 
3.4%
공 46
 
3.3%
원 45
 
3.2%
앞 43
 
3.1%
릉 43
 
3.1%
리 36
 
2.6%
구 35
 
2.5%
Other values (161) 725
51.8%
Decimal Number
ValueCountFrequency (%)
1 149
19.8%
2 118
15.7%
4 78
10.4%
3 73
9.7%
7 70
9.3%
6 65
8.7%
5 65
8.7%
8 50
 
6.7%
0 46
 
6.1%
9 37
 
4.9%
Uppercase Letter
ValueCountFrequency (%)
G 3
25.0%
A 2
16.7%
P 2
16.7%
T 2
16.7%
S 2
16.7%
L 1
 
8.3%
Other Punctuation
ValueCountFrequency (%)
, 16
84.2%
@ 1
 
5.3%
. 1
 
5.3%
/ 1
 
5.3%
Space Separator
ValueCountFrequency (%)
413
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 119
100.0%
Close Punctuation
ValueCountFrequency (%)
) 18
100.0%
Open Punctuation
ValueCountFrequency (%)
( 18
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1399
50.9%
Common 1338
48.7%
Latin 12
 
0.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 167
 
11.9%
계 149
 
10.7%
상 63
 
4.5%
중 47
 
3.4%
공 46
 
3.3%
원 45
 
3.2%
앞 43
 
3.1%
릉 43
 
3.1%
리 36
 
2.6%
구 35
 
2.5%
Other values (161) 725
51.8%
Common
ValueCountFrequency (%)
413
30.9%
1 149
 
11.1%
- 119
 
8.9%
2 118
 
8.8%
4 78
 
5.8%
3 73
 
5.5%
7 70
 
5.2%
6 65
 
4.9%
5 65
 
4.9%
8 50
 
3.7%
Other values (8) 138
 
10.3%
Latin
ValueCountFrequency (%)
G 3
25.0%
A 2
16.7%
P 2
16.7%
T 2
16.7%
S 2
16.7%
L 1
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1399
50.9%
ASCII 1350
49.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
413
30.6%
1 149
 
11.0%
- 119
 
8.8%
2 118
 
8.7%
4 78
 
5.8%
3 73
 
5.4%
7 70
 
5.2%
6 65
 
4.8%
5 65
 
4.8%
8 50
 
3.7%
Other values (14) 150
 
11.1%
Hangul
ValueCountFrequency (%)
동 167
 
11.9%
계 149
 
10.7%
상 63
 
4.5%
중 47
 
3.4%
공 46
 
3.3%
원 45
 
3.2%
앞 43
 
3.1%
릉 43
 
3.1%
리 36
 
2.6%
구 35
 
2.5%
Other values (161) 725
51.8%

위도
Real number (ℝ)

Distinct140
Distinct (%)89.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.646148
Minimum37.61497
Maximum37.682246
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2024-04-06T22:46:36.368214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.61497
5-th percentile37.619477
Q137.629053
median37.648303
Q337.659276
95-th percentile37.673057
Maximum37.682246
Range0.06727582
Interquartile range (IQR)0.030223184

Descriptive statistics

Standard deviation0.017552747
Coefficient of variation (CV)0.00046625612
Kurtosis-1.1723329
Mean37.646148
Median Absolute Deviation (MAD)0.015942754
Skewness-0.054209222
Sum5872.7991
Variance0.00030809893
MonotonicityNot monotonic
2024-04-06T22:46:36.491684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.61497005 3
 
1.9%
37.62701873 3
 
1.9%
37.63139447 3
 
1.9%
37.62235828 3
 
1.9%
37.62782213 3
 
1.9%
37.64363763 3
 
1.9%
37.62473909 3
 
1.9%
37.6581397 3
 
1.9%
37.6585119914383 1
 
0.6%
37.66703786 1
 
0.6%
Other values (130) 130
83.3%
ValueCountFrequency (%)
37.61497005 3
1.9%
37.61551435 1
 
0.6%
37.61777031 1
 
0.6%
37.61849222 1
 
0.6%
37.6189175 1
 
0.6%
37.61934623 1
 
0.6%
37.61951993 1
 
0.6%
37.62100708 1
 
0.6%
37.6211079 1
 
0.6%
37.62113646 1
 
0.6%
ValueCountFrequency (%)
37.68224587 1
0.6%
37.67842129 1
0.6%
37.67759293 1
0.6%
37.67557646 1
0.6%
37.67492775 1
0.6%
37.67437515 1
0.6%
37.67396161 1
0.6%
37.67380943 1
0.6%
37.67280675 1
0.6%
37.6722888633529 1
0.6%

경도
Real number (ℝ)

Distinct140
Distinct (%)89.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.06899
Minimum127.04434
Maximum127.09142
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2024-04-06T22:46:36.627023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum127.04434
5-th percentile127.05206
Q1127.06113
median127.07023
Q3127.07637
95-th percentile127.08388
Maximum127.09142
Range0.0470792
Interquartile range (IQR)0.01524345

Descriptive statistics

Standard deviation0.0099053773
Coefficient of variation (CV)7.7952749 × 10-5
Kurtosis-0.64787241
Mean127.06899
Median Absolute Deviation (MAD)0.0071011482
Skewness-0.11196323
Sum19822.763
Variance9.81165 × 10-5
MonotonicityNot monotonic
2024-04-06T22:46:36.760646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.0647458 3
 
1.9%
127.0766611 3
 
1.9%
127.0610884 3
 
1.9%
127.0868014 3
 
1.9%
127.0499072 3
 
1.9%
127.0729715 3
 
1.9%
127.0889875 3
 
1.9%
127.072772 3
 
1.9%
127.07213667908154 1
 
0.6%
127.0664496 1
 
0.6%
Other values (130) 130
83.3%
ValueCountFrequency (%)
127.0443408 1
 
0.6%
127.0499072 3
1.9%
127.050555895568 1
 
0.6%
127.0508208 1
 
0.6%
127.0509658 1
 
0.6%
127.0520232 1
 
0.6%
127.05207233901088 1
 
0.6%
127.0525355 1
 
0.6%
127.0535037 1
 
0.6%
127.053889693074 1
 
0.6%
ValueCountFrequency (%)
127.09142 1
 
0.6%
127.0889875 3
1.9%
127.0868014 3
1.9%
127.085427 1
 
0.6%
127.0833644 1
 
0.6%
127.0830689 1
 
0.6%
127.0821858 1
 
0.6%
127.0821526 1
 
0.6%
127.081464578054 1
 
0.6%
127.081143 1
 
0.6%

자치구
Categorical

CONSTANT 

Distinct1
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
노원구
156 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
노원구 156
100.0%

Length

2024-04-06T22:46:36.879095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T22:46:36.961052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
노원구 156
100.0%

단속지점명
Text

UNIQUE 

Distinct156
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
2024-04-06T22:46:37.126227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length35
Median length29.5
Mean length24.846154
Min length13

Characters and Unicode

Total characters3876
Distinct characters222
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique156 ?
Unique (%)100.0%

Sample

1st row주정차106.(당고개)덕릉로112길68 덕암초등학교앞
2nd row주정차107.중계동157-5 데미안빌딩
3rd row주정차108.중계동435-2 중계2차 현대아파트정문
4th row주정차109(1).노원역1번,10번 출구앞(1번)
5th row주정차109(2).노원역1번,10번출구앞(10번)
ValueCountFrequency (%)
앞 11
 
2.9%
주변 11
 
2.9%
사거리 6
 
1.6%
입구 3
 
0.8%
정문 2
 
0.5%
주정차 2
 
0.5%
삼거리주변 2
 
0.5%
주정차083.(노원역)동일로216길66 1
 
0.3%
주정차073.d덕릉로805 1
 
0.3%
296-3 1
 
0.3%
Other values (340) 340
89.5%
2024-04-06T22:46:37.441604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
226
 
5.8%
1 200
 
5.2%
주 191
 
4.9%
0 186
 
4.8%
정 179
 
4.6%
차 164
 
4.2%
2 147
 
3.8%
. 136
 
3.5%
로 131
 
3.4%
3 102
 
2.6%
Other values (212) 2214
57.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2226
57.4%
Decimal Number 1040
26.8%
Space Separator 226
 
5.8%
Other Punctuation 161
 
4.2%
Open Punctuation 101
 
2.6%
Close Punctuation 101
 
2.6%
Uppercase Letter 12
 
0.3%
Dash Punctuation 9
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 191
 
8.6%
정 179
 
8.0%
차 164
 
7.4%
로 131
 
5.9%
계 69
 
3.1%
길 65
 
2.9%
원 59
 
2.7%
동 48
 
2.2%
앞 39
 
1.8%
릉 39
 
1.8%
Other values (188) 1242
55.8%
Decimal Number
ValueCountFrequency (%)
1 200
19.2%
0 186
17.9%
2 147
14.1%
3 102
9.8%
4 78
 
7.5%
5 76
 
7.3%
7 64
 
6.2%
6 63
 
6.1%
8 63
 
6.1%
9 61
 
5.9%
Uppercase Letter
ValueCountFrequency (%)
D 2
16.7%
G 2
16.7%
L 2
16.7%
P 2
16.7%
C 1
8.3%
S 1
8.3%
K 1
8.3%
T 1
8.3%
Other Punctuation
ValueCountFrequency (%)
. 136
84.5%
, 25
 
15.5%
Space Separator
ValueCountFrequency (%)
226
100.0%
Open Punctuation
ValueCountFrequency (%)
( 101
100.0%
Close Punctuation
ValueCountFrequency (%)
) 101
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2226
57.4%
Common 1638
42.3%
Latin 12
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 191
 
8.6%
정 179
 
8.0%
차 164
 
7.4%
로 131
 
5.9%
계 69
 
3.1%
길 65
 
2.9%
원 59
 
2.7%
동 48
 
2.2%
앞 39
 
1.8%
릉 39
 
1.8%
Other values (188) 1242
55.8%
Common
ValueCountFrequency (%)
226
13.8%
1 200
12.2%
0 186
11.4%
2 147
9.0%
. 136
8.3%
3 102
 
6.2%
( 101
 
6.2%
) 101
 
6.2%
4 78
 
4.8%
5 76
 
4.6%
Other values (6) 285
17.4%
Latin
ValueCountFrequency (%)
D 2
16.7%
G 2
16.7%
L 2
16.7%
P 2
16.7%
C 1
8.3%
S 1
8.3%
K 1
8.3%
T 1
8.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2226
57.4%
ASCII 1650
42.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
226
13.7%
1 200
12.1%
0 186
11.3%
2 147
8.9%
. 136
8.2%
3 102
 
6.2%
( 101
 
6.1%
) 101
 
6.1%
4 78
 
4.7%
5 76
 
4.6%
Other values (14) 297
18.0%
Hangul
ValueCountFrequency (%)
주 191
 
8.6%
정 179
 
8.0%
차 164
 
7.4%
로 131
 
5.9%
계 69
 
3.1%
길 65
 
2.9%
원 59
 
2.7%
동 48
 
2.2%
앞 39
 
1.8%
릉 39
 
1.8%
Other values (188) 1242
55.8%

현장구분
Categorical

CONSTANT 

Distinct1
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
불법주정차구역
156 

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 156
100.0%

Length

2024-04-06T22:46:37.557994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T22:46:37.638828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
불법주정차구역 156
100.0%

Interactions

2024-04-06T22:46:35.354379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T22:46:35.188822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T22:46:35.432102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T22:46:35.274328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-06T22:46:37.693908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위도경도
위도1.0000.681
경도0.6811.000
2024-04-06T22:46:37.777930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위도경도
위도1.000-0.188
경도-0.1881.000

Missing values

2024-04-06T22:46:35.526482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-06T22:46:35.614635image/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
0상계동 127437.666826127.080874노원구주정차106.(당고개)덕릉로112길68 덕암초등학교앞불법주정차구역
1중계동 157-537.65827127.074329노원구주정차107.중계동157-5 데미안빌딩불법주정차구역
2중계동 435-237.655892127.073576노원구주정차108.중계동435-2 중계2차 현대아파트정문불법주정차구역
3상계동 348-537.656524127.064377노원구주정차109(1).노원역1번,10번 출구앞(1번)불법주정차구역
4상계동 348-537.656572127.064363노원구주정차109(2).노원역1번,10번출구앞(10번)불법주정차구역
5월계동 774-237.633995127.052023노원구주정차110.월계동774-2 초안어린이공원앞불법주정차구역
6월계동 77437.63567127.052536노원구주정차111.초안산로89 청백1단지앞(방범857폴)불법주정차구역
7노원구 상계동 666-237.664331127.055175노원구주정차112.한글비석로 571불법주정차구역
8노원구 중계동 356(노원어린이도서관 앞)37.658323127.076856노원구주정차113.한글비석로 346불법주정차구역
9노원구 하계동 256-2737.644585127.074969노원구주정차114.한글비석로5길 18불법주정차구역
고정형CCTV지번주소위도경도자치구단속지점명현장구분
146서울 노원구 중계동359-1537.650713127.075368노원구주정차128, 은행사거리불법주정차구역
147서울 노원구 상계동 128537.667636127.080548노원구주정차132, 신기아파트 앞불법주정차구역
148서울 노원구 동일로210길 1-237.648161127.065393노원구주정차129, LPG충전소불법주정차구역
149서울시 노원구 중계동 584-137.658512127.072137노원구주정차133, 삼창타워프라자 아파트 앞불법주정차구역
150서울 노원구 중계동51537.645452127.064037노원구주정차130, 중계역2번출구불법주정차구역
151서울 노원구 상계동 72037.653798127.064749노원구주정차135, 상계주공6단지앞불법주정차구역
152서울 노원구 하계동 179-2737.63629127.072342노원구주정차136, 온유한교회맞은편불법주정차구역
153상경초등학교 앞37.670985127.052072노원구주정차137, 상경초등학교 앞불법주정차구역
154상계초교 사거리37.657134127.066365노원구주정차138, 상계초교사거리불법주정차구역
155서울 노원구 중계동 514-337.646509127.06901노원구주정차134, 노원아동복지관불법주정차구역