Overview

Dataset statistics

Number of variables6
Number of observations118
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.9 KiB
Average record size in memory51.1 B

Variable types

Text2
Numeric2
Categorical2

Dataset

Description고정형CCTV지번주소,위도,경도,자치구,단속지점명,현장구분
Author양천구
URLhttps://data.seoul.go.kr/dataList/OA-20486/S/1/datasetView.do

Alerts

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

Reproduction

Analysis started2024-04-20 23:15:22.757862
Analysis finished2024-04-20 23:15:24.502673
Duration1.74 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

고정형CCTV지번주소
Text

UNIQUE 

Distinct118
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2024-04-21T08:15:24.761957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length17
Mean length16.423729
Min length13

Characters and Unicode

Total characters1938
Distinct characters22
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

Unique118 ?
Unique (%)100.0%

Sample

1st row서울 양천구 목4동 762-10
2nd row서울 양천구 목4동 797-8
3rd row서울 양천구 신월2동 496
4th row서울 양천구 신월7동 928-1
5th row서울 양천구 목1동 917
ValueCountFrequency (%)
서울 118
25.0%
양천구 118
25.0%
신정동 20
 
4.2%
신월동 16
 
3.4%
목동 15
 
3.2%
목4동 10
 
2.1%
목1동 8
 
1.7%
신월1동 7
 
1.5%
목5동 5
 
1.1%
목2동 5
 
1.1%
Other values (131) 150
31.8%
2024-04-21T08:15:25.172902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
354
18.3%
1 126
 
6.5%
서 118
 
6.1%
울 118
 
6.1%
양 118
 
6.1%
천 118
 
6.1%
구 118
 
6.1%
동 118
 
6.1%
- 104
 
5.4%
신 72
 
3.7%
Other values (12) 574
29.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 898
46.3%
Decimal Number 582
30.0%
Space Separator 354
 
18.3%
Dash Punctuation 104
 
5.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 126
21.6%
2 71
12.2%
4 63
10.8%
9 59
10.1%
0 55
9.5%
7 47
 
8.1%
3 46
 
7.9%
5 42
 
7.2%
8 38
 
6.5%
6 35
 
6.0%
Other Letter
ValueCountFrequency (%)
서 118
13.1%
울 118
13.1%
양 118
13.1%
천 118
13.1%
구 118
13.1%
동 118
13.1%
신 72
8.0%
목 46
 
5.1%
정 37
 
4.1%
월 35
 
3.9%
Space Separator
ValueCountFrequency (%)
354
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 104
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1040
53.7%
Hangul 898
46.3%

Most frequent character per script

Common
ValueCountFrequency (%)
354
34.0%
1 126
 
12.1%
- 104
 
10.0%
2 71
 
6.8%
4 63
 
6.1%
9 59
 
5.7%
0 55
 
5.3%
7 47
 
4.5%
3 46
 
4.4%
5 42
 
4.0%
Other values (2) 73
 
7.0%
Hangul
ValueCountFrequency (%)
서 118
13.1%
울 118
13.1%
양 118
13.1%
천 118
13.1%
구 118
13.1%
동 118
13.1%
신 72
8.0%
목 46
 
5.1%
정 37
 
4.1%
월 35
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1040
53.7%
Hangul 898
46.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
354
34.0%
1 126
 
12.1%
- 104
 
10.0%
2 71
 
6.8%
4 63
 
6.1%
9 59
 
5.7%
0 55
 
5.3%
7 47
 
4.5%
3 46
 
4.4%
5 42
 
4.0%
Other values (2) 73
 
7.0%
Hangul
ValueCountFrequency (%)
서 118
13.1%
울 118
13.1%
양 118
13.1%
천 118
13.1%
구 118
13.1%
동 118
13.1%
신 72
8.0%
목 46
 
5.1%
정 37
 
4.1%
월 35
 
3.9%

위도
Real number (ℝ)

Distinct117
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.527549
Minimum37.505352
Maximum37.54918
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-04-21T08:15:25.310649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.505352
5-th percentile37.511824
Q137.520245
median37.52579
Q337.536105
95-th percentile37.54657
Maximum37.54918
Range0.043827606
Interquartile range (IQR)0.015859855

Descriptive statistics

Standard deviation0.010250432
Coefficient of variation (CV)0.00027314419
Kurtosis-0.6507252
Mean37.527549
Median Absolute Deviation (MAD)0.0078246675
Skewness0.15255248
Sum4428.2508
Variance0.00010507136
MonotonicityNot monotonic
2024-04-21T08:15:25.442188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.536176 2
 
1.7%
37.535891 1
 
0.8%
37.52708196841768 1
 
0.8%
37.543661802976025 1
 
0.8%
37.51756735332346 1
 
0.8%
37.525638416107086 1
 
0.8%
37.5180058033238 1
 
0.8%
37.53085871053519 1
 
0.8%
37.51957978745644 1
 
0.8%
37.51792527769135 1
 
0.8%
Other values (107) 107
90.7%
ValueCountFrequency (%)
37.50535239387332 1
0.8%
37.508343 1
0.8%
37.509141 1
0.8%
37.509904 1
0.8%
37.509942085219485 1
0.8%
37.51022005985399 1
0.8%
37.512107 1
0.8%
37.512275 1
0.8%
37.512463 1
0.8%
37.512658 1
0.8%
ValueCountFrequency (%)
37.54918 1
0.8%
37.548024 1
0.8%
37.5475866621708 1
0.8%
37.546821 1
0.8%
37.54678 1
0.8%
37.546704 1
0.8%
37.54654621032682 1
0.8%
37.546099 1
0.8%
37.545292 1
0.8%
37.543661802976025 1
0.8%

경도
Real number (ℝ)

Distinct117
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.85756
Minimum126.82453
Maximum126.88694
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-04-21T08:15:25.572709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.82453
5-th percentile126.83074
Q1126.84222
median126.86302
Q3126.87122
95-th percentile126.87904
Maximum126.88694
Range0.062414188
Interquartile range (IQR)0.028999952

Descriptive statistics

Standard deviation0.016904002
Coefficient of variation (CV)0.00013325183
Kurtosis-1.172726
Mean126.85756
Median Absolute Deviation (MAD)0.011406311
Skewness-0.40039324
Sum14969.192
Variance0.00028574528
MonotonicityNot monotonic
2024-04-21T08:15:25.726951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.867045 2
 
1.7%
126.870566 1
 
0.8%
126.85876934482356 1
 
0.8%
126.87397021382048 1
 
0.8%
126.84270076534077 1
 
0.8%
126.8516662000189 1
 
0.8%
126.84551645032226 1
 
0.8%
126.83081053921876 1
 
0.8%
126.8557957283748 1
 
0.8%
126.85301907250955 1
 
0.8%
Other values (107) 107
90.7%
ValueCountFrequency (%)
126.82452681195376 1
0.8%
126.826727 1
0.8%
126.82722 1
0.8%
126.828938 1
0.8%
126.829128 1
0.8%
126.830421 1
0.8%
126.830799 1
0.8%
126.83081053921876 1
0.8%
126.831321 1
0.8%
126.831572 1
0.8%
ValueCountFrequency (%)
126.886941 1
0.8%
126.88525139288114 1
0.8%
126.882787 1
0.8%
126.881427 1
0.8%
126.88078367626863 1
0.8%
126.880352 1
0.8%
126.878808 1
0.8%
126.877275 1
0.8%
126.877254 1
0.8%
126.877113 1
0.8%

자치구
Categorical

CONSTANT 

Distinct1
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
양천구
118 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
양천구 118
100.0%

Length

2024-04-21T08:15:25.849741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T08:15:25.937859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
양천구 118
100.0%

단속지점명
Text

UNIQUE 

Distinct118
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2024-04-21T08:15:26.102871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length16
Mean length12.466102
Min length5

Characters and Unicode

Total characters1471
Distinct characters217
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique118 ?
Unique (%)100.0%

Sample

1st row목4동 영도초등학교 주변
2nd row목4동 태학관 주변
3rd row신월2동 양강초교 후문 주변
4th row신월7동 우성상가 주변
5th row목1동 파라곤(SBS) 주변
ValueCountFrequency (%)
주변 90
26.1%
앞 13
 
3.8%
사거리 10
 
2.9%
목4동 10
 
2.9%
목1동 9
 
2.6%
삼거리 8
 
2.3%
신월1동 7
 
2.0%
목5동 6
 
1.7%
목2동 5
 
1.4%
신정7동 4
 
1.2%
Other values (153) 183
53.0%
2024-04-21T08:15:26.468844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
227
 
15.4%
동 100
 
6.8%
주 96
 
6.5%
변 90
 
6.1%
목 55
 
3.7%
신 50
 
3.4%
교 32
 
2.2%
정 27
 
1.8%
리 25
 
1.7%
1 25
 
1.7%
Other values (207) 744
50.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1133
77.0%
Space Separator 227
 
15.4%
Decimal Number 99
 
6.7%
Uppercase Letter 10
 
0.7%
Open Punctuation 1
 
0.1%
Close Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 100
 
8.8%
주 96
 
8.5%
변 90
 
7.9%
목 55
 
4.9%
신 50
 
4.4%
교 32
 
2.8%
정 27
 
2.4%
리 25
 
2.2%
월 25
 
2.2%
트 21
 
1.9%
Other values (187) 612
54.0%
Decimal Number
ValueCountFrequency (%)
1 25
25.3%
4 19
19.2%
2 12
12.1%
5 12
12.1%
3 12
12.1%
7 10
 
10.1%
0 5
 
5.1%
6 3
 
3.0%
9 1
 
1.0%
Uppercase Letter
ValueCountFrequency (%)
S 3
30.0%
C 1
 
10.0%
W 1
 
10.0%
M 1
 
10.0%
A 1
 
10.0%
P 1
 
10.0%
T 1
 
10.0%
B 1
 
10.0%
Space Separator
ValueCountFrequency (%)
227
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1133
77.0%
Common 328
 
22.3%
Latin 10
 
0.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 100
 
8.8%
주 96
 
8.5%
변 90
 
7.9%
목 55
 
4.9%
신 50
 
4.4%
교 32
 
2.8%
정 27
 
2.4%
리 25
 
2.2%
월 25
 
2.2%
트 21
 
1.9%
Other values (187) 612
54.0%
Common
ValueCountFrequency (%)
227
69.2%
1 25
 
7.6%
4 19
 
5.8%
2 12
 
3.7%
5 12
 
3.7%
3 12
 
3.7%
7 10
 
3.0%
0 5
 
1.5%
6 3
 
0.9%
9 1
 
0.3%
Other values (2) 2
 
0.6%
Latin
ValueCountFrequency (%)
S 3
30.0%
C 1
 
10.0%
W 1
 
10.0%
M 1
 
10.0%
A 1
 
10.0%
P 1
 
10.0%
T 1
 
10.0%
B 1
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1133
77.0%
ASCII 338
 
23.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
227
67.2%
1 25
 
7.4%
4 19
 
5.6%
2 12
 
3.6%
5 12
 
3.6%
3 12
 
3.6%
7 10
 
3.0%
0 5
 
1.5%
S 3
 
0.9%
6 3
 
0.9%
Other values (10) 10
 
3.0%
Hangul
ValueCountFrequency (%)
동 100
 
8.8%
주 96
 
8.5%
변 90
 
7.9%
목 55
 
4.9%
신 50
 
4.4%
교 32
 
2.8%
정 27
 
2.4%
리 25
 
2.2%
월 25
 
2.2%
트 21
 
1.9%
Other values (187) 612
54.0%

현장구분
Categorical

CONSTANT 

Distinct1
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
불법주정차구역
118 

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 118
100.0%

Length

2024-04-21T08:15:26.613379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T08:15:26.705587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
불법주정차구역 118
100.0%

Interactions

2024-04-21T08:15:24.197147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T08:15:23.984277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T08:15:24.269668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T08:15:24.111363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-21T08:15:26.753321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위도경도
위도1.0000.636
경도0.6361.000
2024-04-21T08:15:26.821344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위도경도
위도1.0000.237
경도0.2371.000

Missing values

2024-04-21T08:15:24.372039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-21T08:15:24.461495image/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
0서울 양천구 목4동 762-1037.535891126.870566양천구목4동 영도초등학교 주변불법주정차구역
1서울 양천구 목4동 797-837.53274126.867191양천구목4동 태학관 주변불법주정차구역
2서울 양천구 신월2동 49637.524535126.848224양천구신월2동 양강초교 후문 주변불법주정차구역
3서울 양천구 신월7동 928-137.522349126.833529양천구신월7동 우성상가 주변불법주정차구역
4서울 양천구 목1동 91737.528982126.874597양천구목1동 파라곤(SBS) 주변불법주정차구역
5서울 양천구 목3동 602-1037.548024126.866839양천구목3동 롯데캐슬프라자 주변불법주정차구역
6서울 양천구 신월1동 131-737.531127126.831572양천구신월1동 대흥가스 주변불법주정차구역
7서울 양천구 신정7동 325-837.509141126.861695양천구신정7동 봉영여자중학교 주변불법주정차구역
8서울 양천구 신정2동 119-137.522205126.877254양천구신정2동 파리바게트 주변불법주정차구역
9서울 양천구 목4동 766-1637.536176126.867045양천구목4동 한신빌딩 앞 사거리불법주정차구역
고정형CCTV지번주소위도경도자치구단속지점명현장구분
108서울 양천구 신정동 1191-1437.52073126.851559양천구레미안목동아텔리체 APT 109동 주변불법주정차구역
109서울 양천구 신월동 21-437.540119126.832285양천구전주식당 주변불법주정차구역
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