Overview

Dataset statistics

Number of variables4
Number of observations384
Missing cells1012
Missing cells (%)65.9%
Duplicate rows1
Duplicate rows (%)0.3%
Total size in memory12.9 KiB
Average record size in memory34.3 B

Variable types

Text2
Numeric2

Dataset

Description인천광역시 UTIS(지능형교통정보시스템)의 CCTV 설치위치(위치,주소,위경도)에 관련된 데이터 입니다.
Author인천광역시
URLhttps://data.incheon.go.kr/findData/publicDataDetail?dataId=15089908&srcSe=7661IVAWM27C61E190

Alerts

Dataset has 1 (0.3%) duplicate rowsDuplicates
CCTV위치 has 253 (65.9%) missing valuesMissing
설치주소 has 253 (65.9%) missing valuesMissing
경도 has 253 (65.9%) missing valuesMissing
위도 has 253 (65.9%) missing valuesMissing

Reproduction

Analysis started2024-04-21 08:19:23.733061
Analysis finished2024-04-21 08:19:25.939146
Duration2.21 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

CCTV위치
Text

MISSING 

Distinct131
Distinct (%)100.0%
Missing253
Missing (%)65.9%
Memory size3.1 KiB
2024-04-21T17:19:26.713846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length5
Mean length5.8244275
Min length3

Characters and Unicode

Total characters763
Distinct characters167
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique131 ?
Unique (%)100.0%

Sample

1st row가재울사거리
2nd row가정사거리
3rd row가정오거리
4th row가좌I.C
5th row가좌IC
ValueCountFrequency (%)
계양우체국 1
 
0.7%
숭의시장사거리 1
 
0.7%
옛시민회관 1
 
0.7%
염곡길사거리 1
 
0.7%
연수사거리 1
 
0.7%
앵고개길사거리 1
 
0.7%
알미골사거리 1
 
0.7%
안말사거리 1
 
0.7%
안동포사거리 1
 
0.7%
아시아드사거리 1
 
0.7%
Other values (124) 124
92.5%
2024-04-21T17:19:28.078734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
리 96
 
12.6%
거 95
 
12.5%
사 82
 
10.7%
교 18
 
2.4%
C 14
 
1.8%
인 12
 
1.6%
구 12
 
1.6%
I 11
 
1.4%
석 10
 
1.3%
삼 10
 
1.3%
Other values (157) 403
52.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 709
92.9%
Uppercase Letter 30
 
3.9%
Other Punctuation 8
 
1.0%
Decimal Number 7
 
0.9%
Space Separator 3
 
0.4%
Close Punctuation 3
 
0.4%
Open Punctuation 3
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 96
 
13.5%
거 95
 
13.4%
사 82
 
11.6%
교 18
 
2.5%
인 12
 
1.7%
구 12
 
1.7%
석 10
 
1.4%
삼 10
 
1.4%
동 10
 
1.4%
정 9
 
1.3%
Other values (146) 355
50.1%
Uppercase Letter
ValueCountFrequency (%)
C 14
46.7%
I 11
36.7%
J 3
 
10.0%
T 2
 
6.7%
Decimal Number
ValueCountFrequency (%)
2 4
57.1%
1 2
28.6%
3 1
 
14.3%
Other Punctuation
ValueCountFrequency (%)
. 8
100.0%
Space Separator
ValueCountFrequency (%)
3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 709
92.9%
Latin 30
 
3.9%
Common 24
 
3.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 96
 
13.5%
거 95
 
13.4%
사 82
 
11.6%
교 18
 
2.5%
인 12
 
1.7%
구 12
 
1.7%
석 10
 
1.4%
삼 10
 
1.4%
동 10
 
1.4%
정 9
 
1.3%
Other values (146) 355
50.1%
Common
ValueCountFrequency (%)
. 8
33.3%
2 4
16.7%
3
 
12.5%
) 3
 
12.5%
( 3
 
12.5%
1 2
 
8.3%
3 1
 
4.2%
Latin
ValueCountFrequency (%)
C 14
46.7%
I 11
36.7%
J 3
 
10.0%
T 2
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 709
92.9%
ASCII 54
 
7.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
리 96
 
13.5%
거 95
 
13.4%
사 82
 
11.6%
교 18
 
2.5%
인 12
 
1.7%
구 12
 
1.7%
석 10
 
1.4%
삼 10
 
1.4%
동 10
 
1.4%
정 9
 
1.3%
Other values (146) 355
50.1%
ASCII
ValueCountFrequency (%)
C 14
25.9%
I 11
20.4%
. 8
14.8%
2 4
 
7.4%
3
 
5.6%
J 3
 
5.6%
) 3
 
5.6%
( 3
 
5.6%
T 2
 
3.7%
1 2
 
3.7%

설치주소
Text

MISSING 

Distinct128
Distinct (%)97.7%
Missing253
Missing (%)65.9%
Memory size3.1 KiB
2024-04-21T17:19:29.290209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length24
Mean length12.251908
Min length4

Characters and Unicode

Total characters1605
Distinct characters127
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

Unique125 ?
Unique (%)95.4%

Sample

1st row서구 가좌동 257-2
2nd row서구 원창동 2
3rd row서구 가정동 570
4th row서구 가좌동 177-1
5th row가좌IC
ValueCountFrequency (%)
서구 28
 
6.9%
남동구 26
 
6.5%
남구 19
 
4.7%
계양구 14
 
3.5%
부평구 13
 
3.2%
연수구 10
 
2.5%
중구 9
 
2.2%
주안동 6
 
1.5%
고잔동 5
 
1.2%
석남동 5
 
1.2%
Other values (206) 268
66.5%
2024-04-21T17:19:30.714861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
273
17.0%
동 155
 
9.7%
구 128
 
8.0%
1 85
 
5.3%
- 72
 
4.5%
2 66
 
4.1%
5 56
 
3.5%
남 54
 
3.4%
3 54
 
3.4%
7 42
 
2.6%
Other values (117) 620
38.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 780
48.6%
Decimal Number 464
28.9%
Space Separator 273
 
17.0%
Dash Punctuation 72
 
4.5%
Uppercase Letter 13
 
0.8%
Other Punctuation 3
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 155
19.9%
구 128
16.4%
남 54
 
6.9%
서 37
 
4.7%
부 17
 
2.2%
수 16
 
2.1%
계 16
 
2.1%
평 16
 
2.1%
양 14
 
1.8%
가 13
 
1.7%
Other values (100) 314
40.3%
Decimal Number
ValueCountFrequency (%)
1 85
18.3%
2 66
14.2%
5 56
12.1%
3 54
11.6%
7 42
9.1%
4 39
8.4%
6 34
 
7.3%
9 33
 
7.1%
0 31
 
6.7%
8 24
 
5.2%
Uppercase Letter
ValueCountFrequency (%)
C 6
46.2%
I 5
38.5%
J 1
 
7.7%
T 1
 
7.7%
Space Separator
ValueCountFrequency (%)
273
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 72
100.0%
Other Punctuation
ValueCountFrequency (%)
. 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 812
50.6%
Hangul 780
48.6%
Latin 13
 
0.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 155
19.9%
구 128
16.4%
남 54
 
6.9%
서 37
 
4.7%
부 17
 
2.2%
수 16
 
2.1%
계 16
 
2.1%
평 16
 
2.1%
양 14
 
1.8%
가 13
 
1.7%
Other values (100) 314
40.3%
Common
ValueCountFrequency (%)
273
33.6%
1 85
 
10.5%
- 72
 
8.9%
2 66
 
8.1%
5 56
 
6.9%
3 54
 
6.7%
7 42
 
5.2%
4 39
 
4.8%
6 34
 
4.2%
9 33
 
4.1%
Other values (3) 58
 
7.1%
Latin
ValueCountFrequency (%)
C 6
46.2%
I 5
38.5%
J 1
 
7.7%
T 1
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 825
51.4%
Hangul 780
48.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
273
33.1%
1 85
 
10.3%
- 72
 
8.7%
2 66
 
8.0%
5 56
 
6.8%
3 54
 
6.5%
7 42
 
5.1%
4 39
 
4.7%
6 34
 
4.1%
9 33
 
4.0%
Other values (7) 71
 
8.6%
Hangul
ValueCountFrequency (%)
동 155
19.9%
구 128
16.4%
남 54
 
6.9%
서 37
 
4.7%
부 17
 
2.2%
수 16
 
2.1%
계 16
 
2.1%
평 16
 
2.1%
양 14
 
1.8%
가 13
 
1.7%
Other values (100) 314
40.3%

경도
Real number (ℝ)

MISSING 

Distinct131
Distinct (%)100.0%
Missing253
Missing (%)65.9%
Infinite0
Infinite (%)0.0%
Mean126.67773
Minimum126.37618
Maximum126.75349
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2024-04-21T17:19:30.958334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.37618
5-th percentile126.62279
Q1126.66114
median126.6802
Q3126.71341
95-th percentile126.74402
Maximum126.75349
Range0.37731
Interquartile range (IQR)0.052275

Descriptive statistics

Standard deviation0.060719614
Coefficient of variation (CV)0.00047932352
Kurtosis9.0552358
Mean126.67773
Median Absolute Deviation (MAD)0.02879
Skewness-2.521381
Sum16594.782
Variance0.0036868715
MonotonicityNot monotonic
2024-04-21T17:19:31.421206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.69016 1
 
0.3%
126.67989 1
 
0.3%
126.66803 1
 
0.3%
126.68318 1
 
0.3%
126.66252 1
 
0.3%
126.492 1
 
0.3%
126.69229 1
 
0.3%
126.6551 1
 
0.3%
126.66358 1
 
0.3%
126.69166 1
 
0.3%
Other values (121) 121
31.5%
(Missing) 253
65.9%
ValueCountFrequency (%)
126.37618 1
0.3%
126.41363 1
0.3%
126.42544 1
0.3%
126.492 1
0.3%
126.4934 1
0.3%
126.53278 1
0.3%
126.62201 1
0.3%
126.62358 1
0.3%
126.62672 1
0.3%
126.62772 1
0.3%
ValueCountFrequency (%)
126.75349 1
0.3%
126.75312 1
0.3%
126.75257 1
0.3%
126.75159 1
0.3%
126.74659 1
0.3%
126.74567 1
0.3%
126.74536 1
0.3%
126.74267 1
0.3%
126.73938 1
0.3%
126.73916 1
0.3%

위도
Real number (ℝ)

MISSING 

Distinct131
Distinct (%)100.0%
Missing253
Missing (%)65.9%
Infinite0
Infinite (%)0.0%
Mean37.484808
Minimum37.38517
Maximum37.74248
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2024-04-21T17:19:31.685397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.38517
5-th percentile37.40309
Q137.44571
median37.4698
Q337.52422
95-th percentile37.58879
Maximum37.74248
Range0.35731
Interquartile range (IQR)0.07851

Descriptive statistics

Standard deviation0.060161044
Coefficient of variation (CV)0.0016049447
Kurtosis1.6207943
Mean37.484808
Median Absolute Deviation (MAD)0.03639
Skewness0.9595788
Sum4910.5098
Variance0.0036193512
MonotonicityNot monotonic
2024-04-21T17:19:31.951828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.45052 1
 
0.3%
37.45861 1
 
0.3%
37.5067 1
 
0.3%
37.40751 1
 
0.3%
37.41283 1
 
0.3%
37.74248 1
 
0.3%
37.39691 1
 
0.3%
37.5917 1
 
0.3%
37.55204 1
 
0.3%
37.47292 1
 
0.3%
Other values (121) 121
31.5%
(Missing) 253
65.9%
ValueCountFrequency (%)
37.38517 1
0.3%
37.38762 1
0.3%
37.39254 1
0.3%
37.39299 1
0.3%
37.39691 1
0.3%
37.39717 1
0.3%
37.39867 1
0.3%
37.40751 1
0.3%
37.408 1
0.3%
37.40848 1
0.3%
ValueCountFrequency (%)
37.74248 1
0.3%
37.63725 1
0.3%
37.62755 1
0.3%
37.6021 1
0.3%
37.59344 1
0.3%
37.59313 1
0.3%
37.5917 1
0.3%
37.58588 1
0.3%
37.57762 1
0.3%
37.57677 1
0.3%

Interactions

2024-04-21T17:19:24.600296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T17:19:24.070501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T17:19:24.865755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T17:19:24.336137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-21T17:19:32.123973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
경도위도
경도1.0000.709
위도0.7091.000
2024-04-21T17:19:32.263451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
경도위도
경도1.000-0.022
위도-0.0221.000

Missing values

2024-04-21T17:19:25.215228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-21T17:19:25.500486image/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.
2024-04-21T17:19:25.782555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

CCTV위치설치주소경도위도
0가재울사거리서구 가좌동 257-2126.680237.48734
1가정사거리서구 원창동 2126.6622837.52527
2가정오거리서구 가정동 570126.676737.52431
3가좌I.C서구 가좌동 177-1126.6751537.4843
4가좌IC가좌IC126.6749937.48348
5가좌가정교가좌가정교126.6764237.50826
6간석사거리남동구 간석동 923-3126.7199237.46095
7간석오거리남동구 간석동 215-1126.7076537.46702
8거잠포사거리인천광역시 중구 운서동 2854번지 15호 철탑126.4254437.42379
9건지사거리서구 가좌동 69126.6837837.49654
CCTV위치설치주소경도위도
374<NA><NA><NA><NA>
375<NA><NA><NA><NA>
376<NA><NA><NA><NA>
377<NA><NA><NA><NA>
378<NA><NA><NA><NA>
379<NA><NA><NA><NA>
380<NA><NA><NA><NA>
381<NA><NA><NA><NA>
382<NA><NA><NA><NA>
383<NA><NA><NA><NA>

Duplicate rows

Most frequently occurring

CCTV위치설치주소경도위도# duplicates
0<NA><NA><NA><NA>253