Overview

Dataset statistics

Number of variables6
Number of observations97
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.9 KiB
Average record size in memory51.4 B

Variable types

Numeric2
Categorical2
Text1
DateTime1

Dataset

Description인천광역시 중구 무단투기 CCTV 설치현황입니다.
Author인천광역시 중구
URLhttps://data.incheon.go.kr/findData/publicDataDetail?dataId=15103422&srcSe=7661IVAWM27C61E190

Alerts

데이터 기준일자 has constant value ""Constant
연번 is highly overall correlated with 설치연도 and 2 other fieldsHigh correlation
설치연도 is highly overall correlated with 연번 and 1 other fieldsHigh correlation
구분 is highly overall correlated with 연번 and 1 other fieldsHigh correlation
설치동 is highly overall correlated with 연번High correlation
연번 has unique valuesUnique
위치 has unique valuesUnique

Reproduction

Analysis started2024-04-17 19:29:38.756154
Analysis finished2024-04-17 19:29:39.414191
Duration0.66 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct97
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49
Minimum1
Maximum97
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1005.0 B
2024-04-18T04:29:39.479841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5.8
Q125
median49
Q373
95-th percentile92.2
Maximum97
Range96
Interquartile range (IQR)48

Descriptive statistics

Standard deviation28.145456
Coefficient of variation (CV)0.57439705
Kurtosis-1.2
Mean49
Median Absolute Deviation (MAD)24
Skewness0
Sum4753
Variance792.16667
MonotonicityStrictly increasing
2024-04-18T04:29:39.589938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.0%
74 1
 
1.0%
72 1
 
1.0%
71 1
 
1.0%
70 1
 
1.0%
69 1
 
1.0%
68 1
 
1.0%
67 1
 
1.0%
66 1
 
1.0%
65 1
 
1.0%
Other values (87) 87
89.7%
ValueCountFrequency (%)
1 1
1.0%
2 1
1.0%
3 1
1.0%
4 1
1.0%
5 1
1.0%
6 1
1.0%
7 1
1.0%
8 1
1.0%
9 1
1.0%
10 1
1.0%
ValueCountFrequency (%)
97 1
1.0%
96 1
1.0%
95 1
1.0%
94 1
1.0%
93 1
1.0%
92 1
1.0%
91 1
1.0%
90 1
1.0%
89 1
1.0%
88 1
1.0%

구분
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size908.0 B
고정식
61 
이동식
36 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
고정식 61
62.9%
이동식 36
37.1%

Length

2024-04-18T04:29:39.686363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T04:29:39.754252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
고정식 61
62.9%
이동식 36
37.1%

설치동
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Memory size908.0 B
신포동
16 
신흥동
16 
동인천동
16 
연안동
15 
개항동
13 
Other values (2)
21 

Length

Max length4
Median length3
Mean length3.1649485
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
신포동 16
16.5%
신흥동 16
16.5%
동인천동 16
16.5%
연안동 15
15.5%
개항동 13
13.4%
도원동 12
12.4%
율목동 9
9.3%

Length

2024-04-18T04:29:39.827991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T04:29:39.909871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
신포동 16
16.5%
신흥동 16
16.5%
동인천동 16
16.5%
연안동 15
15.5%
개항동 13
13.4%
도원동 12
12.4%
율목동 9
9.3%

위치
Text

UNIQUE 

Distinct97
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size908.0 B
2024-04-18T04:29:40.148780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length27
Median length24
Mean length18.731959
Min length13

Characters and Unicode

Total characters1817
Distinct characters74
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

Unique97 ?
Unique (%)100.0%

Sample

1st row인천광역시 중구 답동 52-1
2nd row인천광역시 중구 사동 26-20
3rd row인천광역시 중구 인중로164번길 18
4th row인천광역시 중구 우현로 20번길 58
5th row인천광역시 중구 우현로 35번길 26
ValueCountFrequency (%)
인천광역시 97
24.1%
중구 97
24.1%
항동7가 12
 
3.0%
도원동 6
 
1.5%
신흥동3가 4
 
1.0%
율목동 4
 
1.0%
우현로 4
 
1.0%
14 3
 
0.7%
도원로 3
 
0.7%
15 3
 
0.7%
Other values (146) 170
42.2%
2024-04-18T04:29:40.490259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
307
16.9%
중 101
 
5.6%
인 100
 
5.5%
광 97
 
5.3%
역 97
 
5.3%
시 97
 
5.3%
구 97
 
5.3%
천 97
 
5.3%
1 79
 
4.3%
동 58
 
3.2%
Other values (64) 687
37.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1072
59.0%
Decimal Number 380
 
20.9%
Space Separator 307
 
16.9%
Dash Punctuation 58
 
3.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
중 101
 
9.4%
인 100
 
9.3%
광 97
 
9.0%
역 97
 
9.0%
시 97
 
9.0%
구 97
 
9.0%
천 97
 
9.0%
동 58
 
5.4%
로 40
 
3.7%
길 32
 
3.0%
Other values (52) 256
23.9%
Decimal Number
ValueCountFrequency (%)
1 79
20.8%
2 57
15.0%
3 43
11.3%
4 43
11.3%
7 38
10.0%
6 29
 
7.6%
8 29
 
7.6%
5 29
 
7.6%
0 18
 
4.7%
9 15
 
3.9%
Space Separator
ValueCountFrequency (%)
307
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 58
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1072
59.0%
Common 745
41.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
중 101
 
9.4%
인 100
 
9.3%
광 97
 
9.0%
역 97
 
9.0%
시 97
 
9.0%
구 97
 
9.0%
천 97
 
9.0%
동 58
 
5.4%
로 40
 
3.7%
길 32
 
3.0%
Other values (52) 256
23.9%
Common
ValueCountFrequency (%)
307
41.2%
1 79
 
10.6%
- 58
 
7.8%
2 57
 
7.7%
3 43
 
5.8%
4 43
 
5.8%
7 38
 
5.1%
6 29
 
3.9%
8 29
 
3.9%
5 29
 
3.9%
Other values (2) 33
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1072
59.0%
ASCII 745
41.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
307
41.2%
1 79
 
10.6%
- 58
 
7.8%
2 57
 
7.7%
3 43
 
5.8%
4 43
 
5.8%
7 38
 
5.1%
6 29
 
3.9%
8 29
 
3.9%
5 29
 
3.9%
Other values (2) 33
 
4.4%
Hangul
ValueCountFrequency (%)
중 101
 
9.4%
인 100
 
9.3%
광 97
 
9.0%
역 97
 
9.0%
시 97
 
9.0%
구 97
 
9.0%
천 97
 
9.0%
동 58
 
5.4%
로 40
 
3.7%
길 32
 
3.0%
Other values (52) 256
23.9%

설치연도
Real number (ℝ)

HIGH CORRELATION 

Distinct12
Distinct (%)12.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2016.5361
Minimum2008
Maximum2023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1005.0 B
2024-04-18T04:29:40.582708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2008
5-th percentile2008
Q12012
median2017
Q32022
95-th percentile2023
Maximum2023
Range15
Interquartile range (IQR)10

Descriptive statistics

Standard deviation5.4621078
Coefficient of variation (CV)0.0027086586
Kurtosis-1.4147891
Mean2016.5361
Median Absolute Deviation (MAD)5
Skewness-0.26297289
Sum195604
Variance29.834622
MonotonicityNot monotonic
2024-04-18T04:29:40.677474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
2022 21
21.6%
2023 15
15.5%
2008 12
12.4%
2014 12
12.4%
2018 9
9.3%
2017 7
 
7.2%
2011 4
 
4.1%
2010 4
 
4.1%
2009 4
 
4.1%
2013 3
 
3.1%
Other values (2) 6
 
6.2%
ValueCountFrequency (%)
2008 12
12.4%
2009 4
 
4.1%
2010 4
 
4.1%
2011 4
 
4.1%
2012 3
 
3.1%
2013 3
 
3.1%
2014 12
12.4%
2017 7
7.2%
2018 9
9.3%
2019 3
 
3.1%
ValueCountFrequency (%)
2023 15
15.5%
2022 21
21.6%
2019 3
 
3.1%
2018 9
9.3%
2017 7
 
7.2%
2014 12
12.4%
2013 3
 
3.1%
2012 3
 
3.1%
2011 4
 
4.1%
2010 4
 
4.1%

데이터 기준일자
Date

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size908.0 B
Minimum2023-08-08 00:00:00
Maximum2023-08-08 00:00:00
2024-04-18T04:29:40.772366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T04:29:40.841229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2024-04-18T04:29:39.140116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T04:29:38.972738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T04:29:39.201851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T04:29:39.069517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-18T04:29:40.896651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번구분설치동위치설치연도
연번1.0000.9970.8561.0000.602
구분0.9971.0000.0721.0001.000
설치동0.8560.0721.0001.0000.000
위치1.0001.0001.0001.0001.000
설치연도0.6021.0000.0001.0001.000
2024-04-18T04:29:40.967974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
설치동구분
설치동1.0000.070
구분0.0701.000
2024-04-18T04:29:41.030497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번설치연도구분설치동
연번1.0000.7480.9070.652
설치연도0.7481.0000.9680.000
구분0.9070.9681.0000.070
설치동0.6520.0000.0701.000

Missing values

2024-04-18T04:29:39.294539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-18T04:29:39.374493image/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

연번구분설치동위치설치연도데이터 기준일자
01고정식신포동인천광역시 중구 답동 52-120082023-08-08
12고정식신포동인천광역시 중구 사동 26-2020082023-08-08
23고정식신포동인천광역시 중구 인중로164번길 1820142023-08-08
34고정식신포동인천광역시 중구 우현로 20번길 5820112023-08-08
45고정식신포동인천광역시 중구 우현로 35번길 2620142023-08-08
56고정식신포동인천광역시 중구 우현로 20번길 3120112023-08-08
67고정식신포동인천광역시 중구 관동 2-820132023-08-08
78고정식신포동인천광역시 중구 신포로27번길20142023-08-08
89고정식신포동인천광역시 중구 답동로 2320172023-08-08
910고정식신포동인천광역시 중구 사동 15-2220172023-08-08
연번구분설치동위치설치연도데이터 기준일자
8788이동식율목동인천광역시 중구 도원서길 9520232023-08-08
8889이동식동인천동인천광역시 중구 큰우물로 22-120222023-08-08
8990이동식동인천동인천광역시 중구 경동 42-120222023-08-08
9091이동식동인천동인천광역시 중구 자유공원로 1220232023-08-08
9192이동식동인천동인천광역시 중구 용동 174-420222023-08-08
9293이동식개항동인천광역시 중구 북성동1가 98-33920222023-08-08
9394이동식개항동인천광역시 중구 북성동 1가 98-36820222023-08-08
9495이동식개항동인천광역시 중구 북성동 1가 98-1020222023-08-08
9596이동식개항동인천광역시 중구 북성동 1가 98-36720232023-08-08
9697이동식개항동인천광역시 중구 북성동1가 98-36720232023-08-08