Overview

Dataset statistics

Number of variables6
Number of observations140
Missing cells111
Missing cells (%)13.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.8 KiB
Average record size in memory49.9 B

Variable types

Text2
Categorical1
DateTime2
Numeric1

Dataset

Description인천광역시 부평구 무단투기단속 이동형CCTV 현황 데이터는 CCTV 설치동, 설치장소, 설치일자, 이동횟수 등에 대한 데이터를 제공합니다.
URLhttps://www.data.go.kr/data/15102522/fileData.do

Alerts

설치일자(최근이전) has 58 (41.4%) missing valuesMissing
이동횟수 has 53 (37.9%) missing valuesMissing
관리번호 has unique valuesUnique
설치장소 has unique valuesUnique

Reproduction

Analysis started2023-12-12 16:58:33.594443
Analysis finished2023-12-12 16:58:34.430479
Duration0.84 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

관리번호
Text

UNIQUE 

Distinct140
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
2023-12-13T01:58:34.755748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length4.2285714
Min length3

Characters and Unicode

Total characters592
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique140 ?
Unique (%)100.0%

Sample

1st rowD-23
2nd rowD-40
3rd rowD-41
4th rowD-42
5th rowD-84
ValueCountFrequency (%)
d-23 1
 
0.7%
d-134 1
 
0.7%
d-20 1
 
0.7%
d-136 1
 
0.7%
d-108 1
 
0.7%
d-107 1
 
0.7%
d-71 1
 
0.7%
d-70 1
 
0.7%
d-69 1
 
0.7%
d-104 1
 
0.7%
Other values (130) 130
92.9%
2023-12-13T01:58:35.280995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
D 140
23.6%
- 140
23.6%
1 75
12.7%
2 34
 
5.7%
3 34
 
5.7%
4 25
 
4.2%
7 24
 
4.1%
6 24
 
4.1%
5 24
 
4.1%
8 24
 
4.1%
Other values (2) 48
 
8.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 312
52.7%
Uppercase Letter 140
23.6%
Dash Punctuation 140
23.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 75
24.0%
2 34
10.9%
3 34
10.9%
4 25
 
8.0%
7 24
 
7.7%
6 24
 
7.7%
5 24
 
7.7%
8 24
 
7.7%
9 24
 
7.7%
0 24
 
7.7%
Uppercase Letter
ValueCountFrequency (%)
D 140
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 140
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 452
76.4%
Latin 140
 
23.6%

Most frequent character per script

Common
ValueCountFrequency (%)
- 140
31.0%
1 75
16.6%
2 34
 
7.5%
3 34
 
7.5%
4 25
 
5.5%
7 24
 
5.3%
6 24
 
5.3%
5 24
 
5.3%
8 24
 
5.3%
9 24
 
5.3%
Latin
ValueCountFrequency (%)
D 140
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
D 140
23.6%
- 140
23.6%
1 75
12.7%
2 34
 
5.7%
3 34
 
5.7%
4 25
 
4.2%
7 24
 
4.1%
6 24
 
4.1%
5 24
 
4.1%
8 24
 
4.1%
Other values (2) 48
 
8.1%

설치동
Categorical

Distinct22
Distinct (%)15.7%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
부평2동
14 
부평5동
11 
부평6동
9 
산곡1동
9 
청천1동
9 
Other values (17)
88 

Length

Max length4
Median length4
Mean length3.9571429
Min length3

Unique

Unique1 ?
Unique (%)0.7%

Sample

1st row부평1동
2nd row부평1동
3rd row부평1동
4th row부평1동
5th row부평1동

Common Values

ValueCountFrequency (%)
부평2동 14
 
10.0%
부평5동 11
 
7.9%
부평6동 9
 
6.4%
산곡1동 9
 
6.4%
청천1동 9
 
6.4%
부평1동 8
 
5.7%
십정1동 8
 
5.7%
갈산2동 7
 
5.0%
산곡3동 7
 
5.0%
갈산1동 6
 
4.3%
Other values (12) 52
37.1%

Length

2023-12-13T01:58:35.464552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
부평2동 14
 
10.0%
부평5동 11
 
7.9%
부평6동 9
 
6.4%
산곡1동 9
 
6.4%
청천1동 9
 
6.4%
부평1동 8
 
5.7%
십정1동 8
 
5.7%
갈산2동 7
 
5.0%
산곡3동 7
 
5.0%
일신동 6
 
4.3%
Other values (12) 52
37.1%

설치장소
Text

UNIQUE 

Distinct140
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
2023-12-13T01:58:35.795891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length38
Median length32
Mean length25.021429
Min length14

Characters and Unicode

Total characters3503
Distinct characters224
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

Unique140 ?
Unique (%)100.0%

Sample

1st row인천광역시 부평구 장제로 45
2nd row인천광역시 부평구 부흥로267번길 57
3rd row인천광역시 부평구 부흥로267번길 39
4th row인천광역시 부평구 부평대로51번길 7
5th row인천광역시 부평구 경원대로1347번길 19
ValueCountFrequency (%)
인천광역시 140
21.1%
부평구 140
21.1%
앞 32
 
4.8%
전봇대 6
 
0.9%
경인로 6
 
0.9%
평천로 4
 
0.6%
담장 4
 
0.6%
부평동 4
 
0.6%
46 3
 
0.5%
27 3
 
0.5%
Other values (287) 322
48.5%
2023-12-13T01:58:36.363886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
535
 
15.3%
부 183
 
5.2%
인 162
 
4.6%
평 162
 
4.6%
천 151
 
4.3%
역 146
 
4.2%
시 146
 
4.2%
구 144
 
4.1%
광 142
 
4.1%
로 128
 
3.7%
Other values (214) 1604
45.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2238
63.9%
Decimal Number 576
 
16.4%
Space Separator 535
 
15.3%
Close Punctuation 49
 
1.4%
Open Punctuation 49
 
1.4%
Dash Punctuation 46
 
1.3%
Other Punctuation 5
 
0.1%
Uppercase Letter 5
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
부 183
 
8.2%
인 162
 
7.2%
평 162
 
7.2%
천 151
 
6.7%
역 146
 
6.5%
시 146
 
6.5%
구 144
 
6.4%
광 142
 
6.3%
로 128
 
5.7%
길 92
 
4.1%
Other values (194) 782
34.9%
Decimal Number
ValueCountFrequency (%)
1 127
22.0%
2 80
13.9%
3 62
10.8%
4 53
9.2%
9 49
 
8.5%
7 48
 
8.3%
6 46
 
8.0%
5 40
 
6.9%
0 40
 
6.9%
8 31
 
5.4%
Uppercase Letter
ValueCountFrequency (%)
B 1
20.0%
S 1
20.0%
G 1
20.0%
T 1
20.0%
K 1
20.0%
Space Separator
ValueCountFrequency (%)
535
100.0%
Close Punctuation
ValueCountFrequency (%)
) 49
100.0%
Open Punctuation
ValueCountFrequency (%)
( 49
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 46
100.0%
Other Punctuation
ValueCountFrequency (%)
, 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2238
63.9%
Common 1260
36.0%
Latin 5
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
부 183
 
8.2%
인 162
 
7.2%
평 162
 
7.2%
천 151
 
6.7%
역 146
 
6.5%
시 146
 
6.5%
구 144
 
6.4%
광 142
 
6.3%
로 128
 
5.7%
길 92
 
4.1%
Other values (194) 782
34.9%
Common
ValueCountFrequency (%)
535
42.5%
1 127
 
10.1%
2 80
 
6.3%
3 62
 
4.9%
4 53
 
4.2%
) 49
 
3.9%
( 49
 
3.9%
9 49
 
3.9%
7 48
 
3.8%
6 46
 
3.7%
Other values (5) 162
 
12.9%
Latin
ValueCountFrequency (%)
B 1
20.0%
S 1
20.0%
G 1
20.0%
T 1
20.0%
K 1
20.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2238
63.9%
ASCII 1265
36.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
535
42.3%
1 127
 
10.0%
2 80
 
6.3%
3 62
 
4.9%
4 53
 
4.2%
) 49
 
3.9%
( 49
 
3.9%
9 49
 
3.9%
7 48
 
3.8%
6 46
 
3.6%
Other values (10) 167
 
13.2%
Hangul
ValueCountFrequency (%)
부 183
 
8.2%
인 162
 
7.2%
평 162
 
7.2%
천 151
 
6.7%
역 146
 
6.5%
시 146
 
6.5%
구 144
 
6.4%
광 142
 
6.3%
로 128
 
5.7%
길 92
 
4.1%
Other values (194) 782
34.9%

설치일자(신규)
Date

Distinct17
Distinct (%)12.1%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
Minimum2019-07-10 00:00:00
Maximum2023-01-16 00:00:00
2023-12-13T01:58:36.491981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:58:36.623043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)

설치일자(최근이전)
Date

MISSING 

Distinct66
Distinct (%)80.5%
Missing58
Missing (%)41.4%
Memory size1.2 KiB
Minimum2020-09-16 00:00:00
Maximum2023-06-19 00:00:00
2023-12-13T01:58:37.001008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:58:37.123153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

이동횟수
Real number (ℝ)

MISSING 

Distinct16
Distinct (%)18.4%
Missing53
Missing (%)37.9%
Infinite0
Infinite (%)0.0%
Mean4.1954023
Minimum0
Maximum20
Zeros1
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-13T01:58:37.235687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q36
95-th percentile10.7
Maximum20
Range20
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.8725002
Coefficient of variation (CV)0.92303429
Kurtosis5.6996799
Mean4.1954023
Median Absolute Deviation (MAD)2
Skewness2.2031772
Sum365
Variance14.996258
MonotonicityNot monotonic
2023-12-13T01:58:37.341991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
2 19
 
13.6%
1 17
 
12.1%
3 15
 
10.7%
6 7
 
5.0%
4 6
 
4.3%
5 6
 
4.3%
7 5
 
3.6%
8 3
 
2.1%
9 2
 
1.4%
20 1
 
0.7%
Other values (6) 6
 
4.3%
(Missing) 53
37.9%
ValueCountFrequency (%)
0 1
 
0.7%
1 17
12.1%
2 19
13.6%
3 15
10.7%
4 6
 
4.3%
5 6
 
4.3%
6 7
 
5.0%
7 5
 
3.6%
8 3
 
2.1%
9 2
 
1.4%
ValueCountFrequency (%)
20 1
 
0.7%
19 1
 
0.7%
17 1
 
0.7%
15 1
 
0.7%
11 1
 
0.7%
10 1
 
0.7%
9 2
 
1.4%
8 3
2.1%
7 5
3.6%
6 7
5.0%

Interactions

2023-12-13T01:58:33.932267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T01:58:37.422470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
설치동설치일자(신규)설치일자(최근이전)이동횟수
설치동1.0000.6990.9900.000
설치일자(신규)0.6991.0000.9100.000
설치일자(최근이전)0.9900.9101.0000.657
이동횟수0.0000.0000.6571.000
2023-12-13T01:58:37.502294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
이동횟수설치동
이동횟수1.0000.000
설치동0.0001.000

Missing values

2023-12-13T01:58:34.094864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T01:58:34.249195image/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.
2023-12-13T01:58:34.363129image/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

관리번호설치동설치장소설치일자(신규)설치일자(최근이전)이동횟수
0D-23부평1동인천광역시 부평구 장제로 452020-08-122021-07-214
1D-40부평1동인천광역시 부평구 부흥로267번길 572021-05-032021-12-312
2D-41부평1동인천광역시 부평구 부흥로267번길 392021-05-032021-11-112
3D-42부평1동인천광역시 부평구 부평대로51번길 72021-05-03<NA><NA>
4D-84부평1동인천광역시 부평구 경원대로1347번길 192022-06-09<NA><NA>
5D-85부평1동인천광역시 부평구 경원대로1377번길 532022-06-09<NA><NA>
6D-124부평1동인천광역시 부평구 경원대로 13692023-01-04<NA><NA>
7D-125부평1동인천광역시 부평구 원적로471번길 282023-01-04<NA><NA>
8D-6부평2동인천광역시 부평구 부평동 673-1 민들레쉼터2020-04-272021-11-262
9D-7부평2동인천광역시 부평구 동수북로108번길 19-4 부근2020-04-272022-01-265
관리번호설치동설치장소설치일자(신규)설치일자(최근이전)이동횟수
130D-38십정1동인천광역시 부평구 백범로 5422020-08-12<NA><NA>
131D-79십정1동인천광역시 부평구 배곶남로9번길8-82021-05-04<NA><NA>
132D-121십정1동인천광역시 부평구 열우물공원2022-06-08<NA><NA>
133D-122십정1동인천광역시 부평구 방죽공원2022-06-08<NA><NA>
134D-123십정1동인천광역시 부평구 열우물숲 녹지2022-06-08<NA><NA>
135D-139십정1동인천광역시 부평구 경원대로 994번길 462023-01-04<NA><NA>
136D-140십정1동인천광역시 부평구 이규보로 1012023-01-04<NA><NA>
137D-5십정2동인천광역시 부평구 백범로422번길 572019-07-102021-10-293
138D-22십정2동인천광역시 부평구 열우물로38번길 12-52020-04-272021-02-191
139D-39십정2동인천광역시 부평구 십정동 459-932020-08-12<NA><NA>