Overview

Dataset statistics

Number of variables5
Number of observations81
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.4 KiB
Average record size in memory42.6 B

Variable types

Numeric1
Categorical3
Text1

Dataset

Description인천광역시 부평구 청사방호 CCTV 설치현황 데이터는 부평 청사 내의 CCTV 설치 장소, 층, 상세 위치, 설치일의 데이터를 제공합니다.
URLhttps://www.data.go.kr/data/15118775/fileData.do

Alerts

연번 is highly overall correlated with 설치장소High correlation
설치장소 is highly overall correlated with 연번 and 1 other fieldsHigh correlation
설치일 is highly overall correlated with 설치장소High correlation
연번 has unique valuesUnique

Reproduction

Analysis started2023-12-11 23:01:51.310284
Analysis finished2023-12-11 23:01:51.771189
Duration0.46 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct81
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41
Minimum1
Maximum81
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size861.0 B
2023-12-12T08:01:51.862440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q121
median41
Q361
95-th percentile77
Maximum81
Range80
Interquartile range (IQR)40

Descriptive statistics

Standard deviation23.526581
Coefficient of variation (CV)0.57381904
Kurtosis-1.2
Mean41
Median Absolute Deviation (MAD)20
Skewness0
Sum3321
Variance553.5
MonotonicityStrictly increasing
2023-12-12T08:01:52.253613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.2%
62 1
 
1.2%
60 1
 
1.2%
59 1
 
1.2%
58 1
 
1.2%
57 1
 
1.2%
56 1
 
1.2%
55 1
 
1.2%
54 1
 
1.2%
53 1
 
1.2%
Other values (71) 71
87.7%
ValueCountFrequency (%)
1 1
1.2%
2 1
1.2%
3 1
1.2%
4 1
1.2%
5 1
1.2%
6 1
1.2%
7 1
1.2%
8 1
1.2%
9 1
1.2%
10 1
1.2%
ValueCountFrequency (%)
81 1
1.2%
80 1
1.2%
79 1
1.2%
78 1
1.2%
77 1
1.2%
76 1
1.2%
75 1
1.2%
74 1
1.2%
73 1
1.2%
72 1
1.2%

설치장소
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Memory size780.0 B
본관 내부
44 
신관 내부
18 
부평구의회
10 
청사 외곽
9 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row본관 내부
2nd row본관 내부
3rd row본관 내부
4th row본관 내부
5th row본관 내부

Common Values

ValueCountFrequency (%)
본관 내부 44
54.3%
신관 내부 18
22.2%
부평구의회 10
 
12.3%
청사 외곽 9
 
11.1%

Length

2023-12-12T08:01:52.379977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:01:52.470126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
내부 62
40.8%
본관 44
28.9%
신관 18
 
11.8%
부평구의회 10
 
6.6%
청사 9
 
5.9%
외곽 9
 
5.9%

층
Categorical

Distinct11
Distinct (%)13.6%
Missing0
Missing (%)0.0%
Memory size780.0 B
1층
25 
3층
15 
4층
12 
5층
8 
2층
7 
Other values (6)
14 

Length

Max length4
Median length2
Mean length2.0493827
Min length2

Unique

Unique2 ?
Unique (%)2.5%

Sample

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

Common Values

ValueCountFrequency (%)
1층 25
30.9%
3층 15
18.5%
4층 12
14.8%
5층 8
 
9.9%
2층 7
 
8.6%
지하 6
 
7.4%
6층 2
 
2.5%
7층 2
 
2.5%
8층 2
 
2.5%
<NA> 1
 
1.2%

Length

2023-12-12T08:01:52.597760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1층 25
30.9%
3층 15
18.5%
4층 12
14.8%
5층 8
 
9.9%
2층 7
 
8.6%
지하 6
 
7.4%
6층 2
 
2.5%
7층 2
 
2.5%
8층 2
 
2.5%
na 1
 
1.2%

상세 위치
Text

Distinct75
Distinct (%)92.6%
Missing0
Missing (%)0.0%
Memory size780.0 B
2023-12-12T08:01:52.826146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length12
Mean length7.9382716
Min length2

Characters and Unicode

Total characters643
Distinct characters109
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

Unique71 ?
Unique (%)87.7%

Sample

1st row지하1층 우측 E/V 앞
2nd row지하주차장 출입구
3rd row갤러리 외곽
4th row갤러리 내부
5th row지하1층 좌측 E/V 앞
ValueCountFrequency (%)
앞 29
 
14.8%
e/v 20
 
10.2%
복도 9
 
4.6%
우측 8
 
4.1%
좌측 8
 
4.1%
출입구 6
 
3.1%
내부 6
 
3.1%
외곽 5
 
2.6%
4층 5
 
2.6%
입구 4
 
2.0%
Other values (56) 96
49.0%
2023-12-12T08:01:53.246102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
115
 
17.9%
앞 29
 
4.5%
층 28
 
4.4%
V 25
 
3.9%
E 24
 
3.7%
/ 23
 
3.6%
실 16
 
2.5%
측 16
 
2.5%
장 14
 
2.2%
구 14
 
2.2%
Other values (99) 339
52.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 415
64.5%
Space Separator 115
 
17.9%
Uppercase Letter 52
 
8.1%
Decimal Number 36
 
5.6%
Other Punctuation 23
 
3.6%
Close Punctuation 1
 
0.2%
Open Punctuation 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
앞 29
 
7.0%
층 28
 
6.7%
실 16
 
3.9%
측 16
 
3.9%
장 14
 
3.4%
구 14
 
3.4%
과 13
 
3.1%
복 12
 
2.9%
도 10
 
2.4%
입 10
 
2.4%
Other values (84) 253
61.0%
Decimal Number
ValueCountFrequency (%)
1 10
27.8%
2 8
22.2%
4 5
13.9%
3 5
13.9%
5 4
 
11.1%
6 2
 
5.6%
7 2
 
5.6%
Uppercase Letter
ValueCountFrequency (%)
V 25
48.1%
E 24
46.2%
C 2
 
3.8%
T 1
 
1.9%
Space Separator
ValueCountFrequency (%)
115
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 23
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 415
64.5%
Common 176
27.4%
Latin 52
 
8.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
앞 29
 
7.0%
층 28
 
6.7%
실 16
 
3.9%
측 16
 
3.9%
장 14
 
3.4%
구 14
 
3.4%
과 13
 
3.1%
복 12
 
2.9%
도 10
 
2.4%
입 10
 
2.4%
Other values (84) 253
61.0%
Common
ValueCountFrequency (%)
115
65.3%
/ 23
 
13.1%
1 10
 
5.7%
2 8
 
4.5%
4 5
 
2.8%
3 5
 
2.8%
5 4
 
2.3%
6 2
 
1.1%
7 2
 
1.1%
) 1
 
0.6%
Latin
ValueCountFrequency (%)
V 25
48.1%
E 24
46.2%
C 2
 
3.8%
T 1
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 415
64.5%
ASCII 228
35.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
115
50.4%
V 25
 
11.0%
E 24
 
10.5%
/ 23
 
10.1%
1 10
 
4.4%
2 8
 
3.5%
4 5
 
2.2%
3 5
 
2.2%
5 4
 
1.8%
C 2
 
0.9%
Other values (5) 7
 
3.1%
Hangul
ValueCountFrequency (%)
앞 29
 
7.0%
층 28
 
6.7%
실 16
 
3.9%
측 16
 
3.9%
장 14
 
3.4%
구 14
 
3.4%
과 13
 
3.1%
복 12
 
2.9%
도 10
 
2.4%
입 10
 
2.4%
Other values (84) 253
61.0%

설치일
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)7.4%
Missing0
Missing (%)0.0%
Memory size780.0 B
2019-01-01
47 
2022-03-01
20 
2013-07-01
6 
2023-01-01
 
4
2012-08-01
 
3

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique1 ?
Unique (%)1.2%

Sample

1st row2019-01-01
2nd row2023-01-01
3rd row2022-08-01
4th row2019-01-01
5th row2019-01-01

Common Values

ValueCountFrequency (%)
2019-01-01 47
58.0%
2022-03-01 20
24.7%
2013-07-01 6
 
7.4%
2023-01-01 4
 
4.9%
2012-08-01 3
 
3.7%
2022-08-01 1
 
1.2%

Length

2023-12-12T08:01:53.394809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:01:53.500905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2019-01-01 47
58.0%
2022-03-01 20
24.7%
2013-07-01 6
 
7.4%
2023-01-01 4
 
4.9%
2012-08-01 3
 
3.7%
2022-08-01 1
 
1.2%

Interactions

2023-12-12T08:01:51.559947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T08:01:53.575450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번설치장소층상세 위치설치일
연번1.0000.9170.8950.9730.636
설치장소0.9171.0000.5090.9670.707
층0.8950.5091.0000.9860.272
상세 위치0.9730.9670.9861.0000.831
설치일0.6360.7070.2720.8311.000
2023-12-12T08:01:53.661727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
층설치장소설치일
층1.0000.3140.137
설치장소0.3141.0000.531
설치일0.1370.5311.000
2023-12-12T08:01:53.746080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번설치장소층설치일
연번1.0000.7800.4820.396
설치장소0.7801.0000.3140.531
층0.4820.3141.0000.137
설치일0.3960.5310.1371.000

Missing values

2023-12-12T08:01:51.667387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T08:01:51.740886image/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

연번설치장소층상세 위치설치일
01본관 내부지하지하1층 우측 E/V 앞2019-01-01
12본관 내부지하지하주차장 출입구2023-01-01
23본관 내부지하갤러리 외곽2022-08-01
34본관 내부지하갤러리 내부2019-01-01
45본관 내부지하지하1층 좌측 E/V 앞2019-01-01
56본관 내부지하지하1층 전기자동차 충전구역2023-01-01
67본관 내부1층정문 출입구 외곽2013-07-01
78본관 내부1층커피인 앞2012-08-01
89본관 내부1층후문 출입구 외곽2013-07-01
910본관 내부1층1층 우측 EV 앞2019-01-01
연번설치장소층상세 위치설치일
7172신관 내부4층4층 E/V 앞2022-03-01
7273신관 내부4층4층 복도2022-03-01
7374신관 내부4층복지정책과2022-03-01
7475신관 내부4층복지정책과 상담실12022-03-01
7576신관 내부4층복지정책과 상담실22022-03-01
7677신관 내부5층5층E/V 앞2022-03-01
7778신관 내부5층5층 복도2022-03-01
7879신관 내부5층사회보장과2022-03-01
7980신관 내부5층사회보장과 상담실12022-03-01
8081신관 내부5층사회보장과 상담실22022-03-01