Overview

Dataset statistics

Number of variables8
Number of observations30
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.2 KiB
Average record size in memory73.4 B

Variable types

Categorical5
Text1
Numeric2

Dataset

Description샘플 데이터
Author경기콘텐츠진흥원
URLhttps://www.bigdata-region.kr/#/dataset/1a8207cb-882f-4819-8d15-9efca01d82c3

Alerts

시도명 has constant value ""Constant
기준년도 has constant value ""Constant
치안시설상태지수(현황) has constant value ""Constant
어린이집상태지수(현황) has constant value ""Constant
행정동명 has unique valuesUnique
CCTV지수(현황) has 14 (46.7%) zerosZeros
보안등상태지수(현황) has 7 (23.3%) zerosZeros

Reproduction

Analysis started2023-12-10 14:04:36.521857
Analysis finished2023-12-10 14:04:37.727428
Duration1.21 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
경기도
30 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경기도 30
100.0%

Length

2023-12-10T23:04:37.853792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:04:38.015336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경기도 30
100.0%

시군구명
Categorical

Distinct9
Distinct (%)30.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
파주시
8 
시흥시
6 
하남시
5 
포천시
3 
과천시
3 
Other values (4)
5 

Length

Max length8
Median length3
Mean length3.5
Min length3

Unique

Unique3 ?
Unique (%)10.0%

Sample

1st row남양주시
2nd row포천시
3rd row파주시
4th row하남시
5th row파주시

Common Values

ValueCountFrequency (%)
파주시 8
26.7%
시흥시 6
20.0%
하남시 5
16.7%
포천시 3
 
10.0%
과천시 3
 
10.0%
남양주시 2
 
6.7%
수원시 장안구 1
 
3.3%
고양시 일산서구 1
 
3.3%
파주시 파주시 1
 
3.3%

Length

2023-12-10T23:04:38.181777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:04:38.380599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
파주시 10
30.3%
시흥시 6
18.2%
하남시 5
15.2%
포천시 3
 
9.1%
과천시 3
 
9.1%
남양주시 2
 
6.1%
수원시 1
 
3.0%
장안구 1
 
3.0%
고양시 1
 
3.0%
일산서구 1
 
3.0%

행정동명
Text

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T23:04:38.734205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.9333333
Min length2

Characters and Unicode

Total characters88
Distinct characters44
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique30 ?
Unique (%)100.0%

Sample

1st row지금동
2nd row포천동
3rd row교하읍
4th row초이동
5th row금촌동
ValueCountFrequency (%)
지금동 1
 
3.3%
포천동 1
 
3.3%
대야동 1
 
3.3%
주암동 1
 
3.3%
신촌동 1
 
3.3%
구산동 1
 
3.3%
장단면 1
 
3.3%
와동동 1
 
3.3%
야동동 1
 
3.3%
월곶동 1
 
3.3%
Other values (20) 20
66.7%
2023-12-10T23:04:39.293656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 28
31.8%
금 3
 
3.4%
신 3
 
3.4%
산 3
 
3.4%
면 2
 
2.3%
자 2
 
2.3%
미 2
 
2.3%
암 2
 
2.3%
구 2
 
2.3%
단 2
 
2.3%
Other values (34) 39
44.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 88
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 28
31.8%
금 3
 
3.4%
신 3
 
3.4%
산 3
 
3.4%
면 2
 
2.3%
자 2
 
2.3%
미 2
 
2.3%
암 2
 
2.3%
구 2
 
2.3%
단 2
 
2.3%
Other values (34) 39
44.3%

Most occurring scripts

ValueCountFrequency (%)
Hangul 88
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 28
31.8%
금 3
 
3.4%
신 3
 
3.4%
산 3
 
3.4%
면 2
 
2.3%
자 2
 
2.3%
미 2
 
2.3%
암 2
 
2.3%
구 2
 
2.3%
단 2
 
2.3%
Other values (34) 39
44.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 88
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 28
31.8%
금 3
 
3.4%
신 3
 
3.4%
산 3
 
3.4%
면 2
 
2.3%
자 2
 
2.3%
미 2
 
2.3%
암 2
 
2.3%
구 2
 
2.3%
단 2
 
2.3%
Other values (34) 39
44.3%

기준년도
Categorical

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
2017
30 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2017
2nd row2017
3rd row2017
4th row2017
5th row2017

Common Values

ValueCountFrequency (%)
2017 30
100.0%

Length

2023-12-10T23:04:39.522145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:04:39.684207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2017 30
100.0%

CCTV지수(현황)
Real number (ℝ)

ZEROS 

Distinct14
Distinct (%)46.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.2
Minimum0
Maximum124
Zeros14
Zeros (%)46.7%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:04:39.836518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1.5
Q313.25
95-th percentile74.4
Maximum124
Range124
Interquartile range (IQR)13.25

Descriptive statistics

Standard deviation29.484537
Coefficient of variation (CV)1.9397722
Kurtosis6.0943469
Mean15.2
Median Absolute Deviation (MAD)1.5
Skewness2.4654289
Sum456
Variance869.33793
MonotonicityNot monotonic
2023-12-10T23:04:40.039829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0 14
46.7%
2 3
 
10.0%
5 2
 
6.7%
11 1
 
3.3%
1 1
 
3.3%
70 1
 
3.3%
23 1
 
3.3%
52 1
 
3.3%
43 1
 
3.3%
16 1
 
3.3%
Other values (4) 4
 
13.3%
ValueCountFrequency (%)
0 14
46.7%
1 1
 
3.3%
2 3
 
10.0%
5 2
 
6.7%
8 1
 
3.3%
11 1
 
3.3%
14 1
 
3.3%
16 1
 
3.3%
23 1
 
3.3%
43 1
 
3.3%
ValueCountFrequency (%)
124 1
3.3%
78 1
3.3%
70 1
3.3%
52 1
3.3%
43 1
3.3%
23 1
3.3%
16 1
3.3%
14 1
3.3%
11 1
3.3%
8 1
3.3%

치안시설상태지수(현황)
Categorical

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
0
30 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 30
100.0%

Length

2023-12-10T23:04:40.246964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:04:40.399087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 30
100.0%

보안등상태지수(현황)
Real number (ℝ)

ZEROS 

Distinct21
Distinct (%)70.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean117.1
Minimum0
Maximum571
Zeros7
Zeros (%)23.3%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:04:40.556202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median27.5
Q3159.5
95-th percentile550.3
Maximum571
Range571
Interquartile range (IQR)158.5

Descriptive statistics

Standard deviation170.91626
Coefficient of variation (CV)1.4595753
Kurtosis2.455779
Mean117.1
Median Absolute Deviation (MAD)27.5
Skewness1.7873747
Sum3513
Variance29212.369
MonotonicityNot monotonic
2023-12-10T23:04:40.782228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0 7
23.3%
3 3
 
10.0%
1 2
 
6.7%
263 1
 
3.3%
40 1
 
3.3%
161 1
 
3.3%
553 1
 
3.3%
213 1
 
3.3%
11 1
 
3.3%
133 1
 
3.3%
Other values (11) 11
36.7%
ValueCountFrequency (%)
0 7
23.3%
1 2
 
6.7%
3 3
10.0%
11 1
 
3.3%
12 1
 
3.3%
15 1
 
3.3%
40 1
 
3.3%
85 1
 
3.3%
86 1
 
3.3%
88 1
 
3.3%
ValueCountFrequency (%)
571 1
3.3%
553 1
3.3%
547 1
3.3%
280 1
3.3%
263 1
3.3%
213 1
3.3%
161 1
3.3%
160 1
3.3%
158 1
3.3%
133 1
3.3%

어린이집상태지수(현황)
Categorical

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
0
30 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 30
100.0%

Length

2023-12-10T23:04:40.983991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:04:41.170891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 30
100.0%

Interactions

2023-12-10T23:04:37.133322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:04:36.874744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:04:37.266850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:04:37.010556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T23:04:41.379587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구명행정동명CCTV지수(현황)보안등상태지수(현황)
시군구명1.0001.0000.0000.425
행정동명1.0001.0001.0001.000
CCTV지수(현황)0.0001.0001.0000.000
보안등상태지수(현황)0.4251.0000.0001.000
2023-12-10T23:04:41.542618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
CCTV지수(현황)보안등상태지수(현황)시군구명
CCTV지수(현황)1.000-0.1140.000
보안등상태지수(현황)-0.1141.0000.186
시군구명0.0000.1861.000

Missing values

2023-12-10T23:04:37.434230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:04:37.639767image/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

시도명시군구명행정동명기준년도CCTV지수(현황)치안시설상태지수(현황)보안등상태지수(현황)어린이집상태지수(현황)
0경기도남양주시지금동201711000
1경기도포천시포천동20171000
2경기도파주시교하읍201700850
3경기도하남시초이동2017002630
4경기도파주시금촌동2017700400
5경기도수원시 장안구정자동2017005710
6경기도남양주시금곡동20172305470
7경기도시흥시군자동2017501600
8경기도과천시막계동201700120
9경기도포천시선단동201752000
시도명시군구명행정동명기준년도CCTV지수(현황)치안시설상태지수(현황)보안등상태지수(현황)어린이집상태지수(현황)
20경기도시흥시신현동20170030
21경기도시흥시월곶동20171401330
22경기도파주시야동동201750110
23경기도파주시와동동2017124000
24경기도파주시장단면20170010
25경기도고양시 일산서구구산동20172000
26경기도파주시 파주시신촌동20170010
27경기도과천시주암동2017002130
28경기도시흥시대야동20177805530
29경기도시흥시도창동2017801610