Overview

Dataset statistics

Number of variables12
Number of observations62
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.1 KiB
Average record size in memory101.1 B

Variable types

Categorical4
Text4
Numeric3
Boolean1

Dataset

Description파일 다운로드
Author동작구
URLhttps://data.seoul.go.kr/dataList/OA-13279/F/1/datasetView.do

Alerts

관리기관명 has constant value ""Constant
관할경찰서명 has constant value ""Constant
CCTV설치여부 has constant value ""Constant
데이터기준일자 has constant value ""Constant
위도 is highly overall correlated with 시설종류High correlation
시설종류 is highly overall correlated with 위도High correlation
대상시설명 has unique valuesUnique

Reproduction

Analysis started2024-04-17 10:53:10.095229
Analysis finished2024-04-17 10:53:11.266381
Duration1.17 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시설종류
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)8.1%
Missing0
Missing (%)0.0%
Memory size628.0 B
초등학교
21 
유치원
20 
어린이집
14 
학원
6 
특수학교
 
1

Length

Max length4
Median length4
Mean length3.483871
Min length2

Unique

Unique1 ?
Unique (%)1.6%

Sample

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

Common Values

ValueCountFrequency (%)
초등학교 21
33.9%
유치원 20
32.3%
어린이집 14
22.6%
학원 6
 
9.7%
특수학교 1
 
1.6%

Length

2024-04-17T19:53:11.328033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T19:53:11.425327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
초등학교 21
33.9%
유치원 20
32.3%
어린이집 14
22.6%
학원 6
 
9.7%
특수학교 1
 
1.6%

대상시설명
Text

UNIQUE 

Distinct62
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size628.0 B
2024-04-17T19:53:11.616617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length11
Mean length6.2580645
Min length5

Characters and Unicode

Total characters388
Distinct characters96
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique62 ?
Unique (%)100.0%

Sample

1st row신상도초등학교
2nd row강남초등학교
3rd row남사초등학교
4th row남성초등학교
5th row노량진초등학교
ValueCountFrequency (%)
병설유치원 2
 
3.1%
은로초등학교 2
 
3.1%
신상도초등학교 1
 
1.6%
다문화어린이집 1
 
1.6%
강남유치원 1
 
1.6%
상도유치원 1
 
1.6%
행복유치원 1
 
1.6%
신남성초교 1
 
1.6%
샛별유치원 1
 
1.6%
시현유치원 1
 
1.6%
Other values (52) 52
81.2%
2024-04-17T19:53:11.929159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
학 28
 
7.2%
원 25
 
6.4%
교 24
 
6.2%
초 23
 
5.9%
등 22
 
5.7%
유 21
 
5.4%
치 20
 
5.2%
이 16
 
4.1%
어 16
 
4.1%
린 14
 
3.6%
Other values (86) 179
46.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 385
99.2%
Space Separator 2
 
0.5%
Decimal Number 1
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
학 28
 
7.3%
원 25
 
6.5%
교 24
 
6.2%
초 23
 
6.0%
등 22
 
5.7%
유 21
 
5.5%
치 20
 
5.2%
이 16
 
4.2%
어 16
 
4.2%
린 14
 
3.6%
Other values (84) 176
45.7%
Space Separator
ValueCountFrequency (%)
2
100.0%
Decimal Number
ValueCountFrequency (%)
4 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 385
99.2%
Common 3
 
0.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
학 28
 
7.3%
원 25
 
6.5%
교 24
 
6.2%
초 23
 
6.0%
등 22
 
5.7%
유 21
 
5.5%
치 20
 
5.2%
이 16
 
4.2%
어 16
 
4.2%
린 14
 
3.6%
Other values (84) 176
45.7%
Common
ValueCountFrequency (%)
2
66.7%
4 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 385
99.2%
ASCII 3
 
0.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
학 28
 
7.3%
원 25
 
6.5%
교 24
 
6.2%
초 23
 
6.0%
등 22
 
5.7%
유 21
 
5.5%
치 20
 
5.2%
이 16
 
4.2%
어 16
 
4.2%
린 14
 
3.6%
Other values (84) 176
45.7%
ASCII
ValueCountFrequency (%)
2
66.7%
4 1
33.3%

소재지도로명주소
Text

Distinct60
Distinct (%)96.8%
Missing0
Missing (%)0.0%
Memory size628.0 B
2024-04-17T19:53:12.151362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length21
Mean length18.709677
Min length15

Characters and Unicode

Total characters1160
Distinct characters63
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

Unique58 ?
Unique (%)93.5%

Sample

1st row서울특별시 동작구 장승배기로 14
2nd row서울특별시 동작구 강남초등길 15
3rd row서울특별시 동작구 동작대로13길 22
4th row서울특별시 동작구 사당로23길 57-14
5th row서울특별시 동작구 장승배기로 160
ValueCountFrequency (%)
서울특별시 62
26.1%
동작구 60
25.2%
15 4
 
1.7%
서달로 3
 
1.3%
28 3
 
1.3%
사당로23길 2
 
0.8%
성대로21길 2
 
0.8%
27 2
 
0.8%
21 2
 
0.8%
14 2
 
0.8%
Other values (89) 96
40.3%
2024-04-17T19:53:12.496400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
176
15.2%
동 71
 
6.1%
작 69
 
5.9%
서 66
 
5.7%
시 63
 
5.4%
울 62
 
5.3%
특 62
 
5.3%
별 62
 
5.3%
구 62
 
5.3%
로 57
 
4.9%
Other values (53) 410
35.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 761
65.6%
Decimal Number 216
 
18.6%
Space Separator 176
 
15.2%
Dash Punctuation 7
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 71
9.3%
작 69
9.1%
서 66
8.7%
시 63
8.3%
울 62
8.1%
특 62
8.1%
별 62
8.1%
구 62
8.1%
로 57
7.5%
길 45
 
5.9%
Other values (41) 142
18.7%
Decimal Number
ValueCountFrequency (%)
1 47
21.8%
2 35
16.2%
6 26
12.0%
3 26
12.0%
4 23
10.6%
5 15
 
6.9%
7 14
 
6.5%
0 11
 
5.1%
8 11
 
5.1%
9 8
 
3.7%
Space Separator
ValueCountFrequency (%)
176
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 761
65.6%
Common 399
34.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 71
9.3%
작 69
9.1%
서 66
8.7%
시 63
8.3%
울 62
8.1%
특 62
8.1%
별 62
8.1%
구 62
8.1%
로 57
7.5%
길 45
 
5.9%
Other values (41) 142
18.7%
Common
ValueCountFrequency (%)
176
44.1%
1 47
 
11.8%
2 35
 
8.8%
6 26
 
6.5%
3 26
 
6.5%
4 23
 
5.8%
5 15
 
3.8%
7 14
 
3.5%
0 11
 
2.8%
8 11
 
2.8%
Other values (2) 15
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 761
65.6%
ASCII 399
34.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
176
44.1%
1 47
 
11.8%
2 35
 
8.8%
6 26
 
6.5%
3 26
 
6.5%
4 23
 
5.8%
5 15
 
3.8%
7 14
 
3.5%
0 11
 
2.8%
8 11
 
2.8%
Other values (2) 15
 
3.8%
Hangul
ValueCountFrequency (%)
동 71
9.3%
작 69
9.1%
서 66
8.7%
시 63
8.3%
울 62
8.1%
특 62
8.1%
별 62
8.1%
구 62
8.1%
로 57
7.5%
길 45
 
5.9%
Other values (41) 142
18.7%

소재지지번주소
Text

Distinct58
Distinct (%)93.5%
Missing0
Missing (%)0.0%
Memory size628.0 B
2024-04-17T19:53:12.697886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length20
Mean length19.33871
Min length16

Characters and Unicode

Total characters1199
Distinct characters34
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

Unique54 ?
Unique (%)87.1%

Sample

1st row서울특별시 동작구 상도2동 산65
2nd row서울특별시 동작구 상도1동 501
3rd row서울특별시 동작구 사당1동 1011-1
4th row서울특별시 동작구 사당3동 산24
5th row서울특별시 동작구 노량진1동 238
ValueCountFrequency (%)
서울특별시 62
25.4%
동작구 62
25.4%
상도동 7
 
2.9%
사당동 5
 
2.0%
상도4동 5
 
2.0%
흑석동 5
 
2.0%
대방동 5
 
2.0%
흑석1동 4
 
1.6%
신대방1동 4
 
1.6%
상도1동 4
 
1.6%
Other values (68) 81
33.2%
2024-04-17T19:53:13.003182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
182
15.2%
동 126
 
10.5%
1 76
 
6.3%
작 64
 
5.3%
서 62
 
5.2%
특 62
 
5.2%
별 62
 
5.2%
시 62
 
5.2%
구 62
 
5.2%
울 62
 
5.2%
Other values (24) 379
31.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 694
57.9%
Decimal Number 280
23.4%
Space Separator 182
 
15.2%
Dash Punctuation 43
 
3.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 126
18.2%
작 64
9.2%
서 62
8.9%
특 62
8.9%
별 62
8.9%
시 62
8.9%
구 62
8.9%
울 62
8.9%
상 19
 
2.7%
도 19
 
2.7%
Other values (12) 94
13.5%
Decimal Number
ValueCountFrequency (%)
1 76
27.1%
2 40
14.3%
3 36
12.9%
4 34
12.1%
0 22
 
7.9%
5 21
 
7.5%
6 21
 
7.5%
8 15
 
5.4%
7 10
 
3.6%
9 5
 
1.8%
Space Separator
ValueCountFrequency (%)
182
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 43
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 694
57.9%
Common 505
42.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 126
18.2%
작 64
9.2%
서 62
8.9%
특 62
8.9%
별 62
8.9%
시 62
8.9%
구 62
8.9%
울 62
8.9%
상 19
 
2.7%
도 19
 
2.7%
Other values (12) 94
13.5%
Common
ValueCountFrequency (%)
182
36.0%
1 76
15.0%
- 43
 
8.5%
2 40
 
7.9%
3 36
 
7.1%
4 34
 
6.7%
0 22
 
4.4%
5 21
 
4.2%
6 21
 
4.2%
8 15
 
3.0%
Other values (2) 15
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 694
57.9%
ASCII 505
42.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
182
36.0%
1 76
15.0%
- 43
 
8.5%
2 40
 
7.9%
3 36
 
7.1%
4 34
 
6.7%
0 22
 
4.4%
5 21
 
4.2%
6 21
 
4.2%
8 15
 
3.0%
Other values (2) 15
 
3.0%
Hangul
ValueCountFrequency (%)
동 126
18.2%
작 64
9.2%
서 62
8.9%
특 62
8.9%
별 62
8.9%
시 62
8.9%
구 62
8.9%
울 62
8.9%
상 19
 
2.7%
도 19
 
2.7%
Other values (12) 94
13.5%

위도
Real number (ℝ)

HIGH CORRELATION 

Distinct60
Distinct (%)96.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.468714
Minimum37.2848
Maximum37.511751
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size690.0 B
2024-04-17T19:53:13.121768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.2848
5-th percentile37.293005
Q137.484733
median37.497642
Q337.505248
95-th percentile37.51007
Maximum37.511751
Range0.22695119
Interquartile range (IQR)0.020514932

Descriptive statistics

Standard deviation0.073156879
Coefficient of variation (CV)0.0019524791
Kurtosis2.22771
Mean37.468714
Median Absolute Deviation (MAD)0.009456485
Skewness-2.009384
Sum2323.0602
Variance0.005351929
MonotonicityNot monotonic
2024-04-17T19:53:13.247509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.5031946 2
 
3.2%
37.48815083 2
 
3.2%
37.50034631 1
 
1.6%
37.50144862 1
 
1.6%
37.4956159 1
 
1.6%
37.49804586 1
 
1.6%
37.51030525 1
 
1.6%
37.48858884 1
 
1.6%
37.5069456 1
 
1.6%
37.50461575 1
 
1.6%
Other values (50) 50
80.6%
ValueCountFrequency (%)
37.2848 1
1.6%
37.2849 1
1.6%
37.2918 1
1.6%
37.2929 1
1.6%
37.295 1
1.6%
37.2951 1
1.6%
37.2957 1
1.6%
37.3018 1
1.6%
37.3035 1
1.6%
37.480666 1
1.6%
ValueCountFrequency (%)
37.51175119 1
1.6%
37.51129715 1
1.6%
37.51030525 1
1.6%
37.51007796 1
1.6%
37.50992616 1
1.6%
37.50937692 1
1.6%
37.50934081 1
1.6%
37.5089258 1
1.6%
37.50890703 1
1.6%
37.50835746 1
1.6%

경도
Real number (ℝ)

Distinct59
Distinct (%)95.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.89553
Minimum126.5527
Maximum126.98179
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size690.0 B
2024-04-17T19:53:13.378520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.5527
5-th percentile126.57332
Q1126.92524
median126.94664
Q3126.96365
95-th percentile126.9782
Maximum126.98179
Range0.4290887
Interquartile range (IQR)0.038416425

Descriptive statistics

Standard deviation0.13541561
Coefficient of variation (CV)0.0010671425
Kurtosis2.1926302
Mean126.89553
Median Absolute Deviation (MAD)0.01835375
Skewness-1.99358
Sum7867.5226
Variance0.018337387
MonotonicityNot monotonic
2024-04-17T19:53:13.533811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.9600008 2
 
3.2%
126.9648709 2
 
3.2%
126.5818 2
 
3.2%
126.9512421 1
 
1.6%
126.9724116 1
 
1.6%
126.9787548 1
 
1.6%
126.9464077 1
 
1.6%
126.9775139 1
 
1.6%
126.9229536 1
 
1.6%
126.9446175 1
 
1.6%
Other values (49) 49
79.0%
ValueCountFrequency (%)
126.5527 1
1.6%
126.5604 1
1.6%
126.5613 1
1.6%
126.5733 1
1.6%
126.5736 1
1.6%
126.5818 2
3.2%
126.5837 1
1.6%
126.585 1
1.6%
126.9073747 1
1.6%
126.9116916 1
1.6%
ValueCountFrequency (%)
126.9817887 1
1.6%
126.9793688 1
1.6%
126.9787548 1
1.6%
126.9782332 1
1.6%
126.9775139 1
1.6%
126.9765109 1
1.6%
126.9754872 1
1.6%
126.975172 1
1.6%
126.973077 1
1.6%
126.9724116 1
1.6%

관리기관명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size628.0 B
서울특별시 동작구청
62 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row서울특별시 동작구청
2nd row서울특별시 동작구청
3rd row서울특별시 동작구청
4th row서울특별시 동작구청
5th row서울특별시 동작구청

Common Values

ValueCountFrequency (%)
서울특별시 동작구청 62
100.0%

Length

2024-04-17T19:53:13.677566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T19:53:13.772725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서울특별시 62
50.0%
동작구청 62
50.0%

관할경찰서명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size628.0 B
동작경찰서
62 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
동작경찰서 62
100.0%

Length

2024-04-17T19:53:13.867312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T19:53:13.964417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
동작경찰서 62
100.0%

CCTV설치여부
Boolean

CONSTANT 

Distinct1
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size194.0 B
True
62 
ValueCountFrequency (%)
True 62
100.0%
2024-04-17T19:53:14.042623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

CCTV설치대수
Real number (ℝ)

Distinct6
Distinct (%)9.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6774194
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size690.0 B
2024-04-17T19:53:14.129161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile4
Maximum6
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1703186
Coefficient of variation (CV)0.69768995
Kurtosis3.4150691
Mean1.6774194
Median Absolute Deviation (MAD)0
Skewness1.9332754
Sum104
Variance1.3696457
MonotonicityNot monotonic
2024-04-17T19:53:14.243771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 41
66.1%
2 9
 
14.5%
3 7
 
11.3%
4 2
 
3.2%
5 2
 
3.2%
6 1
 
1.6%
ValueCountFrequency (%)
1 41
66.1%
2 9
 
14.5%
3 7
 
11.3%
4 2
 
3.2%
5 2
 
3.2%
6 1
 
1.6%
ValueCountFrequency (%)
6 1
 
1.6%
5 2
 
3.2%
4 2
 
3.2%
3 7
 
11.3%
2 9
 
14.5%
1 41
66.1%

보호구역도로폭
Text

Distinct38
Distinct (%)61.3%
Missing0
Missing (%)0.0%
Memory size628.0 B
2024-04-17T19:53:14.446336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length2.7580645
Min length1

Characters and Unicode

Total characters171
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21 ?
Unique (%)33.9%

Sample

1st row4~17
2nd row4~10
3rd row3~6
4th row4~15
5th row4~24
ValueCountFrequency (%)
7 4
 
6.5%
6 3
 
4.8%
8 3
 
4.8%
6~7 3
 
4.8%
12 3
 
4.8%
3~6 3
 
4.8%
4~5 2
 
3.2%
5~7 2
 
3.2%
4~7 2
 
3.2%
5 2
 
3.2%
Other values (28) 35
56.5%
2024-04-17T19:53:14.709722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
~ 38
22.2%
1 31
18.1%
7 16
9.4%
4 16
9.4%
5 15
 
8.8%
2 13
 
7.6%
6 12
 
7.0%
3 11
 
6.4%
8 9
 
5.3%
0 5
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 133
77.8%
Math Symbol 38
 
22.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 31
23.3%
7 16
12.0%
4 16
12.0%
5 15
11.3%
2 13
9.8%
6 12
 
9.0%
3 11
 
8.3%
8 9
 
6.8%
0 5
 
3.8%
9 5
 
3.8%
Math Symbol
ValueCountFrequency (%)
~ 38
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 171
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
~ 38
22.2%
1 31
18.1%
7 16
9.4%
4 16
9.4%
5 15
 
8.8%
2 13
 
7.6%
6 12
 
7.0%
3 11
 
6.4%
8 9
 
5.3%
0 5
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 171
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
~ 38
22.2%
1 31
18.1%
7 16
9.4%
4 16
9.4%
5 15
 
8.8%
2 13
 
7.6%
6 12
 
7.0%
3 11
 
6.4%
8 9
 
5.3%
0 5
 
2.9%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size628.0 B
2022-09-20
62 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022-09-20
2nd row2022-09-20
3rd row2022-09-20
4th row2022-09-20
5th row2022-09-20

Common Values

ValueCountFrequency (%)
2022-09-20 62
100.0%

Length

2024-04-17T19:53:14.810358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T19:53:14.883513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022-09-20 62
100.0%

Interactions

2024-04-17T19:53:10.823996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T19:53:10.462847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T19:53:10.649752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T19:53:10.894593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T19:53:10.521838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T19:53:10.709036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T19:53:10.968002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T19:53:10.582307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T19:53:10.765797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-17T19:53:14.935513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시설종류대상시설명소재지도로명주소소재지지번주소위도경도CCTV설치대수보호구역도로폭
시설종류1.0001.0000.9330.0000.5670.5270.5210.786
대상시설명1.0001.0001.0001.0001.0001.0001.0001.000
소재지도로명주소0.9331.0001.0001.0001.0001.0000.8961.000
소재지지번주소0.0001.0001.0001.0000.9531.0000.9130.997
위도0.5671.0001.0000.9531.0000.9470.0000.727
경도0.5271.0001.0001.0000.9471.0000.0000.743
CCTV설치대수0.5211.0000.8960.9130.0000.0001.0000.944
보호구역도로폭0.7861.0001.0000.9970.7270.7430.9441.000
2024-04-17T19:53:15.286994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위도경도CCTV설치대수시설종류
위도1.0000.1210.3010.503
경도0.1211.0000.2110.457
CCTV설치대수0.3010.2111.0000.382
시설종류0.5030.4570.3821.000

Missing values

2024-04-17T19:53:11.084299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-17T19:53:11.216552image/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

시설종류대상시설명소재지도로명주소소재지지번주소위도경도관리기관명관할경찰서명CCTV설치여부CCTV설치대수보호구역도로폭데이터기준일자
0초등학교신상도초등학교서울특별시 동작구 장승배기로 14서울특별시 동작구 상도2동 산6537.500346126.944618서울특별시 동작구청동작경찰서Y44~172022-09-20
1초등학교강남초등학교서울특별시 동작구 강남초등길 15서울특별시 동작구 상도1동 50137.50616126.953358서울특별시 동작구청동작경찰서Y54~102022-09-20
2초등학교남사초등학교서울특별시 동작구 동작대로13길 22서울특별시 동작구 사당1동 1011-137.481944126.978233서울특별시 동작구청동작경찰서Y23~62022-09-20
3초등학교남성초등학교서울특별시 동작구 사당로23길 57-14서울특별시 동작구 사당3동 산2437.484596126.975487서울특별시 동작구청동작경찰서Y34~152022-09-20
4초등학교노량진초등학교서울특별시 동작구 장승배기로 160서울특별시 동작구 노량진1동 23837.511751126.940822서울특별시 동작구청동작경찰서Y44~242022-09-20
5초등학교대림초등학교서울특별시 동작구 대방동1길 22서울특별시 동작구 대방동391-6237.500613126.924885서울특별시 동작구청동작경찰서Y14~242022-09-20
6초등학교동작초등학교서울특별시 동작구 동작대로29길 214서울특별시 동작구 동작동 산9-137.494115126.976511서울특별시 동작구청동작경찰서Y33~112022-09-20
7초등학교문창초등학교서울특별시 동작구 신대방2길 14서울특별시 동작구 신대방1동 64037.488983126.914843서울특별시 동작구청동작경찰서Y24~52022-09-20
8초등학교보라매초등학교서울특별시 동작구 여의대방로16길 30서울특별시 동작구 신대방1동 485-137.495853126.916146서울특별시 동작구청동작경찰서Y25~122022-09-20
9초등학교본동초등학교서울특별시 동작구 노량진로26길 16-40서울특별시 동작구 본동 13337.509926126.953853서울특별시 동작구청동작경찰서Y24~92022-09-20
시설종류대상시설명소재지도로명주소소재지지번주소위도경도관리기관명관할경찰서명CCTV설치여부CCTV설치대수보호구역도로폭데이터기준일자
52어린이집흑석어린이집서울특별시 동작구 서달로61서울특별시 동작구 흑석동 257-837.2957126.5736서울특별시 동작구청동작경찰서Y1122022-09-20
53어린이집도레미어린이집서울특별시 동작구흑석로13길25서울특별시 동작구 흑석동 341-237.3035126.5733서울특별시 동작구청동작경찰서Y182022-09-20
54어린이집이수어린이집서울특별시 동작구동작대로35길36서울특별시 동작구 사당동 4137.2929126.585서울특별시 동작구청동작경찰서Y1122022-09-20
55어린이집윤슬어린이집서울특별시 동작구 성대로 16길 21서울특별시 동작구 상도동 264-6037.295126.5613서울특별시 동작구청동작경찰서Y172022-09-20
56학원미래연미술서울특별시 동작구 사당로16길80서울특별시 동작구 사당동 300-7937.2849126.5818서울특별시 동작구청동작경찰서Y172022-09-20
57학원튼튼영어마스터학원서울특별시 동작구 사당로16길90서울특별시 동작구 사당동 300-337.2848126.5818서울특별시 동작구청동작경찰서Y172022-09-20
58학원동작시사어학원서울특별시 동작구 동작대로29길77서울특별시 동작구 사당동12037.2918126.5837서울특별시 동작구청동작경찰서Y182022-09-20
59학원대방유투엠학원서울특별시 동작구 대방동길83서울특별시 동작구 대방동381-4837.3018126.5527서울특별시 동작구청동작경찰서Y1102022-09-20
60학원참음악학원서울특별시 동작구 성대로69서울특별시 동작구 상도동 260-1437.2951126.5604서울특별시 동작구청동작경찰서Y1102022-09-20
61학원하랑숲아이들학원서울특별시 동작구 동작대로41길 7서울특별시 동작구 동작동 102-6037.49601126.981789서울특별시 동작구청동작경찰서Y162022-09-20