Overview

Dataset statistics

Number of variables15
Number of observations10000
Missing cells12087
Missing cells (%)8.1%
Duplicate rows241
Duplicate rows (%)2.4%
Total size in memory1.3 MiB
Average record size in memory132.0 B

Variable types

Text7
Categorical2
Numeric4
DateTime2

Dataset

Description- 공공데이터 제공 표준 기준, 지자체에서 관리하는 CCTV 정보<br/>- 링크된 페이지의 데이터기준일자 상단 EXCEL버튼(초록색)을 클릭하여 데이터 다운로드 가능 「개인정보보호법」 등에 따라 교통정보, 범죄예방 등의 공공목적용으로 실외에 설치된 CCTV(개인정보보호법시행령 제24조 제4항 각 호에 해당하는 시설의 CCTV는 개방대상에서 제외 가능)
Author행정안전부
URLhttps://www.data.go.kr/data/15013094/standard.do

Alerts

Dataset has 241 (2.4%) duplicate rowsDuplicates
위도 is highly overall correlated with 경도High correlation
경도 is highly overall correlated with 위도High correlation
설치목적구분 is highly imbalanced (56.1%)Imbalance
카메라대수 is highly imbalanced (66.1%)Imbalance
소재지도로명주소 has 3938 (39.4%) missing valuesMissing
소재지지번주소 has 1140 (11.4%) missing valuesMissing
카메라화소수 has 956 (9.6%) missing valuesMissing
촬영방면정보 has 2963 (29.6%) missing valuesMissing
보관일수 has 393 (3.9%) missing valuesMissing
설치연월 has 2523 (25.2%) missing valuesMissing
보관일수 is highly skewed (γ1 = 26.00018702)Skewed

Reproduction

Analysis started2023-12-11 23:12:16.118139
Analysis finished2023-12-11 23:12:19.973742
Duration3.86 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

관리기관명
Text

Distinct268
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T08:12:20.178816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length19
Mean length8.6369
Min length2

Characters and Unicode

Total characters86369
Distinct characters176
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique46 ?
Unique (%)0.5%

Sample

1st row서울특별시 노원구청
2nd row대구광역시 북구청 정보통신과
3rd row강원도 속초시청
4th row전라북도 군산시
5th row서울특별시 마포구청
ValueCountFrequency (%)
경기도 1515
 
8.0%
서울특별시 1344
 
7.1%
대구광역시 906
 
4.8%
경상남도 792
 
4.2%
부산광역시 666
 
3.5%
경상북도 603
 
3.2%
이천시청 545
 
2.9%
울산광역시 510
 
2.7%
북구청 464
 
2.5%
강원도 379
 
2.0%
Other values (249) 11150
59.1%
2023-12-12T08:12:20.555305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
8874
 
10.3%
시 7937
 
9.2%
청 7783
 
9.0%
구 5436
 
6.3%
도 4580
 
5.3%
경 3370
 
3.9%
광 3143
 
3.6%
역 2835
 
3.3%
남 2122
 
2.5%
울 2058
 
2.4%
Other values (166) 38231
44.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 77302
89.5%
Space Separator 8874
 
10.3%
Close Punctuation 70
 
0.1%
Open Punctuation 70
 
0.1%
Connector Punctuation 39
 
< 0.1%
Other Punctuation 7
 
< 0.1%
Decimal Number 7
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 7937
 
10.3%
청 7783
 
10.1%
구 5436
 
7.0%
도 4580
 
5.9%
경 3370
 
4.4%
광 3143
 
4.1%
역 2835
 
3.7%
남 2122
 
2.7%
울 2058
 
2.7%
북 1948
 
2.5%
Other values (159) 36090
46.7%
Decimal Number
ValueCountFrequency (%)
1 6
85.7%
2 1
 
14.3%
Space Separator
ValueCountFrequency (%)
8874
100.0%
Close Punctuation
ValueCountFrequency (%)
) 70
100.0%
Open Punctuation
ValueCountFrequency (%)
( 70
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 39
100.0%
Other Punctuation
ValueCountFrequency (%)
· 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 77302
89.5%
Common 9067
 
10.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 7937
 
10.3%
청 7783
 
10.1%
구 5436
 
7.0%
도 4580
 
5.9%
경 3370
 
4.4%
광 3143
 
4.1%
역 2835
 
3.7%
남 2122
 
2.7%
울 2058
 
2.7%
북 1948
 
2.5%
Other values (159) 36090
46.7%
Common
ValueCountFrequency (%)
8874
97.9%
) 70
 
0.8%
( 70
 
0.8%
_ 39
 
0.4%
· 7
 
0.1%
1 6
 
0.1%
2 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 77302
89.5%
ASCII 9060
 
10.5%
None 7
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8874
97.9%
) 70
 
0.8%
( 70
 
0.8%
_ 39
 
0.4%
1 6
 
0.1%
2 1
 
< 0.1%
Hangul
ValueCountFrequency (%)
시 7937
 
10.3%
청 7783
 
10.1%
구 5436
 
7.0%
도 4580
 
5.9%
경 3370
 
4.4%
광 3143
 
4.1%
역 2835
 
3.7%
남 2122
 
2.7%
울 2058
 
2.7%
북 1948
 
2.5%
Other values (159) 36090
46.7%
None
ValueCountFrequency (%)
· 7
100.0%

소재지도로명주소
Text

MISSING 

Distinct5552
Distinct (%)91.6%
Missing3938
Missing (%)39.4%
Memory size156.2 KiB
2023-12-12T08:12:20.900033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length57
Median length43
Mean length20.322336
Min length3

Characters and Unicode

Total characters123194
Distinct characters543
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5175 ?
Unique (%)85.4%

Sample

1st row서울특별시 노원구 노원로 253 하계1주민센터앞
2nd row대구광역시 북구 대현남로서4길 18
3rd row강원도 속초시 중앙동 474-18
4th row전라북도 군산시 옥도면 무녀도1길 11
5th row새터산근린공원
ValueCountFrequency (%)
서울특별시 1356
 
5.2%
대구광역시 840
 
3.2%
경기도 794
 
3.1%
부산광역시 481
 
1.9%
인천광역시 433
 
1.7%
북구 386
 
1.5%
울산광역시 385
 
1.5%
노원구 330
 
1.3%
중구 330
 
1.3%
남구 318
 
1.2%
Other values (7605) 20263
78.2%
2023-12-12T08:12:21.393320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
19857
 
16.1%
시 5456
 
4.4%
구 5135
 
4.2%
1 4850
 
3.9%
로 4736
 
3.8%
길 3590
 
2.9%
2 3285
 
2.7%
광 2611
 
2.1%
3 2562
 
2.1%
도 2393
 
1.9%
Other values (533) 68719
55.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 77685
63.1%
Decimal Number 22381
 
18.2%
Space Separator 19857
 
16.1%
Dash Punctuation 1618
 
1.3%
Open Punctuation 725
 
0.6%
Close Punctuation 724
 
0.6%
Other Punctuation 155
 
0.1%
Uppercase Letter 45
 
< 0.1%
Lowercase Letter 2
 
< 0.1%
Connector Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 5456
 
7.0%
구 5135
 
6.6%
로 4736
 
6.1%
길 3590
 
4.6%
광 2611
 
3.4%
도 2393
 
3.1%
역 2362
 
3.0%
동 2306
 
3.0%
대 1866
 
2.4%
산 1859
 
2.4%
Other values (498) 45371
58.4%
Uppercase Letter
ValueCountFrequency (%)
A 19
42.2%
T 5
 
11.1%
P 4
 
8.9%
C 3
 
6.7%
K 3
 
6.7%
I 3
 
6.7%
S 2
 
4.4%
B 2
 
4.4%
G 1
 
2.2%
H 1
 
2.2%
Other values (2) 2
 
4.4%
Decimal Number
ValueCountFrequency (%)
1 4850
21.7%
2 3285
14.7%
3 2562
11.4%
4 2127
9.5%
5 1895
 
8.5%
6 1827
 
8.2%
7 1666
 
7.4%
8 1482
 
6.6%
0 1386
 
6.2%
9 1301
 
5.8%
Other Punctuation
ValueCountFrequency (%)
, 141
91.0%
. 6
 
3.9%
? 6
 
3.9%
· 1
 
0.6%
/ 1
 
0.6%
Lowercase Letter
ValueCountFrequency (%)
t 1
50.0%
k 1
50.0%
Space Separator
ValueCountFrequency (%)
19857
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1618
100.0%
Open Punctuation
ValueCountFrequency (%)
( 725
100.0%
Close Punctuation
ValueCountFrequency (%)
) 724
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 77685
63.1%
Common 45462
36.9%
Latin 47
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 5456
 
7.0%
구 5135
 
6.6%
로 4736
 
6.1%
길 3590
 
4.6%
광 2611
 
3.4%
도 2393
 
3.1%
역 2362
 
3.0%
동 2306
 
3.0%
대 1866
 
2.4%
산 1859
 
2.4%
Other values (498) 45371
58.4%
Common
ValueCountFrequency (%)
19857
43.7%
1 4850
 
10.7%
2 3285
 
7.2%
3 2562
 
5.6%
4 2127
 
4.7%
5 1895
 
4.2%
6 1827
 
4.0%
7 1666
 
3.7%
- 1618
 
3.6%
8 1482
 
3.3%
Other values (11) 4293
 
9.4%
Latin
ValueCountFrequency (%)
A 19
40.4%
T 5
 
10.6%
P 4
 
8.5%
C 3
 
6.4%
K 3
 
6.4%
I 3
 
6.4%
S 2
 
4.3%
B 2
 
4.3%
G 1
 
2.1%
H 1
 
2.1%
Other values (4) 4
 
8.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 77685
63.1%
ASCII 45508
36.9%
None 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
19857
43.6%
1 4850
 
10.7%
2 3285
 
7.2%
3 2562
 
5.6%
4 2127
 
4.7%
5 1895
 
4.2%
6 1827
 
4.0%
7 1666
 
3.7%
- 1618
 
3.6%
8 1482
 
3.3%
Other values (24) 4339
 
9.5%
Hangul
ValueCountFrequency (%)
시 5456
 
7.0%
구 5135
 
6.6%
로 4736
 
6.1%
길 3590
 
4.6%
광 2611
 
3.4%
도 2393
 
3.1%
역 2362
 
3.0%
동 2306
 
3.0%
대 1866
 
2.4%
산 1859
 
2.4%
Other values (498) 45371
58.4%
None
ValueCountFrequency (%)
· 1
100.0%

소재지지번주소
Text

MISSING 

Distinct8180
Distinct (%)92.3%
Missing1140
Missing (%)11.4%
Memory size156.2 KiB
2023-12-12T08:12:21.720579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length66
Median length60
Mean length20.592551
Min length7

Characters and Unicode

Total characters182450
Distinct characters565
Distinct categories14 ?
Distinct scripts3 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7638 ?
Unique (%)86.2%

Sample

1st row서울특별시 노원구 하계1동 251-6하계1동 주민센터 앞
2nd row대구광역시 북구 대현동 328-17
3rd row강원도 속초시 중앙동 474-18
4th row전라북도 군산시 옥도면 무녀도리 산 126-2
5th row성산동 177-16
ValueCountFrequency (%)
경기도 1757
 
4.4%
서울특별시 1238
 
3.1%
경상남도 759
 
1.9%
대구광역시 745
 
1.9%
부산광역시 630
 
1.6%
경상북도 617
 
1.5%
이천시 545
 
1.4%
인천광역시 447
 
1.1%
충청남도 440
 
1.1%
강원도 369
 
0.9%
Other values (10957) 32390
81.1%
2023-12-12T08:12:22.139671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
31083
 
17.0%
1 7462
 
4.1%
시 7215
 
4.0%
동 7145
 
3.9%
- 6815
 
3.7%
구 5242
 
2.9%
도 5174
 
2.8%
2 4904
 
2.7%
3 4212
 
2.3%
4 3614
 
2.0%
Other values (555) 99584
54.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 106288
58.3%
Decimal Number 36958
 
20.3%
Space Separator 31083
 
17.0%
Dash Punctuation 6815
 
3.7%
Close Punctuation 500
 
0.3%
Open Punctuation 499
 
0.3%
Other Punctuation 219
 
0.1%
Uppercase Letter 72
 
< 0.1%
Lowercase Letter 7
 
< 0.1%
Math Symbol 4
 
< 0.1%
Other values (4) 5
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 7215
 
6.8%
동 7145
 
6.7%
구 5242
 
4.9%
도 5174
 
4.9%
경 3419
 
3.2%
리 3132
 
2.9%
광 2953
 
2.8%
산 2767
 
2.6%
남 2436
 
2.3%
역 2426
 
2.3%
Other values (505) 64379
60.6%
Uppercase Letter
ValueCountFrequency (%)
A 20
27.8%
G 8
 
11.1%
C 8
 
11.1%
L 7
 
9.7%
I 7
 
9.7%
S 4
 
5.6%
P 4
 
5.6%
T 4
 
5.6%
E 2
 
2.8%
K 2
 
2.8%
Other values (5) 6
 
8.3%
Decimal Number
ValueCountFrequency (%)
1 7462
20.2%
2 4904
13.3%
3 4212
11.4%
4 3614
9.8%
5 3336
9.0%
6 3191
8.6%
7 2860
 
7.7%
8 2549
 
6.9%
0 2436
 
6.6%
9 2394
 
6.5%
Other Punctuation
ValueCountFrequency (%)
, 205
93.6%
? 6
 
2.7%
: 2
 
0.9%
. 2
 
0.9%
@ 2
 
0.9%
' 1
 
0.5%
/ 1
 
0.5%
Lowercase Letter
ValueCountFrequency (%)
m 3
42.9%
s 1
 
14.3%
k 1
 
14.3%
y 1
 
14.3%
e 1
 
14.3%
Math Symbol
ValueCountFrequency (%)
~ 2
50.0%
↔ 1
25.0%
→ 1
25.0%
Close Punctuation
ValueCountFrequency (%)
) 496
99.2%
] 4
 
0.8%
Open Punctuation
ValueCountFrequency (%)
( 495
99.2%
[ 4
 
0.8%
Space Separator
ValueCountFrequency (%)
31083
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 6815
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 2
100.0%
Final Punctuation
ValueCountFrequency (%)
” 1
100.0%
Initial Punctuation
ValueCountFrequency (%)
“ 1
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 106289
58.3%
Common 76082
41.7%
Latin 79
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 7215
 
6.8%
동 7145
 
6.7%
구 5242
 
4.9%
도 5174
 
4.9%
경 3419
 
3.2%
리 3132
 
2.9%
광 2953
 
2.8%
산 2767
 
2.6%
남 2436
 
2.3%
역 2426
 
2.3%
Other values (506) 64380
60.6%
Common
ValueCountFrequency (%)
31083
40.9%
1 7462
 
9.8%
- 6815
 
9.0%
2 4904
 
6.4%
3 4212
 
5.5%
4 3614
 
4.8%
5 3336
 
4.4%
6 3191
 
4.2%
7 2860
 
3.8%
8 2549
 
3.4%
Other values (19) 6056
 
8.0%
Latin
ValueCountFrequency (%)
A 20
25.3%
G 8
 
10.1%
C 8
 
10.1%
L 7
 
8.9%
I 7
 
8.9%
S 4
 
5.1%
P 4
 
5.1%
T 4
 
5.1%
m 3
 
3.8%
E 2
 
2.5%
Other values (10) 12
15.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 106288
58.3%
ASCII 76157
41.7%
Punctuation 2
 
< 0.1%
Arrows 2
 
< 0.1%
None 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
31083
40.8%
1 7462
 
9.8%
- 6815
 
8.9%
2 4904
 
6.4%
3 4212
 
5.5%
4 3614
 
4.7%
5 3336
 
4.4%
6 3191
 
4.2%
7 2860
 
3.8%
8 2549
 
3.3%
Other values (35) 6131
 
8.1%
Hangul
ValueCountFrequency (%)
시 7215
 
6.8%
동 7145
 
6.7%
구 5242
 
4.9%
도 5174
 
4.9%
경 3419
 
3.2%
리 3132
 
2.9%
광 2953
 
2.8%
산 2767
 
2.6%
남 2436
 
2.3%
역 2426
 
2.3%
Other values (505) 64379
60.6%
Punctuation
ValueCountFrequency (%)
” 1
50.0%
“ 1
50.0%
Arrows
ValueCountFrequency (%)
↔ 1
50.0%
→ 1
50.0%
None
ValueCountFrequency (%)
㈜ 1
100.0%

설치목적구분
Categorical

IMBALANCE 

Distinct25
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
생활방범
6291 
어린이보호
1272 
다목적
749 
시설물관리
 
451
교통단속
 
272
Other values (20)
965 

Length

Max length12
Median length4
Mean length4.1383
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row교통단속
2nd row생활방범
3rd row시설물관리
4th row생활방범
5th row방범(어린이보호구역)

Common Values

ValueCountFrequency (%)
생활방범 6291
62.9%
어린이보호 1272
 
12.7%
다목적 749
 
7.5%
시설물관리 451
 
4.5%
교통단속 272
 
2.7%
재난재해 239
 
2.4%
쓰레기단속 206
 
2.1%
차량방범 204
 
2.0%
교통정보수집 104
 
1.0%
기타 57
 
0.6%
Other values (15) 155
 
1.6%

Length

2023-12-12T08:12:22.260585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
생활방범 6291
62.8%
어린이보호 1274
 
12.7%
다목적 749
 
7.5%
시설물관리 451
 
4.5%
교통단속 272
 
2.7%
재난재해 239
 
2.4%
쓰레기단속 206
 
2.1%
차량방범 204
 
2.0%
교통정보수집 104
 
1.0%
기타 57
 
0.6%
Other values (18) 164
 
1.6%

카메라대수
Categorical

IMBALANCE 

Distinct31
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
6541 
2
1164 
3
978 
4
799 
5
 
403
Other values (26)
 
115

Length

Max length2
Median length1
Mean length1.0031
Min length1

Unique

Unique13 ?
Unique (%)0.1%

Sample

1st row1
2nd row1
3rd row1
4th row3
5th row1

Common Values

ValueCountFrequency (%)
1 6541
65.4%
2 1164
 
11.6%
3 978
 
9.8%
4 799
 
8.0%
5 403
 
4.0%
6 51
 
0.5%
7 17
 
0.2%
8 10
 
0.1%
9 4
 
< 0.1%
12 3
 
< 0.1%
Other values (21) 30
 
0.3%

Length

2023-12-12T08:12:22.362019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1 6541
65.4%
2 1164
 
11.6%
3 978
 
9.8%
4 799
 
8.0%
5 403
 
4.0%
6 51
 
0.5%
7 17
 
0.2%
8 10
 
0.1%
9 4
 
< 0.1%
12 3
 
< 0.1%
Other values (21) 30
 
0.3%

카메라화소수
Real number (ℝ)

MISSING 

Distinct41
Distinct (%)0.5%
Missing956
Missing (%)9.6%
Infinite0
Infinite (%)0.0%
Mean194.57898
Minimum1
Maximum900
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T08:12:22.472967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile52
Q1200
median200
Q3200
95-th percentile200
Maximum900
Range899
Interquartile range (IQR)0

Descriptive statistics

Standard deviation65.495639
Coefficient of variation (CV)0.33660182
Kurtosis45.529059
Mean194.57898
Median Absolute Deviation (MAD)0
Skewness4.2967244
Sum1759772.3
Variance4289.6787
MonotonicityNot monotonic
2023-12-12T08:12:22.781891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
200.0 7529
75.3%
130.0 601
 
6.0%
41.0 404
 
4.0%
300.0 194
 
1.9%
500.0 45
 
0.4%
900.0 26
 
0.3%
100.0 23
 
0.2%
400.0 21
 
0.2%
210.0 20
 
0.2%
420.0 17
 
0.2%
Other values (31) 164
 
1.6%
(Missing) 956
 
9.6%
ValueCountFrequency (%)
1.0 1
 
< 0.1%
1.3 1
 
< 0.1%
19.0 1
 
< 0.1%
33.0 3
 
< 0.1%
34.0 6
 
0.1%
37.0 1
 
< 0.1%
40.0 13
 
0.1%
41.0 404
4.0%
42.0 5
 
0.1%
45.0 1
 
< 0.1%
ValueCountFrequency (%)
900.0 26
0.3%
740.0 4
 
< 0.1%
700.0 6
 
0.1%
650.0 2
 
< 0.1%
600.0 1
 
< 0.1%
500.0 45
0.4%
420.0 17
 
0.2%
400.0 21
0.2%
340.0 2
 
< 0.1%
330.0 14
 
0.1%

촬영방면정보
Text

MISSING 

Distinct2022
Distinct (%)28.7%
Missing2963
Missing (%)29.6%
Memory size156.2 KiB
2023-12-12T08:12:23.070096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length68
Median length57
Mean length7.2933068
Min length2

Characters and Unicode

Total characters51323
Distinct characters609
Distinct categories12 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1843 ?
Unique (%)26.2%

Sample

1st row회전
2nd row중앙시장 건물 내부
3rd row360도 전방면
4th row360도전방면
5th row문재5가길 22 방향
ValueCountFrequency (%)
360도 1207
 
10.7%
전방면 907
 
8.0%
360도전방면 896
 
7.9%
전방 427
 
3.8%
120 365
 
3.2%
360 337
 
3.0%
회전 328
 
2.9%
앞 249
 
2.2%
고정 244
 
2.2%
고정형 170
 
1.5%
Other values (2827) 6144
54.5%
2023-12-12T08:12:23.517186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4266
 
8.3%
0 4029
 
7.9%
방 3104
 
6.0%
전 3071
 
6.0%
3 3034
 
5.9%
6 2792
 
5.4%
도 2666
 
5.2%
면 2073
 
4.0%
1 1272
 
2.5%
2 762
 
1.5%
Other values (599) 24254
47.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 31815
62.0%
Decimal Number 12827
25.0%
Space Separator 4266
 
8.3%
Close Punctuation 701
 
1.4%
Other Punctuation 401
 
0.8%
Open Punctuation 288
 
0.6%
Uppercase Letter 273
 
0.5%
Lowercase Letter 210
 
0.4%
Dash Punctuation 207
 
0.4%
Connector Punctuation 144
 
0.3%
Other values (2) 191
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
방 3104
 
9.8%
전 3071
 
9.7%
도 2666
 
8.4%
면 2073
 
6.5%
정 607
 
1.9%
회 601
 
1.9%
고 525
 
1.7%
향 507
 
1.6%
로 446
 
1.4%
동 435
 
1.4%
Other values (536) 17780
55.9%
Uppercase Letter
ValueCountFrequency (%)
M 85
31.1%
C 43
15.8%
A 24
 
8.8%
P 22
 
8.1%
T 15
 
5.5%
B 14
 
5.1%
I 13
 
4.8%
U 9
 
3.3%
S 8
 
2.9%
G 8
 
2.9%
Other values (11) 32
 
11.7%
Decimal Number
ValueCountFrequency (%)
0 4029
31.4%
3 3034
23.7%
6 2792
21.8%
1 1272
 
9.9%
2 762
 
5.9%
5 448
 
3.5%
4 231
 
1.8%
8 123
 
1.0%
7 71
 
0.6%
9 65
 
0.5%
Lowercase Letter
ValueCountFrequency (%)
m 198
94.3%
e 2
 
1.0%
l 2
 
1.0%
o 2
 
1.0%
t 2
 
1.0%
a 1
 
0.5%
p 1
 
0.5%
j 1
 
0.5%
i 1
 
0.5%
Other Punctuation
ValueCountFrequency (%)
· 217
54.1%
/ 69
 
17.2%
, 67
 
16.7%
. 34
 
8.5%
: 12
 
3.0%
@ 1
 
0.2%
& 1
 
0.2%
Math Symbol
ValueCountFrequency (%)
~ 89
79.5%
> 11
 
9.8%
= 5
 
4.5%
⇔ 4
 
3.6%
↔ 1
 
0.9%
< 1
 
0.9%
→ 1
 
0.9%
Close Punctuation
ValueCountFrequency (%)
) 692
98.7%
] 9
 
1.3%
Open Punctuation
ValueCountFrequency (%)
( 279
96.9%
[ 9
 
3.1%
Other Symbol
ValueCountFrequency (%)
° 77
97.5%
㈜ 2
 
2.5%
Space Separator
ValueCountFrequency (%)
4266
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 207
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 144
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 31817
62.0%
Common 19023
37.1%
Latin 483
 
0.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
방 3104
 
9.8%
전 3071
 
9.7%
도 2666
 
8.4%
면 2073
 
6.5%
정 607
 
1.9%
회 601
 
1.9%
고 525
 
1.7%
향 507
 
1.6%
로 446
 
1.4%
동 435
 
1.4%
Other values (537) 17782
55.9%
Common
ValueCountFrequency (%)
4266
22.4%
0 4029
21.2%
3 3034
15.9%
6 2792
14.7%
1 1272
 
6.7%
2 762
 
4.0%
) 692
 
3.6%
5 448
 
2.4%
( 279
 
1.5%
4 231
 
1.2%
Other values (22) 1218
 
6.4%
Latin
ValueCountFrequency (%)
m 198
41.0%
M 85
17.6%
C 43
 
8.9%
A 24
 
5.0%
P 22
 
4.6%
T 15
 
3.1%
B 14
 
2.9%
I 13
 
2.7%
U 9
 
1.9%
S 8
 
1.7%
Other values (20) 52
 
10.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 31815
62.0%
ASCII 19206
37.4%
None 296
 
0.6%
Arrows 6
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4266
22.2%
0 4029
21.0%
3 3034
15.8%
6 2792
14.5%
1 1272
 
6.6%
2 762
 
4.0%
) 692
 
3.6%
5 448
 
2.3%
( 279
 
1.5%
4 231
 
1.2%
Other values (47) 1401
 
7.3%
Hangul
ValueCountFrequency (%)
방 3104
 
9.8%
전 3071
 
9.7%
도 2666
 
8.4%
면 2073
 
6.5%
정 607
 
1.9%
회 601
 
1.9%
고 525
 
1.7%
향 507
 
1.6%
로 446
 
1.4%
동 435
 
1.4%
Other values (536) 17780
55.9%
None
ValueCountFrequency (%)
· 217
73.3%
° 77
 
26.0%
㈜ 2
 
0.7%
Arrows
ValueCountFrequency (%)
⇔ 4
66.7%
↔ 1
 
16.7%
→ 1
 
16.7%

보관일수
Real number (ℝ)

MISSING  SKEWED 

Distinct14
Distinct (%)0.1%
Missing393
Missing (%)3.9%
Infinite0
Infinite (%)0.0%
Mean32.049859
Minimum0
Maximum1825
Zeros96
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T08:12:23.613259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile30
Q130
median30
Q330
95-th percentile30
Maximum1825
Range1825
Interquartile range (IQR)0

Descriptive statistics

Standard deviation68.62149
Coefficient of variation (CV)2.1410855
Kurtosis676.65573
Mean32.049859
Median Absolute Deviation (MAD)0
Skewness26.000187
Sum307903
Variance4708.9089
MonotonicityNot monotonic
2023-12-12T08:12:23.703872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
30 9355
93.5%
0 96
 
1.0%
7 42
 
0.4%
15 26
 
0.3%
5 26
 
0.3%
3 16
 
0.2%
20 16
 
0.2%
1825 14
 
0.1%
1 7
 
0.1%
90 4
 
< 0.1%
Other values (4) 5
 
0.1%
(Missing) 393
 
3.9%
ValueCountFrequency (%)
0 96
 
1.0%
1 7
 
0.1%
3 16
 
0.2%
5 26
 
0.3%
7 42
 
0.4%
14 2
 
< 0.1%
15 26
 
0.3%
20 16
 
0.2%
26 1
 
< 0.1%
30 9355
93.5%
ValueCountFrequency (%)
1825 14
 
0.1%
90 4
 
< 0.1%
60 1
 
< 0.1%
40 1
 
< 0.1%
30 9355
93.5%
26 1
 
< 0.1%
20 16
 
0.2%
15 26
 
0.3%
14 2
 
< 0.1%
7 42
 
0.4%

설치연월
Date

MISSING 

Distinct192
Distinct (%)2.6%
Missing2523
Missing (%)25.2%
Memory size156.2 KiB
Minimum1905-07-01 00:00:00
Maximum2023-11-01 00:00:00
2023-12-12T08:12:23.835475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:12:23.942222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

관리기관전화번호
Text

Distinct416
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T08:12:24.172669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length11.9752
Min length11

Characters and Unicode

Total characters119752
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

Unique139 ?
Unique (%)1.4%

Sample

1st row02-2116-4087
2nd row053-665-4481
3rd row033-639-3805
4th row063-454-7922
5th row02-3153-8432
ValueCountFrequency (%)
031-644-2942 544
 
5.4%
02-901-7266 274
 
2.7%
052-226-5572 263
 
2.6%
053-665-4481 252
 
2.5%
032-453-6180 221
 
2.2%
055-330-4741 196
 
2.0%
02-2116-4917 185
 
1.8%
052-209-3145 155
 
1.6%
031-760-2243 140
 
1.4%
054-550-6651 131
 
1.3%
Other values (406) 7639
76.4%
2023-12-12T08:12:24.513308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 20000
16.7%
0 18514
15.5%
2 12273
10.2%
3 11923
10.0%
5 11201
9.4%
4 10561
8.8%
1 9829
8.2%
6 9484
7.9%
8 5612
 
4.7%
9 5375
 
4.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 99752
83.3%
Dash Punctuation 20000
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 18514
18.6%
2 12273
12.3%
3 11923
12.0%
5 11201
11.2%
4 10561
10.6%
1 9829
9.9%
6 9484
9.5%
8 5612
 
5.6%
9 5375
 
5.4%
7 4980
 
5.0%
Dash Punctuation
ValueCountFrequency (%)
- 20000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 119752
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 20000
16.7%
0 18514
15.5%
2 12273
10.2%
3 11923
10.0%
5 11201
9.4%
4 10561
8.8%
1 9829
8.2%
6 9484
7.9%
8 5612
 
4.7%
9 5375
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 119752
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 20000
16.7%
0 18514
15.5%
2 12273
10.2%
3 11923
10.0%
5 11201
9.4%
4 10561
8.8%
1 9829
8.2%
6 9484
7.9%
8 5612
 
4.7%
9 5375
 
4.5%

위도
Real number (ℝ)

HIGH CORRELATION 

Distinct9217
Distinct (%)92.9%
Missing82
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean36.498427
Minimum33.114006
Maximum39.913945
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T08:12:24.671609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum33.114006
5-th percentile35.07755
Q135.55855
median36.620474
Q337.465208
95-th percentile37.731123
Maximum39.913945
Range6.799939
Interquartile range (IQR)1.9066583

Descriptive statistics

Standard deviation1.0475987
Coefficient of variation (CV)0.028702571
Kurtosis-0.44372183
Mean36.498427
Median Absolute Deviation (MAD)0.86209668
Skewness-0.53488663
Sum361991.4
Variance1.097463
MonotonicityNot monotonic
2023-12-12T08:12:24.823100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.465061 62
 
0.6%
38.204471 11
 
0.1%
35.484031 8
 
0.1%
35.885724 7
 
0.1%
35.290223 6
 
0.1%
37.90527228 6
 
0.1%
37.4955112265 6
 
0.1%
37.5051613933 6
 
0.1%
35.885726 6
 
0.1%
37.448474 5
 
0.1%
Other values (9207) 9795
98.0%
(Missing) 82
 
0.8%
ValueCountFrequency (%)
33.114006 1
< 0.1%
33.22115 1
< 0.1%
33.227312 1
< 0.1%
33.227711 1
< 0.1%
33.230676 1
< 0.1%
33.2348203 1
< 0.1%
33.235748 1
< 0.1%
33.236546 2
< 0.1%
33.240061 1
< 0.1%
33.242062 1
< 0.1%
ValueCountFrequency (%)
39.913945 1
< 0.1%
38.4486596 1
< 0.1%
38.221457 1
< 0.1%
38.215985 1
< 0.1%
38.215592 1
< 0.1%
38.211917 1
< 0.1%
38.211517 1
< 0.1%
38.2088832 1
< 0.1%
38.208552578 1
< 0.1%
38.208303 2
< 0.1%

경도
Real number (ℝ)

HIGH CORRELATION 

Distinct9253
Distinct (%)93.4%
Missing92
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean127.73819
Minimum124.63007
Maximum130.90752
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T08:12:24.948787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum124.63007
5-th percentile126.67564
Q1127.01355
median127.44316
Q3128.58849
95-th percentile129.29035
Maximum130.90752
Range6.2774441
Interquartile range (IQR)1.574933

Descriptive statistics

Standard deviation0.88396117
Coefficient of variation (CV)0.0069201011
Kurtosis-1.0533129
Mean127.73819
Median Absolute Deviation (MAD)0.59984035
Skewness0.40945536
Sum1265630
Variance0.78138736
MonotonicityNot monotonic
2023-12-12T08:12:25.128240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
128.590194 11
 
0.1%
128.5829108 9
 
0.1%
127.127886 8
 
0.1%
127.2064391 6
 
0.1%
128.1047763909 6
 
0.1%
126.8882890872 6
 
0.1%
126.775693 6
 
0.1%
127.111111 5
 
0.1%
127.0300217 5
 
0.1%
126.699829 5
 
0.1%
Other values (9243) 9841
98.4%
(Missing) 92
 
0.9%
ValueCountFrequency (%)
124.6300735537 1
< 0.1%
124.6936336001 1
< 0.1%
124.699657935 2
< 0.1%
124.7062676443 1
< 0.1%
124.7122072055 1
< 0.1%
124.7136237335 1
< 0.1%
124.7161149514 1
< 0.1%
124.7197742433 1
< 0.1%
124.7350011535 1
< 0.1%
125.7004571322 1
< 0.1%
ValueCountFrequency (%)
130.9075176325 1
< 0.1%
130.906427353 1
< 0.1%
130.9057519901 1
< 0.1%
130.9035147987 1
< 0.1%
130.9015497148 1
< 0.1%
130.893493384 1
< 0.1%
130.8423625191 1
< 0.1%
130.7975556735 1
< 0.1%
129.712025 1
< 0.1%
129.470121 1
< 0.1%

데이터기준일자
Date

Distinct136
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2015-11-01 00:00:00
Maximum2020-12-24 00:00:00
2023-12-12T08:12:25.263931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:12:25.400027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

제공기관코드
Text

Distinct181
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T08:12:25.715779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters70000
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)0.1%

Sample

1st row3100000
2nd row3450000
3rd row4230000
4th row4670000
5th row3130000
ValueCountFrequency (%)
4070000 544
 
5.4%
3450000 347
 
3.5%
3100000 331
 
3.3%
3080000 282
 
2.8%
3700000 263
 
2.6%
3410000 242
 
2.4%
3530000 221
 
2.2%
5350000 196
 
2.0%
5120000 180
 
1.8%
3820000 175
 
1.8%
Other values (171) 7219
72.2%
2023-12-12T08:12:26.154175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 42786
61.1%
3 7165
 
10.2%
4 5072
 
7.2%
5 4381
 
6.3%
1 2390
 
3.4%
2 2051
 
2.9%
7 1858
 
2.7%
6 1693
 
2.4%
8 1317
 
1.9%
9 1259
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69972
> 99.9%
Uppercase Letter 28
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 42786
61.1%
3 7165
 
10.2%
4 5072
 
7.2%
5 4381
 
6.3%
1 2390
 
3.4%
2 2051
 
2.9%
7 1858
 
2.7%
6 1693
 
2.4%
8 1317
 
1.9%
9 1259
 
1.8%
Uppercase Letter
ValueCountFrequency (%)
B 28
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 69972
> 99.9%
Latin 28
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 42786
61.1%
3 7165
 
10.2%
4 5072
 
7.2%
5 4381
 
6.3%
1 2390
 
3.4%
2 2051
 
2.9%
7 1858
 
2.7%
6 1693
 
2.4%
8 1317
 
1.9%
9 1259
 
1.8%
Latin
ValueCountFrequency (%)
B 28
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 70000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 42786
61.1%
3 7165
 
10.2%
4 5072
 
7.2%
5 4381
 
6.3%
1 2390
 
3.4%
2 2051
 
2.9%
7 1858
 
2.7%
6 1693
 
2.4%
8 1317
 
1.9%
9 1259
 
1.8%

제공기관명
Text

Distinct181
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T08:12:26.544013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length13
Mean length8.0742
Min length5

Characters and Unicode

Total characters80742
Distinct characters127
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)0.1%

Sample

1st row서울특별시 노원구
2nd row대구광역시 북구
3rd row강원도 속초시
4th row전라북도 군산시
5th row서울특별시 마포구
ValueCountFrequency (%)
경기도 1760
 
9.0%
서울특별시 1552
 
7.9%
대구광역시 987
 
5.0%
경상남도 792
 
4.1%
경상북도 764
 
3.9%
부산광역시 665
 
3.4%
인천광역시 619
 
3.2%
이천시 544
 
2.8%
울산광역시 510
 
2.6%
북구 469
 
2.4%
Other values (167) 10888
55.7%
2023-12-12T08:12:27.144571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9550
 
11.8%
시 8266
 
10.2%
구 5469
 
6.8%
도 5216
 
6.5%
경 3711
 
4.6%
광 3415
 
4.2%
역 3104
 
3.8%
남 2289
 
2.8%
울 2166
 
2.7%
북 2043
 
2.5%
Other values (117) 35513
44.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 71192
88.2%
Space Separator 9550
 
11.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 8266
 
11.6%
구 5469
 
7.7%
도 5216
 
7.3%
경 3711
 
5.2%
광 3415
 
4.8%
역 3104
 
4.4%
남 2289
 
3.2%
울 2166
 
3.0%
북 2043
 
2.9%
서 1969
 
2.8%
Other values (116) 33544
47.1%
Space Separator
ValueCountFrequency (%)
9550
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 71192
88.2%
Common 9550
 
11.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 8266
 
11.6%
구 5469
 
7.7%
도 5216
 
7.3%
경 3711
 
5.2%
광 3415
 
4.8%
역 3104
 
4.4%
남 2289
 
3.2%
울 2166
 
3.0%
북 2043
 
2.9%
서 1969
 
2.8%
Other values (116) 33544
47.1%
Common
ValueCountFrequency (%)
9550
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 71192
88.2%
ASCII 9550
 
11.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9550
100.0%
Hangul
ValueCountFrequency (%)
시 8266
 
11.6%
구 5469
 
7.7%
도 5216
 
7.3%
경 3711
 
5.2%
광 3415
 
4.8%
역 3104
 
4.4%
남 2289
 
3.2%
울 2166
 
3.0%
북 2043
 
2.9%
서 1969
 
2.8%
Other values (116) 33544
47.1%

Interactions

2023-12-12T08:12:19.032503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:12:17.820795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:12:18.198318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:12:18.629109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:12:19.126304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:12:17.895340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:12:18.284239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:12:18.713252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:12:19.238359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:12:17.980239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:12:18.431271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:12:18.827578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:12:19.333235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:12:18.102543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:12:18.534197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:12:18.938859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T08:12:27.284554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
설치목적구분카메라대수카메라화소수보관일수위도경도
설치목적구분1.0000.1770.6240.2640.3580.304
카메라대수0.1771.0000.0000.0000.2920.245
카메라화소수0.6240.0001.0000.1840.1810.269
보관일수0.2640.0000.1841.0000.3450.125
위도0.3580.2920.1810.3451.0000.599
경도0.3040.2450.2690.1250.5991.000
2023-12-12T08:12:27.417121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
설치목적구분카메라대수
설치목적구분1.0000.044
카메라대수0.0441.000
2023-12-12T08:12:27.507850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
카메라화소수보관일수위도경도설치목적구분카메라대수
카메라화소수1.000-0.023-0.0240.0560.2730.000
보관일수-0.0231.0000.0060.0080.2280.000
위도-0.0240.0061.000-0.5040.1450.113
경도0.0560.008-0.5041.0000.1120.088
설치목적구분0.2730.2280.1450.1121.0000.044
카메라대수0.0000.0000.1130.0880.0441.000

Missing values

2023-12-12T08:12:19.464649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T08:12:19.682938image/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.
2023-12-12T08:12:19.880245image/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

관리기관명소재지도로명주소소재지지번주소설치목적구분카메라대수카메라화소수촬영방면정보보관일수설치연월관리기관전화번호위도경도데이터기준일자제공기관코드제공기관명
9502서울특별시 노원구청서울특별시 노원구 노원로 253 하계1주민센터앞서울특별시 노원구 하계1동 251-6하계1동 주민센터 앞교통단속1200.0회전302009-0202-2116-408737.640136127.0729342019-04-083100000서울특별시 노원구
16724대구광역시 북구청 정보통신과대구광역시 북구 대현남로서4길 18대구광역시 북구 대현동 328-17생활방범1200.0<NA>30<NA>053-665-448135.878809128.6072322020-01-083450000대구광역시 북구
22888강원도 속초시청강원도 속초시 중앙동 474-18강원도 속초시 중앙동 474-18시설물관리141.0중앙시장 건물 내부302020-06033-639-380538.204471128.5901942020-06-124230000강원도 속초시
29503전라북도 군산시전라북도 군산시 옥도면 무녀도1길 11전라북도 군산시 옥도면 무녀도리 산 126-2생활방범3200.0360도 전방면302018-05063-454-792235.806176126.4188112020-03-124670000전라북도 군산시
2850서울특별시 마포구청새터산근린공원성산동 177-16방범(어린이보호구역)1130.0360도전방면302014-0502-3153-843237.566422126.9045572020-05-203130000서울특별시 마포구
32062홍천군청강원도 홍천군 홍천읍 연봉리 산3-11강원도 홍천군 홍천읍 연봉리 산3-11(연봉리 국도 굴다리)생활방범1200.0<NA>302018-06033-430-230137.686234127.8860542019-06-304250000강원도 홍천군
12004울산광역시 동구청울산광역시 동구 문재5가길 22울산광역시 동구 방어동 1092-3생활방범1200.0문재5가길 22 방향302019-09052-209-314535.485918129.4181662020-10-303710000울산광역시 동구
46106경상남도 양산시 안전총괄과<NA>경상남도 양산시 물금읍 범어리 630-6어린이보호4200.0서남어린이공원302019-06055-392-771335.328518128.9977672020-09-235380000경상남도 양산시
21283경기도 고양시<NA>경기도 고양시 덕양구 원당동 170-2 (남강사철탕 사거리 주변)생활방범4200.0360도전방면30<NA>031-8075-257937.672756126.859752020-01-093940000경기도 고양시
30261경기도 광주시청<NA>경기도 광주시 곤지암읍 삼리 615생활방범4300.0주택가·도로·골목길302017-07031-760-224337.351957127.3337672020-03-165540000경기도 광주시
관리기관명소재지도로명주소소재지지번주소설치목적구분카메라대수카메라화소수촬영방면정보보관일수설치연월관리기관전화번호위도경도데이터기준일자제공기관코드제공기관명
48043부산광역시 금정구청부산광역시 금정구 서동로34번길 14부산광역시 금정구 부곡동 779-2생활방범1200.0360도 전방면302020-05051-519-573135.219938129.0917022020-08-123350000부산광역시 금정구
8661서울특별시 성북구청서울특별시 성북구 종암로25길 22-12서울특별시 성북구 종암동 82-10생활방범3200.0360도전방면302017-1202-2241-456237.600922127.0316572020-10-013070000서울특별시 성북구
13915대구광역시 중구청대구광역시 중구 관덕정길 77대구광역시 중구 남산동 614-18쓰레기단속1200.0남산목욕탕앞72016-11053-661-328135.862194128.5911852018-06-303410000대구광역시 중구
2473경상북도 구미시<NA>경상북도 구미시 남통동 2-3다목적1200.0남통동 2-3(2)302016-01054-480-669336.129346128.3231012020-04-225080000경상북도 구미시
38975경상북도 예천군청<NA>경상북도 예천군 지보면 소화리 503-13생활방범2200.0설치장소 카메라 전방302020-06054-650-898136.545054128.3868722020-09-225230000경상북도 예천군
44119부산광역시 동구청영초길191번길 12초량동 994-552생활방범1200.0360도302019-01051-440-4701<NA><NA>2020-09-233270000부산광역시 동구
43221강원도 강릉시강원도 강릉시 솔올로 66강원도 강릉시 교동 1829-2교통단속1200.0택지 솔올로 상302013-05033-640-533237.767788128.8789852020-01-014200000강원도 강릉시
23550서울특별시 금천구서울특별시 금천구 금하로 668서울특별시 금천구 시흥1동 893다목적4200.0360도전방면30<NA>02-2627-190337.465061126.9043432020-07-313170000서울특별시 금천구
6277대구광역시 수성구청<NA>대구광역시 수성구 중동 171어린이보호1200.0<NA>30<NA>053-666-249335.841561128.6153332020-07-073460000대구광역시 수성구
49668경기도 안양시(교통정책과)경기도 안양시 동안구 부림로 170번길 20<NA>생활방범4<NA><NA>302014-11031-8045-559937.398631126.9643592019-09-013830000경기도 안양시

Duplicate rows

Most frequently occurring

관리기관명소재지도로명주소소재지지번주소설치목적구분카메라대수카메라화소수촬영방면정보보관일수설치연월관리기관전화번호위도경도데이터기준일자제공기관코드제공기관명# duplicates
112대구광역시 북구청 총무과대구광역시 북구 옥산로 65, 북구청 청사내외<NA>시설물관리1200.0<NA>30<NA>053-665-221435.885724128.5829112020-01-083450000대구광역시 북구7
37경기도 포천시<NA>경기도 포천시 신북면 가채리768-2시설물관리141.0<NA><NA><NA>031-538-391737.905272127.2064392019-09-195600000경기도 포천시6
10강원도 횡성군청<NA>강원도 횡성군 횡성읍 읍하리 58-1시설물관리141.0<NA>302004-01033-340-211037.491703127.9850472020-08-144260000강원도 횡성군5
3강원도 횡성군청<NA>강원도 횡성군 공근면 학담리 113어린이보호1200.0<NA>302019-10033-340-211037.534326127.9620212020-08-144260000강원도 횡성군4
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151서울특별시 구로구청서울특별시 구로구 가마산로 245서울특별시 구로구 구로동 435시설물관리1200.0<NA>302009-0902-860-343737.495511126.8882892020-10-233160000서울특별시 구로구4
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