Overview

Dataset statistics

Number of variables10
Number of observations1141
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory93.7 KiB
Average record size in memory84.1 B

Variable types

Numeric4
Categorical2
Text3
DateTime1

Dataset

Description서울특별시 양천구 안심이 CCTV 설치현황 자료입니다. 주소, CCTV 용도, 위도, 경도, CCTV 수량 등의 정보를 포함하고 있습니다.
Author공공데이터포털
URLhttps://www.data.go.kr/data/15084064/fileData.do

Alerts

기준일 has constant value ""Constant
연번 is highly overall correlated with 용도High correlation
위도 is highly overall correlated with 동별High correlation
경도 is highly overall correlated with 동별High correlation
용도 is highly overall correlated with 연번High correlation
동별 is highly overall correlated with 위도 and 1 other fieldsHigh correlation
연번 has unique valuesUnique
관리번호 has unique valuesUnique

Reproduction

Analysis started2024-04-17 13:28:33.637142
Analysis finished2024-04-17 13:28:35.643980
Duration2.01 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct1141
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean571
Minimum1
Maximum1141
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.2 KiB
2024-04-17T22:28:35.717555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile58
Q1286
median571
Q3856
95-th percentile1084
Maximum1141
Range1140
Interquartile range (IQR)570

Descriptive statistics

Standard deviation329.52263
Coefficient of variation (CV)0.57709743
Kurtosis-1.2
Mean571
Median Absolute Deviation (MAD)285
Skewness0
Sum651511
Variance108585.17
MonotonicityStrictly increasing
2024-04-17T22:28:35.867903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.1%
760 1
 
0.1%
766 1
 
0.1%
765 1
 
0.1%
764 1
 
0.1%
763 1
 
0.1%
762 1
 
0.1%
761 1
 
0.1%
759 1
 
0.1%
768 1
 
0.1%
Other values (1131) 1131
99.1%
ValueCountFrequency (%)
1 1
0.1%
2 1
0.1%
3 1
0.1%
4 1
0.1%
5 1
0.1%
6 1
0.1%
7 1
0.1%
8 1
0.1%
9 1
0.1%
10 1
0.1%
ValueCountFrequency (%)
1141 1
0.1%
1140 1
0.1%
1139 1
0.1%
1138 1
0.1%
1137 1
0.1%
1136 1
0.1%
1135 1
0.1%
1134 1
0.1%
1133 1
0.1%
1132 1
0.1%

용도
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size9.0 KiB
방범
761 
어린이보호
228 
공원
152 

Length

Max length5
Median length2
Mean length2.5994741
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
방범 761
66.7%
어린이보호 228
 
20.0%
공원 152
 
13.3%

Length

2024-04-17T22:28:35.998350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T22:28:36.093649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
방범 761
66.7%
어린이보호 228
 
20.0%
공원 152
 
13.3%

동별
Categorical

HIGH CORRELATION 

Distinct18
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size9.0 KiB
신정3동
131 
신정4동
122 
목2동
106 
신월1동
75 
목4동
72 
Other values (13)
635 

Length

Max length4
Median length4
Mean length3.704645
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row목1동
2nd row목1동
3rd row목1동
4th row목1동
5th row목5동

Common Values

ValueCountFrequency (%)
신정3동 131
 
11.5%
신정4동 122
 
10.7%
목2동 106
 
9.3%
신월1동 75
 
6.6%
목4동 72
 
6.3%
신월7동 66
 
5.8%
신월3동 64
 
5.6%
목3동 60
 
5.3%
신월2동 60
 
5.3%
신정7동 52
 
4.6%
Other values (8) 333
29.2%

Length

2024-04-17T22:28:36.186376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
신정3동 131
 
11.5%
신정4동 122
 
10.7%
목2동 106
 
9.3%
신월1동 75
 
6.6%
목4동 72
 
6.3%
신월7동 66
 
5.8%
신월3동 64
 
5.6%
목3동 60
 
5.3%
신월2동 60
 
5.3%
신정7동 52
 
4.6%
Other values (8) 333
29.2%

관리번호
Text

UNIQUE 

Distinct1141
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size9.0 KiB
2024-04-17T22:28:36.444310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length5
Mean length5.3356705
Min length4

Characters and Unicode

Total characters6088
Distinct characters19
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

Unique1141 ?
Unique (%)100.0%

Sample

1st row목001
2nd row목002
3rd row목003
4th row목004
5th row목005
ValueCountFrequency (%)
목001 1
 
0.1%
어목008 1
 
0.1%
어목005 1
 
0.1%
어목004 1
 
0.1%
어목003 1
 
0.1%
어목002 1
 
0.1%
어목001 1
 
0.1%
공원097-1 1
 
0.1%
어신정066-1 1
 
0.1%
어목002-1 1
 
0.1%
Other values (1131) 1131
99.1%
2024-04-17T22:28:36.862785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1224
20.1%
신 691
11.4%
1 509
 
8.4%
월 350
 
5.7%
정 341
 
5.6%
고 320
 
5.3%
목 298
 
4.9%
2 256
 
4.2%
3 246
 
4.0%
4 240
 
3.9%
Other values (9) 1613
26.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3484
57.2%
Other Letter 2543
41.8%
Dash Punctuation 61
 
1.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1224
35.1%
1 509
14.6%
2 256
 
7.3%
3 246
 
7.1%
4 240
 
6.9%
5 218
 
6.3%
6 207
 
5.9%
7 202
 
5.8%
8 197
 
5.7%
9 185
 
5.3%
Other Letter
ValueCountFrequency (%)
신 691
27.2%
월 350
13.8%
정 341
13.4%
고 320
12.6%
목 298
11.7%
어 239
 
9.4%
공 152
 
6.0%
원 152
 
6.0%
Dash Punctuation
ValueCountFrequency (%)
- 61
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3545
58.2%
Hangul 2543
41.8%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1224
34.5%
1 509
14.4%
2 256
 
7.2%
3 246
 
6.9%
4 240
 
6.8%
5 218
 
6.1%
6 207
 
5.8%
7 202
 
5.7%
8 197
 
5.6%
9 185
 
5.2%
Hangul
ValueCountFrequency (%)
신 691
27.2%
월 350
13.8%
정 341
13.4%
고 320
12.6%
목 298
11.7%
어 239
 
9.4%
공 152
 
6.0%
원 152
 
6.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3545
58.2%
Hangul 2543
41.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1224
34.5%
1 509
14.4%
2 256
 
7.2%
3 246
 
6.9%
4 240
 
6.8%
5 218
 
6.1%
6 207
 
5.8%
7 202
 
5.7%
8 197
 
5.6%
9 185
 
5.2%
Hangul
ValueCountFrequency (%)
신 691
27.2%
월 350
13.8%
정 341
13.4%
고 320
12.6%
목 298
11.7%
어 239
 
9.4%
공 152
 
6.0%
원 152
 
6.0%

소재지주소
Text

Distinct1039
Distinct (%)91.1%
Missing0
Missing (%)0.0%
Memory size9.0 KiB
2024-04-17T22:28:37.196783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length21
Mean length19.028046
Min length15

Characters and Unicode

Total characters21711
Distinct characters33
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

Unique969 ?
Unique (%)84.9%

Sample

1st row서울특별시 양천구 목동 917-9
2nd row서울특별시 양천구 목동 406-35
3rd row서울특별시 양천구 목동 809-1
4th row서울특별시 양천구 목동 933
5th row서울특별시 양천구 목동 902
ValueCountFrequency (%)
서울특별시 1141
25.0%
양천구 1141
25.0%
신정동 415
 
9.1%
신월동 379
 
8.3%
목동 335
 
7.3%
906 8
 
0.2%
320 7
 
0.2%
1321-11 5
 
0.1%
871-3 4
 
0.1%
1286 4
 
0.1%
Other values (1030) 1125
24.6%
2024-04-17T22:28:37.646755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3430
15.8%
1 1163
 
5.4%
서 1141
 
5.3%
특 1141
 
5.3%
별 1141
 
5.3%
시 1141
 
5.3%
양 1141
 
5.3%
천 1141
 
5.3%
구 1141
 
5.3%
울 1141
 
5.3%
Other values (23) 7990
36.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 12227
56.3%
Decimal Number 5062
23.3%
Space Separator 3430
 
15.8%
Dash Punctuation 992
 
4.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
서 1141
9.3%
특 1141
9.3%
별 1141
9.3%
시 1141
9.3%
양 1141
9.3%
천 1141
9.3%
구 1141
9.3%
울 1141
9.3%
동 1140
9.3%
신 805
6.6%
Other values (11) 1154
9.4%
Decimal Number
ValueCountFrequency (%)
1 1163
23.0%
2 643
12.7%
3 495
9.8%
9 492
9.7%
5 407
 
8.0%
0 406
 
8.0%
7 399
 
7.9%
4 384
 
7.6%
6 354
 
7.0%
8 319
 
6.3%
Space Separator
ValueCountFrequency (%)
3430
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 992
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 12227
56.3%
Common 9484
43.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
서 1141
9.3%
특 1141
9.3%
별 1141
9.3%
시 1141
9.3%
양 1141
9.3%
천 1141
9.3%
구 1141
9.3%
울 1141
9.3%
동 1140
9.3%
신 805
6.6%
Other values (11) 1154
9.4%
Common
ValueCountFrequency (%)
3430
36.2%
1 1163
 
12.3%
- 992
 
10.5%
2 643
 
6.8%
3 495
 
5.2%
9 492
 
5.2%
5 407
 
4.3%
0 406
 
4.3%
7 399
 
4.2%
4 384
 
4.0%
Other values (2) 673
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 12227
56.3%
ASCII 9484
43.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3430
36.2%
1 1163
 
12.3%
- 992
 
10.5%
2 643
 
6.8%
3 495
 
5.2%
9 492
 
5.2%
5 407
 
4.3%
0 406
 
4.3%
7 399
 
4.2%
4 384
 
4.0%
Other values (2) 673
 
7.1%
Hangul
ValueCountFrequency (%)
서 1141
9.3%
특 1141
9.3%
별 1141
9.3%
시 1141
9.3%
양 1141
9.3%
천 1141
9.3%
구 1141
9.3%
울 1141
9.3%
동 1140
9.3%
신 805
6.6%
Other values (11) 1154
9.4%

상세주소
Text

Distinct544
Distinct (%)47.7%
Missing0
Missing (%)0.0%
Memory size9.0 KiB
2024-04-17T22:28:37.930036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length31
Median length29
Mean length7.4460999
Min length1

Characters and Unicode

Total characters8496
Distinct characters386
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique505 ?
Unique (%)44.3%

Sample

1st row목동41타워 앞
2nd row이디아 커피숖 앞
3rd row목동타운빌딩 베스킨라빈스 앞
4th row삼익아파트 102동 목동세계로약국 앞
5th row아파트 2단지 201동 앞
ValueCountFrequency (%)
앞 340
 
14.4%
사거리 279
 
11.8%
삼거리 269
 
11.4%
건물 121
 
5.1%
인도 74
 
3.1%
옆 72
 
3.1%
도로 56
 
2.4%
입구 37
 
1.6%
안 35
 
1.5%
전봇대 29
 
1.2%
Other values (611) 1047
44.4%
2024-04-17T22:28:38.332541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1282
 
15.1%
리 598
 
7.0%
거 564
 
6.6%
앞 354
 
4.2%
사 317
 
3.7%
삼 278
 
3.3%
원 204
 
2.4%
공 191
 
2.2%
도 173
 
2.0%
로 137
 
1.6%
Other values (376) 4398
51.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 6808
80.1%
Space Separator 1282
 
15.1%
Decimal Number 301
 
3.5%
Close Punctuation 24
 
0.3%
Uppercase Letter 24
 
0.3%
Open Punctuation 23
 
0.3%
Other Punctuation 16
 
0.2%
Dash Punctuation 10
 
0.1%
Lowercase Letter 5
 
0.1%
Math Symbol 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 598
 
8.8%
거 564
 
8.3%
앞 354
 
5.2%
사 317
 
4.7%
삼 278
 
4.1%
원 204
 
3.0%
공 191
 
2.8%
도 173
 
2.5%
로 137
 
2.0%
건 136
 
2.0%
Other values (343) 3856
56.6%
Decimal Number
ValueCountFrequency (%)
1 82
27.2%
0 61
20.3%
2 45
15.0%
3 35
11.6%
4 22
 
7.3%
5 19
 
6.3%
9 11
 
3.7%
7 11
 
3.7%
6 11
 
3.7%
8 4
 
1.3%
Uppercase Letter
ValueCountFrequency (%)
S 6
25.0%
A 5
20.8%
I 2
 
8.3%
U 2
 
8.3%
G 2
 
8.3%
B 2
 
8.3%
C 2
 
8.3%
L 1
 
4.2%
O 1
 
4.2%
K 1
 
4.2%
Lowercase Letter
ValueCountFrequency (%)
k 1
20.0%
s 1
20.0%
u 1
20.0%
c 1
20.0%
m 1
20.0%
Other Punctuation
ValueCountFrequency (%)
, 15
93.8%
. 1
 
6.2%
Space Separator
ValueCountFrequency (%)
1282
100.0%
Close Punctuation
ValueCountFrequency (%)
) 24
100.0%
Open Punctuation
ValueCountFrequency (%)
( 23
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 10
100.0%
Math Symbol
ValueCountFrequency (%)
~ 2
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 6808
80.1%
Common 1659
 
19.5%
Latin 29
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 598
 
8.8%
거 564
 
8.3%
앞 354
 
5.2%
사 317
 
4.7%
삼 278
 
4.1%
원 204
 
3.0%
공 191
 
2.8%
도 173
 
2.5%
로 137
 
2.0%
건 136
 
2.0%
Other values (343) 3856
56.6%
Common
ValueCountFrequency (%)
1282
77.3%
1 82
 
4.9%
0 61
 
3.7%
2 45
 
2.7%
3 35
 
2.1%
) 24
 
1.4%
( 23
 
1.4%
4 22
 
1.3%
5 19
 
1.1%
, 15
 
0.9%
Other values (8) 51
 
3.1%
Latin
ValueCountFrequency (%)
S 6
20.7%
A 5
17.2%
I 2
 
6.9%
U 2
 
6.9%
G 2
 
6.9%
B 2
 
6.9%
C 2
 
6.9%
L 1
 
3.4%
k 1
 
3.4%
s 1
 
3.4%
Other values (5) 5
17.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 6808
80.1%
ASCII 1688
 
19.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1282
75.9%
1 82
 
4.9%
0 61
 
3.6%
2 45
 
2.7%
3 35
 
2.1%
) 24
 
1.4%
( 23
 
1.4%
4 22
 
1.3%
5 19
 
1.1%
, 15
 
0.9%
Other values (23) 80
 
4.7%
Hangul
ValueCountFrequency (%)
리 598
 
8.8%
거 564
 
8.3%
앞 354
 
5.2%
사 317
 
4.7%
삼 278
 
4.1%
원 204
 
3.0%
공 191
 
2.8%
도 173
 
2.5%
로 137
 
2.0%
건 136
 
2.0%
Other values (343) 3856
56.6%

카메라수
Real number (ℝ)

Distinct6
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2217353
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.2 KiB
2024-04-17T22:28:38.441791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median3
Q34
95-th percentile5
Maximum6
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.0917959
Coefficient of variation (CV)0.33888443
Kurtosis-0.51528077
Mean3.2217353
Median Absolute Deviation (MAD)1
Skewness-0.20484778
Sum3676
Variance1.1920184
MonotonicityNot monotonic
2024-04-17T22:28:38.529540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3 400
35.1%
4 328
28.7%
2 190
16.7%
5 139
 
12.2%
1 83
 
7.3%
6 1
 
0.1%
ValueCountFrequency (%)
1 83
 
7.3%
2 190
16.7%
3 400
35.1%
4 328
28.7%
5 139
 
12.2%
6 1
 
0.1%
ValueCountFrequency (%)
6 1
 
0.1%
5 139
 
12.2%
4 328
28.7%
3 400
35.1%
2 190
16.7%
1 83
 
7.3%

위도
Real number (ℝ)

HIGH CORRELATION 

Distinct1107
Distinct (%)97.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.527206
Minimum37.5018
Maximum37.550829
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.2 KiB
2024-04-17T22:28:38.638890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.5018
5-th percentile37.510598
Q137.519714
median37.525486
Q337.535834
95-th percentile37.545173
Maximum37.550829
Range0.049029
Interquartile range (IQR)0.01612

Descriptive statistics

Standard deviation0.010660715
Coefficient of variation (CV)0.00028407964
Kurtosis-0.87881738
Mean37.527206
Median Absolute Deviation (MAD)0.008016
Skewness0.15448845
Sum42818.542
Variance0.00011365085
MonotonicityNot monotonic
2024-04-17T22:28:38.776433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.526253 4
 
0.4%
37.532108 2
 
0.2%
37.52399 2
 
0.2%
37.54402975 2
 
0.2%
37.5322491 2
 
0.2%
37.52586 2
 
0.2%
37.537712 2
 
0.2%
37.51116 2
 
0.2%
37.53166 2
 
0.2%
37.52304 2
 
0.2%
Other values (1097) 1119
98.1%
ValueCountFrequency (%)
37.5018 1
0.1%
37.5048976 1
0.1%
37.505455 1
0.1%
37.5055 1
0.1%
37.505562 1
0.1%
37.5058721 1
0.1%
37.505885 1
0.1%
37.50594 1
0.1%
37.506587 1
0.1%
37.506722 1
0.1%
ValueCountFrequency (%)
37.550829 1
0.1%
37.5504274 1
0.1%
37.550008 1
0.1%
37.549662 1
0.1%
37.5493052 1
0.1%
37.549211 1
0.1%
37.548807 1
0.1%
37.5485319 1
0.1%
37.548492 1
0.1%
37.54804 1
0.1%

경도
Real number (ℝ)

HIGH CORRELATION 

Distinct1107
Distinct (%)97.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.85402
Minimum126.82303
Maximum126.888
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.2 KiB
2024-04-17T22:28:38.899533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.82303
5-th percentile126.8284
Q1126.83817
median126.85527
Q3126.86834
95-th percentile126.87726
Maximum126.888
Range0.0649737
Interquartile range (IQR)0.03017

Descriptive statistics

Standard deviation0.016685472
Coefficient of variation (CV)0.00013153286
Kurtosis-1.2815929
Mean126.85402
Median Absolute Deviation (MAD)0.014695
Skewness-0.11511423
Sum144740.43
Variance0.00027840497
MonotonicityNot monotonic
2024-04-17T22:28:39.030134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.840702 4
 
0.4%
126.853862 2
 
0.2%
126.868731 2
 
0.2%
126.873519 2
 
0.2%
126.88236 2
 
0.2%
126.87419 2
 
0.2%
126.862005 2
 
0.2%
126.87619 2
 
0.2%
126.868446 2
 
0.2%
126.87974 2
 
0.2%
Other values (1097) 1119
98.1%
ValueCountFrequency (%)
126.82303 1
0.1%
126.823878 1
0.1%
126.824084 1
0.1%
126.82413 1
0.1%
126.824303 1
0.1%
126.824464 1
0.1%
126.824478 1
0.1%
126.824656 2
0.2%
126.824703 1
0.1%
126.824755 1
0.1%
ValueCountFrequency (%)
126.8880037 1
0.1%
126.88697 1
0.1%
126.886469 1
0.1%
126.88623 1
0.1%
126.885742 1
0.1%
126.8851549 1
0.1%
126.8847513 1
0.1%
126.8843423 1
0.1%
126.8842203 1
0.1%
126.884017 1
0.1%

기준일
Date

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size9.0 KiB
Minimum2023-08-10 00:00:00
Maximum2023-08-10 00:00:00
2024-04-17T22:28:39.128801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T22:28:39.205384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2024-04-17T22:28:35.082020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T22:28:34.131546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T22:28:34.427231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T22:28:34.740953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T22:28:35.173514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T22:28:34.202326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T22:28:34.502950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T22:28:34.822892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T22:28:35.256205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T22:28:34.272747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T22:28:34.578053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T22:28:34.901307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T22:28:35.341134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T22:28:34.348171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T22:28:34.657047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T22:28:34.981792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-17T22:28:39.269423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번용도동별카메라수위도경도
연번1.0000.9200.7280.3260.6270.700
용도0.9201.0000.3790.5450.2860.119
동별0.7280.3791.0000.2160.8700.895
카메라수0.3260.5450.2161.0000.1610.000
위도0.6270.2860.8700.1611.0000.648
경도0.7000.1190.8950.0000.6481.000
2024-04-17T22:28:39.356337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
용도동별
용도1.0000.187
동별0.1871.000
2024-04-17T22:28:39.427542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번카메라수위도경도용도동별
연번1.000-0.139-0.465-0.1400.8850.382
카메라수-0.1391.0000.0870.0070.2660.087
위도-0.4650.0871.0000.1920.1780.571
경도-0.1400.0070.1921.0000.0710.623
용도0.8850.2660.1780.0711.0000.187
동별0.3820.0870.5710.6230.1871.000

Missing values

2024-04-17T22:28:35.460071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-17T22:28:35.595040image/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

연번용도동별관리번호소재지주소상세주소카메라수위도경도기준일
01방범목1동목001서울특별시 양천구 목동 917-9목동41타워 앞537.527618126.8765282023-08-10
12방범목1동목002서울특별시 양천구 목동 406-35이디아 커피숖 앞437.52503126.873212023-08-10
23방범목1동목003서울특별시 양천구 목동 809-1목동타운빌딩 베스킨라빈스 앞537.52756126.864222023-08-10
34방범목1동목004서울특별시 양천구 목동 933삼익아파트 102동 목동세계로약국 앞537.522262126.8772252023-08-10
45방범목5동목005서울특별시 양천구 목동 902아파트 2단지 201동 앞437.536178126.8775812023-08-10
56방범목5동목006서울특별시 양천구 목동 909-9목동능력교회 앞537.536514126.8832112023-08-10
67방범목5동목007서울특별시 양천구 목동 199-66월촌초등학교 앞 교차로437.539722126.8754832023-08-10
78방범목2동목008서울특별시 양천구 목동 231-98GS25 편의점 앞537.541094126.8713282023-08-10
89방범목2동목009서울특별시 양천구 목동 536스마일랄인마트 앞437.544742126.8743152023-08-10
910방범목2동목009-1서울특별시 양천구 목동 526-23삼거리137.545303126.873812023-08-10
연번용도동별관리번호소재지주소상세주소카메라수위도경도기준일
11311132공원목2동공원138서울특별시 양천구 목동 202-16용왕산공원, 건영배드민턴장 앞237.544155126.8773462023-08-10
11321133공원신정3동공원139서울특별시 양천구 신정동 521-11계남공원, 다락골 놀이터237.515471126.8508262023-08-10
11331134공원신정7동공원140서울특별시 양천구 신정동 163-55갈산공원, 분수광장237.508191126.8693152023-08-10
11341135공원신정7동공원141서울특별시 양천구 신정동 177-8갈산공원, 향림사앞237.505455126.8669662023-08-10
11351136공원신정3동공원142서울특별시 양천구 신정동 814-3온수공원, 매봉약수터237.506722126.8393322023-08-10
11361137공원신월2동공원143서울특별시 양천구 신월동 894장수공원, 해병대컨테이너 앞237.522702126.8495862023-08-10
11371138공원신월2동공원143-1서울특별시 양천구 신월동 894장수공원, 공중화장실 앞137.522549126.8497692023-08-10
11381139공원신정2동공원144서울특별시 양천구 신정동 871-3신정교 하부 주차장437.516927126.8779332023-08-10
11391140공원신정2동공원145서울특별시 양천구 신정동 871-3신정교 하부 주차장437.51682126.8788492023-08-10
11401141공원신정2동공원146서울특별시 양천구 신정동 871-3해마루축구장 근처 공중화장실 옆437.518474126.8776132023-08-10