Overview

Dataset statistics

Number of variables13
Number of observations566
Missing cells1698
Missing cells (%)23.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory60.9 KiB
Average record size in memory110.2 B

Variable types

Categorical6
Text2
Numeric3
Unsupported2

Dataset

Description파일 다운로드
Author동작구
URLhttps://data.seoul.go.kr/dataList/OA-13272/F/1/datasetView.do

Alerts

관리기관명 has constant value ""Constant
보관일수 has constant value ""Constant
관리기관전화번호 has constant value ""Constant
데이터기준일자 has constant value ""Constant
카메라대수 is highly overall correlated with 촬영방면정보High correlation
설치목적구분 is highly overall correlated with 촬영방면정보High correlation
촬영방면정보 is highly overall correlated with 카메라대수 and 1 other fieldsHigh correlation
촬영방면정보 is highly imbalanced (95.2%)Imbalance
소재지도로명주소 has 107 (18.9%) missing valuesMissing
소재지지번주소 has 459 (81.1%) missing valuesMissing
카메라화소수 has 566 (100.0%) missing valuesMissing
설치년월 has 566 (100.0%) missing valuesMissing
카메라화소수 is an unsupported type, check if it needs cleaning or further analysisUnsupported
설치년월 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-12-11 05:03:30.641171
Analysis finished2023-12-11 05:03:34.272888
Duration3.63 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

관리기관명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.6 KiB
서울특별시 동작구청
566 

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 566
100.0%

Length

2023-12-11T14:03:34.383604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T14:03:34.550185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서울특별시 566
50.0%
동작구청 566
50.0%

소재지도로명주소
Text

MISSING 

Distinct459
Distinct (%)100.0%
Missing107
Missing (%)18.9%
Memory size4.6 KiB
2023-12-11T14:03:34.956904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length50
Median length44
Mean length33.520697
Min length17

Characters and Unicode

Total characters15386
Distinct characters245
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique459 ?
Unique (%)100.0%

Sample

1st row서울특별시 동작구 장승배기로23길 10
2nd row서울특별시 동작구 노량진로8길 40
3rd row서울특별시 동작구 장승배기로18길 27
4th row서울특별시 동작구 만양로14다길 7
5th row서울특별시 동작구 장승배기로22길 44
ValueCountFrequency (%)
서울특별시 459
 
17.7%
동작구 459
 
17.7%
앞 31
 
1.2%
상도로 14
 
0.5%
상도4동 12
 
0.5%
노량진1동 9
 
0.3%
대방동 8
 
0.3%
만양로 8
 
0.3%
장승배기로 8
 
0.3%
성대로 8
 
0.3%
Other values (1250) 1577
60.8%
2023-12-11T14:03:35.685260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2155
 
14.0%
동 935
 
6.1%
1 858
 
5.6%
2 698
 
4.5%
작 506
 
3.3%
서 483
 
3.1%
구 468
 
3.0%
시 465
 
3.0%
울 461
 
3.0%
별 460
 
3.0%
Other values (235) 7897
51.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 8120
52.8%
Decimal Number 3861
25.1%
Space Separator 2155
 
14.0%
Open Punctuation 416
 
2.7%
Close Punctuation 415
 
2.7%
Dash Punctuation 415
 
2.7%
Uppercase Letter 3
 
< 0.1%
Other Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 935
 
11.5%
작 506
 
6.2%
서 483
 
5.9%
구 468
 
5.8%
시 465
 
5.7%
울 461
 
5.7%
별 460
 
5.7%
특 459
 
5.7%
길 412
 
5.1%
로 400
 
4.9%
Other values (217) 3071
37.8%
Decimal Number
ValueCountFrequency (%)
1 858
22.2%
2 698
18.1%
3 418
10.8%
4 410
10.6%
6 286
 
7.4%
0 274
 
7.1%
5 265
 
6.9%
7 220
 
5.7%
8 217
 
5.6%
9 215
 
5.6%
Uppercase Letter
ValueCountFrequency (%)
A 1
33.3%
P 1
33.3%
T 1
33.3%
Space Separator
ValueCountFrequency (%)
2155
100.0%
Open Punctuation
ValueCountFrequency (%)
( 416
100.0%
Close Punctuation
ValueCountFrequency (%)
) 415
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 415
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 8120
52.8%
Common 7263
47.2%
Latin 3
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 935
 
11.5%
작 506
 
6.2%
서 483
 
5.9%
구 468
 
5.8%
시 465
 
5.7%
울 461
 
5.7%
별 460
 
5.7%
특 459
 
5.7%
길 412
 
5.1%
로 400
 
4.9%
Other values (217) 3071
37.8%
Common
ValueCountFrequency (%)
2155
29.7%
1 858
 
11.8%
2 698
 
9.6%
3 418
 
5.8%
( 416
 
5.7%
) 415
 
5.7%
- 415
 
5.7%
4 410
 
5.6%
6 286
 
3.9%
0 274
 
3.8%
Other values (5) 918
12.6%
Latin
ValueCountFrequency (%)
A 1
33.3%
P 1
33.3%
T 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 8120
52.8%
ASCII 7266
47.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2155
29.7%
1 858
 
11.8%
2 698
 
9.6%
3 418
 
5.8%
( 416
 
5.7%
) 415
 
5.7%
- 415
 
5.7%
4 410
 
5.6%
6 286
 
3.9%
0 274
 
3.8%
Other values (8) 921
12.7%
Hangul
ValueCountFrequency (%)
동 935
 
11.5%
작 506
 
6.2%
서 483
 
5.9%
구 468
 
5.8%
시 465
 
5.7%
울 461
 
5.7%
별 460
 
5.7%
특 459
 
5.7%
길 412
 
5.1%
로 400
 
4.9%
Other values (217) 3071
37.8%

소재지지번주소
Text

MISSING 

Distinct106
Distinct (%)99.1%
Missing459
Missing (%)81.1%
Memory size4.6 KiB
2023-12-11T14:03:36.130243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length49
Median length39
Mean length30.28972
Min length18

Characters and Unicode

Total characters3241
Distinct characters188
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique105 ?
Unique (%)98.1%

Sample

1st row서울특별시 동작구 대방동 391-269
2nd row서울특별시 동작구 신대방1동 631-38 문창초등학교 옆
3rd row서울특별시 동작구 신대방1동 459-5 문창초등학교 옆
4th row서울특별시 동작구 상도4동 279-481 주변(산65-189) 재개발구역
5th row서울특별시 동작구 상도4동 279-477 주변(산65) 재개발구역
ValueCountFrequency (%)
동작구 114
20.8%
서울특별시 107
19.5%
대방동 15
 
2.7%
상도4동 11
 
2.0%
흑석동 10
 
1.8%
상도1동 10
 
1.8%
신대방1동 9
 
1.6%
노량진1동 8
 
1.5%
앞 8
 
1.5%
상도3동 8
 
1.5%
Other values (206) 249
45.4%
2023-12-11T14:03:36.844990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
446
 
13.8%
동 234
 
7.2%
1 126
 
3.9%
구 119
 
3.7%
작 117
 
3.6%
별 110
 
3.4%
서 108
 
3.3%
울 108
 
3.3%
특 107
 
3.3%
시 107
 
3.3%
Other values (178) 1659
51.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1997
61.6%
Decimal Number 590
 
18.2%
Space Separator 446
 
13.8%
Dash Punctuation 97
 
3.0%
Close Punctuation 55
 
1.7%
Open Punctuation 53
 
1.6%
Other Punctuation 3
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 234
 
11.7%
구 119
 
6.0%
작 117
 
5.9%
별 110
 
5.5%
서 108
 
5.4%
울 108
 
5.4%
특 107
 
5.4%
시 107
 
5.4%
원 59
 
3.0%
도 51
 
2.6%
Other values (163) 877
43.9%
Decimal Number
ValueCountFrequency (%)
1 126
21.4%
2 97
16.4%
3 85
14.4%
4 65
11.0%
5 49
 
8.3%
6 47
 
8.0%
0 41
 
6.9%
9 31
 
5.3%
7 25
 
4.2%
8 24
 
4.1%
Space Separator
ValueCountFrequency (%)
446
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 97
100.0%
Close Punctuation
ValueCountFrequency (%)
) 55
100.0%
Open Punctuation
ValueCountFrequency (%)
( 53
100.0%
Other Punctuation
ValueCountFrequency (%)
, 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1997
61.6%
Common 1244
38.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 234
 
11.7%
구 119
 
6.0%
작 117
 
5.9%
별 110
 
5.5%
서 108
 
5.4%
울 108
 
5.4%
특 107
 
5.4%
시 107
 
5.4%
원 59
 
3.0%
도 51
 
2.6%
Other values (163) 877
43.9%
Common
ValueCountFrequency (%)
446
35.9%
1 126
 
10.1%
- 97
 
7.8%
2 97
 
7.8%
3 85
 
6.8%
4 65
 
5.2%
) 55
 
4.4%
( 53
 
4.3%
5 49
 
3.9%
6 47
 
3.8%
Other values (5) 124
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1997
61.6%
ASCII 1244
38.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
446
35.9%
1 126
 
10.1%
- 97
 
7.8%
2 97
 
7.8%
3 85
 
6.8%
4 65
 
5.2%
) 55
 
4.4%
( 53
 
4.3%
5 49
 
3.9%
6 47
 
3.8%
Other values (5) 124
 
10.0%
Hangul
ValueCountFrequency (%)
동 234
 
11.7%
구 119
 
6.0%
작 117
 
5.9%
별 110
 
5.5%
서 108
 
5.4%
울 108
 
5.4%
특 107
 
5.4%
시 107
 
5.4%
원 59
 
3.0%
도 51
 
2.6%
Other values (163) 877
43.9%

설치목적구분
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size4.6 KiB
생활방범
366 
어린이보호
89 
교통단속
83 
쓰레기단속
 
18
시설물관리
 
10

Length

Max length5
Median length4
Mean length4.2067138
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
생활방범 366
64.7%
어린이보호 89
 
15.7%
교통단속 83
 
14.7%
쓰레기단속 18
 
3.2%
시설물관리 10
 
1.8%

Length

2023-12-11T14:03:37.092140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T14:03:37.262260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
생활방범 366
64.7%
어린이보호 89
 
15.7%
교통단속 83
 
14.7%
쓰레기단속 18
 
3.2%
시설물관리 10
 
1.8%

카메라대수
Real number (ℝ)

HIGH CORRELATION 

Distinct9
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2738516
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.1 KiB
2023-12-11T14:03:37.438374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median3
Q33
95-th percentile4
Maximum9
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.2943396
Coefficient of variation (CV)0.56922784
Kurtosis0.77954292
Mean2.2738516
Median Absolute Deviation (MAD)1
Skewness0.69438775
Sum1287
Variance1.6753151
MonotonicityNot monotonic
2023-12-11T14:03:37.637307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1 253
44.7%
3 200
35.3%
4 76
 
13.4%
2 22
 
3.9%
5 10
 
1.8%
6 2
 
0.4%
7 1
 
0.2%
8 1
 
0.2%
9 1
 
0.2%
ValueCountFrequency (%)
1 253
44.7%
2 22
 
3.9%
3 200
35.3%
4 76
 
13.4%
5 10
 
1.8%
6 2
 
0.4%
7 1
 
0.2%
8 1
 
0.2%
9 1
 
0.2%
ValueCountFrequency (%)
9 1
 
0.2%
8 1
 
0.2%
7 1
 
0.2%
6 2
 
0.4%
5 10
 
1.8%
4 76
 
13.4%
3 200
35.3%
2 22
 
3.9%
1 253
44.7%

카메라화소수
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing566
Missing (%)100.0%
Memory size5.1 KiB

촬영방면정보
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.6 KiB
360도전방면
563 
90도전방면
 
3

Length

Max length9
Median length9
Mean length8.9946996
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row 360도전방면
2nd row 360도전방면
3rd row 360도전방면
4th row 360도전방면
5th row 360도전방면

Common Values

ValueCountFrequency (%)
360도전방면 563
99.5%
90도전방면 3
 
0.5%

Length

2023-12-11T14:03:37.846489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T14:03:37.999820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
360도전방면 563
99.5%
90도전방면 3
 
0.5%

보관일수
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.6 KiB
30
566 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row30
2nd row30
3rd row30
4th row30
5th row30

Common Values

ValueCountFrequency (%)
30 566
100.0%

Length

2023-12-11T14:03:38.168353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T14:03:38.309702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
30 566
100.0%

설치년월
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing566
Missing (%)100.0%
Memory size5.1 KiB

관리기관전화번호
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.6 KiB
02-820-1118
566 

Length

Max length13
Median length13
Mean length13
Min length13

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row 02-820-1118
2nd row 02-820-1118
3rd row 02-820-1118
4th row 02-820-1118
5th row 02-820-1118

Common Values

ValueCountFrequency (%)
02-820-1118 566
100.0%

Length

2023-12-11T14:03:38.467422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T14:03:38.648899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
02-820-1118 566
100.0%

위도
Real number (ℝ)

Distinct560
Distinct (%)98.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.496964
Minimum37.476405
Maximum37.51617
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.1 KiB
2023-12-11T14:03:38.870363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.476405
5-th percentile37.479985
Q137.488705
median37.497577
Q337.50486
95-th percentile37.511499
Maximum37.51617
Range0.039765
Interquartile range (IQR)0.016155

Descriptive statistics

Standard deviation0.0099698943
Coefficient of variation (CV)0.00026588537
Kurtosis-0.94530429
Mean37.496964
Median Absolute Deviation (MAD)0.0079965
Skewness-0.2163663
Sum21223.282
Variance9.9398793 × 10-5
MonotonicityNot monotonic
2023-12-11T14:03:39.159647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.497724 2
 
0.4%
37.488631 2
 
0.4%
37.507985 2
 
0.4%
37.505574 2
 
0.4%
37.498074 2
 
0.4%
37.497235 2
 
0.4%
37.510141 1
 
0.2%
37.499834 1
 
0.2%
37.499773 1
 
0.2%
37.506059 1
 
0.2%
Other values (550) 550
97.2%
ValueCountFrequency (%)
37.476405 1
0.2%
37.476951 1
0.2%
37.47706 1
0.2%
37.47716 1
0.2%
37.47717 1
0.2%
37.477318 1
0.2%
37.47732 1
0.2%
37.477351 1
0.2%
37.477396 1
0.2%
37.477563 1
0.2%
ValueCountFrequency (%)
37.51617 1
0.2%
37.51531 1
0.2%
37.515285 1
0.2%
37.515045 1
0.2%
37.514706 1
0.2%
37.514322 1
0.2%
37.513961 1
0.2%
37.513898 1
0.2%
37.513546 1
0.2%
37.51348 1
0.2%

경도
Real number (ℝ)

Distinct562
Distinct (%)99.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.94902
Minimum126.90498
Maximum127.06116
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.1 KiB
2023-12-11T14:03:39.461899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.90498
5-th percentile126.9129
Q1126.93308
median126.9466
Q3126.96832
95-th percentile126.98002
Maximum127.06116
Range0.156179
Interquartile range (IQR)0.0352385

Descriptive statistics

Standard deviation0.021094528
Coefficient of variation (CV)0.00016616535
Kurtosis0.21524412
Mean126.94902
Median Absolute Deviation (MAD)0.0164705
Skewness0.16941474
Sum71853.145
Variance0.00044497912
MonotonicityNot monotonic
2023-12-11T14:03:39.808740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.966237 2
 
0.4%
126.938819 2
 
0.4%
126.936413 2
 
0.4%
126.950306 2
 
0.4%
126.978436 1
 
0.2%
126.95266 1
 
0.2%
126.944076 1
 
0.2%
126.936805 1
 
0.2%
126.92676 1
 
0.2%
126.915991 1
 
0.2%
Other values (552) 552
97.5%
ValueCountFrequency (%)
126.904984 1
0.2%
126.905984 1
0.2%
126.906306 1
0.2%
126.906977 1
0.2%
126.907536 1
0.2%
126.907773 1
0.2%
126.907861 1
0.2%
126.908275 1
0.2%
126.908593 1
0.2%
126.90881 1
0.2%
ValueCountFrequency (%)
127.061163 1
0.2%
126.982458 1
0.2%
126.981921 1
0.2%
126.981918 1
0.2%
126.981689 1
0.2%
126.981628 1
0.2%
126.981507 1
0.2%
126.981353 1
0.2%
126.981307 1
0.2%
126.981138 1
0.2%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.6 KiB
2016-05-31
566 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2016-05-31
2nd row2016-05-31
3rd row2016-05-31
4th row2016-05-31
5th row2016-05-31

Common Values

ValueCountFrequency (%)
2016-05-31 566
100.0%

Length

2023-12-11T14:03:40.161324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T14:03:40.311807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2016-05-31 566
100.0%

Interactions

2023-12-11T14:03:32.599026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:03:31.403356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:03:32.002146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:03:32.802183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:03:31.595571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:03:32.199935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:03:32.984113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:03:31.806192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:03:32.407782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T14:03:40.429873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
설치목적구분카메라대수촬영방면정보위도경도
설치목적구분1.0000.6250.4440.5100.187
카메라대수0.6251.0000.7920.1950.000
촬영방면정보0.4440.7921.0000.0860.051
위도0.5100.1950.0861.0000.684
경도0.1870.0000.0510.6841.000
2023-12-11T14:03:40.577986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
촬영방면정보설치목적구분
촬영방면정보1.0000.538
설치목적구분0.5381.000
2023-12-11T14:03:40.716480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
카메라대수위도경도설치목적구분촬영방면정보
카메라대수1.0000.122-0.0280.4220.808
위도0.1221.000-0.3610.2350.065
경도-0.028-0.3611.0000.1270.036
설치목적구분0.4220.2350.1271.0000.538
촬영방면정보0.8080.0650.0360.5381.000

Missing values

2023-12-11T14:03:33.670908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T14:03:33.972522image/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.
2023-12-11T14:03:34.177947image/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

관리기관명소재지도로명주소소재지지번주소설치목적구분카메라대수카메라화소수촬영방면정보보관일수설치년월관리기관전화번호위도경도데이터기준일자
0서울특별시 동작구청서울특별시 동작구 장승배기로23길 10<NA>생활방범3<NA>360도전방면30<NA>02-820-111837.510141126.9394522016-05-31
1서울특별시 동작구청서울특별시 동작구 노량진로8길 40<NA>생활방범1<NA>360도전방면30<NA>02-820-111837.512401126.9375882016-05-31
2서울특별시 동작구청서울특별시 동작구 장승배기로18길 27<NA>생활방범1<NA>360도전방면30<NA>02-820-111837.507402126.9415992016-05-31
3서울특별시 동작구청서울특별시 동작구 만양로14다길 7<NA>생활방범1<NA>360도전방면30<NA>02-820-111837.511299126.945312016-05-31
4서울특별시 동작구청서울특별시 동작구 장승배기로22길 44<NA>생활방범1<NA>360도전방면30<NA>02-820-111837.508595126.9415612016-05-31
5서울특별시 동작구청서울특별시 동작구 장승배기로19다길 24<NA>생활방범1<NA>360도전방면30<NA>02-820-111837.509271126.9375722016-05-31
6서울특별시 동작구청서울특별시 동작구 장승배기로24가길 39<NA>생활방범1<NA>360도전방면30<NA>02-820-111837.511253126.9420722016-05-31
7서울특별시 동작구청서울특별시 동작구 노량진로26길 65<NA>생활방범1<NA>360도전방면30<NA>02-820-111837.5097126.9555222016-05-31
8서울특별시 동작구청서울특별시 동작구 노량진로18길 20<NA>생활방범3<NA>360도전방면30<NA>02-820-111837.511993126.950972016-05-31
9서울특별시 동작구청서울특별시 동작구 상도로60길 35<NA>생활방범3<NA>360도전방면30<NA>02-820-111837.492257126.9545962016-05-31
관리기관명소재지도로명주소소재지지번주소설치목적구분카메라대수카메라화소수촬영방면정보보관일수설치년월관리기관전화번호위도경도데이터기준일자
556서울특별시 동작구청서울특별시 동작구 상도로 31길 19 (상도2동 527)<NA>교통단속1<NA>360도전방면30<NA>02-820-111837.50524126.9439912016-05-31
557서울특별시 동작구청서울특별시 동작구 장승배기로 4길 9 (상도2동 521)<NA>교통단속1<NA>360도전방면30<NA>02-820-111837.501251126.9412422016-05-31
558서울특별시 동작구청서울특별시 동작구 상도로 50길 33 (상도1동 470-8)<NA>교통단속1<NA>360도전방면30<NA>02-820-111837.497235126.9503062016-05-31
559서울특별시 동작구청서울특별시 동작구 사당로 300 (사당1동 147-29)<NA>교통단속1<NA>360도전방면30<NA>02-820-111837.484234126.9809542016-05-31
560서울특별시 동작구청서울특별시 동작구 여의대방로 54길 12 (대방동 344-10)<NA>교통단속1<NA>360도전방면30<NA>02-820-111837.512694126.9252772016-05-31
561서울특별시 동작구청서울특별시 동작구 노량진수산시장(노량진2동 13-6)<NA>교통단속1<NA>360도전방면30<NA>02-820-111837.514706126.9359082016-05-31
562서울특별시 동작구청서울특별시 동작구 노량진수산시장(노량진2동 13-6)<NA>교통단속1<NA>360도전방면30<NA>02-820-111837.51531126.9385152016-05-31
563서울특별시 동작구청서울특별시 동작구 상도로15길 120(상도2동 365-7호)<NA>교통단속1<NA>360도전방면30<NA>02-820-111837.504629126.9374072016-05-31
564서울특별시 동작구청서울특별시 동작구 사당로16길 35(사당4동 316-4) 농협앞<NA>교통단속1<NA>360도전방면30<NA>02-820-111837.481815126.9726082016-05-31
565서울특별시 동작구청서울특별시 동작구 여의대방로22길 77(신대방2동 361-2)<NA>교통단속1<NA>360도전방면30<NA>02-820-111837.497801126.9240362016-05-31