Overview

Dataset statistics

Number of variables13
Number of observations985
Missing cells0
Missing cells (%)0.0%
Duplicate rows2
Duplicate rows (%)0.2%
Total size in memory105.0 KiB
Average record size in memory109.1 B

Variable types

Categorical8
Text2
Numeric3

Dataset

Description대구광역시_동구_CCTV_20180821
Author대구광역시 동구
URLhttp://data.daegu.go.kr/open/data/dataView.do?dataSetId=15013094&dataSetDetailId=150130941bd3ed199096b&provdMethod=FILE

Alerts

관리기관명 has constant value ""Constant
관리기관전화번호 has constant value ""Constant
데이터기준일자 has constant value ""Constant
Dataset has 2 (0.2%) duplicate rowsDuplicates
카메라대수 is highly overall correlated with 설치년월High correlation
설치목적구분 is highly overall correlated with 설치년월High correlation
카메라화소수 is highly overall correlated with 촬영방면정보 and 1 other fieldsHigh correlation
촬영방면정보 is highly overall correlated with 카메라화소수 and 1 other fieldsHigh correlation
설치년월 is highly overall correlated with 카메라대수 and 3 other fieldsHigh correlation
보관일수 is highly imbalanced (98.5%)Imbalance

Reproduction

Analysis started2023-09-29 01:09:44.839081
Analysis finished2023-09-29 01:09:57.705886
Duration12.87 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

관리기관명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size7.8 KiB
대구광역시 동구청
985 

Length

Max length9
Median length9
Mean length9
Min length9

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row대구광역시 동구청
2nd row대구광역시 동구청
3rd row대구광역시 동구청
4th row대구광역시 동구청
5th row대구광역시 동구청

Common Values

ValueCountFrequency (%)
대구광역시 동구청 985
100.0%

Length

2023-09-29T01:09:58.117604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-29T01:09:58.675650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
대구광역시 985
50.0%
동구청 985
50.0%

소재지도로명주소
Text

Distinct967
Distinct (%)98.2%
Missing0
Missing (%)0.0%
Memory size7.8 KiB
2023-09-29T01:09:59.764581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length22
Mean length17.770558
Min length13

Characters and Unicode

Total characters17504
Distinct characters118
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique949 ?
Unique (%)96.3%

Sample

1st row대구광역시 동구 금강로 235
2nd row대구광역시 동구 동부로 2
3rd row대구광역시 동구 팔공로 511
4th row대구광역시 동구 덕곡동56
5th row대구광역시 동구 신무동 634-3
ValueCountFrequency (%)
동구 989
24.9%
대구광역시 985
24.8%
팔공로 32
 
0.8%
각산동 32
 
0.8%
아양로 28
 
0.7%
a 20
 
0.5%
산 16
 
0.4%
26 14
 
0.4%
신암남로 14
 
0.4%
20 14
 
0.4%
Other values (911) 1823
46.0%
2023-09-29T01:10:02.535785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2984
17.0%
구 1979
 
11.3%
동 1367
 
7.8%
대 1003
 
5.7%
광 989
 
5.7%
역 987
 
5.6%
시 985
 
5.6%
로 793
 
4.5%
1 764
 
4.4%
길 587
 
3.4%
Other values (108) 5066
28.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 10662
60.9%
Decimal Number 3564
 
20.4%
Space Separator 2984
 
17.0%
Dash Punctuation 261
 
1.5%
Uppercase Letter 31
 
0.2%
Open Punctuation 1
 
< 0.1%
Close Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 1979
18.6%
동 1367
12.8%
대 1003
9.4%
광 989
9.3%
역 987
9.3%
시 985
9.2%
로 793
7.4%
길 587
 
5.5%
신 139
 
1.3%
공 92
 
0.9%
Other values (92) 1741
16.3%
Decimal Number
ValueCountFrequency (%)
1 764
21.4%
2 527
14.8%
3 371
10.4%
5 366
10.3%
6 306
8.6%
4 287
 
8.1%
7 273
 
7.7%
0 231
 
6.5%
9 230
 
6.5%
8 209
 
5.9%
Uppercase Letter
ValueCountFrequency (%)
A 29
93.5%
B 2
 
6.5%
Space Separator
ValueCountFrequency (%)
2984
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 261
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 10662
60.9%
Common 6811
38.9%
Latin 31
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 1979
18.6%
동 1367
12.8%
대 1003
9.4%
광 989
9.3%
역 987
9.3%
시 985
9.2%
로 793
7.4%
길 587
 
5.5%
신 139
 
1.3%
공 92
 
0.9%
Other values (92) 1741
16.3%
Common
ValueCountFrequency (%)
2984
43.8%
1 764
 
11.2%
2 527
 
7.7%
3 371
 
5.4%
5 366
 
5.4%
6 306
 
4.5%
4 287
 
4.2%
7 273
 
4.0%
- 261
 
3.8%
0 231
 
3.4%
Other values (4) 441
 
6.5%
Latin
ValueCountFrequency (%)
A 29
93.5%
B 2
 
6.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 10662
60.9%
ASCII 6842
39.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2984
43.6%
1 764
 
11.2%
2 527
 
7.7%
3 371
 
5.4%
5 366
 
5.3%
6 306
 
4.5%
4 287
 
4.2%
7 273
 
4.0%
- 261
 
3.8%
0 231
 
3.4%
Other values (6) 472
 
6.9%
Hangul
ValueCountFrequency (%)
구 1979
18.6%
동 1367
12.8%
대 1003
9.4%
광 989
9.3%
역 987
9.3%
시 985
9.2%
로 793
7.4%
길 587
 
5.5%
신 139
 
1.3%
공 92
 
0.9%
Other values (92) 1741
16.3%

소재지지번주소
Text

Distinct963
Distinct (%)97.8%
Missing0
Missing (%)0.0%
Memory size7.8 KiB
2023-09-29T01:10:04.277090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length21
Mean length18.086294
Min length14

Characters and Unicode

Total characters17815
Distinct characters84
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique943 ?
Unique (%)95.7%

Sample

1st row대구광역시 동구 금강동 1166
2nd row대구광역시 동구 신천1동 1291-70
3rd row대구광역시 동구 지묘동 188-1
4th row대구광역시 동구 덕곡동56
5th row대구광역시 동구 신무동 634-3
ValueCountFrequency (%)
동구 987
25.0%
대구광역시 985
24.9%
신암동 136
 
3.4%
방촌동 77
 
2.0%
효목동 72
 
1.8%
신천동 69
 
1.7%
율하동 63
 
1.6%
신서동 48
 
1.2%
각산동 44
 
1.1%
불로동 39
 
1.0%
Other values (1000) 1428
36.2%
2023-09-29T01:10:07.381524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2970
16.7%
동 1984
11.1%
구 1972
11.1%
대 1001
 
5.6%
광 989
 
5.6%
역 985
 
5.5%
시 985
 
5.5%
1 875
 
4.9%
- 763
 
4.3%
2 459
 
2.6%
Other values (74) 4832
27.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 9872
55.4%
Decimal Number 4205
23.6%
Space Separator 2970
 
16.7%
Dash Punctuation 763
 
4.3%
Uppercase Letter 5
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 1984
20.1%
구 1972
20.0%
대 1001
10.1%
광 989
10.0%
역 985
10.0%
시 985
10.0%
신 328
 
3.3%
암 155
 
1.6%
천 93
 
0.9%
목 80
 
0.8%
Other values (61) 1300
13.2%
Decimal Number
ValueCountFrequency (%)
1 875
20.8%
2 459
10.9%
3 432
10.3%
5 407
9.7%
4 388
9.2%
6 363
8.6%
7 337
 
8.0%
0 326
 
7.8%
9 310
 
7.4%
8 308
 
7.3%
Space Separator
ValueCountFrequency (%)
2970
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 763
100.0%
Uppercase Letter
ValueCountFrequency (%)
A 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 9872
55.4%
Common 7938
44.6%
Latin 5
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 1984
20.1%
구 1972
20.0%
대 1001
10.1%
광 989
10.0%
역 985
10.0%
시 985
10.0%
신 328
 
3.3%
암 155
 
1.6%
천 93
 
0.9%
목 80
 
0.8%
Other values (61) 1300
13.2%
Common
ValueCountFrequency (%)
2970
37.4%
1 875
 
11.0%
- 763
 
9.6%
2 459
 
5.8%
3 432
 
5.4%
5 407
 
5.1%
4 388
 
4.9%
6 363
 
4.6%
7 337
 
4.2%
0 326
 
4.1%
Other values (2) 618
 
7.8%
Latin
ValueCountFrequency (%)
A 5
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 9872
55.4%
ASCII 7943
44.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2970
37.4%
1 875
 
11.0%
- 763
 
9.6%
2 459
 
5.8%
3 432
 
5.4%
5 407
 
5.1%
4 388
 
4.9%
6 363
 
4.6%
7 337
 
4.2%
0 326
 
4.1%
Other values (3) 623
 
7.8%
Hangul
ValueCountFrequency (%)
동 1984
20.1%
구 1972
20.0%
대 1001
10.1%
광 989
10.0%
역 985
10.0%
시 985
10.0%
신 328
 
3.3%
암 155
 
1.6%
천 93
 
0.9%
목 80
 
0.8%
Other values (61) 1300
13.2%

설치목적구분
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size7.8 KiB
어린이보호
505 
생활방범
370 
쓰레기단속
 
44
재난재해
 
23
시설물관리
 
22

Length

Max length5
Median length5
Mean length4.5796954
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
어린이보호 505
51.3%
생활방범 370
37.6%
쓰레기단속 44
 
4.5%
재난재해 23
 
2.3%
시설물관리 22
 
2.2%
교통단속 21
 
2.1%

Length

2023-09-29T01:10:07.991438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-29T01:10:08.798824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
어린이보호 505
51.3%
생활방범 370
37.6%
쓰레기단속 44
 
4.5%
재난재해 23
 
2.3%
시설물관리 22
 
2.2%
교통단속 21
 
2.1%

카메라대수
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2233503
Minimum1
Maximum23
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.8 KiB
2023-09-29T01:10:09.253835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile2
Maximum23
Range22
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.87237582
Coefficient of variation (CV)0.71310389
Kurtosis398.72518
Mean1.2233503
Median Absolute Deviation (MAD)0
Skewness16.751321
Sum1205
Variance0.76103958
MonotonicityNot monotonic
2023-09-29T01:10:09.709043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 824
83.7%
2 132
 
13.4%
3 22
 
2.2%
4 5
 
0.5%
8 1
 
0.1%
23 1
 
0.1%
ValueCountFrequency (%)
1 824
83.7%
2 132
 
13.4%
3 22
 
2.2%
4 5
 
0.5%
8 1
 
0.1%
23 1
 
0.1%
ValueCountFrequency (%)
23 1
 
0.1%
8 1
 
0.1%
4 5
 
0.5%
3 22
 
2.2%
2 132
 
13.4%
1 824
83.7%

카메라화소수
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.8 KiB
200
830 
130
155 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row200
2nd row200
3rd row200
4th row200
5th row200

Common Values

ValueCountFrequency (%)
200 830
84.3%
130 155
 
15.7%

Length

2023-09-29T01:10:10.303082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-29T01:10:10.969677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
200 830
84.3%
130 155
 
15.7%

촬영방면정보
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.8 KiB
회전형
836 
고정형
149 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
회전형 836
84.9%
고정형 149
 
15.1%

Length

2023-09-29T01:10:11.532108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-29T01:10:11.976635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
회전형 836
84.9%
고정형 149
 
15.1%

보관일수
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size7.8 KiB
30
983 
15
 
1
20
 
1

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique2 ?
Unique (%)0.2%

Sample

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

Common Values

ValueCountFrequency (%)
30 983
99.8%
15 1
 
0.1%
20 1
 
0.1%

Length

2023-09-29T01:10:12.506718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-29T01:10:13.074515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
30 983
99.8%
15 1
 
0.1%
20 1
 
0.1%

설치년월
Categorical

HIGH CORRELATION 

Distinct42
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Memory size7.8 KiB
2017-01
278 
2017-09
78 
2014-01
76 
2015-08
74 
2016-03
54 
Other values (37)
425 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique6 ?
Unique (%)0.6%

Sample

1st row2015-04
2nd row2015-04
3rd row2015-04
4th row2015-04
5th row2015-04

Common Values

ValueCountFrequency (%)
2017-01 278
28.2%
2017-09 78
 
7.9%
2014-01 76
 
7.7%
2015-08 74
 
7.5%
2016-03 54
 
5.5%
2016-06 52
 
5.3%
2018-08 35
 
3.6%
2013-01 28
 
2.8%
2017-06 26
 
2.6%
2011-04 25
 
2.5%
Other values (32) 259
26.3%

Length

2023-09-29T01:10:13.415439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2017-01 278
28.2%
2017-09 78
 
7.9%
2014-01 76
 
7.7%
2015-08 74
 
7.5%
2016-03 54
 
5.5%
2016-06 52
 
5.3%
2018-08 35
 
3.6%
2013-01 28
 
2.8%
2017-06 26
 
2.6%
2011-04 25
 
2.5%
Other values (32) 259
26.3%

관리기관전화번호
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size7.8 KiB
053-662-4314
985 

Length

Max length12
Median length12
Mean length12
Min length12

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row053-662-4314
2nd row053-662-4314
3rd row053-662-4314
4th row053-662-4314
5th row053-662-4314

Common Values

ValueCountFrequency (%)
053-662-4314 985
100.0%

Length

2023-09-29T01:10:14.029231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-29T01:10:14.649347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
053-662-4314 985
100.0%

위도
Real number (ℝ)

Distinct901
Distinct (%)91.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.900277
Minimum33.425934
Maximum38.870721
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.8 KiB
2023-09-29T01:10:15.184714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum33.425934
5-th percentile35.865137
Q135.873899
median35.88207
Q335.891391
95-th percentile35.956884
Maximum38.870721
Range5.4447873
Interquartile range (IQR)0.0174925

Descriptive statistics

Standard deviation0.231565
Coefficient of variation (CV)0.0064502287
Kurtosis67.008153
Mean35.900277
Median Absolute Deviation (MAD)0.008597
Skewness4.1488178
Sum35361.773
Variance0.053622348
MonotonicityNot monotonic
2023-09-29T01:10:16.141740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.8666447 16
 
1.6%
35.885859 13
 
1.3%
35.9263636 7
 
0.7%
35.8599806 4
 
0.4%
35.8855687 4
 
0.4%
35.9094591 3
 
0.3%
35.8846141 3
 
0.3%
35.8739089 3
 
0.3%
35.9844619 2
 
0.2%
35.9151537 2
 
0.2%
Other values (891) 928
94.2%
ValueCountFrequency (%)
33.4259337 1
0.1%
34.839254 1
0.1%
34.8399163 1
0.1%
34.8439168 1
0.1%
35.0911188 1
0.1%
35.0921816 1
0.1%
35.0944662 2
0.2%
35.1528291 1
0.1%
35.1648217 1
0.1%
35.1686168 1
0.1%
ValueCountFrequency (%)
38.870721 1
0.1%
37.7439309 1
0.1%
37.6168316 1
0.1%
37.6075769 1
0.1%
37.6031085 1
0.1%
37.5037061 1
0.1%
37.4988565 1
0.1%
37.4803053 1
0.1%
37.4416658 1
0.1%
37.4378687 1
0.1%

경도
Real number (ℝ)

Distinct902
Distinct (%)91.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128.63867
Minimum126.28471
Maximum129.35114
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.8 KiB
2023-09-29T01:10:16.845284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.28471
5-th percentile128.61552
Q1128.63253
median128.64927
Q3128.69831
95-th percentile128.73408
Maximum129.35114
Range3.0664317
Interquartile range (IQR)0.065776

Descriptive statistics

Standard deviation0.2257687
Coefficient of variation (CV)0.001755061
Kurtosis60.178716
Mean128.63867
Median Absolute Deviation (MAD)0.026094
Skewness-7.5068706
Sum126709.09
Variance0.050971507
MonotonicityNot monotonic
2023-09-29T01:10:17.818942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
128.6918387 16
 
1.6%
128.7172742 13
 
1.3%
128.6434628 7
 
0.7%
128.6271994 4
 
0.4%
128.7307423 4
 
0.4%
128.6943129 4
 
0.4%
128.6419423 3
 
0.3%
128.7476216 3
 
0.3%
128.7122758 3
 
0.3%
128.6118018 2
 
0.2%
Other values (892) 926
94.0%
ValueCountFrequency (%)
126.284711 1
0.1%
126.6866359 1
0.1%
126.686818 1
0.1%
126.6972662 1
0.1%
126.7789728 1
0.1%
126.7867688 1
0.1%
126.7904463 1
0.1%
126.885162 1
0.1%
126.8958437 1
0.1%
126.948702 1
0.1%
ValueCountFrequency (%)
129.3511427 1
0.1%
128.9790979 1
0.1%
128.977629 1
0.1%
128.9773547 2
0.2%
128.8917598 1
0.1%
128.8160358 1
0.1%
128.8110978 1
0.1%
128.7779937 1
0.1%
128.7572402 1
0.1%
128.7558899 1
0.1%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size7.8 KiB
2018-08-21
985 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2018-08-21
2nd row2018-08-21
3rd row2018-08-21
4th row2018-08-21
5th row2018-08-21

Common Values

ValueCountFrequency (%)
2018-08-21 985
100.0%

Length

2023-09-29T01:10:18.730618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-29T01:10:19.294956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2018-08-21 985
100.0%

Interactions

2023-09-29T01:09:53.744228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:09:50.178077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:09:51.792978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:09:54.250040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:09:50.661509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:09:52.432580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:09:54.857975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:09:51.220750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:09:53.126919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-09-29T01:10:19.782291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
설치목적구분카메라대수카메라화소수촬영방면정보보관일수설치년월위도경도
설치목적구분1.0000.1350.5190.5010.0000.9780.0440.094
카메라대수0.1351.0000.1050.1100.0000.8740.0000.056
카메라화소수0.5190.1051.0000.9120.0570.9620.0000.173
촬영방면정보0.5010.1100.9121.0000.0580.9300.0000.221
보관일수0.0000.0000.0570.0581.0000.1200.0000.000
설치년월0.9780.8740.9620.9300.1201.0000.0000.304
위도0.0440.0000.0000.0000.0000.0001.0000.926
경도0.0940.0560.1730.2210.0000.3040.9261.000
2023-09-29T01:10:21.030938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
촬영방면정보보관일수설치목적구분설치년월카메라화소수
촬영방면정보1.0000.0970.3610.7960.732
보관일수0.0971.0000.0000.0540.094
설치목적구분0.3610.0001.0000.8050.374
설치년월0.7960.0540.8051.0000.846
카메라화소수0.7320.0940.3740.8461.000
2023-09-29T01:10:22.219865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
카메라대수위도경도설치목적구분카메라화소수촬영방면정보보관일수설치년월
카메라대수1.000-0.1190.2230.0870.0700.0730.0000.639
위도-0.1191.000-0.3180.0240.0000.0000.0000.000
경도0.223-0.3181.0000.0520.1300.1660.0000.118
설치목적구분0.0870.0240.0521.0000.3740.3610.0000.805
카메라화소수0.0700.0000.1300.3741.0000.7320.0940.846
촬영방면정보0.0730.0000.1660.3610.7321.0000.0970.796
보관일수0.0000.0000.0000.0000.0940.0971.0000.054
설치년월0.6390.0000.1180.8050.8460.7960.0541.000

Missing values

2023-09-29T01:09:56.161262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-09-29T01:09:57.352288image/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

관리기관명소재지도로명주소소재지지번주소설치목적구분카메라대수카메라화소수촬영방면정보보관일수설치년월관리기관전화번호위도경도데이터기준일자
0대구광역시 동구청대구광역시 동구 금강로 235대구광역시 동구 금강동 1166재난재해1200회전형302015-04053-662-431435.858714128.7386382018-08-21
1대구광역시 동구청대구광역시 동구 동부로 2대구광역시 동구 신천1동 1291-70재난재해1200회전형302015-04053-662-431435.873607128.6128792018-08-21
2대구광역시 동구청대구광역시 동구 팔공로 511대구광역시 동구 지묘동 188-1재난재해1200회전형302015-04053-662-431435.943731128.6464122018-08-21
3대구광역시 동구청대구광역시 동구 덕곡동56대구광역시 동구 덕곡동56재난재해1200회전형302015-04053-662-431435.995024128.6116882018-08-21
4대구광역시 동구청대구광역시 동구 신무동 634-3대구광역시 동구 신무동 634-3재난재해1200회전형302015-04053-662-431435.931075128.6796092018-08-21
5대구광역시 동구청대구광역시 동구 지묘동 1030대구광역시 동구 지묘동 1030재난재해1200회전형302015-04053-662-431435.946098128.6265332018-08-21
6대구광역시 동구청대구광역시 동구 진인동 606-2대구광역시 동구 진인동 606-2재난재해1200회전형302015-04053-662-431435.956897128.709062018-08-21
7대구광역시 동구청대구광역시 동구 파계로 500대구광역시 동구 중대동 399-1재난재해1200회전형302015-04053-662-431435.977024128.6313852018-08-21
8대구광역시 동구청대구광역시 동구 능성동78-2대구광역시 동구 능성동78-2재난재해1200회전형302015-04053-662-431435.963561128.7413212018-08-21
9대구광역시 동구청대구광역시 동구 율하동 1626대구광역시 동구 율하동 1626재난재해1200회전형302015-04053-662-431435.859981128.6943132018-08-21
관리기관명소재지도로명주소소재지지번주소설치목적구분카메라대수카메라화소수촬영방면정보보관일수설치년월관리기관전화번호위도경도데이터기준일자
975대구광역시 동구청대구광역시 동구 율하동로55대구광역시 동구 율하동 1468어린이보호1200회전형302018-08053-662-431435.864817128.7006452018-08-21
976대구광역시 동구청대구광역시 동구 금호강변로3길 22대구광역시 동구 율하동 1093어린이보호1200회전형302018-08053-662-431435.869049128.6877792018-08-21
977대구광역시 동구청대구광역시 동구 반야월로47-6대구광역시 동구 용계동 156-2어린이보호1200회전형302018-08053-662-431435.874624128.6940532018-08-21
978대구광역시 동구청대구광역시 동구 화랑로91길18-18대구광역시 동구 용계동422-32어린이보호1200회전형302018-08053-662-431435.877019128.6753612018-08-21
979대구광역시 동구청대구광역시 동구 반야월로 370-10대구광역시 동구 신서동517-1어린이보호1200회전형302018-08053-662-431435.871061128.7283812018-08-21
980대구광역시 동구청대구광역시 동구 반야월북로 301대구광역시 동구 신서동 471-5어린이보호1200회전형302018-08053-662-431435.873106128.7305622018-08-21
981대구광역시 동구청대구광역시 동구 금강로100대구광역시 동구 신서동577-1어린이보호1200회전형302018-08053-662-431435.865085128.7327722018-08-21
982대구광역시 동구청대구광역시 동구 과학로13길 5대구광역시 동구 각산동1059어린이보호1200회전형302018-08053-662-431435.884644128.7170722018-08-21
983대구광역시 동구청대구광역시 동구 이노밸리로54길 10대구광역시 동구 신서동1161-4어린이보호1200회전형302018-08053-662-431435.877465128.7253152018-08-21
984대구광역시 동구청대구광역시 동구 팔공산로2길 6대구광역시 동구 덕곡동 71어린이보호1200회전형302018-08053-662-431435.995345128.6110362018-08-21

Duplicate rows

Most frequently occurring

관리기관명소재지도로명주소소재지지번주소설치목적구분카메라대수카메라화소수촬영방면정보보관일수설치년월관리기관전화번호위도경도데이터기준일자# duplicates
0대구광역시 동구청대구광역시 동구 불로동 338대구광역시 동구 불로동 338시설물관리1200회전형302017-08053-662-431435.914253128.6461172018-08-212
1대구광역시 동구청대구광역시 동구 해동로 82대구광역시 동구 지저동 930생활방범1200회전형302015-11053-662-431435.890386128.6381432018-08-212