Overview

Dataset statistics

Number of variables13
Number of observations1549
Missing cells42
Missing cells (%)0.2%
Duplicate rows45
Duplicate rows (%)2.9%
Total size in memory165.0 KiB
Average record size in memory109.1 B

Variable types

Categorical8
Text2
Numeric2
DateTime1

Dataset

Description대구광역시 달서구_CCTV 설치 위치_20210614
Author대구광역시 달서구
URLhttp://data.daegu.go.kr/open/data/dataView.do?dataSetId=15083776&dataSetDetailId=150837761bd3ea8eedbff&provdMethod=FILE

Alerts

관리기관명 has constant value ""Constant
보관일수 has constant value ""Constant
데이터기준일자 has constant value ""Constant
Dataset has 45 (2.9%) duplicate rowsDuplicates
설치목적구분 is highly overall correlated with 설치연도High correlation
촬영방면정보 is highly overall correlated with 설치연도High correlation
설치연도 is highly overall correlated with 설치목적구분 and 2 other fieldsHigh correlation
관리기관전화번호 is highly overall correlated with 설치연도High correlation
카메라대수 is highly imbalanced (97.8%)Imbalance
카메라화소수 is highly imbalanced (98.0%)Imbalance
관리기관전화번호 is highly imbalanced (61.7%)Imbalance
소재지도로명주소 has 39 (2.5%) missing valuesMissing

Reproduction

Analysis started2024-04-21 11:19:34.782587
Analysis finished2024-04-21 11:19:38.016252
Duration3.23 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

관리기관명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size12.2 KiB
대구광역시 달서구청
1549 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
대구광역시 달서구청 1549
100.0%

Length

2024-04-21T20:19:38.220442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T20:19:38.528316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
대구광역시 1549
50.0%
달서구청 1549
50.0%

소재지도로명주소
Text

MISSING 

Distinct1375
Distinct (%)91.1%
Missing39
Missing (%)2.5%
Memory size12.2 KiB
2024-04-21T20:19:39.384452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length19
Mean length13.378808
Min length6

Characters and Unicode

Total characters20202
Distinct characters115
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1266 ?
Unique (%)83.8%

Sample

1st row두류남길 60(두류동)
2nd row본리서3길 5(본리동)
3rd row호산동로36북길 60(호산동)
4th row월배로68길 48(송현동)
5th row상화로15길 27(진천동)
ValueCountFrequency (%)
도원동 29
 
0.9%
진천동 22
 
0.7%
월배로 20
 
0.6%
용산동 19
 
0.6%
미리샘길 19
 
0.6%
이곡동 17
 
0.5%
장기로 16
 
0.5%
송현로 16
 
0.5%
야외음악당로 14
 
0.5%
대명천로 13
 
0.4%
Other values (1737) 2916
94.0%
2024-04-21T20:19:40.498064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1604
 
7.9%
동 1512
 
7.5%
1 1284
 
6.4%
( 1206
 
6.0%
) 1206
 
6.0%
로 1121
 
5.5%
길 1006
 
5.0%
2 864
 
4.3%
3 719
 
3.6%
4 591
 
2.9%
Other values (105) 9089
45.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 9868
48.8%
Decimal Number 5922
29.3%
Space Separator 1604
 
7.9%
Open Punctuation 1206
 
6.0%
Close Punctuation 1206
 
6.0%
Dash Punctuation 378
 
1.9%
Other Punctuation 16
 
0.1%
Uppercase Letter 1
 
< 0.1%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 1512
 
15.3%
로 1121
 
11.4%
길 1006
 
10.2%
당 362
 
3.7%
산 281
 
2.8%
월 250
 
2.5%
성 244
 
2.5%
상 238
 
2.4%
현 224
 
2.3%
송 224
 
2.3%
Other values (87) 4406
44.6%
Decimal Number
ValueCountFrequency (%)
1 1284
21.7%
2 864
14.6%
3 719
12.1%
4 591
10.0%
5 522
8.8%
6 417
 
7.0%
7 401
 
6.8%
9 382
 
6.5%
0 377
 
6.4%
8 365
 
6.2%
Other Punctuation
ValueCountFrequency (%)
, 9
56.2%
. 7
43.8%
Space Separator
ValueCountFrequency (%)
1604
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1206
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1206
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 378
100.0%
Uppercase Letter
ValueCountFrequency (%)
X 1
100.0%
Math Symbol
ValueCountFrequency (%)
> 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 10333
51.1%
Hangul 9868
48.8%
Latin 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 1512
 
15.3%
로 1121
 
11.4%
길 1006
 
10.2%
당 362
 
3.7%
산 281
 
2.8%
월 250
 
2.5%
성 244
 
2.5%
상 238
 
2.4%
현 224
 
2.3%
송 224
 
2.3%
Other values (87) 4406
44.6%
Common
ValueCountFrequency (%)
1604
15.5%
1 1284
12.4%
( 1206
11.7%
) 1206
11.7%
2 864
8.4%
3 719
7.0%
4 591
 
5.7%
5 522
 
5.1%
6 417
 
4.0%
7 401
 
3.9%
Other values (7) 1519
14.7%
Latin
ValueCountFrequency (%)
X 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10334
51.2%
Hangul 9868
48.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1604
15.5%
1 1284
12.4%
( 1206
11.7%
) 1206
11.7%
2 864
8.4%
3 719
7.0%
4 591
 
5.7%
5 522
 
5.1%
6 417
 
4.0%
7 401
 
3.9%
Other values (8) 1520
14.7%
Hangul
ValueCountFrequency (%)
동 1512
 
15.3%
로 1121
 
11.4%
길 1006
 
10.2%
당 362
 
3.7%
산 281
 
2.8%
월 250
 
2.5%
성 244
 
2.5%
상 238
 
2.4%
현 224
 
2.3%
송 224
 
2.3%
Other values (87) 4406
44.6%

소재지지번주소
Text

Distinct1383
Distinct (%)89.5%
Missing3
Missing (%)0.2%
Memory size12.2 KiB
2024-04-21T20:19:41.348028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length15
Mean length9.3609314
Min length6

Characters and Unicode

Total characters14472
Distinct characters54
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1253 ?
Unique (%)81.0%

Sample

1st row두류동 804-141
2nd row본리동 1210-1
3rd row호산동 357-41
4th row송현1동 798-321
5th row진천동 460
ValueCountFrequency (%)
감삼동 100
 
3.2%
두류동 99
 
3.2%
진천동 96
 
3.1%
성당동 93
 
3.0%
송현동 80
 
2.6%
신당동 75
 
2.4%
장기동 71
 
2.3%
상인동 67
 
2.2%
송현1동 66
 
2.1%
도원동 64
 
2.1%
Other values (1367) 2279
73.8%
2024-04-21T20:19:42.420673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 1735
 
12.0%
1547
 
10.7%
동 1546
 
10.7%
- 1221
 
8.4%
2 948
 
6.6%
3 667
 
4.6%
4 651
 
4.5%
5 607
 
4.2%
9 513
 
3.5%
6 504
 
3.5%
Other values (44) 4533
31.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7087
49.0%
Other Letter 4599
31.8%
Space Separator 1547
 
10.7%
Dash Punctuation 1221
 
8.4%
Other Punctuation 18
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 1546
33.6%
송 185
 
4.0%
현 185
 
4.0%
당 171
 
3.7%
산 169
 
3.7%
성 156
 
3.4%
인 153
 
3.3%
상 153
 
3.3%
류 139
 
3.0%
두 139
 
3.0%
Other values (31) 1603
34.9%
Decimal Number
ValueCountFrequency (%)
1 1735
24.5%
2 948
13.4%
3 667
 
9.4%
4 651
 
9.2%
5 607
 
8.6%
9 513
 
7.2%
6 504
 
7.1%
0 492
 
6.9%
7 492
 
6.9%
8 478
 
6.7%
Space Separator
ValueCountFrequency (%)
1547
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1221
100.0%
Other Punctuation
ValueCountFrequency (%)
, 18
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 9873
68.2%
Hangul 4599
31.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 1546
33.6%
송 185
 
4.0%
현 185
 
4.0%
당 171
 
3.7%
산 169
 
3.7%
성 156
 
3.4%
인 153
 
3.3%
상 153
 
3.3%
류 139
 
3.0%
두 139
 
3.0%
Other values (31) 1603
34.9%
Common
ValueCountFrequency (%)
1 1735
17.6%
1547
15.7%
- 1221
12.4%
2 948
9.6%
3 667
 
6.8%
4 651
 
6.6%
5 607
 
6.1%
9 513
 
5.2%
6 504
 
5.1%
0 492
 
5.0%
Other values (3) 988
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9873
68.2%
Hangul 4599
31.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1735
17.6%
1547
15.7%
- 1221
12.4%
2 948
9.6%
3 667
 
6.8%
4 651
 
6.6%
5 607
 
6.1%
9 513
 
5.2%
6 504
 
5.1%
0 492
 
5.0%
Other values (3) 988
10.0%
Hangul
ValueCountFrequency (%)
동 1546
33.6%
송 185
 
4.0%
현 185
 
4.0%
당 171
 
3.7%
산 169
 
3.7%
성 156
 
3.4%
인 153
 
3.3%
상 153
 
3.3%
류 139
 
3.0%
두 139
 
3.0%
Other values (31) 1603
34.9%

설치목적구분
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size12.2 KiB
생활안전
1043 
어린이보호
316 
아동안전
190 

Length

Max length5
Median length4
Mean length4.2040026
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
생활안전 1043
67.3%
어린이보호 316
 
20.4%
아동안전 190
 
12.3%

Length

2024-04-21T20:19:42.645886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T20:19:42.826858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
생활안전 1043
67.3%
어린이보호 316
 
20.4%
아동안전 190
 
12.3%

카메라대수
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size12.2 KiB
1
1544 
3
 
3
2
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1 1544
99.7%
3 3
 
0.2%
2 2
 
0.1%

Length

2024-04-21T20:19:43.018222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T20:19:43.197233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 1544
99.7%
3 3
 
0.2%
2 2
 
0.1%

카메라화소수
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size12.2 KiB
200
1546 
41
 
3

Length

Max length3
Median length3
Mean length2.9980633
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
200 1546
99.8%
41 3
 
0.2%

Length

2024-04-21T20:19:43.388148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T20:19:43.563936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
200 1546
99.8%
41 3
 
0.2%

촬영방면정보
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size12.2 KiB
회전
1134 
고정
415 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row회전
2nd row회전
3rd row회전
4th row고정
5th row회전

Common Values

ValueCountFrequency (%)
회전 1134
73.2%
고정 415
 
26.8%

Length

2024-04-21T20:19:43.740230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T20:19:44.117989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
회전 1134
73.2%
고정 415
 
26.8%

보관일수
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size12.2 KiB
30
1549 

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 1549
100.0%

Length

2024-04-21T20:19:44.295723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T20:19:44.461121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
30 1549
100.0%

설치연도
Categorical

HIGH CORRELATION 

Distinct12
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size12.2 KiB
2015년
396 
2020년
188 
2017년
182 
2014년
160 
2018년
129 
Other values (7)
494 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2015년
2nd row2015년
3rd row2015년
4th row2020년
5th row2015년

Common Values

ValueCountFrequency (%)
2015년 396
25.6%
2020년 188
12.1%
2017년 182
11.7%
2014년 160
10.3%
2018년 129
 
8.3%
2013년 128
 
8.3%
2021년 100
 
6.5%
2016년 79
 
5.1%
2019년 76
 
4.9%
2011년 46
 
3.0%
Other values (2) 65
 
4.2%

Length

2024-04-21T20:19:44.629272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2015년 396
25.6%
2020년 188
12.1%
2017년 182
11.7%
2014년 160
10.3%
2018년 129
 
8.3%
2013년 128
 
8.3%
2021년 100
 
6.5%
2016년 79
 
5.1%
2019년 76
 
4.9%
2011년 46
 
3.0%
Other values (2) 65
 
4.2%

관리기관전화번호
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size12.2 KiB
053-667-3900
1379 
053-667-3048
 
100
053-667-2747
 
70

Length

Max length12
Median length12
Mean length12
Min length12

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row053-667-3900
2nd row053-667-3900
3rd row053-667-3900
4th row053-667-3900
5th row053-667-3900

Common Values

ValueCountFrequency (%)
053-667-3900 1379
89.0%
053-667-3048 100
 
6.5%
053-667-2747 70
 
4.5%

Length

2024-04-21T20:19:44.908134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T20:19:45.082730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
053-667-3900 1379
89.0%
053-667-3048 100
 
6.5%
053-667-2747 70
 
4.5%

위도
Real number (ℝ)

Distinct1395
Distinct (%)90.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.8373
Minimum35.790842
Maximum35.86425
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.7 KiB
2024-04-21T20:19:45.288067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.790842
5-th percentile35.807058
Q135.821636
median35.841047
Q335.853981
95-th percentile35.859649
Maximum35.86425
Range0.07340833
Interquartile range (IQR)0.03234445

Descriptive statistics

Standard deviation0.018120859
Coefficient of variation (CV)0.0005056424
Kurtosis-1.1866102
Mean35.8373
Median Absolute Deviation (MAD)0.01425488
Skewness-0.41073462
Sum55511.978
Variance0.00032836552
MonotonicityNot monotonic
2024-04-21T20:19:45.544904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.8553021 4
 
0.3%
35.8526 4
 
0.3%
35.8567124 4
 
0.3%
35.8217574 4
 
0.3%
35.8557371 4
 
0.3%
35.85404444 4
 
0.3%
35.80705833 3
 
0.2%
35.82933333 3
 
0.2%
35.80699722 3
 
0.2%
35.80771667 3
 
0.2%
Other values (1385) 1513
97.7%
ValueCountFrequency (%)
35.79084167 1
0.1%
35.791525 1
0.1%
35.79415278 1
0.1%
35.79566389 1
0.1%
35.7956966 1
0.1%
35.7959 1
0.1%
35.7971303 1
0.1%
35.79814167 1
0.1%
35.79871667 1
0.1%
35.798982 1
0.1%
ValueCountFrequency (%)
35.86425 1
0.1%
35.86388889 1
0.1%
35.86348889 1
0.1%
35.86275833 1
0.1%
35.86266111 1
0.1%
35.86251667 1
0.1%
35.86235833 1
0.1%
35.86234722 1
0.1%
35.8620662 2
0.1%
35.86198889 1
0.1%

경도
Real number (ℝ)

Distinct1329
Distinct (%)85.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128.53353
Minimum128.47236
Maximum128.57416
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.7 KiB
2024-04-21T20:19:45.810708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum128.47236
5-th percentile128.49374
Q1128.52196
median128.5358
Q3128.5482
95-th percentile128.56859
Maximum128.57416
Range0.1018
Interquartile range (IQR)0.0262445

Descriptive statistics

Standard deviation0.021066097
Coefficient of variation (CV)0.00016389573
Kurtosis0.20608455
Mean128.53353
Median Absolute Deviation (MAD)0.0131966
Skewness-0.58502079
Sum199098.44
Variance0.00044378044
MonotonicityNot monotonic
2024-04-21T20:19:46.057951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
128.544725 5
 
0.3%
128.5264917 4
 
0.3%
128.5393583 4
 
0.3%
128.5317694 4
 
0.3%
128.5244694 4
 
0.3%
128.54978 4
 
0.3%
128.5432028 4
 
0.3%
128.5320472 4
 
0.3%
128.5436585 4
 
0.3%
128.5366062 4
 
0.3%
Other values (1319) 1508
97.4%
ValueCountFrequency (%)
128.4723639 1
 
0.1%
128.4733944 1
 
0.1%
128.47375 2
0.1%
128.4743944 3
0.2%
128.4748167 1
 
0.1%
128.4761722 2
0.1%
128.4772722 1
 
0.1%
128.4775389 1
 
0.1%
128.4778167 1
 
0.1%
128.4778278 1
 
0.1%
ValueCountFrequency (%)
128.5741639 1
0.1%
128.5740972 1
0.1%
128.5738528 1
0.1%
128.5738409 2
0.1%
128.5737861 1
0.1%
128.573786 1
0.1%
128.5735861 1
0.1%
128.5734742 1
0.1%
128.5734306 1
0.1%
128.5734194 1
0.1%

데이터기준일자
Date

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size12.2 KiB
Minimum2021-06-14 00:00:00
Maximum2021-06-14 00:00:00
2024-04-21T20:19:46.343298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:19:46.644069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2024-04-21T20:19:36.312204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:19:35.772103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:19:36.585797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:19:36.051423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-21T20:19:46.860354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
설치목적구분카메라대수카메라화소수촬영방면정보설치연도관리기관전화번호위도경도
설치목적구분1.0000.0000.0000.2280.7860.3290.1870.230
카메라대수0.0001.0000.0000.0000.0000.0000.0000.000
카메라화소수0.0000.0001.0000.0790.1080.0990.0000.157
촬영방면정보0.2280.0000.0791.0000.9550.2800.0860.141
설치연도0.7860.0000.1080.9551.0000.9460.2550.220
관리기관전화번호0.3290.0000.0990.2800.9461.0000.1660.247
위도0.1870.0000.0000.0860.2550.1661.0000.648
경도0.2300.0000.1570.1410.2200.2470.6481.000
2024-04-21T20:19:47.160363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
촬영방면정보카메라대수관리기관전화번호카메라화소수설치목적구분설치연도
촬영방면정보1.0000.0000.4540.0500.3730.820
카메라대수0.0001.0000.0000.0000.0000.000
관리기관전화번호0.4540.0001.0000.1640.1130.735
카메라화소수0.0500.0000.1641.0000.0000.083
설치목적구분0.3730.0000.1130.0001.0000.506
설치연도0.8200.0000.7350.0830.5061.000
2024-04-21T20:19:47.445943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위도경도설치목적구분카메라대수카메라화소수촬영방면정보설치연도관리기관전화번호
위도1.000-0.1110.1130.0000.0000.0650.1100.099
경도-0.1111.0000.1400.0000.1220.1120.0950.159
설치목적구분0.1130.1401.0000.0000.0000.3730.5060.113
카메라대수0.0000.0000.0001.0000.0000.0000.0000.000
카메라화소수0.0000.1220.0000.0001.0000.0500.0830.164
촬영방면정보0.0650.1120.3730.0000.0501.0000.8200.454
설치연도0.1100.0950.5060.0000.0830.8201.0000.735
관리기관전화번호0.0990.1590.1130.0000.1640.4540.7351.000

Missing values

2024-04-21T20:19:36.953933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-21T20:19:37.514915image/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.
2024-04-21T20:19:37.863273image/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

관리기관명소재지도로명주소소재지지번주소설치목적구분카메라대수카메라화소수촬영방면정보보관일수설치연도관리기관전화번호위도경도데이터기준일자
0대구광역시 달서구청두류남길 60(두류동)두류동 804-141생활안전1200회전302015년053-667-390035.857225128.5712892021-06-14
1대구광역시 달서구청본리서3길 5(본리동)본리동 1210-1생활안전1200회전302015년053-667-390035.839547128.5297922021-06-14
2대구광역시 달서구청호산동로36북길 60(호산동)호산동 357-41생활안전1200회전302015년053-667-390035.851494128.4880282021-06-14
3대구광역시 달서구청월배로68길 48(송현동)송현1동 798-321생활안전1200고정302020년053-667-390035.824289128.5492032021-06-14
4대구광역시 달서구청상화로15길 27(진천동)진천동 460생활안전1200회전302015년053-667-390035.809103128.5222922021-06-14
5대구광역시 달서구청월배로32길 58(상인동)상인2동 344-15생활안전1200고정302012년053-667-390035.814472128.5328692021-06-14
6대구광역시 달서구청당산로45길 17(감삼동)감삼동 31-5생활안전1200회전302015년053-667-390035.85645128.5465112021-06-14
7대구광역시 달서구청죽전4길 95(감삼동)감삼동 32-34생활안전1200회전302015년053-667-390035.855578128.54522021-06-14
8대구광역시 달서구청달구벌대로323길 68(감삼동)감삼동 35-1생활안전1200회전302015년053-667-390035.855506128.5419672021-06-14
9대구광역시 달서구청달구벌대로323길 55(감삼동)감삼동 37-25생활안전1200회전302015년053-667-390035.854864128.5418782021-06-14
관리기관명소재지도로명주소소재지지번주소설치목적구분카메라대수카메라화소수촬영방면정보보관일수설치연도관리기관전화번호위도경도데이터기준일자
1539대구광역시 달서구청와룡로53길 8죽전동 366-17생활안전1200고정302021년053-667-274735.856712128.5366062021-06-14
1540대구광역시 달서구청와룡로53길 8죽전동 366-17생활안전1200고정302021년053-667-274735.856712128.5366062021-06-14
1541대구광역시 달서구청야외음악당로 215-10두류3동 476-5생활안전1200고정302021년053-667-274735.854839128.5579812021-06-14
1542대구광역시 달서구청야외음악당로 215-10두류3동 476-5생활안전1200고정302021년053-667-274735.854839128.5579812021-06-14
1543대구광역시 달서구청두류남7길 28-21두류1,2동 804-41생활안전1200고정302021년053-667-274735.857945128.5720342021-06-14
1544대구광역시 달서구청두류남7길 28-21두류1,2동 804-41생활안전1200고정302021년053-667-274735.857945128.5720342021-06-14
1545대구광역시 달서구청성당로51길 12두류1,2동 777-63생활안전1200고정302021년053-667-274735.855302128.5732332021-06-14
1546대구광역시 달서구청성당로51길 12두류1,2동 777-63생활안전1200고정302021년053-667-274735.855302128.5732332021-06-14
1547대구광역시 달서구청성당로51길 12두류1,2동 777-63생활안전1200고정302021년053-667-274735.855302128.5732332021-06-14
1548대구광역시 달서구청성당로51길 12두류1,2동 777-63생활안전1200고정302021년053-667-274735.855302128.5732332021-06-14

Duplicate rows

Most frequently occurring

관리기관명소재지도로명주소소재지지번주소설치목적구분카메라대수카메라화소수촬영방면정보보관일수설치연도관리기관전화번호위도경도데이터기준일자# duplicates
8대구광역시 달서구청당산로45길 71감삼동 36-24생활안전1200고정302021년053-667-274735.855737128.5436582021-06-144
19대구광역시 달서구청성당로51길 12두류1,2동 777-63생활안전1200고정302021년053-667-274735.855302128.5732332021-06-144
24대구광역시 달서구청송현로4길 10송현1동 1950생활안전1200고정302021년053-667-274735.821757128.549782021-06-144
28대구광역시 달서구청와룡로53길 8죽전동 366-17생활안전1200고정302021년053-667-274735.856712128.5366062021-06-144
3대구광역시 달서구청달구벌대로203길 59호산동 146생활안전1200고정302020년053-667-304835.855872128.4787172021-06-143
11대구광역시 달서구청문화회관3길 11장기동 270-5생활안전1200고정302021년053-667-274735.847296128.5223132021-06-143
15대구광역시 달서구청미리샘길 7도원동 1446생활안전1200고정302020년053-667-304835.806997128.5432032021-06-143
16대구광역시 달서구청미리샘길 80도원동 1446-10생활안전1200고정302020년053-667-304835.807717128.5447252021-06-143
30대구광역시 달서구청월배로67길 46-18송현2동 1910-9생활안전1200고정302021년053-667-274735.827034128.544772021-06-143
35대구광역시 달서구청조암로14안길 16월암동 740생활안전1200고정302021년053-667-274735.824405128.5207742021-06-143