Overview

Dataset statistics

Number of variables16
Number of observations672
Missing cells22
Missing cells (%)0.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory90.0 KiB
Average record size in memory137.2 B

Variable types

Numeric6
Text2
Categorical8

Dataset

Description대전광역시 유성구 CCTV 설치 현황 상세정보 조회하는 데이터로 관리번호, 카메라대수, 설치위치 등을 제공합니다.사용좌표계: EPSG 2097
Author대전광역시 유성구
URLhttps://www.data.go.kr/data/15108945/fileData.do

Alerts

시도코드 has constant value ""Constant
시도이름 has constant value ""Constant
시군구코드 has constant value ""Constant
시군구이름 has constant value ""Constant
기준일자 has constant value ""Constant
번호 is highly overall correlated with 카메라대수High correlation
카메라대수 is highly overall correlated with 번호High correlation
위도 is highly overall correlated with 경도 and 4 other fieldsHigh correlation
경도 is highly overall correlated with 위도 and 4 other fieldsHigh correlation
행정동코드 is highly overall correlated with 위도 and 4 other fieldsHigh correlation
법정동코드 is highly overall correlated with 위도 and 4 other fieldsHigh correlation
행정동이름 is highly overall correlated with 위도 and 4 other fieldsHigh correlation
법정동이름 is highly overall correlated with 위도 and 4 other fieldsHigh correlation
관리번호 has 19 (2.8%) missing valuesMissing
법정동코드 is highly skewed (γ1 = -25.91749745)Skewed
번호 has unique valuesUnique

Reproduction

Analysis started2023-12-12 03:49:01.515102
Analysis finished2023-12-12 03:49:08.658176
Duration7.14 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct672
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean336.5
Minimum1
Maximum672
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.0 KiB
2023-12-12T12:49:08.740053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile34.55
Q1168.75
median336.5
Q3504.25
95-th percentile638.45
Maximum672
Range671
Interquartile range (IQR)335.5

Descriptive statistics

Standard deviation194.13397
Coefficient of variation (CV)0.57692117
Kurtosis-1.2
Mean336.5
Median Absolute Deviation (MAD)168
Skewness0
Sum226128
Variance37688
MonotonicityNot monotonic
2023-12-12T12:49:08.927981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.1%
452 1
 
0.1%
444 1
 
0.1%
445 1
 
0.1%
446 1
 
0.1%
447 1
 
0.1%
448 1
 
0.1%
449 1
 
0.1%
450 1
 
0.1%
451 1
 
0.1%
Other values (662) 662
98.5%
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 (%)
672 1
0.1%
671 1
0.1%
670 1
0.1%
669 1
0.1%
668 1
0.1%
667 1
0.1%
666 1
0.1%
665 1
0.1%
664 1
0.1%
663 1
0.1%

관리번호
Text

MISSING 

Distinct653
Distinct (%)100.0%
Missing19
Missing (%)2.8%
Memory size5.4 KiB
2023-12-12T12:49:09.405195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters3918
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

Unique653 ?
Unique (%)100.0%

Sample

1st row08-001
2nd row08-002
3rd row08-003
4th row08-004
5th row08-005
ValueCountFrequency (%)
11-042 1
 
0.2%
16-465 1
 
0.2%
16-450 1
 
0.2%
16-442 1
 
0.2%
16-444 1
 
0.2%
16-445 1
 
0.2%
16-446 1
 
0.2%
16-447 1
 
0.2%
16-448 1
 
0.2%
16-449 1
 
0.2%
Other values (643) 643
98.5%
2023-12-12T12:49:09.970273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 946
24.1%
- 653
16.7%
2 340
 
8.7%
5 327
 
8.3%
3 307
 
7.8%
0 297
 
7.6%
6 277
 
7.1%
4 262
 
6.7%
9 179
 
4.6%
8 172
 
4.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3265
83.3%
Dash Punctuation 653
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 946
29.0%
2 340
 
10.4%
5 327
 
10.0%
3 307
 
9.4%
0 297
 
9.1%
6 277
 
8.5%
4 262
 
8.0%
9 179
 
5.5%
8 172
 
5.3%
7 158
 
4.8%
Dash Punctuation
ValueCountFrequency (%)
- 653
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3918
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 946
24.1%
- 653
16.7%
2 340
 
8.7%
5 327
 
8.3%
3 307
 
7.8%
0 297
 
7.6%
6 277
 
7.1%
4 262
 
6.7%
9 179
 
4.6%
8 172
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3918
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 946
24.1%
- 653
16.7%
2 340
 
8.7%
5 327
 
8.3%
3 307
 
7.8%
0 297
 
7.6%
6 277
 
7.1%
4 262
 
6.7%
9 179
 
4.6%
8 172
 
4.4%

유형
Categorical

Distinct4
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size5.4 KiB
방범용
417 
도시공원놀이터
128 
어린이보호구역
108 
차량번호인식
 
19

Length

Max length7
Median length3
Mean length4.4895833
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
방범용 417
62.1%
도시공원놀이터 128
 
19.0%
어린이보호구역 108
 
16.1%
차량번호인식 19
 
2.8%

Length

2023-12-12T12:49:10.168853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T12:49:10.317368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
방범용 417
62.1%
도시공원놀이터 128
 
19.0%
어린이보호구역 108
 
16.1%
차량번호인식 19
 
2.8%

카메라대수
Real number (ℝ)

HIGH CORRELATION 

Distinct7
Distinct (%)1.0%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean1.6065574
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.0 KiB
2023-12-12T12:49:10.456435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q32
95-th percentile3
Maximum7
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.69554139
Coefficient of variation (CV)0.43293903
Kurtosis8.2146335
Mean1.6065574
Median Absolute Deviation (MAD)1
Skewness1.8037952
Sum1078
Variance0.48377783
MonotonicityNot monotonic
2023-12-12T12:49:10.593969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1 320
47.6%
2 311
46.3%
3 30
 
4.5%
4 7
 
1.0%
6 1
 
0.1%
5 1
 
0.1%
7 1
 
0.1%
(Missing) 1
 
0.1%
ValueCountFrequency (%)
1 320
47.6%
2 311
46.3%
3 30
 
4.5%
4 7
 
1.0%
5 1
 
0.1%
6 1
 
0.1%
7 1
 
0.1%
ValueCountFrequency (%)
7 1
 
0.1%
6 1
 
0.1%
5 1
 
0.1%
4 7
 
1.0%
3 30
 
4.5%
2 311
46.3%
1 320
47.6%
Distinct659
Distinct (%)98.1%
Missing0
Missing (%)0.0%
Memory size5.4 KiB
2023-12-12T12:49:10.957983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length76
Median length58
Mean length32.135417
Min length16

Characters and Unicode

Total characters21595
Distinct characters333
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique648 ?
Unique (%)96.4%

Sample

1st row대전광역시 유성구 구암동 598-1 유성대로 680번길 46
2nd row대전광역시 유성구 구암동 619-1 월드컵대로 275번길 34
3rd row대전광역시 유성구 장대동 317-5 유성대로 822번길 20
4th row대전광역시 유성구 궁동 1-11 궁동로68
5th row대전광역시 유성구 궁동 417-7궁동로2번길 9-2
ValueCountFrequency (%)
대전광역시 672
 
18.5%
유성구 672
 
18.5%
장대동 45
 
1.2%
구암동 45
 
1.2%
봉명동 40
 
1.1%
주변 32
 
0.9%
지족동 30
 
0.8%
관평동 29
 
0.8%
덕명동 26
 
0.7%
궁동 26
 
0.7%
Other values (1324) 2012
55.4%
2023-12-12T12:49:11.571913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3519
 
16.3%
906
 
4.2%
803
 
3.7%
789
 
3.7%
1 786
 
3.6%
780
 
3.6%
746
 
3.5%
722
 
3.3%
680
 
3.1%
675
 
3.1%
Other values (323) 11189
51.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 12334
57.1%
Decimal Number 4373
 
20.3%
Space Separator 3519
 
16.3%
Dash Punctuation 552
 
2.6%
Close Punctuation 386
 
1.8%
Open Punctuation 386
 
1.8%
Uppercase Letter 23
 
0.1%
Other Punctuation 18
 
0.1%
Math Symbol 4
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
906
 
7.3%
803
 
6.5%
789
 
6.4%
780
 
6.3%
746
 
6.0%
722
 
5.9%
680
 
5.5%
675
 
5.5%
673
 
5.5%
496
 
4.0%
Other values (293) 5064
41.1%
Uppercase Letter
ValueCountFrequency (%)
C 6
26.1%
I 5
21.7%
G 2
 
8.7%
A 2
 
8.7%
B 2
 
8.7%
K 1
 
4.3%
U 1
 
4.3%
S 1
 
4.3%
J 1
 
4.3%
T 1
 
4.3%
Decimal Number
ValueCountFrequency (%)
1 786
18.0%
2 529
12.1%
5 488
11.2%
3 480
11.0%
4 439
10.0%
6 397
9.1%
0 339
7.8%
8 337
7.7%
7 319
7.3%
9 259
 
5.9%
Other Punctuation
ValueCountFrequency (%)
/ 13
72.2%
. 4
 
22.2%
@ 1
 
5.6%
Math Symbol
ValueCountFrequency (%)
~ 2
50.0%
2
50.0%
Space Separator
ValueCountFrequency (%)
3519
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 552
100.0%
Close Punctuation
ValueCountFrequency (%)
) 386
100.0%
Open Punctuation
ValueCountFrequency (%)
( 386
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 12334
57.1%
Common 9238
42.8%
Latin 23
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
906
 
7.3%
803
 
6.5%
789
 
6.4%
780
 
6.3%
746
 
6.0%
722
 
5.9%
680
 
5.5%
675
 
5.5%
673
 
5.5%
496
 
4.0%
Other values (293) 5064
41.1%
Common
ValueCountFrequency (%)
3519
38.1%
1 786
 
8.5%
- 552
 
6.0%
2 529
 
5.7%
5 488
 
5.3%
3 480
 
5.2%
4 439
 
4.8%
6 397
 
4.3%
) 386
 
4.2%
( 386
 
4.2%
Other values (9) 1276
 
13.8%
Latin
ValueCountFrequency (%)
C 6
26.1%
I 5
21.7%
G 2
 
8.7%
A 2
 
8.7%
B 2
 
8.7%
K 1
 
4.3%
U 1
 
4.3%
S 1
 
4.3%
J 1
 
4.3%
T 1
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 12334
57.1%
ASCII 9259
42.9%
Arrows 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3519
38.0%
1 786
 
8.5%
- 552
 
6.0%
2 529
 
5.7%
5 488
 
5.3%
3 480
 
5.2%
4 439
 
4.7%
6 397
 
4.3%
) 386
 
4.2%
( 386
 
4.2%
Other values (19) 1297
 
14.0%
Hangul
ValueCountFrequency (%)
906
 
7.3%
803
 
6.5%
789
 
6.4%
780
 
6.3%
746
 
6.0%
722
 
5.9%
680
 
5.5%
675
 
5.5%
673
 
5.5%
496
 
4.0%
Other values (293) 5064
41.1%
Arrows
ValueCountFrequency (%)
2
100.0%

위도
Real number (ℝ)

HIGH CORRELATION 

Distinct610
Distinct (%)90.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean340851.82
Minimum328613
Maximum347375
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.0 KiB
2023-12-12T12:49:11.853047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum328613
5-th percentile337255.15
Q1338841.5
median340505.5
Q3342059.25
95-th percentile346233.15
Maximum347375
Range18762
Interquartile range (IQR)3217.75

Descriptive statistics

Standard deviation2869.8199
Coefficient of variation (CV)0.008419553
Kurtosis0.16406897
Mean340851.82
Median Absolute Deviation (MAD)1631
Skewness0.30008857
Sum2.2905242 × 108
Variance8235866.4
MonotonicityNot monotonic
2023-12-12T12:49:12.143106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
343149 12
 
1.8%
338621 4
 
0.6%
344695 4
 
0.6%
340557 3
 
0.4%
337537 3
 
0.4%
340023 3
 
0.4%
338053 3
 
0.4%
340152 3
 
0.4%
340243 3
 
0.4%
339151 3
 
0.4%
Other values (600) 631
93.9%
ValueCountFrequency (%)
328613 1
0.1%
332831 1
0.1%
332849 1
0.1%
333276 1
0.1%
333662 1
0.1%
333873 1
0.1%
334150 1
0.1%
334214 1
0.1%
334372 1
0.1%
334603 1
0.1%
ValueCountFrequency (%)
347375 1
0.1%
347235 1
0.1%
347189 1
0.1%
347148 1
0.1%
347074 1
0.1%
347011 1
0.1%
347000 1
0.1%
346998 1
0.1%
346976 1
0.1%
346914 1
0.1%

경도
Real number (ℝ)

HIGH CORRELATION 

Distinct614
Distinct (%)91.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean418763.24
Minimum407849
Maximum432778
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.0 KiB
2023-12-12T12:49:12.426212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum407849
5-th percentile411309.55
Q1416521.75
median418148
Q3421202.75
95-th percentile426082.4
Maximum432778
Range24929
Interquartile range (IQR)4681

Descriptive statistics

Standard deviation4171.9337
Coefficient of variation (CV)0.0099625118
Kurtosis0.27926059
Mean418763.24
Median Absolute Deviation (MAD)2631.5
Skewness0.10902689
Sum2.8140889 × 108
Variance17405031
MonotonicityNot monotonic
2023-12-12T12:49:13.085970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
420015 12
 
1.8%
424672 4
 
0.6%
417244 3
 
0.4%
414037 3
 
0.4%
421246 3
 
0.4%
422721 3
 
0.4%
417198 3
 
0.4%
412892 3
 
0.4%
420925 2
 
0.3%
417959 2
 
0.3%
Other values (604) 634
94.3%
ValueCountFrequency (%)
407849 1
0.1%
407992 1
0.1%
408302 1
0.1%
408309 1
0.1%
409054 1
0.1%
409075 1
0.1%
409116 1
0.1%
409209 1
0.1%
409432 1
0.1%
409506 1
0.1%
ValueCountFrequency (%)
432778 1
0.1%
432682 1
0.1%
431807 1
0.1%
430561 1
0.1%
430476 1
0.1%
428681 1
0.1%
428314 1
0.1%
427608 1
0.1%
427596 1
0.1%
427539 1
0.1%

시도코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size5.4 KiB
3000000000
672 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3000000000
2nd row3000000000
3rd row3000000000
4th row3000000000
5th row3000000000

Common Values

ValueCountFrequency (%)
3000000000 672
100.0%

Length

2023-12-12T12:49:13.288320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T12:49:13.446931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3000000000 672
100.0%

시도이름
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size5.4 KiB
대전광역시
672 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
대전광역시 672
100.0%

Length

2023-12-12T12:49:13.620403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T12:49:13.759422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
대전광역시 672
100.0%

시군구코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size5.4 KiB
3020000000
672 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3020000000
2nd row3020000000
3rd row3020000000
4th row3020000000
5th row3020000000

Common Values

ValueCountFrequency (%)
3020000000 672
100.0%

Length

2023-12-12T12:49:13.910377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T12:49:14.047910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3020000000 672
100.0%

시군구이름
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size5.4 KiB
유성구
672 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
유성구 672
100.0%

Length

2023-12-12T12:49:14.195117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T12:49:14.347547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
유성구 672
100.0%

행정동코드
Real number (ℝ)

HIGH CORRELATION 

Distinct15
Distinct (%)2.2%
Missing2
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean3.0200639 × 109
Minimum3.0170586 × 109
Maximum3.023058 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.0 KiB
2023-12-12T12:49:14.483752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.0170586 × 109
5-th percentile3.020052 × 109
Q13.020053 × 109
median3.0200546 × 109
Q33.020055 × 109
95-th percentile3.020061 × 109
Maximum3.023058 × 109
Range5999400
Interquartile range (IQR)2000

Descriptive statistics

Standard deviation231921.67
Coefficient of variation (CV)7.679363 × 10-5
Kurtosis165.1972
Mean3.0200639 × 109
Median Absolute Deviation (MAD)1600
Skewness6.4150746
Sum2.0234428 × 1012
Variance5.3787662 × 1010
MonotonicityNot monotonic
2023-12-12T12:49:14.659813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
3020052000 105
15.6%
3020053000 101
15.0%
3020054000 92
13.7%
3020055000 67
10.0%
3020054700 48
7.1%
3020061000 48
7.1%
3020058000 47
7.0%
3020060000 37
 
5.5%
3020054600 37
 
5.5%
3020057000 31
 
4.6%
Other values (5) 57
8.5%
ValueCountFrequency (%)
3017058600 1
 
0.1%
3020052000 105
15.6%
3020052600 24
 
3.6%
3020052700 5
 
0.7%
3020053000 101
15.0%
3020054000 92
13.7%
3020054600 37
 
5.5%
3020054700 48
7.1%
3020054800 24
 
3.6%
3020055000 67
10.0%
ValueCountFrequency (%)
3023058000 3
 
0.4%
3020061000 48
7.1%
3020060000 37
5.5%
3020058000 47
7.0%
3020057000 31
 
4.6%
3020055000 67
10.0%
3020054800 24
 
3.6%
3020054700 48
7.1%
3020054600 37
5.5%
3020054000 92
13.7%

행정동이름
Categorical

HIGH CORRELATION 

Distinct16
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size5.4 KiB
진잠동
105 
온천1동
101 
온천2동
92 
신성동
67 
노은2동
48 
Other values (11)
259 

Length

Max length4
Median length4
Mean length3.5252976
Min length3

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row온천1동
2nd row온천1동
3rd row온천2동
4th row온천2동
5th row온천2동

Common Values

ValueCountFrequency (%)
진잠동 105
15.6%
온천1동 101
15.0%
온천2동 92
13.7%
신성동 67
10.0%
노은2동 48
7.1%
원신흥동 48
7.1%
구즉동 47
7.0%
관평동 37
 
5.5%
노은1동 37
 
5.5%
전민동 31
 
4.6%
Other values (6) 59
8.8%

Length

2023-12-12T12:49:14.861229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
진잠동 105
15.6%
온천1동 101
15.0%
온천2동 92
13.7%
신성동 67
10.0%
노은2동 48
7.1%
원신흥동 48
7.1%
구즉동 47
7.0%
관평동 37
 
5.5%
노은1동 37
 
5.5%
전민동 31
 
4.6%
Other values (6) 59
8.8%

법정동코드
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct48
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0200077 × 109
Minimum3.0170113 × 109
Maximum3.0200153 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.0 KiB
2023-12-12T12:49:15.062829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.0170113 × 109
5-th percentile3.0200102 × 109
Q13.0200112 × 109
median3.0200119 × 109
Q33.0200132 × 109
95-th percentile3.0200146 × 109
Maximum3.0200153 × 109
Range3004000
Interquartile range (IQR)2000

Descriptive statistics

Standard deviation115769
Coefficient of variation (CV)3.8334009 × 10-5
Kurtosis671.81085
Mean3.0200077 × 109
Median Absolute Deviation (MAD)800
Skewness-25.917497
Sum2.0294452 × 1012
Variance1.3402462 × 1010
MonotonicityNot monotonic
2023-12-12T12:49:15.273813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
3020011100 50
 
7.4%
3020011200 48
 
7.1%
3020011700 43
 
6.4%
3020012000 31
 
4.6%
3020014600 29
 
4.3%
3020010100 28
 
4.2%
3020011300 27
 
4.0%
3020012500 26
 
3.9%
3020012200 26
 
3.9%
3020014100 24
 
3.6%
Other values (38) 340
50.6%
ValueCountFrequency (%)
3017011300 1
 
0.1%
3020010100 28
4.2%
3020010200 12
1.8%
3020010300 10
 
1.5%
3020010400 9
 
1.3%
3020010500 13
1.9%
3020010600 16
2.4%
3020010700 2
 
0.3%
3020010800 4
 
0.6%
3020010900 7
 
1.0%
ValueCountFrequency (%)
3020015300 1
 
0.1%
3020015000 2
 
0.3%
3020014900 2
 
0.3%
3020014800 2
 
0.3%
3020014700 21
3.1%
3020014600 29
4.3%
3020014500 19
2.8%
3020014400 6
 
0.9%
3020014300 5
 
0.7%
3020014200 2
 
0.3%

법정동이름
Categorical

HIGH CORRELATION 

Distinct48
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Memory size5.4 KiB
봉명동
50 
구암동
48 
장대동
 
43
지족동
 
31
관평동
 
29
Other values (43)
471 

Length

Max length4
Median length3
Mean length2.9375
Min length2

Unique

Unique5 ?
Unique (%)0.7%

Sample

1st row구암동
2nd row구암동
3rd row장대동
4th row궁동
5th row궁동

Common Values

ValueCountFrequency (%)
봉명동 50
 
7.4%
구암동 48
 
7.1%
장대동 43
 
6.4%
지족동 31
 
4.6%
관평동 29
 
4.3%
원내동 28
 
4.2%
덕명동 27
 
4.0%
신성동 26
 
3.9%
궁동 26
 
3.9%
전민동 24
 
3.6%
Other values (38) 340
50.6%

Length

2023-12-12T12:49:15.489866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
봉명동 50
 
7.4%
구암동 48
 
7.1%
장대동 43
 
6.4%
지족동 31
 
4.6%
관평동 29
 
4.3%
원내동 28
 
4.2%
덕명동 27
 
4.0%
신성동 26
 
3.9%
궁동 26
 
3.9%
전민동 24
 
3.6%
Other values (38) 340
50.6%

기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size5.4 KiB
2022
672 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022
2nd row2022
3rd row2022
4th row2022
5th row2022

Common Values

ValueCountFrequency (%)
2022 672
100.0%

Length

2023-12-12T12:49:15.647602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T12:49:15.757407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022 672
100.0%

Interactions

2023-12-12T12:49:07.204475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:49:02.601232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:49:03.440355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:49:04.467794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:49:05.391866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:49:06.249995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:49:07.373714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:49:02.723462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:49:03.563133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:49:04.575424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:49:05.559152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:49:06.503803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:49:07.545444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:49:02.873125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:49:04.013400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:49:04.689616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:49:05.710814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:49:06.642910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:49:07.715364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:49:03.048575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:49:04.128418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:49:04.809017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:49:05.846774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:49:06.816483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:49:07.845194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:49:03.182256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:49:04.235801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:49:04.934367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:49:05.974436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:49:06.933044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:49:07.977747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:49:03.301161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:49:04.368869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:49:05.153289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:49:06.124823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:49:07.079563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T12:49:15.866158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호유형카메라대수위도경도행정동코드행정동이름법정동코드법정동이름
번호1.0000.5850.4450.3010.4280.2870.494NaN0.665
유형0.5851.0000.3620.2160.2270.0900.273NaN0.560
카메라대수0.4450.3621.0000.1930.0000.1190.212NaN0.781
위도0.3010.2160.1931.0000.7380.4250.853NaN0.980
경도0.4280.2270.0000.7381.0000.5660.899NaN0.976
행정동코드0.2870.0900.1190.4250.5661.0001.000NaN0.882
행정동이름0.4940.2730.2120.8530.8991.0001.000NaN0.984
법정동코드NaNNaNNaNNaNNaNNaNNaN1.000NaN
법정동이름0.6650.5600.7810.9800.9760.8820.984NaN1.000
2023-12-12T12:49:16.032070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정동이름법정동이름유형
행정동이름1.0000.8100.157
법정동이름0.8101.0000.288
유형0.1570.2881.000
2023-12-12T12:49:16.155334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호카메라대수위도경도행정동코드법정동코드유형행정동이름법정동이름
번호1.0000.558-0.154-0.144-0.065-0.1280.3900.2070.286
카메라대수0.5581.000-0.208-0.013-0.137-0.0520.2560.0970.436
위도-0.154-0.2081.0000.5560.6370.6030.0920.5430.732
경도-0.144-0.0130.5561.0000.6940.9410.1380.6100.805
행정동코드-0.065-0.1370.6370.6941.0000.7700.0600.9920.783
법정동코드-0.128-0.0520.6030.9410.7701.0000.0000.9900.965
유형0.3900.2560.0920.1380.0600.0001.0000.1570.288
행정동이름0.2070.0970.5430.6100.9920.9900.1571.0000.810
법정동이름0.2860.4360.7320.8050.7830.9650.2880.8101.000

Missing values

2023-12-12T12:49:08.177902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T12:49:08.398957image/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.
2023-12-12T12:49:08.587059image/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

번호관리번호유형카메라대수설치위치위도경도시도코드시도이름시군구코드시군구이름행정동코드행정동이름법정동코드법정동이름기준일자
0108-001방범용2대전광역시 유성구 구암동 598-1 유성대로 680번길 463403214172243000000000대전광역시3020000000유성구3020053000온천1동3020011200구암동2022
1208-002방범용1대전광역시 유성구 구암동 619-1 월드컵대로 275번길 343401974168253000000000대전광역시3020000000유성구3020053000온천1동3020011200구암동2022
2308-003방범용1대전광역시 유성구 장대동 317-5 유성대로 822번길 203409464184193000000000대전광역시3020000000유성구3020054000온천2동3020011700장대동2022
3408-004방범용1대전광역시 유성구 궁동 1-11 궁동로683420504183403000000000대전광역시3020000000유성구3020054000온천2동3020012200궁동2022
4508-005방범용1대전광역시 유성구 궁동 417-7궁동로2번길 9-23416954180733000000000대전광역시3020000000유성구3020054000온천2동3020012200궁동2022
5608-006방범용1대전광역시 유성구 궁동 427-11대학로159번길 23419634179753000000000대전광역시3020000000유성구3020054000온천2동3020012200궁동2022
6708-007방범용1대전광역시 유성구 구암동 544-23유성대로668번길 1073405494169963000000000대전광역시3020000000유성구3020053000온천1동3020011200구암동2022
7808-008방범용1대전광역시 유성구 구암동 622-12월드컵대로253번안길 13400034168283000000000대전광역시3020000000유성구3020053000온천1동3020011200구암동2022
8908-009방범용1대전광역시 유성구 원내동 213-5원내로 53388264112573000000000대전광역시3020000000유성구3020052000진잠동3020010100원내동2022
91008-010방범용2대전광역시 유성구 궁동 458-13문화원로47번길 473411434181433000000000대전광역시3020000000유성구3020054000온천2동3020012200궁동2022
번호관리번호유형카메라대수설치위치위도경도시도코드시도이름시군구코드시군구이름행정동코드행정동이름법정동코드법정동이름기준일자
66266721-667방범용2대전광역시 유성구 구암동 98-63400724175503000000000대전광역시3020000000유성구3020053000온천1동3020011200구암동2022
66366821-668방범용2대전광역시 유성구 구암동 83-33400304172443000000000대전광역시3020000000유성구3020053000온천1동3020011200구암동2022
66466921-669방범용2대전광역시 유성구 구암동 83-33400304172443000000000대전광역시3020000000유성구3020053000온천1동3020011200구암동2022
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