Overview

Dataset statistics

Number of variables19
Number of observations250
Missing cells6
Missing cells (%)0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory39.2 KiB
Average record size in memory160.5 B

Variable types

Numeric8
Text6
DateTime3
Categorical2

Dataset

Description파주시 첨단교통관리시스템(ATMS) 노드 간 월별 통계에 대한 데이터로 시작노드와 종료노드의 아이디, 주소, 위경도 및 노드 관련한 정보를 제공합니다.
Author경기도 파주시
URLhttps://www.data.go.kr/data/15119529/fileData.do

Alerts

통계시작연월일 has constant value ""Constant
통계종료연월일 has constant value ""Constant
관리기관명 has constant value ""Constant
관리기관전화번호 has constant value ""Constant
데이터기준일자 has constant value ""Constant
시작노드위도 is highly overall correlated with 시작노드경도 and 2 other fieldsHigh correlation
시작노드경도 is highly overall correlated with 시작노드위도 and 2 other fieldsHigh correlation
종료노드위도 is highly overall correlated with 시작노드위도 and 2 other fieldsHigh correlation
종료노드경도 is highly overall correlated with 시작노드위도 and 2 other fieldsHigh correlation
시작노드도로명주소 has 3 (1.2%) missing valuesMissing
종료노드도로명주소 has 3 (1.2%) missing valuesMissing
속도 has unique valuesUnique

Reproduction

Analysis started2024-04-17 14:19:20.313931
Analysis finished2024-04-17 14:19:27.498421
Duration7.18 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시작노드아이디(ID)
Real number (ℝ)

Distinct87
Distinct (%)34.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2900667 × 109
Minimum2.290001 × 109
Maximum2.2901933 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2024-04-17T23:19:27.561632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.290001 × 109
5-th percentile2.2900039 × 109
Q12.2900092 × 109
median2.2900598 × 109
Q32.2901148 × 109
95-th percentile2.2901613 × 109
Maximum2.2901933 × 109
Range192300
Interquartile range (IQR)105572

Descriptive statistics

Standard deviation56818.647
Coefficient of variation (CV)2.4810913 × 10-5
Kurtosis-1.1105272
Mean2.2900667 × 109
Median Absolute Deviation (MAD)53350
Skewness0.39124232
Sum5.7251668 × 1011
Variance3.2283587 × 109
MonotonicityNot monotonic
2024-04-17T23:19:27.679694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2290003900 6
 
2.4%
2290006200 5
 
2.0%
2290005700 5
 
2.0%
2290070000 5
 
2.0%
2290005501 5
 
2.0%
2290112900 5
 
2.0%
2290059700 5
 
2.0%
2290122200 5
 
2.0%
2290011300 5
 
2.0%
2290123500 5
 
2.0%
Other values (77) 199
79.6%
ValueCountFrequency (%)
2290001000 4
1.6%
2290002300 2
 
0.8%
2290002800 1
 
0.4%
2290003801 2
 
0.8%
2290003900 6
2.4%
2290004000 3
1.2%
2290004600 3
1.2%
2290004701 4
1.6%
2290004803 1
 
0.4%
2290004805 3
1.2%
ValueCountFrequency (%)
2290193300 3
1.2%
2290179400 3
1.2%
2290179000 3
1.2%
2290175400 2
0.8%
2290161600 2
0.8%
2290161000 3
1.2%
2290160800 3
1.2%
2290160600 4
1.6%
2290160000 3
1.2%
2290134700 1
 
0.4%

시작노드명
Text

Distinct86
Distinct (%)34.4%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
2024-04-17T23:19:27.879236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length9
Mean length6.108
Min length3

Characters and Unicode

Total characters1527
Distinct characters154
Distinct categories4 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique15 ?
Unique (%)6.0%

Sample

1st rowLCD서문교차로
2nd rowLCD서문교차로
3rd rowLCD서문교차로
4th rowPX마을앞교차로
5th rowPX마을앞교차로
ValueCountFrequency (%)
금촌교차로 6
 
2.4%
한빛마을5단지 5
 
2.0%
뜨란채1단지입구 5
 
2.0%
금촌사거리 5
 
2.0%
미주골재중기 5
 
2.0%
갈현교차로 5
 
2.0%
능산교차로 5
 
2.0%
와동교차로 5
 
2.0%
통일공원앞교차로 5
 
2.0%
문산제일고삼거리 5
 
2.0%
Other values (76) 199
79.6%
2024-04-17T23:19:28.192178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
교 103
 
6.7%
리 94
 
6.2%
차 89
 
5.8%
로 82
 
5.4%
거 80
 
5.2%
사 61
 
4.0%
금 40
 
2.6%
동 38
 
2.5%
산 31
 
2.0%
지 29
 
1.9%
Other values (144) 880
57.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1409
92.3%
Decimal Number 74
 
4.8%
Uppercase Letter 39
 
2.6%
Dash Punctuation 5
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
교 103
 
7.3%
리 94
 
6.7%
차 89
 
6.3%
로 82
 
5.8%
거 80
 
5.7%
사 61
 
4.3%
금 40
 
2.8%
동 38
 
2.7%
산 31
 
2.2%
지 29
 
2.1%
Other values (126) 762
54.1%
Decimal Number
ValueCountFrequency (%)
1 19
25.7%
9 13
17.6%
5 9
12.2%
7 8
10.8%
2 7
 
9.5%
4 6
 
8.1%
8 5
 
6.8%
0 4
 
5.4%
3 3
 
4.1%
Uppercase Letter
ValueCountFrequency (%)
C 11
28.2%
I 8
20.5%
K 4
 
10.3%
T 4
 
10.3%
P 3
 
7.7%
X 3
 
7.7%
L 3
 
7.7%
D 3
 
7.7%
Dash Punctuation
ValueCountFrequency (%)
- 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1409
92.3%
Common 79
 
5.2%
Latin 39
 
2.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
교 103
 
7.3%
리 94
 
6.7%
차 89
 
6.3%
로 82
 
5.8%
거 80
 
5.7%
사 61
 
4.3%
금 40
 
2.8%
동 38
 
2.7%
산 31
 
2.2%
지 29
 
2.1%
Other values (126) 762
54.1%
Common
ValueCountFrequency (%)
1 19
24.1%
9 13
16.5%
5 9
11.4%
7 8
10.1%
2 7
 
8.9%
4 6
 
7.6%
8 5
 
6.3%
- 5
 
6.3%
0 4
 
5.1%
3 3
 
3.8%
Latin
ValueCountFrequency (%)
C 11
28.2%
I 8
20.5%
K 4
 
10.3%
T 4
 
10.3%
P 3
 
7.7%
X 3
 
7.7%
L 3
 
7.7%
D 3
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1409
92.3%
ASCII 118
 
7.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
교 103
 
7.3%
리 94
 
6.7%
차 89
 
6.3%
로 82
 
5.8%
거 80
 
5.7%
사 61
 
4.3%
금 40
 
2.8%
동 38
 
2.7%
산 31
 
2.2%
지 29
 
2.1%
Other values (126) 762
54.1%
ASCII
ValueCountFrequency (%)
1 19
16.1%
9 13
11.0%
C 11
9.3%
5 9
 
7.6%
7 8
 
6.8%
I 8
 
6.8%
2 7
 
5.9%
4 6
 
5.1%
8 5
 
4.2%
- 5
 
4.2%
Other values (8) 27
22.9%

시작노드도로명주소
Text

MISSING 

Distinct84
Distinct (%)34.0%
Missing3
Missing (%)1.2%
Memory size2.1 KiB
2024-04-17T23:19:28.440187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length22
Mean length17.117409
Min length13

Characters and Unicode

Total characters4228
Distinct characters115
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

Unique13 ?
Unique (%)5.3%

Sample

1st row경기도 파주시 월롱면 엘지로 408-3
2nd row경기도 파주시 월롱면 엘지로 408-3
3rd row경기도 파주시 월롱면 엘지로 408-3
4th row경기도 파주시 통일로 549
5th row경기도 파주시 통일로 549
ValueCountFrequency (%)
경기도 247
22.9%
파주시 247
22.9%
월롱면 23
 
2.1%
경의로 20
 
1.9%
탄현면 20
 
1.9%
통일로 19
 
1.8%
파주읍 19
 
1.8%
조리읍 15
 
1.4%
새꽃로 14
 
1.3%
와석순환로 13
 
1.2%
Other values (129) 441
40.9%
2024-04-17T23:19:28.812994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
831
19.7%
경 267
 
6.3%
파 267
 
6.3%
주 267
 
6.3%
시 250
 
5.9%
기 250
 
5.9%
도 247
 
5.8%
로 219
 
5.2%
1 175
 
4.1%
5 90
 
2.1%
Other values (105) 1365
32.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2589
61.2%
Space Separator 831
 
19.7%
Decimal Number 765
 
18.1%
Dash Punctuation 43
 
1.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
경 267
 
10.3%
파 267
 
10.3%
주 267
 
10.3%
시 250
 
9.7%
기 250
 
9.7%
도 247
 
9.5%
로 219
 
8.5%
면 46
 
1.8%
읍 44
 
1.7%
길 32
 
1.2%
Other values (93) 700
27.0%
Decimal Number
ValueCountFrequency (%)
1 175
22.9%
5 90
11.8%
9 79
10.3%
4 77
10.1%
2 77
10.1%
7 76
9.9%
3 72
9.4%
0 51
 
6.7%
6 41
 
5.4%
8 27
 
3.5%
Space Separator
ValueCountFrequency (%)
831
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 43
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2589
61.2%
Common 1639
38.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
경 267
 
10.3%
파 267
 
10.3%
주 267
 
10.3%
시 250
 
9.7%
기 250
 
9.7%
도 247
 
9.5%
로 219
 
8.5%
면 46
 
1.8%
읍 44
 
1.7%
길 32
 
1.2%
Other values (93) 700
27.0%
Common
ValueCountFrequency (%)
831
50.7%
1 175
 
10.7%
5 90
 
5.5%
9 79
 
4.8%
4 77
 
4.7%
2 77
 
4.7%
7 76
 
4.6%
3 72
 
4.4%
0 51
 
3.1%
- 43
 
2.6%
Other values (2) 68
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2589
61.2%
ASCII 1639
38.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
831
50.7%
1 175
 
10.7%
5 90
 
5.5%
9 79
 
4.8%
4 77
 
4.7%
2 77
 
4.7%
7 76
 
4.6%
3 72
 
4.4%
0 51
 
3.1%
- 43
 
2.6%
Other values (2) 68
 
4.1%
Hangul
ValueCountFrequency (%)
경 267
 
10.3%
파 267
 
10.3%
주 267
 
10.3%
시 250
 
9.7%
기 250
 
9.7%
도 247
 
9.5%
로 219
 
8.5%
면 46
 
1.8%
읍 44
 
1.7%
길 32
 
1.2%
Other values (93) 700
27.0%

시작노드지번주소
Text

Distinct79
Distinct (%)31.6%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
2024-04-17T23:19:29.048099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length20
Mean length18.32
Min length15

Characters and Unicode

Total characters4580
Distinct characters80
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

Unique12 ?
Unique (%)4.8%

Sample

1st row경기도 파주시 월롱면 덕은리 772-41
2nd row경기도 파주시 월롱면 덕은리 772-41
3rd row경기도 파주시 월롱면 덕은리 772-41
4th row경기도 파주시 아동동 49-3
5th row경기도 파주시 아동동 49-3
ValueCountFrequency (%)
경기도 250
22.6%
파주시 250
22.6%
금촌동 44
 
4.0%
목동동 26
 
2.4%
월롱면 23
 
2.1%
탄현면 20
 
1.8%
파주읍 19
 
1.7%
조리읍 15
 
1.4%
산 14
 
1.3%
와동동 14
 
1.3%
Other values (118) 429
38.9%
2024-04-17T23:19:29.413170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
854
18.6%
파 273
 
6.0%
주 273
 
6.0%
도 253
 
5.5%
경 250
 
5.5%
시 250
 
5.5%
기 250
 
5.5%
동 231
 
5.0%
- 178
 
3.9%
1 173
 
3.8%
Other values (70) 1595
34.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2537
55.4%
Decimal Number 1011
 
22.1%
Space Separator 854
 
18.6%
Dash Punctuation 178
 
3.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
파 273
10.8%
주 273
10.8%
도 253
10.0%
경 250
9.9%
시 250
9.9%
기 250
9.9%
동 231
9.1%
리 103
 
4.1%
금 53
 
2.1%
면 46
 
1.8%
Other values (58) 555
21.9%
Decimal Number
ValueCountFrequency (%)
1 173
17.1%
2 149
14.7%
3 108
10.7%
5 104
10.3%
9 88
8.7%
4 87
8.6%
0 78
7.7%
6 76
7.5%
7 75
7.4%
8 73
7.2%
Space Separator
ValueCountFrequency (%)
854
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 178
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2537
55.4%
Common 2043
44.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
파 273
10.8%
주 273
10.8%
도 253
10.0%
경 250
9.9%
시 250
9.9%
기 250
9.9%
동 231
9.1%
리 103
 
4.1%
금 53
 
2.1%
면 46
 
1.8%
Other values (58) 555
21.9%
Common
ValueCountFrequency (%)
854
41.8%
- 178
 
8.7%
1 173
 
8.5%
2 149
 
7.3%
3 108
 
5.3%
5 104
 
5.1%
9 88
 
4.3%
4 87
 
4.3%
0 78
 
3.8%
6 76
 
3.7%
Other values (2) 148
 
7.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2537
55.4%
ASCII 2043
44.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
854
41.8%
- 178
 
8.7%
1 173
 
8.5%
2 149
 
7.3%
3 108
 
5.3%
5 104
 
5.1%
9 88
 
4.3%
4 87
 
4.3%
0 78
 
3.8%
6 76
 
3.7%
Other values (2) 148
 
7.2%
Hangul
ValueCountFrequency (%)
파 273
10.8%
주 273
10.8%
도 253
10.0%
경 250
9.9%
시 250
9.9%
기 250
9.9%
동 231
9.1%
리 103
 
4.1%
금 53
 
2.1%
면 46
 
1.8%
Other values (58) 555
21.9%

시작노드위도
Real number (ℝ)

HIGH CORRELATION 

Distinct87
Distinct (%)34.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.764126
Minimum37.706598
Maximum37.86021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2024-04-17T23:19:29.529346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.706598
5-th percentile37.711128
Q137.72964
median37.75762
Q337.782749
95-th percentile37.848199
Maximum37.86021
Range0.1536117
Interquartile range (IQR)0.053109247

Descriptive statistics

Standard deviation0.041316099
Coefficient of variation (CV)0.0010940568
Kurtosis-0.35018352
Mean37.764126
Median Absolute Deviation (MAD)0.02796
Skewness0.74138755
Sum9441.0316
Variance0.0017070201
MonotonicityNot monotonic
2024-04-17T23:19:29.646268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.74939 6
 
2.4%
37.76999584 5
 
2.0%
37.76548903 5
 
2.0%
37.77169 5
 
2.0%
37.76282457 5
 
2.0%
37.71711759 5
 
2.0%
37.75772 5
 
2.0%
37.74146 5
 
2.0%
37.84819881 5
 
2.0%
37.83079 5
 
2.0%
Other values (77) 199
79.6%
ValueCountFrequency (%)
37.706598 3
1.2%
37.70692 3
1.2%
37.7070311 3
1.2%
37.70834 1
 
0.4%
37.71007615 3
1.2%
37.71241435 4
1.6%
37.71696356 3
1.2%
37.71711759 5
2.0%
37.71714 4
1.6%
37.71811121 4
1.6%
ValueCountFrequency (%)
37.8602097 2
 
0.8%
37.86003422 3
1.2%
37.85916856 2
 
0.8%
37.85435191 3
1.2%
37.84930658 1
 
0.4%
37.84819881 5
2.0%
37.84559 2
 
0.8%
37.83975313 1
 
0.4%
37.83079 5
2.0%
37.82856 4
1.6%

시작노드경도
Real number (ℝ)

HIGH CORRELATION 

Distinct87
Distinct (%)34.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.76357
Minimum126.68996
Maximum126.87297
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2024-04-17T23:19:29.762909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.68996
5-th percentile126.71733
Q1126.74284
median126.76493
Q3126.78181
95-th percentile126.81545
Maximum126.87297
Range0.1830069
Interquartile range (IQR)0.03897

Descriptive statistics

Standard deviation0.030077101
Coefficient of variation (CV)0.00023726928
Kurtosis0.37556606
Mean126.76357
Median Absolute Deviation (MAD)0.01917965
Skewness0.25017181
Sum31690.893
Variance0.00090463203
MonotonicityNot monotonic
2024-04-17T23:19:29.883865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.79753 6
 
2.4%
126.7628612 5
 
2.0%
126.7694168 5
 
2.0%
126.72397 5
 
2.0%
126.766269 5
 
2.0%
126.7599769 5
 
2.0%
126.76863 5
 
2.0%
126.75247 5
 
2.0%
126.7885548 5
 
2.0%
126.7787 5
 
2.0%
Other values (77) 199
79.6%
ValueCountFrequency (%)
126.68996 2
0.8%
126.6924043 1
 
0.4%
126.7017229 1
 
0.4%
126.7020046 1
 
0.4%
126.70577 2
0.8%
126.71094 2
0.8%
126.71578 3
1.2%
126.7173271 1
 
0.4%
126.7173361 2
0.8%
126.7185091 1
 
0.4%
ValueCountFrequency (%)
126.8729669 1
 
0.4%
126.8463679 1
 
0.4%
126.839989 2
0.8%
126.8337663 2
0.8%
126.8189 3
1.2%
126.81598 4
1.6%
126.8148054 1
 
0.4%
126.8142693 1
 
0.4%
126.8141765 1
 
0.4%
126.80901 2
0.8%

종료노드아이디(ID)
Real number (ℝ)

Distinct86
Distinct (%)34.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.289667 × 109
Minimum2.1900308 × 109
Maximum2.2901933 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2024-04-17T23:19:29.999266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.1900308 × 109
5-th percentile2.2900039 × 109
Q12.2900092 × 109
median2.2900598 × 109
Q32.2901148 × 109
95-th percentile2.2901616 × 109
Maximum2.2901933 × 109
Range1.001625 × 108
Interquartile range (IQR)105572

Descriptive statistics

Standard deviation6327110.2
Coefficient of variation (CV)0.002763332
Kurtosis249.95908
Mean2.289667 × 109
Median Absolute Deviation (MAD)53350
Skewness-15.809455
Sum5.7241675 × 1011
Variance4.0032323 × 1013
MonotonicityNot monotonic
2024-04-17T23:19:30.116830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2290003900 6
 
2.4%
2290011300 6
 
2.4%
2290123500 5
 
2.0%
2290070000 5
 
2.0%
2290112900 5
 
2.0%
2290006200 5
 
2.0%
2290059700 5
 
2.0%
2290005700 5
 
2.0%
2290005501 5
 
2.0%
2290065900 4
 
1.6%
Other values (76) 199
79.6%
ValueCountFrequency (%)
2190030800 1
 
0.4%
2290001000 4
1.6%
2290002300 2
 
0.8%
2290002800 1
 
0.4%
2290003800 1
 
0.4%
2290003801 1
 
0.4%
2290003900 6
2.4%
2290004000 3
1.2%
2290004600 3
1.2%
2290004701 4
1.6%
ValueCountFrequency (%)
2290193300 3
1.2%
2290179400 3
1.2%
2290179000 3
1.2%
2290175400 2
0.8%
2290161600 3
1.2%
2290161000 3
1.2%
2290160800 3
1.2%
2290160600 4
1.6%
2290160000 3
1.2%
2290134700 1
 
0.4%

종료노드명
Text

Distinct85
Distinct (%)34.0%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
2024-04-17T23:19:30.341886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length9
Mean length6.096
Min length3

Characters and Unicode

Total characters1524
Distinct characters153
Distinct categories4 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique14 ?
Unique (%)5.6%

Sample

1st row능산교차로
2nd row덕은5리사거리
3rd row비석사거리
4th row금촌신사거리
5th row등원고가차도
ValueCountFrequency (%)
통일공원앞교차로 6
 
2.4%
금촌교차로 6
 
2.4%
능산교차로 5
 
2.0%
갈현교차로 5
 
2.0%
뜨란채1단지입구 5
 
2.0%
금촌사거리 5
 
2.0%
문산제일고삼거리 5
 
2.0%
한빛마을5단지 5
 
2.0%
미주골재중기 5
 
2.0%
동패동1749 4
 
1.6%
Other values (75) 199
79.6%
2024-04-17T23:19:30.671436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
교 103
 
6.8%
리 92
 
6.0%
차 90
 
5.9%
로 82
 
5.4%
거 78
 
5.1%
사 61
 
4.0%
금 40
 
2.6%
동 35
 
2.3%
산 31
 
2.0%
지 27
 
1.8%
Other values (143) 885
58.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1413
92.7%
Decimal Number 67
 
4.4%
Uppercase Letter 40
 
2.6%
Dash Punctuation 4
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
교 103
 
7.3%
리 92
 
6.5%
차 90
 
6.4%
로 82
 
5.8%
거 78
 
5.5%
사 61
 
4.3%
금 40
 
2.8%
동 35
 
2.5%
산 31
 
2.2%
지 27
 
1.9%
Other values (124) 774
54.8%
Decimal Number
ValueCountFrequency (%)
1 17
25.4%
9 12
17.9%
5 9
13.4%
7 8
11.9%
2 7
10.4%
4 5
 
7.5%
8 4
 
6.0%
3 3
 
4.5%
0 2
 
3.0%
Uppercase Letter
ValueCountFrequency (%)
C 11
27.5%
I 8
20.0%
K 4
 
10.0%
T 4
 
10.0%
X 3
 
7.5%
P 3
 
7.5%
D 3
 
7.5%
L 3
 
7.5%
A 1
 
2.5%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1413
92.7%
Common 71
 
4.7%
Latin 40
 
2.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
교 103
 
7.3%
리 92
 
6.5%
차 90
 
6.4%
로 82
 
5.8%
거 78
 
5.5%
사 61
 
4.3%
금 40
 
2.8%
동 35
 
2.5%
산 31
 
2.2%
지 27
 
1.9%
Other values (124) 774
54.8%
Common
ValueCountFrequency (%)
1 17
23.9%
9 12
16.9%
5 9
12.7%
7 8
11.3%
2 7
9.9%
4 5
 
7.0%
- 4
 
5.6%
8 4
 
5.6%
3 3
 
4.2%
0 2
 
2.8%
Latin
ValueCountFrequency (%)
C 11
27.5%
I 8
20.0%
K 4
 
10.0%
T 4
 
10.0%
X 3
 
7.5%
P 3
 
7.5%
D 3
 
7.5%
L 3
 
7.5%
A 1
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1413
92.7%
ASCII 111
 
7.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
교 103
 
7.3%
리 92
 
6.5%
차 90
 
6.4%
로 82
 
5.8%
거 78
 
5.5%
사 61
 
4.3%
금 40
 
2.8%
동 35
 
2.5%
산 31
 
2.2%
지 27
 
1.9%
Other values (124) 774
54.8%
ASCII
ValueCountFrequency (%)
1 17
15.3%
9 12
10.8%
C 11
9.9%
5 9
 
8.1%
7 8
 
7.2%
I 8
 
7.2%
2 7
 
6.3%
4 5
 
4.5%
- 4
 
3.6%
8 4
 
3.6%
Other values (9) 26
23.4%

종료노드도로명주소
Text

MISSING 

Distinct82
Distinct (%)33.2%
Missing3
Missing (%)1.2%
Memory size2.1 KiB
2024-04-17T23:19:30.912629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length22
Mean length17.137652
Min length13

Characters and Unicode

Total characters4233
Distinct characters117
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

Unique10 ?
Unique (%)4.0%

Sample

1st row경기도 파주시 월롱면 휴암로 449-1
2nd row경기도 파주시 월롱면 엘지로 155
3rd row경기도 파주시 탄현면 정승로 21
4th row경기도 파주시 월롱면 통일로 694
5th row경기도 파주시 조리읍 등원로 47-19
ValueCountFrequency (%)
경기도 247
22.9%
파주시 246
22.8%
월롱면 23
 
2.1%
탄현면 20
 
1.9%
경의로 20
 
1.9%
파주읍 19
 
1.8%
통일로 19
 
1.8%
조리읍 15
 
1.4%
새꽃로 14
 
1.3%
와석순환로 13
 
1.2%
Other values (130) 443
41.1%
2024-04-17T23:19:31.311113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
832
19.7%
경 267
 
6.3%
파 265
 
6.3%
주 265
 
6.3%
시 250
 
5.9%
기 250
 
5.9%
도 247
 
5.8%
로 217
 
5.1%
1 178
 
4.2%
5 90
 
2.1%
Other values (107) 1372
32.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2593
61.3%
Space Separator 832
 
19.7%
Decimal Number 765
 
18.1%
Dash Punctuation 43
 
1.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
경 267
 
10.3%
파 265
 
10.2%
주 265
 
10.2%
시 250
 
9.6%
기 250
 
9.6%
도 247
 
9.5%
로 217
 
8.4%
면 46
 
1.8%
읍 44
 
1.7%
길 34
 
1.3%
Other values (95) 708
27.3%
Decimal Number
ValueCountFrequency (%)
1 178
23.3%
5 90
11.8%
9 79
10.3%
7 77
10.1%
2 77
10.1%
4 76
9.9%
3 72
9.4%
0 49
 
6.4%
6 41
 
5.4%
8 26
 
3.4%
Space Separator
ValueCountFrequency (%)
832
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 43
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2593
61.3%
Common 1640
38.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
경 267
 
10.3%
파 265
 
10.2%
주 265
 
10.2%
시 250
 
9.6%
기 250
 
9.6%
도 247
 
9.5%
로 217
 
8.4%
면 46
 
1.8%
읍 44
 
1.7%
길 34
 
1.3%
Other values (95) 708
27.3%
Common
ValueCountFrequency (%)
832
50.7%
1 178
 
10.9%
5 90
 
5.5%
9 79
 
4.8%
7 77
 
4.7%
2 77
 
4.7%
4 76
 
4.6%
3 72
 
4.4%
0 49
 
3.0%
- 43
 
2.6%
Other values (2) 67
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2593
61.3%
ASCII 1640
38.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
832
50.7%
1 178
 
10.9%
5 90
 
5.5%
9 79
 
4.8%
7 77
 
4.7%
2 77
 
4.7%
4 76
 
4.6%
3 72
 
4.4%
0 49
 
3.0%
- 43
 
2.6%
Other values (2) 67
 
4.1%
Hangul
ValueCountFrequency (%)
경 267
 
10.3%
파 265
 
10.2%
주 265
 
10.2%
시 250
 
9.6%
기 250
 
9.6%
도 247
 
9.5%
로 217
 
8.4%
면 46
 
1.8%
읍 44
 
1.7%
길 34
 
1.3%
Other values (95) 708
27.3%

종료노드지번주소
Text

Distinct78
Distinct (%)31.2%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
2024-04-17T23:19:31.552227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length21
Mean length18.336
Min length15

Characters and Unicode

Total characters4584
Distinct characters83
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

Unique11 ?
Unique (%)4.4%

Sample

1st row경기도 파주시 월롱면 능산리 665
2nd row경기도 파주시 월롱면 덕은리 1058-2
3rd row경기도 파주시 탄현면 금승리 185-16
4th row경기도 파주시 월롱면 영태리 502-1
5th row경기도 파주시 조리읍 등원리 249-1
ValueCountFrequency (%)
경기도 250
22.6%
파주시 249
22.5%
금촌동 44
 
4.0%
목동동 26
 
2.4%
월롱면 23
 
2.1%
탄현면 20
 
1.8%
파주읍 19
 
1.7%
조리읍 15
 
1.4%
산 14
 
1.3%
와동동 14
 
1.3%
Other values (120) 431
39.0%
2024-04-17T23:19:31.917851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
855
18.7%
파 271
 
5.9%
주 271
 
5.9%
도 253
 
5.5%
경 250
 
5.5%
시 250
 
5.5%
기 250
 
5.5%
동 229
 
5.0%
- 179
 
3.9%
1 174
 
3.8%
Other values (73) 1602
34.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2540
55.4%
Decimal Number 1010
 
22.0%
Space Separator 855
 
18.7%
Dash Punctuation 179
 
3.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
파 271
10.7%
주 271
10.7%
도 253
10.0%
경 250
9.8%
시 250
9.8%
기 250
9.8%
동 229
9.0%
리 103
 
4.1%
금 53
 
2.1%
면 46
 
1.8%
Other values (61) 564
22.2%
Decimal Number
ValueCountFrequency (%)
1 174
17.2%
2 148
14.7%
3 112
11.1%
5 103
10.2%
9 88
8.7%
4 87
8.6%
0 80
7.9%
7 74
7.3%
6 73
7.2%
8 71
7.0%
Space Separator
ValueCountFrequency (%)
855
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 179
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2540
55.4%
Common 2044
44.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
파 271
10.7%
주 271
10.7%
도 253
10.0%
경 250
9.8%
시 250
9.8%
기 250
9.8%
동 229
9.0%
리 103
 
4.1%
금 53
 
2.1%
면 46
 
1.8%
Other values (61) 564
22.2%
Common
ValueCountFrequency (%)
855
41.8%
- 179
 
8.8%
1 174
 
8.5%
2 148
 
7.2%
3 112
 
5.5%
5 103
 
5.0%
9 88
 
4.3%
4 87
 
4.3%
0 80
 
3.9%
7 74
 
3.6%
Other values (2) 144
 
7.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2540
55.4%
ASCII 2044
44.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
855
41.8%
- 179
 
8.8%
1 174
 
8.5%
2 148
 
7.2%
3 112
 
5.5%
5 103
 
5.0%
9 88
 
4.3%
4 87
 
4.3%
0 80
 
3.9%
7 74
 
3.6%
Other values (2) 144
 
7.0%
Hangul
ValueCountFrequency (%)
파 271
10.7%
주 271
10.7%
도 253
10.0%
경 250
9.8%
시 250
9.8%
기 250
9.8%
동 229
9.0%
리 103
 
4.1%
금 53
 
2.1%
면 46
 
1.8%
Other values (61) 564
22.2%

종료노드위도
Real number (ℝ)

HIGH CORRELATION 

Distinct86
Distinct (%)34.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.764168
Minimum37.691482
Maximum37.86021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2024-04-17T23:19:32.049162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.691482
5-th percentile37.711128
Q137.72964
median37.75767
Q337.782749
95-th percentile37.848199
Maximum37.86021
Range0.1687277
Interquartile range (IQR)0.053109247

Descriptive statistics

Standard deviation0.04153545
Coefficient of variation (CV)0.0010998641
Kurtosis-0.34350188
Mean37.764168
Median Absolute Deviation (MAD)0.02791
Skewness0.73364187
Sum9441.0419
Variance0.0017251936
MonotonicityNot monotonic
2024-04-17T23:19:32.195134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.74939 6
 
2.4%
37.84819881 6
 
2.4%
37.83079 5
 
2.0%
37.77169 5
 
2.0%
37.71711759 5
 
2.0%
37.76999584 5
 
2.0%
37.75772 5
 
2.0%
37.76548903 5
 
2.0%
37.76282457 5
 
2.0%
37.76507 4
 
1.6%
Other values (76) 199
79.6%
ValueCountFrequency (%)
37.691482 1
 
0.4%
37.706598 2
 
0.8%
37.70692 3
1.2%
37.7070311 3
1.2%
37.70834 1
 
0.4%
37.71007615 3
1.2%
37.71241435 4
1.6%
37.71696356 3
1.2%
37.71711759 5
2.0%
37.71714 4
1.6%
ValueCountFrequency (%)
37.8602097 2
 
0.8%
37.86003422 3
1.2%
37.85916856 2
 
0.8%
37.85435191 3
1.2%
37.84930658 1
 
0.4%
37.84819881 6
2.4%
37.84559 2
 
0.8%
37.83868021 1
 
0.4%
37.83079 5
2.0%
37.82856 3
1.2%

종료노드경도
Real number (ℝ)

HIGH CORRELATION 

Distinct86
Distinct (%)34.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.76329
Minimum126.68433
Maximum126.87297
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2024-04-17T23:19:32.320354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.68433
5-th percentile126.71648
Q1126.74284
median126.76493
Q3126.78181
95-th percentile126.81456
Maximum126.87297
Range0.1886369
Interquartile range (IQR)0.03897

Descriptive statistics

Standard deviation0.030292249
Coefficient of variation (CV)0.00023896704
Kurtosis0.44228762
Mean126.76329
Median Absolute Deviation (MAD)0.01917965
Skewness0.18989274
Sum31690.823
Variance0.00091762038
MonotonicityNot monotonic
2024-04-17T23:19:32.454381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.79753 6
 
2.4%
126.7885548 6
 
2.4%
126.7787 5
 
2.0%
126.72397 5
 
2.0%
126.7599769 5
 
2.0%
126.7628612 5
 
2.0%
126.76863 5
 
2.0%
126.7694168 5
 
2.0%
126.766269 5
 
2.0%
126.77384 4
 
1.6%
Other values (76) 199
79.6%
ValueCountFrequency (%)
126.68433 1
 
0.4%
126.68996 2
0.8%
126.6924043 1
 
0.4%
126.7017229 1
 
0.4%
126.7020046 1
 
0.4%
126.70577 2
0.8%
126.71094 2
0.8%
126.71578 3
1.2%
126.7173271 2
0.8%
126.7173361 2
0.8%
ValueCountFrequency (%)
126.8729669 1
 
0.4%
126.8463679 1
 
0.4%
126.839989 2
0.8%
126.8337663 2
0.8%
126.8189 3
1.2%
126.81598 3
1.2%
126.8148054 1
 
0.4%
126.8142693 1
 
0.4%
126.8141765 1
 
0.4%
126.80901 2
0.8%

기기수
Real number (ℝ)

Distinct249
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean76256.348
Minimum140
Maximum358266
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2024-04-17T23:19:32.587286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum140
5-th percentile2493.3
Q125279.25
median57435.5
Q3103261
95-th percentile228135.35
Maximum358266
Range358126
Interquartile range (IQR)77981.75

Descriptive statistics

Standard deviation72691.426
Coefficient of variation (CV)0.95325082
Kurtosis2.8495589
Mean76256.348
Median Absolute Deviation (MAD)38258.5
Skewness1.6485141
Sum19064087
Variance5.2840435 × 109
MonotonicityNot monotonic
2024-04-17T23:19:32.704623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40011 2
 
0.8%
1207 1
 
0.4%
44010 1
 
0.4%
27507 1
 
0.4%
26447 1
 
0.4%
198612 1
 
0.4%
124785 1
 
0.4%
76179 1
 
0.4%
170223 1
 
0.4%
9749 1
 
0.4%
Other values (239) 239
95.6%
ValueCountFrequency (%)
140 1
0.4%
152 1
0.4%
192 1
0.4%
266 1
0.4%
361 1
0.4%
820 1
0.4%
1207 1
0.4%
1317 1
0.4%
1453 1
0.4%
1556 1
0.4%
ValueCountFrequency (%)
358266 1
0.4%
354783 1
0.4%
332288 1
0.4%
329721 1
0.4%
304716 1
0.4%
301290 1
0.4%
293581 1
0.4%
287118 1
0.4%
260115 1
0.4%
248260 1
0.4%

속도
Real number (ℝ)

UNIQUE 

Distinct250
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.213426
Minimum11.632768
Maximum97.252431
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2024-04-17T23:19:32.838276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11.632768
5-th percentile15.842542
Q125.341611
median34.521933
Q346.094203
95-th percentile68.220848
Maximum97.252431
Range85.619662
Interquartile range (IQR)20.752592

Descriptive statistics

Standard deviation15.962094
Coefficient of variation (CV)0.42893374
Kurtosis0.9872826
Mean37.213426
Median Absolute Deviation (MAD)9.8795499
Skewness0.97626861
Sum9303.3566
Variance254.78845
MonotonicityNot monotonic
2024-04-17T23:19:32.959706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
28.50769231 1
 
0.4%
38.47012956 1
 
0.4%
32.36852647 1
 
0.4%
21.45276779 1
 
0.4%
29.11819166 1
 
0.4%
24.50745726 1
 
0.4%
37.05940347 1
 
0.4%
33.51265978 1
 
0.4%
39.44446859 1
 
0.4%
59.97903618 1
 
0.4%
Other values (240) 240
96.0%
ValueCountFrequency (%)
11.63276836 1
0.4%
12.80707245 1
0.4%
13.91020115 1
0.4%
14.45717993 1
0.4%
14.95046818 1
0.4%
15.03189013 1
0.4%
15.03975457 1
0.4%
15.12728221 1
0.4%
15.18011672 1
0.4%
15.57148067 1
0.4%
ValueCountFrequency (%)
97.25243068 1
0.4%
93.22873975 1
0.4%
83.81329321 1
0.4%
80.69107164 1
0.4%
79.75627152 1
0.4%
77.13546759 1
0.4%
76.70180322 1
0.4%
75.71296858 1
0.4%
72.98115355 1
0.4%
72.05462236 1
0.4%

통계시작연월일
Date

CONSTANT 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
Minimum2024-02-01 00:00:00
Maximum2024-02-01 00:00:00
2024-04-17T23:19:33.054184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:19:33.127977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

통계종료연월일
Date

CONSTANT 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
Minimum2024-02-29 00:00:00
Maximum2024-02-29 00:00:00
2024-04-17T23:19:33.196604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:19:33.269053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

관리기관명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
경기도 파주시청
250 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row경기도 파주시청
2nd row경기도 파주시청
3rd row경기도 파주시청
4th row경기도 파주시청
5th row경기도 파주시청

Common Values

ValueCountFrequency (%)
경기도 파주시청 250
100.0%

Length

2024-04-17T23:19:33.354505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T23:19:33.436282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경기도 250
50.0%
파주시청 250
50.0%

관리기관전화번호
Categorical

CONSTANT 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
031-940-5253
250 

Length

Max length12
Median length12
Mean length12
Min length12

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row031-940-5253
2nd row031-940-5253
3rd row031-940-5253
4th row031-940-5253
5th row031-940-5253

Common Values

ValueCountFrequency (%)
031-940-5253 250
100.0%

Length

2024-04-17T23:19:33.788898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T23:19:33.862600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
031-940-5253 250
100.0%

데이터기준일자
Date

CONSTANT 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
Minimum2024-03-13 00:00:00
Maximum2024-03-13 00:00:00
2024-04-17T23:19:33.930168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:19:34.015690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2024-04-17T23:19:26.275143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:19:21.441412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:19:22.081016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:19:22.998056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:19:23.718575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:19:24.298568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:19:24.918604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:19:25.603424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:19:26.351094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:19:21.522309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:19:22.168713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:19:23.086571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:19:23.794894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:19:24.382834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:19:25.000941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:19:25.702799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:19:26.428129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:19:21.616881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:19:22.249212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:19:23.181007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:19:23.878307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:19:24.464836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:19:25.089702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:19:25.814507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:19:26.498999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:19:21.701610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:19:22.331503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:19:23.278011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:19:23.953283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:19:24.546262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:19:25.176884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:19:25.897875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:19:26.560981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:19:21.772420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:19:22.406410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:19:23.370150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:19:24.014932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:19:24.617523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:19:25.254306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:19:25.973689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:19:26.634113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:19:21.850400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:19:22.739674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:19:23.462708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:19:24.084560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:19:24.690676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:19:25.344882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:19:26.049756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:19:26.726211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:19:21.932653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:19:22.837866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:19:23.550889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:19:24.161093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:19:24.773918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:19:25.435609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:19:26.135354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:19:26.796952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:19:22.011946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:19:22.922326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:19:23.645221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:19:24.235713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:19:24.848756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:19:25.526329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:19:26.206793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-17T23:19:34.087854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시작노드아이디(ID)시작노드명시작노드도로명주소시작노드지번주소시작노드위도시작노드경도종료노드아이디(ID)종료노드명종료노드도로명주소종료노드지번주소종료노드위도종료노드경도기기수속도
시작노드아이디(ID)1.0001.0001.0001.0000.7130.664NaN0.8250.8290.8370.6520.5780.3140.366
시작노드명1.0001.0001.0001.0001.0001.000NaN0.6010.6380.6830.8700.9030.6400.705
시작노드도로명주소1.0001.0001.0001.0001.0001.000NaN0.5750.6090.6580.8700.8900.5450.713
시작노드지번주소1.0001.0001.0001.0000.9991.000NaN0.6830.6880.7800.8780.9050.6290.730
시작노드위도0.7131.0001.0000.9991.0000.818NaN0.8520.8540.8570.9100.7030.4760.421
시작노드경도0.6641.0001.0001.0000.8181.000NaN0.8980.8830.8970.7180.9040.4280.483
종료노드아이디(ID)NaNNaNNaNNaNNaNNaN1.000NaNNaNNaNNaNNaNNaNNaN
종료노드명0.8250.6010.5750.6830.8520.898NaN1.0001.0001.0001.0001.0000.5140.824
종료노드도로명주소0.8290.6380.6090.6880.8540.883NaN1.0001.0001.0001.0001.0000.2360.745
종료노드지번주소0.8370.6830.6580.7800.8570.897NaN1.0001.0001.0001.0001.0000.4920.795
종료노드위도0.6520.8700.8700.8780.9100.718NaN1.0001.0001.0001.0000.8170.3330.394
종료노드경도0.5780.9030.8900.9050.7030.904NaN1.0001.0001.0000.8171.0000.3530.461
기기수0.3140.6400.5450.6290.4760.428NaN0.5140.2360.4920.3330.3531.0000.597
속도0.3660.7050.7130.7300.4210.483NaN0.8240.7450.7950.3940.4610.5971.000
2024-04-17T23:19:34.218923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시작노드아이디(ID)시작노드위도시작노드경도종료노드아이디(ID)종료노드위도종료노드경도기기수속도
시작노드아이디(ID)1.000-0.311-0.3080.438-0.311-0.2830.183-0.040
시작노드위도-0.3111.0000.531-0.2780.9570.523-0.4240.159
시작노드경도-0.3080.5311.000-0.2750.5240.907-0.198-0.035
종료노드아이디(ID)0.438-0.278-0.2751.000-0.285-0.2850.189-0.005
종료노드위도-0.3110.9570.524-0.2851.0000.532-0.4220.165
종료노드경도-0.2830.5230.907-0.2850.5321.000-0.186-0.023
기기수0.183-0.424-0.1980.189-0.422-0.1861.0000.394
속도-0.0400.159-0.035-0.0050.165-0.0230.3941.000

Missing values

2024-04-17T23:19:26.895092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-17T23:19:27.349423image/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.
2024-04-17T23:19:27.453101image/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

시작노드아이디(ID)시작노드명시작노드도로명주소시작노드지번주소시작노드위도시작노드경도종료노드아이디(ID)종료노드명종료노드도로명주소종료노드지번주소종료노드위도종료노드경도기기수속도통계시작연월일통계종료연월일관리기관명관리기관전화번호데이터기준일자
02290085000LCD서문교차로경기도 파주시 월롱면 엘지로 408-3경기도 파주시 월롱면 덕은리 772-4137.81899126.755932290123500능산교차로경기도 파주시 월롱면 휴암로 449-1경기도 파주시 월롱면 능산리 66537.83079126.7787120728.5076922024-02-012024-02-29경기도 파주시청031-940-52532024-03-13
12290085000LCD서문교차로경기도 파주시 월롱면 엘지로 408-3경기도 파주시 월롱면 덕은리 772-4137.81899126.755932290083500덕은5리사거리경기도 파주시 월롱면 엘지로 155경기도 파주시 월롱면 덕은리 1058-237.80972126.781812372235.7728812024-02-012024-02-29경기도 파주시청031-940-52532024-03-13
22290085000LCD서문교차로경기도 파주시 월롱면 엘지로 408-3경기도 파주시 월롱면 덕은리 772-4137.81899126.755932290085800비석사거리경기도 파주시 탄현면 정승로 21경기도 파주시 탄현면 금승리 185-1637.822107126.7453575952746.479282024-02-012024-02-29경기도 파주시청031-940-52532024-03-13
32290004805PX마을앞교차로경기도 파주시 통일로 549경기도 파주시 아동동 49-337.75942126.795552290006300금촌신사거리경기도 파주시 월롱면 통일로 694경기도 파주시 월롱면 영태리 502-137.7713126.79049910047658.4485912024-02-012024-02-29경기도 파주시청031-940-52532024-03-13
42290004805PX마을앞교차로경기도 파주시 통일로 549경기도 파주시 아동동 49-337.75942126.795552290005402등원고가차도경기도 파주시 조리읍 등원로 47-19경기도 파주시 조리읍 등원리 249-137.75813126.8059893199325.9211822024-02-012024-02-29경기도 파주시청031-940-52532024-03-13
52290004805PX마을앞교차로경기도 파주시 통일로 549경기도 파주시 아동동 49-337.75942126.795552290003900금촌교차로경기도 파주시 조리읍 봉천로 129경기도 파주시 조리읍 등원리 50037.74939126.797531785135.0019312024-02-012024-02-29경기도 파주시청031-940-52532024-03-13
62290117400운정소방서사거리경기도 파주시 미래로 596경기도 파주시 목동동 64537.72964126.751672290117600와동동1329경기도 파주시 경의로 1250경기도 파주시 와동동 132937.729887126.76055410124821.2198972024-02-012024-02-29경기도 파주시청031-940-52532024-03-13
72290117400운정소방서사거리경기도 파주시 미래로 596경기도 파주시 목동동 64537.72964126.751672290114800운정행복센타사거리경기도 파주시 와석순환로 415경기도 파주시 목동동 67637.72317126.749746832527.0619022024-02-012024-02-29경기도 파주시청031-940-52532024-03-13
82290117400운정소방서사거리경기도 파주시 미래로 596경기도 파주시 목동동 64537.72964126.751672290179400홈플러스경기도 파주시 산내로104번길 20-15경기도 파주시 목동동 95137.728966126.7379129932632.6414452024-02-012024-02-29경기도 파주시청031-940-52532024-03-13
92290070000갈현교차로경기도 파주시 탄현면 평화로 538경기도 파주시 탄현면 갈현리 1783-337.77169126.723972290074500검산삼거리경기도 파주시 검산로 2경기도 파주시 검산동 297-237.77499126.739699768242.2899262024-02-012024-02-29경기도 파주시청031-940-52532024-03-13
시작노드아이디(ID)시작노드명시작노드도로명주소시작노드지번주소시작노드위도시작노드경도종료노드아이디(ID)종료노드명종료노드도로명주소종료노드지번주소종료노드위도종료노드경도기기수속도통계시작연월일통계종료연월일관리기관명관리기관전화번호데이터기준일자
2402290114800운정행복센타사거리경기도 파주시 와석순환로 415경기도 파주시 목동동 67637.72317126.749742290117400운정소방서사거리경기도 파주시 미래로 596경기도 파주시 목동동 64537.72964126.751677051626.7624242024-02-012024-02-29경기도 파주시청031-940-52532024-03-13
2412290114800운정행복센타사거리경기도 파주시 와석순환로 415경기도 파주시 목동동 67637.72317126.749742290114700산내초등학교경기도 파주시 와석순환로 286경기도 파주시 목동동 105037.722659126.7378952609624.1826612024-02-012024-02-29경기도 파주시청031-940-52532024-03-13
2422290114800운정행복센타사거리경기도 파주시 와석순환로 415경기도 파주시 목동동 67637.72317126.749742290115100유비파크앞경기도 파주시 와석순환로 507경기도 파주시 와동동 132937.724244126.7609636855840.8498912024-02-012024-02-29경기도 파주시청031-940-52532024-03-13
2432290114800운정행복센타사거리경기도 파주시 와석순환로 415경기도 파주시 목동동 67637.72317126.749742290113000동패동1749경기도 파주시 교하로 239경기도 파주시 동패동 174937.71714126.746811883832.7707682024-02-012024-02-29경기도 파주시청031-940-52532024-03-13
2442290179400홈플러스경기도 파주시 산내로104번길 20-15경기도 파주시 목동동 95137.728966126.7379122290117400운정소방서사거리경기도 파주시 미래로 596경기도 파주시 목동동 64537.72964126.7516710488127.0728192024-02-012024-02-29경기도 파주시청031-940-52532024-03-13
2452290179400홈플러스경기도 파주시 산내로104번길 20-15경기도 파주시 목동동 95137.728966126.7379122290001000다율교차로경기도 파주시 청암로 75경기도 파주시 다율동 산 66-137.734559126.7337563658119.3746842024-02-012024-02-29경기도 파주시청031-940-52532024-03-13
2462290179400홈플러스경기도 파주시 산내로104번길 20-15경기도 파주시 목동동 95137.728966126.7379122290179000삼성프라자경기도 파주시 교하로 87경기도 파주시 목동동 95137.72714126.73385315778222.4403272024-02-012024-02-29경기도 파주시청031-940-52532024-03-13
2472290055900흰돌마을앞경기도 파주시 중앙로 179-1경기도 파주시 금릉동 216-337.75427126.78362290003900금촌교차로경기도 파주시 조리읍 봉천로 129경기도 파주시 조리읍 등원리 50037.74939126.797539024729.3030122024-02-012024-02-29경기도 파주시청031-940-52532024-03-13
2482290055900흰돌마을앞경기도 파주시 중앙로 179-1경기도 파주시 금릉동 216-337.75427126.78362290061800시청앞사거리경기도 파주시 중앙로 263경기도 파주시 금촌동 803-437.75953126.777728753730.0797772024-02-012024-02-29경기도 파주시청031-940-52532024-03-13
2492290055900흰돌마을앞경기도 파주시 중앙로 179-1경기도 파주시 금릉동 216-337.75427126.78362290055300금향초교입구경기도 파주시 가나무로 109경기도 파주시 금촌동 978-2237.754081126.7776226507921.7268392024-02-012024-02-29경기도 파주시청031-940-52532024-03-13