Overview

Dataset statistics

Number of variables9
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory839.8 KiB
Average record size in memory86.0 B

Variable types

Numeric6
Categorical1
DateTime1
Text1

Dataset

Description2021년 8월 21일부터 9월 17일까지 창원시 주요도로 부속물(노면표시, 시선유도봉) 훼손 현황 데이터입니다.
Author경상남도 창원시
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15096117

Alerts

위도 is highly overall correlated with 경도High correlation
경도 is highly overall correlated with 위도High correlation
노드링크 is highly overall correlated with 시작노드 and 1 other fieldsHigh correlation
시작노드 is highly overall correlated with 노드링크 and 1 other fieldsHigh correlation
끝노드 is highly overall correlated with 노드링크 and 1 other fieldsHigh correlation
위험물유형 is highly imbalanced (78.1%)Imbalance
노드링크 is highly skewed (γ1 = -44.60945952)Skewed
시작노드 is highly skewed (γ1 = -44.33265089)Skewed
끝노드 is highly skewed (γ1 = -44.89111245)Skewed
분류번호 has unique valuesUnique

Reproduction

Analysis started2023-12-10 23:58:22.818683
Analysis finished2023-12-10 23:58:28.893304
Duration6.07 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

분류번호
Real number (ℝ)

UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39722.755
Minimum22
Maximum80387
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T08:58:28.977004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum22
5-th percentile3932.65
Q119370.25
median39006
Q360338.75
95-th percentile76203.2
Maximum80387
Range80365
Interquartile range (IQR)40968.5

Descriptive statistics

Standard deviation23283.602
Coefficient of variation (CV)0.58615275
Kurtosis-1.2164607
Mean39722.755
Median Absolute Deviation (MAD)20414.5
Skewness0.028875736
Sum3.9722755 × 108
Variance5.4212613 × 108
MonotonicityNot monotonic
2023-12-11T08:58:29.177839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35516 1
 
< 0.1%
14295 1
 
< 0.1%
56727 1
 
< 0.1%
35909 1
 
< 0.1%
80087 1
 
< 0.1%
10744 1
 
< 0.1%
51935 1
 
< 0.1%
22244 1
 
< 0.1%
8341 1
 
< 0.1%
72315 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
22 1
< 0.1%
25 1
< 0.1%
27 1
< 0.1%
30 1
< 0.1%
33 1
< 0.1%
39 1
< 0.1%
46 1
< 0.1%
48 1
< 0.1%
71 1
< 0.1%
73 1
< 0.1%
ValueCountFrequency (%)
80387 1
< 0.1%
80353 1
< 0.1%
80326 1
< 0.1%
80291 1
< 0.1%
80288 1
< 0.1%
80283 1
< 0.1%
80271 1
< 0.1%
80257 1
< 0.1%
80255 1
< 0.1%
80250 1
< 0.1%

위험물유형
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
노면표시훼손
9649 
시선유도봉파손
 
351

Length

Max length7
Median length6
Mean length6.0351
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row노면표시훼손
2nd row노면표시훼손
3rd row시선유도봉파손
4th row노면표시훼손
5th row노면표시훼손

Common Values

ValueCountFrequency (%)
노면표시훼손 9649
96.5%
시선유도봉파손 351
 
3.5%

Length

2023-12-11T08:58:29.309972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:58:29.411196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
노면표시훼손 9649
96.5%
시선유도봉파손 351
 
3.5%

수집날짜
Date

Distinct4817
Distinct (%)48.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2021-08-21 03:54:00
Maximum2021-09-17 22:47:00
2023-12-11T08:58:29.532809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:58:29.708039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

위도
Real number (ℝ)

HIGH CORRELATION 

Distinct5380
Distinct (%)53.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.216109
Minimum35.076385
Maximum35.307436
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T08:58:29.847566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.076385
5-th percentile35.182631
Q135.203802
median35.215725
Q335.225596
95-th percentile35.256458
Maximum35.307436
Range0.231051
Interquartile range (IQR)0.0217945

Descriptive statistics

Standard deviation0.020559676
Coefficient of variation (CV)0.00058381453
Kurtosis2.1528651
Mean35.216109
Median Absolute Deviation (MAD)0.011714
Skewness-0.084622533
Sum352161.09
Variance0.00042270028
MonotonicityNot monotonic
2023-12-11T08:58:29.998668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.210614 30
 
0.3%
35.215591 25
 
0.2%
35.210796 23
 
0.2%
35.219377 22
 
0.2%
35.217564 22
 
0.2%
35.215838 21
 
0.2%
35.211048 21
 
0.2%
35.211045 21
 
0.2%
35.257009 20
 
0.2%
35.225723 19
 
0.2%
Other values (5370) 9776
97.8%
ValueCountFrequency (%)
35.076385 1
 
< 0.1%
35.076427 1
 
< 0.1%
35.10111 1
 
< 0.1%
35.101317 1
 
< 0.1%
35.10142 2
< 0.1%
35.105362 1
 
< 0.1%
35.115639 1
 
< 0.1%
35.115692 2
< 0.1%
35.115766 1
 
< 0.1%
35.116219 4
< 0.1%
ValueCountFrequency (%)
35.307436 1
 
< 0.1%
35.305808 1
 
< 0.1%
35.303158 1
 
< 0.1%
35.302592 1
 
< 0.1%
35.302568 1
 
< 0.1%
35.302302 3
< 0.1%
35.293831 1
 
< 0.1%
35.290213 1
 
< 0.1%
35.281386 1
 
< 0.1%
35.2778 1
 
< 0.1%

경도
Real number (ℝ)

HIGH CORRELATION 

Distinct2429
Distinct (%)24.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128.58834
Minimum128.34898
Maximum129.05907
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T08:58:30.155706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum128.34898
5-th percentile128.56068
Q1128.57074
median128.57616
Q3128.58434
95-th percentile128.68242
Maximum129.05907
Range0.71009
Interquartile range (IQR)0.0136

Descriptive statistics

Standard deviation0.038784636
Coefficient of variation (CV)0.0003016186
Kurtosis13.496852
Mean128.58834
Median Absolute Deviation (MAD)0.00557
Skewness2.7930653
Sum1285883.4
Variance0.001504248
MonotonicityNot monotonic
2023-12-11T08:58:30.311574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
128.57057 97
 
1.0%
128.57196 80
 
0.8%
128.57097 79
 
0.8%
128.57059 70
 
0.7%
128.57613 69
 
0.7%
128.57056 68
 
0.7%
128.57072 63
 
0.6%
128.57071 56
 
0.6%
128.57095 55
 
0.5%
128.57098 54
 
0.5%
Other values (2419) 9309
93.1%
ValueCountFrequency (%)
128.34898 1
< 0.1%
128.38023 1
< 0.1%
128.40828 1
< 0.1%
128.46455 1
< 0.1%
128.46466 1
< 0.1%
128.48341 1
< 0.1%
128.48735 2
< 0.1%
128.48747 1
< 0.1%
128.48749 1
< 0.1%
128.48755 1
< 0.1%
ValueCountFrequency (%)
129.05907 1
< 0.1%
129.04921 1
< 0.1%
128.92853 1
< 0.1%
128.87381 1
< 0.1%
128.87378 2
< 0.1%
128.87372 1
< 0.1%
128.87282 1
< 0.1%
128.87262 2
< 0.1%
128.87257 1
< 0.1%
128.86897 1
< 0.1%

노드링크
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct1022
Distinct (%)10.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.1579761 × 109
Minimum1.3200184 × 109
Maximum4.1803765 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T08:58:30.492580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.3200184 × 109
5-th percentile4.140131 × 109
Q14.1601324 × 109
median4.1601591 × 109
Q34.1700205 × 109
95-th percentile4.1700634 × 109
Maximum4.1803765 × 109
Range2.8603581 × 109
Interquartile range (IQR)9888100

Descriptive statistics

Standard deviation47199658
Coefficient of variation (CV)0.011351594
Kurtosis2586.5236
Mean4.1579761 × 109
Median Absolute Deviation (MAD)35000
Skewness-44.60946
Sum4.1579761 × 1013
Variance2.2278077 × 1015
MonotonicityNot monotonic
2023-12-11T08:58:30.667122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4160152500 319
 
3.2%
4170020500 253
 
2.5%
4170020400 219
 
2.2%
4160166200 193
 
1.9%
4160159700 182
 
1.8%
4170025800 177
 
1.8%
4160166100 172
 
1.7%
4160169600 164
 
1.6%
4170025900 163
 
1.6%
4160150300 144
 
1.4%
Other values (1012) 8014
80.1%
ValueCountFrequency (%)
1320018400 1
 
< 0.1%
1340013500 1
 
< 0.1%
3850000311 1
 
< 0.1%
3850002600 2
< 0.1%
3850004002 1
 
< 0.1%
3850007400 3
< 0.1%
3850041101 1
 
< 0.1%
3850044100 1
 
< 0.1%
3850047600 1
 
< 0.1%
3850077700 1
 
< 0.1%
ValueCountFrequency (%)
4180376500 1
 
< 0.1%
4180376300 1
 
< 0.1%
4180376000 4
< 0.1%
4180370600 1
 
< 0.1%
4180361600 5
0.1%
4180361200 2
 
< 0.1%
4180360500 1
 
< 0.1%
4180360000 1
 
< 0.1%
4180350600 1
 
< 0.1%
4180350500 1
 
< 0.1%

시작노드
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct703
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.1576082 × 109
Minimum1.3200064 × 109
Maximum4.1801035 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T08:58:30.807297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.3200064 × 109
5-th percentile4.1400379 × 109
Q14.1600483 × 109
median4.1600597 × 109
Q34.1700233 × 109
95-th percentile4.1700345 × 109
Maximum4.1801035 × 109
Range2.8600971 × 109
Interquartile range (IQR)9975000

Descriptive statistics

Standard deviation47299892
Coefficient of variation (CV)0.011376708
Kurtosis2563.4548
Mean4.1576082 × 109
Median Absolute Deviation (MAD)13200
Skewness-44.332651
Sum4.1576082 × 1013
Variance2.2372798 × 1015
MonotonicityNot monotonic
2023-12-11T08:58:30.956586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4170025800 506
 
5.1%
4160063300 349
 
3.5%
4160060100 319
 
3.2%
4160057200 277
 
2.8%
4160055900 255
 
2.5%
4160061100 228
 
2.3%
4160056000 224
 
2.2%
4160060700 219
 
2.2%
4170025900 184
 
1.8%
4160059800 173
 
1.7%
Other values (693) 7266
72.7%
ValueCountFrequency (%)
1320006400 1
 
< 0.1%
1340004300 1
 
< 0.1%
3850000201 1
 
< 0.1%
3850001200 2
< 0.1%
3850002100 1
 
< 0.1%
3850003400 3
< 0.1%
3850020600 1
 
< 0.1%
3850020800 1
 
< 0.1%
3850022300 1
 
< 0.1%
3850040300 4
< 0.1%
ValueCountFrequency (%)
4180103500 2
< 0.1%
4180103200 1
 
< 0.1%
4180103100 1
 
< 0.1%
4180103000 1
 
< 0.1%
4180074300 1
 
< 0.1%
4180073600 1
 
< 0.1%
4180073500 1
 
< 0.1%
4180073300 1
 
< 0.1%
4180072900 3
< 0.1%
4180072700 2
< 0.1%

끝노드
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct679
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.1575803 × 109
Minimum1.3200063 × 109
Maximum4.1801062 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T08:58:31.131753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.3200063 × 109
5-th percentile4.1400379 × 109
Q14.1600473 × 109
median4.1600591 × 109
Q34.1700142 × 109
95-th percentile4.1700337 × 109
Maximum4.1801062 × 109
Range2.8600999 × 109
Interquartile range (IQR)9966900

Descriptive statistics

Standard deviation47087644
Coefficient of variation (CV)0.011325733
Kurtosis2609.7051
Mean4.1575803 × 109
Median Absolute Deviation (MAD)12100
Skewness-44.891112
Sum4.1575803 × 1013
Variance2.2172462 × 1015
MonotonicityNot monotonic
2023-12-11T08:58:31.273793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4160063300 487
 
4.9%
4170025800 436
 
4.4%
4160061100 364
 
3.6%
4160055900 291
 
2.9%
4160060700 279
 
2.8%
4160057100 258
 
2.6%
4170025900 225
 
2.2%
4160057200 221
 
2.2%
4170031000 181
 
1.8%
4170028200 176
 
1.8%
Other values (669) 7082
70.8%
ValueCountFrequency (%)
1320006300 1
 
< 0.1%
1340011200 1
 
< 0.1%
3850000600 1
 
< 0.1%
3850001300 2
< 0.1%
3850001901 1
 
< 0.1%
3850003200 3
< 0.1%
3850004300 1
 
< 0.1%
3850018700 1
 
< 0.1%
3850020500 1
 
< 0.1%
3850022200 1
 
< 0.1%
ValueCountFrequency (%)
4180106200 1
 
< 0.1%
4180103100 3
< 0.1%
4180103000 1
 
< 0.1%
4180075400 2
 
< 0.1%
4180074100 1
 
< 0.1%
4180073600 1
 
< 0.1%
4180073400 1
 
< 0.1%
4180073300 2
 
< 0.1%
4180072900 7
0.1%
4180072400 1
 
< 0.1%

도로명주소
Text

Distinct292
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-11T08:58:31.616593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length24
Mean length18.6612
Min length12

Characters and Unicode

Total characters186612
Distinct characters160
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

Unique61 ?
Unique (%)0.6%

Sample

1st row경상남도 창원시 마산합포구 용마로
2nd row경상남도 창원시 마산회원구 3.15대로
3rd row경상남도 창원시 마산합포구 삼호로
4th row경상남도 창원시 마산합포구 자산삼거리로
5th row경상남도 창원시 마산회원구 3.15대로
ValueCountFrequency (%)
경상남도 9998
25.0%
창원시 9939
24.9%
마산합포구 5400
13.5%
마산회원구 2810
 
7.0%
3.15대로 1207
 
3.0%
북성로 1050
 
2.6%
의창구 1039
 
2.6%
합포로 821
 
2.1%
용마로 636
 
1.6%
성산구 627
 
1.6%
Other values (266) 6412
16.1%
2023-12-11T08:58:32.402113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
29939
16.0%
원 13437
 
7.2%
창 11384
 
6.1%
남 10477
 
5.6%
시 10146
 
5.4%
상 10120
 
5.4%
도 10064
 
5.4%
구 10047
 
5.4%
경 10039
 
5.4%
산 9386
 
5.0%
Other values (150) 61573
33.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 150516
80.7%
Space Separator 29939
 
16.0%
Decimal Number 4890
 
2.6%
Other Punctuation 1207
 
0.6%
Dash Punctuation 60
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
원 13437
 
8.9%
창 11384
 
7.6%
남 10477
 
7.0%
시 10146
 
6.7%
상 10120
 
6.7%
도 10064
 
6.7%
구 10047
 
6.7%
경 10039
 
6.7%
산 9386
 
6.2%
로 9240
 
6.1%
Other values (137) 46176
30.7%
Decimal Number
ValueCountFrequency (%)
1 1536
31.4%
3 1387
28.4%
5 1331
27.2%
2 263
 
5.4%
8 107
 
2.2%
4 104
 
2.1%
6 80
 
1.6%
7 40
 
0.8%
0 23
 
0.5%
9 19
 
0.4%
Space Separator
ValueCountFrequency (%)
29939
100.0%
Other Punctuation
ValueCountFrequency (%)
. 1207
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 60
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 150516
80.7%
Common 36096
 
19.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
원 13437
 
8.9%
창 11384
 
7.6%
남 10477
 
7.0%
시 10146
 
6.7%
상 10120
 
6.7%
도 10064
 
6.7%
구 10047
 
6.7%
경 10039
 
6.7%
산 9386
 
6.2%
로 9240
 
6.1%
Other values (137) 46176
30.7%
Common
ValueCountFrequency (%)
29939
82.9%
1 1536
 
4.3%
3 1387
 
3.8%
5 1331
 
3.7%
. 1207
 
3.3%
2 263
 
0.7%
8 107
 
0.3%
4 104
 
0.3%
6 80
 
0.2%
- 60
 
0.2%
Other values (3) 82
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 150516
80.7%
ASCII 36096
 
19.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
29939
82.9%
1 1536
 
4.3%
3 1387
 
3.8%
5 1331
 
3.7%
. 1207
 
3.3%
2 263
 
0.7%
8 107
 
0.3%
4 104
 
0.3%
6 80
 
0.2%
- 60
 
0.2%
Other values (3) 82
 
0.2%
Hangul
ValueCountFrequency (%)
원 13437
 
8.9%
창 11384
 
7.6%
남 10477
 
7.0%
시 10146
 
6.7%
상 10120
 
6.7%
도 10064
 
6.7%
구 10047
 
6.7%
경 10039
 
6.7%
산 9386
 
6.2%
로 9240
 
6.1%
Other values (137) 46176
30.7%

Interactions

2023-12-11T08:58:27.989203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:58:24.022190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:58:24.656942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:58:25.377575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:58:26.523592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:58:27.229455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:58:28.110315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:58:24.122451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:58:24.766799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:58:25.841446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:58:26.661597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:58:27.336174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:58:28.204247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:58:24.214078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:58:24.860011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:58:25.958229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:58:26.784618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:58:27.442615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:58:28.303096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:58:24.332059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:58:24.959949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:58:26.090237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:58:26.893989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:58:27.582870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:58:28.397971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:58:24.439161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:58:25.066954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:58:26.215640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:58:27.001564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:58:27.733150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:58:28.512173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:58:24.536975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:58:25.193495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:58:26.367645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:58:27.112055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:58:27.871039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T08:58:32.497513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
분류번호위험물유형위도경도노드링크시작노드끝노드
분류번호1.0000.0480.1700.2070.0910.0910.091
위험물유형0.0481.0000.2080.1440.0210.0210.021
위도0.1700.2081.0000.8120.2770.2770.277
경도0.2070.1440.8121.0000.9610.9610.961
노드링크0.0910.0210.2770.9611.0001.0001.000
시작노드0.0910.0210.2770.9611.0001.0001.000
끝노드0.0910.0210.2770.9611.0001.0001.000
2023-12-11T08:58:32.606014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
분류번호위도경도노드링크시작노드끝노드위험물유형
분류번호1.0000.0130.047-0.020-0.014-0.0110.037
위도0.0131.0000.5620.2550.1740.1540.159
경도0.0470.5621.000-0.148-0.045-0.0210.110
노드링크-0.0200.255-0.1481.0000.9230.8990.050
시작노드-0.0140.174-0.0450.9231.0000.8990.050
끝노드-0.0110.154-0.0210.8990.8991.0000.050
위험물유형0.0370.1590.1100.0500.0500.0501.000

Missing values

2023-12-11T08:58:28.668893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T08:58:28.828402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

분류번호위험물유형수집날짜위도경도노드링크시작노드끝노드도로명주소
3551535516노면표시훼손2021-08-30 22:2335.218924128.58102416016960041600648004160063300경상남도 창원시 마산합포구 용마로
1764617647노면표시훼손2021-08-26 07:3335.231096128.57697417005100041700282004170029000경상남도 창원시 마산회원구 3.15대로
4598245983시선유도봉파손2021-09-01 07:5635.221015128.58653416017100041600667004160067400경상남도 창원시 마산합포구 삼호로
3245732458노면표시훼손2021-08-30 01:1735.207116128.5656416015190041600505004160048400경상남도 창원시 마산합포구 자산삼거리로
4315343154노면표시훼손2021-09-01 00:5735.23234128.57794417005100041700282004170029000경상남도 창원시 마산회원구 3.15대로
3638936390노면표시훼손2021-08-30 23:3635.200921128.57143416014590041600567004160057200경상남도 창원시 마산합포구 서성로
1304913050노면표시훼손2021-08-24 23:5835.250992128.63971414012800041400478004140049700경상남도 창원시 의창구 도계로
1694616947노면표시훼손2021-08-26 03:0135.202893128.57202416014980041600580004160057200경상남도 창원시 마산합포구 합포로
4506945070노면표시훼손2021-09-01 04:3435.219377128.57834416016980041600620004160063300경상남도 창원시 마산합포구 회원동북로
5953259533노면표시훼손2021-09-04 11:2435.185782128.56079416013240041600451004160045900경상남도 창원시 마산합포구 고운로
분류번호위험물유형수집날짜위도경도노드링크시작노드끝노드도로명주소
5302453025노면표시훼손2021-09-03 08:1835.221506128.57098417002580041700258004170025900경상남도 창원시 마산회원구 북성로
2805428055노면표시훼손2021-08-29 00:1435.218424128.58052416016960041600648004160063300경상남도 창원시 마산합포구 용마로
6645066451노면표시훼손2021-09-06 06:5135.243383128.58658417007700041700321004170032400경상남도 창원시 마산회원구 3.15대로
4647646477노면표시훼손2021-09-01 15:3135.232035128.58311417005470041700296004170031000경상남도 창원시 마산회원구 양덕로
5781557816노면표시훼손2021-09-04 06:0435.181558128.56491416012410041600502004160051400경상남도 창원시 마산합포구 해안대로
4421144212노면표시훼손2021-09-01 02:4035.218238128.57709417002410041600620004170027400경상남도 창원시 마산회원구 회원동25길
53865387노면표시훼손2021-08-22 03:2335.247329128.65251414011780041400568004140058100경상남도 창원시 의창구 지귀로
3559035591노면표시훼손2021-08-30 22:2635.221703128.57092417002590041700259004170025800경상남도 창원시 마산회원구 북성로
2914429145노면표시훼손2021-08-29 04:0435.18228128.5633416012460041600495004160050200경상남도 창원시 마산합포구 월영동15길
1397313974노면표시훼손2021-08-25 05:0035.217643128.60522417001720041700403004170040200경상남도 창원시 마산회원구 봉암공단3길