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 수집한 창원시 주요도로의 도로 위험물 정보(포트홀, 수직균열, 수평균열, 피로균열, 쓰레기(낙하물), 도로수리훼손) 현황 데이터입니다.
Author경상남도 창원시
URLhttps://www.data.go.kr/data/15096114/fileData.do

Alerts

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
노드링크 is highly skewed (γ1 = -21.31264002)Skewed
시작노드 is highly skewed (γ1 = -20.88793117)Skewed
끝노드 is highly skewed (γ1 = -21.29015203)Skewed
분류번호 has unique valuesUnique

Reproduction

Analysis started2023-12-12 05:07:34.283338
Analysis finished2023-12-12 05:07:41.335938
Duration7.05 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%
Mean38743.093
Minimum2
Maximum77365
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T14:07:41.417561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3780.95
Q119236.25
median38781
Q358466.25
95-th percentile73680.15
Maximum77365
Range77363
Interquartile range (IQR)39230

Descriptive statistics

Standard deviation22464.798
Coefficient of variation (CV)0.57984008
Kurtosis-1.2126203
Mean38743.093
Median Absolute Deviation (MAD)19607.5
Skewness-0.0070505451
Sum3.8743093 × 108
Variance5.0466716 × 108
MonotonicityNot monotonic
2023-12-12T14:07:41.594319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
46892 1
 
< 0.1%
50902 1
 
< 0.1%
69131 1
 
< 0.1%
2685 1
 
< 0.1%
73783 1
 
< 0.1%
24083 1
 
< 0.1%
27503 1
 
< 0.1%
41971 1
 
< 0.1%
26854 1
 
< 0.1%
49503 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
2 1
< 0.1%
4 1
< 0.1%
10 1
< 0.1%
20 1
< 0.1%
29 1
< 0.1%
32 1
< 0.1%
37 1
< 0.1%
38 1
< 0.1%
50 1
< 0.1%
52 1
< 0.1%
ValueCountFrequency (%)
77365 1
< 0.1%
77363 1
< 0.1%
77358 1
< 0.1%
77313 1
< 0.1%
77311 1
< 0.1%
77302 1
< 0.1%
77279 1
< 0.1%
77273 1
< 0.1%
77266 1
< 0.1%
77253 1
< 0.1%

위험물유형
Categorical

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
수직균열
4799 
피로균열
3196 
수평균열
932 
포트홀
674 
쓰레기
 
229

Length

Max length6
Median length4
Mean length3.9437
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row수평균열
2nd row수직균열
3rd row수직균열
4th row수직균열
5th row쓰레기

Common Values

ValueCountFrequency (%)
수직균열 4799
48.0%
피로균열 3196
32.0%
수평균열 932
 
9.3%
포트홀 674
 
6.7%
쓰레기 229
 
2.3%
도로수리훼손 170
 
1.7%

Length

2023-12-12T14:07:41.762149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T14:07:41.877623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
수직균열 4799
48.0%
피로균열 3196
32.0%
수평균열 932
 
9.3%
포트홀 674
 
6.7%
쓰레기 229
 
2.3%
도로수리훼손 170
 
1.7%

수집날짜
Date

Distinct5931
Distinct (%)59.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2021-08-21 02:53:00
Maximum2021-09-17 23:56:00
2023-12-12T14:07:42.021781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:07:42.179855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

위도
Real number (ℝ)

Distinct7845
Distinct (%)78.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.216806
Minimum35.006566
Maximum35.354009
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T14:07:42.344895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.006566
5-th percentile35.178816
Q135.203217
median35.216307
Q335.232916
95-th percentile35.256731
Maximum35.354009
Range0.347443
Interquartile range (IQR)0.02969925

Descriptive statistics

Standard deviation0.025869358
Coefficient of variation (CV)0.00073457423
Kurtosis3.3348221
Mean35.216806
Median Absolute Deviation (MAD)0.0149575
Skewness-0.69235735
Sum352168.06
Variance0.00066922369
MonotonicityNot monotonic
2023-12-12T14:07:42.554593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.216307 80
 
0.8%
35.215391 35
 
0.4%
35.216189 26
 
0.3%
35.232319 17
 
0.2%
35.265044 14
 
0.1%
35.216184 13
 
0.1%
35.212909 12
 
0.1%
35.210732 12
 
0.1%
35.21631 11
 
0.1%
35.206927 11
 
0.1%
Other values (7835) 9769
97.7%
ValueCountFrequency (%)
35.006566 1
< 0.1%
35.008453 1
< 0.1%
35.057667 1
< 0.1%
35.064369 1
< 0.1%
35.067642 1
< 0.1%
35.068576 1
< 0.1%
35.069224 1
< 0.1%
35.069278 1
< 0.1%
35.069331 1
< 0.1%
35.06943 1
< 0.1%
ValueCountFrequency (%)
35.354009 1
< 0.1%
35.353896 1
< 0.1%
35.349193 1
< 0.1%
35.327721 1
< 0.1%
35.327522 1
< 0.1%
35.309011 1
< 0.1%
35.307699 1
< 0.1%
35.305881 1
< 0.1%
35.305178 1
< 0.1%
35.305025 2
< 0.1%

경도
Real number (ℝ)

Distinct4337
Distinct (%)43.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128.59648
Minimum127.98857
Maximum129.13335
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T14:07:42.759045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum127.98857
5-th percentile128.5584
Q1128.5688
median128.57639
Q3128.60721
95-th percentile128.69283
Maximum129.13335
Range1.14478
Interquartile range (IQR)0.038415

Descriptive statistics

Standard deviation0.053767173
Coefficient of variation (CV)0.00041810767
Kurtosis14.850948
Mean128.59648
Median Absolute Deviation (MAD)0.01169
Skewness2.3584158
Sum1285964.8
Variance0.0028909089
MonotonicityNot monotonic
2023-12-12T14:07:42.989615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
128.57045 25
 
0.2%
128.56471 24
 
0.2%
128.57018 24
 
0.2%
128.56473 23
 
0.2%
128.57634 23
 
0.2%
128.57631 22
 
0.2%
128.56502 22
 
0.2%
128.57639 22
 
0.2%
128.56467 21
 
0.2%
128.57076 21
 
0.2%
Other values (4327) 9773
97.7%
ValueCountFrequency (%)
127.98857 1
< 0.1%
128.22708 1
< 0.1%
128.32443 1
< 0.1%
128.34105 1
< 0.1%
128.34523 1
< 0.1%
128.34589 1
< 0.1%
128.35612 1
< 0.1%
128.37361 1
< 0.1%
128.3895 1
< 0.1%
128.39502 1
< 0.1%
ValueCountFrequency (%)
129.13335 1
< 0.1%
129.13329 1
< 0.1%
129.12701 1
< 0.1%
129.12444 1
< 0.1%
129.11897 1
< 0.1%
129.11755 1
< 0.1%
129.11696 1
< 0.1%
129.11632 1
< 0.1%
129.05931 1
< 0.1%
129.05923 1
< 0.1%

노드링크
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct1666
Distinct (%)16.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.1501641 × 109
Minimum1.3200212 × 109
Maximum4.180377 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T14:07:43.170795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.3200212 × 109
5-th percentile4.1401026 × 109
Q14.1600936 × 109
median4.1601552 × 109
Q34.1700245 × 109
95-th percentile4.1700974 × 109
Maximum4.180377 × 109
Range2.8603558 × 109
Interquartile range (IQR)9930925

Descriptive statistics

Standard deviation1.1005837 × 108
Coefficient of variation (CV)0.026519041
Kurtosis525.64795
Mean4.1501641 × 109
Median Absolute Deviation (MAD)9868250
Skewness-21.31264
Sum4.1501641 × 1013
Variance1.2112845 × 1016
MonotonicityNot monotonic
2023-12-12T14:07:43.383559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4160144200 176
 
1.8%
4170051000 133
 
1.3%
4170024500 119
 
1.2%
4160150800 108
 
1.1%
4160168600 107
 
1.1%
4140139400 100
 
1.0%
4160151800 97
 
1.0%
4160160400 95
 
0.9%
4160137400 93
 
0.9%
4160163600 89
 
0.9%
Other values (1656) 8883
88.8%
ValueCountFrequency (%)
1320021200 1
< 0.1%
1340005200 1
< 0.1%
1370002501 1
< 0.1%
1370227300 1
< 0.1%
1380005701 1
< 0.1%
1380036703 2
< 0.1%
1380054400 1
< 0.1%
1380056700 1
< 0.1%
1380079502 1
< 0.1%
1380079602 1
< 0.1%
ValueCountFrequency (%)
4180377000 1
 
< 0.1%
4180376900 11
0.1%
4180376800 3
 
< 0.1%
4180376700 1
 
< 0.1%
4180376500 2
 
< 0.1%
4180376400 16
0.2%
4180376300 1
 
< 0.1%
4180376000 1
 
< 0.1%
4180375700 10
0.1%
4180375500 8
0.1%

시작노드
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct1056
Distinct (%)10.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.1497077 × 109
Minimum1.3200073 × 109
Maximum4.1801062 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T14:07:43.585634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.3200073 × 109
5-th percentile4.1400314 × 109
Q14.1600396 × 109
median4.1600566 × 109
Q34.1700258 × 109
95-th percentile4.1700391 × 109
Maximum4.1801062 × 109
Range2.8600989 × 109
Interquartile range (IQR)9986200

Descriptive statistics

Standard deviation1.1333291 × 108
Coefficient of variation (CV)0.027311058
Kurtosis499.9961
Mean4.1497077 × 109
Median Absolute Deviation (MAD)9969200
Skewness-20.887931
Sum4.1497077 × 1013
Variance1.2844348 × 1016
MonotonicityNot monotonic
2023-12-12T14:07:43.796856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4170025800 206
 
2.1%
4160050500 189
 
1.9%
4170028200 178
 
1.8%
4160060800 177
 
1.8%
4160060700 153
 
1.5%
4140047700 111
 
1.1%
4160056600 108
 
1.1%
4160067500 107
 
1.1%
4160053200 106
 
1.1%
4170036900 104
 
1.0%
Other values (1046) 8561
85.6%
ValueCountFrequency (%)
1320007300 1
< 0.1%
1340000100 1
< 0.1%
1370001000 1
< 0.1%
1370090300 1
< 0.1%
1380012301 2
< 0.1%
1380019000 2
< 0.1%
1380028201 1
< 0.1%
1380069900 1
< 0.1%
1380070000 1
< 0.1%
1400001200 1
< 0.1%
ValueCountFrequency (%)
4180106200 2
 
< 0.1%
4180103200 3
< 0.1%
4180103100 1
 
< 0.1%
4180103000 4
< 0.1%
4180100800 1
 
< 0.1%
4180099700 5
0.1%
4180092800 1
 
< 0.1%
4180090100 2
 
< 0.1%
4180087400 1
 
< 0.1%
4180083000 1
 
< 0.1%

끝노드
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct1045
Distinct (%)10.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.1499112 × 109
Minimum1.3200071 × 109
Maximum4.1801062 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T14:07:44.312889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.3200071 × 109
5-th percentile4.1400313 × 109
Q14.1600396 × 109
median4.1600564 × 109
Q34.1700259 × 109
95-th percentile4.1700391 × 109
Maximum4.1801062 × 109
Range2.8600991 × 109
Interquartile range (IQR)9986300

Descriptive statistics

Standard deviation1.1009183 × 108
Coefficient of variation (CV)0.026528719
Kurtosis524.83876
Mean4.1499112 × 109
Median Absolute Deviation (MAD)9969400
Skewness-21.290152
Sum4.1499112 × 1013
Variance1.212021 × 1016
MonotonicityNot monotonic
2023-12-12T14:07:44.484735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4160055900 197
 
2.0%
4160058800 191
 
1.9%
4170029000 154
 
1.5%
4160066200 148
 
1.5%
4170027400 143
 
1.4%
4170028200 122
 
1.2%
4170025800 120
 
1.2%
4160051800 114
 
1.1%
4160060700 109
 
1.1%
4140044300 101
 
1.0%
Other values (1035) 8601
86.0%
ValueCountFrequency (%)
1320007100 1
< 0.1%
1340002100 1
< 0.1%
1370001900 1
< 0.1%
1370090600 1
< 0.1%
1380012302 2
< 0.1%
1380018200 1
< 0.1%
1380022400 1
< 0.1%
1380028700 1
< 0.1%
1380029700 1
< 0.1%
1380069900 1
< 0.1%
ValueCountFrequency (%)
4180106200 2
 
< 0.1%
4180103200 2
 
< 0.1%
4180103100 5
0.1%
4180103000 1
 
< 0.1%
4180102000 1
 
< 0.1%
4180100800 5
0.1%
4180094000 1
 
< 0.1%
4180091900 2
 
< 0.1%
4180083300 1
 
< 0.1%
4180081200 1
 
< 0.1%

도로명주소
Text

Distinct399
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T14:07:44.864374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length22
Mean length18.8242
Min length10

Characters and Unicode

Total characters188242
Distinct characters180
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique101 ?
Unique (%)1.0%

Sample

1st row경상남도 창원시 의창구 소계로
2nd row경상남도 창원시 마산합포구 해안대로
3rd row경상남도 창원시 마산합포구 자산삼거리로
4th row경상남도 창원시 마산회원구 3.15대로
5th row경상남도 창원시 마산회원구 마산역광장로
ValueCountFrequency (%)
경상남도 9987
25.1%
창원시 9773
24.6%
마산합포구 4235
10.6%
마산회원구 3206
 
8.1%
3.15대로 1330
 
3.3%
의창구 1260
 
3.2%
성산구 865
 
2.2%
해안대로 749
 
1.9%
북성로 470
 
1.2%
무학로 297
 
0.7%
Other values (368) 7601
19.1%
2023-12-12T14:07:45.474657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
29773
15.8%
원 14039
 
7.5%
창 11424
 
6.1%
남 11081
 
5.9%
시 10205
 
5.4%
상 10204
 
5.4%
도 10093
 
5.4%
경 10048
 
5.3%
구 9942
 
5.3%
산 9155
 
4.9%
Other values (170) 62278
33.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 150496
79.9%
Space Separator 29773
 
15.8%
Decimal Number 6581
 
3.5%
Other Punctuation 1330
 
0.7%
Dash Punctuation 54
 
< 0.1%
Uppercase Letter 8
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
원 14039
 
9.3%
창 11424
 
7.6%
남 11081
 
7.4%
시 10205
 
6.8%
상 10204
 
6.8%
도 10093
 
6.7%
경 10048
 
6.7%
구 9942
 
6.6%
산 9155
 
6.1%
로 8598
 
5.7%
Other values (153) 45707
30.4%
Decimal Number
ValueCountFrequency (%)
1 2053
31.2%
3 1552
23.6%
5 1530
23.2%
2 467
 
7.1%
8 419
 
6.4%
4 325
 
4.9%
7 99
 
1.5%
6 59
 
0.9%
0 39
 
0.6%
9 38
 
0.6%
Uppercase Letter
ValueCountFrequency (%)
C 2
25.0%
E 2
25.0%
P 2
25.0%
A 2
25.0%
Space Separator
ValueCountFrequency (%)
29773
100.0%
Other Punctuation
ValueCountFrequency (%)
. 1330
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 54
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 150496
79.9%
Common 37738
 
20.0%
Latin 8
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
원 14039
 
9.3%
창 11424
 
7.6%
남 11081
 
7.4%
시 10205
 
6.8%
상 10204
 
6.8%
도 10093
 
6.7%
경 10048
 
6.7%
구 9942
 
6.6%
산 9155
 
6.1%
로 8598
 
5.7%
Other values (153) 45707
30.4%
Common
ValueCountFrequency (%)
29773
78.9%
1 2053
 
5.4%
3 1552
 
4.1%
5 1530
 
4.1%
. 1330
 
3.5%
2 467
 
1.2%
8 419
 
1.1%
4 325
 
0.9%
7 99
 
0.3%
6 59
 
0.2%
Other values (3) 131
 
0.3%
Latin
ValueCountFrequency (%)
C 2
25.0%
E 2
25.0%
P 2
25.0%
A 2
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 150496
79.9%
ASCII 37746
 
20.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
29773
78.9%
1 2053
 
5.4%
3 1552
 
4.1%
5 1530
 
4.1%
. 1330
 
3.5%
2 467
 
1.2%
8 419
 
1.1%
4 325
 
0.9%
7 99
 
0.3%
6 59
 
0.2%
Other values (7) 139
 
0.4%
Hangul
ValueCountFrequency (%)
원 14039
 
9.3%
창 11424
 
7.6%
남 11081
 
7.4%
시 10205
 
6.8%
상 10204
 
6.8%
도 10093
 
6.7%
경 10048
 
6.7%
구 9942
 
6.6%
산 9155
 
6.1%
로 8598
 
5.7%
Other values (153) 45707
30.4%

Interactions

2023-12-12T14:07:40.201705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:07:35.844029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:07:36.605037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:07:37.788462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:07:38.554567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:07:39.415085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:07:40.362127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:07:35.959127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:07:36.776510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:07:37.928537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:07:38.709721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:07:39.557325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:07:40.499370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:07:36.095523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:07:36.928223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:07:38.051980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:07:38.872122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:07:39.702396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:07:40.649851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:07:36.237907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:07:37.082438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:07:38.154862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:07:39.021839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:07:39.829690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:07:40.796929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:07:36.370652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:07:37.207468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:07:38.291039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:07:39.144859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:07:39.940722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:07:40.909509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:07:36.487494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:07:37.642924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:07:38.437153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:07:39.277785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:07:40.060440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T14:07:45.613624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
분류번호위험물유형위도경도노드링크시작노드끝노드
분류번호1.0000.1600.2310.1680.1800.1780.180
위험물유형0.1601.0000.0860.1210.0670.0800.070
위도0.2310.0861.0000.4750.2160.2140.216
경도0.1680.1210.4751.0000.9960.9930.996
노드링크0.1800.0670.2160.9961.0001.0001.000
시작노드0.1780.0800.2140.9931.0001.0001.000
끝노드0.1800.0700.2160.9961.0001.0001.000
2023-12-12T14:07:45.779860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
분류번호위도경도노드링크시작노드끝노드위험물유형
분류번호1.000-0.0200.040-0.044-0.037-0.0320.085
위도-0.0201.0000.4340.1470.0680.0650.045
경도0.0400.4341.000-0.232-0.126-0.1280.060
노드링크-0.0440.147-0.2321.0000.9310.9320.027
시작노드-0.0370.068-0.1260.9311.0000.9640.032
끝노드-0.0320.065-0.1280.9320.9641.0000.028
위험물유형0.0850.0450.0600.0270.0320.0281.000

Missing values

2023-12-12T14:07:41.071754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T14:07:41.241239image/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
4689146892수평균열2021-09-05 02:3135.256489128.59908414014130041400222004140022600경상남도 창원시 의창구 소계로
6106461065수직균열2021-09-10 07:5235.196149128.56999416013740041600562004160055600경상남도 창원시 마산합포구 해안대로
4515945160수직균열2021-09-04 08:5835.203326128.565416015080041600505004160051800경상남도 창원시 마산합포구 자산삼거리로
71287129수직균열2021-08-22 21:5035.232852128.5781417005110041700290004170028200경상남도 창원시 마산회원구 3.15대로
3171831719쓰레기2021-08-31 23:3035.234872128.57945417006260041700290004170028500경상남도 창원시 마산회원구 마산역광장로
2153321534수직균열2021-08-28 04:3335.217921128.66966415010140041500171004150018200경상남도 창원시 성산구 창원대로
4206942070수직균열2021-09-03 23:0935.220128128.56474417002350041700248004170024700경상남도 창원시 마산회원구 회원북24길
5211952120수직균열2021-09-07 01:3535.235134128.58228417005450041700309004170031000경상남도 창원시 마산회원구 합성옛길
27182719수직균열2021-09-14 08:5035.18071128.56464416011880041600514004160053100경상남도 창원시 마산합포구 드림베이대로
2885628857수직균열2021-08-30 22:3935.203231128.56522416015080041600505004160051800경상남도 창원시 마산합포구 자산삼거리로
분류번호위험물유형수집날짜위도경도노드링크시작노드끝노드도로명주소
3170031701포트홀2021-08-31 23:2535.216728128.58725416016900041600673004160067500경상남도 창원시 마산합포구 합포로
4932449325피로균열2021-09-06 06:1535.203349128.56993416015100041600558004160055100경상남도 창원시 마산합포구 남성로
4182741828수직균열2021-09-03 22:4835.197496128.57024416014040041600563004160056200경상남도 창원시 마산합포구 서성로
1932719328수평균열2021-08-27 06:5835.255245128.59901417009810041700391004170036900경상남도 창원시 마산회원구 금강로
4003740038수직균열2021-09-03 05:3035.216248128.585416016870041600656004160065500경상남도 창원시 마산합포구 산호북1길
5340153402포트홀2021-09-07 06:4335.234491128.56302417006090041700237004170024200경상남도 창원시 마산회원구 남해고속도로제1지선
1267612677피로균열2021-08-24 23:2635.196024128.63058415003500041500084004150008500경상남도 창원시 성산구 봉양로
3180431805피로균열2021-08-31 23:4935.261755128.61388414014890041400332004140035000경상남도 창원시 의창구 의창대로
1842418425피로균열2021-08-27 00:5635.194908128.56964416013460041600555004160049800경상남도 창원시 마산합포구 해안대로
1241912420피로균열2021-08-24 22:4335.1814128.56473416012410041600502004160051400경상남도 창원시 마산합포구 해안대로