Overview

Dataset statistics

Number of variables13
Number of observations1290
Missing cells231
Missing cells (%)1.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory145.0 KiB
Average record size in memory115.1 B

Variable types

Numeric7
Categorical5
Text1

Dataset

Description경상남도 도로대장전산화 시스템 데이터의 중장기개방계획에 따른 데이터입니다. 시스템 상에서의 각 도로별 시설물 기본정보를 가지고 있으며, 도로대장의 신호등 데이터를 포함하고있습니다.
Author경상남도
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15091918

Alerts

관리기관 has constant value ""Constant
설치일자 is highly overall correlated with 이력코드High correlation
이력코드 is highly overall correlated with 식별번호 and 1 other fieldsHigh correlation
식별번호 is highly overall correlated with 노선번호 and 2 other fieldsHigh correlation
관리번호 is highly overall correlated with 노선번호 and 1 other fieldsHigh correlation
노선번호 is highly overall correlated with 식별번호 and 2 other fieldsHigh correlation
도로종류 is highly overall correlated with 식별번호 and 2 other fieldsHigh correlation
이력코드 is highly imbalanced (80.8%)Imbalance
설치일자 is highly imbalanced (69.5%)Imbalance
비고 has 231 (17.9%) missing valuesMissing
식별번호 has unique valuesUnique
관리번호 has 93 (7.2%) zerosZeros
위치 has 17 (1.3%) zerosZeros

Reproduction

Analysis started2023-12-11 00:50:06.487904
Analysis finished2023-12-11 00:50:12.488442
Duration6 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

식별번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct1290
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean645.5
Minimum1
Maximum1290
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2023-12-11T09:50:12.578392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile65.45
Q1323.25
median645.5
Q3967.75
95-th percentile1225.55
Maximum1290
Range1289
Interquartile range (IQR)644.5

Descriptive statistics

Standard deviation372.53523
Coefficient of variation (CV)0.57712662
Kurtosis-1.2
Mean645.5
Median Absolute Deviation (MAD)322.5
Skewness0
Sum832695
Variance138782.5
MonotonicityStrictly increasing
2023-12-11T09:50:12.697455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.1%
888 1
 
0.1%
866 1
 
0.1%
865 1
 
0.1%
864 1
 
0.1%
863 1
 
0.1%
862 1
 
0.1%
861 1
 
0.1%
860 1
 
0.1%
859 1
 
0.1%
Other values (1280) 1280
99.2%
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 (%)
1290 1
0.1%
1289 1
0.1%
1288 1
0.1%
1287 1
0.1%
1286 1
0.1%
1285 1
0.1%
1284 1
0.1%
1283 1
0.1%
1282 1
0.1%
1281 1
0.1%

관리번호
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1196
Distinct (%)92.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8292353.2
Minimum0
Maximum10890050
Zeros93
Zeros (%)7.2%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2023-12-11T09:50:12.821194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q110030011
median10200004
Q310420060
95-th percentile10840070
Maximum10890050
Range10890050
Interquartile range (IQR)390048.5

Descriptive statistics

Standard deviation4045911.9
Coefficient of variation (CV)0.48790878
Kurtosis0.12964016
Mean8292353.2
Median Absolute Deviation (MAD)219997
Skewness-1.4475445
Sum1.0697136 × 1010
Variance1.6369403 × 1013
MonotonicityNot monotonic
2023-12-11T09:50:12.937116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 93
 
7.2%
10180014 2
 
0.2%
670031 2
 
0.2%
10200002 1
 
0.1%
10010024 1
 
0.1%
10060002 1
 
0.1%
10060001 1
 
0.1%
10010026 1
 
0.1%
10010023 1
 
0.1%
10010022 1
 
0.1%
Other values (1186) 1186
91.9%
ValueCountFrequency (%)
0 93
7.2%
300001 1
 
0.1%
300002 1
 
0.1%
300003 1
 
0.1%
300004 1
 
0.1%
300005 1
 
0.1%
300006 1
 
0.1%
300007 1
 
0.1%
300008 1
 
0.1%
300009 1
 
0.1%
ValueCountFrequency (%)
10890050 1
0.1%
10890049 1
0.1%
10890048 1
0.1%
10890047 1
0.1%
10890046 1
0.1%
10890045 1
0.1%
10890044 1
0.1%
10890043 1
0.1%
10890042 1
0.1%
10890041 1
0.1%

관리기관
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size10.2 KiB
1683
1290 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1683
2nd row1683
3rd row1683
4th row1683
5th row1683

Common Values

ValueCountFrequency (%)
1683 1290
100.0%

Length

2023-12-11T09:50:13.058929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:50:13.379174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1683 1290
100.0%

도로종류
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size10.2 KiB
1504
1118 
1507
172 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1504
2nd row1504
3rd row1504
4th row1504
5th row1504

Common Values

ValueCountFrequency (%)
1504 1118
86.7%
1507 172
 
13.3%

Length

2023-12-11T09:50:13.458763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:50:13.540769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1504 1118
86.7%
1507 172
 
13.3%

노선번호
Real number (ℝ)

HIGH CORRELATION 

Distinct37
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean905.5062
Minimum30
Maximum1099
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2023-12-11T09:50:13.651059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile58
Q11007
median1020
Q31042
95-th percentile1084
Maximum1099
Range1069
Interquartile range (IQR)35

Descriptive statistics

Standard deviation334.77786
Coefficient of variation (CV)0.36971349
Kurtosis2.6035991
Mean905.5062
Median Absolute Deviation (MAD)20.5
Skewness-2.1295552
Sum1168103
Variance112076.21
MonotonicityNot monotonic
2023-12-11T09:50:13.784749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
1042 167
 
12.9%
1020 144
 
11.2%
1084 107
 
8.3%
1077 97
 
7.5%
1004 79
 
6.1%
1018 72
 
5.6%
1021 67
 
5.2%
1089 50
 
3.9%
67 49
 
3.8%
30 40
 
3.1%
Other values (27) 418
32.4%
ValueCountFrequency (%)
30 40
3.1%
37 5
 
0.4%
58 26
2.0%
60 25
1.9%
67 49
3.8%
69 27
2.1%
907 3
 
0.2%
1001 38
2.9%
1002 6
 
0.5%
1003 18
 
1.4%
ValueCountFrequency (%)
1099 5
 
0.4%
1089 50
 
3.9%
1084 107
8.3%
1080 28
 
2.2%
1077 97
7.5%
1051 1
 
0.1%
1049 2
 
0.2%
1047 1
 
0.1%
1042 167
12.9%
1041 12
 
0.9%

구간번호
Real number (ℝ)

Distinct16
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.0170543
Minimum1
Maximum16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2023-12-11T09:50:13.889789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median3
Q36
95-th percentile11
Maximum16
Range15
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.3476628
Coefficient of variation (CV)0.8333626
Kurtosis1.1821412
Mean4.0170543
Median Absolute Deviation (MAD)2
Skewness1.3417441
Sum5182
Variance11.206846
MonotonicityNot monotonic
2023-12-11T09:50:13.994069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
1 356
27.6%
3 207
16.0%
2 189
14.7%
4 171
13.3%
7 118
 
9.1%
10 42
 
3.3%
6 40
 
3.1%
8 34
 
2.6%
5 29
 
2.2%
9 27
 
2.1%
Other values (6) 77
 
6.0%
ValueCountFrequency (%)
1 356
27.6%
2 189
14.7%
3 207
16.0%
4 171
13.3%
5 29
 
2.2%
6 40
 
3.1%
7 118
 
9.1%
8 34
 
2.6%
9 27
 
2.1%
10 42
 
3.3%
ValueCountFrequency (%)
16 1
 
0.1%
15 8
 
0.6%
14 18
 
1.4%
13 25
 
1.9%
12 9
 
0.7%
11 16
 
1.2%
10 42
 
3.3%
9 27
 
2.1%
8 34
 
2.6%
7 118
9.1%

이력코드
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size10.2 KiB
0
1252 
1
 
38

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1252
97.1%
1 38
 
2.9%

Length

2023-12-11T09:50:14.106587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:50:14.188933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 1252
97.1%
1 38
 
2.9%

위치
Real number (ℝ)

ZEROS 

Distinct1061
Distinct (%)82.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.0349481
Minimum0
Maximum19.361
Zeros17
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2023-12-11T09:50:14.281697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.015
Q11.125
median3.95
Q38
95-th percentile13.21755
Maximum19.361
Range19.361
Interquartile range (IQR)6.875

Descriptive statistics

Standard deviation4.4182998
Coefficient of variation (CV)0.87752639
Kurtosis0.030604946
Mean5.0349481
Median Absolute Deviation (MAD)3.133
Skewness0.82220835
Sum6495.083
Variance19.521373
MonotonicityNot monotonic
2023-12-11T09:50:14.438846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 17
 
1.3%
8.0 12
 
0.9%
7.0 9
 
0.7%
0.01 7
 
0.5%
0.015 5
 
0.4%
6.0 5
 
0.4%
0.013 5
 
0.4%
0.008 5
 
0.4%
0.002 5
 
0.4%
0.022 4
 
0.3%
Other values (1051) 1216
94.3%
ValueCountFrequency (%)
0.0 17
1.3%
0.001 3
 
0.2%
0.002 5
 
0.4%
0.003 3
 
0.2%
0.005 3
 
0.2%
0.006 1
 
0.1%
0.007 3
 
0.2%
0.008 5
 
0.4%
0.009 4
 
0.3%
0.01 7
0.5%
ValueCountFrequency (%)
19.361 1
0.1%
19.172 1
0.1%
19.164 1
0.1%
19.138 1
0.1%
19.129 1
0.1%
18.86 1
0.1%
18.852 1
0.1%
18.828 1
0.1%
18.815 1
0.1%
18.38 1
0.1%

위치_방향
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size10.2 KiB
1
648 
0
642 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1 648
50.2%
0 642
49.8%

Length

2023-12-11T09:50:14.593125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:50:14.688051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 648
50.2%
0 642
49.8%

종류
Real number (ℝ)

Distinct7
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4110.2899
Minimum4100
Maximum4199
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2023-12-11T09:50:14.771679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4100
5-th percentile4101
Q14101
median4102
Q34105
95-th percentile4199
Maximum4199
Range99
Interquartile range (IQR)4

Descriptive statistics

Standard deviation26.456187
Coefficient of variation (CV)0.0064365745
Kurtosis7.3469205
Mean4110.2899
Median Absolute Deviation (MAD)1
Skewness3.0490199
Sum5302274
Variance699.92984
MonotonicityNot monotonic
2023-12-11T09:50:14.861993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
4101 457
35.4%
4102 281
21.8%
4105 222
17.2%
4103 146
 
11.3%
4199 105
 
8.1%
4104 78
 
6.0%
4100 1
 
0.1%
ValueCountFrequency (%)
4100 1
 
0.1%
4101 457
35.4%
4102 281
21.8%
4103 146
 
11.3%
4104 78
 
6.0%
4105 222
17.2%
4199 105
 
8.1%
ValueCountFrequency (%)
4199 105
 
8.1%
4105 222
17.2%
4104 78
 
6.0%
4103 146
 
11.3%
4102 281
21.8%
4101 457
35.4%
4100 1
 
0.1%

설치형식
Real number (ℝ)

Distinct9
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1406.8442
Minimum1400
Maximum1499
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2023-12-11T09:50:14.955945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1400
5-th percentile1401
Q11403
median1403
Q31407
95-th percentile1409
Maximum1499
Range99
Interquartile range (IQR)4

Descriptive statistics

Standard deviation15.800582
Coefficient of variation (CV)0.011231224
Kurtosis29.466301
Mean1406.8442
Median Absolute Deviation (MAD)1
Skewness5.5360602
Sum1814829
Variance249.6584
MonotonicityNot monotonic
2023-12-11T09:50:15.060362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1403 579
44.9%
1407 232
18.0%
1401 166
 
12.9%
1404 126
 
9.8%
1409 100
 
7.8%
1408 39
 
3.0%
1499 36
 
2.8%
1402 11
 
0.9%
1400 1
 
0.1%
ValueCountFrequency (%)
1400 1
 
0.1%
1401 166
 
12.9%
1402 11
 
0.9%
1403 579
44.9%
1404 126
 
9.8%
1407 232
18.0%
1408 39
 
3.0%
1409 100
 
7.8%
1499 36
 
2.8%
ValueCountFrequency (%)
1499 36
 
2.8%
1409 100
 
7.8%
1408 39
 
3.0%
1407 232
18.0%
1404 126
 
9.8%
1403 579
44.9%
1402 11
 
0.9%
1401 166
 
12.9%
1400 1
 
0.1%

설치일자
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct10
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size10.2 KiB
1900-01-01
1098 
2007-12-31
 
50
20090326
 
39
2016-03-31
 
38
20121219
 
23
Other values (5)
 
42

Length

Max length10
Median length10
Mean length9.8682171
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1900-01-01
2nd row1900-01-01
3rd row1900-01-01
4th row1900-01-01
5th row1900-01-01

Common Values

ValueCountFrequency (%)
1900-01-01 1098
85.1%
2007-12-31 50
 
3.9%
20090326 39
 
3.0%
2016-03-31 38
 
2.9%
20121219 23
 
1.8%
2007-10-12 18
 
1.4%
20091209 11
 
0.9%
20120630 6
 
0.5%
2012.03.31 4
 
0.3%
201006 3
 
0.2%

Length

2023-12-11T09:50:15.179467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:50:15.290258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1900-01-01 1098
85.1%
2007-12-31 50
 
3.9%
20090326 39
 
3.0%
2016-03-31 38
 
2.9%
20121219 23
 
1.8%
2007-10-12 18
 
1.4%
20091209 11
 
0.9%
20120630 6
 
0.5%
2012.03.31 4
 
0.3%
201006 3
 
0.2%

비고
Text

MISSING 

Distinct213
Distinct (%)20.1%
Missing231
Missing (%)17.9%
Memory size10.2 KiB
2023-12-11T09:50:15.505685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length29
Median length27
Mean length6.8375826
Min length2

Characters and Unicode

Total characters7241
Distinct characters59
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique102 ?
Unique (%)9.6%

Sample

1st row2개
2nd row2개
3rd row2개
4th row2개
5th row2개
ValueCountFrequency (%)
2ea 201
 
16.4%
2색보행등 102
 
8.3%
3ea 60
 
4.9%
3 49
 
4.0%
1ea 46
 
3.8%
2 41
 
3.4%
2색점멸등 30
 
2.5%
4ea 28
 
2.3%
6ea 28
 
2.3%
2개 26
 
2.1%
Other values (166) 611
50.0%
2023-12-11T09:50:15.847635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 890
12.3%
A 889
12.3%
2 841
11.6%
색 745
10.3%
등 684
 
9.4%
3 445
 
6.1%
4 287
 
4.0%
, 257
 
3.5%
1 200
 
2.8%
( 189
 
2.6%
Other values (49) 1814
25.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2510
34.7%
Decimal Number 1868
25.8%
Uppercase Letter 1779
24.6%
Other Punctuation 539
 
7.4%
Open Punctuation 189
 
2.6%
Close Punctuation 189
 
2.6%
Space Separator 163
 
2.3%
Math Symbol 4
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
색 745
29.7%
등 684
27.3%
보 185
 
7.4%
행 184
 
7.3%
호 154
 
6.1%
신 153
 
6.1%
점 133
 
5.3%
멸 133
 
5.3%
개 45
 
1.8%
뒤 28
 
1.1%
Other values (28) 66
 
2.6%
Decimal Number
ValueCountFrequency (%)
2 841
45.0%
3 445
23.8%
4 287
 
15.4%
1 200
 
10.7%
6 66
 
3.5%
5 15
 
0.8%
8 12
 
0.6%
0 1
 
0.1%
7 1
 
0.1%
Other Punctuation
ValueCountFrequency (%)
, 257
47.7%
@ 177
32.8%
: 89
 
16.5%
. 6
 
1.1%
/ 5
 
0.9%
* 5
 
0.9%
Uppercase Letter
ValueCountFrequency (%)
E 890
50.0%
A 889
50.0%
Open Punctuation
ValueCountFrequency (%)
( 189
100.0%
Close Punctuation
ValueCountFrequency (%)
) 189
100.0%
Space Separator
ValueCountFrequency (%)
163
100.0%
Math Symbol
ValueCountFrequency (%)
+ 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2952
40.8%
Hangul 2510
34.7%
Latin 1779
24.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
색 745
29.7%
등 684
27.3%
보 185
 
7.4%
행 184
 
7.3%
호 154
 
6.1%
신 153
 
6.1%
점 133
 
5.3%
멸 133
 
5.3%
개 45
 
1.8%
뒤 28
 
1.1%
Other values (28) 66
 
2.6%
Common
ValueCountFrequency (%)
2 841
28.5%
3 445
15.1%
4 287
 
9.7%
, 257
 
8.7%
1 200
 
6.8%
( 189
 
6.4%
) 189
 
6.4%
@ 177
 
6.0%
163
 
5.5%
: 89
 
3.0%
Other values (9) 115
 
3.9%
Latin
ValueCountFrequency (%)
E 890
50.0%
A 889
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4731
65.3%
Hangul 2510
34.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 890
18.8%
A 889
18.8%
2 841
17.8%
3 445
9.4%
4 287
 
6.1%
, 257
 
5.4%
1 200
 
4.2%
( 189
 
4.0%
) 189
 
4.0%
@ 177
 
3.7%
Other values (11) 367
7.8%
Hangul
ValueCountFrequency (%)
색 745
29.7%
등 684
27.3%
보 185
 
7.4%
행 184
 
7.3%
호 154
 
6.1%
신 153
 
6.1%
점 133
 
5.3%
멸 133
 
5.3%
개 45
 
1.8%
뒤 28
 
1.1%
Other values (28) 66
 
2.6%

Interactions

2023-12-11T09:50:11.508436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:50:07.150620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:50:08.086393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:50:08.768767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:50:09.433717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:50:10.106768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:50:10.839051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:50:11.641985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:50:07.245651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:50:08.197467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:50:08.879628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:50:09.523622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:50:10.199133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:50:10.934241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:50:11.734785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:50:07.346632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:50:08.313979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:50:08.986130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:50:09.613978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:50:10.288912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:50:11.024433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:50:11.813359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:50:07.449501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:50:08.409894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:50:09.084305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:50:09.698968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:50:10.384473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:50:11.107739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:50:11.902066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:50:07.792411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:50:08.512825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:50:09.182278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:50:09.796351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:50:10.494423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:50:11.202687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:50:12.001509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:50:07.908430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:50:08.601365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:50:09.274851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:50:09.891124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:50:10.621489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:50:11.306360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:50:12.098829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:50:07.997160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:50:08.679437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:50:09.352161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:50:10.009260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:50:10.727870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:50:11.405788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T09:50:15.938341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
식별번호관리번호도로종류노선번호구간번호이력코드위치위치_방향종류설치형식설치일자
식별번호1.0000.6530.8550.6460.7650.6660.6760.0000.2850.3290.826
관리번호0.6531.0000.5090.9920.3650.0480.2640.0000.0670.0210.567
도로종류0.8550.5091.0001.0000.4380.0860.1860.0000.1440.0000.423
노선번호0.6460.9921.0001.0000.3760.0340.1490.0000.0560.0000.348
구간번호0.7650.3650.4380.3761.0000.2430.5540.0000.1540.6370.798
이력코드0.6660.0480.0860.0340.2431.0000.1920.0000.0530.0001.000
위치0.6760.2640.1860.1490.5540.1921.0000.0000.1590.0590.434
위치_방향0.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.000
종류0.2850.0670.1440.0560.1540.0530.1590.0001.0000.3560.131
설치형식0.3290.0210.0000.0000.6370.0000.0590.0000.3561.0000.000
설치일자0.8260.5670.4230.3480.7981.0000.4340.0000.1310.0001.000
2023-12-11T09:50:16.061418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
도로종류설치일자이력코드위치_방향
도로종류1.0000.3240.0550.000
설치일자0.3241.0000.9970.000
이력코드0.0550.9971.0000.000
위치_방향0.0000.0000.0001.000
2023-12-11T09:50:16.163158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
식별번호관리번호노선번호구간번호위치종류설치형식도로종류이력코드위치_방향설치일자
식별번호1.0000.3770.716-0.034-0.049-0.1020.0210.6850.5160.0000.393
관리번호0.3771.0000.731-0.1140.0580.004-0.2640.7710.0800.0000.409
노선번호0.7160.7311.000-0.105-0.021-0.058-0.1151.0000.0570.0000.222
구간번호-0.034-0.114-0.1051.0000.1800.0970.0040.3260.1860.0000.315
위치-0.0490.058-0.0210.1801.0000.031-0.1140.1420.1470.0000.146
종류-0.1020.004-0.0580.0970.0311.000-0.1100.0920.0330.0000.100
설치형식0.021-0.264-0.1150.004-0.114-0.1101.0000.0000.0000.0000.000
도로종류0.6850.7711.0000.3260.1420.0920.0001.0000.0550.0000.324
이력코드0.5160.0800.0570.1860.1470.0330.0000.0551.0000.0000.997
위치_방향0.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.000
설치일자0.3930.4090.2220.3150.1460.1000.0000.3240.9970.0001.000

Missing values

2023-12-11T09:50:12.242587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T09:50:12.419227image/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

식별번호관리번호관리기관도로종류노선번호구간번호이력코드위치위치_방향종류설치형식설치일자비고
0110200002168315041020100.4470410114031900-01-01<NA>
1210200001168315041020100.3581410114031900-01-01<NA>
2310200031168315041020102.4271410514011900-01-01<NA>
3410200032168315041020102.3960410514011900-01-01<NA>
4510200040168315041020102.731410214031900-01-01<NA>
5610200039168315041020102.730410214031900-01-012개
6710200038168315041020102.710410214031900-01-01<NA>
7810200037168315041020102.7141410514011900-01-01<NA>
8910200036168315041020102.4380410514011900-01-01<NA>
91010200027168315041020102.1420410114031900-01-012개
식별번호관리번호관리기관도로종류노선번호구간번호이력코드위치위치_방향종류설치형식설치일자비고
1280128110420121168315041042111.1241410114072016-03-312EA
1281128210420120168315041042111.1061410114072016-03-31<NA>
1282128310420102168315041042111.1050410114072016-03-31<NA>
1283128410420101168315041042111.0740410114072016-03-312EA
1284128510420100168315041042110.7790410114072016-03-31<NA>
1285128610420099168315041042110.7580410114072016-03-312EA
1286128710420098168315041042110.2270410114072016-03-31<NA>
1287128810420097168315041042110.2110410114072016-03-312EA
1288128910420096168315041042110.090410114072016-03-31<NA>
1289129010420095168315041042110.0640410114072016-03-312EA