Overview

Dataset statistics

Number of variables15
Number of observations3425
Missing cells2841
Missing cells (%)5.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory441.6 KiB
Average record size in memory132.0 B

Variable types

Numeric8
Categorical4
DateTime1
Text2

Dataset

Description경상남도 도로대장전산화 시스템 데이터의 중장기개방계획에 따른 데이터입니다. 시스템 상에서의 각 도로별 시설물 기본정보를 가지고 있으며, 도로대장의 가로수 데이터를 포함하고있습니다.
Author경상남도
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15091926

Alerts

식별번호 is highly overall correlated with 노선번호 and 1 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
위치_시점 is highly overall correlated with 위치_종점High correlation
위치_종점 is highly overall correlated with 위치_시점High correlation
도로종류 is highly overall correlated with 식별번호 and 2 other fieldsHigh correlation
관리기관 is highly imbalanced (87.6%)Imbalance
이력코드 is highly imbalanced (97.4%)Imbalance
사진 has 459 (13.4%) missing valuesMissing
비고 has 2382 (69.5%) missing valuesMissing
수량 is highly skewed (γ1 = 32.37376281)Skewed
식별번호 has unique valuesUnique
관리번호 has 127 (3.7%) zerosZeros

Reproduction

Analysis started2023-12-11 00:13:34.761349
Analysis finished2023-12-11 00:13:44.365985
Duration9.6 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

식별번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct3425
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1713
Minimum1
Maximum3425
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size30.2 KiB
2023-12-11T09:13:44.454179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile172.2
Q1857
median1713
Q32569
95-th percentile3253.8
Maximum3425
Range3424
Interquartile range (IQR)1712

Descriptive statistics

Standard deviation988.85666
Coefficient of variation (CV)0.577266
Kurtosis-1.2
Mean1713
Median Absolute Deviation (MAD)856
Skewness0
Sum5867025
Variance977837.5
MonotonicityNot monotonic
2023-12-11T09:13:44.593358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
< 0.1%
3231 1
 
< 0.1%
3233 1
 
< 0.1%
3234 1
 
< 0.1%
3235 1
 
< 0.1%
3236 1
 
< 0.1%
3237 1
 
< 0.1%
3238 1
 
< 0.1%
3239 1
 
< 0.1%
3240 1
 
< 0.1%
Other values (3415) 3415
99.7%
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 (%)
3425 1
< 0.1%
3424 1
< 0.1%
3423 1
< 0.1%
3422 1
< 0.1%
3421 1
< 0.1%
3420 1
< 0.1%
3419 1
< 0.1%
3418 1
< 0.1%
3417 1
< 0.1%
3416 1
< 0.1%

관리번호
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct3259
Distinct (%)95.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8586805.7
Minimum0
Maximum10990081
Zeros127
Zeros (%)3.7%
Negative0
Negative (%)0.0%
Memory size30.2 KiB
2023-12-11T09:13:44.741830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile300033.2
Q110010249
median10100189
Q310280006
95-th percentile10890088
Maximum10990081
Range10990081
Interquartile range (IQR)269757

Descriptive statistics

Standard deviation3714547.5
Coefficient of variation (CV)0.43258781
Kurtosis1.0587329
Mean8586805.7
Median Absolute Deviation (MAD)99815
Skewness-1.7338734
Sum2.940981 × 1010
Variance1.3797863 × 1013
MonotonicityNot monotonic
2023-12-11T09:13:44.867011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 127
 
3.7%
10020003 4
 
0.1%
690002 2
 
0.1%
600001 2
 
0.1%
10240001 2
 
0.1%
600009 2
 
0.1%
10240000 2
 
0.1%
600007 2
 
0.1%
600006 2
 
0.1%
600008 2
 
0.1%
Other values (3249) 3278
95.7%
ValueCountFrequency (%)
0 127
3.7%
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%
ValueCountFrequency (%)
10990081 1
< 0.1%
10990080 1
< 0.1%
10990079 1
< 0.1%
10990078 1
< 0.1%
10990077 1
< 0.1%
10990076 1
< 0.1%
10990075 1
< 0.1%
10990074 1
< 0.1%
10990073 1
< 0.1%
10990072 1
< 0.1%

관리기관
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size26.9 KiB
1683
3367 
1684
 
58

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 3367
98.3%
1684 58
 
1.7%

Length

2023-12-11T09:13:44.982774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:13:45.074769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1683 3367
98.3%
1684 58
 
1.7%

도로종류
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size26.9 KiB
1504
2999 
1507
426 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1507
2nd row1507
3rd row1507
4th row1507
5th row1507

Common Values

ValueCountFrequency (%)
1504 2999
87.6%
1507 426
 
12.4%

Length

2023-12-11T09:13:45.178352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:13:45.277442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1504 2999
87.6%
1507 426
 
12.4%

노선번호
Real number (ℝ)

HIGH CORRELATION 

Distinct41
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean897.77956
Minimum30
Maximum1099
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size30.2 KiB
2023-12-11T09:13:45.387746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile58
Q11003
median1011
Q31034
95-th percentile1089
Maximum1099
Range1069
Interquartile range (IQR)31

Descriptive statistics

Standard deviation331.82605
Coefficient of variation (CV)0.36960749
Kurtosis2.5940168
Mean897.77956
Median Absolute Deviation (MAD)10
Skewness-2.1259675
Sum3074895
Variance110108.53
MonotonicityNot monotonic
2023-12-11T09:13:45.515103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
1010 297
 
8.7%
1001 251
 
7.3%
1018 249
 
7.3%
1003 215
 
6.3%
1034 180
 
5.3%
1089 178
 
5.2%
60 146
 
4.3%
1049 125
 
3.6%
1080 121
 
3.5%
1011 119
 
3.5%
Other values (31) 1544
45.1%
ValueCountFrequency (%)
30 34
 
1.0%
37 101
2.9%
58 42
 
1.2%
60 146
4.3%
67 99
 
2.9%
69 34
 
1.0%
907 26
 
0.8%
1001 251
7.3%
1002 80
 
2.3%
1003 215
6.3%
ValueCountFrequency (%)
1099 81
2.4%
1089 178
5.2%
1084 112
3.3%
1080 121
3.5%
1077 32
 
0.9%
1049 125
3.6%
1042 25
 
0.7%
1041 26
 
0.8%
1040 50
 
1.5%
1037 31
 
0.9%

구간번호
Real number (ℝ)

Distinct17
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.2087591
Minimum1
Maximum19
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size30.2 KiB
2023-12-11T09:13:45.609303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q37
95-th percentile13
Maximum19
Range18
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.7639824
Coefficient of variation (CV)0.72262554
Kurtosis0.47756037
Mean5.2087591
Median Absolute Deviation (MAD)2
Skewness1.0213615
Sum17840
Variance14.167563
MonotonicityNot monotonic
2023-12-11T09:13:45.696931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
1 548
16.0%
5 443
12.9%
2 427
12.5%
3 400
11.7%
4 389
11.4%
9 326
9.5%
7 252
7.4%
6 176
 
5.1%
13 88
 
2.6%
10 82
 
2.4%
Other values (7) 294
8.6%
ValueCountFrequency (%)
1 548
16.0%
2 427
12.5%
3 400
11.7%
4 389
11.4%
5 443
12.9%
6 176
 
5.1%
7 252
7.4%
8 51
 
1.5%
9 326
9.5%
10 82
 
2.4%
ValueCountFrequency (%)
19 9
 
0.3%
16 20
 
0.6%
15 59
 
1.7%
14 79
 
2.3%
13 88
 
2.6%
12 23
 
0.7%
11 53
 
1.5%
10 82
 
2.4%
9 326
9.5%
8 51
 
1.5%

이력코드
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size26.9 KiB
0
3416 
1
 
9

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 3416
99.7%
1 9
 
0.3%

Length

2023-12-11T09:13:45.805835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:13:45.887259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 3416
99.7%
1 9
 
0.3%

위치_시점
Real number (ℝ)

HIGH CORRELATION 

Distinct2660
Distinct (%)77.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.3563673
Minimum0
Maximum28.084
Zeros23
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size30.2 KiB
2023-12-11T09:13:45.973874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.26
Q12.212
median4.782
Q37.936
95-th percentile12.344
Maximum28.084
Range28.084
Interquartile range (IQR)5.724

Descriptive statistics

Standard deviation3.9216865
Coefficient of variation (CV)0.73215414
Kurtosis1.684863
Mean5.3563673
Median Absolute Deviation (MAD)2.822
Skewness0.97312881
Sum18345.558
Variance15.379625
MonotonicityNot monotonic
2023-12-11T09:13:46.108623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 23
 
0.7%
7.0 8
 
0.2%
1.96 6
 
0.2%
5.24 6
 
0.2%
2.34 6
 
0.2%
2.16 6
 
0.2%
1.7 6
 
0.2%
0.78 5
 
0.1%
4.782 5
 
0.1%
8.55 5
 
0.1%
Other values (2650) 3349
97.8%
ValueCountFrequency (%)
0.0 23
0.7%
0.001 1
 
< 0.1%
0.002 1
 
< 0.1%
0.003 4
 
0.1%
0.004 2
 
0.1%
0.008 2
 
0.1%
0.009 1
 
< 0.1%
0.01 1
 
< 0.1%
0.012 1
 
< 0.1%
0.013 1
 
< 0.1%
ValueCountFrequency (%)
28.084 1
< 0.1%
27.255 1
< 0.1%
27.192 1
< 0.1%
24.7 1
< 0.1%
24.682 1
< 0.1%
24.11 2
0.1%
23.78 1
< 0.1%
23.741 1
< 0.1%
23.345 1
< 0.1%
22.805 1
< 0.1%

위치_종점
Real number (ℝ)

HIGH CORRELATION 

Distinct2686
Distinct (%)78.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.6724993
Minimum0
Maximum28.76
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size30.2 KiB
2023-12-11T09:13:46.250127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.6002
Q12.508
median5.08
Q38.265
95-th percentile12.618
Maximum28.76
Range28.76
Interquartile range (IQR)5.757

Descriptive statistics

Standard deviation3.9244685
Coefficient of variation (CV)0.69184117
Kurtosis1.8165197
Mean5.6724993
Median Absolute Deviation (MAD)2.831
Skewness0.9853087
Sum19428.31
Variance15.401453
MonotonicityNot monotonic
2023-12-11T09:13:46.356081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.0 8
 
0.2%
2.5 7
 
0.2%
3.64 6
 
0.2%
4.93 6
 
0.2%
4.0 5
 
0.1%
8.54 5
 
0.1%
3.5 5
 
0.1%
4.1 5
 
0.1%
4.32 5
 
0.1%
1.08 5
 
0.1%
Other values (2676) 3368
98.3%
ValueCountFrequency (%)
0.0 1
< 0.1%
0.014 1
< 0.1%
0.027 1
< 0.1%
0.06 1
< 0.1%
0.069 1
< 0.1%
0.07 1
< 0.1%
0.079 1
< 0.1%
0.08 1
< 0.1%
0.085 1
< 0.1%
0.088 1
< 0.1%
ValueCountFrequency (%)
28.76 1
< 0.1%
28.725 1
< 0.1%
27.565 1
< 0.1%
25.2 1
< 0.1%
25.14 1
< 0.1%
24.392 2
0.1%
24.391 1
< 0.1%
23.809 1
< 0.1%
23.61 1
< 0.1%
23.426 1
< 0.1%

위치_방향
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size26.9 KiB
1
1736 
0
1687 
2
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1 1736
50.7%
0 1687
49.3%
2 2
 
0.1%

Length

2023-12-11T09:13:46.466794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:13:46.556472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 1736
50.7%
0 1687
49.3%
2 2
 
0.1%

종류
Real number (ℝ)

Distinct12
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3635.8642
Minimum3600
Maximum3699
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size30.2 KiB
2023-12-11T09:13:46.646213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3600
5-th percentile3601
Q13606
median3606
Q33699
95-th percentile3699
Maximum3699
Range99
Interquartile range (IQR)93

Descriptive statistics

Standard deviation43.685306
Coefficient of variation (CV)0.012015109
Kurtosis-1.4310557
Mean3635.8642
Median Absolute Deviation (MAD)3
Skewness0.74903922
Sum12452835
Variance1908.4059
MonotonicityNot monotonic
2023-12-11T09:13:46.744906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
3606 1641
47.9%
3699 1106
32.3%
3601 242
 
7.1%
3609 237
 
6.9%
3602 97
 
2.8%
3607 44
 
1.3%
3603 19
 
0.6%
3616 13
 
0.4%
3608 12
 
0.4%
3605 10
 
0.3%
Other values (2) 4
 
0.1%
ValueCountFrequency (%)
3600 1
 
< 0.1%
3601 242
 
7.1%
3602 97
 
2.8%
3603 19
 
0.6%
3605 10
 
0.3%
3606 1641
47.9%
3607 44
 
1.3%
3608 12
 
0.4%
3609 237
 
6.9%
3616 13
 
0.4%
ValueCountFrequency (%)
3699 1106
32.3%
3669 3
 
0.1%
3616 13
 
0.4%
3609 237
 
6.9%
3608 12
 
0.4%
3607 44
 
1.3%
3606 1641
47.9%
3605 10
 
0.3%
3603 19
 
0.6%
3602 97
 
2.8%

수량
Real number (ℝ)

SKEWED 

Distinct253
Distinct (%)7.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.77781
Minimum0
Maximum8168
Zeros22
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size30.2 KiB
2023-12-11T09:13:46.867781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q110
median21
Q345
95-th percentile139
Maximum8168
Range8168
Interquartile range (IQR)35

Descriptive statistics

Standard deviation183.11453
Coefficient of variation (CV)4.0000718
Kurtosis1284.8375
Mean45.77781
Median Absolute Deviation (MAD)13
Skewness32.373763
Sum156789
Variance33530.93
MonotonicityNot monotonic
2023-12-11T09:13:47.027498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 123
 
3.6%
9 116
 
3.4%
6 106
 
3.1%
8 102
 
3.0%
5 101
 
2.9%
14 99
 
2.9%
11 98
 
2.9%
13 98
 
2.9%
15 97
 
2.8%
12 95
 
2.8%
Other values (243) 2390
69.8%
ValueCountFrequency (%)
0 22
 
0.6%
1 19
 
0.6%
2 40
 
1.2%
3 63
1.8%
4 90
2.6%
5 101
2.9%
6 106
3.1%
7 91
2.7%
8 102
3.0%
9 116
3.4%
ValueCountFrequency (%)
8168 1
< 0.1%
4750 1
< 0.1%
3190 1
< 0.1%
1629 1
< 0.1%
966 1
< 0.1%
827 1
< 0.1%
785 1
< 0.1%
760 1
< 0.1%
757 1
< 0.1%
702 2
0.1%
Distinct8
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size26.9 KiB
Minimum1900-01-01 00:00:00
Maximum2016-03-31 00:00:00
2023-12-11T09:13:47.145508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:13:47.254668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)

사진
Text

MISSING 

Distinct2944
Distinct (%)99.3%
Missing459
Missing (%)13.4%
Memory size26.9 KiB
2023-12-11T09:13:47.485267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length13
Mean length13
Min length13

Characters and Unicode

Total characters38558
Distinct characters14
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2925 ?
Unique (%)98.6%

Sample

1st row006701R00634D
2nd row006701R00369U
3rd row006701R01367D
4th row006701R01559U
5th row006701R01361U
ValueCountFrequency (%)
108409r06195u 3
 
0.1%
108409r00306u 3
 
0.1%
108401r04782d 3
 
0.1%
108404r12360d 2
 
0.1%
108401r04651d 2
 
0.1%
108401r04980d 2
 
0.1%
109902r03285d 2
 
0.1%
109902r02785u 2
 
0.1%
102004r00072d 2
 
0.1%
108404r08869d 2
 
0.1%
Other values (2934) 2943
99.2%
2023-12-11T09:13:47.842183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 12223
31.7%
1 6008
15.6%
R 2717
 
7.0%
3 2225
 
5.8%
4 2019
 
5.2%
2 1915
 
5.0%
5 1754
 
4.5%
8 1693
 
4.4%
9 1622
 
4.2%
7 1606
 
4.2%
Other values (4) 4776
 
12.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 32626
84.6%
Uppercase Letter 5932
 
15.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 12223
37.5%
1 6008
18.4%
3 2225
 
6.8%
4 2019
 
6.2%
2 1915
 
5.9%
5 1754
 
5.4%
8 1693
 
5.2%
9 1622
 
5.0%
7 1606
 
4.9%
6 1561
 
4.8%
Uppercase Letter
ValueCountFrequency (%)
R 2717
45.8%
D 1502
25.3%
U 1464
24.7%
S 249
 
4.2%

Most occurring scripts

ValueCountFrequency (%)
Common 32626
84.6%
Latin 5932
 
15.4%

Most frequent character per script

Common
ValueCountFrequency (%)
0 12223
37.5%
1 6008
18.4%
3 2225
 
6.8%
4 2019
 
6.2%
2 1915
 
5.9%
5 1754
 
5.4%
8 1693
 
5.2%
9 1622
 
5.0%
7 1606
 
4.9%
6 1561
 
4.8%
Latin
ValueCountFrequency (%)
R 2717
45.8%
D 1502
25.3%
U 1464
24.7%
S 249
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 38558
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 12223
31.7%
1 6008
15.6%
R 2717
 
7.0%
3 2225
 
5.8%
4 2019
 
5.2%
2 1915
 
5.0%
5 1754
 
4.5%
8 1693
 
4.4%
9 1622
 
4.2%
7 1606
 
4.2%
Other values (4) 4776
 
12.4%

비고
Text

MISSING 

Distinct113
Distinct (%)10.8%
Missing2382
Missing (%)69.5%
Memory size26.9 KiB
2023-12-11T09:13:48.042040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length20
Mean length4.346117
Min length2

Characters and Unicode

Total characters4533
Distinct characters110
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique66 ?
Unique (%)6.3%

Sample

1st row소나무
2nd row소나무
3rd row소나무
4th row소나무
5th row소나무
ValueCountFrequency (%)
동백나무 215
19.4%
소나무 145
13.1%
무궁화 127
11.4%
벗나무 85
 
7.7%
이팝나무 81
 
7.3%
기타 72
 
6.5%
단풍나무 62
 
5.6%
해송 32
 
2.9%
백일홍 30
 
2.7%
이팝나무(물푸레나무과 20
 
1.8%
Other values (101) 242
21.8%
2023-12-11T09:13:48.671873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
964
21.3%
838
18.5%
263
 
5.8%
232
 
5.1%
172
 
3.8%
129
 
2.8%
129
 
2.8%
112
 
2.5%
105
 
2.3%
102
 
2.3%
Other values (100) 1487
32.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3993
88.1%
Decimal Number 154
 
3.4%
Other Punctuation 107
 
2.4%
Uppercase Letter 96
 
2.1%
Space Separator 68
 
1.5%
Open Punctuation 48
 
1.1%
Close Punctuation 48
 
1.1%
Math Symbol 13
 
0.3%
Lowercase Letter 6
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
964
24.1%
838
21.0%
263
 
6.6%
232
 
5.8%
172
 
4.3%
129
 
3.2%
129
 
3.2%
112
 
2.8%
105
 
2.6%
102
 
2.6%
Other values (78) 947
23.7%
Decimal Number
ValueCountFrequency (%)
1 33
21.4%
2 25
16.2%
5 25
16.2%
3 22
14.3%
6 14
9.1%
4 8
 
5.2%
7 7
 
4.5%
9 7
 
4.5%
8 7
 
4.5%
0 6
 
3.9%
Uppercase Letter
ValueCountFrequency (%)
E 36
37.5%
A 36
37.5%
U 12
 
12.5%
C 12
 
12.5%
Other Punctuation
ValueCountFrequency (%)
, 95
88.8%
\ 12
 
11.2%
Lowercase Letter
ValueCountFrequency (%)
a 3
50.0%
e 3
50.0%
Space Separator
ValueCountFrequency (%)
68
100.0%
Open Punctuation
ValueCountFrequency (%)
( 48
100.0%
Close Punctuation
ValueCountFrequency (%)
) 48
100.0%
Math Symbol
ValueCountFrequency (%)
+ 13
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3993
88.1%
Common 438
 
9.7%
Latin 102
 
2.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
964
24.1%
838
21.0%
263
 
6.6%
232
 
5.8%
172
 
4.3%
129
 
3.2%
129
 
3.2%
112
 
2.8%
105
 
2.6%
102
 
2.6%
Other values (78) 947
23.7%
Common
ValueCountFrequency (%)
, 95
21.7%
68
15.5%
( 48
11.0%
) 48
11.0%
1 33
 
7.5%
2 25
 
5.7%
5 25
 
5.7%
3 22
 
5.0%
6 14
 
3.2%
+ 13
 
3.0%
Other values (6) 47
10.7%
Latin
ValueCountFrequency (%)
E 36
35.3%
A 36
35.3%
U 12
 
11.8%
C 12
 
11.8%
a 3
 
2.9%
e 3
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3993
88.1%
ASCII 540
 
11.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
964
24.1%
838
21.0%
263
 
6.6%
232
 
5.8%
172
 
4.3%
129
 
3.2%
129
 
3.2%
112
 
2.8%
105
 
2.6%
102
 
2.6%
Other values (78) 947
23.7%
ASCII
ValueCountFrequency (%)
, 95
17.6%
68
12.6%
( 48
8.9%
) 48
8.9%
E 36
 
6.7%
A 36
 
6.7%
1 33
 
6.1%
2 25
 
4.6%
5 25
 
4.6%
3 22
 
4.1%
Other values (12) 104
19.3%

Interactions

2023-12-11T09:13:42.654810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:13:35.998670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:13:36.779788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:13:37.755873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:13:38.675425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:13:39.969340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:13:40.868079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:13:41.752952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:13:42.787171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:13:36.107306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:13:36.871389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:13:37.847957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:13:38.805669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:13:40.104253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:13:40.980282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:13:41.893682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:13:42.891084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:13:36.221165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:13:36.985053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:13:37.950891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:13:38.918890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:13:40.225144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:13:41.078824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:13:42.017712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:13:42.989845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:13:36.311973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:13:37.081186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:13:38.073499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:13:39.041444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:13:40.318154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:13:41.170716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:13:42.118156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:13:43.438398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:13:36.403770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:13:37.181592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:13:38.199335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:13:39.281412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:13:40.420082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:13:41.281413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:13:42.217576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:13:43.584636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:13:36.499098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:13:37.282123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:13:38.297137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:13:39.478118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:13:40.537032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:13:41.371488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:13:42.323081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:13:43.679127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:13:36.588807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:13:37.492333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:13:38.418260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:13:39.652803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:13:40.643617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:13:41.498997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:13:42.413733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:13:43.793515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:13:36.674668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:13:37.629705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:13:38.552733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:13:39.812012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:13:40.757979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:13:41.624204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:13:42.521440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T09:13:48.793902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
식별번호관리번호관리기관도로종류노선번호구간번호이력코드위치_시점위치_종점위치_방향종류수량설치일자
식별번호1.0000.6360.5080.9160.6900.6370.1890.3690.3680.0650.3710.0450.291
관리번호0.6361.0000.0340.5630.9970.2450.0000.1000.0950.0000.0130.0560.228
관리기관0.5080.0341.0000.0670.0280.1560.0000.5800.5970.0000.0000.0000.000
도로종류0.9160.5630.0671.0000.7240.2100.0000.1120.1020.0000.0000.0000.031
노선번호0.6900.9970.0280.7241.0000.2810.0000.0930.0840.0120.0180.0710.253
구간번호0.6370.2450.1560.2100.2811.0000.0830.3040.2950.0000.1940.0590.663
이력코드0.1890.0000.0000.0000.0000.0831.0000.0230.0370.0000.0000.0001.000
위치_시점0.3690.1000.5800.1120.0930.3040.0231.0000.9980.0000.0960.0000.017
위치_종점0.3680.0950.5970.1020.0840.2950.0370.9981.0000.0000.0980.0000.031
위치_방향0.0650.0000.0000.0000.0120.0000.0000.0000.0001.0000.0260.0000.026
종류0.3710.0130.0000.0000.0180.1940.0000.0960.0980.0261.0000.0000.801
수량0.0450.0560.0000.0000.0710.0590.0000.0000.0000.0000.0001.0000.451
설치일자0.2910.2280.0000.0310.2530.6631.0000.0170.0310.0260.8010.4511.000
2023-12-11T09:13:48.934714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
이력코드도로종류관리기관위치_방향
이력코드1.0000.0000.0000.000
도로종류0.0001.0000.0430.000
관리기관0.0000.0431.0000.000
위치_방향0.0000.0000.0001.000
2023-12-11T09:13:49.040634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
식별번호관리번호노선번호구간번호위치_시점위치_종점종류수량관리기관도로종류이력코드위치_방향
식별번호1.0000.4870.5940.042-0.021-0.0080.1440.1030.3910.7570.1450.038
관리번호0.4871.0000.8460.0060.0530.064-0.0030.0500.0560.8320.0000.000
노선번호0.5940.8461.000-0.1300.0440.052-0.0420.0070.0470.9620.0000.004
구간번호0.0420.006-0.1301.0000.0380.0370.109-0.0460.1200.1620.0630.000
위치_시점-0.0210.0530.0440.0381.0000.989-0.038-0.0450.4470.0860.0180.000
위치_종점-0.0080.0640.0520.0370.9891.000-0.0450.0300.4610.0780.0280.000
종류0.144-0.003-0.0420.109-0.038-0.0451.0000.0920.0000.0000.0000.024
수량0.1030.0500.007-0.046-0.0450.0300.0921.0000.0000.0000.0000.000
관리기관0.3910.0560.0470.1200.4470.4610.0000.0001.0000.0430.0000.000
도로종류0.7570.8320.9620.1620.0860.0780.0000.0000.0431.0000.0000.000
이력코드0.1450.0000.0000.0630.0180.0280.0000.0000.0000.0001.0000.000
위치_방향0.0380.0000.0040.0000.0000.0000.0240.0000.0000.0000.0001.000

Missing values

2023-12-11T09:13:44.009248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T09:13:44.200063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-12-11T09:13:44.314739image/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

식별번호관리번호관리기관도로종류노선번호구간번호이력코드위치_시점위치_종점위치_방향종류수량설치일자사진비고
016700041683150767100.6340.83413606251900-01-01006701R00634D<NA>
126700031683150767100.3690.83303606581900-01-01006701R00369U<NA>
236700091683150767101.3671.48313606111900-01-01006701R01367D<NA>
346700101683150767101.5591.5850360631900-01-01006701R01559U<NA>
456700081683150767101.3611.48703606111900-01-01006701R01361U<NA>
566700071683150767101.0631.25303606191900-01-01006701R01063U<NA>
676700051683150767100.8431.34713606501900-01-01006701R00843D<NA>
786700061683150767100.8481.05103606211900-01-01006701R00848U<NA>
896700021683150767100.160.30803606191900-01-01006701R00160U<NA>
9106700011683150767100.00.58213606681900-01-01006701R00000D<NA>
식별번호관리번호관리기관도로종류노선번호구간번호이력코드위치_시점위치_종점위치_방향종류수량설치일자사진비고
3415315510890021168315041089305.375.4203601121900-01-01108903R05370U<NA>
3416315610890024168315041089306.2156.7213601441900-01-01108903R06215D<NA>
3417315710890025168315041089306.3426.5203601151900-01-01108903R06342U<NA>
3418315810890026168315041089306.6926.7780360171900-01-01108903R06692U<NA>
3419315910890027168315041089306.7976.8751360191900-01-01<NA><NA>
3420316010890028168315041089307.868.1813601281900-01-01108903R07860D<NA>
3421316110890029168315041089308.1968.27203606111900-01-01<NA><NA>
3422316210890031168315041089308.5348.87803606281900-01-01108903R08534U<NA>
3423316310890030168315041089308.348.5213601211900-01-01<NA><NA>
3424316410890015168315041089303.8524.17213601411900-01-01108903R03852D벗나무11개