Overview

Dataset statistics

Number of variables18
Number of observations1451
Missing cells102
Missing cells (%)0.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory225.4 KiB
Average record size in memory159.1 B

Variable types

Numeric9
Categorical8
Text1

Dataset

Description경상남도 도로대장전산화 시스템 데이터의 중장기개방계획에 따른 데이터입니다. 시스템 상에서의 각 도로별 시설물 기본정보를 가지고 있으며, 도로대장의 가로등 데이터를 포함하고있습니다.
Author경상남도
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15091922

Alerts

관리기관 has constant value ""Constant
설치일자 is highly overall correlated with 이력코드 and 2 other fieldsHigh correlation
이력코드 is highly overall correlated with 광원용량 and 2 other fieldsHigh correlation
비고 is highly overall correlated with 식별번호 and 8 other fieldsHigh correlation
도로종류 is highly overall correlated with 식별번호 and 3 other fieldsHigh correlation
식별번호 is highly overall correlated with 노선번호 and 3 other fieldsHigh correlation
관리번호 is highly overall correlated with 노선번호 and 1 other fieldsHigh correlation
노선번호 is highly overall correlated with 식별번호 and 3 other fieldsHigh correlation
구간번호 is highly overall correlated with 비고High correlation
위치_시점 is highly overall correlated with 위치_종점High correlation
위치_종점 is highly overall correlated with 위치_시점High correlation
등주형식 is highly overall correlated with 비고High correlation
등주재질 is highly overall correlated with 비고High correlation
광원용량 is highly overall correlated with 식별번호 and 3 other fieldsHigh correlation
이력코드 is highly imbalanced (89.9%)Imbalance
광원종류 is highly imbalanced (64.0%)Imbalance
설치일자 is highly imbalanced (84.2%)Imbalance
비고 is highly imbalanced (85.0%)Imbalance
사진 has 102 (7.0%) missing valuesMissing
식별번호 has unique valuesUnique
관리번호 has 90 (6.2%) zerosZeros
위치_종점 has 24 (1.7%) zerosZeros

Reproduction

Analysis started2023-12-10 23:56:43.066218
Analysis finished2023-12-10 23:56:55.208349
Duration12.14 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

식별번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct1451
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean726
Minimum1
Maximum1451
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.9 KiB
2023-12-11T08:56:55.294870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile73.5
Q1363.5
median726
Q31088.5
95-th percentile1378.5
Maximum1451
Range1450
Interquartile range (IQR)725

Descriptive statistics

Standard deviation419.01193
Coefficient of variation (CV)0.57715142
Kurtosis-1.2
Mean726
Median Absolute Deviation (MAD)363
Skewness0
Sum1053426
Variance175571
MonotonicityNot monotonic
2023-12-11T08:56:55.445709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.1%
827 1
 
0.1%
825 1
 
0.1%
824 1
 
0.1%
823 1
 
0.1%
822 1
 
0.1%
821 1
 
0.1%
820 1
 
0.1%
819 1
 
0.1%
818 1
 
0.1%
Other values (1441) 1441
99.3%
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 (%)
1451 1
0.1%
1450 1
0.1%
1449 1
0.1%
1448 1
0.1%
1447 1
0.1%
1446 1
0.1%
1445 1
0.1%
1444 1
0.1%
1443 1
0.1%
1442 1
0.1%

관리번호
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1341
Distinct (%)92.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7729109.7
Minimum0
Maximum10990009
Zeros90
Zeros (%)6.2%
Negative0
Negative (%)0.0%
Memory size12.9 KiB
2023-12-11T08:56:55.591800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1690024.5
median10100014
Q310215014
95-th percentile10840012
Maximum10990009
Range10990009
Interquartile range (IQR)9524989.5

Descriptive statistics

Standard deviation4315222.5
Coefficient of variation (CV)0.55830783
Kurtosis-0.72883773
Mean7729109.7
Median Absolute Deviation (MAD)119991
Skewness-1.1186955
Sum1.1214938 × 1010
Variance1.8621145 × 1013
MonotonicityNot monotonic
2023-12-11T08:56:56.081350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 90
 
6.2%
10220001 2
 
0.1%
600004 2
 
0.1%
600005 2
 
0.1%
600007 2
 
0.1%
10770027 2
 
0.1%
690001 2
 
0.1%
690004 2
 
0.1%
690002 2
 
0.1%
690006 2
 
0.1%
Other values (1331) 1343
92.6%
ValueCountFrequency (%)
0 90
6.2%
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 (%)
10990009 1
0.1%
10990008 1
0.1%
10990007 1
0.1%
10990006 1
0.1%
10990005 1
0.1%
10990004 1
0.1%
10990003 1
0.1%
10990002 1
0.1%
10990001 1
0.1%
10890025 1
0.1%

관리기관
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
1683
1451 

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 1451
100.0%

Length

2023-12-11T08:56:56.237465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:56:56.361890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1683 1451
100.0%

도로종류
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
1504
1195 
1507
256 

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 1195
82.4%
1507 256
 
17.6%

Length

2023-12-11T08:56:56.497310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:56:56.630889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1504 1195
82.4%
1507 256
 
17.6%

노선번호
Real number (ℝ)

HIGH CORRELATION 

Distinct41
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean832.77395
Minimum30
Maximum1099
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.9 KiB
2023-12-11T08:56:56.774743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile30
Q11001
median1011
Q31029
95-th percentile1084
Maximum1099
Range1069
Interquartile range (IQR)28

Descriptive statistics

Standard deviation390.5976
Coefficient of variation (CV)0.46903196
Kurtosis0.29209877
Mean832.77395
Median Absolute Deviation (MAD)11
Skewness-1.5039224
Sum1208355
Variance152566.48
MonotonicityNot monotonic
2023-12-11T08:56:56.929393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
1018 112
 
7.7%
1011 101
 
7.0%
1001 76
 
5.2%
30 76
 
5.2%
37 74
 
5.1%
1084 71
 
4.9%
1007 70
 
4.8%
1003 64
 
4.4%
1010 61
 
4.2%
1029 59
 
4.1%
Other values (31) 687
47.3%
ValueCountFrequency (%)
30 76
5.2%
37 74
5.1%
58 19
 
1.3%
60 37
2.5%
67 44
3.0%
69 37
2.5%
907 6
 
0.4%
1001 76
5.2%
1002 7
 
0.5%
1003 64
4.4%
ValueCountFrequency (%)
1099 9
 
0.6%
1089 25
 
1.7%
1084 71
4.9%
1080 39
2.7%
1077 40
2.8%
1051 3
 
0.2%
1049 29
2.0%
1047 2
 
0.1%
1042 44
3.0%
1041 3
 
0.2%

구간번호
Real number (ℝ)

HIGH CORRELATION 

Distinct17
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.9104066
Minimum1
Maximum19
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.9 KiB
2023-12-11T08:56:57.062020image/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 deviation3.6421245
Coefficient of variation (CV)0.74171546
Kurtosis1.1273152
Mean4.9104066
Median Absolute Deviation (MAD)2
Skewness1.1425084
Sum7125
Variance13.265071
MonotonicityNot monotonic
2023-12-11T08:56:57.200659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
1 266
18.3%
2 200
13.8%
3 177
12.2%
7 168
11.6%
4 165
11.4%
6 105
 
7.2%
5 98
 
6.8%
9 71
 
4.9%
8 46
 
3.2%
10 41
 
2.8%
Other values (7) 114
7.9%
ValueCountFrequency (%)
1 266
18.3%
2 200
13.8%
3 177
12.2%
4 165
11.4%
5 98
 
6.8%
6 105
 
7.2%
7 168
11.6%
8 46
 
3.2%
9 71
 
4.9%
10 41
 
2.8%
ValueCountFrequency (%)
19 9
 
0.6%
16 2
 
0.1%
15 14
 
1.0%
14 27
 
1.9%
13 37
2.5%
12 8
 
0.6%
11 17
 
1.2%
10 41
2.8%
9 71
4.9%
8 46
3.2%

이력코드
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1432 
1
 
19

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 1432
98.7%
1 19
 
1.3%

Length

2023-12-11T08:56:57.355593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:56:57.470692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 1432
98.7%
1 19
 
1.3%

위치_시점
Real number (ℝ)

HIGH CORRELATION 

Distinct1285
Distinct (%)88.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.1844997
Minimum0
Maximum19.526
Zeros8
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size12.9 KiB
2023-12-11T08:56:57.624134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.06
Q11.4605
median4.186
Q37.813
95-th percentile13.27
Maximum19.526
Range19.526
Interquartile range (IQR)6.3525

Descriptive statistics

Standard deviation4.3201209
Coefficient of variation (CV)0.83327634
Kurtosis-0.15121521
Mean5.1844997
Median Absolute Deviation (MAD)3.009
Skewness0.77402682
Sum7522.709
Variance18.663445
MonotonicityNot monotonic
2023-12-11T08:56:57.838787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 8
 
0.6%
0.065 8
 
0.6%
0.02 5
 
0.3%
0.16 4
 
0.3%
0.095 4
 
0.3%
0.007 4
 
0.3%
0.06 4
 
0.3%
0.011 3
 
0.2%
0.508 3
 
0.2%
7.67 3
 
0.2%
Other values (1275) 1405
96.8%
ValueCountFrequency (%)
0.0 8
0.6%
0.002 3
 
0.2%
0.003 1
 
0.1%
0.004 2
 
0.1%
0.005 2
 
0.1%
0.006 1
 
0.1%
0.007 4
0.3%
0.008 2
 
0.1%
0.009 2
 
0.1%
0.01 1
 
0.1%
ValueCountFrequency (%)
19.526 1
0.1%
19.478 1
0.1%
19.406 1
0.1%
19.38 1
0.1%
18.726 1
0.1%
18.626 1
0.1%
18.564 1
0.1%
18.516 1
0.1%
18.458 1
0.1%
17.729 1
0.1%

위치_종점
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1303
Distinct (%)89.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.294499
Minimum0
Maximum20.5
Zeros24
Zeros (%)1.7%
Negative0
Negative (%)0.0%
Memory size12.9 KiB
2023-12-11T08:56:58.007197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.095
Q11.467
median4.29
Q38.0135
95-th percentile13.6115
Maximum20.5
Range20.5
Interquartile range (IQR)6.5465

Descriptive statistics

Standard deviation4.3807988
Coefficient of variation (CV)0.82742461
Kurtosis-0.11784581
Mean5.294499
Median Absolute Deviation (MAD)3.066
Skewness0.78560453
Sum7682.318
Variance19.191398
MonotonicityNot monotonic
2023-12-11T08:56:58.163252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 24
 
1.7%
11.36 4
 
0.3%
0.4 4
 
0.3%
0.095 4
 
0.3%
2.87 3
 
0.2%
3.11 3
 
0.2%
1.15 3
 
0.2%
0.18 3
 
0.2%
7.845 2
 
0.1%
5.84 2
 
0.1%
Other values (1293) 1399
96.4%
ValueCountFrequency (%)
0.0 24
1.7%
0.002 2
 
0.1%
0.003 1
 
0.1%
0.005 1
 
0.1%
0.007 2
 
0.1%
0.009 2
 
0.1%
0.011 1
 
0.1%
0.016 1
 
0.1%
0.018 2
 
0.1%
0.019 1
 
0.1%
ValueCountFrequency (%)
20.5 1
0.1%
19.526 1
0.1%
19.478 1
0.1%
19.406 1
0.1%
19.36 1
0.1%
19.136 1
0.1%
19.08 1
0.1%
18.726 1
0.1%
18.626 1
0.1%
18.564 1
0.1%

위치_방향
Categorical

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
768 
1
682 
<NA>
 
1

Length

Max length4
Median length1
Mean length1.0020675
Min length1

Unique

Unique1 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
0 768
52.9%
1 682
47.0%
<NA> 1
 
0.1%

Length

2023-12-11T08:56:58.351743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:56:58.492547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 768
52.9%
1 682
47.0%
na 1
 
0.1%

등주형식
Real number (ℝ)

HIGH CORRELATION 

Distinct7
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3201.519
Minimum3021
Maximum3299
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.9 KiB
2023-12-11T08:56:58.605856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3021
5-th percentile3201
Q13201
median3201
Q33201
95-th percentile3202
Maximum3299
Range278
Interquartile range (IQR)0

Descriptive statistics

Standard deviation10.525707
Coefficient of variation (CV)0.0032877228
Kurtosis167.76356
Mean3201.519
Median Absolute Deviation (MAD)0
Skewness-1.4817826
Sum4645404
Variance110.7905
MonotonicityNot monotonic
2023-12-11T08:56:58.734279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
3201 1307
90.1%
3202 102
 
7.0%
3203 19
 
1.3%
3299 10
 
0.7%
3200 10
 
0.7%
3021 2
 
0.1%
3204 1
 
0.1%
ValueCountFrequency (%)
3021 2
 
0.1%
3200 10
 
0.7%
3201 1307
90.1%
3202 102
 
7.0%
3203 19
 
1.3%
3204 1
 
0.1%
3299 10
 
0.7%
ValueCountFrequency (%)
3299 10
 
0.7%
3204 1
 
0.1%
3203 19
 
1.3%
3202 102
 
7.0%
3201 1307
90.1%
3200 10
 
0.7%
3021 2
 
0.1%

등주재질
Real number (ℝ)

HIGH CORRELATION 

Distinct8
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3305.3825
Minimum3300
Maximum3399
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.9 KiB
2023-12-11T08:56:58.868991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3300
5-th percentile3301
Q13303
median3304
Q33304
95-th percentile3305
Maximum3399
Range99
Interquartile range (IQR)1

Descriptive statistics

Standard deviation13.427474
Coefficient of variation (CV)0.0040623055
Kurtosis44.450131
Mean3305.3825
Median Absolute Deviation (MAD)1
Skewness6.7810005
Sum4796110
Variance180.29705
MonotonicityNot monotonic
2023-12-11T08:56:58.995104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
3304 419
28.9%
3303 415
28.6%
3305 320
22.1%
3302 146
 
10.1%
3301 108
 
7.4%
3399 29
 
2.0%
3300 11
 
0.8%
3306 3
 
0.2%
ValueCountFrequency (%)
3300 11
 
0.8%
3301 108
 
7.4%
3302 146
 
10.1%
3303 415
28.6%
3304 419
28.9%
3305 320
22.1%
3306 3
 
0.2%
3399 29
 
2.0%
ValueCountFrequency (%)
3399 29
 
2.0%
3306 3
 
0.2%
3305 320
22.1%
3304 419
28.9%
3303 415
28.6%
3302 146
 
10.1%
3301 108
 
7.4%
3300 11
 
0.8%

광원종류
Categorical

IMBALANCE 

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
3403
1221 
3401
207 
3402
 
12
3400
 
11

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3403
2nd row3403
3rd row3403
4th row3403
5th row3403

Common Values

ValueCountFrequency (%)
3403 1221
84.1%
3401 207
 
14.3%
3402 12
 
0.8%
3400 11
 
0.8%

Length

2023-12-11T08:56:59.118287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:56:59.268316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3403 1221
84.1%
3401 207
 
14.3%
3402 12
 
0.8%
3400 11
 
0.8%

광원용량
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
3505
844 
3500
484 
3506
109 
3507
 
11
3503
 
3

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3505
2nd row3505
3rd row3505
4th row3505
5th row3505

Common Values

ValueCountFrequency (%)
3505 844
58.2%
3500 484
33.4%
3506 109
 
7.5%
3507 11
 
0.8%
3503 3
 
0.2%

Length

2023-12-11T08:56:59.417258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:56:59.552593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3505 844
58.2%
3500 484
33.4%
3506 109
 
7.5%
3507 11
 
0.8%
3503 3
 
0.2%

등기수 수량
Real number (ℝ)

Distinct46
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2033081
Minimum0
Maximum110
Zeros2
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size12.9 KiB
2023-12-11T08:56:59.707769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q32
95-th percentile12
Maximum110
Range110
Interquartile range (IQR)1

Descriptive statistics

Standard deviation7.9918503
Coefficient of variation (CV)2.4948741
Kurtosis80.556867
Mean3.2033081
Median Absolute Deviation (MAD)0
Skewness7.8783906
Sum4648
Variance63.869672
MonotonicityNot monotonic
2023-12-11T08:56:59.889552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
1 1039
71.6%
2 117
 
8.1%
3 52
 
3.6%
4 41
 
2.8%
5 29
 
2.0%
7 29
 
2.0%
6 19
 
1.3%
8 17
 
1.2%
9 16
 
1.1%
11 8
 
0.6%
Other values (36) 84
 
5.8%
ValueCountFrequency (%)
0 2
 
0.1%
1 1039
71.6%
2 117
 
8.1%
3 52
 
3.6%
4 41
 
2.8%
5 29
 
2.0%
6 19
 
1.3%
7 29
 
2.0%
8 17
 
1.2%
9 16
 
1.1%
ValueCountFrequency (%)
110 1
0.1%
107 1
0.1%
102 1
0.1%
90 1
0.1%
71 1
0.1%
69 1
0.1%
62 1
0.1%
58 1
0.1%
57 1
0.1%
54 1
0.1%

설치일자
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct15
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
1900-01-01
1346 
20121219
 
21
2016-03-31
 
19
20101130
 
13
20090326
 
12
Other values (10)
 
40

Length

Max length10
Median length10
Mean length9.8855961
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 1346
92.8%
20121219 21
 
1.4%
2016-03-31 19
 
1.3%
20101130 13
 
0.9%
20090326 12
 
0.8%
20090515 11
 
0.8%
20100104 7
 
0.5%
201006 5
 
0.3%
2007-12-31 3
 
0.2%
20091209 3
 
0.2%
Other values (5) 11
 
0.8%

Length

2023-12-11T08:57:00.056980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1900-01-01 1346
92.8%
20121219 21
 
1.4%
2016-03-31 19
 
1.3%
20101130 13
 
0.9%
20090326 12
 
0.8%
20090515 11
 
0.8%
20100104 7
 
0.5%
201006 5
 
0.3%
2007-12-31 3
 
0.2%
20091209 3
 
0.2%
Other values (5) 11
 
0.8%

사진
Text

MISSING 

Distinct1340
Distinct (%)99.3%
Missing102
Missing (%)7.0%
Memory size11.5 KiB
2023-12-11T08:57:00.325310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length13
Mean length13
Min length13

Characters and Unicode

Total characters17537
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

Unique1336 ?
Unique (%)99.0%

Sample

1st row006701O00011D
2nd row006701O00025U
3rd row006701O00072U
4th row006701O00116U
5th row006701O00160U
ValueCountFrequency (%)
100501o04871u 5
 
0.4%
100501o04903d 4
 
0.3%
006703o01813u 2
 
0.1%
107704o10790d 2
 
0.1%
006010o09800d 1
 
0.1%
101403o08636d 1
 
0.1%
101807o16218d 1
 
0.1%
101811o03110d 1
 
0.1%
101811o05780d 1
 
0.1%
101811o07134u 1
 
0.1%
Other values (1330) 1330
98.6%
2023-12-11T08:57:00.720209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 5582
31.8%
1 2718
15.5%
O 1347
 
7.7%
2 1014
 
5.8%
3 898
 
5.1%
4 880
 
5.0%
7 879
 
5.0%
8 763
 
4.4%
6 750
 
4.3%
5 718
 
4.1%
Other values (4) 1988
 
11.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14840
84.6%
Uppercase Letter 2697
 
15.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5582
37.6%
1 2718
18.3%
2 1014
 
6.8%
3 898
 
6.1%
4 880
 
5.9%
7 879
 
5.9%
8 763
 
5.1%
6 750
 
5.1%
5 718
 
4.8%
9 638
 
4.3%
Uppercase Letter
ValueCountFrequency (%)
O 1347
49.9%
U 717
26.6%
D 632
23.4%
P 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 14840
84.6%
Latin 2697
 
15.4%

Most frequent character per script

Common
ValueCountFrequency (%)
0 5582
37.6%
1 2718
18.3%
2 1014
 
6.8%
3 898
 
6.1%
4 880
 
5.9%
7 879
 
5.9%
8 763
 
5.1%
6 750
 
5.1%
5 718
 
4.8%
9 638
 
4.3%
Latin
ValueCountFrequency (%)
O 1347
49.9%
U 717
26.6%
D 632
23.4%
P 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 17537
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 5582
31.8%
1 2718
15.5%
O 1347
 
7.7%
2 1014
 
5.8%
3 898
 
5.1%
4 880
 
5.0%
7 879
 
5.0%
8 763
 
4.4%
6 750
 
4.3%
5 718
 
4.1%
Other values (4) 1988
 
11.3%

비고
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct48
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
<NA>
1327 
2EA
 
14
보안등
 
11
전주
 
11
원형철주
 
10
Other values (43)
 
78

Length

Max length13
Median length4
Mean length4.0778773
Min length2

Unique

Unique27 ?
Unique (%)1.9%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 1327
91.5%
2EA 14
 
1.0%
보안등 11
 
0.8%
전주 11
 
0.8%
원형철주 10
 
0.7%
원형(2EA) 8
 
0.6%
3EA 7
 
0.5%
팔각주 7
 
0.5%
팔각철주 4
 
0.3%
(4EA) 3
 
0.2%
Other values (38) 49
 
3.4%

Length

2023-12-11T08:57:00.946737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 1327
91.1%
2ea 16
 
1.1%
보안등 11
 
0.8%
전주 11
 
0.8%
원형철주 10
 
0.7%
팔각주 9
 
0.6%
원형(2ea 8
 
0.5%
3ea 7
 
0.5%
팔각철주 4
 
0.3%
원형 3
 
0.2%
Other values (37) 51
 
3.5%

Interactions

2023-12-11T08:56:53.631616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:45.057601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:46.241514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:47.179127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:48.134704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:49.418924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:50.516920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:51.468706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:52.556818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:53.784213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:45.190776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:46.358012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:47.274719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:48.261294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:49.545082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:50.639952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:51.598763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:52.682838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:53.904129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:45.338494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:46.474021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:47.376253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:48.375204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:49.659573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:50.743487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:51.712167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:52.802823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:54.027267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:45.470182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:46.568141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:47.481094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:48.475775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:49.868833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:50.836242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:51.815309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:52.920860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:54.161211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:45.588731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:46.670186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:47.630775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:48.588659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:49.978866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:50.954356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:51.946509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:53.055689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:54.284103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:45.761943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:46.768530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:47.776863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:48.718865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:50.100981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:51.053862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:52.081418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:53.197767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:54.410532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:45.885751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:46.860665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:47.863599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:48.803938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:50.206006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:51.137828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:52.205385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:53.310745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:54.556522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:46.003719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:46.983079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:47.951501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:48.921570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:50.309223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:51.241784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:52.328440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:53.413787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:54.666274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:46.138936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:47.085425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:48.038052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:49.317277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:50.410136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:51.354666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:52.448809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:53.521642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T08:57:01.088734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
식별번호관리번호도로종류노선번호구간번호이력코드위치_시점위치_종점위치_방향등주형식등주재질광원종류광원용량등기수 수량설치일자비고
식별번호1.0000.8710.9380.6870.6520.4390.4660.4270.1050.0600.1900.5380.8960.1540.5870.972
관리번호0.8711.0000.9460.5900.3360.0850.2280.2410.0990.0000.0970.3420.2720.0680.4490.645
도로종류0.9380.9461.0000.6760.2850.0580.2620.2390.1050.0140.0850.2940.1420.0000.125NaN
노선번호0.6870.5900.6761.0000.3600.0270.2980.2500.0450.0120.0370.1780.2040.0550.4140.904
구간번호0.6520.3360.2850.3601.0000.1930.4150.3730.0740.0000.1220.3040.4890.0000.7730.899
이력코드0.4390.0850.0580.0270.1931.0000.0590.0520.0000.0000.0000.0310.4270.0001.000NaN
위치_시점0.4660.2280.2620.2980.4150.0591.0000.9900.1070.0000.0380.1820.1850.2650.2350.753
위치_종점0.4270.2410.2390.2500.3730.0520.9901.0000.1080.0450.0000.1770.1840.2050.2270.860
위치_방향0.1050.0990.1050.0450.0740.0000.1070.1081.0000.0000.0630.0000.0000.0000.0000.000
등주형식0.0600.0000.0140.0120.0000.0000.0000.0450.0001.0000.0000.0000.0000.0000.0000.783
등주재질0.1900.0970.0850.0370.1220.0000.0380.0000.0630.0001.0000.0290.1090.0000.0000.965
광원종류0.5380.3420.2940.1780.3040.0310.1820.1770.0000.0000.0291.0000.1570.1490.0000.730
광원용량0.8960.2720.1420.2040.4890.4270.1850.1840.0000.0000.1090.1571.0000.2080.9381.000
등기수 수량0.1540.0680.0000.0550.0000.0000.2650.2050.0000.0000.0000.1490.2081.0000.3330.691
설치일자0.5870.4490.1250.4140.7731.0000.2350.2270.0000.0000.0000.0000.9380.3331.0001.000
비고0.9720.645NaN0.9040.899NaN0.7530.8600.0000.7830.9650.7301.0000.6911.0001.000
2023-12-11T08:57:01.252862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
설치일자이력코드광원용량광원종류비고도로종류위치_방향
설치일자1.0000.9960.6800.0000.7940.1140.000
이력코드0.9961.0000.5190.0211.0000.0370.000
광원용량0.6800.5191.0000.1280.7980.1730.000
광원종류0.0000.0210.1281.0000.3920.1960.000
비고0.7941.0000.7980.3921.0001.0000.000
도로종류0.1140.0370.1730.1961.0001.0000.067
위치_방향0.0000.0000.0000.0000.0000.0671.000
2023-12-11T08:57:01.377067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
식별번호관리번호노선번호구간번호위치_시점위치_종점등주형식등주재질등기수 수량도로종류이력코드위치_방향광원종류광원용량설치일자비고
식별번호1.0000.3470.5950.147-0.073-0.094-0.141-0.1090.1720.7870.3370.0800.3510.5810.2620.680
관리번호0.3471.0000.762-0.108-0.050-0.084-0.1450.0050.0330.7890.0540.0630.2280.3320.4090.431
노선번호0.5950.7621.000-0.124-0.108-0.143-0.1330.0080.0190.9320.0440.0750.1690.1560.2070.584
구간번호0.147-0.108-0.1241.0000.1550.130-0.075-0.002-0.0490.2180.1480.0530.1910.1970.3930.524
위치_시점-0.073-0.050-0.1080.1551.0000.955-0.0330.080-0.1590.2000.0450.0820.1090.0770.0890.300
위치_종점-0.094-0.084-0.1430.1300.9551.000-0.0270.026-0.0650.1830.0400.0820.1060.0770.0860.415
등주형식-0.141-0.145-0.133-0.075-0.033-0.0271.000-0.0480.2660.0200.0000.0310.0000.0820.2780.535
등주재질-0.1090.0050.008-0.0020.0800.026-0.0481.000-0.2110.0540.0000.0400.0420.1340.0000.726
등기수 수량0.1720.0330.019-0.049-0.159-0.0650.266-0.2111.0000.0000.0000.0000.1070.1100.1240.358
도로종류0.7870.7890.9320.2180.2000.1830.0200.0540.0001.0000.0370.0670.1960.1730.1141.000
이력코드0.3370.0540.0440.1480.0450.0400.0000.0000.0000.0371.0000.0000.0210.5190.9961.000
위치_방향0.0800.0630.0750.0530.0820.0820.0310.0400.0000.0670.0001.0000.0000.0000.0000.000
광원종류0.3510.2280.1690.1910.1090.1060.0000.0420.1070.1960.0210.0001.0000.1280.0000.392
광원용량0.5810.3320.1560.1970.0770.0770.0820.1340.1100.1730.5190.0000.1281.0000.6800.798
설치일자0.2620.4090.2070.3930.0890.0860.2780.0000.1240.1140.9960.0000.0000.6801.0000.794
비고0.6800.4310.5840.5240.3000.4150.5350.7260.3581.0001.0000.0000.3920.7980.7941.000

Missing values

2023-12-11T08:56:54.840885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T08:56:55.117334image/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

식별번호관리번호관리기관도로종류노선번호구간번호이력코드위치_시점위치_종점위치_방향등주형식등주재질광원종류광원용량등기수 수량설치일자사진비고
016700011683150767100.0110.60213201330334033505181900-01-01006701O00011D<NA>
126700021683150767100.0250.0250320233013403350511900-01-01006701O00025U<NA>
236700031683150767100.0720.0720320133033403350511900-01-01006701O00072U<NA>
346700041683150767100.1160.1160320133033403350511900-01-01006701O00116U<NA>
456700051683150767100.160.310320133033403350551900-01-01006701O00160U<NA>
566700061683150767100.3690.82803201330334033505131900-01-01006701O00369U<NA>
676700071683150767100.6480.8061320133033403350551900-01-01006701O00648D<NA>
786700081683150767100.8431.0461320133033403350571900-01-01006701O00843D<NA>
896700091683150767100.8570.8570320433033403350511900-01-01006701O00857U<NA>
9106700101683150767100.8671.0260320133033403350551900-01-01006701O00867U<NA>
식별번호관리번호관리기관도로종류노선번호구간번호이력코드위치_시점위치_종점위치_방향등주형식등주재질광원종류광원용량등기수 수량설치일자사진비고
1441144210420022168315041042114.1414.4570320133033403350682016-03-31104201O04141U<NA>
1442144310420021168315041042113.6613.9470320133033403350682016-03-31104201O03661U<NA>
1443144410420020168315041042112.4822.8450320133033403350692016-03-31104201O02482U<NA>
1444144510420019168315041042112.1252.3530320133033403350662016-03-31104201O02125U<NA>
1445144610420018168315041042111.8281.8280320133033403350312016-03-31104201O01828U<NA>
1446144710420017168315041042111.4651.4650320133033403350312016-03-31104201O01465U<NA>
1447144810420016168315041042111.0161.99403201330334033506232016-03-31104201O01016U<NA>
1448144910420031168315041042114.1844.457<NA>320133033403350672016-03-31104201O04184D<NA>
1449145060002916831507601908.8459.59513201330334033506222013-10-31006019O08845D<NA>
1450145160002816831507601908.8459.58803201330334033506212013-10-31006019O08845U<NA>