Overview

Dataset statistics

Number of variables16
Number of observations1596
Missing cells9407
Missing cells (%)36.8%
Duplicate rows84
Duplicate rows (%)5.3%
Total size in memory219.9 KiB
Average record size in memory141.1 B

Variable types

Numeric8
Unsupported5
Categorical2
Text1

Dataset

Description경기도_도로대장 전산화 시스템_도로 절개면
Author경기도
URLhttps://data.gg.go.kr/portal/data/service/selectServicePage.do?&infId=J3B5HRMD0QV3X2LYYU0P33883882&infSeq=1

Alerts

Dataset has 84 (5.3%) duplicate rowsDuplicates
위치-시점 is highly overall correlated with 위치-종점High correlation
위치-종점 is highly overall correlated with 위치-시점High correlation
높이-최대 is highly overall correlated with 경사High correlation
경사 is highly overall correlated with 높이-최대High correlation
위치-방향 is highly imbalanced (52.1%)Imbalance
플래그 has 1596 (100.0%) missing valuesMissing
공간경도시점 has 1596 (100.0%) missing valuesMissing
공간위도시점 has 1596 (100.0%) missing valuesMissing
공간경도종점 has 1596 (100.0%) missing valuesMissing
공간위도종점 has 1596 (100.0%) missing valuesMissing
비고 has 1427 (89.4%) missing valuesMissing
위치-시점 is highly skewed (γ1 = 39.92028742)Skewed
위치-종점 is highly skewed (γ1 = 39.9208585)Skewed
플래그 is an unsupported type, check if it needs cleaning or further analysisUnsupported
공간경도시점 is an unsupported type, check if it needs cleaning or further analysisUnsupported
공간위도시점 is an unsupported type, check if it needs cleaning or further analysisUnsupported
공간경도종점 is an unsupported type, check if it needs cleaning or further analysisUnsupported
공간위도종점 is an unsupported type, check if it needs cleaning or further analysisUnsupported
연장 has 39 (2.4%) zerosZeros
높이-최대 has 557 (34.9%) zerosZeros
높이-최소 has 1206 (75.6%) zerosZeros
경사 has 719 (45.1%) zerosZeros

Reproduction

Analysis started2023-12-10 22:30:33.609766
Analysis finished2023-12-10 22:30:40.915166
Duration7.31 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

노선번호
Real number (ℝ)

Distinct44
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean272.56203
Minimum23
Maximum391
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.2 KiB
2023-12-11T07:30:40.980790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum23
5-th percentile56
Q1301
median315
Q3341
95-th percentile383
Maximum391
Range368
Interquartile range (IQR)40

Descriptive statistics

Standard deviation116.43372
Coefficient of variation (CV)0.42718246
Kurtosis-0.32532931
Mean272.56203
Median Absolute Deviation (MAD)26
Skewness-1.1622526
Sum435009
Variance13556.811
MonotonicityNot monotonic
2023-12-11T07:30:41.093639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
318 158
 
9.9%
309 152
 
9.5%
78 96
 
6.0%
56 86
 
5.4%
371 66
 
4.1%
313 65
 
4.1%
23 62
 
3.9%
364 59
 
3.7%
333 59
 
3.7%
341 51
 
3.2%
Other values (34) 742
46.5%
ValueCountFrequency (%)
23 62
3.9%
56 86
5.4%
57 12
 
0.8%
70 34
 
2.1%
78 96
6.0%
82 19
 
1.2%
86 29
 
1.8%
88 11
 
0.7%
98 14
 
0.9%
301 37
 
2.3%
ValueCountFrequency (%)
391 50
3.1%
387 23
 
1.4%
383 43
2.7%
379 4
 
0.3%
376 5
 
0.3%
375 49
3.1%
372 27
1.7%
371 66
4.1%
368 3
 
0.2%
367 3
 
0.2%

구간번호
Real number (ℝ)

Distinct18
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.6472431
Minimum1
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.2 KiB
2023-12-11T07:30:41.196460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median5
Q39
95-th percentile14
Maximum99
Range98
Interquartile range (IQR)6

Descriptive statistics

Standard deviation7.7230428
Coefficient of variation (CV)1.1618415
Kurtosis100.10999
Mean6.6472431
Median Absolute Deviation (MAD)3
Skewness8.6325463
Sum10609
Variance59.645391
MonotonicityNot monotonic
2023-12-11T07:30:41.287439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
4 241
15.1%
1 185
11.6%
2 156
9.8%
9 153
9.6%
14 146
9.1%
5 129
8.1%
3 123
7.7%
6 122
7.6%
7 94
 
5.9%
8 91
 
5.7%
Other values (8) 156
9.8%
ValueCountFrequency (%)
1 185
11.6%
2 156
9.8%
3 123
7.7%
4 241
15.1%
5 129
8.1%
6 122
7.6%
7 94
 
5.9%
8 91
 
5.7%
9 153
9.6%
10 41
 
2.6%
ValueCountFrequency (%)
99 8
 
0.5%
18 1
 
0.1%
16 1
 
0.1%
15 21
 
1.3%
14 146
9.1%
13 45
 
2.8%
12 30
 
1.9%
11 9
 
0.6%
10 41
 
2.6%
9 153
9.6%

플래그
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1596
Missing (%)100.0%
Memory size14.2 KiB

위치-시점
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct1299
Distinct (%)81.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.007384
Minimum0
Maximum7229
Zeros9
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size14.2 KiB
2023-12-11T07:30:41.385246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.38475
Q12.37
median4.602
Q38.12825
95-th percentile13.3375
Maximum7229
Range7229
Interquartile range (IQR)5.75825

Descriptive statistics

Standard deviation180.85888
Coefficient of variation (CV)18.072543
Kurtosis1594.4177
Mean10.007384
Median Absolute Deviation (MAD)2.713
Skewness39.920287
Sum15971.785
Variance32709.935
MonotonicityNot monotonic
2023-12-11T07:30:41.502765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 9
 
0.6%
3.71 6
 
0.4%
3.8 5
 
0.3%
1.8 5
 
0.3%
3.36 5
 
0.3%
1.25 4
 
0.3%
11.046 4
 
0.3%
11.135 4
 
0.3%
0.3 4
 
0.3%
2.71 4
 
0.3%
Other values (1289) 1546
96.9%
ValueCountFrequency (%)
0.0 9
0.6%
0.003 1
 
0.1%
0.005 1
 
0.1%
0.01 1
 
0.1%
0.016 1
 
0.1%
0.017 1
 
0.1%
0.02 1
 
0.1%
0.021 1
 
0.1%
0.022 1
 
0.1%
0.024 1
 
0.1%
ValueCountFrequency (%)
7229.0 1
0.1%
20.03 1
0.1%
19.57 1
0.1%
17.1 1
0.1%
16.307 1
0.1%
16.293 1
0.1%
16.23 1
0.1%
16.047 1
0.1%
15.889 2
0.1%
15.761 2
0.1%

위치-종점
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct1313
Distinct (%)82.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.209534
Minimum0.016
Maximum7314
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.2 KiB
2023-12-11T07:30:41.621068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.016
5-th percentile0.51125
Q12.50225
median4.7395
Q38.281
95-th percentile13.60975
Maximum7314
Range7313.984
Interquartile range (IQR)5.77875

Descriptive statistics

Standard deviation182.98247
Coefficient of variation (CV)17.922706
Kurtosis1594.4482
Mean10.209534
Median Absolute Deviation (MAD)2.7375
Skewness39.920859
Sum16294.416
Variance33482.584
MonotonicityNot monotonic
2023-12-11T07:30:41.733440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.73 6
 
0.4%
3.28 4
 
0.3%
15.251 4
 
0.3%
1.74 4
 
0.3%
10.443 4
 
0.3%
12.705 4
 
0.3%
1.65 3
 
0.2%
1.49 3
 
0.2%
4.22 3
 
0.2%
1.5 3
 
0.2%
Other values (1303) 1558
97.6%
ValueCountFrequency (%)
0.016 1
0.1%
0.033 1
0.1%
0.042 1
0.1%
0.046 1
0.1%
0.049 1
0.1%
0.062 1
0.1%
0.07 1
0.1%
0.093 1
0.1%
0.095 1
0.1%
0.102 1
0.1%
ValueCountFrequency (%)
7314.0 1
0.1%
20.255 1
0.1%
19.72 1
0.1%
17.7 1
0.1%
17.24 1
0.1%
16.36 1
0.1%
16.32 1
0.1%
16.081 1
0.1%
16.06 1
0.1%
16.04 1
0.1%

위치-방향
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size12.6 KiB
상행
800 
하행
773 
<NA>
 
13
양방향
 
9
중앙
 
1

Length

Max length4
Median length2
Mean length2.0219298
Min length2

Unique

Unique1 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
상행 800
50.1%
하행 773
48.4%
<NA> 13
 
0.8%
양방향 9
 
0.6%
중앙 1
 
0.1%

Length

2023-12-11T07:30:41.848891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T07:30:41.940629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
상행 800
50.1%
하행 773
48.4%
na 13
 
0.8%
양방향 9
 
0.6%
중앙 1
 
0.1%

공간경도시점
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1596
Missing (%)100.0%
Memory size14.2 KiB

공간위도시점
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1596
Missing (%)100.0%
Memory size14.2 KiB

공간경도종점
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1596
Missing (%)100.0%
Memory size14.2 KiB

공간위도종점
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1596
Missing (%)100.0%
Memory size14.2 KiB

연장
Real number (ℝ)

ZEROS 

Distinct625
Distinct (%)39.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean116.72728
Minimum0
Maximum1470
Zeros39
Zeros (%)2.4%
Negative0
Negative (%)0.0%
Memory size14.2 KiB
2023-12-11T07:30:42.037591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.11
Q135
median80
Q3158.075
95-th percentile363
Maximum1470
Range1470
Interquartile range (IQR)123.075

Descriptive statistics

Standard deviation130.41063
Coefficient of variation (CV)1.1172249
Kurtosis15.021094
Mean116.72728
Median Absolute Deviation (MAD)56.19
Skewness2.9150722
Sum186296.74
Variance17006.932
MonotonicityNot monotonic
2023-12-11T07:30:42.160248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 39
 
2.4%
60.0 27
 
1.7%
80.0 24
 
1.5%
120.0 22
 
1.4%
100.0 21
 
1.3%
40.0 20
 
1.3%
90.0 17
 
1.1%
140.0 16
 
1.0%
20.0 14
 
0.9%
160.0 13
 
0.8%
Other values (615) 1383
86.7%
ValueCountFrequency (%)
0.0 39
2.4%
0.01 1
 
0.1%
0.02 1
 
0.1%
0.04 3
 
0.2%
0.05 4
 
0.3%
0.06 2
 
0.1%
0.07 8
 
0.5%
0.08 10
 
0.6%
0.09 5
 
0.3%
0.1 5
 
0.3%
ValueCountFrequency (%)
1470.0 1
0.1%
1030.0 1
0.1%
944.0 1
0.1%
900.0 2
0.1%
880.0 1
0.1%
866.0 1
0.1%
860.0 1
0.1%
731.64 1
0.1%
730.0 1
0.1%
690.0 1
0.1%

종류
Categorical

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size12.6 KiB
혼합사면
989 
흙사면
339 
기타
203 
암사면
 
34
<NA>
 
31

Length

Max length4
Median length4
Mean length3.5119048
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row흙사면
2nd row흙사면
3rd row혼합사면
4th row혼합사면
5th row혼합사면

Common Values

ValueCountFrequency (%)
혼합사면 989
62.0%
흙사면 339
 
21.2%
기타 203
 
12.7%
암사면 34
 
2.1%
<NA> 31
 
1.9%

Length

2023-12-11T07:30:42.274656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T07:30:42.368687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
혼합사면 989
62.0%
흙사면 339
 
21.2%
기타 203
 
12.7%
암사면 34
 
2.1%
na 31
 
1.9%

높이-최대
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct691
Distinct (%)43.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.2090652
Minimum0
Maximum191
Zeros557
Zeros (%)34.9%
Negative0
Negative (%)0.0%
Memory size14.2 KiB
2023-12-11T07:30:42.466619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3.3
Q310.9625
95-th percentile30
Maximum191
Range191
Interquartile range (IQR)10.9625

Descriptive statistics

Standard deviation14.782805
Coefficient of variation (CV)1.8007903
Kurtosis39.977009
Mean8.2090652
Median Absolute Deviation (MAD)3.3
Skewness5.0742384
Sum13101.668
Variance218.53132
MonotonicityNot monotonic
2023-12-11T07:30:42.590959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 557
34.9%
2.5 13
 
0.8%
20.0 13
 
0.8%
25.0 12
 
0.8%
15.0 11
 
0.7%
5.0 11
 
0.7%
7.0 9
 
0.6%
22.0 8
 
0.5%
3.5 8
 
0.5%
10.0 8
 
0.5%
Other values (681) 946
59.3%
ValueCountFrequency (%)
0.0 557
34.9%
0.02 1
 
0.1%
0.08 1
 
0.1%
0.19 1
 
0.1%
0.24 1
 
0.1%
0.29 1
 
0.1%
0.42 3
 
0.2%
0.45 1
 
0.1%
0.46 1
 
0.1%
0.49 1
 
0.1%
ValueCountFrequency (%)
191.0 1
0.1%
159.0 1
0.1%
148.0 1
0.1%
142.0 1
0.1%
130.0 1
0.1%
104.0 1
0.1%
100.0 1
0.1%
99.0 2
0.1%
97.0 2
0.1%
96.0 1
0.1%

높이-최소
Real number (ℝ)

ZEROS 

Distinct203
Distinct (%)12.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4727381
Minimum0
Maximum187
Zeros1206
Zeros (%)75.6%
Negative0
Negative (%)0.0%
Memory size14.2 KiB
2023-12-11T07:30:42.703393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile4
Maximum187
Range187
Interquartile range (IQR)0

Descriptive statistics

Standard deviation10.333992
Coefficient of variation (CV)7.0168567
Kurtosis149.12033
Mean1.4727381
Median Absolute Deviation (MAD)0
Skewness11.522259
Sum2350.49
Variance106.7914
MonotonicityNot monotonic
2023-12-11T07:30:42.843241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 1206
75.6%
1.0 37
 
2.3%
0.1 25
 
1.6%
5.0 13
 
0.8%
0.5 8
 
0.5%
4.0 7
 
0.4%
1.5 5
 
0.3%
0.74 4
 
0.3%
2.0 4
 
0.3%
0.87 4
 
0.3%
Other values (193) 283
 
17.7%
ValueCountFrequency (%)
0.0 1206
75.6%
0.01 2
 
0.1%
0.02 1
 
0.1%
0.03 3
 
0.2%
0.04 1
 
0.1%
0.05 4
 
0.3%
0.06 3
 
0.2%
0.07 3
 
0.2%
0.08 3
 
0.2%
0.09 3
 
0.2%
ValueCountFrequency (%)
187.0 1
 
0.1%
143.0 1
 
0.1%
138.0 1
 
0.1%
122.0 1
 
0.1%
101.0 1
 
0.1%
98.0 1
 
0.1%
96.0 4
0.3%
93.0 1
 
0.1%
62.21 1
 
0.1%
59.26 1
 
0.1%

경사
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct265
Distinct (%)16.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.024421
Minimum-6.89
Maximum200
Zeros719
Zeros (%)45.1%
Negative17
Negative (%)1.1%
Memory size14.2 KiB
2023-12-11T07:30:43.058672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-6.89
5-th percentile0
Q10
median7.1
Q383.3
95-th percentile83.3
Maximum200
Range206.89
Interquartile range (IQR)83.3

Descriptive statistics

Standard deviation38.810722
Coefficient of variation (CV)1.1081046
Kurtosis-1.6116085
Mean35.024421
Median Absolute Deviation (MAD)7.1
Skewness0.37214703
Sum55898.976
Variance1506.2721
MonotonicityNot monotonic
2023-12-11T07:30:43.241516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 719
45.1%
83.3 524
32.8%
83.0 15
 
0.9%
66.0 13
 
0.8%
83.33 12
 
0.8%
45.0 11
 
0.7%
70.0 9
 
0.6%
75.0 5
 
0.3%
60.0 4
 
0.3%
40.0 3
 
0.2%
Other values (255) 281
 
17.6%
ValueCountFrequency (%)
-6.89 1
0.1%
-5.9 2
0.1%
-4.0 1
0.1%
-3.18 1
0.1%
-2.5 2
0.1%
-1.8 1
0.1%
-1.71 1
0.1%
-1.6 2
0.1%
-1.44 2
0.1%
-1.09 1
0.1%
ValueCountFrequency (%)
200.0 1
 
0.1%
83.8 1
 
0.1%
83.33 12
 
0.8%
83.3 524
32.8%
83.28 1
 
0.1%
83.0 15
 
0.9%
82.93 1
 
0.1%
80.0 1
 
0.1%
75.0 5
 
0.3%
73.77 1
 
0.1%

비고
Text

MISSING 

Distinct53
Distinct (%)31.4%
Missing1427
Missing (%)89.4%
Memory size12.6 KiB
2023-12-11T07:30:43.437108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length9
Mean length4.5680473
Min length2

Characters and Unicode

Total characters772
Distinct characters67
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique35 ?
Unique (%)20.7%

Sample

1st row이화1
2nd row금대5
3rd row이화2
4th row이화3-1,2
5th row시드스프레이
ValueCountFrequency (%)
2단 34
20.1%
3단 20
 
11.8%
분리(우측 17
 
10.1%
4단 14
 
8.3%
분리(좌측 13
 
7.7%
2011 6
 
3.6%
7단 4
 
2.4%
6단 4
 
2.4%
분리(하행 3
 
1.8%
분리(상행 3
 
1.8%
Other values (43) 51
30.2%
2023-12-11T07:30:44.044706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
80
 
10.4%
0 70
 
9.1%
2 57
 
7.4%
1 39
 
5.1%
( 36
 
4.7%
) 36
 
4.7%
36
 
4.7%
36
 
4.7%
3 35
 
4.5%
30
 
3.9%
Other values (57) 317
41.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 358
46.4%
Decimal Number 301
39.0%
Open Punctuation 36
 
4.7%
Close Punctuation 36
 
4.7%
Uppercase Letter 32
 
4.1%
Dash Punctuation 5
 
0.6%
Other Punctuation 4
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
80
22.3%
36
 
10.1%
36
 
10.1%
30
 
8.4%
17
 
4.7%
13
 
3.6%
11
 
3.1%
11
 
3.1%
6
 
1.7%
6
 
1.7%
Other values (36) 112
31.3%
Decimal Number
ValueCountFrequency (%)
0 70
23.3%
2 57
18.9%
1 39
13.0%
3 35
11.6%
4 24
 
8.0%
7 18
 
6.0%
6 18
 
6.0%
8 16
 
5.3%
5 15
 
5.0%
9 9
 
3.0%
Uppercase Letter
ValueCountFrequency (%)
G 15
46.9%
D 10
31.2%
R 3
 
9.4%
A 1
 
3.1%
C 1
 
3.1%
U 1
 
3.1%
B 1
 
3.1%
Open Punctuation
ValueCountFrequency (%)
( 36
100.0%
Close Punctuation
ValueCountFrequency (%)
) 36
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5
100.0%
Other Punctuation
ValueCountFrequency (%)
, 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 382
49.5%
Hangul 358
46.4%
Latin 32
 
4.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
80
22.3%
36
 
10.1%
36
 
10.1%
30
 
8.4%
17
 
4.7%
13
 
3.6%
11
 
3.1%
11
 
3.1%
6
 
1.7%
6
 
1.7%
Other values (36) 112
31.3%
Common
ValueCountFrequency (%)
0 70
18.3%
2 57
14.9%
1 39
10.2%
( 36
9.4%
) 36
9.4%
3 35
9.2%
4 24
 
6.3%
7 18
 
4.7%
6 18
 
4.7%
8 16
 
4.2%
Other values (4) 33
8.6%
Latin
ValueCountFrequency (%)
G 15
46.9%
D 10
31.2%
R 3
 
9.4%
A 1
 
3.1%
C 1
 
3.1%
U 1
 
3.1%
B 1
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 414
53.6%
Hangul 358
46.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
80
22.3%
36
 
10.1%
36
 
10.1%
30
 
8.4%
17
 
4.7%
13
 
3.6%
11
 
3.1%
11
 
3.1%
6
 
1.7%
6
 
1.7%
Other values (36) 112
31.3%
ASCII
ValueCountFrequency (%)
0 70
16.9%
2 57
13.8%
1 39
9.4%
( 36
8.7%
) 36
8.7%
3 35
8.5%
4 24
 
5.8%
7 18
 
4.3%
6 18
 
4.3%
8 16
 
3.9%
Other values (11) 65
15.7%

Interactions

2023-12-11T07:30:39.980214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:30:34.516255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:30:35.240666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:30:35.923566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:30:36.698109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:30:37.442143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:30:38.208055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:30:39.042803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:30:40.062286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:30:34.634190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:30:35.320205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:30:36.016281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:30:36.805873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:30:37.549064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:30:38.317205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:30:39.158408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:30:40.142339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:30:34.711571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:30:35.403035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:30:36.106511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:30:36.886376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:30:37.626801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:30:38.401185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:30:39.243822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:30:40.225971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:30:34.799853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:30:35.492567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:30:36.214499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:30:36.975931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:30:37.712040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:30:38.501619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:30:39.334088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:30:40.308878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:30:34.885664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:30:35.580354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:30:36.308192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:30:37.061861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:30:37.803111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:30:38.622741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:30:39.654978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:30:40.391020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:30:34.972716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:30:35.671715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:30:36.394660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:30:37.145567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:30:37.885537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:30:38.749017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:30:39.736804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:30:40.473025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:30:35.073938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:30:35.760757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:30:36.482786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:30:37.233670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:30:37.993553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:30:38.856624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:30:39.824994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:30:40.560465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:30:35.158402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:30:35.850156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:30:36.588093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:30:37.343215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:30:38.111696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:30:38.947310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:30:39.906516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T07:30:44.160027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
노선번호구간번호위치-시점위치-종점위치-방향연장종류높이-최대높이-최소경사비고
노선번호1.0000.6430.0700.0700.1100.1970.4690.1720.0600.3940.951
구간번호0.6431.0000.0000.0000.0000.0000.2020.1720.1620.2410.950
위치-시점0.0700.0001.0000.7060.0000.0000.0300.0000.0000.173NaN
위치-종점0.0700.0000.7061.0000.0000.0000.0300.0000.0000.173NaN
위치-방향0.1100.0000.0000.0001.0000.0000.0000.0000.0000.0310.000
연장0.1970.0000.0000.0000.0001.0000.3410.3210.0000.1770.622
종류0.4690.2020.0300.0300.0000.3411.0000.1770.0000.3970.990
높이-최대0.1720.1720.0000.0000.0000.3210.1771.0000.9340.2320.883
높이-최소0.0600.1620.0000.0000.0000.0000.0000.9341.0000.2130.898
경사0.3940.2410.1730.1730.0310.1770.3970.2320.2131.0000.882
비고0.9510.950NaNNaN0.0000.6220.9900.8830.8980.8821.000
2023-12-11T07:30:44.291691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위치-방향종류
위치-방향1.0000.000
종류0.0001.000
2023-12-11T07:30:44.411677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
노선번호구간번호위치-시점위치-종점연장높이-최대높이-최소경사위치-방향종류
노선번호1.0000.0750.2290.2240.102-0.085-0.027-0.0030.0710.320
구간번호0.0751.0000.1420.1410.002-0.065-0.1220.0550.0000.192
위치-시점0.2290.1421.0000.9990.095-0.086-0.0470.0150.0000.020
위치-종점0.2240.1410.9991.0000.109-0.080-0.0420.0070.0000.020
연장0.1020.0020.0950.1091.0000.4130.1690.1430.0000.224
높이-최대-0.085-0.065-0.086-0.0800.4131.0000.4570.5390.0000.106
높이-최소-0.027-0.122-0.047-0.0420.1690.4571.000-0.1990.0000.000
경사-0.0030.0550.0150.0070.1430.539-0.1991.0000.0200.265
위치-방향0.0710.0000.0000.0000.0000.0000.0000.0201.0000.000
종류0.3200.1920.0200.0200.2240.1060.0000.2650.0001.000

Missing values

2023-12-11T07:30:40.673088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T07:30:40.849837image/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

노선번호구간번호플래그위치-시점위치-종점위치-방향공간경도시점공간위도시점공간경도종점공간위도종점연장종류높이-최대높이-최소경사비고
03914<NA>11.52811.64하행<NA><NA><NA><NA>114.56흙사면11.50.075.0<NA>
13916<NA>7.8367.928하행<NA><NA><NA><NA>92.0흙사면12.00.083.3이화1
23916<NA>7.9978.088하행<NA><NA><NA><NA>91.0혼합사면21.00.083.3금대5
33916<NA>8.1558.265하행<NA><NA><NA><NA>110.0혼합사면25.00.083.3이화2
43916<NA>8.2898.526하행<NA><NA><NA><NA>237.0혼합사면25.00.083.3이화3-1,2
53916<NA>0.6550.709하행<NA><NA><NA><NA>54.0흙사면12.015.050.0시드스프레이
63916<NA>0.70.889하행<NA><NA><NA><NA>189.0흙사면15.015.050.0시드스프레이
73916<NA>1.0081.076하행<NA><NA><NA><NA>68.0암사면12.012.070.0<NA>
83916<NA>1.3981.535하행<NA><NA><NA><NA>137.0흙사면20.020.045.0<NA>
93916<NA>3.5453.738상행<NA><NA><NA><NA>193.0흙사면20.020.045.0<NA>
노선번호구간번호플래그위치-시점위치-종점위치-방향공간경도시점공간위도시점공간경도종점공간위도종점연장종류높이-최대높이-최소경사비고
15863229<NA>1.561.63상행<NA><NA><NA><NA>70.0혼합사면1.410.192.21<NA>
15873229<NA>1.7931.82하행<NA><NA><NA><NA>27.0혼합사면0.990.21.54<NA>
15883229<NA>1.81.837상행<NA><NA><NA><NA>37.0혼합사면0.880.41.37<NA>
15893251<NA>4.764.8상행<NA><NA><NA><NA>40.0혼합사면3.682.485.21<NA>
15903251<NA>6.1246.254상행<NA><NA><NA><NA>130.0혼합사면3.450.54.88<NA>
15913258<NA>0.870.949<NA><NA><NA><NA><NA>79.0혼합사면26.580.083.3<NA>
15923258<NA>1.5371.609<NA><NA><NA><NA><NA>72.0혼합사면18.80.083.3<NA>
159332511<NA>1.3131.392상행<NA><NA><NA><NA>79.0흙사면16.30.083.3<NA>
159432511<NA>6.0546.126상행<NA><NA><NA><NA>0.0흙사면5.01.00.0<NA>
159532511<NA>6.2196.274하행<NA><NA><NA><NA>0.0흙사면5.00.50.0<NA>

Duplicate rows

Most frequently occurring

노선번호구간번호위치-시점위치-종점위치-방향연장종류높이-최대높이-최소경사비고# duplicates
07020.440.96하행0.0혼합사면30.02.00.0<NA>2
17020.620.94상행0.0혼합사면29.02.00.0<NA>2
27021.0751.24하행0.0혼합사면15.05.00.0<NA>2
37021.561.74상행0.0혼합사면22.04.00.0<NA>2
47021.571.74하행0.0혼합사면17.05.00.0<NA>2
57021.82.17상행0.0혼합사면29.01.00.0<NA>2
67021.82.2하행0.0혼합사면36.05.00.0<NA>2
77023.713.98상행0.0혼합사면36.04.00.0<NA>2
87023.714.0하행0.0혼합사면36.05.00.0<NA>2
98243.043.6상행0.0혼합사면26.05.00.0<NA>2