Overview

Dataset statistics

Number of variables10
Number of observations497
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory40.9 KiB
Average record size in memory84.3 B

Variable types

Categorical4
Text2
Numeric4

Dataset

Description경기도 포천시에서 제공하는 미끄럼방지시설 현황(시도명, 시군구명, 수치치형도 도엽번호, 넓이, 연장, 방지시설형식, 차도포장재질, 설치주소, 위도, 경도) 데이터 입니다.
Author경기도 포천시
URLhttps://www.data.go.kr/data/15105676/fileData.do

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
위도 is highly overall correlated with 경도 and 1 other fieldsHigh correlation
경도 is highly overall correlated with 위도High correlation
차도포장재질 is highly overall correlated with 위도High correlation
위도 has unique valuesUnique
경도 has unique valuesUnique

Reproduction

Analysis started2023-12-12 15:04:19.167136
Analysis finished2023-12-12 15:04:21.264980
Duration2.1 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
경기도
497 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경기도 497
100.0%

Length

2023-12-13T00:04:21.326553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:04:21.436364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경기도 497
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
포천시
497 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
포천시 497
100.0%

Length

2023-12-13T00:04:21.525076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:04:21.608651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
포천시 497
100.0%

수치지형도 도엽번호
Text

Distinct213
Distinct (%)42.9%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-13T00:04:21.817405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length9.9839034
Min length9

Characters and Unicode

Total characters4962
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

Unique103 ?
Unique (%)20.7%

Sample

1st row3871423441
2nd row3871423441
3rd row3871423441
4th row3871423441
5th row3871423441
ValueCountFrequency (%)
3770110742 47
 
9.5%
3770118492 13
 
2.6%
3770114913 12
 
2.4%
3770114813 10
 
2.0%
3770104173 9
 
1.8%
3871406813 8
 
1.6%
3770110911 8
 
1.6%
3770118483 7
 
1.4%
3770125264 7
 
1.4%
3770114812 6
 
1.2%
Other values (203) 370
74.4%
2023-12-13T00:04:22.169933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
7 1046
21.1%
1 1014
20.4%
3 746
15.0%
0 634
12.8%
4 396
 
8.0%
2 373
 
7.5%
8 277
 
5.6%
9 175
 
3.5%
5 167
 
3.4%
6 103
 
2.1%
Other values (4) 31
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4931
99.4%
Uppercase Letter 31
 
0.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
7 1046
21.2%
1 1014
20.6%
3 746
15.1%
0 634
12.9%
4 396
 
8.0%
2 373
 
7.6%
8 277
 
5.6%
9 175
 
3.5%
5 167
 
3.4%
6 103
 
2.1%
Uppercase Letter
ValueCountFrequency (%)
A 17
54.8%
D 5
 
16.1%
B 5
 
16.1%
C 4
 
12.9%

Most occurring scripts

ValueCountFrequency (%)
Common 4931
99.4%
Latin 31
 
0.6%

Most frequent character per script

Common
ValueCountFrequency (%)
7 1046
21.2%
1 1014
20.6%
3 746
15.1%
0 634
12.9%
4 396
 
8.0%
2 373
 
7.6%
8 277
 
5.6%
9 175
 
3.5%
5 167
 
3.4%
6 103
 
2.1%
Latin
ValueCountFrequency (%)
A 17
54.8%
D 5
 
16.1%
B 5
 
16.1%
C 4
 
12.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4962
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
7 1046
21.1%
1 1014
20.4%
3 746
15.0%
0 634
12.8%
4 396
 
8.0%
2 373
 
7.5%
8 277
 
5.6%
9 175
 
3.5%
5 167
 
3.4%
6 103
 
2.1%
Other values (4) 31
 
0.6%

넓이
Real number (ℝ)

Distinct300
Distinct (%)60.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.7029779
Minimum1
Maximum48.18
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-13T00:04:22.303689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.898
Q13.32
median4.57
Q36.54
95-th percentile11.88
Maximum48.18
Range47.18
Interquartile range (IQR)3.22

Descriptive statistics

Standard deviation4.4351596
Coefficient of variation (CV)0.77769188
Kurtosis45.995116
Mean5.7029779
Median Absolute Deviation (MAD)1.5
Skewness5.7686629
Sum2834.38
Variance19.67064
MonotonicityNot monotonic
2023-12-13T00:04:22.430219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.5 16
 
3.2%
3.0 14
 
2.8%
3.4 14
 
2.8%
3.6 9
 
1.8%
3.1 8
 
1.6%
3.07 6
 
1.2%
3.8 6
 
1.2%
3.55 5
 
1.0%
3.18 5
 
1.0%
2.98 5
 
1.0%
Other values (290) 409
82.3%
ValueCountFrequency (%)
1.0 1
0.2%
2.28 1
0.2%
2.32 1
0.2%
2.44 1
0.2%
2.45 1
0.2%
2.49 1
0.2%
2.5 1
0.2%
2.55 1
0.2%
2.59 1
0.2%
2.6 1
0.2%
ValueCountFrequency (%)
48.18 1
0.2%
47.66 1
0.2%
42.66 1
0.2%
32.47 1
0.2%
30.56 1
0.2%
22.3 1
0.2%
17.69 1
0.2%
15.52 1
0.2%
14.35 1
0.2%
14.22 1
0.2%

연장
Real number (ℝ)

Distinct438
Distinct (%)88.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean58.58501
Minimum0.9
Maximum1130.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-13T00:04:22.854069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.9
5-th percentile1
Q116.97
median35.12
Q367.76
95-th percentile171.704
Maximum1130.4
Range1129.5
Interquartile range (IQR)50.79

Descriptive statistics

Standard deviation90.854076
Coefficient of variation (CV)1.5508076
Kurtosis59.62076
Mean58.58501
Median Absolute Deviation (MAD)23.42
Skewness6.3911737
Sum29116.75
Variance8254.4631
MonotonicityNot monotonic
2023-12-13T00:04:22.999352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.0 35
 
7.0%
0.9 11
 
2.2%
16.98 3
 
0.6%
1.1 3
 
0.6%
51.99 2
 
0.4%
3.0 2
 
0.4%
58.35 2
 
0.4%
1.77 2
 
0.4%
16.97 2
 
0.4%
17.0 2
 
0.4%
Other values (428) 433
87.1%
ValueCountFrequency (%)
0.9 11
 
2.2%
0.94 1
 
0.2%
0.99 1
 
0.2%
1.0 35
7.0%
1.01 1
 
0.2%
1.02 1
 
0.2%
1.03 1
 
0.2%
1.04 1
 
0.2%
1.09 1
 
0.2%
1.1 3
 
0.6%
ValueCountFrequency (%)
1130.4 1
0.2%
927.6 1
0.2%
589.3 1
0.2%
515.18 1
0.2%
475.11 1
0.2%
464.35 1
0.2%
295.6 1
0.2%
293.66 1
0.2%
279.1 1
0.2%
259.96 1
0.2%

방지시설형식
Categorical

Distinct6
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
전면처리형식
300 
이격식 1-3방식
165 
기타
 
11
복합식
 
10
미분류
 
7

Length

Max length9
Median length6
Mean length6.8289738
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
전면처리형식 300
60.4%
이격식 1-3방식 165
33.2%
기타 11
 
2.2%
복합식 10
 
2.0%
미분류 7
 
1.4%
이격식 3-6방식 4
 
0.8%

Length

2023-12-13T00:04:23.139849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:04:23.266147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전면처리형식 300
45.0%
이격식 169
25.4%
1-3방식 165
24.8%
기타 11
 
1.7%
복합식 10
 
1.5%
미분류 7
 
1.1%
3-6방식 4
 
0.6%

차도포장재질
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
특수성AS
267 
아스팔트콘크리트
165 
기타
65 

Length

Max length8
Median length5
Mean length5.6036217
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
특수성AS 267
53.7%
아스팔트콘크리트 165
33.2%
기타 65
 
13.1%

Length

2023-12-13T00:04:23.429961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:04:23.535732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
특수성as 267
53.7%
아스팔트콘크리트 165
33.2%
기타 65
 
13.1%

설치 주소
Text

Distinct349
Distinct (%)70.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-13T00:04:23.765381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length23
Mean length20.442656
Min length15

Characters and Unicode

Total characters10160
Distinct characters100
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique280 ?
Unique (%)56.3%

Sample

1st row경기도 포천시 이동면 장암리 252-7
2nd row경기도 포천시 이동면 장암리 252-3
3rd row경기도 포천시 이동면 장암리 252-3
4th row경기도 포천시 이동면 장암리 252-3
5th row경기도 포천시 이동면 장암리 252-3
ValueCountFrequency (%)
경기도 497
20.3%
포천시 497
20.3%
소흘읍 106
 
4.3%
신북면 82
 
3.4%
가채리 56
 
2.3%
송우리 50
 
2.0%
가산면 41
 
1.7%
군내면 40
 
1.6%
산 32
 
1.3%
선단동 28
 
1.1%
Other values (407) 1015
41.5%
2023-12-13T00:04:24.122118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1947
19.2%
기 512
 
5.0%
천 512
 
5.0%
도 503
 
5.0%
시 497
 
4.9%
경 497
 
4.9%
포 497
 
4.9%
- 441
 
4.3%
리 424
 
4.2%
1 325
 
3.2%
Other values (90) 4005
39.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5799
57.1%
Decimal Number 1973
 
19.4%
Space Separator 1947
 
19.2%
Dash Punctuation 441
 
4.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
기 512
 
8.8%
천 512
 
8.8%
도 503
 
8.7%
시 497
 
8.6%
경 497
 
8.6%
포 497
 
8.6%
리 424
 
7.3%
면 319
 
5.5%
동 168
 
2.9%
읍 130
 
2.2%
Other values (78) 1740
30.0%
Decimal Number
ValueCountFrequency (%)
1 325
16.5%
2 282
14.3%
3 235
11.9%
5 193
9.8%
7 191
9.7%
6 178
9.0%
4 177
9.0%
9 170
8.6%
8 122
 
6.2%
0 100
 
5.1%
Space Separator
ValueCountFrequency (%)
1947
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 441
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5799
57.1%
Common 4361
42.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
기 512
 
8.8%
천 512
 
8.8%
도 503
 
8.7%
시 497
 
8.6%
경 497
 
8.6%
포 497
 
8.6%
리 424
 
7.3%
면 319
 
5.5%
동 168
 
2.9%
읍 130
 
2.2%
Other values (78) 1740
30.0%
Common
ValueCountFrequency (%)
1947
44.6%
- 441
 
10.1%
1 325
 
7.5%
2 282
 
6.5%
3 235
 
5.4%
5 193
 
4.4%
7 191
 
4.4%
6 178
 
4.1%
4 177
 
4.1%
9 170
 
3.9%
Other values (2) 222
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5799
57.1%
ASCII 4361
42.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1947
44.6%
- 441
 
10.1%
1 325
 
7.5%
2 282
 
6.5%
3 235
 
5.4%
5 193
 
4.4%
7 191
 
4.4%
6 178
 
4.1%
4 177
 
4.1%
9 170
 
3.9%
Other values (2) 222
 
5.1%
Hangul
ValueCountFrequency (%)
기 512
 
8.8%
천 512
 
8.8%
도 503
 
8.7%
시 497
 
8.6%
경 497
 
8.6%
포 497
 
8.6%
리 424
 
7.3%
면 319
 
5.5%
동 168
 
2.9%
읍 130
 
2.2%
Other values (78) 1740
30.0%

위도
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct497
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.904725
Minimum37.772708
Maximum38.157274
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-13T00:04:24.272753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.772708
5-th percentile37.789423
Q137.829899
median37.890781
Q337.957952
95-th percentile38.090531
Maximum38.157274
Range0.38456629
Interquartile range (IQR)0.12805281

Descriptive statistics

Standard deviation0.089808425
Coefficient of variation (CV)0.0023693201
Kurtosis0.09914284
Mean37.904725
Median Absolute Deviation (MAD)0.06240203
Skewness0.840426
Sum18838.648
Variance0.0080655532
MonotonicityNot monotonic
2023-12-13T00:04:24.406491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
38.02994698 1
 
0.2%
37.82890107 1
 
0.2%
37.8287504 1
 
0.2%
37.82887246 1
 
0.2%
37.83068333 1
 
0.2%
37.82477364 1
 
0.2%
37.82759291 1
 
0.2%
37.82769104 1
 
0.2%
37.82775901 1
 
0.2%
37.82793137 1
 
0.2%
Other values (487) 487
98.0%
ValueCountFrequency (%)
37.77270776 1
0.2%
37.77385345 1
0.2%
37.77440723 1
0.2%
37.77668332 1
0.2%
37.78417672 1
0.2%
37.78429219 1
0.2%
37.78496104 1
0.2%
37.78503019 1
0.2%
37.78523225 1
0.2%
37.78544597 1
0.2%
ValueCountFrequency (%)
38.15727405 1
0.2%
38.15703631 1
0.2%
38.1568422 1
0.2%
38.15678983 1
0.2%
38.1565361 1
0.2%
38.15648113 1
0.2%
38.15627103 1
0.2%
38.15617872 1
0.2%
38.15594655 1
0.2%
38.15567512 1
0.2%

경도
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct497
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.20668
Minimum127.11769
Maximum127.38578
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-13T00:04:24.559993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum127.11769
5-th percentile127.13424
Q1127.15477
median127.2024
Q3127.22953
95-th percentile127.35813
Maximum127.38578
Range0.2680857
Interquartile range (IQR)0.0747572

Descriptive statistics

Standard deviation0.060374696
Coefficient of variation (CV)0.00047461889
Kurtosis0.93934984
Mean127.20668
Median Absolute Deviation (MAD)0.0373065
Skewness1.0602579
Sum63221.722
Variance0.0036451039
MonotonicityNot monotonic
2023-12-13T00:04:24.700676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.3671325 1
 
0.2%
127.1386406 1
 
0.2%
127.143678 1
 
0.2%
127.1433714 1
 
0.2%
127.1392789 1
 
0.2%
127.1369785 1
 
0.2%
127.141237 1
 
0.2%
127.141012 1
 
0.2%
127.1408564 1
 
0.2%
127.1404135 1
 
0.2%
Other values (487) 487
98.0%
ValueCountFrequency (%)
127.1176929 1
0.2%
127.1199961 1
0.2%
127.1220488 1
0.2%
127.12242 1
0.2%
127.1224435 1
0.2%
127.1227328 1
0.2%
127.1228915 1
0.2%
127.1231621 1
0.2%
127.1232407 1
0.2%
127.1244682 1
0.2%
ValueCountFrequency (%)
127.3857786 1
0.2%
127.3819407 1
0.2%
127.3818474 1
0.2%
127.3817462 1
0.2%
127.3816259 1
0.2%
127.3778836 1
0.2%
127.3695618 1
0.2%
127.3693993 1
0.2%
127.3692318 1
0.2%
127.3691839 1
0.2%

Interactions

2023-12-13T00:04:20.657718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:04:19.538799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:04:19.923625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:04:20.295916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:04:20.756624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:04:19.631053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:04:20.023879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:04:20.387975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:04:20.844370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:04:19.714292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:04:20.100095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:04:20.472485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:04:20.927480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:04:19.826294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:04:20.205448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:04:20.574290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T00:04:24.804031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
넓이연장방지시설형식차도포장재질위도경도
넓이1.0000.0000.6000.3240.3200.282
연장0.0001.0000.3080.0700.0770.000
방지시설형식0.6000.3081.0000.6910.4550.388
차도포장재질0.3240.0700.6911.0000.6640.604
위도0.3200.0770.4550.6641.0000.903
경도0.2820.0000.3880.6040.9031.000
2023-12-13T00:04:24.890739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
방지시설형식차도포장재질
방지시설형식1.0000.373
차도포장재질0.3731.000
2023-12-13T00:04:24.985234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
넓이연장위도경도방지시설형식차도포장재질
넓이1.0000.015-0.142-0.0490.3870.217
연장0.0151.000-0.097-0.0500.1880.046
위도-0.142-0.0971.0000.7310.2590.511
경도-0.049-0.0500.7311.0000.2150.445
방지시설형식0.3870.1880.2590.2151.0000.373
차도포장재질0.2170.0460.5110.4450.3731.000

Missing values

2023-12-13T00:04:21.041081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T00:04:21.210104image/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

시도명시군구명수치지형도 도엽번호넓이연장방지시설형식차도포장재질설치 주소위도경도
0경기도포천시38714234413.555.37전면처리형식아스팔트콘크리트경기도 포천시 이동면 장암리 252-738.029947127.367132
1경기도포천시38714234413.825.89전면처리형식아스팔트콘크리트경기도 포천시 이동면 장암리 252-338.029362127.3674
2경기도포천시38714234413.4821.71전면처리형식아스팔트콘크리트경기도 포천시 이동면 장암리 252-338.029119127.367483
3경기도포천시38714234413.5615.22전면처리형식아스팔트콘크리트경기도 포천시 이동면 장암리 252-338.02977127.367221
4경기도포천시38714234413.5515.13전면처리형식아스팔트콘크리트경기도 포천시 이동면 장암리 252-338.029608127.367296
5경기도포천시387142344215.5211.11전면처리형식아스팔트콘크리트경기도 포천시 이동면 장암리 252-338.029419127.367507
6경기도포천시38714234423.3926.88전면처리형식아스팔트콘크리트경기도 포천시 이동면 장암리 254-538.028809127.367565
7경기도포천시38714210834.6170.81이격식 1-3방식특수성AS경기도 포천시 영북면 야미리 34-238.04614127.287236
8경기도포천시38714211426.82211.52이격식 1-3방식특수성AS경기도 포천시 영북면 야미리 46938.044569127.268236
9경기도포천시38713256433.2132.85전면처리형식아스팔트콘크리트경기도 포천시 영중면 영평리 253-338.015208127.21664
시도명시군구명수치지형도 도엽번호넓이연장방지시설형식차도포장재질설치 주소위도경도
487경기도포천시37701232549.3542.09전면처리형식특수성AS경기도 포천시 소흘읍 이동교리 606-137.785547127.122891
488경기도포천시37701188643.23464.35전면처리형식특수성AS경기도 포천시 소흘읍 이동교리 산 59-2037.80555127.129315
489경기도포천시377011886410.25259.96전면처리형식특수성AS경기도 포천시 소흘읍 이동교리 산 59-2137.805624127.129747
490경기도포천시37701232533.55142.5이격식 1-3방식특수성AS경기도 포천시 소흘읍 이동교리 산 9637.787369127.122049
491경기도포천시37701232513.8216.09이격식 1-3방식특수성AS경기도 포천시 소흘읍 이동교리 산 9637.787538127.12242
492경기도포천시37701188749.5317.97이격식 1-3방식아스팔트콘크리트경기도 포천시 소흘읍 이동교리 781-4137.805269127.132852
493경기도포천시377011887312.2970.81전면처리형식특수성AS경기도 포천시 소흘읍 이동교리 294-237.805464127.132149
494경기도포천시37701232616.9248.77전면처리형식특수성AS경기도 포천시 소흘읍 이동교리 423-337.789897127.125985
495경기도포천시37701207245.4916.98이격식 1-3방식특수성AS경기도 포천시 내촌면 진목리 309-137.811159127.208815
496경기도포천시377012072412.3785.51전면처리형식특수성AS경기도 포천시 내촌면 진목리 312-437.810748127.209431