Overview

Dataset statistics

Number of variables10
Number of observations25
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.2 KiB
Average record size in memory91.3 B

Variable types

Categorical3
Text1
Numeric6

Dataset

Description도로관리 업무에 따른 제주특별자치도 제주시 지방도로 관련 현황 데이터를 제공합니다.
Author제주특별자치도 제주시
URLhttps://www.data.go.kr/data/3083564/fileData.do

Alerts

데이터기준일자 has constant value ""Constant
노선수 is highly overall correlated with 연장(미터) and 2 other fieldsHigh correlation
연장(미터) is highly overall correlated with 노선수 and 2 other fieldsHigh correlation
면적 is highly overall correlated with 연장(미터) and 4 other fieldsHigh correlation
개설(미터) is highly overall correlated with 노선수 and 2 other fieldsHigh correlation
미개설(미터) is highly overall correlated with 면적 and 1 other fieldsHigh correlation
일부개설(미터) is highly overall correlated with 미개설(미터) and 2 other fieldsHigh correlation
유형 is highly overall correlated with 노선수 and 3 other fieldsHigh correlation
폭원(미터) is highly overall correlated with 면적 and 2 other fieldsHigh correlation
구분 has unique valuesUnique
노선수 has unique valuesUnique
연장(미터) has unique valuesUnique
면적 has unique valuesUnique
개설(미터) has unique valuesUnique
미개설(미터) has 3 (12.0%) zerosZeros
일부개설(미터) has 4 (16.0%) zerosZeros

Reproduction

Analysis started2023-12-12 03:44:36.571178
Analysis finished2023-12-12 03:44:42.027769
Duration5.46 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

유형
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
소로망
19 
중로급이상 가로망
6 

Length

Max length9
Median length3
Mean length4.44
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row중로급이상 가로망
2nd row중로급이상 가로망
3rd row중로급이상 가로망
4th row중로급이상 가로망
5th row중로급이상 가로망

Common Values

ValueCountFrequency (%)
소로망 19
76.0%
중로급이상 가로망 6
 
24.0%

Length

2023-12-12T12:44:42.134277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T12:44:42.302452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
소로망 19
61.3%
중로급이상 6
 
19.4%
가로망 6
 
19.4%

구분
Text

UNIQUE 

Distinct25
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
2023-12-12T12:44:42.505719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.52
Min length2

Characters and Unicode

Total characters88
Distinct characters31
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique25 ?
Unique (%)100.0%

Sample

1st row광로3류
2nd row대로1류
3rd row대로3류
4th row중로1류
5th row중로2류
ValueCountFrequency (%)
광로3류 1
 
4.0%
용담2동 1
 
4.0%
이호동 1
 
4.0%
외도동 1
 
4.0%
노형동 1
 
4.0%
연동 1
 
4.0%
오라동 1
 
4.0%
아라동 1
 
4.0%
봉개동 1
 
4.0%
삼양동 1
 
4.0%
Other values (15) 15
60.0%
2023-12-12T12:44:42.944314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 19
21.6%
도 8
 
9.1%
로 6
 
6.8%
류 6
 
6.8%
1 6
 
6.8%
2 5
 
5.7%
삼 3
 
3.4%
이 3
 
3.4%
중 3
 
3.4%
3 3
 
3.4%
Other values (21) 26
29.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 74
84.1%
Decimal Number 14
 
15.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 19
25.7%
도 8
 
10.8%
로 6
 
8.1%
류 6
 
8.1%
삼 3
 
4.1%
이 3
 
4.1%
중 3
 
4.1%
일 2
 
2.7%
라 2
 
2.7%
대 2
 
2.7%
Other values (18) 20
27.0%
Decimal Number
ValueCountFrequency (%)
1 6
42.9%
2 5
35.7%
3 3
21.4%

Most occurring scripts

ValueCountFrequency (%)
Hangul 74
84.1%
Common 14
 
15.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 19
25.7%
도 8
 
10.8%
로 6
 
8.1%
류 6
 
8.1%
삼 3
 
4.1%
이 3
 
4.1%
중 3
 
4.1%
일 2
 
2.7%
라 2
 
2.7%
대 2
 
2.7%
Other values (18) 20
27.0%
Common
ValueCountFrequency (%)
1 6
42.9%
2 5
35.7%
3 3
21.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 74
84.1%
ASCII 14
 
15.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 19
25.7%
도 8
 
10.8%
로 6
 
8.1%
류 6
 
8.1%
삼 3
 
4.1%
이 3
 
4.1%
중 3
 
4.1%
일 2
 
2.7%
라 2
 
2.7%
대 2
 
2.7%
Other values (18) 20
27.0%
ASCII
ValueCountFrequency (%)
1 6
42.9%
2 5
35.7%
3 3
21.4%

폭원(미터)
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)28.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
3~10
19 
40
 
1
35
 
1
25
 
1
20
 
1
Other values (2)
2 

Length

Max length4
Median length4
Mean length3.52
Min length2

Unique

Unique6 ?
Unique (%)24.0%

Sample

1st row40
2nd row35
3rd row25
4th row20
5th row15

Common Values

ValueCountFrequency (%)
3~10 19
76.0%
40 1
 
4.0%
35 1
 
4.0%
25 1
 
4.0%
20 1
 
4.0%
15 1
 
4.0%
12 1
 
4.0%

Length

2023-12-12T12:44:43.163668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T12:44:43.355743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3~10 19
76.0%
40 1
 
4.0%
35 1
 
4.0%
25 1
 
4.0%
20 1
 
4.0%
15 1
 
4.0%
12 1
 
4.0%

노선수
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct25
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean150.52
Minimum2
Maximum394
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2023-12-12T12:44:43.513028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile12
Q140
median99
Q3248
95-th percentile355.4
Maximum394
Range392
Interquartile range (IQR)208

Descriptive statistics

Standard deviation119.7964
Coefficient of variation (CV)0.79588358
Kurtosis-0.98441176
Mean150.52
Median Absolute Deviation (MAD)76
Skewness0.53563973
Sum3763
Variance14351.177
MonotonicityNot monotonic
2023-12-12T12:44:43.707188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
2 1
 
4.0%
9 1
 
4.0%
95 1
 
4.0%
94 1
 
4.0%
220 1
 
4.0%
394 1
 
4.0%
279 1
 
4.0%
212 1
 
4.0%
364 1
 
4.0%
218 1
 
4.0%
Other values (15) 15
60.0%
ValueCountFrequency (%)
2 1
4.0%
9 1
4.0%
24 1
4.0%
30 1
4.0%
32 1
4.0%
35 1
4.0%
40 1
4.0%
61 1
4.0%
78 1
4.0%
81 1
4.0%
ValueCountFrequency (%)
394 1
4.0%
364 1
4.0%
321 1
4.0%
287 1
4.0%
279 1
4.0%
256 1
4.0%
248 1
4.0%
220 1
4.0%
218 1
4.0%
212 1
4.0%

연장(미터)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct25
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52147.4
Minimum4886
Maximum180884
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2023-12-12T12:44:43.926888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4886
5-th percentile6280.2
Q119012
median41863
Q372094
95-th percentile141774.4
Maximum180884
Range175998
Interquartile range (IQR)53082

Descriptive statistics

Standard deviation44066.123
Coefficient of variation (CV)0.84503011
Kurtosis2.2638303
Mean52147.4
Median Absolute Deviation (MAD)23804
Skewness1.4639163
Sum1303685
Variance1.9418232 × 109
MonotonicityNot monotonic
2023-12-12T12:44:44.097631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
4886 1
 
4.0%
76835 1
 
4.0%
21655 1
 
4.0%
29355 1
 
4.0%
61300 1
 
4.0%
149791 1
 
4.0%
81692 1
 
4.0%
50195 1
 
4.0%
180884 1
 
4.0%
109708 1
 
4.0%
Other values (15) 15
60.0%
ValueCountFrequency (%)
4886 1
4.0%
5243 1
4.0%
10429 1
4.0%
11722 1
4.0%
18059 1
4.0%
18242 1
4.0%
19012 1
4.0%
19163 1
4.0%
21655 1
4.0%
29355 1
4.0%
ValueCountFrequency (%)
180884 1
4.0%
149791 1
4.0%
109708 1
4.0%
81692 1
4.0%
76835 1
4.0%
73512 1
4.0%
72094 1
4.0%
65978 1
4.0%
61300 1
4.0%
59561 1
4.0%

면적
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct25
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean453405.16
Minimum35565
Maximum2689225
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2023-12-12T12:44:44.258706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35565
5-th percentile74339.2
Q1140573
median229956
Q3563504
95-th percentile1339052
Maximum2689225
Range2653660
Interquartile range (IQR)422931

Descriptive statistics

Standard deviation577676.81
Coefficient of variation (CV)1.2740852
Kurtosis9.2430547
Mean453405.16
Median Absolute Deviation (MAD)145572
Skewness2.8413999
Sum11335129
Variance3.337105 × 1011
MonotonicityNot monotonic
2023-12-12T12:44:44.454128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
195440 1
 
4.0%
2689225 1
 
4.0%
73208 1
 
4.0%
140573 1
 
4.0%
256084 1
 
4.0%
613633 1
 
4.0%
563504 1
 
4.0%
174898 1
 
4.0%
583394 1
 
4.0%
372861 1
 
4.0%
Other values (15) 15
60.0%
ValueCountFrequency (%)
35565 1
4.0%
73208 1
4.0%
78864 1
4.0%
84384 1
4.0%
113632 1
4.0%
136008 1
4.0%
140573 1
4.0%
145524 1
4.0%
174898 1
4.0%
188574 1
4.0%
ValueCountFrequency (%)
2689225 1
4.0%
1343925 1
4.0%
1319560 1
4.0%
627945 1
4.0%
613633 1
4.0%
583394 1
4.0%
563504 1
4.0%
403676 1
4.0%
379223 1
4.0%
372861 1
4.0%

개설(미터)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct25
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44077.24
Minimum3302
Maximum173058
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2023-12-12T12:44:44.623941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3302
5-th percentile5699.8
Q116214
median26655
Q356662
95-th percentile139970.6
Maximum173058
Range169756
Interquartile range (IQR)40448

Descriptive statistics

Standard deviation43123.457
Coefficient of variation (CV)0.97836111
Kurtosis3.1629466
Mean44077.24
Median Absolute Deviation (MAD)18156
Skewness1.8169677
Sum1101931
Variance1.8596326 × 109
MonotonicityNot monotonic
2023-12-12T12:44:44.796983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
3302 1
 
4.0%
31713 1
 
4.0%
21655 1
 
4.0%
20209 1
 
4.0%
61300 1
 
4.0%
149383 1
 
4.0%
80704 1
 
4.0%
48346 1
 
4.0%
173058 1
 
4.0%
102321 1
 
4.0%
Other values (15) 15
60.0%
ValueCountFrequency (%)
3302 1
4.0%
5000 1
4.0%
8499 1
4.0%
9057 1
4.0%
13850 1
4.0%
15400 1
4.0%
16214 1
4.0%
18072 1
4.0%
20209 1
4.0%
21655 1
4.0%
ValueCountFrequency (%)
173058 1
4.0%
149383 1
4.0%
102321 1
4.0%
80704 1
4.0%
61300 1
4.0%
58585 1
4.0%
56662 1
4.0%
56398 1
4.0%
48346 1
4.0%
45540 1
4.0%

미개설(미터)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct23
Distinct (%)92.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4263.32
Minimum0
Maximum13693
Zeros3
Zeros (%)12.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2023-12-12T12:44:44.931832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1454
median1318
Q37627
95-th percentile12436.2
Maximum13693
Range13693
Interquartile range (IQR)7173

Descriptive statistics

Standard deviation4654.9907
Coefficient of variation (CV)1.0918699
Kurtosis-0.85869339
Mean4263.32
Median Absolute Deviation (MAD)1318
Skewness0.79203237
Sum106583
Variance21668939
MonotonicityNot monotonic
2023-12-12T12:44:45.089015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0 3
 
12.0%
846 1
 
4.0%
5848 1
 
4.0%
8910 1
 
4.0%
408 1
 
4.0%
988 1
 
4.0%
1447 1
 
4.0%
7065 1
 
4.0%
6726 1
 
4.0%
12269 1
 
4.0%
Other values (13) 13
52.0%
ValueCountFrequency (%)
0 3
12.0%
328 1
 
4.0%
370 1
 
4.0%
408 1
 
4.0%
454 1
 
4.0%
537 1
 
4.0%
846 1
 
4.0%
988 1
 
4.0%
1023 1
 
4.0%
1110 1
 
4.0%
ValueCountFrequency (%)
13693 1
4.0%
12478 1
4.0%
12269 1
4.0%
11158 1
4.0%
8910 1
4.0%
8093 1
4.0%
7627 1
4.0%
7065 1
4.0%
6726 1
4.0%
5848 1
4.0%

일부개설(미터)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct22
Distinct (%)88.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3806.84
Minimum0
Maximum33964
Zeros4
Zeros (%)16.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2023-12-12T12:44:45.257069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1402
median822
Q32658
95-th percentile15509.8
Maximum33964
Range33964
Interquartile range (IQR)2256

Descriptive statistics

Standard deviation7482.4268
Coefficient of variation (CV)1.9655218
Kurtosis11.259837
Mean3806.84
Median Absolute Deviation (MAD)822
Skewness3.1911505
Sum95171
Variance55986711
MonotonicityNot monotonic
2023-12-12T12:44:45.427254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
0 4
 
16.0%
738 1
 
4.0%
1905 1
 
4.0%
236 1
 
4.0%
402 1
 
4.0%
761 1
 
4.0%
661 1
 
4.0%
2658 1
 
4.0%
3218 1
 
4.0%
822 1
 
4.0%
Other values (12) 12
48.0%
ValueCountFrequency (%)
0 4
16.0%
236 1
 
4.0%
243 1
 
4.0%
402 1
 
4.0%
409 1
 
4.0%
570 1
 
4.0%
661 1
 
4.0%
738 1
 
4.0%
761 1
 
4.0%
822 1
 
4.0%
ValueCountFrequency (%)
33964 1
4.0%
16301 1
4.0%
12345 1
4.0%
8676 1
4.0%
4824 1
4.0%
3218 1
4.0%
2658 1
4.0%
2445 1
4.0%
1905 1
4.0%
1732 1
4.0%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
2021-03-31
25 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021-03-31
2nd row2021-03-31
3rd row2021-03-31
4th row2021-03-31
5th row2021-03-31

Common Values

ValueCountFrequency (%)
2021-03-31 25
100.0%

Length

2023-12-12T12:44:45.610715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T12:44:46.061572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021-03-31 25
100.0%

Interactions

2023-12-12T12:44:40.804199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:44:36.864983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:44:37.828051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:44:38.520690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:44:39.234706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:44:40.009025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:44:40.943070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:44:36.967591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:44:37.940216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:44:38.612490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:44:39.375152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:44:40.146437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:44:41.081681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:44:37.075253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:44:38.038839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:44:38.718203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:44:39.502878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:44:40.305589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:44:41.228936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:44:37.164035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:44:38.162520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:44:38.832230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:44:39.629213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:44:40.432521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:44:41.370616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:44:37.273798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:44:38.302131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:44:38.969288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:44:39.734220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:44:40.558511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:44:41.533252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:44:37.369745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:44:38.422220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:44:39.102804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:44:39.866933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:44:40.673402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T12:44:46.157561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
유형구분폭원(미터)노선수연장(미터)면적개설(미터)미개설(미터)일부개설(미터)
유형1.0001.0001.0000.8240.0000.5020.0000.6070.895
구분1.0001.0001.0001.0001.0001.0001.0001.0001.000
폭원(미터)1.0001.0001.0000.0000.0000.7940.0000.6920.931
노선수0.8241.0000.0001.0000.5600.6170.6130.0000.000
연장(미터)0.0001.0000.0000.5601.0000.7250.9790.5960.299
면적0.5021.0000.7940.6170.7251.0000.6510.5350.799
개설(미터)0.0001.0000.0000.6130.9790.6511.0000.0000.000
미개설(미터)0.6071.0000.6920.0000.5960.5350.0001.0000.644
일부개설(미터)0.8951.0000.9310.0000.2990.7990.0000.6441.000
2023-12-12T12:44:46.315522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
폭원(미터)유형
폭원(미터)1.0000.885
유형0.8851.000
2023-12-12T12:44:46.426196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
노선수연장(미터)면적개설(미터)미개설(미터)일부개설(미터)유형폭원(미터)
노선수1.0000.5770.1260.739-0.095-0.3040.7070.000
연장(미터)0.5771.0000.7960.9540.3980.0640.0000.000
면적0.1260.7961.0000.6540.5630.4250.5700.637
개설(미터)0.7390.9540.6541.0000.199-0.1050.0000.000
미개설(미터)-0.0950.3980.5630.1991.0000.6960.3830.436
일부개설(미터)-0.3040.0640.425-0.1050.6961.0000.6440.834
유형0.7070.0000.5700.0000.3830.6441.0000.885
폭원(미터)0.0000.0000.6370.0000.4360.8340.8851.000

Missing values

2023-12-12T12:44:41.723075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T12:44:41.958111image/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

유형구분폭원(미터)노선수연장(미터)면적개설(미터)미개설(미터)일부개설(미터)데이터기준일자
0중로급이상 가로망광로3류402488619544033028467382021-03-31
1중로급이상 가로망대로1류3597683526892253171311158339642021-03-31
2중로급이상 가로망대로3류25245375713439252376313693163012021-03-31
3중로급이상 가로망중로1류2032659781319560455408093123452021-03-31
4중로급이상 가로망중로2류15404186362794525560762786762021-03-31
5중로급이상 가로망중로3류12351916322995613850388714262021-03-31
6소로망일도1동3~1030524335565500002432021-03-31
7소로망일도2동3~1017531422229053306853284092021-03-31
8소로망이도1동3~1061104297886490575378352021-03-31
9소로망이도2동3~10321595613564205666245424452021-03-31
유형구분폭원(미터)노선수연장(미터)면적개설(미터)미개설(미터)일부개설(미터)데이터기준일자
15소로망화북동3~1028772094403676563981247832182021-03-31
16소로망삼양동3~1024873512379223585851226926582021-03-31
17소로망봉개동3~1021810970837286110232167266612021-03-31
18소로망아라동3~1036418088458339417305870657612021-03-31
19소로망오라동3~10212501951748984834614474022021-03-31
20소로망연동3~10279816925635048070498802021-03-31
21소로망노형동3~1039414979161363314938340802021-03-31
22소로망외도동3~102206130025608461300002021-03-31
23소로망이호동3~1094293551405732020989102362021-03-31
24소로망도두동3~1095216557320821655002021-03-31