Overview

Dataset statistics

Number of variables16
Number of observations234
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory31.2 KiB
Average record size in memory136.6 B

Variable types

Categorical5
Text2
DateTime1
Numeric8

Dataset

Description보행도로 관련 시설현황
Author인천광역시 동구
URLhttps://www.data.go.kr/data/15007199/fileData.do

Alerts

전체 결정연장 is highly overall correlated with 전체 결정면적 and 3 other fieldsHigh correlation
전체 결정면적 is highly overall correlated with 전체 결정연장 and 4 other fieldsHigh correlation
전체 집행연장 is highly overall correlated with 전체 결정연장 and 3 other fieldsHigh correlation
전체 집행면적 is highly overall correlated with 전체 결정연장 and 3 other fieldsHigh correlation
집행 최초 결정일 is highly overall correlated with 최초 고시번호High correlation
미집행 연장 is highly overall correlated with 미집행 국-공유지 면적 and 2 other fieldsHigh correlation
미집행 국-공유지 면적 is highly overall correlated with 미집행 연장 and 1 other fieldsHigh correlation
미집행 사유지 면적 is highly overall correlated with 미집행 연장 and 3 other fieldsHigh correlation
규모 is highly overall correlated with 전체 결정연장 and 5 other fieldsHigh correlation
사용 및 형태 is highly overall correlated with 기능High correlation
기능 is highly overall correlated with 전체 결정면적 and 2 other fieldsHigh correlation
최초 고시번호 is highly overall correlated with 집행 최초 결정일 and 1 other fieldsHigh correlation
최종변경 고시번호 is highly overall correlated with 최초 고시번호High correlation
사용 및 형태 is highly imbalanced (91.0%)Imbalance
기능 is highly imbalanced (63.8%)Imbalance
최종변경 고시번호 is highly imbalanced (73.3%)Imbalance
전체 집행연장 has 16 (6.8%) zerosZeros
전체 집행면적 has 16 (6.8%) zerosZeros
미집행 연장 has 216 (92.3%) zerosZeros
미집행 국-공유지 면적 has 216 (92.3%) zerosZeros
미집행 사유지 면적 has 226 (96.6%) zerosZeros

Reproduction

Analysis started2023-12-12 14:08:36.970078
Analysis finished2023-12-12 14:08:45.866379
Duration8.9 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

규모
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
소로
176 
중로
47 
대로
 
10
광로
 
1

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique1 ?
Unique (%)0.4%

Sample

1st row소로
2nd row중로
3rd row중로
4th row중로
5th row중로

Common Values

ValueCountFrequency (%)
소로 176
75.2%
중로 47
 
20.1%
대로 10
 
4.3%
광로 1
 
0.4%

Length

2023-12-12T23:08:45.948391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:08:46.078381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
소로 176
75.2%
중로 47
 
20.1%
대로 10
 
4.3%
광로 1
 
0.4%

사용 및 형태
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
일반도로
230 
보행자전용도로
 
2
지하도로
 
2

Length

Max length7
Median length4
Mean length4.025641
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
일반도로 230
98.3%
보행자전용도로 2
 
0.9%
지하도로 2
 
0.9%

Length

2023-12-12T23:08:46.193097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:08:46.308214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
일반도로 230
98.3%
보행자전용도로 2
 
0.9%
지하도로 2
 
0.9%

기능
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
국지도로
198 
집산도로
21 
보조 간선도로
 
11
특수도로
 
2
주간선도로
 
2

Length

Max length7
Median length4
Mean length4.1495726
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row국지도로
2nd row보조 간선도로
3rd row보조 간선도로
4th row보조 간선도로
5th row집산도로

Common Values

ValueCountFrequency (%)
국지도로 198
84.6%
집산도로 21
 
9.0%
보조 간선도로 11
 
4.7%
특수도로 2
 
0.9%
주간선도로 2
 
0.9%

Length

2023-12-12T23:08:46.432344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:08:46.566622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
국지도로 198
80.8%
집산도로 21
 
8.6%
보조 11
 
4.5%
간선도로 11
 
4.5%
특수도로 2
 
0.8%
주간선도로 2
 
0.8%

시설명
Text

Distinct174
Distinct (%)74.4%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
2023-12-12T23:08:46.869127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length11
Mean length9.017094
Min length3

Characters and Unicode

Total characters2110
Distinct characters23
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique146 ?
Unique (%)62.4%

Sample

1st row 도로(2-4)
2nd row 도로(1-185)
3rd row 도로(1-6)
4th row 도로(1-7)
5th row 도로(1-181)
ValueCountFrequency (%)
도로(1-1 6
 
2.6%
도로(3-2 6
 
2.6%
도로(3-5 5
 
2.1%
도로(3-1 5
 
2.1%
도로(3-3 5
 
2.1%
도로(2-2 5
 
2.1%
도로(1-7 4
 
1.7%
도로(3-4 4
 
1.7%
도로(3-6 4
 
1.7%
도로(2-1 4
 
1.7%
Other values (159) 186
79.5%
2023-12-12T23:08:47.310358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
257
12.2%
도 236
11.2%
로 236
11.2%
( 233
11.0%
- 231
10.9%
) 229
10.9%
3 173
8.2%
1 165
7.8%
2 110
5.2%
4 44
 
2.1%
Other values (13) 196
9.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 679
32.2%
Other Letter 481
22.8%
Space Separator 257
 
12.2%
Open Punctuation 233
 
11.0%
Dash Punctuation 231
 
10.9%
Close Punctuation 229
 
10.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 173
25.5%
1 165
24.3%
2 110
16.2%
4 44
 
6.5%
6 40
 
5.9%
8 37
 
5.4%
5 33
 
4.9%
7 30
 
4.4%
9 26
 
3.8%
0 21
 
3.1%
Other Letter
ValueCountFrequency (%)
도 236
49.1%
로 236
49.1%
지 2
 
0.4%
하 2
 
0.4%
배 1
 
0.2%
다 1
 
0.2%
리 1
 
0.2%
송 1
 
0.2%
림 1
 
0.2%
Space Separator
ValueCountFrequency (%)
257
100.0%
Open Punctuation
ValueCountFrequency (%)
( 233
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 231
100.0%
Close Punctuation
ValueCountFrequency (%)
) 229
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1629
77.2%
Hangul 481
 
22.8%

Most frequent character per script

Common
ValueCountFrequency (%)
257
15.8%
( 233
14.3%
- 231
14.2%
) 229
14.1%
3 173
10.6%
1 165
10.1%
2 110
6.8%
4 44
 
2.7%
6 40
 
2.5%
8 37
 
2.3%
Other values (4) 110
6.8%
Hangul
ValueCountFrequency (%)
도 236
49.1%
로 236
49.1%
지 2
 
0.4%
하 2
 
0.4%
배 1
 
0.2%
다 1
 
0.2%
리 1
 
0.2%
송 1
 
0.2%
림 1
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1629
77.2%
Hangul 481
 
22.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
257
15.8%
( 233
14.3%
- 231
14.2%
) 229
14.1%
3 173
10.6%
1 165
10.1%
2 110
6.8%
4 44
 
2.7%
6 40
 
2.5%
8 37
 
2.3%
Other values (4) 110
6.8%
Hangul
ValueCountFrequency (%)
도 236
49.1%
로 236
49.1%
지 2
 
0.4%
하 2
 
0.4%
배 1
 
0.2%
다 1
 
0.2%
리 1
 
0.2%
송 1
 
0.2%
림 1
 
0.2%

최종 변경일
Date

Distinct63
Distinct (%)26.9%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
Minimum1964-12-17 00:00:00
Maximum2019-05-20 00:00:00
2023-12-12T23:08:47.459918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:47.595114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

최초 고시번호
Categorical

HIGH CORRELATION 

Distinct45
Distinct (%)19.2%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
확인중
84 
인고 제157호
20 
인동고 제10호
 
9
인동고 제16호
 
9
인동고 제14호
 
6
Other values (40)
106 

Length

Max length9
Median length8
Mean length6.1282051
Min length3

Unique

Unique14 ?
Unique (%)6.0%

Sample

1st row인고 제157호
2nd row인고 제208호
3rd row건고 제54호
4th row건고 제54호
5th row확인중

Common Values

ValueCountFrequency (%)
확인중 84
35.9%
인고 제157호 20
 
8.5%
인동고 제10호 9
 
3.8%
인동고 제16호 9
 
3.8%
인동고 제14호 6
 
2.6%
인동고 제13호 6
 
2.6%
건고 제1275호 6
 
2.6%
인고 제127호 6
 
2.6%
인고 제208호 5
 
2.1%
인고 제295호 5
 
2.1%
Other values (35) 78
33.3%

Length

2023-12-12T23:08:47.730820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
확인중 84
21.9%
인고 82
21.4%
인동고 57
14.8%
제157호 20
 
5.2%
건고 11
 
2.9%
제10호 9
 
2.3%
제16호 9
 
2.3%
제14호 6
 
1.6%
제13호 6
 
1.6%
제1275호 6
 
1.6%
Other values (38) 94
24.5%

최종변경 고시번호
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct19
Distinct (%)8.1%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
확인중
201 
인고 제47호
 
5
인고 제157호
 
4
인동고 제69호
 
4
인동고 제81호
 
3
Other values (14)
 
17

Length

Max length9
Median length3
Mean length3.6752137
Min length3

Unique

Unique11 ?
Unique (%)4.7%

Sample

1st row확인중
2nd row인동고 제81호
3rd row인고 제157호
4th row확인중
5th row확인중

Common Values

ValueCountFrequency (%)
확인중 201
85.9%
인고 제47호 5
 
2.1%
인고 제157호 4
 
1.7%
인동고 제69호 4
 
1.7%
인동고 제81호 3
 
1.3%
인고 제158호 2
 
0.9%
인동고 제24호 2
 
0.9%
인동고제 2호 2
 
0.9%
인동고 제32호 1
 
0.4%
인동고 제6호 1
 
0.4%
Other values (9) 9
 
3.8%

Length

2023-12-12T23:08:47.863144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
확인중 201
75.3%
인동고 17
 
6.4%
인고 14
 
5.2%
제47호 5
 
1.9%
제157호 4
 
1.5%
제69호 4
 
1.5%
제81호 3
 
1.1%
제158호 2
 
0.7%
제24호 2
 
0.7%
인동고제 2
 
0.7%
Other values (12) 13
 
4.9%

위치
Text

Distinct210
Distinct (%)89.7%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
2023-12-12T23:08:48.044578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length34
Median length25
Mean length13.311966
Min length4

Characters and Unicode

Total characters3115
Distinct characters75
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique194 ?
Unique (%)82.9%

Sample

1st row중1-6~송현동98-211
2nd row광3-2~대림목제저목장
3rd row광3-2~만석동2-254
4th row광3-2~중2-6
5th row대2-39~송림동95
ValueCountFrequency (%)
화수동 15
 
4.7%
송림동 11
 
3.5%
소1-1 5
 
1.6%
소3-1 5
 
1.6%
중2-55~화수동 4
 
1.3%
중1-6~중2-6 4
 
1.3%
광3-2~중2-6 3
 
0.9%
중1-6~소3-184 3
 
0.9%
중1-6~소3-124 3
 
0.9%
소3-3 3
 
0.9%
Other values (232) 260
82.3%
2023-12-12T23:08:48.408219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 458
14.7%
1 292
 
9.4%
2 284
 
9.1%
3 234
 
7.5%
~ 230
 
7.4%
중 140
 
4.5%
동 131
 
4.2%
4 110
 
3.5%
6 110
 
3.5%
소 109
 
3.5%
Other values (65) 1017
32.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1327
42.6%
Other Letter 975
31.3%
Dash Punctuation 458
 
14.7%
Math Symbol 230
 
7.4%
Space Separator 102
 
3.3%
Open Punctuation 10
 
0.3%
Close Punctuation 9
 
0.3%
Uppercase Letter 4
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
중 140
14.4%
동 131
13.4%
소 109
11.2%
대 88
 
9.0%
송 61
 
6.3%
광 47
 
4.8%
림 45
 
4.6%
화 43
 
4.4%
수 32
 
3.3%
로 30
 
3.1%
Other values (48) 249
25.5%
Decimal Number
ValueCountFrequency (%)
1 292
22.0%
2 284
21.4%
3 234
17.6%
4 110
 
8.3%
6 110
 
8.3%
5 92
 
6.9%
9 61
 
4.6%
7 52
 
3.9%
8 50
 
3.8%
0 42
 
3.2%
Uppercase Letter
ValueCountFrequency (%)
C 2
50.0%
I 2
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 458
100.0%
Math Symbol
ValueCountFrequency (%)
~ 230
100.0%
Space Separator
ValueCountFrequency (%)
102
100.0%
Open Punctuation
ValueCountFrequency (%)
( 10
100.0%
Close Punctuation
ValueCountFrequency (%)
) 9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2136
68.6%
Hangul 975
31.3%
Latin 4
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
중 140
14.4%
동 131
13.4%
소 109
11.2%
대 88
 
9.0%
송 61
 
6.3%
광 47
 
4.8%
림 45
 
4.6%
화 43
 
4.4%
수 32
 
3.3%
로 30
 
3.1%
Other values (48) 249
25.5%
Common
ValueCountFrequency (%)
- 458
21.4%
1 292
13.7%
2 284
13.3%
3 234
11.0%
~ 230
10.8%
4 110
 
5.1%
6 110
 
5.1%
102
 
4.8%
5 92
 
4.3%
9 61
 
2.9%
Other values (5) 163
 
7.6%
Latin
ValueCountFrequency (%)
C 2
50.0%
I 2
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2140
68.7%
Hangul 975
31.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 458
21.4%
1 292
13.6%
2 284
13.3%
3 234
10.9%
~ 230
10.7%
4 110
 
5.1%
6 110
 
5.1%
102
 
4.8%
5 92
 
4.3%
9 61
 
2.9%
Other values (7) 167
 
7.8%
Hangul
ValueCountFrequency (%)
중 140
14.4%
동 131
13.4%
소 109
11.2%
대 88
 
9.0%
송 61
 
6.3%
광 47
 
4.8%
림 45
 
4.6%
화 43
 
4.4%
수 32
 
3.3%
로 30
 
3.1%
Other values (48) 249
25.5%

전체 결정연장
Real number (ℝ)

HIGH CORRELATION 

Distinct161
Distinct (%)68.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean254.10684
Minimum17
Maximum6750
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2023-12-12T23:08:48.536976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum17
5-th percentile30.65
Q166.5
median112.5
Q3250
95-th percentile818.4
Maximum6750
Range6733
Interquartile range (IQR)183.5

Descriptive statistics

Standard deviation531.52775
Coefficient of variation (CV)2.0917491
Kurtosis98.875131
Mean254.10684
Median Absolute Deviation (MAD)67.5
Skewness8.7698355
Sum59461
Variance282521.74
MonotonicityNot monotonic
2023-12-12T23:08:48.664721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 8
 
3.4%
45 5
 
2.1%
190 5
 
2.1%
90 5
 
2.1%
31 4
 
1.7%
115 4
 
1.7%
110 3
 
1.3%
120 3
 
1.3%
38 3
 
1.3%
56 3
 
1.3%
Other values (151) 191
81.6%
ValueCountFrequency (%)
17 1
0.4%
18 1
0.4%
19 1
0.4%
20 1
0.4%
23 1
0.4%
24 1
0.4%
25 1
0.4%
26 1
0.4%
28 2
0.9%
29 1
0.4%
ValueCountFrequency (%)
6750 1
0.4%
2952 1
0.4%
1865 1
0.4%
1500 1
0.4%
1450 1
0.4%
1040 1
0.4%
1010 1
0.4%
1005 1
0.4%
1004 1
0.4%
950 1
0.4%

전체 결정면적
Real number (ℝ)

HIGH CORRELATION 

Distinct205
Distinct (%)87.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4454.9017
Minimum40
Maximum270000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2023-12-12T23:08:48.822628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum40
5-th percentile150.95
Q1385.5
median842.5
Q32854.5
95-th percentile12758.5
Maximum270000
Range269960
Interquartile range (IQR)2469

Descriptive statistics

Standard deviation18715.621
Coefficient of variation (CV)4.20113
Kurtosis175.72823
Mean4454.9017
Median Absolute Deviation (MAD)619
Skewness12.527235
Sum1042447
Variance3.5027448 × 108
MonotonicityNot monotonic
2023-12-12T23:08:48.959858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
600 4
 
1.7%
660 3
 
1.3%
1020 3
 
1.3%
270 3
 
1.3%
2160 2
 
0.9%
560 2
 
0.9%
152 2
 
0.9%
720 2
 
0.9%
1856 2
 
0.9%
408 2
 
0.9%
Other values (195) 209
89.3%
ValueCountFrequency (%)
40 1
0.4%
45 1
0.4%
62 1
0.4%
72 1
0.4%
84 1
0.4%
87 1
0.4%
92 1
0.4%
93 1
0.4%
112 1
0.4%
136 1
0.4%
ValueCountFrequency (%)
270000 1
0.4%
45000 1
0.4%
38250 1
0.4%
37720 1
0.4%
36680 1
0.4%
32015 1
0.4%
31200 1
0.4%
30300 1
0.4%
27450 1
0.4%
21600 1
0.4%

전체 집행연장
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct151
Distinct (%)64.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean226.71795
Minimum0
Maximum5430
Zeros16
Zeros (%)6.8%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2023-12-12T23:08:49.115060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q155
median100
Q3236.5
95-th percentile674.5
Maximum5430
Range5430
Interquartile range (IQR)181.5

Descriptive statistics

Standard deviation460.75629
Coefficient of variation (CV)2.0322885
Kurtosis75.326141
Mean226.71795
Median Absolute Deviation (MAD)65
Skewness7.5846888
Sum53052
Variance212296.36
MonotonicityNot monotonic
2023-12-12T23:08:49.276193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 16
 
6.8%
100 8
 
3.4%
90 5
 
2.1%
45 5
 
2.1%
190 4
 
1.7%
31 4
 
1.7%
115 4
 
1.7%
120 3
 
1.3%
360 3
 
1.3%
38 3
 
1.3%
Other values (141) 179
76.5%
ValueCountFrequency (%)
0 16
6.8%
17 1
 
0.4%
18 1
 
0.4%
19 1
 
0.4%
20 1
 
0.4%
23 1
 
0.4%
24 1
 
0.4%
25 1
 
0.4%
26 1
 
0.4%
28 2
 
0.9%
ValueCountFrequency (%)
5430 1
0.4%
2952 1
0.4%
1865 1
0.4%
1500 1
0.4%
1450 1
0.4%
1010 1
0.4%
1005 1
0.4%
915 1
0.4%
886 1
0.4%
782 1
0.4%

전체 집행면적
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct192
Distinct (%)82.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3838.6111
Minimum0
Maximum209900
Zeros16
Zeros (%)6.8%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2023-12-12T23:08:49.461283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1318.25
median697.5
Q32243
95-th percentile12469
Maximum209900
Range209900
Interquartile range (IQR)1924.75

Descriptive statistics

Standard deviation14980.466
Coefficient of variation (CV)3.9025745
Kurtosis155.29254
Mean3838.6111
Median Absolute Deviation (MAD)515.5
Skewness11.561309
Sum898235
Variance2.2441436 × 108
MonotonicityNot monotonic
2023-12-12T23:08:49.627808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 16
 
6.8%
600 4
 
1.7%
660 3
 
1.3%
270 3
 
1.3%
1020 3
 
1.3%
224 2
 
0.9%
1150 2
 
0.9%
408 2
 
0.9%
720 2
 
0.9%
1056 2
 
0.9%
Other values (182) 195
83.3%
ValueCountFrequency (%)
0 16
6.8%
40 1
 
0.4%
45 1
 
0.4%
62 1
 
0.4%
72 1
 
0.4%
84 1
 
0.4%
87 1
 
0.4%
92 1
 
0.4%
93 1
 
0.4%
112 1
 
0.4%
ValueCountFrequency (%)
209900 1
0.4%
45000 1
0.4%
37720 1
0.4%
36680 1
0.4%
33450 1
0.4%
32015 1
0.4%
30300 1
0.4%
27450 1
0.4%
21600 1
0.4%
12830 1
0.4%

집행 최초 결정일
Real number (ℝ)

HIGH CORRELATION 

Distinct64
Distinct (%)27.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35161.615
Minimum23728
Maximum42387
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2023-12-12T23:08:49.783153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum23728
5-th percentile23728
Q131684
median37932
Q339923
95-th percentile41547
Maximum42387
Range18659
Interquartile range (IQR)8239

Descriptive statistics

Standard deviation6479.9359
Coefficient of variation (CV)0.18429005
Kurtosis-0.78564902
Mean35161.615
Median Absolute Deviation (MAD)2192
Skewness-0.92645524
Sum8227818
Variance41989569
MonotonicityNot monotonic
2023-12-12T23:08:49.947093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23728 42
 
17.9%
41547 20
 
8.5%
37403 9
 
3.8%
25608 8
 
3.4%
39923 8
 
3.4%
37515 8
 
3.4%
40049 7
 
3.0%
39262 7
 
3.0%
39790 7
 
3.0%
38348 6
 
2.6%
Other values (54) 112
47.9%
ValueCountFrequency (%)
23728 42
17.9%
25134 1
 
0.4%
25414 1
 
0.4%
25608 8
 
3.4%
25672 1
 
0.4%
28913 2
 
0.9%
29036 1
 
0.4%
30088 1
 
0.4%
30389 1
 
0.4%
31684 2
 
0.9%
ValueCountFrequency (%)
42387 2
 
0.9%
41708 1
 
0.4%
41547 20
8.5%
41096 2
 
0.9%
41071 5
 
2.1%
40802 1
 
0.4%
40659 3
 
1.3%
40294 3
 
1.3%
40217 3
 
1.3%
40063 5
 
2.1%

미집행 연장
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct18
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.794872
Minimum0
Maximum1320
Zeros216
Zeros (%)92.3%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2023-12-12T23:08:50.113746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile136.4
Maximum1320
Range1320
Interquartile range (IQR)0

Descriptive statistics

Standard deviation137.99232
Coefficient of variation (CV)5.1499526
Kurtosis55.634144
Mean26.794872
Median Absolute Deviation (MAD)0
Skewness7.1260141
Sum6270
Variance19041.88
MonotonicityNot monotonic
2023-12-12T23:08:50.256532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
0 216
92.3%
121 2
 
0.9%
46 1
 
0.4%
1040 1
 
0.4%
480 1
 
0.4%
1320 1
 
0.4%
42 1
 
0.4%
88 1
 
0.4%
350 1
 
0.4%
192 1
 
0.4%
Other values (8) 8
 
3.4%
ValueCountFrequency (%)
0 216
92.3%
42 1
 
0.4%
46 1
 
0.4%
73 1
 
0.4%
88 1
 
0.4%
121 2
 
0.9%
165 1
 
0.4%
190 1
 
0.4%
192 1
 
0.4%
228 1
 
0.4%
ValueCountFrequency (%)
1320 1
0.4%
1040 1
0.4%
1004 1
0.4%
480 1
0.4%
350 1
0.4%
307 1
0.4%
270 1
0.4%
233 1
0.4%
228 1
0.4%
192 1
0.4%

미집행 국-공유지 면적
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct19
Distinct (%)8.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean481.00427
Minimum0
Maximum31200
Zeros216
Zeros (%)92.3%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2023-12-12T23:08:50.370696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1758.25
Maximum31200
Range31200
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3091.3071
Coefficient of variation (CV)6.4267768
Kurtosis82.226717
Mean481.00427
Median Absolute Deviation (MAD)0
Skewness8.8183116
Sum112555
Variance9556179.7
MonotonicityNot monotonic
2023-12-12T23:08:50.497107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0 216
92.3%
15842 1
 
0.4%
31200 1
 
0.4%
4800 1
 
0.4%
30580 1
 
0.4%
493 1
 
0.4%
252 1
 
0.4%
5 1
 
0.4%
3480 1
 
0.4%
1718 1
 
0.4%
Other values (9) 9
 
3.8%
ValueCountFrequency (%)
0 216
92.3%
5 1
 
0.4%
252 1
 
0.4%
460 1
 
0.4%
493 1
 
0.4%
803 1
 
0.4%
1718 1
 
0.4%
1833 1
 
0.4%
2475 1
 
0.4%
2850 1
 
0.4%
ValueCountFrequency (%)
31200 1
0.4%
30580 1
0.4%
15842 1
0.4%
4800 1
0.4%
4699 1
0.4%
4660 1
0.4%
3480 1
0.4%
3240 1
0.4%
3165 1
0.4%
2850 1
0.4%

미집행 사유지 면적
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean135.2906
Minimum0
Maximum29520
Zeros226
Zeros (%)96.6%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2023-12-12T23:08:50.621919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum29520
Range29520
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1930.4553
Coefficient of variation (CV)14.268954
Kurtosis233.37576
Mean135.2906
Median Absolute Deviation (MAD)0
Skewness15.267026
Sum31658
Variance3726657.7
MonotonicityNot monotonic
2023-12-12T23:08:50.728165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 226
96.6%
222 1
 
0.4%
33 1
 
0.4%
844 1
 
0.4%
202 1
 
0.4%
20 1
 
0.4%
523 1
 
0.4%
294 1
 
0.4%
29520 1
 
0.4%
ValueCountFrequency (%)
0 226
96.6%
20 1
 
0.4%
33 1
 
0.4%
202 1
 
0.4%
222 1
 
0.4%
294 1
 
0.4%
523 1
 
0.4%
844 1
 
0.4%
29520 1
 
0.4%
ValueCountFrequency (%)
29520 1
 
0.4%
844 1
 
0.4%
523 1
 
0.4%
294 1
 
0.4%
222 1
 
0.4%
202 1
 
0.4%
33 1
 
0.4%
20 1
 
0.4%
0 226
96.6%

Interactions

2023-12-12T23:08:44.237636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:38.086945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:39.281699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:40.050735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:40.969878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:41.760125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:42.600748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:43.427960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:44.346518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:38.205156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:39.376197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:40.194454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:41.063833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:41.878458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:42.714476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:43.516222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:44.456644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:38.650274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:39.467865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:40.349232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:41.144163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:41.965874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:42.807782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:43.611536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:44.564631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:38.758245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:39.571048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:40.454783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:41.256797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:42.057625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:42.899036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:43.711135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:44.642386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:38.856917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:39.668697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:40.555251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:41.343473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:42.150255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:43.002793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:43.814377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:44.743028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:38.972085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:39.752914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:40.662968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:41.463750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:42.268399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:43.117517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:43.944253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:44.856695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:39.067176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:39.838742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:40.771775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:41.567834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:42.369274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:43.231214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:44.041934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:45.302286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:39.177807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:39.950976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:40.873290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:41.662993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:42.510030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:43.326997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:44.132887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T23:08:50.819607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
규모사용 및 형태기능최종 변경일최초 고시번호최종변경 고시번호전체 결정연장전체 결정면적전체 집행연장전체 집행면적집행 최초 결정일미집행 연장미집행 국-공유지 면적미집행 사유지 면적
규모1.0000.0000.5130.9010.0000.0000.7300.7880.8090.9460.7020.7820.8011.000
사용 및 형태0.0001.0000.7140.8380.0000.0000.0000.0000.0000.0000.4070.0000.0000.000
기능0.5130.7141.0000.8840.0000.0000.7750.6050.5180.5320.7590.4710.3720.579
최종 변경일0.9010.8380.8841.0000.9910.9900.7310.8900.6330.6960.9540.8000.7541.000
최초 고시번호0.0000.0000.0000.9911.0000.9620.0000.0000.0000.0000.9290.0000.0000.000
최종변경 고시번호0.0000.0000.0000.9900.9621.0000.4010.0000.2350.0000.5400.3950.5580.000
전체 결정연장0.7300.0000.7750.7310.0000.4011.0000.8730.9350.8210.8160.6920.5251.000
전체 결정면적0.7880.0000.6050.8900.0000.0000.8731.0000.9890.8640.7440.9620.5241.000
전체 집행연장0.8090.0000.5180.6330.0000.2350.9350.9891.0000.9110.6960.8170.5491.000
전체 집행면적0.9460.0000.5320.6960.0000.0000.8210.8640.9111.0000.8110.7650.7641.000
집행 최초 결정일0.7020.4070.7590.9540.9290.5400.8160.7440.6960.8111.0000.5390.4360.855
미집행 연장0.7820.0000.4710.8000.0000.3950.6920.9620.8170.7650.5391.0000.9281.000
미집행 국-공유지 면적0.8010.0000.3720.7540.0000.5580.5250.5240.5490.7640.4360.9281.0000.891
미집행 사유지 면적1.0000.0000.5791.0000.0000.0001.0001.0001.0001.0000.8551.0000.8911.000
2023-12-12T23:08:51.321418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
최초 고시번호사용 및 형태최종변경 고시번호규모기능
최초 고시번호1.0000.0000.6180.0000.000
사용 및 형태0.0001.0000.0000.0000.698
최종변경 고시번호0.6180.0001.0000.0000.000
규모0.0000.0000.0001.0000.440
기능0.0000.6980.0000.4401.000
2023-12-12T23:08:51.435916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
전체 결정연장전체 결정면적전체 집행연장전체 집행면적집행 최초 결정일미집행 연장미집행 국-공유지 면적미집행 사유지 면적규모사용 및 형태기능최초 고시번호최종변경 고시번호
전체 결정연장1.0000.9360.8220.765-0.0070.1670.1670.1550.6700.0000.3960.0000.205
전체 결정면적0.9361.0000.7470.804-0.0010.2040.2040.1450.8450.0000.5560.0000.000
전체 집행연장0.8220.7471.0000.947-0.033-0.358-0.359-0.2230.6580.0000.3830.0000.106
전체 집행면적0.7650.8040.9471.000-0.033-0.353-0.353-0.2230.6900.0000.4580.0000.000
집행 최초 결정일-0.007-0.001-0.033-0.0331.0000.0570.0560.0060.3200.2810.3190.6080.212
미집행 연장0.1670.204-0.358-0.3530.0571.0001.0000.6580.6240.0000.3420.0000.188
미집행 국-공유지 면적0.1670.204-0.359-0.3530.0561.0001.0000.6520.4410.0000.3110.0000.328
미집행 사유지 면적0.1550.145-0.223-0.2230.0060.6580.6521.0000.9960.0000.6950.0000.000
규모0.6700.8450.6580.6900.3200.6240.4410.9961.0000.0000.4400.0000.000
사용 및 형태0.0000.0000.0000.0000.2810.0000.0000.0000.0001.0000.6980.0000.000
기능0.3960.5560.3830.4580.3190.3420.3110.6950.4400.6981.0000.0000.000
최초 고시번호0.0000.0000.0000.0000.6080.0000.0000.0000.0000.0000.0001.0000.618
최종변경 고시번호0.2050.0000.1060.0000.2120.1880.3280.0000.0000.0000.0000.6181.000

Missing values

2023-12-12T23:08:45.466269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T23:08:45.757600image/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

규모사용 및 형태기능시설명최종 변경일최초 고시번호최종변경 고시번호위치전체 결정연장전체 결정면적전체 집행연장전체 집행면적집행 최초 결정일미집행 연장미집행 국-공유지 면적미집행 사유지 면적
0소로일반도로국지도로도로(2-4)2013-09-30인고 제157호확인중중1-6~송현동98-211947439474341547000
1중로일반도로보조 간선도로도로(1-185)2018-12-12인고 제208호인동고 제81호광3-2~대림목제저목장1004160640037932100415842222
2중로일반도로보조 간선도로도로(1-6)2013-09-30건고 제54호인고 제157호광3-2~만석동2-25429523772029523772025608000
3중로일반도로보조 간선도로도로(1-7)1970-02-09건고 제54호확인중광3-2~중2-63206400320640025608000
4중로일반도로집산도로도로(1-181)2004-12-27확인중확인중대2-39~송림동951242190124219038348000
5중로일반도로집산도로도로(1-184)2004-12-27확인중확인중대2-39~소1-582164082164038338000
6중로일반도로집산도로도로(1-186)2004-01-07인동고 제1호확인중대2-1~송림동2-15776152076152037993000
7중로일반도로국지도로도로(1-324)2008-05-06확인중확인중대2-39~중2-44998055499805539574000
8중로일반도로국지도로도로(1-329)2013-09-30인고 제157호확인중화평동528-4~중1-61022170102217041547000
9중로일반도로국지도로도로(1-394)2009-08-24확인중확인중대1-18~중1-3954188360418836040049000
규모사용 및 형태기능시설명최종 변경일최초 고시번호최종변경 고시번호위치전체 결정연장전체 결정면적전체 집행연장전체 집행면적집행 최초 결정일미집행 연장미집행 국-공유지 면적미집행 사유지 면적
224소로일반도로국지도로도로(1-1964-12-17확인중확인중대3-3~대2-391151150115115023728000
225소로일반도로국지도로도로1964-12-17확인중확인중소1-4~송현57-373252232522323728000
226소로일반도로국지도로도로(2-1)1964-12-17확인중확인중금곡42-10~금곡48-1012401920240192023728000
227대로일반도로보조 간선도로도로(2-2)2013-09-30확인중확인중광3-2~동인천역4호광장915274509152745034740000
228대로일반도로국지도로도로(2-41)2013-09-30확인중확인중동인천역4호광장~광3-115004500015004500030389000
229대로일반도로집산도로도로(2-49)2013-09-30확인중확인중송현동 광3-2~화평동 중1-6650117006501170028913000
230대로일반도로국지도로도로(2-43)2013-09-30확인중확인중광3-2~중1-6720216007202160031684000
231중로일반도로국지도로도로(3-264)2010-02-08건고 제1275호인고 제47호소2-18~금곡27-31071284107128423728000
232중로일반도로국지도로도로(3-324)2016-01-18인동고 제40호인동고제 2호중1-96~화수동 7-151번지1632024163202442387000
233소로일반도로국지도로도로(3-145)2016-01-18인동고 제40호인동고제 2호중3-324~화수동 7-359번지312053120542387000