Overview

Dataset statistics

Number of variables20
Number of observations500
Missing cells500
Missing cells (%)5.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory86.1 KiB
Average record size in memory176.3 B

Variable types

Numeric12
Text4
Categorical3
Unsupported1

Dataset

Description샘플 데이터
Author서울시
URLhttps://bigdata.seoul.go.kr/data/selectSampleData.do?sample_data_seq=31

Alerts

도로위계기능구분(ROA_CLS_SE) is highly imbalanced (60.9%)Imbalance
광역도로구분코드(WDR_RD_CD) is highly imbalanced (71.2%)Imbalance
기준년월일(STD_YMD) has 500 (100.0%) missing valuesMissing
도로코드(ROAD_CD) is highly skewed (γ1 = 21.79079786)Skewed
기준년월일(STD_YMD) is an unsupported type, check if it needs cleaning or further analysisUnsupported
도로폭(ROAD_BT) has 14 (2.8%) zerosZeros

Reproduction

Analysis started2023-12-10 15:00:42.986859
Analysis finished2023-12-10 15:01:24.888561
Duration41.9 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기초간격(BSI_INT)
Real number (ℝ)

Distinct20
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.676
Minimum4
Maximum25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-11T00:01:25.020687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile8
Q110
median10
Q311
95-th percentile20
Maximum25
Range21
Interquartile range (IQR)1

Descriptive statistics

Standard deviation4.0233567
Coefficient of variation (CV)0.34458348
Kurtosis0.96790455
Mean11.676
Median Absolute Deviation (MAD)0
Skewness1.42404
Sum5838
Variance16.187399
MonotonicityNot monotonic
2023-12-11T00:01:25.277062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
10 289
57.8%
20 56
 
11.2%
11 55
 
11.0%
9 25
 
5.0%
21 16
 
3.2%
5 14
 
2.8%
8 10
 
2.0%
12 8
 
1.6%
16 8
 
1.6%
17 4
 
0.8%
Other values (10) 15
 
3.0%
ValueCountFrequency (%)
4 1
 
0.2%
5 14
 
2.8%
6 1
 
0.2%
7 2
 
0.4%
8 10
 
2.0%
9 25
 
5.0%
10 289
57.8%
11 55
 
11.0%
12 8
 
1.6%
13 2
 
0.4%
ValueCountFrequency (%)
25 1
 
0.2%
24 1
 
0.2%
22 2
 
0.4%
21 16
 
3.2%
20 56
11.2%
19 1
 
0.2%
17 4
 
0.8%
16 8
 
1.6%
15 2
 
0.4%
14 2
 
0.4%
Distinct484
Distinct (%)96.8%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-11T00:01:25.866484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length27
Median length23
Mean length16.996
Min length6

Characters and Unicode

Total characters8498
Distinct characters52
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique471 ?
Unique (%)94.2%

Sample

1st rowInsubong-ro 57-gil
2nd rowGukhoe-daero
3rd rowMusumak 5ga-gil
4th rowDongmak-ro 12-gil
5th rowMapo-daero 14-gil
ValueCountFrequency (%)
2-gil 16
 
1.7%
12-gil 16
 
1.7%
3-gil 13
 
1.4%
deongneung-ro 11
 
1.2%
dobong-ro 11
 
1.2%
4-gil 10
 
1.1%
dongil-ro 9
 
1.0%
5-gil 9
 
1.0%
7-gil 8
 
0.9%
14-gil 8
 
0.9%
Other values (438) 828
88.2%
2023-12-11T00:01:26.904137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 900
 
10.6%
- 900
 
10.6%
g 869
 
10.2%
n 604
 
7.1%
i 554
 
6.5%
a 525
 
6.2%
l 519
 
6.1%
r 506
 
6.0%
439
 
5.2%
e 358
 
4.2%
Other values (42) 2324
27.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5846
68.8%
Dash Punctuation 900
 
10.6%
Decimal Number 810
 
9.5%
Uppercase Letter 501
 
5.9%
Space Separator 439
 
5.2%
Other Punctuation 2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 900
15.4%
g 869
14.9%
n 604
10.3%
i 554
9.5%
a 525
9.0%
l 519
8.9%
r 506
8.7%
e 358
 
6.1%
u 186
 
3.2%
h 129
 
2.2%
Other values (11) 696
11.9%
Uppercase Letter
ValueCountFrequency (%)
S 88
17.6%
D 74
14.8%
G 59
11.8%
B 37
7.4%
J 33
 
6.6%
H 32
 
6.4%
M 30
 
6.0%
Y 30
 
6.0%
N 24
 
4.8%
C 22
 
4.4%
Other values (8) 72
14.4%
Decimal Number
ValueCountFrequency (%)
1 169
20.9%
2 129
15.9%
3 87
10.7%
4 86
10.6%
5 76
9.4%
7 61
 
7.5%
6 58
 
7.2%
9 52
 
6.4%
0 48
 
5.9%
8 44
 
5.4%
Dash Punctuation
ValueCountFrequency (%)
- 900
100.0%
Space Separator
ValueCountFrequency (%)
439
100.0%
Other Punctuation
ValueCountFrequency (%)
. 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 6347
74.7%
Common 2151
 
25.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 900
14.2%
g 869
13.7%
n 604
9.5%
i 554
8.7%
a 525
8.3%
l 519
8.2%
r 506
8.0%
e 358
 
5.6%
u 186
 
2.9%
h 129
 
2.0%
Other values (29) 1197
18.9%
Common
ValueCountFrequency (%)
- 900
41.8%
439
20.4%
1 169
 
7.9%
2 129
 
6.0%
3 87
 
4.0%
4 86
 
4.0%
5 76
 
3.5%
7 61
 
2.8%
6 58
 
2.7%
9 52
 
2.4%
Other values (3) 94
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8498
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 900
 
10.6%
- 900
 
10.6%
g 869
 
10.2%
n 604
 
7.1%
i 554
 
6.5%
a 525
 
6.2%
l 519
 
6.1%
r 506
 
6.0%
439
 
5.2%
e 358
 
4.2%
Other values (42) 2324
27.3%

고시일자(NTFC_DE)
Real number (ℝ)

Distinct38
Distinct (%)7.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20100566
Minimum19991231
Maximum20140224
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-11T00:01:27.198859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19991231
5-th percentile20100422
Q120100531
median20100610
Q320100617
95-th percentile20101203
Maximum20140224
Range148993
Interquartile range (IQR)86

Descriptive statistics

Standard deviation6114.5125
Coefficient of variation (CV)0.00030419604
Kurtosis212.18361
Mean20100566
Median Absolute Deviation (MAD)7
Skewness-10.339313
Sum1.0050283 × 1010
Variance37387264
MonotonicityNot monotonic
2023-12-11T00:01:27.493361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
20100610 123
24.6%
20100603 52
10.4%
20100617 50
10.0%
20100527 38
 
7.6%
20100630 31
 
6.2%
20100422 29
 
5.8%
20100702 20
 
4.0%
20100528 19
 
3.8%
20100716 18
 
3.6%
20100531 17
 
3.4%
Other values (28) 103
20.6%
ValueCountFrequency (%)
19991231 1
 
0.2%
20090710 12
 
2.4%
20091028 2
 
0.4%
20091217 3
 
0.6%
20100315 1
 
0.2%
20100422 29
5.8%
20100520 13
 
2.6%
20100526 1
 
0.2%
20100527 38
7.6%
20100528 19
3.8%
ValueCountFrequency (%)
20140224 1
 
0.2%
20140217 1
 
0.2%
20120713 1
 
0.2%
20111031 1
 
0.2%
20110526 1
 
0.2%
20110407 2
0.4%
20110321 2
0.4%
20110317 4
0.8%
20110303 1
 
0.2%
20110302 3
0.6%
Distinct488
Distinct (%)97.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-11T00:01:28.155586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length27
Median length20
Mean length9.762
Min length5

Characters and Unicode

Total characters4881
Distinct characters176
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique477 ?
Unique (%)95.4%

Sample

1st row숭인동 1251
2nd row진관동 128-28
3rd row장위동 68-1019대
4th row수유동 279-326
5th row화곡동 1051-2대
ValueCountFrequency (%)
미아동 15
 
1.6%
15
 
1.6%
수유동 12
 
1.2%
신길동 11
 
1.1%
정릉동 9
 
0.9%
화곡동 8
 
0.8%
답십리동 8
 
0.8%
성내동 7
 
0.7%
번동 7
 
0.7%
연희동 7
 
0.7%
Other values (667) 865
89.7%
2023-12-11T00:01:29.139945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
495
 
10.1%
1 472
 
9.7%
463
 
9.5%
- 445
 
9.1%
2 279
 
5.7%
3 258
 
5.3%
4 247
 
5.1%
6 199
 
4.1%
5 187
 
3.8%
7 155
 
3.2%
Other values (166) 1681
34.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2225
45.6%
Other Letter 1743
35.7%
Space Separator 463
 
9.5%
Dash Punctuation 445
 
9.1%
Control 2
 
< 0.1%
Other Punctuation 1
 
< 0.1%
Open Punctuation 1
 
< 0.1%
Close Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
495
28.4%
137
 
7.9%
61
 
3.5%
59
 
3.4%
37
 
2.1%
26
 
1.5%
24
 
1.4%
23
 
1.3%
21
 
1.2%
21
 
1.2%
Other values (149) 839
48.1%
Decimal Number
ValueCountFrequency (%)
1 472
21.2%
2 279
12.5%
3 258
11.6%
4 247
11.1%
6 199
8.9%
5 187
 
8.4%
7 155
 
7.0%
0 145
 
6.5%
9 143
 
6.4%
8 140
 
6.3%
Control
ValueCountFrequency (%)
1
50.0%
1
50.0%
Space Separator
ValueCountFrequency (%)
463
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 445
100.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3138
64.3%
Hangul 1743
35.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
495
28.4%
137
 
7.9%
61
 
3.5%
59
 
3.4%
37
 
2.1%
26
 
1.5%
24
 
1.4%
23
 
1.3%
21
 
1.2%
21
 
1.2%
Other values (149) 839
48.1%
Common
ValueCountFrequency (%)
1 472
15.0%
463
14.8%
- 445
14.2%
2 279
8.9%
3 258
8.2%
4 247
7.9%
6 199
6.3%
5 187
 
6.0%
7 155
 
4.9%
0 145
 
4.6%
Other values (7) 288
9.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3138
64.3%
Hangul 1743
35.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
495
28.4%
137
 
7.9%
61
 
3.5%
59
 
3.4%
37
 
2.1%
26
 
1.5%
24
 
1.4%
23
 
1.3%
21
 
1.2%
21
 
1.2%
Other values (149) 839
48.1%
ASCII
ValueCountFrequency (%)
1 472
15.0%
463
14.8%
- 445
14.2%
2 279
8.9%
3 258
8.2%
4 247
7.9%
6 199
6.3%
5 187
 
6.0%
7 155
 
4.9%
0 145
 
4.6%
Other values (7) 288
9.2%
Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
1
373 
0
82 
2
45 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1 373
74.6%
0 82
 
16.4%
2 45
 
9.0%

Length

2023-12-11T00:01:29.484128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T00:01:29.696383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 373
74.6%
0 82
 
16.4%
2 45
 
9.0%
Distinct471
Distinct (%)94.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1659.138
Minimum14
Maximum5130
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-11T00:01:29.925424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile172.9
Q1736.5
median1576
Q32414.25
95-th percentile3594.45
Maximum5130
Range5116
Interquartile range (IQR)1677.75

Descriptive statistics

Standard deviation1082.6598
Coefficient of variation (CV)0.65254357
Kurtosis-0.34558955
Mean1659.138
Median Absolute Deviation (MAD)840
Skewness0.54341191
Sum829569
Variance1172152.3
MonotonicityNot monotonic
2023-12-11T00:01:30.239637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1253 3
 
0.6%
247 3
 
0.6%
1647 2
 
0.4%
723 2
 
0.4%
1566 2
 
0.4%
2619 2
 
0.4%
245 2
 
0.4%
1646 2
 
0.4%
150 2
 
0.4%
3125 2
 
0.4%
Other values (461) 478
95.6%
ValueCountFrequency (%)
14 1
0.2%
35 1
0.2%
42 1
0.2%
56 1
0.2%
63 1
0.2%
73 1
0.2%
75 1
0.2%
77 1
0.2%
87 1
0.2%
91 1
0.2%
ValueCountFrequency (%)
5130 1
0.2%
5041 1
0.2%
4715 1
0.2%
4627 1
0.2%
4565 1
0.2%
4450 1
0.2%
4261 1
0.2%
4207 1
0.2%
4119 1
0.2%
4049 1
0.2%
Distinct484
Distinct (%)96.8%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-11T00:01:30.769593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length14
Mean length9.634
Min length5

Characters and Unicode

Total characters4817
Distinct characters179
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

Unique470 ?
Unique (%)94.0%

Sample

1st row미아동 791-1051
2nd row중화동248-66
3rd row체부동 98-1
4th row영등포동4가 103
5th row면목동137-3
ValueCountFrequency (%)
미아동 13
 
1.3%
13
 
1.3%
신길동 13
 
1.3%
상계동 11
 
1.1%
수유동 10
 
1.0%
정릉동 9
 
0.9%
상도동 8
 
0.8%
화곡동 7
 
0.7%
홍은동 7
 
0.7%
이문동 7
 
0.7%
Other values (657) 869
89.9%
2023-12-11T00:01:31.896811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
499
 
10.4%
467
 
9.7%
1 449
 
9.3%
- 425
 
8.8%
2 310
 
6.4%
3 255
 
5.3%
4 205
 
4.3%
5 187
 
3.9%
7 175
 
3.6%
6 164
 
3.4%
Other values (169) 1681
34.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2171
45.1%
Other Letter 1752
36.4%
Space Separator 467
 
9.7%
Dash Punctuation 425
 
8.8%
Open Punctuation 1
 
< 0.1%
Close Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
499
28.5%
146
 
8.3%
57
 
3.3%
47
 
2.7%
33
 
1.9%
23
 
1.3%
23
 
1.3%
21
 
1.2%
20
 
1.1%
20
 
1.1%
Other values (155) 863
49.3%
Decimal Number
ValueCountFrequency (%)
1 449
20.7%
2 310
14.3%
3 255
11.7%
4 205
9.4%
5 187
8.6%
7 175
 
8.1%
6 164
 
7.6%
8 151
 
7.0%
9 139
 
6.4%
0 136
 
6.3%
Space Separator
ValueCountFrequency (%)
467
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 425
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3065
63.6%
Hangul 1752
36.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
499
28.5%
146
 
8.3%
57
 
3.3%
47
 
2.7%
33
 
1.9%
23
 
1.3%
23
 
1.3%
21
 
1.2%
20
 
1.1%
20
 
1.1%
Other values (155) 863
49.3%
Common
ValueCountFrequency (%)
467
15.2%
1 449
14.6%
- 425
13.9%
2 310
10.1%
3 255
8.3%
4 205
6.7%
5 187
6.1%
7 175
 
5.7%
6 164
 
5.4%
8 151
 
4.9%
Other values (4) 277
9.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3065
63.6%
Hangul 1752
36.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
499
28.5%
146
 
8.3%
57
 
3.3%
47
 
2.7%
33
 
1.9%
23
 
1.3%
23
 
1.3%
21
 
1.2%
20
 
1.1%
20
 
1.1%
Other values (155) 863
49.3%
ASCII
ValueCountFrequency (%)
467
15.2%
1 449
14.6%
- 425
13.9%
2 310
10.1%
3 255
8.3%
4 205
6.7%
5 187
6.1%
7 175
 
5.7%
6 164
 
5.4%
8 151
 
4.9%
Other values (4) 277
9.0%
Distinct475
Distinct (%)95.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-11T00:01:32.383281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length9
Mean length5.984
Min length3

Characters and Unicode

Total characters2992
Distinct characters231
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

Unique452 ?
Unique (%)90.4%

Sample

1st row신촌로24안길
2nd row사당로14다길
3rd row구로동로35가길
4th row긴고랑로9길
5th row토정로
ValueCountFrequency (%)
북악산로 3
 
0.6%
백범로 3
 
0.6%
종암로36길 2
 
0.4%
삼각산로 2
 
0.4%
연희로 2
 
0.4%
소월로40길 2
 
0.4%
갈현로25길 2
 
0.4%
원신2길 2
 
0.4%
양재대로110길 2
 
0.4%
시흥대로 2
 
0.4%
Other values (465) 478
95.6%
2023-12-11T00:01:33.754401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
461
 
15.4%
423
 
14.1%
1 157
 
5.2%
2 139
 
4.6%
4 75
 
2.5%
3 75
 
2.5%
5 72
 
2.4%
6 66
 
2.2%
62
 
2.1%
51
 
1.7%
Other values (221) 1411
47.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2232
74.6%
Decimal Number 760
 
25.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
461
20.7%
423
19.0%
62
 
2.8%
51
 
2.3%
43
 
1.9%
32
 
1.4%
30
 
1.3%
28
 
1.3%
28
 
1.3%
21
 
0.9%
Other values (211) 1053
47.2%
Decimal Number
ValueCountFrequency (%)
1 157
20.7%
2 139
18.3%
4 75
9.9%
3 75
9.9%
5 72
9.5%
6 66
8.7%
7 47
 
6.2%
0 46
 
6.1%
8 45
 
5.9%
9 38
 
5.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2232
74.6%
Common 760
 
25.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
461
20.7%
423
19.0%
62
 
2.8%
51
 
2.3%
43
 
1.9%
32
 
1.4%
30
 
1.3%
28
 
1.3%
28
 
1.3%
21
 
0.9%
Other values (211) 1053
47.2%
Common
ValueCountFrequency (%)
1 157
20.7%
2 139
18.3%
4 75
9.9%
3 75
9.9%
5 72
9.5%
6 66
8.7%
7 47
 
6.2%
0 46
 
6.1%
8 45
 
5.9%
9 38
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2232
74.6%
ASCII 760
 
25.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
461
20.7%
423
19.0%
62
 
2.8%
51
 
2.3%
43
 
1.9%
32
 
1.4%
30
 
1.3%
28
 
1.3%
28
 
1.3%
21
 
0.9%
Other values (211) 1053
47.2%
ASCII
ValueCountFrequency (%)
1 157
20.7%
2 139
18.3%
4 75
9.9%
3 75
9.9%
5 72
9.5%
6 66
8.7%
7 47
 
6.2%
0 46
 
6.1%
8 45
 
5.9%
9 38
 
5.0%

도로명코드(RN_CD)
Real number (ℝ)

Distinct477
Distinct (%)95.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3996726.6
Minimum2000003
Maximum4172433
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-11T00:01:34.111863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2000003
5-th percentile3005053.7
Q14112085.8
median4124376.5
Q34148454.8
95-th percentile4169066.1
Maximum4172433
Range2172430
Interquartile range (IQR)36369

Descriptive statistics

Standard deviation383074.52
Coefficient of variation (CV)0.095847067
Kurtosis6.789248
Mean3996726.6
Median Absolute Deviation (MAD)20692
Skewness-2.7101797
Sum1.9983633 × 109
Variance1.4674609 × 1011
MonotonicityNot monotonic
2023-12-11T00:01:34.421248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3005028 3
 
0.6%
4148055 3
 
0.6%
4112085 2
 
0.4%
4139085 2
 
0.4%
3005083 2
 
0.4%
4115097 2
 
0.4%
4163196 2
 
0.4%
4121724 2
 
0.4%
4112474 2
 
0.4%
4148228 2
 
0.4%
Other values (467) 478
95.6%
ValueCountFrequency (%)
2000003 1
0.2%
2005007 1
0.2%
2005010 1
0.2%
2113001 1
0.2%
3000004 1
0.2%
3000007 1
0.2%
3000008 1
0.2%
3000009 1
0.2%
3000019 1
0.2%
3000023 1
0.2%
ValueCountFrequency (%)
4172433 1
0.2%
4172431 1
0.2%
4172384 1
0.2%
4172357 1
0.2%
4172343 1
0.2%
4172335 1
0.2%
4172292 1
0.2%
4172259 1
0.2%
4172222 1
0.2%
4172168 1
0.2%

도로폭(ROAD_BT)
Real number (ℝ)

ZEROS 

Distinct29
Distinct (%)5.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.963
Minimum0
Maximum50
Zeros14
Zeros (%)2.8%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-11T00:01:34.673062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median4
Q36
95-th percentile11.05
Maximum50
Range50
Interquartile range (IQR)3

Descriptive statistics

Standard deviation4.951987
Coefficient of variation (CV)0.99778098
Kurtosis29.178116
Mean4.963
Median Absolute Deviation (MAD)2
Skewness4.6387872
Sum2481.5
Variance24.522175
MonotonicityNot monotonic
2023-12-11T00:01:35.086562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
3.0 107
21.4%
4.0 95
19.0%
2.0 71
14.2%
6.0 62
12.4%
5.0 42
 
8.4%
8.0 30
 
6.0%
1.0 22
 
4.4%
7.0 19
 
3.8%
0.0 14
 
2.8%
9.0 6
 
1.2%
Other values (19) 32
 
6.4%
ValueCountFrequency (%)
0.0 14
 
2.8%
1.0 22
 
4.4%
1.5 1
 
0.2%
2.0 71
14.2%
3.0 107
21.4%
4.0 95
19.0%
5.0 42
 
8.4%
6.0 62
12.4%
7.0 19
 
3.8%
8.0 30
 
6.0%
ValueCountFrequency (%)
50.0 1
0.2%
40.0 1
0.2%
39.0 1
0.2%
36.0 1
0.2%
30.0 2
0.4%
28.0 1
0.2%
25.0 1
0.2%
23.0 1
0.2%
21.0 1
0.2%
20.0 2
0.4%

도로길이(ROAD_LT)
Real number (ℝ)

Distinct232
Distinct (%)46.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean610.68396
Minimum9
Maximum57569
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-11T00:01:35.528185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile15
Q131
median62.5
Q3158.75
95-th percentile2902
Maximum57569
Range57560
Interquartile range (IQR)127.75

Descriptive statistics

Standard deviation3390.2595
Coefficient of variation (CV)5.5515778
Kurtosis178.31274
Mean610.68396
Median Absolute Deviation (MAD)40.5
Skewness12.134878
Sum305341.98
Variance11493860
MonotonicityNot monotonic
2023-12-11T00:01:35.898255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24.0 11
 
2.2%
26.0 10
 
2.0%
23.0 10
 
2.0%
12.0 8
 
1.6%
34.0 8
 
1.6%
16.0 8
 
1.6%
21.0 8
 
1.6%
38.0 7
 
1.4%
15.0 7
 
1.4%
18.0 7
 
1.4%
Other values (222) 416
83.2%
ValueCountFrequency (%)
9.0 2
 
0.4%
10.0 3
 
0.6%
10.594 1
 
0.2%
11.0 1
 
0.2%
12.0 8
1.6%
13.0 6
1.2%
14.0 3
 
0.6%
15.0 7
1.4%
16.0 8
1.6%
17.0 6
1.2%
ValueCountFrequency (%)
57569.0 1
0.2%
28595.0 2
0.4%
11840.0 1
0.2%
11419.0 1
0.2%
10433.0 1
0.2%
9999.0 1
0.2%
9100.0 1
0.2%
7600.0 1
0.2%
6846.0 1
0.2%
5898.0 2
0.4%
Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
4
433 
3
62 
2
 
5

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row4
4th row4
5th row4

Common Values

ValueCountFrequency (%)
4 433
86.6%
3 62
 
12.4%
2 5
 
1.0%

Length

2023-12-11T00:01:36.170966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T00:01:36.392832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
4 433
86.6%
3 62
 
12.4%
2 5
 
1.0%

시군구코드(SIG_CD)
Real number (ℝ)

Distinct25
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11387.65
Minimum11110
Maximum11740
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-11T00:01:36.595449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11110
5-th percentile11110
Q111230
median11350
Q311545
95-th percentile11710
Maximum11740
Range630
Interquartile range (IQR)315

Descriptive statistics

Standard deviation181.0029
Coefficient of variation (CV)0.015894667
Kurtosis-1.0543656
Mean11387.65
Median Absolute Deviation (MAD)150
Skewness0.28987582
Sum5693825
Variance32762.052
MonotonicityNot monotonic
2023-12-11T00:01:36.934855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
11260 30
 
6.0%
11305 30
 
6.0%
11110 29
 
5.8%
11290 26
 
5.2%
11215 26
 
5.2%
11440 25
 
5.0%
11230 24
 
4.8%
11590 23
 
4.6%
11410 22
 
4.4%
11380 21
 
4.2%
Other values (15) 244
48.8%
ValueCountFrequency (%)
11110 29
5.8%
11140 21
4.2%
11170 15
3.0%
11200 17
3.4%
11215 26
5.2%
11230 24
4.8%
11260 30
6.0%
11290 26
5.2%
11305 30
6.0%
11320 20
4.0%
ValueCountFrequency (%)
11740 13
2.6%
11710 17
3.4%
11680 16
3.2%
11650 12
2.4%
11620 18
3.6%
11590 23
4.6%
11560 16
3.2%
11545 13
2.6%
11530 21
4.2%
11500 20
4.0%
Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
3
460 
2
 
33
1
 
7

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3 460
92.0%
2 33
 
6.6%
1 7
 
1.4%

Length

2023-12-11T00:01:37.422635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T00:01:37.638415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3 460
92.0%
2 33
 
6.6%
1 7
 
1.4%

MBR_X최소좌표(XMIN)
Real number (ℝ)

Distinct490
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean311171.91
Minimum295271
Maximum325661
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-11T00:01:38.008828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum295271
5-th percentile301283.65
Q1305796.25
median312436
Q3315625.25
95-th percentile320781.3
Maximum325661
Range30390
Interquartile range (IQR)9829

Descriptive statistics

Standard deviation6563.0084
Coefficient of variation (CV)0.021091262
Kurtosis-0.6504209
Mean311171.91
Median Absolute Deviation (MAD)5339
Skewness-0.14926589
Sum1.5558596 × 108
Variance43073079
MonotonicityNot monotonic
2023-12-11T00:01:38.314560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
303058 2
 
0.4%
313516 2
 
0.4%
312698 2
 
0.4%
305584 2
 
0.4%
314912 2
 
0.4%
305485 2
 
0.4%
309012 2
 
0.4%
313464 2
 
0.4%
307560 2
 
0.4%
308387 2
 
0.4%
Other values (480) 480
96.0%
ValueCountFrequency (%)
295271 1
0.2%
295327 1
0.2%
295368 1
0.2%
295762 1
0.2%
296183 1
0.2%
296365 1
0.2%
296450 1
0.2%
296576 1
0.2%
296863 1
0.2%
297102 1
0.2%
ValueCountFrequency (%)
325661 1
0.2%
325574 1
0.2%
325488 1
0.2%
325182 1
0.2%
324312 1
0.2%
324066 1
0.2%
324007 1
0.2%
323736 1
0.2%
323731 1
0.2%
323685 1
0.2%

MBR_Y최소좌표(YMIN)
Real number (ℝ)

Distinct494
Distinct (%)98.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean550993.8
Minimum537997
Maximum565392
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-11T00:01:38.712471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum537997
5-th percentile541838.3
Q1546068
median551147
Q3555016.75
95-th percentile560255.1
Maximum565392
Range27395
Interquartile range (IQR)8948.75

Descriptive statistics

Standard deviation5772.8648
Coefficient of variation (CV)0.010477187
Kurtosis-0.64137476
Mean550993.8
Median Absolute Deviation (MAD)4110
Skewness0.012913539
Sum2.754969 × 108
Variance33325968
MonotonicityNot monotonic
2023-12-11T00:01:39.060175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
550611 2
 
0.4%
554603 2
 
0.4%
550804 2
 
0.4%
544681 2
 
0.4%
555978 2
 
0.4%
555064 2
 
0.4%
542447 1
 
0.2%
542792 1
 
0.2%
552822 1
 
0.2%
548247 1
 
0.2%
Other values (484) 484
96.8%
ValueCountFrequency (%)
537997 1
0.2%
538934 1
0.2%
539086 1
0.2%
539124 1
0.2%
539126 1
0.2%
539155 1
0.2%
539271 1
0.2%
540006 1
0.2%
540052 1
0.2%
540062 1
0.2%
ValueCountFrequency (%)
565392 1
0.2%
564976 1
0.2%
564475 1
0.2%
564385 1
0.2%
563861 1
0.2%
563340 1
0.2%
563173 1
0.2%
563029 1
0.2%
562708 1
0.2%
562599 1
0.2%

MBR_X최대좌표(XMAX)
Real number (ℝ)

Distinct495
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean311096.23
Minimum294982
Maximum336337
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-11T00:01:39.420479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum294982
5-th percentile299010.8
Q1305039.75
median311996
Q3316718
95-th percentile322075.45
Maximum336337
Range41355
Interquartile range (IQR)11678.25

Descriptive statistics

Standard deviation7235.4072
Coefficient of variation (CV)0.023257778
Kurtosis-0.6678526
Mean311096.23
Median Absolute Deviation (MAD)6043
Skewness-0.035930926
Sum1.5554812 × 108
Variance52351117
MonotonicityNot monotonic
2023-12-11T00:01:39.766087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
315996 2
 
0.4%
298335 2
 
0.4%
318749 2
 
0.4%
305285 2
 
0.4%
313770 2
 
0.4%
304085 1
 
0.2%
313325 1
 
0.2%
307272 1
 
0.2%
303358 1
 
0.2%
309052 1
 
0.2%
Other values (485) 485
97.0%
ValueCountFrequency (%)
294982 1
0.2%
295109 1
0.2%
295201 1
0.2%
295352 1
0.2%
295374 1
0.2%
295387 1
0.2%
295516 1
0.2%
295693 1
0.2%
295801 1
0.2%
296648 1
0.2%
ValueCountFrequency (%)
336337 1
0.2%
325667 1
0.2%
325436 1
0.2%
325392 1
0.2%
325202 1
0.2%
325013 1
0.2%
324940 1
0.2%
324924 1
0.2%
324610 1
0.2%
324534 1
0.2%

MBR_Y최대좌표(YMAX)
Real number (ℝ)

Distinct493
Distinct (%)98.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean551182.29
Minimum538517
Maximum565822
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-11T00:01:40.047213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum538517
5-th percentile542067.35
Q1547508
median551345
Q3554968.75
95-th percentile560007.05
Maximum565822
Range27305
Interquartile range (IQR)7460.75

Descriptive statistics

Standard deviation5542.5553
Coefficient of variation (CV)0.010055757
Kurtosis-0.44460293
Mean551182.29
Median Absolute Deviation (MAD)3693.5
Skewness0.031282224
Sum2.7559115 × 108
Variance30719919
MonotonicityNot monotonic
2023-12-11T00:01:40.412724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
549587 2
 
0.4%
554959 2
 
0.4%
547684 2
 
0.4%
551278 2
 
0.4%
548839 2
 
0.4%
550749 2
 
0.4%
552894 2
 
0.4%
549034 1
 
0.2%
547862 1
 
0.2%
551813 1
 
0.2%
Other values (483) 483
96.6%
ValueCountFrequency (%)
538517 1
0.2%
539282 1
0.2%
539514 1
0.2%
539630 1
0.2%
539875 1
0.2%
539971 1
0.2%
540830 1
0.2%
540902 1
0.2%
541010 1
0.2%
541078 1
0.2%
ValueCountFrequency (%)
565822 1
0.2%
564790 1
0.2%
564516 1
0.2%
564068 1
0.2%
564067 1
0.2%
563981 1
0.2%
563829 1
0.2%
563781 1
0.2%
563679 1
0.2%
563550 1
0.2%

기준년월일(STD_YMD)
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing500
Missing (%)100.0%
Memory size4.5 KiB

도로코드(ROAD_CD)
Real number (ℝ)

SKEWED 

Distinct490
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1460727 × 1011
Minimum1.11103 × 1011
Maximum4.14503 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-11T00:01:40.744594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.11103 × 1011
5-th percentile1.114041 × 1011
Q11.1260412 × 1011
median1.1380311 × 1011
Q31.1560415 × 1011
95-th percentile1.1710312 × 1011
Maximum4.14503 × 1011
Range3.034 × 1011
Interquartile range (IQR)3.000036 × 109

Descriptive statistics

Standard deviation1.3554393 × 1010
Coefficient of variation (CV)0.11826818
Kurtosis483.03412
Mean1.1460727 × 1011
Median Absolute Deviation (MAD)1.5010372 × 109
Skewness21.790798
Sum5.7303633 × 1013
Variance1.8372156 × 1020
MonotonicityNot monotonic
2023-12-11T00:01:41.088134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
113054124517 2
 
0.4%
112003103001 2
 
0.4%
113203109008 2
 
0.4%
116203005083 2
 
0.4%
115453000027 2
 
0.4%
114103005057 2
 
0.4%
111104100471 2
 
0.4%
115003115012 2
 
0.4%
113203109001 2
 
0.4%
113204127052 2
 
0.4%
Other values (480) 480
96.0%
ValueCountFrequency (%)
111103000008 1
0.2%
111103005007 1
0.2%
111103100023 1
0.2%
111104100028 1
0.2%
111104100093 1
0.2%
111104100112 1
0.2%
111104100224 1
0.2%
111104100272 1
0.2%
111104100302 1
0.2%
111104100346 1
0.2%
ValueCountFrequency (%)
414503000034 1
0.2%
117404172437 1
0.2%
117404172379 1
0.2%
117404172307 1
0.2%
117404172275 1
0.2%
117404172164 1
0.2%
117404172163 1
0.2%
117404172123 1
0.2%
117404172026 1
0.2%
117404172002 1
0.2%

Interactions

2023-12-11T00:01:21.277531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:49.687035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:52.334730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:55.392007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:58.340160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:01:01.906698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:01:04.617793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:01:07.540907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:01:10.258164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:01:13.261925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:01:16.265712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:01:18.865577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:01:21.477726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:49.980266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:52.571292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:55.601535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:58.565385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:01:02.122576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:01:04.820432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:01:07.853701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:01:10.484449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:01:13.457285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:01:16.518923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:01:19.037271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:01:21.701240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:50.246146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:52.815156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:55.947932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:58.878904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:01:02.392871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:01:05.044782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:01:08.085238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:01:10.770326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:01:13.659001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:01:16.782543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:01:19.253922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:01:21.918244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:50.457141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:53.084709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:56.272249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:59.173775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:01:02.611113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:01:05.373183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:01:08.330461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:01:11.070662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:01:13.846592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:01:16.970983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:01:19.453304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:01:22.123226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:50.642267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:53.391893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:56.517688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:59.488100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:01:02.797913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:01:05.602065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:01:08.621573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:01:11.333813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:01:14.059983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:01:17.219145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:01:19.665772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:01:22.398710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:50.830307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:53.663690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:56.725472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:59.693081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:01:02.996703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:01:05.777355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:01:08.805926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:01:11.614444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:01:14.242578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:01:17.406067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:01:19.847974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:01:22.585315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:51.030050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:53.890398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:56.920448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:59.917181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:01:03.199860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:01:06.058048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:01:09.023057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:01:11.872792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:01:14.446684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:01:17.646306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:01:20.047477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:01:22.834368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:51.226579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:54.131809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:57.141566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:01:00.109233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:01:03.397551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:01:06.386840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:01:09.254991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:01:12.197421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:01:14.662085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:01:17.870284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:01:20.250887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:01:23.056600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:51.453861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:54.360142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:57.362606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:01:00.368388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:01:03.640817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:01:06.606242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:01:09.486828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:01:12.438806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:01:14.858591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:01:18.121242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:01:20.461506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:01:23.246835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:51.711189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:54.602416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:57.595745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:01:00.624156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:01:03.892741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:01:06.921987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:01:09.675994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:01:12.651393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:01:15.104223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:01:18.347628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:01:20.681169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:01:23.443522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:51.906330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:54.901936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:57.801321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:01:00.889383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:01:04.169696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:01:07.116696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:01:09.859768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:01:12.823443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:01:15.736252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:01:18.504481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:01:20.854450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:01:23.692270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:52.106639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:55.115465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:58.010728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:01:01.232124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:01:04.392865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:01:07.316055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:01:10.036763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:01:13.023366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:01:15.988177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:01:18.680321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:01:21.062151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T00:01:41.347492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기초간격(BSI_INT)고시일자(NTFC_DE)도로구간종속구분(RDS_DPN_SE)도로구간일련번호(RDS_MAN_NO)도로명코드(RN_CD)도로폭(ROAD_BT)도로길이(ROAD_LT)도로위계기능구분(ROA_CLS_SE)시군구코드(SIG_CD)광역도로구분코드(WDR_RD_CD)MBR_X최소좌표(XMIN)MBR_Y최소좌표(YMIN)MBR_X최대좌표(XMAX)MBR_Y최대좌표(YMAX)도로코드(ROAD_CD)
기초간격(BSI_INT)1.0000.0000.1390.0000.0000.2210.0000.0000.1390.0000.0000.0750.1240.0000.000
고시일자(NTFC_DE)0.0001.0000.0000.0000.0000.0000.0000.0000.0940.0000.0000.0000.0000.0000.781
도로구간종속구분(RDS_DPN_SE)0.1390.0001.0000.1420.0390.1290.0000.0000.1310.0000.0000.0000.0000.1640.000
도로구간일련번호(RDS_MAN_NO)0.0000.0000.1421.0000.0000.0000.3550.2370.1810.0000.1970.1470.0570.0000.026
도로명코드(RN_CD)0.0000.0000.0390.0001.0000.0000.0000.1350.0000.0000.0000.0000.1060.1340.000
도로폭(ROAD_BT)0.2210.0000.1290.0000.0001.0000.0860.0000.1040.0000.0000.0000.0000.0000.000
도로길이(ROAD_LT)0.0000.0000.0000.3550.0000.0861.0000.0000.0000.0000.3170.0000.0000.2180.000
도로위계기능구분(ROA_CLS_SE)0.0000.0000.0000.2370.1350.0000.0001.0000.2520.0000.2040.0000.1670.0000.000
시군구코드(SIG_CD)0.1390.0940.1310.1810.0000.1040.0000.2521.0000.0290.0000.0000.1260.1790.030
광역도로구분코드(WDR_RD_CD)0.0000.0000.0000.0000.0000.0000.0000.0000.0291.0000.0000.1020.0000.0710.000
MBR_X최소좌표(XMIN)0.0000.0000.0000.1970.0000.0000.3170.2040.0000.0001.0000.0000.0000.0740.000
MBR_Y최소좌표(YMIN)0.0750.0000.0000.1470.0000.0000.0000.0000.0000.1020.0001.0000.0000.0000.000
MBR_X최대좌표(XMAX)0.1240.0000.0000.0570.1060.0000.0000.1670.1260.0000.0000.0001.0000.0700.000
MBR_Y최대좌표(YMAX)0.0000.0000.1640.0000.1340.0000.2180.0000.1790.0710.0740.0000.0701.0000.000
도로코드(ROAD_CD)0.0000.7810.0000.0260.0000.0000.0000.0000.0300.0000.0000.0000.0000.0001.000
2023-12-11T00:01:41.781050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
광역도로구분코드(WDR_RD_CD)도로구간종속구분(RDS_DPN_SE)도로위계기능구분(ROA_CLS_SE)
광역도로구분코드(WDR_RD_CD)1.0000.0000.000
도로구간종속구분(RDS_DPN_SE)0.0001.0000.000
도로위계기능구분(ROA_CLS_SE)0.0000.0001.000
2023-12-11T00:01:42.054220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기초간격(BSI_INT)고시일자(NTFC_DE)도로구간일련번호(RDS_MAN_NO)도로명코드(RN_CD)도로폭(ROAD_BT)도로길이(ROAD_LT)시군구코드(SIG_CD)MBR_X최소좌표(XMIN)MBR_Y최소좌표(YMIN)MBR_X최대좌표(XMAX)MBR_Y최대좌표(YMAX)도로코드(ROAD_CD)도로구간종속구분(RDS_DPN_SE)도로위계기능구분(ROA_CLS_SE)광역도로구분코드(WDR_RD_CD)
기초간격(BSI_INT)1.000-0.0490.0670.053-0.1730.0160.063-0.0270.058-0.0000.013-0.0430.0820.0000.000
고시일자(NTFC_DE)-0.0491.0000.0320.006-0.0860.0040.073-0.007-0.049-0.040-0.0800.0230.0000.0000.000
도로구간일련번호(RDS_MAN_NO)0.0670.0321.0000.0060.0350.0140.019-0.024-0.054-0.0790.0060.0110.0840.1440.000
도로명코드(RN_CD)0.0530.0060.0061.000-0.021-0.023-0.044-0.0830.0460.0140.083-0.0630.0320.1160.000
도로폭(ROAD_BT)-0.173-0.0860.035-0.0211.000-0.0130.0120.063-0.0170.030-0.018-0.0230.0580.0000.000
도로길이(ROAD_LT)0.0160.0040.014-0.023-0.0131.000-0.0250.0120.059-0.020-0.033-0.0140.0000.0000.000
시군구코드(SIG_CD)0.0630.0730.019-0.0440.012-0.0251.0000.0050.0530.007-0.0640.0120.0700.1600.038
MBR_X최소좌표(XMIN)-0.027-0.007-0.024-0.0830.0630.0120.0051.0000.024-0.027-0.0250.0370.0000.1200.000
MBR_Y최소좌표(YMIN)0.058-0.049-0.0540.046-0.0170.0590.0530.0241.000-0.0700.0240.0150.0000.0000.055
MBR_X최대좌표(XMAX)-0.000-0.040-0.0790.0140.030-0.0200.007-0.027-0.0701.0000.0370.0210.0000.0740.000
MBR_Y최대좌표(YMAX)0.013-0.0800.0060.083-0.018-0.033-0.064-0.0250.0240.0371.0000.0500.0930.0000.035
도로코드(ROAD_CD)-0.0430.0230.011-0.063-0.023-0.0140.0120.0370.0150.0210.0501.0000.0000.0000.000
도로구간종속구분(RDS_DPN_SE)0.0820.0000.0840.0320.0580.0000.0700.0000.0000.0000.0930.0001.0000.0000.000
도로위계기능구분(ROA_CLS_SE)0.0000.0000.1440.1160.0000.0000.1600.1200.0000.0740.0000.0000.0001.0000.000
광역도로구분코드(WDR_RD_CD)0.0000.0000.0000.0000.0000.0000.0380.0000.0550.0000.0350.0000.0000.0001.000

Missing values

2023-12-11T00:01:24.068250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T00:01:24.655530image/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

기초간격(BSI_INT)영문도로명(ENG_RN)고시일자(NTFC_DE)종점(REP_CN)도로구간종속구분(RDS_DPN_SE)도로구간일련번호(RDS_MAN_NO)기점(RBP_CN)도로명(RN)도로명코드(RN_CD)도로폭(ROAD_BT)도로길이(ROAD_LT)도로위계기능구분(ROA_CLS_SE)시군구코드(SIG_CD)광역도로구분코드(WDR_RD_CD)MBR_X최소좌표(XMIN)MBR_Y최소좌표(YMIN)MBR_X최대좌표(XMAX)MBR_Y최대좌표(YMAX)기준년월일(STD_YMD)도로코드(ROAD_CD)
08Insubong-ro 57-gil20100610숭인동 125122126미아동 791-1051신촌로24안길31120155.044.04113503318800552408296648554184<NA>114404139537
120Gukhoe-daero20100610진관동 128-282927중화동248-66사당로14다길30050292.041.04112303305826542937305230541377<NA>116203005083
210Musumak 5ga-gil20100603장위동 68-1019대1247체부동 98-1구로동로35가길41003124.012.04112303312974546322316751549601<NA>115004145369
310Dongmak-ro 12-gil20100610수유동 279-326087영등포동4가 103긴고랑로9길41246302.012.04113803307965554022310425539971<NA>114404139353
410Mapo-daero 14-gil20111031화곡동 1051-2대1491면목동137-3토정로41273233.033.04114403296863553895304172547041<NA>116204160666
510Cheongpa-ro 43da-gil20100603소격동 5-111945청파동1가 89-65대효령로31길41240674.0122.04111703317536554603306999548272<NA>116204160461
610Bongujae-ro 64-gil20100610거여동 130-20대1739공릉동 240-38조정대로31번길41217793.0479.04116203308878554603323517541151<NA>115454151198
711Galhyeon-ro 11ga-gil20100610자양동 249-512371개화동 산70-1임동광로46길41574953.0124.04113803313227548824314912552150<NA>113504130341
811Supyo-ro 28-gil20100610신사동 270-4712569대림동 972-7디지털로64길41182793.068.04115303318339559350318067556566<NA>114103005057
911Yonsei-ro20100531길음동 1171대1884행당동3-3역말로9가길30050011.020.04111103301583545400301523553024<NA>112904121135
기초간격(BSI_INT)영문도로명(ENG_RN)고시일자(NTFC_DE)종점(REP_CN)도로구간종속구분(RDS_DPN_SE)도로구간일련번호(RDS_MAN_NO)기점(RBP_CN)도로명(RN)도로명코드(RN_CD)도로폭(ROAD_BT)도로길이(ROAD_LT)도로위계기능구분(ROA_CLS_SE)시군구코드(SIG_CD)광역도로구분코드(WDR_RD_CD)MBR_X최소좌표(XMIN)MBR_Y최소좌표(YMIN)MBR_X최대좌표(XMAX)MBR_Y최대좌표(YMAX)기준년월일(STD_YMD)도로코드(ROAD_CD)
49010Tongil-ro 12ga-gil20100610광희동1가 65-3대01314연건동 1-3도림로141다길41153121.0126.04116503305837550185297348551097<NA>111403005006
49111Bongujae-ro 22-gil20100531우면동 2111672암사동 595잡거북골로12길416040319.064.04117403305237551364305345563001<NA>112904121187
49212Seonyu-ro 40-gil20100528역삼동 814-611639미아동 55-72한천로36길41330963.048.04113503305776555064319972550227<NA>111704106554
49310Sangdo-ro 15sa-gil20100422구로동 26-2대1881독산동 198-34문성로32길41573846.059.04117403297811553450314246561164<NA>112304115408
4949Deongneung-ro20140224쌍문동 443-14대1976면목동172-85방화동로12가길31040088.012.04115903313076551703303153554938<NA>114404139479
49510Gwangpyeong-ro 19-gil20100630홍제동 6-6911023하왕십리동984-183우사단로2가길41451955.017.04112903313014540052308501543557<NA>112604118280
49611Saemal-ro 17-gil20100527신길동 146-441562홍은동 356-31한강대로80길41480363.016.04111403303058543269310967554870<NA>112904121237
49710Deongneung-ro 83-gil20100531홍익동35211262동소문동5가 6대신길로60마길41482288.0382.03117403316994549797323413554944<NA>115304148113
49810Dangsan-ro 54-gil20100610대림동 991-191200개봉동 132-6대국사봉길41244046.056.04115303315510554305299370550777<NA>117403124002
49910Deongneung-ro20100610화곡동 29-57대1622천호동 537대종로43길41484522.0482.04111703313817544834315281550889<NA>113504130180