Overview

Dataset statistics

Number of variables17
Number of observations10000
Missing cells30975
Missing cells (%)18.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.5 MiB
Average record size in memory155.0 B

Variable types

Numeric8
Text4
Categorical5

Dataset

Description시군구_코드,도로_일련번호,법정동_일련번호,도로_명,영문_도로_명,시도_명,시군구_명,법정동_구분,법정동_코드,법정동_명,상위_도로_일련번호,상위_도로_명,새주소_사용여부,변경_이력_사유_코드,변경_이력_정보,법정동_번호,도로_코드
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-15411/S/1/datasetView.do

Alerts

시도_명 has constant value ""Constant
새주소_사용여부 is highly overall correlated with 변경_이력_정보 and 1 other fieldsHigh correlation
변경_이력_사유_코드 is highly overall correlated with 법정동_일련번호 and 3 other fieldsHigh correlation
시군구_코드 is highly overall correlated with 도로_일련번호 and 3 other fieldsHigh correlation
도로_일련번호 is highly overall correlated with 시군구_코드 and 2 other fieldsHigh correlation
법정동_일련번호 is highly overall correlated with 법정동_코드 and 1 other fieldsHigh correlation
법정동_코드 is highly overall correlated with 법정동_일련번호 and 2 other fieldsHigh correlation
변경_이력_정보 is highly overall correlated with 시군구_코드 and 6 other fieldsHigh correlation
법정동_번호 is highly overall correlated with 법정동_코드 and 2 other fieldsHigh correlation
도로_코드 is highly overall correlated with 시군구_코드 and 3 other fieldsHigh correlation
시군구_명 is highly overall correlated with 시군구_코드 and 3 other fieldsHigh correlation
법정동_구분 is highly overall correlated with 법정동_코드 and 1 other fieldsHigh correlation
새주소_사용여부 is highly imbalanced (78.8%)Imbalance
변경_이력_사유_코드 is highly imbalanced (89.0%)Imbalance
법정동_명 has 4668 (46.7%) missing valuesMissing
상위_도로_일련번호 has 1902 (19.0%) missing valuesMissing
상위_도로_명 has 9791 (97.9%) missing valuesMissing
변경_이력_정보 has 9945 (99.5%) missing valuesMissing
법정동_번호 has 4668 (46.7%) missing valuesMissing
법정동_일련번호 has 4668 (46.7%) zerosZeros
법정동_코드 has 4668 (46.7%) zerosZeros

Reproduction

Analysis started2024-05-18 06:02:00.302684
Analysis finished2024-05-18 06:02:21.734984
Duration21.43 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군구_코드
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11419.033
Minimum11110
Maximum11740
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T15:02:21.916705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11110
5-th percentile11140
Q111230
median11440
Q311590
95-th percentile11710
Maximum11740
Range630
Interquartile range (IQR)360

Descriptive statistics

Standard deviation191.32253
Coefficient of variation (CV)0.016754705
Kurtosis-1.3240632
Mean11419.033
Median Absolute Deviation (MAD)180
Skewness0.015185822
Sum1.1419033 × 108
Variance36604.31
MonotonicityNot monotonic
2024-05-18T15:02:22.284175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
11560 605
 
6.0%
11290 601
 
6.0%
11680 553
 
5.5%
11620 550
 
5.5%
11230 490
 
4.9%
11110 472
 
4.7%
11440 455
 
4.5%
11170 449
 
4.5%
11590 446
 
4.5%
11650 426
 
4.3%
Other values (15) 4953
49.5%
ValueCountFrequency (%)
11110 472
4.7%
11140 372
3.7%
11170 449
4.5%
11200 418
4.2%
11215 347
3.5%
11230 490
4.9%
11260 377
3.8%
11290 601
6.0%
11305 405
4.0%
11320 199
 
2.0%
ValueCountFrequency (%)
11740 323
3.2%
11710 365
3.6%
11680 553
5.5%
11650 426
4.3%
11620 550
5.5%
11590 446
4.5%
11560 605
6.0%
11545 262
2.6%
11530 330
3.3%
11500 395
4.0%

도로_일련번호
Real number (ℝ)

HIGH CORRELATION 

Distinct7977
Distinct (%)79.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4052014.1
Minimum1000001
Maximum4861546
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T15:02:22.613776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1000001
5-th percentile3111001
Q14112359.8
median4133297.5
Q34157081.2
95-th percentile4169302
Maximum4861546
Range3861545
Interquartile range (IQR)44721.5

Descriptive statistics

Standard deviation340514.67
Coefficient of variation (CV)0.084035905
Kurtosis15.724546
Mean4052014.1
Median Absolute Deviation (MAD)21282
Skewness-3.6057387
Sum4.0520141 × 1010
Variance1.1595024 × 1011
MonotonicityNot monotonic
2024-05-18T15:02:22.942713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3000008 12
 
0.1%
2000003 12
 
0.1%
3100010 10
 
0.1%
3101011 8
 
0.1%
3005016 8
 
0.1%
3100021 6
 
0.1%
2000006 6
 
0.1%
2100001 6
 
0.1%
2005008 6
 
0.1%
3101009 6
 
0.1%
Other values (7967) 9920
99.2%
ValueCountFrequency (%)
1000001 1
 
< 0.1%
1000027 1
 
< 0.1%
1000028 1
 
< 0.1%
1000033 1
 
< 0.1%
1000105 1
 
< 0.1%
2000001 1
 
< 0.1%
2000003 12
0.1%
2000004 1
 
< 0.1%
2000006 6
0.1%
2000008 6
0.1%
ValueCountFrequency (%)
4861546 1
< 0.1%
4861545 1
< 0.1%
4861342 1
< 0.1%
4860970 2
< 0.1%
4860969 1
< 0.1%
4860966 1
< 0.1%
4860176 1
< 0.1%
4860174 1
< 0.1%
4860173 1
< 0.1%
4859725 1
< 0.1%

법정동_일련번호
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct20
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6902
Minimum0
Maximum27
Zeros4668
Zeros (%)46.7%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T15:02:23.335083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile2
Maximum27
Range27
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.0705786
Coefficient of variation (CV)1.5511136
Kurtosis123.72554
Mean0.6902
Median Absolute Deviation (MAD)1
Skewness7.6379945
Sum6902
Variance1.1461386
MonotonicityNot monotonic
2024-05-18T15:02:23.660859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0 4668
46.7%
1 4591
45.9%
2 454
 
4.5%
3 121
 
1.2%
4 61
 
0.6%
5 33
 
0.3%
6 28
 
0.3%
7 11
 
0.1%
8 10
 
0.1%
9 6
 
0.1%
Other values (10) 17
 
0.2%
ValueCountFrequency (%)
0 4668
46.7%
1 4591
45.9%
2 454
 
4.5%
3 121
 
1.2%
4 61
 
0.6%
5 33
 
0.3%
6 28
 
0.3%
7 11
 
0.1%
8 10
 
0.1%
9 6
 
0.1%
ValueCountFrequency (%)
27 1
 
< 0.1%
26 1
 
< 0.1%
24 1
 
< 0.1%
19 1
 
< 0.1%
15 2
< 0.1%
14 1
 
< 0.1%
13 1
 
< 0.1%
12 4
< 0.1%
11 1
 
< 0.1%
10 4
< 0.1%
Distinct7945
Distinct (%)79.5%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-18T15:02:24.344158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length9
Mean length6.2289
Min length2

Characters and Unicode

Total characters62289
Distinct characters315
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6160 ?
Unique (%)61.6%

Sample

1st row숭인로8가길
2nd row고산자로29길
3rd row도봉로180길
4th row답십리로12길
5th row돌곶이로34길
ValueCountFrequency (%)
남부순환로 12
 
0.1%
통일로 12
 
0.1%
율곡로 10
 
0.1%
퇴계로 8
 
0.1%
백범로 8
 
0.1%
양재대로 6
 
0.1%
충무로 6
 
0.1%
삼일대로 6
 
0.1%
청계천로 6
 
0.1%
천호대로 6
 
0.1%
Other values (7935) 9920
99.2%
2024-05-18T15:02:25.480808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9306
 
14.9%
9116
 
14.6%
1 3568
 
5.7%
2 2515
 
4.0%
3 2012
 
3.2%
4 1628
 
2.6%
5 1492
 
2.4%
6 1267
 
2.0%
1217
 
2.0%
7 1165
 
1.9%
Other values (305) 29003
46.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 45701
73.4%
Decimal Number 16579
 
26.6%
Other Punctuation 9
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
9306
20.4%
9116
19.9%
1217
 
2.7%
1114
 
2.4%
761
 
1.7%
736
 
1.6%
607
 
1.3%
578
 
1.3%
491
 
1.1%
425
 
0.9%
Other values (294) 21350
46.7%
Decimal Number
ValueCountFrequency (%)
1 3568
21.5%
2 2515
15.2%
3 2012
12.1%
4 1628
9.8%
5 1492
9.0%
6 1267
 
7.6%
7 1165
 
7.0%
8 1041
 
6.3%
9 974
 
5.9%
0 917
 
5.5%
Other Punctuation
ValueCountFrequency (%)
. 9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 45701
73.4%
Common 16588
 
26.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
9306
20.4%
9116
19.9%
1217
 
2.7%
1114
 
2.4%
761
 
1.7%
736
 
1.6%
607
 
1.3%
578
 
1.3%
491
 
1.1%
425
 
0.9%
Other values (294) 21350
46.7%
Common
ValueCountFrequency (%)
1 3568
21.5%
2 2515
15.2%
3 2012
12.1%
4 1628
9.8%
5 1492
9.0%
6 1267
 
7.6%
7 1165
 
7.0%
8 1041
 
6.3%
9 974
 
5.9%
0 917
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 45701
73.4%
ASCII 16588
 
26.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
9306
20.4%
9116
19.9%
1217
 
2.7%
1114
 
2.4%
761
 
1.7%
736
 
1.6%
607
 
1.3%
578
 
1.3%
491
 
1.1%
425
 
0.9%
Other values (294) 21350
46.7%
ASCII
ValueCountFrequency (%)
1 3568
21.5%
2 2515
15.2%
3 2012
12.1%
4 1628
9.8%
5 1492
9.0%
6 1267
 
7.6%
7 1165
 
7.0%
8 1041
 
6.3%
9 974
 
5.9%
0 917
 
5.5%
Distinct7952
Distinct (%)79.5%
Missing1
Missing (%)< 0.1%
Memory size156.2 KiB
2024-05-18T15:02:26.159768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length38
Median length34
Mean length18.747075
Min length6

Characters and Unicode

Total characters187452
Distinct characters58
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6174 ?
Unique (%)61.7%

Sample

1st rowSungin-ro 8ga-gil
2nd rowGosanja-ro 29(isipgu)-gil
3rd rowDobong-ro 180-gil
4th rowDapsimni-ro 12(sibi)-gil
5th rowDolgoji-ro 34-gil
ValueCountFrequency (%)
gil 505
 
2.6%
2-gil 188
 
1.0%
nambusunhwan-ro 169
 
0.9%
3-gil 166
 
0.8%
1-gil 163
 
0.8%
6-gil 158
 
0.8%
4-gil 155
 
0.8%
8-gil 155
 
0.8%
5-gil 150
 
0.8%
dongil-ro 140
 
0.7%
Other values (2007) 17828
90.1%
2024-05-18T15:02:27.370826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
g 18724
 
10.0%
o 18147
 
9.7%
- 17596
 
9.4%
i 13501
 
7.2%
n 12950
 
6.9%
a 11968
 
6.4%
l 11394
 
6.1%
11076
 
5.9%
r 9938
 
5.3%
e 7236
 
3.9%
Other values (48) 54922
29.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 129352
69.0%
Dash Punctuation 17596
 
9.4%
Decimal Number 16583
 
8.8%
Space Separator 11076
 
5.9%
Uppercase Letter 10078
 
5.4%
Open Punctuation 1379
 
0.7%
Close Punctuation 1379
 
0.7%
Other Punctuation 9
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
g 18724
14.5%
o 18147
14.0%
i 13501
10.4%
n 12950
10.0%
a 11968
9.3%
l 11394
8.8%
r 9938
7.7%
e 7236
 
5.6%
u 4137
 
3.2%
s 3704
 
2.9%
Other values (14) 17653
13.6%
Uppercase Letter
ValueCountFrequency (%)
S 1870
18.6%
D 1358
13.5%
G 1095
10.9%
Y 716
 
7.1%
J 713
 
7.1%
H 705
 
7.0%
B 652
 
6.5%
M 619
 
6.1%
N 505
 
5.0%
C 410
 
4.1%
Other values (9) 1435
14.2%
Decimal Number
ValueCountFrequency (%)
1 3561
21.5%
2 2510
15.1%
3 2015
12.2%
4 1633
9.8%
5 1496
9.0%
6 1265
 
7.6%
7 1171
 
7.1%
8 1041
 
6.3%
9 979
 
5.9%
0 912
 
5.5%
Dash Punctuation
ValueCountFrequency (%)
- 17596
100.0%
Space Separator
ValueCountFrequency (%)
11076
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1379
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1379
100.0%
Other Punctuation
ValueCountFrequency (%)
. 9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 139430
74.4%
Common 48022
 
25.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
g 18724
13.4%
o 18147
13.0%
i 13501
9.7%
n 12950
9.3%
a 11968
8.6%
l 11394
8.2%
r 9938
 
7.1%
e 7236
 
5.2%
u 4137
 
3.0%
s 3704
 
2.7%
Other values (33) 27731
19.9%
Common
ValueCountFrequency (%)
- 17596
36.6%
11076
23.1%
1 3561
 
7.4%
2 2510
 
5.2%
3 2015
 
4.2%
4 1633
 
3.4%
5 1496
 
3.1%
( 1379
 
2.9%
) 1379
 
2.9%
6 1265
 
2.6%
Other values (5) 4112
 
8.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 187452
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
g 18724
 
10.0%
o 18147
 
9.7%
- 17596
 
9.4%
i 13501
 
7.2%
n 12950
 
6.9%
a 11968
 
6.4%
l 11394
 
6.1%
11076
 
5.9%
r 9938
 
5.3%
e 7236
 
3.9%
Other values (48) 54922
29.3%

시도_명
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
서울특별시
10000 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
서울특별시 10000
100.0%

Length

2024-05-18T15:02:27.779612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T15:02:27.986917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서울특별시 10000
100.0%

시군구_명
Categorical

HIGH CORRELATION 

Distinct25
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
영등포구
 
605
성북구
 
601
강남구
 
553
관악구
 
550
동대문구
 
490
Other values (20)
7201 

Length

Max length4
Median length3
Mean length3.1053
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row성북구
2nd row동대문구
3rd row도봉구
4th row동대문구
5th row성북구

Common Values

ValueCountFrequency (%)
영등포구 605
 
6.0%
성북구 601
 
6.0%
강남구 553
 
5.5%
관악구 550
 
5.5%
동대문구 490
 
4.9%
종로구 472
 
4.7%
마포구 455
 
4.5%
용산구 449
 
4.5%
동작구 446
 
4.5%
서초구 426
 
4.3%
Other values (15) 4953
49.5%

Length

2024-05-18T15:02:28.175878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
영등포구 605
 
6.0%
성북구 601
 
6.0%
강남구 553
 
5.5%
관악구 550
 
5.5%
동대문구 490
 
4.9%
종로구 472
 
4.7%
마포구 455
 
4.5%
용산구 449
 
4.5%
동작구 446
 
4.5%
서초구 426
 
4.3%
Other values (15) 4953
49.5%

법정동_구분
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
5332 
2
4668 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1 5332
53.3%
2 4668
46.7%

Length

2024-05-18T15:02:28.406211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T15:02:28.574007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 5332
53.3%
2 4668
46.7%

법정동_코드
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct322
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6038.4502
Minimum0
Maximum18706
Zeros4668
Zeros (%)46.7%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T15:02:28.914087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median10101
Q310701
95-th percentile13601
Maximum18706
Range18706
Interquartile range (IQR)10701

Descriptive statistics

Standard deviation5782.4057
Coefficient of variation (CV)0.95759765
Kurtosis-1.7147948
Mean6038.4502
Median Absolute Deviation (MAD)4201
Skewness0.027793004
Sum60384502
Variance33436216
MonotonicityNot monotonic
2024-05-18T15:02:29.217709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4668
46.7%
10101 668
 
6.7%
10201 612
 
6.1%
10301 507
 
5.1%
10501 312
 
3.1%
10801 252
 
2.5%
10701 250
 
2.5%
10601 204
 
2.0%
10901 169
 
1.7%
10401 159
 
1.6%
Other values (312) 2199
22.0%
ValueCountFrequency (%)
0 4668
46.7%
10101 668
 
6.7%
10102 4
 
< 0.1%
10201 612
 
6.1%
10202 39
 
0.4%
10301 507
 
5.1%
10302 38
 
0.4%
10303 7
 
0.1%
10304 1
 
< 0.1%
10401 159
 
1.6%
ValueCountFrequency (%)
18706 1
 
< 0.1%
18701 1
 
< 0.1%
18603 1
 
< 0.1%
18602 3
< 0.1%
18601 2
 
< 0.1%
18502 2
 
< 0.1%
18501 1
 
< 0.1%
18412 1
 
< 0.1%
18402 1
 
< 0.1%
18401 6
0.1%

법정동_명
Text

MISSING 

Distinct435
Distinct (%)8.2%
Missing4668
Missing (%)46.7%
Memory size156.2 KiB
2024-05-18T15:02:29.757103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length3
Mean length3.2573143
Min length2

Characters and Unicode

Total characters17368
Distinct characters208
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

Unique47 ?
Unique (%)0.9%

Sample

1st row장위동
2nd row미아동
3rd row신대방동
4th row홍제동
5th row동선동4가
ValueCountFrequency (%)
신림동 173
 
3.2%
봉천동 92
 
1.7%
미아동 86
 
1.6%
신길동 84
 
1.6%
화곡동 80
 
1.5%
수유동 80
 
1.5%
신월동 70
 
1.3%
독산동 69
 
1.3%
면목동 67
 
1.3%
상계동 64
 
1.2%
Other values (425) 4467
83.8%
2024-05-18T15:02:30.587072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5219
30.0%
702
 
4.0%
622
 
3.6%
272
 
1.6%
215
 
1.2%
211
 
1.2%
203
 
1.2%
203
 
1.2%
200
 
1.2%
196
 
1.1%
Other values (198) 9325
53.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 16740
96.4%
Decimal Number 628
 
3.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
5219
31.2%
702
 
4.2%
622
 
3.7%
272
 
1.6%
215
 
1.3%
211
 
1.3%
203
 
1.2%
203
 
1.2%
200
 
1.2%
196
 
1.2%
Other values (190) 8697
52.0%
Decimal Number
ValueCountFrequency (%)
1 196
31.2%
2 167
26.6%
3 111
17.7%
4 58
 
9.2%
5 48
 
7.6%
6 29
 
4.6%
7 16
 
2.5%
8 3
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Hangul 16740
96.4%
Common 628
 
3.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
5219
31.2%
702
 
4.2%
622
 
3.7%
272
 
1.6%
215
 
1.3%
211
 
1.3%
203
 
1.2%
203
 
1.2%
200
 
1.2%
196
 
1.2%
Other values (190) 8697
52.0%
Common
ValueCountFrequency (%)
1 196
31.2%
2 167
26.6%
3 111
17.7%
4 58
 
9.2%
5 48
 
7.6%
6 29
 
4.6%
7 16
 
2.5%
8 3
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 16740
96.4%
ASCII 628
 
3.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
5219
31.2%
702
 
4.2%
622
 
3.7%
272
 
1.6%
215
 
1.3%
211
 
1.3%
203
 
1.2%
203
 
1.2%
200
 
1.2%
196
 
1.2%
Other values (190) 8697
52.0%
ASCII
ValueCountFrequency (%)
1 196
31.2%
2 167
26.6%
3 111
17.7%
4 58
 
9.2%
5 48
 
7.6%
6 29
 
4.6%
7 16
 
2.5%
8 3
 
0.5%

상위_도로_일련번호
Real number (ℝ)

MISSING 

Distinct427
Distinct (%)5.3%
Missing1902
Missing (%)19.0%
Infinite0
Infinite (%)0.0%
Mean2966875.7
Minimum2000003
Maximum3124009
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T15:02:30.861212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2000003
5-th percentile2005007
Q13005029
median3104007
Q33116003
95-th percentile3122011
Maximum3124009
Range1124006
Interquartile range (IQR)110974

Descriptive statistics

Standard deviation325054.32
Coefficient of variation (CV)0.10956115
Kurtosis4.2901002
Mean2966875.7
Median Absolute Deviation (MAD)17003
Skewness-2.4610235
Sum2.4025759 × 1010
Variance1.0566031 × 1011
MonotonicityNot monotonic
2024-05-18T15:02:31.126603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2000003 157
 
1.6%
3000001 147
 
1.5%
3005039 124
 
1.2%
3005041 112
 
1.1%
2000008 99
 
1.0%
3005038 95
 
0.9%
2005010 91
 
0.9%
3000004 82
 
0.8%
3117004 79
 
0.8%
3000008 69
 
0.7%
Other values (417) 7043
70.4%
(Missing) 1902
 
19.0%
ValueCountFrequency (%)
2000003 157
1.6%
2000006 42
 
0.4%
2000007 2
 
< 0.1%
2000008 99
1.0%
2005001 13
 
0.1%
2005002 12
 
0.1%
2005005 54
 
0.5%
2005007 50
 
0.5%
2005008 17
 
0.2%
2005009 45
 
0.4%
ValueCountFrequency (%)
3124009 23
0.2%
3124008 5
 
0.1%
3124007 8
 
0.1%
3124006 13
0.1%
3124005 1
 
< 0.1%
3124004 11
 
0.1%
3124003 28
0.3%
3124002 8
 
0.1%
3124001 23
0.2%
3123024 26
0.3%

상위_도로_명
Text

MISSING 

Distinct72
Distinct (%)34.4%
Missing9791
Missing (%)97.9%
Memory size156.2 KiB
2024-05-18T15:02:31.545598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length3.3492823
Min length3

Characters and Unicode

Total characters700
Distinct characters108
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique28 ?
Unique (%)13.4%

Sample

1st row숭인로
2nd row한천로
3rd row숭인로
4th row북아현로
5th row숭인로
ValueCountFrequency (%)
한천로 21
 
10.0%
통일로 12
 
5.7%
금호로 11
 
5.3%
신길로 9
 
4.3%
북아현로 8
 
3.8%
천호대로 6
 
2.9%
용마산로 6
 
2.9%
장월로 5
 
2.4%
서달로 5
 
2.4%
증가로 5
 
2.4%
Other values (62) 121
57.9%
2024-05-18T15:02:32.461637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
209
29.9%
34
 
4.9%
24
 
3.4%
22
 
3.1%
18
 
2.6%
17
 
2.4%
17
 
2.4%
16
 
2.3%
14
 
2.0%
12
 
1.7%
Other values (98) 317
45.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 700
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
209
29.9%
34
 
4.9%
24
 
3.4%
22
 
3.1%
18
 
2.6%
17
 
2.4%
17
 
2.4%
16
 
2.3%
14
 
2.0%
12
 
1.7%
Other values (98) 317
45.3%

Most occurring scripts

ValueCountFrequency (%)
Hangul 700
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
209
29.9%
34
 
4.9%
24
 
3.4%
22
 
3.1%
18
 
2.6%
17
 
2.4%
17
 
2.4%
16
 
2.3%
14
 
2.0%
12
 
1.7%
Other values (98) 317
45.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 700
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
209
29.9%
34
 
4.9%
24
 
3.4%
22
 
3.1%
18
 
2.6%
17
 
2.4%
17
 
2.4%
16
 
2.3%
14
 
2.0%
12
 
1.7%
Other values (98) 317
45.3%

새주소_사용여부
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
0
9665 
1
 
335

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 9665
96.7%
1 335
 
3.4%

Length

2024-05-18T15:02:32.860135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T15:02:33.147104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 9665
96.7%
1 335
 
3.4%

변경_이력_사유_코드
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9687 
1
 
267
0
 
45
9
 
1

Length

Max length4
Median length4
Mean length3.9061
Min length1

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row1
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 9687
96.9%
1 267
 
2.7%
0 45
 
0.4%
9 1
 
< 0.1%

Length

2024-05-18T15:02:33.477355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T15:02:33.793273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9687
96.9%
1 267
 
2.7%
0 45
 
0.4%
9 1
 
< 0.1%

변경_이력_정보
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct55
Distinct (%)100.0%
Missing9945
Missing (%)99.5%
Infinite0
Infinite (%)0.0%
Mean1.1394891 × 1013
Minimum1.1230412 × 1013
Maximum1.1740417 × 1013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T15:02:34.187655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.1230412 × 1013
5-th percentile1.1272358 × 1013
Q11.1290486 × 1013
median1.1305412 × 1013
Q31.1440414 × 1013
95-th percentile1.1710417 × 1013
Maximum1.1740417 × 1013
Range5.1000567 × 1011
Interquartile range (IQR)1.4992831 × 1011

Descriptive statistics

Standard deviation1.6702478 × 1011
Coefficient of variation (CV)0.014657865
Kurtosis-0.30182051
Mean1.1394891 × 1013
Median Absolute Deviation (MAD)1.4926803 × 1010
Skewness1.1772301
Sum6.2671903 × 1014
Variance2.7897277 × 1022
MonotonicityNot monotonic
2024-05-18T15:02:34.619070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11740417244300 1
 
< 0.1%
11290412180400 1
 
< 0.1%
11305412462400 1
 
< 0.1%
11680416687600 1
 
< 0.1%
11560415483600 1
 
< 0.1%
11680416687700 1
 
< 0.1%
11305412463100 1
 
< 0.1%
11290485659000 1
 
< 0.1%
11290485660401 1
 
< 0.1%
11305412462700 1
 
< 0.1%
Other values (45) 45
 
0.4%
(Missing) 9945
99.5%
ValueCountFrequency (%)
11230411571000 1
< 0.1%
11230411571001 1
< 0.1%
11230412179801 1
< 0.1%
11290335220700 1
< 0.1%
11290412180400 1
< 0.1%
11290485658302 1
< 0.1%
11290485658500 1
< 0.1%
11290485658900 1
< 0.1%
11290485658901 1
< 0.1%
11290485659000 1
< 0.1%
ValueCountFrequency (%)
11740417244301 1
< 0.1%
11740417244300 1
< 0.1%
11710416955401 1
< 0.1%
11710416955301 1
< 0.1%
11710416955200 1
< 0.1%
11710416955101 1
< 0.1%
11680485836201 1
< 0.1%
11680485836200 1
< 0.1%
11680416687700 1
< 0.1%
11680416687600 1
< 0.1%

법정동_번호
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct87
Distinct (%)1.6%
Missing4668
Missing (%)46.7%
Infinite0
Infinite (%)0.0%
Mean113.23631
Minimum101
Maximum187
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T15:02:34.945051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum101
5-th percentile101
Q1103
median107
Q3117
95-th percentile154
Maximum187
Range86
Interquartile range (IQR)14

Descriptive statistics

Standard deviation16.831596
Coefficient of variation (CV)0.14864133
Kurtosis4.456945
Mean113.23631
Median Absolute Deviation (MAD)5
Skewness2.1308684
Sum603776
Variance283.30262
MonotonicityNot monotonic
2024-05-18T15:02:35.207835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
101 672
 
6.7%
102 651
 
6.5%
103 553
 
5.5%
105 347
 
3.5%
108 290
 
2.9%
107 287
 
2.9%
106 226
 
2.3%
109 215
 
2.1%
104 180
 
1.8%
110 122
 
1.2%
Other values (77) 1789
 
17.9%
(Missing) 4668
46.7%
ValueCountFrequency (%)
101 672
6.7%
102 651
6.5%
103 553
5.5%
104 180
 
1.8%
105 347
3.5%
106 226
 
2.3%
107 287
2.9%
108 290
2.9%
109 215
 
2.1%
110 122
 
1.2%
ValueCountFrequency (%)
187 2
 
< 0.1%
186 6
0.1%
185 3
 
< 0.1%
184 8
0.1%
183 14
0.1%
182 5
 
0.1%
181 2
 
< 0.1%
180 2
 
< 0.1%
179 2
 
< 0.1%
178 3
 
< 0.1%

도로_코드
Real number (ℝ)

HIGH CORRELATION 

Distinct8075
Distinct (%)80.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1419438 × 1011
Minimum1.1110201 × 1011
Maximum1.1740486 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T15:02:35.663756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.1110201 × 1011
5-th percentile1.114031 × 1011
Q11.1230412 × 1011
median1.1440311 × 1011
Q31.1590416 × 1011
95-th percentile1.1710417 × 1011
Maximum1.1740486 × 1011
Range6.302856 × 109
Interquartile range (IQR)3.6000416 × 109

Descriptive statistics

Standard deviation1.9132521 × 109
Coefficient of variation (CV)0.016754345
Kurtosis-1.3240415
Mean1.1419438 × 1011
Median Absolute Deviation (MAD)1.7989946 × 109
Skewness0.015160995
Sum1.1419438 × 1015
Variance3.6605337 × 1018
MonotonicityNot monotonic
2024-05-18T15:02:35.928065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
111103100010 10
 
0.1%
111403101011 8
 
0.1%
111403101009 6
 
0.1%
111103000008 5
 
0.1%
111703102009 5
 
0.1%
115603118023 5
 
0.1%
111103100013 5
 
0.1%
115603118024 5
 
0.1%
111703102004 5
 
0.1%
115603005074 5
 
0.1%
Other values (8065) 9941
99.4%
ValueCountFrequency (%)
111102005001 1
 
< 0.1%
111102100001 1
 
< 0.1%
111102100002 2
 
< 0.1%
111103000008 5
0.1%
111103005001 1
 
< 0.1%
111103005002 1
 
< 0.1%
111103005004 1
 
< 0.1%
111103005005 4
< 0.1%
111103005006 1
 
< 0.1%
111103005008 2
 
< 0.1%
ValueCountFrequency (%)
117404860970 2
< 0.1%
117404860969 1
< 0.1%
117404860966 1
< 0.1%
117404859648 1
< 0.1%
117404858126 1
< 0.1%
117404858050 1
< 0.1%
117404858049 2
< 0.1%
117404858048 2
< 0.1%
117404858046 1
< 0.1%
117404172446 1
< 0.1%

Interactions

2024-05-18T15:02:18.228285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:02:04.393973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:02:06.657665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:02:08.999517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:02:11.130709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:02:12.810380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:02:14.502720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:02:16.261059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:02:18.408629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:02:04.668952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:02:06.955462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:02:09.277683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:02:11.406507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:02:12.986840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:02:14.650021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:02:16.503537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:02:18.731526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:02:04.907440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:02:07.258494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:02:09.577093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:02:11.670162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:02:13.185198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:02:14.914813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:02:16.803472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:02:19.001378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:02:05.253982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:02:07.547891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:02:09.848070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:02:11.843860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:02:13.360296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:02:15.176133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:02:17.056343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:02:19.279038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:02:05.515341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:02:07.852097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:02:10.116565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:02:12.027767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:02:13.607797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:02:15.417057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:02:17.301572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:02:19.542764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:02:05.786757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:02:08.135899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:02:10.383023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:02:12.205625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:02:13.919994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:02:15.657912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:02:17.557685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:02:19.776818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:02:06.088974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:02:08.334522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:02:10.614958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:02:12.353618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:02:14.158393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:02:15.892180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:02:17.814722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:02:19.975841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:02:06.363773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:02:08.696477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:02:10.860419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:02:12.562564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:02:14.323879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:02:16.116133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:02:18.039070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-18T15:02:36.133630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구_코드도로_일련번호법정동_일련번호시군구_명법정동_구분법정동_코드상위_도로_일련번호상위_도로_명새주소_사용여부변경_이력_사유_코드변경_이력_정보법정동_번호도로_코드
시군구_코드1.0000.1170.1151.0000.0610.4500.4340.9960.1350.6010.9730.5871.000
도로_일련번호0.1171.0000.3420.1880.1310.1080.053NaN0.0510.3880.8850.1230.123
법정동_일련번호0.1150.3421.0000.1940.1590.2650.000NaN0.015NaNNaN0.2530.136
시군구_명1.0000.1880.1941.0000.0680.6970.5790.9960.1660.9301.0000.8071.000
법정동_구분0.0610.1310.1590.0681.0001.0000.0000.0000.0260.0100.000NaN0.076
법정동_코드0.4500.1080.2650.6971.0001.0000.0900.8820.0560.2720.5100.9960.488
상위_도로_일련번호0.4340.0530.0000.5790.0000.0901.0001.0000.0000.2600.6420.1290.430
상위_도로_명0.996NaNNaN0.9960.0000.8821.0001.0000.7720.9071.0000.9670.993
새주소_사용여부0.1350.0510.0150.1660.0260.0560.0000.7721.0001.000NaN0.0770.141
변경_이력_사유_코드0.6010.388NaN0.9300.0100.2720.2600.9071.0001.0000.8150.3640.602
변경_이력_정보0.9730.885NaN1.0000.0000.5100.6421.000NaN0.8151.0000.5760.973
법정동_번호0.5870.1230.2530.807NaN0.9960.1290.9670.0770.3640.5761.0000.745
도로_코드1.0000.1230.1361.0000.0760.4880.4300.9930.1410.6020.9730.7451.000
2024-05-18T15:02:36.397849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
새주소_사용여부변경_이력_사유_코드시군구_명법정동_구분
새주소_사용여부1.0000.9980.1430.016
변경_이력_사유_코드0.9981.0000.7990.017
시군구_명0.1430.7991.0000.059
법정동_구분0.0160.0170.0591.000
2024-05-18T15:02:36.622294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구_코드도로_일련번호법정동_일련번호법정동_코드상위_도로_일련번호변경_이력_정보법정동_번호도로_코드시군구_명법정동_구분새주소_사용여부변경_이력_사유_코드
시군구_코드1.0000.850-0.075-0.1460.4250.963-0.3600.9990.9990.0590.1080.441
도로_일련번호0.8501.000-0.140-0.1670.4300.737-0.3280.8600.0750.0990.0360.378
법정동_일련번호-0.075-0.1401.0000.909-0.009-0.0440.252-0.0790.0750.1580.0151.000
법정동_코드-0.146-0.1670.9091.000-0.011-0.1341.000-0.1480.4011.0000.0400.116
상위_도로_일련번호0.4250.430-0.009-0.0111.000-0.420-0.0330.4290.3390.0000.0000.172
변경_이력_정보0.9630.737-0.044-0.134-0.4201.000-0.6140.9250.9780.0001.0000.796
법정동_번호-0.360-0.3280.2521.000-0.033-0.6141.000-0.3580.4391.0000.0590.244
도로_코드0.9990.860-0.079-0.1480.4290.925-0.3581.0000.9990.0590.1080.441
시군구_명0.9990.0750.0750.4010.3390.9780.4390.9991.0000.0590.1430.799
법정동_구분0.0590.0990.1581.0000.0000.0001.0000.0590.0591.0000.0160.017
새주소_사용여부0.1080.0360.0150.0400.0001.0000.0590.1080.1430.0161.0000.998
변경_이력_사유_코드0.4410.3781.0000.1160.1720.7960.2440.4410.7990.0170.9981.000

Missing values

2024-05-18T15:02:20.587087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-18T15:02:21.258928image/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.
2024-05-18T15:02:21.567067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

시군구_코드도로_일련번호법정동_일련번호도로_명영문_도로_명시도_명시군구_명법정동_구분법정동_코드법정동_명상위_도로_일련번호상위_도로_명새주소_사용여부변경_이력_사유_코드변경_이력_정보법정동_번호도로_코드
321291129041214010숭인로8가길Sungin-ro 8ga-gil서울특별시성북구20<NA>3005043숭인로11<NA><NA>112904121401
152401123041150260고산자로29길Gosanja-ro 29(isipgu)-gil서울특별시동대문구20<NA>3005030<NA>0<NA><NA><NA>112304115026
233001132041271830도봉로180길Dobong-ro 180-gil서울특별시도봉구20<NA>3005039<NA>0<NA><NA><NA>113204127183
142411123041150530답십리로12길Dapsimni-ro 12(sibi)-gil서울특별시동대문구20<NA>3005025<NA>0<NA><NA><NA>112304115053
154831129041210581돌곶이로34길Dolgoji-ro 34-gil서울특별시성북구113801장위동3107002<NA>0<NA><NA>138112904121058
235581130541241121도봉로53가길Dobong-ro 53ga-gil서울특별시강북구110101미아동3005039<NA>0<NA><NA>101113054124112
137421126041182360면목로60길Myeonmok-ro 60(yuksip)-gil서울특별시중랑구20<NA>3005027<NA>0<NA><NA><NA>112604118236
11381159041574811신대방16길Sindaebang 16-gil서울특별시동작구110901신대방동<NA><NA>0<NA><NA>109115904157481
130571123041154480전농로34길Jeonnong-ro 34(samsipsa)-gil서울특별시동대문구20<NA>3105013<NA>0<NA><NA><NA>112304115448
156601129041211940보문로14길Bomun-ro 14-gil서울특별시성북구20<NA>3005003<NA>0<NA><NA><NA>112904121194
시군구_코드도로_일련번호법정동_일련번호도로_명영문_도로_명시도_명시군구_명법정동_구분법정동_코드법정동_명상위_도로_일련번호상위_도로_명새주소_사용여부변경_이력_사유_코드변경_이력_정보법정동_번호도로_코드
11491159041574860신대방1가길Sindaebang 1 ga-gil서울특별시동작구20<NA><NA><NA>0<NA><NA><NA>115904157486
291491117041060751두텁바위로75길Duteopbawi-ro 75-gil서울특별시용산구110101후암동3102002<NA>0<NA><NA>101111704106075
24931162041607481행운1마길Haengun 1ma-gil서울특별시관악구110101봉천동<NA><NA>0<NA><NA>101116204160748
98171168041660561개포로24길Gaepo-ro 24 gil서울특별시강남구110301개포동3122001<NA>0<NA><NA>103116804166056
303281114041032180손기정로12길Songijeong-ro 12-gil서울특별시중구20<NA>3101019<NA>0<NA><NA><NA>111404103218
71491147041421251목동서로8길Mokdongseo-ro 8-gil서울특별시양천구110201목동3114003<NA>0<NA><NA>102114704142125
78811147041421910목동중앙북로2길Mokdongjungangbuk-ro 2-gil서울특별시양천구20<NA>3114007<NA>0<NA><NA><NA>114704142191
109661174041720420구천면로12길Gucheonmyeon-ro 12-gil서울특별시강동구20<NA>3016054<NA>0<NA><NA><NA>117404172042
86321165041635600주흥길Juheung-gil서울특별시서초구20<NA><NA><NA>0<NA><NA><NA>116504163560
75521144041390700독막로18길Dongmak-ro 18-gil서울특별시마포구20<NA>3113005<NA>0<NA><NA><NA>114404139070