Overview

Dataset statistics

Number of variables19
Number of observations500
Missing cells654
Missing cells (%)6.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory79.7 KiB
Average record size in memory163.3 B

Variable types

Numeric8
Text5
Categorical6

Dataset

Description샘플 데이터
Author빅밸류
URLhttps://bigdata.seoul.go.kr/data/selectSampleData.do?sample_data_seq=47

Alerts

대지구분(DAEJI) has constant value ""Constant
지상지하구분(FLOORTYPE) is highly imbalanced (61.2%)Imbalance
건물골조(GUJONAME) is highly imbalanced (66.1%)Imbalance
지붕구조(ROOFNAME) is highly imbalanced (88.8%)Imbalance
건물이름(BLDGNAME) has 266 (53.2%) missing valuesMissing
동이름(DONGNAME) has 388 (77.6%) missing valuesMissing
번지2(BUNJI2) has 21 (4.2%) zerosZeros

Reproduction

Analysis started2023-12-10 14:57:44.865439
Analysis finished2023-12-10 14:58:01.942484
Duration17.08 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

년월(KEYMONTH)
Real number (ℝ)

Distinct24
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean201960.85
Minimum201901
Maximum202012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:58:02.077854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum201901
5-th percentile201902
Q1201907
median202001
Q3202006.25
95-th percentile202011
Maximum202012
Range111
Interquartile range (IQR)99.25

Descriptive statistics

Standard deviation49.729471
Coefficient of variation (CV)0.00024623323
Kurtosis-1.9565913
Mean201960.85
Median Absolute Deviation (MAD)10
Skewness-0.17612454
Sum1.0098042 × 108
Variance2473.0203
MonotonicityNot monotonic
2023-12-10T23:58:02.335698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
202005 30
 
6.0%
202003 28
 
5.6%
202011 27
 
5.4%
201912 25
 
5.0%
202002 25
 
5.0%
202001 25
 
5.0%
201910 24
 
4.8%
201903 23
 
4.6%
202010 23
 
4.6%
202004 22
 
4.4%
Other values (14) 248
49.6%
ValueCountFrequency (%)
201901 19
3.8%
201902 13
2.6%
201903 23
4.6%
201904 20
4.0%
201905 17
3.4%
201906 19
3.8%
201907 17
3.4%
201908 19
3.8%
201909 12
2.4%
201910 24
4.8%
ValueCountFrequency (%)
202012 17
3.4%
202011 27
5.4%
202010 23
4.6%
202009 17
3.4%
202008 21
4.2%
202007 20
4.0%
202006 17
3.4%
202005 30
6.0%
202004 22
4.4%
202003 28
5.6%
Distinct497
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-10T23:58:02.710832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length24
Mean length24
Min length24

Characters and Unicode

Total characters12000
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique494 ?
Unique (%)98.8%

Sample

1st rowBV1174010800104420005000
2nd rowBV1150010900101680070000
3rd rowBV1147010300109570009000
4th rowBV1141011700101510151000
5th rowBV1168010100106630030000
ValueCountFrequency (%)
bv1141011100101360011000 2
 
0.4%
bv1123010400105970011000 2
 
0.4%
bv1121510500106840010000 2
 
0.4%
bv1162010200106100225000 1
 
0.2%
bv1121510100100330060000 1
 
0.2%
bv1147010200107190046000 1
 
0.2%
bv1159010100102180098000 1
 
0.2%
bv1147010300109660004000 1
 
0.2%
bv1168011400106270002000 1
 
0.2%
bv1162010100100630016001 1
 
0.2%
Other values (487) 487
97.4%
2023-12-10T23:58:03.283828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 5285
44.0%
1 2692
22.4%
2 524
 
4.4%
3 522
 
4.3%
B 500
 
4.2%
V 500
 
4.2%
5 444
 
3.7%
4 404
 
3.4%
7 321
 
2.7%
6 313
 
2.6%
Other values (2) 495
 
4.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 11000
91.7%
Uppercase Letter 1000
 
8.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5285
48.0%
1 2692
24.5%
2 524
 
4.8%
3 522
 
4.7%
5 444
 
4.0%
4 404
 
3.7%
7 321
 
2.9%
6 313
 
2.8%
8 253
 
2.3%
9 242
 
2.2%
Uppercase Letter
ValueCountFrequency (%)
B 500
50.0%
V 500
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 11000
91.7%
Latin 1000
 
8.3%

Most frequent character per script

Common
ValueCountFrequency (%)
0 5285
48.0%
1 2692
24.5%
2 524
 
4.8%
3 522
 
4.7%
5 444
 
4.0%
4 404
 
3.7%
7 321
 
2.9%
6 313
 
2.8%
8 253
 
2.3%
9 242
 
2.2%
Latin
ValueCountFrequency (%)
B 500
50.0%
V 500
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 5285
44.0%
1 2692
22.4%
2 524
 
4.4%
3 522
 
4.3%
B 500
 
4.2%
V 500
 
4.2%
5 444
 
3.7%
4 404
 
3.4%
7 321
 
2.7%
6 313
 
2.6%
Other values (2) 495
 
4.1%
Distinct29
Distinct (%)5.8%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
HO002
73 
HO001
59 
HO000
49 
HO006
47 
HO004
46 
Other values (24)
226 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique8 ?
Unique (%)1.6%

Sample

1st rowHO006
2nd rowHO000
3rd rowHO002
4th rowHO008
5th rowHO003

Common Values

ValueCountFrequency (%)
HO002 73
14.6%
HO001 59
11.8%
HO000 49
9.8%
HO006 47
9.4%
HO004 46
9.2%
HO007 44
8.8%
HO003 36
7.2%
HO005 32
6.4%
HO008 29
 
5.8%
HO009 16
 
3.2%
Other values (19) 69
13.8%

Length

2023-12-10T23:58:03.519120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ho002 73
14.6%
ho001 59
11.8%
ho000 49
9.8%
ho006 47
9.4%
ho004 46
9.2%
ho007 44
8.8%
ho003 36
7.2%
ho005 32
6.4%
ho008 29
 
5.8%
ho009 16
 
3.2%
Other values (19) 69
13.8%
Distinct494
Distinct (%)98.8%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-10T23:58:04.236916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length20
Mean length20.172
Min length14

Characters and Unicode

Total characters10086
Distinct characters155
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique488 ?
Unique (%)97.6%

Sample

1st row서울특별시 종로구 평창동 1*0*
2nd row서울특별시 송파구 송파동 1*2*2*
3rd row서울특별시 성북구 안암동2가 1*3*1*
4th row서울특별시 서대문구 북가좌동 7*-*9*
5th row서울특별시 도봉구 창동 6*1*9*
ValueCountFrequency (%)
서울특별시 500
25.0%
은평구 40
 
2.0%
송파구 37
 
1.9%
강서구 36
 
1.8%
서대문구 30
 
1.5%
관악구 28
 
1.4%
강남구 26
 
1.3%
강북구 26
 
1.3%
성북구 25
 
1.3%
도봉구 24
 
1.2%
Other values (530) 1227
61.4%
2023-12-10T23:58:05.281917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
* 1504
14.9%
1500
14.9%
591
 
5.9%
562
 
5.6%
532
 
5.3%
505
 
5.0%
500
 
5.0%
500
 
5.0%
500
 
5.0%
1 308
 
3.1%
Other values (145) 3084
30.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5559
55.1%
Other Punctuation 1504
 
14.9%
Space Separator 1500
 
14.9%
Decimal Number 1413
 
14.0%
Dash Punctuation 110
 
1.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
591
 
10.6%
562
 
10.1%
532
 
9.6%
505
 
9.1%
500
 
9.0%
500
 
9.0%
500
 
9.0%
108
 
1.9%
64
 
1.2%
56
 
1.0%
Other values (132) 1641
29.5%
Decimal Number
ValueCountFrequency (%)
1 308
21.8%
2 200
14.2%
3 162
11.5%
4 154
10.9%
6 120
 
8.5%
5 101
 
7.1%
8 101
 
7.1%
9 100
 
7.1%
7 100
 
7.1%
0 67
 
4.7%
Other Punctuation
ValueCountFrequency (%)
* 1504
100.0%
Space Separator
ValueCountFrequency (%)
1500
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 110
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5559
55.1%
Common 4527
44.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
591
 
10.6%
562
 
10.1%
532
 
9.6%
505
 
9.1%
500
 
9.0%
500
 
9.0%
500
 
9.0%
108
 
1.9%
64
 
1.2%
56
 
1.0%
Other values (132) 1641
29.5%
Common
ValueCountFrequency (%)
* 1504
33.2%
1500
33.1%
1 308
 
6.8%
2 200
 
4.4%
3 162
 
3.6%
4 154
 
3.4%
6 120
 
2.7%
- 110
 
2.4%
5 101
 
2.2%
8 101
 
2.2%
Other values (3) 267
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5559
55.1%
ASCII 4527
44.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
* 1504
33.2%
1500
33.1%
1 308
 
6.8%
2 200
 
4.4%
3 162
 
3.6%
4 154
 
3.4%
6 120
 
2.7%
- 110
 
2.4%
5 101
 
2.2%
8 101
 
2.2%
Other values (3) 267
 
5.9%
Hangul
ValueCountFrequency (%)
591
 
10.6%
562
 
10.1%
532
 
9.6%
505
 
9.1%
500
 
9.0%
500
 
9.0%
500
 
9.0%
108
 
1.9%
64
 
1.2%
56
 
1.0%
Other values (132) 1641
29.5%

법정동_구코드(SREG)
Real number (ℝ)

Distinct25
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11457.52
Minimum11110
Maximum11740
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:58:05.543192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11110
5-th percentile11170
Q111305
median11470
Q311620
95-th percentile11740
Maximum11740
Range630
Interquartile range (IQR)315

Descriptive statistics

Standard deviation182.46297
Coefficient of variation (CV)0.015925171
Kurtosis-1.2214215
Mean11457.52
Median Absolute Deviation (MAD)165
Skewness-0.014585531
Sum5728760
Variance33292.735
MonotonicityNot monotonic
2023-12-10T23:58:05.812712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
11710 46
 
9.2%
11380 43
 
8.6%
11500 30
 
6.0%
11740 29
 
5.8%
11215 28
 
5.6%
11470 25
 
5.0%
11260 24
 
4.8%
11545 24
 
4.8%
11305 22
 
4.4%
11680 22
 
4.4%
Other values (15) 207
41.4%
ValueCountFrequency (%)
11110 4
 
0.8%
11140 12
2.4%
11170 13
2.6%
11200 8
 
1.6%
11215 28
5.6%
11230 13
2.6%
11260 24
4.8%
11290 20
4.0%
11305 22
4.4%
11320 11
 
2.2%
ValueCountFrequency (%)
11740 29
5.8%
11710 46
9.2%
11680 22
4.4%
11650 19
3.8%
11620 20
4.0%
11590 18
 
3.6%
11560 9
 
1.8%
11545 24
4.8%
11530 15
 
3.0%
11500 30
6.0%

법정동_동코드(SEUB)
Real number (ℝ)

Distinct38
Distinct (%)7.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10791.2
Minimum10100
Maximum18600
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:58:06.078067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10100
5-th percentile10100
Q110200
median10500
Q310800
95-th percentile13200
Maximum18600
Range8500
Interquartile range (IQR)600

Descriptive statistics

Standard deviation1102.0306
Coefficient of variation (CV)0.10212308
Kurtosis18.388894
Mean10791.2
Median Absolute Deviation (MAD)300
Skewness3.7298229
Sum5395600
Variance1214471.5
MonotonicityNot monotonic
2023-12-10T23:58:06.346817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
10200 88
17.6%
10300 72
14.4%
10100 69
13.8%
10800 41
8.2%
10500 36
7.2%
10700 32
 
6.4%
10600 28
 
5.6%
10400 20
 
4.0%
10900 19
 
3.8%
13300 11
 
2.2%
Other values (28) 84
16.8%
ValueCountFrequency (%)
10100 69
13.8%
10200 88
17.6%
10300 72
14.4%
10400 20
 
4.0%
10500 36
7.2%
10600 28
 
5.6%
10700 32
 
6.4%
10800 41
8.2%
10900 19
 
3.8%
11000 11
 
2.2%
ValueCountFrequency (%)
18600 1
 
0.2%
18300 1
 
0.2%
18200 1
 
0.2%
17300 1
 
0.2%
17200 1
 
0.2%
16200 1
 
0.2%
13800 4
 
0.8%
13500 2
 
0.4%
13300 11
2.2%
13200 4
 
0.8%

대지구분(DAEJI)
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
1
500 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1 500
100.0%

Length

2023-12-10T23:58:06.582259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:58:06.786593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 500
100.0%

번지1(BUNJI1)
Real number (ℝ)

Distinct361
Distinct (%)72.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean385.918
Minimum1
Maximum1709
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:58:07.015576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile12
Q1122
median292
Q3541.25
95-th percentile1046.25
Maximum1709
Range1708
Interquartile range (IQR)419.25

Descriptive statistics

Standard deviation349.13195
Coefficient of variation (CV)0.90467911
Kurtosis2.0726028
Mean385.918
Median Absolute Deviation (MAD)202.5
Skewness1.404059
Sum192959
Variance121893.12
MonotonicityNot monotonic
2023-12-10T23:58:07.393023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
55 7
 
1.4%
13 5
 
1.0%
324 4
 
0.8%
10 4
 
0.8%
193 4
 
0.8%
35 4
 
0.8%
127 3
 
0.6%
54 3
 
0.6%
9 3
 
0.6%
3 3
 
0.6%
Other values (351) 460
92.0%
ValueCountFrequency (%)
1 3
0.6%
2 1
 
0.2%
3 3
0.6%
4 2
0.4%
5 1
 
0.2%
6 2
0.4%
7 1
 
0.2%
8 2
0.4%
9 3
0.6%
10 4
0.8%
ValueCountFrequency (%)
1709 1
0.2%
1672 2
0.4%
1667 1
0.2%
1628 1
0.2%
1624 1
0.2%
1595 1
0.2%
1584 1
0.2%
1580 1
0.2%
1524 1
0.2%
1476 1
0.2%

번지2(BUNJI2)
Real number (ℝ)

ZEROS 

Distinct145
Distinct (%)29.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.786
Minimum0
Maximum1456
Zeros21
Zeros (%)4.2%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:58:07.675962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q16
median17
Q345.25
95-th percentile215
Maximum1456
Range1456
Interquartile range (IQR)39.25

Descriptive statistics

Standard deviation110.59256
Coefficient of variation (CV)2.177619
Kurtosis82.861008
Mean50.786
Median Absolute Deviation (MAD)13
Skewness7.6240853
Sum25393
Variance12230.714
MonotonicityNot monotonic
2023-12-10T23:58:07.986944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 23
 
4.6%
6 22
 
4.4%
12 22
 
4.4%
0 21
 
4.2%
2 20
 
4.0%
13 17
 
3.4%
5 17
 
3.4%
9 14
 
2.8%
4 14
 
2.8%
7 13
 
2.6%
Other values (135) 317
63.4%
ValueCountFrequency (%)
0 21
4.2%
1 23
4.6%
2 20
4.0%
3 12
2.4%
4 14
2.8%
5 17
3.4%
6 22
4.4%
7 13
2.6%
8 8
 
1.6%
9 14
2.8%
ValueCountFrequency (%)
1456 1
0.2%
1280 1
0.2%
469 1
0.2%
411 1
0.2%
395 1
0.2%
383 1
0.2%
367 1
0.2%
364 1
0.2%
356 1
0.2%
354 1
0.2%
Distinct221
Distinct (%)94.4%
Missing266
Missing (%)53.2%
Memory size4.0 KiB
2023-12-10T23:58:08.641411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length18
Mean length6.1452991
Min length2

Characters and Unicode

Total characters1438
Distinct characters191
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique210 ?
Unique (%)89.7%

Sample

1st row신*계*르*2*
2nd row헤*하*스*
3rd row나*하*
4th row엘*시*파*
5th row유*바*
ValueCountFrequency (%)
대*빌 3
 
1.2%
로*빌 3
 
1.2%
3
 
1.2%
리*하*스 2
 
0.8%
한*팰*스 2
 
0.8%
스*이 2
 
0.8%
현*빌 2
 
0.8%
스*트 2
 
0.8%
우*빌 2
 
0.8%
건*빌 2
 
0.8%
Other values (228) 233
91.0%
2023-12-10T23:58:09.494127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
* 719
50.0%
89
 
6.2%
39
 
2.7%
27
 
1.9%
22
 
1.5%
18
 
1.3%
16
 
1.1%
16
 
1.1%
15
 
1.0%
14
 
1.0%
Other values (181) 463
32.2%

Most occurring categories

ValueCountFrequency (%)
Other Punctuation 719
50.0%
Other Letter 654
45.5%
Space Separator 22
 
1.5%
Uppercase Letter 17
 
1.2%
Decimal Number 12
 
0.8%
Lowercase Letter 7
 
0.5%
Dash Punctuation 3
 
0.2%
Close Punctuation 2
 
0.1%
Letter Number 1
 
0.1%
Open Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
89
 
13.6%
39
 
6.0%
27
 
4.1%
18
 
2.8%
16
 
2.4%
16
 
2.4%
15
 
2.3%
14
 
2.1%
13
 
2.0%
13
 
2.0%
Other values (156) 394
60.2%
Uppercase Letter
ValueCountFrequency (%)
D 4
23.5%
L 3
17.6%
B 3
17.6%
O 2
11.8%
S 2
11.8%
E 1
 
5.9%
H 1
 
5.9%
J 1
 
5.9%
Decimal Number
ValueCountFrequency (%)
2 3
25.0%
1 3
25.0%
0 2
16.7%
9 1
 
8.3%
7 1
 
8.3%
8 1
 
8.3%
3 1
 
8.3%
Lowercase Letter
ValueCountFrequency (%)
s 3
42.9%
l 2
28.6%
h 1
 
14.3%
e 1
 
14.3%
Other Punctuation
ValueCountFrequency (%)
* 719
100.0%
Space Separator
ValueCountFrequency (%)
22
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Letter Number
ValueCountFrequency (%)
1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 759
52.8%
Hangul 654
45.5%
Latin 25
 
1.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
89
 
13.6%
39
 
6.0%
27
 
4.1%
18
 
2.8%
16
 
2.4%
16
 
2.4%
15
 
2.3%
14
 
2.1%
13
 
2.0%
13
 
2.0%
Other values (156) 394
60.2%
Latin
ValueCountFrequency (%)
D 4
16.0%
L 3
12.0%
s 3
12.0%
B 3
12.0%
O 2
8.0%
S 2
8.0%
l 2
8.0%
1
 
4.0%
h 1
 
4.0%
E 1
 
4.0%
Other values (3) 3
12.0%
Common
ValueCountFrequency (%)
* 719
94.7%
22
 
2.9%
2 3
 
0.4%
1 3
 
0.4%
- 3
 
0.4%
) 2
 
0.3%
0 2
 
0.3%
( 1
 
0.1%
9 1
 
0.1%
7 1
 
0.1%
Other values (2) 2
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 783
54.5%
Hangul 654
45.5%
Number Forms 1
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
* 719
91.8%
22
 
2.8%
D 4
 
0.5%
2 3
 
0.4%
1 3
 
0.4%
L 3
 
0.4%
s 3
 
0.4%
- 3
 
0.4%
B 3
 
0.4%
O 2
 
0.3%
Other values (14) 18
 
2.3%
Hangul
ValueCountFrequency (%)
89
 
13.6%
39
 
6.0%
27
 
4.1%
18
 
2.8%
16
 
2.4%
16
 
2.4%
15
 
2.3%
14
 
2.1%
13
 
2.0%
13
 
2.0%
Other values (156) 394
60.2%
Number Forms
ValueCountFrequency (%)
1
100.0%

동이름(DONGNAME)
Text

MISSING 

Distinct60
Distinct (%)53.6%
Missing388
Missing (%)77.6%
Memory size4.0 KiB
2023-12-10T23:58:09.906982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length12
Mean length3.8928571
Min length2

Characters and Unicode

Total characters436
Distinct characters84
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique45 ?
Unique (%)40.2%

Sample

1st row에*원*
2nd rowA*
3rd row1*
4th rowA*
5th row1*
ValueCountFrequency (%)
a 12
 
10.7%
8
 
7.1%
1 7
 
6.2%
b 6
 
5.4%
1*2 6
 
5.4%
1*1 6
 
5.4%
5
 
4.5%
1*4 3
 
2.7%
에*원 2
 
1.8%
삼*빌 2
 
1.8%
Other values (50) 55
49.1%
2023-12-10T23:58:10.664798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
* 218
50.0%
1 30
 
6.9%
22
 
5.0%
A 13
 
3.0%
10
 
2.3%
2 8
 
1.8%
B 6
 
1.4%
5
 
1.1%
5
 
1.1%
4
 
0.9%
Other values (74) 115
26.4%

Most occurring categories

ValueCountFrequency (%)
Other Punctuation 219
50.2%
Other Letter 138
31.7%
Decimal Number 48
 
11.0%
Uppercase Letter 24
 
5.5%
Lowercase Letter 7
 
1.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
22
 
15.9%
10
 
7.2%
5
 
3.6%
5
 
3.6%
4
 
2.9%
4
 
2.9%
4
 
2.9%
3
 
2.2%
3
 
2.2%
3
 
2.2%
Other values (55) 75
54.3%
Decimal Number
ValueCountFrequency (%)
1 30
62.5%
2 8
 
16.7%
4 4
 
8.3%
3 3
 
6.2%
5 2
 
4.2%
8 1
 
2.1%
Uppercase Letter
ValueCountFrequency (%)
A 13
54.2%
B 6
25.0%
C 2
 
8.3%
E 1
 
4.2%
J 1
 
4.2%
N 1
 
4.2%
Lowercase Letter
ValueCountFrequency (%)
l 2
28.6%
r 2
28.6%
h 1
14.3%
i 1
14.3%
w 1
14.3%
Other Punctuation
ValueCountFrequency (%)
* 218
99.5%
. 1
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Common 267
61.2%
Hangul 138
31.7%
Latin 31
 
7.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
22
 
15.9%
10
 
7.2%
5
 
3.6%
5
 
3.6%
4
 
2.9%
4
 
2.9%
4
 
2.9%
3
 
2.2%
3
 
2.2%
3
 
2.2%
Other values (55) 75
54.3%
Latin
ValueCountFrequency (%)
A 13
41.9%
B 6
19.4%
l 2
 
6.5%
r 2
 
6.5%
C 2
 
6.5%
E 1
 
3.2%
J 1
 
3.2%
N 1
 
3.2%
h 1
 
3.2%
i 1
 
3.2%
Common
ValueCountFrequency (%)
* 218
81.6%
1 30
 
11.2%
2 8
 
3.0%
4 4
 
1.5%
3 3
 
1.1%
5 2
 
0.7%
8 1
 
0.4%
. 1
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 298
68.3%
Hangul 138
31.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
* 218
73.2%
1 30
 
10.1%
A 13
 
4.4%
2 8
 
2.7%
B 6
 
2.0%
4 4
 
1.3%
3 3
 
1.0%
l 2
 
0.7%
r 2
 
0.7%
5 2
 
0.7%
Other values (9) 10
 
3.4%
Hangul
ValueCountFrequency (%)
22
 
15.9%
10
 
7.2%
5
 
3.6%
5
 
3.6%
4
 
2.9%
4
 
2.9%
4
 
2.9%
3
 
2.2%
3
 
2.2%
3
 
2.2%
Other values (55) 75
54.3%

지상지하구분(FLOORTYPE)
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
20
462 
10
 
38

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20
2nd row20
3rd row20
4th row20
5th row20

Common Values

ValueCountFrequency (%)
20 462
92.4%
10 38
 
7.6%

Length

2023-12-10T23:58:10.919551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:58:11.099490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20 462
92.4%
10 38
 
7.6%

층번호(FLOORNUMBER)
Real number (ℝ)

Distinct12
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.83
Minimum-1
Maximum16
Zeros0
Zeros (%)0.0%
Negative25
Negative (%)5.0%
Memory size4.5 KiB
2023-12-10T23:58:11.689835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile0.9
Q12
median3
Q34
95-th percentile5
Maximum16
Range17
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.8131948
Coefficient of variation (CV)0.64070487
Kurtosis9.1395469
Mean2.83
Median Absolute Deviation (MAD)1
Skewness1.3400229
Sum1415
Variance3.2876754
MonotonicityNot monotonic
2023-12-10T23:58:11.925249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
2 130
26.0%
3 114
22.8%
4 87
17.4%
1 69
13.8%
5 57
11.4%
-1 25
 
5.0%
6 12
 
2.4%
9 2
 
0.4%
15 1
 
0.2%
7 1
 
0.2%
Other values (2) 2
 
0.4%
ValueCountFrequency (%)
-1 25
 
5.0%
1 69
13.8%
2 130
26.0%
3 114
22.8%
4 87
17.4%
5 57
11.4%
6 12
 
2.4%
7 1
 
0.2%
8 1
 
0.2%
9 2
 
0.4%
ValueCountFrequency (%)
16 1
 
0.2%
15 1
 
0.2%
9 2
 
0.4%
8 1
 
0.2%
7 1
 
0.2%
6 12
 
2.4%
5 57
11.4%
4 87
17.4%
3 114
22.8%
2 130
26.0%
Distinct61
Distinct (%)12.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-10T23:58:12.290499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length3
Mean length3.008
Min length1

Characters and Unicode

Total characters1504
Distinct characters23
Distinct categories4 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique31 ?
Unique (%)6.2%

Sample

1st row601
2nd row104
3rd row302
4th rowB101
5th row203
ValueCountFrequency (%)
201 68
13.6%
202 54
10.8%
301 48
9.6%
302 48
9.6%
401 42
 
8.4%
101 40
 
8.0%
402 25
 
5.0%
501 21
 
4.2%
102 18
 
3.6%
502 16
 
3.2%
Other values (50) 119
23.8%
2023-12-10T23:58:13.053216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 476
31.6%
2 323
21.5%
1 321
21.3%
3 156
 
10.4%
4 95
 
6.3%
5 44
 
2.9%
B 21
 
1.4%
6 19
 
1.3%
8
 
0.5%
7
 
0.5%
Other values (13) 34
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1447
96.2%
Other Letter 34
 
2.3%
Uppercase Letter 22
 
1.5%
Space Separator 1
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 476
32.9%
2 323
22.3%
1 321
22.2%
3 156
 
10.8%
4 95
 
6.6%
5 44
 
3.0%
6 19
 
1.3%
7 6
 
0.4%
9 4
 
0.3%
8 3
 
0.2%
Other Letter
ValueCountFrequency (%)
8
23.5%
7
20.6%
6
17.6%
3
 
8.8%
3
 
8.8%
2
 
5.9%
2
 
5.9%
1
 
2.9%
1
 
2.9%
1
 
2.9%
Uppercase Letter
ValueCountFrequency (%)
B 21
95.5%
A 1
 
4.5%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1448
96.3%
Hangul 34
 
2.3%
Latin 22
 
1.5%

Most frequent character per script

Common
ValueCountFrequency (%)
0 476
32.9%
2 323
22.3%
1 321
22.2%
3 156
 
10.8%
4 95
 
6.6%
5 44
 
3.0%
6 19
 
1.3%
7 6
 
0.4%
9 4
 
0.3%
8 3
 
0.2%
Hangul
ValueCountFrequency (%)
8
23.5%
7
20.6%
6
17.6%
3
 
8.8%
3
 
8.8%
2
 
5.9%
2
 
5.9%
1
 
2.9%
1
 
2.9%
1
 
2.9%
Latin
ValueCountFrequency (%)
B 21
95.5%
A 1
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1470
97.7%
Hangul 34
 
2.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 476
32.4%
2 323
22.0%
1 321
21.8%
3 156
 
10.6%
4 95
 
6.5%
5 44
 
3.0%
B 21
 
1.4%
6 19
 
1.3%
7 6
 
0.4%
9 4
 
0.3%
Other values (3) 5
 
0.3%
Hangul
ValueCountFrequency (%)
8
23.5%
7
20.6%
6
17.6%
3
 
8.8%
3
 
8.8%
2
 
5.9%
2
 
5.9%
1
 
2.9%
1
 
2.9%
1
 
2.9%

건물골조(GUJONAME)
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
철근콘크리트구조
443 
벽돌구조
55 
철골콘크리트구조
 
2

Length

Max length8
Median length8
Mean length7.56
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row철근콘크리트구조
2nd row철근콘크리트구조
3rd row철근콘크리트구조
4th row철근콘크리트구조
5th row벽돌구조

Common Values

ValueCountFrequency (%)
철근콘크리트구조 443
88.6%
벽돌구조 55
 
11.0%
철골콘크리트구조 2
 
0.4%

Length

2023-12-10T23:58:13.341473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:58:13.584571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
철근콘크리트구조 443
88.6%
벽돌구조 55
 
11.0%
철골콘크리트구조 2
 
0.4%

지붕구조(ROOFNAME)
Categorical

IMBALANCE 

Distinct5
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
(철근)콘크리트
483 
기타지붕
 
11
기와
 
4
슬레이트
 
1
 
1

Length

Max length8
Median length8
Mean length7.842
Min length1

Unique

Unique2 ?
Unique (%)0.4%

Sample

1st row(철근)콘크리트
2nd row(철근)콘크리트
3rd row(철근)콘크리트
4th row(철근)콘크리트
5th row(철근)콘크리트

Common Values

ValueCountFrequency (%)
(철근)콘크리트 483
96.6%
기타지붕 11
 
2.2%
기와 4
 
0.8%
슬레이트 1
 
0.2%
1
 
0.2%

Length

2023-12-10T23:58:13.805060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:58:14.022035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
철근)콘크리트 483
96.8%
기타지붕 11
 
2.2%
기와 4
 
0.8%
슬레이트 1
 
0.2%

전용면적(JYAREA)
Real number (ℝ)

Distinct469
Distinct (%)93.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.48808
Minimum11.9
Maximum356.46
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:58:14.248056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11.9
5-th percentile17.9145
Q134.49
median45.395
Q357.845
95-th percentile84.3385
Maximum356.46
Range344.56
Interquartile range (IQR)23.355

Descriptive statistics

Standard deviation30.116508
Coefficient of variation (CV)0.60856084
Kurtosis36.34248
Mean49.48808
Median Absolute Deviation (MAD)11.89
Skewness4.7581443
Sum24744.04
Variance907.00404
MonotonicityNot monotonic
2023-12-10T23:58:14.595705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29.88 3
 
0.6%
37.02 3
 
0.6%
36.97 2
 
0.4%
55.17 2
 
0.4%
28.26 2
 
0.4%
51.19 2
 
0.4%
36.6 2
 
0.4%
41.7 2
 
0.4%
29.96 2
 
0.4%
43.69 2
 
0.4%
Other values (459) 478
95.6%
ValueCountFrequency (%)
11.9 1
0.2%
12.21 1
0.2%
12.61 1
0.2%
12.83 1
0.2%
13.06 1
0.2%
13.44 1
0.2%
14.0 1
0.2%
14.01 1
0.2%
14.1 1
0.2%
14.76 1
0.2%
ValueCountFrequency (%)
356.46 1
0.2%
282.25 1
0.2%
249.8 1
0.2%
240.83 1
0.2%
195.22 1
0.2%
181.73 1
0.2%
172.6 1
0.2%
120.46 1
0.2%
113.0 1
0.2%
110.22 1
0.2%
Distinct474
Distinct (%)94.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.15456
Minimum3.49
Maximum247.17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:58:14.991959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.49
5-th percentile10.5715
Q119.7575
median27.155
Q334.525
95-th percentile68.092
Maximum247.17
Range243.68
Interquartile range (IQR)14.7675

Descriptive statistics

Standard deviation22.943419
Coefficient of variation (CV)0.73643857
Kurtosis24.475792
Mean31.15456
Median Absolute Deviation (MAD)7.42
Skewness4.0194972
Sum15577.28
Variance526.4005
MonotonicityNot monotonic
2023-12-10T23:58:15.280246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16.03 2
 
0.4%
20.95 2
 
0.4%
23.71 2
 
0.4%
18.37 2
 
0.4%
26.84 2
 
0.4%
30.34 2
 
0.4%
23.7 2
 
0.4%
18.24 2
 
0.4%
19.0 2
 
0.4%
30.57 2
 
0.4%
Other values (464) 480
96.0%
ValueCountFrequency (%)
3.49 1
0.2%
3.5 1
0.2%
3.7 1
0.2%
3.76 1
0.2%
5.13 1
0.2%
5.84 1
0.2%
6.15 1
0.2%
6.17 1
0.2%
6.25 1
0.2%
6.88 1
0.2%
ValueCountFrequency (%)
247.17 1
0.2%
177.19 1
0.2%
152.3 1
0.2%
151.83 1
0.2%
146.59 1
0.2%
135.96 1
0.2%
128.63 1
0.2%
124.0 1
0.2%
111.17 1
0.2%
104.22 1
0.2%
Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
1
409 
0
91 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1 409
81.8%
0 91
 
18.2%

Length

2023-12-10T23:58:15.530294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:58:15.718791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 409
81.8%
0 91
 
18.2%

Interactions

2023-12-10T23:57:58.910686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:46.720716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:48.175519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:49.761664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:51.202797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:52.714038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:55.438527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:57.373424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:59.169206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:46.898281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:48.362362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:49.963553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:51.383043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:53.044740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:55.649820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:57.557389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:59.450788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:47.078900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:48.546255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:50.132969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:51.591410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:53.568177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:55.835041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:57.728574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:59.665534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:47.250452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:48.737266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:50.303295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:51.760528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:54.002589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:56.008946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:57.880798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:00.019175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:47.442873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:48.951563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:50.472886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:51.947959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:54.456777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:56.210225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:58.070602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:00.243229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:47.631194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:49.159884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:50.646382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:52.147084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:54.802116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:56.412227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:58.261216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:00.478185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:47.813815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:49.357885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:50.840509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:52.321257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:55.051626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:56.993599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:58.449405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:00.693254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:48.003386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:49.570861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:51.030410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:52.515141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:55.256413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:57.162686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:58.657299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T23:58:15.869853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
년월(KEYMONTH)호코드(KEY_HO)법정동_구코드(SREG)법정동_동코드(SEUB)번지1(BUNJI1)번지2(BUNJI2)동이름(DONGNAME)지상지하구분(FLOORTYPE)층번호(FLOORNUMBER)호이름(HONAME)건물골조(GUJONAME)지붕구조(ROOFNAME)전용면적(JYAREA)대지권면적(DEJIGUNAREA)해당호기준_시세존재유무(HAVE_SISE)
년월(KEYMONTH)1.0000.0000.0670.0000.1330.0000.0000.0000.0000.0000.0000.0000.0000.0910.066
호코드(KEY_HO)0.0001.0000.0920.0000.1510.0000.8810.0780.0000.5230.0000.2170.0000.0000.000
법정동_구코드(SREG)0.0670.0921.0000.0000.0000.0680.4220.0450.0860.1680.0000.1540.1540.0000.105
법정동_동코드(SEUB)0.0000.0000.0001.0000.2940.2490.8740.0000.0000.0000.0000.0000.0000.0000.000
번지1(BUNJI1)0.1330.1510.0000.2941.0000.0000.7390.0000.1430.4540.0000.0000.0000.3790.000
번지2(BUNJI2)0.0000.0000.0680.2490.0001.0000.8580.0000.0000.0000.0000.0000.0000.0000.065
동이름(DONGNAME)0.0000.8810.4220.8740.7390.8581.0000.6630.0000.0000.7120.0000.8010.3930.000
지상지하구분(FLOORTYPE)0.0000.0780.0450.0000.0000.0000.6631.0000.0000.0000.0000.0000.0000.0000.000
층번호(FLOORNUMBER)0.0000.0000.0860.0000.1430.0000.0000.0001.0000.0000.0000.0000.0000.0000.000
호이름(HONAME)0.0000.5230.1680.0000.4540.0000.0000.0000.0001.0000.0000.7230.8130.4320.000
건물골조(GUJONAME)0.0000.0000.0000.0000.0000.0000.7120.0000.0000.0001.0000.0000.2470.0000.000
지붕구조(ROOFNAME)0.0000.2170.1540.0000.0000.0000.0000.0000.0000.7230.0001.0000.0000.0000.068
전용면적(JYAREA)0.0000.0000.1540.0000.0000.0000.8010.0000.0000.8130.2470.0001.0000.0000.000
대지권면적(DEJIGUNAREA)0.0910.0000.0000.0000.3790.0000.3930.0000.0000.4320.0000.0000.0001.0000.000
해당호기준_시세존재유무(HAVE_SISE)0.0660.0000.1050.0000.0000.0650.0000.0000.0000.0000.0000.0680.0000.0001.000
2023-12-10T23:58:16.187968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지붕구조(ROOFNAME)지상지하구분(FLOORTYPE)건물골조(GUJONAME)호코드(KEY_HO)해당호기준_시세존재유무(HAVE_SISE)
지붕구조(ROOFNAME)1.0000.0000.0000.1010.082
지상지하구분(FLOORTYPE)0.0001.0000.0000.0640.000
건물골조(GUJONAME)0.0000.0001.0000.0000.000
호코드(KEY_HO)0.1010.0640.0001.0000.000
해당호기준_시세존재유무(HAVE_SISE)0.0820.0000.0000.0001.000
2023-12-10T23:58:16.420424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
년월(KEYMONTH)법정동_구코드(SREG)법정동_동코드(SEUB)번지1(BUNJI1)번지2(BUNJI2)층번호(FLOORNUMBER)전용면적(JYAREA)대지권면적(DEJIGUNAREA)호코드(KEY_HO)지상지하구분(FLOORTYPE)건물골조(GUJONAME)지붕구조(ROOFNAME)해당호기준_시세존재유무(HAVE_SISE)
년월(KEYMONTH)1.000-0.0450.0210.016-0.0370.019-0.0240.0060.0000.0000.0000.0000.044
법정동_구코드(SREG)-0.0451.0000.0550.046-0.0290.107-0.0150.0510.0330.0360.0000.0520.091
법정동_동코드(SEUB)0.0210.0551.0000.042-0.081-0.0150.052-0.0020.0000.0000.0000.0000.000
번지1(BUNJI1)0.0160.0460.0421.000-0.049-0.0760.0100.0310.0520.0000.0000.0000.000
번지2(BUNJI2)-0.037-0.029-0.081-0.0491.000-0.078-0.039-0.0490.0000.0000.0000.0000.046
층번호(FLOORNUMBER)0.0190.107-0.015-0.076-0.0781.000-0.018-0.0240.0000.0000.0000.0000.000
전용면적(JYAREA)-0.024-0.0150.0520.010-0.039-0.0181.000-0.0030.0000.0000.1110.0000.000
대지권면적(DEJIGUNAREA)0.0060.051-0.0020.031-0.049-0.024-0.0031.0000.0000.0000.0000.0000.000
호코드(KEY_HO)0.0000.0330.0000.0520.0000.0000.0000.0001.0000.0640.0000.1010.000
지상지하구분(FLOORTYPE)0.0000.0360.0000.0000.0000.0000.0000.0000.0641.0000.0000.0000.000
건물골조(GUJONAME)0.0000.0000.0000.0000.0000.0000.1110.0000.0000.0001.0000.0000.000
지붕구조(ROOFNAME)0.0000.0520.0000.0000.0000.0000.0000.0000.1010.0000.0001.0000.082
해당호기준_시세존재유무(HAVE_SISE)0.0440.0910.0000.0000.0460.0000.0000.0000.0000.0000.0000.0821.000

Missing values

2023-12-10T23:58:01.004035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:58:01.548243image/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.
2023-12-10T23:58:01.844735image/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

년월(KEYMONTH)지번주소+동코드(KEY_DONG)호코드(KEY_HO)주소(ADDRESS)법정동_구코드(SREG)법정동_동코드(SEUB)대지구분(DAEJI)번지1(BUNJI1)번지2(BUNJI2)건물이름(BLDGNAME)동이름(DONGNAME)지상지하구분(FLOORTYPE)층번호(FLOORNUMBER)호이름(HONAME)건물골조(GUJONAME)지붕구조(ROOFNAME)전용면적(JYAREA)대지권면적(DEJIGUNAREA)해당호기준_시세존재유무(HAVE_SISE)
0201903BV1174010800104420005000HO006서울특별시 종로구 평창동 1*0*115001080014866<NA>에*원*202601철근콘크리트구조(철근)콘크리트33.8515.71
1202009BV1150010900101680070000HO000서울특별시 송파구 송파동 1*2*2*1162010500111646신*계*르*2*<NA>201104철근콘크리트구조(철근)콘크리트55.4819.781
2201912BV1147010300109570009000HO002서울특별시 성북구 안암동2가 1*3*1*112601080017262<NA><NA>201302철근콘크리트구조(철근)콘크리트36.0666.461
3201903BV1141011700101510151000HO008서울특별시 서대문구 북가좌동 7*-*9*1132010200141035<NA>A*202B101철근콘크리트구조(철근)콘크리트36.9630.741
4202012BV1168010100106630030000HO003서울특별시 도봉구 창동 6*1*9*1126010100116921헤*하*스*<NA>205203벽돌구조(철근)콘크리트29.8124.31
5202003BV1126010500105140028000HO000서울특별시 중랑구 묵동 1*2*8*1138010800115718<NA><NA>202101철근콘크리트구조(철근)콘크리트33.0713.41
6201901BV1123010600100930030000HO008서울특별시 도봉구 방학동 5*5*5*1153012400117323나*하*<NA>204202철근콘크리트구조(철근)콘크리트57.0721.391
7202004BV1171010600100540013000HO007서울특별시 서초구 방배동 9*4*1*11380103001384383<NA><NA>202B1철근콘크리트구조(철근)콘크리트43.0728.790
8202009BV1138010500101770088000HO006서울특별시 금천구 시흥동 8*1*9*1135010800165엘*시*파*<NA>204101철근콘크리트구조(철근)콘크리트54.4234.780
9202001BV1138010400104800035000HO000서울특별시 은평구 응암동 1*8*2*113201020011809유*바*<NA>201101벽돌구조(철근)콘크리트48.1136.161
년월(KEYMONTH)지번주소+동코드(KEY_DONG)호코드(KEY_HO)주소(ADDRESS)법정동_구코드(SREG)법정동_동코드(SEUB)대지구분(DAEJI)번지1(BUNJI1)번지2(BUNJI2)건물이름(BLDGNAME)동이름(DONGNAME)지상지하구분(FLOORTYPE)층번호(FLOORNUMBER)호이름(HONAME)건물골조(GUJONAME)지붕구조(ROOFNAME)전용면적(JYAREA)대지권면적(DEJIGUNAREA)해당호기준_시세존재유무(HAVE_SISE)
490201902BV1162010200106100012000HO006서울특별시 서대문구 충정로3가 3*-*11290103001664<NA><NA>205303철근콘크리트구조(철근)콘크리트51.1920.861
491202005BV1147010200105240010002HO288서울특별시 광진구 구의동 2*1*2*115001060011326명*하*빌*쌍*프*임*202301철근콘크리트구조(철근)콘크리트41.3427.781
492201906BV1162010100115110040000HO001서울특별시 송파구 방이동 1*7*1*1121511600114022화*트*빌*<NA>20-1B2벽돌구조(철근)콘크리트43.648.550
493201908BV1120011400106850196000HO004서울특별시 강서구 화곡동 1*4*-*116801020019963대*스*이*<NA>204402철근콘크리트구조(철근)콘크리트45.7924.061
494202012BV1174010700104780082000HO002서울특별시 서대문구 홍제동 3*2*2*1168010700153135중*클*식*우*<NA>20-1401철근콘크리트구조(철근)콘크리트59.9227.771
495202006BV1138010700100890041000HO001서울특별시 강북구 번동 1*8*2*3*11305104001556드*하*빌*<NA>203B1철근콘크리트구조(철근)콘크리트27.9825.551
496202006BV1168010500100410012000HO006서울특별시 강북구 우이동 4*-*1117010100153020<NA><NA>20-1501철근콘크리트구조(철근)콘크리트54.3837.31
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