Overview

Dataset statistics

Number of variables29
Number of observations500
Missing cells713
Missing cells (%)4.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory123.7 KiB
Average record size in memory253.3 B

Variable types

Numeric18
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
주용도코드(JYONGDO_CODE) is highly imbalanced (97.9%)Imbalance
주용도명(JYONGDO) is highly imbalanced (96.3%)Imbalance
건물_이름(BLDGNAME) has 299 (59.8%) missing valuesMissing
동_이름(DONGNAME) has 414 (82.8%) missing valuesMissing
지번주소코드(KEY_ADDRESS) has unique valuesUnique
지번주소+동코드(KEY_DONG) has unique valuesUnique
경도(LNG) has unique valuesUnique
번지2(BUNJI2) has 14 (2.8%) zerosZeros
건축면적(GCAREA) has 17 (3.4%) zerosZeros
건폐율(GPRATE) has 89 (17.8%) zerosZeros
지상연면적(JSYRATE) has 38 (7.6%) zerosZeros
용적률(YJAREA) has 87 (17.4%) zerosZeros
높이(HIGH) has 99 (19.8%) zerosZeros
시세산성_세대수(HO_COUNT) has 16 (3.2%) zerosZeros

Reproduction

Analysis started2023-12-10 14:57:21.918063
Analysis finished2023-12-10 14:57:22.723780
Duration0.81 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%
Mean201952.86
Minimum201901
Maximum202012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:57:22.831982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum201901
5-th percentile201902
Q1201906
median201911.5
Q3202006
95-th percentile202011
Maximum202012
Range111
Interquartile range (IQR)100

Descriptive statistics

Standard deviation50.056514
Coefficient of variation (CV)0.00024786237
Kurtosis-1.969373
Mean201952.86
Median Absolute Deviation (MAD)10.5
Skewness0.143553
Sum1.0097643 × 108
Variance2505.6546
MonotonicityNot monotonic
2023-12-10T23:57:23.078473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
201905 36
 
7.2%
202005 35
 
7.0%
201903 31
 
6.2%
201910 28
 
5.6%
202003 25
 
5.0%
201911 24
 
4.8%
201904 22
 
4.4%
201906 21
 
4.2%
202006 21
 
4.2%
201901 20
 
4.0%
Other values (14) 237
47.4%
ValueCountFrequency (%)
201901 20
4.0%
201902 15
3.0%
201903 31
6.2%
201904 22
4.4%
201905 36
7.2%
201906 21
4.2%
201907 15
3.0%
201908 20
4.0%
201909 18
3.6%
201910 28
5.6%
ValueCountFrequency (%)
202012 18
3.6%
202011 19
3.8%
202010 18
3.6%
202009 15
3.0%
202008 20
4.0%
202007 20
4.0%
202006 21
4.2%
202005 35
7.0%
202004 10
 
2.0%
202003 25
5.0%

지번주소코드(KEY_ADDRESS)
Text

UNIQUE 

Distinct500
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-10T23:57:23.428743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length21
Mean length21
Min length21

Characters and Unicode

Total characters10500
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

Unique500 ?
Unique (%)100.0%

Sample

1st rowBV1126010200102000011
2nd rowBV1171010700101710009
3rd rowBV1159010700101780001
4th rowBV1130510300101300039
5th rowBV1147010300105160012
ValueCountFrequency (%)
bv1126010200102000011 1
 
0.2%
bv1130510300102520044 1
 
0.2%
bv1130510100102580609 1
 
0.2%
bv1117012100100380186 1
 
0.2%
bv1130510100102580461 1
 
0.2%
bv1130510100102580086 1
 
0.2%
bv1165010700100940011 1
 
0.2%
bv1150010300101540014 1
 
0.2%
bv1162010200104120185 1
 
0.2%
bv1117013100100010240 1
 
0.2%
Other values (490) 490
98.0%
2023-12-10T23:57:24.062897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 3795
36.1%
1 2691
25.6%
3 524
 
5.0%
2 514
 
4.9%
B 500
 
4.8%
V 500
 
4.8%
5 437
 
4.2%
4 407
 
3.9%
6 326
 
3.1%
7 311
 
3.0%
Other values (2) 495
 
4.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9500
90.5%
Uppercase Letter 1000
 
9.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3795
39.9%
1 2691
28.3%
3 524
 
5.5%
2 514
 
5.4%
5 437
 
4.6%
4 407
 
4.3%
6 326
 
3.4%
7 311
 
3.3%
8 273
 
2.9%
9 222
 
2.3%
Uppercase Letter
ValueCountFrequency (%)
B 500
50.0%
V 500
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 9500
90.5%
Latin 1000
 
9.5%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3795
39.9%
1 2691
28.3%
3 524
 
5.5%
2 514
 
5.4%
5 437
 
4.6%
4 407
 
4.3%
6 326
 
3.4%
7 311
 
3.3%
8 273
 
2.9%
9 222
 
2.3%
Latin
ValueCountFrequency (%)
B 500
50.0%
V 500
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3795
36.1%
1 2691
25.6%
3 524
 
5.0%
2 514
 
4.9%
B 500
 
4.8%
V 500
 
4.8%
5 437
 
4.2%
4 407
 
3.9%
6 326
 
3.1%
7 311
 
3.0%
Other values (2) 495
 
4.7%

지번주소+동코드(KEY_DONG)
Text

UNIQUE 

Distinct500
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-10T23:57:24.555718image/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

Unique500 ?
Unique (%)100.0%

Sample

1st rowBV1150010200106430044000
2nd rowBV1147010300109240003000
3rd rowBV1141010200100030090000
4th rowBV1121510900101600005000
5th rowBV1147010300101070045000
ValueCountFrequency (%)
bv1150010200106430044000 1
 
0.2%
bv1121510100100580004000 1
 
0.2%
bv1147010300109350009000 1
 
0.2%
bv1129013300100160504000 1
 
0.2%
bv1153010700103070014000 1
 
0.2%
bv1132010600106040011000 1
 
0.2%
bv1159010200102990246000 1
 
0.2%
bv1141011900102920006000 1
 
0.2%
bv1171010700100050001000 1
 
0.2%
bv1165010800114870128000 1
 
0.2%
Other values (490) 490
98.0%
2023-12-10T23:57:25.278301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 5235
43.6%
1 2683
22.4%
2 532
 
4.4%
3 522
 
4.3%
B 500
 
4.2%
V 500
 
4.2%
5 444
 
3.7%
4 426
 
3.5%
7 330
 
2.8%
6 301
 
2.5%
Other values (2) 527
 
4.4%

Most occurring categories

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

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5235
47.6%
1 2683
24.4%
2 532
 
4.8%
3 522
 
4.7%
5 444
 
4.0%
4 426
 
3.9%
7 330
 
3.0%
6 301
 
2.7%
8 292
 
2.7%
9 235
 
2.1%
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 5235
47.6%
1 2683
24.4%
2 532
 
4.8%
3 522
 
4.7%
5 444
 
4.0%
4 426
 
3.9%
7 330
 
3.0%
6 301
 
2.7%
8 292
 
2.7%
9 235
 
2.1%
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 5235
43.6%
1 2683
22.4%
2 532
 
4.4%
3 522
 
4.3%
B 500
 
4.2%
V 500
 
4.2%
5 444
 
3.7%
4 426
 
3.5%
7 330
 
2.8%
6 301
 
2.5%
Other values (2) 527
 
4.4%

주소(ADDRESS)
Text

Distinct489
Distinct (%)97.8%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-10T23:57:26.038983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length20
Mean length20.106
Min length16

Characters and Unicode

Total characters10053
Distinct characters160
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

Unique478 ?
Unique (%)95.6%

Sample

1st row서울특별시 관악구 봉천동 4*-*6*
2nd row서울특별시 금천구 독산동 3*8*5*
3rd row서울특별시 노원구 중계동 5*0*2*
4th row서울특별시 도봉구 방학동 6*8*1*
5th row서울특별시 송파구 가락동 4*-*
ValueCountFrequency (%)
서울특별시 500
25.0%
송파구 53
 
2.6%
은평구 39
 
1.9%
강서구 31
 
1.6%
양천구 30
 
1.5%
관악구 28
 
1.4%
강남구 27
 
1.4%
강북구 27
 
1.4%
서대문구 22
 
1.1%
구로구 22
 
1.1%
Other values (527) 1221
61.1%
2023-12-10T23:57:27.143916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1500
14.9%
* 1495
14.9%
서 582
 
5.8%
동 559
 
5.6%
구 535
 
5.3%
시 512
 
5.1%
울 500
 
5.0%
별 500
 
5.0%
특 500
 
5.0%
1 302
 
3.0%
Other values (150) 3068
30.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5548
55.2%
Space Separator 1500
 
14.9%
Other Punctuation 1495
 
14.9%
Decimal Number 1412
 
14.0%
Dash Punctuation 98
 
1.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
서 582
 
10.5%
동 559
 
10.1%
구 535
 
9.6%
시 512
 
9.2%
울 500
 
9.0%
별 500
 
9.0%
특 500
 
9.0%
강 107
 
1.9%
천 66
 
1.2%
파 64
 
1.2%
Other values (137) 1623
29.3%
Decimal Number
ValueCountFrequency (%)
1 302
21.4%
2 220
15.6%
3 167
11.8%
4 140
9.9%
5 132
9.3%
9 104
 
7.4%
6 103
 
7.3%
7 97
 
6.9%
8 87
 
6.2%
0 60
 
4.2%
Space Separator
ValueCountFrequency (%)
1500
100.0%
Other Punctuation
ValueCountFrequency (%)
* 1495
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 98
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5548
55.2%
Common 4505
44.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
서 582
 
10.5%
동 559
 
10.1%
구 535
 
9.6%
시 512
 
9.2%
울 500
 
9.0%
별 500
 
9.0%
특 500
 
9.0%
강 107
 
1.9%
천 66
 
1.2%
파 64
 
1.2%
Other values (137) 1623
29.3%
Common
ValueCountFrequency (%)
1500
33.3%
* 1495
33.2%
1 302
 
6.7%
2 220
 
4.9%
3 167
 
3.7%
4 140
 
3.1%
5 132
 
2.9%
9 104
 
2.3%
6 103
 
2.3%
- 98
 
2.2%
Other values (3) 244
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5548
55.2%
ASCII 4505
44.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1500
33.3%
* 1495
33.2%
1 302
 
6.7%
2 220
 
4.9%
3 167
 
3.7%
4 140
 
3.1%
5 132
 
2.9%
9 104
 
2.3%
6 103
 
2.3%
- 98
 
2.2%
Other values (3) 244
 
5.4%
Hangul
ValueCountFrequency (%)
서 582
 
10.5%
동 559
 
10.1%
구 535
 
9.6%
시 512
 
9.2%
울 500
 
9.0%
별 500
 
9.0%
특 500
 
9.0%
강 107
 
1.9%
천 66
 
1.2%
파 64
 
1.2%
Other values (137) 1623
29.3%

법정동_구코드(SREG)
Real number (ℝ)

Distinct25
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11455.06
Minimum11110
Maximum11740
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:57:27.423056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11110
5-th percentile11170
Q111305
median11470
Q311620
95-th percentile11710
Maximum11740
Range630
Interquartile range (IQR)315

Descriptive statistics

Standard deviation181.76985
Coefficient of variation (CV)0.015868083
Kurtosis-1.2034158
Mean11455.06
Median Absolute Deviation (MAD)165
Skewness-0.043847867
Sum5727530
Variance33040.277
MonotonicityNot monotonic
2023-12-10T23:57:27.670864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
11710 54
 
10.8%
11380 42
 
8.4%
11500 36
 
7.2%
11470 27
 
5.4%
11680 26
 
5.2%
11215 23
 
4.6%
11650 23
 
4.6%
11620 22
 
4.4%
11410 21
 
4.2%
11260 21
 
4.2%
Other values (15) 205
41.0%
ValueCountFrequency (%)
11110 11
2.2%
11140 4
 
0.8%
11170 15
3.0%
11200 11
2.2%
11215 23
4.6%
11230 18
3.6%
11260 21
4.2%
11290 18
3.6%
11305 19
3.8%
11320 19
3.8%
ValueCountFrequency (%)
11740 15
 
3.0%
11710 54
10.8%
11680 26
5.2%
11650 23
4.6%
11620 22
4.4%
11590 17
 
3.4%
11560 9
 
1.8%
11545 10
 
2.0%
11530 16
 
3.2%
11500 36
7.2%

법정동_동코드(SEUB)
Real number (ℝ)

Distinct44
Distinct (%)8.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10902.4
Minimum10100
Maximum18600
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:57:27.943758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10100
5-th percentile10100
Q110200
median10500
Q310900
95-th percentile13300
Maximum18600
Range8500
Interquartile range (IQR)700

Descriptive statistics

Standard deviation1307.3993
Coefficient of variation (CV)0.11991848
Kurtosis12.513999
Mean10902.4
Median Absolute Deviation (MAD)300
Skewness3.2812038
Sum5451200
Variance1709292.8
MonotonicityNot monotonic
2023-12-10T23:57:28.240565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
10300 88
17.6%
10200 69
13.8%
10100 68
13.6%
10700 38
 
7.6%
10500 34
 
6.8%
10800 25
 
5.0%
10400 23
 
4.6%
10600 23
 
4.6%
10900 20
 
4.0%
11100 13
 
2.6%
Other values (34) 99
19.8%
ValueCountFrequency (%)
10100 68
13.6%
10200 69
13.8%
10300 88
17.6%
10400 23
 
4.6%
10500 34
 
6.8%
10600 23
 
4.6%
10700 38
7.6%
10800 25
 
5.0%
10900 20
 
4.0%
11000 6
 
1.2%
ValueCountFrequency (%)
18600 1
 
0.2%
18300 1
 
0.2%
18100 2
 
0.4%
17400 1
 
0.2%
16900 1
 
0.2%
16800 1
 
0.2%
16500 1
 
0.2%
16200 5
1.0%
14400 1
 
0.2%
13800 3
0.6%

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

2023-12-10T23:57:28.509839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:57:28.738781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 500
100.0%

번지1(BUNJI1)
Real number (ℝ)

Distinct353
Distinct (%)70.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean349.584
Minimum1
Maximum1690
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:57:28.957338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile11.95
Q1104.25
median252.5
Q3512.25
95-th percentile972.05
Maximum1690
Range1689
Interquartile range (IQR)408

Descriptive statistics

Standard deviation315.56574
Coefficient of variation (CV)0.9026893
Kurtosis1.6549619
Mean349.584
Median Absolute Deviation (MAD)180
Skewness1.2852863
Sum174792
Variance99581.734
MonotonicityNot monotonic
2023-12-10T23:57:29.251725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
38 5
 
1.0%
105 5
 
1.0%
1 5
 
1.0%
136 4
 
0.8%
5 4
 
0.8%
102 4
 
0.8%
3 4
 
0.8%
610 4
 
0.8%
79 4
 
0.8%
410 4
 
0.8%
Other values (343) 457
91.4%
ValueCountFrequency (%)
1 5
1.0%
2 1
 
0.2%
3 4
0.8%
5 4
0.8%
6 1
 
0.2%
7 1
 
0.2%
8 2
 
0.4%
9 3
0.6%
10 1
 
0.2%
11 3
0.6%
ValueCountFrequency (%)
1690 1
0.2%
1643 1
0.2%
1628 1
0.2%
1471 1
0.2%
1465 1
0.2%
1265 1
0.2%
1233 1
0.2%
1219 1
0.2%
1170 1
0.2%
1163 1
0.2%

번지2(BUNJI2)
Real number (ℝ)

ZEROS 

Distinct142
Distinct (%)28.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean60.968
Minimum0
Maximum3146
Zeros14
Zeros (%)2.8%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:57:29.560245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q16
median17
Q348
95-th percentile232.4
Maximum3146
Range3146
Interquartile range (IQR)42

Descriptive statistics

Standard deviation189.44315
Coefficient of variation (CV)3.1072555
Kurtosis152.23145
Mean60.968
Median Absolute Deviation (MAD)14
Skewness10.718616
Sum30484
Variance35888.708
MonotonicityNot monotonic
2023-12-10T23:57:29.873895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 26
 
5.2%
2 22
 
4.4%
3 20
 
4.0%
5 17
 
3.4%
13 15
 
3.0%
4 14
 
2.8%
0 14
 
2.8%
8 14
 
2.8%
12 13
 
2.6%
9 13
 
2.6%
Other values (132) 332
66.4%
ValueCountFrequency (%)
0 14
2.8%
1 26
5.2%
2 22
4.4%
3 20
4.0%
4 14
2.8%
5 17
3.4%
6 13
2.6%
7 12
2.4%
8 14
2.8%
9 13
2.6%
ValueCountFrequency (%)
3146 1
0.2%
1531 1
0.2%
1171 1
0.2%
1129 1
0.2%
679 1
0.2%
624 1
0.2%
553 1
0.2%
538 1
0.2%
470 1
0.2%
456 1
0.2%

경도(LNG)
Real number (ℝ)

UNIQUE 

Distinct500
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.99004
Minimum126.80712
Maximum127.16888
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:57:30.167050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.80712
5-th percentile126.84098
Q1126.91349
median127.00395
Q3127.07187
95-th percentile127.13297
Maximum127.16888
Range0.36176054
Interquartile range (IQR)0.15837235

Descriptive statistics

Standard deviation0.092312867
Coefficient of variation (CV)0.00072693001
Kurtosis-1.1140357
Mean126.99004
Median Absolute Deviation (MAD)0.079149027
Skewness-0.08758758
Sum63495.018
Variance0.0085216655
MonotonicityNot monotonic
2023-12-10T23:57:30.942931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.859805780407 1
 
0.2%
126.918720141685 1
 
0.2%
126.90805271286 1
 
0.2%
127.065040549775 1
 
0.2%
127.014769611928 1
 
0.2%
126.865680324873 1
 
0.2%
127.076328050523 1
 
0.2%
126.845914184568 1
 
0.2%
127.130515166772 1
 
0.2%
127.036094292826 1
 
0.2%
Other values (490) 490
98.0%
ValueCountFrequency (%)
126.807119264193 1
0.2%
126.810182323403 1
0.2%
126.8153634622 1
0.2%
126.821925078296 1
0.2%
126.824197082237 1
0.2%
126.825252513742 1
0.2%
126.829693797081 1
0.2%
126.830125157666 1
0.2%
126.831169267803 1
0.2%
126.831434097295 1
0.2%
ValueCountFrequency (%)
127.168879806572 1
0.2%
127.167480108319 1
0.2%
127.156144892312 1
0.2%
127.155360272905 1
0.2%
127.154977823019 1
0.2%
127.154889137306 1
0.2%
127.151531297361 1
0.2%
127.149227395595 1
0.2%
127.147545201036 1
0.2%
127.142519704906 1
0.2%

위도(LAT)
Real number (ℝ)

Distinct499
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.549531
Minimum37.4521
Maximum37.685784
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:57:31.293734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.4521
5-th percentile37.476996
Q137.504573
median37.546866
Q337.588651
95-th percentile37.640328
Maximum37.685784
Range0.23368464
Interquartile range (IQR)0.08407813

Descriptive statistics

Standard deviation0.051487843
Coefficient of variation (CV)0.001371198
Kurtosis-0.64672056
Mean37.549531
Median Absolute Deviation (MAD)0.042206634
Skewness0.3299062
Sum18774.766
Variance0.0026509979
MonotonicityNot monotonic
2023-12-10T23:57:31.598536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.570735839682 2
 
0.4%
37.575635588884 1
 
0.2%
37.552096032164 1
 
0.2%
37.5221930647774 1
 
0.2%
37.493832880135 1
 
0.2%
37.564841131463 1
 
0.2%
37.532555779853 1
 
0.2%
37.498682029351 1
 
0.2%
37.596131858959 1
 
0.2%
37.5121651975239 1
 
0.2%
Other values (489) 489
97.8%
ValueCountFrequency (%)
37.45209968645 1
0.2%
37.4523405874692 1
0.2%
37.453425784594 1
0.2%
37.453820104023 1
0.2%
37.454001794347 1
0.2%
37.455268462272 1
0.2%
37.457066476504 1
0.2%
37.457793364286 1
0.2%
37.460622409478 1
0.2%
37.461130667858 1
0.2%
ValueCountFrequency (%)
37.685784324242 1
0.2%
37.675893809965 1
0.2%
37.6753599459096 1
0.2%
37.675304078603 1
0.2%
37.673657770013 1
0.2%
37.672906943674 1
0.2%
37.672442246296 1
0.2%
37.667414721001 1
0.2%
37.664647867723 1
0.2%
37.661943973473 1
0.2%

건물_이름(BLDGNAME)
Text

MISSING 

Distinct188
Distinct (%)93.5%
Missing299
Missing (%)59.8%
Memory size4.0 KiB
2023-12-10T23:57:32.296368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length40
Median length14
Mean length5.7412935
Min length2

Characters and Unicode

Total characters1154
Distinct characters172
Distinct categories9 ?
Distinct scripts4 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique175 ?
Unique (%)87.1%

Sample

1st row동*팰*스*
2nd row동*팰*스*
3rd row면*동*다*대*택*신*공*
4th row해*치*
5th row드*캐*
ValueCountFrequency (%)
3
 
1.4%
진*빌 2
 
1.0%
우*빌 2
 
1.0%
미*홈*운 2
 
1.0%
예*팰*스*4 2
 
1.0%
힐*우 2
 
1.0%
청*빌 2
 
1.0%
삼*아*빌 2
 
1.0%
한*그*빌 2
 
1.0%
동*팰*스 2
 
1.0%
Other values (184) 188
90.0%
2023-12-10T23:57:33.265063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
* 577
50.0%
빌 74
 
6.4%
스 32
 
2.8%
동 23
 
2.0%
하 20
 
1.7%
대 14
 
1.2%
아 13
 
1.1%
리 11
 
1.0%
그 11
 
1.0%
트 10
 
0.9%
Other values (162) 369
32.0%

Most occurring categories

ValueCountFrequency (%)
Other Punctuation 578
50.1%
Other Letter 541
46.9%
Decimal Number 12
 
1.0%
Space Separator 8
 
0.7%
Uppercase Letter 8
 
0.7%
Lowercase Letter 3
 
0.3%
Open Punctuation 2
 
0.2%
Close Punctuation 1
 
0.1%
Dash Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
빌 74
 
13.7%
스 32
 
5.9%
동 23
 
4.3%
하 20
 
3.7%
대 14
 
2.6%
아 13
 
2.4%
리 11
 
2.0%
그 11
 
2.0%
트 10
 
1.8%
주 10
 
1.8%
Other values (137) 323
59.7%
Decimal Number
ValueCountFrequency (%)
4 3
25.0%
9 2
16.7%
2 2
16.7%
0 1
 
8.3%
7 1
 
8.3%
3 1
 
8.3%
1 1
 
8.3%
8 1
 
8.3%
Uppercase Letter
ValueCountFrequency (%)
M 1
12.5%
G 1
12.5%
U 1
12.5%
A 1
12.5%
J 1
12.5%
D 1
12.5%
L 1
12.5%
I 1
12.5%
Lowercase Letter
ValueCountFrequency (%)
a 1
33.3%
n 1
33.3%
b 1
33.3%
Other Punctuation
ValueCountFrequency (%)
* 577
99.8%
. 1
 
0.2%
Space Separator
ValueCountFrequency (%)
8
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 602
52.2%
Hangul 540
46.8%
Latin 11
 
1.0%
Han 1
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
빌 74
 
13.7%
스 32
 
5.9%
동 23
 
4.3%
하 20
 
3.7%
대 14
 
2.6%
아 13
 
2.4%
리 11
 
2.0%
그 11
 
2.0%
트 10
 
1.9%
주 10
 
1.9%
Other values (136) 322
59.6%
Common
ValueCountFrequency (%)
* 577
95.8%
8
 
1.3%
4 3
 
0.5%
( 2
 
0.3%
9 2
 
0.3%
2 2
 
0.3%
0 1
 
0.2%
. 1
 
0.2%
7 1
 
0.2%
) 1
 
0.2%
Other values (4) 4
 
0.7%
Latin
ValueCountFrequency (%)
M 1
9.1%
a 1
9.1%
G 1
9.1%
n 1
9.1%
b 1
9.1%
U 1
9.1%
A 1
9.1%
J 1
9.1%
D 1
9.1%
L 1
9.1%
Han
ValueCountFrequency (%)
休 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 613
53.1%
Hangul 540
46.8%
CJK 1
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
* 577
94.1%
8
 
1.3%
4 3
 
0.5%
( 2
 
0.3%
9 2
 
0.3%
2 2
 
0.3%
M 1
 
0.2%
0 1
 
0.2%
. 1
 
0.2%
a 1
 
0.2%
Other values (15) 15
 
2.4%
Hangul
ValueCountFrequency (%)
빌 74
 
13.7%
스 32
 
5.9%
동 23
 
4.3%
하 20
 
3.7%
대 14
 
2.6%
아 13
 
2.4%
리 11
 
2.0%
그 11
 
2.0%
트 10
 
1.9%
주 10
 
1.9%
Other values (136) 322
59.6%
CJK
ValueCountFrequency (%)
休 1
100.0%

동_이름(DONGNAME)
Text

MISSING 

Distinct50
Distinct (%)58.1%
Missing414
Missing (%)82.8%
Memory size4.0 KiB
2023-12-10T23:57:33.682019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length10
Mean length3.8837209
Min length2

Characters and Unicode

Total characters334
Distinct characters66
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique40 ?
Unique (%)46.5%

Sample

1st row한*스*이* *동*
2nd row대*아*빌*
3rd row3*
4th row청*빌*
5th row나*
ValueCountFrequency (%)
b 10
 
11.2%
a 8
 
9.0%
나 5
 
5.6%
1 5
 
5.6%
가 4
 
4.5%
1*2 4
 
4.5%
1*1 4
 
4.5%
청*빌 2
 
2.2%
3 2
 
2.2%
1*4 2
 
2.2%
Other values (43) 43
48.3%
2023-12-10T23:57:34.378719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
* 167
50.0%
1 22
 
6.6%
빌 18
 
5.4%
B 11
 
3.3%
A 9
 
2.7%
2 6
 
1.8%
나 5
 
1.5%
트 5
 
1.5%
동 5
 
1.5%
가 4
 
1.2%
Other values (56) 82
24.6%

Most occurring categories

ValueCountFrequency (%)
Other Punctuation 167
50.0%
Other Letter 105
31.4%
Decimal Number 36
 
10.8%
Uppercase Letter 21
 
6.3%
Space Separator 3
 
0.9%
Dash Punctuation 1
 
0.3%
Open Punctuation 1
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
빌 18
 
17.1%
나 5
 
4.8%
트 5
 
4.8%
동 5
 
4.8%
가 4
 
3.8%
아 3
 
2.9%
스 3
 
2.9%
이 3
 
2.9%
우 3
 
2.9%
청 3
 
2.9%
Other values (43) 53
50.5%
Decimal Number
ValueCountFrequency (%)
1 22
61.1%
2 6
 
16.7%
3 3
 
8.3%
4 3
 
8.3%
0 1
 
2.8%
7 1
 
2.8%
Uppercase Letter
ValueCountFrequency (%)
B 11
52.4%
A 9
42.9%
D 1
 
4.8%
Other Punctuation
ValueCountFrequency (%)
* 167
100.0%
Space Separator
ValueCountFrequency (%)
3
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 208
62.3%
Hangul 105
31.4%
Latin 21
 
6.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
빌 18
 
17.1%
나 5
 
4.8%
트 5
 
4.8%
동 5
 
4.8%
가 4
 
3.8%
아 3
 
2.9%
스 3
 
2.9%
이 3
 
2.9%
우 3
 
2.9%
청 3
 
2.9%
Other values (43) 53
50.5%
Common
ValueCountFrequency (%)
* 167
80.3%
1 22
 
10.6%
2 6
 
2.9%
3
 
1.4%
3 3
 
1.4%
4 3
 
1.4%
- 1
 
0.5%
0 1
 
0.5%
( 1
 
0.5%
7 1
 
0.5%
Latin
ValueCountFrequency (%)
B 11
52.4%
A 9
42.9%
D 1
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 229
68.6%
Hangul 105
31.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
* 167
72.9%
1 22
 
9.6%
B 11
 
4.8%
A 9
 
3.9%
2 6
 
2.6%
3
 
1.3%
3 3
 
1.3%
4 3
 
1.3%
- 1
 
0.4%
0 1
 
0.4%
Other values (3) 3
 
1.3%
Hangul
ValueCountFrequency (%)
빌 18
 
17.1%
나 5
 
4.8%
트 5
 
4.8%
동 5
 
4.8%
가 4
 
3.8%
아 3
 
2.9%
스 3
 
2.9%
이 3
 
2.9%
우 3
 
2.9%
청 3
 
2.9%
Other values (43) 53
50.5%

주용도코드(JYONGDO_CODE)
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2000
499 
3000
 
1

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st row2000
2nd row2000
3rd row2000
4th row2000
5th row2000

Common Values

ValueCountFrequency (%)
2000 499
99.8%
3000 1
 
0.2%

Length

2023-12-10T23:57:34.657298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:57:34.840354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2000 499
99.8%
3000 1
 
0.2%

주용도명(JYONGDO)
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
공동주택
497 
업무시설
 
2
제1종근린생활시설
 
1

Length

Max length9
Median length4
Mean length4.01
Min length4

Unique

Unique1 ?
Unique (%)0.2%

Sample

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

Common Values

ValueCountFrequency (%)
공동주택 497
99.4%
업무시설 2
 
0.4%
제1종근린생활시설 1
 
0.2%

Length

2023-12-10T23:57:35.068793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:57:35.273460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
공동주택 497
99.4%
업무시설 2
 
0.4%
제1종근린생활시설 1
 
0.2%

대지면적(DJAREA)
Real number (ℝ)

Distinct440
Distinct (%)88.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean317.82898
Minimum62
Maximum3401
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:57:35.501688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum62
5-th percentile115.885
Q1184.575
median236.85
Q3329.6
95-th percentile728.4935
Maximum3401
Range3339
Interquartile range (IQR)145.025

Descriptive statistics

Standard deviation330.48858
Coefficient of variation (CV)1.0398315
Kurtosis40.524902
Mean317.82898
Median Absolute Deviation (MAD)64.95
Skewness5.6328504
Sum158914.49
Variance109222.7
MonotonicityNot monotonic
2023-12-10T23:57:35.799661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
168.0 4
 
0.8%
255.0 4
 
0.8%
172.0 4
 
0.8%
198.0 3
 
0.6%
347.0 3
 
0.6%
201.7 3
 
0.6%
218.0 3
 
0.6%
200.0 3
 
0.6%
192.0 3
 
0.6%
261.0 3
 
0.6%
Other values (430) 467
93.4%
ValueCountFrequency (%)
62.0 1
0.2%
65.0 1
0.2%
66.0 1
0.2%
69.0 1
0.2%
69.1 1
0.2%
82.0 1
0.2%
83.0 1
0.2%
86.0 1
0.2%
88.7 1
0.2%
88.9 1
0.2%
ValueCountFrequency (%)
3401.0 1
0.2%
3334.3 1
0.2%
2821.0 1
0.2%
2231.0 1
0.2%
2119.6 1
0.2%
1809.0 1
0.2%
1793.1 1
0.2%
1713.9 1
0.2%
1548.2 1
0.2%
1267.55 1
0.2%

건축면적(GCAREA)
Real number (ℝ)

ZEROS 

Distinct471
Distinct (%)94.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean140.92358
Minimum0
Maximum593.46
Zeros17
Zeros (%)3.4%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:57:36.079619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile53.796
Q196.48
median134.345
Q3169.24
95-th percentile262.1135
Maximum593.46
Range593.46
Interquartile range (IQR)72.76

Descriptive statistics

Standard deviation74.580659
Coefficient of variation (CV)0.52922768
Kurtosis7.7202256
Mean140.92358
Median Absolute Deviation (MAD)36.995
Skewness1.9242481
Sum70461.79
Variance5562.2747
MonotonicityNot monotonic
2023-12-10T23:57:36.337448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 17
 
3.4%
116.52 2
 
0.4%
108.0 2
 
0.4%
149.4 2
 
0.4%
132.84 2
 
0.4%
169.2 2
 
0.4%
124.6 2
 
0.4%
76.43 2
 
0.4%
110.08 2
 
0.4%
157.44 2
 
0.4%
Other values (461) 465
93.0%
ValueCountFrequency (%)
0.0 17
3.4%
34.32 1
 
0.2%
38.11 1
 
0.2%
41.2 1
 
0.2%
44.95 1
 
0.2%
49.56 1
 
0.2%
49.68 1
 
0.2%
49.83 1
 
0.2%
53.72 1
 
0.2%
53.8 1
 
0.2%
ValueCountFrequency (%)
593.46 1
0.2%
543.6 1
0.2%
527.74 1
0.2%
495.93 1
0.2%
456.1 1
0.2%
431.81 1
0.2%
422.92 1
0.2%
389.09 1
0.2%
370.89 1
0.2%
362.2 1
0.2%

건폐율(GPRATE)
Real number (ℝ)

ZEROS 

Distinct292
Distinct (%)58.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.48632
Minimum0
Maximum78.51
Zeros89
Zeros (%)17.8%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:57:36.611941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q145.965
median56.855
Q359.62
95-th percentile59.9605
Maximum78.51
Range78.51
Interquartile range (IQR)13.655

Descriptive statistics

Standard deviation22.450537
Coefficient of variation (CV)0.49356679
Kurtosis0.18305002
Mean45.48632
Median Absolute Deviation (MAD)3.085
Skewness-1.3613835
Sum22743.16
Variance504.02661
MonotonicityNot monotonic
2023-12-10T23:57:36.861079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 89
 
17.8%
59.96 8
 
1.6%
59.94 8
 
1.6%
59.88 7
 
1.4%
59.83 7
 
1.4%
59.95 6
 
1.2%
59.97 5
 
1.0%
59.93 5
 
1.0%
59.89 4
 
0.8%
59.64 4
 
0.8%
Other values (282) 357
71.4%
ValueCountFrequency (%)
0.0 89
17.8%
13.11 1
 
0.2%
16.59 1
 
0.2%
16.83 1
 
0.2%
18.81 1
 
0.2%
20.79 1
 
0.2%
21.19 1
 
0.2%
22.39 1
 
0.2%
22.8 1
 
0.2%
25.13 1
 
0.2%
ValueCountFrequency (%)
78.51 1
0.2%
77.54 1
0.2%
76.77 1
0.2%
75.05 1
0.2%
73.96 1
0.2%
69.59 1
0.2%
69.0 1
0.2%
66.55 1
0.2%
66.42 1
0.2%
65.69 1
0.2%

연면적(YAREA)
Real number (ℝ)

Distinct499
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean589.04446
Minimum67.61
Maximum4460.34
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:57:37.107944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum67.61
5-th percentile215.994
Q1376.7975
median532.055
Q3658.91
95-th percentile1128.354
Maximum4460.34
Range4392.73
Interquartile range (IQR)282.1125

Descriptive statistics

Standard deviation423.6926
Coefficient of variation (CV)0.71928797
Kurtosis25.731655
Mean589.04446
Median Absolute Deviation (MAD)128.225
Skewness4.2174377
Sum294522.23
Variance179515.42
MonotonicityNot monotonic
2023-12-10T23:57:37.375575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
659.76 2
 
0.4%
555.12 1
 
0.2%
1645.03 1
 
0.2%
345.2 1
 
0.2%
491.89 1
 
0.2%
2664.52 1
 
0.2%
432.62 1
 
0.2%
543.27 1
 
0.2%
247.26 1
 
0.2%
2333.83 1
 
0.2%
Other values (489) 489
97.8%
ValueCountFrequency (%)
67.61 1
0.2%
78.94 1
0.2%
101.44 1
0.2%
119.02 1
0.2%
127.83 1
0.2%
133.54 1
0.2%
138.88 1
0.2%
141.48 1
0.2%
147.12 1
0.2%
149.58 1
0.2%
ValueCountFrequency (%)
4460.34 1
0.2%
3560.25 1
0.2%
3155.99 1
0.2%
2841.46 1
0.2%
2664.52 1
0.2%
2438.35 1
0.2%
2428.76 1
0.2%
2370.37 1
0.2%
2333.83 1
0.2%
2234.7 1
0.2%

지상연면적(JSYRATE)
Real number (ℝ)

ZEROS 

Distinct461
Distinct (%)92.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean465.86004
Minimum0
Maximum2533.32
Zeros38
Zeros (%)7.6%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:57:37.630109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1329.7675
median470.655
Q3589.5
95-th percentile916.9935
Maximum2533.32
Range2533.32
Interquartile range (IQR)259.7325

Descriptive statistics

Standard deviation280.2451
Coefficient of variation (CV)0.60156501
Kurtosis10.154767
Mean465.86004
Median Absolute Deviation (MAD)131
Skewness1.8088977
Sum232930.02
Variance78537.315
MonotonicityNot monotonic
2023-12-10T23:57:37.863208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 38
 
7.6%
504.78 2
 
0.4%
659.27 2
 
0.4%
397.38 1
 
0.2%
339.6 1
 
0.2%
514.13 1
 
0.2%
433.53 1
 
0.2%
482.5 1
 
0.2%
363.14 1
 
0.2%
882.28 1
 
0.2%
Other values (451) 451
90.2%
ValueCountFrequency (%)
0.0 38
7.6%
41.24 1
 
0.2%
93.59 1
 
0.2%
96.58 1
 
0.2%
99.0 1
 
0.2%
101.25 1
 
0.2%
112.38 1
 
0.2%
119.0 1
 
0.2%
128.32 1
 
0.2%
134.64 1
 
0.2%
ValueCountFrequency (%)
2533.32 1
0.2%
2293.92 1
0.2%
1771.59 1
0.2%
1441.68 1
0.2%
1339.65 1
0.2%
1309.32 1
0.2%
1260.57 1
0.2%
1226.36 1
0.2%
1198.57 1
0.2%
1170.58 1
0.2%

용적률(YJAREA)
Real number (ℝ)

ZEROS 

Distinct391
Distinct (%)78.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean157.99814
Minimum0
Maximum379.63
Zeros87
Zeros (%)17.4%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:57:38.074029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1122.5225
median192.9
Q3207.005
95-th percentile249.5705
Maximum379.63
Range379.63
Interquartile range (IQR)84.4825

Descriptive statistics

Standard deviation84.320898
Coefficient of variation (CV)0.53368285
Kurtosis-0.19768636
Mean157.99814
Median Absolute Deviation (MAD)30.41
Skewness-0.79709125
Sum78999.07
Variance7110.0138
MonotonicityNot monotonic
2023-12-10T23:57:38.346720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 87
 
17.4%
199.98 4
 
0.8%
199.85 3
 
0.6%
199.79 3
 
0.6%
195.3 2
 
0.4%
191.65 2
 
0.4%
199.88 2
 
0.4%
149.9 2
 
0.4%
199.41 2
 
0.4%
199.84 2
 
0.4%
Other values (381) 391
78.2%
ValueCountFrequency (%)
0.0 87
17.4%
61.27 1
 
0.2%
61.31 1
 
0.2%
62.66 1
 
0.2%
74.17 1
 
0.2%
77.69 1
 
0.2%
79.73 1
 
0.2%
81.67 1
 
0.2%
84.76 1
 
0.2%
85.69 1
 
0.2%
ValueCountFrequency (%)
379.63 1
0.2%
370.98 1
0.2%
351.51 1
0.2%
344.51 1
0.2%
322.2 1
0.2%
314.19 1
0.2%
312.47 1
0.2%
299.87 1
0.2%
294.39 1
0.2%
287.42 1
0.2%

높이(HIGH)
Real number (ℝ)

ZEROS 

Distinct166
Distinct (%)33.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.18658
Minimum0
Maximum74.6
Zeros99
Zeros (%)19.8%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:57:38.591979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q110.3375
median12.9
Q314.5
95-th percentile18.6
Maximum74.6
Range74.6
Interquartile range (IQR)4.1625

Descriptive statistics

Standard deviation6.7353915
Coefficient of variation (CV)0.60209568
Kurtosis14.870443
Mean11.18658
Median Absolute Deviation (MAD)2.1
Skewness1.0463577
Sum5593.29
Variance45.365498
MonotonicityNot monotonic
2023-12-10T23:57:38.852205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 99
 
19.8%
12.8 18
 
3.6%
13.2 17
 
3.4%
13.1 13
 
2.6%
11.7 12
 
2.4%
10.5 10
 
2.0%
14.5 9
 
1.8%
14.4 9
 
1.8%
13.0 8
 
1.6%
11.6 7
 
1.4%
Other values (156) 298
59.6%
ValueCountFrequency (%)
0.0 99
19.8%
3.9 1
 
0.2%
5.4 1
 
0.2%
6.5 1
 
0.2%
7.3 1
 
0.2%
7.5 1
 
0.2%
7.6 1
 
0.2%
7.7 3
 
0.6%
7.9 3
 
0.6%
7.95 1
 
0.2%
ValueCountFrequency (%)
74.6 1
0.2%
29.6 1
0.2%
27.4 1
0.2%
23.0 1
0.2%
22.35 1
0.2%
22.15 1
0.2%
21.2 1
0.2%
21.0 1
0.2%
19.9 2
0.4%
19.6 1
0.2%

지상층수(JSFLOOR)
Real number (ℝ)

Distinct10
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.342
Minimum2
Maximum26
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:57:39.056431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2
Q14
median4
Q35
95-th percentile6
Maximum26
Range24
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.5728903
Coefficient of variation (CV)0.36225019
Kurtosis71.807726
Mean4.342
Median Absolute Deviation (MAD)1
Skewness5.2990763
Sum2171
Variance2.473984
MonotonicityNot monotonic
2023-12-10T23:57:39.246216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
5 182
36.4%
4 147
29.4%
3 58
 
11.6%
2 54
 
10.8%
6 50
 
10.0%
7 4
 
0.8%
8 2
 
0.4%
10 1
 
0.2%
11 1
 
0.2%
26 1
 
0.2%
ValueCountFrequency (%)
2 54
 
10.8%
3 58
 
11.6%
4 147
29.4%
5 182
36.4%
6 50
 
10.0%
7 4
 
0.8%
8 2
 
0.4%
10 1
 
0.2%
11 1
 
0.2%
26 1
 
0.2%
ValueCountFrequency (%)
26 1
 
0.2%
11 1
 
0.2%
10 1
 
0.2%
8 2
 
0.4%
7 4
 
0.8%
6 50
 
10.0%
5 182
36.4%
4 147
29.4%
3 58
 
11.6%
2 54
 
10.8%

지하층수(JHFLOOR)
Categorical

Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
1
252 
0
243 
2
 
5

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 (%)
1 252
50.4%
0 243
48.6%
2 5
 
1.0%

Length

2023-12-10T23:57:39.469353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:57:39.643002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 252
50.4%
0 243
48.6%
2 5
 
1.0%

세대수(SEDECNT)
Real number (ℝ)

Distinct23
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.696
Minimum2
Maximum28
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:57:39.810025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3.95
Q16
median8
Q310
95-th percentile16
Maximum28
Range26
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.9133378
Coefficient of variation (CV)0.45001584
Kurtosis2.7293955
Mean8.696
Median Absolute Deviation (MAD)2
Skewness1.2548932
Sum4348
Variance15.314212
MonotonicityNot monotonic
2023-12-10T23:57:40.019889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
8 93
18.6%
10 68
13.6%
6 60
12.0%
9 46
9.2%
7 43
8.6%
4 36
 
7.2%
12 28
 
5.6%
5 25
 
5.0%
3 15
 
3.0%
11 14
 
2.8%
Other values (13) 72
14.4%
ValueCountFrequency (%)
2 10
 
2.0%
3 15
 
3.0%
4 36
 
7.2%
5 25
 
5.0%
6 60
12.0%
7 43
8.6%
8 93
18.6%
9 46
9.2%
10 68
13.6%
11 14
 
2.8%
ValueCountFrequency (%)
28 1
 
0.2%
24 3
 
0.6%
23 1
 
0.2%
22 1
 
0.2%
20 4
 
0.8%
19 5
1.0%
18 5
1.0%
17 1
 
0.2%
16 10
2.0%
15 11
2.2%

사용승인일(SYDATE)
Real number (ℝ)

Distinct491
Distinct (%)98.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20021700
Minimum19750822
Maximum20200713
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:57:40.286794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19750822
5-th percentile19851107
Q119930919
median20021014
Q320120942
95-th percentile20180423
Maximum20200713
Range449891
Interquartile range (IQR)190023.25

Descriptive statistics

Standard deviation110139.68
Coefficient of variation (CV)0.0055010154
Kurtosis-0.8746273
Mean20021700
Median Absolute Deviation (MAD)99444
Skewness-0.21332203
Sum1.001085 × 1010
Variance1.213075 × 1010
MonotonicityNot monotonic
2023-12-10T23:57:40.550947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19890622 2
 
0.4%
20020729 2
 
0.4%
19931217 2
 
0.4%
20020814 2
 
0.4%
20180329 2
 
0.4%
20000509 2
 
0.4%
20020509 2
 
0.4%
19941012 2
 
0.4%
19861008 2
 
0.4%
20160810 1
 
0.2%
Other values (481) 481
96.2%
ValueCountFrequency (%)
19750822 1
0.2%
19751007 1
0.2%
19760929 1
0.2%
19761014 1
0.2%
19761224 1
0.2%
19770706 1
0.2%
19771019 1
0.2%
19771101 1
0.2%
19780622 1
0.2%
19791120 1
0.2%
ValueCountFrequency (%)
20200713 1
0.2%
20200601 1
0.2%
20200518 1
0.2%
20200401 1
0.2%
20200324 1
0.2%
20200323 1
0.2%
20200206 1
0.2%
20200129 1
0.2%
20191023 1
0.2%
20191015 1
0.2%

건축년도(BUILDYEAR)
Categorical

Distinct23
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
0
227 
2002
58 
2015
28 
2008
 
18
2016
 
17
Other values (18)
152 

Length

Max length4
Median length4
Mean length2.638
Min length1

Unique

Unique4 ?
Unique (%)0.8%

Sample

1st row2010
2nd row2013
3rd row2002
4th row2018
5th row2002

Common Values

ValueCountFrequency (%)
0 227
45.4%
2002 58
 
11.6%
2015 28
 
5.6%
2008 18
 
3.6%
2016 17
 
3.4%
2014 16
 
3.2%
2017 16
 
3.2%
2018 14
 
2.8%
2007 14
 
2.8%
2003 13
 
2.6%
Other values (13) 79
 
15.8%

Length

2023-12-10T23:57:41.180261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0 227
45.4%
2002 58
 
11.6%
2015 28
 
5.6%
2008 18
 
3.6%
2016 17
 
3.4%
2014 16
 
3.2%
2017 16
 
3.2%
2018 14
 
2.8%
2007 14
 
2.8%
2013 13
 
2.6%
Other values (13) 79
 
15.8%

지역지구(JYJG)
Categorical

Distinct10
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
제2종일반주거지역
155 
127 
일반주거지역
117 
제1종일반주거지역
40 
도시지역
30 
Other values (5)
31 

Length

Max length9
Median length6
Mean length5.85
Min length1

Unique

Unique2 ?
Unique (%)0.4%

Sample

1st row
2nd row제2종일반주거지역
3rd row
4th row
5th row일반주거지역

Common Values

ValueCountFrequency (%)
제2종일반주거지역 155
31.0%
127
25.4%
일반주거지역 117
23.4%
제1종일반주거지역 40
 
8.0%
도시지역 30
 
6.0%
제3종일반주거지역 16
 
3.2%
준주거지역 7
 
1.4%
준공업지역 6
 
1.2%
근린상업지역 1
 
0.2%
일반상업지역 1
 
0.2%

Length

2023-12-10T23:57:41.470057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:57:41.687765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
제2종일반주거지역 155
41.6%
일반주거지역 117
31.4%
제1종일반주거지역 40
 
10.7%
도시지역 30
 
8.0%
제3종일반주거지역 16
 
4.3%
준주거지역 7
 
1.9%
준공업지역 6
 
1.6%
근린상업지역 1
 
0.3%
일반상업지역 1
 
0.3%

시세산성_세대수(HO_COUNT)
Real number (ℝ)

ZEROS 

Distinct23
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.62
Minimum0
Maximum39
Zeros16
Zeros (%)3.2%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:57:41.908450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.95
Q15
median8
Q39
95-th percentile15.05
Maximum39
Range39
Interquartile range (IQR)4

Descriptive statistics

Standard deviation4.4039061
Coefficient of variation (CV)0.57794043
Kurtosis9.5853065
Mean7.62
Median Absolute Deviation (MAD)2
Skewness1.8665441
Sum3810
Variance19.394389
MonotonicityNot monotonic
2023-12-10T23:57:42.132779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
8 119
23.8%
4 59
11.8%
6 59
11.8%
7 46
 
9.2%
12 35
 
7.0%
10 29
 
5.8%
3 21
 
4.2%
5 17
 
3.4%
2 17
 
3.4%
0 16
 
3.2%
Other values (13) 82
16.4%
ValueCountFrequency (%)
0 16
 
3.2%
1 9
 
1.8%
2 17
 
3.4%
3 21
 
4.2%
4 59
11.8%
5 17
 
3.4%
6 59
11.8%
7 46
 
9.2%
8 119
23.8%
9 16
 
3.2%
ValueCountFrequency (%)
39 1
 
0.2%
38 1
 
0.2%
24 2
 
0.4%
20 3
 
0.6%
19 2
 
0.4%
18 4
 
0.8%
16 12
2.4%
15 6
1.2%
14 10
2.0%
13 4
 
0.8%

Sample

년월(KEYMONTH)지번주소코드(KEY_ADDRESS)지번주소+동코드(KEY_DONG)주소(ADDRESS)법정동_구코드(SREG)법정동_동코드(SEUB)대지구분(DAEJI)번지1(BUNJI1)번지2(BUNJI2)경도(LNG)위도(LAT)건물_이름(BLDGNAME)동_이름(DONGNAME)주용도코드(JYONGDO_CODE)주용도명(JYONGDO)대지면적(DJAREA)건축면적(GCAREA)건폐율(GPRATE)연면적(YAREA)지상연면적(JSYRATE)용적률(YJAREA)높이(HIGH)지상층수(JSFLOOR)지하층수(JHFLOOR)세대수(SEDECNT)사용승인일(SYDATE)건축년도(BUILDYEAR)지역지구(JYJG)시세산성_세대수(HO_COUNT)
0201911BV1126010200102000011BV1150010200106430044000서울특별시 관악구 봉천동 4*-*6*1150010900120670126.85980637.575636<NA><NA>2000공동주택280.275.9659.49555.120.099.57.761131999052420105
1202010BV1171010700101710009BV1147010300109240003000서울특별시 금천구 독산동 3*8*5*11230132001665184127.0958237.526916동*팰*스*<NA>2000공동주택1267.55129.480.0149.581032.14199.7614.71408199201062013제2종일반주거지역7
2201903BV1159010700101780001BV1141010200100030090000서울특별시 노원구 중계동 5*0*2*113501010011624127.10040137.535172동*팰*스*<NA>2000공동주택187.8136.9459.88247.35659.21242.2211.75062014101620022
3202005BV1130510300101300039BV1121510900101600005000서울특별시 도봉구 방학동 6*8*1*11230133001102679126.99117937.591878면*동*다*대*택*신*공*<NA>2000공동주택243.4164.240.0875.33149.64221.4710.44081987123020188
4202010BV1147010300105160012BV1147010300101070045000서울특별시 송파구 가락동 4*-*11410104001947126.95149437.578389해*치*<NA>2000공동주택307.65166.559.77489.12593.65180.8510.8506200208142002일반주거지역0
5201903BV1138010900100010075BV1123010900102930036000서울특별시 은평구 갈현동 5*2*3*1132010600115022127.06082737.539259<NA><NA>2000공동주택215.90.00.0480.88544.69161.590.0505199011010일반주거지역10
6202005BV1150010300110530017BV1130510300101300057000서울특별시 서초구 반포동 7*1*3*117101110019197127.13446237.599712드*캐*<NA>2000공동주택337.2128.6559.51538.02430.68194.5217.55171990041420140
7201910BV1138010400103270095BV1154510300108940008000서울특별시 양천구 신월동 9*-*1*1144010200149421126.87273837.531857운*하*스*<NA>2000공동주택227.8151.8159.52656.32398.5181.911.7518200106070일반주거지역5
8202006BV1117011900100050305BV1126010300103230065000서울특별시 송파구 오금동 5*114401020012058126.81536337.457066시*그*빌*<NA>2000공동주택92.099.4556.73659.26543.84109.4912.73010200311102007도시지역4
9201905BV1144012500100040003BV1150010300108690009000서울특별시 동작구 상도동 3*8*1*1159010300152045126.95019137.554184<NA><NA>2000공동주택195.40.057.271116.34626.02192.7112.5413201910152007제2종일반주거지역6
년월(KEYMONTH)지번주소코드(KEY_ADDRESS)지번주소+동코드(KEY_DONG)주소(ADDRESS)법정동_구코드(SREG)법정동_동코드(SEUB)대지구분(DAEJI)번지1(BUNJI1)번지2(BUNJI2)경도(LNG)위도(LAT)건물_이름(BLDGNAME)동_이름(DONGNAME)주용도코드(JYONGDO_CODE)주용도명(JYONGDO)대지면적(DJAREA)건축면적(GCAREA)건폐율(GPRATE)연면적(YAREA)지상연면적(JSYRATE)용적률(YJAREA)높이(HIGH)지상층수(JSFLOOR)지하층수(JHFLOOR)세대수(SEDECNT)사용승인일(SYDATE)건축년도(BUILDYEAR)지역지구(JYJG)시세산성_세대수(HO_COUNT)
490202003BV1117010900100040003BV1150010300111130005000서울특별시 구로구 개봉동 3*0*1*1159010300113829127.06245737.525846<NA><NA>2000공동주택323.6893.60.0402.94418.85223.8113.2506197707062002일반주거지역10
491202001BV1126010100101420012BV1138010800100380054000서울특별시 성동구 응봉동 2*5*7*1171010100141048127.12950437.498283<NA><NA>2000공동주택318.783.9559.93465.31819.72279.8618.5226199211240준주거지역2
492202004BV1130510300102910189BV1147010100108750020000서울특별시 중랑구 면목동 1*2*1*4*113801080011856126.89966337.607049예*크*시*루*2000공동주택260.1171.50.0483.52426.0199.4418.92110200302150일반주거지역4
493202012BV1171010800101060003BV1165010100104510037000서울특별시 중랑구 중화동 3*0*3*1165016200110652127.1674837.608116동*씨*빌*<NA>2000공동주택171.9137.1359.33720.690.0178.8111.55508199312172002일반주거지역2
494202002BV1159010400100480025BV1171010600100070015000서울특별시 은평구 갈현동 2*5*1*0*1171011400147511126.93302737.615782동*쉐*빌*<NA>2000공동주택216.1527.740.0433.55645.32169.140.03010201410222014일반주거지역8
495202012BV1159010500102780000BV1171010700100440006000서울특별시 관악구 신림동 6*5*4*116801050014591127.04167437.520902<NA><NA>2000공동주택201.3179.459.98298.86657.9361.3110.551319930624201714
496201906BV1141012000103260020BV1144012400104870263000서울특별시 송파구 문정동 6*-*116501050014841531127.09131437.519431상*스*빌*<NA>2000공동주택683.0123.059.72249.6347.41112.1414.54131991072720035
497201901BV1153010200107920044BV1121510300105870055002서울특별시 마포구 염리동 4*7*2*1117010100121212127.0363437.461131<NA><NA>2000공동주택210.0253.270.0643.99326.32156.3216.9518199803062013제2종일반주거지역11
498201902BV1138010800100310020BV1159010700110210033000서울특별시 서초구 방배동 9*6*2*11440105001101812127.15614537.476036<NA><NA>2000공동주택141.5285.6857.78292.97333.41199.540.04110198705060제2종일반주거지역8
499201903BV1144012400103700023BV1174010800103860008000서울특별시 관악구 남현동 6*2*2*4*1150010300145610126.91796737.484471<NA><NA>2000공동주택1713.9161.6676.77323.51554.37128.9212.85110200110202002일반주거지역4