Overview

Dataset statistics

Number of variables18
Number of observations199
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory30.3 KiB
Average record size in memory155.7 B

Variable types

Text4
Numeric12
Categorical2

Alerts

1130510300004480027 is highly overall correlated with 1130510300104480027000001 and 1 other fieldsHigh correlation
1130510300104480027000001 is highly overall correlated with 1130510300004480027 and 1 other fieldsHigh correlation
313370 is highly overall correlated with 559665High correlation
559665 is highly overall correlated with 1130510300004480027 and 2 other fieldsHigh correlation
-99999 is highly overall correlated with 15000 and 2 other fieldsHigh correlation
15000 is highly overall correlated with -99999 and 2 other fieldsHigh correlation
0 is highly overall correlated with -99999 and 2 other fieldsHigh correlation
전세 is highly overall correlated with -99999 and 2 other fieldsHigh correlation
0 has 90 (45.2%) zerosZeros

Reproduction

Analysis started2023-12-10 06:17:35.667728
Analysis finished2023-12-10 06:18:33.995487
Duration58.33 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

서울특별시 강북구 수유동 448-27 상아주택(448-27)
Text

Distinct190
Distinct (%)95.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-12-10T15:18:34.409702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length45
Median length36
Mean length27.175879
Min length19

Characters and Unicode

Total characters5408
Distinct characters251
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique182 ?
Unique (%)91.5%

Sample

1st row서울특별시 강북구 오패산로60길 8 유림빌라(258-306)
2nd row서울특별시 강북구 미아동 1357 삼각산아이원
3rd row서울특별시 강남구 역삼로70길 19-11 효산그린빌
4th row서울특별시 강서구 등촌로13자길 51 미주
5th row서울특별시 강북구 미아동 1356 경남아너스빌(1356-0)
ValueCountFrequency (%)
서울특별시 199
 
19.3%
강서구 107
 
10.4%
강남구 67
 
6.5%
강북구 25
 
2.4%
화곡동 10
 
1.0%
미아동 8
 
0.8%
대치동 8
 
0.8%
등촌동 8
 
0.8%
역삼동 7
 
0.7%
가양동 7
 
0.7%
Other values (464) 583
56.7%
2023-12-10T15:18:35.390747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
830
 
15.3%
서 321
 
5.9%
강 220
 
4.1%
시 210
 
3.9%
구 207
 
3.8%
울 201
 
3.7%
특 199
 
3.7%
별 199
 
3.7%
1 177
 
3.3%
로 122
 
2.3%
Other values (241) 2722
50.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3494
64.6%
Decimal Number 922
 
17.0%
Space Separator 830
 
15.3%
Dash Punctuation 93
 
1.7%
Close Punctuation 28
 
0.5%
Open Punctuation 28
 
0.5%
Uppercase Letter 8
 
0.1%
Math Symbol 2
 
< 0.1%
Other Punctuation 2
 
< 0.1%
Lowercase Letter 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
서 321
 
9.2%
강 220
 
6.3%
시 210
 
6.0%
구 207
 
5.9%
울 201
 
5.8%
특 199
 
5.7%
별 199
 
5.7%
로 122
 
3.5%
동 104
 
3.0%
곡 77
 
2.2%
Other values (219) 1634
46.8%
Decimal Number
ValueCountFrequency (%)
1 177
19.2%
2 113
12.3%
5 105
11.4%
4 97
10.5%
3 91
9.9%
6 81
8.8%
7 73
7.9%
0 66
 
7.2%
8 63
 
6.8%
9 56
 
6.1%
Uppercase Letter
ValueCountFrequency (%)
B 5
62.5%
M 1
 
12.5%
S 1
 
12.5%
K 1
 
12.5%
Other Punctuation
ValueCountFrequency (%)
: 1
50.0%
& 1
50.0%
Space Separator
ValueCountFrequency (%)
830
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 93
100.0%
Close Punctuation
ValueCountFrequency (%)
) 28
100.0%
Open Punctuation
ValueCountFrequency (%)
( 28
100.0%
Math Symbol
ValueCountFrequency (%)
~ 2
100.0%
Lowercase Letter
ValueCountFrequency (%)
e 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3494
64.6%
Common 1905
35.2%
Latin 9
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
서 321
 
9.2%
강 220
 
6.3%
시 210
 
6.0%
구 207
 
5.9%
울 201
 
5.8%
특 199
 
5.7%
별 199
 
5.7%
로 122
 
3.5%
동 104
 
3.0%
곡 77
 
2.2%
Other values (219) 1634
46.8%
Common
ValueCountFrequency (%)
830
43.6%
1 177
 
9.3%
2 113
 
5.9%
5 105
 
5.5%
4 97
 
5.1%
- 93
 
4.9%
3 91
 
4.8%
6 81
 
4.3%
7 73
 
3.8%
0 66
 
3.5%
Other values (7) 179
 
9.4%
Latin
ValueCountFrequency (%)
B 5
55.6%
M 1
 
11.1%
S 1
 
11.1%
K 1
 
11.1%
e 1
 
11.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3494
64.6%
ASCII 1914
35.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
830
43.4%
1 177
 
9.2%
2 113
 
5.9%
5 105
 
5.5%
4 97
 
5.1%
- 93
 
4.9%
3 91
 
4.8%
6 81
 
4.2%
7 73
 
3.8%
0 66
 
3.4%
Other values (12) 188
 
9.8%
Hangul
ValueCountFrequency (%)
서 321
 
9.2%
강 220
 
6.3%
시 210
 
6.0%
구 207
 
5.9%
울 201
 
5.8%
특 199
 
5.7%
별 199
 
5.7%
로 122
 
3.5%
동 104
 
3.0%
곡 77
 
2.2%
Other values (219) 1634
46.8%

상아주택(448-27)
Text

Distinct182
Distinct (%)91.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-12-10T15:18:35.931797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length17
Mean length7.4120603
Min length2

Characters and Unicode

Total characters1475
Distinct characters226
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique169 ?
Unique (%)84.9%

Sample

1st row유림빌라(258-306)
2nd row삼각산아이원
3rd row효산그린빌
4th row미주
5th row경남아너스빌(1356-0)
ValueCountFrequency (%)
마곡엠밸리8단지 4
 
1.7%
강남 4
 
1.7%
오피스텔 4
 
1.7%
마곡수명산파크5단지 3
 
1.3%
엘에이치강남아이파크 3
 
1.3%
강변 2
 
0.9%
삼환 2
 
0.9%
까치마을 2
 
0.9%
화곡푸르지오 2
 
0.9%
푸르지오시티 2
 
0.9%
Other values (194) 205
88.0%
2023-12-10T15:18:36.702718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
스 44
 
3.0%
지 43
 
2.9%
이 41
 
2.8%
빌 37
 
2.5%
1 36
 
2.4%
34
 
2.3%
2 33
 
2.2%
마 32
 
2.2%
트 32
 
2.2%
곡 32
 
2.2%
Other values (216) 1111
75.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1168
79.2%
Decimal Number 182
 
12.3%
Space Separator 34
 
2.3%
Open Punctuation 28
 
1.9%
Close Punctuation 28
 
1.9%
Dash Punctuation 22
 
1.5%
Uppercase Letter 8
 
0.5%
Math Symbol 2
 
0.1%
Other Punctuation 2
 
0.1%
Lowercase Letter 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
스 44
 
3.8%
지 43
 
3.7%
이 41
 
3.5%
빌 37
 
3.2%
마 32
 
2.7%
트 32
 
2.7%
곡 32
 
2.7%
파 29
 
2.5%
아 27
 
2.3%
대 24
 
2.1%
Other values (194) 827
70.8%
Decimal Number
ValueCountFrequency (%)
1 36
19.8%
2 33
18.1%
5 22
12.1%
3 17
9.3%
8 17
9.3%
4 15
8.2%
9 13
 
7.1%
6 12
 
6.6%
0 11
 
6.0%
7 6
 
3.3%
Uppercase Letter
ValueCountFrequency (%)
B 5
62.5%
K 1
 
12.5%
M 1
 
12.5%
S 1
 
12.5%
Other Punctuation
ValueCountFrequency (%)
: 1
50.0%
& 1
50.0%
Space Separator
ValueCountFrequency (%)
34
100.0%
Open Punctuation
ValueCountFrequency (%)
( 28
100.0%
Close Punctuation
ValueCountFrequency (%)
) 28
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 22
100.0%
Math Symbol
ValueCountFrequency (%)
~ 2
100.0%
Lowercase Letter
ValueCountFrequency (%)
e 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1168
79.2%
Common 298
 
20.2%
Latin 9
 
0.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
스 44
 
3.8%
지 43
 
3.7%
이 41
 
3.5%
빌 37
 
3.2%
마 32
 
2.7%
트 32
 
2.7%
곡 32
 
2.7%
파 29
 
2.5%
아 27
 
2.3%
대 24
 
2.1%
Other values (194) 827
70.8%
Common
ValueCountFrequency (%)
1 36
12.1%
34
11.4%
2 33
11.1%
( 28
9.4%
) 28
9.4%
- 22
7.4%
5 22
7.4%
3 17
 
5.7%
8 17
 
5.7%
4 15
 
5.0%
Other values (7) 46
15.4%
Latin
ValueCountFrequency (%)
B 5
55.6%
K 1
 
11.1%
e 1
 
11.1%
M 1
 
11.1%
S 1
 
11.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1168
79.2%
ASCII 307
 
20.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
스 44
 
3.8%
지 43
 
3.7%
이 41
 
3.5%
빌 37
 
3.2%
마 32
 
2.7%
트 32
 
2.7%
곡 32
 
2.7%
파 29
 
2.5%
아 27
 
2.3%
대 24
 
2.1%
Other values (194) 827
70.8%
ASCII
ValueCountFrequency (%)
1 36
11.7%
34
11.1%
2 33
10.7%
( 28
9.1%
) 28
9.1%
- 22
 
7.2%
5 22
 
7.2%
3 17
 
5.5%
8 17
 
5.5%
4 15
 
4.9%
Other values (12) 55
17.9%

2009
Real number (ℝ)

Distinct38
Distinct (%)19.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2006.0754
Minimum1976
Maximum2020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-10T15:18:37.039279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1976
5-th percentile1986.8
Q11999
median2008
Q32016
95-th percentile2020
Maximum2020
Range44
Interquartile range (IQR)17

Descriptive statistics

Standard deviation11.013736
Coefficient of variation (CV)0.0054901903
Kurtosis-0.34216844
Mean2006.0754
Median Absolute Deviation (MAD)8
Skewness-0.63862833
Sum399209
Variance121.30237
MonotonicityNot monotonic
2023-12-10T15:18:37.339998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
2016 16
 
8.0%
2020 14
 
7.0%
2008 14
 
7.0%
2002 13
 
6.5%
2014 11
 
5.5%
2015 10
 
5.0%
2019 10
 
5.0%
2017 10
 
5.0%
1992 9
 
4.5%
1999 8
 
4.0%
Other values (28) 84
42.2%
ValueCountFrequency (%)
1976 3
1.5%
1977 1
 
0.5%
1980 1
 
0.5%
1982 1
 
0.5%
1983 2
1.0%
1985 2
1.0%
1987 1
 
0.5%
1988 2
1.0%
1990 2
1.0%
1991 3
1.5%
ValueCountFrequency (%)
2020 14
7.0%
2019 10
5.0%
2018 5
 
2.5%
2017 10
5.0%
2016 16
8.0%
2015 10
5.0%
2014 11
5.5%
2013 5
 
2.5%
2012 2
 
1.0%
2011 2
 
1.0%

1130510300004480027
Real number (ℝ)

HIGH CORRELATION 

Distinct178
Distinct (%)89.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1536211 × 1018
Minimum1.1305101 × 1018
Maximum1.1680118 × 1018
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-10T15:18:37.658190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.1305101 × 1018
5-th percentile1.1305101 × 1018
Q11.1500103 × 1018
median1.1500105 × 1018
Q31.1680104 × 1018
95-th percentile1.1680114 × 1018
Maximum1.1680118 × 1018
Range3.75017 × 1016
Interquartile range (IQR)1.80001 × 1016

Descriptive statistics

Standard deviation1.2022931 × 1016
Coefficient of variation (CV)0.010421906
Kurtosis-0.54885776
Mean1.1536211 × 1018
Median Absolute Deviation (MAD)4.0000084 × 1011
Skewness-0.33465357
Sum8.2096623 × 1018
Variance1.4455086 × 1032
MonotonicityNot monotonic
2023-12-10T15:18:37.996634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1150010500007410000 4
 
2.0%
1150010600007490000 3
 
1.5%
1168011200006020000 3
 
1.5%
1168011800005270000 2
 
1.0%
1150010500007730000 2
 
1.0%
1150010300010910000 2
 
1.0%
1150010400014750000 2
 
1.0%
1168011000003690001 2
 
1.0%
1150010500007760000 2
 
1.0%
1168011000004260000 2
 
1.0%
Other values (168) 175
87.9%
ValueCountFrequency (%)
1130510100000040000 1
0.5%
1130510100000840039 1
0.5%
1130510100000870015 1
0.5%
1130510100001600003 1
0.5%
1130510100002580306 1
0.5%
1130510100002580592 1
0.5%
1130510100003190053 1
0.5%
1130510100004740000 1
0.5%
1130510100007080006 1
0.5%
1130510100007911666 1
0.5%
ValueCountFrequency (%)
1168011800009480026 1
0.5%
1168011800005270000 2
1.0%
1168011500007460000 2
1.0%
1168011500007380000 1
0.5%
1168011500007360000 1
0.5%
1168011500005370000 1
0.5%
1168011400007390000 1
0.5%
1168011400007160000 1
0.5%
1168011400006250010 1
0.5%
1168011200006620000 1
0.5%

1130510300104480027000001
Real number (ℝ)

HIGH CORRELATION 

Distinct174
Distinct (%)87.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1536211 × 1024
Minimum1.1305101 × 1024
Maximum1.1680118 × 1024
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-10T15:18:38.258894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.1305101 × 1024
5-th percentile1.1305101 × 1024
Q11.1500103 × 1024
median1.1500105 × 1024
Q31.1680104 × 1024
95-th percentile1.1680114 × 1024
Maximum1.1680118 × 1024
Range3.75017 × 1022
Interquartile range (IQR)1.80001 × 1022

Descriptive statistics

Standard deviation1.202293 × 1022
Coefficient of variation (CV)0.010421906
Kurtosis-0.54885723
Mean1.1536211 × 1024
Median Absolute Deviation (MAD)4.000009 × 1017
Skewness-0.33465395
Sum2.2957059 × 1026
Variance1.4455085 × 1044
MonotonicityNot monotonic
2023-12-10T15:18:38.969481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.15001050010741e+24 4
 
2.0%
1.16801120010602e+24 3
 
1.5%
1.15001050010776e+24 3
 
1.5%
1.15001060010749e+24 3
 
1.5%
1.13051010011357e+24 2
 
1.0%
1.16801180010527e+24 2
 
1.0%
1.15001050010773e+24 2
 
1.0%
1.1305103001025202e+24 2
 
1.0%
1.16801060010511e+24 2
 
1.0%
1.15001030010078e+24 2
 
1.0%
Other values (164) 174
87.4%
ValueCountFrequency (%)
1.13051010010001e+24 1
0.5%
1.13051010010084e+24 1
0.5%
1.13051010010087e+24 1
0.5%
1.1305101001016e+24 1
0.5%
1.1305101001025804e+24 2
1.0%
1.13051010010319e+24 1
0.5%
1.13051010010474e+24 1
0.5%
1.13051010010708e+24 1
0.5%
1.1305101001079116e+24 1
0.5%
1.13051010010811e+24 1
0.5%
ValueCountFrequency (%)
1.16801180010948e+24 1
0.5%
1.16801180010527e+24 2
1.0%
1.16801150010746e+24 2
1.0%
1.16801150010738e+24 1
0.5%
1.16801150010736e+24 1
0.5%
1.16801150010537e+24 1
0.5%
1.16801140010739e+24 1
0.5%
1.16801140010716e+24 1
0.5%
1.16801140010625e+24 1
0.5%
1.16801120010662e+24 1
0.5%

서울특별시 강북구 수유동 448-27번지 상아주택(448-27)
Text

Distinct182
Distinct (%)91.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-12-10T15:18:39.642603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length47
Median length37
Mean length28.894472
Min length21

Characters and Unicode

Total characters5750
Distinct characters233
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique169 ?
Unique (%)84.9%

Sample

1st row서울특별시 강북구 미아동 258-306번지 유림빌라(258-306)
2nd row서울특별시 강북구 미아동 1357번지 삼각산아이원
3rd row서울특별시 강남구 대치동 918-5번지 효산그린빌
4th row서울특별시 강서구 화곡동 474-1번지 미주
5th row서울특별시 강북구 미아동 1356번지 경남아너스빌(1356-0)
ValueCountFrequency (%)
서울특별시 199
 
19.3%
강서구 107
 
10.4%
강남구 67
 
6.5%
화곡동 33
 
3.2%
강북구 25
 
2.4%
마곡동 19
 
1.8%
미아동 16
 
1.6%
가양동 16
 
1.6%
등촌동 15
 
1.5%
대치동 13
 
1.3%
Other values (398) 519
50.4%
2023-12-10T15:18:40.446683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
830
 
14.4%
서 314
 
5.5%
지 242
 
4.2%
동 214
 
3.7%
강 213
 
3.7%
시 210
 
3.7%
구 207
 
3.6%
번 202
 
3.5%
울 201
 
3.5%
특 199
 
3.5%
Other values (223) 2918
50.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3766
65.5%
Decimal Number 955
 
16.6%
Space Separator 830
 
14.4%
Dash Punctuation 130
 
2.3%
Open Punctuation 28
 
0.5%
Close Punctuation 28
 
0.5%
Uppercase Letter 8
 
0.1%
Math Symbol 2
 
< 0.1%
Other Punctuation 2
 
< 0.1%
Lowercase Letter 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
서 314
 
8.3%
지 242
 
6.4%
동 214
 
5.7%
강 213
 
5.7%
시 210
 
5.6%
구 207
 
5.5%
번 202
 
5.4%
울 201
 
5.3%
특 199
 
5.3%
별 199
 
5.3%
Other values (201) 1565
41.6%
Decimal Number
ValueCountFrequency (%)
1 168
17.6%
4 102
10.7%
3 102
10.7%
2 102
10.7%
5 97
10.2%
7 94
9.8%
6 80
8.4%
8 75
7.9%
9 74
7.7%
0 61
 
6.4%
Uppercase Letter
ValueCountFrequency (%)
B 5
62.5%
K 1
 
12.5%
M 1
 
12.5%
S 1
 
12.5%
Other Punctuation
ValueCountFrequency (%)
: 1
50.0%
& 1
50.0%
Space Separator
ValueCountFrequency (%)
830
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 130
100.0%
Open Punctuation
ValueCountFrequency (%)
( 28
100.0%
Close Punctuation
ValueCountFrequency (%)
) 28
100.0%
Math Symbol
ValueCountFrequency (%)
~ 2
100.0%
Lowercase Letter
ValueCountFrequency (%)
e 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3766
65.5%
Common 1975
34.3%
Latin 9
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
서 314
 
8.3%
지 242
 
6.4%
동 214
 
5.7%
강 213
 
5.7%
시 210
 
5.6%
구 207
 
5.5%
번 202
 
5.4%
울 201
 
5.3%
특 199
 
5.3%
별 199
 
5.3%
Other values (201) 1565
41.6%
Common
ValueCountFrequency (%)
830
42.0%
1 168
 
8.5%
- 130
 
6.6%
4 102
 
5.2%
3 102
 
5.2%
2 102
 
5.2%
5 97
 
4.9%
7 94
 
4.8%
6 80
 
4.1%
8 75
 
3.8%
Other values (7) 195
 
9.9%
Latin
ValueCountFrequency (%)
B 5
55.6%
e 1
 
11.1%
K 1
 
11.1%
M 1
 
11.1%
S 1
 
11.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3766
65.5%
ASCII 1984
34.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
830
41.8%
1 168
 
8.5%
- 130
 
6.6%
4 102
 
5.1%
3 102
 
5.1%
2 102
 
5.1%
5 97
 
4.9%
7 94
 
4.7%
6 80
 
4.0%
8 75
 
3.8%
Other values (12) 204
 
10.3%
Hangul
ValueCountFrequency (%)
서 314
 
8.3%
지 242
 
6.4%
동 214
 
5.7%
강 213
 
5.7%
시 210
 
5.6%
구 207
 
5.5%
번 202
 
5.4%
울 201
 
5.3%
특 199
 
5.3%
별 199
 
5.3%
Other values (201) 1565
41.6%

서울특별시 강북구 삼양로87길 21-7 상아주택(448-27)
Text

Distinct182
Distinct (%)91.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-12-10T15:18:41.039643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length45
Median length37
Mean length27.432161
Min length3

Characters and Unicode

Total characters5459
Distinct characters256
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique169 ?
Unique (%)84.9%

Sample

1st row서울특별시 강북구 오패산로60길 8 유림빌라(258-306)
2nd row서울특별시 강북구 삼양로19길 113 삼각산아이원
3rd row서울특별시 강남구 역삼로70길 19-11 효산그린빌
4th row서울특별시 강서구 등촌로13자길 51 미주
5th row서울특별시 강북구 오패산로30길 30 경남아너스빌(1356-0)
ValueCountFrequency (%)
서울특별시 197
 
19.3%
강서구 105
 
10.3%
강남구 67
 
6.6%
강북구 25
 
2.4%
자곡로 8
 
0.8%
양천로 7
 
0.7%
허준로 6
 
0.6%
마곡서1로 5
 
0.5%
강서로 5
 
0.5%
선릉로 5
 
0.5%
Other values (464) 591
57.9%
2023-12-10T15:18:41.854078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
822
 
15.1%
서 321
 
5.9%
강 222
 
4.1%
시 208
 
3.8%
로 206
 
3.8%
구 205
 
3.8%
1 201
 
3.7%
울 199
 
3.6%
별 197
 
3.6%
특 197
 
3.6%
Other values (246) 2681
49.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3566
65.3%
Decimal Number 935
 
17.1%
Space Separator 822
 
15.1%
Dash Punctuation 67
 
1.2%
Open Punctuation 28
 
0.5%
Close Punctuation 28
 
0.5%
Uppercase Letter 8
 
0.1%
Math Symbol 2
 
< 0.1%
Other Punctuation 2
 
< 0.1%
Lowercase Letter 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
서 321
 
9.0%
강 222
 
6.2%
시 208
 
5.8%
로 206
 
5.8%
구 205
 
5.7%
울 199
 
5.6%
별 197
 
5.5%
특 197
 
5.5%
길 124
 
3.5%
남 78
 
2.2%
Other values (224) 1609
45.1%
Decimal Number
ValueCountFrequency (%)
1 201
21.5%
2 130
13.9%
3 102
10.9%
5 99
10.6%
4 84
9.0%
6 77
 
8.2%
8 67
 
7.2%
0 64
 
6.8%
7 59
 
6.3%
9 52
 
5.6%
Uppercase Letter
ValueCountFrequency (%)
B 5
62.5%
K 1
 
12.5%
S 1
 
12.5%
M 1
 
12.5%
Other Punctuation
ValueCountFrequency (%)
& 1
50.0%
: 1
50.0%
Space Separator
ValueCountFrequency (%)
822
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 67
100.0%
Open Punctuation
ValueCountFrequency (%)
( 28
100.0%
Close Punctuation
ValueCountFrequency (%)
) 28
100.0%
Math Symbol
ValueCountFrequency (%)
~ 2
100.0%
Lowercase Letter
ValueCountFrequency (%)
e 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3566
65.3%
Common 1884
34.5%
Latin 9
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
서 321
 
9.0%
강 222
 
6.2%
시 208
 
5.8%
로 206
 
5.8%
구 205
 
5.7%
울 199
 
5.6%
별 197
 
5.5%
특 197
 
5.5%
길 124
 
3.5%
남 78
 
2.2%
Other values (224) 1609
45.1%
Common
ValueCountFrequency (%)
822
43.6%
1 201
 
10.7%
2 130
 
6.9%
3 102
 
5.4%
5 99
 
5.3%
4 84
 
4.5%
6 77
 
4.1%
8 67
 
3.6%
- 67
 
3.6%
0 64
 
3.4%
Other values (7) 171
 
9.1%
Latin
ValueCountFrequency (%)
B 5
55.6%
e 1
 
11.1%
K 1
 
11.1%
S 1
 
11.1%
M 1
 
11.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3566
65.3%
ASCII 1893
34.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
822
43.4%
1 201
 
10.6%
2 130
 
6.9%
3 102
 
5.4%
5 99
 
5.2%
4 84
 
4.4%
6 77
 
4.1%
8 67
 
3.5%
- 67
 
3.5%
0 64
 
3.4%
Other values (12) 180
 
9.5%
Hangul
ValueCountFrequency (%)
서 321
 
9.0%
강 222
 
6.2%
시 208
 
5.8%
로 206
 
5.8%
구 205
 
5.7%
울 199
 
5.6%
별 197
 
5.5%
특 197
 
5.5%
길 124
 
3.5%
남 78
 
2.2%
Other values (224) 1609
45.1%

313370
Real number (ℝ)

HIGH CORRELATION 

Distinct176
Distinct (%)88.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean306396.28
Minimum295209
Maximum321620
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-10T15:18:42.089969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum295209
5-th percentile295735.4
Q1297765.5
median299741
Q3315801
95-th percentile320188.9
Maximum321620
Range26411
Interquartile range (IQR)18035.5

Descriptive statistics

Standard deviation9444.1004
Coefficient of variation (CV)0.030823156
Kurtosis-1.7970342
Mean306396.28
Median Absolute Deviation (MAD)4224
Skewness0.20116721
Sum60972859
Variance89191032
MonotonicityNot monotonic
2023-12-10T15:18:42.334128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
295924 4
 
2.0%
320720 3
 
1.5%
296542 3
 
1.5%
318426 2
 
1.0%
297709 2
 
1.0%
313969 2
 
1.0%
316249 2
 
1.0%
319518 2
 
1.0%
297647 2
 
1.0%
313504 2
 
1.0%
Other values (166) 175
87.9%
ValueCountFrequency (%)
295209 1
0.5%
295292 1
0.5%
295332 1
0.5%
295335 1
0.5%
295337 1
0.5%
295347 1
0.5%
295396 1
0.5%
295517 1
0.5%
295522 1
0.5%
295550 1
0.5%
ValueCountFrequency (%)
321620 1
 
0.5%
321076 1
 
0.5%
320907 1
 
0.5%
320828 1
 
0.5%
320720 3
1.5%
320608 1
 
0.5%
320487 1
 
0.5%
320296 1
 
0.5%
320177 1
 
0.5%
320114 1
 
0.5%

559665
Real number (ℝ)

HIGH CORRELATION 

Distinct178
Distinct (%)89.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean549717.58
Minimum540886
Maximum560820
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-10T15:18:42.577622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum540886
5-th percentile542026.6
Q1545890.5
median550196
Q3552064
95-th percentile558737
Maximum560820
Range19934
Interquartile range (IQR)6173.5

Descriptive statistics

Standard deviation4750.2748
Coefficient of variation (CV)0.0086413005
Kurtosis-0.31158726
Mean549717.58
Median Absolute Deviation (MAD)2374
Skewness0.2586252
Sum1.093938 × 108
Variance22565111
MonotonicityNot monotonic
2023-12-10T15:18:42.869406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
551918 4
 
2.0%
550626 3
 
1.5%
541746 3
 
1.5%
543913 2
 
1.0%
551550 2
 
1.0%
549788 2
 
1.0%
552058 2
 
1.0%
548284 2
 
1.0%
552444 2
 
1.0%
547557 2
 
1.0%
Other values (168) 175
87.9%
ValueCountFrequency (%)
540886 1
 
0.5%
540909 1
 
0.5%
541063 1
 
0.5%
541176 1
 
0.5%
541720 1
 
0.5%
541746 3
1.5%
541791 1
 
0.5%
541924 1
 
0.5%
542038 1
 
0.5%
542435 1
 
0.5%
ValueCountFrequency (%)
560820 1
0.5%
560694 1
0.5%
560274 1
0.5%
559952 1
0.5%
559794 1
0.5%
559547 1
0.5%
559329 1
0.5%
559172 1
0.5%
558861 1
0.5%
558746 1
0.5%

219095
Real number (ℝ)

Distinct175
Distinct (%)87.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean197452.53
Minimum1700
Maximum509648
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-10T15:18:43.132164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1700
5-th percentile14595.2
Q116420.5
median220301
Q3355114
95-th percentile421587.8
Maximum509648
Range507948
Interquartile range (IQR)338693.5

Descriptive statistics

Standard deviation171316
Coefficient of variation (CV)0.86763134
Kurtosis-1.554502
Mean197452.53
Median Absolute Deviation (MAD)194245
Skewness0.17315051
Sum39293053
Variance2.9349172 × 1010
MonotonicityNot monotonic
2023-12-10T15:18:43.373279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
413691 4
 
2.0%
509648 3
 
1.5%
414546 3
 
1.5%
414023 3
 
1.5%
415213 3
 
1.5%
273304 2
 
1.0%
361621 2
 
1.0%
17349 2
 
1.0%
27659 2
 
1.0%
32908 2
 
1.0%
Other values (165) 173
86.9%
ValueCountFrequency (%)
1700 1
0.5%
2076 1
0.5%
8216 2
1.0%
9908 1
0.5%
11823 1
0.5%
13477 1
0.5%
13791 1
0.5%
14112 1
0.5%
14489 1
0.5%
14607 1
0.5%
ValueCountFrequency (%)
509648 3
1.5%
509643 1
 
0.5%
509640 1
 
0.5%
502131 1
 
0.5%
422551 1
 
0.5%
422494 1
 
0.5%
422367 1
 
0.5%
421991 1
 
0.5%
421543 1
 
0.5%
419847 1
 
0.5%

3
Real number (ℝ)

Distinct21
Distinct (%)10.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.120603
Minimum-1
Maximum22
Zeros0
Zeros (%)0.0%
Negative2
Negative (%)1.0%
Memory size1.9 KiB
2023-12-10T15:18:43.596047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile1
Q13
median5
Q38.5
95-th percentile14
Maximum22
Range23
Interquartile range (IQR)5.5

Descriptive statistics

Standard deviation4.1555035
Coefficient of variation (CV)0.67893695
Kurtosis1.1095816
Mean6.120603
Median Absolute Deviation (MAD)2
Skewness1.1239416
Sum1218
Variance17.26821
MonotonicityNot monotonic
2023-12-10T15:18:43.762516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
5 28
14.1%
2 26
13.1%
4 24
12.1%
3 22
11.1%
6 21
10.6%
7 13
6.5%
9 10
 
5.0%
1 9
 
4.5%
10 9
 
4.5%
12 7
 
3.5%
Other values (11) 30
15.1%
ValueCountFrequency (%)
-1 2
 
1.0%
1 9
 
4.5%
2 26
13.1%
3 22
11.1%
4 24
12.1%
5 28
14.1%
6 21
10.6%
7 13
6.5%
8 4
 
2.0%
9 10
 
5.0%
ValueCountFrequency (%)
22 1
 
0.5%
20 1
 
0.5%
18 1
 
0.5%
17 2
 
1.0%
16 1
 
0.5%
15 1
 
0.5%
14 6
3.0%
13 7
3.5%
12 7
3.5%
11 4
2.0%

전세
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
전세
90 
매매
59 
월세
50 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row매매
2nd row매매
3rd row전세
4th row매매
5th row매매

Common Values

ValueCountFrequency (%)
전세 90
45.2%
매매 59
29.6%
월세 50
25.1%

Length

2023-12-10T15:18:43.973196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:18:44.162882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전세 90
45.2%
매매 59
29.6%
월세 50
25.1%

60.88
Real number (ℝ)

Distinct178
Distinct (%)89.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean55.799015
Minimum13.425
Maximum198.41
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-10T15:18:44.382140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum13.425
5-th percentile18.73979
Q129.92
median49.77
Q383.775
95-th percentile111.392
Maximum198.41
Range184.985
Interquartile range (IQR)53.855

Descriptive statistics

Standard deviation30.117238
Coefficient of variation (CV)0.53974499
Kurtosis1.5502102
Mean55.799015
Median Absolute Deviation (MAD)21.44
Skewness0.98708528
Sum11104.004
Variance907.04805
MonotonicityNot monotonic
2023-12-10T15:18:44.659413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
59.79 3
 
1.5%
84.99 3
 
1.5%
49.5 3
 
1.5%
84.97 3
 
1.5%
59.84 3
 
1.5%
22.26 2
 
1.0%
84.87 2
 
1.0%
21.25 2
 
1.0%
59.96 2
 
1.0%
84.88 2
 
1.0%
Other values (168) 174
87.4%
ValueCountFrequency (%)
13.425 1
0.5%
14.31 1
0.5%
14.57 1
0.5%
15.92 1
0.5%
17.44 1
0.5%
17.59 1
0.5%
17.96 1
0.5%
18.15 1
0.5%
18.38 1
0.5%
18.6479 1
0.5%
ValueCountFrequency (%)
198.41 1
0.5%
131.48 1
0.5%
130.279 1
0.5%
124.5792 1
0.5%
121.69 1
0.5%
119.85 1
0.5%
115.23 1
0.5%
114.922 1
0.5%
114.88 1
0.5%
111.5 1
0.5%

20200321
Real number (ℝ)

Distinct158
Distinct (%)79.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20200695
Minimum20200102
Maximum20201229
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-10T15:18:44.950845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20200102
5-th percentile20200131
Q120200426
median20200708
Q320201009
95-th percentile20201212
Maximum20201229
Range1127
Interquartile range (IQR)583

Descriptive statistics

Standard deviation334.55287
Coefficient of variation (CV)1.6561453 × 10-5
Kurtosis-1.045062
Mean20200695
Median Absolute Deviation (MAD)297
Skewness-0.090868175
Sum4.0199384 × 109
Variance111925.62
MonotonicityNot monotonic
2023-12-10T15:18:45.200443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20200704 5
 
2.5%
20201017 4
 
2.0%
20200709 3
 
1.5%
20200701 3
 
1.5%
20200820 3
 
1.5%
20200205 3
 
1.5%
20200708 3
 
1.5%
20200610 3
 
1.5%
20200525 3
 
1.5%
20200710 2
 
1.0%
Other values (148) 167
83.9%
ValueCountFrequency (%)
20200102 1
0.5%
20200106 1
0.5%
20200109 1
0.5%
20200110 1
0.5%
20200111 1
0.5%
20200117 2
1.0%
20200128 2
1.0%
20200129 1
0.5%
20200131 1
0.5%
20200201 1
0.5%
ValueCountFrequency (%)
20201229 1
0.5%
20201228 1
0.5%
20201226 2
1.0%
20201224 1
0.5%
20201221 1
0.5%
20201219 1
0.5%
20201217 1
0.5%
20201216 1
0.5%
20201214 1
0.5%
20201212 2
1.0%

-99999
Real number (ℝ)

HIGH CORRELATION 

Distinct53
Distinct (%)26.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-50136.482
Minimum-99999
Maximum500000
Zeros0
Zeros (%)0.0%
Negative140
Negative (%)70.4%
Memory size1.9 KiB
2023-12-10T15:18:45.423294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-99999
5-th percentile-99999
Q1-99999
median-99999
Q320250
95-th percentile89820
Maximum500000
Range599999
Interquartile range (IQR)120249

Descriptive statistics

Standard deviation88382.982
Coefficient of variation (CV)-1.7628477
Kurtosis8.0654688
Mean-50136.482
Median Absolute Deviation (MAD)0
Skewness2.3094787
Sum-9977160
Variance7.8115516 × 109
MonotonicityNot monotonic
2023-12-10T15:18:45.665957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-99999 140
70.4%
34000 2
 
1.0%
29500 2
 
1.0%
23000 2
 
1.0%
21500 2
 
1.0%
24000 2
 
1.0%
50000 2
 
1.0%
29000 2
 
1.0%
90000 1
 
0.5%
21950 1
 
0.5%
Other values (43) 43
 
21.6%
ValueCountFrequency (%)
-99999 140
70.4%
5800 1
 
0.5%
9500 1
 
0.5%
11000 1
 
0.5%
11800 1
 
0.5%
15000 1
 
0.5%
16400 1
 
0.5%
17300 1
 
0.5%
17500 1
 
0.5%
19000 1
 
0.5%
ValueCountFrequency (%)
500000 1
0.5%
280000 1
0.5%
274000 1
0.5%
210000 1
0.5%
170000 1
0.5%
105000 1
0.5%
98500 1
0.5%
96250 1
0.5%
94700 1
0.5%
90000 1
0.5%

15000
Real number (ℝ)

HIGH CORRELATION 

Distinct91
Distinct (%)45.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-7652.5779
Minimum-99999
Maximum170000
Zeros0
Zeros (%)0.0%
Negative59
Negative (%)29.6%
Memory size1.9 KiB
2023-12-10T15:18:45.905915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-99999
5-th percentile-99999
Q1-99999
median12300
Q330250
95-th percentile85000
Maximum170000
Range269999
Interquartile range (IQR)130249

Descriptive statistics

Standard deviation65481.671
Coefficient of variation (CV)-8.5568121
Kurtosis-0.83603124
Mean-7652.5779
Median Absolute Deviation (MAD)24700
Skewness-0.27773609
Sum-1522863
Variance4.2878492 × 109
MonotonicityNot monotonic
2023-12-10T15:18:46.156812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-99999 59
29.6%
1000 11
 
5.5%
10000 4
 
2.0%
5000 4
 
2.0%
40000 4
 
2.0%
70000 3
 
1.5%
20000 3
 
1.5%
28000 3
 
1.5%
4000 3
 
1.5%
18000 3
 
1.5%
Other values (81) 102
51.3%
ValueCountFrequency (%)
-99999 59
29.6%
500 3
 
1.5%
1000 11
 
5.5%
1497 1
 
0.5%
2000 3
 
1.5%
2500 1
 
0.5%
3000 2
 
1.0%
4000 3
 
1.5%
5000 4
 
2.0%
5300 1
 
0.5%
ValueCountFrequency (%)
170000 1
0.5%
135000 1
0.5%
133000 1
0.5%
120000 1
0.5%
110000 1
0.5%
103000 1
0.5%
100000 1
0.5%
95000 1
0.5%
89000 1
0.5%
85000 2
1.0%

0
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct36
Distinct (%)18.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-29627.94
Minimum-99999
Maximum460
Zeros90
Zeros (%)45.2%
Negative59
Negative (%)29.6%
Memory size1.9 KiB
2023-12-10T15:18:46.438762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-99999
5-th percentile-99999
Q1-99999
median0
Q33
95-th percentile115.5
Maximum460
Range100459
Interquartile range (IQR)100002

Descriptive statistics

Standard deviation45798.397
Coefficient of variation (CV)-1.5457841
Kurtosis-1.2057748
Mean-29627.94
Median Absolute Deviation (MAD)35
Skewness-0.89802005
Sum-5895960
Variance2.0974932 × 109
MonotonicityNot monotonic
2023-12-10T15:18:46.653332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
0 90
45.2%
-99999 59
29.6%
40 7
 
3.5%
50 4
 
2.0%
80 4
 
2.0%
120 2
 
1.0%
15 2
 
1.0%
35 2
 
1.0%
85 2
 
1.0%
160 1
 
0.5%
Other values (26) 26
 
13.1%
ValueCountFrequency (%)
-99999 59
29.6%
0 90
45.2%
6 1
 
0.5%
8 1
 
0.5%
15 2
 
1.0%
19 1
 
0.5%
20 1
 
0.5%
22 1
 
0.5%
23 1
 
0.5%
25 1
 
0.5%
ValueCountFrequency (%)
460 1
0.5%
230 1
0.5%
220 1
0.5%
180 1
0.5%
160 1
0.5%
150 1
0.5%
147 1
0.5%
140 1
0.5%
120 2
1.0%
115 1
0.5%

연립/다세대
Categorical

Distinct3
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
아파트
100 
연립/다세대
67 
기타
32 

Length

Max length6
Median length3
Mean length3.8492462
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row연립/다세대
2nd row아파트
3rd row연립/다세대
4th row아파트
5th row아파트

Common Values

ValueCountFrequency (%)
아파트 100
50.3%
연립/다세대 67
33.7%
기타 32
 
16.1%

Length

2023-12-10T15:18:46.893795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:18:47.158938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
아파트 100
50.3%
연립/다세대 67
33.7%
기타 32
 
16.1%

Interactions

2023-12-10T15:18:29.828204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:17:37.927987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:17:41.230489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:17:44.969965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:18:00.826418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:18:04.064494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:18:07.736487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:18:12.040920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:18:15.432210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:18:19.194661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:18:22.682460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:18:26.009350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:18:29.959425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:17:38.087613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:17:41.379996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:17:46.122305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:18:00.980504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:18:04.228527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:18:07.954703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:18:12.191775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:18:15.567584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:18:19.346644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:18:22.819764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:18:26.242741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:18:30.148551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:17:38.268022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:17:41.560292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:17:47.280360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:18:01.142718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:18:04.444095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:18:08.190407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:18:12.371683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:18:15.743819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:18:19.575827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:18:23.013303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:18:26.431033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:18:31.739474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:17:39.852566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:17:43.443902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:17:49.778620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:18:02.703550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:18:06.100621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:18:10.579157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:18:14.049855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:18:17.343078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:18:21.196738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:18:24.630619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:18:28.314337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:18:31.917513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:17:40.009296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:17:43.611021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:17:50.905305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:18:02.855461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:18:06.269716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:18:10.735960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:18:14.188943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:18:17.491593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:18:21.346754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:18:24.796421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:18:28.472983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:18:32.122402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:17:40.162375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:17:43.774929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:17:52.125902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:18:03.026437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:18:06.498475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:18:10.912272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:18:14.364533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:18:17.663438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:18:21.497785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:18:24.962902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:18:28.770493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:18:32.260728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:17:40.310810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:17:43.922758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:17:53.302366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:18:03.179961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:18:06.650411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:18:11.060156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:18:14.495695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:18:17.799097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:18:21.646987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:18:25.101568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:18:28.935336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:18:32.453977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:17:40.451561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:17:44.075294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:17:54.422093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:18:03.313312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:18:06.792678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:18:11.220084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:18:14.659101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:18:17.955038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:18:21.867465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:18:25.222076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:18:29.068166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:18:32.666444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:17:40.616765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:17:44.256662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:17:55.975682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:18:03.471383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:18:07.033226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:18:11.378396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:18:14.820014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:18:18.107049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:18:22.053396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:18:25.390821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:18:29.232572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:18:32.868529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:17:40.765593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:17:44.412198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:17:57.032014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:18:03.625291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:18:07.196686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:18:11.538988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:18:14.977927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:18:18.242312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:18:22.200025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:18:25.553325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:18:29.377389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:18:33.020815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:17:40.917514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:17:44.592879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:17:58.092243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:18:03.782408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:18:07.353395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:18:11.690403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:18:15.139350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:18:18.390962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:18:22.354547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:18:25.727425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:18:29.541391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:18:33.166877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:17:41.085017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:17:44.750236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:17:59.210411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:18:03.918802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:18:07.544471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:18:11.859146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:18:15.283230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:18:18.568211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:18:22.547106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:18:25.875513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:18:29.691409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T15:18:47.330757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2009113051030000448002711305103001044800270000013133705596652190953전세60.8820200321-99999150000연립/다세대
20091.0000.4170.4170.4080.4560.5090.2100.1250.4490.0000.3140.341NaN0.450
11305103000044800270.4171.0001.0000.8660.9990.9620.1650.4150.2690.2300.4950.396NaN0.466
11305103001044800270000010.4171.0001.0000.8660.9990.9620.1650.4150.2690.2300.4950.396NaN0.466
3133700.4080.8660.8661.0000.7720.6980.1990.2720.2250.0250.5510.499NaN0.305
5596650.4560.9990.9990.7721.0000.8570.1860.4140.2540.2360.4810.194NaN0.411
2190950.5090.9620.9620.6980.8571.0000.0000.3250.2850.1090.3460.558NaN0.466
30.2100.1650.1650.1990.1860.0001.0000.2460.2470.2300.7610.353NaN0.487
전세0.1250.4150.4150.2720.4140.3250.2461.0000.2080.000NaN0.578NaN0.461
60.880.4490.2690.2690.2250.2540.2850.2470.2081.0000.0000.8400.826NaN0.619
202003210.0000.2300.2300.0250.2360.1090.2300.0000.0001.0000.4020.000NaN0.000
-999990.3140.4950.4950.5510.4810.3460.761NaN0.8400.4021.000NaNNaN0.404
150000.3410.3960.3960.4990.1940.5580.3530.5780.8260.000NaN1.000NaN0.481
0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1.000NaN
연립/다세대0.4500.4660.4660.3050.4110.4660.4870.4610.6190.0000.4040.481NaN1.000
2023-12-10T15:18:47.933892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연립/다세대전세
연립/다세대1.0000.180
전세0.1801.000
2023-12-10T15:18:48.092992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
200911305103000044800271130510300104480027000001313370559665219095360.8820200321-99999150000전세연립/다세대
20091.000-0.167-0.168-0.2580.0830.2150.052-0.4650.125-0.142-0.0100.1040.0540.309
1130510300004480027-0.1671.0000.9980.399-0.7210.3320.1860.148-0.038-0.1490.3450.2010.1450.179
1130510300104480027000001-0.1680.9981.0000.400-0.7230.3340.1810.145-0.036-0.1520.3440.2020.1810.327
313370-0.2580.3990.4001.000-0.6070.181-0.0310.181-0.145-0.0220.2200.0840.1830.207
5596650.083-0.721-0.723-0.6071.000-0.209-0.048-0.1040.0750.098-0.321-0.1400.1970.197
2190950.2150.3320.3340.181-0.2091.0000.039-0.059-0.005-0.0850.1100.1210.1490.230
30.0520.1860.181-0.031-0.0480.0391.0000.1450.076-0.0500.1790.1030.1480.329
60.88-0.4650.1480.1450.181-0.104-0.0590.1451.0000.0070.1620.246-0.0950.1320.483
202003210.125-0.038-0.036-0.1450.075-0.0050.0760.0071.0000.044-0.048-0.0200.0000.000
-99999-0.142-0.149-0.152-0.0220.098-0.085-0.0500.1620.0441.000-0.785-0.8260.6850.221
15000-0.0100.3450.3440.220-0.3210.1100.1790.246-0.048-0.7851.0000.5270.8230.354
00.1040.2010.2020.084-0.1400.1210.103-0.095-0.020-0.8260.5271.0000.9970.201
전세0.0540.1450.1810.1830.1970.1490.1480.1320.0000.6850.8230.9971.0000.180
연립/다세대0.3090.1790.3270.2070.1970.2300.3290.4830.0000.2210.3540.2010.1801.000

Missing values

2023-12-10T15:18:33.387133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T15:18:33.823659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

서울특별시 강북구 수유동 448-27 상아주택(448-27)상아주택(448-27)200911305103000044800271130510300104480027000001서울특별시 강북구 수유동 448-27번지 상아주택(448-27)서울특별시 강북구 삼양로87길 21-7 상아주택(448-27)3133705596652190953전세60.8820200321-99999150000연립/다세대
0서울특별시 강북구 오패산로60길 8 유림빌라(258-306)유림빌라(258-306)200211305101000025803061130510100102580306031266서울특별시 강북구 미아동 258-306번지 유림빌라(258-306)서울특별시 강북구 오패산로60길 8 유림빌라(258-306)3145845586192204753매매42.122020101719000-99999-99999연립/다세대
1서울특별시 강북구 미아동 1357 삼각산아이원삼각산아이원200311305101000135700001130510100113570000035071서울특별시 강북구 미아동 1357번지 삼각산아이원서울특별시 강북구 삼양로19길 113 삼각산아이원31350455741422076120매매114.882020063074000-99999-99999아파트
2서울특별시 강남구 역삼로70길 19-11 효산그린빌효산그린빌200211680106000091800051168010600109180005013400서울특별시 강남구 대치동 918-5번지 효산그린빌서울특별시 강남구 역삼로70길 19-11 효산그린빌3165785446872651273전세83.1620201021-99999472500연립/다세대
3서울특별시 강서구 등촌로13자길 51 미주미주198811500103000047400011150010300104740001019464서울특별시 강서구 화곡동 474-1번지 미주서울특별시 강서구 등촌로13자길 51 미주299029548975154053매매48.32020080117300-99999-99999아파트
4서울특별시 강북구 미아동 1356 경남아너스빌(1356-0)경남아너스빌(1356-0)200311305101000135600001130510100113560000029077서울특별시 강북구 미아동 1356번지 경남아너스빌(1356-0)서울특별시 강북구 오패산로30길 30 경남아너스빌(1356-0)3150655574174105331매매114.9222020010960000-99999-99999아파트
5서울특별시 강서구 화곡동 1100-3 휴&유빌휴&유빌202011500103000110000031150010300111000003014666서울특별시 강서구 화곡동 1100-3번지 휴&유빌서울특별시 강서구 화곡로59길 29-12 휴&유빌298514550834154816전세40.420200604-99999360000연립/다세대
6서울특별시 강북구 오패산로52자길 27 (258-592)(258-592)198711305101000025805921130510100102580592029827서울특별시 강북구 미아동 258-592번지 (258-592)서울특별시 강북구 오패산로52자길 27 (258-592)31460555886117002매매32.462020070117500-99999-99999연립/다세대
7서울특별시 강서구 양천로 489 가양우성가양우성199011500104000001400031150010400100140003009639서울특별시 강서구 가양동 14-3번지 가양우성서울특별시 강서구 양천로 489 가양우성2989715517261648012전세75.4820200820-99999420000아파트
8서울특별시 강남구 도곡로 405 삼환 아르누보2삼환 아르누보2200411680106000093800001168010600109380024014224서울특별시 강남구 대치동 938번지 삼환 아르누보2서울특별시 강남구 도곡로 405 삼환 아르누보23166045443384190646월세26.4220201221-99999200096기타
9서울특별시 강서구 수명로1길 16 마곡수명산파크4단지마곡수명산파크4단지200811500106000075300001150010600107530000128108서울특별시 강서구 내발산동 753번지 마곡수명산파크4단지서울특별시 강서구 수명로1길 16 마곡수명산파크4단지2962325505323486117매매84.532020081787000-99999-99999아파트
서울특별시 강북구 수유동 448-27 상아주택(448-27)상아주택(448-27)200911305103000044800271130510300104480027000001서울특별시 강북구 수유동 448-27번지 상아주택(448-27)서울특별시 강북구 삼양로87길 21-7 상아주택(448-27)3133705596652190953전세60.8820200321-99999150000연립/다세대
189서울특별시 강서구 등촌동 506-22 다솔하이츠B동(506-22)다솔하이츠B동(506-22)200211500102000050600221150010200105060022026332서울특별시 강서구 등촌동 506-22번지 다솔하이츠B동(506-22)서울특별시 강서구 등촌로55길 12 다솔하이츠B동(506-22)299741550416146612매매25.022020042029000-99999-99999연립/다세대
190서울특별시 강남구 삼성동 99 삼부아파트 102동삼부아파트 102동199811680105000009900001168010500100990000027901서울특별시 강남구 삼성동 99번지 삼부아파트 102동서울특별시 강남구 봉은사로111길 34 삼부아파트 102동3175305465492661536전세59.6120200201-99999450000아파트
191서울특별시 강서구 양천로47길 12 마곡보타닉투웨니퍼스트마곡보타닉투웨니퍼스트202011500104000025700011150010400102570001010762서울특별시 강서구 가양동 257-1번지 마곡보타닉투웨니퍼스트서울특별시 강서구 양천로47길 12 마곡보타닉투웨니퍼스트2975845527201634213전세28.3320200807-99999245000기타
192서울특별시 강서구 등촌동 510-2 한울팰리스한울팰리스201711500102000051000021150010200105100002026590서울특별시 강서구 등촌동 510-2번지 한울팰리스서울특별시 강서구 등촌로 211 한울팰리스29973255019699087전세26.7320200128-99999255000연립/다세대
193서울특별시 강서구 가양동 1480-4 미씨엘로오피스텔미씨엘로오피스텔201011500104000148000041150010400114800004000001서울특별시 강서구 가양동 1480-4번지 미씨엘로오피스텔서울특별시 강서구 양천로 469-13 미씨엘로오피스텔298891551802164645월세29.5820200525-99999100050기타
194서울특별시 강남구 수서동 736 신동아신동아199211680115000073600001168011500107360000001470서울특별시 강남구 수서동 736번지 신동아서울특별시 강남구 광평로47길 17 신동아3204875431481517267매매33.182020020586000-99999-99999아파트
195서울특별시 강서구 등촌동 75-1 삼부르네상스삼부르네상스200411500102000007500011150010200100750001025384서울특별시 강서구 등촌동 75-1번지 삼부르네상스서울특별시 강서구 양천로 482 삼부르네상스298948551622152313전세63.8620200608-99999290000기타
196서울특별시 강서구 마곡중앙로 33 마곡엠벨리(14단지)마곡엠벨리(14단지)201411500105000075000001150010700100670001000001서울특별시 강서구 마곡동 750번지 마곡엠벨리(14단지)서울특별시 강서구 마곡중앙로 33 마곡엠벨리(14단지)29633155117550213115전세84.8820200704-99999440000아파트
197서울특별시 강서구 양천로28길 11 블루힐블루힐201511500109000017500121150010900101750012003618서울특별시 강서구 방화동 175-12번지 블루힐서울특별시 강서구 양천로28길 11 블루힐296015552923167382전세29.2620201027-99999192150연립/다세대
198서울특별시 강서구 화곡동 1054-2 미래빌라트미래빌라트200011500103000105400021150010300110540002019020서울특별시 강서구 화곡동 1054-2번지 미래빌라트서울특별시 강서구 강서로34길 15-16 미래빌라트297701549653175191전세74.0820200106-99999180000연립/다세대