Overview

Dataset statistics

Number of variables17
Number of observations10000
Missing cells6251
Missing cells (%)3.7%
Duplicate rows195
Duplicate rows (%)1.9%
Total size in memory1.5 MiB
Average record size in memory153.0 B

Variable types

Numeric8
Categorical7
Text1
DateTime1

Dataset

Description서울특별시 구로구 행정동별 부동산 실거래가 정보로 자 법정동명, 지번구분, 본번, 부번, 건물용도, 건물명, 물건금액 등의 정보를 제공합니다. - 출처 : 서울시 부동산 실거래가 정보(서울 열린데이터 광장), 서울부동산정보광장
URLhttps://www.data.go.kr/data/15120851/fileData.do

Alerts

자치구명 has constant value ""Constant
데이터 기준일자 has constant value ""Constant
Dataset has 195 (1.9%) duplicate rowsDuplicates
법정동명 is highly overall correlated with 지번구분 and 1 other fieldsHigh correlation
지번구분 is highly overall correlated with 접수연도 and 11 other fieldsHigh correlation
건물용도 is highly overall correlated with 지번구분 and 1 other fieldsHigh correlation
신고구분 is highly overall correlated with 건축년도 and 2 other fieldsHigh correlation
지번구분명 is highly overall correlated with 접수연도 and 11 other fieldsHigh correlation
접수연도 is highly overall correlated with 지번구분 and 1 other fieldsHigh correlation
본번 is highly overall correlated with 지번구분 and 1 other fieldsHigh correlation
부번 is highly overall correlated with 지번구분 and 1 other fieldsHigh correlation
층 is highly overall correlated with 지번구분 and 1 other fieldsHigh correlation
건물면적(m2) is highly overall correlated with 물건금액(만원) and 2 other fieldsHigh correlation
토지면적(m2) is highly overall correlated with 지번구분 and 1 other fieldsHigh correlation
건축년도 is highly overall correlated with 지번구분 and 2 other fieldsHigh correlation
물건금액(만원) is highly overall correlated with 건물면적(m2) and 2 other fieldsHigh correlation
지번구분 is highly imbalanced (66.9%)Imbalance
지번구분명 is highly imbalanced (66.9%)Imbalance
신고구분 is highly imbalanced (56.5%)Imbalance
본번 has 608 (6.1%) missing valuesMissing
부번 has 608 (6.1%) missing valuesMissing
건물명 has 608 (6.1%) missing valuesMissing
층 has 608 (6.1%) missing valuesMissing
토지면적(m2) has 3806 (38.1%) missing valuesMissing
부번 has 3656 (36.6%) zerosZeros
토지면적(m2) has 1407 (14.1%) zerosZeros

Reproduction

Analysis started2023-12-12 12:57:38.465351
Analysis finished2023-12-12 12:57:48.461781
Duration10 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

접수연도
Real number (ℝ)

HIGH CORRELATION 

Distinct18
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2014.8972
Minimum2006
Maximum2023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T21:57:48.523780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2006
5-th percentile2006
Q12010
median2016
Q32020
95-th percentile2022
Maximum2023
Range17
Interquartile range (IQR)10

Descriptive statistics

Standard deviation5.2819525
Coefficient of variation (CV)0.0026214501
Kurtosis-1.2055668
Mean2014.8972
Median Absolute Deviation (MAD)4
Skewness-0.25368484
Sum20148972
Variance27.899022
MonotonicityNot monotonic
2023-12-12T21:57:48.627937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
2022 1095
 
10.9%
2020 766
 
7.7%
2015 743
 
7.4%
2016 730
 
7.3%
2006 711
 
7.1%
2017 656
 
6.6%
2018 625
 
6.2%
2007 607
 
6.1%
2021 596
 
6.0%
2008 585
 
5.9%
Other values (8) 2886
28.9%
ValueCountFrequency (%)
2006 711
7.1%
2007 607
6.1%
2008 585
5.9%
2009 422
4.2%
2010 291
 
2.9%
2011 397
4.0%
2012 277
 
2.8%
2013 388
3.9%
2014 516
5.2%
2015 743
7.4%
ValueCountFrequency (%)
2023 163
 
1.6%
2022 1095
10.9%
2021 596
6.0%
2020 766
7.7%
2019 432
 
4.3%
2018 625
6.2%
2017 656
6.6%
2016 730
7.3%
2015 743
7.4%
2014 516
5.2%

자치구명
Categorical

CONSTANT 

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

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
구로구 10000
100.0%

Length

2023-12-12T21:57:48.763312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T21:57:48.856088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
구로구 10000
100.0%

법정동명
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
구로동
4612 
개봉동
3028 
고척동
1457 
궁동
623 
가리봉동
 
280

Length

Max length4
Median length3
Mean length2.9657
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row개봉동
2nd row개봉동
3rd row궁동
4th row구로동
5th row개봉동

Common Values

ValueCountFrequency (%)
구로동 4612
46.1%
개봉동 3028
30.3%
고척동 1457
 
14.6%
궁동 623
 
6.2%
가리봉동 280
 
2.8%

Length

2023-12-12T21:57:48.957560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T21:57:49.063629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
구로동 4612
46.1%
개봉동 3028
30.3%
고척동 1457
 
14.6%
궁동 623
 
6.2%
가리봉동 280
 
2.8%

지번구분
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
9392 
<NA>
 
608

Length

Max length4
Median length1
Mean length1.1824
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1 9392
93.9%
<NA> 608
 
6.1%

Length

2023-12-12T21:57:49.179013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T21:57:49.276526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 9392
93.9%
na 608
 
6.1%

지번구분명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
대지
9392 
<NA>
 
608

Length

Max length4
Median length2
Mean length2.1216
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
대지 9392
93.9%
<NA> 608
 
6.1%

Length

2023-12-12T21:57:49.390945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T21:57:49.491494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
대지 9392
93.9%
na 608
 
6.1%

본번
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct438
Distinct (%)4.7%
Missing608
Missing (%)6.1%
Infinite0
Infinite (%)0.0%
Mean456.38448
Minimum2
Maximum1289
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T21:57:49.610787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile46
Q1171
median339
Q3642
95-th percentile1267
Maximum1289
Range1287
Interquartile range (IQR)471

Descriptive statistics

Standard deviation366.13771
Coefficient of variation (CV)0.80225715
Kurtosis0.25899797
Mean456.38448
Median Absolute Deviation (MAD)200
Skewness1.0900404
Sum4286363
Variance134056.82
MonotonicityNot monotonic
2023-12-12T21:57:49.754040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
685 399
 
4.0%
481 250
 
2.5%
1265 216
 
2.2%
476 184
 
1.8%
478 161
 
1.6%
97 151
 
1.5%
650 151
 
1.5%
241 131
 
1.3%
98 131
 
1.3%
1267 128
 
1.3%
Other values (428) 7490
74.9%
(Missing) 608
 
6.1%
ValueCountFrequency (%)
2 23
0.2%
3 1
 
< 0.1%
5 2
 
< 0.1%
8 1
 
< 0.1%
10 6
 
0.1%
11 11
0.1%
13 2
 
< 0.1%
15 4
 
< 0.1%
17 2
 
< 0.1%
18 2
 
< 0.1%
ValueCountFrequency (%)
1289 2
 
< 0.1%
1288 3
 
< 0.1%
1287 14
 
0.1%
1286 13
 
0.1%
1285 12
 
0.1%
1284 2
 
< 0.1%
1283 11
 
0.1%
1282 9
 
0.1%
1281 49
0.5%
1280 50
0.5%

부번
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct201
Distinct (%)2.1%
Missing608
Missing (%)6.1%
Infinite0
Infinite (%)0.0%
Mean22.938671
Minimum0
Maximum480
Zeros3656
Zeros (%)36.6%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T21:57:49.975551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4
Q319
95-th percentile124
Maximum480
Range480
Interquartile range (IQR)19

Descriptive statistics

Standard deviation50.295666
Coefficient of variation (CV)2.1926146
Kurtosis13.956151
Mean22.938671
Median Absolute Deviation (MAD)4
Skewness3.499783
Sum215440
Variance2529.654
MonotonicityNot monotonic
2023-12-12T21:57:50.128189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3656
36.6%
4 542
 
5.4%
1 503
 
5.0%
9 278
 
2.8%
2 263
 
2.6%
8 257
 
2.6%
5 201
 
2.0%
10 185
 
1.8%
3 154
 
1.5%
6 146
 
1.5%
Other values (191) 3207
32.1%
(Missing) 608
 
6.1%
ValueCountFrequency (%)
0 3656
36.6%
1 503
 
5.0%
2 263
 
2.6%
3 154
 
1.5%
4 542
 
5.4%
5 201
 
2.0%
6 146
 
1.5%
7 126
 
1.3%
8 257
 
2.6%
9 278
 
2.8%
ValueCountFrequency (%)
480 2
 
< 0.1%
468 2
 
< 0.1%
371 2
 
< 0.1%
356 4
 
< 0.1%
349 1
 
< 0.1%
348 2
 
< 0.1%
343 2
 
< 0.1%
341 1
 
< 0.1%
332 13
0.1%
313 2
 
< 0.1%

건물용도
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
아파트
5213 
연립다세대
3091 
오피스텔
1088 
단독다가구
608 

Length

Max length5
Median length3
Mean length3.8486
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row연립다세대
2nd row연립다세대
3rd row연립다세대
4th row아파트
5th row연립다세대

Common Values

ValueCountFrequency (%)
아파트 5213
52.1%
연립다세대 3091
30.9%
오피스텔 1088
 
10.9%
단독다가구 608
 
6.1%

Length

2023-12-12T21:57:50.306799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T21:57:50.440958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
아파트 5213
52.1%
연립다세대 3091
30.9%
오피스텔 1088
 
10.9%
단독다가구 608
 
6.1%

건물명
Text

MISSING 

Distinct1981
Distinct (%)21.1%
Missing608
Missing (%)6.1%
Memory size156.2 KiB
2023-12-12T21:57:50.735854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length18
Mean length6.3968271
Min length2

Characters and Unicode

Total characters60079
Distinct characters387
Distinct categories13 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique929 ?
Unique (%)9.9%

Sample

1st row휴미락나동
2nd row양지쉐르빌(310-5)
3rd row현대빌라(207-55)
4th row신도림롯데아파트
5th row그레이스빌2차
ValueCountFrequency (%)
현대 287
 
2.9%
구로두산 216
 
2.2%
한마을 170
 
1.7%
한진 161
 
1.6%
sk 151
 
1.5%
허브수 151
 
1.5%
신도림태영타운 128
 
1.3%
비즈트위트 126
 
1.3%
삼성래미안 119
 
1.2%
현대홈타운 114
 
1.1%
Other values (2003) 8362
83.7%
2023-12-12T21:57:51.222724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
( 2137
 
3.6%
) 2136
 
3.6%
1 2132
 
3.5%
빌 2029
 
3.4%
- 1916
 
3.2%
2 1585
 
2.6%
3 1476
 
2.5%
스 1325
 
2.2%
대 1296
 
2.2%
동 1105
 
1.8%
Other values (377) 42942
71.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 41463
69.0%
Decimal Number 10604
 
17.7%
Open Punctuation 2137
 
3.6%
Close Punctuation 2136
 
3.6%
Dash Punctuation 1916
 
3.2%
Uppercase Letter 1079
 
1.8%
Space Separator 601
 
1.0%
Other Punctuation 70
 
0.1%
Control 39
 
0.1%
Letter Number 21
 
< 0.1%
Other values (3) 13
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
빌 2029
 
4.9%
스 1325
 
3.2%
대 1296
 
3.1%
동 1105
 
2.7%
라 1074
 
2.6%
이 997
 
2.4%
트 961
 
2.3%
성 933
 
2.3%
우 916
 
2.2%
현 840
 
2.0%
Other values (334) 29987
72.3%
Uppercase Letter
ValueCountFrequency (%)
S 238
22.1%
K 157
14.6%
B 95
 
8.8%
L 85
 
7.9%
A 84
 
7.8%
P 84
 
7.8%
O 81
 
7.5%
U 78
 
7.2%
G 75
 
7.0%
I 51
 
4.7%
Other values (8) 51
 
4.7%
Decimal Number
ValueCountFrequency (%)
1 2132
20.1%
2 1585
14.9%
3 1476
13.9%
7 1076
10.1%
4 902
8.5%
0 849
 
8.0%
8 739
 
7.0%
5 705
 
6.6%
9 580
 
5.5%
6 560
 
5.3%
Letter Number
ValueCountFrequency (%)
Ⅲ 15
71.4%
Ⅱ 3
 
14.3%
Ⅴ 2
 
9.5%
Ⅰ 1
 
4.8%
Other Punctuation
ValueCountFrequency (%)
, 69
98.6%
# 1
 
1.4%
Lowercase Letter
ValueCountFrequency (%)
e 3
60.0%
l 2
40.0%
Open Punctuation
ValueCountFrequency (%)
( 2137
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2136
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1916
100.0%
Space Separator
ValueCountFrequency (%)
601
100.0%
Control
ValueCountFrequency (%)
39
100.0%
Math Symbol
ValueCountFrequency (%)
~ 7
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 41463
69.0%
Common 17511
29.1%
Latin 1105
 
1.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
빌 2029
 
4.9%
스 1325
 
3.2%
대 1296
 
3.1%
동 1105
 
2.7%
라 1074
 
2.6%
이 997
 
2.4%
트 961
 
2.3%
성 933
 
2.3%
우 916
 
2.2%
현 840
 
2.0%
Other values (334) 29987
72.3%
Latin
ValueCountFrequency (%)
S 238
21.5%
K 157
14.2%
B 95
 
8.6%
L 85
 
7.7%
A 84
 
7.6%
P 84
 
7.6%
O 81
 
7.3%
U 78
 
7.1%
G 75
 
6.8%
I 51
 
4.6%
Other values (14) 77
 
7.0%
Common
ValueCountFrequency (%)
( 2137
12.2%
) 2136
12.2%
1 2132
12.2%
- 1916
10.9%
2 1585
9.1%
3 1476
8.4%
7 1076
6.1%
4 902
 
5.2%
0 849
 
4.8%
8 739
 
4.2%
Other values (9) 2563
14.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 41463
69.0%
ASCII 18595
31.0%
Number Forms 21
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
( 2137
11.5%
) 2136
11.5%
1 2132
11.5%
- 1916
10.3%
2 1585
8.5%
3 1476
7.9%
7 1076
 
5.8%
4 902
 
4.9%
0 849
 
4.6%
8 739
 
4.0%
Other values (29) 3647
19.6%
Hangul
ValueCountFrequency (%)
빌 2029
 
4.9%
스 1325
 
3.2%
대 1296
 
3.1%
동 1105
 
2.7%
라 1074
 
2.6%
이 997
 
2.4%
트 961
 
2.3%
성 933
 
2.3%
우 916
 
2.2%
현 840
 
2.0%
Other values (334) 29987
72.3%
Number Forms
ValueCountFrequency (%)
Ⅲ 15
71.4%
Ⅱ 3
 
14.3%
Ⅴ 2
 
9.5%
Ⅰ 1
 
4.8%

계약일
Date

Distinct4417
Distinct (%)44.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2006-01-03 00:00:00
Maximum2023-08-21 00:00:00
2023-12-12T21:57:51.369464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:57:51.535372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

층
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct37
Distinct (%)0.4%
Missing608
Missing (%)6.1%
Infinite0
Infinite (%)0.0%
Mean7.2096465
Minimum-1
Maximum36
Zeros0
Zeros (%)0.0%
Negative194
Negative (%)1.9%
Memory size166.0 KiB
2023-12-12T21:57:51.709258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile1
Q13
median5
Q311
95-th percentile20
Maximum36
Range37
Interquartile range (IQR)8

Descriptive statistics

Standard deviation6.0048272
Coefficient of variation (CV)0.83288788
Kurtosis0.63324056
Mean7.2096465
Median Absolute Deviation (MAD)3
Skewness1.1125463
Sum67713
Variance36.05795
MonotonicityNot monotonic
2023-12-12T21:57:51.869878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
2 1212
12.1%
3 1107
11.1%
4 1078
10.8%
5 837
 
8.4%
1 731
 
7.3%
6 451
 
4.5%
12 347
 
3.5%
10 336
 
3.4%
9 331
 
3.3%
7 329
 
3.3%
Other values (27) 2633
26.3%
(Missing) 608
 
6.1%
ValueCountFrequency (%)
-1 194
 
1.9%
1 731
7.3%
2 1212
12.1%
3 1107
11.1%
4 1078
10.8%
5 837
8.4%
6 451
 
4.5%
7 329
 
3.3%
8 326
 
3.3%
9 331
 
3.3%
ValueCountFrequency (%)
36 1
 
< 0.1%
35 3
 
< 0.1%
34 2
 
< 0.1%
33 1
 
< 0.1%
32 1
 
< 0.1%
31 1
 
< 0.1%
30 3
 
< 0.1%
29 3
 
< 0.1%
28 4
 
< 0.1%
27 11
0.1%

건물면적(m2)
Real number (ℝ)

HIGH CORRELATION 

Distinct2845
Distinct (%)28.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65.466572
Minimum8.26
Maximum869.42
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T21:57:52.090029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8.26
5-th percentile20.9
Q141.52
median59.5
Q384.17
95-th percentile123.903
Maximum869.42
Range861.16
Interquartile range (IQR)42.65

Descriptive statistics

Standard deviation43.481889
Coefficient of variation (CV)0.6641846
Kurtosis38.014454
Mean65.466572
Median Absolute Deviation (MAD)20.14
Skewness4.4055287
Sum654665.72
Variance1890.6747
MonotonicityNot monotonic
2023-12-12T21:57:52.242844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
84.96 194
 
1.9%
84.99 175
 
1.8%
44.64 143
 
1.4%
59.94 143
 
1.4%
84.98 140
 
1.4%
59.95 130
 
1.3%
84.87 122
 
1.2%
59.57 102
 
1.0%
84.88 101
 
1.0%
59.96 77
 
0.8%
Other values (2835) 8673
86.7%
ValueCountFrequency (%)
8.26 1
 
< 0.1%
11.53 1
 
< 0.1%
12.32 1
 
< 0.1%
12.49 2
 
< 0.1%
12.5 1
 
< 0.1%
12.55 2
 
< 0.1%
12.6 2
 
< 0.1%
12.9 3
 
< 0.1%
13.22 2
 
< 0.1%
13.23 8
0.1%
ValueCountFrequency (%)
869.42 1
< 0.1%
642.72 1
< 0.1%
635.93 1
< 0.1%
545.58 1
< 0.1%
543.86 1
< 0.1%
532.54 1
< 0.1%
526.94 1
< 0.1%
511.04 1
< 0.1%
509.3 1
< 0.1%
494.44 1
< 0.1%

토지면적(m2)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct1989
Distinct (%)32.1%
Missing3806
Missing (%)38.1%
Infinite0
Infinite (%)0.0%
Mean36.712036
Minimum0
Maximum509
Zeros1407
Zeros (%)14.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T21:57:52.690645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q113.08
median28.155
Q341.6475
95-th percentile122
Maximum509
Range509
Interquartile range (IQR)28.5675

Descriptive statistics

Standard deviation41.435455
Coefficient of variation (CV)1.1286613
Kurtosis10.081952
Mean36.712036
Median Absolute Deviation (MAD)14.385
Skewness2.5150552
Sum227394.35
Variance1716.8969
MonotonicityNot monotonic
2023-12-12T21:57:52.833341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 1407
 
14.1%
46.4 66
 
0.7%
40.59 61
 
0.6%
73.08 56
 
0.6%
70.28 36
 
0.4%
95.06 28
 
0.3%
35.04 23
 
0.2%
77.7 22
 
0.2%
71.76 20
 
0.2%
37.43 19
 
0.2%
Other values (1979) 4456
44.6%
(Missing) 3806
38.1%
ValueCountFrequency (%)
0.0 1407
14.1%
4.02 2
 
< 0.1%
6.5 1
 
< 0.1%
6.66 1
 
< 0.1%
7.0 1
 
< 0.1%
7.02 2
 
< 0.1%
7.5 1
 
< 0.1%
7.63 4
 
< 0.1%
7.82 1
 
< 0.1%
7.87 1
 
< 0.1%
ValueCountFrequency (%)
509.0 1
< 0.1%
400.0 1
< 0.1%
348.1 1
< 0.1%
327.0 1
< 0.1%
317.1 1
< 0.1%
313.4 1
< 0.1%
304.8 1
< 0.1%
297.0 1
< 0.1%
295.0 1
< 0.1%
294.0 1
< 0.1%

건축년도
Real number (ℝ)

HIGH CORRELATION 

Distinct66
Distinct (%)0.7%
Missing13
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean1989.8624
Minimum0
Maximum2023
Zeros53
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T21:57:52.975584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1983
Q11994
median2001
Q32006
95-th percentile2019
Maximum2023
Range2023
Interquartile range (IQR)12

Descriptive statistics

Standard deviation145.74361
Coefficient of variation (CV)0.073243058
Kurtosis181.53466
Mean1989.8624
Median Absolute Deviation (MAD)6
Skewness-13.509127
Sum19872756
Variance21241.199
MonotonicityNot monotonic
2023-12-12T21:57:53.121125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2001 622
 
6.2%
2002 539
 
5.4%
1998 524
 
5.2%
2004 479
 
4.8%
1999 455
 
4.5%
2006 436
 
4.4%
2000 432
 
4.3%
2003 400
 
4.0%
2005 340
 
3.4%
1994 327
 
3.3%
Other values (56) 5433
54.3%
ValueCountFrequency (%)
0 53
0.5%
1900 2
 
< 0.1%
1945 3
 
< 0.1%
1960 1
 
< 0.1%
1961 9
 
0.1%
1963 3
 
< 0.1%
1964 1
 
< 0.1%
1965 4
 
< 0.1%
1966 2
 
< 0.1%
1967 7
 
0.1%
ValueCountFrequency (%)
2023 13
 
0.1%
2022 191
1.9%
2021 171
1.7%
2020 82
0.8%
2019 65
 
0.7%
2018 122
1.2%
2017 87
0.9%
2016 163
1.6%
2015 131
1.3%
2014 198
2.0%

신고구분
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
8677 
중개거래
 
824
직거래
 
499

Length

Max length4
Median length4
Mean length3.9501
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 8677
86.8%
중개거래 824
 
8.2%
직거래 499
 
5.0%

Length

2023-12-12T21:57:53.258162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T21:57:53.351504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 8677
86.8%
중개거래 824
 
8.2%
직거래 499
 
5.0%

물건금액(만원)
Real number (ℝ)

HIGH CORRELATION 

Distinct1280
Distinct (%)12.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29393.908
Minimum700
Maximum300000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T21:57:53.454250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum700
5-th percentile9000
Q116000
median25000
Q337300
95-th percentile64810
Maximum300000
Range299300
Interquartile range (IQR)21300

Descriptive statistics

Standard deviation19623.102
Coefficient of variation (CV)0.66759078
Kurtosis18.561515
Mean29393.908
Median Absolute Deviation (MAD)10000
Skewness2.7449002
Sum2.9393908 × 108
Variance3.8506614 × 108
MonotonicityNot monotonic
2023-12-12T21:57:53.586394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20000 136
 
1.4%
16000 124
 
1.2%
30000 112
 
1.1%
12000 111
 
1.1%
15000 97
 
1.0%
22000 97
 
1.0%
23000 93
 
0.9%
25000 93
 
0.9%
18000 92
 
0.9%
21000 92
 
0.9%
Other values (1270) 8953
89.5%
ValueCountFrequency (%)
700 1
 
< 0.1%
2000 2
< 0.1%
2100 1
 
< 0.1%
2700 1
 
< 0.1%
2900 3
< 0.1%
2950 1
 
< 0.1%
3000 4
< 0.1%
3100 1
 
< 0.1%
3300 2
< 0.1%
3400 1
 
< 0.1%
ValueCountFrequency (%)
300000 1
< 0.1%
289000 1
< 0.1%
270000 1
< 0.1%
260000 1
< 0.1%
236740 1
< 0.1%
230000 1
< 0.1%
228000 1
< 0.1%
193000 1
< 0.1%
181500 1
< 0.1%
171550 1
< 0.1%

데이터 기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-08-28
10000 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-08-28
2nd row2023-08-28
3rd row2023-08-28
4th row2023-08-28
5th row2023-08-28

Common Values

ValueCountFrequency (%)
2023-08-28 10000
100.0%

Length

2023-12-12T21:57:53.734503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T21:57:53.827276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-08-28 10000
100.0%

Interactions

2023-12-12T21:57:47.165487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:57:40.932545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:57:41.731361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:57:42.442009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:57:43.222417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:57:44.113432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:57:45.166440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:57:45.982391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:57:47.270992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:57:41.026099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:57:41.827587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:57:42.549440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:57:43.314172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:57:44.235994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:57:45.265205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:57:46.090723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:57:47.383851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:57:41.126776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:57:41.911850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:57:42.647097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:57:43.400092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:57:44.350734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:57:45.362403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:57:46.194282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:57:47.492111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:57:41.226858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:57:41.991421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:57:42.745180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:57:43.488286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:57:44.599897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:57:45.477985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:57:46.297490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:57:47.591491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:57:41.338050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:57:42.088667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:57:42.840662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:57:43.603534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:57:44.759706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:57:45.588274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:57:46.411355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:57:47.684715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:57:41.436585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:57:42.166870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:57:42.920998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:57:43.718148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:57:44.848745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:57:45.677239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:57:46.796824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:57:47.770349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:57:41.530549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:57:42.256616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:57:43.012060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:57:43.846609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:57:44.954235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:57:45.774468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:57:46.908684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:57:47.872325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:57:41.646035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:57:42.348518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:57:43.112864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:57:43.978811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:57:45.060027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:57:45.885888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:57:47.050704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T21:57:53.916986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
접수연도법정동명본번부번건물용도층건물면적(m2)토지면적(m2)건축년도신고구분물건금액(만원)
접수연도1.0000.1840.1760.0730.2630.1770.0590.1930.1430.0000.326
법정동명0.1841.0000.6830.2380.3610.3560.0920.2150.0530.2520.132
본번0.1760.6831.0000.5340.7440.3330.2410.2490.0820.2850.321
부번0.0730.2380.5341.0000.3150.1390.0760.0840.0040.1880.098
건물용도0.2630.3610.7440.3151.0000.5850.5930.6610.1020.3270.496
층0.1770.3560.3330.1390.5851.0000.4340.1030.1000.2610.404
건물면적(m2)0.0590.0920.2410.0760.5930.4341.0000.8370.0000.2130.688
토지면적(m2)0.1930.2150.2490.0840.6610.1030.8371.0000.0270.1970.682
건축년도0.1430.0530.0820.0040.1020.1000.0000.0271.000NaN0.000
신고구분0.0000.2520.2850.1880.3270.2610.2130.197NaN1.0000.383
물건금액(만원)0.3260.1320.3210.0980.4960.4040.6880.6820.0000.3831.000
2023-12-12T21:57:54.062662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정동명지번구분건물용도신고구분지번구분명
법정동명1.0001.0000.3020.3081.000
지번구분1.0001.0001.0001.0001.000
건물용도0.3021.0001.0000.2181.000
신고구분0.3081.0000.2181.0001.000
지번구분명1.0001.0001.0001.0001.000
2023-12-12T21:57:54.182532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
접수연도본번부번층건물면적(m2)토지면적(m2)건축년도물건금액(만원)법정동명지번구분지번구분명건물용도신고구분
접수연도1.000-0.1250.061-0.056-0.177-0.3000.2670.2920.0761.0001.0000.1580.000
본번-0.1251.000-0.2320.1270.213-0.267-0.1750.2550.4821.0001.0000.4490.284
부번0.061-0.2321.000-0.414-0.4050.170-0.066-0.4670.1381.0001.0000.1440.138
층-0.0560.127-0.4141.0000.236-0.2080.0700.3490.1561.0001.0000.4280.196
건물면적(m2)-0.1770.213-0.4050.2361.0000.212-0.2990.7040.0531.0001.0000.4240.153
토지면적(m2)-0.300-0.2670.170-0.2080.2121.000-0.123-0.0700.1251.0001.0000.4910.142
건축년도0.267-0.175-0.0660.070-0.299-0.1231.000-0.0410.0651.0001.0000.0681.000
물건금액(만원)0.2920.255-0.4670.3490.704-0.070-0.0411.0000.0551.0001.0000.3190.287
법정동명0.0760.4820.1380.1560.0530.1250.0650.0551.0001.0001.0000.3020.308
지번구분1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
지번구분명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
건물용도0.1580.4490.1440.4280.4240.4910.0680.3190.3021.0001.0001.0000.218
신고구분0.0000.2840.1380.1960.1530.1421.0000.2870.3081.0001.0000.2181.000

Missing values

2023-12-12T21:57:47.997988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T21:57:48.189759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-12-12T21:57:48.355881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

접수연도자치구명법정동명지번구분지번구분명본번부번건물용도건물명계약일층건물면적(m2)토지면적(m2)건축년도신고구분물건금액(만원)데이터 기준일자
51802022구로구개봉동1대지139216연립다세대휴미락나동2022-03-22359.2430.452022직거래400002023-08-28
101512019구로구개봉동1대지3105연립다세대양지쉐르빌(310-5)2019-11-08354.3730.272011<NA>230002023-08-28
939042011구로구궁동1대지20755연립다세대현대빌라(207-55)2011-07-13446.222.482001<NA>162002023-08-28
765822011구로구구로동1대지12630아파트신도림롯데아파트2011-01-11859.96<NA>1999<NA>312502023-08-28
218942013구로구개봉동1대지3249연립다세대그레이스빌2차2013-04-11372.4546.562010<NA>290002023-08-28
822032008구로구구로동1대지12710아파트보람쉬움2008-03-13781.09<NA>2003<NA>313002023-08-28
309402006구로구개봉동1대지4810아파트현대2006-03-081184.99<NA>2001<NA>260002023-08-28
379592016구로구고척동1대지3480연립다세대명품하이츠빌(348번지)2016-03-03352.0332.32015<NA>268002023-08-28
587772018구로구구로동1대지685223아파트주공22018-03-27132.390.01987<NA>236002023-08-28
779932010구로구구로동1대지7430아파트현대2010-02-06856.49<NA>1988<NA>258502023-08-28
접수연도자치구명법정동명지번구분지번구분명본번부번건물용도건물명계약일층건물면적(m2)토지면적(m2)건축년도신고구분물건금액(만원)데이터 기준일자
208372014구로구개봉동1대지31920연립다세대오션빌2013-12-31323.2816.342013<NA>103502023-08-28
510622021구로구구로동1대지12650아파트구로두산2021-05-28651.840.01998<NA>619002023-08-28
70252021구로구개봉동1대지4700아파트삼환2021-05-11259.40.01995<NA>600002023-08-28
396752014구로구고척동1대지2960아파트대우2014-05-221184.98<NA>1999<NA>315002023-08-28
365242017구로구고척동1대지579아파트산업인2017-06-12147.010.01976<NA>157502023-08-28
229312011구로구개봉동1대지4760아파트한마을2011-11-221559.57<NA>1999<NA>295002023-08-28
514342021구로구구로동1대지79344아파트(793-44)2021-04-05533.840.01996<NA>230002023-08-28
860132006구로구구로동1대지41537아파트한일퍼스트빌(415-37)2006-12-04782.62<NA>2004<NA>191002023-08-28
931272015구로구궁동1대지18412연립다세대대명그린빌2동(184-12)2015-06-26276.251.882004<NA>199002023-08-28
796802009구로구구로동1대지12650아파트구로두산2009-05-281944.64<NA>1998<NA>213702023-08-28

Duplicate rows

Most frequently occurring

접수연도자치구명법정동명지번구분지번구분명본번부번건물용도건물명계약일층건물면적(m2)토지면적(m2)건축년도신고구분물건금액(만원)데이터 기준일자# duplicates
262021구로구개봉동1대지40318오피스텔진오피스텔2021-08-17519.0519.052012<NA>97772023-08-286
1152022구로구구로동1대지1062아파트대림역포스큐2022-09-072014.580.02015직거래119002023-08-284
1712022구로구궁동1대지16915연립다세대아르누보12022-01-18545.5128.232021직거래317002023-08-284
22009구로구고척동1대지764오피스텔홈런오피스텔2009-08-13526.9133.582003<NA>65002023-08-283
202019구로구개봉동1대지1994아파트림괄2019-06-14455.65<NA>1988<NA>290002023-08-283
312022구로구가리봉동1대지12536연립다세대호암빌2022-02-08558.3229.72015중개거래260002023-08-283
342022구로구개봉동1대지3372연립다세대하늘사랑채2022-07-19320.913.082022직거래160002023-08-283
662022구로구개봉동1대지36515연립다세대다원아트5차2022-03-19231.0719.82015중개거래190002023-08-283
852022구로구고척동1대지241116연립다세대하늘사랑채5차B동2022-08-12219.612.642022직거래136502023-08-283
892022구로구고척동1대지253244연립다세대청호파크빌2022-05-16555.9232.252002중개거래275002023-08-283