Overview

Dataset statistics

Number of variables18
Number of observations200
Missing cells200
Missing cells (%)5.6%
Duplicate rows4
Duplicate rows (%)2.0%
Total size in memory29.6 KiB
Average record size in memory151.7 B

Variable types

Text6
Categorical5
Numeric6
Unsupported1

Alerts

Dataset has 4 (2.0%) duplicate rowsDuplicates
BUILD_STDYM is highly overall correlated with HOUS_ID and 7 other fieldsHigh correlation
FLOOR is highly overall correlated with HOUS_ID and 4 other fieldsHigh correlation
CONT_CLSS is highly overall correlated with HOUS_ID and 5 other fieldsHigh correlation
BLD_CLSS is highly overall correlated with BLD_CD and 4 other fieldsHigh correlation
HOUS_ID is highly overall correlated with BLD_CD and 4 other fieldsHigh correlation
BLD_CD is highly overall correlated with HOUS_ID and 4 other fieldsHigh correlation
Y_AXIS is highly overall correlated with BUILD_STDYM and 2 other fieldsHigh correlation
BLK_CD is highly overall correlated with HOUS_ID and 2 other fieldsHigh correlation
CONT_DATE is highly overall correlated with BUILD_STDYM and 2 other fieldsHigh correlation
AMOUNT is highly overall correlated with BUILD_STDYM and 2 other fieldsHigh correlation
CONT_CLSS is highly imbalanced (95.5%)Imbalance
DEPOSIT is highly imbalanced (95.5%)Imbalance
RENT_AMOUNT has 200 (100.0%) missing valuesMissing
RENT_AMOUNT is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-12-10 06:34:45.250860
Analysis finished2023-12-10 06:34:59.978712
Duration14.73 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

ADDRESS
Text

Distinct67
Distinct (%)33.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-12-10T15:35:00.192398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length38
Median length35
Mean length26.67
Min length20

Characters and Unicode

Total characters5334
Distinct characters159
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique44 ?
Unique (%)22.0%

Sample

1st row대구광역시 동구 입석동 893-29 센트로파크A동
2nd row인천광역시 서구 왕길동 660-2 타워팰리스2
3rd row인천광역시 서구 왕길동 660-2 타워팰리스2
4th row인천광역시 서구 왕길동 660-2 타워팰리스2
5th row인천광역시 서구 왕길동 660-2 타워팰리스2
ValueCountFrequency (%)
서울특별시 170
17.4%
동대문구 167
17.1%
장안동 110
 
11.3%
청량리동 43
 
4.4%
453-8 35
 
3.6%
서도휴빌3차 35
 
3.6%
409-1 26
 
2.7%
홀가하우스 26
 
2.7%
235-1 13
 
1.3%
미주 13
 
1.3%
Other values (143) 339
34.7%
2023-12-10T15:35:00.842875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
777
 
14.6%
동 379
 
7.1%
서 212
 
4.0%
구 201
 
3.8%
시 200
 
3.7%
대 177
 
3.3%
특 172
 
3.2%
별 172
 
3.2%
- 171
 
3.2%
울 171
 
3.2%
Other values (149) 2702
50.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3354
62.9%
Decimal Number 949
 
17.8%
Space Separator 777
 
14.6%
Dash Punctuation 171
 
3.2%
Close Punctuation 37
 
0.7%
Open Punctuation 37
 
0.7%
Uppercase Letter 8
 
0.1%
Other Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 379
 
11.3%
서 212
 
6.3%
구 201
 
6.0%
시 200
 
6.0%
대 177
 
5.3%
특 172
 
5.1%
별 172
 
5.1%
울 171
 
5.1%
문 167
 
5.0%
장 139
 
4.1%
Other values (130) 1364
40.7%
Decimal Number
ValueCountFrequency (%)
4 156
16.4%
5 137
14.4%
3 129
13.6%
1 109
11.5%
6 96
10.1%
2 94
9.9%
0 79
8.3%
8 60
 
6.3%
9 58
 
6.1%
7 31
 
3.3%
Uppercase Letter
ValueCountFrequency (%)
S 3
37.5%
A 2
25.0%
B 2
25.0%
J 1
 
12.5%
Space Separator
ValueCountFrequency (%)
777
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 171
100.0%
Close Punctuation
ValueCountFrequency (%)
) 37
100.0%
Open Punctuation
ValueCountFrequency (%)
( 37
100.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3354
62.9%
Common 1972
37.0%
Latin 8
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 379
 
11.3%
서 212
 
6.3%
구 201
 
6.0%
시 200
 
6.0%
대 177
 
5.3%
특 172
 
5.1%
별 172
 
5.1%
울 171
 
5.1%
문 167
 
5.0%
장 139
 
4.1%
Other values (130) 1364
40.7%
Common
ValueCountFrequency (%)
777
39.4%
- 171
 
8.7%
4 156
 
7.9%
5 137
 
6.9%
3 129
 
6.5%
1 109
 
5.5%
6 96
 
4.9%
2 94
 
4.8%
0 79
 
4.0%
8 60
 
3.0%
Other values (5) 164
 
8.3%
Latin
ValueCountFrequency (%)
S 3
37.5%
A 2
25.0%
B 2
25.0%
J 1
 
12.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3354
62.9%
ASCII 1980
37.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
777
39.2%
- 171
 
8.6%
4 156
 
7.9%
5 137
 
6.9%
3 129
 
6.5%
1 109
 
5.5%
6 96
 
4.8%
2 94
 
4.7%
0 79
 
4.0%
8 60
 
3.0%
Other values (9) 172
 
8.7%
Hangul
ValueCountFrequency (%)
동 379
 
11.3%
서 212
 
6.3%
구 201
 
6.0%
시 200
 
6.0%
대 177
 
5.3%
특 172
 
5.1%
별 172
 
5.1%
울 171
 
5.1%
문 167
 
5.0%
장 139
 
4.1%
Other values (130) 1364
40.7%

APT_NM
Text

Distinct67
Distinct (%)33.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-12-10T15:35:01.223705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length16
Mean length6.33
Min length2

Characters and Unicode

Total characters1266
Distinct characters134
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique44 ?
Unique (%)22.0%

Sample

1st row센트로파크A동
2nd row타워팰리스2
3rd row타워팰리스2
4th row타워팰리스2
5th row타워팰리스2
ValueCountFrequency (%)
서도휴빌3차 35
17.1%
홀가하우스 26
 
12.7%
미주 13
 
6.3%
장안푸르미에 11
 
5.4%
괴정엔스타 11
 
5.4%
제일풍경채에듀파크2단지 10
 
4.9%
한신 10
 
4.9%
신부파스칼텔 5
 
2.4%
타워팰리스2 4
 
2.0%
신부파스카(563 4
 
2.0%
Other values (60) 76
37.1%
2023-12-10T15:35:02.011376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
스 69
 
5.5%
빌 59
 
4.7%
3 48
 
3.8%
차 43
 
3.4%
( 37
 
2.9%
) 36
 
2.8%
서 35
 
2.8%
도 35
 
2.8%
휴 35
 
2.8%
하 30
 
2.4%
Other values (124) 839
66.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 964
76.1%
Decimal Number 190
 
15.0%
Open Punctuation 37
 
2.9%
Close Punctuation 36
 
2.8%
Dash Punctuation 25
 
2.0%
Uppercase Letter 8
 
0.6%
Space Separator 5
 
0.4%
Other Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
스 69
 
7.2%
빌 59
 
6.1%
차 43
 
4.5%
서 35
 
3.6%
도 35
 
3.6%
휴 35
 
3.6%
하 30
 
3.1%
가 29
 
3.0%
우 28
 
2.9%
파 26
 
2.7%
Other values (105) 575
59.6%
Decimal Number
ValueCountFrequency (%)
3 48
25.3%
2 29
15.3%
4 22
11.6%
0 20
10.5%
1 19
 
10.0%
5 18
 
9.5%
6 15
 
7.9%
8 8
 
4.2%
9 7
 
3.7%
7 4
 
2.1%
Uppercase Letter
ValueCountFrequency (%)
S 3
37.5%
A 2
25.0%
B 2
25.0%
J 1
 
12.5%
Open Punctuation
ValueCountFrequency (%)
( 37
100.0%
Close Punctuation
ValueCountFrequency (%)
) 36
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 25
100.0%
Space Separator
ValueCountFrequency (%)
5
100.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 964
76.1%
Common 294
 
23.2%
Latin 8
 
0.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
스 69
 
7.2%
빌 59
 
6.1%
차 43
 
4.5%
서 35
 
3.6%
도 35
 
3.6%
휴 35
 
3.6%
하 30
 
3.1%
가 29
 
3.0%
우 28
 
2.9%
파 26
 
2.7%
Other values (105) 575
59.6%
Common
ValueCountFrequency (%)
3 48
16.3%
( 37
12.6%
) 36
12.2%
2 29
9.9%
- 25
8.5%
4 22
7.5%
0 20
6.8%
1 19
 
6.5%
5 18
 
6.1%
6 15
 
5.1%
Other values (5) 25
8.5%
Latin
ValueCountFrequency (%)
S 3
37.5%
A 2
25.0%
B 2
25.0%
J 1
 
12.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 964
76.1%
ASCII 302
 
23.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
스 69
 
7.2%
빌 59
 
6.1%
차 43
 
4.5%
서 35
 
3.6%
도 35
 
3.6%
휴 35
 
3.6%
하 30
 
3.1%
가 29
 
3.0%
우 28
 
2.9%
파 26
 
2.7%
Other values (105) 575
59.6%
ASCII
ValueCountFrequency (%)
3 48
15.9%
( 37
12.3%
) 36
11.9%
2 29
9.6%
- 25
8.3%
4 22
7.3%
0 20
6.6%
1 19
 
6.3%
5 18
 
6.0%
6 15
 
5.0%
Other values (9) 33
10.9%

BUILD_STDYM
Categorical

HIGH CORRELATION 

Distinct28
Distinct (%)14.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2019
61 
2015
27 
2013
13 
1978
13 
2011
12 
Other values (23)
74 

Length

Max length6
Median length4
Mean length4.01
Min length4

Unique

Unique11 ?
Unique (%)5.5%

Sample

1st row2015
2nd row2015
3rd row2015
4th row2015
5th row2015

Common Values

ValueCountFrequency (%)
2019 61
30.5%
2015 27
13.5%
2013 13
 
6.5%
1978 13
 
6.5%
2011 12
 
6.0%
2005 11
 
5.5%
1997 10
 
5.0%
2003 8
 
4.0%
2002 6
 
3.0%
2014 5
 
2.5%
Other values (18) 34
17.0%

Length

2023-12-10T15:35:02.317836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2019 61
30.5%
2015 27
13.5%
2013 13
 
6.5%
1978 13
 
6.5%
2011 12
 
6.0%
2005 11
 
5.5%
1997 10
 
5.0%
2003 8
 
4.0%
2002 6
 
3.0%
2014 5
 
2.5%
Other values (18) 34
17.0%

HOUS_ID
Real number (ℝ)

HIGH CORRELATION 

Distinct67
Distinct (%)33.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3747056 × 1018
Minimum2014
Maximum3.6110107 × 1018
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-10T15:35:02.547685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2014
5-th percentile1.1230106 × 1018
Q11.1230106 × 1018
median1.1230106 × 1018
Q31.1230107 × 1018
95-th percentile2.915511 × 1018
Maximum3.6110107 × 1018
Range3.6110107 × 1018
Interquartile range (IQR)9.999804 × 1010

Descriptive statistics

Standard deviation6.272076 × 1017
Coefficient of variation (CV)0.45624867
Kurtosis2.592567
Mean1.3747056 × 1018
Median Absolute Deviation (MAD)569984
Skewness1.9870784
Sum-1.7600424 × 1018
Variance3.9338937 × 1035
MonotonicityNot monotonic
2023-12-10T15:35:02.911163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1123010600004530008 35
17.5%
1123010600004090001 26
 
13.0%
1123010700002350001 13
 
6.5%
2638010100012750000 11
 
5.5%
1123010600004660005 11
 
5.5%
2915511000005620000 10
 
5.0%
1123010700000600000 10
 
5.0%
1123010600004310005 5
 
2.5%
1123010600005630000 4
 
2.0%
2826012000006600002 4
 
2.0%
Other values (57) 71
35.5%
ValueCountFrequency (%)
2014 1
 
0.5%
1123010600003940007 1
 
0.5%
1123010600003970002 1
 
0.5%
1123010600004000001 1
 
0.5%
1123010600004050016 1
 
0.5%
1123010600004060001 2
 
1.0%
1123010600004060002 1
 
0.5%
1123010600004090001 26
13.0%
1123010600004100013 1
 
0.5%
1123010600004160002 2
 
1.0%
ValueCountFrequency (%)
3611010700007130000 1
 
0.5%
3611010600009470000 1
 
0.5%
2915511000005620000 10
5.0%
2826012000006600002 4
 
2.0%
2826010300005250010 2
 
1.0%
2714010900008930029 1
 
0.5%
2638010100012750000 11
5.5%
1153010900000970009 2
 
1.0%
1150010500007930006 1
 
0.5%
1123010800000650000 1
 
0.5%

BLD_CD
Real number (ℝ)

HIGH CORRELATION 

Distinct63
Distinct (%)31.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3790157 × 1024
Minimum1.1230106 × 1018
Maximum4.473033 × 1024
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-10T15:35:03.207005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.1230106 × 1018
5-th percentile1.1230106 × 1024
Q11.1230106 × 1024
median1.1230106 × 1024
Q31.1230107 × 1024
95-th percentile2.915511 × 1024
Maximum4.473033 × 1024
Range4.4730319 × 1024
Interquartile range (IQR)9.999804 × 1016

Descriptive statistics

Standard deviation6.4535191 × 1023
Coefficient of variation (CV)0.4679801
Kurtosis3.9304275
Mean1.3790157 × 1024
Median Absolute Deviation (MAD)5.7002269 × 1011
Skewness2.1543943
Sum2.7580314 × 1026
Variance4.1647909 × 1047
MonotonicityNot monotonic
2023-12-10T15:35:03.481645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.12301060010453e+24 35
17.5%
1.12301060010409e+24 26
 
13.0%
1.12301070010235e+24 13
 
6.5%
1.12301060010466e+24 12
 
6.0%
2.63801010011016e+24 11
 
5.5%
1.1230107001006e+24 10
 
5.0%
2.91551100010562e+24 10
 
5.0%
1.12301060010431e+24 7
 
3.5%
1.12301060010563e+24 4
 
2.0%
2.8260120001066e+24 4
 
2.0%
Other values (53) 68
34.0%
ValueCountFrequency (%)
1.12301060000394e+18 1
 
0.5%
1.12301060010192e+24 1
 
0.5%
1.1230106001034e+24 1
 
0.5%
1.12301060010394e+24 1
 
0.5%
1.12301060010397e+24 1
 
0.5%
1.123010600104e+24 1
 
0.5%
1.12301060010405e+24 1
 
0.5%
1.12301060010406e+24 3
 
1.5%
1.12301060010409e+24 26
13.0%
1.1230106001041e+24 1
 
0.5%
ValueCountFrequency (%)
4.4730330331054305e+24 1
 
0.5%
3.61101070010488e+24 1
 
0.5%
2.91551100010562e+24 10
5.0%
2.8260120001066e+24 4
 
2.0%
2.82601030010525e+24 2
 
1.0%
2.71401090010893e+24 1
 
0.5%
2.63801010011016e+24 11
5.5%
1.15301090010097e+24 2
 
1.0%
1.15001050010793e+24 1
 
0.5%
1.12301080010065e+24 1
 
0.5%

HOUS_ADDR
Text

Distinct67
Distinct (%)33.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-12-10T15:35:03.802343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length40
Median length37
Mean length28.745
Min length23

Characters and Unicode

Total characters5749
Distinct characters160
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique44 ?
Unique (%)22.0%

Sample

1st row대구광역시 동구 입석동 893-29번지 센트로파크A동
2nd row인천광역시 서구 왕길동 660-2번지 타워팰리스2
3rd row인천광역시 서구 왕길동 660-2번지 타워팰리스2
4th row인천광역시 서구 왕길동 660-2번지 타워팰리스2
5th row인천광역시 서구 왕길동 660-2번지 타워팰리스2
ValueCountFrequency (%)
서울특별시 169
16.9%
동대문구 166
16.6%
장안동 120
 
12.0%
청량리동 43
 
4.3%
453-8번지 35
 
3.5%
서도휴빌3차 35
 
3.5%
409-1번지 26
 
2.6%
홀가하우스 26
 
2.6%
235-1번지 13
 
1.3%
미주 13
 
1.3%
Other values (151) 353
35.3%
2023-12-10T15:35:04.394519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
799
 
13.9%
동 376
 
6.5%
지 212
 
3.7%
서 211
 
3.7%
구 200
 
3.5%
시 199
 
3.5%
번 199
 
3.5%
대 176
 
3.1%
특 171
 
3.0%
별 171
 
3.0%
Other values (150) 3035
52.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3733
64.9%
Decimal Number 968
 
16.8%
Space Separator 799
 
13.9%
Dash Punctuation 168
 
2.9%
Close Punctuation 36
 
0.6%
Open Punctuation 36
 
0.6%
Uppercase Letter 8
 
0.1%
Other Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 376
 
10.1%
지 212
 
5.7%
서 211
 
5.7%
구 200
 
5.4%
시 199
 
5.3%
번 199
 
5.3%
대 176
 
4.7%
특 171
 
4.6%
별 171
 
4.6%
울 170
 
4.6%
Other values (131) 1648
44.1%
Decimal Number
ValueCountFrequency (%)
4 153
15.8%
5 139
14.4%
3 130
13.4%
1 114
11.8%
6 99
10.2%
2 93
9.6%
0 88
9.1%
8 60
 
6.2%
9 58
 
6.0%
7 34
 
3.5%
Uppercase Letter
ValueCountFrequency (%)
S 3
37.5%
A 2
25.0%
B 2
25.0%
J 1
 
12.5%
Space Separator
ValueCountFrequency (%)
799
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 168
100.0%
Close Punctuation
ValueCountFrequency (%)
) 36
100.0%
Open Punctuation
ValueCountFrequency (%)
( 36
100.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3733
64.9%
Common 2008
34.9%
Latin 8
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 376
 
10.1%
지 212
 
5.7%
서 211
 
5.7%
구 200
 
5.4%
시 199
 
5.3%
번 199
 
5.3%
대 176
 
4.7%
특 171
 
4.6%
별 171
 
4.6%
울 170
 
4.6%
Other values (131) 1648
44.1%
Common
ValueCountFrequency (%)
799
39.8%
- 168
 
8.4%
4 153
 
7.6%
5 139
 
6.9%
3 130
 
6.5%
1 114
 
5.7%
6 99
 
4.9%
2 93
 
4.6%
0 88
 
4.4%
8 60
 
3.0%
Other values (5) 165
 
8.2%
Latin
ValueCountFrequency (%)
S 3
37.5%
A 2
25.0%
B 2
25.0%
J 1
 
12.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3733
64.9%
ASCII 2016
35.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
799
39.6%
- 168
 
8.3%
4 153
 
7.6%
5 139
 
6.9%
3 130
 
6.4%
1 114
 
5.7%
6 99
 
4.9%
2 93
 
4.6%
0 88
 
4.4%
8 60
 
3.0%
Other values (9) 173
 
8.6%
Hangul
ValueCountFrequency (%)
동 376
 
10.1%
지 212
 
5.7%
서 211
 
5.7%
구 200
 
5.4%
시 199
 
5.3%
번 199
 
5.3%
대 176
 
4.7%
특 171
 
4.6%
별 171
 
4.6%
울 170
 
4.6%
Other values (131) 1648
44.1%

ROAD_ADDR
Text

Distinct67
Distinct (%)33.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-12-10T15:35:04.832833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length42
Median length34
Mean length27.085
Min length21

Characters and Unicode

Total characters5417
Distinct characters164
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique44 ?
Unique (%)22.0%

Sample

1st row대구광역시 동구 동촌역사로3길 28 센트로파크A동
2nd row인천광역시 서구 완정로117번길 61 타워팰리스2
3rd row인천광역시 서구 완정로117번길 61 타워팰리스2
4th row인천광역시 서구 완정로117번길 61 타워팰리스2
5th row인천광역시 서구 완정로117번길 61 타워팰리스2
ValueCountFrequency (%)
서울특별시 170
 
16.9%
동대문구 167
 
16.7%
17 36
 
3.6%
천호대로93길 35
 
3.5%
서도휴빌3차 35
 
3.5%
한천로6길 29
 
2.9%
26 28
 
2.8%
홀가하우스 26
 
2.6%
약령시로 13
 
1.3%
147 13
 
1.3%
Other values (178) 451
45.0%
2023-12-10T15:35:05.483717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
803
 
14.8%
대 260
 
4.8%
시 216
 
4.0%
서 213
 
3.9%
구 201
 
3.7%
로 200
 
3.7%
동 192
 
3.5%
별 172
 
3.2%
특 172
 
3.2%
울 171
 
3.2%
Other values (154) 2817
52.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3576
66.0%
Decimal Number 910
 
16.8%
Space Separator 803
 
14.8%
Dash Punctuation 45
 
0.8%
Close Punctuation 37
 
0.7%
Open Punctuation 37
 
0.7%
Uppercase Letter 8
 
0.1%
Other Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
대 260
 
7.3%
시 216
 
6.0%
서 213
 
6.0%
구 201
 
5.6%
로 200
 
5.6%
동 192
 
5.4%
별 172
 
4.8%
특 172
 
4.8%
울 171
 
4.8%
문 168
 
4.7%
Other values (135) 1611
45.1%
Decimal Number
ValueCountFrequency (%)
3 160
17.6%
1 145
15.9%
2 127
14.0%
6 106
11.6%
7 88
9.7%
9 78
8.6%
4 69
7.6%
0 49
 
5.4%
5 46
 
5.1%
8 42
 
4.6%
Uppercase Letter
ValueCountFrequency (%)
S 3
37.5%
A 2
25.0%
B 2
25.0%
J 1
 
12.5%
Space Separator
ValueCountFrequency (%)
803
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 45
100.0%
Close Punctuation
ValueCountFrequency (%)
) 37
100.0%
Open Punctuation
ValueCountFrequency (%)
( 37
100.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3576
66.0%
Common 1833
33.8%
Latin 8
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
대 260
 
7.3%
시 216
 
6.0%
서 213
 
6.0%
구 201
 
5.6%
로 200
 
5.6%
동 192
 
5.4%
별 172
 
4.8%
특 172
 
4.8%
울 171
 
4.8%
문 168
 
4.7%
Other values (135) 1611
45.1%
Common
ValueCountFrequency (%)
803
43.8%
3 160
 
8.7%
1 145
 
7.9%
2 127
 
6.9%
6 106
 
5.8%
7 88
 
4.8%
9 78
 
4.3%
4 69
 
3.8%
0 49
 
2.7%
5 46
 
2.5%
Other values (5) 162
 
8.8%
Latin
ValueCountFrequency (%)
S 3
37.5%
A 2
25.0%
B 2
25.0%
J 1
 
12.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3576
66.0%
ASCII 1841
34.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
803
43.6%
3 160
 
8.7%
1 145
 
7.9%
2 127
 
6.9%
6 106
 
5.8%
7 88
 
4.8%
9 78
 
4.2%
4 69
 
3.7%
0 49
 
2.7%
5 46
 
2.5%
Other values (9) 170
 
9.2%
Hangul
ValueCountFrequency (%)
대 260
 
7.3%
시 216
 
6.0%
서 213
 
6.0%
구 201
 
5.6%
로 200
 
5.6%
동 192
 
5.4%
별 172
 
4.8%
특 172
 
4.8%
울 171
 
4.8%
문 168
 
4.7%
Other values (135) 1611
45.1%

X_AXIS
Text

Distinct66
Distinct (%)33.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-12-10T15:35:05.889367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length30
Median length6
Mean length6.12
Min length6

Characters and Unicode

Total characters1224
Distinct characters32
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

Unique42 ?
Unique (%)21.0%

Sample

1st row458963
2nd row281589
3rd row281589
4th row281589
5th row281589
ValueCountFrequency (%)
317983 35
17.2%
317220 26
 
12.7%
315994 13
 
6.4%
318025 11
 
5.4%
490886 11
 
5.4%
298686 10
 
4.9%
316242 10
 
4.9%
317613 5
 
2.5%
318032 4
 
2.0%
281589 4
 
2.0%
Other values (60) 75
36.8%
2023-12-10T15:35:06.634480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 235
19.2%
1 206
16.8%
8 130
10.6%
7 120
9.8%
2 120
9.8%
9 109
8.9%
6 85
 
6.9%
0 76
 
6.2%
5 60
 
4.9%
4 57
 
4.7%
Other values (22) 26
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1198
97.9%
Other Letter 20
 
1.6%
Space Separator 4
 
0.3%
Open Punctuation 1
 
0.1%
Close Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 2
 
10.0%
특 1
 
5.0%
별 1
 
5.0%
다 1
 
5.0%
가 1
 
5.0%
스 1
 
5.0%
리 1
 
5.0%
팰 1
 
5.0%
광 1
 
5.0%
길 1
 
5.0%
Other values (9) 9
45.0%
Decimal Number
ValueCountFrequency (%)
3 235
19.6%
1 206
17.2%
8 130
10.9%
7 120
10.0%
2 120
10.0%
9 109
9.1%
6 85
 
7.1%
0 76
 
6.3%
5 60
 
5.0%
4 57
 
4.8%
Space Separator
ValueCountFrequency (%)
4
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1204
98.4%
Hangul 20
 
1.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 2
 
10.0%
특 1
 
5.0%
별 1
 
5.0%
다 1
 
5.0%
가 1
 
5.0%
스 1
 
5.0%
리 1
 
5.0%
팰 1
 
5.0%
광 1
 
5.0%
길 1
 
5.0%
Other values (9) 9
45.0%
Common
ValueCountFrequency (%)
3 235
19.5%
1 206
17.1%
8 130
10.8%
7 120
10.0%
2 120
10.0%
9 109
9.1%
6 85
 
7.1%
0 76
 
6.3%
5 60
 
5.0%
4 57
 
4.7%
Other values (3) 6
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1204
98.4%
Hangul 20
 
1.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 235
19.5%
1 206
17.1%
8 130
10.8%
7 120
10.0%
2 120
10.0%
9 109
9.1%
6 85
 
7.1%
0 76
 
6.3%
5 60
 
5.0%
4 57
 
4.7%
Other values (3) 6
 
0.5%
Hangul
ValueCountFrequency (%)
동 2
 
10.0%
특 1
 
5.0%
별 1
 
5.0%
다 1
 
5.0%
가 1
 
5.0%
스 1
 
5.0%
리 1
 
5.0%
팰 1
 
5.0%
광 1
 
5.0%
길 1
 
5.0%
Other values (9) 9
45.0%

Y_AXIS
Real number (ℝ)

HIGH CORRELATION 

Distinct66
Distinct (%)33.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean520294.59
Minimum278376
Maximum556276
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-10T15:35:06.897720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum278376
5-th percentile278376
Q1551533
median551666
Q3553566.5
95-th percentile554438.35
Maximum556276
Range277900
Interquartile range (IQR)2033.5

Descriptive statistics

Standard deviation86492.844
Coefficient of variation (CV)0.16623822
Kurtosis3.8325839
Mean520294.59
Median Absolute Deviation (MAD)213
Skewness-2.3879549
Sum1.0405892 × 108
Variance7.4810121 × 109
MonotonicityNot monotonic
2023-12-10T15:35:07.138519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
551533 35
17.5%
551666 26
 
13.0%
553952 13
 
6.5%
551418 11
 
5.5%
278376 11
 
5.5%
278667 10
 
5.0%
554387 10
 
5.0%
551658 5
 
2.5%
556276 4
 
2.0%
554055 4
 
2.0%
Other values (56) 71
35.5%
ValueCountFrequency (%)
278376 11
5.5%
278667 10
5.0%
317720 1
 
0.5%
365766 1
 
0.5%
431210 1
 
0.5%
431625 1
 
0.5%
544772 2
 
1.0%
551403 1
 
0.5%
551418 11
5.5%
551480 2
 
1.0%
ValueCountFrequency (%)
556276 4
 
2.0%
555191 1
 
0.5%
554980 1
 
0.5%
554772 1
 
0.5%
554722 1
 
0.5%
554563 1
 
0.5%
554464 1
 
0.5%
554437 2
 
1.0%
554410 1
 
0.5%
554387 10
5.0%

BLK_CD
Real number (ℝ)

HIGH CORRELATION 

Distinct62
Distinct (%)31.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean306560.5
Minimum17938
Maximum552189
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-10T15:35:07.410318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum17938
5-th percentile76013.95
Q1168932.5
median346436
Q3363173
95-th percentile516861
Maximum552189
Range534251
Interquartile range (IQR)194240.5

Descriptive statistics

Standard deviation145174.85
Coefficient of variation (CV)0.47356018
Kurtosis-0.75520399
Mean306560.5
Median Absolute Deviation (MAD)41311
Skewness-0.69759706
Sum61312100
Variance2.1075736 × 1010
MonotonicityNot monotonic
2023-12-10T15:35:07.691451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
79595 35
17.5%
344817 26
 
13.0%
363173 13
 
6.5%
349020 12
 
6.0%
486814 11
 
5.5%
413133 10
 
5.0%
516861 10
 
5.0%
344807 7
 
3.5%
412321 4
 
2.0%
361986 4
 
2.0%
Other values (52) 68
34.0%
ValueCountFrequency (%)
17938 2
 
1.0%
35276 1
 
0.5%
39606 1
 
0.5%
39711 1
 
0.5%
40607 2
 
1.0%
50834 1
 
0.5%
70256 2
 
1.0%
76317 1
 
0.5%
79595 35
17.5%
79597 2
 
1.0%
ValueCountFrequency (%)
552189 1
 
0.5%
516861 10
5.0%
501325 2
 
1.0%
486814 11
5.5%
449885 1
 
0.5%
449433 1
 
0.5%
414605 1
 
0.5%
413825 1
 
0.5%
413766 1
 
0.5%
413221 1
 
0.5%

FLOOR
Categorical

HIGH CORRELATION 

Distinct25
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
4
34 
5
33 
3
30 
2
28 
7
12 
Other values (20)
63 

Length

Max length2
Median length1
Mean length1.155
Min length1

Unique

Unique11 ?
Unique (%)5.5%

Sample

1st row7
2nd row7
3rd row7
4th row7
5th row6

Common Values

ValueCountFrequency (%)
4 34
17.0%
5 33
16.5%
3 30
15.0%
2 28
14.0%
7 12
 
6.0%
1 10
 
5.0%
6 10
 
5.0%
8 6
 
3.0%
9 6
 
3.0%
10 6
 
3.0%
Other values (15) 25
12.5%

Length

2023-12-10T15:35:07.972680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
4 34
17.0%
5 33
16.5%
3 30
15.0%
2 28
14.0%
7 12
 
6.0%
1 11
 
5.5%
6 10
 
5.0%
8 6
 
3.0%
9 6
 
3.0%
10 6
 
3.0%
Other values (14) 24
12.0%

CONT_CLSS
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
매매
199 
2
 
1

Length

Max length2
Median length2
Mean length1.995
Min length1

Unique

Unique1 ?
Unique (%)0.5%

Sample

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

Common Values

ValueCountFrequency (%)
매매 199
99.5%
2 1
 
0.5%

Length

2023-12-10T15:35:08.245219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:35:08.446579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
매매 199
99.5%
2 1
 
0.5%

AREA
Text

Distinct113
Distinct (%)56.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-12-10T15:35:08.904309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length5
Mean length5.285
Min length2

Characters and Unicode

Total characters1057
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique85 ?
Unique (%)42.5%

Sample

1st row83.6835
2nd row48.5523
3rd row49.8441
4th row49.2521
5th row49.2521
ValueCountFrequency (%)
30.04 26
 
13.0%
84.9184 10
 
5.0%
12.9463 8
 
4.0%
59.97 5
 
2.5%
56.15 5
 
2.5%
84.9226 5
 
2.5%
56.41 5
 
2.5%
84.99 4
 
2.0%
84.92 4
 
2.0%
101.62 3
 
1.5%
Other values (103) 125
62.5%
2023-12-10T15:35:09.780134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 197
18.6%
4 144
13.6%
3 96
9.1%
5 96
9.1%
9 89
8.4%
1 87
8.2%
2 85
8.0%
8 82
7.8%
0 75
 
7.1%
6 66
 
6.2%
Other values (2) 40
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 858
81.2%
Other Punctuation 197
 
18.6%
Other Letter 2
 
0.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 144
16.8%
3 96
11.2%
5 96
11.2%
9 89
10.4%
1 87
10.1%
2 85
9.9%
8 82
9.6%
0 75
8.7%
6 66
7.7%
7 38
 
4.4%
Other Punctuation
ValueCountFrequency (%)
. 197
100.0%
Other Letter
ValueCountFrequency (%)
매 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1055
99.8%
Hangul 2
 
0.2%

Most frequent character per script

Common
ValueCountFrequency (%)
. 197
18.7%
4 144
13.6%
3 96
9.1%
5 96
9.1%
9 89
8.4%
1 87
8.2%
2 85
8.1%
8 82
7.8%
0 75
 
7.1%
6 66
 
6.3%
Hangul
ValueCountFrequency (%)
매 2
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1055
99.8%
Hangul 2
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 197
18.7%
4 144
13.6%
3 96
9.1%
5 96
9.1%
9 89
8.4%
1 87
8.2%
2 85
8.1%
8 82
7.8%
0 75
 
7.1%
6 66
 
6.3%
Hangul
ValueCountFrequency (%)
매 2
100.0%

CONT_DATE
Real number (ℝ)

HIGH CORRELATION 

Distinct105
Distinct (%)52.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20089441
Minimum51.04
Maximum20190629
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-10T15:35:10.078676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum51.04
5-th percentile20190118
Q120190314
median20190419
Q320190513
95-th percentile20190617
Maximum20190629
Range20190578
Interquartile range (IQR)199.25

Descriptive statistics

Standard deviation1427672.8
Coefficient of variation (CV)0.071065828
Kurtosis200
Mean20089441
Median Absolute Deviation (MAD)101.5
Skewness-14.142135
Sum4.0178883 × 109
Variance2.0382496 × 1012
MonotonicityNot monotonic
2023-12-10T15:35:10.368991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20190419.0 37
 
18.5%
20190323.0 6
 
3.0%
20190613.0 6
 
3.0%
20190612.0 5
 
2.5%
20190316.0 5
 
2.5%
20190228.0 4
 
2.0%
20190422.0 4
 
2.0%
20190325.0 4
 
2.0%
20190130.0 3
 
1.5%
20190614.0 3
 
1.5%
Other values (95) 123
61.5%
ValueCountFrequency (%)
51.04 1
0.5%
20190101.0 1
0.5%
20190103.0 1
0.5%
20190107.0 1
0.5%
20190110.0 1
0.5%
20190112.0 1
0.5%
20190114.0 1
0.5%
20190115.0 1
0.5%
20190117.0 1
0.5%
20190118.0 2
1.0%
ValueCountFrequency (%)
20190629.0 2
1.0%
20190628.0 1
0.5%
20190626.0 1
0.5%
20190625.0 1
0.5%
20190624.0 1
0.5%
20190622.0 1
0.5%
20190620.0 1
0.5%
20190619.0 1
0.5%
20190618.0 1
0.5%
20190617.0 1
0.5%

AMOUNT
Real number (ℝ)

HIGH CORRELATION 

Distinct121
Distinct (%)60.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean135284.71
Minimum8000
Maximum20190323
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-10T15:35:10.651936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8000
5-th percentile11895
Q125112.5
median29425
Q340200
95-th percentile83025
Maximum20190323
Range20182323
Interquartile range (IQR)15087.5

Descriptive statistics

Standard deviation1425361.8
Coefficient of variation (CV)10.536015
Kurtosis199.9258
Mean135284.71
Median Absolute Deviation (MAD)7150
Skewness14.138224
Sum27056943
Variance2.0316561 × 1012
MonotonicityNot monotonic
2023-12-10T15:35:11.297869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29000 17
 
8.5%
28000 11
 
5.5%
29500 5
 
2.5%
31000 4
 
2.0%
51000 4
 
2.0%
83000 4
 
2.0%
12000 4
 
2.0%
29450 3
 
1.5%
50000 3
 
1.5%
55000 3
 
1.5%
Other values (111) 142
71.0%
ValueCountFrequency (%)
8000 1
0.5%
8400 1
0.5%
8500 1
0.5%
8600 1
0.5%
8700 1
0.5%
10600 2
1.0%
11000 1
0.5%
11800 2
1.0%
11900 2
1.0%
11950 1
0.5%
ValueCountFrequency (%)
20190323 1
0.5%
115000 1
0.5%
110000 1
0.5%
89500 1
0.5%
89000 1
0.5%
88000 1
0.5%
87000 1
0.5%
85000 1
0.5%
84500 1
0.5%
83500 1
0.5%

DEPOSIT
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
<NA>
199 
29000
 
1

Length

Max length5
Median length4
Mean length4.005
Min length4

Unique

Unique1 ?
Unique (%)0.5%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 199
99.5%
29000 1
 
0.5%

Length

2023-12-10T15:35:11.543022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:35:11.757305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 199
99.5%
29000 1
 
0.5%

RENT_AMOUNT
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing200
Missing (%)100.0%
Memory size1.9 KiB

BLD_CLSS
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
연립/다세대
102 
아파트
84 
기타
13 
<NA>
 
1

Length

Max length6
Median length6
Mean length4.47
Min length2

Unique

Unique1 ?
Unique (%)0.5%

Sample

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

Common Values

ValueCountFrequency (%)
연립/다세대 102
51.0%
아파트 84
42.0%
기타 13
 
6.5%
<NA> 1
 
0.5%

Length

2023-12-10T15:35:11.964607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:35:12.174071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
연립/다세대 102
51.0%
아파트 84
42.0%
기타 13
 
6.5%
na 1
 
0.5%

Interactions

2023-12-10T15:34:57.256590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:34:46.957560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:34:48.800895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:34:52.663843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:34:54.160111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:34:55.607484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:34:57.469372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:34:47.144554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:34:49.381396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:34:52.874486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:34:54.326590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:34:55.815644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:34:58.575850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:34:47.864595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:34:50.674152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:34:53.547276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:34:54.959804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:34:56.518128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:34:58.741806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:34:48.160752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:34:51.149777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:34:53.679052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:34:55.098228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:34:56.711137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:34:58.970904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:34:48.369050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:34:51.602763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:34:53.833503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:34:55.245380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:34:56.890088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:34:59.152873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:34:48.613594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:34:52.088610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:34:54.011781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:34:55.418129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:34:57.067101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T15:35:12.333222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ADDRESSAPT_NMBUILD_STDYMHOUS_IDBLD_CDHOUS_ADDRROAD_ADDRX_AXISY_AXISBLK_CDFLOORCONT_CLSSCONT_DATEAMOUNTBLD_CLSS
ADDRESS1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.4761.0001.0001.0001.000
APT_NM1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.4761.0001.0001.0001.000
BUILD_STDYM1.0001.0001.0000.7570.8221.0001.0000.9990.8560.8890.8011.0001.0001.0000.969
HOUS_ID1.0001.0000.7571.0000.8881.0001.0001.0000.9870.8610.800NaNNaNNaN0.248
BLD_CD1.0001.0000.8220.8881.0001.0001.0001.0000.9600.9310.749NaNNaNNaN0.341
HOUS_ADDR1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.4761.0001.0001.0001.000
ROAD_ADDR1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.4761.0001.0001.0001.000
X_AXIS1.0001.0000.9991.0001.0001.0001.0001.0001.0001.0000.4891.0001.0001.0000.999
Y_AXIS1.0001.0000.8560.9870.9601.0001.0001.0001.0000.9860.8161.0001.0001.0000.238
BLK_CD1.0001.0000.8890.8610.9311.0001.0001.0000.9861.0000.5140.2180.2180.2170.563
FLOOR0.4760.4760.8010.8000.7490.4760.4760.4890.8160.5141.0001.0001.0001.0000.619
CONT_CLSS1.0001.0001.000NaNNaN1.0001.0001.0001.0000.2181.0001.0000.7000.700NaN
CONT_DATE1.0001.0001.000NaNNaN1.0001.0001.0001.0000.2181.0000.7001.0000.700NaN
AMOUNT1.0001.0001.000NaNNaN1.0001.0001.0001.0000.2171.0000.7000.7001.000NaN
BLD_CLSS1.0001.0000.9690.2480.3411.0001.0000.9990.2380.5630.619NaNNaNNaN1.000
2023-12-10T15:35:12.607998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
BUILD_STDYMFLOORCONT_CLSSDEPOSITBLD_CLSS
BUILD_STDYM1.0000.3170.932NaN0.769
FLOOR0.3171.0000.940NaN0.340
CONT_CLSS0.9320.9401.000NaN1.000
DEPOSITNaNNaNNaN1.000NaN
BLD_CLSS0.7690.3401.000NaN1.000
2023-12-10T15:35:12.856321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
HOUS_IDBLD_CDY_AXISBLK_CDCONT_DATEAMOUNTBUILD_STDYMFLOORCONT_CLSSDEPOSITBLD_CLSS
HOUS_ID1.0000.9790.0260.638-0.0640.2370.6240.6290.992NaN0.236
BLD_CD0.9791.0000.0040.651-0.0670.2260.8790.0940.820NaN0.824
Y_AXIS0.0260.0041.0000.124-0.0750.3040.5510.6040.992NaN0.298
BLK_CD0.6380.6510.1241.000-0.0330.2440.5460.1940.165NaN0.381
CONT_DATE-0.064-0.067-0.075-0.0331.0000.0340.9320.9400.494NaN1.000
AMOUNT0.2370.2260.3040.2440.0341.0000.9320.9400.494NaN1.000
BUILD_STDYM0.6240.8790.5510.5460.9320.9321.0000.3170.932NaN0.769
FLOOR0.6290.0940.6040.1940.9400.9400.3171.0000.940NaN0.340
CONT_CLSS0.9920.8200.9920.1650.4940.4940.9320.9401.000NaN1.000
DEPOSITNaNNaNNaNNaNNaNNaNNaNNaNNaN1.0000.000
BLD_CLSS0.2360.8240.2980.3811.0001.0000.7690.3401.0000.0001.000

Missing values

2023-12-10T15:34:59.435918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T15:34:59.829024image/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

ADDRESSAPT_NMBUILD_STDYMHOUS_IDBLD_CDHOUS_ADDRROAD_ADDRX_AXISY_AXISBLK_CDFLOORCONT_CLSSAREACONT_DATEAMOUNTDEPOSITRENT_AMOUNTBLD_CLSS
0대구광역시 동구 입석동 893-29 센트로파크A동센트로파크A동201527140109000089300292714010900108930029000001대구광역시 동구 입석동 893-29번지 센트로파크A동대구광역시 동구 동촌역사로3길 28 센트로파크A동4589633657662554707매매83.683520190325.023000<NA><NA>아파트
1인천광역시 서구 왕길동 660-2 타워팰리스2타워팰리스2201528260120000066000022826012000106600002000001인천광역시 서구 왕길동 660-2번지 타워팰리스2인천광역시 서구 완정로117번길 61 타워팰리스22815895562764123217매매48.552320190524.013300<NA><NA>연립/다세대
2인천광역시 서구 왕길동 660-2 타워팰리스2타워팰리스2201528260120000066000022826012000106600002000001인천광역시 서구 왕길동 660-2번지 타워팰리스2인천광역시 서구 완정로117번길 61 타워팰리스22815895562764123217매매49.844120190418.014500<NA><NA>연립/다세대
3인천광역시 서구 왕길동 660-2 타워팰리스2타워팰리스2201528260120000066000022826012000106600002000001인천광역시 서구 왕길동 660-2번지 타워팰리스2인천광역시 서구 완정로117번길 61 타워팰리스22815895562764123217매매49.252120190130.013500<NA><NA>연립/다세대
4인천광역시 서구 왕길동 660-2 타워팰리스2타워팰리스2201528260120000066000022826012000106600002000001인천광역시 서구 왕길동 660-2번지 타워팰리스2인천광역시 서구 완정로117번길 61 타워팰리스22815895562764123216매매49.252120190126.012000<NA><NA>연립/다세대
5인천광역시 서구 검암동 525-10 베르데힐(525-10)베르데힐(525-10)201528260103000052500102826010300105250010000001인천광역시 서구 검암동 525-10번지 베르데힐(525-10)인천광역시 서구 허암길 5-3 베르데힐(525-10)2834015520625013254매매55.4320190302.018500<NA><NA>연립/다세대
6인천광역시 서구 검암동 525-10 베르데힐(525-10)베르데힐(525-10)201528260103000052500102826010300105250010000001인천광역시 서구 검암동 525-10번지 베르데힐(525-10)인천광역시 서구 허암길 5-3 베르데힐(525-10)2834015520625013253매매5620190130.016000<NA><NA>연립/다세대
7광주광역시 남구 행암동 562 제일풍경채에듀파크2단지제일풍경채에듀파크2단지201529155110000056200002915511000105620000000001광주광역시 남구 행암동 562번지 제일풍경채에듀파크2단지광주광역시 남구 효우2로 46 제일풍경채에듀파크2단지29868627866751686112매매84.918420190518.035900<NA><NA>아파트
8광주광역시 남구 행암동 562 제일풍경채에듀파크2단지제일풍경채에듀파크2단지201529155110000056200002915511000105620000000001광주광역시 남구 행암동 562번지 제일풍경채에듀파크2단지광주광역시 남구 효우2로 46 제일풍경채에듀파크2단지2986862786675168616매매84.918420190419.035500<NA><NA>아파트
9광주광역시 남구 행암동 562 제일풍경채에듀파크2단지제일풍경채에듀파크2단지201529155110000056200002915511000105620000000001광주광역시 남구 행암동 562번지 제일풍경채에듀파크2단지광주광역시 남구 효우2로 46 제일풍경채에듀파크2단지2986862786675168616매매84.918420190618.041900<NA><NA>아파트
ADDRESSAPT_NMBUILD_STDYMHOUS_IDBLD_CDHOUS_ADDRROAD_ADDRX_AXISY_AXISBLK_CDFLOORCONT_CLSSAREACONT_DATEAMOUNTDEPOSITRENT_AMOUNTBLD_CLSS
190서울특별시 동대문구 청량리동 829 (829-0)(829-0)198011230107000082900001123010700108290000012176서울특별시 동대문구 청량리동 829번지 (829-0)서울특별시 동대문구 제기로31길 32-13 (829-0)3160585544643631121매매53.9520190323.049000<NA><NA>연립/다세대
191서울특별시 동대문구 청량리동 834 (834-0)(834-0)198011230107000083400001123010700108340000012236서울특별시 동대문구 청량리동 834번지 (834-0)서울특별시 동대문구 제기로31길 32-3 (834-0)3160265544373631121매매86.1820190125.041200<NA><NA>연립/다세대
192서울특별시 동대문구 청량리동 834 (834-0)(834-0)198011230107000083400001123010700108340000012236서울특별시 동대문구 청량리동 834번지 (834-0)서울특별시 동대문구 제기로31길 32-3 (834-0)3160265544373631122매매86.1820190119.043292<NA><NA>연립/다세대
193서울특별시 동대문구 청량리동 868 (868-0)(868-0)197911230107000086800001123010700108680000012291서울특별시 동대문구 청량리동 868번지 (868-0)서울특별시 동대문구 홍릉로24길 50-4 (868-0)3159185544103631022매매54.9820190228.047500<NA><NA>연립/다세대
194서울특별시 동대문구 청량리동 905 (905-0)(905-0)198111230107000090500001123010700109050000012634서울특별시 동대문구 청량리동 905번지 (905-0)서울특별시 동대문구 제기로29길 20-3 (905-0)3159805543093626861매매32.8320190406.029000<NA><NA>연립/다세대
195서울특별시 동대문구 청량리동 926 (926-0)(926-0)198111230107000092600001123010700109260000013130서울특별시 동대문구 청량리동 926번지 (926-0)서울특별시 동대문구 홍릉로22길 38 (926-0)3158605543643626741매매51.2120190521.036500<NA><NA>연립/다세대
196서울특별시 동대문구 청량리동 949 상그레빌상그레빌200411230107000094900001123010700109490000000001서울특별시 동대문구 청량리동 949번지 상그레빌서울특별시 동대문구 회기로5길 100 상그레빌3155045551914126936매매80.1620190612.051500<NA><NA>아파트
197서울특별시 동대문구 회기동 54-47 탑스빌탑스빌200211230108000005400471123010800100540047011492서울특별시 동대문구 회기동 54-47번지 탑스빌서울특별시 동대문구 회기로23다길 18 탑스빌3167975549803620912매매58.3320190509.022000<NA><NA>연립/다세대
198서울특별시 동대문구 회기동 60-218 동일아트맨션19차나동동일아트맨션19차나동200311230108000006002181123010800100600218011504서울특별시 동대문구 회기동 60-218번지 동일아트맨션19차나동서울특별시 동대문구 회기로 108-10 동일아트맨션19차나동3161165547723621165매매52.5320190123.020500<NA><NA>연립/다세대
199서울특별시 동대문구 회기동 65 신현대신현대198911230108000006500001123010800100650000010212서울특별시 동대문구 회기동 65번지 신현대서울특별시 동대문구 이문로1길 21 신현대31645555456336218511매매84.9620190331.055000<NA><NA>아파트

Duplicate rows

Most frequently occurring

ADDRESSAPT_NMBUILD_STDYMHOUS_IDBLD_CDHOUS_ADDRROAD_ADDRX_AXISY_AXISBLK_CDFLOORCONT_CLSSAREACONT_DATEAMOUNTDEPOSITBLD_CLSS# duplicates
2서울특별시 동대문구 장안동 409-1 홀가하우스홀가하우스201911230106000040900011123010600104090001018025서울특별시 동대문구 장안동 409-1번지 홀가하우스서울특별시 동대문구 한천로6길 26 홀가하우스3172205516663448174매매30.0420190316.029000<NA>연립/다세대3
0서울특별시 동대문구 장안동 409-1 홀가하우스홀가하우스201911230106000040900011123010600104090001018025서울특별시 동대문구 장안동 409-1번지 홀가하우스서울특별시 동대문구 한천로6길 26 홀가하우스3172205516663448173매매30.0420190430.028000<NA>연립/다세대2
1서울특별시 동대문구 장안동 409-1 홀가하우스홀가하우스201911230106000040900011123010600104090001018025서울특별시 동대문구 장안동 409-1번지 홀가하우스서울특별시 동대문구 한천로6길 26 홀가하우스3172205516663448173매매30.0420190517.028000<NA>연립/다세대2
3서울특별시 동대문구 장안동 409-1 홀가하우스홀가하우스201911230106000040900011123010600104090001018025서울특별시 동대문구 장안동 409-1번지 홀가하우스서울특별시 동대문구 한천로6길 26 홀가하우스3172205516663448175매매30.0420190325.029000<NA>연립/다세대2