Overview

Dataset statistics

Number of variables7
Number of observations10000
Missing cells103
Missing cells (%)0.1%
Duplicate rows5
Duplicate rows (%)0.1%
Total size in memory654.3 KiB
Average record size in memory67.0 B

Variable types

Text3
Numeric3
Categorical1

Dataset

DescriptionSample
Author한국토지주택공사
URLhttps://www.bigdata-realestate.kr/rebpp/usr/prd/prdInfoDetail.do?req_productId=6

Alerts

Dataset has 5 (0.1%) duplicate rowsDuplicates
LTOUT_CTRT_YMD is highly overall correlated with AGEHigh correlation
AGE is highly overall correlated with LTOUT_CTRT_YMDHigh correlation
CNTRR_RSDNC_NM has 103 (1.0%) missing valuesMissing

Reproduction

Analysis started2023-12-11 22:30:42.149256
Analysis finished2023-12-11 22:30:43.479699
Duration1.33 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

BSNS_DISTRICT_NM
Text

Distinct420
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T07:30:43.623813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length13
Mean length8.7105
Min length2

Characters and Unicode

Total characters87105
Distinct characters259
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

Unique12 ?
Unique (%)0.1%

Sample

1st row수원고등(05,주환3)
2nd row평택고덕국제화계획
3rd row울산옥현
4th row부천옥길(09,보금1)
5th row창원반송2(재건축)
ValueCountFrequency (%)
행정중심복합도시 237
 
2.3%
하남미사(09,보금3 233
 
2.3%
화성동탄2 189
 
1.9%
인천서창2(05,택2 125
 
1.2%
수원호매실(경기03,gb2 116
 
1.1%
성남금광1(재개발 103
 
1.0%
시흥은계(09,보금4 100
 
1.0%
평택고덕국제화계획 96
 
0.9%
위례 94
 
0.9%
행복도시 94
 
0.9%
Other values (413) 8724
86.3%
2023-12-12T07:30:43.919981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
( 6452
 
7.4%
) 6452
 
7.4%
, 4239
 
4.9%
0 3902
 
4.5%
2 3027
 
3.5%
주 2533
 
2.9%
1 1967
 
2.3%
택 1956
 
2.2%
천 1833
 
2.1%
산 1575
 
1.8%
Other values (249) 53169
61.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 52129
59.8%
Decimal Number 14675
 
16.8%
Open Punctuation 6452
 
7.4%
Close Punctuation 6452
 
7.4%
Other Punctuation 4273
 
4.9%
Uppercase Letter 2657
 
3.1%
Dash Punctuation 356
 
0.4%
Space Separator 111
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 2533
 
4.9%
택 1956
 
3.8%
천 1833
 
3.5%
산 1575
 
3.0%
남 1365
 
2.6%
대 1334
 
2.6%
성 1253
 
2.4%
환 1105
 
2.1%
양 1054
 
2.0%
원 995
 
1.9%
Other values (223) 37126
71.2%
Decimal Number
ValueCountFrequency (%)
0 3902
26.6%
2 3027
20.6%
1 1967
13.4%
3 1256
 
8.6%
5 1254
 
8.5%
9 1237
 
8.4%
4 641
 
4.4%
6 537
 
3.7%
7 482
 
3.3%
8 372
 
2.5%
Uppercase Letter
ValueCountFrequency (%)
B 1193
44.9%
G 791
29.8%
L 379
 
14.3%
A 191
 
7.2%
M 27
 
1.0%
P 27
 
1.0%
C 25
 
0.9%
X 8
 
0.3%
T 8
 
0.3%
K 8
 
0.3%
Other Punctuation
ValueCountFrequency (%)
, 4239
99.2%
. 34
 
0.8%
Open Punctuation
ValueCountFrequency (%)
( 6452
100.0%
Close Punctuation
ValueCountFrequency (%)
) 6452
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 356
100.0%
Space Separator
ValueCountFrequency (%)
111
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 52129
59.8%
Common 32319
37.1%
Latin 2657
 
3.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 2533
 
4.9%
택 1956
 
3.8%
천 1833
 
3.5%
산 1575
 
3.0%
남 1365
 
2.6%
대 1334
 
2.6%
성 1253
 
2.4%
환 1105
 
2.1%
양 1054
 
2.0%
원 995
 
1.9%
Other values (223) 37126
71.2%
Common
ValueCountFrequency (%)
( 6452
20.0%
) 6452
20.0%
, 4239
13.1%
0 3902
12.1%
2 3027
9.4%
1 1967
 
6.1%
3 1256
 
3.9%
5 1254
 
3.9%
9 1237
 
3.8%
4 641
 
2.0%
Other values (6) 1892
 
5.9%
Latin
ValueCountFrequency (%)
B 1193
44.9%
G 791
29.8%
L 379
 
14.3%
A 191
 
7.2%
M 27
 
1.0%
P 27
 
1.0%
C 25
 
0.9%
X 8
 
0.3%
T 8
 
0.3%
K 8
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 52129
59.8%
ASCII 34976
40.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
( 6452
18.4%
) 6452
18.4%
, 4239
12.1%
0 3902
11.2%
2 3027
8.7%
1 1967
 
5.6%
3 1256
 
3.6%
5 1254
 
3.6%
9 1237
 
3.5%
B 1193
 
3.4%
Other values (16) 3997
11.4%
Hangul
ValueCountFrequency (%)
주 2533
 
4.9%
택 1956
 
3.8%
천 1833
 
3.5%
산 1575
 
3.0%
남 1365
 
2.6%
대 1334
 
2.6%
성 1253
 
2.4%
환 1105
 
2.1%
양 1054
 
2.0%
원 995
 
1.9%
Other values (223) 37126
71.2%

BSNS_DISTRICT_ISE_BLCK_NM
Text

Distinct210
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T07:30:44.207361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length7
Mean length2.3186
Min length1

Characters and Unicode

Total characters23186
Distinct characters38
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

Unique5 ?
Unique (%)< 0.1%

Sample

1st rowA-1
2nd rowAa54
3rd row01
4th rowS1
5th row01
ValueCountFrequency (%)
1 1535
 
15.3%
01 1147
 
11.5%
2 415
 
4.2%
3 318
 
3.2%
b-1 282
 
2.8%
a-1 276
 
2.8%
b1 236
 
2.4%
b-2 235
 
2.4%
02 223
 
2.2%
b2 175
 
1.8%
Other values (199) 5158
51.6%
2023-12-12T07:30:44.607656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 5642
24.3%
- 3099
13.4%
A 2671
11.5%
2 2360
10.2%
0 2102
 
9.1%
B 1593
 
6.9%
3 1290
 
5.6%
4 945
 
4.1%
6 523
 
2.3%
8 430
 
1.9%
Other values (28) 2531
10.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14178
61.1%
Uppercase Letter 5320
 
22.9%
Dash Punctuation 3099
 
13.4%
Other Punctuation 301
 
1.3%
Lowercase Letter 186
 
0.8%
Other Letter 66
 
0.3%
Open Punctuation 18
 
0.1%
Close Punctuation 18
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 5642
39.8%
2 2360
16.6%
0 2102
 
14.8%
3 1290
 
9.1%
4 945
 
6.7%
6 523
 
3.7%
8 430
 
3.0%
5 419
 
3.0%
7 302
 
2.1%
9 165
 
1.2%
Other Letter
ValueCountFrequency (%)
라 18
27.3%
신 18
27.3%
규 13
19.7%
혼 5
 
7.6%
마 3
 
4.5%
주 3
 
4.5%
상 3
 
4.5%
복 1
 
1.5%
합 1
 
1.5%
나 1
 
1.5%
Uppercase Letter
ValueCountFrequency (%)
A 2671
50.2%
B 1593
29.9%
S 332
 
6.2%
L 263
 
4.9%
C 261
 
4.9%
M 175
 
3.3%
R 12
 
0.2%
H 12
 
0.2%
D 1
 
< 0.1%
Lowercase Letter
ValueCountFrequency (%)
a 89
47.8%
b 59
31.7%
c 25
 
13.4%
l 13
 
7.0%
Other Punctuation
ValueCountFrequency (%)
* 290
96.3%
, 11
 
3.7%
Dash Punctuation
ValueCountFrequency (%)
- 3099
100.0%
Open Punctuation
ValueCountFrequency (%)
( 18
100.0%
Close Punctuation
ValueCountFrequency (%)
) 18
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 17614
76.0%
Latin 5506
 
23.7%
Hangul 66
 
0.3%

Most frequent character per script

Common
ValueCountFrequency (%)
1 5642
32.0%
- 3099
17.6%
2 2360
13.4%
0 2102
 
11.9%
3 1290
 
7.3%
4 945
 
5.4%
6 523
 
3.0%
8 430
 
2.4%
5 419
 
2.4%
7 302
 
1.7%
Other values (5) 502
 
2.9%
Latin
ValueCountFrequency (%)
A 2671
48.5%
B 1593
28.9%
S 332
 
6.0%
L 263
 
4.8%
C 261
 
4.7%
M 175
 
3.2%
a 89
 
1.6%
b 59
 
1.1%
c 25
 
0.5%
l 13
 
0.2%
Other values (3) 25
 
0.5%
Hangul
ValueCountFrequency (%)
라 18
27.3%
신 18
27.3%
규 13
19.7%
혼 5
 
7.6%
마 3
 
4.5%
주 3
 
4.5%
상 3
 
4.5%
복 1
 
1.5%
합 1
 
1.5%
나 1
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23120
99.7%
Hangul 66
 
0.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 5642
24.4%
- 3099
13.4%
A 2671
11.6%
2 2360
10.2%
0 2102
 
9.1%
B 1593
 
6.9%
3 1290
 
5.6%
4 945
 
4.1%
6 523
 
2.3%
8 430
 
1.9%
Other values (18) 2465
10.7%
Hangul
ValueCountFrequency (%)
라 18
27.3%
신 18
27.3%
규 13
19.7%
혼 5
 
7.6%
마 3
 
4.5%
주 3
 
4.5%
상 3
 
4.5%
복 1
 
1.5%
합 1
 
1.5%
나 1
 
1.5%

LEGALDONG_NM
Real number (ℝ)

Distinct599
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean591.4292
Minimum4
Maximum8010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T07:30:44.737449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile103
Q1125
median310
Q3609
95-th percentile2109
Maximum8010
Range8006
Interquartile range (IQR)484

Descriptive statistics

Standard deviation849.75978
Coefficient of variation (CV)1.4367904
Kurtosis24.819412
Mean591.4292
Median Absolute Deviation (MAD)200
Skewness4.2850883
Sum5914292
Variance722091.68
MonotonicityNot monotonic
2023-12-12T07:30:44.847145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
103 247
 
2.5%
102 241
 
2.4%
105 237
 
2.4%
101 235
 
2.4%
106 228
 
2.3%
104 226
 
2.3%
107 166
 
1.7%
203 161
 
1.6%
204 161
 
1.6%
201 159
 
1.6%
Other values (589) 7939
79.4%
ValueCountFrequency (%)
4 2
 
< 0.1%
5 1
 
< 0.1%
7 1
 
< 0.1%
101 235
2.4%
102 241
2.4%
103 247
2.5%
104 226
2.3%
105 237
2.4%
106 228
2.3%
107 166
1.7%
ValueCountFrequency (%)
8010 3
< 0.1%
8005 2
< 0.1%
8004 3
< 0.1%
8003 1
 
< 0.1%
8002 2
< 0.1%
8001 2
< 0.1%
7502 2
< 0.1%
7501 1
 
< 0.1%
7406 3
< 0.1%
7405 4
< 0.1%

LTOUT_CTRT_YMD
Real number (ℝ)

HIGH CORRELATION 

Distinct3385
Distinct (%)33.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20091623
Minimum19941007
Maximum20230919
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T07:30:44.952397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19941007
5-th percentile19970328
Q120020424
median20100630
Q320160114
95-th percentile20211108
Maximum20230919
Range289912
Interquartile range (IQR)139689.5

Descriptive statistics

Standard deviation79154.283
Coefficient of variation (CV)0.0039396659
Kurtosis-1.1913008
Mean20091623
Median Absolute Deviation (MAD)69800.5
Skewness-0.025370095
Sum2.0091623 × 1011
Variance6.2654005 × 109
MonotonicityNot monotonic
2023-12-12T07:30:45.054130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20140704 64
 
0.6%
20080829 52
 
0.5%
20020724 44
 
0.4%
20190626 44
 
0.4%
20050418 43
 
0.4%
19951229 37
 
0.4%
20020424 37
 
0.4%
19971224 37
 
0.4%
20021212 37
 
0.4%
20190624 36
 
0.4%
Other values (3375) 9569
95.7%
ValueCountFrequency (%)
19941007 1
 
< 0.1%
19950727 22
0.2%
19950822 17
0.2%
19951023 1
 
< 0.1%
19951031 1
 
< 0.1%
19951109 9
0.1%
19951110 4
 
< 0.1%
19951111 2
 
< 0.1%
19951113 1
 
< 0.1%
19951115 1
 
< 0.1%
ValueCountFrequency (%)
20230919 1
 
< 0.1%
20230912 2
 
< 0.1%
20230901 5
0.1%
20230831 1
 
< 0.1%
20230711 1
 
< 0.1%
20230706 1
 
< 0.1%
20230630 1
 
< 0.1%
20230628 2
 
< 0.1%
20230612 3
< 0.1%
20230605 2
 
< 0.1%

AGE
Real number (ℝ)

HIGH CORRELATION 

Distinct87
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean55.8979
Minimum13
Maximum113
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T07:30:45.156534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile36
Q147
median55
Q364
95-th percentile79
Maximum113
Range100
Interquartile range (IQR)17

Descriptive statistics

Standard deviation12.939305
Coefficient of variation (CV)0.23148106
Kurtosis0.28319625
Mean55.8979
Median Absolute Deviation (MAD)9
Skewness0.43616494
Sum558979
Variance167.42562
MonotonicityNot monotonic
2023-12-12T07:30:45.260931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
55 355
 
3.5%
54 350
 
3.5%
56 350
 
3.5%
53 341
 
3.4%
52 322
 
3.2%
57 317
 
3.2%
51 314
 
3.1%
58 299
 
3.0%
62 293
 
2.9%
63 288
 
2.9%
Other values (77) 6771
67.7%
ValueCountFrequency (%)
13 1
 
< 0.1%
22 1
 
< 0.1%
23 2
 
< 0.1%
24 2
 
< 0.1%
25 2
 
< 0.1%
26 1
 
< 0.1%
27 8
 
0.1%
28 9
 
0.1%
29 31
0.3%
30 27
0.3%
ValueCountFrequency (%)
113 1
 
< 0.1%
112 1
 
< 0.1%
110 1
 
< 0.1%
106 2
 
< 0.1%
104 2
 
< 0.1%
103 1
 
< 0.1%
101 4
 
< 0.1%
100 5
0.1%
99 6
0.1%
98 10
0.1%

CNTRR_SEX_NM
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
남자
6551 
여자
3449 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row남자
2nd row남자
3rd row여자
4th row여자
5th row남자

Common Values

ValueCountFrequency (%)
남자 6551
65.5%
여자 3449
34.5%

Length

2023-12-12T07:30:45.364911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T07:30:45.436714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
남자 6551
65.5%
여자 3449
34.5%

CNTRR_RSDNC_NM
Text

MISSING 

Distinct1595
Distinct (%)16.1%
Missing103
Missing (%)1.0%
Memory size156.2 KiB
2023-12-12T07:30:45.669699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length46
Median length32
Mean length10.593513
Min length4

Characters and Unicode

Total characters104844
Distinct characters300
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique811 ?
Unique (%)8.2%

Sample

1st row경기도 수원시 권선구
2nd row경기도 평택시 고덕면
3rd row울산광역시 북구
4th row경기도 부천시 원미구
5th row경남 마산시 해운동
ValueCountFrequency (%)
경기도 2680
 
10.8%
서울특별시 1137
 
4.6%
경기 930
 
3.8%
수원시 494
 
2.0%
인천광역시 484
 
2.0%
성남시 463
 
1.9%
대구광역시 430
 
1.7%
서구 405
 
1.6%
대전광역시 385
 
1.6%
동구 362
 
1.5%
Other values (1364) 16996
68.6%
2023-12-12T07:30:46.020404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
25495
24.3%
시 8710
 
8.3%
구 7156
 
6.8%
경 4369
 
4.2%
도 4161
 
4.0%
기 3762
 
3.6%
동 2908
 
2.8%
광 2681
 
2.6%
남 2358
 
2.2%
서 2250
 
2.1%
Other values (290) 40994
39.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 78902
75.3%
Space Separator 25495
 
24.3%
Decimal Number 424
 
0.4%
Other Punctuation 14
 
< 0.1%
Open Punctuation 4
 
< 0.1%
Math Symbol 2
 
< 0.1%
Dash Punctuation 1
 
< 0.1%
Uppercase Letter 1
 
< 0.1%
Close Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 8710
 
11.0%
구 7156
 
9.1%
경 4369
 
5.5%
도 4161
 
5.3%
기 3762
 
4.8%
동 2908
 
3.7%
광 2681
 
3.4%
남 2358
 
3.0%
서 2250
 
2.9%
역 1958
 
2.5%
Other values (272) 38589
48.9%
Decimal Number
ValueCountFrequency (%)
2 145
34.2%
1 122
28.8%
3 73
17.2%
4 29
 
6.8%
5 15
 
3.5%
6 9
 
2.1%
7 9
 
2.1%
0 8
 
1.9%
9 7
 
1.7%
8 7
 
1.7%
Other Punctuation
ValueCountFrequency (%)
. 9
64.3%
, 5
35.7%
Space Separator
ValueCountFrequency (%)
25495
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%
Math Symbol
ValueCountFrequency (%)
< 2
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%
Uppercase Letter
ValueCountFrequency (%)
A 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 78902
75.3%
Common 25941
 
24.7%
Latin 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 8710
 
11.0%
구 7156
 
9.1%
경 4369
 
5.5%
도 4161
 
5.3%
기 3762
 
4.8%
동 2908
 
3.7%
광 2681
 
3.4%
남 2358
 
3.0%
서 2250
 
2.9%
역 1958
 
2.5%
Other values (272) 38589
48.9%
Common
ValueCountFrequency (%)
25495
98.3%
2 145
 
0.6%
1 122
 
0.5%
3 73
 
0.3%
4 29
 
0.1%
5 15
 
0.1%
6 9
 
< 0.1%
7 9
 
< 0.1%
. 9
 
< 0.1%
0 8
 
< 0.1%
Other values (7) 27
 
0.1%
Latin
ValueCountFrequency (%)
A 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 78902
75.3%
ASCII 25942
 
24.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
25495
98.3%
2 145
 
0.6%
1 122
 
0.5%
3 73
 
0.3%
4 29
 
0.1%
5 15
 
0.1%
6 9
 
< 0.1%
7 9
 
< 0.1%
. 9
 
< 0.1%
0 8
 
< 0.1%
Other values (8) 28
 
0.1%
Hangul
ValueCountFrequency (%)
시 8710
 
11.0%
구 7156
 
9.1%
경 4369
 
5.5%
도 4161
 
5.3%
기 3762
 
4.8%
동 2908
 
3.7%
광 2681
 
3.4%
남 2358
 
3.0%
서 2250
 
2.9%
역 1958
 
2.5%
Other values (272) 38589
48.9%

Interactions

2023-12-12T07:30:43.124161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:30:42.671007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:30:42.895753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:30:43.199115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:30:42.749920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:30:42.975811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:30:43.266081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:30:42.826212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:30:43.059120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T07:30:46.097211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
LEGALDONG_NMLTOUT_CTRT_YMDAGECNTRR_SEX_NM
LEGALDONG_NM1.0000.4150.1610.028
LTOUT_CTRT_YMD0.4151.0000.6070.125
AGE0.1610.6071.0000.114
CNTRR_SEX_NM0.0280.1250.1141.000
2023-12-12T07:30:46.164552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
LEGALDONG_NMLTOUT_CTRT_YMDAGECNTRR_SEX_NM
LEGALDONG_NM1.0000.114-0.0920.022
LTOUT_CTRT_YMD0.1141.000-0.5480.096
AGE-0.092-0.5481.0000.091
CNTRR_SEX_NM0.0220.0960.0911.000

Missing values

2023-12-12T07:30:43.352769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T07:30:43.438756image/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

BSNS_DISTRICT_NMBSNS_DISTRICT_ISE_BLCK_NMLEGALDONG_NMLTOUT_CTRT_YMDAGECNTRR_SEX_NMCNTRR_RSDNC_NM
57459수원고등(05,주환3)A-11192019041644남자경기도 수원시 권선구
18830평택고덕국제화계획Aa5411092021100535남자경기도 평택시 고덕면
8738울산옥현011102000041254여자울산광역시 북구
69621부천옥길(09,보금1)S17112015082646여자경기도 부천시 원미구
15118창원반송2(재건축)012262004062866남자경남 마산시 해운동
36722남양주별내A-25BLA-2517042020021836여자경기도 남양주시
35287성남금광1(재개발)A44042019071059남자경기도 성남시 중원구
42382대전도안서남부(01.택)66052009041668여자대전 유성구 용산동
62834안성아양B-12032021090934남자경기도 안성시 대덕면
45379행정중심복합도시63M25122022021774남자충청남도 논산시 은진면
BSNS_DISTRICT_NMBSNS_DISTRICT_ISE_BLCK_NMLEGALDONG_NMLTOUT_CTRT_YMDAGECNTRR_SEX_NMCNTRR_RSDNC_NM
59229안동용상(6)66071997111357남자경북 안동시 남선면
4743의왕포일2(05,국민2)B-13062009111851남자경기 의왕시 포일동
11427서울강남(09,보금)A11102011102883여자경기 성남시 분당구
10940하남미사(09,보금3)A1818172013080663남자경기도 하남시
47519화성동탄2A-6666102014110747남자경기도 화성시
28870광명소하(02,GB)C-12062008081954남자경기도 광명시
23624대구신천1-2(01,주환)15032010061544남자대구광역시 북구
55834대전노은3(05,국민,GB)A22032013090936여자대전광역시 유성구
15018광주운남1(택)15142002090263남자경기도 수원시 장안구
48508인천영종A401112021112034남자서울특별시 강서구

Duplicate rows

Most frequently occurring

BSNS_DISTRICT_NMBSNS_DISTRICT_ISE_BLCK_NMLEGALDONG_NMLTOUT_CTRT_YMDAGECNTRR_SEX_NMCNTRR_RSDNC_NM# duplicates
0김해율하2B-11072017012342남자경상남도 김해시2
1인천서창2(05,택2)1010022015052751남자인천광역시 남동구2
2진해석동223052001120564남자경상남도 창원시 진해구2
3청주개신B-13012002070555남자충청북도 청주시 흥덕구2
4파주금촌(PM)11182001073157여자경기도 파주시2