Overview

Dataset statistics

Number of variables6
Number of observations10000
Missing cells333
Missing cells (%)0.6%
Duplicate rows57
Duplicate rows (%)0.6%
Total size in memory566.4 KiB
Average record size in memory58.0 B

Variable types

Text3
Numeric2
Categorical1

Dataset

DescriptionSample
Author한국토지주택공사
URLhttps://www.bigdata-realestate.kr/rebpp/usr/prd/prdInfoDetail.do?req_productId=8

Alerts

Dataset has 57 (0.6%) 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 332 (3.3%) missing valuesMissing

Reproduction

Analysis started2023-12-11 22:32:12.716534
Analysis finished2023-12-11 22:32:13.734462
Duration1.02 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

BSNS_DISTRICT_NM
Text

Distinct542
Distinct (%)5.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T07:32:13.910646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length15
Mean length5.8794
Min length2

Characters and Unicode

Total characters58794
Distinct characters287
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique67 ?
Unique (%)0.7%

Sample

1st row충북지사비축토지
2nd row광주수완
3rd row충남도청이전도시
4th row용인동백코아루아파트
5th row청주동남(05,택)
ValueCountFrequency (%)
고양일산 246
 
2.4%
대전둔산1 176
 
1.8%
행정중심복합도시 154
 
1.5%
인천영종 144
 
1.4%
김해장유 137
 
1.4%
화성동탄 130
 
1.3%
파주운정(01,택 129
 
1.3%
양산물금2 129
 
1.3%
평택고덕국제화계획 122
 
1.2%
화성동탄2 109
 
1.1%
Other values (537) 8571
85.3%
2023-12-12T07:32:14.245441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
산 2246
 
3.8%
주 2136
 
3.6%
( 1629
 
2.8%
) 1627
 
2.8%
2 1504
 
2.6%
, 1349
 
2.3%
양 1321
 
2.2%
대 1307
 
2.2%
천 1224
 
2.1%
0 1216
 
2.1%
Other values (277) 43235
73.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 47727
81.2%
Decimal Number 5688
 
9.7%
Open Punctuation 1629
 
2.8%
Close Punctuation 1627
 
2.8%
Other Punctuation 1411
 
2.4%
Uppercase Letter 636
 
1.1%
Space Separator 47
 
0.1%
Lowercase Letter 19
 
< 0.1%
Connector Punctuation 7
 
< 0.1%
Dash Punctuation 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 2246
 
4.7%
주 2136
 
4.5%
양 1321
 
2.8%
대 1307
 
2.7%
천 1224
 
2.6%
택 1173
 
2.5%
남 1049
 
2.2%
동 964
 
2.0%
전 955
 
2.0%
구 872
 
1.8%
Other values (245) 34480
72.2%
Uppercase Letter
ValueCountFrequency (%)
G 275
43.2%
B 275
43.2%
R 36
 
5.7%
D 36
 
5.7%
T 3
 
0.5%
K 3
 
0.5%
X 3
 
0.5%
M 2
 
0.3%
C 1
 
0.2%
A 1
 
0.2%
Decimal Number
ValueCountFrequency (%)
2 1504
26.4%
0 1216
21.4%
1 1212
21.3%
3 573
 
10.1%
5 386
 
6.8%
9 358
 
6.3%
6 163
 
2.9%
7 128
 
2.3%
4 103
 
1.8%
8 45
 
0.8%
Other Punctuation
ValueCountFrequency (%)
, 1349
95.6%
. 42
 
3.0%
& 17
 
1.2%
: 3
 
0.2%
Open Punctuation
ValueCountFrequency (%)
( 1629
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1627
100.0%
Space Separator
ValueCountFrequency (%)
47
100.0%
Lowercase Letter
ValueCountFrequency (%)
n 19
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 7
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%
Letter Number
ValueCountFrequency (%)
Ⅱ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 47727
81.2%
Common 10411
 
17.7%
Latin 656
 
1.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 2246
 
4.7%
주 2136
 
4.5%
양 1321
 
2.8%
대 1307
 
2.7%
천 1224
 
2.6%
택 1173
 
2.5%
남 1049
 
2.2%
동 964
 
2.0%
전 955
 
2.0%
구 872
 
1.8%
Other values (245) 34480
72.2%
Common
ValueCountFrequency (%)
( 1629
15.6%
) 1627
15.6%
2 1504
14.4%
, 1349
13.0%
0 1216
11.7%
1 1212
11.6%
3 573
 
5.5%
5 386
 
3.7%
9 358
 
3.4%
6 163
 
1.6%
Other values (9) 394
 
3.8%
Latin
ValueCountFrequency (%)
G 275
41.9%
B 275
41.9%
R 36
 
5.5%
D 36
 
5.5%
n 19
 
2.9%
T 3
 
0.5%
K 3
 
0.5%
X 3
 
0.5%
M 2
 
0.3%
C 1
 
0.2%
Other values (3) 3
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 47727
81.2%
ASCII 11066
 
18.8%
Number Forms 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
산 2246
 
4.7%
주 2136
 
4.5%
양 1321
 
2.8%
대 1307
 
2.7%
천 1224
 
2.6%
택 1173
 
2.5%
남 1049
 
2.2%
동 964
 
2.0%
전 955
 
2.0%
구 872
 
1.8%
Other values (245) 34480
72.2%
ASCII
ValueCountFrequency (%)
( 1629
14.7%
) 1627
14.7%
2 1504
13.6%
, 1349
12.2%
0 1216
11.0%
1 1212
11.0%
3 573
 
5.2%
5 386
 
3.5%
9 358
 
3.2%
G 275
 
2.5%
Other values (21) 937
8.5%
Number Forms
ValueCountFrequency (%)
Ⅱ 1
100.0%

LAND_SPL_PRPOS_NM
Text

Distinct69
Distinct (%)0.7%
Missing1
Missing (%)< 0.1%
Memory size156.2 KiB
2023-12-12T07:32:14.396968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length11
Mean length9.9253925
Min length2

Characters and Unicode

Total characters99244
Distinct characters112
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique24 ?
Unique (%)0.2%

Sample

1st row기타용도
2nd row실수요자택지 점포겸용
3rd row이주자 택지 점포겸용
4th row분양아파트(85㎡초과 공동)
5th row이주자 택지 점포겸용
ValueCountFrequency (%)
점포겸용 5786
29.5%
실수요자택지 4423
22.5%
택지 1935
 
9.9%
이주자 1934
 
9.9%
주거전용 1780
 
9.1%
협의양도인택지 1210
 
6.2%
일반상업용지 507
 
2.6%
근린생활시설 474
 
2.4%
준주거용지 246
 
1.3%
중심상업용지 213
 
1.1%
Other values (62) 1110
 
5.7%
2023-12-12T07:32:14.669010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9619
 
9.7%
지 8984
 
9.1%
용 8963
 
9.0%
택 7749
 
7.8%
자 6367
 
6.4%
점 5786
 
5.8%
포 5786
 
5.8%
겸 5786
 
5.8%
실 4423
 
4.5%
수 4423
 
4.5%
Other values (102) 31358
31.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 88720
89.4%
Space Separator 9619
 
9.7%
Decimal Number 322
 
0.3%
Close Punctuation 211
 
0.2%
Open Punctuation 211
 
0.2%
Other Symbol 112
 
0.1%
Math Symbol 48
 
< 0.1%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 8984
 
10.1%
용 8963
 
10.1%
택 7749
 
8.7%
자 6367
 
7.2%
점 5786
 
6.5%
포 5786
 
6.5%
겸 5786
 
6.5%
실 4423
 
5.0%
수 4423
 
5.0%
요 4423
 
5.0%
Other values (92) 26030
29.3%
Decimal Number
ValueCountFrequency (%)
8 110
34.2%
5 110
34.2%
0 51
15.8%
6 51
15.8%
Space Separator
ValueCountFrequency (%)
9619
100.0%
Close Punctuation
ValueCountFrequency (%)
) 211
100.0%
Open Punctuation
ValueCountFrequency (%)
( 211
100.0%
Other Symbol
ValueCountFrequency (%)
㎡ 112
100.0%
Math Symbol
ValueCountFrequency (%)
~ 48
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 88720
89.4%
Common 10524
 
10.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 8984
 
10.1%
용 8963
 
10.1%
택 7749
 
8.7%
자 6367
 
7.2%
점 5786
 
6.5%
포 5786
 
6.5%
겸 5786
 
6.5%
실 4423
 
5.0%
수 4423
 
5.0%
요 4423
 
5.0%
Other values (92) 26030
29.3%
Common
ValueCountFrequency (%)
9619
91.4%
) 211
 
2.0%
( 211
 
2.0%
㎡ 112
 
1.1%
8 110
 
1.0%
5 110
 
1.0%
0 51
 
0.5%
6 51
 
0.5%
~ 48
 
0.5%
- 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 88720
89.4%
ASCII 10412
 
10.5%
CJK Compat 112
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9619
92.4%
) 211
 
2.0%
( 211
 
2.0%
8 110
 
1.1%
5 110
 
1.1%
0 51
 
0.5%
6 51
 
0.5%
~ 48
 
0.5%
- 1
 
< 0.1%
Hangul
ValueCountFrequency (%)
지 8984
 
10.1%
용 8963
 
10.1%
택 7749
 
8.7%
자 6367
 
7.2%
점 5786
 
6.5%
포 5786
 
6.5%
겸 5786
 
6.5%
실 4423
 
5.0%
수 4423
 
5.0%
요 4423
 
5.0%
Other values (92) 26030
29.3%
CJK Compat
ValueCountFrequency (%)
㎡ 112
100.0%

LTOUT_CTRT_YMD
Real number (ℝ)

HIGH CORRELATION 

Distinct4117
Distinct (%)41.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20045603
Minimum19790917
Maximum20230920
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T07:32:14.804045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19790917
5-th percentile19891130
Q119961209
median20050624
Q320130422
95-th percentile20181672
Maximum20230920
Range440003
Interquartile range (IQR)169213

Descriptive statistics

Standard deviation97341.988
Coefficient of variation (CV)0.004856027
Kurtosis-1.0781285
Mean20045603
Median Absolute Deviation (MAD)80116.5
Skewness-0.18874605
Sum2.0045603 × 1011
Variance9.4754627 × 109
MonotonicityNot monotonic
2023-12-12T07:32:14.925269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19911122 58
 
0.6%
19900921 46
 
0.5%
20040629 41
 
0.4%
20000630 40
 
0.4%
20040712 39
 
0.4%
19901129 37
 
0.4%
19911125 31
 
0.3%
20161207 30
 
0.3%
20031219 29
 
0.3%
20050527 27
 
0.3%
Other values (4107) 9622
96.2%
ValueCountFrequency (%)
19790917 1
 
< 0.1%
19801101 2
 
< 0.1%
19810228 1
 
< 0.1%
19810326 6
0.1%
19810616 2
 
< 0.1%
19810814 1
 
< 0.1%
19811202 1
 
< 0.1%
19811209 1
 
< 0.1%
19820111 1
 
< 0.1%
19820609 1
 
< 0.1%
ValueCountFrequency (%)
20230920 1
 
< 0.1%
20230728 1
 
< 0.1%
20230725 1
 
< 0.1%
20230717 3
< 0.1%
20230629 2
< 0.1%
20230628 1
 
< 0.1%
20230623 1
 
< 0.1%
20230613 1
 
< 0.1%
20230609 3
< 0.1%
20230526 1
 
< 0.1%

AGE
Real number (ℝ)

HIGH CORRELATION 

Distinct96
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean66.1805
Minimum11
Maximum118
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T07:32:15.323747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile45
Q158
median65
Q374
95-th percentile89
Maximum118
Range107
Interquartile range (IQR)16

Descriptive statistics

Standard deviation13.097445
Coefficient of variation (CV)0.1979049
Kurtosis0.43152034
Mean66.1805
Median Absolute Deviation (MAD)8
Skewness0.25883184
Sum661805
Variance171.54307
MonotonicityNot monotonic
2023-12-12T07:32:15.512054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
64 407
 
4.1%
63 390
 
3.9%
69 363
 
3.6%
62 355
 
3.5%
65 351
 
3.5%
67 334
 
3.3%
66 331
 
3.3%
68 317
 
3.2%
70 298
 
3.0%
72 287
 
2.9%
Other values (86) 6567
65.7%
ValueCountFrequency (%)
11 1
 
< 0.1%
13 3
< 0.1%
21 1
 
< 0.1%
22 1
 
< 0.1%
25 1
 
< 0.1%
26 1
 
< 0.1%
27 3
< 0.1%
28 5
0.1%
29 4
< 0.1%
30 4
< 0.1%
ValueCountFrequency (%)
118 1
 
< 0.1%
117 1
 
< 0.1%
114 5
0.1%
113 5
0.1%
112 4
< 0.1%
111 5
0.1%
110 7
0.1%
109 4
< 0.1%
108 4
< 0.1%
107 2
 
< 0.1%

CNTRR_SEX_NM
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
남자
6570 
여자
3430 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
남자 6570
65.7%
여자 3430
34.3%

Length

2023-12-12T07:32:15.815630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T07:32:15.947994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
남자 6570
65.7%
여자 3430
34.3%

CNTRR_RSDNC_NM
Text

MISSING 

Distinct2191
Distinct (%)22.7%
Missing332
Missing (%)3.3%
Memory size156.2 KiB
2023-12-12T07:32:16.355469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length33
Median length31
Mean length10.895842
Min length5

Characters and Unicode

Total characters105341
Distinct characters367
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1526 ?
Unique (%)15.8%

Sample

1st row충청북도 청주시 흥덕구
2nd row광주광역시 광산구
3rd row충청남도 예산군 삽교읍
4th row경기도 과천시
5th row충청북도 청주시 상당구
ValueCountFrequency (%)
경기도 1974
 
8.1%
서울특별시 838
 
3.5%
경상남도 598
 
2.5%
대구광역시 595
 
2.5%
서구 452
 
1.9%
경기 431
 
1.8%
대전광역시 418
 
1.7%
북구 379
 
1.6%
인천광역시 363
 
1.5%
광주광역시 356
 
1.5%
Other values (2203) 17853
73.6%
2023-12-12T07:32:16.828428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
24326
23.1%
시 8664
 
8.2%
구 6714
 
6.4%
도 4218
 
4.0%
경 3549
 
3.4%
광 3039
 
2.9%
기 2544
 
2.4%
동 2500
 
2.4%
남 2368
 
2.2%
역 2263
 
2.1%
Other values (357) 45156
42.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 78675
74.7%
Space Separator 24332
 
23.1%
Decimal Number 1911
 
1.8%
Dash Punctuation 224
 
0.2%
Uppercase Letter 100
 
0.1%
Other Punctuation 49
 
< 0.1%
Close Punctuation 18
 
< 0.1%
Open Punctuation 16
 
< 0.1%
Control 9
 
< 0.1%
Lowercase Letter 7
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 8664
 
11.0%
구 6714
 
8.5%
도 4218
 
5.4%
경 3549
 
4.5%
광 3039
 
3.9%
기 2544
 
3.2%
동 2500
 
3.2%
남 2368
 
3.0%
역 2263
 
2.9%
서 2219
 
2.8%
Other values (315) 40597
51.6%
Uppercase Letter
ValueCountFrequency (%)
A 41
41.0%
P 18
18.0%
T 17
17.0%
D 5
 
5.0%
R 3
 
3.0%
B 2
 
2.0%
N 2
 
2.0%
C 2
 
2.0%
L 2
 
2.0%
O 2
 
2.0%
Other values (6) 6
 
6.0%
Decimal Number
ValueCountFrequency (%)
1 431
22.6%
2 316
16.5%
3 248
13.0%
0 174
9.1%
4 166
 
8.7%
5 165
 
8.6%
8 113
 
5.9%
7 113
 
5.9%
6 100
 
5.2%
9 85
 
4.4%
Other Punctuation
ValueCountFrequency (%)
/ 22
44.9%
. 21
42.9%
, 5
 
10.2%
@ 1
 
2.0%
Lowercase Letter
ValueCountFrequency (%)
a 4
57.1%
b 1
 
14.3%
p 1
 
14.3%
t 1
 
14.3%
Space Separator
ValueCountFrequency (%)
24326
> 99.9%
　 6
 
< 0.1%
Close Punctuation
ValueCountFrequency (%)
) 17
94.4%
] 1
 
5.6%
Control
ValueCountFrequency (%)
7
77.8%
 2
 
22.2%
Dash Punctuation
ValueCountFrequency (%)
- 224
100.0%
Open Punctuation
ValueCountFrequency (%)
( 16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 78675
74.7%
Common 26559
 
25.2%
Latin 107
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 8664
 
11.0%
구 6714
 
8.5%
도 4218
 
5.4%
경 3549
 
4.5%
광 3039
 
3.9%
기 2544
 
3.2%
동 2500
 
3.2%
남 2368
 
3.0%
역 2263
 
2.9%
서 2219
 
2.8%
Other values (315) 40597
51.6%
Common
ValueCountFrequency (%)
24326
91.6%
1 431
 
1.6%
2 316
 
1.2%
3 248
 
0.9%
- 224
 
0.8%
0 174
 
0.7%
4 166
 
0.6%
5 165
 
0.6%
8 113
 
0.4%
7 113
 
0.4%
Other values (12) 283
 
1.1%
Latin
ValueCountFrequency (%)
A 41
38.3%
P 18
16.8%
T 17
15.9%
D 5
 
4.7%
a 4
 
3.7%
R 3
 
2.8%
B 2
 
1.9%
N 2
 
1.9%
C 2
 
1.9%
L 2
 
1.9%
Other values (10) 11
 
10.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 78675
74.7%
ASCII 26660
 
25.3%
None 6
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
24326
91.2%
1 431
 
1.6%
2 316
 
1.2%
3 248
 
0.9%
- 224
 
0.8%
0 174
 
0.7%
4 166
 
0.6%
5 165
 
0.6%
8 113
 
0.4%
7 113
 
0.4%
Other values (31) 384
 
1.4%
Hangul
ValueCountFrequency (%)
시 8664
 
11.0%
구 6714
 
8.5%
도 4218
 
5.4%
경 3549
 
4.5%
광 3039
 
3.9%
기 2544
 
3.2%
동 2500
 
3.2%
남 2368
 
3.0%
역 2263
 
2.9%
서 2219
 
2.8%
Other values (315) 40597
51.6%
None
ValueCountFrequency (%)
　 6
100.0%

Interactions

2023-12-12T07:32:13.361529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:32:13.224486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:32:13.434446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:32:13.292303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T07:32:16.922069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
LAND_SPL_PRPOS_NMLTOUT_CTRT_YMDAGECNTRR_SEX_NM
LAND_SPL_PRPOS_NM1.0000.6320.3300.126
LTOUT_CTRT_YMD0.6321.0000.5300.284
AGE0.3300.5301.0000.198
CNTRR_SEX_NM0.1260.2840.1981.000
2023-12-12T07:32:17.007306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
LTOUT_CTRT_YMDAGECNTRR_SEX_NM
LTOUT_CTRT_YMD1.000-0.5090.218
AGE-0.5091.0000.152
CNTRR_SEX_NM0.2180.1521.000

Missing values

2023-12-12T07:32:13.527747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T07:32:13.613486image/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.
2023-12-12T07:32:13.694973image/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

BSNS_DISTRICT_NMLAND_SPL_PRPOS_NMLTOUT_CTRT_YMDAGECNTRR_SEX_NMCNTRR_RSDNC_NM
71558충북지사비축토지기타용도2004062658남자충청북도 청주시 흥덕구
45474광주수완실수요자택지 점포겸용2010100567여자광주광역시 광산구
25785충남도청이전도시이주자 택지 점포겸용2011102856남자충청남도 예산군 삽교읍
66701용인동백코아루아파트분양아파트(85㎡초과 공동)2003081983여자경기도 과천시
826청주동남(05,택)이주자 택지 점포겸용2016120791여자충청북도 청주시 상당구
97940남양주진접실수요자택지 주거전용2011110859여자경기 남양주시 진접읍
14662부산쌍용아파트분양아파트(85㎡초과 공동)2001053158여자부산광역시 부산진구
14426광주첨단지원시설용지(산단)2000072784남자광주 북구 중흥2동
44367양산물금1협의양도인택지 점포겸용2004062974여자<NA>
58433군장군산2일반상업용지2002043064여자충북 청주시 상당구
BSNS_DISTRICT_NMLAND_SPL_PRPOS_NMLTOUT_CTRT_YMDAGECNTRR_SEX_NMCNTRR_RSDNC_NM
90994인천연수실수요자택지 점포겸용1990121769남자인천광역시 남동구
50359춘천퇴계1,2이주자 택지 점포겸용19911226113남자퇴계동 414-2
26785충북혁신도시협의양도인택지 주거전용2012072776여자충북 진천군 덕산면
48848파주운정(01,택)실수요자택지 주거전용2015082634여자경기도 남양주시
97286용인동백근린상업용지2003121281여자경기도 용인시 처인구
54863여천돌산불용잔지2002020572여자전라남도 여수시
12296대구신서이주자 택지 점포겸용2011092644여자서울특별시 광진구
55424청주율량2이주자 택지 점포겸용2009112768남자충청북도 청주시 청원구
56220동두천생연일반상업용지2001082285여자서울특별시 송파구
85257대전도안실수요자택지 점포겸용2013090566여자대전광역시 서구

Duplicate rows

Most frequently occurring

BSNS_DISTRICT_NMLAND_SPL_PRPOS_NMLTOUT_CTRT_YMDAGECNTRR_SEX_NMCNTRR_RSDNC_NM# duplicates
22서귀포서호실수요자택지 점포겸용1990112868남자제주특별자치도 서귀포시3
41진해이동실수요자택지 점포겸용1990112966남자경상남도 창원시 진해구3
0고양삼송일반상업용지2013061772남자서울 마포구 공덕동2
1고양일산중심업무시설1999102377남자서울특별시 종로구2
2군산수송실수요자택지 점포겸용2006102358여자전라북도 군산시2
3군장군산1일반상업용지2002011264여자경기 안산시 사동2
4김해내동실수요자택지 점포겸용1987052069남자부산광역시 서구2
5김해율하이주자 택지 점포겸용2004122162남자경상남도 김해시2
6김해율하2일반상업용지2016050950여자부산광역시 수영구2
7김해장유실수요자택지 점포겸용1997061056남자경상남도 김해시2