Overview

Dataset statistics

Number of variables15
Number of observations100
Missing cells21
Missing cells (%)1.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory12.5 KiB
Average record size in memory128.3 B

Variable types

Text5
Categorical3
Numeric7

Alerts

regist_de is highly overall correlated with instl_deHigh correlation
instl_de is highly overall correlated with regist_deHigh correlation
ctprvn_cd is highly overall correlated with signgu_cd and 3 other fieldsHigh correlation
signgu_cd is highly overall correlated with ctprvn_cd and 3 other fieldsHigh correlation
adstrd_cd is highly overall correlated with ctprvn_cd and 3 other fieldsHigh correlation
lo_val is highly overall correlated with la_val and 2 other fieldsHigh correlation
la_val is highly overall correlated with lo_val and 2 other fieldsHigh correlation
ctprvn_nm is highly overall correlated with ctprvn_cd and 5 other fieldsHigh correlation
signgu_nm is highly overall correlated with ctprvn_cd and 5 other fieldsHigh correlation
cl is highly imbalanced (53.8%)Imbalance
regist_de has 14 (14.0%) missing valuesMissing
instl_de has 6 (6.0%) missing valuesMissing

Reproduction

Analysis started2023-12-10 10:10:07.669635
Analysis finished2023-12-10 10:10:19.879117
Duration12.21 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

prdct_nm
Text

Distinct98
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:10:20.317393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length33
Median length18
Mean length6.79
Min length1

Characters and Unicode

Total characters679
Distinct characters219
Distinct categories9 ?
Distinct scripts4 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique97 ?
Unique (%)97.0%

Sample

1st rowVISOIN TREE
2nd row力, 昇
3rd row상승하는 미래
4th row바람이 불어도 가야한다
5th row향수
ValueCountFrequency (%)
space 4
 
2.5%
비상 3
 
1.9%
of 3
 
1.9%
숲 2
 
1.2%
love 2
 
1.2%
the 2
 
1.2%
dream 2
 
1.2%
harmony 2
 
1.2%
상승하는 2
 
1.2%
구조 2
 
1.2%
Other values (134) 136
85.0%
2023-12-10T19:10:21.338706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
60
 
8.8%
e 29
 
4.3%
i 18
 
2.7%
n 17
 
2.5%
a 17
 
2.5%
o 16
 
2.4%
의 14
 
2.1%
r 13
 
1.9%
t 11
 
1.6%
- 11
 
1.6%
Other values (209) 473
69.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 298
43.9%
Lowercase Letter 198
29.2%
Uppercase Letter 71
 
10.5%
Space Separator 60
 
8.8%
Decimal Number 25
 
3.7%
Dash Punctuation 11
 
1.6%
Other Punctuation 8
 
1.2%
Close Punctuation 4
 
0.6%
Open Punctuation 4
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
의 14
 
4.7%
상 8
 
2.7%
이 8
 
2.7%
연 5
 
1.7%
바 5
 
1.7%
람 5
 
1.7%
다 5
 
1.7%
는 5
 
1.7%
어 5
 
1.7%
화 4
 
1.3%
Other values (149) 234
78.5%
Uppercase Letter
ValueCountFrequency (%)
N 8
 
11.3%
S 8
 
11.3%
I 6
 
8.5%
L 5
 
7.0%
V 4
 
5.6%
O 4
 
5.6%
E 4
 
5.6%
C 4
 
5.6%
T 4
 
5.6%
U 3
 
4.2%
Other values (12) 21
29.6%
Lowercase Letter
ValueCountFrequency (%)
e 29
14.6%
i 18
 
9.1%
n 17
 
8.6%
a 17
 
8.6%
o 16
 
8.1%
r 13
 
6.6%
t 11
 
5.6%
c 11
 
5.6%
h 10
 
5.1%
s 8
 
4.0%
Other values (11) 48
24.2%
Decimal Number
ValueCountFrequency (%)
0 8
32.0%
1 6
24.0%
2 6
24.0%
7 2
 
8.0%
3 1
 
4.0%
9 1
 
4.0%
4 1
 
4.0%
Other Punctuation
ValueCountFrequency (%)
. 4
50.0%
, 2
25.0%
/ 1
 
12.5%
& 1
 
12.5%
Close Punctuation
ValueCountFrequency (%)
) 3
75.0%
] 1
 
25.0%
Open Punctuation
ValueCountFrequency (%)
( 3
75.0%
[ 1
 
25.0%
Space Separator
ValueCountFrequency (%)
60
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 296
43.6%
Latin 269
39.6%
Common 112
 
16.5%
Han 2
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
의 14
 
4.7%
상 8
 
2.7%
이 8
 
2.7%
연 5
 
1.7%
바 5
 
1.7%
람 5
 
1.7%
다 5
 
1.7%
는 5
 
1.7%
어 5
 
1.7%
화 4
 
1.4%
Other values (147) 232
78.4%
Latin
ValueCountFrequency (%)
e 29
 
10.8%
i 18
 
6.7%
n 17
 
6.3%
a 17
 
6.3%
o 16
 
5.9%
r 13
 
4.8%
t 11
 
4.1%
c 11
 
4.1%
h 10
 
3.7%
s 8
 
3.0%
Other values (33) 119
44.2%
Common
ValueCountFrequency (%)
60
53.6%
- 11
 
9.8%
0 8
 
7.1%
1 6
 
5.4%
2 6
 
5.4%
. 4
 
3.6%
) 3
 
2.7%
( 3
 
2.7%
, 2
 
1.8%
7 2
 
1.8%
Other values (7) 7
 
6.2%
Han
ValueCountFrequency (%)
昇 1
50.0%
力 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 381
56.1%
Hangul 296
43.6%
CJK 2
 
0.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
60
 
15.7%
e 29
 
7.6%
i 18
 
4.7%
n 17
 
4.5%
a 17
 
4.5%
o 16
 
4.2%
r 13
 
3.4%
t 11
 
2.9%
- 11
 
2.9%
c 11
 
2.9%
Other values (50) 178
46.7%
Hangul
ValueCountFrequency (%)
의 14
 
4.7%
상 8
 
2.7%
이 8
 
2.7%
연 5
 
1.7%
바 5
 
1.7%
람 5
 
1.7%
다 5
 
1.7%
는 5
 
1.7%
어 5
 
1.7%
화 4
 
1.4%
Other values (147) 232
78.4%
CJK
ValueCountFrequency (%)
昇 1
50.0%
力 1
50.0%

writr_nm
Text

Distinct86
Distinct (%)86.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:10:21.841691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length3
Mean length3.03
Min length2

Characters and Unicode

Total characters303
Distinct characters108
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

Unique77 ?
Unique (%)77.0%

Sample

1st row한성수
2nd row송근배
3rd row박춘근
4th row김성복
5th row김종은
ValueCountFrequency (%)
김계현 5
 
5.0%
김봉수 3
 
3.0%
이철희 3
 
3.0%
안태영 2
 
2.0%
이찬우 2
 
2.0%
안승혁 2
 
2.0%
유주희 2
 
2.0%
윤성필 2
 
2.0%
김병진 2
 
2.0%
김태인 1
 
1.0%
Other values (77) 77
76.2%
2023-12-10T19:10:22.561415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
김 30
 
9.9%
이 16
 
5.3%
희 11
 
3.6%
영 10
 
3.3%
성 10
 
3.3%
현 9
 
3.0%
호 8
 
2.6%
최 7
 
2.3%
수 6
 
2.0%
선 6
 
2.0%
Other values (98) 190
62.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 301
99.3%
Other Punctuation 1
 
0.3%
Space Separator 1
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
김 30
 
10.0%
이 16
 
5.3%
희 11
 
3.7%
영 10
 
3.3%
성 10
 
3.3%
현 9
 
3.0%
호 8
 
2.7%
최 7
 
2.3%
수 6
 
2.0%
선 6
 
2.0%
Other values (96) 188
62.5%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 301
99.3%
Common 2
 
0.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
김 30
 
10.0%
이 16
 
5.3%
희 11
 
3.7%
영 10
 
3.3%
성 10
 
3.3%
현 9
 
3.0%
호 8
 
2.7%
최 7
 
2.3%
수 6
 
2.0%
선 6
 
2.0%
Other values (96) 188
62.5%
Common
ValueCountFrequency (%)
, 1
50.0%
1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 301
99.3%
ASCII 2
 
0.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
김 30
 
10.0%
이 16
 
5.3%
희 11
 
3.7%
영 10
 
3.3%
성 10
 
3.3%
현 9
 
3.0%
호 8
 
2.7%
최 7
 
2.3%
수 6
 
2.0%
선 6
 
2.0%
Other values (96) 188
62.5%
ASCII
ValueCountFrequency (%)
, 1
50.0%
1
50.0%

cl
Categorical

IMBALANCE 

Distinct7
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
조각
75 
회화
13 
미디어
 
6
기타
 
2
벽화
 
2
Other values (2)
 
2

Length

Max length3
Median length2
Mean length2.07
Min length2

Unique

Unique2 ?
Unique (%)2.0%

Sample

1st row조각
2nd row조각
3rd row조각
4th row조각
5th row회화

Common Values

ValueCountFrequency (%)
조각 75
75.0%
회화 13
 
13.0%
미디어 6
 
6.0%
기타 2
 
2.0%
벽화 2
 
2.0%
분수대 1
 
1.0%
공예 1
 
1.0%

Length

2023-12-10T19:10:22.853258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:10:23.075484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
조각 75
75.0%
회화 13
 
13.0%
미디어 6
 
6.0%
기타 2
 
2.0%
벽화 2
 
2.0%
분수대 1
 
1.0%
공예 1
 
1.0%

ctprvn_nm
Categorical

HIGH CORRELATION 

Distinct13
Distinct (%)13.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
부산광역시
23 
경기도
20 
인천광역시
19 
대구광역시
11 
서울특별시
5 
Other values (8)
22 

Length

Max length5
Median length5
Mean length4.41
Min length3

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row인천광역시
2nd row부산광역시
3rd row인천광역시
4th row충청북도
5th row인천광역시

Common Values

ValueCountFrequency (%)
부산광역시 23
23.0%
경기도 20
20.0%
인천광역시 19
19.0%
대구광역시 11
11.0%
서울특별시 5
 
5.0%
경상남도 5
 
5.0%
광주광역시 4
 
4.0%
강원도 4
 
4.0%
충청북도 2
 
2.0%
대전광역시 2
 
2.0%
Other values (3) 5
 
5.0%

Length

2023-12-10T19:10:23.378817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
부산광역시 23
23.0%
경기도 20
20.0%
인천광역시 19
19.0%
대구광역시 11
11.0%
서울특별시 5
 
5.0%
경상남도 5
 
5.0%
광주광역시 4
 
4.0%
강원도 4
 
4.0%
충청북도 2
 
2.0%
대전광역시 2
 
2.0%
Other values (3) 5
 
5.0%

signgu_nm
Categorical

HIGH CORRELATION 

Distinct42
Distinct (%)42.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
강서구
13 
서구
11 
동구
8 
부평구
 
5
수성구
 
5
Other values (37)
58 

Length

Max length9
Median length3
Mean length3.15
Min length2

Unique

Unique23 ?
Unique (%)23.0%

Sample

1st row부평구
2nd row사상구
3rd row부평구
4th row청주시 흥덕구
5th row미추홀구

Common Values

ValueCountFrequency (%)
강서구 13
 
13.0%
서구 11
 
11.0%
동구 8
 
8.0%
부평구 5
 
5.0%
수성구 5
 
5.0%
중구 4
 
4.0%
평택시 4
 
4.0%
미추홀구 4
 
4.0%
북구 3
 
3.0%
안양시 동안구 2
 
2.0%
Other values (32) 41
41.0%

Length

2023-12-10T19:10:23.739329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
강서구 13
 
12.0%
서구 11
 
10.2%
동구 8
 
7.4%
부평구 5
 
4.6%
수성구 5
 
4.6%
중구 4
 
3.7%
평택시 4
 
3.7%
미추홀구 4
 
3.7%
북구 3
 
2.8%
광명시 2
 
1.9%
Other values (38) 49
45.4%

bild_nm
Text

Distinct79
Distinct (%)79.8%
Missing1
Missing (%)1.0%
Memory size932.0 B
2023-12-10T19:10:24.388921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length14
Mean length8.5353535
Min length3

Characters and Unicode

Total characters845
Distinct characters231
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

Unique69 ?
Unique (%)69.7%

Sample

1st row유 하임 오피스텔
2nd row사상철물판매업협동상가
3rd row대명벨리온 오피스텔
4th row베스티안병원
5th row숭의동 75-5
ValueCountFrequency (%)
디엠시티 7
 
4.3%
대방 7
 
4.3%
명지 6
 
3.7%
센텀오션2 5
 
3.1%
명지플러스시네마 5
 
3.1%
검단금호어울림센트럴 3
 
1.9%
방촌역 3
 
1.9%
태왕아너스 3
 
1.9%
수성알파시티청아람 3
 
1.9%
검단신도시 3
 
1.9%
Other values (106) 117
72.2%
2023-12-10T19:10:25.521523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
63
 
7.5%
스 32
 
3.8%
시 24
 
2.8%
아 16
 
1.9%
트 16
 
1.9%
명 16
 
1.9%
티 16
 
1.9%
지 15
 
1.8%
파 15
 
1.8%
오 14
 
1.7%
Other values (221) 618
73.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 715
84.6%
Space Separator 63
 
7.5%
Uppercase Letter 27
 
3.2%
Decimal Number 25
 
3.0%
Dash Punctuation 6
 
0.7%
Open Punctuation 3
 
0.4%
Close Punctuation 3
 
0.4%
Other Punctuation 2
 
0.2%
Lowercase Letter 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
스 32
 
4.5%
시 24
 
3.4%
아 16
 
2.2%
트 16
 
2.2%
명 16
 
2.2%
티 16
 
2.2%
지 15
 
2.1%
파 15
 
2.1%
오 14
 
2.0%
대 14
 
2.0%
Other values (195) 537
75.1%
Uppercase Letter
ValueCountFrequency (%)
S 5
18.5%
A 5
18.5%
B 4
14.8%
G 2
 
7.4%
E 1
 
3.7%
D 1
 
3.7%
W 1
 
3.7%
I 1
 
3.7%
K 1
 
3.7%
V 1
 
3.7%
Other values (5) 5
18.5%
Decimal Number
ValueCountFrequency (%)
2 9
36.0%
1 8
32.0%
5 5
20.0%
7 2
 
8.0%
3 1
 
4.0%
Space Separator
ValueCountFrequency (%)
63
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 6
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Other Punctuation
ValueCountFrequency (%)
. 2
100.0%
Lowercase Letter
ValueCountFrequency (%)
s 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 715
84.6%
Common 102
 
12.1%
Latin 28
 
3.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
스 32
 
4.5%
시 24
 
3.4%
아 16
 
2.2%
트 16
 
2.2%
명 16
 
2.2%
티 16
 
2.2%
지 15
 
2.1%
파 15
 
2.1%
오 14
 
2.0%
대 14
 
2.0%
Other values (195) 537
75.1%
Latin
ValueCountFrequency (%)
S 5
17.9%
A 5
17.9%
B 4
14.3%
G 2
 
7.1%
E 1
 
3.6%
D 1
 
3.6%
W 1
 
3.6%
I 1
 
3.6%
K 1
 
3.6%
V 1
 
3.6%
Other values (6) 6
21.4%
Common
ValueCountFrequency (%)
63
61.8%
2 9
 
8.8%
1 8
 
7.8%
- 6
 
5.9%
5 5
 
4.9%
( 3
 
2.9%
) 3
 
2.9%
. 2
 
2.0%
7 2
 
2.0%
3 1
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 715
84.6%
ASCII 130
 
15.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
63
48.5%
2 9
 
6.9%
1 8
 
6.2%
- 6
 
4.6%
5 5
 
3.8%
S 5
 
3.8%
A 5
 
3.8%
B 4
 
3.1%
( 3
 
2.3%
) 3
 
2.3%
Other values (16) 19
 
14.6%
Hangul
ValueCountFrequency (%)
스 32
 
4.5%
시 24
 
3.4%
아 16
 
2.2%
트 16
 
2.2%
명 16
 
2.2%
티 16
 
2.2%
지 15
 
2.1%
파 15
 
2.1%
오 14
 
2.0%
대 14
 
2.0%
Other values (195) 537
75.1%

bild_addr
Text

Distinct80
Distinct (%)80.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:10:26.238608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length54
Median length34
Mean length19.62
Min length11

Characters and Unicode

Total characters1962
Distinct characters179
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

Unique70 ?
Unique (%)70.0%

Sample

1st row인천 부평구 부평동 758-38
2nd row부산시 사상구 괘법동 578
3rd row인천 부평구 부평동 47-2
4th row충북 청주시 흥덕구 오송읍 연제리 682-1
5th row인천 미추홀구 숭의동 75-5
ValueCountFrequency (%)
부산 18
 
3.9%
인천 18
 
3.9%
경기 15
 
3.2%
명지동 13
 
2.8%
강서구 13
 
2.8%
서구 11
 
2.4%
대구 11
 
2.4%
원당동 9
 
1.9%
동구 9
 
1.9%
3586-4 5
 
1.1%
Other values (247) 343
73.8%
2023-12-10T19:10:27.196711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
365
 
18.6%
동 118
 
6.0%
구 91
 
4.6%
1 72
 
3.7%
- 67
 
3.4%
3 63
 
3.2%
2 51
 
2.6%
시 45
 
2.3%
4 43
 
2.2%
5 42
 
2.1%
Other values (169) 1005
51.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1059
54.0%
Decimal Number 424
21.6%
Space Separator 365
 
18.6%
Dash Punctuation 67
 
3.4%
Uppercase Letter 19
 
1.0%
Close Punctuation 12
 
0.6%
Open Punctuation 12
 
0.6%
Other Punctuation 4
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 118
 
11.1%
구 91
 
8.6%
시 45
 
4.2%
산 41
 
3.9%
지 35
 
3.3%
서 31
 
2.9%
부 31
 
2.9%
천 29
 
2.7%
경 25
 
2.4%
인 22
 
2.1%
Other values (150) 591
55.8%
Decimal Number
ValueCountFrequency (%)
1 72
17.0%
3 63
14.9%
2 51
12.0%
4 43
10.1%
5 42
9.9%
8 37
8.7%
7 32
7.5%
6 32
7.5%
9 27
 
6.4%
0 25
 
5.9%
Uppercase Letter
ValueCountFrequency (%)
B 9
47.4%
A 6
31.6%
L 3
 
15.8%
S 1
 
5.3%
Space Separator
ValueCountFrequency (%)
365
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 67
100.0%
Close Punctuation
ValueCountFrequency (%)
) 12
100.0%
Open Punctuation
ValueCountFrequency (%)
( 12
100.0%
Other Punctuation
ValueCountFrequency (%)
, 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1059
54.0%
Common 884
45.1%
Latin 19
 
1.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 118
 
11.1%
구 91
 
8.6%
시 45
 
4.2%
산 41
 
3.9%
지 35
 
3.3%
서 31
 
2.9%
부 31
 
2.9%
천 29
 
2.7%
경 25
 
2.4%
인 22
 
2.1%
Other values (150) 591
55.8%
Common
ValueCountFrequency (%)
365
41.3%
1 72
 
8.1%
- 67
 
7.6%
3 63
 
7.1%
2 51
 
5.8%
4 43
 
4.9%
5 42
 
4.8%
8 37
 
4.2%
7 32
 
3.6%
6 32
 
3.6%
Other values (5) 80
 
9.0%
Latin
ValueCountFrequency (%)
B 9
47.4%
A 6
31.6%
L 3
 
15.8%
S 1
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1059
54.0%
ASCII 903
46.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
365
40.4%
1 72
 
8.0%
- 67
 
7.4%
3 63
 
7.0%
2 51
 
5.6%
4 43
 
4.8%
5 42
 
4.7%
8 37
 
4.1%
7 32
 
3.5%
6 32
 
3.5%
Other values (9) 99
 
11.0%
Hangul
ValueCountFrequency (%)
동 118
 
11.1%
구 91
 
8.6%
시 45
 
4.2%
산 41
 
3.9%
지 35
 
3.3%
서 31
 
2.9%
부 31
 
2.9%
천 29
 
2.7%
경 25
 
2.4%
인 22
 
2.1%
Other values (150) 591
55.8%

regist_de
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct65
Distinct (%)75.6%
Missing14
Missing (%)14.0%
Infinite0
Infinite (%)0.0%
Mean20152551
Minimum19910813
Maximum20210128
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:10:27.460934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19910813
5-th percentile20005868
Q120110438
median20190226
Q320200909
95-th percentile20207898
Maximum20210128
Range299315
Interquartile range (IQR)90471.5

Descriptive statistics

Standard deviation70948.846
Coefficient of variation (CV)0.0035205887
Kurtosis1.8981753
Mean20152551
Median Absolute Deviation (MAD)10886
Skewness-1.5890973
Sum1.7331194 × 109
Variance5.0337387 × 109
MonotonicityNot monotonic
2023-12-10T19:10:28.306448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20201112 5
 
5.0%
20200909 5
 
5.0%
20200701 3
 
3.0%
20180726 3
 
3.0%
20200918 3
 
3.0%
20190328 3
 
3.0%
20200331 2
 
2.0%
20210128 2
 
2.0%
20210126 2
 
2.0%
20201028 2
 
2.0%
Other values (55) 56
56.0%
(Missing) 14
 
14.0%
ValueCountFrequency (%)
19910813 1
1.0%
19940607 1
1.0%
19960412 1
1.0%
19960626 1
1.0%
20000819 1
1.0%
20021016 1
1.0%
20031218 1
1.0%
20050701 1
1.0%
20050824 1
1.0%
20060118 1
1.0%
ValueCountFrequency (%)
20210128 2
 
2.0%
20210126 2
 
2.0%
20210121 1
 
1.0%
20201228 1
 
1.0%
20201130 1
 
1.0%
20201112 5
5.0%
20201029 1
 
1.0%
20201028 2
 
2.0%
20200918 3
3.0%
20200910 1
 
1.0%

instl_de
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct74
Distinct (%)78.7%
Missing6
Missing (%)6.0%
Infinite0
Infinite (%)0.0%
Mean20149456
Minimum19890520
Maximum20211015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:10:28.650728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19890520
5-th percentile19997527
Q120100252
median20210164
Q320210315
95-th percentile20210354
Maximum20211015
Range320495
Interquartile range (IQR)110063

Descriptive statistics

Standard deviation81123.913
Coefficient of variation (CV)0.0040261094
Kurtosis0.34220038
Mean20149456
Median Absolute Deviation (MAD)808
Skewness-1.167965
Sum1.8940488 × 109
Variance6.5810892 × 109
MonotonicityNot monotonic
2023-12-10T19:10:29.065909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20210316 6
 
6.0%
20210322 5
 
5.0%
20210205 3
 
3.0%
20210306 3
 
3.0%
20210217 3
 
3.0%
20210305 2
 
2.0%
20210323 2
 
2.0%
20210315 2
 
2.0%
20210204 2
 
2.0%
20210225 2
 
2.0%
Other values (64) 64
64.0%
(Missing) 6
 
6.0%
ValueCountFrequency (%)
19890520 1
1.0%
19950224 1
1.0%
19960626 1
1.0%
19961030 1
1.0%
19991207 1
1.0%
20000930 1
1.0%
20001120 1
1.0%
20010921 1
1.0%
20011214 1
1.0%
20021115 1
1.0%
ValueCountFrequency (%)
20211015 1
 
1.0%
20210930 1
 
1.0%
20210409 1
 
1.0%
20210407 1
 
1.0%
20210401 1
 
1.0%
20210329 1
 
1.0%
20210327 1
 
1.0%
20210325 1
 
1.0%
20210323 2
 
2.0%
20210322 5
5.0%

ctprvn_cd
Real number (ℝ)

HIGH CORRELATION 

Distinct13
Distinct (%)13.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.35
Minimum11
Maximum38
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:10:29.444978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile20.5
Q121
median23
Q331
95-th percentile36.1
Maximum38
Range27
Interquartile range (IQR)10

Descriptive statistics

Standard deviation6.233463
Coefficient of variation (CV)0.24589598
Kurtosis-0.047894284
Mean25.35
Median Absolute Deviation (MAD)2
Skewness0.11386122
Sum2535
Variance38.856061
MonotonicityNot monotonic
2023-12-10T19:10:29.755986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
21 23
23.0%
31 20
20.0%
23 19
19.0%
22 11
11.0%
11 5
 
5.0%
38 5
 
5.0%
24 4
 
4.0%
32 4
 
4.0%
33 2
 
2.0%
25 2
 
2.0%
Other values (3) 5
 
5.0%
ValueCountFrequency (%)
11 5
 
5.0%
21 23
23.0%
22 11
11.0%
23 19
19.0%
24 4
 
4.0%
25 2
 
2.0%
26 1
 
1.0%
31 20
20.0%
32 4
 
4.0%
33 2
 
2.0%
ValueCountFrequency (%)
38 5
 
5.0%
36 2
 
2.0%
35 2
 
2.0%
33 2
 
2.0%
32 4
 
4.0%
31 20
20.0%
26 1
 
1.0%
25 2
 
2.0%
24 4
 
4.0%
23 19
19.0%

signgu_cd
Real number (ℝ)

HIGH CORRELATION 

Distinct52
Distinct (%)52.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25429.81
Minimum11020
Maximum38113
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:10:30.058438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11020
5-th percentile20531.5
Q121135
median23080
Q331075.5
95-th percentile36130
Maximum38113
Range27093
Interquartile range (IQR)9940.5

Descriptive statistics

Standard deviation6232.9864
Coefficient of variation (CV)0.2451055
Kurtosis-0.058381128
Mean25429.81
Median Absolute Deviation (MAD)1960
Skewness0.11479835
Sum2542981
Variance38850119
MonotonicityNot monotonic
2023-12-10T19:10:30.311715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21120 13
 
13.0%
23080 8
 
8.0%
23060 5
 
5.0%
22060 5
 
5.0%
23090 4
 
4.0%
31070 4
 
4.0%
22020 4
 
4.0%
31240 2
 
2.0%
24010 2
 
2.0%
21060 2
 
2.0%
Other values (42) 51
51.0%
ValueCountFrequency (%)
11020 1
1.0%
11040 1
1.0%
11070 1
1.0%
11080 1
1.0%
11250 1
1.0%
21020 1
1.0%
21030 1
1.0%
21060 2
2.0%
21080 2
2.0%
21090 1
1.0%
ValueCountFrequency (%)
38113 1
1.0%
38080 1
1.0%
38070 1
1.0%
38060 1
1.0%
38030 1
1.0%
36030 2
2.0%
35020 2
2.0%
33043 1
1.0%
33020 1
1.0%
32320 1
1.0%

adstrd_cd
Real number (ℝ)

HIGH CORRELATION 

Distinct72
Distinct (%)72.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2543040.2
Minimum1102055
Maximum3811364
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:10:30.568949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1102055
5-th percentile2053218.1
Q12113556.8
median2308082
Q33107614.5
95-th percentile3613066.3
Maximum3811364
Range2709309
Interquartile range (IQR)994057.75

Descriptive statistics

Standard deviation623295.74
Coefficient of variation (CV)0.24509866
Kurtosis-0.058340418
Mean2543040.2
Median Absolute Deviation (MAD)196023
Skewness0.11479574
Sum2.5430402 × 108
Variance3.8849758 × 1011
MonotonicityNot monotonic
2023-12-10T19:10:30.815178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2112059 13
 
13.0%
2308082 6
 
6.0%
2202069 3
 
3.0%
2206072 3
 
3.0%
2306051 2
 
2.0%
2309062 2
 
2.0%
2115057 2
 
2.0%
3107064 2
 
2.0%
2306053 2
 
2.0%
3603066 2
 
2.0%
Other values (62) 63
63.0%
ValueCountFrequency (%)
1102055 1
1.0%
1104055 1
1.0%
1107064 1
1.0%
1108061 1
1.0%
1125070 1
1.0%
2102068 1
1.0%
2103057 1
1.0%
2106051 1
1.0%
2106055 1
1.0%
2108051 1
1.0%
ValueCountFrequency (%)
3811364 1
1.0%
3808054 1
1.0%
3807054 1
1.0%
3806055 1
1.0%
3803072 1
1.0%
3603066 2
2.0%
3502072 1
1.0%
3502069 1
1.0%
3304311 1
1.0%
3302054 1
1.0%

adstrd_nm
Text

Distinct72
Distinct (%)72.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:10:31.231275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length3.51
Min length2

Characters and Unicode

Total characters351
Distinct characters94
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique61 ?
Unique (%)61.0%

Sample

1st row부평1동
2nd row괘법동
3rd row부평1동
4th row오송읍
5th row숭의1·3동
ValueCountFrequency (%)
명지1동 13
 
13.0%
원당동 6
 
6.0%
해안동 3
 
3.0%
고산2동 3
 
3.0%
왕조1동 2
 
2.0%
부평1동 2
 
2.0%
광명1동 2
 
2.0%
세교동 2
 
2.0%
도화1동 2
 
2.0%
부평3동 2
 
2.0%
Other values (62) 63
63.0%
2023-12-10T19:10:31.980670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 96
27.4%
1 36
 
10.3%
명 16
 
4.6%
지 14
 
4.0%
2 11
 
3.1%
산 9
 
2.6%
원 7
 
2.0%
당 7
 
2.0%
수 6
 
1.7%
3 6
 
1.7%
Other values (84) 143
40.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 296
84.3%
Decimal Number 54
 
15.4%
Other Punctuation 1
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 96
32.4%
명 16
 
5.4%
지 14
 
4.7%
산 9
 
3.0%
원 7
 
2.4%
당 7
 
2.4%
수 6
 
2.0%
고 6
 
2.0%
성 6
 
2.0%
교 4
 
1.4%
Other values (79) 125
42.2%
Decimal Number
ValueCountFrequency (%)
1 36
66.7%
2 11
 
20.4%
3 6
 
11.1%
4 1
 
1.9%
Other Punctuation
ValueCountFrequency (%)
· 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 296
84.3%
Common 55
 
15.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 96
32.4%
명 16
 
5.4%
지 14
 
4.7%
산 9
 
3.0%
원 7
 
2.4%
당 7
 
2.4%
수 6
 
2.0%
고 6
 
2.0%
성 6
 
2.0%
교 4
 
1.4%
Other values (79) 125
42.2%
Common
ValueCountFrequency (%)
1 36
65.5%
2 11
 
20.0%
3 6
 
10.9%
· 1
 
1.8%
4 1
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 296
84.3%
ASCII 54
 
15.4%
None 1
 
0.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 96
32.4%
명 16
 
5.4%
지 14
 
4.7%
산 9
 
3.0%
원 7
 
2.4%
당 7
 
2.4%
수 6
 
2.0%
고 6
 
2.0%
성 6
 
2.0%
교 4
 
1.4%
Other values (79) 125
42.2%
ASCII
ValueCountFrequency (%)
1 36
66.7%
2 11
 
20.4%
3 6
 
11.1%
4 1
 
1.9%
None
ValueCountFrequency (%)
· 1
100.0%

lo_val
Real number (ℝ)

HIGH CORRELATION 

Distinct80
Distinct (%)80.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.76961
Minimum126.55437
Maximum129.33099
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:10:32.290378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.55437
5-th percentile126.66112
Q1126.85094
median127.40878
Q3128.87673
95-th percentile129.08139
Maximum129.33099
Range2.7766207
Interquartile range (IQR)2.0257844

Descriptive statistics

Standard deviation0.97549837
Coefficient of variation (CV)0.0076348229
Kurtosis-1.768281
Mean127.76961
Median Absolute Deviation (MAD)0.72390805
Skewness0.20745885
Sum12776.961
Variance0.95159707
MonotonicityNot monotonic
2023-12-10T19:10:32.519500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
128.9031205 5
 
5.0%
128.9116633 5
 
5.0%
126.7130035 3
 
3.0%
126.7046878 3
 
3.0%
128.6681993 3
 
3.0%
128.6967516 3
 
3.0%
128.9100384 2
 
2.0%
128.9794917 2
 
2.0%
126.6611244 2
 
2.0%
126.8557545 2
 
2.0%
Other values (70) 70
70.0%
ValueCountFrequency (%)
126.5543722 1
 
1.0%
126.6487365 1
 
1.0%
126.6529256 1
 
1.0%
126.6532645 1
 
1.0%
126.6611244 2
2.0%
126.6641592 1
 
1.0%
126.6708435 1
 
1.0%
126.6778217 1
 
1.0%
126.6919311 1
 
1.0%
126.7046878 3
3.0%
ValueCountFrequency (%)
129.3309929 1
1.0%
129.1794269 1
1.0%
129.1192003 1
1.0%
129.1182898 1
1.0%
129.0826812 1
1.0%
129.0813268 1
1.0%
129.047436 1
1.0%
129.0269811 1
1.0%
129.0125352 1
1.0%
129.0013057 1
1.0%

la_val
Real number (ℝ)

HIGH CORRELATION 

Distinct80
Distinct (%)80.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.406077
Minimum34.949536
Maximum37.809036
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:10:32.830090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum34.949536
5-th percentile35.092003
Q135.175999
median36.493206
Q337.484607
95-th percentile37.617313
Maximum37.809036
Range2.8595007
Interquartile range (IQR)2.3086073

Descriptive statistics

Standard deviation1.0771013
Coefficient of variation (CV)0.029585756
Kurtosis-1.7918365
Mean36.406077
Median Absolute Deviation (MAD)1.0379282
Skewness-0.10510257
Sum3640.6077
Variance1.1601473
MonotonicityNot monotonic
2023-12-10T19:10:33.111910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.0944378 5
 
5.0%
35.0928421 5
 
5.0%
37.5990848 3
 
3.0%
37.5969893 3
 
3.0%
35.8805007 3
 
3.0%
35.8346344 3
 
3.0%
35.0925869 2
 
2.0%
35.1597767 2
 
2.0%
37.466474 2
 
2.0%
37.4733494 2
 
2.0%
Other values (70) 70
70.0%
ValueCountFrequency (%)
34.9495356 1
 
1.0%
34.9553308 1
 
1.0%
34.9612203 1
 
1.0%
35.0625971 1
 
1.0%
35.0808997 1
 
1.0%
35.0925869 2
 
2.0%
35.0928421 5
5.0%
35.0944378 5
5.0%
35.1285858 1
 
1.0%
35.1503491 1
 
1.0%
ValueCountFrequency (%)
37.8090363 1
 
1.0%
37.7616987 1
 
1.0%
37.759715 1
 
1.0%
37.7559467 1
 
1.0%
37.6694 1
 
1.0%
37.6145711 1
 
1.0%
37.5990848 3
3.0%
37.5971845 1
 
1.0%
37.5969893 3
3.0%
37.5841641 1
 
1.0%

Interactions

2023-12-10T19:10:17.844375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:10:09.912696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:10:11.363306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:10:12.516937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:10:13.984011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:10:15.245749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:10:16.299650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:10:18.039560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:10:10.080640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:10:11.530748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:10:13.047216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:10:14.147764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:10:15.402172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:10:16.526389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:10:18.214428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:10:10.242104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:10:11.704334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:10:13.221795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:10:14.297305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:10:15.556181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:10:16.692027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:10:18.365456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:10:10.642923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:10:11.862620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:10:13.371224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:10:14.449949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:10:15.711717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:10:16.897251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:10:18.551300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:10:10.895824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:10:12.033634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:10:13.509314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:10:14.729595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:10:15.872018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:10:17.071194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:10:18.744667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:10:11.062336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:10:12.189614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:10:13.683235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:10:14.884757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:10:16.005643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:10:17.293971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:10:18.930245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:10:11.215107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:10:12.346395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:10:13.845037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:10:15.076063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:10:16.146666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:10:17.541725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:10:33.293090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
prdct_nmwritr_nmclctprvn_nmsigngu_nmbild_nmbild_addrregist_deinstl_dectprvn_cdsigngu_cdadstrd_cdadstrd_nmlo_valla_val
prdct_nm1.0000.9831.0000.9290.0000.9680.9691.0000.7650.9870.9820.9820.9800.8800.955
writr_nm0.9831.0001.0000.9910.9930.9950.9950.9260.9480.9740.9710.9710.9950.9890.985
cl1.0001.0001.0000.0000.7320.9840.9840.3530.2890.4180.4290.4290.9540.3080.494
ctprvn_nm0.9290.9910.0001.0000.9691.0001.0000.0000.4681.0001.0001.0001.0000.9040.939
signgu_nm0.0000.9930.7320.9691.0001.0001.0000.7840.7450.9770.9840.9841.0000.9810.945
bild_nm0.9680.9950.9841.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
bild_addr0.9690.9950.9841.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
regist_de1.0000.9260.3530.0000.7841.0001.0001.0000.9900.0000.0000.0000.9830.1230.000
instl_de0.7650.9480.2890.4680.7451.0001.0000.9901.0000.3590.3220.3220.9920.3190.066
ctprvn_cd0.9870.9740.4181.0000.9771.0001.0000.0000.3591.0001.0001.0001.0000.7770.840
signgu_cd0.9820.9710.4291.0000.9841.0001.0000.0000.3221.0001.0001.0001.0000.7760.837
adstrd_cd0.9820.9710.4291.0000.9841.0001.0000.0000.3221.0001.0001.0001.0000.7760.837
adstrd_nm0.9800.9950.9541.0001.0001.0001.0000.9830.9921.0001.0001.0001.0001.0001.000
lo_val0.8800.9890.3080.9040.9811.0001.0000.1230.3190.7770.7760.7761.0001.0000.875
la_val0.9550.9850.4940.9390.9451.0001.0000.0000.0660.8400.8370.8371.0000.8751.000
2023-12-10T19:10:33.564863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
clsigngu_nmctprvn_nm
cl1.0000.2890.000
signgu_nm0.2891.0000.624
ctprvn_nm0.0000.6241.000
2023-12-10T19:10:33.734266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
regist_deinstl_dectprvn_cdsigngu_cdadstrd_cdlo_valla_valclctprvn_nmsigngu_nm
regist_de1.0000.8270.0780.1310.1300.201-0.3110.3140.0000.333
instl_de0.8271.000-0.140-0.106-0.1090.130-0.1810.4110.1750.250
ctprvn_cd0.078-0.1401.0000.9870.987-0.3610.2580.1540.9670.635
signgu_cd0.131-0.1060.9871.0001.000-0.3580.2510.1540.9670.635
adstrd_cd0.130-0.1090.9871.0001.000-0.3570.2520.1540.9670.635
lo_val0.2010.130-0.361-0.358-0.3571.000-0.6580.1630.6790.687
la_val-0.311-0.1810.2580.2510.252-0.6581.0000.2830.7680.574
cl0.3140.4110.1540.1540.1540.1630.2831.0000.0000.289
ctprvn_nm0.0000.1750.9670.9670.9670.6790.7680.0001.0000.624
signgu_nm0.3330.2500.6350.6350.6350.6870.5740.2890.6241.000

Missing values

2023-12-10T19:10:19.162127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:10:19.512777image/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.
2023-12-10T19:10:19.778039image/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

prdct_nmwritr_nmclctprvn_nmsigngu_nmbild_nmbild_addrregist_deinstl_dectprvn_cdsigngu_cdadstrd_cdadstrd_nmlo_valla_val
0VISOIN TREE한성수조각인천광역시부평구유 하임 오피스텔인천 부평구 부평동 758-38201901242020083123230602306051부평1동126.71482437.486298
1力, 昇송근배조각부산광역시사상구사상철물판매업협동상가부산시 사상구 괘법동 578<NA><NA>21211502115057괘법동128.97949235.159777
2상승하는 미래박춘근조각인천광역시부평구대명벨리온 오피스텔인천 부평구 부평동 47-2201712222019042223230602306051부평1동126.72101237.505143
3바람이 불어도 가야한다김성복조각충청북도청주시 흥덕구베스티안병원충북 청주시 흥덕구 오송읍 연제리 682-1201804132018092033330433304311오송읍127.32165836.636241
4향수김종은회화인천광역시미추홀구숭의동 75-5인천 미추홀구 숭의동 75-5201712272018070423230902309075숭의1·3동126.65326537.465839
5자본의순환최원석기타경기도안양시 동안구안양금융센터A.F.C경기 안양시 동안구 관양동 1591-12201802222018051631310423104257관양2동126.95955537.393112
6SPACE 2017-숲김정희조각서울특별시성북구<NA>서울 성북구 보문동4가 1-1외 110필지201803232018032311110801108061보문동127.02095937.584164
7축제이두식회화부산광역시사상구사상철물판매업협동상가부산시 사상구 괘법동 578<NA><NA>21211502115057괘법동128.97949235.159777
8꽃반지이찬우조각경기도안산시 단원구엔즈타워경기 안산시 단원구 초지동 743201704122017051231310923109266초지동126.80983237.302565
9Sphere고관호조각대구광역시동구신세계백화점대구 동구 신천동 329-5201610042016112722220202202052신암2동128.62729335.877025
prdct_nmwritr_nmclctprvn_nmsigngu_nmbild_nmbild_addrregist_deinstl_dectprvn_cdsigngu_cdadstrd_cdadstrd_nmlo_valla_val
90하모니강선흥조각강원도강릉시강릉 유승한내들 더퍼스트강원 강릉시 유천동 778202006042021022532320303203067경포동128.86855437.761699
91INFINITY-공간분할이혜선조각강원도횡성군횡성 코아루 하우스토리강원 횡성군 횡성읍 앞들서3로 77 (읍하리, 횡성 코아루 하우스토리)202101262021022332323203232011횡성읍127.97457237.484974
92해피트리최정우조각인천광역시미추홀구용현학익7블럭 A1 (힐스테이트)인천 미추홀구 학익동 587-37202005062021021923230902309061학익2동126.64873637.444336
93Connection최원순조각강원도강릉시강릉 벽산블루밍 오션힐스강원 강릉시 성산면 위촌리 809-2202004232021021732320303203031성산면128.83562537.759715
94Blue유주희회화대구광역시동구방촌역 태왕아너스대구 동구 방촌동 876-1202007012021020522220202202069해안동128.66819935.880501
95바람이 불어오는 곳방준호조각대구광역시동구방촌역 태왕아너스대구 동구 방촌동 876-1202007012021020522220202202069해안동128.66819935.880501
96Landscape over being유주희회화대구광역시동구방촌역 태왕아너스대구 동구 방촌동 876-1202007012021020522220202202069해안동128.66819935.880501
97Study of Green-White Birch(Ver.3)강홍구회화경상남도밀양시밀양온천관광호텔경남 밀양시 삼문강변로 82 (삼문동)밀양온천관광호텔202101212021012538380803808054삼문동128.75381835.478966
98The space of happiness신윤자회화인천광역시미추홀구더프레스티움인천 미추홀구 도화동 642-17202008262021020423230902309062도화1동126.66112437.466474
99휴일의 아침박인우조각인천광역시미추홀구더프레스티움인천 미추홀구 도화동 642-17202010282021020423230902309062도화1동126.66112437.466474