Overview

Dataset statistics

Number of variables13
Number of observations10000
Missing cells16653
Missing cells (%)12.8%
Duplicate rows5
Duplicate rows (%)0.1%
Total size in memory1.1 MiB
Average record size in memory115.0 B

Variable types

Categorical7
Text5
Numeric1

Alerts

lastupdtdt has constant value ""Constant
Dataset has 5 (0.1%) duplicate rowsDuplicates
ofcpssecodenm is highly overall correlated with brkrasortcode and 2 other fieldsHigh correlation
ldcodenm is highly overall correlated with ldcode and 1 other fieldsHigh correlation
brkrasortcode is highly overall correlated with brkrasortcodenm and 2 other fieldsHigh correlation
ofcpssecode is highly overall correlated with brkrasortcode and 2 other fieldsHigh correlation
last_load_dttm is highly overall correlated with ldcode and 1 other fieldsHigh correlation
brkrasortcodenm is highly overall correlated with brkrasortcode and 2 other fieldsHigh correlation
ldcode is highly overall correlated with ldcodenm and 1 other fieldsHigh correlation
bsnmcmpnm has 4168 (41.7%) missing valuesMissing
crqfcacqdt has 4207 (42.1%) missing valuesMissing
crqfcno has 4110 (41.1%) missing valuesMissing
jurirno has 4168 (41.7%) missing valuesMissing

Reproduction

Analysis started2024-04-16 10:25:29.899498
Analysis finished2024-04-16 10:25:31.539409
Duration1.64 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

brkrasortcode
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2
6507 
4
3277 
1
 
215
3
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row4
2nd row2
3rd row4
4th row2
5th row4

Common Values

ValueCountFrequency (%)
2 6507
65.1%
4 3277
32.8%
1 215
 
2.1%
3 1
 
< 0.1%

Length

2024-04-16T19:25:31.600885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T19:25:31.703076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 6507
65.1%
4 3277
32.8%
1 215
 
2.1%
3 1
 
< 0.1%

brkrasortcodenm
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
공인중개사
6507 
중개보조원
3277 
중개인
 
215
법인
 
1

Length

Max length5
Median length5
Mean length4.9567
Min length2

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row중개보조원
2nd row공인중개사
3rd row중개보조원
4th row공인중개사
5th row중개보조원

Common Values

ValueCountFrequency (%)
공인중개사 6507
65.1%
중개보조원 3277
32.8%
중개인 215
 
2.1%
법인 1
 
< 0.1%

Length

2024-04-16T19:25:31.794127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T19:25:31.876623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
공인중개사 6507
65.1%
중개보조원 3277
32.8%
중개인 215
 
2.1%
법인 1
 
< 0.1%

brkrnm
Text

Distinct7938
Distinct (%)79.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-04-16T19:25:32.139766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length3
Mean length3.0132
Min length2

Characters and Unicode

Total characters30132
Distinct characters391
Distinct categories6 ?
Distinct scripts4 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6700 ?
Unique (%)67.0%

Sample

1st row김정숙
2nd row박상기
3rd row문두홍
4th row김혜영
5th row유인규
ValueCountFrequency (%)
김정숙 15
 
0.1%
김미경 13
 
0.1%
김영희 13
 
0.1%
김정희 12
 
0.1%
김미숙 11
 
0.1%
이영주 11
 
0.1%
김경희 10
 
0.1%
정영희 10
 
0.1%
김명희 10
 
0.1%
이정숙 9
 
0.1%
Other values (7934) 9892
98.9%
2024-04-16T19:25:32.575351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
김 2134
 
7.1%
이 1522
 
5.1%
정 1336
 
4.4%
영 1022
 
3.4%
박 950
 
3.2%
희 683
 
2.3%
경 602
 
2.0%
성 538
 
1.8%
숙 536
 
1.8%
미 522
 
1.7%
Other values (381) 20287
67.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 30029
99.7%
Open Punctuation 37
 
0.1%
Close Punctuation 37
 
0.1%
Uppercase Letter 13
 
< 0.1%
Lowercase Letter 10
 
< 0.1%
Space Separator 6
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
김 2134
 
7.1%
이 1522
 
5.1%
정 1336
 
4.4%
영 1022
 
3.4%
박 950
 
3.2%
희 683
 
2.3%
경 602
 
2.0%
성 538
 
1.8%
숙 536
 
1.8%
미 522
 
1.7%
Other values (362) 20184
67.2%
Uppercase Letter
ValueCountFrequency (%)
N 2
15.4%
A 2
15.4%
Y 2
15.4%
T 2
15.4%
E 1
7.7%
H 1
7.7%
L 1
7.7%
C 1
7.7%
S 1
7.7%
Lowercase Letter
ValueCountFrequency (%)
e 4
40.0%
g 1
 
10.0%
a 1
 
10.0%
s 1
 
10.0%
y 1
 
10.0%
u 1
 
10.0%
n 1
 
10.0%
Open Punctuation
ValueCountFrequency (%)
( 37
100.0%
Close Punctuation
ValueCountFrequency (%)
) 37
100.0%
Space Separator
ValueCountFrequency (%)
6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 29919
99.3%
Han 110
 
0.4%
Common 80
 
0.3%
Latin 23
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
김 2134
 
7.1%
이 1522
 
5.1%
정 1336
 
4.5%
영 1022
 
3.4%
박 950
 
3.2%
희 683
 
2.3%
경 602
 
2.0%
성 538
 
1.8%
숙 536
 
1.8%
미 522
 
1.7%
Other values (283) 20074
67.1%
Han
ValueCountFrequency (%)
金 9
 
8.2%
崔 4
 
3.6%
順 3
 
2.7%
榮 3
 
2.7%
子 3
 
2.7%
鄭 3
 
2.7%
李 3
 
2.7%
朴 2
 
1.8%
永 2
 
1.8%
東 2
 
1.8%
Other values (69) 76
69.1%
Latin
ValueCountFrequency (%)
e 4
17.4%
N 2
 
8.7%
A 2
 
8.7%
Y 2
 
8.7%
T 2
 
8.7%
E 1
 
4.3%
H 1
 
4.3%
L 1
 
4.3%
C 1
 
4.3%
g 1
 
4.3%
Other values (6) 6
26.1%
Common
ValueCountFrequency (%)
( 37
46.2%
) 37
46.2%
6
 
7.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 29919
99.3%
CJK 104
 
0.3%
ASCII 103
 
0.3%
CJK Compat Ideographs 6
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
김 2134
 
7.1%
이 1522
 
5.1%
정 1336
 
4.5%
영 1022
 
3.4%
박 950
 
3.2%
희 683
 
2.3%
경 602
 
2.0%
성 538
 
1.8%
숙 536
 
1.8%
미 522
 
1.7%
Other values (283) 20074
67.1%
ASCII
ValueCountFrequency (%)
( 37
35.9%
) 37
35.9%
6
 
5.8%
e 4
 
3.9%
N 2
 
1.9%
A 2
 
1.9%
Y 2
 
1.9%
T 2
 
1.9%
E 1
 
1.0%
H 1
 
1.0%
Other values (9) 9
 
8.7%
CJK
ValueCountFrequency (%)
金 9
 
8.7%
崔 4
 
3.8%
順 3
 
2.9%
榮 3
 
2.9%
子 3
 
2.9%
鄭 3
 
2.9%
朴 2
 
1.9%
永 2
 
1.9%
東 2
 
1.9%
文 2
 
1.9%
Other values (65) 71
68.3%
CJK Compat Ideographs
ValueCountFrequency (%)
李 3
50.0%
連 1
 
16.7%
梁 1
 
16.7%
林 1
 
16.7%

bsnmcmpnm
Text

MISSING 

Distinct2711
Distinct (%)46.5%
Missing4168
Missing (%)41.7%
Memory size156.2 KiB
2024-04-16T19:25:32.776852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length22
Mean length11.356653
Min length4

Characters and Unicode

Total characters66232
Distinct characters543
Distinct categories10 ?
Distinct scripts4 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1611 ?
Unique (%)27.6%

Sample

1st row나이스부동산공인중개사사무소
2nd row대한공인중개사사무소
3rd row럭키합동공인중개사사무소
4th row주식회사 부동산중개법인 더트럼프
5th row아주공인중개사사무소
ValueCountFrequency (%)
주식회사 140
 
2.3%
공인중개사사무소 59
 
1.0%
주)부동산중개법인개벽 45
 
0.7%
사무소 42
 
0.7%
현대공인중개사사무소 35
 
0.6%
대명합동공인중개사사무소 32
 
0.5%
삼오부동산중개법인 31
 
0.5%
조은부동산중개 30
 
0.5%
주)온나라부동산중개법인 29
 
0.5%
삼성공인중개사사무소 26
 
0.4%
Other values (2709) 5679
92.4%
2024-04-16T19:25:33.135234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
사 9854
14.9%
개 5877
 
8.9%
중 5843
 
8.8%
소 5121
 
7.7%
무 5096
 
7.7%
인 4941
 
7.5%
공 4535
 
6.8%
동 2621
 
4.0%
부 2396
 
3.6%
산 2391
 
3.6%
Other values (533) 17557
26.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 64294
97.1%
Uppercase Letter 743
 
1.1%
Space Separator 393
 
0.6%
Decimal Number 249
 
0.4%
Close Punctuation 194
 
0.3%
Open Punctuation 194
 
0.3%
Lowercase Letter 136
 
0.2%
Other Punctuation 23
 
< 0.1%
Dash Punctuation 4
 
< 0.1%
Letter Number 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
사 9854
15.3%
개 5877
 
9.1%
중 5843
 
9.1%
소 5121
 
8.0%
무 5096
 
7.9%
인 4941
 
7.7%
공 4535
 
7.1%
동 2621
 
4.1%
부 2396
 
3.7%
산 2391
 
3.7%
Other values (479) 15619
24.3%
Uppercase Letter
ValueCountFrequency (%)
K 123
16.6%
S 78
10.5%
L 68
 
9.2%
T 64
 
8.6%
C 53
 
7.1%
B 42
 
5.7%
H 39
 
5.2%
G 32
 
4.3%
O 30
 
4.0%
W 29
 
3.9%
Other values (13) 185
24.9%
Lowercase Letter
ValueCountFrequency (%)
e 63
46.3%
h 22
 
16.2%
w 10
 
7.4%
t 10
 
7.4%
s 8
 
5.9%
c 7
 
5.1%
k 6
 
4.4%
b 5
 
3.7%
y 2
 
1.5%
i 1
 
0.7%
Other values (2) 2
 
1.5%
Decimal Number
ValueCountFrequency (%)
1 96
38.6%
2 37
 
14.9%
4 31
 
12.4%
8 28
 
11.2%
3 24
 
9.6%
9 19
 
7.6%
5 6
 
2.4%
6 4
 
1.6%
7 3
 
1.2%
0 1
 
0.4%
Other Punctuation
ValueCountFrequency (%)
& 20
87.0%
· 1
 
4.3%
# 1
 
4.3%
. 1
 
4.3%
Space Separator
ValueCountFrequency (%)
393
100.0%
Close Punctuation
ValueCountFrequency (%)
) 194
100.0%
Open Punctuation
ValueCountFrequency (%)
( 194
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%
Letter Number
ValueCountFrequency (%)
Ⅱ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 64286
97.1%
Common 1057
 
1.6%
Latin 881
 
1.3%
Han 8
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
사 9854
15.3%
개 5877
 
9.1%
중 5843
 
9.1%
소 5121
 
8.0%
무 5096
 
7.9%
인 4941
 
7.7%
공 4535
 
7.1%
동 2621
 
4.1%
부 2396
 
3.7%
산 2391
 
3.7%
Other values (471) 15611
24.3%
Latin
ValueCountFrequency (%)
K 123
14.0%
S 78
 
8.9%
L 68
 
7.7%
T 64
 
7.3%
e 63
 
7.2%
C 53
 
6.0%
B 42
 
4.8%
H 39
 
4.4%
G 32
 
3.6%
O 30
 
3.4%
Other values (26) 289
32.8%
Common
ValueCountFrequency (%)
393
37.2%
) 194
18.4%
( 194
18.4%
1 96
 
9.1%
2 37
 
3.5%
4 31
 
2.9%
8 28
 
2.6%
3 24
 
2.3%
& 20
 
1.9%
9 19
 
1.8%
Other values (8) 21
 
2.0%
Han
ValueCountFrequency (%)
本 1
12.5%
利 1
12.5%
太 1
12.5%
甲 1
12.5%
堂 1
12.5%
明 1
12.5%
秀 1
12.5%
福 1
12.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 64286
97.1%
ASCII 1935
 
2.9%
CJK 8
 
< 0.1%
Number Forms 2
 
< 0.1%
None 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
사 9854
15.3%
개 5877
 
9.1%
중 5843
 
9.1%
소 5121
 
8.0%
무 5096
 
7.9%
인 4941
 
7.7%
공 4535
 
7.1%
동 2621
 
4.1%
부 2396
 
3.7%
산 2391
 
3.7%
Other values (471) 15611
24.3%
ASCII
ValueCountFrequency (%)
393
20.3%
) 194
 
10.0%
( 194
 
10.0%
K 123
 
6.4%
1 96
 
5.0%
S 78
 
4.0%
L 68
 
3.5%
T 64
 
3.3%
e 63
 
3.3%
C 53
 
2.7%
Other values (42) 609
31.5%
Number Forms
ValueCountFrequency (%)
Ⅱ 2
100.0%
None
ValueCountFrequency (%)
· 1
100.0%
CJK
ValueCountFrequency (%)
本 1
12.5%
利 1
12.5%
太 1
12.5%
甲 1
12.5%
堂 1
12.5%
明 1
12.5%
秀 1
12.5%
福 1
12.5%

crqfcacqdt
Text

MISSING 

Distinct632
Distinct (%)10.9%
Missing4207
Missing (%)42.1%
Memory size156.2 KiB
2024-04-16T19:25:33.400610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length9.9993095
Min length8

Characters and Unicode

Total characters57926
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique401 ?
Unique (%)6.9%

Sample

1st row1985-10-25
2nd row2013-10-27
3rd row2016-10-29
4th row2003-11-07
5th row2021-03-22
ValueCountFrequency (%)
2005-07-20 391
 
6.7%
2017-12-11 332
 
5.7%
2016-12-12 321
 
5.5%
2019-12-09 238
 
4.1%
2003-11-07 198
 
3.4%
2015-12-09 197
 
3.4%
2018-12-10 183
 
3.2%
2005-12-12 182
 
3.1%
2001-12-10 160
 
2.8%
2007-12-17 150
 
2.6%
Other values (622) 3441
59.4%
2024-04-16T19:25:33.790408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 13253
22.9%
0 12042
20.8%
- 11582
20.0%
2 11067
19.1%
9 2650
 
4.6%
5 1685
 
2.9%
7 1613
 
2.8%
8 1394
 
2.4%
3 1072
 
1.9%
6 938
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 46344
80.0%
Dash Punctuation 11582
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 13253
28.6%
0 12042
26.0%
2 11067
23.9%
9 2650
 
5.7%
5 1685
 
3.6%
7 1613
 
3.5%
8 1394
 
3.0%
3 1072
 
2.3%
6 938
 
2.0%
4 630
 
1.4%
Dash Punctuation
ValueCountFrequency (%)
- 11582
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 57926
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 13253
22.9%
0 12042
20.8%
- 11582
20.0%
2 11067
19.1%
9 2650
 
4.6%
5 1685
 
2.9%
7 1613
 
2.8%
8 1394
 
2.4%
3 1072
 
1.9%
6 938
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 57926
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 13253
22.9%
0 12042
20.8%
- 11582
20.0%
2 11067
19.1%
9 2650
 
4.6%
5 1685
 
2.9%
7 1613
 
2.8%
8 1394
 
2.4%
3 1072
 
1.9%
6 938
 
1.6%

crqfcno
Text

MISSING 

Distinct5699
Distinct (%)96.8%
Missing4110
Missing (%)41.1%
Memory size156.2 KiB
2024-04-16T19:25:34.066438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length19
Mean length9.2239389
Min length1

Characters and Unicode

Total characters54329
Distinct characters60
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5526 ?
Unique (%)93.8%

Sample

1st row410
2nd row24-0646(부산)
3rd row26-2016-01102(부산)
4th row[부산]449
5th row26-2020-00828
ValueCountFrequency (%)
부산 355
 
5.5%
부산시 49
 
0.8%
부산광역시장 24
 
0.4%
부산광역시 22
 
0.3%
경남 13
 
0.2%
울산 9
 
0.1%
1154 5
 
0.1%
경기도 4
 
0.1%
제 4
 
0.1%
6 4
 
0.1%
Other values (5657) 5918
92.4%
2024-04-16T19:25:34.486088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 8812
16.2%
2 6923
12.7%
1 6609
12.2%
- 6089
11.2%
6 3851
 
7.1%
3 2427
 
4.5%
4 2427
 
4.5%
8 2233
 
4.1%
5 2176
 
4.0%
9 2168
 
4.0%
Other values (50) 10614
19.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 39793
73.2%
Other Letter 6483
 
11.9%
Dash Punctuation 6089
 
11.2%
Open Punctuation 711
 
1.3%
Close Punctuation 711
 
1.3%
Space Separator 524
 
1.0%
Other Punctuation 17
 
< 0.1%
Uppercase Letter 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 1954
30.1%
부 1935
29.8%
호 498
 
7.7%
제 439
 
6.8%
시 368
 
5.7%
광 281
 
4.3%
역 280
 
4.3%
장 161
 
2.5%
경 123
 
1.9%
남 94
 
1.4%
Other values (29) 350
 
5.4%
Decimal Number
ValueCountFrequency (%)
0 8812
22.1%
2 6923
17.4%
1 6609
16.6%
6 3851
9.7%
3 2427
 
6.1%
4 2427
 
6.1%
8 2233
 
5.6%
5 2176
 
5.5%
9 2168
 
5.4%
7 2167
 
5.4%
Other Punctuation
ValueCountFrequency (%)
. 6
35.3%
, 6
35.3%
: 3
17.6%
？ 2
 
11.8%
Open Punctuation
ValueCountFrequency (%)
( 658
92.5%
[ 53
 
7.5%
Close Punctuation
ValueCountFrequency (%)
) 658
92.5%
] 53
 
7.5%
Dash Punctuation
ValueCountFrequency (%)
- 6089
100.0%
Space Separator
ValueCountFrequency (%)
524
100.0%
Uppercase Letter
ValueCountFrequency (%)
Ы 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 47845
88.1%
Hangul 6483
 
11.9%
Cyrillic 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 1954
30.1%
부 1935
29.8%
호 498
 
7.7%
제 439
 
6.8%
시 368
 
5.7%
광 281
 
4.3%
역 280
 
4.3%
장 161
 
2.5%
경 123
 
1.9%
남 94
 
1.4%
Other values (29) 350
 
5.4%
Common
ValueCountFrequency (%)
0 8812
18.4%
2 6923
14.5%
1 6609
13.8%
- 6089
12.7%
6 3851
8.0%
3 2427
 
5.1%
4 2427
 
5.1%
8 2233
 
4.7%
5 2176
 
4.5%
9 2168
 
4.5%
Other values (10) 4130
8.6%
Cyrillic
ValueCountFrequency (%)
Ы 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 47843
88.1%
Hangul 6483
 
11.9%
None 2
 
< 0.1%
Cyrillic 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 8812
18.4%
2 6923
14.5%
1 6609
13.8%
- 6089
12.7%
6 3851
8.0%
3 2427
 
5.1%
4 2427
 
5.1%
8 2233
 
4.7%
5 2176
 
4.5%
9 2168
 
4.5%
Other values (9) 4128
8.6%
Hangul
ValueCountFrequency (%)
산 1954
30.1%
부 1935
29.8%
호 498
 
7.7%
제 439
 
6.8%
시 368
 
5.7%
광 281
 
4.3%
역 280
 
4.3%
장 161
 
2.5%
경 123
 
1.9%
남 94
 
1.4%
Other values (29) 350
 
5.4%
None
ValueCountFrequency (%)
？ 2
100.0%
Cyrillic
ValueCountFrequency (%)
Ы 1
100.0%

jurirno
Text

MISSING 

Distinct3684
Distinct (%)63.2%
Missing4168
Missing (%)41.7%
Memory size156.2 KiB
2024-04-16T19:25:34.891856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length16
Mean length13.835219
Min length6

Characters and Unicode

Total characters80687
Distinct characters14
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2609 ?
Unique (%)44.7%

Sample

1st row26410-2019-00041
2nd row26140-2017-00014
3rd row가-05-2297
4th row가-05-3636
5th row가-7-1843
ValueCountFrequency (%)
26470-2018-00085 45
 
0.8%
26470-2015-00027 32
 
0.5%
26230-2016-00137 31
 
0.5%
26470-2016-00066 29
 
0.5%
26470-2021-00017 29
 
0.5%
26470-2018-00103 21
 
0.4%
26230-2020-00171 19
 
0.3%
26290-2017-00018 18
 
0.3%
가-05-3566 18
 
0.3%
26230-2016-00096 17
 
0.3%
Other values (3677) 5577
95.6%
2024-04-16T19:25:35.187692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 23155
28.7%
2 13049
16.2%
- 11619
14.4%
1 8392
 
10.4%
6 6151
 
7.6%
3 3318
 
4.1%
4 3289
 
4.1%
5 2997
 
3.7%
7 2806
 
3.5%
9 2282
 
2.8%
Other values (4) 3629
 
4.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 67355
83.5%
Dash Punctuation 11619
 
14.4%
Other Letter 1709
 
2.1%
Space Separator 4
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 23155
34.4%
2 13049
19.4%
1 8392
 
12.5%
6 6151
 
9.1%
3 3318
 
4.9%
4 3289
 
4.9%
5 2997
 
4.4%
7 2806
 
4.2%
9 2282
 
3.4%
8 1916
 
2.8%
Other Letter
ValueCountFrequency (%)
가 1689
98.8%
나 20
 
1.2%
Dash Punctuation
ValueCountFrequency (%)
- 11619
100.0%
Space Separator
ValueCountFrequency (%)
4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 78978
97.9%
Hangul 1709
 
2.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 23155
29.3%
2 13049
16.5%
- 11619
14.7%
1 8392
 
10.6%
6 6151
 
7.8%
3 3318
 
4.2%
4 3289
 
4.2%
5 2997
 
3.8%
7 2806
 
3.6%
9 2282
 
2.9%
Other values (2) 1920
 
2.4%
Hangul
ValueCountFrequency (%)
가 1689
98.8%
나 20
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 78978
97.9%
Hangul 1709
 
2.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 23155
29.3%
2 13049
16.5%
- 11619
14.7%
1 8392
 
10.6%
6 6151
 
7.8%
3 3318
 
4.2%
4 3289
 
4.2%
5 2997
 
3.8%
7 2806
 
3.6%
9 2282
 
2.9%
Other values (2) 1920
 
2.4%
Hangul
ValueCountFrequency (%)
가 1689
98.8%
나 20
 
1.2%

lastupdtdt
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2021-03-29
10000 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021-03-29
2nd row2021-03-29
3rd row2021-03-29
4th row2021-03-29
5th row2021-03-29

Common Values

ValueCountFrequency (%)
2021-03-29 10000
100.0%

Length

2024-04-16T19:25:35.308315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T19:25:35.388879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021-03-29 10000
100.0%

ldcode
Real number (ℝ)

HIGH CORRELATION 

Distinct16
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26362.876
Minimum26110
Maximum26710
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-16T19:25:35.453149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum26110
5-th percentile26170
Q126260
median26350
Q326440
95-th percentile26530
Maximum26710
Range600
Interquartile range (IQR)180

Descriptive statistics

Standard deviation128.9436
Coefficient of variation (CV)0.0048911051
Kurtosis0.50592679
Mean26362.876
Median Absolute Deviation (MAD)90
Skewness0.62322584
Sum2.6362876 × 108
Variance16626.451
MonotonicityNot monotonic
2024-04-16T19:25:35.540010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
26230 1454
14.5%
26350 1383
13.8%
26260 1041
10.4%
26470 948
9.5%
26410 819
8.2%
26440 763
7.6%
26380 668
6.7%
26500 620
6.2%
26290 514
 
5.1%
26710 483
 
4.8%
Other values (6) 1307
13.1%
ValueCountFrequency (%)
26110 173
 
1.7%
26140 165
 
1.7%
26170 181
 
1.8%
26200 169
 
1.7%
26230 1454
14.5%
26260 1041
10.4%
26290 514
 
5.1%
26320 324
 
3.2%
26350 1383
13.8%
26380 668
6.7%
ValueCountFrequency (%)
26710 483
 
4.8%
26530 295
 
2.9%
26500 620
6.2%
26470 948
9.5%
26440 763
7.6%
26410 819
8.2%
26380 668
6.7%
26350 1383
13.8%
26320 324
 
3.2%
26290 514
 
5.1%

ldcodenm
Categorical

HIGH CORRELATION 

Distinct16
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
부산광역시 부산진구
1454 
부산광역시 해운대구
1383 
부산광역시 동래구
1041 
부산광역시 연제구
948 
부산광역시 금정구
819 
Other values (11)
4355 

Length

Max length10
Median length9
Mean length9.148
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row부산광역시 금정구
2nd row부산광역시 서구
3rd row부산광역시 부산진구
4th row부산광역시 부산진구
5th row부산광역시 북구

Common Values

ValueCountFrequency (%)
부산광역시 부산진구 1454
14.5%
부산광역시 해운대구 1383
13.8%
부산광역시 동래구 1041
10.4%
부산광역시 연제구 948
9.5%
부산광역시 금정구 819
8.2%
부산광역시 강서구 763
7.6%
부산광역시 사하구 668
6.7%
부산광역시 수영구 620
6.2%
부산광역시 남구 514
 
5.1%
부산광역시 기장군 483
 
4.8%
Other values (6) 1307
13.1%

Length

2024-04-16T19:25:35.651409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
부산광역시 10000
50.0%
부산진구 1454
 
7.3%
해운대구 1383
 
6.9%
동래구 1041
 
5.2%
연제구 948
 
4.7%
금정구 819
 
4.1%
강서구 763
 
3.8%
사하구 668
 
3.3%
수영구 620
 
3.1%
남구 514
 
2.6%
Other values (7) 1790
 
8.9%

ofcpssecode
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
3722 
4
3604 
<NA>
2648 
3
 
16
2
 
10

Length

Max length4
Median length1
Mean length1.7944
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row1
3rd row4
4th row2
5th row4

Common Values

ValueCountFrequency (%)
1 3722
37.2%
4 3604
36.0%
<NA> 2648
26.5%
3 16
 
0.2%
2 10
 
0.1%

Length

2024-04-16T19:25:35.778505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T19:25:35.875056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 3722
37.2%
4 3604
36.0%
na 2648
26.5%
3 16
 
0.2%
2 10
 
0.1%

ofcpssecodenm
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
대표
3722 
일반
3604 
<NA>
2648 
이사
 
16
감사
 
10

Length

Max length4
Median length2
Mean length2.5296
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row일반
2nd row대표
3rd row일반
4th row감사
5th row일반

Common Values

ValueCountFrequency (%)
대표 3722
37.2%
일반 3604
36.0%
<NA> 2648
26.5%
이사 16
 
0.2%
감사 10
 
0.1%

Length

2024-04-16T19:25:35.978685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T19:25:36.078509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
대표 3722
37.2%
일반 3604
36.0%
na 2648
26.5%
이사 16
 
0.2%
감사 10
 
0.1%

last_load_dttm
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2021-04-01 06:22:03
5005 
2021-04-01 06:22:04
4995 

Length

Max length19
Median length19
Mean length19
Min length19

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021-04-01 06:22:04
2nd row2021-04-01 06:22:03
3rd row2021-04-01 06:22:03
4th row2021-04-01 06:22:03
5th row2021-04-01 06:22:03

Common Values

ValueCountFrequency (%)
2021-04-01 06:22:03 5005
50.0%
2021-04-01 06:22:04 4995
50.0%

Length

2024-04-16T19:25:36.169240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T19:25:36.247871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021-04-01 10000
50.0%
06:22:03 5005
25.0%
06:22:04 4995
25.0%

Interactions

2024-04-16T19:25:31.013539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-16T19:25:36.310829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
brkrasortcodebrkrasortcodenmldcodeldcodenmofcpssecodeofcpssecodenmlast_load_dttm
brkrasortcode1.0001.0000.2430.3000.5740.5740.056
brkrasortcodenm1.0001.0000.2430.3000.5740.5740.056
ldcode0.2430.2431.0001.0000.2150.2150.995
ldcodenm0.3000.3001.0001.0000.2550.2550.995
ofcpssecode0.5740.5740.2150.2551.0001.0000.047
ofcpssecodenm0.5740.5740.2150.2551.0001.0000.047
last_load_dttm0.0560.0560.9950.9950.0470.0471.000
2024-04-16T19:25:36.432366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ofcpssecodenmldcodenmbrkrasortcodeofcpssecodelast_load_dttmbrkrasortcodenm
ofcpssecodenm1.0000.1220.5841.0000.0310.584
ldcodenm0.1221.0000.1450.1220.9410.145
brkrasortcode0.5840.1451.0000.5840.0371.000
ofcpssecode1.0000.1220.5841.0000.0310.584
last_load_dttm0.0310.9410.0370.0311.0000.037
brkrasortcodenm0.5840.1451.0000.5840.0371.000
2024-04-16T19:25:36.524013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ldcodebrkrasortcodebrkrasortcodenmldcodenmofcpssecodeofcpssecodenmlast_load_dttm
ldcode1.0000.1120.1121.0000.0970.0970.937
brkrasortcode0.1121.0001.0000.1450.5840.5840.037
brkrasortcodenm0.1121.0001.0000.1450.5840.5840.037
ldcodenm1.0000.1450.1451.0000.1220.1220.941
ofcpssecode0.0970.5840.5840.1221.0001.0000.031
ofcpssecodenm0.0970.5840.5840.1221.0001.0000.031
last_load_dttm0.9370.0370.0370.9410.0310.0311.000

Missing values

2024-04-16T19:25:31.155699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-16T19:25:31.334585image/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.
2024-04-16T19:25:31.463050image/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

brkrasortcodebrkrasortcodenmbrkrnmbsnmcmpnmcrqfcacqdtcrqfcnojurirnolastupdtdtldcodeldcodenmofcpssecodeofcpssecodenmlast_load_dttm
133854중개보조원김정숙나이스부동산공인중개사사무소<NA><NA>26410-2019-000412021-03-2926410부산광역시 금정구4일반2021-04-01 06:22:04
5782공인중개사박상기대한공인중개사사무소1985-10-2541026140-2017-000142021-03-2926140부산광역시 서구1대표2021-04-01 06:22:03
33414중개보조원문두홍럭키합동공인중개사사무소<NA><NA>가-05-22972021-03-2926230부산광역시 부산진구4일반2021-04-01 06:22:03
33902공인중개사김혜영주식회사 부동산중개법인 더트럼프2013-10-2724-0646(부산)가-05-36362021-03-2926230부산광역시 부산진구2감사2021-04-01 06:22:03
76034중개보조원유인규<NA><NA><NA><NA>2021-03-2926320부산광역시 북구4일반2021-04-01 06:22:03
27582공인중개사신준호<NA>2016-10-2926-2016-01102(부산)<NA>2021-03-2926230부산광역시 부산진구1대표2021-04-01 06:22:03
113342공인중개사이선미<NA>2003-11-07[부산]449<NA>2021-03-2926380부산광역시 사하구<NA><NA>2021-04-01 06:22:04
118854중개보조원김미숙<NA><NA><NA><NA>2021-03-2926380부산광역시 사하구4일반2021-04-01 06:22:04
2742공인중개사김외선<NA><NA><NA><NA>2021-03-2926110부산광역시 중구<NA><NA>2021-04-01 06:22:03
100012공인중개사김혜린<NA>2021-03-2226-2020-00828<NA>2021-03-2926350부산광역시 해운대구1대표2021-04-01 06:22:04
brkrasortcodebrkrasortcodenmbrkrnmbsnmcmpnmcrqfcacqdtcrqfcnojurirnolastupdtdtldcodeldcodenmofcpssecodeofcpssecodenmlast_load_dttm
38911중개인김동수밀양부동산중개사무소<NA><NA>나-05-1552021-03-2926230부산광역시 부산진구1대표2021-04-01 06:22:03
119044중개보조원김복희<NA><NA><NA><NA>2021-03-2926380부산광역시 사하구4일반2021-04-01 06:22:04
69274중개보조원박현숙W럭키부동산중개사무소<NA><NA>26290-2019-000512021-03-2926290부산광역시 남구4일반2021-04-01 06:22:03
195124중개보조원윤병수금나라부동산공인중개사사무소<NA><NA>26710-2015-001022021-03-2926710부산광역시 기장군4일반2021-04-01 06:22:04
57912공인중개사김흥도<NA><NA><NA><NA>2021-03-2926260부산광역시 동래구<NA><NA>2021-04-01 06:22:03
52882공인중개사안성희<NA>2017-12-1126-2017-00352<NA>2021-03-2926260부산광역시 동래구4일반2021-04-01 06:22:03
116292공인중개사박미숙<NA><NA><NA><NA>2021-03-2926380부산광역시 사하구<NA><NA>2021-04-01 06:22:04
13364중개보조원고윤미신도 공인 중개사 사무소<NA><NA>가-4-3912021-03-2926200부산광역시 영도구4일반2021-04-01 06:22:03
106272공인중개사권은정센텀에이스공인중개사사무소2003-11-06부산1486가-10-16602021-03-2926350부산광역시 해운대구1대표2021-04-01 06:22:04
68424중개보조원선정주대우공인중개사사무소<NA><NA>가-7-15862021-03-2926290부산광역시 남구4일반2021-04-01 06:22:03

Duplicate rows

Most frequently occurring

brkrasortcodebrkrasortcodenmbrkrnmbsnmcmpnmcrqfcacqdtcrqfcnojurirnolastupdtdtldcodeldcodenmofcpssecodeofcpssecodenmlast_load_dttm# duplicates
02공인중개사김미정<NA><NA><NA><NA>2021-03-2926260부산광역시 동래구<NA><NA>2021-04-01 06:22:032
12공인중개사김성수<NA><NA><NA><NA>2021-03-2926350부산광역시 해운대구<NA><NA>2021-04-01 06:22:042
22공인중개사박창호<NA><NA><NA><NA>2021-03-2926260부산광역시 동래구<NA><NA>2021-04-01 06:22:032
34중개보조원김명자<NA><NA><NA><NA>2021-03-2926230부산광역시 부산진구<NA><NA>2021-04-01 06:22:032
44중개보조원최혜빈(주)고명부동산중개법인<NA><NA>가-12-11152021-03-2926440부산광역시 강서구4일반2021-04-01 06:22:042