Overview

Dataset statistics

Number of variables13
Number of observations10000
Missing cells13602
Missing cells (%)10.5%
Duplicate rows1
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 1 (< 0.1%) duplicate rowsDuplicates
ofcpssecodenm is highly overall correlated with ofcpssecodeHigh correlation
ldcodenm is highly overall correlated with ldcode and 1 other fieldsHigh correlation
brkrasortcode is highly overall correlated with brkrasortcodenmHigh correlation
ofcpssecode is highly overall correlated with ofcpssecodenmHigh correlation
last_load_dttm is highly overall correlated with ldcode and 1 other fieldsHigh correlation
brkrasortcodenm is highly overall correlated with brkrasortcodeHigh correlation
ldcode is highly overall correlated with ldcodenm and 1 other fieldsHigh correlation
bsnmcmpnm has 2700 (27.0%) missing valuesMissing
crqfcacqdt has 4150 (41.5%) missing valuesMissing
crqfcno has 4052 (40.5%) missing valuesMissing
jurirno has 2700 (27.0%) missing valuesMissing

Reproduction

Analysis started2024-04-16 10:25:38.490955
Analysis finished2024-04-16 10:25:40.039558
Duration1.55 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
6601 
4
3191 
1
 
206
3
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
2 6601
66.0%
4 3191
31.9%
1 206
 
2.1%
3 2
 
< 0.1%

Length

2024-04-16T19:25:40.101415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T19:25:40.198643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 6601
66.0%
4 3191
31.9%
1 206
 
2.1%
3 2
 
< 0.1%

brkrasortcodenm
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
공인중개사
6601 
중개보조원
3191 
중개인
 
206
법인
 
2

Length

Max length5
Median length5
Mean length4.9582
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
공인중개사 6601
66.0%
중개보조원 3191
31.9%
중개인 206
 
2.1%
법인 2
 
< 0.1%

Length

2024-04-16T19:25:40.305677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T19:25:40.389477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
공인중개사 6601
66.0%
중개보조원 3191
31.9%
중개인 206
 
2.1%
법인 2
 
< 0.1%

brkrnm
Text

Distinct7901
Distinct (%)79.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-04-16T19:25:40.668241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length3
Mean length3.0158
Min length2

Characters and Unicode

Total characters30158
Distinct characters378
Distinct categories5 ?
Distinct scripts4 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6638 ?
Unique (%)66.4%

Sample

1st row채영주
2nd row박동수
3rd row이상윤
4th row원종환
5th row김병관
ValueCountFrequency (%)
김정희 13
 
0.1%
김경희 13
 
0.1%
김정숙 12
 
0.1%
이영주 11
 
0.1%
김영희 11
 
0.1%
김영숙 11
 
0.1%
박정수 11
 
0.1%
이정희 11
 
0.1%
이경숙 10
 
0.1%
김성희 10
 
0.1%
Other values (7899) 9897
98.9%
2024-04-16T19:25:41.057149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
김 2117
 
7.0%
이 1459
 
4.8%
정 1369
 
4.5%
영 1035
 
3.4%
박 926
 
3.1%
희 716
 
2.4%
경 623
 
2.1%
미 555
 
1.8%
숙 525
 
1.7%
현 522
 
1.7%
Other values (368) 20311
67.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 30048
99.6%
Close Punctuation 40
 
0.1%
Open Punctuation 40
 
0.1%
Uppercase Letter 18
 
0.1%
Space Separator 12
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
김 2117
 
7.0%
이 1459
 
4.9%
정 1369
 
4.6%
영 1035
 
3.4%
박 926
 
3.1%
희 716
 
2.4%
경 623
 
2.1%
미 555
 
1.8%
숙 525
 
1.7%
현 522
 
1.7%
Other values (358) 20201
67.2%
Uppercase Letter
ValueCountFrequency (%)
N 4
22.2%
Y 3
16.7%
A 3
16.7%
T 3
16.7%
I 3
16.7%
J 1
 
5.6%
E 1
 
5.6%
Close Punctuation
ValueCountFrequency (%)
) 40
100.0%
Open Punctuation
ValueCountFrequency (%)
( 40
100.0%
Space Separator
ValueCountFrequency (%)
12
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 29932
99.3%
Han 116
 
0.4%
Common 92
 
0.3%
Latin 18
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
김 2117
 
7.1%
이 1459
 
4.9%
정 1369
 
4.6%
영 1035
 
3.5%
박 926
 
3.1%
희 716
 
2.4%
경 623
 
2.1%
미 555
 
1.9%
숙 525
 
1.8%
현 522
 
1.7%
Other values (280) 20085
67.1%
Han
ValueCountFrequency (%)
金 12
 
10.3%
李 7
 
6.0%
順 4
 
3.4%
英 3
 
2.6%
崔 3
 
2.6%
榮 3
 
2.6%
慶 3
 
2.6%
朴 3
 
2.6%
子 2
 
1.7%
淑 2
 
1.7%
Other values (68) 74
63.8%
Latin
ValueCountFrequency (%)
N 4
22.2%
Y 3
16.7%
A 3
16.7%
T 3
16.7%
I 3
16.7%
J 1
 
5.6%
E 1
 
5.6%
Common
ValueCountFrequency (%)
) 40
43.5%
( 40
43.5%
12
 
13.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 29931
99.2%
ASCII 110
 
0.4%
CJK 108
 
0.4%
CJK Compat Ideographs 8
 
< 0.1%
Compat Jamo 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
김 2117
 
7.1%
이 1459
 
4.9%
정 1369
 
4.6%
영 1035
 
3.5%
박 926
 
3.1%
희 716
 
2.4%
경 623
 
2.1%
미 555
 
1.9%
숙 525
 
1.8%
현 522
 
1.7%
Other values (279) 20084
67.1%
ASCII
ValueCountFrequency (%)
) 40
36.4%
( 40
36.4%
12
 
10.9%
N 4
 
3.6%
Y 3
 
2.7%
A 3
 
2.7%
T 3
 
2.7%
I 3
 
2.7%
J 1
 
0.9%
E 1
 
0.9%
CJK
ValueCountFrequency (%)
金 12
 
11.1%
順 4
 
3.7%
英 3
 
2.8%
崔 3
 
2.8%
榮 3
 
2.8%
慶 3
 
2.8%
朴 3
 
2.8%
子 2
 
1.9%
淑 2
 
1.9%
泰 2
 
1.9%
Other values (66) 71
65.7%
CJK Compat Ideographs
ValueCountFrequency (%)
李 7
87.5%
林 1
 
12.5%
Compat Jamo
ValueCountFrequency (%)
ㅂ 1
100.0%

bsnmcmpnm
Text

MISSING 

Distinct3397
Distinct (%)46.5%
Missing2700
Missing (%)27.0%
Memory size156.2 KiB
2024-04-16T19:25:41.259956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length22
Mean length11.3
Min length4

Characters and Unicode

Total characters82490
Distinct characters577
Distinct categories11 ?
Distinct scripts4 ?
Distinct blocks6 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2108 ?
Unique (%)28.9%

Sample

1st row천지공인중개사사무소
2nd row부산상가채널공인중개사사무소
3rd row태평양부동산중개사무소
4th row동서남북공인중개사사무소
5th row(주)온나라부동산중개법인
ValueCountFrequency (%)
주식회사 99
 
1.3%
공인중개사사무소 87
 
1.1%
사무소 66
 
0.9%
주)온나라부동산중개법인 40
 
0.5%
주)부동산중개법인개벽 38
 
0.5%
태양공인중개사사무소 30
 
0.4%
삼오부동산중개법인 28
 
0.4%
삼성공인중개사사무소 28
 
0.4%
현대공인중개사사무소 27
 
0.4%
가은부동산중개 26
 
0.3%
Other values (3388) 7163
93.9%
2024-04-16T19:25:41.610794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
사 12596
15.3%
개 7342
 
8.9%
중 7319
 
8.9%
소 6568
 
8.0%
무 6533
 
7.9%
인 6254
 
7.6%
공 5841
 
7.1%
동 3085
 
3.7%
부 2830
 
3.4%
산 2804
 
3.4%
Other values (567) 21318
25.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 80281
97.3%
Uppercase Letter 796
 
1.0%
Space Separator 408
 
0.5%
Decimal Number 325
 
0.4%
Open Punctuation 231
 
0.3%
Close Punctuation 231
 
0.3%
Lowercase Letter 181
 
0.2%
Other Punctuation 27
 
< 0.1%
Dash Punctuation 7
 
< 0.1%
Letter Number 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
사 12596
15.7%
개 7342
 
9.1%
중 7319
 
9.1%
소 6568
 
8.2%
무 6533
 
8.1%
인 6254
 
7.8%
공 5841
 
7.3%
동 3085
 
3.8%
부 2830
 
3.5%
산 2804
 
3.5%
Other values (502) 19109
23.8%
Uppercase Letter
ValueCountFrequency (%)
K 125
15.7%
S 82
 
10.3%
T 69
 
8.7%
L 63
 
7.9%
B 44
 
5.5%
H 44
 
5.5%
C 43
 
5.4%
O 43
 
5.4%
W 40
 
5.0%
E 28
 
3.5%
Other values (15) 215
27.0%
Lowercase Letter
ValueCountFrequency (%)
e 79
43.6%
h 27
 
14.9%
t 16
 
8.8%
c 15
 
8.3%
w 9
 
5.0%
k 6
 
3.3%
s 6
 
3.3%
i 5
 
2.8%
n 4
 
2.2%
m 2
 
1.1%
Other values (8) 12
 
6.6%
Decimal Number
ValueCountFrequency (%)
1 138
42.5%
2 41
 
12.6%
8 41
 
12.6%
4 35
 
10.8%
3 28
 
8.6%
9 18
 
5.5%
5 8
 
2.5%
0 7
 
2.2%
7 5
 
1.5%
6 4
 
1.2%
Other Punctuation
ValueCountFrequency (%)
& 17
63.0%
. 3
 
11.1%
· 3
 
11.1%
, 2
 
7.4%
! 1
 
3.7%
# 1
 
3.7%
Space Separator
ValueCountFrequency (%)
408
100.0%
Open Punctuation
ValueCountFrequency (%)
( 231
100.0%
Close Punctuation
ValueCountFrequency (%)
) 231
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 7
100.0%
Letter Number
ValueCountFrequency (%)
Ⅱ 2
100.0%
Other Symbol
ValueCountFrequency (%)
ⓡ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 80270
97.3%
Common 1230
 
1.5%
Latin 979
 
1.2%
Han 11
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
사 12596
15.7%
개 7342
 
9.1%
중 7319
 
9.1%
소 6568
 
8.2%
무 6533
 
8.1%
인 6254
 
7.8%
공 5841
 
7.3%
동 3085
 
3.8%
부 2830
 
3.5%
산 2804
 
3.5%
Other values (494) 19098
23.8%
Latin
ValueCountFrequency (%)
K 125
 
12.8%
S 82
 
8.4%
e 79
 
8.1%
T 69
 
7.0%
L 63
 
6.4%
B 44
 
4.5%
H 44
 
4.5%
C 43
 
4.4%
O 43
 
4.4%
W 40
 
4.1%
Other values (34) 347
35.4%
Common
ValueCountFrequency (%)
408
33.2%
( 231
18.8%
) 231
18.8%
1 138
 
11.2%
2 41
 
3.3%
8 41
 
3.3%
4 35
 
2.8%
3 28
 
2.3%
9 18
 
1.5%
& 17
 
1.4%
Other values (11) 42
 
3.4%
Han
ValueCountFrequency (%)
福 3
27.3%
本 2
18.2%
該 1
 
9.1%
甲 1
 
9.1%
利 1
 
9.1%
秀 1
 
9.1%
炫 1
 
9.1%
太 1
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 80270
97.3%
ASCII 2203
 
2.7%
CJK 11
 
< 0.1%
None 3
 
< 0.1%
Number Forms 2
 
< 0.1%
Enclosed Alphanum 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
사 12596
15.7%
개 7342
 
9.1%
중 7319
 
9.1%
소 6568
 
8.2%
무 6533
 
8.1%
인 6254
 
7.8%
공 5841
 
7.3%
동 3085
 
3.8%
부 2830
 
3.5%
산 2804
 
3.5%
Other values (494) 19098
23.8%
ASCII
ValueCountFrequency (%)
408
18.5%
( 231
 
10.5%
) 231
 
10.5%
1 138
 
6.3%
K 125
 
5.7%
S 82
 
3.7%
e 79
 
3.6%
T 69
 
3.1%
L 63
 
2.9%
B 44
 
2.0%
Other values (52) 733
33.3%
CJK
ValueCountFrequency (%)
福 3
27.3%
本 2
18.2%
該 1
 
9.1%
甲 1
 
9.1%
利 1
 
9.1%
秀 1
 
9.1%
炫 1
 
9.1%
太 1
 
9.1%
None
ValueCountFrequency (%)
· 3
100.0%
Number Forms
ValueCountFrequency (%)
Ⅱ 2
100.0%
Enclosed Alphanum
ValueCountFrequency (%)
ⓡ 1
100.0%

crqfcacqdt
Text

MISSING 

Distinct649
Distinct (%)11.1%
Missing4150
Missing (%)41.5%
Memory size156.2 KiB
2024-04-16T19:25:41.872030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length9.9986325
Min length8

Characters and Unicode

Total characters58492
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique407 ?
Unique (%)7.0%

Sample

1st row2008-12-15
2nd row2003-09-21
3rd row2004-06-03
4th row2010-12-13
5th row2000-11-20
ValueCountFrequency (%)
2005-07-20 373
 
6.4%
2017-12-11 348
 
5.9%
2016-12-12 315
 
5.4%
2019-12-09 223
 
3.8%
2003-11-07 201
 
3.4%
2018-12-10 197
 
3.4%
2015-12-09 188
 
3.2%
2005-12-12 180
 
3.1%
2001-12-10 171
 
2.9%
2000-11-20 156
 
2.7%
Other values (639) 3498
59.8%
2024-04-16T19:25:42.257121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 13481
23.0%
0 12211
20.9%
- 11692
20.0%
2 11079
18.9%
9 2694
 
4.6%
5 1694
 
2.9%
7 1625
 
2.8%
8 1442
 
2.5%
3 1068
 
1.8%
6 932
 
1.6%
Other values (2) 574
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 46796
80.0%
Dash Punctuation 11692
 
20.0%
Space Separator 4
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 13481
28.8%
0 12211
26.1%
2 11079
23.7%
9 2694
 
5.8%
5 1694
 
3.6%
7 1625
 
3.5%
8 1442
 
3.1%
3 1068
 
2.3%
6 932
 
2.0%
4 570
 
1.2%
Dash Punctuation
ValueCountFrequency (%)
- 11692
100.0%
Space Separator
ValueCountFrequency (%)
4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 58492
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 13481
23.0%
0 12211
20.9%
- 11692
20.0%
2 11079
18.9%
9 2694
 
4.6%
5 1694
 
2.9%
7 1625
 
2.8%
8 1442
 
2.5%
3 1068
 
1.8%
6 932
 
1.6%
Other values (2) 574
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 58492
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 13481
23.0%
0 12211
20.9%
- 11692
20.0%
2 11079
18.9%
9 2694
 
4.6%
5 1694
 
2.9%
7 1625
 
2.8%
8 1442
 
2.5%
3 1068
 
1.8%
6 932
 
1.6%
Other values (2) 574
 
1.0%

crqfcno
Text

MISSING 

Distinct5697
Distinct (%)95.8%
Missing4052
Missing (%)40.5%
Memory size156.2 KiB
2024-04-16T19:25:42.752984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length20
Mean length9.1772024
Min length1

Characters and Unicode

Total characters54586
Distinct characters58
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5480 ?
Unique (%)92.1%

Sample

1st row부산 19-245
2nd row14-611
3rd row2103
4th row21-00681(부산광역시)
5th row246
ValueCountFrequency (%)
부산 334
 
5.2%
부산시 46
 
0.7%
부산광역시장 18
 
0.3%
부산광역시 17
 
0.3%
경남 16
 
0.2%
울산 5
 
0.1%
경상남도 5
 
0.1%
662 4
 
0.1%
794 4
 
0.1%
410 4
 
0.1%
Other values (5656) 5974
93.0%
2024-04-16T19:25:43.152014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 8798
16.1%
2 6829
12.5%
1 6543
12.0%
- 6134
11.2%
6 3882
 
7.1%
4 2504
 
4.6%
3 2495
 
4.6%
8 2307
 
4.2%
5 2210
 
4.0%
7 2202
 
4.0%
Other values (48) 10682
19.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 39937
73.2%
Other Letter 6578
 
12.1%
Dash Punctuation 6134
 
11.2%
Close Punctuation 719
 
1.3%
Open Punctuation 719
 
1.3%
Space Separator 487
 
0.9%
Other Punctuation 12
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 1945
29.6%
부 1928
29.3%
호 517
 
7.9%
제 475
 
7.2%
시 374
 
5.7%
광 283
 
4.3%
역 282
 
4.3%
장 157
 
2.4%
경 136
 
2.1%
남 111
 
1.7%
Other values (29) 370
 
5.6%
Decimal Number
ValueCountFrequency (%)
0 8798
22.0%
2 6829
17.1%
1 6543
16.4%
6 3882
9.7%
4 2504
 
6.3%
3 2495
 
6.2%
8 2307
 
5.8%
5 2210
 
5.5%
7 2202
 
5.5%
9 2167
 
5.4%
Other Punctuation
ValueCountFrequency (%)
. 8
66.7%
, 3
 
25.0%
: 1
 
8.3%
Close Punctuation
ValueCountFrequency (%)
) 670
93.2%
] 49
 
6.8%
Open Punctuation
ValueCountFrequency (%)
( 670
93.2%
[ 49
 
6.8%
Dash Punctuation
ValueCountFrequency (%)
- 6134
100.0%
Space Separator
ValueCountFrequency (%)
487
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 48008
87.9%
Hangul 6578
 
12.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 1945
29.6%
부 1928
29.3%
호 517
 
7.9%
제 475
 
7.2%
시 374
 
5.7%
광 283
 
4.3%
역 282
 
4.3%
장 157
 
2.4%
경 136
 
2.1%
남 111
 
1.7%
Other values (29) 370
 
5.6%
Common
ValueCountFrequency (%)
0 8798
18.3%
2 6829
14.2%
1 6543
13.6%
- 6134
12.8%
6 3882
8.1%
4 2504
 
5.2%
3 2495
 
5.2%
8 2307
 
4.8%
5 2210
 
4.6%
7 2202
 
4.6%
Other values (9) 4104
8.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 48008
87.9%
Hangul 6578
 
12.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 8798
18.3%
2 6829
14.2%
1 6543
13.6%
- 6134
12.8%
6 3882
8.1%
4 2504
 
5.2%
3 2495
 
5.2%
8 2307
 
4.8%
5 2210
 
4.6%
7 2202
 
4.6%
Other values (9) 4104
8.5%
Hangul
ValueCountFrequency (%)
산 1945
29.6%
부 1928
29.3%
호 517
 
7.9%
제 475
 
7.2%
시 374
 
5.7%
광 283
 
4.3%
역 282
 
4.3%
장 157
 
2.4%
경 136
 
2.1%
남 111
 
1.7%
Other values (29) 370
 
5.6%

jurirno
Text

MISSING 

Distinct4780
Distinct (%)65.5%
Missing2700
Missing (%)27.0%
Memory size156.2 KiB
2024-04-16T19:25:43.345185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length16
Mean length13.741781
Min length6

Characters and Unicode

Total characters100315
Distinct characters13
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

Unique3462 ?
Unique (%)47.4%

Sample

1st row가-08-1713
2nd row26500-2018-00097
3rd row26470-2020-00116
4th row가-11-1667
5th row26470-2016-00066
ValueCountFrequency (%)
26470-2016-00066 40
 
0.5%
26470-2018-00085 38
 
0.5%
26230-2016-00137 28
 
0.4%
26530-2017-00027 26
 
0.4%
26470-2015-00027 24
 
0.3%
26470-2018-00103 22
 
0.3%
가-05-4212 17
 
0.2%
가-05-3566 16
 
0.2%
가-08-2037 15
 
0.2%
가-13-1947 14
 
0.2%
Other values (4770) 7060
96.7%
2024-04-16T19:25:43.637159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 28791
28.7%
2 16337
16.3%
- 14540
14.5%
1 9984
 
10.0%
6 7870
 
7.8%
3 4270
 
4.3%
4 3774
 
3.8%
5 3521
 
3.5%
7 3338
 
3.3%
9 2931
 
2.9%
Other values (3) 4959
 
4.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 83531
83.3%
Dash Punctuation 14540
 
14.5%
Other Letter 2244
 
2.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 28791
34.5%
2 16337
19.6%
1 9984
 
12.0%
6 7870
 
9.4%
3 4270
 
5.1%
4 3774
 
4.5%
5 3521
 
4.2%
7 3338
 
4.0%
9 2931
 
3.5%
8 2715
 
3.3%
Other Letter
ValueCountFrequency (%)
가 2222
99.0%
나 22
 
1.0%
Dash Punctuation
ValueCountFrequency (%)
- 14540
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 98071
97.8%
Hangul 2244
 
2.2%

Most frequent character per script

Common
ValueCountFrequency (%)
0 28791
29.4%
2 16337
16.7%
- 14540
14.8%
1 9984
 
10.2%
6 7870
 
8.0%
3 4270
 
4.4%
4 3774
 
3.8%
5 3521
 
3.6%
7 3338
 
3.4%
9 2931
 
3.0%
Hangul
ValueCountFrequency (%)
가 2222
99.0%
나 22
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 98071
97.8%
Hangul 2244
 
2.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 28791
29.4%
2 16337
16.7%
- 14540
14.8%
1 9984
 
10.2%
6 7870
 
8.0%
3 4270
 
4.4%
4 3774
 
3.8%
5 3521
 
3.6%
7 3338
 
3.4%
9 2931
 
3.0%
Hangul
ValueCountFrequency (%)
가 2222
99.0%
나 22
 
1.0%

lastupdtdt
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2021-02-02
10000 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021-02-02
2nd row2021-02-02
3rd row2021-02-02
4th row2021-02-02
5th row2021-02-02

Common Values

ValueCountFrequency (%)
2021-02-02 10000
100.0%

Length

2024-04-16T19:25:43.751201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T19:25:43.823624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021-02-02 10000
100.0%

ldcode
Real number (ℝ)

HIGH CORRELATION 

Distinct16
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26362.102
Minimum26110
Maximum26710
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-16T19:25:43.898711image/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 deviation128.71681
Coefficient of variation (CV)0.0048826461
Kurtosis0.51497213
Mean26362.102
Median Absolute Deviation (MAD)90
Skewness0.62893602
Sum2.6362102 × 108
Variance16568.018
MonotonicityNot monotonic
2024-04-16T19:25:44.000639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
26230 1428
14.3%
26350 1338
13.4%
26260 1086
10.9%
26470 891
8.9%
26410 845
8.5%
26440 750
7.5%
26380 685
6.9%
26500 637
6.4%
26290 553
 
5.5%
26710 476
 
4.8%
Other values (6) 1311
13.1%
ValueCountFrequency (%)
26110 176
 
1.8%
26140 167
 
1.7%
26170 183
 
1.8%
26200 161
 
1.6%
26230 1428
14.3%
26260 1086
10.9%
26290 553
 
5.5%
26320 320
 
3.2%
26350 1338
13.4%
26380 685
6.9%
ValueCountFrequency (%)
26710 476
 
4.8%
26530 304
 
3.0%
26500 637
6.4%
26470 891
8.9%
26440 750
7.5%
26410 845
8.5%
26380 685
6.9%
26350 1338
13.4%
26320 320
 
3.2%
26290 553
5.5%

ldcodenm
Categorical

HIGH CORRELATION 

Distinct16
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
부산광역시 부산진구
1428 
부산광역시 해운대구
1338 
부산광역시 동래구
1086 
부산광역시 연제구
891 
부산광역시 금정구
845 
Other values (11)
4412 

Length

Max length10
Median length9
Mean length9.1367
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row부산광역시 사하구
2nd row부산광역시 기장군
3rd row부산광역시 수영구
4th row부산광역시 사하구
5th row부산광역시 부산진구

Common Values

ValueCountFrequency (%)
부산광역시 부산진구 1428
14.3%
부산광역시 해운대구 1338
13.4%
부산광역시 동래구 1086
10.9%
부산광역시 연제구 891
8.9%
부산광역시 금정구 845
8.5%
부산광역시 강서구 750
7.5%
부산광역시 사하구 685
6.9%
부산광역시 수영구 637
6.4%
부산광역시 남구 553
 
5.5%
부산광역시 기장군 476
 
4.8%
Other values (6) 1311
13.1%

Length

2024-04-16T19:25:44.115010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
부산광역시 10000
50.0%
부산진구 1428
 
7.1%
해운대구 1338
 
6.7%
동래구 1086
 
5.4%
연제구 891
 
4.5%
금정구 845
 
4.2%
강서구 750
 
3.8%
사하구 685
 
3.4%
수영구 637
 
3.2%
남구 553
 
2.8%
Other values (7) 1787
 
8.9%

ofcpssecode
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
3745 
4
3539 
<NA>
2695 
3
 
13
2
 
8

Length

Max length4
Median length1
Mean length1.8085
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row<NA>
3rd row4
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
1 3745
37.5%
4 3539
35.4%
<NA> 2695
27.0%
3 13
 
0.1%
2 8
 
0.1%

Length

2024-04-16T19:25:44.221794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T19:25:44.322142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 3745
37.5%
4 3539
35.4%
na 2695
27.0%
3 13
 
0.1%
2 8
 
0.1%

ofcpssecodenm
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
대표
3745 
일반
3539 
<NA>
2695 
이사
 
13
감사
 
8

Length

Max length4
Median length2
Mean length2.539
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
대표 3745
37.5%
일반 3539
35.4%
<NA> 2695
27.0%
이사 13
 
0.1%
감사 8
 
0.1%

Length

2024-04-16T19:25:44.423577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T19:25:44.523851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
대표 3745
37.5%
일반 3539
35.4%
na 2695
27.0%
이사 13
 
0.1%
감사 8
 
0.1%

last_load_dttm
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2021-03-01 06:22:03
5099 
2021-03-01 06:22:04
4901 

Length

Max length19
Median length19
Mean length19
Min length19

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
2021-03-01 06:22:03 5099
51.0%
2021-03-01 06:22:04 4901
49.0%

Length

2024-04-16T19:25:44.615247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T19:25:44.691544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021-03-01 10000
50.0%
06:22:03 5099
25.5%
06:22:04 4901
24.5%

Interactions

2024-04-16T19:25:39.533748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-16T19:25:44.755957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
brkrasortcodebrkrasortcodenmldcodeldcodenmofcpssecodeofcpssecodenmlast_load_dttm
brkrasortcode1.0001.0000.2120.2820.8320.8320.043
brkrasortcodenm1.0001.0000.2120.2820.8320.8320.043
ldcode0.2120.2121.0001.0000.2050.2050.996
ldcodenm0.2820.2821.0001.0000.2360.2360.997
ofcpssecode0.8320.8320.2050.2361.0001.0000.032
ofcpssecodenm0.8320.8320.2050.2361.0001.0000.032
last_load_dttm0.0430.0430.9960.9970.0320.0321.000
2024-04-16T19:25:44.853477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ofcpssecodenmldcodenmbrkrasortcodeofcpssecodelast_load_dttmbrkrasortcodenm
ofcpssecodenm1.0000.1130.4791.0000.0210.479
ldcodenm0.1131.0000.1350.1130.9500.135
brkrasortcode0.4790.1351.0000.4790.0281.000
ofcpssecode1.0000.1130.4791.0000.0210.479
last_load_dttm0.0210.9500.0280.0211.0000.028
brkrasortcodenm0.4790.1351.0000.4790.0281.000
2024-04-16T19:25:44.961940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ldcodebrkrasortcodebrkrasortcodenmldcodenmofcpssecodeofcpssecodenmlast_load_dttm
ldcode1.0000.0960.0961.0000.0920.0920.948
brkrasortcode0.0961.0001.0000.1350.4790.4790.028
brkrasortcodenm0.0961.0001.0000.1350.4790.4790.028
ldcodenm1.0000.1350.1351.0000.1130.1130.950
ofcpssecode0.0920.4790.4790.1131.0001.0000.021
ofcpssecodenm0.0920.4790.4790.1131.0001.0000.021
last_load_dttm0.9480.0280.0280.9500.0210.0211.000

Missing values

2024-04-16T19:25:39.655779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-16T19:25:39.824492image/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.
2024-04-16T19:25:39.952173image/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
107462공인중개사채영주천지공인중개사사무소2008-12-15부산 19-245가-08-17132021-02-0226380부산광역시 사하구1대표2021-03-01 06:22:04
194264중개보조원박동수<NA><NA><NA><NA>2021-02-0226710부산광역시 기장군<NA><NA>2021-03-01 06:22:04
173644중개보조원이상윤부산상가채널공인중개사사무소<NA><NA>26500-2018-000972021-02-0226500부산광역시 수영구4일반2021-03-01 06:22:04
112232공인중개사원종환<NA>2003-09-2114-611<NA>2021-02-0226380부산광역시 사하구<NA><NA>2021-03-01 06:22:04
37362공인중개사김병관<NA>2004-06-032103<NA>2021-02-0226230부산광역시 부산진구<NA><NA>2021-03-01 06:22:03
161574중개보조원서시호태평양부동산중개사무소<NA><NA>26470-2020-001162021-02-0226470부산광역시 연제구4일반2021-03-01 06:22:04
124232공인중개사전진혁동서남북공인중개사사무소2010-12-1321-00681(부산광역시)가-11-16672021-02-0226410부산광역시 금정구1대표2021-03-01 06:22:04
156874중개보조원임승현(주)온나라부동산중개법인<NA><NA>26470-2016-000662021-02-0226470부산광역시 연제구4일반2021-03-01 06:22:04
56932공인중개사김현희삼성래미안공인중개사사무소2000-11-2024626260-2019-001332021-02-0226260부산광역시 동래구1대표2021-03-01 06:22:03
180794중개보조원공미영더존부동산공인중개사사무소<NA><NA>26500-2019-000102021-02-0226500부산광역시 수영구4일반2021-03-01 06:22:04
brkrasortcodebrkrasortcodenmbrkrnmbsnmcmpnmcrqfcacqdtcrqfcnojurirnolastupdtdtldcodeldcodenmofcpssecodeofcpssecodenmlast_load_dttm
149782공인중개사김명희창조부동산공인중개사사무소2016-12-1226-2016-0214426440-2017-001362021-02-0226440부산광역시 강서구1대표2021-03-01 06:22:04
71112공인중개사김경희우리공인중개사사무소2015-12-092620150057526290-2019-000642021-02-0226290부산광역시 남구1대표2021-03-01 06:22:03
136852공인중개사한현주명지박사공인중개사사무소2015-12-0926-2015-0141026440-2017-000012021-02-0226440부산광역시 강서구1대표2021-03-01 06:22:04
131962공인중개사문성식<NA>2003-11-0703-1690<NA>2021-02-0226410부산광역시 금정구<NA><NA>2021-03-01 06:22:04
9634중개보조원김세일세여부동산중개<NA><NA>26170-2019-000502021-02-0226170부산광역시 동구4일반2021-03-01 06:22:03
85222공인중개사전재철<NA>2005-07-20부산1895<NA>2021-02-0226350부산광역시 해운대구<NA><NA>2021-03-01 06:22:03
72862공인중개사한미숙올레공인중개사사무소2012-12-1023-0026626320-2015-000062021-02-0226320부산광역시 북구1대표2021-03-01 06:22:03
3182공인중개사김봉제코모도부동산중개2019-12-0926-2019-0137526110-2020-000232021-02-0226110부산광역시 중구1대표2021-03-01 06:22:03
115942공인중개사김주은<NA>2009-12-14[부산]20-00402<NA>2021-02-0226380부산광역시 사하구<NA><NA>2021-03-01 06:22:04
162004중개보조원박지홍이루다공인중개사사무소<NA><NA>26470-2020-001012021-02-0226470부산광역시 연제구4일반2021-03-01 06:22:04

Duplicate rows

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brkrasortcodebrkrasortcodenmbrkrnmbsnmcmpnmcrqfcacqdtcrqfcnojurirnolastupdtdtldcodeldcodenmofcpssecodeofcpssecodenmlast_load_dttm# duplicates
02공인중개사권의현<NA><NA><NA><NA>2021-02-0226410부산광역시 금정구<NA><NA>2021-03-01 06:22:042