Overview

Dataset statistics

Number of variables13
Number of observations10000
Missing cells13528
Missing cells (%)10.4%
Duplicate rows4
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 4 (< 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 2712 (27.1%) missing valuesMissing
crqfcacqdt has 4104 (41.0%) missing valuesMissing
crqfcno has 4000 (40.0%) missing valuesMissing
jurirno has 2712 (27.1%) missing valuesMissing

Reproduction

Analysis started2024-04-16 10:25:46.930675
Analysis finished2024-04-16 10:25:48.429954
Duration1.5 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
6621 
4
3174 
1
 
201
3
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
2 6621
66.2%
4 3174
31.7%
1 201
 
2.0%
3 4
 
< 0.1%

Length

2024-04-16T19:25:48.492779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T19:25:48.592990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 6621
66.2%
4 3174
31.7%
1 201
 
2.0%
3 4
 
< 0.1%

brkrasortcodenm
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
공인중개사
6621 
중개보조원
3174 
중개인
 
201
법인
 
4

Length

Max length5
Median length5
Mean length4.9586
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
공인중개사 6621
66.2%
중개보조원 3174
31.7%
중개인 201
 
2.0%
법인 4
 
< 0.1%

Length

2024-04-16T19:25:48.700667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T19:25:48.791902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
공인중개사 6621
66.2%
중개보조원 3174
31.7%
중개인 201
 
2.0%
법인 4
 
< 0.1%

brkrnm
Text

Distinct7909
Distinct (%)79.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-04-16T19:25:49.074372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length3
Mean length3.013
Min length2

Characters and Unicode

Total characters30130
Distinct characters374
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

Unique6631 ?
Unique (%)66.3%

Sample

1st row여미화
2nd row김정희
3rd row송민금
4th row박순근
5th row이관훈
ValueCountFrequency (%)
김영희 17
 
0.2%
김정희 15
 
0.1%
김선희 13
 
0.1%
이영주 12
 
0.1%
김정숙 12
 
0.1%
이정희 11
 
0.1%
정영희 11
 
0.1%
김미숙 11
 
0.1%
이미경 10
 
0.1%
김미경 10
 
0.1%
Other values (7909) 9890
98.8%
2024-04-16T19:25:49.499500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
김 2197
 
7.3%
이 1492
 
5.0%
정 1344
 
4.5%
영 1032
 
3.4%
박 910
 
3.0%
희 690
 
2.3%
경 631
 
2.1%
숙 552
 
1.8%
미 542
 
1.8%
수 542
 
1.8%
Other values (364) 20198
67.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 30011
99.6%
Close Punctuation 37
 
0.1%
Open Punctuation 37
 
0.1%
Uppercase Letter 22
 
0.1%
Space Separator 13
 
< 0.1%
Lowercase Letter 10
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
김 2197
 
7.3%
이 1492
 
5.0%
정 1344
 
4.5%
영 1032
 
3.4%
박 910
 
3.0%
희 690
 
2.3%
경 631
 
2.1%
숙 552
 
1.8%
미 542
 
1.8%
수 542
 
1.8%
Other values (343) 20079
66.9%
Uppercase Letter
ValueCountFrequency (%)
N 4
18.2%
Y 3
13.6%
A 3
13.6%
T 3
13.6%
I 3
13.6%
J 1
 
4.5%
E 1
 
4.5%
H 1
 
4.5%
S 1
 
4.5%
C 1
 
4.5%
Lowercase Letter
ValueCountFrequency (%)
e 4
40.0%
g 1
 
10.0%
n 1
 
10.0%
u 1
 
10.0%
y 1
 
10.0%
s 1
 
10.0%
a 1
 
10.0%
Close Punctuation
ValueCountFrequency (%)
) 37
100.0%
Open Punctuation
ValueCountFrequency (%)
( 37
100.0%
Space Separator
ValueCountFrequency (%)
13
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 29902
99.2%
Han 109
 
0.4%
Common 87
 
0.3%
Latin 32
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
김 2197
 
7.3%
이 1492
 
5.0%
정 1344
 
4.5%
영 1032
 
3.5%
박 910
 
3.0%
희 690
 
2.3%
경 631
 
2.1%
숙 552
 
1.8%
미 542
 
1.8%
수 542
 
1.8%
Other values (273) 19970
66.8%
Han
ValueCountFrequency (%)
金 10
 
9.2%
李 7
 
6.4%
鄭 4
 
3.7%
崔 4
 
3.7%
永 3
 
2.8%
泰 3
 
2.8%
慶 3
 
2.8%
朴 2
 
1.8%
守 2
 
1.8%
東 2
 
1.8%
Other values (60) 69
63.3%
Latin
ValueCountFrequency (%)
e 4
12.5%
N 4
12.5%
Y 3
 
9.4%
A 3
 
9.4%
T 3
 
9.4%
I 3
 
9.4%
J 1
 
3.1%
E 1
 
3.1%
H 1
 
3.1%
g 1
 
3.1%
Other values (8) 8
25.0%
Common
ValueCountFrequency (%)
) 37
42.5%
( 37
42.5%
13
 
14.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 29902
99.2%
ASCII 119
 
0.4%
CJK 100
 
0.3%
CJK Compat Ideographs 9
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
김 2197
 
7.3%
이 1492
 
5.0%
정 1344
 
4.5%
영 1032
 
3.5%
박 910
 
3.0%
희 690
 
2.3%
경 631
 
2.1%
숙 552
 
1.8%
미 542
 
1.8%
수 542
 
1.8%
Other values (273) 19970
66.8%
ASCII
ValueCountFrequency (%)
) 37
31.1%
( 37
31.1%
13
 
10.9%
e 4
 
3.4%
N 4
 
3.4%
Y 3
 
2.5%
A 3
 
2.5%
T 3
 
2.5%
I 3
 
2.5%
J 1
 
0.8%
Other values (11) 11
 
9.2%
CJK
ValueCountFrequency (%)
金 10
 
10.0%
鄭 4
 
4.0%
崔 4
 
4.0%
永 3
 
3.0%
泰 3
 
3.0%
慶 3
 
3.0%
朴 2
 
2.0%
守 2
 
2.0%
東 2
 
2.0%
淑 2
 
2.0%
Other values (57) 65
65.0%
CJK Compat Ideographs
ValueCountFrequency (%)
李 7
77.8%
梁 1
 
11.1%
連 1
 
11.1%

bsnmcmpnm
Text

MISSING 

Distinct3322
Distinct (%)45.6%
Missing2712
Missing (%)27.1%
Memory size156.2 KiB
2024-04-16T19:25:49.687012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length20
Mean length11.280461
Min length4

Characters and Unicode

Total characters82212
Distinct characters580
Distinct categories12 ?
Distinct scripts4 ?
Distinct blocks6 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2029 ?
Unique (%)27.8%

Sample

1st row행복한부동산공인중개사사무소
2nd row거산공인중개사사무소
3rd row신우공인중개사사무소
4th row더원공인중개사사무소
5th row하늘부동산공인중개사사무소
ValueCountFrequency (%)
주식회사 96
 
1.3%
공인중개사사무소 72
 
0.9%
사무소 72
 
0.9%
주)부동산중개법인개벽 41
 
0.5%
주)온나라부동산중개법인 38
 
0.5%
삼성공인중개사사무소 36
 
0.5%
현대공인중개사사무소 33
 
0.4%
굿모닝공인중개사사무소 33
 
0.4%
대명합동공인중개사사무소 30
 
0.4%
우리공인중개사사무소 28
 
0.4%
Other values (3320) 7124
93.7%
2024-04-16T19:25:50.002930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
사 12590
15.3%
개 7336
 
8.9%
중 7306
 
8.9%
소 6587
 
8.0%
무 6541
 
8.0%
인 6247
 
7.6%
공 5835
 
7.1%
동 3031
 
3.7%
부 2800
 
3.4%
산 2783
 
3.4%
Other values (570) 21156
25.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 80122
97.5%
Uppercase Letter 792
 
1.0%
Space Separator 396
 
0.5%
Decimal Number 299
 
0.4%
Open Punctuation 211
 
0.3%
Close Punctuation 211
 
0.3%
Lowercase Letter 149
 
0.2%
Other Punctuation 26
 
< 0.1%
Dash Punctuation 3
 
< 0.1%
Letter Number 1
 
< 0.1%
Other values (2) 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
사 12590
15.7%
개 7336
 
9.2%
중 7306
 
9.1%
소 6587
 
8.2%
무 6541
 
8.2%
인 6247
 
7.8%
공 5835
 
7.3%
동 3031
 
3.8%
부 2800
 
3.5%
산 2783
 
3.5%
Other values (504) 19066
23.8%
Uppercase Letter
ValueCountFrequency (%)
K 149
18.8%
S 93
11.7%
T 62
 
7.8%
L 57
 
7.2%
C 48
 
6.1%
B 46
 
5.8%
H 43
 
5.4%
W 38
 
4.8%
O 38
 
4.8%
G 25
 
3.2%
Other values (15) 193
24.4%
Lowercase Letter
ValueCountFrequency (%)
e 55
36.9%
h 22
 
14.8%
t 11
 
7.4%
s 8
 
5.4%
c 7
 
4.7%
w 7
 
4.7%
k 7
 
4.7%
i 6
 
4.0%
b 5
 
3.4%
o 5
 
3.4%
Other values (8) 16
 
10.7%
Decimal Number
ValueCountFrequency (%)
1 129
43.1%
2 42
 
14.0%
8 39
 
13.0%
4 34
 
11.4%
3 25
 
8.4%
9 11
 
3.7%
5 9
 
3.0%
6 6
 
2.0%
0 4
 
1.3%
Other Punctuation
ValueCountFrequency (%)
& 15
57.7%
. 3
 
11.5%
# 2
 
7.7%
· 2
 
7.7%
？ 2
 
7.7%
! 1
 
3.8%
, 1
 
3.8%
Space Separator
ValueCountFrequency (%)
396
100.0%
Open Punctuation
ValueCountFrequency (%)
( 211
100.0%
Close Punctuation
ValueCountFrequency (%)
) 211
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%
Letter Number
ValueCountFrequency (%)
Ⅱ 1
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 1
100.0%
Other Symbol
ValueCountFrequency (%)
ⓡ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 80111
97.4%
Common 1148
 
1.4%
Latin 942
 
1.1%
Han 11
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
사 12590
15.7%
개 7336
 
9.2%
중 7306
 
9.1%
소 6587
 
8.2%
무 6541
 
8.2%
인 6247
 
7.8%
공 5835
 
7.3%
동 3031
 
3.8%
부 2800
 
3.5%
산 2783
 
3.5%
Other values (493) 19055
23.8%
Latin
ValueCountFrequency (%)
K 149
15.8%
S 93
 
9.9%
T 62
 
6.6%
L 57
 
6.1%
e 55
 
5.8%
C 48
 
5.1%
B 46
 
4.9%
H 43
 
4.6%
W 38
 
4.0%
O 38
 
4.0%
Other values (34) 313
33.2%
Common
ValueCountFrequency (%)
396
34.5%
( 211
18.4%
) 211
18.4%
1 129
 
11.2%
2 42
 
3.7%
8 39
 
3.4%
4 34
 
3.0%
3 25
 
2.2%
& 15
 
1.3%
9 11
 
1.0%
Other values (12) 35
 
3.0%
Han
ValueCountFrequency (%)
人 1
9.1%
利 1
9.1%
太 1
9.1%
秀 1
9.1%
本 1
9.1%
氷 1
9.1%
明 1
9.1%
堂 1
9.1%
甲 1
9.1%
該 1
9.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 80111
97.4%
ASCII 2084
 
2.5%
CJK 11
 
< 0.1%
None 4
 
< 0.1%
Number Forms 1
 
< 0.1%
Enclosed Alphanum 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
사 12590
15.7%
개 7336
 
9.2%
중 7306
 
9.1%
소 6587
 
8.2%
무 6541
 
8.2%
인 6247
 
7.8%
공 5835
 
7.3%
동 3031
 
3.8%
부 2800
 
3.5%
산 2783
 
3.5%
Other values (493) 19055
23.8%
ASCII
ValueCountFrequency (%)
396
19.0%
( 211
 
10.1%
) 211
 
10.1%
K 149
 
7.1%
1 129
 
6.2%
S 93
 
4.5%
T 62
 
3.0%
L 57
 
2.7%
e 55
 
2.6%
C 48
 
2.3%
Other values (52) 673
32.3%
None
ValueCountFrequency (%)
· 2
50.0%
？ 2
50.0%
CJK
ValueCountFrequency (%)
人 1
9.1%
利 1
9.1%
太 1
9.1%
秀 1
9.1%
本 1
9.1%
氷 1
9.1%
明 1
9.1%
堂 1
9.1%
甲 1
9.1%
該 1
9.1%
Number Forms
ValueCountFrequency (%)
Ⅱ 1
100.0%
Enclosed Alphanum
ValueCountFrequency (%)
ⓡ 1
100.0%

crqfcacqdt
Text

MISSING 

Distinct658
Distinct (%)11.2%
Missing4104
Missing (%)41.0%
Memory size156.2 KiB
2024-04-16T19:25:50.260719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length9.9989824
Min length8

Characters and Unicode

Total characters58954
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

Unique405 ?
Unique (%)6.9%

Sample

1st row2016-12-15
2nd row2019-12-09
3rd row1985-11-06
4th row2016-12-12
5th row1985-11-18
ValueCountFrequency (%)
2005-07-20 377
 
6.4%
2017-12-11 330
 
5.6%
2016-12-12 306
 
5.2%
2019-12-09 242
 
4.1%
2015-12-09 220
 
3.7%
2003-11-07 212
 
3.6%
2005-12-12 191
 
3.2%
2018-12-10 184
 
3.1%
2000-11-20 154
 
2.6%
2001-12-10 154
 
2.6%
Other values (648) 3526
59.8%
2024-04-16T19:25:50.837990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 13421
22.8%
0 12350
20.9%
- 11786
20.0%
2 11189
19.0%
9 2800
 
4.7%
5 1704
 
2.9%
7 1632
 
2.8%
8 1425
 
2.4%
3 1136
 
1.9%
6 944
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 47168
80.0%
Dash Punctuation 11786
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 13421
28.5%
0 12350
26.2%
2 11189
23.7%
9 2800
 
5.9%
5 1704
 
3.6%
7 1632
 
3.5%
8 1425
 
3.0%
3 1136
 
2.4%
6 944
 
2.0%
4 567
 
1.2%
Dash Punctuation
ValueCountFrequency (%)
- 11786
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 58954
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 13421
22.8%
0 12350
20.9%
- 11786
20.0%
2 11189
19.0%
9 2800
 
4.7%
5 1704
 
2.9%
7 1632
 
2.8%
8 1425
 
2.4%
3 1136
 
1.9%
6 944
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 58954
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 13421
22.8%
0 12350
20.9%
- 11786
20.0%
2 11189
19.0%
9 2800
 
4.7%
5 1704
 
2.9%
7 1632
 
2.8%
8 1425
 
2.4%
3 1136
 
1.9%
6 944
 
1.6%

crqfcno
Text

MISSING 

Distinct5774
Distinct (%)96.2%
Missing4000
Missing (%)40.0%
Memory size156.2 KiB
2024-04-16T19:25:51.128383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length20
Mean length9.2301667
Min length1

Characters and Unicode

Total characters55381
Distinct characters63
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

Unique5575 ?
Unique (%)92.9%

Sample

1st row26-2016-01898
2nd row26-2019-00518
3rd row2560
4th row26-2016-01358
5th row4988
ValueCountFrequency (%)
부산 361
 
5.5%
부산시 52
 
0.8%
부산광역시 25
 
0.4%
부산광역시장 18
 
0.3%
경남 16
 
0.2%
울산 5
 
0.1%
455 4
 
0.1%
제 4
 
0.1%
경상남도 4
 
0.1%
662 4
 
0.1%
Other values (5730) 6023
92.4%
2024-04-16T19:25:51.524909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 8902
16.1%
2 6995
12.6%
1 6693
12.1%
- 6177
11.2%
6 3938
 
7.1%
4 2539
 
4.6%
3 2445
 
4.4%
5 2260
 
4.1%
8 2246
 
4.1%
7 2235
 
4.0%
Other values (53) 10951
19.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 40434
73.0%
Other Letter 6830
 
12.3%
Dash Punctuation 6177
 
11.2%
Close Punctuation 703
 
1.3%
Open Punctuation 703
 
1.3%
Space Separator 518
 
0.9%
Other Punctuation 16
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 2016
29.5%
부 1999
29.3%
호 543
 
8.0%
제 484
 
7.1%
시 395
 
5.8%
광 300
 
4.4%
역 299
 
4.4%
장 167
 
2.4%
경 142
 
2.1%
남 117
 
1.7%
Other values (33) 368
 
5.4%
Decimal Number
ValueCountFrequency (%)
0 8902
22.0%
2 6995
17.3%
1 6693
16.6%
6 3938
9.7%
4 2539
 
6.3%
3 2445
 
6.0%
5 2260
 
5.6%
8 2246
 
5.6%
7 2235
 
5.5%
9 2181
 
5.4%
Other Punctuation
ValueCountFrequency (%)
. 8
50.0%
, 4
25.0%
: 3
 
18.8%
? 1
 
6.2%
Close Punctuation
ValueCountFrequency (%)
) 657
93.5%
] 46
 
6.5%
Open Punctuation
ValueCountFrequency (%)
( 657
93.5%
[ 46
 
6.5%
Dash Punctuation
ValueCountFrequency (%)
- 6177
100.0%
Space Separator
ValueCountFrequency (%)
518
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 48551
87.7%
Hangul 6830
 
12.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 2016
29.5%
부 1999
29.3%
호 543
 
8.0%
제 484
 
7.1%
시 395
 
5.8%
광 300
 
4.4%
역 299
 
4.4%
장 167
 
2.4%
경 142
 
2.1%
남 117
 
1.7%
Other values (33) 368
 
5.4%
Common
ValueCountFrequency (%)
0 8902
18.3%
2 6995
14.4%
1 6693
13.8%
- 6177
12.7%
6 3938
8.1%
4 2539
 
5.2%
3 2445
 
5.0%
5 2260
 
4.7%
8 2246
 
4.6%
7 2235
 
4.6%
Other values (10) 4121
8.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 48551
87.7%
Hangul 6830
 
12.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 8902
18.3%
2 6995
14.4%
1 6693
13.8%
- 6177
12.7%
6 3938
8.1%
4 2539
 
5.2%
3 2445
 
5.0%
5 2260
 
4.7%
8 2246
 
4.6%
7 2235
 
4.6%
Other values (10) 4121
8.5%
Hangul
ValueCountFrequency (%)
산 2016
29.5%
부 1999
29.3%
호 543
 
8.0%
제 484
 
7.1%
시 395
 
5.8%
광 300
 
4.4%
역 299
 
4.4%
장 167
 
2.4%
경 142
 
2.1%
남 117
 
1.7%
Other values (33) 368
 
5.4%

jurirno
Text

MISSING 

Distinct4756
Distinct (%)65.3%
Missing2712
Missing (%)27.1%
Memory size156.2 KiB
2024-04-16T19:25:51.719004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length16
Mean length13.727772
Min length6

Characters and Unicode

Total characters100048
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

Unique3434 ?
Unique (%)47.1%

Sample

1st row26170-2020-00034
2nd row26500-2019-00125
3rd row26140-2017-00010
4th row26230-2019-00034
5th row26440-2020-00034
ValueCountFrequency (%)
26470-2018-00085 41
 
0.6%
26470-2016-00066 38
 
0.5%
26470-2015-00027 30
 
0.4%
26530-2017-00027 25
 
0.3%
가-13-1750 23
 
0.3%
26470-2018-00103 23
 
0.3%
가-05-4212 23
 
0.3%
26230-2016-00137 20
 
0.3%
26230-2020-00171 18
 
0.2%
26290-2017-00018 18
 
0.2%
Other values (4747) 7030
96.4%
2024-04-16T19:25:52.018729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 28702
28.7%
2 16338
16.3%
- 14534
14.5%
1 9889
 
9.9%
6 7847
 
7.8%
3 4313
 
4.3%
4 3812
 
3.8%
5 3538
 
3.5%
7 3278
 
3.3%
9 2922
 
2.9%
Other values (4) 4875
 
4.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 83256
83.2%
Dash Punctuation 14534
 
14.5%
Other Letter 2257
 
2.3%
Space Separator 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 28702
34.5%
2 16338
19.6%
1 9889
 
11.9%
6 7847
 
9.4%
3 4313
 
5.2%
4 3812
 
4.6%
5 3538
 
4.2%
7 3278
 
3.9%
9 2922
 
3.5%
8 2617
 
3.1%
Other Letter
ValueCountFrequency (%)
가 2231
98.8%
나 26
 
1.2%
Dash Punctuation
ValueCountFrequency (%)
- 14534
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 97791
97.7%
Hangul 2257
 
2.3%

Most frequent character per script

Common
ValueCountFrequency (%)
0 28702
29.4%
2 16338
16.7%
- 14534
14.9%
1 9889
 
10.1%
6 7847
 
8.0%
3 4313
 
4.4%
4 3812
 
3.9%
5 3538
 
3.6%
7 3278
 
3.4%
9 2922
 
3.0%
Other values (2) 2618
 
2.7%
Hangul
ValueCountFrequency (%)
가 2231
98.8%
나 26
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 97791
97.7%
Hangul 2257
 
2.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 28702
29.4%
2 16338
16.7%
- 14534
14.9%
1 9889
 
10.1%
6 7847
 
8.0%
3 4313
 
4.4%
4 3812
 
3.9%
5 3538
 
3.6%
7 3278
 
3.4%
9 2922
 
3.0%
Other values (2) 2618
 
2.7%
Hangul
ValueCountFrequency (%)
가 2231
98.8%
나 26
 
1.2%

lastupdtdt
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2021-01-31
10000 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021-01-31
2nd row2021-01-31
3rd row2021-01-31
4th row2021-01-31
5th row2021-01-31

Common Values

ValueCountFrequency (%)
2021-01-31 10000
100.0%

Length

2024-04-16T19:25:52.136374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T19:25:52.213591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021-01-31 10000
100.0%

ldcode
Real number (ℝ)

HIGH CORRELATION 

Distinct16
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26361.157
Minimum26110
Maximum26710
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-16T19:25:52.278757image/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 deviation127.02151
Coefficient of variation (CV)0.0048185106
Kurtosis0.57838593
Mean26361.157
Median Absolute Deviation (MAD)90
Skewness0.63547105
Sum2.6361157 × 108
Variance16134.465
MonotonicityNot monotonic
2024-04-16T19:25:52.368498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
26230 1417
14.2%
26350 1388
13.9%
26260 1086
10.9%
26470 900
9.0%
26410 881
8.8%
26440 773
7.7%
26380 650
6.5%
26500 591
5.9%
26290 552
 
5.5%
26710 455
 
4.5%
Other values (6) 1307
13.1%
ValueCountFrequency (%)
26110 161
 
1.6%
26140 177
 
1.8%
26170 175
 
1.8%
26200 164
 
1.6%
26230 1417
14.2%
26260 1086
10.9%
26290 552
 
5.5%
26320 340
 
3.4%
26350 1388
13.9%
26380 650
6.5%
ValueCountFrequency (%)
26710 455
 
4.5%
26530 290
 
2.9%
26500 591
5.9%
26470 900
9.0%
26440 773
7.7%
26410 881
8.8%
26380 650
6.5%
26350 1388
13.9%
26320 340
 
3.4%
26290 552
 
5.5%

ldcodenm
Categorical

HIGH CORRELATION 

Distinct16
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
부산광역시 부산진구
1417 
부산광역시 해운대구
1388 
부산광역시 동래구
1086 
부산광역시 연제구
900 
부산광역시 금정구
881 
Other values (11)
4328 

Length

Max length10
Median length9
Mean length9.14
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row부산광역시 동구
2nd row부산광역시 수영구
3rd row부산광역시 서구
4th row부산광역시 부산진구
5th row부산광역시 강서구

Common Values

ValueCountFrequency (%)
부산광역시 부산진구 1417
14.2%
부산광역시 해운대구 1388
13.9%
부산광역시 동래구 1086
10.9%
부산광역시 연제구 900
9.0%
부산광역시 금정구 881
8.8%
부산광역시 강서구 773
7.7%
부산광역시 사하구 650
6.5%
부산광역시 수영구 591
5.9%
부산광역시 남구 552
 
5.5%
부산광역시 기장군 455
 
4.5%
Other values (6) 1307
13.1%

Length

2024-04-16T19:25:52.484075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
부산광역시 10000
50.0%
부산진구 1417
 
7.1%
해운대구 1388
 
6.9%
동래구 1086
 
5.4%
연제구 900
 
4.5%
금정구 881
 
4.4%
강서구 773
 
3.9%
사하구 650
 
3.2%
수영구 591
 
3.0%
남구 552
 
2.8%
Other values (7) 1762
 
8.8%

ofcpssecode
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
3739 
4
3535 
<NA>
2706 
2
 
10
3
 
10

Length

Max length4
Median length1
Mean length1.8118
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1 3739
37.4%
4 3535
35.4%
<NA> 2706
27.1%
2 10
 
0.1%
3 10
 
0.1%

Length

2024-04-16T19:25:52.590552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T19:25:52.674198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 3739
37.4%
4 3535
35.4%
na 2706
27.1%
2 10
 
0.1%
3 10
 
0.1%

ofcpssecodenm
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
대표
3739 
일반
3535 
<NA>
2706 
감사
 
10
이사
 
10

Length

Max length4
Median length2
Mean length2.5412
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
대표 3739
37.4%
일반 3535
35.4%
<NA> 2706
27.1%
감사 10
 
0.1%
이사 10
 
0.1%

Length

2024-04-16T19:25:52.776043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T19:25:52.869049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
대표 3739
37.4%
일반 3535
35.4%
na 2706
27.1%
감사 10
 
0.1%
이사 10
 
0.1%

last_load_dttm
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2021-02-01 06:22:03
5134 
2021-02-01 06:22:04
4866 

Length

Max length19
Median length19
Mean length19
Min length19

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021-02-01 06:22:03
2nd row2021-02-01 06:22:04
3rd row2021-02-01 06:22:03
4th row2021-02-01 06:22:03
5th row2021-02-01 06:22:04

Common Values

ValueCountFrequency (%)
2021-02-01 06:22:03 5134
51.3%
2021-02-01 06:22:04 4866
48.7%

Length

2024-04-16T19:25:52.959878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T19:25:53.043055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021-02-01 10000
50.0%
06:22:03 5134
25.7%
06:22:04 4866
24.3%

Interactions

2024-04-16T19:25:47.965231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-16T19:25:53.115051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
brkrasortcodebrkrasortcodenmldcodeldcodenmofcpssecodeofcpssecodenmlast_load_dttm
brkrasortcode1.0001.0000.2310.2930.8290.8290.048
brkrasortcodenm1.0001.0000.2310.2930.8290.8290.048
ldcode0.2310.2311.0001.0000.2170.2170.996
ldcodenm0.2930.2931.0001.0000.2450.2450.997
ofcpssecode0.8290.8290.2170.2451.0001.0000.034
ofcpssecodenm0.8290.8290.2170.2451.0001.0000.034
last_load_dttm0.0480.0480.9960.9970.0340.0341.000
2024-04-16T19:25:53.212771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ofcpssecodenmldcodenmbrkrasortcodeofcpssecodelast_load_dttmbrkrasortcodenm
ofcpssecodenm1.0000.1170.4761.0000.0220.476
ldcodenm0.1171.0000.1410.1170.9480.141
brkrasortcode0.4760.1411.0000.4760.0321.000
ofcpssecode1.0000.1170.4761.0000.0220.476
last_load_dttm0.0220.9480.0320.0221.0000.032
brkrasortcodenm0.4760.1411.0000.4760.0321.000
2024-04-16T19:25:53.314754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ldcodebrkrasortcodebrkrasortcodenmldcodenmofcpssecodeofcpssecodenmlast_load_dttm
ldcode1.0000.1070.1071.0000.0990.0990.945
brkrasortcode0.1071.0001.0000.1410.4760.4760.032
brkrasortcodenm0.1071.0001.0000.1410.4760.4760.032
ldcodenm1.0000.1410.1411.0000.1170.1170.948
ofcpssecode0.0990.4760.4760.1171.0001.0000.022
ofcpssecodenm0.0990.4760.4760.1171.0001.0000.022
last_load_dttm0.9450.0320.0320.9480.0220.0221.000

Missing values

2024-04-16T19:25:48.076034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-16T19:25:48.226576image/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.
2024-04-16T19:25:48.353964image/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
8082공인중개사여미화행복한부동산공인중개사사무소2016-12-1526-2016-0189826170-2020-000342021-01-3126170부산광역시 동구1대표2021-02-01 06:22:03
178884중개보조원김정희거산공인중개사사무소<NA><NA>26500-2019-001252021-01-3126500부산광역시 수영구4일반2021-02-01 06:22:04
5402공인중개사송민금신우공인중개사사무소2019-12-0926-2019-0051826140-2017-000102021-01-3126140부산광역시 서구4일반2021-02-01 06:22:03
32124중개보조원박순근더원공인중개사사무소<NA><NA>26230-2019-000342021-01-3126230부산광역시 부산진구4일반2021-02-01 06:22:03
142054중개보조원이관훈하늘부동산공인중개사사무소<NA><NA>26440-2020-000342021-01-3126440부산광역시 강서구4일반2021-02-01 06:22:04
49634중개보조원윤효덕유림부동산공인중개사사무소<NA><NA>가-06-43422021-01-3126260부산광역시 동래구4일반2021-02-01 06:22:03
17204중개보조원조현태굿모닝공인중개사사무소<NA><NA>가-05-42122021-01-3126230부산광역시 부산진구4일반2021-02-01 06:22:03
175842공인중개사석영태YT부동산공인중개사사무소1985-11-062560가-14-9932021-01-3126500부산광역시 수영구1대표2021-02-01 06:22:04
153232공인중개사하주현우정부동산공인중개사사무소2016-12-1226-2016-0135826470-2017-000782021-01-3126470부산광역시 연제구1대표2021-02-01 06:22:04
122332공인중개사조동식<NA>1985-11-184988<NA>2021-01-3126410부산광역시 금정구<NA><NA>2021-02-01 06:22:04
brkrasortcodebrkrasortcodenmbrkrnmbsnmcmpnmcrqfcacqdtcrqfcnojurirnolastupdtdtldcodeldcodenmofcpssecodeofcpssecodenmlast_load_dttm
149152공인중개사김숙<NA>1991-12-2097<NA>2021-01-3126440부산광역시 강서구<NA><NA>2021-02-01 06:22:04
60322공인중개사김미정그레이공인중개사사무소2019-12-0926-2019-0163226260-2020-000912021-01-3126260부산광역시 동래구4일반2021-02-01 06:22:03
56052공인중개사류계향<NA><NA><NA><NA>2021-01-3126260부산광역시 동래구<NA><NA>2021-02-01 06:22:03
164642공인중개사노재호복덩이공인중개사사무소1989-02-134-70726470-2020-001392021-01-3126470부산광역시 연제구4일반2021-02-01 06:22:04
109194중개보조원정민주장수공인중개사사무소<NA><NA>가-08-7612021-01-3126380부산광역시 사하구4일반2021-02-01 06:22:04
127824중개보조원양기영조은 공인중개사사무소<NA><NA>가-11-20312021-01-3126410부산광역시 금정구4일반2021-02-01 06:22:04
63304중개보조원하귀선홈런공인중개사사무소<NA><NA>26290-2017-001702021-01-3126290부산광역시 남구4일반2021-02-01 06:22:03
139842공인중개사장재원한라공인중개사사무소2016-12-1248-2016-0128426440-2015-001602021-01-3126440부산광역시 강서구4일반2021-02-01 06:22:04
189652공인중개사정경희<NA>2005-07-201451(부산)<NA>2021-01-3126710부산광역시 기장군<NA><NA>2021-02-01 06:22:04
72724중개보조원최정원e편한세상공인중개사사무소<NA><NA>26320-2020-000022021-01-3126320부산광역시 북구4일반2021-02-01 06:22:03

Duplicate rows

Most frequently occurring

brkrasortcodebrkrasortcodenmbrkrnmbsnmcmpnmcrqfcacqdtcrqfcnojurirnolastupdtdtldcodeldcodenmofcpssecodeofcpssecodenmlast_load_dttm# duplicates
02공인중개사김미정<NA><NA><NA><NA>2021-01-3126260부산광역시 동래구<NA><NA>2021-02-01 06:22:032
12공인중개사김성수<NA><NA><NA><NA>2021-01-3126350부산광역시 해운대구<NA><NA>2021-02-01 06:22:042
24중개보조원박영만대원부동산공인중개사사무소<NA><NA>가-05-21152021-01-3126230부산광역시 부산진구4일반2021-02-01 06:22:032
34중개보조원이기옥<NA><NA><NA><NA>2021-01-3126410부산광역시 금정구<NA><NA>2021-02-01 06:22:042