Overview

Dataset statistics

Number of variables13
Number of observations10000
Missing cells13436
Missing cells (%)10.3%
Duplicate rows2
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 2 (< 0.1%) duplicate rowsDuplicates
ofcpssecodenm is highly overall correlated with brkrasortcode and 2 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
brkrasortcodenm is highly overall correlated with brkrasortcode and 2 other fieldsHigh correlation
ldcode is highly overall correlated with ldcodenm and 1 other fieldsHigh correlation
ldcodenm is highly overall correlated with ldcode and 1 other fieldsHigh correlation
last_load_dttm is highly overall correlated with ldcode and 1 other fieldsHigh correlation
bsnmcmpnm has 2653 (26.5%) missing valuesMissing
crqfcacqdt has 4111 (41.1%) missing valuesMissing
crqfcno has 4019 (40.2%) missing valuesMissing
jurirno has 2653 (26.5%) missing valuesMissing

Reproduction

Analysis started2024-04-16 10:25:21.629505
Analysis finished2024-04-16 10:25:23.180534
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
6624 
4
3171 
1
 
203
3
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
2 6624
66.2%
4 3171
31.7%
1 203
 
2.0%
3 2
 
< 0.1%

Length

2024-04-16T19:25:23.234772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T19:25:23.310944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 6624
66.2%
4 3171
31.7%
1 203
 
2.0%
3 2
 
< 0.1%

brkrasortcodenm
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
공인중개사
6624 
중개보조원
3171 
중개인
 
203
법인
 
2

Length

Max length5
Median length5
Mean length4.9588
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
공인중개사 6624
66.2%
중개보조원 3171
31.7%
중개인 203
 
2.0%
법인 2
 
< 0.1%

Length

2024-04-16T19:25:23.398440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T19:25:23.490138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
공인중개사 6624
66.2%
중개보조원 3171
31.7%
중개인 203
 
2.0%
법인 2
 
< 0.1%

brkrnm
Text

Distinct7994
Distinct (%)79.9%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-04-16T19:25:23.740377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length3
Mean length3.0146
Min length2

Characters and Unicode

Total characters30146
Distinct characters401
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

Unique6779 ?
Unique (%)67.8%

Sample

1st row최진순
2nd row이일환
3rd row제귀환
4th row김창록
5th row이범
ValueCountFrequency (%)
김정희 22
 
0.2%
김영희 17
 
0.2%
김미숙 12
 
0.1%
이명희 11
 
0.1%
이정희 10
 
0.1%
박정수 10
 
0.1%
김정숙 10
 
0.1%
김혜영 9
 
0.1%
김민정 9
 
0.1%
이영주 8
 
0.1%
Other values (7998) 9898
98.8%
2024-04-16T19:25:24.133872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
김 2141
 
7.1%
이 1500
 
5.0%
정 1304
 
4.3%
영 1063
 
3.5%
박 916
 
3.0%
희 689
 
2.3%
경 615
 
2.0%
수 558
 
1.9%
숙 532
 
1.8%
현 520
 
1.7%
Other values (391) 20308
67.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 30000
99.5%
Open Punctuation 38
 
0.1%
Close Punctuation 38
 
0.1%
Uppercase Letter 38
 
0.1%
Space Separator 17
 
0.1%
Lowercase Letter 15
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
김 2141
 
7.1%
이 1500
 
5.0%
정 1304
 
4.3%
영 1063
 
3.5%
박 916
 
3.1%
희 689
 
2.3%
경 615
 
2.1%
수 558
 
1.9%
숙 532
 
1.8%
현 520
 
1.7%
Other values (361) 20162
67.2%
Uppercase Letter
ValueCountFrequency (%)
N 6
15.8%
A 5
13.2%
Y 4
10.5%
I 4
10.5%
L 3
7.9%
T 3
7.9%
K 2
 
5.3%
E 2
 
5.3%
B 1
 
2.6%
J 1
 
2.6%
Other values (7) 7
18.4%
Lowercase Letter
ValueCountFrequency (%)
e 4
26.7%
a 2
13.3%
y 2
13.3%
m 1
 
6.7%
k 1
 
6.7%
i 1
 
6.7%
g 1
 
6.7%
n 1
 
6.7%
u 1
 
6.7%
s 1
 
6.7%
Open Punctuation
ValueCountFrequency (%)
( 38
100.0%
Close Punctuation
ValueCountFrequency (%)
) 38
100.0%
Space Separator
ValueCountFrequency (%)
17
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 29887
99.1%
Han 113
 
0.4%
Common 93
 
0.3%
Latin 53
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
김 2141
 
7.2%
이 1500
 
5.0%
정 1304
 
4.4%
영 1063
 
3.6%
박 916
 
3.1%
희 689
 
2.3%
경 615
 
2.1%
수 558
 
1.9%
숙 532
 
1.8%
현 520
 
1.7%
Other values (282) 20049
67.1%
Han
ValueCountFrequency (%)
金 10
 
8.8%
李 7
 
6.2%
朴 4
 
3.5%
基 3
 
2.7%
子 3
 
2.7%
英 3
 
2.7%
美 3
 
2.7%
姜 2
 
1.8%
鄭 2
 
1.8%
惠 2
 
1.8%
Other values (69) 74
65.5%
Latin
ValueCountFrequency (%)
N 6
 
11.3%
A 5
 
9.4%
Y 4
 
7.5%
I 4
 
7.5%
e 4
 
7.5%
L 3
 
5.7%
T 3
 
5.7%
K 2
 
3.8%
a 2
 
3.8%
y 2
 
3.8%
Other values (17) 18
34.0%
Common
ValueCountFrequency (%)
( 38
40.9%
) 38
40.9%
17
18.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 29887
99.1%
ASCII 146
 
0.5%
CJK 104
 
0.3%
CJK Compat Ideographs 9
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
김 2141
 
7.2%
이 1500
 
5.0%
정 1304
 
4.4%
영 1063
 
3.6%
박 916
 
3.1%
희 689
 
2.3%
경 615
 
2.1%
수 558
 
1.9%
숙 532
 
1.8%
현 520
 
1.7%
Other values (282) 20049
67.1%
ASCII
ValueCountFrequency (%)
( 38
26.0%
) 38
26.0%
17
11.6%
N 6
 
4.1%
A 5
 
3.4%
Y 4
 
2.7%
I 4
 
2.7%
e 4
 
2.7%
L 3
 
2.1%
T 3
 
2.1%
Other values (20) 24
16.4%
CJK
ValueCountFrequency (%)
金 10
 
9.6%
朴 4
 
3.8%
基 3
 
2.9%
子 3
 
2.9%
英 3
 
2.9%
美 3
 
2.9%
姜 2
 
1.9%
鄭 2
 
1.9%
惠 2
 
1.9%
玉 2
 
1.9%
Other values (66) 70
67.3%
CJK Compat Ideographs
ValueCountFrequency (%)
李 7
77.8%
梁 1
 
11.1%
連 1
 
11.1%

bsnmcmpnm
Text

MISSING 

Distinct3391
Distinct (%)46.2%
Missing2653
Missing (%)26.5%
Memory size156.2 KiB
2024-04-16T19:25:24.324398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length21
Mean length11.287056
Min length4

Characters and Unicode

Total characters82926
Distinct characters569
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

Unique2080 ?
Unique (%)28.3%

Sample

1st row길공인중개사사무소
2nd row한결공인중개사사무소
3rd row국민공인중개사사무소
4th row복산아세아부동산공인중개사사무소
5th row꿈있는부동산공인중개사사무소
ValueCountFrequency (%)
주식회사 115
 
1.5%
공인중개사사무소 75
 
1.0%
사무소 68
 
0.9%
주)부동산중개법인개벽 41
 
0.5%
가은부동산중개 36
 
0.5%
태양공인중개사사무소 35
 
0.5%
현대공인중개사사무소 35
 
0.5%
대명합동공인중개사사무소 33
 
0.4%
삼성공인중개사사무소 32
 
0.4%
행운공인중개사사무소 31
 
0.4%
Other values (3380) 7188
93.5%
2024-04-16T19:25:24.628107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
사 12670
15.3%
개 7398
 
8.9%
중 7375
 
8.9%
소 6609
 
8.0%
무 6563
 
7.9%
인 6262
 
7.6%
공 5854
 
7.1%
동 3133
 
3.8%
부 2910
 
3.5%
산 2856
 
3.4%
Other values (559) 21296
25.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 80845
97.5%
Uppercase Letter 758
 
0.9%
Space Separator 449
 
0.5%
Decimal Number 258
 
0.3%
Close Punctuation 198
 
0.2%
Open Punctuation 198
 
0.2%
Lowercase Letter 182
 
0.2%
Other Punctuation 31
 
< 0.1%
Dash Punctuation 4
 
< 0.1%
Other Symbol 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
사 12670
15.7%
개 7398
 
9.2%
중 7375
 
9.1%
소 6609
 
8.2%
무 6563
 
8.1%
인 6262
 
7.7%
공 5854
 
7.2%
동 3133
 
3.9%
부 2910
 
3.6%
산 2856
 
3.5%
Other values (496) 19215
23.8%
Uppercase Letter
ValueCountFrequency (%)
K 128
16.9%
S 84
11.1%
T 68
 
9.0%
L 68
 
9.0%
B 49
 
6.5%
W 46
 
6.1%
C 44
 
5.8%
O 41
 
5.4%
H 31
 
4.1%
N 24
 
3.2%
Other values (15) 175
23.1%
Lowercase Letter
ValueCountFrequency (%)
e 72
39.6%
h 24
 
13.2%
t 18
 
9.9%
c 17
 
9.3%
w 15
 
8.2%
k 10
 
5.5%
s 7
 
3.8%
n 4
 
2.2%
o 4
 
2.2%
a 2
 
1.1%
Other values (6) 9
 
4.9%
Decimal Number
ValueCountFrequency (%)
1 123
47.7%
8 31
 
12.0%
2 30
 
11.6%
4 27
 
10.5%
3 18
 
7.0%
9 12
 
4.7%
5 8
 
3.1%
7 4
 
1.6%
6 3
 
1.2%
0 2
 
0.8%
Other Punctuation
ValueCountFrequency (%)
& 17
54.8%
. 5
 
16.1%
· 4
 
12.9%
! 2
 
6.5%
# 2
 
6.5%
, 1
 
3.2%
Space Separator
ValueCountFrequency (%)
449
100.0%
Close Punctuation
ValueCountFrequency (%)
) 198
100.0%
Open Punctuation
ValueCountFrequency (%)
( 198
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%
Other Symbol
ValueCountFrequency (%)
ⓡ 2
100.0%
Letter Number
ValueCountFrequency (%)
Ⅱ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 80838
97.5%
Common 1140
 
1.4%
Latin 941
 
1.1%
Han 7
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
사 12670
15.7%
개 7398
 
9.2%
중 7375
 
9.1%
소 6609
 
8.2%
무 6563
 
8.1%
인 6262
 
7.7%
공 5854
 
7.2%
동 3133
 
3.9%
부 2910
 
3.6%
산 2856
 
3.5%
Other values (490) 19208
23.8%
Latin
ValueCountFrequency (%)
K 128
13.6%
S 84
 
8.9%
e 72
 
7.7%
T 68
 
7.2%
L 68
 
7.2%
B 49
 
5.2%
W 46
 
4.9%
C 44
 
4.7%
O 41
 
4.4%
H 31
 
3.3%
Other values (32) 310
32.9%
Common
ValueCountFrequency (%)
449
39.4%
) 198
17.4%
( 198
17.4%
1 123
 
10.8%
8 31
 
2.7%
2 30
 
2.6%
4 27
 
2.4%
3 18
 
1.6%
& 17
 
1.5%
9 12
 
1.1%
Other values (11) 37
 
3.2%
Han
ValueCountFrequency (%)
福 2
28.6%
該 1
14.3%
明 1
14.3%
堂 1
14.3%
人 1
14.3%
炫 1
14.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 80838
97.5%
ASCII 2074
 
2.5%
CJK 7
 
< 0.1%
None 4
 
< 0.1%
Enclosed Alphanum 2
 
< 0.1%
Number Forms 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
사 12670
15.7%
개 7398
 
9.2%
중 7375
 
9.1%
소 6609
 
8.2%
무 6563
 
8.1%
인 6262
 
7.7%
공 5854
 
7.2%
동 3133
 
3.9%
부 2910
 
3.6%
산 2856
 
3.5%
Other values (490) 19208
23.8%
ASCII
ValueCountFrequency (%)
449
21.6%
) 198
 
9.5%
( 198
 
9.5%
K 128
 
6.2%
1 123
 
5.9%
S 84
 
4.1%
e 72
 
3.5%
T 68
 
3.3%
L 68
 
3.3%
B 49
 
2.4%
Other values (50) 637
30.7%
None
ValueCountFrequency (%)
· 4
100.0%
CJK
ValueCountFrequency (%)
福 2
28.6%
該 1
14.3%
明 1
14.3%
堂 1
14.3%
人 1
14.3%
炫 1
14.3%
Enclosed Alphanum
ValueCountFrequency (%)
ⓡ 2
100.0%
Number Forms
ValueCountFrequency (%)
Ⅱ 1
100.0%

crqfcacqdt
Text

MISSING 

Distinct657
Distinct (%)11.2%
Missing4111
Missing (%)41.1%
Memory size156.2 KiB
2024-04-16T19:25:24.882938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length9.9993208
Min length8

Characters and Unicode

Total characters58886
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

Unique428 ?
Unique (%)7.3%

Sample

1st row2003-09-21
2nd row2005-07-20
3rd row2017-12-11
4th row1985-11-11
5th row2019-12-09
ValueCountFrequency (%)
2005-07-20 371
 
6.3%
2017-12-11 327
 
5.6%
2016-12-12 303
 
5.1%
2019-12-09 258
 
4.4%
2003-11-07 221
 
3.8%
2015-12-09 219
 
3.7%
2005-12-12 214
 
3.6%
2018-12-10 195
 
3.3%
2000-11-20 154
 
2.6%
2001-12-10 153
 
2.6%
Other values (647) 3474
59.0%
2024-04-16T19:25:25.278251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 13372
22.7%
0 12364
21.0%
- 11774
20.0%
2 11237
19.1%
9 2750
 
4.7%
5 1735
 
2.9%
7 1583
 
2.7%
8 1459
 
2.5%
3 1094
 
1.9%
6 922
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 47112
80.0%
Dash Punctuation 11774
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 13372
28.4%
0 12364
26.2%
2 11237
23.9%
9 2750
 
5.8%
5 1735
 
3.7%
7 1583
 
3.4%
8 1459
 
3.1%
3 1094
 
2.3%
6 922
 
2.0%
4 596
 
1.3%
Dash Punctuation
ValueCountFrequency (%)
- 11774
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 58886
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 13372
22.7%
0 12364
21.0%
- 11774
20.0%
2 11237
19.1%
9 2750
 
4.7%
5 1735
 
2.9%
7 1583
 
2.7%
8 1459
 
2.5%
3 1094
 
1.9%
6 922
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 58886
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 13372
22.7%
0 12364
21.0%
- 11774
20.0%
2 11237
19.1%
9 2750
 
4.7%
5 1735
 
2.9%
7 1583
 
2.7%
8 1459
 
2.5%
3 1094
 
1.9%
6 922
 
1.6%

crqfcno
Text

MISSING 

Distinct5756
Distinct (%)96.2%
Missing4019
Missing (%)40.2%
Memory size156.2 KiB
2024-04-16T19:25:25.483611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length20
Mean length9.292426
Min length1

Characters and Unicode

Total characters55578
Distinct characters59
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

Unique5556 ?
Unique (%)92.9%

Sample

1st row14-1855
2nd row부산 제271호
3rd row48-2017-01379
4th row1-3472(부산)
5th row26-2019-01358
ValueCountFrequency (%)
부산 347
 
5.3%
부산시 63
 
1.0%
부산광역시 23
 
0.4%
경남 22
 
0.3%
부산광역시장 19
 
0.3%
454 5
 
0.1%
경상남도 5
 
0.1%
326 5
 
0.1%
287 4
 
0.1%
628 4
 
0.1%
Other values (5719) 6008
92.4%
2024-04-16T19:25:25.789611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 8977
16.2%
2 7061
12.7%
1 6662
12.0%
- 6232
11.2%
6 3993
 
7.2%
3 2464
 
4.4%
4 2422
 
4.4%
8 2268
 
4.1%
5 2252
 
4.1%
7 2223
 
4.0%
Other values (49) 11024
19.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 40532
72.9%
Other Letter 6777
 
12.2%
Dash Punctuation 6232
 
11.2%
Open Punctuation 748
 
1.3%
Close Punctuation 747
 
1.3%
Space Separator 527
 
0.9%
Other Punctuation 14
 
< 0.1%
Uppercase Letter 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 2008
29.6%
부 1990
29.4%
호 505
 
7.5%
제 465
 
6.9%
시 416
 
6.1%
역 309
 
4.6%
광 308
 
4.5%
장 172
 
2.5%
경 138
 
2.0%
남 118
 
1.7%
Other values (29) 348
 
5.1%
Decimal Number
ValueCountFrequency (%)
0 8977
22.1%
2 7061
17.4%
1 6662
16.4%
6 3993
9.9%
3 2464
 
6.1%
4 2422
 
6.0%
8 2268
 
5.6%
5 2252
 
5.6%
7 2223
 
5.5%
9 2210
 
5.5%
Other Punctuation
ValueCountFrequency (%)
, 7
50.0%
. 5
35.7%
？ 2
 
14.3%
Open Punctuation
ValueCountFrequency (%)
( 692
92.5%
[ 56
 
7.5%
Close Punctuation
ValueCountFrequency (%)
) 691
92.5%
] 56
 
7.5%
Dash Punctuation
ValueCountFrequency (%)
- 6232
100.0%
Space Separator
ValueCountFrequency (%)
527
100.0%
Uppercase Letter
ValueCountFrequency (%)
Ы 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 48800
87.8%
Hangul 6777
 
12.2%
Cyrillic 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 2008
29.6%
부 1990
29.4%
호 505
 
7.5%
제 465
 
6.9%
시 416
 
6.1%
역 309
 
4.6%
광 308
 
4.5%
장 172
 
2.5%
경 138
 
2.0%
남 118
 
1.7%
Other values (29) 348
 
5.1%
Common
ValueCountFrequency (%)
0 8977
18.4%
2 7061
14.5%
1 6662
13.7%
- 6232
12.8%
6 3993
8.2%
3 2464
 
5.0%
4 2422
 
5.0%
8 2268
 
4.6%
5 2252
 
4.6%
7 2223
 
4.6%
Other values (9) 4246
8.7%
Cyrillic
ValueCountFrequency (%)
Ы 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 48798
87.8%
Hangul 6777
 
12.2%
None 2
 
< 0.1%
Cyrillic 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 8977
18.4%
2 7061
14.5%
1 6662
13.7%
- 6232
12.8%
6 3993
8.2%
3 2464
 
5.0%
4 2422
 
5.0%
8 2268
 
4.6%
5 2252
 
4.6%
7 2223
 
4.6%
Other values (8) 4244
8.7%
Hangul
ValueCountFrequency (%)
산 2008
29.6%
부 1990
29.4%
호 505
 
7.5%
제 465
 
6.9%
시 416
 
6.1%
역 309
 
4.6%
광 308
 
4.5%
장 172
 
2.5%
경 138
 
2.0%
남 118
 
1.7%
Other values (29) 348
 
5.1%
None
ValueCountFrequency (%)
？ 2
100.0%
Cyrillic
ValueCountFrequency (%)
Ы 1
100.0%

jurirno
Text

MISSING 

Distinct4808
Distinct (%)65.4%
Missing2653
Missing (%)26.5%
Memory size156.2 KiB
2024-04-16T19:25:25.990507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length16
Mean length13.808765
Min length6

Characters and Unicode

Total characters101453
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

Unique3477 ?
Unique (%)47.3%

Sample

1st row가-09-1560
2nd row26260-2020-00114
3rd row가-05-1348
4th row26260-2020-00203
5th row26350-2015-00049
ValueCountFrequency (%)
26470-2018-00085 41
 
0.6%
26530-2017-00027 36
 
0.5%
26470-2015-00027 33
 
0.4%
26470-2016-00066 30
 
0.4%
26470-2021-00017 26
 
0.4%
26470-2018-00103 23
 
0.3%
26230-2016-00137 21
 
0.3%
가-05-4212 17
 
0.2%
26470-2020-00016 16
 
0.2%
가-13-1750 15
 
0.2%
Other values (4798) 7089
96.5%
2024-04-16T19:25:26.285949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 29187
28.8%
2 16811
16.6%
- 14640
14.4%
1 10008
 
9.9%
6 7971
 
7.9%
3 4342
 
4.3%
4 3856
 
3.8%
5 3548
 
3.5%
7 3379
 
3.3%
9 2909
 
2.9%
Other values (3) 4802
 
4.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 84626
83.4%
Dash Punctuation 14640
 
14.4%
Other Letter 2187
 
2.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 29187
34.5%
2 16811
19.9%
1 10008
 
11.8%
6 7971
 
9.4%
3 4342
 
5.1%
4 3856
 
4.6%
5 3548
 
4.2%
7 3379
 
4.0%
9 2909
 
3.4%
8 2615
 
3.1%
Other Letter
ValueCountFrequency (%)
가 2167
99.1%
나 20
 
0.9%
Dash Punctuation
ValueCountFrequency (%)
- 14640
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 99266
97.8%
Hangul 2187
 
2.2%

Most frequent character per script

Common
ValueCountFrequency (%)
0 29187
29.4%
2 16811
16.9%
- 14640
14.7%
1 10008
 
10.1%
6 7971
 
8.0%
3 4342
 
4.4%
4 3856
 
3.9%
5 3548
 
3.6%
7 3379
 
3.4%
9 2909
 
2.9%
Hangul
ValueCountFrequency (%)
가 2167
99.1%
나 20
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 99266
97.8%
Hangul 2187
 
2.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 29187
29.4%
2 16811
16.9%
- 14640
14.7%
1 10008
 
10.1%
6 7971
 
8.0%
3 4342
 
4.4%
4 3856
 
3.9%
5 3548
 
3.6%
7 3379
 
3.4%
9 2909
 
2.9%
Hangul
ValueCountFrequency (%)
가 2167
99.1%
나 20
 
0.9%

lastupdtdt
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2021-04-29
10000 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021-04-29
2nd row2021-04-29
3rd row2021-04-29
4th row2021-04-29
5th row2021-04-29

Common Values

ValueCountFrequency (%)
2021-04-29 10000
100.0%

Length

2024-04-16T19:25:26.397855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T19:25:26.477434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021-04-29 10000
100.0%

ldcode
Real number (ℝ)

HIGH CORRELATION 

Distinct16
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26364.028
Minimum26110
Maximum26710
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-16T19:25:26.548894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum26110
5-th percentile26200
Q126260
median26350
Q326440
95-th percentile26530
Maximum26710
Range600
Interquartile range (IQR)180

Descriptive statistics

Standard deviation127.91533
Coefficient of variation (CV)0.0048518887
Kurtosis0.49156183
Mean26364.028
Median Absolute Deviation (MAD)90
Skewness0.59957785
Sum2.6364028 × 108
Variance16362.331
MonotonicityNot monotonic
2024-04-16T19:25:26.846212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
26230 1411
14.1%
26350 1382
13.8%
26260 1057
10.6%
26470 940
9.4%
26410 842
8.4%
26440 781
7.8%
26380 657
6.6%
26500 652
6.5%
26290 528
 
5.3%
26710 466
 
4.7%
Other values (6) 1284
12.8%
ValueCountFrequency (%)
26110 166
 
1.7%
26140 169
 
1.7%
26170 163
 
1.6%
26200 161
 
1.6%
26230 1411
14.1%
26260 1057
10.6%
26290 528
 
5.3%
26320 315
 
3.1%
26350 1382
13.8%
26380 657
6.6%
ValueCountFrequency (%)
26710 466
 
4.7%
26530 310
 
3.1%
26500 652
6.5%
26470 940
9.4%
26440 781
7.8%
26410 842
8.4%
26380 657
6.6%
26350 1382
13.8%
26320 315
 
3.1%
26290 528
 
5.3%

ldcodenm
Categorical

HIGH CORRELATION 

Distinct16
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
부산광역시 부산진구
1411 
부산광역시 해운대구
1382 
부산광역시 동래구
1057 
부산광역시 연제구
940 
부산광역시 금정구
842 
Other values (11)
4368 

Length

Max length10
Median length9
Mean length9.1452
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row부산광역시 북구
2nd row부산광역시 해운대구
3rd row부산광역시 동래구
4th row부산광역시 부산진구
5th row부산광역시 동래구

Common Values

ValueCountFrequency (%)
부산광역시 부산진구 1411
14.1%
부산광역시 해운대구 1382
13.8%
부산광역시 동래구 1057
10.6%
부산광역시 연제구 940
9.4%
부산광역시 금정구 842
8.4%
부산광역시 강서구 781
7.8%
부산광역시 사하구 657
6.6%
부산광역시 수영구 652
6.5%
부산광역시 남구 528
 
5.3%
부산광역시 기장군 466
 
4.7%
Other values (6) 1284
12.8%

Length

2024-04-16T19:25:26.956700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
부산광역시 10000
50.0%
부산진구 1411
 
7.1%
해운대구 1382
 
6.9%
동래구 1057
 
5.3%
연제구 940
 
4.7%
금정구 842
 
4.2%
강서구 781
 
3.9%
사하구 657
 
3.3%
수영구 652
 
3.3%
남구 528
 
2.6%
Other values (7) 1750
 
8.8%

ofcpssecode
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
3757 
4
3578 
<NA>
2649 
3
 
10
2
 
6

Length

Max length4
Median length1
Mean length1.7947
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1 3757
37.6%
4 3578
35.8%
<NA> 2649
26.5%
3 10
 
0.1%
2 6
 
0.1%

Length

2024-04-16T19:25:27.082051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T19:25:27.174067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 3757
37.6%
4 3578
35.8%
na 2649
26.5%
3 10
 
0.1%
2 6
 
0.1%

ofcpssecodenm
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
대표
3757 
일반
3578 
<NA>
2649 
이사
 
10
감사
 
6

Length

Max length4
Median length2
Mean length2.5298
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
대표 3757
37.6%
일반 3578
35.8%
<NA> 2649
26.5%
이사 10
 
0.1%
감사 6
 
0.1%

Length

2024-04-16T19:25:27.280339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T19:25:27.387047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
대표 3757
37.6%
일반 3578
35.8%
na 2649
26.5%
이사 10
 
0.1%
감사 6
 
0.1%

last_load_dttm
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2021-05-01 06:22:04
5002 
2021-05-01 06:22:03
4959 
2021-05-01 06:22:05
 
39

Length

Max length19
Median length19
Mean length19
Min length19

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021-05-01 06:22:03
2nd row2021-05-01 06:22:03
3rd row2021-05-01 06:22:03
4th row2021-05-01 06:22:03
5th row2021-05-01 06:22:03

Common Values

ValueCountFrequency (%)
2021-05-01 06:22:04 5002
50.0%
2021-05-01 06:22:03 4959
49.6%
2021-05-01 06:22:05 39
 
0.4%

Length

2024-04-16T19:25:27.478650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T19:25:27.558098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021-05-01 10000
50.0%
06:22:04 5002
25.0%
06:22:03 4959
24.8%
06:22:05 39
 
0.2%

Interactions

2024-04-16T19:25:22.689673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-16T19:25:27.626871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
brkrasortcodebrkrasortcodenmldcodeldcodenmofcpssecodeofcpssecodenmlast_load_dttm
brkrasortcode1.0001.0000.2290.2910.5690.5690.016
brkrasortcodenm1.0001.0000.2290.2910.5690.5690.016
ldcode0.2290.2291.0001.0000.2100.2100.776
ldcodenm0.2910.2911.0001.0000.2420.2420.840
ofcpssecode0.5690.5690.2100.2421.0001.0000.000
ofcpssecodenm0.5690.5690.2100.2421.0001.0000.000
last_load_dttm0.0160.0160.7760.8400.0000.0001.000
2024-04-16T19:25:27.744568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ofcpssecodenmldcodenmbrkrasortcodeofcpssecodelast_load_dttmbrkrasortcodenm
ofcpssecodenm1.0000.1160.5791.0000.0000.579
ldcodenm0.1161.0000.1400.1160.6930.140
brkrasortcode0.5790.1401.0000.5790.0151.000
ofcpssecode1.0000.1160.5791.0000.0000.579
last_load_dttm0.0000.6930.0150.0001.0000.015
brkrasortcodenm0.5790.1401.0000.5790.0151.000
2024-04-16T19:25:27.842113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ldcodebrkrasortcodebrkrasortcodenmldcodenmofcpssecodeofcpssecodenmlast_load_dttm
ldcode1.0000.1060.1061.0000.0950.0950.691
brkrasortcode0.1061.0001.0000.1400.5790.5790.015
brkrasortcodenm0.1061.0001.0000.1400.5790.5790.015
ldcodenm1.0000.1400.1401.0000.1160.1160.693
ofcpssecode0.0950.5790.5790.1161.0001.0000.000
ofcpssecodenm0.0950.5790.5790.1161.0001.0000.000
last_load_dttm0.6910.0150.0150.6930.0000.0001.000

Missing values

2024-04-16T19:25:22.796526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-16T19:25:22.958365image/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.
2024-04-16T19:25:23.100892image/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
73692공인중개사최진순길공인중개사사무소2003-09-2114-1855가-09-15602021-04-2926320부산광역시 북구1대표2021-05-01 06:22:03
89212공인중개사이일환<NA>2005-07-20부산 제271호<NA>2021-04-2926350부산광역시 해운대구<NA><NA>2021-05-01 06:22:03
45192공인중개사제귀환한결공인중개사사무소2017-12-1148-2017-0137926260-2020-001142021-04-2926260부산광역시 동래구1대표2021-05-01 06:22:03
35022공인중개사김창록국민공인중개사사무소1985-11-111-3472(부산)가-05-13482021-04-2926230부산광역시 부산진구1대표2021-05-01 06:22:03
50052공인중개사이범복산아세아부동산공인중개사사무소2019-12-0926-2019-0135826260-2020-002032021-04-2926260부산광역시 동래구4일반2021-05-01 06:22:03
82532공인중개사추성모꿈있는부동산공인중개사사무소2003-09-2114-60826350-2015-000492021-04-2926350부산광역시 해운대구1대표2021-05-01 06:22:03
110702공인중개사전가윤당근공인중개사사무소2019-12-1926-2019-0174626380-2019-000292021-04-2926380부산광역시 사하구4일반2021-05-01 06:22:04
92604중개보조원안화옥<NA><NA><NA><NA>2021-04-2926350부산광역시 해운대구<NA><NA>2021-05-01 06:22:03
53642공인중개사변창대대륙공인중개사사무소1989-02-134-70426260-2020-001052021-04-2926260부산광역시 동래구1대표2021-05-01 06:22:03
165814중개보조원박상미공유부동산중개사무소<NA><NA>26470-2020-000272021-04-2926470부산광역시 연제구4일반2021-05-01 06:22:04
brkrasortcodebrkrasortcodenmbrkrnmbsnmcmpnmcrqfcacqdtcrqfcnojurirnolastupdtdtldcodeldcodenmofcpssecodeofcpssecodenmlast_load_dttm
174162공인중개사최성열<NA>2000-09-241086<NA>2021-04-2926500부산광역시 수영구<NA><NA>2021-05-01 06:22:04
83832공인중개사지민아프라임부동산공인중개사사무소<NA><NA>가-10-38392021-04-2926350부산광역시 해운대구4일반2021-05-01 06:22:03
12864중개보조원황태수프로합동공인중개사사무소<NA><NA>가-05-35662021-04-2926230부산광역시 부산진구4일반2021-05-01 06:22:03
37132공인중개사김연화양정123명가부동산공인중개사사무소2015-12-0926-2015-00529(부산)26230-2020-001622021-04-2926230부산광역시 부산진구1대표2021-05-01 06:22:03
8412공인중개사박조연동일부동산공인중개사사무소2016-12-1226-2016-00260(부산)26170-2017-000222021-04-2926170부산광역시 동구1대표2021-05-01 06:22:03
88002공인중개사이정임Lct태양공인중개사사무소2014-10-262014-0421(부산)26350-2021-000502021-04-2926350부산광역시 해운대구1대표2021-05-01 06:22:03
14482공인중개사한순임동일스위트공인중개사사무소2005-10-3016-261(부산광역시장)가-05-39852021-04-2926230부산광역시 부산진구1대표2021-05-01 06:22:03
169802공인중개사김연수한솔공인중개사사무소2012-12-1023-00417(부산광역시)가-13-21542021-04-2926470부산광역시 연제구1대표2021-05-01 06:22:04
48342공인중개사이장룡<NA><NA><NA><NA>2021-04-2926260부산광역시 동래구<NA><NA>2021-05-01 06:22:03
44022공인중개사최재원사직플래티넘공인중개사사무소2016-12-1226-2016-00939 (부산)26260-2019-000602021-04-2926260부산광역시 동래구1대표2021-05-01 06:22:03

Duplicate rows

Most frequently occurring

brkrasortcodebrkrasortcodenmbrkrnmbsnmcmpnmcrqfcacqdtcrqfcnojurirnolastupdtdtldcodeldcodenmofcpssecodeofcpssecodenmlast_load_dttm# duplicates
02공인중개사김성수<NA><NA><NA><NA>2021-04-2926350부산광역시 해운대구<NA><NA>2021-05-01 06:22:042
14중개보조원이기옥<NA><NA><NA><NA>2021-04-2926410부산광역시 금정구<NA><NA>2021-05-01 06:22:042