Overview

Dataset statistics

Number of variables13
Number of observations10000
Missing cells13647
Missing cells (%)10.5%
Duplicate rows3
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 3 (< 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 2721 (27.2%) missing valuesMissing
crqfcacqdt has 4152 (41.5%) missing valuesMissing
crqfcno has 4053 (40.5%) missing valuesMissing
jurirno has 2721 (27.2%) missing valuesMissing

Reproduction

Analysis started2024-04-16 10:25:55.305514
Analysis finished2024-04-16 10:25:56.781231
Duration1.48 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
6560 
4
3236 
1
 
202
3
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
2 6560
65.6%
4 3236
32.4%
1 202
 
2.0%
3 2
 
< 0.1%

Length

2024-04-16T19:25:56.837134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T19:25:56.919679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 6560
65.6%
4 3236
32.4%
1 202
 
2.0%
3 2
 
< 0.1%

brkrasortcodenm
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
공인중개사
6560 
중개보조원
3236 
중개인
 
202
법인
 
2

Length

Max length5
Median length5
Mean length4.959
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
공인중개사 6560
65.6%
중개보조원 3236
32.4%
중개인 202
 
2.0%
법인 2
 
< 0.1%

Length

2024-04-16T19:25:57.222819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T19:25:57.308063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
공인중개사 6560
65.6%
중개보조원 3236
32.4%
중개인 202
 
2.0%
법인 2
 
< 0.1%

brkrnm
Text

Distinct7906
Distinct (%)79.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-04-16T19:25:57.561463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length3
Mean length3.0163
Min length2

Characters and Unicode

Total characters30163
Distinct characters396
Distinct categories6 ?
Distinct scripts4 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6643 ?
Unique (%)66.4%

Sample

1st row한정욱
2nd row김길홍
3rd row정진홍
4th row김홍석
5th row변용현
ValueCountFrequency (%)
김정희 18
 
0.2%
김영희 16
 
0.2%
김경희 12
 
0.1%
이정희 11
 
0.1%
박정희 10
 
0.1%
김현주 10
 
0.1%
김은희 10
 
0.1%
이경숙 10
 
0.1%
김정숙 9
 
0.1%
김미경 9
 
0.1%
Other values (7901) 9890
98.9%
2024-04-16T19:25:57.954720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
김 2201
 
7.3%
이 1512
 
5.0%
정 1315
 
4.4%
영 994
 
3.3%
박 889
 
2.9%
희 702
 
2.3%
경 623
 
2.1%
현 557
 
1.8%
숙 532
 
1.8%
미 528
 
1.8%
Other values (386) 20310
67.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 30047
99.6%
Open Punctuation 43
 
0.1%
Close Punctuation 43
 
0.1%
Uppercase Letter 19
 
0.1%
Space Separator 6
 
< 0.1%
Lowercase Letter 5
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
김 2201
 
7.3%
이 1512
 
5.0%
정 1315
 
4.4%
영 994
 
3.3%
박 889
 
3.0%
희 702
 
2.3%
경 623
 
2.1%
현 557
 
1.9%
숙 532
 
1.8%
미 528
 
1.8%
Other values (367) 20194
67.2%
Uppercase Letter
ValueCountFrequency (%)
N 4
21.1%
I 4
21.1%
A 3
15.8%
B 1
 
5.3%
Z 1
 
5.3%
H 1
 
5.3%
W 1
 
5.3%
E 1
 
5.3%
Y 1
 
5.3%
T 1
 
5.3%
Lowercase Letter
ValueCountFrequency (%)
y 1
20.0%
m 1
20.0%
k 1
20.0%
i 1
20.0%
a 1
20.0%
Open Punctuation
ValueCountFrequency (%)
( 43
100.0%
Close Punctuation
ValueCountFrequency (%)
) 43
100.0%
Space Separator
ValueCountFrequency (%)
6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 29919
99.2%
Han 128
 
0.4%
Common 92
 
0.3%
Latin 24
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
김 2201
 
7.4%
이 1512
 
5.1%
정 1315
 
4.4%
영 994
 
3.3%
박 889
 
3.0%
희 702
 
2.3%
경 623
 
2.1%
현 557
 
1.9%
숙 532
 
1.8%
미 528
 
1.8%
Other values (283) 20066
67.1%
Han
ValueCountFrequency (%)
金 12
 
9.4%
李 11
 
8.6%
崔 4
 
3.1%
慶 4
 
3.1%
惠 3
 
2.3%
順 3
 
2.3%
子 3
 
2.3%
美 2
 
1.6%
朴 2
 
1.6%
鄭 2
 
1.6%
Other values (74) 82
64.1%
Latin
ValueCountFrequency (%)
N 4
16.7%
I 4
16.7%
A 3
12.5%
B 1
 
4.2%
Z 1
 
4.2%
H 1
 
4.2%
W 1
 
4.2%
y 1
 
4.2%
m 1
 
4.2%
E 1
 
4.2%
Other values (6) 6
25.0%
Common
ValueCountFrequency (%)
( 43
46.7%
) 43
46.7%
6
 
6.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 29918
99.2%
CJK 117
 
0.4%
ASCII 116
 
0.4%
CJK Compat Ideographs 11
 
< 0.1%
Compat Jamo 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
김 2201
 
7.4%
이 1512
 
5.1%
정 1315
 
4.4%
영 994
 
3.3%
박 889
 
3.0%
희 702
 
2.3%
경 623
 
2.1%
현 557
 
1.9%
숙 532
 
1.8%
미 528
 
1.8%
Other values (282) 20065
67.1%
ASCII
ValueCountFrequency (%)
( 43
37.1%
) 43
37.1%
6
 
5.2%
N 4
 
3.4%
I 4
 
3.4%
A 3
 
2.6%
B 1
 
0.9%
Z 1
 
0.9%
H 1
 
0.9%
W 1
 
0.9%
Other values (9) 9
 
7.8%
CJK
ValueCountFrequency (%)
金 12
 
10.3%
崔 4
 
3.4%
慶 4
 
3.4%
惠 3
 
2.6%
順 3
 
2.6%
子 3
 
2.6%
美 2
 
1.7%
朴 2
 
1.7%
鄭 2
 
1.7%
泰 2
 
1.7%
Other values (73) 80
68.4%
CJK Compat Ideographs
ValueCountFrequency (%)
李 11
100.0%
Compat Jamo
ValueCountFrequency (%)
ㅂ 1
100.0%

bsnmcmpnm
Text

MISSING 

Distinct3309
Distinct (%)45.5%
Missing2721
Missing (%)27.2%
Memory size156.2 KiB
2024-04-16T19:25:58.151642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length22
Mean length11.248798
Min length4

Characters and Unicode

Total characters81880
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 (%)27.9%

Sample

1st row정인공인중개사사무소
2nd row골드공인중개사사무소
3rd row삼보공인중개사사무소
4th row유한회사맥비스타부동산중개법인
5th row신세계공인중개사사무소
ValueCountFrequency (%)
주식회사 90
 
1.2%
공인중개사사무소 80
 
1.1%
사무소 61
 
0.8%
조은공인중개사사무소 51
 
0.7%
주)부동산중개법인개벽 40
 
0.5%
삼성공인중개사사무소 37
 
0.5%
현대공인중개사사무소 37
 
0.5%
삼오부동산중개법인 33
 
0.4%
태양공인중개사사무소 31
 
0.4%
주)온나라부동산중개법인 31
 
0.4%
Other values (3302) 7081
93.5%
2024-04-16T19:25:58.474959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
사 12599
15.4%
개 7326
 
8.9%
중 7302
 
8.9%
소 6583
 
8.0%
무 6532
 
8.0%
인 6232
 
7.6%
공 5820
 
7.1%
동 3029
 
3.7%
부 2765
 
3.4%
산 2746
 
3.4%
Other values (570) 20946
25.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 79922
97.6%
Uppercase Letter 734
 
0.9%
Space Separator 377
 
0.5%
Decimal Number 297
 
0.4%
Open Punctuation 189
 
0.2%
Close Punctuation 189
 
0.2%
Lowercase Letter 142
 
0.2%
Other Punctuation 22
 
< 0.1%
Dash Punctuation 4
 
< 0.1%
Letter Number 2
 
< 0.1%
Other values (2) 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
사 12599
15.8%
개 7326
 
9.2%
중 7302
 
9.1%
소 6583
 
8.2%
무 6532
 
8.2%
인 6232
 
7.8%
공 5820
 
7.3%
동 3029
 
3.8%
부 2765
 
3.5%
산 2746
 
3.4%
Other values (507) 18988
23.8%
Uppercase Letter
ValueCountFrequency (%)
K 128
17.4%
S 91
12.4%
L 61
 
8.3%
T 57
 
7.8%
B 46
 
6.3%
H 42
 
5.7%
W 42
 
5.7%
O 35
 
4.8%
C 33
 
4.5%
E 23
 
3.1%
Other values (14) 176
24.0%
Lowercase Letter
ValueCountFrequency (%)
e 60
42.3%
h 20
 
14.1%
t 13
 
9.2%
c 10
 
7.0%
k 9
 
6.3%
s 8
 
5.6%
w 5
 
3.5%
o 3
 
2.1%
i 3
 
2.1%
l 2
 
1.4%
Other values (6) 9
 
6.3%
Decimal Number
ValueCountFrequency (%)
1 129
43.4%
8 43
 
14.5%
2 32
 
10.8%
4 28
 
9.4%
3 25
 
8.4%
9 16
 
5.4%
5 9
 
3.0%
0 8
 
2.7%
6 6
 
2.0%
7 1
 
0.3%
Other Punctuation
ValueCountFrequency (%)
& 12
54.5%
. 5
22.7%
？ 2
 
9.1%
! 1
 
4.5%
· 1
 
4.5%
, 1
 
4.5%
Space Separator
ValueCountFrequency (%)
377
100.0%
Open Punctuation
ValueCountFrequency (%)
( 189
100.0%
Close Punctuation
ValueCountFrequency (%)
) 189
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%
Letter Number
ValueCountFrequency (%)
Ⅱ 2
100.0%
Other Symbol
ValueCountFrequency (%)
ⓡ 1
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 79913
97.6%
Common 1080
 
1.3%
Latin 878
 
1.1%
Han 9
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
사 12599
15.8%
개 7326
 
9.2%
중 7302
 
9.1%
소 6583
 
8.2%
무 6532
 
8.2%
인 6232
 
7.8%
공 5820
 
7.3%
동 3029
 
3.8%
부 2765
 
3.5%
산 2746
 
3.4%
Other values (498) 18979
23.7%
Latin
ValueCountFrequency (%)
K 128
14.6%
S 91
 
10.4%
L 61
 
6.9%
e 60
 
6.8%
T 57
 
6.5%
B 46
 
5.2%
H 42
 
4.8%
W 42
 
4.8%
O 35
 
4.0%
C 33
 
3.8%
Other values (31) 283
32.2%
Common
ValueCountFrequency (%)
377
34.9%
( 189
17.5%
) 189
17.5%
1 129
 
11.9%
8 43
 
4.0%
2 32
 
3.0%
4 28
 
2.6%
3 25
 
2.3%
9 16
 
1.5%
& 12
 
1.1%
Other values (12) 40
 
3.7%
Han
ValueCountFrequency (%)
秀 1
11.1%
氷 1
11.1%
甲 1
11.1%
福 1
11.1%
該 1
11.1%
太 1
11.1%
利 1
11.1%
本 1
11.1%
炫 1
11.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 79913
97.6%
ASCII 1952
 
2.4%
CJK 9
 
< 0.1%
None 3
 
< 0.1%
Number Forms 2
 
< 0.1%
Enclosed Alphanum 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
사 12599
15.8%
개 7326
 
9.2%
중 7302
 
9.1%
소 6583
 
8.2%
무 6532
 
8.2%
인 6232
 
7.8%
공 5820
 
7.3%
동 3029
 
3.8%
부 2765
 
3.5%
산 2746
 
3.4%
Other values (498) 18979
23.7%
ASCII
ValueCountFrequency (%)
377
19.3%
( 189
 
9.7%
) 189
 
9.7%
1 129
 
6.6%
K 128
 
6.6%
S 91
 
4.7%
L 61
 
3.1%
e 60
 
3.1%
T 57
 
2.9%
B 46
 
2.4%
Other values (49) 625
32.0%
Number Forms
ValueCountFrequency (%)
Ⅱ 2
100.0%
None
ValueCountFrequency (%)
？ 2
66.7%
· 1
33.3%
CJK
ValueCountFrequency (%)
秀 1
11.1%
氷 1
11.1%
甲 1
11.1%
福 1
11.1%
該 1
11.1%
太 1
11.1%
利 1
11.1%
本 1
11.1%
炫 1
11.1%
Enclosed Alphanum
ValueCountFrequency (%)
ⓡ 1
100.0%

crqfcacqdt
Text

MISSING 

Distinct644
Distinct (%)11.0%
Missing4152
Missing (%)41.5%
Memory size156.2 KiB
2024-04-16T19:25:58.762881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length9.999316
Min length8

Characters and Unicode

Total characters58476
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

Unique425 ?
Unique (%)7.3%

Sample

1st row1989-02-08
2nd row2015-10-24
3rd row1985-11-14
4th row2019-12-09
5th row1985-11-04
ValueCountFrequency (%)
2005-07-20 419
 
7.2%
2017-12-11 318
 
5.4%
2016-12-12 311
 
5.3%
2019-12-09 243
 
4.2%
2015-12-09 211
 
3.6%
2003-11-07 200
 
3.4%
2018-12-10 194
 
3.3%
2005-12-12 191
 
3.3%
2001-12-10 158
 
2.7%
2000-11-20 154
 
2.6%
Other values (634) 3449
59.0%
2024-04-16T19:25:59.221922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 13265
22.7%
0 12335
21.1%
- 11692
20.0%
2 11069
18.9%
9 2725
 
4.7%
5 1785
 
3.1%
7 1647
 
2.8%
8 1434
 
2.5%
3 1088
 
1.9%
6 919
 
1.6%
Other values (2) 517
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 46780
80.0%
Dash Punctuation 11692
 
20.0%
Space Separator 4
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 13265
28.4%
0 12335
26.4%
2 11069
23.7%
9 2725
 
5.8%
5 1785
 
3.8%
7 1647
 
3.5%
8 1434
 
3.1%
3 1088
 
2.3%
6 919
 
2.0%
4 513
 
1.1%
Dash Punctuation
ValueCountFrequency (%)
- 11692
100.0%
Space Separator
ValueCountFrequency (%)
4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 58476
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 13265
22.7%
0 12335
21.1%
- 11692
20.0%
2 11069
18.9%
9 2725
 
4.7%
5 1785
 
3.1%
7 1647
 
2.8%
8 1434
 
2.5%
3 1088
 
1.9%
6 919
 
1.6%
Other values (2) 517
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 58476
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 13265
22.7%
0 12335
21.1%
- 11692
20.0%
2 11069
18.9%
9 2725
 
4.7%
5 1785
 
3.1%
7 1647
 
2.8%
8 1434
 
2.5%
3 1088
 
1.9%
6 919
 
1.6%
Other values (2) 517
 
0.9%

crqfcno
Text

MISSING 

Distinct5713
Distinct (%)96.1%
Missing4053
Missing (%)40.5%
Memory size156.2 KiB
2024-04-16T19:25:59.523063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length20
Mean length9.151505
Min length1

Characters and Unicode

Total characters54424
Distinct characters55
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

Unique5497 ?
Unique (%)92.4%

Sample

1st row부산88-536
2nd row26-2015-978(부산)
3rd row4335
4th row26-2019-01910
5th row1727
ValueCountFrequency (%)
부산 352
 
5.4%
부산시 57
 
0.9%
부산광역시 30
 
0.5%
부산광역시장 23
 
0.4%
경남 18
 
0.3%
경상남도 6
 
0.1%
21 5
 
0.1%
1236 4
 
0.1%
제 4
 
0.1%
410 4
 
0.1%
Other values (5674) 5963
92.2%
2024-04-16T19:25:59.929256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 8581
15.8%
2 6752
12.4%
1 6667
12.3%
- 6065
11.1%
6 3804
 
7.0%
4 2461
 
4.5%
3 2459
 
4.5%
8 2256
 
4.1%
5 2229
 
4.1%
9 2216
 
4.1%
Other values (45) 10934
20.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 39631
72.8%
Other Letter 6793
 
12.5%
Dash Punctuation 6065
 
11.1%
Open Punctuation 702
 
1.3%
Close Punctuation 702
 
1.3%
Space Separator 525
 
1.0%
Other Punctuation 6
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 2019
29.7%
부 2002
29.5%
호 512
 
7.5%
제 458
 
6.7%
시 410
 
6.0%
광 310
 
4.6%
역 310
 
4.6%
장 167
 
2.5%
경 144
 
2.1%
남 121
 
1.8%
Other values (27) 340
 
5.0%
Decimal Number
ValueCountFrequency (%)
0 8581
21.7%
2 6752
17.0%
1 6667
16.8%
6 3804
9.6%
4 2461
 
6.2%
3 2459
 
6.2%
8 2256
 
5.7%
5 2229
 
5.6%
9 2216
 
5.6%
7 2206
 
5.6%
Open Punctuation
ValueCountFrequency (%)
( 654
93.2%
[ 48
 
6.8%
Close Punctuation
ValueCountFrequency (%)
) 654
93.2%
] 48
 
6.8%
Other Punctuation
ValueCountFrequency (%)
, 4
66.7%
: 2
33.3%
Dash Punctuation
ValueCountFrequency (%)
- 6065
100.0%
Space Separator
ValueCountFrequency (%)
525
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 47631
87.5%
Hangul 6793
 
12.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 2019
29.7%
부 2002
29.5%
호 512
 
7.5%
제 458
 
6.7%
시 410
 
6.0%
광 310
 
4.6%
역 310
 
4.6%
장 167
 
2.5%
경 144
 
2.1%
남 121
 
1.8%
Other values (27) 340
 
5.0%
Common
ValueCountFrequency (%)
0 8581
18.0%
2 6752
14.2%
1 6667
14.0%
- 6065
12.7%
6 3804
8.0%
4 2461
 
5.2%
3 2459
 
5.2%
8 2256
 
4.7%
5 2229
 
4.7%
9 2216
 
4.7%
Other values (8) 4141
8.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 47631
87.5%
Hangul 6793
 
12.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 8581
18.0%
2 6752
14.2%
1 6667
14.0%
- 6065
12.7%
6 3804
8.0%
4 2461
 
5.2%
3 2459
 
5.2%
8 2256
 
4.7%
5 2229
 
4.7%
9 2216
 
4.7%
Other values (8) 4141
8.7%
Hangul
ValueCountFrequency (%)
산 2019
29.7%
부 2002
29.5%
호 512
 
7.5%
제 458
 
6.7%
시 410
 
6.0%
광 310
 
4.6%
역 310
 
4.6%
장 167
 
2.5%
경 144
 
2.1%
남 121
 
1.8%
Other values (27) 340
 
5.0%

jurirno
Text

MISSING 

Distinct4742
Distinct (%)65.1%
Missing2721
Missing (%)27.2%
Memory size156.2 KiB
2024-04-16T19:26:00.131094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length16
Mean length13.720841
Min length6

Characters and Unicode

Total characters99874
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

Unique3463 ?
Unique (%)47.6%

Sample

1st row26290-2018-00022
2nd row26290-2017-00083
3rd row26230-2017-00191
4th row26350-2018-00140
5th row가-08-1292
ValueCountFrequency (%)
26470-2018-00085 40
 
0.5%
26230-2016-00137 33
 
0.5%
26470-2016-00066 31
 
0.4%
가-13-1490 31
 
0.4%
26470-2015-00027 28
 
0.4%
26530-2017-00027 28
 
0.4%
가-13-1750 25
 
0.3%
가-05-3566 23
 
0.3%
26470-2018-00103 22
 
0.3%
가-13-1947 15
 
0.2%
Other values (4735) 7007
96.2%
2024-04-16T19:26:00.485098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 28514
28.5%
2 16072
16.1%
- 14513
14.5%
1 9939
 
10.0%
6 7805
 
7.8%
3 4408
 
4.4%
4 3729
 
3.7%
5 3631
 
3.6%
7 3312
 
3.3%
9 2978
 
3.0%
Other values (4) 4973
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 83091
83.2%
Dash Punctuation 14513
 
14.5%
Other Letter 2266
 
2.3%
Space Separator 4
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 28514
34.3%
2 16072
19.3%
1 9939
 
12.0%
6 7805
 
9.4%
3 4408
 
5.3%
4 3729
 
4.5%
5 3631
 
4.4%
7 3312
 
4.0%
9 2978
 
3.6%
8 2703
 
3.3%
Other Letter
ValueCountFrequency (%)
가 2245
99.1%
나 21
 
0.9%
Dash Punctuation
ValueCountFrequency (%)
- 14513
100.0%
Space Separator
ValueCountFrequency (%)
4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 97608
97.7%
Hangul 2266
 
2.3%

Most frequent character per script

Common
ValueCountFrequency (%)
0 28514
29.2%
2 16072
16.5%
- 14513
14.9%
1 9939
 
10.2%
6 7805
 
8.0%
3 4408
 
4.5%
4 3729
 
3.8%
5 3631
 
3.7%
7 3312
 
3.4%
9 2978
 
3.1%
Other values (2) 2707
 
2.8%
Hangul
ValueCountFrequency (%)
가 2245
99.1%
나 21
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 97608
97.7%
Hangul 2266
 
2.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 28514
29.2%
2 16072
16.5%
- 14513
14.9%
1 9939
 
10.2%
6 7805
 
8.0%
3 4408
 
4.5%
4 3729
 
3.8%
5 3631
 
3.7%
7 3312
 
3.4%
9 2978
 
3.1%
Other values (2) 2707
 
2.8%
Hangul
ValueCountFrequency (%)
가 2245
99.1%
나 21
 
0.9%

lastupdtdt
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2021-01-02
10000 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021-01-02
2nd row2021-01-02
3rd row2021-01-02
4th row2021-01-02
5th row2021-01-02

Common Values

ValueCountFrequency (%)
2021-01-02 10000
100.0%

Length

2024-04-16T19:26:00.594340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T19:26:00.669285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021-01-02 10000
100.0%

ldcode
Real number (ℝ)

HIGH CORRELATION 

Distinct16
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26361.871
Minimum26110
Maximum26710
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-16T19:26:00.733165image/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 deviation127.01418
Coefficient of variation (CV)0.004818102
Kurtosis0.56325798
Mean26361.871
Median Absolute Deviation (MAD)90
Skewness0.6251349
Sum2.6361871 × 108
Variance16132.603
MonotonicityNot monotonic
2024-04-16T19:26:00.823515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
26230 1436
14.4%
26350 1414
14.1%
26260 1078
10.8%
26470 925
9.2%
26410 839
8.4%
26440 749
7.5%
26380 679
6.8%
26500 615
6.2%
26290 545
 
5.5%
26710 454
 
4.5%
Other values (6) 1266
12.7%
ValueCountFrequency (%)
26110 174
 
1.7%
26140 164
 
1.6%
26170 159
 
1.6%
26200 151
 
1.5%
26230 1436
14.4%
26260 1078
10.8%
26290 545
 
5.5%
26320 323
 
3.2%
26350 1414
14.1%
26380 679
6.8%
ValueCountFrequency (%)
26710 454
 
4.5%
26530 295
 
2.9%
26500 615
6.2%
26470 925
9.2%
26440 749
7.5%
26410 839
8.4%
26380 679
6.8%
26350 1414
14.1%
26320 323
 
3.2%
26290 545
 
5.5%

ldcodenm
Categorical

HIGH CORRELATION 

Distinct16
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
부산광역시 부산진구
1436 
부산광역시 해운대구
1414 
부산광역시 동래구
1078 
부산광역시 연제구
925 
부산광역시 금정구
839 
Other values (11)
4308 

Length

Max length10
Median length9
Mean length9.1485
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row부산광역시 남구
2nd row부산광역시 해운대구
3rd row부산광역시 남구
4th row부산광역시 기장군
5th row부산광역시 부산진구

Common Values

ValueCountFrequency (%)
부산광역시 부산진구 1436
14.4%
부산광역시 해운대구 1414
14.1%
부산광역시 동래구 1078
10.8%
부산광역시 연제구 925
9.2%
부산광역시 금정구 839
8.4%
부산광역시 강서구 749
7.5%
부산광역시 사하구 679
6.8%
부산광역시 수영구 615
6.2%
부산광역시 남구 545
 
5.5%
부산광역시 기장군 454
 
4.5%
Other values (6) 1266
12.7%

Length

2024-04-16T19:26:00.933190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
부산광역시 10000
50.0%
부산진구 1436
 
7.2%
해운대구 1414
 
7.1%
동래구 1078
 
5.4%
연제구 925
 
4.6%
금정구 839
 
4.2%
강서구 749
 
3.7%
사하구 679
 
3.4%
수영구 615
 
3.1%
남구 545
 
2.7%
Other values (7) 1720
 
8.6%

ofcpssecode
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
3686 
4
3576 
<NA>
2717 
3
 
15
2
 
6

Length

Max length4
Median length1
Mean length1.8151
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1 3686
36.9%
4 3576
35.8%
<NA> 2717
27.2%
3 15
 
0.1%
2 6
 
0.1%

Length

2024-04-16T19:26:01.045594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T19:26:01.135091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 3686
36.9%
4 3576
35.8%
na 2717
27.2%
3 15
 
0.1%
2 6
 
0.1%

ofcpssecodenm
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
대표
3686 
일반
3576 
<NA>
2717 
이사
 
15
감사
 
6

Length

Max length4
Median length2
Mean length2.5434
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
대표 3686
36.9%
일반 3576
35.8%
<NA> 2717
27.2%
이사 15
 
0.1%
감사 6
 
0.1%

Length

2024-04-16T19:26:01.243492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T19:26:01.385535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
대표 3686
36.9%
일반 3576
35.8%
na 2717
27.2%
이사 15
 
0.1%
감사 6
 
0.1%

last_load_dttm
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2021-01-06 13:28:01
5447 
2021-01-06 13:28:02
3779 
2021-01-06 13:28:00
774 

Length

Max length19
Median length19
Mean length19
Min length19

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021-01-06 13:28:01
2nd row2021-01-06 13:28:01
3rd row2021-01-06 13:28:01
4th row2021-01-06 13:28:02
5th row2021-01-06 13:28:01

Common Values

ValueCountFrequency (%)
2021-01-06 13:28:01 5447
54.5%
2021-01-06 13:28:02 3779
37.8%
2021-01-06 13:28:00 774
 
7.7%

Length

2024-04-16T19:26:01.510608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T19:26:01.606755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021-01-06 10000
50.0%
13:28:01 5447
27.2%
13:28:02 3779
 
18.9%
13:28:00 774
 
3.9%

Interactions

2024-04-16T19:25:56.315067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-16T19:26:01.667135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
brkrasortcodebrkrasortcodenmldcodeldcodenmofcpssecodeofcpssecodenmlast_load_dttm
brkrasortcode1.0001.0000.2330.2870.8310.8310.032
brkrasortcodenm1.0001.0000.2330.2870.8310.8310.032
ldcode0.2330.2331.0001.0000.2260.2260.873
ldcodenm0.2870.2871.0001.0000.2510.2510.971
ofcpssecode0.8310.8310.2260.2511.0001.0000.008
ofcpssecodenm0.8310.8310.2260.2511.0001.0000.008
last_load_dttm0.0320.0320.8730.9710.0080.0081.000
2024-04-16T19:26:01.777455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ofcpssecodenmldcodenmbrkrasortcodeofcpssecodelast_load_dttmbrkrasortcodenm
ofcpssecodenm1.0000.1200.4791.0000.0070.479
ldcodenm0.1201.0000.1380.1200.9460.138
brkrasortcode0.4790.1381.0000.4790.0301.000
ofcpssecode1.0000.1200.4791.0000.0070.479
last_load_dttm0.0070.9460.0300.0071.0000.030
brkrasortcodenm0.4790.1381.0000.4790.0301.000
2024-04-16T19:26:01.890547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ldcodebrkrasortcodebrkrasortcodenmldcodenmofcpssecodeofcpssecodenmlast_load_dttm
ldcode1.0000.1060.1061.0000.1020.1020.864
brkrasortcode0.1061.0001.0000.1380.4790.4790.030
brkrasortcodenm0.1061.0001.0000.1380.4790.4790.030
ldcodenm1.0000.1380.1381.0000.1200.1200.946
ofcpssecode0.1020.4790.4790.1201.0001.0000.007
ofcpssecodenm0.1020.4790.4790.1201.0001.0000.007
last_load_dttm0.8640.0300.0300.9460.0070.0071.000

Missing values

2024-04-16T19:25:56.421876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-16T19:25:56.569006image/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.
2024-04-16T19:25:56.698407image/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
62914중개보조원한정욱정인공인중개사사무소<NA><NA>26290-2018-000222021-01-0226290부산광역시 남구4일반2021-01-06 13:28:01
103372공인중개사김길홍<NA>1989-02-08부산88-536<NA>2021-01-0226350부산광역시 해운대구<NA><NA>2021-01-06 13:28:01
64332공인중개사정진홍골드공인중개사사무소2015-10-2426-2015-978(부산)26290-2017-000832021-01-0226290부산광역시 남구1대표2021-01-06 13:28:01
193472공인중개사김홍석<NA>1985-11-144335<NA>2021-01-0226710부산광역시 기장군<NA><NA>2021-01-06 13:28:02
29364중개보조원변용현삼보공인중개사사무소<NA><NA>26230-2017-001912021-01-0226230부산광역시 부산진구4일반2021-01-06 13:28:01
100192공인중개사김윤경유한회사맥비스타부동산중개법인2019-12-0926-2019-0191026350-2018-001402021-01-0226350부산광역시 해운대구4일반2021-01-06 13:28:01
41792공인중개사강권칠<NA>1985-11-041727<NA>2021-01-0226230부산광역시 부산진구<NA><NA>2021-01-06 13:28:01
102332공인중개사김순희<NA>2003-11-19서울14-07930<NA>2021-01-0226350부산광역시 해운대구<NA><NA>2021-01-06 13:28:01
111022공인중개사이동찬<NA>2005-12-12제16-822호<NA>2021-01-0226380부산광역시 사하구<NA><NA>2021-01-06 13:28:01
116982공인중개사김종대신세계공인중개사사무소2005-12-12부산16-828가-08-12922021-01-0226380부산광역시 사하구1대표2021-01-06 13:28:01
brkrasortcodebrkrasortcodenmbrkrnmbsnmcmpnmcrqfcacqdtcrqfcnojurirnolastupdtdtldcodeldcodenmofcpssecodeofcpssecodenmlast_load_dttm
51812공인중개사신옥자<NA>2005-07-20336<NA>2021-01-0226260부산광역시 동래구<NA><NA>2021-01-06 13:28:01
167094중개보조원김병주탑공인중개사사무소<NA><NA>26470-2020-000122021-01-0226470부산광역시 연제구4일반2021-01-06 13:28:02
184174중개보조원신영자대흥공인중개사 사무소<NA><NA>가-15-9082021-01-0226530부산광역시 사상구4일반2021-01-06 13:28:02
56922공인중개사김종득새우성공인중개사사무소1985-11-062468가-06-23032021-01-0226260부산광역시 동래구1대표2021-01-06 13:28:01
41332공인중개사홍승휘<NA>2000-11-20부산시 11-143<NA>2021-01-0226260부산광역시 동래구<NA><NA>2021-01-06 13:28:01
123292공인중개사임수경(任壽京)<NA>2005-07-201145(부산광역시)<NA>2021-01-0226410부산광역시 금정구<NA><NA>2021-01-06 13:28:02
86872공인중개사이종석<NA>2005-07-20부산124<NA>2021-01-0226350부산광역시 해운대구<NA><NA>2021-01-06 13:28:01
56694중개보조원노병흡박사공인중개사사무소<NA><NA>26260-2020-000422021-01-0226260부산광역시 동래구4일반2021-01-06 13:28:01
24252공인중개사이경준주식회사 삼오부동산중개법인2014-12-1026-2014-72626230-2016-001372021-01-0226230부산광역시 부산진구4일반2021-01-06 13:28:01
192594중개보조원송정숙예율공인중개사사무소<NA><NA>26710-2017-000202021-01-0226710부산광역시 기장군4일반2021-01-06 13:28:02

Duplicate rows

Most frequently occurring

brkrasortcodebrkrasortcodenmbrkrnmbsnmcmpnmcrqfcacqdtcrqfcnojurirnolastupdtdtldcodeldcodenmofcpssecodeofcpssecodenmlast_load_dttm# duplicates
02공인중개사박창호<NA><NA><NA><NA>2021-01-0226260부산광역시 동래구<NA><NA>2021-01-06 13:28:012
14중개보조원김덕환<NA><NA><NA><NA>2021-01-0226410부산광역시 금정구<NA><NA>2021-01-06 13:28:022
24중개보조원김승모부산상가채널공인중개사사무소<NA><NA>26500-2018-000972021-01-0226500부산광역시 수영구4일반2021-01-06 13:28:022