Overview

Dataset statistics

Number of variables13
Number of observations7431
Missing cells7788
Missing cells (%)8.1%
Duplicate rows4
Duplicate rows (%)0.1%
Total size in memory776.6 KiB
Average record size in memory107.0 B

Variable types

Text3
DateTime3
Categorical5
Numeric1
Unsupported1

Alerts

lastupdtdt has constant value ""Constant
last_load_dttm has constant value ""Constant
Dataset has 4 (0.1%) duplicate rowsDuplicates
sttussecode is highly overall correlated with sttussecodenmHigh correlation
sttussecodenm is highly overall correlated with sttussecodeHigh correlation
ldcode is highly overall correlated with ldcodenmHigh correlation
ldcodenm is highly overall correlated with ldcodeHigh correlation
sttussecode is highly imbalanced (97.7%)Imbalance
sttussecodenm is highly imbalanced (97.7%)Imbalance
estbsbeginde has 91 (1.2%) missing valuesMissing
estbsendde has 91 (1.2%) missing valuesMissing
telnolist has 7431 (100.0%) missing valuesMissing
telnolist is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2024-04-17 02:16:29.123060
Analysis finished2024-04-17 02:16:30.384960
Duration1.26 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

brkrnm
Text

Distinct6138
Distinct (%)83.3%
Missing63
Missing (%)0.8%
Memory size58.2 KiB
2024-04-17T11:16:30.600340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length3
Mean length3.002443
Min length2

Characters and Unicode

Total characters22122
Distinct characters328
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

Unique5402 ?
Unique (%)73.3%

Sample

1st row김홍숙
2nd row강영지
3rd row정인수
4th row박은미
5th row주광형
ValueCountFrequency (%)
김정희 16
 
0.2%
김미경 11
 
0.1%
이영주 11
 
0.1%
김영희 10
 
0.1%
김경희 10
 
0.1%
김민정 10
 
0.1%
이미경 10
 
0.1%
이정희 10
 
0.1%
김미숙 10
 
0.1%
김영미 8
 
0.1%
Other values (6136) 7271
98.6%
2024-04-17T11:16:30.980697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
김 1592
 
7.2%
이 1120
 
5.1%
정 1070
 
4.8%
영 769
 
3.5%
박 660
 
3.0%
희 556
 
2.5%
경 537
 
2.4%
미 470
 
2.1%
숙 442
 
2.0%
현 404
 
1.8%
Other values (318) 14502
65.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 22069
99.8%
Lowercase Letter 15
 
0.1%
Close Punctuation 11
 
< 0.1%
Open Punctuation 11
 
< 0.1%
Space Separator 10
 
< 0.1%
Uppercase Letter 6
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
김 1592
 
7.2%
이 1120
 
5.1%
정 1070
 
4.8%
영 769
 
3.5%
박 660
 
3.0%
희 556
 
2.5%
경 537
 
2.4%
미 470
 
2.1%
숙 442
 
2.0%
현 404
 
1.8%
Other values (299) 14449
65.5%
Lowercase Letter
ValueCountFrequency (%)
e 4
26.7%
a 2
13.3%
y 2
13.3%
g 1
 
6.7%
n 1
 
6.7%
s 1
 
6.7%
u 1
 
6.7%
i 1
 
6.7%
k 1
 
6.7%
m 1
 
6.7%
Uppercase Letter
ValueCountFrequency (%)
H 1
16.7%
L 1
16.7%
S 1
16.7%
C 1
16.7%
A 1
16.7%
B 1
16.7%
Close Punctuation
ValueCountFrequency (%)
) 11
100.0%
Open Punctuation
ValueCountFrequency (%)
( 11
100.0%
Space Separator
ValueCountFrequency (%)
10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 22037
99.6%
Common 32
 
0.1%
Han 32
 
0.1%
Latin 21
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
김 1592
 
7.2%
이 1120
 
5.1%
정 1070
 
4.9%
영 769
 
3.5%
박 660
 
3.0%
희 556
 
2.5%
경 537
 
2.4%
미 470
 
2.1%
숙 442
 
2.0%
현 404
 
1.8%
Other values (272) 14417
65.4%
Han
ValueCountFrequency (%)
金 3
 
9.4%
美 2
 
6.2%
崔 2
 
6.2%
順 2
 
6.2%
忠 1
 
3.1%
孝 1
 
3.1%
張 1
 
3.1%
慶 1
 
3.1%
銀 1
 
3.1%
榮 1
 
3.1%
Other values (17) 17
53.1%
Latin
ValueCountFrequency (%)
e 4
19.0%
a 2
 
9.5%
y 2
 
9.5%
H 1
 
4.8%
g 1
 
4.8%
n 1
 
4.8%
L 1
 
4.8%
S 1
 
4.8%
s 1
 
4.8%
C 1
 
4.8%
Other values (6) 6
28.6%
Common
ValueCountFrequency (%)
) 11
34.4%
( 11
34.4%
10
31.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 22037
99.6%
ASCII 53
 
0.2%
CJK 31
 
0.1%
CJK Compat Ideographs 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
김 1592
 
7.2%
이 1120
 
5.1%
정 1070
 
4.9%
영 769
 
3.5%
박 660
 
3.0%
희 556
 
2.5%
경 537
 
2.4%
미 470
 
2.1%
숙 442
 
2.0%
현 404
 
1.8%
Other values (272) 14417
65.4%
ASCII
ValueCountFrequency (%)
) 11
20.8%
( 11
20.8%
10
18.9%
e 4
 
7.5%
a 2
 
3.8%
y 2
 
3.8%
H 1
 
1.9%
g 1
 
1.9%
n 1
 
1.9%
L 1
 
1.9%
Other values (9) 9
17.0%
CJK
ValueCountFrequency (%)
金 3
 
9.7%
美 2
 
6.5%
崔 2
 
6.5%
順 2
 
6.5%
忠 1
 
3.2%
孝 1
 
3.2%
張 1
 
3.2%
慶 1
 
3.2%
銀 1
 
3.2%
榮 1
 
3.2%
Other values (16) 16
51.6%
CJK Compat Ideographs
ValueCountFrequency (%)
李 1
100.0%

bsnmcmpnm
Text

Distinct4815
Distinct (%)65.3%
Missing56
Missing (%)0.8%
Memory size58.2 KiB
2024-04-17T11:16:31.190576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length22
Mean length11.306576
Min length4

Characters and Unicode

Total characters83386
Distinct characters631
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

Unique3972 ?
Unique (%)53.9%

Sample

1st row부원공인중개사
2nd row신창부동산
3rd row금터공인중개사
4th row천진공인중개사 사무소
5th row재덕공인중개사 사무소
ValueCountFrequency (%)
공인중개사사무소 83
 
1.1%
사무소 83
 
1.1%
현대공인중개사사무소 41
 
0.5%
삼성공인중개사사무소 36
 
0.5%
행운공인중개사사무소 33
 
0.4%
미래공인중개사사무소 33
 
0.4%
태양공인중개사사무소 30
 
0.4%
탑공인중개사사무소 28
 
0.4%
하나공인중개사사무소 25
 
0.3%
신세계공인중개사사무소 25
 
0.3%
Other values (4799) 7216
94.5%
2024-04-17T11:16:31.486865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
사 13295
15.9%
중 7405
 
8.9%
개 7388
 
8.9%
소 6968
 
8.4%
무 6927
 
8.3%
인 6460
 
7.7%
공 6276
 
7.5%
동 2806
 
3.4%
부 2549
 
3.1%
산 2497
 
3.0%
Other values (621) 20815
25.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 81502
97.7%
Uppercase Letter 789
 
0.9%
Space Separator 338
 
0.4%
Decimal Number 333
 
0.4%
Lowercase Letter 171
 
0.2%
Open Punctuation 110
 
0.1%
Close Punctuation 110
 
0.1%
Other Punctuation 21
 
< 0.1%
Dash Punctuation 9
 
< 0.1%
Modifier Symbol 1
 
< 0.1%
Other values (2) 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
사 13295
16.3%
중 7405
 
9.1%
개 7388
 
9.1%
소 6968
 
8.5%
무 6927
 
8.5%
인 6460
 
7.9%
공 6276
 
7.7%
동 2806
 
3.4%
부 2549
 
3.1%
산 2497
 
3.1%
Other values (552) 18931
23.2%
Uppercase Letter
ValueCountFrequency (%)
K 146
18.5%
S 100
12.7%
T 69
 
8.7%
L 67
 
8.5%
C 52
 
6.6%
O 45
 
5.7%
W 39
 
4.9%
E 33
 
4.2%
H 30
 
3.8%
G 27
 
3.4%
Other values (16) 181
22.9%
Lowercase Letter
ValueCountFrequency (%)
e 75
43.9%
h 27
 
15.8%
t 13
 
7.6%
c 11
 
6.4%
k 8
 
4.7%
w 8
 
4.7%
s 7
 
4.1%
i 4
 
2.3%
o 4
 
2.3%
n 3
 
1.8%
Other values (9) 11
 
6.4%
Decimal Number
ValueCountFrequency (%)
1 144
43.2%
8 45
 
13.5%
4 38
 
11.4%
2 31
 
9.3%
3 24
 
7.2%
9 20
 
6.0%
5 12
 
3.6%
6 7
 
2.1%
0 6
 
1.8%
7 6
 
1.8%
Other Punctuation
ValueCountFrequency (%)
& 7
33.3%
. 6
28.6%
？ 2
 
9.5%
· 2
 
9.5%
# 2
 
9.5%
, 1
 
4.8%
! 1
 
4.8%
Space Separator
ValueCountFrequency (%)
338
100.0%
Open Punctuation
ValueCountFrequency (%)
( 110
100.0%
Close Punctuation
ValueCountFrequency (%)
) 110
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 9
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 1
100.0%
Letter Number
ValueCountFrequency (%)
Ⅱ 1
100.0%
Other Symbol
ValueCountFrequency (%)
ⓡ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 81489
97.7%
Latin 961
 
1.2%
Common 923
 
1.1%
Han 13
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
사 13295
16.3%
중 7405
 
9.1%
개 7388
 
9.1%
소 6968
 
8.6%
무 6927
 
8.5%
인 6460
 
7.9%
공 6276
 
7.7%
동 2806
 
3.4%
부 2549
 
3.1%
산 2497
 
3.1%
Other values (540) 18918
23.2%
Latin
ValueCountFrequency (%)
K 146
15.2%
S 100
 
10.4%
e 75
 
7.8%
T 69
 
7.2%
L 67
 
7.0%
C 52
 
5.4%
O 45
 
4.7%
W 39
 
4.1%
E 33
 
3.4%
H 30
 
3.1%
Other values (36) 305
31.7%
Common
ValueCountFrequency (%)
338
36.6%
1 144
15.6%
( 110
 
11.9%
) 110
 
11.9%
8 45
 
4.9%
4 38
 
4.1%
2 31
 
3.4%
3 24
 
2.6%
9 20
 
2.2%
5 12
 
1.3%
Other values (13) 51
 
5.5%
Han
ValueCountFrequency (%)
福 2
15.4%
太 1
7.7%
秀 1
7.7%
利 1
7.7%
人 1
7.7%
堂 1
7.7%
明 1
7.7%
甲 1
7.7%
本 1
7.7%
氷 1
7.7%
Other values (2) 2
15.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 81489
97.7%
ASCII 1878
 
2.3%
CJK 13
 
< 0.1%
None 4
 
< 0.1%
Number Forms 1
 
< 0.1%
Enclosed Alphanum 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
사 13295
16.3%
중 7405
 
9.1%
개 7388
 
9.1%
소 6968
 
8.6%
무 6927
 
8.5%
인 6460
 
7.9%
공 6276
 
7.7%
동 2806
 
3.4%
부 2549
 
3.1%
산 2497
 
3.1%
Other values (540) 18918
23.2%
ASCII
ValueCountFrequency (%)
338
18.0%
K 146
 
7.8%
1 144
 
7.7%
( 110
 
5.9%
) 110
 
5.9%
S 100
 
5.3%
e 75
 
4.0%
T 69
 
3.7%
L 67
 
3.6%
C 52
 
2.8%
Other values (55) 667
35.5%
None
ValueCountFrequency (%)
？ 2
50.0%
· 2
50.0%
CJK
ValueCountFrequency (%)
福 2
15.4%
太 1
7.7%
秀 1
7.7%
利 1
7.7%
人 1
7.7%
堂 1
7.7%
明 1
7.7%
甲 1
7.7%
本 1
7.7%
氷 1
7.7%
Other values (2) 2
15.4%
Number Forms
ValueCountFrequency (%)
Ⅱ 1
100.0%
Enclosed Alphanum
ValueCountFrequency (%)
ⓡ 1
100.0%

estbsbeginde
Date

MISSING 

Distinct514
Distinct (%)7.0%
Missing91
Missing (%)1.2%
Memory size58.2 KiB
Minimum2013-03-24 00:00:00
Maximum2021-12-16 00:00:00
2024-04-17T11:16:31.602268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T11:16:31.725335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

estbsendde
Date

MISSING 

Distinct510
Distinct (%)6.9%
Missing91
Missing (%)1.2%
Memory size58.2 KiB
Minimum2014-03-23 00:00:00
Maximum2024-11-15 00:00:00
2024-04-17T11:16:32.049618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T11:16:32.157221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

jurirno
Text

Distinct7370
Distinct (%)99.9%
Missing56
Missing (%)0.8%
Memory size58.2 KiB
2024-04-17T11:16:32.337156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length16
Mean length13.469559
Min length6

Characters and Unicode

Total characters99338
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

Unique7365 ?
Unique (%)99.9%

Sample

1st row가-01-595
2nd row가-01-573
3rd row가-01-606
4th row가-01-612
5th row가-01-613
ValueCountFrequency (%)
26440-2019-00095 2
 
< 0.1%
26470-2015-00086 2
 
< 0.1%
26230-2017-00146 2
 
< 0.1%
26410-2016-00056 2
 
< 0.1%
가-11-2094 2
 
< 0.1%
2
 
< 0.1%
가 2
 
< 0.1%
26440-2020-00098 1
 
< 0.1%
26440-2020-00094 1
 
< 0.1%
26440-2020-00093 1
 
< 0.1%
Other values (7363) 7363
99.8%
2024-04-17T11:16:32.626347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 28027
28.2%
2 15830
15.9%
- 14680
14.8%
1 10012
 
10.1%
6 7716
 
7.8%
3 4198
 
4.2%
4 3889
 
3.9%
5 3674
 
3.7%
7 3152
 
3.2%
9 2971
 
3.0%
Other values (4) 5189
 
5.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 82129
82.7%
Dash Punctuation 14680
 
14.8%
Other Letter 2524
 
2.5%
Space Separator 5
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 28027
34.1%
2 15830
19.3%
1 10012
 
12.2%
6 7716
 
9.4%
3 4198
 
5.1%
4 3889
 
4.7%
5 3674
 
4.5%
7 3152
 
3.8%
9 2971
 
3.6%
8 2660
 
3.2%
Other Letter
ValueCountFrequency (%)
가 2494
98.8%
나 30
 
1.2%
Dash Punctuation
ValueCountFrequency (%)
- 14680
100.0%
Space Separator
ValueCountFrequency (%)
5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 96814
97.5%
Hangul 2524
 
2.5%

Most frequent character per script

Common
ValueCountFrequency (%)
0 28027
28.9%
2 15830
16.4%
- 14680
15.2%
1 10012
 
10.3%
6 7716
 
8.0%
3 4198
 
4.3%
4 3889
 
4.0%
5 3674
 
3.8%
7 3152
 
3.3%
9 2971
 
3.1%
Other values (2) 2665
 
2.8%
Hangul
ValueCountFrequency (%)
가 2494
98.8%
나 30
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 96814
97.5%
Hangul 2524
 
2.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 28027
28.9%
2 15830
16.4%
- 14680
15.2%
1 10012
 
10.3%
6 7716
 
8.0%
3 4198
 
4.3%
4 3889
 
4.0%
5 3674
 
3.8%
7 3152
 
3.3%
9 2971
 
3.1%
Other values (2) 2665
 
2.8%
Hangul
ValueCountFrequency (%)
가 2494
98.8%
나 30
 
1.2%

lastupdtdt
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size58.2 KiB
2021-02-02
7431 

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 7431
100.0%

Length

2024-04-17T11:16:32.733618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T11:16:32.805959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021-02-02 7431
100.0%

ldcode
Real number (ℝ)

HIGH CORRELATION 

Distinct16
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26369.839
Minimum26110
Maximum26710
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size65.4 KiB
2024-04-17T11:16:32.868172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum26110
5-th percentile26170
Q126260
median26350
Q326470
95-th percentile26710
Maximum26710
Range600
Interquartile range (IQR)210

Descriptive statistics

Standard deviation133.87868
Coefficient of variation (CV)0.0050769623
Kurtosis0.37376885
Mean26369.839
Median Absolute Deviation (MAD)90
Skewness0.61099133
Sum1.9595427 × 108
Variance17923.5
MonotonicityNot monotonic
2024-04-17T11:16:32.956698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
26350 928
12.5%
26230 843
11.3%
26260 673
9.1%
26470 601
8.1%
26440 591
8.0%
26500 535
7.2%
26410 534
7.2%
26290 525
7.1%
26380 469
 
6.3%
26710 434
 
5.8%
Other values (6) 1298
17.5%
ValueCountFrequency (%)
26110 123
 
1.7%
26140 155
 
2.1%
26170 162
 
2.2%
26200 130
 
1.7%
26230 843
11.3%
26260 673
9.1%
26290 525
7.1%
26320 417
5.6%
26350 928
12.5%
26380 469
6.3%
ValueCountFrequency (%)
26710 434
5.8%
26530 311
 
4.2%
26500 535
7.2%
26470 601
8.1%
26440 591
8.0%
26410 534
7.2%
26380 469
6.3%
26350 928
12.5%
26320 417
5.6%
26290 525
7.1%

ldcodenm
Categorical

HIGH CORRELATION 

Distinct16
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size58.2 KiB
부산광역시 해운대구
928 
부산광역시 부산진구
843 
부산광역시 동래구
673 
부산광역시 연제구
601 
부산광역시 강서구
591 
Other values (11)
3795 

Length

Max length10
Median length9
Mean length9.0523483
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row부산광역시 중구
2nd row부산광역시 중구
3rd row부산광역시 중구
4th row부산광역시 중구
5th row부산광역시 중구

Common Values

ValueCountFrequency (%)
부산광역시 해운대구 928
12.5%
부산광역시 부산진구 843
11.3%
부산광역시 동래구 673
9.1%
부산광역시 연제구 601
8.1%
부산광역시 강서구 591
8.0%
부산광역시 수영구 535
7.2%
부산광역시 금정구 534
7.2%
부산광역시 남구 525
7.1%
부산광역시 사하구 469
 
6.3%
부산광역시 기장군 434
 
5.8%
Other values (6) 1298
17.5%

Length

2024-04-17T11:16:33.057939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
부산광역시 7431
50.0%
해운대구 928
 
6.2%
부산진구 843
 
5.7%
동래구 673
 
4.5%
연제구 601
 
4.0%
강서구 591
 
4.0%
수영구 535
 
3.6%
금정구 534
 
3.6%
남구 525
 
3.5%
사하구 469
 
3.2%
Other values (7) 1732
 
11.7%

registde
Date

Distinct3194
Distinct (%)43.0%
Missing0
Missing (%)0.0%
Memory size58.2 KiB
Minimum1984-04-24 00:00:00
Maximum2021-01-30 00:00:00
2024-04-17T11:16:33.158228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T11:16:33.264258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

sttussecode
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size58.2 KiB
1
7398 
2
 
24
3
 
5
8
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1 7398
99.6%
2 24
 
0.3%
3 5
 
0.1%
8 4
 
0.1%

Length

2024-04-17T11:16:33.368392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T11:16:33.450474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 7398
99.6%
2 24
 
0.3%
3 5
 
0.1%
8 4
 
0.1%

sttussecodenm
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size58.2 KiB
영업중
7398 
휴업
 
24
휴업연장
 
5
업무정지
 
4

Length

Max length4
Median length3
Mean length2.9979814
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
영업중 7398
99.6%
휴업 24
 
0.3%
휴업연장 5
 
0.1%
업무정지 4
 
0.1%

Length

2024-04-17T11:16:33.544783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T11:16:33.650887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
영업중 7398
99.6%
휴업 24
 
0.3%
휴업연장 5
 
0.1%
업무정지 4
 
0.1%

telnolist
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing7431
Missing (%)100.0%
Memory size65.4 KiB

last_load_dttm
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size58.2 KiB
2021-03-01 06:23:03
7431 

Length

Max length19
Median length19
Mean length19
Min length19

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021-03-01 06:23:03
2nd row2021-03-01 06:23:03
3rd row2021-03-01 06:23:03
4th row2021-03-01 06:23:03
5th row2021-03-01 06:23:03

Common Values

ValueCountFrequency (%)
2021-03-01 06:23:03 7431
100.0%

Length

2024-04-17T11:16:33.736427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T11:16:33.811440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021-03-01 7431
50.0%
06:23:03 7431
50.0%

Interactions

2024-04-17T11:16:29.895864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-17T11:16:33.860263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ldcodeldcodenmsttussecodesttussecodenm
ldcode1.0001.0000.0470.047
ldcodenm1.0001.0000.0620.062
sttussecode0.0470.0621.0001.000
sttussecodenm0.0470.0621.0001.000
2024-04-17T11:16:33.936620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
sttussecodeldcodenmsttussecodenm
sttussecode1.0000.0291.000
ldcodenm0.0291.0000.029
sttussecodenm1.0000.0291.000
2024-04-17T11:16:34.005673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ldcodeldcodenmsttussecodesttussecodenm
ldcode1.0000.9990.0200.020
ldcodenm0.9991.0000.0290.029
sttussecode0.0200.0291.0001.000
sttussecodenm0.0200.0291.0001.000

Missing values

2024-04-17T11:16:30.016479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-17T11:16:30.203000image/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.
2024-04-17T11:16:30.319350image/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

brkrnmbsnmcmpnmestbsbegindeestbsenddejurirnolastupdtdtldcodeldcodenmregistdesttussecodesttussecodenmtelnolistlast_load_dttm
0김홍숙부원공인중개사2020-12-182021-12-17가-01-5952021-02-0226110부산광역시 중구2009-07-201영업중<NA>2021-03-01 06:23:03
1강영지신창부동산2020-09-042021-09-03가-01-5732021-02-0226110부산광역시 중구2006-09-061영업중<NA>2021-03-01 06:23:03
2정인수금터공인중개사2020-11-232021-11-22가-01-6062021-02-0226110부산광역시 중구2009-11-131영업중<NA>2021-03-01 06:23:03
3박은미천진공인중개사 사무소2020-02-172021-02-16가-01-6122021-02-0226110부산광역시 중구2010-02-171영업중<NA>2021-03-01 06:23:03
4주광형재덕공인중개사 사무소2020-03-082021-03-07가-01-6132021-02-0226110부산광역시 중구2010-03-041영업중<NA>2021-03-01 06:23:03
5최난영탑공인중개사사무소2020-09-092021-09-08가-01-6172021-02-0226110부산광역시 중구2010-09-091영업중<NA>2021-03-01 06:23:03
6정영희천봉공인중개사2021-01-042022-01-03가-01-6192021-02-0226110부산광역시 중구2011-01-041영업중<NA>2021-03-01 06:23:03
7이지미중앙공인중개사무소2021-01-132022-01-12가-01-6202021-02-0226110부산광역시 중구2011-01-131영업중<NA>2021-03-01 06:23:03
8김덕길마당발부동산중개사무소2020-04-062021-04-05가-01-6212021-02-0226110부산광역시 중구2006-04-101영업중<NA>2021-03-01 06:23:03
9천순철참조은공인중개사사무소2020-08-112021-08-10가-01-6242021-02-0226110부산광역시 중구2008-08-111영업중<NA>2021-03-01 06:23:03
brkrnmbsnmcmpnmestbsbegindeestbsenddejurirnolastupdtdtldcodeldcodenmregistdesttussecodesttussecodenmtelnolistlast_load_dttm
7421이서영일광퀸부동산공인중개사사무소2020-03-272021-03-26가16-9662021-02-0226710부산광역시 기장군2012-03-271영업중<NA>2021-03-01 06:23:03
7422하화정강남공인중개사사무소2020-05-012021-04-30가16-9722021-02-0226710부산광역시 기장군2012-04-241영업중<NA>2021-03-01 06:23:03
7423서창열부자공인중개사사무소2020-06-082021-06-07가16-9842021-02-0226710부산광역시 기장군2012-06-071영업중<NA>2021-03-01 06:23:03
7424선은미롯데캐슬부동산공인중개사사무소2020-08-162021-08-15가16-9922021-02-0226710부산광역시 기장군2012-08-161영업중<NA>2021-03-01 06:23:03
7425한성희크로바공인중개사사무소2020-12-202021-12-19가16-10142021-02-0226710부산광역시 기장군2012-12-201영업중<NA>2021-03-01 06:23:03
7426김성훈여명컨설팅공인중개사사무소2020-12-232021-12-22가16-10172021-02-0226710부산광역시 기장군2012-12-211영업중<NA>2021-03-01 06:23:03
7427윤학순기장우리들공인중개사사무소2021-01-062022-01-05가-16-10272021-02-0226710부산광역시 기장군2013-01-041영업중<NA>2021-03-01 06:23:03
7428최해정제일부동산공인중개사사무소2021-01-152022-01-14가16-10302021-02-0226710부산광역시 기장군2013-01-151영업중<NA>2021-03-01 06:23:03
7429황숙이서희스타힐스부동산공인중개사사무소2021-02-042022-02-03가16-10372021-02-0226710부산광역시 기장군2013-02-041영업중<NA>2021-03-01 06:23:03
7430최권세알파부동산공인중개사사무소2020-04-082021-04-07가16-10492021-02-0226710부산광역시 기장군2013-03-081영업중<NA>2021-03-01 06:23:03

Duplicate rows

Most frequently occurring

brkrnmbsnmcmpnmestbsbegindeestbsenddejurirnolastupdtdtldcodeldcodenmregistdesttussecodesttussecodenmlast_load_dttm# duplicates
3<NA><NA><NA><NA><NA>2021-02-0226380부산광역시 사하구2012-10-291영업중2021-03-01 06:23:036
0<NA>삼성명가부동산중개2014-06-122015-06-11가-11-20942021-02-0226410부산광역시 금정구2013-06-121영업중2021-03-01 06:23:032
1<NA><NA><NA><NA><NA>2021-02-0226140부산광역시 서구2015-06-101영업중2021-03-01 06:23:032
2<NA><NA><NA><NA><NA>2021-02-0226170부산광역시 동구2013-08-281영업중2021-03-01 06:23:032