Overview

Dataset statistics

Number of variables49
Number of observations10000
Missing cells22626
Missing cells (%)4.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.9 MiB
Average record size in memory405.0 B

Variable types

Numeric7
Text7
Categorical34
DateTime1

Alerts

opnsvcid is highly imbalanced (71.1%)Imbalance
updategbn is highly imbalanced (99.1%)Imbalance
opnsvcnm is highly imbalanced (92.0%)Imbalance
clgstdt is highly imbalanced (98.5%)Imbalance
clgenddt is highly imbalanced (98.5%)Imbalance
ropnymd is highly imbalanced (98.4%)Imbalance
uptaenm is highly imbalanced (55.7%)Imbalance
sitetel is highly imbalanced (98.4%)Imbalance
bdngownsenm is highly imbalanced (96.3%)Imbalance
fctyowkepcnt is highly imbalanced (96.5%)Imbalance
fctypdtjobepcnt is highly imbalanced (96.5%)Imbalance
fctysiljobepcnt is highly imbalanced (96.5%)Imbalance
rgtmbdsno is highly imbalanced (61.7%)Imbalance
wtrsplyfacilsenm is highly imbalanced (76.2%)Imbalance
maneipcnt is highly imbalanced (82.0%)Imbalance
multusnupsoyn is highly imbalanced (98.9%)Imbalance
lvsenm is highly imbalanced (83.5%)Imbalance
isream is highly imbalanced (97.1%)Imbalance
hoffepcnt is highly imbalanced (96.5%)Imbalance
equsiz is highly imbalanced (96.1%)Imbalance
wmeipcnt is highly imbalanced (83.0%)Imbalance
trdpjubnsenm is highly imbalanced (81.9%)Imbalance
monam is highly imbalanced (97.1%)Imbalance
sntuptaenm is highly imbalanced (73.5%)Imbalance
jtupsomainedf is highly imbalanced (96.1%)Imbalance
jtupsoasgnno is highly imbalanced (97.4%)Imbalance
lindprcbgbnnm is highly imbalanced (67.2%)Imbalance
lindjobgbnnm is highly imbalanced (97.9%)Imbalance
lindseqno is highly imbalanced (96.1%)Imbalance
homepage is highly imbalanced (96.1%)Imbalance
sitepostno has 7553 (75.5%) missing valuesMissing
sitewhladdr has 316 (3.2%) missing valuesMissing
rdnwhladdr has 1650 (16.5%) missing valuesMissing
dcbymd has 4878 (48.8%) missing valuesMissing
x has 358 (3.6%) missing valuesMissing
y has 358 (3.6%) missing valuesMissing
faciltotscp has 7512 (75.1%) missing valuesMissing
skey has unique valuesUnique

Reproduction

Analysis started2024-04-16 08:58:06.666761
Analysis finished2024-04-16 08:58:09.113520
Duration2.45 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

skey
Real number (ℝ)

UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6534.0959
Minimum1
Maximum19776
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-16T17:58:09.176151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile655.95
Q13229.75
median6350.5
Q39629.25
95-th percentile12216.05
Maximum19776
Range19775
Interquartile range (IQR)6399.5

Descriptive statistics

Standard deviation3975.8728
Coefficient of variation (CV)0.60848094
Kurtosis-0.31871236
Mean6534.0959
Median Absolute Deviation (MAD)3198
Skewness0.36271255
Sum65340959
Variance15807564
MonotonicityNot monotonic
2024-04-16T17:58:09.291119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5698 1
 
< 0.1%
6023 1
 
< 0.1%
806 1
 
< 0.1%
11669 1
 
< 0.1%
4489 1
 
< 0.1%
12286 1
 
< 0.1%
7050 1
 
< 0.1%
9649 1
 
< 0.1%
4288 1
 
< 0.1%
3710 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
19776 1
< 0.1%
19756 1
< 0.1%
19751 1
< 0.1%
19704 1
< 0.1%
19703 1
< 0.1%
19700 1
< 0.1%
19699 1
< 0.1%
19698 1
< 0.1%
19658 1
< 0.1%
19655 1
< 0.1%

opnsfteamcode
Real number (ℝ)

Distinct16
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3331196
Minimum3250000
Maximum3400000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-16T17:58:09.408938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3250000
5-th percentile3260000
Q13300000
median3330000
Q33360000
95-th percentile3390000
Maximum3400000
Range150000
Interquartile range (IQR)60000

Descriptive statistics

Standard deviation39859.108
Coefficient of variation (CV)0.011965404
Kurtosis-0.88064618
Mean3331196
Median Absolute Deviation (MAD)30000
Skewness0.020061412
Sum3.331196 × 1010
Variance1.5887485 × 109
MonotonicityNot monotonic
2024-04-16T17:58:09.513433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
3330000 1015
10.2%
3290000 991
9.9%
3320000 970
9.7%
3390000 908
9.1%
3340000 881
8.8%
3300000 853
8.5%
3350000 709
 
7.1%
3310000 644
 
6.4%
3370000 591
 
5.9%
3380000 499
 
5.0%
Other values (6) 1939
19.4%
ValueCountFrequency (%)
3250000 225
 
2.2%
3260000 284
 
2.8%
3270000 323
 
3.2%
3280000 270
 
2.7%
3290000 991
9.9%
3300000 853
8.5%
3310000 644
6.4%
3320000 970
9.7%
3330000 1015
10.2%
3340000 881
8.8%
ValueCountFrequency (%)
3400000 484
4.8%
3390000 908
9.1%
3380000 499
5.0%
3370000 591
5.9%
3360000 353
 
3.5%
3350000 709
7.1%
3340000 881
8.8%
3330000 1015
10.2%
3320000 970
9.7%
3310000 644
6.4%

mgtno
Text

Distinct9939
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-04-16T17:58:09.695605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length18
Mean length18.9948
Min length18

Characters and Unicode

Total characters189948
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

Unique9889 ?
Unique (%)98.9%

Sample

1st row334000000920140022
2nd row332000000920000020
3rd row333000000920020012
4th row330000000920020031
5th row332000000920000017
ValueCountFrequency (%)
3250000-107-2019-00112 3
 
< 0.1%
3320000-107-2019-00008 3
 
< 0.1%
3290000-107-2019-00009 3
 
< 0.1%
3320000-107-2019-00268 3
 
< 0.1%
3310000-107-2019-00096 3
 
< 0.1%
3300000-107-2020-00055 3
 
< 0.1%
3370000-107-2020-00064 3
 
< 0.1%
3340000-107-2019-00117 3
 
< 0.1%
3290000-107-2019-00147 3
 
< 0.1%
3340000-107-2020-00071 3
 
< 0.1%
Other values (9929) 9970
99.7%
2024-04-16T17:58:10.003959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 95896
50.5%
3 21407
 
11.3%
1 16547
 
8.7%
2 16239
 
8.5%
9 16022
 
8.4%
- 7461
 
3.9%
4 3756
 
2.0%
8 3487
 
1.8%
7 3317
 
1.7%
5 3080
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 182487
96.1%
Dash Punctuation 7461
 
3.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 95896
52.5%
3 21407
 
11.7%
1 16547
 
9.1%
2 16239
 
8.9%
9 16022
 
8.8%
4 3756
 
2.1%
8 3487
 
1.9%
7 3317
 
1.8%
5 3080
 
1.7%
6 2736
 
1.5%
Dash Punctuation
ValueCountFrequency (%)
- 7461
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 189948
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 95896
50.5%
3 21407
 
11.3%
1 16547
 
8.7%
2 16239
 
8.5%
9 16022
 
8.4%
- 7461
 
3.9%
4 3756
 
2.0%
8 3487
 
1.8%
7 3317
 
1.7%
5 3080
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 189948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 95896
50.5%
3 21407
 
11.3%
1 16547
 
8.7%
2 16239
 
8.5%
9 16022
 
8.4%
- 7461
 
3.9%
4 3756
 
2.0%
8 3487
 
1.8%
7 3317
 
1.7%
5 3080
 
1.6%

opnsvcid
Categorical

IMBALANCE 

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
07_22_04_P
7511 
07_22_18_P
2124 
07_22_19_P
 
286
07_22_03_P
 
36
07_22_01_P
 
29
Other values (6)
 
14

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st row07_22_04_P
2nd row07_22_04_P
3rd row07_22_04_P
4th row07_22_04_P
5th row07_22_04_P

Common Values

ValueCountFrequency (%)
07_22_04_P 7511
75.1%
07_22_18_P 2124
 
21.2%
07_22_19_P 286
 
2.9%
07_22_03_P 36
 
0.4%
07_22_01_P 29
 
0.3%
07_22_02_P 7
 
0.1%
07_22_11_P 3
 
< 0.1%
07_22_10_P 1
 
< 0.1%
07_22_25_P 1
 
< 0.1%
07_22_08_P 1
 
< 0.1%

Length

2024-04-16T17:58:10.114228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
07_22_04_p 7511
75.1%
07_22_18_p 2124
 
21.2%
07_22_19_p 286
 
2.9%
07_22_03_p 36
 
0.4%
07_22_01_p 29
 
0.3%
07_22_02_p 7
 
0.1%
07_22_11_p 3
 
< 0.1%
07_22_10_p 1
 
< 0.1%
07_22_25_p 1
 
< 0.1%
07_22_08_p 1
 
< 0.1%

updategbn
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
I
9992 
U
 
8

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
I 9992
99.9%
U 8
 
0.1%

Length

2024-04-16T17:58:10.207259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:58:10.295122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
i 9992
99.9%
u 8
 
0.1%
Distinct217
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2018-08-31 23:59:59
Maximum2021-02-24 00:23:01
2024-04-16T17:58:10.377267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T17:58:10.489211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

opnsvcnm
Categorical

IMBALANCE 

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9629 
즉석판매제조가공업
 
286
건강기능식품일반판매업
 
36
집단급식소식품판매업
 
29
건강기능식품유통전문판매업
 
7
Other values (6)
 
13

Length

Max length13
Median length4
Mean length4.1943
Min length4

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 9629
96.3%
즉석판매제조가공업 286
 
2.9%
건강기능식품일반판매업 36
 
0.4%
집단급식소식품판매업 29
 
0.3%
건강기능식품유통전문판매업 7
 
0.1%
축산판매업 6
 
0.1%
식품제조가공업 3
 
< 0.1%
식품자동판매기업 1
 
< 0.1%
축산물운반업 1
 
< 0.1%
식품소분업 1
 
< 0.1%

Length

2024-04-16T17:58:10.593877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 9629
96.3%
즉석판매제조가공업 286
 
2.9%
건강기능식품일반판매업 36
 
0.4%
집단급식소식품판매업 29
 
0.3%
건강기능식품유통전문판매업 7
 
0.1%
축산판매업 6
 
0.1%
식품제조가공업 3
 
< 0.1%
식품자동판매기업 1
 
< 0.1%
축산물운반업 1
 
< 0.1%
식품소분업 1
 
< 0.1%

bplcnm
Text

Distinct7721
Distinct (%)77.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-04-16T17:58:10.806145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length41
Median length30
Mean length6.4883
Min length2

Characters and Unicode

Total characters64883
Distinct characters816
Distinct categories12 ?
Distinct scripts4 ?
Distinct blocks7 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6674 ?
Unique (%)66.7%

Sample

1st row연세우유부산사하대리점
2nd row부산미트벨리
3rd row부산우유 재송3보급소
4th row하나로식품
5th row(주)케이앤제이위스
ValueCountFrequency (%)
주식회사 81
 
0.7%
파리바게뜨 80
 
0.7%
베이커리 71
 
0.6%
부산우유 56
 
0.5%
뚜레쥬르 47
 
0.4%
탑플러스마트 44
 
0.4%
파리바게트 44
 
0.4%
식육점 42
 
0.4%
한우식육점 33
 
0.3%
제일식육점 30
 
0.3%
Other values (7867) 11234
95.5%
2024-04-16T17:58:11.187023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3874
 
6.0%
2742
 
4.2%
2516
 
3.9%
1765
 
2.7%
1727
 
2.7%
1484
 
2.3%
1479
 
2.3%
1475
 
2.3%
1219
 
1.9%
1182
 
1.8%
Other values (806) 45420
70.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 59565
91.8%
Space Separator 1765
 
2.7%
Close Punctuation 1107
 
1.7%
Open Punctuation 1103
 
1.7%
Uppercase Letter 546
 
0.8%
Lowercase Letter 401
 
0.6%
Decimal Number 276
 
0.4%
Other Punctuation 94
 
0.1%
Dash Punctuation 17
 
< 0.1%
Math Symbol 5
 
< 0.1%
Other values (2) 4
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
3874
 
6.5%
2742
 
4.6%
2516
 
4.2%
1727
 
2.9%
1484
 
2.5%
1479
 
2.5%
1475
 
2.5%
1219
 
2.0%
1182
 
2.0%
1155
 
1.9%
Other values (730) 40712
68.3%
Uppercase Letter
ValueCountFrequency (%)
S 76
13.9%
M 44
 
8.1%
K 42
 
7.7%
D 38
 
7.0%
C 31
 
5.7%
J 30
 
5.5%
T 29
 
5.3%
N 26
 
4.8%
B 24
 
4.4%
A 24
 
4.4%
Other values (15) 182
33.3%
Lowercase Letter
ValueCountFrequency (%)
e 55
13.7%
o 41
 
10.2%
a 38
 
9.5%
s 28
 
7.0%
r 23
 
5.7%
n 22
 
5.5%
i 22
 
5.5%
l 19
 
4.7%
t 17
 
4.2%
k 17
 
4.2%
Other values (14) 119
29.7%
Decimal Number
ValueCountFrequency (%)
2 84
30.4%
1 72
26.1%
3 25
 
9.1%
0 19
 
6.9%
5 17
 
6.2%
9 15
 
5.4%
7 13
 
4.7%
8 12
 
4.3%
6 10
 
3.6%
4 9
 
3.3%
Other Punctuation
ValueCountFrequency (%)
. 31
33.0%
& 26
27.7%
, 14
14.9%
' 10
 
10.6%
" 4
 
4.3%
· 4
 
4.3%
! 2
 
2.1%
2
 
2.1%
/ 1
 
1.1%
Other Symbol
ValueCountFrequency (%)
1
50.0%
1
50.0%
Space Separator
ValueCountFrequency (%)
1765
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1107
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1103
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 17
100.0%
Math Symbol
ValueCountFrequency (%)
+ 5
100.0%
Letter Number
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 59561
91.8%
Common 4368
 
6.7%
Latin 949
 
1.5%
Han 5
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
3874
 
6.5%
2742
 
4.6%
2516
 
4.2%
1727
 
2.9%
1484
 
2.5%
1479
 
2.5%
1475
 
2.5%
1219
 
2.0%
1182
 
2.0%
1155
 
1.9%
Other values (727) 40708
68.3%
Latin
ValueCountFrequency (%)
S 76
 
8.0%
e 55
 
5.8%
M 44
 
4.6%
K 42
 
4.4%
o 41
 
4.3%
a 38
 
4.0%
D 38
 
4.0%
C 31
 
3.3%
J 30
 
3.2%
T 29
 
3.1%
Other values (40) 525
55.3%
Common
ValueCountFrequency (%)
1765
40.4%
) 1107
25.3%
( 1103
25.3%
2 84
 
1.9%
1 72
 
1.6%
. 31
 
0.7%
& 26
 
0.6%
3 25
 
0.6%
0 19
 
0.4%
5 17
 
0.4%
Other values (15) 119
 
2.7%
Han
ValueCountFrequency (%)
2
40.0%
1
20.0%
1
20.0%
1
20.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 59559
91.8%
ASCII 5308
 
8.2%
None 7
 
< 0.1%
CJK 5
 
< 0.1%
Number Forms 2
 
< 0.1%
Specials 1
 
< 0.1%
Compat Jamo 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
3874
 
6.5%
2742
 
4.6%
2516
 
4.2%
1727
 
2.9%
1484
 
2.5%
1479
 
2.5%
1475
 
2.5%
1219
 
2.0%
1182
 
2.0%
1155
 
1.9%
Other values (725) 40706
68.3%
ASCII
ValueCountFrequency (%)
1765
33.3%
) 1107
20.9%
( 1103
20.8%
2 84
 
1.6%
S 76
 
1.4%
1 72
 
1.4%
e 55
 
1.0%
M 44
 
0.8%
K 42
 
0.8%
o 41
 
0.8%
Other values (61) 919
17.3%
None
ValueCountFrequency (%)
· 4
57.1%
2
28.6%
1
 
14.3%
Number Forms
ValueCountFrequency (%)
2
100.0%
CJK
ValueCountFrequency (%)
2
40.0%
1
20.0%
1
20.0%
1
20.0%
Specials
ValueCountFrequency (%)
1
100.0%
Compat Jamo
ValueCountFrequency (%)
1
100.0%

sitepostno
Text

MISSING 

Distinct605
Distinct (%)24.7%
Missing7553
Missing (%)75.5%
Memory size156.2 KiB
2024-04-16T17:58:11.467817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters14682
Distinct characters15
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique171 ?
Unique (%)7.0%

Sample

1st row617808
2nd row606809
3rd row612030
4th row612836
5th row618440
ValueCountFrequency (%)
612020 78
 
3.2%
616852 28
 
1.1%
617808 24
 
1.0%
607815 24
 
1.0%
600017 23
 
0.9%
618814 22
 
0.9%
604822 20
 
0.8%
612824 19
 
0.8%
607831 18
 
0.7%
604851 17
 
0.7%
Other values (595) 2174
88.8%
2024-04-16T17:58:11.847823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6 2964
20.2%
1 2420
16.5%
8 2416
16.5%
0 2381
16.2%
2 1205
8.2%
4 992
 
6.8%
3 712
 
4.8%
7 703
 
4.8%
9 508
 
3.5%
5 369
 
2.5%
Other values (5) 12
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14670
99.9%
Other Letter 12
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
6 2964
20.2%
1 2420
16.5%
8 2416
16.5%
0 2381
16.2%
2 1205
8.2%
4 992
 
6.8%
3 712
 
4.9%
7 703
 
4.8%
9 508
 
3.5%
5 369
 
2.5%
Other Letter
ValueCountFrequency (%)
4
33.3%
2
16.7%
2
16.7%
2
16.7%
2
16.7%

Most occurring scripts

ValueCountFrequency (%)
Common 14670
99.9%
Hangul 12
 
0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
6 2964
20.2%
1 2420
16.5%
8 2416
16.5%
0 2381
16.2%
2 1205
8.2%
4 992
 
6.8%
3 712
 
4.9%
7 703
 
4.8%
9 508
 
3.5%
5 369
 
2.5%
Hangul
ValueCountFrequency (%)
4
33.3%
2
16.7%
2
16.7%
2
16.7%
2
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14670
99.9%
Hangul 12
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6 2964
20.2%
1 2420
16.5%
8 2416
16.5%
0 2381
16.2%
2 1205
8.2%
4 992
 
6.8%
3 712
 
4.9%
7 703
 
4.8%
9 508
 
3.5%
5 369
 
2.5%
Hangul
ValueCountFrequency (%)
4
33.3%
2
16.7%
2
16.7%
2
16.7%
2
16.7%

sitewhladdr
Text

MISSING 

Distinct7976
Distinct (%)82.4%
Missing316
Missing (%)3.2%
Memory size156.2 KiB
2024-04-16T17:58:12.125744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length60
Median length53
Mean length24.548534
Min length13

Characters and Unicode

Total characters237728
Distinct characters462
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6980 ?
Unique (%)72.1%

Sample

1st row부산광역시 사하구 다대동 950-16번지
2nd row부산광역시 북구 구포동 1188-5 번지
3rd row부산광역시 해운대구 재송동 1041-9번지 이화맨션상가 8호
4th row부산광역시 동래구 안락동 152-87번지
5th row부산광역시 북구 만덕동 287-2번지
ValueCountFrequency (%)
부산광역시 9684
 
22.6%
해운대구 952
 
2.2%
부산진구 936
 
2.2%
북구 906
 
2.1%
사상구 900
 
2.1%
사하구 864
 
2.0%
동래구 851
 
2.0%
금정구 697
 
1.6%
남구 643
 
1.5%
연제구 575
 
1.3%
Other values (8471) 25857
60.3%
2024-04-16T17:58:12.523997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
42501
17.9%
11385
 
4.8%
11328
 
4.8%
11143
 
4.7%
1 11044
 
4.6%
10026
 
4.2%
9936
 
4.2%
9856
 
4.1%
9836
 
4.1%
9691
 
4.1%
Other values (452) 100982
42.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 138806
58.4%
Decimal Number 46948
 
19.7%
Space Separator 42501
 
17.9%
Dash Punctuation 8482
 
3.6%
Uppercase Letter 311
 
0.1%
Other Punctuation 236
 
0.1%
Open Punctuation 208
 
0.1%
Close Punctuation 204
 
0.1%
Lowercase Letter 19
 
< 0.1%
Math Symbol 13
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
11385
 
8.2%
11328
 
8.2%
11143
 
8.0%
10026
 
7.2%
9936
 
7.2%
9856
 
7.1%
9836
 
7.1%
9691
 
7.0%
9479
 
6.8%
2014
 
1.5%
Other values (399) 44112
31.8%
Uppercase Letter
ValueCountFrequency (%)
B 85
27.3%
A 54
17.4%
G 30
 
9.6%
S 28
 
9.0%
K 17
 
5.5%
C 14
 
4.5%
L 13
 
4.2%
D 11
 
3.5%
P 11
 
3.5%
T 10
 
3.2%
Other values (10) 38
12.2%
Decimal Number
ValueCountFrequency (%)
1 11044
23.5%
2 6156
13.1%
3 4810
10.2%
4 4388
 
9.3%
5 4277
 
9.1%
0 3659
 
7.8%
6 3363
 
7.2%
7 3235
 
6.9%
8 3165
 
6.7%
9 2851
 
6.1%
Lowercase Letter
ValueCountFrequency (%)
e 4
21.1%
s 4
21.1%
k 3
15.8%
g 2
10.5%
b 1
 
5.3%
p 1
 
5.3%
o 1
 
5.3%
u 1
 
5.3%
c 1
 
5.3%
h 1
 
5.3%
Other Punctuation
ValueCountFrequency (%)
, 177
75.0%
@ 29
 
12.3%
. 20
 
8.5%
/ 7
 
3.0%
' 2
 
0.8%
: 1
 
0.4%
Open Punctuation
ValueCountFrequency (%)
( 207
99.5%
[ 1
 
0.5%
Close Punctuation
ValueCountFrequency (%)
) 203
99.5%
] 1
 
0.5%
Space Separator
ValueCountFrequency (%)
42501
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 8482
100.0%
Math Symbol
ValueCountFrequency (%)
~ 13
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 138806
58.4%
Common 98592
41.5%
Latin 330
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
11385
 
8.2%
11328
 
8.2%
11143
 
8.0%
10026
 
7.2%
9936
 
7.2%
9856
 
7.1%
9836
 
7.1%
9691
 
7.0%
9479
 
6.8%
2014
 
1.5%
Other values (399) 44112
31.8%
Latin
ValueCountFrequency (%)
B 85
25.8%
A 54
16.4%
G 30
 
9.1%
S 28
 
8.5%
K 17
 
5.2%
C 14
 
4.2%
L 13
 
3.9%
D 11
 
3.3%
P 11
 
3.3%
T 10
 
3.0%
Other values (20) 57
17.3%
Common
ValueCountFrequency (%)
42501
43.1%
1 11044
 
11.2%
- 8482
 
8.6%
2 6156
 
6.2%
3 4810
 
4.9%
4 4388
 
4.5%
5 4277
 
4.3%
0 3659
 
3.7%
6 3363
 
3.4%
7 3235
 
3.3%
Other values (13) 6677
 
6.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 138806
58.4%
ASCII 98922
41.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
42501
43.0%
1 11044
 
11.2%
- 8482
 
8.6%
2 6156
 
6.2%
3 4810
 
4.9%
4 4388
 
4.4%
5 4277
 
4.3%
0 3659
 
3.7%
6 3363
 
3.4%
7 3235
 
3.3%
Other values (43) 7007
 
7.1%
Hangul
ValueCountFrequency (%)
11385
 
8.2%
11328
 
8.2%
11143
 
8.0%
10026
 
7.2%
9936
 
7.2%
9856
 
7.1%
9836
 
7.1%
9691
 
7.0%
9479
 
6.8%
2014
 
1.5%
Other values (399) 44112
31.8%

rdnpostno
Real number (ℝ)

Distinct1426
Distinct (%)14.3%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean48341.726
Minimum46002
Maximum49527
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-16T17:58:12.638881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum46002
5-th percentile46318
Q147810
median48947
Q348947
95-th percentile49219.1
Maximum49527
Range3525
Interquartile range (IQR)1137

Descriptive statistics

Standard deviation929.82961
Coefficient of variation (CV)0.019234514
Kurtosis-0.15006276
Mean48341.726
Median Absolute Deviation (MAD)0
Skewness-1.0867736
Sum4.8336892 × 108
Variance864583.11
MonotonicityNot monotonic
2024-04-16T17:58:12.760299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
48947 5122
51.2%
46504 80
 
0.8%
48058 65
 
0.7%
47052 45
 
0.4%
46901 44
 
0.4%
46916 33
 
0.3%
46702 26
 
0.3%
46703 25
 
0.2%
47251 25
 
0.2%
48944 23
 
0.2%
Other values (1416) 4511
45.1%
ValueCountFrequency (%)
46002 2
 
< 0.1%
46003 1
 
< 0.1%
46004 6
0.1%
46006 2
 
< 0.1%
46007 4
 
< 0.1%
46008 7
0.1%
46012 2
 
< 0.1%
46013 4
 
< 0.1%
46015 12
0.1%
46016 2
 
< 0.1%
ValueCountFrequency (%)
49527 1
 
< 0.1%
49526 1
 
< 0.1%
49525 1
 
< 0.1%
49524 4
 
< 0.1%
49523 1
 
< 0.1%
49522 1
 
< 0.1%
49521 1
 
< 0.1%
49520 7
0.1%
49519 17
0.2%
49518 5
 
0.1%

rdnwhladdr
Text

MISSING 

Distinct6997
Distinct (%)83.8%
Missing1650
Missing (%)16.5%
Memory size156.2 KiB
2024-04-16T17:58:13.068937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length62
Median length56
Mean length28.312335
Min length19

Characters and Unicode

Total characters236408
Distinct characters498
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6203 ?
Unique (%)74.3%

Sample

1st row부산광역시 사하구 윤공단로 47 (다대동)
2nd row부산광역시 북구 덕천로 299 (만덕동)
3rd row부산광역시 해운대구 선수촌로 80-1 (반여동)
4th row부산광역시 영도구 중리북로29번길 2 (동삼동)
5th row부산광역시 연제구 금련로 25 (연산동)
ValueCountFrequency (%)
부산광역시 8350
 
18.4%
부산진구 890
 
2.0%
북구 780
 
1.7%
동래구 757
 
1.7%
사상구 735
 
1.6%
사하구 723
 
1.6%
해운대구 650
 
1.4%
금정구 641
 
1.4%
남구 576
 
1.3%
1층 556
 
1.2%
Other values (5608) 30698
67.7%
2024-04-16T17:58:13.457918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
37022
 
15.7%
10654
 
4.5%
10122
 
4.3%
10058
 
4.3%
8850
 
3.7%
8737
 
3.7%
1 8711
 
3.7%
8679
 
3.7%
8362
 
3.5%
( 8093
 
3.4%
Other values (488) 117120
49.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 142791
60.4%
Space Separator 37022
 
15.7%
Decimal Number 35982
 
15.2%
Open Punctuation 8093
 
3.4%
Close Punctuation 8090
 
3.4%
Other Punctuation 2932
 
1.2%
Dash Punctuation 1183
 
0.5%
Uppercase Letter 286
 
0.1%
Lowercase Letter 15
 
< 0.1%
Math Symbol 14
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
10654
 
7.5%
10122
 
7.1%
10058
 
7.0%
8850
 
6.2%
8737
 
6.1%
8679
 
6.1%
8362
 
5.9%
8027
 
5.6%
4498
 
3.2%
4217
 
3.0%
Other values (438) 60587
42.4%
Uppercase Letter
ValueCountFrequency (%)
B 75
26.2%
A 45
15.7%
S 27
 
9.4%
C 22
 
7.7%
G 22
 
7.7%
K 18
 
6.3%
E 16
 
5.6%
P 12
 
4.2%
N 10
 
3.5%
D 7
 
2.4%
Other values (11) 32
11.2%
Decimal Number
ValueCountFrequency (%)
1 8711
24.2%
2 4911
13.6%
3 4070
11.3%
5 3254
 
9.0%
4 3110
 
8.6%
0 2786
 
7.7%
6 2528
 
7.0%
7 2513
 
7.0%
8 2145
 
6.0%
9 1954
 
5.4%
Lowercase Letter
ValueCountFrequency (%)
e 4
26.7%
k 2
13.3%
s 2
13.3%
b 2
13.3%
o 1
 
6.7%
p 1
 
6.7%
c 1
 
6.7%
a 1
 
6.7%
u 1
 
6.7%
Other Punctuation
ValueCountFrequency (%)
, 2906
99.1%
. 11
 
0.4%
@ 9
 
0.3%
/ 5
 
0.2%
' 1
 
< 0.1%
Space Separator
ValueCountFrequency (%)
37022
100.0%
Open Punctuation
ValueCountFrequency (%)
( 8093
100.0%
Close Punctuation
ValueCountFrequency (%)
) 8090
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1183
100.0%
Math Symbol
ValueCountFrequency (%)
~ 14
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 142791
60.4%
Common 93316
39.5%
Latin 301
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
10654
 
7.5%
10122
 
7.1%
10058
 
7.0%
8850
 
6.2%
8737
 
6.1%
8679
 
6.1%
8362
 
5.9%
8027
 
5.6%
4498
 
3.2%
4217
 
3.0%
Other values (438) 60587
42.4%
Latin
ValueCountFrequency (%)
B 75
24.9%
A 45
15.0%
S 27
 
9.0%
C 22
 
7.3%
G 22
 
7.3%
K 18
 
6.0%
E 16
 
5.3%
P 12
 
4.0%
N 10
 
3.3%
D 7
 
2.3%
Other values (20) 47
15.6%
Common
ValueCountFrequency (%)
37022
39.7%
1 8711
 
9.3%
( 8093
 
8.7%
) 8090
 
8.7%
2 4911
 
5.3%
3 4070
 
4.4%
5 3254
 
3.5%
4 3110
 
3.3%
, 2906
 
3.1%
0 2786
 
3.0%
Other values (10) 10363
 
11.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 142791
60.4%
ASCII 93617
39.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
37022
39.5%
1 8711
 
9.3%
( 8093
 
8.6%
) 8090
 
8.6%
2 4911
 
5.2%
3 4070
 
4.3%
5 3254
 
3.5%
4 3110
 
3.3%
, 2906
 
3.1%
0 2786
 
3.0%
Other values (40) 10664
 
11.4%
Hangul
ValueCountFrequency (%)
10654
 
7.5%
10122
 
7.1%
10058
 
7.0%
8850
 
6.2%
8737
 
6.1%
8679
 
6.1%
8362
 
5.9%
8027
 
5.6%
4498
 
3.2%
4217
 
3.0%
Other values (438) 60587
42.4%

apvpermymd
Real number (ℝ)

Distinct5696
Distinct (%)57.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20044164
Minimum19631010
Maximum20210222
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-16T17:58:13.599189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19631010
5-th percentile19850418
Q119981120
median20060414
Q320120806
95-th percentile20180406
Maximum20210222
Range579212
Interquartile range (IQR)139686.5

Descriptive statistics

Standard deviation101158.21
Coefficient of variation (CV)0.0050467664
Kurtosis0.36014166
Mean20044164
Median Absolute Deviation (MAD)69801
Skewness-0.78283248
Sum2.0044164 × 1011
Variance1.0232984 × 1010
MonotonicityNot monotonic
2024-04-16T17:58:13.735830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19980713 88
 
0.9%
20120406 12
 
0.1%
20060414 10
 
0.1%
20050315 9
 
0.1%
20141106 9
 
0.1%
19960802 9
 
0.1%
20190809 8
 
0.1%
20190104 8
 
0.1%
20090212 7
 
0.1%
20120320 7
 
0.1%
Other values (5686) 9833
98.3%
ValueCountFrequency (%)
19631010 2
 
< 0.1%
19651010 5
0.1%
19651024 1
 
< 0.1%
19651116 2
 
< 0.1%
19651124 1
 
< 0.1%
19651215 1
 
< 0.1%
19660415 1
 
< 0.1%
19660916 1
 
< 0.1%
19661001 2
 
< 0.1%
19670302 1
 
< 0.1%
ValueCountFrequency (%)
20210222 1
 
< 0.1%
20210219 2
< 0.1%
20210215 1
 
< 0.1%
20210213 4
< 0.1%
20210206 2
< 0.1%
20210205 1
 
< 0.1%
20210204 1
 
< 0.1%
20210130 1
 
< 0.1%
20210126 2
< 0.1%
20210122 1
 
< 0.1%

dcbymd
Text

MISSING 

Distinct2697
Distinct (%)52.7%
Missing4878
Missing (%)48.8%
Memory size156.2 KiB
2024-04-16T17:58:13.997820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length7.9672003
Min length4

Characters and Unicode

Total characters40808
Distinct characters14
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1362 ?
Unique (%)26.6%

Sample

1st row20131014
2nd row20020313
3rd row20031229
4th row20130227
5th row20100201
ValueCountFrequency (%)
폐업일자 42
 
0.8%
20131222 40
 
0.8%
20121213 29
 
0.6%
20060216 26
 
0.5%
20140820 23
 
0.4%
20170131 17
 
0.3%
20130607 14
 
0.3%
20111230 13
 
0.3%
20090807 13
 
0.3%
20130409 10
 
0.2%
Other values (2687) 4895
95.6%
2024-04-16T17:58:14.399409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 13468
33.0%
2 8712
21.3%
1 7933
19.4%
3 2041
 
5.0%
4 1579
 
3.9%
6 1571
 
3.8%
7 1537
 
3.8%
5 1351
 
3.3%
8 1297
 
3.2%
9 1151
 
2.8%
Other values (4) 168
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 40640
99.6%
Other Letter 168
 
0.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 13468
33.1%
2 8712
21.4%
1 7933
19.5%
3 2041
 
5.0%
4 1579
 
3.9%
6 1571
 
3.9%
7 1537
 
3.8%
5 1351
 
3.3%
8 1297
 
3.2%
9 1151
 
2.8%
Other Letter
ValueCountFrequency (%)
42
25.0%
42
25.0%
42
25.0%
42
25.0%

Most occurring scripts

ValueCountFrequency (%)
Common 40640
99.6%
Hangul 168
 
0.4%

Most frequent character per script

Common
ValueCountFrequency (%)
0 13468
33.1%
2 8712
21.4%
1 7933
19.5%
3 2041
 
5.0%
4 1579
 
3.9%
6 1571
 
3.9%
7 1537
 
3.8%
5 1351
 
3.3%
8 1297
 
3.2%
9 1151
 
2.8%
Hangul
ValueCountFrequency (%)
42
25.0%
42
25.0%
42
25.0%
42
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 40640
99.6%
Hangul 168
 
0.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 13468
33.1%
2 8712
21.4%
1 7933
19.5%
3 2041
 
5.0%
4 1579
 
3.9%
6 1571
 
3.9%
7 1537
 
3.8%
5 1351
 
3.3%
8 1297
 
3.2%
9 1151
 
2.8%
Hangul
ValueCountFrequency (%)
42
25.0%
42
25.0%
42
25.0%
42
25.0%

clgstdt
Categorical

IMBALANCE 

Distinct22
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9938 
휴업시작일자
 
42
20160331
 
1
20170313
 
1
20110607
 
1
Other values (17)
 
17

Length

Max length8
Median length4
Mean length4.0164
Min length4

Unique

Unique20 ?
Unique (%)0.2%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 9938
99.4%
휴업시작일자 42
 
0.4%
20160331 1
 
< 0.1%
20170313 1
 
< 0.1%
20110607 1
 
< 0.1%
20120215 1
 
< 0.1%
20150101 1
 
< 0.1%
20060420 1
 
< 0.1%
20130318 1
 
< 0.1%
20130314 1
 
< 0.1%
Other values (12) 12
 
0.1%

Length

2024-04-16T17:58:14.556603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 9938
99.4%
휴업시작일자 42
 
0.4%
20110408 1
 
< 0.1%
20050331 1
 
< 0.1%
20050428 1
 
< 0.1%
20070621 1
 
< 0.1%
20180817 1
 
< 0.1%
20130528 1
 
< 0.1%
20160406 1
 
< 0.1%
20101018 1
 
< 0.1%
Other values (12) 12
 
0.1%

clgenddt
Categorical

IMBALANCE 

Distinct17
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9942 
휴업종료일자
 
42
20171231
 
2
20111010
 
1
20151231
 
1
Other values (12)
 
12

Length

Max length8
Median length4
Mean length4.0148
Min length4

Unique

Unique14 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 9942
99.4%
휴업종료일자 42
 
0.4%
20171231 2
 
< 0.1%
20111010 1
 
< 0.1%
20151231 1
 
< 0.1%
20060831 1
 
< 0.1%
20160317 1
 
< 0.1%
20140313 1
 
< 0.1%
20121012 1
 
< 0.1%
20131231 1
 
< 0.1%
Other values (7) 7
 
0.1%

Length

2024-04-16T17:58:14.669371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 9942
99.4%
휴업종료일자 42
 
0.4%
20171231 2
 
< 0.1%
20061231 1
 
< 0.1%
20161006 1
 
< 0.1%
20070331 1
 
< 0.1%
20060429 1
 
< 0.1%
20071231 1
 
< 0.1%
20210816 1
 
< 0.1%
20121012 1
 
< 0.1%
Other values (7) 7
 
0.1%

ropnymd
Categorical

IMBALANCE 

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9952 
재개업일자
 
42
20180619
 
1
20130806
 
1
20070607
 
1
Other values (3)
 
3

Length

Max length8
Median length4
Mean length4.0066
Min length4

Unique

Unique6 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 9952
99.5%
재개업일자 42
 
0.4%
20180619 1
 
< 0.1%
20130806 1
 
< 0.1%
20070607 1
 
< 0.1%
20061030 1
 
< 0.1%
20180531 1
 
< 0.1%
20030327 1
 
< 0.1%

Length

2024-04-16T17:58:14.787987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:58:14.897780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9952
99.5%
재개업일자 42
 
0.4%
20180619 1
 
< 0.1%
20130806 1
 
< 0.1%
20070607 1
 
< 0.1%
20061030 1
 
< 0.1%
20180531 1
 
< 0.1%
20030327 1
 
< 0.1%

trdstatenm
Categorical

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
0002
3934 
0000
3180 
02
1184 
01
940 
0004
 
368
Other values (6)
394 

Length

Max length5
Median length4
Mean length3.6109
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0000
2nd row0000
3rd row0002
4th row0002
5th row0002

Common Values

ValueCountFrequency (%)
0002 3934
39.3%
0000 3180
31.8%
02 1184
 
11.8%
01 940
 
9.4%
0004 368
 
3.7%
영업/정상 361
 
3.6%
0001 18
 
0.2%
<NA> 5
 
0.1%
0003 5
 
0.1%
영업상태 3
 
< 0.1%

Length

2024-04-16T17:58:14.997637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0002 3934
39.3%
0000 3180
31.8%
02 1184
 
11.8%
01 940
 
9.4%
0004 368
 
3.7%
영업/정상 361
 
3.6%
0001 18
 
0.2%
na 5
 
< 0.1%
0003 5
 
< 0.1%
영업상태 3
 
< 0.1%

dtlstatenm
Categorical

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
폐업
5120 
정상
3188 
영업
1301 
말소
 
368
휴업
 
18

Length

Max length4
Median length2
Mean length2.001
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row정상
2nd row정상
3rd row폐업
4th row폐업
5th row폐업

Common Values

ValueCountFrequency (%)
폐업 5120
51.2%
정상 3188
31.9%
영업 1301
 
13.0%
말소 368
 
3.7%
휴업 18
 
0.2%
행정처분 5
 
0.1%

Length

2024-04-16T17:58:15.096982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:58:15.187306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 5120
51.2%
정상 3188
31.9%
영업 1301
 
13.0%
말소 368
 
3.7%
휴업 18
 
0.2%
행정처분 5
 
< 0.1%

x
Real number (ℝ)

MISSING 

Distinct7324
Distinct (%)76.0%
Missing358
Missing (%)3.6%
Infinite0
Infinite (%)0.0%
Mean387658.79
Minimum365157.28
Maximum409429.53
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-16T17:58:15.284148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum365157.28
5-th percentile379402.9
Q1382808.83
median387984.8
Q3391613.05
95-th percentile398132.91
Maximum409429.53
Range44272.255
Interquartile range (IQR)8804.2238

Descriptive statistics

Standard deviation5985.2547
Coefficient of variation (CV)0.015439492
Kurtosis0.091957835
Mean387658.79
Median Absolute Deviation (MAD)4296.3336
Skewness0.1707515
Sum3.737806 × 109
Variance35823274
MonotonicityNot monotonic
2024-04-16T17:58:15.398145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
381223.937708 40
 
0.4%
381150.240625 34
 
0.3%
394015.453851 32
 
0.3%
381168.291599 25
 
0.2%
381172.695874 21
 
0.2%
381201.909278 20
 
0.2%
381275.335615 20
 
0.2%
387443.214568 20
 
0.2%
381022.722372 17
 
0.2%
381211.78269 13
 
0.1%
Other values (7314) 9400
94.0%
(Missing) 358
 
3.6%
ValueCountFrequency (%)
365157.276407 1
< 0.1%
365673.424344 1
< 0.1%
366798.269419272 1
< 0.1%
366852.689513 1
< 0.1%
366871.978797 1
< 0.1%
366902.34539 1
< 0.1%
367088.710957 1
< 0.1%
367337.078664 1
< 0.1%
367523.898965 2
< 0.1%
367585.061894 2
< 0.1%
ValueCountFrequency (%)
409429.531583 1
< 0.1%
409316.302152 1
< 0.1%
407942.799729 1
< 0.1%
407781.091682 1
< 0.1%
407631.694095 1
< 0.1%
407504.543476 2
< 0.1%
407499.627476 2
< 0.1%
407372.101586 1
< 0.1%
407318.950507 1
< 0.1%
407318.32285 1
< 0.1%

y
Real number (ℝ)

MISSING 

Distinct7323
Distinct (%)75.9%
Missing358
Missing (%)3.6%
Infinite0
Infinite (%)0.0%
Mean187985.78
Minimum169678.05
Maximum210621.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-16T17:58:15.765115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum169678.05
5-th percentile178544.27
Q1184159.1
median188204.91
Q3191460.68
95-th percentile197616.62
Maximum210621.3
Range40943.253
Interquartile range (IQR)7301.578

Descriptive statistics

Standard deviation5879.7157
Coefficient of variation (CV)0.031277449
Kurtosis0.19903239
Mean187985.78
Median Absolute Deviation (MAD)3635.0926
Skewness0.19554958
Sum1.8125589 × 109
Variance34571056
MonotonicityNot monotonic
2024-04-16T17:58:15.865801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
184549.28339 40
 
0.4%
190717.703956 34
 
0.3%
187900.939617 32
 
0.3%
190674.256248 25
 
0.2%
190737.643277 21
 
0.2%
186484.775084 20
 
0.2%
184537.273724 20
 
0.2%
190613.077363 20
 
0.2%
190346.147267 17
 
0.2%
187602.933160728 13
 
0.1%
Other values (7313) 9400
94.0%
(Missing) 358
 
3.6%
ValueCountFrequency (%)
169678.048271107 1
< 0.1%
171205.308829 1
< 0.1%
171853.030476 1
< 0.1%
174139.062155 1
< 0.1%
174271.863625 1
< 0.1%
174278.713602 2
< 0.1%
174304.335355 1
< 0.1%
174308.655044 1
< 0.1%
174404.947794 1
< 0.1%
174408.450507 2
< 0.1%
ValueCountFrequency (%)
210621.30088 1
< 0.1%
209959.0 1
< 0.1%
209931.20783 1
< 0.1%
208940.818093 1
< 0.1%
208076.65376 1
< 0.1%
207600.052706 1
< 0.1%
207277.243876 1
< 0.1%
207197.92457 1
< 0.1%
206945.800615 2
< 0.1%
206599.783800745 1
< 0.1%

lastmodts
Real number (ℝ)

Distinct9766
Distinct (%)97.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0121178 × 1013
Minimum1.9990303 × 1013
Maximum2.0210222 × 1013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-16T17:58:15.969154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.9990303 × 1013
5-th percentile2.0030808 × 1013
Q12.0081116 × 1013
median2.0130725 × 1013
Q32.0161026 × 1013
95-th percentile2.0180821 × 1013
Maximum2.0210222 × 1013
Range2.1991915 × 1011
Interquartile range (IQR)7.9909747 × 1010

Descriptive statistics

Standard deviation4.8899986 × 1010
Coefficient of variation (CV)0.0024302745
Kurtosis-0.80217303
Mean2.0121178 × 1013
Median Absolute Deviation (MAD)3.9490011 × 1010
Skewness-0.49153165
Sum2.0121178 × 1017
Variance2.3912086 × 1021
MonotonicityNot monotonic
2024-04-16T17:58:16.077217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20061226000000 22
 
0.2%
20040823000000 15
 
0.1%
20050614000000 12
 
0.1%
20020802000000 9
 
0.1%
19990317000000 8
 
0.1%
20050615000000 8
 
0.1%
19990318000000 8
 
0.1%
20010803000000 7
 
0.1%
20050909000000 7
 
0.1%
20020801000000 6
 
0.1%
Other values (9756) 9898
99.0%
ValueCountFrequency (%)
19990303000000 1
 
< 0.1%
19990315000000 5
0.1%
19990316000000 5
0.1%
19990317000000 8
0.1%
19990318000000 8
0.1%
19990319000000 6
0.1%
19990323000000 2
 
< 0.1%
19990324000000 1
 
< 0.1%
19990511000000 3
 
< 0.1%
19990610000000 1
 
< 0.1%
ValueCountFrequency (%)
20210222152111 1
< 0.1%
20210219181535 1
< 0.1%
20210219131617 1
< 0.1%
20210215115201 1
< 0.1%
20210213222945 1
< 0.1%
20210213222556 1
< 0.1%
20210213222209 1
< 0.1%
20210213221807 1
< 0.1%
20210206092836 1
< 0.1%
20210206091849 1
< 0.1%

uptaenm
Categorical

IMBALANCE 

Distinct16
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
식육판매업
6058 
제과점영업
2124 
우유류판매업
914 
즉석판매제조가공업
 
283
축산물유통전문판매업
 
220
Other values (11)
 
401

Length

Max length13
Median length5
Mean length5.4394
Min length2

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row우유류판매업
2nd row축산물수입판매업
3rd row우유류판매업
4th row식육판매업
5th row축산물수입판매업

Common Values

ValueCountFrequency (%)
식육판매업 6058
60.6%
제과점영업 2124
 
21.2%
우유류판매업 914
 
9.1%
즉석판매제조가공업 283
 
2.8%
축산물유통전문판매업 220
 
2.2%
축산물수입판매업 186
 
1.9%
식용란수집판매업 90
 
0.9%
식육부산물전문판매업 43
 
0.4%
<NA> 35
 
0.4%
집단급식소 식품판매업 29
 
0.3%
Other values (6) 18
 
0.2%

Length

2024-04-16T17:58:16.175890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
식육판매업 6058
60.4%
제과점영업 2124
 
21.2%
우유류판매업 914
 
9.1%
즉석판매제조가공업 283
 
2.8%
축산물유통전문판매업 220
 
2.2%
축산물수입판매업 186
 
1.9%
식용란수집판매업 90
 
0.9%
식육부산물전문판매업 43
 
0.4%
na 35
 
0.3%
집단급식소 29
 
0.3%
Other values (7) 50
 
0.5%

sitetel
Categorical

IMBALANCE 

Distinct17
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
051-123-1234
9937 
<NA>
 
45
전화번호
 
3
051 896 2777
 
2
051 256 6649
 
1
Other values (12)
 
12

Length

Max length12
Median length12
Mean length11.9616
Min length4

Unique

Unique13 ?
Unique (%)0.1%

Sample

1st row051-123-1234
2nd row051-123-1234
3rd row051-123-1234
4th row051-123-1234
5th row051-123-1234

Common Values

ValueCountFrequency (%)
051-123-1234 9937
99.4%
<NA> 45
 
0.4%
전화번호 3
 
< 0.1%
051 896 2777 2
 
< 0.1%
051 256 6649 1
 
< 0.1%
062 224 2300 1
 
< 0.1%
062 524 5248 1
 
< 0.1%
031 793 5113 1
 
< 0.1%
051 519 6424 1
 
< 0.1%
051 831 5624 1
 
< 0.1%
Other values (7) 7
 
0.1%

Length

2024-04-16T17:58:16.285475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
051-123-1234 9937
99.1%
na 45
 
0.4%
051 11
 
0.1%
전화번호 3
 
< 0.1%
896 2
 
< 0.1%
2777 2
 
< 0.1%
831 2
 
< 0.1%
062 2
 
< 0.1%
8388 1
 
< 0.1%
971 1
 
< 0.1%
Other values (23) 23
 
0.2%

bdngownsenm
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9924 
건물소유구분명
 
40
자가
 
32
임대
 
4

Length

Max length7
Median length4
Mean length4.0048
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 9924
99.2%
건물소유구분명 40
 
0.4%
자가 32
 
0.3%
임대 4
 
< 0.1%

Length

2024-04-16T17:58:16.382374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:58:16.464182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9924
99.2%
건물소유구분명 40
 
0.4%
자가 32
 
0.3%
임대 4
 
< 0.1%

fctyowkepcnt
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9943 
공장사무직종업원수
 
42
0
 
15

Length

Max length9
Median length4
Mean length4.0165
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 9943
99.4%
공장사무직종업원수 42
 
0.4%
0 15
 
0.1%

Length

2024-04-16T17:58:16.567501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:58:16.651669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9943
99.4%
공장사무직종업원수 42
 
0.4%
0 15
 
0.1%

fctypdtjobepcnt
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9943 
공장생산직종업원수
 
42
0
 
15

Length

Max length9
Median length4
Mean length4.0165
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 9943
99.4%
공장생산직종업원수 42
 
0.4%
0 15
 
0.1%

Length

2024-04-16T17:58:16.753290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:58:16.837976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9943
99.4%
공장생산직종업원수 42
 
0.4%
0 15
 
0.1%

fctysiljobepcnt
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9943 
공장판매직종업원수
 
42
0
 
15

Length

Max length9
Median length4
Mean length4.0165
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 9943
99.4%
공장판매직종업원수 42
 
0.4%
0 15
 
0.1%

Length

2024-04-16T17:58:16.931259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:58:17.017903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9943
99.4%
공장판매직종업원수 42
 
0.4%
0 15
 
0.1%

rgtmbdsno
Categorical

IMBALANCE 

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
000
6731 
<NA>
2446 
L00
770 
권리주체일련번호
 
41
100
 
4
Other values (4)
 
8

Length

Max length8
Median length3
Mean length3.2651
Min length3

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row000
2nd row000
3rd row000
4th row000
5th row000

Common Values

ValueCountFrequency (%)
000 6731
67.3%
<NA> 2446
 
24.5%
L00 770
 
7.7%
권리주체일련번호 41
 
0.4%
100 4
 
< 0.1%
F00 3
 
< 0.1%
L01 2
 
< 0.1%
010 2
 
< 0.1%
200 1
 
< 0.1%

Length

2024-04-16T17:58:17.128050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:58:17.223265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
000 6731
67.3%
na 2446
 
24.5%
l00 770
 
7.7%
권리주체일련번호 41
 
0.4%
100 4
 
< 0.1%
f00 3
 
< 0.1%
l01 2
 
< 0.1%
010 2
 
< 0.1%
200 1
 
< 0.1%

wtrsplyfacilsenm
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
8817 
상수도전용
1139 
급수시설구분명
 
42
간이상수도
 
1
지하수전용
 
1

Length

Max length7
Median length4
Mean length4.1267
Min length4

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 8817
88.2%
상수도전용 1139
 
11.4%
급수시설구분명 42
 
0.4%
간이상수도 1
 
< 0.1%
지하수전용 1
 
< 0.1%

Length

2024-04-16T17:58:17.323289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:58:17.403072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 8817
88.2%
상수도전용 1139
 
11.4%
급수시설구분명 42
 
0.4%
간이상수도 1
 
< 0.1%
지하수전용 1
 
< 0.1%

maneipcnt
Categorical

IMBALANCE 

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9088 
0
 
803
1
 
53
남성종사자수
 
42
2
 
11
Other values (2)
 
3

Length

Max length6
Median length4
Mean length3.7474
Min length1

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 9088
90.9%
0 803
 
8.0%
1 53
 
0.5%
남성종사자수 42
 
0.4%
2 11
 
0.1%
3 2
 
< 0.1%
4 1
 
< 0.1%

Length

2024-04-16T17:58:17.490775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:58:17.594388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9088
90.9%
0 803
 
8.0%
1 53
 
0.5%
남성종사자수 42
 
0.4%
2 11
 
0.1%
3 2
 
< 0.1%
4 1
 
< 0.1%

multusnupsoyn
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
N
9981 
Y
 
14
<NA>
 
4
 
1

Length

Max length4
Median length1
Mean length1.0012
Min length1

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
N 9981
99.8%
Y 14
 
0.1%
<NA> 4
 
< 0.1%
1
 
< 0.1%

Length

2024-04-16T17:58:17.705359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:58:17.786040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
n 9981
99.8%
y 14
 
0.1%
na 4
 
< 0.1%
1
 
< 0.1%

lvsenm
Categorical

IMBALANCE 

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9271 
기타
 
492
자율
 
190
등급구분명
 
42
지도
 
2
Other values (2)
 
3

Length

Max length5
Median length4
Mean length3.8668
Min length2

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 9271
92.7%
기타 492
 
4.9%
자율 190
 
1.9%
등급구분명 42
 
0.4%
지도 2
 
< 0.1%
우수 2
 
< 0.1%
관리 1
 
< 0.1%

Length

2024-04-16T17:58:17.869185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:58:17.955299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9271
92.7%
기타 492
 
4.9%
자율 190
 
1.9%
등급구분명 42
 
0.4%
지도 2
 
< 0.1%
우수 2
 
< 0.1%
관리 1
 
< 0.1%

isream
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9953 
보증액
 
42
0
 
5

Length

Max length4
Median length4
Mean length3.9943
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 9953
99.5%
보증액 42
 
0.4%
0 5
 
0.1%

Length

2024-04-16T17:58:18.051726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:58:18.132645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9953
99.5%
보증액 42
 
0.4%
0 5
 
< 0.1%

hoffepcnt
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9943 
본사종업원수
 
42
0
 
15

Length

Max length6
Median length4
Mean length4.0039
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 9943
99.4%
본사종업원수 42
 
0.4%
0 15
 
0.1%

Length

2024-04-16T17:58:18.213306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:58:18.290242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9943
99.4%
본사종업원수 42
 
0.4%
0 15
 
0.1%

equsiz
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9958 
설비규격
 
42

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 9958
99.6%
설비규격 42
 
0.4%

Length

2024-04-16T17:58:18.373794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:58:18.446227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9958
99.6%
설비규격 42
 
0.4%

faciltotscp
Text

MISSING 

Distinct1564
Distinct (%)62.9%
Missing7512
Missing (%)75.1%
Memory size156.2 KiB
2024-04-16T17:58:18.711455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length3.7475884
Min length1

Characters and Unicode

Total characters9324
Distinct characters16
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

Unique1311 ?
Unique (%)52.7%

Sample

1st row37.33
2nd row36
3rd row34.3
4th row118.74
5th row29.6
ValueCountFrequency (%)
0 479
 
19.3%
24 13
 
0.5%
33 11
 
0.4%
20 9
 
0.4%
25 9
 
0.4%
26 8
 
0.3%
3.3 8
 
0.3%
12 8
 
0.3%
30 8
 
0.3%
14 7
 
0.3%
Other values (1553) 1928
77.5%
2024-04-16T17:58:19.128749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 1750
18.8%
2 995
10.7%
1 938
10.1%
0 880
9.4%
3 802
8.6%
5 779
8.4%
4 773
8.3%
6 732
7.9%
8 605
 
6.5%
9 557
 
6.0%
Other values (6) 513
 
5.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7569
81.2%
Other Punctuation 1750
 
18.8%
Other Letter 5
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 995
13.1%
1 938
12.4%
0 880
11.6%
3 802
10.6%
5 779
10.3%
4 773
10.2%
6 732
9.7%
8 605
8.0%
9 557
7.4%
7 508
6.7%
Other Letter
ValueCountFrequency (%)
1
20.0%
1
20.0%
1
20.0%
1
20.0%
1
20.0%
Other Punctuation
ValueCountFrequency (%)
. 1750
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 9319
99.9%
Hangul 5
 
0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
. 1750
18.8%
2 995
10.7%
1 938
10.1%
0 880
9.4%
3 802
8.6%
5 779
8.4%
4 773
8.3%
6 732
7.9%
8 605
 
6.5%
9 557
 
6.0%
Hangul
ValueCountFrequency (%)
1
20.0%
1
20.0%
1
20.0%
1
20.0%
1
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9319
99.9%
Hangul 5
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 1750
18.8%
2 995
10.7%
1 938
10.1%
0 880
9.4%
3 802
8.6%
5 779
8.4%
4 773
8.3%
6 732
7.9%
8 605
 
6.5%
9 557
 
6.0%
Hangul
ValueCountFrequency (%)
1
20.0%
1
20.0%
1
20.0%
1
20.0%
1
20.0%

wmeipcnt
Categorical

IMBALANCE 

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9084 
0
 
798
1
 
62
여성종사자수
 
42
2
 
10
Other values (3)
 
4

Length

Max length6
Median length4
Mean length3.7463
Min length1

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 9084
90.8%
0 798
 
8.0%
1 62
 
0.6%
여성종사자수 42
 
0.4%
2 10
 
0.1%
4 2
 
< 0.1%
3 1
 
< 0.1%
11 1
 
< 0.1%

Length

2024-04-16T17:58:19.240469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:58:19.326748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9084
90.8%
0 798
 
8.0%
1 62
 
0.6%
여성종사자수 42
 
0.4%
2 10
 
0.1%
4 2
 
< 0.1%
3 1
 
< 0.1%
11 1
 
< 0.1%

trdpjubnsenm
Categorical

IMBALANCE 

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9154 
기타
 
583
주택가주변
 
111
아파트지역
 
81
영업장주변구분명
 
42
Other values (3)
 
29

Length

Max length8
Median length4
Mean length3.9309
Min length2

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 9154
91.5%
기타 583
 
5.8%
주택가주변 111
 
1.1%
아파트지역 81
 
0.8%
영업장주변구분명 42
 
0.4%
유흥업소밀집지역 23
 
0.2%
학교정화(상대) 5
 
0.1%
결혼예식장주변 1
 
< 0.1%

Length

2024-04-16T17:58:19.425876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:58:19.513075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9154
91.5%
기타 583
 
5.8%
주택가주변 111
 
1.1%
아파트지역 81
 
0.8%
영업장주변구분명 42
 
0.4%
유흥업소밀집지역 23
 
0.2%
학교정화(상대 5
 
< 0.1%
결혼예식장주변 1
 
< 0.1%

monam
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9953 
월세액
 
42
0
 
5

Length

Max length4
Median length4
Mean length3.9943
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 9953
99.5%
월세액 42
 
0.4%
0 5
 
0.1%

Length

2024-04-16T17:58:19.613219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:58:19.700725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9953
99.5%
월세액 42
 
0.4%
0 5
 
< 0.1%

sntuptaenm
Categorical

IMBALANCE 

Distinct14
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
7512 
제과점영업
2124 
즉석판매제조가공업
 
283
집단급식소 식품판매업
 
29
전자상거래(통신판매업)
 
20
Other values (9)
 
32

Length

Max length13
Median length4
Mean length4.3999
Min length2

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 7512
75.1%
제과점영업 2124
 
21.2%
즉석판매제조가공업 283
 
2.8%
집단급식소 식품판매업 29
 
0.3%
전자상거래(통신판매업) 20
 
0.2%
영업장판매 11
 
0.1%
건강기능식품유통전문판매업 7
 
0.1%
방문판매 4
 
< 0.1%
기타 3
 
< 0.1%
기타 식품제조가공업 3
 
< 0.1%
Other values (4) 4
 
< 0.1%

Length

2024-04-16T17:58:19.798953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 7512
74.9%
제과점영업 2124
 
21.2%
즉석판매제조가공업 283
 
2.8%
집단급식소 29
 
0.3%
식품판매업 29
 
0.3%
전자상거래(통신판매업 20
 
0.2%
영업장판매 11
 
0.1%
건강기능식품유통전문판매업 7
 
0.1%
기타 6
 
0.1%
방문판매 4
 
< 0.1%
Other values (6) 8
 
0.1%

jtupsomainedf
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9958 
전통업소주된음식
 
42

Length

Max length8
Median length4
Mean length4.0168
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 9958
99.6%
전통업소주된음식 42
 
0.4%

Length

2024-04-16T17:58:19.917454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:58:20.012901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9958
99.6%
전통업소주된음식 42
 
0.4%

jtupsoasgnno
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9957 
전통업소지정번호
 
42
-+
 
1

Length

Max length8
Median length4
Mean length4.0166
Min length2

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 9957
99.6%
전통업소지정번호 42
 
0.4%
-+ 1
 
< 0.1%

Length

2024-04-16T17:58:20.100605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:58:20.180158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9957
99.6%
전통업소지정번호 42
 
0.4%
1
 
< 0.1%

totepnum
Categorical

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
식육판매업
6054 
<NA>
2453 
우유류판매업
913 
축산물유통전문판매업
 
219
축산물수입판매업
 
186
Other values (3)
 
175

Length

Max length10
Median length5
Mean length5.0598
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row우유류판매업
2nd row축산물수입판매업
3rd row우유류판매업
4th row식육판매업
5th row축산물수입판매업

Common Values

ValueCountFrequency (%)
식육판매업 6054
60.5%
<NA> 2453
24.5%
우유류판매업 913
 
9.1%
축산물유통전문판매업 219
 
2.2%
축산물수입판매업 186
 
1.9%
식용란수집판매업 90
 
0.9%
식육부산물전문판매업 43
 
0.4%
총종업원수 42
 
0.4%

Length

2024-04-16T17:58:20.263243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:58:20.352239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
식육판매업 6054
60.5%
na 2453
24.5%
우유류판매업 913
 
9.1%
축산물유통전문판매업 219
 
2.2%
축산물수입판매업 186
 
1.9%
식용란수집판매업 90
 
0.9%
식육부산물전문판매업 43
 
0.4%
총종업원수 42
 
0.4%

lindprcbgbnnm
Categorical

IMBALANCE 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
축산물판매업
7505 
<NA>
2448 
축산물가공업구분명
 
41
식육판매업
 
4
축산물유통전문판매업
 
1

Length

Max length10
Median length6
Mean length5.5227
Min length4

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
축산물판매업 7505
75.0%
<NA> 2448
 
24.5%
축산물가공업구분명 41
 
0.4%
식육판매업 4
 
< 0.1%
축산물유통전문판매업 1
 
< 0.1%
우유류판매업 1
 
< 0.1%

Length

2024-04-16T17:58:20.458435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:58:20.547964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
축산물판매업 7505
75.0%
na 2448
 
24.5%
축산물가공업구분명 41
 
0.4%
식육판매업 4
 
< 0.1%
축산물유통전문판매업 1
 
< 0.1%
우유류판매업 1
 
< 0.1%

lindjobgbnnm
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9951 
축산업무구분명
 
41
축산물판매업
 
6
축산물운반업
 
1
축산물보관업
 
1

Length

Max length7
Median length4
Mean length4.0139
Min length4

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 9951
99.5%
축산업무구분명 41
 
0.4%
축산물판매업 6
 
0.1%
축산물운반업 1
 
< 0.1%
축산물보관업 1
 
< 0.1%

Length

2024-04-16T17:58:20.649420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:58:20.734483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9951
99.5%
축산업무구분명 41
 
0.4%
축산물판매업 6
 
0.1%
축산물운반업 1
 
< 0.1%
축산물보관업 1
 
< 0.1%

lindseqno
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9958 
축산일련번호
 
42

Length

Max length6
Median length4
Mean length4.0084
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 9958
99.6%
축산일련번호 42
 
0.4%

Length

2024-04-16T17:58:20.830289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:58:20.909736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9958
99.6%
축산일련번호 42
 
0.4%

homepage
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9958 
홈페이지
 
42

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 9958
99.6%
홈페이지 42
 
0.4%

Length

2024-04-16T17:58:20.986638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:58:21.056688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9958
99.6%
홈페이지 42
 
0.4%

last_load_dttm
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2021-03-01 05:26:04
3936 
2021-03-01 05:26:03
3857 
2021-03-01 05:26:05
2207 

Length

Max length19
Median length19
Mean length19
Min length19

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021-03-01 05:26:04
2nd row2021-03-01 05:26:03
3rd row2021-03-01 05:26:04
4th row2021-03-01 05:26:03
5th row2021-03-01 05:26:03

Common Values

ValueCountFrequency (%)
2021-03-01 05:26:04 3936
39.4%
2021-03-01 05:26:03 3857
38.6%
2021-03-01 05:26:05 2207
22.1%

Length

2024-04-16T17:58:21.145477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:58:21.230001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021-03-01 10000
50.0%
05:26:04 3936
 
19.7%
05:26:03 3857
 
19.3%
05:26:05 2207
 
11.0%

Sample

skeyopnsfteamcodemgtnoopnsvcidupdategbnupdatedtopnsvcnmbplcnmsitepostnositewhladdrrdnpostnordnwhladdrapvpermymddcbymdclgstdtclgenddtropnymdtrdstatenmdtlstatenmxylastmodtsuptaenmsitetelbdngownsenmfctyowkepcntfctypdtjobepcntfctysiljobepcntrgtmbdsnowtrsplyfacilsenmmaneipcntmultusnupsoynlvsenmisreamhoffepcntequsizfaciltotscpwmeipcnttrdpjubnsenmmonamsntuptaenmjtupsomainedfjtupsoasgnnototepnumlindprcbgbnnmlindjobgbnnmlindseqnohomepagelast_load_dttm
56915698334000033400000092014002207_22_04_PI2018-08-31 23:59:59.0<NA>연세우유부산사하대리점<NA>부산광역시 사하구 다대동 950-16번지49495부산광역시 사하구 윤공단로 47 (다대동)20141008<NA><NA><NA><NA>0000정상379532.52786500000175152.1969000000020141008145300우유류판매업051-123-1234<NA><NA><NA><NA>000<NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>우유류판매업축산물판매업<NA><NA><NA>2021-03-01 05:26:04
37123705332000033200000092000002007_22_04_PI2018-08-31 23:59:59.0<NA>부산미트벨리<NA>부산광역시 북구 구포동 1188-5 번지48947<NA>20001007<NA><NA><NA><NA>0000정상<NA><NA>20030327143829축산물수입판매업051-123-1234<NA><NA><NA><NA>000<NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>축산물수입판매업축산물판매업<NA><NA><NA>2021-03-01 05:26:03
49154916333000033300000092002001207_22_04_PI2018-08-31 23:59:59.0<NA>부산우유 재송3보급소<NA>부산광역시 해운대구 재송동 1041-9번지 이화맨션상가 8호48947<NA>2002042320131014<NA><NA><NA>0002폐업393651.563137190482.92910220131014151249우유류판매업051-123-1234<NA><NA><NA><NA>000<NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>우유류판매업축산물판매업<NA><NA><NA>2021-03-01 05:26:04
25222519330000033000000092002003107_22_04_PI2018-08-31 23:59:59.0<NA>하나로식품<NA>부산광역시 동래구 안락동 152-87번지48947<NA>2002030220020313<NA><NA><NA>0002폐업391914.808137190469.32541320031202194116식육판매업051-123-1234<NA><NA><NA><NA>000<NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>식육판매업축산물판매업<NA><NA><NA>2021-03-01 05:26:03
37093702332000033200000092000001707_22_04_PI2018-08-31 23:59:59.0<NA>(주)케이앤제이위스<NA>부산광역시 북구 만덕동 287-2번지48947부산광역시 북구 덕천로 299 (만덕동)2000012420031229<NA><NA><NA>0002폐업385450.95468100000192265.4325220000020031229134043축산물수입판매업051-123-1234<NA><NA><NA><NA>000<NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>축산물수입판매업축산물판매업<NA><NA><NA>2021-03-01 05:26:03
44024397333000033300000091986000507_22_04_PI2018-08-31 23:59:59.0<NA>부전식육점<NA>부산광역시 해운대구 반송동 250-248번지48947<NA>1986070920130227<NA><NA><NA>0002폐업395745.085056194113.12900720130227170019식육판매업051-123-1234<NA><NA><NA><NA>000<NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>식육판매업축산물판매업<NA><NA><NA>2021-03-01 05:26:03
121791223933900003390000-121-1998-0000207_22_18_PI2018-08-31 23:59:59.0<NA>리첼617808부산광역시 사상구 괘법동 516-3번지 한신2차상가 12동 105호48947<NA>1998102720100201<NA><NA><NA>02폐업380306.426506187560.58344120071101133204제과점영업051-123-1234<NA><NA><NA><NA><NA>상수도전용0N기타<NA><NA><NA>37.330아파트지역<NA>제과점영업<NA><NA><NA><NA><NA><NA><NA>2021-03-01 05:26:05
47404738333000033300000092006000507_22_04_PI2018-08-31 23:59:59.0<NA>우리한우마을<NA>부산광역시 해운대구 반여동 1441-84번지48037부산광역시 해운대구 선수촌로 80-1 (반여동)20060215<NA><NA><NA><NA>0004말소393009.65893300000191436.5724970000020160408094812식육판매업051-123-1234<NA><NA><NA><NA>000<NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>식육판매업축산물판매업<NA><NA><NA>2021-03-01 05:26:03
9957997432800003280000-121-1994-0000207_22_18_PI2018-08-31 23:59:59.0<NA>밀타운제과606809부산광역시 영도구 동삼동 545-1번지49118부산광역시 영도구 중리북로29번길 2 (동삼동)1991101120130618<NA><NA><NA>02폐업388589.75747700000176969.7059400000020130625143737제과점영업051-123-1234<NA><NA><NA><NA><NA><NA>0N<NA><NA><NA><NA>360<NA><NA>제과점영업<NA><NA><NA><NA><NA><NA><NA>2021-03-01 05:26:04
75227513337000033700000092015000707_22_04_PI2018-08-31 23:59:59.0<NA>새벽달걀<NA>부산광역시 연제구 연산동 1811-143번지47616부산광역시 연제구 금련로 25 (연산동)20150728<NA><NA><NA><NA>0000정상390997.45081900000188173.7121840000020151027090540식용란수집판매업051-123-1234<NA><NA><NA><NA>000<NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>식용란수집판매업축산물판매업<NA><NA><NA>2021-03-01 05:26:04
skeyopnsfteamcodemgtnoopnsvcidupdategbnupdatedtopnsvcnmbplcnmsitepostnositewhladdrrdnpostnordnwhladdrapvpermymddcbymdclgstdtclgenddtropnymdtrdstatenmdtlstatenmxylastmodtsuptaenmsitetelbdngownsenmfctyowkepcntfctypdtjobepcntfctysiljobepcntrgtmbdsnowtrsplyfacilsenmmaneipcntmultusnupsoynlvsenmisreamhoffepcntequsizfaciltotscpwmeipcnttrdpjubnsenmmonamsntuptaenmjtupsomainedfjtupsoasgnnototepnumlindprcbgbnnmlindjobgbnnmlindseqnohomepagelast_load_dttm
53915390334000033400000091984000807_22_04_PI2018-08-31 23:59:59.0<NA>성호식육점<NA>부산광역시 사하구 다대동 541-2번지48947<NA>1984082520050216<NA><NA><NA>0002폐업<NA><NA>20050216104311식육판매업051-123-1234<NA><NA><NA><NA>000<NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>식육판매업축산물판매업<NA><NA><NA>2021-03-01 05:26:04
53595358334000033400000091987000407_22_04_PI2018-08-31 23:59:59.0<NA>선미식육점<NA>부산광역시 사하구 당리동 327-1번지48947부산광역시 사하구 괴정로 86 (당리동)1987030620121026<NA><NA><NA>0002폐업379950.64646700000180177.0307900000020121213145624식육판매업051-123-1234<NA><NA><NA><NA>000<NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>식육판매업축산물판매업<NA><NA><NA>2021-03-01 05:26:04
279281326000032600000092010000307_22_04_PI2018-08-31 23:59:59.0<NA>승학마루<NA>부산광역시 서구 서대신동3가 7-57번지49200부산광역시 서구 꽃마을로163번길 34, 201호 (서대신동3가)2010102520141121<NA><NA><NA>0002폐업382893.86034200000183088.2958680000020141121102448식육판매업051-123-1234<NA><NA><NA><NA>000<NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>식육판매업축산물판매업<NA><NA><NA>2021-03-01 05:26:03
108401085333200003320000-121-2010-0001107_22_18_PI2018-08-31 23:59:59.0<NA>몽블랑제616811부산광역시 북구 금곡동 57-9번지 홈플러스익스프레스 부산금곡점 내46512부산광역시 북구 효열로 222 (금곡동,홈플러스익스프레스 부산금곡점 내)2010102020150508<NA><NA><NA>02폐업383791.64901200000198214.6184820000020131025152156제과점영업051-123-1234<NA><NA><NA><NA><NA>상수도전용<NA>N<NA><NA><NA><NA>9<NA><NA><NA>제과점영업<NA><NA><NA><NA><NA><NA><NA>2021-03-01 05:26:05
57645771334000033400000092001001907_22_04_PI2018-08-31 23:59:59.0<NA>주공식육점<NA>부산광역시 사하구 다대동 86-6번지 주공복합상가 108호48947<NA>2001091920100519<NA><NA><NA>0002폐업380892.903206176108.83371120100519155651식육판매업051-123-1234<NA><NA><NA><NA>000<NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>식육판매업축산물판매업<NA><NA><NA>2021-03-01 05:26:04
85088511339000033900000091995000807_22_04_PI2018-08-31 23:59:59.0<NA>영아식품<NA>부산광역시 사상구 학장동 570-2번지48947부산광역시 사상구 학감대로 109-10 (학장동)1995080520110429<NA><NA><NA>0002폐업381178.78237800000184515.0153470000020110429131535식육판매업051-123-1234<NA><NA><NA><NA>000<NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>식육판매업축산물판매업<NA><NA><NA>2021-03-01 05:26:04
85678570339000033900000092009000207_22_04_PI2018-08-31 23:59:59.0<NA>성황리마트<NA>부산광역시 사상구 덕포동 421-13번지48947부산광역시 사상구 사상로293번길 12 (덕포동)2009011520091013<NA><NA><NA>0002폐업380592.32023000000187908.0431760000020091013092806식육판매업051-123-1234<NA><NA><NA><NA>000<NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>식육판매업축산물판매업<NA><NA><NA>2021-03-01 05:26:04
56605666334000033400000092012003607_22_04_PI2018-08-31 23:59:59.0<NA>성원마트(정육코너)<NA>부산광역시 사하구 장림동 599-8번지49483부산광역시 사하구 장평로 121 (장림동)20120824<NA><NA><NA><NA>0000정상379923.14261500000177407.5435480000020161115163737식육판매업051-123-1234<NA><NA><NA><NA>000<NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>식육판매업축산물판매업<NA><NA><NA>2021-03-01 05:26:04
247249326000032600000092002001207_22_04_PI2018-08-31 23:59:59.0<NA>새벽식육점<NA>부산광역시 서구 충무동1가 32-38번지49254부산광역시 서구 충무대로276번길 6 (충무동1가)20020605<NA><NA><NA><NA>0000정상384619.63117900000179508.1736200000020120213165907식육판매업051-123-1234<NA><NA><NA><NA>000<NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>식육판매업축산물판매업<NA><NA><NA>2021-03-01 05:26:03
34113402331000033100000092002001407_22_04_PI2018-08-31 23:59:59.0<NA>대연에스마켓식육검<NA>부산광역시 남구 대연동 1170-2번지 대연대우아파트지하상가 1호48947부산광역시 남구 홍곡로 343, 1호 (대연동,대연대우아파트지하상가)2002040420030814<NA><NA><NA>0002폐업390471.10538600000182779.8525350000020040110105832식육판매업051-123-1234<NA><NA><NA><NA>000<NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>식육판매업축산물판매업<NA><NA><NA>2021-03-01 05:26:03