Overview

Dataset statistics

Number of variables49
Number of observations10000
Missing cells18736
Missing cells (%)3.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.9 MiB
Average record size in memory404.0 B

Variable types

Numeric4
Text10
Categorical32
DateTime2
Unsupported1

Alerts

opnsvcid is highly imbalanced (55.9%)Imbalance
updategbn is highly imbalanced (98.7%)Imbalance
opnsvcnm is highly imbalanced (67.2%)Imbalance
clgstdt is highly imbalanced (93.5%)Imbalance
clgenddt is highly imbalanced (93.3%)Imbalance
ropnymd is highly imbalanced (89.0%)Imbalance
bdngownsenm is highly imbalanced (65.7%)Imbalance
fctyowkepcnt is highly imbalanced (87.5%)Imbalance
fctypdtjobepcnt is highly imbalanced (88.5%)Imbalance
fctysiljobepcnt is highly imbalanced (85.4%)Imbalance
rgtmbdsno is highly imbalanced (51.9%)Imbalance
wtrsplyfacilsenm is highly imbalanced (68.9%)Imbalance
maneipcnt is highly imbalanced (80.4%)Imbalance
multusnupsoyn is highly imbalanced (97.2%)Imbalance
lvsenm is highly imbalanced (81.6%)Imbalance
isream is highly imbalanced (91.2%)Imbalance
hoffepcnt is highly imbalanced (85.7%)Imbalance
equsiz is highly imbalanced (78.2%)Imbalance
wmeipcnt is highly imbalanced (80.4%)Imbalance
trdpjubnsenm is highly imbalanced (79.3%)Imbalance
monam is highly imbalanced (91.2%)Imbalance
sntuptaenm is highly imbalanced (58.3%)Imbalance
jtupsomainedf is highly imbalanced (78.2%)Imbalance
jtupsoasgnno is highly imbalanced (78.2%)Imbalance
totepnum is highly imbalanced (52.5%)Imbalance
lindprcbgbnnm is highly imbalanced (62.8%)Imbalance
lindjobgbnnm is highly imbalanced (88.9%)Imbalance
lindseqno is highly imbalanced (78.2%)Imbalance
homepage is highly imbalanced (86.2%)Imbalance
sitepostno has 5204 (52.0%) missing valuesMissing
sitewhladdr has 213 (2.1%) missing valuesMissing
rdnwhladdr has 1111 (11.1%) missing valuesMissing
dcbymd has 6248 (62.5%) missing valuesMissing
x has 290 (2.9%) missing valuesMissing
y has 290 (2.9%) missing valuesMissing
sitetel has 296 (3.0%) missing valuesMissing
faciltotscp has 5084 (50.8%) missing valuesMissing
skey has unique valuesUnique
rdnpostno is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2024-04-16 08:58:49.117740
Analysis finished2024-04-16 08:58:51.697946
Duration2.58 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%
Mean9680.7816
Minimum2
Maximum19327
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-16T17:58:51.755068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile952.95
Q14829.75
median9703
Q314518.75
95-th percentile18364.1
Maximum19327
Range19325
Interquartile range (IQR)9689

Descriptive statistics

Standard deviation5586.8125
Coefficient of variation (CV)0.57710346
Kurtosis-1.2047282
Mean9680.7816
Median Absolute Deviation (MAD)4840.5
Skewness-0.0052221239
Sum96807816
Variance31212474
MonotonicityNot monotonic
2024-04-16T17:58:51.869771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10697 1
 
< 0.1%
1163 1
 
< 0.1%
1909 1
 
< 0.1%
366 1
 
< 0.1%
2301 1
 
< 0.1%
18491 1
 
< 0.1%
17329 1
 
< 0.1%
17533 1
 
< 0.1%
4548 1
 
< 0.1%
15536 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
2 1
< 0.1%
3 1
< 0.1%
5 1
< 0.1%
7 1
< 0.1%
9 1
< 0.1%
15 1
< 0.1%
16 1
< 0.1%
17 1
< 0.1%
18 1
< 0.1%
22 1
< 0.1%
ValueCountFrequency (%)
19327 1
< 0.1%
19326 1
< 0.1%
19324 1
< 0.1%
19322 1
< 0.1%
19318 1
< 0.1%
19316 1
< 0.1%
19315 1
< 0.1%
19314 1
< 0.1%
19313 1
< 0.1%
19312 1
< 0.1%

opnsfteamcode
Real number (ℝ)

Distinct214
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3589419.2
Minimum3000000
Maximum6520000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-16T17:58:51.996518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3000000
5-th percentile3209500
Q13300000
median3340000
Q33450000
95-th percentile5310000
Maximum6520000
Range3520000
Interquartile range (IQR)150000

Descriptive statistics

Standard deviation613531.1
Coefficient of variation (CV)0.17092768
Kurtosis5.1693433
Mean3589419.2
Median Absolute Deviation (MAD)50000
Skewness2.4004433
Sum3.5894192 × 1010
Variance3.7642041 × 1011
MonotonicityNot monotonic
2024-04-16T17:58:52.110286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3290000 657
 
6.6%
3330000 652
 
6.5%
3320000 630
 
6.3%
3390000 602
 
6.0%
3300000 565
 
5.7%
3340000 556
 
5.6%
3350000 454
 
4.5%
3310000 439
 
4.4%
3370000 404
 
4.0%
3380000 355
 
3.5%
Other values (204) 4686
46.9%
ValueCountFrequency (%)
3000000 10
 
0.1%
3010000 34
0.3%
3020000 20
0.2%
3030000 17
0.2%
3040000 24
0.2%
3050000 27
0.3%
3060000 17
0.2%
3070000 20
0.2%
3080000 15
0.1%
3090000 12
 
0.1%
ValueCountFrequency (%)
6520000 10
 
0.1%
6510000 28
 
0.3%
6450000 2
 
< 0.1%
6440000 1
 
< 0.1%
6410000 1
 
< 0.1%
5710000 70
0.7%
5700000 5
 
0.1%
5690000 33
0.3%
5680000 8
 
0.1%
5670000 55
0.5%

mgtno
Text

Distinct9645
Distinct (%)96.5%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-04-16T17:58:52.278417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length18
Mean length19.9541
Min length18

Characters and Unicode

Total characters199541
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

Unique9321 ?
Unique (%)93.2%

Sample

1st row3310000-121-2007-00001
2nd row356000000920190094
3rd row331000000920120008
4th row330000000919990006
5th row335000000919870009
ValueCountFrequency (%)
3080000-107-2019-00020 3
 
< 0.1%
571000000720200002 3
 
< 0.1%
3220000-107-2020-00041 3
 
< 0.1%
3320000-107-2019-00007 3
 
< 0.1%
4070000-107-2019-00092 3
 
< 0.1%
4140000-107-2020-00016 3
 
< 0.1%
5340000-107-2020-00036 3
 
< 0.1%
5530000-107-2019-00077 3
 
< 0.1%
3620000-107-2019-00027 3
 
< 0.1%
3250000-107-2019-00112 3
 
< 0.1%
Other values (9635) 9970
99.7%
2024-04-16T17:58:52.567826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 94555
47.4%
1 18365
 
9.2%
3 18220
 
9.1%
2 17843
 
8.9%
- 14652
 
7.3%
9 13375
 
6.7%
7 6053
 
3.0%
4 5057
 
2.5%
5 4283
 
2.1%
8 3786
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 184889
92.7%
Dash Punctuation 14652
 
7.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 94555
51.1%
1 18365
 
9.9%
3 18220
 
9.9%
2 17843
 
9.7%
9 13375
 
7.2%
7 6053
 
3.3%
4 5057
 
2.7%
5 4283
 
2.3%
8 3786
 
2.0%
6 3352
 
1.8%
Dash Punctuation
ValueCountFrequency (%)
- 14652
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 199541
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 94555
47.4%
1 18365
 
9.2%
3 18220
 
9.1%
2 17843
 
8.9%
- 14652
 
7.3%
9 13375
 
6.7%
7 6053
 
3.0%
4 5057
 
2.5%
5 4283
 
2.1%
8 3786
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 199541
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 94555
47.4%
1 18365
 
9.2%
3 18220
 
9.1%
2 17843
 
8.9%
- 14652
 
7.3%
9 13375
 
6.7%
7 6053
 
3.0%
4 5057
 
2.5%
5 4283
 
2.1%
8 3786
 
1.9%

opnsvcid
Categorical

IMBALANCE 

Distinct18
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
07_22_04_P
5023 
07_22_19_P
2888 
07_22_18_P
1413 
07_22_03_P
 
351
07_22_01_P
 
97
Other values (13)
 
228

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row07_22_18_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 5023
50.2%
07_22_19_P 2888
28.9%
07_22_18_P 1413
 
14.1%
07_22_03_P 351
 
3.5%
07_22_01_P 97
 
1.0%
07_22_02_P 67
 
0.7%
07_22_25_P 49
 
0.5%
07_22_11_P 34
 
0.3%
07_22_24_P 26
 
0.3%
07_22_10_P 16
 
0.2%
Other values (8) 36
 
0.4%

Length

2024-04-16T17:58:52.682433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
07_22_04_p 5023
50.2%
07_22_19_p 2888
28.9%
07_22_18_p 1413
 
14.1%
07_22_03_p 351
 
3.5%
07_22_01_p 97
 
1.0%
07_22_02_p 67
 
0.7%
07_22_25_p 49
 
0.5%
07_22_11_p 34
 
0.3%
07_22_24_p 26
 
0.3%
07_22_10_p 16
 
0.2%
Other values (8) 36
 
0.4%

updategbn
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
I
9988 
U
 
12

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 9988
99.9%
U 12
 
0.1%

Length

2024-04-16T17:58:52.776296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:58:52.848881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
i 9988
99.9%
u 12
 
0.1%
Distinct559
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2018-08-31 23:59:59
Maximum2021-01-05 00:23:05
2024-04-16T17:58:52.930215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T17:58:53.036476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

opnsvcnm
Categorical

IMBALANCE 

Distinct19
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
6386 
즉석판매제조가공업
2888 
건강기능식품일반판매업
 
351
집단급식소식품판매업
 
97
건강기능식품유통전문판매업
 
67
Other values (14)
 
211

Length

Max length13
Median length4
Mean length5.8534
Min length3

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 6386
63.9%
즉석판매제조가공업 2888
28.9%
건강기능식품일반판매업 351
 
3.5%
집단급식소식품판매업 97
 
1.0%
건강기능식품유통전문판매업 67
 
0.7%
축산물운반업 49
 
0.5%
축산판매업 48
 
0.5%
식품제조가공업 34
 
0.3%
축산물보관업 26
 
0.3%
식품자동판매기업 16
 
0.2%
Other values (9) 38
 
0.4%

Length

2024-04-16T17:58:53.147507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 6386
63.9%
즉석판매제조가공업 2888
28.9%
건강기능식품일반판매업 351
 
3.5%
집단급식소식품판매업 97
 
1.0%
건강기능식품유통전문판매업 67
 
0.7%
축산물운반업 49
 
0.5%
축산판매업 48
 
0.5%
식품제조가공업 34
 
0.3%
축산물보관업 26
 
0.3%
식품자동판매기업 16
 
0.2%
Other values (9) 38
 
0.4%

bplcnm
Text

Distinct7409
Distinct (%)74.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-04-16T17:58:53.354310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length41
Median length27
Mean length6.714
Min length2

Characters and Unicode

Total characters67140
Distinct characters895
Distinct categories13 ?
Distinct scripts4 ?
Distinct blocks7 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6334 ?
Unique (%)63.3%

Sample

1st row따삐오 베이커리
2nd row신선유통
3rd row한우외식센타
4th row봉계유통
5th row세원식육점
ValueCountFrequency (%)
주식회사 337
 
2.8%
파리바게뜨 46
 
0.4%
베이커리 42
 
0.4%
수라원 41
 
0.3%
주)한울에프엔비 40
 
0.3%
부산우유 37
 
0.3%
더원씨푸드 36
 
0.3%
주)와이에이비커머스 35
 
0.3%
뚜레쥬르 34
 
0.3%
현승유통 32
 
0.3%
Other values (7763) 11210
94.3%
2024-04-16T17:58:53.713917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2736
 
4.1%
2358
 
3.5%
1950
 
2.9%
1891
 
2.8%
) 1755
 
2.6%
( 1742
 
2.6%
1696
 
2.5%
1436
 
2.1%
1275
 
1.9%
1249
 
1.9%
Other values (885) 49052
73.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 60066
89.5%
Space Separator 1891
 
2.8%
Close Punctuation 1755
 
2.6%
Open Punctuation 1742
 
2.6%
Uppercase Letter 617
 
0.9%
Lowercase Letter 533
 
0.8%
Decimal Number 292
 
0.4%
Other Punctuation 131
 
0.2%
Dash Punctuation 105
 
0.2%
Modifier Symbol 3
 
< 0.1%
Other values (3) 5
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
2736
 
4.6%
2358
 
3.9%
1950
 
3.2%
1696
 
2.8%
1436
 
2.4%
1275
 
2.1%
1249
 
2.1%
1110
 
1.8%
1028
 
1.7%
1020
 
1.7%
Other values (808) 44208
73.6%
Uppercase Letter
ValueCountFrequency (%)
S 80
 
13.0%
M 43
 
7.0%
G 38
 
6.2%
T 35
 
5.7%
N 35
 
5.7%
D 34
 
5.5%
B 31
 
5.0%
C 31
 
5.0%
K 31
 
5.0%
J 30
 
4.9%
Other values (14) 229
37.1%
Lowercase Letter
ValueCountFrequency (%)
e 83
15.6%
a 53
 
9.9%
o 47
 
8.8%
n 34
 
6.4%
s 33
 
6.2%
i 32
 
6.0%
l 30
 
5.6%
r 28
 
5.3%
y 22
 
4.1%
t 21
 
3.9%
Other values (13) 150
28.1%
Other Punctuation
ValueCountFrequency (%)
& 46
35.1%
. 26
19.8%
, 21
16.0%
? 14
 
10.7%
' 11
 
8.4%
" 4
 
3.1%
3
 
2.3%
: 2
 
1.5%
! 1
 
0.8%
; 1
 
0.8%
Other values (2) 2
 
1.5%
Decimal Number
ValueCountFrequency (%)
1 75
25.7%
2 71
24.3%
3 32
11.0%
5 20
 
6.8%
0 19
 
6.5%
6 19
 
6.5%
9 16
 
5.5%
8 15
 
5.1%
4 13
 
4.5%
7 12
 
4.1%
Space Separator
ValueCountFrequency (%)
1891
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1755
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1742
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 105
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 3
100.0%
Math Symbol
ValueCountFrequency (%)
+ 3
100.0%
Letter Number
ValueCountFrequency (%)
1
100.0%
Other Symbol
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 60059
89.5%
Common 5922
 
8.8%
Latin 1151
 
1.7%
Han 8
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
2736
 
4.6%
2358
 
3.9%
1950
 
3.2%
1696
 
2.8%
1436
 
2.4%
1275
 
2.1%
1249
 
2.1%
1110
 
1.8%
1028
 
1.7%
1020
 
1.7%
Other values (801) 44201
73.6%
Latin
ValueCountFrequency (%)
e 83
 
7.2%
S 80
 
7.0%
a 53
 
4.6%
o 47
 
4.1%
M 43
 
3.7%
G 38
 
3.3%
T 35
 
3.0%
N 35
 
3.0%
D 34
 
3.0%
n 34
 
3.0%
Other values (38) 669
58.1%
Common
ValueCountFrequency (%)
1891
31.9%
) 1755
29.6%
( 1742
29.4%
- 105
 
1.8%
1 75
 
1.3%
2 71
 
1.2%
& 46
 
0.8%
3 32
 
0.5%
. 26
 
0.4%
, 21
 
0.4%
Other values (18) 158
 
2.7%
Han
ValueCountFrequency (%)
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 60056
89.4%
ASCII 7068
 
10.5%
CJK 7
 
< 0.1%
None 5
 
< 0.1%
Compat Jamo 2
 
< 0.1%
Number Forms 1
 
< 0.1%
CJK Compat Ideographs 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
2736
 
4.6%
2358
 
3.9%
1950
 
3.2%
1696
 
2.8%
1436
 
2.4%
1275
 
2.1%
1249
 
2.1%
1110
 
1.8%
1028
 
1.7%
1020
 
1.7%
Other values (798) 44198
73.6%
ASCII
ValueCountFrequency (%)
1891
26.8%
) 1755
24.8%
( 1742
24.6%
- 105
 
1.5%
e 83
 
1.2%
S 80
 
1.1%
1 75
 
1.1%
2 71
 
1.0%
a 53
 
0.7%
o 47
 
0.7%
Other values (63) 1166
16.5%
None
ValueCountFrequency (%)
3
60.0%
1
 
20.0%
· 1
 
20.0%
Number Forms
ValueCountFrequency (%)
1
100.0%
CJK
ValueCountFrequency (%)
1
14.3%
1
14.3%
1
14.3%
1
14.3%
1
14.3%
1
14.3%
1
14.3%
CJK Compat Ideographs
ValueCountFrequency (%)
1
100.0%
Compat Jamo
ValueCountFrequency (%)
1
50.0%
1
50.0%

sitepostno
Text

MISSING 

Distinct2120
Distinct (%)44.2%
Missing5204
Missing (%)52.0%
Memory size156.2 KiB
2024-04-16T17:58:54.176110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters28776
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

Unique1153 ?
Unique (%)24.0%

Sample

1st row608807
2nd row150034
3rd row611823
4th row760310
5th row136719
ValueCountFrequency (%)
612020 47
 
1.0%
지번우편번호 44
 
0.9%
463420 23
 
0.5%
600017 20
 
0.4%
406081 20
 
0.4%
411410 19
 
0.4%
618814 18
 
0.4%
617808 17
 
0.4%
616852 17
 
0.4%
608832 15
 
0.3%
Other values (2110) 4556
95.0%
2024-04-16T17:58:54.599253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 4558
15.8%
1 4109
14.3%
8 3895
13.5%
6 3382
11.8%
4 2795
9.7%
3 2482
8.6%
2 2448
8.5%
5 1784
 
6.2%
7 1758
 
6.1%
9 1301
 
4.5%
Other values (5) 264
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 28512
99.1%
Other Letter 264
 
0.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4558
16.0%
1 4109
14.4%
8 3895
13.7%
6 3382
11.9%
4 2795
9.8%
3 2482
8.7%
2 2448
8.6%
5 1784
 
6.3%
7 1758
 
6.2%
9 1301
 
4.6%
Other Letter
ValueCountFrequency (%)
88
33.3%
44
16.7%
44
16.7%
44
16.7%
44
16.7%

Most occurring scripts

ValueCountFrequency (%)
Common 28512
99.1%
Hangul 264
 
0.9%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4558
16.0%
1 4109
14.4%
8 3895
13.7%
6 3382
11.9%
4 2795
9.8%
3 2482
8.7%
2 2448
8.6%
5 1784
 
6.3%
7 1758
 
6.2%
9 1301
 
4.6%
Hangul
ValueCountFrequency (%)
88
33.3%
44
16.7%
44
16.7%
44
16.7%
44
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28512
99.1%
Hangul 264
 
0.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4558
16.0%
1 4109
14.4%
8 3895
13.7%
6 3382
11.9%
4 2795
9.8%
3 2482
8.7%
2 2448
8.6%
5 1784
 
6.3%
7 1758
 
6.2%
9 1301
 
4.6%
Hangul
ValueCountFrequency (%)
88
33.3%
44
16.7%
44
16.7%
44
16.7%
44
16.7%

sitewhladdr
Text

MISSING 

Distinct7946
Distinct (%)81.2%
Missing213
Missing (%)2.1%
Memory size156.2 KiB
2024-04-16T17:58:54.912041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length72
Median length53
Mean length25.888117
Min length13

Characters and Unicode

Total characters253367
Distinct characters609
Distinct categories12 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6851 ?
Unique (%)70.0%

Sample

1st row부산광역시 남구 대연동 1742-4번지 외 1필지
2nd row인천광역시 서구 오류동 1654번지 (주)신선식품
3rd row부산광역시 남구 용호동 495-14번지
4th row부산광역시 동래구 온천동 729-12번지
5th row부산광역시 금정구 장전동 418-10번지
ValueCountFrequency (%)
부산광역시 6409
 
13.6%
경기도 1001
 
2.1%
서울특별시 794
 
1.7%
북구 657
 
1.4%
부산진구 614
 
1.3%
해운대구 608
 
1.3%
사상구 596
 
1.3%
동래구 563
 
1.2%
사하구 547
 
1.2%
남구 492
 
1.0%
Other values (10997) 34938
74.0%
2024-04-16T17:58:55.345395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
46755
 
18.5%
10824
 
4.3%
1 10631
 
4.2%
9836
 
3.9%
9544
 
3.8%
8853
 
3.5%
8834
 
3.5%
8449
 
3.3%
7890
 
3.1%
- 7722
 
3.0%
Other values (599) 124029
49.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 151608
59.8%
Space Separator 46755
 
18.5%
Decimal Number 45777
 
18.1%
Dash Punctuation 7722
 
3.0%
Uppercase Letter 629
 
0.2%
Other Punctuation 298
 
0.1%
Open Punctuation 221
 
0.1%
Close Punctuation 219
 
0.1%
Lowercase Letter 124
 
< 0.1%
Math Symbol 12
 
< 0.1%
Other values (2) 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
10824
 
7.1%
9836
 
6.5%
9544
 
6.3%
8853
 
5.8%
8834
 
5.8%
8449
 
5.6%
7890
 
5.2%
7382
 
4.9%
7019
 
4.6%
2443
 
1.6%
Other values (530) 70534
46.5%
Uppercase Letter
ValueCountFrequency (%)
A 87
13.8%
B 86
13.7%
S 67
10.7%
G 52
 
8.3%
E 48
 
7.6%
C 43
 
6.8%
K 39
 
6.2%
T 26
 
4.1%
M 22
 
3.5%
R 21
 
3.3%
Other values (15) 138
21.9%
Lowercase Letter
ValueCountFrequency (%)
e 19
15.3%
s 15
12.1%
l 10
 
8.1%
a 9
 
7.3%
g 8
 
6.5%
t 7
 
5.6%
i 7
 
5.6%
o 6
 
4.8%
u 5
 
4.0%
n 5
 
4.0%
Other values (9) 33
26.6%
Decimal Number
ValueCountFrequency (%)
1 10631
23.2%
2 5828
12.7%
3 4684
10.2%
4 4225
 
9.2%
5 4077
 
8.9%
0 3753
 
8.2%
6 3514
 
7.7%
7 3226
 
7.0%
8 2984
 
6.5%
9 2855
 
6.2%
Other Punctuation
ValueCountFrequency (%)
, 210
70.5%
? 35
 
11.7%
. 28
 
9.4%
@ 19
 
6.4%
/ 2
 
0.7%
' 2
 
0.7%
: 1
 
0.3%
& 1
 
0.3%
Space Separator
ValueCountFrequency (%)
46755
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 7722
100.0%
Open Punctuation
ValueCountFrequency (%)
( 221
100.0%
Close Punctuation
ValueCountFrequency (%)
) 219
100.0%
Math Symbol
ValueCountFrequency (%)
~ 12
100.0%
Letter Number
ValueCountFrequency (%)
1
100.0%
Other Symbol
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 151609
59.8%
Common 101004
39.9%
Latin 754
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
10824
 
7.1%
9836
 
6.5%
9544
 
6.3%
8853
 
5.8%
8834
 
5.8%
8449
 
5.6%
7890
 
5.2%
7382
 
4.9%
7019
 
4.6%
2443
 
1.6%
Other values (531) 70535
46.5%
Latin
ValueCountFrequency (%)
A 87
 
11.5%
B 86
 
11.4%
S 67
 
8.9%
G 52
 
6.9%
E 48
 
6.4%
C 43
 
5.7%
K 39
 
5.2%
T 26
 
3.4%
M 22
 
2.9%
R 21
 
2.8%
Other values (35) 263
34.9%
Common
ValueCountFrequency (%)
46755
46.3%
1 10631
 
10.5%
- 7722
 
7.6%
2 5828
 
5.8%
3 4684
 
4.6%
4 4225
 
4.2%
5 4077
 
4.0%
0 3753
 
3.7%
6 3514
 
3.5%
7 3226
 
3.2%
Other values (13) 6589
 
6.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 151608
59.8%
ASCII 101757
40.2%
Number Forms 1
 
< 0.1%
None 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
46755
45.9%
1 10631
 
10.4%
- 7722
 
7.6%
2 5828
 
5.7%
3 4684
 
4.6%
4 4225
 
4.2%
5 4077
 
4.0%
0 3753
 
3.7%
6 3514
 
3.5%
7 3226
 
3.2%
Other values (57) 7342
 
7.2%
Hangul
ValueCountFrequency (%)
10824
 
7.1%
9836
 
6.5%
9544
 
6.3%
8853
 
5.8%
8834
 
5.8%
8449
 
5.6%
7890
 
5.2%
7382
 
4.9%
7019
 
4.6%
2443
 
1.6%
Other values (530) 70534
46.5%
Number Forms
ValueCountFrequency (%)
1
100.0%
None
ValueCountFrequency (%)
1
100.0%

rdnpostno
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size156.2 KiB

rdnwhladdr
Text

MISSING 

Distinct7377
Distinct (%)83.0%
Missing1111
Missing (%)11.1%
Memory size156.2 KiB
2024-04-16T17:58:55.659174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length67
Median length59
Mean length30.935538
Min length5

Characters and Unicode

Total characters274986
Distinct characters671
Distinct categories12 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6445 ?
Unique (%)72.5%

Sample

1st row부산광역시 남구 못골로 78 (대연동)
2nd row인천광역시 서구 검단로 107, (주)신선식품 (오류동)
3rd row부산광역시 남구 용호로 191 (용호동)
4th row부산광역시 동래구 충렬대로95번길 56 (온천동)
5th row부산광역시 금정구 부산대학로50번길 23 (장전동)
ValueCountFrequency (%)
부산광역시 5514
 
10.1%
1층 1321
 
2.4%
경기도 1001
 
1.8%
서울특별시 794
 
1.5%
부산진구 587
 
1.1%
북구 584
 
1.1%
지하1층 571
 
1.1%
동래구 504
 
0.9%
사상구 489
 
0.9%
사하구 448
 
0.8%
Other values (9565) 42516
78.3%
2024-04-16T17:58:56.144011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
45474
 
16.5%
10935
 
4.0%
1 10488
 
3.8%
9260
 
3.4%
8649
 
3.1%
( 8238
 
3.0%
) 8236
 
3.0%
8168
 
3.0%
7779
 
2.8%
7343
 
2.7%
Other values (661) 150416
54.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 166383
60.5%
Space Separator 45474
 
16.5%
Decimal Number 39245
 
14.3%
Open Punctuation 8239
 
3.0%
Close Punctuation 8237
 
3.0%
Other Punctuation 5409
 
2.0%
Dash Punctuation 1205
 
0.4%
Uppercase Letter 691
 
0.3%
Lowercase Letter 87
 
< 0.1%
Math Symbol 14
 
< 0.1%
Other values (2) 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
10935
 
6.6%
9260
 
5.6%
8649
 
5.2%
8168
 
4.9%
7779
 
4.7%
7343
 
4.4%
6655
 
4.0%
6184
 
3.7%
4072
 
2.4%
3604
 
2.2%
Other values (590) 93734
56.3%
Uppercase Letter
ValueCountFrequency (%)
B 125
18.1%
A 90
13.0%
S 82
11.9%
G 59
8.5%
C 50
 
7.2%
E 46
 
6.7%
K 44
 
6.4%
P 24
 
3.5%
N 22
 
3.2%
M 21
 
3.0%
Other values (15) 128
18.5%
Lowercase Letter
ValueCountFrequency (%)
e 17
19.5%
s 16
18.4%
g 10
11.5%
l 6
 
6.9%
o 5
 
5.7%
u 5
 
5.7%
c 4
 
4.6%
a 4
 
4.6%
m 3
 
3.4%
i 3
 
3.4%
Other values (7) 14
16.1%
Decimal Number
ValueCountFrequency (%)
1 10488
26.7%
2 5498
14.0%
3 4104
 
10.5%
0 3428
 
8.7%
5 3165
 
8.1%
4 3141
 
8.0%
6 2672
 
6.8%
7 2563
 
6.5%
8 2189
 
5.6%
9 1997
 
5.1%
Other Punctuation
ValueCountFrequency (%)
, 5340
98.7%
? 39
 
0.7%
. 15
 
0.3%
@ 8
 
0.1%
/ 2
 
< 0.1%
# 1
 
< 0.1%
· 1
 
< 0.1%
* 1
 
< 0.1%
' 1
 
< 0.1%
& 1
 
< 0.1%
Open Punctuation
ValueCountFrequency (%)
( 8238
> 99.9%
[ 1
 
< 0.1%
Close Punctuation
ValueCountFrequency (%)
) 8236
> 99.9%
] 1
 
< 0.1%
Space Separator
ValueCountFrequency (%)
45474
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1205
100.0%
Math Symbol
ValueCountFrequency (%)
~ 14
100.0%
Letter Number
ValueCountFrequency (%)
1
100.0%
Other Symbol
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 166384
60.5%
Common 107823
39.2%
Latin 779
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
10935
 
6.6%
9260
 
5.6%
8649
 
5.2%
8168
 
4.9%
7779
 
4.7%
7343
 
4.4%
6655
 
4.0%
6184
 
3.7%
4072
 
2.4%
3604
 
2.2%
Other values (591) 93735
56.3%
Latin
ValueCountFrequency (%)
B 125
16.0%
A 90
11.6%
S 82
 
10.5%
G 59
 
7.6%
C 50
 
6.4%
E 46
 
5.9%
K 44
 
5.6%
P 24
 
3.1%
N 22
 
2.8%
M 21
 
2.7%
Other values (33) 216
27.7%
Common
ValueCountFrequency (%)
45474
42.2%
1 10488
 
9.7%
( 8238
 
7.6%
) 8236
 
7.6%
2 5498
 
5.1%
, 5340
 
5.0%
3 4104
 
3.8%
0 3428
 
3.2%
5 3165
 
2.9%
4 3141
 
2.9%
Other values (17) 10711
 
9.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 166383
60.5%
ASCII 108600
39.5%
None 2
 
< 0.1%
Number Forms 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
45474
41.9%
1 10488
 
9.7%
( 8238
 
7.6%
) 8236
 
7.6%
2 5498
 
5.1%
, 5340
 
4.9%
3 4104
 
3.8%
0 3428
 
3.2%
5 3165
 
2.9%
4 3141
 
2.9%
Other values (58) 11488
 
10.6%
Hangul
ValueCountFrequency (%)
10935
 
6.6%
9260
 
5.6%
8649
 
5.2%
8168
 
4.9%
7779
 
4.7%
7343
 
4.4%
6655
 
4.0%
6184
 
3.7%
4072
 
2.4%
3604
 
2.2%
Other values (590) 93734
56.3%
None
ValueCountFrequency (%)
· 1
50.0%
1
50.0%
Number Forms
ValueCountFrequency (%)
1
100.0%

apvpermymd
Real number (ℝ)

Distinct4842
Distinct (%)48.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20095843
Minimum19631010
Maximum20210104
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-16T17:58:56.258008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19631010
5-th percentile19880728
Q120030115
median20121024
Q320190611
95-th percentile20200915
Maximum20210104
Range579094
Interquartile range (IQR)160496.25

Descriptive statistics

Standard deviation107539.74
Coefficient of variation (CV)0.0053513427
Kurtosis0.48839466
Mean20095843
Median Absolute Deviation (MAD)70104
Skewness-1.023853
Sum2.0095843 × 1011
Variance1.1564796 × 1010
MonotonicityNot monotonic
2024-04-16T17:58:56.368270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19980713 51
 
0.5%
20190315 23
 
0.2%
20190208 20
 
0.2%
20191213 20
 
0.2%
20200131 20
 
0.2%
20201120 20
 
0.2%
20190104 20
 
0.2%
20190412 19
 
0.2%
20190322 19
 
0.2%
20200327 19
 
0.2%
Other values (4832) 9769
97.7%
ValueCountFrequency (%)
19631010 1
 
< 0.1%
19651010 5
0.1%
19651024 1
 
< 0.1%
19651116 1
 
< 0.1%
19651124 1
 
< 0.1%
19651215 1
 
< 0.1%
19660916 1
 
< 0.1%
19661125 1
 
< 0.1%
19670814 1
 
< 0.1%
19680422 2
 
< 0.1%
ValueCountFrequency (%)
20210104 1
 
< 0.1%
20210103 1
 
< 0.1%
20210102 2
 
< 0.1%
20210101 2
 
< 0.1%
20201231 5
0.1%
20201230 2
 
< 0.1%
20201229 5
0.1%
20201228 4
< 0.1%
20201227 1
 
< 0.1%
20201226 1
 
< 0.1%

dcbymd
Text

MISSING 

Distinct2161
Distinct (%)57.6%
Missing6248
Missing (%)62.5%
Memory size156.2 KiB
2024-04-16T17:58:56.596519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length7.6289979
Min length4

Characters and Unicode

Total characters28624
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

Unique1390 ?
Unique (%)37.0%

Sample

1st row20151111
2nd row20141226
3rd row20160630
4th row19990323
5th row20091126
ValueCountFrequency (%)
폐업일자 348
 
9.3%
20131222 30
 
0.8%
20121213 22
 
0.6%
20060216 19
 
0.5%
20140820 15
 
0.4%
20170131 9
 
0.2%
20050526 9
 
0.2%
20060412 9
 
0.2%
20111230 8
 
0.2%
20130607 8
 
0.2%
Other values (2151) 3275
87.3%
2024-04-16T17:58:56.956338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 9032
31.6%
2 5838
20.4%
1 5294
18.5%
3 1368
 
4.8%
6 1100
 
3.8%
4 1036
 
3.6%
7 1019
 
3.6%
5 920
 
3.2%
8 868
 
3.0%
9 757
 
2.6%
Other values (4) 1392
 
4.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 27232
95.1%
Other Letter 1392
 
4.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 9032
33.2%
2 5838
21.4%
1 5294
19.4%
3 1368
 
5.0%
6 1100
 
4.0%
4 1036
 
3.8%
7 1019
 
3.7%
5 920
 
3.4%
8 868
 
3.2%
9 757
 
2.8%
Other Letter
ValueCountFrequency (%)
348
25.0%
348
25.0%
348
25.0%
348
25.0%

Most occurring scripts

ValueCountFrequency (%)
Common 27232
95.1%
Hangul 1392
 
4.9%

Most frequent character per script

Common
ValueCountFrequency (%)
0 9032
33.2%
2 5838
21.4%
1 5294
19.4%
3 1368
 
5.0%
6 1100
 
4.0%
4 1036
 
3.8%
7 1019
 
3.7%
5 920
 
3.4%
8 868
 
3.2%
9 757
 
2.8%
Hangul
ValueCountFrequency (%)
348
25.0%
348
25.0%
348
25.0%
348
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 27232
95.1%
Hangul 1392
 
4.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 9032
33.2%
2 5838
21.4%
1 5294
19.4%
3 1368
 
5.0%
6 1100
 
4.0%
4 1036
 
3.8%
7 1019
 
3.7%
5 920
 
3.4%
8 868
 
3.2%
9 757
 
2.8%
Hangul
ValueCountFrequency (%)
348
25.0%
348
25.0%
348
25.0%
348
25.0%

clgstdt
Categorical

IMBALANCE 

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9642 
휴업시작일자
 
348
20101103
 
1
20130528
 
1
20050428
 
1
Other values (7)
 
7

Length

Max length8
Median length4
Mean length4.0736
Min length4

Unique

Unique10 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 9642
96.4%
휴업시작일자 348
 
3.5%
20101103 1
 
< 0.1%
20130528 1
 
< 0.1%
20050428 1
 
< 0.1%
20160406 1
 
< 0.1%
20060707 1
 
< 0.1%
20130314 1
 
< 0.1%
20111013 1
 
< 0.1%
20110408 1
 
< 0.1%
Other values (2) 2
 
< 0.1%

Length

2024-04-16T17:58:57.090545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 9642
96.4%
휴업시작일자 348
 
3.5%
20101103 1
 
< 0.1%
20130528 1
 
< 0.1%
20050428 1
 
< 0.1%
20160406 1
 
< 0.1%
20060707 1
 
< 0.1%
20130314 1
 
< 0.1%
20111013 1
 
< 0.1%
20110408 1
 
< 0.1%
Other values (2) 2
 
< 0.1%

clgenddt
Categorical

IMBALANCE 

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9643 
휴업종료일자
 
348
20111102
 
1
20131231
 
1
20060429
 
1
Other values (6)
 
6

Length

Max length8
Median length4
Mean length4.0732
Min length4

Unique

Unique9 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 9643
96.4%
휴업종료일자 348
 
3.5%
20111102 1
 
< 0.1%
20131231 1
 
< 0.1%
20060429 1
 
< 0.1%
20161006 1
 
< 0.1%
20061231 1
 
< 0.1%
20140313 1
 
< 0.1%
20121012 1
 
< 0.1%
20120324 1
 
< 0.1%

Length

2024-04-16T17:58:57.198860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 9643
96.4%
휴업종료일자 348
 
3.5%
20111102 1
 
< 0.1%
20131231 1
 
< 0.1%
20060429 1
 
< 0.1%
20161006 1
 
< 0.1%
20061231 1
 
< 0.1%
20140313 1
 
< 0.1%
20121012 1
 
< 0.1%
20120324 1
 
< 0.1%

ropnymd
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9650 
재개업일자
 
348
20081007
 
1
20130806
 
1

Length

Max length8
Median length4
Mean length4.0356
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> 9650
96.5%
재개업일자 348
 
3.5%
20081007 1
 
< 0.1%
20130806 1
 
< 0.1%

Length

2024-04-16T17:58:57.310641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:58:57.428076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9650
96.5%
재개업일자 348
 
3.5%
20081007 1
 
< 0.1%
20130806 1
 
< 0.1%

trdstatenm
Categorical

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
영업/정상
3556 
0002
2607 
0000
2114 
02
813 
01
598 
Other values (6)
 
312

Length

Max length5
Median length4
Mean length4.072
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row02
2nd row영업/정상
3rd row0002
4th row0002
5th row0002

Common Values

ValueCountFrequency (%)
영업/정상 3556
35.6%
0002 2607
26.1%
0000 2114
21.1%
02 813
 
8.1%
01 598
 
6.0%
0004 243
 
2.4%
<NA> 44
 
0.4%
0001 9
 
0.1%
폐업 7
 
0.1%
영업상태 7
 
0.1%

Length

2024-04-16T17:58:57.535095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
영업/정상 3556
35.6%
0002 2607
26.1%
0000 2114
21.1%
02 813
 
8.1%
01 598
 
6.0%
0004 243
 
2.4%
na 44
 
0.4%
0001 9
 
0.1%
폐업 7
 
0.1%
영업상태 7
 
0.1%

dtlstatenm
Categorical

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
영업
4063 
폐업
3427 
정상
2249 
말소
 
243
휴업
 
9
Other values (3)
 
9

Length

Max length4
Median length2
Mean length2.0009
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
영업 4063
40.6%
폐업 3427
34.3%
정상 2249
22.5%
말소 243
 
2.4%
휴업 9
 
0.1%
영업중 5
 
0.1%
?? 2
 
< 0.1%
행정처분 2
 
< 0.1%

Length

2024-04-16T17:58:57.642647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:58:57.739141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
영업 4063
40.6%
폐업 3427
34.3%
정상 2249
22.5%
말소 243
 
2.4%
휴업 9
 
0.1%
영업중 5
 
< 0.1%
2
 
< 0.1%
행정처분 2
 
< 0.1%

x
Text

MISSING 

Distinct7185
Distinct (%)74.0%
Missing290
Missing (%)2.9%
Memory size156.2 KiB
2024-04-16T17:58:57.946844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length20
Mean length19.994645
Min length7

Characters and Unicode

Total characters194148
Distinct characters19
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5892 ?
Unique (%)60.7%

Sample

1st row390580.53369500000
2nd row166274.138994991
3rd row392474.24138100000
4th row388680.63587400000
5th row389913.11285600000
ValueCountFrequency (%)
381223.93770800000 30
 
0.3%
209850.446960528 22
 
0.2%
381150.24062500000 18
 
0.2%
394015.45385100000 17
 
0.2%
381201.909278 16
 
0.2%
381168.29159900000 16
 
0.2%
190107.045415333 14
 
0.1%
202358.505687227 14
 
0.1%
178006.080301401 14
 
0.1%
200250.447804795 14
 
0.1%
Other values (7175) 9535
98.2%
2024-04-16T17:58:58.275495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 37569
19.4%
34767
17.9%
3 17272
8.9%
8 14314
 
7.4%
9 13293
 
6.8%
2 12438
 
6.4%
1 12426
 
6.4%
7 11104
 
5.7%
5 10508
 
5.4%
4 10411
 
5.4%
Other values (9) 20046
10.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 149724
77.1%
Space Separator 34767
 
17.9%
Other Punctuation 9629
 
5.0%
Other Letter 16
 
< 0.1%
Open Punctuation 4
 
< 0.1%
Uppercase Letter 4
 
< 0.1%
Close Punctuation 4
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 37569
25.1%
3 17272
11.5%
8 14314
 
9.6%
9 13293
 
8.9%
2 12438
 
8.3%
1 12426
 
8.3%
7 11104
 
7.4%
5 10508
 
7.0%
4 10411
 
7.0%
6 10389
 
6.9%
Other Letter
ValueCountFrequency (%)
4
25.0%
4
25.0%
4
25.0%
4
25.0%
Space Separator
ValueCountFrequency (%)
34767
100.0%
Other Punctuation
ValueCountFrequency (%)
. 9629
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%
Uppercase Letter
ValueCountFrequency (%)
X 4
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 194128
> 99.9%
Hangul 16
 
< 0.1%
Latin 4
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 37569
19.4%
34767
17.9%
3 17272
8.9%
8 14314
 
7.4%
9 13293
 
6.8%
2 12438
 
6.4%
1 12426
 
6.4%
7 11104
 
5.7%
5 10508
 
5.4%
4 10411
 
5.4%
Other values (4) 20026
10.3%
Hangul
ValueCountFrequency (%)
4
25.0%
4
25.0%
4
25.0%
4
25.0%
Latin
ValueCountFrequency (%)
X 4
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 194132
> 99.9%
Hangul 16
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 37569
19.4%
34767
17.9%
3 17272
8.9%
8 14314
 
7.4%
9 13293
 
6.8%
2 12438
 
6.4%
1 12426
 
6.4%
7 11104
 
5.7%
5 10508
 
5.4%
4 10411
 
5.4%
Other values (5) 20030
10.3%
Hangul
ValueCountFrequency (%)
4
25.0%
4
25.0%
4
25.0%
4
25.0%

y
Text

MISSING 

Distinct7184
Distinct (%)74.0%
Missing290
Missing (%)2.9%
Memory size156.2 KiB
2024-04-16T17:58:58.489355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length20
Mean length19.994645
Min length7

Characters and Unicode

Total characters194148
Distinct characters21
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5890 ?
Unique (%)60.7%

Sample

1st row184167.07016700000
2nd row455084.787859596
3rd row181919.67284200000
4th row192411.99154100000
5th row194880.99658600000
ValueCountFrequency (%)
184549.28339000000 30
 
0.3%
432304.149379043 22
 
0.2%
190717.70395600000 18
 
0.2%
187900.93961700000 17
 
0.2%
190674.25624800000 16
 
0.2%
184537.273724 16
 
0.2%
444683.220506107 14
 
0.1%
447232.955697694 14
 
0.1%
445157.626366229 14
 
0.1%
462865.812502378 14
 
0.1%
Other values (7174) 9535
98.2%
2024-04-16T17:58:58.812668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 36516
18.8%
34767
17.9%
1 17453
9.0%
8 13749
 
7.1%
4 13126
 
6.8%
9 13010
 
6.7%
7 11583
 
6.0%
3 11257
 
5.8%
2 11165
 
5.8%
6 11022
 
5.7%
Other values (11) 20500
10.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 149693
77.1%
Space Separator 34767
 
17.9%
Other Punctuation 9630
 
5.0%
Dash Punctuation 26
 
< 0.1%
Other Letter 16
 
< 0.1%
Close Punctuation 8
 
< 0.1%
Open Punctuation 4
 
< 0.1%
Uppercase Letter 4
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 36516
24.4%
1 17453
11.7%
8 13749
 
9.2%
4 13126
 
8.8%
9 13010
 
8.7%
7 11583
 
7.7%
3 11257
 
7.5%
2 11165
 
7.5%
6 11022
 
7.4%
5 10812
 
7.2%
Other Letter
ValueCountFrequency (%)
4
25.0%
4
25.0%
4
25.0%
4
25.0%
Close Punctuation
ValueCountFrequency (%)
) 4
50.0%
] 4
50.0%
Space Separator
ValueCountFrequency (%)
34767
100.0%
Other Punctuation
ValueCountFrequency (%)
. 9630
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 26
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%
Uppercase Letter
ValueCountFrequency (%)
Y 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 194128
> 99.9%
Hangul 16
 
< 0.1%
Latin 4
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 36516
18.8%
34767
17.9%
1 17453
9.0%
8 13749
 
7.1%
4 13126
 
6.8%
9 13010
 
6.7%
7 11583
 
6.0%
3 11257
 
5.8%
2 11165
 
5.8%
6 11022
 
5.7%
Other values (6) 20480
10.5%
Hangul
ValueCountFrequency (%)
4
25.0%
4
25.0%
4
25.0%
4
25.0%
Latin
ValueCountFrequency (%)
Y 4
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 194132
> 99.9%
Hangul 16
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 36516
18.8%
34767
17.9%
1 17453
9.0%
8 13749
 
7.1%
4 13126
 
6.8%
9 13010
 
6.7%
7 11583
 
6.0%
3 11257
 
5.8%
2 11165
 
5.8%
6 11022
 
5.7%
Other values (7) 20484
10.6%
Hangul
ValueCountFrequency (%)
4
25.0%
4
25.0%
4
25.0%
4
25.0%

lastmodts
Real number (ℝ)

Distinct9532
Distinct (%)95.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0146072 × 1013
Minimum1.9990315 × 1013
Maximum2.0210103 × 1013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-16T17:58:58.941854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.9990315 × 1013
5-th percentile2.0040402 × 1013
Q12.0111114 × 1013
median2.016111 × 1013
Q32.0190611 × 1013
95-th percentile2.0200915 × 1013
Maximum2.0210103 × 1013
Range2.1978817 × 1011
Interquartile range (IQR)7.9496969 × 1010

Descriptive statistics

Standard deviation5.2729921 × 1010
Coefficient of variation (CV)0.0026173797
Kurtosis-0.41142193
Mean2.0146072 × 1013
Median Absolute Deviation (MAD)3.038506 × 1010
Skewness-0.84865462
Sum2.0146072 × 1017
Variance2.7804446 × 1021
MonotonicityNot monotonic
2024-04-16T17:58:59.057499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20061226000000 16
 
0.2%
20050614000000 10
 
0.1%
20020802000000 7
 
0.1%
20040823000000 7
 
0.1%
20050615000000 7
 
0.1%
19990319000000 7
 
0.1%
20050525000000 6
 
0.1%
20020801000000 5
 
0.1%
19990318000000 5
 
0.1%
20020531000000 5
 
0.1%
Other values (9522) 9925
99.2%
ValueCountFrequency (%)
19990315000000 3
< 0.1%
19990316000000 3
< 0.1%
19990317000000 4
< 0.1%
19990318000000 5
0.1%
19990319000000 7
0.1%
19990323000000 1
 
< 0.1%
19990324000000 1
 
< 0.1%
19990511000000 2
 
< 0.1%
19990520000000 1
 
< 0.1%
19990610000000 1
 
< 0.1%
ValueCountFrequency (%)
20210103171443 1
< 0.1%
20210103141601 1
< 0.1%
20210102151319 1
< 0.1%
20210102150412 1
< 0.1%
20210101102502 1
< 0.1%
20201231203956 1
< 0.1%
20201231163331 1
< 0.1%
20201231145850 1
< 0.1%
20201231141227 1
< 0.1%
20201231140741 1
< 0.1%

uptaenm
Categorical

Distinct21
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
식육판매업
4028 
즉석판매제조가공업
2870 
제과점영업
1413 
우유류판매업
605 
<NA>
 
398
Other values (16)
686 

Length

Max length13
Median length5
Mean length6.458
Min length2

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row제과점영업
2nd row식용란수집판매업
3rd row식육판매업
4th row식육판매업
5th row식육판매업

Common Values

ValueCountFrequency (%)
식육판매업 4028
40.3%
즉석판매제조가공업 2870
28.7%
제과점영업 1413
 
14.1%
우유류판매업 605
 
6.0%
<NA> 398
 
4.0%
축산물유통전문판매업 165
 
1.7%
축산물수입판매업 122
 
1.2%
집단급식소 식품판매업 97
 
1.0%
건강기능식품유통전문판매업 67
 
0.7%
식용란수집판매업 66
 
0.7%
Other values (11) 169
 
1.7%

Length

2024-04-16T17:58:59.161459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
식육판매업 4028
39.8%
즉석판매제조가공업 2870
28.3%
제과점영업 1413
 
13.9%
우유류판매업 605
 
6.0%
na 398
 
3.9%
축산물유통전문판매업 165
 
1.6%
축산물수입판매업 122
 
1.2%
집단급식소 97
 
1.0%
식품판매업 97
 
1.0%
건강기능식품유통전문판매업 67
 
0.7%
Other values (12) 269
 
2.7%

sitetel
Text

MISSING 

Distinct101
Distinct (%)1.0%
Missing296
Missing (%)3.0%
Memory size156.2 KiB
2024-04-16T17:58:59.427516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length11.972795
Min length4

Characters and Unicode

Total characters116184
Distinct characters16
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

Unique83 ?
Unique (%)0.9%

Sample

1st row051-123-1234
2nd row051-123-1234
3rd row051-123-1234
4th row051-123-1234
5th row051-123-1234
ValueCountFrequency (%)
051-123-1234 9552
96.8%
전화번호 30
 
0.3%
02 18
 
0.2%
070 14
 
0.1%
031 13
 
0.1%
055 10
 
0.1%
041 9
 
0.1%
062 7
 
0.1%
032 6
 
0.1%
78118500 4
 
< 0.1%
Other values (152) 202
 
2.0%
2024-04-16T17:58:59.808911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 28772
24.8%
2 19256
16.6%
3 19243
16.6%
- 19133
16.5%
0 9783
 
8.4%
5 9696
 
8.3%
4 9652
 
8.3%
179
 
0.2%
7 105
 
0.1%
8 104
 
0.1%
Other values (6) 261
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 96752
83.3%
Dash Punctuation 19133
 
16.5%
Space Separator 179
 
0.2%
Other Letter 120
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 28772
29.7%
2 19256
19.9%
3 19243
19.9%
0 9783
 
10.1%
5 9696
 
10.0%
4 9652
 
10.0%
7 105
 
0.1%
8 104
 
0.1%
6 83
 
0.1%
9 58
 
0.1%
Other Letter
ValueCountFrequency (%)
30
25.0%
30
25.0%
30
25.0%
30
25.0%
Dash Punctuation
ValueCountFrequency (%)
- 19133
100.0%
Space Separator
ValueCountFrequency (%)
179
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 116064
99.9%
Hangul 120
 
0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
1 28772
24.8%
2 19256
16.6%
3 19243
16.6%
- 19133
16.5%
0 9783
 
8.4%
5 9696
 
8.4%
4 9652
 
8.3%
179
 
0.2%
7 105
 
0.1%
8 104
 
0.1%
Other values (2) 141
 
0.1%
Hangul
ValueCountFrequency (%)
30
25.0%
30
25.0%
30
25.0%
30
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 116064
99.9%
Hangul 120
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 28772
24.8%
2 19256
16.6%
3 19243
16.6%
- 19133
16.5%
0 9783
 
8.4%
5 9696
 
8.4%
4 9652
 
8.3%
179
 
0.2%
7 105
 
0.1%
8 104
 
0.1%
Other values (2) 141
 
0.1%
Hangul
ValueCountFrequency (%)
30
25.0%
30
25.0%
30
25.0%
30
25.0%

bdngownsenm
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
8564 
자가
 
812
임대
 
390
건물소유구분명
 
233
??
 
1

Length

Max length7
Median length4
Mean length3.8293
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> 8564
85.6%
자가 812
 
8.1%
임대 390
 
3.9%
건물소유구분명 233
 
2.3%
?? 1
 
< 0.1%

Length

2024-04-16T17:58:59.917497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:58:59.997286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 8564
85.6%
자가 812
 
8.1%
임대 390
 
3.9%
건물소유구분명 233
 
2.3%
1
 
< 0.1%

fctyowkepcnt
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9574 
공장사무직종업원수
 
334
0
 
86
1
 
4
2
 
2

Length

Max length9
Median length4
Mean length4.1394
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> 9574
95.7%
공장사무직종업원수 334
 
3.3%
0 86
 
0.9%
1 4
 
< 0.1%
2 2
 
< 0.1%

Length

2024-04-16T17:59:00.096189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:59:00.189854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9574
95.7%
공장사무직종업원수 334
 
3.3%
0 86
 
0.9%
1 4
 
< 0.1%
2 2
 
< 0.1%

fctypdtjobepcnt
Categorical

IMBALANCE 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9570 
공장생산직종업원수
 
330
0
 
85
1
 
10
2
 
4

Length

Max length9
Median length4
Mean length4.135
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> 9570
95.7%
공장생산직종업원수 330
 
3.3%
0 85
 
0.9%
1 10
 
0.1%
2 4
 
< 0.1%
5 1
 
< 0.1%

Length

2024-04-16T17:59:00.304038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:59:00.408324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9570
95.7%
공장생산직종업원수 330
 
3.3%
0 85
 
0.9%
1 10
 
0.1%
2 4
 
< 0.1%
5 1
 
< 0.1%

fctysiljobepcnt
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9572 
공장판매직종업원수
 
335
0
 
85
1
 
8

Length

Max length9
Median length4
Mean length4.1396
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> 9572
95.7%
공장판매직종업원수 335
 
3.4%
0 85
 
0.9%
1 8
 
0.1%

Length

2024-04-16T17:59:00.538507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:59:00.638080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9572
95.7%
공장판매직종업원수 335
 
3.4%
0 85
 
0.9%
1 8
 
0.1%

rgtmbdsno
Categorical

IMBALANCE 

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
4569 
000
4523 
L00
579 
권리주체일련번호
 
320
100
 
5
Other values (3)
 
4

Length

Max length8
Median length3
Mean length3.6169
Min length3

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row<NA>
2nd row000
3rd row000
4th row000
5th row000

Common Values

ValueCountFrequency (%)
<NA> 4569
45.7%
000 4523
45.2%
L00 579
 
5.8%
권리주체일련번호 320
 
3.2%
100 5
 
0.1%
F00 2
 
< 0.1%
010 1
 
< 0.1%
L01 1
 
< 0.1%

Length

2024-04-16T17:59:00.728607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:59:00.826316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 4569
45.7%
000 4523
45.2%
l00 579
 
5.8%
권리주체일련번호 320
 
3.2%
100 5
 
< 0.1%
f00 2
 
< 0.1%
010 1
 
< 0.1%
l01 1
 
< 0.1%

wtrsplyfacilsenm
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
8535 
상수도전용
1147 
급수시설구분명
 
302
지하수전용
 
14
간이상수도
 
2

Length

Max length7
Median length4
Mean length4.2069
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> 8535
85.4%
상수도전용 1147
 
11.5%
급수시설구분명 302
 
3.0%
지하수전용 14
 
0.1%
간이상수도 2
 
< 0.1%

Length

2024-04-16T17:59:00.937299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:59:01.026245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 8535
85.4%
상수도전용 1147
 
11.5%
급수시설구분명 302
 
3.0%
지하수전용 14
 
0.1%
간이상수도 2
 
< 0.1%

maneipcnt
Categorical

IMBALANCE 

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9103 
0
 
502
남성종사자수
 
348
1
 
38
2
 
7
Other values (2)
 
2

Length

Max length6
Median length4
Mean length3.9049
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> 9103
91.0%
0 502
 
5.0%
남성종사자수 348
 
3.5%
1 38
 
0.4%
2 7
 
0.1%
3 1
 
< 0.1%
4 1
 
< 0.1%

Length

2024-04-16T17:59:01.140061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:59:01.223495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9103
91.0%
0 502
 
5.0%
남성종사자수 348
 
3.5%
1 38
 
0.4%
2 7
 
0.1%
3 1
 
< 0.1%
4 1
 
< 0.1%

multusnupsoyn
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
N
9946 
<NA>
 
33
 
12
Y
 
9

Length

Max length4
Median length1
Mean length1.0099
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
N 9946
99.5%
<NA> 33
 
0.3%
12
 
0.1%
Y 9
 
0.1%

Length

2024-04-16T17:59:01.315140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:59:01.396347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
n 9946
99.5%
na 33
 
0.3%
12
 
0.1%
y 9
 
0.1%

lvsenm
Categorical

IMBALANCE 

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9216 
등급구분명
 
348
기타
 
305
자율
 
128
지도
 
1
Other values (2)
 
2

Length

Max length5
Median length4
Mean length3.9476
Min length2

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 9216
92.2%
등급구분명 348
 
3.5%
기타 305
 
3.0%
자율 128
 
1.3%
지도 1
 
< 0.1%
우수 1
 
< 0.1%
관리 1
 
< 0.1%

Length

2024-04-16T17:59:01.484235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:59:01.574204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9216
92.2%
등급구분명 348
 
3.5%
기타 305
 
3.0%
자율 128
 
1.3%
지도 1
 
< 0.1%
우수 1
 
< 0.1%
관리 1
 
< 0.1%

isream
Categorical

IMBALANCE 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9645 
보증액
 
347
0
 
5
5000000
 
1
10000000
 
1

Length

Max length8
Median length4
Mean length3.9648
Min length1

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 9645
96.5%
보증액 347
 
3.5%
0 5
 
0.1%
5000000 1
 
< 0.1%
10000000 1
 
< 0.1%
2000000 1
 
< 0.1%

Length

2024-04-16T17:59:01.690553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:59:02.041412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9645
96.5%
보증액 347
 
3.5%
0 5
 
< 0.1%
5000000 1
 
< 0.1%
10000000 1
 
< 0.1%
2000000 1
 
< 0.1%

hoffepcnt
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9575 
본사종업원수
 
336
0
 
87
1
 
2

Length

Max length6
Median length4
Mean length4.0405
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> 9575
95.8%
본사종업원수 336
 
3.4%
0 87
 
0.9%
1 2
 
< 0.1%

Length

2024-04-16T17:59:02.127224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:59:02.216341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9575
95.8%
본사종업원수 336
 
3.4%
0 87
 
0.9%
1 2
 
< 0.1%

equsiz
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9652 
설비규격
 
348

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> 9652
96.5%
설비규격 348
 
3.5%

Length

2024-04-16T17:59:02.321053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:59:02.400282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9652
96.5%
설비규격 348
 
3.5%

faciltotscp
Text

MISSING 

Distinct1223
Distinct (%)24.9%
Missing5084
Missing (%)50.8%
Memory size156.2 KiB
2024-04-16T17:59:02.656337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length1
Mean length2.1175753
Min length1

Characters and Unicode

Total characters10410
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

Unique1022 ?
Unique (%)20.8%

Sample

1st row33.66
2nd row0
3rd row17.64
4th row0
5th row0
ValueCountFrequency (%)
0 3230
65.7%
3.3 40
 
0.8%
시설총규모 32
 
0.7%
10 18
 
0.4%
6.6 18
 
0.4%
33 10
 
0.2%
20 10
 
0.2%
24 9
 
0.2%
30 9
 
0.2%
5 7
 
0.1%
Other values (1212) 1533
31.2%
2024-04-16T17:59:03.068222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 3569
34.3%
. 1390
 
13.4%
2 767
 
7.4%
1 754
 
7.2%
3 698
 
6.7%
4 608
 
5.8%
6 590
 
5.7%
5 581
 
5.6%
8 481
 
4.6%
9 407
 
3.9%
Other values (6) 565
 
5.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8860
85.1%
Other Punctuation 1390
 
13.4%
Other Letter 160
 
1.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3569
40.3%
2 767
 
8.7%
1 754
 
8.5%
3 698
 
7.9%
4 608
 
6.9%
6 590
 
6.7%
5 581
 
6.6%
8 481
 
5.4%
9 407
 
4.6%
7 405
 
4.6%
Other Letter
ValueCountFrequency (%)
32
20.0%
32
20.0%
32
20.0%
32
20.0%
32
20.0%
Other Punctuation
ValueCountFrequency (%)
. 1390
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 10250
98.5%
Hangul 160
 
1.5%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3569
34.8%
. 1390
 
13.6%
2 767
 
7.5%
1 754
 
7.4%
3 698
 
6.8%
4 608
 
5.9%
6 590
 
5.8%
5 581
 
5.7%
8 481
 
4.7%
9 407
 
4.0%
Hangul
ValueCountFrequency (%)
32
20.0%
32
20.0%
32
20.0%
32
20.0%
32
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10250
98.5%
Hangul 160
 
1.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3569
34.8%
. 1390
 
13.6%
2 767
 
7.5%
1 754
 
7.4%
3 698
 
6.8%
4 608
 
5.9%
6 590
 
5.8%
5 581
 
5.7%
8 481
 
4.7%
9 407
 
4.0%
Hangul
ValueCountFrequency (%)
32
20.0%
32
20.0%
32
20.0%
32
20.0%
32
20.0%

wmeipcnt
Categorical

IMBALANCE 

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9101 
0
 
501
여성종사자수
 
348
1
 
41
2
 
7
Other values (2)
 
2

Length

Max length6
Median length4
Mean length3.9044
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> 9101
91.0%
0 501
 
5.0%
여성종사자수 348
 
3.5%
1 41
 
0.4%
2 7
 
0.1%
11 1
 
< 0.1%
4 1
 
< 0.1%

Length

2024-04-16T17:59:03.190734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:59:03.273396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9101
91.0%
0 501
 
5.0%
여성종사자수 348
 
3.5%
1 41
 
0.4%
2 7
 
0.1%
11 1
 
< 0.1%
4 1
 
< 0.1%

trdpjubnsenm
Categorical

IMBALANCE 

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9129 
기타
 
364
영업장주변구분명
 
348
주택가주변
 
85
아파트지역
 
53
Other values (2)
 
21

Length

Max length8
Median length4
Mean length4.0886
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> 9129
91.3%
기타 364
 
3.6%
영업장주변구분명 348
 
3.5%
주택가주변 85
 
0.9%
아파트지역 53
 
0.5%
유흥업소밀집지역 15
 
0.1%
학교정화(상대) 6
 
0.1%

Length

2024-04-16T17:59:03.365980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:59:03.469154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9129
91.3%
기타 364
 
3.6%
영업장주변구분명 348
 
3.5%
주택가주변 85
 
0.9%
아파트지역 53
 
0.5%
유흥업소밀집지역 15
 
0.1%
학교정화(상대 6
 
0.1%

monam
Categorical

IMBALANCE 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9645 
월세액
 
347
0
 
5
500000
 
1
600000
 
1

Length

Max length6
Median length4
Mean length3.9644
Min length1

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 9645
96.5%
월세액 347
 
3.5%
0 5
 
0.1%
500000 1
 
< 0.1%
600000 1
 
< 0.1%
100000 1
 
< 0.1%

Length

2024-04-16T17:59:03.566885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:59:03.650018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9645
96.5%
월세액 347
 
3.5%
0 5
 
< 0.1%
500000 1
 
< 0.1%
600000 1
 
< 0.1%
100000 1
 
< 0.1%

sntuptaenm
Categorical

IMBALANCE 

Distinct22
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
5084 
즉석판매제조가공업
2870 
제과점영업
1413 
전자상거래(통신판매업)
 
200
영업장판매
 
101
Other values (17)
 
332

Length

Max length14
Median length4
Mean length5.915
Min length2

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 5084
50.8%
즉석판매제조가공업 2870
28.7%
제과점영업 1413
 
14.1%
전자상거래(통신판매업) 200
 
2.0%
영업장판매 101
 
1.0%
집단급식소 식품판매업 97
 
1.0%
건강기능식품유통전문판매업 67
 
0.7%
기타 식품제조가공업 34
 
0.3%
위생업태명 32
 
0.3%
방문판매 26
 
0.3%
Other values (12) 76
 
0.8%

Length

2024-04-16T17:59:03.746153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 5084
50.1%
즉석판매제조가공업 2870
28.3%
제과점영업 1413
 
13.9%
전자상거래(통신판매업 200
 
2.0%
영업장판매 101
 
1.0%
집단급식소 97
 
1.0%
식품판매업 97
 
1.0%
건강기능식품유통전문판매업 67
 
0.7%
기타 55
 
0.5%
식품제조가공업 34
 
0.3%
Other values (14) 122
 
1.2%

jtupsomainedf
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9652 
전통업소주된음식
 
348

Length

Max length8
Median length4
Mean length4.1392
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> 9652
96.5%
전통업소주된음식 348
 
3.5%

Length

2024-04-16T17:59:03.851894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:59:03.939787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9652
96.5%
전통업소주된음식 348
 
3.5%

jtupsoasgnno
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9652 
전통업소지정번호
 
348

Length

Max length8
Median length4
Mean length4.1392
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> 9652
96.5%
전통업소지정번호 348
 
3.5%

Length

2024-04-16T17:59:04.049138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:59:04.134884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9652
96.5%
전통업소지정번호 348
 
3.5%

totepnum
Categorical

IMBALANCE 

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
4674 
식육판매업
4000 
우유류판매업
602 
총종업원수
 
347
축산물유통전문판매업
 
152
Other values (7)
 
225

Length

Max length10
Median length8
Mean length4.7406
Min length1

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 4674
46.7%
식육판매업 4000
40.0%
우유류판매업 602
 
6.0%
총종업원수 347
 
3.5%
축산물유통전문판매업 152
 
1.5%
축산물수입판매업 122
 
1.2%
식용란수집판매업 64
 
0.6%
식육부산물전문판매업 35
 
0.4%
4 1
 
< 0.1%
6 1
 
< 0.1%
Other values (2) 2
 
< 0.1%

Length

2024-04-16T17:59:04.215465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 4674
46.7%
식육판매업 4000
40.0%
우유류판매업 602
 
6.0%
총종업원수 347
 
3.5%
축산물유통전문판매업 152
 
1.5%
축산물수입판매업 122
 
1.2%
식용란수집판매업 64
 
0.6%
식육부산물전문판매업 35
 
0.4%
4 1
 
< 0.1%
6 1
 
< 0.1%
Other values (2) 2
 
< 0.1%

lindprcbgbnnm
Categorical

IMBALANCE 

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
축산물판매업
4975 
<NA>
4632 
축산물가공업구분명
 
333
식육판매업
 
28
축산물유통전문판매업
 
13
Other values (5)
 
19

Length

Max length10
Median length9
Mean length5.1777
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
축산물판매업 4975
49.8%
<NA> 4632
46.3%
축산물가공업구분명 333
 
3.3%
식육판매업 28
 
0.3%
축산물유통전문판매업 13
 
0.1%
식육포장처리업 9
 
0.1%
식육가공업 3
 
< 0.1%
우유류판매업 3
 
< 0.1%
식용란수집판매업 2
 
< 0.1%
식육부산물전문판매업 2
 
< 0.1%

Length

2024-04-16T17:59:04.324515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:59:04.418463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
축산물판매업 4975
49.8%
na 4632
46.3%
축산물가공업구분명 333
 
3.3%
식육판매업 28
 
0.3%
축산물유통전문판매업 13
 
0.1%
식육포장처리업 9
 
0.1%
식육가공업 3
 
< 0.1%
우유류판매업 3
 
< 0.1%
식용란수집판매업 2
 
< 0.1%
식육부산물전문판매업 2
 
< 0.1%

lindjobgbnnm
Categorical

IMBALANCE 

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9544 
축산업무구분명
 
320
축산물운반업
 
49
축산물판매업
 
48
축산물보관업
 
26
Other values (3)
 
13

Length

Max length7
Median length4
Mean length4.1238
Min length3

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 9544
95.4%
축산업무구분명 320
 
3.2%
축산물운반업 49
 
0.5%
축산물판매업 48
 
0.5%
축산물보관업 26
 
0.3%
식육포장처리업 9
 
0.1%
축산물가공업 3
 
< 0.1%
집유업 1
 
< 0.1%

Length

2024-04-16T17:59:04.535649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:59:04.634368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9544
95.4%
축산업무구분명 320
 
3.2%
축산물운반업 49
 
0.5%
축산물판매업 48
 
0.5%
축산물보관업 26
 
0.3%
식육포장처리업 9
 
0.1%
축산물가공업 3
 
< 0.1%
집유업 1
 
< 0.1%

lindseqno
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9652 
축산일련번호
 
348

Length

Max length6
Median length4
Mean length4.0696
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> 9652
96.5%
축산일련번호 348
 
3.5%

Length

2024-04-16T17:59:04.741381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:59:04.826864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9652
96.5%
축산일련번호 348
 
3.5%

homepage
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9651 
홈페이지
 
348
https://smartstore.naver.com/navidstore
 
1

Length

Max length39
Median length4
Mean length4.0035
Min length4

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> 9651
96.5%
홈페이지 348
 
3.5%
https://smartstore.naver.com/navidstore 1
 
< 0.1%

Length

2024-04-16T17:59:04.911810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:59:04.997087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9651
96.5%
홈페이지 348
 
3.5%
https://smartstore.naver.com/navidstore 1
 
< 0.1%
Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2021-01-05 10:31:03
Maximum2021-01-05 10:31:07
2024-04-16T17:59:05.075018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T17:59:05.168084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=5)

Sample

skeyopnsfteamcodemgtnoopnsvcidupdategbnupdatedtopnsvcnmbplcnmsitepostnositewhladdrrdnpostnordnwhladdrapvpermymddcbymdclgstdtclgenddtropnymdtrdstatenmdtlstatenmxylastmodtsuptaenmsitetelbdngownsenmfctyowkepcntfctypdtjobepcntfctysiljobepcntrgtmbdsnowtrsplyfacilsenmmaneipcntmultusnupsoynlvsenmisreamhoffepcntequsizfaciltotscpwmeipcnttrdpjubnsenmmonamsntuptaenmjtupsomainedfjtupsoasgnnototepnumlindprcbgbnnmlindjobgbnnmlindseqnohomepagelast_load_dttm
106961069733100003310000-121-2007-0000107_22_18_PI2018-08-31 23:59:59.0<NA>따삐오 베이커리608807부산광역시 남구 대연동 1742-4번지 외 1필지48445부산광역시 남구 못골로 78 (대연동)2007041820151111<NA><NA><NA>02폐업390580.53369500000184167.0701670000020140408143443제과점영업051-123-1234<NA><NA><NA><NA><NA><NA><NA>N<NA><NA><NA><NA>33.66<NA><NA><NA>제과점영업<NA><NA><NA><NA><NA><NA><NA>2021-01-05 10:31:05
1607416076356000035600000092019009407_22_04_PI2019-12-27 00:23:37.0축산판매업신선유통<NA>인천광역시 서구 오류동 1654번지 (주)신선식품22653인천광역시 서구 검단로 107, (주)신선식품 (오류동)20191225<NA><NA><NA><NA>영업/정상정상166274.138994991455084.78785959620191225155324식용란수집판매업051-123-1234<NA><NA><NA><NA>000<NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>식용란수집판매업축산물판매업<NA><NA>2021-01-05 10:31:06
33133305331000033100000092012000807_22_04_PI2018-08-31 23:59:59.0<NA>한우외식센타<NA>부산광역시 남구 용호동 495-14번지48587부산광역시 남구 용호로 191 (용호동)2012052120141226<NA><NA><NA>0002폐업392474.24138100000181919.6728420000020141226160011식육판매업051-123-1234<NA><NA><NA><NA>000<NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>식육판매업축산물판매업<NA><NA><NA>2021-01-05 10:31:04
23552352330000033000000091999000607_22_04_PI2018-08-31 23:59:59.0<NA>봉계유통<NA>부산광역시 동래구 온천동 729-12번지47730부산광역시 동래구 충렬대로95번길 56 (온천동)1999040620160630<NA><NA><NA>0002폐업388680.63587400000192411.9915410000020160629101024식육판매업051-123-1234<NA><NA><NA><NA>000<NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>식육판매업축산물판매업<NA><NA><NA>2021-01-05 10:31:03
66386636335000033500000091987000907_22_04_PI2018-08-31 23:59:59.0<NA>세원식육점<NA>부산광역시 금정구 장전동 418-10번지48947부산광역시 금정구 부산대학로50번길 23 (장전동)1987082219990323<NA><NA><NA>0002폐업389913.11285600000194880.9965860000020050131155818식육판매업051-123-1234<NA><NA><NA><NA>000<NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>식육판매업축산물판매업<NA><NA><NA>2021-01-05 10:31:04
178751787731800003180000-107-2020-0034707_22_19_PI2020-07-19 00:23:15.0즉석판매제조가공업사랑섬김 사회적 협동조합150034서울특별시 영등포구 영등포동4가 426-207305서울특별시 영등포구 경인로 823-2 (영등포동4가)20200717<NA><NA><NA><NA>영업/정상영업191478.014118646445967.32655522120200717145213즉석판매제조가공업051-123-1234자가<NA><NA><NA><NA><NA><NA>N<NA><NA><NA><NA>0<NA><NA><NA>즉석판매제조가공업<NA><NA><NA><NA><NA><NA><NA>2021-01-05 10:31:06
37693762332000033200000092016000907_22_04_PI2018-08-31 23:59:59.0<NA>예우축산<NA><NA>46504부산광역시 북구 사상로577번길 14 (구포동)20160407<NA><NA><NA><NA>0000정상381222.30580500000190634.5653980000020160407175032식육판매업051-123-1234<NA><NA><NA><NA>000<NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>식육판매업축산물판매업<NA><NA><NA>2021-01-05 10:31:04
68046798336000033600000092002000107_22_04_PI2018-08-31 23:59:59.0<NA>평강식육점<NA>부산광역시 강서구 대저1동 1748-70번지48947부산광역시 강서구 낙동북로188번길 26 (대저1동)20021116<NA><NA><NA><NA>0000정상377382.06709100000192520.9765550000020130628175030식육판매업051-123-1234<NA><NA><NA><NA>000<NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>식육판매업축산물판매업<NA><NA><NA>2021-01-05 10:31:04
118951189733700003370000-121-1998-0000107_22_18_PI2018-08-31 23:59:59.0<NA>빠리앙스제과점611823부산광역시 연제구 연산동 767-7번지48947<NA>1998012220091126<NA><NA><NA>02폐업389868.770150189639.43683620061226000000제과점영업051-123-1234<NA><NA><NA><NA><NA>상수도전용0N기타<NA><NA><NA>17.640기타<NA>제과점영업<NA><NA><NA><NA><NA><NA><NA>2021-01-05 10:31:05
138541385550700005070000-134-2019-0000907_22_03_PI2019-04-04 02:20:12.0건강기능식품일반판매업아모레퍼스픽760310경상북도 안동시 옥동 800-3번지 이마트36663경상북도 안동시 옥동1길 2, 이마트 지하2층 (옥동)20190403폐업일자휴업시작일자휴업종료일자재개업일자영업/정상영업352101.301410573340941.20763512320190402194437업태구분명051-123-1234자가공장사무직종업원수공장생산직종업원수공장판매직종업원수권리주체일련번호급수시설구분명남성종사자수N등급구분명보증액본사종업원수설비규격0여성종사자수영업장주변구분명월세액방문판매전통업소주된음식전통업소지정번호총종업원수축산물가공업구분명축산업무구분명축산일련번호홈페이지2021-01-05 10:31:06
skeyopnsfteamcodemgtnoopnsvcidupdategbnupdatedtopnsvcnmbplcnmsitepostnositewhladdrrdnpostnordnwhladdrapvpermymddcbymdclgstdtclgenddtropnymdtrdstatenmdtlstatenmxylastmodtsuptaenmsitetelbdngownsenmfctyowkepcntfctypdtjobepcntfctysiljobepcntrgtmbdsnowtrsplyfacilsenmmaneipcntmultusnupsoynlvsenmisreamhoffepcntequsizfaciltotscpwmeipcnttrdpjubnsenmmonamsntuptaenmjtupsomainedfjtupsoasgnnototepnumlindprcbgbnnmlindjobgbnnmlindseqnohomepagelast_load_dttm
148421484451300005130000-135-2019-0000207_22_02_PI2019-07-14 02:21:36.0건강기능식품유통전문판매업(주)이엘농업법인712881경상북도 경산시 남천면 협석리 423-1번지 다동38695경상북도 경산시 남천면 협석2길 25, 다동20190712<NA><NA><NA><NA>영업/정상영업356225.839399379255813.97566040620190712131327건강기능식품유통전문판매업051-123-1234자가<NA><NA><NA><NA>상수도전용<NA>N<NA><NA><NA><NA>0<NA><NA><NA>건강기능식품유통전문판매업<NA><NA><NA><NA><NA><NA><NA>2021-01-05 10:31:06
37623755332000033200000091996000707_22_04_PI2018-08-31 23:59:59.0<NA>금곡스토아<NA>부산광역시 북구 금곡동 1110번지48947<NA>1996071220050831<NA><NA><NA>0002폐업383444.793733196568.89360120050831161151식육판매업051-123-1234<NA><NA><NA><NA>000<NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>식육판매업축산물판매업<NA><NA><NA>2021-01-05 10:31:04
168891689131400003140000-107-2020-0015807_22_19_PI2020-03-22 00:23:22.0즉석판매제조가공업(주)햇살드림158050서울특별시 양천구 목동 916번지 현대백화점 목동점 지하2층07998서울특별시 양천구 목동동로 257, 지하2층 (목동, 현대하이페리온)20200320<NA><NA><NA><NA>영업/정상영업188884.075622342447186.88860430620200320145648즉석판매제조가공업051-123-1234<NA><NA><NA><NA><NA><NA><NA>N<NA><NA><NA><NA>0<NA><NA><NA>즉석판매제조가공업<NA><NA><NA><NA><NA><NA><NA>2021-01-05 10:31:06
137139325000032500000092004000107_22_04_PI2018-08-31 23:59:59.0<NA>남양유업중구가정대리점<NA>부산광역시 중구 영주동 282-24번지 12통4반48947<NA>2004040720090515<NA><NA><NA>0002폐업385025.109745180944.89624720090515165816우유류판매업051-123-1234<NA><NA><NA><NA>000<NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>우유류판매업축산물판매업<NA><NA><NA>2021-01-05 10:31:03
181631816436900003690000-107-2020-0008107_22_19_PI2020-08-27 00:23:13.0즉석판매제조가공업양금반찬681815울산광역시 중구 우정동 283-344467울산광역시 중구 장춘로 12, 1층 (우정동)20200825<NA><NA><NA><NA>영업/정상영업409460.763531293230891.5297869420200825102843즉석판매제조가공업051-123-1234자가<NA><NA><NA><NA><NA><NA>N<NA><NA><NA><NA>10.8<NA><NA><NA>즉석판매제조가공업<NA><NA><NA><NA><NA><NA><NA>2021-01-05 10:31:06
121961219933900003390000-121-2011-0000907_22_18_PI2018-08-31 23:59:59.0<NA>몽블랑제서부산점행사매대617808부산광역시 사상구 괘법동 529-1번지 홈플러스서부산점 지하 1층46970부산광역시 사상구 광장로 7, 지하 1층 (괘법동, 홈플러스서부산점)2011081020180608<NA><NA><NA>02폐업380245.68145100000187128.2756260000020180608171833제과점영업051-123-1234<NA><NA><NA><NA><NA><NA><NA>N<NA><NA><NA><NA>2.69<NA><NA><NA>제과점영업<NA><NA><NA><NA><NA><NA><NA>2021-01-05 10:31:06
44954490333000033300000091995001007_22_04_PI2018-08-31 23:59:59.0<NA>해강식육점<NA>부산광역시 해운대구 우동 1388-10번지48091부산광역시 해운대구 해운대해변로 83 (우동)19951011<NA><NA><NA><NA>0000정상395198.78523300000187130.0233360000020170914131841식육판매업051-123-1234<NA><NA><NA><NA>000<NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>식육판매업축산물판매업<NA><NA><NA>2021-01-05 10:31:04
82338232339000033900000092009001407_22_04_PI2018-08-31 23:59:59.0<NA>시골장터식육점<NA>부산광역시 사상구 괘법동 273-1번지48947부산광역시 사상구 사상로224번길 14 (괘법동)2009060320140610<NA><NA><NA>0002폐업380788.80772300000187216.9003880000020140610175520식육판매업051-123-1234<NA><NA><NA><NA>000<NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>식육판매업축산물판매업<NA><NA><NA>2021-01-05 10:31:04
138911389231000003100000-107-2019-0018107_22_19_PI2019-04-07 02:20:15.0즉석판매제조가공업마더앤피쉬139708서울특별시 노원구 상계동 713번지 롯데백화점 지하1층1695서울특별시 노원구 동일로 1414, 롯데백화점 지하1층 (상계동)20190405<NA><NA><NA><NA>영업/정상영업205320.28476675461419.88179500420190405162846즉석판매제조가공업051-123-1234<NA><NA><NA><NA><NA><NA><NA>N<NA><NA><NA><NA>0<NA><NA><NA>즉석판매제조가공업<NA><NA><NA><NA><NA><NA><NA>2021-01-05 10:31:06
10141019328000032800000092000000407_22_04_PI2018-08-31 23:59:59.0<NA>미주마트식육점<NA>부산광역시 영도구 청학동 69-83번지 미주비치아파트 상가동49090부산광역시 영도구 일산봉로 64 (청학동, 미주비치아파트 상가동)20001201<NA><NA><NA><NA>0000정상388310.19209000000178786.1680520000020120217105930식육판매업051-123-1234<NA><NA><NA><NA>000<NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>식육판매업축산물판매업<NA><NA><NA>2021-01-05 10:31:03