Overview

Dataset statistics

Number of variables49
Number of observations10000
Missing cells22438
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

Numeric5
Text10
Categorical33
DateTime1

Alerts

opnsvcid is highly imbalanced (69.5%)Imbalance
updategbn is highly imbalanced (87.6%)Imbalance
opnsvcnm is highly imbalanced (85.2%)Imbalance
clgstdt is highly imbalanced (98.4%)Imbalance
clgenddt is highly imbalanced (98.4%)Imbalance
ropnymd is highly imbalanced (98.2%)Imbalance
uptaenm is highly imbalanced (54.7%)Imbalance
bdngownsenm is highly imbalanced (94.3%)Imbalance
fctyowkepcnt is highly imbalanced (93.8%)Imbalance
fctypdtjobepcnt is highly imbalanced (93.8%)Imbalance
fctysiljobepcnt is highly imbalanced (93.8%)Imbalance
rgtmbdsno is highly imbalanced (60.3%)Imbalance
wtrsplyfacilsenm is highly imbalanced (78.9%)Imbalance
maneipcnt is highly imbalanced (82.8%)Imbalance
multusnupsoyn is highly imbalanced (96.6%)Imbalance
lvsenm is highly imbalanced (84.4%)Imbalance
isream is highly imbalanced (97.0%)Imbalance
hoffepcnt is highly imbalanced (93.8%)Imbalance
equsiz is highly imbalanced (95.7%)Imbalance
wmeipcnt is highly imbalanced (82.6%)Imbalance
trdpjubnsenm is highly imbalanced (82.9%)Imbalance
monam is highly imbalanced (97.0%)Imbalance
sntuptaenm is highly imbalanced (70.8%)Imbalance
jtupsomainedf is highly imbalanced (95.7%)Imbalance
jtupsoasgnno is highly imbalanced (97.2%)Imbalance
lindprcbgbnnm is highly imbalanced (70.1%)Imbalance
lindjobgbnnm is highly imbalanced (96.5%)Imbalance
lindseqno is highly imbalanced (95.7%)Imbalance
homepage is highly imbalanced (95.7%)Imbalance
sitepostno has 7224 (72.2%) missing valuesMissing
sitewhladdr has 298 (3.0%) missing valuesMissing
rdnwhladdr has 1583 (15.8%) missing valuesMissing
dcbymd has 4999 (50.0%) missing valuesMissing
x has 345 (3.5%) missing valuesMissing
y has 345 (3.5%) missing valuesMissing
sitetel has 457 (4.6%) missing valuesMissing
faciltotscp has 7174 (71.7%) missing valuesMissing
opnsfteamcode is highly skewed (γ1 = 27.07883207)Skewed
rdnpostno is highly skewed (γ1 = 95.99375402)Skewed
skey has unique valuesUnique

Reproduction

Analysis started2024-04-16 08:57:45.536402
Analysis finished2024-04-16 08:57:47.748946
Duration2.21 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%
Mean7206.2085
Minimum1
Maximum20452
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-16T17:57:47.805455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile650.95
Q13333.75
median6738.5
Q310139.75
95-th percentile19784.1
Maximum20452
Range20451
Interquartile range (IQR)6806

Descriptive statistics

Standard deviation4891.0314
Coefficient of variation (CV)0.67872465
Kurtosis0.53905183
Mean7206.2085
Median Absolute Deviation (MAD)3404
Skewness0.83657057
Sum72062085
Variance23922188
MonotonicityNot monotonic
2024-04-16T17:57:47.917720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7269 1
 
< 0.1%
11680 1
 
< 0.1%
2784 1
 
< 0.1%
2137 1
 
< 0.1%
9838 1
 
< 0.1%
290 1
 
< 0.1%
3261 1
 
< 0.1%
5183 1
 
< 0.1%
3961 1
 
< 0.1%
10268 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%
8 1
< 0.1%
10 1
< 0.1%
13 1
< 0.1%
14 1
< 0.1%
15 1
< 0.1%
ValueCountFrequency (%)
20452 1
< 0.1%
20450 1
< 0.1%
20449 1
< 0.1%
20448 1
< 0.1%
20445 1
< 0.1%
20444 1
< 0.1%
20442 1
< 0.1%
20441 1
< 0.1%
20440 1
< 0.1%
20436 1
< 0.1%

opnsfteamcode
Real number (ℝ)

SKEWED 

Distinct17
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3332851
Minimum3250000
Maximum6260000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-16T17:57:48.013563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3250000
5-th percentile3260000
Q13300000
median3330000
Q33370000
95-th percentile3400000
Maximum6260000
Range3010000
Interquartile range (IQR)70000

Descriptive statistics

Standard deviation82209.474
Coefficient of variation (CV)0.024666411
Kurtosis962.16338
Mean3332851
Median Absolute Deviation (MAD)30000
Skewness27.078832
Sum3.332851 × 1010
Variance6.7583976 × 109
MonotonicityNot monotonic
2024-04-16T17:57:48.099061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
3290000 1012
10.1%
3330000 1006
10.1%
3320000 906
9.1%
3390000 882
8.8%
3300000 868
8.7%
3340000 856
8.6%
3350000 719
 
7.2%
3310000 634
 
6.3%
3370000 606
 
6.1%
3380000 506
 
5.1%
Other values (7) 2005
20.1%
ValueCountFrequency (%)
3250000 233
 
2.3%
3260000 298
 
3.0%
3270000 339
 
3.4%
3280000 256
 
2.6%
3290000 1012
10.1%
3300000 868
8.7%
3310000 634
6.3%
3320000 906
9.1%
3330000 1006
10.1%
3340000 856
8.6%
ValueCountFrequency (%)
6260000 6
 
0.1%
3400000 506
5.1%
3390000 882
8.8%
3380000 506
5.1%
3370000 606
6.1%
3360000 367
 
3.7%
3350000 719
7.2%
3340000 856
8.6%
3330000 1006
10.1%
3320000 906
9.1%

mgtno
Text

Distinct9880
Distinct (%)98.8%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-04-16T17:57:48.255533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length18
Mean length19.1296
Min length18

Characters and Unicode

Total characters191296
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

Unique9773 ?
Unique (%)97.7%

Sample

1st row337000000920070005
2nd row3320000-107-2019-00049
3rd row339000000919980017
4th row333000000920140016
5th row334000000920000013
ValueCountFrequency (%)
3400000-107-2020-00056 3
 
< 0.1%
3330000-107-2020-00126 3
 
< 0.1%
3300000-107-2020-00055 3
 
< 0.1%
3370000-107-2020-00064 3
 
< 0.1%
3320000-107-2019-00268 3
 
< 0.1%
3310000-107-2019-00096 3
 
< 0.1%
3250000-107-2019-00112 3
 
< 0.1%
3290000-107-2019-00147 3
 
< 0.1%
3340000-107-2019-00005 3
 
< 0.1%
3280000-107-2020-00002 3
 
< 0.1%
Other values (9870) 9970
99.7%
2024-04-16T17:57:48.558787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 95726
50.0%
3 21440
 
11.2%
1 16778
 
8.8%
2 16694
 
8.7%
9 15367
 
8.0%
- 8472
 
4.4%
4 3943
 
2.1%
7 3573
 
1.9%
8 3430
 
1.8%
5 3077
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 182824
95.6%
Dash Punctuation 8472
 
4.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 95726
52.4%
3 21440
 
11.7%
1 16778
 
9.2%
2 16694
 
9.1%
9 15367
 
8.4%
4 3943
 
2.2%
7 3573
 
2.0%
8 3430
 
1.9%
5 3077
 
1.7%
6 2796
 
1.5%
Dash Punctuation
ValueCountFrequency (%)
- 8472
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 191296
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 95726
50.0%
3 21440
 
11.2%
1 16778
 
8.8%
2 16694
 
8.7%
9 15367
 
8.0%
- 8472
 
4.4%
4 3943
 
2.1%
7 3573
 
1.9%
8 3430
 
1.8%
5 3077
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 191296
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 95726
50.0%
3 21440
 
11.2%
1 16778
 
8.8%
2 16694
 
8.7%
9 15367
 
8.0%
- 8472
 
4.4%
4 3943
 
2.1%
7 3573
 
1.9%
8 3430
 
1.8%
5 3077
 
1.6%

opnsvcid
Categorical

IMBALANCE 

Distinct17
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
07_22_04_P
7163 
07_22_18_P
2024 
07_22_19_P
 
513
07_22_03_P
 
179
07_22_01_P
 
41
Other values (12)
 
80

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row07_22_04_P
2nd row07_22_19_P
3rd row07_22_04_P
4th row07_22_04_P
5th row07_22_04_P

Common Values

ValueCountFrequency (%)
07_22_04_P 7163
71.6%
07_22_18_P 2024
 
20.2%
07_22_19_P 513
 
5.1%
07_22_03_P 179
 
1.8%
07_22_01_P 41
 
0.4%
07_22_10_P 18
 
0.2%
07_22_17_P 15
 
0.1%
07_22_11_P 13
 
0.1%
07_22_02_P 8
 
0.1%
07_22_08_P 8
 
0.1%
Other values (7) 18
 
0.2%

Length

2024-04-16T17:57:48.684155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
07_22_04_p 7163
71.6%
07_22_18_p 2024
 
20.2%
07_22_19_p 513
 
5.1%
07_22_03_p 179
 
1.8%
07_22_01_p 41
 
0.4%
07_22_10_p 18
 
0.2%
07_22_17_p 15
 
0.1%
07_22_11_p 13
 
0.1%
07_22_08_p 8
 
0.1%
07_22_02_p 8
 
0.1%
Other values (7) 18
 
0.2%

updategbn
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
I
9831 
U
 
169

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 9831
98.3%
U 169
 
1.7%

Length

2024-04-16T17:57:48.771514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:57:48.842599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
i 9831
98.3%
u 169
 
1.7%
Distinct238
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2018-08-31 23:59:59
Maximum2021-04-01 02:40:00
2024-04-16T17:57:48.925178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T17:57:49.049197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

opnsvcnm
Categorical

IMBALANCE 

Distinct18
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9116 
즉석판매제조가공업
 
513
건강기능식품일반판매업
 
179
축산판매업
 
42
집단급식소식품판매업
 
41
Other values (13)
 
109

Length

Max length13
Median length4
Mean length4.4413
Min length4

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 9116
91.2%
즉석판매제조가공업 513
 
5.1%
건강기능식품일반판매업 179
 
1.8%
축산판매업 42
 
0.4%
집단급식소식품판매업 41
 
0.4%
제과점영업 29
 
0.3%
식품자동판매기업 18
 
0.2%
유통전문판매업 15
 
0.1%
식품제조가공업 13
 
0.1%
건강기능식품유통전문판매업 8
 
0.1%
Other values (8) 26
 
0.3%

Length

2024-04-16T17:57:49.156282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 9116
91.2%
즉석판매제조가공업 513
 
5.1%
건강기능식품일반판매업 179
 
1.8%
축산판매업 42
 
0.4%
집단급식소식품판매업 41
 
0.4%
제과점영업 29
 
0.3%
식품자동판매기업 18
 
0.2%
유통전문판매업 15
 
0.1%
식품제조가공업 13
 
0.1%
식품소분업 8
 
0.1%
Other values (8) 26
 
0.3%

bplcnm
Text

Distinct7705
Distinct (%)77.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-04-16T17:57:49.392894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length41
Median length30
Mean length6.4825
Min length1

Characters and Unicode

Total characters64825
Distinct characters836
Distinct categories12 ?
Distinct scripts4 ?
Distinct blocks8 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6630 ?
Unique (%)66.3%

Sample

1st row청춘정육점
2nd row신우에프앤지(주)
3rd row(주)명성유통
4th row필봉 한우암소 판매점
5th row동신유통
ValueCountFrequency (%)
주식회사 112
 
0.9%
파리바게뜨 74
 
0.6%
베이커리 56
 
0.5%
부산우유 54
 
0.5%
뚜레쥬르 50
 
0.4%
탑플러스마트 40
 
0.3%
식육점 38
 
0.3%
파리바게트 35
 
0.3%
한우식육점 31
 
0.3%
제일식육점 27
 
0.2%
Other values (7895) 11305
95.6%
2024-04-16T17:57:49.803215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3717
 
5.7%
2654
 
4.1%
2364
 
3.6%
1826
 
2.8%
1684
 
2.6%
1478
 
2.3%
1439
 
2.2%
1410
 
2.2%
1212
 
1.9%
1191
 
1.8%
Other values (826) 45850
70.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 59186
91.3%
Space Separator 1826
 
2.8%
Close Punctuation 1169
 
1.8%
Open Punctuation 1162
 
1.8%
Uppercase Letter 619
 
1.0%
Lowercase Letter 464
 
0.7%
Decimal Number 294
 
0.5%
Other Punctuation 89
 
0.1%
Dash Punctuation 9
 
< 0.1%
Math Symbol 5
 
< 0.1%
Other values (2) 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
3717
 
6.3%
2654
 
4.5%
2364
 
4.0%
1684
 
2.8%
1478
 
2.5%
1439
 
2.4%
1410
 
2.4%
1212
 
2.0%
1191
 
2.0%
1157
 
2.0%
Other values (751) 40880
69.1%
Uppercase Letter
ValueCountFrequency (%)
S 81
 
13.1%
C 41
 
6.6%
D 41
 
6.6%
M 39
 
6.3%
T 33
 
5.3%
K 33
 
5.3%
J 33
 
5.3%
A 32
 
5.2%
O 30
 
4.8%
N 30
 
4.8%
Other values (14) 226
36.5%
Lowercase Letter
ValueCountFrequency (%)
e 67
14.4%
a 52
 
11.2%
o 43
 
9.3%
n 32
 
6.9%
i 30
 
6.5%
s 28
 
6.0%
r 23
 
5.0%
m 20
 
4.3%
l 19
 
4.1%
c 19
 
4.1%
Other values (13) 131
28.2%
Other Punctuation
ValueCountFrequency (%)
. 29
32.6%
& 27
30.3%
, 8
 
9.0%
' 7
 
7.9%
· 5
 
5.6%
" 4
 
4.5%
3
 
3.4%
! 2
 
2.2%
: 2
 
2.2%
/ 1
 
1.1%
Decimal Number
ValueCountFrequency (%)
2 90
30.6%
1 79
26.9%
3 32
 
10.9%
5 20
 
6.8%
8 14
 
4.8%
4 14
 
4.8%
9 13
 
4.4%
0 12
 
4.1%
7 11
 
3.7%
6 9
 
3.1%
Space Separator
ValueCountFrequency (%)
1826
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1169
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1162
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 9
100.0%
Math Symbol
ValueCountFrequency (%)
+ 5
100.0%
Other Symbol
ValueCountFrequency (%)
1
100.0%
Letter Number
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 59178
91.3%
Common 4555
 
7.0%
Latin 1084
 
1.7%
Han 8
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
3717
 
6.3%
2654
 
4.5%
2364
 
4.0%
1684
 
2.8%
1478
 
2.5%
1439
 
2.4%
1410
 
2.4%
1212
 
2.0%
1191
 
2.0%
1157
 
2.0%
Other values (744) 40872
69.1%
Latin
ValueCountFrequency (%)
S 81
 
7.5%
e 67
 
6.2%
a 52
 
4.8%
o 43
 
4.0%
C 41
 
3.8%
D 41
 
3.8%
M 39
 
3.6%
T 33
 
3.0%
K 33
 
3.0%
J 33
 
3.0%
Other values (38) 621
57.3%
Common
ValueCountFrequency (%)
1826
40.1%
) 1169
25.7%
( 1162
25.5%
2 90
 
2.0%
1 79
 
1.7%
3 32
 
0.7%
. 29
 
0.6%
& 27
 
0.6%
5 20
 
0.4%
8 14
 
0.3%
Other values (17) 107
 
2.3%
Han
ValueCountFrequency (%)
2
25.0%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 59177
91.3%
ASCII 5629
 
8.7%
None 8
 
< 0.1%
CJK 7
 
< 0.1%
Specials 1
 
< 0.1%
Number Forms 1
 
< 0.1%
CJK Compat Ideographs 1
 
< 0.1%
Compat Jamo 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
3717
 
6.3%
2654
 
4.5%
2364
 
4.0%
1684
 
2.8%
1478
 
2.5%
1439
 
2.4%
1410
 
2.4%
1212
 
2.0%
1191
 
2.0%
1157
 
2.0%
Other values (743) 40871
69.1%
ASCII
ValueCountFrequency (%)
1826
32.4%
) 1169
20.8%
( 1162
20.6%
2 90
 
1.6%
S 81
 
1.4%
1 79
 
1.4%
e 67
 
1.2%
a 52
 
0.9%
o 43
 
0.8%
C 41
 
0.7%
Other values (61) 1019
18.1%
None
ValueCountFrequency (%)
· 5
62.5%
3
37.5%
CJK
ValueCountFrequency (%)
2
28.6%
1
14.3%
1
14.3%
1
14.3%
1
14.3%
1
14.3%
Specials
ValueCountFrequency (%)
1
100.0%
Number Forms
ValueCountFrequency (%)
1
100.0%
CJK Compat Ideographs
ValueCountFrequency (%)
1
100.0%
Compat Jamo
ValueCountFrequency (%)
1
100.0%

sitepostno
Text

MISSING 

Distinct636
Distinct (%)22.9%
Missing7224
Missing (%)72.2%
Memory size156.2 KiB
2024-04-16T17:57:50.068001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters16656
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

Unique173 ?
Unique (%)6.2%

Sample

1st row616814
2nd row604843
3rd row614831
4th row612819
5th row602827
ValueCountFrequency (%)
612020 89
 
3.2%
600017 40
 
1.4%
614847 31
 
1.1%
616852 25
 
0.9%
611811 23
 
0.8%
617808 23
 
0.8%
608832 22
 
0.8%
607815 22
 
0.8%
612836 22
 
0.8%
607831 21
 
0.8%
Other values (626) 2458
88.5%
2024-04-16T17:57:50.465445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6 3326
20.0%
1 2801
16.8%
8 2744
16.5%
0 2672
16.0%
2 1365
8.2%
4 1119
 
6.7%
7 833
 
5.0%
3 810
 
4.9%
9 568
 
3.4%
5 406
 
2.4%
Other values (5) 12
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 16644
99.9%
Other Letter 12
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
6 3326
20.0%
1 2801
16.8%
8 2744
16.5%
0 2672
16.1%
2 1365
8.2%
4 1119
 
6.7%
7 833
 
5.0%
3 810
 
4.9%
9 568
 
3.4%
5 406
 
2.4%
Other Letter
ValueCountFrequency (%)
4
33.3%
2
16.7%
2
16.7%
2
16.7%
2
16.7%

Most occurring scripts

ValueCountFrequency (%)
Common 16644
99.9%
Hangul 12
 
0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
6 3326
20.0%
1 2801
16.8%
8 2744
16.5%
0 2672
16.1%
2 1365
8.2%
4 1119
 
6.7%
7 833
 
5.0%
3 810
 
4.9%
9 568
 
3.4%
5 406
 
2.4%
Hangul
ValueCountFrequency (%)
4
33.3%
2
16.7%
2
16.7%
2
16.7%
2
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16644
99.9%
Hangul 12
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6 3326
20.0%
1 2801
16.8%
8 2744
16.5%
0 2672
16.1%
2 1365
8.2%
4 1119
 
6.7%
7 833
 
5.0%
3 810
 
4.9%
9 568
 
3.4%
5 406
 
2.4%
Hangul
ValueCountFrequency (%)
4
33.3%
2
16.7%
2
16.7%
2
16.7%
2
16.7%

sitewhladdr
Text

MISSING 

Distinct7918
Distinct (%)81.6%
Missing298
Missing (%)3.0%
Memory size156.2 KiB
2024-04-16T17:57:50.731246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length60
Median length52
Mean length24.60771
Min length13

Characters and Unicode

Total characters238744
Distinct characters469
Distinct categories10 ?
Distinct scripts4 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6868 ?
Unique (%)70.8%

Sample

1st row부산광역시 연제구 연산동 400-42번지
2nd row부산광역시 북구 덕천동 370번지 메가마트 內
3rd row부산광역시 사상구 모라동 283-6번지
4th row부산광역시 사하구 하단동 242-4번지
5th row부산광역시 금정구 서동 111-10번지
ValueCountFrequency (%)
부산광역시 9702
 
22.4%
부산진구 958
 
2.2%
해운대구 954
 
2.2%
사상구 871
 
2.0%
동래구 866
 
2.0%
북구 850
 
2.0%
사하구 838
 
1.9%
금정구 708
 
1.6%
남구 634
 
1.5%
연제구 588
 
1.4%
Other values (8628) 26281
60.8%
2024-04-16T17:57:51.302591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
42869
18.0%
11467
 
4.8%
11402
 
4.8%
11206
 
4.7%
1 11045
 
4.6%
9981
 
4.2%
9890
 
4.1%
9809
 
4.1%
9712
 
4.1%
9515
 
4.0%
Other values (459) 101848
42.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 139372
58.4%
Decimal Number 47044
 
19.7%
Space Separator 42869
 
18.0%
Dash Punctuation 8454
 
3.5%
Uppercase Letter 330
 
0.1%
Other Punctuation 227
 
0.1%
Open Punctuation 208
 
0.1%
Close Punctuation 206
 
0.1%
Lowercase Letter 20
 
< 0.1%
Math Symbol 14
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
11467
 
8.2%
11402
 
8.2%
11206
 
8.0%
9981
 
7.2%
9890
 
7.1%
9809
 
7.0%
9712
 
7.0%
9515
 
6.8%
8961
 
6.4%
1972
 
1.4%
Other values (407) 45457
32.6%
Uppercase Letter
ValueCountFrequency (%)
B 80
24.2%
A 54
16.4%
S 37
11.2%
G 33
10.0%
K 19
 
5.8%
C 15
 
4.5%
E 13
 
3.9%
L 12
 
3.6%
D 11
 
3.3%
T 11
 
3.3%
Other values (10) 45
13.6%
Decimal Number
ValueCountFrequency (%)
1 11045
23.5%
2 6114
13.0%
3 4860
10.3%
4 4377
 
9.3%
5 4297
 
9.1%
0 3689
 
7.8%
6 3395
 
7.2%
7 3330
 
7.1%
8 3090
 
6.6%
9 2847
 
6.1%
Lowercase Letter
ValueCountFrequency (%)
s 5
25.0%
b 3
15.0%
k 3
15.0%
g 3
15.0%
e 2
 
10.0%
l 1
 
5.0%
h 1
 
5.0%
c 1
 
5.0%
u 1
 
5.0%
Other Punctuation
ValueCountFrequency (%)
, 174
76.7%
@ 29
 
12.8%
. 14
 
6.2%
/ 5
 
2.2%
· 3
 
1.3%
' 2
 
0.9%
Open Punctuation
ValueCountFrequency (%)
( 207
99.5%
[ 1
 
0.5%
Close Punctuation
ValueCountFrequency (%)
) 205
99.5%
] 1
 
0.5%
Space Separator
ValueCountFrequency (%)
42869
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 8454
100.0%
Math Symbol
ValueCountFrequency (%)
~ 14
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 139371
58.4%
Common 99022
41.5%
Latin 350
 
0.1%
Han 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
11467
 
8.2%
11402
 
8.2%
11206
 
8.0%
9981
 
7.2%
9890
 
7.1%
9809
 
7.0%
9712
 
7.0%
9515
 
6.8%
8961
 
6.4%
1972
 
1.4%
Other values (406) 45456
32.6%
Latin
ValueCountFrequency (%)
B 80
22.9%
A 54
15.4%
S 37
10.6%
G 33
9.4%
K 19
 
5.4%
C 15
 
4.3%
E 13
 
3.7%
L 12
 
3.4%
D 11
 
3.1%
T 11
 
3.1%
Other values (19) 65
18.6%
Common
ValueCountFrequency (%)
42869
43.3%
1 11045
 
11.2%
- 8454
 
8.5%
2 6114
 
6.2%
3 4860
 
4.9%
4 4377
 
4.4%
5 4297
 
4.3%
0 3689
 
3.7%
6 3395
 
3.4%
7 3330
 
3.4%
Other values (13) 6592
 
6.7%
Han
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 139371
58.4%
ASCII 99369
41.6%
None 3
 
< 0.1%
CJK 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
42869
43.1%
1 11045
 
11.1%
- 8454
 
8.5%
2 6114
 
6.2%
3 4860
 
4.9%
4 4377
 
4.4%
5 4297
 
4.3%
0 3689
 
3.7%
6 3395
 
3.4%
7 3330
 
3.4%
Other values (41) 6939
 
7.0%
Hangul
ValueCountFrequency (%)
11467
 
8.2%
11402
 
8.2%
11206
 
8.0%
9981
 
7.2%
9890
 
7.1%
9809
 
7.0%
9712
 
7.0%
9515
 
6.8%
8961
 
6.4%
1972
 
1.4%
Other values (406) 45456
32.6%
None
ValueCountFrequency (%)
· 3
100.0%
CJK
ValueCountFrequency (%)
1
100.0%

rdnpostno
Real number (ℝ)

SKEWED 

Distinct1451
Distinct (%)14.5%
Missing13
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean48371.673
Minimum46002
Maximum618270
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-16T17:57:51.413735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum46002
5-th percentile46303
Q147754.5
median48947
Q348947
95-th percentile49227
Maximum618270
Range572268
Interquartile range (IQR)1192.5

Descriptive statistics

Standard deviation5780.1668
Coefficient of variation (CV)0.11949487
Kurtosis9465.8551
Mean48371.673
Median Absolute Deviation (MAD)19
Skewness95.993754
Sum4.830879 × 108
Variance33410329
MonotonicityNot monotonic
2024-04-16T17:57:51.523797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
48947 4910
49.1%
48058 72
 
0.7%
46504 69
 
0.7%
46901 46
 
0.5%
47052 42
 
0.4%
48944 41
 
0.4%
46916 40
 
0.4%
47285 30
 
0.3%
46702 27
 
0.3%
48735 25
 
0.2%
Other values (1441) 4685
46.9%
ValueCountFrequency (%)
46002 1
 
< 0.1%
46003 1
 
< 0.1%
46004 6
0.1%
46006 2
 
< 0.1%
46007 5
0.1%
46008 10
0.1%
46010 1
 
< 0.1%
46011 1
 
< 0.1%
46012 2
 
< 0.1%
46013 6
0.1%
ValueCountFrequency (%)
618270 1
 
< 0.1%
49526 1
 
< 0.1%
49525 2
 
< 0.1%
49524 4
 
< 0.1%
49523 1
 
< 0.1%
49521 1
 
< 0.1%
49520 7
0.1%
49519 16
0.2%
49518 7
0.1%
49515 2
 
< 0.1%

rdnwhladdr
Text

MISSING 

Distinct6965
Distinct (%)82.7%
Missing1583
Missing (%)15.8%
Memory size156.2 KiB
2024-04-16T17:57:51.804284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length63
Median length56
Mean length28.748248
Min length19

Characters and Unicode

Total characters241974
Distinct characters509
Distinct categories10 ?
Distinct scripts4 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6098 ?
Unique (%)72.4%

Sample

1st row부산광역시 연제구 과정로 224 (연산동)
2nd row부산광역시 북구 기찰로 164, 메가마트 內 1층 (덕천동)
3rd row부산광역시 사상구 사상로531번길 47 (모라동)
4th row부산광역시 해운대구 해운대로76번길 52 (재송동)
5th row부산광역시 사하구 동매로 97-1 (하단동)
ValueCountFrequency (%)
부산광역시 8417
 
18.1%
부산진구 920
 
2.0%
동래구 765
 
1.6%
북구 738
 
1.6%
사상구 719
 
1.5%
사하구 704
 
1.5%
해운대구 670
 
1.4%
1층 662
 
1.4%
금정구 650
 
1.4%
남구 571
 
1.2%
Other values (5737) 31617
68.1%
2024-04-16T17:57:52.235023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
38032
 
15.7%
10818
 
4.5%
10265
 
4.2%
10222
 
4.2%
1 8969
 
3.7%
8946
 
3.7%
8853
 
3.7%
8691
 
3.6%
8433
 
3.5%
( 8139
 
3.4%
Other values (499) 120606
49.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 145987
60.3%
Space Separator 38032
 
15.7%
Decimal Number 36789
 
15.2%
Open Punctuation 8139
 
3.4%
Close Punctuation 8136
 
3.4%
Other Punctuation 3347
 
1.4%
Dash Punctuation 1177
 
0.5%
Uppercase Letter 335
 
0.1%
Lowercase Letter 17
 
< 0.1%
Math Symbol 15
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
10818
 
7.4%
10265
 
7.0%
10222
 
7.0%
8946
 
6.1%
8853
 
6.1%
8691
 
6.0%
8433
 
5.8%
8107
 
5.6%
4454
 
3.1%
4184
 
2.9%
Other values (449) 63014
43.2%
Uppercase Letter
ValueCountFrequency (%)
B 73
21.8%
A 46
13.7%
S 42
12.5%
G 32
9.6%
C 27
 
8.1%
K 21
 
6.3%
E 21
 
6.3%
P 12
 
3.6%
N 12
 
3.6%
D 8
 
2.4%
Other values (11) 41
12.2%
Decimal Number
ValueCountFrequency (%)
1 8969
24.4%
2 4981
13.5%
3 4119
11.2%
5 3348
 
9.1%
4 3119
 
8.5%
0 2968
 
8.1%
6 2575
 
7.0%
7 2549
 
6.9%
8 2141
 
5.8%
9 2020
 
5.5%
Lowercase Letter
ValueCountFrequency (%)
s 5
29.4%
g 3
17.6%
e 2
 
11.8%
b 2
 
11.8%
k 2
 
11.8%
c 1
 
5.9%
a 1
 
5.9%
u 1
 
5.9%
Other Punctuation
ValueCountFrequency (%)
, 3320
99.2%
@ 11
 
0.3%
. 8
 
0.2%
/ 4
 
0.1%
· 3
 
0.1%
' 1
 
< 0.1%
Space Separator
ValueCountFrequency (%)
38032
100.0%
Open Punctuation
ValueCountFrequency (%)
( 8139
100.0%
Close Punctuation
ValueCountFrequency (%)
) 8136
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1177
100.0%
Math Symbol
ValueCountFrequency (%)
~ 15
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 145986
60.3%
Common 95635
39.5%
Latin 352
 
0.1%
Han 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
10818
 
7.4%
10265
 
7.0%
10222
 
7.0%
8946
 
6.1%
8853
 
6.1%
8691
 
6.0%
8433
 
5.8%
8107
 
5.6%
4454
 
3.1%
4184
 
2.9%
Other values (448) 63013
43.2%
Latin
ValueCountFrequency (%)
B 73
20.7%
A 46
13.1%
S 42
11.9%
G 32
9.1%
C 27
 
7.7%
K 21
 
6.0%
E 21
 
6.0%
P 12
 
3.4%
N 12
 
3.4%
D 8
 
2.3%
Other values (19) 58
16.5%
Common
ValueCountFrequency (%)
38032
39.8%
1 8969
 
9.4%
( 8139
 
8.5%
) 8136
 
8.5%
2 4981
 
5.2%
3 4119
 
4.3%
5 3348
 
3.5%
, 3320
 
3.5%
4 3119
 
3.3%
0 2968
 
3.1%
Other values (11) 10504
 
11.0%
Han
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 145986
60.3%
ASCII 95984
39.7%
None 3
 
< 0.1%
CJK 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
38032
39.6%
1 8969
 
9.3%
( 8139
 
8.5%
) 8136
 
8.5%
2 4981
 
5.2%
3 4119
 
4.3%
5 3348
 
3.5%
, 3320
 
3.5%
4 3119
 
3.2%
0 2968
 
3.1%
Other values (39) 10853
 
11.3%
Hangul
ValueCountFrequency (%)
10818
 
7.4%
10265
 
7.0%
10222
 
7.0%
8946
 
6.1%
8853
 
6.1%
8691
 
6.0%
8433
 
5.8%
8107
 
5.6%
4454
 
3.1%
4184
 
2.9%
Other values (448) 63013
43.2%
None
ValueCountFrequency (%)
· 3
100.0%
CJK
ValueCountFrequency (%)
1
100.0%

apvpermymd
Real number (ℝ)

Distinct5552
Distinct (%)55.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20052955
Minimum19631010
Maximum20210330
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-16T17:57:52.348168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19631010
5-th percentile19850819
Q119990520
median20070130
Q320130923
95-th percentile20210114
Maximum20210330
Range579320
Interquartile range (IQR)140402.75

Descriptive statistics

Standard deviation104400.95
Coefficient of variation (CV)0.0052062626
Kurtosis0.2057694
Mean20052955
Median Absolute Deviation (MAD)70279
Skewness-0.69956937
Sum2.0052955 × 1011
Variance1.0899558 × 1010
MonotonicityNot monotonic
2024-04-16T17:57:52.475350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19980713 87
 
0.9%
20210312 45
 
0.4%
20210326 43
 
0.4%
20210305 37
 
0.4%
20210319 35
 
0.4%
20210322 33
 
0.3%
20210329 27
 
0.3%
20210303 25
 
0.2%
20210311 24
 
0.2%
20210330 22
 
0.2%
Other values (5542) 9622
96.2%
ValueCountFrequency (%)
19631010 2
< 0.1%
19651010 3
< 0.1%
19651024 1
 
< 0.1%
19651116 2
< 0.1%
19660415 1
 
< 0.1%
19660916 1
 
< 0.1%
19661001 1
 
< 0.1%
19661125 1
 
< 0.1%
19670811 1
 
< 0.1%
19670814 1
 
< 0.1%
ValueCountFrequency (%)
20210330 22
0.2%
20210329 27
0.3%
20210327 2
 
< 0.1%
20210326 43
0.4%
20210325 13
 
0.1%
20210324 16
 
0.2%
20210323 11
 
0.1%
20210322 33
0.3%
20210319 35
0.4%
20210318 17
 
0.2%

dcbymd
Text

MISSING 

Distinct2717
Distinct (%)54.3%
Missing4999
Missing (%)50.0%
Memory size156.2 KiB
2024-04-16T17:57:52.748426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length7.9640072
Min length4

Characters and Unicode

Total characters39828
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

Unique1452 ?
Unique (%)29.0%

Sample

1st row20180814
2nd row20171101
3rd row20110329
4th row20030207
5th row20100129
ValueCountFrequency (%)
폐업일자 45
 
0.9%
20131222 43
 
0.9%
20121213 23
 
0.5%
20140820 17
 
0.3%
20060216 16
 
0.3%
20130607 15
 
0.3%
20170131 15
 
0.3%
20060412 13
 
0.3%
20210314 11
 
0.2%
20130409 10
 
0.2%
Other values (2707) 4793
95.8%
2024-04-16T17:57:53.131715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 13116
32.9%
2 8579
21.5%
1 7684
19.3%
3 2060
 
5.2%
6 1514
 
3.8%
4 1509
 
3.8%
7 1497
 
3.8%
5 1328
 
3.3%
8 1224
 
3.1%
9 1137
 
2.9%
Other values (4) 180
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 39648
99.5%
Other Letter 180
 
0.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 13116
33.1%
2 8579
21.6%
1 7684
19.4%
3 2060
 
5.2%
6 1514
 
3.8%
4 1509
 
3.8%
7 1497
 
3.8%
5 1328
 
3.3%
8 1224
 
3.1%
9 1137
 
2.9%
Other Letter
ValueCountFrequency (%)
45
25.0%
45
25.0%
45
25.0%
45
25.0%

Most occurring scripts

ValueCountFrequency (%)
Common 39648
99.5%
Hangul 180
 
0.5%

Most frequent character per script

Common
ValueCountFrequency (%)
0 13116
33.1%
2 8579
21.6%
1 7684
19.4%
3 2060
 
5.2%
6 1514
 
3.8%
4 1509
 
3.8%
7 1497
 
3.8%
5 1328
 
3.3%
8 1224
 
3.1%
9 1137
 
2.9%
Hangul
ValueCountFrequency (%)
45
25.0%
45
25.0%
45
25.0%
45
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 39648
99.5%
Hangul 180
 
0.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 13116
33.1%
2 8579
21.6%
1 7684
19.4%
3 2060
 
5.2%
6 1514
 
3.8%
4 1509
 
3.8%
7 1497
 
3.8%
5 1328
 
3.3%
8 1224
 
3.1%
9 1137
 
2.9%
Hangul
ValueCountFrequency (%)
45
25.0%
45
25.0%
45
25.0%
45
25.0%

clgstdt
Categorical

IMBALANCE 

Distinct19
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9936 
휴업시작일자
 
47
20130314
 
1
20120215
 
1
20111013
 
1
Other values (14)
 
14

Length

Max length8
Median length4
Mean length4.0162
Min length4

Unique

Unique17 ?
Unique (%)0.2%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 9936
99.4%
휴업시작일자 47
 
0.5%
20130314 1
 
< 0.1%
20120215 1
 
< 0.1%
20111013 1
 
< 0.1%
20110408 1
 
< 0.1%
20110225 1
 
< 0.1%
20130318 1
 
< 0.1%
20060420 1
 
< 0.1%
20101103 1
 
< 0.1%
Other values (9) 9
 
0.1%

Length

2024-04-16T17:57:53.259981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 9936
99.4%
휴업시작일자 47
 
0.5%
20050428 1
 
< 0.1%
20110607 1
 
< 0.1%
20040325 1
 
< 0.1%
20150101 1
 
< 0.1%
20101018 1
 
< 0.1%
20080720 1
 
< 0.1%
20160406 1
 
< 0.1%
20180817 1
 
< 0.1%
Other values (9) 9
 
0.1%

clgenddt
Categorical

IMBALANCE 

Distinct14
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9941 
휴업종료일자
 
47
20121012
 
1
20160317
 
1
20060831
 
1
Other values (9)
 
9

Length

Max length8
Median length4
Mean length4.0142
Min length4

Unique

Unique12 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 9941
99.4%
휴업종료일자 47
 
0.5%
20121012 1
 
< 0.1%
20160317 1
 
< 0.1%
20060831 1
 
< 0.1%
20111102 1
 
< 0.1%
20140313 1
 
< 0.1%
20060429 1
 
< 0.1%
20210816 1
 
< 0.1%
20161006 1
 
< 0.1%
Other values (4) 4
 
< 0.1%

Length

2024-04-16T17:57:53.364861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 9941
99.4%
휴업종료일자 47
 
0.5%
20121012 1
 
< 0.1%
20160317 1
 
< 0.1%
20060831 1
 
< 0.1%
20111102 1
 
< 0.1%
20140313 1
 
< 0.1%
20060429 1
 
< 0.1%
20210816 1
 
< 0.1%
20161006 1
 
< 0.1%
Other values (4) 4
 
< 0.1%

ropnymd
Categorical

IMBALANCE 

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9948 
재개업일자
 
47
20130806
 
1
20180531
 
1
20180619
 
1
Other values (2)
 
2

Length

Max length8
Median length4
Mean length4.0067
Min length4

Unique

Unique5 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 9948
99.5%
재개업일자 47
 
0.5%
20130806 1
 
< 0.1%
20180531 1
 
< 0.1%
20180619 1
 
< 0.1%
20070607 1
 
< 0.1%
20061030 1
 
< 0.1%

Length

2024-04-16T17:57:53.472767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:57:53.590151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9948
99.5%
재개업일자 47
 
0.5%
20130806 1
 
< 0.1%
20180531 1
 
< 0.1%
20180619 1
 
< 0.1%
20070607 1
 
< 0.1%
20061030 1
 
< 0.1%

trdstatenm
Categorical

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
0002
3744 
0000
3016 
02
1144 
01
851 
영업/정상
781 
Other values (6)
464 

Length

Max length5
Median length4
Mean length3.6601
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0000
2nd row영업/정상
3rd row0004
4th row0002
5th row0000

Common Values

ValueCountFrequency (%)
0002 3744
37.4%
0000 3016
30.2%
02 1144
 
11.4%
01 851
 
8.5%
영업/정상 781
 
7.8%
0004 342
 
3.4%
폐업 95
 
0.9%
0001 14
 
0.1%
<NA> 5
 
0.1%
0003 5
 
0.1%

Length

2024-04-16T17:57:53.704566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0002 3744
37.4%
0000 3016
30.2%
02 1144
 
11.4%
01 851
 
8.5%
영업/정상 781
 
7.8%
0004 342
 
3.4%
폐업 95
 
0.9%
0001 14
 
0.1%
na 5
 
< 0.1%
0003 5
 
< 0.1%

dtlstatenm
Categorical

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
폐업
4983 
정상
3062 
영업
1594 
말소
 
342
휴업
 
14

Length

Max length4
Median length2
Mean length2.001
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
폐업 4983
49.8%
정상 3062
30.6%
영업 1594
 
15.9%
말소 342
 
3.4%
휴업 14
 
0.1%
행정처분 5
 
0.1%

Length

2024-04-16T17:57:53.834038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:57:53.942609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 4983
49.8%
정상 3062
30.6%
영업 1594
 
15.9%
말소 342
 
3.4%
휴업 14
 
0.1%
행정처분 5
 
< 0.1%

x
Text

MISSING 

Distinct7276
Distinct (%)75.4%
Missing345
Missing (%)3.5%
Memory size156.2 KiB
2024-04-16T17:57:54.112375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length20
Mean length19.998654
Min length7

Characters and Unicode

Total characters193087
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

Unique5998 ?
Unique (%)62.1%

Sample

1st row391535.23019400000
2nd row384082.64392147
3rd row380979.44002900000
4th row392871.79818800000
5th row379533.22164200000
ValueCountFrequency (%)
381223.93770800000 36
 
0.4%
381150.24062500000 34
 
0.4%
394015.45385100000 29
 
0.3%
393952.264486105 27
 
0.3%
385590.814676765 27
 
0.3%
381172.69587400000 23
 
0.2%
381168.29159900000 20
 
0.2%
387443.21456800000 17
 
0.2%
381201.909278 17
 
0.2%
387271.299492377 15
 
0.2%
Other values (7266) 9410
97.5%
2024-04-16T17:57:54.417696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 47435
24.6%
28661
14.8%
3 18893
 
9.8%
8 15096
 
7.8%
9 13237
 
6.9%
1 10653
 
5.5%
7 10578
 
5.5%
2 10236
 
5.3%
5 9795
 
5.1%
4 9635
 
5.0%
Other values (9) 18868
 
9.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 154836
80.2%
Space Separator 28661
 
14.8%
Other Punctuation 9583
 
5.0%
Other Letter 4
 
< 0.1%
Open Punctuation 1
 
< 0.1%
Uppercase Letter 1
 
< 0.1%
Close Punctuation 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 47435
30.6%
3 18893
 
12.2%
8 15096
 
9.7%
9 13237
 
8.5%
1 10653
 
6.9%
7 10578
 
6.8%
2 10236
 
6.6%
5 9795
 
6.3%
4 9635
 
6.2%
6 9278
 
6.0%
Other Letter
ValueCountFrequency (%)
1
25.0%
1
25.0%
1
25.0%
1
25.0%
Space Separator
ValueCountFrequency (%)
28661
100.0%
Other Punctuation
ValueCountFrequency (%)
. 9583
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Uppercase Letter
ValueCountFrequency (%)
X 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 193082
> 99.9%
Hangul 4
 
< 0.1%
Latin 1
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 47435
24.6%
28661
14.8%
3 18893
 
9.8%
8 15096
 
7.8%
9 13237
 
6.9%
1 10653
 
5.5%
7 10578
 
5.5%
2 10236
 
5.3%
5 9795
 
5.1%
4 9635
 
5.0%
Other values (4) 18863
 
9.8%
Hangul
ValueCountFrequency (%)
1
25.0%
1
25.0%
1
25.0%
1
25.0%
Latin
ValueCountFrequency (%)
X 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 193083
> 99.9%
Hangul 4
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 47435
24.6%
28661
14.8%
3 18893
 
9.8%
8 15096
 
7.8%
9 13237
 
6.9%
1 10653
 
5.5%
7 10578
 
5.5%
2 10236
 
5.3%
5 9795
 
5.1%
4 9635
 
5.0%
Other values (5) 18864
 
9.8%
Hangul
ValueCountFrequency (%)
1
25.0%
1
25.0%
1
25.0%
1
25.0%

y
Text

MISSING 

Distinct7276
Distinct (%)75.4%
Missing345
Missing (%)3.5%
Memory size156.2 KiB
2024-04-16T17:57:54.619914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length20
Mean length19.998654
Min length7

Characters and Unicode

Total characters193087
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

Unique5998 ?
Unique (%)62.1%

Sample

1st row189969.34774800000
2nd row192420.344583399
3rd row190257.98210000000
4th row190221.81382300000
5th row180229.26595900000
ValueCountFrequency (%)
184549.28339000000 36
 
0.4%
190717.70395600000 34
 
0.4%
187900.93961700000 29
 
0.3%
187602.933160728 27
 
0.3%
179553.867031936 27
 
0.3%
190737.64327700000 23
 
0.2%
190674.25624800000 20
 
0.2%
186484.77508400000 17
 
0.2%
184537.273724 17
 
0.2%
186099.137533193 15
 
0.2%
Other values (7266) 9410
97.5%
2024-04-16T17:57:54.932767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 47245
24.5%
28580
14.8%
1 19363
10.0%
8 14678
 
7.6%
9 13603
 
7.0%
7 11336
 
5.9%
6 9937
 
5.1%
4 9812
 
5.1%
3 9768
 
5.1%
2 9654
 
5.0%
Other values (9) 19111
9.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 154917
80.2%
Space Separator 28580
 
14.8%
Other Punctuation 9583
 
5.0%
Other Letter 4
 
< 0.1%
Open Punctuation 1
 
< 0.1%
Uppercase Letter 1
 
< 0.1%
Close Punctuation 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 47245
30.5%
1 19363
12.5%
8 14678
 
9.5%
9 13603
 
8.8%
7 11336
 
7.3%
6 9937
 
6.4%
4 9812
 
6.3%
3 9768
 
6.3%
2 9654
 
6.2%
5 9521
 
6.1%
Other Letter
ValueCountFrequency (%)
1
25.0%
1
25.0%
1
25.0%
1
25.0%
Space Separator
ValueCountFrequency (%)
28580
100.0%
Other Punctuation
ValueCountFrequency (%)
. 9583
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Uppercase Letter
ValueCountFrequency (%)
Y 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 193082
> 99.9%
Hangul 4
 
< 0.1%
Latin 1
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 47245
24.5%
28580
14.8%
1 19363
10.0%
8 14678
 
7.6%
9 13603
 
7.0%
7 11336
 
5.9%
6 9937
 
5.1%
4 9812
 
5.1%
3 9768
 
5.1%
2 9654
 
5.0%
Other values (4) 19106
9.9%
Hangul
ValueCountFrequency (%)
1
25.0%
1
25.0%
1
25.0%
1
25.0%
Latin
ValueCountFrequency (%)
Y 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 193083
> 99.9%
Hangul 4
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 47245
24.5%
28580
14.8%
1 19363
10.0%
8 14678
 
7.6%
9 13603
 
7.0%
7 11336
 
5.9%
6 9937
 
5.1%
4 9812
 
5.1%
3 9768
 
5.1%
2 9654
 
5.0%
Other values (5) 19107
9.9%
Hangul
ValueCountFrequency (%)
1
25.0%
1
25.0%
1
25.0%
1
25.0%

lastmodts
Real number (ℝ)

Distinct9687
Distinct (%)96.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0125853 × 1013
Minimum1.9990315 × 1013
Maximum2.021033 × 1013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-16T17:57:55.063423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.9990315 × 1013
5-th percentile2.0030823 × 1013
Q12.009051 × 1013
median2.0131227 × 1013
Q32.0170214 × 1013
95-th percentile2.0210305 × 1013
Maximum2.021033 × 1013
Range2.2001518 × 1011
Interquartile range (IQR)7.9703785 × 1010

Descriptive statistics

Standard deviation5.177553 × 1010
Coefficient of variation (CV)0.0025725881
Kurtosis-0.7505193
Mean2.0125853 × 1013
Median Absolute Deviation (MAD)3.9396011 × 1010
Skewness-0.39923674
Sum2.0125853 × 1017
Variance2.6807055 × 1021
MonotonicityNot monotonic
2024-04-16T17:57:55.179145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20061226000000 21
 
0.2%
20050615000000 11
 
0.1%
19990318000000 10
 
0.1%
20020802000000 9
 
0.1%
20040823000000 9
 
0.1%
20050614000000 8
 
0.1%
19990319000000 8
 
0.1%
19990317000000 7
 
0.1%
20010803000000 6
 
0.1%
20050525000000 6
 
0.1%
Other values (9677) 9905
99.1%
ValueCountFrequency (%)
19990315000000 5
0.1%
19990316000000 4
 
< 0.1%
19990317000000 7
0.1%
19990318000000 10
0.1%
19990319000000 8
0.1%
19990323000000 2
 
< 0.1%
19990324000000 1
 
< 0.1%
19990511000000 3
 
< 0.1%
19990520000000 1
 
< 0.1%
19990610000000 1
 
< 0.1%
ValueCountFrequency (%)
20210330175605 1
< 0.1%
20210330171825 1
< 0.1%
20210330170709 1
< 0.1%
20210330170310 1
< 0.1%
20210330164703 1
< 0.1%
20210330164635 1
< 0.1%
20210330163249 1
< 0.1%
20210330161832 1
< 0.1%
20210330160442 1
< 0.1%
20210330155132 1
< 0.1%

uptaenm
Categorical

IMBALANCE 

Distinct21
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
식육판매업
5752 
제과점영업
2024 
우유류판매업
891 
즉석판매제조가공업
 
511
축산물유통전문판매업
 
206
Other values (16)
616 

Length

Max length13
Median length5
Mean length5.5285
Min length2

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row식육판매업
2nd row즉석판매제조가공업
3rd row식육판매업
4th row식육판매업
5th row식육판매업

Common Values

ValueCountFrequency (%)
식육판매업 5752
57.5%
제과점영업 2024
 
20.2%
우유류판매업 891
 
8.9%
즉석판매제조가공업 511
 
5.1%
축산물유통전문판매업 206
 
2.1%
<NA> 180
 
1.8%
축산물수입판매업 171
 
1.7%
식용란수집판매업 104
 
1.0%
집단급식소 식품판매업 41
 
0.4%
식육부산물전문판매업 39
 
0.4%
Other values (11) 81
 
0.8%

Length

2024-04-16T17:57:55.287234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
식육판매업 5752
57.2%
제과점영업 2024
 
20.1%
우유류판매업 891
 
8.9%
즉석판매제조가공업 511
 
5.1%
축산물유통전문판매업 206
 
2.0%
na 180
 
1.8%
축산물수입판매업 171
 
1.7%
식용란수집판매업 104
 
1.0%
집단급식소 41
 
0.4%
식품판매업 41
 
0.4%
Other values (12) 133
 
1.3%

sitetel
Text

MISSING 

Distinct108
Distinct (%)1.1%
Missing457
Missing (%)4.6%
Memory size156.2 KiB
2024-04-16T17:57:55.414405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length11.9825
Min length3

Characters and Unicode

Total characters114349
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

Unique80 ?
Unique (%)0.8%

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 9391
96.2%
051 92
 
0.9%
전화번호 13
 
0.1%
831 9
 
0.1%
031 8
 
0.1%
5711 6
 
0.1%
611 5
 
0.1%
062 5
 
0.1%
055 5
 
0.1%
971 4
 
< 0.1%
Other values (167) 225
 
2.3%
2024-04-16T17:57:55.689757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 28433
24.9%
2 18904
16.5%
3 18882
16.5%
- 18805
16.4%
0 9624
 
8.4%
5 9600
 
8.4%
4 9457
 
8.3%
222
 
0.2%
7 107
 
0.1%
6 106
 
0.1%
Other values (6) 209
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 95270
83.3%
Dash Punctuation 18805
 
16.4%
Space Separator 222
 
0.2%
Other Letter 52
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 28433
29.8%
2 18904
19.8%
3 18882
19.8%
0 9624
 
10.1%
5 9600
 
10.1%
4 9457
 
9.9%
7 107
 
0.1%
6 106
 
0.1%
8 84
 
0.1%
9 73
 
0.1%
Other Letter
ValueCountFrequency (%)
13
25.0%
13
25.0%
13
25.0%
13
25.0%
Dash Punctuation
ValueCountFrequency (%)
- 18805
100.0%
Space Separator
ValueCountFrequency (%)
222
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 114297
> 99.9%
Hangul 52
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
1 28433
24.9%
2 18904
16.5%
3 18882
16.5%
- 18805
16.5%
0 9624
 
8.4%
5 9600
 
8.4%
4 9457
 
8.3%
222
 
0.2%
7 107
 
0.1%
6 106
 
0.1%
Other values (2) 157
 
0.1%
Hangul
ValueCountFrequency (%)
13
25.0%
13
25.0%
13
25.0%
13
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 114297
> 99.9%
Hangul 52
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 28433
24.9%
2 18904
16.5%
3 18882
16.5%
- 18805
16.5%
0 9624
 
8.4%
5 9600
 
8.4%
4 9457
 
8.3%
222
 
0.2%
7 107
 
0.1%
6 106
 
0.1%
Other values (2) 157
 
0.1%
Hangul
ValueCountFrequency (%)
13
25.0%
13
25.0%
13
25.0%
13
25.0%

bdngownsenm
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9872 
자가
 
79
건물소유구분명
 
45
임대
 
4

Length

Max length7
Median length4
Mean length3.9969
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> 9872
98.7%
자가 79
 
0.8%
건물소유구분명 45
 
0.4%
임대 4
 
< 0.1%

Length

2024-04-16T17:57:55.799098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:57:55.887046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9872
98.7%
자가 79
 
0.8%
건물소유구분명 45
 
0.4%
임대 4
 
< 0.1%

fctyowkepcnt
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9890 
0
 
63
공장사무직종업원수
 
47

Length

Max length9
Median length4
Mean length4.0046
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> 9890
98.9%
0 63
 
0.6%
공장사무직종업원수 47
 
0.5%

Length

2024-04-16T17:57:55.982056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:57:56.070658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9890
98.9%
0 63
 
0.6%
공장사무직종업원수 47
 
0.5%

fctypdtjobepcnt
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9890 
0
 
63
공장생산직종업원수
 
47

Length

Max length9
Median length4
Mean length4.0046
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> 9890
98.9%
0 63
 
0.6%
공장생산직종업원수 47
 
0.5%

Length

2024-04-16T17:57:56.163816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:57:56.254266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9890
98.9%
0 63
 
0.6%
공장생산직종업원수 47
 
0.5%

fctysiljobepcnt
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9890 
0
 
63
공장판매직종업원수
 
47

Length

Max length9
Median length4
Mean length4.0046
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> 9890
98.9%
0 63
 
0.6%
공장판매직종업원수 47
 
0.5%

Length

2024-04-16T17:57:56.361339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:57:56.458595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9890
98.9%
0 63
 
0.6%
공장판매직종업원수 47
 
0.5%

rgtmbdsno
Categorical

IMBALANCE 

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
000
6394 
<NA>
2779 
L00
770 
권리주체일련번호
 
45
100
 
5
Other values (4)
 
7

Length

Max length8
Median length3
Mean length3.3004
Min length3

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
000 6394
63.9%
<NA> 2779
27.8%
L00 770
 
7.7%
권리주체일련번호 45
 
0.4%
100 5
 
0.1%
F00 3
 
< 0.1%
L01 2
 
< 0.1%
L02 1
 
< 0.1%
010 1
 
< 0.1%

Length

2024-04-16T17:57:56.543137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:57:56.635066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
000 6394
63.9%
na 2779
27.8%
l00 770
 
7.7%
권리주체일련번호 45
 
0.4%
100 5
 
< 0.1%
f00 3
 
< 0.1%
l01 2
 
< 0.1%
l02 1
 
< 0.1%
010 1
 
< 0.1%

wtrsplyfacilsenm
Categorical

IMBALANCE 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
8855 
상수도전용
1095 
급수시설구분명
 
47
지하수전용
 
1
간이상수도
 
1

Length

Max length17
Median length4
Mean length4.1251
Min length4

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> 8855
88.5%
상수도전용 1095
 
10.9%
급수시설구분명 47
 
0.5%
지하수전용 1
 
< 0.1%
간이상수도 1
 
< 0.1%
상수도(음용)지하수(주방용)겸용 1
 
< 0.1%

Length

2024-04-16T17:57:56.769418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:57:56.875723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 8855
88.5%
상수도전용 1095
 
10.9%
급수시설구분명 47
 
0.5%
지하수전용 1
 
< 0.1%
간이상수도 1
 
< 0.1%
상수도(음용)지하수(주방용)겸용 1
 
< 0.1%

maneipcnt
Categorical

IMBALANCE 

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9151 
0
 
737
1
 
52
남성종사자수
 
47
2
 
9
Other values (2)
 
4

Length

Max length6
Median length4
Mean length3.7688
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> 9151
91.5%
0 737
 
7.4%
1 52
 
0.5%
남성종사자수 47
 
0.5%
2 9
 
0.1%
3 2
 
< 0.1%
4 2
 
< 0.1%

Length

2024-04-16T17:57:57.013872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:57:57.127602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9151
91.5%
0 737
 
7.4%
1 52
 
0.5%
남성종사자수 47
 
0.5%
2 9
 
0.1%
3 2
 
< 0.1%
4 2
 
< 0.1%

multusnupsoyn
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
N
9930 
<NA>
 
50
Y
 
18
 
2

Length

Max length4
Median length1
Mean length1.015
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
N 9930
99.3%
<NA> 50
 
0.5%
Y 18
 
0.2%
2
 
< 0.1%

Length

2024-04-16T17:57:57.252398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:57:57.344661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
n 9930
99.3%
na 50
 
0.5%
y 18
 
0.2%
2
 
< 0.1%

lvsenm
Categorical

IMBALANCE 

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9328 
기타
 
446
자율
 
176
등급구분명
 
47
지도
 
1
Other values (2)
 
2

Length

Max length5
Median length4
Mean length3.8797
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> 9328
93.3%
기타 446
 
4.5%
자율 176
 
1.8%
등급구분명 47
 
0.5%
지도 1
 
< 0.1%
우수 1
 
< 0.1%
관리 1
 
< 0.1%

Length

2024-04-16T17:57:57.464176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:57:57.578414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9328
93.3%
기타 446
 
4.5%
자율 176
 
1.8%
등급구분명 47
 
0.5%
지도 1
 
< 0.1%
우수 1
 
< 0.1%
관리 1
 
< 0.1%

isream
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9950 
보증액
 
47
0
 
3

Length

Max length4
Median length4
Mean length3.9944
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> 9950
99.5%
보증액 47
 
0.5%
0 3
 
< 0.1%

Length

2024-04-16T17:57:57.722267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:57:57.821249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9950
99.5%
보증액 47
 
0.5%
0 3
 
< 0.1%

hoffepcnt
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9890 
0
 
63
본사종업원수
 
47

Length

Max length6
Median length4
Mean length3.9905
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> 9890
98.9%
0 63
 
0.6%
본사종업원수 47
 
0.5%

Length

2024-04-16T17:57:57.918813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:57:57.999149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9890
98.9%
0 63
 
0.6%
본사종업원수 47
 
0.5%

equsiz
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9953 
설비규격
 
47

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> 9953
99.5%
설비규격 47
 
0.5%

Length

2024-04-16T17:57:58.433842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:57:58.532458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9953
99.5%
설비규격 47
 
0.5%

faciltotscp
Text

MISSING 

Distinct1511
Distinct (%)53.5%
Missing7174
Missing (%)71.7%
Memory size156.2 KiB
2024-04-16T17:57:58.861671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length3.3181175
Min length1

Characters and Unicode

Total characters9377
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

Unique1268 ?
Unique (%)44.9%

Sample

1st row0
2nd row12
3rd row22.02
4th row28.47
5th row68.66
ValueCountFrequency (%)
0 883
31.2%
24 12
 
0.4%
33 12
 
0.4%
3.3 10
 
0.4%
25 9
 
0.3%
15 8
 
0.3%
26 8
 
0.3%
23.1 7
 
0.2%
30 7
 
0.2%
50 7
 
0.2%
Other values (1499) 1863
65.9%
2024-04-16T17:57:59.396964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 1682
17.9%
0 1263
13.5%
2 946
10.1%
1 874
9.3%
3 798
8.5%
4 765
8.2%
6 698
7.4%
5 697
7.4%
8 600
 
6.4%
9 533
 
5.7%
Other values (6) 521
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7685
82.0%
Other Punctuation 1682
 
17.9%
Other Letter 10
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1263
16.4%
2 946
12.3%
1 874
11.4%
3 798
10.4%
4 765
10.0%
6 698
9.1%
5 697
9.1%
8 600
7.8%
9 533
6.9%
7 511
6.6%
Other Letter
ValueCountFrequency (%)
2
20.0%
2
20.0%
2
20.0%
2
20.0%
2
20.0%
Other Punctuation
ValueCountFrequency (%)
. 1682
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 9367
99.9%
Hangul 10
 
0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
. 1682
18.0%
0 1263
13.5%
2 946
10.1%
1 874
9.3%
3 798
8.5%
4 765
8.2%
6 698
7.5%
5 697
7.4%
8 600
 
6.4%
9 533
 
5.7%
Hangul
ValueCountFrequency (%)
2
20.0%
2
20.0%
2
20.0%
2
20.0%
2
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9367
99.9%
Hangul 10
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 1682
18.0%
0 1263
13.5%
2 946
10.1%
1 874
9.3%
3 798
8.5%
4 765
8.2%
6 698
7.5%
5 697
7.4%
8 600
 
6.4%
9 533
 
5.7%
Hangul
ValueCountFrequency (%)
2
20.0%
2
20.0%
2
20.0%
2
20.0%
2
20.0%

wmeipcnt
Categorical

IMBALANCE 

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9147 
0
 
733
1
 
62
여성종사자수
 
47
2
 
8
Other values (2)
 
3

Length

Max length6
Median length4
Mean length3.7677
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> 9147
91.5%
0 733
 
7.3%
1 62
 
0.6%
여성종사자수 47
 
0.5%
2 8
 
0.1%
4 2
 
< 0.1%
11 1
 
< 0.1%

Length

2024-04-16T17:57:59.522183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:57:59.616940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9147
91.5%
0 733
 
7.3%
1 62
 
0.6%
여성종사자수 47
 
0.5%
2 8
 
0.1%
4 2
 
< 0.1%
11 1
 
< 0.1%

trdpjubnsenm
Categorical

IMBALANCE 

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9223 
기타
 
528
주택가주변
 
106
아파트지역
 
69
영업장주변구분명
 
47
Other values (3)
 
27

Length

Max length8
Median length4
Mean length3.9414
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> 9223
92.2%
기타 528
 
5.3%
주택가주변 106
 
1.1%
아파트지역 69
 
0.7%
영업장주변구분명 47
 
0.5%
유흥업소밀집지역 19
 
0.2%
학교정화(상대) 7
 
0.1%
결혼예식장주변 1
 
< 0.1%

Length

2024-04-16T17:57:59.735410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:57:59.856106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9223
92.2%
기타 528
 
5.3%
주택가주변 106
 
1.1%
아파트지역 69
 
0.7%
영업장주변구분명 47
 
0.5%
유흥업소밀집지역 19
 
0.2%
학교정화(상대 7
 
0.1%
결혼예식장주변 1
 
< 0.1%

monam
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9950 
월세액
 
47
0
 
3

Length

Max length4
Median length4
Mean length3.9944
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> 9950
99.5%
월세액 47
 
0.5%
0 3
 
< 0.1%

Length

2024-04-16T17:57:59.980557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:58:00.069174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9950
99.5%
월세액 47
 
0.5%
0 3
 
< 0.1%

sntuptaenm
Categorical

IMBALANCE 

Distinct20
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
7174 
제과점영업
2024 
즉석판매제조가공업
 
511
전자상거래(통신판매업)
 
111
영업장판매
 
44
Other values (15)
 
136

Length

Max length13
Median length4
Mean length4.6127
Min length2

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 7174
71.7%
제과점영업 2024
 
20.2%
즉석판매제조가공업 511
 
5.1%
전자상거래(통신판매업) 111
 
1.1%
영업장판매 44
 
0.4%
집단급식소 식품판매업 41
 
0.4%
식품자동판매기영업 18
 
0.2%
방문판매 17
 
0.2%
유통전문판매업 15
 
0.1%
기타 식품제조가공업 13
 
0.1%
Other values (10) 32
 
0.3%

Length

2024-04-16T17:58:00.180046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 7174
71.3%
제과점영업 2024
 
20.1%
즉석판매제조가공업 511
 
5.1%
전자상거래(통신판매업 111
 
1.1%
영업장판매 44
 
0.4%
집단급식소 41
 
0.4%
식품판매업 41
 
0.4%
식품자동판매기영업 18
 
0.2%
방문판매 17
 
0.2%
유통전문판매업 15
 
0.1%
Other values (12) 63
 
0.6%

jtupsomainedf
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9953 
전통업소주된음식
 
47

Length

Max length8
Median length4
Mean length4.0188
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> 9953
99.5%
전통업소주된음식 47
 
0.5%

Length

2024-04-16T17:58:00.301568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:58:00.398946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9953
99.5%
전통업소주된음식 47
 
0.5%

jtupsoasgnno
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9952 
전통업소지정번호
 
47
-+
 
1

Length

Max length8
Median length4
Mean length4.0186
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> 9952
99.5%
전통업소지정번호 47
 
0.5%
-+ 1
 
< 0.1%

Length

2024-04-16T17:58:00.486140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:58:00.576854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9952
99.5%
전통업소지정번호 47
 
0.5%
1
 
< 0.1%

totepnum
Categorical

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
식육판매업
5731 
<NA>
2826 
우유류판매업
880 
축산물유통전문판매업
 
199
축산물수입판매업
 
171
Other values (4)
 
193

Length

Max length10
Median length5
Mean length5.0036
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
식육판매업 5731
57.3%
<NA> 2826
28.3%
우유류판매업 880
 
8.8%
축산물유통전문판매업 199
 
2.0%
축산물수입판매업 171
 
1.7%
식용란수집판매업 101
 
1.0%
총종업원수 47
 
0.5%
식육부산물전문판매업 39
 
0.4%
0 6
 
0.1%

Length

2024-04-16T17:58:00.685901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:58:00.782477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
식육판매업 5731
57.3%
na 2826
28.3%
우유류판매업 880
 
8.8%
축산물유통전문판매업 199
 
2.0%
축산물수입판매업 171
 
1.7%
식용란수집판매업 101
 
1.0%
총종업원수 47
 
0.5%
식육부산물전문판매업 39
 
0.4%
0 6
 
0.1%

lindprcbgbnnm
Categorical

IMBALANCE 

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
축산물판매업
7121 
<NA>
2784 
축산물가공업구분명
 
45
식육판매업
 
21
우유류판매업
 
11
Other values (4)
 
18

Length

Max length10
Median length6
Mean length5.4576
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
축산물판매업 7121
71.2%
<NA> 2784
 
27.8%
축산물가공업구분명 45
 
0.4%
식육판매업 21
 
0.2%
우유류판매업 11
 
0.1%
축산물유통전문판매업 7
 
0.1%
식육가공업 6
 
0.1%
식용란수집판매업 3
 
< 0.1%
식육포장처리업 2
 
< 0.1%

Length

2024-04-16T17:58:00.902369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:58:00.998584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
축산물판매업 7121
71.2%
na 2784
 
27.8%
축산물가공업구분명 45
 
0.4%
식육판매업 21
 
0.2%
우유류판매업 11
 
0.1%
축산물유통전문판매업 7
 
0.1%
식육가공업 6
 
0.1%
식용란수집판매업 3
 
< 0.1%
식육포장처리업 2
 
< 0.1%

lindjobgbnnm
Categorical

IMBALANCE 

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9900 
축산업무구분명
 
45
축산물판매업
 
42
축산물가공업
 
6
축산물운반업
 
3
Other values (2)
 
4

Length

Max length7
Median length4
Mean length4.0247
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> 9900
99.0%
축산업무구분명 45
 
0.4%
축산물판매업 42
 
0.4%
축산물가공업 6
 
0.1%
축산물운반업 3
 
< 0.1%
축산물보관업 2
 
< 0.1%
식육포장처리업 2
 
< 0.1%

Length

2024-04-16T17:58:01.119966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:58:01.207395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9900
99.0%
축산업무구분명 45
 
0.4%
축산물판매업 42
 
0.4%
축산물가공업 6
 
0.1%
축산물운반업 3
 
< 0.1%
축산물보관업 2
 
< 0.1%
식육포장처리업 2
 
< 0.1%

lindseqno
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9953 
축산일련번호
 
47

Length

Max length6
Median length4
Mean length4.0094
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> 9953
99.5%
축산일련번호 47
 
0.5%

Length

2024-04-16T17:58:01.328508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:58:01.420328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9953
99.5%
축산일련번호 47
 
0.5%

homepage
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9953 
홈페이지
 
47

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> 9953
99.5%
홈페이지 47
 
0.5%

Length

2024-04-16T17:58:01.504338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:58:01.584603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9953
99.5%
홈페이지 47
 
0.5%

last_load_dttm
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2021-04-01 05:26:04
3798 
2021-04-01 05:26:03
3613 
2021-04-01 05:26:05
1611 
2021-04-01 05:26:06
978 

Length

Max length19
Median length19
Mean length19
Min length19

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021-04-01 05:26:04
2nd row2021-04-01 05:26:06
3rd row2021-04-01 05:26:04
4th row2021-04-01 05:26:03
5th row2021-04-01 05:26:04

Common Values

ValueCountFrequency (%)
2021-04-01 05:26:04 3798
38.0%
2021-04-01 05:26:03 3613
36.1%
2021-04-01 05:26:05 1611
16.1%
2021-04-01 05:26:06 978
 
9.8%

Length

2024-04-16T17:58:01.681483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:58:01.767911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021-04-01 10000
50.0%
05:26:04 3798
 
19.0%
05:26:03 3613
 
18.1%
05:26:05 1611
 
8.1%
05:26:06 978
 
4.9%

Sample

skeyopnsfteamcodemgtnoopnsvcidupdategbnupdatedtopnsvcnmbplcnmsitepostnositewhladdrrdnpostnordnwhladdrapvpermymddcbymdclgstdtclgenddtropnymdtrdstatenmdtlstatenmxylastmodtsuptaenmsitetelbdngownsenmfctyowkepcntfctypdtjobepcntfctysiljobepcntrgtmbdsnowtrsplyfacilsenmmaneipcntmultusnupsoynlvsenmisreamhoffepcntequsizfaciltotscpwmeipcnttrdpjubnsenmmonamsntuptaenmjtupsomainedfjtupsoasgnnototepnumlindprcbgbnnmlindjobgbnnmlindseqnohomepagelast_load_dttm
72757269337000033700000092007000507_22_04_PI2018-08-31 23:59:59.0<NA>청춘정육점<NA>부산광역시 연제구 연산동 400-42번지48947부산광역시 연제구 과정로 224 (연산동)20070412<NA><NA><NA><NA>0000정상391535.23019400000189969.3477480000020160425095157식육판매업051-123-1234<NA><NA><NA><NA>000<NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>식육판매업축산물판매업<NA><NA><NA>2021-04-01 05:26:04
124461359333200003320000-107-2019-0004907_22_19_PI2019-03-08 02:21:43.0즉석판매제조가공업신우에프앤지(주)616814부산광역시 북구 덕천동 370번지 메가마트 內46554부산광역시 북구 기찰로 164, 메가마트 內 1층 (덕천동)20190306<NA><NA><NA><NA>영업/정상영업384082.64392147192420.34458339920190306162928즉석판매제조가공업051-123-1234<NA><NA><NA><NA><NA><NA><NA>N<NA><NA><NA><NA>0<NA><NA><NA>즉석판매제조가공업<NA><NA><NA><NA><NA><NA><NA>2021-04-01 05:26:06
89788979339000033900000091998001707_22_04_PI2018-08-31 23:59:59.0<NA>(주)명성유통<NA>부산광역시 사상구 모라동 283-6번지48947부산광역시 사상구 사상로531번길 47 (모라동)19981226<NA><NA><NA><NA>0004말소380979.44002900000190257.9821000000020140110094901식육판매업051-123-1234<NA><NA><NA><NA>000<NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>식육판매업축산물판매업<NA><NA><NA>2021-04-01 05:26:04
48314830333000033300000092014001607_22_04_PI2018-08-31 23:59:59.0<NA>필봉 한우암소 판매점<NA><NA>48047부산광역시 해운대구 해운대로76번길 52 (재송동)2014061820180814<NA><NA><NA>0002폐업392871.79818800000190221.8138230000020180816091830식육판매업051-123-1234<NA><NA><NA><NA>000<NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>식육판매업축산물판매업<NA><NA><NA>2021-04-01 05:26:03
54865486334000033400000092000001307_22_04_PI2018-08-31 23:59:59.0<NA>동신유통<NA>부산광역시 사하구 하단동 242-4번지49413부산광역시 사하구 동매로 97-1 (하단동)20001227<NA><NA><NA><NA>0000정상379533.22164200000180229.2659590000020161116115318식육판매업051-123-1234<NA><NA><NA><NA>000<NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>식육판매업축산물판매업<NA><NA><NA>2021-04-01 05:26:04
62856284335000033500000091984000207_22_04_PI2018-08-31 23:59:59.0<NA>연일식육점<NA>부산광역시 금정구 서동 111-10번지48947부산광역시 금정구 금사로51번길 40 (서동)19840308<NA><NA><NA><NA>0004말소391928.16505000000193444.8521620000020150126142725식육판매업051-123-1234<NA><NA><NA><NA>000<NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>식육판매업축산물판매업<NA><NA><NA>2021-04-01 05:26:04
2325325000032500000092013000207_22_04_PI2018-08-31 23:59:59.0<NA>일품정육점<NA>부산광역시 중구 부평동2가 68-1번지48977부산광역시 중구 중구로39번길 41 (부평동2가)2013030420171101<NA><NA><NA>0002폐업384697.76632000000180210.3331810000020171101180556식육판매업051-123-1234<NA><NA><NA><NA>000<NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>식육판매업축산물판매업<NA><NA><NA>2021-04-01 05:26:03
16331631329000032900000092005002507_22_04_PI2018-08-31 23:59:59.0<NA>(주)호익원푸드코리아<NA>부산광역시 부산진구 전포동 666-6번지 세화빌딩604호48947부산광역시 부산진구 동천로108번길 36 (전포동,세화빌딩604호)20050805<NA><NA><NA><NA>0000정상388103.88048500000186593.9277850000020170119105556축산물수입판매업051-123-1234<NA><NA><NA><NA>000<NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>축산물수입판매업축산물판매업<NA><NA><NA>2021-04-01 05:26:03
53685367334000033400000091990001207_22_04_PI2018-08-31 23:59:59.0<NA>야베스식육점<NA>부산광역시 사하구 감천동 268-3번지48947<NA>1990092520110329<NA><NA><NA>0002폐업383194.618111178093.46648020111201182325식육판매업051-123-1234<NA><NA><NA><NA>000<NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>식육판매업축산물판매업<NA><NA><NA>2021-04-01 05:26:04
10601064329000032900000092002002607_22_04_PI2018-08-31 23:59:59.0<NA>창조물산<NA>부산광역시 부산진구 가야동 414-5번지 414-3048947부산광역시 부산진구 엄광로 198 (가야동,414-30)2002101420030207<NA><NA><NA>0002폐업385503.92525400000185482.6771930000020030516092853식육판매업051-123-1234<NA><NA><NA><NA>000<NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>식육판매업축산물판매업<NA><NA><NA>2021-04-01 05:26:03
skeyopnsfteamcodemgtnoopnsvcidupdategbnupdatedtopnsvcnmbplcnmsitepostnositewhladdrrdnpostnordnwhladdrapvpermymddcbymdclgstdtclgenddtropnymdtrdstatenmdtlstatenmxylastmodtsuptaenmsitetelbdngownsenmfctyowkepcntfctypdtjobepcntfctysiljobepcntrgtmbdsnowtrsplyfacilsenmmaneipcntmultusnupsoynlvsenmisreamhoffepcntequsizfaciltotscpwmeipcnttrdpjubnsenmmonamsntuptaenmjtupsomainedfjtupsoasgnnototepnumlindprcbgbnnmlindjobgbnnmlindseqnohomepagelast_load_dttm
56705677334000033400000092004002607_22_04_PI2018-08-31 23:59:59.0<NA>대신통상<NA>부산광역시 사하구 괴정동 1064-1번지48947부산광역시 사하구 승학로233번길 50 (괴정동)2004080220131128<NA><NA><NA>0002폐업381502.45855300000180500.1545770000020131128140900축산물수입판매업051-123-1234<NA><NA><NA><NA>000<NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>축산물수입판매업축산물판매업<NA><NA><NA>2021-04-01 05:26:04
85468549339000033900000092008000507_22_04_PI2018-08-31 23:59:59.0<NA>매일우유감전가정대리점<NA>부산광역시 사상구 괘법동 584-13번지48947<NA>20080303<NA><NA><NA><NA>0000정상380917.075741186448.76609420140305155507우유류판매업051-123-1234<NA><NA><NA><NA>000<NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>우유류판매업축산물판매업<NA><NA><NA>2021-04-01 05:26:04
107881080033200003320000-121-2003-0003807_22_18_PI2018-08-31 23:59:59.0<NA>드래곤베이커리616820부산광역시 북구 덕천동 400-7번지48947<NA>2003072520060519<NA><NA><NA>02폐업382746.520960192264.60616420050923000000제과점영업051-123-1234<NA><NA><NA><NA><NA>상수도전용0N<NA><NA><NA><NA>49.980<NA><NA>제과점영업<NA><NA><NA><NA><NA><NA><NA>2021-04-01 05:26:05
13591357329000032900000091981000507_22_04_PI2018-08-31 23:59:59.0<NA>매일식육점<NA>부산광역시 부산진구 초읍동 271-6번지48947부산광역시 부산진구 새싹로 207-6 (초읍동)1981112020110822<NA><NA><NA>0002폐업386612.20248000000188421.1240030000020110822152051식육판매업051-123-1234<NA><NA><NA><NA>000<NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>식육판매업축산물판매업<NA><NA><NA>2021-04-01 05:26:03
56145618334000033400000091999002107_22_04_PI2018-08-31 23:59:59.0<NA>한일유통<NA>부산광역시 사하구 감천동 296-44 번지48947<NA>19991206<NA><NA><NA><NA>0000정상<NA><NA>20030513105639식육판매업051-123-1234<NA><NA><NA><NA>000<NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>식육판매업축산물판매업<NA><NA><NA>2021-04-01 05:26:04
80688066338000033800000092007002407_22_04_PI2018-08-31 23:59:59.0<NA>삼성빅마트식육점<NA>부산광역시 수영구 남천동 24-25번지48947부산광역시 수영구 수영로 384 (남천동)2007111920080731<NA><NA><NA>0002폐업392025.82694800000184745.5589440000020080731171844식육판매업051-123-1234<NA><NA><NA><NA>000<NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>식육판매업축산물판매업<NA><NA><NA>2021-04-01 05:26:04
81308126338000033800000092018000107_22_04_PI2018-08-31 23:59:59.0<NA>더 조은 유통<NA>부산광역시 수영구 망미동 805-10번지 1층48235부산광역시 수영구 망미로8번길 39-1, 1층 (망미동)20180223<NA><NA><NA><NA>0000정상391602.54946400000188219.5898270000020180223103630식용란수집판매업051-123-1234<NA><NA><NA><NA>000<NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>식용란수집판매업축산물판매업<NA><NA><NA>2021-04-01 05:26:04
50495047333000033300000092009001007_22_04_PI2018-08-31 23:59:59.0<NA>집나간 암소한마리<NA>부산광역시 해운대구 반여동 1608-16번지48025부산광역시 해운대구 재반로 248 (반여동)20090309<NA><NA><NA><NA>0004말소394128.46118900000191286.4486080000020140710140412식육판매업051-123-1234<NA><NA><NA><NA>000<NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>식육판매업축산물판매업<NA><NA><NA>2021-04-01 05:26:04
31303125331000033100000091982000407_22_04_PI2018-08-31 23:59:59.0<NA>대주식육점<NA>부산광역시 남구 대연동 1379-3번지48947부산광역시 남구 못골로 56 (대연동)19820916<NA><NA><NA><NA>0000정상390347.28976500000184144.4598270000020130725101651식육판매업051-123-1234<NA><NA><NA><NA>000<NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>식육판매업축산물판매업<NA><NA><NA>2021-04-01 05:26:03
120871210033800003380000-121-2017-0000107_22_18_PI2018-08-31 23:59:59.0<NA>이찌방노치즈케이크613802부산광역시 수영구 광안동 116-13번지48270부산광역시 수영구 수영로 634-1, 1층 (광안동)2017032320171228<NA><NA><NA>02폐업392667.52528200000187148.0140630000020171228112830제과점영업051-123-1234<NA><NA><NA><NA><NA><NA><NA>N<NA><NA><NA><NA>5<NA><NA><NA>제과점영업<NA><NA><NA><NA><NA><NA><NA>2021-04-01 05:26:05