Overview

Dataset statistics

Number of variables49
Number of observations10000
Missing cells18906
Missing cells (%)3.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.9 MiB
Average record size in memory404.0 B

Variable types

Numeric4
Text10
Categorical33
DateTime1
Unsupported1

Alerts

opnsvcid is highly imbalanced (55.1%)Imbalance
updategbn is highly imbalanced (99.2%)Imbalance
opnsvcnm is highly imbalanced (66.6%)Imbalance
clgstdt is highly imbalanced (93.5%)Imbalance
clgenddt is highly imbalanced (93.3%)Imbalance
ropnymd is highly imbalanced (91.1%)Imbalance
bdngownsenm is highly imbalanced (60.8%)Imbalance
fctyowkepcnt is highly imbalanced (86.7%)Imbalance
fctypdtjobepcnt is highly imbalanced (88.7%)Imbalance
fctysiljobepcnt is highly imbalanced (84.6%)Imbalance
rgtmbdsno is highly imbalanced (51.7%)Imbalance
wtrsplyfacilsenm is highly imbalanced (68.8%)Imbalance
maneipcnt is highly imbalanced (80.4%)Imbalance
multusnupsoyn is highly imbalanced (97.2%)Imbalance
lvsenm is highly imbalanced (80.1%)Imbalance
isream is highly imbalanced (88.4%)Imbalance
hoffepcnt is highly imbalanced (84.7%)Imbalance
equsiz is highly imbalanced (77.5%)Imbalance
wmeipcnt is highly imbalanced (80.1%)Imbalance
trdpjubnsenm is highly imbalanced (80.7%)Imbalance
monam is highly imbalanced (88.4%)Imbalance
sntuptaenm is highly imbalanced (57.2%)Imbalance
jtupsomainedf is highly imbalanced (77.5%)Imbalance
jtupsoasgnno is highly imbalanced (77.5%)Imbalance
totepnum is highly imbalanced (53.5%)Imbalance
lindprcbgbnnm is highly imbalanced (62.6%)Imbalance
lindjobgbnnm is highly imbalanced (88.3%)Imbalance
lindseqno is highly imbalanced (77.5%)Imbalance
homepage is highly imbalanced (85.7%)Imbalance
sitepostno has 5264 (52.6%) missing valuesMissing
sitewhladdr has 223 (2.2%) missing valuesMissing
rdnwhladdr has 1146 (11.5%) missing valuesMissing
dcbymd has 6228 (62.3%) missing valuesMissing
x has 309 (3.1%) missing valuesMissing
y has 309 (3.1%) missing valuesMissing
sitetel has 278 (2.8%) missing valuesMissing
faciltotscp has 5148 (51.5%) missing valuesMissing
skey has unique valuesUnique
rdnpostno is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2024-04-16 08:59:11.049413
Analysis finished2024-04-16 08:59:13.374141
Duration2.32 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%
Mean9593.2737
Minimum2
Maximum19234
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-16T17:59:13.431097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile966.8
Q14784.75
median9585.5
Q314424
95-th percentile18295.1
Maximum19234
Range19232
Interquartile range (IQR)9639.25

Descriptive statistics

Standard deviation5560.9303
Coefficient of variation (CV)0.57966972
Kurtosis-1.2004773
Mean9593.2737
Median Absolute Deviation (MAD)4816
Skewness0.0083646939
Sum95932737
Variance30923946
MonotonicityNot monotonic
2024-04-16T17:59:13.556162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12069 1
 
< 0.1%
18861 1
 
< 0.1%
2439 1
 
< 0.1%
10095 1
 
< 0.1%
6933 1
 
< 0.1%
474 1
 
< 0.1%
14823 1
 
< 0.1%
5980 1
 
< 0.1%
8071 1
 
< 0.1%
11201 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
2 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
12 1
< 0.1%
15 1
< 0.1%
16 1
< 0.1%
18 1
< 0.1%
20 1
< 0.1%
ValueCountFrequency (%)
19234 1
< 0.1%
19232 1
< 0.1%
19231 1
< 0.1%
19230 1
< 0.1%
19229 1
< 0.1%
19228 1
< 0.1%
19225 1
< 0.1%
19221 1
< 0.1%
19219 1
< 0.1%
19218 1
< 0.1%

opnsfteamcode
Real number (ℝ)

Distinct220
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3593115.1
Minimum3000000
Maximum6520000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-16T17:59:13.693082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3000000
5-th percentile3210000
Q13300000
median3340000
Q33460000
95-th percentile5310000
Maximum6520000
Range3520000
Interquartile range (IQR)160000

Descriptive statistics

Standard deviation615825.36
Coefficient of variation (CV)0.17139038
Kurtosis4.944456
Mean3593115.1
Median Absolute Deviation (MAD)50000
Skewness2.3614496
Sum3.5931152 × 1010
Variance3.7924088 × 1011
MonotonicityNot monotonic
2024-04-16T17:59:13.841312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3290000 678
 
6.8%
3330000 664
 
6.6%
3320000 607
 
6.1%
3300000 588
 
5.9%
3340000 580
 
5.8%
3390000 573
 
5.7%
3350000 483
 
4.8%
3310000 433
 
4.3%
3370000 396
 
4.0%
3380000 356
 
3.6%
Other values (210) 4642
46.4%
ValueCountFrequency (%)
3000000 14
0.1%
3010000 27
0.3%
3020000 17
0.2%
3030000 17
0.2%
3040000 24
0.2%
3050000 22
0.2%
3060000 21
0.2%
3070000 18
0.2%
3080000 13
0.1%
3090000 12
0.1%
ValueCountFrequency (%)
6520000 10
 
0.1%
6510000 26
 
0.3%
6470000 1
 
< 0.1%
6450000 1
 
< 0.1%
6440000 1
 
< 0.1%
6410000 1
 
< 0.1%
6280000 1
 
< 0.1%
5710000 74
0.7%
5700000 5
 
0.1%
5690000 33
0.3%

mgtno
Text

Distinct9639
Distinct (%)96.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-04-16T17:59:14.016684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length18
Mean length19.928
Min length18

Characters and Unicode

Total characters199280
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

Unique9314 ?
Unique (%)93.1%

Sample

1st row3380000-121-2000-03772
2nd row6520000-107-2019-00078
3rd row3210000-134-2020-00392
4th row337000000920010012
5th row338000000920090026
ValueCountFrequency (%)
3740000-107-2019-01607 3
 
< 0.1%
3990000-107-2019-00417 3
 
< 0.1%
3540000-107-2019-00098 3
 
< 0.1%
571000000720200002 3
 
< 0.1%
3210000-107-2019-00920 3
 
< 0.1%
4390000-107-2019-00145 3
 
< 0.1%
3590000-107-2019-00008 3
 
< 0.1%
3020000-107-2020-00059 3
 
< 0.1%
5030000-107-2019-00213 3
 
< 0.1%
495000000920190007 3
 
< 0.1%
Other values (9629) 9970
99.7%
2024-04-16T17:59:14.299775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 94518
47.4%
1 18451
 
9.3%
3 18152
 
9.1%
2 17706
 
8.9%
- 14457
 
7.3%
9 13467
 
6.8%
7 5991
 
3.0%
4 5046
 
2.5%
5 4421
 
2.2%
8 3771
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 184823
92.7%
Dash Punctuation 14457
 
7.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 94518
51.1%
1 18451
 
10.0%
3 18152
 
9.8%
2 17706
 
9.6%
9 13467
 
7.3%
7 5991
 
3.2%
4 5046
 
2.7%
5 4421
 
2.4%
8 3771
 
2.0%
6 3300
 
1.8%
Dash Punctuation
ValueCountFrequency (%)
- 14457
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 199280
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 94518
47.4%
1 18451
 
9.3%
3 18152
 
9.1%
2 17706
 
8.9%
- 14457
 
7.3%
9 13467
 
6.8%
7 5991
 
3.0%
4 5046
 
2.5%
5 4421
 
2.2%
8 3771
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 199280
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 94518
47.4%
1 18451
 
9.3%
3 18152
 
9.1%
2 17706
 
8.9%
- 14457
 
7.3%
9 13467
 
6.8%
7 5991
 
3.0%
4 5046
 
2.5%
5 4421
 
2.2%
8 3771
 
1.9%

opnsvcid
Categorical

IMBALANCE 

Distinct17
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
07_22_04_P
5079 
07_22_19_P
2824 
07_22_18_P
1408 
07_22_03_P
 
353
07_22_01_P
 
108
Other values (12)
 
228

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row07_22_18_P
2nd row07_22_19_P
3rd row07_22_03_P
4th row07_22_04_P
5th row07_22_04_P

Common Values

ValueCountFrequency (%)
07_22_04_P 5079
50.8%
07_22_19_P 2824
28.2%
07_22_18_P 1408
 
14.1%
07_22_03_P 353
 
3.5%
07_22_01_P 108
 
1.1%
07_22_02_P 71
 
0.7%
07_22_25_P 53
 
0.5%
07_22_24_P 34
 
0.3%
07_22_11_P 30
 
0.3%
07_22_08_P 11
 
0.1%
Other values (7) 29
 
0.3%

Length

2024-04-16T17:59:14.423165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
07_22_04_p 5079
50.8%
07_22_19_p 2824
28.2%
07_22_18_p 1408
 
14.1%
07_22_03_p 353
 
3.5%
07_22_01_p 108
 
1.1%
07_22_02_p 71
 
0.7%
07_22_25_p 53
 
0.5%
07_22_24_p 34
 
0.3%
07_22_11_p 30
 
0.3%
07_22_08_p 11
 
0.1%
Other values (7) 29
 
0.3%

updategbn
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
I
9993 
U
 
7

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 9993
99.9%
U 7
 
0.1%

Length

2024-04-16T17:59:14.510958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:59:14.584802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
i 9993
99.9%
u 7
 
0.1%
Distinct552
Distinct (%)5.5%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2018-08-31 23:59:59
Maximum2020-12-22 00:23:05
2024-04-16T17:59:14.667152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T17:59:14.785966image/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>
6433 
즉석판매제조가공업
2824 
건강기능식품일반판매업
 
353
집단급식소식품판매업
 
108
건강기능식품유통전문판매업
 
71
Other values (13)
 
211

Length

Max length13
Median length4
Mean length5.8297
Min length3

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 6433
64.3%
즉석판매제조가공업 2824
28.2%
건강기능식품일반판매업 353
 
3.5%
집단급식소식품판매업 108
 
1.1%
건강기능식품유통전문판매업 71
 
0.7%
축산물운반업 53
 
0.5%
축산판매업 52
 
0.5%
축산물보관업 34
 
0.3%
식품제조가공업 30
 
0.3%
식품소분업 11
 
0.1%
Other values (8) 31
 
0.3%

Length

2024-04-16T17:59:14.924148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 6433
64.3%
즉석판매제조가공업 2824
28.2%
건강기능식품일반판매업 353
 
3.5%
집단급식소식품판매업 108
 
1.1%
건강기능식품유통전문판매업 71
 
0.7%
축산물운반업 53
 
0.5%
축산판매업 52
 
0.5%
축산물보관업 34
 
0.3%
식품제조가공업 30
 
0.3%
식품소분업 11
 
0.1%
Other values (8) 31
 
0.3%

bplcnm
Text

Distinct7401
Distinct (%)74.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-04-16T17:59:15.181685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length41
Median length27.5
Mean length6.7224
Min length2

Characters and Unicode

Total characters67224
Distinct characters901
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

Unique6314 ?
Unique (%)63.1%

Sample

1st row크라운베이커리 부산 남천점
2nd row바타타식탁
3rd row(주)케이비맵
4th row부광식육점
5th row영남해장국식육점
ValueCountFrequency (%)
주식회사 319
 
2.7%
베이커리 55
 
0.5%
파리바게뜨 51
 
0.4%
수라원 46
 
0.4%
부산우유 42
 
0.4%
더원씨푸드 41
 
0.3%
주)와이에이비커머스 36
 
0.3%
현승유통 35
 
0.3%
뚜레쥬르 31
 
0.3%
주)한울에프엔비 29
 
0.2%
Other values (7754) 11187
94.2%
2024-04-16T17:59:15.596964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2782
 
4.1%
2344
 
3.5%
1913
 
2.8%
1876
 
2.8%
) 1752
 
2.6%
( 1742
 
2.6%
1682
 
2.5%
1457
 
2.2%
1280
 
1.9%
1251
 
1.9%
Other values (891) 49145
73.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 60082
89.4%
Space Separator 1876
 
2.8%
Close Punctuation 1752
 
2.6%
Open Punctuation 1742
 
2.6%
Uppercase Letter 651
 
1.0%
Lowercase Letter 561
 
0.8%
Decimal Number 331
 
0.5%
Other Punctuation 119
 
0.2%
Dash Punctuation 100
 
0.1%
Math Symbol 4
 
< 0.1%
Other values (3) 6
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
2782
 
4.6%
2344
 
3.9%
1913
 
3.2%
1682
 
2.8%
1457
 
2.4%
1280
 
2.1%
1251
 
2.1%
1128
 
1.9%
1094
 
1.8%
1016
 
1.7%
Other values (815) 44135
73.5%
Uppercase Letter
ValueCountFrequency (%)
S 85
 
13.1%
M 44
 
6.8%
T 43
 
6.6%
N 38
 
5.8%
A 36
 
5.5%
D 34
 
5.2%
I 33
 
5.1%
K 31
 
4.8%
G 29
 
4.5%
O 29
 
4.5%
Other values (16) 249
38.2%
Lowercase Letter
ValueCountFrequency (%)
e 84
15.0%
a 52
 
9.3%
o 51
 
9.1%
s 45
 
8.0%
n 30
 
5.3%
i 29
 
5.2%
r 28
 
5.0%
l 26
 
4.6%
t 24
 
4.3%
d 23
 
4.1%
Other values (12) 169
30.1%
Decimal Number
ValueCountFrequency (%)
2 85
25.7%
1 77
23.3%
3 41
12.4%
5 27
 
8.2%
0 22
 
6.6%
8 21
 
6.3%
6 17
 
5.1%
9 16
 
4.8%
7 14
 
4.2%
4 11
 
3.3%
Other Punctuation
ValueCountFrequency (%)
& 45
37.8%
. 30
25.2%
' 16
 
13.4%
, 15
 
12.6%
? 4
 
3.4%
" 4
 
3.4%
# 2
 
1.7%
/ 1
 
0.8%
· 1
 
0.8%
1
 
0.8%
Space Separator
ValueCountFrequency (%)
1876
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1752
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1742
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 100
100.0%
Math Symbol
ValueCountFrequency (%)
+ 4
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 4
100.0%
Letter Number
ValueCountFrequency (%)
1
100.0%
Other Symbol
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 60072
89.4%
Common 5929
 
8.8%
Latin 1213
 
1.8%
Han 10
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
2782
 
4.6%
2344
 
3.9%
1913
 
3.2%
1682
 
2.8%
1457
 
2.4%
1280
 
2.1%
1251
 
2.1%
1128
 
1.9%
1094
 
1.8%
1016
 
1.7%
Other values (807) 44125
73.5%
Latin
ValueCountFrequency (%)
S 85
 
7.0%
e 84
 
6.9%
a 52
 
4.3%
o 51
 
4.2%
s 45
 
3.7%
M 44
 
3.6%
T 43
 
3.5%
N 38
 
3.1%
A 36
 
3.0%
D 34
 
2.8%
Other values (39) 701
57.8%
Common
ValueCountFrequency (%)
1876
31.6%
) 1752
29.5%
( 1742
29.4%
- 100
 
1.7%
2 85
 
1.4%
1 77
 
1.3%
& 45
 
0.8%
3 41
 
0.7%
. 30
 
0.5%
5 27
 
0.5%
Other values (17) 154
 
2.6%
Han
ValueCountFrequency (%)
2
20.0%
2
20.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 60072
89.4%
ASCII 7138
 
10.6%
CJK 9
 
< 0.1%
None 2
 
< 0.1%
Number Forms 1
 
< 0.1%
Specials 1
 
< 0.1%
CJK Compat Ideographs 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
2782
 
4.6%
2344
 
3.9%
1913
 
3.2%
1682
 
2.8%
1457
 
2.4%
1280
 
2.1%
1251
 
2.1%
1128
 
1.9%
1094
 
1.8%
1016
 
1.7%
Other values (807) 44125
73.5%
ASCII
ValueCountFrequency (%)
1876
26.3%
) 1752
24.5%
( 1742
24.4%
- 100
 
1.4%
2 85
 
1.2%
S 85
 
1.2%
e 84
 
1.2%
1 77
 
1.1%
a 52
 
0.7%
o 51
 
0.7%
Other values (62) 1234
17.3%
CJK
ValueCountFrequency (%)
2
22.2%
2
22.2%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
Number Forms
ValueCountFrequency (%)
1
100.0%
Specials
ValueCountFrequency (%)
1
100.0%
CJK Compat Ideographs
ValueCountFrequency (%)
1
100.0%
None
ValueCountFrequency (%)
· 1
50.0%
1
50.0%

sitepostno
Text

MISSING 

Distinct2074
Distinct (%)43.8%
Missing5264
Missing (%)52.6%
Memory size156.2 KiB
2024-04-16T17:59:15.883036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters28416
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

Unique1124 ?
Unique (%)23.7%

Sample

1st row613815
2nd row699914
3rd row137895
4th row607804
5th row607804
ValueCountFrequency (%)
612020 57
 
1.2%
지번우편번호 46
 
1.0%
411410 24
 
0.5%
406081 21
 
0.4%
600017 19
 
0.4%
463420 18
 
0.4%
608832 17
 
0.4%
617808 17
 
0.4%
616852 16
 
0.3%
612824 15
 
0.3%
Other values (2064) 4486
94.7%
2024-04-16T17:59:16.263589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 4529
15.9%
1 4018
14.1%
8 3793
13.3%
6 3356
11.8%
4 2712
9.5%
2 2474
8.7%
3 2426
8.5%
5 1777
 
6.3%
7 1760
 
6.2%
9 1295
 
4.6%
Other values (5) 276
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 28140
99.0%
Other Letter 276
 
1.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4529
16.1%
1 4018
14.3%
8 3793
13.5%
6 3356
11.9%
4 2712
9.6%
2 2474
8.8%
3 2426
8.6%
5 1777
 
6.3%
7 1760
 
6.3%
9 1295
 
4.6%
Other Letter
ValueCountFrequency (%)
92
33.3%
46
16.7%
46
16.7%
46
16.7%
46
16.7%

Most occurring scripts

ValueCountFrequency (%)
Common 28140
99.0%
Hangul 276
 
1.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4529
16.1%
1 4018
14.3%
8 3793
13.5%
6 3356
11.9%
4 2712
9.6%
2 2474
8.8%
3 2426
8.6%
5 1777
 
6.3%
7 1760
 
6.3%
9 1295
 
4.6%
Hangul
ValueCountFrequency (%)
92
33.3%
46
16.7%
46
16.7%
46
16.7%
46
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28140
99.0%
Hangul 276
 
1.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4529
16.1%
1 4018
14.3%
8 3793
13.5%
6 3356
11.9%
4 2712
9.6%
2 2474
8.8%
3 2426
8.6%
5 1777
 
6.3%
7 1760
 
6.3%
9 1295
 
4.6%
Hangul
ValueCountFrequency (%)
92
33.3%
46
16.7%
46
16.7%
46
16.7%
46
16.7%

sitewhladdr
Text

MISSING 

Distinct7977
Distinct (%)81.6%
Missing223
Missing (%)2.2%
Memory size156.2 KiB
2024-04-16T17:59:16.550865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length53
Median length49
Mean length25.776823
Min length13

Characters and Unicode

Total characters252020
Distinct characters616
Distinct categories12 ?
Distinct scripts3 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6918 ?
Unique (%)70.8%

Sample

1st row부산광역시 수영구 남천동 29-13번지
2nd row제주특별자치도 서귀포시 표선면 표선리 484번지
3rd row서울특별시 서초구 양재동 287-4 동주빌딩
4th row부산광역시 연제구 연산동 767-42번지
5th row부산광역시 수영구 남천동 280번지
ValueCountFrequency (%)
부산광역시 6430
 
13.7%
경기도 960
 
2.0%
서울특별시 752
 
1.6%
북구 650
 
1.4%
부산진구 636
 
1.4%
해운대구 623
 
1.3%
동래구 586
 
1.2%
사상구 566
 
1.2%
사하구 566
 
1.2%
남구 487
 
1.0%
Other values (10911) 34694
73.9%
2024-04-16T17:59:16.981375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
46531
 
18.5%
10848
 
4.3%
1 10601
 
4.2%
9825
 
3.9%
9583
 
3.8%
8864
 
3.5%
8847
 
3.5%
8476
 
3.4%
7894
 
3.1%
- 7713
 
3.1%
Other values (606) 122838
48.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 150816
59.8%
Space Separator 46531
 
18.5%
Decimal Number 45599
 
18.1%
Dash Punctuation 7713
 
3.1%
Uppercase Letter 585
 
0.2%
Other Punctuation 273
 
0.1%
Open Punctuation 197
 
0.1%
Close Punctuation 196
 
0.1%
Lowercase Letter 93
 
< 0.1%
Math Symbol 13
 
< 0.1%
Other values (2) 4
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
10848
 
7.2%
9825
 
6.5%
9583
 
6.4%
8864
 
5.9%
8847
 
5.9%
8476
 
5.6%
7894
 
5.2%
7402
 
4.9%
7033
 
4.7%
2413
 
1.6%
Other values (535) 69631
46.2%
Uppercase Letter
ValueCountFrequency (%)
B 82
14.0%
A 76
13.0%
S 64
10.9%
E 51
8.7%
G 50
8.5%
K 38
 
6.5%
C 29
 
5.0%
R 25
 
4.3%
T 23
 
3.9%
M 23
 
3.9%
Other values (15) 124
21.2%
Lowercase Letter
ValueCountFrequency (%)
e 14
15.1%
s 14
15.1%
g 10
10.8%
a 9
9.7%
r 6
 
6.5%
n 5
 
5.4%
l 5
 
5.4%
t 4
 
4.3%
m 4
 
4.3%
i 4
 
4.3%
Other values (8) 18
19.4%
Decimal Number
ValueCountFrequency (%)
1 10601
23.2%
2 5931
13.0%
3 4595
10.1%
4 4184
 
9.2%
5 4122
 
9.0%
0 3701
 
8.1%
6 3420
 
7.5%
7 3161
 
6.9%
8 2983
 
6.5%
9 2901
 
6.4%
Other Punctuation
ValueCountFrequency (%)
, 206
75.5%
. 27
 
9.9%
@ 18
 
6.6%
? 14
 
5.1%
/ 4
 
1.5%
' 2
 
0.7%
& 1
 
0.4%
: 1
 
0.4%
Open Punctuation
ValueCountFrequency (%)
( 196
99.5%
[ 1
 
0.5%
Close Punctuation
ValueCountFrequency (%)
) 195
99.5%
] 1
 
0.5%
Letter Number
ValueCountFrequency (%)
1
50.0%
1
50.0%
Space Separator
ValueCountFrequency (%)
46531
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 7713
100.0%
Math Symbol
ValueCountFrequency (%)
~ 13
100.0%
Other Symbol
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 150818
59.8%
Common 100522
39.9%
Latin 680
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
10848
 
7.2%
9825
 
6.5%
9583
 
6.4%
8864
 
5.9%
8847
 
5.9%
8476
 
5.6%
7894
 
5.2%
7402
 
4.9%
7033
 
4.7%
2413
 
1.6%
Other values (536) 69633
46.2%
Latin
ValueCountFrequency (%)
B 82
 
12.1%
A 76
 
11.2%
S 64
 
9.4%
E 51
 
7.5%
G 50
 
7.4%
K 38
 
5.6%
C 29
 
4.3%
R 25
 
3.7%
T 23
 
3.4%
M 23
 
3.4%
Other values (35) 219
32.2%
Common
ValueCountFrequency (%)
46531
46.3%
1 10601
 
10.5%
- 7713
 
7.7%
2 5931
 
5.9%
3 4595
 
4.6%
4 4184
 
4.2%
5 4122
 
4.1%
0 3701
 
3.7%
6 3420
 
3.4%
7 3161
 
3.1%
Other values (15) 6563
 
6.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 150815
59.8%
ASCII 101200
40.2%
None 2
 
< 0.1%
Number Forms 2
 
< 0.1%
Compat Jamo 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
46531
46.0%
1 10601
 
10.5%
- 7713
 
7.6%
2 5931
 
5.9%
3 4595
 
4.5%
4 4184
 
4.1%
5 4122
 
4.1%
0 3701
 
3.7%
6 3420
 
3.4%
7 3161
 
3.1%
Other values (58) 7241
 
7.2%
Hangul
ValueCountFrequency (%)
10848
 
7.2%
9825
 
6.5%
9583
 
6.4%
8864
 
5.9%
8847
 
5.9%
8476
 
5.6%
7894
 
5.2%
7402
 
4.9%
7033
 
4.7%
2413
 
1.6%
Other values (534) 69630
46.2%
None
ValueCountFrequency (%)
2
100.0%
Number Forms
ValueCountFrequency (%)
1
50.0%
1
50.0%
Compat Jamo
ValueCountFrequency (%)
1
100.0%

rdnpostno
Unsupported

REJECTED  UNSUPPORTED 

Missing1
Missing (%)< 0.1%
Memory size156.2 KiB

rdnwhladdr
Text

MISSING 

Distinct7380
Distinct (%)83.4%
Missing1146
Missing (%)11.5%
Memory size156.2 KiB
2024-04-16T17:59:17.285786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length67
Median length57
Mean length30.831263
Min length18

Characters and Unicode

Total characters272980
Distinct characters677
Distinct categories12 ?
Distinct scripts3 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6449 ?
Unique (%)72.8%

Sample

1st row제주특별자치도 서귀포시 표선면 표선동서로264번길 22, 2층
2nd row서울특별시 서초구 논현로11길 6-7, 동주빌딩 302호 (양재동)
3rd row부산광역시 연제구 고분로20번길 6 (연산동)
4th row부산광역시 수영구 황령대로489번길 19 (남천동)
5th row부산광역시 동래구 명륜로 117 (명륜동)
ValueCountFrequency (%)
부산광역시 5513
 
10.2%
1층 1273
 
2.4%
경기도 959
 
1.8%
서울특별시 752
 
1.4%
부산진구 600
 
1.1%
북구 575
 
1.1%
지하1층 572
 
1.1%
동래구 519
 
1.0%
사하구 484
 
0.9%
사상구 448
 
0.8%
Other values (9424) 42250
78.3%
2024-04-16T17:59:17.728485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
45123
 
16.5%
10903
 
4.0%
1 10396
 
3.8%
9220
 
3.4%
8625
 
3.2%
( 8239
 
3.0%
) 8235
 
3.0%
8126
 
3.0%
7783
 
2.9%
7304
 
2.7%
Other values (667) 149026
54.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 165266
60.5%
Space Separator 45123
 
16.5%
Decimal Number 38859
 
14.2%
Open Punctuation 8241
 
3.0%
Close Punctuation 8237
 
3.0%
Other Punctuation 5244
 
1.9%
Dash Punctuation 1223
 
0.4%
Uppercase Letter 663
 
0.2%
Lowercase Letter 105
 
< 0.1%
Math Symbol 15
 
< 0.1%
Other values (2) 4
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
10903
 
6.6%
9220
 
5.6%
8625
 
5.2%
8126
 
4.9%
7783
 
4.7%
7304
 
4.4%
6652
 
4.0%
6175
 
3.7%
4053
 
2.5%
3595
 
2.2%
Other values (594) 92830
56.2%
Uppercase Letter
ValueCountFrequency (%)
B 122
18.4%
A 90
13.6%
S 77
11.6%
G 60
9.0%
E 50
7.5%
K 40
 
6.0%
C 36
 
5.4%
M 22
 
3.3%
N 21
 
3.2%
T 21
 
3.2%
Other values (14) 124
18.7%
Lowercase Letter
ValueCountFrequency (%)
e 15
14.3%
s 13
12.4%
g 12
11.4%
a 11
10.5%
c 6
 
5.7%
m 6
 
5.7%
n 5
 
4.8%
r 5
 
4.8%
l 5
 
4.8%
t 5
 
4.8%
Other values (9) 22
21.0%
Decimal Number
ValueCountFrequency (%)
1 10396
26.8%
2 5384
13.9%
3 4102
 
10.6%
0 3360
 
8.6%
4 3198
 
8.2%
5 3139
 
8.1%
6 2632
 
6.8%
7 2512
 
6.5%
8 2177
 
5.6%
9 1959
 
5.0%
Other Punctuation
ValueCountFrequency (%)
, 5198
99.1%
. 17
 
0.3%
? 16
 
0.3%
@ 5
 
0.1%
/ 2
 
< 0.1%
* 2
 
< 0.1%
# 1
 
< 0.1%
& 1
 
< 0.1%
' 1
 
< 0.1%
· 1
 
< 0.1%
Open Punctuation
ValueCountFrequency (%)
( 8239
> 99.9%
[ 2
 
< 0.1%
Close Punctuation
ValueCountFrequency (%)
) 8235
> 99.9%
] 2
 
< 0.1%
Letter Number
ValueCountFrequency (%)
1
50.0%
1
50.0%
Space Separator
ValueCountFrequency (%)
45123
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1223
100.0%
Math Symbol
ValueCountFrequency (%)
~ 15
100.0%
Other Symbol
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 165268
60.5%
Common 106942
39.2%
Latin 770
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
10903
 
6.6%
9220
 
5.6%
8625
 
5.2%
8126
 
4.9%
7783
 
4.7%
7304
 
4.4%
6652
 
4.0%
6175
 
3.7%
4053
 
2.5%
3595
 
2.2%
Other values (595) 92832
56.2%
Latin
ValueCountFrequency (%)
B 122
15.8%
A 90
 
11.7%
S 77
 
10.0%
G 60
 
7.8%
E 50
 
6.5%
K 40
 
5.2%
C 36
 
4.7%
M 22
 
2.9%
N 21
 
2.7%
T 21
 
2.7%
Other values (35) 231
30.0%
Common
ValueCountFrequency (%)
45123
42.2%
1 10396
 
9.7%
( 8239
 
7.7%
) 8235
 
7.7%
2 5384
 
5.0%
, 5198
 
4.9%
3 4102
 
3.8%
0 3360
 
3.1%
4 3198
 
3.0%
5 3139
 
2.9%
Other values (17) 10568
 
9.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 165265
60.5%
ASCII 107709
39.5%
None 3
 
< 0.1%
Number Forms 2
 
< 0.1%
Compat Jamo 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
45123
41.9%
1 10396
 
9.7%
( 8239
 
7.6%
) 8235
 
7.6%
2 5384
 
5.0%
, 5198
 
4.8%
3 4102
 
3.8%
0 3360
 
3.1%
4 3198
 
3.0%
5 3139
 
2.9%
Other values (59) 11335
 
10.5%
Hangul
ValueCountFrequency (%)
10903
 
6.6%
9220
 
5.6%
8625
 
5.2%
8126
 
4.9%
7783
 
4.7%
7304
 
4.4%
6652
 
4.0%
6175
 
3.7%
4053
 
2.5%
3595
 
2.2%
Other values (593) 92829
56.2%
None
ValueCountFrequency (%)
2
66.7%
· 1
33.3%
Compat Jamo
ValueCountFrequency (%)
1
100.0%
Number Forms
ValueCountFrequency (%)
1
50.0%
1
50.0%

apvpermymd
Real number (ℝ)

Distinct4785
Distinct (%)47.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20094288
Minimum19631010
Maximum20201220
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-16T17:59:17.854757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19631010
5-th percentile19871030
Q120021118
median20120820
Q320190531
95-th percentile20200907
Maximum20201220
Range570210
Interquartile range (IQR)169413

Descriptive statistics

Standard deviation108626.88
Coefficient of variation (CV)0.0054058588
Kurtosis0.47112184
Mean20094288
Median Absolute Deviation (MAD)70287.5
Skewness-1.0250926
Sum2.0094288 × 1011
Variance1.17998 × 1010
MonotonicityNot monotonic
2024-04-16T17:59:17.976495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19980713 58
 
0.6%
20181116 22
 
0.2%
20201106 21
 
0.2%
20190412 21
 
0.2%
20200117 20
 
0.2%
20191227 20
 
0.2%
20190503 19
 
0.2%
20190208 19
 
0.2%
20190125 19
 
0.2%
20200515 19
 
0.2%
Other values (4775) 9762
97.6%
ValueCountFrequency (%)
19631010 1
< 0.1%
19651010 2
< 0.1%
19651116 1
< 0.1%
19651124 1
< 0.1%
19651215 1
< 0.1%
19660916 1
< 0.1%
19661001 2
< 0.1%
19661125 1
< 0.1%
19670302 1
< 0.1%
19670811 1
< 0.1%
ValueCountFrequency (%)
20201220 1
 
< 0.1%
20201219 2
 
< 0.1%
20201218 16
0.2%
20201217 6
 
0.1%
20201216 5
 
0.1%
20201215 7
0.1%
20201214 6
 
0.1%
20201213 1
 
< 0.1%
20201212 1
 
< 0.1%
20201211 13
0.1%

dcbymd
Text

MISSING 

Distinct2213
Distinct (%)58.7%
Missing6228
Missing (%)62.3%
Memory size156.2 KiB
2024-04-16T17:59:18.204993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length7.6161188
Min length4

Characters and Unicode

Total characters28728
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

Unique1454 ?
Unique (%)38.5%

Sample

1st row20110401
2nd row20050118
3rd row20110603
4th row20131120
5th row20171207
ValueCountFrequency (%)
폐업일자 362
 
9.6%
20131222 30
 
0.8%
20170131 16
 
0.4%
20121213 16
 
0.4%
20140820 14
 
0.4%
20060216 14
 
0.4%
20130607 9
 
0.2%
20090807 8
 
0.2%
20060412 8
 
0.2%
20111230 8
 
0.2%
Other values (2203) 3287
87.1%
2024-04-16T17:59:18.766994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 9109
31.7%
2 5830
20.3%
1 5211
18.1%
3 1351
 
4.7%
6 1123
 
3.9%
7 1078
 
3.8%
4 1050
 
3.7%
5 916
 
3.2%
8 844
 
2.9%
9 768
 
2.7%
Other values (4) 1448
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 27280
95.0%
Other Letter 1448
 
5.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 9109
33.4%
2 5830
21.4%
1 5211
19.1%
3 1351
 
5.0%
6 1123
 
4.1%
7 1078
 
4.0%
4 1050
 
3.8%
5 916
 
3.4%
8 844
 
3.1%
9 768
 
2.8%
Other Letter
ValueCountFrequency (%)
362
25.0%
362
25.0%
362
25.0%
362
25.0%

Most occurring scripts

ValueCountFrequency (%)
Common 27280
95.0%
Hangul 1448
 
5.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 9109
33.4%
2 5830
21.4%
1 5211
19.1%
3 1351
 
5.0%
6 1123
 
4.1%
7 1078
 
4.0%
4 1050
 
3.8%
5 916
 
3.4%
8 844
 
3.1%
9 768
 
2.8%
Hangul
ValueCountFrequency (%)
362
25.0%
362
25.0%
362
25.0%
362
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 27280
95.0%
Hangul 1448
 
5.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 9109
33.4%
2 5830
21.4%
1 5211
19.1%
3 1351
 
5.0%
6 1123
 
4.1%
7 1078
 
4.0%
4 1050
 
3.8%
5 916
 
3.4%
8 844
 
3.1%
9 768
 
2.8%
Hangul
ValueCountFrequency (%)
362
25.0%
362
25.0%
362
25.0%
362
25.0%

clgstdt
Categorical

IMBALANCE 

Distinct13
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9627 
휴업시작일자
 
362
20060707
 
1
20130318
 
1
20110325
 
1
Other values (8)
 
8

Length

Max length8
Median length4
Mean length4.0768
Min length4

Unique

Unique11 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 9627
96.3%
휴업시작일자 362
 
3.6%
20060707 1
 
< 0.1%
20130318 1
 
< 0.1%
20110325 1
 
< 0.1%
20101103 1
 
< 0.1%
20070621 1
 
< 0.1%
20111013 1
 
< 0.1%
20170313 1
 
< 0.1%
20060420 1
 
< 0.1%
Other values (3) 3
 
< 0.1%

Length

2024-04-16T17:59:18.900340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 9627
96.3%
휴업시작일자 362
 
3.6%
20060707 1
 
< 0.1%
20130318 1
 
< 0.1%
20110325 1
 
< 0.1%
20101103 1
 
< 0.1%
20070621 1
 
< 0.1%
20111013 1
 
< 0.1%
20170313 1
 
< 0.1%
20060420 1
 
< 0.1%
Other values (3) 3
 
< 0.1%

clgenddt
Categorical

IMBALANCE 

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9627 
휴업종료일자
 
362
20171231
 
2
20061231
 
1
20160317
 
1
Other values (7)
 
7

Length

Max length8
Median length4
Mean length4.0768
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> 9627
96.3%
휴업종료일자 362
 
3.6%
20171231 2
 
< 0.1%
20061231 1
 
< 0.1%
20160317 1
 
< 0.1%
20120324 1
 
< 0.1%
20111102 1
 
< 0.1%
20071231 1
 
< 0.1%
20121012 1
 
< 0.1%
20060831 1
 
< 0.1%
Other values (2) 2
 
< 0.1%

Length

2024-04-16T17:59:19.045528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 9627
96.3%
휴업종료일자 362
 
3.6%
20171231 2
 
< 0.1%
20061231 1
 
< 0.1%
20160317 1
 
< 0.1%
20120324 1
 
< 0.1%
20111102 1
 
< 0.1%
20071231 1
 
< 0.1%
20121012 1
 
< 0.1%
20060831 1
 
< 0.1%
Other values (2) 2
 
< 0.1%

ropnymd
Categorical

IMBALANCE 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9634 
재개업일자
 
362
20070607
 
1
20061030
 
1
20180619
 
1

Length

Max length8
Median length4
Mean length4.0378
Min length4

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 9634
96.3%
재개업일자 362
 
3.6%
20070607 1
 
< 0.1%
20061030 1
 
< 0.1%
20180619 1
 
< 0.1%
20081007 1
 
< 0.1%

Length

2024-04-16T17:59:19.161774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:59:19.264107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9634
96.3%
재개업일자 362
 
3.6%
20070607 1
 
< 0.1%
20061030 1
 
< 0.1%
20180619 1
 
< 0.1%
20081007 1
 
< 0.1%

trdstatenm
Categorical

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
영업/정상
3495 
0002
2612 
0000
2147 
02
796 
01
610 
Other values (6)
 
340

Length

Max length5
Median length4
Mean length4.0661
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
영업/정상 3495
34.9%
0002 2612
26.1%
0000 2147
21.5%
02 796
 
8.0%
01 610
 
6.1%
0004 252
 
2.5%
<NA> 58
 
0.6%
폐업 11
 
0.1%
0001 10
 
0.1%
0003 6
 
0.1%

Length

2024-04-16T17:59:19.383682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
영업/정상 3495
34.9%
0002 2612
26.1%
0000 2147
21.5%
02 796
 
8.0%
01 610
 
6.1%
0004 252
 
2.5%
na 58
 
0.6%
폐업 11
 
0.1%
0001 10
 
0.1%
0003 6
 
0.1%

dtlstatenm
Categorical

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
영업
4012 
폐업
3419 
정상
2296 
말소
 
252
휴업
 
10
Other values (3)
 
11

Length

Max length4
Median length2
Mean length2.0016
Min length2

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
영업 4012
40.1%
폐업 3419
34.2%
정상 2296
23.0%
말소 252
 
2.5%
휴업 10
 
0.1%
행정처분 6
 
0.1%
영업중 4
 
< 0.1%
?? 1
 
< 0.1%

Length

2024-04-16T17:59:19.514334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:59:19.619009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
영업 4012
40.1%
폐업 3419
34.2%
정상 2296
23.0%
말소 252
 
2.5%
휴업 10
 
0.1%
행정처분 6
 
0.1%
영업중 4
 
< 0.1%
1
 
< 0.1%

x
Text

MISSING 

Distinct7198
Distinct (%)74.3%
Missing309
Missing (%)3.1%
Memory size156.2 KiB
2024-04-16T17:59:19.838674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length20
Mean length19.991951
Min length7

Characters and Unicode

Total characters193742
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

Unique5932 ?
Unique (%)61.2%

Sample

1st row392353.090923
2nd row184649.620428
3rd row204145.273488218
4th row389869.54051300000
5th row392128.24573200000
ValueCountFrequency (%)
381223.93770800000 25
 
0.3%
394015.45385100000 24
 
0.2%
178006.080301401 19
 
0.2%
209850.446960528 18
 
0.2%
381150.24062500000 18
 
0.2%
200250.447804795 15
 
0.2%
190107.045415333 15
 
0.2%
381201.909278 15
 
0.2%
387443.21456800000 14
 
0.1%
238047.286715 13
 
0.1%
Other values (7188) 9515
98.2%
2024-04-16T17:59:20.155425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 37520
19.4%
34871
18.0%
3 17270
8.9%
8 14237
 
7.3%
9 13076
 
6.7%
1 12375
 
6.4%
2 12140
 
6.3%
7 11232
 
5.8%
4 10547
 
5.4%
5 10527
 
5.4%
Other values (9) 19947
10.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 149226
77.0%
Space Separator 34871
 
18.0%
Other Punctuation 9603
 
5.0%
Other Letter 24
 
< 0.1%
Open Punctuation 6
 
< 0.1%
Uppercase Letter 6
 
< 0.1%
Close Punctuation 6
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 37520
25.1%
3 17270
11.6%
8 14237
 
9.5%
9 13076
 
8.8%
1 12375
 
8.3%
2 12140
 
8.1%
7 11232
 
7.5%
4 10547
 
7.1%
5 10527
 
7.1%
6 10302
 
6.9%
Other Letter
ValueCountFrequency (%)
6
25.0%
6
25.0%
6
25.0%
6
25.0%
Space Separator
ValueCountFrequency (%)
34871
100.0%
Other Punctuation
ValueCountFrequency (%)
. 9603
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6
100.0%
Uppercase Letter
ValueCountFrequency (%)
X 6
100.0%
Close Punctuation
ValueCountFrequency (%)
) 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 193712
> 99.9%
Hangul 24
 
< 0.1%
Latin 6
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 37520
19.4%
34871
18.0%
3 17270
8.9%
8 14237
 
7.3%
9 13076
 
6.8%
1 12375
 
6.4%
2 12140
 
6.3%
7 11232
 
5.8%
4 10547
 
5.4%
5 10527
 
5.4%
Other values (4) 19917
10.3%
Hangul
ValueCountFrequency (%)
6
25.0%
6
25.0%
6
25.0%
6
25.0%
Latin
ValueCountFrequency (%)
X 6
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 193718
> 99.9%
Hangul 24
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 37520
19.4%
34871
18.0%
3 17270
8.9%
8 14237
 
7.3%
9 13076
 
6.8%
1 12375
 
6.4%
2 12140
 
6.3%
7 11232
 
5.8%
4 10547
 
5.4%
5 10527
 
5.4%
Other values (5) 19923
10.3%
Hangul
ValueCountFrequency (%)
6
25.0%
6
25.0%
6
25.0%
6
25.0%

y
Text

MISSING 

Distinct7197
Distinct (%)74.3%
Missing309
Missing (%)3.1%
Memory size156.2 KiB
2024-04-16T17:59:20.366433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length20
Mean length19.991951
Min length7

Characters and Unicode

Total characters193742
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

Unique5930 ?
Unique (%)61.2%

Sample

1st row185461.806335
2nd row-18767.8647766
3rd row441308.474903399
4th row189653.15547800000
5th row184563.69511100000
ValueCountFrequency (%)
184549.28339000000 25
 
0.3%
187900.93961700000 24
 
0.2%
462865.812502378 19
 
0.2%
432304.149379043 18
 
0.2%
190717.70395600000 18
 
0.2%
444683.220506107 15
 
0.2%
445157.626366229 15
 
0.2%
184537.273724 15
 
0.2%
186484.77508400000 14
 
0.1%
349368.880808 13
 
0.1%
Other values (7187) 9515
98.2%
2024-04-16T17:59:20.681211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 36546
18.9%
34908
18.0%
1 17427
9.0%
8 13726
 
7.1%
4 13106
 
6.8%
9 12949
 
6.7%
7 11498
 
5.9%
3 11162
 
5.8%
2 11071
 
5.7%
6 10957
 
5.7%
Other values (11) 20392
10.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 149161
77.0%
Space Separator 34908
 
18.0%
Other Punctuation 9604
 
5.0%
Other Letter 24
 
< 0.1%
Dash Punctuation 23
 
< 0.1%
Close Punctuation 10
 
< 0.1%
Open Punctuation 6
 
< 0.1%
Uppercase Letter 6
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 36546
24.5%
1 17427
11.7%
8 13726
 
9.2%
4 13106
 
8.8%
9 12949
 
8.7%
7 11498
 
7.7%
3 11162
 
7.5%
2 11071
 
7.4%
6 10957
 
7.3%
5 10719
 
7.2%
Other Letter
ValueCountFrequency (%)
6
25.0%
6
25.0%
6
25.0%
6
25.0%
Close Punctuation
ValueCountFrequency (%)
) 6
60.0%
] 4
40.0%
Space Separator
ValueCountFrequency (%)
34908
100.0%
Other Punctuation
ValueCountFrequency (%)
. 9604
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 23
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6
100.0%
Uppercase Letter
ValueCountFrequency (%)
Y 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 193712
> 99.9%
Hangul 24
 
< 0.1%
Latin 6
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 36546
18.9%
34908
18.0%
1 17427
9.0%
8 13726
 
7.1%
4 13106
 
6.8%
9 12949
 
6.7%
7 11498
 
5.9%
3 11162
 
5.8%
2 11071
 
5.7%
6 10957
 
5.7%
Other values (6) 20362
10.5%
Hangul
ValueCountFrequency (%)
6
25.0%
6
25.0%
6
25.0%
6
25.0%
Latin
ValueCountFrequency (%)
Y 6
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 193718
> 99.9%
Hangul 24
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 36546
18.9%
34908
18.0%
1 17427
9.0%
8 13726
 
7.1%
4 13106
 
6.8%
9 12949
 
6.7%
7 11498
 
5.9%
3 11162
 
5.8%
2 11071
 
5.7%
6 10957
 
5.7%
Other values (7) 20368
10.5%
Hangul
ValueCountFrequency (%)
6
25.0%
6
25.0%
6
25.0%
6
25.0%

lastmodts
Real number (ℝ)

Distinct9531
Distinct (%)95.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0145558 × 1013
Minimum1.9990315 × 1013
Maximum2.020122 × 1013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-16T17:59:20.807476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.9990315 × 1013
5-th percentile2.0040324 × 1013
Q12.011111 × 1013
median2.016102 × 1013
Q32.0190531 × 1013
95-th percentile2.0200907 × 1013
Maximum2.020122 × 1013
Range2.1090514 × 1011
Interquartile range (IQR)7.9420991 × 1010

Descriptive statistics

Standard deviation5.280007 × 1010
Coefficient of variation (CV)0.0026209286
Kurtosis-0.44030177
Mean2.0145558 × 1013
Median Absolute Deviation (MAD)3.0198999 × 1010
Skewness-0.84309904
Sum2.0145558 × 1017
Variance2.7878474 × 1021
MonotonicityNot monotonic
2024-04-16T17:59:20.922072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20061226000000 16
 
0.2%
20050614000000 11
 
0.1%
20020802000000 9
 
0.1%
20040823000000 7
 
0.1%
20050525000000 6
 
0.1%
20050615000000 5
 
0.1%
20020704000000 5
 
0.1%
19990318000000 4
 
< 0.1%
20190503161545 4
 
< 0.1%
19990317000000 4
 
< 0.1%
Other values (9521) 9929
99.3%
ValueCountFrequency (%)
19990315000000 3
< 0.1%
19990316000000 3
< 0.1%
19990317000000 4
< 0.1%
19990318000000 4
< 0.1%
19990319000000 4
< 0.1%
19990323000000 1
 
< 0.1%
19990324000000 1
 
< 0.1%
19990520000000 1
 
< 0.1%
19990610000000 1
 
< 0.1%
19990716000000 1
 
< 0.1%
ValueCountFrequency (%)
20201220142437 1
< 0.1%
20201219173343 1
< 0.1%
20201219173013 1
< 0.1%
20201218174754 1
< 0.1%
20201218172026 2
< 0.1%
20201218164433 1
< 0.1%
20201218152229 2
< 0.1%
20201218143910 1
< 0.1%
20201218141525 1
< 0.1%
20201218135303 1
< 0.1%

uptaenm
Categorical

Distinct20
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
식육판매업
4028 
즉석판매제조가공업
2810 
제과점영업
1408 
우유류판매업
683 
<NA>
 
397
Other values (15)
674 

Length

Max length13
Median length5
Mean length6.436
Min length2

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
식육판매업 4028
40.3%
즉석판매제조가공업 2810
28.1%
제과점영업 1408
 
14.1%
우유류판매업 683
 
6.8%
<NA> 397
 
4.0%
축산물유통전문판매업 143
 
1.4%
축산물수입판매업 125
 
1.2%
집단급식소 식품판매업 108
 
1.1%
건강기능식품유통전문판매업 71
 
0.7%
식용란수집판매업 66
 
0.7%
Other values (10) 161
 
1.6%

Length

2024-04-16T17:59:21.032851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
식육판매업 4028
39.7%
즉석판매제조가공업 2810
27.7%
제과점영업 1408
 
13.9%
우유류판매업 683
 
6.7%
na 397
 
3.9%
축산물유통전문판매업 143
 
1.4%
축산물수입판매업 125
 
1.2%
집단급식소 108
 
1.1%
식품판매업 108
 
1.1%
건강기능식품유통전문판매업 71
 
0.7%
Other values (11) 257
 
2.5%

sitetel
Text

MISSING 

Distinct84
Distinct (%)0.9%
Missing278
Missing (%)2.8%
Memory size156.2 KiB
2024-04-16T17:59:21.150902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length11.964616
Min length4

Characters and Unicode

Total characters116320
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

Unique72 ?
Unique (%)0.7%

Sample

1st row051-123-1234
2nd row051-123-1234
3rd row070 46105577
4th row051-123-1234
5th row051-123-1234
ValueCountFrequency (%)
051-123-1234 9587
97.4%
전화번호 40
 
0.4%
02 18
 
0.2%
070 12
 
0.1%
031 10
 
0.1%
062 6
 
0.1%
054 4
 
< 0.1%
26629884 4
 
< 0.1%
051 4
 
< 0.1%
041 4
 
< 0.1%
Other values (126) 154
 
1.6%
2024-04-16T17:59:21.420105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 28852
24.8%
2 19300
16.6%
3 19268
16.6%
- 19203
16.5%
0 9758
 
8.4%
5 9696
 
8.3%
4 9673
 
8.3%
137
 
0.1%
8 83
 
0.1%
6 69
 
0.1%
Other values (6) 281
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 96820
83.2%
Dash Punctuation 19203
 
16.5%
Other Letter 160
 
0.1%
Space Separator 137
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 28852
29.8%
2 19300
19.9%
3 19268
19.9%
0 9758
 
10.1%
5 9696
 
10.0%
4 9673
 
10.0%
8 83
 
0.1%
6 69
 
0.1%
7 61
 
0.1%
9 60
 
0.1%
Other Letter
ValueCountFrequency (%)
40
25.0%
40
25.0%
40
25.0%
40
25.0%
Dash Punctuation
ValueCountFrequency (%)
- 19203
100.0%
Space Separator
ValueCountFrequency (%)
137
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 116160
99.9%
Hangul 160
 
0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
1 28852
24.8%
2 19300
16.6%
3 19268
16.6%
- 19203
16.5%
0 9758
 
8.4%
5 9696
 
8.3%
4 9673
 
8.3%
137
 
0.1%
8 83
 
0.1%
6 69
 
0.1%
Other values (2) 121
 
0.1%
Hangul
ValueCountFrequency (%)
40
25.0%
40
25.0%
40
25.0%
40
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 116160
99.9%
Hangul 160
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 28852
24.8%
2 19300
16.6%
3 19268
16.6%
- 19203
16.5%
0 9758
 
8.4%
5 9696
 
8.3%
4 9673
 
8.3%
137
 
0.1%
8 83
 
0.1%
6 69
 
0.1%
Other values (2) 121
 
0.1%
Hangul
ValueCountFrequency (%)
40
25.0%
40
25.0%
40
25.0%
40
25.0%

bdngownsenm
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
8606 
자가
 
742
임대
 
408
건물소유구분명
 
244

Length

Max length7
Median length4
Mean length3.8432
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 8606
86.1%
자가 742
 
7.4%
임대 408
 
4.1%
건물소유구분명 244
 
2.4%

Length

2024-04-16T17:59:21.524289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:59:21.604276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 8606
86.1%
자가 742
 
7.4%
임대 408
 
4.1%
건물소유구분명 244
 
2.4%

fctyowkepcnt
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9542 
공장사무직종업원수
 
347
0
 
105
1
 
4
2
 
2

Length

Max length9
Median length4
Mean length4.1402
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> 9542
95.4%
공장사무직종업원수 347
 
3.5%
0 105
 
1.1%
1 4
 
< 0.1%
2 2
 
< 0.1%

Length

2024-04-16T17:59:21.695679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:59:21.796674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9542
95.4%
공장사무직종업원수 347
 
3.5%
0 105
 
1.1%
1 4
 
< 0.1%
2 2
 
< 0.1%

fctypdtjobepcnt
Categorical

IMBALANCE 

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9535 
공장생산직종업원수
 
345
0
 
105
1
 
11
2
 
2
Other values (2)
 
2

Length

Max length9
Median length4
Mean length4.1365
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> 9535
95.3%
공장생산직종업원수 345
 
3.5%
0 105
 
1.1%
1 11
 
0.1%
2 2
 
< 0.1%
6 1
 
< 0.1%
4 1
 
< 0.1%

Length

2024-04-16T17:59:21.890998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:59:21.986803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9535
95.3%
공장생산직종업원수 345
 
3.5%
0 105
 
1.1%
1 11
 
0.1%
2 2
 
< 0.1%
6 1
 
< 0.1%
4 1
 
< 0.1%

fctysiljobepcnt
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9542 
공장판매직종업원수
 
348
0
 
106
1
 
4

Length

Max length9
Median length4
Mean length4.141
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> 9542
95.4%
공장판매직종업원수 348
 
3.5%
0 106
 
1.1%
1 4
 
< 0.1%

Length

2024-04-16T17:59:22.086414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:59:22.169752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9542
95.4%
공장판매직종업원수 348
 
3.5%
0 106
 
1.1%
1 4
 
< 0.1%

rgtmbdsno
Categorical

IMBALANCE 

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
000
4580 
<NA>
4492 
L00
587 
권리주체일련번호
 
331
100
 
3
Other values (3)
 
7

Length

Max length8
Median length3
Mean length3.6147
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
000 4580
45.8%
<NA> 4492
44.9%
L00 587
 
5.9%
권리주체일련번호 331
 
3.3%
100 3
 
< 0.1%
010 3
 
< 0.1%
L01 2
 
< 0.1%
F00 2
 
< 0.1%

Length

2024-04-16T17:59:22.255501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:59:22.349510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
000 4580
45.8%
na 4492
44.9%
l00 587
 
5.9%
권리주체일련번호 331
 
3.3%
100 3
 
< 0.1%
010 3
 
< 0.1%
l01 2
 
< 0.1%
f00 2
 
< 0.1%

wtrsplyfacilsenm
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
8540 
상수도전용
1123 
급수시설구분명
 
320
지하수전용
 
13
간이상수도
 
4

Length

Max length7
Median length4
Mean length4.21
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 8540
85.4%
상수도전용 1123
 
11.2%
급수시설구분명 320
 
3.2%
지하수전용 13
 
0.1%
간이상수도 4
 
< 0.1%

Length

2024-04-16T17:59:22.459168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:59:22.547351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 8540
85.4%
상수도전용 1123
 
11.2%
급수시설구분명 320
 
3.2%
지하수전용 13
 
0.1%
간이상수도 4
 
< 0.1%

maneipcnt
Categorical

IMBALANCE 

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9092 
0
 
507
남성종사자수
 
362
1
 
30
2
 
6
Other values (2)
 
3

Length

Max length6
Median length4
Mean length3.9086
Min length1

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 9092
90.9%
0 507
 
5.1%
남성종사자수 362
 
3.6%
1 30
 
0.3%
2 6
 
0.1%
3 2
 
< 0.1%
4 1
 
< 0.1%

Length

2024-04-16T17:59:22.659225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:59:22.767650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9092
90.9%
0 507
 
5.1%
남성종사자수 362
 
3.6%
1 30
 
0.3%
2 6
 
0.1%
3 2
 
< 0.1%
4 1
 
< 0.1%

multusnupsoyn
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
N
9946 
<NA>
 
30
 
13
Y
 
11

Length

Max length4
Median length1
Mean length1.009
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> 30
 
0.3%
13
 
0.1%
Y 11
 
0.1%

Length

2024-04-16T17:59:22.884793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:59:22.979046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
n 9946
99.5%
na 30
 
0.3%
13
 
0.1%
y 11
 
0.1%

lvsenm
Categorical

IMBALANCE 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9218 
등급구분명
 
362
기타
 
293
자율
 
124
우수
 
2

Length

Max length5
Median length4
Mean length3.9522
Min length2

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 9218
92.2%
등급구분명 362
 
3.6%
기타 293
 
2.9%
자율 124
 
1.2%
우수 2
 
< 0.1%
관리 1
 
< 0.1%

Length

2024-04-16T17:59:23.073458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:59:23.183914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9218
92.2%
등급구분명 362
 
3.6%
기타 293
 
2.9%
자율 124
 
1.2%
우수 2
 
< 0.1%
관리 1
 
< 0.1%

isream
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9632 
보증액
 
362
0
 
5
5000000
 
1

Length

Max length7
Median length4
Mean length3.9626
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> 9632
96.3%
보증액 362
 
3.6%
0 5
 
0.1%
5000000 1
 
< 0.1%

Length

2024-04-16T17:59:23.304862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:59:23.392061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9632
96.3%
보증액 362
 
3.6%
0 5
 
< 0.1%
5000000 1
 
< 0.1%

hoffepcnt
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9543 
본사종업원수
 
348
0
 
107
1
 
2

Length

Max length6
Median length4
Mean length4.0369
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> 9543
95.4%
본사종업원수 348
 
3.5%
0 107
 
1.1%
1 2
 
< 0.1%

Length

2024-04-16T17:59:23.491357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:59:23.583490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9543
95.4%
본사종업원수 348
 
3.5%
0 107
 
1.1%
1 2
 
< 0.1%

equsiz
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9638 
설비규격
 
362

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> 9638
96.4%
설비규격 362
 
3.6%

Length

2024-04-16T17:59:23.678318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:59:23.756409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9638
96.4%
설비규격 362
 
3.6%

faciltotscp
Text

MISSING 

Distinct1207
Distinct (%)24.9%
Missing5148
Missing (%)51.5%
Memory size156.2 KiB
2024-04-16T17:59:23.999315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length1
Mean length2.1032564
Min length1

Characters and Unicode

Total characters10205
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

Unique1030 ?
Unique (%)21.2%

Sample

1st row50.34
2nd row0
3rd row0
4th row82.27
5th row19
ValueCountFrequency (%)
0 3192
65.8%
3.3 46
 
0.9%
시설총규모 33
 
0.7%
10 20
 
0.4%
6.6 19
 
0.4%
20 10
 
0.2%
33 9
 
0.2%
25 9
 
0.2%
36 8
 
0.2%
9.9 8
 
0.2%
Other values (1196) 1498
30.9%
2024-04-16T17:59:24.382094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 3518
34.5%
. 1374
 
13.5%
2 749
 
7.3%
1 711
 
7.0%
3 659
 
6.5%
4 581
 
5.7%
6 580
 
5.7%
5 570
 
5.6%
8 473
 
4.6%
9 422
 
4.1%
Other values (6) 568
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8666
84.9%
Other Punctuation 1374
 
13.5%
Other Letter 165
 
1.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3518
40.6%
2 749
 
8.6%
1 711
 
8.2%
3 659
 
7.6%
4 581
 
6.7%
6 580
 
6.7%
5 570
 
6.6%
8 473
 
5.5%
9 422
 
4.9%
7 403
 
4.7%
Other Letter
ValueCountFrequency (%)
33
20.0%
33
20.0%
33
20.0%
33
20.0%
33
20.0%
Other Punctuation
ValueCountFrequency (%)
. 1374
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 10040
98.4%
Hangul 165
 
1.6%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3518
35.0%
. 1374
 
13.7%
2 749
 
7.5%
1 711
 
7.1%
3 659
 
6.6%
4 581
 
5.8%
6 580
 
5.8%
5 570
 
5.7%
8 473
 
4.7%
9 422
 
4.2%
Hangul
ValueCountFrequency (%)
33
20.0%
33
20.0%
33
20.0%
33
20.0%
33
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10040
98.4%
Hangul 165
 
1.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3518
35.0%
. 1374
 
13.7%
2 749
 
7.5%
1 711
 
7.1%
3 659
 
6.6%
4 581
 
5.8%
6 580
 
5.8%
5 570
 
5.7%
8 473
 
4.7%
9 422
 
4.2%
Hangul
ValueCountFrequency (%)
33
20.0%
33
20.0%
33
20.0%
33
20.0%
33
20.0%

wmeipcnt
Categorical

IMBALANCE 

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9086 
0
 
504
여성종사자수
 
362
1
 
39
2
 
6
Other values (2)
 
3

Length

Max length6
Median length4
Mean length3.9069
Min length1

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 9086
90.9%
0 504
 
5.0%
여성종사자수 362
 
3.6%
1 39
 
0.4%
2 6
 
0.1%
4 2
 
< 0.1%
11 1
 
< 0.1%

Length

2024-04-16T17:59:24.505092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:59:24.596151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9086
90.9%
0 504
 
5.0%
여성종사자수 362
 
3.6%
1 39
 
0.4%
2 6
 
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>
9133 
영업장주변구분명
 
362
기타
 
351
주택가주변
 
75
아파트지역
 
57
Other values (3)
 
22

Length

Max length8
Median length4
Mean length4.0965
Min length2

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 9133
91.3%
영업장주변구분명 362
 
3.6%
기타 351
 
3.5%
주택가주변 75
 
0.8%
아파트지역 57
 
0.6%
유흥업소밀집지역 16
 
0.2%
학교정화(상대) 5
 
0.1%
결혼예식장주변 1
 
< 0.1%

Length

2024-04-16T17:59:24.700710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:59:24.804691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9133
91.3%
영업장주변구분명 362
 
3.6%
기타 351
 
3.5%
주택가주변 75
 
0.8%
아파트지역 57
 
0.6%
유흥업소밀집지역 16
 
0.2%
학교정화(상대 5
 
< 0.1%
결혼예식장주변 1
 
< 0.1%

monam
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9632 
월세액
 
362
0
 
5
400000
 
1

Length

Max length6
Median length4
Mean length3.9625
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> 9632
96.3%
월세액 362
 
3.6%
0 5
 
0.1%
400000 1
 
< 0.1%

Length

2024-04-16T17:59:24.913130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:59:24.995147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9632
96.3%
월세액 362
 
3.6%
0 5
 
< 0.1%
400000 1
 
< 0.1%

sntuptaenm
Categorical

IMBALANCE 

Distinct20
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
5148 
즉석판매제조가공업
2810 
제과점영업
1408 
전자상거래(통신판매업)
 
190
집단급식소 식품판매업
 
108
Other values (15)
 
336

Length

Max length14
Median length4
Mean length5.8824
Min length2

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row제과점영업
2nd row즉석판매제조가공업
3rd row전자상거래(통신판매업)
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 5148
51.5%
즉석판매제조가공업 2810
28.1%
제과점영업 1408
 
14.1%
전자상거래(통신판매업) 190
 
1.9%
집단급식소 식품판매업 108
 
1.1%
영업장판매 102
 
1.0%
건강기능식품유통전문판매업 71
 
0.7%
방문판매 36
 
0.4%
위생업태명 33
 
0.3%
기타 식품제조가공업 30
 
0.3%
Other values (10) 64
 
0.6%

Length

2024-04-16T17:59:25.086787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 5148
50.7%
즉석판매제조가공업 2810
27.7%
제과점영업 1408
 
13.9%
전자상거래(통신판매업 190
 
1.9%
집단급식소 108
 
1.1%
식품판매업 108
 
1.1%
영업장판매 102
 
1.0%
건강기능식품유통전문판매업 71
 
0.7%
기타 47
 
0.5%
방문판매 36
 
0.4%
Other values (12) 120
 
1.2%

jtupsomainedf
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9638 
전통업소주된음식
 
362

Length

Max length8
Median length4
Mean length4.1448
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> 9638
96.4%
전통업소주된음식 362
 
3.6%

Length

2024-04-16T17:59:25.199793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:59:25.283545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9638
96.4%
전통업소주된음식 362
 
3.6%

jtupsoasgnno
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9638 
전통업소지정번호
 
362

Length

Max length8
Median length4
Mean length4.1448
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> 9638
96.4%
전통업소지정번호 362
 
3.6%

Length

2024-04-16T17:59:25.367716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:59:25.452783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9638
96.4%
전통업소지정번호 362
 
3.6%

totepnum
Categorical

IMBALANCE 

Distinct13
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
4607 
식육판매업
3998 
우유류판매업
680 
총종업원수
 
361
축산물유통전문판매업
 
130
Other values (8)
 
224

Length

Max length10
Median length8
Mean length4.7424
Min length1

Unique

Unique5 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 4607
46.1%
식육판매업 3998
40.0%
우유류판매업 680
 
6.8%
총종업원수 361
 
3.6%
축산물유통전문판매업 130
 
1.3%
축산물수입판매업 125
 
1.2%
식용란수집판매업 63
 
0.6%
식육부산물전문판매업 31
 
0.3%
3 1
 
< 0.1%
6 1
 
< 0.1%
Other values (3) 3
 
< 0.1%

Length

2024-04-16T17:59:25.536719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 4607
46.1%
식육판매업 3998
40.0%
우유류판매업 680
 
6.8%
총종업원수 361
 
3.6%
축산물유통전문판매업 130
 
1.3%
축산물수입판매업 125
 
1.2%
식용란수집판매업 63
 
0.6%
식육부산물전문판매업 31
 
0.3%
3 1
 
< 0.1%
6 1
 
< 0.1%
Other values (3) 3
 
< 0.1%

lindprcbgbnnm
Categorical

IMBALANCE 

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
축산물판매업
5027 
<NA>
4562 
축산물가공업구분명
 
349
식육판매업
 
30
축산물유통전문판매업
 
13
Other values (5)
 
19

Length

Max length10
Median length6
Mean length5.1963
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
축산물판매업 5027
50.3%
<NA> 4562
45.6%
축산물가공업구분명 349
 
3.5%
식육판매업 30
 
0.3%
축산물유통전문판매업 13
 
0.1%
식육가공업 5
 
0.1%
식육포장처리업 5
 
0.1%
식용란수집판매업 3
 
< 0.1%
식육부산물전문판매업 3
 
< 0.1%
우유류판매업 3
 
< 0.1%

Length

2024-04-16T17:59:25.957198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:59:26.067937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
축산물판매업 5027
50.3%
na 4562
45.6%
축산물가공업구분명 349
 
3.5%
식육판매업 30
 
0.3%
축산물유통전문판매업 13
 
0.1%
식육가공업 5
 
< 0.1%
식육포장처리업 5
 
< 0.1%
식용란수집판매업 3
 
< 0.1%
식육부산물전문판매업 3
 
< 0.1%
우유류판매업 3
 
< 0.1%

lindjobgbnnm
Categorical

IMBALANCE 

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9519 
축산업무구분명
 
331
축산물운반업
 
53
축산물판매업
 
52
축산물보관업
 
34
Other values (3)
 
11

Length

Max length7
Median length4
Mean length4.1295
Min length3

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> 9519
95.2%
축산업무구분명 331
 
3.3%
축산물운반업 53
 
0.5%
축산물판매업 52
 
0.5%
축산물보관업 34
 
0.3%
축산물가공업 5
 
0.1%
식육포장처리업 5
 
0.1%
집유업 1
 
< 0.1%

Length

2024-04-16T17:59:26.188507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:59:26.279921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9519
95.2%
축산업무구분명 331
 
3.3%
축산물운반업 53
 
0.5%
축산물판매업 52
 
0.5%
축산물보관업 34
 
0.3%
축산물가공업 5
 
< 0.1%
식육포장처리업 5
 
< 0.1%
집유업 1
 
< 0.1%

lindseqno
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9638 
축산일련번호
 
362

Length

Max length6
Median length4
Mean length4.0724
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> 9638
96.4%
축산일련번호 362
 
3.6%

Length

2024-04-16T17:59:26.395598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:59:26.482744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9638
96.4%
축산일련번호 362
 
3.6%

homepage
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9637 
홈페이지
 
362
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> 9637
96.4%
홈페이지 362
 
3.6%
https://smartstore.naver.com/navidstore 1
 
< 0.1%

Length

2024-04-16T17:59:26.571181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:59:26.651985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9637
96.4%
홈페이지 362
 
3.6%
https://smartstore.naver.com/navidstore 1
 
< 0.1%

last_load_dttm
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2020-12-22 14:06:00
2649 
2020-12-22 14:06:01
2563 
2020-12-22 14:06:02
2458 
2020-12-22 14:06:03
2330 

Length

Max length19
Median length19
Mean length19
Min length19

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020-12-22 14:06:02
2nd row2020-12-22 14:06:02
3rd row2020-12-22 14:06:03
4th row2020-12-22 14:06:01
5th row2020-12-22 14:06:01

Common Values

ValueCountFrequency (%)
2020-12-22 14:06:00 2649
26.5%
2020-12-22 14:06:01 2563
25.6%
2020-12-22 14:06:02 2458
24.6%
2020-12-22 14:06:03 2330
23.3%

Length

2024-04-16T17:59:26.740174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:59:26.830669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020-12-22 10000
50.0%
14:06:00 2649
 
13.2%
14:06:01 2563
 
12.8%
14:06:02 2458
 
12.3%
14:06:03 2330
 
11.7%

Sample

skeyopnsfteamcodemgtnoopnsvcidupdategbnupdatedtopnsvcnmbplcnmsitepostnositewhladdrrdnpostnordnwhladdrapvpermymddcbymdclgstdtclgenddtropnymdtrdstatenmdtlstatenmxylastmodtsuptaenmsitetelbdngownsenmfctyowkepcntfctypdtjobepcntfctysiljobepcntrgtmbdsnowtrsplyfacilsenmmaneipcntmultusnupsoynlvsenmisreamhoffepcntequsizfaciltotscpwmeipcnttrdpjubnsenmmonamsntuptaenmjtupsomainedfjtupsoasgnnototepnumlindprcbgbnnmlindjobgbnnmlindseqnohomepagelast_load_dttm
120661206933800003380000-121-2000-0377207_22_18_PI2018-08-31 23:59:59.0<NA>크라운베이커리 부산 남천점613815부산광역시 수영구 남천동 29-13번지48947<NA>2000032220110401<NA><NA><NA>02폐업392353.090923185461.80633520041008000000제과점영업051-123-1234<NA><NA><NA><NA><NA><NA>0N자율<NA><NA><NA>50.340유흥업소밀집지역<NA>제과점영업<NA><NA><NA><NA><NA><NA><NA>2020-12-22 14:06:02
146331463465200006520000-107-2019-0007807_22_19_PI2019-06-23 02:21:37.0즉석판매제조가공업바타타식탁699914제주특별자치도 서귀포시 표선면 표선리 484번지63629제주특별자치도 서귀포시 표선면 표선동서로264번길 22, 2층20190621<NA><NA><NA><NA>영업/정상영업184649.620428-18767.864776620190621165026즉석판매제조가공업051-123-1234자가<NA><NA><NA><NA>상수도전용<NA>N<NA><NA><NA><NA>0<NA><NA><NA>즉석판매제조가공업<NA><NA><NA><NA><NA><NA><NA>2020-12-22 14:06:02
186841868532100003210000-134-2020-0039207_22_03_PI2020-10-25 00:23:29.0건강기능식품일반판매업(주)케이비맵137895서울특별시 서초구 양재동 287-4 동주빌딩06782서울특별시 서초구 논현로11길 6-7, 동주빌딩 302호 (양재동)20201023<NA><NA><NA><NA>영업/정상영업204145.273488218441308.47490339920201023160531<NA>070 46105577<NA><NA><NA><NA><NA><NA><NA>N<NA><NA><NA><NA>0<NA><NA><NA>전자상거래(통신판매업)<NA><NA><NA><NA><NA><NA><NA>2020-12-22 14:06:03
73827375337000033700000092001001207_22_04_PI2018-08-31 23:59:59.0<NA>부광식육점<NA>부산광역시 연제구 연산동 767-42번지48947부산광역시 연제구 고분로20번길 6 (연산동)2001112720050118<NA><NA><NA>0002폐업389869.54051300000189653.1554780000020050118170657식육판매업051-123-1234<NA><NA><NA><NA>000<NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>식육판매업축산물판매업<NA><NA><NA>2020-12-22 14:06:01
80258025338000033800000092009002607_22_04_PI2018-08-31 23:59:59.0<NA>영남해장국식육점<NA>부산광역시 수영구 남천동 280번지48947부산광역시 수영구 황령대로489번길 19 (남천동)2009072320110603<NA><NA><NA>0002폐업392128.24573200000184563.6951110000020110603181548식육판매업051-123-1234<NA><NA><NA><NA>000<NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>식육판매업축산물판매업<NA><NA><NA>2020-12-22 14:06:01
104331043533000003300000-121-2002-0000307_22_18_PI2018-08-31 23:59:59.0<NA>던킨도너츠명륜점607804부산광역시 동래구 명륜동 446-4번지47738부산광역시 동래구 명륜로 117 (명륜동)2002050720131120<NA><NA><NA>02폐업389656.43299700000191730.4272180000020030317000000제과점영업051-123-1234<NA><NA><NA><NA><NA>상수도전용0N<NA><NA><NA><NA>82.270유흥업소밀집지역<NA>제과점영업<NA><NA><NA><NA><NA><NA><NA>2020-12-22 14:06:02
8083325000032500000092017000607_22_04_PI2018-08-31 23:59:59.0<NA>금오생고기<NA><NA>48966부산광역시 중구 보수대로 94 (보수동3가, 보수종합시장)2016051320171207<NA><NA><NA>0002폐업384345.89484100000180527.1244140000020171207133708식육판매업051-123-1234<NA><NA><NA><NA>000<NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>식육판매업축산물판매업<NA><NA><NA>2020-12-22 14:06:00
95529558340000034000000092007001607_22_04_PI2018-08-31 23:59:59.0<NA>동양마트월내점<NA>부산광역시 기장군 장안읍 길천리 207-3번지48947부산광역시 기장군 장안읍 해맞이로 4242007122620120318<NA><NA><NA>0002폐업407499.62747600000206002.5065320000020120402133151식육판매업051-123-1234<NA><NA><NA><NA>000<NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>식육판매업축산물판매업<NA><NA><NA>2020-12-22 14:06:01
104601046133000003300000-121-1993-0000107_22_18_PI2018-08-31 23:59:59.0<NA>신라당 명과607804부산광역시 동래구 명륜동 553-12번지48947<NA>1993042220080416<NA><NA><NA>02폐업389473.737572191889.08387019990317000000제과점영업051-123-1234<NA><NA><NA><NA><NA><NA>0N기타<NA><NA><NA>190기타<NA>제과점영업<NA><NA><NA><NA><NA><NA><NA>2020-12-22 14:06:02
152601526130200003020000-107-2019-0012507_22_19_PI2019-10-06 00:22:43.0즉석판매제조가공업아띠몽140780서울특별시 용산구 한강로3가 40-999번지4377서울특별시 용산구 한강대로23길 55, 용산아이파크몰 6층 (한강로3가)20191004<NA><NA><NA><NA>영업/정상영업196762.077394917447480.03957735920191004172527즉석판매제조가공업051-123-1234<NA><NA><NA><NA><NA><NA><NA>N<NA><NA><NA><NA>0<NA><NA><NA>즉석판매제조가공업<NA><NA><NA><NA><NA><NA><NA>2020-12-22 14:06:03
skeyopnsfteamcodemgtnoopnsvcidupdategbnupdatedtopnsvcnmbplcnmsitepostnositewhladdrrdnpostnordnwhladdrapvpermymddcbymdclgstdtclgenddtropnymdtrdstatenmdtlstatenmxylastmodtsuptaenmsitetelbdngownsenmfctyowkepcntfctypdtjobepcntfctysiljobepcntrgtmbdsnowtrsplyfacilsenmmaneipcntmultusnupsoynlvsenmisreamhoffepcntequsizfaciltotscpwmeipcnttrdpjubnsenmmonamsntuptaenmjtupsomainedfjtupsoasgnnototepnumlindprcbgbnnmlindjobgbnnmlindseqnohomepagelast_load_dttm
17631759329000032900000092005001007_22_04_PI2018-08-31 23:59:59.0<NA>삼길유통<NA>부산광역시 부산진구 전포동 191-109번지 대동연립1동 지하1층48947부산광역시 부산진구 동성로96번길 59 (전포동,대동연립1동 지하1층)20050321<NA><NA><NA><NA>0000정상388603.68925700000186919.9069880000020050321092510식육판매업051-123-1234<NA><NA><NA><NA>000<NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>식육판매업축산물판매업<NA><NA><NA>2020-12-22 14:06:00
59973995332000033200000092001000707_22_04_PI2018-08-31 23:59:59.0<NA>매일식육점<NA>부산광역시 북구 만덕동 909-9번지48947부산광역시 북구 만덕3로48번가길 21 (만덕동)2001122020060323<NA><NA><NA>0002폐업384630.81187700000192444.8249160000020060323180041식육판매업051-123-1234<NA><NA><NA><NA>000<NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>식육판매업축산물판매업<NA><NA><NA>2020-12-22 14:06:01
189471894831800003180000-107-2020-0056907_22_19_PI2020-11-22 00:23:08.0즉석판매제조가공업주)위찬150796서울특별시 영등포구 여의도동 42 한양아파트07340서울특별시 영등포구 국제금융로 79 (여의도동, 한양아파트)20201120<NA><NA><NA><NA>영업/정상영업193963.563089797446584.55023756920201120141611즉석판매제조가공업<NA><NA><NA><NA><NA><NA><NA><NA>N<NA><NA><NA><NA>0<NA><NA><NA>즉석판매제조가공업<NA><NA><NA><NA><NA><NA><NA>2020-12-22 14:06:03
115481154733500003350000-121-2015-0001107_22_18_PI2018-08-31 23:59:59.0<NA>본슈아라크렘609839부산광역시 금정구 장전동 292-4번지46291부산광역시 금정구 장전로12번길 55, 1116호,1117호,1118호,1119호 (장전동, 라퓨타아일랜드)20151116<NA><NA><NA><NA>01영업390099.23809100000194622.2011310000020151116144111제과점영업051-123-1234<NA><NA><NA><NA><NA><NA><NA>N<NA><NA><NA><NA>45<NA><NA><NA>제과점영업<NA><NA><NA><NA><NA><NA><NA>2020-12-22 14:06:02
52905288334000033400000091997000807_22_04_PI2018-08-31 23:59:59.0<NA>영천식육점<NA>부산광역시 사하구 하단동 870-24번지48947부산광역시 사하구 낙동남로1353번길 24 (하단동)1997081820131222<NA><NA><NA>0002폐업378697.51830000000180795.1690380000020140414175422식육판매업051-123-1234<NA><NA><NA><NA>000<NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>식육판매업축산물판매업<NA><NA><NA>2020-12-22 14:06:01
84728475339000033900000092003001707_22_04_PI2018-08-31 23:59:59.0<NA>합천식육점<NA>부산광역시 사상구 괘법동 551-13번지48947부산광역시 사상구 광장로104번길 40 (괘법동)20030503<NA><NA><NA><NA>0004말소381148.98377400000186690.1353720000020140110101947식육판매업051-123-1234<NA><NA><NA><NA>000<NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>식육판매업축산물판매업<NA><NA><NA>2020-12-22 14:06:01
84448446339000033900000091994001207_22_04_PI2018-08-31 23:59:59.0<NA>항도유통<NA>부산광역시 사상구 엄궁동 645-3번지 상가 79호47032부산광역시 사상구 농산물시장로25번길 70 (엄궁동)19940520<NA><NA><NA><NA>0000정상379415.47295000000183348.0107030000020180716143427식육판매업051-123-1234<NA><NA><NA><NA>000<NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>식육판매업축산물판매업<NA><NA><NA>2020-12-22 14:06:01
163701637139700003970000-107-2020-0000707_22_19_PI2020-01-30 00:23:24.0즉석판매제조가공업농촌사랑 주식회사427800경기도 과천시 별양동 1-19번지 이마트 과천점13837경기도 과천시 별양상가3로 11, 이마트 과천점 지하1층 (별양동)20200128<NA><NA><NA><NA>영업/정상영업199209.021144538436019.96541891820200128173838즉석판매제조가공업051-123-1234<NA><NA><NA><NA><NA><NA><NA>N<NA><NA><NA><NA>0<NA><NA><NA>즉석판매제조가공업<NA><NA><NA><NA><NA><NA><NA>2020-12-22 14:06:03
106581066033100003310000-121-2003-0000207_22_18_PI2018-08-31 23:59:59.0<NA>옵스608832부산광역시 남구 용호동 176-30번지 엘지메트로시티 1005동 111호48516부산광역시 남구 분포로 113, 1005동 111호 (용호동, 엘지메트로시티)20031021<NA><NA><NA><NA>01영업392456.73180800000183776.5252640000020151229162418제과점영업051-123-1234<NA><NA><NA><NA><NA>상수도전용0N<NA><NA><NA><NA>00아파트지역<NA>제과점영업<NA><NA><NA><NA><NA><NA><NA>2020-12-22 14:06:02
94579462340000034000000092001001007_22_04_PI2018-08-31 23:59:59.0<NA>이진식육점<NA>부산광역시 기장군 기장읍 서부리 392-1번지48947부산광역시 기장군 기장읍 배산로8번길 482001112120060704<NA><NA><NA>0002폐업401100.42451000000197150.4600010000020060704144656식육판매업051-123-1234<NA><NA><NA><NA>000<NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>식육판매업축산물판매업<NA><NA><NA>2020-12-22 14:06:01