Overview

Dataset statistics

Number of variables49
Number of observations10000
Missing cells18739
Missing cells (%)3.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.9 MiB
Average record size in memory404.0 B

Variable types

Numeric4
Text10
Categorical32
DateTime2
Unsupported1

Alerts

opnsvcid is highly imbalanced (56.3%)Imbalance
updategbn is highly imbalanced (99.1%)Imbalance
opnsvcnm is highly imbalanced (67.2%)Imbalance
clgstdt is highly imbalanced (93.7%)Imbalance
clgenddt is highly imbalanced (93.1%)Imbalance
ropnymd is highly imbalanced (91.3%)Imbalance
bdngownsenm is highly imbalanced (60.0%)Imbalance
fctyowkepcnt is highly imbalanced (86.1%)Imbalance
fctypdtjobepcnt is highly imbalanced (88.8%)Imbalance
fctysiljobepcnt is highly imbalanced (83.7%)Imbalance
rgtmbdsno is highly imbalanced (54.2%)Imbalance
wtrsplyfacilsenm is highly imbalanced (68.6%)Imbalance
maneipcnt is highly imbalanced (79.5%)Imbalance
multusnupsoyn is highly imbalanced (97.0%)Imbalance
lvsenm is highly imbalanced (80.6%)Imbalance
isream is highly imbalanced (91.7%)Imbalance
hoffepcnt is highly imbalanced (84.0%)Imbalance
equsiz is highly imbalanced (76.3%)Imbalance
wmeipcnt is highly imbalanced (79.5%)Imbalance
trdpjubnsenm is highly imbalanced (78.5%)Imbalance
monam is highly imbalanced (90.5%)Imbalance
sntuptaenm is highly imbalanced (58.5%)Imbalance
jtupsomainedf is highly imbalanced (76.3%)Imbalance
jtupsoasgnno is highly imbalanced (76.3%)Imbalance
totepnum is highly imbalanced (53.9%)Imbalance
lindprcbgbnnm is highly imbalanced (62.1%)Imbalance
lindjobgbnnm is highly imbalanced (88.0%)Imbalance
lindseqno is highly imbalanced (76.3%)Imbalance
homepage is highly imbalanced (84.9%)Imbalance
sitepostno has 5172 (51.7%) missing valuesMissing
sitewhladdr has 229 (2.3%) missing valuesMissing
rdnwhladdr has 1111 (11.1%) missing valuesMissing
dcbymd has 6220 (62.2%) missing valuesMissing
x has 284 (2.8%) missing valuesMissing
y has 284 (2.8%) missing valuesMissing
sitetel has 380 (3.8%) missing valuesMissing
faciltotscp has 5056 (50.6%) missing valuesMissing
skey has unique valuesUnique
rdnpostno is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2024-04-16 08:58:27.326873
Analysis finished2024-04-16 08:58:29.915797
Duration2.59 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%
Mean9753.7978
Minimum5
Maximum19590
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-16T17:58:29.976688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile1019.9
Q14823
median9762.5
Q314633.75
95-th percentile18607.05
Maximum19590
Range19585
Interquartile range (IQR)9810.75

Descriptive statistics

Standard deviation5644.0923
Coefficient of variation (CV)0.57865587
Kurtosis-1.2050265
Mean9753.7978
Median Absolute Deviation (MAD)4910
Skewness0.016849086
Sum97537978
Variance31855778
MonotonicityNot monotonic
2024-04-16T17:58:30.096174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16278 1
 
< 0.1%
13962 1
 
< 0.1%
1580 1
 
< 0.1%
12261 1
 
< 0.1%
17809 1
 
< 0.1%
3245 1
 
< 0.1%
1786 1
 
< 0.1%
15937 1
 
< 0.1%
16608 1
 
< 0.1%
16197 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
9 1
< 0.1%
12 1
< 0.1%
15 1
< 0.1%
19 1
< 0.1%
20 1
< 0.1%
22 1
< 0.1%
23 1
< 0.1%
ValueCountFrequency (%)
19590 1
< 0.1%
19588 1
< 0.1%
19584 1
< 0.1%
19583 1
< 0.1%
19582 1
< 0.1%
19581 1
< 0.1%
19580 1
< 0.1%
19578 1
< 0.1%
19576 1
< 0.1%
19575 1
< 0.1%

opnsfteamcode
Real number (ℝ)

Distinct219
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3598656.8
Minimum3000000
Maximum6520000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-16T17:58:30.211565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3000000
5-th percentile3200000
Q13300000
median3340000
Q33510500
95-th percentile5330000
Maximum6520000
Range3520000
Interquartile range (IQR)210500

Descriptive statistics

Standard deviation623263.84
Coefficient of variation (CV)0.17319347
Kurtosis5.0434158
Mean3598656.8
Median Absolute Deviation (MAD)50000
Skewness2.3699564
Sum3.5986568 × 1010
Variance3.8845782 × 1011
MonotonicityNot monotonic
2024-04-16T17:58:30.500475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3330000 686
 
6.9%
3320000 635
 
6.3%
3290000 627
 
6.3%
3300000 591
 
5.9%
3390000 586
 
5.9%
3340000 574
 
5.7%
3350000 486
 
4.9%
3310000 428
 
4.3%
3370000 402
 
4.0%
3380000 362
 
3.6%
Other values (209) 4623
46.2%
ValueCountFrequency (%)
3000000 9
 
0.1%
3010000 32
0.3%
3020000 18
0.2%
3030000 16
0.2%
3040000 22
0.2%
3050000 24
0.2%
3060000 24
0.2%
3070000 26
0.3%
3080000 14
0.1%
3090000 10
 
0.1%
ValueCountFrequency (%)
6520000 14
 
0.1%
6510000 32
0.3%
6470000 1
 
< 0.1%
6450000 1
 
< 0.1%
6440000 1
 
< 0.1%
6410000 1
 
< 0.1%
6280000 2
 
< 0.1%
5710000 65
0.7%
5700000 6
 
0.1%
5690000 32
0.3%

mgtno
Text

Distinct9647
Distinct (%)96.5%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-04-16T17:58:30.680876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length18
Mean length19.9645
Min length18

Characters and Unicode

Total characters199645
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

Unique9326 ?
Unique (%)93.3%

Sample

1st row3230000-107-2020-00045
2nd row4000000-107-2019-00050
3rd row326000000920140004
4th row330000000920070011
5th row5690000-107-2020-00206
ValueCountFrequency (%)
5030000-107-2019-00028 3
 
< 0.1%
5060000-107-2019-00005 3
 
< 0.1%
4400000-134-2020-00007 3
 
< 0.1%
4030000-107-2018-00123 3
 
< 0.1%
5380000-107-2019-00446 3
 
< 0.1%
3150000-107-2019-00210 3
 
< 0.1%
3900000-107-2020-00148 3
 
< 0.1%
3560000-107-2019-00120 3
 
< 0.1%
5380000-107-2019-00080 3
 
< 0.1%
339000000920190028 3
 
< 0.1%
Other values (9637) 9970
99.7%
2024-04-16T17:58:30.969826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 94419
47.3%
1 18448
 
9.2%
3 18178
 
9.1%
2 17883
 
9.0%
- 14730
 
7.4%
9 13303
 
6.7%
7 5975
 
3.0%
4 5127
 
2.6%
5 4383
 
2.2%
8 3882
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 184915
92.6%
Dash Punctuation 14730
 
7.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 94419
51.1%
1 18448
 
10.0%
3 18178
 
9.8%
2 17883
 
9.7%
9 13303
 
7.2%
7 5975
 
3.2%
4 5127
 
2.8%
5 4383
 
2.4%
8 3882
 
2.1%
6 3317
 
1.8%
Dash Punctuation
ValueCountFrequency (%)
- 14730
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 199645
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 94419
47.3%
1 18448
 
9.2%
3 18178
 
9.1%
2 17883
 
9.0%
- 14730
 
7.4%
9 13303
 
6.7%
7 5975
 
3.0%
4 5127
 
2.6%
5 4383
 
2.2%
8 3882
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 199645
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 94419
47.3%
1 18448
 
9.2%
3 18178
 
9.1%
2 17883
 
9.0%
- 14730
 
7.4%
9 13303
 
6.7%
7 5975
 
3.0%
4 5127
 
2.6%
5 4383
 
2.2%
8 3882
 
1.9%

opnsvcid
Categorical

IMBALANCE 

Distinct19
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
07_22_04_P
4996 
07_22_19_P
2884 
07_22_18_P
1406 
07_22_03_P
 
370
07_22_01_P
 
112
Other values (14)
 
232

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
07_22_04_P 4996
50.0%
07_22_19_P 2884
28.8%
07_22_18_P 1406
 
14.1%
07_22_03_P 370
 
3.7%
07_22_01_P 112
 
1.1%
07_22_02_P 73
 
0.7%
07_22_25_P 50
 
0.5%
07_22_11_P 36
 
0.4%
07_22_24_P 26
 
0.3%
07_22_08_P 12
 
0.1%
Other values (9) 35
 
0.4%

Length

2024-04-16T17:58:31.111274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
07_22_04_p 4996
50.0%
07_22_19_p 2884
28.8%
07_22_18_p 1406
 
14.1%
07_22_03_p 370
 
3.7%
07_22_01_p 112
 
1.1%
07_22_02_p 73
 
0.7%
07_22_25_p 50
 
0.5%
07_22_11_p 36
 
0.4%
07_22_24_p 26
 
0.3%
07_22_08_p 12
 
0.1%
Other values (9) 35
 
0.4%

updategbn
Categorical

IMBALANCE 

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

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

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

Length

2024-04-16T17:58:31.214942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:58:31.289826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
i 9992
99.9%
u 8
 
0.1%
Distinct589
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2018-08-31 23:59:59
Maximum2021-01-31 00:23:03
2024-04-16T17:58:31.373527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T17:58:31.490699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

opnsvcnm
Categorical

IMBALANCE 

Distinct20
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
6343 
즉석판매제조가공업
2884 
건강기능식품일반판매업
 
370
집단급식소식품판매업
 
112
건강기능식품유통전문판매업
 
73
Other values (15)
 
218

Length

Max length13
Median length4
Mean length5.8776
Min length3

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 6343
63.4%
즉석판매제조가공업 2884
28.8%
건강기능식품일반판매업 370
 
3.7%
집단급식소식품판매업 112
 
1.1%
건강기능식품유통전문판매업 73
 
0.7%
축산판매업 57
 
0.6%
축산물운반업 50
 
0.5%
식품제조가공업 36
 
0.4%
축산물보관업 26
 
0.3%
식품소분업 12
 
0.1%
Other values (10) 37
 
0.4%

Length

2024-04-16T17:58:31.609331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 6343
63.4%
즉석판매제조가공업 2884
28.8%
건강기능식품일반판매업 370
 
3.7%
집단급식소식품판매업 112
 
1.1%
건강기능식품유통전문판매업 73
 
0.7%
축산판매업 57
 
0.6%
축산물운반업 50
 
0.5%
식품제조가공업 36
 
0.4%
축산물보관업 26
 
0.3%
식품소분업 12
 
0.1%
Other values (10) 37
 
0.4%

bplcnm
Text

Distinct7407
Distinct (%)74.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-04-16T17:58:31.794074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length30
Median length27
Mean length6.713
Min length1

Characters and Unicode

Total characters67130
Distinct characters903
Distinct categories12 ?
Distinct scripts4 ?
Distinct blocks6 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6363 ?
Unique (%)63.6%

Sample

1st row명류당티에프
2nd row(주)만구
3rd row비락우유 서구 대리점
4th row한국야쿠르트 온천점
5th row대산유통시스템
ValueCountFrequency (%)
주식회사 308
 
2.6%
베이커리 48
 
0.4%
파리바게뜨 46
 
0.4%
주)와이에이비커머스 43
 
0.4%
부산우유 42
 
0.4%
수라원 37
 
0.3%
현승유통 36
 
0.3%
더원씨푸드 35
 
0.3%
주)한울에프엔비 33
 
0.3%
뚜레쥬르 33
 
0.3%
Other values (7762) 11186
94.4%
2024-04-16T17:58:32.120399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2744
 
4.1%
2306
 
3.4%
1939
 
2.9%
1850
 
2.8%
) 1808
 
2.7%
( 1795
 
2.7%
1667
 
2.5%
1438
 
2.1%
1304
 
1.9%
1254
 
1.9%
Other values (893) 49025
73.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 59942
89.3%
Space Separator 1850
 
2.8%
Close Punctuation 1808
 
2.7%
Open Punctuation 1795
 
2.7%
Uppercase Letter 630
 
0.9%
Lowercase Letter 538
 
0.8%
Decimal Number 318
 
0.5%
Other Punctuation 139
 
0.2%
Dash Punctuation 101
 
0.2%
Math Symbol 6
 
< 0.1%
Other values (2) 3
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
2744
 
4.6%
2306
 
3.8%
1939
 
3.2%
1667
 
2.8%
1438
 
2.4%
1304
 
2.2%
1254
 
2.1%
1083
 
1.8%
1052
 
1.8%
1011
 
1.7%
Other values (812) 44144
73.6%
Uppercase Letter
ValueCountFrequency (%)
S 74
 
11.7%
D 44
 
7.0%
A 43
 
6.8%
N 42
 
6.7%
T 39
 
6.2%
M 31
 
4.9%
K 29
 
4.6%
I 28
 
4.4%
C 28
 
4.4%
G 28
 
4.4%
Other values (15) 244
38.7%
Lowercase Letter
ValueCountFrequency (%)
e 90
16.7%
o 53
 
9.9%
a 47
 
8.7%
s 40
 
7.4%
r 31
 
5.8%
l 29
 
5.4%
t 28
 
5.2%
i 27
 
5.0%
m 25
 
4.6%
n 21
 
3.9%
Other values (13) 147
27.3%
Other Punctuation
ValueCountFrequency (%)
& 47
33.8%
. 38
27.3%
, 21
15.1%
' 11
 
7.9%
? 4
 
2.9%
· 4
 
2.9%
! 3
 
2.2%
: 3
 
2.2%
/ 2
 
1.4%
2
 
1.4%
Other values (3) 4
 
2.9%
Decimal Number
ValueCountFrequency (%)
2 86
27.0%
1 75
23.6%
3 34
 
10.7%
5 25
 
7.9%
9 22
 
6.9%
6 18
 
5.7%
0 17
 
5.3%
8 17
 
5.3%
4 14
 
4.4%
7 10
 
3.1%
Math Symbol
ValueCountFrequency (%)
+ 4
66.7%
< 1
 
16.7%
> 1
 
16.7%
Other Symbol
ValueCountFrequency (%)
1
50.0%
1
50.0%
Space Separator
ValueCountFrequency (%)
1850
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1808
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1795
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 101
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 59931
89.3%
Common 6019
 
9.0%
Latin 1168
 
1.7%
Han 12
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
2744
 
4.6%
2306
 
3.8%
1939
 
3.2%
1667
 
2.8%
1438
 
2.4%
1304
 
2.2%
1254
 
2.1%
1083
 
1.8%
1052
 
1.8%
1011
 
1.7%
Other values (802) 44133
73.6%
Latin
ValueCountFrequency (%)
e 90
 
7.7%
S 74
 
6.3%
o 53
 
4.5%
a 47
 
4.0%
D 44
 
3.8%
A 43
 
3.7%
N 42
 
3.6%
s 40
 
3.4%
T 39
 
3.3%
r 31
 
2.7%
Other values (38) 665
56.9%
Common
ValueCountFrequency (%)
1850
30.7%
) 1808
30.0%
( 1795
29.8%
- 101
 
1.7%
2 86
 
1.4%
1 75
 
1.2%
& 47
 
0.8%
. 38
 
0.6%
3 34
 
0.6%
5 25
 
0.4%
Other values (22) 160
 
2.7%
Han
ValueCountFrequency (%)
2
16.7%
1
8.3%
1
8.3%
1
8.3%
1
8.3%
1
8.3%
1
8.3%
1
8.3%
1
8.3%
1
8.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 59930
89.3%
ASCII 7180
 
10.7%
CJK 11
 
< 0.1%
None 7
 
< 0.1%
Specials 1
 
< 0.1%
CJK Compat Ideographs 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
2744
 
4.6%
2306
 
3.8%
1939
 
3.2%
1667
 
2.8%
1438
 
2.4%
1304
 
2.2%
1254
 
2.1%
1083
 
1.8%
1052
 
1.8%
1011
 
1.7%
Other values (801) 44132
73.6%
ASCII
ValueCountFrequency (%)
1850
25.8%
) 1808
25.2%
( 1795
25.0%
- 101
 
1.4%
e 90
 
1.3%
2 86
 
1.2%
1 75
 
1.0%
S 74
 
1.0%
o 53
 
0.7%
a 47
 
0.7%
Other values (67) 1201
16.7%
None
ValueCountFrequency (%)
· 4
57.1%
2
28.6%
1
 
14.3%
CJK
ValueCountFrequency (%)
2
18.2%
1
9.1%
1
9.1%
1
9.1%
1
9.1%
1
9.1%
1
9.1%
1
9.1%
1
9.1%
1
9.1%
Specials
ValueCountFrequency (%)
1
100.0%
CJK Compat Ideographs
ValueCountFrequency (%)
1
100.0%

sitepostno
Text

MISSING 

Distinct2140
Distinct (%)44.3%
Missing5172
Missing (%)51.7%
Memory size156.2 KiB
2024-04-16T17:58:32.413300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters28968
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

Unique1184 ?
Unique (%)24.5%

Sample

1st row138934
2nd row447805
3rd row339854
4th row137893
5th row619901
ValueCountFrequency (%)
612020 52
 
1.1%
지번우편번호 46
 
1.0%
463420 25
 
0.5%
411410 22
 
0.5%
406081 20
 
0.4%
135724 17
 
0.4%
608832 16
 
0.3%
617808 16
 
0.3%
152706 16
 
0.3%
600017 15
 
0.3%
Other values (2130) 4583
94.9%
2024-04-16T17:58:32.833536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 4602
15.9%
1 4117
14.2%
8 3909
13.5%
6 3414
11.8%
4 2803
9.7%
2 2519
8.7%
3 2431
8.4%
5 1834
 
6.3%
7 1762
 
6.1%
9 1301
 
4.5%
Other values (5) 276
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 28692
99.0%
Other Letter 276
 
1.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4602
16.0%
1 4117
14.3%
8 3909
13.6%
6 3414
11.9%
4 2803
9.8%
2 2519
8.8%
3 2431
8.5%
5 1834
 
6.4%
7 1762
 
6.1%
9 1301
 
4.5%
Other Letter
ValueCountFrequency (%)
92
33.3%
46
16.7%
46
16.7%
46
16.7%
46
16.7%

Most occurring scripts

ValueCountFrequency (%)
Common 28692
99.0%
Hangul 276
 
1.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4602
16.0%
1 4117
14.3%
8 3909
13.6%
6 3414
11.9%
4 2803
9.8%
2 2519
8.8%
3 2431
8.5%
5 1834
 
6.4%
7 1762
 
6.1%
9 1301
 
4.5%
Hangul
ValueCountFrequency (%)
92
33.3%
46
16.7%
46
16.7%
46
16.7%
46
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28692
99.0%
Hangul 276
 
1.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4602
16.0%
1 4117
14.3%
8 3909
13.6%
6 3414
11.9%
4 2803
9.8%
2 2519
8.8%
3 2431
8.5%
5 1834
 
6.4%
7 1762
 
6.1%
9 1301
 
4.5%
Hangul
ValueCountFrequency (%)
92
33.3%
46
16.7%
46
16.7%
46
16.7%
46
16.7%

sitewhladdr
Text

MISSING 

Distinct7992
Distinct (%)81.8%
Missing229
Missing (%)2.3%
Memory size156.2 KiB
2024-04-16T17:58:33.162357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length72
Median length52
Mean length25.797667
Min length13

Characters and Unicode

Total characters252069
Distinct characters614
Distinct categories12 ?
Distinct scripts4 ?
Distinct blocks6 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6944 ?
Unique (%)71.1%

Sample

1st row서울특별시 송파구 신천동 29번지 롯데월드타워앤드롯데월드몰
2nd row경기도 오산시 원동 360-6번지 오산이마트 1층
3rd row부산광역시 서구 부용동2가 30-8번지
4th row부산광역시 동래구 온천동 307-9번지
5th row세종특별자치시 전의면 읍내리 214-8
ValueCountFrequency (%)
부산광역시 6351
 
13.5%
경기도 1031
 
2.2%
서울특별시 766
 
1.6%
북구 664
 
1.4%
해운대구 635
 
1.4%
동래구 590
 
1.3%
부산진구 582
 
1.2%
사상구 580
 
1.2%
사하구 560
 
1.2%
남구 484
 
1.0%
Other values (10979) 34783
74.0%
2024-04-16T17:58:33.588767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
46582
 
18.5%
10875
 
4.3%
1 10649
 
4.2%
9831
 
3.9%
9397
 
3.7%
8865
 
3.5%
8691
 
3.4%
8413
 
3.3%
7768
 
3.1%
- 7731
 
3.1%
Other values (604) 123267
48.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 150640
59.8%
Space Separator 46582
 
18.5%
Decimal Number 45772
 
18.2%
Dash Punctuation 7731
 
3.1%
Uppercase Letter 546
 
0.2%
Other Punctuation 270
 
0.1%
Open Punctuation 196
 
0.1%
Close Punctuation 194
 
0.1%
Lowercase Letter 128
 
0.1%
Math Symbol 6
 
< 0.1%
Other values (2) 4
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
10875
 
7.2%
9831
 
6.5%
9397
 
6.2%
8865
 
5.9%
8691
 
5.8%
8413
 
5.6%
7768
 
5.2%
7324
 
4.9%
6957
 
4.6%
2459
 
1.6%
Other values (534) 70060
46.5%
Uppercase Letter
ValueCountFrequency (%)
B 78
14.3%
A 77
14.1%
S 70
12.8%
G 53
9.7%
E 43
7.9%
K 32
 
5.9%
C 27
 
4.9%
T 23
 
4.2%
M 21
 
3.8%
R 18
 
3.3%
Other values (14) 104
19.0%
Lowercase Letter
ValueCountFrequency (%)
e 21
16.4%
s 19
14.8%
l 11
 
8.6%
o 9
 
7.0%
m 8
 
6.2%
g 7
 
5.5%
t 7
 
5.5%
a 6
 
4.7%
i 6
 
4.7%
u 6
 
4.7%
Other values (9) 28
21.9%
Decimal Number
ValueCountFrequency (%)
1 10649
23.3%
2 5739
12.5%
3 4669
10.2%
4 4271
9.3%
5 4104
 
9.0%
0 3713
 
8.1%
6 3442
 
7.5%
7 3211
 
7.0%
8 3066
 
6.7%
9 2908
 
6.4%
Other Punctuation
ValueCountFrequency (%)
, 216
80.0%
@ 21
 
7.8%
. 14
 
5.2%
? 14
 
5.2%
/ 3
 
1.1%
· 1
 
0.4%
' 1
 
0.4%
Open Punctuation
ValueCountFrequency (%)
( 195
99.5%
[ 1
 
0.5%
Close Punctuation
ValueCountFrequency (%)
) 193
99.5%
] 1
 
0.5%
Letter Number
ValueCountFrequency (%)
1
50.0%
1
50.0%
Space Separator
ValueCountFrequency (%)
46582
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 7731
100.0%
Math Symbol
ValueCountFrequency (%)
~ 6
100.0%
Other Symbol
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 150641
59.8%
Common 100751
40.0%
Latin 676
 
0.3%
Han 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
10875
 
7.2%
9831
 
6.5%
9397
 
6.2%
8865
 
5.9%
8691
 
5.8%
8413
 
5.6%
7768
 
5.2%
7324
 
4.9%
6957
 
4.6%
2459
 
1.6%
Other values (534) 70061
46.5%
Latin
ValueCountFrequency (%)
B 78
 
11.5%
A 77
 
11.4%
S 70
 
10.4%
G 53
 
7.8%
E 43
 
6.4%
K 32
 
4.7%
C 27
 
4.0%
T 23
 
3.4%
e 21
 
3.1%
M 21
 
3.1%
Other values (35) 231
34.2%
Common
ValueCountFrequency (%)
46582
46.2%
1 10649
 
10.6%
- 7731
 
7.7%
2 5739
 
5.7%
3 4669
 
4.6%
4 4271
 
4.2%
5 4104
 
4.1%
0 3713
 
3.7%
6 3442
 
3.4%
7 3211
 
3.2%
Other values (14) 6640
 
6.6%
Han
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 150638
59.8%
ASCII 101424
40.2%
None 3
 
< 0.1%
Number Forms 2
 
< 0.1%
CJK 1
 
< 0.1%
Compat Jamo 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
46582
45.9%
1 10649
 
10.5%
- 7731
 
7.6%
2 5739
 
5.7%
3 4669
 
4.6%
4 4271
 
4.2%
5 4104
 
4.0%
0 3713
 
3.7%
6 3442
 
3.4%
7 3211
 
3.2%
Other values (56) 7313
 
7.2%
Hangul
ValueCountFrequency (%)
10875
 
7.2%
9831
 
6.5%
9397
 
6.2%
8865
 
5.9%
8691
 
5.8%
8413
 
5.6%
7768
 
5.2%
7324
 
4.9%
6957
 
4.6%
2459
 
1.6%
Other values (532) 70058
46.5%
None
ValueCountFrequency (%)
2
66.7%
· 1
33.3%
Number Forms
ValueCountFrequency (%)
1
50.0%
1
50.0%
CJK
ValueCountFrequency (%)
1
100.0%
Compat Jamo
ValueCountFrequency (%)
1
100.0%

rdnpostno
Unsupported

REJECTED  UNSUPPORTED 

Missing3
Missing (%)< 0.1%
Memory size156.2 KiB

rdnwhladdr
Text

MISSING 

Distinct7423
Distinct (%)83.5%
Missing1111
Missing (%)11.1%
Memory size156.2 KiB
2024-04-16T17:58:33.903836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length65
Median length56
Mean length30.900776
Min length5

Characters and Unicode

Total characters274677
Distinct characters671
Distinct categories12 ?
Distinct scripts4 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6531 ?
Unique (%)73.5%

Sample

1st row서울특별시 송파구 올림픽로 300, 롯데마트 월드타워점 (신천동)
2nd row경기도 오산시 경기대로 181, 오산이마트 1층 (원동)
3rd row부산광역시 동래구 차밭골로 43-1 (온천동)
4th row세종특별자치시 전의면 읍내길 10, 전의농협 하나로마트
5th row서울특별시 서초구 매헌로16길 6, 푸른솔빌딩 지하1층 (양재동)
ValueCountFrequency (%)
부산광역시 5474
 
10.1%
1층 1330
 
2.5%
경기도 1030
 
1.9%
서울특별시 766
 
1.4%
지하1층 579
 
1.1%
북구 574
 
1.1%
부산진구 561
 
1.0%
동래구 532
 
1.0%
사상구 477
 
0.9%
사하구 473
 
0.9%
Other values (9549) 42472
78.3%
2024-04-16T17:58:34.338223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
45411
 
16.5%
11036
 
4.0%
1 10374
 
3.8%
9264
 
3.4%
8690
 
3.2%
( 8287
 
3.0%
) 8286
 
3.0%
8198
 
3.0%
7766
 
2.8%
7273
 
2.6%
Other values (661) 150092
54.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 166293
60.5%
Space Separator 45411
 
16.5%
Decimal Number 39049
 
14.2%
Open Punctuation 8290
 
3.0%
Close Punctuation 8289
 
3.0%
Other Punctuation 5357
 
2.0%
Dash Punctuation 1221
 
0.4%
Uppercase Letter 638
 
0.2%
Lowercase Letter 115
 
< 0.1%
Math Symbol 10
 
< 0.1%
Other values (2) 4
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
11036
 
6.6%
9264
 
5.6%
8690
 
5.2%
8198
 
4.9%
7766
 
4.7%
7273
 
4.4%
6634
 
4.0%
6134
 
3.7%
4089
 
2.5%
3554
 
2.1%
Other values (590) 93655
56.3%
Uppercase Letter
ValueCountFrequency (%)
B 115
18.0%
S 93
14.6%
A 79
12.4%
G 77
12.1%
E 41
 
6.4%
K 38
 
6.0%
C 35
 
5.5%
M 19
 
3.0%
N 18
 
2.8%
P 16
 
2.5%
Other values (15) 107
16.8%
Lowercase Letter
ValueCountFrequency (%)
s 20
17.4%
e 18
15.7%
m 10
8.7%
g 8
 
7.0%
o 8
 
7.0%
l 8
 
7.0%
a 7
 
6.1%
u 7
 
6.1%
p 6
 
5.2%
t 5
 
4.3%
Other values (8) 18
15.7%
Decimal Number
ValueCountFrequency (%)
1 10374
26.6%
2 5412
13.9%
3 4163
10.7%
0 3328
 
8.5%
5 3163
 
8.1%
4 3146
 
8.1%
6 2720
 
7.0%
7 2577
 
6.6%
8 2141
 
5.5%
9 2025
 
5.2%
Other Punctuation
ValueCountFrequency (%)
, 5311
99.1%
? 16
 
0.3%
. 15
 
0.3%
@ 8
 
0.1%
* 3
 
0.1%
/ 2
 
< 0.1%
' 1
 
< 0.1%
# 1
 
< 0.1%
Open Punctuation
ValueCountFrequency (%)
( 8287
> 99.9%
[ 3
 
< 0.1%
Close Punctuation
ValueCountFrequency (%)
) 8286
> 99.9%
] 3
 
< 0.1%
Letter Number
ValueCountFrequency (%)
1
50.0%
1
50.0%
Space Separator
ValueCountFrequency (%)
45411
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1221
100.0%
Math Symbol
ValueCountFrequency (%)
~ 10
100.0%
Other Symbol
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 166294
60.5%
Common 107627
39.2%
Latin 755
 
0.3%
Han 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
11036
 
6.6%
9264
 
5.6%
8690
 
5.2%
8198
 
4.9%
7766
 
4.7%
7273
 
4.4%
6634
 
4.0%
6134
 
3.7%
4089
 
2.5%
3554
 
2.1%
Other values (590) 93656
56.3%
Latin
ValueCountFrequency (%)
B 115
15.2%
S 93
12.3%
A 79
 
10.5%
G 77
 
10.2%
E 41
 
5.4%
K 38
 
5.0%
C 35
 
4.6%
s 20
 
2.6%
M 19
 
2.5%
e 18
 
2.4%
Other values (35) 220
29.1%
Common
ValueCountFrequency (%)
45411
42.2%
1 10374
 
9.6%
( 8287
 
7.7%
) 8286
 
7.7%
2 5412
 
5.0%
, 5311
 
4.9%
3 4163
 
3.9%
0 3328
 
3.1%
5 3163
 
2.9%
4 3146
 
2.9%
Other values (15) 10746
 
10.0%
Han
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 166292
60.5%
ASCII 108380
39.5%
None 2
 
< 0.1%
Number Forms 2
 
< 0.1%
CJK 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
45411
41.9%
1 10374
 
9.6%
( 8287
 
7.6%
) 8286
 
7.6%
2 5412
 
5.0%
, 5311
 
4.9%
3 4163
 
3.8%
0 3328
 
3.1%
5 3163
 
2.9%
4 3146
 
2.9%
Other values (58) 11499
 
10.6%
Hangul
ValueCountFrequency (%)
11036
 
6.6%
9264
 
5.6%
8690
 
5.2%
8198
 
4.9%
7766
 
4.7%
7273
 
4.4%
6634
 
4.0%
6134
 
3.7%
4089
 
2.5%
3554
 
2.1%
Other values (589) 93654
56.3%
None
ValueCountFrequency (%)
2
100.0%
CJK
ValueCountFrequency (%)
1
100.0%
Number Forms
ValueCountFrequency (%)
1
50.0%
1
50.0%

apvpermymd
Real number (ℝ)

Distinct4845
Distinct (%)48.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20094921
Minimum19631010
Maximum20210129
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-16T17:58:34.450424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19631010
5-th percentile19871218
Q120021023
median20121212
Q320190621
95-th percentile20201016
Maximum20210129
Range579119
Interquartile range (IQR)169598

Descriptive statistics

Standard deviation109338.15
Coefficient of variation (CV)0.0054410839
Kurtosis0.35010108
Mean20094921
Median Absolute Deviation (MAD)70015
Skewness-0.99751575
Sum2.0094921 × 1011
Variance1.1954832 × 1010
MonotonicityNot monotonic
2024-04-16T17:58:34.562973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19980713 57
 
0.6%
20190927 23
 
0.2%
20200110 21
 
0.2%
20191206 20
 
0.2%
20190118 20
 
0.2%
20190531 20
 
0.2%
20181207 19
 
0.2%
20181123 19
 
0.2%
20190201 19
 
0.2%
20190628 19
 
0.2%
Other values (4835) 9763
97.6%
ValueCountFrequency (%)
19631010 2
< 0.1%
19651010 2
< 0.1%
19651011 1
< 0.1%
19651024 1
< 0.1%
19651116 1
< 0.1%
19660415 1
< 0.1%
19661001 1
< 0.1%
19661125 1
< 0.1%
19670814 1
< 0.1%
19680422 2
< 0.1%
ValueCountFrequency (%)
20210129 10
0.1%
20210128 6
0.1%
20210127 7
0.1%
20210126 4
 
< 0.1%
20210125 5
0.1%
20210124 1
 
< 0.1%
20210122 9
0.1%
20210121 7
0.1%
20210120 2
 
< 0.1%
20210119 7
0.1%

dcbymd
Text

MISSING 

Distinct2173
Distinct (%)57.5%
Missing6220
Missing (%)62.2%
Memory size156.2 KiB
2024-04-16T17:58:34.771573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length7.5883598
Min length4

Characters and Unicode

Total characters28684
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

Unique1395 ?
Unique (%)36.9%

Sample

1st row20050920
2nd row20000720
3rd row20071231
4th row20171228
5th row20150508
ValueCountFrequency (%)
폐업일자 389
 
10.3%
20131222 20
 
0.5%
20060216 19
 
0.5%
20121213 18
 
0.5%
20140820 15
 
0.4%
20170131 14
 
0.4%
20130607 10
 
0.3%
20060412 9
 
0.2%
20130409 9
 
0.2%
20050526 8
 
0.2%
Other values (2163) 3269
86.5%
2024-04-16T17:58:35.129348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 9044
31.5%
2 5849
20.4%
1 5260
18.3%
3 1325
 
4.6%
4 1071
 
3.7%
6 1052
 
3.7%
7 994
 
3.5%
5 909
 
3.2%
8 863
 
3.0%
9 761
 
2.7%
Other values (4) 1556
 
5.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 27128
94.6%
Other Letter 1556
 
5.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 9044
33.3%
2 5849
21.6%
1 5260
19.4%
3 1325
 
4.9%
4 1071
 
3.9%
6 1052
 
3.9%
7 994
 
3.7%
5 909
 
3.4%
8 863
 
3.2%
9 761
 
2.8%
Other Letter
ValueCountFrequency (%)
389
25.0%
389
25.0%
389
25.0%
389
25.0%

Most occurring scripts

ValueCountFrequency (%)
Common 27128
94.6%
Hangul 1556
 
5.4%

Most frequent character per script

Common
ValueCountFrequency (%)
0 9044
33.3%
2 5849
21.6%
1 5260
19.4%
3 1325
 
4.9%
4 1071
 
3.9%
6 1052
 
3.9%
7 994
 
3.7%
5 909
 
3.4%
8 863
 
3.2%
9 761
 
2.8%
Hangul
ValueCountFrequency (%)
389
25.0%
389
25.0%
389
25.0%
389
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 27128
94.6%
Hangul 1556
 
5.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 9044
33.3%
2 5849
21.6%
1 5260
19.4%
3 1325
 
4.9%
4 1071
 
3.9%
6 1052
 
3.9%
7 994
 
3.7%
5 909
 
3.4%
8 863
 
3.2%
9 761
 
2.8%
Hangul
ValueCountFrequency (%)
389
25.0%
389
25.0%
389
25.0%
389
25.0%

clgstdt
Categorical

IMBALANCE 

Distinct17
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9596 
휴업시작일자
 
389
20110325
 
1
20110225
 
1
20040325
 
1
Other values (12)
 
12

Length

Max length8
Median length4
Mean length4.0838
Min length4

Unique

Unique15 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 9596
96.0%
휴업시작일자 389
 
3.9%
20110325 1
 
< 0.1%
20110225 1
 
< 0.1%
20040325 1
 
< 0.1%
20160331 1
 
< 0.1%
20060420 1
 
< 0.1%
20130314 1
 
< 0.1%
20050428 1
 
< 0.1%
20170313 1
 
< 0.1%
Other values (7) 7
 
0.1%

Length

2024-04-16T17:58:35.255356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 9596
96.0%
휴업시작일자 389
 
3.9%
20170313 1
 
< 0.1%
20180817 1
 
< 0.1%
20070621 1
 
< 0.1%
20160406 1
 
< 0.1%
20050331 1
 
< 0.1%
20060707 1
 
< 0.1%
20120215 1
 
< 0.1%
20050428 1
 
< 0.1%
Other values (7) 7
 
0.1%

clgenddt
Categorical

IMBALANCE 

Distinct13
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9599 
휴업종료일자
 
389
20171231
 
2
20060831
 
1
20140313
 
1
Other values (8)
 
8

Length

Max length8
Median length4
Mean length4.0826
Min length4

Unique

Unique10 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 9599
96.0%
휴업종료일자 389
 
3.9%
20171231 2
 
< 0.1%
20060831 1
 
< 0.1%
20140313 1
 
< 0.1%
20060429 1
 
< 0.1%
20120324 1
 
< 0.1%
20210816 1
 
< 0.1%
20061231 1
 
< 0.1%
20070331 1
 
< 0.1%
Other values (3) 3
 
< 0.1%

Length

2024-04-16T17:58:35.357853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 9599
96.0%
휴업종료일자 389
 
3.9%
20171231 2
 
< 0.1%
20060831 1
 
< 0.1%
20140313 1
 
< 0.1%
20060429 1
 
< 0.1%
20120324 1
 
< 0.1%
20210816 1
 
< 0.1%
20061231 1
 
< 0.1%
20070331 1
 
< 0.1%
Other values (3) 3
 
< 0.1%

ropnymd
Categorical

IMBALANCE 

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9606 
재개업일자
 
389
20061030
 
1
20081007
 
1
20030327
 
1
Other values (2)
 
2

Length

Max length8
Median length4
Mean length4.0409
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> 9606
96.1%
재개업일자 389
 
3.9%
20061030 1
 
< 0.1%
20081007 1
 
< 0.1%
20030327 1
 
< 0.1%
20130806 1
 
< 0.1%
20070607 1
 
< 0.1%

Length

2024-04-16T17:58:35.458906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:58:35.548258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9606
96.1%
재개업일자 389
 
3.9%
20061030 1
 
< 0.1%
20081007 1
 
< 0.1%
20030327 1
 
< 0.1%
20130806 1
 
< 0.1%
20070607 1
 
< 0.1%

trdstatenm
Categorical

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
영업/정상
3602 
0002
2602 
0000
2070 
02
795 
01
609 
Other values (6)
 
322

Length

Max length5
Median length4
Mean length4.0774
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
영업/정상 3602
36.0%
0002 2602
26.0%
0000 2070
20.7%
02 795
 
8.0%
01 609
 
6.1%
0004 251
 
2.5%
<NA> 42
 
0.4%
0001 14
 
0.1%
폐업 10
 
0.1%
영업상태 3
 
< 0.1%

Length

2024-04-16T17:58:35.641799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
영업/정상 3602
36.0%
0002 2602
26.0%
0000 2070
20.7%
02 795
 
8.0%
01 609
 
6.1%
0004 251
 
2.5%
na 42
 
0.4%
0001 14
 
0.1%
폐업 10
 
0.1%
영업상태 3
 
< 0.1%

dtlstatenm
Categorical

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
영업
4104 
폐업
3407 
정상
2216 
말소
 
251
휴업
 
14
Other values (3)
 
8

Length

Max length4
Median length2
Mean length2.0009
Min length2

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
영업 4104
41.0%
폐업 3407
34.1%
정상 2216
22.2%
말소 251
 
2.5%
휴업 14
 
0.1%
영업중 5
 
0.1%
행정처분 2
 
< 0.1%
?? 1
 
< 0.1%

Length

2024-04-16T17:58:35.739944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:58:35.832602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
영업 4104
41.0%
폐업 3407
34.1%
정상 2216
22.2%
말소 251
 
2.5%
휴업 14
 
0.1%
영업중 5
 
< 0.1%
행정처분 2
 
< 0.1%
1
 
< 0.1%

x
Text

MISSING 

Distinct7231
Distinct (%)74.4%
Missing284
Missing (%)2.8%
Memory size156.2 KiB
2024-04-16T17:58:36.020002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length20
Mean length19.995986
Min length7

Characters and Unicode

Total characters194281
Distinct characters20
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

Unique5982 ?
Unique (%)61.6%

Sample

1st row209074.900840074
2nd row206383.775609107
3rd row384086.906530
4th row389119.89976100000
5th row218237.744534084
ValueCountFrequency (%)
381223.93770800000 25
 
0.3%
209850.446960528 24
 
0.2%
394015.45385100000 23
 
0.2%
178006.080301401 19
 
0.2%
202358.505687227 17
 
0.2%
190107.045415333 16
 
0.2%
381150.24062500000 16
 
0.2%
188884.075622342 14
 
0.1%
381172.69587400000 14
 
0.1%
381201.909278 14
 
0.1%
Other values (7221) 9534
98.1%
2024-04-16T17:58:36.349166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 37334
19.2%
34943
18.0%
3 17448
9.0%
8 14487
 
7.5%
9 13196
 
6.8%
1 12323
 
6.3%
2 12269
 
6.3%
7 11124
 
5.7%
4 10570
 
5.4%
5 10543
 
5.4%
Other values (10) 20044
10.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 149683
77.0%
Space Separator 34943
 
18.0%
Other Punctuation 9633
 
5.0%
Other Letter 12
 
< 0.1%
Open Punctuation 3
 
< 0.1%
Uppercase Letter 3
 
< 0.1%
Close Punctuation 3
 
< 0.1%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 37334
24.9%
3 17448
11.7%
8 14487
 
9.7%
9 13196
 
8.8%
1 12323
 
8.2%
2 12269
 
8.2%
7 11124
 
7.4%
4 10570
 
7.1%
5 10543
 
7.0%
6 10389
 
6.9%
Other Letter
ValueCountFrequency (%)
3
25.0%
3
25.0%
3
25.0%
3
25.0%
Space Separator
ValueCountFrequency (%)
34943
100.0%
Other Punctuation
ValueCountFrequency (%)
. 9633
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Uppercase Letter
ValueCountFrequency (%)
X 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 194266
> 99.9%
Hangul 12
 
< 0.1%
Latin 3
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 37334
19.2%
34943
18.0%
3 17448
9.0%
8 14487
 
7.5%
9 13196
 
6.8%
1 12323
 
6.3%
2 12269
 
6.3%
7 11124
 
5.7%
4 10570
 
5.4%
5 10543
 
5.4%
Other values (5) 20029
10.3%
Hangul
ValueCountFrequency (%)
3
25.0%
3
25.0%
3
25.0%
3
25.0%
Latin
ValueCountFrequency (%)
X 3
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 194269
> 99.9%
Hangul 12
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 37334
19.2%
34943
18.0%
3 17448
9.0%
8 14487
 
7.5%
9 13196
 
6.8%
1 12323
 
6.3%
2 12269
 
6.3%
7 11124
 
5.7%
4 10570
 
5.4%
5 10543
 
5.4%
Other values (6) 20032
10.3%
Hangul
ValueCountFrequency (%)
3
25.0%
3
25.0%
3
25.0%
3
25.0%

y
Text

MISSING 

Distinct7231
Distinct (%)74.4%
Missing284
Missing (%)2.8%
Memory size156.2 KiB
2024-04-16T17:58:36.539621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length20
Mean length19.995986
Min length7

Characters and Unicode

Total characters194281
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

Unique5982 ?
Unique (%)61.6%

Sample

1st row445657.80932984
2nd row404319.688193021
3rd row180888.571802
4th row193179.78102800000
5th row353235.355878044
ValueCountFrequency (%)
184549.28339000000 25
 
0.3%
432304.149379043 24
 
0.2%
187900.93961700000 23
 
0.2%
462865.812502378 19
 
0.2%
447232.955697694 17
 
0.2%
445157.626366229 16
 
0.2%
190717.70395600000 16
 
0.2%
447186.888604306 14
 
0.1%
190737.64327700000 14
 
0.1%
184537.273724 14
 
0.1%
Other values (7221) 9534
98.1%
2024-04-16T17:58:36.824978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 36304
18.7%
34915
18.0%
1 17479
9.0%
8 13865
 
7.1%
4 13216
 
6.8%
9 13036
 
6.7%
7 11539
 
5.9%
2 11326
 
5.8%
3 11083
 
5.7%
6 10967
 
5.6%
Other values (11) 20551
10.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 149678
77.0%
Space Separator 34915
 
18.0%
Other Punctuation 9633
 
5.0%
Dash Punctuation 30
 
< 0.1%
Other Letter 12
 
< 0.1%
Close Punctuation 7
 
< 0.1%
Open Punctuation 3
 
< 0.1%
Uppercase Letter 3
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 36304
24.3%
1 17479
11.7%
8 13865
 
9.3%
4 13216
 
8.8%
9 13036
 
8.7%
7 11539
 
7.7%
2 11326
 
7.6%
3 11083
 
7.4%
6 10967
 
7.3%
5 10863
 
7.3%
Other Letter
ValueCountFrequency (%)
3
25.0%
3
25.0%
3
25.0%
3
25.0%
Close Punctuation
ValueCountFrequency (%)
] 4
57.1%
) 3
42.9%
Space Separator
ValueCountFrequency (%)
34915
100.0%
Other Punctuation
ValueCountFrequency (%)
. 9633
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 30
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Uppercase Letter
ValueCountFrequency (%)
Y 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 194266
> 99.9%
Hangul 12
 
< 0.1%
Latin 3
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 36304
18.7%
34915
18.0%
1 17479
9.0%
8 13865
 
7.1%
4 13216
 
6.8%
9 13036
 
6.7%
7 11539
 
5.9%
2 11326
 
5.8%
3 11083
 
5.7%
6 10967
 
5.6%
Other values (6) 20536
10.6%
Hangul
ValueCountFrequency (%)
3
25.0%
3
25.0%
3
25.0%
3
25.0%
Latin
ValueCountFrequency (%)
Y 3
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 194269
> 99.9%
Hangul 12
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 36304
18.7%
34915
18.0%
1 17479
9.0%
8 13865
 
7.1%
4 13216
 
6.8%
9 13036
 
6.7%
7 11539
 
5.9%
2 11326
 
5.8%
3 11083
 
5.7%
6 10967
 
5.6%
Other values (7) 20539
10.6%
Hangul
ValueCountFrequency (%)
3
25.0%
3
25.0%
3
25.0%
3
25.0%

lastmodts
Real number (ℝ)

Distinct9525
Distinct (%)95.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0146091 × 1013
Minimum1.9990315 × 1013
Maximum2.0210129 × 1013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-16T17:58:36.943464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.9990315 × 1013
5-th percentile2.0040322 × 1013
Q12.0111114 × 1013
median2.0161122 × 1013
Q32.0190621 × 1013
95-th percentile2.0201016 × 1013
Maximum2.0210129 × 1013
Range2.1981418 × 1011
Interquartile range (IQR)7.950701 × 1010

Descriptive statistics

Standard deviation5.3376943 × 1010
Coefficient of variation (CV)0.0026494938
Kurtosis-0.42246649
Mean2.0146091 × 1013
Median Absolute Deviation (MAD)3.0600059 × 1010
Skewness-0.84359965
Sum2.0146091 × 1017
Variance2.849098 × 1021
MonotonicityNot monotonic
2024-04-16T17:58:37.306242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20061226000000 15
 
0.1%
20040823000000 10
 
0.1%
20050614000000 10
 
0.1%
19990319000000 7
 
0.1%
20010803000000 7
 
0.1%
19990318000000 7
 
0.1%
19990317000000 6
 
0.1%
20020802000000 6
 
0.1%
20050525000000 6
 
0.1%
20050615000000 6
 
0.1%
Other values (9515) 9920
99.2%
ValueCountFrequency (%)
19990315000000 3
< 0.1%
19990316000000 4
< 0.1%
19990317000000 6
0.1%
19990318000000 7
0.1%
19990319000000 7
0.1%
19990323000000 1
 
< 0.1%
19990511000000 2
 
< 0.1%
19990520000000 1
 
< 0.1%
19990610000000 1
 
< 0.1%
19990716000000 1
 
< 0.1%
ValueCountFrequency (%)
20210129175608 2
< 0.1%
20210129155415 2
< 0.1%
20210129114423 1
< 0.1%
20210129110703 1
< 0.1%
20210129104447 1
< 0.1%
20210129095732 1
< 0.1%
20210129095256 1
< 0.1%
20210129092925 1
< 0.1%
20210128173601 1
< 0.1%
20210128154453 1
< 0.1%

uptaenm
Categorical

Distinct22
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
식육판매업
4022 
즉석판매제조가공업
2868 
제과점영업
1406 
우유류판매업
611 
<NA>
404 
Other values (17)
689 

Length

Max length13
Median length5
Mean length6.4565
Min length2

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
식육판매업 4022
40.2%
즉석판매제조가공업 2868
28.7%
제과점영업 1406
 
14.1%
우유류판매업 611
 
6.1%
<NA> 404
 
4.0%
축산물유통전문판매업 143
 
1.4%
축산물수입판매업 123
 
1.2%
집단급식소 식품판매업 112
 
1.1%
건강기능식품유통전문판매업 73
 
0.7%
식용란수집판매업 62
 
0.6%
Other values (12) 176
 
1.8%

Length

2024-04-16T17:58:37.422068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
식육판매업 4022
39.6%
즉석판매제조가공업 2868
28.3%
제과점영업 1406
 
13.9%
우유류판매업 611
 
6.0%
na 404
 
4.0%
축산물유통전문판매업 143
 
1.4%
축산물수입판매업 123
 
1.2%
집단급식소 112
 
1.1%
식품판매업 112
 
1.1%
건강기능식품유통전문판매업 73
 
0.7%
Other values (13) 274
 
2.7%

sitetel
Text

MISSING 

Distinct124
Distinct (%)1.3%
Missing380
Missing (%)3.8%
Memory size156.2 KiB
2024-04-16T17:58:37.560534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length11.957277
Min length4

Characters and Unicode

Total characters115029
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

Unique105 ?
Unique (%)1.1%

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 9429
96.2%
전화번호 46
 
0.5%
02 21
 
0.2%
070 12
 
0.1%
031 12
 
0.1%
051 10
 
0.1%
055 8
 
0.1%
041 8
 
0.1%
062 7
 
0.1%
032 6
 
0.1%
Other values (189) 245
 
2.5%
2024-04-16T17:58:37.809119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 28433
24.7%
2 19040
16.6%
3 19027
16.5%
- 18897
16.4%
0 9697
 
8.4%
5 9594
 
8.3%
4 9539
 
8.3%
202
 
0.2%
8 134
 
0.1%
7 115
 
0.1%
Other values (6) 351
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 95746
83.2%
Dash Punctuation 18897
 
16.4%
Space Separator 202
 
0.2%
Other Letter 184
 
0.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 28433
29.7%
2 19040
19.9%
3 19027
19.9%
0 9697
 
10.1%
5 9594
 
10.0%
4 9539
 
10.0%
8 134
 
0.1%
7 115
 
0.1%
6 97
 
0.1%
9 70
 
0.1%
Other Letter
ValueCountFrequency (%)
46
25.0%
46
25.0%
46
25.0%
46
25.0%
Dash Punctuation
ValueCountFrequency (%)
- 18897
100.0%
Space Separator
ValueCountFrequency (%)
202
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 114845
99.8%
Hangul 184
 
0.2%

Most frequent character per script

Common
ValueCountFrequency (%)
1 28433
24.8%
2 19040
16.6%
3 19027
16.6%
- 18897
16.5%
0 9697
 
8.4%
5 9594
 
8.4%
4 9539
 
8.3%
202
 
0.2%
8 134
 
0.1%
7 115
 
0.1%
Other values (2) 167
 
0.1%
Hangul
ValueCountFrequency (%)
46
25.0%
46
25.0%
46
25.0%
46
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 114845
99.8%
Hangul 184
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 28433
24.8%
2 19040
16.6%
3 19027
16.6%
- 18897
16.5%
0 9697
 
8.4%
5 9594
 
8.4%
4 9539
 
8.3%
202
 
0.2%
8 134
 
0.1%
7 115
 
0.1%
Other values (2) 167
 
0.1%
Hangul
ValueCountFrequency (%)
46
25.0%
46
25.0%
46
25.0%
46
25.0%

bdngownsenm
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
8563 
자가
 
783
임대
 
389
건물소유구분명
 
265

Length

Max length7
Median length4
Mean length3.8451
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 8563
85.6%
자가 783
 
7.8%
임대 389
 
3.9%
건물소유구분명 265
 
2.6%

Length

2024-04-16T17:58:37.913146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:58:38.006974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 8563
85.6%
자가 783
 
7.8%
임대 389
 
3.9%
건물소유구분명 265
 
2.6%

fctyowkepcnt
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9513 
공장사무직종업원수
 
372
0
 
111
1
 
3
2
 
1

Length

Max length9
Median length4
Mean length4.1515
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> 9513
95.1%
공장사무직종업원수 372
 
3.7%
0 111
 
1.1%
1 3
 
< 0.1%
2 1
 
< 0.1%

Length

2024-04-16T17:58:38.102706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:58:38.199856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9513
95.1%
공장사무직종업원수 372
 
3.7%
0 111
 
1.1%
1 3
 
< 0.1%
2 1
 
< 0.1%

fctypdtjobepcnt
Categorical

IMBALANCE 

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9505 
공장생산직종업원수
 
366
0
 
108
1
 
15
2
 
3
Other values (3)
 
3

Length

Max length9
Median length4
Mean length4.1443
Min length1

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 9505
95.0%
공장생산직종업원수 366
 
3.7%
0 108
 
1.1%
1 15
 
0.1%
2 3
 
< 0.1%
6 1
 
< 0.1%
3 1
 
< 0.1%
4 1
 
< 0.1%

Length

2024-04-16T17:58:38.303278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:58:38.409916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9505
95.0%
공장생산직종업원수 366
 
3.7%
0 108
 
1.1%
1 15
 
0.1%
2 3
 
< 0.1%
6 1
 
< 0.1%
3 1
 
< 0.1%
4 1
 
< 0.1%

fctysiljobepcnt
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9508 
공장판매직종업원수
 
372
0
 
112
1
 
8

Length

Max length9
Median length4
Mean length4.15
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> 9508
95.1%
공장판매직종업원수 372
 
3.7%
0 112
 
1.1%
1 8
 
0.1%

Length

2024-04-16T17:58:38.542586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:58:38.641812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9508
95.1%
공장판매직종업원수 372
 
3.7%
0 112
 
1.1%
1 8
 
0.1%

rgtmbdsno
Categorical

IMBALANCE 

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
4558 
000
4513 
L00
563 
권리주체일련번호
 
357
100
 
3
Other values (4)
 
6

Length

Max length8
Median length3
Mean length3.6343
Min length3

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 4558
45.6%
000 4513
45.1%
L00 563
 
5.6%
권리주체일련번호 357
 
3.6%
100 3
 
< 0.1%
010 3
 
< 0.1%
L01 1
 
< 0.1%
F00 1
 
< 0.1%
L02 1
 
< 0.1%

Length

2024-04-16T17:58:38.729253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:58:38.816468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 4558
45.6%
000 4513
45.1%
l00 563
 
5.6%
권리주체일련번호 357
 
3.6%
100 3
 
< 0.1%
010 3
 
< 0.1%
l01 1
 
< 0.1%
f00 1
 
< 0.1%
l02 1
 
< 0.1%

wtrsplyfacilsenm
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
8526 
상수도전용
1124 
급수시설구분명
 
334
지하수전용
 
14
간이상수도
 
2

Length

Max length7
Median length4
Mean length4.2142
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> 8526
85.3%
상수도전용 1124
 
11.2%
급수시설구분명 334
 
3.3%
지하수전용 14
 
0.1%
간이상수도 2
 
< 0.1%

Length

2024-04-16T17:58:38.913474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:58:38.996338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 8526
85.3%
상수도전용 1124
 
11.2%
급수시설구분명 334
 
3.3%
지하수전용 14
 
0.1%
간이상수도 2
 
< 0.1%

maneipcnt
Categorical

IMBALANCE 

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9037 
0
 
537
남성종사자수
 
389
1
 
26
2
 
8
Other values (2)
 
3

Length

Max length6
Median length4
Mean length3.9056
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> 9037
90.4%
0 537
 
5.4%
남성종사자수 389
 
3.9%
1 26
 
0.3%
2 8
 
0.1%
3 2
 
< 0.1%
4 1
 
< 0.1%

Length

2024-04-16T17:58:39.088701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:58:39.178574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9037
90.4%
0 537
 
5.4%
남성종사자수 389
 
3.9%
1 26
 
0.3%
2 8
 
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
9940 
<NA>
 
36
 
16
Y
 
8

Length

Max length4
Median length1
Mean length1.0108
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
N 9940
99.4%
<NA> 36
 
0.4%
16
 
0.2%
Y 8
 
0.1%

Length

2024-04-16T17:58:39.286095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:58:39.367434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
n 9940
99.4%
na 36
 
0.4%
16
 
0.2%
y 8
 
0.1%

lvsenm
Categorical

IMBALANCE 

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9155 
등급구분명
 
389
기타
 
324
자율
 
129
지도
 
1
Other values (2)
 
2

Length

Max length5
Median length4
Mean length3.9477
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> 9155
91.5%
등급구분명 389
 
3.9%
기타 324
 
3.2%
자율 129
 
1.3%
지도 1
 
< 0.1%
관리 1
 
< 0.1%
우수 1
 
< 0.1%

Length

2024-04-16T17:58:39.451290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:58:39.536011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9155
91.5%
등급구분명 389
 
3.9%
기타 324
 
3.2%
자율 129
 
1.3%
지도 1
 
< 0.1%
관리 1
 
< 0.1%
우수 1
 
< 0.1%

isream
Categorical

IMBALANCE 

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9604 
보증액
 
387
0
 
4
210000000
 
1
10000000
 
1
Other values (3)
 
3

Length

Max length9
Median length4
Mean length3.9621
Min length1

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> 9604
96.0%
보증액 387
 
3.9%
0 4
 
< 0.1%
210000000 1
 
< 0.1%
10000000 1
 
< 0.1%
3000000 1
 
< 0.1%
2000000 1
 
< 0.1%
700000000 1
 
< 0.1%

Length

2024-04-16T17:58:39.657671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:58:39.761674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9604
96.0%
보증액 387
 
3.9%
0 4
 
< 0.1%
210000000 1
 
< 0.1%
10000000 1
 
< 0.1%
3000000 1
 
< 0.1%
2000000 1
 
< 0.1%
700000000 1
 
< 0.1%

hoffepcnt
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9513 
본사종업원수
 
373
0
 
112
1
 
2

Length

Max length6
Median length4
Mean length4.0404
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> 9513
95.1%
본사종업원수 373
 
3.7%
0 112
 
1.1%
1 2
 
< 0.1%

Length

2024-04-16T17:58:39.869895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:58:39.964087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9513
95.1%
본사종업원수 373
 
3.7%
0 112
 
1.1%
1 2
 
< 0.1%

equsiz
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9611 
설비규격
 
389

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> 9611
96.1%
설비규격 389
 
3.9%

Length

2024-04-16T17:58:40.065836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:58:40.154204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9611
96.1%
설비규격 389
 
3.9%

faciltotscp
Text

MISSING 

Distinct1223
Distinct (%)24.7%
Missing5056
Missing (%)50.6%
Memory size156.2 KiB
2024-04-16T17:58:40.391935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length1
Mean length2.1041667
Min length1

Characters and Unicode

Total characters10403
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

Unique1044 ?
Unique (%)21.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row95.79
ValueCountFrequency (%)
0 3251
65.8%
3.3 48
 
1.0%
시설총규모 36
 
0.7%
10 20
 
0.4%
6.6 19
 
0.4%
20 12
 
0.2%
33 11
 
0.2%
24 11
 
0.2%
15 9
 
0.2%
9.9 9
 
0.2%
Other values (1212) 1518
30.7%
2024-04-16T17:58:40.752017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 3579
34.4%
. 1395
 
13.4%
1 755
 
7.3%
2 729
 
7.0%
3 718
 
6.9%
6 604
 
5.8%
4 569
 
5.5%
5 551
 
5.3%
8 498
 
4.8%
9 447
 
4.3%
Other values (6) 558
 
5.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8828
84.9%
Other Punctuation 1395
 
13.4%
Other Letter 180
 
1.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3579
40.5%
1 755
 
8.6%
2 729
 
8.3%
3 718
 
8.1%
6 604
 
6.8%
4 569
 
6.4%
5 551
 
6.2%
8 498
 
5.6%
9 447
 
5.1%
7 378
 
4.3%
Other Letter
ValueCountFrequency (%)
36
20.0%
36
20.0%
36
20.0%
36
20.0%
36
20.0%
Other Punctuation
ValueCountFrequency (%)
. 1395
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 10223
98.3%
Hangul 180
 
1.7%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3579
35.0%
. 1395
 
13.6%
1 755
 
7.4%
2 729
 
7.1%
3 718
 
7.0%
6 604
 
5.9%
4 569
 
5.6%
5 551
 
5.4%
8 498
 
4.9%
9 447
 
4.4%
Hangul
ValueCountFrequency (%)
36
20.0%
36
20.0%
36
20.0%
36
20.0%
36
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10223
98.3%
Hangul 180
 
1.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3579
35.0%
. 1395
 
13.6%
1 755
 
7.4%
2 729
 
7.1%
3 718
 
7.0%
6 604
 
5.9%
4 569
 
5.6%
5 551
 
5.4%
8 498
 
4.9%
9 447
 
4.4%
Hangul
ValueCountFrequency (%)
36
20.0%
36
20.0%
36
20.0%
36
20.0%
36
20.0%

wmeipcnt
Categorical

IMBALANCE 

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9037 
0
 
537
여성종사자수
 
389
1
 
29
2
 
6
Other values (2)
 
2

Length

Max length6
Median length4
Mean length3.9056
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> 9037
90.4%
0 537
 
5.4%
여성종사자수 389
 
3.9%
1 29
 
0.3%
2 6
 
0.1%
3 1
 
< 0.1%
4 1
 
< 0.1%

Length

2024-04-16T17:58:40.885983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:58:40.973932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9037
90.4%
0 537
 
5.4%
여성종사자수 389
 
3.9%
1 29
 
0.3%
2 6
 
0.1%
3 1
 
< 0.1%
4 1
 
< 0.1%

trdpjubnsenm
Categorical

IMBALANCE 

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9079 
영업장주변구분명
 
389
기타
 
385
주택가주변
 
73
아파트지역
 
51
Other values (2)
 
23

Length

Max length8
Median length4
Mean length4.1002
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> 9079
90.8%
영업장주변구분명 389
 
3.9%
기타 385
 
3.9%
주택가주변 73
 
0.7%
아파트지역 51
 
0.5%
유흥업소밀집지역 18
 
0.2%
학교정화(상대) 5
 
0.1%

Length

2024-04-16T17:58:41.088542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:58:41.196850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9079
90.8%
영업장주변구분명 389
 
3.9%
기타 385
 
3.9%
주택가주변 73
 
0.7%
아파트지역 51
 
0.5%
유흥업소밀집지역 18
 
0.2%
학교정화(상대 5
 
< 0.1%

monam
Categorical

IMBALANCE 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9605 
월세액
 
388
0
 
4
600000
 
1
250000
 
1

Length

Max length6
Median length4
Mean length3.9606
Min length1

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 9605
96.0%
월세액 388
 
3.9%
0 4
 
< 0.1%
600000 1
 
< 0.1%
250000 1
 
< 0.1%
100000 1
 
< 0.1%

Length

2024-04-16T17:58:41.316080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:58:41.399731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9605
96.0%
월세액 388
 
3.9%
0 4
 
< 0.1%
600000 1
 
< 0.1%
250000 1
 
< 0.1%
100000 1
 
< 0.1%

sntuptaenm
Categorical

IMBALANCE 

Distinct23
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
5056 
즉석판매제조가공업
2866 
제과점영업
1406 
전자상거래(통신판매업)
 
208
영업장판매
 
115
Other values (18)
 
349

Length

Max length14
Median length4
Mean length5.9317
Min length2

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 5056
50.6%
즉석판매제조가공업 2866
28.7%
제과점영업 1406
 
14.1%
전자상거래(통신판매업) 208
 
2.1%
영업장판매 115
 
1.1%
집단급식소 식품판매업 112
 
1.1%
건강기능식품유통전문판매업 73
 
0.7%
위생업태명 36
 
0.4%
기타 식품제조가공업 36
 
0.4%
방문판매 29
 
0.3%
Other values (13) 63
 
0.6%

Length

2024-04-16T17:58:41.491819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 5056
49.8%
즉석판매제조가공업 2866
28.2%
제과점영업 1406
 
13.8%
전자상거래(통신판매업 208
 
2.0%
영업장판매 115
 
1.1%
집단급식소 112
 
1.1%
식품판매업 112
 
1.1%
건강기능식품유통전문판매업 73
 
0.7%
기타 53
 
0.5%
위생업태명 36
 
0.4%
Other values (15) 116
 
1.1%

jtupsomainedf
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9611 
전통업소주된음식
 
389

Length

Max length8
Median length4
Mean length4.1556
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> 9611
96.1%
전통업소주된음식 389
 
3.9%

Length

2024-04-16T17:58:41.594569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:58:41.672377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9611
96.1%
전통업소주된음식 389
 
3.9%

jtupsoasgnno
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9611 
전통업소지정번호
 
389

Length

Max length8
Median length4
Mean length4.1556
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> 9611
96.1%
전통업소지정번호 389
 
3.9%

Length

2024-04-16T17:58:41.755352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:58:41.838788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9611
96.1%
전통업소지정번호 389
 
3.9%

totepnum
Categorical

IMBALANCE 

Distinct13
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
4669 
식육판매업
3986 
우유류판매업
608 
총종업원수
 
386
축산물유통전문판매업
 
132
Other values (8)
 
219

Length

Max length10
Median length8
Mean length4.7277
Min length1

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 4669
46.7%
식육판매업 3986
39.9%
우유류판매업 608
 
6.1%
총종업원수 386
 
3.9%
축산물유통전문판매업 132
 
1.3%
축산물수입판매업 123
 
1.2%
식용란수집판매업 59
 
0.6%
식육부산물전문판매업 31
 
0.3%
3 2
 
< 0.1%
16 1
 
< 0.1%
Other values (3) 3
 
< 0.1%

Length

2024-04-16T17:58:41.923128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 4669
46.7%
식육판매업 3986
39.9%
우유류판매업 608
 
6.1%
총종업원수 386
 
3.9%
축산물유통전문판매업 132
 
1.3%
축산물수입판매업 123
 
1.2%
식용란수집판매업 59
 
0.6%
식육부산물전문판매업 31
 
0.3%
3 2
 
< 0.1%
16 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
4939 
<NA>
4619 
축산물가공업구분명
 
373
식육판매업
 
36
축산물유통전문판매업
 
11
Other values (5)
 
22

Length

Max length10
Median length9
Mean length5.1913
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
축산물판매업 4939
49.4%
<NA> 4619
46.2%
축산물가공업구분명 373
 
3.7%
식육판매업 36
 
0.4%
축산물유통전문판매업 11
 
0.1%
식육포장처리업 7
 
0.1%
식육가공업 5
 
0.1%
식육부산물전문판매업 4
 
< 0.1%
식용란수집판매업 3
 
< 0.1%
우유류판매업 3
 
< 0.1%

Length

2024-04-16T17:58:42.025905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:58:42.131096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
축산물판매업 4939
49.4%
na 4619
46.2%
축산물가공업구분명 373
 
3.7%
식육판매업 36
 
0.4%
축산물유통전문판매업 11
 
0.1%
식육포장처리업 7
 
0.1%
식육가공업 5
 
< 0.1%
식육부산물전문판매업 4
 
< 0.1%
식용란수집판매업 3
 
< 0.1%
우유류판매업 3
 
< 0.1%

lindjobgbnnm
Categorical

IMBALANCE 

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9497 
축산업무구분명
 
357
축산물판매업
 
57
축산물운반업
 
50
축산물보관업
 
26
Other values (3)
 
13

Length

Max length7
Median length4
Mean length4.1367
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> 9497
95.0%
축산업무구분명 357
 
3.6%
축산물판매업 57
 
0.6%
축산물운반업 50
 
0.5%
축산물보관업 26
 
0.3%
식육포장처리업 7
 
0.1%
축산물가공업 5
 
0.1%
집유업 1
 
< 0.1%

Length

2024-04-16T17:58:42.249056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:58:42.346321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9497
95.0%
축산업무구분명 357
 
3.6%
축산물판매업 57
 
0.6%
축산물운반업 50
 
0.5%
축산물보관업 26
 
0.3%
식육포장처리업 7
 
0.1%
축산물가공업 5
 
< 0.1%
집유업 1
 
< 0.1%

lindseqno
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9611 
축산일련번호
 
389

Length

Max length6
Median length4
Mean length4.0778
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> 9611
96.1%
축산일련번호 389
 
3.9%

Length

2024-04-16T17:58:42.466955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:58:42.555878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9611
96.1%
축산일련번호 389
 
3.9%

homepage
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9610 
홈페이지
 
389
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> 9610
96.1%
홈페이지 389
 
3.9%
https://smartstore.naver.com/navidstore 1
 
< 0.1%

Length

2024-04-16T17:58:42.641171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:58:42.721886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9610
96.1%
홈페이지 389
 
3.9%
https://smartstore.naver.com/navidstore 1
 
< 0.1%
Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2021-02-01 05:26:03
Maximum2021-02-01 05:26:07
2024-04-16T17:58:42.792382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T17:58:42.878289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=5)

Sample

skeyopnsfteamcodemgtnoopnsvcidupdategbnupdatedtopnsvcnmbplcnmsitepostnositewhladdrrdnpostnordnwhladdrapvpermymddcbymdclgstdtclgenddtropnymdtrdstatenmdtlstatenmxylastmodtsuptaenmsitetelbdngownsenmfctyowkepcntfctypdtjobepcntfctysiljobepcntrgtmbdsnowtrsplyfacilsenmmaneipcntmultusnupsoynlvsenmisreamhoffepcntequsizfaciltotscpwmeipcnttrdpjubnsenmmonamsntuptaenmjtupsomainedfjtupsoasgnnototepnumlindprcbgbnnmlindjobgbnnmlindseqnohomepagelast_load_dttm
162761627832300003230000-107-2020-0004507_22_19_PI2020-01-17 00:23:25.0즉석판매제조가공업명류당티에프138934서울특별시 송파구 신천동 29번지 롯데월드타워앤드롯데월드몰5551서울특별시 송파구 올림픽로 300, 롯데마트 월드타워점 (신천동)20200115<NA><NA><NA><NA>영업/정상영업209074.900840074445657.8093298420200115154958즉석판매제조가공업051-123-1234임대<NA><NA><NA><NA><NA><NA>N<NA><NA><NA><NA>0<NA><NA><NA>즉석판매제조가공업<NA><NA><NA><NA><NA><NA><NA>2021-02-01 05:26:06
139191392040000004000000-107-2019-0005007_22_19_PI2019-04-10 02:20:18.0즉석판매제조가공업(주)만구447805경기도 오산시 원동 360-6번지 오산이마트 1층18143경기도 오산시 경기대로 181, 오산이마트 1층 (원동)20190408<NA><NA><NA><NA>영업/정상영업206383.775609107404319.68819302120190408161435즉석판매제조가공업051-123-1234임대<NA><NA><NA><NA>상수도전용<NA>N<NA><NA><NA><NA>0<NA><NA><NA>즉석판매제조가공업<NA><NA><NA><NA><NA><NA><NA>2021-02-01 05:26:06
349350326000032600000092014000407_22_04_PI2018-08-31 23:59:59.0<NA>비락우유 서구 대리점<NA>부산광역시 서구 부용동2가 30-8번지48947<NA>20140430<NA><NA><NA><NA>0000정상384086.906530180888.57180220150205135633우유류판매업051-123-1234<NA><NA><NA><NA>000<NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>우유류판매업축산물판매업<NA><NA><NA>2021-02-01 05:26:03
22082205330000033000000092007001107_22_04_PI2018-08-31 23:59:59.0<NA>한국야쿠르트 온천점<NA>부산광역시 동래구 온천동 307-9번지47706부산광역시 동래구 차밭골로 43-1 (온천동)20070330<NA><NA><NA><NA>0000정상389119.89976100000193179.7810280000020150609090717우유류판매업051-123-1234<NA><NA><NA><NA>000<NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>우유류판매업축산물판매업<NA><NA><NA>2021-02-01 05:26:03
180551805656900005690000-107-2020-0020607_22_19_PI2020-08-15 00:23:13.0즉석판매제조가공업대산유통시스템339854세종특별자치시 전의면 읍내리 214-830004세종특별자치시 전의면 읍내길 10, 전의농협 하나로마트20200813<NA><NA><NA><NA>영업/정상영업218237.744534084353235.35587804420200813151640즉석판매제조가공업051-123-1234<NA><NA><NA><NA><NA><NA><NA>N<NA><NA><NA><NA>0<NA><NA><NA>즉석판매제조가공업<NA><NA><NA><NA><NA><NA><NA>2021-02-01 05:26:06
147231472532100003210000-107-2019-0043807_22_19_PI2019-07-03 02:21:41.0즉석판매제조가공업(주)인네이처137893서울특별시 서초구 양재동 204번지 푸른솔빌딩6770서울특별시 서초구 매헌로16길 6, 푸른솔빌딩 지하1층 (양재동)20190701<NA><NA><NA><NA>영업/정상영업203021.730728799440569.77096803620190701163429즉석판매제조가공업051-123-1234<NA><NA><NA><NA><NA><NA><NA>N<NA><NA><NA><NA>0<NA><NA><NA>즉석판매제조가공업<NA><NA><NA><NA><NA><NA><NA>2021-02-01 05:26:06
123001230434000003400000-121-2003-0000607_22_18_PI2018-08-31 23:59:59.0<NA>샹크미과자점619901부산광역시 기장군 기장읍 교리 345-5번지48947<NA>2003100720050920<NA><NA><NA>02폐업401759.117029197329.55961920031007000000제과점영업051-123-1234<NA><NA><NA><NA><NA>상수도전용0N자율<NA><NA><NA>95.790주택가주변<NA>제과점영업<NA><NA><NA><NA><NA><NA><NA>2021-02-01 05:26:06
195191952143900004390000-107-2021-0001407_22_19_PI2021-01-24 00:23:04.0즉석판매제조가공업(주)와이에이비커머스380884충청북도 충주시 주덕읍 신양리 550 농협하나로마트27462충청북도 충주시 주덕읍 신양로 34, 농협하나로마트20210122<NA><NA><NA><NA>영업/정상영업270406.537514385725.53794220210122103515즉석판매제조가공업07078118500<NA><NA><NA><NA><NA><NA><NA>N<NA><NA><NA><NA>0<NA><NA><NA>즉석판매제조가공업<NA><NA><NA><NA><NA><NA><NA>2021-02-01 05:26:07
158641586651000005100000-107-2019-0007507_22_19_PI2019-12-06 00:23:27.0즉석판매제조가공업주식회사 인네이처770240경상북도 영천시 오수동 286번지38840경상북도 영천시 최무선로 83, 1층 (오수동)20191204<NA><NA><NA><NA>영업/정상영업372336.077240811274785.01620730520191204174405즉석판매제조가공업051-123-1234<NA><NA><NA><NA><NA><NA><NA>N<NA><NA><NA><NA>0<NA><NA><NA>즉석판매제조가공업<NA><NA><NA><NA><NA><NA><NA>2021-02-01 05:26:06
127651276737800003800000-134-2018-0004307_22_03_PI2018-12-07 02:20:08.0건강기능식품일반판매업대박때깔462834경기도 성남시 중원구 은행동 2280번지 동남빌라 402호13163경기도 성남시 중원구 자혜로57번길 21-9, 402호 (은행동, 동남빌라)20181205<NA><NA><NA><NA>영업/정상영업214748.262764904438914.59420337320181205150030<NA>051-123-1234<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>2021-02-01 05:26:06
skeyopnsfteamcodemgtnoopnsvcidupdategbnupdatedtopnsvcnmbplcnmsitepostnositewhladdrrdnpostnordnwhladdrapvpermymddcbymdclgstdtclgenddtropnymdtrdstatenmdtlstatenmxylastmodtsuptaenmsitetelbdngownsenmfctyowkepcntfctypdtjobepcntfctysiljobepcntrgtmbdsnowtrsplyfacilsenmmaneipcntmultusnupsoynlvsenmisreamhoffepcntequsizfaciltotscpwmeipcnttrdpjubnsenmmonamsntuptaenmjtupsomainedfjtupsoasgnnototepnumlindprcbgbnnmlindjobgbnnmlindseqnohomepagelast_load_dttm
71427136337000033700000092010000807_22_04_PI2018-08-31 23:59:59.0<NA>삼성빅마트<NA>부산광역시 연제구 연산동 1255-2번지48947부산광역시 연제구 월드컵대로153번길 18 (연산동)20100302<NA><NA><NA><NA>0004말소389368.86502900000189711.1651260000020160122180530식육판매업051-123-1234<NA><NA><NA><NA>000<NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>식육판매업축산물판매업<NA><NA><NA>2021-02-01 05:26:04
44314426333000033300000091985000507_22_04_PI2018-08-31 23:59:59.0<NA>정육식육점<NA>부산광역시 해운대구 반여동 1217-15번지48038부산광역시 해운대구 선수촌로 109 (반여동)19850520<NA><NA><NA><NA>0004말소393019.20926300000191734.1638250000020140710140048식육판매업051-123-1234<NA><NA><NA><NA>000<NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>식육판매업축산물판매업<NA><NA><NA>2021-02-01 05:26:04
102541025632900003290000-121-2000-0686107_22_18_PI2018-08-31 23:59:59.0<NA>이드프랑스제과점614823부산광역시 부산진구 당감동 797-0번지 나동 103호 주공상가48947<NA>2000011420070103<NA><NA><NA>02폐업384725.502542187928.97068220020621000000제과점영업051-123-1234<NA><NA><NA><NA><NA><NA>0N기타<NA><NA><NA>420기타<NA>제과점영업<NA><NA><NA><NA><NA><NA><NA>2021-02-01 05:26:05
16121609329000032900000092010000807_22_04_PI2018-08-31 23:59:59.0<NA>참존후레쉬마트<NA>부산광역시 부산진구 전포동 190-25번지48947<NA>2010032920110429<NA><NA><NA>0002폐업388688.017890186929.54721720110503162918식육판매업051-123-1234<NA><NA><NA><NA>000<NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>식육판매업축산물판매업<NA><NA><NA>2021-02-01 05:26:03
17781774329000032900000092013004407_22_04_PI2018-08-31 23:59:59.0<NA>후레쉬마트<NA>부산광역시 부산진구 연지동 50-6번지47120부산광역시 부산진구 동평로291번길 30 (연지동, 화인아파트)20131205<NA><NA><NA><NA>0004말소387668.06311200000188352.5372980000020150805130725식육판매업051-123-1234<NA><NA><NA><NA>000<NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>식육판매업축산물판매업<NA><NA><NA>2021-02-01 05:26:03
104611046233000003300000-121-1993-0000207_22_18_PI2018-08-31 23:59:59.0<NA>르네상스베이커리607809부산광역시 동래구 명장동 145-17번지47775부산광역시 동래구 명장로 54-4 (명장동)1993051220130516<NA><NA><NA>02폐업391857.16855200000191809.9573410000019990520000000제과점영업051-123-1234<NA><NA><NA><NA><NA><NA>0N기타<NA><NA><NA>240기타<NA>제과점영업<NA><NA><NA><NA><NA><NA><NA>2021-02-01 05:26:05
57785784334000033400000092011002907_22_04_PI2018-08-31 23:59:59.0<NA>가람슈퍼렛<NA>부산광역시 사하구 하단동 529-1번지 EF골든빌라1층상가48947부산광역시 사하구 낙동대로486번길 6 (하단동,EF골든빌라1층상가)2011082520111010<NA><NA><NA>0002폐업379433.48378900000180847.6119920000020111201181727식육판매업051-123-1234<NA><NA><NA><NA>000<NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>식육판매업축산물판매업<NA><NA><NA>2021-02-01 05:26:04
148981490056900005690000-134-2019-0005407_22_03_PI2019-07-21 02:21:42.0건강기능식품일반판매업(주)부원산업339813세종특별자치시 연서면 봉암리 397번지30047세종특별자치시 연서면 서원길 27-2220190719<NA><NA><NA><NA>영업/정상영업225825.975530657339883.63720369320190719093339<NA>051-123-1234<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>2021-02-01 05:26:06
74207413337000033700000092016001207_22_04_PI2018-08-31 23:59:59.0<NA>남양상회<NA><NA>47543부산광역시 연제구 거제대로178번길 40 (거제동)20160805<NA><NA><NA><NA>0000정상388775.26567800000189486.7793410000020160805174924식용란수집판매업051-123-1234<NA><NA><NA><NA>000<NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>식용란수집판매업축산물판매업<NA><NA><NA>2021-02-01 05:26:04
16991696329000032900000092004000807_22_04_PI2018-08-31 23:59:59.0<NA>대발유통<NA>부산광역시 부산진구 초읍동 272-21번지48947부산광역시 부산진구 초읍천로108번길 82 (초읍동)2004080220060721<NA><NA><NA>0002폐업386485.69532900000188767.6262670000020060721111150식육판매업051-123-1234<NA><NA><NA><NA>000<NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>식육판매업축산물판매업<NA><NA><NA>2021-02-01 05:26:03