Overview

Dataset statistics

Number of variables49
Number of observations10000
Missing cells22201
Missing cells (%)4.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.9 MiB
Average record size in memory404.0 B

Variable types

Numeric4
Text11
Categorical32
DateTime2

Alerts

opnsvcid is highly imbalanced (65.2%)Imbalance
updategbn is highly imbalanced (73.2%)Imbalance
opnsvcnm is highly imbalanced (78.9%)Imbalance
clgstdt is highly imbalanced (98.2%)Imbalance
clgenddt is highly imbalanced (98.1%)Imbalance
ropnymd is highly imbalanced (97.2%)Imbalance
uptaenm is highly imbalanced (52.2%)Imbalance
bdngownsenm is highly imbalanced (91.4%)Imbalance
fctyowkepcnt is highly imbalanced (91.8%)Imbalance
fctypdtjobepcnt is highly imbalanced (91.8%)Imbalance
fctysiljobepcnt is highly imbalanced (89.7%)Imbalance
rgtmbdsno is highly imbalanced (59.4%)Imbalance
wtrsplyfacilsenm is highly imbalanced (76.1%)Imbalance
maneipcnt is highly imbalanced (82.8%)Imbalance
multusnupsoyn is highly imbalanced (94.4%)Imbalance
lvsenm is highly imbalanced (84.3%)Imbalance
isream is highly imbalanced (96.4%)Imbalance
hoffepcnt is highly imbalanced (91.8%)Imbalance
equsiz is highly imbalanced (94.8%)Imbalance
wmeipcnt is highly imbalanced (83.8%)Imbalance
trdpjubnsenm is highly imbalanced (82.7%)Imbalance
monam is highly imbalanced (96.4%)Imbalance
sntuptaenm is highly imbalanced (67.5%)Imbalance
jtupsomainedf is highly imbalanced (94.8%)Imbalance
jtupsoasgnno is highly imbalanced (96.6%)Imbalance
totepnum is highly imbalanced (50.4%)Imbalance
lindprcbgbnnm is highly imbalanced (67.1%)Imbalance
lindjobgbnnm is highly imbalanced (94.6%)Imbalance
lindseqno is highly imbalanced (94.8%)Imbalance
homepage is highly imbalanced (94.8%)Imbalance
sitepostno has 6931 (69.3%) missing valuesMissing
sitewhladdr has 295 (2.9%) missing valuesMissing
rdnwhladdr has 1516 (15.2%) missing valuesMissing
dcbymd has 5106 (51.1%) missing valuesMissing
x has 344 (3.4%) missing valuesMissing
y has 344 (3.4%) missing valuesMissing
sitetel has 784 (7.8%) missing valuesMissing
faciltotscp has 6862 (68.6%) missing valuesMissing
opnsfteamcode is highly skewed (γ1 = 26.52727362)Skewed
skey has unique valuesUnique

Reproduction

Analysis started2024-04-16 08:57:23.075210
Analysis finished2024-04-16 08:57:25.710900
Duration2.64 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%
Mean7844.9097
Minimum1
Maximum21070
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-16T17:57:25.767979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile732.95
Q13544.75
median7096.5
Q310623.25
95-th percentile20388.05
Maximum21070
Range21069
Interquartile range (IQR)7078.5

Descriptive statistics

Standard deviation5527.9644
Coefficient of variation (CV)0.70465622
Kurtosis0.20672481
Mean7844.9097
Median Absolute Deviation (MAD)3537
Skewness0.88063086
Sum78449097
Variance30558390
MonotonicityNot monotonic
2024-04-16T17:57:25.881758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1588 1
 
< 0.1%
40 1
 
< 0.1%
6511 1
 
< 0.1%
4831 1
 
< 0.1%
3562 1
 
< 0.1%
7446 1
 
< 0.1%
6966 1
 
< 0.1%
2062 1
 
< 0.1%
1073 1
 
< 0.1%
9557 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
1 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
10 1
< 0.1%
11 1
< 0.1%
12 1
< 0.1%
ValueCountFrequency (%)
21070 1
< 0.1%
21068 1
< 0.1%
21067 1
< 0.1%
21066 1
< 0.1%
21065 1
< 0.1%
21062 1
< 0.1%
21061 1
< 0.1%
21060 1
< 0.1%
21059 1
< 0.1%
21057 1
< 0.1%

opnsfteamcode
Real number (ℝ)

SKEWED 

Distinct17
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3333343
Minimum3250000
Maximum6260000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-16T17:57:25.984565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3250000
5-th percentile3270000
Q13300000
median3330000
Q33370000
95-th percentile3400000
Maximum6260000
Range3010000
Interquartile range (IQR)70000

Descriptive statistics

Standard deviation87126.307
Coefficient of variation (CV)0.026137816
Kurtosis888.94291
Mean3333343
Median Absolute Deviation (MAD)30000
Skewness26.527274
Sum3.333343 × 1010
Variance7.5909935 × 109
MonotonicityNot monotonic
2024-04-16T17:57:26.071383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
3330000 1052
10.5%
3290000 1015
10.2%
3320000 916
9.2%
3300000 896
9.0%
3390000 865
8.6%
3340000 846
8.5%
3350000 684
 
6.8%
3370000 627
 
6.3%
3310000 626
 
6.3%
3400000 517
 
5.2%
Other values (7) 1956
19.6%
ValueCountFrequency (%)
3250000 220
 
2.2%
3260000 273
 
2.7%
3270000 334
 
3.3%
3280000 249
 
2.5%
3290000 1015
10.2%
3300000 896
9.0%
3310000 626
6.3%
3320000 916
9.2%
3330000 1052
10.5%
3340000 846
8.5%
ValueCountFrequency (%)
6260000 7
 
0.1%
3400000 517
5.2%
3390000 865
8.6%
3380000 489
4.9%
3370000 627
6.3%
3360000 384
 
3.8%
3350000 684
6.8%
3340000 846
8.5%
3330000 1052
10.5%
3320000 916
9.2%

mgtno
Text

Distinct9845
Distinct (%)98.5%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-04-16T17:57:26.244611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length18
Mean length19.2542
Min length18

Characters and Unicode

Total characters192542
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

Unique9696 ?
Unique (%)97.0%

Sample

1st row329000000920080024
2nd row333000000920010022
3rd row3370000-107-2021-00034
4th row3290000-107-2021-00059
5th row329000000920030021
ValueCountFrequency (%)
3330000-134-2020-00021 3
 
< 0.1%
3400000-107-2019-00012 3
 
< 0.1%
3370000-107-2020-00064 3
 
< 0.1%
3320000-107-2019-00268 3
 
< 0.1%
3340000-107-2019-00006 3
 
< 0.1%
3340000-107-2019-00005 3
 
< 0.1%
3250000-134-2020-00049 2
 
< 0.1%
3400000-107-2021-00034 2
 
< 0.1%
3330000-134-2021-00105 2
 
< 0.1%
3340000-107-2021-00041 2
 
< 0.1%
Other values (9835) 9974
99.7%
2024-04-16T17:57:26.552116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 95460
49.6%
3 21608
 
11.2%
1 17205
 
8.9%
2 17128
 
8.9%
9 14819
 
7.7%
- 9405
 
4.9%
4 4045
 
2.1%
7 3726
 
1.9%
8 3297
 
1.7%
5 3029
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 183137
95.1%
Dash Punctuation 9405
 
4.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 95460
52.1%
3 21608
 
11.8%
1 17205
 
9.4%
2 17128
 
9.4%
9 14819
 
8.1%
4 4045
 
2.2%
7 3726
 
2.0%
8 3297
 
1.8%
5 3029
 
1.7%
6 2820
 
1.5%
Dash Punctuation
ValueCountFrequency (%)
- 9405
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 192542
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 95460
49.6%
3 21608
 
11.2%
1 17205
 
8.9%
2 17128
 
8.9%
9 14819
 
7.7%
- 9405
 
4.9%
4 4045
 
2.1%
7 3726
 
1.9%
8 3297
 
1.7%
5 3029
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 192542
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 95460
49.6%
3 21608
 
11.2%
1 17205
 
8.9%
2 17128
 
8.9%
9 14819
 
7.7%
- 9405
 
4.9%
4 4045
 
2.1%
7 3726
 
1.9%
8 3297
 
1.7%
5 3029
 
1.6%

opnsvcid
Categorical

IMBALANCE 

Distinct17
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
07_22_04_P
6845 
07_22_18_P
1962 
07_22_19_P
716 
07_22_03_P
 
284
07_22_01_P
 
41
Other values (12)
 
152

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
07_22_04_P 6845
68.5%
07_22_18_P 1962
 
19.6%
07_22_19_P 716
 
7.2%
07_22_03_P 284
 
2.8%
07_22_01_P 41
 
0.4%
07_22_17_P 38
 
0.4%
07_22_10_P 33
 
0.3%
07_22_11_P 26
 
0.3%
07_22_08_P 22
 
0.2%
07_22_02_P 8
 
0.1%
Other values (7) 25
 
0.2%

Length

2024-04-16T17:57:26.659220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
07_22_04_p 6845
68.5%
07_22_18_p 1962
 
19.6%
07_22_19_p 716
 
7.2%
07_22_03_p 284
 
2.8%
07_22_01_p 41
 
0.4%
07_22_17_p 38
 
0.4%
07_22_10_p 33
 
0.3%
07_22_11_p 26
 
0.3%
07_22_08_p 22
 
0.2%
07_22_02_p 8
 
0.1%
Other values (7) 25
 
0.2%

updategbn
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
I
9543 
U
 
457

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
I 9543
95.4%
U 457
 
4.6%

Length

2024-04-16T17:57:26.746957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:57:26.820840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
i 9543
95.4%
u 457
 
4.6%
Distinct283
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2018-08-31 23:59:59
Maximum2021-05-01 02:40:00
2024-04-16T17:57:26.908167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T17:57:27.024116image/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>
8643 
즉석판매제조가공업
 
716
건강기능식품일반판매업
 
284
축산판매업
 
97
제과점영업
 
67
Other values (13)
 
193

Length

Max length13
Median length4
Mean length4.6461
Min length4

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 8643
86.4%
즉석판매제조가공업 716
 
7.2%
건강기능식품일반판매업 284
 
2.8%
축산판매업 97
 
1.0%
제과점영업 67
 
0.7%
집단급식소식품판매업 41
 
0.4%
유통전문판매업 38
 
0.4%
식품자동판매기업 33
 
0.3%
식품제조가공업 26
 
0.3%
식품소분업 22
 
0.2%
Other values (8) 33
 
0.3%

Length

2024-04-16T17:57:27.129373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 8643
86.4%
즉석판매제조가공업 716
 
7.2%
건강기능식품일반판매업 284
 
2.8%
축산판매업 97
 
1.0%
제과점영업 67
 
0.7%
집단급식소식품판매업 41
 
0.4%
유통전문판매업 38
 
0.4%
식품자동판매기업 33
 
0.3%
식품제조가공업 26
 
0.3%
식품소분업 22
 
0.2%
Other values (8) 33
 
0.3%

bplcnm
Text

Distinct7737
Distinct (%)77.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-04-16T17:57:27.418260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length41
Median length28
Mean length6.4927
Min length1

Characters and Unicode

Total characters64927
Distinct characters864
Distinct categories12 ?
Distinct scripts4 ?
Distinct blocks8 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6678 ?
Unique (%)66.8%

Sample

1st row신우유통
2nd row철마성호정육
3rd row덕유산유통
4th row당감찬들
5th row자운식육점
ValueCountFrequency (%)
주식회사 117
 
1.0%
파리바게뜨 79
 
0.7%
베이커리 66
 
0.6%
부산우유 57
 
0.5%
뚜레쥬르 46
 
0.4%
파리바게트 41
 
0.3%
식육점 37
 
0.3%
탑플러스마트 35
 
0.3%
한우식육점 29
 
0.2%
대성식육점 28
 
0.2%
Other values (7998) 11353
95.5%
2024-04-16T17:57:28.193680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3652
 
5.6%
2528
 
3.9%
2269
 
3.5%
1891
 
2.9%
1696
 
2.6%
1446
 
2.2%
1395
 
2.1%
1352
 
2.1%
1227
 
1.9%
) 1189
 
1.8%
Other values (854) 46282
71.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 59137
91.1%
Space Separator 1891
 
2.9%
Close Punctuation 1189
 
1.8%
Open Punctuation 1183
 
1.8%
Uppercase Letter 636
 
1.0%
Lowercase Letter 488
 
0.8%
Decimal Number 285
 
0.4%
Other Punctuation 103
 
0.2%
Dash Punctuation 10
 
< 0.1%
Letter Number 2
 
< 0.1%
Other values (2) 3
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
3652
 
6.2%
2528
 
4.3%
2269
 
3.8%
1696
 
2.9%
1446
 
2.4%
1395
 
2.4%
1352
 
2.3%
1227
 
2.1%
1154
 
2.0%
1136
 
1.9%
Other values (773) 41282
69.8%
Uppercase Letter
ValueCountFrequency (%)
S 83
 
13.1%
M 49
 
7.7%
C 43
 
6.8%
K 37
 
5.8%
D 37
 
5.8%
T 37
 
5.8%
G 35
 
5.5%
F 33
 
5.2%
N 30
 
4.7%
A 28
 
4.4%
Other values (16) 224
35.2%
Lowercase Letter
ValueCountFrequency (%)
e 75
15.4%
a 57
 
11.7%
o 43
 
8.8%
n 30
 
6.1%
s 27
 
5.5%
i 25
 
5.1%
r 23
 
4.7%
l 20
 
4.1%
m 20
 
4.1%
c 19
 
3.9%
Other values (14) 149
30.5%
Other Punctuation
ValueCountFrequency (%)
& 33
32.0%
. 28
27.2%
, 16
15.5%
' 10
 
9.7%
· 4
 
3.9%
: 2
 
1.9%
2
 
1.9%
! 2
 
1.9%
" 2
 
1.9%
% 1
 
1.0%
Other values (3) 3
 
2.9%
Decimal Number
ValueCountFrequency (%)
2 90
31.6%
1 71
24.9%
3 30
 
10.5%
5 21
 
7.4%
0 18
 
6.3%
9 15
 
5.3%
4 12
 
4.2%
7 10
 
3.5%
8 10
 
3.5%
6 8
 
2.8%
Other Symbol
ValueCountFrequency (%)
1
50.0%
1
50.0%
Space Separator
ValueCountFrequency (%)
1891
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1189
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1183
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 10
100.0%
Letter Number
ValueCountFrequency (%)
2
100.0%
Math Symbol
ValueCountFrequency (%)
+ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 59130
91.1%
Common 4663
 
7.2%
Latin 1126
 
1.7%
Han 8
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
3652
 
6.2%
2528
 
4.3%
2269
 
3.8%
1696
 
2.9%
1446
 
2.4%
1395
 
2.4%
1352
 
2.3%
1227
 
2.1%
1154
 
2.0%
1136
 
1.9%
Other values (767) 41275
69.8%
Latin
ValueCountFrequency (%)
S 83
 
7.4%
e 75
 
6.7%
a 57
 
5.1%
M 49
 
4.4%
o 43
 
3.8%
C 43
 
3.8%
K 37
 
3.3%
D 37
 
3.3%
T 37
 
3.3%
G 35
 
3.1%
Other values (41) 630
56.0%
Common
ValueCountFrequency (%)
1891
40.6%
) 1189
25.5%
( 1183
25.4%
2 90
 
1.9%
1 71
 
1.5%
& 33
 
0.7%
3 30
 
0.6%
. 28
 
0.6%
5 21
 
0.5%
0 18
 
0.4%
Other values (19) 109
 
2.3%
Han
ValueCountFrequency (%)
2
25.0%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 59128
91.1%
ASCII 5780
 
8.9%
None 7
 
< 0.1%
CJK 7
 
< 0.1%
Number Forms 2
 
< 0.1%
Specials 1
 
< 0.1%
Compat Jamo 1
 
< 0.1%
CJK Compat Ideographs 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
3652
 
6.2%
2528
 
4.3%
2269
 
3.8%
1696
 
2.9%
1446
 
2.4%
1395
 
2.4%
1352
 
2.3%
1227
 
2.1%
1154
 
2.0%
1136
 
1.9%
Other values (765) 41273
69.8%
ASCII
ValueCountFrequency (%)
1891
32.7%
) 1189
20.6%
( 1183
20.5%
2 90
 
1.6%
S 83
 
1.4%
e 75
 
1.3%
1 71
 
1.2%
a 57
 
1.0%
M 49
 
0.8%
o 43
 
0.7%
Other values (66) 1049
18.1%
None
ValueCountFrequency (%)
· 4
57.1%
2
28.6%
1
 
14.3%
Number Forms
ValueCountFrequency (%)
2
100.0%
CJK
ValueCountFrequency (%)
2
28.6%
1
14.3%
1
14.3%
1
14.3%
1
14.3%
1
14.3%
Specials
ValueCountFrequency (%)
1
100.0%
Compat Jamo
ValueCountFrequency (%)
1
100.0%
CJK Compat Ideographs
ValueCountFrequency (%)
1
100.0%

sitepostno
Text

MISSING 

Distinct654
Distinct (%)21.3%
Missing6931
Missing (%)69.3%
Memory size156.2 KiB
2024-04-16T17:57:28.492269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters18414
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

Unique177 ?
Unique (%)5.8%

Sample

1st row611811
2nd row614838
3rd row616827
4th row608817
5th row612769
ValueCountFrequency (%)
612020 106
 
3.5%
600017 39
 
1.3%
614847 36
 
1.2%
617808 33
 
1.1%
618200 31
 
1.0%
612824 31
 
1.0%
607831 28
 
0.9%
618814 26
 
0.8%
607804 25
 
0.8%
616852 24
 
0.8%
Other values (644) 2690
87.7%
2024-04-16T17:57:28.896131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6 3703
20.1%
1 3087
16.8%
8 3004
16.3%
0 2942
16.0%
2 1512
8.2%
4 1263
 
6.9%
7 946
 
5.1%
3 879
 
4.8%
9 626
 
3.4%
5 434
 
2.4%
Other values (5) 18
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 18396
99.9%
Other Letter 18
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
6 3703
20.1%
1 3087
16.8%
8 3004
16.3%
0 2942
16.0%
2 1512
8.2%
4 1263
 
6.9%
7 946
 
5.1%
3 879
 
4.8%
9 626
 
3.4%
5 434
 
2.4%
Other Letter
ValueCountFrequency (%)
6
33.3%
3
16.7%
3
16.7%
3
16.7%
3
16.7%

Most occurring scripts

ValueCountFrequency (%)
Common 18396
99.9%
Hangul 18
 
0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
6 3703
20.1%
1 3087
16.8%
8 3004
16.3%
0 2942
16.0%
2 1512
8.2%
4 1263
 
6.9%
7 946
 
5.1%
3 879
 
4.8%
9 626
 
3.4%
5 434
 
2.4%
Hangul
ValueCountFrequency (%)
6
33.3%
3
16.7%
3
16.7%
3
16.7%
3
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 18396
99.9%
Hangul 18
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6 3703
20.1%
1 3087
16.8%
8 3004
16.3%
0 2942
16.0%
2 1512
8.2%
4 1263
 
6.9%
7 946
 
5.1%
3 879
 
4.8%
9 626
 
3.4%
5 434
 
2.4%
Hangul
ValueCountFrequency (%)
6
33.3%
3
16.7%
3
16.7%
3
16.7%
3
16.7%

sitewhladdr
Text

MISSING 

Distinct7950
Distinct (%)81.9%
Missing295
Missing (%)2.9%
Memory size156.2 KiB
2024-04-16T17:57:29.178534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length60
Median length52
Mean length24.661721
Min length13

Characters and Unicode

Total characters239342
Distinct characters484
Distinct categories10 ?
Distinct scripts4 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6979 ?
Unique (%)71.9%

Sample

1st row부산광역시 부산진구 개금동 481-8번지
2nd row부산광역시 해운대구 반송동 40-1063번지
3rd row부산광역시 연제구 연산동 105-1 홈플러스 부산연산점
4th row부산광역시 부산진구 부암동 332
5th row부산광역시 부산진구 개금동 154-1번지
ValueCountFrequency (%)
부산광역시 9705
 
22.3%
해운대구 996
 
2.3%
부산진구 962
 
2.2%
동래구 894
 
2.1%
북구 863
 
2.0%
사상구 855
 
2.0%
사하구 829
 
1.9%
금정구 673
 
1.5%
남구 625
 
1.4%
연제구 611
 
1.4%
Other values (8761) 26540
60.9%
2024-04-16T17:57:29.565593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
43143
18.0%
11496
 
4.8%
11470
 
4.8%
11236
 
4.7%
1 10953
 
4.6%
9976
 
4.2%
9916
 
4.1%
9789
 
4.1%
9719
 
4.1%
9076
 
3.8%
Other values (474) 102568
42.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 139828
58.4%
Decimal Number 46988
 
19.6%
Space Separator 43143
 
18.0%
Dash Punctuation 8367
 
3.5%
Uppercase Letter 375
 
0.2%
Other Punctuation 212
 
0.1%
Open Punctuation 200
 
0.1%
Close Punctuation 197
 
0.1%
Lowercase Letter 19
 
< 0.1%
Math Symbol 13
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
11496
 
8.2%
11470
 
8.2%
11236
 
8.0%
9976
 
7.1%
9916
 
7.1%
9789
 
7.0%
9719
 
7.0%
9076
 
6.5%
8503
 
6.1%
1944
 
1.4%
Other values (422) 46703
33.4%
Uppercase Letter
ValueCountFrequency (%)
B 77
20.5%
A 60
16.0%
S 46
12.3%
G 35
9.3%
K 26
 
6.9%
E 20
 
5.3%
C 18
 
4.8%
T 13
 
3.5%
L 12
 
3.2%
P 12
 
3.2%
Other values (13) 56
14.9%
Decimal Number
ValueCountFrequency (%)
1 10953
23.3%
2 6122
13.0%
3 4817
10.3%
5 4421
9.4%
4 4372
 
9.3%
0 3736
 
8.0%
7 3355
 
7.1%
6 3319
 
7.1%
8 3084
 
6.6%
9 2809
 
6.0%
Lowercase Letter
ValueCountFrequency (%)
e 5
26.3%
s 4
21.1%
g 3
15.8%
c 2
 
10.5%
b 2
 
10.5%
l 1
 
5.3%
k 1
 
5.3%
u 1
 
5.3%
Other Punctuation
ValueCountFrequency (%)
, 159
75.0%
@ 28
 
13.2%
. 15
 
7.1%
· 5
 
2.4%
/ 4
 
1.9%
' 1
 
0.5%
Space Separator
ValueCountFrequency (%)
43143
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 8367
100.0%
Open Punctuation
ValueCountFrequency (%)
( 200
100.0%
Close Punctuation
ValueCountFrequency (%)
) 197
100.0%
Math Symbol
ValueCountFrequency (%)
~ 13
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 139827
58.4%
Common 99120
41.4%
Latin 394
 
0.2%
Han 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
11496
 
8.2%
11470
 
8.2%
11236
 
8.0%
9976
 
7.1%
9916
 
7.1%
9789
 
7.0%
9719
 
7.0%
9076
 
6.5%
8503
 
6.1%
1944
 
1.4%
Other values (421) 46702
33.4%
Latin
ValueCountFrequency (%)
B 77
19.5%
A 60
15.2%
S 46
11.7%
G 35
8.9%
K 26
 
6.6%
E 20
 
5.1%
C 18
 
4.6%
T 13
 
3.3%
L 12
 
3.0%
P 12
 
3.0%
Other values (21) 75
19.0%
Common
ValueCountFrequency (%)
43143
43.5%
1 10953
 
11.1%
- 8367
 
8.4%
2 6122
 
6.2%
3 4817
 
4.9%
5 4421
 
4.5%
4 4372
 
4.4%
0 3736
 
3.8%
7 3355
 
3.4%
6 3319
 
3.3%
Other values (11) 6515
 
6.6%
Han
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 139827
58.4%
ASCII 99509
41.6%
None 5
 
< 0.1%
CJK 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
43143
43.4%
1 10953
 
11.0%
- 8367
 
8.4%
2 6122
 
6.2%
3 4817
 
4.8%
5 4421
 
4.4%
4 4372
 
4.4%
0 3736
 
3.8%
7 3355
 
3.4%
6 3319
 
3.3%
Other values (41) 6904
 
6.9%
Hangul
ValueCountFrequency (%)
11496
 
8.2%
11470
 
8.2%
11236
 
8.0%
9976
 
7.1%
9916
 
7.1%
9789
 
7.0%
9719
 
7.0%
9076
 
6.5%
8503
 
6.1%
1944
 
1.4%
Other values (421) 46702
33.4%
None
ValueCountFrequency (%)
· 5
100.0%
CJK
ValueCountFrequency (%)
1
100.0%
Distinct1459
Distinct (%)14.6%
Missing19
Missing (%)0.2%
Memory size156.2 KiB
2024-04-16T17:57:29.836538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length5
Mean length5.0004008
Min length5

Characters and Unicode

Total characters49909
Distinct characters17
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

Unique515 ?
Unique (%)5.2%

Sample

1st row48947
2nd row48947
3rd row47552
4th row47139
5th row48947
ValueCountFrequency (%)
48947 4639
46.5%
48058 88
 
0.9%
46504 63
 
0.6%
47052 45
 
0.5%
46901 44
 
0.4%
48944 40
 
0.4%
46916 38
 
0.4%
47285 37
 
0.4%
48735 30
 
0.3%
46702 30
 
0.3%
Other values (1449) 4927
49.4%
2024-04-16T17:57:30.254632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4 15980
32.0%
7 7799
15.6%
8 7422
14.9%
9 6676
13.4%
6 2600
 
5.2%
0 2394
 
4.8%
5 1999
 
4.0%
2 1914
 
3.8%
1 1662
 
3.3%
3 1456
 
2.9%
Other values (7) 7
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 49902
> 99.9%
Other Letter 7
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 15980
32.0%
7 7799
15.6%
8 7422
14.9%
9 6676
13.4%
6 2600
 
5.2%
0 2394
 
4.8%
5 1999
 
4.0%
2 1914
 
3.8%
1 1662
 
3.3%
3 1456
 
2.9%
Other Letter
ValueCountFrequency (%)
1
14.3%
1
14.3%
1
14.3%
1
14.3%
1
14.3%
1
14.3%
1
14.3%

Most occurring scripts

ValueCountFrequency (%)
Common 49902
> 99.9%
Hangul 7
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
4 15980
32.0%
7 7799
15.6%
8 7422
14.9%
9 6676
13.4%
6 2600
 
5.2%
0 2394
 
4.8%
5 1999
 
4.0%
2 1914
 
3.8%
1 1662
 
3.3%
3 1456
 
2.9%
Hangul
ValueCountFrequency (%)
1
14.3%
1
14.3%
1
14.3%
1
14.3%
1
14.3%
1
14.3%
1
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 49902
> 99.9%
Hangul 7
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 15980
32.0%
7 7799
15.6%
8 7422
14.9%
9 6676
13.4%
6 2600
 
5.2%
0 2394
 
4.8%
5 1999
 
4.0%
2 1914
 
3.8%
1 1662
 
3.3%
3 1456
 
2.9%
Hangul
ValueCountFrequency (%)
1
14.3%
1
14.3%
1
14.3%
1
14.3%
1
14.3%
1
14.3%
1
14.3%

rdnwhladdr
Text

MISSING 

Distinct7008
Distinct (%)82.6%
Missing1516
Missing (%)15.2%
Memory size156.2 KiB
2024-04-16T17:57:30.573674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length63
Median length56
Mean length29.133074
Min length19

Characters and Unicode

Total characters247165
Distinct characters516
Distinct categories10 ?
Distinct scripts4 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6173 ?
Unique (%)72.8%

Sample

1st row부산광역시 부산진구 백양대로313번길 9 (개금동)
2nd row부산광역시 연제구 반송로 88, 홈플러스 부산연산점 (연산동)
3rd row부산광역시 부산진구 동평로 129, 1층 (부암동)
4th row부산광역시 부산진구 가야대로507번길 11 (개금동)
5th row부산광역시 북구 덕천로276번길 32-14 (만덕동)
ValueCountFrequency (%)
부산광역시 8484
 
17.9%
부산진구 920
 
1.9%
동래구 801
 
1.7%
1층 772
 
1.6%
북구 752
 
1.6%
해운대구 724
 
1.5%
사상구 707
 
1.5%
사하구 701
 
1.5%
금정구 622
 
1.3%
남구 570
 
1.2%
Other values (5869) 32399
68.3%
2024-04-16T17:57:31.078712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
38984
 
15.8%
10916
 
4.4%
10354
 
4.2%
10344
 
4.2%
1 9252
 
3.7%
9046
 
3.7%
8918
 
3.6%
8728
 
3.5%
8499
 
3.4%
( 8209
 
3.3%
Other values (506) 123915
50.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 148921
60.3%
Space Separator 38984
 
15.8%
Decimal Number 37569
 
15.2%
Open Punctuation 8209
 
3.3%
Close Punctuation 8206
 
3.3%
Other Punctuation 3690
 
1.5%
Dash Punctuation 1170
 
0.5%
Uppercase Letter 381
 
0.2%
Lowercase Letter 21
 
< 0.1%
Math Symbol 14
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
10916
 
7.3%
10354
 
7.0%
10344
 
6.9%
9046
 
6.1%
8918
 
6.0%
8728
 
5.9%
8499
 
5.7%
8198
 
5.5%
4456
 
3.0%
4202
 
2.8%
Other values (456) 65260
43.8%
Uppercase Letter
ValueCountFrequency (%)
B 81
21.3%
A 57
15.0%
S 50
13.1%
G 38
10.0%
E 27
 
7.1%
C 25
 
6.6%
K 24
 
6.3%
P 13
 
3.4%
N 12
 
3.1%
D 8
 
2.1%
Other values (12) 46
12.1%
Decimal Number
ValueCountFrequency (%)
1 9252
24.6%
2 5194
13.8%
3 4263
11.3%
5 3348
 
8.9%
4 3175
 
8.5%
0 3033
 
8.1%
7 2581
 
6.9%
6 2549
 
6.8%
8 2142
 
5.7%
9 2032
 
5.4%
Lowercase Letter
ValueCountFrequency (%)
s 6
28.6%
g 5
23.8%
e 5
23.8%
c 2
 
9.5%
k 1
 
4.8%
a 1
 
4.8%
u 1
 
4.8%
Other Punctuation
ValueCountFrequency (%)
, 3663
99.3%
. 10
 
0.3%
@ 9
 
0.2%
· 5
 
0.1%
/ 2
 
0.1%
' 1
 
< 0.1%
Space Separator
ValueCountFrequency (%)
38984
100.0%
Open Punctuation
ValueCountFrequency (%)
( 8209
100.0%
Close Punctuation
ValueCountFrequency (%)
) 8206
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1170
100.0%
Math Symbol
ValueCountFrequency (%)
~ 14
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 148920
60.3%
Common 97842
39.6%
Latin 402
 
0.2%
Han 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
10916
 
7.3%
10354
 
7.0%
10344
 
6.9%
9046
 
6.1%
8918
 
6.0%
8728
 
5.9%
8499
 
5.7%
8198
 
5.5%
4456
 
3.0%
4202
 
2.8%
Other values (455) 65259
43.8%
Latin
ValueCountFrequency (%)
B 81
20.1%
A 57
14.2%
S 50
12.4%
G 38
9.5%
E 27
 
6.7%
C 25
 
6.2%
K 24
 
6.0%
P 13
 
3.2%
N 12
 
3.0%
D 8
 
2.0%
Other values (19) 67
16.7%
Common
ValueCountFrequency (%)
38984
39.8%
1 9252
 
9.5%
( 8209
 
8.4%
) 8206
 
8.4%
2 5194
 
5.3%
3 4263
 
4.4%
, 3663
 
3.7%
5 3348
 
3.4%
4 3175
 
3.2%
0 3033
 
3.1%
Other values (11) 10515
 
10.7%
Han
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 148920
60.3%
ASCII 98239
39.7%
None 5
 
< 0.1%
CJK 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
38984
39.7%
1 9252
 
9.4%
( 8209
 
8.4%
) 8206
 
8.4%
2 5194
 
5.3%
3 4263
 
4.3%
, 3663
 
3.7%
5 3348
 
3.4%
4 3175
 
3.2%
0 3033
 
3.1%
Other values (39) 10912
 
11.1%
Hangul
ValueCountFrequency (%)
10916
 
7.3%
10354
 
7.0%
10344
 
6.9%
9046
 
6.1%
8918
 
6.0%
8728
 
5.9%
8499
 
5.7%
8198
 
5.5%
4456
 
3.0%
4202
 
2.8%
Other values (455) 65259
43.8%
None
ValueCountFrequency (%)
· 5
100.0%
CJK
ValueCountFrequency (%)
1
100.0%

apvpermymd
Real number (ℝ)

Distinct5426
Distinct (%)54.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20061143
Minimum19631010
Maximum20210429
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-16T17:57:31.257853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19631010
5-th percentile19859664
Q119991122
median20071214
Q320140925
95-th percentile20210326
Maximum20210429
Range579419
Interquartile range (IQR)149803

Descriptive statistics

Standard deviation106372.28
Coefficient of variation (CV)0.0053024038
Kurtosis0.14858031
Mean20061143
Median Absolute Deviation (MAD)70793
Skewness-0.68254078
Sum2.0061143 × 1011
Variance1.1315062 × 1010
MonotonicityNot monotonic
2024-04-16T17:57:31.446178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19980713 81
 
0.8%
20210312 46
 
0.5%
20210319 37
 
0.4%
20210326 35
 
0.4%
20210402 33
 
0.3%
20210305 33
 
0.3%
20210322 31
 
0.3%
20210409 29
 
0.3%
20210426 29
 
0.3%
20210423 29
 
0.3%
Other values (5416) 9617
96.2%
ValueCountFrequency (%)
19631010 1
 
< 0.1%
19651010 3
< 0.1%
19651011 1
 
< 0.1%
19651116 2
< 0.1%
19651124 1
 
< 0.1%
19661001 1
 
< 0.1%
19661125 1
 
< 0.1%
19670302 1
 
< 0.1%
19670811 1
 
< 0.1%
19670814 1
 
< 0.1%
ValueCountFrequency (%)
20210429 22
0.2%
20210428 13
0.1%
20210427 17
0.2%
20210426 29
0.3%
20210425 1
 
< 0.1%
20210424 1
 
< 0.1%
20210423 29
0.3%
20210422 18
0.2%
20210421 16
0.2%
20210420 23
0.2%

dcbymd
Text

MISSING 

Distinct2647
Distinct (%)54.1%
Missing5106
Missing (%)51.1%
Memory size156.2 KiB
2024-04-16T17:57:31.778278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length7.9542297
Min length4

Characters and Unicode

Total characters38928
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

Unique1446 ?
Unique (%)29.5%

Sample

1st row20090430
2nd row20050614
3rd row20210407
4th row20141222
5th row20131016
ValueCountFrequency (%)
폐업일자 56
 
1.1%
20131222 40
 
0.8%
20121213 26
 
0.5%
20060216 25
 
0.5%
20210414 22
 
0.4%
20140820 19
 
0.4%
20170131 16
 
0.3%
20210421 12
 
0.2%
20210410 12
 
0.2%
20130607 11
 
0.2%
Other values (2637) 4655
95.1%
2024-04-16T17:57:32.304001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 12754
32.8%
2 8471
21.8%
1 7493
19.2%
3 1992
 
5.1%
4 1626
 
4.2%
6 1469
 
3.8%
7 1432
 
3.7%
5 1234
 
3.2%
8 1188
 
3.1%
9 1045
 
2.7%
Other values (4) 224
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 38704
99.4%
Other Letter 224
 
0.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 12754
33.0%
2 8471
21.9%
1 7493
19.4%
3 1992
 
5.1%
4 1626
 
4.2%
6 1469
 
3.8%
7 1432
 
3.7%
5 1234
 
3.2%
8 1188
 
3.1%
9 1045
 
2.7%
Other Letter
ValueCountFrequency (%)
56
25.0%
56
25.0%
56
25.0%
56
25.0%

Most occurring scripts

ValueCountFrequency (%)
Common 38704
99.4%
Hangul 224
 
0.6%

Most frequent character per script

Common
ValueCountFrequency (%)
0 12754
33.0%
2 8471
21.9%
1 7493
19.4%
3 1992
 
5.1%
4 1626
 
4.2%
6 1469
 
3.8%
7 1432
 
3.7%
5 1234
 
3.2%
8 1188
 
3.1%
9 1045
 
2.7%
Hangul
ValueCountFrequency (%)
56
25.0%
56
25.0%
56
25.0%
56
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 38704
99.4%
Hangul 224
 
0.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 12754
33.0%
2 8471
21.9%
1 7493
19.4%
3 1992
 
5.1%
4 1626
 
4.2%
6 1469
 
3.8%
7 1432
 
3.7%
5 1234
 
3.2%
8 1188
 
3.1%
9 1045
 
2.7%
Hangul
ValueCountFrequency (%)
56
25.0%
56
25.0%
56
25.0%
56
25.0%

clgstdt
Categorical

IMBALANCE 

Distinct16
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9927 
휴업시작일자
 
59
20160406
 
1
20180817
 
1
20170313
 
1
Other values (11)
 
11

Length

Max length8
Median length4
Mean length4.0174
Min length4

Unique

Unique14 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 9927
99.3%
휴업시작일자 59
 
0.6%
20160406 1
 
< 0.1%
20180817 1
 
< 0.1%
20170313 1
 
< 0.1%
20040325 1
 
< 0.1%
20130318 1
 
< 0.1%
20110225 1
 
< 0.1%
20060420 1
 
< 0.1%
20110408 1
 
< 0.1%
Other values (6) 6
 
0.1%

Length

2024-04-16T17:57:32.563475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 9927
99.3%
휴업시작일자 59
 
0.6%
20160406 1
 
< 0.1%
20180817 1
 
< 0.1%
20170313 1
 
< 0.1%
20040325 1
 
< 0.1%
20130318 1
 
< 0.1%
20110225 1
 
< 0.1%
20060420 1
 
< 0.1%
20110408 1
 
< 0.1%
Other values (6) 6
 
0.1%

clgenddt
Categorical

IMBALANCE 

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9931 
휴업종료일자
 
59
20161006
 
1
20210816
 
1
20171231
 
1
Other values (7)
 
7

Length

Max length8
Median length4
Mean length4.0158
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> 9931
99.3%
휴업종료일자 59
 
0.6%
20161006 1
 
< 0.1%
20210816 1
 
< 0.1%
20171231 1
 
< 0.1%
20160317 1
 
< 0.1%
20060831 1
 
< 0.1%
20120324 1
 
< 0.1%
20111010 1
 
< 0.1%
20121012 1
 
< 0.1%
Other values (2) 2
 
< 0.1%

Length

2024-04-16T17:57:32.858998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 9931
99.3%
휴업종료일자 59
 
0.6%
20161006 1
 
< 0.1%
20210816 1
 
< 0.1%
20171231 1
 
< 0.1%
20160317 1
 
< 0.1%
20060831 1
 
< 0.1%
20120324 1
 
< 0.1%
20111010 1
 
< 0.1%
20121012 1
 
< 0.1%
Other values (2) 2
 
< 0.1%

ropnymd
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9939 
재개업일자
 
59
20081007
 
1
20180531
 
1

Length

Max length8
Median length4
Mean length4.0067
Min length4

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 9939
99.4%
재개업일자 59
 
0.6%
20081007 1
 
< 0.1%
20180531 1
 
< 0.1%

Length

2024-04-16T17:57:33.100981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:57:33.212606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9939
99.4%
재개업일자 59
 
0.6%
20081007 1
 
< 0.1%
20180531 1
 
< 0.1%

trdstatenm
Categorical

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
0002
3551 
0000
2838 
영업/정상
1124 
02
1091 
01
804 
Other values (7)
592 

Length

Max length14
Median length4
Mean length3.689
Min length2

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
0002 3551
35.5%
0000 2838
28.4%
영업/정상 1124
 
11.2%
02 1091
 
10.9%
01 804
 
8.0%
0004 343
 
3.4%
폐업 227
 
2.3%
0001 13
 
0.1%
<NA> 4
 
< 0.1%
0003 3
 
< 0.1%
Other values (2) 2
 
< 0.1%

Length

2024-04-16T17:57:33.361097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0002 3551
35.5%
0000 2838
28.4%
영업/정상 1124
 
11.2%
02 1091
 
10.9%
01 804
 
8.0%
0004 343
 
3.4%
폐업 227
 
2.3%
0001 13
 
0.1%
na 4
 
< 0.1%
0003 3
 
< 0.1%
Other values (2) 2
 
< 0.1%

dtlstatenm
Categorical

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
폐업
4869 
정상
2942 
영업
1827 
말소
 
344
휴업
 
13
Other values (2)
 
5

Length

Max length4
Median length2
Mean length2.0008
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
폐업 4869
48.7%
정상 2942
29.4%
영업 1827
 
18.3%
말소 344
 
3.4%
휴업 13
 
0.1%
행정처분 3
 
< 0.1%
영업중 2
 
< 0.1%

Length

2024-04-16T17:57:33.474104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:57:33.591129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 4869
48.7%
정상 2942
29.4%
영업 1827
 
18.3%
말소 344
 
3.4%
휴업 13
 
0.1%
행정처분 3
 
< 0.1%
영업중 2
 
< 0.1%

x
Text

MISSING 

Distinct7280
Distinct (%)75.4%
Missing344
Missing (%)3.4%
Memory size156.2 KiB
2024-04-16T17:57:33.803862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length20
Mean length19.994615
Min length7

Characters and Unicode

Total characters193068
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

Unique6079 ?
Unique (%)63.0%

Sample

1st row383848.17053700000
2nd row396217.102225
3rd row390220.95452402
4th row386124.196932643
5th row384763.13178000000
ValueCountFrequency (%)
393952.264486105 43
 
0.4%
381223.93770800000 37
 
0.4%
381150.24062500000 30
 
0.3%
385590.814676765 26
 
0.3%
394015.45385100000 25
 
0.3%
387271.299492377 23
 
0.2%
387443.21456800000 18
 
0.2%
387539.767677801 18
 
0.2%
381201.909278 17
 
0.2%
380816.783733047 17
 
0.2%
Other values (7270) 9402
97.4%
2024-04-16T17:57:34.137713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 45542
23.6%
29290
15.2%
3 19079
9.9%
8 15212
 
7.9%
9 13341
 
6.9%
7 10818
 
5.6%
1 10659
 
5.5%
2 10232
 
5.3%
5 10022
 
5.2%
4 9697
 
5.0%
Other values (9) 19176
9.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 154165
79.9%
Space Separator 29290
 
15.2%
Other Punctuation 9585
 
5.0%
Other Letter 16
 
< 0.1%
Open Punctuation 4
 
< 0.1%
Uppercase Letter 4
 
< 0.1%
Close Punctuation 4
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 45542
29.5%
3 19079
12.4%
8 15212
 
9.9%
9 13341
 
8.7%
7 10818
 
7.0%
1 10659
 
6.9%
2 10232
 
6.6%
5 10022
 
6.5%
4 9697
 
6.3%
6 9563
 
6.2%
Other Letter
ValueCountFrequency (%)
4
25.0%
4
25.0%
4
25.0%
4
25.0%
Space Separator
ValueCountFrequency (%)
29290
100.0%
Other Punctuation
ValueCountFrequency (%)
. 9585
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%
Uppercase Letter
ValueCountFrequency (%)
X 4
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 193048
> 99.9%
Hangul 16
 
< 0.1%
Latin 4
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 45542
23.6%
29290
15.2%
3 19079
9.9%
8 15212
 
7.9%
9 13341
 
6.9%
7 10818
 
5.6%
1 10659
 
5.5%
2 10232
 
5.3%
5 10022
 
5.2%
4 9697
 
5.0%
Other values (4) 19156
9.9%
Hangul
ValueCountFrequency (%)
4
25.0%
4
25.0%
4
25.0%
4
25.0%
Latin
ValueCountFrequency (%)
X 4
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 193052
> 99.9%
Hangul 16
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 45542
23.6%
29290
15.2%
3 19079
9.9%
8 15212
 
7.9%
9 13341
 
6.9%
7 10818
 
5.6%
1 10659
 
5.5%
2 10232
 
5.3%
5 10022
 
5.2%
4 9697
 
5.0%
Other values (5) 19160
9.9%
Hangul
ValueCountFrequency (%)
4
25.0%
4
25.0%
4
25.0%
4
25.0%

y
Text

MISSING 

Distinct7280
Distinct (%)75.4%
Missing344
Missing (%)3.4%
Memory size156.2 KiB
2024-04-16T17:57:34.357201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length20
Mean length19.994615
Min length7

Characters and Unicode

Total characters193068
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

Unique6079 ?
Unique (%)63.0%

Sample

1st row186012.39549000000
2nd row194891.400984
3rd row189976.508572936
4th row187156.935956832
5th row186066.82398600000
ValueCountFrequency (%)
187602.933160728 43
 
0.4%
184549.28339000000 37
 
0.4%
190717.70395600000 30
 
0.3%
179553.867031936 26
 
0.3%
187900.93961700000 25
 
0.3%
186099.137533193 23
 
0.2%
186484.77508400000 18
 
0.2%
184402.96650913 18
 
0.2%
184537.273724 17
 
0.2%
175515.467301635 17
 
0.2%
Other values (7270) 9402
97.4%
2024-04-16T17:57:34.678775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 45391
23.5%
29187
15.1%
1 19418
10.1%
8 14654
 
7.6%
9 13832
 
7.2%
7 11480
 
5.9%
6 10120
 
5.2%
3 10023
 
5.2%
4 9911
 
5.1%
2 9818
 
5.1%
Other values (9) 19234
10.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 154268
79.9%
Space Separator 29187
 
15.1%
Other Punctuation 9585
 
5.0%
Other Letter 16
 
< 0.1%
Open Punctuation 4
 
< 0.1%
Uppercase Letter 4
 
< 0.1%
Close Punctuation 4
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 45391
29.4%
1 19418
12.6%
8 14654
 
9.5%
9 13832
 
9.0%
7 11480
 
7.4%
6 10120
 
6.6%
3 10023
 
6.5%
4 9911
 
6.4%
2 9818
 
6.4%
5 9621
 
6.2%
Other Letter
ValueCountFrequency (%)
4
25.0%
4
25.0%
4
25.0%
4
25.0%
Space Separator
ValueCountFrequency (%)
29187
100.0%
Other Punctuation
ValueCountFrequency (%)
. 9585
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%
Uppercase Letter
ValueCountFrequency (%)
Y 4
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 193048
> 99.9%
Hangul 16
 
< 0.1%
Latin 4
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 45391
23.5%
29187
15.1%
1 19418
10.1%
8 14654
 
7.6%
9 13832
 
7.2%
7 11480
 
5.9%
6 10120
 
5.2%
3 10023
 
5.2%
4 9911
 
5.1%
2 9818
 
5.1%
Other values (4) 19214
10.0%
Hangul
ValueCountFrequency (%)
4
25.0%
4
25.0%
4
25.0%
4
25.0%
Latin
ValueCountFrequency (%)
Y 4
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 193052
> 99.9%
Hangul 16
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 45391
23.5%
29187
15.1%
1 19418
10.1%
8 14654
 
7.6%
9 13832
 
7.2%
7 11480
 
5.9%
6 10120
 
5.2%
3 10023
 
5.2%
4 9911
 
5.1%
2 9818
 
5.1%
Other values (5) 19218
10.0%
Hangul
ValueCountFrequency (%)
4
25.0%
4
25.0%
4
25.0%
4
25.0%

lastmodts
Real number (ℝ)

Distinct9592
Distinct (%)95.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0130505 × 1013
Minimum1.9990303 × 1013
Maximum2.0210429 × 1013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-16T17:57:34.808233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.9990303 × 1013
5-th percentile2.0031009 × 1013
Q12.0090915 × 1013
median2.0140414 × 1013
Q32.0170808 × 1013
95-th percentile2.0210409 × 1013
Maximum2.0210429 × 1013
Range2.2012618 × 1011
Interquartile range (IQR)7.9893233 × 1010

Descriptive statistics

Standard deviation5.360046 × 1010
Coefficient of variation (CV)0.0026626486
Kurtosis-0.73967297
Mean2.0130505 × 1013
Median Absolute Deviation (MAD)3.9306536 × 1010
Skewness-0.3904265
Sum2.0130505 × 1017
Variance2.8730093 × 1021
MonotonicityNot monotonic
2024-04-16T17:57:34.931122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20061226000000 18
 
0.2%
20210415041509 17
 
0.2%
20040823000000 12
 
0.1%
19990318000000 12
 
0.1%
20210422041509 10
 
0.1%
20050615000000 9
 
0.1%
19990317000000 9
 
0.1%
20050525000000 9
 
0.1%
19990319000000 9
 
0.1%
20050614000000 8
 
0.1%
Other values (9582) 9887
98.9%
ValueCountFrequency (%)
19990303000000 1
 
< 0.1%
19990315000000 6
0.1%
19990316000000 5
0.1%
19990317000000 9
0.1%
19990318000000 12
0.1%
19990319000000 9
0.1%
19990323000000 1
 
< 0.1%
19990324000000 1
 
< 0.1%
19990511000000 3
 
< 0.1%
19990610000000 1
 
< 0.1%
ValueCountFrequency (%)
20210429182318 1
< 0.1%
20210429180705 1
< 0.1%
20210429180201 1
< 0.1%
20210429175803 1
< 0.1%
20210429175226 2
< 0.1%
20210429173504 1
< 0.1%
20210429173008 1
< 0.1%
20210429171009 1
< 0.1%
20210429163143 1
< 0.1%
20210429163106 1
< 0.1%

uptaenm
Categorical

IMBALANCE 

Distinct22
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
식육판매업
5485 
제과점영업
1962 
우유류판매업
857 
즉석판매제조가공업
714 
<NA>
 
284
Other values (17)
698 

Length

Max length13
Median length5
Mean length5.6059
Min length2

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
식육판매업 5485
54.9%
제과점영업 1962
 
19.6%
우유류판매업 857
 
8.6%
즉석판매제조가공업 714
 
7.1%
<NA> 284
 
2.8%
축산물유통전문판매업 206
 
2.1%
축산물수입판매업 170
 
1.7%
식용란수집판매업 94
 
0.9%
집단급식소 식품판매업 41
 
0.4%
유통전문판매업 38
 
0.4%
Other values (12) 149
 
1.5%

Length

2024-04-16T17:57:35.039210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
식육판매업 5485
54.5%
제과점영업 1962
 
19.5%
우유류판매업 857
 
8.5%
즉석판매제조가공업 714
 
7.1%
na 284
 
2.8%
축산물유통전문판매업 206
 
2.0%
축산물수입판매업 170
 
1.7%
식용란수집판매업 94
 
0.9%
집단급식소 41
 
0.4%
식품판매업 41
 
0.4%
Other values (12) 211
 
2.1%

sitetel
Text

MISSING 

Distinct229
Distinct (%)2.5%
Missing784
Missing (%)7.8%
Memory size156.2 KiB
2024-04-16T17:57:35.170800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length12
Mean length11.965929
Min length3

Characters and Unicode

Total characters110278
Distinct characters17
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique190 ?
Unique (%)2.1%

Sample

1st row051-123-1234
2nd row051-123-1234
3rd row051-123-1234
4th row051 362 6305
5th row051-123-1234
ValueCountFrequency (%)
051-123-1234 8913
92.7%
051 172
 
1.8%
전화번호 21
 
0.2%
831 17
 
0.2%
5711 13
 
0.1%
031 12
 
0.1%
062 9
 
0.1%
055 7
 
0.1%
070 7
 
0.1%
02 6
 
0.1%
Other values (335) 441
 
4.6%
2024-04-16T17:57:35.415201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 27230
24.7%
2 18069
16.4%
3 18042
16.4%
- 17901
16.2%
0 9388
 
8.5%
5 9322
 
8.5%
4 9069
 
8.2%
412
 
0.4%
6 204
 
0.2%
7 204
 
0.2%
Other values (7) 437
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 91880
83.3%
Dash Punctuation 17901
 
16.2%
Space Separator 412
 
0.4%
Other Letter 84
 
0.1%
Other Punctuation 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 27230
29.6%
2 18069
19.7%
3 18042
19.6%
0 9388
 
10.2%
5 9322
 
10.1%
4 9069
 
9.9%
6 204
 
0.2%
7 204
 
0.2%
8 180
 
0.2%
9 172
 
0.2%
Other Letter
ValueCountFrequency (%)
21
25.0%
21
25.0%
21
25.0%
21
25.0%
Dash Punctuation
ValueCountFrequency (%)
- 17901
100.0%
Space Separator
ValueCountFrequency (%)
412
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 110194
99.9%
Hangul 84
 
0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
1 27230
24.7%
2 18069
16.4%
3 18042
16.4%
- 17901
16.2%
0 9388
 
8.5%
5 9322
 
8.5%
4 9069
 
8.2%
412
 
0.4%
6 204
 
0.2%
7 204
 
0.2%
Other values (3) 353
 
0.3%
Hangul
ValueCountFrequency (%)
21
25.0%
21
25.0%
21
25.0%
21
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 110194
99.9%
Hangul 84
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 27230
24.7%
2 18069
16.4%
3 18042
16.4%
- 17901
16.2%
0 9388
 
8.5%
5 9322
 
8.5%
4 9069
 
8.2%
412
 
0.4%
6 204
 
0.2%
7 204
 
0.2%
Other values (3) 353
 
0.3%
Hangul
ValueCountFrequency (%)
21
25.0%
21
25.0%
21
25.0%
21
25.0%

bdngownsenm
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9785 
자가
 
153
건물소유구분명
 
55
임대
 
7

Length

Max length7
Median length4
Mean length3.9845
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> 9785
97.9%
자가 153
 
1.5%
건물소유구분명 55
 
0.5%
임대 7
 
0.1%

Length

2024-04-16T17:57:35.515178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:57:35.592403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9785
97.9%
자가 153
 
1.5%
건물소유구분명 55
 
0.5%
임대 7
 
0.1%

fctyowkepcnt
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9792 
0
 
148
공장사무직종업원수
 
59
1
 
1

Length

Max length9
Median length4
Mean length3.9848
Min length1

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 9792
97.9%
0 148
 
1.5%
공장사무직종업원수 59
 
0.6%
1 1
 
< 0.1%

Length

2024-04-16T17:57:35.684962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:57:35.768013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9792
97.9%
0 148
 
1.5%
공장사무직종업원수 59
 
0.6%
1 1
 
< 0.1%

fctypdtjobepcnt
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9793 
0
 
147
공장생산직종업원수
 
59
1
 
1

Length

Max length9
Median length4
Mean length3.9851
Min length1

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 9793
97.9%
0 147
 
1.5%
공장생산직종업원수 59
 
0.6%
1 1
 
< 0.1%

Length

2024-04-16T17:57:35.861314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:57:35.947911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9793
97.9%
0 147
 
1.5%
공장생산직종업원수 59
 
0.6%
1 1
 
< 0.1%

fctysiljobepcnt
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9793 
0
 
148
공장판매직종업원수
 
59

Length

Max length9
Median length4
Mean length3.9851
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 9793
97.9%
0 148
 
1.5%
공장판매직종업원수 59
 
0.6%

Length

2024-04-16T17:57:36.033325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:57:36.111661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9793
97.9%
0 148
 
1.5%
공장판매직종업원수 59
 
0.6%

rgtmbdsno
Categorical

IMBALANCE 

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
000
6123 
<NA>
3081 
L00
730 
권리주체일련번호
 
56
100
 
4
Other values (4)
 
6

Length

Max length8
Median length3
Mean length3.3361
Min length3

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
000 6123
61.2%
<NA> 3081
30.8%
L00 730
 
7.3%
권리주체일련번호 56
 
0.6%
100 4
 
< 0.1%
010 2
 
< 0.1%
L01 2
 
< 0.1%
200 1
 
< 0.1%
F00 1
 
< 0.1%

Length

2024-04-16T17:57:36.215084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:57:36.328760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
000 6123
61.2%
na 3081
30.8%
l00 730
 
7.3%
권리주체일련번호 56
 
0.6%
100 4
 
< 0.1%
010 2
 
< 0.1%
l01 2
 
< 0.1%
200 1
 
< 0.1%
f00 1
 
< 0.1%

wtrsplyfacilsenm
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
8839 
상수도전용
1100 
급수시설구분명
 
58
지하수전용
 
2
간이상수도
 
1

Length

Max length7
Median length4
Mean length4.1277
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> 8839
88.4%
상수도전용 1100
 
11.0%
급수시설구분명 58
 
0.6%
지하수전용 2
 
< 0.1%
간이상수도 1
 
< 0.1%

Length

2024-04-16T17:57:36.689203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:57:36.767357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 8839
88.4%
상수도전용 1100
 
11.0%
급수시설구분명 58
 
0.6%
지하수전용 2
 
< 0.1%
간이상수도 1
 
< 0.1%

maneipcnt
Categorical

IMBALANCE 

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9163 
0
 
717
남성종사자수
 
59
1
 
49
2
 
7
Other values (2)
 
5

Length

Max length6
Median length4
Mean length3.7784
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> 9163
91.6%
0 717
 
7.2%
남성종사자수 59
 
0.6%
1 49
 
0.5%
2 7
 
0.1%
3 3
 
< 0.1%
4 2
 
< 0.1%

Length

2024-04-16T17:57:36.851390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:57:36.931178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9163
91.6%
0 717
 
7.2%
남성종사자수 59
 
0.6%
1 49
 
0.5%
2 7
 
0.1%
3 3
 
< 0.1%
4 2
 
< 0.1%

multusnupsoyn
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
N
9867 
<NA>
 
112
Y
 
19
 
2

Length

Max length4
Median length1
Mean length1.0336
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
N 9867
98.7%
<NA> 112
 
1.1%
Y 19
 
0.2%
2
 
< 0.1%

Length

2024-04-16T17:57:37.042213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:57:37.142022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
n 9867
98.7%
na 112
 
1.1%
y 19
 
0.2%
2
 
< 0.1%

lvsenm
Categorical

IMBALANCE 

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9329 
기타
 
430
자율
 
177
등급구분명
 
59
우수
 
2
Other values (2)
 
3

Length

Max length5
Median length4
Mean length3.8835
Min length2

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 9329
93.3%
기타 430
 
4.3%
자율 177
 
1.8%
등급구분명 59
 
0.6%
우수 2
 
< 0.1%
지도 2
 
< 0.1%
관리 1
 
< 0.1%

Length

2024-04-16T17:57:37.250494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:57:37.354925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9329
93.3%
기타 430
 
4.3%
자율 177
 
1.8%
등급구분명 59
 
0.6%
우수 2
 
< 0.1%
지도 2
 
< 0.1%
관리 1
 
< 0.1%

isream
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9937 
보증액
 
59
0
 
4

Length

Max length4
Median length4
Mean length3.9929
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> 9937
99.4%
보증액 59
 
0.6%
0 4
 
< 0.1%

Length

2024-04-16T17:57:37.449424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:57:37.527321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9937
99.4%
보증액 59
 
0.6%
0 4
 
< 0.1%

hoffepcnt
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9792 
0
 
148
본사종업원수
 
59
3
 
1

Length

Max length6
Median length4
Mean length3.9671
Min length1

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 9792
97.9%
0 148
 
1.5%
본사종업원수 59
 
0.6%
3 1
 
< 0.1%

Length

2024-04-16T17:57:37.609449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:57:37.690933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9792
97.9%
0 148
 
1.5%
본사종업원수 59
 
0.6%
3 1
 
< 0.1%

equsiz
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9941 
설비규격
 
59

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> 9941
99.4%
설비규격 59
 
0.6%

Length

2024-04-16T17:57:37.774647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:57:37.852803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9941
99.4%
설비규격 59
 
0.6%

faciltotscp
Text

MISSING 

Distinct1500
Distinct (%)47.8%
Missing6862
Missing (%)68.6%
Memory size156.2 KiB
2024-04-16T17:57:38.129616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length3.0764818
Min length1

Characters and Unicode

Total characters9654
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

Unique1263 ?
Unique (%)40.2%

Sample

1st row0
2nd row0
3rd row129.48
4th row0
5th row0
ValueCountFrequency (%)
0 1213
38.7%
33 11
 
0.4%
24 10
 
0.3%
66 9
 
0.3%
30 8
 
0.3%
20 8
 
0.3%
25 8
 
0.3%
3 7
 
0.2%
26 7
 
0.2%
22 7
 
0.2%
Other values (1488) 1850
59.0%
2024-04-16T17:57:38.557767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 1665
17.2%
0 1594
16.5%
2 943
9.8%
1 893
9.3%
3 766
7.9%
4 718
7.4%
6 715
7.4%
5 700
7.3%
8 596
 
6.2%
9 525
 
5.4%
Other values (6) 539
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7974
82.6%
Other Punctuation 1665
 
17.2%
Other Letter 15
 
0.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1594
20.0%
2 943
11.8%
1 893
11.2%
3 766
9.6%
4 718
9.0%
6 715
9.0%
5 700
8.8%
8 596
 
7.5%
9 525
 
6.6%
7 524
 
6.6%
Other Letter
ValueCountFrequency (%)
3
20.0%
3
20.0%
3
20.0%
3
20.0%
3
20.0%
Other Punctuation
ValueCountFrequency (%)
. 1665
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 9639
99.8%
Hangul 15
 
0.2%

Most frequent character per script

Common
ValueCountFrequency (%)
. 1665
17.3%
0 1594
16.5%
2 943
9.8%
1 893
9.3%
3 766
7.9%
4 718
7.4%
6 715
7.4%
5 700
7.3%
8 596
 
6.2%
9 525
 
5.4%
Hangul
ValueCountFrequency (%)
3
20.0%
3
20.0%
3
20.0%
3
20.0%
3
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9639
99.8%
Hangul 15
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 1665
17.3%
0 1594
16.5%
2 943
9.8%
1 893
9.3%
3 766
7.9%
4 718
7.4%
6 715
7.4%
5 700
7.3%
8 596
 
6.2%
9 525
 
5.4%
Hangul
ValueCountFrequency (%)
3
20.0%
3
20.0%
3
20.0%
3
20.0%
3
20.0%

wmeipcnt
Categorical

IMBALANCE 

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9159 
0
 
714
여성종사자수
 
59
1
 
55
2
 
9
Other values (3)
 
4

Length

Max length6
Median length4
Mean length3.7773
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> 9159
91.6%
0 714
 
7.1%
여성종사자수 59
 
0.6%
1 55
 
0.5%
2 9
 
0.1%
4 2
 
< 0.1%
3 1
 
< 0.1%
11 1
 
< 0.1%

Length

2024-04-16T17:57:38.664994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:57:38.746985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9159
91.6%
0 714
 
7.1%
여성종사자수 59
 
0.6%
1 55
 
0.5%
2 9
 
0.1%
4 2
 
< 0.1%
3 1
 
< 0.1%
11 1
 
< 0.1%

trdpjubnsenm
Categorical

IMBALANCE 

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9223 
기타
 
512
주택가주변
 
99
아파트지역
 
75
영업장주변구분명
 
59
Other values (3)
 
32

Length

Max length8
Median length4
Mean length3.9513
Min length2

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 9223
92.2%
기타 512
 
5.1%
주택가주변 99
 
1.0%
아파트지역 75
 
0.8%
영업장주변구분명 59
 
0.6%
유흥업소밀집지역 24
 
0.2%
학교정화(상대) 7
 
0.1%
결혼예식장주변 1
 
< 0.1%

Length

2024-04-16T17:57:38.839927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:57:38.926589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9223
92.2%
기타 512
 
5.1%
주택가주변 99
 
1.0%
아파트지역 75
 
0.8%
영업장주변구분명 59
 
0.6%
유흥업소밀집지역 24
 
0.2%
학교정화(상대 7
 
0.1%
결혼예식장주변 1
 
< 0.1%

monam
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9937 
월세액
 
59
0
 
4

Length

Max length4
Median length4
Mean length3.9929
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> 9937
99.4%
월세액 59
 
0.6%
0 4
 
< 0.1%

Length

2024-04-16T17:57:39.023821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:57:39.098763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9937
99.4%
월세액 59
 
0.6%
0 4
 
< 0.1%

sntuptaenm
Categorical

IMBALANCE 

Distinct22
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
6862 
제과점영업
1962 
즉석판매제조가공업
714 
전자상거래(통신판매업)
 
185
영업장판매
 
62
Other values (17)
 
215

Length

Max length14
Median length4
Mean length4.8005
Min length2

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 6862
68.6%
제과점영업 1962
 
19.6%
즉석판매제조가공업 714
 
7.1%
전자상거래(통신판매업) 185
 
1.8%
영업장판매 62
 
0.6%
집단급식소 식품판매업 41
 
0.4%
유통전문판매업 38
 
0.4%
식품자동판매기영업 33
 
0.3%
기타 식품제조가공업 24
 
0.2%
식품소분업 22
 
0.2%
Other values (12) 57
 
0.6%

Length

2024-04-16T17:57:39.204584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 6862
68.1%
제과점영업 1962
 
19.5%
즉석판매제조가공업 714
 
7.1%
전자상거래(통신판매업 185
 
1.8%
영업장판매 62
 
0.6%
집단급식소 41
 
0.4%
식품판매업 41
 
0.4%
유통전문판매업 38
 
0.4%
식품자동판매기영업 33
 
0.3%
기타 33
 
0.3%
Other values (13) 107
 
1.1%

jtupsomainedf
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9941 
전통업소주된음식
 
59

Length

Max length8
Median length4
Mean length4.0236
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> 9941
99.4%
전통업소주된음식 59
 
0.6%

Length

2024-04-16T17:57:39.338039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:57:39.420087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9941
99.4%
전통업소주된음식 59
 
0.6%

jtupsoasgnno
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9940 
전통업소지정번호
 
59
-+
 
1

Length

Max length8
Median length4
Mean length4.0234
Min length2

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 9940
99.4%
전통업소지정번호 59
 
0.6%
-+ 1
 
< 0.1%

Length

2024-04-16T17:57:39.500932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:57:39.589709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9940
99.4%
전통업소지정번호 59
 
0.6%
1
 
< 0.1%

totepnum
Categorical

IMBALANCE 

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
식육판매업
5448 
<NA>
3186 
우유류판매업
818 
축산물유통전문판매업
 
192
축산물수입판매업
 
170
Other values (5)
 
186

Length

Max length10
Median length5
Mean length4.95
Min length1

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
식육판매업 5448
54.5%
<NA> 3186
31.9%
우유류판매업 818
 
8.2%
축산물유통전문판매업 192
 
1.9%
축산물수입판매업 170
 
1.7%
식용란수집판매업 87
 
0.9%
총종업원수 59
 
0.6%
식육부산물전문판매업 33
 
0.3%
0 6
 
0.1%
1 1
 
< 0.1%

Length

2024-04-16T17:57:39.676377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:57:39.772487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
식육판매업 5448
54.5%
na 3186
31.9%
우유류판매업 818
 
8.2%
축산물유통전문판매업 192
 
1.9%
축산물수입판매업 170
 
1.7%
식용란수집판매업 87
 
0.9%
총종업원수 59
 
0.6%
식육부산물전문판매업 33
 
0.3%
0 6
 
0.1%
1 1
 
< 0.1%

lindprcbgbnnm
Categorical

IMBALANCE 

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
축산물판매업
6748 
<NA>
3086 
축산물가공업구분명
 
57
우유류판매업
 
39
식육판매업
 
37
Other values (4)
 
33

Length

Max length10
Median length6
Mean length5.403
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
축산물판매업 6748
67.5%
<NA> 3086
30.9%
축산물가공업구분명 57
 
0.6%
우유류판매업 39
 
0.4%
식육판매업 37
 
0.4%
축산물유통전문판매업 14
 
0.1%
식용란수집판매업 7
 
0.1%
식육가공업 7
 
0.1%
식육포장처리업 5
 
0.1%

Length

2024-04-16T17:57:39.888107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:57:39.981115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
축산물판매업 6748
67.5%
na 3086
30.9%
축산물가공업구분명 57
 
0.6%
우유류판매업 39
 
0.4%
식육판매업 37
 
0.4%
축산물유통전문판매업 14
 
0.1%
식용란수집판매업 7
 
0.1%
식육가공업 7
 
0.1%
식육포장처리업 5
 
< 0.1%

lindjobgbnnm
Categorical

IMBALANCE 

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9829 
축산물판매업
 
97
축산업무구분명
 
56
축산물가공업
 
7
식육포장처리업
 
5
Other values (2)
 
6

Length

Max length7
Median length4
Mean length4.0403
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> 9829
98.3%
축산물판매업 97
 
1.0%
축산업무구분명 56
 
0.6%
축산물가공업 7
 
0.1%
식육포장처리업 5
 
0.1%
축산물운반업 4
 
< 0.1%
축산물보관업 2
 
< 0.1%

Length

2024-04-16T17:57:40.083251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:57:40.167535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9829
98.3%
축산물판매업 97
 
1.0%
축산업무구분명 56
 
0.6%
축산물가공업 7
 
0.1%
식육포장처리업 5
 
< 0.1%
축산물운반업 4
 
< 0.1%
축산물보관업 2
 
< 0.1%

lindseqno
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9941 
축산일련번호
 
59

Length

Max length6
Median length4
Mean length4.0118
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> 9941
99.4%
축산일련번호 59
 
0.6%

Length

2024-04-16T17:57:40.269626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:57:40.355234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9941
99.4%
축산일련번호 59
 
0.6%

homepage
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9941 
홈페이지
 
59

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> 9941
99.4%
홈페이지 59
 
0.6%

Length

2024-04-16T17:57:40.461902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:57:40.552384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9941
99.4%
홈페이지 59
 
0.6%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2021-05-01 05:26:03
Maximum2021-05-01 05:26:06
2024-04-16T17:57:40.635246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T17:57:40.722182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=4)

Sample

skeyopnsfteamcodemgtnoopnsvcidupdategbnupdatedtopnsvcnmbplcnmsitepostnositewhladdrrdnpostnordnwhladdrapvpermymddcbymdclgstdtclgenddtropnymdtrdstatenmdtlstatenmxylastmodtsuptaenmsitetelbdngownsenmfctyowkepcntfctypdtjobepcntfctysiljobepcntrgtmbdsnowtrsplyfacilsenmmaneipcntmultusnupsoynlvsenmisreamhoffepcntequsizfaciltotscpwmeipcnttrdpjubnsenmmonamsntuptaenmjtupsomainedfjtupsoasgnnototepnumlindprcbgbnnmlindjobgbnnmlindseqnohomepagelast_load_dttm
15911588329000032900000092008002407_22_04_PI2018-08-31 23:59:59.0<NA>신우유통<NA>부산광역시 부산진구 개금동 481-8번지48947부산광역시 부산진구 백양대로313번길 9 (개금동)2008082120090430<NA><NA><NA>0002폐업383848.17053700000186012.3954900000020090430153527식육판매업051-123-1234<NA><NA><NA><NA>000<NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>식육판매업축산물판매업<NA><NA><NA>2021-05-01 05:26:03
46224618333000033300000092001002207_22_04_PI2018-08-31 23:59:59.0<NA>철마성호정육<NA>부산광역시 해운대구 반송동 40-1063번지48947<NA>2001061520050614<NA><NA><NA>0002폐업396217.102225194891.40098420050614105507식육판매업051-123-1234<NA><NA><NA><NA>000<NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>식육판매업축산물판매업<NA><NA><NA>2021-05-01 05:26:04
139521994433700003370000-107-2021-0003407_22_19_PU2021-04-10 02:40:00.0즉석판매제조가공업덕유산유통611811부산광역시 연제구 연산동 105-1 홈플러스 부산연산점47552부산광역시 연제구 반송로 88, 홈플러스 부산연산점 (연산동)2021030920210407<NA><NA><NA>폐업폐업390220.95452402189976.50857293620210408041508즉석판매제조가공업<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>2021-05-01 05:26:06
137312016032900003290000-107-2021-0005907_22_19_PU2021-04-11 02:40:00.0즉석판매제조가공업당감찬들614838부산광역시 부산진구 부암동 33247139부산광역시 부산진구 동평로 129, 1층 (부암동)20210318<NA><NA><NA><NA>영업/정상영업386124.196932643187156.93595683220210409174128즉석판매제조가공업<NA><NA>000<NA><NA><NA>N<NA><NA>0<NA>0<NA><NA><NA>즉석판매제조가공업<NA><NA><NA><NA><NA><NA><NA>2021-05-01 05:26:06
10671071329000032900000092003002107_22_04_PI2018-08-31 23:59:59.0<NA>자운식육점<NA>부산광역시 부산진구 개금동 154-1번지48947부산광역시 부산진구 가야대로507번길 11 (개금동)20030429<NA><NA><NA><NA>0000정상384763.13178000000186066.8239860000020090629160658식육판매업051-123-1234<NA><NA><NA><NA>000<NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>식육판매업축산물판매업<NA><NA><NA>2021-05-01 05:26:03
108671087933200003320000-121-2015-0001107_22_18_PU2021-04-22 02:40:00.0제과점영업드래곤과자점(동문점)616827부산광역시 북구 만덕동 835-346611부산광역시 북구 덕천로276번길 32-14 (만덕동)20151026<NA><NA><NA><NA>영업/정상영업385091.793401198191787.62237309220210420141532제과점영업051 362 6305<NA><NA><NA><NA><NA>상수도전용<NA>N<NA><NA><NA><NA>129.48<NA><NA><NA>제과점영업<NA><NA><NA><NA><NA><NA><NA>2021-05-01 05:26:05
88438842339000033900000091989000707_22_04_PI2018-08-31 23:59:59.0<NA>만물식육점<NA>부산광역시 사상구 학장동 565-1번지47052부산광역시 사상구 학감대로 111 (학장동)1989110620141222<NA><NA><NA>0002폐업381223.93770800000184549.2833900000020141223151733식육판매업051-123-1234<NA><NA><NA><NA>000<NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>식육판매업축산물판매업<NA><NA><NA>2021-05-01 05:26:04
31633159331000033100000091984000307_22_04_PI2018-08-31 23:59:59.0<NA>중앙식육점<NA>부산광역시 남구 대연동 1781-59번지48947부산광역시 남구 중앙고길 167 (대연동)1984082820131016<NA><NA><NA>0002폐업389366.70264700000184371.5684390000020140106141855식육판매업051-123-1234<NA><NA><NA><NA>000<NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>식육판매업축산물판매업<NA><NA><NA>2021-05-01 05:26:03
69326924336000033600000092016003107_22_04_PI2018-08-31 23:59:59.0<NA>대한푸드<NA><NA>46703부산광역시 강서구 체육공원로6번길 56 (대저1동)20161220<NA><NA><NA><NA>0000정상380043.61206600000191740.6112230000020170612153527축산물유통전문판매업051-123-1234<NA><NA><NA><NA>000<NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>축산물유통전문판매업축산물판매업<NA><NA><NA>2021-05-01 05:26:04
124891429833100003310000-134-2019-0001907_22_03_PI2019-05-19 02:20:50.0건강기능식품일반판매업이치방 유통608817부산광역시 남구 대연동 1578-48번지 302호48448부산광역시 남구 진남로154번길 10, 3층 302호 (대연동)20190517<NA><NA><NA><NA>영업/정상영업389509.731252298184346.3634992520190517140825<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>2021-05-01 05:26:05
skeyopnsfteamcodemgtnoopnsvcidupdategbnupdatedtopnsvcnmbplcnmsitepostnositewhladdrrdnpostnordnwhladdrapvpermymddcbymdclgstdtclgenddtropnymdtrdstatenmdtlstatenmxylastmodtsuptaenmsitetelbdngownsenmfctyowkepcntfctypdtjobepcntfctysiljobepcntrgtmbdsnowtrsplyfacilsenmmaneipcntmultusnupsoynlvsenmisreamhoffepcntequsizfaciltotscpwmeipcnttrdpjubnsenmmonamsntuptaenmjtupsomainedfjtupsoasgnnototepnumlindprcbgbnnmlindjobgbnnmlindseqnohomepagelast_load_dttm
88488847339000033900000091984000207_22_04_PI2018-08-31 23:59:59.0<NA>대성식육점<NA>부산광역시 사상구 덕포동 422-6번지48947부산광역시 사상구 사상로293번길 17 (덕포동)1984041120170706<NA><NA><NA>0002폐업380558.52596200000187878.5551470000020170706102942식육판매업051-123-1234<NA><NA><NA><NA>000<NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>식육판매업축산물판매업<NA><NA><NA>2021-05-01 05:26:04
1352619206340000034000000092020002507_22_04_PI2020-12-20 00:23:06.0축산판매업부산상회<NA>부산광역시 기장군 정관읍 방곡리 427-346022부산광역시 기장군 정관읍 방곡5로 2-2320201218<NA><NA><NA><NA>영업/정상정상398684.41527569204815.97339345720201218105041우유류판매업<NA><NA><NA><NA><NA>000<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>우유류판매업축산물판매업<NA><NA>2021-05-01 05:26:06
95749580340000034000000092013002107_22_04_PI2018-08-31 23:59:59.0<NA>태양플러스 축산<NA>부산광역시 기장군 장안읍 월내리 265-5번지46037부산광역시 기장군 장안읍 해맞이로 41320130719<NA><NA><NA><NA>0000정상407372.101586206012.33549320180125164405식육판매업051-123-1234<NA><NA><NA><NA>000<NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>식육판매업축산물판매업<NA><NA><NA>2021-05-01 05:26:04
89658967339000033900000091998000507_22_04_PI2018-08-31 23:59:59.0<NA>(주)미진식품<NA>부산광역시 사상구 삼락동 51-4번지48947부산광역시 사상구 낙동대로 1554-8 (삼락동)1998112720121213<NA><NA><NA>0002폐업380989.08016600000190610.5515430000020121213104028식육판매업051-123-1234<NA><NA><NA><NA>L00<NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>식육판매업축산물판매업<NA><NA><NA>2021-05-01 05:26:04
123661239733400003340000-107-2018-0017407_22_19_PI2018-10-21 02:37:05.0즉석판매제조가공업수지INT's604822부산광역시 사하구 다대동 120-19번지49519부산광역시 사하구 다송로 58 (다대동)20181019<NA><NA><NA><NA><NA>영업380816.783733047175515.46730163520181019172813즉석판매제조가공업051-123-1234<NA><NA><NA><NA><NA><NA><NA>N<NA><NA><NA><NA>0<NA><NA><NA>즉석판매제조가공업<NA><NA><NA><NA><NA><NA><NA>2021-05-01 05:26:05
38033796332000033200000091995003407_22_04_PI2018-08-31 23:59:59.0<NA>주야식육점<NA>부산광역시 북구 구포동 1187-1번지48947부산광역시 북구 낙동대로1570번나길 17 (구포동)1995101120140501<NA><NA><NA>0002폐업381206.39575100000190672.6210450000020140616113512식육부산물전문판매업051-123-1234<NA><NA><NA><NA>000<NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>식육부산물전문판매업축산물판매업<NA><NA><NA>2021-05-01 05:26:03
46794676333000033300000092001001607_22_04_PI2018-08-31 23:59:59.0<NA>우진유통<NA>부산광역시 해운대구 좌동 425-11번지48079부산광역시 해운대구 대천로 132 (좌동)20010320<NA><NA><NA><NA>0004말소398281.52880000000188578.6830870000020140710134116식육판매업051-123-1234<NA><NA><NA><NA>000<NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>식육판매업축산물판매업<NA><NA><NA>2021-05-01 05:26:04
118421185433700003370000-121-2012-0000707_22_18_PI2018-08-31 23:59:59.0<NA>(주)쿱스토어부산 연산토곡점2611817부산광역시 연제구 연산동 400-14번지47565부산광역시 연제구 과정로 216 (연산동)2012061920170511<NA><NA><NA>02폐업391620.76403200000190002.3320000000020170511110838제과점영업051-123-1234<NA><NA><NA><NA><NA>상수도전용<NA>N<NA><NA><NA><NA>32.98<NA><NA><NA>제과점영업<NA><NA><NA><NA><NA><NA><NA>2021-05-01 05:26:05
54875487334000033400000092000001407_22_04_PI2018-08-31 23:59:59.0<NA>화인유통<NA>부산광역시 사하구 하단동 457-6번지48947<NA>2000051620060629<NA><NA><NA>0002폐업<NA><NA>20060629154607식육판매업051-123-1234<NA><NA><NA><NA>000<NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>식육판매업축산물판매업<NA><NA><NA>2021-05-01 05:26:04
70907084336000033600000092018002207_22_04_PI2018-08-31 23:59:59.0<NA>아진미트<NA>부산광역시 강서구 대저1동 3177-1번지46703부산광역시 강서구 신덕길2번길 53, 1층 (대저1동)20180618<NA><NA><NA><NA>0000정상379496.82494100000191355.8185810000020180618174322축산물유통전문판매업051-123-1234<NA><NA><NA><NA>000<NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>축산물유통전문판매업축산물판매업<NA><NA><NA>2021-05-01 05:26:04