Overview

Dataset statistics

Number of variables34
Number of observations7270
Missing cells9903
Missing cells (%)4.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.9 MiB
Average record size in memory276.0 B

Variable types

Text11
Numeric4
Categorical18
DateTime1

Alerts

updategbn is highly imbalanced (89.1%)Imbalance
opnsvcnm is highly imbalanced (80.8%)Imbalance
clgstdt is highly imbalanced (96.8%)Imbalance
clgenddt is highly imbalanced (96.8%)Imbalance
ropnymd is highly imbalanced (93.6%)Imbalance
trdstatenm is highly imbalanced (54.0%)Imbalance
dtlstatenm is highly imbalanced (57.7%)Imbalance
uptaenm is highly imbalanced (81.0%)Imbalance
bdngdngnum is highly imbalanced (71.4%)Imbalance
puprsenm is highly imbalanced (97.6%)Imbalance
bupnm is highly imbalanced (90.1%)Imbalance
insurjnyncode is highly imbalanced (55.3%)Imbalance
drmkcobnm is highly imbalanced (90.1%)Imbalance
ldercnt is highly imbalanced (68.7%)Imbalance
memcolltotstfnum is highly imbalanced (96.6%)Imbalance
last_load_dttm is highly imbalanced (99.6%)Imbalance
sitepostno has 1777 (24.4%) missing valuesMissing
sitewhladdr has 141 (1.9%) missing valuesMissing
rdnwhladdr has 349 (4.8%) missing valuesMissing
dcbymd has 3904 (53.7%) missing valuesMissing
x has 133 (1.8%) missing valuesMissing
y has 133 (1.8%) missing valuesMissing
sitetel has 108 (1.5%) missing valuesMissing
bdngyarea has 3336 (45.9%) missing valuesMissing
opnsfteamcode is highly skewed (γ1 = -21.7036597)Skewed
rdnpostno is highly skewed (γ1 = 59.01486431)Skewed
apvpermymd is highly skewed (γ1 = -59.01044049)Skewed
skey has unique valuesUnique

Reproduction

Analysis started2024-04-17 21:56:12.363003
Analysis finished2024-04-17 21:56:14.121438
Duration1.76 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

skey
Text

UNIQUE 

Distinct7270
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size56.9 KiB
2024-04-18T06:56:14.404298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length4
Mean length3.8909216
Min length1

Characters and Unicode

Total characters28287
Distinct characters19
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7270 ?
Unique (%)100.0%

Sample

1st row4
2nd row5
3rd row6
4th row7
5th row8
ValueCountFrequency (%)
4 1
 
< 0.1%
4837 1
 
< 0.1%
4864 1
 
< 0.1%
4863 1
 
< 0.1%
4862 1
 
< 0.1%
4861 1
 
< 0.1%
4860 1
 
< 0.1%
4859 1
 
< 0.1%
4858 1
 
< 0.1%
4857 1
 
< 0.1%
Other values (7261) 7261
99.9%
2024-04-18T06:56:15.271648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 3573
12.6%
3 3318
11.7%
2 3272
11.6%
4 3213
11.4%
5 3212
11.4%
6 2970
10.5%
7 2236
7.9%
8 2182
7.7%
0 2153
7.6%
9 2149
7.6%
Other values (9) 9
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 28278
> 99.9%
Lowercase Letter 5
 
< 0.1%
Uppercase Letter 3
 
< 0.1%
Space Separator 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 3573
12.6%
3 3318
11.7%
2 3272
11.6%
4 3213
11.4%
5 3212
11.4%
6 2970
10.5%
7 2236
7.9%
8 2182
7.7%
0 2153
7.6%
9 2149
7.6%
Lowercase Letter
ValueCountFrequency (%)
t 1
20.0%
u 1
20.0%
d 1
20.0%
i 1
20.0%
o 1
20.0%
Uppercase Letter
ValueCountFrequency (%)
P 1
33.3%
T 1
33.3%
S 1
33.3%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 28279
> 99.9%
Latin 8
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
1 3573
12.6%
3 3318
11.7%
2 3272
11.6%
4 3213
11.4%
5 3212
11.4%
6 2970
10.5%
7 2236
7.9%
8 2182
7.7%
0 2153
7.6%
9 2149
7.6%
Latin
ValueCountFrequency (%)
P 1
12.5%
T 1
12.5%
S 1
12.5%
t 1
12.5%
u 1
12.5%
d 1
12.5%
i 1
12.5%
o 1
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28287
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 3573
12.6%
3 3318
11.7%
2 3272
11.6%
4 3213
11.4%
5 3212
11.4%
6 2970
10.5%
7 2236
7.9%
8 2182
7.7%
0 2153
7.6%
9 2149
7.6%
Other values (9) 9
 
< 0.1%

opnsfteamcode
Real number (ℝ)

SKEWED 

Distinct17
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3326853.5
Minimum614853
Maximum3400000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size64.0 KiB
2024-04-18T06:56:15.400065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum614853
5-th percentile3260000
Q13300000
median3330000
Q33350000
95-th percentile3390000
Maximum3400000
Range2785147
Interquartile range (IQR)50000

Descriptive statistics

Standard deviation50136.759
Coefficient of variation (CV)0.015070324
Kurtosis1176.5319
Mean3326853.5
Median Absolute Deviation (MAD)30000
Skewness-21.70366
Sum2.4186225 × 1010
Variance2.5136946 × 109
MonotonicityNot monotonic
2024-04-18T06:56:15.497740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
3300000 837
11.5%
3290000 776
10.7%
3330000 704
9.7%
3310000 699
9.6%
3340000 617
8.5%
3350000 594
8.2%
3390000 508
 
7.0%
3320000 483
 
6.6%
3370000 411
 
5.7%
3380000 319
 
4.4%
Other values (7) 1322
18.2%
ValueCountFrequency (%)
614853 1
 
< 0.1%
3250000 205
 
2.8%
3260000 188
 
2.6%
3270000 184
 
2.5%
3280000 238
 
3.3%
3290000 776
10.7%
3300000 837
11.5%
3310000 699
9.6%
3320000 483
6.6%
3330000 704
9.7%
ValueCountFrequency (%)
3400000 280
 
3.9%
3390000 508
7.0%
3380000 319
4.4%
3370000 411
5.7%
3360000 226
 
3.1%
3350000 594
8.2%
3340000 617
8.5%
3330000 704
9.7%
3320000 483
6.6%
3310000 699
9.6%

mgtno
Text

Distinct1781
Distinct (%)24.5%
Missing0
Missing (%)0.0%
Memory size56.9 KiB
2024-04-18T06:56:15.678908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length20
Mean length20.0011
Min length20

Characters and Unicode

Total characters145408
Distinct characters31
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

Unique691 ?
Unique (%)9.5%

Sample

1st rowCDFH3301051997000002
2nd rowCDFH3301051999000001
3rd rowCDFH3301052003000001
4th rowCDFH3301052003000002
5th rowCDFH3301052004000001
ValueCountFrequency (%)
cdfh3301062019000002 17
 
0.2%
cdfh3301082003000001 16
 
0.2%
cdfh3301052009000001 16
 
0.2%
cdfh3301082003000002 16
 
0.2%
cdfh3301082010000002 16
 
0.2%
cdfh3301052010000001 16
 
0.2%
cdfh3301082010000001 16
 
0.2%
cdfh3301082016000001 16
 
0.2%
cdfh3301082014000001 16
 
0.2%
cdfh3301082009000005 16
 
0.2%
Other values (1775) 7113
97.8%
2024-04-18T06:56:15.996650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 59230
40.7%
3 16578
 
11.4%
1 14422
 
9.9%
2 9592
 
6.6%
C 7269
 
5.0%
D 7269
 
5.0%
F 7269
 
5.0%
H 7269
 
5.0%
9 4474
 
3.1%
8 4462
 
3.1%
Other values (21) 7574
 
5.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 116309
80.0%
Uppercase Letter 29076
 
20.0%
Other Letter 15
 
< 0.1%
Space Separator 5
 
< 0.1%
Dash Punctuation 1
 
< 0.1%
Open Punctuation 1
 
< 0.1%
Close Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
2
13.3%
2
13.3%
1
 
6.7%
1
 
6.7%
1
 
6.7%
1
 
6.7%
1
 
6.7%
1
 
6.7%
1
 
6.7%
1
 
6.7%
Other values (3) 3
20.0%
Decimal Number
ValueCountFrequency (%)
0 59230
50.9%
3 16578
 
14.3%
1 14422
 
12.4%
2 9592
 
8.2%
9 4474
 
3.8%
8 4462
 
3.8%
6 2651
 
2.3%
5 2220
 
1.9%
4 1453
 
1.2%
7 1227
 
1.1%
Uppercase Letter
ValueCountFrequency (%)
C 7269
25.0%
D 7269
25.0%
F 7269
25.0%
H 7269
25.0%
Space Separator
ValueCountFrequency (%)
5
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 116317
80.0%
Latin 29076
 
20.0%
Hangul 15
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 59230
50.9%
3 16578
 
14.3%
1 14422
 
12.4%
2 9592
 
8.2%
9 4474
 
3.8%
8 4462
 
3.8%
6 2651
 
2.3%
5 2220
 
1.9%
4 1453
 
1.2%
7 1227
 
1.1%
Other values (4) 8
 
< 0.1%
Hangul
ValueCountFrequency (%)
2
13.3%
2
13.3%
1
 
6.7%
1
 
6.7%
1
 
6.7%
1
 
6.7%
1
 
6.7%
1
 
6.7%
1
 
6.7%
1
 
6.7%
Other values (3) 3
20.0%
Latin
ValueCountFrequency (%)
C 7269
25.0%
D 7269
25.0%
F 7269
25.0%
H 7269
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 145393
> 99.9%
Hangul 15
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 59230
40.7%
3 16578
 
11.4%
1 14422
 
9.9%
2 9592
 
6.6%
C 7269
 
5.0%
D 7269
 
5.0%
F 7269
 
5.0%
H 7269
 
5.0%
9 4474
 
3.1%
8 4462
 
3.1%
Other values (8) 7559
 
5.2%
Hangul
ValueCountFrequency (%)
2
13.3%
2
13.3%
1
 
6.7%
1
 
6.7%
1
 
6.7%
1
 
6.7%
1
 
6.7%
1
 
6.7%
1
 
6.7%
1
 
6.7%
Other values (3) 3
20.0%

opnsvcid
Categorical

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size56.9 KiB
10_32_01_P
3059 
10_41_01_P
1745 
10_42_01_P
1404 
10_31_01_P
920 
10_35_01_P
 
95
Other values (5)
 
47

Length

Max length10
Median length10
Mean length9.9993122
Min length5

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row10_31_01_P
2nd row10_31_01_P
3rd row10_31_01_P
4th row10_31_01_P
5th row10_31_01_P

Common Values

ValueCountFrequency (%)
10_32_01_P 3059
42.1%
10_41_01_P 1745
24.0%
10_42_01_P 1404
19.3%
10_31_01_P 920
 
12.7%
10_35_01_P 95
 
1.3%
10_37_01_P 34
 
0.5%
10_33_02_P 8
 
0.1%
10_39_01_P 3
 
< 0.1%
47213 1
 
< 0.1%
10_33_01_P 1
 
< 0.1%

Length

2024-04-18T06:56:16.175989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T06:56:16.308342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
10_32_01_p 3059
42.1%
10_41_01_p 1745
24.0%
10_42_01_p 1404
19.3%
10_31_01_p 920
 
12.7%
10_35_01_p 95
 
1.3%
10_37_01_p 34
 
0.5%
10_33_02_p 8
 
0.1%
10_39_01_p 3
 
< 0.1%
47213 1
 
< 0.1%
10_33_01_p 1
 
< 0.1%

updategbn
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size56.9 KiB
I
7085 
U
 
184
부산광역시 부산진구 중앙대로 923-1, 2층 (양정동)
 
1

Length

Max length31
Median length1
Mean length1.0041265
Min length1

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
I 7085
97.5%
U 184
 
2.5%
부산광역시 부산진구 중앙대로 923-1, 2층 (양정동) 1
 
< 0.1%

Length

2024-04-18T06:56:16.487529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T06:56:16.594649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
i 7085
97.4%
u 184
 
2.5%
부산광역시 1
 
< 0.1%
부산진구 1
 
< 0.1%
중앙대로 1
 
< 0.1%
923-1 1
 
< 0.1%
2층 1
 
< 0.1%
양정동 1
 
< 0.1%
Distinct319
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Memory size56.9 KiB
Minimum2013-12-05 00:00:00
Maximum2021-05-01 02:40:00
2024-04-18T06:56:16.709091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T06:56:16.835531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

opnsvcnm
Categorical

IMBALANCE 

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size56.9 KiB
<NA>
6612 
체력단련장업
 
220
당구장업
 
160
체육도장업
 
150
골프연습장업
 
98
Other values (5)
 
30

Length

Max length7
Median length4
Mean length4.1116919
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> 6612
90.9%
체력단련장업 220
 
3.0%
당구장업 160
 
2.2%
체육도장업 150
 
2.1%
골프연습장업 98
 
1.3%
수영장업 12
 
0.2%
무도학원업 8
 
0.1%
종합체육시설업 6
 
0.1%
썰매장업 3
 
< 0.1%
무도장업 1
 
< 0.1%

Length

2024-04-18T06:56:16.953160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T06:56:17.062225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 6612
90.9%
체력단련장업 220
 
3.0%
당구장업 160
 
2.2%
체육도장업 150
 
2.1%
골프연습장업 98
 
1.3%
수영장업 12
 
0.2%
무도학원업 8
 
0.1%
종합체육시설업 6
 
0.1%
썰매장업 3
 
< 0.1%
무도장업 1
 
< 0.1%

bplcnm
Text

Distinct5468
Distinct (%)75.2%
Missing1
Missing (%)< 0.1%
Memory size56.9 KiB
2024-04-18T06:56:17.325262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length27
Mean length6.660476
Min length1

Characters and Unicode

Total characters48415
Distinct characters754
Distinct categories14 ?
Distinct scripts4 ?
Distinct blocks9 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4579 ?
Unique (%)63.0%

Sample

1st row광복실내골프연습장
2nd row마린골프연습장
3rd row포시즌 골프연습장
4th row에스에스 골프연습장
5th row가나다라골프연습장
ValueCountFrequency (%)
당구장 254
 
2.7%
당구클럽 243
 
2.6%
태권도 116
 
1.2%
휘트니스 84
 
0.9%
태권도장 63
 
0.7%
골프 53
 
0.6%
스크린골프 42
 
0.4%
헬스 39
 
0.4%
피트니스 37
 
0.4%
골프연습장 35
 
0.4%
Other values (5534) 8435
89.7%
2024-04-18T06:56:17.698506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2984
 
6.2%
2953
 
6.1%
2734
 
5.6%
2172
 
4.5%
2132
 
4.4%
1185
 
2.4%
1184
 
2.4%
1111
 
2.3%
994
 
2.1%
917
 
1.9%
Other values (744) 30049
62.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 42872
88.6%
Space Separator 2132
 
4.4%
Uppercase Letter 1921
 
4.0%
Lowercase Letter 460
 
1.0%
Decimal Number 387
 
0.8%
Close Punctuation 236
 
0.5%
Open Punctuation 235
 
0.5%
Other Punctuation 142
 
0.3%
Dash Punctuation 15
 
< 0.1%
Letter Number 6
 
< 0.1%
Other values (4) 9
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
2984
 
7.0%
2953
 
6.9%
2734
 
6.4%
2172
 
5.1%
1185
 
2.8%
1184
 
2.8%
1111
 
2.6%
994
 
2.3%
917
 
2.1%
869
 
2.0%
Other values (661) 25769
60.1%
Uppercase Letter
ValueCountFrequency (%)
M 146
 
7.6%
S 132
 
6.9%
G 131
 
6.8%
P 127
 
6.6%
T 123
 
6.4%
A 119
 
6.2%
K 117
 
6.1%
I 104
 
5.4%
O 102
 
5.3%
J 98
 
5.1%
Other values (16) 722
37.6%
Lowercase Letter
ValueCountFrequency (%)
i 47
 
10.2%
e 47
 
10.2%
o 45
 
9.8%
n 40
 
8.7%
s 32
 
7.0%
l 29
 
6.3%
a 25
 
5.4%
r 23
 
5.0%
m 22
 
4.8%
t 22
 
4.8%
Other values (15) 128
27.8%
Decimal Number
ValueCountFrequency (%)
2 139
35.9%
0 64
16.5%
1 58
15.0%
3 29
 
7.5%
5 28
 
7.2%
7 23
 
5.9%
4 20
 
5.2%
8 14
 
3.6%
9 7
 
1.8%
6 5
 
1.3%
Other Punctuation
ValueCountFrequency (%)
. 64
45.1%
& 53
37.3%
, 8
 
5.6%
· 5
 
3.5%
' 5
 
3.5%
# 2
 
1.4%
2
 
1.4%
: 2
 
1.4%
/ 1
 
0.7%
Letter Number
ValueCountFrequency (%)
5
83.3%
1
 
16.7%
Math Symbol
ValueCountFrequency (%)
+ 3
75.0%
1
 
25.0%
Other Number
ValueCountFrequency (%)
1
50.0%
1
50.0%
Other Symbol
ValueCountFrequency (%)
1
50.0%
1
50.0%
Space Separator
ValueCountFrequency (%)
2132
100.0%
Close Punctuation
ValueCountFrequency (%)
) 236
100.0%
Open Punctuation
ValueCountFrequency (%)
( 235
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 15
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 42869
88.5%
Common 3156
 
6.5%
Latin 2387
 
4.9%
Han 3
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
2984
 
7.0%
2953
 
6.9%
2734
 
6.4%
2172
 
5.1%
1185
 
2.8%
1184
 
2.8%
1111
 
2.6%
994
 
2.3%
917
 
2.1%
869
 
2.0%
Other values (658) 25766
60.1%
Latin
ValueCountFrequency (%)
M 146
 
6.1%
S 132
 
5.5%
G 131
 
5.5%
P 127
 
5.3%
T 123
 
5.2%
A 119
 
5.0%
K 117
 
4.9%
I 104
 
4.4%
O 102
 
4.3%
J 98
 
4.1%
Other values (43) 1188
49.8%
Common
ValueCountFrequency (%)
2132
67.6%
) 236
 
7.5%
( 235
 
7.4%
2 139
 
4.4%
0 64
 
2.0%
. 64
 
2.0%
1 58
 
1.8%
& 53
 
1.7%
3 29
 
0.9%
5 28
 
0.9%
Other values (20) 118
 
3.7%
Han
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 42869
88.5%
ASCII 5525
 
11.4%
None 8
 
< 0.1%
Number Forms 6
 
< 0.1%
CJK 3
 
< 0.1%
Letterlike Symbols 1
 
< 0.1%
Enclosed Alphanum 1
 
< 0.1%
Specials 1
 
< 0.1%
Arrows 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
2984
 
7.0%
2953
 
6.9%
2734
 
6.4%
2172
 
5.1%
1185
 
2.8%
1184
 
2.8%
1111
 
2.6%
994
 
2.3%
917
 
2.1%
869
 
2.0%
Other values (658) 25766
60.1%
ASCII
ValueCountFrequency (%)
2132
38.6%
) 236
 
4.3%
( 235
 
4.3%
M 146
 
2.6%
2 139
 
2.5%
S 132
 
2.4%
G 131
 
2.4%
P 127
 
2.3%
T 123
 
2.2%
A 119
 
2.2%
Other values (64) 2005
36.3%
Number Forms
ValueCountFrequency (%)
5
83.3%
1
 
16.7%
None
ValueCountFrequency (%)
· 5
62.5%
2
 
25.0%
1
 
12.5%
CJK
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%
Letterlike Symbols
ValueCountFrequency (%)
1
100.0%
Enclosed Alphanum
ValueCountFrequency (%)
1
100.0%
Specials
ValueCountFrequency (%)
1
100.0%
Arrows
ValueCountFrequency (%)
1
100.0%

sitepostno
Text

MISSING 

Distinct871
Distinct (%)15.9%
Missing1777
Missing (%)24.4%
Memory size56.9 KiB
2024-04-18T06:56:17.985970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters32958
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

Unique234 ?
Unique (%)4.3%

Sample

1st row600092
2nd row600100
3rd row600092
4th row600031
5th row600816
ValueCountFrequency (%)
지번우편번호 90
 
1.6%
608805 66
 
1.2%
616852 53
 
1.0%
604851 52
 
0.9%
609839 49
 
0.9%
619963 43
 
0.8%
608810 42
 
0.8%
607815 41
 
0.7%
619903 39
 
0.7%
619905 39
 
0.7%
Other values (861) 4979
90.6%
2024-04-18T06:56:18.372017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6 6626
20.1%
8 5708
17.3%
0 5427
16.5%
1 4832
14.7%
2 2305
 
7.0%
4 1888
 
5.7%
7 1787
 
5.4%
3 1714
 
5.2%
9 1267
 
3.8%
5 862
 
2.6%
Other values (6) 542
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 32416
98.4%
Other Letter 540
 
1.6%
Space Separator 2
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
6 6626
20.4%
8 5708
17.6%
0 5427
16.7%
1 4832
14.9%
2 2305
 
7.1%
4 1888
 
5.8%
7 1787
 
5.5%
3 1714
 
5.3%
9 1267
 
3.9%
5 862
 
2.7%
Other Letter
ValueCountFrequency (%)
180
33.3%
90
16.7%
90
16.7%
90
16.7%
90
16.7%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 32418
98.4%
Hangul 540
 
1.6%

Most frequent character per script

Common
ValueCountFrequency (%)
6 6626
20.4%
8 5708
17.6%
0 5427
16.7%
1 4832
14.9%
2 2305
 
7.1%
4 1888
 
5.8%
7 1787
 
5.5%
3 1714
 
5.3%
9 1267
 
3.9%
5 862
 
2.7%
Hangul
ValueCountFrequency (%)
180
33.3%
90
16.7%
90
16.7%
90
16.7%
90
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 32418
98.4%
Hangul 540
 
1.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6 6626
20.4%
8 5708
17.6%
0 5427
16.7%
1 4832
14.9%
2 2305
 
7.1%
4 1888
 
5.8%
7 1787
 
5.5%
3 1714
 
5.3%
9 1267
 
3.9%
5 862
 
2.7%
Hangul
ValueCountFrequency (%)
180
33.3%
90
16.7%
90
16.7%
90
16.7%
90
16.7%

sitewhladdr
Text

MISSING 

Distinct6076
Distinct (%)85.2%
Missing141
Missing (%)1.9%
Memory size56.9 KiB
2024-04-18T06:56:18.665071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length73
Median length51
Mean length25.122878
Min length4

Characters and Unicode

Total characters179101
Distinct characters464
Distinct categories11 ?
Distinct scripts4 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5270 ?
Unique (%)73.9%

Sample

1st row부산광역시 중구 대청동2가 34-1번지
2nd row부산광역시 중구 중앙동4가 79-1번지 마린센터 지하107호,110호
3rd row부산광역시 중구 대창동2가 36-5번지
4th row부산광역시 중구 대청동2가 7-1번지 5층
5th row부산광역시 중구 신창동3가 13-1번지
ValueCountFrequency (%)
부산광역시 7128
 
21.6%
동래구 837
 
2.5%
부산진구 775
 
2.3%
해운대구 694
 
2.1%
남구 687
 
2.1%
금정구 591
 
1.8%
사하구 574
 
1.7%
사상구 502
 
1.5%
북구 479
 
1.4%
3층 462
 
1.4%
Other values (6375) 20333
61.5%
2024-04-18T06:56:19.092341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
32554
18.2%
8448
 
4.7%
8413
 
4.7%
8375
 
4.7%
1 7570
 
4.2%
7300
 
4.1%
7240
 
4.0%
7199
 
4.0%
7133
 
4.0%
7100
 
4.0%
Other values (454) 77769
43.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 103004
57.5%
Decimal Number 36262
 
20.2%
Space Separator 32554
 
18.2%
Dash Punctuation 6426
 
3.6%
Uppercase Letter 247
 
0.1%
Other Punctuation 243
 
0.1%
Open Punctuation 161
 
0.1%
Close Punctuation 160
 
0.1%
Math Symbol 36
 
< 0.1%
Lowercase Letter 7
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
8448
 
8.2%
8413
 
8.2%
8375
 
8.1%
7300
 
7.1%
7240
 
7.0%
7199
 
7.0%
7133
 
6.9%
7100
 
6.9%
6760
 
6.6%
1817
 
1.8%
Other values (403) 33219
32.3%
Uppercase Letter
ValueCountFrequency (%)
B 70
28.3%
A 28
 
11.3%
S 19
 
7.7%
C 16
 
6.5%
K 14
 
5.7%
I 13
 
5.3%
E 9
 
3.6%
T 8
 
3.2%
P 8
 
3.2%
G 8
 
3.2%
Other values (12) 54
21.9%
Decimal Number
ValueCountFrequency (%)
1 7570
20.9%
2 5161
14.2%
3 4371
12.1%
4 3656
10.1%
5 3239
8.9%
0 2881
 
7.9%
6 2556
 
7.0%
7 2528
 
7.0%
8 2261
 
6.2%
9 2039
 
5.6%
Other Punctuation
ValueCountFrequency (%)
, 197
81.1%
. 24
 
9.9%
@ 7
 
2.9%
/ 7
 
2.9%
& 3
 
1.2%
? 2
 
0.8%
2
 
0.8%
· 1
 
0.4%
Lowercase Letter
ValueCountFrequency (%)
e 3
42.9%
s 1
 
14.3%
k 1
 
14.3%
b 1
 
14.3%
g 1
 
14.3%
Space Separator
ValueCountFrequency (%)
32554
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 6426
100.0%
Open Punctuation
ValueCountFrequency (%)
( 161
100.0%
Close Punctuation
ValueCountFrequency (%)
) 160
100.0%
Math Symbol
ValueCountFrequency (%)
~ 36
100.0%
Letter Number
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 103003
57.5%
Common 75842
42.3%
Latin 255
 
0.1%
Han 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
8448
 
8.2%
8413
 
8.2%
8375
 
8.1%
7300
 
7.1%
7240
 
7.0%
7199
 
7.0%
7133
 
6.9%
7100
 
6.9%
6760
 
6.6%
1817
 
1.8%
Other values (402) 33218
32.2%
Latin
ValueCountFrequency (%)
B 70
27.5%
A 28
 
11.0%
S 19
 
7.5%
C 16
 
6.3%
K 14
 
5.5%
I 13
 
5.1%
E 9
 
3.5%
T 8
 
3.1%
P 8
 
3.1%
G 8
 
3.1%
Other values (18) 62
24.3%
Common
ValueCountFrequency (%)
32554
42.9%
1 7570
 
10.0%
- 6426
 
8.5%
2 5161
 
6.8%
3 4371
 
5.8%
4 3656
 
4.8%
5 3239
 
4.3%
0 2881
 
3.8%
6 2556
 
3.4%
7 2528
 
3.3%
Other values (13) 4900
 
6.5%
Han
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 103003
57.5%
ASCII 76093
42.5%
None 3
 
< 0.1%
Number Forms 1
 
< 0.1%
CJK Compat Ideographs 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
32554
42.8%
1 7570
 
9.9%
- 6426
 
8.4%
2 5161
 
6.8%
3 4371
 
5.7%
4 3656
 
4.8%
5 3239
 
4.3%
0 2881
 
3.8%
6 2556
 
3.4%
7 2528
 
3.3%
Other values (38) 5151
 
6.8%
Hangul
ValueCountFrequency (%)
8448
 
8.2%
8413
 
8.2%
8375
 
8.1%
7300
 
7.1%
7240
 
7.0%
7199
 
7.0%
7133
 
6.9%
7100
 
6.9%
6760
 
6.6%
1817
 
1.8%
Other values (402) 33218
32.2%
None
ValueCountFrequency (%)
2
66.7%
· 1
33.3%
Number Forms
ValueCountFrequency (%)
1
100.0%
CJK Compat Ideographs
ValueCountFrequency (%)
1
100.0%

rdnpostno
Real number (ℝ)

SKEWED 

Distinct1169
Distinct (%)16.1%
Missing18
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean48517.328
Minimum13
Maximum619962
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size64.0 KiB
2024-04-18T06:56:19.212337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile46277
Q147780.75
median48947
Q348947
95-th percentile49009
Maximum619962
Range619949
Interquartile range (IQR)1166.25

Descriptive statistics

Standard deviation9544.5056
Coefficient of variation (CV)0.19672364
Kurtosis3529.9906
Mean48517.328
Median Absolute Deviation (MAD)0
Skewness59.014864
Sum3.5184766 × 108
Variance91097587
MonotonicityNot monotonic
2024-04-18T06:56:19.334204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
48947 4054
55.8%
46726 59
 
0.8%
46759 24
 
0.3%
46764 20
 
0.3%
46015 19
 
0.3%
48111 17
 
0.2%
46061 16
 
0.2%
46765 15
 
0.2%
48512 15
 
0.2%
46230 15
 
0.2%
Other values (1159) 2998
41.2%
(Missing) 18
 
0.2%
ValueCountFrequency (%)
13 1
 
< 0.1%
46004 3
 
< 0.1%
46006 1
 
< 0.1%
46007 1
 
< 0.1%
46008 13
0.2%
46011 1
 
< 0.1%
46012 8
0.1%
46013 2
 
< 0.1%
46014 2
 
< 0.1%
46015 19
0.3%
ValueCountFrequency (%)
619962 1
 
< 0.1%
618814 1
 
< 0.1%
49524 1
 
< 0.1%
49523 1
 
< 0.1%
49521 8
0.1%
49520 2
 
< 0.1%
49519 1
 
< 0.1%
49518 4
0.1%
49516 3
 
< 0.1%
49515 1
 
< 0.1%

rdnwhladdr
Text

MISSING 

Distinct6281
Distinct (%)90.8%
Missing349
Missing (%)4.8%
Memory size56.9 KiB
2024-04-18T06:56:19.650204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length70
Median length55
Mean length29.327409
Min length3

Characters and Unicode

Total characters202975
Distinct characters531
Distinct categories11 ?
Distinct scripts4 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5760 ?
Unique (%)83.2%

Sample

1st row부산광역시 중구 광복중앙로 28-1 (대청동2가)
2nd row부산광역시 중구 충장대로9번길 52, 지1층 (중앙동4가, 마린센터빌딩)
3rd row부산광역시 중구 중앙대로 133 (대창동2가)
4th row부산광역시 중구 대청로 107, 5층 (대청동2가)
5th row부산광역시 중구 광복로35번길 18 (신창동3가)
ValueCountFrequency (%)
부산광역시 6920
 
17.8%
동래구 791
 
2.0%
부산진구 758
 
1.9%
해운대구 686
 
1.8%
남구 656
 
1.7%
사하구 600
 
1.5%
금정구 576
 
1.5%
사상구 476
 
1.2%
3층 467
 
1.2%
북구 464
 
1.2%
Other values (4864) 26524
68.2%
2024-04-18T06:56:20.104783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
34763
 
17.1%
8857
 
4.4%
8323
 
4.1%
8285
 
4.1%
7323
 
3.6%
7293
 
3.6%
7021
 
3.5%
6926
 
3.4%
6832
 
3.4%
( 6805
 
3.4%
Other values (521) 100547
49.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 119182
58.7%
Space Separator 34763
 
17.1%
Decimal Number 29809
 
14.7%
Open Punctuation 6807
 
3.4%
Close Punctuation 6805
 
3.4%
Other Punctuation 4477
 
2.2%
Dash Punctuation 824
 
0.4%
Uppercase Letter 242
 
0.1%
Math Symbol 45
 
< 0.1%
Lowercase Letter 20
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
8857
 
7.4%
8323
 
7.0%
8285
 
7.0%
7323
 
6.1%
7293
 
6.1%
7021
 
5.9%
6926
 
5.8%
6832
 
5.7%
3148
 
2.6%
2578
 
2.2%
Other values (459) 52596
44.1%
Uppercase Letter
ValueCountFrequency (%)
B 87
36.0%
A 23
 
9.5%
S 21
 
8.7%
K 18
 
7.4%
C 14
 
5.8%
I 12
 
5.0%
E 7
 
2.9%
P 6
 
2.5%
G 6
 
2.5%
W 5
 
2.1%
Other values (14) 43
17.8%
Lowercase Letter
ValueCountFrequency (%)
e 4
20.0%
s 3
15.0%
b 3
15.0%
a 2
10.0%
k 2
10.0%
z 1
 
5.0%
l 1
 
5.0%
v 1
 
5.0%
i 1
 
5.0%
m 1
 
5.0%
Decimal Number
ValueCountFrequency (%)
1 5960
20.0%
2 4796
16.1%
3 3761
12.6%
4 2836
9.5%
0 2606
8.7%
5 2456
8.2%
6 2159
 
7.2%
7 1875
 
6.3%
8 1748
 
5.9%
9 1612
 
5.4%
Other Punctuation
ValueCountFrequency (%)
, 4437
99.1%
. 18
 
0.4%
@ 6
 
0.1%
/ 4
 
0.1%
· 3
 
0.1%
& 3
 
0.1%
2
 
< 0.1%
* 2
 
< 0.1%
? 2
 
< 0.1%
Open Punctuation
ValueCountFrequency (%)
( 6805
> 99.9%
[ 2
 
< 0.1%
Close Punctuation
ValueCountFrequency (%)
) 6803
> 99.9%
] 2
 
< 0.1%
Space Separator
ValueCountFrequency (%)
34763
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 824
100.0%
Math Symbol
ValueCountFrequency (%)
~ 45
100.0%
Letter Number
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 119181
58.7%
Common 83530
41.2%
Latin 263
 
0.1%
Han 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
8857
 
7.4%
8323
 
7.0%
8285
 
7.0%
7323
 
6.1%
7293
 
6.1%
7021
 
5.9%
6926
 
5.8%
6832
 
5.7%
3148
 
2.6%
2578
 
2.2%
Other values (458) 52595
44.1%
Latin
ValueCountFrequency (%)
B 87
33.1%
A 23
 
8.7%
S 21
 
8.0%
K 18
 
6.8%
C 14
 
5.3%
I 12
 
4.6%
E 7
 
2.7%
P 6
 
2.3%
G 6
 
2.3%
W 5
 
1.9%
Other values (26) 64
24.3%
Common
ValueCountFrequency (%)
34763
41.6%
( 6805
 
8.1%
) 6803
 
8.1%
1 5960
 
7.1%
2 4796
 
5.7%
, 4437
 
5.3%
3 3761
 
4.5%
4 2836
 
3.4%
0 2606
 
3.1%
5 2456
 
2.9%
Other values (16) 8307
 
9.9%
Han
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 119181
58.7%
ASCII 83787
41.3%
None 5
 
< 0.1%
Number Forms 1
 
< 0.1%
CJK Compat Ideographs 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
34763
41.5%
( 6805
 
8.1%
) 6803
 
8.1%
1 5960
 
7.1%
2 4796
 
5.7%
, 4437
 
5.3%
3 3761
 
4.5%
4 2836
 
3.4%
0 2606
 
3.1%
5 2456
 
2.9%
Other values (49) 8564
 
10.2%
Hangul
ValueCountFrequency (%)
8857
 
7.4%
8323
 
7.0%
8285
 
7.0%
7323
 
6.1%
7293
 
6.1%
7021
 
5.9%
6926
 
5.8%
6832
 
5.7%
3148
 
2.6%
2578
 
2.2%
Other values (458) 52595
44.1%
None
ValueCountFrequency (%)
· 3
60.0%
2
40.0%
Number Forms
ValueCountFrequency (%)
1
100.0%
CJK Compat Ideographs
ValueCountFrequency (%)
1
100.0%

apvpermymd
Real number (ℝ)

SKEWED 

Distinct4269
Distinct (%)58.7%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean20053798
Minimum388631.59
Maximum20210429
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size64.0 KiB
2024-04-18T06:56:20.253997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum388631.59
5-th percentile19921114
Q120000426
median20060111
Q320120215
95-th percentile20190614
Maximum20210429
Range19821797
Interquartile range (IQR)119789

Descriptive statistics

Standard deviation271922.38
Coefficient of variation (CV)0.013559645
Kurtosis4020.8745
Mean20053798
Median Absolute Deviation (MAD)59798
Skewness-59.01044
Sum1.4577106 × 1011
Variance7.3941779 × 1010
MonotonicityNot monotonic
2024-04-18T06:56:20.381360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20030204.0 124
 
1.7%
20030206.0 66
 
0.9%
20030203.0 55
 
0.8%
20030205.0 36
 
0.5%
19890731.0 31
 
0.4%
20030123.0 21
 
0.3%
20030124.0 19
 
0.3%
20030210.0 12
 
0.2%
19891116.0 12
 
0.2%
20191213.0 11
 
0.2%
Other values (4259) 6882
94.7%
ValueCountFrequency (%)
388631.593406 1
< 0.1%
10001126.0 1
< 0.1%
19711022.0 1
< 0.1%
19720503.0 2
< 0.1%
19730112.0 1
< 0.1%
19750416.0 1
< 0.1%
19750503.0 1
< 0.1%
19750519.0 1
< 0.1%
19751001.0 1
< 0.1%
19770203.0 1
< 0.1%
ValueCountFrequency (%)
20210429.0 1
 
< 0.1%
20210428.0 1
 
< 0.1%
20210423.0 2
< 0.1%
20210421.0 2
< 0.1%
20210420.0 4
0.1%
20210419.0 2
< 0.1%
20210416.0 2
< 0.1%
20210415.0 2
< 0.1%
20210413.0 1
 
< 0.1%
20210412.0 2
< 0.1%

dcbymd
Text

MISSING 

Distinct1879
Distinct (%)55.8%
Missing3904
Missing (%)53.7%
Memory size56.9 KiB
2024-04-18T06:56:20.650771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length8
Mean length7.8954248
Min length4

Characters and Unicode

Total characters26576
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

Unique1329 ?
Unique (%)39.5%

Sample

1st row20020727
2nd row20040228
3rd row20130408
4th row20150608
5th row20070605
ValueCountFrequency (%)
20180302 96
 
2.9%
폐업일자 91
 
2.7%
20040504 65
 
1.9%
20140411 54
 
1.6%
20151231 51
 
1.5%
20070801 39
 
1.2%
20030613 28
 
0.8%
20021212 24
 
0.7%
20061215 20
 
0.6%
20180703 19
 
0.6%
Other values (1869) 2879
85.5%
2024-04-18T06:56:21.068855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 8614
32.4%
2 5378
20.2%
1 5124
19.3%
3 1300
 
4.9%
4 1045
 
3.9%
9 998
 
3.8%
7 989
 
3.7%
8 968
 
3.6%
5 954
 
3.6%
6 839
 
3.2%
Other values (6) 367
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 26209
98.6%
Other Letter 364
 
1.4%
Space Separator 2
 
< 0.1%
Other Punctuation 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 8614
32.9%
2 5378
20.5%
1 5124
19.6%
3 1300
 
5.0%
4 1045
 
4.0%
9 998
 
3.8%
7 989
 
3.8%
8 968
 
3.7%
5 954
 
3.6%
6 839
 
3.2%
Other Letter
ValueCountFrequency (%)
91
25.0%
91
25.0%
91
25.0%
91
25.0%
Space Separator
ValueCountFrequency (%)
2
100.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 26212
98.6%
Hangul 364
 
1.4%

Most frequent character per script

Common
ValueCountFrequency (%)
0 8614
32.9%
2 5378
20.5%
1 5124
19.5%
3 1300
 
5.0%
4 1045
 
4.0%
9 998
 
3.8%
7 989
 
3.8%
8 968
 
3.7%
5 954
 
3.6%
6 839
 
3.2%
Other values (2) 3
 
< 0.1%
Hangul
ValueCountFrequency (%)
91
25.0%
91
25.0%
91
25.0%
91
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 26212
98.6%
Hangul 364
 
1.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 8614
32.9%
2 5378
20.5%
1 5124
19.5%
3 1300
 
5.0%
4 1045
 
4.0%
9 998
 
3.8%
7 989
 
3.8%
8 968
 
3.7%
5 954
 
3.6%
6 839
 
3.2%
Other values (2) 3
 
< 0.1%
Hangul
ValueCountFrequency (%)
91
25.0%
91
25.0%
91
25.0%
91
25.0%

clgstdt
Categorical

IMBALANCE 

Distinct16
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size56.9 KiB
<NA>
7163 
휴업시작일자
 
93
20180808
 
1
20180701
 
1
20090701
 
1
Other values (11)
 
11

Length

Max length14
Median length4
Mean length4.0341128
Min length4

Unique

Unique14 ?
Unique (%)0.2%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 7163
98.5%
휴업시작일자 93
 
1.3%
20180808 1
 
< 0.1%
20180701 1
 
< 0.1%
20090701 1
 
< 0.1%
20140115112341 1
 
< 0.1%
20070801 1
 
< 0.1%
20030108 1
 
< 0.1%
20171025 1
 
< 0.1%
20130122 1
 
< 0.1%
Other values (6) 6
 
0.1%

Length

2024-04-18T06:56:21.193588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 7163
98.5%
휴업시작일자 93
 
1.3%
20180808 1
 
< 0.1%
20180701 1
 
< 0.1%
20090701 1
 
< 0.1%
20140115112341 1
 
< 0.1%
20070801 1
 
< 0.1%
20030108 1
 
< 0.1%
20171025 1
 
< 0.1%
20130122 1
 
< 0.1%
Other values (6) 6
 
0.1%

clgenddt
Categorical

IMBALANCE 

Distinct15
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size56.9 KiB
<NA>
7164 
휴업종료일자
 
93
20190630
 
1
20181231
 
1
20110630
 
1
Other values (10)
 
10

Length

Max length8
Median length4
Mean length4.0327373
Min length4

Unique

Unique13 ?
Unique (%)0.2%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 7164
98.5%
휴업종료일자 93
 
1.3%
20190630 1
 
< 0.1%
20181231 1
 
< 0.1%
20110630 1
 
< 0.1%
20421031 1
 
< 0.1%
20031231 1
 
< 0.1%
20181025 1
 
< 0.1%
20130714 1
 
< 0.1%
20100123 1
 
< 0.1%
Other values (5) 5
 
0.1%

Length

2024-04-18T06:56:21.314626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 7164
98.5%
휴업종료일자 93
 
1.3%
20190630 1
 
< 0.1%
20181231 1
 
< 0.1%
20110630 1
 
< 0.1%
20421031 1
 
< 0.1%
20031231 1
 
< 0.1%
20181025 1
 
< 0.1%
20130714 1
 
< 0.1%
20100123 1
 
< 0.1%
Other values (5) 5
 
0.1%

ropnymd
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size56.9 KiB
<NA>
7176 
재개업일자
 
93
051-123-1234
 
1

Length

Max length12
Median length4
Mean length4.0138927
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> 7176
98.7%
재개업일자 93
 
1.3%
051-123-1234 1
 
< 0.1%

Length

2024-04-18T06:56:21.456180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T06:56:21.559850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 7176
98.7%
재개업일자 93
 
1.3%
051-123-1234 1
 
< 0.1%

trdstatenm
Categorical

IMBALANCE 

Distinct12
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size56.9 KiB
13
3231 
03
2962 
영업/정상
622 
35
402 
폐업
 
23
Other values (7)
 
30

Length

Max length14
Median length2
Mean length2.2682256
Min length2

Unique

Unique4 ?
Unique (%)0.1%

Sample

1st row03
2nd row13
3rd row03
4th row13
5th row13

Common Values

ValueCountFrequency (%)
13 3231
44.4%
03 2962
40.7%
영업/정상 622
 
8.6%
35 402
 
5.5%
폐업 23
 
0.3%
02 13
 
0.2%
<NA> 8
 
0.1%
취소/말소/만료/정지/중지 5
 
0.1%
32 1
 
< 0.1%
33 1
 
< 0.1%
Other values (2) 2
 
< 0.1%

Length

2024-04-18T06:56:21.684067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
13 3231
44.4%
03 2962
40.7%
영업/정상 622
 
8.6%
35 402
 
5.5%
폐업 23
 
0.3%
02 13
 
0.2%
na 8
 
0.1%
취소/말소/만료/정지/중지 5
 
0.1%
32 1
 
< 0.1%
33 1
 
< 0.1%
Other values (2) 2
 
< 0.1%

dtlstatenm
Categorical

IMBALANCE 

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size56.9 KiB
영업중
3860 
폐업
2985 
직권말소
407 
휴업
 
13
<NA>
 
2
Other values (3)
 
3

Length

Max length4
Median length3
Mean length2.6440165
Min length2

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
영업중 3860
53.1%
폐업 2985
41.1%
직권말소 407
 
5.6%
휴업 13
 
0.2%
<NA> 2
 
< 0.1%
신고취소 1
 
< 0.1%
지정취소 1
 
< 0.1%
전출 1
 
< 0.1%

Length

2024-04-18T06:56:21.804343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T06:56:21.930346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
영업중 3860
53.1%
폐업 2985
41.1%
직권말소 407
 
5.6%
휴업 13
 
0.2%
na 2
 
< 0.1%
신고취소 1
 
< 0.1%
지정취소 1
 
< 0.1%
전출 1
 
< 0.1%

x
Text

MISSING 

Distinct5128
Distinct (%)71.9%
Missing133
Missing (%)1.8%
Memory size56.9 KiB
2024-04-18T06:56:22.124542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length20
Mean length19.993835
Min length2

Characters and Unicode

Total characters142696
Distinct characters21
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

Unique3773 ?
Unique (%)52.9%

Sample

1st row385198.60018500000
2nd row385898.66824500000
3rd row385707.34088600000
4th row385250.02502800000
5th row385004.37293600000
ValueCountFrequency (%)
380613.87795500000 11
 
0.2%
373579.04761500000 9
 
0.1%
395308.61045334 8
 
0.1%
390129.64468900000 8
 
0.1%
393357.80475400000 8
 
0.1%
387962.62914200000 7
 
0.1%
385599.32170700000 7
 
0.1%
383255.80791500000 7
 
0.1%
387811.67602200000 7
 
0.1%
379127.98256500000 6
 
0.1%
Other values (5118) 7059
98.9%
2024-04-18T06:56:22.476268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 38182
26.8%
18049
12.6%
3 14088
 
9.9%
8 11315
 
7.9%
9 9914
 
6.9%
7 7875
 
5.5%
1 7582
 
5.3%
2 7359
 
5.2%
4 7268
 
5.1%
5 7136
 
5.0%
Other values (11) 13928
 
9.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 117572
82.4%
Space Separator 18049
 
12.6%
Other Punctuation 7059
 
4.9%
Other Letter 10
 
< 0.1%
Open Punctuation 2
 
< 0.1%
Uppercase Letter 2
 
< 0.1%
Close Punctuation 2
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 38182
32.5%
3 14088
 
12.0%
8 11315
 
9.6%
9 9914
 
8.4%
7 7875
 
6.7%
1 7582
 
6.4%
2 7359
 
6.3%
4 7268
 
6.2%
5 7136
 
6.1%
6 6853
 
5.8%
Other Letter
ValueCountFrequency (%)
2
20.0%
2
20.0%
2
20.0%
2
20.0%
1
10.0%
1
10.0%
Space Separator
ValueCountFrequency (%)
18049
100.0%
Other Punctuation
ValueCountFrequency (%)
. 7059
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Uppercase Letter
ValueCountFrequency (%)
X 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 142684
> 99.9%
Hangul 10
 
< 0.1%
Latin 2
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 38182
26.8%
18049
12.6%
3 14088
 
9.9%
8 11315
 
7.9%
9 9914
 
6.9%
7 7875
 
5.5%
1 7582
 
5.3%
2 7359
 
5.2%
4 7268
 
5.1%
5 7136
 
5.0%
Other values (4) 13916
 
9.8%
Hangul
ValueCountFrequency (%)
2
20.0%
2
20.0%
2
20.0%
2
20.0%
1
10.0%
1
10.0%
Latin
ValueCountFrequency (%)
X 2
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 142686
> 99.9%
Hangul 10
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 38182
26.8%
18049
12.6%
3 14088
 
9.9%
8 11315
 
7.9%
9 9914
 
6.9%
7 7875
 
5.5%
1 7582
 
5.3%
2 7359
 
5.2%
4 7268
 
5.1%
5 7136
 
5.0%
Other values (5) 13918
 
9.8%
Hangul
ValueCountFrequency (%)
2
20.0%
2
20.0%
2
20.0%
2
20.0%
1
10.0%
1
10.0%

y
Text

MISSING 

Distinct5127
Distinct (%)71.8%
Missing133
Missing (%)1.8%
Memory size56.9 KiB
2024-04-18T06:56:22.669830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length20
Mean length19.994395
Min length6

Characters and Unicode

Total characters142700
Distinct characters25
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

Unique3773 ?
Unique (%)52.9%

Sample

1st row180287.48839500000
2nd row181213.88172000000
3rd row181152.27052700000
4th row180424.29203400000
5th row180162.69448800000
ValueCountFrequency (%)
175596.00351700000 11
 
0.2%
178017.84297300000 9
 
0.1%
197260.59223600000 8
 
0.1%
191804.07825600000 8
 
0.1%
186564.639113751 8
 
0.1%
187606.00717200000 7
 
0.1%
190262.68201300000 7
 
0.1%
188403.67614400000 7
 
0.1%
194331.52968600000 7
 
0.1%
187408.08665500000 6
 
0.1%
Other values (5117) 7059
98.9%
2024-04-18T06:56:22.979381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 38004
26.6%
18024
12.6%
1 14377
 
10.1%
8 11045
 
7.7%
9 9862
 
6.9%
7 8362
 
5.9%
6 7378
 
5.2%
4 7302
 
5.1%
5 7181
 
5.0%
. 7059
 
4.9%
Other values (15) 14106
 
9.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 117597
82.4%
Space Separator 18024
 
12.6%
Other Punctuation 7059
 
4.9%
Other Letter 14
 
< 0.1%
Open Punctuation 2
 
< 0.1%
Close Punctuation 2
 
< 0.1%
Uppercase Letter 2
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 38004
32.3%
1 14377
 
12.2%
8 11045
 
9.4%
9 9862
 
8.4%
7 8362
 
7.1%
6 7378
 
6.3%
4 7302
 
6.2%
5 7181
 
6.1%
3 7056
 
6.0%
2 7030
 
6.0%
Other Letter
ValueCountFrequency (%)
2
14.3%
2
14.3%
2
14.3%
2
14.3%
1
7.1%
1
7.1%
1
7.1%
1
7.1%
1
7.1%
1
7.1%
Space Separator
ValueCountFrequency (%)
18024
100.0%
Other Punctuation
ValueCountFrequency (%)
. 7059
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Uppercase Letter
ValueCountFrequency (%)
Y 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 142684
> 99.9%
Hangul 14
 
< 0.1%
Latin 2
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 38004
26.6%
18024
12.6%
1 14377
 
10.1%
8 11045
 
7.7%
9 9862
 
6.9%
7 8362
 
5.9%
6 7378
 
5.2%
4 7302
 
5.1%
5 7181
 
5.0%
. 7059
 
4.9%
Other values (4) 14090
 
9.9%
Hangul
ValueCountFrequency (%)
2
14.3%
2
14.3%
2
14.3%
2
14.3%
1
7.1%
1
7.1%
1
7.1%
1
7.1%
1
7.1%
1
7.1%
Latin
ValueCountFrequency (%)
Y 2
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 142686
> 99.9%
Hangul 14
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 38004
26.6%
18024
12.6%
1 14377
 
10.1%
8 11045
 
7.7%
9 9862
 
6.9%
7 8362
 
5.9%
6 7378
 
5.2%
4 7302
 
5.1%
5 7181
 
5.0%
. 7059
 
4.9%
Other values (5) 14092
 
9.9%
Hangul
ValueCountFrequency (%)
2
14.3%
2
14.3%
2
14.3%
2
14.3%
1
7.1%
1
7.1%
1
7.1%
1
7.1%
1
7.1%
1
7.1%

lastmodts
Real number (ℝ)

Distinct7079
Distinct (%)97.4%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean2.0132816 × 1013
Minimum2.0021018 × 1013
Maximum2.0210429 × 1013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size64.0 KiB
2024-04-18T06:56:23.117689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0021018 × 1013
5-th percentile2.0031124 × 1013
Q12.011012 × 1013
median2.0140611 × 1013
Q32.0170617 × 1013
95-th percentile2.0200508 × 1013
Maximum2.0210429 × 1013
Range1.8941102 × 1011
Interquartile range (IQR)6.0497301 × 1010

Descriptive statistics

Standard deviation4.8157458 × 1010
Coefficient of variation (CV)0.0023919882
Kurtosis-0.53279142
Mean2.0132816 × 1013
Median Absolute Deviation (MAD)3.0287965 × 1010
Skewness-0.57464933
Sum1.463253 × 1017
Variance2.3191407 × 1021
MonotonicityNot monotonic
2024-04-18T06:56:23.240975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20021018132120 62
 
0.9%
20191129084358 3
 
< 0.1%
20190412102302 3
 
< 0.1%
20200313173159 3
 
< 0.1%
20190329091410 3
 
< 0.1%
20190809140635 3
 
< 0.1%
20181207152524 3
 
< 0.1%
20190809182139 3
 
< 0.1%
20190809132354 3
 
< 0.1%
20190329091733 3
 
< 0.1%
Other values (7069) 7179
98.7%
ValueCountFrequency (%)
20021018132120 62
0.9%
20021226152409 1
 
< 0.1%
20021226155826 1
 
< 0.1%
20021226160855 1
 
< 0.1%
20021226163050 1
 
< 0.1%
20021227103144 1
 
< 0.1%
20021227115048 1
 
< 0.1%
20021227135543 1
 
< 0.1%
20021227140112 1
 
< 0.1%
20021227140309 1
 
< 0.1%
ValueCountFrequency (%)
20210429155036 1
< 0.1%
20210429141644 1
< 0.1%
20210429105442 1
< 0.1%
20210429101817 1
< 0.1%
20210429095044 1
< 0.1%
20210428171512 1
< 0.1%
20210428114550 1
< 0.1%
20210428093742 1
< 0.1%
20210426181817 1
< 0.1%
20210426131909 1
< 0.1%

uptaenm
Categorical

IMBALANCE 

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size56.9 KiB
<NA>
6637 
태권도
 
376
업태구분명
 
65
권투
 
54
유도
 
45
Other values (4)
 
93

Length

Max length5
Median length4
Mean length3.9110041
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> 6637
91.3%
태권도 376
 
5.2%
업태구분명 65
 
0.9%
권투 54
 
0.7%
유도 45
 
0.6%
합기도 40
 
0.6%
검도 38
 
0.5%
레슬링 8
 
0.1%
우슈 7
 
0.1%

Length

2024-04-18T06:56:23.363868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T06:56:23.474967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 6637
91.3%
태권도 376
 
5.2%
업태구분명 65
 
0.9%
권투 54
 
0.7%
유도 45
 
0.6%
합기도 40
 
0.6%
검도 38
 
0.5%
레슬링 8
 
0.1%
우슈 7
 
0.1%

sitetel
Text

MISSING 

Distinct145
Distinct (%)2.0%
Missing108
Missing (%)1.5%
Memory size56.9 KiB
2024-04-18T06:56:23.784207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length11.940938
Min length4

Characters and Unicode

Total characters85521
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

Unique133 ?
Unique (%)1.9%

Sample

1st row051-123-1234
2nd row051-123-1234
3rd row051-123-1234
4th row051-123-1234
5th row051-123-1234
ValueCountFrequency (%)
051-123-1234 6968
97.3%
전화번호 41
 
0.6%
051-911-0202 2
 
< 0.1%
051-747-0336 2
 
< 0.1%
051-925-0909 2
 
< 0.1%
051)515-1369 2
 
< 0.1%
051-582-8779 2
 
< 0.1%
051-723-5896 2
 
< 0.1%
051-996-6565 2
 
< 0.1%
051-731-1469 2
 
< 0.1%
Other values (135) 137
 
1.9%
2024-04-18T06:56:24.175719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 21142
24.7%
- 14209
16.6%
3 14052
16.4%
2 14025
16.4%
0 7235
 
8.5%
5 7192
 
8.4%
4 7053
 
8.2%
7 132
 
0.2%
8 118
 
0.1%
6 99
 
0.1%
Other values (7) 264
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 71144
83.2%
Dash Punctuation 14209
 
16.6%
Other Letter 164
 
0.2%
Close Punctuation 3
 
< 0.1%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 21142
29.7%
3 14052
19.8%
2 14025
19.7%
0 7235
 
10.2%
5 7192
 
10.1%
4 7053
 
9.9%
7 132
 
0.2%
8 118
 
0.2%
6 99
 
0.1%
9 96
 
0.1%
Other Letter
ValueCountFrequency (%)
41
25.0%
41
25.0%
41
25.0%
41
25.0%
Dash Punctuation
ValueCountFrequency (%)
- 14209
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 85357
99.8%
Hangul 164
 
0.2%

Most frequent character per script

Common
ValueCountFrequency (%)
1 21142
24.8%
- 14209
16.6%
3 14052
16.5%
2 14025
16.4%
0 7235
 
8.5%
5 7192
 
8.4%
4 7053
 
8.3%
7 132
 
0.2%
8 118
 
0.1%
6 99
 
0.1%
Other values (3) 100
 
0.1%
Hangul
ValueCountFrequency (%)
41
25.0%
41
25.0%
41
25.0%
41
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 85357
99.8%
Hangul 164
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 21142
24.8%
- 14209
16.6%
3 14052
16.5%
2 14025
16.4%
0 7235
 
8.5%
5 7192
 
8.4%
4 7053
 
8.3%
7 132
 
0.2%
8 118
 
0.1%
6 99
 
0.1%
Other values (3) 100
 
0.1%
Hangul
ValueCountFrequency (%)
41
25.0%
41
25.0%
41
25.0%
41
25.0%

bdngdngnum
Categorical

IMBALANCE 

Distinct11
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size56.9 KiB
<NA>
5787 
1
1046 
0
 
309
건축물동수
 
83
2
 
24
Other values (6)
 
21

Length

Max length5
Median length4
Mean length3.4341128
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> 5787
79.6%
1 1046
 
14.4%
0 309
 
4.3%
건축물동수 83
 
1.1%
2 24
 
0.3%
3 8
 
0.1%
4 6
 
0.1%
5 4
 
0.1%
302 1
 
< 0.1%
12 1
 
< 0.1%

Length

2024-04-18T06:56:24.311161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 5787
79.6%
1 1046
 
14.4%
0 309
 
4.3%
건축물동수 83
 
1.1%
2 24
 
0.3%
3 8
 
0.1%
4 6
 
0.1%
5 4
 
0.1%
302 1
 
< 0.1%
12 1
 
< 0.1%

bdngyarea
Text

MISSING 

Distinct2904
Distinct (%)73.8%
Missing3336
Missing (%)45.9%
Memory size56.9 KiB
2024-04-18T06:56:24.609181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length8
Mean length5.1489578
Min length1

Characters and Unicode

Total characters20256
Distinct characters17
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

Unique2557 ?
Unique (%)65.0%

Sample

1st row20120.65
2nd row4130.27
3rd row1155.7
4th row5404.88
5th row8245.54
ValueCountFrequency (%)
0 306
 
7.8%
1 85
 
2.2%
건축물연면적 52
 
1.3%
150 36
 
0.9%
160 25
 
0.6%
120 23
 
0.6%
140 16
 
0.4%
99 14
 
0.4%
158 12
 
0.3%
165 11
 
0.3%
Other values (2894) 3354
85.3%
2024-04-18T06:56:25.041774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 2969
14.7%
1 2838
14.0%
2 1938
9.6%
9 1671
8.2%
4 1650
8.1%
3 1586
7.8%
8 1543
7.6%
5 1492
7.4%
0 1442
7.1%
6 1427
7.0%
Other values (7) 1700
8.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 16975
83.8%
Other Punctuation 2969
 
14.7%
Other Letter 312
 
1.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2838
16.7%
2 1938
11.4%
9 1671
9.8%
4 1650
9.7%
3 1586
9.3%
8 1543
9.1%
5 1492
8.8%
0 1442
8.5%
6 1427
8.4%
7 1388
8.2%
Other Letter
ValueCountFrequency (%)
52
16.7%
52
16.7%
52
16.7%
52
16.7%
52
16.7%
52
16.7%
Other Punctuation
ValueCountFrequency (%)
. 2969
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 19944
98.5%
Hangul 312
 
1.5%

Most frequent character per script

Common
ValueCountFrequency (%)
. 2969
14.9%
1 2838
14.2%
2 1938
9.7%
9 1671
8.4%
4 1650
8.3%
3 1586
8.0%
8 1543
7.7%
5 1492
7.5%
0 1442
7.2%
6 1427
7.2%
Hangul
ValueCountFrequency (%)
52
16.7%
52
16.7%
52
16.7%
52
16.7%
52
16.7%
52
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 19944
98.5%
Hangul 312
 
1.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 2969
14.9%
1 2838
14.2%
2 1938
9.7%
9 1671
8.4%
4 1650
8.3%
3 1586
8.0%
8 1543
7.7%
5 1492
7.5%
0 1442
7.2%
6 1427
7.2%
Hangul
ValueCountFrequency (%)
52
16.7%
52
16.7%
52
16.7%
52
16.7%
52
16.7%
52
16.7%

puprsenm
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size56.9 KiB
사립
7228 
<NA>
 
34
공립
 
6
2021-05-01 05:22:03
 
1
공사립구분명
 
1

Length

Max length19
Median length2
Mean length2.0122421
Min length2

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
사립 7228
99.4%
<NA> 34
 
0.5%
공립 6
 
0.1%
2021-05-01 05:22:03 1
 
< 0.1%
공사립구분명 1
 
< 0.1%

Length

2024-04-18T06:56:25.165186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T06:56:25.259453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
사립 7228
99.4%
na 34
 
0.5%
공립 6
 
0.1%
2021-05-01 1
 
< 0.1%
05:22:03 1
 
< 0.1%
공사립구분명 1
 
< 0.1%

culphyedcobnm
Categorical

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size56.9 KiB
당구장업
3059 
체육도장업
1745 
체력단련장업
1403 
골프연습장업
920 
수영장업
 
95
Other values (5)
 
48

Length

Max length7
Median length6
Mean length4.8806052
Min length4

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
당구장업 3059
42.1%
체육도장업 1745
24.0%
체력단련장업 1403
19.3%
골프연습장업 920
 
12.7%
수영장업 95
 
1.3%
<NA> 35
 
0.5%
무도학원업 8
 
0.1%
썰매장업 3
 
< 0.1%
문화체육업종명 1
 
< 0.1%
무도장업 1
 
< 0.1%

Length

2024-04-18T06:56:25.362110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T06:56:25.470326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
당구장업 3059
42.1%
체육도장업 1745
24.0%
체력단련장업 1403
19.3%
골프연습장업 920
 
12.7%
수영장업 95
 
1.3%
na 35
 
0.5%
무도학원업 8
 
0.1%
썰매장업 3
 
< 0.1%
문화체육업종명 1
 
< 0.1%
무도장업 1
 
< 0.1%

bupnm
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size56.9 KiB
<NA>
7177 
법인명
 
93

Length

Max length4
Median length4
Mean length3.9872077
Min length3

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> 7177
98.7%
법인명 93
 
1.3%

Length

2024-04-18T06:56:25.603969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T06:56:25.689528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 7177
98.7%
법인명 93
 
1.3%

insurjnyncode
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size56.9 KiB
<NA>
5199 
0
1857 
Y
 
102
 
91
1
 
21

Length

Max length4
Median length4
Mean length3.145392
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 5199
71.5%
0 1857
 
25.5%
Y 102
 
1.4%
91
 
1.3%
1 21
 
0.3%

Length

2024-04-18T06:56:25.773702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T06:56:25.880913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 5199
71.5%
0 1857
 
25.5%
y 102
 
1.4%
91
 
1.3%
1 21
 
0.3%

drmkcobnm
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size56.9 KiB
<NA>
7177 
세부업종명
 
93

Length

Max length5
Median length4
Mean length4.0127923
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> 7177
98.7%
세부업종명 93
 
1.3%

Length

2024-04-18T06:56:25.983468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T06:56:26.064689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 7177
98.7%
세부업종명 93
 
1.3%

ldercnt
Categorical

IMBALANCE 

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size56.9 KiB
<NA>
5800 
1
1043 
2
 
186
0
 
178
지도자수
 
56
Other values (4)
 
7

Length

Max length4
Median length4
Mean length3.4165062
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> 5800
79.8%
1 1043
 
14.3%
2 186
 
2.6%
0 178
 
2.4%
지도자수 56
 
0.8%
3 4
 
0.1%
5 1
 
< 0.1%
4 1
 
< 0.1%
7 1
 
< 0.1%

Length

2024-04-18T06:56:26.152759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T06:56:26.264412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 5800
79.8%
1 1043
 
14.3%
2 186
 
2.6%
0 178
 
2.4%
지도자수 56
 
0.8%
3 4
 
0.1%
5 1
 
< 0.1%
4 1
 
< 0.1%
7 1
 
< 0.1%

memcolltotstfnum
Categorical

IMBALANCE 

Distinct16
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size56.9 KiB
<NA>
7157 
회원모집총인원
 
93
60
 
3
100
 
2
20
 
2
Other values (11)
 
13

Length

Max length7
Median length4
Mean length4.033425
Min length1

Unique

Unique9 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 7157
98.4%
회원모집총인원 93
 
1.3%
60 3
 
< 0.1%
100 2
 
< 0.1%
20 2
 
< 0.1%
50 2
 
< 0.1%
30 2
 
< 0.1%
300 1
 
< 0.1%
70 1
 
< 0.1%
400 1
 
< 0.1%
Other values (6) 6
 
0.1%

Length

2024-04-18T06:56:26.375001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 7157
98.4%
회원모집총인원 93
 
1.3%
60 3
 
< 0.1%
100 2
 
< 0.1%
20 2
 
< 0.1%
50 2
 
< 0.1%
30 2
 
< 0.1%
300 1
 
< 0.1%
70 1
 
< 0.1%
400 1
 
< 0.1%
Other values (6) 6
 
0.1%

last_load_dttm
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size56.9 KiB
2021-05-01 05:22:03
7268 
<NA>
 
2

Length

Max length19
Median length19
Mean length18.995873
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021-05-01 05:22:03
2nd row2021-05-01 05:22:03
3rd row2021-05-01 05:22:03
4th row2021-05-01 05:22:03
5th row2021-05-01 05:22:03

Common Values

ValueCountFrequency (%)
2021-05-01 05:22:03 7268
> 99.9%
<NA> 2
 
< 0.1%

Length

2024-04-18T06:56:26.490225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T06:56:27.163419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021-05-01 7268
50.0%
05:22:03 7268
50.0%
na 2
 
< 0.1%

Sample

skeyopnsfteamcodemgtnoopnsvcidupdategbnupdatedtopnsvcnmbplcnmsitepostnositewhladdrrdnpostnordnwhladdrapvpermymddcbymdclgstdtclgenddtropnymdtrdstatenmdtlstatenmxylastmodtsuptaenmsitetelbdngdngnumbdngyareapuprsenmculphyedcobnmbupnminsurjnyncodedrmkcobnmldercntmemcolltotstfnumlast_load_dttm
043250000CDFH330105199700000210_31_01_PI2018-08-31 23:59:59.0<NA>광복실내골프연습장600092부산광역시 중구 대청동2가 34-1번지48947부산광역시 중구 광복중앙로 28-1 (대청동2가)19971211.020020727<NA><NA><NA>03폐업385198.60018500000180287.4883950000020040727102048<NA>051-123-1234<NA><NA>사립골프연습장업<NA>0<NA><NA><NA>2021-05-01 05:22:03
153250000CDFH330105199900000110_31_01_PI2018-08-31 23:59:59.0<NA>마린골프연습장<NA>부산광역시 중구 중앙동4가 79-1번지 마린센터 지하107호,110호48936부산광역시 중구 충장대로9번길 52, 지1층 (중앙동4가, 마린센터빌딩)19990513.0<NA><NA><NA><NA>13영업중385898.66824500000181213.8817200000020170124143354<NA>051-123-1234<NA>20120.65사립골프연습장업<NA><NA><NA><NA><NA>2021-05-01 05:22:03
263250000CDFH330105200300000110_31_01_PI2018-08-31 23:59:59.0<NA>포시즌 골프연습장600100부산광역시 중구 대창동2가 36-5번지48947부산광역시 중구 중앙대로 133 (대창동2가)20030305.020040228<NA><NA><NA>03폐업385707.34088600000181152.2705270000020040709121811<NA>051-123-1234<NA><NA>사립골프연습장업<NA>0<NA><NA><NA>2021-05-01 05:22:03
373250000CDFH330105200300000210_31_01_PI2018-08-31 23:59:59.0<NA>에스에스 골프연습장<NA>부산광역시 중구 대청동2가 7-1번지 5층48933부산광역시 중구 대청로 107, 5층 (대청동2가)20031124.0<NA><NA><NA><NA>13영업중385250.02502800000180424.2920340000020161205154150<NA>051-123-1234<NA>4130.27사립골프연습장업<NA><NA><NA><NA><NA>2021-05-01 05:22:03
483250000CDFH330105200400000110_31_01_PI2018-08-31 23:59:59.0<NA>가나다라골프연습장<NA>부산광역시 중구 신창동3가 13-1번지48946부산광역시 중구 광복로35번길 18 (신창동3가)20040413.0<NA><NA><NA><NA>13영업중385004.37293600000180162.6944880000020180202153954<NA>051-123-1234<NA>1155.7사립골프연습장업<NA><NA><NA><NA><NA>2021-05-01 05:22:03
593250000CDFH330105200400000210_31_01_PI2018-08-31 23:59:59.0<NA>테일러메이드 실내골프 연습장600092부산광역시 중구 대청동2가 34-1번지48947부산광역시 중구 광복중앙로 28-1 (대청동2가)20040727.020130408<NA><NA><NA>35직권말소385198.60018500000180287.4883950000020130408094631<NA>051-123-1234<NA><NA>사립골프연습장업<NA><NA><NA><NA><NA>2021-05-01 05:22:03
6103250000CDFH330105200400000310_31_01_PI2018-08-31 23:59:59.0<NA>(주)이지알앤에스 실내골프연습장600031부산광역시 중구 광복동1가 7번지48947부산광역시 중구 광복로85번길 5-10 (광복동1가)20040811.020150608<NA><NA><NA>03폐업385448.75499800000179977.3282280000020150608132227<NA>051-123-1234<NA><NA>사립골프연습장업<NA><NA><NA><NA><NA>2021-05-01 05:22:03
7113250000CDFH330105200500000110_31_01_PI2018-08-31 23:59:59.0<NA>리드 실내골프연습장600816부산광역시 중구 중앙동4가 76-23번지48947부산광역시 중구 중앙대로 148 (중앙동4가)20050328.020070605<NA><NA><NA>03폐업385805.96072100000181267.3847210000020070605140559<NA>051-123-1234<NA><NA>사립골프연습장업<NA>0<NA><NA><NA>2021-05-01 05:22:03
8123250000CDFH330105201400000210_31_01_PI2018-08-31 23:59:59.0<NA>우리동네스크린골프<NA>부산광역시 중구 동광동2가 1번지48955부산광역시 중구 광복로97번길 26-2, 2층 (동광동2가)20140429.0<NA><NA><NA><NA>13영업중385546.63989500000180191.5029580000020180126153016<NA>051-123-1234<NA>5404.88사립골프연습장업<NA><NA><NA><NA><NA>2021-05-01 05:22:03
9133250000CDFH330105201400000310_31_01_PI2018-08-31 23:59:59.0<NA>광복스크린600042부산광역시 중구 남포동2가 25-10번지48954부산광역시 중구 구덕로34번길 4 (남포동2가)20140508.0<NA><NA><NA><NA>13영업중385314.95814000000179888.7646780000020161125110922<NA>051-123-1234<NA>8245.54사립골프연습장업<NA><NA><NA><NA><NA>2021-05-01 05:22:03
skeyopnsfteamcodemgtnoopnsvcidupdategbnupdatedtopnsvcnmbplcnmsitepostnositewhladdrrdnpostnordnwhladdrapvpermymddcbymdclgstdtclgenddtropnymdtrdstatenmdtlstatenmxylastmodtsuptaenmsitetelbdngdngnumbdngyareapuprsenmculphyedcobnmbupnminsurjnyncodedrmkcobnmldercntmemcolltotstfnumlast_load_dttm
7260137933280000CDFH330102202100000110_41_01_PI2021-03-07 00:23:00.0체육도장업크로스멀티짐(CROSS MULTI GYM)지번우편번호부산광역시 영도구 동삼동 219-61 장원빌라49092부산광역시 영도구 동삼서로 52, 지하1층 (동삼동, 장원빌라)20210305.0폐업일자휴업시작일자휴업종료일자재개업일자영업/정상영업중388729.846932294177945.50785130820210305143730유도051-996-6565건축물동수건축물연면적사립체육도장업법인명세부업종명2회원모집총인원2021-05-01 05:22:03
7261137943280000CDFH330102202100000110_41_01_PI2021-03-07 00:23:00.0체육도장업크로스멀티짐(CROSS MULTI GYM)지번우편번호부산광역시 영도구 동삼동 219-61 장원빌라49092부산광역시 영도구 동삼서로 52, 지하1층 (동삼동, 장원빌라)20210305.0폐업일자휴업시작일자휴업종료일자재개업일자영업/정상영업중388729.846932294177945.50785130820210305143730유도051-996-6565건축물동수건축물연면적사립체육도장업법인명세부업종명2회원모집총인원2021-05-01 05:22:03
7262137953380000CDFH330106202100000110_42_01_PI2021-03-11 00:23:00.0체력단련장업비기닝하루지번우편번호부산광역시 수영구 남천동 3-4 세진빌딩48304부산광역시 수영구 남천바다로 34, 세진빌딩 5층 (남천동)20210309.0폐업일자휴업시작일자휴업종료일자재개업일자영업/정상영업중392569.973712727185302.93129763120210309085632업태구분명전화번호건축물동수건축물연면적사립체력단련장업법인명세부업종명1회원모집총인원2021-05-01 05:22:03
7263137963360000CDFH330102202100000210_41_01_PI2021-03-12 00:23:00.0체육도장업기품태권도지번우편번호부산광역시 강서구 신호동 215-1546759부산광역시 강서구 신호산단2로27번길 3, 채움더테라스 상가동 2층 202,203호 (신호동)20210310.0폐업일자휴업시작일자휴업종료일자재개업일자영업/정상영업중371179.51398421178017.34667939120210310131708태권도051-973-7002건축물동수건축물연면적사립체육도장업법인명세부업종명지도자수회원모집총인원2021-05-01 05:22:03
7264137973330000CDFH330106202100000610_42_01_PU2021-04-21 02:40:00.0체력단련장업워너짐 반여<NA>부산광역시 해운대구 반여동 1199-11 센텀대림아파트 상가12동 지하1~5호48038부산광역시 해운대구 선수촌로 95, 상가12동 지하1층 1~5호 (반여동, 센텀대림아파트)20210310.0<NA><NA><NA><NA>영업/정상영업중392834.111355574191144.53043606420210419203949<NA>051-531-1516<NA>2404.06사립체력단련장업<NA><NA><NA><NA><NA>2021-05-01 05:22:03
7265137983350000CDFH330106202100000210_42_01_PI2021-03-13 00:23:00.0체력단련장업마이너짐지번우편번호부산광역시 금정구 구서동 1013-546235부산광역시 금정구 금샘로 419, 2층 (구서동)20210311.0폐업일자휴업시작일자휴업종료일자재개업일자영업/정상영업중389597.400218161197663.50314821520210311115544업태구분명전화번호건축물동수558.67사립체력단련장업법인명세부업종명1회원모집총인원2021-05-01 05:22:03
7266137993370000CDFH330102202100000110_41_01_PI2021-03-14 00:23:00.0체육도장업GTI태권도지번우편번호부산광역시 연제구 연산동 2027-2447608부산광역시 연제구 황령산로605번길 35, 2층 (연산동)20210312.0폐업일자휴업시작일자휴업종료일자재개업일자영업/정상영업중389597.015225215187856.75804528520210312115913태권도전화번호건축물동수건축물연면적사립체육도장업법인명세부업종명1회원모집총인원2021-05-01 05:22:03
7267138003370000CDFH330102202100000110_41_01_PI2021-03-14 00:23:00.0체육도장업GTI태권도지번우편번호부산광역시 연제구 연산동 2027-2447608부산광역시 연제구 황령산로605번길 35, 2층 (연산동)20210312.0폐업일자휴업시작일자휴업종료일자재개업일자영업/정상영업중389597.015225215187856.75804528520210312115913태권도전화번호건축물동수건축물연면적사립체육도장업법인명세부업종명1회원모집총인원2021-05-01 05:22:03
7268138013400000CDFH330106202100000410_42_01_PI2021-03-18 00:22:59.0체력단련장업운동J GYM지번우편번호부산광역시 기장군 정관읍 매학리 717-1 스타빌딩 3층 306호46015부산광역시 기장군 정관읍 정관로 579, 스타빌딩 306호20210316.0폐업일자휴업시작일자휴업종료일자재개업일자영업/정상영업중397914.744296503204614.4023687620210316161825업태구분명전화번호113040.64사립체력단련장업법인명세부업종명1회원모집총인원2021-05-01 05:22:03
7269137783340000CDFH330108202100000210_32_01_PI2021-02-24 00:23:01.0당구장업해오름 당구클럽<NA>부산광역시 사하구 다대동 1552-549506부산광역시 사하구 다대로 694-2, 5층 (다대동)20210222.0<NA><NA><NA><NA>영업/정상영업중379257.467705859173968.57461559120210222090253<NA><NA><NA><NA>사립당구장업<NA><NA><NA><NA><NA>2021-05-01 05:22:03