Overview

Dataset statistics

Number of variables47
Number of observations1815
Missing cells3433
Missing cells (%)4.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory689.6 KiB
Average record size in memory389.1 B

Variable types

Numeric9
Text8
Categorical27
DateTime1
Boolean2

Alerts

opnsvcid has constant value ""Constant
last_load_dttm has constant value ""Constant
clgstdt is highly imbalanced (93.1%)Imbalance
clgenddt is highly imbalanced (93.1%)Imbalance
ropnymd is highly imbalanced (93.1%)Imbalance
uptaenm is highly imbalanced (63.2%)Imbalance
maneipcnt is highly imbalanced (90.8%)Imbalance
multusnupsoyn is highly imbalanced (86.6%)Imbalance
useunderendflr is highly imbalanced (58.1%)Imbalance
useunderstflr is highly imbalanced (50.6%)Imbalance
wmeipcnt is highly imbalanced (91.5%)Imbalance
sntuptaenm is highly imbalanced (63.2%)Imbalance
cndpermstymd is highly imbalanced (95.2%)Imbalance
cndpermntwhy is highly imbalanced (95.2%)Imbalance
cndpermendymd is highly imbalanced (95.2%)Imbalance
rdnwhladdr has 618 (34.0%) missing valuesMissing
dcbymd has 840 (46.3%) missing valuesMissing
x has 75 (4.1%) missing valuesMissing
y has 75 (4.1%) missing valuesMissing
bdngjisgflrcnt has 464 (25.6%) missing valuesMissing
bdngunderflrcnt has 705 (38.8%) missing valuesMissing
yoksilcnt has 641 (35.3%) missing valuesMissing
skey has unique valuesUnique
bdngjisgflrcnt has 321 (17.7%) zerosZeros
bdngunderflrcnt has 509 (28.0%) zerosZeros
yoksilcnt has 590 (32.5%) zerosZeros

Reproduction

Analysis started2024-04-16 08:11:23.306391
Analysis finished2024-04-16 08:11:24.212015
Duration0.91 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

skey
Real number (ℝ)

UNIQUE 

Distinct1815
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean913.05069
Minimum3
Maximum2150
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.1 KiB
2024-04-16T17:11:24.266654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile93.7
Q1456.5
median910
Q31363.5
95-th percentile1726.3
Maximum2150
Range2147
Interquartile range (IQR)907

Descriptive statistics

Standard deviation530.00889
Coefficient of variation (CV)0.58048135
Kurtosis-1.1102675
Mean913.05069
Median Absolute Deviation (MAD)454
Skewness0.047715113
Sum1657187
Variance280909.43
MonotonicityNot monotonic
2024-04-16T17:11:24.367368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 1
 
0.1%
1194 1
 
0.1%
1220 1
 
0.1%
1219 1
 
0.1%
1218 1
 
0.1%
1217 1
 
0.1%
1216 1
 
0.1%
1215 1
 
0.1%
1214 1
 
0.1%
1213 1
 
0.1%
Other values (1805) 1805
99.4%
ValueCountFrequency (%)
3 1
0.1%
4 1
0.1%
5 1
0.1%
6 1
0.1%
7 1
0.1%
8 1
0.1%
9 1
0.1%
10 1
0.1%
11 1
0.1%
12 1
0.1%
ValueCountFrequency (%)
2150 1
0.1%
2143 1
0.1%
2136 1
0.1%
2131 1
0.1%
2104 1
0.1%
2102 1
0.1%
2101 1
0.1%
2100 1
0.1%
2097 1
0.1%
2095 1
0.1%

opnsfteamcode
Real number (ℝ)

Distinct16
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3323002.8
Minimum3250000
Maximum3400000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.1 KiB
2024-04-16T17:11:24.453631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3250000
5-th percentile3260000
Q13290000
median3320000
Q33350000
95-th percentile3390000
Maximum3400000
Range150000
Interquartile range (IQR)60000

Descriptive statistics

Standard deviation39911.251
Coefficient of variation (CV)0.012010598
Kurtosis-0.91112888
Mean3323002.8
Median Absolute Deviation (MAD)30000
Skewness0.13424002
Sum6.03125 × 109
Variance1.5929079 × 109
MonotonicityNot monotonic
2024-04-16T17:11:24.542373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
3290000 221
12.2%
3340000 171
9.4%
3300000 164
9.0%
3330000 159
8.8%
3310000 148
 
8.2%
3370000 131
 
7.2%
3320000 123
 
6.8%
3350000 116
 
6.4%
3380000 112
 
6.2%
3270000 93
 
5.1%
Other values (6) 377
20.8%
ValueCountFrequency (%)
3250000 62
 
3.4%
3260000 72
 
4.0%
3270000 93
5.1%
3280000 74
 
4.1%
3290000 221
12.2%
3300000 164
9.0%
3310000 148
8.2%
3320000 123
6.8%
3330000 159
8.8%
3340000 171
9.4%
ValueCountFrequency (%)
3400000 45
 
2.5%
3390000 93
5.1%
3380000 112
6.2%
3370000 131
7.2%
3360000 31
 
1.7%
3350000 116
6.4%
3340000 171
9.4%
3330000 159
8.8%
3320000 123
6.8%
3310000 148
8.2%

mgtno
Text

Distinct1806
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Memory size14.3 KiB
2024-04-16T17:11:24.715989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters39930
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

Unique1801 ?
Unique (%)99.2%

Sample

1st row3250000-202-2002-00001
2nd row3250000-202-1985-00156
3rd row3250000-202-2001-00013
4th row3250000-202-2007-00001
5th row3250000-202-1988-00159
ValueCountFrequency (%)
3360000-202-2019-00003 3
 
0.2%
3310000-202-2020-00001 3
 
0.2%
3350000-202-2019-00001 3
 
0.2%
3280000-202-2020-00001 3
 
0.2%
3370000-202-2018-00003 2
 
0.1%
3340000-202-1996-00553 1
 
0.1%
3340000-202-1978-00421 1
 
0.1%
3340000-202-1989-00041 1
 
0.1%
3340000-202-2003-00005 1
 
0.1%
3340000-202-2000-01140 1
 
0.1%
Other values (1796) 1796
99.0%
2024-04-16T17:11:24.998677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 15391
38.5%
2 5647
 
14.1%
- 5445
 
13.6%
3 3940
 
9.9%
1 2746
 
6.9%
9 2541
 
6.4%
8 1144
 
2.9%
4 944
 
2.4%
7 837
 
2.1%
5 695
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 34485
86.4%
Dash Punctuation 5445
 
13.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15391
44.6%
2 5647
 
16.4%
3 3940
 
11.4%
1 2746
 
8.0%
9 2541
 
7.4%
8 1144
 
3.3%
4 944
 
2.7%
7 837
 
2.4%
5 695
 
2.0%
6 600
 
1.7%
Dash Punctuation
ValueCountFrequency (%)
- 5445
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 39930
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15391
38.5%
2 5647
 
14.1%
- 5445
 
13.6%
3 3940
 
9.9%
1 2746
 
6.9%
9 2541
 
6.4%
8 1144
 
2.9%
4 944
 
2.4%
7 837
 
2.1%
5 695
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 39930
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15391
38.5%
2 5647
 
14.1%
- 5445
 
13.6%
3 3940
 
9.9%
1 2746
 
6.9%
9 2541
 
6.4%
8 1144
 
2.9%
4 944
 
2.4%
7 837
 
2.1%
5 695
 
1.7%

opnsvcid
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size14.3 KiB
11_44_01_P
1815 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row11_44_01_P
2nd row11_44_01_P
3rd row11_44_01_P
4th row11_44_01_P
5th row11_44_01_P

Common Values

ValueCountFrequency (%)
11_44_01_P 1815
100.0%

Length

2024-04-16T17:11:25.105917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:11:25.178802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
11_44_01_p 1815
100.0%

updategbn
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size14.3 KiB
I
1541 
U
274 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
I 1541
84.9%
U 274
 
15.1%

Length

2024-04-16T17:11:25.253225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:11:25.338803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
i 1541
84.9%
u 274
 
15.1%
Distinct184
Distinct (%)10.1%
Missing0
Missing (%)0.0%
Memory size14.3 KiB
Minimum2018-08-31 23:59:59
Maximum2021-02-27 02:40:00
2024-04-16T17:11:25.422865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T17:11:25.529936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

opnsvcnm
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size14.3 KiB
<NA>
1534 
목욕장업
281 

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> 1534
84.5%
목욕장업 281
 
15.5%

Length

2024-04-16T17:11:25.630555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:11:25.708975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 1534
84.5%
목욕장업 281
 
15.5%

bplcnm
Text

Distinct1135
Distinct (%)62.5%
Missing0
Missing (%)0.0%
Memory size14.3 KiB
2024-04-16T17:11:25.948404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length3
Mean length4.138292
Min length2

Characters and Unicode

Total characters7511
Distinct characters385
Distinct categories9 ?
Distinct scripts4 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique888 ?
Unique (%)48.9%

Sample

1st row옥샘탕
2nd row금수탕
3rd row백록담
4th row유나목욕탕
5th row녹수탕
ValueCountFrequency (%)
사우나 23
 
1.2%
청수탕 21
 
1.1%
현대탕 21
 
1.1%
옥천탕 19
 
1.0%
산수탕 15
 
0.8%
천수탕 15
 
0.8%
목욕탕 15
 
0.8%
장수탕 14
 
0.7%
제일탕 13
 
0.7%
정수탕 13
 
0.7%
Other values (1191) 1799
91.4%
2024-04-16T17:11:26.318489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1367
 
18.2%
300
 
4.0%
226
 
3.0%
181
 
2.4%
177
 
2.4%
165
 
2.2%
155
 
2.1%
154
 
2.1%
122
 
1.6%
117
 
1.6%
Other values (375) 4547
60.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 7229
96.2%
Space Separator 154
 
2.1%
Close Punctuation 40
 
0.5%
Open Punctuation 37
 
0.5%
Decimal Number 20
 
0.3%
Uppercase Letter 20
 
0.3%
Lowercase Letter 7
 
0.1%
Dash Punctuation 2
 
< 0.1%
Other Punctuation 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
1367
 
18.9%
300
 
4.1%
226
 
3.1%
181
 
2.5%
177
 
2.4%
165
 
2.3%
155
 
2.1%
122
 
1.7%
117
 
1.6%
112
 
1.5%
Other values (351) 4307
59.6%
Uppercase Letter
ValueCountFrequency (%)
G 5
25.0%
L 4
20.0%
M 2
 
10.0%
O 2
 
10.0%
B 1
 
5.0%
Y 1
 
5.0%
A 1
 
5.0%
W 1
 
5.0%
F 1
 
5.0%
S 1
 
5.0%
Lowercase Letter
ValueCountFrequency (%)
n 2
28.6%
o 2
28.6%
d 1
14.3%
u 1
14.3%
r 1
14.3%
Decimal Number
ValueCountFrequency (%)
2 11
55.0%
4 9
45.0%
Other Punctuation
ValueCountFrequency (%)
, 1
50.0%
. 1
50.0%
Space Separator
ValueCountFrequency (%)
154
100.0%
Close Punctuation
ValueCountFrequency (%)
) 40
100.0%
Open Punctuation
ValueCountFrequency (%)
( 37
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 7228
96.2%
Common 255
 
3.4%
Latin 27
 
0.4%
Han 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
1367
 
18.9%
300
 
4.2%
226
 
3.1%
181
 
2.5%
177
 
2.4%
165
 
2.3%
155
 
2.1%
122
 
1.7%
117
 
1.6%
112
 
1.5%
Other values (350) 4306
59.6%
Latin
ValueCountFrequency (%)
G 5
18.5%
L 4
14.8%
M 2
 
7.4%
n 2
 
7.4%
o 2
 
7.4%
O 2
 
7.4%
B 1
 
3.7%
Y 1
 
3.7%
A 1
 
3.7%
W 1
 
3.7%
Other values (6) 6
22.2%
Common
ValueCountFrequency (%)
154
60.4%
) 40
 
15.7%
( 37
 
14.5%
2 11
 
4.3%
4 9
 
3.5%
- 2
 
0.8%
, 1
 
0.4%
. 1
 
0.4%
Han
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 7228
96.2%
ASCII 282
 
3.8%
CJK 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
1367
 
18.9%
300
 
4.2%
226
 
3.1%
181
 
2.5%
177
 
2.4%
165
 
2.3%
155
 
2.1%
122
 
1.7%
117
 
1.6%
112
 
1.5%
Other values (350) 4306
59.6%
ASCII
ValueCountFrequency (%)
154
54.6%
) 40
 
14.2%
( 37
 
13.1%
2 11
 
3.9%
4 9
 
3.2%
G 5
 
1.8%
L 4
 
1.4%
M 2
 
0.7%
n 2
 
0.7%
o 2
 
0.7%
Other values (14) 16
 
5.7%
CJK
ValueCountFrequency (%)
1
100.0%

sitepostno
Real number (ℝ)

Distinct629
Distinct (%)34.8%
Missing5
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean610432.57
Minimum600011
Maximum619953
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.1 KiB
2024-04-16T17:11:26.432691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum600011
5-th percentile601811
Q1606812.5
median611781
Q3614820
95-th percentile617834
Maximum619953
Range19942
Interquartile range (IQR)8007.5

Descriptive statistics

Standard deviation5157.3185
Coefficient of variation (CV)0.0084486292
Kurtosis-0.91043145
Mean610432.57
Median Absolute Deviation (MAD)3942.5
Skewness-0.20716839
Sum1.104883 × 109
Variance26597934
MonotonicityNot monotonic
2024-04-16T17:11:26.540135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
604851 15
 
0.8%
612846 12
 
0.7%
612847 12
 
0.7%
608828 10
 
0.6%
608808 10
 
0.6%
607833 10
 
0.6%
614822 10
 
0.6%
607826 9
 
0.5%
613805 9
 
0.5%
613832 9
 
0.5%
Other values (619) 1704
93.9%
ValueCountFrequency (%)
600011 1
 
0.1%
600012 1
 
0.1%
600017 1
 
0.1%
600021 1
 
0.1%
600022 4
0.2%
600023 1
 
0.1%
600025 1
 
0.1%
600032 1
 
0.1%
600042 1
 
0.1%
600044 1
 
0.1%
ValueCountFrequency (%)
619953 2
 
0.1%
619952 3
0.2%
619951 4
0.2%
619913 1
 
0.1%
619912 2
 
0.1%
619911 2
 
0.1%
619906 3
0.2%
619905 5
0.3%
619904 3
0.2%
619903 7
0.4%
Distinct1724
Distinct (%)95.0%
Missing0
Missing (%)0.0%
Memory size14.3 KiB
2024-04-16T17:11:26.843456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length55
Median length49
Mean length24.627548
Min length17

Characters and Unicode

Total characters44699
Distinct characters271
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1649 ?
Unique (%)90.9%

Sample

1st row부산광역시 중구 창선동1가 9-9번지
2nd row부산광역시 중구 중앙동4가 78-2번지
3rd row부산광역시 중구 대청동1가 34-1번지
4th row부산광역시 중구 신창동1가 5-1번지 (5~8층)
5th row부산광역시 중구 신창동2가 21-2번지
ValueCountFrequency (%)
부산광역시 1815
 
22.4%
t통b반 335
 
4.1%
부산진구 221
 
2.7%
사하구 171
 
2.1%
동래구 164
 
2.0%
해운대구 159
 
2.0%
남구 148
 
1.8%
연제구 131
 
1.6%
북구 123
 
1.5%
금정구 116
 
1.4%
Other values (2140) 4702
58.2%
2024-04-16T17:11:27.254981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
8057
18.0%
2164
 
4.8%
2158
 
4.8%
2112
 
4.7%
1 1909
 
4.3%
1885
 
4.2%
1836
 
4.1%
1828
 
4.1%
1817
 
4.1%
1759
 
3.9%
Other values (261) 19174
42.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 25224
56.4%
Decimal Number 8804
 
19.7%
Space Separator 8057
 
18.0%
Dash Punctuation 1687
 
3.8%
Uppercase Letter 683
 
1.5%
Other Punctuation 114
 
0.3%
Close Punctuation 61
 
0.1%
Open Punctuation 61
 
0.1%
Math Symbol 7
 
< 0.1%
Letter Number 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
2164
 
8.6%
2158
 
8.6%
2112
 
8.4%
1885
 
7.5%
1836
 
7.3%
1828
 
7.2%
1817
 
7.2%
1759
 
7.0%
1689
 
6.7%
386
 
1.5%
Other values (235) 7590
30.1%
Decimal Number
ValueCountFrequency (%)
1 1909
21.7%
2 1139
12.9%
3 990
11.2%
4 890
10.1%
5 785
8.9%
6 685
 
7.8%
7 648
 
7.4%
8 609
 
6.9%
0 608
 
6.9%
9 541
 
6.1%
Uppercase Letter
ValueCountFrequency (%)
B 342
50.1%
T 335
49.0%
A 2
 
0.3%
W 1
 
0.1%
I 1
 
0.1%
G 1
 
0.1%
L 1
 
0.1%
Other Punctuation
ValueCountFrequency (%)
, 112
98.2%
. 1
 
0.9%
@ 1
 
0.9%
Space Separator
ValueCountFrequency (%)
8057
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1687
100.0%
Close Punctuation
ValueCountFrequency (%)
) 61
100.0%
Open Punctuation
ValueCountFrequency (%)
( 61
100.0%
Math Symbol
ValueCountFrequency (%)
~ 7
100.0%
Letter Number
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 25224
56.4%
Common 18791
42.0%
Latin 684
 
1.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
2164
 
8.6%
2158
 
8.6%
2112
 
8.4%
1885
 
7.5%
1836
 
7.3%
1828
 
7.2%
1817
 
7.2%
1759
 
7.0%
1689
 
6.7%
386
 
1.5%
Other values (235) 7590
30.1%
Common
ValueCountFrequency (%)
8057
42.9%
1 1909
 
10.2%
- 1687
 
9.0%
2 1139
 
6.1%
3 990
 
5.3%
4 890
 
4.7%
5 785
 
4.2%
6 685
 
3.6%
7 648
 
3.4%
8 609
 
3.2%
Other values (8) 1392
 
7.4%
Latin
ValueCountFrequency (%)
B 342
50.0%
T 335
49.0%
A 2
 
0.3%
W 1
 
0.1%
I 1
 
0.1%
G 1
 
0.1%
L 1
 
0.1%
1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 25224
56.4%
ASCII 19474
43.6%
Number Forms 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8057
41.4%
1 1909
 
9.8%
- 1687
 
8.7%
2 1139
 
5.8%
3 990
 
5.1%
4 890
 
4.6%
5 785
 
4.0%
6 685
 
3.5%
7 648
 
3.3%
8 609
 
3.1%
Other values (15) 2075
 
10.7%
Hangul
ValueCountFrequency (%)
2164
 
8.6%
2158
 
8.6%
2112
 
8.4%
1885
 
7.5%
1836
 
7.3%
1828
 
7.2%
1817
 
7.2%
1759
 
7.0%
1689
 
6.7%
386
 
1.5%
Other values (235) 7590
30.1%
Number Forms
ValueCountFrequency (%)
1
100.0%

rdnpostno
Real number (ℝ)

Distinct877
Distinct (%)48.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48267.839
Minimum46002
Maximum49523
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.1 KiB
2024-04-16T17:11:27.365633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum46002
5-th percentile46317.7
Q147567.5
median48815
Q348947
95-th percentile49323.3
Maximum49523
Range3521
Interquartile range (IQR)1379.5

Descriptive statistics

Standard deviation935.89115
Coefficient of variation (CV)0.019389539
Kurtosis-0.54870799
Mean48267.839
Median Absolute Deviation (MAD)394
Skewness-0.84195123
Sum87606127
Variance875892.24
MonotonicityNot monotonic
2024-04-16T17:11:27.680949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
48947 670
36.9%
48099 8
 
0.4%
47709 8
 
0.4%
46327 5
 
0.3%
47248 5
 
0.3%
48531 4
 
0.2%
47142 4
 
0.2%
46308 4
 
0.2%
47712 4
 
0.2%
46275 4
 
0.2%
Other values (867) 1099
60.6%
ValueCountFrequency (%)
46002 1
0.1%
46008 1
0.1%
46015 1
0.1%
46017 1
0.1%
46020 1
0.1%
46032 1
0.1%
46033 1
0.1%
46036 2
0.1%
46037 2
0.1%
46040 1
0.1%
ValueCountFrequency (%)
49523 1
0.1%
49522 1
0.1%
49521 1
0.1%
49518 2
0.1%
49515 2
0.1%
49511 2
0.1%
49506 1
0.1%
49505 1
0.1%
49504 1
0.1%
49503 2
0.1%

rdnwhladdr
Text

MISSING 

Distinct1174
Distinct (%)98.1%
Missing618
Missing (%)34.0%
Memory size14.3 KiB
2024-04-16T17:11:27.993736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length59
Median length50
Mean length27.452799
Min length20

Characters and Unicode

Total characters32861
Distinct characters331
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1157 ?
Unique (%)96.7%

Sample

1st row부산광역시 중구 광복로55번길 14-2 (창선동1가)
2nd row부산광역시 중구 광복중앙로 25 (신창동1가)
3rd row부산광역시 중구 광복로43번길 12 (신창동2가)
4th row부산광역시 중구 흑교로31번길 3-1 (부평동3가, 외 2필지)
5th row부산광역시 중구 비프광장로 20, 10층 (남포동6가)
ValueCountFrequency (%)
부산광역시 1197
 
19.1%
부산진구 153
 
2.4%
남구 104
 
1.7%
해운대구 103
 
1.6%
사하구 100
 
1.6%
동래구 99
 
1.6%
연제구 85
 
1.4%
금정구 77
 
1.2%
북구 75
 
1.2%
사상구 71
 
1.1%
Other values (1709) 4201
67.1%
2024-04-16T17:11:28.446067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5069
 
15.4%
1519
 
4.6%
1451
 
4.4%
1450
 
4.4%
1264
 
3.8%
1256
 
3.8%
1226
 
3.7%
1200
 
3.7%
( 1182
 
3.6%
) 1182
 
3.6%
Other values (321) 16062
48.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 19798
60.2%
Space Separator 5069
 
15.4%
Decimal Number 5059
 
15.4%
Open Punctuation 1185
 
3.6%
Close Punctuation 1185
 
3.6%
Other Punctuation 343
 
1.0%
Dash Punctuation 198
 
0.6%
Math Symbol 12
 
< 0.1%
Uppercase Letter 11
 
< 0.1%
Letter Number 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
1519
 
7.7%
1451
 
7.3%
1450
 
7.3%
1264
 
6.4%
1256
 
6.3%
1226
 
6.2%
1200
 
6.1%
1141
 
5.8%
709
 
3.6%
667
 
3.4%
Other values (293) 7915
40.0%
Decimal Number
ValueCountFrequency (%)
1 1142
22.6%
2 722
14.3%
3 626
12.4%
5 441
 
8.7%
4 435
 
8.6%
6 392
 
7.7%
0 377
 
7.5%
7 353
 
7.0%
9 287
 
5.7%
8 284
 
5.6%
Uppercase Letter
ValueCountFrequency (%)
B 4
36.4%
A 4
36.4%
I 1
 
9.1%
W 1
 
9.1%
G 1
 
9.1%
Other Punctuation
ValueCountFrequency (%)
, 337
98.3%
* 3
 
0.9%
. 2
 
0.6%
@ 1
 
0.3%
Open Punctuation
ValueCountFrequency (%)
( 1182
99.7%
[ 3
 
0.3%
Close Punctuation
ValueCountFrequency (%)
) 1182
99.7%
] 3
 
0.3%
Math Symbol
ValueCountFrequency (%)
~ 11
91.7%
1
 
8.3%
Space Separator
ValueCountFrequency (%)
5069
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 198
100.0%
Letter Number
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 19798
60.2%
Common 13051
39.7%
Latin 12
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
1519
 
7.7%
1451
 
7.3%
1450
 
7.3%
1264
 
6.4%
1256
 
6.3%
1226
 
6.2%
1200
 
6.1%
1141
 
5.8%
709
 
3.6%
667
 
3.4%
Other values (293) 7915
40.0%
Common
ValueCountFrequency (%)
5069
38.8%
( 1182
 
9.1%
) 1182
 
9.1%
1 1142
 
8.8%
2 722
 
5.5%
3 626
 
4.8%
5 441
 
3.4%
4 435
 
3.3%
6 392
 
3.0%
0 377
 
2.9%
Other values (12) 1483
 
11.4%
Latin
ValueCountFrequency (%)
B 4
33.3%
A 4
33.3%
1
 
8.3%
I 1
 
8.3%
W 1
 
8.3%
G 1
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 19798
60.2%
ASCII 13061
39.7%
Number Forms 1
 
< 0.1%
None 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5069
38.8%
( 1182
 
9.0%
) 1182
 
9.0%
1 1142
 
8.7%
2 722
 
5.5%
3 626
 
4.8%
5 441
 
3.4%
4 435
 
3.3%
6 392
 
3.0%
0 377
 
2.9%
Other values (16) 1493
 
11.4%
Hangul
ValueCountFrequency (%)
1519
 
7.7%
1451
 
7.3%
1450
 
7.3%
1264
 
6.4%
1256
 
6.3%
1226
 
6.2%
1200
 
6.1%
1141
 
5.8%
709
 
3.6%
667
 
3.4%
Other values (293) 7915
40.0%
Number Forms
ValueCountFrequency (%)
1
100.0%
None
ValueCountFrequency (%)
1
100.0%

apvpermymd
Real number (ℝ)

Distinct1521
Distinct (%)83.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19910120
Minimum19540131
Maximum20201013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.1 KiB
2024-04-16T17:11:28.559847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19540131
5-th percentile19700483
Q119830516
median19901201
Q320010322
95-th percentile20103892
Maximum20201013
Range660882
Interquartile range (IQR)179805.5

Descriptive statistics

Standard deviation123302.36
Coefficient of variation (CV)0.0061929491
Kurtosis-0.35712397
Mean19910120
Median Absolute Deviation (MAD)80899
Skewness-0.05323414
Sum3.6136868 × 1010
Variance1.5203472 × 1010
MonotonicityNot monotonic
2024-04-16T17:11:28.699299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19630110 15
 
0.8%
20001130 9
 
0.5%
19921202 8
 
0.4%
19960710 6
 
0.3%
19971227 6
 
0.3%
20030115 5
 
0.3%
20000420 5
 
0.3%
19820928 5
 
0.3%
19830304 5
 
0.3%
19630610 4
 
0.2%
Other values (1511) 1747
96.3%
ValueCountFrequency (%)
19540131 1
 
0.1%
19601210 3
 
0.2%
19630108 1
 
0.1%
19630109 3
 
0.2%
19630110 15
0.8%
19630610 4
 
0.2%
19631001 1
 
0.1%
19640211 1
 
0.1%
19640915 1
 
0.1%
19641015 1
 
0.1%
ValueCountFrequency (%)
20201013 1
 
0.1%
20200924 1
 
0.1%
20200908 1
 
0.1%
20200827 1
 
0.1%
20200708 1
 
0.1%
20200703 3
0.2%
20200624 1
 
0.1%
20200619 3
0.2%
20200514 1
 
0.1%
20191211 1
 
0.1%

dcbymd
Text

MISSING 

Distinct808
Distinct (%)82.9%
Missing840
Missing (%)46.3%
Memory size14.3 KiB
2024-04-16T17:11:28.968553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length7.9466667
Min length4

Characters and Unicode

Total characters7748
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

Unique696 ?
Unique (%)71.4%

Sample

1st row20070612
2nd row20120221
3rd row20140401
4th row20131227
5th row20030703
ValueCountFrequency (%)
폐업일자 13
 
1.3%
20050121 12
 
1.2%
20051017 7
 
0.7%
20001130 7
 
0.7%
20170310 5
 
0.5%
20030401 5
 
0.5%
20030122 4
 
0.4%
20141030 4
 
0.4%
20120621 4
 
0.4%
20051019 3
 
0.3%
Other values (798) 911
93.4%
2024-04-16T17:11:29.351938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 2601
33.6%
2 1587
20.5%
1 1395
18.0%
9 355
 
4.6%
3 345
 
4.5%
7 299
 
3.9%
5 293
 
3.8%
6 280
 
3.6%
8 277
 
3.6%
4 264
 
3.4%
Other values (4) 52
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7696
99.3%
Other Letter 52
 
0.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2601
33.8%
2 1587
20.6%
1 1395
18.1%
9 355
 
4.6%
3 345
 
4.5%
7 299
 
3.9%
5 293
 
3.8%
6 280
 
3.6%
8 277
 
3.6%
4 264
 
3.4%
Other Letter
ValueCountFrequency (%)
13
25.0%
13
25.0%
13
25.0%
13
25.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7696
99.3%
Hangul 52
 
0.7%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2601
33.8%
2 1587
20.6%
1 1395
18.1%
9 355
 
4.6%
3 345
 
4.5%
7 299
 
3.9%
5 293
 
3.8%
6 280
 
3.6%
8 277
 
3.6%
4 264
 
3.4%
Hangul
ValueCountFrequency (%)
13
25.0%
13
25.0%
13
25.0%
13
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7696
99.3%
Hangul 52
 
0.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2601
33.8%
2 1587
20.6%
1 1395
18.1%
9 355
 
4.6%
3 345
 
4.5%
7 299
 
3.9%
5 293
 
3.8%
6 280
 
3.6%
8 277
 
3.6%
4 264
 
3.4%
Hangul
ValueCountFrequency (%)
13
25.0%
13
25.0%
13
25.0%
13
25.0%

clgstdt
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size14.3 KiB
<NA>
1800 
휴업시작일자
 
15

Length

Max length6
Median length4
Mean length4.0165289
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> 1800
99.2%
휴업시작일자 15
 
0.8%

Length

2024-04-16T17:11:29.476469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:11:29.561751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 1800
99.2%
휴업시작일자 15
 
0.8%

clgenddt
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size14.3 KiB
<NA>
1800 
휴업종료일자
 
15

Length

Max length6
Median length4
Mean length4.0165289
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> 1800
99.2%
휴업종료일자 15
 
0.8%

Length

2024-04-16T17:11:29.649405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:11:29.731551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 1800
99.2%
휴업종료일자 15
 
0.8%

ropnymd
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size14.3 KiB
<NA>
1800 
재개업일자
 
15

Length

Max length5
Median length4
Mean length4.0082645
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> 1800
99.2%
재개업일자 15
 
0.8%

Length

2024-04-16T17:11:29.812644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:11:29.892911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 1800
99.2%
재개업일자 15
 
0.8%

trdstatenm
Categorical

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size14.3 KiB
02
925 
01
609 
영업/정상
242 
폐업
 
37
<NA>
 
2

Length

Max length5
Median length2
Mean length2.4022039
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row01
2nd row02
3rd row02
4th row02
5th row01

Common Values

ValueCountFrequency (%)
02 925
51.0%
01 609
33.6%
영업/정상 242
 
13.3%
폐업 37
 
2.0%
<NA> 2
 
0.1%

Length

2024-04-16T17:11:29.986505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:11:30.069797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
02 925
51.0%
01 609
33.6%
영업/정상 242
 
13.3%
폐업 37
 
2.0%
na 2
 
0.1%

dtlstatenm
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size14.3 KiB
폐업
962 
영업
853 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
폐업 962
53.0%
영업 853
47.0%

Length

2024-04-16T17:11:30.158282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:11:30.253300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 962
53.0%
영업 853
47.0%

x
Text

MISSING 

Distinct1670
Distinct (%)96.0%
Missing75
Missing (%)4.1%
Memory size14.3 KiB
2024-04-16T17:11:30.442935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length20
Mean length19.992529
Min length7

Characters and Unicode

Total characters34787
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

Unique1613 ?
Unique (%)92.7%

Sample

1st row385089.38491100000
2nd row385793.29931
3rd row385208.257554
4th row385157.86566400000
5th row385086.62014400000
ValueCountFrequency (%)
387810.283167969 4
 
0.2%
389727.58350700000 4
 
0.2%
389394.976154 4
 
0.2%
390974.056399381 4
 
0.2%
386501.470234 3
 
0.2%
390237.659277726 3
 
0.2%
370674.351397752 3
 
0.2%
386099.535234 3
 
0.2%
391906.711100 3
 
0.2%
378667.112847 2
 
0.1%
Other values (1660) 1707
98.1%
2024-04-16T17:11:30.736793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6744
19.4%
0 6385
18.4%
3 3521
10.1%
8 2860
8.2%
9 2514
 
7.2%
7 1929
 
5.5%
2 1860
 
5.3%
4 1829
 
5.3%
1 1812
 
5.2%
5 1794
 
5.2%
Other values (9) 3539
10.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 26297
75.6%
Space Separator 6744
 
19.4%
Other Punctuation 1739
 
5.0%
Other Letter 4
 
< 0.1%
Open Punctuation 1
 
< 0.1%
Uppercase Letter 1
 
< 0.1%
Close Punctuation 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 6385
24.3%
3 3521
13.4%
8 2860
10.9%
9 2514
 
9.6%
7 1929
 
7.3%
2 1860
 
7.1%
4 1829
 
7.0%
1 1812
 
6.9%
5 1794
 
6.8%
6 1793
 
6.8%
Other Letter
ValueCountFrequency (%)
1
25.0%
1
25.0%
1
25.0%
1
25.0%
Space Separator
ValueCountFrequency (%)
6744
100.0%
Other Punctuation
ValueCountFrequency (%)
. 1739
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Uppercase Letter
ValueCountFrequency (%)
X 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 34782
> 99.9%
Hangul 4
 
< 0.1%
Latin 1
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
6744
19.4%
0 6385
18.4%
3 3521
10.1%
8 2860
8.2%
9 2514
 
7.2%
7 1929
 
5.5%
2 1860
 
5.3%
4 1829
 
5.3%
1 1812
 
5.2%
5 1794
 
5.2%
Other values (4) 3534
10.2%
Hangul
ValueCountFrequency (%)
1
25.0%
1
25.0%
1
25.0%
1
25.0%
Latin
ValueCountFrequency (%)
X 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 34783
> 99.9%
Hangul 4
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6744
19.4%
0 6385
18.4%
3 3521
10.1%
8 2860
8.2%
9 2514
 
7.2%
7 1929
 
5.5%
2 1860
 
5.3%
4 1829
 
5.3%
1 1812
 
5.2%
5 1794
 
5.2%
Other values (5) 3535
10.2%
Hangul
ValueCountFrequency (%)
1
25.0%
1
25.0%
1
25.0%
1
25.0%

y
Text

MISSING 

Distinct1670
Distinct (%)96.0%
Missing75
Missing (%)4.1%
Memory size14.3 KiB
2024-04-16T17:11:30.926932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length20
Mean length19.992529
Min length7

Characters and Unicode

Total characters34787
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

Unique1613 ?
Unique (%)92.7%

Sample

1st row180062.56761800000
2nd row180910.386636
3rd row180108.034984
4th row180248.21457300000
5th row180119.33406400000
ValueCountFrequency (%)
179178.742589729 4
 
0.2%
191654.34294400000 4
 
0.2%
193519.152183 4
 
0.2%
182797.894969615 4
 
0.2%
189887.738381 3
 
0.2%
195579.465881184 3
 
0.2%
184448.198703368 3
 
0.2%
181890.404844 3
 
0.2%
189495.497384 3
 
0.2%
181019.683831 2
 
0.1%
Other values (1660) 1707
98.1%
2024-04-16T17:11:31.216916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6732
19.4%
0 6342
18.2%
1 3547
10.2%
8 2767
8.0%
9 2399
 
6.9%
7 2237
 
6.4%
6 1864
 
5.4%
4 1833
 
5.3%
3 1831
 
5.3%
2 1748
 
5.0%
Other values (9) 3487
10.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 26309
75.6%
Space Separator 6732
 
19.4%
Other Punctuation 1739
 
5.0%
Other Letter 4
 
< 0.1%
Open Punctuation 1
 
< 0.1%
Uppercase Letter 1
 
< 0.1%
Close Punctuation 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 6342
24.1%
1 3547
13.5%
8 2767
10.5%
9 2399
 
9.1%
7 2237
 
8.5%
6 1864
 
7.1%
4 1833
 
7.0%
3 1831
 
7.0%
2 1748
 
6.6%
5 1741
 
6.6%
Other Letter
ValueCountFrequency (%)
1
25.0%
1
25.0%
1
25.0%
1
25.0%
Space Separator
ValueCountFrequency (%)
6732
100.0%
Other Punctuation
ValueCountFrequency (%)
. 1739
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Uppercase Letter
ValueCountFrequency (%)
Y 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 34782
> 99.9%
Hangul 4
 
< 0.1%
Latin 1
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
6732
19.4%
0 6342
18.2%
1 3547
10.2%
8 2767
8.0%
9 2399
 
6.9%
7 2237
 
6.4%
6 1864
 
5.4%
4 1833
 
5.3%
3 1831
 
5.3%
2 1748
 
5.0%
Other values (4) 3482
10.0%
Hangul
ValueCountFrequency (%)
1
25.0%
1
25.0%
1
25.0%
1
25.0%
Latin
ValueCountFrequency (%)
Y 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 34783
> 99.9%
Hangul 4
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6732
19.4%
0 6342
18.2%
1 3547
10.2%
8 2767
8.0%
9 2399
 
6.9%
7 2237
 
6.4%
6 1864
 
5.4%
4 1833
 
5.3%
3 1831
 
5.3%
2 1748
 
5.0%
Other values (5) 3483
10.0%
Hangul
ValueCountFrequency (%)
1
25.0%
1
25.0%
1
25.0%
1
25.0%

lastmodts
Real number (ℝ)

Distinct1482
Distinct (%)81.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0112594 × 1013
Minimum1.999021 × 1013
Maximum2.0210225 × 1013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.1 KiB
2024-04-16T17:11:31.336235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.999021 × 1013
5-th percentile2.0020422 × 1013
Q12.0041208 × 1013
median2.0120905 × 1013
Q32.0170412 × 1013
95-th percentile2.0200915 × 1013
Maximum2.0210225 × 1013
Range2.2001515 × 1011
Interquartile range (IQR)1.2920414 × 1011

Descriptive statistics

Standard deviation6.3514292 × 1010
Coefficient of variation (CV)0.0031579363
Kurtosis-1.3127751
Mean2.0112594 × 1013
Median Absolute Deviation (MAD)5.9604058 × 1010
Skewness-0.20091306
Sum3.6504358 × 1016
Variance4.0340653 × 1021
MonotonicityNot monotonic
2024-04-16T17:11:31.440450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20030409000000 29
 
1.6%
20020418000000 27
 
1.5%
20040318000000 16
 
0.9%
20050415000000 14
 
0.8%
20031217000000 12
 
0.7%
20030303000000 12
 
0.7%
20040324000000 11
 
0.6%
20020422000000 10
 
0.6%
20041208000000 9
 
0.5%
20030722000000 8
 
0.4%
Other values (1472) 1667
91.8%
ValueCountFrequency (%)
19990210000000 2
 
0.1%
19990212000000 1
 
0.1%
19990302000000 7
0.4%
19990310000000 6
0.3%
19990315000000 1
 
0.1%
19990325000000 2
 
0.1%
19990420000000 2
 
0.1%
19990421000000 7
0.4%
19990422000000 1
 
0.1%
19990427000000 1
 
0.1%
ValueCountFrequency (%)
20210225153932 1
0.1%
20210224092430 1
0.1%
20210218140116 1
0.1%
20210218113852 1
0.1%
20210208111352 1
0.1%
20210208111312 1
0.1%
20210208105516 1
0.1%
20210208101335 1
0.1%
20210203110646 1
0.1%
20210202173923 1
0.1%

uptaenm
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size14.3 KiB
공동탕업
1537 
목욕장업 기타
154 
공동탕업+찜질시설서비스영업
 
78
한증막업
 
35
찜질시설서비스영업
 
11

Length

Max length14
Median length4
Mean length4.7146006
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row공동탕업+찜질시설서비스영업
2nd row한증막업
3rd row공동탕업+찜질시설서비스영업
4th row공동탕업+찜질시설서비스영업
5th row공동탕업

Common Values

ValueCountFrequency (%)
공동탕업 1537
84.7%
목욕장업 기타 154
 
8.5%
공동탕업+찜질시설서비스영업 78
 
4.3%
한증막업 35
 
1.9%
찜질시설서비스영업 11
 
0.6%

Length

2024-04-16T17:11:31.536484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:11:31.618045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
공동탕업 1537
78.1%
목욕장업 154
 
7.8%
기타 154
 
7.8%
공동탕업+찜질시설서비스영업 78
 
4.0%
한증막업 35
 
1.8%
찜질시설서비스영업 11
 
0.6%
Distinct66
Distinct (%)3.7%
Missing8
Missing (%)0.4%
Memory size14.3 KiB
2024-04-16T17:11:31.813907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length11.980631
Min length7

Characters and Unicode

Total characters21649
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique63 ?
Unique (%)3.5%

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 1739
91.1%
051 64
 
3.4%
962 3
 
0.2%
4011 3
 
0.2%
667 2
 
0.1%
271 2
 
0.1%
7667 2
 
0.1%
204 2
 
0.1%
997 1
 
0.1%
338 1
 
0.1%
Other values (89) 89
 
4.7%
2024-04-16T17:11:32.114442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 5340
24.7%
2 3525
16.3%
3 3524
16.3%
- 3478
16.1%
0 1867
 
8.6%
5 1848
 
8.5%
4 1778
 
8.2%
103
 
0.5%
6 53
 
0.2%
9 49
 
0.2%
Other values (2) 84
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 18068
83.5%
Dash Punctuation 3478
 
16.1%
Space Separator 103
 
0.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 5340
29.6%
2 3525
19.5%
3 3524
19.5%
0 1867
 
10.3%
5 1848
 
10.2%
4 1778
 
9.8%
6 53
 
0.3%
9 49
 
0.3%
7 44
 
0.2%
8 40
 
0.2%
Dash Punctuation
ValueCountFrequency (%)
- 3478
100.0%
Space Separator
ValueCountFrequency (%)
103
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 21649
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 5340
24.7%
2 3525
16.3%
3 3524
16.3%
- 3478
16.1%
0 1867
 
8.6%
5 1848
 
8.5%
4 1778
 
8.2%
103
 
0.5%
6 53
 
0.2%
9 49
 
0.2%
Other values (2) 84
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21649
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 5340
24.7%
2 3525
16.3%
3 3524
16.3%
- 3478
16.1%
0 1867
 
8.6%
5 1848
 
8.5%
4 1778
 
8.2%
103
 
0.5%
6 53
 
0.2%
9 49
 
0.2%
Other values (2) 84
 
0.4%

bdngownsenm
Categorical

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size14.3 KiB
<NA>
1405 
자가
273 
임대
 
127
건물소유구분명
 
10

Length

Max length7
Median length4
Mean length3.5757576
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 1405
77.4%
자가 273
 
15.0%
임대 127
 
7.0%
건물소유구분명 10
 
0.6%

Length

2024-04-16T17:11:32.225950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:11:32.308064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 1405
77.4%
자가 273
 
15.0%
임대 127
 
7.0%
건물소유구분명 10
 
0.6%

bdngjisgflrcnt
Real number (ℝ)

MISSING  ZEROS 

Distinct33
Distinct (%)2.4%
Missing464
Missing (%)25.6%
Infinite0
Infinite (%)0.0%
Mean3.8327165
Minimum0
Maximum42
Zeros321
Zeros (%)17.7%
Negative0
Negative (%)0.0%
Memory size16.1 KiB
2024-04-16T17:11:32.404519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q35
95-th percentile10
Maximum42
Range42
Interquartile range (IQR)4

Descriptive statistics

Standard deviation4.4399027
Coefficient of variation (CV)1.1584219
Kurtosis16.277461
Mean3.8327165
Median Absolute Deviation (MAD)2
Skewness3.3094555
Sum5178
Variance19.712736
MonotonicityNot monotonic
2024-04-16T17:11:32.498475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
0 321
17.7%
3 300
16.5%
4 208
11.5%
2 133
 
7.3%
5 132
 
7.3%
6 51
 
2.8%
7 48
 
2.6%
8 29
 
1.6%
1 25
 
1.4%
9 20
 
1.1%
Other values (23) 84
 
4.6%
(Missing) 464
25.6%
ValueCountFrequency (%)
0 321
17.7%
1 25
 
1.4%
2 133
7.3%
3 300
16.5%
4 208
11.5%
5 132
7.3%
6 51
 
2.8%
7 48
 
2.6%
8 29
 
1.6%
9 20
 
1.1%
ValueCountFrequency (%)
42 1
 
0.1%
37 1
 
0.1%
34 1
 
0.1%
33 1
 
0.1%
32 1
 
0.1%
30 1
 
0.1%
29 1
 
0.1%
28 3
0.2%
27 1
 
0.1%
25 2
0.1%

bdngunderflrcnt
Real number (ℝ)

MISSING  ZEROS 

Distinct8
Distinct (%)0.7%
Missing705
Missing (%)38.8%
Infinite0
Infinite (%)0.0%
Mean0.78468468
Minimum0
Maximum7
Zeros509
Zeros (%)28.0%
Negative0
Negative (%)0.0%
Memory size16.1 KiB
2024-04-16T17:11:32.578562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile3
Maximum7
Range7
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.0400196
Coefficient of variation (CV)1.3253981
Kurtosis7.6567211
Mean0.78468468
Median Absolute Deviation (MAD)1
Skewness2.3568018
Sum871
Variance1.0816408
MonotonicityNot monotonic
2024-04-16T17:11:32.658873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 509
28.0%
1 460
25.3%
2 77
 
4.2%
3 29
 
1.6%
4 16
 
0.9%
5 9
 
0.5%
6 9
 
0.5%
7 1
 
0.1%
(Missing) 705
38.8%
ValueCountFrequency (%)
0 509
28.0%
1 460
25.3%
2 77
 
4.2%
3 29
 
1.6%
4 16
 
0.9%
5 9
 
0.5%
6 9
 
0.5%
7 1
 
0.1%
ValueCountFrequency (%)
7 1
 
0.1%
6 9
 
0.5%
5 9
 
0.5%
4 16
 
0.9%
3 29
 
1.6%
2 77
 
4.2%
1 460
25.3%
0 509
28.0%

maneipcnt
Categorical

IMBALANCE 

Distinct6
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size14.3 KiB
<NA>
1757 
0
 
46
남성종사자수
 
7
1
 
3
4
 
1

Length

Max length6
Median length4
Mean length3.923416
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> 1757
96.8%
0 46
 
2.5%
남성종사자수 7
 
0.4%
1 3
 
0.2%
4 1
 
0.1%
5 1
 
0.1%

Length

2024-04-16T17:11:32.753973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:11:32.840087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 1757
96.8%
0 46
 
2.5%
남성종사자수 7
 
0.4%
1 3
 
0.2%
4 1
 
0.1%
5 1
 
0.1%

multusnupsoyn
Boolean

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
False
1781 
True
 
34
ValueCountFrequency (%)
False 1781
98.1%
True 34
 
1.9%
2024-04-16T17:11:32.934048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Distinct2
Distinct (%)0.1%
Missing2
Missing (%)0.1%
Memory size3.7 KiB
False
1359 
True
454 
(Missing)
 
2
ValueCountFrequency (%)
False 1359
74.9%
True 454
 
25.0%
(Missing) 2
 
0.1%
2024-04-16T17:11:33.001198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

usejisgendflr
Categorical

Distinct14
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size14.3 KiB
<NA>
796 
2
383 
3
208 
0
155 
1
92 
Other values (9)
181 

Length

Max length6
Median length1
Mean length2.3261708
Min length1

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row0
2nd row<NA>
3rd row<NA>
4th row8
5th row4

Common Values

ValueCountFrequency (%)
<NA> 796
43.9%
2 383
21.1%
3 208
 
11.5%
0 155
 
8.5%
1 92
 
5.1%
4 84
 
4.6%
5 48
 
2.6%
6 21
 
1.2%
7 11
 
0.6%
8 5
 
0.3%
Other values (4) 12
 
0.7%

Length

2024-04-16T17:11:33.082791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 796
43.9%
2 383
21.1%
3 208
 
11.5%
0 155
 
8.5%
1 92
 
5.1%
4 84
 
4.6%
5 48
 
2.6%
6 21
 
1.2%
7 11
 
0.6%
8 5
 
0.3%
Other values (4) 12
 
0.7%

useunderendflr
Categorical

IMBALANCE 

Distinct7
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size14.3 KiB
<NA>
1240 
0
478 
1
 
68
2
 
21
사용끝지하층
 
4
Other values (2)
 
4

Length

Max length6
Median length4
Mean length3.0606061
Min length1

Unique

Unique1 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 1240
68.3%
0 478
 
26.3%
1 68
 
3.7%
2 21
 
1.2%
사용끝지하층 4
 
0.2%
3 3
 
0.2%
4 1
 
0.1%

Length

2024-04-16T17:11:33.173184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:11:33.266588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 1240
68.3%
0 478
 
26.3%
1 68
 
3.7%
2 21
 
1.2%
사용끝지하층 4
 
0.2%
3 3
 
0.2%
4 1
 
0.1%

usejisgstflr
Categorical

Distinct12
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size14.3 KiB
<NA>
655 
1
368 
0
325 
2
302 
3
79 
Other values (7)
86 

Length

Max length7
Median length1
Mean length2.0909091
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row<NA>
3rd row<NA>
4th row5
5th row2

Common Values

ValueCountFrequency (%)
<NA> 655
36.1%
1 368
20.3%
0 325
17.9%
2 302
16.6%
3 79
 
4.4%
4 42
 
2.3%
5 20
 
1.1%
6 13
 
0.7%
8 4
 
0.2%
10 3
 
0.2%
Other values (2) 4
 
0.2%

Length

2024-04-16T17:11:33.360751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 655
36.1%
1 368
20.3%
0 325
17.9%
2 302
16.6%
3 79
 
4.4%
4 42
 
2.3%
5 20
 
1.1%
6 13
 
0.7%
8 4
 
0.2%
10 3
 
0.2%
Other values (2) 4
 
0.2%

useunderstflr
Categorical

IMBALANCE 

Distinct6
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size14.3 KiB
<NA>
978 
0
742 
1
 
79
2
 
9
사용시작지하층
 
4

Length

Max length7
Median length4
Mean length2.6297521
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 978
53.9%
0 742
40.9%
1 79
 
4.4%
2 9
 
0.5%
사용시작지하층 4
 
0.2%
3 3
 
0.2%

Length

2024-04-16T17:11:33.454394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:11:33.536697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 978
53.9%
0 742
40.9%
1 79
 
4.4%
2 9
 
0.5%
사용시작지하층 4
 
0.2%
3 3
 
0.2%

washmccnt
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size14.3 KiB
<NA>
1243 
0
572 

Length

Max length4
Median length4
Mean length3.0545455
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 1243
68.5%
0 572
31.5%

Length

2024-04-16T17:11:33.632021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:11:33.711887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 1243
68.5%
0 572
31.5%

yangsilcnt
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size14.3 KiB
<NA>
976 
0
839 

Length

Max length4
Median length4
Mean length2.6132231
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 976
53.8%
0 839
46.2%

Length

2024-04-16T17:11:33.791152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:11:33.868129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 976
53.8%
0 839
46.2%

wmeipcnt
Categorical

IMBALANCE 

Distinct7
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size14.3 KiB
<NA>
1757 
0
 
46
여성종사자수
 
7
2
 
2
1
 
1
Other values (2)
 
2

Length

Max length6
Median length4
Mean length3.923416
Min length1

Unique

Unique3 ?
Unique (%)0.2%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 1757
96.8%
0 46
 
2.5%
여성종사자수 7
 
0.4%
2 2
 
0.1%
1 1
 
0.1%
3 1
 
0.1%
5 1
 
0.1%

Length

2024-04-16T17:11:33.950258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:11:34.285328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 1757
96.8%
0 46
 
2.5%
여성종사자수 7
 
0.4%
2 2
 
0.1%
1 1
 
0.1%
3 1
 
0.1%
5 1
 
0.1%

yoksilcnt
Real number (ℝ)

MISSING  ZEROS 

Distinct17
Distinct (%)1.4%
Missing641
Missing (%)35.3%
Infinite0
Infinite (%)0.0%
Mean1.3620102
Minimum0
Maximum26
Zeros590
Zeros (%)32.5%
Negative0
Negative (%)0.0%
Memory size16.1 KiB
2024-04-16T17:11:34.368851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile5
Maximum26
Range26
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.1695297
Coefficient of variation (CV)1.592888
Kurtosis31.024572
Mean1.3620102
Median Absolute Deviation (MAD)0
Skewness4.3037022
Sum1599
Variance4.706859
MonotonicityNot monotonic
2024-04-16T17:11:34.454247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0 590
32.5%
2 472
26.0%
6 28
 
1.5%
4 27
 
1.5%
1 19
 
1.0%
8 13
 
0.7%
10 6
 
0.3%
3 5
 
0.3%
5 3
 
0.2%
18 2
 
0.1%
Other values (7) 9
 
0.5%
(Missing) 641
35.3%
ValueCountFrequency (%)
0 590
32.5%
1 19
 
1.0%
2 472
26.0%
3 5
 
0.3%
4 27
 
1.5%
5 3
 
0.2%
6 28
 
1.5%
8 13
 
0.7%
9 2
 
0.1%
10 6
 
0.3%
ValueCountFrequency (%)
26 1
 
0.1%
22 1
 
0.1%
18 2
 
0.1%
15 1
 
0.1%
14 1
 
0.1%
12 2
 
0.1%
11 1
 
0.1%
10 6
0.3%
9 2
 
0.1%
8 13
0.7%

sntuptaenm
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size14.3 KiB
공동탕업
1537 
목욕장업 기타
154 
공동탕업+찜질시설서비스영업
 
78
한증막업
 
35
찜질시설서비스영업
 
11

Length

Max length14
Median length4
Mean length4.7146006
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row공동탕업+찜질시설서비스영업
2nd row한증막업
3rd row공동탕업+찜질시설서비스영업
4th row공동탕업+찜질시설서비스영업
5th row공동탕업

Common Values

ValueCountFrequency (%)
공동탕업 1537
84.7%
목욕장업 기타 154
 
8.5%
공동탕업+찜질시설서비스영업 78
 
4.3%
한증막업 35
 
1.9%
찜질시설서비스영업 11
 
0.6%

Length

2024-04-16T17:11:34.547544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:11:34.629719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
공동탕업 1537
78.1%
목욕장업 154
 
7.8%
기타 154
 
7.8%
공동탕업+찜질시설서비스영업 78
 
4.0%
한증막업 35
 
1.8%
찜질시설서비스영업 11
 
0.6%

chaircnt
Categorical

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size14.3 KiB
<NA>
976 
0
838 
2
 
1

Length

Max length4
Median length4
Mean length2.6132231
Min length1

Unique

Unique1 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 976
53.8%
0 838
46.2%
2 1
 
0.1%

Length

2024-04-16T17:11:34.718619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:11:34.798246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 976
53.8%
0 838
46.2%
2 1
 
0.1%

cndpermstymd
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size14.3 KiB
<NA>
1799 
조건부허가시작일자
 
15
20190501
 
1

Length

Max length9
Median length4
Mean length4.0435262
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> 1799
99.1%
조건부허가시작일자 15
 
0.8%
20190501 1
 
0.1%

Length

2024-04-16T17:11:34.880834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:11:34.957976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 1799
99.1%
조건부허가시작일자 15
 
0.8%
20190501 1
 
0.1%

cndpermntwhy
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size14.3 KiB
<NA>
1799 
조건부허가신고사유
 
15
건축과-16824(2019.4.15),가설건축물 존치기간연장 신고
 
1

Length

Max length36
Median length4
Mean length4.0589532
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> 1799
99.1%
조건부허가신고사유 15
 
0.8%
건축과-16824(2019.4.15),가설건축물 존치기간연장 신고 1
 
0.1%

Length

2024-04-16T17:11:35.049026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:11:35.127983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 1799
99.0%
조건부허가신고사유 15
 
0.8%
건축과-16824(2019.4.15),가설건축물 1
 
0.1%
존치기간연장 1
 
0.1%
신고 1
 
0.1%

cndpermendymd
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size14.3 KiB
<NA>
1799 
조건부허가종료일자
 
15
20210421
 
1

Length

Max length9
Median length4
Mean length4.0435262
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> 1799
99.1%
조건부허가종료일자 15
 
0.8%
20210421 1
 
0.1%

Length

2024-04-16T17:11:35.219963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:11:35.304755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 1799
99.1%
조건부허가종료일자 15
 
0.8%
20210421 1
 
0.1%

abedcnt
Categorical

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size14.3 KiB
<NA>
1270 
0
544 
침대수
 
1

Length

Max length4
Median length4
Mean length3.1002755
Min length1

Unique

Unique1 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 1270
70.0%
0 544
30.0%
침대수 1
 
0.1%

Length

2024-04-16T17:11:35.391392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:11:35.470541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 1270
70.0%
0 544
30.0%
침대수 1
 
0.1%

hanshilcnt
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size14.3 KiB
<NA>
976 
0
839 

Length

Max length4
Median length4
Mean length2.6132231
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 976
53.8%
0 839
46.2%

Length

2024-04-16T17:11:35.570123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:11:35.645752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 976
53.8%
0 839
46.2%

rcvdryncnt
Categorical

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size14.3 KiB
<NA>
1269 
0
545 
회수건조수
 
1

Length

Max length5
Median length4
Mean length3.0997245
Min length1

Unique

Unique1 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 1269
69.9%
0 545
30.0%
회수건조수 1
 
0.1%

Length

2024-04-16T17:11:35.731824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:11:35.817148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 1269
69.9%
0 545
30.0%
회수건조수 1
 
0.1%

last_load_dttm
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size14.3 KiB
2021-03-01 05:11:03
1815 

Length

Max length19
Median length19
Mean length19
Min length19

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021-03-01 05:11:03
2nd row2021-03-01 05:11:03
3rd row2021-03-01 05:11:03
4th row2021-03-01 05:11:03
5th row2021-03-01 05:11:03

Common Values

ValueCountFrequency (%)
2021-03-01 05:11:03 1815
100.0%

Length

2024-04-16T17:11:35.897059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:11:35.970544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021-03-01 1815
50.0%
05:11:03 1815
50.0%

Sample

skeyopnsfteamcodemgtnoopnsvcidupdategbnupdatedtopnsvcnmbplcnmsitepostnositewhladdrrdnpostnordnwhladdrapvpermymddcbymdclgstdtclgenddtropnymdtrdstatenmdtlstatenmxylastmodtsuptaenmsitetelbdngownsenmbdngjisgflrcntbdngunderflrcntmaneipcntmultusnupsoynbalhansilynusejisgendflruseunderendflrusejisgstflruseunderstflrwashmccntyangsilcntwmeipcntyoksilcntsntuptaenmchaircntcndpermstymdcndpermntwhycndpermendymdabedcnthanshilcntrcvdryncntlast_load_dttm
0332500003250000-202-2002-0000111_44_01_PI2018-08-31 23:59:59.0<NA>옥샘탕600051부산광역시 중구 창선동1가 9-9번지48947부산광역시 중구 광복로55번길 14-2 (창선동1가)20020513<NA><NA><NA><NA>01영업385089.38491100000180062.5676180000020180724131551공동탕업+찜질시설서비스영업051-123-1234<NA>00<NA>NN000000<NA>0공동탕업+찜질시설서비스영업0<NA><NA><NA>0002021-03-01 05:11:03
1432500003250000-202-1985-0015611_44_01_PI2018-08-31 23:59:59.0<NA>금수탕600816부산광역시 중구 중앙동4가 78-2번지48947<NA>1985040920070612<NA><NA><NA>02폐업385793.29931180910.38663620041116000000한증막업051-123-1234<NA><NA><NA><NA>NN<NA><NA><NA><NA><NA><NA><NA><NA>한증막업<NA><NA><NA><NA><NA><NA><NA>2021-03-01 05:11:03
2532500003250000-202-2001-0001311_44_01_PI2018-08-31 23:59:59.0<NA>백록담600091부산광역시 중구 대청동1가 34-1번지48947<NA>2001111620120221<NA><NA><NA>02폐업385208.257554180108.03498420120227093749공동탕업+찜질시설서비스영업051-123-1234<NA><NA><NA><NA>NN<NA><NA><NA><NA><NA><NA><NA><NA>공동탕업+찜질시설서비스영업<NA><NA><NA><NA><NA><NA><NA>2021-03-01 05:11:03
3632500003250000-202-2007-0000111_44_01_PI2018-08-31 23:59:59.0<NA>유나목욕탕600061부산광역시 중구 신창동1가 5-1번지 (5~8층)48948부산광역시 중구 광복중앙로 25 (신창동1가)2007040620140401<NA><NA><NA>02폐업385157.86566400000180248.2145730000020130208113918공동탕업+찜질시설서비스영업051-123-1234자가93<NA>NY805000<NA>2공동탕업+찜질시설서비스영업0<NA><NA><NA>0002021-03-01 05:11:03
4732500003250000-202-1988-0015911_44_01_PI2018-08-31 23:59:59.0<NA>녹수탕600062부산광역시 중구 신창동2가 21-2번지48947부산광역시 중구 광복로43번길 12 (신창동2가)19880913<NA><NA><NA><NA>01영업385086.62014400000180119.3340640000020130208111503공동탕업051-123-1234임대41<NA>NN402000<NA>0공동탕업0<NA><NA><NA>0002021-03-01 05:11:03
5832500003250000-202-1960-0014411_44_01_PU2018-11-30 02:40:00.0목욕장업금강스파600808부산광역시 중구 부평동3가 22-1번지 외 2필지48976부산광역시 중구 흑교로31번길 3-1 (부평동3가, 외 2필지)19601210<NA><NA><NA><NA>영업/정상영업384542.121201746179994.20210389820181128093116공동탕업+찜질시설서비스영업051-123-1234<NA>51<NA>NN515100<NA>0공동탕업+찜질시설서비스영업0<NA><NA><NA>0002021-03-01 05:11:03
6932500003250000-202-2009-0000111_44_01_PI2018-08-31 23:59:59.0<NA>오투(O2)600046부산광역시 중구 남포동6가 85번지 (10층)48981부산광역시 중구 비프광장로 20, 10층 (남포동6가)2009022320131227<NA><NA><NA>02폐업384892.63184400000179895.5128170000020130208114221공동탕업+찜질시설서비스영업051-123-1234임대104<NA>NY10010000<NA>0공동탕업+찜질시설서비스영업0<NA><NA><NA>0002021-03-01 05:11:03
71032500003250000-202-1984-0015411_44_01_PI2018-08-31 23:59:59.0<NA>영진사우나600045부산광역시 중구 남포동5가 88번지48947<NA>1984020120030703<NA><NA><NA>02폐업384910.466574179447.22650620030703000000한증막업051-123-1234<NA><NA><NA><NA>NN<NA><NA><NA><NA><NA><NA><NA><NA>한증막업<NA><NA><NA><NA><NA><NA><NA>2021-03-01 05:11:03
81132500003250000-202-1984-0015211_44_01_PI2018-08-31 23:59:59.0<NA>거북목욕탕600110부산광역시 중구 영주동 292-10번지48916부산광역시 중구 영주로 20 (영주동)19840217<NA><NA><NA><NA>01영업385241.59002600000181147.1401790000020130208111357공동탕업051-123-1234<NA>00<NA>NN000000<NA>0공동탕업0<NA><NA><NA>0002021-03-01 05:11:03
91232500003250000-202-1973-0015011_44_01_PI2018-08-31 23:59:59.0<NA>부원탕600101부산광역시 중구 대창동1가 54-2번지48947<NA>1973011220051013<NA><NA><NA>02폐업<NA><NA>20040531000000공동탕업051-123-1234<NA><NA><NA><NA>NN<NA><NA><NA><NA><NA><NA><NA><NA>공동탕업<NA><NA><NA><NA><NA><NA><NA>2021-03-01 05:11:03
skeyopnsfteamcodemgtnoopnsvcidupdategbnupdatedtopnsvcnmbplcnmsitepostnositewhladdrrdnpostnordnwhladdrapvpermymddcbymdclgstdtclgenddtropnymdtrdstatenmdtlstatenmxylastmodtsuptaenmsitetelbdngownsenmbdngjisgflrcntbdngunderflrcntmaneipcntmultusnupsoynbalhansilynusejisgendflruseunderendflrusejisgstflruseunderstflrwashmccntyangsilcntwmeipcntyoksilcntsntuptaenmchaircntcndpermstymdcndpermntwhycndpermendymdabedcnthanshilcntrcvdryncntlast_load_dttm
1805209533100003310000-202-2020-0000111_44_01_PI2020-06-21 00:23:27.0목욕장업문화불한증막608812부산광역시 남구 대연동 735-7번지 지하1층48531부산광역시 남구 유엔평화로 111, 지하1층 (대연동)20200619<NA><NA><NA><NA>영업/정상영업390974.056399381182797.89496961520200619150720한증막업051-123-1234임대410NN<NA>1<NA>10001한증막업0<NA><NA><NA>0002021-03-01 05:11:03
1806209733300003330000-202-2020-0000111_44_01_PI2020-06-26 00:23:17.0목욕장업(주)호텔롯데 시그니엘 부산612010부산광역시 해운대구 중동 1829 엘시티48099부산광역시 해운대구 달맞이길 30, 포디움동 6층 602호 (중동, 엘시티)20200624폐업일자휴업시작일자휴업종료일자재개업일자영업/정상영업좌표정보(X)좌표정보(Y)20200624175736목욕장업 기타051-123-1234건물소유구분명000NN사용끝지상층사용끝지하층사용시작지상층사용시작지하층0006목욕장업 기타0조건부허가시작일자조건부허가신고사유조건부허가종료일자0002021-03-01 05:11:03
1807210032800003280000-202-2020-0000111_44_01_PU2021-01-20 02:40:00.0목욕장업김천탕606822부산광역시 영도구 청학동 252-13649014부산광역시 영도구 청학로 62 (청학동)[*미고시]20200703<NA><NA><NA><NA>영업/정상영업387810.283167969179178.74258972920210118091801목욕장업 기타051 962 4011<NA>00<NA>NY302000<NA>2목욕장업 기타0<NA><NA><NA>0002021-03-01 05:11:03
1808210132800003280000-202-2020-0000111_44_01_PU2021-01-20 02:40:00.0목욕장업김천탕606822부산광역시 영도구 청학동 252-13649014부산광역시 영도구 청학로 62 (청학동)[*미고시]20200703<NA><NA><NA><NA>영업/정상영업387810.283167969179178.74258972920210118091801목욕장업 기타051 962 4011<NA>00<NA>NY302000<NA>2목욕장업 기타0<NA><NA><NA>0002021-03-01 05:11:03
1809210232800003280000-202-2020-0000111_44_01_PU2021-01-20 02:40:00.0목욕장업김천탕606822부산광역시 영도구 청학동 252-13649014부산광역시 영도구 청학로 62 (청학동)[*미고시]20200703<NA><NA><NA><NA>영업/정상영업387810.283167969179178.74258972920210118091801목욕장업 기타051 962 4011<NA>00<NA>NY302000<NA>2목욕장업 기타0<NA><NA><NA>0002021-03-01 05:11:03
1810210432900003290000-202-2020-0000111_44_01_PU2020-08-08 02:40:00.0목욕장업우성스마트사우나614859부산광역시 부산진구 연지동 42-4 연지우성스마트시티뷰47123부산광역시 부산진구 동평로 269, 2층 201호 (연지동, 연지우성스마트시티뷰)20200708<NA><NA><NA><NA>영업/정상영업387301.611272345187722.85966300320200806111714공동탕업051-123-1234<NA>2110NN20200002공동탕업0<NA><NA><NA>0002021-03-01 05:11:03
1811213133100003310000-202-2020-0000211_44_01_PU2020-10-15 02:40:00.0목욕장업(주)벽승 명성사우나608830부산광역시 남구 용당동 564-5 한신문화타운 2층48535부산광역시 남구 유엔평화로 150, 한신문화타운 2층 (용당동)20200827<NA><NA><NA><NA>영업/정상영업391103.422349355182446.17047299120201013154141공동탕업<NA>자가410NN2<NA>2<NA>0002공동탕업0<NA><NA><NA>0002021-03-01 05:11:03
1812213633300003330000-202-2020-0000211_44_01_PU2021-01-27 02:40:00.0목욕장업(주)조선호텔앤리조트 그랜드조선 부산 사우나612846부산광역시 해운대구 중동 1405-16 그랜드 조선 부산48099부산광역시 해운대구 해운대해변로 292, 그랜드 조선 부산 6층 (중동)20200908<NA><NA><NA><NA>영업/정상영업397022.971550345186692.0646219620210125144914목욕장업 기타<NA><NA>000NN<NA><NA>6<NA>0006목욕장업 기타0<NA><NA><NA>0002021-03-01 05:11:03
1813214334000003400000-202-2020-0000211_44_01_PU2021-02-04 02:40:00.0목욕장업월내탕619951부산광역시 기장군 장안읍 월내리 132-4946037부산광역시 기장군 장안읍 해맞이로 368, 3~4층20200924<NA><NA><NA><NA>영업/정상영업406982.053033795205366.77000216120210202173923공동탕업<NA><NA>000NN4<NA>3<NA>0000공동탕업0<NA><NA><NA>0002021-03-01 05:11:03
1814215032700003270000-202-2020-0000111_44_01_PU2020-11-12 02:40:00.0목욕장업주)더스포 현대백화점 부산점 별관 현대휘트니스601803부산광역시 동구 범일동 62-26048734부산광역시 동구 범일일길 47, 현대휘트니스센터 지하1층 (범일동)20201013<NA><NA><NA><NA>영업/정상영업387485.832510158184380.86148619420201110135045공동탕업+찜질시설서비스영업051 667 0098<NA>520NY<NA>2<NA>20004공동탕업+찜질시설서비스영업0<NA><NA><NA>0002021-03-01 05:11:03