Overview

Dataset statistics

Number of variables16
Number of observations230
Missing cells237
Missing cells (%)6.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory30.7 KiB
Average record size in memory136.6 B

Variable types

Categorical4
Text3
Numeric8
Boolean1

Dataset

Description숙박업체 현황(일반-일반호텔)
Author행정안전부
URLhttps://data.gg.go.kr/portal/data/service/selectServicePage.do?&infId=E3PA798S0MU468B423D4826400&infSeq=1

Alerts

다중이용업소여부 has constant value ""Constant
시군명 is highly overall correlated with 소재지우편번호 and 4 other fieldsHigh correlation
위생업종명 is highly overall correlated with 인허가일자 and 10 other fieldsHigh correlation
영업상태명 is highly overall correlated with 폐업일자 and 2 other fieldsHigh correlation
위생업태명 is highly overall correlated with 인허가일자 and 10 other fieldsHigh correlation
인허가일자 is highly overall correlated with 년도 and 2 other fieldsHigh correlation
폐업일자 is highly overall correlated with 영업상태명 and 2 other fieldsHigh correlation
년도 is highly overall correlated with 인허가일자 and 2 other fieldsHigh correlation
양실수(개) is highly overall correlated with 위생업종명 and 1 other fieldsHigh correlation
한실수(개) is highly overall correlated with 위생업종명 and 1 other fieldsHigh correlation
소재지우편번호 is highly overall correlated with WGS84경도 and 3 other fieldsHigh correlation
WGS84위도 is highly overall correlated with 시군명 and 2 other fieldsHigh correlation
WGS84경도 is highly overall correlated with 소재지우편번호 and 3 other fieldsHigh correlation
위생업종명 is highly imbalanced (78.2%)Imbalance
위생업태명 is highly imbalanced (78.2%)Imbalance
폐업일자 has 159 (69.1%) missing valuesMissing
년도 has 8 (3.5%) missing valuesMissing
다중이용업소여부 has 8 (3.5%) missing valuesMissing
양실수(개) has 8 (3.5%) missing valuesMissing
한실수(개) has 32 (13.9%) missing valuesMissing
소재지도로명주소 has 14 (6.1%) missing valuesMissing
WGS84위도 has 4 (1.7%) missing valuesMissing
WGS84경도 has 4 (1.7%) missing valuesMissing
양실수(개) has 11 (4.8%) zerosZeros
한실수(개) has 130 (56.5%) zerosZeros

Reproduction

Analysis started2023-12-10 22:19:46.436485
Analysis finished2023-12-10 22:19:52.438396
Duration6 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군명
Categorical

HIGH CORRELATION 

Distinct26
Distinct (%)11.3%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
수원시
38 
용인시
26 
화성시
23 
남양주시
18 
가평군
15 
Other values (21)
110 

Length

Max length4
Median length3
Mean length3.1695652
Min length3

Unique

Unique3 ?
Unique (%)1.3%

Sample

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

Common Values

ValueCountFrequency (%)
수원시 38
16.5%
용인시 26
11.3%
화성시 23
 
10.0%
남양주시 18
 
7.8%
가평군 15
 
6.5%
동두천시 14
 
6.1%
부천시 12
 
5.2%
고양시 11
 
4.8%
김포시 8
 
3.5%
안산시 7
 
3.0%
Other values (16) 58
25.2%

Length

2023-12-11T07:19:52.486354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
수원시 38
16.5%
용인시 26
11.3%
화성시 23
 
10.0%
남양주시 18
 
7.8%
가평군 15
 
6.5%
동두천시 14
 
6.1%
부천시 12
 
5.2%
고양시 11
 
4.8%
김포시 8
 
3.5%
이천시 7
 
3.0%
Other values (16) 58
25.2%

사업장명
Text

Distinct226
Distinct (%)98.3%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
2023-12-11T07:19:52.648439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length27
Mean length5.5652174
Min length1

Characters and Unicode

Total characters1280
Distinct characters259
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique222 ?
Unique (%)96.5%

Sample

1st row양지카운티
2nd row힐타운모텔
3rd row뉴월드호텔
4th row브라운모텔
5th row로즈모텔
ValueCountFrequency (%)
호텔 13
 
4.8%
hotel 3
 
1.1%
모텔 3
 
1.1%
동탄 2
 
0.7%
드라이브인하비비 2
 
0.7%
신라장여관 2
 
0.7%
호텔야자 2
 
0.7%
레인보우호텔 2
 
0.7%
나우호텔 2
 
0.7%
디자인 1
 
0.4%
Other values (239) 239
88.2%
2023-12-11T07:19:52.922388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
텔 168
 
13.1%
호 124
 
9.7%
스 47
 
3.7%
41
 
3.2%
모 40
 
3.1%
이 32
 
2.5%
리 32
 
2.5%
라 21
 
1.6%
아 18
 
1.4%
) 17
 
1.3%
Other values (249) 740
57.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1095
85.5%
Lowercase Letter 57
 
4.5%
Space Separator 41
 
3.2%
Uppercase Letter 39
 
3.0%
Close Punctuation 17
 
1.3%
Open Punctuation 16
 
1.2%
Decimal Number 9
 
0.7%
Other Punctuation 3
 
0.2%
Dash Punctuation 3
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
텔 168
 
15.3%
호 124
 
11.3%
스 47
 
4.3%
모 40
 
3.7%
이 32
 
2.9%
리 32
 
2.9%
라 21
 
1.9%
아 18
 
1.6%
레 14
 
1.3%
인 14
 
1.3%
Other values (204) 585
53.4%
Lowercase Letter
ValueCountFrequency (%)
e 8
14.0%
a 7
12.3%
o 6
10.5%
l 6
10.5%
t 6
10.5%
n 4
 
7.0%
i 4
 
7.0%
r 2
 
3.5%
d 2
 
3.5%
m 2
 
3.5%
Other values (8) 10
17.5%
Uppercase Letter
ValueCountFrequency (%)
M 6
15.4%
H 4
10.3%
T 4
10.3%
G 3
7.7%
I 3
7.7%
J 3
7.7%
E 3
7.7%
W 2
 
5.1%
K 2
 
5.1%
L 2
 
5.1%
Other values (7) 7
17.9%
Decimal Number
ValueCountFrequency (%)
2 4
44.4%
7 2
22.2%
1 1
 
11.1%
4 1
 
11.1%
5 1
 
11.1%
Space Separator
ValueCountFrequency (%)
41
100.0%
Close Punctuation
ValueCountFrequency (%)
) 17
100.0%
Open Punctuation
ValueCountFrequency (%)
( 16
100.0%
Other Punctuation
ValueCountFrequency (%)
. 3
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1095
85.5%
Latin 96
 
7.5%
Common 89
 
7.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
텔 168
 
15.3%
호 124
 
11.3%
스 47
 
4.3%
모 40
 
3.7%
이 32
 
2.9%
리 32
 
2.9%
라 21
 
1.9%
아 18
 
1.6%
레 14
 
1.3%
인 14
 
1.3%
Other values (204) 585
53.4%
Latin
ValueCountFrequency (%)
e 8
 
8.3%
a 7
 
7.3%
o 6
 
6.2%
l 6
 
6.2%
M 6
 
6.2%
t 6
 
6.2%
H 4
 
4.2%
T 4
 
4.2%
n 4
 
4.2%
i 4
 
4.2%
Other values (25) 41
42.7%
Common
ValueCountFrequency (%)
41
46.1%
) 17
19.1%
( 16
 
18.0%
2 4
 
4.5%
. 3
 
3.4%
- 3
 
3.4%
7 2
 
2.2%
1 1
 
1.1%
4 1
 
1.1%
5 1
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1095
85.5%
ASCII 185
 
14.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
텔 168
 
15.3%
호 124
 
11.3%
스 47
 
4.3%
모 40
 
3.7%
이 32
 
2.9%
리 32
 
2.9%
라 21
 
1.9%
아 18
 
1.6%
레 14
 
1.3%
인 14
 
1.3%
Other values (204) 585
53.4%
ASCII
ValueCountFrequency (%)
41
22.2%
) 17
 
9.2%
( 16
 
8.6%
e 8
 
4.3%
a 7
 
3.8%
o 6
 
3.2%
l 6
 
3.2%
M 6
 
3.2%
t 6
 
3.2%
H 4
 
2.2%
Other values (35) 68
36.8%

인허가일자
Real number (ℝ)

HIGH CORRELATION 

Distinct221
Distinct (%)96.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20030535
Minimum19810720
Maximum20180827
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2023-12-11T07:19:53.031369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19810720
5-th percentile19870613
Q119980749
median20030311
Q320090256
95-th percentile20170716
Maximum20180827
Range370107
Interquartile range (IQR)109506.5

Descriptive statistics

Standard deviation88030.787
Coefficient of variation (CV)0.0043948294
Kurtosis-0.39155489
Mean20030535
Median Absolute Deviation (MAD)50546
Skewness-0.19540361
Sum4.6070231 × 109
Variance7.7494194 × 109
MonotonicityNot monotonic
2023-12-11T07:19:53.141648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20030623 3
 
1.3%
20040827 2
 
0.9%
20030224 2
 
0.9%
20171010 2
 
0.9%
20040819 2
 
0.9%
20030225 2
 
0.9%
20001109 2
 
0.9%
20020731 2
 
0.9%
20170403 1
 
0.4%
19951222 1
 
0.4%
Other values (211) 211
91.7%
ValueCountFrequency (%)
19810720 1
0.4%
19811023 1
0.4%
19820122 1
0.4%
19830211 1
0.4%
19840601 1
0.4%
19840613 1
0.4%
19850222 1
0.4%
19850810 1
0.4%
19861119 1
0.4%
19870319 1
0.4%
ValueCountFrequency (%)
20180827 1
0.4%
20180625 1
0.4%
20171110 1
0.4%
20171107 1
0.4%
20171010 2
0.9%
20170921 1
0.4%
20170901 1
0.4%
20170828 1
0.4%
20170808 1
0.4%
20170721 1
0.4%

영업상태명
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
운영중
159 
폐업 등
71 

Length

Max length4
Median length3
Mean length3.3086957
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
운영중 159
69.1%
폐업 등 71
30.9%

Length

2023-12-11T07:19:53.247427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T07:19:53.330643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
운영중 159
52.8%
폐업 71
23.6%
등 71
23.6%

폐업일자
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct67
Distinct (%)94.4%
Missing159
Missing (%)69.1%
Infinite0
Infinite (%)0.0%
Mean20100394
Minimum19981028
Maximum20180809
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2023-12-11T07:19:53.422804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19981028
5-th percentile20005412
Q120065558
median20110210
Q320141012
95-th percentile20170516
Maximum20180809
Range199781
Interquartile range (IQR)75453.5

Descriptive statistics

Standard deviation53596.074
Coefficient of variation (CV)0.0026664191
Kurtosis-0.77565166
Mean20100394
Median Absolute Deviation (MAD)39896
Skewness-0.42029418
Sum1.427128 × 109
Variance2.8725392 × 109
MonotonicityNot monotonic
2023-12-11T07:19:53.536513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20140822 3
 
1.3%
20021111 2
 
0.9%
20140219 2
 
0.9%
20161219 1
 
0.4%
20120326 1
 
0.4%
20120327 1
 
0.4%
20081217 1
 
0.4%
20161123 1
 
0.4%
20081128 1
 
0.4%
20040407 1
 
0.4%
Other values (57) 57
 
24.8%
(Missing) 159
69.1%
ValueCountFrequency (%)
19981028 1
0.4%
19990310 1
0.4%
19990901 1
0.4%
20000222 1
0.4%
20010601 1
0.4%
20020823 1
0.4%
20021021 1
0.4%
20021111 2
0.9%
20030304 1
0.4%
20030318 1
0.4%
ValueCountFrequency (%)
20180809 1
0.4%
20170915 1
0.4%
20170824 1
0.4%
20170816 1
0.4%
20170217 1
0.4%
20170214 1
0.4%
20170125 1
0.4%
20170124 1
0.4%
20161219 1
0.4%
20161123 1
0.4%

년도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct36
Distinct (%)16.2%
Missing8
Missing (%)3.5%
Infinite0
Infinite (%)0.0%
Mean2003.3559
Minimum1981
Maximum2018
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2023-12-11T07:19:53.640314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1981
5-th percentile1989
Q11998.25
median2003
Q32009.75
95-th percentile2017
Maximum2018
Range37
Interquartile range (IQR)11.5

Descriptive statistics

Standard deviation8.5820204
Coefficient of variation (CV)0.0042838223
Kurtosis-0.40896572
Mean2003.3559
Median Absolute Deviation (MAD)5
Skewness-0.1607864
Sum444745
Variance73.651074
MonotonicityNot monotonic
2023-12-11T07:19:53.747762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
2003 29
 
12.6%
2004 19
 
8.3%
2017 17
 
7.4%
2001 14
 
6.1%
2002 11
 
4.8%
2000 11
 
4.8%
2014 10
 
4.3%
2005 9
 
3.9%
1998 8
 
3.5%
2015 7
 
3.0%
Other values (26) 87
37.8%
(Missing) 8
 
3.5%
ValueCountFrequency (%)
1981 1
 
0.4%
1982 1
 
0.4%
1983 1
 
0.4%
1984 2
 
0.9%
1985 1
 
0.4%
1987 3
1.3%
1989 5
2.2%
1990 5
2.2%
1991 4
1.7%
1992 4
1.7%
ValueCountFrequency (%)
2018 2
 
0.9%
2017 17
7.4%
2016 6
 
2.6%
2015 7
3.0%
2014 10
4.3%
2013 5
 
2.2%
2012 6
 
2.6%
2011 2
 
0.9%
2010 1
 
0.4%
2009 3
 
1.3%

다중이용업소여부
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)0.5%
Missing8
Missing (%)3.5%
Memory size592.0 B
False
222 
(Missing)
 
8
ValueCountFrequency (%)
False 222
96.5%
(Missing) 8
 
3.5%
2023-12-11T07:19:54.030009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

양실수(개)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct69
Distinct (%)31.1%
Missing8
Missing (%)3.5%
Infinite0
Infinite (%)0.0%
Mean38.126126
Minimum0
Maximum300
Zeros11
Zeros (%)4.8%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2023-12-11T07:19:54.109917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q123
median34.5
Q342
95-th percentile68.85
Maximum300
Range300
Interquartile range (IQR)19

Descriptive statistics

Standard deviation36.448468
Coefficient of variation (CV)0.95599715
Kurtosis31.106694
Mean38.126126
Median Absolute Deviation (MAD)10.5
Skewness4.9424385
Sum8464
Variance1328.4908
MonotonicityNot monotonic
2023-12-11T07:19:54.212339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 11
 
4.8%
40 11
 
4.8%
42 11
 
4.8%
30 10
 
4.3%
36 8
 
3.5%
20 7
 
3.0%
32 7
 
3.0%
41 7
 
3.0%
28 6
 
2.6%
35 6
 
2.6%
Other values (59) 138
60.0%
(Missing) 8
 
3.5%
ValueCountFrequency (%)
0 11
4.8%
3 2
 
0.9%
5 1
 
0.4%
7 1
 
0.4%
8 2
 
0.9%
9 1
 
0.4%
10 1
 
0.4%
12 2
 
0.9%
13 1
 
0.4%
14 1
 
0.4%
ValueCountFrequency (%)
300 1
0.4%
287 1
0.4%
286 1
0.4%
188 1
0.4%
116 1
0.4%
114 1
0.4%
99 1
0.4%
93 1
0.4%
80 1
0.4%
72 1
0.4%

위생업종명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
숙박업(일반)
222 
<NA>
 
8

Length

Max length7
Median length7
Mean length6.8956522
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row숙박업(일반)
2nd row숙박업(일반)
3rd row숙박업(일반)
4th row숙박업(일반)
5th row숙박업(일반)

Common Values

ValueCountFrequency (%)
숙박업(일반) 222
96.5%
<NA> 8
 
3.5%

Length

2023-12-11T07:19:54.331191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T07:19:54.424172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
숙박업(일반 222
96.5%
na 8
 
3.5%

위생업태명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
일반호텔
222 
<NA>
 
8

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
일반호텔 222
96.5%
<NA> 8
 
3.5%

Length

2023-12-11T07:19:54.509302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T07:19:54.577378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
일반호텔 222
96.5%
na 8
 
3.5%

한실수(개)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct24
Distinct (%)12.1%
Missing32
Missing (%)13.9%
Infinite0
Infinite (%)0.0%
Mean3.7525253
Minimum0
Maximum64
Zeros130
Zeros (%)56.5%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2023-12-11T07:19:54.649440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33
95-th percentile18.3
Maximum64
Range64
Interquartile range (IQR)3

Descriptive statistics

Standard deviation9.0033479
Coefficient of variation (CV)2.3992771
Kurtosis19.412117
Mean3.7525253
Median Absolute Deviation (MAD)0
Skewness3.9743496
Sum743
Variance81.060273
MonotonicityNot monotonic
2023-12-11T07:19:54.746917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0 130
56.5%
4 11
 
4.8%
1 7
 
3.0%
3 7
 
3.0%
10 6
 
2.6%
2 5
 
2.2%
5 4
 
1.7%
16 3
 
1.3%
17 3
 
1.3%
8 3
 
1.3%
Other values (14) 19
 
8.3%
(Missing) 32
 
13.9%
ValueCountFrequency (%)
0 130
56.5%
1 7
 
3.0%
2 5
 
2.2%
3 7
 
3.0%
4 11
 
4.8%
5 4
 
1.7%
7 3
 
1.3%
8 3
 
1.3%
10 6
 
2.6%
13 1
 
0.4%
ValueCountFrequency (%)
64 1
0.4%
59 1
0.4%
45 1
0.4%
38 1
0.4%
30 1
0.4%
26 1
0.4%
24 1
0.4%
22 1
0.4%
20 2
0.9%
18 1
0.4%

소재지도로명주소
Text

MISSING 

Distinct212
Distinct (%)98.1%
Missing14
Missing (%)6.1%
Memory size1.9 KiB
2023-12-11T07:19:54.974919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length53
Median length38.5
Mean length26.083333
Min length15

Characters and Unicode

Total characters5634
Distinct characters227
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique208 ?
Unique (%)96.3%

Sample

1st row경기도 가평군 북면 백둔로560번길 26-4
2nd row경기도 가평군 설악면 유명로 1734
3rd row경기도 가평군 청평면 은행나무길 19
4th row경기도 가평군 상면 축령로 145
5th row경기도 가평군 가평읍 북한강변로 1027-11 (,136-1,136-2)
ValueCountFrequency (%)
경기도 216
 
17.9%
수원시 38
 
3.2%
팔달구 28
 
2.3%
용인시 22
 
1.8%
화성시 22
 
1.8%
인계동 18
 
1.5%
남양주시 16
 
1.3%
동두천시 14
 
1.2%
처인구 13
 
1.1%
가평군 12
 
1.0%
Other values (509) 805
66.9%
2023-12-11T07:19:55.346361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
988
 
17.5%
경 231
 
4.1%
도 226
 
4.0%
기 223
 
4.0%
1 222
 
3.9%
로 212
 
3.8%
시 203
 
3.6%
동 150
 
2.7%
2 133
 
2.4%
( 125
 
2.2%
Other values (217) 2921
51.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3223
57.2%
Decimal Number 1020
 
18.1%
Space Separator 988
 
17.5%
Open Punctuation 125
 
2.2%
Close Punctuation 125
 
2.2%
Dash Punctuation 82
 
1.5%
Other Punctuation 53
 
0.9%
Math Symbol 16
 
0.3%
Uppercase Letter 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
경 231
 
7.2%
도 226
 
7.0%
기 223
 
6.9%
로 212
 
6.6%
시 203
 
6.3%
동 150
 
4.7%
길 104
 
3.2%
번 93
 
2.9%
구 90
 
2.8%
원 59
 
1.8%
Other values (199) 1632
50.6%
Decimal Number
ValueCountFrequency (%)
1 222
21.8%
2 133
13.0%
3 125
12.3%
4 95
9.3%
6 93
9.1%
7 81
 
7.9%
5 78
 
7.6%
8 70
 
6.9%
0 67
 
6.6%
9 56
 
5.5%
Uppercase Letter
ValueCountFrequency (%)
A 1
50.0%
B 1
50.0%
Space Separator
ValueCountFrequency (%)
988
100.0%
Open Punctuation
ValueCountFrequency (%)
( 125
100.0%
Close Punctuation
ValueCountFrequency (%)
) 125
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 82
100.0%
Other Punctuation
ValueCountFrequency (%)
, 53
100.0%
Math Symbol
ValueCountFrequency (%)
~ 16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3223
57.2%
Common 2409
42.8%
Latin 2
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
경 231
 
7.2%
도 226
 
7.0%
기 223
 
6.9%
로 212
 
6.6%
시 203
 
6.3%
동 150
 
4.7%
길 104
 
3.2%
번 93
 
2.9%
구 90
 
2.8%
원 59
 
1.8%
Other values (199) 1632
50.6%
Common
ValueCountFrequency (%)
988
41.0%
1 222
 
9.2%
2 133
 
5.5%
( 125
 
5.2%
3 125
 
5.2%
) 125
 
5.2%
4 95
 
3.9%
6 93
 
3.9%
- 82
 
3.4%
7 81
 
3.4%
Other values (6) 340
 
14.1%
Latin
ValueCountFrequency (%)
A 1
50.0%
B 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3223
57.2%
ASCII 2411
42.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
988
41.0%
1 222
 
9.2%
2 133
 
5.5%
( 125
 
5.2%
3 125
 
5.2%
) 125
 
5.2%
4 95
 
3.9%
6 93
 
3.9%
- 82
 
3.4%
7 81
 
3.4%
Other values (8) 342
 
14.2%
Hangul
ValueCountFrequency (%)
경 231
 
7.2%
도 226
 
7.0%
기 223
 
6.9%
로 212
 
6.6%
시 203
 
6.3%
동 150
 
4.7%
길 104
 
3.2%
번 93
 
2.9%
구 90
 
2.8%
원 59
 
1.8%
Other values (199) 1632
50.6%

소재지지번주소
Text

Distinct227
Distinct (%)98.7%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
2023-12-11T07:19:55.584696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length50
Median length37
Mean length24.373913
Min length17

Characters and Unicode

Total characters5606
Distinct characters186
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique225 ?
Unique (%)97.8%

Sample

1st row경기도 가평군 북면 백둔리 420번지
2nd row경기도 가평군 설악면 회곡리 737-9번지
3rd row경기도 가평군 설악면 선촌리 410번지
4th row경기도 가평군 청평면 상천리 산 207-5번지
5th row경기도 가평군 상면 항사리 291-2번지
ValueCountFrequency (%)
경기도 230
 
19.7%
수원시 38
 
3.2%
팔달구 28
 
2.4%
용인시 26
 
2.2%
화성시 23
 
2.0%
인계동 20
 
1.7%
남양주시 18
 
1.5%
처인구 15
 
1.3%
가평군 15
 
1.3%
동두천시 14
 
1.2%
Other values (484) 743
63.5%
2023-12-11T07:19:55.938234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
940
 
16.8%
지 257
 
4.6%
도 244
 
4.4%
1 243
 
4.3%
기 236
 
4.2%
경 230
 
4.1%
번 230
 
4.1%
- 215
 
3.8%
시 215
 
3.8%
동 178
 
3.2%
Other values (176) 2618
46.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3277
58.5%
Decimal Number 1097
 
19.6%
Space Separator 940
 
16.8%
Dash Punctuation 215
 
3.8%
Other Punctuation 47
 
0.8%
Math Symbol 14
 
0.2%
Open Punctuation 7
 
0.1%
Close Punctuation 7
 
0.1%
Uppercase Letter 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 257
 
7.8%
도 244
 
7.4%
기 236
 
7.2%
경 230
 
7.0%
번 230
 
7.0%
시 215
 
6.6%
동 178
 
5.4%
구 95
 
2.9%
리 92
 
2.8%
인 61
 
1.9%
Other values (158) 1439
43.9%
Decimal Number
ValueCountFrequency (%)
1 243
22.2%
2 139
12.7%
3 135
12.3%
4 114
10.4%
5 91
 
8.3%
7 84
 
7.7%
6 82
 
7.5%
0 79
 
7.2%
9 76
 
6.9%
8 54
 
4.9%
Uppercase Letter
ValueCountFrequency (%)
A 1
50.0%
B 1
50.0%
Space Separator
ValueCountFrequency (%)
940
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 215
100.0%
Other Punctuation
ValueCountFrequency (%)
, 47
100.0%
Math Symbol
ValueCountFrequency (%)
~ 14
100.0%
Open Punctuation
ValueCountFrequency (%)
( 7
100.0%
Close Punctuation
ValueCountFrequency (%)
) 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3277
58.5%
Common 2327
41.5%
Latin 2
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 257
 
7.8%
도 244
 
7.4%
기 236
 
7.2%
경 230
 
7.0%
번 230
 
7.0%
시 215
 
6.6%
동 178
 
5.4%
구 95
 
2.9%
리 92
 
2.8%
인 61
 
1.9%
Other values (158) 1439
43.9%
Common
ValueCountFrequency (%)
940
40.4%
1 243
 
10.4%
- 215
 
9.2%
2 139
 
6.0%
3 135
 
5.8%
4 114
 
4.9%
5 91
 
3.9%
7 84
 
3.6%
6 82
 
3.5%
0 79
 
3.4%
Other values (6) 205
 
8.8%
Latin
ValueCountFrequency (%)
A 1
50.0%
B 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3277
58.5%
ASCII 2329
41.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
940
40.4%
1 243
 
10.4%
- 215
 
9.2%
2 139
 
6.0%
3 135
 
5.8%
4 114
 
4.9%
5 91
 
3.9%
7 84
 
3.6%
6 82
 
3.5%
0 79
 
3.4%
Other values (8) 207
 
8.9%
Hangul
ValueCountFrequency (%)
지 257
 
7.8%
도 244
 
7.4%
기 236
 
7.2%
경 230
 
7.0%
번 230
 
7.0%
시 215
 
6.6%
동 178
 
5.4%
구 95
 
2.9%
리 92
 
2.8%
인 61
 
1.9%
Other values (158) 1439
43.9%

소재지우편번호
Real number (ℝ)

HIGH CORRELATION 

Distinct142
Distinct (%)61.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean425528.85
Minimum11187
Maximum487833
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2023-12-11T07:19:56.058416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11187
5-th percentile14752.25
Q1441822
median446597.5
Q3472842
95-th percentile483030
Maximum487833
Range476646
Interquartile range (IQR)31020

Descriptive statistics

Standard deviation106884.99
Coefficient of variation (CV)0.25118154
Kurtosis10.682873
Mean425528.85
Median Absolute Deviation (MAD)20596
Skewness-3.4610666
Sum97871636
Variance1.1424401 × 1010
MonotonicityNot monotonic
2023-12-11T07:19:56.178213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
442834 12
 
5.2%
472842 8
 
3.5%
442835 8
 
3.5%
449815 6
 
2.6%
445160 6
 
2.6%
410835 5
 
2.2%
483050 4
 
1.7%
441822 4
 
1.7%
413841 4
 
1.7%
445883 4
 
1.7%
Other values (132) 169
73.5%
ValueCountFrequency (%)
11187 1
 
0.4%
14543 1
 
0.4%
14547 3
1.3%
14643 3
1.3%
14704 1
 
0.4%
14705 1
 
0.4%
14711 1
 
0.4%
14750 1
 
0.4%
14755 1
 
0.4%
18472 1
 
0.4%
ValueCountFrequency (%)
487833 1
 
0.4%
487831 1
 
0.4%
487821 1
 
0.4%
483801 1
 
0.4%
483800 1
 
0.4%
483050 4
1.7%
483040 1
 
0.4%
483030 3
1.3%
483020 2
0.9%
483010 2
0.9%

WGS84위도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct220
Distinct (%)97.3%
Missing4
Missing (%)1.7%
Infinite0
Infinite (%)0.0%
Mean37.448707
Minimum36.947893
Maximum37.94629
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2023-12-11T07:19:56.301607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.947893
5-th percentile37.081731
Q137.262149
median37.373598
Q337.662454
95-th percentile37.890953
Maximum37.94629
Range0.99839757
Interquartile range (IQR)0.40030436

Descriptive statistics

Standard deviation0.25401009
Coefficient of variation (CV)0.0067828801
Kurtosis-1.1206642
Mean37.448707
Median Absolute Deviation (MAD)0.17924834
Skewness0.22064899
Sum8463.4079
Variance0.064521127
MonotonicityNot monotonic
2023-12-11T07:19:56.428836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.2645266795 3
 
1.3%
37.6438675151 2
 
0.9%
37.12658573 2
 
0.9%
37.8185402182 2
 
0.9%
37.2595792414 2
 
0.9%
37.2987686934 1
 
0.4%
37.3256515511 1
 
0.4%
37.297134504 1
 
0.4%
37.143099332 1
 
0.4%
37.2677979238 1
 
0.4%
Other values (210) 210
91.3%
(Missing) 4
 
1.7%
ValueCountFrequency (%)
36.9478928374 1
0.4%
36.959508969 1
0.4%
36.9660233717 1
0.4%
36.9764445126 1
0.4%
36.9940166905 1
0.4%
36.9950234772 1
0.4%
37.0146794649 1
0.4%
37.0451802393 1
0.4%
37.0547685569 1
0.4%
37.055067954 1
0.4%
ValueCountFrequency (%)
37.9462904073 1
0.4%
37.9114636299 1
0.4%
37.9081471372 1
0.4%
37.9060446876 1
0.4%
37.9033942005 1
0.4%
37.8969181594 1
0.4%
37.896846484 1
0.4%
37.8958314177 1
0.4%
37.8949124767 1
0.4%
37.8920711288 1
0.4%

WGS84경도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct220
Distinct (%)97.3%
Missing4
Missing (%)1.7%
Infinite0
Infinite (%)0.0%
Mean127.06092
Minimum126.5477
Maximum127.61257
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2023-12-11T07:19:56.546550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.5477
5-th percentile126.68839
Q1126.88708
median127.05345
Q3127.21519
95-th percentile127.47176
Maximum127.61257
Range1.0648643
Interquartile range (IQR)0.32811527

Descriptive statistics

Standard deviation0.23074768
Coefficient of variation (CV)0.0018160398
Kurtosis-0.32123953
Mean127.06092
Median Absolute Deviation (MAD)0.16234653
Skewness0.12892847
Sum28715.767
Variance0.053244494
MonotonicityNot monotonic
2023-12-11T07:19:56.660720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.0284387388 3
 
1.3%
126.547701149 2
 
0.9%
127.6125654228 2
 
0.9%
127.5071807705 2
 
0.9%
127.0332962799 2
 
0.9%
127.2204010091 1
 
0.4%
127.1025187485 1
 
0.4%
127.1088067843 1
 
0.4%
127.4202108703 1
 
0.4%
127.1072329758 1
 
0.4%
Other values (210) 210
91.3%
(Missing) 4
 
1.7%
ValueCountFrequency (%)
126.547701149 2
0.9%
126.5534578204 1
0.4%
126.5882483286 1
0.4%
126.6216928961 1
0.4%
126.6267288372 1
0.4%
126.6598709317 1
0.4%
126.6610073395 1
0.4%
126.6611870747 1
0.4%
126.6838669884 1
0.4%
126.6876928223 1
0.4%
ValueCountFrequency (%)
127.6125654228 2
0.9%
127.5755805892 1
0.4%
127.5750802418 1
0.4%
127.5308501617 1
0.4%
127.5244234387 1
0.4%
127.5071807705 2
0.9%
127.5000909217 1
0.4%
127.4816187925 1
0.4%
127.4784763176 1
0.4%
127.4776037828 1
0.4%

Interactions

2023-12-11T07:19:51.440333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:19:47.198131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:19:47.782523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:19:48.343740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:19:48.927334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:19:49.500942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:19:50.269649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:19:50.835354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:19:51.518988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:19:47.273836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:19:47.859332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:19:48.421188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:19:49.005745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:19:49.573379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:19:50.347158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:19:50.923541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:19:51.594997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:19:47.349560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:19:47.936327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:19:48.497144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:19:49.077482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:19:49.659805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:19:50.420875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:19:51.011317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:19:51.667032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:19:47.423711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:19:48.004466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:19:48.567167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:19:49.164034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:19:49.730335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:19:50.487841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:19:51.092059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:19:51.734163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:19:47.506064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:19:48.075108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:19:48.633900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:19:49.228581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:19:49.990941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:19:50.554463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:19:51.162670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:19:51.798294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:19:47.574757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:19:48.138324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:19:48.705107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:19:49.297221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:19:50.055735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:19:50.623358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:19:51.236417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:19:51.880858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:19:47.644768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:19:48.207737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:19:48.770483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:19:49.375244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:19:50.120648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:19:50.689845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:19:51.299711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:19:51.948840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:19:47.711592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:19:48.274326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:19:48.840585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:19:49.435815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:19:50.184333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:19:50.767328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:19:51.362392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T07:19:56.739126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명인허가일자영업상태명폐업일자년도양실수(개)한실수(개)소재지우편번호WGS84위도WGS84경도
시군명1.0000.5310.3680.6550.4820.2430.6520.9910.9670.922
인허가일자0.5311.0000.2500.6191.0000.3820.3550.3630.3840.579
영업상태명0.3680.2501.000NaN0.1940.2090.2480.1450.2660.100
폐업일자0.6550.619NaN1.0000.5470.1670.0000.6070.5580.394
년도0.4821.0000.1940.5471.0000.3910.3950.1590.3440.564
양실수(개)0.2430.3820.2090.1670.3911.0000.1390.1230.0000.000
한실수(개)0.6520.3550.2480.0000.3950.1391.0000.0000.0170.294
소재지우편번호0.9910.3630.1450.6070.1590.1230.0001.0000.7280.750
WGS84위도0.9670.3840.2660.5580.3440.0000.0170.7281.0000.712
WGS84경도0.9220.5790.1000.3940.5640.0000.2940.7500.7121.000
2023-12-11T07:19:56.838731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명위생업종명영업상태명위생업태명
시군명1.0001.0000.2761.000
위생업종명1.0001.0001.0001.000
영업상태명0.2761.0001.0001.000
위생업태명1.0001.0001.0001.000
2023-12-11T07:19:56.920135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
인허가일자폐업일자년도양실수(개)한실수(개)소재지우편번호WGS84위도WGS84경도시군명영업상태명위생업종명위생업태명
인허가일자1.0000.3420.9970.205-0.376-0.206-0.210-0.2670.2090.1831.0001.000
폐업일자0.3421.0000.1690.045-0.0600.284-0.0010.2330.3091.0001.0001.000
년도0.9970.1691.0000.206-0.374-0.288-0.205-0.3250.1880.1481.0001.000
양실수(개)0.2050.0450.2061.000-0.434-0.263-0.046-0.2520.1200.1391.0001.000
한실수(개)-0.376-0.060-0.374-0.4341.0000.2360.0410.3110.3100.1831.0001.000
소재지우편번호-0.2060.284-0.288-0.2630.2361.0000.2810.7520.9120.2231.0001.000
WGS84위도-0.210-0.001-0.205-0.0460.0410.2811.0000.0820.7860.2001.0001.000
WGS84경도-0.2670.233-0.325-0.2520.3110.7520.0821.0000.6410.0741.0001.000
시군명0.2090.3090.1880.1200.3100.9120.7860.6411.0000.2761.0001.000
영업상태명0.1831.0000.1480.1390.1830.2230.2000.0740.2761.0001.0001.000
위생업종명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
위생업태명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000

Missing values

2023-12-11T07:19:52.045253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T07:19:52.213046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-12-11T07:19:52.343787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

시군명사업장명인허가일자영업상태명폐업일자년도다중이용업소여부양실수(개)위생업종명위생업태명한실수(개)소재지도로명주소소재지지번주소소재지우편번호WGS84위도WGS84경도
0가평군양지카운티20020710운영중<NA>2002N0숙박업(일반)일반호텔3경기도 가평군 북면 백둔로560번길 26-4경기도 가평군 북면 백둔리 420번지47784237.908147127.449924
1가평군힐타운모텔20040731운영중<NA>2004N14숙박업(일반)일반호텔3<NA>경기도 가평군 설악면 회곡리 737-9번지47785437.713413127.440865
2가평군뉴월드호텔19910711운영중<NA>1991N15숙박업(일반)일반호텔15경기도 가평군 설악면 유명로 1734경기도 가평군 설악면 선촌리 410번지47785437.676327127.478476
3가평군브라운모텔20020731운영중<NA>2002N0숙박업(일반)일반호텔22<NA>경기도 가평군 청평면 상천리 산 207-5번지477814<NA><NA>
4가평군로즈모텔19950805운영중<NA>1995N28숙박업(일반)일반호텔0<NA>경기도 가평군 상면 항사리 291-2번지477821<NA><NA>
5가평군트라움19900827운영중<NA>1990N16숙박업(일반)일반호텔17경기도 가평군 청평면 은행나무길 19경기도 가평군 청평면 대성리 631-2번지47781237.679976127.375274
6가평군청평별빛마을20000221운영중<NA>2000N5숙박업(일반)일반호텔16경기도 가평군 상면 축령로 145경기도 가평군 상면 행현리 804-2번지47782437.774885127.35798
7가평군아이리스20020326운영중<NA>2002N3숙박업(일반)일반호텔26경기도 가평군 가평읍 북한강변로 1027-11 (,136-1,136-2)경기도 가평군 가평읍 달전리 134번지 ,136-1,136-247780437.804153127.524423
8가평군(주)호명청평산장호텔19820122운영중<NA>1982N17숙박업(일반)일반호텔17경기도 가평군 청평면 청군로 70경기도 가평군 청평면 하천리 531-1번지47781637.749689127.42138
9가평군나우호텔20100812운영중<NA>2010N36숙박업(일반)일반호텔5경기도 가평군 가평읍 석봉로3번길 14경기도 가평군 가평읍 대곡리 411-3번지47780437.81854127.507181
시군명사업장명인허가일자영업상태명폐업일자년도다중이용업소여부양실수(개)위생업종명위생업태명한실수(개)소재지도로명주소소재지지번주소소재지우편번호WGS84위도WGS84경도
220화성시J모텔20170921운영중<NA>2017N0숙박업(일반)일반호텔0경기도 화성시 동탄지성로 12, 7층 701,702,703호 (반송동, 월드프라자)경기도 화성시 반송동 91-7번지 월드프라자 701,702,703호44516037.205042127.073255
221화성시세화호텔19941008운영중<NA>1994N23숙박업(일반)일반호텔7경기도 화성시 팔탄면 온천로 314경기도 화성시 팔탄면 덕천리 121-1번지44591937.146631126.872583
222화성시칼튼20030415운영중<NA>2003N25숙박업(일반)일반호텔0경기도 화성시 정남면 세자로303번길 15-46 (외1필지)경기도 화성시 정남면 보통리 89-1번지 외1필지44596337.192754126.978272
223화성시싱가폴모텔20030310운영중<NA>2003N38숙박업(일반)일반호텔1경기도 화성시 팔탄면 버들로1597번길 4-6경기도 화성시 팔탄면 월문리 235-17번지44597137.114722126.877844
224화성시화성스파빌호텔20011020폐업 등201701242001N51숙박업(일반)일반호텔<NA>경기도 화성시 팔탄면 온천로 29경기도 화성시 팔탄면 덕우리 233-1번지44591837.128289126.859417
225화성시타임모텔20040430폐업 등201104272004N40숙박업(일반)일반호텔<NA>경기도 화성시 서신면 제부로 327-17경기도 화성시 서신면 송교리 476-7번지44588337.16747126.661187
226화성시호텔쉐르빌20000526폐업 등201102102000N34숙박업(일반)일반호텔0<NA>경기도 화성시 동탄면 영천리 623-844번지18472<NA><NA>
227화성시조아텔화성점20030312폐업 등201109022003N20숙박업(일반)일반호텔<NA>경기도 화성시 정남면 세자로303번길 14-16경기도 화성시 정남면 보통리 79-5번지44596337.194316126.978825
228화성시로보텔19960810폐업 등200703141996N39숙박업(일반)일반호텔0경기도 화성시 서신면 해안길 406경기도 화성시 서신면 제부리 288-43번지44588337.175666126.621693
229화성시again motel20040401폐업 등200605232004N23숙박업(일반)일반호텔<NA>경기도 화성시 서신면 당성로 228-7경기도 화성시 서신면 전곡리 189-1번지44588337.194383126.6945