Overview

Dataset statistics

Number of variables17
Number of observations8401
Missing cells34017
Missing cells (%)23.8%
Duplicate rows2
Duplicate rows (%)< 0.1%
Total size in memory1.1 MiB
Average record size in memory143.0 B

Variable types

Categorical5
Text3
DateTime2
Numeric6
Unsupported1

Dataset

Description숙박업체 현황(일반-관광호텔)
Author행정안전부
URLhttps://data.gg.go.kr/portal/data/service/selectServicePage.do?&infId=1KXXP77628MJ8H1VQFSK678986&infSeq=1

Alerts

Dataset has 2 (< 0.1%) duplicate rowsDuplicates
건물소유구분명 is highly overall correlated with 위생업종명High correlation
위생업종명 is highly overall correlated with 년도 and 9 other fieldsHigh correlation
위생업태명 is highly overall correlated with 년도 and 1 other fieldsHigh correlation
시군명 is highly overall correlated with 소재지우편번호 and 3 other fieldsHigh correlation
영업상태명 is highly overall correlated with 위생업종명High correlation
년도 is highly overall correlated with 위생업종명 and 1 other fieldsHigh correlation
양실수(개) is highly overall correlated with 위생업종명High correlation
한실수(개) is highly overall correlated with 위생업종명High correlation
소재지우편번호 is highly overall correlated with WGS84위도 and 2 other fieldsHigh correlation
WGS84위도 is highly overall correlated with 소재지우편번호 and 2 other fieldsHigh correlation
WGS84경도 is highly overall correlated with 시군명 and 1 other fieldsHigh correlation
건물소유구분명 is highly imbalanced (65.5%)Imbalance
위생업종명 is highly imbalanced (90.5%)Imbalance
폐업일자 has 6518 (77.6%) missing valuesMissing
년도 has 8298 (98.8%) missing valuesMissing
다중이용업소여부 has 8401 (100.0%) missing valuesMissing
양실수(개) has 5339 (63.6%) missing valuesMissing
한실수(개) has 5347 (63.6%) missing valuesMissing
다중이용업소여부 is an unsupported type, check if it needs cleaning or further analysisUnsupported
양실수(개) has 378 (4.5%) zerosZeros
한실수(개) has 1982 (23.6%) zerosZeros

Reproduction

Analysis started2023-12-10 22:24:17.556883
Analysis finished2023-12-10 22:24:22.948201
Duration5.39 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군명
Categorical

HIGH CORRELATION 

Distinct31
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size65.8 KiB
가평군
2041 
양평군
1016 
안산시
989 
수원시
520 
포천시
449 
Other values (26)
3386 

Length

Max length4
Median length3
Mean length3.0421378
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
가평군 2041
24.3%
양평군 1016
12.1%
안산시 989
11.8%
수원시 520
 
6.2%
포천시 449
 
5.3%
파주시 378
 
4.5%
화성시 346
 
4.1%
용인시 304
 
3.6%
성남시 290
 
3.5%
남양주시 213
 
2.5%
Other values (21) 1855
22.1%

Length

2023-12-11T07:24:22.996573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
가평군 2041
24.3%
양평군 1016
12.1%
안산시 989
11.8%
수원시 520
 
6.2%
포천시 449
 
5.3%
파주시 378
 
4.5%
화성시 346
 
4.1%
용인시 304
 
3.6%
성남시 290
 
3.5%
남양주시 213
 
2.5%
Other values (21) 1855
22.1%

사업장명
Text

Distinct7449
Distinct (%)88.7%
Missing0
Missing (%)0.0%
Memory size65.8 KiB
2023-12-11T07:24:23.277758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length36
Median length34
Mean length5.6878943
Min length1

Characters and Unicode

Total characters47784
Distinct characters921
Distinct categories13 ?
Distinct scripts4 ?
Distinct blocks8 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6722 ?
Unique (%)80.0%

Sample

1st row가평빠지워터플레이
2nd row스포츠텔
3rd row폴라리스키즈펜션
4th row더키즈풀빌라
5th row라라키즈풀빌라
ValueCountFrequency (%)
펜션 236
 
2.3%
호텔 95
 
0.9%
민박 70
 
0.7%
모텔 39
 
0.4%
캠핑장 38
 
0.4%
hotel 37
 
0.4%
하우스 36
 
0.3%
스테이 35
 
0.3%
더 31
 
0.3%
풀빌라 27
 
0.3%
Other values (7741) 9730
93.8%
2023-12-11T07:24:23.703948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2031
 
4.3%
션 1433
 
3.0%
텔 1427
 
3.0%
스 1416
 
3.0%
펜 1392
 
2.9%
이 1101
 
2.3%
리 971
 
2.0%
호 886
 
1.9%
라 796
 
1.7%
모 732
 
1.5%
Other values (911) 35599
74.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 41280
86.4%
Space Separator 2031
 
4.3%
Uppercase Letter 1514
 
3.2%
Lowercase Letter 1086
 
2.3%
Decimal Number 964
 
2.0%
Close Punctuation 355
 
0.7%
Open Punctuation 354
 
0.7%
Other Punctuation 134
 
0.3%
Dash Punctuation 50
 
0.1%
Letter Number 9
 
< 0.1%
Other values (3) 7
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
션 1433
 
3.5%
텔 1427
 
3.5%
스 1416
 
3.4%
펜 1392
 
3.4%
이 1101
 
2.7%
리 971
 
2.4%
호 886
 
2.1%
라 796
 
1.9%
모 732
 
1.8%
하 590
 
1.4%
Other values (828) 30536
74.0%
Lowercase Letter
ValueCountFrequency (%)
e 156
14.4%
o 123
11.3%
a 98
9.0%
t 90
 
8.3%
l 83
 
7.6%
s 78
 
7.2%
i 64
 
5.9%
n 56
 
5.2%
r 52
 
4.8%
u 50
 
4.6%
Other values (16) 236
21.7%
Uppercase Letter
ValueCountFrequency (%)
A 129
 
8.5%
O 126
 
8.3%
H 125
 
8.3%
T 120
 
7.9%
E 113
 
7.5%
L 93
 
6.1%
B 82
 
5.4%
M 80
 
5.3%
S 80
 
5.3%
N 62
 
4.1%
Other values (16) 504
33.3%
Decimal Number
ValueCountFrequency (%)
2 260
27.0%
1 202
21.0%
5 81
 
8.4%
3 79
 
8.2%
0 78
 
8.1%
4 61
 
6.3%
6 59
 
6.1%
7 58
 
6.0%
9 49
 
5.1%
8 37
 
3.8%
Other Punctuation
ValueCountFrequency (%)
. 51
38.1%
& 40
29.9%
, 21
15.7%
· 7
 
5.2%
' 7
 
5.2%
/ 3
 
2.2%
; 2
 
1.5%
@ 1
 
0.7%
? 1
 
0.7%
# 1
 
0.7%
Math Symbol
ValueCountFrequency (%)
+ 3
60.0%
→ 1
 
20.0%
> 1
 
20.0%
Letter Number
ValueCountFrequency (%)
Ⅱ 6
66.7%
Ⅰ 3
33.3%
Space Separator
ValueCountFrequency (%)
2031
100.0%
Close Punctuation
ValueCountFrequency (%)
) 355
100.0%
Open Punctuation
ValueCountFrequency (%)
( 354
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 50
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 1
100.0%
Other Symbol
ValueCountFrequency (%)
℃ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 41274
86.4%
Common 3895
 
8.2%
Latin 2609
 
5.5%
Han 6
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
션 1433
 
3.5%
텔 1427
 
3.5%
스 1416
 
3.4%
펜 1392
 
3.4%
이 1101
 
2.7%
리 971
 
2.4%
호 886
 
2.1%
라 796
 
1.9%
모 732
 
1.8%
하 590
 
1.4%
Other values (822) 30530
74.0%
Latin
ValueCountFrequency (%)
e 156
 
6.0%
A 129
 
4.9%
O 126
 
4.8%
H 125
 
4.8%
o 123
 
4.7%
T 120
 
4.6%
E 113
 
4.3%
a 98
 
3.8%
L 93
 
3.6%
t 90
 
3.4%
Other values (44) 1436
55.0%
Common
ValueCountFrequency (%)
2031
52.1%
) 355
 
9.1%
( 354
 
9.1%
2 260
 
6.7%
1 202
 
5.2%
5 81
 
2.1%
3 79
 
2.0%
0 78
 
2.0%
4 61
 
1.6%
6 59
 
1.5%
Other values (19) 335
 
8.6%
Han
ValueCountFrequency (%)
家 1
16.7%
休 1
16.7%
來 1
16.7%
月 1
16.7%
夢 1
16.7%
美 1
16.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 41273
86.4%
ASCII 6486
 
13.6%
Number Forms 9
 
< 0.1%
None 7
 
< 0.1%
CJK 6
 
< 0.1%
Arrows 1
 
< 0.1%
Compat Jamo 1
 
< 0.1%
Letterlike Symbols 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2031
31.3%
) 355
 
5.5%
( 354
 
5.5%
2 260
 
4.0%
1 202
 
3.1%
e 156
 
2.4%
A 129
 
2.0%
O 126
 
1.9%
H 125
 
1.9%
o 123
 
1.9%
Other values (68) 2625
40.5%
Hangul
ValueCountFrequency (%)
션 1433
 
3.5%
텔 1427
 
3.5%
스 1416
 
3.4%
펜 1392
 
3.4%
이 1101
 
2.7%
리 971
 
2.4%
호 886
 
2.1%
라 796
 
1.9%
모 732
 
1.8%
하 590
 
1.4%
Other values (821) 30529
74.0%
None
ValueCountFrequency (%)
· 7
100.0%
Number Forms
ValueCountFrequency (%)
Ⅱ 6
66.7%
Ⅰ 3
33.3%
Arrows
ValueCountFrequency (%)
→ 1
100.0%
CJK
ValueCountFrequency (%)
家 1
16.7%
休 1
16.7%
來 1
16.7%
月 1
16.7%
夢 1
16.7%
美 1
16.7%
Compat Jamo
ValueCountFrequency (%)
ㅣ 1
100.0%
Letterlike Symbols
ValueCountFrequency (%)
℃ 1
100.0%

인허가일자
Date

Distinct4152
Distinct (%)49.4%
Missing1
Missing (%)< 0.1%
Memory size65.8 KiB
Minimum1946-11-23 00:00:00
Maximum2023-12-05 00:00:00
2023-12-11T07:24:23.815389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:24:23.947515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

영업상태명
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size65.8 KiB
정상
3520 
영업
2486 
폐업
1841 
영업중
421 
운영중
 
62
Other values (2)
 
71

Length

Max length4
Median length2
Mean length2.067492
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
정상 3520
41.9%
영업 2486
29.6%
폐업 1841
21.9%
영업중 421
 
5.0%
운영중 62
 
0.7%
폐업 등 42
 
0.5%
휴업 29
 
0.3%

Length

2023-12-11T07:24:24.055955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T07:24:24.160142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
정상 3520
41.7%
영업 2486
29.4%
폐업 1883
22.3%
영업중 421
 
5.0%
운영중 62
 
0.7%
등 42
 
0.5%
휴업 29
 
0.3%

폐업일자
Date

MISSING 

Distinct864
Distinct (%)45.9%
Missing6518
Missing (%)77.6%
Memory size65.8 KiB
Minimum1997-11-20 00:00:00
Maximum2023-12-05 00:00:00
2023-12-11T07:24:24.269116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:24:24.374321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

건물소유구분명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size65.8 KiB
<NA>
7583 
자가
 
574
임대
 
244

Length

Max length4
Median length4
Mean length3.8052613
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 7583
90.3%
자가 574
 
6.8%
임대 244
 
2.9%

Length

2023-12-11T07:24:24.482193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T07:24:24.590940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 7583
90.3%
자가 574
 
6.8%
임대 244
 
2.9%

년도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct33
Distinct (%)32.0%
Missing8298
Missing (%)98.8%
Infinite0
Infinite (%)0.0%
Mean2002.0777
Minimum1973
Maximum2018
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size74.0 KiB
2023-12-11T07:24:24.746982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1973
5-th percentile1984
Q11995
median2002
Q32013
95-th percentile2017
Maximum2018
Range45
Interquartile range (IQR)18

Descriptive statistics

Standard deviation11.250873
Coefficient of variation (CV)0.0056195987
Kurtosis-0.71675662
Mean2002.0777
Median Absolute Deviation (MAD)9
Skewness-0.39544436
Sum206214
Variance126.58214
MonotonicityNot monotonic
2023-12-11T07:24:24.858878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
2015 9
 
0.1%
2001 7
 
0.1%
1997 6
 
0.1%
2016 6
 
0.1%
2003 6
 
0.1%
2017 5
 
0.1%
1998 4
 
< 0.1%
1990 4
 
< 0.1%
2013 4
 
< 0.1%
1988 4
 
< 0.1%
Other values (23) 48
 
0.6%
(Missing) 8298
98.8%
ValueCountFrequency (%)
1973 1
 
< 0.1%
1977 2
< 0.1%
1983 1
 
< 0.1%
1984 3
< 0.1%
1985 1
 
< 0.1%
1986 3
< 0.1%
1987 3
< 0.1%
1988 4
< 0.1%
1990 4
< 0.1%
1994 2
< 0.1%
ValueCountFrequency (%)
2018 2
 
< 0.1%
2017 5
0.1%
2016 6
0.1%
2015 9
0.1%
2014 2
 
< 0.1%
2013 4
< 0.1%
2012 1
 
< 0.1%
2011 3
 
< 0.1%
2010 1
 
< 0.1%
2009 2
 
< 0.1%

다중이용업소여부
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing8401
Missing (%)100.0%
Memory size74.0 KiB

양실수(개)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct149
Distinct (%)4.9%
Missing5339
Missing (%)63.6%
Infinite0
Infinite (%)0.0%
Mean25.141084
Minimum0
Maximum826
Zeros378
Zeros (%)4.5%
Negative0
Negative (%)0.0%
Memory size74.0 KiB
2023-12-11T07:24:24.969177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16
median18
Q331.75
95-th percentile56
Maximum826
Range826
Interquartile range (IQR)25.75

Descriptive statistics

Standard deviation41.943931
Coefficient of variation (CV)1.6683422
Kurtosis81.253958
Mean25.141084
Median Absolute Deviation (MAD)12
Skewness7.3754909
Sum76982
Variance1759.2934
MonotonicityNot monotonic
2023-12-11T07:24:25.110072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 378
 
4.5%
10 104
 
1.2%
12 89
 
1.1%
15 88
 
1.0%
18 87
 
1.0%
19 86
 
1.0%
30 84
 
1.0%
1 83
 
1.0%
6 79
 
0.9%
20 77
 
0.9%
Other values (139) 1907
 
22.7%
(Missing) 5339
63.6%
ValueCountFrequency (%)
0 378
4.5%
1 83
 
1.0%
2 72
 
0.9%
3 46
 
0.5%
4 66
 
0.8%
5 68
 
0.8%
6 79
 
0.9%
7 52
 
0.6%
8 56
 
0.7%
9 45
 
0.5%
ValueCountFrequency (%)
826 1
< 0.1%
495 1
< 0.1%
476 1
< 0.1%
440 1
< 0.1%
432 1
< 0.1%
422 1
< 0.1%
421 1
< 0.1%
416 1
< 0.1%
401 1
< 0.1%
343 1
< 0.1%

위생업종명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size65.8 KiB
<NA>
8298 
숙박업(일반)
 
103

Length

Max length7
Median length4
Mean length4.0367813
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> 8298
98.8%
숙박업(일반) 103
 
1.2%

Length

2023-12-11T07:24:25.232230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T07:24:25.321182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 8298
98.8%
숙박업(일반 103
 
1.2%

위생업태명
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size65.8 KiB
<NA>
5336 
여관업
1527 
숙박업(생활)
588 
여인숙업
 
345
일반호텔
 
302
Other values (3)
 
303

Length

Max length8
Median length4
Mean length4.0546364
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row숙박업 기타
2nd row여관업
3rd row숙박업(생활)
4th row숙박업(생활)
5th row숙박업(생활)

Common Values

ValueCountFrequency (%)
<NA> 5336
63.5%
여관업 1527
 
18.2%
숙박업(생활) 588
 
7.0%
여인숙업 345
 
4.1%
일반호텔 302
 
3.6%
관광호텔 198
 
2.4%
숙박업 기타 99
 
1.2%
휴양콘도미니엄업 6
 
0.1%

Length

2023-12-11T07:24:25.424376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T07:24:25.530897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 5336
62.8%
여관업 1527
 
18.0%
숙박업(생활 588
 
6.9%
여인숙업 345
 
4.1%
일반호텔 302
 
3.6%
관광호텔 198
 
2.3%
숙박업 99
 
1.2%
기타 99
 
1.2%
휴양콘도미니엄업 6
 
0.1%

한실수(개)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct62
Distinct (%)2.0%
Missing5347
Missing (%)63.6%
Infinite0
Infinite (%)0.0%
Mean3.2393582
Minimum0
Maximum281
Zeros1982
Zeros (%)23.6%
Negative0
Negative (%)0.0%
Memory size74.0 KiB
2023-12-11T07:24:25.660634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33
95-th percentile13
Maximum281
Range281
Interquartile range (IQR)3

Descriptive statistics

Standard deviation11.286781
Coefficient of variation (CV)3.4842646
Kurtosis278.90249
Mean3.2393582
Median Absolute Deviation (MAD)0
Skewness14.012157
Sum9893
Variance127.39143
MonotonicityNot monotonic
2023-12-11T07:24:25.786805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1982
 
23.6%
2 132
 
1.6%
4 105
 
1.2%
3 93
 
1.1%
1 86
 
1.0%
5 86
 
1.0%
10 83
 
1.0%
6 81
 
1.0%
8 67
 
0.8%
7 64
 
0.8%
Other values (52) 275
 
3.3%
(Missing) 5347
63.6%
ValueCountFrequency (%)
0 1982
23.6%
1 86
 
1.0%
2 132
 
1.6%
3 93
 
1.1%
4 105
 
1.2%
5 86
 
1.0%
6 81
 
1.0%
7 64
 
0.8%
8 67
 
0.8%
9 62
 
0.7%
ValueCountFrequency (%)
281 1
< 0.1%
273 1
< 0.1%
185 1
< 0.1%
151 1
< 0.1%
143 1
< 0.1%
125 1
< 0.1%
120 1
< 0.1%
107 1
< 0.1%
91 1
< 0.1%
83 1
< 0.1%

소재지도로명주소
Text

Distinct7728
Distinct (%)92.6%
Missing51
Missing (%)0.6%
Memory size65.8 KiB
2023-12-11T07:24:26.069470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length89
Median length71
Mean length25.754371
Min length14

Characters and Unicode

Total characters215049
Distinct characters647
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7197 ?
Unique (%)86.2%

Sample

1st row경기도 가평군 가평읍 북한강변로 882, A,B,C동 각1~2층
2nd row경기도 가평군 청평면 경춘로 1436
3rd row경기도 가평군 북면 백둔로 132
4th row경기도 가평군 가평읍 경반안로 357-1, 총5개동 1~5동 각1~2층
5th row경기도 가평군 상면 임초밤안골로 162-13, A,B동 각1~2층
ValueCountFrequency (%)
경기도 8350
 
18.0%
가평군 2027
 
4.4%
양평군 1013
 
2.2%
안산시 987
 
2.1%
단원구 927
 
2.0%
수원시 518
 
1.1%
북면 480
 
1.0%
가평읍 453
 
1.0%
포천시 439
 
0.9%
파주시 370
 
0.8%
Other values (8096) 30851
66.5%
2023-12-11T07:24:26.582216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
38077
 
17.7%
도 8535
 
4.0%
기 8529
 
4.0%
경 8511
 
4.0%
1 8214
 
3.8%
2 5951
 
2.8%
로 5840
 
2.7%
시 5306
 
2.5%
길 5140
 
2.4%
동 4601
 
2.1%
Other values (637) 116345
54.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 123856
57.6%
Decimal Number 38770
 
18.0%
Space Separator 38077
 
17.7%
Dash Punctuation 4368
 
2.0%
Close Punctuation 3308
 
1.5%
Open Punctuation 3308
 
1.5%
Other Punctuation 2508
 
1.2%
Math Symbol 475
 
0.2%
Uppercase Letter 335
 
0.2%
Lowercase Letter 41
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 8535
 
6.9%
기 8529
 
6.9%
경 8511
 
6.9%
로 5840
 
4.7%
시 5306
 
4.3%
길 5140
 
4.1%
동 4601
 
3.7%
평 4331
 
3.5%
면 4283
 
3.5%
군 3317
 
2.7%
Other values (572) 65463
52.9%
Uppercase Letter
ValueCountFrequency (%)
A 98
29.3%
B 85
25.4%
C 37
 
11.0%
E 19
 
5.7%
D 16
 
4.8%
T 11
 
3.3%
H 10
 
3.0%
O 8
 
2.4%
F 7
 
2.1%
I 6
 
1.8%
Other values (12) 38
 
11.3%
Lowercase Letter
ValueCountFrequency (%)
t 6
14.6%
n 6
14.6%
e 5
12.2%
m 3
7.3%
r 3
7.3%
o 3
7.3%
a 3
7.3%
h 2
 
4.9%
v 2
 
4.9%
i 2
 
4.9%
Other values (5) 6
14.6%
Decimal Number
ValueCountFrequency (%)
1 8214
21.2%
2 5951
15.3%
3 4121
10.6%
4 3506
9.0%
5 3236
 
8.3%
6 3003
 
7.7%
7 2934
 
7.6%
8 2708
 
7.0%
0 2599
 
6.7%
9 2498
 
6.4%
Other Punctuation
ValueCountFrequency (%)
, 2464
98.2%
. 24
 
1.0%
* 6
 
0.2%
/ 6
 
0.2%
: 5
 
0.2%
· 2
 
0.1%
& 1
 
< 0.1%
Math Symbol
ValueCountFrequency (%)
~ 473
99.6%
< 1
 
0.2%
> 1
 
0.2%
Close Punctuation
ValueCountFrequency (%)
) 3302
99.8%
] 6
 
0.2%
Open Punctuation
ValueCountFrequency (%)
( 3302
99.8%
[ 6
 
0.2%
Letter Number
ValueCountFrequency (%)
Ⅰ 2
66.7%
Ⅱ 1
33.3%
Space Separator
ValueCountFrequency (%)
38077
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4368
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 123856
57.6%
Common 90814
42.2%
Latin 379
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 8535
 
6.9%
기 8529
 
6.9%
경 8511
 
6.9%
로 5840
 
4.7%
시 5306
 
4.3%
길 5140
 
4.1%
동 4601
 
3.7%
평 4331
 
3.5%
면 4283
 
3.5%
군 3317
 
2.7%
Other values (572) 65463
52.9%
Latin
ValueCountFrequency (%)
A 98
25.9%
B 85
22.4%
C 37
 
9.8%
E 19
 
5.0%
D 16
 
4.2%
T 11
 
2.9%
H 10
 
2.6%
O 8
 
2.1%
F 7
 
1.8%
I 6
 
1.6%
Other values (29) 82
21.6%
Common
ValueCountFrequency (%)
38077
41.9%
1 8214
 
9.0%
2 5951
 
6.6%
- 4368
 
4.8%
3 4121
 
4.5%
4 3506
 
3.9%
) 3302
 
3.6%
( 3302
 
3.6%
5 3236
 
3.6%
6 3003
 
3.3%
Other values (16) 13734
 
15.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 123856
57.6%
ASCII 91188
42.4%
Number Forms 3
 
< 0.1%
None 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
38077
41.8%
1 8214
 
9.0%
2 5951
 
6.5%
- 4368
 
4.8%
3 4121
 
4.5%
4 3506
 
3.8%
) 3302
 
3.6%
( 3302
 
3.6%
5 3236
 
3.5%
6 3003
 
3.3%
Other values (52) 14108
 
15.5%
Hangul
ValueCountFrequency (%)
도 8535
 
6.9%
기 8529
 
6.9%
경 8511
 
6.9%
로 5840
 
4.7%
시 5306
 
4.3%
길 5140
 
4.1%
동 4601
 
3.7%
평 4331
 
3.5%
면 4283
 
3.5%
군 3317
 
2.7%
Other values (572) 65463
52.9%
Number Forms
ValueCountFrequency (%)
Ⅰ 2
66.7%
Ⅱ 1
33.3%
None
ValueCountFrequency (%)
· 2
100.0%

소재지지번주소
Text

Distinct7921
Distinct (%)94.3%
Missing2
Missing (%)< 0.1%
Memory size65.8 KiB
2023-12-11T07:24:27.156641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length83
Median length72
Mean length22.840457
Min length14

Characters and Unicode

Total characters191837
Distinct characters531
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7498 ?
Unique (%)89.3%

Sample

1st row경기도 가평군 가평읍 이화리 36-1 ,A,B,C동 각1~2층
2nd row경기도 가평군 청평면 상천리 277
3rd row경기도 가평군 북면 백둔리 30-1
4th row경기도 가평군 가평읍 경반리 583-2 1~5동 1~2층(총5개동)
5th row경기도 가평군 상면 임초리 170-4 외2필지(172,174-41) A,B동 각1~2층
ValueCountFrequency (%)
경기도 8399
 
19.1%
가평군 2041
 
4.6%
양평군 1016
 
2.3%
안산시 989
 
2.3%
단원구 929
 
2.1%
수원시 520
 
1.2%
북면 483
 
1.1%
가평읍 453
 
1.0%
포천시 449
 
1.0%
대부남동 449
 
1.0%
Other values (8507) 28179
64.2%
2023-12-11T07:24:27.635582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
38879
20.3%
도 8739
 
4.6%
기 8548
 
4.5%
경 8439
 
4.4%
1 7580
 
4.0%
- 6681
 
3.5%
리 5447
 
2.8%
시 5281
 
2.8%
2 5141
 
2.7%
동 4378
 
2.3%
Other values (521) 92724
48.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 106862
55.7%
Space Separator 38879
 
20.3%
Decimal Number 37436
 
19.5%
Dash Punctuation 6681
 
3.5%
Other Punctuation 835
 
0.4%
Math Symbol 360
 
0.2%
Uppercase Letter 257
 
0.1%
Open Punctuation 246
 
0.1%
Close Punctuation 244
 
0.1%
Lowercase Letter 36
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 8739
 
8.2%
기 8548
 
8.0%
경 8439
 
7.9%
리 5447
 
5.1%
시 5281
 
4.9%
동 4378
 
4.1%
면 4305
 
4.0%
평 4288
 
4.0%
군 3277
 
3.1%
가 2864
 
2.7%
Other values (462) 51296
48.0%
Uppercase Letter
ValueCountFrequency (%)
A 73
28.4%
B 64
24.9%
C 30
11.7%
E 14
 
5.4%
D 10
 
3.9%
T 10
 
3.9%
H 9
 
3.5%
O 8
 
3.1%
Y 5
 
1.9%
I 5
 
1.9%
Other values (10) 29
 
11.3%
Lowercase Letter
ValueCountFrequency (%)
t 5
13.9%
n 4
11.1%
m 4
11.1%
e 4
11.1%
o 3
8.3%
a 3
8.3%
h 2
 
5.6%
u 2
 
5.6%
i 2
 
5.6%
r 2
 
5.6%
Other values (4) 5
13.9%
Decimal Number
ValueCountFrequency (%)
1 7580
20.2%
2 5141
13.7%
3 4275
11.4%
4 3576
9.6%
6 3417
9.1%
5 3279
8.8%
7 2862
 
7.6%
8 2554
 
6.8%
0 2460
 
6.6%
9 2292
 
6.1%
Other Punctuation
ValueCountFrequency (%)
, 806
96.5%
. 20
 
2.4%
/ 4
 
0.5%
· 2
 
0.2%
: 1
 
0.1%
; 1
 
0.1%
& 1
 
0.1%
Math Symbol
ValueCountFrequency (%)
~ 356
98.9%
> 2
 
0.6%
< 2
 
0.6%
Space Separator
ValueCountFrequency (%)
38879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 6681
100.0%
Open Punctuation
ValueCountFrequency (%)
( 246
100.0%
Close Punctuation
ValueCountFrequency (%)
) 244
100.0%
Letter Number
ValueCountFrequency (%)
Ⅱ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 106862
55.7%
Common 84681
44.1%
Latin 294
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 8739
 
8.2%
기 8548
 
8.0%
경 8439
 
7.9%
리 5447
 
5.1%
시 5281
 
4.9%
동 4378
 
4.1%
면 4305
 
4.0%
평 4288
 
4.0%
군 3277
 
3.1%
가 2864
 
2.7%
Other values (462) 51296
48.0%
Latin
ValueCountFrequency (%)
A 73
24.8%
B 64
21.8%
C 30
10.2%
E 14
 
4.8%
D 10
 
3.4%
T 10
 
3.4%
H 9
 
3.1%
O 8
 
2.7%
Y 5
 
1.7%
I 5
 
1.7%
Other values (25) 66
22.4%
Common
ValueCountFrequency (%)
38879
45.9%
1 7580
 
9.0%
- 6681
 
7.9%
2 5141
 
6.1%
3 4275
 
5.0%
4 3576
 
4.2%
6 3417
 
4.0%
5 3279
 
3.9%
7 2862
 
3.4%
8 2554
 
3.0%
Other values (14) 6437
 
7.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 106862
55.7%
ASCII 84972
44.3%
None 2
 
< 0.1%
Number Forms 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
38879
45.8%
1 7580
 
8.9%
- 6681
 
7.9%
2 5141
 
6.1%
3 4275
 
5.0%
4 3576
 
4.2%
6 3417
 
4.0%
5 3279
 
3.9%
7 2862
 
3.4%
8 2554
 
3.0%
Other values (47) 6728
 
7.9%
Hangul
ValueCountFrequency (%)
도 8739
 
8.2%
기 8548
 
8.0%
경 8439
 
7.9%
리 5447
 
5.1%
시 5281
 
4.9%
동 4378
 
4.1%
면 4305
 
4.0%
평 4288
 
4.0%
군 3277
 
3.1%
가 2864
 
2.7%
Other values (462) 51296
48.0%
None
ValueCountFrequency (%)
· 2
100.0%
Number Forms
ValueCountFrequency (%)
Ⅱ 1
100.0%

소재지우편번호
Real number (ℝ)

HIGH CORRELATION 

Distinct1144
Distinct (%)13.6%
Missing20
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean13639.324
Minimum10000
Maximum18635
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size74.0 KiB
2023-12-11T07:24:27.764598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10000
5-th percentile10862
Q112406
median12512
Q315650
95-th percentile17948
Maximum18635
Range8635
Interquartile range (IQR)3244

Descriptive statistics

Standard deviation2242.8346
Coefficient of variation (CV)0.16443885
Kurtosis-0.71886426
Mean13639.324
Median Absolute Deviation (MAD)1182
Skewness0.70064852
Sum1.1431117 × 108
Variance5030307.2
MonotonicityNot monotonic
2023-12-11T07:24:27.888693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15650 241
 
2.9%
18553 159
 
1.9%
15651 154
 
1.8%
12448 146
 
1.7%
13246 117
 
1.4%
15653 116
 
1.4%
12405 107
 
1.3%
12406 93
 
1.1%
12473 85
 
1.0%
12457 81
 
1.0%
Other values (1134) 7082
84.3%
ValueCountFrequency (%)
10000 2
< 0.1%
10002 1
 
< 0.1%
10005 2
< 0.1%
10006 2
< 0.1%
10013 1
 
< 0.1%
10018 3
< 0.1%
10020 3
< 0.1%
10021 1
 
< 0.1%
10029 4
< 0.1%
10041 2
< 0.1%
ValueCountFrequency (%)
18635 3
 
< 0.1%
18630 1
 
< 0.1%
18626 1
 
< 0.1%
18623 6
0.1%
18606 1
 
< 0.1%
18595 3
 
< 0.1%
18593 3
 
< 0.1%
18584 1
 
< 0.1%
18577 8
0.1%
18573 1
 
< 0.1%

WGS84위도
Real number (ℝ)

HIGH CORRELATION 

Distinct7420
Distinct (%)88.5%
Missing20
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean37.548259
Minimum36.903248
Maximum38.230563
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size74.0 KiB
2023-12-11T07:24:28.007309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.903248
5-th percentile37.142687
Q137.275873
median37.553445
Q337.775468
95-th percentile37.97197
Maximum38.230563
Range1.3273155
Interquartile range (IQR)0.49959468

Descriptive statistics

Standard deviation0.28058326
Coefficient of variation (CV)0.0074726038
Kurtosis-1.1065919
Mean37.548259
Median Absolute Deviation (MAD)0.25077697
Skewness-0.007000282
Sum314691.96
Variance0.078726967
MonotonicityNot monotonic
2023-12-11T07:24:28.175852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
38.0937203006 10
 
0.1%
37.7941598866 7
 
0.1%
37.5908224952 6
 
0.1%
38.0803017413 5
 
0.1%
37.327693903 5
 
0.1%
37.5327047109 4
 
< 0.1%
37.7848178714 4
 
< 0.1%
37.59769209 4
 
< 0.1%
37.4359619354 4
 
< 0.1%
37.4358629046 4
 
< 0.1%
Other values (7410) 8328
99.1%
(Missing) 20
 
0.2%
ValueCountFrequency (%)
36.9032479894 1
< 0.1%
36.9087254069 1
< 0.1%
36.9120927477 1
< 0.1%
36.915604861 1
< 0.1%
36.9162708577 1
< 0.1%
36.9178004878 1
< 0.1%
36.9202835686 1
< 0.1%
36.9402626726 1
< 0.1%
36.9434918567 1
< 0.1%
36.9501561633 1
< 0.1%
ValueCountFrequency (%)
38.2305634409 1
< 0.1%
38.2145976156 1
< 0.1%
38.2144854264 1
< 0.1%
38.2124865269 1
< 0.1%
38.2106053092 1
< 0.1%
38.1862048375 1
< 0.1%
38.1855182519 1
< 0.1%
38.184973564 1
< 0.1%
38.1813305573 1
< 0.1%
38.1718579327 1
< 0.1%

WGS84경도
Real number (ℝ)

HIGH CORRELATION 

Distinct7420
Distinct (%)88.5%
Missing20
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean127.1616
Minimum126.39105
Maximum127.79744
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size74.0 KiB
2023-12-11T07:24:28.296553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.39105
5-th percentile126.59742
Q1126.90979
median127.22034
Q3127.44976
95-th percentile127.60462
Maximum127.79744
Range1.4063935
Interquartile range (IQR)0.5399673

Descriptive statistics

Standard deviation0.33352507
Coefficient of variation (CV)0.0026228442
Kurtosis-1.0866027
Mean127.1616
Median Absolute Deviation (MAD)0.25383955
Skewness-0.35050552
Sum1065741.4
Variance0.11123897
MonotonicityNot monotonic
2023-12-11T07:24:28.421823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.3190921499 10
 
0.1%
127.3872699728 7
 
0.1%
127.5864040662 6
 
0.1%
127.320025996 5
 
0.1%
126.8018777724 5
 
0.1%
127.6248082871 4
 
< 0.1%
126.6866581651 4
 
< 0.1%
127.1386630585 4
 
< 0.1%
127.3600811215 4
 
< 0.1%
127.3601830402 4
 
< 0.1%
Other values (7410) 8328
99.1%
(Missing) 20
 
0.2%
ValueCountFrequency (%)
126.3910509784 1
< 0.1%
126.391690975 1
< 0.1%
126.3919915331 1
< 0.1%
126.3924075156 1
< 0.1%
126.4494001465 1
< 0.1%
126.4506324893 1
< 0.1%
126.5277844892 1
< 0.1%
126.5310472614 1
< 0.1%
126.5329698727 1
< 0.1%
126.5380751612 1
< 0.1%
ValueCountFrequency (%)
127.797444475 3
< 0.1%
127.7947934275 1
 
< 0.1%
127.7946897469 1
 
< 0.1%
127.7945659916 1
 
< 0.1%
127.7941692302 2
< 0.1%
127.7914677089 1
 
< 0.1%
127.7912363328 1
 
< 0.1%
127.7899900354 1
 
< 0.1%
127.7896839147 1
 
< 0.1%
127.7880417331 2
< 0.1%

Interactions

2023-12-11T07:24:21.783693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:24:19.513570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:24:19.961792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:24:20.452391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:24:20.901759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:24:21.356884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:24:21.854358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:24:19.577243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:24:20.039433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:24:20.532163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:24:20.964407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:24:21.417919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:24:21.928080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:24:19.666572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:24:20.117621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:24:20.603849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:24:21.030008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:24:21.488929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:24:22.003526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:24:19.750146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:24:20.191588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:24:20.677840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:24:21.103697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:24:21.560974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:24:22.077081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:24:19.816967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:24:20.261590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:24:20.748730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:24:21.167503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:24:21.633976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:24:22.342104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:24:19.895567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:24:20.357822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:24:20.823745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:24:21.265059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:24:21.707673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T07:24:28.512480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명영업상태명건물소유구분명년도양실수(개)위생업태명한실수(개)소재지우편번호WGS84위도WGS84경도
시군명1.0000.5470.1030.4050.1910.6880.0490.9950.9270.923
영업상태명0.5471.0000.1400.3750.1860.6070.0000.3820.2310.415
건물소유구분명0.1030.1401.0000.5320.0000.1650.0510.0000.0000.000
년도0.4050.3750.5321.0000.000NaN0.2080.2310.6090.000
양실수(개)0.1910.1860.0000.0001.0000.5060.0890.1190.0620.000
위생업태명0.6880.6070.165NaN0.5061.0000.3650.5330.4150.467
한실수(개)0.0490.0000.0510.2080.0890.3651.0000.0850.1240.000
소재지우편번호0.9950.3820.0000.2310.1190.5330.0851.0000.9010.914
WGS84위도0.9270.2310.0000.6090.0620.4150.1240.9011.0000.749
WGS84경도0.9230.4150.0000.0000.0000.4670.0000.9140.7491.000
2023-12-11T07:24:28.621431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
건물소유구분명위생업종명위생업태명시군명영업상태명
건물소유구분명1.0001.0000.1760.0860.093
위생업종명1.0001.0001.0001.0001.000
위생업태명0.1761.0001.0000.3690.466
시군명0.0861.0000.3691.0000.263
영업상태명0.0931.0000.4660.2631.000
2023-12-11T07:24:28.715196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
년도양실수(개)한실수(개)소재지우편번호WGS84위도WGS84경도시군명영업상태명건물소유구분명위생업종명위생업태명
년도1.0000.261-0.3210.067-0.048-0.1590.1560.3780.3451.0001.000
양실수(개)0.2611.000-0.3730.208-0.251-0.3620.0800.1290.0001.0000.197
한실수(개)-0.321-0.3731.0000.094-0.1580.0510.0190.0000.0621.0000.205
소재지우편번호0.0670.2080.0941.000-0.925-0.2960.9560.2040.0001.0000.305
WGS84위도-0.048-0.251-0.158-0.9251.0000.4300.6630.1180.0001.0000.224
WGS84경도-0.159-0.3620.051-0.2960.4301.0000.6520.2250.0001.0000.269
시군명0.1560.0800.0190.9560.6630.6521.0000.2630.0861.0000.369
영업상태명0.3780.1290.0000.2040.1180.2250.2631.0000.0931.0000.466
건물소유구분명0.3450.0000.0620.0000.0000.0000.0860.0931.0001.0000.176
위생업종명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
위생업태명1.0000.1970.2050.3050.2240.2690.3690.4660.1761.0001.000

Missing values

2023-12-11T07:24:22.459234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T07:24:22.646201image/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.
2023-12-11T07:24:22.814376image/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

시군명사업장명인허가일자영업상태명폐업일자건물소유구분명년도다중이용업소여부양실수(개)위생업종명위생업태명한실수(개)소재지도로명주소소재지지번주소소재지우편번호WGS84위도WGS84경도
0가평군가평빠지워터플레이20200915영업<NA><NA><NA><NA>6<NA>숙박업 기타0경기도 가평군 가평읍 북한강변로 882, A,B,C동 각1~2층경기도 가평군 가평읍 이화리 36-1 ,A,B,C동 각1~2층1242737.79652127.517947
1가평군스포츠텔1998-01-15영업<NA><NA><NA><NA>5<NA>여관업16경기도 가평군 청평면 경춘로 1436경기도 가평군 청평면 상천리 2771244937.7773127.464468
2가평군폴라리스키즈펜션20180330영업<NA>자가<NA><NA>7<NA>숙박업(생활)0경기도 가평군 북면 백둔로 132경기도 가평군 북면 백둔리 30-11240637.897684127.490207
3가평군더키즈풀빌라20210727영업<NA><NA><NA><NA>10<NA>숙박업(생활)0경기도 가평군 가평읍 경반안로 357-1, 총5개동 1~5동 각1~2층경기도 가평군 가평읍 경반리 583-2 1~5동 1~2층(총5개동)1241537.831137127.470613
4가평군라라키즈풀빌라20210714영업<NA><NA><NA><NA>6<NA>숙박업(생활)0경기도 가평군 상면 임초밤안골로 162-13, A,B동 각1~2층경기도 가평군 상면 임초리 170-4 외2필지(172,174-41) A,B동 각1~2층1244737.75607127.370072
5가평군(주)호명청평산장호텔19820122영업<NA><NA><NA><NA>17<NA>일반호텔17경기도 가평군 청평면 청군로 70경기도 가평군 청평면 하천리 531-11245037.749689127.42138
6가평군아토키즈풀빌라20200522영업<NA>자가<NA><NA>5<NA>숙박업(생활)0경기도 가평군 상면 수목원로150번길 28-9, 1동~2동(각1층~2층),3동(지하1층~2층)경기도 가평군 상면 행현리 398-4 ,1동~2동(각1층~2층),3동(지하1층~2층)1244837.763882127.360215
7가평군포도밭댕댕이2021-04-02영업<NA><NA><NA><NA>5<NA>숙박업(생활)0경기도 가평군 상면 축령로45번길 101, 1~2층경기도 가평군 상면 행현리 334-3 1~2층1244837.767248127.363183
8가평군아베크제이20210927영업<NA><NA><NA><NA>10<NA>숙박업(생활)0경기도 가평군 가평읍 태봉두밀로 173, 175 (각1층~2층)경기도 가평군 가평읍 하색리 758-18 ,758-23 (각1층~2층)1242437.813328127.473949
9가평군꿈속에서20210317영업<NA><NA><NA><NA>2<NA>숙박업(생활)0경기도 가평군 상면 수목원로386번길 10-2, 1-2층경기도 가평군 상면 행현리 663-14 1-2층1244837.748316127.352741
시군명사업장명인허가일자영업상태명폐업일자건물소유구분명년도다중이용업소여부양실수(개)위생업종명위생업태명한실수(개)소재지도로명주소소재지지번주소소재지우편번호WGS84위도WGS84경도
8391화성시우리민박2017-06-01폐업2018-05-14<NA><NA><NA><NA><NA><NA><NA>경기도 화성시 서신면 해안길 240-4경기도 화성시 서신면 제부리 190-681855337.162483126.618602
8392화성시그린민박2006-05-04폐업2022-09-22<NA><NA><NA><NA><NA><NA><NA>경기도 화성시 서신면 해안길 292경기도 화성시 서신면 제부리 190-521855337.167054126.61758
8393화성시해오름민박2009-04-27폐업2021-12-22<NA><NA><NA><NA><NA><NA><NA>경기도 화성시 서신면 전곡항로50번길 11-12경기도 화성시 서신면 전곡리 9991855437.188472126.656076
8394화성시빌라 파우제2017-10-11폐업2018-08-10<NA><NA><NA><NA><NA><NA><NA>경기도 화성시 우정읍 매향웃말길 61-8경기도 화성시 우정읍 매향리 352-31857037.052588126.761681
8395화성시거슨 힐링 캠프2018-07-25폐업2019-05-10<NA><NA><NA><NA><NA><NA><NA>경기도 화성시 정남면 관항길254번길 49경기도 화성시 정남면 관항리 4081852037.184339126.959122
8396화성시더 존 민박2012-04-09폐업2019-08-19<NA><NA><NA><NA><NA><NA><NA>경기도 화성시 서신면 전곡항로14번길 12-35경기도 화성시 서신면 전곡리 9481855437.184125126.6519
8397화성시꽃뜨락2017-12-14폐업2018-05-11<NA><NA><NA><NA><NA><NA><NA>경기도 화성시 서신면 수문개길 96-8경기도 화성시 서신면 궁평리 480-111855637.125171126.68624
8398화성시마지막2006-05-04폐업2018-08-01<NA><NA><NA><NA><NA><NA><NA>경기도 화성시 서신면 궁평항로 1265경기도 화성시 서신면 궁평리 322-11855637.131547126.69457
8399화성시롤링힐스호텔20100114폐업 등20150528<NA>2010<NA>242숙박업(일반)관광호텔0경기도 화성시 남양읍 시청로 290 (활초동)경기도 화성시 남양읍 활초리 4-21번지1827837.190029126.838638
8400화성시이화관광호텔19940923폐업 등20170124<NA>1994<NA>76숙박업(일반)관광호텔0경기도 화성시 우정읍 남양만로 662경기도 화성시 우정읍 이화리 439-1번지1857337.042361126.798045

Duplicate rows

Most frequently occurring

시군명사업장명인허가일자영업상태명폐업일자건물소유구분명년도양실수(개)위생업종명위생업태명한실수(개)소재지도로명주소소재지지번주소소재지우편번호WGS84위도WGS84경도# duplicates
1평택시해달2022-05-19폐업2023-11-23<NA><NA><NA><NA><NA><NA>경기도 평택시 진위면 견산2길 10-3경기도 평택시 진위면 견산리 17-11771437.106473127.080823
0양주시별뜨락2019-12-18폐업2022-09-16<NA><NA><NA><NA><NA><NA>경기도 양주시 장흥면 권율로 95-1경기도 양주시 장흥면 일영리 55-31152037.723401126.947682