Overview

Dataset statistics

Number of variables8
Number of observations3247
Missing cells15885
Missing cells (%)61.2%
Duplicate rows1
Duplicate rows (%)< 0.1%
Total size in memory206.2 KiB
Average record size in memory65.0 B

Variable types

Categorical3
Text3
DateTime1
Numeric1

Dataset

Description진천군에 소재된 숙박업소의 업소명, 도로명 주소, 지번주소, 영업시작일, 업태명, 객실수 등 진천군 숙박업소의 일반현황에 대한 자료입니다.
Author충청북도 진천군
URLhttps://www.data.go.kr/data/15127531/fileData.do

Alerts

Dataset has 1 (< 0.1%) duplicate rowsDuplicates
업태명 is highly overall correlated with 업종명 and 1 other fieldsHigh correlation
기준일자 is highly overall correlated with 객실수 and 2 other fieldsHigh correlation
업종명 is highly overall correlated with 업태명 and 1 other fieldsHigh correlation
객실수 is highly overall correlated with 기준일자High correlation
업종명 is highly imbalanced (90.2%)Imbalance
업태명 is highly imbalanced (90.2%)Imbalance
기준일자 is highly imbalanced (85.0%)Imbalance
업소명 has 3177 (97.8%) missing valuesMissing
소재지(도로명) has 3177 (97.8%) missing valuesMissing
소재지(지번) has 3177 (97.8%) missing valuesMissing
영업자시작일 has 3177 (97.8%) missing valuesMissing
객실수 has 3177 (97.8%) missing valuesMissing

Reproduction

Analysis started2024-04-21 02:33:28.387845
Analysis finished2024-04-21 02:33:31.274376
Duration2.89 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

업종명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size25.5 KiB
<NA>
3177 
숙박업(일반)
 
67
숙박업(생활)
 
3

Length

Max length7
Median length4
Mean length4.0646751
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 (%)
<NA> 3177
97.8%
숙박업(일반) 67
 
2.1%
숙박업(생활) 3
 
0.1%

Length

2024-04-21T11:33:31.355751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T11:33:31.476302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 3177
97.8%
숙박업(일반 67
 
2.1%
숙박업(생활 3
 
0.1%

업소명
Text

MISSING 

Distinct70
Distinct (%)100.0%
Missing3177
Missing (%)97.8%
Memory size25.5 KiB
2024-04-21T11:33:31.728410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length9
Mean length5.4285714
Min length1

Characters and Unicode

Total characters380
Distinct characters136
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique70 ?
Unique (%)100.0%

Sample

1st row문화여관
2nd row홍운장여관
3rd row신화장여관
4th row봉림장 여관
5th row영빈장 여관
ValueCountFrequency (%)
호텔 4
 
4.8%
g7 3
 
3.6%
여관 2
 
2.4%
2s모텔 1
 
1.2%
아일랜드모텔 1
 
1.2%
올림피아 1
 
1.2%
이팝무인텔 1
 
1.2%
스테이 1
 
1.2%
두바이무인텔 1
 
1.2%
주)아름다은 1
 
1.2%
Other values (67) 67
80.7%
2024-04-21T11:33:32.202261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
텔 50
 
13.2%
모 27
 
7.1%
호 15
 
3.9%
14
 
3.7%
스 14
 
3.7%
인 10
 
2.6%
무 9
 
2.4%
아 8
 
2.1%
이 7
 
1.8%
여 7
 
1.8%
Other values (126) 219
57.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 327
86.1%
Uppercase Letter 19
 
5.0%
Space Separator 14
 
3.7%
Open Punctuation 6
 
1.6%
Close Punctuation 6
 
1.6%
Decimal Number 4
 
1.1%
Letter Number 4
 
1.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
텔 50
 
15.3%
모 27
 
8.3%
호 15
 
4.6%
스 14
 
4.3%
인 10
 
3.1%
무 9
 
2.8%
아 8
 
2.4%
이 7
 
2.1%
여 7
 
2.1%
리 6
 
1.8%
Other values (108) 174
53.2%
Uppercase Letter
ValueCountFrequency (%)
L 3
15.8%
G 3
15.8%
E 2
10.5%
T 2
10.5%
O 2
10.5%
H 2
10.5%
U 1
 
5.3%
I 1
 
5.3%
P 1
 
5.3%
S 1
 
5.3%
Decimal Number
ValueCountFrequency (%)
7 3
75.0%
2 1
 
25.0%
Letter Number
ValueCountFrequency (%)
Ⅰ 2
50.0%
Ⅱ 2
50.0%
Space Separator
ValueCountFrequency (%)
14
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6
100.0%
Close Punctuation
ValueCountFrequency (%)
) 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 327
86.1%
Common 30
 
7.9%
Latin 23
 
6.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
텔 50
 
15.3%
모 27
 
8.3%
호 15
 
4.6%
스 14
 
4.3%
인 10
 
3.1%
무 9
 
2.8%
아 8
 
2.4%
이 7
 
2.1%
여 7
 
2.1%
리 6
 
1.8%
Other values (108) 174
53.2%
Latin
ValueCountFrequency (%)
L 3
13.0%
G 3
13.0%
E 2
8.7%
T 2
8.7%
Ⅰ 2
8.7%
Ⅱ 2
8.7%
O 2
8.7%
H 2
8.7%
U 1
 
4.3%
I 1
 
4.3%
Other values (3) 3
13.0%
Common
ValueCountFrequency (%)
14
46.7%
( 6
20.0%
) 6
20.0%
7 3
 
10.0%
2 1
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 327
86.1%
ASCII 49
 
12.9%
Number Forms 4
 
1.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
텔 50
 
15.3%
모 27
 
8.3%
호 15
 
4.6%
스 14
 
4.3%
인 10
 
3.1%
무 9
 
2.8%
아 8
 
2.4%
이 7
 
2.1%
여 7
 
2.1%
리 6
 
1.8%
Other values (108) 174
53.2%
ASCII
ValueCountFrequency (%)
14
28.6%
( 6
12.2%
) 6
12.2%
7 3
 
6.1%
L 3
 
6.1%
G 3
 
6.1%
E 2
 
4.1%
T 2
 
4.1%
O 2
 
4.1%
H 2
 
4.1%
Other values (6) 6
12.2%
Number Forms
ValueCountFrequency (%)
Ⅰ 2
50.0%
Ⅱ 2
50.0%

소재지(도로명)
Text

MISSING 

Distinct68
Distinct (%)97.1%
Missing3177
Missing (%)97.8%
Memory size25.5 KiB
2024-04-21T11:33:32.557153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length31
Median length28
Mean length21.857143
Min length18

Characters and Unicode

Total characters1530
Distinct characters75
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique66 ?
Unique (%)94.3%

Sample

1st row충청북도 진천군 진천읍 중앙동6길 6-3
2nd row충청북도 진천군 진천읍 상산로 53-1
3rd row충청북도 진천군 진천읍 중앙서2길 21-11
4th row충청북도 진천군 이월면 진광로 782
5th row충청북도 진천군 광혜원면 장기길 99
ValueCountFrequency (%)
충청북도 70
19.8%
진천군 70
19.8%
진천읍 25
 
7.1%
광혜원면 16
 
4.5%
덕산읍 11
 
3.1%
진광로 8
 
2.3%
문진로 7
 
2.0%
문백면 6
 
1.7%
초평면 6
 
1.7%
이월면 5
 
1.4%
Other values (104) 130
36.7%
2024-04-21T11:33:33.151861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
284
18.6%
진 111
 
7.3%
천 96
 
6.3%
북 72
 
4.7%
충 70
 
4.6%
군 70
 
4.6%
청 70
 
4.6%
도 70
 
4.6%
1 57
 
3.7%
2 41
 
2.7%
Other values (65) 589
38.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 958
62.6%
Space Separator 284
 
18.6%
Decimal Number 246
 
16.1%
Dash Punctuation 35
 
2.3%
Other Punctuation 3
 
0.2%
Math Symbol 2
 
0.1%
Close Punctuation 1
 
0.1%
Open Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
진 111
 
11.6%
천 96
 
10.0%
북 72
 
7.5%
충 70
 
7.3%
군 70
 
7.3%
청 70
 
7.3%
도 70
 
7.3%
읍 36
 
3.8%
로 36
 
3.8%
길 35
 
3.7%
Other values (49) 292
30.5%
Decimal Number
ValueCountFrequency (%)
1 57
23.2%
2 41
16.7%
3 28
11.4%
5 27
11.0%
9 21
 
8.5%
6 19
 
7.7%
7 17
 
6.9%
4 15
 
6.1%
8 13
 
5.3%
0 8
 
3.3%
Space Separator
ValueCountFrequency (%)
284
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 35
100.0%
Other Punctuation
ValueCountFrequency (%)
, 3
100.0%
Math Symbol
ValueCountFrequency (%)
~ 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 958
62.6%
Common 572
37.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
진 111
 
11.6%
천 96
 
10.0%
북 72
 
7.5%
충 70
 
7.3%
군 70
 
7.3%
청 70
 
7.3%
도 70
 
7.3%
읍 36
 
3.8%
로 36
 
3.8%
길 35
 
3.7%
Other values (49) 292
30.5%
Common
ValueCountFrequency (%)
284
49.7%
1 57
 
10.0%
2 41
 
7.2%
- 35
 
6.1%
3 28
 
4.9%
5 27
 
4.7%
9 21
 
3.7%
6 19
 
3.3%
7 17
 
3.0%
4 15
 
2.6%
Other values (6) 28
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 958
62.6%
ASCII 572
37.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
284
49.7%
1 57
 
10.0%
2 41
 
7.2%
- 35
 
6.1%
3 28
 
4.9%
5 27
 
4.7%
9 21
 
3.7%
6 19
 
3.3%
7 17
 
3.0%
4 15
 
2.6%
Other values (6) 28
 
4.9%
Hangul
ValueCountFrequency (%)
진 111
 
11.6%
천 96
 
10.0%
북 72
 
7.5%
충 70
 
7.3%
군 70
 
7.3%
청 70
 
7.3%
도 70
 
7.3%
읍 36
 
3.8%
로 36
 
3.8%
길 35
 
3.7%
Other values (49) 292
30.5%

소재지(지번)
Text

MISSING 

Distinct68
Distinct (%)97.1%
Missing3177
Missing (%)97.8%
Memory size25.5 KiB
2024-04-21T11:33:33.515455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length31
Median length30
Mean length23
Min length17

Characters and Unicode

Total characters1610
Distinct characters63
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique66 ?
Unique (%)94.3%

Sample

1st row충청북도 진천군 진천읍 읍내리 144-8
2nd row충청북도 진천군 진천읍 읍내리 425-1
3rd row충청북도 진천군 진천읍 읍내리 88
4th row충청북도 진천군 이월면 송림리 689
5th row충청북도 진천군 광혜원면 광혜원리 262-3
ValueCountFrequency (%)
충청북도 70
19.9%
진천군 70
19.9%
진천읍 25
 
7.1%
읍내리 16
 
4.5%
광혜원면 16
 
4.5%
덕산읍 11
 
3.1%
광혜원리 11
 
3.1%
초평면 6
 
1.7%
문백면 6
 
1.7%
도하리 5
 
1.4%
Other values (92) 116
33.0%
2024-04-21T11:33:33.926959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
352
21.9%
진 99
 
6.1%
천 95
 
5.9%
도 75
 
4.7%
충 70
 
4.3%
청 70
 
4.3%
북 70
 
4.3%
군 70
 
4.3%
리 67
 
4.2%
1 55
 
3.4%
Other values (53) 587
36.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 941
58.4%
Space Separator 352
 
21.9%
Decimal Number 266
 
16.5%
Dash Punctuation 51
 
3.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
진 99
10.5%
천 95
 
10.1%
도 75
 
8.0%
충 70
 
7.4%
청 70
 
7.4%
북 70
 
7.4%
군 70
 
7.4%
리 67
 
7.1%
읍 52
 
5.5%
면 34
 
3.6%
Other values (41) 239
25.4%
Decimal Number
ValueCountFrequency (%)
1 55
20.7%
2 43
16.2%
3 26
9.8%
9 24
9.0%
5 24
9.0%
6 22
 
8.3%
4 22
 
8.3%
8 21
 
7.9%
0 16
 
6.0%
7 13
 
4.9%
Space Separator
ValueCountFrequency (%)
352
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 51
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 941
58.4%
Common 669
41.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
진 99
10.5%
천 95
 
10.1%
도 75
 
8.0%
충 70
 
7.4%
청 70
 
7.4%
북 70
 
7.4%
군 70
 
7.4%
리 67
 
7.1%
읍 52
 
5.5%
면 34
 
3.6%
Other values (41) 239
25.4%
Common
ValueCountFrequency (%)
352
52.6%
1 55
 
8.2%
- 51
 
7.6%
2 43
 
6.4%
3 26
 
3.9%
9 24
 
3.6%
5 24
 
3.6%
6 22
 
3.3%
4 22
 
3.3%
8 21
 
3.1%
Other values (2) 29
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 941
58.4%
ASCII 669
41.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
352
52.6%
1 55
 
8.2%
- 51
 
7.6%
2 43
 
6.4%
3 26
 
3.9%
9 24
 
3.6%
5 24
 
3.6%
6 22
 
3.3%
4 22
 
3.3%
8 21
 
3.1%
Other values (2) 29
 
4.3%
Hangul
ValueCountFrequency (%)
진 99
10.5%
천 95
 
10.1%
도 75
 
8.0%
충 70
 
7.4%
청 70
 
7.4%
북 70
 
7.4%
군 70
 
7.4%
리 67
 
7.1%
읍 52
 
5.5%
면 34
 
3.6%
Other values (41) 239
25.4%

영업자시작일
Date

MISSING 

Distinct67
Distinct (%)95.7%
Missing3177
Missing (%)97.8%
Memory size25.5 KiB
Minimum1985-12-06 00:00:00
Maximum2024-03-18 00:00:00
2024-04-21T11:33:34.077694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:33:34.209863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

업태명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size25.5 KiB
<NA>
3177 
여관업
 
67
숙박업(생활)
 
3

Length

Max length7
Median length4
Mean length3.9821374
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 3177
97.8%
여관업 67
 
2.1%
숙박업(생활) 3
 
0.1%

Length

2024-04-21T11:33:34.346659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T11:33:34.454114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 3177
97.8%
여관업 67
 
2.1%
숙박업(생활 3
 
0.1%

기준일자
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size25.5 KiB
<NA>
3177 
2024-04-03
 
70

Length

Max length10
Median length4
Mean length4.1293502
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2024-04-03
2nd row2024-04-03
3rd row2024-04-03
4th row2024-04-03
5th row2024-04-03

Common Values

ValueCountFrequency (%)
<NA> 3177
97.8%
2024-04-03 70
 
2.2%

Length

2024-04-21T11:33:34.557162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T11:33:34.647711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 3177
97.8%
2024-04-03 70
 
2.2%

객실수
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct37
Distinct (%)52.9%
Missing3177
Missing (%)97.8%
Infinite0
Infinite (%)0.0%
Mean25.814286
Minimum6
Maximum61
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size28.7 KiB
2024-04-21T11:33:34.748462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile10.45
Q117
median24.5
Q334.5
95-th percentile44.1
Maximum61
Range55
Interquartile range (IQR)17.5

Descriptive statistics

Standard deviation11.611205
Coefficient of variation (CV)0.44979765
Kurtosis0.063663236
Mean25.814286
Median Absolute Deviation (MAD)8
Skewness0.55516408
Sum1807
Variance134.82008
MonotonicityNot monotonic
2024-04-21T11:33:34.862099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
29 4
 
0.1%
12 4
 
0.1%
35 4
 
0.1%
30 4
 
0.1%
18 4
 
0.1%
20 3
 
0.1%
27 3
 
0.1%
17 3
 
0.1%
37 3
 
0.1%
24 2
 
0.1%
Other values (27) 36
 
1.1%
(Missing) 3177
97.8%
ValueCountFrequency (%)
6 1
 
< 0.1%
7 1
 
< 0.1%
9 1
 
< 0.1%
10 1
 
< 0.1%
11 2
0.1%
12 4
0.1%
13 2
0.1%
14 1
 
< 0.1%
15 1
 
< 0.1%
16 2
0.1%
ValueCountFrequency (%)
61 1
 
< 0.1%
52 1
 
< 0.1%
49 1
 
< 0.1%
45 1
 
< 0.1%
43 2
0.1%
42 1
 
< 0.1%
40 2
0.1%
38 1
 
< 0.1%
37 3
0.1%
36 1
 
< 0.1%

Interactions

2024-04-21T11:33:30.683260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-21T11:33:35.006632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업종명업소명소재지(도로명)소재지(지번)영업자시작일업태명객실수
업종명1.0001.0001.0001.0001.0000.9620.000
업소명1.0001.0001.0001.0001.0001.0001.000
소재지(도로명)1.0001.0001.0000.9941.0001.0000.975
소재지(지번)1.0001.0000.9941.0000.9971.0000.988
영업자시작일1.0001.0001.0000.9971.0001.0000.984
업태명0.9621.0001.0001.0001.0001.0000.000
객실수0.0001.0000.9750.9880.9840.0001.000
2024-04-21T11:33:35.156747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업태명기준일자업종명
업태명1.0001.0000.823
기준일자1.0001.0001.000
업종명0.8231.0001.000
2024-04-21T11:33:35.243158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
객실수업종명업태명기준일자
객실수1.0000.0000.0001.000
업종명0.0001.0000.8231.000
업태명0.0000.8231.0001.000
기준일자1.0001.0001.0001.000

Missing values

2024-04-21T11:33:30.873928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-21T11:33:31.018337image/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.
2024-04-21T11:33:31.164291image/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

업종명업소명소재지(도로명)소재지(지번)영업자시작일업태명기준일자객실수
0숙박업(일반)문화여관충청북도 진천군 진천읍 중앙동6길 6-3충청북도 진천군 진천읍 읍내리 144-81987-08-03여관업2024-04-039
1숙박업(일반)홍운장여관충청북도 진천군 진천읍 상산로 53-1충청북도 진천군 진천읍 읍내리 425-12019-10-28여관업2024-04-0313
2숙박업(일반)신화장여관충청북도 진천군 진천읍 중앙서2길 21-11충청북도 진천군 진천읍 읍내리 881985-12-06여관업2024-04-0323
3숙박업(일반)봉림장 여관충청북도 진천군 이월면 진광로 782충청북도 진천군 이월면 송림리 6892020-09-28여관업2024-04-0310
4숙박업(일반)영빈장 여관충청북도 진천군 광혜원면 장기길 99충청북도 진천군 광혜원면 광혜원리 262-32009-12-18여관업2024-04-0317
5숙박업(일반)중앙파크충청북도 진천군 덕산읍 용몽3길 42충청북도 진천군 덕산읍 용몽리 589-61988-04-29여관업2024-04-0321
6숙박업(일반)그랜드모텔충청북도 진천군 진천읍 중앙서로 28-1충청북도 진천군 진천읍 읍내리 2842011-01-12여관업2024-04-0324
7숙박업(일반)갤럭시모텔충청북도 진천군 이월면 진광로 778충청북도 진천군 이월면 송림리 7022007-03-23여관업2024-04-0320
8숙박업(일반)큐모텔충청북도 진천군 진천읍 중앙동2길 9충청북도 진천군 진천읍 읍내리 385-22008-03-26여관업2024-04-0329
9숙박업(일반)이룸스테이충청북도 진천군 진천읍 백사천길 59충청북도 진천군 진천읍 읍내리 171-62022-08-10여관업2024-04-0320
업종명업소명소재지(도로명)소재지(지번)영업자시작일업태명기준일자객실수
3237<NA><NA><NA><NA><NA><NA><NA><NA>
3238<NA><NA><NA><NA><NA><NA><NA><NA>
3239<NA><NA><NA><NA><NA><NA><NA><NA>
3240<NA><NA><NA><NA><NA><NA><NA><NA>
3241<NA><NA><NA><NA><NA><NA><NA><NA>
3242<NA><NA><NA><NA><NA><NA><NA><NA>
3243<NA><NA><NA><NA><NA><NA><NA><NA>
3244<NA><NA><NA><NA><NA><NA><NA><NA>
3245<NA><NA><NA><NA><NA><NA><NA><NA>
3246<NA><NA><NA><NA><NA><NA><NA><NA>

Duplicate rows

Most frequently occurring

업종명업소명소재지(도로명)소재지(지번)영업자시작일업태명기준일자객실수# duplicates
0<NA><NA><NA><NA><NA><NA><NA><NA>3177