Overview

Dataset statistics

Number of variables9
Number of observations200
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory15.6 KiB
Average record size in memory79.7 B

Variable types

Numeric6
Categorical2
Text1

Dataset

Description부산광역시 빅데이터플랫폼에 있는 국세청과 관련된 자료로 2014년에서 2017년사이 100대업소에 대한 순위 현황에 대한 항목을 제공하고 있습니다.
Author부산광역시
URLhttps://www.data.go.kr/data/15063703/fileData.do

Alerts

변경년도 has constant value ""Constant
코드 is highly overall correlated with 랭킹 and 2 other fieldsHigh correlation
랭킹 is highly overall correlated with 코드 and 2 other fieldsHigh correlation
변경랭킹 is highly overall correlated with 코드 and 2 other fieldsHigh correlation
증감율 is highly overall correlated with 코드 and 2 other fieldsHigh correlation
코드 has unique valuesUnique
변경랭킹 has unique valuesUnique

Reproduction

Analysis started2023-12-12 19:42:34.397634
Analysis finished2023-12-12 19:42:38.611342
Duration4.21 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

코드
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct200
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100.5
Minimum1
Maximum200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-13T04:42:38.694214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile10.95
Q150.75
median100.5
Q3150.25
95-th percentile190.05
Maximum200
Range199
Interquartile range (IQR)99.5

Descriptive statistics

Standard deviation57.879185
Coefficient of variation (CV)0.57591228
Kurtosis-1.2
Mean100.5
Median Absolute Deviation (MAD)50
Skewness0
Sum20100
Variance3350
MonotonicityStrictly increasing
2023-12-13T04:42:38.858634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.5%
139 1
 
0.5%
129 1
 
0.5%
130 1
 
0.5%
131 1
 
0.5%
132 1
 
0.5%
133 1
 
0.5%
134 1
 
0.5%
135 1
 
0.5%
136 1
 
0.5%
Other values (190) 190
95.0%
ValueCountFrequency (%)
1 1
0.5%
2 1
0.5%
3 1
0.5%
4 1
0.5%
5 1
0.5%
6 1
0.5%
7 1
0.5%
8 1
0.5%
9 1
0.5%
10 1
0.5%
ValueCountFrequency (%)
200 1
0.5%
199 1
0.5%
198 1
0.5%
197 1
0.5%
196 1
0.5%
195 1
0.5%
194 1
0.5%
193 1
0.5%
192 1
0.5%
191 1
0.5%

랭킹
Real number (ℝ)

HIGH CORRELATION 

Distinct100
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.5
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-13T04:42:39.011676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5.95
Q125.75
median50.5
Q375.25
95-th percentile95.05
Maximum100
Range99
Interquartile range (IQR)49.5

Descriptive statistics

Standard deviation28.938507
Coefficient of variation (CV)0.57303974
Kurtosis-1.2001846
Mean50.5
Median Absolute Deviation (MAD)25
Skewness0
Sum10100
Variance837.43719
MonotonicityIncreasing
2023-12-13T04:42:39.205102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 2
 
1.0%
65 2
 
1.0%
75 2
 
1.0%
74 2
 
1.0%
73 2
 
1.0%
72 2
 
1.0%
71 2
 
1.0%
70 2
 
1.0%
69 2
 
1.0%
68 2
 
1.0%
Other values (90) 180
90.0%
ValueCountFrequency (%)
1 2
1.0%
2 2
1.0%
3 2
1.0%
4 2
1.0%
5 2
1.0%
6 2
1.0%
7 2
1.0%
8 2
1.0%
9 2
1.0%
10 2
1.0%
ValueCountFrequency (%)
100 2
1.0%
99 2
1.0%
98 2
1.0%
97 2
1.0%
96 2
1.0%
95 2
1.0%
94 2
1.0%
93 2
1.0%
92 2
1.0%
91 2
1.0%

구분
Real number (ℝ)

Distinct100
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.5
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-13T04:42:39.367604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5.95
Q125.75
median50.5
Q375.25
95-th percentile95.05
Maximum100
Range99
Interquartile range (IQR)49.5

Descriptive statistics

Standard deviation28.938507
Coefficient of variation (CV)0.57303974
Kurtosis-1.2001846
Mean50.5
Median Absolute Deviation (MAD)25
Skewness0
Sum10100
Variance837.43719
MonotonicityNot monotonic
2023-12-13T04:42:39.512714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
48 2
 
1.0%
26 2
 
1.0%
3 2
 
1.0%
13 2
 
1.0%
27 2
 
1.0%
7 2
 
1.0%
51 2
 
1.0%
43 2
 
1.0%
88 2
 
1.0%
76 2
 
1.0%
Other values (90) 180
90.0%
ValueCountFrequency (%)
1 2
1.0%
2 2
1.0%
3 2
1.0%
4 2
1.0%
5 2
1.0%
6 2
1.0%
7 2
1.0%
8 2
1.0%
9 2
1.0%
10 2
1.0%
ValueCountFrequency (%)
100 2
1.0%
99 2
1.0%
98 2
1.0%
97 2
1.0%
96 2
1.0%
95 2
1.0%
94 2
1.0%
93 2
1.0%
92 2
1.0%
91 2
1.0%

수치
Real number (ℝ)

Distinct198
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21022.355
Minimum441
Maximum375152
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-13T04:42:39.692393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum441
5-th percentile1266.1
Q14591.25
median10591.5
Q321372.75
95-th percentile63070.6
Maximum375152
Range374711
Interquartile range (IQR)16781.5

Descriptive statistics

Standard deviation41592.565
Coefficient of variation (CV)1.9784922
Kurtosis45.497121
Mean21022.355
Median Absolute Deviation (MAD)7584
Skewness6.1427326
Sum4204471
Variance1.7299415 × 109
MonotonicityNot monotonic
2023-12-13T04:42:40.199787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1603 2
 
1.0%
1524 2
 
1.0%
2132 1
 
0.5%
8234 1
 
0.5%
21191 1
 
0.5%
21918 1
 
0.5%
35301 1
 
0.5%
36405 1
 
0.5%
19480 1
 
0.5%
19752 1
 
0.5%
Other values (188) 188
94.0%
ValueCountFrequency (%)
441 1
0.5%
712 1
0.5%
749 1
0.5%
793 1
0.5%
932 1
0.5%
940 1
0.5%
946 1
0.5%
1052 1
0.5%
1057 1
0.5%
1192 1
0.5%
ValueCountFrequency (%)
375152 1
0.5%
346352 1
0.5%
187809 1
0.5%
128342 1
0.5%
115717 1
0.5%
94908 1
0.5%
92126 1
0.5%
91077 1
0.5%
89892 1
0.5%
83013 1
0.5%

년도
Categorical

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2014
100 
2017
100 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2014
2nd row2017
3rd row2014
4th row2017
5th row2014

Common Values

ValueCountFrequency (%)
2014 100
50.0%
2017 100
50.0%

Length

2023-12-13T04:42:40.333039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:42:40.433889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2014 100
50.0%
2017 100
50.0%

변경년도
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2014-2017
200 

Length

Max length9
Median length9
Mean length9
Min length9

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2014-2017
2nd row2014-2017
3rd row2014-2017
4th row2014-2017
5th row2014-2017

Common Values

ValueCountFrequency (%)
2014-2017 200
100.0%

Length

2023-12-13T04:42:40.533240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:42:40.622362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2014-2017 200
100.0%

변경랭킹
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct200
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100.5
Minimum1
Maximum200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-13T04:42:40.733963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile10.95
Q150.75
median100.5
Q3150.25
95-th percentile190.05
Maximum200
Range199
Interquartile range (IQR)99.5

Descriptive statistics

Standard deviation57.879185
Coefficient of variation (CV)0.57591228
Kurtosis-1.2
Mean100.5
Median Absolute Deviation (MAD)50
Skewness0
Sum20100
Variance3350
MonotonicityStrictly increasing
2023-12-13T04:42:40.900048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.5%
139 1
 
0.5%
129 1
 
0.5%
130 1
 
0.5%
131 1
 
0.5%
132 1
 
0.5%
133 1
 
0.5%
134 1
 
0.5%
135 1
 
0.5%
136 1
 
0.5%
Other values (190) 190
95.0%
ValueCountFrequency (%)
1 1
0.5%
2 1
0.5%
3 1
0.5%
4 1
0.5%
5 1
0.5%
6 1
0.5%
7 1
0.5%
8 1
0.5%
9 1
0.5%
10 1
0.5%
ValueCountFrequency (%)
200 1
0.5%
199 1
0.5%
198 1
0.5%
197 1
0.5%
196 1
0.5%
195 1
0.5%
194 1
0.5%
193 1
0.5%
192 1
0.5%
191 1
0.5%

구분설명
Text

Distinct100
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-12-13T04:42:41.145502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length7
Mean length4.94
Min length2

Characters and Unicode

Total characters988
Distinct characters175
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row스포츠시설운영업
2nd row스포츠시설운영업
3rd row펜션ㆍ게스트하우스
4th row펜션ㆍ게스트하우스
5th row애완용품점
ValueCountFrequency (%)
스포츠시설운영업 2
 
1.0%
독서실 2
 
1.0%
곡물가게 2
 
1.0%
노래방 2
 
1.0%
간판광고물업 2
 
1.0%
시계ㆍ귀금속점 2
 
1.0%
서점 2
 
1.0%
컴퓨터판매점 2
 
1.0%
정육점 2
 
1.0%
화장품가게 2
 
1.0%
Other values (90) 180
90.0%
2023-12-13T04:42:41.546551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
점 58
 
5.9%
가 42
 
4.3%
원 36
 
3.6%
게 36
 
3.6%
의 26
 
2.6%
과 24
 
2.4%
사 22
 
2.2%
식 22
 
2.2%
전 18
 
1.8%
ㆍ 18
 
1.8%
Other values (165) 686
69.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 978
99.0%
Uppercase Letter 6
 
0.6%
Lowercase Letter 4
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
점 58
 
5.9%
가 42
 
4.3%
원 36
 
3.7%
게 36
 
3.7%
의 26
 
2.7%
과 24
 
2.5%
사 22
 
2.2%
식 22
 
2.2%
전 18
 
1.8%
ㆍ 18
 
1.8%
Other values (160) 676
69.1%
Uppercase Letter
ValueCountFrequency (%)
G 2
33.3%
P 2
33.3%
L 2
33.3%
Lowercase Letter
ValueCountFrequency (%)
c 2
50.0%
p 2
50.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 978
99.0%
Latin 10
 
1.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
점 58
 
5.9%
가 42
 
4.3%
원 36
 
3.7%
게 36
 
3.7%
의 26
 
2.7%
과 24
 
2.5%
사 22
 
2.2%
식 22
 
2.2%
전 18
 
1.8%
ㆍ 18
 
1.8%
Other values (160) 676
69.1%
Latin
ValueCountFrequency (%)
c 2
20.0%
p 2
20.0%
G 2
20.0%
P 2
20.0%
L 2
20.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 960
97.2%
Compat Jamo 18
 
1.8%
ASCII 10
 
1.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
점 58
 
6.0%
가 42
 
4.4%
원 36
 
3.8%
게 36
 
3.8%
의 26
 
2.7%
과 24
 
2.5%
사 22
 
2.3%
식 22
 
2.3%
전 18
 
1.9%
스 16
 
1.7%
Other values (159) 660
68.8%
Compat Jamo
ValueCountFrequency (%)
ㆍ 18
100.0%
ASCII
ValueCountFrequency (%)
c 2
20.0%
p 2
20.0%
G 2
20.0%
P 2
20.0%
L 2
20.0%

증감율
Real number (ℝ)

HIGH CORRELATION 

Distinct53
Distinct (%)26.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean93.84
Minimum75
Maximum240
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-13T04:42:41.679177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum75
5-th percentile75
Q175
median75
Q3108
95-th percentile139.1
Maximum240
Range165
Interquartile range (IQR)33

Descriptive statistics

Standard deviation25.315386
Coefficient of variation (CV)0.2697718
Kurtosis6.2262716
Mean93.84
Median Absolute Deviation (MAD)0
Skewness1.9720784
Sum18768
Variance640.86874
MonotonicityDecreasing
2023-12-13T04:42:41.821071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
75 101
50.5%
106 7
 
3.5%
109 6
 
3.0%
101 5
 
2.5%
108 5
 
2.5%
117 4
 
2.0%
98 4
 
2.0%
95 3
 
1.5%
114 3
 
1.5%
119 3
 
1.5%
Other values (43) 59
29.5%
ValueCountFrequency (%)
75 101
50.5%
76 1
 
0.5%
80 1
 
0.5%
84 1
 
0.5%
87 1
 
0.5%
88 2
 
1.0%
89 1
 
0.5%
90 1
 
0.5%
91 1
 
0.5%
93 1
 
0.5%
ValueCountFrequency (%)
240 1
0.5%
189 1
0.5%
180 1
0.5%
173 1
0.5%
161 1
0.5%
159 1
0.5%
149 1
0.5%
146 1
0.5%
145 1
0.5%
141 1
0.5%

Interactions

2023-12-13T04:42:37.653461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:42:34.748046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:42:35.307861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:42:35.845875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:42:36.389729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:42:36.959677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:42:37.772490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:42:34.842666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:42:35.407392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:42:35.941743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:42:36.499238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:42:37.074873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:42:37.913990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:42:34.943115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:42:35.503907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:42:36.035383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:42:36.594016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:42:37.169615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:42:38.027550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:42:35.028476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:42:35.577553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:42:36.115620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:42:36.682707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:42:37.276239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:42:38.139009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:42:35.120446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:42:35.663097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:42:36.202840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:42:36.769013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:42:37.440619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:42:38.249762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:42:35.212634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:42:35.751133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:42:36.297395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:42:36.873164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:42:37.554949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T04:42:41.920064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
코드랭킹구분수치년도변경랭킹구분설명증감율
코드1.0001.0000.5860.2320.0001.0001.0000.797
랭킹1.0001.0000.5860.2320.0001.0001.0000.797
구분0.5860.5861.0000.2840.0000.5861.0000.266
수치0.2320.2320.2841.0000.0000.2320.9330.256
년도0.0000.0000.0000.0001.0000.0000.0000.000
변경랭킹1.0001.0000.5860.2320.0001.0001.0000.797
구분설명1.0001.0001.0000.9330.0001.0001.0000.965
증감율0.7970.7970.2660.2560.0000.7970.9651.000
2023-12-13T04:42:42.038426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
코드랭킹구분수치변경랭킹증감율년도
코드1.0001.000-0.0980.0841.000-0.9330.000
랭킹1.0001.000-0.0980.0841.000-0.9330.000
구분-0.098-0.0981.0000.177-0.0980.0400.000
수치0.0840.0840.1771.0000.084-0.0970.000
변경랭킹1.0001.000-0.0980.0841.000-0.9330.000
증감율-0.933-0.9330.040-0.097-0.9331.0000.000
년도0.0000.0000.0000.0000.0000.0001.000

Missing values

2023-12-13T04:42:38.381593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T04:42:38.553108image/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.

Sample

코드랭킹구분수치년도변경년도변경랭킹구분설명증감율
01148213220142014-20171스포츠시설운영업240
12148512320172014-20172스포츠시설운영업189
23291470620142014-20173펜션ㆍ게스트하우스180
34291890020172014-20174펜션ㆍ게스트하우스173
45360374020142014-20175애완용품점161
56360673920172014-20176애완용품점159
674872515120142014-20177커피음료점149
784874345720172014-20178커피음료점146
8951444120142014-20179공인노무사145
91051471220172014-201710공인노무사141
코드랭킹구분수치년도변경년도변경랭킹구분설명증감율
1901919654836420142014-2017191신발가게75
1911929654729820172014-2017192신발가게75
1921939761984920142014-2017193간이주점75
1931949761673320172014-2017194간이주점75
19419598281917820142014-2017195담배가게75
19519698281536620172014-2017196담배가게75
1961979957341920142014-2017197실외골프연습장75
1971989957259420172014-2017198실외골프연습장75
198199100193501120142014-2017199구내식당75
199200100192620220172014-2017200구내식당75