Overview

Dataset statistics

Number of variables13
Number of observations100
Missing cells105
Missing cells (%)8.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory10.7 KiB
Average record size in memory109.3 B

Variable types

Categorical2
Text6
Numeric4
DateTime1

Alerts

base_ymd has constant value ""Constant
city_do_cd is highly overall correlated with city_gn_gu_cd and 2 other fieldsHigh correlation
city_gn_gu_cd is highly overall correlated with city_do_cd and 2 other fieldsHigh correlation
ypos_la is highly overall correlated with city_do_cd and 2 other fieldsHigh correlation
area_nm is highly overall correlated with city_do_cd and 2 other fieldsHigh correlation
homepage_url has 12 (12.0%) missing valuesMissing
tel_no has 13 (13.0%) missing valuesMissing
menu_pc has 4 (4.0%) missing valuesMissing
sales_tm has 72 (72.0%) missing valuesMissing
entrp_nm has unique valuesUnique
load_addr has unique valuesUnique

Reproduction

Analysis started2023-12-10 10:11:09.275496
Analysis finished2023-12-10 10:11:14.915256
Duration5.64 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

se_nm
Categorical

Distinct4
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
템플스테이
60 
궁중음식점
28 
사찰음식점
9 
전통차
 
3

Length

Max length5
Median length5
Mean length4.94
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row템플스테이
2nd row전통차
3rd row템플스테이
4th row궁중음식점
5th row템플스테이

Common Values

ValueCountFrequency (%)
템플스테이 60
60.0%
궁중음식점 28
28.0%
사찰음식점 9
 
9.0%
전통차 3
 
3.0%

Length

2023-12-10T19:11:15.036581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:11:15.216651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
템플스테이 60
60.0%
궁중음식점 28
28.0%
사찰음식점 9
 
9.0%
전통차 3
 
3.0%

entrp_nm
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:11:15.708693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length3
Mean length4.06
Min length2

Characters and Unicode

Total characters406
Distinct characters161
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

Unique100 ?
Unique (%)100.0%

Sample

1st row영평사
2nd row다인 전통찻집
3rd row선운사
4th row단원 한정식
5th row건봉사
ValueCountFrequency (%)
한정식 4
 
3.5%
향적원 2
 
1.8%
영평사 1
 
0.9%
다인 1
 
0.9%
흥국사(고양 1
 
0.9%
백담사 1
 
0.9%
칠량 1
 
0.9%
법주사 1
 
0.9%
연곡사 1
 
0.9%
장미식당 1
 
0.9%
Other values (99) 99
87.6%
2023-12-10T19:11:16.554394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
사 57
 
14.0%
정 14
 
3.4%
13
 
3.2%
원 11
 
2.7%
) 9
 
2.2%
( 9
 
2.2%
한 9
 
2.2%
식 7
 
1.7%
산 7
 
1.7%
국 6
 
1.5%
Other values (151) 264
65.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 375
92.4%
Space Separator 13
 
3.2%
Close Punctuation 9
 
2.2%
Open Punctuation 9
 
2.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
사 57
 
15.2%
정 14
 
3.7%
원 11
 
2.9%
한 9
 
2.4%
식 7
 
1.9%
산 7
 
1.9%
국 6
 
1.6%
연 6
 
1.6%
화 6
 
1.6%
수 6
 
1.6%
Other values (148) 246
65.6%
Space Separator
ValueCountFrequency (%)
13
100.0%
Close Punctuation
ValueCountFrequency (%)
) 9
100.0%
Open Punctuation
ValueCountFrequency (%)
( 9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 375
92.4%
Common 31
 
7.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
사 57
 
15.2%
정 14
 
3.7%
원 11
 
2.9%
한 9
 
2.4%
식 7
 
1.9%
산 7
 
1.9%
국 6
 
1.6%
연 6
 
1.6%
화 6
 
1.6%
수 6
 
1.6%
Other values (148) 246
65.6%
Common
ValueCountFrequency (%)
13
41.9%
) 9
29.0%
( 9
29.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 375
92.4%
ASCII 31
 
7.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
사 57
 
15.2%
정 14
 
3.7%
원 11
 
2.9%
한 9
 
2.4%
식 7
 
1.9%
산 7
 
1.9%
국 6
 
1.6%
연 6
 
1.6%
화 6
 
1.6%
수 6
 
1.6%
Other values (148) 246
65.6%
ASCII
ValueCountFrequency (%)
13
41.9%
) 9
29.0%
( 9
29.0%

load_addr
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:11:17.197284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length39
Median length28
Mean length22.47
Min length16

Characters and Unicode

Total characters2247
Distinct characters201
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique100 ?
Unique (%)100.0%

Sample

1st row세종특별자치시 장군면 영평사길 124
2nd row전라북도 고창군 고창읍 월곡로 51
3rd row전라북도 고창군 아산면 선운사로 250(선운사)
4th row경기도 안산시 단원구 광덕대로 251
5th row강원도 고성군 거진읍 건봉사로 723
ValueCountFrequency (%)
서울특별시 19
 
4.0%
경기도 14
 
2.9%
경상북도 11
 
2.3%
전라남도 10
 
2.1%
전라북도 8
 
1.7%
강남구 7
 
1.5%
강원도 7
 
1.5%
경주시 7
 
1.5%
충청북도 6
 
1.3%
경상남도 6
 
1.3%
Other values (329) 381
80.0%
2023-12-10T19:11:18.227839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
377
 
16.8%
1 76
 
3.4%
도 75
 
3.3%
로 74
 
3.3%
시 74
 
3.3%
사 50
 
2.2%
2 48
 
2.1%
구 46
 
2.0%
길 45
 
2.0%
3 43
 
1.9%
Other values (191) 1339
59.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1419
63.2%
Space Separator 377
 
16.8%
Decimal Number 354
 
15.8%
Open Punctuation 36
 
1.6%
Close Punctuation 36
 
1.6%
Dash Punctuation 23
 
1.0%
Uppercase Letter 1
 
< 0.1%
Other Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 75
 
5.3%
로 74
 
5.2%
시 74
 
5.2%
사 50
 
3.5%
구 46
 
3.2%
길 45
 
3.2%
면 43
 
3.0%
경 41
 
2.9%
남 38
 
2.7%
북 37
 
2.6%
Other values (175) 896
63.1%
Decimal Number
ValueCountFrequency (%)
1 76
21.5%
2 48
13.6%
3 43
12.1%
5 35
9.9%
0 32
9.0%
4 31
8.8%
6 31
8.8%
8 24
 
6.8%
9 18
 
5.1%
7 16
 
4.5%
Space Separator
ValueCountFrequency (%)
377
100.0%
Open Punctuation
ValueCountFrequency (%)
( 36
100.0%
Close Punctuation
ValueCountFrequency (%)
) 36
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 23
100.0%
Uppercase Letter
ValueCountFrequency (%)
E 1
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1419
63.2%
Common 827
36.8%
Latin 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 75
 
5.3%
로 74
 
5.2%
시 74
 
5.2%
사 50
 
3.5%
구 46
 
3.2%
길 45
 
3.2%
면 43
 
3.0%
경 41
 
2.9%
남 38
 
2.7%
북 37
 
2.6%
Other values (175) 896
63.1%
Common
ValueCountFrequency (%)
377
45.6%
1 76
 
9.2%
2 48
 
5.8%
3 43
 
5.2%
( 36
 
4.4%
) 36
 
4.4%
5 35
 
4.2%
0 32
 
3.9%
4 31
 
3.7%
6 31
 
3.7%
Other values (5) 82
 
9.9%
Latin
ValueCountFrequency (%)
E 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1419
63.2%
ASCII 828
36.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
377
45.5%
1 76
 
9.2%
2 48
 
5.8%
3 43
 
5.2%
( 36
 
4.3%
) 36
 
4.3%
5 35
 
4.2%
0 32
 
3.9%
4 31
 
3.7%
6 31
 
3.7%
Other values (6) 83
 
10.0%
Hangul
ValueCountFrequency (%)
도 75
 
5.3%
로 74
 
5.2%
시 74
 
5.2%
사 50
 
3.5%
구 46
 
3.2%
길 45
 
3.2%
면 43
 
3.0%
경 41
 
2.9%
남 38
 
2.7%
북 37
 
2.6%
Other values (175) 896
63.1%

city_do_cd
Real number (ℝ)

HIGH CORRELATION 

Distinct14
Distinct (%)14.1%
Missing1
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean36.363636
Minimum11
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:11:18.573979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile11
Q127
median42
Q346
95-th percentile48
Maximum50
Range39
Interquartile range (IQR)19

Descriptive statistics

Standard deviation13.648245
Coefficient of variation (CV)0.37532673
Kurtosis-0.44671578
Mean36.363636
Median Absolute Deviation (MAD)4
Skewness-1.0957872
Sum3600
Variance186.27458
MonotonicityNot monotonic
2023-12-10T19:11:18.850163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
11 19
19.0%
41 15
15.0%
47 11
11.0%
46 10
10.0%
45 8
8.0%
42 7
 
7.0%
48 6
 
6.0%
43 6
 
6.0%
26 5
 
5.0%
44 4
 
4.0%
Other values (4) 8
8.0%
ValueCountFrequency (%)
11 19
19.0%
26 5
 
5.0%
27 2
 
2.0%
29 2
 
2.0%
31 1
 
1.0%
41 15
15.0%
42 7
 
7.0%
43 6
 
6.0%
44 4
 
4.0%
45 8
8.0%
ValueCountFrequency (%)
50 3
 
3.0%
48 6
 
6.0%
47 11
11.0%
46 10
10.0%
45 8
8.0%
44 4
 
4.0%
43 6
 
6.0%
42 7
7.0%
41 15
15.0%
31 1
 
1.0%

city_gn_gu_cd
Real number (ℝ)

HIGH CORRELATION 

Distinct60
Distinct (%)60.6%
Missing1
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean36787.697
Minimum11110
Maximum50110
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:11:19.114928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11110
5-th percentile11303.5
Q127470
median42820
Q346750
95-th percentile48842
Maximum50110
Range39000
Interquartile range (IQR)19280

Descriptive statistics

Standard deviation13637.159
Coefficient of variation (CV)0.3706989
Kurtosis-0.4550911
Mean36787.697
Median Absolute Deviation (MAD)4000
Skewness-1.0931572
Sum3641982
Variance1.859721 × 108
MonotonicityNot monotonic
2023-12-10T19:11:19.434575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11680 7
 
7.0%
47130 7
 
7.0%
44150 4
 
4.0%
46730 3
 
3.0%
11110 3
 
3.0%
41281 3
 
3.0%
41210 3
 
3.0%
50110 3
 
3.0%
11650 3
 
3.0%
43800 3
 
3.0%
Other values (50) 60
60.0%
ValueCountFrequency (%)
11110 3
3.0%
11140 1
 
1.0%
11290 1
 
1.0%
11305 1
 
1.0%
11320 1
 
1.0%
11350 1
 
1.0%
11500 1
 
1.0%
11650 3
3.0%
11680 7
7.0%
26140 1
 
1.0%
ValueCountFrequency (%)
50110 3
3.0%
48860 2
 
2.0%
48840 1
 
1.0%
48820 1
 
1.0%
48270 1
 
1.0%
48170 1
 
1.0%
47290 1
 
1.0%
47190 2
 
2.0%
47170 1
 
1.0%
47130 7
7.0%

xpos_lo
Real number (ℝ)

Distinct99
Distinct (%)100.0%
Missing1
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean127.64766
Minimum126.42414
Maximum129.40894
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:11:19.689681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.42414
5-th percentile126.68312
Q1127.00852
median127.27537
Q3128.37661
95-th percentile129.20903
Maximum129.40894
Range2.984797
Interquartile range (IQR)1.3680855

Descriptive statistics

Standard deviation0.85417269
Coefficient of variation (CV)0.0066916437
Kurtosis-0.91649132
Mean127.64766
Median Absolute Deviation (MAD)0.427216
Skewness0.68299541
Sum12637.119
Variance0.72961098
MonotonicityNot monotonic
2023-12-10T19:11:19.942010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.227574 1
 
1.0%
127.497572 1
 
1.0%
127.514072 1
 
1.0%
127.13292 1
 
1.0%
126.939671 1
 
1.0%
128.373955 1
 
1.0%
127.054784 1
 
1.0%
127.833737 1
 
1.0%
127.588181 1
 
1.0%
128.484578 1
 
1.0%
Other values (89) 89
89.0%
ValueCountFrequency (%)
126.42414 1
1.0%
126.558101 1
1.0%
126.578075 1
1.0%
126.598633 1
1.0%
126.649657 1
1.0%
126.68684 1
1.0%
126.706202 1
1.0%
126.710814 1
1.0%
126.74759 1
1.0%
126.753577 1
1.0%
ValueCountFrequency (%)
129.408937 1
1.0%
129.331319 1
1.0%
129.307733 1
1.0%
129.218147 1
1.0%
129.216878 1
1.0%
129.208158 1
1.0%
129.186389 1
1.0%
129.177961 1
1.0%
129.096382 1
1.0%
129.079302 1
1.0%

ypos_la
Real number (ℝ)

HIGH CORRELATION 

Distinct99
Distinct (%)100.0%
Missing1
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean36.390022
Minimum33.423472
Maximum38.402208
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:11:20.208877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum33.423472
5-th percentile34.633645
Q135.386179
median36.365046
Q337.52168
95-th percentile37.794248
Maximum38.402208
Range4.978736
Interquartile range (IQR)2.1355005

Descriptive statistics

Standard deviation1.1900657
Coefficient of variation (CV)0.032703077
Kurtosis-0.82237199
Mean36.390022
Median Absolute Deviation (MAD)1.119827
Skewness-0.33669176
Sum3602.6122
Variance1.4162563
MonotonicityNot monotonic
2023-12-10T19:11:20.486131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36.47284 1
 
1.0%
35.256578 1
 
1.0%
35.061928 1
 
1.0%
34.961457 1
 
1.0%
37.66374 1
 
1.0%
38.165364 1
 
1.0%
37.523013 1
 
1.0%
36.542393 1
 
1.0%
35.254477 1
 
1.0%
37.042068 1
 
1.0%
Other values (89) 89
89.0%
ValueCountFrequency (%)
33.423472 1
1.0%
33.448754 1
1.0%
33.553816 1
1.0%
34.574407 1
1.0%
34.588079 1
1.0%
34.638708 1
1.0%
34.738421 1
1.0%
34.787972 1
1.0%
34.961457 1
1.0%
35.003307 1
1.0%
ValueCountFrequency (%)
38.402208 1
1.0%
38.165364 1
1.0%
38.124816 1
1.0%
37.894508 1
1.0%
37.888257 1
1.0%
37.783803 1
1.0%
37.777053 1
1.0%
37.736782 1
1.0%
37.735285 1
1.0%
37.694419 1
1.0%

area_nm
Categorical

HIGH CORRELATION 

Distinct15
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
서울
19 
경기
15 
경북
11 
전남
10 
전북
8 
Other values (10)
37 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique2 ?
Unique (%)2.0%

Sample

1st row세종
2nd row전북
3rd row전북
4th row경기
5th row강원

Common Values

ValueCountFrequency (%)
서울 19
19.0%
경기 15
15.0%
경북 11
11.0%
전남 10
10.0%
전북 8
8.0%
강원 7
 
7.0%
경남 6
 
6.0%
충북 6
 
6.0%
부산 5
 
5.0%
충남 4
 
4.0%
Other values (5) 9
9.0%

Length

2023-12-10T19:11:20.744927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
서울 19
19.0%
경기 15
15.0%
경북 11
11.0%
전남 10
10.0%
전북 8
8.0%
강원 7
 
7.0%
경남 6
 
6.0%
충북 6
 
6.0%
부산 5
 
5.0%
충남 4
 
4.0%
Other values (5) 9
9.0%

homepage_url
Text

MISSING 

Distinct86
Distinct (%)97.7%
Missing12
Missing (%)12.0%
Memory size932.0 B
2023-12-10T19:11:21.163996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length68
Median length49
Mean length40.988636
Min length19

Characters and Unicode

Total characters3607
Distinct characters47
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique85 ?
Unique (%)96.6%

Sample

1st rowhttp://kb5.templestay.com/index.asp?t_id=ypsa
2nd rowhttp://kb3.templestay.com/index.asp?t_id=sg9893
3rd rowhttp://www.seokparang.co.kr/
4th rowhttp://kb1.templestay.com/index.asp?t_id=gunbongsa
5th rowhttp://kb1.templestay.com/index.asp?t_id=jungtosa
ValueCountFrequency (%)
http://www.jinpoongjeong.com 3
 
3.4%
http://kb1.templestay.com/index.asp?t_id=guinsa 1
 
1.1%
http://kb1.templestay.com/index.asp?t_id=jeonghyesa 1
 
1.1%
http://kb1.templestay.com/index.asp?t_id=daewonsab 1
 
1.1%
http://kb1.templestay.com/index.asp?t_id=heungguksa 1
 
1.1%
http://kb1.templestay.com/index.asp?t_id=baekdamsa 1
 
1.1%
http://www.태능경회루.kr 1
 
1.1%
http://kb1.templestay.com/index.asp?t_id=beopjusa 1
 
1.1%
http://kb4.templestay.com/index.asp?t_id=yeongoksa 1
 
1.1%
http://www.bohyunsa.or.kr 1
 
1.1%
Other values (76) 76
86.4%
2023-12-10T19:11:21.851770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
t 344
 
9.5%
/ 268
 
7.4%
. 239
 
6.6%
e 207
 
5.7%
a 204
 
5.7%
p 203
 
5.6%
s 202
 
5.6%
o 166
 
4.6%
m 149
 
4.1%
i 140
 
3.9%
Other values (37) 1485
41.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2701
74.9%
Other Punctuation 645
 
17.9%
Decimal Number 143
 
4.0%
Connector Punctuation 54
 
1.5%
Math Symbol 52
 
1.4%
Uppercase Letter 6
 
0.2%
Other Letter 5
 
0.1%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 344
12.7%
e 207
 
7.7%
a 204
 
7.6%
p 203
 
7.5%
s 202
 
7.5%
o 166
 
6.1%
m 149
 
5.5%
i 140
 
5.2%
n 140
 
5.2%
d 122
 
4.5%
Other values (14) 824
30.5%
Decimal Number
ValueCountFrequency (%)
1 43
30.1%
2 18
12.6%
3 17
 
11.9%
4 13
 
9.1%
0 12
 
8.4%
8 11
 
7.7%
6 11
 
7.7%
9 8
 
5.6%
5 7
 
4.9%
7 3
 
2.1%
Other Letter
ValueCountFrequency (%)
태 1
20.0%
능 1
20.0%
경 1
20.0%
회 1
20.0%
루 1
20.0%
Other Punctuation
ValueCountFrequency (%)
/ 268
41.6%
. 239
37.1%
: 86
 
13.3%
? 52
 
8.1%
Connector Punctuation
ValueCountFrequency (%)
_ 54
100.0%
Math Symbol
ValueCountFrequency (%)
= 52
100.0%
Uppercase Letter
ValueCountFrequency (%)
P 6
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2707
75.0%
Common 895
 
24.8%
Hangul 5
 
0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 344
12.7%
e 207
 
7.6%
a 204
 
7.5%
p 203
 
7.5%
s 202
 
7.5%
o 166
 
6.1%
m 149
 
5.5%
i 140
 
5.2%
n 140
 
5.2%
d 122
 
4.5%
Other values (15) 830
30.7%
Common
ValueCountFrequency (%)
/ 268
29.9%
. 239
26.7%
: 86
 
9.6%
_ 54
 
6.0%
= 52
 
5.8%
? 52
 
5.8%
1 43
 
4.8%
2 18
 
2.0%
3 17
 
1.9%
4 13
 
1.5%
Other values (7) 53
 
5.9%
Hangul
ValueCountFrequency (%)
태 1
20.0%
능 1
20.0%
경 1
20.0%
회 1
20.0%
루 1
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3602
99.9%
Hangul 5
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 344
 
9.6%
/ 268
 
7.4%
. 239
 
6.6%
e 207
 
5.7%
a 204
 
5.7%
p 203
 
5.6%
s 202
 
5.6%
o 166
 
4.6%
m 149
 
4.1%
i 140
 
3.9%
Other values (32) 1480
41.1%
Hangul
ValueCountFrequency (%)
태 1
20.0%
능 1
20.0%
경 1
20.0%
회 1
20.0%
루 1
20.0%

tel_no
Text

MISSING 

Distinct87
Distinct (%)100.0%
Missing13
Missing (%)13.0%
Memory size932.0 B
2023-12-10T19:11:22.290126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length12
Mean length11.666667
Min length9

Characters and Unicode

Total characters1015
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique87 ?
Unique (%)100.0%

Sample

1st row044-854-1854
2nd row635643660
3rd row063-561-1375
4th row02-395-2500
5th row033-682-8103
ValueCountFrequency (%)
044-854-1854 1
 
1.1%
061-853-1755 1
 
1.1%
061-755-8484 1
 
1.1%
033-462-5565 1
 
1.1%
02-972-2774 1
 
1.1%
043-544-5656 1
 
1.1%
043-423-7255 1
 
1.1%
043-423-7100 1
 
1.1%
061-782-7600 1
 
1.1%
051-517-3352 1
 
1.1%
Other values (77) 77
88.5%
2023-12-10T19:11:23.095062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 168
16.6%
0 142
14.0%
3 107
10.5%
5 104
10.2%
2 102
10.0%
4 80
7.9%
7 77
7.6%
6 76
7.5%
1 65
 
6.4%
8 57
 
5.6%
Other values (2) 37
 
3.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 846
83.3%
Dash Punctuation 168
 
16.6%
Math Symbol 1
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 142
16.8%
3 107
12.6%
5 104
12.3%
2 102
12.1%
4 80
9.5%
7 77
9.1%
6 76
9.0%
1 65
7.7%
8 57
6.7%
9 36
 
4.3%
Dash Punctuation
ValueCountFrequency (%)
- 168
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1015
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 168
16.6%
0 142
14.0%
3 107
10.5%
5 104
10.2%
2 102
10.0%
4 80
7.9%
7 77
7.6%
6 76
7.5%
1 65
 
6.4%
8 57
 
5.6%
Other values (2) 37
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1015
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 168
16.6%
0 142
14.0%
3 107
10.5%
5 104
10.2%
2 102
10.0%
4 80
7.9%
7 77
7.6%
6 76
7.5%
1 65
 
6.4%
8 57
 
5.6%
Other values (2) 37
 
3.6%

menu_pc
Text

MISSING 

Distinct49
Distinct (%)51.0%
Missing4
Missing (%)4.0%
Memory size932.0 B
2023-12-10T19:11:23.538648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length171
Median length6
Mean length24.65625
Min length6

Characters and Unicode

Total characters2367
Distinct characters183
Distinct categories9 ?
Distinct scripts4 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique41 ?
Unique (%)42.7%

Sample

1st row110 000
2nd row60 000
3rd row궁중수라, 160 000 원, 석파, 130 000 원, 만세, 110 000 원, 점심 수복, 66 000 원, 진찬, 88 000 원
4th row40 000
5th row50 000
ValueCountFrequency (%)
000 126
22.0%
원 49
 
8.5%
000원 37
 
6.4%
60 21
 
3.7%
50 20
 
3.5%
70 7
 
1.2%
7
 
1.2%
120 6
 
1.0%
55 6
 
1.0%
4인 6
 
1.0%
Other values (200) 289
50.3%
2023-12-10T19:11:24.317080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 634
26.8%
479
20.2%
, 177
 
7.5%
원 88
 
3.7%
5 59
 
2.5%
1 48
 
2.0%
정 45
 
1.9%
6 39
 
1.6%
식 38
 
1.6%
8 37
 
1.6%
Other values (173) 723
30.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 938
39.6%
Other Letter 699
29.5%
Space Separator 479
20.2%
Other Punctuation 180
 
7.6%
Close Punctuation 25
 
1.1%
Open Punctuation 25
 
1.1%
Uppercase Letter 12
 
0.5%
Math Symbol 8
 
0.3%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
원 88
 
12.6%
정 45
 
6.4%
식 38
 
5.4%
상 33
 
4.7%
스 32
 
4.6%
코 29
 
4.1%
진 26
 
3.7%
수 16
 
2.3%
인 13
 
1.9%
심 13
 
1.9%
Other values (153) 366
52.4%
Decimal Number
ValueCountFrequency (%)
0 634
67.6%
5 59
 
6.3%
1 48
 
5.1%
6 39
 
4.2%
8 37
 
3.9%
2 33
 
3.5%
4 27
 
2.9%
3 25
 
2.7%
7 19
 
2.0%
9 17
 
1.8%
Other Punctuation
ValueCountFrequency (%)
, 177
98.3%
· 2
 
1.1%
/ 1
 
0.6%
Uppercase Letter
ValueCountFrequency (%)
A 7
58.3%
B 5
41.7%
Space Separator
ValueCountFrequency (%)
479
100.0%
Close Punctuation
ValueCountFrequency (%)
) 25
100.0%
Open Punctuation
ValueCountFrequency (%)
( 25
100.0%
Math Symbol
ValueCountFrequency (%)
~ 8
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1656
70.0%
Hangul 681
28.8%
Han 18
 
0.8%
Latin 12
 
0.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
원 88
 
12.9%
정 45
 
6.6%
식 38
 
5.6%
상 33
 
4.8%
스 32
 
4.7%
코 29
 
4.3%
진 26
 
3.8%
수 16
 
2.3%
인 13
 
1.9%
심 13
 
1.9%
Other values (139) 348
51.1%
Common
ValueCountFrequency (%)
0 634
38.3%
479
28.9%
, 177
 
10.7%
5 59
 
3.6%
1 48
 
2.9%
6 39
 
2.4%
8 37
 
2.2%
2 33
 
2.0%
4 27
 
1.6%
) 25
 
1.5%
Other values (8) 98
 
5.9%
Han
ValueCountFrequency (%)
餐 3
16.7%
一 2
11.1%
晩 2
11.1%
伐 1
 
5.6%
品 1
 
5.6%
秀 1
 
5.6%
羅 1
 
5.6%
徐 1
 
5.6%
選 1
 
5.6%
親 1
 
5.6%
Other values (4) 4
22.2%
Latin
ValueCountFrequency (%)
A 7
58.3%
B 5
41.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1666
70.4%
Hangul 681
28.8%
CJK 18
 
0.8%
None 2
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 634
38.1%
479
28.8%
, 177
 
10.6%
5 59
 
3.5%
1 48
 
2.9%
6 39
 
2.3%
8 37
 
2.2%
2 33
 
2.0%
4 27
 
1.6%
) 25
 
1.5%
Other values (9) 108
 
6.5%
Hangul
ValueCountFrequency (%)
원 88
 
12.9%
정 45
 
6.6%
식 38
 
5.6%
상 33
 
4.8%
스 32
 
4.7%
코 29
 
4.3%
진 26
 
3.8%
수 16
 
2.3%
인 13
 
1.9%
심 13
 
1.9%
Other values (139) 348
51.1%
CJK
ValueCountFrequency (%)
餐 3
16.7%
一 2
11.1%
晩 2
11.1%
伐 1
 
5.6%
品 1
 
5.6%
秀 1
 
5.6%
羅 1
 
5.6%
徐 1
 
5.6%
選 1
 
5.6%
親 1
 
5.6%
Other values (4) 4
22.2%
None
ValueCountFrequency (%)
· 2
100.0%

sales_tm
Text

MISSING 

Distinct26
Distinct (%)92.9%
Missing72
Missing (%)72.0%
Memory size932.0 B
2023-12-10T19:11:24.656727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length61
Median length40.5
Mean length29.642857
Min length7

Characters and Unicode

Total characters830
Distinct characters53
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique24 ?
Unique (%)85.7%

Sample

1st row매일12:00 ~ 22:00
2nd row매일12:00 ~ 22:00
3rd row오찬 12:00 ~ 14:00, 만찬 17:00 ~ 21:00
4th row매일 11:30 - 22:00 Break time 15:00~17:00
5th row매일 11:30 - 22:00 Break time 15:00~17:00
ValueCountFrequency (%)
37
23.0%
22:00 17
 
10.6%
매일 12
 
7.5%
11:30 9
 
5.6%
15:00~17:00 6
 
3.7%
12:00 6
 
3.7%
time 5
 
3.1%
15:00 4
 
2.5%
11:00 4
 
2.5%
매일12:00 3
 
1.9%
Other values (39) 58
36.0%
2023-12-10T19:11:25.299402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 169
20.4%
136
16.4%
: 94
11.3%
1 86
10.4%
2 65
 
7.8%
~ 33
 
4.0%
3 25
 
3.0%
일 20
 
2.4%
매 17
 
2.0%
- 14
 
1.7%
Other values (43) 171
20.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 376
45.3%
Space Separator 136
 
16.4%
Other Letter 116
 
14.0%
Other Punctuation 107
 
12.9%
Lowercase Letter 41
 
4.9%
Math Symbol 33
 
4.0%
Dash Punctuation 14
 
1.7%
Uppercase Letter 4
 
0.5%
Open Punctuation 2
 
0.2%
Close Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
일 20
17.2%
매 17
 
14.7%
주 7
 
6.0%
중 5
 
4.3%
휴 4
 
3.4%
식 4
 
3.4%
시 4
 
3.4%
임 3
 
2.6%
녁 3
 
2.6%
저 3
 
2.6%
Other values (19) 46
39.7%
Decimal Number
ValueCountFrequency (%)
0 169
44.9%
1 86
22.9%
2 65
 
17.3%
3 25
 
6.6%
5 14
 
3.7%
7 9
 
2.4%
8 5
 
1.3%
4 3
 
0.8%
Lowercase Letter
ValueCountFrequency (%)
e 10
24.4%
m 5
12.2%
i 5
12.2%
t 5
12.2%
k 5
12.2%
a 5
12.2%
r 5
12.2%
b 1
 
2.4%
Other Punctuation
ValueCountFrequency (%)
: 94
87.9%
, 13
 
12.1%
Space Separator
ValueCountFrequency (%)
136
100.0%
Math Symbol
ValueCountFrequency (%)
~ 33
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 14
100.0%
Uppercase Letter
ValueCountFrequency (%)
B 4
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 669
80.6%
Hangul 116
 
14.0%
Latin 45
 
5.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
일 20
17.2%
매 17
 
14.7%
주 7
 
6.0%
중 5
 
4.3%
휴 4
 
3.4%
식 4
 
3.4%
시 4
 
3.4%
임 3
 
2.6%
녁 3
 
2.6%
저 3
 
2.6%
Other values (19) 46
39.7%
Common
ValueCountFrequency (%)
0 169
25.3%
136
20.3%
: 94
14.1%
1 86
12.9%
2 65
 
9.7%
~ 33
 
4.9%
3 25
 
3.7%
- 14
 
2.1%
5 14
 
2.1%
, 13
 
1.9%
Other values (5) 20
 
3.0%
Latin
ValueCountFrequency (%)
e 10
22.2%
m 5
11.1%
i 5
11.1%
t 5
11.1%
k 5
11.1%
a 5
11.1%
r 5
11.1%
B 4
 
8.9%
b 1
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 714
86.0%
Hangul 116
 
14.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 169
23.7%
136
19.0%
: 94
13.2%
1 86
12.0%
2 65
 
9.1%
~ 33
 
4.6%
3 25
 
3.5%
- 14
 
2.0%
5 14
 
2.0%
, 13
 
1.8%
Other values (14) 65
 
9.1%
Hangul
ValueCountFrequency (%)
일 20
17.2%
매 17
 
14.7%
주 7
 
6.0%
중 5
 
4.3%
휴 4
 
3.4%
식 4
 
3.4%
시 4
 
3.4%
임 3
 
2.6%
녁 3
 
2.6%
저 3
 
2.6%
Other values (19) 46
39.7%

base_ymd
Date

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
Minimum2020-12-31 00:00:00
Maximum2020-12-31 00:00:00
2023-12-10T19:11:25.506125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:25.774203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2023-12-10T19:11:13.466369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:10.960043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:11.662789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:12.747843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:13.639631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:11.154823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:11.846897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:12.909909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:13.798774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:11.334908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:12.071189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:13.113877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:13.950667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:11.508928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:12.591711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:13.315461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:11:26.000006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
se_nmentrp_nmload_addrcity_do_cdcity_gn_gu_cdxpos_loypos_laarea_nmhomepage_urltel_nomenu_pcsales_tm
se_nm1.0001.0001.0000.5030.5030.4840.2410.6371.0001.0001.000NaN
entrp_nm1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
load_addr1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
city_do_cd0.5031.0001.0001.0000.9980.6600.6751.0001.0001.0000.9171.000
city_gn_gu_cd0.5031.0001.0000.9981.0000.6140.6891.0001.0001.0000.9251.000
xpos_lo0.4841.0001.0000.6600.6141.0000.6010.8220.7551.0000.0000.832
ypos_la0.2411.0001.0000.6750.6890.6011.0000.9030.9101.0000.0000.201
area_nm0.6371.0001.0001.0001.0000.8220.9031.0001.0001.0000.8201.000
homepage_url1.0001.0001.0001.0001.0000.7550.9101.0001.0001.0000.9720.996
tel_no1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
menu_pc1.0001.0001.0000.9170.9250.0000.0000.8200.9721.0001.0000.982
sales_tmNaN1.0001.0001.0001.0000.8320.2011.0000.9961.0000.9821.000
2023-12-10T19:11:26.626996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
area_nmse_nm
area_nm1.0000.393
se_nm0.3931.000
2023-12-10T19:11:26.868786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
city_do_cdcity_gn_gu_cdxpos_loypos_lase_nmarea_nm
city_do_cd1.0000.9930.257-0.6160.3630.961
city_gn_gu_cd0.9931.0000.253-0.6080.3410.934
xpos_lo0.2570.2531.000-0.1130.2980.463
ypos_la-0.616-0.608-0.1131.0000.1490.640
se_nm0.3630.3410.2980.1491.0000.393
area_nm0.9610.9340.4630.6400.3931.000

Missing values

2023-12-10T19:11:14.151352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:11:14.455648image/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.
2023-12-10T19:11:14.738340image/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

se_nmentrp_nmload_addrcity_do_cdcity_gn_gu_cdxpos_loypos_laarea_nmhomepage_urltel_nomenu_pcsales_tmbase_ymd
0템플스테이영평사세종특별자치시 장군면 영평사길 124<NA><NA>127.22757436.47284세종http://kb5.templestay.com/index.asp?t_id=ypsa044-854-1854110 000<NA>2020-12-31
1전통차다인 전통찻집전라북도 고창군 고창읍 월곡로 514545790126.71081435.436294전북<NA>635643660<NA><NA>2020.12.31
2템플스테이선운사전라북도 고창군 아산면 선운사로 250(선운사)4545790126.57807535.496939전북http://kb3.templestay.com/index.asp?t_id=sg9893063-561-137560 000<NA>2020-12-31
3궁중음식점단원 한정식경기도 안산시 단원구 광덕대로 2514141273126.82988137.319155경기http://www.seokparang.co.kr/02-395-2500궁중수라, 160 000 원, 석파, 130 000 원, 만세, 110 000 원, 점심 수복, 66 000 원, 진찬, 88 000 원매일12:00 ~ 22:002020-12-31
4템플스테이건봉사강원도 고성군 거진읍 건봉사로 7234242820128.37925938.402208강원http://kb1.templestay.com/index.asp?t_id=gunbongsa033-682-810340 000<NA>2020-12-31
5템플스테이정토사경기도 성남시 수정구 옛골로 42번길 34141131127.06811937.426724경기http://kb1.templestay.com/index.asp?t_id=jungtosa031-723-979750 000<NA>2020-12-31
6궁중음식점요리경기도 파주시 탄현면 법흥로 1064141480126.70620237.777053경기http://www.philkyungjae.co.kr/rain/index.php02-445-2115미정식, 54 000 원, 죽정식, 84 000 원, 국화정식, 102 000 원, 난정식, 132 000 원, 매화정식, 165 000 원매일12:00 ~ 22:002020-12-31
7전통차달리아아트문화센터경기도 광명시 하안로 60, 광명테크노파크 E동 207일부호 (소하동)4141210<NA><NA>경기<NA>220600088<NA><NA>2020.12.31
8궁중음식점송화한정식경상남도 진주시 도동천로 824848170128.10950435.179129경남http://www.koreahouse.or.kr/02-2266-9101~3녹음정식 68 000 원 , 청우정식 88 000 원, 해린정식 122 000 원, 어진정식 150 000 원오찬 12:00 ~ 14:00, 만찬 17:00 ~ 21:002020-12-31
9궁중음식점요석궁경상북도 경주시 교촌안길 19-44747130129.21687835.830354경북http://www.jinpoongjeong.com02-538-7733진연코스, 49 000원, 진풍정A, 68 000원, 진찬코스, 38 000원, 진풍정B, 55 000원, 어상B코스, 80 000원매일 11:30 - 22:00 Break time 15:00~17:002020-12-31
se_nmentrp_nmload_addrcity_do_cdcity_gn_gu_cdxpos_loypos_laarea_nmhomepage_urltel_nomenu_pcsales_tmbase_ymd
90템플스테이송광사(순천)전라남도 순천시 송광면 송광사안길 1004646150127.2753735.003307전남http://kb1.templestay.com/index.asp?t_id=songgwangsa061-755-535060 000<NA>2020-12-31
91템플스테이도리사경상북도 구미시 해평면 도리사로 526(도리사)4747190128.39876536.255615경북http://kb2.templestay.com/index.asp?t_id=dorisa054-474-387770 000<NA>2020-12-31
92템플스테이반야사충청북도 영동군 황간면 백화산로 652(반야사)4343740127.90933136.280607충북http://kb1.templestay.com/index.asp?t_id=bys4199<NA>60 000<NA>2020-12-31
93템플스테이봉인사경기도 남양주시 진건읍 사릉로156번길 295(봉인사)4141360127.21802737.66194경기http://kb1.templestay.com/index.asp?t_id=bonginsa031-528-5585180 000<NA>2020-12-31
94템플스테이천축사서울특별시 도봉구 도봉산길92-21111320127.01975237.694419서울http://www.cheonchuksa.kr/<NA>50 000<NA>2020-12-31
95템플스테이문수암경상남도 산청군 시천면 마근담길 173-17(문수암)4848860127.85364535.288199경남https://www.munsuam.org/<NA>60 000<NA>2020-12-31
96템플스테이삼운사강원도 춘천시 후석로441번길 12 (후평동)4242110127.7417637.888257강원http://kb1.templestay.com/index.asp?t_id=samwoon033-253-654230 000<NA>2020-12-31
97궁중음식점용수산서울특별시 서초구 서초중앙로 188 아크로비스타1111650127.01341337.498085서울http://www.seoulgaon.co.kr/02-733-3276궁, 40 000 원, 상, 50 000 원, 각, 60 000 원, 평일점심특선, 30 000 원매일, 11:30 ~ 22:002020-12-31
98템플스테이삼화사강원도 동해시 삼화로 584 (삼화동 삼화사)4242170129.01436637.463646강원http://kb1.templestay.com/index.asp?t_id=534-7661033-534-7676100 000<NA>2020-12-31
99템플스테이마곡사충청남도 공주시 사곡면 마곡사로 966(마곡사)4444150127.01200736.558594충남http://kb1.templestay.com/index.asp?t_id=magoksa041-841-622660 000<NA>2020-12-31