Overview

Dataset statistics

Number of variables10
Number of observations100
Missing cells91
Missing cells (%)9.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.3 KiB
Average record size in memory85.3 B

Variable types

Text4
Numeric4
Categorical2

Alerts

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
xpos_lo is highly overall correlated with area_nmHigh 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 3 other fieldsHigh correlation
base_ymd is highly imbalanced (71.4%)Imbalance
homepage_url has 17 (17.0%) missing valuesMissing
tel_no has 72 (72.0%) missing valuesMissing

Reproduction

Analysis started2023-12-10 09:52:41.920169
Analysis finished2023-12-10 09:52:46.846842
Duration4.93 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

entrp_nm
Text

Distinct99
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T18:52:47.053639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length12
Mean length7.56
Min length2

Characters and Unicode

Total characters756
Distinct characters193
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique98 ?
Unique (%)98.0%

Sample

1st row강릉오죽한옥마을
2nd row올모스트홈스테이 청송점
3rd row동해한옥 동안재
4th row태백산 한옥펜션
5th row화천한옥학교
ValueCountFrequency (%)
한옥 9
 
6.1%
경주 3
 
2.0%
한옥펜션 3
 
2.0%
펜션 2
 
1.4%
한옥체험관 2
 
1.4%
공주한옥마을 2
 
1.4%
태양옥민박 1
 
0.7%
산들바다애 1
 
0.7%
농부삼촌 1
 
0.7%
오동재 1
 
0.7%
Other values (123) 123
83.1%
2023-12-10T18:52:47.691640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
한 86
 
11.4%
옥 84
 
11.1%
48
 
6.3%
스 21
 
2.8%
주 16
 
2.1%
펜 15
 
2.0%
션 15
 
2.0%
험 12
 
1.6%
체 12
 
1.6%
을 12
 
1.6%
Other values (183) 435
57.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 697
92.2%
Space Separator 48
 
6.3%
Decimal Number 4
 
0.5%
Close Punctuation 3
 
0.4%
Open Punctuation 3
 
0.4%
Other Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
한 86
 
12.3%
옥 84
 
12.1%
스 21
 
3.0%
주 16
 
2.3%
펜 15
 
2.2%
션 15
 
2.2%
험 12
 
1.7%
체 12
 
1.7%
을 12
 
1.7%
마 12
 
1.7%
Other values (176) 412
59.1%
Decimal Number
ValueCountFrequency (%)
2 2
50.0%
1 1
25.0%
0 1
25.0%
Space Separator
ValueCountFrequency (%)
48
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Other Punctuation
ValueCountFrequency (%)
& 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 697
92.2%
Common 59
 
7.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
한 86
 
12.3%
옥 84
 
12.1%
스 21
 
3.0%
주 16
 
2.3%
펜 15
 
2.2%
션 15
 
2.2%
험 12
 
1.7%
체 12
 
1.7%
을 12
 
1.7%
마 12
 
1.7%
Other values (176) 412
59.1%
Common
ValueCountFrequency (%)
48
81.4%
) 3
 
5.1%
( 3
 
5.1%
2 2
 
3.4%
1 1
 
1.7%
0 1
 
1.7%
& 1
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 697
92.2%
ASCII 59
 
7.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
한 86
 
12.3%
옥 84
 
12.1%
스 21
 
3.0%
주 16
 
2.3%
펜 15
 
2.2%
션 15
 
2.2%
험 12
 
1.7%
체 12
 
1.7%
을 12
 
1.7%
마 12
 
1.7%
Other values (176) 412
59.1%
ASCII
ValueCountFrequency (%)
48
81.4%
) 3
 
5.1%
( 3
 
5.1%
2 2
 
3.4%
1 1
 
1.7%
0 1
 
1.7%
& 1
 
1.7%

load_addr
Text

Distinct98
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T18:52:48.654555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length35
Median length29
Mean length24.02
Min length17

Characters and Unicode

Total characters2402
Distinct characters200
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique96 ?
Unique (%)96.0%

Sample

1st row강원도 강릉시 죽헌길 114(죽헌동)
2nd row경상북도 청송군 주왕산면 주왕산로 494
3rd row강원도 동해시 천곡1길 74-2(천곡동)
4th row강원도 태백시 소롯골길 34(소도동)
5th row강원도 화천군 간동면 모현동로 182-35
ValueCountFrequency (%)
전라북도 20
 
4.3%
전라남도 18
 
3.8%
완산구 16
 
3.4%
전주시 16
 
3.4%
경상북도 16
 
3.4%
경주시 12
 
2.6%
서울특별시 12
 
2.6%
종로구 10
 
2.1%
경상남도 7
 
1.5%
경기도 7
 
1.5%
Other values (283) 336
71.5%
2023-12-10T18:52:49.449085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
370
 
15.4%
도 91
 
3.8%
시 79
 
3.3%
1 74
 
3.1%
동 73
 
3.0%
길 70
 
2.9%
( 63
 
2.6%
) 63
 
2.6%
로 61
 
2.5%
전 58
 
2.4%
Other values (190) 1400
58.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1489
62.0%
Space Separator 370
 
15.4%
Decimal Number 361
 
15.0%
Open Punctuation 63
 
2.6%
Close Punctuation 63
 
2.6%
Dash Punctuation 55
 
2.3%
Other Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 91
 
6.1%
시 79
 
5.3%
동 73
 
4.9%
길 70
 
4.7%
로 61
 
4.1%
전 58
 
3.9%
남 50
 
3.4%
구 45
 
3.0%
주 45
 
3.0%
경 44
 
3.0%
Other values (175) 873
58.6%
Decimal Number
ValueCountFrequency (%)
1 74
20.5%
2 53
14.7%
3 40
11.1%
4 36
10.0%
6 35
9.7%
5 33
9.1%
8 24
 
6.6%
0 23
 
6.4%
7 22
 
6.1%
9 21
 
5.8%
Space Separator
ValueCountFrequency (%)
370
100.0%
Open Punctuation
ValueCountFrequency (%)
( 63
100.0%
Close Punctuation
ValueCountFrequency (%)
) 63
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 55
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1489
62.0%
Common 913
38.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 91
 
6.1%
시 79
 
5.3%
동 73
 
4.9%
길 70
 
4.7%
로 61
 
4.1%
전 58
 
3.9%
남 50
 
3.4%
구 45
 
3.0%
주 45
 
3.0%
경 44
 
3.0%
Other values (175) 873
58.6%
Common
ValueCountFrequency (%)
370
40.5%
1 74
 
8.1%
( 63
 
6.9%
) 63
 
6.9%
- 55
 
6.0%
2 53
 
5.8%
3 40
 
4.4%
4 36
 
3.9%
6 35
 
3.8%
5 33
 
3.6%
Other values (5) 91
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1489
62.0%
ASCII 913
38.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
370
40.5%
1 74
 
8.1%
( 63
 
6.9%
) 63
 
6.9%
- 55
 
6.0%
2 53
 
5.8%
3 40
 
4.4%
4 36
 
3.9%
6 35
 
3.8%
5 33
 
3.6%
Other values (5) 91
 
10.0%
Hangul
ValueCountFrequency (%)
도 91
 
6.1%
시 79
 
5.3%
동 73
 
4.9%
길 70
 
4.7%
로 61
 
4.1%
전 58
 
3.9%
남 50
 
3.4%
구 45
 
3.0%
주 45
 
3.0%
경 44
 
3.0%
Other values (175) 873
58.6%

city_do_cd
Real number (ℝ)

HIGH CORRELATION 

Distinct13
Distinct (%)13.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.85
Minimum11
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:52:49.677219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile11
Q141
median45
Q346.25
95-th percentile48
Maximum50
Range39
Interquartile range (IQR)5.25

Descriptive statistics

Standard deviation11.891237
Coefficient of variation (CV)0.29839992
Kurtosis1.539331
Mean39.85
Median Absolute Deviation (MAD)2
Skewness-1.7401805
Sum3985
Variance141.40152
MonotonicityNot monotonic
2023-12-10T18:52:49.898936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
45 20
20.0%
46 18
18.0%
47 16
16.0%
11 12
12.0%
41 7
 
7.0%
48 7
 
7.0%
42 5
 
5.0%
27 4
 
4.0%
44 4
 
4.0%
28 3
 
3.0%
Other values (3) 4
 
4.0%
ValueCountFrequency (%)
11 12
12.0%
27 4
 
4.0%
28 3
 
3.0%
29 1
 
1.0%
41 7
 
7.0%
42 5
 
5.0%
43 1
 
1.0%
44 4
 
4.0%
45 20
20.0%
46 18
18.0%
ValueCountFrequency (%)
50 2
 
2.0%
48 7
 
7.0%
47 16
16.0%
46 18
18.0%
45 20
20.0%
44 4
 
4.0%
43 1
 
1.0%
42 5
 
5.0%
41 7
 
7.0%
29 1
 
1.0%

city_gn_gu_cd
Real number (ℝ)

HIGH CORRELATION 

Distinct44
Distinct (%)44.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40178.51
Minimum11110
Maximum50130
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:52:50.163849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11110
5-th percentile11110
Q141475.75
median45111
Q346957.5
95-th percentile48300
Maximum50130
Range39020
Interquartile range (IQR)5481.75

Descriptive statistics

Standard deviation11952.522
Coefficient of variation (CV)0.29748544
Kurtosis1.5573787
Mean40178.51
Median Absolute Deviation (MAD)2019
Skewness-1.7447184
Sum4017851
Variance1.4286278 × 108
MonotonicityNot monotonic
2023-12-10T18:52:50.545641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
45111 16
 
16.0%
47130 12
 
12.0%
11110 10
 
10.0%
46150 4
 
4.0%
46820 4
 
4.0%
28710 3
 
3.0%
44150 3
 
3.0%
27110 3
 
3.0%
45710 3
 
3.0%
48220 2
 
2.0%
Other values (34) 40
40.0%
ValueCountFrequency (%)
11110 10
10.0%
11290 1
 
1.0%
11380 1
 
1.0%
27110 3
 
3.0%
27290 1
 
1.0%
28710 3
 
3.0%
29110 1
 
1.0%
41115 1
 
1.0%
41450 1
 
1.0%
41461 2
 
2.0%
ValueCountFrequency (%)
50130 1
1.0%
50110 1
1.0%
48880 1
1.0%
48870 2
2.0%
48270 1
1.0%
48250 1
1.0%
48220 2
2.0%
47750 2
2.0%
47290 1
1.0%
47170 1
1.0%

xpos_lo
Real number (ℝ)

HIGH CORRELATION 

Distinct96
Distinct (%)97.0%
Missing1
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean127.59264
Minimum126.16128
Maximum129.50312
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:52:50.881731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.16128
5-th percentile126.48314
Q1126.98646
median127.15412
Q3128.48878
95-th percentile129.21315
Maximum129.50312
Range3.341848
Interquartile range (IQR)1.5023165

Descriptive statistics

Standard deviation0.92774767
Coefficient of variation (CV)0.0072711691
Kurtosis-0.8315179
Mean127.59264
Median Absolute Deviation (MAD)0.386052
Skewness0.75092847
Sum12631.672
Variance0.86071574
MonotonicityNot monotonic
2023-12-10T18:52:51.209651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
129.107187 2
 
2.0%
127.108831 2
 
2.0%
129.20369 2
 
2.0%
126.677806 1
 
1.0%
127.233306 1
 
1.0%
127.625702 1
 
1.0%
126.661611 1
 
1.0%
126.613616 1
 
1.0%
126.632948 1
 
1.0%
126.633391 1
 
1.0%
Other values (86) 86
86.0%
ValueCountFrequency (%)
126.161276 1
1.0%
126.277085 1
1.0%
126.295113 1
1.0%
126.446949 1
1.0%
126.460626 1
1.0%
126.485636 1
1.0%
126.60692 1
1.0%
126.613616 1
1.0%
126.632948 1
1.0%
126.633391 1
1.0%
ValueCountFrequency (%)
129.503124 1
1.0%
129.303211 1
1.0%
129.286921 1
1.0%
129.218382 1
1.0%
129.214054 1
1.0%
129.213055 1
1.0%
129.212023 1
1.0%
129.204963 1
1.0%
129.20369 2
2.0%
129.196643 1
1.0%

ypos_la
Real number (ℝ)

HIGH CORRELATION 

Distinct96
Distinct (%)97.0%
Missing1
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean36.065949
Minimum33.276086
Maximum38.096749
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:52:51.514219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum33.276086
5-th percentile34.434482
Q135.508392
median35.820564
Q337.197609
95-th percentile37.745836
Maximum38.096749
Range4.820663
Interquartile range (IQR)1.689217

Descriptive statistics

Standard deviation1.1083062
Coefficient of variation (CV)0.030729989
Kurtosis-0.64549053
Mean36.065949
Median Absolute Deviation (MAD)0.754594
Skewness0.033478612
Sum3570.529
Variance1.2283427
MonotonicityNot monotonic
2023-12-10T18:52:51.796185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36.377957 2
 
2.0%
36.464487 2
 
2.0%
35.780773 2
 
2.0%
34.396479 1
 
1.0%
35.979648 1
 
1.0%
35.418358 1
 
1.0%
34.515998 1
 
1.0%
34.501562 1
 
1.0%
34.438516 1
 
1.0%
34.438702 1
 
1.0%
Other values (86) 86
86.0%
ValueCountFrequency (%)
33.276086 1
1.0%
33.488607 1
1.0%
34.373184 1
1.0%
34.396479 1
1.0%
34.39818 1
1.0%
34.438516 1
1.0%
34.438702 1
1.0%
34.501562 1
1.0%
34.515998 1
1.0%
34.725445 1
1.0%
ValueCountFrequency (%)
38.096749 1
1.0%
38.038838 1
1.0%
37.777114 1
1.0%
37.762631 1
1.0%
37.754596 1
1.0%
37.744863 1
1.0%
37.643198 1
1.0%
37.619394 1
1.0%
37.592815 1
1.0%
37.583303 1
1.0%

area_nm
Categorical

HIGH CORRELATION 

Distinct13
Distinct (%)13.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
전북
20 
전남
18 
경북
16 
서울
12 
경기
7 
Other values (8)
27 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique2 ?
Unique (%)2.0%

Sample

1st row강원
2nd row경북
3rd row강원
4th row강원
5th row강원

Common Values

ValueCountFrequency (%)
전북 20
20.0%
전남 18
18.0%
경북 16
16.0%
서울 12
12.0%
경기 7
 
7.0%
경남 7
 
7.0%
강원 5
 
5.0%
대구 4
 
4.0%
충남 4
 
4.0%
인천 3
 
3.0%
Other values (3) 4
 
4.0%

Length

2023-12-10T18:52:52.089414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
전북 20
20.0%
전남 18
18.0%
경북 16
16.0%
서울 12
12.0%
경기 7
 
7.0%
경남 7
 
7.0%
강원 5
 
5.0%
대구 4
 
4.0%
충남 4
 
4.0%
인천 3
 
3.0%
Other values (3) 4
 
4.0%

homepage_url
Text

MISSING 

Distinct83
Distinct (%)100.0%
Missing17
Missing (%)17.0%
Memory size932.0 B
2023-12-10T18:52:52.596268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length74
Median length32
Mean length26.843373
Min length13

Characters and Unicode

Total characters2228
Distinct characters76
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique83 ?
Unique (%)100.0%

Sample

1st rowhttp://www.ojuk.or.kr
2nd rowhttp://www.donganjae.com
3rd rowhttp://www.ok114.co.kr/0335522367
4th rowhttp://www.hanokschool.co.kr
5th rowhttps://dagagadahanok.modoo.at/
ValueCountFrequency (%)
http://hanokpension.com 1
 
1.2%
http://www.leegahanok.com 1
 
1.2%
https://sanalehanok.modoo.at 1
 
1.2%
http://www.stayhanok.com 1
 
1.2%
http://www.한옥펜션.한국/main 1
 
1.2%
http://jhanok21.modoo.at 1
 
1.2%
http://www.개울한옥.com 1
 
1.2%
https://heanamsun.modoo.at 1
 
1.2%
https://sandeulbadaae.modoo.at 1
 
1.2%
http://www.esamchon.co.kr 1
 
1.2%
Other values (73) 73
88.0%
2023-12-10T18:52:53.325352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 231
 
10.4%
t 208
 
9.3%
o 185
 
8.3%
. 174
 
7.8%
w 144
 
6.5%
h 143
 
6.4%
a 119
 
5.3%
p 103
 
4.6%
n 93
 
4.2%
: 82
 
3.7%
Other values (66) 746
33.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1616
72.5%
Other Punctuation 490
 
22.0%
Decimal Number 59
 
2.6%
Other Letter 58
 
2.6%
Math Symbol 2
 
0.1%
Dash Punctuation 2
 
0.1%
Connector Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
한 8
 
13.8%
션 7
 
12.1%
옥 7
 
12.1%
펜 7
 
12.1%
을 2
 
3.4%
잠 1
 
1.7%
힐 1
 
1.7%
꿀 1
 
1.7%
온 1
 
1.7%
도 1
 
1.7%
Other values (22) 22
37.9%
Lowercase Letter
ValueCountFrequency (%)
t 208
12.9%
o 185
11.4%
w 144
 
8.9%
h 143
 
8.8%
a 119
 
7.4%
p 103
 
6.4%
n 93
 
5.8%
m 79
 
4.9%
k 71
 
4.4%
e 68
 
4.2%
Other values (15) 403
24.9%
Decimal Number
ValueCountFrequency (%)
1 12
20.3%
0 11
18.6%
2 10
16.9%
3 7
11.9%
5 5
8.5%
4 4
 
6.8%
6 3
 
5.1%
8 3
 
5.1%
7 3
 
5.1%
9 1
 
1.7%
Other Punctuation
ValueCountFrequency (%)
/ 231
47.1%
. 174
35.5%
: 82
 
16.7%
# 1
 
0.2%
& 1
 
0.2%
? 1
 
0.2%
Math Symbol
ValueCountFrequency (%)
= 2
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1616
72.5%
Common 554
 
24.9%
Hangul 58
 
2.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
한 8
 
13.8%
션 7
 
12.1%
옥 7
 
12.1%
펜 7
 
12.1%
을 2
 
3.4%
잠 1
 
1.7%
힐 1
 
1.7%
꿀 1
 
1.7%
온 1
 
1.7%
도 1
 
1.7%
Other values (22) 22
37.9%
Latin
ValueCountFrequency (%)
t 208
12.9%
o 185
11.4%
w 144
 
8.9%
h 143
 
8.8%
a 119
 
7.4%
p 103
 
6.4%
n 93
 
5.8%
m 79
 
4.9%
k 71
 
4.4%
e 68
 
4.2%
Other values (15) 403
24.9%
Common
ValueCountFrequency (%)
/ 231
41.7%
. 174
31.4%
: 82
 
14.8%
1 12
 
2.2%
0 11
 
2.0%
2 10
 
1.8%
3 7
 
1.3%
5 5
 
0.9%
4 4
 
0.7%
6 3
 
0.5%
Other values (9) 15
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2170
97.4%
Hangul 58
 
2.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/ 231
 
10.6%
t 208
 
9.6%
o 185
 
8.5%
. 174
 
8.0%
w 144
 
6.6%
h 143
 
6.6%
a 119
 
5.5%
p 103
 
4.7%
n 93
 
4.3%
: 82
 
3.8%
Other values (34) 688
31.7%
Hangul
ValueCountFrequency (%)
한 8
 
13.8%
션 7
 
12.1%
옥 7
 
12.1%
펜 7
 
12.1%
을 2
 
3.4%
잠 1
 
1.7%
힐 1
 
1.7%
꿀 1
 
1.7%
온 1
 
1.7%
도 1
 
1.7%
Other values (22) 22
37.9%

tel_no
Text

MISSING 

Distinct28
Distinct (%)100.0%
Missing72
Missing (%)72.0%
Memory size932.0 B
2023-12-10T18:52:53.949131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length11.535714
Min length9

Characters and Unicode

Total characters323
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique28 ?
Unique (%)100.0%

Sample

1st row033-655-1117
2nd row236777121
3rd row033-552-2367
4th row033-442-3366
5th row033-441-1488
ValueCountFrequency (%)
559605162 1
 
3.6%
033-552-2367 1
 
3.6%
054-821-8589 1
 
3.6%
317928000 1
 
3.6%
031-792-8000 1
 
3.6%
041-840-8903 1
 
3.6%
041-840-8900 1
 
3.6%
064-733-8869 1
 
3.6%
063-636-1003 1
 
3.6%
061-554-7736 1
 
3.6%
Other values (18) 18
64.3%
2023-12-10T18:52:54.701517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 53
16.4%
- 46
14.2%
5 33
10.2%
3 33
10.2%
1 26
8.0%
4 26
8.0%
6 24
7.4%
7 23
7.1%
8 22
6.8%
9 19
 
5.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 277
85.8%
Dash Punctuation 46
 
14.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 53
19.1%
5 33
11.9%
3 33
11.9%
1 26
9.4%
4 26
9.4%
6 24
8.7%
7 23
8.3%
8 22
7.9%
9 19
 
6.9%
2 18
 
6.5%
Dash Punctuation
ValueCountFrequency (%)
- 46
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 323
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 53
16.4%
- 46
14.2%
5 33
10.2%
3 33
10.2%
1 26
8.0%
4 26
8.0%
6 24
7.4%
7 23
7.1%
8 22
6.8%
9 19
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 323
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 53
16.4%
- 46
14.2%
5 33
10.2%
3 33
10.2%
1 26
8.0%
4 26
8.0%
6 24
7.4%
7 23
7.1%
8 22
6.8%
9 19
 
5.9%

base_ymd
Categorical

IMBALANCE 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2020-12-31
95 
2020.12.31
 
5

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020-12-31
2nd row2020.12.31
3rd row2020-12-31
4th row2020-12-31
5th row2020-12-31

Common Values

ValueCountFrequency (%)
2020-12-31 95
95.0%
2020.12.31 5
 
5.0%

Length

2023-12-10T18:52:55.033666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:52:55.267204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020-12-31 95
95.0%
2020.12.31 5
 
5.0%

Interactions

2023-12-10T18:52:45.334781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:52:42.980628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:52:43.804302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:52:44.600757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:52:45.571827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:52:43.229268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:52:44.031497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:52:44.787725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:52:45.751776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:52:43.431765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:52:44.251343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:52:44.975067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:52:45.971443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:52:43.630909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:52:44.431766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:52:45.151842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T18:52:55.428624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
entrp_nmload_addrcity_do_cdcity_gn_gu_cdxpos_loypos_laarea_nmhomepage_urltel_nobase_ymd
entrp_nm1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
load_addr1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
city_do_cd1.0001.0001.0000.9960.8830.7171.0001.0001.0000.062
city_gn_gu_cd1.0001.0000.9961.0000.8350.6820.9721.0001.0000.000
xpos_lo1.0001.0000.8830.8351.0000.6760.8561.0001.0000.000
ypos_la1.0001.0000.7170.6820.6761.0000.9191.0001.0000.356
area_nm1.0001.0001.0000.9720.8560.9191.0001.0001.0000.239
homepage_url1.0001.0001.0001.0001.0001.0001.0001.0001.000NaN
tel_no1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
base_ymd1.0001.0000.0620.0000.0000.3560.239NaN1.0001.000
2023-12-10T18:52:55.762575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
base_ymdarea_nm
base_ymd1.0000.207
area_nm0.2071.000
2023-12-10T18:52:56.029794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
city_do_cdcity_gn_gu_cdxpos_loypos_laarea_nmbase_ymd
city_do_cd1.0000.9930.413-0.7210.9570.072
city_gn_gu_cd0.9931.0000.390-0.7070.9010.000
xpos_lo0.4130.3901.000-0.0000.5710.000
ypos_la-0.721-0.707-0.0001.0000.7130.342
area_nm0.9570.9010.5710.7131.0000.207
base_ymd0.0720.0000.0000.3420.2071.000

Missing values

2023-12-10T18:52:46.240542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T18:52:46.508590image/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.
2023-12-10T18:52:46.732059image/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

entrp_nmload_addrcity_do_cdcity_gn_gu_cdxpos_loypos_laarea_nmhomepage_urltel_nobase_ymd
0강릉오죽한옥마을강원도 강릉시 죽헌길 114(죽헌동)4242150128.87861237.777114강원http://www.ojuk.or.kr033-655-11172020-12-31
1올모스트홈스테이 청송점경상북도 청송군 주왕산면 주왕산로 4944747750129.10718736.377957경북<NA>2367771212020.12.31
2동해한옥 동안재강원도 동해시 천곡1길 74-2(천곡동)4242170129.10838637.528262강원http://www.donganjae.com<NA>2020-12-31
3태백산 한옥펜션강원도 태백시 소롯골길 34(소도동)4242190128.95198937.134189강원http://www.ok114.co.kr/0335522367033-552-23672020-12-31
4화천한옥학교강원도 화천군 간동면 모현동로 182-354242790127.78927438.038838강원http://www.hanokschool.co.kr033-442-33662020-12-31
5다가가다한옥강원도 화천군 화천읍 평화로 1674242790127.72568238.096749강원https://dagagadahanok.modoo.at/033-441-14882020-12-31
6한국민속촌경기도 용인시 기흥구 민속촌로 90 한국민속촌4141463127.12065737.259318경기http://www.koreanfolk.co.kr/031-2888-00002020-12-31
7(주)인산가 죽림지점 한옥체험관경상남도 함양군 함양읍 삼봉로 292-90 (영호당, 보인당)4848870<NA><NA>경남<NA>5596399912020.12.31
8신풍재경기도 수원시 팔달구 화서문로42번길 34(신풍동)4141115127.0135137.283857경기<NA><NA>2020-12-31
9양평이포한옥경기도 양평군 개군면 여양로 21144141830127.54016937.413337경기<NA><NA>2020-12-31
entrp_nmload_addrcity_do_cdcity_gn_gu_cdxpos_loypos_laarea_nmhomepage_urltel_nobase_ymd
90한옥 혜윰전라북도 전주시 완산구 향교길 42-5(교동)4545111127.15204735.811496전북http://www.hanokhyeyum.com/<NA>2020-12-31
91마당예쁜집전라북도 전주시 완산구 향교길 79-8(교동)4545111127.15388635.812302전북http://www.madangzip.kr/<NA>2020-12-31
92남극노인성제주특별자치도 서귀포시 하신상로 216(상효동)5050130126.6069233.276086제주https://canopusjeju.modoo.at/064-733-88692020-12-31
93먼나머루휴양펜션제주특별자치도 제주시 조천읍 남조로 25285050110126.65060133.488607제주https://mnmr.modoo.at/<NA>2020-12-31
94공주한옥마을충청남도 공주시 관광단지길 12(웅진동)4444150127.10883136.464487충남http://www.gongju.go.kr/hanok041-840-89002020-12-31
95공주한옥마을충청남도 공주시 관광단지길 12(웅진동)4444150127.10883136.464487충남http://hanok.gongju.go.kr/#popup041-840-89032020-12-31
96한채당 한옥체험관충청남도 태안군 소원면 송의로 695-94444825126.16127636.830031충남http://hancorp.co.kr/stay/031-792-80002020-12-31
97가영충청북도 청주시 청원구 오창읍 미래지로 71-544343110127.40549836.735082충북<NA><NA>2020-12-31
98한채당한옥체험관(석천재)경기도 하남시 윗배알미길 138 (배알미동)4141450127.27653237.514794경기<NA>3179280002020.12.31
99공주하숙마을충청남도 공주시 제민천1길 73-1 (반죽동)4444150127.12286836.452479충남<NA>4185247472020.12.31