Overview

Dataset statistics

Number of variables14
Number of observations45
Missing cells61
Missing cells (%)9.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.1 KiB
Average record size in memory114.9 B

Variable types

Text8
Categorical6

Dataset

Description우수숙박시설굿스테이현황_150828
Author전라북도
URLhttps://www.bigdatahub.go.kr/opendata/dataSet/detail.nm?contentId=37&rlik=49451aebf056b486&serviceId=201648

Alerts

Unnamed: 12 has constant value ""Constant
Unnamed: 1 is highly overall correlated with Unnamed: 4 and 1 other fieldsHigh correlation
Unnamed: 3 is highly overall correlated with Unnamed: 4 and 1 other fieldsHigh correlation
Unnamed: 4 is highly overall correlated with Unnamed: 1 and 3 other fieldsHigh correlation
Unnamed: 9 is highly overall correlated with Unnamed: 1 and 3 other fieldsHigh correlation
Unnamed: 10 is highly overall correlated with Unnamed: 4 and 1 other fieldsHigh correlation
Unnamed: 13 is highly overall correlated with Unnamed: 4 and 1 other fieldsHigh correlation
Unnamed: 1 is highly imbalanced (80.6%)Imbalance
Unnamed: 3 is highly imbalanced (80.6%)Imbalance
Unnamed: 10 is highly imbalanced (80.6%)Imbalance
Unnamed: 13 is highly imbalanced (80.6%)Imbalance
우수숙박시설(굿스테이) 현황(2015년 8월) has 1 (2.2%) missing valuesMissing
Unnamed: 2 has 1 (2.2%) missing valuesMissing
Unnamed: 5 has 1 (2.2%) missing valuesMissing
Unnamed: 6 has 1 (2.2%) missing valuesMissing
Unnamed: 7 has 1 (2.2%) missing valuesMissing
Unnamed: 8 has 1 (2.2%) missing valuesMissing
Unnamed: 11 has 11 (24.4%) missing valuesMissing
Unnamed: 12 has 44 (97.8%) missing valuesMissing

Reproduction

Analysis started2024-03-14 02:31:08.358945
Analysis finished2024-03-14 02:31:09.498719
Duration1.14 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

우수숙박시설(굿스테이) 현황(2015년 8월)
Text

MISSING 

Distinct44
Distinct (%)100.0%
Missing1
Missing (%)2.2%
Memory size492.0 B
2024-03-14T11:31:09.637043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length2
Median length2
Mean length1.7954545
Min length1

Characters and Unicode

Total characters79
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique44 ?
Unique (%)100.0%

Sample

1st row연번
2nd row1
3rd row2
4th row3
5th row4
ValueCountFrequency (%)
연번 1
 
2.3%
1 1
 
2.3%
38 1
 
2.3%
39 1
 
2.3%
40 1
 
2.3%
41 1
 
2.3%
42 1
 
2.3%
43 1
 
2.3%
44 1
 
2.3%
45 1
 
2.3%
Other values (34) 34
77.3%
2024-03-14T11:31:09.922710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4 15
19.0%
3 13
16.5%
5 13
16.5%
1 12
15.2%
2 5
 
6.3%
6 5
 
6.3%
7 5
 
6.3%
8 3
 
3.8%
9 3
 
3.8%
0 3
 
3.8%
Other values (2) 2
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 77
97.5%
Other Letter 2
 
2.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 15
19.5%
3 13
16.9%
5 13
16.9%
1 12
15.6%
2 5
 
6.5%
6 5
 
6.5%
7 5
 
6.5%
8 3
 
3.9%
9 3
 
3.9%
0 3
 
3.9%
Other Letter
ValueCountFrequency (%)
연 1
50.0%
번 1
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 77
97.5%
Hangul 2
 
2.5%

Most frequent character per script

Common
ValueCountFrequency (%)
4 15
19.5%
3 13
16.9%
5 13
16.9%
1 12
15.6%
2 5
 
6.5%
6 5
 
6.5%
7 5
 
6.5%
8 3
 
3.9%
9 3
 
3.9%
0 3
 
3.9%
Hangul
ValueCountFrequency (%)
연 1
50.0%
번 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 77
97.5%
Hangul 2
 
2.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 15
19.5%
3 13
16.9%
5 13
16.9%
1 12
15.6%
2 5
 
6.5%
6 5
 
6.5%
7 5
 
6.5%
8 3
 
3.9%
9 3
 
3.9%
0 3
 
3.9%
Hangul
ValueCountFrequency (%)
연 1
50.0%
번 1
50.0%

Unnamed: 1
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size492.0 B
숙박업
43 
업종명
 
1
<NA>
 
1

Length

Max length4
Median length3
Mean length3.0222222
Min length3

Unique

Unique2 ?
Unique (%)4.4%

Sample

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

Common Values

ValueCountFrequency (%)
숙박업 43
95.6%
업종명 1
 
2.2%
<NA> 1
 
2.2%

Length

2024-03-14T11:31:10.035787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T11:31:10.145360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
숙박업 43
95.6%
업종명 1
 
2.2%
na 1
 
2.2%

Unnamed: 2
Text

MISSING 

Distinct44
Distinct (%)100.0%
Missing1
Missing (%)2.2%
Memory size492.0 B
2024-03-14T11:31:10.357934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length10.5
Mean length5.7727273
Min length3

Characters and Unicode

Total characters254
Distinct characters117
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

Unique44 ?
Unique (%)100.0%

Sample

1st row영업소명(공식상)
2nd row무주이리스모텔
3rd row세르빌호텔
4th row제일산장
5th row채석리조텔오크빌
ValueCountFrequency (%)
영업소명(공식상 1
 
1.9%
그린토피아 1
 
1.9%
샤르모텔 1
 
1.9%
s모텔(구 1
 
1.9%
금수장 1
 
1.9%
왕궁온천 1
 
1.9%
전주호텔(구 1
 
1.9%
아테네 1
 
1.9%
마이산콘도빌 1
 
1.9%
펜션설화 1
 
1.9%
Other values (43) 43
81.1%
2024-03-14T11:31:10.648191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
텔 27
 
10.6%
호 15
 
5.9%
모 11
 
4.3%
9
 
3.5%
스 8
 
3.1%
리 7
 
2.8%
산 6
 
2.4%
( 5
 
2.0%
아 5
 
2.0%
) 5
 
2.0%
Other values (107) 156
61.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 228
89.8%
Space Separator 9
 
3.5%
Open Punctuation 5
 
2.0%
Close Punctuation 5
 
2.0%
Decimal Number 3
 
1.2%
Uppercase Letter 3
 
1.2%
Math Symbol 1
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
텔 27
 
11.8%
호 15
 
6.6%
모 11
 
4.8%
스 8
 
3.5%
리 7
 
3.1%
산 6
 
2.6%
아 5
 
2.2%
빌 4
 
1.8%
우 4
 
1.8%
션 3
 
1.3%
Other values (98) 138
60.5%
Uppercase Letter
ValueCountFrequency (%)
S 1
33.3%
W 1
33.3%
H 1
33.3%
Decimal Number
ValueCountFrequency (%)
1 2
66.7%
5 1
33.3%
Space Separator
ValueCountFrequency (%)
9
100.0%
Open Punctuation
ValueCountFrequency (%)
( 5
100.0%
Close Punctuation
ValueCountFrequency (%)
) 5
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 228
89.8%
Common 23
 
9.1%
Latin 3
 
1.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
텔 27
 
11.8%
호 15
 
6.6%
모 11
 
4.8%
스 8
 
3.5%
리 7
 
3.1%
산 6
 
2.6%
아 5
 
2.2%
빌 4
 
1.8%
우 4
 
1.8%
션 3
 
1.3%
Other values (98) 138
60.5%
Common
ValueCountFrequency (%)
9
39.1%
( 5
21.7%
) 5
21.7%
1 2
 
8.7%
5 1
 
4.3%
~ 1
 
4.3%
Latin
ValueCountFrequency (%)
S 1
33.3%
W 1
33.3%
H 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 228
89.8%
ASCII 26
 
10.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
텔 27
 
11.8%
호 15
 
6.6%
모 11
 
4.8%
스 8
 
3.5%
리 7
 
3.1%
산 6
 
2.6%
아 5
 
2.2%
빌 4
 
1.8%
우 4
 
1.8%
션 3
 
1.3%
Other values (98) 138
60.5%
ASCII
ValueCountFrequency (%)
9
34.6%
( 5
19.2%
) 5
19.2%
1 2
 
7.7%
S 1
 
3.8%
W 1
 
3.8%
H 1
 
3.8%
5 1
 
3.8%
~ 1
 
3.8%

Unnamed: 3
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size492.0 B
전북
43 
광역시/도
 
1
<NA>
 
1

Length

Max length5
Median length2
Mean length2.1111111
Min length2

Unique

Unique2 ?
Unique (%)4.4%

Sample

1st row광역시/도
2nd row<NA>
3rd row전북
4th row전북
5th row전북

Common Values

ValueCountFrequency (%)
전북 43
95.6%
광역시/도 1
 
2.2%
<NA> 1
 
2.2%

Length

2024-03-14T11:31:10.754344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T11:31:10.836315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전북 43
95.6%
광역시/도 1
 
2.2%
na 1
 
2.2%

Unnamed: 4
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)31.1%
Missing0
Missing (%)0.0%
Memory size492.0 B
전주시
9 
무주군
7 
군산시
6 
남원시
6 
부안군
3 
Other values (9)
14 

Length

Max length4
Median length3
Mean length3.0222222
Min length3

Unique

Unique5 ?
Unique (%)11.1%

Sample

1st row시군구
2nd row<NA>
3rd row무주군
4th row정읍시
5th row무주군

Common Values

ValueCountFrequency (%)
전주시 9
20.0%
무주군 7
15.6%
군산시 6
13.3%
남원시 6
13.3%
부안군 3
 
6.7%
고창군 3
 
6.7%
정읍시 2
 
4.4%
진안군 2
 
4.4%
장수군 2
 
4.4%
시군구 1
 
2.2%
Other values (4) 4
8.9%

Length

2024-03-14T11:31:10.933541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
전주시 9
20.0%
무주군 7
15.6%
군산시 6
13.3%
남원시 6
13.3%
부안군 3
 
6.7%
고창군 3
 
6.7%
정읍시 2
 
4.4%
진안군 2
 
4.4%
장수군 2
 
4.4%
시군구 1
 
2.2%
Other values (4) 4
8.9%

Unnamed: 5
Text

MISSING 

Distinct44
Distinct (%)100.0%
Missing1
Missing (%)2.2%
Memory size492.0 B
2024-03-14T11:31:11.154056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length38
Median length28.5
Mean length22.636364
Min length2

Characters and Unicode

Total characters996
Distinct characters111
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

Unique44 ?
Unique (%)100.0%

Sample

1st row주소
2nd row전라북도 무주군 무주읍 한풍루로 381-7
3rd row전라북도 정읍시 내장산로 937
4th row전라북도 무주군 설천면 구천동1로 156
5th row전라북도 부안군 변산면 격포로 196
ValueCountFrequency (%)
전라북도 43
 
19.7%
전주시 9
 
4.1%
무주군 7
 
3.2%
덕진구 7
 
3.2%
남원시 6
 
2.8%
군산시 6
 
2.8%
설천면 4
 
1.8%
구천동로 3
 
1.4%
고창군 3
 
1.4%
산정2길 3
 
1.4%
Other values (113) 127
58.3%
2024-03-14T11:31:11.552111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
174
 
17.5%
전 54
 
5.4%
도 46
 
4.6%
라 43
 
4.3%
북 43
 
4.3%
1 36
 
3.6%
2 30
 
3.0%
군 25
 
2.5%
주 24
 
2.4%
시 24
 
2.4%
Other values (101) 497
49.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 616
61.8%
Space Separator 174
 
17.5%
Decimal Number 154
 
15.5%
Dash Punctuation 21
 
2.1%
Open Punctuation 14
 
1.4%
Close Punctuation 14
 
1.4%
Other Punctuation 3
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
전 54
 
8.8%
도 46
 
7.5%
라 43
 
7.0%
북 43
 
7.0%
군 25
 
4.1%
주 24
 
3.9%
시 24
 
3.9%
로 23
 
3.7%
산 22
 
3.6%
면 18
 
2.9%
Other values (86) 294
47.7%
Decimal Number
ValueCountFrequency (%)
1 36
23.4%
2 30
19.5%
3 17
11.0%
6 16
10.4%
4 14
 
9.1%
9 13
 
8.4%
7 9
 
5.8%
0 7
 
4.5%
8 6
 
3.9%
5 6
 
3.9%
Space Separator
ValueCountFrequency (%)
174
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 21
100.0%
Open Punctuation
ValueCountFrequency (%)
( 14
100.0%
Close Punctuation
ValueCountFrequency (%)
) 14
100.0%
Other Punctuation
ValueCountFrequency (%)
? 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 616
61.8%
Common 380
38.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
전 54
 
8.8%
도 46
 
7.5%
라 43
 
7.0%
북 43
 
7.0%
군 25
 
4.1%
주 24
 
3.9%
시 24
 
3.9%
로 23
 
3.7%
산 22
 
3.6%
면 18
 
2.9%
Other values (86) 294
47.7%
Common
ValueCountFrequency (%)
174
45.8%
1 36
 
9.5%
2 30
 
7.9%
- 21
 
5.5%
3 17
 
4.5%
6 16
 
4.2%
( 14
 
3.7%
4 14
 
3.7%
) 14
 
3.7%
9 13
 
3.4%
Other values (5) 31
 
8.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 616
61.8%
ASCII 380
38.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
174
45.8%
1 36
 
9.5%
2 30
 
7.9%
- 21
 
5.5%
3 17
 
4.5%
6 16
 
4.2%
( 14
 
3.7%
4 14
 
3.7%
) 14
 
3.7%
9 13
 
3.4%
Other values (5) 31
 
8.2%
Hangul
ValueCountFrequency (%)
전 54
 
8.8%
도 46
 
7.5%
라 43
 
7.0%
북 43
 
7.0%
군 25
 
4.1%
주 24
 
3.9%
시 24
 
3.9%
로 23
 
3.7%
산 22
 
3.6%
면 18
 
2.9%
Other values (86) 294
47.7%

Unnamed: 6
Text

MISSING 

Distinct30
Distinct (%)68.2%
Missing1
Missing (%)2.2%
Memory size492.0 B
2024-03-14T11:31:11.930280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length2
Mean length2.0681818
Min length1

Characters and Unicode

Total characters91
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21 ?
Unique (%)47.7%

Sample

1st row총객실수
2nd row50
3rd row32
4th row20
5th row30
ValueCountFrequency (%)
32 5
 
11.4%
30 5
 
11.4%
31 3
 
6.8%
38 2
 
4.5%
35 2
 
4.5%
28 2
 
4.5%
41 2
 
4.5%
33 2
 
4.5%
34 2
 
4.5%
50 2
 
4.5%
Other values (17) 17
38.6%
2024-03-14T11:31:12.174272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 26
28.6%
2 14
15.4%
0 10
 
11.0%
1 8
 
8.8%
4 7
 
7.7%
5 6
 
6.6%
8 5
 
5.5%
6 4
 
4.4%
3
 
3.3%
9 2
 
2.2%
Other values (5) 6
 
6.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 84
92.3%
Other Letter 4
 
4.4%
Space Separator 3
 
3.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 26
31.0%
2 14
16.7%
0 10
 
11.9%
1 8
 
9.5%
4 7
 
8.3%
5 6
 
7.1%
8 5
 
6.0%
6 4
 
4.8%
9 2
 
2.4%
7 2
 
2.4%
Other Letter
ValueCountFrequency (%)
총 1
25.0%
객 1
25.0%
실 1
25.0%
수 1
25.0%
Space Separator
ValueCountFrequency (%)
3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 87
95.6%
Hangul 4
 
4.4%

Most frequent character per script

Common
ValueCountFrequency (%)
3 26
29.9%
2 14
16.1%
0 10
 
11.5%
1 8
 
9.2%
4 7
 
8.0%
5 6
 
6.9%
8 5
 
5.7%
6 4
 
4.6%
3
 
3.4%
9 2
 
2.3%
Hangul
ValueCountFrequency (%)
총 1
25.0%
객 1
25.0%
실 1
25.0%
수 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 87
95.6%
Hangul 4
 
4.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 26
29.9%
2 14
16.1%
0 10
 
11.5%
1 8
 
9.2%
4 7
 
8.0%
5 6
 
6.9%
8 5
 
5.7%
6 4
 
4.6%
3
 
3.4%
9 2
 
2.3%
Hangul
ValueCountFrequency (%)
총 1
25.0%
객 1
25.0%
실 1
25.0%
수 1
25.0%

Unnamed: 7
Text

MISSING 

Distinct44
Distinct (%)100.0%
Missing1
Missing (%)2.2%
Memory size492.0 B
2024-03-14T11:31:12.397321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length32
Median length12
Mean length12.272727
Min length4

Characters and Unicode

Total characters540
Distinct characters24
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique44 ?
Unique (%)100.0%

Sample

1st row전화번호
2nd row063-324-3400
3rd row063-538-9487
4th row063-322-3100
5th row063-583-8046
ValueCountFrequency (%)
전화번호 1
 
2.2%
063-273-2233 1
 
2.2%
063-538-5656 1
 
2.2%
063-636-7200 1
 
2.2%
063-584-9931 1
 
2.2%
063-653-3960 1
 
2.2%
063-291-5000 1
 
2.2%
063-291-4747(사무실 1
 
2.2%
063-247-3333 1
 
2.2%
063-432-4201 1
 
2.2%
Other values (35) 35
77.8%
2024-03-14T11:31:12.728506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 89
16.5%
- 87
16.1%
0 83
15.4%
6 75
13.9%
2 52
9.6%
4 35
 
6.5%
5 32
 
5.9%
1 20
 
3.7%
9 19
 
3.5%
8 18
 
3.3%
Other values (14) 30
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 439
81.3%
Dash Punctuation 87
 
16.1%
Other Letter 7
 
1.3%
Space Separator 2
 
0.4%
Open Punctuation 1
 
0.2%
Close Punctuation 1
 
0.2%
Control 1
 
0.2%
Other Punctuation 1
 
0.2%
Math Symbol 1
 
0.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 89
20.3%
0 83
18.9%
6 75
17.1%
2 52
11.8%
4 35
 
8.0%
5 32
 
7.3%
1 20
 
4.6%
9 19
 
4.3%
8 18
 
4.1%
7 16
 
3.6%
Other Letter
ValueCountFrequency (%)
실 1
14.3%
무 1
14.3%
사 1
14.3%
전 1
14.3%
화 1
14.3%
호 1
14.3%
번 1
14.3%
Dash Punctuation
ValueCountFrequency (%)
- 87
100.0%
Space Separator
ValueCountFrequency (%)
2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Control
ValueCountFrequency (%)
1
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 533
98.7%
Hangul 7
 
1.3%

Most frequent character per script

Common
ValueCountFrequency (%)
3 89
16.7%
- 87
16.3%
0 83
15.6%
6 75
14.1%
2 52
9.8%
4 35
 
6.6%
5 32
 
6.0%
1 20
 
3.8%
9 19
 
3.6%
8 18
 
3.4%
Other values (7) 23
 
4.3%
Hangul
ValueCountFrequency (%)
실 1
14.3%
무 1
14.3%
사 1
14.3%
전 1
14.3%
화 1
14.3%
호 1
14.3%
번 1
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 533
98.7%
Hangul 7
 
1.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 89
16.7%
- 87
16.3%
0 83
15.6%
6 75
14.1%
2 52
9.8%
4 35
 
6.6%
5 32
 
6.0%
1 20
 
3.8%
9 19
 
3.6%
8 18
 
3.4%
Other values (7) 23
 
4.3%
Hangul
ValueCountFrequency (%)
실 1
14.3%
무 1
14.3%
사 1
14.3%
전 1
14.3%
화 1
14.3%
호 1
14.3%
번 1
14.3%

Unnamed: 8
Text

MISSING 

Distinct32
Distinct (%)72.7%
Missing1
Missing (%)2.2%
Memory size492.0 B
2024-03-14T11:31:12.882490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length42
Median length30
Mean length15.477273
Min length1

Characters and Unicode

Total characters681
Distinct characters44
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique31 ?
Unique (%)70.5%

Sample

1st row홈페이지
2nd row-
3rd rowhttp://063-538-9487.114service.co.kr
4th rowwww.jlsj.co.kr/
5th rowwww.csr063.com/
ValueCountFrequency (%)
13
29.5%
www.hiddenmotel.com 1
 
2.3%
www.seolguk.com 1
 
2.3%
http://www.063-273-2233.kti114.net/idx.htm 1
 
2.3%
www.jungeup.com/538-5656 1
 
2.3%
http://cafe.daum.net/charmotel 1
 
2.3%
wgspa.co.kr 1
 
2.3%
www.pensionsulwha.com 1
 
2.3%
www.hhillsresort.co.kr 1
 
2.3%
www.mujulaube.com 1
 
2.3%
Other values (22) 22
50.0%
2024-03-14T11:31:13.156809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
w 80
 
11.7%
. 69
 
10.1%
o 45
 
6.6%
t 42
 
6.2%
/ 33
 
4.8%
e 33
 
4.8%
c 31
 
4.6%
r 28
 
4.1%
m 27
 
4.0%
h 26
 
3.8%
Other values (34) 267
39.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 500
73.4%
Other Punctuation 113
 
16.6%
Decimal Number 40
 
5.9%
Dash Punctuation 21
 
3.1%
Other Letter 4
 
0.6%
Space Separator 3
 
0.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
w 80
16.0%
o 45
 
9.0%
t 42
 
8.4%
e 33
 
6.6%
c 31
 
6.2%
r 28
 
5.6%
m 27
 
5.4%
h 26
 
5.2%
a 26
 
5.2%
n 24
 
4.8%
Other values (15) 138
27.6%
Decimal Number
ValueCountFrequency (%)
3 8
20.0%
6 6
15.0%
1 6
15.0%
4 4
10.0%
5 4
10.0%
2 3
 
7.5%
8 3
 
7.5%
0 3
 
7.5%
7 2
 
5.0%
9 1
 
2.5%
Other Letter
ValueCountFrequency (%)
이 1
25.0%
지 1
25.0%
페 1
25.0%
홈 1
25.0%
Other Punctuation
ValueCountFrequency (%)
. 69
61.1%
/ 33
29.2%
: 11
 
9.7%
Dash Punctuation
ValueCountFrequency (%)
- 21
100.0%
Space Separator
ValueCountFrequency (%)
3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 500
73.4%
Common 177
 
26.0%
Hangul 4
 
0.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
w 80
16.0%
o 45
 
9.0%
t 42
 
8.4%
e 33
 
6.6%
c 31
 
6.2%
r 28
 
5.6%
m 27
 
5.4%
h 26
 
5.2%
a 26
 
5.2%
n 24
 
4.8%
Other values (15) 138
27.6%
Common
ValueCountFrequency (%)
. 69
39.0%
/ 33
18.6%
- 21
 
11.9%
: 11
 
6.2%
3 8
 
4.5%
6 6
 
3.4%
1 6
 
3.4%
4 4
 
2.3%
5 4
 
2.3%
2 3
 
1.7%
Other values (5) 12
 
6.8%
Hangul
ValueCountFrequency (%)
이 1
25.0%
지 1
25.0%
페 1
25.0%
홈 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 677
99.4%
Hangul 4
 
0.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
w 80
 
11.8%
. 69
 
10.2%
o 45
 
6.6%
t 42
 
6.2%
/ 33
 
4.9%
e 33
 
4.9%
c 31
 
4.6%
r 28
 
4.1%
m 27
 
4.0%
h 26
 
3.8%
Other values (30) 263
38.8%
Hangul
ValueCountFrequency (%)
이 1
25.0%
지 1
25.0%
페 1
25.0%
홈 1
25.0%

Unnamed: 9
Categorical

HIGH CORRELATION 

Distinct21
Distinct (%)46.7%
Missing0
Missing (%)0.0%
Memory size492.0 B
30대
7 
20대
6 
40대
5 
50대
4 
25대
4 
Other values (16)
19 

Length

Max length5
Median length3
Mean length3.2
Min length3

Unique

Unique13 ?
Unique (%)28.9%

Sample

1st row주차장유무
2nd row<NA>
3rd row40대
4th row50대
5th row20대

Common Values

ValueCountFrequency (%)
30대 7
15.6%
20대 6
13.3%
40대 5
11.1%
50대 4
 
8.9%
25대 4
 
8.9%
15대 2
 
4.4%
100대 2
 
4.4%
120대 2
 
4.4%
35대 1
 
2.2%
17대 1
 
2.2%
Other values (11) 11
24.4%

Length

2024-03-14T11:31:13.286897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
30대 7
15.6%
20대 6
13.3%
40대 5
11.1%
50대 4
 
8.9%
25대 4
 
8.9%
15대 2
 
4.4%
100대 2
 
4.4%
120대 2
 
4.4%
45대 1
 
2.2%
주차장유무 1
 
2.2%
Other values (11) 11
24.4%

Unnamed: 10
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size492.0 B
신용카드, 현금
43 
결제방법
 
1
<NA>
 
1

Length

Max length8
Median length8
Mean length7.8222222
Min length4

Unique

Unique2 ?
Unique (%)4.4%

Sample

1st row결제방법
2nd row<NA>
3rd row신용카드, 현금
4th row신용카드, 현금
5th row신용카드, 현금

Common Values

ValueCountFrequency (%)
신용카드, 현금 43
95.6%
결제방법 1
 
2.2%
<NA> 1
 
2.2%

Length

2024-03-14T11:31:13.397782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T11:31:13.485729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
신용카드 43
48.9%
현금 43
48.9%
결제방법 1
 
1.1%
na 1
 
1.1%

Unnamed: 11
Text

MISSING 

Distinct22
Distinct (%)64.7%
Missing11
Missing (%)24.4%
Memory size492.0 B
2024-03-14T11:31:13.644540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length11
Mean length6.4411765
Min length2

Characters and Unicode

Total characters219
Distinct characters46
Distinct categories4 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique14 ?
Unique (%)41.2%

Sample

1st row부대시설
2nd row한식당
3rd row세미나실, 족구장 등
4th row식당
5th row휴게소
ValueCountFrequency (%)
세미나실 13
24.1%
사우나실 6
11.1%
식음료장 5
 
9.3%
노래방 5
 
9.3%
등 4
 
7.4%
식당 3
 
5.6%
조식 3
 
5.6%
카페 2
 
3.7%
한식당 2
 
3.7%
족구장 2
 
3.7%
Other values (8) 9
16.7%
2024-03-14T11:31:13.939779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
실 22
 
10.0%
20
 
9.1%
나 19
 
8.7%
, 16
 
7.3%
식 14
 
6.4%
세 13
 
5.9%
미 13
 
5.9%
장 8
 
3.7%
당 6
 
2.7%
사 6
 
2.7%
Other values (36) 82
37.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 177
80.8%
Space Separator 20
 
9.1%
Other Punctuation 16
 
7.3%
Lowercase Letter 6
 
2.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
실 22
 
12.4%
나 19
 
10.7%
식 14
 
7.9%
세 13
 
7.3%
미 13
 
7.3%
장 8
 
4.5%
당 6
 
3.4%
사 6
 
3.4%
우 6
 
3.4%
래 5
 
2.8%
Other values (32) 65
36.7%
Lowercase Letter
ValueCountFrequency (%)
p 3
50.0%
c 3
50.0%
Space Separator
ValueCountFrequency (%)
20
100.0%
Other Punctuation
ValueCountFrequency (%)
, 16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 177
80.8%
Common 36
 
16.4%
Latin 6
 
2.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
실 22
 
12.4%
나 19
 
10.7%
식 14
 
7.9%
세 13
 
7.3%
미 13
 
7.3%
장 8
 
4.5%
당 6
 
3.4%
사 6
 
3.4%
우 6
 
3.4%
래 5
 
2.8%
Other values (32) 65
36.7%
Common
ValueCountFrequency (%)
20
55.6%
, 16
44.4%
Latin
ValueCountFrequency (%)
p 3
50.0%
c 3
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 177
80.8%
ASCII 42
 
19.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
실 22
 
12.4%
나 19
 
10.7%
식 14
 
7.9%
세 13
 
7.3%
미 13
 
7.3%
장 8
 
4.5%
당 6
 
3.4%
사 6
 
3.4%
우 6
 
3.4%
래 5
 
2.8%
Other values (32) 65
36.7%
ASCII
ValueCountFrequency (%)
20
47.6%
, 16
38.1%
p 3
 
7.1%
c 3
 
7.1%

Unnamed: 12
Text

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing44
Missing (%)97.8%
Memory size492.0 B
2024-03-14T11:31:14.057729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters6
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row주변관광정보
ValueCountFrequency (%)
주변관광정보 1
100.0%
2024-03-14T11:31:14.258117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
주 1
16.7%
변 1
16.7%
관 1
16.7%
광 1
16.7%
정 1
16.7%
보 1
16.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 6
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 1
16.7%
변 1
16.7%
관 1
16.7%
광 1
16.7%
정 1
16.7%
보 1
16.7%

Most occurring scripts

ValueCountFrequency (%)
Hangul 6
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 1
16.7%
변 1
16.7%
관 1
16.7%
광 1
16.7%
정 1
16.7%
보 1
16.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 6
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
주 1
16.7%
변 1
16.7%
관 1
16.7%
광 1
16.7%
정 1
16.7%
보 1
16.7%

Unnamed: 13
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size492.0 B
2015. 8.
43 
데이터기준일자
 
1
<NA>
 
1

Length

Max length8
Median length8
Mean length7.8888889
Min length4

Unique

Unique2 ?
Unique (%)4.4%

Sample

1st row데이터기준일자
2nd row<NA>
3rd row2015. 8.
4th row2015. 8.
5th row2015. 8.

Common Values

ValueCountFrequency (%)
2015. 8. 43
95.6%
데이터기준일자 1
 
2.2%
<NA> 1
 
2.2%

Length

2024-03-14T11:31:14.377279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T11:31:14.515454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2015 43
48.9%
8 43
48.9%
데이터기준일자 1
 
1.1%
na 1
 
1.1%

Correlations

2024-03-14T11:31:14.603148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
우수숙박시설(굿스테이) 현황(2015년 8월)Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 7Unnamed: 8Unnamed: 9Unnamed: 10Unnamed: 11Unnamed: 13
우수숙박시설(굿스테이) 현황(2015년 8월)1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
Unnamed: 11.0001.0001.0000.6731.0001.0001.0001.0001.0001.0000.6731.0000.673
Unnamed: 21.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
Unnamed: 31.0000.6731.0001.0001.0001.0001.0001.0001.0001.0000.6731.0000.673
Unnamed: 41.0001.0001.0001.0001.0001.0000.0001.0000.6910.3681.0000.7221.000
Unnamed: 51.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
Unnamed: 61.0001.0001.0001.0000.0001.0001.0001.0000.9190.0001.0000.9541.000
Unnamed: 71.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
Unnamed: 81.0001.0001.0001.0000.6911.0000.9191.0001.0000.7421.0000.8591.000
Unnamed: 91.0001.0001.0001.0000.3681.0000.0001.0000.7421.0001.0000.4931.000
Unnamed: 101.0000.6731.0000.6731.0001.0001.0001.0001.0001.0001.0001.0000.673
Unnamed: 111.0001.0001.0001.0000.7221.0000.9541.0000.8590.4931.0001.0001.000
Unnamed: 131.0000.6731.0000.6731.0001.0001.0001.0001.0001.0000.6731.0001.000
2024-03-14T11:31:14.796307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Unnamed: 4Unnamed: 13Unnamed: 10Unnamed: 9Unnamed: 3Unnamed: 1
Unnamed: 41.0000.8590.8590.0000.8590.859
Unnamed: 130.8591.0000.4690.7560.4690.469
Unnamed: 100.8590.4691.0000.7560.4690.469
Unnamed: 90.0000.7560.7561.0000.7560.756
Unnamed: 30.8590.4690.4690.7561.0000.469
Unnamed: 10.8590.4690.4690.7560.4691.000
2024-03-14T11:31:14.939482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Unnamed: 1Unnamed: 3Unnamed: 4Unnamed: 9Unnamed: 10Unnamed: 13
Unnamed: 11.0000.4690.8590.7560.4690.469
Unnamed: 30.4691.0000.8590.7560.4690.469
Unnamed: 40.8590.8591.0000.0000.8590.859
Unnamed: 90.7560.7560.0001.0000.7560.756
Unnamed: 100.4690.4690.8590.7561.0000.469
Unnamed: 130.4690.4690.8590.7560.4691.000

Missing values

2024-03-14T11:31:08.972206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T11:31:09.145701image/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.
2024-03-14T11:31:09.332458image/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

우수숙박시설(굿스테이) 현황(2015년 8월)Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 7Unnamed: 8Unnamed: 9Unnamed: 10Unnamed: 11Unnamed: 12Unnamed: 13
0연번업종명영업소명(공식상)광역시/도시군구주소총객실수전화번호홈페이지주차장유무결제방법부대시설주변관광정보데이터기준일자
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21숙박업무주이리스모텔전북무주군전라북도 무주군 무주읍 한풍루로 381-750063-324-3400-40대신용카드, 현금<NA><NA>2015. 8.
32숙박업세르빌호텔전북정읍시전라북도 정읍시 내장산로 93732063-538-9487http://063-538-9487.114service.co.kr50대신용카드, 현금한식당<NA>2015. 8.
43숙박업제일산장전북무주군전라북도 무주군 설천면 구천동1로 15620063-322-3100www.jlsj.co.kr/20대신용카드, 현금세미나실, 족구장 등<NA>2015. 8.
54숙박업채석리조텔오크빌전북부안군전라북도 부안군 변산면 격포로 19630063-583-8046www.csr063.com/15대신용카드, 현금식당<NA>2015. 8.
65숙박업모악산모텔전북완주군전라북도 완주군 구이면 모악산길 104-1026063-222-2023http://www.moakmotel.com30대신용카드, 현금<NA><NA>2015. 8.
76숙박업웨스턴호텔전북군산시전라북도 군산시 옥서면 선연길 1733063-471-0715www.western-inn.kr/30대신용카드, 현금휴게소<NA>2015. 8.
87숙박업지리산칸호텔전북남원시전라북도 남원시 산내면 지리산로 81536063-626-2114http://www.jirisankhanhotel.com40대신용카드, 현금세미나실, 노래방<NA>2015. 8.
98숙박업휴모텔(군산)전북군산시전라북도 군산시 가도안1길 6140063-464-6081-40대신용카드, 현금<NA><NA>2015. 8.
우수숙박시설(굿스테이) 현황(2015년 8월)Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 7Unnamed: 8Unnamed: 9Unnamed: 10Unnamed: 11Unnamed: 12Unnamed: 13
3548숙박업설국펜션전북무주군전라북도 무주군 설천면 원삼공2길 9-715063-324-2220www.seolguk.com20대신용카드, 현금세미나실, 스포츠시설<NA>2015. 8.
3649숙박업로베펜션전북무주군전라북도 무주군 설천면 구천동1로 12910063-322-9914www.mujulaube.com18대신용카드, 현금세미나실, 노래방<NA>2015. 8.
3750숙박업무주 덕유산 레저바이크텔전북무주군전라북도 무주군 설천면 구천동로 9686063-320-2575www.mj1614.com40대신용카드, 현금세미나실, 사우나실, 노래방 등<NA>2015. 8.
3851숙박업타코마 팜 리조트전북장수군전라북도 장수군 계남면 장수로 2662-1150063-353-8200www.tacomaresort.co.kr200대신용카드, 현금세미나실, 사우나실, 노래방 등<NA>2015. 8.
3952숙박업넥스텔전북고창군전라북도 고창군 고창읍 월암수월길 112 (월암리)38063-564-8999-45대신용카드, 현금<NA><NA>2015. 8.
4053숙박업자라게스트하우스전북남원시전라북도 남원시 인월면 인월장터로 6 (인월리)7063-626-2129www.zaraguest.com20대신용카드, 현금조식<NA>2015. 8.
4154숙박업W 호텔전북군산시전라북도 군산시 소룡1길 58 (소룡동)41063-464-6205~6-32대신용카드, 현금공공pc실, 카페<NA>2015. 8.
4255숙박업솔호텔전북전주시전라북도 전주시 덕진구 아중2길 22-4 (우아동2가 928-5번지)30063-261-7000www.hotelsol.co.kr25대신용카드, 현금<NA><NA>2015. 8.
4356숙박업르시엘전북전주시전라북도 전주시 덕진구 산정2길 23(산정동)31063-245-4848http://www.lecielhotel.com50대신용카드, 현금노래방<NA>2015. 8.
4457숙박업남원호텔전북남원시전라북도 남원시 주천면 정령치로 122-11(주천면)35063-626-3535www.namwonhotel.com100대신용카드, 현금뷔폐식당<NA>2015. 8.