Overview

Dataset statistics

Number of variables6
Number of observations135
Missing cells312
Missing cells (%)38.5%
Duplicate rows1
Duplicate rows (%)0.7%
Total size in memory6.5 KiB
Average record size in memory49.0 B

Variable types

Categorical2
Text3
DateTime1

Dataset

Description이 데이터는 충청남도 금산군의 숙박업(업종구분, 업소명, 행정구역, 주소, 전화번호, 데이터기준일자)에 대한 정보를 제공합니다.
Author충청남도
URLhttps://alldam.chungnam.go.kr/index.chungnam?menuCd=DOM_000000201001001001&st=&cds=&orgCd=&apiType=&isOpen=Y&pageIndex=104&beforeMenuCd=DOM_000000201001001000&publicdatapk=15099798

Alerts

데이터기준일자 has constant value ""Constant
Dataset has 1 (0.7%) duplicate rowsDuplicates
업소명 has 76 (56.3%) missing valuesMissing
주소 has 76 (56.3%) missing valuesMissing
전화번호 has 84 (62.2%) missing valuesMissing
데이터기준일자 has 76 (56.3%) missing valuesMissing

Reproduction

Analysis started2024-01-09 20:24:07.744076
Analysis finished2024-01-09 20:24:08.441500
Duration0.7 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

업종구분
Categorical

Distinct3
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
<NA>
76 
일반
57 
생활
 
2

Length

Max length4
Median length4
Mean length3.1259259
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 76
56.3%
일반 57
42.2%
생활 2
 
1.5%

Length

2024-01-10T05:24:08.500503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T05:24:08.588252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 76
56.3%
일반 57
42.2%
생활 2
 
1.5%

업소명
Text

MISSING 

Distinct58
Distinct (%)98.3%
Missing76
Missing (%)56.3%
Memory size1.2 KiB
2024-01-10T05:24:08.757808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length9
Mean length5.2372881
Min length1

Characters and Unicode

Total characters309
Distinct characters126
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

Unique57 ?
Unique (%)96.6%

Sample

1st row연신여인숙
2nd row장수여인숙
3rd row산장여관
4th row힐튼모텔
5th row거북장여관
ValueCountFrequency (%)
나인모텔 2
 
3.3%
골프모텔원 1
 
1.7%
더슈나애견펜션 1
 
1.7%
금산한방스파(주 1
 
1.7%
월영산모텔 1
 
1.7%
스테이인터뷰금산 1
 
1.7%
서대산드림리조트 1
 
1.7%
다니아모텔 1
 
1.7%
심천자연휴양림콘도 1
 
1.7%
큐(q)모텔 1
 
1.7%
Other values (49) 49
81.7%
2024-01-10T05:24:09.077944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
텔 25
 
8.1%
모 18
 
5.8%
인 12
 
3.9%
여 11
 
3.6%
산 11
 
3.6%
관 11
 
3.6%
장 10
 
3.2%
크 9
 
2.9%
파 9
 
2.9%
무 6
 
1.9%
Other values (116) 187
60.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 301
97.4%
Close Punctuation 2
 
0.6%
Open Punctuation 2
 
0.6%
Decimal Number 2
 
0.6%
Uppercase Letter 1
 
0.3%
Space Separator 1
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
텔 25
 
8.3%
모 18
 
6.0%
인 12
 
4.0%
여 11
 
3.7%
산 11
 
3.7%
관 11
 
3.7%
장 10
 
3.3%
크 9
 
3.0%
파 9
 
3.0%
무 6
 
2.0%
Other values (110) 179
59.5%
Decimal Number
ValueCountFrequency (%)
2 1
50.0%
1 1
50.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Uppercase Letter
ValueCountFrequency (%)
Q 1
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 301
97.4%
Common 7
 
2.3%
Latin 1
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
텔 25
 
8.3%
모 18
 
6.0%
인 12
 
4.0%
여 11
 
3.7%
산 11
 
3.7%
관 11
 
3.7%
장 10
 
3.3%
크 9
 
3.0%
파 9
 
3.0%
무 6
 
2.0%
Other values (110) 179
59.5%
Common
ValueCountFrequency (%)
) 2
28.6%
( 2
28.6%
2 1
14.3%
1 1
14.3%
1
14.3%
Latin
ValueCountFrequency (%)
Q 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 301
97.4%
ASCII 8
 
2.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
텔 25
 
8.3%
모 18
 
6.0%
인 12
 
4.0%
여 11
 
3.7%
산 11
 
3.7%
관 11
 
3.7%
장 10
 
3.3%
크 9
 
3.0%
파 9
 
3.0%
무 6
 
2.0%
Other values (110) 179
59.5%
ASCII
ValueCountFrequency (%)
) 2
25.0%
( 2
25.0%
Q 1
12.5%
2 1
12.5%
1 1
12.5%
1
12.5%

행정구역
Categorical

Distinct9
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
<NA>
76 
금산읍
18 
진산면
16 
복수면
10 
추부면
 
7
Other values (4)
8 

Length

Max length4
Median length4
Mean length3.562963
Min length3

Unique

Unique2 ?
Unique (%)1.5%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 76
56.3%
금산읍 18
 
13.3%
진산면 16
 
11.9%
복수면 10
 
7.4%
추부면 7
 
5.2%
금성면 3
 
2.2%
남일면 3
 
2.2%
제원면 1
 
0.7%
군북면 1
 
0.7%

Length

2024-01-10T05:24:09.199296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T05:24:09.297707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 76
56.3%
금산읍 18
 
13.3%
진산면 16
 
11.9%
복수면 10
 
7.4%
추부면 7
 
5.2%
금성면 3
 
2.2%
남일면 3
 
2.2%
제원면 1
 
0.7%
군북면 1
 
0.7%

주소
Text

MISSING 

Distinct58
Distinct (%)98.3%
Missing76
Missing (%)56.3%
Memory size1.2 KiB
2024-01-10T05:24:09.521076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length34
Median length29
Mean length21.661017
Min length18

Characters and Unicode

Total characters1278
Distinct characters92
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

Unique57 ?
Unique (%)96.6%

Sample

1st row충청남도 금산군 금산읍 뒷담말길 17-5
2nd row충청남도 금산군 금산읍 뒷담말길 9-4
3rd row충청남도 금산군 금산읍 비호로 76
4th row충청남도 금산군 금산읍 금산로 1514
5th row충청남도 금산군 금산읍 인삼로 109
ValueCountFrequency (%)
충청남도 59
19.5%
금산군 59
19.5%
금산읍 18
 
5.9%
진산면 16
 
5.3%
복수로 11
 
3.6%
복수면 10
 
3.3%
추부면 7
 
2.3%
금산로 5
 
1.7%
대둔산로 5
 
1.7%
산내로 4
 
1.3%
Other values (89) 109
36.0%
2024-01-10T05:24:09.875921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
247
19.3%
산 113
 
8.8%
금 86
 
6.7%
청 62
 
4.9%
남 62
 
4.9%
군 61
 
4.8%
도 60
 
4.7%
충 59
 
4.6%
로 45
 
3.5%
면 41
 
3.2%
Other values (82) 442
34.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 815
63.8%
Space Separator 247
 
19.3%
Decimal Number 186
 
14.6%
Dash Punctuation 18
 
1.4%
Other Punctuation 6
 
0.5%
Close Punctuation 3
 
0.2%
Open Punctuation 3
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 113
13.9%
금 86
10.6%
청 62
 
7.6%
남 62
 
7.6%
군 61
 
7.5%
도 60
 
7.4%
충 59
 
7.2%
로 45
 
5.5%
면 41
 
5.0%
복 22
 
2.7%
Other values (67) 204
25.0%
Decimal Number
ValueCountFrequency (%)
1 36
19.4%
2 24
12.9%
4 24
12.9%
3 20
10.8%
5 17
9.1%
7 15
8.1%
0 14
 
7.5%
9 14
 
7.5%
6 13
 
7.0%
8 9
 
4.8%
Space Separator
ValueCountFrequency (%)
247
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 18
100.0%
Other Punctuation
ValueCountFrequency (%)
, 6
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 815
63.8%
Common 463
36.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 113
13.9%
금 86
10.6%
청 62
 
7.6%
남 62
 
7.6%
군 61
 
7.5%
도 60
 
7.4%
충 59
 
7.2%
로 45
 
5.5%
면 41
 
5.0%
복 22
 
2.7%
Other values (67) 204
25.0%
Common
ValueCountFrequency (%)
247
53.3%
1 36
 
7.8%
2 24
 
5.2%
4 24
 
5.2%
3 20
 
4.3%
- 18
 
3.9%
5 17
 
3.7%
7 15
 
3.2%
0 14
 
3.0%
9 14
 
3.0%
Other values (5) 34
 
7.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 815
63.8%
ASCII 463
36.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
247
53.3%
1 36
 
7.8%
2 24
 
5.2%
4 24
 
5.2%
3 20
 
4.3%
- 18
 
3.9%
5 17
 
3.7%
7 15
 
3.2%
0 14
 
3.0%
9 14
 
3.0%
Other values (5) 34
 
7.3%
Hangul
ValueCountFrequency (%)
산 113
13.9%
금 86
10.6%
청 62
 
7.6%
남 62
 
7.6%
군 61
 
7.5%
도 60
 
7.4%
충 59
 
7.2%
로 45
 
5.5%
면 41
 
5.0%
복 22
 
2.7%
Other values (67) 204
25.0%

전화번호
Text

MISSING 

Distinct50
Distinct (%)98.0%
Missing84
Missing (%)62.2%
Memory size1.2 KiB
2024-01-10T05:24:10.042392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length14
Mean length14
Min length14

Characters and Unicode

Total characters714
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

Unique49 ?
Unique (%)96.1%

Sample

1st row 041- 754-2321
2nd row 041-751 -0581
3rd row 041- 752-1580
4th row 041- 752-1107
5th row 041- 753-2828
ValueCountFrequency (%)
041 49
40.8%
752 5
 
4.2%
751 4
 
3.3%
753 4
 
3.3%
750 2
 
1.7%
754 2
 
1.7%
0010 2
 
1.7%
734 1
 
0.8%
754-1568 1
 
0.8%
8868 1
 
0.8%
Other values (49) 49
40.8%
2024-01-10T05:24:10.316054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
102
14.3%
- 102
14.3%
0 100
14.0%
1 88
12.3%
4 77
10.8%
7 66
9.2%
5 65
9.1%
3 32
 
4.5%
2 29
 
4.1%
8 23
 
3.2%
Other values (2) 30
 
4.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 510
71.4%
Space Separator 102
 
14.3%
Dash Punctuation 102
 
14.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 100
19.6%
1 88
17.3%
4 77
15.1%
7 66
12.9%
5 65
12.7%
3 32
 
6.3%
2 29
 
5.7%
8 23
 
4.5%
6 16
 
3.1%
9 14
 
2.7%
Space Separator
ValueCountFrequency (%)
102
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 102
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 714
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
102
14.3%
- 102
14.3%
0 100
14.0%
1 88
12.3%
4 77
10.8%
7 66
9.2%
5 65
9.1%
3 32
 
4.5%
2 29
 
4.1%
8 23
 
3.2%
Other values (2) 30
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 714
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
102
14.3%
- 102
14.3%
0 100
14.0%
1 88
12.3%
4 77
10.8%
7 66
9.2%
5 65
9.1%
3 32
 
4.5%
2 29
 
4.1%
8 23
 
3.2%
Other values (2) 30
 
4.2%

데이터기준일자
Date

CONSTANT  MISSING 

Distinct1
Distinct (%)1.7%
Missing76
Missing (%)56.3%
Memory size1.2 KiB
Minimum2022-03-31 00:00:00
Maximum2022-03-31 00:00:00
2024-01-10T05:24:10.423072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:24:10.493810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Correlations

2024-01-10T05:24:10.554818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업종구분업소명행정구역주소전화번호
업종구분1.0001.0000.0001.0001.000
업소명1.0001.0001.0000.9971.000
행정구역0.0001.0001.0001.0001.000
주소1.0000.9971.0001.0001.000
전화번호1.0001.0001.0001.0001.000
2024-01-10T05:24:10.845113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정구역업종구분
행정구역1.0000.000
업종구분0.0001.000
2024-01-10T05:24:10.914833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업종구분행정구역
업종구분1.0000.000
행정구역0.0001.000

Missing values

2024-01-10T05:24:08.197561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-10T05:24:08.285999image/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.
2024-01-10T05:24:08.383883image/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

업종구분업소명행정구역주소전화번호데이터기준일자
0일반연신여인숙금산읍충청남도 금산군 금산읍 뒷담말길 17-5041- 754-23212022-03-31
1일반장수여인숙금산읍충청남도 금산군 금산읍 뒷담말길 9-4<NA>2022-03-31
2일반산장여관금산읍충청남도 금산군 금산읍 비호로 76041-751 -05812022-03-31
3일반힐튼모텔금산읍충청남도 금산군 금산읍 금산로 1514041- 752-15802022-03-31
4일반거북장여관금산읍충청남도 금산군 금산읍 인삼로 109041- 752-11072022-03-31
5일반황금장여관금산읍충청남도 금산군 금산읍 인삼로 120 (,30)041- 753-28282022-03-31
6일반세종금산읍충청남도 금산군 금산읍 금산로 1542041- 751-24002022-03-31
7일반물돌장여관금산읍충청남도 금산군 금산읍 향군길 9041- 751-18102022-03-31
8일반호정장금산읍충청남도 금산군 금산읍 중도리 481041- 751-03952022-03-31
9일반신데렐라 파크추부면충청남도 금산군 추부면 산내로 20041- 752-24662022-03-31
업종구분업소명행정구역주소전화번호데이터기준일자
125<NA><NA><NA><NA><NA><NA>
126<NA><NA><NA><NA><NA><NA>
127<NA><NA><NA><NA><NA><NA>
128<NA><NA><NA><NA><NA><NA>
129<NA><NA><NA><NA><NA><NA>
130<NA><NA><NA><NA><NA><NA>
131<NA><NA><NA><NA><NA><NA>
132<NA><NA><NA><NA><NA><NA>
133<NA><NA><NA><NA><NA><NA>
134<NA><NA><NA><NA><NA><NA>

Duplicate rows

Most frequently occurring

업종구분업소명행정구역주소전화번호데이터기준일자# duplicates
0<NA><NA><NA><NA><NA><NA>76