Overview

Dataset statistics

Number of variables15
Number of observations34
Missing cells120
Missing cells (%)23.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.3 KiB
Average record size in memory129.9 B

Variable types

Numeric4
Categorical3
Text7
Unsupported1

Dataset

Description대구광역시_외국인관광 도시민박업(게스트하우스) 업체 현황_20200930
Author대구광역시
URLhttp://data.daegu.go.kr/open/data/dataView.do?dataSetId=15054190&dataSetDetailId=150541901fcfeae52d5ad&provdMethod=FILE

Alerts

연번 is highly overall correlated with 구군High correlation
전체 객실수 is highly overall correlated with 최대수용인원 and 2 other fieldsHigh correlation
최대수용인원 is highly overall correlated with 전체 객실수 and 1 other fieldsHigh correlation
주택연면적 is highly overall correlated with 전체 객실수High correlation
구군 is highly overall correlated with 연번High correlation
지정 객실수 is highly overall correlated with 전체 객실수 and 1 other fieldsHigh correlation
대지면적 has 24 (70.6%) missing valuesMissing
연락처 has 31 (91.2%) missing valuesMissing
자본금(원) has 31 (91.2%) missing valuesMissing
비고(휴업 등 여부) has 34 (100.0%) missing valuesMissing
연번 has unique valuesUnique
상호명 has unique valuesUnique
소재지 has unique valuesUnique
대표자 has unique valuesUnique
주택연면적 has unique valuesUnique
비고(휴업 등 여부) is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2024-04-18 07:08:53.246339
Analysis finished2024-04-18 07:08:55.478940
Duration2.23 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct34
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.5
Minimum1
Maximum34
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2024-04-18T16:08:55.535125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.65
Q19.25
median17.5
Q325.75
95-th percentile32.35
Maximum34
Range33
Interquartile range (IQR)16.5

Descriptive statistics

Standard deviation9.9582462
Coefficient of variation (CV)0.56904264
Kurtosis-1.2
Mean17.5
Median Absolute Deviation (MAD)8.5
Skewness0
Sum595
Variance99.166667
MonotonicityStrictly increasing
2024-04-18T16:08:55.658672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
1 1
 
2.9%
27 1
 
2.9%
21 1
 
2.9%
22 1
 
2.9%
23 1
 
2.9%
24 1
 
2.9%
25 1
 
2.9%
26 1
 
2.9%
28 1
 
2.9%
19 1
 
2.9%
Other values (24) 24
70.6%
ValueCountFrequency (%)
1 1
2.9%
2 1
2.9%
3 1
2.9%
4 1
2.9%
5 1
2.9%
6 1
2.9%
7 1
2.9%
8 1
2.9%
9 1
2.9%
10 1
2.9%
ValueCountFrequency (%)
34 1
2.9%
33 1
2.9%
32 1
2.9%
31 1
2.9%
30 1
2.9%
29 1
2.9%
28 1
2.9%
27 1
2.9%
26 1
2.9%
25 1
2.9%

구군
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)17.6%
Missing0
Missing (%)0.0%
Memory size404.0 B
중구
15 
남구
10 
동구
4 
서구
2 
북구
2 

Length

Max length3
Median length2
Mean length2.0294118
Min length2

Unique

Unique1 ?
Unique (%)2.9%

Sample

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

Common Values

ValueCountFrequency (%)
중구 15
44.1%
남구 10
29.4%
동구 4
 
11.8%
서구 2
 
5.9%
북구 2
 
5.9%
달서구 1
 
2.9%

Length

2024-04-18T16:08:55.780169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T16:08:55.884333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
중구 15
44.1%
남구 10
29.4%
동구 4
 
11.8%
서구 2
 
5.9%
북구 2
 
5.9%
달서구 1
 
2.9%

상호명
Text

UNIQUE 

Distinct34
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size404.0 B
2024-04-18T16:08:56.061918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length14
Mean length7.5882353
Min length2

Characters and Unicode

Total characters258
Distinct characters110
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

Unique34 ?
Unique (%)100.0%

Sample

1st row더스타일
2nd row청라언덕 게스트하우스
3rd row봉산게스트하우스
4th row캐주얼하우스 소노
5th row어반덴
ValueCountFrequency (%)
게스트하우스 5
 
9.4%
house 3
 
5.7%
더스타일 1
 
1.9%
the 1
 
1.9%
미라벨 1
 
1.9%
청라언덕 1
 
1.9%
멋진 1
 
1.9%
뷰가 1
 
1.9%
있는 1
 
1.9%
강변 1
 
1.9%
Other values (37) 37
69.8%
2024-04-18T16:08:56.402722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
스 28
 
10.9%
19
 
7.4%
우 16
 
6.2%
하 16
 
6.2%
트 12
 
4.7%
게 11
 
4.3%
e 8
 
3.1%
o 7
 
2.7%
u 6
 
2.3%
라 5
 
1.9%
Other values (100) 130
50.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 173
67.1%
Lowercase Letter 51
 
19.8%
Space Separator 19
 
7.4%
Uppercase Letter 10
 
3.9%
Modifier Symbol 1
 
0.4%
Other Punctuation 1
 
0.4%
Open Punctuation 1
 
0.4%
Letter Number 1
 
0.4%
Close Punctuation 1
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
스 28
 
16.2%
우 16
 
9.2%
하 16
 
9.2%
트 12
 
6.9%
게 11
 
6.4%
라 5
 
2.9%
유 2
 
1.2%
가 2
 
1.2%
청 2
 
1.2%
네 2
 
1.2%
Other values (67) 77
44.5%
Lowercase Letter
ValueCountFrequency (%)
e 8
15.7%
o 7
13.7%
u 6
11.8%
a 4
7.8%
s 4
7.8%
h 4
7.8%
n 3
 
5.9%
l 3
 
5.9%
g 2
 
3.9%
d 1
 
2.0%
Other values (9) 9
17.6%
Uppercase Letter
ValueCountFrequency (%)
B 2
20.0%
G 2
20.0%
J 1
10.0%
T 1
10.0%
K 1
10.0%
L 1
10.0%
U 1
10.0%
S 1
10.0%
Space Separator
ValueCountFrequency (%)
19
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 1
100.0%
Other Punctuation
ValueCountFrequency (%)
& 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Letter Number
ValueCountFrequency (%)
Ⅲ 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 172
66.7%
Latin 62
 
24.0%
Common 23
 
8.9%
Han 1
 
0.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
스 28
 
16.3%
우 16
 
9.3%
하 16
 
9.3%
트 12
 
7.0%
게 11
 
6.4%
라 5
 
2.9%
유 2
 
1.2%
가 2
 
1.2%
청 2
 
1.2%
네 2
 
1.2%
Other values (66) 76
44.2%
Latin
ValueCountFrequency (%)
e 8
12.9%
o 7
 
11.3%
u 6
 
9.7%
a 4
 
6.5%
s 4
 
6.5%
h 4
 
6.5%
n 3
 
4.8%
l 3
 
4.8%
B 2
 
3.2%
G 2
 
3.2%
Other values (18) 19
30.6%
Common
ValueCountFrequency (%)
19
82.6%
` 1
 
4.3%
& 1
 
4.3%
( 1
 
4.3%
) 1
 
4.3%
Han
ValueCountFrequency (%)
休 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 172
66.7%
ASCII 84
32.6%
CJK 1
 
0.4%
Number Forms 1
 
0.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
스 28
 
16.3%
우 16
 
9.3%
하 16
 
9.3%
트 12
 
7.0%
게 11
 
6.4%
라 5
 
2.9%
유 2
 
1.2%
가 2
 
1.2%
청 2
 
1.2%
네 2
 
1.2%
Other values (66) 76
44.2%
ASCII
ValueCountFrequency (%)
19
22.6%
e 8
 
9.5%
o 7
 
8.3%
u 6
 
7.1%
a 4
 
4.8%
s 4
 
4.8%
h 4
 
4.8%
n 3
 
3.6%
l 3
 
3.6%
B 2
 
2.4%
Other values (22) 24
28.6%
CJK
ValueCountFrequency (%)
休 1
100.0%
Number Forms
ValueCountFrequency (%)
Ⅲ 1
100.0%

소재지
Text

UNIQUE 

Distinct34
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size404.0 B
2024-04-18T16:08:56.659901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length46
Median length34
Mean length27.470588
Min length16

Characters and Unicode

Total characters934
Distinct characters98
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

Unique34 ?
Unique (%)100.0%

Sample

1st row대구시 중구 서성로14길 26(서내동)
2nd row대구시 중구 달구벌대로401길 22(동산동)
3rd row대구시 중구 봉산문화1길 16-12(봉산동)
4th row대구광역시 중구 명륜동 86, 2층(남산동)
5th row대구광역시 중구 동덕로8길 40-16, 3층(대봉동)
ValueCountFrequency (%)
대구광역시 31
 
18.3%
중구 15
 
8.9%
남구 10
 
5.9%
동구 4
 
2.4%
대구시 3
 
1.8%
북내동 3
 
1.8%
서성로16길 2
 
1.2%
대명동 2
 
1.2%
달구벌대로 2
 
1.2%
3층 2
 
1.2%
Other values (91) 95
56.2%
2024-04-18T16:08:57.042603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
136
 
14.6%
구 71
 
7.6%
대 51
 
5.5%
1 47
 
5.0%
동 39
 
4.2%
시 34
 
3.6%
광 31
 
3.3%
역 31
 
3.3%
로 30
 
3.2%
( 29
 
3.1%
Other values (88) 435
46.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 507
54.3%
Decimal Number 194
 
20.8%
Space Separator 136
 
14.6%
Open Punctuation 29
 
3.1%
Close Punctuation 29
 
3.1%
Dash Punctuation 20
 
2.1%
Other Punctuation 19
 
2.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 71
14.0%
대 51
 
10.1%
동 39
 
7.7%
시 34
 
6.7%
광 31
 
6.1%
역 31
 
6.1%
로 30
 
5.9%
길 28
 
5.5%
중 20
 
3.9%
남 14
 
2.8%
Other values (73) 158
31.2%
Decimal Number
ValueCountFrequency (%)
1 47
24.2%
2 27
13.9%
3 26
13.4%
4 20
10.3%
0 18
 
9.3%
6 18
 
9.3%
5 13
 
6.7%
8 10
 
5.2%
9 9
 
4.6%
7 6
 
3.1%
Space Separator
ValueCountFrequency (%)
136
100.0%
Open Punctuation
ValueCountFrequency (%)
( 29
100.0%
Close Punctuation
ValueCountFrequency (%)
) 29
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 20
100.0%
Other Punctuation
ValueCountFrequency (%)
, 19
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 507
54.3%
Common 427
45.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 71
14.0%
대 51
 
10.1%
동 39
 
7.7%
시 34
 
6.7%
광 31
 
6.1%
역 31
 
6.1%
로 30
 
5.9%
길 28
 
5.5%
중 20
 
3.9%
남 14
 
2.8%
Other values (73) 158
31.2%
Common
ValueCountFrequency (%)
136
31.9%
1 47
 
11.0%
( 29
 
6.8%
) 29
 
6.8%
2 27
 
6.3%
3 26
 
6.1%
- 20
 
4.7%
4 20
 
4.7%
, 19
 
4.4%
0 18
 
4.2%
Other values (5) 56
13.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 507
54.3%
ASCII 427
45.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
136
31.9%
1 47
 
11.0%
( 29
 
6.8%
) 29
 
6.8%
2 27
 
6.3%
3 26
 
6.1%
- 20
 
4.7%
4 20
 
4.7%
, 19
 
4.4%
0 18
 
4.2%
Other values (5) 56
13.1%
Hangul
ValueCountFrequency (%)
구 71
14.0%
대 51
 
10.1%
동 39
 
7.7%
시 34
 
6.7%
광 31
 
6.1%
역 31
 
6.1%
로 30
 
5.9%
길 28
 
5.5%
중 20
 
3.9%
남 14
 
2.8%
Other values (73) 158
31.2%

전체 객실수
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)17.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.8235294
Minimum2
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2024-04-18T16:08:57.161617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2
Q13
median3
Q35
95-th percentile6
Maximum8
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.4453811
Coefficient of variation (CV)0.37802275
Kurtosis0.57848825
Mean3.8235294
Median Absolute Deviation (MAD)1
Skewness0.90297346
Sum130
Variance2.0891266
MonotonicityNot monotonic
2024-04-18T16:08:57.257289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3 13
38.2%
4 6
17.6%
2 5
 
14.7%
5 5
 
14.7%
6 4
 
11.8%
8 1
 
2.9%
ValueCountFrequency (%)
2 5
 
14.7%
3 13
38.2%
4 6
17.6%
5 5
 
14.7%
6 4
 
11.8%
8 1
 
2.9%
ValueCountFrequency (%)
8 1
 
2.9%
6 4
 
11.8%
5 5
 
14.7%
4 6
17.6%
3 13
38.2%
2 5
 
14.7%

지정 객실수
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)14.7%
Missing0
Missing (%)0.0%
Memory size404.0 B
2
12 
1
9 
3
6 
4
4 
5
3 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row1
4th row4
5th row3

Common Values

ValueCountFrequency (%)
2 12
35.3%
1 9
26.5%
3 6
17.6%
4 4
 
11.8%
5 3
 
8.8%

Length

2024-04-18T16:08:57.365410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T16:08:57.460999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 12
35.3%
1 9
26.5%
3 6
17.6%
4 4
 
11.8%
5 3
 
8.8%

최대수용인원
Real number (ℝ)

HIGH CORRELATION 

Distinct14
Distinct (%)41.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.941176
Minimum1
Maximum32
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2024-04-18T16:08:57.552388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q15
median8
Q315
95-th percentile24.7
Maximum32
Range31
Interquartile range (IQR)10

Descriptive statistics

Standard deviation7.4790259
Coefficient of variation (CV)0.68356689
Kurtosis0.58101137
Mean10.941176
Median Absolute Deviation (MAD)4
Skewness0.94061805
Sum372
Variance55.935829
MonotonicityNot monotonic
2024-04-18T16:08:57.651304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
15 5
14.7%
8 5
14.7%
4 5
14.7%
12 3
8.8%
6 3
8.8%
14 2
 
5.9%
2 2
 
5.9%
20 2
 
5.9%
5 2
 
5.9%
26 1
 
2.9%
Other values (4) 4
11.8%
ValueCountFrequency (%)
1 1
 
2.9%
2 2
 
5.9%
4 5
14.7%
5 2
 
5.9%
6 3
8.8%
8 5
14.7%
12 3
8.8%
14 2
 
5.9%
15 5
14.7%
18 1
 
2.9%
ValueCountFrequency (%)
32 1
 
2.9%
26 1
 
2.9%
24 1
 
2.9%
20 2
 
5.9%
18 1
 
2.9%
15 5
14.7%
14 2
 
5.9%
12 3
8.8%
8 5
14.7%
6 3
8.8%

대표자
Text

UNIQUE 

Distinct34
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size404.0 B
2024-04-18T16:08:57.830843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters102
Distinct characters55
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

Unique34 ?
Unique (%)100.0%

Sample

1st row김경호
2nd row김연순
3rd row오선경
4th row장우정
5th row배상애
ValueCountFrequency (%)
김경호 1
 
2.9%
김연순 1
 
2.9%
진희정 1
 
2.9%
김성자 1
 
2.9%
김주현 1
 
2.9%
이우주 1
 
2.9%
김정우 1
 
2.9%
김혜정 1
 
2.9%
정재욱 1
 
2.9%
양혜영 1
 
2.9%
Other values (24) 24
70.6%
2024-04-18T16:08:58.127863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
김 14
 
13.7%
정 7
 
6.9%
이 6
 
5.9%
은 4
 
3.9%
영 4
 
3.9%
우 3
 
2.9%
경 3
 
2.9%
욱 3
 
2.9%
배 3
 
2.9%
재 2
 
2.0%
Other values (45) 53
52.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 102
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
김 14
 
13.7%
정 7
 
6.9%
이 6
 
5.9%
은 4
 
3.9%
영 4
 
3.9%
우 3
 
2.9%
경 3
 
2.9%
욱 3
 
2.9%
배 3
 
2.9%
재 2
 
2.0%
Other values (45) 53
52.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 102
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
김 14
 
13.7%
정 7
 
6.9%
이 6
 
5.9%
은 4
 
3.9%
영 4
 
3.9%
우 3
 
2.9%
경 3
 
2.9%
욱 3
 
2.9%
배 3
 
2.9%
재 2
 
2.0%
Other values (45) 53
52.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 102
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
김 14
 
13.7%
정 7
 
6.9%
이 6
 
5.9%
은 4
 
3.9%
영 4
 
3.9%
우 3
 
2.9%
경 3
 
2.9%
욱 3
 
2.9%
배 3
 
2.9%
재 2
 
2.0%
Other values (45) 53
52.0%

주택종류(단독,다가구, 아파트, 연립, 다세대)
Categorical

Distinct5
Distinct (%)14.7%
Missing0
Missing (%)0.0%
Memory size404.0 B
단독
17 
다가구
12 
아파트
3 
다세대
 
1
단독주택
 
1

Length

Max length4
Median length3.5
Mean length2.5294118
Min length2

Unique

Unique2 ?
Unique (%)5.9%

Sample

1st row다가구
2nd row다가구
3rd row단독
4th row다가구
5th row다가구

Common Values

ValueCountFrequency (%)
단독 17
50.0%
다가구 12
35.3%
아파트 3
 
8.8%
다세대 1
 
2.9%
단독주택 1
 
2.9%

Length

2024-04-18T16:08:58.260228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T16:08:58.396931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
단독 17
50.0%
다가구 12
35.3%
아파트 3
 
8.8%
다세대 1
 
2.9%
단독주택 1
 
2.9%

대지면적
Text

MISSING 

Distinct10
Distinct (%)100.0%
Missing24
Missing (%)70.6%
Memory size404.0 B
2024-04-18T16:08:58.548756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5.5
Mean length3.6
Min length2

Characters and Unicode

Total characters36
Distinct characters12
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

Unique10 ?
Unique (%)100.0%

Sample

1st row147
2nd row199
3rd row186
4th row130.9
5th row170
ValueCountFrequency (%)
147 1
10.0%
199 1
10.0%
186 1
10.0%
130.9 1
10.0%
170 1
10.0%
85 1
10.0%
226.34 1
10.0%
36 1
10.0%
23,646 1
10.0%
163 1
10.0%
2024-04-18T16:08:58.851223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 6
16.7%
6 6
16.7%
3 5
13.9%
4 3
8.3%
9 3
8.3%
2 3
8.3%
7 2
 
5.6%
8 2
 
5.6%
0 2
 
5.6%
. 2
 
5.6%
Other values (2) 2
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 33
91.7%
Other Punctuation 3
 
8.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 6
18.2%
6 6
18.2%
3 5
15.2%
4 3
9.1%
9 3
9.1%
2 3
9.1%
7 2
 
6.1%
8 2
 
6.1%
0 2
 
6.1%
5 1
 
3.0%
Other Punctuation
ValueCountFrequency (%)
. 2
66.7%
, 1
33.3%

Most occurring scripts

ValueCountFrequency (%)
Common 36
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 6
16.7%
6 6
16.7%
3 5
13.9%
4 3
8.3%
9 3
8.3%
2 3
8.3%
7 2
 
5.6%
8 2
 
5.6%
0 2
 
5.6%
. 2
 
5.6%
Other values (2) 2
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 36
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 6
16.7%
6 6
16.7%
3 5
13.9%
4 3
8.3%
9 3
8.3%
2 3
8.3%
7 2
 
5.6%
8 2
 
5.6%
0 2
 
5.6%
. 2
 
5.6%
Other values (2) 2
 
5.6%

주택연면적
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct34
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean115.61882
Minimum31.17
Maximum229
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2024-04-18T16:08:58.968697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum31.17
5-th percentile44.3035
Q179.8975
median107.085
Q3157.1475
95-th percentile208.567
Maximum229
Range197.83
Interquartile range (IQR)77.25

Descriptive statistics

Standard deviation54.731201
Coefficient of variation (CV)0.47337621
Kurtosis-0.76327692
Mean115.61882
Median Absolute Deviation (MAD)48.24
Skewness0.44496675
Sum3931.04
Variance2995.5043
MonotonicityNot monotonic
2024-04-18T16:08:59.088032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
103.0 1
 
2.9%
52.23 1
 
2.9%
121.64 1
 
2.9%
96.53 1
 
2.9%
125.0 1
 
2.9%
101.28 1
 
2.9%
204.99 1
 
2.9%
158.14 1
 
2.9%
94.47 1
 
2.9%
111.78 1
 
2.9%
Other values (24) 24
70.6%
ValueCountFrequency (%)
31.17 1
2.9%
39.37 1
2.9%
46.96 1
2.9%
52.23 1
2.9%
54.0 1
2.9%
56.0 1
2.9%
57.5 1
2.9%
57.69 1
2.9%
78.53 1
2.9%
84.0 1
2.9%
ValueCountFrequency (%)
229.0 1
2.9%
215.21 1
2.9%
204.99 1
2.9%
195.44 1
2.9%
187.6 1
2.9%
186.0 1
2.9%
180.77 1
2.9%
159.3 1
2.9%
158.14 1
2.9%
154.17 1
2.9%

연락처
Text

MISSING 

Distinct3
Distinct (%)100.0%
Missing31
Missing (%)91.2%
Memory size404.0 B
2024-04-18T16:08:59.228031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters36
Distinct characters10
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

Unique3 ?
Unique (%)100.0%

Sample

1st row053-423-7778
2nd row053-423-7780
3rd row053-741-7973
ValueCountFrequency (%)
053-423-7778 1
33.3%
053-423-7780 1
33.3%
053-741-7973 1
33.3%
2024-04-18T16:08:59.466038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
7 8
22.2%
3 6
16.7%
- 6
16.7%
0 4
11.1%
5 3
 
8.3%
4 3
 
8.3%
2 2
 
5.6%
8 2
 
5.6%
1 1
 
2.8%
9 1
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30
83.3%
Dash Punctuation 6
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
7 8
26.7%
3 6
20.0%
0 4
13.3%
5 3
 
10.0%
4 3
 
10.0%
2 2
 
6.7%
8 2
 
6.7%
1 1
 
3.3%
9 1
 
3.3%
Dash Punctuation
ValueCountFrequency (%)
- 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 36
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
7 8
22.2%
3 6
16.7%
- 6
16.7%
0 4
11.1%
5 3
 
8.3%
4 3
 
8.3%
2 2
 
5.6%
8 2
 
5.6%
1 1
 
2.8%
9 1
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 36
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
7 8
22.2%
3 6
16.7%
- 6
16.7%
0 4
11.1%
5 3
 
8.3%
4 3
 
8.3%
2 2
 
5.6%
8 2
 
5.6%
1 1
 
2.8%
9 1
 
2.8%

등록년월일(변경등록일)
Text

Distinct33
Distinct (%)97.1%
Missing0
Missing (%)0.0%
Memory size404.0 B
2024-04-18T16:08:59.644444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length10
Mean length10.352941
Min length10

Characters and Unicode

Total characters352
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique32 ?
Unique (%)94.1%

Sample

1st row2013-10-18(2017-08-08)
2nd row2014-01-06
3rd row2014-11-03
4th row2015-12-17
5th row2016-04-18
ValueCountFrequency (%)
2017-11-08 2
 
5.9%
2019-02-18 1
 
2.9%
2016-01-12 1
 
2.9%
2020-09-04 1
 
2.9%
2017-02-27 1
 
2.9%
2018-02-28 1
 
2.9%
2018-04-17 1
 
2.9%
2018-04-23 1
 
2.9%
2013-10-18(2017-08-08 1
 
2.9%
2018-05-09 1
 
2.9%
Other values (23) 23
67.6%
2024-04-18T16:08:59.945051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 79
22.4%
- 70
19.9%
1 68
19.3%
2 49
13.9%
9 19
 
5.4%
8 15
 
4.3%
7 12
 
3.4%
6 12
 
3.4%
5 10
 
2.8%
4 9
 
2.6%
Other values (3) 9
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 280
79.5%
Dash Punctuation 70
 
19.9%
Open Punctuation 1
 
0.3%
Close Punctuation 1
 
0.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 79
28.2%
1 68
24.3%
2 49
17.5%
9 19
 
6.8%
8 15
 
5.4%
7 12
 
4.3%
6 12
 
4.3%
5 10
 
3.6%
4 9
 
3.2%
3 7
 
2.5%
Dash Punctuation
ValueCountFrequency (%)
- 70
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 352
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 79
22.4%
- 70
19.9%
1 68
19.3%
2 49
13.9%
9 19
 
5.4%
8 15
 
4.3%
7 12
 
3.4%
6 12
 
3.4%
5 10
 
2.8%
4 9
 
2.6%
Other values (3) 9
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 352
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 79
22.4%
- 70
19.9%
1 68
19.3%
2 49
13.9%
9 19
 
5.4%
8 15
 
4.3%
7 12
 
3.4%
6 12
 
3.4%
5 10
 
2.8%
4 9
 
2.6%
Other values (3) 9
 
2.6%

자본금(원)
Text

MISSING 

Distinct3
Distinct (%)100.0%
Missing31
Missing (%)91.2%
Memory size404.0 B
2024-04-18T16:09:00.082395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length9.6666667
Min length9

Characters and Unicode

Total characters29
Distinct characters4
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

Unique3 ?
Unique (%)100.0%

Sample

1st row5,000,000
2nd row50,000,000
3rd row70,000,000
ValueCountFrequency (%)
5,000,000 1
33.3%
50,000,000 1
33.3%
70,000,000 1
33.3%
2024-04-18T16:09:00.652159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 20
69.0%
, 6
 
20.7%
5 2
 
6.9%
7 1
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23
79.3%
Other Punctuation 6
 
20.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 20
87.0%
5 2
 
8.7%
7 1
 
4.3%
Other Punctuation
ValueCountFrequency (%)
, 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 29
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 20
69.0%
, 6
 
20.7%
5 2
 
6.9%
7 1
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 29
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 20
69.0%
, 6
 
20.7%
5 2
 
6.9%
7 1
 
3.4%

비고(휴업 등 여부)
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing34
Missing (%)100.0%
Memory size438.0 B

Interactions

2024-04-18T16:08:54.749937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T16:08:53.742913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T16:08:54.086164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T16:08:54.427457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T16:08:54.836559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T16:08:53.826863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T16:08:54.178272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T16:08:54.513056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T16:08:54.921211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T16:08:53.905303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T16:08:54.254598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T16:08:54.592008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T16:08:55.008005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T16:08:53.986150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T16:08:54.345094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T16:08:54.667210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-18T16:09:00.751286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번구군상호명소재지전체 객실수지정 객실수최대수용인원대표자주택종류(단독,다가구, 아파트, 연립, 다세대)대지면적주택연면적연락처등록년월일(변경등록일)자본금(원)
연번1.0000.7871.0001.0000.5230.7730.6401.0000.3201.0000.5481.0000.9281.000
구군0.7871.0001.0001.0000.7150.4270.4541.0000.5501.0000.5291.0000.9711.000
상호명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
소재지1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
전체 객실수0.5230.7151.0001.0001.0000.8560.7721.0000.6341.0000.5031.0001.0001.000
지정 객실수0.7730.4271.0001.0000.8561.0000.9711.0000.4781.0000.3841.0001.000NaN
최대수용인원0.6400.4541.0001.0000.7720.9711.0001.0000.0001.0000.2791.0001.000NaN
대표자1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
주택종류(단독,다가구, 아파트, 연립, 다세대)0.3200.5501.0001.0000.6340.4780.0001.0001.0001.0000.4631.0001.0001.000
대지면적1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000NaN1.0001.000
주택연면적0.5480.5291.0001.0000.5030.3840.2791.0000.4631.0001.0001.0000.9611.000
연락처1.0001.0001.0001.0001.0001.0001.0001.0001.000NaN1.0001.0001.000NaN
등록년월일(변경등록일)0.9280.9711.0001.0001.0001.0001.0001.0001.0001.0000.9611.0001.0001.000
자본금(원)1.0001.0001.0001.0001.000NaNNaN1.0001.0001.0001.000NaN1.0001.000
2024-04-18T16:09:00.901733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
주택종류(단독,다가구, 아파트, 연립, 다세대)지정 객실수구군
주택종류(단독,다가구, 아파트, 연립, 다세대)1.0000.1850.400
지정 객실수0.1851.0000.293
구군0.4000.2931.000
2024-04-18T16:09:00.992448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번전체 객실수최대수용인원주택연면적구군지정 객실수주택종류(단독,다가구, 아파트, 연립, 다세대)
연번1.000-0.218-0.4730.0570.5210.3800.083
전체 객실수-0.2181.0000.7120.5160.3240.7550.481
최대수용인원-0.4730.7121.0000.2300.2210.6890.000
주택연면적0.0570.5160.2301.0000.2740.1220.166
구군0.5210.3240.2210.2741.0000.2930.400
지정 객실수0.3800.7550.6890.1220.2931.0000.185
주택종류(단독,다가구, 아파트, 연립, 다세대)0.0830.4810.0000.1660.4000.1851.000

Missing values

2024-04-18T16:08:55.135784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-18T16:08:55.308320image/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.
2024-04-18T16:08:55.423168image/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

연번구군상호명소재지전체 객실수지정 객실수최대수용인원대표자주택종류(단독,다가구, 아파트, 연립, 다세대)대지면적주택연면적연락처등록년월일(변경등록일)자본금(원)비고(휴업 등 여부)
01중구더스타일대구시 중구 서성로14길 26(서내동)4326김경호다가구147103.0<NA>2013-10-18(2017-08-08)<NA><NA>
12중구청라언덕 게스트하우스대구시 중구 달구벌대로401길 22(동산동)4314김연순다가구199122.0<NA>2014-01-06<NA><NA>
23중구봉산게스트하우스대구시 중구 봉산문화1길 16-12(봉산동)6115오선경단독186186.0<NA>2014-11-03<NA><NA>
34중구캐주얼하우스 소노대구광역시 중구 명륜동 86, 2층(남산동)6424장우정다가구<NA>229.0053-423-77782015-12-17<NA><NA>
45중구어반덴대구광역시 중구 동덕로8길 40-16, 3층(대봉동)4315배상애다가구<NA>86.88<NA>2016-04-18<NA><NA>
56중구온나 게스트하우스대구광역시 중구 남성로 4, 2층(남성로)6532이희곤단독<NA>195.44053-423-77802016-06-09<NA><NA>
67중구청라게스트하우스대구광역시 중구 달구벌대로 401길 164315이용구다가구<NA>78.53<NA>2016-07-29<NA><NA>
78중구게스트하우스 만나대구광역시 중구 명륜로 121-37, 3층(봉산동)3212김종태단독<NA>39.37<NA>2017-08-11<NA><NA>
89중구애가Ⅲ(코지트)대구광역시 중구 서성로16길 46-8 (북내동)326김경하단독<NA>84.0<NA>2017-11-08<NA><NA>
910중구레터프롬대구광역시 중구 동덕로14길 35, 2층(대봉동)212양혜영단독<NA>31.17<NA>2018-04-16<NA><NA>
연번구군상호명소재지전체 객실수지정 객실수최대수용인원대표자주택종류(단독,다가구, 아파트, 연립, 다세대)대지면적주택연면적연락처등록년월일(변경등록일)자본금(원)비고(휴업 등 여부)
2425남구유원게스트하우스대구광역시 남구 앞산순환로 93길 48514박준지단독<NA>204.99<NA>2018-04-17<NA><NA>
2526남구상상하우스대구광역시 남구 매자안길 39312배성호다가구<NA>158.14<NA>2018-04-23<NA><NA>
2627남구멋진 뷰가 있는 강변 타운대구광역시 남구 명덕로 68길 19,102동 1105호(강변타운)214정재욱아파트<NA>52.23<NA>2019-02-18<NA><NA>
2728남구미라벨대구광역시 남구 두류공원로 12길 17-40,미라벨 501호328김혜정다가구<NA>94.47<NA>2019-03-01<NA><NA>
2829남구Jung`s house대구광역시 남구 대명로54길 6-3, 3층 (대명동)214김정우다가구226.34180.77<NA>2019-07-0550,000,000<NA>
2930남구사라네집대구광역시 남구 중앙대로40길 48-6 (대명동)214이우주단독3657.5<NA>2019-09-1970,000,000<NA>
3031남구休U대구광역시 남구 대명로53길 35-6, 3층(대명동)3214김주현다가구<NA>56.0<NA>2020-09-15<NA><NA>
3132북구오페라게스트하우스대구광역시 북구 침산로21길 23, 103동1805호(칠성동2가, 침산1차푸르지오)526김성자아파트23,646135.62<NA>2016-03-29<NA><NA>
3233북구패밀리민박대구광역시 북구 대학로9길 68-2(산격동)8512진희정단독주택<NA>187.6<NA>2019-11-05<NA><NA>
3334달서구다온하우스대구광역시 달서구 죽전길 31-10(죽전동)6520김남영단독163154.17<NA>2016-06-15<NA><NA>