Overview

Dataset statistics

Number of variables18
Number of observations38
Missing cells77
Missing cells (%)11.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.7 KiB
Average record size in memory154.5 B

Variable types

Numeric6
Categorical4
Text6
DateTime2

Dataset

Description대구지역 호텔업 현황(지역, 업종분류, 호텔명, 소재지, 전화번호 등)일부 업체의 연락처는 개인정보(휴대전화)가 포함되어 제공되지 않음을 양해바랍니다.
Author대구광역시
URLhttps://www.data.go.kr/data/15054187/fileData.do

Alerts

지역 is highly overall correlated with 연번 and 1 other fieldsHigh correlation
등급(이전등급) is highly overall correlated with 업종분류 and 1 other fieldsHigh correlation
객실수_기타 is highly overall correlated with 연번 and 8 other fieldsHigh correlation
업종분류 is highly overall correlated with 객실수_소계 and 2 other fieldsHigh correlation
연번 is highly overall correlated with 지역 and 1 other fieldsHigh correlation
객실수_스탠더드 is highly overall correlated with 객실수_소계 and 1 other fieldsHigh correlation
객실수_디럭스 is highly overall correlated with 객실수_소계 and 1 other fieldsHigh correlation
객실수_스위트 is highly overall correlated with 객실수_소계 and 1 other fieldsHigh correlation
객실수_한실 is highly overall correlated with 객실수_기타High correlation
객실수_소계 is highly overall correlated with 객실수_스탠더드 and 4 other fieldsHigh correlation
객실수_기타 is highly imbalanced (73.8%)Imbalance
법인명 has 13 (34.2%) missing valuesMissing
객실수_스탠더드 has 3 (7.9%) missing valuesMissing
객실수_디럭스 has 4 (10.5%) missing valuesMissing
객실수_스위트 has 3 (7.9%) missing valuesMissing
객실수_한실 has 3 (7.9%) missing valuesMissing
전화번호 has 5 (13.2%) missing valuesMissing
팩스번호 has 23 (60.5%) missing valuesMissing
변경등록 has 23 (60.5%) missing valuesMissing
연번 has unique valuesUnique
호텔명 has unique valuesUnique
소재지 has unique valuesUnique
대표자(총지배인) has unique valuesUnique
객실수_스탠더드 has 4 (10.5%) zerosZeros
객실수_디럭스 has 7 (18.4%) zerosZeros
객실수_스위트 has 12 (31.6%) zerosZeros
객실수_한실 has 20 (52.6%) zerosZeros

Reproduction

Analysis started2023-12-12 05:00:25.300775
Analysis finished2023-12-12 05:00:31.296779
Duration6 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct38
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.5
Minimum1
Maximum38
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size474.0 B
2023-12-12T14:00:31.378101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.85
Q110.25
median19.5
Q328.75
95-th percentile36.15
Maximum38
Range37
Interquartile range (IQR)18.5

Descriptive statistics

Standard deviation11.113055
Coefficient of variation (CV)0.56990028
Kurtosis-1.2
Mean19.5
Median Absolute Deviation (MAD)9.5
Skewness0
Sum741
Variance123.5
MonotonicityStrictly increasing
2023-12-12T14:00:31.528804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
1 1
 
2.6%
30 1
 
2.6%
23 1
 
2.6%
24 1
 
2.6%
25 1
 
2.6%
26 1
 
2.6%
27 1
 
2.6%
28 1
 
2.6%
29 1
 
2.6%
31 1
 
2.6%
Other values (28) 28
73.7%
ValueCountFrequency (%)
1 1
2.6%
2 1
2.6%
3 1
2.6%
4 1
2.6%
5 1
2.6%
6 1
2.6%
7 1
2.6%
8 1
2.6%
9 1
2.6%
10 1
2.6%
ValueCountFrequency (%)
38 1
2.6%
37 1
2.6%
36 1
2.6%
35 1
2.6%
34 1
2.6%
33 1
2.6%
32 1
2.6%
31 1
2.6%
30 1
2.6%
29 1
2.6%

지역
Categorical

HIGH CORRELATION 

Distinct9
Distinct (%)23.7%
Missing0
Missing (%)0.0%
Memory size436.0 B
동구
11 
중구
8 
수성구
5 
경제자유구역
4 
달서구
3 
Other values (4)
7 

Length

Max length6
Median length2
Mean length2.7368421
Min length2

Unique

Unique2 ?
Unique (%)5.3%

Sample

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

Common Values

ValueCountFrequency (%)
동구 11
28.9%
중구 8
21.1%
수성구 5
13.2%
경제자유구역 4
 
10.5%
달서구 3
 
7.9%
달성군 3
 
7.9%
남구 2
 
5.3%
북구 1
 
2.6%
군위군 1
 
2.6%

Length

2023-12-12T14:00:31.732071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T14:00:31.901459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
동구 11
28.9%
중구 8
21.1%
수성구 5
13.2%
경제자유구역 4
 
10.5%
달서구 3
 
7.9%
달성군 3
 
7.9%
남구 2
 
5.3%
북구 1
 
2.6%
군위군 1
 
2.6%

업종분류
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Memory size436.0 B
관광호텔업
32 
호스텔업
6 

Length

Max length5
Median length5
Mean length4.8421053
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
관광호텔업 32
84.2%
호스텔업 6
 
15.8%

Length

2023-12-12T14:00:32.100790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T14:00:32.209368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
관광호텔업 32
84.2%
호스텔업 6
 
15.8%

등급(이전등급)
Categorical

HIGH CORRELATION 

Distinct12
Distinct (%)31.6%
Missing0
Missing (%)0.0%
Memory size436.0 B
2성
8 
<NA>
6 
3성
5 
등급없음
4 
4성
3 
Other values (7)
12 

Length

Max length8
Median length2
Mean length3.6315789
Min length2

Unique

Unique3 ?
Unique (%)7.9%

Sample

1st row4성
2nd row등급없음(2성)
3rd row2성
4th row2성
5th row3성

Common Values

ValueCountFrequency (%)
2성 8
21.1%
<NA> 6
15.8%
3성 5
13.2%
등급없음 4
10.5%
4성 3
 
7.9%
등급없음(2성) 3
 
7.9%
5성 2
 
5.3%
등급없음(3성) 2
 
5.3%
1성 2
 
5.3%
등급없음(4성) 1
 
2.6%
Other values (2) 2
 
5.3%

Length

2023-12-12T14:00:32.333181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2성 8
21.1%
na 6
15.8%
3성 5
13.2%
등급없음 4
10.5%
4성 3
 
7.9%
등급없음(2성 3
 
7.9%
5성 2
 
5.3%
등급없음(3성 2
 
5.3%
1성 2
 
5.3%
등급없음(4성 1
 
2.6%
Other values (2) 2
 
5.3%

호텔명
Text

UNIQUE 

Distinct38
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size436.0 B
2023-12-12T14:00:32.608798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length9
Mean length7.2631579
Min length3

Characters and Unicode

Total characters276
Distinct characters97
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique38 ?
Unique (%)100.0%

Sample

1st row엘디스리젠트호텔
2nd row유니온관광호텔
3rd row토요코인 대구동성로
4th row노블스테이
5th row리버틴호텔
ValueCountFrequency (%)
호텔 4
 
7.5%
호스텔 3
 
5.7%
관광호텔 2
 
3.8%
엘디스리젠트호텔 1
 
1.9%
주)호텔라온제나 1
 
1.9%
하우스오브갤러리 1
 
1.9%
브라운도트호텔현풍점 1
 
1.9%
주)인터불고 1
 
1.9%
엑스코 1
 
1.9%
호텔인터불고 1
 
1.9%
Other values (37) 37
69.8%
2023-12-12T14:00:33.093532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
호 33
 
12.0%
텔 33
 
12.0%
15
 
5.4%
스 13
 
4.7%
관 9
 
3.3%
광 8
 
2.9%
리 8
 
2.9%
아 6
 
2.2%
온 5
 
1.8%
이 5
 
1.8%
Other values (87) 141
51.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 245
88.8%
Space Separator 15
 
5.4%
Close Punctuation 4
 
1.4%
Open Punctuation 4
 
1.4%
Uppercase Letter 4
 
1.4%
Other Symbol 2
 
0.7%
Other Punctuation 1
 
0.4%
Lowercase Letter 1
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
호 33
 
13.5%
텔 33
 
13.5%
스 13
 
5.3%
관 9
 
3.7%
광 8
 
3.3%
리 8
 
3.3%
아 6
 
2.4%
온 5
 
2.0%
이 5
 
2.0%
코 4
 
1.6%
Other values (77) 121
49.4%
Uppercase Letter
ValueCountFrequency (%)
A 1
25.0%
C 1
25.0%
G 1
25.0%
T 1
25.0%
Space Separator
ValueCountFrequency (%)
15
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 2
100.0%
Other Punctuation
ValueCountFrequency (%)
' 1
100.0%
Lowercase Letter
ValueCountFrequency (%)
s 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 247
89.5%
Common 24
 
8.7%
Latin 5
 
1.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
호 33
 
13.4%
텔 33
 
13.4%
스 13
 
5.3%
관 9
 
3.6%
광 8
 
3.2%
리 8
 
3.2%
아 6
 
2.4%
온 5
 
2.0%
이 5
 
2.0%
코 4
 
1.6%
Other values (78) 123
49.8%
Latin
ValueCountFrequency (%)
A 1
20.0%
C 1
20.0%
G 1
20.0%
s 1
20.0%
T 1
20.0%
Common
ValueCountFrequency (%)
15
62.5%
) 4
 
16.7%
( 4
 
16.7%
' 1
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 245
88.8%
ASCII 29
 
10.5%
None 2
 
0.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
호 33
 
13.5%
텔 33
 
13.5%
스 13
 
5.3%
관 9
 
3.7%
광 8
 
3.3%
리 8
 
3.3%
아 6
 
2.4%
온 5
 
2.0%
이 5
 
2.0%
코 4
 
1.6%
Other values (77) 121
49.4%
ASCII
ValueCountFrequency (%)
15
51.7%
) 4
 
13.8%
( 4
 
13.8%
A 1
 
3.4%
C 1
 
3.4%
G 1
 
3.4%
' 1
 
3.4%
s 1
 
3.4%
T 1
 
3.4%
None
ValueCountFrequency (%)
㈜ 2
100.0%

법인명
Text

MISSING 

Distinct25
Distinct (%)100.0%
Missing13
Missing (%)34.2%
Memory size436.0 B
2023-12-12T14:00:33.380659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length12
Mean length8.72
Min length5

Characters and Unicode

Total characters218
Distinct characters90
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique25 ?
Unique (%)100.0%

Sample

1st row㈜엘디스리젠트호텔
2nd row㈜유니온관광호텔
3rd row토요코인코리아 주식회사
4th row㈜노블스테이
5th row주식회사 로프
ValueCountFrequency (%)
주식회사 7
 
18.4%
㈜엘디스리젠트호텔 1
 
2.6%
㈜즐거운 1
 
2.6%
엑스코 1
 
2.6%
주)인터불고 1
 
2.6%
㈜서정에스엠 1
 
2.6%
알파개발 1
 
2.6%
온천호텔 1
 
2.6%
㈜파라다이스 1
 
2.6%
㈜크리스탈관광호텔 1
 
2.6%
Other values (22) 22
57.9%
2023-12-12T14:00:33.767057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
㈜ 16
 
7.3%
15
 
6.9%
스 11
 
5.0%
사 10
 
4.6%
호 9
 
4.1%
텔 9
 
4.1%
회 8
 
3.7%
주 8
 
3.7%
식 7
 
3.2%
이 7
 
3.2%
Other values (80) 118
54.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 185
84.9%
Other Symbol 16
 
7.3%
Space Separator 15
 
6.9%
Open Punctuation 1
 
0.5%
Close Punctuation 1
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
스 11
 
5.9%
사 10
 
5.4%
호 9
 
4.9%
텔 9
 
4.9%
회 8
 
4.3%
주 8
 
4.3%
식 7
 
3.8%
이 7
 
3.8%
리 5
 
2.7%
코 4
 
2.2%
Other values (76) 107
57.8%
Other Symbol
ValueCountFrequency (%)
㈜ 16
100.0%
Space Separator
ValueCountFrequency (%)
15
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 201
92.2%
Common 17
 
7.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
㈜ 16
 
8.0%
스 11
 
5.5%
사 10
 
5.0%
호 9
 
4.5%
텔 9
 
4.5%
회 8
 
4.0%
주 8
 
4.0%
식 7
 
3.5%
이 7
 
3.5%
리 5
 
2.5%
Other values (77) 111
55.2%
Common
ValueCountFrequency (%)
15
88.2%
( 1
 
5.9%
) 1
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 185
84.9%
ASCII 17
 
7.8%
None 16
 
7.3%

Most frequent character per block

None
ValueCountFrequency (%)
㈜ 16
100.0%
ASCII
ValueCountFrequency (%)
15
88.2%
( 1
 
5.9%
) 1
 
5.9%
Hangul
ValueCountFrequency (%)
스 11
 
5.9%
사 10
 
5.4%
호 9
 
4.9%
텔 9
 
4.9%
회 8
 
4.3%
주 8
 
4.3%
식 7
 
3.8%
이 7
 
3.8%
리 5
 
2.7%
코 4
 
2.2%
Other values (76) 107
57.8%

객실수_스탠더드
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct28
Distinct (%)80.0%
Missing3
Missing (%)7.9%
Infinite0
Infinite (%)0.0%
Mean40.857143
Minimum0
Maximum231
Zeros4
Zeros (%)10.5%
Negative0
Negative (%)0.0%
Memory size474.0 B
2023-12-12T14:00:33.915937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q114
median25
Q343
95-th percentile155.4
Maximum231
Range231
Interquartile range (IQR)29

Descriptive statistics

Standard deviation51.835227
Coefficient of variation (CV)1.2686944
Kurtosis6.4629745
Mean40.857143
Median Absolute Deviation (MAD)16
Skewness2.5023618
Sum1430
Variance2686.8908
MonotonicityNot monotonic
2023-12-12T14:00:34.060489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0 4
 
10.5%
20 3
 
7.9%
19 2
 
5.3%
38 2
 
5.3%
11 1
 
2.6%
17 1
 
2.6%
42 1
 
2.6%
41 1
 
2.6%
22 1
 
2.6%
50 1
 
2.6%
Other values (18) 18
47.4%
(Missing) 3
 
7.9%
ValueCountFrequency (%)
0 4
10.5%
4 1
 
2.6%
5 1
 
2.6%
6 1
 
2.6%
11 1
 
2.6%
12 1
 
2.6%
16 1
 
2.6%
17 1
 
2.6%
19 2
5.3%
20 3
7.9%
ValueCountFrequency (%)
231 1
2.6%
196 1
2.6%
138 1
2.6%
95 1
2.6%
74 1
2.6%
60 1
2.6%
55 1
2.6%
50 1
2.6%
44 1
2.6%
42 1
2.6%

객실수_디럭스
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct22
Distinct (%)64.7%
Missing4
Missing (%)10.5%
Infinite0
Infinite (%)0.0%
Mean24.058824
Minimum0
Maximum195
Zeros7
Zeros (%)18.4%
Negative0
Negative (%)0.0%
Memory size474.0 B
2023-12-12T14:00:34.199955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13.25
median10.5
Q325
95-th percentile89.35
Maximum195
Range195
Interquartile range (IQR)21.75

Descriptive statistics

Standard deviation41.3565
Coefficient of variation (CV)1.7189743
Kurtosis11.032272
Mean24.058824
Median Absolute Deviation (MAD)10.5
Skewness3.2444784
Sum818
Variance1710.3601
MonotonicityNot monotonic
2023-12-12T14:00:34.379450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
0 7
18.4%
6 4
 
10.5%
10 2
 
5.3%
14 2
 
5.3%
25 2
 
5.3%
3 1
 
2.6%
11 1
 
2.6%
37 1
 
2.6%
39 1
 
2.6%
20 1
 
2.6%
Other values (12) 12
31.6%
(Missing) 4
 
10.5%
ValueCountFrequency (%)
0 7
18.4%
2 1
 
2.6%
3 1
 
2.6%
4 1
 
2.6%
6 4
10.5%
8 1
 
2.6%
10 2
 
5.3%
11 1
 
2.6%
12 1
 
2.6%
14 2
 
5.3%
ValueCountFrequency (%)
195 1
2.6%
155 1
2.6%
54 1
2.6%
51 1
2.6%
39 1
2.6%
37 1
2.6%
34 1
2.6%
30 1
2.6%
25 2
5.3%
24 1
2.6%

객실수_스위트
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct16
Distinct (%)45.7%
Missing3
Missing (%)7.9%
Infinite0
Infinite (%)0.0%
Mean9.6
Minimum0
Maximum116
Zeros12
Zeros (%)31.6%
Negative0
Negative (%)0.0%
Memory size474.0 B
2023-12-12T14:00:34.541819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q39
95-th percentile35.3
Maximum116
Range116
Interquartile range (IQR)9

Descriptive statistics

Standard deviation20.61724
Coefficient of variation (CV)2.1476292
Kurtosis21.812909
Mean9.6
Median Absolute Deviation (MAD)3
Skewness4.3733578
Sum336
Variance425.07059
MonotonicityNot monotonic
2023-12-12T14:00:34.680740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 12
31.6%
3 4
 
10.5%
6 3
 
7.9%
4 2
 
5.3%
10 2
 
5.3%
7 2
 
5.3%
14 1
 
2.6%
2 1
 
2.6%
11 1
 
2.6%
21 1
 
2.6%
Other values (6) 6
15.8%
(Missing) 3
 
7.9%
ValueCountFrequency (%)
0 12
31.6%
1 1
 
2.6%
2 1
 
2.6%
3 4
 
10.5%
4 2
 
5.3%
6 3
 
7.9%
7 2
 
5.3%
8 1
 
2.6%
10 2
 
5.3%
11 1
 
2.6%
ValueCountFrequency (%)
116 1
2.6%
36 1
2.6%
35 1
2.6%
21 1
2.6%
20 1
2.6%
14 1
2.6%
11 1
2.6%
10 2
5.3%
8 1
2.6%
7 2
5.3%

객실수_한실
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct13
Distinct (%)37.1%
Missing3
Missing (%)7.9%
Infinite0
Infinite (%)0.0%
Mean3.8857143
Minimum0
Maximum24
Zeros20
Zeros (%)52.6%
Negative0
Negative (%)0.0%
Memory size474.0 B
2023-12-12T14:00:34.829033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q36
95-th percentile14.9
Maximum24
Range24
Interquartile range (IQR)6

Descriptive statistics

Standard deviation6.1537718
Coefficient of variation (CV)1.5836913
Kurtosis2.2349348
Mean3.8857143
Median Absolute Deviation (MAD)0
Skewness1.655246
Sum136
Variance37.868908
MonotonicityNot monotonic
2023-12-12T14:00:34.976385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0 20
52.6%
6 2
 
5.3%
1 2
 
5.3%
11 2
 
5.3%
2 1
 
2.6%
9 1
 
2.6%
24 1
 
2.6%
13 1
 
2.6%
5 1
 
2.6%
12 1
 
2.6%
Other values (3) 3
 
7.9%
(Missing) 3
 
7.9%
ValueCountFrequency (%)
0 20
52.6%
1 2
 
5.3%
2 1
 
2.6%
4 1
 
2.6%
5 1
 
2.6%
6 2
 
5.3%
9 1
 
2.6%
11 2
 
5.3%
12 1
 
2.6%
13 1
 
2.6%
ValueCountFrequency (%)
24 1
2.6%
17 1
2.6%
14 1
2.6%
13 1
2.6%
12 1
2.6%
11 2
5.3%
9 1
2.6%
6 2
5.3%
5 1
2.6%
4 1
2.6%

객실수_기타
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)10.5%
Missing0
Missing (%)0.0%
Memory size436.0 B
<NA>
35 
24
 
1
15
 
1
4
 
1

Length

Max length4
Median length4
Mean length3.8157895
Min length1

Unique

Unique3 ?
Unique (%)7.9%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 35
92.1%
24 1
 
2.6%
15 1
 
2.6%
4 1
 
2.6%

Length

2023-12-12T14:00:35.126473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T14:00:35.240008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 35
92.1%
24 1
 
2.6%
15 1
 
2.6%
4 1
 
2.6%

객실수_소계
Real number (ℝ)

HIGH CORRELATION 

Distinct32
Distinct (%)84.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean77.815789
Minimum4
Maximum325
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size474.0 B
2023-12-12T14:00:35.356699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile15.25
Q144
median53
Q371.5
95-th percentile226.5
Maximum325
Range321
Interquartile range (IQR)27.5

Descriptive statistics

Standard deviation73.458889
Coefficient of variation (CV)0.94401007
Kurtosis4.4362123
Mean77.815789
Median Absolute Deviation (MAD)17
Skewness2.1540092
Sum2957
Variance5396.2084
MonotonicityNot monotonic
2023-12-12T14:00:35.485190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
54 3
 
7.9%
20 2
 
5.3%
44 2
 
5.3%
70 2
 
5.3%
51 2
 
5.3%
60 1
 
2.6%
80 1
 
2.6%
30 1
 
2.6%
49 1
 
2.6%
4 1
 
2.6%
Other values (22) 22
57.9%
ValueCountFrequency (%)
4 1
2.6%
11 1
2.6%
16 1
2.6%
20 2
5.3%
30 1
2.6%
33 1
2.6%
36 1
2.6%
38 1
2.6%
44 2
5.3%
45 1
2.6%
ValueCountFrequency (%)
325 1
2.6%
303 1
2.6%
213 1
2.6%
190 1
2.6%
181 1
2.6%
150 1
2.6%
110 1
2.6%
94 1
2.6%
80 1
2.6%
72 1
2.6%

소재지
Text

UNIQUE 

Distinct38
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size436.0 B
2023-12-12T14:00:35.750305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length25
Mean length22.552632
Min length18

Characters and Unicode

Total characters857
Distinct characters99
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

Unique38 ?
Unique (%)100.0%

Sample

1st row대구광역시 중구 달구벌대로 2033(동산동)
2nd row대구광역시 중구 태평로 117(태평로2가)
3rd row대구광역시 중구 동성로1길 15 (동성로3가)
4th row대구광역시 중구 국채보상로 123길 23 (동문동)
5th row대구광역시 중구 경상감영길 193 (동문동)
ValueCountFrequency (%)
대구광역시 37
22.4%
동구 11
 
6.7%
중구 8
 
4.8%
수성구 7
 
4.2%
달성군 5
 
3.0%
달서구 3
 
1.8%
율암로 3
 
1.8%
달성2차동1로 2
 
1.2%
남구 2
 
1.2%
동대구로 2
 
1.2%
Other values (79) 85
51.5%
2023-12-12T14:00:36.176263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
128
 
14.9%
구 76
 
8.9%
동 53
 
6.2%
대 47
 
5.5%
시 38
 
4.4%
광 37
 
4.3%
역 37
 
4.3%
로 34
 
4.0%
1 34
 
4.0%
( 29
 
3.4%
Other values (89) 344
40.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 536
62.5%
Decimal Number 129
 
15.1%
Space Separator 128
 
14.9%
Open Punctuation 29
 
3.4%
Close Punctuation 29
 
3.4%
Dash Punctuation 6
 
0.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 76
14.2%
동 53
 
9.9%
대 47
 
8.8%
시 38
 
7.1%
광 37
 
6.9%
역 37
 
6.9%
로 34
 
6.3%
성 19
 
3.5%
달 12
 
2.2%
길 11
 
2.1%
Other values (75) 172
32.1%
Decimal Number
ValueCountFrequency (%)
1 34
26.4%
2 22
17.1%
4 14
10.9%
3 12
 
9.3%
5 11
 
8.5%
6 9
 
7.0%
0 9
 
7.0%
7 8
 
6.2%
8 6
 
4.7%
9 4
 
3.1%
Space Separator
ValueCountFrequency (%)
128
100.0%
Open Punctuation
ValueCountFrequency (%)
( 29
100.0%
Close Punctuation
ValueCountFrequency (%)
) 29
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 536
62.5%
Common 321
37.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 76
14.2%
동 53
 
9.9%
대 47
 
8.8%
시 38
 
7.1%
광 37
 
6.9%
역 37
 
6.9%
로 34
 
6.3%
성 19
 
3.5%
달 12
 
2.2%
길 11
 
2.1%
Other values (75) 172
32.1%
Common
ValueCountFrequency (%)
128
39.9%
1 34
 
10.6%
( 29
 
9.0%
) 29
 
9.0%
2 22
 
6.9%
4 14
 
4.4%
3 12
 
3.7%
5 11
 
3.4%
6 9
 
2.8%
0 9
 
2.8%
Other values (4) 24
 
7.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 536
62.5%
ASCII 321
37.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
128
39.9%
1 34
 
10.6%
( 29
 
9.0%
) 29
 
9.0%
2 22
 
6.9%
4 14
 
4.4%
3 12
 
3.7%
5 11
 
3.4%
6 9
 
2.8%
0 9
 
2.8%
Other values (4) 24
 
7.5%
Hangul
ValueCountFrequency (%)
구 76
14.2%
동 53
 
9.9%
대 47
 
8.8%
시 38
 
7.1%
광 37
 
6.9%
역 37
 
6.9%
로 34
 
6.3%
성 19
 
3.5%
달 12
 
2.2%
길 11
 
2.1%
Other values (75) 172
32.1%

대표자(총지배인)
Text

UNIQUE 

Distinct38
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size436.0 B
2023-12-12T14:00:36.422842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length3
Mean length4.6578947
Min length2

Characters and Unicode

Total characters177
Distinct characters77
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

Unique38 ?
Unique (%)100.0%

Sample

1st row김도헌
2nd row김재홍
3rd row홍지명
4th row은영미
5th row이원호
ValueCountFrequency (%)
김도헌 1
 
2.6%
나홍열+공석 1
 
2.6%
서정심 1
 
2.6%
이진수 1
 
2.6%
김성곤+박경수 1
 
2.6%
서윤자+조준건 1
 
2.6%
김영미+서정호 1
 
2.6%
김윤식+곽순호 1
 
2.6%
정문호+홍보지훈 1
 
2.6%
김건훈+이응원 1
 
2.6%
Other values (28) 28
73.7%
2023-12-12T14:00:36.819280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
김 14
 
7.9%
+ 13
 
7.3%
정 8
 
4.5%
호 7
 
4.0%
홍 6
 
3.4%
영 6
 
3.4%
이 5
 
2.8%
박 4
 
2.3%
조 4
 
2.3%
종 4
 
2.3%
Other values (67) 106
59.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 162
91.5%
Math Symbol 13
 
7.3%
Decimal Number 2
 
1.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
김 14
 
8.6%
정 8
 
4.9%
호 7
 
4.3%
홍 6
 
3.7%
영 6
 
3.7%
이 5
 
3.1%
박 4
 
2.5%
조 4
 
2.5%
종 4
 
2.5%
성 4
 
2.5%
Other values (65) 100
61.7%
Math Symbol
ValueCountFrequency (%)
+ 13
100.0%
Decimal Number
ValueCountFrequency (%)
1 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 162
91.5%
Common 15
 
8.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
김 14
 
8.6%
정 8
 
4.9%
호 7
 
4.3%
홍 6
 
3.7%
영 6
 
3.7%
이 5
 
3.1%
박 4
 
2.5%
조 4
 
2.5%
종 4
 
2.5%
성 4
 
2.5%
Other values (65) 100
61.7%
Common
ValueCountFrequency (%)
+ 13
86.7%
1 2
 
13.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 162
91.5%
ASCII 15
 
8.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
김 14
 
8.6%
정 8
 
4.9%
호 7
 
4.3%
홍 6
 
3.7%
영 6
 
3.7%
이 5
 
3.1%
박 4
 
2.5%
조 4
 
2.5%
종 4
 
2.5%
성 4
 
2.5%
Other values (65) 100
61.7%
ASCII
ValueCountFrequency (%)
+ 13
86.7%
1 2
 
13.3%

전화번호
Text

MISSING 

Distinct33
Distinct (%)100.0%
Missing5
Missing (%)13.2%
Memory size436.0 B
2023-12-12T14:00:37.062339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12.030303
Min length12

Characters and Unicode

Total characters397
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

Unique33 ?
Unique (%)100.0%

Sample

1st row053-253-7711
2nd row053-252-2221
3rd row053-428-1045
4th row053-421-5007
5th row053-269-4000
ValueCountFrequency (%)
053-623-1000 1
 
3.0%
053-218-6655 1
 
3.0%
053-589-6700 1
 
3.0%
053-380-0114 1
 
3.0%
053-602-7114 1
 
3.0%
053-742-0001 1
 
3.0%
053-770-5412 1
 
3.0%
053-765-7776 1
 
3.0%
053-718-7000 1
 
3.0%
053-655-7799 1
 
3.0%
Other values (23) 23
69.7%
2023-12-12T14:00:37.452076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 82
20.7%
- 66
16.6%
5 59
14.9%
3 45
11.3%
2 29
 
7.3%
7 29
 
7.3%
1 28
 
7.1%
9 16
 
4.0%
4 15
 
3.8%
6 14
 
3.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 331
83.4%
Dash Punctuation 66
 
16.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 82
24.8%
5 59
17.8%
3 45
13.6%
2 29
 
8.8%
7 29
 
8.8%
1 28
 
8.5%
9 16
 
4.8%
4 15
 
4.5%
6 14
 
4.2%
8 14
 
4.2%
Dash Punctuation
ValueCountFrequency (%)
- 66
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 397
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 82
20.7%
- 66
16.6%
5 59
14.9%
3 45
11.3%
2 29
 
7.3%
7 29
 
7.3%
1 28
 
7.1%
9 16
 
4.0%
4 15
 
3.8%
6 14
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 397
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 82
20.7%
- 66
16.6%
5 59
14.9%
3 45
11.3%
2 29
 
7.3%
7 29
 
7.3%
1 28
 
7.1%
9 16
 
4.0%
4 15
 
3.8%
6 14
 
3.5%

팩스번호
Text

MISSING 

Distinct15
Distinct (%)100.0%
Missing23
Missing (%)60.5%
Memory size436.0 B
2023-12-12T14:00:37.678088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters180
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

Unique15 ?
Unique (%)100.0%

Sample

1st row053-256-0406
2nd row053-253-8571
3rd row053-282-1011
4th row053-984-2200
5th row053-985-8097
ValueCountFrequency (%)
053-256-0406 1
 
6.7%
053-253-8571 1
 
6.7%
053-282-1011 1
 
6.7%
053-984-2200 1
 
6.7%
053-985-8097 1
 
6.7%
053-327-7770 1
 
6.7%
053-472-7900 1
 
6.7%
053-380-0409 1
 
6.7%
053-953-2008 1
 
6.7%
053-742-0002 1
 
6.7%
Other values (5) 5
33.3%
2023-12-12T14:00:38.036617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 39
21.7%
- 30
16.7%
5 26
14.4%
3 22
12.2%
7 18
10.0%
2 11
 
6.1%
8 8
 
4.4%
6 7
 
3.9%
9 7
 
3.9%
4 6
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 150
83.3%
Dash Punctuation 30
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 39
26.0%
5 26
17.3%
3 22
14.7%
7 18
12.0%
2 11
 
7.3%
8 8
 
5.3%
6 7
 
4.7%
9 7
 
4.7%
4 6
 
4.0%
1 6
 
4.0%
Dash Punctuation
ValueCountFrequency (%)
- 30
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 180
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 39
21.7%
- 30
16.7%
5 26
14.4%
3 22
12.2%
7 18
10.0%
2 11
 
6.1%
8 8
 
4.4%
6 7
 
3.9%
9 7
 
3.9%
4 6
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 180
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 39
21.7%
- 30
16.7%
5 26
14.4%
3 22
12.2%
7 18
10.0%
2 11
 
6.1%
8 8
 
4.4%
6 7
 
3.9%
9 7
 
3.9%
4 6
 
3.3%

등록년월일
Date

Distinct37
Distinct (%)97.4%
Missing0
Missing (%)0.0%
Memory size436.0 B
Minimum1964-12-16 00:00:00
Maximum2022-08-31 00:00:00
2023-12-12T14:00:38.205317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:00:38.355460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)

변경등록
Date

MISSING 

Distinct15
Distinct (%)100.0%
Missing23
Missing (%)60.5%
Memory size436.0 B
Minimum2007-09-27 00:00:00
Maximum2023-05-15 00:00:00
2023-12-12T14:00:38.488040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:00:38.613661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)

Interactions

2023-12-12T14:00:29.583582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:00:26.205900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:00:26.844863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:00:27.441009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:00:28.173884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:00:28.898748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:00:29.704300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:00:26.318528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:00:26.964590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:00:27.577654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:00:28.304555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:00:29.007368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:00:29.799240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:00:26.414623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:00:27.049496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:00:27.708809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:00:28.440917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:00:29.126788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:00:29.896008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:00:26.526870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:00:27.145669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:00:27.830190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:00:28.570681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:00:29.257672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:00:29.992785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:00:26.622091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:00:27.234555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:00:27.933072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:00:28.688954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:00:29.358607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:00:30.085748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:00:26.719588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:00:27.335827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:00:28.039534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:00:28.804852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:00:29.463926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T14:00:38.751139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번지역업종분류등급(이전등급)호텔명법인명객실수_스탠더드객실수_디럭스객실수_스위트객실수_한실객실수_기타객실수_소계소재지대표자(총지배인)전화번호팩스번호등록년월일변경등록
연번1.0000.9160.6360.1641.0001.0000.0000.4440.5980.000NaN0.5011.0001.0001.0001.0000.9361.000
지역0.9161.0000.0000.7131.0001.0000.5180.0000.3830.636NaN0.0001.0001.0001.0001.0000.8111.000
업종분류0.6360.0001.000NaN1.0001.0000.0000.0000.0000.000NaN0.7331.0001.0001.000NaN1.0001.000
등급(이전등급)0.1640.713NaN1.0001.0001.0000.5710.7120.0000.6321.0000.6591.0001.0001.0001.0000.9191.000
호텔명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
법인명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000NaN1.0001.0001.0001.0001.0001.0001.000
객실수_스탠더드0.0000.5180.0000.5711.0001.0001.0000.5560.8150.7311.0000.9461.0001.0001.0001.0001.0001.000
객실수_디럭스0.4440.0000.0000.7121.0001.0000.5561.0000.4260.1621.0000.6711.0001.0001.0001.0000.7211.000
객실수_스위트0.5980.3830.0000.0001.0001.0000.8150.4261.0000.0001.0000.9181.0001.0001.0001.0001.0001.000
객실수_한실0.0000.6360.0000.6321.0001.0000.7310.1620.0001.000NaN0.4591.0001.0001.0001.0000.0001.000
객실수_기타NaNNaNNaN1.0001.000NaN1.0001.0001.000NaN1.0001.0001.0001.0001.000NaN1.000NaN
객실수_소계0.5010.0000.7330.6591.0001.0000.9460.6710.9180.4591.0001.0001.0001.0001.0001.0000.9781.000
소재지1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
대표자(총지배인)1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
전화번호1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
팩스번호1.0001.000NaN1.0001.0001.0001.0001.0001.0001.000NaN1.0001.0001.0001.0001.0001.0001.000
등록년월일0.9360.8111.0000.9191.0001.0001.0000.7211.0000.0001.0000.9781.0001.0001.0001.0001.0001.000
변경등록1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000NaN1.0001.0001.0001.0001.0001.0001.000
2023-12-12T14:00:38.981136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지역등급(이전등급)객실수_기타업종분류
지역1.0000.3921.0000.000
등급(이전등급)0.3921.0001.0001.000
객실수_기타1.0001.0001.0001.000
업종분류0.0001.0001.0001.000
2023-12-12T14:00:39.120265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번객실수_스탠더드객실수_디럭스객실수_스위트객실수_한실객실수_소계지역업종분류등급(이전등급)객실수_기타
연번1.0000.129-0.132-0.0130.2440.0480.7200.4300.0001.000
객실수_스탠더드0.1291.000-0.0250.205-0.0500.5380.2660.0000.2661.000
객실수_디럭스-0.132-0.0251.0000.4670.1570.7160.0000.0000.3321.000
객실수_스위트-0.0130.2050.4671.0000.1260.6240.2170.0000.0001.000
객실수_한실0.244-0.0500.1570.1261.0000.1080.3400.0000.2961.000
객실수_소계0.0480.5380.7160.6240.1081.0000.0000.5100.3461.000
지역0.7200.2660.0000.2170.3400.0001.0000.0000.3921.000
업종분류0.4300.0000.0000.0000.0000.5100.0001.0001.0001.000
등급(이전등급)0.0000.2660.3320.0000.2960.3460.3921.0001.0001.000
객실수_기타1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000

Missing values

2023-12-12T14:00:30.247702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T14:00:30.569295image/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.
2023-12-12T14:00:31.150833image/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

연번지역업종분류등급(이전등급)호텔명법인명객실수_스탠더드객실수_디럭스객실수_스위트객실수_한실객실수_기타객실수_소계소재지대표자(총지배인)전화번호팩스번호등록년월일변경등록
01중구관광호텔업4성엘디스리젠트호텔㈜엘디스리젠트호텔445466<NA>110대구광역시 중구 달구벌대로 2033(동산동)김도헌053-253-7711053-256-04061984-07-302020-06-15
12중구관광호텔업등급없음(2성)유니온관광호텔㈜유니온관광호텔3025100<NA>65대구광역시 중구 태평로 117(태평로2가)김재홍053-252-2221053-253-85711989-01-312022-07-29
23중구관광호텔업2성토요코인 대구동성로토요코인코리아 주식회사1961430<NA>213대구광역시 중구 동성로1길 15 (동성로3가)홍지명053-428-1045<NA>2019-04-30<NA>
34중구관광호텔업2성노블스테이㈜노블스테이05100<NA>51대구광역시 중구 국채보상로 123길 23 (동문동)은영미053-421-5007<NA>2019-11-132021-07-19
45중구관광호텔업3성리버틴호텔<NA>1717142<NA>50대구광역시 중구 경상감영길 193 (동문동)이원호053-269-4000<NA>2020-01-06<NA>
56중구호스텔업<NA>대구동성로호스텔주식회사 로프11000<NA>11대구광역시 중구 동성로 12길 9(동문동)조현정070-7708-3145<NA>2015-12-152018-05-24
67중구호스텔업<NA>대구미드타운 호스텔<NA>20000<NA>20대구광역시 중구 중앙대로77길 47(장관동)양승욱053-719-3450<NA>2017-11-01<NA>
78중구호스텔업<NA>경's 호스텔<NA>16000<NA>16대구광역시 중구 북성로4길 61 (인교동)최은경<NA><NA>2019-05-13<NA>
89동구관광호텔업3성퀸벨호텔㈜생활속행복55601<NA>62대구광역시 동구 동촌로 200(방촌동)박완053-282-1000053-282-10112002-05-302018-12-17
910동구관광호텔업2성인스타관광호텔<NA>203020<NA>52대구광역시 동구 율암로 164(상매동)홍정필+김준호053-213-8080<NA>2017-01-112023-02-17
연번지역업종분류등급(이전등급)호텔명법인명객실수_스탠더드객실수_디럭스객실수_스위트객실수_한실객실수_기타객실수_소계소재지대표자(총지배인)전화번호팩스번호등록년월일변경등록
2829달서구관광호텔업등급없음크리스탈관광호텔㈜크리스탈관광호텔1937014<NA>70대구광역시 달서구 달구벌대로 1910(두류동)박성배+최성호053-655-7799053-655-70071989-04-142007-09-27
2930달서구관광호텔업2성호텔 엘리시아<NA>501100<NA>61대구광역시 달서구 갈밭남로 45(대곡동)김건훈+이응원053-625-9100<NA>2019-06-04<NA>
3031달성군관광호텔업등급없음㈜파라다이스 온천호텔㈜파라다이스 온천호텔220011<NA>33대구광역시 달성군 논공읍 약산덧재길 81한명환053-555-7221<NA>2004-10-152015-04-21
3132달성군관광호텔업1성호텔G<NA>41300<NA>44대구광역시 달성군 구지면 달성2차동1로 145나근숙053-263-5511<NA>2017-02-272020-12-04
3233달성군관광호텔업1성칸 호텔<NA>38600<NA>44대구광역시 달성군 구지면 달성2차동1로 141김종호053-263-0777<NA>2017-03-20<NA>
3334군위군관광호텔업등급없음(1성)백송온천관광호텔<NA>010317<NA>30대구시 군위군 부계면 한티로 2244김민재+김종식054-382-1400<NA>2011-03-23<NA>
3435경제자유구역호스텔업<NA>노블 호스텔<NA><NA><NA><NA><NA><NA>54대구광역시 달성군 현풍읍 중리(504-01)곽은렬외1인<NA><NA>2021-04-02<NA>
3536경제자유구역관광호텔업3성ACT 관광호텔주식회사 알파개발<NA><NA><NA><NA><NA>80대구광역시 수성구 대흥동 843-1주식회사알파개발<NA><NA>2022-01-06<NA>
3637경제자유구역관광호텔업2성브라운도트호텔현풍점㈜서정에스엠<NA><NA><NA><NA><NA>60대구광역시 달성군 현풍읍 테크노공원로 35서정심<NA><NA>2022-08-31<NA>
3738경제자유구역관광호텔업신청더아르코호텔㈜대명디스플레이42070<NA>49대구광역시 수성구 대흥동 842번지이상광+조홍환053-791-9000<NA>2022-03-27<NA>