Overview

Dataset statistics

Number of variables7
Number of observations525
Missing cells538
Missing cells (%)14.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory30.9 KiB
Average record size in memory60.3 B

Variable types

Numeric4
Categorical1
Text2

Dataset

Description전라남도 순천시의 숙박업소에 관한 업종구분, 업소명, 도로명주소, 객실수, 한실수, 양실수에 대한 정보를 제공하는 공공데이터 입니다.
Author공공데이터포털
URLhttps://www.data.go.kr/data/15117756/fileData.do

Alerts

연번 is highly overall correlated with 객실수 and 2 other fieldsHigh correlation
객실수 is highly overall correlated with 연번 and 1 other fieldsHigh correlation
한실수 is highly overall correlated with 연번High correlation
양실수 is highly overall correlated with 객실수High correlation
업종구분 is highly overall correlated with 연번High correlation
한실수 has 269 (51.2%) missing valuesMissing
양실수 has 269 (51.2%) missing valuesMissing
연번 has unique valuesUnique
한실수 has 85 (16.2%) zerosZeros
양실수 has 60 (11.4%) zerosZeros

Reproduction

Analysis started2024-04-16 16:17:39.995898
Analysis finished2024-04-16 16:17:42.374304
Duration2.38 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct525
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean263
Minimum1
Maximum525
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.7 KiB
2024-04-17T01:17:42.436861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile27.2
Q1132
median263
Q3394
95-th percentile498.8
Maximum525
Range524
Interquartile range (IQR)262

Descriptive statistics

Standard deviation151.69871
Coefficient of variation (CV)0.5768012
Kurtosis-1.2
Mean263
Median Absolute Deviation (MAD)131
Skewness0
Sum138075
Variance23012.5
MonotonicityStrictly increasing
2024-04-17T01:17:42.554626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.2%
362 1
 
0.2%
360 1
 
0.2%
359 1
 
0.2%
358 1
 
0.2%
357 1
 
0.2%
356 1
 
0.2%
355 1
 
0.2%
354 1
 
0.2%
353 1
 
0.2%
Other values (515) 515
98.1%
ValueCountFrequency (%)
1 1
0.2%
2 1
0.2%
3 1
0.2%
4 1
0.2%
5 1
0.2%
6 1
0.2%
7 1
0.2%
8 1
0.2%
9 1
0.2%
10 1
0.2%
ValueCountFrequency (%)
525 1
0.2%
524 1
0.2%
523 1
0.2%
522 1
0.2%
521 1
0.2%
520 1
0.2%
519 1
0.2%
518 1
0.2%
517 1
0.2%
516 1
0.2%

업종구분
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
농어촌민박
205 
숙박업(일반)
183 
숙박업(생활)
73 
한옥체험
62 
외국인관광민박
 
2

Length

Max length7
Median length5
Mean length5.8647619
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row숙박업(일반)
2nd row숙박업(일반)
3rd row숙박업(일반)
4th row숙박업(일반)
5th row숙박업(일반)

Common Values

ValueCountFrequency (%)
농어촌민박 205
39.0%
숙박업(일반) 183
34.9%
숙박업(생활) 73
 
13.9%
한옥체험 62
 
11.8%
외국인관광민박 2
 
0.4%

Length

2024-04-17T01:17:42.677469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T01:17:42.786906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
농어촌민박 205
39.0%
숙박업(일반 183
34.9%
숙박업(생활 73
 
13.9%
한옥체험 62
 
11.8%
외국인관광민박 2
 
0.4%

업소명
Text

Distinct519
Distinct (%)98.9%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
2024-04-17T01:17:43.023859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length13
Mean length5.4380952
Min length2

Characters and Unicode

Total characters2855
Distinct characters418
Distinct categories10 ?
Distinct scripts4 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique513 ?
Unique (%)97.7%

Sample

1st row금광여관
2nd row남원여인숙
3rd row현대장모텔
4th row강남여관
5th row대흥여인숙
ValueCountFrequency (%)
순천만 21
 
3.5%
리조트 7
 
1.2%
호스텔 5
 
0.8%
여인숙 4
 
0.7%
순천게스트하우스 3
 
0.5%
무인텔 3
 
0.5%
순천 3
 
0.5%
게스트하우스 2
 
0.3%
순천점 2
 
0.3%
여기어때 2
 
0.3%
Other values (542) 556
91.4%
2024-04-17T01:17:43.390441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
텔 130
 
4.6%
민 114
 
4.0%
박 113
 
4.0%
90
 
3.2%
천 88
 
3.1%
스 81
 
2.8%
순 80
 
2.8%
호 71
 
2.5%
모 63
 
2.2%
만 57
 
2.0%
Other values (408) 1968
68.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2623
91.9%
Space Separator 90
 
3.2%
Uppercase Letter 51
 
1.8%
Decimal Number 49
 
1.7%
Lowercase Letter 12
 
0.4%
Close Punctuation 10
 
0.4%
Open Punctuation 10
 
0.4%
Other Punctuation 5
 
0.2%
Dash Punctuation 3
 
0.1%
Letter Number 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
텔 130
 
5.0%
민 114
 
4.3%
박 113
 
4.3%
천 88
 
3.4%
스 81
 
3.1%
순 80
 
3.0%
호 71
 
2.7%
모 63
 
2.4%
만 57
 
2.2%
여 46
 
1.8%
Other values (364) 1780
67.9%
Uppercase Letter
ValueCountFrequency (%)
H 6
11.8%
T 5
9.8%
O 5
9.8%
S 5
9.8%
E 5
9.8%
A 4
7.8%
C 3
 
5.9%
L 3
 
5.9%
B 3
 
5.9%
J 3
 
5.9%
Other values (6) 9
17.6%
Decimal Number
ValueCountFrequency (%)
2 14
28.6%
4 7
14.3%
1 7
14.3%
3 6
12.2%
6 5
 
10.2%
8 3
 
6.1%
7 3
 
6.1%
5 2
 
4.1%
9 1
 
2.0%
0 1
 
2.0%
Lowercase Letter
ValueCountFrequency (%)
s 2
16.7%
e 2
16.7%
t 2
16.7%
a 1
8.3%
y 1
8.3%
o 1
8.3%
l 1
8.3%
m 1
8.3%
u 1
8.3%
Other Punctuation
ValueCountFrequency (%)
: 2
40.0%
. 2
40.0%
, 1
20.0%
Letter Number
ValueCountFrequency (%)
Ⅰ 1
50.0%
Ⅱ 1
50.0%
Space Separator
ValueCountFrequency (%)
90
100.0%
Close Punctuation
ValueCountFrequency (%)
) 10
100.0%
Open Punctuation
ValueCountFrequency (%)
( 10
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2621
91.8%
Common 167
 
5.8%
Latin 65
 
2.3%
Han 2
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
텔 130
 
5.0%
민 114
 
4.3%
박 113
 
4.3%
천 88
 
3.4%
스 81
 
3.1%
순 80
 
3.1%
호 71
 
2.7%
모 63
 
2.4%
만 57
 
2.2%
여 46
 
1.8%
Other values (362) 1778
67.8%
Latin
ValueCountFrequency (%)
H 6
 
9.2%
T 5
 
7.7%
O 5
 
7.7%
S 5
 
7.7%
E 5
 
7.7%
A 4
 
6.2%
C 3
 
4.6%
L 3
 
4.6%
B 3
 
4.6%
J 3
 
4.6%
Other values (17) 23
35.4%
Common
ValueCountFrequency (%)
90
53.9%
2 14
 
8.4%
) 10
 
6.0%
( 10
 
6.0%
4 7
 
4.2%
1 7
 
4.2%
3 6
 
3.6%
6 5
 
3.0%
8 3
 
1.8%
- 3
 
1.8%
Other values (7) 12
 
7.2%
Han
ValueCountFrequency (%)
好 1
50.0%
泊 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2621
91.8%
ASCII 230
 
8.1%
Number Forms 2
 
0.1%
CJK 2
 
0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
텔 130
 
5.0%
민 114
 
4.3%
박 113
 
4.3%
천 88
 
3.4%
스 81
 
3.1%
순 80
 
3.1%
호 71
 
2.7%
모 63
 
2.4%
만 57
 
2.2%
여 46
 
1.8%
Other values (362) 1778
67.8%
ASCII
ValueCountFrequency (%)
90
39.1%
2 14
 
6.1%
) 10
 
4.3%
( 10
 
4.3%
4 7
 
3.0%
1 7
 
3.0%
3 6
 
2.6%
H 6
 
2.6%
T 5
 
2.2%
O 5
 
2.2%
Other values (32) 70
30.4%
Number Forms
ValueCountFrequency (%)
Ⅰ 1
50.0%
Ⅱ 1
50.0%
CJK
ValueCountFrequency (%)
好 1
50.0%
泊 1
50.0%

도로명 주소
Text

Distinct520
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
2024-04-17T01:17:43.647223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length53
Median length34
Mean length20.979048
Min length10

Characters and Unicode

Total characters11014
Distinct characters197
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique515 ?
Unique (%)98.1%

Sample

1st row전라남도 순천시 송광면 송광사안길 89 ((1~2층))
2nd row전라남도 순천시 역전길 1 (조곡동 (2층))
3rd row전라남도 순천시 팔마로 109 (조곡동)
4th row전라남도 순천시 역전광장2길 3 (조곡동)
5th row전라남도 순천시 풍덕주택길 11 (조곡동 외1필지 (2층))
ValueCountFrequency (%)
순천시 520
 
20.9%
전라남도 256
 
10.3%
낙안면 77
 
3.1%
순천만길 53
 
2.1%
대대동 43
 
1.7%
조곡동 42
 
1.7%
연향동 32
 
1.3%
풍덕동 30
 
1.2%
해룡면 29
 
1.2%
장천동 29
 
1.2%
Other values (612) 1380
55.4%
2024-04-17T01:17:44.039511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2052
18.6%
천 638
 
5.8%
순 582
 
5.3%
시 525
 
4.8%
길 489
 
4.4%
1 401
 
3.6%
( 328
 
3.0%
) 328
 
3.0%
동 325
 
3.0%
전 306
 
2.8%
Other values (187) 5040
45.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 6208
56.4%
Space Separator 2052
 
18.6%
Decimal Number 1838
 
16.7%
Open Punctuation 328
 
3.0%
Close Punctuation 328
 
3.0%
Dash Punctuation 162
 
1.5%
Math Symbol 73
 
0.7%
Lowercase Letter 13
 
0.1%
Other Punctuation 10
 
0.1%
Uppercase Letter 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
천 638
 
10.3%
순 582
 
9.4%
시 525
 
8.5%
길 489
 
7.9%
동 325
 
5.2%
전 306
 
4.9%
남 272
 
4.4%
라 260
 
4.2%
도 258
 
4.2%
면 212
 
3.4%
Other values (158) 2341
37.7%
Decimal Number
ValueCountFrequency (%)
1 401
21.8%
2 302
16.4%
3 261
14.2%
6 188
10.2%
4 157
 
8.5%
7 123
 
6.7%
5 120
 
6.5%
0 103
 
5.6%
8 100
 
5.4%
9 83
 
4.5%
Lowercase Letter
ValueCountFrequency (%)
n 2
15.4%
l 2
15.4%
e 2
15.4%
u 1
7.7%
c 1
7.7%
h 1
7.7%
o 1
7.7%
d 1
7.7%
a 1
7.7%
i 1
7.7%
Other Punctuation
ValueCountFrequency (%)
, 6
60.0%
. 4
40.0%
Uppercase Letter
ValueCountFrequency (%)
S 1
50.0%
V 1
50.0%
Space Separator
ValueCountFrequency (%)
2052
100.0%
Open Punctuation
ValueCountFrequency (%)
( 328
100.0%
Close Punctuation
ValueCountFrequency (%)
) 328
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 162
100.0%
Math Symbol
ValueCountFrequency (%)
~ 73
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 6208
56.4%
Common 4791
43.5%
Latin 15
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
천 638
 
10.3%
순 582
 
9.4%
시 525
 
8.5%
길 489
 
7.9%
동 325
 
5.2%
전 306
 
4.9%
남 272
 
4.4%
라 260
 
4.2%
도 258
 
4.2%
면 212
 
3.4%
Other values (158) 2341
37.7%
Common
ValueCountFrequency (%)
2052
42.8%
1 401
 
8.4%
( 328
 
6.8%
) 328
 
6.8%
2 302
 
6.3%
3 261
 
5.4%
6 188
 
3.9%
- 162
 
3.4%
4 157
 
3.3%
7 123
 
2.6%
Other values (7) 489
 
10.2%
Latin
ValueCountFrequency (%)
n 2
13.3%
l 2
13.3%
e 2
13.3%
S 1
6.7%
u 1
6.7%
c 1
6.7%
h 1
6.7%
o 1
6.7%
d 1
6.7%
a 1
6.7%
Other values (2) 2
13.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 6208
56.4%
ASCII 4806
43.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2052
42.7%
1 401
 
8.3%
( 328
 
6.8%
) 328
 
6.8%
2 302
 
6.3%
3 261
 
5.4%
6 188
 
3.9%
- 162
 
3.4%
4 157
 
3.3%
7 123
 
2.6%
Other values (19) 504
 
10.5%
Hangul
ValueCountFrequency (%)
천 638
 
10.3%
순 582
 
9.4%
시 525
 
8.5%
길 489
 
7.9%
동 325
 
5.2%
전 306
 
4.9%
남 272
 
4.4%
라 260
 
4.2%
도 258
 
4.2%
면 212
 
3.4%
Other values (158) 2341
37.7%

객실수
Real number (ℝ)

HIGH CORRELATION 

Distinct54
Distinct (%)10.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.23619
Minimum1
Maximum104
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.7 KiB
2024-04-17T01:17:44.160969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q319
95-th percentile40
Maximum104
Range103
Interquartile range (IQR)17

Descriptive statistics

Standard deviation14.417488
Coefficient of variation (CV)1.178266
Kurtosis3.7179137
Mean12.23619
Median Absolute Deviation (MAD)3
Skewness1.6812346
Sum6424
Variance207.86395
MonotonicityNot monotonic
2024-04-17T01:17:44.271385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 96
18.3%
1 67
 
12.8%
3 53
 
10.1%
4 48
 
9.1%
6 23
 
4.4%
35 18
 
3.4%
7 16
 
3.0%
5 14
 
2.7%
15 11
 
2.1%
12 10
 
1.9%
Other values (44) 169
32.2%
ValueCountFrequency (%)
1 67
12.8%
2 96
18.3%
3 53
10.1%
4 48
9.1%
5 14
 
2.7%
6 23
 
4.4%
7 16
 
3.0%
8 8
 
1.5%
9 8
 
1.5%
10 8
 
1.5%
ValueCountFrequency (%)
104 1
0.2%
82 1
0.2%
60 1
0.2%
56 1
0.2%
54 1
0.2%
53 1
0.2%
51 1
0.2%
48 2
0.4%
47 1
0.2%
45 1
0.2%

한실수
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct29
Distinct (%)11.3%
Missing269
Missing (%)51.2%
Infinite0
Infinite (%)0.0%
Mean5.9179688
Minimum0
Maximum33
Zeros85
Zeros (%)16.2%
Negative0
Negative (%)0.0%
Memory size4.7 KiB
2024-04-17T01:17:44.386069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4
Q39
95-th percentile19.25
Maximum33
Range33
Interquartile range (IQR)9

Descriptive statistics

Standard deviation6.8419918
Coefficient of variation (CV)1.1561386
Kurtosis1.4948028
Mean5.9179688
Median Absolute Deviation (MAD)4
Skewness1.3554707
Sum1515
Variance46.812852
MonotonicityNot monotonic
2024-04-17T01:17:44.482972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
0 85
 
16.2%
3 18
 
3.4%
8 17
 
3.2%
5 16
 
3.0%
4 14
 
2.7%
6 13
 
2.5%
2 11
 
2.1%
12 10
 
1.9%
1 10
 
1.9%
15 9
 
1.7%
Other values (19) 53
 
10.1%
(Missing) 269
51.2%
ValueCountFrequency (%)
0 85
16.2%
1 10
 
1.9%
2 11
 
2.1%
3 18
 
3.4%
4 14
 
2.7%
5 16
 
3.0%
6 13
 
2.5%
7 6
 
1.1%
8 17
 
3.2%
9 4
 
0.8%
ValueCountFrequency (%)
33 1
0.2%
28 1
0.2%
27 2
0.4%
26 2
0.4%
25 1
0.2%
24 1
0.2%
23 1
0.2%
22 1
0.2%
21 1
0.2%
20 2
0.4%

양실수
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct50
Distinct (%)19.5%
Missing269
Missing (%)51.2%
Infinite0
Infinite (%)0.0%
Mean16.410156
Minimum0
Maximum104
Zeros60
Zeros (%)11.4%
Negative0
Negative (%)0.0%
Memory size4.7 KiB
2024-04-17T01:17:44.595567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median12
Q329
95-th percentile41
Maximum104
Range104
Interquartile range (IQR)26

Descriptive statistics

Standard deviation15.952864
Coefficient of variation (CV)0.97213356
Kurtosis3.1235588
Mean16.410156
Median Absolute Deviation (MAD)12
Skewness1.2246396
Sum4201
Variance254.49386
MonotonicityNot monotonic
2024-04-17T01:17:44.714664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 60
 
11.4%
4 17
 
3.2%
7 10
 
1.9%
6 9
 
1.7%
30 9
 
1.7%
36 8
 
1.5%
8 7
 
1.3%
5 7
 
1.3%
24 7
 
1.3%
31 6
 
1.1%
Other values (40) 116
22.1%
(Missing) 269
51.2%
ValueCountFrequency (%)
0 60
11.4%
1 1
 
0.2%
3 4
 
0.8%
4 17
 
3.2%
5 7
 
1.3%
6 9
 
1.7%
7 10
 
1.9%
8 7
 
1.3%
9 6
 
1.1%
10 5
 
1.0%
ValueCountFrequency (%)
104 1
 
0.2%
82 1
 
0.2%
60 1
 
0.2%
51 1
 
0.2%
48 1
 
0.2%
47 1
 
0.2%
44 2
0.4%
43 1
 
0.2%
42 3
0.6%
41 2
0.4%

Interactions

2024-04-17T01:17:41.444621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:17:40.389313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:17:40.668638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:17:41.056924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:17:41.542760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:17:40.455624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:17:40.761488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:17:41.157243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:17:41.640963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:17:40.523477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:17:40.874684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:17:41.265566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:17:41.731810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:17:40.597690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:17:40.969515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:17:41.355964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-17T01:17:44.788554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번업종구분객실수한실수양실수
연번1.0000.9780.6520.7550.502
업종구분0.9781.0000.5910.5940.515
객실수0.6520.5911.0000.5270.970
한실수0.7550.5940.5271.0000.201
양실수0.5020.5150.9700.2011.000
2024-04-17T01:17:44.876865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번객실수한실수양실수업종구분
연번1.000-0.765-0.7030.1480.785
객실수-0.7651.0000.1610.8120.414
한실수-0.7030.1611.000-0.3800.452
양실수0.1480.812-0.3801.0000.384
업종구분0.7850.4140.4520.3841.000

Missing values

2024-04-17T01:17:42.119896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-17T01:17:42.253445image/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.
2024-04-17T01:17:42.334438image/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

연번업종구분업소명도로명 주소객실수한실수양실수
01숙박업(일반)금광여관전라남도 순천시 송광면 송광사안길 89 ((1~2층))25250
12숙박업(일반)남원여인숙전라남도 순천시 역전길 1 (조곡동 (2층))880
23숙박업(일반)현대장모텔전라남도 순천시 팔마로 109 (조곡동)15150
34숙박업(일반)강남여관전라남도 순천시 역전광장2길 3 (조곡동)10100
45숙박업(일반)대흥여인숙전라남도 순천시 풍덕주택길 11 (조곡동 외1필지 (2층))11110
56숙박업(일반)장미여관전라남도 순천시 역전광장2길 14 (풍덕동)11110
67숙박업(일반)광주여인숙전라남도 순천시 역전광장1길 7 (풍덕동)990
78숙박업(일반)제일 여인숙전라남도 순천시 역전광장3길 8 (조곡동)880
89숙박업(일반)오복여인숙전라남도 순천시 역전길 37 (조곡동 (2 3층))1064
910숙박업(일반)아젤리아모텔전라남도 순천시 북문길 202 (매곡동 (2 3층))15150
연번업종구분업소명도로명 주소객실수한실수양실수
515516한옥체험순천, 한가로이순천시 옥천길 2022<NA><NA>
516517한옥체험쉼한옥민박순천시 상사면 응령길 203<NA><NA>
517518한옥체험농어촌한옥체험관순천시 송광면 덕동길 1092<NA><NA>
518519한옥체험유룡고택순천시 해룡면 유룡길 121<NA><NA>
519520한옥체험노을한옥순천시 해룡면 와온2길 76<NA><NA>
520521한옥체험에코촌 유스호스텔순천시 해룡면 생태배움길123(해룡면)20<NA><NA>
521522한옥체험꽃뜨라래순천시 해룡면 와온3길 443<NA><NA>
522523한옥체험나모:온순천시 호남길 1402<NA><NA>
523524한옥체험어여와순천시 은하길 692<NA><NA>
524525한옥체험도담헌순천시 상인제길 352<NA><NA>