Overview

Dataset statistics

Number of variables9
Number of observations1110
Missing cells624
Missing cells (%)6.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory80.3 KiB
Average record size in memory74.1 B

Variable types

Categorical3
Text4
Numeric2

Dataset

Description여수시 관내 숙박업 현황(2022. 6. 24.기준)에 대한 데이터로, 농어촌민박업과 관광숙박업과 일반숙박업 현황을 제공합니다.
Author전라남도 여수시
URLhttps://www.data.go.kr/data/15074636/fileData.do

Alerts

비고 is highly overall correlated with 업종명High correlation
업종명 is highly overall correlated with 우편번호(도로명) and 2 other fieldsHigh correlation
우편번호(도로명) is highly overall correlated with 업종명 and 1 other fieldsHigh correlation
행정동명(지번) is highly overall correlated with 우편번호(도로명) and 1 other fieldsHigh correlation
비고 is highly imbalanced (66.4%)Imbalance
소재지전화 has 621 (55.9%) missing valuesMissing

Reproduction

Analysis started2023-12-11 22:55:09.445406
Analysis finished2023-12-11 22:55:10.971220
Duration1.53 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

업종명
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size8.8 KiB
농어촌민박업
572 
숙박업(일반)
274 
숙박업(생활)
264 

Length

Max length7
Median length6
Mean length6.4846847
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
농어촌민박업 572
51.5%
숙박업(일반) 274
24.7%
숙박업(생활) 264
23.8%

Length

2023-12-12T07:55:11.033413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T07:55:11.128652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
농어촌민박업 572
51.5%
숙박업(일반 274
24.7%
숙박업(생활 264
23.8%

업소명
Text

Distinct1090
Distinct (%)98.2%
Missing0
Missing (%)0.0%
Memory size8.8 KiB
2023-12-12T07:55:11.470442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length16
Mean length6.0765766
Min length1

Characters and Unicode

Total characters6745
Distinct characters535
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

Unique1073 ?
Unique (%)96.7%

Sample

1st row하나여인숙
2nd row쉼표 더하기
3rd row나비잠
4th row제이미 모텔
5th row엘디아 호텔
ValueCountFrequency (%)
호스텔 44
 
2.8%
민박 37
 
2.3%
여수 37
 
2.3%
펜션 33
 
2.1%
호텔 26
 
1.6%
비고리조트 19
 
1.2%
풀빌라 14
 
0.9%
모텔 13
 
0.8%
리베라 12
 
0.8%
포시즌리조트 11
 
0.7%
Other values (1183) 1351
84.6%
2023-12-12T07:55:12.078522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
492
 
7.3%
박 286
 
4.2%
민 282
 
4.2%
텔 257
 
3.8%
스 220
 
3.3%
션 188
 
2.8%
펜 165
 
2.4%
수 146
 
2.2%
호 142
 
2.1%
모 140
 
2.1%
Other values (525) 4427
65.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5763
85.4%
Space Separator 492
 
7.3%
Uppercase Letter 193
 
2.9%
Decimal Number 138
 
2.0%
Lowercase Letter 56
 
0.8%
Close Punctuation 36
 
0.5%
Open Punctuation 33
 
0.5%
Other Punctuation 27
 
0.4%
Dash Punctuation 6
 
0.1%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
박 286
 
5.0%
민 282
 
4.9%
텔 257
 
4.5%
스 220
 
3.8%
션 188
 
3.3%
펜 165
 
2.9%
수 146
 
2.5%
호 142
 
2.5%
모 140
 
2.4%
리 131
 
2.3%
Other values (459) 3806
66.0%
Uppercase Letter
ValueCountFrequency (%)
A 21
 
10.9%
E 19
 
9.8%
H 18
 
9.3%
T 14
 
7.3%
S 13
 
6.7%
B 12
 
6.2%
C 10
 
5.2%
J 10
 
5.2%
F 8
 
4.1%
G 7
 
3.6%
Other values (14) 61
31.6%
Lowercase Letter
ValueCountFrequency (%)
o 10
17.9%
e 9
16.1%
t 5
8.9%
i 5
8.9%
u 4
 
7.1%
a 4
 
7.1%
n 3
 
5.4%
s 3
 
5.4%
h 3
 
5.4%
l 2
 
3.6%
Other values (7) 8
14.3%
Decimal Number
ValueCountFrequency (%)
1 39
28.3%
2 29
21.0%
3 16
11.6%
5 11
 
8.0%
7 11
 
8.0%
9 9
 
6.5%
4 7
 
5.1%
0 6
 
4.3%
6 5
 
3.6%
8 5
 
3.6%
Other Punctuation
ValueCountFrequency (%)
& 14
51.9%
, 5
 
18.5%
. 3
 
11.1%
# 1
 
3.7%
' 1
 
3.7%
! 1
 
3.7%
· 1
 
3.7%
: 1
 
3.7%
Close Punctuation
ValueCountFrequency (%)
) 35
97.2%
] 1
 
2.8%
Open Punctuation
ValueCountFrequency (%)
( 32
97.0%
[ 1
 
3.0%
Space Separator
ValueCountFrequency (%)
492
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 6
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5761
85.4%
Common 733
 
10.9%
Latin 249
 
3.7%
Han 2
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
박 286
 
5.0%
민 282
 
4.9%
텔 257
 
4.5%
스 220
 
3.8%
션 188
 
3.3%
펜 165
 
2.9%
수 146
 
2.5%
호 142
 
2.5%
모 140
 
2.4%
리 131
 
2.3%
Other values (457) 3804
66.0%
Latin
ValueCountFrequency (%)
A 21
 
8.4%
E 19
 
7.6%
H 18
 
7.2%
T 14
 
5.6%
S 13
 
5.2%
B 12
 
4.8%
C 10
 
4.0%
J 10
 
4.0%
o 10
 
4.0%
e 9
 
3.6%
Other values (31) 113
45.4%
Common
ValueCountFrequency (%)
492
67.1%
1 39
 
5.3%
) 35
 
4.8%
( 32
 
4.4%
2 29
 
4.0%
3 16
 
2.2%
& 14
 
1.9%
5 11
 
1.5%
7 11
 
1.5%
9 9
 
1.2%
Other values (15) 45
 
6.1%
Han
ValueCountFrequency (%)
愛 1
50.0%
夜 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5761
85.4%
ASCII 981
 
14.5%
CJK 2
 
< 0.1%
None 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
492
50.2%
1 39
 
4.0%
) 35
 
3.6%
( 32
 
3.3%
2 29
 
3.0%
A 21
 
2.1%
E 19
 
1.9%
H 18
 
1.8%
3 16
 
1.6%
T 14
 
1.4%
Other values (55) 266
27.1%
Hangul
ValueCountFrequency (%)
박 286
 
5.0%
민 282
 
4.9%
텔 257
 
4.5%
스 220
 
3.8%
션 188
 
3.3%
펜 165
 
2.9%
수 146
 
2.5%
호 142
 
2.5%
모 140
 
2.4%
리 131
 
2.3%
Other values (457) 3804
66.0%
CJK
ValueCountFrequency (%)
愛 1
50.0%
夜 1
50.0%
None
ValueCountFrequency (%)
· 1
100.0%

영업소 주소(도로명)
Text

Distinct1079
Distinct (%)97.2%
Missing0
Missing (%)0.0%
Memory size8.8 KiB
2023-12-12T07:55:12.510435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length40
Median length32
Mean length21.907207
Min length10

Characters and Unicode

Total characters24317
Distinct characters212
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

Unique1058 ?
Unique (%)95.3%

Sample

1st row전라남도 여수시 교동시장4길 16-3 (교동)
2nd row전라남도 여수시 동문로 77 (관문동,1~5층)
3rd row전라남도 여수시 시청동3길 35-12 (학동)
4th row전라남도 여수시 충무로 51-1 (충무동)
5th row전라남도 여수시 시청동3길 24-7 (학동)
ValueCountFrequency (%)
전라남도 1110
20.1%
여수시 1107
20.0%
돌산읍 350
 
6.3%
남면 108
 
2.0%
화양면 91
 
1.6%
돌산로 63
 
1.1%
삼산면 57
 
1.0%
화정면 55
 
1.0%
소라면 47
 
0.8%
봉산동 46
 
0.8%
Other values (1055) 2502
45.2%
2023-12-12T07:55:13.094597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4457
18.3%
남 1334
 
5.5%
도 1169
 
4.8%
수 1163
 
4.8%
라 1157
 
4.8%
여 1146
 
4.7%
시 1144
 
4.7%
전 1119
 
4.6%
1 850
 
3.5%
길 659
 
2.7%
Other values (202) 10119
41.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 14954
61.5%
Space Separator 4457
 
18.3%
Decimal Number 3632
 
14.9%
Dash Punctuation 478
 
2.0%
Open Punctuation 373
 
1.5%
Close Punctuation 373
 
1.5%
Other Punctuation 36
 
0.1%
Math Symbol 10
 
< 0.1%
Uppercase Letter 3
 
< 0.1%
Lowercase Letter 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
남 1334
 
8.9%
도 1169
 
7.8%
수 1163
 
7.8%
라 1157
 
7.7%
여 1146
 
7.7%
시 1144
 
7.7%
전 1119
 
7.5%
길 659
 
4.4%
산 593
 
4.0%
동 526
 
3.5%
Other values (183) 4944
33.1%
Decimal Number
ValueCountFrequency (%)
1 850
23.4%
2 496
13.7%
3 487
13.4%
5 361
9.9%
4 297
 
8.2%
6 290
 
8.0%
7 240
 
6.6%
0 208
 
5.7%
8 206
 
5.7%
9 197
 
5.4%
Uppercase Letter
ValueCountFrequency (%)
A 2
66.7%
F 1
33.3%
Space Separator
ValueCountFrequency (%)
4457
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 478
100.0%
Open Punctuation
ValueCountFrequency (%)
( 373
100.0%
Close Punctuation
ValueCountFrequency (%)
) 373
100.0%
Other Punctuation
ValueCountFrequency (%)
, 36
100.0%
Math Symbol
ValueCountFrequency (%)
~ 10
100.0%
Lowercase Letter
ValueCountFrequency (%)
c 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 14954
61.5%
Common 9359
38.5%
Latin 4
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
남 1334
 
8.9%
도 1169
 
7.8%
수 1163
 
7.8%
라 1157
 
7.7%
여 1146
 
7.7%
시 1144
 
7.7%
전 1119
 
7.5%
길 659
 
4.4%
산 593
 
4.0%
동 526
 
3.5%
Other values (183) 4944
33.1%
Common
ValueCountFrequency (%)
4457
47.6%
1 850
 
9.1%
2 496
 
5.3%
3 487
 
5.2%
- 478
 
5.1%
( 373
 
4.0%
) 373
 
4.0%
5 361
 
3.9%
4 297
 
3.2%
6 290
 
3.1%
Other values (6) 897
 
9.6%
Latin
ValueCountFrequency (%)
A 2
50.0%
c 1
25.0%
F 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 14954
61.5%
ASCII 9363
38.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4457
47.6%
1 850
 
9.1%
2 496
 
5.3%
3 487
 
5.2%
- 478
 
5.1%
( 373
 
4.0%
) 373
 
4.0%
5 361
 
3.9%
4 297
 
3.2%
6 290
 
3.1%
Other values (9) 901
 
9.6%
Hangul
ValueCountFrequency (%)
남 1334
 
8.9%
도 1169
 
7.8%
수 1163
 
7.8%
라 1157
 
7.7%
여 1146
 
7.7%
시 1144
 
7.7%
전 1119
 
7.5%
길 659
 
4.4%
산 593
 
4.0%
동 526
 
3.5%
Other values (183) 4944
33.1%

우편번호(도로명)
Real number (ℝ)

HIGH CORRELATION 

Distinct102
Distinct (%)9.2%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean59739.803
Minimum59601
Maximum59792
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.9 KiB
2023-12-12T07:55:13.261356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum59601
5-th percentile59621
Q159718
median59769
Q359778
95-th percentile59788
Maximum59792
Range191
Interquartile range (IQR)60

Descriptive statistics

Standard deviation54.287706
Coefficient of variation (CV)0.00090873594
Kurtosis0.087686549
Mean59739.803
Median Absolute Deviation (MAD)17
Skewness-1.1934554
Sum66251441
Variance2947.155
MonotonicityNot monotonic
2023-12-12T07:55:13.399241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
59771 99
 
8.9%
59786 68
 
6.1%
59790 46
 
4.1%
59763 40
 
3.6%
59769 38
 
3.4%
59767 36
 
3.2%
59772 34
 
3.1%
59774 33
 
3.0%
59651 32
 
2.9%
59778 31
 
2.8%
Other values (92) 652
58.7%
ValueCountFrequency (%)
59601 3
 
0.3%
59603 8
 
0.7%
59604 3
 
0.3%
59606 2
 
0.2%
59607 25
2.3%
59608 1
 
0.1%
59617 2
 
0.2%
59619 7
 
0.6%
59620 1
 
0.1%
59621 9
 
0.8%
ValueCountFrequency (%)
59792 2
 
0.2%
59790 46
4.1%
59789 4
 
0.4%
59788 7
 
0.6%
59787 11
 
1.0%
59786 68
6.1%
59785 23
 
2.1%
59784 6
 
0.5%
59783 4
 
0.4%
59782 30
2.7%

영업소 주소(지번)
Text

Distinct1072
Distinct (%)96.8%
Missing2
Missing (%)0.2%
Memory size8.8 KiB
2023-12-12T07:55:13.654627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length32
Median length29
Mean length21.203971
Min length15

Characters and Unicode

Total characters23494
Distinct characters133
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1046 ?
Unique (%)94.4%

Sample

1st row전라남도 여수시 교동 622-18
2nd row전라남도 여수시 관문동 909 1~5층
3rd row전라남도 여수시 학동 181-1
4th row전라남도 여수시 충무동 512
5th row전라남도 여수시 학동 178-2
ValueCountFrequency (%)
전라남도 1108
21.1%
여수시 1107
21.0%
돌산읍 351
 
6.7%
평사리 153
 
2.9%
남면 108
 
2.1%
우두리 99
 
1.9%
화양면 91
 
1.7%
삼산면 58
 
1.1%
화정면 55
 
1.0%
소라면 46
 
0.9%
Other values (1162) 2086
39.6%
2023-12-12T07:55:13.992105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4702
20.0%
남 1218
 
5.2%
도 1170
 
5.0%
라 1155
 
4.9%
수 1147
 
4.9%
여 1118
 
4.8%
전 1115
 
4.7%
시 1108
 
4.7%
1 974
 
4.1%
- 785
 
3.3%
Other values (123) 9002
38.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 13376
56.9%
Space Separator 4702
 
20.0%
Decimal Number 4610
 
19.6%
Dash Punctuation 785
 
3.3%
Other Punctuation 7
 
< 0.1%
Math Symbol 5
 
< 0.1%
Open Punctuation 4
 
< 0.1%
Close Punctuation 4
 
< 0.1%
Uppercase Letter 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
남 1218
 
9.1%
도 1170
 
8.7%
라 1155
 
8.6%
수 1147
 
8.6%
여 1118
 
8.4%
전 1115
 
8.3%
시 1108
 
8.3%
리 721
 
5.4%
산 506
 
3.8%
동 395
 
3.0%
Other values (106) 3723
27.8%
Decimal Number
ValueCountFrequency (%)
1 974
21.1%
2 577
12.5%
4 445
9.7%
3 415
9.0%
0 409
8.9%
5 402
8.7%
9 360
 
7.8%
7 360
 
7.8%
6 346
 
7.5%
8 322
 
7.0%
Space Separator
ValueCountFrequency (%)
4702
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 785
100.0%
Other Punctuation
ValueCountFrequency (%)
, 7
100.0%
Math Symbol
ValueCountFrequency (%)
~ 5
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%
Uppercase Letter
ValueCountFrequency (%)
D 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 13376
56.9%
Common 10117
43.1%
Latin 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
남 1218
 
9.1%
도 1170
 
8.7%
라 1155
 
8.6%
수 1147
 
8.6%
여 1118
 
8.4%
전 1115
 
8.3%
시 1108
 
8.3%
리 721
 
5.4%
산 506
 
3.8%
동 395
 
3.0%
Other values (106) 3723
27.8%
Common
ValueCountFrequency (%)
4702
46.5%
1 974
 
9.6%
- 785
 
7.8%
2 577
 
5.7%
4 445
 
4.4%
3 415
 
4.1%
0 409
 
4.0%
5 402
 
4.0%
9 360
 
3.6%
7 360
 
3.6%
Other values (6) 688
 
6.8%
Latin
ValueCountFrequency (%)
D 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 13376
56.9%
ASCII 10118
43.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4702
46.5%
1 974
 
9.6%
- 785
 
7.8%
2 577
 
5.7%
4 445
 
4.4%
3 415
 
4.1%
0 409
 
4.0%
5 402
 
4.0%
9 360
 
3.6%
7 360
 
3.6%
Other values (7) 689
 
6.8%
Hangul
ValueCountFrequency (%)
남 1218
 
9.1%
도 1170
 
8.7%
라 1155
 
8.6%
수 1147
 
8.6%
여 1118
 
8.4%
전 1115
 
8.3%
시 1108
 
8.3%
리 721
 
5.4%
산 506
 
3.8%
동 395
 
3.0%
Other values (106) 3723
27.8%

소재지전화
Text

MISSING 

Distinct469
Distinct (%)95.9%
Missing621
Missing (%)55.9%
Memory size8.8 KiB
2023-12-12T07:55:14.211062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length14
Mean length13.464213
Min length8

Characters and Unicode

Total characters6584
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

Unique458 ?
Unique (%)93.7%

Sample

1st row 061- 641-1122
2nd row 061- 641-1230
3rd row 061- 641-1252
4th row 061- 641-1447
5th row 061- 641-1451
ValueCountFrequency (%)
061 348
33.8%
644 18
 
1.7%
662 15
 
1.5%
685 14
 
1.4%
1111 13
 
1.3%
666 11
 
1.1%
661 9
 
0.9%
642 9
 
0.9%
665 8
 
0.8%
643 8
 
0.8%
Other values (482) 578
56.1%
2023-12-12T07:55:14.742175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6 1353
20.5%
- 976
14.8%
0 880
13.4%
1 788
12.0%
721
11.0%
4 366
 
5.6%
5 343
 
5.2%
2 270
 
4.1%
8 261
 
4.0%
3 250
 
3.8%
Other values (2) 376
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4887
74.2%
Dash Punctuation 976
 
14.8%
Space Separator 721
 
11.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
6 1353
27.7%
0 880
18.0%
1 788
16.1%
4 366
 
7.5%
5 343
 
7.0%
2 270
 
5.5%
8 261
 
5.3%
3 250
 
5.1%
9 194
 
4.0%
7 182
 
3.7%
Dash Punctuation
ValueCountFrequency (%)
- 976
100.0%
Space Separator
ValueCountFrequency (%)
721
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6584
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
6 1353
20.5%
- 976
14.8%
0 880
13.4%
1 788
12.0%
721
11.0%
4 366
 
5.6%
5 343
 
5.2%
2 270
 
4.1%
8 261
 
4.0%
3 250
 
3.8%
Other values (2) 376
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6584
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6 1353
20.5%
- 976
14.8%
0 880
13.4%
1 788
12.0%
721
11.0%
4 366
 
5.6%
5 343
 
5.2%
2 270
 
4.1%
8 261
 
4.0%
3 250
 
3.8%
Other values (2) 376
 
5.7%

행정동명(지번)
Categorical

HIGH CORRELATION 

Distinct28
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size8.8 KiB
돌산읍
350 
남면
108 
화양면
91 
쌍봉동
65 
삼산면
57 
Other values (23)
439 

Length

Max length4
Median length3
Mean length2.8963964
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row중앙동
2nd row동문동
3rd row쌍봉동
4th row충무동
5th row쌍봉동

Common Values

ValueCountFrequency (%)
돌산읍 350
31.5%
남면 108
 
9.7%
화양면 91
 
8.2%
쌍봉동 65
 
5.9%
삼산면 57
 
5.1%
화정면 55
 
5.0%
한려동 55
 
5.0%
대교동 48
 
4.3%
소라면 47
 
4.2%
만덕동 41
 
3.7%
Other values (18) 193
17.4%

Length

2023-12-12T07:55:14.857741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
돌산읍 350
31.5%
남면 108
 
9.7%
화양면 91
 
8.2%
쌍봉동 65
 
5.9%
삼산면 57
 
5.1%
화정면 55
 
5.0%
한려동 55
 
5.0%
대교동 48
 
4.3%
소라면 47
 
4.2%
만덕동 41
 
3.7%
Other values (18) 193
17.4%

객실수
Real number (ℝ)

Distinct81
Distinct (%)7.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.207207
Minimum0
Maximum427
Zeros2
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size9.9 KiB
2023-12-12T07:55:14.960984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median5
Q316
95-th percentile40
Maximum427
Range427
Interquartile range (IQR)14

Descriptive statistics

Standard deviation31.545927
Coefficient of variation (CV)2.2204172
Kurtosis77.032969
Mean14.207207
Median Absolute Deviation (MAD)3
Skewness7.7341687
Sum15770
Variance995.14548
MonotonicityNot monotonic
2023-12-12T07:55:15.065853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 174
15.7%
3 133
 
12.0%
1 113
 
10.2%
4 91
 
8.2%
5 67
 
6.0%
6 53
 
4.8%
7 45
 
4.1%
8 30
 
2.7%
10 23
 
2.1%
12 22
 
2.0%
Other values (71) 359
32.3%
ValueCountFrequency (%)
0 2
 
0.2%
1 113
10.2%
2 174
15.7%
3 133
12.0%
4 91
8.2%
5 67
 
6.0%
6 53
 
4.8%
7 45
 
4.1%
8 30
 
2.7%
9 17
 
1.5%
ValueCountFrequency (%)
427 1
0.1%
399 1
0.1%
343 1
0.1%
313 1
0.1%
311 1
0.1%
253 1
0.1%
210 1
0.1%
178 2
0.2%
173 1
0.1%
170 1
0.1%

비고
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct7
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size8.8 KiB
<NA>
874 
호스텔
195 
분양형숙박
 
20
관광호텔
 
17
휴양콘도미니엄업
 
2
Other values (2)
 
2

Length

Max length8
Median length4
Mean length3.8504505
Min length3

Unique

Unique2 ?
Unique (%)0.2%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 874
78.7%
호스텔 195
 
17.6%
분양형숙박 20
 
1.8%
관광호텔 17
 
1.5%
휴양콘도미니엄업 2
 
0.2%
소형호텔업 1
 
0.1%
전통호텔 1
 
0.1%

Length

2023-12-12T07:55:15.164538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T07:55:15.251218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 874
78.7%
호스텔 195
 
17.6%
분양형숙박 20
 
1.8%
관광호텔 17
 
1.5%
휴양콘도미니엄업 2
 
0.2%
소형호텔업 1
 
0.1%
전통호텔 1
 
0.1%

Interactions

2023-12-12T07:55:10.420779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:55:10.169624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:55:10.525429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:55:10.301666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T07:55:15.319781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업종명우편번호(도로명)행정동명(지번)객실수비고
업종명1.0000.6810.8580.3590.999
우편번호(도로명)0.6811.0000.9510.2100.388
행정동명(지번)0.8580.9511.0000.6040.623
객실수0.3590.2100.6041.0000.703
비고0.9990.3880.6230.7031.000
2023-12-12T07:55:15.399374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
비고업종명행정동명(지번)
비고1.0000.9650.337
업종명0.9651.0000.612
행정동명(지번)0.3370.6121.000
2023-12-12T07:55:15.470581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
우편번호(도로명)객실수업종명행정동명(지번)비고
우편번호(도로명)1.000-0.3670.5300.7490.202
객실수-0.3671.0000.1690.2440.438
업종명0.5300.1691.0000.6120.965
행정동명(지번)0.7490.2440.6121.0000.337
비고0.2020.4380.9650.3371.000

Missing values

2023-12-12T07:55:10.632392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T07:55:10.767208image/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.
2023-12-12T07:55:10.901039image/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

업종명업소명영업소 주소(도로명)우편번호(도로명)영업소 주소(지번)소재지전화행정동명(지번)객실수비고
0숙박업(일반)하나여인숙전라남도 여수시 교동시장4길 16-3 (교동)59734전라남도 여수시 교동 622-18<NA>중앙동7<NA>
1숙박업(일반)쉼표 더하기전라남도 여수시 동문로 77 (관문동,1~5층)59730전라남도 여수시 관문동 909 1~5층<NA>동문동23<NA>
2숙박업(일반)나비잠전라남도 여수시 시청동3길 35-12 (학동)59689전라남도 여수시 학동 181-1<NA>쌍봉동9<NA>
3숙박업(일반)제이미 모텔전라남도 여수시 충무로 51-1 (충무동)59733전라남도 여수시 충무동 512<NA>충무동4<NA>
4숙박업(일반)엘디아 호텔전라남도 여수시 시청동3길 24-7 (학동)59689전라남도 여수시 학동 178-2<NA>쌍봉동38<NA>
5숙박업(일반)(주)호텔케니여수전라남도 여수시 충무로 54-2 (충무동)59732전라남도 여수시 충무동 502-1<NA>충무동178<NA>
6숙박업(일반)별 달전라남도 여수시 통제영1길 5-2, 2-3층 (충무동)59732전라남도 여수시 충무동 628-4<NA>충무동10<NA>
7숙박업(일반)모텔 밤바다전라남도 여수시 봉산남7길 11-20 (봉산동)59763전라남도 여수시 봉산동 261-4<NA>대교동29<NA>
8숙박업(생활)달링하버전라남도 여수시 돌산읍 신추길 959767전라남도 여수시 돌산읍 우두리 755-50<NA>돌산읍10<NA>
9숙박업(생활)로미하우스전라남도 여수시 동문로 35-1, 2~3층 (관문동)59730전라남도 여수시 관문동 377<NA>동문동2<NA>
업종명업소명영업소 주소(도로명)우편번호(도로명)영업소 주소(지번)소재지전화행정동명(지번)객실수비고
1100농어촌민박업올레 비치전라남도 여수시 화양면 옥천로 663-1559652전라남도 여수시 화양면 옥적리 1539-15번지 외 1필지<NA>화양면4<NA>
1101농어촌민박업백야 코지전라남도 여수시 화정면 백야로 156-3659782전라남도 여수시 화정면 백야리 271-1번지<NA>화정면2<NA>
1102농어촌민박업페이지전라남도 여수시 화양면 용주길 121-2259659전라남도 여수시 화양면 용주리 478-6번지<NA>화양면2<NA>
1103농어촌민박업아른 독채 풀빌라전라남도 여수시 화양면 세포당머리길 959780전라남도 여수시 화양면 안포리 358-2번지 외 1 필지<NA>화양면2<NA>
1104농어촌민박업나진 스테이전라남도 여수시 화양면 화양로 1436-1559779전라남도 여수시 화양면 나진리 524<NA>화양면3<NA>
1105농어촌민박업장등을 밝히다전라남도 여수시 화양면 장수로 13959780전라남도 여수시 화양면 장수리 10-7번지 외 1필지<NA>화양면2<NA>
1106농어촌민박업삼각산 민박전라남도 여수시 삼산면 손죽길 16859788전라남도 여수시 삼산면 손죽리 산 1621<NA>삼산면1<NA>
1107농어촌민박업다온 민박전라남도 여수시 돌산읍 상하동길 3059770전라남도 여수시 돌산읍 우두리 504-84번지 외 1필지<NA>돌산읍2<NA>
1108농어촌민박업일상의 작은변화전라남도 여수시 소라면 대곡해안길 203-959648전라남도 여수시 소라면 복산리 921번지 외 1필지<NA>소라면2<NA>
1109농어촌민박업공정 1390전라남도 여수시 화양면 장수로 645-2159780전라남도 여수시 화양면 장수리 1390<NA>화양면1<NA>