Overview

Dataset statistics

Number of variables15
Number of observations1383
Missing cells1678
Missing cells (%)8.1%
Duplicate rows16
Duplicate rows (%)1.2%
Total size in memory177.1 KiB
Average record size in memory131.1 B

Variable types

Text3
Categorical2
Numeric10

Dataset

Description전라남도 여수시 관광홈페이지 관광지 시설의 객실정보 데이터로서 전라남도 여수시_여수시문화관광 객실의 종류, 전라남도 여수시 여수시문화관광_관광정보_객실정보 선호도, 전라남도 여수시의 관광 객실정보의 기준인원 등을 제공합니다.
Author전라남도 여수시
URLhttps://www.data.go.kr/data/15040852/fileData.do

Alerts

Dataset has 16 (1.2%) duplicate rowsDuplicates
기준인원 is highly overall correlated with 최대인원 and 1 other fieldsHigh correlation
최대인원 is highly overall correlated with 기준인원 and 1 other fieldsHigh correlation
추가비용 is highly overall correlated with 객실종류영어High correlation
규모 is highly overall correlated with 객실종류영어High correlation
성수기주중요금 is highly overall correlated with 성수기주말요금 and 5 other fieldsHigh correlation
성수기주말요금 is highly overall correlated with 성수기주중요금 and 5 other fieldsHigh correlation
극성수기주중요금 is highly overall correlated with 성수기주중요금 and 5 other fieldsHigh correlation
극성수기주말요금 is highly overall correlated with 성수기주중요금 and 5 other fieldsHigh correlation
비수기주중요금 is highly overall correlated with 성수기주중요금 and 5 other fieldsHigh correlation
비수기주말요금 is highly overall correlated with 성수기주중요금 and 5 other fieldsHigh correlation
객실종류영어 is highly overall correlated with 기준인원 and 9 other fieldsHigh correlation
객실종류영어 is highly imbalanced (89.6%)Imbalance
기타객실정보 has 446 (32.2%) missing valuesMissing
추가객실정보 has 1232 (89.1%) missing valuesMissing
규모 is highly skewed (γ1 = 37.18844707)Skewed
기준인원 has 359 (26.0%) zerosZeros
최대인원 has 535 (38.7%) zerosZeros
추가비용 has 697 (50.4%) zerosZeros
규모 has 965 (69.8%) zerosZeros
성수기주중요금 has 268 (19.4%) zerosZeros
성수기주말요금 has 272 (19.7%) zerosZeros
극성수기주중요금 has 464 (33.6%) zerosZeros
극성수기주말요금 has 470 (34.0%) zerosZeros
비수기주중요금 has 263 (19.0%) zerosZeros
비수기주말요금 has 270 (19.5%) zerosZeros

Reproduction

Analysis started2023-12-12 14:40:16.190472
Analysis finished2023-12-12 14:40:29.020753
Duration12.83 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Distinct786
Distinct (%)56.8%
Missing0
Missing (%)0.0%
Memory size10.9 KiB
2023-12-12T23:40:29.245901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length50
Median length39
Mean length5.1489516
Min length1

Characters and Unicode

Total characters7121
Distinct characters427
Distinct categories11 ?
Distinct scripts4 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique662 ?
Unique (%)47.9%

Sample

1st row양실
2nd row온돌
3rd row일반
4th row특실
5th row트윈룸
ValueCountFrequency (%)
일반실 111
 
6.0%
특실 83
 
4.5%
온돌 74
 
4.0%
디럭스 41
 
2.2%
마을회관 39
 
2.1%
트윈 36
 
2.0%
스위트 29
 
1.6%
일반 29
 
1.6%
스탠다드 29
 
1.6%
한실 27
 
1.5%
Other values (764) 1345
73.0%
2023-12-12T23:40:29.692163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
493
 
6.9%
465
 
6.5%
200
 
2.8%
199
 
2.8%
195
 
2.7%
193
 
2.7%
0 189
 
2.7%
2 189
 
2.7%
) 168
 
2.4%
( 167
 
2.3%
Other values (417) 4663
65.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4869
68.4%
Decimal Number 781
 
11.0%
Space Separator 493
 
6.9%
Lowercase Letter 243
 
3.4%
Uppercase Letter 238
 
3.3%
Close Punctuation 168
 
2.4%
Open Punctuation 168
 
2.4%
Other Punctuation 116
 
1.6%
Math Symbol 17
 
0.2%
Dash Punctuation 16
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
465
 
9.6%
200
 
4.1%
199
 
4.1%
195
 
4.0%
193
 
4.0%
124
 
2.5%
122
 
2.5%
121
 
2.5%
117
 
2.4%
110
 
2.3%
Other values (353) 3023
62.1%
Lowercase Letter
ValueCountFrequency (%)
e 32
13.2%
i 27
11.1%
o 26
10.7%
l 22
 
9.1%
u 15
 
6.2%
n 14
 
5.8%
m 14
 
5.8%
a 13
 
5.3%
p 12
 
4.9%
t 9
 
3.7%
Other values (11) 59
24.3%
Uppercase Letter
ValueCountFrequency (%)
B 35
14.7%
P 34
14.3%
A 29
12.2%
I 27
11.3%
V 27
11.3%
D 16
6.7%
T 13
 
5.5%
F 13
 
5.5%
C 12
 
5.0%
R 9
 
3.8%
Other values (7) 23
9.7%
Decimal Number
ValueCountFrequency (%)
0 189
24.2%
2 189
24.2%
1 161
20.6%
3 85
10.9%
4 52
 
6.7%
5 47
 
6.0%
6 39
 
5.0%
8 9
 
1.2%
7 7
 
0.9%
9 3
 
0.4%
Other Punctuation
ValueCountFrequency (%)
, 98
84.5%
? 9
 
7.8%
/ 3
 
2.6%
. 3
 
2.6%
: 2
 
1.7%
* 1
 
0.9%
Math Symbol
ValueCountFrequency (%)
~ 14
82.4%
+ 1
 
5.9%
> 1
 
5.9%
< 1
 
5.9%
Open Punctuation
ValueCountFrequency (%)
( 167
99.4%
{ 1
 
0.6%
Space Separator
ValueCountFrequency (%)
493
100.0%
Close Punctuation
ValueCountFrequency (%)
) 168
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 16
100.0%
Control
ValueCountFrequency (%)
12
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4861
68.3%
Common 1771
 
24.9%
Latin 481
 
6.8%
Han 8
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
465
 
9.6%
200
 
4.1%
199
 
4.1%
195
 
4.0%
193
 
4.0%
124
 
2.6%
122
 
2.5%
121
 
2.5%
117
 
2.4%
110
 
2.3%
Other values (347) 3015
62.0%
Latin
ValueCountFrequency (%)
B 35
 
7.3%
P 34
 
7.1%
e 32
 
6.7%
A 29
 
6.0%
I 27
 
5.6%
i 27
 
5.6%
V 27
 
5.6%
o 26
 
5.4%
l 22
 
4.6%
D 16
 
3.3%
Other values (28) 206
42.8%
Common
ValueCountFrequency (%)
493
27.8%
0 189
 
10.7%
2 189
 
10.7%
) 168
 
9.5%
( 167
 
9.4%
1 161
 
9.1%
, 98
 
5.5%
3 85
 
4.8%
4 52
 
2.9%
5 47
 
2.7%
Other values (16) 122
 
6.9%
Han
ValueCountFrequency (%)
2
25.0%
2
25.0%
1
12.5%
1
12.5%
1
12.5%
1
12.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4845
68.0%
ASCII 2252
31.6%
Compat Jamo 16
 
0.2%
CJK 8
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
493
21.9%
0 189
 
8.4%
2 189
 
8.4%
) 168
 
7.5%
( 167
 
7.4%
1 161
 
7.1%
, 98
 
4.4%
3 85
 
3.8%
4 52
 
2.3%
5 47
 
2.1%
Other values (54) 603
26.8%
Hangul
ValueCountFrequency (%)
465
 
9.6%
200
 
4.1%
199
 
4.1%
195
 
4.0%
193
 
4.0%
124
 
2.6%
122
 
2.5%
121
 
2.5%
117
 
2.4%
110
 
2.3%
Other values (340) 2999
61.9%
Compat Jamo
ValueCountFrequency (%)
5
31.2%
4
25.0%
2
 
12.5%
2
 
12.5%
1
 
6.2%
1
 
6.2%
1
 
6.2%
CJK
ValueCountFrequency (%)
2
25.0%
2
25.0%
1
12.5%
1
12.5%
1
12.5%
1
12.5%

객실종류영어
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct22
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size10.9 KiB
<NA>
1314 
Western Style Room
 
12
Korean Style Room
 
12
Special Room
 
12
Standard Room
 
9
Other values (17)
 
24

Length

Max length25
Median length4
Mean length4.505423
Min length4

Unique

Unique13 ?
Unique (%)0.9%

Sample

1st row<NA>
2nd rowKorean Style Room
3rd rowStandard Room
4th rowSpecial Room
5th rowSpecial Room

Common Values

ValueCountFrequency (%)
<NA> 1314
95.0%
Western Style Room 12
 
0.9%
Korean Style Room 12
 
0.9%
Special Room 12
 
0.9%
Standard Room 9
 
0.7%
VIP Room 4
 
0.3%
Deluxe Room 3
 
0.2%
Twin Room 2
 
0.1%
Suite Room 2
 
0.1%
Special Room2 1
 
0.1%
Other values (12) 12
 
0.9%

Length

2023-12-12T23:40:29.820869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 1314
88.8%
room 64
 
4.3%
style 27
 
1.8%
korean 15
 
1.0%
special 15
 
1.0%
western 12
 
0.8%
standard 10
 
0.7%
suite 4
 
0.3%
vip 4
 
0.3%
deluxe 3
 
0.2%
Other values (10) 12
 
0.8%

선호도
Categorical

Distinct5
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size10.9 KiB
<NA>
694 
1
429 
2
144 
4
 
69
3
 
47

Length

Max length4
Median length4
Mean length2.505423
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 694
50.2%
1 429
31.0%
2 144
 
10.4%
4 69
 
5.0%
3 47
 
3.4%

Length

2023-12-12T23:40:29.948213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:40:30.071566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 694
50.2%
1 429
31.0%
2 144
 
10.4%
4 69
 
5.0%
3 47
 
3.4%

기타객실정보
Text

MISSING 

Distinct340
Distinct (%)36.3%
Missing446
Missing (%)32.2%
Memory size10.9 KiB
2023-12-12T23:40:30.310759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length44
Median length35
Mean length26.640342
Min length1

Characters and Unicode

Total characters24962
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

Unique201 ?
Unique (%)21.5%

Sample

1st row01,02,03,04,05,07,08,09,10,11,14,15
2nd row01,03,04,07,09,10,14,15
3rd row01,03,04,07,10,14,15
4th row01,02,03,04,05,06,07,08,09,10,11,14,15
5th row01,02,03,04,05,07,09,10,11,14,15,16
ValueCountFrequency (%)
01,02,03,04,05,07,08,09,10,11,14,15 48
 
5.1%
01,02,03,04,05,06,07,08,09,10,11,14,15 35
 
3.7%
02,04,12 24
 
2.6%
01,02,03,04,07,08,09,10,12,13,15 22
 
2.3%
02,03,04,07,08,10,11,12,13 16
 
1.7%
02,03,04,07,09,10,15 16
 
1.7%
01,02,03,04,07,08,10,11,12,13 16
 
1.7%
01,02,03,04,05,06,07,08,09,10,11,15 14
 
1.5%
02,12,13,14 13
 
1.4%
01,02,03,04,07,10,12,13 13
 
1.4%
Other values (330) 720
76.8%
2023-12-12T23:40:31.093255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 7700
30.8%
0 6194
24.8%
1 4312
17.3%
4 1392
 
5.6%
3 1265
 
5.1%
2 1233
 
4.9%
7 757
 
3.0%
5 732
 
2.9%
9 591
 
2.4%
8 526
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17262
69.2%
Other Punctuation 7700
30.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 6194
35.9%
1 4312
25.0%
4 1392
 
8.1%
3 1265
 
7.3%
2 1233
 
7.1%
7 757
 
4.4%
5 732
 
4.2%
9 591
 
3.4%
8 526
 
3.0%
6 260
 
1.5%
Other Punctuation
ValueCountFrequency (%)
, 7700
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 24962
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
, 7700
30.8%
0 6194
24.8%
1 4312
17.3%
4 1392
 
5.6%
3 1265
 
5.1%
2 1233
 
4.9%
7 757
 
3.0%
5 732
 
2.9%
9 591
 
2.4%
8 526
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 24962
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
, 7700
30.8%
0 6194
24.8%
1 4312
17.3%
4 1392
 
5.6%
3 1265
 
5.1%
2 1233
 
4.9%
7 757
 
3.0%
5 732
 
2.9%
9 591
 
2.4%
8 526
 
2.1%

기준인원
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct15
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.670282
Minimum0
Maximum24
Zeros359
Zeros (%)26.0%
Negative0
Negative (%)0.0%
Memory size12.3 KiB
2023-12-12T23:40:31.268292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q34
95-th percentile8
Maximum24
Range24
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.9505878
Coefficient of variation (CV)1.1049724
Kurtosis11.187243
Mean2.670282
Median Absolute Deviation (MAD)2
Skewness2.6688049
Sum3693
Variance8.7059686
MonotonicityNot monotonic
2023-12-12T23:40:31.409064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
2 575
41.6%
0 359
26.0%
4 210
 
15.2%
8 64
 
4.6%
6 47
 
3.4%
3 43
 
3.1%
5 27
 
2.0%
10 17
 
1.2%
12 11
 
0.8%
1 11
 
0.8%
Other values (5) 19
 
1.4%
ValueCountFrequency (%)
0 359
26.0%
1 11
 
0.8%
2 575
41.6%
3 43
 
3.1%
4 210
 
15.2%
5 27
 
2.0%
6 47
 
3.4%
7 1
 
0.1%
8 64
 
4.6%
10 17
 
1.2%
ValueCountFrequency (%)
24 2
 
0.1%
20 7
 
0.5%
16 2
 
0.1%
15 7
 
0.5%
12 11
 
0.8%
10 17
 
1.2%
8 64
4.6%
7 1
 
0.1%
6 47
3.4%
5 27
2.0%

최대인원
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct22
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.6464208
Minimum0
Maximum40
Zeros535
Zeros (%)38.7%
Negative0
Negative (%)0.0%
Memory size12.3 KiB
2023-12-12T23:40:31.553164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q35
95-th percentile12
Maximum40
Range40
Interquartile range (IQR)5

Descriptive statistics

Standard deviation4.4841012
Coefficient of variation (CV)1.2297267
Kurtosis9.5969902
Mean3.6464208
Median Absolute Deviation (MAD)3
Skewness2.381847
Sum5043
Variance20.107163
MonotonicityNot monotonic
2023-12-12T23:40:31.707311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
0 535
38.7%
4 254
18.4%
2 110
 
8.0%
6 110
 
8.0%
8 90
 
6.5%
3 80
 
5.8%
5 59
 
4.3%
10 39
 
2.8%
12 25
 
1.8%
7 23
 
1.7%
Other values (12) 58
 
4.2%
ValueCountFrequency (%)
0 535
38.7%
1 1
 
0.1%
2 110
 
8.0%
3 80
 
5.8%
4 254
18.4%
5 59
 
4.3%
6 110
 
8.0%
7 23
 
1.7%
8 90
 
6.5%
9 3
 
0.2%
ValueCountFrequency (%)
40 1
 
0.1%
30 6
 
0.4%
25 2
 
0.1%
24 1
 
0.1%
20 14
1.0%
16 2
 
0.1%
15 22
1.6%
14 3
 
0.2%
13 1
 
0.1%
12 25
1.8%

추가비용
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct10
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5592.1909
Minimum0
Maximum350000
Zeros697
Zeros (%)50.4%
Negative0
Negative (%)0.0%
Memory size12.3 KiB
2023-12-12T23:40:31.848864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q310000
95-th percentile10000
Maximum350000
Range350000
Interquartile range (IQR)10000

Descriptive statistics

Standard deviation12149.932
Coefficient of variation (CV)2.1726604
Kurtosis479.54601
Mean5592.1909
Median Absolute Deviation (MAD)0
Skewness18.221839
Sum7734000
Variance1.4762084 × 108
MonotonicityNot monotonic
2023-12-12T23:40:31.990050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 697
50.4%
10000 587
42.4%
5000 53
 
3.8%
20000 19
 
1.4%
15000 12
 
0.9%
100000 5
 
0.4%
25000 4
 
0.3%
22000 4
 
0.3%
1000 1
 
0.1%
350000 1
 
0.1%
ValueCountFrequency (%)
0 697
50.4%
1000 1
 
0.1%
5000 53
 
3.8%
10000 587
42.4%
15000 12
 
0.9%
20000 19
 
1.4%
22000 4
 
0.3%
25000 4
 
0.3%
100000 5
 
0.4%
350000 1
 
0.1%
ValueCountFrequency (%)
350000 1
 
0.1%
100000 5
 
0.4%
25000 4
 
0.3%
22000 4
 
0.3%
20000 19
 
1.4%
15000 12
 
0.9%
10000 587
42.4%
5000 53
 
3.8%
1000 1
 
0.1%
0 697
50.4%

규모
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct79
Distinct (%)5.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean316.79899
Minimum0
Maximum420000
Zeros965
Zeros (%)69.8%
Negative0
Negative (%)0.0%
Memory size12.3 KiB
2023-12-12T23:40:32.148715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q320
95-th percentile62.8
Maximum420000
Range420000
Interquartile range (IQR)20

Descriptive statistics

Standard deviation11293.424
Coefficient of variation (CV)35.648549
Kurtosis1382.9871
Mean316.79899
Median Absolute Deviation (MAD)0
Skewness37.188447
Sum438133
Variance1.2754143 × 108
MonotonicityNot monotonic
2023-12-12T23:40:32.292625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 965
69.8%
33 36
 
2.6%
53 31
 
2.2%
20 19
 
1.4%
43 17
 
1.2%
26 17
 
1.2%
30 16
 
1.2%
52 16
 
1.2%
16 13
 
0.9%
59 12
 
0.9%
Other values (69) 241
 
17.4%
ValueCountFrequency (%)
0 965
69.8%
3 3
 
0.2%
8 3
 
0.2%
10 5
 
0.4%
11 4
 
0.3%
12 4
 
0.3%
13 7
 
0.5%
14 1
 
0.1%
15 3
 
0.2%
16 13
 
0.9%
ValueCountFrequency (%)
420000 1
 
0.1%
173 2
0.1%
148 1
 
0.1%
132 1
 
0.1%
129 1
 
0.1%
122 1
 
0.1%
120 1
 
0.1%
116 3
0.2%
115 1
 
0.1%
109 1
 
0.1%

성수기주중요금
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct114
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean110906
Minimum0
Maximum1100000
Zeros268
Zeros (%)19.4%
Negative0
Negative (%)0.0%
Memory size12.3 KiB
2023-12-12T23:40:32.439743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q140000
median70000
Q3150000
95-th percentile350000
Maximum1100000
Range1100000
Interquartile range (IQR)110000

Descriptive statistics

Standard deviation125029.07
Coefficient of variation (CV)1.1273427
Kurtosis9.8045897
Mean110906
Median Absolute Deviation (MAD)50000
Skewness2.4977389
Sum1.53383 × 108
Variance1.5632269 × 1010
MonotonicityNot monotonic
2023-12-12T23:40:32.585117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 268
19.4%
50000 117
 
8.5%
60000 90
 
6.5%
80000 75
 
5.4%
70000 73
 
5.3%
40000 67
 
4.8%
100000 66
 
4.8%
150000 54
 
3.9%
120000 52
 
3.8%
200000 41
 
3.0%
Other values (104) 480
34.7%
ValueCountFrequency (%)
0 268
19.4%
2000 1
 
0.1%
10000 1
 
0.1%
15000 1
 
0.1%
17000 1
 
0.1%
20000 6
 
0.4%
22000 7
 
0.5%
23000 1
 
0.1%
25000 10
 
0.7%
30000 17
 
1.2%
ValueCountFrequency (%)
1100000 1
0.1%
990000 1
0.1%
900000 1
0.1%
861000 1
0.1%
830000 1
0.1%
790000 1
0.1%
730000 1
0.1%
710000 1
0.1%
682000 1
0.1%
630000 1
0.1%

성수기주말요금
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct107
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean118947.94
Minimum0
Maximum1100000
Zeros272
Zeros (%)19.7%
Negative0
Negative (%)0.0%
Memory size12.3 KiB
2023-12-12T23:40:32.721801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q140000
median80000
Q3150000
95-th percentile369000
Maximum1100000
Range1100000
Interquartile range (IQR)110000

Descriptive statistics

Standard deviation128010.59
Coefficient of variation (CV)1.07619
Kurtosis7.8172702
Mean118947.94
Median Absolute Deviation (MAD)60000
Skewness2.2152202
Sum1.64505 × 108
Variance1.6386711 × 1010
MonotonicityNot monotonic
2023-12-12T23:40:32.876239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 272
19.7%
70000 90
 
6.5%
50000 80
 
5.8%
60000 79
 
5.7%
80000 76
 
5.5%
100000 75
 
5.4%
150000 63
 
4.6%
120000 60
 
4.3%
250000 49
 
3.5%
90000 39
 
2.8%
Other values (97) 500
36.2%
ValueCountFrequency (%)
0 272
19.7%
10000 3
 
0.2%
20000 5
 
0.4%
22000 6
 
0.4%
23000 1
 
0.1%
25000 10
 
0.7%
28000 1
 
0.1%
30000 16
 
1.2%
35000 8
 
0.6%
40000 35
 
2.5%
ValueCountFrequency (%)
1100000 1
 
0.1%
990000 1
 
0.1%
861000 1
 
0.1%
830000 1
 
0.1%
800000 1
 
0.1%
730000 1
 
0.1%
710000 1
 
0.1%
700000 2
0.1%
682000 1
 
0.1%
600000 3
0.2%

극성수기주중요금
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct92
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean92704.989
Minimum0
Maximum1000000
Zeros464
Zeros (%)33.6%
Negative0
Negative (%)0.0%
Memory size12.3 KiB
2023-12-12T23:40:33.068764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median60000
Q3120000
95-th percentile319900
Maximum1000000
Range1000000
Interquartile range (IQR)120000

Descriptive statistics

Standard deviation118404.17
Coefficient of variation (CV)1.2772146
Kurtosis11.160343
Mean92704.989
Median Absolute Deviation (MAD)60000
Skewness2.618812
Sum1.28211 × 108
Variance1.4019547 × 1010
MonotonicityNot monotonic
2023-12-12T23:40:33.242193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 464
33.6%
50000 75
 
5.4%
70000 73
 
5.3%
100000 72
 
5.2%
80000 70
 
5.1%
60000 67
 
4.8%
150000 61
 
4.4%
120000 54
 
3.9%
40000 38
 
2.7%
90000 35
 
2.5%
Other values (82) 374
27.0%
ValueCountFrequency (%)
0 464
33.6%
20000 4
 
0.3%
22000 7
 
0.5%
25000 9
 
0.7%
30000 10
 
0.7%
35000 8
 
0.6%
40000 38
 
2.7%
45000 9
 
0.7%
50000 75
 
5.4%
55000 5
 
0.4%
ValueCountFrequency (%)
1000000 1
0.1%
990000 1
0.1%
903000 1
0.1%
900000 1
0.1%
800000 1
0.1%
790000 1
0.1%
720000 1
0.1%
600000 1
0.1%
599000 1
0.1%
596000 1
0.1%

극성수기주말요금
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct92
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean95274.62
Minimum0
Maximum1000000
Zeros470
Zeros (%)34.0%
Negative0
Negative (%)0.0%
Memory size12.3 KiB
2023-12-12T23:40:33.407842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median70000
Q3130000
95-th percentile330000
Maximum1000000
Range1000000
Interquartile range (IQR)130000

Descriptive statistics

Standard deviation120864
Coefficient of variation (CV)1.2685855
Kurtosis10.56387
Mean95274.62
Median Absolute Deviation (MAD)70000
Skewness2.5379942
Sum1.317648 × 108
Variance1.4608107 × 1010
MonotonicityNot monotonic
2023-12-12T23:40:33.583149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 470
34.0%
70000 79
 
5.7%
80000 74
 
5.4%
60000 72
 
5.2%
150000 64
 
4.6%
100000 62
 
4.5%
50000 52
 
3.8%
120000 49
 
3.5%
90000 35
 
2.5%
250000 31
 
2.2%
Other values (82) 395
28.6%
ValueCountFrequency (%)
0 470
34.0%
20000 4
 
0.3%
22000 6
 
0.4%
23000 1
 
0.1%
25000 9
 
0.7%
30000 10
 
0.7%
35000 8
 
0.6%
40000 24
 
1.7%
45000 10
 
0.7%
50000 52
 
3.8%
ValueCountFrequency (%)
1000000 1
0.1%
990000 1
0.1%
903000 1
0.1%
900000 2
0.1%
800000 1
0.1%
680000 1
0.1%
600000 1
0.1%
599000 1
0.1%
596000 1
0.1%
580000 1
0.1%

비수기주중요금
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct106
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean85532.176
Minimum0
Maximum1100000
Zeros263
Zeros (%)19.0%
Negative0
Negative (%)0.0%
Memory size12.3 KiB
2023-12-12T23:40:33.769095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q135000
median55000
Q3100000
95-th percentile271000
Maximum1100000
Range1100000
Interquartile range (IQR)65000

Descriptive statistics

Standard deviation104847.73
Coefficient of variation (CV)1.2258279
Kurtosis17.710704
Mean85532.176
Median Absolute Deviation (MAD)35000
Skewness3.3633143
Sum1.18291 × 108
Variance1.0993047 × 1010
MonotonicityNot monotonic
2023-12-12T23:40:33.943145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 263
19.0%
50000 155
 
11.2%
40000 127
 
9.2%
60000 92
 
6.7%
80000 79
 
5.7%
70000 75
 
5.4%
100000 61
 
4.4%
120000 49
 
3.5%
130000 40
 
2.9%
30000 36
 
2.6%
Other values (96) 406
29.4%
ValueCountFrequency (%)
0 263
19.0%
5000 1
 
0.1%
10000 2
 
0.1%
15000 2
 
0.1%
17000 1
 
0.1%
18000 4
 
0.3%
20000 13
 
0.9%
21000 1
 
0.1%
22000 4
 
0.3%
25000 10
 
0.7%
ValueCountFrequency (%)
1100000 1
0.1%
990000 1
0.1%
830000 1
0.1%
730000 1
0.1%
710000 1
0.1%
682000 1
0.1%
600000 1
0.1%
590000 1
0.1%
580000 1
0.1%
572000 1
0.1%

비수기주말요금
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct111
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100303.69
Minimum0
Maximum1100000
Zeros270
Zeros (%)19.5%
Negative0
Negative (%)0.0%
Memory size12.3 KiB
2023-12-12T23:40:34.121562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q140000
median70000
Q3130000
95-th percentile300000
Maximum1100000
Range1100000
Interquartile range (IQR)90000

Descriptive statistics

Standard deviation114398.17
Coefficient of variation (CV)1.1405181
Kurtosis12.120202
Mean100303.69
Median Absolute Deviation (MAD)47000
Skewness2.7554966
Sum1.3872 × 108
Variance1.3086941 × 1010
MonotonicityNot monotonic
2023-12-12T23:40:34.306825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 270
19.5%
50000 129
 
9.3%
60000 91
 
6.6%
80000 85
 
6.1%
70000 84
 
6.1%
100000 76
 
5.5%
150000 69
 
5.0%
40000 68
 
4.9%
120000 39
 
2.8%
90000 31
 
2.2%
Other values (101) 441
31.9%
ValueCountFrequency (%)
0 270
19.5%
2000 1
 
0.1%
10000 2
 
0.1%
15000 1
 
0.1%
20000 7
 
0.5%
22000 5
 
0.4%
23000 1
 
0.1%
25000 14
 
1.0%
30000 18
 
1.3%
35000 14
 
1.0%
ValueCountFrequency (%)
1100000 1
0.1%
990000 1
0.1%
830000 1
0.1%
730000 1
0.1%
710000 1
0.1%
682000 1
0.1%
653000 1
0.1%
630000 1
0.1%
620000 1
0.1%
600000 2
0.1%

추가객실정보
Text

MISSING 

Distinct59
Distinct (%)39.1%
Missing1232
Missing (%)89.1%
Memory size10.9 KiB
2023-12-12T23:40:34.652452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length158
Median length69
Mean length22
Min length5

Characters and Unicode

Total characters3322
Distinct characters187
Distinct categories12 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique39 ?
Unique (%)25.8%

Sample

1st row 대인 10,000원 소인 5,000원
2nd row 대인 10,000원 소인 5,000원
3rd row 대인 10,000원 소인 5,000원
4th row대인 10,000원 소인 5,000원
5th row대인 10,000원 소인 5,000원
ValueCountFrequency (%)
소인 52
 
8.3%
46
 
7.3%
10,000원 39
 
6.2%
추가요금 32
 
5.1%
5,000원 28
 
4.5%
객실수 27
 
4.3%
대인 19
 
3.0%
가격 15
 
2.4%
업체 14
 
2.2%
문의 14
 
2.2%
Other values (194) 340
54.3%
2023-12-12T23:40:35.163577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
537
 
16.2%
0 447
 
13.5%
, 189
 
5.7%
1 145
 
4.4%
123
 
3.7%
115
 
3.5%
) 62
 
1.9%
( 62
 
1.9%
62
 
1.9%
61
 
1.8%
Other values (177) 1519
45.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1419
42.7%
Decimal Number 831
25.0%
Space Separator 537
 
16.2%
Other Punctuation 317
 
9.5%
Close Punctuation 62
 
1.9%
Open Punctuation 62
 
1.9%
Lowercase Letter 30
 
0.9%
Math Symbol 28
 
0.8%
Control 16
 
0.5%
Other Symbol 14
 
0.4%
Other values (2) 6
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
123
 
8.7%
115
 
8.1%
62
 
4.4%
61
 
4.3%
51
 
3.6%
47
 
3.3%
47
 
3.3%
46
 
3.2%
45
 
3.2%
45
 
3.2%
Other values (143) 777
54.8%
Decimal Number
ValueCountFrequency (%)
0 447
53.8%
1 145
 
17.4%
2 60
 
7.2%
5 57
 
6.9%
3 34
 
4.1%
4 28
 
3.4%
6 24
 
2.9%
8 15
 
1.8%
9 13
 
1.6%
7 8
 
1.0%
Lowercase Letter
ValueCountFrequency (%)
r 10
33.3%
b 9
30.0%
a 4
 
13.3%
x 2
 
6.7%
t 2
 
6.7%
d 2
 
6.7%
o 1
 
3.3%
Other Punctuation
ValueCountFrequency (%)
, 189
59.6%
: 57
 
18.0%
/ 30
 
9.5%
. 29
 
9.1%
* 11
 
3.5%
1
 
0.3%
Math Symbol
ValueCountFrequency (%)
~ 8
28.6%
< 7
25.0%
> 7
25.0%
+ 6
21.4%
Space Separator
ValueCountFrequency (%)
537
100.0%
Close Punctuation
ValueCountFrequency (%)
) 62
100.0%
Open Punctuation
ValueCountFrequency (%)
( 62
100.0%
Control
ValueCountFrequency (%)
16
100.0%
Other Symbol
ValueCountFrequency (%)
14
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%
Uppercase Letter
ValueCountFrequency (%)
E 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1871
56.3%
Hangul 1419
42.7%
Latin 32
 
1.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
123
 
8.7%
115
 
8.1%
62
 
4.4%
61
 
4.3%
51
 
3.6%
47
 
3.3%
47
 
3.3%
46
 
3.2%
45
 
3.2%
45
 
3.2%
Other values (143) 777
54.8%
Common
ValueCountFrequency (%)
537
28.7%
0 447
23.9%
, 189
 
10.1%
1 145
 
7.7%
) 62
 
3.3%
( 62
 
3.3%
2 60
 
3.2%
: 57
 
3.0%
5 57
 
3.0%
3 34
 
1.8%
Other values (16) 221
11.8%
Latin
ValueCountFrequency (%)
r 10
31.2%
b 9
28.1%
a 4
 
12.5%
x 2
 
6.2%
t 2
 
6.2%
d 2
 
6.2%
E 2
 
6.2%
o 1
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1888
56.8%
Hangul 1419
42.7%
CJK Compat 14
 
0.4%
Punctuation 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
537
28.4%
0 447
23.7%
, 189
 
10.0%
1 145
 
7.7%
) 62
 
3.3%
( 62
 
3.3%
2 60
 
3.2%
: 57
 
3.0%
5 57
 
3.0%
3 34
 
1.8%
Other values (22) 238
12.6%
Hangul
ValueCountFrequency (%)
123
 
8.7%
115
 
8.1%
62
 
4.4%
61
 
4.3%
51
 
3.6%
47
 
3.3%
47
 
3.3%
46
 
3.2%
45
 
3.2%
45
 
3.2%
Other values (143) 777
54.8%
CJK Compat
ValueCountFrequency (%)
14
100.0%
Punctuation
ValueCountFrequency (%)
1
100.0%

Interactions

2023-12-12T23:40:27.335321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:40:17.294173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:40:18.314045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:40:19.537277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:40:20.605349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:40:21.562469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:40:22.629567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:40:24.118915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:40:25.270894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:40:26.237396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2023-12-12T23:40:17.373556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:40:18.443457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:40:19.641980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:40:20.702590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:40:21.651513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:40:22.721266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:40:24.237575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:40:25.358297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:40:26.332969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:40:27.570788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:40:17.477739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:40:18.558435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:40:19.762733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:40:20.819779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:40:21.769379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:40:22.852157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:40:24.361846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:40:25.461360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:40:26.440597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:40:27.673578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:40:17.574672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:40:18.699011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:40:19.891315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:40:20.912245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:40:21.904409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:40:23.313799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:40:24.494867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:40:25.567415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:40:26.550831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:40:27.765630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:40:17.659893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:40:18.825668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:40:19.998709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:40:21.011269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:40:21.999101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:40:23.429380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:40:24.590674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:40:25.654554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:40:26.665468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:40:27.864229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:40:17.745324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:40:18.946434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:40:20.122068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:40:21.116801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:40:22.098346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:40:23.560543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:40:24.699920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:40:25.749374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:40:26.760156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:40:27.953411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:40:17.857418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:40:19.043839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:40:20.215732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:40:21.201921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:40:22.203692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:40:23.676678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:40:24.796237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:40:25.836834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:40:26.869102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:40:28.097028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:40:17.954162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:40:19.163000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:40:20.322881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:40:21.283819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:40:22.319117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:40:23.781119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:40:24.899855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:40:25.942357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:40:26.974918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:40:28.242728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:40:18.040964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:40:19.288779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:40:20.416366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:40:21.368173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:40:22.423310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:40:23.889743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:40:25.023165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:40:26.053708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:40:27.097250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:40:28.371312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:40:18.195637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:40:19.427685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:40:20.518156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:40:21.465109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:40:22.533769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:40:24.002348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:40:25.167027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:40:26.146439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:40:27.215547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T23:40:35.297993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
객실종류영어선호도기준인원최대인원추가비용규모성수기주중요금성수기주말요금극성수기주중요금극성수기주말요금비수기주중요금비수기주말요금추가객실정보
객실종류영어1.0000.7630.9770.693NaNNaN0.9810.9810.9580.9500.9370.8811.000
선호도0.7631.0000.4240.3710.0000.0000.0990.1100.0000.0780.1290.1150.465
기준인원0.9770.4241.0000.8640.0000.0000.3440.4140.4220.3310.3750.3830.860
최대인원0.6930.3710.8641.0000.0000.0310.2960.3400.3640.2740.3360.3510.900
추가비용NaN0.0000.0000.0001.0001.0000.1120.0880.0360.1200.1090.000NaN
규모NaN0.0000.0000.0311.0001.0000.1500.1330.0610.1350.1430.076NaN
성수기주중요금0.9810.0990.3440.2960.1120.1501.0000.9870.7690.8600.9790.9850.956
성수기주말요금0.9810.1100.4140.3400.0880.1330.9871.0000.8030.9110.9780.9830.964
극성수기주중요금0.9580.0000.4220.3640.0360.0610.7690.8031.0000.9540.6790.7450.876
극성수기주말요금0.9500.0780.3310.2740.1200.1350.8600.9110.9541.0000.8150.8690.887
비수기주중요금0.9370.1290.3750.3360.1090.1430.9790.9780.6790.8151.0000.9940.957
비수기주말요금0.8810.1150.3830.3510.0000.0760.9850.9830.7450.8690.9941.0000.950
추가객실정보1.0000.4650.8600.900NaNNaN0.9560.9640.8760.8870.9570.9501.000
2023-12-12T23:40:35.458591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
객실종류영어선호도
객실종류영어1.0000.420
선호도0.4201.000
2023-12-12T23:40:35.571869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준인원최대인원추가비용규모성수기주중요금성수기주말요금극성수기주중요금극성수기주말요금비수기주중요금비수기주말요금객실종류영어선호도
기준인원1.0000.730-0.1140.3310.3370.3240.1670.1820.3350.3180.7120.276
최대인원0.7301.000-0.1200.3520.3040.2950.1350.1520.3120.2960.5080.235
추가비용-0.114-0.1201.000-0.2440.0520.0270.1440.119-0.0020.0021.0000.000
규모0.3310.352-0.2441.0000.3240.3370.1640.1790.3510.3551.0000.000
성수기주중요금0.3370.3040.0520.3241.0000.9540.6120.5870.8790.8790.7230.063
성수기주말요금0.3240.2950.0270.3370.9541.0000.6080.6360.8790.9080.7230.070
극성수기주중요금0.1670.1350.1440.1640.6120.6081.0000.9730.6180.6330.7430.000
극성수기주말요금0.1820.1520.1190.1790.5870.6360.9731.0000.6210.6490.7260.046
비수기주중요금0.3350.312-0.0020.3510.8790.8790.6180.6211.0000.9670.7630.058
비수기주말요금0.3180.2960.0020.3550.8790.9080.6330.6490.9671.0000.6960.052
객실종류영어0.7120.5081.0001.0000.7230.7230.7430.7260.7630.6961.0000.420
선호도0.2760.2350.0000.0000.0630.0700.0000.0460.0580.0520.4201.000

Missing values

2023-12-12T23:40:28.540945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T23:40:28.783955image/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.
2023-12-12T23:40:28.928118image/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

객실종류객실종류영어선호도기타객실정보기준인원최대인원추가비용규모성수기주중요금성수기주말요금극성수기주중요금극성수기주말요금비수기주중요금비수기주말요금추가객실정보
0양실<NA>101,02,03,04,05,07,08,09,10,11,14,1523100000500005000050000500004000040000<NA>
1온돌Korean Style Room101,03,04,07,09,10,14,152000450005500045000550003500045000<NA>
2일반Standard Room101,03,04,07,10,14,152000450005500045000550003500045000<NA>
3특실Special Room101,02,03,04,05,06,07,08,09,10,11,14,15201000005500065000004500055000<NA>
4트윈룸Special Room101,02,03,04,05,07,09,10,11,14,15,1624100000700007000070000700007000070000<NA>
5일반실Western Style Room101,02,03,04,05,07,09,10,11,14,1520100000400004000040000400004000040000<NA>
6한실Korean Style Room101,02,03,04,05,07,09,10,11,14,1524100000000000<NA>
7한실<NA>101,03,04,05,07,08,09,10,11,14,152450000000000<NA>
8특실<NA>101,03,04,05,06,07,08,09,10,11,14,152410000080000100000004000050000<NA>
9일반실<NA>101,03,04,05,07,08,09,10,11,14,1523100000600007000060000700004000050000<NA>
객실종류객실종류영어선호도기타객실정보기준인원최대인원추가비용규모성수기주중요금성수기주말요금극성수기주중요금극성수기주말요금비수기주중요금비수기주말요금추가객실정보
1373일반실B<NA><NA><NA>00100000450007000075000750004500070000대인:10,000(평일), 15,000원(주말) / 소인:5,000(평일), 10,000(주말)
1374디럭스<NA><NA><NA>00100000500008000090000900005000080000대인:10,000(평일), 15,000원(주말) / 소인:5,000(평일), 10,000(주말)
1375트윈<NA><NA><NA>0010000065000900001200001200006500090000대인:10,000(평일), 15,000원(주말) / 소인:5,000(평일), 10,000(주말)
13762PC룸<NA><NA><NA>0010000065000900001200001200006500090000대인:10,000(평일), 15,000원(주말) / 소인:5,000(평일), 10,000(주말)
1377일반<NA><NA><NA>00100000500005000060000600004000040000<NA>
1378일반실 침대<NA><NA><NA>00100000550006500075000750003500045000<NA>
1379일반실 온돌<NA><NA><NA>00100000600007000080000800004000050000<NA>
1380특실<NA><NA><NA>00100000750008500095000950004500065000<NA>
1381트윈실<NA><NA><NA>0010000080000900001000001000006000070000<NA>
1382가족실<NA><NA><NA>0010000080000900001000001000006000070000<NA>

Duplicate rows

Most frequently occurring

객실종류객실종류영어선호도기타객실정보기준인원최대인원추가비용규모성수기주중요금성수기주말요금극성수기주중요금극성수기주말요금비수기주중요금비수기주말요금추가객실정보# duplicates
14펜션<NA>101,02,03,04,05,07,08,09,10,12,13,140000000000<NA>6
10짱구<NA>301,02,03,04,07,10,12,130000000000<NA>4
8일반실<NA><NA><NA>20100000500005000050000500004000040000<NA>3
0B1, B2<NA>101,02,03,04,07,08,10,11,12,132404315000021000000130000180000<NA>2
1F3<NA>201,02,03,04,07,08,10,11,12,132204320000026000000180000230000<NA>2
2객실<NA>101,02,03,04,07,10,12,13,140000000000<NA>2
3돔하우스 12평형<NA><NA><NA>0000000000<NA>2
4일반실<NA><NA>02,04,07,09,11,12,130000000000<NA>2
5일반실<NA><NA><NA>0000000000<NA>2
6일반실<NA><NA><NA>00100000600007000080000800005000060000<NA>2