Overview

Dataset statistics

Number of variables8
Number of observations100
Missing cells30
Missing cells (%)3.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.6 KiB
Average record size in memory67.3 B

Variable types

Text4
Numeric2
Categorical2

Alerts

base_ymd has constant value ""Constant
xpos_lo is highly overall correlated with area_nmHigh correlation
ypos_la is highly overall correlated with area_nmHigh correlation
area_nm is highly overall correlated with xpos_lo and 1 other fieldsHigh correlation
area_nm is highly imbalanced (80.6%)Imbalance
xpos_lo has 3 (3.0%) missing valuesMissing
ypos_la has 3 (3.0%) missing valuesMissing
homepage_url has 11 (11.0%) missing valuesMissing
tel_no has 13 (13.0%) missing valuesMissing
entrp_nm has unique valuesUnique

Reproduction

Analysis started2023-12-10 09:58:13.008267
Analysis finished2023-12-10 09:58:16.257202
Duration3.25 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

entrp_nm
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T18:58:16.630922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length43
Median length20
Mean length9.09
Min length4

Characters and Unicode

Total characters909
Distinct characters225
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique100 ?
Unique (%)100.0%

Sample

1st row화이트캐슬리조트
2nd row[chaeum2101! HOUSE] ONE ROOM !! 간섭NO!!
3rd row썬크루즈리조트
4th row썬크루즈리조트 비치크루즈
5th row송정콘도
ValueCountFrequency (%)
용평리조트 3
 
2.0%
하이원 2
 
1.3%
엘리시안강촌 2
 
1.3%
썬크루즈리조트 2
 
1.3%
켄싱턴리조트 2
 
1.3%
양양 2
 
1.3%
쏠비치 2
 
1.3%
알펜시아리조트 2
 
1.3%
설악 2
 
1.3%
리조트 2
 
1.3%
Other values (129) 130
86.1%
2023-12-10T18:58:17.409557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
리 86
 
9.5%
트 77
 
8.5%
조 71
 
7.8%
52
 
5.7%
스 25
 
2.8%
도 22
 
2.4%
콘 21
 
2.3%
이 18
 
2.0%
설 14
 
1.5%
악 13
 
1.4%
Other values (215) 510
56.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 805
88.6%
Space Separator 52
 
5.7%
Uppercase Letter 21
 
2.3%
Lowercase Letter 10
 
1.1%
Decimal Number 9
 
1.0%
Other Punctuation 6
 
0.7%
Open Punctuation 3
 
0.3%
Close Punctuation 3
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 86
 
10.7%
트 77
 
9.6%
조 71
 
8.8%
스 25
 
3.1%
도 22
 
2.7%
콘 21
 
2.6%
이 18
 
2.2%
설 14
 
1.7%
악 13
 
1.6%
평 11
 
1.4%
Other values (186) 447
55.5%
Uppercase Letter
ValueCountFrequency (%)
O 5
23.8%
C 3
14.3%
N 2
 
9.5%
E 2
 
9.5%
H 2
 
9.5%
S 2
 
9.5%
R 1
 
4.8%
U 1
 
4.8%
M 1
 
4.8%
K 1
 
4.8%
Lowercase Letter
ValueCountFrequency (%)
e 3
30.0%
t 1
 
10.0%
v 1
 
10.0%
m 1
 
10.0%
a 1
 
10.0%
u 1
 
10.0%
h 1
 
10.0%
c 1
 
10.0%
Decimal Number
ValueCountFrequency (%)
0 3
33.3%
7 2
22.2%
1 2
22.2%
2 1
 
11.1%
3 1
 
11.1%
Other Punctuation
ValueCountFrequency (%)
! 5
83.3%
& 1
 
16.7%
Space Separator
ValueCountFrequency (%)
52
100.0%
Open Punctuation
ValueCountFrequency (%)
[ 3
100.0%
Close Punctuation
ValueCountFrequency (%)
] 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 805
88.6%
Common 73
 
8.0%
Latin 31
 
3.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 86
 
10.7%
트 77
 
9.6%
조 71
 
8.8%
스 25
 
3.1%
도 22
 
2.7%
콘 21
 
2.6%
이 18
 
2.2%
설 14
 
1.7%
악 13
 
1.6%
평 11
 
1.4%
Other values (186) 447
55.5%
Latin
ValueCountFrequency (%)
O 5
16.1%
C 3
 
9.7%
e 3
 
9.7%
N 2
 
6.5%
E 2
 
6.5%
H 2
 
6.5%
S 2
 
6.5%
R 1
 
3.2%
t 1
 
3.2%
U 1
 
3.2%
Other values (9) 9
29.0%
Common
ValueCountFrequency (%)
52
71.2%
! 5
 
6.8%
0 3
 
4.1%
[ 3
 
4.1%
] 3
 
4.1%
7 2
 
2.7%
1 2
 
2.7%
2 1
 
1.4%
3 1
 
1.4%
& 1
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 805
88.6%
ASCII 104
 
11.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
리 86
 
10.7%
트 77
 
9.6%
조 71
 
8.8%
스 25
 
3.1%
도 22
 
2.7%
콘 21
 
2.6%
이 18
 
2.2%
설 14
 
1.7%
악 13
 
1.6%
평 11
 
1.4%
Other values (186) 447
55.5%
ASCII
ValueCountFrequency (%)
52
50.0%
! 5
 
4.8%
O 5
 
4.8%
0 3
 
2.9%
C 3
 
2.9%
e 3
 
2.9%
[ 3
 
2.9%
] 3
 
2.9%
7 2
 
1.9%
N 2
 
1.9%
Other values (19) 23
22.1%

load_addr
Text

Distinct94
Distinct (%)94.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T18:58:18.114183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length21
Mean length18.65
Min length8

Characters and Unicode

Total characters1865
Distinct characters154
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique89 ?
Unique (%)89.0%

Sample

1st row강원 강릉시 강동면 산두양지길 7-16
2nd row인천광역시 남구 용현1동
3rd row강원 강릉시 강동면 헌화로 950-39
4th row강원 강릉시 강동면 헌화로 950-39
5th row강원 강릉시 창해로 141
ValueCountFrequency (%)
강원 97
 
20.5%
평창군 16
 
3.4%
속초시 15
 
3.2%
춘천시 15
 
3.2%
고성군 14
 
3.0%
토성면 11
 
2.3%
남산면 10
 
2.1%
양양군 9
 
1.9%
봉평면 8
 
1.7%
동해대로 7
 
1.5%
Other values (194) 271
57.3%
2023-12-10T18:58:19.339543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
373
20.0%
강 108
 
5.8%
원 105
 
5.6%
면 64
 
3.4%
로 57
 
3.1%
군 55
 
2.9%
1 50
 
2.7%
시 46
 
2.5%
3 44
 
2.4%
2 43
 
2.3%
Other values (144) 920
49.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1117
59.9%
Space Separator 373
 
20.0%
Decimal Number 342
 
18.3%
Dash Punctuation 33
 
1.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
강 108
 
9.7%
원 105
 
9.4%
면 64
 
5.7%
로 57
 
5.1%
군 55
 
4.9%
시 46
 
4.1%
길 42
 
3.8%
평 32
 
2.9%
성 27
 
2.4%
양 26
 
2.3%
Other values (132) 555
49.7%
Decimal Number
ValueCountFrequency (%)
1 50
14.6%
3 44
12.9%
2 43
12.6%
4 38
11.1%
6 37
10.8%
7 31
9.1%
0 29
8.5%
5 28
8.2%
8 27
7.9%
9 15
 
4.4%
Space Separator
ValueCountFrequency (%)
373
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 33
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1117
59.9%
Common 748
40.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
강 108
 
9.7%
원 105
 
9.4%
면 64
 
5.7%
로 57
 
5.1%
군 55
 
4.9%
시 46
 
4.1%
길 42
 
3.8%
평 32
 
2.9%
성 27
 
2.4%
양 26
 
2.3%
Other values (132) 555
49.7%
Common
ValueCountFrequency (%)
373
49.9%
1 50
 
6.7%
3 44
 
5.9%
2 43
 
5.7%
4 38
 
5.1%
6 37
 
4.9%
- 33
 
4.4%
7 31
 
4.1%
0 29
 
3.9%
5 28
 
3.7%
Other values (2) 42
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1117
59.9%
ASCII 748
40.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
373
49.9%
1 50
 
6.7%
3 44
 
5.9%
2 43
 
5.7%
4 38
 
5.1%
6 37
 
4.9%
- 33
 
4.4%
7 31
 
4.1%
0 29
 
3.9%
5 28
 
3.7%
Other values (2) 42
 
5.6%
Hangul
ValueCountFrequency (%)
강 108
 
9.7%
원 105
 
9.4%
면 64
 
5.7%
로 57
 
5.1%
군 55
 
4.9%
시 46
 
4.1%
길 42
 
3.8%
평 32
 
2.9%
성 27
 
2.4%
양 26
 
2.3%
Other values (132) 555
49.7%

xpos_lo
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct91
Distinct (%)93.8%
Missing3
Missing (%)3.0%
Infinite0
Infinite (%)0.0%
Mean128.40673
Minimum127.28184
Maximum129.22079
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:58:19.680025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum127.28184
5-th percentile127.53812
Q1128.31863
median128.54137
Q3128.67278
95-th percentile128.99564
Maximum129.22079
Range1.938955
Interquartile range (IQR)0.354153

Descriptive statistics

Standard deviation0.46563011
Coefficient of variation (CV)0.0036262127
Kurtosis-0.1442798
Mean128.40673
Median Absolute Deviation (MAD)0.192127
Skewness-0.88006858
Sum12455.453
Variance0.2168114
MonotonicityNot monotonic
2023-12-10T18:58:20.512005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
128.686881 3
 
3.0%
128.338475 2
 
2.0%
128.600822 2
 
2.0%
128.67278 2
 
2.0%
129.042907 2
 
2.0%
128.381817 1
 
1.0%
127.530251 1
 
1.0%
127.538846 1
 
1.0%
127.539384 1
 
1.0%
127.546124 1
 
1.0%
Other values (81) 81
81.0%
(Missing) 3
 
3.0%
ValueCountFrequency (%)
127.281839 1
1.0%
127.299336 1
1.0%
127.527876 1
1.0%
127.530251 1
1.0%
127.535238 1
1.0%
127.538846 1
1.0%
127.539352 1
1.0%
127.539384 1
1.0%
127.546124 1
1.0%
127.579634 1
1.0%
ValueCountFrequency (%)
129.220794 1
1.0%
129.164779 1
1.0%
129.077946 1
1.0%
129.042907 2
2.0%
128.983822 1
1.0%
128.94578 1
1.0%
128.934751 1
1.0%
128.905552 1
1.0%
128.90028 1
1.0%
128.883011 1
1.0%

ypos_la
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct91
Distinct (%)93.8%
Missing3
Missing (%)3.0%
Infinite0
Infinite (%)0.0%
Mean37.853473
Minimum37.140319
Maximum38.510316
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:58:20.800736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.140319
5-th percentile37.206117
Q137.646872
median37.822534
Q338.181411
95-th percentile38.243367
Maximum38.510316
Range1.369997
Interquartile range (IQR)0.534539

Descriptive statistics

Standard deviation0.32965821
Coefficient of variation (CV)0.008708797
Kurtosis-0.73463272
Mean37.853473
Median Absolute Deviation (MAD)0.26527
Skewness-0.36293374
Sum3671.7868
Variance0.10867454
MonotonicityNot monotonic
2023-12-10T18:58:21.087865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.646872 3
 
3.0%
37.571418 2
 
2.0%
38.189 2
 
2.0%
37.65773 2
 
2.0%
37.683547 2
 
2.0%
37.601525 1
 
1.0%
37.798807 1
 
1.0%
37.777018 1
 
1.0%
37.776218 1
 
1.0%
37.769343 1
 
1.0%
Other values (81) 81
81.0%
(Missing) 3
 
3.0%
ValueCountFrequency (%)
37.140319 1
1.0%
37.169787 1
1.0%
37.196348 1
1.0%
37.19693 1
1.0%
37.204431 1
1.0%
37.206539 1
1.0%
37.211528 1
1.0%
37.217394 1
1.0%
37.395926 1
1.0%
37.402708 1
1.0%
ValueCountFrequency (%)
38.510316 1
1.0%
38.317084 1
1.0%
38.315956 1
1.0%
38.245059 1
1.0%
38.244611 1
1.0%
38.243056 1
1.0%
38.240561 1
1.0%
38.238878 1
1.0%
38.237683 1
1.0%
38.234166 1
1.0%

area_nm
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
강원
97 
인천
 
3

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row강원
2nd row인천
3rd row강원
4th row강원
5th row강원

Common Values

ValueCountFrequency (%)
강원 97
97.0%
인천 3
 
3.0%

Length

2023-12-10T18:58:21.322016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:58:21.501621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
강원 97
97.0%
인천 3
 
3.0%

homepage_url
Text

MISSING 

Distinct87
Distinct (%)97.8%
Missing11
Missing (%)11.0%
Memory size932.0 B
2023-12-10T18:58:21.849393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length108
Median length53
Mean length33.573034
Min length17

Characters and Unicode

Total characters2988
Distinct characters69
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique85 ?
Unique (%)95.5%

Sample

1st rowhttp://www.wcastle.net/
2nd rowhttps://www.airbnb.co.kr/rooms/36307472?source_impression_id=p3_1576460821_DICcB8YhfxJhaEjf&s_tag=INpf1v7_
3rd rowhttp://www.esuncruise.com/
4th rowhttp://www.esuncruise.com/
5th rowhttp://www.welfare.mil.kr/hotel/content/content.do?m_code=190
ValueCountFrequency (%)
http://www.elysian.co.kr 2
 
2.2%
http://www.esuncruise.com 2
 
2.2%
http://www.phoenixhnr.co.kr/pyeongchang/index 1
 
1.1%
http://godaresort.com 1
 
1.1%
http://www.hileisure.net 1
 
1.1%
http://ok7788.kr 1
 
1.1%
http://smruvillresort.com 1
 
1.1%
http://www.hiresort.co.kr 1
 
1.1%
http://bfourseason.com 1
 
1.1%
http://www.clubvivaldi.com 1
 
1.1%
Other values (77) 77
86.5%
2023-12-10T18:58:22.621322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 294
 
9.8%
t 258
 
8.6%
o 238
 
8.0%
w 225
 
7.5%
. 218
 
7.3%
r 171
 
5.7%
e 144
 
4.8%
h 137
 
4.6%
p 128
 
4.3%
c 121
 
4.0%
Other values (59) 1054
35.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2203
73.7%
Other Punctuation 613
 
20.5%
Decimal Number 90
 
3.0%
Uppercase Letter 37
 
1.2%
Connector Punctuation 21
 
0.7%
Math Symbol 11
 
0.4%
Dash Punctuation 7
 
0.2%
Other Letter 6
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 258
11.7%
o 238
10.8%
w 225
10.2%
r 171
 
7.8%
e 144
 
6.5%
h 137
 
6.2%
p 128
 
5.8%
c 121
 
5.5%
s 118
 
5.4%
n 106
 
4.8%
Other values (15) 557
25.3%
Uppercase Letter
ValueCountFrequency (%)
H 6
16.2%
I 4
10.8%
B 4
10.8%
N 3
 
8.1%
C 2
 
5.4%
D 2
 
5.4%
L 2
 
5.4%
E 2
 
5.4%
K 2
 
5.4%
M 1
 
2.7%
Other values (9) 9
24.3%
Decimal Number
ValueCountFrequency (%)
1 17
18.9%
7 15
16.7%
0 10
11.1%
6 10
11.1%
8 8
8.9%
2 8
8.9%
3 7
7.8%
4 6
 
6.7%
5 5
 
5.6%
9 4
 
4.4%
Other Punctuation
ValueCountFrequency (%)
/ 294
48.0%
. 218
35.6%
: 89
 
14.5%
? 8
 
1.3%
& 3
 
0.5%
% 1
 
0.2%
Other Letter
ValueCountFrequency (%)
악 1
16.7%
트 1
16.7%
조 1
16.7%
리 1
16.7%
산 1
16.7%
설 1
16.7%
Connector Punctuation
ValueCountFrequency (%)
_ 21
100.0%
Math Symbol
ValueCountFrequency (%)
= 11
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2240
75.0%
Common 742
 
24.8%
Hangul 6
 
0.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 258
11.5%
o 238
10.6%
w 225
 
10.0%
r 171
 
7.6%
e 144
 
6.4%
h 137
 
6.1%
p 128
 
5.7%
c 121
 
5.4%
s 118
 
5.3%
n 106
 
4.7%
Other values (34) 594
26.5%
Common
ValueCountFrequency (%)
/ 294
39.6%
. 218
29.4%
: 89
 
12.0%
_ 21
 
2.8%
1 17
 
2.3%
7 15
 
2.0%
= 11
 
1.5%
0 10
 
1.3%
6 10
 
1.3%
? 8
 
1.1%
Other values (9) 49
 
6.6%
Hangul
ValueCountFrequency (%)
악 1
16.7%
트 1
16.7%
조 1
16.7%
리 1
16.7%
산 1
16.7%
설 1
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2982
99.8%
Hangul 6
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/ 294
 
9.9%
t 258
 
8.7%
o 238
 
8.0%
w 225
 
7.5%
. 218
 
7.3%
r 171
 
5.7%
e 144
 
4.8%
h 137
 
4.6%
p 128
 
4.3%
c 121
 
4.1%
Other values (53) 1048
35.1%
Hangul
ValueCountFrequency (%)
악 1
16.7%
트 1
16.7%
조 1
16.7%
리 1
16.7%
산 1
16.7%
설 1
16.7%

tel_no
Text

MISSING 

Distinct81
Distinct (%)93.1%
Missing13
Missing (%)13.0%
Memory size932.0 B
2023-12-10T18:58:23.072334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length11.517241
Min length9

Characters and Unicode

Total characters1002
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

Unique77 ?
Unique (%)88.5%

Sample

1st row033-644-6444
2nd row033-610-7000
3rd row033-652-7573
4th row033-644-7123
5th row1644-3001
ValueCountFrequency (%)
1588-7789 3
 
3.4%
1588-4888 3
 
3.4%
033-339-0000 2
 
2.3%
033-260-2000 2
 
2.3%
033-269-8888 1
 
1.1%
010-9204-5080 1
 
1.1%
033-263-7227 1
 
1.1%
033-263-4091 1
 
1.1%
070-4104-5255 1
 
1.1%
033-263-4767 1
 
1.1%
Other values (71) 71
81.6%
2023-12-10T18:58:23.877550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 215
21.5%
0 189
18.9%
- 159
15.9%
6 76
 
7.6%
7 67
 
6.7%
8 65
 
6.5%
1 63
 
6.3%
5 56
 
5.6%
4 45
 
4.5%
2 41
 
4.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 843
84.1%
Dash Punctuation 159
 
15.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 215
25.5%
0 189
22.4%
6 76
 
9.0%
7 67
 
7.9%
8 65
 
7.7%
1 63
 
7.5%
5 56
 
6.6%
4 45
 
5.3%
2 41
 
4.9%
9 26
 
3.1%
Dash Punctuation
ValueCountFrequency (%)
- 159
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1002
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 215
21.5%
0 189
18.9%
- 159
15.9%
6 76
 
7.6%
7 67
 
6.7%
8 65
 
6.5%
1 63
 
6.3%
5 56
 
5.6%
4 45
 
4.5%
2 41
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1002
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 215
21.5%
0 189
18.9%
- 159
15.9%
6 76
 
7.6%
7 67
 
6.7%
8 65
 
6.5%
1 63
 
6.3%
5 56
 
5.6%
4 45
 
4.5%
2 41
 
4.1%

base_ymd
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2019-12-09
100 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2019-12-09
2nd row2019-12-09
3rd row2019-12-09
4th row2019-12-09
5th row2019-12-09

Common Values

ValueCountFrequency (%)
2019-12-09 100
100.0%

Length

2023-12-10T18:58:24.135658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:58:24.276953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2019-12-09 100
100.0%

Interactions

2023-12-10T18:58:15.079115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:58:14.604346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:58:15.280393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:58:14.871047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T18:58:24.390873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
entrp_nmload_addrxpos_loypos_laarea_nmhomepage_urltel_no
entrp_nm1.0001.0001.0001.0001.0001.0001.000
load_addr1.0001.0001.0001.0001.0000.9980.992
xpos_lo1.0001.0001.0000.873NaN1.0000.968
ypos_la1.0001.0000.8731.000NaN0.9650.932
area_nm1.0001.000NaNNaN1.0001.000NaN
homepage_url1.0000.9981.0000.9651.0001.0001.000
tel_no1.0000.9920.9680.932NaN1.0001.000
2023-12-10T18:58:24.604180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
xpos_loypos_laarea_nm
xpos_lo1.000-0.1631.000
ypos_la-0.1631.0001.000
area_nm1.0001.0001.000

Missing values

2023-12-10T18:58:15.529056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T18:58:15.796323image/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.
2023-12-10T18:58:16.057258image/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

entrp_nmload_addrxpos_loypos_laarea_nmhomepage_urltel_nobase_ymd
0화이트캐슬리조트강원 강릉시 강동면 산두양지길 7-16128.98382237.720825강원http://www.wcastle.net/033-644-64442019-12-09
1[chaeum2101! HOUSE] ONE ROOM !! 간섭NO!!인천광역시 남구 용현1동<NA><NA>인천https://www.airbnb.co.kr/rooms/36307472?source_impression_id=p3_1576460821_DICcB8YhfxJhaEjf&s_tag=INpf1v7_<NA>2019-12-09
2썬크루즈리조트강원 강릉시 강동면 헌화로 950-39129.04290737.683547강원http://www.esuncruise.com/033-610-70002019-12-09
3썬크루즈리조트 비치크루즈강원 강릉시 강동면 헌화로 950-39129.04290737.683547강원http://www.esuncruise.com/<NA>2019-12-09
4송정콘도강원 강릉시 창해로 141128.93475137.781142강원http://www.welfare.mil.kr/hotel/content/content.do?m_code=190033-652-75732019-12-09
5경포산장콘도강원 강릉시 하남길 345-18128.9002837.809729강원https://kpcondo.modoo.at/033-644-71232019-12-09
6라카이샌드파인리조트강원 강릉시 해안로 536128.90555237.806148강원http://www.lakaisandpine.co.kr/1644-30012019-12-09
7[Steve]합리적인 가격으로 누리는 부평 베이스캠프 헨리하우스 부평점인천광역시 부평구<NA><NA>인천https://www.airbnb.co.kr/rooms/21841919?source_impression_id=p3_1576460840_H0vK1EBftyzBRy7%2B&s_tag=INpf1v7_<NA>2019-12-09
8오션투유리조트 속초설악비치 호텔앤콘도강원 고성군 죽왕면 삼포해변길 9128.53138.317084강원http://www.ocean2you.co.kr/070-7688-89502019-12-09
9설악썬밸리골프리조트강원 고성군 죽왕면 순포로 188128.51117538.315956강원<NA>033-638-53622019-12-09
entrp_nmload_addrxpos_loypos_laarea_nmhomepage_urltel_nobase_ymd
90휘닉스평창강원 평창군 봉평면 태기로 174128.33847537.571418강원http://www.phoenixhnr.co.kr/pyeongchang/index1588-28282019-12-09
91휘닉스평창 스카이콘도강원 평창군 봉평면 태기로 174128.33847537.571418강원https://phoenixhnr.co.kr/pyeongchang/room/condo033-333-60002019-12-09
92한화리조트 평창강원 평창군 봉평면 태기로 228-33128.33136437.578881강원http://www.hanwharesort.co.kr/033-334-61002019-12-09
93다온리조트강원 평창군 봉평면 태기로 283-2128.33812137.578249강원http://www.daonresort.co.kr/033-333-58702019-12-09
94오리엔트리조트강원 평창군 봉평면 태기로 80-56128.31862737.586827강원http://www.orientresort.co.kr/1544-13802019-12-09
95베리온리조트강원 평창군 봉평면 평온길 14-13128.38181737.601525강원http://www.berion.co.kr/033-335-80012019-12-09
96평창그린힐리조트강원 평창군 용평면 신약수로 252-26128.49862637.671246강원http://www.pyeongchang-greenhill.com/033-333-41112019-12-09
97로하스포레스트강원 평창군 용평면 작은도사길 162-49128.45801737.642757강원http://www.lohas-forest.co.kr/070-4607-52672019-12-09
98가람밸리리조트강원 홍천군 두촌면 부채들길 29128.01915237.836207강원http://www.garamvalley.co.kr/070-4348-38802019-12-09
99몬테리오리조트강원 홍천군 서면 마곡길 220127.57963437.728686강원http://www.riveraroma.com/033-436-10002019-12-09