Overview

Dataset statistics

Number of variables15
Number of observations100
Missing cells20
Missing cells (%)1.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory12.3 KiB
Average record size in memory126.3 B

Variable types

Text4
Numeric4
Categorical7

Alerts

base_ymd has constant value ""Constant
middle_prefer_trrsrt_nm is highly overall correlated with city_do_cd and 7 other fieldsHigh correlation
europe_prefer_trrsrt_nm is highly overall correlated with city_do_cd and 7 other fieldsHigh correlation
asia_prefer_trrsrt_nm is highly overall correlated with city_do_cd and 7 other fieldsHigh correlation
america_prefer_trrsrt_nm is highly overall correlated with city_do_cd and 7 other fieldsHigh correlation
city_do_cd is highly overall correlated with city_gn_gu_cd and 6 other fieldsHigh correlation
city_gn_gu_cd is highly overall correlated with city_do_cd and 6 other fieldsHigh correlation
xpos_lo is highly overall correlated with area_nm and 4 other fieldsHigh correlation
ypos_la is highly overall correlated with city_do_cd and 6 other fieldsHigh correlation
area_nm is highly overall correlated with city_do_cd and 7 other fieldsHigh correlation
tel_no has 5 (5.0%) missing valuesMissing
homepage_url has 15 (15.0%) missing valuesMissing
entrp_nm has unique valuesUnique
load_addr has unique valuesUnique

Reproduction

Analysis started2023-12-10 10:03:03.409184
Analysis finished2023-12-10 10:03:09.922875
Duration6.51 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
2023-12-10T19:03:10.209394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length15
Mean length9.17
Min length3

Characters and Unicode

Total characters917
Distinct characters165
Distinct categories4 ?
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 rowE호텔
2nd row호텔시에나
3rd row나인트리프리미어호텔명동2
4th row나인트리호텔명동
5th row라마다서울동대문
ValueCountFrequency (%)
호텔 53
23.9%
서울 7
 
3.2%
프리미어 7
 
3.2%
베스트웨스턴 5
 
2.3%
동대문 4
 
1.8%
강남 4
 
1.8%
송도 4
 
1.8%
인천 3
 
1.4%
명동 3
 
1.4%
관광 3
 
1.4%
Other values (118) 129
58.1%
2023-12-10T19:03:10.908758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
122
 
13.3%
호 85
 
9.3%
텔 82
 
8.9%
스 53
 
5.8%
이 21
 
2.3%
트 21
 
2.3%
리 19
 
2.1%
라 16
 
1.7%
서 15
 
1.6%
아 14
 
1.5%
Other values (155) 469
51.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 785
85.6%
Space Separator 122
 
13.3%
Decimal Number 5
 
0.5%
Uppercase Letter 5
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
호 85
 
10.8%
텔 82
 
10.4%
스 53
 
6.8%
이 21
 
2.7%
트 21
 
2.7%
리 19
 
2.4%
라 16
 
2.0%
서 15
 
1.9%
아 14
 
1.8%
어 13
 
1.7%
Other values (146) 446
56.8%
Uppercase Letter
ValueCountFrequency (%)
T 1
20.0%
M 1
20.0%
I 1
20.0%
S 1
20.0%
E 1
20.0%
Decimal Number
ValueCountFrequency (%)
2 3
60.0%
7 1
 
20.0%
1 1
 
20.0%
Space Separator
ValueCountFrequency (%)
122
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 785
85.6%
Common 127
 
13.8%
Latin 5
 
0.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
호 85
 
10.8%
텔 82
 
10.4%
스 53
 
6.8%
이 21
 
2.7%
트 21
 
2.7%
리 19
 
2.4%
라 16
 
2.0%
서 15
 
1.9%
아 14
 
1.8%
어 13
 
1.7%
Other values (146) 446
56.8%
Latin
ValueCountFrequency (%)
T 1
20.0%
M 1
20.0%
I 1
20.0%
S 1
20.0%
E 1
20.0%
Common
ValueCountFrequency (%)
122
96.1%
2 3
 
2.4%
7 1
 
0.8%
1 1
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 785
85.6%
ASCII 132
 
14.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
122
92.4%
2 3
 
2.3%
T 1
 
0.8%
M 1
 
0.8%
I 1
 
0.8%
S 1
 
0.8%
E 1
 
0.8%
7 1
 
0.8%
1 1
 
0.8%
Hangul
ValueCountFrequency (%)
호 85
 
10.8%
텔 82
 
10.4%
스 53
 
6.8%
이 21
 
2.7%
트 21
 
2.7%
리 19
 
2.4%
라 16
 
2.0%
서 15
 
1.9%
아 14
 
1.8%
어 13
 
1.7%
Other values (146) 446
56.8%

load_addr
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:03:11.420620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length46
Median length31
Mean length21.39
Min length12

Characters and Unicode

Total characters2139
Distinct characters199
Distinct categories6 ?
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 row서울특별시 서초구 반포대로18길 40 E 호텔
2nd row경기도 파주시 소리천로 31
3rd row서울특별시 중구 마른내로 28
4th row서울특별시 종로구 인사동길 49
5th row서울특별시 중구 동호로 354
ValueCountFrequency (%)
서울특별시 50
 
11.1%
중구 19
 
4.2%
경기도 14
 
3.1%
인천광역시 12
 
2.7%
부산광역시 10
 
2.2%
제주특별자치도 10
 
2.2%
서귀포시 9
 
2.0%
강남구 8
 
1.8%
강서구 6
 
1.3%
수원시 5
 
1.1%
Other values (255) 309
68.4%
2023-12-10T19:03:12.294372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
352
 
16.5%
시 104
 
4.9%
로 99
 
4.6%
구 86
 
4.0%
1 72
 
3.4%
서 70
 
3.3%
특 61
 
2.9%
별 61
 
2.9%
울 52
 
2.4%
길 42
 
2.0%
Other values (189) 1140
53.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1427
66.7%
Space Separator 352
 
16.5%
Decimal Number 332
 
15.5%
Dash Punctuation 16
 
0.7%
Uppercase Letter 8
 
0.4%
Lowercase Letter 4
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 104
 
7.3%
로 99
 
6.9%
구 86
 
6.0%
서 70
 
4.9%
특 61
 
4.3%
별 61
 
4.3%
울 52
 
3.6%
길 42
 
2.9%
광 36
 
2.5%
중 35
 
2.5%
Other values (168) 781
54.7%
Decimal Number
ValueCountFrequency (%)
1 72
21.7%
2 40
12.0%
3 35
10.5%
6 30
9.0%
9 29
8.7%
8 28
 
8.4%
5 27
 
8.1%
4 26
 
7.8%
7 25
 
7.5%
0 20
 
6.0%
Uppercase Letter
ValueCountFrequency (%)
A 2
25.0%
C 2
25.0%
E 2
25.0%
P 1
12.5%
H 1
12.5%
Lowercase Letter
ValueCountFrequency (%)
l 1
25.0%
e 1
25.0%
t 1
25.0%
o 1
25.0%
Space Separator
ValueCountFrequency (%)
352
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1427
66.7%
Common 700
32.7%
Latin 12
 
0.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 104
 
7.3%
로 99
 
6.9%
구 86
 
6.0%
서 70
 
4.9%
특 61
 
4.3%
별 61
 
4.3%
울 52
 
3.6%
길 42
 
2.9%
광 36
 
2.5%
중 35
 
2.5%
Other values (168) 781
54.7%
Common
ValueCountFrequency (%)
352
50.3%
1 72
 
10.3%
2 40
 
5.7%
3 35
 
5.0%
6 30
 
4.3%
9 29
 
4.1%
8 28
 
4.0%
5 27
 
3.9%
4 26
 
3.7%
7 25
 
3.6%
Other values (2) 36
 
5.1%
Latin
ValueCountFrequency (%)
A 2
16.7%
C 2
16.7%
E 2
16.7%
P 1
8.3%
l 1
8.3%
e 1
8.3%
t 1
8.3%
o 1
8.3%
H 1
8.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1427
66.7%
ASCII 712
33.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
352
49.4%
1 72
 
10.1%
2 40
 
5.6%
3 35
 
4.9%
6 30
 
4.2%
9 29
 
4.1%
8 28
 
3.9%
5 27
 
3.8%
4 26
 
3.7%
7 25
 
3.5%
Other values (11) 48
 
6.7%
Hangul
ValueCountFrequency (%)
시 104
 
7.3%
로 99
 
6.9%
구 86
 
6.0%
서 70
 
4.9%
특 61
 
4.3%
별 61
 
4.3%
울 52
 
3.6%
길 42
 
2.9%
광 36
 
2.5%
중 35
 
2.5%
Other values (168) 781
54.7%

city_do_cd
Real number (ℝ)

HIGH CORRELATION 

Distinct9
Distinct (%)9.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.92
Minimum11
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:03:12.525958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile11
Q111
median18.5
Q341
95-th percentile50
Maximum50
Range39
Interquartile range (IQR)30

Descriptive statistics

Standard deviation14.607788
Coefficient of variation (CV)0.61069348
Kurtosis-1.1899489
Mean23.92
Median Absolute Deviation (MAD)7.5
Skewness0.58723114
Sum2392
Variance213.38747
MonotonicityNot monotonic
2023-12-10T19:03:12.778126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
11 50
50.0%
41 14
 
14.0%
28 12
 
12.0%
50 10
 
10.0%
26 10
 
10.0%
31 1
 
1.0%
46 1
 
1.0%
48 1
 
1.0%
47 1
 
1.0%
ValueCountFrequency (%)
11 50
50.0%
26 10
 
10.0%
28 12
 
12.0%
31 1
 
1.0%
41 14
 
14.0%
46 1
 
1.0%
47 1
 
1.0%
48 1
 
1.0%
50 10
 
10.0%
ValueCountFrequency (%)
50 10
 
10.0%
48 1
 
1.0%
47 1
 
1.0%
46 1
 
1.0%
41 14
 
14.0%
31 1
 
1.0%
28 12
 
12.0%
26 10
 
10.0%
11 50
50.0%

city_gn_gu_cd
Real number (ℝ)

HIGH CORRELATION 

Distinct41
Distinct (%)41.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24223.37
Minimum11110
Maximum50130
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:03:13.058087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11110
5-th percentile11138.5
Q111500
median18910
Q341113.5
95-th percentile50130
Maximum50130
Range39020
Interquartile range (IQR)29613.5

Descriptive statistics

Standard deviation14516.335
Coefficient of variation (CV)0.59926985
Kurtosis-1.1871659
Mean24223.37
Median Absolute Deviation (MAD)7770
Skewness0.5903281
Sum2422337
Variance2.1072399 × 108
MonotonicityNot monotonic
2023-12-10T19:03:13.425976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
11140 13
 
13.0%
50130 9
 
9.0%
11680 8
 
8.0%
11500 6
 
6.0%
11110 5
 
5.0%
28110 5
 
5.0%
28185 5
 
5.0%
26350 4
 
4.0%
11560 4
 
4.0%
11650 3
 
3.0%
Other values (31) 38
38.0%
ValueCountFrequency (%)
11110 5
 
5.0%
11140 13
13.0%
11170 1
 
1.0%
11215 1
 
1.0%
11290 1
 
1.0%
11350 1
 
1.0%
11380 1
 
1.0%
11440 1
 
1.0%
11500 6
6.0%
11530 1
 
1.0%
ValueCountFrequency (%)
50130 9
9.0%
50110 1
 
1.0%
48310 1
 
1.0%
47113 1
 
1.0%
46130 1
 
1.0%
41590 1
 
1.0%
41500 1
 
1.0%
41480 1
 
1.0%
41390 1
 
1.0%
41370 1
 
1.0%

xpos_lo
Real number (ℝ)

HIGH CORRELATION 

Distinct99
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.18539
Minimum126.40835
Maximum129.35736
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:03:13.815277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.40835
5-th percentile126.52188
Q1126.8394
median126.98795
Q3127.03575
95-th percentile129.15657
Maximum129.35736
Range2.9490161
Interquartile range (IQR)0.19635875

Descriptive statistics

Standard deviation0.77175323
Coefficient of variation (CV)0.0060679394
Kurtosis2.5317503
Mean127.18539
Median Absolute Deviation (MAD)0.08262305
Skewness1.9748162
Sum12718.539
Variance0.59560305
MonotonicityNot monotonic
2023-12-10T19:03:14.182561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.0366394 2
 
2.0%
127.0126078 1
 
1.0%
126.8142917 1
 
1.0%
127.0087236 1
 
1.0%
126.5575562 1
 
1.0%
126.58147 1
 
1.0%
126.5977517 1
 
1.0%
126.7402653 1
 
1.0%
128.6034968 1
 
1.0%
127.0055188 1
 
1.0%
Other values (89) 89
89.0%
ValueCountFrequency (%)
126.4083476 1
1.0%
126.4131379 1
1.0%
126.4305938 1
1.0%
126.4557688 1
1.0%
126.519265 1
1.0%
126.522019 1
1.0%
126.5575562 1
1.0%
126.578421 1
1.0%
126.58147 1
1.0%
126.5977517 1
1.0%
ValueCountFrequency (%)
129.3573637 1
1.0%
129.3473935 1
1.0%
129.165879 1
1.0%
129.1611261 1
1.0%
129.1569244 1
1.0%
129.1565486 1
1.0%
129.1179799 1
1.0%
129.058122 1
1.0%
129.057318 1
1.0%
129.0418332 1
1.0%

ypos_la
Real number (ℝ)

HIGH CORRELATION 

Distinct99
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.750864
Minimum33.247674
Maximum37.715623
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:03:14.545934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum33.247674
5-th percentile33.427692
Q137.200041
median37.486884
Q337.553208
95-th percentile37.574556
Maximum37.715623
Range4.4679489
Interquartile range (IQR)0.35316722

Descriptive statistics

Standard deviation1.394222
Coefficient of variation (CV)0.037937121
Kurtosis1.0391406
Mean36.750864
Median Absolute Deviation (MAD)0.08351027
Skewness-1.5900356
Sum3675.0864
Variance1.9438549
MonotonicityNot monotonic
2023-12-10T19:03:14.986535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.52738988 2
 
2.0%
37.48697203 1
 
1.0%
37.56157776 1
 
1.0%
37.56412429 1
 
1.0%
33.24770083 1
 
1.0%
37.49027215 1
 
1.0%
37.47399441 1
 
1.0%
37.39046414 1
 
1.0%
34.88593306 1
 
1.0%
37.56299991 1
 
1.0%
Other values (89) 89
89.0%
ValueCountFrequency (%)
33.24767393 1
1.0%
33.24770083 1
1.0%
33.24905001 1
1.0%
33.25447711 1
1.0%
33.25743821 1
1.0%
33.4366529 1
1.0%
33.45210482 1
1.0%
33.46235117 1
1.0%
33.46598674 1
1.0%
33.512769 1
1.0%
ValueCountFrequency (%)
37.7156228 1
1.0%
37.65519408 1
1.0%
37.59989609 1
1.0%
37.59361073 1
1.0%
37.57625021 1
1.0%
37.57446681 1
1.0%
37.57400036 1
1.0%
37.57382681 1
1.0%
37.57332038 1
1.0%
37.56797554 1
1.0%

area_nm
Categorical

HIGH CORRELATION 

Distinct9
Distinct (%)9.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
서울
50 
경기
14 
인천
12 
제주
10 
부산
10 
Other values (4)
 
4

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique4 ?
Unique (%)4.0%

Sample

1st row서울
2nd row경기
3rd row서울
4th row서울
5th row서울

Common Values

ValueCountFrequency (%)
서울 50
50.0%
경기 14
 
14.0%
인천 12
 
12.0%
제주 10
 
10.0%
부산 10
 
10.0%
울산 1
 
1.0%
전남 1
 
1.0%
경남 1
 
1.0%
경북 1
 
1.0%

Length

2023-12-10T19:03:15.218906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:03:15.404778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서울 50
50.0%
경기 14
 
14.0%
인천 12
 
12.0%
제주 10
 
10.0%
부산 10
 
10.0%
울산 1
 
1.0%
전남 1
 
1.0%
경남 1
 
1.0%
경북 1
 
1.0%

hotel_grad
Categorical

Distinct6
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
3
42 
4
23 
2
20 
<NA>
8 
5
 
4

Length

Max length4
Median length1
Mean length1.24
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row4
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3 42
42.0%
4 23
23.0%
2 20
20.0%
<NA> 8
 
8.0%
5 4
 
4.0%
1 3
 
3.0%

Length

2023-12-10T19:03:15.642556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:03:15.871375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3 42
42.0%
4 23
23.0%
2 20
20.0%
na 8
 
8.0%
5 4
 
4.0%
1 3
 
3.0%

tel_no
Text

MISSING 

Distinct95
Distinct (%)100.0%
Missing5
Missing (%)5.0%
Memory size932.0 B
2023-12-10T19:03:16.316532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length11.789474
Min length9

Characters and Unicode

Total characters1120
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

Unique95 ?
Unique (%)100.0%

Sample

1st row02-521-6555
2nd row031-943-7260
3rd row02-6967-0999
4th row02-750-0999
5th row02-2276-3500
ValueCountFrequency (%)
02-521-6555 1
 
1.1%
02-2079-8888 1
 
1.1%
064-763-2299 1
 
1.1%
064-741-0000 1
 
1.1%
032-890-0000 1
 
1.1%
032-777-7272 1
 
1.1%
031-318-0743 1
 
1.1%
02-867-9345 1
 
1.1%
02-2277-1141 1
 
1.1%
061-664-0001 1
 
1.1%
Other values (85) 85
89.5%
2023-12-10T19:03:17.074828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 270
24.1%
- 189
16.9%
2 132
11.8%
1 88
 
7.9%
3 81
 
7.2%
5 73
 
6.5%
7 72
 
6.4%
6 67
 
6.0%
8 54
 
4.8%
4 53
 
4.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 931
83.1%
Dash Punctuation 189
 
16.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 270
29.0%
2 132
14.2%
1 88
 
9.5%
3 81
 
8.7%
5 73
 
7.8%
7 72
 
7.7%
6 67
 
7.2%
8 54
 
5.8%
4 53
 
5.7%
9 41
 
4.4%
Dash Punctuation
ValueCountFrequency (%)
- 189
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 270
24.1%
- 189
16.9%
2 132
11.8%
1 88
 
7.9%
3 81
 
7.2%
5 73
 
6.5%
7 72
 
6.4%
6 67
 
6.0%
8 54
 
4.8%
4 53
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 270
24.1%
- 189
16.9%
2 132
11.8%
1 88
 
7.9%
3 81
 
7.2%
5 73
 
6.5%
7 72
 
6.4%
6 67
 
6.0%
8 54
 
4.8%
4 53
 
4.7%

homepage_url
Text

MISSING 

Distinct82
Distinct (%)96.5%
Missing15
Missing (%)15.0%
Memory size932.0 B
2023-12-10T19:03:17.616723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length76
Median length41
Mean length33.070588
Min length19

Characters and Unicode

Total characters2811
Distinct characters45
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique81 ?
Unique (%)95.3%

Sample

1st rowhttp://www.e-hotel.kr/
2nd rowhttp://www.hotelsienna.com/
3rd rowhttp://www.ninetreehotels.com/nth2/
4th rowhttps://ninetreehotel.com/
5th rowhttp://www.ramadaddm.com/main/
ValueCountFrequency (%)
http://www.aroomi.co.kr/kor/index.do 4
 
4.7%
http://www.cshotelseoul.com 1
 
1.2%
https://www.ambatel.com/ibisbudget/dongdaemun/ko/main.do 1
 
1.2%
http://www.hotelwinstory.com 1
 
1.2%
http://www.westerngracehotel.net 1
 
1.2%
http://www.premiers.kr 1
 
1.2%
http://www.youngbinhotel.co.kr 1
 
1.2%
http://www.yeosuvenezia.com 1
 
1.2%
http://mshotel.alltheway.kr 1
 
1.2%
http://www.afirsthotelgroup.com/ko 1
 
1.2%
Other values (72) 72
84.7%
2023-12-10T19:03:18.399088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 289
 
10.3%
t 269
 
9.6%
w 221
 
7.9%
o 221
 
7.9%
. 207
 
7.4%
h 164
 
5.8%
e 159
 
5.7%
p 124
 
4.4%
r 109
 
3.9%
a 107
 
3.8%
Other values (35) 941
33.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2178
77.5%
Other Punctuation 590
 
21.0%
Decimal Number 25
 
0.9%
Uppercase Letter 11
 
0.4%
Dash Punctuation 5
 
0.2%
Math Symbol 1
 
< 0.1%
Connector Punctuation 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 269
12.4%
w 221
 
10.1%
o 221
 
10.1%
h 164
 
7.5%
e 159
 
7.3%
p 124
 
5.7%
r 109
 
5.0%
a 107
 
4.9%
c 103
 
4.7%
m 99
 
4.5%
Other values (15) 602
27.6%
Other Punctuation
ValueCountFrequency (%)
/ 289
49.0%
. 207
35.1%
: 87
 
14.7%
% 4
 
0.7%
? 2
 
0.3%
# 1
 
0.2%
Decimal Number
ValueCountFrequency (%)
2 7
28.0%
1 6
24.0%
0 5
20.0%
3 3
12.0%
5 3
12.0%
7 1
 
4.0%
Uppercase Letter
ValueCountFrequency (%)
A 3
27.3%
F 3
27.3%
M 2
18.2%
R 2
18.2%
J 1
 
9.1%
Dash Punctuation
ValueCountFrequency (%)
- 5
100.0%
Math Symbol
ValueCountFrequency (%)
= 1
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2189
77.9%
Common 622
 
22.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 269
12.3%
w 221
 
10.1%
o 221
 
10.1%
h 164
 
7.5%
e 159
 
7.3%
p 124
 
5.7%
r 109
 
5.0%
a 107
 
4.9%
c 103
 
4.7%
m 99
 
4.5%
Other values (20) 613
28.0%
Common
ValueCountFrequency (%)
/ 289
46.5%
. 207
33.3%
: 87
 
14.0%
2 7
 
1.1%
1 6
 
1.0%
- 5
 
0.8%
0 5
 
0.8%
% 4
 
0.6%
3 3
 
0.5%
5 3
 
0.5%
Other values (5) 6
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2811
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/ 289
 
10.3%
t 269
 
9.6%
w 221
 
7.9%
o 221
 
7.9%
. 207
 
7.4%
h 164
 
5.8%
e 159
 
5.7%
p 124
 
4.4%
r 109
 
3.9%
a 107
 
3.8%
Other values (35) 941
33.5%

asia_prefer_trrsrt_nm
Categorical

HIGH CORRELATION 

Distinct37
Distinct (%)37.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
강남역거리
16 
성산일출봉
9 
여의도(63빌딩)
8 
수원화성
6 
서울식물원
6 
Other values (32)
55 

Length

Max length11
Median length8.5
Mean length5.51
Min length2

Unique

Unique22 ?
Unique (%)22.0%

Sample

1st row강화도
2nd row수원화성
3rd row강남역거리
4th row강남역거리
5th row강남역거리

Common Values

ValueCountFrequency (%)
강남역거리 16
16.0%
성산일출봉 9
 
9.0%
여의도(63빌딩) 8
 
8.0%
수원화성 6
 
6.0%
서울식물원 6
 
6.0%
송도 센트럴파크 5
 
5.0%
고투몰 5
 
5.0%
해운대해수욕장 4
 
4.0%
소래포구 4
 
4.0%
안양예술공원 4
 
4.0%
Other values (27) 33
33.0%

Length

2023-12-10T19:03:18.716785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
강남역거리 16
 
14.3%
성산일출봉 9
 
8.0%
여의도(63빌딩 8
 
7.1%
수원화성 6
 
5.4%
서울식물원 6
 
5.4%
송도 5
 
4.5%
센트럴파크 5
 
4.5%
고투몰 5
 
4.5%
해운대해수욕장 4
 
3.6%
소래포구 4
 
3.6%
Other values (33) 44
39.3%

europe_prefer_trrsrt_nm
Categorical

HIGH CORRELATION 

Distinct37
Distinct (%)37.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
강남역거리
16 
성산일출봉
9 
여의도(63빌딩)
8 
수원화성
6 
서울식물원
6 
Other values (32)
55 

Length

Max length11
Median length9.5
Mean length5.61
Min length2

Unique

Unique22 ?
Unique (%)22.0%

Sample

1st row강화도
2nd row수원화성
3rd row강남역거리
4th row강남역거리
5th row강남역거리

Common Values

ValueCountFrequency (%)
강남역거리 16
16.0%
성산일출봉 9
 
9.0%
여의도(63빌딩) 8
 
8.0%
수원화성 6
 
6.0%
서울식물원 6
 
6.0%
송도 센트럴파크 5
 
5.0%
방배동 5
 
5.0%
해운대해수욕장 4
 
4.0%
소래포구 4
 
4.0%
안양예술공원 4
 
4.0%
Other values (27) 33
33.0%

Length

2023-12-10T19:03:19.156744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
강남역거리 16
 
13.6%
성산일출봉 9
 
7.6%
여의도(63빌딩 8
 
6.8%
수원화성 6
 
5.1%
서울식물원 6
 
5.1%
송도 5
 
4.2%
센트럴파크 5
 
4.2%
방배동 5
 
4.2%
안양예술공원 4
 
3.4%
해운대해수욕장 4
 
3.4%
Other values (35) 50
42.4%

america_prefer_trrsrt_nm
Categorical

HIGH CORRELATION 

Distinct37
Distinct (%)37.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
강남역거리
16 
성산일출봉
9 
여의도(63빌딩)
8 
수원화성
6 
서울식물원
6 
Other values (32)
55 

Length

Max length11
Median length9
Mean length5.71
Min length2

Unique

Unique22 ?
Unique (%)22.0%

Sample

1st row강화도
2nd row수원화성
3rd row강남역거리
4th row강남역거리
5th row강남역거리

Common Values

ValueCountFrequency (%)
강남역거리 16
16.0%
성산일출봉 9
 
9.0%
여의도(63빌딩) 8
 
8.0%
수원화성 6
 
6.0%
서울식물원 6
 
6.0%
송도 센트럴파크 5
 
5.0%
고투몰 5
 
5.0%
해운대해수욕장 4
 
4.0%
소래포구 4
 
4.0%
안양예술공원 4
 
4.0%
Other values (27) 33
33.0%

Length

2023-12-10T19:03:19.390066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
강남역거리 16
 
13.7%
성산일출봉 9
 
7.7%
여의도(63빌딩 8
 
6.8%
수원화성 6
 
5.1%
서울식물원 6
 
5.1%
송도 5
 
4.3%
센트럴파크 5
 
4.3%
고투몰 5
 
4.3%
안양예술공원 4
 
3.4%
해운대해수욕장 4
 
3.4%
Other values (34) 49
41.9%

middle_prefer_trrsrt_nm
Categorical

HIGH CORRELATION 

Distinct37
Distinct (%)37.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
강남역거리
16 
성산일출봉
9 
여의도(63빌딩)
8 
수원화성
6 
등촌동
6 
Other values (32)
55 

Length

Max length9
Median length8
Mean length5.16
Min length2

Unique

Unique22 ?
Unique (%)22.0%

Sample

1st row강화도
2nd row수원화성
3rd row강남역거리
4th row강남역거리
5th row강남역거리

Common Values

ValueCountFrequency (%)
강남역거리 16
16.0%
성산일출봉 9
 
9.0%
여의도(63빌딩) 8
 
8.0%
수원화성 6
 
6.0%
등촌동 6
 
6.0%
송도 센트럴파크 5
 
5.0%
고투몰 5
 
5.0%
해운대해수욕장 4
 
4.0%
소래포구 4
 
4.0%
안양예술공원 4
 
4.0%
Other values (27) 33
33.0%

Length

2023-12-10T19:03:19.995846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
강남역거리 16
15.0%
성산일출봉 9
 
8.4%
여의도(63빌딩 8
 
7.5%
수원화성 6
 
5.6%
등촌동 6
 
5.6%
송도 5
 
4.7%
센트럴파크 5
 
4.7%
고투몰 5
 
4.7%
해운대해수욕장 4
 
3.7%
소래포구 4
 
3.7%
Other values (30) 39
36.4%

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

2023-12-10T19:03:20.234312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:03:20.407827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2019-12-09 100
100.0%

Interactions

2023-12-10T19:03:08.445287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:05.554231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:06.883960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:07.572773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:08.682266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:06.199130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:07.078968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:07.746496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:08.871621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:06.430877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:07.247277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:07.983962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:09.014282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:06.691386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:07.428672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:08.200544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:03:20.548913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
entrp_nmload_addrcity_do_cdcity_gn_gu_cdxpos_loypos_laarea_nmhotel_gradtel_nohomepage_urlasia_prefer_trrsrt_nmeurope_prefer_trrsrt_nmamerica_prefer_trrsrt_nmmiddle_prefer_trrsrt_nm
entrp_nm1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
load_addr1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
city_do_cd1.0001.0001.0001.0000.8420.9811.0000.0521.0001.0000.9960.9960.9960.996
city_gn_gu_cd1.0001.0001.0001.0000.8410.9811.0000.0291.0001.0000.9960.9960.9960.996
xpos_lo1.0001.0000.8420.8411.0000.8070.8970.0001.0000.9700.9160.9160.9160.916
ypos_la1.0001.0000.9810.9810.8071.0000.9710.0001.0001.0000.9870.9870.9870.987
area_nm1.0001.0001.0001.0000.8970.9711.0000.0001.0001.0000.9980.9980.9980.998
hotel_grad1.0001.0000.0520.0290.0000.0000.0001.0001.0000.9550.4760.4760.4760.476
tel_no1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
homepage_url1.0001.0001.0001.0000.9701.0001.0000.9551.0001.0001.0001.0001.0001.000
asia_prefer_trrsrt_nm1.0001.0000.9960.9960.9160.9870.9980.4761.0001.0001.0001.0001.0001.000
europe_prefer_trrsrt_nm1.0001.0000.9960.9960.9160.9870.9980.4761.0001.0001.0001.0001.0001.000
america_prefer_trrsrt_nm1.0001.0000.9960.9960.9160.9870.9980.4761.0001.0001.0001.0001.0001.000
middle_prefer_trrsrt_nm1.0001.0000.9960.9960.9160.9870.9980.4761.0001.0001.0001.0001.0001.000
2023-12-10T19:03:20.860842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
hotel_gradarea_nmmiddle_prefer_trrsrt_nmeurope_prefer_trrsrt_nmasia_prefer_trrsrt_nmamerica_prefer_trrsrt_nm
hotel_grad1.0000.0000.1850.1850.1850.185
area_nm0.0001.0000.8150.8150.8150.815
middle_prefer_trrsrt_nm0.1850.8151.0001.0001.0001.000
europe_prefer_trrsrt_nm0.1850.8151.0001.0001.0001.000
asia_prefer_trrsrt_nm0.1850.8151.0001.0001.0001.000
america_prefer_trrsrt_nm0.1850.8151.0001.0001.0001.000
2023-12-10T19:03:21.086386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
city_do_cdcity_gn_gu_cdxpos_loypos_laarea_nmhotel_gradasia_prefer_trrsrt_nmeurope_prefer_trrsrt_nmamerica_prefer_trrsrt_nmmiddle_prefer_trrsrt_nm
city_do_cd1.0000.934-0.208-0.8180.9890.0170.8000.8000.8000.800
city_gn_gu_cd0.9341.000-0.156-0.8630.9890.0170.8000.8000.8000.800
xpos_lo-0.208-0.1561.000-0.0430.7170.0000.5500.5500.5500.550
ypos_la-0.818-0.863-0.0431.0000.9320.0000.7580.7580.7580.758
area_nm0.9890.9890.7170.9321.0000.0000.8150.8150.8150.815
hotel_grad0.0170.0170.0000.0000.0001.0000.1850.1850.1850.185
asia_prefer_trrsrt_nm0.8000.8000.5500.7580.8150.1851.0001.0001.0001.000
europe_prefer_trrsrt_nm0.8000.8000.5500.7580.8150.1851.0001.0001.0001.000
america_prefer_trrsrt_nm0.8000.8000.5500.7580.8150.1851.0001.0001.0001.000
middle_prefer_trrsrt_nm0.8000.8000.5500.7580.8150.1851.0001.0001.0001.000

Missing values

2023-12-10T19:03:09.254103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:03:09.610083image/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.
2023-12-10T19:03:09.818164image/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_addrcity_do_cdcity_gn_gu_cdxpos_loypos_laarea_nmhotel_gradtel_nohomepage_urlasia_prefer_trrsrt_nmeurope_prefer_trrsrt_nmamerica_prefer_trrsrt_nmmiddle_prefer_trrsrt_nmbase_ymd
0E호텔서울특별시 서초구 반포대로18길 40 E 호텔1111650127.01260837.486972서울302-521-6555http://www.e-hotel.kr/강화도강화도강화도강화도2019-12-09
1호텔시에나경기도 파주시 소리천로 314141480126.76187237.715623경기3031-943-7260http://www.hotelsienna.com/수원화성수원화성수원화성수원화성2019-12-09
2나인트리프리미어호텔명동2서울특별시 중구 마른내로 281111140126.99081937.564337서울402-6967-0999http://www.ninetreehotels.com/nth2/강남역거리강남역거리강남역거리강남역거리2019-12-09
3나인트리호텔명동서울특별시 종로구 인사동길 491111110126.98355337.574467서울302-750-0999https://ninetreehotel.com/강남역거리강남역거리강남역거리강남역거리2019-12-09
4라마다서울동대문서울특별시 중구 동호로 3541111140127.002837.5659서울302-2276-3500http://www.ramadaddm.com/main/강남역거리강남역거리강남역거리강남역거리2019-12-09
5라마다앙코르서울마곡서울특별시 강서구 마곡중앙로 161-11 힐스테이트에코마곡나루역라마다앙코르서울특별시1111500126.82672237.567976서울302-2161-9000http://www.ramadaencore-seoulmagok.com/강남역거리강남역거리강남역거리강남역거리2019-12-09
6라마다앙코르제주서귀포제주특별자치도 서귀포시 서호중로 555050130126.51926533.254477제주4064-735-2000https://www.ramadaencorejejuseogwipo.com/성산일출봉성산일출봉성산일출봉성산일출봉2019-12-09
7호텔이루다경기도 안양시 만안구 만안로 714141171126.93293137.389511경기3031-443-2700http://www.hoteliruda.com/수원화성수원화성수원화성수원화성2019-12-09
8라비타호텔서울특별시 강남구 영동대로 7121111680127.05688437.521414서울202-545-0015<NA>강남역거리강남역거리강남역거리강남역거리2019-12-09
9레지던스 강남서울특별시 강남구 학동로4길 151111680127.02353637.510515서울<NA>02-3485-3100<NA>여의도(63빌딩)여의도(63빌딩)여의도(63빌딩)여의도(63빌딩)2019-12-09
entrp_nmload_addrcity_do_cdcity_gn_gu_cdxpos_loypos_laarea_nmhotel_gradtel_nohomepage_urlasia_prefer_trrsrt_nmeurope_prefer_trrsrt_nmamerica_prefer_trrsrt_nmmiddle_prefer_trrsrt_nmbase_ymd
90프린세스 호텔서울특별시 강남구 압구정로46길 171111680127.03663937.52739서울202-544-0366http://www.princesshotel.co.kr/xe/page_nRij55이태원관광특구이태원관광특구이태원관광특구남산N서울타워2019-12-09
91필로스 호텔경북 포항시 북구 죽파로 64747113129.35736436.029035경북4054-250-2000http://www.philoshotel.kr/차이나타운부산역차이나타운부산역2019-12-09
92한강 호텔서울특별시 광진구 광장동 188-21111215127.03663937.52739서울4<NA><NA>이태원관광특구이태원관광특구이태원관광특구남산N서울타워2019-12-09
93해담채 가산서울특별시 금천구 벚꽃로56길 1901111545126.88872737.4795서울302-853-7111https://www.hotelhaedamchae.com/은평한옥마을은평한옥마을은평한옥마을은평한옥마을2019-12-09
94해담채 스테이서울특별시 강서구 곰달래로 70-11111500126.84059637.530015서울1070-4632-3500http://www.haedamchaestay.com/경복궁, 인사동 일대경복궁, 인사동 일대경복궁, 인사동 일대경복궁2019-12-09
95해운대 비지니스호텔S부산광역시 해운대구 구남로8번길 492626350129.15692435.1616부산3051-741-5009http://www.businesshotelshaeundae.com/해운대해수욕장해운대해수욕장해운대해수욕장해운대해수욕장2019-12-09
96해운대골든튤립부산광역시 해운대구 해운대해변로 3222626350129.16587935.162277부산4051-795-7000http://www.goldentulip-haeundae.com/강남역거리강남역거리강남역거리강남역거리2019-12-09
97호매실 호텔경기도 수원시 권선구 금곡로 197번길 17-104141113126.95131837.274486경기2<NA><NA>안양예술공원안양예술공원안양예술공원안양예술공원2019-12-09
98호메르스 호텔부산광역시 수영구 광안해변로 217 호메르스관광호텔2626500129.1179835.153426부산3051-750-8000http://www.homershotel.com/강남역거리강남역거리강남역거리강남역거리2019-12-09
99호텔 노블레스 서울서울특별시 노원구 노해로77길 22 노블레스관광호텔1111350127.0593537.655194서울102-558-1202http://www.noblesse.com/home/main.php#경복궁, 인사동 일대경복궁, 인사동 일대경복궁, 인사동 일대경복궁2019-12-09