Overview

Dataset statistics

Number of variables12
Number of observations100
Missing cells23
Missing cells (%)1.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.7 KiB
Average record size in memory99.3 B

Variable types

Categorical6
Text4
Numeric2

Alerts

eng_lang_area_nm has constant value ""Constant
kor_lang_area_nm has constant value ""Constant
jan_lang_area_nm has constant value ""Constant
chg_lang_area_nm has constant value ""Constant
BASE_YMD has constant value ""Constant
lo is highly overall correlated with la and 1 other fieldsHigh correlation
la is highly overall correlated with loHigh correlation
se_nm is highly overall correlated with loHigh correlation
se_nm is highly imbalanced (58.7%)Imbalance
eng_lang_hotel_nm has 14 (14.0%) missing valuesMissing
tel_no has 7 (7.0%) missing valuesMissing
kor_lang_hotel_nm has unique valuesUnique
rn_adres has unique valuesUnique

Reproduction

Analysis started2023-12-10 09:39:10.021041
Analysis finished2023-12-10 09:39:13.893347
Duration3.87 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

se_nm
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
호텔
81 
게스트하우스
13 
기타
 
3
료칸
 
2
모텔
 
1

Length

Max length6
Median length2
Mean length2.52
Min length2

Unique

Unique1 ?
Unique (%)1.0%

Sample

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

Common Values

ValueCountFrequency (%)
호텔 81
81.0%
게스트하우스 13
 
13.0%
기타 3
 
3.0%
료칸 2
 
2.0%
모텔 1
 
1.0%

Length

2023-12-10T18:39:14.017302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:39:14.188288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
호텔 81
81.0%
게스트하우스 13
 
13.0%
기타 3
 
3.0%
료칸 2
 
2.0%
모텔 1
 
1.0%

eng_lang_hotel_nm
Text

MISSING 

Distinct86
Distinct (%)100.0%
Missing14
Missing (%)14.0%
Memory size932.0 B
2023-12-10T18:39:14.701035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length42
Median length32
Mean length24.418605
Min length8

Characters and Unicode

Total characters2100
Distinct characters57
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

Unique86 ?
Unique (%)100.0%

Sample

1st rowHotel Sunroute Namba
2nd rowDormy Inn Premium Namba Natural Hot Spring
3rd rowOsaka Fujiya Hotel
4th rowHilton Okinawa Chatan Resort
5th rowSun White Hotel
ValueCountFrequency (%)
hotel 55
 
16.6%
osaka 15
 
4.5%
okinawa 9
 
2.7%
inn 9
 
2.7%
resort 8
 
2.4%
namba 8
 
2.4%
hakata 8
 
2.4%
7
 
2.1%
fukuoka 7
 
2.1%
spring 6
 
1.8%
Other values (142) 200
60.2%
2023-12-10T18:39:15.416821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
246
 
11.7%
a 214
 
10.2%
o 166
 
7.9%
e 160
 
7.6%
t 144
 
6.9%
n 100
 
4.8%
i 96
 
4.6%
l 86
 
4.1%
r 85
 
4.0%
H 82
 
3.9%
Other values (47) 721
34.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1499
71.4%
Uppercase Letter 337
 
16.0%
Space Separator 246
 
11.7%
Other Punctuation 9
 
0.4%
Other Letter 5
 
0.2%
Dash Punctuation 4
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 214
14.3%
o 166
11.1%
e 160
10.7%
t 144
9.6%
n 100
 
6.7%
i 96
 
6.4%
l 86
 
5.7%
r 85
 
5.7%
s 82
 
5.5%
k 72
 
4.8%
Other values (14) 294
19.6%
Uppercase Letter
ValueCountFrequency (%)
H 82
24.3%
N 37
11.0%
O 34
10.1%
S 32
 
9.5%
C 19
 
5.6%
M 17
 
5.0%
I 15
 
4.5%
R 13
 
3.9%
T 11
 
3.3%
F 10
 
3.0%
Other values (13) 67
19.9%
Other Letter
ValueCountFrequency (%)
別 1
20.0%
石 1
20.0%
水 1
20.0%
亭 1
20.0%
登 1
20.0%
Other Punctuation
ValueCountFrequency (%)
& 7
77.8%
/ 1
 
11.1%
. 1
 
11.1%
Space Separator
ValueCountFrequency (%)
246
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1836
87.4%
Common 259
 
12.3%
Han 5
 
0.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 214
 
11.7%
o 166
 
9.0%
e 160
 
8.7%
t 144
 
7.8%
n 100
 
5.4%
i 96
 
5.2%
l 86
 
4.7%
r 85
 
4.6%
H 82
 
4.5%
s 82
 
4.5%
Other values (37) 621
33.8%
Common
ValueCountFrequency (%)
246
95.0%
& 7
 
2.7%
- 4
 
1.5%
/ 1
 
0.4%
. 1
 
0.4%
Han
ValueCountFrequency (%)
別 1
20.0%
石 1
20.0%
水 1
20.0%
亭 1
20.0%
登 1
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2095
99.8%
CJK 5
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
246
 
11.7%
a 214
 
10.2%
o 166
 
7.9%
e 160
 
7.6%
t 144
 
6.9%
n 100
 
4.8%
i 96
 
4.6%
l 86
 
4.1%
r 85
 
4.1%
H 82
 
3.9%
Other values (42) 716
34.2%
CJK
ValueCountFrequency (%)
別 1
20.0%
石 1
20.0%
水 1
20.0%
亭 1
20.0%
登 1
20.0%

kor_lang_hotel_nm
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T18:39:16.069812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length17
Mean length12.89
Min length4

Characters and Unicode

Total characters1289
Distinct characters186
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 row호텔 선루트 오사카 난바
2nd row도미 인 프리미엄 난바 내추럴 핫 스프링
3rd row오사카 후지야호텔
4th row힐튼 오키나와 차탄 리조트
5th row호텔 선 화이트
ValueCountFrequency (%)
호텔 48
 
13.6%
오사카 17
 
4.8%
오키나와 10
 
2.8%
하카타 10
 
2.8%
리조트 7
 
2.0%
인 7
 
2.0%
난바 7
 
2.0%
후쿠오카 6
 
1.7%
나하 5
 
1.4%
핫 5
 
1.4%
Other values (185) 231
65.4%
2023-12-10T18:39:17.070443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
253
 
19.6%
호 63
 
4.9%
텔 63
 
4.9%
스 53
 
4.1%
오 45
 
3.5%
카 40
 
3.1%
이 38
 
2.9%
리 29
 
2.2%
트 28
 
2.2%
나 25
 
1.9%
Other values (176) 652
50.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1020
79.1%
Space Separator 253
 
19.6%
Other Punctuation 7
 
0.5%
Uppercase Letter 6
 
0.5%
Dash Punctuation 2
 
0.2%
Decimal Number 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
호 63
 
6.2%
텔 63
 
6.2%
스 53
 
5.2%
오 45
 
4.4%
카 40
 
3.9%
이 38
 
3.7%
리 29
 
2.8%
트 28
 
2.7%
나 25
 
2.5%
사 25
 
2.5%
Other values (164) 611
59.9%
Uppercase Letter
ValueCountFrequency (%)
M 1
16.7%
E 1
16.7%
R 1
16.7%
G 1
16.7%
B 1
16.7%
S 1
16.7%
Other Punctuation
ValueCountFrequency (%)
& 3
42.9%
, 3
42.9%
. 1
 
14.3%
Space Separator
ValueCountFrequency (%)
253
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%
Decimal Number
ValueCountFrequency (%)
2 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1020
79.1%
Common 263
 
20.4%
Latin 6
 
0.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
호 63
 
6.2%
텔 63
 
6.2%
스 53
 
5.2%
오 45
 
4.4%
카 40
 
3.9%
이 38
 
3.7%
리 29
 
2.8%
트 28
 
2.7%
나 25
 
2.5%
사 25
 
2.5%
Other values (164) 611
59.9%
Common
ValueCountFrequency (%)
253
96.2%
& 3
 
1.1%
, 3
 
1.1%
- 2
 
0.8%
2 1
 
0.4%
. 1
 
0.4%
Latin
ValueCountFrequency (%)
M 1
16.7%
E 1
16.7%
R 1
16.7%
G 1
16.7%
B 1
16.7%
S 1
16.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1020
79.1%
ASCII 269
 
20.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
253
94.1%
& 3
 
1.1%
, 3
 
1.1%
- 2
 
0.7%
M 1
 
0.4%
E 1
 
0.4%
R 1
 
0.4%
G 1
 
0.4%
2 1
 
0.4%
B 1
 
0.4%
Other values (2) 2
 
0.7%
Hangul
ValueCountFrequency (%)
호 63
 
6.2%
텔 63
 
6.2%
스 53
 
5.2%
오 45
 
4.4%
카 40
 
3.9%
이 38
 
3.7%
리 29
 
2.8%
트 28
 
2.7%
나 25
 
2.5%
사 25
 
2.5%
Other values (164) 611
59.9%

eng_lang_area_nm
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
Japan
100 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
Japan 100
100.0%

Length

2023-12-10T18:39:17.267251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:39:17.403252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
japan 100
100.0%

kor_lang_area_nm
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
일본
100 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
일본 100
100.0%

Length

2023-12-10T18:39:17.556071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:39:17.680755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
일본 100
100.0%

jan_lang_area_nm
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
日本
100 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row日本
2nd row日本
3rd row日本
4th row日本
5th row日本

Common Values

ValueCountFrequency (%)
日本 100
100.0%

Length

2023-12-10T18:39:17.826330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:39:17.960185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
日本 100
100.0%

chg_lang_area_nm
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
日本
100 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row日本
2nd row日本
3rd row日本
4th row日本
5th row日本

Common Values

ValueCountFrequency (%)
日本 100
100.0%

Length

2023-12-10T18:39:18.143381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:39:18.295275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
日本 100
100.0%

rn_adres
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T18:39:18.573946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length99
Median length70.5
Mean length60.17
Min length22

Characters and Unicode

Total characters6017
Distinct characters249
Distinct categories10 ?
Distinct scripts6 ?
Distinct blocks7 ?
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 row일본 〒542-0073 Ōsaka-fu, Ōsaka-shi, Chūō-ku, Nipponbashi, 1 Chome−1−１３ ホテルサンルート大阪なんば
2nd row일본 〒542-0082 Ōsaka-fu, Ōsaka-shi, Chūō-ku, Shimanouchi, 2 Chome−14−２３ 天然温泉夕霧の湯
3rd row2 Chome-2-2 Higashishinsaibashi, Chūō-ku, Ōsaka-shi, Ōsaka-fu 542-0083 일본
4th row40 Mihama, Chatan-chō, Nakagami-gun, Okinawa-ken 904-0115 일본
5th row3 Chome-7-4 Tanimachi, Chūō-ku, Ōsaka-shi, Ōsaka-fu 540-0012 일본
ValueCountFrequency (%)
일본 100
 
12.6%
2 28
 
3.5%
chūō-ku 28
 
3.5%
ōsaka-fu 26
 
3.3%
ōsaka-shi 25
 
3.2%
1 19
 
2.4%
시 17
 
2.1%
okinawa-ken 16
 
2.0%
fukuoka-ken 15
 
1.9%
fukuoka-shi 15
 
1.9%
Other values (328) 503
63.5%
2023-12-10T18:39:19.258183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
692
 
11.5%
a 415
 
6.9%
- 382
 
6.3%
k 294
 
4.9%
i 273
 
4.5%
, 261
 
4.3%
h 250
 
4.2%
u 214
 
3.6%
s 195
 
3.2%
0 178
 
3.0%
Other values (239) 2863
47.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2678
44.5%
Decimal Number 810
 
13.5%
Other Letter 745
 
12.4%
Space Separator 692
 
11.5%
Uppercase Letter 385
 
6.4%
Dash Punctuation 382
 
6.3%
Other Punctuation 263
 
4.4%
Math Symbol 31
 
0.5%
Other Symbol 24
 
0.4%
Modifier Letter 7
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
본 100
 
13.4%
일 100
 
13.4%
오 35
 
4.7%
카 30
 
4.0%
시 23
 
3.1%
ル 18
 
2.4%
사 15
 
2.0%
나 14
 
1.9%
현 13
 
1.7%
와 13
 
1.7%
Other values (167) 384
51.5%
Uppercase Letter
ValueCountFrequency (%)
C 94
24.4%
Ō 54
14.0%
F 33
 
8.6%
H 32
 
8.3%
N 32
 
8.3%
K 31
 
8.1%
O 29
 
7.5%
S 19
 
4.9%
M 14
 
3.6%
T 12
 
3.1%
Other values (14) 35
 
9.1%
Lowercase Letter
ValueCountFrequency (%)
a 415
15.5%
k 294
11.0%
i 273
10.2%
h 250
9.3%
u 214
8.0%
s 195
7.3%
o 174
 
6.5%
n 163
 
6.1%
e 149
 
5.6%
m 116
 
4.3%
Other values (13) 435
16.2%
Decimal Number
ValueCountFrequency (%)
0 178
22.0%
1 147
18.1%
2 109
13.5%
5 75
9.3%
4 74
9.1%
3 64
 
7.9%
8 44
 
5.4%
6 37
 
4.6%
7 33
 
4.1%
9 32
 
4.0%
Other values (7) 17
 
2.1%
Other Punctuation
ValueCountFrequency (%)
, 261
99.2%
＆ 1
 
0.4%
・ 1
 
0.4%
Space Separator
ValueCountFrequency (%)
692
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 382
100.0%
Math Symbol
ValueCountFrequency (%)
− 31
100.0%
Other Symbol
ValueCountFrequency (%)
〒 24
100.0%
Modifier Letter
ValueCountFrequency (%)
ー 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3063
50.9%
Common 2209
36.7%
Hangul 484
 
8.0%
Katakana 133
 
2.2%
Han 106
 
1.8%
Hiragana 22
 
0.4%

Most frequent character per script

Han
ValueCountFrequency (%)
大 4
 
3.8%
号 4
 
3.8%
阪 4
 
3.8%
目 3
 
2.8%
丁 3
 
2.8%
泉 3
 
2.8%
温 3
 
2.8%
館 2
 
1.9%
宿 2
 
1.9%
覇 2
 
1.9%
Other values (63) 76
71.7%
Hangul
ValueCountFrequency (%)
본 100
20.7%
일 100
20.7%
오 35
 
7.2%
카 30
 
6.2%
시 23
 
4.8%
사 15
 
3.1%
나 14
 
2.9%
현 13
 
2.7%
와 13
 
2.7%
구 11
 
2.3%
Other values (39) 130
26.9%
Latin
ValueCountFrequency (%)
a 415
13.5%
k 294
 
9.6%
i 273
 
8.9%
h 250
 
8.2%
u 214
 
7.0%
s 195
 
6.4%
o 174
 
5.7%
n 163
 
5.3%
e 149
 
4.9%
m 116
 
3.8%
Other values (37) 820
26.8%
Katakana
ValueCountFrequency (%)
ル 18
 
13.5%
テ 13
 
9.8%
ホ 11
 
8.3%
ス 9
 
6.8%
イ 7
 
5.3%
ン 6
 
4.5%
ト 6
 
4.5%
ビ 5
 
3.8%
リ 4
 
3.0%
ラ 4
 
3.0%
Other values (31) 50
37.6%
Common
ValueCountFrequency (%)
692
31.3%
- 382
17.3%
, 261
 
11.8%
0 178
 
8.1%
1 147
 
6.7%
2 109
 
4.9%
5 75
 
3.4%
4 74
 
3.3%
3 64
 
2.9%
8 44
 
2.0%
Other values (15) 183
 
8.3%
Hiragana
ValueCountFrequency (%)
の 3
13.6%
ろ 3
13.6%
く 2
9.1%
な 2
9.1%
ば 2
9.1%
ん 2
9.1%
お 1
 
4.5%
ち 1
 
4.5%
ま 1
 
4.5%
も 1
 
4.5%
Other values (4) 4
18.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5017
83.4%
Hangul 484
 
8.0%
None 216
 
3.6%
Katakana 141
 
2.3%
CJK 106
 
1.8%
Math Operators 31
 
0.5%
Hiragana 22
 
0.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
692
 
13.8%
a 415
 
8.3%
- 382
 
7.6%
k 294
 
5.9%
i 273
 
5.4%
, 261
 
5.2%
h 250
 
5.0%
u 214
 
4.3%
s 195
 
3.9%
0 178
 
3.5%
Other values (45) 1863
37.1%
Hangul
ValueCountFrequency (%)
본 100
20.7%
일 100
20.7%
오 35
 
7.2%
카 30
 
6.2%
시 23
 
4.8%
사 15
 
3.1%
나 14
 
2.9%
현 13
 
2.7%
와 13
 
2.7%
구 11
 
2.3%
Other values (39) 130
26.9%
None
ValueCountFrequency (%)
ō 88
40.7%
Ō 54
25.0%
ū 30
 
13.9%
〒 24
 
11.1%
２ 4
 
1.9%
１ 3
 
1.4%
５ 3
 
1.4%
６ 3
 
1.4%
３ 2
 
0.9%
＆ 1
 
0.5%
Other values (4) 4
 
1.9%
Math Operators
ValueCountFrequency (%)
− 31
100.0%
Katakana
ValueCountFrequency (%)
ル 18
 
12.8%
テ 13
 
9.2%
ホ 11
 
7.8%
ス 9
 
6.4%
イ 7
 
5.0%
ー 7
 
5.0%
ン 6
 
4.3%
ト 6
 
4.3%
ビ 5
 
3.5%
リ 4
 
2.8%
Other values (33) 55
39.0%
CJK
ValueCountFrequency (%)
大 4
 
3.8%
号 4
 
3.8%
阪 4
 
3.8%
目 3
 
2.8%
丁 3
 
2.8%
泉 3
 
2.8%
温 3
 
2.8%
館 2
 
1.9%
宿 2
 
1.9%
覇 2
 
1.9%
Other values (63) 76
71.7%
Hiragana
ValueCountFrequency (%)
の 3
13.6%
ろ 3
13.6%
く 2
9.1%
な 2
9.1%
ば 2
9.1%
ん 2
9.1%
お 1
 
4.5%
ち 1
 
4.5%
ま 1
 
4.5%
も 1
 
4.5%
Other values (4) 4
18.2%

lo
Real number (ℝ)

HIGH CORRELATION 

Distinct97
Distinct (%)98.0%
Missing1
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean133.1788
Minimum127.64041
Maximum141.35708
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:39:19.481772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum127.64041
5-th percentile127.69425
Q1130.39573
median135.48862
Q3135.50631
95-th percentile141.14403
Maximum141.35708
Range13.716662
Interquartile range (IQR)5.1105767

Descriptive statistics

Standard deviation4.190212
Coefficient of variation (CV)0.031463056
Kurtosis-0.95409453
Mean133.1788
Median Absolute Deviation (MAD)4.3047504
Skewness0.2206638
Sum13184.701
Variance17.557877
MonotonicityNot monotonic
2023-12-10T18:39:20.021700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.7567273 2
 
2.0%
141.1440337 2
 
2.0%
135.5062901 1
 
1.0%
141.3370059 1
 
1.0%
130.3968076 1
 
1.0%
130.4025448 1
 
1.0%
130.4197392 1
 
1.0%
127.678168 1
 
1.0%
135.4982514 1
 
1.0%
139.7685053 1
 
1.0%
Other values (87) 87
87.0%
ValueCountFrequency (%)
127.6404145 1
1.0%
127.6761642 1
1.0%
127.678168 1
1.0%
127.680166 1
1.0%
127.6859595 1
1.0%
127.6951687 1
1.0%
127.697698 1
1.0%
127.7067909 1
1.0%
127.7334956 1
1.0%
127.7558047 1
1.0%
ValueCountFrequency (%)
141.3570763 1
1.0%
141.3557659 1
1.0%
141.3487327 1
1.0%
141.3370059 1
1.0%
141.1440337 2
2.0%
140.9948291 1
1.0%
139.7933711 1
1.0%
139.7685053 1
1.0%
139.7228219 1
1.0%
139.7025527 1
1.0%

la
Real number (ℝ)

HIGH CORRELATION 

Distinct97
Distinct (%)98.0%
Missing1
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean33.129291
Minimum26.176547
Maximum43.198447
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:39:20.274040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum26.176547
5-th percentile26.21893
Q133.264774
median34.658349
Q334.671291
95-th percentile42.549468
Maximum43.198447
Range17.021901
Interquartile range (IQR)1.4065176

Descriptive statistics

Standard deviation4.3612313
Coefficient of variation (CV)0.13164276
Kurtosis0.30219438
Mean33.129291
Median Absolute Deviation (MAD)1.0646424
Skewness-0.038665659
Sum3279.7998
Variance19.020339
MonotonicityNot monotonic
2023-12-10T18:39:20.503493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26.3157855 2
 
2.0%
42.4945528 2
 
2.0%
34.6683088 1
 
1.0%
43.0537709 1
 
1.0%
33.5921749 1
 
1.0%
33.5925045 1
 
1.0%
33.5925209 1
 
1.0%
26.213362 1
 
1.0%
34.6698801 1
 
1.0%
35.6972552 1
 
1.0%
Other values (87) 87
87.0%
ValueCountFrequency (%)
26.1765465 1
1.0%
26.206835 1
1.0%
26.213362 1
1.0%
26.2144384 1
1.0%
26.2157869 1
1.0%
26.2192788 1
1.0%
26.222995 1
1.0%
26.2252346 1
1.0%
26.273873 1
1.0%
26.3064071 1
1.0%
ValueCountFrequency (%)
43.1984473 1
1.0%
43.0570961 1
1.0%
43.0558198 1
1.0%
43.0537709 1
1.0%
43.043703 1
1.0%
42.4945528 2
2.0%
35.7038754 1
1.0%
35.6972552 1
1.0%
35.695275 1
1.0%
35.686809 1
1.0%

tel_no
Text

MISSING 

Distinct93
Distinct (%)100.0%
Missing7
Missing (%)7.0%
Memory size932.0 B
2023-12-10T18:39:20.856767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length12
Mean length13.086022
Min length10

Characters and Unicode

Total characters1217
Distinct characters16
Distinct categories6 ?
Distinct scripts1 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique93 ?
Unique (%)100.0%

Sample

1st row+81-6-6211-3610
2nd row+6-6214-5489
3rd row06-6211-5522
4th row098-901-1111
5th row06-6942-3711
ValueCountFrequency (%)
81 22
 
18.0%
– 3
 
2.5%
092-433-2305 1
 
0.8%
011-232-11 1
 
0.8%
092-712-5858 1
 
0.8%
098-866-3811 1
 
0.8%
80-675-881 1
 
0.8%
06-6211-8151 1
 
0.8%
3-5289-3939 1
 
0.8%
098-935-1500 1
 
0.8%
Other values (89) 89
73.0%
2023-12-10T18:39:21.433868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 205
16.8%
- 189
15.5%
8 122
10.0%
0 111
9.1%
6 96
7.9%
9 87
7.1%
2 86
7.1%
7 67
 
5.5%
3 65
 
5.3%
5 63
 
5.2%
Other values (6) 126
10.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 954
78.4%
Dash Punctuation 192
 
15.8%
Math Symbol 40
 
3.3%
Space Separator 29
 
2.4%
Close Punctuation 1
 
0.1%
Other Punctuation 1
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 205
21.5%
8 122
12.8%
0 111
11.6%
6 96
10.1%
9 87
9.1%
2 86
9.0%
7 67
 
7.0%
3 65
 
6.8%
5 63
 
6.6%
4 52
 
5.5%
Dash Punctuation
ValueCountFrequency (%)
- 189
98.4%
– 3
 
1.6%
Math Symbol
ValueCountFrequency (%)
+ 40
100.0%
Space Separator
ValueCountFrequency (%)
29
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1217
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 205
16.8%
- 189
15.5%
8 122
10.0%
0 111
9.1%
6 96
7.9%
9 87
7.1%
2 86
7.1%
7 67
 
5.5%
3 65
 
5.3%
5 63
 
5.2%
Other values (6) 126
10.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1214
99.8%
Punctuation 3
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 205
16.9%
- 189
15.6%
8 122
10.0%
0 111
9.1%
6 96
7.9%
9 87
7.2%
2 86
7.1%
7 67
 
5.5%
3 65
 
5.4%
5 63
 
5.2%
Other values (5) 123
10.1%
Punctuation
ValueCountFrequency (%)
– 3
100.0%

BASE_YMD
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2020-12-09
100 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020-12-09
2nd row2020-12-09
3rd row2020-12-09
4th row2020-12-09
5th row2020-12-09

Common Values

ValueCountFrequency (%)
2020-12-09 100
100.0%

Length

2023-12-10T18:39:21.680994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:39:21.819137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020-12-09 100
100.0%

Interactions

2023-12-10T18:39:12.587821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:39:12.006663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:39:12.784815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:39:12.375242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T18:39:21.924818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
se_nmeng_lang_hotel_nmkor_lang_hotel_nmrn_adreslolatel_no
se_nm1.0001.0001.0001.0000.6870.2031.000
eng_lang_hotel_nm1.0001.0001.0001.0001.0001.0001.000
kor_lang_hotel_nm1.0001.0001.0001.0001.0001.0001.000
rn_adres1.0001.0001.0001.0001.0001.0001.000
lo0.6871.0001.0001.0001.0000.9941.000
la0.2031.0001.0001.0000.9941.0001.000
tel_no1.0001.0001.0001.0001.0001.0001.000
2023-12-10T18:39:22.101867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
lolase_nm
lo1.0000.9210.504
la0.9211.0000.165
se_nm0.5040.1651.000

Missing values

2023-12-10T18:39:13.025135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T18:39:13.431902image/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.
2023-12-10T18:39:13.766722image/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

se_nmeng_lang_hotel_nmkor_lang_hotel_nmeng_lang_area_nmkor_lang_area_nmjan_lang_area_nmchg_lang_area_nmrn_adreslolatel_noBASE_YMD
0호텔Hotel Sunroute Namba호텔 선루트 오사카 난바Japan일본日本日本일본 〒542-0073 Ōsaka-fu, Ōsaka-shi, Chūō-ku, Nipponbashi, 1 Chome−1−１３ ホテルサンルート大阪なんば135.5062934.668309+81-6-6211-36102020-12-09
1호텔Dormy Inn Premium Namba Natural Hot Spring도미 인 프리미엄 난바 내추럴 핫 스프링Japan일본日本日本일본 〒542-0082 Ōsaka-fu, Ōsaka-shi, Chūō-ku, Shimanouchi, 2 Chome−14−２３ 天然温泉夕霧の湯135.50642334.670992+6-6214-54892020-12-09
2호텔Osaka Fujiya Hotel오사카 후지야호텔Japan일본日本日本2 Chome-2-2 Higashishinsaibashi, Chūō-ku, Ōsaka-shi, Ōsaka-fu 542-0083 일본135.50592634.67088906-6211-55222020-12-09
3호텔Hilton Okinawa Chatan Resort힐튼 오키나와 차탄 리조트Japan일본日本日本40 Mihama, Chatan-chō, Nakagami-gun, Okinawa-ken 904-0115 일본127.75672726.315786098-901-11112020-12-09
4호텔Sun White Hotel호텔 선 화이트Japan일본日本日本3 Chome-7-4 Tanimachi, Chūō-ku, Ōsaka-shi, Ōsaka-fu 540-0012 일본135.51699734.68422606-6942-37112020-12-09
5호텔Canal City Fukuoka Washington Hotel캐널 시티 워싱턴 호텔Japan일본日本日本1 Chome-2-20 Sumiyoshi, Hakata-ku, Fukuoka-shi, Fukuoka-ken 812-0018 일본130.40889533.581859092-282-88002020-12-09
6호텔Hotel Orion Motobu Resort & Spa오리온 모토부 리조트 앤 스파Japan일본日本日本148 Bise, Motobu-chō, Kunigami-gun, Okinawa-ken, 일본127.88173426.7011780980-51-73002020-12-09
7호텔<NA>리잔시파크 호텔 탄챠베이Japan일본日本日本일본 Okinawa-ken, Kunigami-gun, Onna-son 国道58号線127.84310226.479278098-964-66112020-12-09
8호텔the Beach Tower Okinawa Hotel더 비치 타워 오키나와 호텔Japan일본日本日本일본 오키나와 현 나카가미 군 자탄 초 미하마 8−6127.7561526.314133098-921-77112020-12-09
9호텔<NA>레드 루프 플러스 오사카 남바Japan일본日本日本일본 〒542-0074 Ōsaka-fu, Ōsaka-shi, Chūō-ku, Sennichimae, 1 Chome−9−7, ライブハウスアナザードリーム135.50394634.66751+81 6-6484-03392020-12-09
se_nmeng_lang_hotel_nmkor_lang_hotel_nmeng_lang_area_nmkor_lang_area_nmjan_lang_area_nmchg_lang_area_nmrn_adreslolatel_noBASE_YMD
90호텔Hotel Mercure Sapporo머큐어 호텔 삿포로Japan일본日本日本2 Chome-2-4 Minami 4 Jōnishi, Chūō-ku, Sapporo-shi, Hokkaidō, 일본141.35576643.05582011-513-11002020-12-09
91호텔Daiwa Roynet Hotel Naha Omoromachi다이와 로이네트 호텔 나하 오모로마치Japan일본日本日本일본 〒900-0006 Okinawa-ken, Naha-shi, Omoromachi, 1 Chome−1 ダイワロイネットホテル那覇おもろまち127.69516926.222995098-862-45552020-12-09
92호텔Hakata Miyako Hotel하카타 미야코 호텔Japan일본日本日本2 Chome-1-1 Hakataekihigashi, Hakata-ku, Fukuoka-shi, Fukuoka-ken 812-0013 일본130.42282233.5897692-441-31112020-12-09
93호텔Hotel Moon Beach Okinawa호텔 문비치 오키나와Japan일본日本日本1203 Maeganeku, Onna-son, Kunigami-gun, Okinawa-ken, 일본127.80271326.450762+81 98-964-35122020-12-09
94호텔Hotel Taiyo호텔 타이요Japan일본日本日本1 Chome-2-23 Taishi, Nishinari-ku, Ōsaka-shi, Ōsaka-fu, 일본135.50477634.648264<NA>2020-12-09
95호텔Dormy Inn Premium Otaru Natural Hot Spring도미 인 프리미움 오타루 핫 스프링Japan일본日本日本3 Chome-9-1 Inaho, Otaru-shi, Hokkaidō 047-0032 일본140.99482943.198447+81 134-21-54892020-12-09
96호텔Toko Hotel도코, 토코 호텔Japan일본日本日本일본 〒141-0031 Tōkyō-to, Shinagawa-ku, Nishigotanda, 2 Chome−6−8 ホテルマイステイズ五反田駅前139.72282235.626463+81-3-3494-10502020-12-09
97호텔Hotel Hillarys호텔 힐라리스Japan일본日本日本3 Chome-4-10 Nipponbashi, Naniwa-ku, Ōsaka-shi, Ōsaka-fu 556-0005 일본135.50613534.661463<NA>2020-12-09
98호텔Cross Hotel Osaka크로스호텔 오사카Japan일본日本日本일본 〒542-0085 Ōsaka-fu, Ōsaka-shi, Chūō-ku, Shinsaibashisuji, 2 Chome−5−15 小林ビルディングクロスホテル大阪135.50083334.66966+81 6 6213 82812020-12-09
99호텔Kobe Meriken Park Oriental Hotel고베 메리켄파크 오리엔탈 호텔Japan일본日本日本5-6 Hatobachō, Chūō-ku, Kōbe-shi, Hyōgo-ken 650-0042 일본135.18410934.683926078-325-81112020-12-09