Overview

Dataset statistics

Number of variables12
Number of observations100
Missing cells209
Missing cells (%)17.4%
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
se_nm is highly imbalanced (91.9%)Imbalance
eng_lang_hotel_nm has 45 (45.0%) missing valuesMissing
kor_lang_hotel_nm has 54 (54.0%) missing valuesMissing
rn_adres has 2 (2.0%) missing valuesMissing
lo has 5 (5.0%) missing valuesMissing
la has 5 (5.0%) missing valuesMissing
tel_no has 98 (98.0%) missing valuesMissing

Reproduction

Analysis started2023-12-10 09:58:22.506236
Analysis finished2023-12-10 09:58:24.513675
Duration2.01 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

se_nm
Categorical

IMBALANCE 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
호텔
99 
에어비앤비
 
1

Length

Max length5
Median length2
Mean length2.03
Min length2

Unique

Unique1 ?
Unique (%)1.0%

Sample

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

Common Values

ValueCountFrequency (%)
호텔 99
99.0%
에어비앤비 1
 
1.0%

Length

2023-12-10T18:58:24.654118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:58:24.847903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
호텔 99
99.0%
에어비앤비 1
 
1.0%

eng_lang_hotel_nm
Text

MISSING 

Distinct55
Distinct (%)100.0%
Missing45
Missing (%)45.0%
Memory size932.0 B
2023-12-10T18:58:25.375474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length50
Median length38
Mean length29.854545
Min length10

Characters and Unicode

Total characters1642
Distinct characters72
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique55 ?
Unique (%)100.0%

Sample

1st row@12Haven- Stunning Seaside Luxury Villa. Sleeps 12
2nd row1000 miles
3rd row15 minutes to Kuala Lumpur City Centre
4th row1-5 pax 5mins IOI Mall LRT Cozy Apartment Puchong
5th row1805 Condo D'Savoy Homestay
ValueCountFrequency (%)
10
 
3.5%
suites 7
 
2.4%
apartment 6
 
2.1%
2br 6
 
2.1%
hotel 5
 
1.7%
homestay 5
 
1.7%
klcc 5
 
1.7%
pax 5
 
1.7%
lrt 4
 
1.4%
city 4
 
1.4%
Other values (169) 232
80.3%
2023-12-10T18:58:26.260694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
234
 
14.3%
e 121
 
7.4%
a 96
 
5.8%
t 87
 
5.3%
o 85
 
5.2%
i 71
 
4.3%
n 71
 
4.3%
r 58
 
3.5%
s 57
 
3.5%
u 46
 
2.8%
Other values (62) 716
43.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 966
58.8%
Uppercase Letter 307
 
18.7%
Space Separator 234
 
14.3%
Decimal Number 95
 
5.8%
Other Punctuation 21
 
1.3%
Dash Punctuation 12
 
0.7%
Math Symbol 3
 
0.2%
Close Punctuation 2
 
0.1%
Open Punctuation 2
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 121
12.5%
a 96
9.9%
t 87
 
9.0%
o 85
 
8.8%
i 71
 
7.3%
n 71
 
7.3%
r 58
 
6.0%
s 57
 
5.9%
u 46
 
4.8%
l 45
 
4.7%
Other values (16) 229
23.7%
Uppercase Letter
ValueCountFrequency (%)
C 34
11.1%
S 33
10.7%
A 33
10.7%
H 25
 
8.1%
L 25
 
8.1%
B 22
 
7.2%
R 18
 
5.9%
K 15
 
4.9%
P 12
 
3.9%
I 12
 
3.9%
Other values (14) 78
25.4%
Decimal Number
ValueCountFrequency (%)
2 16
16.8%
1 15
15.8%
5 13
13.7%
8 11
11.6%
3 9
9.5%
7 7
7.4%
0 7
7.4%
6 7
7.4%
4 6
 
6.3%
9 4
 
4.2%
Other Punctuation
ValueCountFrequency (%)
@ 8
38.1%
* 4
19.0%
, 3
 
14.3%
' 3
 
14.3%
/ 1
 
4.8%
& 1
 
4.8%
. 1
 
4.8%
Space Separator
ValueCountFrequency (%)
234
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 12
100.0%
Math Symbol
ValueCountFrequency (%)
| 3
100.0%
Close Punctuation
ValueCountFrequency (%)
] 2
100.0%
Open Punctuation
ValueCountFrequency (%)
[ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1273
77.5%
Common 369
 
22.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 121
 
9.5%
a 96
 
7.5%
t 87
 
6.8%
o 85
 
6.7%
i 71
 
5.6%
n 71
 
5.6%
r 58
 
4.6%
s 57
 
4.5%
u 46
 
3.6%
l 45
 
3.5%
Other values (40) 536
42.1%
Common
ValueCountFrequency (%)
234
63.4%
2 16
 
4.3%
1 15
 
4.1%
5 13
 
3.5%
- 12
 
3.3%
8 11
 
3.0%
3 9
 
2.4%
@ 8
 
2.2%
7 7
 
1.9%
0 7
 
1.9%
Other values (12) 37
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1642
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
234
 
14.3%
e 121
 
7.4%
a 96
 
5.8%
t 87
 
5.3%
o 85
 
5.2%
i 71
 
4.3%
n 71
 
4.3%
r 58
 
3.5%
s 57
 
3.5%
u 46
 
2.8%
Other values (62) 716
43.6%

kor_lang_hotel_nm
Text

MISSING 

Distinct45
Distinct (%)97.8%
Missing54
Missing (%)54.0%
Memory size932.0 B
2023-12-10T18:58:26.733504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length35
Median length19
Mean length12.173913
Min length3

Characters and Unicode

Total characters560
Distinct characters135
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique44 ?
Unique (%)95.7%

Sample

1st row1 다마이 레지던스 - 더 럭셔리 3 베드룸 스위트 앳 KLCC
2nd row1 데이 카 호텔
3rd row1 리바란 호텔
4th row1 바론 모텔
5th row1 보니오 타워 B 서비스 콘도
ValueCountFrequency (%)
호텔 28
 
15.0%
1 10
 
5.3%
인 9
 
4.8%
1st 6
 
3.2%
6
 
3.2%
부티크 4
 
2.1%
7 4
 
2.1%
알람 3
 
1.6%
게스트하우스 3
 
1.6%
샤 3
 
1.6%
Other values (95) 111
59.4%
2023-12-10T18:58:27.534805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
141
25.2%
1 32
 
5.7%
텔 30
 
5.4%
호 29
 
5.2%
스 18
 
3.2%
인 10
 
1.8%
트 10
 
1.8%
이 10
 
1.8%
티 9
 
1.6%
3 8
 
1.4%
Other values (125) 263
47.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 317
56.6%
Space Separator 141
25.2%
Decimal Number 71
 
12.7%
Lowercase Letter 12
 
2.1%
Uppercase Letter 12
 
2.1%
Other Punctuation 4
 
0.7%
Dash Punctuation 3
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
텔 30
 
9.5%
호 29
 
9.1%
스 18
 
5.7%
인 10
 
3.2%
트 10
 
3.2%
이 10
 
3.2%
티 9
 
2.8%
라 6
 
1.9%
부 6
 
1.9%
리 6
 
1.9%
Other values (104) 183
57.7%
Decimal Number
ValueCountFrequency (%)
1 32
45.1%
3 8
 
11.3%
0 7
 
9.9%
7 6
 
8.5%
9 5
 
7.0%
8 4
 
5.6%
6 3
 
4.2%
2 3
 
4.2%
5 3
 
4.2%
Uppercase Letter
ValueCountFrequency (%)
A 4
33.3%
C 2
16.7%
B 2
16.7%
F 1
 
8.3%
K 1
 
8.3%
L 1
 
8.3%
G 1
 
8.3%
Lowercase Letter
ValueCountFrequency (%)
t 6
50.0%
s 6
50.0%
Space Separator
ValueCountFrequency (%)
141
100.0%
Other Punctuation
ValueCountFrequency (%)
@ 4
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 317
56.6%
Common 219
39.1%
Latin 24
 
4.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
텔 30
 
9.5%
호 29
 
9.1%
스 18
 
5.7%
인 10
 
3.2%
트 10
 
3.2%
이 10
 
3.2%
티 9
 
2.8%
라 6
 
1.9%
부 6
 
1.9%
리 6
 
1.9%
Other values (104) 183
57.7%
Common
ValueCountFrequency (%)
141
64.4%
1 32
 
14.6%
3 8
 
3.7%
0 7
 
3.2%
7 6
 
2.7%
9 5
 
2.3%
@ 4
 
1.8%
8 4
 
1.8%
6 3
 
1.4%
- 3
 
1.4%
Other values (2) 6
 
2.7%
Latin
ValueCountFrequency (%)
t 6
25.0%
s 6
25.0%
A 4
16.7%
C 2
 
8.3%
B 2
 
8.3%
F 1
 
4.2%
K 1
 
4.2%
L 1
 
4.2%
G 1
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 317
56.6%
ASCII 243
43.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
141
58.0%
1 32
 
13.2%
3 8
 
3.3%
0 7
 
2.9%
t 6
 
2.5%
7 6
 
2.5%
s 6
 
2.5%
9 5
 
2.1%
@ 4
 
1.6%
8 4
 
1.6%
Other values (11) 24
 
9.9%
Hangul
ValueCountFrequency (%)
텔 30
 
9.5%
호 29
 
9.1%
스 18
 
5.7%
인 10
 
3.2%
트 10
 
3.2%
이 10
 
3.2%
티 9
 
2.8%
라 6
 
1.9%
부 6
 
1.9%
리 6
 
1.9%
Other values (104) 183
57.7%

eng_lang_area_nm
Categorical

CONSTANT 

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

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
말레이시아 100
100.0%

Length

2023-12-10T18:58:27.794365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:58:27.977966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
말레이시아 100
100.0%

kor_lang_area_nm
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
マレーシア
100 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowマレーシア
2nd rowマレーシア
3rd rowマレーシア
4th rowマレーシア
5th rowマレーシア

Common Values

ValueCountFrequency (%)
マレーシア 100
100.0%

Length

2023-12-10T18:58:28.206943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:58:28.453611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
マレーシア 100
100.0%

jan_lang_area_nm
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
馬來西亞
100 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row馬來西亞
2nd row馬來西亞
3rd row馬來西亞
4th row馬來西亞
5th row馬來西亞

Common Values

ValueCountFrequency (%)
馬來西亞 100
100.0%

Length

2023-12-10T18:58:28.724833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:58:28.928114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
馬來西亞 100
100.0%

chg_lang_area_nm
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
馬來西亞
100 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row馬來西亞
2nd row馬來西亞
3rd row馬來西亞
4th row馬來西亞
5th row馬來西亞

Common Values

ValueCountFrequency (%)
馬來西亞 100
100.0%

Length

2023-12-10T18:58:29.113792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:58:29.284381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
馬來西亞 100
100.0%

rn_adres
Text

MISSING 

Distinct94
Distinct (%)95.9%
Missing2
Missing (%)2.0%
Memory size932.0 B
2023-12-10T18:58:30.019066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length128
Median length55.5
Mean length38.479592
Min length11

Characters and Unicode

Total characters3771
Distinct characters84
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique92 ?
Unique (%)93.9%

Sample

1st rowLorong Tj 5
2nd rowLot 3, Lorong Damai 7, Jalan Damai, Off Jalan Ampang
3rd row154, Jalan Pengkalan Barat 32,
4th rowLorong Labuk Jaya C1, Block C7, Bandar Labuk Jaya, Sandakan
5th rowNo.64,Bandar Baru Baron (Upper Floor)
ValueCountFrequency (%)
jalan 76
 
11.8%
no 18
 
2.8%
kuala 11
 
1.7%
lumpur 10
 
1.6%
jaya 9
 
1.4%
말레이시아 9
 
1.4%
bukit 8
 
1.2%
sultan 8
 
1.2%
taman 8
 
1.2%
persiaran 8
 
1.2%
Other values (326) 478
74.3%
2023-12-10T18:58:31.113977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
545
 
14.5%
a 511
 
13.6%
n 257
 
6.8%
l 171
 
4.5%
e 133
 
3.5%
, 130
 
3.4%
u 116
 
3.1%
i 113
 
3.0%
r 108
 
2.9%
o 107
 
2.8%
Other values (74) 1580
41.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2079
55.1%
Uppercase Letter 567
 
15.0%
Space Separator 545
 
14.5%
Decimal Number 317
 
8.4%
Other Punctuation 169
 
4.5%
Other Letter 69
 
1.8%
Dash Punctuation 17
 
0.5%
Close Punctuation 4
 
0.1%
Open Punctuation 4
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 511
24.6%
n 257
12.4%
l 171
 
8.2%
e 133
 
6.4%
u 116
 
5.6%
i 113
 
5.4%
r 108
 
5.2%
o 107
 
5.1%
t 95
 
4.6%
m 77
 
3.7%
Other values (16) 391
18.8%
Uppercase Letter
ValueCountFrequency (%)
J 96
16.9%
S 61
10.8%
B 55
9.7%
L 46
 
8.1%
K 39
 
6.9%
P 38
 
6.7%
A 33
 
5.8%
T 30
 
5.3%
C 27
 
4.8%
N 24
 
4.2%
Other values (12) 118
20.8%
Other Letter
ValueCountFrequency (%)
말 9
13.0%
레 9
13.0%
이 9
13.0%
시 9
13.0%
아 9
13.0%
주 3
 
4.3%
라 3
 
4.3%
조 2
 
2.9%
지 2
 
2.9%
타 2
 
2.9%
Other values (7) 12
17.4%
Decimal Number
ValueCountFrequency (%)
1 63
19.9%
2 59
18.6%
0 58
18.3%
5 28
8.8%
3 27
8.5%
7 22
 
6.9%
4 17
 
5.4%
9 15
 
4.7%
6 14
 
4.4%
8 14
 
4.4%
Other Punctuation
ValueCountFrequency (%)
, 130
76.9%
/ 24
 
14.2%
. 12
 
7.1%
& 2
 
1.2%
@ 1
 
0.6%
Space Separator
ValueCountFrequency (%)
545
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 17
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2646
70.2%
Common 1056
 
28.0%
Hangul 69
 
1.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 511
19.3%
n 257
 
9.7%
l 171
 
6.5%
e 133
 
5.0%
u 116
 
4.4%
i 113
 
4.3%
r 108
 
4.1%
o 107
 
4.0%
J 96
 
3.6%
t 95
 
3.6%
Other values (38) 939
35.5%
Common
ValueCountFrequency (%)
545
51.6%
, 130
 
12.3%
1 63
 
6.0%
2 59
 
5.6%
0 58
 
5.5%
5 28
 
2.7%
3 27
 
2.6%
/ 24
 
2.3%
7 22
 
2.1%
4 17
 
1.6%
Other values (9) 83
 
7.9%
Hangul
ValueCountFrequency (%)
말 9
13.0%
레 9
13.0%
이 9
13.0%
시 9
13.0%
아 9
13.0%
주 3
 
4.3%
라 3
 
4.3%
조 2
 
2.9%
지 2
 
2.9%
타 2
 
2.9%
Other values (7) 12
17.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3702
98.2%
Hangul 69
 
1.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
545
 
14.7%
a 511
 
13.8%
n 257
 
6.9%
l 171
 
4.6%
e 133
 
3.6%
, 130
 
3.5%
u 116
 
3.1%
i 113
 
3.1%
r 108
 
2.9%
o 107
 
2.9%
Other values (57) 1511
40.8%
Hangul
ValueCountFrequency (%)
말 9
13.0%
레 9
13.0%
이 9
13.0%
시 9
13.0%
아 9
13.0%
주 3
 
4.3%
라 3
 
4.3%
조 2
 
2.9%
지 2
 
2.9%
타 2
 
2.9%
Other values (7) 12
17.4%

lo
Real number (ℝ)

MISSING 

Distinct91
Distinct (%)95.8%
Missing5
Missing (%)5.0%
Infinite0
Infinite (%)0.0%
Mean100.60321
Minimum-120.00028
Maximum118.0493
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)1.0%
Memory size1.0 KiB
2023-12-10T18:58:31.541308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-120.00028
5-th percentile100.30674
Q1101.44995
median101.69672
Q3102.02515
95-th percentile116.07493
Maximum118.0493
Range238.04958
Interquartile range (IQR)0.5751993

Descriptive statistics

Standard deviation23.243968
Coefficient of variation (CV)0.23104599
Kurtosis88.922701
Mean100.60321
Median Absolute Deviation (MAD)0.256765
Skewness-9.2644932
Sum9557.3048
Variance540.28205
MonotonicityNot monotonic
2023-12-10T18:58:32.378058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
101.6967223 4
 
4.0%
103.6591988 2
 
2.0%
100.4154654 1
 
1.0%
103.0338169 1
 
1.0%
100.3079791 1
 
1.0%
100.3070986 1
 
1.0%
101.7048279 1
 
1.0%
101.6196247 1
 
1.0%
100.3351286 1
 
1.0%
103.7649038 1
 
1.0%
Other values (81) 81
81.0%
(Missing) 5
 
5.0%
ValueCountFrequency (%)
-120.000278 1
1.0%
99.8436768 1
1.0%
100.1927617 1
1.0%
100.242224 1
1.0%
100.3058889 1
1.0%
100.3070986 1
1.0%
100.3079791 1
1.0%
100.3084019 1
1.0%
100.309787 1
1.0%
100.3166503 1
1.0%
ValueCountFrequency (%)
118.0493004 1
1.0%
116.8396655 1
1.0%
116.1953518 1
1.0%
116.1298227 1
1.0%
116.0761132 1
1.0%
116.0744249 1
1.0%
113.0468986 1
1.0%
110.354363 1
1.0%
110.3529436 1
1.0%
110.3315929 1
1.0%

la
Real number (ℝ)

MISSING 

Distinct91
Distinct (%)95.8%
Missing5
Missing (%)5.0%
Infinite0
Infinite (%)0.0%
Mean4.1132916
Minimum1.469816
Maximum47.224109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:58:32.679315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.469816
5-th percentile1.5000434
Q13.0570935
median3.1538242
Q34.9453173
95-th percentile6.1922284
Maximum47.224109
Range45.754293
Interquartile range (IQR)1.8882237

Descriptive statistics

Standard deviation4.6839596
Coefficient of variation (CV)1.1387375
Kurtosis78.295627
Mean4.1132916
Median Absolute Deviation (MAD)0.1902326
Skewness8.4615487
Sum390.76271
Variance21.939478
MonotonicityNot monotonic
2023-12-10T18:58:33.095778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.1599264 4
 
4.0%
1.4826883 2
 
2.0%
6.2366503 1
 
1.0%
3.930931 1
 
1.0%
5.4561444 1
 
1.0%
5.3443129 1
 
1.0%
3.1587854 1
 
1.0%
3.043131 1
 
1.0%
5.420126 1
 
1.0%
1.4811164 1
 
1.0%
Other values (81) 81
81.0%
(Missing) 5
 
5.0%
ValueCountFrequency (%)
1.469816 1
1.0%
1.4811164 1
1.0%
1.4826883 2
2.0%
1.495784 1
1.0%
1.5018689 1
1.0%
1.515134 1
1.0%
1.5543301 1
1.0%
1.6254236 1
1.0%
2.1939746 1
1.0%
2.1943933 1
1.0%
ValueCountFrequency (%)
47.2241095 1
1.0%
6.9476873 1
1.0%
6.4510554 1
1.0%
6.3248605 1
1.0%
6.2366503 1
1.0%
6.1731905 1
1.0%
6.1297642 1
1.0%
6.1142175 1
1.0%
6.0893366 1
1.0%
6.0368481 1
1.0%

tel_no
Text

MISSING 

Distinct2
Distinct (%)100.0%
Missing98
Missing (%)98.0%
Memory size932.0 B
2023-12-10T18:58:33.494585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length13.5
Mean length13.5
Min length13

Characters and Unicode

Total characters27
Distinct characters12
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st row60 (4) 9608000
2nd row603 2781 8888
ValueCountFrequency (%)
60 1
16.7%
4 1
16.7%
9608000 1
16.7%
603 1
16.7%
2781 1
16.7%
8888 1
16.7%
2023-12-10T18:58:34.186631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 6
22.2%
8 6
22.2%
4
14.8%
6 3
11.1%
( 1
 
3.7%
4 1
 
3.7%
) 1
 
3.7%
9 1
 
3.7%
3 1
 
3.7%
2 1
 
3.7%
Other values (2) 2
 
7.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21
77.8%
Space Separator 4
 
14.8%
Open Punctuation 1
 
3.7%
Close Punctuation 1
 
3.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 6
28.6%
8 6
28.6%
6 3
14.3%
4 1
 
4.8%
9 1
 
4.8%
3 1
 
4.8%
2 1
 
4.8%
7 1
 
4.8%
1 1
 
4.8%
Space Separator
ValueCountFrequency (%)
4
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 27
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 6
22.2%
8 6
22.2%
4
14.8%
6 3
11.1%
( 1
 
3.7%
4 1
 
3.7%
) 1
 
3.7%
9 1
 
3.7%
3 1
 
3.7%
2 1
 
3.7%
Other values (2) 2
 
7.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 27
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 6
22.2%
8 6
22.2%
4
14.8%
6 3
11.1%
( 1
 
3.7%
4 1
 
3.7%
) 1
 
3.7%
9 1
 
3.7%
3 1
 
3.7%
2 1
 
3.7%
Other values (2) 2
 
7.4%

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

2023-12-10T18:58:34.554448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:58:34.720153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020-12-09 100
100.0%

Interactions

2023-12-10T18:58:23.474622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:58:23.176209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:58:23.622716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:58:23.327227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T18:58:34.855603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
se_nmeng_lang_hotel_nmkor_lang_hotel_nmrn_adreslolatel_no
se_nm1.0001.000NaN1.0000.0000.000NaN
eng_lang_hotel_nm1.0001.000NaN1.0001.0001.0000.000
kor_lang_hotel_nmNaNNaN1.0001.000NaN1.000NaN
rn_adres1.0001.0001.0001.0001.0001.0000.000
lo0.0001.000NaN1.0001.0001.000NaN
la0.0001.0001.0001.0001.0001.0000.000
tel_noNaN0.000NaN0.000NaN0.0001.000
2023-12-10T18:58:35.076292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
lolase_nm
lo1.000-0.4270.000
la-0.4271.0000.000
se_nm0.0000.0001.000

Missing values

2023-12-10T18:58:23.854734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T18:58:24.186301image/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.
2023-12-10T18:58:24.379516image/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
0호텔@12Haven- Stunning Seaside Luxury Villa. Sleeps 12<NA>말레이시아マレーシア馬來西亞馬來西亞Lorong Tj 5101.7939362.553577<NA>2020-12-09
1호텔<NA>1 다마이 레지던스 - 더 럭셔리 3 베드룸 스위트 앳 KLCC말레이시아マレーシア馬來西亞馬來西亞Lot 3, Lorong Damai 7, Jalan Damai, Off Jalan Ampang101.7240423.163213<NA>2020-12-09
2호텔<NA>1 데이 카 호텔말레이시아マレーシア馬來西亞馬來西亞154, Jalan Pengkalan Barat 32,101.0680364.548675<NA>2020-12-09
3호텔<NA>1 리바란 호텔말레이시아マレーシア馬來西亞馬來西亞Lorong Labuk Jaya C1, Block C7, Bandar Labuk Jaya, Sandakan118.04935.881305<NA>2020-12-09
4호텔<NA>1 바론 모텔말레이시아マレーシア馬來西亞馬來西亞No.64,Bandar Baru Baron (Upper Floor)<NA><NA><NA>2020-12-09
5호텔<NA>1 보니오 타워 B 서비스 콘도말레이시아マレーシア馬來西亞馬來西亞Jalan Sulaman116.1953526.17319<NA>2020-12-09
6호텔<NA>1 시티 호텔말레이시아マレーシア馬來西亞馬來西亞Lot 1, Block B, Segama Complex116.0744255.983968<NA>2020-12-09
7호텔<NA>1 테브라우 스위트말레이시아マレーシア馬來西亞馬來西亞Jalan Seri Setanggi, Taman Seri Setanggi103.7669191.501869<NA>2020-12-09
8호텔<NA>1 호텔 쿠차이 라마말레이시아マレーシア馬來西亞馬來西亞No. J-0-1, Jalan 1/127 Kuchai Business Park101.6913343.085168<NA>2020-12-09
9호텔<NA>1 호텔 타만 콘노트말레이시아マレーシア馬來西亞馬來西亞17, Jalan Menara Gading 1, Taman Connaught101.7330083.081037<NA>2020-12-09
se_nmeng_lang_hotel_nmkor_lang_hotel_nmeng_lang_area_nmkor_lang_area_nmjan_lang_area_nmchg_lang_area_nmrn_adreslolatel_noBASE_YMD
90호텔Acton VACATION Home<NA>말레이시아マレーシア馬來西亞馬來西亞279 Lorong Seri Koh 9100.381776.114217<NA>2020-12-09
91호텔Adam Soffea Homestay<NA>말레이시아マレーシア馬來西亞馬來西亞7/27 Jalan Sri Cemerlang102.2473046.129764<NA>2020-12-09
92호텔Adya Hotel Langkawi<NA>말레이시아マレーシア馬來西亞馬來西亞Persiaran Mutiara 2, Kuah, 07000 Langkawi, Kedah, 말레이시아99.8436776.3248660 (4) 96080002020-12-09
93호텔<NA>AF 게스트하우스 @ 트로피카나 시티 몰말레이시아マレーシア馬來西亞馬來西亞no3 The Tropics, Jalan 20/27 , petaling jaya101.6268433.130545<NA>2020-12-09
94호텔A'Famosa Resort에이파모사 리조트말레이시아マレーシア馬來西亞馬來西亞Jalan Kemus / Sempang Ampat, 78000 Alor Gajah, 믈라카 주 말레이시아102.2062272.44557603 2781 88882020-12-09
95호텔Affordable walk to the Beach Apartment @ Ferringhi<NA>말레이시아マレーシア馬來西亞馬來西亞Bayu Emas Apartment100.2422245.46592<NA>2020-12-09
96호텔<NA>AG 호텔말레이시아マレーシア馬來西亞馬來西亞350-e, Jalan Sultan Azlan Shah100.3097875.368982<NA>2020-12-09
97호텔Ahyu Hotel<NA>말레이시아マレーシア馬來西亞馬來西亞34, Leboh Ampang, City Centre, 50100 Kuala Lumpur, Wilayah Persekutuan Kuala Lumpur, 말레이시아101.69763.149583<NA>2020-12-09
98호텔Aiji Home @ Central Residences<NA>말레이시아マレーシア馬來西亞馬來西亞Jalan Sungai Besi101.7088193.122622<NA>2020-12-09
99호텔Aira Hotel<NA>말레이시아マレーシア馬來西亞馬來西亞290, Jalan Raja Laut, Chow Kit, 50350 Kuala Lumpur, Wilayah Persekutuan Kuala Lumpur, 말레이시아101.6961773.162864<NA>2020-12-09