Overview

Dataset statistics

Number of variables12
Number of observations100
Missing cells101
Missing cells (%)8.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.7 KiB
Average record size in memory99.3 B

Variable types

Categorical5
Text4
Numeric2
DateTime1

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 laHigh correlation
la is highly overall correlated with loHigh correlation
eng_lang_hotel_nm has 8 (8.0%) missing valuesMissing
kor_lang_hotel_nm has 18 (18.0%) missing valuesMissing
tel_no has 75 (75.0%) missing valuesMissing

Reproduction

Analysis started2023-12-10 10:09:56.028597
Analysis finished2023-12-10 10:09:58.664375
Duration2.64 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

se_nm
Categorical

Distinct4
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
호텔
66 
게스트하우스
26 
기타
 
4
에어비앤비
 
4

Length

Max length6
Median length2
Mean length3.16
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
호텔 66
66.0%
게스트하우스 26
 
26.0%
기타 4
 
4.0%
에어비앤비 4
 
4.0%

Length

2023-12-10T19:09:58.802164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:09:58.982240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
호텔 66
66.0%
게스트하우스 26
 
26.0%
기타 4
 
4.0%
에어비앤비 4
 
4.0%

eng_lang_hotel_nm
Text

MISSING 

Distinct92
Distinct (%)100.0%
Missing8
Missing (%)8.0%
Memory size932.0 B
2023-12-10T19:09:59.346630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length42
Median length31
Mean length17.880435
Min length6

Characters and Unicode

Total characters1645
Distinct characters71
Distinct categories5 ?
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 (%)100.0%

Sample

1st rowAzimutHotelVladivostok
2nd rowSuperstarguestHosue
3rd rowHotelHyundai
4th rowSibirskoePodvorie
5th rowhostelIZBA
ValueCountFrequency (%)
sunriseaparthotel 1
 
1.1%
noblesuiteshotel 1
 
1.1%
hostelbravo 1
 
1.1%
veliyhotelmokhovayamoscow 1
 
1.1%
capsulehotelaloha 1
 
1.1%
goodmoodhostel 1
 
1.1%
courtyardbymarriottirkutskcitycenter 1
 
1.1%
coronahotel 1
 
1.1%
radissoncollectionhotelmoscow 1
 
1.1%
zhostel 1
 
1.1%
Other values (82) 82
89.1%
2023-12-10T19:10:00.008085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 173
 
10.5%
e 169
 
10.3%
t 144
 
8.8%
l 112
 
6.8%
a 108
 
6.6%
s 95
 
5.8%
r 84
 
5.1%
i 78
 
4.7%
n 71
 
4.3%
H 55
 
3.3%
Other values (61) 556
33.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1370
83.3%
Uppercase Letter 256
 
15.6%
Other Punctuation 9
 
0.5%
Dash Punctuation 7
 
0.4%
Decimal Number 3
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 173
12.6%
e 169
12.3%
t 144
10.5%
l 112
 
8.2%
a 108
 
7.9%
s 95
 
6.9%
r 84
 
6.1%
i 78
 
5.7%
n 71
 
5.2%
u 41
 
3.0%
Other values (30) 295
21.5%
Uppercase Letter
ValueCountFrequency (%)
H 55
21.5%
S 27
10.5%
A 26
10.2%
M 24
9.4%
C 15
 
5.9%
B 12
 
4.7%
V 12
 
4.7%
P 11
 
4.3%
I 9
 
3.5%
L 9
 
3.5%
Other values (13) 56
21.9%
Other Punctuation
ValueCountFrequency (%)
, 6
66.7%
. 1
 
11.1%
& 1
 
11.1%
' 1
 
11.1%
Decimal Number
ValueCountFrequency (%)
1 1
33.3%
5 1
33.3%
0 1
33.3%
Dash Punctuation
ValueCountFrequency (%)
- 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1595
97.0%
Cyrillic 31
 
1.9%
Common 19
 
1.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 173
 
10.8%
e 169
 
10.6%
t 144
 
9.0%
l 112
 
7.0%
a 108
 
6.8%
s 95
 
6.0%
r 84
 
5.3%
i 78
 
4.9%
n 71
 
4.5%
H 55
 
3.4%
Other values (36) 506
31.7%
Cyrillic
ValueCountFrequency (%)
и 3
9.7%
т 3
9.7%
а 3
9.7%
р 3
9.7%
о 2
 
6.5%
д 2
 
6.5%
с 2
 
6.5%
м 2
 
6.5%
в 2
 
6.5%
е 2
 
6.5%
Other values (7) 7
22.6%
Common
ValueCountFrequency (%)
- 7
36.8%
, 6
31.6%
1 1
 
5.3%
. 1
 
5.3%
& 1
 
5.3%
' 1
 
5.3%
5 1
 
5.3%
0 1
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1614
98.1%
Cyrillic 31
 
1.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 173
 
10.7%
e 169
 
10.5%
t 144
 
8.9%
l 112
 
6.9%
a 108
 
6.7%
s 95
 
5.9%
r 84
 
5.2%
i 78
 
4.8%
n 71
 
4.4%
H 55
 
3.4%
Other values (44) 525
32.5%
Cyrillic
ValueCountFrequency (%)
и 3
9.7%
т 3
9.7%
а 3
9.7%
р 3
9.7%
о 2
 
6.5%
д 2
 
6.5%
с 2
 
6.5%
м 2
 
6.5%
в 2
 
6.5%
е 2
 
6.5%
Other values (7) 7
22.6%

kor_lang_hotel_nm
Text

MISSING 

Distinct80
Distinct (%)97.6%
Missing18
Missing (%)18.0%
Memory size932.0 B
2023-12-10T19:10:00.522404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length16.5
Mean length8.3414634
Min length3

Characters and Unicode

Total characters684
Distinct characters152
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

Unique78 ?
Unique (%)95.1%

Sample

1st row아지무트호텔블라디보스토크
2nd row수퍼스타게스트하우스
3rd row롯데호텔구,현대호텔
4th row시비르스코에포드보리에
5th row이즈바호스텔
ValueCountFrequency (%)
이퀘이터호텔 2
 
2.4%
에어익스프레스 2
 
2.4%
아지무트호텔블라디보스토크 1
 
1.2%
필린i소바미니호텔 1
 
1.2%
알로하캡슐호텔 1
 
1.2%
굿무드호스텔 1
 
1.2%
코트야드바이메리어트이르쿠츠크시티센터 1
 
1.2%
코로나호텔 1
 
1.2%
래디슨컬렉션호텔모스코바 1
 
1.2%
소프카호텔 1
 
1.2%
Other values (70) 70
85.4%
2023-12-10T19:10:01.183638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
스 65
 
9.5%
텔 54
 
7.9%
호 52
 
7.6%
트 26
 
3.8%
바 24
 
3.5%
이 23
 
3.4%
아 16
 
2.3%
모 15
 
2.2%
크 13
 
1.9%
레 13
 
1.9%
Other values (142) 383
56.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 679
99.3%
Other Punctuation 3
 
0.4%
Uppercase Letter 1
 
0.1%
Decimal Number 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
스 65
 
9.6%
텔 54
 
8.0%
호 52
 
7.7%
트 26
 
3.8%
바 24
 
3.5%
이 23
 
3.4%
아 16
 
2.4%
모 15
 
2.2%
크 13
 
1.9%
레 13
 
1.9%
Other values (138) 378
55.7%
Other Punctuation
ValueCountFrequency (%)
, 2
66.7%
& 1
33.3%
Uppercase Letter
ValueCountFrequency (%)
I 1
100.0%
Decimal Number
ValueCountFrequency (%)
1 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 679
99.3%
Common 4
 
0.6%
Latin 1
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
스 65
 
9.6%
텔 54
 
8.0%
호 52
 
7.7%
트 26
 
3.8%
바 24
 
3.5%
이 23
 
3.4%
아 16
 
2.4%
모 15
 
2.2%
크 13
 
1.9%
레 13
 
1.9%
Other values (138) 378
55.7%
Common
ValueCountFrequency (%)
, 2
50.0%
& 1
25.0%
1 1
25.0%
Latin
ValueCountFrequency (%)
I 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 679
99.3%
ASCII 5
 
0.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
스 65
 
9.6%
텔 54
 
8.0%
호 52
 
7.7%
트 26
 
3.8%
바 24
 
3.5%
이 23
 
3.4%
아 16
 
2.4%
모 15
 
2.2%
크 13
 
1.9%
레 13
 
1.9%
Other values (138) 378
55.7%
ASCII
ValueCountFrequency (%)
, 2
40.0%
I 1
20.0%
& 1
20.0%
1 1
20.0%

eng_lang_area_nm
Categorical

CONSTANT 

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

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
Russia 100
100.0%

Length

2023-12-10T19:10:01.457654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:10:01.625932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
russia 100
100.0%

kor_lang_area_nm
Categorical

CONSTANT 

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

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
러시아 100
100.0%

Length

2023-12-10T19:10:01.797846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:10:01.960510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
러시아 100
100.0%

jan_lang_area_nm
Categorical

CONSTANT 

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

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowロシア
2nd rowロシア
3rd rowロシア
4th rowロシア
5th rowロシア

Common Values

ValueCountFrequency (%)
ロシア 100
100.0%

Length

2023-12-10T19:10:02.146888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:10:02.318588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
ロシア 100
100.0%

chg_lang_area_nm
Categorical

CONSTANT 

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

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row俄羅斯
2nd row俄羅斯
3rd row俄羅斯
4th row俄羅斯
5th row俄羅斯

Common Values

ValueCountFrequency (%)
俄羅斯 100
100.0%

Length

2023-12-10T19:10:02.500179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:10:02.699438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
俄羅斯 100
100.0%

rn_adres
Text

Distinct98
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:10:03.201288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length67
Median length55
Mean length49
Min length19

Characters and Unicode

Total characters4900
Distinct characters100
Distinct categories6 ?
Distinct scripts4 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique96 ?
Unique (%)96.0%

Sample

1st rownab.ul.,10,Vladivostok,Primorskiykray,러시아690003
2nd rowUlitsaAdmiralaFokina,8А,Vladivostok,Primorskiykray,러시아690091
3rd rowSemenovskayaUlitsa,29,Vladivostok,Primorskiykray,러시아690091
4th rowOkeanskiypr.,26,Vladivostok,Primorskiykray,러시아690091
5th rowul.Mordovtseva,3,Vladivostok,Primorskiykray,러시아690091
ValueCountFrequency (%)
tigrovayaul.,16,vladivostok,primorskiykray,러시아690090 2
 
2.0%
pos'yetskayaulitsa,14,vladivostok,primorskiykray,러시아690003 2
 
2.0%
teatralnyypr-d,3с3,moskva,러시아107031 1
 
1.0%
kutuzovskiypr.,2/1,moskva,러시아121248 1
 
1.0%
nab.ul.,10,vladivostok,primorskiykray,러시아690003 1
 
1.0%
naberezhnayapirogovskaya,5세인트피터즈버그러시아194044 1
 
1.0%
tigrovayaul.,9,vladivostok,primorskiykray,러시아690090 1
 
1.0%
stremyannayaul.,20,sankt-peterburg,러시아191025 1
 
1.0%
mokhovayast,10,moskva,러시아119019 1
 
1.0%
ulitsapraporshchikakomarova,18,vladivostok,primorskiykray,러시아690091 1
 
1.0%
Other values (88) 88
88.0%
2023-12-10T19:10:04.129725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 414
 
8.4%
, 343
 
7.0%
k 296
 
6.0%
o 284
 
5.8%
s 268
 
5.5%
r 257
 
5.2%
i 239
 
4.9%
0 212
 
4.3%
y 211
 
4.3%
t 174
 
3.6%
Other values (90) 2202
44.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3024
61.7%
Decimal Number 780
 
15.9%
Other Punctuation 408
 
8.3%
Other Letter 351
 
7.2%
Uppercase Letter 324
 
6.6%
Dash Punctuation 13
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 414
13.7%
k 296
9.8%
o 284
9.4%
s 268
8.9%
r 257
8.5%
i 239
7.9%
y 211
 
7.0%
t 174
 
5.8%
l 159
 
5.3%
v 130
 
4.3%
Other values (25) 592
19.6%
Other Letter
ValueCountFrequency (%)
시 99
28.2%
아 99
28.2%
러 99
28.2%
트 5
 
1.4%
그 4
 
1.1%
세 4
 
1.1%
버 4
 
1.1%
터 4
 
1.1%
피 4
 
1.1%
인 4
 
1.1%
Other values (17) 25
 
7.1%
Uppercase Letter
ValueCountFrequency (%)
P 69
21.3%
V 49
15.1%
M 38
11.7%
S 35
10.8%
U 33
10.2%
K 24
 
7.4%
I 19
 
5.9%
T 11
 
3.4%
G 8
 
2.5%
B 8
 
2.5%
Other values (13) 30
9.3%
Decimal Number
ValueCountFrequency (%)
0 212
27.2%
1 149
19.1%
9 120
15.4%
6 92
11.8%
2 50
 
6.4%
4 43
 
5.5%
3 38
 
4.9%
5 36
 
4.6%
8 22
 
2.8%
7 18
 
2.3%
Other Punctuation
ValueCountFrequency (%)
, 343
84.1%
. 36
 
8.8%
' 19
 
4.7%
/ 10
 
2.5%
Dash Punctuation
ValueCountFrequency (%)
- 13
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3309
67.5%
Common 1201
 
24.5%
Hangul 351
 
7.2%
Cyrillic 39
 
0.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 414
12.5%
k 296
 
8.9%
o 284
 
8.6%
s 268
 
8.1%
r 257
 
7.8%
i 239
 
7.2%
y 211
 
6.4%
t 174
 
5.3%
l 159
 
4.8%
v 130
 
3.9%
Other values (29) 877
26.5%
Hangul
ValueCountFrequency (%)
시 99
28.2%
아 99
28.2%
러 99
28.2%
트 5
 
1.4%
그 4
 
1.1%
세 4
 
1.1%
버 4
 
1.1%
터 4
 
1.1%
피 4
 
1.1%
인 4
 
1.1%
Other values (17) 25
 
7.1%
Cyrillic
ValueCountFrequency (%)
с 6
15.4%
е 5
12.8%
а 4
10.3%
А 3
 
7.7%
н 2
 
5.1%
к 2
 
5.1%
Г 2
 
5.1%
р 2
 
5.1%
и 2
 
5.1%
о 2
 
5.1%
Other values (9) 9
23.1%
Common
ValueCountFrequency (%)
, 343
28.6%
0 212
17.7%
1 149
12.4%
9 120
 
10.0%
6 92
 
7.7%
2 50
 
4.2%
4 43
 
3.6%
3 38
 
3.2%
. 36
 
3.0%
5 36
 
3.0%
Other values (5) 82
 
6.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4510
92.0%
Hangul 351
 
7.2%
Cyrillic 39
 
0.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 414
 
9.2%
, 343
 
7.6%
k 296
 
6.6%
o 284
 
6.3%
s 268
 
5.9%
r 257
 
5.7%
i 239
 
5.3%
0 212
 
4.7%
y 211
 
4.7%
t 174
 
3.9%
Other values (44) 1812
40.2%
Hangul
ValueCountFrequency (%)
시 99
28.2%
아 99
28.2%
러 99
28.2%
트 5
 
1.4%
그 4
 
1.1%
세 4
 
1.1%
버 4
 
1.1%
터 4
 
1.1%
피 4
 
1.1%
인 4
 
1.1%
Other values (17) 25
 
7.1%
Cyrillic
ValueCountFrequency (%)
с 6
15.4%
е 5
12.8%
а 4
10.3%
А 3
 
7.7%
н 2
 
5.1%
к 2
 
5.1%
Г 2
 
5.1%
р 2
 
5.1%
и 2
 
5.1%
о 2
 
5.1%
Other values (9) 9
23.1%

lo
Real number (ℝ)

HIGH CORRELATION 

Distinct86
Distinct (%)86.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean89.978263
Minimum-82.640292
Maximum135.11385
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)1.0%
Memory size1.0 KiB
2023-12-10T19:10:04.388454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-82.640292
5-th percentile30.311398
Q137.589796
median131.80722
Q3131.87946
95-th percentile135.0268
Maximum135.11385
Range217.75414
Interquartile range (IQR)94.289664

Descriptive statistics

Standard deviation49.125139
Coefficient of variation (CV)0.54596675
Kurtosis-0.51129609
Mean89.978263
Median Absolute Deviation (MAD)3.282343
Skewness-0.66052879
Sum8997.8263
Variance2413.2793
MonotonicityNot monotonic
2023-12-10T19:10:04.713598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
131.8581034 8
 
8.0%
131.8593012 4
 
4.0%
131.8072228 2
 
2.0%
131.8805529 2
 
2.0%
37.6380537 2
 
2.0%
131.878643 2
 
2.0%
37.56088 1
 
1.0%
104.2735241 1
 
1.0%
30.306485 1
 
1.0%
30.3508545 1
 
1.0%
Other values (76) 76
76.0%
ValueCountFrequency (%)
-82.6402915 1
1.0%
30.288402 1
1.0%
30.3028072 1
1.0%
30.305979 1
1.0%
30.306485 1
1.0%
30.3116561 1
1.0%
30.3204751 1
1.0%
30.3369363 1
1.0%
30.3412056 1
1.0%
30.34729 1
1.0%
ValueCountFrequency (%)
135.1138507 1
1.0%
135.065281 1
1.0%
135.060996 1
1.0%
135.0581372 1
1.0%
135.0562564 1
1.0%
135.0252476 1
1.0%
131.9809739 1
1.0%
131.904768 1
1.0%
131.8981356 1
1.0%
131.8936387 1
1.0%

la
Real number (ℝ)

HIGH CORRELATION 

Distinct86
Distinct (%)86.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.415859
Minimum27.767601
Maximum59.951411
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:10:05.074996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum27.767601
5-th percentile43.109016
Q143.118799
median48.473003
Q355.759159
95-th percentile59.933934
Maximum59.951411
Range32.18381
Interquartile range (IQR)12.64036

Descriptive statistics

Standard deviation6.8861692
Coefficient of variation (CV)0.1393514
Kurtosis-0.84260465
Mean49.415859
Median Absolute Deviation (MAD)5.3578138
Skewness-0.046283252
Sum4941.5859
Variance47.419326
MonotonicityNot monotonic
2023-12-10T19:10:05.487300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
43.1404899 8
 
8.0%
43.1238028 4
 
4.0%
43.1497541 2
 
2.0%
43.11695 2
 
2.0%
55.7623302 2
 
2.0%
43.1090159 2
 
2.0%
55.750933 1
 
1.0%
52.2823163 1
 
1.0%
59.933905 1
 
1.0%
59.9320066 1
 
1.0%
Other values (76) 76
76.0%
ValueCountFrequency (%)
27.7676008 1
1.0%
43.0863029 1
1.0%
43.099682 1
1.0%
43.1061737 1
1.0%
43.1090159 2
2.0%
43.1126209 1
1.0%
43.113677 1
1.0%
43.1143591 1
1.0%
43.114709 1
1.0%
43.114712 1
1.0%
ValueCountFrequency (%)
59.9514105 1
1.0%
59.941675 1
1.0%
59.938108 1
1.0%
59.9372745 1
1.0%
59.9344853 1
1.0%
59.933905 1
1.0%
59.9320066 1
1.0%
59.9317214 1
1.0%
59.931218 1
1.0%
59.9279197 1
1.0%

tel_no
Text

MISSING 

Distinct25
Distinct (%)100.0%
Missing75
Missing (%)75.0%
Memory size932.0 B
2023-12-10T19:10:06.007582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length14
Mean length13.96
Min length12

Characters and Unicode

Total characters349
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique25 ?
Unique (%)100.0%

Sample

1st row+7-914-667-5645
2nd row+7423240-72-01
3rd row+7924738-67-90
4th row+7423290-95-55
5th row+7423250-20-55
ValueCountFrequency (%)
7-914-667-5645 1
 
4.0%
7495626-59-00 1
 
4.0%
7-916-716-90-13 1
 
4.0%
7421220-98-87 1
 
4.0%
7395225-07-00 1
 
4.0%
7495221-55-55 1
 
4.0%
7902515-25-35 1
 
4.0%
7983693-93-97 1
 
4.0%
79084548488 1
 
4.0%
78-926-221-4388 1
 
4.0%
Other values (15) 15
60.0%
2023-12-10T19:10:06.681126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 48
13.8%
7 43
12.3%
2 38
10.9%
4 33
9.5%
9 29
8.3%
0 29
8.3%
5 27
7.7%
+ 25
7.2%
3 22
6.3%
1 21
6.0%
Other values (2) 34
9.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 276
79.1%
Dash Punctuation 48
 
13.8%
Math Symbol 25
 
7.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
7 43
15.6%
2 38
13.8%
4 33
12.0%
9 29
10.5%
0 29
10.5%
5 27
9.8%
3 22
8.0%
1 21
7.6%
8 18
6.5%
6 16
 
5.8%
Dash Punctuation
ValueCountFrequency (%)
- 48
100.0%
Math Symbol
ValueCountFrequency (%)
+ 25
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 349
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 48
13.8%
7 43
12.3%
2 38
10.9%
4 33
9.5%
9 29
8.3%
0 29
8.3%
5 27
7.7%
+ 25
7.2%
3 22
6.3%
1 21
6.0%
Other values (2) 34
9.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 349
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 48
13.8%
7 43
12.3%
2 38
10.9%
4 33
9.5%
9 29
8.3%
0 29
8.3%
5 27
7.7%
+ 25
7.2%
3 22
6.3%
1 21
6.0%
Other values (2) 34
9.7%

BASE_YMD
Date

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
Minimum2020-12-09 00:00:00
Maximum2020-12-09 00:00:00
2023-12-10T19:10:06.867426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:10:07.006986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2023-12-10T19:09:57.665798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:57.396441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:57.798020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:57.517941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:10:07.119031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
se_nmeng_lang_hotel_nmkor_lang_hotel_nmrn_adreslolatel_no
se_nm1.0001.0000.9760.0000.0000.0001.000
eng_lang_hotel_nm1.0001.0001.0001.0001.0001.0001.000
kor_lang_hotel_nm0.9761.0001.0000.9960.9370.9891.000
rn_adres0.0001.0000.9961.0001.0001.0001.000
lo0.0001.0000.9371.0001.0001.0001.000
la0.0001.0000.9891.0001.0001.0001.000
tel_no1.0001.0001.0001.0001.0001.0001.000
2023-12-10T19:10:07.292432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
lolase_nm
lo1.000-0.7640.000
la-0.7641.0000.000
se_nm0.0000.0001.000

Missing values

2023-12-10T19:09:58.032123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:09:58.373806image/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.
2023-12-10T19:09:58.572003image/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
0호텔AzimutHotelVladivostok아지무트호텔블라디보스토크Russia러시아ロシア俄羅斯nab.ul.,10,Vladivostok,Primorskiykray,러시아690003131.80722343.149754<NA>2020-12-09
1게스트하우스SuperstarguestHosue수퍼스타게스트하우스Russia러시아ロシア俄羅斯UlitsaAdmiralaFokina,8А,Vladivostok,Primorskiykray,러시아690091131.88191343.117201+7-914-667-56452020-12-09
2호텔HotelHyundai롯데호텔구,현대호텔Russia러시아ロシア俄羅斯SemenovskayaUlitsa,29,Vladivostok,Primorskiykray,러시아690091131.85810343.14049+7423240-72-012020-12-09
3호텔SibirskoePodvorie시비르스코에포드보리에Russia러시아ロシア俄羅斯Okeanskiypr.,26,Vladivostok,Primorskiykray,러시아690091131.88739343.120377<NA>2020-12-09
4게스트하우스hostelIZBA이즈바호스텔Russia러시아ロシア俄羅斯ul.Mordovtseva,3,Vladivostok,Primorskiykray,러시아690091131.88414843.119493<NA>2020-12-09
5호텔Arbat-Vladivostok아파트호텔아르바트블라디보스톡Russia러시아ロシア俄羅斯PogranichnayaUlitsa,4,Vladivostok,Primorskiykray,러시아690091131.8799243.117583<NA>2020-12-09
6게스트하우스GuestHouseGallery&More갤러리&모어게스트하우스Russia러시아ロシア俄羅斯ul.AdmiralaFokina,4Г,Vladivostok,Primorskiykray,러시아690091131.88058643.1174+7924738-67-902020-12-09
7호텔AirExpressSheremetyevo에어익스프레스Russia러시아ロシア俄羅斯Sheremetyevo,Khimki,Moskovskayaoblast',러시아14142537.41250355.973648<NA>2020-12-09
8게스트하우스<NA>에어익스프레스Russia러시아ロシア俄羅斯Tigrovayaul.,16,Vladivostok,Primorskiykray,러시아690090131.85930143.123803<NA>2020-12-09
9호텔TeploHotel테플로호텔Russia러시아ロシア俄羅斯Posyetskayaul.,16,Vladivostok,Primorskiykray,러시아690003131.80722343.149754+7423290-95-552020-12-09
se_nmeng_lang_hotel_nmkor_lang_hotel_nmeng_lang_area_nmkor_lang_area_nmjan_lang_area_nmchg_lang_area_nmrn_adreslolatel_noBASE_YMD
90게스트하우스Mattress,sailorandalbatross<NA>Russia러시아ロシア俄羅斯Svetlanskayaul.,31/3,Vladivostok,Primorskiykray,러시아690091131.88748743.115942<NA>2020-12-09
91호텔HotelSukharevsky호텔수카레브스키Russia러시아ロシア俄羅斯1-yKoptelskiyper.,2/7с4,Moskva,러시아12909037.63905155.776621<NA>2020-12-09
92호텔<NA>바이칼우드호텔Russia러시아ロシア俄羅斯Baykal'skayaUlitsa,64,Khuzhir,Irkutskayaoblast',러시아666137107.35399153.194229<NA>2020-12-09
93호텔AzimutHotelSmolenskayaMoscow아지무트호텔스몰렌스카야모스크바Russia러시아ロシア俄羅斯SmolenskayaUlitsa,8,Moskva,러시아12109937.58043555.746209<NA>2020-12-09
94호텔ArbatHouse아르바트하우스Russia러시아ロシア俄羅斯SkatertnyyPereulok,14,Moskva,러시아12106937.58997855.756782<NA>2020-12-09
95호텔NovotelMoscowCentreHotel노보텔모스코센터호텔Russia러시아ロシア俄羅斯MetroMendeleyevskaya,Moskva,러시아12705537.59938955.785952<NA>2020-12-09
96게스트하우스fasolhostel<NA>Russia러시아ロシア俄羅斯Arkhangelskiyper.,11/16с3,Moskva,러시아10100037.63805455.76233<NA>2020-12-09
97게스트하우스Valenkihostel발렌키호스텔Russia러시아ロシア俄羅斯ulitsaGruzinskiyVal,28/45,Moskva,러시아12305637.58316355.772236<NA>2020-12-09
98호텔GoldenRingHotel골든링호텔Russia러시아ロシア俄羅斯SmolenskayaUlitsa,5,Moskva,러시아11912137.58053255.7453<NA>2020-12-09
99호텔HOTELSputnik<NA>Russia러시아ロシア俄羅斯prospektLeninskiy,45Встроение2모스크바러시아11904937.57623655.701896<NA>2020-12-09