Overview

Dataset statistics

Number of variables17
Number of observations100
Missing cells196
Missing cells (%)11.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory13.8 KiB
Average record size in memory141.3 B

Variable types

Text8
Numeric4
Categorical4
DateTime1

Alerts

base_ymd has constant value ""Constant
city_do_jan_lang_nm is highly overall correlated with xpos_lo and 6 other fieldsHigh correlation
city_gn_gu_jan_lang_nm is highly overall correlated with xpos_lo and 6 other fieldsHigh correlation
city_gn_gu_kor_lang_nm is highly overall correlated with xpos_lo and 6 other fieldsHigh correlation
city_do_kor_lang_nm is highly overall correlated with xpos_lo and 6 other fieldsHigh correlation
xpos_lo is highly overall correlated with city_do_cd and 5 other fieldsHigh correlation
ypos_la is highly overall correlated with city_do_kor_lang_nm and 3 other fieldsHigh correlation
city_do_cd is highly overall correlated with xpos_lo and 5 other fieldsHigh correlation
city_gn_gu_cd is highly overall correlated with xpos_lo and 5 other fieldsHigh correlation
eng_lang_nm has 37 (37.0%) missing valuesMissing
jan_lang_nm has 58 (58.0%) missing valuesMissing
chg_lang_nm has 37 (37.0%) missing valuesMissing
chb_lang_nm has 60 (60.0%) missing valuesMissing
gov_dn_jan_lang_nm has 4 (4.0%) missing valuesMissing
xpos_lo has unique valuesUnique
ypos_la has unique valuesUnique

Reproduction

Analysis started2023-12-10 10:04:27.855257
Analysis finished2023-12-10 10:04:34.902839
Duration7.05 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

entrp_nm
Text

Distinct99
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:04:35.324830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length8
Mean length4.41
Min length1

Characters and Unicode

Total characters441
Distinct characters156
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

Unique98 ?
Unique (%)98.0%

Sample

1st row강변파크
2nd row파크여관
3rd row로얄파크
4th row리버풀파크
5th row명지산파크
ValueCountFrequency (%)
꿈의궁전모텔 2
 
2.0%
쿠페 1
 
1.0%
파랑새 1
 
1.0%
비너스모텔 1
 
1.0%
효성파크 1
 
1.0%
첼로모텔 1
 
1.0%
샵모텔 1
 
1.0%
뜨락모텔 1
 
1.0%
드라마 1
 
1.0%
계룡파크 1
 
1.0%
Other values (90) 90
89.1%
2023-12-10T19:04:36.211569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
텔 45
 
10.2%
모 31
 
7.0%
스 24
 
5.4%
파 21
 
4.8%
크 18
 
4.1%
호 10
 
2.3%
라 9
 
2.0%
타 7
 
1.6%
장 6
 
1.4%
유 6
 
1.4%
Other values (146) 264
59.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 432
98.0%
Other Punctuation 6
 
1.4%
Uppercase Letter 1
 
0.2%
Decimal Number 1
 
0.2%
Space Separator 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
텔 45
 
10.4%
모 31
 
7.2%
스 24
 
5.6%
파 21
 
4.9%
크 18
 
4.2%
호 10
 
2.3%
라 9
 
2.1%
타 7
 
1.6%
장 6
 
1.4%
유 6
 
1.4%
Other values (142) 255
59.0%
Other Punctuation
ValueCountFrequency (%)
/ 6
100.0%
Uppercase Letter
ValueCountFrequency (%)
K 1
100.0%
Decimal Number
ValueCountFrequency (%)
2 1
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 432
98.0%
Common 8
 
1.8%
Latin 1
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
텔 45
 
10.4%
모 31
 
7.2%
스 24
 
5.6%
파 21
 
4.9%
크 18
 
4.2%
호 10
 
2.3%
라 9
 
2.1%
타 7
 
1.6%
장 6
 
1.4%
유 6
 
1.4%
Other values (142) 255
59.0%
Common
ValueCountFrequency (%)
/ 6
75.0%
2 1
 
12.5%
1
 
12.5%
Latin
ValueCountFrequency (%)
K 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 432
98.0%
ASCII 9
 
2.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
텔 45
 
10.4%
모 31
 
7.2%
스 24
 
5.6%
파 21
 
4.9%
크 18
 
4.2%
호 10
 
2.3%
라 9
 
2.1%
타 7
 
1.6%
장 6
 
1.4%
유 6
 
1.4%
Other values (142) 255
59.0%
ASCII
ValueCountFrequency (%)
/ 6
66.7%
K 1
 
11.1%
2 1
 
11.1%
1
 
11.1%

xpos_lo
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean965684.79
Minimum126.93581
Maximum1178598.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:04:36.612249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.93581
5-th percentile928041.61
Q1941839.68
median959378.91
Q3995680.59
95-th percentile1165056.8
Maximum1178598.2
Range1178471.3
Interquartile range (IQR)53840.91

Descriptive statistics

Standard deviation187892.73
Coefficient of variation (CV)0.19456942
Kurtosis19.549181
Mean965684.79
Median Absolute Deviation (MAD)19065.57
Skewness-4.0133687
Sum96568479
Variance3.5303676 × 1010
MonotonicityNot monotonic
2023-12-10T19:04:36.927142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
993285.25 1
 
1.0%
1165068.25 1
 
1.0%
1083186.31 1
 
1.0%
1088218.0 1
 
1.0%
1080156.951 1
 
1.0%
930295.63 1
 
1.0%
931557.72 1
 
1.0%
932850.568 1
 
1.0%
931387.69 1
 
1.0%
931238.56 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
126.935806 1
1.0%
127.007369 1
1.0%
128.160928 1
1.0%
899907.81 1
1.0%
928019.94 1
1.0%
928042.75 1
1.0%
928166.0 1
1.0%
928318.5 1
1.0%
930295.63 1
1.0%
931238.56 1
1.0%
ValueCountFrequency (%)
1178598.23 1
1.0%
1165175.55 1
1.0%
1165153.34 1
1.0%
1165101.91 1
1.0%
1165068.25 1
1.0%
1165056.21 1
1.0%
1165018.26 1
1.0%
1164958.1 1
1.0%
1160834.38 1
1.0%
1155780.25 1
1.0%

ypos_la
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1850581.5
Minimum36.417723
Maximum2049662.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:04:37.327811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.417723
5-th percentile1723981.7
Q11824451
median1947930.3
Q31958566.9
95-th percentile1979780.8
Maximum2049662.4
Range2049626
Interquartile range (IQR)134115.95

Descriptive statistics

Standard deviation338714.49
Coefficient of variation (CV)0.18303138
Kurtosis25.605753
Mean1850581.5
Median Absolute Deviation (MAD)15873.495
Skewness-5.0238298
Sum1.8505815 × 108
Variance1.147275 × 1011
MonotonicityNot monotonic
2023-12-10T19:04:37.645248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1970591.5 1
 
1.0%
1755942.49 1
 
1.0%
2049662.41 1
 
1.0%
2039678.0 1
 
1.0%
1750992.756 1
 
1.0%
1947490.38 1
 
1.0%
1949077.71 1
 
1.0%
1947599.154 1
 
1.0%
1949084.66 1
 
1.0%
1948081.5 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
36.417723 1
1.0%
37.463207 1
1.0%
37.638335 1
1.0%
1672976.54 1
1.0%
1695437.47 1
1.0%
1725484.0 1
1.0%
1725734.5 1
1.0%
1726047.38 1
1.0%
1726067.38 1
1.0%
1743602.49 1
1.0%
ValueCountFrequency (%)
2049662.41 1
1.0%
2039678.0 1
1.0%
1987444.15 1
1.0%
1980014.89 1
1.0%
1979972.73 1
1.0%
1979770.75 1
1.0%
1978464.85 1
1.0%
1978459.0 1
1.0%
1977647.0 1
1.0%
1975603.71 1
1.0%

kor_lang_nm
Text

Distinct99
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:04:38.181994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length7
Mean length4.41
Min length2

Characters and Unicode

Total characters441
Distinct characters155
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

Unique98 ?
Unique (%)98.0%

Sample

1st row강변파크
2nd row파크여관
3rd row로얄파크
4th row리버풀파크
5th row명지산파크
ValueCountFrequency (%)
꿈의궁전모텔 2
 
2.0%
쿠페 1
 
1.0%
파랑새 1
 
1.0%
비너스모텔 1
 
1.0%
효성파크 1
 
1.0%
첼로모텔 1
 
1.0%
샵모텔 1
 
1.0%
뜨락모텔 1
 
1.0%
드라마 1
 
1.0%
계룡파크 1
 
1.0%
Other values (90) 90
89.1%
2023-12-10T19:04:39.001300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
텔 48
 
10.9%
모 32
 
7.3%
스 24
 
5.4%
파 21
 
4.8%
크 18
 
4.1%
호 12
 
2.7%
라 9
 
2.0%
타 7
 
1.6%
장 6
 
1.4%
유 6
 
1.4%
Other values (145) 258
58.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 438
99.3%
Uppercase Letter 1
 
0.2%
Decimal Number 1
 
0.2%
Space Separator 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
텔 48
 
11.0%
모 32
 
7.3%
스 24
 
5.5%
파 21
 
4.8%
크 18
 
4.1%
호 12
 
2.7%
라 9
 
2.1%
타 7
 
1.6%
장 6
 
1.4%
유 6
 
1.4%
Other values (142) 255
58.2%
Uppercase Letter
ValueCountFrequency (%)
K 1
100.0%
Decimal Number
ValueCountFrequency (%)
2 1
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 438
99.3%
Common 2
 
0.5%
Latin 1
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
텔 48
 
11.0%
모 32
 
7.3%
스 24
 
5.5%
파 21
 
4.8%
크 18
 
4.1%
호 12
 
2.7%
라 9
 
2.1%
타 7
 
1.6%
장 6
 
1.4%
유 6
 
1.4%
Other values (142) 255
58.2%
Common
ValueCountFrequency (%)
2 1
50.0%
1
50.0%
Latin
ValueCountFrequency (%)
K 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 438
99.3%
ASCII 3
 
0.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
텔 48
 
11.0%
모 32
 
7.3%
스 24
 
5.5%
파 21
 
4.8%
크 18
 
4.1%
호 12
 
2.7%
라 9
 
2.1%
타 7
 
1.6%
장 6
 
1.4%
유 6
 
1.4%
Other values (142) 255
58.2%
ASCII
ValueCountFrequency (%)
K 1
33.3%
2 1
33.3%
1
33.3%

eng_lang_nm
Text

MISSING 

Distinct54
Distinct (%)85.7%
Missing37
Missing (%)37.0%
Memory size932.0 B
2023-12-10T19:04:39.408945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length14
Mean length9.5873016
Min length2

Characters and Unicode

Total characters604
Distinct characters48
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique52 ?
Unique (%)82.5%

Sample

1st rowMotel
2nd rowMotel
3rd rowGaya Hotel
4th rowCutee
5th rowRitz Motel
ValueCountFrequency (%)
motel 28
26.9%
hotel 7
 
6.7%
park 3
 
2.9%
s 2
 
1.9%
hilton 2
 
1.9%
kkumuigungjeon 2
 
1.9%
croce 1
 
1.0%
mote 1
 
1.0%
line 1
 
1.0%
rameses 1
 
1.0%
Other values (56) 56
53.8%
2023-12-10T19:04:40.121697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 81
13.4%
o 58
 
9.6%
l 54
 
8.9%
t 51
 
8.4%
41
 
6.8%
a 37
 
6.1%
n 33
 
5.5%
M 32
 
5.3%
i 23
 
3.8%
r 20
 
3.3%
Other values (38) 174
28.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 457
75.7%
Uppercase Letter 104
 
17.2%
Space Separator 41
 
6.8%
Dash Punctuation 1
 
0.2%
Decimal Number 1
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 81
17.7%
o 58
12.7%
l 54
11.8%
t 51
11.2%
a 37
8.1%
n 33
7.2%
i 23
 
5.0%
r 20
 
4.4%
s 18
 
3.9%
g 14
 
3.1%
Other values (15) 68
14.9%
Uppercase Letter
ValueCountFrequency (%)
M 32
30.8%
H 10
 
9.6%
P 9
 
8.7%
R 7
 
6.7%
S 7
 
6.7%
L 6
 
5.8%
C 6
 
5.8%
G 6
 
5.8%
T 5
 
4.8%
K 3
 
2.9%
Other values (10) 13
12.5%
Space Separator
ValueCountFrequency (%)
41
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%
Decimal Number
ValueCountFrequency (%)
2 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 561
92.9%
Common 43
 
7.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 81
14.4%
o 58
 
10.3%
l 54
 
9.6%
t 51
 
9.1%
a 37
 
6.6%
n 33
 
5.9%
M 32
 
5.7%
i 23
 
4.1%
r 20
 
3.6%
s 18
 
3.2%
Other values (35) 154
27.5%
Common
ValueCountFrequency (%)
41
95.3%
- 1
 
2.3%
2 1
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 604
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 81
13.4%
o 58
 
9.6%
l 54
 
8.9%
t 51
 
8.4%
41
 
6.8%
a 37
 
6.1%
n 33
 
5.5%
M 32
 
5.3%
i 23
 
3.8%
r 20
 
3.3%
Other values (38) 174
28.8%

jan_lang_nm
Text

MISSING 

Distinct36
Distinct (%)85.7%
Missing58
Missing (%)58.0%
Memory size932.0 B
2023-12-10T19:04:40.462765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length9
Mean length6.6904762
Min length2

Characters and Unicode

Total characters281
Distinct characters77
Distinct categories8 ?
Distinct scripts4 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique33 ?
Unique (%)78.6%

Sample

1st rowモ?テル
2nd rowロイヤルパ?ク
3rd row伽揶Hotel
4th rowリッツ
5th rowベリシックスホテル
ValueCountFrequency (%)
モ?テル 5
 
11.4%
ヒルトンモ?テル 2
 
4.5%
クミグンジョンモ?テル 2
 
4.5%
白?館 1
 
2.3%
オトゥモテル 1
 
2.3%
ヴィ?ナスモテル 1
 
2.3%
エバ?グリ?ン 1
 
2.3%
ソウルパ?ク 1
 
2.3%
ラグジュアリ?モ?テル 1
 
2.3%
ラムセス?ホテル 1
 
2.3%
Other values (28) 28
63.6%
2023-12-10T19:04:41.125964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
? 33
 
11.7%
ル 30
 
10.7%
テ 25
 
8.9%
モ 21
 
7.5%
ン 16
 
5.7%
ス 8
 
2.8%
ク 8
 
2.8%
ラ 8
 
2.8%
グ 6
 
2.1%
リ 6
 
2.1%
Other values (67) 120
42.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 219
77.9%
Other Punctuation 33
 
11.7%
Lowercase Letter 16
 
5.7%
Uppercase Letter 8
 
2.8%
Space Separator 2
 
0.7%
Open Punctuation 1
 
0.4%
Close Punctuation 1
 
0.4%
Decimal Number 1
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
ル 30
 
13.7%
テ 25
 
11.4%
モ 21
 
9.6%
ン 16
 
7.3%
ス 8
 
3.7%
ク 8
 
3.7%
ラ 8
 
3.7%
グ 6
 
2.7%
リ 6
 
2.7%
パ 6
 
2.7%
Other values (50) 85
38.8%
Lowercase Letter
ValueCountFrequency (%)
e 5
31.2%
t 4
25.0%
o 3
18.8%
l 2
 
12.5%
v 1
 
6.2%
r 1
 
6.2%
Uppercase Letter
ValueCountFrequency (%)
L 2
25.0%
M 2
25.0%
K 1
12.5%
H 1
12.5%
T 1
12.5%
S 1
12.5%
Other Punctuation
ValueCountFrequency (%)
? 33
100.0%
Space Separator
ValueCountFrequency (%)
2
100.0%
Open Punctuation
ValueCountFrequency (%)
「 1
100.0%
Close Punctuation
ValueCountFrequency (%)
」 1
100.0%
Decimal Number
ValueCountFrequency (%)
2 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Katakana 210
74.7%
Common 38
 
13.5%
Latin 24
 
8.5%
Han 9
 
3.2%

Most frequent character per script

Katakana
ValueCountFrequency (%)
ル 30
 
14.3%
テ 25
 
11.9%
モ 21
 
10.0%
ン 16
 
7.6%
ス 8
 
3.8%
ク 8
 
3.8%
ラ 8
 
3.8%
グ 6
 
2.9%
リ 6
 
2.9%
パ 6
 
2.9%
Other values (41) 76
36.2%
Latin
ValueCountFrequency (%)
e 5
20.8%
t 4
16.7%
o 3
12.5%
L 2
 
8.3%
l 2
 
8.3%
M 2
 
8.3%
K 1
 
4.2%
H 1
 
4.2%
v 1
 
4.2%
r 1
 
4.2%
Other values (2) 2
 
8.3%
Han
ValueCountFrequency (%)
白 1
11.1%
地 1
11.1%
館 1
11.1%
王 1
11.1%
影 1
11.1%
撮 1
11.1%
製 1
11.1%
伽 1
11.1%
揶 1
11.1%
Common
ValueCountFrequency (%)
? 33
86.8%
2
 
5.3%
「 1
 
2.6%
」 1
 
2.6%
2 1
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
Katakana 210
74.7%
ASCII 60
 
21.4%
CJK 9
 
3.2%
None 2
 
0.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
? 33
55.0%
e 5
 
8.3%
t 4
 
6.7%
o 3
 
5.0%
L 2
 
3.3%
2
 
3.3%
l 2
 
3.3%
M 2
 
3.3%
K 1
 
1.7%
H 1
 
1.7%
Other values (5) 5
 
8.3%
Katakana
ValueCountFrequency (%)
ル 30
 
14.3%
テ 25
 
11.9%
モ 21
 
10.0%
ン 16
 
7.6%
ス 8
 
3.8%
ク 8
 
3.8%
ラ 8
 
3.8%
グ 6
 
2.9%
リ 6
 
2.9%
パ 6
 
2.9%
Other values (41) 76
36.2%
CJK
ValueCountFrequency (%)
白 1
11.1%
地 1
11.1%
館 1
11.1%
王 1
11.1%
影 1
11.1%
撮 1
11.1%
製 1
11.1%
伽 1
11.1%
揶 1
11.1%
None
ValueCountFrequency (%)
「 1
50.0%
」 1
50.0%

chg_lang_nm
Text

MISSING 

Distinct55
Distinct (%)87.3%
Missing37
Missing (%)37.0%
Memory size932.0 B
2023-12-10T19:04:41.504044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length12
Mean length5.1269841
Min length2

Characters and Unicode

Total characters323
Distinct characters107
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

Unique53 ?
Unique (%)84.1%

Sample

1st row汽?旅?
2nd row汽?旅?
3rd row伽倻Hotel
4th row故地
5th rowRitz酒店
ValueCountFrequency (%)
汽?旅 8
 
11.9%
2
 
3.0%
色 2
 
3.0%
中殿堂旅 2
 
3.0%
旅 1
 
1.5%
希??旅 1
 
1.5%
amigos朋友 1
 
1.5%
明洞公 1
 
1.5%
斯汽?旅 1
 
1.5%
城 1
 
1.5%
Other values (47) 47
70.1%
2023-12-10T19:04:42.166209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
? 101
31.3%
旅 29
 
9.0%
汽 11
 
3.4%
斯 6
 
1.9%
i 6
 
1.9%
l 6
 
1.9%
o 6
 
1.9%
t 6
 
1.9%
n 5
 
1.5%
4
 
1.2%
Other values (97) 143
44.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 137
42.4%
Other Punctuation 101
31.3%
Lowercase Letter 53
 
16.4%
Uppercase Letter 19
 
5.9%
Space Separator 4
 
1.2%
Dash Punctuation 3
 
0.9%
Decimal Number 2
 
0.6%
Open Punctuation 2
 
0.6%
Close Punctuation 2
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
旅 29
21.2%
汽 11
 
8.0%
斯 6
 
4.4%
店 4
 
2.9%
色 3
 
2.2%
明 3
 
2.2%
拉 3
 
2.2%
酒 3
 
2.2%
提 2
 
1.5%
山 2
 
1.5%
Other values (64) 71
51.8%
Lowercase Letter
ValueCountFrequency (%)
i 6
11.3%
l 6
11.3%
o 6
11.3%
t 6
11.3%
n 5
9.4%
a 4
7.5%
r 4
7.5%
e 4
7.5%
s 3
5.7%
g 2
 
3.8%
Other values (6) 7
13.2%
Uppercase Letter
ValueCountFrequency (%)
H 3
15.8%
A 2
10.5%
T 2
10.5%
M 2
10.5%
S 2
10.5%
C 2
10.5%
R 2
10.5%
O 1
 
5.3%
P 1
 
5.3%
D 1
 
5.3%
Other Punctuation
ValueCountFrequency (%)
? 101
100.0%
Space Separator
ValueCountFrequency (%)
4
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%
Decimal Number
ValueCountFrequency (%)
2 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Han 137
42.4%
Common 114
35.3%
Latin 72
22.3%

Most frequent character per script

Han
ValueCountFrequency (%)
旅 29
21.2%
汽 11
 
8.0%
斯 6
 
4.4%
店 4
 
2.9%
色 3
 
2.2%
明 3
 
2.2%
拉 3
 
2.2%
酒 3
 
2.2%
提 2
 
1.5%
山 2
 
1.5%
Other values (64) 71
51.8%
Latin
ValueCountFrequency (%)
i 6
 
8.3%
l 6
 
8.3%
o 6
 
8.3%
t 6
 
8.3%
n 5
 
6.9%
a 4
 
5.6%
r 4
 
5.6%
e 4
 
5.6%
H 3
 
4.2%
s 3
 
4.2%
Other values (17) 25
34.7%
Common
ValueCountFrequency (%)
? 101
88.6%
4
 
3.5%
- 3
 
2.6%
2 2
 
1.8%
( 2
 
1.8%
) 2
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 186
57.6%
CJK 137
42.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
? 101
54.3%
i 6
 
3.2%
l 6
 
3.2%
o 6
 
3.2%
t 6
 
3.2%
n 5
 
2.7%
4
 
2.2%
a 4
 
2.2%
r 4
 
2.2%
e 4
 
2.2%
Other values (23) 40
 
21.5%
CJK
ValueCountFrequency (%)
旅 29
21.2%
汽 11
 
8.0%
斯 6
 
4.4%
店 4
 
2.9%
色 3
 
2.2%
明 3
 
2.2%
拉 3
 
2.2%
酒 3
 
2.2%
提 2
 
1.5%
山 2
 
1.5%
Other values (64) 71
51.8%

chb_lang_nm
Text

MISSING 

Distinct29
Distinct (%)72.5%
Missing60
Missing (%)60.0%
Memory size932.0 B
2023-12-10T19:04:42.509648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length10
Mean length5.075
Min length2

Characters and Unicode

Total characters203
Distinct characters79
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

Unique27 ?
Unique (%)67.5%

Sample

1st row汽車旅館
2nd row汽車旅館
3rd row伽倻Hotel
4th row利?酒店
5th row非常之六飯店
ValueCountFrequency (%)
汽車旅館 11
25.6%
motel 2
 
4.7%
夢想宮殿汽車旅館 2
 
4.7%
莎樂望汽車旅館 1
 
2.3%
白堊館 1
 
2.3%
star 1
 
2.3%
豪華汽車旅館 1
 
2.3%
維納斯汽車旅館 1
 
2.3%
大提琴汽車旅館 1
 
2.3%
鳥 1
 
2.3%
Other values (21) 21
48.8%
2023-12-10T19:04:43.109991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
旅 23
 
11.3%
館 23
 
11.3%
汽 21
 
10.3%
車 21
 
10.3%
e 6
 
3.0%
t 6
 
3.0%
L 4
 
2.0%
o 4
 
2.0%
? 4
 
2.0%
- 3
 
1.5%
Other values (69) 88
43.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 154
75.9%
Lowercase Letter 25
 
12.3%
Uppercase Letter 13
 
6.4%
Other Punctuation 4
 
2.0%
Dash Punctuation 3
 
1.5%
Space Separator 3
 
1.5%
Decimal Number 1
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
旅 23
14.9%
館 23
14.9%
汽 21
13.6%
車 21
13.6%
色 3
 
1.9%
綠 3
 
1.9%
宮 2
 
1.3%
殿 2
 
1.3%
夢 2
 
1.3%
店 2
 
1.3%
Other values (49) 52
33.8%
Lowercase Letter
ValueCountFrequency (%)
e 6
24.0%
t 6
24.0%
o 4
16.0%
l 3
12.0%
r 2
 
8.0%
a 1
 
4.0%
v 1
 
4.0%
i 1
 
4.0%
n 1
 
4.0%
Uppercase Letter
ValueCountFrequency (%)
L 4
30.8%
M 3
23.1%
S 2
15.4%
H 1
 
7.7%
A 1
 
7.7%
T 1
 
7.7%
K 1
 
7.7%
Other Punctuation
ValueCountFrequency (%)
? 4
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%
Space Separator
ValueCountFrequency (%)
3
100.0%
Decimal Number
ValueCountFrequency (%)
2 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Han 154
75.9%
Latin 38
 
18.7%
Common 11
 
5.4%

Most frequent character per script

Han
ValueCountFrequency (%)
旅 23
14.9%
館 23
14.9%
汽 21
13.6%
車 21
13.6%
色 3
 
1.9%
綠 3
 
1.9%
宮 2
 
1.3%
殿 2
 
1.3%
夢 2
 
1.3%
店 2
 
1.3%
Other values (49) 52
33.8%
Latin
ValueCountFrequency (%)
e 6
15.8%
t 6
15.8%
L 4
10.5%
o 4
10.5%
M 3
7.9%
l 3
7.9%
S 2
 
5.3%
r 2
 
5.3%
a 1
 
2.6%
v 1
 
2.6%
Other values (6) 6
15.8%
Common
ValueCountFrequency (%)
? 4
36.4%
- 3
27.3%
3
27.3%
2 1
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
CJK 154
75.9%
ASCII 49
 
24.1%

Most frequent character per block

CJK
ValueCountFrequency (%)
旅 23
14.9%
館 23
14.9%
汽 21
13.6%
車 21
13.6%
色 3
 
1.9%
綠 3
 
1.9%
宮 2
 
1.3%
殿 2
 
1.3%
夢 2
 
1.3%
店 2
 
1.3%
Other values (49) 52
33.8%
ASCII
ValueCountFrequency (%)
e 6
12.2%
t 6
12.2%
L 4
 
8.2%
o 4
 
8.2%
? 4
 
8.2%
- 3
 
6.1%
M 3
 
6.1%
3
 
6.1%
l 3
 
6.1%
S 2
 
4.1%
Other values (10) 11
22.4%

city_do_cd
Real number (ℝ)

HIGH CORRELATION 

Distinct9
Distinct (%)9.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.08
Minimum11
Maximum48
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:04:43.352056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile11
Q111
median28
Q344
95-th percentile47
Maximum48
Range37
Interquartile range (IQR)33

Descriptive statistics

Standard deviation16.048965
Coefficient of variation (CV)0.57154435
Kurtosis-1.9183864
Mean28.08
Median Absolute Deviation (MAD)17
Skewness-0.050935582
Sum2808
Variance257.56929
MonotonicityNot monotonic
2023-12-10T19:04:43.548223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
11 45
45.0%
41 14
 
14.0%
47 13
 
13.0%
42 9
 
9.0%
44 7
 
7.0%
28 6
 
6.0%
45 4
 
4.0%
48 1
 
1.0%
46 1
 
1.0%
ValueCountFrequency (%)
11 45
45.0%
28 6
 
6.0%
41 14
 
14.0%
42 9
 
9.0%
44 7
 
7.0%
45 4
 
4.0%
46 1
 
1.0%
47 13
 
13.0%
48 1
 
1.0%
ValueCountFrequency (%)
48 1
 
1.0%
47 13
 
13.0%
46 1
 
1.0%
45 4
 
4.0%
44 7
 
7.0%
42 9
 
9.0%
41 14
 
14.0%
28 6
 
6.0%
11 45
45.0%

city_gn_gu_cd
Real number (ℝ)

HIGH CORRELATION 

Distinct20
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28555.43
Minimum11305
Maximum48820
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:04:43.745209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11305
5-th percentile11500
Q111620
median28477.5
Q344150
95-th percentile47130
Maximum48820
Range37515
Interquartile range (IQR)32530

Descriptive statistics

Standard deviation15962.363
Coefficient of variation (CV)0.55899571
Kurtosis-1.9207579
Mean28555.43
Median Absolute Deviation (MAD)16827.5
Skewness-0.051052376
Sum2855543
Variance2.5479703 × 108
MonotonicityNot monotonic
2023-12-10T19:04:44.010247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
11500 19
19.0%
47130 11
11.0%
11680 10
10.0%
41820 9
9.0%
11620 9
9.0%
42150 7
 
7.0%
44150 6
 
6.0%
28245 5
 
5.0%
11740 4
 
4.0%
45790 4
 
4.0%
Other values (10) 16
16.0%
ValueCountFrequency (%)
11305 3
 
3.0%
11500 19
19.0%
11620 9
9.0%
11680 10
10.0%
11740 4
 
4.0%
28245 5
 
5.0%
28710 1
 
1.0%
41281 2
 
2.0%
41287 3
 
3.0%
41820 9
9.0%
ValueCountFrequency (%)
48820 1
 
1.0%
47830 1
 
1.0%
47250 1
 
1.0%
47130 11
11.0%
46720 1
 
1.0%
45790 4
 
4.0%
44250 1
 
1.0%
44150 6
6.0%
42820 2
 
2.0%
42150 7
7.0%

city_do_kor_lang_nm
Categorical

HIGH CORRELATION 

Distinct9
Distinct (%)9.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
서울
45 
경기
14 
경북
13 
강원
9 
충남
7 
Other values (4)
12 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique2 ?
Unique (%)2.0%

Sample

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

Common Values

ValueCountFrequency (%)
서울 45
45.0%
경기 14
 
14.0%
경북 13
 
13.0%
강원 9
 
9.0%
충남 7
 
7.0%
인천 6
 
6.0%
전북 4
 
4.0%
경남 1
 
1.0%
전남 1
 
1.0%

Length

2023-12-10T19:04:44.232409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:04:44.446779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서울 45
45.0%
경기 14
 
14.0%
경북 13
 
13.0%
강원 9
 
9.0%
충남 7
 
7.0%
인천 6
 
6.0%
전북 4
 
4.0%
경남 1
 
1.0%
전남 1
 
1.0%

city_gn_gu_kor_lang_nm
Categorical

HIGH CORRELATION 

Distinct19
Distinct (%)19.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
강서구
19 
경주시
11 
강남구
10 
관악구
9 
가평군
9 
Other values (14)
42 

Length

Max length8
Median length3
Mean length3.23
Min length3

Unique

Unique5 ?
Unique (%)5.0%

Sample

1st row가평군
2nd row상주시
3rd row가평군
4th row가평군
5th row가평군

Common Values

ValueCountFrequency (%)
강서구 19
19.0%
경주시 11
11.0%
강남구 10
10.0%
관악구 9
9.0%
가평군 9
9.0%
강릉시 7
 
7.0%
공주시 6
 
6.0%
계양구 5
 
5.0%
고창군 4
 
4.0%
강동구 4
 
4.0%
Other values (9) 16
16.0%

Length

2023-12-10T19:04:44.660882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
강서구 19
18.1%
경주시 11
10.5%
강남구 10
9.5%
관악구 9
8.6%
가평군 9
8.6%
강릉시 7
 
6.7%
공주시 6
 
5.7%
고양시 5
 
4.8%
계양구 5
 
4.8%
고창군 4
 
3.8%
Other values (10) 20
19.0%

gov_dn_kor_lang_nm
Text

Distinct58
Distinct (%)58.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:04:45.024748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length3.36
Min length2

Characters and Unicode

Total characters336
Distinct characters78
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique42 ?
Unique (%)42.0%

Sample

1st row청평면
2nd row서성동
3rd row조종면
4th row가평읍
5th row북면
ValueCountFrequency (%)
화곡1동 10
 
10.0%
불국동 7
 
7.0%
청평면 5
 
5.0%
신림동 5
 
5.0%
반포면 4
 
4.0%
흥덕면 4
 
4.0%
화곡본동 4
 
4.0%
역삼2동 3
 
3.0%
일산2동 2
 
2.0%
길동 2
 
2.0%
Other values (48) 54
54.0%
2023-12-10T19:04:45.721650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 71
21.1%
면 26
 
7.7%
화 19
 
5.7%
곡 18
 
5.4%
1 16
 
4.8%
2 13
 
3.9%
산 8
 
2.4%
삼 8
 
2.4%
평 7
 
2.1%
신 7
 
2.1%
Other values (68) 143
42.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 304
90.5%
Decimal Number 32
 
9.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 71
23.4%
면 26
 
8.6%
화 19
 
6.2%
곡 18
 
5.9%
산 8
 
2.6%
삼 8
 
2.6%
평 7
 
2.3%
신 7
 
2.3%
불 7
 
2.3%
성 7
 
2.3%
Other values (64) 126
41.4%
Decimal Number
ValueCountFrequency (%)
1 16
50.0%
2 13
40.6%
6 2
 
6.2%
4 1
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
Hangul 304
90.5%
Common 32
 
9.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 71
23.4%
면 26
 
8.6%
화 19
 
6.2%
곡 18
 
5.9%
산 8
 
2.6%
삼 8
 
2.6%
평 7
 
2.3%
신 7
 
2.3%
불 7
 
2.3%
성 7
 
2.3%
Other values (64) 126
41.4%
Common
ValueCountFrequency (%)
1 16
50.0%
2 13
40.6%
6 2
 
6.2%
4 1
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 304
90.5%
ASCII 32
 
9.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 71
23.4%
면 26
 
8.6%
화 19
 
6.2%
곡 18
 
5.9%
산 8
 
2.6%
삼 8
 
2.6%
평 7
 
2.3%
신 7
 
2.3%
불 7
 
2.3%
성 7
 
2.3%
Other values (64) 126
41.4%
ASCII
ValueCountFrequency (%)
1 16
50.0%
2 13
40.6%
6 2
 
6.2%
4 1
 
3.1%

city_do_jan_lang_nm
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
ソウル特別市
43 
京畿道
13 
慶?北道
12 
江原道
9 
忠?南道
7 
Other values (5)
16 

Length

Max length6
Median length5
Mean length4.7
Min length3

Unique

Unique2 ?
Unique (%)2.0%

Sample

1st row京畿道
2nd row<NA>
3rd row<NA>
4th row京畿道
5th row京畿道

Common Values

ValueCountFrequency (%)
ソウル特別市 43
43.0%
京畿道 13
 
13.0%
慶?北道 12
 
12.0%
江原道 9
 
9.0%
忠?南道 7
 
7.0%
仁川?域市 6
 
6.0%
<NA> 4
 
4.0%
全羅北道 4
 
4.0%
慶?南道 1
 
1.0%
全羅南道 1
 
1.0%

Length

2023-12-10T19:04:45.994308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:04:46.233037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
ソウル特別市 43
43.0%
京畿道 13
 
13.0%
慶?北道 12
 
12.0%
江原道 9
 
9.0%
忠?南道 7
 
7.0%
仁川?域市 6
 
6.0%
na 4
 
4.0%
全羅北道 4
 
4.0%
慶?南道 1
 
1.0%
全羅南道 1
 
1.0%

city_gn_gu_jan_lang_nm
Categorical

HIGH CORRELATION 

Distinct19
Distinct (%)19.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
江西?
19 
慶州市
11 
江南?
10 
加平郡
8 
冠岳?
8 
Other values (14)
44 

Length

Max length7
Median length3
Mean length3.21
Min length3

Unique

Unique4 ?
Unique (%)4.0%

Sample

1st row加平郡
2nd row<NA>
3rd row<NA>
4th row加平郡
5th row加平郡

Common Values

ValueCountFrequency (%)
江西? 19
19.0%
慶州市 11
11.0%
江南? 10
10.0%
加平郡 8
8.0%
冠岳? 8
8.0%
江陵市 7
 
7.0%
公州市 6
 
6.0%
桂陽? 5
 
5.0%
高敞郡 4
 
4.0%
<NA> 4
 
4.0%
Other values (9) 18
18.0%

Length

2023-12-10T19:04:46.490009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
江西 19
18.1%
慶州市 11
10.5%
江南 10
9.5%
加平郡 8
 
7.6%
冠岳 8
 
7.6%
江陵市 7
 
6.7%
公州市 6
 
5.7%
桂陽 5
 
4.8%
高陽市 5
 
4.8%
江東 4
 
3.8%
Other values (10) 22
21.0%

gov_dn_jan_lang_nm
Text

MISSING 

Distinct55
Distinct (%)57.3%
Missing4
Missing (%)4.0%
Memory size932.0 B
2023-12-10T19:04:46.881136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length3.3645833
Min length2

Characters and Unicode

Total characters323
Distinct characters77
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique39 ?
Unique (%)40.6%

Sample

1st row?平面
2nd row加平邑
3rd row北面
4th row加平邑
5th row?平面
ValueCountFrequency (%)
禾谷1洞 10
 
10.4%
洞 7
 
7.3%
平面 5
 
5.2%
禾谷本洞 4
 
4.2%
興?面 4
 
4.2%
盤浦面 4
 
4.2%
神林洞 4
 
4.2%
三2洞 3
 
3.1%
吉洞 2
 
2.1%
桂山1洞 2
 
2.1%
Other values (45) 51
53.1%
2023-12-10T19:04:47.479508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
洞 67
20.7%
? 33
 
10.2%
面 25
 
7.7%
谷 18
 
5.6%
禾 16
 
5.0%
1 16
 
5.0%
2 13
 
4.0%
三 8
 
2.5%
山 8
 
2.5%
平 7
 
2.2%
Other values (67) 112
34.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 258
79.9%
Other Punctuation 33
 
10.2%
Decimal Number 32
 
9.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
洞 67
26.0%
面 25
 
9.7%
谷 18
 
7.0%
禾 16
 
6.2%
三 8
 
3.1%
山 8
 
3.1%
平 7
 
2.7%
浦 6
 
2.3%
本 4
 
1.6%
盤 4
 
1.6%
Other values (62) 95
36.8%
Decimal Number
ValueCountFrequency (%)
1 16
50.0%
2 13
40.6%
6 2
 
6.2%
4 1
 
3.1%
Other Punctuation
ValueCountFrequency (%)
? 33
100.0%

Most occurring scripts

ValueCountFrequency (%)
Han 258
79.9%
Common 65
 
20.1%

Most frequent character per script

Han
ValueCountFrequency (%)
洞 67
26.0%
面 25
 
9.7%
谷 18
 
7.0%
禾 16
 
6.2%
三 8
 
3.1%
山 8
 
3.1%
平 7
 
2.7%
浦 6
 
2.3%
本 4
 
1.6%
盤 4
 
1.6%
Other values (62) 95
36.8%
Common
ValueCountFrequency (%)
? 33
50.8%
1 16
24.6%
2 13
 
20.0%
6 2
 
3.1%
4 1
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
CJK 258
79.9%
ASCII 65
 
20.1%

Most frequent character per block

CJK
ValueCountFrequency (%)
洞 67
26.0%
面 25
 
9.7%
谷 18
 
7.0%
禾 16
 
6.2%
三 8
 
3.1%
山 8
 
3.1%
平 7
 
2.7%
浦 6
 
2.3%
本 4
 
1.6%
盤 4
 
1.6%
Other values (62) 95
36.8%
ASCII
ValueCountFrequency (%)
? 33
50.8%
1 16
24.6%
2 13
 
20.0%
6 2
 
3.1%
4 1
 
1.5%

base_ymd
Date

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
Minimum2020-12-31 00:00:00
Maximum2020-12-31 00:00:00
2023-12-10T19:04:47.705346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:04:48.004740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2023-12-10T19:04:32.735413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:04:30.464159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:04:31.254232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:04:32.006606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:04:32.902942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:04:30.628187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:04:31.426258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:04:32.251431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:04:33.076154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:04:30.882164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:04:31.568680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:04:32.407806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:04:33.276790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:04:31.051272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:04:31.752123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:04:32.570842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:04:48.164366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
entrp_nmxpos_loypos_lakor_lang_nmeng_lang_nmjan_lang_nmchg_lang_nmchb_lang_nmcity_do_cdcity_gn_gu_cdcity_do_kor_lang_nmcity_gn_gu_kor_lang_nmgov_dn_kor_lang_nmcity_do_jan_lang_nmcity_gn_gu_jan_lang_nmgov_dn_jan_lang_nm
entrp_nm1.0000.9330.8181.0001.0001.0001.0001.0000.8620.9360.7770.9760.9940.7990.9700.993
xpos_lo0.9331.0000.7170.9330.0000.3870.0000.4390.7650.5410.7580.9560.9790.9631.0001.000
ypos_la0.8180.7171.0000.8180.0000.0000.0000.0000.5630.6160.9200.9140.9511.0000.9991.000
kor_lang_nm1.0000.9330.8181.0001.0001.0001.0001.0000.8620.9360.7770.9760.9940.7990.9700.993
eng_lang_nm1.0000.0000.0001.0001.0000.9961.0001.0000.0000.0000.0000.0000.0000.0000.0000.000
jan_lang_nm1.0000.3870.0001.0000.9961.0000.9910.9980.0000.0000.0000.0000.4710.0000.0000.200
chg_lang_nm1.0000.0000.0001.0001.0000.9911.0001.0000.0000.0000.0000.0000.0000.0000.0000.000
chb_lang_nm1.0000.4390.0001.0001.0000.9981.0001.0000.0000.0000.0000.0000.0000.0000.0000.000
city_do_cd0.8620.7650.5630.8620.0000.0000.0000.0001.0001.0001.0000.9941.0001.0000.9991.000
city_gn_gu_cd0.9360.5410.6160.9360.0000.0000.0000.0001.0001.0000.9450.9941.0000.9470.9941.000
city_do_kor_lang_nm0.7770.7580.9200.7770.0000.0000.0000.0001.0000.9451.0000.9901.0001.0000.9991.000
city_gn_gu_kor_lang_nm0.9760.9560.9140.9760.0000.0000.0000.0000.9940.9940.9901.0001.0000.9991.0001.000
gov_dn_kor_lang_nm0.9940.9790.9510.9940.0000.4710.0000.0001.0001.0001.0001.0001.0001.0001.0001.000
city_do_jan_lang_nm0.7990.9631.0000.7990.0000.0000.0000.0001.0000.9471.0000.9991.0001.0000.9991.000
city_gn_gu_jan_lang_nm0.9701.0000.9990.9700.0000.0000.0000.0000.9990.9940.9991.0001.0000.9991.0001.000
gov_dn_jan_lang_nm0.9931.0001.0000.9930.0000.2000.0000.0001.0001.0001.0001.0001.0001.0001.0001.000
2023-12-10T19:04:48.444663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
city_do_jan_lang_nmcity_gn_gu_jan_lang_nmcity_gn_gu_kor_lang_nmcity_do_kor_lang_nm
city_do_jan_lang_nm1.0000.8920.8921.000
city_gn_gu_jan_lang_nm0.8921.0001.0000.892
city_gn_gu_kor_lang_nm0.8921.0001.0000.888
city_do_kor_lang_nm1.0000.8920.8881.000
2023-12-10T19:04:48.686076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
xpos_loypos_lacity_do_cdcity_gn_gu_cdcity_do_kor_lang_nmcity_gn_gu_kor_lang_nmcity_do_jan_lang_nmcity_gn_gu_jan_lang_nm
xpos_lo1.0000.0310.5150.5740.5880.7970.7460.901
ypos_la0.0311.000-0.283-0.3110.6510.7360.9620.891
city_do_cd0.515-0.2831.0000.9560.9740.9080.9720.909
city_gn_gu_cd0.574-0.3110.9561.0000.8700.9150.8730.917
city_do_kor_lang_nm0.5880.6510.9740.8701.0000.8881.0000.892
city_gn_gu_kor_lang_nm0.7970.7360.9080.9150.8881.0000.8921.000
city_do_jan_lang_nm0.7460.9620.9720.8731.0000.8921.0000.892
city_gn_gu_jan_lang_nm0.9010.8910.9090.9170.8921.0000.8921.000

Missing values

2023-12-10T19:04:33.877682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:04:34.355859image/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.
2023-12-10T19:04:34.700671image/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_nmxpos_loypos_lakor_lang_nmeng_lang_nmjan_lang_nmchg_lang_nmchb_lang_nmcity_do_cdcity_gn_gu_cdcity_do_kor_lang_nmcity_gn_gu_kor_lang_nmgov_dn_kor_lang_nmcity_do_jan_lang_nmcity_gn_gu_jan_lang_nmgov_dn_jan_lang_nmbase_ymd
0강변파크993285.251970591.5강변파크Motelモ?テル汽?旅?汽車旅館4141820경기가평군청평면京畿道加平郡?平面2020-12-31
1파크여관128.16092836.417723파크여관<NA><NA><NA><NA>4747250경북상주시서성동<NA><NA><NA>2020-12-31
2로얄파크986592.51980014.89로얄파크Motelロイヤルパ?ク汽?旅?汽車旅館4141820경기가평군조종면<NA><NA><NA>2020-12-31
3리버풀파크1002196.31978464.85리버풀파크<NA><NA><NA><NA>4141820경기가평군가평읍京畿道加平郡加平邑2020-12-31
4명지산파크1004392.21987444.15명지산파크<NA><NA><NA><NA>4141820경기가평군북면京畿道加平郡北面2020-12-31
5밥스1002262.01978459.0밥스<NA><NA><NA><NA>4141820경기가평군가평읍京畿道加平郡加平邑2020-12-31
6설악파크992072.461969841.4설악파크<NA><NA><NA><NA>4141820경기가평군청평면京畿道加平郡?平面2020-12-31
7은정장 여관127.00736937.638335은정장 여관<NA><NA><NA><NA>1111305서울강북구수유동<NA><NA><NA>2020-12-31
8온천지가/열린모텔991944.561970193.91온천지가열린모텔<NA><NA><NA><NA>4141820경기가평군청평면京畿道加平郡?平面2020-12-31
9파인하우스/모텔989629.251963914.13파인하우스모텔<NA><NA><NA><NA>4141820경기가평군청평면京畿道加平郡?平面2020-12-31
entrp_nmxpos_loypos_lakor_lang_nmeng_lang_nmjan_lang_nmchg_lang_nmchb_lang_nmcity_do_cdcity_gn_gu_cdcity_do_kor_lang_nmcity_gn_gu_kor_lang_nmgov_dn_kor_lang_nmcity_do_jan_lang_nmcity_gn_gu_jan_lang_nmgov_dn_jan_lang_nmbase_ymd
90에스모텔977932.311818939.0에스모텔S MotelSモ?テルS汽?旅?S-汽車旅館4444150충남공주시반포면忠?南道公州市盤浦面2020-12-31
91월드무인텔977698.5241818913.466월드무인텔<NA><NA><NA><NA>4444150충남공주시반포면忠?南道公州市盤浦面2020-12-31
92가든파크949647.741943112.62가든파크Garden Park<NA>花?公?<NA>1111620서울관악구신림동ソウル特別市冠岳?神林洞2020-12-31
93국일949681.311943049.5국일Gukil<NA>?一<NA>1111620서울관악구신림동ソウル特別市冠岳?神林洞2020-12-31
94꿈의궁전모텔951656.951942364.76꿈의궁전모텔Kkumuigungjeon Motelクミグンジョンモ?テル?中殿堂旅?夢想宮殿汽車旅館1111620서울관악구낙성대동ソウル特別市冠岳?落星洞2020-12-31
95다이나모텔954099.181941904.38다이나모텔Dina Mote<NA>Dyna 旅?<NA>1111620서울관악구남현동ソウル特別市冠岳?南峴洞2020-12-31
96라노스949432.251943237.75라노스Ranos<NA>拉?斯旅?<NA>1111620서울관악구신림동ソウル特別市冠岳?神林洞2020-12-31
97램파트954114.51941771.25램파트Ram Part<NA>城?<NA>1111620서울관악구남현동ソウル特別市冠岳?南峴洞2020-12-31
98명동파크951074.871942616.87명동파크Myeongdong Park<NA>明洞公?<NA>1111620서울관악구청룡동ソウル特別市冠岳??龍洞2020-12-31
99명보장모텔949451.311943145.88명보장모텔Myeongbojang Motel<NA>明?山庄旅?<NA>1111620서울관악구신림동ソウル特別市冠岳?神林洞2020-12-31