Overview

Dataset statistics

Number of variables17
Number of observations100
Missing cells164
Missing cells (%)9.6%
Duplicate rows8
Duplicate rows (%)8.0%
Total size in memory13.8 KiB
Average record size in memory141.3 B

Variable types

Text6
Numeric3
Categorical8

Alerts

Dataset has 8 (8.0%) duplicate rowsDuplicates
city_gn_gu_jan_lang_nm is highly overall correlated with xpos_lo and 9 other fieldsHigh correlation
city_do_cd is highly overall correlated with xpos_lo and 9 other fieldsHigh correlation
base_ymd is highly overall correlated with xpos_lo and 9 other fieldsHigh correlation
gov_dn_kor_lang_nm is highly overall correlated with xpos_lo and 9 other fieldsHigh correlation
city_gn_gu_kor_lang_nm is highly overall correlated with xpos_lo and 9 other fieldsHigh correlation
gov_dn_jan_lang_nm is highly overall correlated with xpos_lo and 9 other fieldsHigh correlation
city_do_kor_lang_nm is highly overall correlated with xpos_lo and 9 other fieldsHigh correlation
city_do_jan_lang_nm is highly overall correlated with xpos_lo and 9 other fieldsHigh correlation
xpos_lo is highly overall correlated with city_do_cd and 7 other fieldsHigh correlation
ypos_la is highly overall correlated with city_do_cd and 7 other fieldsHigh correlation
city_gn_gu_cd is highly overall correlated with city_do_cd and 7 other fieldsHigh correlation
city_do_cd is highly imbalanced (87.9%)Imbalance
city_do_kor_lang_nm is highly imbalanced (87.9%)Imbalance
city_do_jan_lang_nm is highly imbalanced (80.6%)Imbalance
base_ymd is highly imbalanced (91.9%)Imbalance
eng_lang_nm has 32 (32.0%) missing valuesMissing
jan_lang_nm has 35 (35.0%) missing valuesMissing
chg_lang_nm has 22 (22.0%) missing valuesMissing
chb_lang_nm has 70 (70.0%) missing valuesMissing

Reproduction

Analysis started2023-12-10 09:44:20.789331
Analysis finished2023-12-10 09:44:26.723394
Duration5.93 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

entrp_nm
Text

Distinct91
Distinct (%)91.9%
Missing1
Missing (%)1.0%
Memory size932.0 B
2023-12-10T18:44:27.045355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length12
Mean length6.4242424
Min length2

Characters and Unicode

Total characters636
Distinct characters158
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

Unique83 ?
Unique (%)83.8%

Sample

1st row강릉관광호텔
2nd row호텔더반
3rd row브이브이호텔
4th row아이리스
5th row주문진호텔
ValueCountFrequency (%)
w호텔 2
 
2.0%
설악산/유스호스텔 2
 
2.0%
일경레저관광 2
 
2.0%
강촌/유스호스텔 2
 
2.0%
치악산/유스호스텔 2
 
2.0%
가고파호텔 2
 
2.0%
낙산/유스호스텔 2
 
2.0%
유스호스텔 2
 
2.0%
한솔오크밸리 1
 
1.0%
치악산호텔 1
 
1.0%
Other values (83) 83
82.2%
2023-12-10T18:44:27.773695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
텔 45
 
7.1%
호 43
 
6.8%
스 36
 
5.7%
도 31
 
4.9%
콘 30
 
4.7%
/ 30
 
4.7%
리 19
 
3.0%
산 19
 
3.0%
악 13
 
2.0%
이 11
 
1.7%
Other values (148) 359
56.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 600
94.3%
Other Punctuation 30
 
4.7%
Uppercase Letter 2
 
0.3%
Space Separator 2
 
0.3%
Close Punctuation 1
 
0.2%
Open Punctuation 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
텔 45
 
7.5%
호 43
 
7.2%
스 36
 
6.0%
도 31
 
5.2%
콘 30
 
5.0%
리 19
 
3.2%
산 19
 
3.2%
악 13
 
2.2%
이 11
 
1.8%
유 10
 
1.7%
Other values (143) 343
57.2%
Other Punctuation
ValueCountFrequency (%)
/ 30
100.0%
Uppercase Letter
ValueCountFrequency (%)
W 2
100.0%
Space Separator
ValueCountFrequency (%)
2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 600
94.3%
Common 34
 
5.3%
Latin 2
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
텔 45
 
7.5%
호 43
 
7.2%
스 36
 
6.0%
도 31
 
5.2%
콘 30
 
5.0%
리 19
 
3.2%
산 19
 
3.2%
악 13
 
2.2%
이 11
 
1.8%
유 10
 
1.7%
Other values (143) 343
57.2%
Common
ValueCountFrequency (%)
/ 30
88.2%
2
 
5.9%
) 1
 
2.9%
( 1
 
2.9%
Latin
ValueCountFrequency (%)
W 2
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 600
94.3%
ASCII 36
 
5.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
텔 45
 
7.5%
호 43
 
7.2%
스 36
 
6.0%
도 31
 
5.2%
콘 30
 
5.0%
리 19
 
3.2%
산 19
 
3.2%
악 13
 
2.2%
이 11
 
1.8%
유 10
 
1.7%
Other values (143) 343
57.2%
ASCII
ValueCountFrequency (%)
/ 30
83.3%
W 2
 
5.6%
2
 
5.6%
) 1
 
2.8%
( 1
 
2.8%

xpos_lo
Real number (ℝ)

HIGH CORRELATION 

Distinct91
Distinct (%)91.9%
Missing1
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean1066046.8
Minimum127.8525
Maximum1151028
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:44:28.054404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum127.8525
5-th percentile1011581.9
Q11053379
median1092691.2
Q31116774
95-th percentile1138919.8
Maximum1151028
Range1150900.1
Interquartile range (IQR)63395

Descriptive statistics

Standard deviation157840.43
Coefficient of variation (CV)0.14806144
Kurtosis42.040679
Mean1066046.8
Median Absolute Deviation (MAD)25914.771
Skewness-6.3970299
Sum1.0553863 × 108
Variance2.49136 × 1010
MonotonicityNot monotonic
2023-12-10T18:44:28.309173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1011446.0 2
 
2.0%
1037791.0 2
 
2.0%
1098771.0 2
 
2.0%
1128440.0 2
 
2.0%
1090085.0 2
 
2.0%
1093477.0 2
 
2.0%
1049533.0 2
 
2.0%
1047262.0 2
 
2.0%
1046013.0 1
 
1.0%
1056810.0 1
 
1.0%
Other values (81) 81
81.0%
ValueCountFrequency (%)
127.852495 1
1.0%
129.17674 1
1.0%
1007909.0 1
1.0%
1011446.0 2
2.0%
1011597.0 1
1.0%
1017047.0 1
1.0%
1020091.0 1
1.0%
1028093.0 1
1.0%
1028161.0 1
1.0%
1028355.0 1
1.0%
ValueCountFrequency (%)
1151028.0 1
1.0%
1143230.0 1
1.0%
1142980.0 1
1.0%
1142975.0 1
1.0%
1142806.0 1
1.0%
1138488.0 1
1.0%
1135876.0 1
1.0%
1132350.0 1
1.0%
1130766.0 1
1.0%
1130481.0 1
1.0%

ypos_la
Real number (ℝ)

HIGH CORRELATION 

Distinct91
Distinct (%)91.9%
Missing1
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean1932534.6
Minimum35.162511
Maximum2057004
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:44:28.583330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.162511
5-th percentile1909790.1
Q11924979
median1974978
Q32016540
95-th percentile2027533.2
Maximum2057004
Range2056968.8
Interquartile range (IQR)91561

Descriptive statistics

Standard deviation282530.64
Coefficient of variation (CV)0.14619694
Kurtosis44.401447
Mean1932534.6
Median Absolute Deviation (MAD)45914
Skewness-6.651751
Sum1.9132092 × 108
Variance7.9823561 × 1010
MonotonicityNot monotonic
2023-12-10T18:44:28.888353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1978535.0 2
 
2.0%
1927684.0 2
 
2.0%
2014121.0 2
 
2.0%
1909851.0 2
 
2.0%
2018959.0 2
 
2.0%
2027043.0 2
 
2.0%
1937080.0 2
 
2.0%
1918346.0 2
 
2.0%
1918450.0 1
 
1.0%
1917467.0 1
 
1.0%
Other values (81) 81
81.0%
ValueCountFrequency (%)
35.162511 1
1.0%
36.94929 1
1.0%
1904675.0 1
1.0%
1905518.0 1
1.0%
1909242.0 1
1.0%
1909851.0 2
2.0%
1912703.0 1
1.0%
1912813.0 1
1.0%
1912919.0 1
1.0%
1912946.0 1
1.0%
ValueCountFrequency (%)
2057004.0 1
1.0%
2053091.0 1
1.0%
2035665.0 1
1.0%
2035028.0 1
1.0%
2027670.0 1
1.0%
2027518.0 1
1.0%
2027277.0 1
1.0%
2027043.0 2
2.0%
2025805.0 1
1.0%
2024243.0 1
1.0%

kor_lang_nm
Text

Distinct90
Distinct (%)90.9%
Missing1
Missing (%)1.0%
Memory size932.0 B
2023-12-10T18:44:29.342314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length11
Mean length6.1414141
Min length2

Characters and Unicode

Total characters608
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

Unique81 ?
Unique (%)81.8%

Sample

1st row강릉관광호텔
2nd row호텔더반
3rd row브이브이호텔
4th row아이리스
5th row주문진호텔
ValueCountFrequency (%)
w호텔 2
 
2.0%
설악산유스호스텔 2
 
2.0%
일경레저관광 2
 
2.0%
강촌유스호스텔 2
 
2.0%
치악산유스호스텔 2
 
2.0%
현대수콘도미니엄 2
 
2.0%
가고파호텔 2
 
2.0%
유스호스텔 2
 
2.0%
낙산유스호스텔 2
 
2.0%
한솔오크밸리 1
 
1.0%
Other values (82) 82
81.2%
2023-12-10T18:44:30.335794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
텔 45
 
7.4%
호 43
 
7.1%
스 35
 
5.8%
도 31
 
5.1%
콘 30
 
4.9%
리 20
 
3.3%
산 19
 
3.1%
악 13
 
2.1%
이 12
 
2.0%
유 10
 
1.6%
Other values (146) 350
57.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 602
99.0%
Uppercase Letter 2
 
0.3%
Space Separator 2
 
0.3%
Open Punctuation 1
 
0.2%
Close Punctuation 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
텔 45
 
7.5%
호 43
 
7.1%
스 35
 
5.8%
도 31
 
5.1%
콘 30
 
5.0%
리 20
 
3.3%
산 19
 
3.2%
악 13
 
2.2%
이 12
 
2.0%
유 10
 
1.7%
Other values (142) 344
57.1%
Uppercase Letter
ValueCountFrequency (%)
W 2
100.0%
Space Separator
ValueCountFrequency (%)
2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 602
99.0%
Common 4
 
0.7%
Latin 2
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
텔 45
 
7.5%
호 43
 
7.1%
스 35
 
5.8%
도 31
 
5.1%
콘 30
 
5.0%
리 20
 
3.3%
산 19
 
3.2%
악 13
 
2.2%
이 12
 
2.0%
유 10
 
1.7%
Other values (142) 344
57.1%
Common
ValueCountFrequency (%)
2
50.0%
( 1
25.0%
) 1
25.0%
Latin
ValueCountFrequency (%)
W 2
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 602
99.0%
ASCII 6
 
1.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
텔 45
 
7.5%
호 43
 
7.1%
스 35
 
5.8%
도 31
 
5.1%
콘 30
 
5.0%
리 20
 
3.3%
산 19
 
3.2%
악 13
 
2.2%
이 12
 
2.0%
유 10
 
1.7%
Other values (142) 344
57.1%
ASCII
ValueCountFrequency (%)
W 2
33.3%
2
33.3%
( 1
16.7%
) 1
16.7%

eng_lang_nm
Text

MISSING 

Distinct58
Distinct (%)85.3%
Missing32
Missing (%)32.0%
Memory size932.0 B
2023-12-10T18:44:30.827964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length30
Median length20
Mean length14.455882
Min length5

Characters and Unicode

Total characters983
Distinct characters49
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

Unique54 ?
Unique (%)79.4%

Sample

1st rowGangneung Hotels
2nd rowVV Hotel
3rd rowNamsan Lotte Iris Apt.
4th rowJumunjin Hotel
5th rowKorea Hotels
ValueCountFrequency (%)
hotel 21
 
13.2%
condo 14
 
8.8%
youth 10
 
6.3%
hostel 10
 
6.3%
valley 5
 
3.1%
park 3
 
1.9%
hotels 3
 
1.9%
village 3
 
1.9%
oak 2
 
1.3%
hansol 2
 
1.3%
Other values (76) 86
54.1%
2023-12-10T18:44:31.623749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 110
 
11.2%
91
 
9.3%
e 86
 
8.7%
l 72
 
7.3%
t 64
 
6.5%
n 63
 
6.4%
a 58
 
5.9%
H 44
 
4.5%
u 37
 
3.8%
s 35
 
3.6%
Other values (39) 323
32.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 713
72.5%
Uppercase Letter 175
 
17.8%
Space Separator 91
 
9.3%
Other Punctuation 3
 
0.3%
Dash Punctuation 1
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 110
15.4%
e 86
12.1%
l 72
10.1%
t 64
9.0%
n 63
8.8%
a 58
8.1%
u 37
 
5.2%
s 35
 
4.9%
i 33
 
4.6%
r 28
 
3.9%
Other values (12) 127
17.8%
Uppercase Letter
ValueCountFrequency (%)
H 44
25.1%
S 23
13.1%
C 20
11.4%
V 11
 
6.3%
Y 11
 
6.3%
M 8
 
4.6%
D 7
 
4.0%
G 6
 
3.4%
O 6
 
3.4%
T 5
 
2.9%
Other values (12) 34
19.4%
Other Punctuation
ValueCountFrequency (%)
. 1
33.3%
' 1
33.3%
& 1
33.3%
Space Separator
ValueCountFrequency (%)
91
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 888
90.3%
Common 95
 
9.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 110
 
12.4%
e 86
 
9.7%
l 72
 
8.1%
t 64
 
7.2%
n 63
 
7.1%
a 58
 
6.5%
H 44
 
5.0%
u 37
 
4.2%
s 35
 
3.9%
i 33
 
3.7%
Other values (34) 286
32.2%
Common
ValueCountFrequency (%)
91
95.8%
. 1
 
1.1%
' 1
 
1.1%
- 1
 
1.1%
& 1
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 983
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 110
 
11.2%
91
 
9.3%
e 86
 
8.7%
l 72
 
7.3%
t 64
 
6.5%
n 63
 
6.4%
a 58
 
5.9%
H 44
 
4.5%
u 37
 
3.8%
s 35
 
3.6%
Other values (39) 323
32.9%

jan_lang_nm
Text

MISSING 

Distinct56
Distinct (%)86.2%
Missing35
Missing (%)35.0%
Memory size932.0 B
2023-12-10T18:44:32.503641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length30
Median length20
Mean length9.6461538
Min length4

Characters and Unicode

Total characters627
Distinct characters116
Distinct categories5 ?
Distinct scripts4 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique50 ?
Unique (%)76.9%

Sample

1st row?光ホテル
2nd rowブイブイホテル
3rd rowモ?テル
4th row注文津ホテル
5th rowコリアホテル
ValueCountFrequency (%)
condo 7
 
7.4%
ユ?スホステル 4
 
4.2%
hotel 4
 
4.2%
village 3
 
3.2%
valley 3
 
3.2%
光ホテル 3
 
3.2%
雪岳山ユ?スホステル 2
 
2.1%
airport 2
 
2.1%
江村ユ?スホステル 2
 
2.1%
koresco 2
 
2.1%
Other values (61) 63
66.3%
2023-12-10T18:44:33.412853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 38
 
6.1%
ル 36
 
5.7%
テ 33
 
5.3%
? 33
 
5.3%
ホ 32
 
5.1%
30
 
4.8%
ス 25
 
4.0%
l 25
 
4.0%
n 23
 
3.7%
e 22
 
3.5%
Other values (106) 330
52.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 281
44.8%
Lowercase Letter 213
34.0%
Uppercase Letter 68
 
10.8%
Other Punctuation 35
 
5.6%
Space Separator 30
 
4.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
ル 36
 
12.8%
テ 33
 
11.7%
ホ 32
 
11.4%
ス 25
 
8.9%
ン 10
 
3.6%
ユ 10
 
3.6%
山 9
 
3.2%
岳 7
 
2.5%
イ 7
 
2.5%
リ 6
 
2.1%
Other values (63) 106
37.7%
Lowercase Letter
ValueCountFrequency (%)
o 38
17.8%
l 25
11.7%
n 23
10.8%
e 22
10.3%
a 19
8.9%
i 14
 
6.6%
d 11
 
5.2%
r 11
 
5.2%
t 11
 
5.2%
y 6
 
2.8%
Other values (10) 33
15.5%
Uppercase Letter
ValueCountFrequency (%)
C 12
17.6%
S 8
11.8%
H 7
10.3%
V 6
8.8%
O 5
 
7.4%
M 4
 
5.9%
R 3
 
4.4%
T 3
 
4.4%
G 3
 
4.4%
A 3
 
4.4%
Other values (9) 14
20.6%
Other Punctuation
ValueCountFrequency (%)
? 33
94.3%
& 1
 
2.9%
/ 1
 
2.9%
Space Separator
ValueCountFrequency (%)
30
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 281
44.8%
Katakana 223
35.6%
Common 65
 
10.4%
Han 58
 
9.3%

Most frequent character per script

Katakana
ValueCountFrequency (%)
ル 36
16.1%
テ 33
14.8%
ホ 32
14.3%
ス 25
11.2%
ン 10
 
4.5%
ユ 10
 
4.5%
イ 7
 
3.1%
リ 6
 
2.7%
ド 6
 
2.7%
ア 6
 
2.7%
Other values (32) 52
23.3%
Latin
ValueCountFrequency (%)
o 38
 
13.5%
l 25
 
8.9%
n 23
 
8.2%
e 22
 
7.8%
a 19
 
6.8%
i 14
 
5.0%
C 12
 
4.3%
d 11
 
3.9%
r 11
 
3.9%
t 11
 
3.9%
Other values (29) 95
33.8%
Han
ValueCountFrequency (%)
山 9
15.5%
岳 7
 
12.1%
雪 6
 
10.3%
光 3
 
5.2%
江 3
 
5.2%
村 3
 
5.2%
浦 2
 
3.4%
大 2
 
3.4%
嶺 1
 
1.7%
雉 1
 
1.7%
Other values (21) 21
36.2%
Common
ValueCountFrequency (%)
? 33
50.8%
30
46.2%
& 1
 
1.5%
/ 1
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 346
55.2%
Katakana 223
35.6%
CJK 58
 
9.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 38
 
11.0%
? 33
 
9.5%
30
 
8.7%
l 25
 
7.2%
n 23
 
6.6%
e 22
 
6.4%
a 19
 
5.5%
i 14
 
4.0%
C 12
 
3.5%
d 11
 
3.2%
Other values (33) 119
34.4%
Katakana
ValueCountFrequency (%)
ル 36
16.1%
テ 33
14.8%
ホ 32
14.3%
ス 25
11.2%
ン 10
 
4.5%
ユ 10
 
4.5%
イ 7
 
3.1%
リ 6
 
2.7%
ド 6
 
2.7%
ア 6
 
2.7%
Other values (32) 52
23.3%
CJK
ValueCountFrequency (%)
山 9
15.5%
岳 7
 
12.1%
雪 6
 
10.3%
光 3
 
5.2%
江 3
 
5.2%
村 3
 
5.2%
浦 2
 
3.4%
大 2
 
3.4%
嶺 1
 
1.7%
雉 1
 
1.7%
Other values (21) 21
36.2%

chg_lang_nm
Text

MISSING 

Distinct70
Distinct (%)89.7%
Missing22
Missing (%)22.0%
Memory size932.0 B
2023-12-10T18:44:34.021780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length16
Mean length8.0897436
Min length3

Characters and Unicode

Total characters631
Distinct characters124
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

Unique63 ?
Unique (%)80.8%

Sample

1st row江陵旅游酒店
2nd rowVV酒店
3rd row汽?旅?
4th row注文津酒店
5th row??酒店
ValueCountFrequency (%)
condo 7
 
7.2%
年旅 3
 
3.1%
江村?年旅社 2
 
2.1%
山?年旅 2
 
2.1%
valley 2
 
2.1%
village 2
 
2.1%
valley酒店 2
 
2.1%
旅游酒店 2
 
2.1%
w酒店 2
 
2.1%
雉岳山?年旅 2
 
2.1%
Other values (70) 71
73.2%
2023-12-10T18:44:34.855823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
? 56
 
8.9%
o 41
 
6.5%
店 29
 
4.6%
酒 28
 
4.4%
n 27
 
4.3%
l 23
 
3.6%
e 22
 
3.5%
C 19
 
3.0%
19
 
3.0%
d 18
 
2.9%
Other values (114) 349
55.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 272
43.1%
Lowercase Letter 224
35.5%
Uppercase Letter 58
 
9.2%
Other Punctuation 57
 
9.0%
Space Separator 19
 
3.0%
Decimal Number 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
店 29
 
10.7%
酒 28
 
10.3%
旅 15
 
5.5%
山 15
 
5.5%
村 14
 
5.1%
岳 12
 
4.4%
年 11
 
4.0%
假 10
 
3.7%
度 10
 
3.7%
雪 9
 
3.3%
Other values (74) 119
43.8%
Lowercase Letter
ValueCountFrequency (%)
o 41
18.3%
n 27
12.1%
l 23
10.3%
e 22
9.8%
d 18
8.0%
a 18
8.0%
i 17
7.6%
y 9
 
4.0%
r 9
 
4.0%
t 7
 
3.1%
Other values (11) 33
14.7%
Uppercase Letter
ValueCountFrequency (%)
C 19
32.8%
V 8
13.8%
S 5
 
8.6%
H 5
 
8.6%
M 4
 
6.9%
W 3
 
5.2%
A 2
 
3.4%
G 2
 
3.4%
P 2
 
3.4%
T 2
 
3.4%
Other values (5) 6
 
10.3%
Other Punctuation
ValueCountFrequency (%)
? 56
98.2%
& 1
 
1.8%
Space Separator
ValueCountFrequency (%)
19
100.0%
Decimal Number
ValueCountFrequency (%)
1 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 282
44.7%
Han 272
43.1%
Common 77
 
12.2%

Most frequent character per script

Han
ValueCountFrequency (%)
店 29
 
10.7%
酒 28
 
10.3%
旅 15
 
5.5%
山 15
 
5.5%
村 14
 
5.1%
岳 12
 
4.4%
年 11
 
4.0%
假 10
 
3.7%
度 10
 
3.7%
雪 9
 
3.3%
Other values (74) 119
43.8%
Latin
ValueCountFrequency (%)
o 41
14.5%
n 27
 
9.6%
l 23
 
8.2%
e 22
 
7.8%
C 19
 
6.7%
d 18
 
6.4%
a 18
 
6.4%
i 17
 
6.0%
y 9
 
3.2%
r 9
 
3.2%
Other values (26) 79
28.0%
Common
ValueCountFrequency (%)
? 56
72.7%
19
 
24.7%
1 1
 
1.3%
& 1
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 359
56.9%
CJK 272
43.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
? 56
15.6%
o 41
11.4%
n 27
 
7.5%
l 23
 
6.4%
e 22
 
6.1%
C 19
 
5.3%
19
 
5.3%
d 18
 
5.0%
a 18
 
5.0%
i 17
 
4.7%
Other values (30) 99
27.6%
CJK
ValueCountFrequency (%)
店 29
 
10.7%
酒 28
 
10.3%
旅 15
 
5.5%
山 15
 
5.5%
村 14
 
5.1%
岳 12
 
4.4%
年 11
 
4.0%
假 10
 
3.7%
度 10
 
3.7%
雪 9
 
3.3%
Other values (74) 119
43.8%

chb_lang_nm
Text

MISSING 

Distinct18
Distinct (%)60.0%
Missing70
Missing (%)70.0%
Memory size932.0 B
2023-12-10T18:44:35.223700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length4
Mean length4.9
Min length2

Characters and Unicode

Total characters147
Distinct characters58
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13 ?
Unique (%)43.3%

Sample

1st row觀光飯店
2nd row汽車旅館
3rd row韓國飯店
4th row觀光飯店
5th row渡假村
ValueCountFrequency (%)
年旅館 6
20.0%
觀光飯店 4
13.3%
汽車旅館 3
 
10.0%
雪嶽山?年旅館 2
 
6.7%
年旅館 2
 
6.7%
渡假村 1
 
3.3%
民宿 1
 
3.3%
非常之六飯店 1
 
3.3%
思潮度假村 1
 
3.3%
現代秀度假村 1
 
3.3%
Other values (8) 8
26.7%
2023-12-10T18:44:35.782877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
館 13
 
8.8%
旅 11
 
7.5%
? 10
 
6.8%
年 10
 
6.8%
店 8
 
5.4%
飯 7
 
4.8%
村 7
 
4.8%
假 6
 
4.1%
度 5
 
3.4%
山 4
 
2.7%
Other values (48) 66
44.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 125
85.0%
Other Punctuation 10
 
6.8%
Lowercase Letter 9
 
6.1%
Uppercase Letter 2
 
1.4%
Decimal Number 1
 
0.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
館 13
 
10.4%
旅 11
 
8.8%
年 10
 
8.0%
店 8
 
6.4%
飯 7
 
5.6%
村 7
 
5.6%
假 6
 
4.8%
度 5
 
4.0%
山 4
 
3.2%
觀 4
 
3.2%
Other values (36) 50
40.0%
Lowercase Letter
ValueCountFrequency (%)
i 2
22.2%
g 1
11.1%
l 1
11.1%
y 1
11.1%
s 1
11.1%
a 1
11.1%
n 1
11.1%
h 1
11.1%
Uppercase Letter
ValueCountFrequency (%)
E 1
50.0%
H 1
50.0%
Other Punctuation
ValueCountFrequency (%)
? 10
100.0%
Decimal Number
ValueCountFrequency (%)
1 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Han 125
85.0%
Common 11
 
7.5%
Latin 11
 
7.5%

Most frequent character per script

Han
ValueCountFrequency (%)
館 13
 
10.4%
旅 11
 
8.8%
年 10
 
8.0%
店 8
 
6.4%
飯 7
 
5.6%
村 7
 
5.6%
假 6
 
4.8%
度 5
 
4.0%
山 4
 
3.2%
觀 4
 
3.2%
Other values (36) 50
40.0%
Latin
ValueCountFrequency (%)
i 2
18.2%
g 1
9.1%
E 1
9.1%
l 1
9.1%
y 1
9.1%
s 1
9.1%
a 1
9.1%
n 1
9.1%
H 1
9.1%
h 1
9.1%
Common
ValueCountFrequency (%)
? 10
90.9%
1 1
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
CJK 123
83.7%
ASCII 22
 
15.0%
CJK Compat Ideographs 2
 
1.4%

Most frequent character per block

CJK
ValueCountFrequency (%)
館 13
 
10.6%
旅 11
 
8.9%
年 10
 
8.1%
店 8
 
6.5%
飯 7
 
5.7%
村 7
 
5.7%
假 6
 
4.9%
度 5
 
4.1%
山 4
 
3.3%
觀 4
 
3.3%
Other values (35) 48
39.0%
ASCII
ValueCountFrequency (%)
? 10
45.5%
i 2
 
9.1%
g 1
 
4.5%
E 1
 
4.5%
l 1
 
4.5%
y 1
 
4.5%
s 1
 
4.5%
a 1
 
4.5%
n 1
 
4.5%
H 1
 
4.5%
Other values (2) 2
 
9.1%
CJK Compat Ideographs
ValueCountFrequency (%)
旅 2
100.0%

city_do_cd
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
42
97 
43
 
1
26
 
1
<NA>
 
1

Length

Max length4
Median length2
Mean length2.02
Min length2

Unique

Unique3 ?
Unique (%)3.0%

Sample

1st row42
2nd row43
3rd row42
4th row42
5th row42

Common Values

ValueCountFrequency (%)
42 97
97.0%
43 1
 
1.0%
26 1
 
1.0%
<NA> 1
 
1.0%

Length

2023-12-10T18:44:36.057875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:44:36.245914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
42 97
97.0%
43 1
 
1.0%
26 1
 
1.0%
na 1
 
1.0%

city_gn_gu_cd
Real number (ℝ)

HIGH CORRELATION 

Distinct17
Distinct (%)17.2%
Missing1
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean40940.202
Minimum11680
Maximum48820
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:44:36.413049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11680
5-th percentile11680
Q142130
median42210
Q342830
95-th percentile48820
Maximum48820
Range37140
Interquartile range (IQR)700

Descriptive statistics

Standard deviation8569.4366
Coefficient of variation (CV)0.20931593
Kurtosis7.4209473
Mean40940.202
Median Absolute Deviation (MAD)550
Skewness-2.8432607
Sum4053080
Variance73435243
MonotonicityNot monotonic
2023-12-10T18:44:36.634630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
42130 16
16.0%
48820 13
13.0%
42830 12
12.0%
42210 12
12.0%
42770 11
11.0%
11680 7
7.0%
42110 6
 
6.0%
42170 4
 
4.0%
42190 4
 
4.0%
42750 4
 
4.0%
Other values (7) 10
10.0%
ValueCountFrequency (%)
11680 7
7.0%
26350 1
 
1.0%
42110 6
 
6.0%
42130 16
16.0%
42150 1
 
1.0%
42170 4
 
4.0%
42190 4
 
4.0%
42210 12
12.0%
42230 1
 
1.0%
42750 4
 
4.0%
ValueCountFrequency (%)
48820 13
13.0%
43110 1
 
1.0%
42830 12
12.0%
42810 1
 
1.0%
42800 2
 
2.0%
42770 11
11.0%
42760 3
 
3.0%
42750 4
 
4.0%
42230 1
 
1.0%
42210 12
12.0%

city_do_kor_lang_nm
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
강원
97 
충북
 
1
부산
 
1
<NA>
 
1

Length

Max length4
Median length2
Mean length2.02
Min length2

Unique

Unique3 ?
Unique (%)3.0%

Sample

1st row강원
2nd row충북
3rd row강원
4th row강원
5th row강원

Common Values

ValueCountFrequency (%)
강원 97
97.0%
충북 1
 
1.0%
부산 1
 
1.0%
<NA> 1
 
1.0%

Length

2023-12-10T18:44:36.880418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:44:37.068611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
강원 97
97.0%
충북 1
 
1.0%
부산 1
 
1.0%
na 1
 
1.0%

city_gn_gu_kor_lang_nm
Categorical

HIGH CORRELATION 

Distinct17
Distinct (%)17.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
원주시
16 
고성군
13 
속초시
12 
양양군
12 
정선군
11 
Other values (12)
36 

Length

Max length4
Median length3
Mean length3.02
Min length3

Unique

Unique5 ?
Unique (%)5.0%

Sample

1st row강릉시
2nd row충주시
3rd row강릉시
4th row강릉시
5th row강릉시

Common Values

ValueCountFrequency (%)
원주시 16
16.0%
고성군 13
13.0%
속초시 12
12.0%
양양군 12
12.0%
정선군 11
11.0%
강릉시 8
8.0%
춘천시 6
 
6.0%
동해시 4
 
4.0%
태백시 4
 
4.0%
영월군 4
 
4.0%
Other values (7) 10
10.0%

Length

2023-12-10T18:44:37.271194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
원주시 16
16.0%
고성군 13
13.0%
속초시 12
12.0%
양양군 12
12.0%
정선군 11
11.0%
강릉시 8
8.0%
춘천시 6
 
6.0%
영월군 4
 
4.0%
태백시 4
 
4.0%
동해시 4
 
4.0%
Other values (7) 10
10.0%

gov_dn_kor_lang_nm
Categorical

HIGH CORRELATION 

Distinct46
Distinct (%)46.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
토성면
9 
고한읍
8 
노학동
7 
판부면
 
4
소초면
 
4
Other values (41)
68 

Length

Max length5
Median length3
Mean length3.07
Min length2

Unique

Unique23 ?
Unique (%)23.0%

Sample

1st row포남1동
2nd row대소원면
3rd row교1동
4th row옥계면
5th row주문진읍

Common Values

ValueCountFrequency (%)
토성면 9
 
9.0%
고한읍 8
 
8.0%
노학동 7
 
7.0%
판부면 4
 
4.0%
소초면 4
 
4.0%
남산면 4
 
4.0%
대포동 3
 
3.0%
강현면 3
 
3.0%
사북읍 3
 
3.0%
지정면 3
 
3.0%
Other values (36) 52
52.0%

Length

2023-12-10T18:44:37.490230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
토성면 9
 
9.0%
고한읍 8
 
8.0%
노학동 7
 
7.0%
판부면 4
 
4.0%
소초면 4
 
4.0%
남산면 4
 
4.0%
현북면 3
 
3.0%
천곡동 3
 
3.0%
양양읍 3
 
3.0%
지정면 3
 
3.0%
Other values (36) 52
52.0%

city_do_jan_lang_nm
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
江原道
97 
<NA>
 
3

Length

Max length4
Median length3
Mean length3.03
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row江原道
2nd row<NA>
3rd row江原道
4th row江原道
5th row江原道

Common Values

ValueCountFrequency (%)
江原道 97
97.0%
<NA> 3
 
3.0%

Length

2023-12-10T18:44:37.707456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:44:37.874360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
江原道 97
97.0%
na 3
 
3.0%

city_gn_gu_jan_lang_nm
Categorical

HIGH CORRELATION 

Distinct15
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
原州市
16 
高城郡
13 
束草市
12 
襄陽郡
12 
旌善郡
11 
Other values (10)
36 

Length

Max length4
Median length3
Mean length3.03
Min length3

Unique

Unique2 ?
Unique (%)2.0%

Sample

1st row江陵市
2nd row<NA>
3rd row江陵市
4th row江陵市
5th row江陵市

Common Values

ValueCountFrequency (%)
原州市 16
16.0%
高城郡 13
13.0%
束草市 12
12.0%
襄陽郡 12
12.0%
旌善郡 11
11.0%
江陵市 8
8.0%
春川市 6
 
6.0%
東海市 4
 
4.0%
寧越郡 4
 
4.0%
太白市 4
 
4.0%
Other values (5) 10
10.0%

Length

2023-12-10T18:44:38.058433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
原州市 16
16.0%
高城郡 13
13.0%
束草市 12
12.0%
襄陽郡 12
12.0%
旌善郡 11
11.0%
江陵市 8
8.0%
春川市 6
 
6.0%
東海市 4
 
4.0%
寧越郡 4
 
4.0%
太白市 4
 
4.0%
Other values (5) 10
10.0%

gov_dn_jan_lang_nm
Categorical

HIGH CORRELATION 

Distinct44
Distinct (%)44.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
土城面
9 
古汗邑
8 
蘆鶴洞
7 
南山面
 
4
所草面
 
4
Other values (39)
68 

Length

Max length5
Median length3
Mean length3.09
Min length2

Unique

Unique20 ?
Unique (%)20.0%

Sample

1st row浦南1洞
2nd row<NA>
3rd row校1洞
4th row玉?面
5th row注文津邑

Common Values

ValueCountFrequency (%)
土城面 9
 
9.0%
古汗邑 8
 
8.0%
蘆鶴洞 7
 
7.0%
南山面 4
 
4.0%
所草面 4
 
4.0%
板富面 4
 
4.0%
?北邑 3
 
3.0%
泉谷洞 3
 
3.0%
?北面 3
 
3.0%
大浦洞 3
 
3.0%
Other values (34) 52
52.0%

Length

2023-12-10T18:44:38.308078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
土城面 9
 
9.0%
古汗邑 8
 
8.0%
蘆鶴洞 7
 
7.0%
南山面 4
 
4.0%
所草面 4
 
4.0%
板富面 4
 
4.0%
降峴面 3
 
3.0%
地正面 3
 
3.0%
襄陽邑 3
 
3.0%
na 3
 
3.0%
Other values (34) 52
52.0%

base_ymd
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2020-12-31
99 
<NA>
 
1

Length

Max length10
Median length10
Mean length9.94
Min length4

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row2020-12-31
2nd row2020-12-31
3rd row2020-12-31
4th row2020-12-31
5th row2020-12-31

Common Values

ValueCountFrequency (%)
2020-12-31 99
99.0%
<NA> 1
 
1.0%

Length

2023-12-10T18:44:38.591700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:44:38.764892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020-12-31 99
99.0%
na 1
 
1.0%

Interactions

2023-12-10T18:44:24.603143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:44:23.394974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:44:24.064823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:44:24.781585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:44:23.571701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:44:24.221364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:44:24.938843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:44:23.867468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:44:24.421232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T18:44:38.901934image/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_gn_gu_jan_lang_nmgov_dn_jan_lang_nm
entrp_nm1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
xpos_lo1.0001.0001.0001.0000.0001.0001.0000.4250.9400.8200.9400.9551.0000.9431.000
ypos_la1.0001.0001.0001.000NaNNaNNaNNaN1.0000.4491.0001.0001.000NaNNaN
kor_lang_nm1.0001.0001.0001.0001.0001.0000.9991.0001.0001.0001.0001.0001.0001.0001.000
eng_lang_nm1.0000.000NaN1.0001.0001.0000.9980.988NaN1.000NaN0.9510.9910.9510.991
jan_lang_nm1.0001.000NaN1.0001.0001.0000.9980.993NaN1.000NaN0.9860.9860.9860.986
chg_lang_nm1.0001.000NaN0.9990.9980.9981.0000.988NaN1.000NaN0.9360.9920.9360.992
chb_lang_nm1.0000.425NaN1.0000.9880.9930.9881.000NaN1.000NaN0.8990.9210.8990.921
city_do_cd1.0000.9401.0001.000NaNNaNNaNNaN1.0000.9401.0001.0001.000NaNNaN
city_gn_gu_cd1.0000.8200.4491.0001.0001.0001.0001.0000.9401.0000.9401.0001.0001.0001.000
city_do_kor_lang_nm1.0000.9401.0001.000NaNNaNNaNNaN1.0000.9401.0001.0001.000NaNNaN
city_gn_gu_kor_lang_nm1.0000.9551.0001.0000.9510.9860.9360.8991.0001.0001.0001.0001.0001.0001.000
gov_dn_kor_lang_nm1.0001.0001.0001.0000.9910.9860.9920.9211.0001.0001.0001.0001.0001.0001.000
city_gn_gu_jan_lang_nm1.0000.943NaN1.0000.9510.9860.9360.899NaN1.000NaN1.0001.0001.0001.000
gov_dn_jan_lang_nm1.0001.000NaN1.0000.9910.9860.9920.921NaN1.000NaN1.0001.0001.0001.000
2023-12-10T18:44:39.242660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
city_gn_gu_jan_lang_nmcity_do_cdbase_ymdgov_dn_kor_lang_nmcity_gn_gu_kor_lang_nmgov_dn_jan_lang_nmcity_do_kor_lang_nmcity_do_jan_lang_nm
city_gn_gu_jan_lang_nm1.0001.0001.0000.8071.0000.8071.0001.000
city_do_cd1.0001.0001.0000.7500.9301.0001.0001.000
base_ymd1.0001.0001.0001.0001.0001.0001.0001.000
gov_dn_kor_lang_nm0.8070.7501.0001.0000.8071.0000.7501.000
city_gn_gu_kor_lang_nm1.0000.9301.0000.8071.0000.8070.9301.000
gov_dn_jan_lang_nm0.8071.0001.0001.0000.8071.0001.0001.000
city_do_kor_lang_nm1.0001.0001.0000.7500.9301.0001.0001.000
city_do_jan_lang_nm1.0001.0001.0001.0001.0001.0001.0001.000
2023-12-10T18:44:39.474410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
xpos_loypos_lacity_gn_gu_cdcity_do_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
xpos_lo1.000-0.1250.1250.7000.7000.8440.7501.0000.7560.7541.000
ypos_la-0.1251.0000.4200.9950.9950.9250.7461.0001.0001.0001.000
city_gn_gu_cd0.1250.4201.0000.6930.6930.9090.7541.0000.9020.7581.000
city_do_cd0.7000.9950.6931.0001.0000.9300.7501.0001.0001.0001.000
city_do_kor_lang_nm0.7000.9950.6931.0001.0000.9300.7501.0001.0001.0001.000
city_gn_gu_kor_lang_nm0.8440.9250.9090.9300.9301.0000.8071.0001.0000.8071.000
gov_dn_kor_lang_nm0.7500.7460.7540.7500.7500.8071.0001.0000.8071.0001.000
city_do_jan_lang_nm1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
city_gn_gu_jan_lang_nm0.7561.0000.9021.0001.0001.0000.8071.0001.0000.8071.000
gov_dn_jan_lang_nm0.7541.0000.7581.0001.0000.8071.0001.0000.8071.0001.000
base_ymd1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000

Missing values

2023-12-10T18:44:25.204747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T18:44:25.763705image/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.
2023-12-10T18:44:26.300257image/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
0강릉관광호텔1123609.01974978.0강릉관광호텔Gangneung Hotels?光ホテル江陵旅游酒店觀光飯店4242150강원강릉시포남1동江原道江陵市浦南1洞2020-12-31
1호텔더반127.85249536.94929호텔더반<NA><NA><NA><NA>4343110충북충주시대소원면<NA><NA><NA>2020-12-31
2브이브이호텔1121363.01974950.0브이브이호텔VV HotelブイブイホテルVV酒店<NA>4211680강원강릉시교1동江原道江陵市校1洞2020-12-31
3아이리스1138488.01957504.0아이리스Namsan Lotte Iris Apt.モ?テル汽?旅?汽車旅館4211680강원강릉시옥계면江原道江陵市玉?面2020-12-31
4주문진호텔1116855.01989213.0주문진호텔Jumunjin Hotel注文津ホテル注文津酒店<NA>4211680강원강릉시주문진읍江原道江陵市注文津邑2020-12-31
5코리아호텔1116693.01988025.0코리아호텔Korea Hotelsコリアホテル??酒店韓國飯店4211680강원강릉시주문진읍江原道江陵市注文津邑2020-12-31
6향기1135876.01966262.0향기Scent<NA><NA><NA>4211680강원강릉시강동면江原道江陵市江東面2020-12-31
7(주)호텔롯데 시그니엘 부산129.1767435.162511(주)호텔롯데 시그니엘 부산<NA><NA><NA><NA>2626350부산해운대구중동<NA><NA><NA>2020-12-31
8화이트캐슬/리조트1130766.01970061.0화이트캐슬리조트White Castle Resort?光ホテルWhite Castle度假村觀光飯店4211680강원강릉시강동면江原道江陵市江東面2020-12-31
9경포산장/콘도미니엄1123239.01979820.0경포산장콘도미니엄<NA>鏡浦山?コンドミニアム?浦山庄Condo<NA>4211680강원강릉시경포동江原道江陵市鏡浦洞2020-12-31
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
90라데나/콘도미니엄1017047.01985890.0라데나콘도미니엄<NA><NA>拉戴那Condo<NA>4242110강원춘천시강남동江原道春川市江南洞2020-12-31
91엘리시안강촌/엘스위트1007909.01980288.0엘리시안강촌Elysian Gangchonエリシアン江村Elysian江村Elysian江村4242110강원춘천시남산면江原道春川市南山面2020-12-31
92강촌/유스호스텔1011446.01978535.0강촌유스호스텔Youth Hostel江村ユ?スホステル江村?年旅社?年旅館4242110강원춘천시남산면江原道春川市南山面2020-12-31
93메르디앙호텔1132350.01909242.0메르디앙호텔Merdien Hotelホテル?メルディアン默里迪恩酒店<NA>4242190강원태백시황지동江原道太白市?池洞2020-12-31
94스카이호텔1130481.01904675.0스카이호텔Sky Hotelスカイホテル天空酒店汽車旅館4242190강원태백시문곡소도동江原道太白市文曲所道洞2020-12-31
95유스호스텔1128440.01909851.0유스호스텔Youth Hostelユ?スホステル?年旅??年旅館4242190강원태백시상장동江原道太白市上長洞2020-12-31
96유스호스텔1128440.01909851.0유스호스텔Youth Hostelユ?スホステル?年旅??年旅館4242190강원태백시상장동江原道太白市上長洞2020-12-31
97그린엔블루1106081.01964534.0그린엔블루The Green&BlueThe Green&BlueGreen&Blue酒店<NA>4242760강원평창군대관령면江原道平昌郡大?嶺面2020-12-31
98대관령호텔1106368.01964344.0대관령호텔Daegwanryung Hotel大?嶺ホテル大??酒店<NA>4242760강원평창군대관령면江原道平昌郡大?嶺面2020-12-31
99산정호텔1094279.01962123.0산정호텔Motel山井ホテル山井酒店汽車旅館4242760강원평창군진부면江原道平昌郡珍富面2020-12-31

Duplicate rows

Most frequently occurring

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# duplicates
0W호텔1037791.01927684.0W호텔W HotelWホテルW酒店<NA>4242130강원원주시단계동江原道原州市丹?洞2020-12-312
1가고파호텔1047262.01918346.0가고파호텔<NA><NA><NA><NA>4242130강원원주시판부면江原道原州市板富面2020-12-312
2강촌/유스호스텔1011446.01978535.0강촌유스호스텔Youth Hostel江村ユ?スホステル江村?年旅社?年旅館4242110강원춘천시남산면江原道春川市南山面2020-12-312
3낙산/유스호스텔1098771.02014121.0낙산유스호스텔Youth Hostel?山ユ?スホステル?山?年旅??年旅館4242830강원양양군강현면江原道襄陽郡降峴面2020-12-312
4설악산/유스호스텔1090085.02018959.0설악산유스호스텔Seolarksan Youth Hostel雪岳山ユ?スホステル雪岳山?年旅?雪嶽山?年旅館4242210강원속초시대포동江原道束草市大浦洞2020-12-312
5유스호스텔1128440.01909851.0유스호스텔Youth Hostelユ?スホステル?年旅??年旅館4242190강원태백시상장동江原道太白市上長洞2020-12-312
6일경레저관광1093477.02027043.0일경레저관광<NA><NA><NA><NA>4248820강원고성군토성면江原道高城郡土城面2020-12-312
7치악산/유스호스텔1049533.01937080.0치악산유스호스텔Youth Hostelユ?スホステル雉岳山?年旅??年旅館4242130강원원주시소초면江原道原州市所草面2020-12-312