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

Text6
Numeric4
Categorical6
DateTime1

Alerts

base_ymd has constant value ""Constant
city_gn_gu_jan_lang_nm is highly overall correlated with xpos_lo and 8 other fieldsHigh correlation
gov_dn_kor_lang_nm is highly overall correlated with xpos_lo and 8 other fieldsHigh correlation
city_gn_gu_kor_lang_nm is highly overall correlated with xpos_lo and 8 other fieldsHigh correlation
gov_dn_jan_lang_nm is highly overall correlated with xpos_lo and 8 other fieldsHigh correlation
city_do_kor_lang_nm is highly overall correlated with xpos_lo and 8 other fieldsHigh correlation
city_do_jan_lang_nm is highly overall correlated with xpos_lo and 8 other fieldsHigh correlation
xpos_lo is highly overall correlated with ypos_la and 7 other fieldsHigh correlation
ypos_la is highly overall correlated with xpos_lo and 6 other fieldsHigh correlation
city_do_cd is highly overall correlated with xpos_lo and 7 other fieldsHigh correlation
city_gn_gu_cd is highly overall correlated with city_do_cd and 6 other fieldsHigh correlation
eng_lang_nm has 51 (51.0%) missing valuesMissing
jan_lang_nm has 43 (43.0%) missing valuesMissing
chg_lang_nm has 28 (28.0%) missing valuesMissing
chb_lang_nm has 74 (74.0%) missing valuesMissing

Reproduction

Analysis started2023-12-10 09:54:06.139961
Analysis finished2023-12-10 09:54:13.630796
Duration7.49 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

entrp_nm
Text

Distinct93
Distinct (%)93.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T18:54:13.921883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length11
Mean length7.38
Min length3

Characters and Unicode

Total characters738
Distinct characters202
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

Unique86 ?
Unique (%)86.0%

Sample

1st row가평/쁘띠프랑스
2nd row성산 스튜디오
3rd row금강유원지
4th row번창유원지
5th row생텍쥐페리기념관
ValueCountFrequency (%)
강원바우길/11구간 2
 
2.0%
강원바우길/4구간 2
 
2.0%
강원바우길/6구간 2
 
2.0%
강원바우길/7구간 2
 
2.0%
강원바우길/9구간 2
 
2.0%
강원바우길/2구간 2
 
2.0%
가우도출렁다리 2
 
2.0%
강원바우길/3구간 1
 
1.0%
강릉솔향/수목원 1
 
1.0%
강릉카페거리 1
 
1.0%
Other values (85) 85
83.3%
2023-12-10T18:54:14.673411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
원 54
 
7.3%
/ 41
 
5.6%
지 30
 
4.1%
강 30
 
4.1%
유 27
 
3.7%
구 24
 
3.3%
길 21
 
2.8%
우 20
 
2.7%
간 17
 
2.3%
바 16
 
2.2%
Other values (192) 458
62.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 668
90.5%
Other Punctuation 41
 
5.6%
Decimal Number 18
 
2.4%
Close Punctuation 3
 
0.4%
Open Punctuation 3
 
0.4%
Uppercase Letter 3
 
0.4%
Space Separator 2
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
원 54
 
8.1%
지 30
 
4.5%
강 30
 
4.5%
유 27
 
4.0%
구 24
 
3.6%
길 21
 
3.1%
우 20
 
3.0%
간 17
 
2.5%
바 16
 
2.4%
리 15
 
2.2%
Other values (176) 414
62.0%
Decimal Number
ValueCountFrequency (%)
1 5
27.8%
2 2
 
11.1%
9 2
 
11.1%
7 2
 
11.1%
6 2
 
11.1%
4 2
 
11.1%
5 1
 
5.6%
8 1
 
5.6%
3 1
 
5.6%
Uppercase Letter
ValueCountFrequency (%)
J 1
33.3%
G 1
33.3%
L 1
33.3%
Other Punctuation
ValueCountFrequency (%)
/ 41
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 668
90.5%
Common 67
 
9.1%
Latin 3
 
0.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
원 54
 
8.1%
지 30
 
4.5%
강 30
 
4.5%
유 27
 
4.0%
구 24
 
3.6%
길 21
 
3.1%
우 20
 
3.0%
간 17
 
2.5%
바 16
 
2.4%
리 15
 
2.2%
Other values (176) 414
62.0%
Common
ValueCountFrequency (%)
/ 41
61.2%
1 5
 
7.5%
) 3
 
4.5%
( 3
 
4.5%
2 2
 
3.0%
9 2
 
3.0%
7 2
 
3.0%
6 2
 
3.0%
4 2
 
3.0%
2
 
3.0%
Other values (3) 3
 
4.5%
Latin
ValueCountFrequency (%)
J 1
33.3%
G 1
33.3%
L 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 668
90.5%
ASCII 70
 
9.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
원 54
 
8.1%
지 30
 
4.5%
강 30
 
4.5%
유 27
 
4.0%
구 24
 
3.6%
길 21
 
3.1%
우 20
 
3.0%
간 17
 
2.5%
바 16
 
2.4%
리 15
 
2.2%
Other values (176) 414
62.0%
ASCII
ValueCountFrequency (%)
/ 41
58.6%
1 5
 
7.1%
) 3
 
4.3%
( 3
 
4.3%
2 2
 
2.9%
9 2
 
2.9%
7 2
 
2.9%
6 2
 
2.9%
4 2
 
2.9%
2
 
2.9%
Other values (6) 6
 
8.6%

xpos_lo
Real number (ℝ)

HIGH CORRELATION 

Distinct97
Distinct (%)97.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean992885.51
Minimum126.41931
Maximum1136775
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:54:14.927598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.41931
5-th percentile933196.93
Q1959575
median992469.5
Q31116018.5
95-th percentile1135929
Maximum1136775
Range1136648.6
Interquartile range (IQR)156443.5

Descriptive statistics

Standard deviation189978.53
Coefficient of variation (CV)0.19133982
Kurtosis20.998984
Mean992885.51
Median Absolute Deviation (MAD)34559.5
Skewness-4.3039275
Sum99288551
Variance3.6091842 × 1010
MonotonicityNot monotonic
2023-12-10T18:54:15.256573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
993424.0 2
 
2.0%
1135929.0 2
 
2.0%
1136195.0 2
 
2.0%
999131.0 1
 
1.0%
1124666.0 1
 
1.0%
1125222.0 1
 
1.0%
1117919.0 1
 
1.0%
1115385.0 1
 
1.0%
1111696.0 1
 
1.0%
1111914.0 1
 
1.0%
Other values (87) 87
87.0%
ValueCountFrequency (%)
126.419307 1
1.0%
126.864735 1
1.0%
127.74289 1
1.0%
913252.0 1
1.0%
932416.5771 1
1.0%
933238.0 1
1.0%
934872.0 1
1.0%
936773.0 1
1.0%
938416.0 1
1.0%
938561.0 1
1.0%
ValueCountFrequency (%)
1136775.0 1
1.0%
1136195.0 2
2.0%
1136157.0 1
1.0%
1135929.0 2
2.0%
1135617.0 1
1.0%
1135166.0 1
1.0%
1132529.0 1
1.0%
1132278.0 1
1.0%
1130261.0 1
1.0%
1128166.0 1
1.0%

ypos_la
Real number (ℝ)

HIGH CORRELATION 

Distinct98
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1889101.7
Minimum33.293983
Maximum1998567
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:54:15.644233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum33.293983
5-th percentile1626498
Q11948970.2
median1967732.5
Q31977211.2
95-th percentile1990142.2
Maximum1998567
Range1998533.7
Interquartile range (IQR)28241

Descriptive statistics

Standard deviation343136.83
Coefficient of variation (CV)0.18164021
Kurtosis26.504182
Mean1889101.7
Median Absolute Deviation (MAD)10629.5
Skewness-5.1711357
Sum1.8891017 × 108
Variance1.1774288 × 1011
MonotonicityNot monotonic
2023-12-10T18:54:15.985819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1966040.0 2
 
2.0%
1965962.0 2
 
2.0%
1968379.0 1
 
1.0%
1975643.0 1
 
1.0%
1980175.0 1
 
1.0%
1977211.0 1
 
1.0%
1973858.0 1
 
1.0%
1967109.0 1
 
1.0%
1968883.0 1
 
1.0%
1978240.0 1
 
1.0%
Other values (88) 88
88.0%
ValueCountFrequency (%)
33.293983 1
1.0%
33.418029 1
1.0%
34.756786 1
1.0%
1615151.0 1
1.0%
1615953.0 1
1.0%
1627053.0 1
1.0%
1628902.2 1
1.0%
1679426.0 1
1.0%
1685479.0 1
1.0%
1944378.156 1
1.0%
ValueCountFrequency (%)
1998567.0 1
1.0%
1996419.0 1
1.0%
1996250.0 1
1.0%
1993360.0 1
1.0%
1990830.0 1
1.0%
1990106.0 1
1.0%
1989544.0 1
1.0%
1989085.0 1
1.0%
1984266.0 1
1.0%
1983504.0 1
1.0%

kor_lang_nm
Text

Distinct92
Distinct (%)92.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T18:54:16.383812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length12.5
Mean length6.97
Min length3

Characters and Unicode

Total characters697
Distinct characters201
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

Unique84 ?
Unique (%)84.0%

Sample

1st row가평쁘띠프랑스
2nd row성산 스튜디오
3rd row금강유원지
4th row번창유원지
5th row생텍쥐페리기념관
ValueCountFrequency (%)
강원바우길11구간 2
 
2.0%
강원바우길4구간 2
 
2.0%
강원바우길6구간 2
 
2.0%
강원바우길7구간 2
 
2.0%
강원바우길9구간 2
 
2.0%
청평호반매운탕촌 2
 
2.0%
강원바우길2구간 2
 
2.0%
가우도출렁다리 2
 
2.0%
용원사파리동산 1
 
1.0%
안목카페거리 1
 
1.0%
Other values (84) 84
82.4%
2023-12-10T18:54:17.051826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
원 54
 
7.7%
강 30
 
4.3%
지 30
 
4.3%
유 27
 
3.9%
구 24
 
3.4%
길 21
 
3.0%
우 20
 
2.9%
간 17
 
2.4%
바 16
 
2.3%
리 15
 
2.2%
Other values (191) 443
63.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 668
95.8%
Decimal Number 18
 
2.6%
Close Punctuation 3
 
0.4%
Open Punctuation 3
 
0.4%
Uppercase Letter 3
 
0.4%
Space Separator 2
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
원 54
 
8.1%
강 30
 
4.5%
지 30
 
4.5%
유 27
 
4.0%
구 24
 
3.6%
길 21
 
3.1%
우 20
 
3.0%
간 17
 
2.5%
바 16
 
2.4%
리 15
 
2.2%
Other values (176) 414
62.0%
Decimal Number
ValueCountFrequency (%)
1 5
27.8%
2 2
 
11.1%
9 2
 
11.1%
7 2
 
11.1%
6 2
 
11.1%
4 2
 
11.1%
3 1
 
5.6%
8 1
 
5.6%
5 1
 
5.6%
Uppercase Letter
ValueCountFrequency (%)
J 1
33.3%
G 1
33.3%
L 1
33.3%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 668
95.8%
Common 26
 
3.7%
Latin 3
 
0.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
원 54
 
8.1%
강 30
 
4.5%
지 30
 
4.5%
유 27
 
4.0%
구 24
 
3.6%
길 21
 
3.1%
우 20
 
3.0%
간 17
 
2.5%
바 16
 
2.4%
리 15
 
2.2%
Other values (176) 414
62.0%
Common
ValueCountFrequency (%)
1 5
19.2%
) 3
11.5%
( 3
11.5%
2 2
 
7.7%
9 2
 
7.7%
7 2
 
7.7%
6 2
 
7.7%
4 2
 
7.7%
2
 
7.7%
3 1
 
3.8%
Other values (2) 2
 
7.7%
Latin
ValueCountFrequency (%)
J 1
33.3%
G 1
33.3%
L 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 668
95.8%
ASCII 29
 
4.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
원 54
 
8.1%
강 30
 
4.5%
지 30
 
4.5%
유 27
 
4.0%
구 24
 
3.6%
길 21
 
3.1%
우 20
 
3.0%
간 17
 
2.5%
바 16
 
2.4%
리 15
 
2.2%
Other values (176) 414
62.0%
ASCII
ValueCountFrequency (%)
1 5
17.2%
) 3
10.3%
( 3
10.3%
2 2
 
6.9%
9 2
 
6.9%
7 2
 
6.9%
6 2
 
6.9%
4 2
 
6.9%
2
 
6.9%
3 1
 
3.4%
Other values (5) 5
17.2%

eng_lang_nm
Text

MISSING 

Distinct39
Distinct (%)79.6%
Missing51
Missing (%)51.0%
Memory size932.0 B
2023-12-10T18:54:17.668722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length47
Median length34
Mean length21.367347
Min length4

Characters and Unicode

Total characters1047
Distinct characters52
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique38 ?
Unique (%)77.6%

Sample

1st rowPetite France
2nd rowGeumgang Resort
3rd rowRecreation Area
4th rowRecreation Area
5th rowRecreation Area
ValueCountFrequency (%)
area 13
 
9.0%
recreation 11
 
7.6%
park 8
 
5.5%
street 4
 
2.8%
road 3
 
2.1%
korean 3
 
2.1%
apgujeong 2
 
1.4%
center 2
 
1.4%
cycle 2
 
1.4%
river 2
 
1.4%
Other values (83) 95
65.5%
2023-12-10T18:54:18.563725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 116
 
11.1%
96
 
9.2%
a 93
 
8.9%
n 80
 
7.6%
r 74
 
7.1%
o 73
 
7.0%
g 52
 
5.0%
i 51
 
4.9%
t 45
 
4.3%
l 35
 
3.3%
Other values (42) 332
31.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 807
77.1%
Uppercase Letter 129
 
12.3%
Space Separator 96
 
9.2%
Dash Punctuation 6
 
0.6%
Close Punctuation 3
 
0.3%
Open Punctuation 3
 
0.3%
Other Punctuation 2
 
0.2%
Decimal Number 1
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 116
14.4%
a 93
11.5%
n 80
9.9%
r 74
9.2%
o 73
9.0%
g 52
 
6.4%
i 51
 
6.3%
t 45
 
5.6%
l 35
 
4.3%
u 33
 
4.1%
Other values (14) 155
19.2%
Uppercase Letter
ValueCountFrequency (%)
A 22
17.1%
R 20
15.5%
S 16
12.4%
P 13
10.1%
C 11
8.5%
G 9
7.0%
B 4
 
3.1%
T 4
 
3.1%
H 4
 
3.1%
M 4
 
3.1%
Other values (12) 22
17.1%
Space Separator
ValueCountFrequency (%)
96
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 6
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Other Punctuation
ValueCountFrequency (%)
& 2
100.0%
Decimal Number
ValueCountFrequency (%)
1 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 936
89.4%
Common 111
 
10.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 116
12.4%
a 93
 
9.9%
n 80
 
8.5%
r 74
 
7.9%
o 73
 
7.8%
g 52
 
5.6%
i 51
 
5.4%
t 45
 
4.8%
l 35
 
3.7%
u 33
 
3.5%
Other values (36) 284
30.3%
Common
ValueCountFrequency (%)
96
86.5%
- 6
 
5.4%
) 3
 
2.7%
( 3
 
2.7%
& 2
 
1.8%
1 1
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1047
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 116
 
11.1%
96
 
9.2%
a 93
 
8.9%
n 80
 
7.6%
r 74
 
7.1%
o 73
 
7.0%
g 52
 
5.0%
i 51
 
4.9%
t 45
 
4.3%
l 35
 
3.3%
Other values (42) 332
31.7%

jan_lang_nm
Text

MISSING 

Distinct55
Distinct (%)96.5%
Missing43
Missing (%)43.0%
Memory size932.0 B
2023-12-10T18:54:19.230367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length12
Mean length7.2280702
Min length3

Characters and Unicode

Total characters412
Distinct characters157
Distinct categories3 ?
Distinct scripts5 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique53 ?
Unique (%)93.0%

Sample

1st rowプチフランス
2nd row錦江遊園地
3rd rowポンチャン遊園地
4th rowエ?デルワイス
5th rowJ芝ガ?デン遊園地
ValueCountFrequency (%)
平遊園地 2
 
3.5%
加牛島つり橋 2
 
3.5%
ロッテモ?ル金浦空港スカイパ?ク 1
 
1.8%
鳳凰閣 1
 
1.8%
康津?恩山ブイランド 1
 
1.8%
耽津江遊園地 1
 
1.8%
彫刻公園 1
 
1.8%
狎?亭ロデオ通り 1
 
1.8%
lgア?トセンタ 1
 
1.8%
イェリムダンア?トホ?ル 1
 
1.8%
Other values (45) 45
78.9%
2023-12-10T18:54:20.024365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
園 38
 
9.2%
? 30
 
7.3%
遊 28
 
6.8%
地 28
 
6.8%
ン 20
 
4.9%
ル 10
 
2.4%
ス 8
 
1.9%
江 6
 
1.5%
公 6
 
1.5%
デ 6
 
1.5%
Other values (147) 232
56.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 379
92.0%
Other Punctuation 30
 
7.3%
Uppercase Letter 3
 
0.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
園 38
 
10.0%
遊 28
 
7.4%
地 28
 
7.4%
ン 20
 
5.3%
ル 10
 
2.6%
ス 8
 
2.1%
江 6
 
1.6%
公 6
 
1.6%
デ 6
 
1.6%
ム 6
 
1.6%
Other values (143) 223
58.8%
Uppercase Letter
ValueCountFrequency (%)
J 1
33.3%
G 1
33.3%
L 1
33.3%
Other Punctuation
ValueCountFrequency (%)
? 30
100.0%

Most occurring scripts

ValueCountFrequency (%)
Han 230
55.8%
Katakana 139
33.7%
Common 30
 
7.3%
Hiragana 10
 
2.4%
Latin 3
 
0.7%

Most frequent character per script

Han
ValueCountFrequency (%)
園 38
 
16.5%
遊 28
 
12.2%
地 28
 
12.2%
江 6
 
2.6%
公 6
 
2.6%
津 5
 
2.2%
牛 4
 
1.7%
平 4
 
1.7%
山 3
 
1.3%
島 3
 
1.3%
Other values (87) 105
45.7%
Katakana
ValueCountFrequency (%)
ン 20
 
14.4%
ル 10
 
7.2%
ス 8
 
5.8%
デ 6
 
4.3%
ム 6
 
4.3%
イ 5
 
3.6%
ド 5
 
3.6%
ト 4
 
2.9%
チ 4
 
2.9%
ク 4
 
2.9%
Other values (40) 67
48.2%
Hiragana
ValueCountFrequency (%)
り 3
30.0%
の 2
20.0%
つ 2
20.0%
ち 1
 
10.0%
も 1
 
10.0%
い 1
 
10.0%
Latin
ValueCountFrequency (%)
J 1
33.3%
G 1
33.3%
L 1
33.3%
Common
ValueCountFrequency (%)
? 30
100.0%

Most occurring blocks

ValueCountFrequency (%)
CJK 229
55.6%
Katakana 139
33.7%
ASCII 33
 
8.0%
Hiragana 10
 
2.4%
CJK Compat Ideographs 1
 
0.2%

Most frequent character per block

CJK
ValueCountFrequency (%)
園 38
 
16.6%
遊 28
 
12.2%
地 28
 
12.2%
江 6
 
2.6%
公 6
 
2.6%
津 5
 
2.2%
牛 4
 
1.7%
平 4
 
1.7%
山 3
 
1.3%
島 3
 
1.3%
Other values (86) 104
45.4%
ASCII
ValueCountFrequency (%)
? 30
90.9%
J 1
 
3.0%
G 1
 
3.0%
L 1
 
3.0%
Katakana
ValueCountFrequency (%)
ン 20
 
14.4%
ル 10
 
7.2%
ス 8
 
5.8%
デ 6
 
4.3%
ム 6
 
4.3%
イ 5
 
3.6%
ド 5
 
3.6%
ト 4
 
2.9%
チ 4
 
2.9%
ク 4
 
2.9%
Other values (40) 67
48.2%
Hiragana
ValueCountFrequency (%)
り 3
30.0%
の 2
20.0%
つ 2
20.0%
ち 1
 
10.0%
も 1
 
10.0%
い 1
 
10.0%
CJK Compat Ideographs
ValueCountFrequency (%)
龍 1
100.0%

chg_lang_nm
Text

MISSING 

Distinct71
Distinct (%)98.6%
Missing28
Missing (%)28.0%
Memory size932.0 B
2023-12-10T18:54:20.567225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length10
Mean length6.4166667
Min length3

Characters and Unicode

Total characters462
Distinct characters153
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique70 ?
Unique (%)97.2%

Sample

1st row加平小法?西
2nd row?江游?地
3rd row繁?游??
4th row?埃克?佩里?念?
5th row草坪花?游??
ValueCountFrequency (%)
游 3
 
4.1%
平湖畔辣??村 2
 
2.7%
加平小法?西 1
 
1.4%
江南旅游信息中心 1
 
1.4%
刺?博物 1
 
1.4%
渡口脚?公 1
 
1.4%
堂里血 1
 
1.4%
竝川米 1
 
1.4%
千?洞?德???街 1
 
1.4%
日出公 1
 
1.4%
Other values (60) 60
82.2%
2023-12-10T18:54:21.510696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
? 176
38.1%
游 27
 
5.8%
街 10
 
2.2%
江 9
 
1.9%
公 9
 
1.9%
平 6
 
1.3%
村 6
 
1.3%
洞 5
 
1.1%
路 5
 
1.1%
津 4
 
0.9%
Other values (143) 205
44.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 265
57.4%
Other Punctuation 176
38.1%
Lowercase Letter 12
 
2.6%
Uppercase Letter 4
 
0.9%
Space Separator 2
 
0.4%
Open Punctuation 1
 
0.2%
Decimal Number 1
 
0.2%
Close Punctuation 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
游 27
 
10.2%
街 10
 
3.8%
江 9
 
3.4%
公 9
 
3.4%
平 6
 
2.3%
村 6
 
2.3%
洞 5
 
1.9%
路 5
 
1.9%
津 4
 
1.5%
正 3
 
1.1%
Other values (125) 181
68.3%
Lowercase Letter
ValueCountFrequency (%)
i 3
25.0%
a 2
16.7%
g 1
 
8.3%
k 1
 
8.3%
b 1
 
8.3%
n 1
 
8.3%
h 1
 
8.3%
r 1
 
8.3%
f 1
 
8.3%
Uppercase Letter
ValueCountFrequency (%)
T 1
25.0%
S 1
25.0%
G 1
25.0%
L 1
25.0%
Other Punctuation
ValueCountFrequency (%)
? 176
100.0%
Space Separator
ValueCountFrequency (%)
2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Decimal Number
ValueCountFrequency (%)
1 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Han 265
57.4%
Common 181
39.2%
Latin 16
 
3.5%

Most frequent character per script

Han
ValueCountFrequency (%)
游 27
 
10.2%
街 10
 
3.8%
江 9
 
3.4%
公 9
 
3.4%
平 6
 
2.3%
村 6
 
2.3%
洞 5
 
1.9%
路 5
 
1.9%
津 4
 
1.5%
正 3
 
1.1%
Other values (125) 181
68.3%
Latin
ValueCountFrequency (%)
i 3
18.8%
a 2
12.5%
g 1
 
6.2%
k 1
 
6.2%
b 1
 
6.2%
n 1
 
6.2%
T 1
 
6.2%
h 1
 
6.2%
r 1
 
6.2%
f 1
 
6.2%
Other values (3) 3
18.8%
Common
ValueCountFrequency (%)
? 176
97.2%
2
 
1.1%
( 1
 
0.6%
1 1
 
0.6%
) 1
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
CJK 265
57.4%
ASCII 197
42.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
? 176
89.3%
i 3
 
1.5%
a 2
 
1.0%
2
 
1.0%
g 1
 
0.5%
k 1
 
0.5%
( 1
 
0.5%
b 1
 
0.5%
n 1
 
0.5%
1 1
 
0.5%
Other values (8) 8
 
4.1%
CJK
ValueCountFrequency (%)
游 27
 
10.2%
街 10
 
3.8%
江 9
 
3.4%
公 9
 
3.4%
平 6
 
2.3%
村 6
 
2.3%
洞 5
 
1.9%
路 5
 
1.9%
津 4
 
1.5%
正 3
 
1.1%
Other values (125) 181
68.3%

chb_lang_nm
Text

MISSING 

Distinct16
Distinct (%)61.5%
Missing74
Missing (%)74.0%
Memory size932.0 B
2023-12-10T18:54:21.859536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length3
Mean length4.6153846
Min length2

Characters and Unicode

Total characters120
Distinct characters68
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

Unique15 ?
Unique (%)57.7%

Sample

1st row錦江遊園區
2nd row聖埃克蘇佩里紀念館
3rd row遊園區
4th row遊園區
5th row遊園區
ValueCountFrequency (%)
遊園區 11
40.7%
竝川?血粉腸 1
 
3.7%
韓國刺繡博物館 1
 
3.7%
牛耳洞遊園地 1
 
3.7%
公園 1
 
3.7%
雕刻公園 1
 
3.7%
正東津 1
 
3.7%
沙漏公園 1
 
3.7%
錦江遊園區 1
 
3.7%
江南旅遊資訊中心 1
 
3.7%
Other values (7) 7
25.9%
2023-12-10T18:54:22.495254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
園 16
 
13.3%
遊 14
 
11.7%
區 12
 
10.0%
? 3
 
2.5%
公 3
 
2.5%
L 2
 
1.7%
腸 2
 
1.7%
江 2
 
1.7%
中 2
 
1.7%
心 2
 
1.7%
Other values (58) 62
51.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 110
91.7%
Uppercase Letter 4
 
3.3%
Other Punctuation 3
 
2.5%
Open Punctuation 1
 
0.8%
Close Punctuation 1
 
0.8%
Space Separator 1
 
0.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
園 16
 
14.5%
遊 14
 
12.7%
區 12
 
10.9%
公 3
 
2.7%
腸 2
 
1.8%
江 2
 
1.8%
中 2
 
1.8%
心 2
 
1.8%
館 2
 
1.8%
血 2
 
1.8%
Other values (52) 53
48.2%
Uppercase Letter
ValueCountFrequency (%)
L 2
50.0%
G 2
50.0%
Other Punctuation
ValueCountFrequency (%)
? 3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Han 110
91.7%
Common 6
 
5.0%
Latin 4
 
3.3%

Most frequent character per script

Han
ValueCountFrequency (%)
園 16
 
14.5%
遊 14
 
12.7%
區 12
 
10.9%
公 3
 
2.7%
腸 2
 
1.8%
江 2
 
1.8%
中 2
 
1.8%
心 2
 
1.8%
館 2
 
1.8%
血 2
 
1.8%
Other values (52) 53
48.2%
Common
ValueCountFrequency (%)
? 3
50.0%
( 1
 
16.7%
) 1
 
16.7%
1
 
16.7%
Latin
ValueCountFrequency (%)
L 2
50.0%
G 2
50.0%

Most occurring blocks

ValueCountFrequency (%)
CJK 110
91.7%
ASCII 10
 
8.3%

Most frequent character per block

CJK
ValueCountFrequency (%)
園 16
 
14.5%
遊 14
 
12.7%
區 12
 
10.9%
公 3
 
2.7%
腸 2
 
1.8%
江 2
 
1.8%
中 2
 
1.8%
心 2
 
1.8%
館 2
 
1.8%
血 2
 
1.8%
Other values (52) 53
48.2%
ASCII
ValueCountFrequency (%)
? 3
30.0%
L 2
20.0%
G 2
20.0%
( 1
 
10.0%
) 1
 
10.0%
1
 
10.0%

city_do_cd
Real number (ℝ)

HIGH CORRELATION 

Distinct7
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.6
Minimum11
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:54:22.785377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile11
Q111
median41
Q342
95-th percentile46
Maximum50
Range39
Interquartile range (IQR)31

Descriptive statistics

Standard deviation14.206273
Coefficient of variation (CV)0.43577523
Kurtosis-1.2354757
Mean32.6
Median Absolute Deviation (MAD)1
Skewness-0.8163069
Sum3260
Variance201.81818
MonotonicityNot monotonic
2023-12-10T18:54:23.051102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
41 31
31.0%
42 30
30.0%
11 29
29.0%
46 5
 
5.0%
50 2
 
2.0%
26 2
 
2.0%
28 1
 
1.0%
ValueCountFrequency (%)
11 29
29.0%
26 2
 
2.0%
28 1
 
1.0%
41 31
31.0%
42 30
30.0%
46 5
 
5.0%
50 2
 
2.0%
ValueCountFrequency (%)
50 2
 
2.0%
46 5
 
5.0%
42 30
30.0%
41 31
31.0%
28 1
 
1.0%
26 2
 
2.0%
11 29
29.0%

city_gn_gu_cd
Real number (ℝ)

HIGH CORRELATION 

Distinct10
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36740.35
Minimum11305
Maximum50130
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:54:23.288436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11305
5-th percentile11305
Q141820
median41820
Q342150
95-th percentile46810
Maximum50130
Range38825
Interquartile range (IQR)330

Descriptive statistics

Standard deviation11857.506
Coefficient of variation (CV)0.32273797
Kurtosis0.74025007
Mean36740.35
Median Absolute Deviation (MAD)330
Skewness-1.5762671
Sum3674035
Variance1.4060045 × 108
MonotonicityNot monotonic
2023-12-10T18:54:23.522211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
41820 43
43.0%
42150 30
30.0%
11740 8
 
8.0%
11305 7
 
7.0%
46810 4
 
4.0%
50130 2
 
2.0%
26440 2
 
2.0%
11500 2
 
2.0%
46130 1
 
1.0%
28710 1
 
1.0%
ValueCountFrequency (%)
11305 7
 
7.0%
11500 2
 
2.0%
11740 8
 
8.0%
26440 2
 
2.0%
28710 1
 
1.0%
41820 43
43.0%
42150 30
30.0%
46130 1
 
1.0%
46810 4
 
4.0%
50130 2
 
2.0%
ValueCountFrequency (%)
50130 2
 
2.0%
46810 4
 
4.0%
46130 1
 
1.0%
42150 30
30.0%
41820 43
43.0%
28710 1
 
1.0%
26440 2
 
2.0%
11740 8
 
8.0%
11500 2
 
2.0%
11305 7
 
7.0%

city_do_kor_lang_nm
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
경기
31 
강원
30 
서울
29 
전남
5 
제주
 
2
Other values (2)
 
3

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique1 ?
Unique (%)1.0%

Sample

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

Common Values

ValueCountFrequency (%)
경기 31
31.0%
강원 30
30.0%
서울 29
29.0%
전남 5
 
5.0%
제주 2
 
2.0%
부산 2
 
2.0%
인천 1
 
1.0%

Length

2023-12-10T18:54:23.847536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:54:24.083344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경기 31
31.0%
강원 30
30.0%
서울 29
29.0%
전남 5
 
5.0%
제주 2
 
2.0%
부산 2
 
2.0%
인천 1
 
1.0%

city_gn_gu_kor_lang_nm
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
가평군
31 
강릉시
30 
강남구
12 
강동구
8 
강북구
7 
Other values (5)
12 

Length

Max length4
Median length3
Mean length3.02
Min length3

Unique

Unique2 ?
Unique (%)2.0%

Sample

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

Common Values

ValueCountFrequency (%)
가평군 31
31.0%
강릉시 30
30.0%
강남구 12
 
12.0%
강동구 8
 
8.0%
강북구 7
 
7.0%
강서구 4
 
4.0%
강진군 4
 
4.0%
서귀포시 2
 
2.0%
여수시 1
 
1.0%
강화군 1
 
1.0%

Length

2023-12-10T18:54:24.450416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:54:24.736833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
가평군 31
31.0%
강릉시 30
30.0%
강남구 12
 
12.0%
강동구 8
 
8.0%
강북구 7
 
7.0%
강서구 4
 
4.0%
강진군 4
 
4.0%
서귀포시 2
 
2.0%
여수시 1
 
1.0%
강화군 1
 
1.0%

gov_dn_kor_lang_nm
Categorical

HIGH CORRELATION 

Distinct46
Distinct (%)46.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
청평면
9 
강동면
9 
북면
7 
조종면
7 
우이동
 
5
Other values (41)
63 

Length

Max length4
Median length3
Mean length3.07
Min length2

Unique

Unique24 ?
Unique (%)24.0%

Sample

1st row청평면
2nd row성산읍
3rd row북면
4th row상면
5th row청평면

Common Values

ValueCountFrequency (%)
청평면 9
 
9.0%
강동면 9
 
9.0%
북면 7
 
7.0%
조종면 7
 
7.0%
우이동 5
 
5.0%
상면 4
 
4.0%
성산면 3
 
3.0%
사천면 3
 
3.0%
경포동 3
 
3.0%
구정면 2
 
2.0%
Other values (36) 48
48.0%

Length

2023-12-10T18:54:25.087195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
청평면 9
 
9.0%
강동면 9
 
9.0%
북면 7
 
7.0%
조종면 7
 
7.0%
우이동 5
 
5.0%
상면 4
 
4.0%
성산면 3
 
3.0%
사천면 3
 
3.0%
경포동 3
 
3.0%
압구정동 2
 
2.0%
Other values (36) 48
48.0%

city_do_jan_lang_nm
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
江原道
30 
ソウル特別市
29 
京畿道
24 
<NA>
10 
全羅南道
4 
Other values (2)
 
3

Length

Max length6
Median length3
Mean length4.07
Min length3

Unique

Unique1 ?
Unique (%)1.0%

Sample

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

Common Values

ValueCountFrequency (%)
江原道 30
30.0%
ソウル特別市 29
29.0%
京畿道 24
24.0%
<NA> 10
 
10.0%
全羅南道 4
 
4.0%
釜山?域市 2
 
2.0%
仁川?域市 1
 
1.0%

Length

2023-12-10T18:54:25.379501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:54:25.683753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
江原道 30
30.0%
ソウル特別市 29
29.0%
京畿道 24
24.0%
na 10
 
10.0%
全羅南道 4
 
4.0%
釜山?域市 2
 
2.0%
仁川?域市 1
 
1.0%

city_gn_gu_jan_lang_nm
Categorical

HIGH CORRELATION 

Distinct9
Distinct (%)9.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
江陵市
30 
加平郡
24 
江南?
12 
<NA>
10 
江東?
8 
Other values (4)
16 

Length

Max length4
Median length3
Mean length3.1
Min length3

Unique

Unique1 ?
Unique (%)1.0%

Sample

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

Common Values

ValueCountFrequency (%)
江陵市 30
30.0%
加平郡 24
24.0%
江南? 12
 
12.0%
<NA> 10
 
10.0%
江東? 8
 
8.0%
江北? 7
 
7.0%
江西? 4
 
4.0%
康津郡 4
 
4.0%
江華郡 1
 
1.0%

Length

2023-12-10T18:54:25.980858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:54:26.212219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
江陵市 30
30.0%
加平郡 24
24.0%
江南 12
 
12.0%
na 10
 
10.0%
江東 8
 
8.0%
江北 7
 
7.0%
江西 4
 
4.0%
康津郡 4
 
4.0%
江華郡 1
 
1.0%

gov_dn_jan_lang_nm
Categorical

HIGH CORRELATION 

Distinct43
Distinct (%)43.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
<NA>
10 
江東面
9 
?平面
9 
北面
7 
牛耳洞
 
5
Other values (38)
60 

Length

Max length4
Median length3
Mean length3.17
Min length2

Unique

Unique21 ?
Unique (%)21.0%

Sample

1st row?平面
2nd row<NA>
3rd row北面
4th row上面
5th row?平面

Common Values

ValueCountFrequency (%)
<NA> 10
 
10.0%
江東面 9
 
9.0%
?平面 9
 
9.0%
北面 7
 
7.0%
牛耳洞 5
 
5.0%
上面 4
 
4.0%
城山面 3
 
3.0%
泗川面 3
 
3.0%
鏡浦洞 3
 
3.0%
千?2洞 2
 
2.0%
Other values (33) 45
45.0%

Length

2023-12-10T18:54:26.521771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 10
 
10.0%
平面 9
 
9.0%
江東面 9
 
9.0%
北面 7
 
7.0%
牛耳洞 5
 
5.0%
上面 4
 
4.0%
城山面 3
 
3.0%
泗川面 3
 
3.0%
鏡浦洞 3
 
3.0%
加平邑 2
 
2.0%
Other values (33) 45
45.0%

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
2023-12-10T18:54:26.747007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:54:26.915233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2023-12-10T18:54:11.315347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:54:08.540288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:54:09.397713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:54:10.271426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:54:11.502793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:54:08.807040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:54:09.596741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:54:10.481068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:54:11.711755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:54:09.016396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:54:09.810810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:54:10.709461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:54:12.296722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:54:09.213941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:54:10.060946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:54:11.114349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T18:54:27.081914image/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_nm1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.9621.0001.0000.960
xpos_lo1.0001.0000.9401.0001.0001.0001.0001.0000.9350.8530.9140.9771.0001.0000.9991.000
ypos_la1.0000.9401.0001.0000.0001.0001.0000.0000.9920.9750.8900.9541.0001.0000.9871.000
kor_lang_nm1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.9751.0001.0000.963
eng_lang_nm1.0001.0000.0001.0001.0001.0001.0001.0000.0000.0000.0000.8270.9620.0000.7850.973
jan_lang_nm1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.9471.0001.0000.958
chg_lang_nm1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.9771.0001.0000.987
chb_lang_nm1.0001.0000.0001.0001.0001.0001.0001.0000.0000.0000.7760.9070.8140.7290.8990.870
city_do_cd1.0000.9350.9921.0000.0001.0001.0000.0001.0000.9861.0000.9791.0001.0000.9491.000
city_gn_gu_cd1.0000.8530.9751.0000.0001.0001.0000.0000.9861.0000.9110.9941.0000.9380.9381.000
city_do_kor_lang_nm1.0000.9140.8901.0000.0001.0001.0000.7761.0000.9111.0000.9781.0001.0000.9801.000
city_gn_gu_kor_lang_nm1.0000.9770.9541.0000.8271.0001.0000.9070.9790.9940.9781.0001.0000.9801.0001.000
gov_dn_kor_lang_nm0.9621.0001.0000.9750.9620.9470.9770.8141.0001.0001.0001.0001.0001.0001.0001.000
city_do_jan_lang_nm1.0001.0001.0001.0000.0001.0001.0000.7291.0000.9381.0000.9801.0001.0000.9801.000
city_gn_gu_jan_lang_nm1.0000.9990.9871.0000.7851.0001.0000.8990.9490.9380.9801.0001.0000.9801.0001.000
gov_dn_jan_lang_nm0.9601.0001.0000.9630.9730.9580.9870.8701.0001.0001.0001.0001.0001.0001.0001.000
2023-12-10T18:54:27.831477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
city_gn_gu_jan_lang_nmgov_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_nm1.0000.7651.0000.7650.9290.929
gov_dn_kor_lang_nm0.7651.0000.7751.0000.7620.756
city_gn_gu_kor_lang_nm1.0000.7751.0000.7650.9340.929
gov_dn_jan_lang_nm0.7651.0000.7651.0000.7560.756
city_do_kor_lang_nm0.9290.7620.9340.7561.0001.000
city_do_jan_lang_nm0.9290.7560.9290.7561.0001.000
2023-12-10T18:54:28.079607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
xpos_loypos_lacity_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
xpos_lo1.0000.5040.5080.4380.9040.9510.7460.9770.9400.739
ypos_la0.5041.0000.2540.0820.8690.9150.7460.9770.8670.739
city_do_cd0.5080.2541.0000.9260.9950.9190.7580.9940.9080.751
city_gn_gu_cd0.4380.0820.9261.0000.8280.9160.7580.9230.9050.751
city_do_kor_lang_nm0.9040.8690.9950.8281.0000.9340.7621.0000.9290.756
city_gn_gu_kor_lang_nm0.9510.9150.9190.9160.9341.0000.7750.9291.0000.765
gov_dn_kor_lang_nm0.7460.7460.7580.7580.7620.7751.0000.7560.7651.000
city_do_jan_lang_nm0.9770.9770.9940.9231.0000.9290.7561.0000.9290.756
city_gn_gu_jan_lang_nm0.9400.8670.9080.9050.9291.0000.7650.9291.0000.765
gov_dn_jan_lang_nm0.7390.7390.7510.7510.7560.7651.0000.7560.7651.000

Missing values

2023-12-10T18:54:12.584730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T18:54:13.023131image/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.
2023-12-10T18:54:13.402227image/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
0가평/쁘띠프랑스999131.01968379.0가평쁘띠프랑스Petite Franceプチフランス加平小法?西<NA>4141820경기가평군청평면京畿道加平郡?平面2020-12-31
1성산 스튜디오126.86473533.418029성산 스튜디오<NA><NA><NA><NA>5050130제주서귀포시성산읍<NA><NA><NA>2020-12-31
2금강유원지1005675.01990830.0금강유원지Geumgang Resort錦江遊園地?江游?地錦江遊園區4141820경기가평군북면京畿道加平郡北面2020-12-31
3번창유원지984864.01979808.0번창유원지<NA>ポンチャン遊園地繁?游??<NA>4141820경기가평군상면京畿道加平郡上面2020-12-31
4생텍쥐페리기념관999177.78551968403.5생텍쥐페리기념관<NA><NA>?埃克?佩里?念?聖埃克蘇佩里紀念館4141820경기가평군청평면京畿道加平郡?平面2020-12-31
5에델바이스995417.431963122.5에델바이스<NA>エ?デルワイス<NA><NA>4141820경기가평군설악면京畿道加平郡雪岳面2020-12-31
6J잔디가든/유원지988480.01989085.0J잔디가든유원지<NA>J芝ガ?デン遊園地草坪花?游??<NA>4141820경기가평군조종면<NA><NA><NA>2020-12-31
7크리에이티브통 제주126.41930733.293983크리에이티브통 제주<NA><NA><NA><NA>5050130제주서귀포시색달동<NA><NA><NA>2020-12-31
8꿈의동산/놀이공원998823.01976005.0꿈의동산놀이공원<NA>ドリ?ム遊園地?之花?游??<NA>4141820경기가평군청평면京畿道加平郡?平面2020-12-31
9녹천유원지986818.01982927.0녹천유원지<NA>ノクチョン遊園地?天游??<NA>4141820경기가평군조종면<NA><NA><NA>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
90봉황각956445.23781962421.742봉황각Bonghwanggak (Pavilion)鳳凰閣?凰?鳳凰閣1111305서울강북구우이동ソウル特別市江北?牛耳洞2020-12-31
91강서오리마을1132529.01685479.0강서오리마을Gangseo Ori-maeul<NA>江西?子村<NA>2626440부산강서구대저2동釜山?域市江西?大渚2洞2020-12-31
92명지선창/회타운1130261.01679426.0명지선창회타운Myongji Seonchang Hoe Town<NA>?旨船?生?片城<NA>2626440부산강서구명지동釜山?域市江西?鳴旨洞2020-12-31
93롯데몰김포공항/스카이파크938416.01951752.0롯데몰김포공항스카이파크Lotte Mall Gimpo International Airport Sky Parkロッテモ?ル金浦空港スカイパ?ク?天?金浦机?天空公?<NA>1111500서울강서구방화2동ソウル特別市江西?傍花2洞2020-12-31
94한강자전거도로(강서지구)938561.01955454.0한강자전거도로(강서지구)Hangang River cycle lane(Gangseo Area)<NA>?江脚?路(江西地?)<NA>1111500서울강서구방화2동ソウル特別市江西?傍花2洞2020-12-31
95가우도출렁다리933238.01615151.0가우도출렁다리<NA>加牛島つり橋<NA><NA>4646810전남강진군도암면全羅南道康津郡道岩面2020-12-31
96가우도출렁다리934872.01615953.0가우도출렁다리<NA>加牛島つり橋<NA><NA>4646810전남강진군대구면全羅南道康津郡大邱面2020-12-31
97탐진강유원지936773.01627053.0탐진강유원지Recreation Area耽津江遊園地游??遊園區4646810전남강진군군동면全羅南道康津郡郡東面2020-12-31
98강진보은산브이랜드932416.57711628902.2강진보은산브이랜드<NA>康津?恩山ブイランド<NA><NA>4646810전남강진군강진읍全羅南道康津郡康津邑2020-12-31
99강화도남단해안도로913252.01970999.0강화도남단해안도로<NA>江華島南端海岸道路<NA><NA>2828710인천강화군강화읍仁川?域市江華郡江華邑2020-12-31