Overview

Dataset statistics

Number of variables15
Number of observations417
Missing cells1593
Missing cells (%)25.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory49.4 KiB
Average record size in memory121.3 B

Variable types

Text7
Categorical5
Numeric1
Boolean1
DateTime1

Dataset

Description국외독립운동사적지 홈페이지에서 사용하는 마스터 코드 정보로 국가코드, 국가별 지역코드(한글, 영어, 일어, 중국어), 교과서 분류 코드 등을 포함하고 있다.
Author독립기념관
URLhttps://www.data.go.kr/data/15122341/fileData.do

Alerts

등록일 has constant value ""Constant
비고 is highly overall correlated with 코드1 and 3 other fieldsHigh correlation
코드1 is highly overall correlated with 상위코드 and 3 other fieldsHigh correlation
구분A is highly overall correlated with 코드1 and 3 other fieldsHigh correlation
사용구분 is highly overall correlated with 코드1 and 3 other fieldsHigh correlation
상위코드 is highly overall correlated with 코드2 and 4 other fieldsHigh correlation
코드2 is highly overall correlated with 상위코드High correlation
코드명_일본어 has 369 (88.5%) missing valuesMissing
코드명_중국어 has 384 (92.1%) missing valuesMissing
코드약어 has 177 (42.4%) missing valuesMissing
구분B has 246 (59.0%) missing valuesMissing
수정일 has 414 (99.3%) missing valuesMissing
코드 has unique valuesUnique

Reproduction

Analysis started2023-12-11 23:53:31.202641
Analysis finished2023-12-11 23:53:32.648628
Duration1.45 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

코드
Text

UNIQUE 

Distinct417
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
2023-12-12T08:53:32.945068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters2502
Distinct characters19
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique417 ?
Unique (%)100.0%

Sample

1st rowARA001
2nd rowARA002
3rd rowARA003
4th rowARA004
5th rowARA005
ValueCountFrequency (%)
ara001 1
 
0.2%
sta031 1
 
0.2%
sta107 1
 
0.2%
sta106 1
 
0.2%
sta105 1
 
0.2%
sta104 1
 
0.2%
sta103 1
 
0.2%
sta102 1
 
0.2%
sta101 1
 
0.2%
sta100 1
 
0.2%
Other values (407) 407
97.6%
2023-12-12T08:53:33.517011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 577
23.1%
0 313
12.5%
1 260
10.4%
S 238
9.5%
T 238
9.5%
R 169
 
6.8%
2 132
 
5.3%
3 93
 
3.7%
4 82
 
3.3%
5 82
 
3.3%
Other values (9) 318
12.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1251
50.0%
Decimal Number 1251
50.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 313
25.0%
1 260
20.8%
2 132
10.6%
3 93
 
7.4%
4 82
 
6.6%
5 82
 
6.6%
6 79
 
6.3%
7 71
 
5.7%
8 70
 
5.6%
9 69
 
5.5%
Uppercase Letter
ValueCountFrequency (%)
A 577
46.1%
S 238
19.0%
T 238
19.0%
R 169
 
13.5%
D 13
 
1.0%
L 7
 
0.6%
G 3
 
0.2%
E 3
 
0.2%
U 3
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 1251
50.0%
Common 1251
50.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 313
25.0%
1 260
20.8%
2 132
10.6%
3 93
 
7.4%
4 82
 
6.6%
5 82
 
6.6%
6 79
 
6.3%
7 71
 
5.7%
8 70
 
5.6%
9 69
 
5.5%
Latin
ValueCountFrequency (%)
A 577
46.1%
S 238
19.0%
T 238
19.0%
R 169
 
13.5%
D 13
 
1.0%
L 7
 
0.6%
G 3
 
0.2%
E 3
 
0.2%
U 3
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2502
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 577
23.1%
0 313
12.5%
1 260
10.4%
S 238
9.5%
T 238
9.5%
R 169
 
6.8%
2 132
 
5.3%
3 93
 
3.7%
4 82
 
3.3%
5 82
 
3.3%
Other values (9) 318
12.7%

코드1
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
STA
238 
ARA
166 
LAD
 
7
EDU
 
3
GRD
 
3

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
STA 238
57.1%
ARA 166
39.8%
LAD 7
 
1.7%
EDU 3
 
0.7%
GRD 3
 
0.7%

Length

2023-12-12T08:53:33.677770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:53:33.814576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
sta 238
57.1%
ara 166
39.8%
lad 7
 
1.7%
edu 3
 
0.7%
grd 3
 
0.7%

코드2
Real number (ℝ)

HIGH CORRELATION 

Distinct239
Distinct (%)57.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean101.51079
Minimum0
Maximum238
Zeros1
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2023-12-12T08:53:33.987344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q146
median98
Q3150
95-th percentile217.2
Maximum238
Range238
Interquartile range (IQR)104

Descriptive statistics

Standard deviation65.113846
Coefficient of variation (CV)0.64144753
Kurtosis-0.93913102
Mean101.51079
Median Absolute Deviation (MAD)52
Skewness0.24241985
Sum42330
Variance4239.813
MonotonicityNot monotonic
2023-12-12T08:53:34.171251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 5
 
1.2%
3 5
 
1.2%
2 5
 
1.2%
4 3
 
0.7%
5 3
 
0.7%
6 3
 
0.7%
7 3
 
0.7%
116 2
 
0.5%
109 2
 
0.5%
110 2
 
0.5%
Other values (229) 384
92.1%
ValueCountFrequency (%)
0 1
 
0.2%
1 5
1.2%
2 5
1.2%
3 5
1.2%
4 3
0.7%
5 3
0.7%
6 3
0.7%
7 3
0.7%
8 2
 
0.5%
9 2
 
0.5%
ValueCountFrequency (%)
238 1
0.2%
237 1
0.2%
236 1
0.2%
235 1
0.2%
234 1
0.2%
233 1
0.2%
232 1
0.2%
231 1
0.2%
230 1
0.2%
229 1
0.2%

코드명
Text

Distinct393
Distinct (%)94.2%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
2023-12-12T08:53:34.560218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length13
Mean length4.2230216
Min length1

Characters and Unicode

Total characters1761
Distinct characters293
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

Unique369 ?
Unique (%)88.5%

Sample

1st row간쑤성
2nd row광둥성
3rd row광시좡족자치구
4th row구이저우성
5th row네이멍구자치구
ValueCountFrequency (%)
군도 8
 
1.7%
세인트 5
 
1.1%
제도 4
 
0.9%
프랑스 3
 
0.6%
미국령 3
 
0.6%
섬 3
 
0.6%
사모아 2
 
0.4%
버진아일랜드 2
 
0.4%
기니 2
 
0.4%
미얀마 2
 
0.4%
Other values (406) 433
92.7%
2023-12-12T08:53:35.107634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
아 86
 
4.9%
이 64
 
3.6%
스 62
 
3.5%
리 51
 
2.9%
50
 
2.8%
현 43
 
2.4%
라 36
 
2.0%
도 35
 
2.0%
시 34
 
1.9%
나 32
 
1.8%
Other values (283) 1268
72.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1704
96.8%
Space Separator 50
 
2.8%
Close Punctuation 2
 
0.1%
Open Punctuation 2
 
0.1%
Uppercase Letter 2
 
0.1%
Other Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
아 86
 
5.0%
이 64
 
3.8%
스 62
 
3.6%
리 51
 
3.0%
현 43
 
2.5%
라 36
 
2.1%
도 35
 
2.1%
시 34
 
2.0%
나 32
 
1.9%
니 31
 
1.8%
Other values (277) 1230
72.2%
Uppercase Letter
ValueCountFrequency (%)
C 1
50.0%
D 1
50.0%
Space Separator
ValueCountFrequency (%)
50
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1704
96.8%
Common 55
 
3.1%
Latin 2
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
아 86
 
5.0%
이 64
 
3.8%
스 62
 
3.6%
리 51
 
3.0%
현 43
 
2.5%
라 36
 
2.1%
도 35
 
2.1%
시 34
 
2.0%
나 32
 
1.9%
니 31
 
1.8%
Other values (277) 1230
72.2%
Common
ValueCountFrequency (%)
50
90.9%
) 2
 
3.6%
( 2
 
3.6%
. 1
 
1.8%
Latin
ValueCountFrequency (%)
C 1
50.0%
D 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1704
96.8%
ASCII 57
 
3.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
아 86
 
5.0%
이 64
 
3.8%
스 62
 
3.6%
리 51
 
3.0%
현 43
 
2.5%
라 36
 
2.1%
도 35
 
2.1%
시 34
 
2.0%
나 32
 
1.9%
니 31
 
1.8%
Other values (277) 1230
72.2%
ASCII
ValueCountFrequency (%)
50
87.7%
) 2
 
3.5%
( 2
 
3.5%
C 1
 
1.8%
. 1
 
1.8%
D 1
 
1.8%

코드명_영어
Text

Distinct411
Distinct (%)99.3%
Missing3
Missing (%)0.7%
Memory size3.4 KiB
2023-12-12T08:53:35.480508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length38
Median length29
Mean length9.7874396
Min length3

Characters and Unicode

Total characters4052
Distinct characters59
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique409 ?
Unique (%)98.8%

Sample

1st rowGansusheng
2nd rowGuangdong
3rd rowGuangxiZhuangzu
4th rowGuizhou
5th rowNeimenggu
ValueCountFrequency (%)
islands 15
 
2.5%
republic 11
 
1.8%
and 10
 
1.7%
of 7
 
1.2%
united 6
 
1.0%
saint 4
 
0.7%
america 3
 
0.5%
the 3
 
0.5%
french 3
 
0.5%
arab 3
 
0.5%
Other values (490) 531
89.1%
2023-12-12T08:53:35.970555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 391
 
9.6%
I 249
 
6.1%
N 239
 
5.9%
197
 
4.9%
a 195
 
4.8%
E 184
 
4.5%
R 158
 
3.9%
S 158
 
3.9%
O 140
 
3.5%
i 140
 
3.5%
Other values (49) 2001
49.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 2635
65.0%
Lowercase Letter 1143
28.2%
Space Separator 197
 
4.9%
Other Punctuation 65
 
1.6%
Open Punctuation 5
 
0.1%
Close Punctuation 5
 
0.1%
Dash Punctuation 2
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 391
14.8%
I 249
 
9.4%
N 239
 
9.1%
E 184
 
7.0%
R 158
 
6.0%
S 158
 
6.0%
O 140
 
5.3%
T 130
 
4.9%
L 129
 
4.9%
U 110
 
4.2%
Other values (16) 747
28.3%
Lowercase Letter
ValueCountFrequency (%)
a 195
17.1%
i 140
12.2%
n 110
 
9.6%
o 87
 
7.6%
e 72
 
6.3%
s 60
 
5.2%
g 54
 
4.7%
h 54
 
4.7%
r 50
 
4.4%
t 43
 
3.8%
Other values (16) 278
24.3%
Other Punctuation
ValueCountFrequency (%)
, 57
87.7%
. 5
 
7.7%
' 3
 
4.6%
Space Separator
ValueCountFrequency (%)
197
100.0%
Open Punctuation
ValueCountFrequency (%)
( 5
100.0%
Close Punctuation
ValueCountFrequency (%)
) 5
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3778
93.2%
Common 274
 
6.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 391
 
10.3%
I 249
 
6.6%
N 239
 
6.3%
a 195
 
5.2%
E 184
 
4.9%
R 158
 
4.2%
S 158
 
4.2%
O 140
 
3.7%
i 140
 
3.7%
T 130
 
3.4%
Other values (42) 1794
47.5%
Common
ValueCountFrequency (%)
197
71.9%
, 57
 
20.8%
. 5
 
1.8%
( 5
 
1.8%
) 5
 
1.8%
' 3
 
1.1%
- 2
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4052
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 391
 
9.6%
I 249
 
6.1%
N 239
 
5.9%
197
 
4.9%
a 195
 
4.8%
E 184
 
4.5%
R 158
 
3.9%
S 158
 
3.9%
O 140
 
3.5%
i 140
 
3.5%
Other values (49) 2001
49.4%

코드명_일본어
Text

MISSING 

Distinct48
Distinct (%)100.0%
Missing369
Missing (%)88.5%
Memory size3.4 KiB
2023-12-12T08:53:36.267114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3
Min length1

Characters and Unicode

Total characters144
Distinct characters78
Distinct categories2 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique48 ?
Unique (%)100.0%

Sample

1st row香川현
2nd row鹿兒島
3rd row神奈川현
4th row高知현
5th row京都府
ValueCountFrequency (%)
香川현 1
 
2.1%
沖승현 1
 
2.1%
愛知현 1
 
2.1%
秋田현 1
 
2.1%
山形현 1
 
2.1%
山口현 1
 
2.1%
山梨현 1
 
2.1%
愛媛현 1
 
2.1%
大阪府 1
 
2.1%
大分縣 1
 
2.1%
Other values (37) 37
78.7%
2023-12-12T08:53:36.662504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
현 41
28.5%
山 6
 
4.2%
島 5
 
3.5%
岡 3
 
2.1%
福 3
 
2.1%
川 3
 
2.1%
府 2
 
1.4%
大 2
 
1.4%
崎 2
 
1.4%
長 2
 
1.4%
Other values (68) 75
52.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 143
99.3%
Space Separator 1
 
0.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
현 41
28.7%
山 6
 
4.2%
島 5
 
3.5%
岡 3
 
2.1%
福 3
 
2.1%
川 3
 
2.1%
府 2
 
1.4%
大 2
 
1.4%
崎 2
 
1.4%
長 2
 
1.4%
Other values (67) 74
51.7%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Han 97
67.4%
Hangul 46
31.9%
Common 1
 
0.7%

Most frequent character per script

Han
ValueCountFrequency (%)
山 6
 
6.2%
島 5
 
5.2%
岡 3
 
3.1%
福 3
 
3.1%
川 3
 
3.1%
府 2
 
2.1%
大 2
 
2.1%
崎 2
 
2.1%
長 2
 
2.1%
宮 2
 
2.1%
Other values (61) 67
69.1%
Hangul
ValueCountFrequency (%)
현 41
89.1%
승 1
 
2.2%
광 1
 
2.2%
청 1
 
2.2%
정 1
 
2.2%
회 1
 
2.2%
Common
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
CJK 95
66.0%
Hangul 46
31.9%
CJK Compat Ideographs 2
 
1.4%
ASCII 1
 
0.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
현 41
89.1%
승 1
 
2.2%
광 1
 
2.2%
청 1
 
2.2%
정 1
 
2.2%
회 1
 
2.2%
CJK
ValueCountFrequency (%)
山 6
 
6.3%
島 5
 
5.3%
岡 3
 
3.2%
福 3
 
3.2%
川 3
 
3.2%
府 2
 
2.1%
大 2
 
2.1%
崎 2
 
2.1%
長 2
 
2.1%
宮 2
 
2.1%
Other values (59) 65
68.4%
CJK Compat Ideographs
ValueCountFrequency (%)
奈 1
50.0%
良 1
50.0%
ASCII
ValueCountFrequency (%)
1
100.0%

코드명_중국어
Text

MISSING 

Distinct33
Distinct (%)100.0%
Missing384
Missing (%)92.1%
Memory size3.4 KiB
2023-12-12T08:53:37.150753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length7
Mean length3.6060606
Min length2

Characters and Unicode

Total characters119
Distinct characters59
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

Unique33 ?
Unique (%)100.0%

Sample

1st row甘肅省
2nd row廣東省
3rd row廣西壯族自治區
4th row貴州省
5th row內蒙古自治區
ValueCountFrequency (%)
山東省 1
 
3.0%
江蘇省 1
 
3.0%
浙江省 1
 
3.0%
吉林省 1
 
3.0%
重慶 1
 
3.0%
靑海省 1
 
3.0%
天津 1
 
3.0%
西藏自治區 1
 
3.0%
海南省 1
 
3.0%
廣東省 1
 
3.0%
Other values (23) 23
69.7%
2023-12-12T08:53:37.485942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
省 22
 
18.5%
7
 
5.9%
自 5
 
4.2%
治 5
 
4.2%
區 5
 
4.2%
西 5
 
4.2%
江 4
 
3.4%
南 4
 
3.4%
海 3
 
2.5%
北 3
 
2.5%
Other values (49) 56
47.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 112
94.1%
Space Separator 7
 
5.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
省 22
19.6%
自 5
 
4.5%
治 5
 
4.5%
區 5
 
4.5%
西 5
 
4.5%
江 4
 
3.6%
南 4
 
3.6%
海 3
 
2.7%
北 3
 
2.7%
山 2
 
1.8%
Other values (48) 54
48.2%
Space Separator
ValueCountFrequency (%)
7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Han 112
94.1%
Common 7
 
5.9%

Most frequent character per script

Han
ValueCountFrequency (%)
省 22
19.6%
自 5
 
4.5%
治 5
 
4.5%
區 5
 
4.5%
西 5
 
4.5%
江 4
 
3.6%
南 4
 
3.6%
海 3
 
2.7%
北 3
 
2.7%
山 2
 
1.8%
Other values (48) 54
48.2%
Common
ValueCountFrequency (%)
7
100.0%

Most occurring blocks

ValueCountFrequency (%)
CJK 112
94.1%
ASCII 7
 
5.9%

Most frequent character per block

CJK
ValueCountFrequency (%)
省 22
19.6%
自 5
 
4.5%
治 5
 
4.5%
區 5
 
4.5%
西 5
 
4.5%
江 4
 
3.6%
南 4
 
3.6%
海 3
 
2.7%
北 3
 
2.7%
山 2
 
1.8%
Other values (48) 54
48.2%
ASCII
ValueCountFrequency (%)
7
100.0%

코드약어
Text

MISSING 

Distinct238
Distinct (%)99.2%
Missing177
Missing (%)42.4%
Memory size3.4 KiB
2023-12-12T08:53:37.865552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters480
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique236 ?
Unique (%)98.3%

Sample

1st rowES
2nd rowMS
3rd rowHS
4th rowXX
5th rowGH
ValueCountFrequency (%)
es 2
 
0.8%
ms 2
 
0.8%
is 1
 
0.4%
iq 1
 
0.4%
hu 1
 
0.4%
ir 1
 
0.4%
au 1
 
0.4%
at 1
 
0.4%
hn 1
 
0.4%
jo 1
 
0.4%
Other values (228) 228
95.0%
2023-12-12T08:53:38.355247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
M 37
 
7.7%
S 32
 
6.7%
G 29
 
6.0%
T 28
 
5.8%
A 27
 
5.6%
C 26
 
5.4%
N 24
 
5.0%
B 23
 
4.8%
E 20
 
4.2%
I 19
 
4.0%
Other values (16) 215
44.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 480
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
M 37
 
7.7%
S 32
 
6.7%
G 29
 
6.0%
T 28
 
5.8%
A 27
 
5.6%
C 26
 
5.4%
N 24
 
5.0%
B 23
 
4.8%
E 20
 
4.2%
I 19
 
4.0%
Other values (16) 215
44.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 480
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
M 37
 
7.7%
S 32
 
6.7%
G 29
 
6.0%
T 28
 
5.8%
A 27
 
5.6%
C 26
 
5.4%
N 24
 
5.0%
B 23
 
4.8%
E 20
 
4.2%
I 19
 
4.0%
Other values (16) 215
44.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 480
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
M 37
 
7.7%
S 32
 
6.7%
G 29
 
6.0%
T 28
 
5.8%
A 27
 
5.6%
C 26
 
5.4%
N 24
 
5.0%
B 23
 
4.8%
E 20
 
4.2%
I 19
 
4.0%
Other values (16) 215
44.8%

상위코드
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
<NA>
251 
america
53 
japan
47 
china
34 
russia
 
14
Other values (2)
 
18

Length

Max length7
Median length4
Mean length4.676259
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 251
60.2%
america 53
 
12.7%
japan 47
 
11.3%
china 34
 
8.2%
russia 14
 
3.4%
asia 11
 
2.6%
europe 7
 
1.7%

Length

2023-12-12T08:53:38.530962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:53:38.660855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 251
60.2%
america 53
 
12.7%
japan 47
 
11.3%
china 34
 
8.2%
russia 14
 
3.4%
asia 11
 
2.6%
europe 7
 
1.7%

구분A
Categorical

HIGH CORRELATION 

Distinct27
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
0
214 
3
52 
4
48 
1
35 
2
 
15
Other values (22)
53 

Length

Max length4
Median length1
Mean length1.0935252
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 214
51.3%
3 52
 
12.5%
4 48
 
11.5%
1 35
 
8.4%
2 15
 
3.6%
10
 
2.4%
<NA> 3
 
0.7%
6 2
 
0.5%
7 2
 
0.5%
8 2
 
0.5%
Other values (17) 34
 
8.2%

Length

2023-12-12T08:53:38.776496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0 214
52.6%
3 52
 
12.8%
4 48
 
11.8%
1 35
 
8.6%
2 15
 
3.7%
na 3
 
0.7%
16 2
 
0.5%
5 2
 
0.5%
17 2
 
0.5%
24 2
 
0.5%
Other values (16) 32
 
7.9%

구분B
Text

MISSING 

Distinct52
Distinct (%)30.4%
Missing246
Missing (%)59.0%
Memory size3.4 KiB
2023-12-12T08:53:38.928228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length2
Median length2
Mean length1.6432749
Min length1

Characters and Unicode

Total characters281
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)2.3%

Sample

1st row1
2nd row2
3rd row3
4th row4
5th row5
ValueCountFrequency (%)
1 24
 
14.5%
9 4
 
2.4%
4 4
 
2.4%
11 4
 
2.4%
5 4
 
2.4%
3 4
 
2.4%
13 4
 
2.4%
2 4
 
2.4%
14 4
 
2.4%
10 4
 
2.4%
Other values (41) 106
63.9%
2023-12-12T08:53:39.283100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 72
25.6%
2 46
16.4%
3 41
14.6%
4 34
12.1%
5 16
 
5.7%
6 14
 
5.0%
7 14
 
5.0%
8 13
 
4.6%
0 13
 
4.6%
9 13
 
4.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 276
98.2%
Space Separator 5
 
1.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 72
26.1%
2 46
16.7%
3 41
14.9%
4 34
12.3%
5 16
 
5.8%
6 14
 
5.1%
7 14
 
5.1%
8 13
 
4.7%
0 13
 
4.7%
9 13
 
4.7%
Space Separator
ValueCountFrequency (%)
5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 281
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 72
25.6%
2 46
16.4%
3 41
14.6%
4 34
12.1%
5 16
 
5.7%
6 14
 
5.0%
7 14
 
5.0%
8 13
 
4.6%
0 13
 
4.6%
9 13
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 281
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 72
25.6%
2 46
16.4%
3 41
14.6%
4 34
12.1%
5 16
 
5.7%
6 14
 
5.0%
7 14
 
5.0%
8 13
 
4.6%
0 13
 
4.6%
9 13
 
4.6%

비고
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
국가별 코드
238 
국가별 지역목록입니다보조항목1 : 코드관리의 국가(카테고리:STA)에서 보조항목1과 해당 항목이 같은 경우 해당 지역 목록을 표시
166 
대륙명
 
7
교과서 분류코드
 
3
등급번호 ( 사적지 고유번호 생성 시 등급 번호에 해당 )
 
3

Length

Max length72
Median length6
Mean length32.42446
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row국가별 지역목록입니다보조항목1 : 코드관리의 국가(카테고리:STA)에서 보조항목1과 해당 항목이 같은 경우 해당 지역 목록을 표시
2nd row국가별 지역목록입니다보조항목1 : 코드관리의 국가(카테고리:STA)에서 보조항목1과 해당 항목이 같은 경우 해당 지역 목록을 표시
3rd row국가별 지역목록입니다보조항목1 : 코드관리의 국가(카테고리:STA)에서 보조항목1과 해당 항목이 같은 경우 해당 지역 목록을 표시
4th row국가별 지역목록입니다보조항목1 : 코드관리의 국가(카테고리:STA)에서 보조항목1과 해당 항목이 같은 경우 해당 지역 목록을 표시
5th row국가별 지역목록입니다보조항목1 : 코드관리의 국가(카테고리:STA)에서 보조항목1과 해당 항목이 같은 경우 해당 지역 목록을 표시

Common Values

ValueCountFrequency (%)
국가별 코드 238
57.1%
국가별 지역목록입니다보조항목1 : 코드관리의 국가(카테고리:STA)에서 보조항목1과 해당 항목이 같은 경우 해당 지역 목록을 표시 166
39.8%
대륙명 7
 
1.7%
교과서 분류코드 3
 
0.7%
등급번호 ( 사적지 고유번호 생성 시 등급 번호에 해당 ) 3
 
0.7%

Length

2023-12-12T08:53:39.426872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:53:39.545175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
국가별 404
14.2%
해당 335
11.8%
코드 238
 
8.4%
172
 
6.0%
같은 166
 
5.8%
표시 166
 
5.8%
지역 166
 
5.8%
경우 166
 
5.8%
목록을 166
 
5.8%
항목이 166
 
5.8%
Other values (14) 698
24.6%

사용구분
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size549.0 B
False
215 
True
202 
ValueCountFrequency (%)
False 215
51.6%
True 202
48.4%
2023-12-12T08:53:39.662979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

등록일
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
2015-11-23
417 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2015-11-23
2nd row2015-11-23
3rd row2015-11-23
4th row2015-11-23
5th row2015-11-23

Common Values

ValueCountFrequency (%)
2015-11-23 417
100.0%

Length

2023-12-12T08:53:39.804814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:53:39.910880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2015-11-23 417
100.0%

수정일
Date

MISSING 

Distinct2
Distinct (%)66.7%
Missing414
Missing (%)99.3%
Memory size3.4 KiB
Minimum2015-12-08 00:00:00
Maximum2017-10-12 00:00:00
2023-12-12T08:53:39.987332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:53:40.122109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=2)

Interactions

2023-12-12T08:53:31.921474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T08:53:40.243060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
코드1코드2코드명_일본어코드명_중국어상위코드구분A구분B비고사용구분수정일
코드11.0000.547NaNNaNNaN0.9271.0001.0000.7500.000
코드20.5471.0001.0001.0000.8710.7530.4100.5470.3811.000
코드명_일본어NaN1.0001.000NaN1.0001.0001.000NaNNaNNaN
코드명_중국어NaN1.000NaN1.000NaNNaN1.000NaNNaNNaN
상위코드NaN0.8711.000NaN1.0001.0000.000NaNNaNNaN
구분A0.9270.7531.000NaN1.0001.0000.0000.9271.0000.000
구분B1.0000.4101.0001.0000.0000.0001.0001.0000.000NaN
비고1.0000.547NaNNaNNaN0.9271.0001.0000.7500.000
사용구분0.7500.381NaNNaNNaN1.0000.0000.7501.0000.000
수정일0.0001.000NaNNaNNaN0.000NaN0.0000.0001.000
2023-12-12T08:53:40.462928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
비고코드1구분A사용구분상위코드
비고1.0001.0000.7500.8811.000
코드11.0001.0000.7500.8811.000
구분A0.7500.7501.0000.9660.942
사용구분0.8810.8810.9661.0001.000
상위코드1.0001.0000.9421.0001.000
2023-12-12T08:53:40.580736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
코드2코드1상위코드구분A비고사용구분
코드21.0000.2560.7420.3820.2560.290
코드10.2561.0001.0000.7501.0000.881
상위코드0.7421.0001.0000.9421.0001.000
구분A0.3820.7500.9421.0000.7500.966
비고0.2561.0001.0000.7501.0000.881
사용구분0.2900.8811.0000.9660.8811.000

Missing values

2023-12-12T08:53:32.059099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T08:53:32.293378image/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.
2023-12-12T08:53:32.531849image/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

코드코드1코드2코드명코드명_영어코드명_일본어코드명_중국어코드약어상위코드구분A구분B비고사용구분등록일수정일
0ARA001ARA1간쑤성Gansusheng<NA>甘肅省<NA>china11국가별 지역목록입니다보조항목1 : 코드관리의 국가(카테고리:STA)에서 보조항목1과 해당 항목이 같은 경우 해당 지역 목록을 표시Y2015-11-23<NA>
1ARA002ARA2광둥성Guangdong<NA>廣東省<NA>china12국가별 지역목록입니다보조항목1 : 코드관리의 국가(카테고리:STA)에서 보조항목1과 해당 항목이 같은 경우 해당 지역 목록을 표시Y2015-11-23<NA>
2ARA003ARA3광시좡족자치구GuangxiZhuangzu<NA>廣西壯族自治區<NA>china13국가별 지역목록입니다보조항목1 : 코드관리의 국가(카테고리:STA)에서 보조항목1과 해당 항목이 같은 경우 해당 지역 목록을 표시Y2015-11-23<NA>
3ARA004ARA4구이저우성Guizhou<NA>貴州省<NA>china14국가별 지역목록입니다보조항목1 : 코드관리의 국가(카테고리:STA)에서 보조항목1과 해당 항목이 같은 경우 해당 지역 목록을 표시Y2015-11-23<NA>
4ARA005ARA5네이멍구자치구Neimenggu<NA>內蒙古自治區<NA>china15국가별 지역목록입니다보조항목1 : 코드관리의 국가(카테고리:STA)에서 보조항목1과 해당 항목이 같은 경우 해당 지역 목록을 표시Y2015-11-23<NA>
5ARA006ARA6닝샤후이족자치구Ningxia<NA>寧夏回族自治區<NA>china16국가별 지역목록입니다보조항목1 : 코드관리의 국가(카테고리:STA)에서 보조항목1과 해당 항목이 같은 경우 해당 지역 목록을 표시Y2015-11-23<NA>
6ARA007ARA7랴오닝성Liaoning<NA>遼寧省<NA>china17국가별 지역목록입니다보조항목1 : 코드관리의 국가(카테고리:STA)에서 보조항목1과 해당 항목이 같은 경우 해당 지역 목록을 표시Y2015-11-23<NA>
7ARA008ARA8마카오Macau<NA>澳門<NA>china18국가별 지역목록입니다보조항목1 : 코드관리의 국가(카테고리:STA)에서 보조항목1과 해당 항목이 같은 경우 해당 지역 목록을 표시Y2015-11-23<NA>
8ARA009ARA9베이징Beijing<NA>北京<NA>china19국가별 지역목록입니다보조항목1 : 코드관리의 국가(카테고리:STA)에서 보조항목1과 해당 항목이 같은 경우 해당 지역 목록을 표시Y2015-11-23<NA>
9ARA010ARA10산둥성Shandong<NA>山東省<NA>china110국가별 지역목록입니다보조항목1 : 코드관리의 국가(카테고리:STA)에서 보조항목1과 해당 항목이 같은 경우 해당 지역 목록을 표시Y2015-11-23<NA>
코드코드1코드2코드명코드명_영어코드명_일본어코드명_중국어코드약어상위코드구분A구분B비고사용구분등록일수정일
407STA229STA229프랑스 남부 지역FRENCH SOUTHERN TERRITORIES<NA><NA>TF<NA>0<NA>국가별 코드N2015-11-23<NA>
408STA230STA230프랑스령 기아나FRENCH GUIANA<NA><NA>GF<NA>0<NA>국가별 코드N2015-11-23<NA>
409STA231STA231프랑스령 폴리네시아FRENCH POLYNESIA<NA><NA>PF<NA>0<NA>국가별 코드N2015-11-23<NA>
410STA232STA232피지FIJI<NA><NA>FJ<NA>0<NA>국가별 코드N2015-11-23<NA>
411STA233STA233핀란드FINLAND<NA><NA>FI<NA>0<NA>국가별 코드N2015-11-23<NA>
412STA234STA234필리핀PHILIPPINES<NA><NA>PH<NA>12<NA>국가별 코드Y2015-11-23<NA>
413STA235STA235핏케언 군도PITCAIRN<NA><NA>PN<NA>0<NA>국가별 코드N2015-11-23<NA>
414STA236STA236허드 섬 및 맥도날드 군도HEARD AND MC DONALD ISLANDS<NA><NA>HM<NA>0<NA>국가별 코드N2015-11-23<NA>
415STA237STA237헝가리HUNGARY<NA><NA>HU<NA>0<NA>국가별 코드N2015-11-23<NA>
416STA238STA238홍콩HONG KONG<NA><NA>HK<NA>0<NA>국가별 코드N2015-11-232015-12-08