Overview

Dataset statistics

Number of variables5
Number of observations76
Missing cells143
Missing cells (%)37.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.2 KiB
Average record size in memory42.7 B

Variable types

Text3
Categorical2

Alerts

FILE_NAME has constant value ""Constant
BASE_YMD has constant value ""Constant
Abbreviation_Korean_NM has 69 (90.8%) missing valuesMissing
Abbreviation_English_NM has 74 (97.4%) missing valuesMissing
Entry_NM has unique valuesUnique

Reproduction

Analysis started2023-12-10 10:01:53.612446
Analysis finished2023-12-10 10:01:54.586468
Duration0.97 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Entry_NM
Text

UNIQUE 

Distinct76
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size740.0 B
2023-12-10T19:01:55.095772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length11
Mean length4.9078947
Min length2

Characters and Unicode

Total characters373
Distinct characters116
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique76 ?
Unique (%)100.0%

Sample

1st row방탄소년단
2nd row블랙핑크
3rd row엑스원
4th row에버글로우
5th row갓세븐
ValueCountFrequency (%)
엔터테인먼트 12
 
12.6%
슈퍼엠 2
 
2.1%
체셔 1
 
1.1%
서클 1
 
1.1%
이너 1
 
1.1%
위즈원 1
 
1.1%
몬베베 1
 
1.1%
아로하 1
 
1.1%
무무 1
 
1.1%
네버랜드 1
 
1.1%
Other values (73) 73
76.8%
2023-12-10T19:01:56.411711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
이 24
 
6.4%
19
 
5.1%
트 16
 
4.3%
엔 14
 
3.8%
테 14
 
3.8%
스 13
 
3.5%
터 13
 
3.5%
인 13
 
3.5%
먼 12
 
3.2%
아 8
 
2.1%
Other values (106) 227
60.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 352
94.4%
Space Separator 19
 
5.1%
Open Punctuation 1
 
0.3%
Close Punctuation 1
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
이 24
 
6.8%
트 16
 
4.5%
엔 14
 
4.0%
테 14
 
4.0%
스 13
 
3.7%
터 13
 
3.7%
인 13
 
3.7%
먼 12
 
3.4%
아 8
 
2.3%
더 7
 
2.0%
Other values (103) 218
61.9%
Space Separator
ValueCountFrequency (%)
19
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 352
94.4%
Common 21
 
5.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
이 24
 
6.8%
트 16
 
4.5%
엔 14
 
4.0%
테 14
 
4.0%
스 13
 
3.7%
터 13
 
3.7%
인 13
 
3.7%
먼 12
 
3.4%
아 8
 
2.3%
더 7
 
2.0%
Other values (103) 218
61.9%
Common
ValueCountFrequency (%)
19
90.5%
( 1
 
4.8%
) 1
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 352
94.4%
ASCII 21
 
5.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
이 24
 
6.8%
트 16
 
4.5%
엔 14
 
4.0%
테 14
 
4.0%
스 13
 
3.7%
터 13
 
3.7%
인 13
 
3.7%
먼 12
 
3.4%
아 8
 
2.3%
더 7
 
2.0%
Other values (103) 218
61.9%
ASCII
ValueCountFrequency (%)
19
90.5%
( 1
 
4.8%
) 1
 
4.8%

Abbreviation_Korean_NM
Text

MISSING 

Distinct7
Distinct (%)100.0%
Missing69
Missing (%)90.8%
Memory size740.0 B
2023-12-10T19:01:56.751606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length2
Mean length2.5714286
Min length2

Characters and Unicode

Total characters18
Distinct characters16
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

Unique7 ?
Unique (%)100.0%

Sample

1st row방탄
2nd row블핑
3rd row슈주
4th row스키즈
5th row레벨
ValueCountFrequency (%)
방탄 1
14.3%
블핑 1
14.3%
슈주 1
14.3%
스키즈 1
14.3%
레벨 1
14.3%
티엑스티 1
14.3%
오마걸 1
14.3%
2023-12-10T19:01:57.321673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
스 2
 
11.1%
티 2
 
11.1%
방 1
 
5.6%
탄 1
 
5.6%
블 1
 
5.6%
핑 1
 
5.6%
슈 1
 
5.6%
주 1
 
5.6%
키 1
 
5.6%
즈 1
 
5.6%
Other values (6) 6
33.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 18
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
스 2
 
11.1%
티 2
 
11.1%
방 1
 
5.6%
탄 1
 
5.6%
블 1
 
5.6%
핑 1
 
5.6%
슈 1
 
5.6%
주 1
 
5.6%
키 1
 
5.6%
즈 1
 
5.6%
Other values (6) 6
33.3%

Most occurring scripts

ValueCountFrequency (%)
Hangul 18
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
스 2
 
11.1%
티 2
 
11.1%
방 1
 
5.6%
탄 1
 
5.6%
블 1
 
5.6%
핑 1
 
5.6%
슈 1
 
5.6%
주 1
 
5.6%
키 1
 
5.6%
즈 1
 
5.6%
Other values (6) 6
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 18
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
스 2
 
11.1%
티 2
 
11.1%
방 1
 
5.6%
탄 1
 
5.6%
블 1
 
5.6%
핑 1
 
5.6%
슈 1
 
5.6%
주 1
 
5.6%
키 1
 
5.6%
즈 1
 
5.6%
Other values (6) 6
33.3%

Abbreviation_English_NM
Text

MISSING 

Distinct2
Distinct (%)100.0%
Missing74
Missing (%)97.4%
Memory size740.0 B
2023-12-10T19:01:57.524513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length2.5
Mean length2.5
Min length2

Characters and Unicode

Total characters5
Distinct characters4
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

Unique2 ?
Unique (%)100.0%

Sample

1st rowSJ
2nd rowTXT
ValueCountFrequency (%)
sj 1
50.0%
txt 1
50.0%
2023-12-10T19:01:57.983121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
T 2
40.0%
S 1
20.0%
J 1
20.0%
X 1
20.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 5
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T 2
40.0%
S 1
20.0%
J 1
20.0%
X 1
20.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 5
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
T 2
40.0%
S 1
20.0%
J 1
20.0%
X 1
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T 2
40.0%
S 1
20.0%
J 1
20.0%
X 1
20.0%

FILE_NAME
Categorical

CONSTANT 

Distinct1
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size740.0 B
KC_DICTIONARY_ABB_INFO_2019
76 

Length

Max length27
Median length27
Mean length27
Min length27

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowKC_DICTIONARY_ABB_INFO_2019
2nd rowKC_DICTIONARY_ABB_INFO_2019
3rd rowKC_DICTIONARY_ABB_INFO_2019
4th rowKC_DICTIONARY_ABB_INFO_2019
5th rowKC_DICTIONARY_ABB_INFO_2019

Common Values

ValueCountFrequency (%)
KC_DICTIONARY_ABB_INFO_2019 76
100.0%

Length

2023-12-10T19:01:58.248057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:01:58.461597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
kc_dictionary_abb_info_2019 76
100.0%

BASE_YMD
Categorical

CONSTANT 

Distinct1
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size740.0 B
2019
76 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2019
2nd row2019
3rd row2019
4th row2019
5th row2019

Common Values

ValueCountFrequency (%)
2019 76
100.0%

Length

2023-12-10T19:01:58.672184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:01:58.872221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2019 76
100.0%

Correlations

2023-12-10T19:01:59.002870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Entry_NMAbbreviation_Korean_NMAbbreviation_English_NM
Entry_NM1.0001.0000.000
Abbreviation_Korean_NM1.0001.0000.000
Abbreviation_English_NM0.0000.0001.000

Missing values

2023-12-10T19:01:54.057707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:01:54.251792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-12-10T19:01:54.459514image/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

Entry_NMAbbreviation_Korean_NMAbbreviation_English_NMFILE_NAMEBASE_YMD
0방탄소년단방탄<NA>KC_DICTIONARY_ABB_INFO_20192019
1블랙핑크블핑<NA>KC_DICTIONARY_ABB_INFO_20192019
2엑스원<NA><NA>KC_DICTIONARY_ABB_INFO_20192019
3에버글로우<NA><NA>KC_DICTIONARY_ABB_INFO_20192019
4갓세븐<NA><NA>KC_DICTIONARY_ABB_INFO_20192019
5슈퍼주니어슈주SJKC_DICTIONARY_ABB_INFO_20192019
6트와이스<NA><NA>KC_DICTIONARY_ABB_INFO_20192019
7제이홉<NA><NA>KC_DICTIONARY_ABB_INFO_20192019
8세븐틴<NA><NA>KC_DICTIONARY_ABB_INFO_20192019
9드림캐쳐<NA><NA>KC_DICTIONARY_ABB_INFO_20192019
Entry_NMAbbreviation_Korean_NMAbbreviation_English_NMFILE_NAMEBASE_YMD
66플레디스 엔터테인먼트<NA><NA>KC_DICTIONARY_ABB_INFO_20192019
67케이큐 엔터테인먼트<NA><NA>KC_DICTIONARY_ABB_INFO_20192019
68더블랙레이블<NA><NA>KC_DICTIONARY_ABB_INFO_20192019
69더블유엠 엔터테인먼트<NA><NA>KC_DICTIONARY_ABB_INFO_20192019
70큐브 엔터테인먼트<NA><NA>KC_DICTIONARY_ABB_INFO_20192019
71알비더블유<NA><NA>KC_DICTIONARY_ABB_INFO_20192019
72스타쉽 엔터테인먼트<NA><NA>KC_DICTIONARY_ABB_INFO_20192019
73오프더레코드 엔터테인먼트<NA><NA>KC_DICTIONARY_ABB_INFO_20192019
74크래커 엔터테인먼트<NA><NA>KC_DICTIONARY_ABB_INFO_20192019
75판타지오<NA><NA>KC_DICTIONARY_ABB_INFO_20192019