Overview

Dataset statistics

Number of variables8
Number of observations22
Missing cells88
Missing cells (%)50.0%
Duplicate rows2
Duplicate rows (%)9.1%
Total size in memory1.5 KiB
Average record size in memory70.0 B

Variable types

Text8

Dataset

Description2013년다중이용업안전관리우수업소현황전북
Author전라북도
URLhttps://www.bigdatahub.go.kr/opendata/dataSet/detail.nm?contentId=37&rlik=49451aebf056b486&serviceId=201676

Alerts

Dataset has 2 (9.1%) duplicate rowsDuplicates
2013년 전북 다중이용업 안전관리 우수업소 현황 has 3 (13.6%) missing valuesMissing
Unnamed: 1 has 9 (40.9%) missing valuesMissing
Unnamed: 2 has 9 (40.9%) missing valuesMissing
Unnamed: 3 has 9 (40.9%) missing valuesMissing
Unnamed: 4 has 9 (40.9%) missing valuesMissing
Unnamed: 5 has 9 (40.9%) missing valuesMissing
Unnamed: 6 has 20 (90.9%) missing valuesMissing
Unnamed: 7 has 20 (90.9%) missing valuesMissing

Reproduction

Analysis started2024-03-14 02:49:05.183261
Analysis finished2024-03-14 02:49:05.856231
Duration0.67 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

2013년 전북 다중이용업 안전관리 우수업소 현황
Text

MISSING 

Distinct15
Distinct (%)78.9%
Missing3
Missing (%)13.6%
Memory size308.0 B
2024-03-14T11:49:05.918482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length7
Mean length2.1578947
Min length1

Characters and Unicode

Total characters41
Distinct characters24
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13 ?
Unique (%)68.4%

Sample

1st row?
2nd row□ 업종별 현황
3rd row소 계
4th row10
5th row?
ValueCountFrequency (%)
4
16.7%
□ 2
 
8.3%
현황 2
 
8.3%
10 2
 
8.3%
3 1
 
4.2%
8 1
 
4.2%
7 1
 
4.2%
6 1
 
4.2%
5 1
 
4.2%
4 1
 
4.2%
Other values (8) 8
33.3%
2024-03-14T11:49:06.159514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9
22.0%
? 4
 
9.8%
1 3
 
7.3%
0 2
 
4.9%
□ 2
 
4.9%
현 2
 
4.9%
황 2
 
4.9%
번 1
 
2.4%
8 1
 
2.4%
7 1
 
2.4%
Other values (14) 14
34.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 13
31.7%
Other Letter 13
31.7%
Space Separator 9
22.0%
Other Punctuation 4
 
9.8%
Other Symbol 2
 
4.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
현 2
15.4%
황 2
15.4%
번 1
7.7%
세 1
7.7%
연 1
7.7%
부 1
7.7%
계 1
7.7%
소 1
7.7%
별 1
7.7%
종 1
7.7%
Decimal Number
ValueCountFrequency (%)
1 3
23.1%
0 2
15.4%
8 1
 
7.7%
7 1
 
7.7%
6 1
 
7.7%
5 1
 
7.7%
4 1
 
7.7%
3 1
 
7.7%
2 1
 
7.7%
9 1
 
7.7%
Space Separator
ValueCountFrequency (%)
9
100.0%
Other Punctuation
ValueCountFrequency (%)
? 4
100.0%
Other Symbol
ValueCountFrequency (%)
□ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 28
68.3%
Hangul 13
31.7%

Most frequent character per script

Common
ValueCountFrequency (%)
9
32.1%
? 4
14.3%
1 3
 
10.7%
0 2
 
7.1%
□ 2
 
7.1%
8 1
 
3.6%
7 1
 
3.6%
6 1
 
3.6%
5 1
 
3.6%
4 1
 
3.6%
Other values (3) 3
 
10.7%
Hangul
ValueCountFrequency (%)
현 2
15.4%
황 2
15.4%
번 1
7.7%
세 1
7.7%
연 1
7.7%
부 1
7.7%
계 1
7.7%
소 1
7.7%
별 1
7.7%
종 1
7.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 26
63.4%
Hangul 13
31.7%
Geometric Shapes 2
 
4.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9
34.6%
? 4
15.4%
1 3
 
11.5%
0 2
 
7.7%
8 1
 
3.8%
7 1
 
3.8%
6 1
 
3.8%
5 1
 
3.8%
4 1
 
3.8%
3 1
 
3.8%
Other values (2) 2
 
7.7%
Geometric Shapes
ValueCountFrequency (%)
□ 2
100.0%
Hangul
ValueCountFrequency (%)
현 2
15.4%
황 2
15.4%
번 1
7.7%
세 1
7.7%
연 1
7.7%
부 1
7.7%
계 1
7.7%
소 1
7.7%
별 1
7.7%
종 1
7.7%

Unnamed: 1
Text

MISSING 

Distinct9
Distinct (%)69.2%
Missing9
Missing (%)40.9%
Memory size308.0 B
2024-03-14T11:49:06.278526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length5
Mean length4.1538462
Min length1

Characters and Unicode

Total characters54
Distinct characters28
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

Unique7 ?
Unique (%)53.8%

Sample

1st row일반음식점
2nd row3
3rd row업종
4th row산후조리원
5th row영화상영관
ValueCountFrequency (%)
일반음식점 4
30.8%
영화상영관 2
15.4%
3 1
 
7.7%
업종 1
 
7.7%
산후조리원 1
 
7.7%
휴게음식점 1
 
7.7%
유흥주점 1
 
7.7%
학원 1
 
7.7%
노래연습장 1
 
7.7%
2024-03-14T11:49:06.510503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
점 6
 
11.1%
음 5
 
9.3%
식 5
 
9.3%
일 4
 
7.4%
영 4
 
7.4%
반 4
 
7.4%
화 2
 
3.7%
상 2
 
3.7%
관 2
 
3.7%
원 2
 
3.7%
Other values (18) 18
33.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 53
98.1%
Decimal Number 1
 
1.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
점 6
 
11.3%
음 5
 
9.4%
식 5
 
9.4%
일 4
 
7.5%
영 4
 
7.5%
반 4
 
7.5%
화 2
 
3.8%
상 2
 
3.8%
관 2
 
3.8%
원 2
 
3.8%
Other values (17) 17
32.1%
Decimal Number
ValueCountFrequency (%)
3 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 53
98.1%
Common 1
 
1.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
점 6
 
11.3%
음 5
 
9.4%
식 5
 
9.4%
일 4
 
7.5%
영 4
 
7.5%
반 4
 
7.5%
화 2
 
3.8%
상 2
 
3.8%
관 2
 
3.8%
원 2
 
3.8%
Other values (17) 17
32.1%
Common
ValueCountFrequency (%)
3 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 53
98.1%
ASCII 1
 
1.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
점 6
 
11.3%
음 5
 
9.4%
식 5
 
9.4%
일 4
 
7.5%
영 4
 
7.5%
반 4
 
7.5%
화 2
 
3.8%
상 2
 
3.8%
관 2
 
3.8%
원 2
 
3.8%
Other values (17) 17
32.1%
ASCII
ValueCountFrequency (%)
3 1
100.0%

Unnamed: 2
Text

MISSING 

Distinct13
Distinct (%)100.0%
Missing9
Missing (%)40.9%
Memory size308.0 B
2024-03-14T11:49:06.700660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length8
Mean length6.4615385
Min length1

Characters and Unicode

Total characters84
Distinct characters65
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

Unique13 ?
Unique (%)100.0%

Sample

1st row영화상영관
2nd row2
3rd row대상명
4th row한나산후조리원
5th row메가박스(전주)
ValueCountFrequency (%)
영화상영관 1
 
7.1%
2 1
 
7.1%
대상명 1
 
7.1%
한나산후조리원 1
 
7.1%
메가박스(전주 1
 
7.1%
엔제리너스 1
 
7.1%
군산수송 1
 
7.1%
vips(익산영등 1
 
7.1%
행복하누 1
 
7.1%
부영음악광장 1
 
7.1%
Other values (4) 4
28.6%
2024-03-14T11:49:07.033447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
영 4
 
4.8%
한 3
 
3.6%
) 3
 
3.6%
리 3
 
3.6%
산 3
 
3.6%
( 3
 
3.6%
상 2
 
2.4%
누 2
 
2.4%
장 2
 
2.4%
시 2
 
2.4%
Other values (55) 57
67.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 72
85.7%
Uppercase Letter 4
 
4.8%
Close Punctuation 3
 
3.6%
Open Punctuation 3
 
3.6%
Decimal Number 1
 
1.2%
Control 1
 
1.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
영 4
 
5.6%
한 3
 
4.2%
리 3
 
4.2%
산 3
 
4.2%
상 2
 
2.8%
누 2
 
2.8%
장 2
 
2.8%
시 2
 
2.8%
지 2
 
2.8%
스 2
 
2.8%
Other values (47) 47
65.3%
Uppercase Letter
ValueCountFrequency (%)
S 1
25.0%
V 1
25.0%
I 1
25.0%
P 1
25.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Decimal Number
ValueCountFrequency (%)
2 1
100.0%
Control
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 72
85.7%
Common 8
 
9.5%
Latin 4
 
4.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
영 4
 
5.6%
한 3
 
4.2%
리 3
 
4.2%
산 3
 
4.2%
상 2
 
2.8%
누 2
 
2.8%
장 2
 
2.8%
시 2
 
2.8%
지 2
 
2.8%
스 2
 
2.8%
Other values (47) 47
65.3%
Common
ValueCountFrequency (%)
) 3
37.5%
( 3
37.5%
2 1
 
12.5%
1
 
12.5%
Latin
ValueCountFrequency (%)
S 1
25.0%
V 1
25.0%
I 1
25.0%
P 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 72
85.7%
ASCII 12
 
14.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
영 4
 
5.6%
한 3
 
4.2%
리 3
 
4.2%
산 3
 
4.2%
상 2
 
2.8%
누 2
 
2.8%
장 2
 
2.8%
시 2
 
2.8%
지 2
 
2.8%
스 2
 
2.8%
Other values (47) 47
65.3%
ASCII
ValueCountFrequency (%)
) 3
25.0%
( 3
25.0%
S 1
 
8.3%
2 1
 
8.3%
1
 
8.3%
V 1
 
8.3%
I 1
 
8.3%
P 1
 
8.3%

Unnamed: 3
Text

MISSING 

Distinct13
Distinct (%)100.0%
Missing9
Missing (%)40.9%
Memory size308.0 B
2024-03-14T11:49:07.195009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length3
Mean length3.4615385
Min length1

Characters and Unicode

Total characters45
Distinct characters39
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13 ?
Unique (%)100.0%

Sample

1st row휴게음식점
2nd row1
3rd row영업주
4th row박용배 외 4명
5th row여환주
ValueCountFrequency (%)
휴게음식점 1
 
6.7%
1 1
 
6.7%
영업주 1
 
6.7%
박용배 1
 
6.7%
외 1
 
6.7%
4명 1
 
6.7%
여환주 1
 
6.7%
주종균 1
 
6.7%
허민회 1
 
6.7%
김상준 1
 
6.7%
Other values (5) 5
33.3%
2024-03-14T11:49:07.463978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
김 4
 
8.9%
주 3
 
6.7%
2
 
4.4%
휴 1
 
2.2%
제 1
 
2.2%
상 1
 
2.2%
준 1
 
2.2%
안 1
 
2.2%
재 1
 
2.2%
열 1
 
2.2%
Other values (29) 29
64.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 41
91.1%
Space Separator 2
 
4.4%
Decimal Number 2
 
4.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
김 4
 
9.8%
주 3
 
7.3%
휴 1
 
2.4%
제 1
 
2.4%
상 1
 
2.4%
준 1
 
2.4%
안 1
 
2.4%
재 1
 
2.4%
열 1
 
2.4%
시 1
 
2.4%
Other values (26) 26
63.4%
Decimal Number
ValueCountFrequency (%)
1 1
50.0%
4 1
50.0%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 41
91.1%
Common 4
 
8.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
김 4
 
9.8%
주 3
 
7.3%
휴 1
 
2.4%
제 1
 
2.4%
상 1
 
2.4%
준 1
 
2.4%
안 1
 
2.4%
재 1
 
2.4%
열 1
 
2.4%
시 1
 
2.4%
Other values (26) 26
63.4%
Common
ValueCountFrequency (%)
2
50.0%
1 1
25.0%
4 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 41
91.1%
ASCII 4
 
8.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
김 4
 
9.8%
주 3
 
7.3%
휴 1
 
2.4%
제 1
 
2.4%
상 1
 
2.4%
준 1
 
2.4%
안 1
 
2.4%
재 1
 
2.4%
열 1
 
2.4%
시 1
 
2.4%
Other values (26) 26
63.4%
ASCII
ValueCountFrequency (%)
2
50.0%
1 1
25.0%
4 1
25.0%

Unnamed: 4
Text

MISSING 

Distinct13
Distinct (%)100.0%
Missing9
Missing (%)40.9%
Memory size308.0 B
2024-03-14T11:49:07.628828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length16
Mean length11.461538
Min length1

Characters and Unicode

Total characters149
Distinct characters62
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

Unique13 ?
Unique (%)100.0%

Sample

1st row노래연습장
2nd row1
3rd row위 치
4th row전주시 덕진구 기린대로 489
5th row전주시 완산구 전주객사4길 74-10
ValueCountFrequency (%)
전주시 2
 
5.1%
노래연습장 1
 
2.6%
22 1
 
2.6%
23-9 1
 
2.6%
김제시 1
 
2.6%
도장로 1
 
2.6%
74 1
 
2.6%
고창군 1
 
2.6%
고창읍 1
 
2.6%
성산로 1
 
2.6%
Other values (28) 28
71.8%
2024-03-14T11:49:07.919148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
28
 
18.8%
시 7
 
4.7%
로 7
 
4.7%
산 6
 
4.0%
1 5
 
3.4%
군 4
 
2.7%
2 4
 
2.7%
장 4
 
2.7%
4 4
 
2.7%
읍 3
 
2.0%
Other values (52) 77
51.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 89
59.7%
Decimal Number 30
 
20.1%
Space Separator 28
 
18.8%
Dash Punctuation 2
 
1.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 7
 
7.9%
로 7
 
7.9%
산 6
 
6.7%
군 4
 
4.5%
장 4
 
4.5%
읍 3
 
3.4%
길 3
 
3.4%
변 3
 
3.4%
주 3
 
3.4%
전 3
 
3.4%
Other values (40) 46
51.7%
Decimal Number
ValueCountFrequency (%)
1 5
16.7%
2 4
13.3%
4 4
13.3%
0 3
10.0%
9 3
10.0%
3 3
10.0%
5 3
10.0%
7 2
 
6.7%
8 2
 
6.7%
6 1
 
3.3%
Space Separator
ValueCountFrequency (%)
28
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 89
59.7%
Common 60
40.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 7
 
7.9%
로 7
 
7.9%
산 6
 
6.7%
군 4
 
4.5%
장 4
 
4.5%
읍 3
 
3.4%
길 3
 
3.4%
변 3
 
3.4%
주 3
 
3.4%
전 3
 
3.4%
Other values (40) 46
51.7%
Common
ValueCountFrequency (%)
28
46.7%
1 5
 
8.3%
2 4
 
6.7%
4 4
 
6.7%
0 3
 
5.0%
9 3
 
5.0%
3 3
 
5.0%
5 3
 
5.0%
- 2
 
3.3%
7 2
 
3.3%
Other values (2) 3
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 89
59.7%
ASCII 60
40.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
28
46.7%
1 5
 
8.3%
2 4
 
6.7%
4 4
 
6.7%
0 3
 
5.0%
9 3
 
5.0%
3 3
 
5.0%
5 3
 
5.0%
- 2
 
3.3%
7 2
 
3.3%
Other values (2) 3
 
5.0%
Hangul
ValueCountFrequency (%)
시 7
 
7.9%
로 7
 
7.9%
산 6
 
6.7%
군 4
 
4.5%
장 4
 
4.5%
읍 3
 
3.4%
길 3
 
3.4%
변 3
 
3.4%
주 3
 
3.4%
전 3
 
3.4%
Other values (40) 46
51.7%

Unnamed: 5
Text

MISSING 

Distinct13
Distinct (%)100.0%
Missing9
Missing (%)40.9%
Memory size308.0 B
2024-03-14T11:49:08.072253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length2
Mean length2.6923077
Min length1

Characters and Unicode

Total characters35
Distinct characters29
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

Unique13 ?
Unique (%)100.0%

Sample

1st row유흥주점
2nd row1
3rd row관할소방서
4th row전주덕진
5th row전주완산
ValueCountFrequency (%)
유흥주점 1
 
7.7%
1 1
 
7.7%
관할소방서 1
 
7.7%
전주덕진 1
 
7.7%
전주완산 1
 
7.7%
군산 1
 
7.7%
익산 1
 
7.7%
정읍 1
 
7.7%
남원 1
 
7.7%
김제 1
 
7.7%
Other values (3) 3
23.1%
2024-03-14T11:49:08.337959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
산 3
 
8.6%
주 3
 
8.6%
전 2
 
5.7%
진 2
 
5.7%
익 1
 
2.9%
무 1
 
2.9%
안 1
 
2.9%
부 1
 
2.9%
창 1
 
2.9%
고 1
 
2.9%
Other values (19) 19
54.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 34
97.1%
Decimal Number 1
 
2.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 3
 
8.8%
주 3
 
8.8%
전 2
 
5.9%
진 2
 
5.9%
익 1
 
2.9%
무 1
 
2.9%
안 1
 
2.9%
부 1
 
2.9%
창 1
 
2.9%
고 1
 
2.9%
Other values (18) 18
52.9%
Decimal Number
ValueCountFrequency (%)
1 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 34
97.1%
Common 1
 
2.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 3
 
8.8%
주 3
 
8.8%
전 2
 
5.9%
진 2
 
5.9%
익 1
 
2.9%
무 1
 
2.9%
안 1
 
2.9%
부 1
 
2.9%
창 1
 
2.9%
고 1
 
2.9%
Other values (18) 18
52.9%
Common
ValueCountFrequency (%)
1 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 34
97.1%
ASCII 1
 
2.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
산 3
 
8.8%
주 3
 
8.8%
전 2
 
5.9%
진 2
 
5.9%
익 1
 
2.9%
무 1
 
2.9%
안 1
 
2.9%
부 1
 
2.9%
창 1
 
2.9%
고 1
 
2.9%
Other values (18) 18
52.9%
ASCII
ValueCountFrequency (%)
1 1
100.0%

Unnamed: 6
Text

MISSING 

Distinct2
Distinct (%)100.0%
Missing20
Missing (%)90.9%
Memory size308.0 B
2024-03-14T11:49:08.428506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length2
Median length1.5
Mean length1.5
Min length1

Characters and Unicode

Total characters3
Distinct characters3
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

Unique2 ?
Unique (%)100.0%

Sample

1st row학원
2nd row1
ValueCountFrequency (%)
학원 1
50.0%
1 1
50.0%
2024-03-14T11:49:08.611185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
학 1
33.3%
원 1
33.3%
1 1
33.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2
66.7%
Decimal Number 1
33.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
학 1
50.0%
원 1
50.0%
Decimal Number
ValueCountFrequency (%)
1 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2
66.7%
Common 1
33.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
학 1
50.0%
원 1
50.0%
Common
ValueCountFrequency (%)
1 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2
66.7%
ASCII 1
33.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
학 1
50.0%
원 1
50.0%
ASCII
ValueCountFrequency (%)
1 1
100.0%

Unnamed: 7
Text

MISSING 

Distinct2
Distinct (%)100.0%
Missing20
Missing (%)90.9%
Memory size308.0 B
2024-03-14T11:49:08.715369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length3
Min length1

Characters and Unicode

Total characters6
Distinct characters6
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

Unique2 ?
Unique (%)100.0%

Sample

1st row산후조리원
2nd row1
ValueCountFrequency (%)
산후조리원 1
50.0%
1 1
50.0%
2024-03-14T11:49:08.972668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
산 1
16.7%
후 1
16.7%
조 1
16.7%
리 1
16.7%
원 1
16.7%
1 1
16.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5
83.3%
Decimal Number 1
 
16.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 1
20.0%
후 1
20.0%
조 1
20.0%
리 1
20.0%
원 1
20.0%
Decimal Number
ValueCountFrequency (%)
1 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5
83.3%
Common 1
 
16.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 1
20.0%
후 1
20.0%
조 1
20.0%
리 1
20.0%
원 1
20.0%
Common
ValueCountFrequency (%)
1 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5
83.3%
ASCII 1
 
16.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
산 1
20.0%
후 1
20.0%
조 1
20.0%
리 1
20.0%
원 1
20.0%
ASCII
ValueCountFrequency (%)
1 1
100.0%

Correlations

2024-03-14T11:49:09.088036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2013년 전북 다중이용업 안전관리 우수업소 현황Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 7
2013년 전북 다중이용업 안전관리 우수업소 현황1.0000.8771.0001.0001.0001.0000.0000.000
Unnamed: 10.8771.0001.0001.0001.0001.0000.0000.000
Unnamed: 21.0001.0001.0001.0001.0001.0000.0000.000
Unnamed: 31.0001.0001.0001.0001.0001.0000.0000.000
Unnamed: 41.0001.0001.0001.0001.0001.0000.0000.000
Unnamed: 51.0001.0001.0001.0001.0001.0000.0000.000
Unnamed: 60.0000.0000.0000.0000.0000.0001.0000.000
Unnamed: 70.0000.0000.0000.0000.0000.0000.0001.000

Missing values

2024-03-14T11:49:05.528621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T11:49:05.629636image/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.
2024-03-14T11:49:05.769890image/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

2013년 전북 다중이용업 안전관리 우수업소 현황Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 7
0?<NA><NA><NA><NA><NA><NA><NA>
1□ 업종별 현황<NA><NA><NA><NA><NA><NA><NA>
2소 계일반음식점영화상영관휴게음식점노래연습장유흥주점학원산후조리원
3103211111
4?<NA><NA><NA><NA><NA><NA><NA>
5□ 세부 현황<NA><NA><NA><NA><NA><NA><NA>
6연번업종대상명영업주위 치관할소방서<NA><NA>
71산후조리원한나산후조리원박용배 외 4명전주시 덕진구 기린대로 489전주덕진<NA><NA>
82영화상영관메가박스(전주)여환주전주시 완산구 전주객사4길 74-10전주완산<NA><NA>
93휴게음식점엔제리너스 (군산수송)주종균군산시 남수송6길 10군산<NA><NA>
2013년 전북 다중이용업 안전관리 우수업소 현황Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 7
126유흥주점부영음악광장안재열남원시 큰들길 23-9남원<NA><NA>
137학원지평선학당김제시장김제시 도장로 74김제<NA><NA>
148일반음식점한우사랑방유덕근고창군 고창읍 성산로 22고창<NA><NA>
159노래연습장뮤직시티노래연습장김은숙부안군 변산면 변산해변로 51부안<NA><NA>
1610영화상영관한누리디지털시네마김선태장수군 장수읍 한누리로 393무진장<NA><NA>
17?<NA><NA><NA><NA><NA><NA><NA>
18?<NA><NA><NA><NA><NA><NA><NA>
19<NA><NA><NA><NA><NA><NA><NA><NA>
20<NA><NA><NA><NA><NA><NA><NA><NA>
21<NA><NA><NA><NA><NA><NA><NA><NA>

Duplicate rows

Most frequently occurring

2013년 전북 다중이용업 안전관리 우수업소 현황Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 7# duplicates
0?<NA><NA><NA><NA><NA><NA><NA>4
1<NA><NA><NA><NA><NA><NA><NA><NA>3