Overview

Dataset statistics

Number of variables5
Number of observations29
Missing cells15
Missing cells (%)10.3%
Duplicate rows1
Duplicate rows (%)3.4%
Total size in memory1.3 KiB
Average record size in memory46.4 B

Variable types

Text3
Numeric2

Dataset

Description전북특별자치도 시군별 영화관 정보(영화관명, 상영관 수, 좌석, 소재지 등)해당영화관이 위치한 전북특별자치도 내 지역명, 해당영화관이 보유한 스크린 수
Author전북특별자치도
URLhttps://www.data.go.kr/data/15055581/fileData.do

Alerts

Dataset has 1 (3.4%) duplicate rowsDuplicates
총 스크린수 is highly overall correlated with 총 좌석수High correlation
총 좌석수 is highly overall correlated with 총 스크린수High correlation
지역명 has 3 (10.3%) missing valuesMissing
영화상영관명 has 3 (10.3%) missing valuesMissing
총 스크린수 has 3 (10.3%) missing valuesMissing
총 좌석수 has 3 (10.3%) missing valuesMissing
주소 has 3 (10.3%) missing valuesMissing

Reproduction

Analysis started2024-03-14 09:53:59.320797
Analysis finished2024-03-14 09:54:01.528155
Duration2.21 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

지역명
Text

MISSING 

Distinct14
Distinct (%)53.8%
Missing3
Missing (%)10.3%
Memory size360.0 B
2024-03-14T18:54:01.910861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters78
Distinct characters24
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

Unique11 ?
Unique (%)42.3%

Sample

1st row전주시
2nd row전주시
3rd row전주시
4th row전주시
5th row전주시
ValueCountFrequency (%)
전주시 10
38.5%
군산시 3
 
11.5%
익산시 2
 
7.7%
정읍시 1
 
3.8%
남원시 1
 
3.8%
김제시 1
 
3.8%
완주군 1
 
3.8%
진안군 1
 
3.8%
무주군 1
 
3.8%
장수군 1
 
3.8%
Other values (4) 4
 
15.4%
2024-03-14T18:54:02.824699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 18
23.1%
주 12
15.4%
군 11
14.1%
전 10
12.8%
산 5
 
6.4%
익 2
 
2.6%
창 2
 
2.6%
안 2
 
2.6%
무 1
 
1.3%
고 1
 
1.3%
Other values (14) 14
17.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 78
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 18
23.1%
주 12
15.4%
군 11
14.1%
전 10
12.8%
산 5
 
6.4%
익 2
 
2.6%
창 2
 
2.6%
안 2
 
2.6%
무 1
 
1.3%
고 1
 
1.3%
Other values (14) 14
17.9%

Most occurring scripts

ValueCountFrequency (%)
Hangul 78
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 18
23.1%
주 12
15.4%
군 11
14.1%
전 10
12.8%
산 5
 
6.4%
익 2
 
2.6%
창 2
 
2.6%
안 2
 
2.6%
무 1
 
1.3%
고 1
 
1.3%
Other values (14) 14
17.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 78
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 18
23.1%
주 12
15.4%
군 11
14.1%
전 10
12.8%
산 5
 
6.4%
익 2
 
2.6%
창 2
 
2.6%
안 2
 
2.6%
무 1
 
1.3%
고 1
 
1.3%
Other values (14) 14
17.9%

영화상영관명
Text

MISSING 

Distinct26
Distinct (%)100.0%
Missing3
Missing (%)10.3%
Memory size360.0 B
2024-03-14T18:54:03.596086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length10
Mean length8.3076923
Min length6

Characters and Unicode

Total characters216
Distinct characters75
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26 ?
Unique (%)100.0%

Sample

1st rowCGV서전주
2nd row메가박스 전주혁신
3rd rowCGV 전주효자
4th rowCGV 전주고사
5th row조이앤시네마 전주점
ValueCountFrequency (%)
롯데시네마 5
 
10.6%
cgv 5
 
10.6%
메가박스 3
 
6.4%
휴 1
 
2.1%
시네마 1
 
2.1%
마이골작은영화관 1
 
2.1%
무주 1
 
2.1%
산골영화관 1
 
2.1%
한누리시네마 1
 
2.1%
작은별 1
 
2.1%
Other values (27) 27
57.4%
2024-03-14T18:54:04.752671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
21
 
9.7%
마 13
 
6.0%
주 11
 
5.1%
네 11
 
5.1%
시 11
 
5.1%
화 9
 
4.2%
전 9
 
4.2%
산 7
 
3.2%
영 6
 
2.8%
롯 6
 
2.8%
Other values (65) 112
51.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 175
81.0%
Space Separator 21
 
9.7%
Uppercase Letter 18
 
8.3%
Open Punctuation 1
 
0.5%
Close Punctuation 1
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
마 13
 
7.4%
주 11
 
6.3%
네 11
 
6.3%
시 11
 
6.3%
화 9
 
5.1%
전 9
 
5.1%
산 7
 
4.0%
영 6
 
3.4%
롯 6
 
3.4%
데 6
 
3.4%
Other values (59) 86
49.1%
Uppercase Letter
ValueCountFrequency (%)
C 6
33.3%
G 6
33.3%
V 6
33.3%
Space Separator
ValueCountFrequency (%)
21
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 175
81.0%
Common 23
 
10.6%
Latin 18
 
8.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
마 13
 
7.4%
주 11
 
6.3%
네 11
 
6.3%
시 11
 
6.3%
화 9
 
5.1%
전 9
 
5.1%
산 7
 
4.0%
영 6
 
3.4%
롯 6
 
3.4%
데 6
 
3.4%
Other values (59) 86
49.1%
Common
ValueCountFrequency (%)
21
91.3%
( 1
 
4.3%
) 1
 
4.3%
Latin
ValueCountFrequency (%)
C 6
33.3%
G 6
33.3%
V 6
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 175
81.0%
ASCII 41
 
19.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
21
51.2%
C 6
 
14.6%
G 6
 
14.6%
V 6
 
14.6%
( 1
 
2.4%
) 1
 
2.4%
Hangul
ValueCountFrequency (%)
마 13
 
7.4%
주 11
 
6.3%
네 11
 
6.3%
시 11
 
6.3%
화 9
 
5.1%
전 9
 
5.1%
산 7
 
4.0%
영 6
 
3.4%
롯 6
 
3.4%
데 6
 
3.4%
Other values (59) 86
49.1%

총 스크린수
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct7
Distinct (%)26.9%
Missing3
Missing (%)10.3%
Infinite0
Infinite (%)0.0%
Mean4.6538462
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size389.0 B
2024-03-14T18:54:05.100754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median5
Q37
95-th percentile8
Maximum8
Range7
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.4485475
Coefficient of variation (CV)0.52613416
Kurtosis-1.7086645
Mean4.6538462
Median Absolute Deviation (MAD)2.5
Skewness-0.059840771
Sum121
Variance5.9953846
MonotonicityNot monotonic
2024-03-14T18:54:05.453848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2 9
31.0%
7 6
20.7%
8 3
 
10.3%
6 3
 
10.3%
5 2
 
6.9%
4 2
 
6.9%
1 1
 
3.4%
(Missing) 3
 
10.3%
ValueCountFrequency (%)
1 1
 
3.4%
2 9
31.0%
4 2
 
6.9%
5 2
 
6.9%
6 3
 
10.3%
7 6
20.7%
8 3
 
10.3%
ValueCountFrequency (%)
8 3
 
10.3%
7 6
20.7%
6 3
 
10.3%
5 2
 
6.9%
4 2
 
6.9%
2 9
31.0%
1 1
 
3.4%

총 좌석수
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct23
Distinct (%)88.5%
Missing3
Missing (%)10.3%
Infinite0
Infinite (%)0.0%
Mean687.38462
Minimum90
Maximum1795
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size389.0 B
2024-03-14T18:54:05.794346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum90
5-th percentile90.75
Q199
median726
Q31073.75
95-th percentile1541.75
Maximum1795
Range1705
Interquartile range (IQR)974.75

Descriptive statistics

Standard deviation539.30867
Coefficient of variation (CV)0.78458066
Kurtosis-1.0674476
Mean687.38462
Median Absolute Deviation (MAD)531
Skewness0.26933362
Sum17872
Variance290853.85
MonotonicityNot monotonic
2024-03-14T18:54:06.167409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
98 2
 
6.9%
90 2
 
6.9%
99 2
 
6.9%
1123 1
 
3.4%
93 1
 
3.4%
149 1
 
3.4%
97 1
 
3.4%
103 1
 
3.4%
615 1
 
3.4%
565 1
 
3.4%
Other values (13) 13
44.8%
(Missing) 3
 
10.3%
ValueCountFrequency (%)
90 2
6.9%
93 1
3.4%
97 1
3.4%
98 2
6.9%
99 2
6.9%
103 1
3.4%
149 1
3.4%
565 1
3.4%
615 1
3.4%
720 1
3.4%
ValueCountFrequency (%)
1795 1
3.4%
1632 1
3.4%
1271 1
3.4%
1243 1
3.4%
1215 1
3.4%
1123 1
3.4%
1085 1
3.4%
1040 1
3.4%
1014 1
3.4%
975 1
3.4%

주소
Text

MISSING 

Distinct26
Distinct (%)100.0%
Missing3
Missing (%)10.3%
Memory size360.0 B
2024-03-14T18:54:07.270190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length46
Median length36.5
Mean length29.076923
Min length16

Characters and Unicode

Total characters756
Distinct characters140
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26 ?
Unique (%)100.0%

Sample

1st row전라북도 전주시 완산구 홍산로 260 엠스퀘어 6층
2nd row전라북도 전주시 덕진구 기지로 77 2층
3rd row전라북도 전주시 완산구 용머리로 45 (효자동1가, Mall of Hyoja 2층)
4th row전라북도 전주시 완산구 전주객사3길 72
5th row전라북도 전주시 완산구 전주객사3길 74-25
ValueCountFrequency (%)
전라북도 26
 
15.5%
전주시 10
 
6.0%
완산구 8
 
4.8%
2층 4
 
2.4%
전주객사3길 4
 
2.4%
군산시 3
 
1.8%
익산시 2
 
1.2%
고사동 2
 
1.2%
백토로 2
 
1.2%
덕진구 2
 
1.2%
Other values (104) 105
62.5%
2024-03-14T18:54:08.891378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
144
 
19.0%
전 42
 
5.6%
도 28
 
3.7%
북 26
 
3.4%
라 26
 
3.4%
로 19
 
2.5%
1 19
 
2.5%
2 19
 
2.5%
산 19
 
2.5%
주 19
 
2.5%
Other values (130) 395
52.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 462
61.1%
Space Separator 144
 
19.0%
Decimal Number 93
 
12.3%
Open Punctuation 16
 
2.1%
Close Punctuation 15
 
2.0%
Other Punctuation 11
 
1.5%
Lowercase Letter 9
 
1.2%
Dash Punctuation 3
 
0.4%
Uppercase Letter 2
 
0.3%
Math Symbol 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
전 42
 
9.1%
도 28
 
6.1%
북 26
 
5.6%
라 26
 
5.6%
로 19
 
4.1%
산 19
 
4.1%
주 19
 
4.1%
시 18
 
3.9%
동 14
 
3.0%
군 12
 
2.6%
Other values (106) 239
51.7%
Decimal Number
ValueCountFrequency (%)
1 19
20.4%
2 19
20.4%
3 14
15.1%
7 9
9.7%
6 8
8.6%
0 7
 
7.5%
4 6
 
6.5%
5 5
 
5.4%
9 3
 
3.2%
8 3
 
3.2%
Lowercase Letter
ValueCountFrequency (%)
l 2
22.2%
o 2
22.2%
a 2
22.2%
f 1
11.1%
y 1
11.1%
j 1
11.1%
Uppercase Letter
ValueCountFrequency (%)
M 1
50.0%
H 1
50.0%
Space Separator
ValueCountFrequency (%)
144
100.0%
Open Punctuation
ValueCountFrequency (%)
( 16
100.0%
Close Punctuation
ValueCountFrequency (%)
) 15
100.0%
Other Punctuation
ValueCountFrequency (%)
, 11
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 462
61.1%
Common 283
37.4%
Latin 11
 
1.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
전 42
 
9.1%
도 28
 
6.1%
북 26
 
5.6%
라 26
 
5.6%
로 19
 
4.1%
산 19
 
4.1%
주 19
 
4.1%
시 18
 
3.9%
동 14
 
3.0%
군 12
 
2.6%
Other values (106) 239
51.7%
Common
ValueCountFrequency (%)
144
50.9%
1 19
 
6.7%
2 19
 
6.7%
( 16
 
5.7%
) 15
 
5.3%
3 14
 
4.9%
, 11
 
3.9%
7 9
 
3.2%
6 8
 
2.8%
0 7
 
2.5%
Other values (6) 21
 
7.4%
Latin
ValueCountFrequency (%)
l 2
18.2%
o 2
18.2%
a 2
18.2%
M 1
9.1%
f 1
9.1%
H 1
9.1%
y 1
9.1%
j 1
9.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 462
61.1%
ASCII 294
38.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
144
49.0%
1 19
 
6.5%
2 19
 
6.5%
( 16
 
5.4%
) 15
 
5.1%
3 14
 
4.8%
, 11
 
3.7%
7 9
 
3.1%
6 8
 
2.7%
0 7
 
2.4%
Other values (14) 32
 
10.9%
Hangul
ValueCountFrequency (%)
전 42
 
9.1%
도 28
 
6.1%
북 26
 
5.6%
라 26
 
5.6%
로 19
 
4.1%
산 19
 
4.1%
주 19
 
4.1%
시 18
 
3.9%
동 14
 
3.0%
군 12
 
2.6%
Other values (106) 239
51.7%

Interactions

2024-03-14T18:54:00.158364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:53:59.640400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:54:00.413965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:53:59.900221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T18:54:09.156014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지역명영화상영관명총 스크린수총 좌석수주소
지역명1.0001.0000.0000.0001.000
영화상영관명1.0001.0001.0001.0001.000
총 스크린수0.0001.0001.0000.8691.000
총 좌석수0.0001.0000.8691.0001.000
주소1.0001.0001.0001.0001.000
2024-03-14T18:54:09.411126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
총 스크린수총 좌석수
총 스크린수1.0000.922
총 좌석수0.9221.000

Missing values

2024-03-14T18:54:00.734432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T18:54:01.043003image/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.
2024-03-14T18:54:01.349134image/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

지역명영화상영관명총 스크린수총 좌석수주소
0전주시CGV서전주5720전라북도 전주시 완산구 홍산로 260 엠스퀘어 6층
1전주시메가박스 전주혁신7732전라북도 전주시 덕진구 기지로 77 2층
2전주시CGV 전주효자71795전라북도 전주시 완산구 용머리로 45 (효자동1가, Mall of Hyoja 2층)
3전주시CGV 전주고사81271전라북도 전주시 완산구 전주객사3길 72
4전주시조이앤시네마 전주점6888전라북도 전주시 완산구 전주객사3길 74-25
5전주시메가박스 송천81215전라북도 전주시 덕진구 동부대로 1215 (송천동2가)
6전주시전주디지털독립영화관198전라북도 전주시 완산구 전주객사3길 22 (고사동, 전주영화제작소 4층
7전주시전주시네마타운6943전라북도 전주시 완산구 전주객사3길 67 (고사동)
8전주시롯데시네마 전주(롯데백화점)81632전라북도 전주시 완산구 온고을로 2 (서신동, 롯데백화점 7층)
9전주시롯데시네마 전주평화61040전라북도 전주시 완산구 백제대로 10 (평화동1가)
지역명영화상영관명총 스크린수총 좌석수주소
19진안군마이골작은영화관298전라북도 진안군 진안읍 중앙로 40
20무주군무주 산골영화관2103전라북도 무주군 무주읍 한풍루로 326-17 (당산리, 무주예체문화관 2층)
21장수군한누리시네마297전라북도 장수군 장수읍 한누리로 393 (두산리, 한누리전당 내 가람관 1층)
22임실군작은별 영화관290전라북도 임실군 임실읍 호국로 1703 (이도리, 임실군민회관 지하1층)
23순창군천재의 공간 영화산책2149전라북도 순창군 순창읍 남계로 83
24고창군고창문화원 동리시네마293전라북도 고창군 고창읍 판소리길 20 (읍내리, 동리국악당 지하)
25부안군부안마실영화관299전라북도 부안군 부안읍 예술회관길 11 (서외리)
26<NA><NA><NA><NA><NA>
27<NA><NA><NA><NA><NA>
28<NA><NA><NA><NA><NA>

Duplicate rows

Most frequently occurring

지역명영화상영관명총 스크린수총 좌석수주소# duplicates
0<NA><NA><NA><NA><NA>3