Overview

Dataset statistics

Number of variables6
Number of observations22
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.2 KiB
Average record size in memory57.0 B

Variable types

Text3
Categorical1
Numeric2

Alerts

영화관등록수 has constant value ""Constant
상영관(스크린) is highly overall correlated with 좌석High correlation
좌석 is highly overall correlated with 상영관(스크린)High correlation
상영영화관명 has unique valuesUnique
소재지 has unique valuesUnique

Reproduction

Analysis started2024-03-14 02:37:08.835135
Analysis finished2024-03-14 02:37:09.407406
Duration0.57 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Text

Distinct14
Distinct (%)63.6%
Missing0
Missing (%)0.0%
Memory size308.0 B
2024-03-14T11:37:09.478966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters66
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

Unique12 ?
Unique (%)54.5%

Sample

1st row전주시
2nd row전주시
3rd row전주시
4th row전주시
5th row전주시
ValueCountFrequency (%)
전주시 8
36.4%
군산시 2
 
9.1%
익산시 1
 
4.5%
정읍시 1
 
4.5%
남원시 1
 
4.5%
김제시 1
 
4.5%
완주군 1
 
4.5%
진안군 1
 
4.5%
무주군 1
 
4.5%
장수군 1
 
4.5%
Other values (4) 4
18.2%
2024-03-14T11:37:09.685197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 14
21.2%
군 10
15.2%
주 10
15.2%
전 8
12.1%
산 3
 
4.5%
안 2
 
3.0%
창 2
 
3.0%
고 1
 
1.5%
순 1
 
1.5%
실 1
 
1.5%
Other values (14) 14
21.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 66
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 14
21.2%
군 10
15.2%
주 10
15.2%
전 8
12.1%
산 3
 
4.5%
안 2
 
3.0%
창 2
 
3.0%
고 1
 
1.5%
순 1
 
1.5%
실 1
 
1.5%
Other values (14) 14
21.2%

Most occurring scripts

ValueCountFrequency (%)
Hangul 66
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 14
21.2%
군 10
15.2%
주 10
15.2%
전 8
12.1%
산 3
 
4.5%
안 2
 
3.0%
창 2
 
3.0%
고 1
 
1.5%
순 1
 
1.5%
실 1
 
1.5%
Other values (14) 14
21.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 66
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 14
21.2%
군 10
15.2%
주 10
15.2%
전 8
12.1%
산 3
 
4.5%
안 2
 
3.0%
창 2
 
3.0%
고 1
 
1.5%
순 1
 
1.5%
실 1
 
1.5%
Other values (14) 14
21.2%

영화관등록수
Categorical

CONSTANT 

Distinct1
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Memory size308.0 B
1
22 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1 22
100.0%

Length

2024-03-14T11:37:09.789320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T11:37:09.863113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 22
100.0%

상영영화관명
Text

UNIQUE 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size308.0 B
2024-03-14T11:37:09.994438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length13
Mean length9.5454545
Min length4

Characters and Unicode

Total characters210
Distinct characters60
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

Unique22 ?
Unique (%)100.0%

Sample

1st row롯데시네마 전주
2nd row롯데시네마 평화점
3rd row메가박스 전주
4th row씨너스 송천
5th row전주시네마타운
ValueCountFrequency (%)
cgv 4
 
12.1%
롯데시네마 3
 
9.1%
전주 3
 
9.1%
메가박스 2
 
6.1%
군산 2
 
6.1%
시네마(작은영화관 1
 
3.0%
동리시네마(작은영화관 1
 
3.0%
영화산책(작은영화관 1
 
3.0%
천재의공간 1
 
3.0%
작은별영화관(작은영화관 1
 
3.0%
Other values (14) 14
42.4%
2024-03-14T11:37:10.255303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
화 16
 
7.6%
영 15
 
7.1%
관 14
 
6.7%
11
 
5.2%
마 10
 
4.8%
은 10
 
4.8%
작 10
 
4.8%
) 9
 
4.3%
( 9
 
4.3%
네 8
 
3.8%
Other values (50) 98
46.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 166
79.0%
Uppercase Letter 15
 
7.1%
Space Separator 11
 
5.2%
Close Punctuation 9
 
4.3%
Open Punctuation 9
 
4.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
화 16
 
9.6%
영 15
 
9.0%
관 14
 
8.4%
마 10
 
6.0%
은 10
 
6.0%
작 10
 
6.0%
네 8
 
4.8%
시 8
 
4.8%
주 6
 
3.6%
전 5
 
3.0%
Other values (44) 64
38.6%
Uppercase Letter
ValueCountFrequency (%)
C 5
33.3%
V 5
33.3%
G 5
33.3%
Space Separator
ValueCountFrequency (%)
11
100.0%
Close Punctuation
ValueCountFrequency (%)
) 9
100.0%
Open Punctuation
ValueCountFrequency (%)
( 9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 166
79.0%
Common 29
 
13.8%
Latin 15
 
7.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
화 16
 
9.6%
영 15
 
9.0%
관 14
 
8.4%
마 10
 
6.0%
은 10
 
6.0%
작 10
 
6.0%
네 8
 
4.8%
시 8
 
4.8%
주 6
 
3.6%
전 5
 
3.0%
Other values (44) 64
38.6%
Common
ValueCountFrequency (%)
11
37.9%
) 9
31.0%
( 9
31.0%
Latin
ValueCountFrequency (%)
C 5
33.3%
V 5
33.3%
G 5
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 166
79.0%
ASCII 44
 
21.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
화 16
 
9.6%
영 15
 
9.0%
관 14
 
8.4%
마 10
 
6.0%
은 10
 
6.0%
작 10
 
6.0%
네 8
 
4.8%
시 8
 
4.8%
주 6
 
3.6%
전 5
 
3.0%
Other values (44) 64
38.6%
ASCII
ValueCountFrequency (%)
11
25.0%
) 9
20.5%
( 9
20.5%
C 5
11.4%
V 5
11.4%
G 5
11.4%

상영관(스크린)
Real number (ℝ)

HIGH CORRELATION 

Distinct9
Distinct (%)40.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.5454545
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2024-03-14T11:37:10.356802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median4
Q37
95-th percentile8.95
Maximum10
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.8238318
Coefficient of variation (CV)0.62124299
Kurtosis-1.2700588
Mean4.5454545
Median Absolute Deviation (MAD)2
Skewness0.44294931
Sum100
Variance7.974026
MonotonicityNot monotonic
2024-03-14T11:37:10.471704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
2 9
40.9%
7 3
 
13.6%
8 2
 
9.1%
6 2
 
9.1%
4 2
 
9.1%
10 1
 
4.5%
1 1
 
4.5%
5 1
 
4.5%
9 1
 
4.5%
ValueCountFrequency (%)
1 1
 
4.5%
2 9
40.9%
4 2
 
9.1%
5 1
 
4.5%
6 2
 
9.1%
7 3
 
13.6%
8 2
 
9.1%
9 1
 
4.5%
10 1
 
4.5%
ValueCountFrequency (%)
10 1
 
4.5%
9 1
 
4.5%
8 2
 
9.1%
7 3
 
13.6%
6 2
 
9.1%
5 1
 
4.5%
4 2
 
9.1%
2 9
40.9%
1 1
 
4.5%

좌석
Real number (ℝ)

HIGH CORRELATION 

Distinct18
Distinct (%)81.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean687.18182
Minimum90
Maximum1795
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2024-03-14T11:37:10.622388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum90
5-th percentile90.15
Q198
median590
Q31209.25
95-th percentile1620.45
Maximum1795
Range1705
Interquartile range (IQR)1111.25

Descriptive statistics

Standard deviation610.92082
Coefficient of variation (CV)0.88902355
Kurtosis-1.4791619
Mean687.18182
Median Absolute Deviation (MAD)496.5
Skewness0.35987003
Sum15118
Variance373224.25
MonotonicityNot monotonic
2024-03-14T11:37:10.929406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
98 3
 
13.6%
90 2
 
9.1%
99 2
 
9.1%
1626 1
 
4.5%
1325 1
 
4.5%
93 1
 
4.5%
149 1
 
4.5%
94 1
 
4.5%
615 1
 
4.5%
565 1
 
4.5%
Other values (8) 8
36.4%
ValueCountFrequency (%)
90 2
9.1%
93 1
 
4.5%
94 1
 
4.5%
98 3
13.6%
99 2
9.1%
149 1
 
4.5%
565 1
 
4.5%
615 1
 
4.5%
922 1
 
4.5%
975 1
 
4.5%
ValueCountFrequency (%)
1795 1
4.5%
1626 1
4.5%
1515 1
4.5%
1325 1
4.5%
1265 1
4.5%
1215 1
4.5%
1192 1
4.5%
1100 1
4.5%
975 1
4.5%
922 1
4.5%

소재지
Text

UNIQUE 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size308.0 B
2024-03-14T11:37:11.139113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length29
Median length22
Mean length18.272727
Min length10

Characters and Unicode

Total characters402
Distinct characters89
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

Unique22 ?
Unique (%)100.0%

Sample

1st row전주시 완산구 서신동 971
2nd row전주시 완산구 평화동 604-1
3rd row전주시 완산구 전주객사4길 74-12
4th row전주시 덕진구 송천동 2가 661-15
5th row전주시 완산구 고사동 340-3
ValueCountFrequency (%)
전주시 8
 
8.7%
완산구 7
 
7.6%
전라북도 4
 
4.3%
나운동 2
 
2.2%
전주객사3길 2
 
2.2%
군산시 2
 
2.2%
143 1
 
1.1%
무주군 1
 
1.1%
무주읍 1
 
1.1%
한풍루로326-17 1
 
1.1%
Other values (63) 63
68.5%
2024-03-14T11:37:11.456819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
71
 
17.7%
1 19
 
4.7%
전 17
 
4.2%
주 15
 
3.7%
시 14
 
3.5%
4 13
 
3.2%
산 12
 
3.0%
군 12
 
3.0%
3 12
 
3.0%
- 11
 
2.7%
Other values (79) 206
51.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 241
60.0%
Decimal Number 79
 
19.7%
Space Separator 71
 
17.7%
Dash Punctuation 11
 
2.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
전 17
 
7.1%
주 15
 
6.2%
시 14
 
5.8%
산 12
 
5.0%
군 12
 
5.0%
동 11
 
4.6%
읍 9
 
3.7%
구 8
 
3.3%
완 8
 
3.3%
리 7
 
2.9%
Other values (67) 128
53.1%
Decimal Number
ValueCountFrequency (%)
1 19
24.1%
4 13
16.5%
3 12
15.2%
2 10
12.7%
5 5
 
6.3%
6 5
 
6.3%
7 5
 
6.3%
0 4
 
5.1%
9 4
 
5.1%
8 2
 
2.5%
Space Separator
ValueCountFrequency (%)
71
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 241
60.0%
Common 161
40.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
전 17
 
7.1%
주 15
 
6.2%
시 14
 
5.8%
산 12
 
5.0%
군 12
 
5.0%
동 11
 
4.6%
읍 9
 
3.7%
구 8
 
3.3%
완 8
 
3.3%
리 7
 
2.9%
Other values (67) 128
53.1%
Common
ValueCountFrequency (%)
71
44.1%
1 19
 
11.8%
4 13
 
8.1%
3 12
 
7.5%
- 11
 
6.8%
2 10
 
6.2%
5 5
 
3.1%
6 5
 
3.1%
7 5
 
3.1%
0 4
 
2.5%
Other values (2) 6
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 241
60.0%
ASCII 161
40.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
71
44.1%
1 19
 
11.8%
4 13
 
8.1%
3 12
 
7.5%
- 11
 
6.8%
2 10
 
6.2%
5 5
 
3.1%
6 5
 
3.1%
7 5
 
3.1%
0 4
 
2.5%
Other values (2) 6
 
3.7%
Hangul
ValueCountFrequency (%)
전 17
 
7.1%
주 15
 
6.2%
시 14
 
5.8%
산 12
 
5.0%
군 12
 
5.0%
동 11
 
4.6%
읍 9
 
3.7%
구 8
 
3.3%
완 8
 
3.3%
리 7
 
2.9%
Other values (67) 128
53.1%

Interactions

2024-03-14T11:37:09.128543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:37:08.991359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:37:09.192555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:37:09.062994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T11:37:11.555748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분상영영화관명상영관(스크린)좌석소재지
구분1.0001.0000.0000.3101.000
상영영화관명1.0001.0001.0001.0001.000
상영관(스크린)0.0001.0001.0000.9531.000
좌석0.3101.0000.9531.0001.000
소재지1.0001.0001.0001.0001.000
2024-03-14T11:37:11.645890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
상영관(스크린)좌석
상영관(스크린)1.0000.907
좌석0.9071.000

Missing values

2024-03-14T11:37:09.294899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T11:37:09.373700image/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.

Sample

구분영화관등록수상영영화관명상영관(스크린)좌석소재지
0전주시1롯데시네마 전주81626전주시 완산구 서신동 971
1전주시1롯데시네마 평화점6922전주시 완산구 평화동 604-1
2전주시1메가박스 전주101515전주시 완산구 전주객사4길 74-12
3전주시1씨너스 송천81215전주시 덕진구 송천동 2가 661-15
4전주시1전주시네마타운71265전주시 완산구 고사동 340-3
5전주시1CGV 전주61192전라북도 전주시 완산구 전주객사3길 72
6전주시1전주디지털독립영화관198전주시 완산구 전주객사3길 22 전주영화제작소
7전주시1CGV효자동71795전주시 완산구 용머리로45
8군산시1롯데시네마 군산5975군산시 나운동 124-4
9군산시1CGV 군산71100군산시 나운동 114-34
구분영화관등록수상영영화관명상영관(스크린)좌석소재지
12남원시1메가박스4615남원시 쌍교동 82-1
13김제시1지평선 시네마(작은영화관)299김제시 검산동 62-1 청소년수련관
14완주군1휴시네마(작은영화관)290전라북도 완주군 봉동읍 둔산3로 94
15진안군1진안마이골영화관(작은영화관)298진안군 진안읍 군하리 143
16무주군1무주산골영화관(작은영화관)298무주군 무주읍 한풍루로326-17
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