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 memory56.0 B

Variable types

Text4
Categorical1
Numeric1

Alerts

영화관등록수 has constant value ""Constant
상영영화관명 has unique valuesUnique
소재지 has unique valuesUnique

Reproduction

Analysis started2024-03-14 02:37:05.672085
Analysis finished2024-03-14 02:37:06.151253
Duration0.48 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
2024-03-14T11:37:06.235837image/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%
2024-03-14T11:37:06.441725image/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

2024-03-14T11:37:06.539725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T11:37:06.615440image/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
2024-03-14T11:37:06.745997image/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%
2024-03-14T11:37:07.031358image/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)

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
2024-03-14T11:37:07.144366image/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
2024-03-14T11:37:07.231819image/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
Text

Distinct18
Distinct (%)81.8%
Missing0
Missing (%)0.0%
Memory size308.0 B
2024-03-14T11:37:07.356138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length3.3181818
Min length2

Characters and Unicode

Total characters73
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

Unique15 ?
Unique (%)68.2%

Sample

1st row1,626
2nd row922
3rd row1,515
4th row1,215
5th row1,265
ValueCountFrequency (%)
98 3
 
13.6%
90 2
 
9.1%
99 2
 
9.1%
1,100 1
 
4.5%
1,626 1
 
4.5%
1,325 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%
2024-03-14T11:37:07.597036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 16
21.9%
1 14
19.2%
5 10
13.7%
, 8
11.0%
2 7
9.6%
6 5
 
6.8%
0 4
 
5.5%
8 3
 
4.1%
7 2
 
2.7%
4 2
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 65
89.0%
Other Punctuation 8
 
11.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 16
24.6%
1 14
21.5%
5 10
15.4%
2 7
10.8%
6 5
 
7.7%
0 4
 
6.2%
8 3
 
4.6%
7 2
 
3.1%
4 2
 
3.1%
3 2
 
3.1%
Other Punctuation
ValueCountFrequency (%)
, 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 73
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
9 16
21.9%
1 14
19.2%
5 10
13.7%
, 8
11.0%
2 7
9.6%
6 5
 
6.8%
0 4
 
5.5%
8 3
 
4.1%
7 2
 
2.7%
4 2
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 73
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 16
21.9%
1 14
19.2%
5 10
13.7%
, 8
11.0%
2 7
9.6%
6 5
 
6.8%
0 4
 
5.5%
8 3
 
4.1%
7 2
 
2.7%
4 2
 
2.7%

소재지
Text

UNIQUE 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size308.0 B
2024-03-14T11:37:07.815673image/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%
2024-03-14T11:37:08.138803image/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

2024-03-14T11:37:05.843993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T11:37:08.293249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분상영영화관명상영관(스크린)좌석소재지
구분1.0001.0000.0000.0001.000
상영영화관명1.0001.0001.0001.0001.000
상영관(스크린)0.0001.0001.0000.9801.000
좌석0.0001.0000.9801.0001.000
소재지1.0001.0001.0001.0001.000

Missing values

2024-03-14T11:37:05.932377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T11:37:06.086265image/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 전주81,626전주시 완산구 서신동 971
1전주시1롯데시네마 평화점6922전주시 완산구 평화동 604-1
2전주시1메가박스 전주101,515전주시 완산구 전주객사4길 74-12
3전주시1씨너스 송천81,215전주시 덕진구 송천동 2가 661-15
4전주시1전주시네마타운71,265전주시 완산구 고사동 340-3
5전주시1CGV 전주61,192전라북도 전주시 완산구 전주객사3길 72
6전주시1전주디지털독립영화관198전주시 완산구 전주객사3길 22 전주영화제작소
7전주시1CGV효자동71,795전주시 완산구 용머리로45
8군산시1롯데시네마 군산5975군산시 나운동 124-4
9군산시1CGV 군산71,100군산시 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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