Overview

Dataset statistics

Number of variables6
Number of observations24
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.3 KiB
Average record size in memory55.5 B

Variable types

Text4
Categorical1
Numeric1

Alerts

영화관등록수 has constant value ""Constant
상영영화관명 has unique valuesUnique

Reproduction

Analysis started2024-03-14 00:29:19.506032
Analysis finished2024-03-14 00:29:19.923876
Duration0.42 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Text

Distinct14
Distinct (%)58.3%
Missing0
Missing (%)0.0%
Memory size324.0 B
2024-03-14T09:29:20.198665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters72
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 (%)50.0%

Sample

1st row전주시
2nd row전주시
3rd row전주시
4th row전주시
5th row전주시
ValueCountFrequency (%)
전주시 10
41.7%
군산시 2
 
8.3%
익산시 1
 
4.2%
정읍시 1
 
4.2%
남원시 1
 
4.2%
김제시 1
 
4.2%
완주군 1
 
4.2%
진안군 1
 
4.2%
무주군 1
 
4.2%
장수군 1
 
4.2%
Other values (4) 4
 
16.7%
2024-03-14T09:29:20.395327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 16
22.2%
주 12
16.7%
전 10
13.9%
군 10
13.9%
산 3
 
4.2%
안 2
 
2.8%
창 2
 
2.8%
고 1
 
1.4%
순 1
 
1.4%
실 1
 
1.4%
Other values (14) 14
19.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 72
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 16
22.2%
주 12
16.7%
전 10
13.9%
군 10
13.9%
산 3
 
4.2%
안 2
 
2.8%
창 2
 
2.8%
고 1
 
1.4%
순 1
 
1.4%
실 1
 
1.4%
Other values (14) 14
19.4%

Most occurring scripts

ValueCountFrequency (%)
Hangul 72
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 16
22.2%
주 12
16.7%
전 10
13.9%
군 10
13.9%
산 3
 
4.2%
안 2
 
2.8%
창 2
 
2.8%
고 1
 
1.4%
순 1
 
1.4%
실 1
 
1.4%
Other values (14) 14
19.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 72
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 16
22.2%
주 12
16.7%
전 10
13.9%
군 10
13.9%
산 3
 
4.2%
안 2
 
2.8%
창 2
 
2.8%
고 1
 
1.4%
순 1
 
1.4%
실 1
 
1.4%
Other values (14) 14
19.4%

영화관등록수
Categorical

CONSTANT 

Distinct1
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size324.0 B
1
24 

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 24
100.0%

Length

2024-03-14T09:29:20.496239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T09:29:20.563158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 24
100.0%

상영영화관명
Text

UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size324.0 B
2024-03-14T09:29:20.728520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length9
Mean length7.8333333
Min length5

Characters and Unicode

Total characters188
Distinct characters65
Distinct categories3 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique24 ?
Unique (%)100.0%

Sample

1st row메가박스 전주
2nd row메가박스 송천
3rd row조이앤시네마 전주점
4th row조이아트시네마
5th row전주디지털독립영화관
ValueCountFrequency (%)
cgv 5
 
11.6%
메가박스 3
 
7.0%
롯데시네마 3
 
7.0%
전주 2
 
4.7%
군산 2
 
4.7%
마이골작은영화관 1
 
2.3%
무주 1
 
2.3%
산골 1
 
2.3%
영화관 1
 
2.3%
한누리디지털시네마 1
 
2.3%
Other values (23) 23
53.5%
2024-03-14T09:29:21.009196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
19
 
10.1%
마 12
 
6.4%
시 10
 
5.3%
네 10
 
5.3%
주 10
 
5.3%
화 8
 
4.3%
전 8
 
4.3%
영 6
 
3.2%
V 5
 
2.7%
G 5
 
2.7%
Other values (55) 95
50.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 154
81.9%
Space Separator 19
 
10.1%
Uppercase Letter 15
 
8.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
마 12
 
7.8%
시 10
 
6.5%
네 10
 
6.5%
주 10
 
6.5%
화 8
 
5.2%
전 8
 
5.2%
영 6
 
3.9%
산 5
 
3.2%
관 5
 
3.2%
지 3
 
1.9%
Other values (51) 77
50.0%
Uppercase Letter
ValueCountFrequency (%)
V 5
33.3%
G 5
33.3%
C 5
33.3%
Space Separator
ValueCountFrequency (%)
19
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 154
81.9%
Common 19
 
10.1%
Latin 15
 
8.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
마 12
 
7.8%
시 10
 
6.5%
네 10
 
6.5%
주 10
 
6.5%
화 8
 
5.2%
전 8
 
5.2%
영 6
 
3.9%
산 5
 
3.2%
관 5
 
3.2%
지 3
 
1.9%
Other values (51) 77
50.0%
Latin
ValueCountFrequency (%)
V 5
33.3%
G 5
33.3%
C 5
33.3%
Common
ValueCountFrequency (%)
19
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 154
81.9%
ASCII 34
 
18.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
19
55.9%
V 5
 
14.7%
G 5
 
14.7%
C 5
 
14.7%
Hangul
ValueCountFrequency (%)
마 12
 
7.8%
시 10
 
6.5%
네 10
 
6.5%
주 10
 
6.5%
화 8
 
5.2%
전 8
 
5.2%
영 6
 
3.9%
산 5
 
3.2%
관 5
 
3.2%
지 3
 
1.9%
Other values (51) 77
50.0%

상영관(스크린수)
Real number (ℝ)

Distinct9
Distinct (%)37.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4583333
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2024-03-14T09:29:21.102250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.15
Q12
median4
Q37
95-th percentile8.85
Maximum10
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.8434389
Coefficient of variation (CV)0.63778069
Kurtosis-1.2320706
Mean4.4583333
Median Absolute Deviation (MAD)2
Skewness0.4559471
Sum107
Variance8.0851449
MonotonicityNot monotonic
2024-03-14T09:29:21.209566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
2 9
37.5%
8 3
 
12.5%
5 2
 
8.3%
1 2
 
8.3%
6 2
 
8.3%
7 2
 
8.3%
4 2
 
8.3%
10 1
 
4.2%
9 1
 
4.2%
ValueCountFrequency (%)
1 2
 
8.3%
2 9
37.5%
4 2
 
8.3%
5 2
 
8.3%
6 2
 
8.3%
7 2
 
8.3%
8 3
 
12.5%
9 1
 
4.2%
10 1
 
4.2%
ValueCountFrequency (%)
10 1
 
4.2%
9 1
 
4.2%
8 3
 
12.5%
7 2
 
8.3%
6 2
 
8.3%
5 2
 
8.3%
4 2
 
8.3%
2 9
37.5%
1 2
 
8.3%

좌석
Text

Distinct20
Distinct (%)83.3%
Missing0
Missing (%)0.0%
Memory size324.0 B
2024-03-14T09:29:21.353711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length3.2083333
Min length2

Characters and Unicode

Total characters77
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

Unique17 ?
Unique (%)70.8%

Sample

1st row1,568
2nd row1,180
3rd row782
4th row103
5th row98
ValueCountFrequency (%)
98 3
 
12.5%
90 2
 
8.3%
99 2
 
8.3%
893 1
 
4.2%
1,568 1
 
4.2%
1,027 1
 
4.2%
149 1
 
4.2%
94 1
 
4.2%
615 1
 
4.2%
564 1
 
4.2%
Other values (10) 10
41.7%
2024-03-14T09:29:21.596094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 16
20.8%
1 11
14.3%
2 8
10.4%
8 7
9.1%
, 7
9.1%
5 6
 
7.8%
0 5
 
6.5%
3 5
 
6.5%
7 4
 
5.2%
4 4
 
5.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 70
90.9%
Other Punctuation 7
 
9.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 16
22.9%
1 11
15.7%
2 8
11.4%
8 7
10.0%
5 6
 
8.6%
0 5
 
7.1%
3 5
 
7.1%
7 4
 
5.7%
4 4
 
5.7%
6 4
 
5.7%
Other Punctuation
ValueCountFrequency (%)
, 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 77
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
9 16
20.8%
1 11
14.3%
2 8
10.4%
8 7
9.1%
, 7
9.1%
5 6
 
7.8%
0 5
 
6.5%
3 5
 
6.5%
7 4
 
5.2%
4 4
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 77
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 16
20.8%
1 11
14.3%
2 8
10.4%
8 7
9.1%
, 7
9.1%
5 6
 
7.8%
0 5
 
6.5%
3 5
 
6.5%
7 4
 
5.2%
4 4
 
5.2%

소재지
Text

Distinct23
Distinct (%)95.8%
Missing0
Missing (%)0.0%
Memory size324.0 B
2024-03-14T09:29:21.798346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length23
Mean length20.5
Min length17

Characters and Unicode

Total characters492
Distinct characters77
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 (%)91.7%

Sample

1st row전라북도 전주시 완산구 고사동 181
2nd row전라북도 전주시 덕진구 송천동2가 661-15
3rd row전라북도 전주시 완산구 고사동 288-2
4th row전라북도 전주시 완산구 고사동 288-2
5th row전라북도 전주시 완산구 전주객사3길 22
ValueCountFrequency (%)
전라북도 24
21.1%
전주시 10
 
8.8%
완산구 9
 
7.9%
고사동 5
 
4.4%
나운동 2
 
1.8%
군산시 2
 
1.8%
288-2 2
 
1.8%
부안읍 1
 
0.9%
부안군 1
 
0.9%
3 1
 
0.9%
Other values (57) 57
50.0%
2024-03-14T09:29:22.132840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
90
18.3%
전 35
 
7.1%
도 26
 
5.3%
북 24
 
4.9%
라 24
 
4.9%
1 20
 
4.1%
시 16
 
3.3%
동 15
 
3.0%
2 14
 
2.8%
주 14
 
2.8%
Other values (67) 214
43.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 302
61.4%
Space Separator 90
 
18.3%
Decimal Number 87
 
17.7%
Dash Punctuation 13
 
2.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
전 35
 
11.6%
도 26
 
8.6%
북 24
 
7.9%
라 24
 
7.9%
시 16
 
5.3%
동 15
 
5.0%
주 14
 
4.6%
산 14
 
4.6%
구 10
 
3.3%
완 10
 
3.3%
Other values (55) 114
37.7%
Decimal Number
ValueCountFrequency (%)
1 20
23.0%
2 14
16.1%
3 12
13.8%
4 12
13.8%
8 7
 
8.0%
9 6
 
6.9%
5 6
 
6.9%
7 5
 
5.7%
6 3
 
3.4%
0 2
 
2.3%
Space Separator
ValueCountFrequency (%)
90
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 13
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 302
61.4%
Common 190
38.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
전 35
 
11.6%
도 26
 
8.6%
북 24
 
7.9%
라 24
 
7.9%
시 16
 
5.3%
동 15
 
5.0%
주 14
 
4.6%
산 14
 
4.6%
구 10
 
3.3%
완 10
 
3.3%
Other values (55) 114
37.7%
Common
ValueCountFrequency (%)
90
47.4%
1 20
 
10.5%
2 14
 
7.4%
- 13
 
6.8%
3 12
 
6.3%
4 12
 
6.3%
8 7
 
3.7%
9 6
 
3.2%
5 6
 
3.2%
7 5
 
2.6%
Other values (2) 5
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 302
61.4%
ASCII 190
38.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
90
47.4%
1 20
 
10.5%
2 14
 
7.4%
- 13
 
6.8%
3 12
 
6.3%
4 12
 
6.3%
8 7
 
3.7%
9 6
 
3.2%
5 6
 
3.2%
7 5
 
2.6%
Other values (2) 5
 
2.6%
Hangul
ValueCountFrequency (%)
전 35
 
11.6%
도 26
 
8.6%
북 24
 
7.9%
라 24
 
7.9%
시 16
 
5.3%
동 15
 
5.0%
주 14
 
4.6%
산 14
 
4.6%
구 10
 
3.3%
완 10
 
3.3%
Other values (55) 114
37.7%

Interactions

2024-03-14T09:29:19.694546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T09:29:22.205765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분상영영화관명상영관(스크린수)좌석소재지
구분1.0001.0000.0000.0001.000
상영영화관명1.0001.0001.0001.0001.000
상영관(스크린수)0.0001.0001.0000.9720.824
좌석0.0001.0000.9721.0000.938
소재지1.0001.0000.8240.9381.000

Missing values

2024-03-14T09:29:19.796230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T09:29:19.885737image/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(스크린수)좌석소재지
0전주시1메가박스 전주101,568전라북도 전주시 완산구 고사동 181
1전주시1메가박스 송천81,180전라북도 전주시 덕진구 송천동2가 661-15
2전주시1조이앤시네마 전주점5782전라북도 전주시 완산구 고사동 288-2
3전주시1조이아트시네마1103전라북도 전주시 완산구 고사동 288-2
4전주시1전주디지털독립영화관198전라북도 전주시 완산구 전주객사3길 22
5전주시1CGV 전주고사81,275전라북도 전주시 완산구 고사동 355-1
6전주시1전주시네마타운6924전라북도 전주시 완산구 고사동 340-7
7전주시1롯데시네마 전주81,632전라북도 전주시 완산구 서신동 971
8전주시1롯데시네마 전주평화6922전라북도 전주시 완산구 평화동1가 604-1
9전주시1CGV 전주효자71,795전라북도 전주시 완산구 효자동1가 434
구분영화관등록수상영영화관명상영관(스크린수)좌석소재지
14남원시1메가박스 남원4615전라북도 남원시 쌍교동 82-1
15김제시1지평선시네마299전라북도 김제시 도작로 224-32
16완주군1완주 휴 시네마290전라북도 완주군 봉동읍 둔산3로 94
17진안군1마이골작은영화관298전라북도 진안군 진안읍 대성길 3
18무주군1무주 산골 영화관298전라북도 무주군 무주읍 당산리 1199-3
19장수군1한누리디지털시네마290전라북도 장수군 장수읍 한누리로 393
20임실군1작은별영화관294전라북도 임실군 임실읍 이도리 277
21순창군1천재의 공간 영화산책2149전라북도 순창군 순창읍 남계로 83
22고창군1고창문화원 동리시네마293전라북도 고창군 고창읍 읍내리 457
23부안군1마실영화관299전라북도 부안군 부안읍 예술회관길 11