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 memory56.5 B

Variable types

Text3
Categorical1
Numeric2

Alerts

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

Reproduction

Analysis started2024-03-14 00:29:22.690544
Analysis finished2024-03-14 00:29:23.341731
Duration0.65 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
2024-03-14T09:29:23.424349image/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%
2024-03-14T09:29:23.643703image/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

2024-03-14T09:29:23.761356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T09:29:23.835757image/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
2024-03-14T09:29:23.984505image/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%
2024-03-14T09:29:24.267494image/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)

HIGH CORRELATION 

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
2024-03-14T09:29:24.365761image/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
2024-03-14T09:29:24.447207image/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
Real number (ℝ)

HIGH CORRELATION 

Distinct20
Distinct (%)83.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean650.54167
Minimum90
Maximum1795
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2024-03-14T09:29:24.528284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum90
5-th percentile90.45
Q198
median589.5
Q31065.25
95-th percentile1622.4
Maximum1795
Range1705
Interquartile range (IQR)967.25

Descriptive statistics

Standard deviation587.53753
Coefficient of variation (CV)0.90315126
Kurtosis-1.1452977
Mean650.54167
Median Absolute Deviation (MAD)491.5
Skewness0.513349
Sum15613
Variance345200.35
MonotonicityNot monotonic
2024-03-14T09:29:24.611473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
98 3
 
12.5%
90 2
 
8.3%
99 2
 
8.3%
1568 1
 
4.2%
1027 1
 
4.2%
93 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%
ValueCountFrequency (%)
90 2
8.3%
93 1
 
4.2%
94 1
 
4.2%
98 3
12.5%
99 2
8.3%
103 1
 
4.2%
149 1
 
4.2%
564 1
 
4.2%
615 1
 
4.2%
782 1
 
4.2%
ValueCountFrequency (%)
1795 1
4.2%
1632 1
4.2%
1568 1
4.2%
1325 1
4.2%
1275 1
4.2%
1180 1
4.2%
1027 1
4.2%
924 1
4.2%
922 1
4.2%
893 1
4.2%

소재지
Text

Distinct23
Distinct (%)95.8%
Missing0
Missing (%)0.0%
Memory size324.0 B
2024-03-14T09:29:24.782988image/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%
2024-03-14T09:29:25.090952image/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

2024-03-14T09:29:23.065912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:29:22.879979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:29:23.128630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:29:22.962463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T09:29:25.224335image/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)0.0001.0001.0000.9760.824
좌석0.0001.0000.9761.0000.973
소재지1.0001.0000.8240.9731.000
2024-03-14T09:29:25.315361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
상영관(스크린수)좌석
상영관(스크린수)1.0000.889
좌석0.8891.000

Missing values

2024-03-14T09:29:23.216379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T09:29:23.296404image/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 전주101568전라북도 전주시 완산구 고사동 181
1전주시1메가박스 송천81180전라북도 전주시 덕진구 송천동2가 661-15
2전주시1조이앤시네마 전주점5782전라북도 전주시 완산구 고사동 288-2
3전주시1조이아트시네마1103전라북도 전주시 완산구 고사동 288-2
4전주시1전주디지털독립영화관198전라북도 전주시 완산구 전주객사3길 22
5전주시1CGV 전주고사81275전라북도 전주시 완산구 고사동 355-1
6전주시1전주시네마타운6924전라북도 전주시 완산구 고사동 340-7
7전주시1롯데시네마 전주81632전라북도 전주시 완산구 서신동 971
8전주시1롯데시네마 전주평화6922전라북도 전주시 완산구 평화동1가 604-1
9전주시1CGV 전주효자71795전라북도 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