Overview

Dataset statistics

Number of variables6
Number of observations22
Missing cells16
Missing cells (%)12.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.2 KiB
Average record size in memory54.0 B

Variable types

Text3
Unsupported3

Alerts

전북도내 영화관 시설 운영현황(2014.11.30기준) has 8 (36.4%) missing valuesMissing
Unnamed: 1 has 8 (36.4%) missing valuesMissing
Unnamed: 2 has unique valuesUnique
Unnamed: 5 has unique valuesUnique
Unnamed: 1 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 3 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 4 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2024-03-14 01:11:28.485604
Analysis finished2024-03-14 01:11:28.885320
Duration0.4 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

전북도내 영화관 시설 운영현황(2014.11.30기준)
Text

MISSING 

Distinct14
Distinct (%)100.0%
Missing8
Missing (%)36.4%
Memory size308.0 B
2024-03-14T10:11:28.990675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.7857143
Min length1

Characters and Unicode

Total characters39
Distinct characters25
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

Unique14 ?
Unique (%)100.0%

Sample

1st row구분
2nd row계
3rd row전주시
4th row군산시
5th row익산시
ValueCountFrequency (%)
구분 1
 
7.1%
계 1
 
7.1%
전주시 1
 
7.1%
군산시 1
 
7.1%
익산시 1
 
7.1%
정읍시 1
 
7.1%
남원시 1
 
7.1%
김제시 1
 
7.1%
완주군 1
 
7.1%
무주군 1
 
7.1%
Other values (4) 4
28.6%
2024-03-14T10:11:29.297766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
군 7
17.9%
시 6
 
15.4%
주 3
 
7.7%
산 2
 
5.1%
구 1
 
2.6%
완 1
 
2.6%
부 1
 
2.6%
창 1
 
2.6%
고 1
 
2.6%
실 1
 
2.6%
Other values (15) 15
38.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 39
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
군 7
17.9%
시 6
 
15.4%
주 3
 
7.7%
산 2
 
5.1%
구 1
 
2.6%
완 1
 
2.6%
부 1
 
2.6%
창 1
 
2.6%
고 1
 
2.6%
실 1
 
2.6%
Other values (15) 15
38.5%

Most occurring scripts

ValueCountFrequency (%)
Hangul 39
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
군 7
17.9%
시 6
 
15.4%
주 3
 
7.7%
산 2
 
5.1%
구 1
 
2.6%
완 1
 
2.6%
부 1
 
2.6%
창 1
 
2.6%
고 1
 
2.6%
실 1
 
2.6%
Other values (15) 15
38.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 39
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
군 7
17.9%
시 6
 
15.4%
주 3
 
7.7%
산 2
 
5.1%
구 1
 
2.6%
완 1
 
2.6%
부 1
 
2.6%
창 1
 
2.6%
고 1
 
2.6%
실 1
 
2.6%
Other values (15) 15
38.5%

Unnamed: 1
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing8
Missing (%)36.4%
Memory size308.0 B

Unnamed: 2
Text

UNIQUE 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size308.0 B
2024-03-14T10:11:29.448936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length12
Mean length9
Min length4

Characters and Unicode

Total characters198
Distinct characters64
Distinct categories7 ?
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 row상영영화관명
2nd row20개소(일반13,작은7)
3rd row롯데시네마 전주
4th row롯데시네마 평화점
5th row메가박스 전주
ValueCountFrequency (%)
cgv 4
 
12.5%
롯데시네마 3
 
9.4%
전주 3
 
9.4%
메가박스 2
 
6.2%
군산 2
 
6.2%
상영영화관명 1
 
3.1%
정읍 1
 
3.1%
동리시네마(작은영화관 1
 
3.1%
작은별영화관(작은영화관 1
 
3.1%
한누리시네마(작은영화관 1
 
3.1%
Other values (13) 13
40.6%
2024-03-14T10:11:29.740806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
화 13
 
6.6%
영 13
 
6.6%
관 12
 
6.1%
10
 
5.1%
은 9
 
4.5%
작 9
 
4.5%
마 9
 
4.5%
시 8
 
4.0%
네 8
 
4.0%
( 8
 
4.0%
Other values (54) 99
50.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 151
76.3%
Uppercase Letter 15
 
7.6%
Space Separator 10
 
5.1%
Open Punctuation 8
 
4.0%
Close Punctuation 8
 
4.0%
Decimal Number 5
 
2.5%
Other Punctuation 1
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
화 13
 
8.6%
영 13
 
8.6%
관 12
 
7.9%
은 9
 
6.0%
작 9
 
6.0%
마 9
 
6.0%
시 8
 
5.3%
네 8
 
5.3%
주 6
 
4.0%
전 5
 
3.3%
Other values (42) 59
39.1%
Decimal Number
ValueCountFrequency (%)
2 1
20.0%
0 1
20.0%
1 1
20.0%
3 1
20.0%
7 1
20.0%
Uppercase Letter
ValueCountFrequency (%)
C 5
33.3%
G 5
33.3%
V 5
33.3%
Space Separator
ValueCountFrequency (%)
10
100.0%
Open Punctuation
ValueCountFrequency (%)
( 8
100.0%
Close Punctuation
ValueCountFrequency (%)
) 8
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 151
76.3%
Common 32
 
16.2%
Latin 15
 
7.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
화 13
 
8.6%
영 13
 
8.6%
관 12
 
7.9%
은 9
 
6.0%
작 9
 
6.0%
마 9
 
6.0%
시 8
 
5.3%
네 8
 
5.3%
주 6
 
4.0%
전 5
 
3.3%
Other values (42) 59
39.1%
Common
ValueCountFrequency (%)
10
31.2%
( 8
25.0%
) 8
25.0%
2 1
 
3.1%
0 1
 
3.1%
1 1
 
3.1%
3 1
 
3.1%
, 1
 
3.1%
7 1
 
3.1%
Latin
ValueCountFrequency (%)
C 5
33.3%
G 5
33.3%
V 5
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 151
76.3%
ASCII 47
 
23.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
화 13
 
8.6%
영 13
 
8.6%
관 12
 
7.9%
은 9
 
6.0%
작 9
 
6.0%
마 9
 
6.0%
시 8
 
5.3%
네 8
 
5.3%
주 6
 
4.0%
전 5
 
3.3%
Other values (42) 59
39.1%
ASCII
ValueCountFrequency (%)
10
21.3%
( 8
17.0%
) 8
17.0%
C 5
10.6%
G 5
10.6%
V 5
10.6%
2 1
 
2.1%
0 1
 
2.1%
1 1
 
2.1%
3 1
 
2.1%
Other values (2) 2
 
4.3%

Unnamed: 3
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size308.0 B

Unnamed: 4
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size308.0 B

Unnamed: 5
Text

UNIQUE 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size308.0 B
2024-03-14T10:11:29.916048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length27
Median length18.5
Mean length15.590909
Min length1

Characters and Unicode

Total characters343
Distinct characters91
Distinct categories6 ?
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 row소재지
2nd row-
3rd row전주시 완산구 서신동 971
4th row전주시 완산구 평화동 604-1
5th row전주시 고사동 181
ValueCountFrequency (%)
전주시 7
 
10.4%
완산구 4
 
6.0%
고사동 3
 
4.5%
나운동 2
 
3.0%
군산시 2
 
3.0%
소재지 1
 
1.5%
무주군 1
 
1.5%
82-1 1
 
1.5%
김제시 1
 
1.5%
검산동 1
 
1.5%
Other values (44) 44
65.7%
2024-03-14T10:11:30.165519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
45
 
13.1%
1 24
 
7.0%
시 14
 
4.1%
동 14
 
4.1%
주 12
 
3.5%
- 11
 
3.2%
전 11
 
3.2%
4 10
 
2.9%
산 10
 
2.9%
군 9
 
2.6%
Other values (81) 183
53.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 206
60.1%
Decimal Number 73
 
21.3%
Space Separator 45
 
13.1%
Dash Punctuation 11
 
3.2%
Close Punctuation 4
 
1.2%
Open Punctuation 4
 
1.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 14
 
6.8%
동 14
 
6.8%
주 12
 
5.8%
전 11
 
5.3%
산 10
 
4.9%
군 9
 
4.4%
읍 7
 
3.4%
고 6
 
2.9%
리 6
 
2.9%
구 5
 
2.4%
Other values (67) 112
54.4%
Decimal Number
ValueCountFrequency (%)
1 24
32.9%
4 10
13.7%
2 9
 
12.3%
8 7
 
9.6%
3 6
 
8.2%
6 5
 
6.8%
0 4
 
5.5%
7 4
 
5.5%
9 2
 
2.7%
5 2
 
2.7%
Space Separator
ValueCountFrequency (%)
45
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 11
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 206
60.1%
Common 137
39.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 14
 
6.8%
동 14
 
6.8%
주 12
 
5.8%
전 11
 
5.3%
산 10
 
4.9%
군 9
 
4.4%
읍 7
 
3.4%
고 6
 
2.9%
리 6
 
2.9%
구 5
 
2.4%
Other values (67) 112
54.4%
Common
ValueCountFrequency (%)
45
32.8%
1 24
17.5%
- 11
 
8.0%
4 10
 
7.3%
2 9
 
6.6%
8 7
 
5.1%
3 6
 
4.4%
6 5
 
3.6%
) 4
 
2.9%
( 4
 
2.9%
Other values (4) 12
 
8.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 206
60.1%
ASCII 137
39.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
45
32.8%
1 24
17.5%
- 11
 
8.0%
4 10
 
7.3%
2 9
 
6.6%
8 7
 
5.1%
3 6
 
4.4%
6 5
 
3.6%
) 4
 
2.9%
( 4
 
2.9%
Other values (4) 12
 
8.8%
Hangul
ValueCountFrequency (%)
시 14
 
6.8%
동 14
 
6.8%
주 12
 
5.8%
전 11
 
5.3%
산 10
 
4.9%
군 9
 
4.4%
읍 7
 
3.4%
고 6
 
2.9%
리 6
 
2.9%
구 5
 
2.4%
Other values (67) 112
54.4%

Correlations

2024-03-14T10:11:30.233599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
전북도내 영화관 시설 운영현황(2014.11.30기준)Unnamed: 2Unnamed: 5
전북도내 영화관 시설 운영현황(2014.11.30기준)1.0001.0001.000
Unnamed: 21.0001.0001.000
Unnamed: 51.0001.0001.000

Missing values

2024-03-14T10:11:28.636190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T10:11:28.720044image/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.
2024-03-14T10:11:28.820053image/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

전북도내 영화관 시설 운영현황(2014.11.30기준)Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5
0구분영화관등록수상영영화관명상영관(스크린)좌석소재지
1계2020개소(일반13,작은7)96관(82/14)14,871\n(14,208/663)-
2전주시8롯데시네마 전주81626전주시 완산구 서신동 971
3<NA>NaN롯데시네마 평화점6922전주시 완산구 평화동 604-1
4<NA>NaN메가박스 전주101515전주시 고사동 181
5<NA>NaN씨너스 송천81215전주시 덕진구 송천동 2가 661-15
6<NA>NaN전주시네마타운71265전주시 완산구 고사동 340-3
7<NA>NaNCGV 전주61192전주시 고사동 288-2
8<NA>NaN전주디지털독립영화관198전주영화제작소 4층(전주시고사동 객사3길18)
9<NA>NaNCGV효자동71795전주시 완산구 용머리로45
전북도내 영화관 시설 운영현황(2014.11.30기준)Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5
12익산시1CGV 익산91325익산시 영등동 149-1
13정읍시1CGV 정읍4565정읍시 중앙1길 1
14남원시1메가박스4615남원시 쌍교동 82-1
15김제시1지평선 시네마(작은영화관)299김제시 검산동 62-1 청소년수련관
16완주군1휴시네마(작은영화관)290완주군 봉동읍 군산리 881
17무주군1무주산골영화관(작은영화관)298무주군 무주읍 한풍루로326-17
18장수군1한누리시네마(작은영화관)290한누리전당내가람관1층장수군(장수읍 두산리 472)
19임실군1작은별영화관(작은영화관)294국민회관지하1층(임실군임실읍호국로1703)
20고창군1동리시네마(작은영화관)293동리국악당지하1층(고창군고창읍판소리길20)
21부안군1마실영화관(작은영화관)299전북 부안군 부안읍 예술화관길 11