Overview

Dataset statistics

Number of variables18
Number of observations143
Missing cells50
Missing cells (%)1.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory21.4 KiB
Average record size in memory152.9 B

Variable types

Categorical12
DateTime1
Numeric4
Text1

Dataset

Description대구광역시_영화상영관 현황_20210826
Author대구광역시
URLhttp://data.daegu.go.kr/open/data/dataView.do?dataSetId=15014093&dataSetDetailId=15014093187e64c163957&provdMethod=FILE

Alerts

개방서비스명 has constant value ""Constant
영업상태구분코드 has constant value ""Constant
총층수 is highly overall correlated with 좌표정보(X) and 9 other fieldsHigh correlation
소재지전체주소 is highly overall correlated with 도로명우편번호 and 12 other fieldsHigh correlation
상세영업상태명 is highly overall correlated with 지상층수 and 8 other fieldsHigh correlation
주변환경명 is highly overall correlated with 도로명우편번호 and 12 other fieldsHigh correlation
도로명전체주소 is highly overall correlated with 도로명우편번호 and 12 other fieldsHigh correlation
통로너비 is highly overall correlated with 좌표정보(X) and 9 other fieldsHigh correlation
소재지전화 is highly overall correlated with 도로명우편번호 and 12 other fieldsHigh correlation
건물용도명 is highly overall correlated with 영업상태명 and 7 other fieldsHigh correlation
지하층수 is highly overall correlated with 도로명우편번호 and 11 other fieldsHigh correlation
영업상태명 is highly overall correlated with 지상층수 and 8 other fieldsHigh correlation
도로명우편번호 is highly overall correlated with 좌표정보(Y) and 5 other fieldsHigh correlation
좌표정보(X) is highly overall correlated with 소재지전화 and 6 other fieldsHigh correlation
좌표정보(Y) is highly overall correlated with 도로명우편번호 and 7 other fieldsHigh correlation
지상층수 is highly overall correlated with 영업상태명 and 8 other fieldsHigh correlation
영업상태명 is highly imbalanced (94.0%)Imbalance
상세영업상태명 is highly imbalanced (94.0%)Imbalance
도로명우편번호 has 10 (7.0%) missing valuesMissing
좌표정보(X) has 6 (4.2%) missing valuesMissing
좌표정보(Y) has 6 (4.2%) missing valuesMissing
지상층수 has 28 (19.6%) missing valuesMissing
사업장명 has unique valuesUnique

Reproduction

Analysis started2024-04-19 06:48:23.246607
Analysis finished2024-04-19 06:48:26.130354
Duration2.88 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

개방서비스명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
영화상영관
143 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
영화상영관 143
100.0%

Length

2024-04-19T15:48:26.187423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-19T15:48:26.270723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
영화상영관 143
100.0%

인허가일자
Date

Distinct23
Distinct (%)16.1%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
Minimum2002-01-22 00:00:00
Maximum2019-06-11 00:00:00
2024-04-19T15:48:26.353233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T15:48:26.447659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)

영업상태구분코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
1
143 

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 (%)
1 143
100.0%

Length

2024-04-19T15:48:26.561104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-19T15:48:26.652683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 143
100.0%

영업상태명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
영업/정상
142 
휴업
 
1

Length

Max length5
Median length5
Mean length4.979021
Min length2

Unique

Unique1 ?
Unique (%)0.7%

Sample

1st row영업/정상
2nd row영업/정상
3rd row영업/정상
4th row영업/정상
5th row영업/정상

Common Values

ValueCountFrequency (%)
영업/정상 142
99.3%
휴업 1
 
0.7%

Length

2024-04-19T15:48:26.782096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-19T15:48:26.896585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
영업/정상 142
99.3%
휴업 1
 
0.7%

상세영업상태명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
영업중
142 
휴업중
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique1 ?
Unique (%)0.7%

Sample

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

Common Values

ValueCountFrequency (%)
영업중 142
99.3%
휴업중 1
 
0.7%

Length

2024-04-19T15:48:26.994112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-19T15:48:27.089396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
영업중 142
99.3%
휴업중 1
 
0.7%

소재지전화
Categorical

HIGH CORRELATION 

Distinct24
Distinct (%)16.8%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
053-222-6600
10 
02-3147-3872
10 
053-422-0900
 
9
053-427-8091
 
9
053-584-1166
 
9
Other values (19)
96 

Length

Max length14
Median length12
Mean length12.181818
Min length12

Unique

Unique3 ?
Unique (%)2.1%

Sample

1st row053-422-0900
2nd row053-422-0900
3rd row053-422-0900
4th row053-422-0900
5th row053-422-0900

Common Values

ValueCountFrequency (%)
053-222-6600 10
 
7.0%
02-3147-3872 10
 
7.0%
053-422-0900 9
 
6.3%
053-427-8091 9
 
6.3%
053-584-1166 9
 
6.3%
070-7491-6291 7
 
4.9%
070-7434-5020 7
 
4.9%
053-431-5609 7
 
4.9%
053-322-1500 7
 
4.9%
053-982-1235 7
 
4.9%
Other values (14) 61
42.7%

Length

2024-04-19T15:48:27.195995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
053-222-6600 10
 
7.0%
02-3147-3872 10
 
7.0%
053-422-0900 9
 
6.3%
053-427-8091 9
 
6.3%
053-584-1166 9
 
6.3%
070-7491-6291 7
 
4.9%
070-7434-5020 7
 
4.9%
053-431-5609 7
 
4.9%
053-322-1500 7
 
4.9%
053-982-1235 7
 
4.9%
Other values (14) 61
42.7%

소재지전체주소
Categorical

HIGH CORRELATION 

Distinct27
Distinct (%)18.9%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
대구광역시 수성구 대흥동 504번지 B2F
10 
대구광역시 북구 칠성동2가 20번지
10 
대구광역시 중구 종로1가 29-4번지
 
9
대구광역시 중구 동성로2가 66-1번지
 
9
대구광역시 달서구 이곡동 1244-1번지
 
9
Other values (22)
96 

Length

Max length34
Median length33
Mean length22.111888
Min length17

Unique

Unique4 ?
Unique (%)2.8%

Sample

1st row대구광역시 중구 종로1가 29-4번지
2nd row대구광역시 중구 종로1가 29-4번지
3rd row대구광역시 중구 종로1가 29-4번지
4th row대구광역시 중구 종로1가 29-4번지
5th row대구광역시 중구 종로1가 29-4번지

Common Values

ValueCountFrequency (%)
대구광역시 수성구 대흥동 504번지 B2F 10
 
7.0%
대구광역시 북구 칠성동2가 20번지 10
 
7.0%
대구광역시 중구 종로1가 29-4번지 9
 
6.3%
대구광역시 중구 동성로2가 66-1번지 9
 
6.3%
대구광역시 달서구 이곡동 1244-1번지 9
 
6.3%
대구광역시 동구 봉무동 1545번지 이시아폴리스라이프스타일센터 7
 
4.9%
대구광역시 달서구 상인동 241번지 7
 
4.9%
대구광역시 북구 동천동 894-1번지 7
 
4.9%
대구광역시 동구 봉무동 1548번지 7
 
4.9%
대구광역시 서구 내당동 463-62번지 M플라자 7
 
4.9%
Other values (17) 61
42.7%

Length

2024-04-19T15:48:27.320165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
대구광역시 143
22.6%
중구 38
 
6.0%
북구 29
 
4.6%
동구 27
 
4.3%
달서구 22
 
3.5%
수성구 14
 
2.2%
동성로2가 14
 
2.2%
봉무동 14
 
2.2%
동천동 13
 
2.1%
대흥동 10
 
1.6%
Other values (53) 308
48.7%

도로명전체주소
Categorical

HIGH CORRELATION 

Distinct26
Distinct (%)18.2%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
대구광역시 수성구 유니버시아드로 140 (대흥동)
10 
대구광역시 북구 침산로 93 (칠성동2가)
10 
대구광역시 중구 국채보상로 547 (종로1가)
 
9
대구광역시 중구 동성로2길 95 (동성로2가)
 
9
대구광역시 달서구 성서로 414 (이곡동)
 
9
Other values (21)
96 

Length

Max length39
Median length34
Mean length25.79021
Min length21

Unique

Unique5 ?
Unique (%)3.5%

Sample

1st row대구광역시 중구 국채보상로 547 (종로1가)
2nd row대구광역시 중구 국채보상로 547 (종로1가)
3rd row대구광역시 중구 국채보상로 547 (종로1가)
4th row대구광역시 중구 국채보상로 547 (종로1가)
5th row대구광역시 중구 국채보상로 547 (종로1가)

Common Values

ValueCountFrequency (%)
대구광역시 수성구 유니버시아드로 140 (대흥동) 10
 
7.0%
대구광역시 북구 침산로 93 (칠성동2가) 10
 
7.0%
대구광역시 중구 국채보상로 547 (종로1가) 9
 
6.3%
대구광역시 중구 동성로2길 95 (동성로2가) 9
 
6.3%
대구광역시 달서구 성서로 414 (이곡동) 9
 
6.3%
대구광역시 서구 달구벌대로 1691 (내당동, M플라자) 7
 
4.9%
대구광역시 달서구 월곡로 247 (상인동) 7
 
4.9%
대구광역시 중구 중앙대로 412 (남일동) 7
 
4.9%
대구광역시 북구 동암로 90 (동천동) 7
 
4.9%
대구광역시 달성군 현풍읍 테크노상업로 62 6
 
4.2%
Other values (16) 62
43.4%

Length

2024-04-19T15:48:27.437088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
대구광역시 143
 
19.1%
중구 38
 
5.1%
북구 29
 
3.9%
동구 27
 
3.6%
달서구 22
 
2.9%
수성구 14
 
1.9%
팔공로49길 14
 
1.9%
동성로2가 14
 
1.9%
동암로 13
 
1.7%
동천동 13
 
1.7%
Other values (68) 422
56.3%

도로명우편번호
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct18
Distinct (%)13.5%
Missing10
Missing (%)7.0%
Infinite0
Infinite (%)0.0%
Mean41900.474
Minimum41007
Maximum43017
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-04-19T15:48:27.535779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum41007
5-th percentile41026
Q141423
median41919
Q342250
95-th percentile42786
Maximum43017
Range2010
Interquartile range (IQR)827

Descriptive statistics

Standard deviation564.55529
Coefficient of variation (CV)0.013473721
Kurtosis-0.76476474
Mean41900.474
Median Absolute Deviation (MAD)495
Skewness0.31701047
Sum5572763
Variance318722.68
MonotonicityNot monotonic
2024-04-19T15:48:27.643774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
41423 13
 
9.1%
41937 12
 
8.4%
41919 10
 
7.0%
41593 10
 
7.0%
42250 10
 
7.0%
41938 9
 
6.3%
42620 9
 
6.3%
41026 8
 
5.6%
42786 7
 
4.9%
41856 7
 
4.9%
Other values (8) 38
26.6%
(Missing) 10
 
7.0%
ValueCountFrequency (%)
41007 1
 
0.7%
41026 8
5.6%
41097 6
4.2%
41229 6
4.2%
41423 13
9.1%
41424 6
4.2%
41593 10
7.0%
41856 7
4.9%
41909 1
 
0.7%
41919 10
7.0%
ValueCountFrequency (%)
43017 6
4.2%
42786 7
4.9%
42757 6
4.2%
42620 9
6.3%
42250 10
7.0%
41938 9
6.3%
41937 12
8.4%
41936 6
4.2%
41919 10
7.0%
41909 1
 
0.7%

사업장명
Text

UNIQUE 

Distinct143
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
2024-04-19T15:48:27.846024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length21
Mean length13.608392
Min length5

Characters and Unicode

Total characters1946
Distinct characters92
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique143 ?
Unique (%)100.0%

Sample

1st row롯데시네마 프리미엄 만경관 7관
2nd row롯데시네마 프리미엄 만경관 6관
3rd row롯데시네마 프리미엄 만경관 5관
4th row롯데시네마 프리미엄 만경관 4관
5th row롯데시네마 프리미엄 만경관 3관
ValueCountFrequency (%)
롯데시네마 52
 
13.5%
cgv 17
 
4.4%
프리미엄 15
 
3.9%
메가박스 13
 
3.4%
메가박스중앙(주 10
 
2.6%
대구지점 10
 
2.6%
만경관 9
 
2.3%
5관 9
 
2.3%
4관 9
 
2.3%
3관 9
 
2.3%
Other values (66) 232
60.3%
2024-04-19T15:48:28.199973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
242
 
12.4%
관 164
 
8.4%
대 79
 
4.1%
제 77
 
4.0%
시 73
 
3.8%
구 73
 
3.8%
데 67
 
3.4%
롯 60
 
3.1%
네 60
 
3.1%
마 59
 
3.0%
Other values (82) 992
51.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1310
67.3%
Space Separator 242
 
12.4%
Uppercase Letter 176
 
9.0%
Decimal Number 152
 
7.8%
Open Punctuation 33
 
1.7%
Close Punctuation 33
 
1.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
관 164
 
12.5%
대 79
 
6.0%
제 77
 
5.9%
시 73
 
5.6%
구 73
 
5.6%
데 67
 
5.1%
롯 60
 
4.6%
네 60
 
4.6%
마 59
 
4.5%
스 46
 
3.5%
Other values (57) 552
42.1%
Uppercase Letter
ValueCountFrequency (%)
C 55
31.2%
G 52
29.5%
V 52
29.5%
O 4
 
2.3%
M 3
 
1.7%
F 2
 
1.1%
R 2
 
1.1%
T 2
 
1.1%
I 1
 
0.6%
N 1
 
0.6%
Other values (2) 2
 
1.1%
Decimal Number
ValueCountFrequency (%)
1 21
13.8%
2 20
13.2%
3 20
13.2%
4 20
13.2%
5 19
12.5%
6 18
11.8%
9 14
9.2%
7 11
7.2%
8 6
 
3.9%
0 3
 
2.0%
Space Separator
ValueCountFrequency (%)
242
100.0%
Open Punctuation
ValueCountFrequency (%)
( 33
100.0%
Close Punctuation
ValueCountFrequency (%)
) 33
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1310
67.3%
Common 460
 
23.6%
Latin 176
 
9.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
관 164
 
12.5%
대 79
 
6.0%
제 77
 
5.9%
시 73
 
5.6%
구 73
 
5.6%
데 67
 
5.1%
롯 60
 
4.6%
네 60
 
4.6%
마 59
 
4.5%
스 46
 
3.5%
Other values (57) 552
42.1%
Common
ValueCountFrequency (%)
242
52.6%
( 33
 
7.2%
) 33
 
7.2%
1 21
 
4.6%
2 20
 
4.3%
3 20
 
4.3%
4 20
 
4.3%
5 19
 
4.1%
6 18
 
3.9%
9 14
 
3.0%
Other values (3) 20
 
4.3%
Latin
ValueCountFrequency (%)
C 55
31.2%
G 52
29.5%
V 52
29.5%
O 4
 
2.3%
M 3
 
1.7%
F 2
 
1.1%
R 2
 
1.1%
T 2
 
1.1%
I 1
 
0.6%
N 1
 
0.6%
Other values (2) 2
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1310
67.3%
ASCII 636
32.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
242
38.1%
C 55
 
8.6%
G 52
 
8.2%
V 52
 
8.2%
( 33
 
5.2%
) 33
 
5.2%
1 21
 
3.3%
2 20
 
3.1%
3 20
 
3.1%
4 20
 
3.1%
Other values (15) 88
 
13.8%
Hangul
ValueCountFrequency (%)
관 164
 
12.5%
대 79
 
6.0%
제 77
 
5.9%
시 73
 
5.6%
구 73
 
5.6%
데 67
 
5.1%
롯 60
 
4.6%
네 60
 
4.6%
마 59
 
4.5%
스 46
 
3.5%
Other values (57) 552
42.1%

좌표정보(X)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct22
Distinct (%)16.1%
Missing6
Missing (%)4.2%
Infinite0
Infinite (%)0.0%
Mean343928.17
Minimum336128.86
Maximum352868.54
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-04-19T15:48:28.314388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum336128.86
5-th percentile336128.86
Q1340718.25
median343588.74
Q3347543.75
95-th percentile352403.89
Maximum352868.54
Range16739.675
Interquartile range (IQR)6825.4932

Descriptive statistics

Standard deviation4542.2249
Coefficient of variation (CV)0.0132069
Kurtosis-0.41230576
Mean343928.17
Median Absolute Deviation (MAD)3448.5064
Skewness0.37705179
Sum47118159
Variance20631807
MonotonicityNot monotonic
2024-04-19T15:48:28.417011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
352335.045 10
 
7.0%
343510.5732 10
 
7.0%
343571.9657 9
 
6.3%
344128.8755 9
 
6.3%
336128.8616 9
 
6.3%
339041.173 7
 
4.9%
343908.6834 7
 
4.9%
347690.4344 7
 
4.9%
347543.7468 7
 
4.9%
340718.2536 7
 
4.9%
Other values (12) 55
38.5%
ValueCountFrequency (%)
336128.8616 9
6.3%
337925.1213 6
4.2%
339041.173 7
4.9%
340011.7376 7
4.9%
340718.2536 7
4.9%
340843.6291 6
4.2%
341042.0641 6
4.2%
343471.4392 1
 
0.7%
343510.5732 10
7.0%
343571.9657 9
6.3%
ValueCountFrequency (%)
352868.5366 6
4.2%
352679.2832 1
 
0.7%
352335.045 10
7.0%
348194.4994 4
 
2.8%
347690.4344 7
4.9%
347543.7468 7
4.9%
347037.242 6
4.2%
344128.8755 9
6.3%
344030.6777 1
 
0.7%
344009.2409 5
3.5%

좌표정보(Y)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct22
Distinct (%)16.1%
Missing6
Missing (%)4.2%
Infinite0
Infinite (%)0.0%
Mean265237.87
Minimum258463.13
Maximum277641.79
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-04-19T15:48:28.525130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum258463.13
5-th percentile259008.24
Q1262620.44
median264507.16
Q3266134.26
95-th percentile272659.98
Maximum277641.79
Range19178.658
Interquartile range (IQR)3513.8244

Descriptive statistics

Standard deviation4375.117
Coefficient of variation (CV)0.016495069
Kurtosis-0.49133914
Mean265237.87
Median Absolute Deviation (MAD)1886.7212
Skewness0.47383189
Sum36337588
Variance19141648
MonotonicityNot monotonic
2024-04-19T15:48:28.886292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
260158.7502 10
 
7.0%
266134.2599 10
 
7.0%
264582.7763 9
 
6.3%
264507.1567 9
 
6.3%
262620.4355 9
 
6.3%
258463.128 7
 
4.9%
264477.417 7
 
4.9%
270271.2257 7
 
4.9%
270181.3331 7
 
4.9%
272659.9751 7
 
4.9%
Other values (12) 55
38.5%
ValueCountFrequency (%)
258463.128 7
4.9%
259144.5193 4
 
2.8%
259346.8508 6
4.2%
260158.7502 10
7.0%
262620.4355 9
6.3%
262835.1995 7
4.9%
264119.0108 6
4.2%
264477.417 7
4.9%
264480.5655 6
4.2%
264507.1567 9
6.3%
ValueCountFrequency (%)
277641.7859 1
 
0.7%
272659.9751 7
4.9%
272656.8378 6
4.2%
272450.2185 6
4.2%
270271.2257 7
4.9%
270181.3331 7
4.9%
266134.2599 10
7.0%
265407.4043 6
4.2%
264814.8322 1
 
0.7%
264582.7763 9
6.3%

총층수
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
<NA>
56 
9
26 
14
18 
11
17 
13
15 

Length

Max length4
Median length2
Mean length2.6013986
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 56
39.2%
9 26
18.2%
14 18
 
12.6%
11 17
 
11.9%
13 15
 
10.5%
10 11
 
7.7%

Length

2024-04-19T15:48:29.013429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-19T15:48:29.132890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 56
39.2%
9 26
18.2%
14 18
 
12.6%
11 17
 
11.9%
13 15
 
10.5%
10 11
 
7.7%

주변환경명
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
<NA>
117 
기타
26 

Length

Max length4
Median length4
Mean length3.6363636
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 117
81.8%
기타 26
 
18.2%

Length

2024-04-19T15:48:29.321054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-19T15:48:29.423054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 117
81.8%
기타 26
 
18.2%

지상층수
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct9
Distinct (%)7.8%
Missing28
Missing (%)19.6%
Infinite0
Infinite (%)0.0%
Mean7.4782609
Minimum3
Maximum11
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-04-19T15:48:29.503159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile4
Q16
median8
Q39
95-th percentile11
Maximum11
Range8
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.1535276
Coefficient of variation (CV)0.28797172
Kurtosis-0.95739851
Mean7.4782609
Median Absolute Deviation (MAD)2
Skewness-0.13063009
Sum860
Variance4.6376812
MonotonicityNot monotonic
2024-04-19T15:48:29.606629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
5 19
13.3%
7 19
13.3%
9 18
12.6%
8 16
11.2%
10 15
10.5%
6 10
 
7.0%
11 9
 
6.3%
4 6
 
4.2%
3 3
 
2.1%
(Missing) 28
19.6%
ValueCountFrequency (%)
3 3
 
2.1%
4 6
 
4.2%
5 19
13.3%
6 10
7.0%
7 19
13.3%
8 16
11.2%
9 18
12.6%
10 15
10.5%
11 9
6.3%
ValueCountFrequency (%)
11 9
6.3%
10 15
10.5%
9 18
12.6%
8 16
11.2%
7 19
13.3%
6 10
7.0%
5 19
13.3%
4 6
 
4.2%
3 3
 
2.1%

지하층수
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
<NA>
60 
3
29 
2
28 
5
14 
1
12 

Length

Max length4
Median length1
Mean length2.2587413
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 60
42.0%
3 29
20.3%
2 28
19.6%
5 14
 
9.8%
1 12
 
8.4%

Length

2024-04-19T15:48:29.722385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-19T15:48:29.821992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 60
42.0%
3 29
20.3%
2 28
19.6%
5 14
 
9.8%
1 12
 
8.4%

건물용도명
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
문화시설
72 
<NA>
69 
근린생활시설
 
2

Length

Max length6
Median length4
Mean length4.027972
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
문화시설 72
50.3%
<NA> 69
48.3%
근린생활시설 2
 
1.4%

Length

2024-04-19T15:48:29.939009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-19T15:48:30.048394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
문화시설 72
50.3%
na 69
48.3%
근린생활시설 2
 
1.4%

통로너비
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
<NA>
101 
1.0
36 
1.1
 
6

Length

Max length4
Median length4
Mean length3.7062937
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
<NA> 101
70.6%
1.0 36
 
25.2%
1.1 6
 
4.2%

Length

2024-04-19T15:48:30.153517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-19T15:48:30.253401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 101
70.6%
1.0 36
 
25.2%
1.1 6
 
4.2%

Interactions

2024-04-19T15:48:25.243617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T15:48:24.133875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T15:48:24.530850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T15:48:24.885355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T15:48:25.347181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T15:48:24.247718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T15:48:24.629554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T15:48:24.978213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T15:48:25.439847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T15:48:24.333225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T15:48:24.717386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T15:48:25.068396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T15:48:25.531254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T15:48:24.427806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T15:48:24.797655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T15:48:25.153491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-19T15:48:30.333091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
인허가일자영업상태명상세영업상태명소재지전화소재지전체주소도로명전체주소도로명우편번호좌표정보(X)좌표정보(Y)총층수지상층수지하층수건물용도명통로너비
인허가일자1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.9971.0001.0001.000
영업상태명1.0001.0000.6971.0001.0001.0000.0000.0000.000NaN0.525NaNNaNNaN
상세영업상태명1.0000.6971.0001.0001.0001.0000.0000.0000.000NaN0.525NaNNaNNaN
소재지전화1.0001.0001.0001.0000.9990.9991.0001.0001.0001.0000.9831.0001.0001.000
소재지전체주소1.0001.0001.0000.9991.0000.9971.0001.0001.0001.0000.9831.0001.0001.000
도로명전체주소1.0001.0001.0000.9990.9971.0001.0001.0001.0001.0000.9841.0001.0001.000
도로명우편번호1.0000.0000.0001.0001.0001.0001.0000.8920.8110.8500.8200.6560.4170.585
좌표정보(X)1.0000.0000.0001.0001.0001.0000.8921.0000.9430.8510.8740.8980.1800.391
좌표정보(Y)1.0000.0000.0001.0001.0001.0000.8110.9431.0000.6360.7880.7730.1660.964
총층수1.000NaNNaN1.0001.0001.0000.8500.8510.6361.0000.8530.7630.1460.272
지상층수0.9970.5250.5250.9830.9830.9840.8200.8740.7880.8531.0000.8670.0001.000
지하층수1.000NaNNaN1.0001.0001.0000.6560.8980.7730.7630.8671.000NaN0.300
건물용도명1.000NaNNaN1.0001.0001.0000.4170.1800.1660.1460.000NaN1.000NaN
통로너비1.000NaNNaN1.0001.0001.0000.5850.3910.9640.2721.0000.300NaN1.000
2024-04-19T15:48:30.479266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
총층수소재지전체주소상세영업상태명주변환경명도로명전체주소통로너비소재지전화건물용도명지하층수영업상태명
총층수1.0000.9311.0001.0000.9370.4300.9440.1700.7541.000
소재지전체주소0.9311.0000.9071.0000.9460.9220.9610.9200.9210.907
상세영업상태명1.0000.9071.0001.0000.9111.0000.9191.0001.0000.491
주변환경명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
도로명전체주소0.9370.9460.9111.0001.0000.9220.9760.9130.9210.911
통로너비0.4300.9221.0001.0000.9221.0000.9351.0000.4741.000
소재지전화0.9440.9610.9191.0000.9760.9351.0000.9200.9350.919
건물용도명0.1700.9201.0001.0000.9131.0000.9201.0001.0001.000
지하층수0.7540.9211.0001.0000.9210.4740.9351.0001.0001.000
영업상태명1.0000.9070.4911.0000.9111.0000.9191.0001.0001.000
2024-04-19T15:48:30.613205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
도로명우편번호좌표정보(X)좌표정보(Y)지상층수영업상태명상세영업상태명소재지전화소재지전체주소도로명전체주소총층수주변환경명지하층수건물용도명통로너비
도로명우편번호1.000-0.366-0.8220.2480.0000.0000.9390.9220.9300.4861.0000.5820.2890.391
좌표정보(X)-0.3661.0000.101-0.4730.0000.0000.9360.9240.9280.5831.0000.7790.2070.593
좌표정보(Y)-0.8220.1011.000-0.0420.0000.0000.9330.9210.9250.5421.0000.7350.2190.807
지상층수0.248-0.473-0.0421.0000.5090.5090.8660.8540.8550.7271.0000.7840.0000.952
영업상태명0.0000.0000.0000.5091.0000.4910.9190.9070.9111.0001.0001.0001.0001.000
상세영업상태명0.0000.0000.0000.5090.4911.0000.9190.9070.9111.0001.0001.0001.0001.000
소재지전화0.9390.9360.9330.8660.9190.9191.0000.9610.9760.9441.0000.9350.9200.935
소재지전체주소0.9220.9240.9210.8540.9070.9070.9611.0000.9460.9311.0000.9210.9200.922
도로명전체주소0.9300.9280.9250.8550.9110.9110.9760.9461.0000.9371.0000.9210.9130.922
총층수0.4860.5830.5420.7271.0001.0000.9440.9310.9371.0001.0000.7540.1700.430
주변환경명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
지하층수0.5820.7790.7350.7841.0001.0000.9350.9210.9210.7541.0001.0001.0000.474
건물용도명0.2890.2070.2190.0001.0001.0000.9200.9200.9130.1701.0001.0001.0001.000
통로너비0.3910.5930.8070.9521.0001.0000.9350.9220.9220.4301.0000.4741.0001.000

Missing values

2024-04-19T15:48:25.671577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-19T15:48:25.880466image/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.
2024-04-19T15:48:26.031967image/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

개방서비스명인허가일자영업상태구분코드영업상태명상세영업상태명소재지전화소재지전체주소도로명전체주소도로명우편번호사업장명좌표정보(X)좌표정보(Y)총층수주변환경명지상층수지하층수건물용도명통로너비
0영화상영관2002-06-261영업/정상영업중053-422-0900대구광역시 중구 종로1가 29-4번지대구광역시 중구 국채보상로 547 (종로1가)41919롯데시네마 프리미엄 만경관 7관343571.9657264582.7763<NA><NA>9<NA><NA>1.0
1영화상영관2002-06-261영업/정상영업중053-422-0900대구광역시 중구 종로1가 29-4번지대구광역시 중구 국채보상로 547 (종로1가)41919롯데시네마 프리미엄 만경관 6관343571.9657264582.7763<NA><NA>9<NA><NA>1.0
2영화상영관2002-06-261영업/정상영업중053-422-0900대구광역시 중구 종로1가 29-4번지대구광역시 중구 국채보상로 547 (종로1가)41919롯데시네마 프리미엄 만경관 5관343571.9657264582.7763<NA><NA>9<NA><NA>1.0
3영화상영관2002-06-261영업/정상영업중053-422-0900대구광역시 중구 종로1가 29-4번지대구광역시 중구 국채보상로 547 (종로1가)41919롯데시네마 프리미엄 만경관 4관343571.9657264582.7763<NA><NA>9<NA><NA>1.0
4영화상영관2002-06-261영업/정상영업중053-422-0900대구광역시 중구 종로1가 29-4번지대구광역시 중구 국채보상로 547 (종로1가)41919롯데시네마 프리미엄 만경관 3관343571.9657264582.7763<NA><NA>9<NA><NA>1.0
5영화상영관2002-06-261영업/정상영업중053-422-0900대구광역시 중구 종로1가 29-4번지대구광역시 중구 국채보상로 547 (종로1가)41919롯데시네마 프리미엄 만경관 2관343571.9657264582.7763<NA><NA>9<NA><NA>1.0
6영화상영관2002-06-261영업/정상영업중053-422-0900대구광역시 중구 종로1가 29-4번지대구광역시 중구 국채보상로 547 (종로1가)41919롯데시네마 프리미엄 만경관 1관343571.9657264582.7763<NA><NA>9<NA><NA><NA>
7영화상영관2002-06-261영업/정상영업중053-422-0900대구광역시 중구 종로1가 29-4번지대구광역시 중구 국채보상로 547 (종로1가)41919롯데시네마 프리미엄 만경관 9관343571.9657264582.7763<NA><NA>9<NA><NA>1.0
8영화상영관2002-06-261영업/정상영업중053-422-0900대구광역시 중구 종로1가 29-4번지대구광역시 중구 국채보상로 547 (종로1가)41919롯데시네마 프리미엄 만경관 8관343571.9657264582.7763<NA><NA>9<NA><NA>1.0
9영화상영관2004-05-131휴업휴업중053-425-2845대구광역시 중구 동성로1가 22-1번지대구광역시 중구 동성로 69 (동성로1가)41909동성아트홀344030.6777264814.8322<NA><NA>3<NA><NA><NA>
개방서비스명인허가일자영업상태구분코드영업상태명상세영업상태명소재지전화소재지전체주소도로명전체주소도로명우편번호사업장명좌표정보(X)좌표정보(Y)총층수주변환경명지상층수지하층수건물용도명통로너비
133영화상영관2016-11-221영업/정상영업중053-635-8312대구광역시 달서구 월성동 1846번지대구광역시 달서구 조암로 29 (월성동)42757CGV 대구월성 4관337925.1213259346.8508<NA><NA><NA><NA><NA><NA>
134영화상영관2016-11-221영업/정상영업중053-635-8312대구광역시 달서구 월성동 1846번지대구광역시 달서구 조암로 29 (월성동)42757CGV 대구월성 6관337925.1213259346.850810<NA>82<NA><NA>
135영화상영관2016-11-221영업/정상영업중053-635-8312대구광역시 달서구 월성동 1846번지대구광역시 달서구 조암로 29 (월성동)42757CGV 대구월성 5관337925.1213259346.850810<NA>82<NA><NA>
136영화상영관2016-11-221영업/정상영업중053-635-8312대구광역시 달서구 월성동 1846번지대구광역시 달서구 조암로 29 (월성동)42757CGV 대구월성 3관337925.1213259346.8508<NA><NA><NA><NA><NA><NA>
137영화상영관2019-06-111영업/정상영업중053-616-5678대구광역시 달성군 현풍읍 중리 490번지대구광역시 달성군 현풍읍 테크노상업로 6243017롯데시네마 대구현풍 제1관<NA><NA>9<NA><NA><NA>문화시설<NA>
138영화상영관2019-06-111영업/정상영업중053-616-5678대구광역시 달성군 현풍읍 중리 490번지대구광역시 달성군 현풍읍 테크노상업로 6243017롯데시네마 대구현풍 제2관<NA><NA>9<NA><NA><NA>문화시설<NA>
139영화상영관2019-06-111영업/정상영업중053-616-5678대구광역시 달성군 현풍읍 중리 490번지대구광역시 달성군 현풍읍 테크노상업로 6243017롯데시네마 대구현풍 제3관<NA><NA>9<NA><NA><NA>문화시설<NA>
140영화상영관2019-06-111영업/정상영업중053-616-5678대구광역시 달성군 현풍읍 중리 490번지대구광역시 달성군 현풍읍 테크노상업로 6243017롯데시네마 대구현풍 제4관<NA><NA>9<NA><NA><NA>문화시설<NA>
141영화상영관2019-06-111영업/정상영업중053-616-5678대구광역시 달성군 현풍읍 중리 490번지대구광역시 달성군 현풍읍 테크노상업로 6243017롯데시네마 대구현풍 제5관<NA><NA>9<NA><NA><NA>문화시설<NA>
142영화상영관2019-06-111영업/정상영업중053-616-5678대구광역시 달성군 현풍읍 중리 490번지대구광역시 달성군 현풍읍 테크노상업로 6243017롯데시네마 대구현풍 제6관<NA><NA>9<NA><NA><NA>문화시설<NA>