Overview

Dataset statistics

Number of variables6
Number of observations218
Missing cells67
Missing cells (%)5.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory10.6 KiB
Average record size in memory49.6 B

Variable types

Numeric1
Categorical2
Text3

Dataset

Description대구광역시 북구 관내에 등록된 체육시설업 현황(헬스장, 골프연습장) 정보(상호, 소재지, 전화번호 등)를 제공합니다.
Author대구광역시 북구
URLhttps://www.data.go.kr/data/15093580/fileData.do

Alerts

기준일자 has constant value ""Constant
연번 is highly overall correlated with 업종High correlation
업종 is highly overall correlated with 연번High correlation
전화번호 has 67 (30.7%) missing valuesMissing
연번 has unique valuesUnique
소재지 has unique valuesUnique

Reproduction

Analysis started2023-12-12 09:12:16.848938
Analysis finished2023-12-12 09:12:17.499429
Duration0.65 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct218
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean109.5
Minimum1
Maximum218
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2023-12-12T18:12:17.583203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile11.85
Q155.25
median109.5
Q3163.75
95-th percentile207.15
Maximum218
Range217
Interquartile range (IQR)108.5

Descriptive statistics

Standard deviation63.075352
Coefficient of variation (CV)0.57603061
Kurtosis-1.2
Mean109.5
Median Absolute Deviation (MAD)54.5
Skewness0
Sum23871
Variance3978.5
MonotonicityStrictly increasing
2023-12-12T18:12:17.832648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.5%
151 1
 
0.5%
140 1
 
0.5%
141 1
 
0.5%
142 1
 
0.5%
143 1
 
0.5%
144 1
 
0.5%
145 1
 
0.5%
146 1
 
0.5%
147 1
 
0.5%
Other values (208) 208
95.4%
ValueCountFrequency (%)
1 1
0.5%
2 1
0.5%
3 1
0.5%
4 1
0.5%
5 1
0.5%
6 1
0.5%
7 1
0.5%
8 1
0.5%
9 1
0.5%
10 1
0.5%
ValueCountFrequency (%)
218 1
0.5%
217 1
0.5%
216 1
0.5%
215 1
0.5%
214 1
0.5%
213 1
0.5%
212 1
0.5%
211 1
0.5%
210 1
0.5%
209 1
0.5%

업종
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
체력단련장업
101 
골프연습장업
80 
가상체험 체육시설업(골프종목)
37 

Length

Max length16
Median length6
Mean length7.6972477
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
체력단련장업 101
46.3%
골프연습장업 80
36.7%
가상체험 체육시설업(골프종목) 37
 
17.0%

Length

2023-12-12T18:12:18.052348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:12:18.214323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
체력단련장업 101
39.6%
골프연습장업 80
31.4%
가상체험 37
 
14.5%
체육시설업(골프종목 37
 
14.5%

상호
Text

Distinct215
Distinct (%)98.6%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
2023-12-12T18:12:18.530796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length19
Mean length8.4770642
Min length2

Characters and Unicode

Total characters1848
Distinct characters268
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique212 ?
Unique (%)97.2%

Sample

1st row동경헬스
2nd row보석스포렉스
3rd rowEASY 피트니스
4th row그린파크
5th rowFM피트니스 동천점
ValueCountFrequency (%)
스크린골프 11
 
3.0%
골프 7
 
1.9%
아카데미 7
 
1.9%
골프연습장 6
 
1.6%
피트니스 6
 
1.6%
골프존파크 6
 
1.6%
스크린 5
 
1.3%
gym 4
 
1.1%
스크린골프연습장 3
 
0.8%
휘트니스 3
 
0.8%
Other values (280) 314
84.4%
2023-12-12T18:12:19.051934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
154
 
8.3%
스 132
 
7.1%
프 106
 
5.7%
골 100
 
5.4%
크 69
 
3.7%
린 55
 
3.0%
이 44
 
2.4%
트 41
 
2.2%
아 31
 
1.7%
카 28
 
1.5%
Other values (258) 1088
58.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1511
81.8%
Space Separator 154
 
8.3%
Uppercase Letter 102
 
5.5%
Lowercase Letter 45
 
2.4%
Decimal Number 16
 
0.9%
Close Punctuation 8
 
0.4%
Open Punctuation 7
 
0.4%
Other Punctuation 4
 
0.2%
Connector Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
스 132
 
8.7%
프 106
 
7.0%
골 100
 
6.6%
크 69
 
4.6%
린 55
 
3.6%
이 44
 
2.9%
트 41
 
2.7%
아 31
 
2.1%
카 28
 
1.9%
연 28
 
1.9%
Other values (212) 877
58.0%
Uppercase Letter
ValueCountFrequency (%)
G 14
13.7%
S 11
10.8%
J 8
 
7.8%
M 8
 
7.8%
Y 8
 
7.8%
K 8
 
7.8%
D 7
 
6.9%
T 6
 
5.9%
B 5
 
4.9%
A 5
 
4.9%
Other values (8) 22
21.6%
Lowercase Letter
ValueCountFrequency (%)
e 7
15.6%
n 5
11.1%
m 5
11.1%
i 5
11.1%
y 4
8.9%
o 4
8.9%
l 2
 
4.4%
t 2
 
4.4%
s 2
 
4.4%
g 2
 
4.4%
Other values (7) 7
15.6%
Decimal Number
ValueCountFrequency (%)
2 8
50.0%
4 4
25.0%
1 1
 
6.2%
3 1
 
6.2%
7 1
 
6.2%
5 1
 
6.2%
Space Separator
ValueCountFrequency (%)
154
100.0%
Close Punctuation
ValueCountFrequency (%)
) 8
100.0%
Open Punctuation
ValueCountFrequency (%)
( 7
100.0%
Other Punctuation
ValueCountFrequency (%)
& 4
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1511
81.8%
Common 190
 
10.3%
Latin 147
 
8.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
스 132
 
8.7%
프 106
 
7.0%
골 100
 
6.6%
크 69
 
4.6%
린 55
 
3.6%
이 44
 
2.9%
트 41
 
2.7%
아 31
 
2.1%
카 28
 
1.9%
연 28
 
1.9%
Other values (212) 877
58.0%
Latin
ValueCountFrequency (%)
G 14
 
9.5%
S 11
 
7.5%
J 8
 
5.4%
M 8
 
5.4%
Y 8
 
5.4%
K 8
 
5.4%
D 7
 
4.8%
e 7
 
4.8%
T 6
 
4.1%
n 5
 
3.4%
Other values (25) 65
44.2%
Common
ValueCountFrequency (%)
154
81.1%
) 8
 
4.2%
2 8
 
4.2%
( 7
 
3.7%
4 4
 
2.1%
& 4
 
2.1%
1 1
 
0.5%
3 1
 
0.5%
7 1
 
0.5%
_ 1
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1511
81.8%
ASCII 337
 
18.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
154
45.7%
G 14
 
4.2%
S 11
 
3.3%
J 8
 
2.4%
M 8
 
2.4%
) 8
 
2.4%
2 8
 
2.4%
Y 8
 
2.4%
K 8
 
2.4%
D 7
 
2.1%
Other values (36) 103
30.6%
Hangul
ValueCountFrequency (%)
스 132
 
8.7%
프 106
 
7.0%
골 100
 
6.6%
크 69
 
4.6%
린 55
 
3.6%
이 44
 
2.9%
트 41
 
2.7%
아 31
 
2.1%
카 28
 
1.9%
연 28
 
1.9%
Other values (212) 877
58.0%

소재지
Text

UNIQUE 

Distinct218
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
2023-12-12T18:12:19.442231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length57
Median length46
Mean length29.449541
Min length21

Characters and Unicode

Total characters6420
Distinct characters152
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique218 ?
Unique (%)100.0%

Sample

1st row대구광역시 북구 침산남로 170 (침산동)
2nd row대구광역시 북구 호암로 28-3 (칠성동2가)
3rd row대구광역시 북구 칠곡중앙대로 322 (태전동)
4th row대구광역시 북구 학정로 439 (동천동)
5th row대구광역시 북구 대천로 90, 지상5층 (동천동)
ValueCountFrequency (%)
대구광역시 218
 
16.1%
북구 218
 
16.1%
동천동 33
 
2.4%
2층 33
 
2.4%
침산동 28
 
2.1%
4층 27
 
2.0%
3층 25
 
1.8%
복현동 20
 
1.5%
5층 19
 
1.4%
산격동 19
 
1.4%
Other values (335) 713
52.7%
2023-12-12T18:12:20.025693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1135
 
17.7%
구 461
 
7.2%
동 322
 
5.0%
대 257
 
4.0%
, 241
 
3.8%
북 235
 
3.7%
시 220
 
3.4%
( 219
 
3.4%
역 219
 
3.4%
) 219
 
3.4%
Other values (142) 2892
45.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3516
54.8%
Space Separator 1135
 
17.7%
Decimal Number 1045
 
16.3%
Other Punctuation 241
 
3.8%
Open Punctuation 219
 
3.4%
Close Punctuation 219
 
3.4%
Dash Punctuation 30
 
0.5%
Uppercase Letter 8
 
0.1%
Math Symbol 7
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 461
13.1%
동 322
 
9.2%
대 257
 
7.3%
북 235
 
6.7%
시 220
 
6.3%
역 219
 
6.2%
광 218
 
6.2%
로 215
 
6.1%
층 174
 
4.9%
산 80
 
2.3%
Other values (120) 1115
31.7%
Decimal Number
ValueCountFrequency (%)
1 205
19.6%
2 191
18.3%
3 139
13.3%
4 105
10.0%
0 96
9.2%
5 87
8.3%
6 66
 
6.3%
8 56
 
5.4%
7 53
 
5.1%
9 47
 
4.5%
Uppercase Letter
ValueCountFrequency (%)
B 3
37.5%
S 1
 
12.5%
K 1
 
12.5%
Y 1
 
12.5%
A 1
 
12.5%
C 1
 
12.5%
Space Separator
ValueCountFrequency (%)
1135
100.0%
Other Punctuation
ValueCountFrequency (%)
, 241
100.0%
Open Punctuation
ValueCountFrequency (%)
( 219
100.0%
Close Punctuation
ValueCountFrequency (%)
) 219
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 30
100.0%
Math Symbol
ValueCountFrequency (%)
~ 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3516
54.8%
Common 2896
45.1%
Latin 8
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 461
13.1%
동 322
 
9.2%
대 257
 
7.3%
북 235
 
6.7%
시 220
 
6.3%
역 219
 
6.2%
광 218
 
6.2%
로 215
 
6.1%
층 174
 
4.9%
산 80
 
2.3%
Other values (120) 1115
31.7%
Common
ValueCountFrequency (%)
1135
39.2%
, 241
 
8.3%
( 219
 
7.6%
) 219
 
7.6%
1 205
 
7.1%
2 191
 
6.6%
3 139
 
4.8%
4 105
 
3.6%
0 96
 
3.3%
5 87
 
3.0%
Other values (6) 259
 
8.9%
Latin
ValueCountFrequency (%)
B 3
37.5%
S 1
 
12.5%
K 1
 
12.5%
Y 1
 
12.5%
A 1
 
12.5%
C 1
 
12.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3516
54.8%
ASCII 2904
45.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1135
39.1%
, 241
 
8.3%
( 219
 
7.5%
) 219
 
7.5%
1 205
 
7.1%
2 191
 
6.6%
3 139
 
4.8%
4 105
 
3.6%
0 96
 
3.3%
5 87
 
3.0%
Other values (12) 267
 
9.2%
Hangul
ValueCountFrequency (%)
구 461
13.1%
동 322
 
9.2%
대 257
 
7.3%
북 235
 
6.7%
시 220
 
6.3%
역 219
 
6.2%
광 218
 
6.2%
로 215
 
6.1%
층 174
 
4.9%
산 80
 
2.3%
Other values (120) 1115
31.7%

전화번호
Text

MISSING 

Distinct147
Distinct (%)97.4%
Missing67
Missing (%)30.7%
Memory size1.8 KiB
2023-12-12T18:12:20.351150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length11.94702
Min length9

Characters and Unicode

Total characters1804
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

Unique143 ?
Unique (%)94.7%

Sample

1st row053-355-7887
2nd row053-355-8080
3rd row053-312-1020
4th row053-326-5560
5th row053-323-8388
ValueCountFrequency (%)
053-313-3184 2
 
1.3%
053-985-0753 2
 
1.3%
053-313-5700 2
 
1.3%
053-314-3333 2
 
1.3%
053-326-1011 1
 
0.7%
053-326-7011 1
 
0.7%
053-311-0705 1
 
0.7%
053-312-0753 1
 
0.7%
053-384-3344 1
 
0.7%
053-956-6660 1
 
0.7%
Other values (137) 137
90.7%
2023-12-12T18:12:20.814534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 354
19.6%
- 299
16.6%
0 287
15.9%
5 274
15.2%
1 114
 
6.3%
2 104
 
5.8%
7 95
 
5.3%
9 76
 
4.2%
8 70
 
3.9%
6 67
 
3.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1505
83.4%
Dash Punctuation 299
 
16.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 354
23.5%
0 287
19.1%
5 274
18.2%
1 114
 
7.6%
2 104
 
6.9%
7 95
 
6.3%
9 76
 
5.0%
8 70
 
4.7%
6 67
 
4.5%
4 64
 
4.3%
Dash Punctuation
ValueCountFrequency (%)
- 299
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1804
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 354
19.6%
- 299
16.6%
0 287
15.9%
5 274
15.2%
1 114
 
6.3%
2 104
 
5.8%
7 95
 
5.3%
9 76
 
4.2%
8 70
 
3.9%
6 67
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1804
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 354
19.6%
- 299
16.6%
0 287
15.9%
5 274
15.2%
1 114
 
6.3%
2 104
 
5.8%
7 95
 
5.3%
9 76
 
4.2%
8 70
 
3.9%
6 67
 
3.7%

기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
2023-10-20
218 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-10-20
2nd row2023-10-20
3rd row2023-10-20
4th row2023-10-20
5th row2023-10-20

Common Values

ValueCountFrequency (%)
2023-10-20 218
100.0%

Length

2023-12-12T18:12:20.953709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:12:21.047628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-10-20 218
100.0%

Interactions

2023-12-12T18:12:17.187252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T18:12:21.113966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번업종
연번1.0000.936
업종0.9361.000
2023-12-12T18:12:21.202699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번업종
연번1.0000.903
업종0.9031.000

Missing values

2023-12-12T18:12:17.327004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T18:12:17.458567image/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

연번업종상호소재지전화번호기준일자
01체력단련장업동경헬스대구광역시 북구 침산남로 170 (침산동)053-355-78872023-10-20
12체력단련장업보석스포렉스대구광역시 북구 호암로 28-3 (칠성동2가)053-355-80802023-10-20
23체력단련장업EASY 피트니스대구광역시 북구 칠곡중앙대로 322 (태전동)053-312-10202023-10-20
34체력단련장업그린파크대구광역시 북구 학정로 439 (동천동)053-326-55602023-10-20
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