Overview

Dataset statistics

Number of variables12
Number of observations182
Missing cells174
Missing cells (%)8.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory18.3 KiB
Average record size in memory102.7 B

Variable types

Numeric4
Categorical3
Text4
DateTime1

Dataset

Description전북특별자치도 전주시의 체력단련장업을 제공하며 업종, 사업장명, 인허가일자, 전화번호, 도로명주소, 지번주소 등을 제공합니다.체력단련장업 : 헬스장과 같은 체격관리 및 근력, 유연성, 지구력 강화 등을 목표로 운동을 할 때 필요한 도구와 기구를 제공하는 업소항목 : 연번, 업종, 사업장명, 인허가일자, 전화번호, 도로명주소, 지번주소, 위도, 경도 등제공부서 : 체육산업과
Author전북특별자치도 전주시
URLhttps://www.data.go.kr/data/15112497/fileData.do

Alerts

업종 has constant value ""Constant
건축물연면적 is highly overall correlated with 회원모집총인원High correlation
회원모집총인원 is highly overall correlated with 건축물연면적High correlation
전화번호 has 96 (52.7%) missing valuesMissing
건축물연면적 has 75 (41.2%) missing valuesMissing
연번 has unique valuesUnique
건축물연면적 has 51 (28.0%) zerosZeros

Reproduction

Analysis started2024-03-14 14:19:01.492788
Analysis finished2024-03-14 14:19:07.209446
Duration5.72 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

UNIQUE 

Distinct182
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean91.5
Minimum1
Maximum182
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2024-03-14T23:19:07.430262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile10.05
Q146.25
median91.5
Q3136.75
95-th percentile172.95
Maximum182
Range181
Interquartile range (IQR)90.5

Descriptive statistics

Standard deviation52.683014
Coefficient of variation (CV)0.57577065
Kurtosis-1.2
Mean91.5
Median Absolute Deviation (MAD)45.5
Skewness0
Sum16653
Variance2775.5
MonotonicityStrictly increasing
2024-03-14T23:19:07.873986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.5%
116 1
 
0.5%
118 1
 
0.5%
119 1
 
0.5%
120 1
 
0.5%
121 1
 
0.5%
122 1
 
0.5%
123 1
 
0.5%
124 1
 
0.5%
125 1
 
0.5%
Other values (172) 172
94.5%
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 (%)
182 1
0.5%
181 1
0.5%
180 1
0.5%
179 1
0.5%
178 1
0.5%
177 1
0.5%
176 1
0.5%
175 1
0.5%
174 1
0.5%
173 1
0.5%

업종
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
체력단련장업
182 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
체력단련장업 182
100.0%

Length

2024-03-14T23:19:08.299508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T23:19:08.612793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
체력단련장업 182
100.0%

사업장명
Text

Distinct177
Distinct (%)97.3%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
2024-03-14T23:19:09.396002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length19
Mean length7.6923077
Min length2

Characters and Unicode

Total characters1400
Distinct characters267
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique172 ?
Unique (%)94.5%

Sample

1st row(유)대해스포츠센터
2nd row(유)스포츠박스
3rd row(유)팀에프엠
4th row(주)피티핏
5th row1.2.3 헬스
ValueCountFrequency (%)
휘트니스 18
 
6.0%
gym 9
 
3.0%
짐 7
 
2.3%
피트니스 6
 
2.0%
헬스클럽 5
 
1.7%
크로스핏 4
 
1.3%
사람 3
 
1.0%
더 3
 
1.0%
피티 3
 
1.0%
바디 3
 
1.0%
Other values (222) 240
79.7%
2024-03-14T23:19:10.668819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
119
 
8.5%
스 114
 
8.1%
트 68
 
4.9%
니 59
 
4.2%
휘 46
 
3.3%
짐 33
 
2.4%
이 29
 
2.1%
헬 27
 
1.9%
M 27
 
1.9%
T 24
 
1.7%
Other values (257) 854
61.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 972
69.4%
Uppercase Letter 188
 
13.4%
Space Separator 119
 
8.5%
Lowercase Letter 54
 
3.9%
Decimal Number 21
 
1.5%
Close Punctuation 16
 
1.1%
Open Punctuation 16
 
1.1%
Other Punctuation 12
 
0.9%
Dash Punctuation 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
스 114
 
11.7%
트 68
 
7.0%
니 59
 
6.1%
휘 46
 
4.7%
짐 33
 
3.4%
이 29
 
3.0%
헬 27
 
2.8%
클 20
 
2.1%
피 18
 
1.9%
럽 17
 
1.7%
Other values (199) 541
55.7%
Uppercase Letter
ValueCountFrequency (%)
M 27
14.4%
T 24
12.8%
G 21
11.2%
Y 20
10.6%
P 19
10.1%
S 14
 
7.4%
E 9
 
4.8%
A 7
 
3.7%
B 5
 
2.7%
H 5
 
2.7%
Other values (14) 37
19.7%
Lowercase Letter
ValueCountFrequency (%)
i 8
14.8%
o 7
13.0%
t 6
11.1%
n 5
9.3%
e 4
7.4%
d 4
7.4%
l 4
7.4%
s 3
 
5.6%
u 3
 
5.6%
m 2
 
3.7%
Other values (6) 8
14.8%
Decimal Number
ValueCountFrequency (%)
1 6
28.6%
8 3
14.3%
3 3
14.3%
0 3
14.3%
2 2
 
9.5%
4 2
 
9.5%
6 1
 
4.8%
5 1
 
4.8%
Other Punctuation
ValueCountFrequency (%)
. 6
50.0%
& 2
 
16.7%
' 1
 
8.3%
! 1
 
8.3%
· 1
 
8.3%
: 1
 
8.3%
Space Separator
ValueCountFrequency (%)
119
100.0%
Close Punctuation
ValueCountFrequency (%)
) 16
100.0%
Open Punctuation
ValueCountFrequency (%)
( 16
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 972
69.4%
Latin 242
 
17.3%
Common 186
 
13.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
스 114
 
11.7%
트 68
 
7.0%
니 59
 
6.1%
휘 46
 
4.7%
짐 33
 
3.4%
이 29
 
3.0%
헬 27
 
2.8%
클 20
 
2.1%
피 18
 
1.9%
럽 17
 
1.7%
Other values (199) 541
55.7%
Latin
ValueCountFrequency (%)
M 27
 
11.2%
T 24
 
9.9%
G 21
 
8.7%
Y 20
 
8.3%
P 19
 
7.9%
S 14
 
5.8%
E 9
 
3.7%
i 8
 
3.3%
o 7
 
2.9%
A 7
 
2.9%
Other values (30) 86
35.5%
Common
ValueCountFrequency (%)
119
64.0%
) 16
 
8.6%
( 16
 
8.6%
1 6
 
3.2%
. 6
 
3.2%
8 3
 
1.6%
3 3
 
1.6%
0 3
 
1.6%
2 2
 
1.1%
4 2
 
1.1%
Other values (8) 10
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 972
69.4%
ASCII 427
30.5%
None 1
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
119
27.9%
M 27
 
6.3%
T 24
 
5.6%
G 21
 
4.9%
Y 20
 
4.7%
P 19
 
4.4%
) 16
 
3.7%
( 16
 
3.7%
S 14
 
3.3%
E 9
 
2.1%
Other values (47) 142
33.3%
Hangul
ValueCountFrequency (%)
스 114
 
11.7%
트 68
 
7.0%
니 59
 
6.1%
휘 46
 
4.7%
짐 33
 
3.4%
이 29
 
3.0%
헬 27
 
2.8%
클 20
 
2.1%
피 18
 
1.9%
럽 17
 
1.7%
Other values (199) 541
55.7%
None
ValueCountFrequency (%)
· 1
100.0%

인허가일자
Date

Distinct178
Distinct (%)97.8%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
Minimum1996-12-07 00:00:00
Maximum2023-01-06 00:00:00
2024-03-14T23:19:11.063524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:19:11.458363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

전화번호
Text

MISSING 

Distinct85
Distinct (%)98.8%
Missing96
Missing (%)52.7%
Memory size1.5 KiB
2024-03-14T23:19:12.327621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12.023256
Min length12

Characters and Unicode

Total characters1034
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

Unique84 ?
Unique (%)97.7%

Sample

1st row063-241-1075
2nd row063-905-7886
3rd row063-223-0987
4th row063-255-2577
5th row063-212-0365
ValueCountFrequency (%)
063-224-3342 2
 
2.3%
063-250-8320 1
 
1.2%
063-241-1075 1
 
1.2%
063-213-9682 1
 
1.2%
063-243-0073 1
 
1.2%
063-281-9302 1
 
1.2%
063-232-0020 1
 
1.2%
063-224-3242 1
 
1.2%
063-214-2500 1
 
1.2%
063-251-9611 1
 
1.2%
Other values (75) 75
87.2%
2024-03-14T23:19:13.609001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 172
16.6%
2 147
14.2%
0 144
13.9%
6 131
12.7%
3 129
12.5%
5 57
 
5.5%
9 55
 
5.3%
7 55
 
5.3%
1 53
 
5.1%
4 46
 
4.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 862
83.4%
Dash Punctuation 172
 
16.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 147
17.1%
0 144
16.7%
6 131
15.2%
3 129
15.0%
5 57
 
6.6%
9 55
 
6.4%
7 55
 
6.4%
1 53
 
6.1%
4 46
 
5.3%
8 45
 
5.2%
Dash Punctuation
ValueCountFrequency (%)
- 172
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1034
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 172
16.6%
2 147
14.2%
0 144
13.9%
6 131
12.7%
3 129
12.5%
5 57
 
5.5%
9 55
 
5.3%
7 55
 
5.3%
1 53
 
5.1%
4 46
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1034
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 172
16.6%
2 147
14.2%
0 144
13.9%
6 131
12.7%
3 129
12.5%
5 57
 
5.5%
9 55
 
5.3%
7 55
 
5.3%
1 53
 
5.1%
4 46
 
4.4%

도로명주소
Text

Distinct176
Distinct (%)97.2%
Missing1
Missing (%)0.5%
Memory size1.5 KiB
2024-03-14T23:19:15.181280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length52
Median length41
Mean length23.668508
Min length21

Characters and Unicode

Total characters4284
Distinct characters160
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique171 ?
Unique (%)94.5%

Sample

1st row전북특별자치도 전주시 덕진구 인교로 65
2nd row전북특별자치도 전주시 덕진구 백제대로 709
3rd row전북특별자치도 전주시 완산구 서원로 2
4th row전북특별자치도 전주시 완산구 유연로 298-1
5th row전북특별자치도 전주시 덕진구 동부대로 803
ValueCountFrequency (%)
전북특별자치도 181
19.8%
전주시 181
19.8%
덕진구 98
 
10.7%
완산구 83
 
9.1%
백제대로 11
 
1.2%
서원로 5
 
0.5%
기지로 5
 
0.5%
안덕원로 5
 
0.5%
홍산로 4
 
0.4%
온고을로 4
 
0.4%
Other values (253) 337
36.9%
2024-03-14T23:19:17.163363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
733
17.1%
전 367
 
8.6%
시 185
 
4.3%
북 183
 
4.3%
구 182
 
4.2%
자 182
 
4.2%
주 182
 
4.2%
특 181
 
4.2%
별 181
 
4.2%
치 181
 
4.2%
Other values (150) 1727
40.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3018
70.4%
Space Separator 733
 
17.1%
Decimal Number 500
 
11.7%
Dash Punctuation 26
 
0.6%
Close Punctuation 2
 
< 0.1%
Other Punctuation 2
 
< 0.1%
Open Punctuation 2
 
< 0.1%
Lowercase Letter 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
전 367
 
12.2%
시 185
 
6.1%
북 183
 
6.1%
구 182
 
6.0%
자 182
 
6.0%
주 182
 
6.0%
특 181
 
6.0%
별 181
 
6.0%
치 181
 
6.0%
도 181
 
6.0%
Other values (134) 1013
33.6%
Decimal Number
ValueCountFrequency (%)
1 98
19.6%
2 79
15.8%
3 54
10.8%
6 48
9.6%
4 47
9.4%
5 42
8.4%
7 40
8.0%
9 31
 
6.2%
8 31
 
6.2%
0 30
 
6.0%
Space Separator
ValueCountFrequency (%)
733
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 26
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Other Punctuation
ValueCountFrequency (%)
, 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Lowercase Letter
ValueCountFrequency (%)
e 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3018
70.4%
Common 1265
29.5%
Latin 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
전 367
 
12.2%
시 185
 
6.1%
북 183
 
6.1%
구 182
 
6.0%
자 182
 
6.0%
주 182
 
6.0%
특 181
 
6.0%
별 181
 
6.0%
치 181
 
6.0%
도 181
 
6.0%
Other values (134) 1013
33.6%
Common
ValueCountFrequency (%)
733
57.9%
1 98
 
7.7%
2 79
 
6.2%
3 54
 
4.3%
6 48
 
3.8%
4 47
 
3.7%
5 42
 
3.3%
7 40
 
3.2%
9 31
 
2.5%
8 31
 
2.5%
Other values (5) 62
 
4.9%
Latin
ValueCountFrequency (%)
e 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3018
70.4%
ASCII 1266
29.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
733
57.9%
1 98
 
7.7%
2 79
 
6.2%
3 54
 
4.3%
6 48
 
3.8%
4 47
 
3.7%
5 42
 
3.3%
7 40
 
3.2%
9 31
 
2.4%
8 31
 
2.4%
Other values (6) 63
 
5.0%
Hangul
ValueCountFrequency (%)
전 367
 
12.2%
시 185
 
6.1%
북 183
 
6.1%
구 182
 
6.0%
자 182
 
6.0%
주 182
 
6.0%
특 181
 
6.0%
별 181
 
6.0%
치 181
 
6.0%
도 181
 
6.0%
Other values (134) 1013
33.6%

지번주소
Text

Distinct177
Distinct (%)97.3%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
2024-03-14T23:19:18.688984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length45
Median length34
Mean length27.093407
Min length23

Characters and Unicode

Total characters4931
Distinct characters71
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

Unique172 ?
Unique (%)94.5%

Sample

1st row전북특별자치도 전주시 덕진구 우아동1가 1093-1
2nd row전북특별자치도 전주시 덕진구 인후동2가 1533
3rd row전북특별자치도 전주시 완산구 효자동3가 1724-4
4th row전북특별자치도 전주시 완산구 중화산동2가 728-4
5th row전북특별자치도 전주시 덕진구 호성동1가 791-1
ValueCountFrequency (%)
전북특별자치도 182
19.9%
전주시 182
19.9%
덕진구 99
 
10.8%
완산구 83
 
9.1%
송천동1가 15
 
1.6%
효자동2가 15
 
1.6%
서신동 14
 
1.5%
송천동2가 13
 
1.4%
중화산동2가 12
 
1.3%
인후동1가 11
 
1.2%
Other values (208) 290
31.7%
2024-03-14T23:19:20.602613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
743
 
15.1%
전 364
 
7.4%
1 217
 
4.4%
자 215
 
4.4%
동 184
 
3.7%
북 184
 
3.7%
특 182
 
3.7%
별 182
 
3.7%
치 182
 
3.7%
도 182
 
3.7%
Other values (61) 2296
46.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3069
62.2%
Decimal Number 949
 
19.2%
Space Separator 743
 
15.1%
Dash Punctuation 168
 
3.4%
Other Punctuation 1
 
< 0.1%
Lowercase Letter 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
전 364
 
11.9%
자 215
 
7.0%
동 184
 
6.0%
북 184
 
6.0%
특 182
 
5.9%
별 182
 
5.9%
치 182
 
5.9%
도 182
 
5.9%
주 182
 
5.9%
시 182
 
5.9%
Other values (47) 1030
33.6%
Decimal Number
ValueCountFrequency (%)
1 217
22.9%
2 155
16.3%
3 98
10.3%
7 90
9.5%
4 81
 
8.5%
5 81
 
8.5%
6 75
 
7.9%
9 60
 
6.3%
8 58
 
6.1%
0 34
 
3.6%
Space Separator
ValueCountFrequency (%)
743
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 168
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%
Lowercase Letter
ValueCountFrequency (%)
e 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3069
62.2%
Common 1861
37.7%
Latin 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
전 364
 
11.9%
자 215
 
7.0%
동 184
 
6.0%
북 184
 
6.0%
특 182
 
5.9%
별 182
 
5.9%
치 182
 
5.9%
도 182
 
5.9%
주 182
 
5.9%
시 182
 
5.9%
Other values (47) 1030
33.6%
Common
ValueCountFrequency (%)
743
39.9%
1 217
 
11.7%
- 168
 
9.0%
2 155
 
8.3%
3 98
 
5.3%
7 90
 
4.8%
4 81
 
4.4%
5 81
 
4.4%
6 75
 
4.0%
9 60
 
3.2%
Other values (3) 93
 
5.0%
Latin
ValueCountFrequency (%)
e 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3069
62.2%
ASCII 1862
37.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
743
39.9%
1 217
 
11.7%
- 168
 
9.0%
2 155
 
8.3%
3 98
 
5.3%
7 90
 
4.8%
4 81
 
4.4%
5 81
 
4.4%
6 75
 
4.0%
9 60
 
3.2%
Other values (4) 94
 
5.0%
Hangul
ValueCountFrequency (%)
전 364
 
11.9%
자 215
 
7.0%
동 184
 
6.0%
북 184
 
6.0%
특 182
 
5.9%
별 182
 
5.9%
치 182
 
5.9%
도 182
 
5.9%
주 182
 
5.9%
시 182
 
5.9%
Other values (47) 1030
33.6%

위도
Real number (ℝ)

Distinct176
Distinct (%)97.2%
Missing1
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean35.832427
Minimum35.783051
Maximum35.879258
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2024-03-14T23:19:21.028002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.783051
5-th percentile35.796851
Q135.816018
median35.834386
Q335.844658
95-th percentile35.869442
Maximum35.879258
Range0.09620705
Interquartile range (IQR)0.02863965

Descriptive statistics

Standard deviation0.022336242
Coefficient of variation (CV)0.00062335275
Kurtosis-0.6451909
Mean35.832427
Median Absolute Deviation (MAD)0.01557399
Skewness-0.015067685
Sum6485.6693
Variance0.00049890771
MonotonicityNot monotonic
2024-03-14T23:19:21.483562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.85298273 2
 
1.1%
35.80585715 2
 
1.1%
35.83812879 2
 
1.1%
35.83769568 2
 
1.1%
35.84533509 2
 
1.1%
35.81714782 1
 
0.5%
35.83516034 1
 
0.5%
35.83233526 1
 
0.5%
35.81603753 1
 
0.5%
35.78824558 1
 
0.5%
Other values (166) 166
91.2%
ValueCountFrequency (%)
35.78305084 1
0.5%
35.78617072 1
0.5%
35.78686647 1
0.5%
35.78824558 1
0.5%
35.78826166 1
0.5%
35.79464359 1
0.5%
35.79510964 1
0.5%
35.79587858 1
0.5%
35.79667927 1
0.5%
35.79685123 1
0.5%
ValueCountFrequency (%)
35.87925789 1
0.5%
35.87519002 1
0.5%
35.87422527 1
0.5%
35.87371223 1
0.5%
35.87279654 1
0.5%
35.87200421 1
0.5%
35.87199165 1
0.5%
35.87082881 1
0.5%
35.870617 1
0.5%
35.86944165 1
0.5%

경도
Real number (ℝ)

Distinct176
Distinct (%)97.2%
Missing1
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean127.12141
Minimum127.05899
Maximum127.17188
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2024-03-14T23:19:21.909340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum127.05899
5-th percentile127.07388
Q1127.10966
median127.12216
Q3127.13639
95-th percentile127.16244
Maximum127.17188
Range0.1128915
Interquartile range (IQR)0.026731

Descriptive statistics

Standard deviation0.025117935
Coefficient of variation (CV)0.00019759013
Kurtosis0.29242485
Mean127.12141
Median Absolute Deviation (MAD)0.0128671
Skewness-0.43245986
Sum23008.974
Variance0.00063091065
MonotonicityNot monotonic
2024-03-14T23:19:22.187008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.1189031 2
 
1.1%
127.1281296 2
 
1.1%
127.06129 2
 
1.1%
127.0599579 2
 
1.1%
127.1460778 2
 
1.1%
127.1485519 1
 
0.5%
127.1202102 1
 
0.5%
127.1319015 1
 
0.5%
127.104989 1
 
0.5%
127.136394 1
 
0.5%
Other values (166) 166
91.2%
ValueCountFrequency (%)
127.0589878 1
0.5%
127.0590138 1
0.5%
127.0593368 1
0.5%
127.0599579 2
1.1%
127.0599762 1
0.5%
127.06129 2
1.1%
127.07165 1
0.5%
127.0738758 1
0.5%
127.0739777 1
0.5%
127.0741728 1
0.5%
ValueCountFrequency (%)
127.1718793 1
0.5%
127.1708285 1
0.5%
127.1707693 1
0.5%
127.1694253 1
0.5%
127.1681718 1
0.5%
127.1677008 1
0.5%
127.1650359 1
0.5%
127.1644446 1
0.5%
127.1633497 1
0.5%
127.1624365 1
0.5%

지도자수
Categorical

Distinct5
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
1
76 
2
63 
<NA>
23 
0
18 
3
 
2

Length

Max length4
Median length1
Mean length1.3791209
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row<NA>
4th row<NA>
5th row2

Common Values

ValueCountFrequency (%)
1 76
41.8%
2 63
34.6%
<NA> 23
 
12.6%
0 18
 
9.9%
3 2
 
1.1%

Length

2024-03-14T23:19:22.464383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T23:19:22.829560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 76
41.8%
2 63
34.6%
na 23
 
12.6%
0 18
 
9.9%
3 2
 
1.1%

건축물연면적
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct57
Distinct (%)53.3%
Missing75
Missing (%)41.2%
Infinite0
Infinite (%)0.0%
Mean25036.492
Minimum0
Maximum2523325
Zeros51
Zeros (%)28.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2024-03-14T23:19:23.215632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median260
Q31896.61
95-th percentile6939.415
Maximum2523325
Range2523325
Interquartile range (IQR)1896.61

Descriptive statistics

Standard deviation243809.51
Coefficient of variation (CV)9.7381658
Kurtosis106.97767
Mean25036.492
Median Absolute Deviation (MAD)260
Skewness10.342479
Sum2678904.6
Variance5.9443077 × 1010
MonotonicityNot monotonic
2024-03-14T23:19:23.662914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 51
28.0%
1941.11 1
 
0.5%
12171.0 1
 
0.5%
833.76 1
 
0.5%
3901.5 1
 
0.5%
1679.68 1
 
0.5%
4309.21 1
 
0.5%
1941.14 1
 
0.5%
762.71 1
 
0.5%
6130.65 1
 
0.5%
Other values (47) 47
25.8%
(Missing) 75
41.2%
ValueCountFrequency (%)
0.0 51
28.0%
185.54 1
 
0.5%
218.01 1
 
0.5%
260.0 1
 
0.5%
323.14 1
 
0.5%
330.0 1
 
0.5%
431.14 1
 
0.5%
440.51 1
 
0.5%
576.72 1
 
0.5%
669.83 1
 
0.5%
ValueCountFrequency (%)
2523325.0 1
0.5%
12171.0 1
0.5%
11596.13 1
0.5%
11043.0 1
0.5%
7377.0 1
0.5%
7112.8 1
0.5%
6534.85 1
0.5%
6130.65 1
0.5%
5458.32 1
0.5%
4895.28 1
0.5%

회원모집총인원
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
<NA>
120 
0
60 
600
 
1
1500
 
1

Length

Max length4
Median length4
Mean length3.0054945
Min length1

Unique

Unique2 ?
Unique (%)1.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 120
65.9%
0 60
33.0%
600 1
 
0.5%
1500 1
 
0.5%

Length

2024-03-14T23:19:24.287675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T23:19:24.627272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 120
65.9%
0 60
33.0%
600 1
 
0.5%
1500 1
 
0.5%

Interactions

2024-03-14T23:19:05.351397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:19:02.322864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:19:03.356337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:19:04.358447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:19:05.822390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:19:02.584426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:19:03.615739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:19:04.616925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:19:05.967042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:19:02.846025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:19:03.860764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:19:04.862710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:19:06.114428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:19:03.100787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:19:04.110781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:19:05.107773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T23:19:24.844615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번전화번호위도경도지도자수건축물연면적회원모집총인원
연번1.0000.9590.1620.0000.0000.2460.000
전화번호0.9591.0000.9670.0001.0001.0001.000
위도0.1620.9671.0000.6750.3000.0000.000
경도0.0000.0000.6751.0000.0000.8370.000
지도자수0.0001.0000.3000.0001.0000.0000.058
건축물연면적0.2461.0000.0000.8370.0001.000NaN
회원모집총인원0.0001.0000.0000.0000.058NaN1.000
2024-03-14T23:19:25.132168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
회원모집총인원지도자수
회원모집총인원1.0000.000
지도자수0.0001.000
2024-03-14T23:19:25.375256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번위도경도건축물연면적지도자수회원모집총인원
연번1.0000.082-0.047-0.0340.0000.000
위도0.0821.0000.1140.1440.1790.000
경도-0.0470.1141.000-0.0170.0000.000
건축물연면적-0.0340.144-0.0171.0000.0001.000
지도자수0.0000.1790.0000.0001.0000.000
회원모집총인원0.0000.0000.0001.0000.0001.000

Missing values

2024-03-14T23:19:06.321415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T23:19:06.611564image/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.
2024-03-14T23:19:06.984587image/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

연번업종사업장명인허가일자전화번호도로명주소지번주소위도경도지도자수건축물연면적회원모집총인원
01체력단련장업(유)대해스포츠센터2004-02-11063-241-1075전북특별자치도 전주시 덕진구 인교로 65전북특별자치도 전주시 덕진구 우아동1가 1093-135.827231127.17187927112.8<NA>
12체력단련장업(유)스포츠박스2018-05-17063-905-7886전북특별자치도 전주시 덕진구 백제대로 709전북특별자치도 전주시 덕진구 인후동2가 153335.845335127.14607821344.0<NA>
23체력단련장업(유)팀에프엠2016-02-12063-223-0987전북특별자치도 전주시 완산구 서원로 2전북특별자치도 전주시 완산구 효자동3가 1724-435.813401127.094696<NA><NA><NA>
34체력단련장업(주)피티핏2017-01-11063-255-2577전북특별자치도 전주시 완산구 유연로 298-1전북특별자치도 전주시 완산구 중화산동2가 728-435.825115127.118008<NA><NA><NA>
45체력단련장업1.2.3 헬스2016-09-09<NA>전북특별자치도 전주시 덕진구 동부대로 803전북특별자치도 전주시 덕진구 호성동1가 791-135.859131127.1541122<NA><NA>
56체력단련장업100휘트니스2020-05-22<NA>전북특별자치도 전주시 완산구 홍산로 263전북특별자치도 전주시 완산구 효자동2가 1239-135.817285127.10548<NA>7377.0<NA>
67체력단련장업241GYM2022-10-28<NA>전북특별자치도 전주시 완산구 유연로 290전북특별자치도 전주시 완산구 중화산동2가 744-635.825116127.11717310.00
78체력단련장업365헬스2017-07-03063-212-0365전북특별자치도 전주시 덕진구 편운로 6전북특별자치도 전주시 덕진구 동산동 70135.870829127.077037<NA><NA><NA>
89체력단련장업3GYM2007-05-29063-223-6153전북특별자치도 전주시 완산구 용리로 46전북특별자치도 전주시 완산구 삼천동1가 285-535.798305127.1151442<NA><NA>
910체력단련장업88GYM2022-06-17<NA>전북특별자치도 전주시 완산구 홍산북로 69-9전북특별자치도 전주시 완산구 효자동3가 1528-1235.818157127.10998310.00
연번업종사업장명인허가일자전화번호도로명주소지번주소위도경도지도자수건축물연면적회원모집총인원
172173체력단련장업핑크팬더즈2016-10-17063-236-1910전북특별자치도 전주시 완산구 안행로 9전북특별자치도 전주시 완산구 삼천동1가 732-1135.799955127.129447<NA><NA><NA>
173174체력단련장업하가 휘트니스2012-11-01<NA>전북특별자치도 전주시 덕진구 가련산로 10전북특별자치도 전주시 덕진구 덕진동2가 695-235.840668127.1105542<NA><NA>
174175체력단련장업한솔보석 헬스클럽2005-06-15063-214-4500전북특별자치도 전주시 덕진구 신복6길 6전북특별자치도 전주시 덕진구 팔복동1가 139-1735.85397127.1055432<NA><NA>
175176체력단련장업한암헬스클럽1999-05-11063-272-8434전북특별자치도 전주시 덕진구 건지2길 15전북특별자치도 전주시 덕진구 금암동 105-39735.844658127.1410291770.6<NA>
176177체력단련장업한양 휘트니스2020-03-09063-282-9686전북특별자치도 전주시 완산구 꽃밭정로 50전북특별자치도 전주시 완산구 평화동1가 580-335.797861127.13794921936.8<NA>
177178체력단련장업헤라휘트니스2003-07-23063-288-1138전북특별자치도 전주시 완산구 공수내로 9전북특별자치도 전주시 완산구 서서학동 113-235.804588127.14848120.00
178179체력단련장업헬스를 배우다! 포비스클럽2022-01-17<NA>전북특별자치도 전주시 덕진구 만성서로 160전북특별자치도 전주시 덕진구 만성동 1340-135.851532127.07647521734.780
179180체력단련장업헬스보이짐 전주송천점 앤 전주사우나2022-10-07<NA>전북특별자치도 전주시 덕진구 송천로 1전북특별자치도 전주시 덕진구 송천동1가 292-735.852983127.11890300.00
180181체력단련장업헬스짐(Hell's Gym)2022-02-04<NA>전북특별자치도 전주시 덕진구 건산로 229전북특별자치도 전주시 덕진구 인후동1가 805-135.834453127.16243600.00
181182체력단련장업힐링 휘트니스2013-06-21063-245-3068전북특별자치도 전주시 덕진구 한배미로 74전북특별자치도 전주시 덕진구 우아동1가 1091-835.82683127.1708281<NA><NA>