Overview

Dataset statistics

Number of variables5
Number of observations59
Missing cells27
Missing cells (%)9.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.5 KiB
Average record size in memory43.2 B

Variable types

Numeric1
Text3
Categorical1

Dataset

Description서대문구 체력단련장(헬스장,피트니스) 현황
Author서울특별시 서대문구
URLhttps://www.data.go.kr/data/15074161/fileData.do

Alerts

기준일 has constant value ""Constant
시설전화번호 has 27 (45.8%) missing valuesMissing
연번 has unique valuesUnique
시설주소(도로명) has unique valuesUnique

Reproduction

Analysis started2023-12-12 14:36:08.431721
Analysis finished2023-12-12 14:36:09.037793
Duration0.61 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

UNIQUE 

Distinct59
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30
Minimum1
Maximum59
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size663.0 B
2023-12-12T23:36:09.113132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.9
Q115.5
median30
Q344.5
95-th percentile56.1
Maximum59
Range58
Interquartile range (IQR)29

Descriptive statistics

Standard deviation17.175564
Coefficient of variation (CV)0.5725188
Kurtosis-1.2
Mean30
Median Absolute Deviation (MAD)15
Skewness0
Sum1770
Variance295
MonotonicityStrictly increasing
2023-12-12T23:36:09.299036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.7%
2 1
 
1.7%
33 1
 
1.7%
34 1
 
1.7%
35 1
 
1.7%
36 1
 
1.7%
37 1
 
1.7%
38 1
 
1.7%
39 1
 
1.7%
40 1
 
1.7%
Other values (49) 49
83.1%
ValueCountFrequency (%)
1 1
1.7%
2 1
1.7%
3 1
1.7%
4 1
1.7%
5 1
1.7%
6 1
1.7%
7 1
1.7%
8 1
1.7%
9 1
1.7%
10 1
1.7%
ValueCountFrequency (%)
59 1
1.7%
58 1
1.7%
57 1
1.7%
56 1
1.7%
55 1
1.7%
54 1
1.7%
53 1
1.7%
52 1
1.7%
51 1
1.7%
50 1
1.7%

상호
Text

Distinct57
Distinct (%)96.6%
Missing0
Missing (%)0.0%
Memory size604.0 B
2023-12-12T23:36:09.606498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length14
Mean length7.5084746
Min length3

Characters and Unicode

Total characters443
Distinct characters142
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

Unique56 ?
Unique (%)94.9%

Sample

1st row월드휘트니스
2nd row중앙헬스
3rd row성은헬스
4th rowJC헬스
5th row바나나휘트니스
ValueCountFrequency (%)
휘트니스 7
 
7.1%
스타칼리 3
 
3.1%
fitness 3
 
3.1%
커브스 2
 
2.0%
홍제점 2
 
2.0%
pt 2
 
2.0%
헬스뱅크 2
 
2.0%
studio 2
 
2.0%
피트니스 2
 
2.0%
클럽 1
 
1.0%
Other values (72) 72
73.5%
2023-12-12T23:36:10.056709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
스 42
 
9.5%
39
 
8.8%
트 18
 
4.1%
짐 18
 
4.1%
니 17
 
3.8%
휘 13
 
2.9%
S 12
 
2.7%
T 9
 
2.0%
이 9
 
2.0%
헬 8
 
1.8%
Other values (132) 258
58.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 299
67.5%
Uppercase Letter 72
 
16.3%
Space Separator 39
 
8.8%
Lowercase Letter 16
 
3.6%
Open Punctuation 5
 
1.1%
Close Punctuation 5
 
1.1%
Decimal Number 4
 
0.9%
Other Punctuation 2
 
0.5%
Dash Punctuation 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
스 42
 
14.0%
트 18
 
6.0%
짐 18
 
6.0%
니 17
 
5.7%
휘 13
 
4.3%
이 9
 
3.0%
헬 8
 
2.7%
크 6
 
2.0%
리 6
 
2.0%
타 6
 
2.0%
Other values (94) 156
52.2%
Uppercase Letter
ValueCountFrequency (%)
S 12
16.7%
T 9
12.5%
I 6
 
8.3%
O 5
 
6.9%
A 4
 
5.6%
U 4
 
5.6%
N 4
 
5.6%
F 4
 
5.6%
P 3
 
4.2%
E 3
 
4.2%
Other values (11) 18
25.0%
Lowercase Letter
ValueCountFrequency (%)
t 4
25.0%
e 3
18.8%
s 2
12.5%
i 2
12.5%
o 1
 
6.2%
n 1
 
6.2%
m 1
 
6.2%
y 1
 
6.2%
h 1
 
6.2%
Decimal Number
ValueCountFrequency (%)
2 2
50.0%
3 2
50.0%
Other Punctuation
ValueCountFrequency (%)
, 1
50.0%
. 1
50.0%
Space Separator
ValueCountFrequency (%)
39
100.0%
Open Punctuation
ValueCountFrequency (%)
( 5
100.0%
Close Punctuation
ValueCountFrequency (%)
) 5
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 299
67.5%
Latin 88
 
19.9%
Common 56
 
12.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
스 42
 
14.0%
트 18
 
6.0%
짐 18
 
6.0%
니 17
 
5.7%
휘 13
 
4.3%
이 9
 
3.0%
헬 8
 
2.7%
크 6
 
2.0%
리 6
 
2.0%
타 6
 
2.0%
Other values (94) 156
52.2%
Latin
ValueCountFrequency (%)
S 12
 
13.6%
T 9
 
10.2%
I 6
 
6.8%
O 5
 
5.7%
t 4
 
4.5%
A 4
 
4.5%
U 4
 
4.5%
N 4
 
4.5%
F 4
 
4.5%
P 3
 
3.4%
Other values (20) 33
37.5%
Common
ValueCountFrequency (%)
39
69.6%
( 5
 
8.9%
) 5
 
8.9%
2 2
 
3.6%
3 2
 
3.6%
, 1
 
1.8%
. 1
 
1.8%
- 1
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 299
67.5%
ASCII 144
32.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
스 42
 
14.0%
트 18
 
6.0%
짐 18
 
6.0%
니 17
 
5.7%
휘 13
 
4.3%
이 9
 
3.0%
헬 8
 
2.7%
크 6
 
2.0%
리 6
 
2.0%
타 6
 
2.0%
Other values (94) 156
52.2%
ASCII
ValueCountFrequency (%)
39
27.1%
S 12
 
8.3%
T 9
 
6.2%
I 6
 
4.2%
O 5
 
3.5%
( 5
 
3.5%
) 5
 
3.5%
t 4
 
2.8%
A 4
 
2.8%
U 4
 
2.8%
Other values (28) 51
35.4%

시설주소(도로명)
Text

UNIQUE 

Distinct59
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size604.0 B
2023-12-12T23:36:10.380720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length49
Median length36
Mean length31.932203
Min length23

Characters and Unicode

Total characters1884
Distinct characters135
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

Unique59 ?
Unique (%)100.0%

Sample

1st row서울특별시 서대문구 증가로 140, 5층 (남가좌동)
2nd row서울특별시 서대문구 북아현로 55 (북아현동)
3rd row서울특별시 서대문구 증가로 246 (북가좌동, 성은빌딩)
4th row서울특별시 서대문구 명물길 16, 3,4층 (창천동)
5th row서울특별시 서대문구 가좌로 84, 4층 (홍은동)
ValueCountFrequency (%)
서울특별시 59
 
15.7%
서대문구 59
 
15.7%
홍제동 13
 
3.5%
연희동 9
 
2.4%
지하1층 9
 
2.4%
창천동 8
 
2.1%
북가좌동 8
 
2.1%
통일로 8
 
2.1%
연희로 7
 
1.9%
남가좌동 6
 
1.6%
Other values (135) 189
50.4%
2023-12-12T23:36:10.841618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
316
 
16.8%
서 118
 
6.3%
, 67
 
3.6%
대 64
 
3.4%
동 62
 
3.3%
문 61
 
3.2%
시 60
 
3.2%
특 59
 
3.1%
별 59
 
3.1%
구 59
 
3.1%
Other values (125) 959
50.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1146
60.8%
Space Separator 316
 
16.8%
Decimal Number 220
 
11.7%
Other Punctuation 67
 
3.6%
Close Punctuation 59
 
3.1%
Open Punctuation 59
 
3.1%
Dash Punctuation 7
 
0.4%
Uppercase Letter 6
 
0.3%
Lowercase Letter 4
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
서 118
 
10.3%
대 64
 
5.6%
동 62
 
5.4%
문 61
 
5.3%
시 60
 
5.2%
특 59
 
5.1%
별 59
 
5.1%
구 59
 
5.1%
울 59
 
5.1%
로 54
 
4.7%
Other values (100) 491
42.8%
Decimal Number
ValueCountFrequency (%)
1 46
20.9%
2 31
14.1%
3 29
13.2%
5 25
11.4%
4 23
10.5%
6 18
 
8.2%
0 15
 
6.8%
8 14
 
6.4%
7 12
 
5.5%
9 7
 
3.2%
Uppercase Letter
ValueCountFrequency (%)
J 1
16.7%
T 1
16.7%
D 1
16.7%
M 1
16.7%
C 1
16.7%
B 1
16.7%
Lowercase Letter
ValueCountFrequency (%)
o 1
25.0%
w 1
25.0%
e 1
25.0%
r 1
25.0%
Space Separator
ValueCountFrequency (%)
316
100.0%
Other Punctuation
ValueCountFrequency (%)
, 67
100.0%
Close Punctuation
ValueCountFrequency (%)
) 59
100.0%
Open Punctuation
ValueCountFrequency (%)
( 59
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1146
60.8%
Common 728
38.6%
Latin 10
 
0.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
서 118
 
10.3%
대 64
 
5.6%
동 62
 
5.4%
문 61
 
5.3%
시 60
 
5.2%
특 59
 
5.1%
별 59
 
5.1%
구 59
 
5.1%
울 59
 
5.1%
로 54
 
4.7%
Other values (100) 491
42.8%
Common
ValueCountFrequency (%)
316
43.4%
, 67
 
9.2%
) 59
 
8.1%
( 59
 
8.1%
1 46
 
6.3%
2 31
 
4.3%
3 29
 
4.0%
5 25
 
3.4%
4 23
 
3.2%
6 18
 
2.5%
Other values (5) 55
 
7.6%
Latin
ValueCountFrequency (%)
J 1
10.0%
T 1
10.0%
o 1
10.0%
w 1
10.0%
e 1
10.0%
r 1
10.0%
D 1
10.0%
M 1
10.0%
C 1
10.0%
B 1
10.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1146
60.8%
ASCII 738
39.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
316
42.8%
, 67
 
9.1%
) 59
 
8.0%
( 59
 
8.0%
1 46
 
6.2%
2 31
 
4.2%
3 29
 
3.9%
5 25
 
3.4%
4 23
 
3.1%
6 18
 
2.4%
Other values (15) 65
 
8.8%
Hangul
ValueCountFrequency (%)
서 118
 
10.3%
대 64
 
5.6%
동 62
 
5.4%
문 61
 
5.3%
시 60
 
5.2%
특 59
 
5.1%
별 59
 
5.1%
구 59
 
5.1%
울 59
 
5.1%
로 54
 
4.7%
Other values (100) 491
42.8%

시설전화번호
Text

MISSING 

Distinct31
Distinct (%)96.9%
Missing27
Missing (%)45.8%
Memory size604.0 B
2023-12-12T23:36:11.053828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12.5
Mean length10.59375
Min length8

Characters and Unicode

Total characters339
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

Unique30 ?
Unique (%)93.8%

Sample

1st row2302-3114
2nd row391-9850
3rd row2392-0988
4th row306-3676
5th row02-333-6644
ValueCountFrequency (%)
02-3141-0922 2
 
6.2%
070-8256-6952 1
 
3.1%
2302-3114 1
 
3.1%
02-312-8296 1
 
3.1%
070-7576-3206 1
 
3.1%
02-733-0717 1
 
3.1%
02-312-2597 1
 
3.1%
02-303-8484 1
 
3.1%
02-396-8283 1
 
3.1%
02-379-8815 1
 
3.1%
Other values (21) 21
65.6%
2023-12-12T23:36:11.345853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 55
16.2%
- 55
16.2%
0 45
13.3%
3 42
12.4%
1 25
7.4%
6 24
7.1%
7 23
6.8%
9 21
 
6.2%
8 19
 
5.6%
5 16
 
4.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 284
83.8%
Dash Punctuation 55
 
16.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 55
19.4%
0 45
15.8%
3 42
14.8%
1 25
8.8%
6 24
8.5%
7 23
8.1%
9 21
 
7.4%
8 19
 
6.7%
5 16
 
5.6%
4 14
 
4.9%
Dash Punctuation
ValueCountFrequency (%)
- 55
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 339
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 55
16.2%
- 55
16.2%
0 45
13.3%
3 42
12.4%
1 25
7.4%
6 24
7.1%
7 23
6.8%
9 21
 
6.2%
8 19
 
5.6%
5 16
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 339
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 55
16.2%
- 55
16.2%
0 45
13.3%
3 42
12.4%
1 25
7.4%
6 24
7.1%
7 23
6.8%
9 21
 
6.2%
8 19
 
5.6%
5 16
 
4.7%

기준일
Categorical

CONSTANT 

Distinct1
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size604.0 B
2020-12-01
59 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020-12-01
2nd row2020-12-01
3rd row2020-12-01
4th row2020-12-01
5th row2020-12-01

Common Values

ValueCountFrequency (%)
2020-12-01 59
100.0%

Length

2023-12-12T23:36:11.473779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:36:11.836853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020-12-01 59
100.0%

Interactions

2023-12-12T23:36:08.736417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T23:36:11.905070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번상호시설주소(도로명)시설전화번호
연번1.0000.8701.0000.947
상호0.8701.0001.0001.000
시설주소(도로명)1.0001.0001.0001.000
시설전화번호0.9471.0001.0001.000

Missing values

2023-12-12T23:36:08.880878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T23:36:08.997959image/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

연번상호시설주소(도로명)시설전화번호기준일
01월드휘트니스서울특별시 서대문구 증가로 140, 5층 (남가좌동)<NA>2020-12-01
12중앙헬스서울특별시 서대문구 북아현로 55 (북아현동)<NA>2020-12-01
23성은헬스서울특별시 서대문구 증가로 246 (북가좌동, 성은빌딩)<NA>2020-12-01
34JC헬스서울특별시 서대문구 명물길 16, 3,4층 (창천동)<NA>2020-12-01
45바나나휘트니스서울특별시 서대문구 가좌로 84, 4층 (홍은동)<NA>2020-12-01
56삼성골드헬스서울특별시 서대문구 증가로 191 (남가좌동, 삼성아파트)2302-31142020-12-01
67헬스뱅크서울특별시 서대문구 간호대로 35, 지하1층 (홍제동)<NA>2020-12-01
78더짐휘트니스서울특별시 서대문구 응암로 54 (북가좌동)<NA>2020-12-01
89올림픽 헬스뱅크서울특별시 서대문구 간호대로 35 (홍제동)391-98502020-12-01
910(주)허공서울특별시 서대문구 독립문로 10, 3층 (영천동, 삼호아파트)2392-09882020-12-01
연번상호시설주소(도로명)시설전화번호기준일
4950익스트림 컴뱃서울특별시 서대문구 통일로 456, 3층 (홍제동)<NA>2020-12-01
5051짐피티 연대이대 3호점서울특별시 서대문구 명물길 74, 에스프리 1,2층 (창천동)<NA>2020-12-01
5152파트너짐 2호점서울특별시 서대문구 연희맛로 22, 2층 (연희동, 삼원빌딩)070-7576-32062020-12-01
5253Fitness W (피트니스 더블유)서울특별시 서대문구 증가로 133, 우리은행 3층 (남가좌동)<NA>2020-12-01
5354셀프메이드짐 2서울특별시 서대문구 연희로 185, 한영빌딩 지하1층 (연희동)<NA>2020-12-01
5455스타칼리 휘트니스서울특별시 서대문구 통일로 413, 연세24시사우나 7층 (홍제동)<NA>2020-12-01
5556운동하는날서울특별시 서대문구 신촌로 231, 백상빌딩 지하1층 8호 (북아현동)02-312-82962020-12-01
5657PT STUDIO, SOOM서울특별시 서대문구 연희로 48-20, 4층 (연희동)<NA>2020-12-01
5758SSAC PT서울특별시 서대문구 거북골로 55, 3층 (남가좌동)02-303-12312020-12-01
58593Y짐서울특별시 서대문구 응암로 108, 지하1층 (북가좌동)<NA>2020-12-01