Overview

Dataset statistics

Number of variables5
Number of observations310
Missing cells45
Missing cells (%)2.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory12.5 KiB
Average record size in memory41.4 B

Variable types

Categorical2
Text2
Numeric1

Dataset

Description이 데이터는 서울특별시 동작구 체육시설업 현황에 관한 것입니다. 이 데이터에는 업종, 상호명, 우편번호, 주소 등이 포함되어 있습니다.
URLhttps://www.data.go.kr/data/15016523/fileData.do

Alerts

데이터갱신일자 has constant value ""Constant
우편번호 has 45 (14.5%) missing valuesMissing

Reproduction

Analysis started2023-10-09 19:10:11.941330
Analysis finished2023-10-09 19:10:13.066226
Duration1.12 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

업종
Categorical

Distinct9
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
체력단련장업
90 
체육도장업
87 
당구장업
77 
골프연습장업
37 
가상체험 체육시설업
 
8
Other values (4)
11 

Length

Max length10
Median length7
Mean length5.2935484
Min length4

Unique

Unique2 ?
Unique (%)0.6%

Sample

1st row수영장업
2nd row수영장업
3rd row체육도장업
4th row체육도장업
5th row체육도장업

Common Values

ValueCountFrequency (%)
체력단련장업 90
29.0%
체육도장업 87
28.1%
당구장업 77
24.8%
골프연습장업 37
11.9%
가상체험 체육시설업 8
 
2.6%
체육교습업 7
 
2.3%
수영장업 2
 
0.6%
종합체육시설업 1
 
0.3%
인공암벽장업 1
 
0.3%

Length

2023-10-10T04:10:13.206500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-10T04:10:13.431101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
체력단련장업 90
28.3%
체육도장업 87
27.4%
당구장업 77
24.2%
골프연습장업 37
11.6%
가상체험 8
 
2.5%
체육시설업 8
 
2.5%
체육교습업 7
 
2.2%
수영장업 2
 
0.6%
종합체육시설업 1
 
0.3%
인공암벽장업 1
 
0.3%

상호
Text

Distinct304
Distinct (%)98.1%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
2023-10-10T04:10:14.030348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length21
Mean length8.0225806
Min length2

Characters and Unicode

Total characters2487
Distinct characters343
Distinct categories9 ?
Distinct scripts4 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique298 ?
Unique (%)96.1%

Sample

1st row오션키즈(사당동)
2nd row오션키즈 대방센터
3rd row영수 태권도장
4th row경문태권도
5th row수월관(검도)
ValueCountFrequency (%)
당구장 20
 
3.6%
태권도 13
 
2.3%
태권도장 13
 
2.3%
당구클럽 12
 
2.2%
휘트니스 11
 
2.0%
gym 8
 
1.4%
피트니스 7
 
1.3%
용인대 6
 
1.1%
스크린골프 5
 
0.9%
한국체대 5
 
0.9%
Other values (377) 454
81.9%
2023-10-10T04:10:14.752833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
244
 
9.8%
스 106
 
4.3%
장 76
 
3.1%
도 74
 
3.0%
구 70
 
2.8%
당 69
 
2.8%
태 56
 
2.3%
권 54
 
2.2%
프 48
 
1.9%
트 47
 
1.9%
Other values (333) 1643
66.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1925
77.4%
Space Separator 244
 
9.8%
Uppercase Letter 150
 
6.0%
Lowercase Letter 70
 
2.8%
Open Punctuation 27
 
1.1%
Close Punctuation 27
 
1.1%
Decimal Number 20
 
0.8%
Other Punctuation 13
 
0.5%
Dash Punctuation 11
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
스 106
 
5.5%
장 76
 
3.9%
도 74
 
3.8%
구 70
 
3.6%
당 69
 
3.6%
태 56
 
2.9%
권 54
 
2.8%
프 48
 
2.5%
트 47
 
2.4%
골 42
 
2.2%
Other values (274) 1283
66.6%
Uppercase Letter
ValueCountFrequency (%)
S 20
13.3%
T 19
12.7%
B 13
 
8.7%
M 11
 
7.3%
G 10
 
6.7%
P 9
 
6.0%
Y 7
 
4.7%
A 6
 
4.0%
L 6
 
4.0%
J 5
 
3.3%
Other values (13) 44
29.3%
Lowercase Letter
ValueCountFrequency (%)
e 10
14.3%
o 7
 
10.0%
i 7
 
10.0%
n 5
 
7.1%
r 4
 
5.7%
a 4
 
5.7%
h 4
 
5.7%
t 3
 
4.3%
x 3
 
4.3%
d 3
 
4.3%
Other values (11) 20
28.6%
Decimal Number
ValueCountFrequency (%)
1 6
30.0%
2 4
20.0%
9 3
15.0%
4 3
15.0%
8 1
 
5.0%
0 1
 
5.0%
3 1
 
5.0%
5 1
 
5.0%
Other Punctuation
ValueCountFrequency (%)
. 7
53.8%
& 5
38.5%
, 1
 
7.7%
Space Separator
ValueCountFrequency (%)
244
100.0%
Open Punctuation
ValueCountFrequency (%)
( 27
100.0%
Close Punctuation
ValueCountFrequency (%)
) 27
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1924
77.4%
Common 342
 
13.8%
Latin 220
 
8.8%
Han 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
스 106
 
5.5%
장 76
 
4.0%
도 74
 
3.8%
구 70
 
3.6%
당 69
 
3.6%
태 56
 
2.9%
권 54
 
2.8%
프 48
 
2.5%
트 47
 
2.4%
골 42
 
2.2%
Other values (273) 1282
66.6%
Latin
ValueCountFrequency (%)
S 20
 
9.1%
T 19
 
8.6%
B 13
 
5.9%
M 11
 
5.0%
e 10
 
4.5%
G 10
 
4.5%
P 9
 
4.1%
o 7
 
3.2%
i 7
 
3.2%
Y 7
 
3.2%
Other values (34) 107
48.6%
Common
ValueCountFrequency (%)
244
71.3%
( 27
 
7.9%
) 27
 
7.9%
- 11
 
3.2%
. 7
 
2.0%
1 6
 
1.8%
& 5
 
1.5%
2 4
 
1.2%
9 3
 
0.9%
4 3
 
0.9%
Other values (5) 5
 
1.5%
Han
ValueCountFrequency (%)
孝 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1924
77.4%
ASCII 562
 
22.6%
CJK 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
244
43.4%
( 27
 
4.8%
) 27
 
4.8%
S 20
 
3.6%
T 19
 
3.4%
B 13
 
2.3%
- 11
 
2.0%
M 11
 
2.0%
e 10
 
1.8%
G 10
 
1.8%
Other values (49) 170
30.2%
Hangul
ValueCountFrequency (%)
스 106
 
5.5%
장 76
 
4.0%
도 74
 
3.8%
구 70
 
3.6%
당 69
 
3.6%
태 56
 
2.9%
권 54
 
2.8%
프 48
 
2.5%
트 47
 
2.4%
골 42
 
2.2%
Other values (273) 1282
66.6%
CJK
ValueCountFrequency (%)
孝 1
100.0%

우편번호
Real number (ℝ)

MISSING 

Distinct105
Distinct (%)39.6%
Missing45
Missing (%)14.5%
Infinite0
Infinite (%)0.0%
Mean6991.434
Minimum6900
Maximum7073
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2023-10-10T04:10:15.003299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6900
5-th percentile6913
Q16946
median6999
Q37037
95-th percentile7071
Maximum7073
Range173
Interquartile range (IQR)91

Descriptive statistics

Standard deviation52.54324
Coefficient of variation (CV)0.0075153738
Kurtosis-1.2446611
Mean6991.434
Median Absolute Deviation (MAD)45
Skewness-0.030513318
Sum1852730
Variance2760.792
MonotonicityNot monotonic
2023-10-10T04:10:15.693401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7071 13
 
4.2%
6999 9
 
2.9%
6979 9
 
2.9%
7008 8
 
2.6%
7013 8
 
2.6%
6913 6
 
1.9%
7055 6
 
1.9%
6919 5
 
1.6%
6921 5
 
1.6%
6904 5
 
1.6%
Other values (95) 191
61.6%
(Missing) 45
 
14.5%
ValueCountFrequency (%)
6900 1
 
0.3%
6902 1
 
0.3%
6904 5
1.6%
6906 2
 
0.6%
6910 1
 
0.3%
6912 2
 
0.6%
6913 6
1.9%
6914 2
 
0.6%
6916 1
 
0.3%
6917 2
 
0.6%
ValueCountFrequency (%)
7073 1
 
0.3%
7072 5
 
1.6%
7071 13
4.2%
7070 1
 
0.3%
7069 3
 
1.0%
7067 2
 
0.6%
7065 4
 
1.3%
7064 3
 
1.0%
7063 1
 
0.3%
7061 2
 
0.6%

시설주소(도로명)
Text

Distinct304
Distinct (%)98.1%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
2023-10-10T04:10:16.281221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length59
Median length51
Mean length33.46129
Min length22

Characters and Unicode

Total characters10373
Distinct characters215
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

Unique298 ?
Unique (%)96.1%

Sample

1st row서울특별시 동작구 동작대로29길 119, 113동 지하1층 (사당동, 극동아파트)
2nd row서울특별시 동작구 노량진로 26, B1호 (대방동, 솔표빌딩)
3rd row서울특별시 동작구 사당로 236 (사당동, 제일약국)
4th row서울특별시 동작구 동작대로29길 50, 3층 (사당동)
5th row서울특별시 동작구 노량진로 4 (대방동, 대방빌딩)
ValueCountFrequency (%)
서울특별시 310
 
15.6%
동작구 310
 
15.6%
상도동 69
 
3.5%
사당동 64
 
3.2%
신대방동 45
 
2.3%
상도로 41
 
2.1%
대방동 37
 
1.9%
3층 32
 
1.6%
2층 31
 
1.6%
지층 28
 
1.4%
Other values (490) 1016
51.2%
2023-10-10T04:10:17.268204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1707
 
16.5%
동 721
 
7.0%
, 370
 
3.6%
작 367
 
3.5%
1 352
 
3.4%
서 326
 
3.1%
( 317
 
3.1%
) 317
 
3.1%
시 316
 
3.0%
울 311
 
3.0%
Other values (205) 5269
50.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 6142
59.2%
Space Separator 1707
 
16.5%
Decimal Number 1453
 
14.0%
Other Punctuation 372
 
3.6%
Open Punctuation 317
 
3.1%
Close Punctuation 317
 
3.1%
Uppercase Letter 32
 
0.3%
Dash Punctuation 20
 
0.2%
Math Symbol 13
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 721
 
11.7%
작 367
 
6.0%
서 326
 
5.3%
시 316
 
5.1%
울 311
 
5.1%
특 310
 
5.0%
별 310
 
5.0%
구 310
 
5.0%
로 296
 
4.8%
층 214
 
3.5%
Other values (184) 2661
43.3%
Decimal Number
ValueCountFrequency (%)
1 352
24.2%
2 278
19.1%
3 167
11.5%
0 123
 
8.5%
4 111
 
7.6%
5 108
 
7.4%
6 93
 
6.4%
9 83
 
5.7%
8 72
 
5.0%
7 66
 
4.5%
Uppercase Letter
ValueCountFrequency (%)
B 20
62.5%
A 8
 
25.0%
T 2
 
6.2%
P 2
 
6.2%
Other Punctuation
ValueCountFrequency (%)
, 370
99.5%
· 2
 
0.5%
Space Separator
ValueCountFrequency (%)
1707
100.0%
Open Punctuation
ValueCountFrequency (%)
( 317
100.0%
Close Punctuation
ValueCountFrequency (%)
) 317
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 20
100.0%
Math Symbol
ValueCountFrequency (%)
~ 13
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 6142
59.2%
Common 4199
40.5%
Latin 32
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 721
 
11.7%
작 367
 
6.0%
서 326
 
5.3%
시 316
 
5.1%
울 311
 
5.1%
특 310
 
5.0%
별 310
 
5.0%
구 310
 
5.0%
로 296
 
4.8%
층 214
 
3.5%
Other values (184) 2661
43.3%
Common
ValueCountFrequency (%)
1707
40.7%
, 370
 
8.8%
1 352
 
8.4%
( 317
 
7.5%
) 317
 
7.5%
2 278
 
6.6%
3 167
 
4.0%
0 123
 
2.9%
4 111
 
2.6%
5 108
 
2.6%
Other values (7) 349
 
8.3%
Latin
ValueCountFrequency (%)
B 20
62.5%
A 8
 
25.0%
T 2
 
6.2%
P 2
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 6142
59.2%
ASCII 4229
40.8%
None 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1707
40.4%
, 370
 
8.7%
1 352
 
8.3%
( 317
 
7.5%
) 317
 
7.5%
2 278
 
6.6%
3 167
 
3.9%
0 123
 
2.9%
4 111
 
2.6%
5 108
 
2.6%
Other values (10) 379
 
9.0%
Hangul
ValueCountFrequency (%)
동 721
 
11.7%
작 367
 
6.0%
서 326
 
5.3%
시 316
 
5.1%
울 311
 
5.1%
특 310
 
5.0%
별 310
 
5.0%
구 310
 
5.0%
로 296
 
4.8%
층 214
 
3.5%
Other values (184) 2661
43.3%
None
ValueCountFrequency (%)
· 2
100.0%

데이터갱신일자
Categorical

CONSTANT 

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
2023-07-12
310 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-07-12
2nd row2023-07-12
3rd row2023-07-12
4th row2023-07-12
5th row2023-07-12

Common Values

ValueCountFrequency (%)
2023-07-12 310
100.0%

Length

2023-10-10T04:10:17.556774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-10T04:10:17.761700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-07-12 310
100.0%

Interactions

2023-10-10T04:10:12.528100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-10-10T04:10:17.887000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업종우편번호
업종1.0000.000
우편번호0.0001.000
2023-10-10T04:10:18.047874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
우편번호업종
우편번호1.0000.000
업종0.0001.000

Missing values

2023-10-10T04:10:12.755893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-10-10T04:10:12.991151image/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

업종상호우편번호시설주소(도로명)데이터갱신일자
0수영장업오션키즈(사당동)6990서울특별시 동작구 동작대로29길 119, 113동 지하1층 (사당동, 극동아파트)2023-07-12
1수영장업오션키즈 대방센터6938서울특별시 동작구 노량진로 26, B1호 (대방동, 솔표빌딩)2023-07-12
2체육도장업영수 태권도장7010서울특별시 동작구 사당로 236 (사당동, 제일약국)2023-07-12
3체육도장업경문태권도6997서울특별시 동작구 동작대로29길 50, 3층 (사당동)2023-07-12
4체육도장업수월관(검도)6938서울특별시 동작구 노량진로 4 (대방동, 대방빌딩)2023-07-12
5체육도장업한국체대 보라매태권도장7061서울특별시 동작구 보라매로 61-1 (신대방동, 보래매약국)2023-07-12
6체육도장업명검대 (검도)6999서울특별시 동작구 동작대로29길 91, 503호 (사당동, 우성상가)2023-07-12
7체육도장업우리태권도7065서울특별시 동작구 여의대방로 22 (신대방동, 우성아파트)2023-07-12
8체육도장업문창체육관(태권도)7067서울특별시 동작구 신대방2가길 1 (신대방동)2023-07-12
9체육도장업동광체육관(태권도)7011서울특별시 동작구 사당로20길 42 (사당동)2023-07-12
업종상호우편번호시설주소(도로명)데이터갱신일자
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