Overview

Dataset statistics

Number of variables6
Number of observations101
Missing cells6
Missing cells (%)1.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.1 KiB
Average record size in memory51.3 B

Variable types

Categorical2
Text2
Numeric2

Dataset

Description충청남도 보령시에 위치한 체육시설업에 대한 데이터로 업종, 상호, 도로명주소, 위도, 경도, 데이터기준일을 제공하고 있습니다.
Author충청남도
URLhttps://alldam.chungnam.go.kr/index.chungnam?menuCd=DOM_000000201001001001&st=&cds=&orgCd=&apiType=&isOpen=Y&pageIndex=44&beforeMenuCd=DOM_000000201001001000&publicdatapk=15114023

Alerts

데이터기준일 has constant value ""Constant
위도 has 3 (3.0%) missing valuesMissing
경도 has 3 (3.0%) missing valuesMissing

Reproduction

Analysis started2024-01-09 20:51:12.540627
Analysis finished2024-01-09 20:51:13.315644
Duration0.78 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

업종
Categorical

Distinct8
Distinct (%)7.9%
Missing0
Missing (%)0.0%
Memory size940.0 B
체육도장업
26 
당구장업
26 
골프연습장업
19 
체력단련장업
14 
골프종목
12 
Other values (3)
4 

Length

Max length6
Median length5
Mean length4.9207921
Min length4

Unique

Unique2 ?
Unique (%)2.0%

Sample

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

Common Values

ValueCountFrequency (%)
체육도장업 26
25.7%
당구장업 26
25.7%
골프연습장업 19
18.8%
체력단련장업 14
13.9%
골프종목 12
11.9%
야구종목 2
 
2.0%
승마장업 1
 
1.0%
줄넘기종목 1
 
1.0%

Length

2024-01-10T05:51:13.375301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T05:51:13.477921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
체육도장업 26
25.7%
당구장업 26
25.7%
골프연습장업 19
18.8%
체력단련장업 14
13.9%
골프종목 12
11.9%
야구종목 2
 
2.0%
승마장업 1
 
1.0%
줄넘기종목 1
 
1.0%

상호
Text

Distinct100
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Memory size940.0 B
2024-01-10T05:51:13.708865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length11
Mean length7.0891089
Min length3

Characters and Unicode

Total characters716
Distinct characters193
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique99 ?
Unique (%)98.0%

Sample

1st row용인대반석태권도교육관
2nd row경희대태권도장
3rd row나래체육관
4th row선인체육관
5th row마스터태권도장
ValueCountFrequency (%)
명천시티 3
 
2.2%
보령 3
 
2.2%
p.t 2
 
1.5%
케이골프 2
 
1.5%
케이골프존 2
 
1.5%
복싱짐 2
 
1.5%
용인대 2
 
1.5%
골프존파크 2
 
1.5%
골프연습장 2
 
1.5%
골프 2
 
1.5%
Other values (110) 113
83.7%
2024-01-10T05:51:14.037795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
34
 
4.7%
장 32
 
4.5%
스 32
 
4.5%
프 31
 
4.3%
골 31
 
4.3%
구 26
 
3.6%
당 24
 
3.4%
도 22
 
3.1%
크 15
 
2.1%
태 14
 
2.0%
Other values (183) 455
63.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 646
90.2%
Space Separator 34
 
4.7%
Uppercase Letter 24
 
3.4%
Other Punctuation 7
 
1.0%
Decimal Number 3
 
0.4%
Other Symbol 2
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
장 32
 
5.0%
스 32
 
5.0%
프 31
 
4.8%
골 31
 
4.8%
구 26
 
4.0%
당 24
 
3.7%
도 22
 
3.4%
크 15
 
2.3%
태 14
 
2.2%
이 14
 
2.2%
Other values (163) 405
62.7%
Uppercase Letter
ValueCountFrequency (%)
T 5
20.8%
S 4
16.7%
P 4
16.7%
C 2
 
8.3%
W 1
 
4.2%
Y 1
 
4.2%
K 1
 
4.2%
L 1
 
4.2%
D 1
 
4.2%
E 1
 
4.2%
Other values (3) 3
12.5%
Decimal Number
ValueCountFrequency (%)
6 1
33.3%
5 1
33.3%
3 1
33.3%
Other Punctuation
ValueCountFrequency (%)
. 6
85.7%
& 1
 
14.3%
Space Separator
ValueCountFrequency (%)
34
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 648
90.5%
Common 44
 
6.1%
Latin 24
 
3.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
장 32
 
4.9%
스 32
 
4.9%
프 31
 
4.8%
골 31
 
4.8%
구 26
 
4.0%
당 24
 
3.7%
도 22
 
3.4%
크 15
 
2.3%
태 14
 
2.2%
이 14
 
2.2%
Other values (164) 407
62.8%
Latin
ValueCountFrequency (%)
T 5
20.8%
S 4
16.7%
P 4
16.7%
C 2
 
8.3%
W 1
 
4.2%
Y 1
 
4.2%
K 1
 
4.2%
L 1
 
4.2%
D 1
 
4.2%
E 1
 
4.2%
Other values (3) 3
12.5%
Common
ValueCountFrequency (%)
34
77.3%
. 6
 
13.6%
6 1
 
2.3%
5 1
 
2.3%
3 1
 
2.3%
& 1
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 646
90.2%
ASCII 68
 
9.5%
None 2
 
0.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
34
50.0%
. 6
 
8.8%
T 5
 
7.4%
S 4
 
5.9%
P 4
 
5.9%
C 2
 
2.9%
6 1
 
1.5%
5 1
 
1.5%
3 1
 
1.5%
W 1
 
1.5%
Other values (9) 9
 
13.2%
Hangul
ValueCountFrequency (%)
장 32
 
5.0%
스 32
 
5.0%
프 31
 
4.8%
골 31
 
4.8%
구 26
 
4.0%
당 24
 
3.7%
도 22
 
3.4%
크 15
 
2.3%
태 14
 
2.2%
이 14
 
2.2%
Other values (163) 405
62.7%
None
ValueCountFrequency (%)
㈜ 2
100.0%

도로명 주소
Text

Distinct93
Distinct (%)92.1%
Missing0
Missing (%)0.0%
Memory size940.0 B
2024-01-10T05:51:14.294376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length38
Median length31
Mean length23.831683
Min length18

Characters and Unicode

Total characters2407
Distinct characters142
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

Unique87 ?
Unique (%)86.1%

Sample

1st row충청남도 보령시 대천로 58 (대천동)
2nd row충청남도 보령시 지장골길 158, 2층 (죽정동)
3rd row충청남도 보령시 주공로 91 (명천동)
4th row충청남도 보령시 동현로 28 (동대동)
5th row충청남도 보령시 해수욕장6길 78 (신흑동)
ValueCountFrequency (%)
충청남도 101
18.6%
보령시 101
18.6%
동대동 35
 
6.4%
2층 16
 
2.9%
대천동 14
 
2.6%
명천동 12
 
2.2%
3층 10
 
1.8%
주공로 10
 
1.8%
신흑동 7
 
1.3%
죽정동 7
 
1.3%
Other values (148) 230
42.4%
2024-01-10T05:51:14.685124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
442
18.4%
동 120
 
5.0%
보 106
 
4.4%
령 105
 
4.4%
남 104
 
4.3%
청 104
 
4.3%
시 103
 
4.3%
충 102
 
4.2%
도 101
 
4.2%
) 81
 
3.4%
Other values (132) 1039
43.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1458
60.6%
Space Separator 442
 
18.4%
Decimal Number 288
 
12.0%
Close Punctuation 81
 
3.4%
Open Punctuation 81
 
3.4%
Other Punctuation 43
 
1.8%
Dash Punctuation 11
 
0.5%
Uppercase Letter 3
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 120
 
8.2%
보 106
 
7.3%
령 105
 
7.2%
남 104
 
7.1%
청 104
 
7.1%
시 103
 
7.1%
충 102
 
7.0%
도 101
 
6.9%
대 66
 
4.5%
로 58
 
4.0%
Other values (113) 489
33.5%
Decimal Number
ValueCountFrequency (%)
1 69
24.0%
2 39
13.5%
3 33
11.5%
5 28
9.7%
8 26
 
9.0%
7 25
 
8.7%
4 24
 
8.3%
6 18
 
6.2%
9 16
 
5.6%
0 10
 
3.5%
Uppercase Letter
ValueCountFrequency (%)
S 1
33.3%
T 1
33.3%
P 1
33.3%
Other Punctuation
ValueCountFrequency (%)
, 42
97.7%
? 1
 
2.3%
Space Separator
ValueCountFrequency (%)
442
100.0%
Close Punctuation
ValueCountFrequency (%)
) 81
100.0%
Open Punctuation
ValueCountFrequency (%)
( 81
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1458
60.6%
Common 946
39.3%
Latin 3
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 120
 
8.2%
보 106
 
7.3%
령 105
 
7.2%
남 104
 
7.1%
청 104
 
7.1%
시 103
 
7.1%
충 102
 
7.0%
도 101
 
6.9%
대 66
 
4.5%
로 58
 
4.0%
Other values (113) 489
33.5%
Common
ValueCountFrequency (%)
442
46.7%
) 81
 
8.6%
( 81
 
8.6%
1 69
 
7.3%
, 42
 
4.4%
2 39
 
4.1%
3 33
 
3.5%
5 28
 
3.0%
8 26
 
2.7%
7 25
 
2.6%
Other values (6) 80
 
8.5%
Latin
ValueCountFrequency (%)
S 1
33.3%
T 1
33.3%
P 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1458
60.6%
ASCII 949
39.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
442
46.6%
) 81
 
8.5%
( 81
 
8.5%
1 69
 
7.3%
, 42
 
4.4%
2 39
 
4.1%
3 33
 
3.5%
5 28
 
3.0%
8 26
 
2.7%
7 25
 
2.6%
Other values (9) 83
 
8.7%
Hangul
ValueCountFrequency (%)
동 120
 
8.2%
보 106
 
7.3%
령 105
 
7.2%
남 104
 
7.1%
청 104
 
7.1%
시 103
 
7.1%
충 102
 
7.0%
도 101
 
6.9%
대 66
 
4.5%
로 58
 
4.0%
Other values (113) 489
33.5%

위도
Real number (ℝ)

MISSING 

Distinct88
Distinct (%)89.8%
Missing3
Missing (%)3.0%
Infinite0
Infinite (%)0.0%
Mean36.340538
Minimum36.232531
Maximum36.474595
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-01-10T05:51:14.813234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.232531
5-th percentile36.294997
Q136.338871
median36.346658
Q336.35075
95-th percentile36.36061
Maximum36.474595
Range0.24206428
Interquartile range (IQR)0.011878972

Descriptive statistics

Standard deviation0.031418879
Coefficient of variation (CV)0.00086456835
Kurtosis8.0248892
Mean36.340538
Median Absolute Deviation (MAD)0.00488264
Skewness-1.2133866
Sum3561.3727
Variance0.00098714594
MonotonicityNot monotonic
2024-01-10T05:51:14.942173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36.34451228 4
 
4.0%
36.34868563 3
 
3.0%
36.35080267 2
 
2.0%
36.3461543 2
 
2.0%
36.3466576 2
 
2.0%
36.34954137 2
 
2.0%
36.23253079 2
 
2.0%
36.3440523 1
 
1.0%
36.34880322 1
 
1.0%
36.34788071 1
 
1.0%
Other values (78) 78
77.2%
(Missing) 3
 
3.0%
ValueCountFrequency (%)
36.23253079 2
2.0%
36.23281587 1
1.0%
36.23307194 1
1.0%
36.239491 1
1.0%
36.3047918 1
1.0%
36.30569837 1
1.0%
36.30860495 1
1.0%
36.30880072 1
1.0%
36.3088912 1
1.0%
36.30917698 1
1.0%
ValueCountFrequency (%)
36.47459507 1
1.0%
36.39116595 1
1.0%
36.37796127 1
1.0%
36.37778184 1
1.0%
36.36145749 1
1.0%
36.36046072 1
1.0%
36.36030484 1
1.0%
36.36027013 1
1.0%
36.35909669 1
1.0%
36.35901395 1
1.0%

경도
Real number (ℝ)

MISSING 

Distinct88
Distinct (%)89.8%
Missing3
Missing (%)3.0%
Infinite0
Infinite (%)0.0%
Mean126.59382
Minimum126.50787
Maximum126.65176
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-01-10T05:51:15.054957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.50787
5-th percentile126.51637
Q1126.59766
median126.60281
Q3126.6075
95-th percentile126.61519
Maximum126.65176
Range0.1438908
Interquartile range (IQR)0.0098397

Descriptive statistics

Standard deviation0.028663172
Coefficient of variation (CV)0.00022641842
Kurtosis3.0291742
Mean126.59382
Median Absolute Deviation (MAD)0.0048619
Skewness-1.9233493
Sum12406.194
Variance0.00082157745
MonotonicityNot monotonic
2024-01-10T05:51:15.181309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.6032042 4
 
4.0%
126.5985363 3
 
3.0%
126.5992367 2
 
2.0%
126.5891197 2
 
2.0%
126.6031966 2
 
2.0%
126.6061434 2
 
2.0%
126.5999447 2
 
2.0%
126.6138695 1
 
1.0%
126.6035549 1
 
1.0%
126.6059592 1
 
1.0%
Other values (78) 78
77.2%
(Missing) 3
 
3.0%
ValueCountFrequency (%)
126.5078661 1
1.0%
126.5130741 1
1.0%
126.5154889 1
1.0%
126.5156931 1
1.0%
126.516299 1
1.0%
126.5163836 1
1.0%
126.5169542 1
1.0%
126.5171071 1
1.0%
126.525108 1
1.0%
126.531532 1
1.0%
ValueCountFrequency (%)
126.6517569 1
1.0%
126.6302741 1
1.0%
126.6228136 1
1.0%
126.6226931 1
1.0%
126.6161764 1
1.0%
126.6150204 1
1.0%
126.6138996 1
1.0%
126.6138695 1
1.0%
126.6111808 1
1.0%
126.6109443 1
1.0%

데이터기준일
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size940.0 B
2023-05-25
101 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-05-25
2nd row2023-05-25
3rd row2023-05-25
4th row2023-05-25
5th row2023-05-25

Common Values

ValueCountFrequency (%)
2023-05-25 101
100.0%

Length

2024-01-10T05:51:15.296999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T05:51:15.626587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-05-25 101
100.0%

Interactions

2024-01-10T05:51:12.969533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:51:12.842342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:51:13.033525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:51:12.901135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-10T05:51:15.672771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업종상호도로명 주소위도경도
업종1.0000.9470.0000.1710.615
상호0.9471.0001.0001.0001.000
도로명 주소0.0001.0001.0001.0001.000
위도0.1711.0001.0001.0000.829
경도0.6151.0001.0000.8291.000
2024-01-10T05:51:15.753792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위도경도업종
위도1.0000.0370.087
경도0.0371.0000.347
업종0.0870.3471.000

Missing values

2024-01-10T05:51:13.124813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-10T05:51:13.204805image/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.
2024-01-10T05:51:13.277144image/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

업종상호도로명 주소위도경도데이터기준일
0체육도장업용인대반석태권도교육관충청남도 보령시 대천로 58 (대천동)36.353105126.5995612023-05-25
1체육도장업경희대태권도장충청남도 보령시 지장골길 158, 2층 (죽정동)36.361457126.599122023-05-25
2체육도장업나래체육관충청남도 보령시 주공로 91 (명천동)36.339838126.608022023-05-25
3체육도장업선인체육관충청남도 보령시 동현로 28 (동대동)36.350591126.610422023-05-25
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