Overview

Dataset statistics

Number of variables6
Number of observations187
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.3 KiB
Average record size in memory50.7 B

Variable types

Categorical2
Text2
Numeric2

Dataset

Description전라남도 내 우수 다중이용업소 현황에 대해 조회(해당 소방관서, 업종, 업소명, 주소, 위도, 경도 등)하실 수 있습니다.
Author전라남도
URLhttps://www.data.go.kr/data/3036099/fileData.do

Alerts

위도 is highly overall correlated with 관서명High correlation
경도 is highly overall correlated with 관서명High correlation
관서명 is highly overall correlated with 위도 and 1 other fieldsHigh correlation

Reproduction

Analysis started2023-12-12 08:00:07.331279
Analysis finished2023-12-12 08:00:08.220959
Duration0.89 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

관서명
Categorical

HIGH CORRELATION 

Distinct16
Distinct (%)8.6%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
여수
38 
순천
35 
목포
31 
광양
22 
해남
10 
Other values (11)
51 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
여수 38
20.3%
순천 35
18.7%
목포 31
16.6%
광양 22
11.8%
해남 10
 
5.3%
강진 10
 
5.3%
화순 7
 
3.7%
고흥 7
 
3.7%
영암 6
 
3.2%
나주 5
 
2.7%
Other values (6) 16
8.6%

Length

2023-12-12T17:00:08.288070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
여수 38
20.3%
순천 35
18.7%
목포 31
16.6%
광양 22
11.8%
해남 10
 
5.3%
강진 10
 
5.3%
화순 7
 
3.7%
고흥 7
 
3.7%
영암 6
 
3.2%
나주 5
 
2.7%
Other values (6) 16
8.6%

업종
Categorical

Distinct17
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
일반음식점
61 
유흥주점
44 
노래연습장
23 
단란주점
13 
휴게음식점
11 
Other values (12)
35 

Length

Max length8
Median length5
Mean length4.4759358
Min length3

Unique

Unique7 ?
Unique (%)3.7%

Sample

1st row휴게음식점
2nd row단란주점
3rd rowPC방
4th row산후조리원
5th row영화관

Common Values

ValueCountFrequency (%)
일반음식점 61
32.6%
유흥주점 44
23.5%
노래연습장 23
 
12.3%
단란주점 13
 
7.0%
휴게음식점 11
 
5.9%
노래방 11
 
5.9%
골프연습장 8
 
4.3%
PC방 4
 
2.1%
pc방 3
 
1.6%
영화관 2
 
1.1%
Other values (7) 7
 
3.7%

Length

2023-12-12T17:00:08.432747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
일반음식점 61
32.6%
유흥주점 44
23.5%
노래연습장 23
 
12.3%
단란주점 13
 
7.0%
휴게음식점 11
 
5.9%
노래방 11
 
5.9%
골프연습장 8
 
4.3%
pc방 7
 
3.7%
영화관 2
 
1.1%
스크린골프연습장 1
 
0.5%
Other values (6) 6
 
3.2%

업소명
Text

Distinct185
Distinct (%)98.9%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
2023-12-12T17:00:08.709959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length12
Mean length6.0106952
Min length2

Characters and Unicode

Total characters1124
Distinct characters311
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

Unique183 ?
Unique (%)97.9%

Sample

1st row피어파이브
2nd row비노래홀단란주점
3rd row케이원피시방
4th row한사랑병원산후조리원
5th row매가박스상영관
ValueCountFrequency (%)
팡팡pc방 2
 
1.0%
노래타운 2
 
1.0%
투썸플레이스 2
 
1.0%
순천만점 2
 
1.0%
유흥주점 2
 
1.0%
뉴sbs노래연습장 1
 
0.5%
입큰개구리노래방 1
 
0.5%
고래고래노래연습장 1
 
0.5%
벌교스크린골프장 1
 
0.5%
피어파이브 1
 
0.5%
Other values (194) 194
92.8%
2023-12-12T17:00:09.149512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
노 41
 
3.6%
래 40
 
3.6%
스 34
 
3.0%
점 29
 
2.6%
리 25
 
2.2%
22
 
2.0%
장 20
 
1.8%
방 19
 
1.7%
이 18
 
1.6%
주 17
 
1.5%
Other values (301) 859
76.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1039
92.4%
Space Separator 22
 
2.0%
Uppercase Letter 17
 
1.5%
Decimal Number 11
 
1.0%
Lowercase Letter 11
 
1.0%
Open Punctuation 9
 
0.8%
Close Punctuation 9
 
0.8%
Other Punctuation 5
 
0.4%
Dash Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
노 41
 
3.9%
래 40
 
3.8%
스 34
 
3.3%
점 29
 
2.8%
리 25
 
2.4%
장 20
 
1.9%
방 19
 
1.8%
이 18
 
1.7%
주 17
 
1.6%
연 17
 
1.6%
Other values (273) 779
75.0%
Lowercase Letter
ValueCountFrequency (%)
c 2
18.2%
p 2
18.2%
t 1
9.1%
h 1
9.1%
e 1
9.1%
b 1
9.1%
a 1
9.1%
r 1
9.1%
k 1
9.1%
Uppercase Letter
ValueCountFrequency (%)
C 5
29.4%
P 5
29.4%
J 2
 
11.8%
S 2
 
11.8%
D 1
 
5.9%
T 1
 
5.9%
B 1
 
5.9%
Decimal Number
ValueCountFrequency (%)
0 4
36.4%
2 3
27.3%
5 2
18.2%
1 1
 
9.1%
3 1
 
9.1%
Other Punctuation
ValueCountFrequency (%)
, 3
60.0%
. 1
 
20.0%
& 1
 
20.0%
Space Separator
ValueCountFrequency (%)
22
100.0%
Open Punctuation
ValueCountFrequency (%)
( 9
100.0%
Close Punctuation
ValueCountFrequency (%)
) 9
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1039
92.4%
Common 57
 
5.1%
Latin 28
 
2.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
노 41
 
3.9%
래 40
 
3.8%
스 34
 
3.3%
점 29
 
2.8%
리 25
 
2.4%
장 20
 
1.9%
방 19
 
1.8%
이 18
 
1.7%
주 17
 
1.6%
연 17
 
1.6%
Other values (273) 779
75.0%
Latin
ValueCountFrequency (%)
C 5
17.9%
P 5
17.9%
J 2
 
7.1%
c 2
 
7.1%
p 2
 
7.1%
S 2
 
7.1%
D 1
 
3.6%
T 1
 
3.6%
B 1
 
3.6%
t 1
 
3.6%
Other values (6) 6
21.4%
Common
ValueCountFrequency (%)
22
38.6%
( 9
15.8%
) 9
15.8%
0 4
 
7.0%
2 3
 
5.3%
, 3
 
5.3%
5 2
 
3.5%
1 1
 
1.8%
. 1
 
1.8%
& 1
 
1.8%
Other values (2) 2
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1039
92.4%
ASCII 85
 
7.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
노 41
 
3.9%
래 40
 
3.8%
스 34
 
3.3%
점 29
 
2.8%
리 25
 
2.4%
장 20
 
1.9%
방 19
 
1.8%
이 18
 
1.7%
주 17
 
1.6%
연 17
 
1.6%
Other values (273) 779
75.0%
ASCII
ValueCountFrequency (%)
22
25.9%
( 9
10.6%
) 9
10.6%
C 5
 
5.9%
P 5
 
5.9%
0 4
 
4.7%
2 3
 
3.5%
, 3
 
3.5%
J 2
 
2.4%
5 2
 
2.4%
Other values (18) 21
24.7%

주소(도로명 주소)
Text

Distinct182
Distinct (%)97.3%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
2023-12-12T17:00:09.555194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length27
Median length23
Mean length18.481283
Min length14

Characters and Unicode

Total characters3456
Distinct characters166
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique177 ?
Unique (%)94.7%

Sample

1st row전라남도 목포시 평화로61번길 2
2nd row전라남도 목포시 원형로 2
3rd row전라남도 목포시 상동로 13
4th row전라남도 목포시 백년대로 335
5th row전라남도 목포시 옥암로 95
ValueCountFrequency (%)
전라남도 187
 
22.8%
여수시 37
 
4.5%
순천시 34
 
4.1%
목포시 30
 
3.7%
광양시 22
 
2.7%
3 10
 
1.2%
광양읍 8
 
1.0%
화순군 7
 
0.9%
고흥군 7
 
0.9%
여서1로 7
 
0.9%
Other values (323) 472
57.5%
2023-12-12T17:00:10.178011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
634
18.3%
남 212
 
6.1%
도 207
 
6.0%
전 187
 
5.4%
라 187
 
5.4%
1 140
 
4.1%
시 138
 
4.0%
길 113
 
3.3%
로 104
 
3.0%
2 73
 
2.1%
Other values (156) 1461
42.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2245
65.0%
Space Separator 634
 
18.3%
Decimal Number 541
 
15.7%
Dash Punctuation 36
 
1.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
남 212
 
9.4%
도 207
 
9.2%
전 187
 
8.3%
라 187
 
8.3%
시 138
 
6.1%
길 113
 
5.0%
로 104
 
4.6%
군 61
 
2.7%
읍 61
 
2.7%
순 51
 
2.3%
Other values (144) 924
41.2%
Decimal Number
ValueCountFrequency (%)
1 140
25.9%
2 73
13.5%
3 69
12.8%
4 51
 
9.4%
6 48
 
8.9%
5 46
 
8.5%
8 35
 
6.5%
9 30
 
5.5%
7 26
 
4.8%
0 23
 
4.3%
Space Separator
ValueCountFrequency (%)
634
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 36
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2245
65.0%
Common 1211
35.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
남 212
 
9.4%
도 207
 
9.2%
전 187
 
8.3%
라 187
 
8.3%
시 138
 
6.1%
길 113
 
5.0%
로 104
 
4.6%
군 61
 
2.7%
읍 61
 
2.7%
순 51
 
2.3%
Other values (144) 924
41.2%
Common
ValueCountFrequency (%)
634
52.4%
1 140
 
11.6%
2 73
 
6.0%
3 69
 
5.7%
4 51
 
4.2%
6 48
 
4.0%
5 46
 
3.8%
- 36
 
3.0%
8 35
 
2.9%
9 30
 
2.5%
Other values (2) 49
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2245
65.0%
ASCII 1211
35.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
634
52.4%
1 140
 
11.6%
2 73
 
6.0%
3 69
 
5.7%
4 51
 
4.2%
6 48
 
4.0%
5 46
 
3.8%
- 36
 
3.0%
8 35
 
2.9%
9 30
 
2.5%
Other values (2) 49
 
4.0%
Hangul
ValueCountFrequency (%)
남 212
 
9.4%
도 207
 
9.2%
전 187
 
8.3%
라 187
 
8.3%
시 138
 
6.1%
길 113
 
5.0%
로 104
 
4.6%
군 61
 
2.7%
읍 61
 
2.7%
순 51
 
2.3%
Other values (144) 924
41.2%

위도
Real number (ℝ)

HIGH CORRELATION 

Distinct182
Distinct (%)97.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.845045
Minimum34.318864
Maximum35.322444
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-12T17:00:10.374035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum34.318864
5-th percentile34.564075
Q134.750722
median34.806046
Q334.955363
95-th percentile35.1352
Maximum35.322444
Range1.0035808
Interquartile range (IQR)0.20464077

Descriptive statistics

Standard deviation0.17861453
Coefficient of variation (CV)0.0051259664
Kurtosis0.87687838
Mean34.845045
Median Absolute Deviation (MAD)0.1328178
Skewness-0.048123166
Sum6516.0233
Variance0.03190315
MonotonicityNot monotonic
2023-12-12T17:00:10.558782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
34.80604562 2
 
1.1%
34.97022524 2
 
1.1%
34.77524756 2
 
1.1%
34.75381763 2
 
1.1%
34.94411231 2
 
1.1%
34.56778276 1
 
0.5%
34.92525161 1
 
0.5%
34.94384574 1
 
0.5%
34.94207969 1
 
0.5%
34.9309829 1
 
0.5%
Other values (172) 172
92.0%
ValueCountFrequency (%)
34.31886353 1
0.5%
34.32103131 1
0.5%
34.32240475 1
0.5%
34.40119008 1
0.5%
34.48046903 1
0.5%
34.52427575 1
0.5%
34.52440448 1
0.5%
34.527742 1
0.5%
34.52886566 1
0.5%
34.56248636 1
0.5%
ValueCountFrequency (%)
35.32244437 1
0.5%
35.29383053 1
0.5%
35.28410556 1
0.5%
35.27479736 1
0.5%
35.27468293 1
0.5%
35.26149188 1
0.5%
35.26059998 1
0.5%
35.21139763 1
0.5%
35.19234405 1
0.5%
35.16366995 1
0.5%

경도
Real number (ℝ)

HIGH CORRELATION 

Distinct182
Distinct (%)97.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.14973
Minimum126.20592
Maximum127.75441
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-12T17:00:10.710973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.20592
5-th percentile126.37878
Q1126.64255
median127.37347
Q3127.65345
95-th percentile127.73134
Maximum127.75441
Range1.5484877
Interquartile range (IQR)1.0109015

Descriptive statistics

Standard deviation0.52190138
Coefficient of variation (CV)0.0041046206
Kurtosis-1.5328524
Mean127.14973
Median Absolute Deviation (MAD)0.3500285
Skewness-0.3491747
Sum23776.999
Variance0.27238105
MonotonicityNot monotonic
2023-12-12T17:00:10.856739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.4012916 2
 
1.1%
127.5225792 2
 
1.1%
127.6491878 2
 
1.1%
127.708389 2
 
1.1%
127.687301 2
 
1.1%
126.6079456 1
 
0.5%
127.6968708 1
 
0.5%
127.6885335 1
 
0.5%
127.6960148 1
 
0.5%
127.6977467 1
 
0.5%
Other values (172) 172
92.0%
ValueCountFrequency (%)
126.2059249 1
0.5%
126.2141918 1
0.5%
126.2653962 1
0.5%
126.3685515 1
0.5%
126.368789 1
0.5%
126.3691397 1
0.5%
126.3711439 1
0.5%
126.3722094 1
0.5%
126.3732774 1
0.5%
126.3765253 1
0.5%
ValueCountFrequency (%)
127.7544126 1
0.5%
127.7509096 1
0.5%
127.7499222 1
0.5%
127.7487125 1
0.5%
127.744737 1
0.5%
127.7337341 1
0.5%
127.7332492 1
0.5%
127.7328907 1
0.5%
127.7324406 1
0.5%
127.7316267 1
0.5%

Interactions

2023-12-12T17:00:07.833060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:00:07.665879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:00:07.919137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:00:07.746783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T17:00:10.965344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관서명업종위도경도
관서명1.0000.7180.9270.936
업종0.7181.0000.4620.462
위도0.9270.4621.0000.920
경도0.9360.4620.9201.000
2023-12-12T17:00:11.061365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업종관서명
업종1.0000.319
관서명0.3191.000
2023-12-12T17:00:11.154863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위도경도관서명업종
위도1.000-0.0750.7030.194
경도-0.0751.0000.7290.194
관서명0.7030.7291.0000.319
업종0.1940.1940.3191.000

Missing values

2023-12-12T17:00:08.053653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T17:00:08.176085image/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

관서명업종업소명주소(도로명 주소)위도경도
0목포휴게음식점피어파이브전라남도 목포시 평화로61번길 234.796273126.431775
1목포단란주점비노래홀단란주점전라남도 목포시 원형로 234.798976126.430851
2목포PC방케이원피시방전라남도 목포시 상동로 1334.811721126.41611
3목포산후조리원한사랑병원산후조리원전라남도 목포시 백년대로 33534.805836126.423631
4목포영화관매가박스상영관전라남도 목포시 옥암로 9534.807303126.425793
5목포일반음식점향토집전라남도 목포시 평화로49번길 434.795896126.430514
6목포단란주점부영단란주점전라남도 목포시 통일대로75번길 1534.801625126.431375
7목포노래방둥근노래연습장전라남도 목포시 삼향천로 4134.80549126.433591
8목포일반음식점호화대반점전라남도 목포시 옥암로46번길 834.803942126.430153
9목포노래방포미노래연습장전라남도 목포시 용해지구로 334.816976126.398799
관서명업종업소명주소(도로명 주소)위도경도
177고흥단란주점커플단란주점전라남도 고흥군 고흥읍 봉동주공길 2634.606199127.289924
178고흥유흥주점미시촌전라남도 고흥군 도양읍 녹동신항1길 3434.524404127.144567
179고흥유흥주점야돈룸가요주점전라남도 고흥군 도양읍 목넘가는길 1634.527742127.133167
180고흥일반음식점맘스터치전라남도 고흥군 고흥읍 고흥로 180034.609885127.289483
181고흥휴게음식점투썸플레이스전라남도 고흥군 도양읍 우주항공로 5434.528866127.140806
182고흥일반음식점노블레스전라남도 고흥군 도양읍 우주항공로 334.524276127.140559
183함평일반음식점함평한우프라자전라남도 함평군 함평읍 서부길 4235.060671126.519971
184함평일반음식점아마떼전라남도 함평군 함평읍 영수길 15635.063019126.520137
185장성일반음식점명가참숯불구이전라남도 장성군 삼계면 능성로 54835.2606126.677153
186장성스크린골프장홍길동스크린골프전라남도 장성군 황룡면 하사1길 5735.293831126.764672