Overview

Dataset statistics

Number of variables6
Number of observations390
Missing cells133
Missing cells (%)5.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory18.4 KiB
Average record size in memory48.3 B

Variable types

Categorical2
DateTime1
Text3

Dataset

Description충청남도 논산시 미용업에 대한 공공데이터입니다. 해당데이터는 업소명, 행정동, 주소, 전화번호 정보를 제공하고 있습니다.
Author충청남도
URLhttps://alldam.chungnam.go.kr/index.chungnam?menuCd=DOM_000000201001001001&st=&cds=&orgCd=&apiType=&isOpen=Y&pageIndex=389&beforeMenuCd=DOM_000000201001001000&publicdatapk=15054218

Alerts

행정구역 is highly imbalanced (52.7%)Imbalance
전화번호 has 133 (34.1%) missing valuesMissing

Reproduction

Analysis started2024-01-09 19:55:08.032213
Analysis finished2024-01-09 19:55:08.681845
Duration0.65 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

업종명
Categorical

Distinct15
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
미용업
164 
일반미용업
112 
피부미용업
55 
네일미용업
 
16
종합미용업
 
15
Other values (10)
28 

Length

Max length23
Median length5
Mean length4.9076923
Min length3

Unique

Unique3 ?
Unique (%)0.8%

Sample

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

Common Values

ValueCountFrequency (%)
미용업 164
42.1%
일반미용업 112
28.7%
피부미용업 55
 
14.1%
네일미용업 16
 
4.1%
종합미용업 15
 
3.8%
네일미용업, 화장ㆍ분장 미용업 7
 
1.8%
일반미용업, 피부미용업 4
 
1.0%
일반미용업, 화장ㆍ분장 미용업 4
 
1.0%
일반미용업, 네일미용업 3
 
0.8%
피부미용업, 네일미용업 3
 
0.8%
Other values (5) 7
 
1.8%

Length

2024-01-10T04:55:08.806842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
미용업 181
41.2%
일반미용업 126
28.7%
피부미용업 66
 
15.0%
네일미용업 34
 
7.7%
화장ㆍ분장 17
 
3.9%
종합미용업 15
 
3.4%

신고일자
Date

Distinct370
Distinct (%)94.9%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
Minimum1966-07-01 00:00:00
Maximum2022-07-18 00:00:00
2024-01-10T04:55:08.991133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T04:55:09.184610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

업소명
Text

Distinct385
Distinct (%)98.7%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
2024-01-10T04:55:09.638850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length18
Mean length5.4128205
Min length2

Characters and Unicode

Total characters2111
Distinct characters375
Distinct categories8 ?
Distinct scripts4 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique380 ?
Unique (%)97.4%

Sample

1st row에덴미용원
2nd row스타미용실
3rd row꽃가마
4th row숙녀
5th row성모
ValueCountFrequency (%)
신신미용실 2
 
0.5%
미용실 2
 
0.5%
더 2
 
0.5%
헤어샵 2
 
0.5%
헤어아트 2
 
0.5%
취암점 2
 
0.5%
에스테틱 2
 
0.5%
우리미용실 2
 
0.5%
스타미용실 2
 
0.5%
손톱달눈썹달 2
 
0.5%
Other values (406) 407
95.3%
2024-01-10T04:55:11.145867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
어 151
 
7.2%
헤 148
 
7.0%
미 103
 
4.9%
실 77
 
3.6%
용 74
 
3.5%
스 56
 
2.7%
리 42
 
2.0%
이 38
 
1.8%
37
 
1.8%
샵 35
 
1.7%
Other values (365) 1350
64.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1905
90.2%
Lowercase Letter 81
 
3.8%
Space Separator 37
 
1.8%
Uppercase Letter 34
 
1.6%
Close Punctuation 15
 
0.7%
Open Punctuation 15
 
0.7%
Other Punctuation 13
 
0.6%
Decimal Number 11
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
어 151
 
7.9%
헤 148
 
7.8%
미 103
 
5.4%
실 77
 
4.0%
용 74
 
3.9%
스 56
 
2.9%
리 42
 
2.2%
이 38
 
2.0%
샵 35
 
1.8%
아 32
 
1.7%
Other values (317) 1149
60.3%
Lowercase Letter
ValueCountFrequency (%)
e 10
12.3%
n 10
12.3%
o 8
9.9%
i 8
9.9%
s 7
8.6%
a 6
 
7.4%
l 5
 
6.2%
h 5
 
6.2%
m 3
 
3.7%
k 3
 
3.7%
Other values (9) 16
19.8%
Uppercase Letter
ValueCountFrequency (%)
S 6
17.6%
J 4
11.8%
O 4
11.8%
M 3
8.8%
N 3
8.8%
B 3
8.8%
L 2
 
5.9%
T 2
 
5.9%
Y 2
 
5.9%
R 1
 
2.9%
Other values (4) 4
11.8%
Decimal Number
ValueCountFrequency (%)
0 4
36.4%
3 2
18.2%
4 1
 
9.1%
9 1
 
9.1%
8 1
 
9.1%
1 1
 
9.1%
5 1
 
9.1%
Other Punctuation
ValueCountFrequency (%)
& 4
30.8%
, 3
23.1%
# 3
23.1%
' 2
15.4%
. 1
 
7.7%
Space Separator
ValueCountFrequency (%)
37
100.0%
Close Punctuation
ValueCountFrequency (%)
) 15
100.0%
Open Punctuation
ValueCountFrequency (%)
( 15
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1903
90.1%
Latin 115
 
5.4%
Common 91
 
4.3%
Han 2
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
어 151
 
7.9%
헤 148
 
7.8%
미 103
 
5.4%
실 77
 
4.0%
용 74
 
3.9%
스 56
 
2.9%
리 42
 
2.2%
이 38
 
2.0%
샵 35
 
1.8%
아 32
 
1.7%
Other values (315) 1147
60.3%
Latin
ValueCountFrequency (%)
e 10
 
8.7%
n 10
 
8.7%
o 8
 
7.0%
i 8
 
7.0%
s 7
 
6.1%
S 6
 
5.2%
a 6
 
5.2%
l 5
 
4.3%
h 5
 
4.3%
J 4
 
3.5%
Other values (23) 46
40.0%
Common
ValueCountFrequency (%)
37
40.7%
) 15
16.5%
( 15
16.5%
& 4
 
4.4%
0 4
 
4.4%
, 3
 
3.3%
# 3
 
3.3%
3 2
 
2.2%
' 2
 
2.2%
4 1
 
1.1%
Other values (5) 5
 
5.5%
Han
ValueCountFrequency (%)
美 1
50.0%
韓 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1903
90.1%
ASCII 206
 
9.8%
CJK 2
 
0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
어 151
 
7.9%
헤 148
 
7.8%
미 103
 
5.4%
실 77
 
4.0%
용 74
 
3.9%
스 56
 
2.9%
리 42
 
2.2%
이 38
 
2.0%
샵 35
 
1.8%
아 32
 
1.7%
Other values (315) 1147
60.3%
ASCII
ValueCountFrequency (%)
37
18.0%
) 15
 
7.3%
( 15
 
7.3%
e 10
 
4.9%
n 10
 
4.9%
o 8
 
3.9%
i 8
 
3.9%
s 7
 
3.4%
S 6
 
2.9%
a 6
 
2.9%
Other values (38) 84
40.8%
CJK
ValueCountFrequency (%)
美 1
50.0%
韓 1
50.0%

행정구역
Categorical

IMBALANCE 

Distinct13
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
취암동
261 
연무읍
43 
강경읍
39 
부창동
 
21
연산면
 
7
Other values (8)
 
19

Length

Max length4
Median length3
Mean length3.0051282
Min length3

Unique

Unique2 ?
Unique (%)0.5%

Sample

1st row연무읍
2nd row연산면
3rd row취암동
4th row취암동
5th row부창동

Common Values

ValueCountFrequency (%)
취암동 261
66.9%
연무읍 43
 
11.0%
강경읍 39
 
10.0%
부창동 21
 
5.4%
연산면 7
 
1.8%
양촌면 6
 
1.5%
성동면 3
 
0.8%
가야곡면 2
 
0.5%
광석면 2
 
0.5%
은진면 2
 
0.5%
Other values (3) 4
 
1.0%

Length

2024-01-10T04:55:11.556425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
취암동 261
66.9%
연무읍 43
 
11.0%
강경읍 39
 
10.0%
부창동 21
 
5.4%
연산면 7
 
1.8%
양촌면 6
 
1.5%
성동면 3
 
0.8%
가야곡면 2
 
0.5%
광석면 2
 
0.5%
은진면 2
 
0.5%
Other values (3) 4
 
1.0%

주소
Text

Distinct382
Distinct (%)97.9%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
2024-01-10T04:55:12.349169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length52
Median length44
Mean length27.033333
Min length18

Characters and Unicode

Total characters10543
Distinct characters152
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

Unique374 ?
Unique (%)95.9%

Sample

1st row충청남도 논산시 연무읍 연무로178번길 2-1
2nd row충청남도 논산시 연산면 황산벌로 1534
3rd row충청남도 논산시 해월로 229 (화지동)
4th row충청남도 논산시 대화로70번길 9-4 (화지동)
5th row충청남도 논산시 중앙로505번길 10 (대교동)
ValueCountFrequency (%)
충청남도 390
17.9%
논산시 390
17.9%
취암동 116
 
5.3%
내동 69
 
3.2%
1층 63
 
2.9%
화지동 45
 
2.1%
연무읍 43
 
2.0%
강경읍 39
 
1.8%
중앙로 38
 
1.7%
반월동 30
 
1.4%
Other values (438) 960
44.0%
2024-01-10T04:55:13.605068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1795
 
17.0%
1 481
 
4.6%
시 470
 
4.5%
산 436
 
4.1%
논 406
 
3.9%
청 395
 
3.7%
남 392
 
3.7%
충 390
 
3.7%
도 390
 
3.7%
로 384
 
3.6%
Other values (142) 5004
47.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5938
56.3%
Decimal Number 1927
 
18.3%
Space Separator 1795
 
17.0%
Close Punctuation 282
 
2.7%
Open Punctuation 282
 
2.7%
Other Punctuation 177
 
1.7%
Dash Punctuation 140
 
1.3%
Uppercase Letter 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 470
 
7.9%
산 436
 
7.3%
논 406
 
6.8%
청 395
 
6.7%
남 392
 
6.6%
충 390
 
6.6%
도 390
 
6.6%
로 384
 
6.5%
동 311
 
5.2%
번 209
 
3.5%
Other values (125) 2155
36.3%
Decimal Number
ValueCountFrequency (%)
1 481
25.0%
2 268
13.9%
3 208
10.8%
0 200
10.4%
4 183
 
9.5%
8 159
 
8.3%
9 137
 
7.1%
5 105
 
5.4%
7 96
 
5.0%
6 90
 
4.7%
Uppercase Letter
ValueCountFrequency (%)
G 1
50.0%
I 1
50.0%
Space Separator
ValueCountFrequency (%)
1795
100.0%
Close Punctuation
ValueCountFrequency (%)
) 282
100.0%
Open Punctuation
ValueCountFrequency (%)
( 282
100.0%
Other Punctuation
ValueCountFrequency (%)
, 177
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 140
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5938
56.3%
Common 4603
43.7%
Latin 2
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 470
 
7.9%
산 436
 
7.3%
논 406
 
6.8%
청 395
 
6.7%
남 392
 
6.6%
충 390
 
6.6%
도 390
 
6.6%
로 384
 
6.5%
동 311
 
5.2%
번 209
 
3.5%
Other values (125) 2155
36.3%
Common
ValueCountFrequency (%)
1795
39.0%
1 481
 
10.4%
) 282
 
6.1%
( 282
 
6.1%
2 268
 
5.8%
3 208
 
4.5%
0 200
 
4.3%
4 183
 
4.0%
, 177
 
3.8%
8 159
 
3.5%
Other values (5) 568
 
12.3%
Latin
ValueCountFrequency (%)
G 1
50.0%
I 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5938
56.3%
ASCII 4605
43.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1795
39.0%
1 481
 
10.4%
) 282
 
6.1%
( 282
 
6.1%
2 268
 
5.8%
3 208
 
4.5%
0 200
 
4.3%
4 183
 
4.0%
, 177
 
3.8%
8 159
 
3.5%
Other values (7) 570
 
12.4%
Hangul
ValueCountFrequency (%)
시 470
 
7.9%
산 436
 
7.3%
논 406
 
6.8%
청 395
 
6.7%
남 392
 
6.6%
충 390
 
6.6%
도 390
 
6.6%
로 384
 
6.5%
동 311
 
5.2%
번 209
 
3.5%
Other values (125) 2155
36.3%

전화번호
Text

MISSING 

Distinct257
Distinct (%)100.0%
Missing133
Missing (%)34.1%
Memory size3.2 KiB
2024-01-10T04:55:14.238883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length11.996109
Min length9

Characters and Unicode

Total characters3083
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

Unique257 ?
Unique (%)100.0%

Sample

1st row041-741-7890
2nd row041-735-0006
3rd row041-735-3089
4th row041-733-0450
5th row041-735-8227
ValueCountFrequency (%)
041-733-5040 1
 
0.4%
041-732-8020 1
 
0.4%
041-741-5848 1
 
0.4%
041-980-8020 1
 
0.4%
041-745-4874 1
 
0.4%
041-745-5669 1
 
0.4%
041-736-8556 1
 
0.4%
041-733-1920 1
 
0.4%
041-733-5431 1
 
0.4%
041-736-5533 1
 
0.4%
Other values (247) 247
96.1%
2024-01-10T04:55:15.113445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 514
16.7%
4 441
14.3%
0 400
13.0%
7 367
11.9%
1 363
11.8%
3 308
10.0%
5 216
7.0%
2 153
 
5.0%
6 127
 
4.1%
8 104
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2569
83.3%
Dash Punctuation 514
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 441
17.2%
0 400
15.6%
7 367
14.3%
1 363
14.1%
3 308
12.0%
5 216
8.4%
2 153
 
6.0%
6 127
 
4.9%
8 104
 
4.0%
9 90
 
3.5%
Dash Punctuation
ValueCountFrequency (%)
- 514
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3083
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 514
16.7%
4 441
14.3%
0 400
13.0%
7 367
11.9%
1 363
11.8%
3 308
10.0%
5 216
7.0%
2 153
 
5.0%
6 127
 
4.1%
8 104
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3083
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 514
16.7%
4 441
14.3%
0 400
13.0%
7 367
11.9%
1 363
11.8%
3 308
10.0%
5 216
7.0%
2 153
 
5.0%
6 127
 
4.1%
8 104
 
3.4%

Correlations

2024-01-10T04:55:15.226295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업종명행정구역
업종명1.0000.000
행정구역0.0001.000
2024-01-10T04:55:15.347313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정구역업종명
행정구역1.0000.000
업종명0.0001.000
2024-01-10T04:55:15.474699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업종명행정구역
업종명1.0000.000
행정구역0.0001.000

Missing values

2024-01-10T04:55:08.468349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-10T04:55:08.632488image/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

업종명신고일자업소명행정구역주소전화번호
0미용업1966-07-01에덴미용원연무읍충청남도 논산시 연무읍 연무로178번길 2-1041-741-7890
1미용업1967-05-12스타미용실연산면충청남도 논산시 연산면 황산벌로 1534041-735-0006
2미용업1968-10-25꽃가마취암동충청남도 논산시 해월로 229 (화지동)<NA>
3미용업1970-12-23숙녀취암동충청남도 논산시 대화로70번길 9-4 (화지동)041-735-3089
4미용업1972-11-08성모부창동충청남도 논산시 중앙로505번길 10 (대교동)041-733-0450
5미용업1974-01-21중앙취암동충청남도 논산시 해월로 170 (반월동)041-735-8227
6미용업1974-06-08귀희강경읍충청남도 논산시 강경읍 대흥로 13041-745-0482
7미용업1976-08-07은지헤어라인강경읍충청남도 논산시 강경읍 대흥로 14041-745-7790
8미용업1978-04-15수정미용실취암동충청남도 논산시 중앙로 498-10 (화지동)041-735-6826
9미용업1979-06-05은희취암동충청남도 논산시 해월로 222 (반월동)041-735-2668
업종명신고일자업소명행정구역주소전화번호
380네일미용업, 화장ㆍ분장 미용업2019-04-17어나더네일취암동충청남도 논산시 중앙로 132, 1층 104호 (내동)<NA>
381네일미용업, 화장ㆍ분장 미용업2020-02-28그리다취암동충청남도 논산시 중앙로384번길 31-11, 1층 101호 (취암동)<NA>
382네일미용업, 화장ㆍ분장 미용업2020-03-27안녕 네일취암동충청남도 논산시 중앙로 404, 세우연립 가동 1층 3호 (취암동)<NA>
383네일미용업, 화장ㆍ분장 미용업2020-06-05네일터취암동충청남도 논산시 중앙로384번길 25-10, 1층 (취암동)<NA>
384네일미용업, 화장ㆍ분장 미용업2022-01-24904네일취암동충청남도 논산시 중앙로410번길 29-3, 1층 (취암동)<NA>
385일반미용업, 피부미용업, 네일미용업2021-01-28루인헤어취암동충청남도 논산시 시민로132번길 7 (내동, 힐스테이트자이논산)<NA>
386일반미용업, 네일미용업, 화장ㆍ분장 미용업2019-06-24해니#취암동충청남도 논산시 중앙로384번길 49, 1층 101호 (취암동)<NA>
387일반미용업, 네일미용업, 화장ㆍ분장 미용업2021-03-05늘다옴헤어취암동충청남도 논산시 시민로 238, 105호 (내동)041-7333-442
388피부미용업, 네일미용업, 화장ㆍ분장 미용업2013-10-28렛미인취암동충청남도 논산시 중앙로398번길 19-12 (취암동)041-733-2761
389피부미용업, 네일미용업, 화장ㆍ분장 미용업2020-12-07네일공간취암동충청남도 논산시 중앙로398번길 18, 102호 (취암동)<NA>