Overview

Dataset statistics

Number of variables6
Number of observations51
Missing cells12
Missing cells (%)3.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.6 KiB
Average record size in memory51.6 B

Variable types

Numeric1
Categorical2
Text3

Dataset

Description전라북도 임실군의 미용업현황 데이터 입니다. 데이터 세부내역에는 순번, 업종명, 업소명, 도로명주소, 전화번호, 업태명을 포함하여 데이터를 제공하고 있습니다.
Author전라북도 임실군
URLhttps://www.data.go.kr/data/15039729/fileData.do

Alerts

순번 is highly overall correlated with 업종명 and 1 other fieldsHigh correlation
업종명 is highly overall correlated with 순번 and 1 other fieldsHigh correlation
업태명 is highly overall correlated with 순번 and 1 other fieldsHigh correlation
업태명 is highly imbalanced (60.3%)Imbalance
전화번호 has 12 (23.5%) missing valuesMissing
순번 has unique valuesUnique
업소명 has unique valuesUnique

Reproduction

Analysis started2023-12-12 11:52:31.693209
Analysis finished2023-12-12 11:52:32.814909
Duration1.12 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

순번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct51
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26
Minimum1
Maximum51
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size591.0 B
2023-12-12T20:52:32.904391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.5
Q113.5
median26
Q338.5
95-th percentile48.5
Maximum51
Range50
Interquartile range (IQR)25

Descriptive statistics

Standard deviation14.866069
Coefficient of variation (CV)0.57177187
Kurtosis-1.2
Mean26
Median Absolute Deviation (MAD)13
Skewness0
Sum1326
Variance221
MonotonicityStrictly increasing
2023-12-12T20:52:33.128593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
2.0%
2 1
 
2.0%
29 1
 
2.0%
30 1
 
2.0%
31 1
 
2.0%
32 1
 
2.0%
33 1
 
2.0%
34 1
 
2.0%
35 1
 
2.0%
36 1
 
2.0%
Other values (41) 41
80.4%
ValueCountFrequency (%)
1 1
2.0%
2 1
2.0%
3 1
2.0%
4 1
2.0%
5 1
2.0%
6 1
2.0%
7 1
2.0%
8 1
2.0%
9 1
2.0%
10 1
2.0%
ValueCountFrequency (%)
51 1
2.0%
50 1
2.0%
49 1
2.0%
48 1
2.0%
47 1
2.0%
46 1
2.0%
45 1
2.0%
44 1
2.0%
43 1
2.0%
42 1
2.0%

업종명
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)7.8%
Missing0
Missing (%)0.0%
Memory size540.0 B
미용업
22 
일반미용업
22 
종합미용업
4 
피부미용업
3 

Length

Max length5
Median length5
Mean length4.1372549
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
미용업 22
43.1%
일반미용업 22
43.1%
종합미용업 4
 
7.8%
피부미용업 3
 
5.9%

Length

2023-12-12T20:52:33.331128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T20:52:33.519480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
미용업 22
43.1%
일반미용업 22
43.1%
종합미용업 4
 
7.8%
피부미용업 3
 
5.9%

업소명
Text

UNIQUE 

Distinct51
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size540.0 B
2023-12-12T20:52:33.822963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length9
Mean length5.2745098
Min length2

Characters and Unicode

Total characters269
Distinct characters110
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique51 ?
Unique (%)100.0%

Sample

1st row중앙미장원
2nd row코리아미장원
3rd row까치미용실
4th row금잔디미용실
5th row아리아미용실
ValueCountFrequency (%)
헤어샵 3
 
4.9%
라움 1
 
1.6%
아담과이브 1
 
1.6%
서울미용실 1
 
1.6%
임실미용실 1
 
1.6%
영화미용실 1
 
1.6%
은장미머리방 1
 
1.6%
반달미용실 1
 
1.6%
서영 1
 
1.6%
강진 1
 
1.6%
Other values (49) 49
80.3%
2023-12-12T20:52:34.373003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
미 29
 
10.8%
용 24
 
8.9%
실 24
 
8.9%
헤 13
 
4.8%
어 13
 
4.8%
10
 
3.7%
리 6
 
2.2%
아 5
 
1.9%
샵 5
 
1.9%
라 4
 
1.5%
Other values (100) 136
50.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 252
93.7%
Space Separator 10
 
3.7%
Lowercase Letter 4
 
1.5%
Dash Punctuation 1
 
0.4%
Uppercase Letter 1
 
0.4%
Other Punctuation 1
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
미 29
 
11.5%
용 24
 
9.5%
실 24
 
9.5%
헤 13
 
5.2%
어 13
 
5.2%
리 6
 
2.4%
아 5
 
2.0%
샵 5
 
2.0%
라 4
 
1.6%
머 4
 
1.6%
Other values (92) 125
49.6%
Lowercase Letter
ValueCountFrequency (%)
s 1
25.0%
k 1
25.0%
i 1
25.0%
n 1
25.0%
Space Separator
ValueCountFrequency (%)
10
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%
Uppercase Letter
ValueCountFrequency (%)
B 1
100.0%
Other Punctuation
ValueCountFrequency (%)
# 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 252
93.7%
Common 12
 
4.5%
Latin 5
 
1.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
미 29
 
11.5%
용 24
 
9.5%
실 24
 
9.5%
헤 13
 
5.2%
어 13
 
5.2%
리 6
 
2.4%
아 5
 
2.0%
샵 5
 
2.0%
라 4
 
1.6%
머 4
 
1.6%
Other values (92) 125
49.6%
Latin
ValueCountFrequency (%)
s 1
20.0%
B 1
20.0%
k 1
20.0%
i 1
20.0%
n 1
20.0%
Common
ValueCountFrequency (%)
10
83.3%
- 1
 
8.3%
# 1
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 252
93.7%
ASCII 17
 
6.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
미 29
 
11.5%
용 24
 
9.5%
실 24
 
9.5%
헤 13
 
5.2%
어 13
 
5.2%
리 6
 
2.4%
아 5
 
2.0%
샵 5
 
2.0%
라 4
 
1.6%
머 4
 
1.6%
Other values (92) 125
49.6%
ASCII
ValueCountFrequency (%)
10
58.8%
- 1
 
5.9%
s 1
 
5.9%
B 1
 
5.9%
k 1
 
5.9%
i 1
 
5.9%
# 1
 
5.9%
n 1
 
5.9%

도로명주소
Text

Distinct48
Distinct (%)94.1%
Missing0
Missing (%)0.0%
Memory size540.0 B
2023-12-12T20:52:34.742725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length43
Median length31
Mean length21.764706
Min length18

Characters and Unicode

Total characters1110
Distinct characters91
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique45 ?
Unique (%)88.2%

Sample

1st row전라북도 임실군 오수면 삼일로 17-3, 2호
2nd row전라북도 임실군 오수면 오수5길 5-1
3rd row전라북도 임실군 신평면 원천2길 2
4th row전라북도 임실군 임실읍 봉황8길 22
5th row전라북도 임실군 오수면 오수로 114
ValueCountFrequency (%)
전라북도 51
19.0%
임실군 51
19.0%
임실읍 19
 
7.1%
오수면 15
 
5.6%
운수로 11
 
4.1%
관촌면 8
 
3.0%
오수로 7
 
2.6%
사선로 7
 
2.6%
강진면 5
 
1.9%
오수4길 4
 
1.5%
Other values (73) 91
33.8%
2023-12-12T20:52:35.194196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
218
19.6%
임 73
 
6.6%
실 73
 
6.6%
전 51
 
4.6%
북 51
 
4.6%
도 51
 
4.6%
군 51
 
4.6%
라 51
 
4.6%
수 39
 
3.5%
로 36
 
3.2%
Other values (81) 416
37.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 717
64.6%
Space Separator 218
 
19.6%
Decimal Number 148
 
13.3%
Dash Punctuation 16
 
1.4%
Other Punctuation 9
 
0.8%
Close Punctuation 1
 
0.1%
Open Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
임 73
 
10.2%
실 73
 
10.2%
전 51
 
7.1%
북 51
 
7.1%
도 51
 
7.1%
군 51
 
7.1%
라 51
 
7.1%
수 39
 
5.4%
로 36
 
5.0%
면 32
 
4.5%
Other values (66) 209
29.1%
Decimal Number
ValueCountFrequency (%)
1 36
24.3%
3 22
14.9%
2 19
12.8%
5 17
11.5%
4 13
 
8.8%
6 11
 
7.4%
7 10
 
6.8%
8 9
 
6.1%
0 8
 
5.4%
9 3
 
2.0%
Space Separator
ValueCountFrequency (%)
218
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 16
100.0%
Other Punctuation
ValueCountFrequency (%)
, 9
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 717
64.6%
Common 393
35.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
임 73
 
10.2%
실 73
 
10.2%
전 51
 
7.1%
북 51
 
7.1%
도 51
 
7.1%
군 51
 
7.1%
라 51
 
7.1%
수 39
 
5.4%
로 36
 
5.0%
면 32
 
4.5%
Other values (66) 209
29.1%
Common
ValueCountFrequency (%)
218
55.5%
1 36
 
9.2%
3 22
 
5.6%
2 19
 
4.8%
5 17
 
4.3%
- 16
 
4.1%
4 13
 
3.3%
6 11
 
2.8%
7 10
 
2.5%
, 9
 
2.3%
Other values (5) 22
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 717
64.6%
ASCII 393
35.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
218
55.5%
1 36
 
9.2%
3 22
 
5.6%
2 19
 
4.8%
5 17
 
4.3%
- 16
 
4.1%
4 13
 
3.3%
6 11
 
2.8%
7 10
 
2.5%
, 9
 
2.3%
Other values (5) 22
 
5.6%
Hangul
ValueCountFrequency (%)
임 73
 
10.2%
실 73
 
10.2%
전 51
 
7.1%
북 51
 
7.1%
도 51
 
7.1%
군 51
 
7.1%
라 51
 
7.1%
수 39
 
5.4%
로 36
 
5.0%
면 32
 
4.5%
Other values (66) 209
29.1%

전화번호
Text

MISSING 

Distinct39
Distinct (%)100.0%
Missing12
Missing (%)23.5%
Memory size540.0 B
2023-12-12T20:52:35.434812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters468
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

Unique39 ?
Unique (%)100.0%

Sample

1st row063-642-1234
2nd row063-642-6834
3rd row063-643-6404
4th row063-643-3166
5th row063-642-7118
ValueCountFrequency (%)
063-642-1234 1
 
2.6%
063-642-5303 1
 
2.6%
063-643-7776 1
 
2.6%
063-642-5416 1
 
2.6%
063-643-3346 1
 
2.6%
063-643-3427 1
 
2.6%
063-643-1686 1
 
2.6%
063-642-0108 1
 
2.6%
063-643-1094 1
 
2.6%
063-937-9850 1
 
2.6%
Other values (29) 29
74.4%
2023-12-12T20:52:35.784489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6 97
20.7%
- 78
16.7%
3 73
15.6%
0 61
13.0%
4 55
11.8%
2 33
 
7.1%
5 20
 
4.3%
7 16
 
3.4%
1 14
 
3.0%
8 14
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 390
83.3%
Dash Punctuation 78
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
6 97
24.9%
3 73
18.7%
0 61
15.6%
4 55
14.1%
2 33
 
8.5%
5 20
 
5.1%
7 16
 
4.1%
1 14
 
3.6%
8 14
 
3.6%
9 7
 
1.8%
Dash Punctuation
ValueCountFrequency (%)
- 78
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 468
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
6 97
20.7%
- 78
16.7%
3 73
15.6%
0 61
13.0%
4 55
11.8%
2 33
 
7.1%
5 20
 
4.3%
7 16
 
3.4%
1 14
 
3.0%
8 14
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 468
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6 97
20.7%
- 78
16.7%
3 73
15.6%
0 61
13.0%
4 55
11.8%
2 33
 
7.1%
5 20
 
4.3%
7 16
 
3.4%
1 14
 
3.0%
8 14
 
3.0%

업태명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Memory size540.0 B
일반미용업
47 
피부미용업
 
4

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
일반미용업 47
92.2%
피부미용업 4
 
7.8%

Length

2023-12-12T20:52:35.920990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T20:52:36.019555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
일반미용업 47
92.2%
피부미용업 4
 
7.8%

Interactions

2023-12-12T20:52:32.099839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T20:52:36.086553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번업종명업소명도로명주소전화번호업태명
순번1.0000.9221.0000.7961.0000.744
업종명0.9221.0001.0001.0001.0000.984
업소명1.0001.0001.0001.0001.0001.000
도로명주소0.7961.0001.0001.0001.0001.000
전화번호1.0001.0001.0001.0001.000NaN
업태명0.7440.9841.0001.000NaN1.000
2023-12-12T20:52:36.204800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업태명업종명
업태명1.0000.867
업종명0.8671.000
2023-12-12T20:52:36.298592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번업종명업태명
순번1.0000.7760.532
업종명0.7761.0000.867
업태명0.5320.8671.000

Missing values

2023-12-12T20:52:32.260150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T20:52:32.754512image/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

순번업종명업소명도로명주소전화번호업태명
01미용업중앙미장원전라북도 임실군 오수면 삼일로 17-3, 2호063-642-1234일반미용업
12미용업코리아미장원전라북도 임실군 오수면 오수5길 5-1063-642-6834일반미용업
23미용업까치미용실전라북도 임실군 신평면 원천2길 2063-643-6404일반미용업
34미용업금잔디미용실전라북도 임실군 임실읍 봉황8길 22063-643-3166일반미용업
45미용업아리아미용실전라북도 임실군 오수면 오수로 114063-642-7118일반미용업
56미용업청웅미용타운전라북도 임실군 청웅면 청웅로 162063-643-7580일반미용업
67미용업화니핀미용실전라북도 임실군 임실읍 운수로 33-2063-642-2686일반미용업
78미용업송희미용실전라북도 임실군 관촌면 사선로 45063-642-1368일반미용업
89미용업로즈헤어커커전라북도 임실군 임실읍 운수로 7063-643-2829일반미용업
910미용업인애머리방전라북도 임실군 오수면 오수4길 19063-642-5524일반미용업
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