Overview

Dataset statistics

Number of variables7
Number of observations201
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory11.3 KiB
Average record size in memory57.7 B

Variable types

Categorical6
Text1

Dataset

DescriptionSample
Author소상공인연합회
URLhttps://www.bigdata-telecom.kr/invoke/SOKBP2603/?goodsCode=KFMECMS012

Alerts

행정구역별 has constant value ""Constant
년도 has constant value ""Constant
항목 is highly overall correlated with 단위High correlation
단위 is highly overall correlated with 항목High correlation

Reproduction

Analysis started2023-12-10 06:21:58.547028
Analysis finished2023-12-10 06:21:59.347842
Duration0.8 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

행정구역별
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
서울
201 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
서울 201
100.0%

Length

2023-12-10T15:21:59.461874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:21:59.652194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서울 201
100.0%

산업별
Categorical

Distinct13
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
제조업(10~34)
18 
건설업(41~42)
18 
도매 및 소매업(45~47)
18 
운수 및 창고업(49~52)
18 
숙박 및 음식점업(55~56)
18 
Other values (8)
111 

Length

Max length28
Median length22
Mean length14.895522
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row농업/임업 및 어업(01~03)
2nd row농업/임업 및 어업(01~03)
3rd row농업/임업 및 어업(01~03)
4th row농업/임업 및 어업(01~03)
5th row농업/임업 및 어업(01~03)

Common Values

ValueCountFrequency (%)
제조업(10~34) 18
9.0%
건설업(41~42) 18
9.0%
도매 및 소매업(45~47) 18
9.0%
운수 및 창고업(49~52) 18
9.0%
숙박 및 음식점업(55~56) 18
9.0%
정보통신업(58~63) 18
9.0%
금융 및 보험업(64~66) 18
9.0%
부동산업(68) 18
9.0%
전기/가스/증기 및 공기조절 공급업(35) 14
7.0%
농업/임업 및 어업(01~03) 12
 
6.0%
Other values (3) 31
15.4%

Length

2023-12-10T15:21:59.884248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
및 121
24.0%
제조업(10~34 18
 
3.6%
도매 18
 
3.6%
소매업(45~47 18
 
3.6%
운수 18
 
3.6%
창고업(49~52 18
 
3.6%
숙박 18
 
3.6%
음식점업(55~56 18
 
3.6%
정보통신업(58~63 18
 
3.6%
금융 18
 
3.6%
Other values (17) 221
43.8%

종사자규모별
Categorical

Distinct9
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
1 - 4명
26 
5 - 9명
26 
10 - 19명
26 
20 - 49명
26 
50 - 99명
24 
Other values (4)
73 

Length

Max length10
Median length8
Mean length8.0497512
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1 - 4명
2nd row1 - 4명
3rd row5 - 9명
4th row5 - 9명
5th row10 - 19명

Common Values

ValueCountFrequency (%)
1 - 4명 26
12.9%
5 - 9명 26
12.9%
10 - 19명 26
12.9%
20 - 49명 26
12.9%
50 - 99명 24
11.9%
100 - 299명 23
11.4%
300 - 499명 18
9.0%
500 - 999명 16
8.0%
1000명 이상 16
8.0%

Length

2023-12-10T15:22:00.242347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:22:00.471956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
185
31.5%
1 26
 
4.4%
4명 26
 
4.4%
5 26
 
4.4%
9명 26
 
4.4%
10 26
 
4.4%
19명 26
 
4.4%
20 26
 
4.4%
49명 26
 
4.4%
99명 24
 
4.1%
Other values (9) 170
29.0%

항목
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
사업체수
101 
종사자수
100 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row사업체수
2nd row종사자수
3rd row사업체수
4th row종사자수
5th row사업체수

Common Values

ValueCountFrequency (%)
사업체수 101
50.2%
종사자수 100
49.8%

Length

2023-12-10T15:22:00.760720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:22:00.910814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
사업체수 101
50.2%
종사자수 100
49.8%

단위
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
개
101 
명
100 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row개
2nd row명
3rd row개
4th row명
5th row개

Common Values

ValueCountFrequency (%)
개 101
50.2%
명 100
49.8%

Length

2023-12-10T15:22:01.087636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:22:01.253543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
개 101
50.2%
명 100
49.8%

년도
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2015
201 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2015
2nd row2015
3rd row2015
4th row2015
5th row2015

Common Values

ValueCountFrequency (%)
2015 201
100.0%

Length

2023-12-10T15:22:01.427490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:22:01.586117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2015 201
100.0%

값
Text

Distinct176
Distinct (%)87.6%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-12-10T15:22:02.105216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length3.5621891
Min length1

Characters and Unicode

Total characters716
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique164 ?
Unique (%)81.6%

Sample

1st row7
2nd row11
3rd row2
4th rowX
5th row3
ValueCountFrequency (%)
0 6
 
3.0%
3 5
 
2.5%
2 4
 
2.0%
x 4
 
2.0%
7 3
 
1.5%
15 3
 
1.5%
10 2
 
1.0%
14 2
 
1.0%
11 2
 
1.0%
32 2
 
1.0%
Other values (166) 168
83.6%
2023-12-10T15:22:02.949518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 106
14.8%
2 83
11.6%
3 80
11.2%
6 69
9.6%
4 67
9.4%
0 66
9.2%
7 66
9.2%
9 66
9.2%
5 65
9.1%
8 44
6.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 712
99.4%
Uppercase Letter 4
 
0.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 106
14.9%
2 83
11.7%
3 80
11.2%
6 69
9.7%
4 67
9.4%
0 66
9.3%
7 66
9.3%
9 66
9.3%
5 65
9.1%
8 44
6.2%
Uppercase Letter
ValueCountFrequency (%)
X 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 712
99.4%
Latin 4
 
0.6%

Most frequent character per script

Common
ValueCountFrequency (%)
1 106
14.9%
2 83
11.7%
3 80
11.2%
6 69
9.7%
4 67
9.4%
0 66
9.3%
7 66
9.3%
9 66
9.3%
5 65
9.1%
8 44
6.2%
Latin
ValueCountFrequency (%)
X 4
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 716
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 106
14.8%
2 83
11.6%
3 80
11.2%
6 69
9.6%
4 67
9.4%
0 66
9.2%
7 66
9.2%
9 66
9.2%
5 65
9.1%
8 44
6.1%

Correlations

2023-12-10T15:22:03.137065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
산업별종사자규모별항목단위
산업별1.0000.0000.0000.000
종사자규모별0.0001.0000.0000.000
항목0.0000.0001.0001.000
단위0.0000.0001.0001.000
2023-12-10T15:22:03.297324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
산업별항목단위종사자규모별
산업별1.0000.0000.0000.000
항목0.0001.0000.9900.000
단위0.0000.9901.0000.000
종사자규모별0.0000.0000.0001.000
2023-12-10T15:22:03.449742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
산업별종사자규모별항목단위
산업별1.0000.0000.0000.000
종사자규모별0.0001.0000.0000.000
항목0.0000.0001.0000.990
단위0.0000.0000.9901.000

Missing values

2023-12-10T15:21:58.962154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T15:21:59.278840image/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

행정구역별산업별종사자규모별항목단위년도값
0서울농업/임업 및 어업(01~03)1 - 4명사업체수개20157
1서울농업/임업 및 어업(01~03)1 - 4명종사자수명201511
2서울농업/임업 및 어업(01~03)5 - 9명사업체수개20152
3서울농업/임업 및 어업(01~03)5 - 9명종사자수명2015X
4서울농업/임업 및 어업(01~03)10 - 19명사업체수개20153
5서울농업/임업 및 어업(01~03)10 - 19명종사자수명201546
6서울농업/임업 및 어업(01~03)20 - 49명사업체수개20153
7서울농업/임업 및 어업(01~03)20 - 49명종사자수명2015105
8서울농업/임업 및 어업(01~03)50 - 99명사업체수개20150
9서울농업/임업 및 어업(01~03)50 - 99명종사자수명20150
행정구역별산업별종사자규모별항목단위년도값
191서울전문/과학 및 기술 서비스업(70~73)1 - 4명종사자수명201554855
192서울전문/과학 및 기술 서비스업(70~73)5 - 9명사업체수개20158375
193서울전문/과학 및 기술 서비스업(70~73)5 - 9명종사자수명201553327
194서울전문/과학 및 기술 서비스업(70~73)10 - 19명사업체수개20153581
195서울전문/과학 및 기술 서비스업(70~73)10 - 19명종사자수명201546706
196서울전문/과학 및 기술 서비스업(70~73)20 - 49명사업체수개20152074
197서울전문/과학 및 기술 서비스업(70~73)20 - 49명종사자수명201560708
198서울전문/과학 및 기술 서비스업(70~73)50 - 99명사업체수개2015636
199서울전문/과학 및 기술 서비스업(70~73)50 - 99명종사자수명201543095
200서울전문/과학 및 기술 서비스업(70~73)100 - 299명사업체수개2015417