Overview

Dataset statistics

Number of variables6
Number of observations21
Missing cells7
Missing cells (%)5.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.1 KiB
Average record size in memory55.3 B

Variable types

Numeric1
Text4
Categorical1

Dataset

Description경기도 포천시에서 제공하는 소독업소현황(업체명, 대표자, 도로명주소, 전화번호, 데이터기준일자)데이터 입니다.
Author경기도 포천시
URLhttps://www.data.go.kr/data/15002324/fileData.do

Alerts

데이터기준일자 has constant value ""Constant
전화번호 has 7 (33.3%) missing valuesMissing
번호 has unique valuesUnique
업체명 has unique valuesUnique
대표자 has unique valuesUnique

Reproduction

Analysis started2023-12-12 17:03:51.330018
Analysis finished2023-12-12 17:03:51.820993
Duration0.49 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

번호
Real number (ℝ)

UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11
Minimum1
Maximum21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-13T02:03:51.870869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q16
median11
Q316
95-th percentile20
Maximum21
Range20
Interquartile range (IQR)10

Descriptive statistics

Standard deviation6.2048368
Coefficient of variation (CV)0.56407607
Kurtosis-1.2
Mean11
Median Absolute Deviation (MAD)5
Skewness0
Sum231
Variance38.5
MonotonicityStrictly increasing
2023-12-13T02:03:51.969517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
1 1
 
4.8%
2 1
 
4.8%
21 1
 
4.8%
20 1
 
4.8%
19 1
 
4.8%
18 1
 
4.8%
17 1
 
4.8%
16 1
 
4.8%
15 1
 
4.8%
14 1
 
4.8%
Other values (11) 11
52.4%
ValueCountFrequency (%)
1 1
4.8%
2 1
4.8%
3 1
4.8%
4 1
4.8%
5 1
4.8%
6 1
4.8%
7 1
4.8%
8 1
4.8%
9 1
4.8%
10 1
4.8%
ValueCountFrequency (%)
21 1
4.8%
20 1
4.8%
19 1
4.8%
18 1
4.8%
17 1
4.8%
16 1
4.8%
15 1
4.8%
14 1
4.8%
13 1
4.8%
12 1
4.8%

업체명
Text

UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size300.0 B
2023-12-13T02:03:52.137129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length9
Mean length5.4285714
Min length3

Characters and Unicode

Total characters114
Distinct characters66
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21 ?
Unique (%)100.0%

Sample

1st row소흘환경
2nd row태양환경방역
3rd row대성환경
4th row엘엔피방역
5th row신우환경
ValueCountFrequency (%)
소흘환경 1
 
4.5%
태양환경방역 1
 
4.5%
미세르방역소독 1
 
4.5%
한신방역소독 1
 
4.5%
이레케어 1
 
4.5%
sy소독방역 1
 
4.5%
대한건설 1
 
4.5%
협동조합 1
 
4.5%
가온누리 1
 
4.5%
이지케어(포천지점 1
 
4.5%
Other values (12) 12
54.5%
2023-12-13T02:03:52.437559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
방 7
 
6.1%
역 6
 
5.3%
소 5
 
4.4%
환 5
 
4.4%
경 5
 
4.4%
독 4
 
3.5%
이 4
 
3.5%
( 3
 
2.6%
) 3
 
2.6%
지 2
 
1.8%
Other values (56) 70
61.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 105
92.1%
Open Punctuation 3
 
2.6%
Close Punctuation 3
 
2.6%
Uppercase Letter 2
 
1.8%
Space Separator 1
 
0.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
방 7
 
6.7%
역 6
 
5.7%
소 5
 
4.8%
환 5
 
4.8%
경 5
 
4.8%
독 4
 
3.8%
이 4
 
3.8%
지 2
 
1.9%
린 2
 
1.9%
리 2
 
1.9%
Other values (51) 63
60.0%
Uppercase Letter
ValueCountFrequency (%)
S 1
50.0%
Y 1
50.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 105
92.1%
Common 7
 
6.1%
Latin 2
 
1.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
방 7
 
6.7%
역 6
 
5.7%
소 5
 
4.8%
환 5
 
4.8%
경 5
 
4.8%
독 4
 
3.8%
이 4
 
3.8%
지 2
 
1.9%
린 2
 
1.9%
리 2
 
1.9%
Other values (51) 63
60.0%
Common
ValueCountFrequency (%)
( 3
42.9%
) 3
42.9%
1
 
14.3%
Latin
ValueCountFrequency (%)
S 1
50.0%
Y 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 105
92.1%
ASCII 9
 
7.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
방 7
 
6.7%
역 6
 
5.7%
소 5
 
4.8%
환 5
 
4.8%
경 5
 
4.8%
독 4
 
3.8%
이 4
 
3.8%
지 2
 
1.9%
린 2
 
1.9%
리 2
 
1.9%
Other values (51) 63
60.0%
ASCII
ValueCountFrequency (%)
( 3
33.3%
) 3
33.3%
1
 
11.1%
S 1
 
11.1%
Y 1
 
11.1%

대표자
Text

UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size300.0 B
2023-12-13T02:03:52.608043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters63
Distinct characters42
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21 ?
Unique (%)100.0%

Sample

1st row송규범
2nd row김재명
3rd row박광선
4th row이재선
5th row최광섭
ValueCountFrequency (%)
송규범 1
 
4.8%
성상현 1
 
4.8%
최정필 1
 
4.8%
곽희선 1
 
4.8%
이우석 1
 
4.8%
홍성용 1
 
4.8%
서광수 1
 
4.8%
이경옥 1
 
4.8%
정헌정 1
 
4.8%
김일두 1
 
4.8%
Other values (11) 11
52.4%
2023-12-13T02:03:52.899305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
이 5
 
7.9%
선 3
 
4.8%
재 3
 
4.8%
광 3
 
4.8%
정 3
 
4.8%
김 3
 
4.8%
서 3
 
4.8%
최 2
 
3.2%
성 2
 
3.2%
희 2
 
3.2%
Other values (32) 34
54.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 63
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
이 5
 
7.9%
선 3
 
4.8%
재 3
 
4.8%
광 3
 
4.8%
정 3
 
4.8%
김 3
 
4.8%
서 3
 
4.8%
최 2
 
3.2%
성 2
 
3.2%
희 2
 
3.2%
Other values (32) 34
54.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 63
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
이 5
 
7.9%
선 3
 
4.8%
재 3
 
4.8%
광 3
 
4.8%
정 3
 
4.8%
김 3
 
4.8%
서 3
 
4.8%
최 2
 
3.2%
성 2
 
3.2%
희 2
 
3.2%
Other values (32) 34
54.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 63
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
이 5
 
7.9%
선 3
 
4.8%
재 3
 
4.8%
광 3
 
4.8%
정 3
 
4.8%
김 3
 
4.8%
서 3
 
4.8%
최 2
 
3.2%
성 2
 
3.2%
희 2
 
3.2%
Other values (32) 34
54.0%

도로명주소
Text

Distinct20
Distinct (%)95.2%
Missing0
Missing (%)0.0%
Memory size300.0 B
2023-12-13T02:03:53.118007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length30
Median length27
Mean length20.714286
Min length16

Characters and Unicode

Total characters435
Distinct characters56
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

Unique19 ?
Unique (%)90.5%

Sample

1st row경기도 포천시 가산면 감암3길 30
2nd row경기도 포천시 원앙로 79 (신읍동)
3rd row경기도 포천시 중앙로 31 (신읍동)
4th row경기도 포천시 영북면 호국로 3428
5th row경기도 포천시 영중면 양문로6길 15
ValueCountFrequency (%)
경기도 21
19.4%
포천시 21
19.4%
소흘읍 6
 
5.6%
호국로 4
 
3.7%
소흘로 3
 
2.8%
신읍동 3
 
2.8%
일동면 2
 
1.9%
원앙로 2
 
1.9%
나동 2
 
1.9%
봉화로 2
 
1.9%
Other values (38) 42
38.9%
2023-12-13T02:03:53.497725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
87
20.0%
경 21
 
4.8%
천 21
 
4.8%
시 21
 
4.8%
기 21
 
4.8%
포 21
 
4.8%
도 21
 
4.8%
로 18
 
4.1%
1 15
 
3.4%
면 11
 
2.5%
Other values (46) 178
40.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 264
60.7%
Space Separator 87
 
20.0%
Decimal Number 72
 
16.6%
Other Punctuation 4
 
0.9%
Close Punctuation 3
 
0.7%
Open Punctuation 3
 
0.7%
Dash Punctuation 2
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
경 21
 
8.0%
천 21
 
8.0%
시 21
 
8.0%
기 21
 
8.0%
포 21
 
8.0%
도 21
 
8.0%
로 18
 
6.8%
면 11
 
4.2%
소 10
 
3.8%
흘 10
 
3.8%
Other values (31) 89
33.7%
Decimal Number
ValueCountFrequency (%)
1 15
20.8%
2 10
13.9%
3 9
12.5%
5 8
11.1%
6 7
9.7%
7 6
 
8.3%
4 6
 
8.3%
0 5
 
6.9%
8 5
 
6.9%
9 1
 
1.4%
Space Separator
ValueCountFrequency (%)
87
100.0%
Other Punctuation
ValueCountFrequency (%)
, 4
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 264
60.7%
Common 171
39.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
경 21
 
8.0%
천 21
 
8.0%
시 21
 
8.0%
기 21
 
8.0%
포 21
 
8.0%
도 21
 
8.0%
로 18
 
6.8%
면 11
 
4.2%
소 10
 
3.8%
흘 10
 
3.8%
Other values (31) 89
33.7%
Common
ValueCountFrequency (%)
87
50.9%
1 15
 
8.8%
2 10
 
5.8%
3 9
 
5.3%
5 8
 
4.7%
6 7
 
4.1%
7 6
 
3.5%
4 6
 
3.5%
0 5
 
2.9%
8 5
 
2.9%
Other values (5) 13
 
7.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 264
60.7%
ASCII 171
39.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
87
50.9%
1 15
 
8.8%
2 10
 
5.8%
3 9
 
5.3%
5 8
 
4.7%
6 7
 
4.1%
7 6
 
3.5%
4 6
 
3.5%
0 5
 
2.9%
8 5
 
2.9%
Other values (5) 13
 
7.6%
Hangul
ValueCountFrequency (%)
경 21
 
8.0%
천 21
 
8.0%
시 21
 
8.0%
기 21
 
8.0%
포 21
 
8.0%
도 21
 
8.0%
로 18
 
6.8%
면 11
 
4.2%
소 10
 
3.8%
흘 10
 
3.8%
Other values (31) 89
33.7%

전화번호
Text

MISSING 

Distinct14
Distinct (%)100.0%
Missing7
Missing (%)33.3%
Memory size300.0 B
2023-12-13T02:03:53.694095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length12
Mean length12
Min length9

Characters and Unicode

Total characters168
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique14 ?
Unique (%)100.0%

Sample

1st row031 531 8887
2nd row031-534-4757
3rd row031-534-7733
4th row031-533-7748
5th row031-538-7746
ValueCountFrequency (%)
031 1
 
6.2%
531 1
 
6.2%
8887 1
 
6.2%
031-534-4757 1
 
6.2%
031-534-7733 1
 
6.2%
031-533-7748 1
 
6.2%
031-538-7746 1
 
6.2%
031-533-8260 1
 
6.2%
031-878-7733 1
 
6.2%
031-541-7172 1
 
6.2%
Other values (6) 6
37.5%
2023-12-13T02:03:54.048842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 26
15.5%
7 24
14.3%
- 24
14.3%
1 23
13.7%
0 19
11.3%
5 16
9.5%
8 10
 
6.0%
4 10
 
6.0%
6 8
 
4.8%
2 5
 
3.0%
Other values (2) 3
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 142
84.5%
Dash Punctuation 24
 
14.3%
Space Separator 2
 
1.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 26
18.3%
7 24
16.9%
1 23
16.2%
0 19
13.4%
5 16
11.3%
8 10
 
7.0%
4 10
 
7.0%
6 8
 
5.6%
2 5
 
3.5%
9 1
 
0.7%
Dash Punctuation
ValueCountFrequency (%)
- 24
100.0%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 168
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 26
15.5%
7 24
14.3%
- 24
14.3%
1 23
13.7%
0 19
11.3%
5 16
9.5%
8 10
 
6.0%
4 10
 
6.0%
6 8
 
4.8%
2 5
 
3.0%
Other values (2) 3
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 168
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 26
15.5%
7 24
14.3%
- 24
14.3%
1 23
13.7%
0 19
11.3%
5 16
9.5%
8 10
 
6.0%
4 10
 
6.0%
6 8
 
4.8%
2 5
 
3.0%
Other values (2) 3
 
1.8%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Memory size300.0 B
2021-08-31
21 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021-08-31
2nd row2021-08-31
3rd row2021-08-31
4th row2021-08-31
5th row2021-08-31

Common Values

ValueCountFrequency (%)
2021-08-31 21
100.0%

Length

2023-12-13T02:03:54.181360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:03:54.308285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021-08-31 21
100.0%

Interactions

2023-12-13T02:03:51.579067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T02:03:54.400488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호업체명대표자도로명주소전화번호
번호1.0001.0001.0000.9481.000
업체명1.0001.0001.0001.0001.000
대표자1.0001.0001.0001.0001.000
도로명주소0.9481.0001.0001.0001.000
전화번호1.0001.0001.0001.0001.000

Missing values

2023-12-13T02:03:51.689190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T02:03:51.783206image/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

번호업체명대표자도로명주소전화번호데이터기준일자
01소흘환경송규범경기도 포천시 가산면 감암3길 30031 531 88872021-08-31
12태양환경방역김재명경기도 포천시 원앙로 79 (신읍동)031-534-47572021-08-31
23대성환경박광선경기도 포천시 중앙로 31 (신읍동)031-534-77332021-08-31
34엘엔피방역이재선경기도 포천시 영북면 호국로 3428031-533-77482021-08-31
45신우환경최광섭경기도 포천시 영중면 양문로6길 15031-538-77462021-08-31
56나래소독한동희경기도 포천시 원동교길 252031-533-82602021-08-31
67영진환경김영배경기도 포천시 화현면 봉화로 816031-878-77332021-08-31
78(주)가우서비스이장미경기도 포천시 소흘읍 무란길 5-1031-541-71722021-08-31
89(주)예스종합관리양진춘경기도 포천시 소흘읍 소흘로 52, 나동0507-1403-71722021-08-31
910유인방제유인서경기도 포천시 소흘읍 소흘로 45<NA>2021-08-31
번호업체명대표자도로명주소전화번호데이터기준일자
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