Overview

Dataset statistics

Number of variables11
Number of observations82
Missing cells45
Missing cells (%)5.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.3 KiB
Average record size in memory91.6 B

Variable types

Categorical2
Text7
Numeric2

Dataset

Description코로나19 대응 관련 사회적기업 리스트
Author경기도
URLhttps://data.gg.go.kr/portal/data/service/selectServicePage.do?&infId=A63IS1UAIY84EGWNXRUQ30136310&infSeq=1

Alerts

위도 is highly overall correlated with 시군명High correlation
경도 is highly overall correlated with 시군명High correlation
시군명 is highly overall correlated with 위도 and 1 other fieldsHigh correlation
팩스번호 has 4 (4.9%) missing valuesMissing
소재지도로명주소 has 1 (1.2%) missing valuesMissing
홈페이지URL has 40 (48.8%) missing valuesMissing
업체명 has unique valuesUnique
대표전화번호 has unique valuesUnique
소재지지번주소 has unique valuesUnique

Reproduction

Analysis started2023-12-10 21:40:26.325180
Analysis finished2023-12-10 21:40:28.215752
Duration1.89 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군명
Categorical

HIGH CORRELATION 

Distinct25
Distinct (%)30.5%
Missing0
Missing (%)0.0%
Memory size788.0 B
성남시
13 
용인시
6 
시흥시
6 
화성시
6 
남양주시
5 
Other values (20)
46 

Length

Max length4
Median length3
Mean length3.0853659
Min length3

Unique

Unique6 ?
Unique (%)7.3%

Sample

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

Common Values

ValueCountFrequency (%)
성남시 13
15.9%
용인시 6
 
7.3%
시흥시 6
 
7.3%
화성시 6
 
7.3%
남양주시 5
 
6.1%
고양시 5
 
6.1%
안양시 5
 
6.1%
광명시 4
 
4.9%
부천시 4
 
4.9%
안산시 3
 
3.7%
Other values (15) 25
30.5%

Length

2023-12-11T06:40:28.285558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
성남시 13
15.9%
시흥시 6
 
7.3%
화성시 6
 
7.3%
용인시 6
 
7.3%
남양주시 5
 
6.1%
고양시 5
 
6.1%
안양시 5
 
6.1%
광명시 4
 
4.9%
부천시 4
 
4.9%
안산시 3
 
3.7%
Other values (15) 25
30.5%

업체명
Text

UNIQUE 

Distinct82
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size788.0 B
2023-12-11T06:40:28.529649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length12.5
Mean length8.6585366
Min length3

Characters and Unicode

Total characters710
Distinct characters176
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique82 ?
Unique (%)100.0%

Sample

1st row(주) 함께나눔
2nd row㈜ 우리티엠에스
3rd row㈜대화
4th row그린피플 주식회사
5th row주식회사 희민개발
ValueCountFrequency (%)
주식회사 31
 
23.1%
주 8
 
6.0%
㈜ 7
 
5.2%
사회적협동조합 2
 
1.5%
㈜크린서비스청 1
 
0.7%
만나 1
 
0.7%
주)씨에스코리아 1
 
0.7%
양평이클린(e-clean 1
 
0.7%
굿데이크리닝서비스 1
 
0.7%
세움주식회사 1
 
0.7%
Other values (80) 80
59.7%
2023-12-11T06:40:28.950696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
52
 
7.3%
주 50
 
7.0%
사 45
 
6.3%
회 41
 
5.8%
식 36
 
5.1%
㈜ 28
 
3.9%
리 15
 
2.1%
스 14
 
2.0%
( 13
 
1.8%
) 13
 
1.8%
Other values (166) 403
56.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 597
84.1%
Space Separator 52
 
7.3%
Other Symbol 28
 
3.9%
Open Punctuation 13
 
1.8%
Close Punctuation 13
 
1.8%
Lowercase Letter 6
 
0.8%
Dash Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 50
 
8.4%
사 45
 
7.5%
회 41
 
6.9%
식 36
 
6.0%
리 15
 
2.5%
스 14
 
2.3%
린 12
 
2.0%
크 10
 
1.7%
경 9
 
1.5%
이 9
 
1.5%
Other values (156) 356
59.6%
Lowercase Letter
ValueCountFrequency (%)
e 2
33.3%
c 1
16.7%
l 1
16.7%
a 1
16.7%
n 1
16.7%
Space Separator
ValueCountFrequency (%)
52
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 28
100.0%
Open Punctuation
ValueCountFrequency (%)
( 13
100.0%
Close Punctuation
ValueCountFrequency (%)
) 13
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 625
88.0%
Common 79
 
11.1%
Latin 6
 
0.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 50
 
8.0%
사 45
 
7.2%
회 41
 
6.6%
식 36
 
5.8%
㈜ 28
 
4.5%
리 15
 
2.4%
스 14
 
2.2%
린 12
 
1.9%
크 10
 
1.6%
경 9
 
1.4%
Other values (157) 365
58.4%
Latin
ValueCountFrequency (%)
e 2
33.3%
c 1
16.7%
l 1
16.7%
a 1
16.7%
n 1
16.7%
Common
ValueCountFrequency (%)
52
65.8%
( 13
 
16.5%
) 13
 
16.5%
- 1
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 597
84.1%
ASCII 85
 
12.0%
None 28
 
3.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
52
61.2%
( 13
 
15.3%
) 13
 
15.3%
e 2
 
2.4%
c 1
 
1.2%
- 1
 
1.2%
l 1
 
1.2%
a 1
 
1.2%
n 1
 
1.2%
Hangul
ValueCountFrequency (%)
주 50
 
8.4%
사 45
 
7.5%
회 41
 
6.9%
식 36
 
6.0%
리 15
 
2.5%
스 14
 
2.3%
린 12
 
2.0%
크 10
 
1.7%
경 9
 
1.5%
이 9
 
1.5%
Other values (156) 356
59.6%
None
ValueCountFrequency (%)
㈜ 28
100.0%

사업내용
Text

Distinct79
Distinct (%)96.3%
Missing0
Missing (%)0.0%
Memory size788.0 B
2023-12-11T06:40:29.317180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length108
Median length47
Mean length29.390244
Min length6

Characters and Unicode

Total characters2410
Distinct characters224
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

Unique76 ?
Unique (%)92.7%

Sample

1st row고독사 현장 등 유품정리, 특수청소, 재활용품 판매
2nd row경비, 청소, 주차장 운영업
3rd row청소 및 위생관리 용역사업, 화장지 제조
4th row위생관리용역, 방역소독, 건물및차량실내소독 등
5th row건물청소 , 경비
ValueCountFrequency (%)
및 24
 
5.5%
청소 13
 
3.0%
청소용역 10
 
2.3%
위생관리용역 9
 
2.1%
소독 9
 
2.1%
등 8
 
1.8%
도시락 7
 
1.6%
위생관리 7
 
1.6%
소독업 7
 
1.6%
건물관리 7
 
1.6%
Other values (249) 332
76.7%
2023-12-11T06:40:29.779257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
351
 
14.6%
, 234
 
9.7%
소 115
 
4.8%
업 96
 
4.0%
리 75
 
3.1%
용 70
 
2.9%
청 68
 
2.8%
관 67
 
2.8%
역 60
 
2.5%
물 47
 
2.0%
Other values (214) 1227
50.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1798
74.6%
Space Separator 351
 
14.6%
Other Punctuation 236
 
9.8%
Close Punctuation 9
 
0.4%
Open Punctuation 9
 
0.4%
Uppercase Letter 7
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
소 115
 
6.4%
업 96
 
5.3%
리 75
 
4.2%
용 70
 
3.9%
청 68
 
3.8%
관 67
 
3.7%
역 60
 
3.3%
물 47
 
2.6%
비 44
 
2.4%
생 40
 
2.2%
Other values (202) 1116
62.1%
Uppercase Letter
ValueCountFrequency (%)
C 2
28.6%
D 1
14.3%
E 1
14.3%
L 1
14.3%
V 1
14.3%
T 1
14.3%
Other Punctuation
ValueCountFrequency (%)
, 234
99.2%
/ 1
 
0.4%
. 1
 
0.4%
Space Separator
ValueCountFrequency (%)
351
100.0%
Close Punctuation
ValueCountFrequency (%)
) 9
100.0%
Open Punctuation
ValueCountFrequency (%)
( 9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1798
74.6%
Common 605
 
25.1%
Latin 7
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
소 115
 
6.4%
업 96
 
5.3%
리 75
 
4.2%
용 70
 
3.9%
청 68
 
3.8%
관 67
 
3.7%
역 60
 
3.3%
물 47
 
2.6%
비 44
 
2.4%
생 40
 
2.2%
Other values (202) 1116
62.1%
Common
ValueCountFrequency (%)
351
58.0%
, 234
38.7%
) 9
 
1.5%
( 9
 
1.5%
/ 1
 
0.2%
. 1
 
0.2%
Latin
ValueCountFrequency (%)
C 2
28.6%
D 1
14.3%
E 1
14.3%
L 1
14.3%
V 1
14.3%
T 1
14.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1798
74.6%
ASCII 612
 
25.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
351
57.4%
, 234
38.2%
) 9
 
1.5%
( 9
 
1.5%
C 2
 
0.3%
D 1
 
0.2%
E 1
 
0.2%
L 1
 
0.2%
/ 1
 
0.2%
V 1
 
0.2%
Other values (2) 2
 
0.3%
Hangul
ValueCountFrequency (%)
소 115
 
6.4%
업 96
 
5.3%
리 75
 
4.2%
용 70
 
3.9%
청 68
 
3.8%
관 67
 
3.7%
역 60
 
3.3%
물 47
 
2.6%
비 44
 
2.4%
생 40
 
2.2%
Other values (202) 1116
62.1%

사업분야
Categorical

Distinct12
Distinct (%)14.6%
Missing0
Missing (%)0.0%
Memory size788.0 B
청소
22 
청소+위생
10 
도시락
10 
위생
8 
청소+위생+소독
8 
Other values (7)
24 

Length

Max length11
Median length8
Mean length4.2439024
Min length2

Unique

Unique1 ?
Unique (%)1.2%

Sample

1st row청소
2nd row청소
3rd row청소+위생
4th row위생+방역+소독
5th row청소

Common Values

ValueCountFrequency (%)
청소 22
26.8%
청소+위생 10
12.2%
도시락 10
12.2%
위생 8
 
9.8%
청소+위생+소독 8
 
9.8%
소독 6
 
7.3%
위생+방역+소독 4
 
4.9%
청소+방역+소독 4
 
4.9%
청소+소독 4
 
4.9%
청소+위생+방역+소독 3
 
3.7%
Other values (2) 3
 
3.7%

Length

2023-12-11T06:40:29.930566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
청소 22
26.8%
청소+위생 10
12.2%
도시락 10
12.2%
위생 8
 
9.8%
청소+위생+소독 8
 
9.8%
소독 6
 
7.3%
위생+방역+소독 4
 
4.9%
청소+방역+소독 4
 
4.9%
청소+소독 4
 
4.9%
청소+위생+방역+소독 3
 
3.7%
Other values (2) 3
 
3.7%

대표전화번호
Text

UNIQUE 

Distinct82
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size788.0 B
2023-12-11T06:40:30.193971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length11.987805
Min length9

Characters and Unicode

Total characters983
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

Unique82 ?
Unique (%)100.0%

Sample

1st row1661-6872
2nd row031-963-8250
3rd row031-908-0055
4th row031-944-0341
5th row031-915-8600
ValueCountFrequency (%)
1661-6872 1
 
1.2%
031-741-5771 1
 
1.2%
070-7825-8896 1
 
1.2%
070-4423-8834 1
 
1.2%
031-775-9600 1
 
1.2%
031-855-2689 1
 
1.2%
031-865-4888 1
 
1.2%
031-346-1147 1
 
1.2%
031-388-6661 1
 
1.2%
031-457-5100 1
 
1.2%
Other values (72) 72
87.8%
2023-12-11T06:40:30.664854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 161
16.4%
0 140
14.2%
1 129
13.1%
3 129
13.1%
4 67
6.8%
7 65
6.6%
5 65
6.6%
8 62
 
6.3%
2 60
 
6.1%
6 54
 
5.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 822
83.6%
Dash Punctuation 161
 
16.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 140
17.0%
1 129
15.7%
3 129
15.7%
4 67
8.2%
7 65
7.9%
5 65
7.9%
8 62
7.5%
2 60
7.3%
6 54
 
6.6%
9 51
 
6.2%
Dash Punctuation
ValueCountFrequency (%)
- 161
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 983
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 161
16.4%
0 140
14.2%
1 129
13.1%
3 129
13.1%
4 67
6.8%
7 65
6.6%
5 65
6.6%
8 62
 
6.3%
2 60
 
6.1%
6 54
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 983
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 161
16.4%
0 140
14.2%
1 129
13.1%
3 129
13.1%
4 67
6.8%
7 65
6.6%
5 65
6.6%
8 62
 
6.3%
2 60
 
6.1%
6 54
 
5.5%

팩스번호
Text

MISSING 

Distinct78
Distinct (%)100.0%
Missing4
Missing (%)4.9%
Memory size788.0 B
2023-12-11T06:40:30.916638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length11.987179
Min length11

Characters and Unicode

Total characters935
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

Unique78 ?
Unique (%)100.0%

Sample

1st row02-749-6727
2nd row031-963-8251
3rd row031-975-7750
4th row031-966-0342
5th row031-915-8855
ValueCountFrequency (%)
031-768-0765 1
 
1.3%
031-332-5672 1
 
1.3%
031-335-4403 1
 
1.3%
031-225-3995 1
 
1.3%
031-374-5046 1
 
1.3%
031-372-1462 1
 
1.3%
031-775-7946 1
 
1.3%
031-855-2689 1
 
1.3%
031-865-4889 1
 
1.3%
031-388-6669 1
 
1.3%
Other values (68) 68
87.2%
2023-12-11T06:40:31.359957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 156
16.7%
3 123
13.2%
0 118
12.6%
1 114
12.2%
5 72
7.7%
2 64
6.8%
7 62
 
6.6%
8 59
 
6.3%
9 56
 
6.0%
4 56
 
6.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 779
83.3%
Dash Punctuation 156
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 123
15.8%
0 118
15.1%
1 114
14.6%
5 72
9.2%
2 64
8.2%
7 62
8.0%
8 59
7.6%
9 56
7.2%
4 56
7.2%
6 55
7.1%
Dash Punctuation
ValueCountFrequency (%)
- 156
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 935
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 156
16.7%
3 123
13.2%
0 118
12.6%
1 114
12.2%
5 72
7.7%
2 64
6.8%
7 62
 
6.6%
8 59
 
6.3%
9 56
 
6.0%
4 56
 
6.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 935
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 156
16.7%
3 123
13.2%
0 118
12.6%
1 114
12.2%
5 72
7.7%
2 64
6.8%
7 62
 
6.6%
8 59
 
6.3%
9 56
 
6.0%
4 56
 
6.0%

소재지도로명주소
Text

MISSING 

Distinct80
Distinct (%)98.8%
Missing1
Missing (%)1.2%
Memory size788.0 B
2023-12-11T06:40:31.633328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length27
Median length23
Mean length19.209877
Min length13

Characters and Unicode

Total characters1556
Distinct characters136
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

Unique79 ?
Unique (%)97.5%

Sample

1st row경기도 고양시 덕양구 마상로134번길 71
2nd row경기도 고양시 일산동구 성현로268번길 108
3rd row경기도 고양시 일산서구 홀트로 66
4th row경기도 고양시 덕양구 고양대로 1387
5th row경기도 고양시 일산서구 산현로17번길 9
ValueCountFrequency (%)
경기도 81
 
21.8%
성남시 13
 
3.5%
중원구 8
 
2.2%
용인시 6
 
1.6%
시흥시 6
 
1.6%
화성시 6
 
1.6%
남양주시 5
 
1.3%
안양시 5
 
1.3%
고양시 5
 
1.3%
18 4
 
1.1%
Other values (185) 232
62.5%
2023-12-11T06:40:32.017477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
290
18.6%
시 90
 
5.8%
경 87
 
5.6%
기 84
 
5.4%
도 82
 
5.3%
로 75
 
4.8%
1 59
 
3.8%
구 40
 
2.6%
길 38
 
2.4%
2 36
 
2.3%
Other values (126) 675
43.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 979
62.9%
Space Separator 290
 
18.6%
Decimal Number 275
 
17.7%
Dash Punctuation 12
 
0.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 90
 
9.2%
경 87
 
8.9%
기 84
 
8.6%
도 82
 
8.4%
로 75
 
7.7%
구 40
 
4.1%
길 38
 
3.9%
번 30
 
3.1%
성 28
 
2.9%
남 27
 
2.8%
Other values (114) 398
40.7%
Decimal Number
ValueCountFrequency (%)
1 59
21.5%
2 36
13.1%
9 32
11.6%
3 27
9.8%
5 26
9.5%
8 21
 
7.6%
7 21
 
7.6%
6 20
 
7.3%
0 18
 
6.5%
4 15
 
5.5%
Space Separator
ValueCountFrequency (%)
290
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 12
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 979
62.9%
Common 577
37.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 90
 
9.2%
경 87
 
8.9%
기 84
 
8.6%
도 82
 
8.4%
로 75
 
7.7%
구 40
 
4.1%
길 38
 
3.9%
번 30
 
3.1%
성 28
 
2.9%
남 27
 
2.8%
Other values (114) 398
40.7%
Common
ValueCountFrequency (%)
290
50.3%
1 59
 
10.2%
2 36
 
6.2%
9 32
 
5.5%
3 27
 
4.7%
5 26
 
4.5%
8 21
 
3.6%
7 21
 
3.6%
6 20
 
3.5%
0 18
 
3.1%
Other values (2) 27
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 979
62.9%
ASCII 577
37.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
290
50.3%
1 59
 
10.2%
2 36
 
6.2%
9 32
 
5.5%
3 27
 
4.7%
5 26
 
4.5%
8 21
 
3.6%
7 21
 
3.6%
6 20
 
3.5%
0 18
 
3.1%
Other values (2) 27
 
4.7%
Hangul
ValueCountFrequency (%)
시 90
 
9.2%
경 87
 
8.9%
기 84
 
8.6%
도 82
 
8.4%
로 75
 
7.7%
구 40
 
4.1%
길 38
 
3.9%
번 30
 
3.1%
성 28
 
2.9%
남 27
 
2.8%
Other values (114) 398
40.7%

소재지지번주소
Text

UNIQUE 

Distinct82
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size788.0 B
2023-12-11T06:40:32.287405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length42
Median length35
Mean length28.268293
Min length17

Characters and Unicode

Total characters2318
Distinct characters193
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

Unique82 ?
Unique (%)100.0%

Sample

1st row경기도 고양시 덕양구 주교동 613-13번지 1층, 2층
2nd row경기도 고양시 일산동구 성석동 53-17번지
3rd row경기도 고양시 일산서구 탄현동 1491-5번지 동화프라자 B101호
4th row경기도 고양시 덕양구 성사동 704-4번지 성원코아 314호
5th row경기도 고양시 일산서구 탄현동 1576-5번지 부건3차 202호
ValueCountFrequency (%)
경기도 82
 
16.8%
성남시 13
 
2.7%
2층 8
 
1.6%
중원구 8
 
1.6%
1층 7
 
1.4%
용인시 6
 
1.2%
시흥시 6
 
1.2%
화성시 6
 
1.2%
안양시 5
 
1.0%
남양주시 5
 
1.0%
Other values (280) 343
70.1%
2023-12-11T06:40:32.717039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
407
 
17.6%
1 112
 
4.8%
시 90
 
3.9%
지 87
 
3.8%
기 85
 
3.7%
도 84
 
3.6%
동 83
 
3.6%
경 82
 
3.5%
번 78
 
3.4%
2 76
 
3.3%
Other values (183) 1134
48.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1354
58.4%
Decimal Number 481
 
20.8%
Space Separator 407
 
17.6%
Dash Punctuation 65
 
2.8%
Uppercase Letter 8
 
0.3%
Other Punctuation 3
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 90
 
6.6%
지 87
 
6.4%
기 85
 
6.3%
도 84
 
6.2%
동 83
 
6.1%
경 82
 
6.1%
번 78
 
5.8%
구 38
 
2.8%
호 35
 
2.6%
성 29
 
2.1%
Other values (165) 663
49.0%
Decimal Number
ValueCountFrequency (%)
1 112
23.3%
2 76
15.8%
4 52
10.8%
3 49
10.2%
0 48
10.0%
5 47
9.8%
7 27
 
5.6%
8 26
 
5.4%
9 24
 
5.0%
6 20
 
4.2%
Uppercase Letter
ValueCountFrequency (%)
B 4
50.0%
L 1
 
12.5%
H 1
 
12.5%
E 1
 
12.5%
F 1
 
12.5%
Space Separator
ValueCountFrequency (%)
407
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 65
100.0%
Other Punctuation
ValueCountFrequency (%)
, 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1354
58.4%
Common 956
41.2%
Latin 8
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 90
 
6.6%
지 87
 
6.4%
기 85
 
6.3%
도 84
 
6.2%
동 83
 
6.1%
경 82
 
6.1%
번 78
 
5.8%
구 38
 
2.8%
호 35
 
2.6%
성 29
 
2.1%
Other values (165) 663
49.0%
Common
ValueCountFrequency (%)
407
42.6%
1 112
 
11.7%
2 76
 
7.9%
- 65
 
6.8%
4 52
 
5.4%
3 49
 
5.1%
0 48
 
5.0%
5 47
 
4.9%
7 27
 
2.8%
8 26
 
2.7%
Other values (3) 47
 
4.9%
Latin
ValueCountFrequency (%)
B 4
50.0%
L 1
 
12.5%
H 1
 
12.5%
E 1
 
12.5%
F 1
 
12.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1354
58.4%
ASCII 964
41.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
407
42.2%
1 112
 
11.6%
2 76
 
7.9%
- 65
 
6.7%
4 52
 
5.4%
3 49
 
5.1%
0 48
 
5.0%
5 47
 
4.9%
7 27
 
2.8%
8 26
 
2.7%
Other values (8) 55
 
5.7%
Hangul
ValueCountFrequency (%)
시 90
 
6.6%
지 87
 
6.4%
기 85
 
6.3%
도 84
 
6.2%
동 83
 
6.1%
경 82
 
6.1%
번 78
 
5.8%
구 38
 
2.8%
호 35
 
2.6%
성 29
 
2.1%
Other values (165) 663
49.0%

홈페이지URL
Text

MISSING 

Distinct42
Distinct (%)100.0%
Missing40
Missing (%)48.8%
Memory size788.0 B
2023-12-11T06:40:32.927594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length21
Mean length16.809524
Min length12

Characters and Unicode

Total characters706
Distinct characters28
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique42 ?
Unique (%)100.0%

Sample

1st rowwww.hamge.co.kr
2nd rowwww.dhclean.co.kr
3rd rowwww.apeople.co.kr
4th rowwww.jd123.co.kr
5th rowwww.cweco.co.kr
ValueCountFrequency (%)
www.sufood.kr 1
 
2.4%
www.hamge.co.kr 1
 
2.4%
www.han-tech.net 1
 
2.4%
www.malgeun.or.kr 1
 
2.4%
www.peopleclean.co.kr 1
 
2.4%
www.iksco.kr 1
 
2.4%
www.seumter.com 1
 
2.4%
www.gooddaycs.com 1
 
2.4%
www.csktotal.co.kr 1
 
2.4%
www.aceled.kr 1
 
2.4%
Other values (32) 32
76.2%
2023-12-11T06:40:33.273983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
w 128
18.1%
. 104
14.7%
o 64
 
9.1%
c 52
 
7.4%
r 46
 
6.5%
e 36
 
5.1%
k 31
 
4.4%
n 31
 
4.4%
a 30
 
4.2%
m 24
 
3.4%
Other values (18) 160
22.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 587
83.1%
Other Punctuation 104
 
14.7%
Decimal Number 11
 
1.6%
Dash Punctuation 4
 
0.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
w 128
21.8%
o 64
10.9%
c 52
8.9%
r 46
 
7.8%
e 36
 
6.1%
k 31
 
5.3%
n 31
 
5.3%
a 30
 
5.1%
m 24
 
4.1%
s 20
 
3.4%
Other values (12) 125
21.3%
Decimal Number
ValueCountFrequency (%)
0 4
36.4%
1 3
27.3%
2 3
27.3%
3 1
 
9.1%
Other Punctuation
ValueCountFrequency (%)
. 104
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 587
83.1%
Common 119
 
16.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
w 128
21.8%
o 64
10.9%
c 52
8.9%
r 46
 
7.8%
e 36
 
6.1%
k 31
 
5.3%
n 31
 
5.3%
a 30
 
5.1%
m 24
 
4.1%
s 20
 
3.4%
Other values (12) 125
21.3%
Common
ValueCountFrequency (%)
. 104
87.4%
- 4
 
3.4%
0 4
 
3.4%
1 3
 
2.5%
2 3
 
2.5%
3 1
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 706
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
w 128
18.1%
. 104
14.7%
o 64
 
9.1%
c 52
 
7.4%
r 46
 
6.5%
e 36
 
5.1%
k 31
 
4.4%
n 31
 
4.4%
a 30
 
4.2%
m 24
 
3.4%
Other values (18) 160
22.7%

위도
Real number (ℝ)

HIGH CORRELATION 

Distinct81
Distinct (%)98.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.428306
Minimum37.004023
Maximum37.904637
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size870.0 B
2023-12-11T06:40:33.433619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.004023
5-th percentile37.151459
Q137.300428
median37.429457
Q337.526002
95-th percentile37.756554
Maximum37.904637
Range0.90061374
Interquartile range (IQR)0.22557406

Descriptive statistics

Standard deviation0.19095991
Coefficient of variation (CV)0.0051020186
Kurtosis-0.16605337
Mean37.428306
Median Absolute Deviation (MAD)0.11571175
Skewness0.23534433
Sum3069.1211
Variance0.036465688
MonotonicityNot monotonic
2023-12-11T06:40:33.610146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.6116470845 2
 
2.4%
37.404583268 1
 
1.2%
37.2567666567 1
 
1.2%
37.2473788597 1
 
1.2%
37.1690883155 1
 
1.2%
37.1523063703 1
 
1.2%
37.1862774459 1
 
1.2%
37.4881372399 1
 
1.2%
37.7943124677 1
 
1.2%
37.8468082722 1
 
1.2%
Other values (71) 71
86.6%
ValueCountFrequency (%)
37.0040233809 1
1.2%
37.0489382703 1
1.2%
37.052430827 1
1.2%
37.1503605628 1
1.2%
37.1514141034 1
1.2%
37.1523063703 1
1.2%
37.1608921076 1
1.2%
37.1690883155 1
1.2%
37.1862774459 1
1.2%
37.2075753416 1
1.2%
ValueCountFrequency (%)
37.9046371205 1
1.2%
37.8468082722 1
1.2%
37.7943124677 1
1.2%
37.7577033842 1
1.2%
37.7566725106 1
1.2%
37.7543089551 1
1.2%
37.7452077789 1
1.2%
37.7008229206 1
1.2%
37.6947107017 1
1.2%
37.6900719167 1
1.2%

경도
Real number (ℝ)

HIGH CORRELATION 

Distinct81
Distinct (%)98.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.01783
Minimum126.70915
Maximum127.59344
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size870.0 B
2023-12-11T06:40:33.766648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.70915
5-th percentile126.76196
Q1126.86315
median127.01846
Q3127.14941
95-th percentile127.25001
Maximum127.59344
Range0.88429921
Interquartile range (IQR)0.28625371

Descriptive statistics

Standard deviation0.181867
Coefficient of variation (CV)0.0014318226
Kurtosis-0.026548311
Mean127.01783
Median Absolute Deviation (MAD)0.14241826
Skewness0.29950988
Sum10415.462
Variance0.033075604
MonotonicityNot monotonic
2023-12-11T06:40:33.931150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.145041882 2
 
2.4%
126.9195063711 1
 
1.2%
127.1678526173 1
 
1.2%
127.2322342118 1
 
1.2%
127.1793264807 1
 
1.2%
127.0694435395 1
 
1.2%
127.0463076997 1
 
1.2%
127.5934444496 1
 
1.2%
127.009183113 1
 
1.2%
127.0546451413 1
 
1.2%
Other values (71) 71
86.6%
ValueCountFrequency (%)
126.7091452369 1
1.2%
126.7205037357 1
1.2%
126.7452191427 1
1.2%
126.761165598 1
1.2%
126.7618903066 1
1.2%
126.7632959556 1
1.2%
126.7717393883 1
1.2%
126.772767699 1
1.2%
126.7765175986 1
1.2%
126.7779582174 1
1.2%
ValueCountFrequency (%)
127.5934444496 1
1.2%
127.4732342709 1
1.2%
127.2821624165 1
1.2%
127.2815770203 1
1.2%
127.2509491651 1
1.2%
127.2322342118 1
1.2%
127.2138702523 1
1.2%
127.2122240544 1
1.2%
127.2114383521 1
1.2%
127.204330172 1
1.2%

Interactions

2023-12-11T06:40:27.715729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:40:27.577591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:40:27.787203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:40:27.645375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T06:40:34.070530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명업체명사업내용사업분야대표전화번호팩스번호소재지도로명주소소재지지번주소홈페이지URL위도경도
시군명1.0001.0000.9710.4161.0001.0001.0001.0001.0000.9670.963
업체명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
사업내용0.9711.0001.0001.0001.0001.0000.9951.0001.0000.8300.963
사업분야0.4161.0001.0001.0001.0001.0000.9871.0001.0000.1460.000
대표전화번호1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
팩스번호1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
소재지도로명주소1.0001.0000.9950.9871.0001.0001.0001.0001.0001.0001.000
소재지지번주소1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
홈페이지URL1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
위도0.9671.0000.8300.1461.0001.0001.0001.0001.0001.0000.444
경도0.9631.0000.9630.0001.0001.0001.0001.0001.0000.4441.000
2023-12-11T06:40:34.241470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명사업분야
시군명1.0000.120
사업분야0.1201.000
2023-12-11T06:40:34.327165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위도경도시군명사업분야
위도1.000-0.0410.7040.042
경도-0.0411.0000.7220.000
시군명0.7040.7221.0000.120
사업분야0.0420.0000.1201.000

Missing values

2023-12-11T06:40:27.902849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T06:40:28.046380image/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.
2023-12-11T06:40:28.153698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

시군명업체명사업내용사업분야대표전화번호팩스번호소재지도로명주소소재지지번주소홈페이지URL위도경도
0고양시(주) 함께나눔고독사 현장 등 유품정리, 특수청소, 재활용품 판매청소1661-687202-749-6727경기도 고양시 덕양구 마상로134번길 71경기도 고양시 덕양구 주교동 613-13번지 1층, 2층www.hamge.co.kr37.656995126.835013
1고양시㈜ 우리티엠에스경비, 청소, 주차장 운영업청소031-963-8250031-963-8251경기도 고양시 일산동구 성현로268번길 108경기도 고양시 일산동구 성석동 53-17번지<NA>37.700823126.814322
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