Overview

Dataset statistics

Number of variables10
Number of observations498
Missing cells449
Missing cells (%)9.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory40.0 KiB
Average record size in memory82.3 B

Variable types

Text4
Categorical3
DateTime1
Numeric2

Dataset

Description경상남도 김해시 직업소개소 현황에 대한 데이터로 사업장명,전화번호,지번주소,도로명주소,위도,경도 등의 항목을 제공합니다.
Author경상남도 김해시
URLhttps://www.data.go.kr/data/15033315/fileData.do

Alerts

구분명 is highly imbalanced (83.6%)Imbalance
법인구분명 is highly imbalanced (59.0%)Imbalance
폐업일자 has 212 (42.6%) missing valuesMissing
전화번호 has 49 (9.8%) missing valuesMissing
지번주소 has 188 (37.8%) missing valuesMissing

Reproduction

Analysis started2024-03-14 20:15:33.952957
Analysis finished2024-03-14 20:15:37.050629
Duration3.1 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

사업장명
Text

Distinct440
Distinct (%)88.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2024-03-15T05:15:38.011295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length17
Mean length6.1546185
Min length2

Characters and Unicode

Total characters3065
Distinct characters331
Distinct categories9 ?
Distinct scripts4 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique396 ?
Unique (%)79.5%

Sample

1st row한신인력
2nd row주식회사 비케이워크
3rd row일과 사람
4th row현대인력
5th row신새벽 인력개발
ValueCountFrequency (%)
주식회사 7
 
1.3%
인력개발 6
 
1.1%
직업소개소 5
 
0.9%
진영인력 4
 
0.7%
행운인력 4
 
0.7%
대성인력 4
 
0.7%
행복직업소개소 3
 
0.5%
대한인력 3
 
0.5%
빨리인력 3
 
0.5%
삼일건설인력 3
 
0.5%
Other values (454) 505
92.3%
2024-03-15T05:15:39.626538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
인 291
 
9.5%
력 273
 
8.9%
소 185
 
6.0%
개 161
 
5.3%
업 105
 
3.4%
직 84
 
2.7%
발 76
 
2.5%
사 73
 
2.4%
49
 
1.6%
대 45
 
1.5%
Other values (321) 1723
56.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2876
93.8%
Uppercase Letter 53
 
1.7%
Space Separator 49
 
1.6%
Close Punctuation 25
 
0.8%
Open Punctuation 25
 
0.8%
Lowercase Letter 18
 
0.6%
Decimal Number 14
 
0.5%
Other Punctuation 4
 
0.1%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
인 291
 
10.1%
력 273
 
9.5%
소 185
 
6.4%
개 161
 
5.6%
업 105
 
3.7%
직 84
 
2.9%
발 76
 
2.6%
사 73
 
2.5%
대 45
 
1.6%
가 39
 
1.4%
Other values (280) 1544
53.7%
Uppercase Letter
ValueCountFrequency (%)
O 8
15.1%
K 7
13.2%
E 5
9.4%
S 4
 
7.5%
W 3
 
5.7%
J 3
 
5.7%
N 3
 
5.7%
M 3
 
5.7%
T 3
 
5.7%
L 2
 
3.8%
Other values (9) 12
22.6%
Lowercase Letter
ValueCountFrequency (%)
e 4
22.2%
t 3
16.7%
r 3
16.7%
n 2
11.1%
m 1
 
5.6%
c 1
 
5.6%
i 1
 
5.6%
u 1
 
5.6%
b 1
 
5.6%
o 1
 
5.6%
Decimal Number
ValueCountFrequency (%)
3 4
28.6%
1 4
28.6%
2 2
14.3%
8 2
14.3%
5 1
 
7.1%
6 1
 
7.1%
Other Punctuation
ValueCountFrequency (%)
. 3
75.0%
& 1
 
25.0%
Space Separator
ValueCountFrequency (%)
49
100.0%
Close Punctuation
ValueCountFrequency (%)
) 25
100.0%
Open Punctuation
ValueCountFrequency (%)
( 25
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2874
93.8%
Common 118
 
3.8%
Latin 71
 
2.3%
Han 2
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
인 291
 
10.1%
력 273
 
9.5%
소 185
 
6.4%
개 161
 
5.6%
업 105
 
3.7%
직 84
 
2.9%
발 76
 
2.6%
사 73
 
2.5%
대 45
 
1.6%
가 39
 
1.4%
Other values (278) 1542
53.7%
Latin
ValueCountFrequency (%)
O 8
 
11.3%
K 7
 
9.9%
E 5
 
7.0%
S 4
 
5.6%
e 4
 
5.6%
t 3
 
4.2%
r 3
 
4.2%
W 3
 
4.2%
J 3
 
4.2%
N 3
 
4.2%
Other values (19) 28
39.4%
Common
ValueCountFrequency (%)
49
41.5%
) 25
21.2%
( 25
21.2%
3 4
 
3.4%
1 4
 
3.4%
. 3
 
2.5%
2 2
 
1.7%
8 2
 
1.7%
- 1
 
0.8%
& 1
 
0.8%
Other values (2) 2
 
1.7%
Han
ValueCountFrequency (%)
星 1
50.0%
金 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2874
93.8%
ASCII 189
 
6.2%
CJK 1
 
< 0.1%
CJK Compat Ideographs 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
인 291
 
10.1%
력 273
 
9.5%
소 185
 
6.4%
개 161
 
5.6%
업 105
 
3.7%
직 84
 
2.9%
발 76
 
2.6%
사 73
 
2.5%
대 45
 
1.6%
가 39
 
1.4%
Other values (278) 1542
53.7%
ASCII
ValueCountFrequency (%)
49
25.9%
) 25
13.2%
( 25
13.2%
O 8
 
4.2%
K 7
 
3.7%
E 5
 
2.6%
3 4
 
2.1%
1 4
 
2.1%
S 4
 
2.1%
e 4
 
2.1%
Other values (31) 54
28.6%
CJK
ValueCountFrequency (%)
星 1
100.0%
CJK Compat Ideographs
ValueCountFrequency (%)
金 1
100.0%

구분명
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
유료
486 
무료
 
12

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
유료 486
97.6%
무료 12
 
2.4%

Length

2024-03-15T05:15:40.034814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T05:15:40.337380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
유료 486
97.6%
무료 12
 
2.4%

상세영업상태명
Categorical

Distinct4
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
폐업
281 
영업중
191 
등록취소
 
18
타시군구이관
 
8

Length

Max length6
Median length2
Mean length2.5200803
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
폐업 281
56.4%
영업중 191
38.4%
등록취소 18
 
3.6%
타시군구이관 8
 
1.6%

Length

2024-03-15T05:15:40.699019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T05:15:41.037392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 281
56.4%
영업중 191
38.4%
등록취소 18
 
3.6%
타시군구이관 8
 
1.6%

폐업일자
Date

MISSING 

Distinct256
Distinct (%)89.5%
Missing212
Missing (%)42.6%
Memory size4.0 KiB
Minimum1999-06-18 00:00:00
Maximum2023-06-21 00:00:00
2024-03-15T05:15:41.305476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:15:41.779577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

전화번호
Text

MISSING 

Distinct176
Distinct (%)39.2%
Missing49
Missing (%)9.8%
Memory size4.0 KiB
2024-03-15T05:15:42.753463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length13
Mean length12.572383
Min length9

Characters and Unicode

Total characters5645
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

Unique163 ?
Unique (%)36.3%

Sample

1st row055-313-2434
2nd row010-****-****
3rd row010-****-****
4th row1670-0621
5th row055-323-1000
ValueCountFrequency (%)
010 260
57.9%
055-343-2204 3
 
0.7%
055-322-8966 3
 
0.7%
055-334-6282 2
 
0.4%
055-322-7674 2
 
0.4%
055-334-1917 2
 
0.4%
055-337-5525 2
 
0.4%
055-325-9924 2
 
0.4%
055-333-8877 2
 
0.4%
055-343-5936 2
 
0.4%
Other values (166) 169
37.6%
2024-03-15T05:15:44.188546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
* 2101
37.2%
- 897
15.9%
0 799
 
14.2%
5 451
 
8.0%
1 388
 
6.9%
3 323
 
5.7%
2 174
 
3.1%
7 123
 
2.2%
4 121
 
2.1%
6 110
 
1.9%
Other values (2) 158
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2647
46.9%
Other Punctuation 2101
37.2%
Dash Punctuation 897
 
15.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 799
30.2%
5 451
17.0%
1 388
14.7%
3 323
12.2%
2 174
 
6.6%
7 123
 
4.6%
4 121
 
4.6%
6 110
 
4.2%
8 91
 
3.4%
9 67
 
2.5%
Other Punctuation
ValueCountFrequency (%)
* 2101
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 897
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5645
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
* 2101
37.2%
- 897
15.9%
0 799
 
14.2%
5 451
 
8.0%
1 388
 
6.9%
3 323
 
5.7%
2 174
 
3.1%
7 123
 
2.2%
4 121
 
2.1%
6 110
 
1.9%
Other values (2) 158
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5645
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
* 2101
37.2%
- 897
15.9%
0 799
 
14.2%
5 451
 
8.0%
1 388
 
6.9%
3 323
 
5.7%
2 174
 
3.1%
7 123
 
2.2%
4 121
 
2.1%
6 110
 
1.9%
Other values (2) 158
 
2.8%

지번주소
Text

MISSING 

Distinct287
Distinct (%)92.6%
Missing188
Missing (%)37.8%
Memory size4.0 KiB
2024-03-15T05:15:45.231552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length48
Median length44
Mean length23.958065
Min length16

Characters and Unicode

Total characters7427
Distinct characters160
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

Unique269 ?
Unique (%)86.8%

Sample

1st row경상남도 김해시 삼정동 618-17
2nd row경상남도 김해시 진영읍 여래리 145번지 2호
3rd row경상남도 김해시 대청동 297-14
4th row경상남도 김해시 진영읍 여래리 700번지 188호
5th row경상남도 김해시 무계동 383번지 8호
ValueCountFrequency (%)
경상남도 308
 
18.1%
김해시 308
 
18.1%
부원동 47
 
2.8%
진영읍 42
 
2.5%
1호 38
 
2.2%
2호 32
 
1.9%
3호 31
 
1.8%
외동 26
 
1.5%
삼정동 23
 
1.3%
5호 22
 
1.3%
Other values (385) 828
48.6%
2024-03-15T05:15:46.640863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1468
19.8%
상 345
 
4.6%
호 320
 
4.3%
1 312
 
4.2%
경 311
 
4.2%
시 311
 
4.2%
해 310
 
4.2%
도 309
 
4.2%
김 309
 
4.2%
남 308
 
4.1%
Other values (150) 3124
42.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4453
60.0%
Decimal Number 1484
 
20.0%
Space Separator 1468
 
19.8%
Uppercase Letter 12
 
0.2%
Dash Punctuation 9
 
0.1%
Other Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
상 345
 
7.7%
호 320
 
7.2%
경 311
 
7.0%
시 311
 
7.0%
해 310
 
7.0%
도 309
 
6.9%
김 309
 
6.9%
남 308
 
6.9%
지 301
 
6.8%
번 292
 
6.6%
Other values (129) 1337
30.0%
Decimal Number
ValueCountFrequency (%)
1 312
21.0%
2 243
16.4%
0 140
9.4%
6 140
9.4%
3 137
9.2%
7 127
8.6%
5 113
 
7.6%
4 97
 
6.5%
8 88
 
5.9%
9 87
 
5.9%
Uppercase Letter
ValueCountFrequency (%)
B 3
25.0%
L 2
16.7%
A 2
16.7%
O 1
 
8.3%
T 1
 
8.3%
C 1
 
8.3%
Y 1
 
8.3%
M 1
 
8.3%
Space Separator
ValueCountFrequency (%)
1468
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 9
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4453
60.0%
Common 2962
39.9%
Latin 12
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
상 345
 
7.7%
호 320
 
7.2%
경 311
 
7.0%
시 311
 
7.0%
해 310
 
7.0%
도 309
 
6.9%
김 309
 
6.9%
남 308
 
6.9%
지 301
 
6.8%
번 292
 
6.6%
Other values (129) 1337
30.0%
Common
ValueCountFrequency (%)
1468
49.6%
1 312
 
10.5%
2 243
 
8.2%
0 140
 
4.7%
6 140
 
4.7%
3 137
 
4.6%
7 127
 
4.3%
5 113
 
3.8%
4 97
 
3.3%
8 88
 
3.0%
Other values (3) 97
 
3.3%
Latin
ValueCountFrequency (%)
B 3
25.0%
L 2
16.7%
A 2
16.7%
O 1
 
8.3%
T 1
 
8.3%
C 1
 
8.3%
Y 1
 
8.3%
M 1
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4453
60.0%
ASCII 2974
40.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1468
49.4%
1 312
 
10.5%
2 243
 
8.2%
0 140
 
4.7%
6 140
 
4.7%
3 137
 
4.6%
7 127
 
4.3%
5 113
 
3.8%
4 97
 
3.3%
8 88
 
3.0%
Other values (11) 109
 
3.7%
Hangul
ValueCountFrequency (%)
상 345
 
7.7%
호 320
 
7.2%
경 311
 
7.0%
시 311
 
7.0%
해 310
 
7.0%
도 309
 
6.9%
김 309
 
6.9%
남 308
 
6.9%
지 301
 
6.8%
번 292
 
6.6%
Other values (129) 1337
30.0%

도로명주소
Text

Distinct444
Distinct (%)89.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2024-03-15T05:15:47.728675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length49
Median length44
Mean length26.753012
Min length16

Characters and Unicode

Total characters13323
Distinct characters187
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

Unique399 ?
Unique (%)80.1%

Sample

1st row경상남도 김해시 분성로48번길 8, 한덕한신타워아파트상가 107호 (외동)
2nd row경상남도 김해시 호계로 498 (동상동)
3rd row경상남도 김해시 금관대로 1275, 2층 (내동)
4th row경상남도 김해시 분성로501번길 49, 2층 (어방동)
5th row경상남도 김해시 구지로 59, 동부아파트 상가동 201호 (내동)
ValueCountFrequency (%)
경상남도 497
 
17.9%
김해시 497
 
17.9%
진영읍 67
 
2.4%
2층 59
 
2.1%
부원동 57
 
2.1%
외동 46
 
1.7%
진영로 32
 
1.2%
무계동 31
 
1.1%
장유로 31
 
1.1%
내동 27
 
1.0%
Other values (614) 1430
51.6%
2024-03-15T05:15:49.622410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2280
 
17.1%
상 559
 
4.2%
1 556
 
4.2%
해 543
 
4.1%
김 540
 
4.1%
경 503
 
3.8%
시 501
 
3.8%
남 500
 
3.8%
도 499
 
3.7%
로 494
 
3.7%
Other values (177) 6348
47.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 7583
56.9%
Decimal Number 2335
 
17.5%
Space Separator 2280
 
17.1%
Close Punctuation 383
 
2.9%
Open Punctuation 383
 
2.9%
Other Punctuation 220
 
1.7%
Dash Punctuation 130
 
1.0%
Uppercase Letter 9
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
상 559
 
7.4%
해 543
 
7.2%
김 540
 
7.1%
경 503
 
6.6%
시 501
 
6.6%
남 500
 
6.6%
도 499
 
6.6%
로 494
 
6.5%
동 448
 
5.9%
번 234
 
3.1%
Other values (156) 2762
36.4%
Decimal Number
ValueCountFrequency (%)
1 556
23.8%
2 403
17.3%
3 264
11.3%
0 209
 
9.0%
5 196
 
8.4%
4 193
 
8.3%
6 141
 
6.0%
7 138
 
5.9%
9 130
 
5.6%
8 105
 
4.5%
Uppercase Letter
ValueCountFrequency (%)
B 4
44.4%
L 2
22.2%
T 1
 
11.1%
O 1
 
11.1%
A 1
 
11.1%
Other Punctuation
ValueCountFrequency (%)
, 219
99.5%
/ 1
 
0.5%
Space Separator
ValueCountFrequency (%)
2280
100.0%
Close Punctuation
ValueCountFrequency (%)
) 383
100.0%
Open Punctuation
ValueCountFrequency (%)
( 383
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 130
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 7583
56.9%
Common 5731
43.0%
Latin 9
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
상 559
 
7.4%
해 543
 
7.2%
김 540
 
7.1%
경 503
 
6.6%
시 501
 
6.6%
남 500
 
6.6%
도 499
 
6.6%
로 494
 
6.5%
동 448
 
5.9%
번 234
 
3.1%
Other values (156) 2762
36.4%
Common
ValueCountFrequency (%)
2280
39.8%
1 556
 
9.7%
2 403
 
7.0%
) 383
 
6.7%
( 383
 
6.7%
3 264
 
4.6%
, 219
 
3.8%
0 209
 
3.6%
5 196
 
3.4%
4 193
 
3.4%
Other values (6) 645
 
11.3%
Latin
ValueCountFrequency (%)
B 4
44.4%
L 2
22.2%
T 1
 
11.1%
O 1
 
11.1%
A 1
 
11.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 7583
56.9%
ASCII 5740
43.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2280
39.7%
1 556
 
9.7%
2 403
 
7.0%
) 383
 
6.7%
( 383
 
6.7%
3 264
 
4.6%
, 219
 
3.8%
0 209
 
3.6%
5 196
 
3.4%
4 193
 
3.4%
Other values (11) 654
 
11.4%
Hangul
ValueCountFrequency (%)
상 559
 
7.4%
해 543
 
7.2%
김 540
 
7.1%
경 503
 
6.6%
시 501
 
6.6%
남 500
 
6.6%
도 499
 
6.6%
로 494
 
6.5%
동 448
 
5.9%
번 234
 
3.1%
Other values (156) 2762
36.4%

법인구분명
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
개인
457 
법인
 
41

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row개인
2nd row법인
3rd row개인
4th row개인
5th row개인

Common Values

ValueCountFrequency (%)
개인 457
91.8%
법인 41
 
8.2%

Length

2024-03-15T05:15:50.034226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T05:15:50.343637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
개인 457
91.8%
법인 41
 
8.2%

위도
Real number (ℝ)

Distinct386
Distinct (%)77.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.244508
Minimum35.175859
Maximum35.325936
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-03-15T05:15:50.712024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.175859
5-th percentile35.195029
Q135.228255
median35.234889
Q335.248615
95-th percentile35.322679
Maximum35.325936
Range0.15007631
Interquartile range (IQR)0.020360643

Descriptive statistics

Standard deviation0.035230846
Coefficient of variation (CV)0.00099961238
Kurtosis-0.011710024
Mean35.244508
Median Absolute Deviation (MAD)0.00775492
Skewness0.82576547
Sum17551.765
Variance0.0012412125
MonotonicityNot monotonic
2024-03-15T05:15:51.205817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.32267888 25
 
5.0%
35.24861537 6
 
1.2%
35.3035019 4
 
0.8%
35.1941109 4
 
0.8%
35.30472316 4
 
0.8%
35.22803514 4
 
0.8%
35.23133141 3
 
0.6%
35.2317321 3
 
0.6%
35.22988737 3
 
0.6%
35.19664184 3
 
0.6%
Other values (376) 439
88.2%
ValueCountFrequency (%)
35.1758593 1
0.2%
35.17660879 1
0.2%
35.17661439 1
0.2%
35.17816033 1
0.2%
35.18295985 1
0.2%
35.18531983 1
0.2%
35.18584712 1
0.2%
35.18959334 1
0.2%
35.19058466 1
0.2%
35.19183311 1
0.2%
ValueCountFrequency (%)
35.32593561 1
 
0.2%
35.32267888 25
5.0%
35.31996824 1
 
0.2%
35.31637143 1
 
0.2%
35.31021491 1
 
0.2%
35.30961876 1
 
0.2%
35.30696305 1
 
0.2%
35.30645352 1
 
0.2%
35.3055762 2
 
0.4%
35.30528473 1
 
0.2%

경도
Real number (ℝ)

Distinct385
Distinct (%)77.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128.84323
Minimum128.71415
Maximum129.11382
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-03-15T05:15:51.748513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum128.71415
5-th percentile128.73333
Q1128.80089
median128.86532
Q3128.88439
95-th percentile128.90956
Maximum129.11382
Range0.3996685
Interquartile range (IQR)0.0835044

Descriptive statistics

Standard deviation0.058239105
Coefficient of variation (CV)0.00045201526
Kurtosis0.108748
Mean128.84323
Median Absolute Deviation (MAD)0.03284135
Skewness-0.5187675
Sum64163.927
Variance0.0033917933
MonotonicityNot monotonic
2024-03-15T05:15:52.337357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
128.7998884 25
 
5.0%
128.753369 6
 
1.2%
128.8938914 4
 
0.8%
128.7333292 4
 
0.8%
128.730816 4
 
0.8%
128.799387 4
 
0.8%
128.8522817 3
 
0.6%
128.8846333 3
 
0.6%
128.8933886 3
 
0.6%
128.8818803 3
 
0.6%
Other values (375) 439
88.2%
ValueCountFrequency (%)
128.7141467 1
0.2%
128.7258416 1
0.2%
128.7260397 1
0.2%
128.7260406 1
0.2%
128.7265999 1
0.2%
128.7270554 1
0.2%
128.7276051 1
0.2%
128.7276256 1
0.2%
128.728117 1
0.2%
128.7283047 2
0.4%
ValueCountFrequency (%)
129.1138152 1
0.2%
128.9293427 1
0.2%
128.9283435 1
0.2%
128.9274147 2
0.4%
128.9272319 1
0.2%
128.9199236 2
0.4%
128.9190432 2
0.4%
128.9189278 2
0.4%
128.9180018 1
0.2%
128.9175474 1
0.2%

Interactions

2024-03-15T05:15:35.628048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:15:35.053078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:15:35.909831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:15:35.338362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-15T05:15:52.641153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분명상세영업상태명법인구분명위도경도
구분명1.0000.0000.7060.0000.000
상세영업상태명0.0001.0000.0000.0000.000
법인구분명0.7060.0001.0000.0410.072
위도0.0000.0000.0411.0000.794
경도0.0000.0000.0720.7941.000
2024-03-15T05:15:52.917943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분명상세영업상태명법인구분명
구분명1.0000.0000.499
상세영업상태명0.0001.0000.000
법인구분명0.4990.0001.000
2024-03-15T05:15:53.180699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위도경도구분명상세영업상태명법인구분명
위도1.000-0.3370.0000.0000.031
경도-0.3371.0000.0000.0000.088
구분명0.0000.0001.0000.0000.499
상세영업상태명0.0000.0000.0001.0000.000
법인구분명0.0310.0880.4990.0001.000

Missing values

2024-03-15T05:15:36.301748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T05:15:36.699826image/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.
2024-03-15T05:15:36.889779image/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

사업장명구분명상세영업상태명폐업일자전화번호지번주소도로명주소법인구분명위도경도
0한신인력유료영업중<NA><NA><NA>경상남도 김해시 분성로48번길 8, 한덕한신타워아파트상가 107호 (외동)개인35.231331128.852282
1주식회사 비케이워크유료영업중<NA><NA><NA>경상남도 김해시 호계로 498 (동상동)법인35.234878128.884633
2일과 사람유료영업중<NA><NA><NA>경상남도 김해시 금관대로 1275, 2층 (내동)개인35.238667128.85959
3현대인력유료영업중<NA><NA><NA>경상남도 김해시 분성로501번길 49, 2층 (어방동)개인35.238977128.90386
4신새벽 인력개발유료영업중<NA><NA><NA>경상남도 김해시 구지로 59, 동부아파트 상가동 201호 (내동)개인35.239713128.870183
5나라종합중기 직업소개소유료영업중<NA><NA><NA>경상남도 김해시 인제로11번길 8, 1층 (어방동)개인35.229709128.90444
6한국협회유료영업중<NA>055-313-2434<NA>경상남도 김해시 번화1로44번길 31, 603호 (대청동)개인35.192215128.803783
7대근인력유료폐업2023-06-21010-****-****<NA>경상남도 김해시 진영읍 진영로 344개인35.300885128.750407
8연지인력유료폐업2023-06-19010-****-****<NA>경상남도 김해시 금관대로1368번길 14, 201호 (내동)개인35.244421128.868163
9드림가유료영업중<NA>1670-0621<NA>경상남도 김해시 금관대로599번길 29, 석봉마을부영아파트상가 3층 301호 (무계동)개인35.204764128.813455
사업장명구분명상세영업상태명폐업일자전화번호지번주소도로명주소법인구분명위도경도
488으뜸직업안내컨설팅유료폐업2002-12-25055-334-0077<NA>경상남도 김해시 금관대로1265번길 6-7 (내동)개인35.238801128.858685
489김해직업소개소유료폐업2002-12-25055-333-3788<NA>경상남도 김해시 가락로 88-1 (서상동)개인35.23441128.881559
490영이직업소개소유료폐업2002-12-25055-322-0201<NA>경상남도 김해시 가락로 41 (부원동)개인35.230251128.88188
491양승걸직업소개소유료폐업2002-12-25055-333-8877<NA>경상남도 김해시 호계로 417 (부원동)개인35.227713128.884993
492허창훈직업소개소유료폐업2000-05-26055-337-9416<NA>경상남도 김해시 김해대로2371번길 12-13 (부원동)개인35.228031128.886628
493이호경직업소개소유료폐업<NA>055-332-3924<NA>경상남도 김해시 가락로 3 (부원동)개인35.226819128.882489
494장명대직업소개소유료폐업<NA>055-333-7774<NA>경상남도 김해시 가락로 31 (부원동)개인35.229339128.882096
495양일술직업소개소유료폐업<NA>055-333-8877<NA>경상남도 김해시 부원동 호 19B4L개인35.229339128.882096
496김진태직업소개소유료폐업1999-12-28055-334-7970<NA>경상남도 김해시 가락로16번길 18, 601호 (부원동, 한도빌라)개인35.227888128.884422
497채유학직업소개소유료폐업1999-06-18055-334-6282<NA>경상남도 김해시 부원동 호 20B10L개인35.229339128.882096