Overview

Dataset statistics

Number of variables10
Number of observations416
Missing cells661
Missing cells (%)15.9%
Duplicate rows1
Duplicate rows (%)0.2%
Total size in memory33.4 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://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15033315

Alerts

Dataset has 1 (0.2%) duplicate rowsDuplicates
구분명 is highly overall correlated with 법인구분명High correlation
법인구분명 is highly overall correlated with 구분명High correlation
구분명 is highly imbalanced (79.9%)Imbalance
법인구분명 is highly imbalanced (63.5%)Imbalance
폐업일자 has 209 (50.2%) missing valuesMissing
전화번호 has 344 (82.7%) missing valuesMissing
지번주소 has 36 (8.7%) missing valuesMissing
위도 has 36 (8.7%) missing valuesMissing
경도 has 36 (8.7%) missing valuesMissing

Reproduction

Analysis started2024-03-13 00:11:21.683761
Analysis finished2024-03-13 00:11:22.757493
Duration1.07 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

사업장명
Text

Distinct377
Distinct (%)90.6%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
2024-03-13T09:11:22.936016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length17
Mean length6.1754808
Min length2

Characters and Unicode

Total characters2569
Distinct characters315
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

Unique346 ?
Unique (%)83.2%

Sample

1st row(사)대한노인회 김해시지회
2nd row가야지역평생교육지원센터
3rd row인적자원개발처
4th row김해노인일자리창출지원센터
5th row김해상공회의소 무료직업소개소
ValueCountFrequency (%)
인력개발 5
 
1.1%
진영인력 4
 
0.9%
대성인력 4
 
0.9%
대한인력 3
 
0.7%
진례인력 3
 
0.7%
대경인력 3
 
0.7%
무료직업소개소 3
 
0.7%
주식회사 3
 
0.7%
행복직업소개소 3
 
0.7%
삼일건설인력 3
 
0.7%
Other values (388) 415
92.4%
2024-03-13T09:11:23.298718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
인 245
 
9.5%
력 230
 
9.0%
소 165
 
6.4%
개 141
 
5.5%
업 94
 
3.7%
직 77
 
3.0%
발 63
 
2.5%
사 62
 
2.4%
대 43
 
1.7%
33
 
1.3%
Other values (305) 1416
55.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2416
94.0%
Uppercase Letter 45
 
1.8%
Space Separator 33
 
1.3%
Lowercase Letter 21
 
0.8%
Close Punctuation 19
 
0.7%
Open Punctuation 19
 
0.7%
Decimal Number 14
 
0.5%
Dash Punctuation 1
 
< 0.1%
Other Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
인 245
 
10.1%
력 230
 
9.5%
소 165
 
6.8%
개 141
 
5.8%
업 94
 
3.9%
직 77
 
3.2%
발 63
 
2.6%
사 62
 
2.6%
대 43
 
1.8%
가 33
 
1.4%
Other values (264) 1263
52.3%
Uppercase Letter
ValueCountFrequency (%)
O 9
20.0%
K 7
15.6%
B 4
8.9%
E 3
 
6.7%
S 3
 
6.7%
N 3
 
6.7%
W 3
 
6.7%
A 2
 
4.4%
T 2
 
4.4%
U 1
 
2.2%
Other values (8) 8
17.8%
Lowercase Letter
ValueCountFrequency (%)
t 3
14.3%
n 3
14.3%
e 2
9.5%
r 2
9.5%
i 2
9.5%
u 2
9.5%
c 2
9.5%
o 1
 
4.8%
g 1
 
4.8%
s 1
 
4.8%
Other values (2) 2
9.5%
Decimal Number
ValueCountFrequency (%)
3 4
28.6%
1 4
28.6%
8 2
14.3%
2 2
14.3%
5 1
 
7.1%
6 1
 
7.1%
Space Separator
ValueCountFrequency (%)
33
100.0%
Close Punctuation
ValueCountFrequency (%)
) 19
100.0%
Open Punctuation
ValueCountFrequency (%)
( 19
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2414
94.0%
Common 87
 
3.4%
Latin 66
 
2.6%
Han 2
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
인 245
 
10.1%
력 230
 
9.5%
소 165
 
6.8%
개 141
 
5.8%
업 94
 
3.9%
직 77
 
3.2%
발 63
 
2.6%
사 62
 
2.6%
대 43
 
1.8%
가 33
 
1.4%
Other values (262) 1261
52.2%
Latin
ValueCountFrequency (%)
O 9
 
13.6%
K 7
 
10.6%
B 4
 
6.1%
t 3
 
4.5%
n 3
 
4.5%
E 3
 
4.5%
S 3
 
4.5%
N 3
 
4.5%
W 3
 
4.5%
e 2
 
3.0%
Other values (20) 26
39.4%
Common
ValueCountFrequency (%)
33
37.9%
) 19
21.8%
( 19
21.8%
3 4
 
4.6%
1 4
 
4.6%
8 2
 
2.3%
2 2
 
2.3%
- 1
 
1.1%
5 1
 
1.1%
6 1
 
1.1%
Han
ValueCountFrequency (%)
星 1
50.0%
金 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2414
94.0%
ASCII 153
 
6.0%
CJK 1
 
< 0.1%
CJK Compat Ideographs 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
인 245
 
10.1%
력 230
 
9.5%
소 165
 
6.8%
개 141
 
5.8%
업 94
 
3.9%
직 77
 
3.2%
발 63
 
2.6%
사 62
 
2.6%
대 43
 
1.8%
가 33
 
1.4%
Other values (262) 1261
52.2%
ASCII
ValueCountFrequency (%)
33
21.6%
) 19
 
12.4%
( 19
 
12.4%
O 9
 
5.9%
K 7
 
4.6%
3 4
 
2.6%
1 4
 
2.6%
B 4
 
2.6%
t 3
 
2.0%
n 3
 
2.0%
Other values (31) 48
31.4%
CJK
ValueCountFrequency (%)
星 1
100.0%
CJK Compat Ideographs
ValueCountFrequency (%)
金 1
100.0%

구분명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
유료
403 
무료
 
13

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
유료 403
96.9%
무료 13
 
3.1%

Length

2024-03-13T09:11:23.400580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T09:11:23.472653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
유료 403
96.9%
무료 13
 
3.1%

상세영업상태명
Categorical

Distinct3
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
폐업
231 
영업중
173 
등록취소
 
12

Length

Max length4
Median length2
Mean length2.4735577
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
폐업 231
55.5%
영업중 173
41.6%
등록취소 12
 
2.9%

Length

2024-03-13T09:11:23.558373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T09:11:23.647026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 231
55.5%
영업중 173
41.6%
등록취소 12
 
2.9%

폐업일자
Date

MISSING 

Distinct188
Distinct (%)90.8%
Missing209
Missing (%)50.2%
Memory size3.4 KiB
Minimum1999-06-18 00:00:00
Maximum2021-06-04 00:00:00
2024-03-13T09:11:23.733594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T09:11:23.844551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

전화번호
Text

MISSING 

Distinct68
Distinct (%)94.4%
Missing344
Missing (%)82.7%
Memory size3.4 KiB
2024-03-13T09:11:24.043190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters864
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

Unique64 ?
Unique (%)88.9%

Sample

1st row055-334-7111
2nd row055-323-6588
3rd row055-337-6001
4th row055-333-6648
5th row055-332-6332
ValueCountFrequency (%)
055-322-8966 2
 
2.8%
055-343-2204 2
 
2.8%
055-345-1604 2
 
2.8%
055-342-8816 2
 
2.8%
055-325-1222 1
 
1.4%
055-346-2251 1
 
1.4%
055-333-8773 1
 
1.4%
055-328-5494 1
 
1.4%
055-326-5833 1
 
1.4%
055-327-1661 1
 
1.4%
Other values (58) 58
80.6%
2024-03-13T09:11:24.368572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 179
20.7%
- 144
16.7%
3 126
14.6%
0 110
12.7%
2 61
 
7.1%
1 61
 
7.1%
4 48
 
5.6%
6 45
 
5.2%
8 35
 
4.1%
7 33
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 720
83.3%
Dash Punctuation 144
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 179
24.9%
3 126
17.5%
0 110
15.3%
2 61
 
8.5%
1 61
 
8.5%
4 48
 
6.7%
6 45
 
6.2%
8 35
 
4.9%
7 33
 
4.6%
9 22
 
3.1%
Dash Punctuation
ValueCountFrequency (%)
- 144
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 864
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 179
20.7%
- 144
16.7%
3 126
14.6%
0 110
12.7%
2 61
 
7.1%
1 61
 
7.1%
4 48
 
5.6%
6 45
 
5.2%
8 35
 
4.1%
7 33
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 864
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 179
20.7%
- 144
16.7%
3 126
14.6%
0 110
12.7%
2 61
 
7.1%
1 61
 
7.1%
4 48
 
5.6%
6 45
 
5.2%
8 35
 
4.1%
7 33
 
3.8%

지번주소
Text

MISSING 

Distinct356
Distinct (%)93.7%
Missing36
Missing (%)8.7%
Memory size3.4 KiB
2024-03-13T09:11:24.669964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length51
Median length45
Mean length24.802632
Min length11

Characters and Unicode

Total characters9425
Distinct characters168
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

Unique334 ?
Unique (%)87.9%

Sample

1st row경상남도 김해시 봉황동 17번지 13호
2nd row경상남도 김해시 구산동 440번지 4호
3rd row경상남도 김해시 어방동 607번지 인제대학교
4th row경상남도 김해시 흥동 415번지 4호
5th row경상남도 김해시 부원동 623번지 1호
ValueCountFrequency (%)
경상남도 378
 
17.1%
김해시 377
 
17.1%
진영읍 51
 
2.3%
1호 42
 
1.9%
외동 42
 
1.9%
2호 38
 
1.7%
3호 33
 
1.5%
부원동 32
 
1.4%
여래리 30
 
1.4%
5호 25
 
1.1%
Other values (522) 1160
52.5%
2024-03-13T09:11:25.079102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1851
19.6%
1 429
 
4.6%
상 418
 
4.4%
남 382
 
4.1%
도 381
 
4.0%
경 380
 
4.0%
시 380
 
4.0%
김 378
 
4.0%
해 378
 
4.0%
호 347
 
3.7%
Other values (158) 4101
43.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5527
58.6%
Decimal Number 2006
 
21.3%
Space Separator 1851
 
19.6%
Dash Punctuation 32
 
0.3%
Uppercase Letter 8
 
0.1%
Other Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
상 418
 
7.6%
남 382
 
6.9%
도 381
 
6.9%
경 380
 
6.9%
시 380
 
6.9%
김 378
 
6.8%
해 378
 
6.8%
호 347
 
6.3%
지 345
 
6.2%
번 333
 
6.0%
Other values (138) 1805
32.7%
Decimal Number
ValueCountFrequency (%)
1 429
21.4%
2 290
14.5%
0 198
9.9%
6 184
9.2%
3 182
9.1%
5 164
 
8.2%
4 156
 
7.8%
7 154
 
7.7%
8 129
 
6.4%
9 120
 
6.0%
Uppercase Letter
ValueCountFrequency (%)
B 2
25.0%
M 1
12.5%
Y 1
12.5%
C 1
12.5%
O 1
12.5%
T 1
12.5%
A 1
12.5%
Space Separator
ValueCountFrequency (%)
1851
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 32
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5527
58.6%
Common 3890
41.3%
Latin 8
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
상 418
 
7.6%
남 382
 
6.9%
도 381
 
6.9%
경 380
 
6.9%
시 380
 
6.9%
김 378
 
6.8%
해 378
 
6.8%
호 347
 
6.3%
지 345
 
6.2%
번 333
 
6.0%
Other values (138) 1805
32.7%
Common
ValueCountFrequency (%)
1851
47.6%
1 429
 
11.0%
2 290
 
7.5%
0 198
 
5.1%
6 184
 
4.7%
3 182
 
4.7%
5 164
 
4.2%
4 156
 
4.0%
7 154
 
4.0%
8 129
 
3.3%
Other values (3) 153
 
3.9%
Latin
ValueCountFrequency (%)
B 2
25.0%
M 1
12.5%
Y 1
12.5%
C 1
12.5%
O 1
12.5%
T 1
12.5%
A 1
12.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5527
58.6%
ASCII 3898
41.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1851
47.5%
1 429
 
11.0%
2 290
 
7.4%
0 198
 
5.1%
6 184
 
4.7%
3 182
 
4.7%
5 164
 
4.2%
4 156
 
4.0%
7 154
 
4.0%
8 129
 
3.3%
Other values (10) 161
 
4.1%
Hangul
ValueCountFrequency (%)
상 418
 
7.6%
남 382
 
6.9%
도 381
 
6.9%
경 380
 
6.9%
시 380
 
6.9%
김 378
 
6.8%
해 378
 
6.8%
호 347
 
6.3%
지 345
 
6.2%
번 333
 
6.0%
Other values (138) 1805
32.7%

도로명주소
Text

Distinct383
Distinct (%)92.1%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
2024-03-13T09:11:25.305994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length49
Median length44
Mean length26.610577
Min length18

Characters and Unicode

Total characters11070
Distinct characters175
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

Unique358 ?
Unique (%)86.1%

Sample

1st row경상남도 김해시 가락로15번길 37 (봉황동, 김해시노인복지회관)
2nd row경상남도 김해시 가락로 276, 2층 (구산동)
3rd row경상남도 김해시 인제로 197 (어방동)
4th row경상남도 김해시 흥동로 150 (흥동)
5th row경상남도 김해시 호계로422번길 24 (부원동)
ValueCountFrequency (%)
경상남도 416
 
18.2%
김해시 416
 
18.2%
진영읍 58
 
2.5%
외동 41
 
1.8%
부원동 36
 
1.6%
2층 31
 
1.4%
진영로 30
 
1.3%
동상동 23
 
1.0%
장유로 22
 
1.0%
금관대로 21
 
0.9%
Other values (576) 1194
52.2%
2024-03-13T09:11:25.662241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1874
 
16.9%
1 468
 
4.2%
상 463
 
4.2%
해 451
 
4.1%
김 449
 
4.1%
남 421
 
3.8%
경 419
 
3.8%
시 419
 
3.8%
도 418
 
3.8%
로 412
 
3.7%
Other values (165) 5276
47.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 6310
57.0%
Decimal Number 1959
 
17.7%
Space Separator 1874
 
16.9%
Close Punctuation 314
 
2.8%
Open Punctuation 314
 
2.8%
Other Punctuation 174
 
1.6%
Dash Punctuation 119
 
1.1%
Uppercase Letter 6
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
상 463
 
7.3%
해 451
 
7.1%
김 449
 
7.1%
남 421
 
6.7%
경 419
 
6.6%
시 419
 
6.6%
도 418
 
6.6%
로 412
 
6.5%
동 365
 
5.8%
번 198
 
3.1%
Other values (145) 2295
36.4%
Decimal Number
ValueCountFrequency (%)
1 468
23.9%
2 333
17.0%
3 210
10.7%
0 184
 
9.4%
4 158
 
8.1%
5 152
 
7.8%
7 136
 
6.9%
9 116
 
5.9%
6 108
 
5.5%
8 94
 
4.8%
Uppercase Letter
ValueCountFrequency (%)
B 3
50.0%
O 1
 
16.7%
T 1
 
16.7%
A 1
 
16.7%
Other Punctuation
ValueCountFrequency (%)
, 173
99.4%
/ 1
 
0.6%
Space Separator
ValueCountFrequency (%)
1874
100.0%
Close Punctuation
ValueCountFrequency (%)
) 314
100.0%
Open Punctuation
ValueCountFrequency (%)
( 314
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 119
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 6310
57.0%
Common 4754
42.9%
Latin 6
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
상 463
 
7.3%
해 451
 
7.1%
김 449
 
7.1%
남 421
 
6.7%
경 419
 
6.6%
시 419
 
6.6%
도 418
 
6.6%
로 412
 
6.5%
동 365
 
5.8%
번 198
 
3.1%
Other values (145) 2295
36.4%
Common
ValueCountFrequency (%)
1874
39.4%
1 468
 
9.8%
2 333
 
7.0%
) 314
 
6.6%
( 314
 
6.6%
3 210
 
4.4%
0 184
 
3.9%
, 173
 
3.6%
4 158
 
3.3%
5 152
 
3.2%
Other values (6) 574
 
12.1%
Latin
ValueCountFrequency (%)
B 3
50.0%
O 1
 
16.7%
T 1
 
16.7%
A 1
 
16.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 6310
57.0%
ASCII 4760
43.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1874
39.4%
1 468
 
9.8%
2 333
 
7.0%
) 314
 
6.6%
( 314
 
6.6%
3 210
 
4.4%
0 184
 
3.9%
, 173
 
3.6%
4 158
 
3.3%
5 152
 
3.2%
Other values (10) 580
 
12.2%
Hangul
ValueCountFrequency (%)
상 463
 
7.3%
해 451
 
7.1%
김 449
 
7.1%
남 421
 
6.7%
경 419
 
6.6%
시 419
 
6.6%
도 418
 
6.6%
로 412
 
6.5%
동 365
 
5.8%
번 198
 
3.1%
Other values (145) 2295
36.4%

법인구분명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
개인
387 
법인
 
29

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
개인 387
93.0%
법인 29
 
7.0%

Length

2024-03-13T09:11:25.763854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T09:11:25.835922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
개인 387
93.0%
법인 29
 
7.0%

위도
Real number (ℝ)

MISSING 

Distinct300
Distinct (%)78.9%
Missing36
Missing (%)8.7%
Infinite0
Infinite (%)0.0%
Mean35.275499
Minimum35.056648
Maximum36.833649
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2024-03-13T09:11:25.922089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.056648
5-th percentile35.1946
Q135.229323
median35.23556
Q335.250604
95-th percentile35.316888
Maximum36.833649
Range1.7770016
Interquartile range (IQR)0.02128056

Descriptive statistics

Standard deviation0.18460299
Coefficient of variation (CV)0.0052331789
Kurtosis36.347721
Mean35.275499
Median Absolute Deviation (MAD)0.007959275
Skewness5.7884155
Sum13404.69
Variance0.034078266
MonotonicityNot monotonic
2024-03-13T09:11:26.041054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.30484915 10
 
2.4%
35.2279096 8
 
1.9%
35.23534347 5
 
1.2%
35.19394144 5
 
1.2%
35.2344948 4
 
1.0%
35.24850543 4
 
1.0%
35.23559551 4
 
1.0%
35.1999806 3
 
0.7%
35.23314508 3
 
0.7%
35.30325911 3
 
0.7%
Other values (290) 331
79.6%
(Missing) 36
 
8.7%
ValueCountFrequency (%)
35.05664785 1
0.2%
35.17761107 1
0.2%
35.18297843 1
0.2%
35.18602138 1
0.2%
35.1896039 1
0.2%
35.19160863 1
0.2%
35.19185203 1
0.2%
35.19227585 1
0.2%
35.19229797 1
0.2%
35.1928086 1
0.2%
ValueCountFrequency (%)
36.83364941 1
0.2%
36.67569825 1
0.2%
36.48220237 1
0.2%
36.47531532 1
0.2%
36.22828904 1
0.2%
36.2208699 2
0.5%
35.96726361 1
0.2%
35.89429232 1
0.2%
35.78177083 2
0.5%
35.78058135 1
0.2%

경도
Real number (ℝ)

MISSING 

Distinct300
Distinct (%)78.9%
Missing36
Missing (%)8.7%
Infinite0
Infinite (%)0.0%
Mean128.82594
Minimum126.84503
Maximum128.98392
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2024-03-13T09:11:26.155720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.84503
5-th percentile128.72976
Q1128.80275
median128.86759
Q3128.88674
95-th percentile128.91493
Maximum128.98392
Range2.1388905
Interquartile range (IQR)0.083992375

Descriptive statistics

Standard deviation0.17398659
Coefficient of variation (CV)0.0013505555
Kurtosis87.260236
Mean128.82594
Median Absolute Deviation (MAD)0.03226495
Skewness-8.3051191
Sum48953.859
Variance0.030271333
MonotonicityNot monotonic
2024-03-13T09:11:26.477291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
128.7349861 10
 
2.4%
128.892796 8
 
1.9%
128.855995 5
 
1.2%
128.8000907 5
 
1.2%
128.8728797 4
 
1.0%
128.7533685 4
 
1.0%
128.9189433 4
 
1.0%
128.8152951 3
 
0.7%
128.871 3
 
0.7%
128.7318026 3
 
0.7%
Other values (290) 331
79.6%
(Missing) 36
 
8.7%
ValueCountFrequency (%)
126.8450333 1
0.2%
126.8659913 1
0.2%
128.1696295 1
0.2%
128.1714307 1
0.2%
128.212253 1
0.2%
128.3846123 2
0.5%
128.385036 1
0.2%
128.3870358 1
0.2%
128.3899489 1
0.2%
128.6874241 1
0.2%
ValueCountFrequency (%)
128.9839238 1
 
0.2%
128.9711175 2
0.5%
128.9656704 1
 
0.2%
128.9634329 1
 
0.2%
128.9299805 3
0.7%
128.9291814 1
 
0.2%
128.9274164 1
 
0.2%
128.9207438 1
 
0.2%
128.9195651 1
 
0.2%
128.9189433 4
1.0%

Interactions

2024-03-13T09:11:22.327170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T09:11:22.170893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T09:11:22.400575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T09:11:22.256660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-13T09:11:26.576333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분명상세영업상태명전화번호법인구분명위도경도
구분명1.0000.0001.0000.7840.0000.000
상세영업상태명0.0001.0000.9570.0000.0000.000
전화번호1.0000.9571.0001.0001.0001.000
법인구분명0.7840.0001.0001.0000.0000.015
위도0.0000.0001.0000.0001.0000.844
경도0.0000.0001.0000.0150.8441.000
2024-03-13T09:11:26.674232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
상세영업상태명법인구분명구분명
상세영업상태명1.0000.0000.000
법인구분명0.0001.0000.573
구분명0.0000.5731.000
2024-03-13T09:11:26.761532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위도경도구분명상세영업상태명법인구분명
위도1.000-0.2410.0000.0000.000
경도-0.2411.0000.0000.0000.017
구분명0.0000.0001.0000.0000.573
상세영업상태명0.0000.0000.0001.0000.000
법인구분명0.0000.0170.5730.0001.000

Missing values

2024-03-13T09:11:22.496569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-13T09:11:22.616931image/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.
2024-03-13T09:11:22.704407image/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
0(사)대한노인회 김해시지회무료영업중<NA><NA>경상남도 김해시 봉황동 17번지 13호경상남도 김해시 가락로15번길 37 (봉황동, 김해시노인복지회관)법인35.2272128.87657
1가야지역평생교육지원센터무료영업중<NA><NA>경상남도 김해시 구산동 440번지 4호경상남도 김해시 가락로 276, 2층 (구산동)법인35.25011128.87298
2인적자원개발처무료영업중<NA>055-334-7111경상남도 김해시 어방동 607번지 인제대학교경상남도 김해시 인제로 197 (어방동)법인35.250401128.901196
3김해노인일자리창출지원센터무료영업중<NA>055-323-6588경상남도 김해시 흥동 415번지 4호경상남도 김해시 흥동로 150 (흥동)개인35.221987128.86443
4김해상공회의소 무료직업소개소무료영업중<NA>055-337-6001경상남도 김해시 부원동 623번지 1호경상남도 김해시 호계로422번길 24 (부원동)법인35.228342128.88922
5아시아문화센터 무료직업소개소무료폐업2020-06-19<NA>경상남도 김해시 봉황동 404번지 김해YMCA경상남도 김해시 분성로 277 (봉황동)법인35.23316128.876231
6사단법인 사람앤희망 무료직업소개소무료폐업2016-08-05<NA>경상남도 김해시 삼계동 1489번지 8호 서주빌딩 602호경상남도 김해시 삼계중앙로 35, 6층 602호 (삼계동, 서주빌딩)법인35.260989128.868623
7한국노총무료취업알선센터무료폐업2013-07-23055-333-6648경상남도 김해시 삼방동 178번지 3호경상남도 김해시 활천로255번길 10 (삼방동)법인35.244148128.907471
8김해시고령자취업알선센터무료폐업2015-01-14055-332-6332경상남도 김해시 구산동 756번지경상남도 김해시 김해대로1902번길 12 (구산동)법인35.256374128.864513
9장애인무료직업소개소무료폐업2013-09-03<NA>경상남도 김해시 한림면 장방리 1313번지 56호 8통 9반경상남도 김해시 한림면 한림로442번길 19-8법인35.326364128.798453
사업장명구분명상세영업상태명폐업일자전화번호지번주소도로명주소법인구분명위도경도
406우리가사원유료폐업2015-11-30<NA>경상남도 김해시 대청동 332번지 4호경상남도 김해시 대청로 117, 305호 (대청동, 대청프라자)개인35.186021128.79775
407희망직업소개소유료폐업2016-07-22<NA><NA>경상남도 김해시 진영읍 진영로136번길 18-5개인<NA><NA>
408뉴 빨리인력유료폐업2020-04-29<NA>경상남도 김해시 부원동 171번지 25호경상남도 김해시 활천로 1 (부원동)개인35.22791128.892796
409(주)바로인력유료폐업2015-06-19055-333-8516경상남도 김해시 외동경상남도 김해시 분성로 16 (외동)법인35.233708128.85994
410보람인력유료폐업2015-08-10<NA><NA>경상남도 김해시 가락로7번길 7-14 (봉황동)개인<NA><NA>
411대신인력유료폐업2016-11-14<NA><NA>경상남도 김해시 호계로 498-1 (동상동)개인<NA><NA>
412태양인력유료폐업2016-06-07<NA><NA>경상남도 김해시 가락로 151-1 (대성동)개인<NA><NA>
413미래휴먼컨설팅유료폐업2018-04-06<NA>경상남도 김해시 삼방동 553번지 7호경상남도 김해시 인제로 272 (삼방동)개인35.253474128.902692
414시민인력공사유료폐업<NA><NA>경상남도 김해시 삼방동 568번지 3호 62통 6반 덕명빌라 B 502경상남도 김해시 삼안로298번길 6-10, B동 502호 (삼방동,덕명빌라)개인35.252482128.906362
415효사랑요양보호사간병협회유료폐업2021-06-04<NA>경상남도 김해시 외동 1034-4경상남도 김해시 분성로 62, 3층 (외동)개인35.231476128.854027

Duplicate rows

Most frequently occurring

사업장명구분명상세영업상태명폐업일자전화번호지번주소도로명주소법인구분명위도경도# duplicates
0친정맘산후도우미유료영업중<NA><NA>경상남도 김해시 외동 1261-9 한국아파트 상가경상남도 김해시 함박로 120, 한국아파트 상가 224호 (외동)개인35.230928128.8675892