Overview

Dataset statistics

Number of variables9
Number of observations87
Missing cells31
Missing cells (%)4.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.5 KiB
Average record size in memory76.5 B

Variable types

Numeric3
Categorical1
Text5

Dataset

Description미추홀구 관내 직업소개소 현황에 대한 데이터로 연번, 상호명, 유형, 전화번호, 도로명주소, 죄표값 등의 항목을 제공합니다.
Author인천광역시 미추홀구
URLhttps://data.incheon.go.kr/findData/publicDataDetail?dataId=15060863&srcSe=7661IVAWM27C61E190

Alerts

구분 is highly imbalanced (68.3%)Imbalance
전화번호 has 25 (28.7%) missing valuesMissing
지번주소 has 6 (6.9%) missing valuesMissing
연번 has unique valuesUnique
법인명 has unique valuesUnique
대표자명 has unique valuesUnique

Reproduction

Analysis started2024-01-28 16:54:37.794021
Analysis finished2024-01-28 16:54:41.105333
Duration3.31 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

UNIQUE 

Distinct87
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44
Minimum1
Maximum87
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size915.0 B
2024-01-29T01:54:41.730769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5.3
Q122.5
median44
Q365.5
95-th percentile82.7
Maximum87
Range86
Interquartile range (IQR)43

Descriptive statistics

Standard deviation25.258662
Coefficient of variation (CV)0.5740605
Kurtosis-1.2
Mean44
Median Absolute Deviation (MAD)22
Skewness0
Sum3828
Variance638
MonotonicityStrictly increasing
2024-01-29T01:54:42.048554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.1%
2 1
 
1.1%
65 1
 
1.1%
64 1
 
1.1%
63 1
 
1.1%
62 1
 
1.1%
61 1
 
1.1%
60 1
 
1.1%
59 1
 
1.1%
58 1
 
1.1%
Other values (77) 77
88.5%
ValueCountFrequency (%)
1 1
1.1%
2 1
1.1%
3 1
1.1%
4 1
1.1%
5 1
1.1%
6 1
1.1%
7 1
1.1%
8 1
1.1%
9 1
1.1%
10 1
1.1%
ValueCountFrequency (%)
87 1
1.1%
86 1
1.1%
85 1
1.1%
84 1
1.1%
83 1
1.1%
82 1
1.1%
81 1
1.1%
80 1
1.1%
79 1
1.1%
78 1
1.1%

구분
Categorical

IMBALANCE 

Distinct2
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size828.0 B
유료
82 
무료
 
5

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 82
94.3%
무료 5
 
5.7%

Length

2024-01-29T01:54:42.296308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-29T01:54:42.510813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
유료 82
94.3%
무료 5
 
5.7%

법인명
Text

UNIQUE 

Distinct87
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size828.0 B
2024-01-29T01:54:42.951245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length13
Mean length7.2068966
Min length2

Characters and Unicode

Total characters627
Distinct characters173
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

Unique87 ?
Unique (%)100.0%

Sample

1st row전기인력 모여라
2nd row주식회사 골드직업소개소
3rd row삼춘네건축
4th row조은
5th row우리인력사무소
ValueCountFrequency (%)
직업소개소 2
 
2.0%
신기인력 2
 
2.0%
주식회사 2
 
2.0%
전기인력 1
 
1.0%
석바위 1
 
1.0%
오성인력개발 1
 
1.0%
태경종합관리시스템 1
 
1.0%
두인인력 1
 
1.0%
미래여성직업소개소 1
 
1.0%
다인인력개발 1
 
1.0%
Other values (87) 87
87.0%
2024-01-29T01:54:43.710477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
인 57
 
9.1%
력 39
 
6.2%
소 34
 
5.4%
개 30
 
4.8%
업 21
 
3.3%
직 15
 
2.4%
13
 
2.1%
사 12
 
1.9%
발 12
 
1.9%
주 11
 
1.8%
Other values (163) 383
61.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 595
94.9%
Space Separator 13
 
2.1%
Close Punctuation 9
 
1.4%
Open Punctuation 8
 
1.3%
Uppercase Letter 2
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
인 57
 
9.6%
력 39
 
6.6%
소 34
 
5.7%
개 30
 
5.0%
업 21
 
3.5%
직 15
 
2.5%
사 12
 
2.0%
발 12
 
2.0%
주 11
 
1.8%
스 10
 
1.7%
Other values (158) 354
59.5%
Uppercase Letter
ValueCountFrequency (%)
S 1
50.0%
J 1
50.0%
Space Separator
ValueCountFrequency (%)
13
100.0%
Close Punctuation
ValueCountFrequency (%)
) 9
100.0%
Open Punctuation
ValueCountFrequency (%)
( 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 595
94.9%
Common 30
 
4.8%
Latin 2
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
인 57
 
9.6%
력 39
 
6.6%
소 34
 
5.7%
개 30
 
5.0%
업 21
 
3.5%
직 15
 
2.5%
사 12
 
2.0%
발 12
 
2.0%
주 11
 
1.8%
스 10
 
1.7%
Other values (158) 354
59.5%
Common
ValueCountFrequency (%)
13
43.3%
) 9
30.0%
( 8
26.7%
Latin
ValueCountFrequency (%)
S 1
50.0%
J 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 595
94.9%
ASCII 32
 
5.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
인 57
 
9.6%
력 39
 
6.6%
소 34
 
5.7%
개 30
 
5.0%
업 21
 
3.5%
직 15
 
2.5%
사 12
 
2.0%
발 12
 
2.0%
주 11
 
1.8%
스 10
 
1.7%
Other values (158) 354
59.5%
ASCII
ValueCountFrequency (%)
13
40.6%
) 9
28.1%
( 8
25.0%
S 1
 
3.1%
J 1
 
3.1%

대표자명
Text

UNIQUE 

Distinct87
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size828.0 B
2024-01-29T01:54:44.267243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters261
Distinct characters93
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

Unique87 ?
Unique (%)100.0%

Sample

1st row이애순
2nd row김성숙
3rd row강선구
4th row이동진
5th row김상우
ValueCountFrequency (%)
이애순 1
 
1.1%
정민수 1
 
1.1%
제창식 1
 
1.1%
김재전 1
 
1.1%
이우철 1
 
1.1%
박창완 1
 
1.1%
유병칠 1
 
1.1%
홍승원 1
 
1.1%
강경화 1
 
1.1%
정경자 1
 
1.1%
Other values (77) 77
88.5%
2024-01-29T01:54:45.110448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
김 19
 
7.3%
이 16
 
6.1%
정 15
 
5.7%
성 7
 
2.7%
희 7
 
2.7%
순 6
 
2.3%
미 5
 
1.9%
진 5
 
1.9%
박 5
 
1.9%
은 5
 
1.9%
Other values (83) 171
65.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 261
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
김 19
 
7.3%
이 16
 
6.1%
정 15
 
5.7%
성 7
 
2.7%
희 7
 
2.7%
순 6
 
2.3%
미 5
 
1.9%
진 5
 
1.9%
박 5
 
1.9%
은 5
 
1.9%
Other values (83) 171
65.5%

Most occurring scripts

ValueCountFrequency (%)
Hangul 261
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
김 19
 
7.3%
이 16
 
6.1%
정 15
 
5.7%
성 7
 
2.7%
희 7
 
2.7%
순 6
 
2.3%
미 5
 
1.9%
진 5
 
1.9%
박 5
 
1.9%
은 5
 
1.9%
Other values (83) 171
65.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 261
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
김 19
 
7.3%
이 16
 
6.1%
정 15
 
5.7%
성 7
 
2.7%
희 7
 
2.7%
순 6
 
2.3%
미 5
 
1.9%
진 5
 
1.9%
박 5
 
1.9%
은 5
 
1.9%
Other values (83) 171
65.5%

전화번호
Text

MISSING 

Distinct62
Distinct (%)100.0%
Missing25
Missing (%)28.7%
Memory size828.0 B
2024-01-29T01:54:45.633147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12.016129
Min length11

Characters and Unicode

Total characters745
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

Unique62 ?
Unique (%)100.0%

Sample

1st row032-874-8323
2nd row032-429-1977
3rd row032-876-3332
4th row032-201-1616
5th row032-886-4880
ValueCountFrequency (%)
032-433-5253 1
 
1.6%
032-866-0788 1
 
1.6%
02-558-2278 1
 
1.6%
032-328-5757 1
 
1.6%
032-875-0800 1
 
1.6%
032-876-8080 1
 
1.6%
032-441-9100 1
 
1.6%
032-0813-4404 1
 
1.6%
032-884-9114 1
 
1.6%
032-864-0707 1
 
1.6%
Other values (52) 52
83.9%
2024-01-29T01:54:46.440995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 124
16.6%
0 105
14.1%
2 104
14.0%
3 99
13.3%
8 85
11.4%
4 56
7.5%
1 46
 
6.2%
7 35
 
4.7%
6 35
 
4.7%
5 31
 
4.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 621
83.4%
Dash Punctuation 124
 
16.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 105
16.9%
2 104
16.7%
3 99
15.9%
8 85
13.7%
4 56
9.0%
1 46
7.4%
7 35
 
5.6%
6 35
 
5.6%
5 31
 
5.0%
9 25
 
4.0%
Dash Punctuation
ValueCountFrequency (%)
- 124
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 745
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 124
16.6%
0 105
14.1%
2 104
14.0%
3 99
13.3%
8 85
11.4%
4 56
7.5%
1 46
 
6.2%
7 35
 
4.7%
6 35
 
4.7%
5 31
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 745
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 124
16.6%
0 105
14.1%
2 104
14.0%
3 99
13.3%
8 85
11.4%
4 56
7.5%
1 46
 
6.2%
7 35
 
4.7%
6 35
 
4.7%
5 31
 
4.2%

지번주소
Text

MISSING 

Distinct77
Distinct (%)95.1%
Missing6
Missing (%)6.9%
Memory size828.0 B
2024-01-29T01:54:47.015592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length34
Median length29
Mean length25.839506
Min length19

Characters and Unicode

Total characters2093
Distinct characters103
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

Unique73 ?
Unique (%)90.1%

Sample

1st row인천광역시 미추홀구 주안동 267-9
2nd row인천광역시 미추홀구 용현동 207-12
3rd row인천광역시 미추홀구 주안동 1482-23
4th row인천광역시 미추홀구 주안동 325-1 간석역프라자
5th row인천광역시 미추홀구 주안동 1580-25
ValueCountFrequency (%)
인천광역시 81
19.0%
미추홀구 81
19.0%
주안동 47
 
11.0%
1호 14
 
3.3%
용현동 11
 
2.6%
도화동 8
 
1.9%
2층 7
 
1.6%
136번지 4
 
0.9%
숭의동 4
 
0.9%
8호 4
 
0.9%
Other values (136) 166
38.9%
2024-01-29T01:54:47.897469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
349
 
16.7%
1 89
 
4.3%
인 84
 
4.0%
미 83
 
4.0%
역 82
 
3.9%
광 81
 
3.9%
시 81
 
3.9%
추 81
 
3.9%
홀 81
 
3.9%
구 81
 
3.9%
Other values (93) 1001
47.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1298
62.0%
Decimal Number 406
 
19.4%
Space Separator 349
 
16.7%
Dash Punctuation 35
 
1.7%
Uppercase Letter 5
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
인 84
 
6.5%
미 83
 
6.4%
역 82
 
6.3%
광 81
 
6.2%
시 81
 
6.2%
추 81
 
6.2%
홀 81
 
6.2%
구 81
 
6.2%
동 81
 
6.2%
천 81
 
6.2%
Other values (76) 482
37.1%
Decimal Number
ValueCountFrequency (%)
1 89
21.9%
2 61
15.0%
4 46
11.3%
3 45
11.1%
6 37
9.1%
5 29
 
7.1%
0 28
 
6.9%
9 27
 
6.7%
7 25
 
6.2%
8 19
 
4.7%
Uppercase Letter
ValueCountFrequency (%)
V 1
20.0%
I 1
20.0%
P 1
20.0%
D 1
20.0%
C 1
20.0%
Space Separator
ValueCountFrequency (%)
349
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 35
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1298
62.0%
Common 790
37.7%
Latin 5
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
인 84
 
6.5%
미 83
 
6.4%
역 82
 
6.3%
광 81
 
6.2%
시 81
 
6.2%
추 81
 
6.2%
홀 81
 
6.2%
구 81
 
6.2%
동 81
 
6.2%
천 81
 
6.2%
Other values (76) 482
37.1%
Common
ValueCountFrequency (%)
349
44.2%
1 89
 
11.3%
2 61
 
7.7%
4 46
 
5.8%
3 45
 
5.7%
6 37
 
4.7%
- 35
 
4.4%
5 29
 
3.7%
0 28
 
3.5%
9 27
 
3.4%
Other values (2) 44
 
5.6%
Latin
ValueCountFrequency (%)
V 1
20.0%
I 1
20.0%
P 1
20.0%
D 1
20.0%
C 1
20.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1298
62.0%
ASCII 795
38.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
349
43.9%
1 89
 
11.2%
2 61
 
7.7%
4 46
 
5.8%
3 45
 
5.7%
6 37
 
4.7%
- 35
 
4.4%
5 29
 
3.6%
0 28
 
3.5%
9 27
 
3.4%
Other values (7) 49
 
6.2%
Hangul
ValueCountFrequency (%)
인 84
 
6.5%
미 83
 
6.4%
역 82
 
6.3%
광 81
 
6.2%
시 81
 
6.2%
추 81
 
6.2%
홀 81
 
6.2%
구 81
 
6.2%
동 81
 
6.2%
천 81
 
6.2%
Other values (76) 482
37.1%

도로명주소
Text

Distinct78
Distinct (%)89.7%
Missing0
Missing (%)0.0%
Memory size828.0 B
2024-01-29T01:54:48.426056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length43
Median length35
Mean length26.264368
Min length23

Characters and Unicode

Total characters2285
Distinct characters102
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

Unique73 ?
Unique (%)83.9%

Sample

1st row인천광역시 미추홀구 주안로 68 (주안동)
2nd row인천광역시 미추홀구 독배로382번길 20(용현동)
3rd row인천광역시 미추홀구 인주대로 423 (주안동)
4th row인천광역시 미추홀구 주안로213번길 15 (주안동)
5th row인천광역시 미추홀구 구월로8번길 42-1 (주안동)
ValueCountFrequency (%)
인천광역시 87
19.7%
미추홀구 87
19.7%
주안동 55
 
12.5%
경인로 13
 
2.9%
용현동 11
 
2.5%
주안로 8
 
1.8%
미추홀대로 8
 
1.8%
도화동 7
 
1.6%
108 6
 
1.4%
학익동 5
 
1.1%
Other values (121) 154
34.9%
2024-01-29T01:54:49.319275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
354
 
15.5%
인 112
 
4.9%
홀 103
 
4.5%
미 101
 
4.4%
추 100
 
4.4%
구 89
 
3.9%
시 88
 
3.9%
천 87
 
3.8%
광 87
 
3.8%
역 87
 
3.8%
Other values (92) 1077
47.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1431
62.6%
Space Separator 354
 
15.5%
Decimal Number 298
 
13.0%
Close Punctuation 87
 
3.8%
Open Punctuation 87
 
3.8%
Dash Punctuation 22
 
1.0%
Other Punctuation 6
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
인 112
 
7.8%
홀 103
 
7.2%
미 101
 
7.1%
추 100
 
7.0%
구 89
 
6.2%
시 88
 
6.1%
천 87
 
6.1%
광 87
 
6.1%
역 87
 
6.1%
동 87
 
6.1%
Other values (77) 490
34.2%
Decimal Number
ValueCountFrequency (%)
1 49
16.4%
3 43
14.4%
2 37
12.4%
4 32
10.7%
5 28
9.4%
6 27
9.1%
8 23
7.7%
7 21
7.0%
0 21
7.0%
9 17
 
5.7%
Space Separator
ValueCountFrequency (%)
354
100.0%
Close Punctuation
ValueCountFrequency (%)
) 87
100.0%
Open Punctuation
ValueCountFrequency (%)
( 87
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 22
100.0%
Other Punctuation
ValueCountFrequency (%)
. 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1431
62.6%
Common 854
37.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
인 112
 
7.8%
홀 103
 
7.2%
미 101
 
7.1%
추 100
 
7.0%
구 89
 
6.2%
시 88
 
6.1%
천 87
 
6.1%
광 87
 
6.1%
역 87
 
6.1%
동 87
 
6.1%
Other values (77) 490
34.2%
Common
ValueCountFrequency (%)
354
41.5%
) 87
 
10.2%
( 87
 
10.2%
1 49
 
5.7%
3 43
 
5.0%
2 37
 
4.3%
4 32
 
3.7%
5 28
 
3.3%
6 27
 
3.2%
8 23
 
2.7%
Other values (5) 87
 
10.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1431
62.6%
ASCII 854
37.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
354
41.5%
) 87
 
10.2%
( 87
 
10.2%
1 49
 
5.7%
3 43
 
5.0%
2 37
 
4.3%
4 32
 
3.7%
5 28
 
3.3%
6 27
 
3.2%
8 23
 
2.7%
Other values (5) 87
 
10.2%
Hangul
ValueCountFrequency (%)
인 112
 
7.8%
홀 103
 
7.2%
미 101
 
7.1%
추 100
 
7.0%
구 89
 
6.2%
시 88
 
6.1%
천 87
 
6.1%
광 87
 
6.1%
역 87
 
6.1%
동 87
 
6.1%
Other values (77) 490
34.2%

위도
Real number (ℝ)

Distinct76
Distinct (%)87.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.457366
Minimum37.437956
Maximum37.467489
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size915.0 B
2024-01-29T01:54:49.640526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.437956
5-th percentile37.440767
Q137.453404
median37.458953
Q337.463411
95-th percentile37.465676
Maximum37.467489
Range0.02953319
Interquartile range (IQR)0.01000692

Descriptive statistics

Standard deviation0.0070848676
Coefficient of variation (CV)0.00018914484
Kurtosis0.80042368
Mean37.457366
Median Absolute Deviation (MAD)0.00470771
Skewness-1.0927956
Sum3258.7908
Variance5.0195349 × 10-5
MonotonicityNot monotonic
2024-01-29T01:54:49.940032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.46366077 6
 
6.9%
37.46341102 3
 
3.4%
37.45803438 2
 
2.3%
37.45808058 2
 
2.3%
37.45965343 2
 
2.3%
37.46057865 2
 
2.3%
37.45895306 1
 
1.1%
37.44879215 1
 
1.1%
37.45183998 1
 
1.1%
37.45083403 1
 
1.1%
Other values (66) 66
75.9%
ValueCountFrequency (%)
37.43795599 1
1.1%
37.43811766 1
1.1%
37.43889382 1
1.1%
37.43913243 1
1.1%
37.43999291 1
1.1%
37.44257161 1
1.1%
37.44533928 1
1.1%
37.44765296 1
1.1%
37.44831019 1
1.1%
37.44832667 1
1.1%
ValueCountFrequency (%)
37.46748918 1
1.1%
37.46691771 1
1.1%
37.46644569 1
1.1%
37.46601105 1
1.1%
37.46569769 1
1.1%
37.46562508 1
1.1%
37.46531887 1
1.1%
37.46443721 1
1.1%
37.46428825 1
1.1%
37.4642261 1
1.1%

경도
Real number (ℝ)

Distinct76
Distinct (%)87.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.67385
Minimum126.63477
Maximum126.69466
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size915.0 B
2024-01-29T01:54:50.253274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.63477
5-th percentile126.63942
Q1126.66871
median126.67888
Q3126.68199
95-th percentile126.6911
Maximum126.69466
Range0.059891
Interquartile range (IQR)0.01328475

Descriptive statistics

Standard deviation0.014917538
Coefficient of variation (CV)0.00011776336
Kurtosis0.60904191
Mean126.67385
Median Absolute Deviation (MAD)0.0048269
Skewness-1.1616576
Sum11020.625
Variance0.00022253294
MonotonicityNot monotonic
2024-01-29T01:54:50.549762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.6817327 6
 
6.9%
126.6817343 3
 
3.4%
126.6831597 2
 
2.3%
126.6772234 2
 
2.3%
126.6798251 2
 
2.3%
126.6822513 2
 
2.3%
126.6754612 1
 
1.1%
126.6708473 1
 
1.1%
126.6691965 1
 
1.1%
126.6801057 1
 
1.1%
Other values (66) 66
75.9%
ValueCountFrequency (%)
126.6347668 1
1.1%
126.6354444 1
1.1%
126.6370275 1
1.1%
126.6381984 1
1.1%
126.6392566 1
1.1%
126.6397858 1
1.1%
126.6464455 1
1.1%
126.6475943 1
1.1%
126.6491376 1
1.1%
126.6495924 1
1.1%
ValueCountFrequency (%)
126.6946578 1
1.1%
126.6935741 1
1.1%
126.693418 1
1.1%
126.6917895 1
1.1%
126.6911718 1
1.1%
126.6909363 1
1.1%
126.6901341 1
1.1%
126.6899169 1
1.1%
126.6898041 1
1.1%
126.689321 1
1.1%

Interactions

2024-01-29T01:54:39.980764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:54:38.874103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:54:39.416616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:54:40.146657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:54:39.031247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:54:39.600895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:54:40.301359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:54:39.231187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:54:39.805021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-29T01:54:50.814581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번구분법인명대표자명전화번호지번주소도로명주소위도경도
연번1.0000.1711.0001.0001.0000.9080.7290.2930.000
구분0.1711.0001.0001.0001.0001.0001.0000.2120.356
법인명1.0001.0001.0001.0001.0001.0001.0001.0001.000
대표자명1.0001.0001.0001.0001.0001.0001.0001.0001.000
전화번호1.0001.0001.0001.0001.0001.0001.0001.0001.000
지번주소0.9081.0001.0001.0001.0001.0001.0001.0001.000
도로명주소0.7291.0001.0001.0001.0001.0001.0001.0001.000
위도0.2930.2121.0001.0001.0001.0001.0001.0000.755
경도0.0000.3561.0001.0001.0001.0001.0000.7551.000
2024-01-29T01:54:51.088279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번위도경도구분
연번1.000-0.204-0.2110.120
위도-0.2041.0000.1820.151
경도-0.2110.1821.0000.258
구분0.1200.1510.2581.000

Missing values

2024-01-29T01:54:40.551104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-29T01:54:40.842700image/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.
2024-01-29T01:54:41.020311image/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

연번구분법인명대표자명전화번호지번주소도로명주소위도경도
01유료전기인력 모여라이애순<NA>인천광역시 미추홀구 주안동 267-9인천광역시 미추홀구 주안로 68 (주안동)37.463929126.67713
12유료주식회사 골드직업소개소김성숙<NA>인천광역시 미추홀구 용현동 207-12인천광역시 미추홀구 독배로382번길 20(용현동)37.453458126.649676
23유료삼춘네건축강선구032-874-8323인천광역시 미추홀구 주안동 1482-23인천광역시 미추홀구 인주대로 423 (주안동)37.451464126.683703
34유료조은이동진032-429-1977인천광역시 미추홀구 주안동 325-1 간석역프라자인천광역시 미추홀구 주안로213번길 15 (주안동)37.464226126.693574
45유료우리인력사무소김상우<NA>인천광역시 미추홀구 주안동 1580-25인천광역시 미추홀구 구월로8번길 42-1 (주안동)37.456622126.693418
56유료주식회사 인천일보아카데미조성호032-876-3332인천광역시 미추홀구 학익동 663-1인천광역시 미추홀구 매소홀로488번길 6-32 (학익동)37.438894126.675113
67유료오케이인력개발김인애<NA>인천광역시 미추홀구 주안동 1419-13인천광역시 미추홀구 인하로 226 (주안동)37.44831126.673118
78유료개미인력인천미추홀구점김한섭<NA>인천광역시 미추홀구 주안동 1586-4인천광역시 미추홀구 구월로 13 (주안동)37.457959126.691789
89유료제이워크이봉회<NA>인천광역시 미추홀구 주안동 169 신성쇼핑인천광역시 미추홀구 주안중로 25 (주안동)37.460579126.682251
910유료일하는사람들조선미032-201-1616인천광역시 미추홀구 주안동 264-10 오현프라자인천광역시 미추홀구 주안로 62 (주안동)37.463988126.676488
연번구분법인명대표자명전화번호지번주소도로명주소위도경도
7778유료도화인력개발심명희<NA>인천광역시 미추홀구 도화동 572번지 21호 2층인천광역시 미추홀구 숙골로 2-11 (도화동)37.464288126.669399
7879유료주안인력공사김정립032-868-0133인천광역시 미추홀구 용현동 117번지 1호인천광역시 미추홀구 수봉남로6번길 154 (용현동)37.455976126.664259
7980유료연합안전직업소개소이금숙032-434-1043인천광역시 미추홀구 주안동 1527번지 6호 2층인천광역시 미추홀구 인하로 369 (주안동)37.448384126.689321
8081유료스마일취업정보타운최인구032-868-4242인천광역시 미추홀구 주안동 197번지 18호 301호인천광역시 미추홀구 주안서로 5 (주안동)37.459271126.677599
8182유료개미건축인력직업소개소이은순032-884-1604인천광역시 미추홀구 용현동 482번지 99호인천광역시 미추홀구 독배로 398-1 (용현동)37.455219126.649592
8283유료신기인력강정화032-862-4888인천광역시 미추홀구 주안동 848번지 8호인천광역시 미추홀구 인하로222번길 13-4 (주안동)37.447653126.672782
8384유료자유건축인력직업소개소정민환032-891-0708인천광역시 미추홀구 용현동 146번지 444호 2층인천광역시 미추홀구 인주대로 171 (용현동)37.456024126.656171
8485유료일진인력개발강영범032-872-0350인천광역시 미추홀구 용현동 70번지 2호인천광역시 미추홀구 인주대로 262 (용현동)37.452705126.665453
8586유료현대직업소개소김을례032-865-1188인천광역시 미추홀구 도화동 433번지 1호인천광역시 미추홀구 경인로 308-1 (도화동)37.459693126.674522
8687무료인천미추홀여성인력개발센터심혜미032-881-6060인천광역시 미추홀구 용현동 624번지 55호 항마스타타워인천광역시 미추홀구 아암대로 57. 항마스타타워 3층 (용현동)37.456232126.635444