Overview

Dataset statistics

Number of variables8
Number of observations106
Missing cells68
Missing cells (%)8.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.9 KiB
Average record size in memory66.2 B

Variable types

Numeric1
Text6
DateTime1

Dataset

Description인천광역시 남동구 유료직업소개소 현황에 대한 데이터로 등록번호, 소개소명칭, 대표자명, 소재지, 전화번호, 팩스번호 등을 제공합니다.
Author인천광역시 남동구
URLhttps://data.incheon.go.kr/findData/publicDataDetail?dataId=15113263&srcSe=7661IVAWM27C61E190

Alerts

데이터기준일 has constant value ""Constant
전화번호 has 26 (24.5%) missing valuesMissing
팩스번호 has 42 (39.6%) missing valuesMissing
연번 has unique valuesUnique
소개소명칭 has unique valuesUnique

Reproduction

Analysis started2024-03-18 05:11:37.902338
Analysis finished2024-03-18 05:11:40.389927
Duration2.49 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

UNIQUE 

Distinct106
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53.5
Minimum1
Maximum106
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-03-18T14:11:40.455369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6.25
Q127.25
median53.5
Q379.75
95-th percentile100.75
Maximum106
Range105
Interquartile range (IQR)52.5

Descriptive statistics

Standard deviation30.743563
Coefficient of variation (CV)0.57464604
Kurtosis-1.2
Mean53.5
Median Absolute Deviation (MAD)26.5
Skewness0
Sum5671
Variance945.16667
MonotonicityStrictly increasing
2024-03-18T14:11:40.598611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.9%
81 1
 
0.9%
79 1
 
0.9%
78 1
 
0.9%
77 1
 
0.9%
76 1
 
0.9%
75 1
 
0.9%
74 1
 
0.9%
73 1
 
0.9%
72 1
 
0.9%
Other values (96) 96
90.6%
ValueCountFrequency (%)
1 1
0.9%
2 1
0.9%
3 1
0.9%
4 1
0.9%
5 1
0.9%
6 1
0.9%
7 1
0.9%
8 1
0.9%
9 1
0.9%
10 1
0.9%
ValueCountFrequency (%)
106 1
0.9%
105 1
0.9%
104 1
0.9%
103 1
0.9%
102 1
0.9%
101 1
0.9%
100 1
0.9%
99 1
0.9%
98 1
0.9%
97 1
0.9%

등록번호
Text

Distinct91
Distinct (%)85.8%
Missing0
Missing (%)0.0%
Memory size980.0 B
2024-03-18T14:11:40.839903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length23
Mean length23.009434
Min length23

Characters and Unicode

Total characters2439
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

Unique85 ?
Unique (%)80.2%

Sample

1st row2002-3530049-11-5-00019
2nd row2002-3530049-11-5-00019
3rd row2003-3530049-11-5-00018
4th row2003-3530049-11-5-00023
5th row2003-3530049-11-5-00025
ValueCountFrequency (%)
2014-3530129-14-5-00001 10
 
9.4%
2017-3530177-14-5-00027 3
 
2.8%
2002-3530049-11-5-00019 2
 
1.9%
2020-3530198-14-5-00028 2
 
1.9%
2020-3530198-14-5-00010 2
 
1.9%
2009-3530066-14-5-00006 2
 
1.9%
2021-3530198-14-5-00018 1
 
0.9%
2021-3530198-14-5-00011 1
 
0.9%
2021-3530198-14-5-00009 1
 
0.9%
2021-3530198-14-5-00008 1
 
0.9%
Other values (81) 81
76.4%
2024-03-18T14:11:41.197346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 642
26.3%
- 424
17.4%
1 324
13.3%
3 224
 
9.2%
5 223
 
9.1%
2 216
 
8.9%
4 127
 
5.2%
9 94
 
3.9%
7 69
 
2.8%
8 63
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2015
82.6%
Dash Punctuation 424
 
17.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 642
31.9%
1 324
16.1%
3 224
 
11.1%
5 223
 
11.1%
2 216
 
10.7%
4 127
 
6.3%
9 94
 
4.7%
7 69
 
3.4%
8 63
 
3.1%
6 33
 
1.6%
Dash Punctuation
ValueCountFrequency (%)
- 424
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2439
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 642
26.3%
- 424
17.4%
1 324
13.3%
3 224
 
9.2%
5 223
 
9.1%
2 216
 
8.9%
4 127
 
5.2%
9 94
 
3.9%
7 69
 
2.8%
8 63
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2439
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 642
26.3%
- 424
17.4%
1 324
13.3%
3 224
 
9.2%
5 223
 
9.1%
2 216
 
8.9%
4 127
 
5.2%
9 94
 
3.9%
7 69
 
2.8%
8 63
 
2.6%

소개소명칭
Text

UNIQUE 

Distinct106
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size980.0 B
2024-03-18T14:11:41.415749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length30
Median length17
Mean length7.9150943
Min length2

Characters and Unicode

Total characters839
Distinct characters191
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique106 ?
Unique (%)100.0%

Sample

1st row만수인력개발
2nd row만수인력개발(지점)
3rd row새벽을여는사람들
4th row지구촌인력
5th row세영직업소개소
ValueCountFrequency (%)
주식회사 14
 
10.6%
채움에이치알디 7
 
5.3%
인천경기간병센터 2
 
1.5%
태강직업소개소 1
 
0.8%
대동인건설.인력사무소 1
 
0.8%
주)가교시스템 1
 
0.8%
다원직업소개소 1
 
0.8%
이노스잡(부천지점 1
 
0.8%
이노스잡 1
 
0.8%
인화케어 1
 
0.8%
Other values (102) 102
77.3%
2024-03-18T14:11:41.755559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
인 52
 
6.2%
소 33
 
3.9%
사 32
 
3.8%
력 30
 
3.6%
26
 
3.1%
개 25
 
3.0%
주 20
 
2.4%
회 19
 
2.3%
업 19
 
2.3%
이 18
 
2.1%
Other values (181) 565
67.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 756
90.1%
Space Separator 26
 
3.1%
Open Punctuation 17
 
2.0%
Close Punctuation 17
 
2.0%
Uppercase Letter 7
 
0.8%
Other Symbol 6
 
0.7%
Lowercase Letter 6
 
0.7%
Other Punctuation 4
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
인 52
 
6.9%
소 33
 
4.4%
사 32
 
4.2%
력 30
 
4.0%
개 25
 
3.3%
주 20
 
2.6%
회 19
 
2.5%
업 19
 
2.5%
이 18
 
2.4%
지 17
 
2.2%
Other values (163) 491
64.9%
Uppercase Letter
ValueCountFrequency (%)
H 2
28.6%
N 1
14.3%
I 1
14.3%
C 1
14.3%
R 1
14.3%
A 1
14.3%
Lowercase Letter
ValueCountFrequency (%)
n 2
33.3%
h 1
16.7%
c 1
16.7%
e 1
16.7%
o 1
16.7%
Other Punctuation
ValueCountFrequency (%)
. 2
50.0%
: 1
25.0%
& 1
25.0%
Space Separator
ValueCountFrequency (%)
26
100.0%
Open Punctuation
ValueCountFrequency (%)
( 17
100.0%
Close Punctuation
ValueCountFrequency (%)
) 17
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 762
90.8%
Common 64
 
7.6%
Latin 13
 
1.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
인 52
 
6.8%
소 33
 
4.3%
사 32
 
4.2%
력 30
 
3.9%
개 25
 
3.3%
주 20
 
2.6%
회 19
 
2.5%
업 19
 
2.5%
이 18
 
2.4%
지 17
 
2.2%
Other values (164) 497
65.2%
Latin
ValueCountFrequency (%)
n 2
15.4%
H 2
15.4%
h 1
7.7%
c 1
7.7%
e 1
7.7%
N 1
7.7%
I 1
7.7%
C 1
7.7%
o 1
7.7%
R 1
7.7%
Common
ValueCountFrequency (%)
26
40.6%
( 17
26.6%
) 17
26.6%
. 2
 
3.1%
: 1
 
1.6%
& 1
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 756
90.1%
ASCII 77
 
9.2%
None 6
 
0.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
인 52
 
6.9%
소 33
 
4.4%
사 32
 
4.2%
력 30
 
4.0%
개 25
 
3.3%
주 20
 
2.6%
회 19
 
2.5%
업 19
 
2.5%
이 18
 
2.4%
지 17
 
2.2%
Other values (163) 491
64.9%
ASCII
ValueCountFrequency (%)
26
33.8%
( 17
22.1%
) 17
22.1%
n 2
 
2.6%
H 2
 
2.6%
. 2
 
2.6%
h 1
 
1.3%
c 1
 
1.3%
e 1
 
1.3%
N 1
 
1.3%
Other values (7) 7
 
9.1%
None
ValueCountFrequency (%)
㈜ 6
100.0%

대표자명
Text

Distinct92
Distinct (%)86.8%
Missing0
Missing (%)0.0%
Memory size980.0 B
2024-03-18T14:11:41.983178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length3
Mean length3
Min length2

Characters and Unicode

Total characters318
Distinct characters102
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique87 ?
Unique (%)82.1%

Sample

1st row채종훈
2nd row채종훈
3rd row강현일
4th row이기숙
5th row김정석
ValueCountFrequency (%)
이수연 10
 
9.4%
김순천 3
 
2.8%
채종훈 2
 
1.9%
윤덕중 2
 
1.9%
이한공 2
 
1.9%
박양주+주형진 1
 
0.9%
김남영 1
 
0.9%
김효선 1
 
0.9%
서지숙 1
 
0.9%
유희경 1
 
0.9%
Other values (82) 82
77.4%
2024-03-18T14:11:42.346949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
이 28
 
8.8%
김 23
 
7.2%
연 15
 
4.7%
수 13
 
4.1%
영 12
 
3.8%
자 7
 
2.2%
순 7
 
2.2%
성 7
 
2.2%
미 7
 
2.2%
박 7
 
2.2%
Other values (92) 192
60.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 317
99.7%
Math Symbol 1
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
이 28
 
8.8%
김 23
 
7.3%
연 15
 
4.7%
수 13
 
4.1%
영 12
 
3.8%
자 7
 
2.2%
순 7
 
2.2%
성 7
 
2.2%
미 7
 
2.2%
박 7
 
2.2%
Other values (91) 191
60.3%
Math Symbol
ValueCountFrequency (%)
+ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 317
99.7%
Common 1
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
이 28
 
8.8%
김 23
 
7.3%
연 15
 
4.7%
수 13
 
4.1%
영 12
 
3.8%
자 7
 
2.2%
순 7
 
2.2%
성 7
 
2.2%
미 7
 
2.2%
박 7
 
2.2%
Other values (91) 191
60.3%
Common
ValueCountFrequency (%)
+ 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 317
99.7%
ASCII 1
 
0.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
이 28
 
8.8%
김 23
 
7.3%
연 15
 
4.7%
수 13
 
4.1%
영 12
 
3.8%
자 7
 
2.2%
순 7
 
2.2%
성 7
 
2.2%
미 7
 
2.2%
박 7
 
2.2%
Other values (91) 191
60.3%
ASCII
ValueCountFrequency (%)
+ 1
100.0%

소재지
Text

Distinct104
Distinct (%)98.1%
Missing0
Missing (%)0.0%
Memory size980.0 B
2024-03-18T14:11:42.602185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length49
Median length39
Mean length30.075472
Min length20

Characters and Unicode

Total characters3188
Distinct characters161
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

Unique102 ?
Unique (%)96.2%

Sample

1st row인천광역시 남동구 만경로8번길 46, 1층 (만수동)
2nd row인천광역시 남구 수봉로 54-5, 지하호 (숭의동)
3rd row인천광역시 남동구 백범로 406, 304호 (간석동)
4th row인천광역시 남동구 석정로 507, 3층 (간석동)
5th row인천광역시 남동구 구월말로 67, 2층(만수동)
ValueCountFrequency (%)
인천광역시 99
 
17.4%
남동구 94
 
16.5%
백범로 15
 
2.6%
2층 9
 
1.6%
만수동 7
 
1.2%
간석동 7
 
1.2%
3층 6
 
1.1%
경기도 6
 
1.1%
구월로 6
 
1.1%
남동대로 5
 
0.9%
Other values (263) 315
55.4%
2024-03-18T14:11:43.049409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
465
 
14.6%
동 205
 
6.4%
구 142
 
4.5%
남 117
 
3.7%
인 111
 
3.5%
시 108
 
3.4%
로 105
 
3.3%
1 104
 
3.3%
천 102
 
3.2%
광 101
 
3.2%
Other values (151) 1628
51.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1824
57.2%
Decimal Number 612
 
19.2%
Space Separator 465
 
14.6%
Other Punctuation 102
 
3.2%
Close Punctuation 82
 
2.6%
Open Punctuation 82
 
2.6%
Dash Punctuation 17
 
0.5%
Uppercase Letter 4
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 205
 
11.2%
구 142
 
7.8%
남 117
 
6.4%
인 111
 
6.1%
시 108
 
5.9%
로 105
 
5.8%
천 102
 
5.6%
광 101
 
5.5%
역 101
 
5.5%
호 71
 
3.9%
Other values (132) 661
36.2%
Decimal Number
ValueCountFrequency (%)
1 104
17.0%
3 97
15.8%
2 94
15.4%
0 86
14.1%
4 55
9.0%
7 43
7.0%
6 42
6.9%
8 38
 
6.2%
5 37
 
6.0%
9 16
 
2.6%
Uppercase Letter
ValueCountFrequency (%)
A 2
50.0%
B 1
25.0%
D 1
25.0%
Other Punctuation
ValueCountFrequency (%)
, 101
99.0%
. 1
 
1.0%
Space Separator
ValueCountFrequency (%)
465
100.0%
Close Punctuation
ValueCountFrequency (%)
) 82
100.0%
Open Punctuation
ValueCountFrequency (%)
( 82
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 17
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1824
57.2%
Common 1360
42.7%
Latin 4
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 205
 
11.2%
구 142
 
7.8%
남 117
 
6.4%
인 111
 
6.1%
시 108
 
5.9%
로 105
 
5.8%
천 102
 
5.6%
광 101
 
5.5%
역 101
 
5.5%
호 71
 
3.9%
Other values (132) 661
36.2%
Common
ValueCountFrequency (%)
465
34.2%
1 104
 
7.6%
, 101
 
7.4%
3 97
 
7.1%
2 94
 
6.9%
0 86
 
6.3%
) 82
 
6.0%
( 82
 
6.0%
4 55
 
4.0%
7 43
 
3.2%
Other values (6) 151
 
11.1%
Latin
ValueCountFrequency (%)
A 2
50.0%
B 1
25.0%
D 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1824
57.2%
ASCII 1364
42.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
465
34.1%
1 104
 
7.6%
, 101
 
7.4%
3 97
 
7.1%
2 94
 
6.9%
0 86
 
6.3%
) 82
 
6.0%
( 82
 
6.0%
4 55
 
4.0%
7 43
 
3.2%
Other values (9) 155
 
11.4%
Hangul
ValueCountFrequency (%)
동 205
 
11.2%
구 142
 
7.8%
남 117
 
6.4%
인 111
 
6.1%
시 108
 
5.9%
로 105
 
5.8%
천 102
 
5.6%
광 101
 
5.5%
역 101
 
5.5%
호 71
 
3.9%
Other values (132) 661
36.2%

전화번호
Text

MISSING 

Distinct73
Distinct (%)91.2%
Missing26
Missing (%)24.5%
Memory size980.0 B
2024-03-18T14:11:43.270840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12.0125
Min length12

Characters and Unicode

Total characters961
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

Unique71 ?
Unique (%)88.8%

Sample

1st row032-461-0950
2nd row032-461-0950
3rd row032-463-1155
4th row032-429-9555
5th row032-471-0405
ValueCountFrequency (%)
032-330-0157 7
 
8.8%
032-461-0950 2
 
2.5%
032-283-1666 1
 
1.2%
032-422-0110 1
 
1.2%
032-467-3781 1
 
1.2%
032-464-5333 1
 
1.2%
032-247-3500 1
 
1.2%
032-432-1300 1
 
1.2%
032-467-8263 1
 
1.2%
032-442-1141 1
 
1.2%
Other values (63) 63
78.8%
2024-03-18T14:11:43.611242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 160
16.6%
0 147
15.3%
2 141
14.7%
3 130
13.5%
4 91
9.5%
1 73
7.6%
7 64
 
6.7%
5 51
 
5.3%
6 43
 
4.5%
8 31
 
3.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 801
83.4%
Dash Punctuation 160
 
16.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 147
18.4%
2 141
17.6%
3 130
16.2%
4 91
11.4%
1 73
9.1%
7 64
8.0%
5 51
 
6.4%
6 43
 
5.4%
8 31
 
3.9%
9 30
 
3.7%
Dash Punctuation
ValueCountFrequency (%)
- 160
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 961
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 160
16.6%
0 147
15.3%
2 141
14.7%
3 130
13.5%
4 91
9.5%
1 73
7.6%
7 64
 
6.7%
5 51
 
5.3%
6 43
 
4.5%
8 31
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 961
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 160
16.6%
0 147
15.3%
2 141
14.7%
3 130
13.5%
4 91
9.5%
1 73
7.6%
7 64
 
6.7%
5 51
 
5.3%
6 43
 
4.5%
8 31
 
3.2%

팩스번호
Text

MISSING 

Distinct62
Distinct (%)96.9%
Missing42
Missing (%)39.6%
Memory size980.0 B
2024-03-18T14:11:43.848185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12.109375
Min length12

Characters and Unicode

Total characters775
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

Unique60 ?
Unique (%)93.8%

Sample

1st row032-461-0951
2nd row032-472-4005
3rd row032-260-3609
4th row032-426-9554
5th row032-466-2619
ValueCountFrequency (%)
032-421-0772 2
 
3.1%
032-508-1754 2
 
3.1%
032-435-0809 1
 
1.6%
032-428-3778 1
 
1.6%
032-461-0951 1
 
1.6%
032-422-1280 1
 
1.6%
032-465-3781 1
 
1.6%
032-468-5332 1
 
1.6%
032-471-2322 1
 
1.6%
032-432-4875 1
 
1.6%
Other values (52) 52
81.2%
2024-03-18T14:11:44.173815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 128
16.5%
2 127
16.4%
0 118
15.2%
3 92
11.9%
4 81
10.5%
1 53
6.8%
7 50
 
6.5%
5 43
 
5.5%
8 32
 
4.1%
6 29
 
3.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 647
83.5%
Dash Punctuation 128
 
16.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 127
19.6%
0 118
18.2%
3 92
14.2%
4 81
12.5%
1 53
8.2%
7 50
 
7.7%
5 43
 
6.6%
8 32
 
4.9%
6 29
 
4.5%
9 22
 
3.4%
Dash Punctuation
ValueCountFrequency (%)
- 128
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 775
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 128
16.5%
2 127
16.4%
0 118
15.2%
3 92
11.9%
4 81
10.5%
1 53
6.8%
7 50
 
6.5%
5 43
 
5.5%
8 32
 
4.1%
6 29
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 775
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 128
16.5%
2 127
16.4%
0 118
15.2%
3 92
11.9%
4 81
10.5%
1 53
6.8%
7 50
 
6.5%
5 43
 
5.5%
8 32
 
4.1%
6 29
 
3.7%

데이터기준일
Date

CONSTANT 

Distinct1
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size980.0 B
Minimum2023-09-07 00:00:00
Maximum2023-09-07 00:00:00
2024-03-18T14:11:44.275356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:11:44.349349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2024-03-18T14:11:39.982072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-18T14:11:44.439120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번등록번호대표자명전화번호팩스번호
연번1.0000.9880.9931.0001.000
등록번호0.9881.0001.0001.0000.992
대표자명0.9931.0001.0001.0000.992
전화번호1.0001.0001.0001.0001.000
팩스번호1.0000.9920.9921.0001.000

Missing values

2024-03-18T14:11:40.148834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-18T14:11:40.260357image/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.
2024-03-18T14:11:40.348153image/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

연번등록번호소개소명칭대표자명소재지전화번호팩스번호데이터기준일
012002-3530049-11-5-00019만수인력개발채종훈인천광역시 남동구 만경로8번길 46, 1층 (만수동)032-461-0950032-461-09512023-09-07
122002-3530049-11-5-00019만수인력개발(지점)채종훈인천광역시 남구 수봉로 54-5, 지하호 (숭의동)032-461-0950032-472-40052023-09-07
232003-3530049-11-5-00018새벽을여는사람들강현일인천광역시 남동구 백범로 406, 304호 (간석동)032-463-1155032-260-36092023-09-07
342003-3530049-11-5-00023지구촌인력이기숙인천광역시 남동구 석정로 507, 3층 (간석동)032-429-9555032-426-95542023-09-07
452003-3530049-11-5-00025세영직업소개소김정석인천광역시 남동구 구월말로 67, 2층(만수동)032-471-0405032-466-26192023-09-07
562004-3530049-11-5-00010해동인력.파출조순희인천광역시 남동구 백범로 213 (만수동)032-464-2467032-465-80102023-09-07
672004-3530049-11-5-00012제일직업소개소(이)이혁인천광역시 남동구 백범로 269-2 (간석동)032-431-6886032-888-38832023-09-07
782004-3530049-11-5-00018제일직업소개소(한)한우리인천광역시 남동구 용천로168번길 5, 3층 (간석동)032-437-7797032-817-29012023-09-07
892006-3530049-11-5-00004바로취업정보서홍열인천광역시 남동구 문화서로3번길 13, 202호 (구월동)032-432-0607032-812-43482023-09-07
9102006-3530066-11-5-00001새벽직업소개소이병희인천광역시 남동구 호구포로810번길 76, 3층 (구월동)032-464-8758<NA>2023-09-07
연번등록번호소개소명칭대표자명소재지전화번호팩스번호데이터기준일
96972022-3530198-14-5-00014블루베리HR김성민인천광역시 남동구 서창남순환로223, A동312호<NA><NA>2023-09-07
97982022-3530198-14-5-00015백마인력김윤환인천광역시 남동구 남동대로765번길17,808호<NA><NA>2023-09-07
98992022-3530198-14-5-00016일가자인력(인천점)이경현인천광역시 남동구 남동대로765번길17,706호<NA><NA>2023-09-07
991002022-3530198-14-5-00017호텔인박성필인천광역시 남동구 남동대로765번길17,906호032-715-5459032-508-17542023-09-07
1001012022-3530198-14-5-00018맘앤터치최미화인천광역시 남동구 석산로 183, 2층032-424-1197<NA>2023-09-07
1011022022-3530198-14-5-00019올잡&모든일이기철인천광역시 남동구 남동대로733번길 8, 205호<NA>032-421-07722023-09-07
1021032022-3530198-14-5-00020힘찬인력사무소윤건인천광역시 남동구 만수로 53, 2층<NA><NA>2023-09-07
1031042022-3530198-14-5-00021제이컴퍼니신영자인천광역시 남동구 논현로26번길 34, 709호<NA><NA>2023-09-07
1041052023-3530198-14-5-00001동양인력임경혜인천광역시 남동구 구월로 358, 2층 202호(만수동)032-462-3111050-4022-08772023-09-07
1051062023-3530198-14-5-00002드림파출최종숙인천광역시 남동구 백범로 338, 301호(간석동)<NA>032-508-17542023-09-07