Overview

Dataset statistics

Number of variables13
Number of observations592
Missing cells1328
Missing cells (%)17.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory65.5 KiB
Average record size in memory113.2 B

Variable types

Numeric7
Text4
Categorical2

Dataset

Description인천광역시 유료 직업소개소 현황(직업소개소 명칭, 종사자 인원, 취업 현황, 주소, 연락처 등)의 항목을 공개하고자 합니다.
URLhttps://www.data.go.kr/data/15054558/fileData.do

Alerts

상담원_운영인력(명) is highly overall correlated with 대표자_운영인력(명)High correlation
대표자_운영인력(명) is highly overall correlated with 상담원_운영인력(명)High correlation
대표자_운영인력(명) is highly imbalanced (89.5%)Imbalance
상담원_운영인력(명) has 125 (21.1%) missing valuesMissing
건설_인력 소개 분야(인원수) has 150 (25.3%) missing valuesMissing
파출_인력 소개 분야(인원수) has 213 (36.0%) missing valuesMissing
간병_인력 소개 분야(인원수) has 235 (39.7%) missing valuesMissing
제조_인력 소개 분야(인원수) has 228 (38.5%) missing valuesMissing
기타_인력 소개 분야(인원수) has 217 (36.7%) missing valuesMissing
전화번호 has 160 (27.0%) missing valuesMissing
연번 has unique valuesUnique
상담원_운영인력(명) has 228 (38.5%) zerosZeros
건설_인력 소개 분야(인원수) has 187 (31.6%) zerosZeros
파출_인력 소개 분야(인원수) has 276 (46.6%) zerosZeros
간병_인력 소개 분야(인원수) has 332 (56.1%) zerosZeros
제조_인력 소개 분야(인원수) has 278 (47.0%) zerosZeros
기타_인력 소개 분야(인원수) has 284 (48.0%) zerosZeros

Reproduction

Analysis started2023-12-12 02:08:19.629498
Analysis finished2023-12-12 02:08:26.993352
Duration7.36 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

UNIQUE 

Distinct592
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean296.5
Minimum1
Maximum592
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.3 KiB
2023-12-12T11:08:27.073418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile30.55
Q1148.75
median296.5
Q3444.25
95-th percentile562.45
Maximum592
Range591
Interquartile range (IQR)295.5

Descriptive statistics

Standard deviation171.03996
Coefficient of variation (CV)0.57686326
Kurtosis-1.2
Mean296.5
Median Absolute Deviation (MAD)148
Skewness0
Sum175528
Variance29254.667
MonotonicityStrictly increasing
2023-12-12T11:08:27.254651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.2%
391 1
 
0.2%
393 1
 
0.2%
394 1
 
0.2%
395 1
 
0.2%
396 1
 
0.2%
397 1
 
0.2%
398 1
 
0.2%
399 1
 
0.2%
400 1
 
0.2%
Other values (582) 582
98.3%
ValueCountFrequency (%)
1 1
0.2%
2 1
0.2%
3 1
0.2%
4 1
0.2%
5 1
0.2%
6 1
0.2%
7 1
0.2%
8 1
0.2%
9 1
0.2%
10 1
0.2%
ValueCountFrequency (%)
592 1
0.2%
591 1
0.2%
590 1
0.2%
589 1
0.2%
588 1
0.2%
587 1
0.2%
586 1
0.2%
585 1
0.2%
584 1
0.2%
583 1
0.2%

기관명
Text

Distinct571
Distinct (%)96.5%
Missing0
Missing (%)0.0%
Memory size4.8 KiB
2023-12-12T11:08:27.603477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length21
Mean length6.8226351
Min length2

Characters and Unicode

Total characters4039
Distinct characters365
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique556 ?
Unique (%)93.9%

Sample

1st row (주)무지개레마(법인)
2nd row (주)스카이피엠씨(법인)
3rd row 0815인력
4th row 21세기인력사무소
5th row OK인력
ValueCountFrequency (%)
주식회사 14
 
2.1%
직업소개소 9
 
1.3%
인력 6
 
0.9%
힘찬인력 4
 
0.6%
든든한파출부 4
 
0.6%
㈜휴먼잡트러스트 4
 
0.6%
동양인력 3
 
0.4%
한빛인력개발 3
 
0.4%
우리인력 3
 
0.4%
희망인력 3
 
0.4%
Other values (594) 621
92.1%
2023-12-12T11:08:28.111176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
인 352
 
8.7%
력 288
 
7.1%
소 205
 
5.1%
192
 
4.8%
개 174
 
4.3%
업 108
 
2.7%
직 89
 
2.2%
발 75
 
1.9%
사 72
 
1.8%
파 60
 
1.5%
Other values (355) 2424
60.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3638
90.1%
Space Separator 192
 
4.8%
Open Punctuation 46
 
1.1%
Close Punctuation 46
 
1.1%
Uppercase Letter 44
 
1.1%
Lowercase Letter 24
 
0.6%
Other Symbol 21
 
0.5%
Decimal Number 20
 
0.5%
Other Punctuation 5
 
0.1%
Math Symbol 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
인 352
 
9.7%
력 288
 
7.9%
소 205
 
5.6%
개 174
 
4.8%
업 108
 
3.0%
직 89
 
2.4%
발 75
 
2.1%
사 72
 
2.0%
파 60
 
1.6%
스 54
 
1.5%
Other values (307) 2161
59.4%
Uppercase Letter
ValueCountFrequency (%)
O 6
13.6%
K 6
13.6%
A 4
9.1%
H 4
9.1%
C 4
9.1%
R 3
6.8%
J 3
6.8%
L 3
6.8%
B 2
 
4.5%
G 2
 
4.5%
Other values (6) 7
15.9%
Lowercase Letter
ValueCountFrequency (%)
h 3
12.5%
n 3
12.5%
l 3
12.5%
i 3
12.5%
o 2
8.3%
g 2
8.3%
x 1
 
4.2%
s 1
 
4.2%
m 1
 
4.2%
t 1
 
4.2%
Other values (4) 4
16.7%
Decimal Number
ValueCountFrequency (%)
1 8
40.0%
3 2
 
10.0%
5 2
 
10.0%
2 2
 
10.0%
7 1
 
5.0%
4 1
 
5.0%
6 1
 
5.0%
9 1
 
5.0%
0 1
 
5.0%
8 1
 
5.0%
Other Punctuation
ValueCountFrequency (%)
. 3
60.0%
& 2
40.0%
Space Separator
ValueCountFrequency (%)
192
100.0%
Open Punctuation
ValueCountFrequency (%)
( 46
100.0%
Close Punctuation
ValueCountFrequency (%)
) 46
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 21
100.0%
Math Symbol
ValueCountFrequency (%)
+ 2
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3659
90.6%
Common 312
 
7.7%
Latin 68
 
1.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
인 352
 
9.6%
력 288
 
7.9%
소 205
 
5.6%
개 174
 
4.8%
업 108
 
3.0%
직 89
 
2.4%
발 75
 
2.0%
사 72
 
2.0%
파 60
 
1.6%
스 54
 
1.5%
Other values (308) 2182
59.6%
Latin
ValueCountFrequency (%)
O 6
 
8.8%
K 6
 
8.8%
A 4
 
5.9%
H 4
 
5.9%
C 4
 
5.9%
h 3
 
4.4%
R 3
 
4.4%
J 3
 
4.4%
n 3
 
4.4%
L 3
 
4.4%
Other values (20) 29
42.6%
Common
ValueCountFrequency (%)
192
61.5%
( 46
 
14.7%
) 46
 
14.7%
1 8
 
2.6%
. 3
 
1.0%
3 2
 
0.6%
& 2
 
0.6%
+ 2
 
0.6%
5 2
 
0.6%
2 2
 
0.6%
Other values (7) 7
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3638
90.1%
ASCII 380
 
9.4%
None 21
 
0.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
인 352
 
9.7%
력 288
 
7.9%
소 205
 
5.6%
개 174
 
4.8%
업 108
 
3.0%
직 89
 
2.4%
발 75
 
2.1%
사 72
 
2.0%
파 60
 
1.6%
스 54
 
1.5%
Other values (307) 2161
59.4%
ASCII
ValueCountFrequency (%)
192
50.5%
( 46
 
12.1%
) 46
 
12.1%
1 8
 
2.1%
O 6
 
1.6%
K 6
 
1.6%
A 4
 
1.1%
H 4
 
1.1%
C 4
 
1.1%
h 3
 
0.8%
Other values (37) 61
 
16.1%
None
ValueCountFrequency (%)
㈜ 21
100.0%

대표자명
Text

Distinct575
Distinct (%)97.1%
Missing0
Missing (%)0.0%
Memory size4.8 KiB
2023-12-12T11:08:28.638468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length5
Mean length4.9949324
Min length4

Characters and Unicode

Total characters2957
Distinct characters184
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique560 ?
Unique (%)94.6%

Sample

1st row 이영배
2nd row 박성우
3rd row 최종근
4th row 변각현
5th row 이태연
ValueCountFrequency (%)
김재순 3
 
0.5%
김미숙 3
 
0.5%
최미화 2
 
0.3%
서정선 2
 
0.3%
이상희 2
 
0.3%
박성우 2
 
0.3%
김성민 2
 
0.3%
임혜영 2
 
0.3%
이상규 2
 
0.3%
김정원 2
 
0.3%
Other values (566) 571
96.3%
2023-12-12T11:08:29.353161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1185
40.1%
김 132
 
4.5%
이 89
 
3.0%
정 63
 
2.1%
영 54
 
1.8%
박 49
 
1.7%
희 43
 
1.5%
성 42
 
1.4%
순 34
 
1.1%
현 32
 
1.1%
Other values (174) 1234
41.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1771
59.9%
Space Separator 1185
40.1%
Other Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
김 132
 
7.5%
이 89
 
5.0%
정 63
 
3.6%
영 54
 
3.0%
박 49
 
2.8%
희 43
 
2.4%
성 42
 
2.4%
순 34
 
1.9%
현 32
 
1.8%
미 32
 
1.8%
Other values (172) 1201
67.8%
Space Separator
ValueCountFrequency (%)
1185
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1771
59.9%
Common 1186
40.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
김 132
 
7.5%
이 89
 
5.0%
정 63
 
3.6%
영 54
 
3.0%
박 49
 
2.8%
희 43
 
2.4%
성 42
 
2.4%
순 34
 
1.9%
현 32
 
1.8%
미 32
 
1.8%
Other values (172) 1201
67.8%
Common
ValueCountFrequency (%)
1185
99.9%
, 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1771
59.9%
ASCII 1186
40.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1185
99.9%
, 1
 
0.1%
Hangul
ValueCountFrequency (%)
김 132
 
7.5%
이 89
 
5.0%
정 63
 
3.6%
영 54
 
3.0%
박 49
 
2.8%
희 43
 
2.4%
성 42
 
2.4%
순 34
 
1.9%
현 32
 
1.8%
미 32
 
1.8%
Other values (172) 1201
67.8%

대표자_운영인력(명)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size4.8 KiB
1
579 
<NA>
 
11
2
 
2

Length

Max length4
Median length1
Mean length1.0557432
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 579
97.8%
<NA> 11
 
1.9%
2 2
 
0.3%

Length

2023-12-12T11:08:29.574698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:08:29.737131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 579
97.8%
na 11
 
1.9%
2 2
 
0.3%

상담원_운영인력(명)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct9
Distinct (%)1.9%
Missing125
Missing (%)21.1%
Infinite0
Infinite (%)0.0%
Mean0.78372591
Minimum0
Maximum17
Zeros228
Zeros (%)38.5%
Negative0
Negative (%)0.0%
Memory size5.3 KiB
2023-12-12T11:08:29.849579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile2
Maximum17
Range17
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.2611361
Coefficient of variation (CV)1.6091545
Kurtosis67.381937
Mean0.78372591
Median Absolute Deviation (MAD)1
Skewness6.2153518
Sum366
Variance1.5904642
MonotonicityNot monotonic
2023-12-12T11:08:30.014255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 228
38.5%
1 164
27.7%
2 52
 
8.8%
3 17
 
2.9%
4 2
 
0.3%
11 1
 
0.2%
17 1
 
0.2%
5 1
 
0.2%
6 1
 
0.2%
(Missing) 125
21.1%
ValueCountFrequency (%)
0 228
38.5%
1 164
27.7%
2 52
 
8.8%
3 17
 
2.9%
4 2
 
0.3%
5 1
 
0.2%
6 1
 
0.2%
11 1
 
0.2%
17 1
 
0.2%
ValueCountFrequency (%)
17 1
 
0.2%
11 1
 
0.2%
6 1
 
0.2%
5 1
 
0.2%
4 2
 
0.3%
3 17
 
2.9%
2 52
 
8.8%
1 164
27.7%
0 228
38.5%

종사자_운영인력(명)
Categorical

Distinct6
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size4.8 KiB
0
274 
<NA>
152 
1
128 
2
 
27
3
 
10

Length

Max length4
Median length1
Mean length1.7702703
Min length1

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st row1
2nd row1
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
0 274
46.3%
<NA> 152
25.7%
1 128
21.6%
2 27
 
4.6%
3 10
 
1.7%
5 1
 
0.2%

Length

2023-12-12T11:08:30.192657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:08:30.350275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 274
46.3%
na 152
25.7%
1 128
21.6%
2 27
 
4.6%
3 10
 
1.7%
5 1
 
0.2%

건설_인력 소개 분야(인원수)
Real number (ℝ)

MISSING  ZEROS 

Distinct203
Distinct (%)45.9%
Missing150
Missing (%)25.3%
Infinite0
Infinite (%)0.0%
Mean807.77602
Minimum0
Maximum23400
Zeros187
Zeros (%)31.6%
Negative0
Negative (%)0.0%
Memory size5.3 KiB
2023-12-12T11:08:30.533034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median30
Q3710.75
95-th percentile3639.9
Maximum23400
Range23400
Interquartile range (IQR)710.75

Descriptive statistics

Standard deviation2052.3065
Coefficient of variation (CV)2.5406876
Kurtosis44.754554
Mean807.77602
Median Absolute Deviation (MAD)30
Skewness5.6569724
Sum357037
Variance4211961.9
MonotonicityNot monotonic
2023-12-12T11:08:30.739184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 187
31.6%
20 9
 
1.5%
50 5
 
0.8%
100 5
 
0.8%
150 5
 
0.8%
1 4
 
0.7%
1200 4
 
0.7%
130 3
 
0.5%
350 3
 
0.5%
5 3
 
0.5%
Other values (193) 214
36.1%
(Missing) 150
25.3%
ValueCountFrequency (%)
0 187
31.6%
1 4
 
0.7%
2 2
 
0.3%
3 2
 
0.3%
5 3
 
0.5%
7 1
 
0.2%
8 1
 
0.2%
9 1
 
0.2%
10 2
 
0.3%
11 1
 
0.2%
ValueCountFrequency (%)
23400 1
0.2%
15118 1
0.2%
13866 1
0.2%
11067 1
0.2%
10515 1
0.2%
8751 1
0.2%
8162 1
0.2%
7865 1
0.2%
7665 1
0.2%
6490 1
0.2%

파출_인력 소개 분야(인원수)
Real number (ℝ)

MISSING  ZEROS 

Distinct86
Distinct (%)22.7%
Missing213
Missing (%)36.0%
Infinite0
Infinite (%)0.0%
Mean279.32718
Minimum0
Maximum23946
Zeros276
Zeros (%)46.6%
Negative0
Negative (%)0.0%
Memory size5.3 KiB
2023-12-12T11:08:30.973658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33.5
95-th percentile1496.4
Maximum23946
Range23946
Interquartile range (IQR)3.5

Descriptive statistics

Standard deviation1425.987
Coefficient of variation (CV)5.1050781
Kurtosis203.3504
Mean279.32718
Median Absolute Deviation (MAD)0
Skewness12.843782
Sum105865
Variance2033439.1
MonotonicityNot monotonic
2023-12-12T11:08:31.173477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 276
46.6%
2 5
 
0.8%
5 5
 
0.8%
15 3
 
0.5%
90 2
 
0.3%
20 2
 
0.3%
3 2
 
0.3%
55 2
 
0.3%
60 2
 
0.3%
10 2
 
0.3%
Other values (76) 78
 
13.2%
(Missing) 213
36.0%
ValueCountFrequency (%)
0 276
46.6%
1 1
 
0.2%
2 5
 
0.8%
3 2
 
0.3%
4 1
 
0.2%
5 5
 
0.8%
6 1
 
0.2%
7 1
 
0.2%
8 1
 
0.2%
9 1
 
0.2%
ValueCountFrequency (%)
23946 1
0.2%
6495 1
0.2%
6397 1
0.2%
4360 1
0.2%
3753 1
0.2%
3600 1
0.2%
3459 1
0.2%
2825 1
0.2%
2622 1
0.2%
2405 1
0.2%

간병_인력 소개 분야(인원수)
Real number (ℝ)

MISSING  ZEROS 

Distinct23
Distinct (%)6.4%
Missing235
Missing (%)39.7%
Infinite0
Infinite (%)0.0%
Mean54.47619
Minimum0
Maximum13751
Zeros332
Zeros (%)56.1%
Negative0
Negative (%)0.0%
Memory size5.3 KiB
2023-12-12T11:08:31.331795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile36
Maximum13751
Range13751
Interquartile range (IQR)0

Descriptive statistics

Standard deviation738.63263
Coefficient of variation (CV)13.558816
Kurtosis334.86307
Mean54.47619
Median Absolute Deviation (MAD)0
Skewness18.070684
Sum19448
Variance545578.16
MonotonicityNot monotonic
2023-12-12T11:08:31.500493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0 332
56.1%
5 3
 
0.5%
59 2
 
0.3%
81 1
 
0.2%
40 1
 
0.2%
1924 1
 
0.2%
12 1
 
0.2%
91 1
 
0.2%
416 1
 
0.2%
35 1
 
0.2%
Other values (13) 13
 
2.2%
(Missing) 235
39.7%
ValueCountFrequency (%)
0 332
56.1%
3 1
 
0.2%
5 3
 
0.5%
12 1
 
0.2%
16 1
 
0.2%
35 1
 
0.2%
40 1
 
0.2%
45 1
 
0.2%
59 2
 
0.3%
70 1
 
0.2%
ValueCountFrequency (%)
13751 1
0.2%
1924 1
0.2%
1162 1
0.2%
916 1
0.2%
416 1
0.2%
180 1
0.2%
132 1
0.2%
121 1
0.2%
120 1
0.2%
111 1
0.2%

제조_인력 소개 분야(인원수)
Real number (ℝ)

MISSING  ZEROS 

Distinct58
Distinct (%)15.9%
Missing228
Missing (%)38.5%
Infinite0
Infinite (%)0.0%
Mean162.94505
Minimum0
Maximum23554
Zeros278
Zeros (%)47.0%
Negative0
Negative (%)0.0%
Memory size5.3 KiB
2023-12-12T11:08:31.675984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile167.35
Maximum23554
Range23554
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1446.1587
Coefficient of variation (CV)8.8751311
Kurtosis196.95821
Mean162.94505
Median Absolute Deviation (MAD)0
Skewness13.18011
Sum59312
Variance2091375.1
MonotonicityNot monotonic
2023-12-12T11:08:31.854464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 278
47.0%
5 6
 
1.0%
2 5
 
0.8%
1 4
 
0.7%
6 4
 
0.7%
3 4
 
0.7%
7 4
 
0.7%
10 3
 
0.5%
105 2
 
0.3%
30 2
 
0.3%
Other values (48) 52
 
8.8%
(Missing) 228
38.5%
ValueCountFrequency (%)
0 278
47.0%
1 4
 
0.7%
2 5
 
0.8%
3 4
 
0.7%
4 2
 
0.3%
5 6
 
1.0%
6 4
 
0.7%
7 4
 
0.7%
8 1
 
0.2%
10 3
 
0.5%
ValueCountFrequency (%)
23554 1
0.2%
9000 1
0.2%
8286 1
0.2%
7200 1
0.2%
2521 1
0.2%
1923 1
0.2%
840 1
0.2%
800 1
0.2%
629 1
0.2%
337 1
0.2%

기타_인력 소개 분야(인원수)
Real number (ℝ)

MISSING  ZEROS 

Distinct50
Distinct (%)13.3%
Missing217
Missing (%)36.7%
Infinite0
Infinite (%)0.0%
Mean89.389333
Minimum0
Maximum13253
Zeros284
Zeros (%)48.0%
Negative0
Negative (%)0.0%
Memory size5.3 KiB
2023-12-12T11:08:32.024275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile84.3
Maximum13253
Range13253
Interquartile range (IQR)0

Descriptive statistics

Standard deviation780.42192
Coefficient of variation (CV)8.7305934
Kurtosis226.10193
Mean89.389333
Median Absolute Deviation (MAD)0
Skewness14.227281
Sum33521
Variance609058.38
MonotonicityNot monotonic
2023-12-12T11:08:32.240022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 284
48.0%
1 10
 
1.7%
2 6
 
1.0%
3 5
 
0.8%
8 4
 
0.7%
10 4
 
0.7%
20 3
 
0.5%
65 3
 
0.5%
27 3
 
0.5%
5 3
 
0.5%
Other values (40) 50
 
8.4%
(Missing) 217
36.7%
ValueCountFrequency (%)
0 284
48.0%
1 10
 
1.7%
2 6
 
1.0%
3 5
 
0.8%
4 3
 
0.5%
5 3
 
0.5%
6 1
 
0.2%
7 1
 
0.2%
8 4
 
0.7%
9 3
 
0.5%
ValueCountFrequency (%)
13253 1
0.2%
5960 1
0.2%
2642 1
0.2%
2078 1
0.2%
1504 1
0.2%
1444 1
0.2%
1180 1
0.2%
1076 1
0.2%
835 1
0.2%
500 1
0.2%

전화번호
Text

MISSING 

Distinct402
Distinct (%)93.1%
Missing160
Missing (%)27.0%
Memory size4.8 KiB
2023-12-12T11:08:32.577108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length12
Mean length11.398148
Min length1

Characters and Unicode

Total characters4924
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique397 ?
Unique (%)91.9%

Sample

1st row 032-746-0086
2nd row 032-746-3332
3rd row 032-752-0815
4th row 032-891-8219
5th row 032-752-7177
ValueCountFrequency (%)
29
 
6.3%
032 13
 
2.8%
032-876-0288 2
 
0.4%
032-431-4218 2
 
0.4%
032-882-5119 2
 
0.4%
032-746-0086 1
 
0.2%
032-521-2773 1
 
0.2%
032-710-5969 1
 
0.2%
032-655-8090 1
 
0.2%
032-423-8258 1
 
0.2%
Other values (405) 405
88.4%
2023-12-12T11:08:33.110024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 768
15.6%
2 655
13.3%
0 644
13.1%
3 599
12.2%
5 374
7.6%
1 358
7.3%
8 343
7.0%
4 314
6.4%
7 284
 
5.8%
6 224
 
4.5%
Other values (3) 361
7.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3974
80.7%
Dash Punctuation 768
 
15.6%
Space Separator 113
 
2.3%
Other Punctuation 69
 
1.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 655
16.5%
0 644
16.2%
3 599
15.1%
5 374
9.4%
1 358
9.0%
8 343
8.6%
4 314
7.9%
7 284
7.1%
6 224
 
5.6%
9 179
 
4.5%
Dash Punctuation
ValueCountFrequency (%)
- 768
100.0%
Space Separator
ValueCountFrequency (%)
113
100.0%
Other Punctuation
ValueCountFrequency (%)
' 69
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4924
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 768
15.6%
2 655
13.3%
0 644
13.1%
3 599
12.2%
5 374
7.6%
1 358
7.3%
8 343
7.0%
4 314
6.4%
7 284
 
5.8%
6 224
 
4.5%
Other values (3) 361
7.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4924
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 768
15.6%
2 655
13.3%
0 644
13.1%
3 599
12.2%
5 374
7.6%
1 358
7.3%
8 343
7.0%
4 314
6.4%
7 284
 
5.8%
6 224
 
4.5%
Other values (3) 361
7.3%

소재지
Text

Distinct585
Distinct (%)98.8%
Missing0
Missing (%)0.0%
Memory size4.8 KiB
2023-12-12T11:08:33.571317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length50
Median length43
Mean length31.038851
Min length17

Characters and Unicode

Total characters18375
Distinct characters340
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique578 ?
Unique (%)97.6%

Sample

1st row 인천광역시 중구 영종대로 108. 2층 201호 (운서동. 비2빌)
2nd row 인천광역시 중구 하늘중앙로225번길17-1 (중산동)
3rd row 인천광역시 중구 영종대로84,신공항파크빌210호일부 (운서동)
4th row 인천광역시 중구 인중로 50. 2층 (신흥동3가)
5th row 인천광역시 중구 신도시남로141번길 7. 신공항프라자 516호 (운서동)
ValueCountFrequency (%)
인천광역시 586
 
16.3%
서구 117
 
3.3%
부평구 108
 
3.0%
남동구 92
 
2.6%
미추홀구 81
 
2.3%
2층 78
 
2.2%
3층 75
 
2.1%
계양구 61
 
1.7%
연수구 53
 
1.5%
주안동 50
 
1.4%
Other values (1127) 2284
63.7%
2023-12-12T11:08:34.171342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3097
 
16.9%
동 688
 
3.7%
인 663
 
3.6%
1 620
 
3.4%
구 619
 
3.4%
천 616
 
3.4%
시 605
 
3.3%
광 596
 
3.2%
역 590
 
3.2%
로 584
 
3.2%
Other values (330) 9697
52.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 10415
56.7%
Decimal Number 3192
 
17.4%
Space Separator 3097
 
16.9%
Other Punctuation 527
 
2.9%
Open Punctuation 499
 
2.7%
Close Punctuation 499
 
2.7%
Dash Punctuation 103
 
0.6%
Uppercase Letter 36
 
0.2%
Lowercase Letter 4
 
< 0.1%
Math Symbol 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 688
 
6.6%
인 663
 
6.4%
구 619
 
5.9%
천 616
 
5.9%
시 605
 
5.8%
광 596
 
5.7%
역 590
 
5.7%
로 584
 
5.6%
호 311
 
3.0%
층 261
 
2.5%
Other values (296) 4882
46.9%
Uppercase Letter
ValueCountFrequency (%)
A 10
27.8%
B 9
25.0%
C 4
 
11.1%
T 3
 
8.3%
D 3
 
8.3%
G 1
 
2.8%
L 1
 
2.8%
S 1
 
2.8%
V 1
 
2.8%
I 1
 
2.8%
Other values (2) 2
 
5.6%
Decimal Number
ValueCountFrequency (%)
1 620
19.4%
2 505
15.8%
3 462
14.5%
0 390
12.2%
4 305
9.6%
6 206
 
6.5%
5 190
 
6.0%
7 188
 
5.9%
8 184
 
5.8%
9 142
 
4.4%
Lowercase Letter
ValueCountFrequency (%)
i 1
25.0%
e 1
25.0%
b 1
25.0%
s 1
25.0%
Other Punctuation
ValueCountFrequency (%)
, 286
54.3%
. 241
45.7%
Space Separator
ValueCountFrequency (%)
3097
100.0%
Open Punctuation
ValueCountFrequency (%)
( 499
100.0%
Close Punctuation
ValueCountFrequency (%)
) 499
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 103
100.0%
Math Symbol
ValueCountFrequency (%)
~ 2
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 10416
56.7%
Common 7919
43.1%
Latin 40
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 688
 
6.6%
인 663
 
6.4%
구 619
 
5.9%
천 616
 
5.9%
시 605
 
5.8%
광 596
 
5.7%
역 590
 
5.7%
로 584
 
5.6%
호 311
 
3.0%
층 261
 
2.5%
Other values (297) 4883
46.9%
Common
ValueCountFrequency (%)
3097
39.1%
1 620
 
7.8%
2 505
 
6.4%
( 499
 
6.3%
) 499
 
6.3%
3 462
 
5.8%
0 390
 
4.9%
4 305
 
3.9%
, 286
 
3.6%
. 241
 
3.0%
Other values (7) 1015
 
12.8%
Latin
ValueCountFrequency (%)
A 10
25.0%
B 9
22.5%
C 4
 
10.0%
T 3
 
7.5%
D 3
 
7.5%
G 1
 
2.5%
L 1
 
2.5%
i 1
 
2.5%
e 1
 
2.5%
b 1
 
2.5%
Other values (6) 6
15.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 10415
56.7%
ASCII 7959
43.3%
None 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3097
38.9%
1 620
 
7.8%
2 505
 
6.3%
( 499
 
6.3%
) 499
 
6.3%
3 462
 
5.8%
0 390
 
4.9%
4 305
 
3.8%
, 286
 
3.6%
. 241
 
3.0%
Other values (23) 1055
 
13.3%
Hangul
ValueCountFrequency (%)
동 688
 
6.6%
인 663
 
6.4%
구 619
 
5.9%
천 616
 
5.9%
시 605
 
5.8%
광 596
 
5.7%
역 590
 
5.7%
로 584
 
5.6%
호 311
 
3.0%
층 261
 
2.5%
Other values (296) 4882
46.9%
None
ValueCountFrequency (%)
㈜ 1
100.0%

Interactions

2023-12-12T11:08:25.571201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:20.567908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:21.387334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:22.141368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:22.750070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:23.395136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:24.378029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:25.685792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:20.704057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:21.496725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:22.222922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:22.850034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:23.547532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:24.496594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:25.821507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:20.835093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:21.610151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:22.312851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:22.943274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:23.724310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:24.635539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:25.932887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:20.946572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:21.714407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:22.388555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:23.022888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:23.868252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:24.737252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:26.029130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:21.041122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:21.831418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:22.483271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:23.119538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:24.002816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:24.842052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:26.142688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:21.152640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:21.935734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:22.569854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:23.219958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:24.131516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:24.994952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:26.254057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:21.272689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:22.030272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:22.654880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:23.298092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:24.246565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:25.458289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T11:08:34.296119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번대표자_운영인력(명)상담원_운영인력(명)종사자_운영인력(명)건설_인력 소개 분야(인원수)파출_인력 소개 분야(인원수)간병_인력 소개 분야(인원수)제조_인력 소개 분야(인원수)기타_인력 소개 분야(인원수)
연번1.0000.0000.1850.4560.1190.0740.0000.0630.000
대표자_운영인력(명)0.0001.0000.8880.0000.0000.0000.0000.0000.000
상담원_운영인력(명)0.1850.8881.0000.2570.7210.0000.0000.0000.039
종사자_운영인력(명)0.4560.0000.2571.0000.5620.3070.0000.0000.000
건설_인력 소개 분야(인원수)0.1190.0000.7210.5621.0000.0000.0000.0000.000
파출_인력 소개 분야(인원수)0.0740.0000.0000.3070.0001.0000.0000.0000.000
간병_인력 소개 분야(인원수)0.0000.0000.0000.0000.0000.0001.0000.0000.000
제조_인력 소개 분야(인원수)0.0630.0000.0000.0000.0000.0000.0001.0000.000
기타_인력 소개 분야(인원수)0.0000.0000.0390.0000.0000.0000.0000.0001.000
2023-12-12T11:08:34.701835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대표자_운영인력(명)종사자_운영인력(명)
대표자_운영인력(명)1.0000.000
종사자_운영인력(명)0.0001.000
2023-12-12T11:08:34.793952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번상담원_운영인력(명)건설_인력 소개 분야(인원수)파출_인력 소개 분야(인원수)간병_인력 소개 분야(인원수)제조_인력 소개 분야(인원수)기타_인력 소개 분야(인원수)대표자_운영인력(명)종사자_운영인력(명)
연번1.000-0.144-0.068-0.058-0.014-0.015-0.1630.0000.205
상담원_운영인력(명)-0.1441.0000.1700.0240.0850.114-0.0270.7000.177
건설_인력 소개 분야(인원수)-0.0680.1701.000-0.267-0.201-0.034-0.1740.0000.277
파출_인력 소개 분야(인원수)-0.0580.024-0.2671.000-0.057-0.096-0.0900.0000.124
간병_인력 소개 분야(인원수)-0.0140.085-0.201-0.0571.000-0.059-0.0460.0000.000
제조_인력 소개 분야(인원수)-0.0150.114-0.034-0.096-0.0591.0000.1200.0000.000
기타_인력 소개 분야(인원수)-0.163-0.027-0.174-0.090-0.0460.1201.0000.0000.000
대표자_운영인력(명)0.0000.7000.0000.0000.0000.0000.0001.0000.000
종사자_운영인력(명)0.2050.1770.2770.1240.0000.0000.0000.0001.000

Missing values

2023-12-12T11:08:26.412658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T11:08:26.680053image/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.
2023-12-12T11:08:26.869225image/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

연번기관명대표자명대표자_운영인력(명)상담원_운영인력(명)종사자_운영인력(명)건설_인력 소개 분야(인원수)파출_인력 소개 분야(인원수)간병_인력 소개 분야(인원수)제조_인력 소개 분야(인원수)기타_인력 소개 분야(인원수)전화번호소재지
01(주)무지개레마(법인)이영배1<NA>13600<NA><NA><NA><NA>032-746-0086인천광역시 중구 영종대로 108. 2층 201호 (운서동. 비2빌)
12(주)스카이피엠씨(법인)박성우1313900<NA><NA><NA><NA>032-746-3332인천광역시 중구 하늘중앙로225번길17-1 (중산동)
230815인력최종근1<NA><NA>2050<NA><NA><NA>102032-752-0815인천광역시 중구 영종대로84,신공항파크빌210호일부 (운서동)
3421세기인력사무소변각현1<NA><NA>50515<NA><NA><NA>032-891-8219인천광역시 중구 인중로 50. 2층 (신흥동3가)
45OK인력이태연1<NA><NA>83<NA><NA><NA><NA>032-752-7177인천광역시 중구 신도시남로141번길 7. 신공항프라자 516호 (운서동)
56공항인력직업소개소박병철1<NA><NA>450<NA><NA><NA><NA><NA>인천광역시 중구 용유서로 172번길 41-31 A동 (을왕동)
67광진선박선원컨설팅오보라1<NA><NA>00000<NA>인천광역시 중구 연안부두로 7. 1층 (항동7가)
78국토개발윤지원1<NA>1<NA><NA><NA><NA><NA>032-777-3320인천광역시 중구 우현로72번길 24. 1층 (용동)
89나는나직업소개소황부연11<NA><NA><NA><NA><NA>8032-888-8689인천광역시 중구 연안부두로33번길 1 (항동7가)
910다온인력지원센터박현미11<NA><NA><NA>916<NA><NA>032-772-8080인천광역시 중구 답동로 27. 2층 (경동)
연번기관명대표자명대표자_운영인력(명)상담원_운영인력(명)종사자_운영인력(명)건설_인력 소개 분야(인원수)파출_인력 소개 분야(인원수)간병_인력 소개 분야(인원수)제조_인력 소개 분야(인원수)기타_인력 소개 분야(인원수)전화번호소재지
582583굿모닝직업소개소우근목1115000000032-932-8944인천광역시 강화군 강화읍 중앙로 43 터미널상가 203호
583584남부인력서성범1001000000032-937-1472인천광역시 강화군 길상면 전등사로 97
584585길상종합인력박규태1002000000<NA>인천광역시 강화군 길상면 강화동로 25
585586강화태양인력박영선10150000080032-933-9949인천광역시 강화군 강화읍 고비고개로8 (강화고등학교 앞)
586587원인력구영희1011000000<NA>인천광역시 강화군 강화읍 남문로 60
587588모아인력김재순100500000032-932-5111인천광역시 강화군 선원면 중앙로 130
588589극화인력양승희100600000032-932-7536인천광역시 강화군 선원면 중앙로 248.
589590발품인력이승숙100600000<NA>인천광역시 강화군 강화읍 대산길126번길 9
590591지구촌인력개발박영기110150150000<NA>인천광역시 강화군 강화읍 강화대로185, 2층
591592백령직업소개소윤희주11000000<NA>인천광역시 옹진군 백령면 백령로 272-1