Overview

Dataset statistics

Number of variables6
Number of observations235
Missing cells2
Missing cells (%)0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory11.4 KiB
Average record size in memory49.6 B

Variable types

Categorical2
Text2
DateTime1
Numeric1

Dataset

Description전라남도 시군에 위치한 노동조합 현황 (조합명, 조합원수, 사업장 명,설립일 등)에 관한 데이터를 조회하실 수 있습니다.
Author전라남도
URLhttps://www.data.go.kr/data/3036101/fileData.do

Alerts

조합원수 is highly overall correlated with 상급단체High correlation
상급단체 is highly overall correlated with 조합원수High correlation
노동조합명 has unique valuesUnique

Reproduction

Analysis started2023-12-12 00:55:03.397473
Analysis finished2023-12-12 00:55:04.323111
Duration0.93 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

행정관청명
Categorical

Distinct21
Distinct (%)8.9%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
여수시
71 
광양시
37 
순천시
21 
영광군
17 
목포시
15 
Other values (16)
74 

Length

Max length4
Median length3
Mean length3.1319149
Min length3

Unique

Unique2 ?
Unique (%)0.9%

Sample

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

Common Values

ValueCountFrequency (%)
여수시 71
30.2%
광양시 37
15.7%
순천시 21
 
8.9%
영광군 17
 
7.2%
목포시 15
 
6.4%
전라남도 14
 
6.0%
영암군 10
 
4.3%
나주시 8
 
3.4%
해남군 7
 
3.0%
장성군 6
 
2.6%
Other values (11) 29
12.3%

Length

2023-12-12T09:55:04.401707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
여수시 71
30.2%
광양시 37
15.7%
순천시 21
 
8.9%
영광군 17
 
7.2%
목포시 15
 
6.4%
전라남도 14
 
6.0%
영암군 10
 
4.3%
나주시 8
 
3.4%
해남군 7
 
3.0%
장성군 6
 
2.6%
Other values (11) 29
12.3%

노동조합명
Text

UNIQUE 

Distinct235
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
2023-12-12T09:55:04.679274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length32
Median length28
Mean length11.523404
Min length4

Characters and Unicode

Total characters2708
Distinct characters259
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique235 ?
Unique (%)100.0%

Sample

1st rowLG Chem 노동조합
2nd row전남서부항운노동조합
3rd row전남지역학교비정규직노동조합
4th row전국예능인노동조합
5th row여수광양항만예선노동조합
ValueCountFrequency (%)
노동조합 61
 
17.3%
한전kps노동조합 3
 
0.8%
여수시도시관리공단 3
 
0.8%
영광지부 3
 
0.8%
영암지부 2
 
0.6%
컨테이너지부 2
 
0.6%
전국금융산업노동조합 2
 
0.6%
한국노총 2
 
0.6%
혁성실업(주 1
 
0.3%
전국금속노동조합 1
 
0.3%
Other values (273) 273
77.3%
2023-12-12T09:55:05.140488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 239
 
8.8%
조 237
 
8.8%
합 235
 
8.7%
노 233
 
8.6%
119
 
4.4%
전 52
 
1.9%
지 43
 
1.6%
남 42
 
1.6%
주 39
 
1.4%
수 38
 
1.4%
Other values (249) 1431
52.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2432
89.8%
Space Separator 119
 
4.4%
Uppercase Letter 61
 
2.3%
Open Punctuation 36
 
1.3%
Close Punctuation 36
 
1.3%
Lowercase Letter 10
 
0.4%
Other Symbol 8
 
0.3%
Other Punctuation 3
 
0.1%
Decimal Number 3
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 239
 
9.8%
조 237
 
9.7%
합 235
 
9.7%
노 233
 
9.6%
전 52
 
2.1%
지 43
 
1.8%
남 42
 
1.7%
주 39
 
1.6%
수 38
 
1.6%
광 33
 
1.4%
Other values (215) 1241
51.0%
Uppercase Letter
ValueCountFrequency (%)
S 9
14.8%
C 8
13.1%
K 7
11.5%
P 5
8.2%
T 5
8.2%
G 5
8.2%
N 5
8.2%
D 3
 
4.9%
R 2
 
3.3%
M 2
 
3.3%
Other values (8) 10
16.4%
Lowercase Letter
ValueCountFrequency (%)
p 2
20.0%
c 2
20.0%
k 2
20.0%
m 1
10.0%
e 1
10.0%
h 1
10.0%
s 1
10.0%
Decimal Number
ValueCountFrequency (%)
2 1
33.3%
1 1
33.3%
3 1
33.3%
Other Punctuation
ValueCountFrequency (%)
. 2
66.7%
& 1
33.3%
Space Separator
ValueCountFrequency (%)
119
100.0%
Open Punctuation
ValueCountFrequency (%)
( 36
100.0%
Close Punctuation
ValueCountFrequency (%)
) 36
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2440
90.1%
Common 197
 
7.3%
Latin 71
 
2.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 239
 
9.8%
조 237
 
9.7%
합 235
 
9.6%
노 233
 
9.5%
전 52
 
2.1%
지 43
 
1.8%
남 42
 
1.7%
주 39
 
1.6%
수 38
 
1.6%
광 33
 
1.4%
Other values (216) 1249
51.2%
Latin
ValueCountFrequency (%)
S 9
12.7%
C 8
 
11.3%
K 7
 
9.9%
P 5
 
7.0%
T 5
 
7.0%
G 5
 
7.0%
N 5
 
7.0%
D 3
 
4.2%
R 2
 
2.8%
p 2
 
2.8%
Other values (15) 20
28.2%
Common
ValueCountFrequency (%)
119
60.4%
( 36
 
18.3%
) 36
 
18.3%
. 2
 
1.0%
2 1
 
0.5%
1 1
 
0.5%
3 1
 
0.5%
& 1
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2432
89.8%
ASCII 268
 
9.9%
None 8
 
0.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 239
 
9.8%
조 237
 
9.7%
합 235
 
9.7%
노 233
 
9.6%
전 52
 
2.1%
지 43
 
1.8%
남 42
 
1.7%
주 39
 
1.6%
수 38
 
1.6%
광 33
 
1.4%
Other values (215) 1241
51.0%
ASCII
ValueCountFrequency (%)
119
44.4%
( 36
 
13.4%
) 36
 
13.4%
S 9
 
3.4%
C 8
 
3.0%
K 7
 
2.6%
P 5
 
1.9%
T 5
 
1.9%
G 5
 
1.9%
N 5
 
1.9%
Other values (23) 33
 
12.3%
None
ValueCountFrequency (%)
㈜ 8
100.0%

사업장명
Text

Distinct218
Distinct (%)93.2%
Missing1
Missing (%)0.4%
Memory size2.0 KiB
2023-12-12T09:55:05.544920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length12
Mean length6.6794872
Min length2

Characters and Unicode

Total characters1563
Distinct characters249
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique207 ?
Unique (%)88.5%

Sample

1st rowLG Chem
2nd row전남서부항운
3rd row전남지역학교
4th row전국예능인노동조합
5th row여수광양항만
ValueCountFrequency (%)
전남권 5
 
1.9%
주식회사 3
 
1.1%
여수광양항만관리(주 3
 
1.1%
여수시도시관리공단 3
 
1.1%
영암지부 3
 
1.1%
노동조합 2
 
0.8%
주 2
 
0.8%
㈜나주교통 2
 
0.8%
남해화학(주 2
 
0.8%
유)해남교통 2
 
0.8%
Other values (228) 234
89.7%
2023-12-12T09:55:06.091770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
( 69
 
4.4%
) 69
 
4.4%
주 61
 
3.9%
남 35
 
2.2%
수 31
 
2.0%
광 30
 
1.9%
28
 
1.8%
한 28
 
1.8%
시 27
 
1.7%
전 26
 
1.7%
Other values (239) 1159
74.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1325
84.8%
Open Punctuation 69
 
4.4%
Close Punctuation 69
 
4.4%
Uppercase Letter 47
 
3.0%
Space Separator 28
 
1.8%
Other Symbol 12
 
0.8%
Lowercase Letter 9
 
0.6%
Decimal Number 3
 
0.2%
Other Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 61
 
4.6%
남 35
 
2.6%
수 31
 
2.3%
광 30
 
2.3%
한 28
 
2.1%
시 27
 
2.0%
전 26
 
2.0%
업 26
 
2.0%
유 24
 
1.8%
여 24
 
1.8%
Other values (207) 1013
76.5%
Uppercase Letter
ValueCountFrequency (%)
S 8
17.0%
K 5
10.6%
T 5
10.6%
G 4
8.5%
N 4
8.5%
P 4
8.5%
C 4
8.5%
D 3
 
6.4%
I 2
 
4.3%
E 1
 
2.1%
Other values (7) 7
14.9%
Lowercase Letter
ValueCountFrequency (%)
c 2
22.2%
k 2
22.2%
m 1
11.1%
p 1
11.1%
e 1
11.1%
h 1
11.1%
s 1
11.1%
Decimal Number
ValueCountFrequency (%)
3 1
33.3%
1 1
33.3%
2 1
33.3%
Open Punctuation
ValueCountFrequency (%)
( 69
100.0%
Close Punctuation
ValueCountFrequency (%)
) 69
100.0%
Space Separator
ValueCountFrequency (%)
28
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 12
100.0%
Other Punctuation
ValueCountFrequency (%)
& 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1337
85.5%
Common 170
 
10.9%
Latin 56
 
3.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 61
 
4.6%
남 35
 
2.6%
수 31
 
2.3%
광 30
 
2.2%
한 28
 
2.1%
시 27
 
2.0%
전 26
 
1.9%
업 26
 
1.9%
유 24
 
1.8%
여 24
 
1.8%
Other values (208) 1025
76.7%
Latin
ValueCountFrequency (%)
S 8
14.3%
K 5
 
8.9%
T 5
 
8.9%
G 4
 
7.1%
N 4
 
7.1%
P 4
 
7.1%
C 4
 
7.1%
D 3
 
5.4%
I 2
 
3.6%
c 2
 
3.6%
Other values (14) 15
26.8%
Common
ValueCountFrequency (%)
( 69
40.6%
) 69
40.6%
28
16.5%
3 1
 
0.6%
1 1
 
0.6%
2 1
 
0.6%
& 1
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1325
84.8%
ASCII 226
 
14.5%
None 12
 
0.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
( 69
30.5%
) 69
30.5%
28
12.4%
S 8
 
3.5%
K 5
 
2.2%
T 5
 
2.2%
G 4
 
1.8%
N 4
 
1.8%
P 4
 
1.8%
C 4
 
1.8%
Other values (21) 26
 
11.5%
Hangul
ValueCountFrequency (%)
주 61
 
4.6%
남 35
 
2.6%
수 31
 
2.3%
광 30
 
2.3%
한 28
 
2.1%
시 27
 
2.0%
전 26
 
2.0%
업 26
 
2.0%
유 24
 
1.8%
여 24
 
1.8%
Other values (207) 1013
76.5%
None
ValueCountFrequency (%)
㈜ 12
100.0%

설립일
Date

Distinct224
Distinct (%)95.7%
Missing1
Missing (%)0.4%
Memory size2.0 KiB
Minimum1958-02-28 00:00:00
Maximum2021-12-29 00:00:00
2023-12-12T09:55:06.291094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:55:06.471758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

조합원수
Real number (ℝ)

HIGH CORRELATION 

Distinct124
Distinct (%)52.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean122.64681
Minimum1
Maximum5250
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2023-12-12T09:55:06.655920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q113
median33
Q393.5
95-th percentile356.2
Maximum5250
Range5249
Interquartile range (IQR)80.5

Descriptive statistics

Standard deviation422.66265
Coefficient of variation (CV)3.4461773
Kurtosis98.663639
Mean122.64681
Median Absolute Deviation (MAD)25
Skewness9.0971394
Sum28822
Variance178643.72
MonotonicityNot monotonic
2023-12-12T09:55:06.835715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 9
 
3.8%
2 7
 
3.0%
8 7
 
3.0%
15 7
 
3.0%
20 6
 
2.6%
10 5
 
2.1%
30 5
 
2.1%
5 5
 
2.1%
11 5
 
2.1%
45 5
 
2.1%
Other values (114) 174
74.0%
ValueCountFrequency (%)
1 1
 
0.4%
2 7
3.0%
3 9
3.8%
4 3
 
1.3%
5 5
2.1%
6 4
1.7%
7 4
1.7%
8 7
3.0%
9 4
1.7%
10 5
2.1%
ValueCountFrequency (%)
5250 1
0.4%
2352 1
0.4%
2176 1
0.4%
1674 1
0.4%
950 1
0.4%
758 1
0.4%
679 1
0.4%
563 1
0.4%
542 1
0.4%
468 1
0.4%

상급단체
Categorical

HIGH CORRELATION 

Distinct33
Distinct (%)14.0%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
미가맹
120 
한)화학노련
24 
한국노총
16 
한)금속노련
 
12
한)전택노련
 
9
Other values (28)
54 

Length

Max length14
Median length3
Mean length4.6085106
Min length3

Unique

Unique17 ?
Unique (%)7.2%

Sample

1st row민)화학섬유연맹
2nd row한)항운노련
3rd row민주노총
4th row예능인노련
5th row미가맹

Common Values

ValueCountFrequency (%)
미가맹 120
51.1%
한)화학노련 24
 
10.2%
한국노총 16
 
6.8%
한)금속노련 12
 
5.1%
한)전택노련 9
 
3.8%
한)항운노련 6
 
2.6%
한)공공노련 5
 
2.1%
민)화섬연맹 5
 
2.1%
한)연합노련 4
 
1.7%
한)한국노총 3
 
1.3%
Other values (23) 31
 
13.2%

Length

2023-12-12T09:55:07.002243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
미가맹 120
50.2%
한)화학노련 24
 
10.0%
한국노총 16
 
6.7%
한)금속노련 12
 
5.0%
한)전택노련 9
 
3.8%
한)항운노련 6
 
2.5%
한)공공노련 5
 
2.1%
민)화섬연맹 5
 
2.1%
한)연합노련 4
 
1.7%
한)한국노총 3
 
1.3%
Other values (25) 35
 
14.6%

Interactions

2023-12-12T09:55:03.830388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T09:55:07.081115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정관청명조합원수상급단체
행정관청명1.0000.4350.626
조합원수0.4351.0000.911
상급단체0.6260.9111.000
2023-12-12T09:55:07.169360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
상급단체행정관청명
상급단체1.0000.196
행정관청명0.1961.000
2023-12-12T09:55:07.260509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
조합원수행정관청명상급단체
조합원수1.0000.2210.669
행정관청명0.2211.0000.196
상급단체0.6690.1961.000

Missing values

2023-12-12T09:55:03.978902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T09:55:04.146701image/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.
2023-12-12T09:55:04.249213image/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

행정관청명노동조합명사업장명설립일조합원수상급단체
0전라남도LG Chem 노동조합LG Chem1997-10-292352민)화학섬유연맹
1전라남도전남서부항운노동조합전남서부항운2002-06-21387한)항운노련
2전라남도전남지역학교비정규직노동조합전남지역학교2010-10-045250민주노총
3전라남도전국예능인노동조합전국예능인노동조합2015-08-2427예능인노련
4전라남도여수광양항만예선노동조합여수광양항만2016-08-0453미가맹
5전라남도전남중소사업장연대노조전남중소사업장2016-11-0144민주노총
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