Overview

Dataset statistics

Number of variables6
Number of observations239
Missing cells24
Missing cells (%)1.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory11.6 KiB
Average record size in memory49.5 B

Variable types

Categorical1
Text3
DateTime1
Numeric1

Dataset

Description전북특별자치도 관내 노동조합 현황 데이터입니다. 노동조합명, 설립일, 조합원수, 상급단체 명 등의 데이터를 제공합니다.
Author전북특별자치도
URLhttps://www.data.go.kr/data/15099617/fileData.do

Alerts

사업장명 has 18 (7.5%) missing valuesMissing
상급단체 has 6 (2.5%) missing valuesMissing
노동조합명 has unique valuesUnique
조합원수 has 3 (1.3%) zerosZeros

Reproduction

Analysis started2024-04-21 11:12:54.094820
Analysis finished2024-04-21 11:12:55.347809
Duration1.25 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

행정관청명
Categorical

Distinct13
Distinct (%)5.4%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
전주시
63 
익산시
45 
군산시
37 
정읍시
22 
완주군
22 
Other values (8)
50 

Length

Max length7
Median length3
Mean length3.3514644
Min length3

Unique

Unique2 ?
Unique (%)0.8%

Sample

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

Common Values

ValueCountFrequency (%)
전주시 63
26.4%
익산시 45
18.8%
군산시 37
15.5%
정읍시 22
 
9.2%
완주군 22
 
9.2%
전북특별자치도 21
 
8.8%
김제시 8
 
3.3%
남원시 7
 
2.9%
부안군 7
 
2.9%
임실군 3
 
1.3%
Other values (3) 4
 
1.7%

Length

2024-04-21T20:12:55.478230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
전주시 63
26.4%
익산시 45
18.8%
군산시 37
15.5%
정읍시 22
 
9.2%
완주군 22
 
9.2%
전북특별자치도 21
 
8.8%
김제시 8
 
3.3%
남원시 7
 
2.9%
부안군 7
 
2.9%
임실군 3
 
1.3%
Other values (3) 4
 
1.7%

노동조합명
Text

UNIQUE 

Distinct239
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
2024-04-21T20:12:56.186154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length18
Mean length11.656904
Min length5

Characters and Unicode

Total characters2786
Distinct characters268
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique239 ?
Unique (%)100.0%

Sample

1st row전북서부항운 노동조합
2nd row전북지역자동차노동조합
3rd row전북특별자치도지역일반노동조합
4th row전주김제완주축협노동조합
5th row전북지역연대노동조합
ValueCountFrequency (%)
노동조합 88
 
22.3%
영업 5
 
1.3%
전주시 3
 
0.8%
공무직 3
 
0.8%
전북지역 3
 
0.8%
한마음노동조합 2
 
0.5%
제일여객 2
 
0.5%
예수병원 2
 
0.5%
기업노조 2
 
0.5%
광전자 2
 
0.5%
Other values (280) 283
71.6%
2024-04-21T20:12:57.127940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 247
 
8.9%
합 244
 
8.8%
조 242
 
8.7%
노 240
 
8.6%
158
 
5.7%
주 63
 
2.3%
전 60
 
2.2%
시 42
 
1.5%
산 41
 
1.5%
) 40
 
1.4%
Other values (258) 1409
50.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2508
90.0%
Space Separator 158
 
5.7%
Close Punctuation 45
 
1.6%
Open Punctuation 45
 
1.6%
Uppercase Letter 19
 
0.7%
Other Symbol 7
 
0.3%
Decimal Number 2
 
0.1%
Lowercase Letter 1
 
< 0.1%
Letter Number 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 247
 
9.8%
합 244
 
9.7%
조 242
 
9.6%
노 240
 
9.6%
주 63
 
2.5%
전 60
 
2.4%
시 42
 
1.7%
산 41
 
1.6%
원 38
 
1.5%
업 31
 
1.2%
Other values (236) 1260
50.2%
Uppercase Letter
ValueCountFrequency (%)
S 4
21.1%
C 4
21.1%
G 2
10.5%
K 1
 
5.3%
F 1
 
5.3%
E 1
 
5.3%
D 1
 
5.3%
L 1
 
5.3%
O 1
 
5.3%
T 1
 
5.3%
Other values (2) 2
10.5%
Close Punctuation
ValueCountFrequency (%)
) 40
88.9%
] 5
 
11.1%
Open Punctuation
ValueCountFrequency (%)
( 40
88.9%
[ 5
 
11.1%
Decimal Number
ValueCountFrequency (%)
1 1
50.0%
2 1
50.0%
Space Separator
ValueCountFrequency (%)
158
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 7
100.0%
Lowercase Letter
ValueCountFrequency (%)
n 1
100.0%
Letter Number
ValueCountFrequency (%)
Ⅱ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2515
90.3%
Common 250
 
9.0%
Latin 21
 
0.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 247
 
9.8%
합 244
 
9.7%
조 242
 
9.6%
노 240
 
9.5%
주 63
 
2.5%
전 60
 
2.4%
시 42
 
1.7%
산 41
 
1.6%
원 38
 
1.5%
업 31
 
1.2%
Other values (237) 1267
50.4%
Latin
ValueCountFrequency (%)
S 4
19.0%
C 4
19.0%
G 2
9.5%
K 1
 
4.8%
F 1
 
4.8%
E 1
 
4.8%
D 1
 
4.8%
L 1
 
4.8%
n 1
 
4.8%
Ⅱ 1
 
4.8%
Other values (4) 4
19.0%
Common
ValueCountFrequency (%)
158
63.2%
) 40
 
16.0%
( 40
 
16.0%
[ 5
 
2.0%
] 5
 
2.0%
1 1
 
0.4%
2 1
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2508
90.0%
ASCII 270
 
9.7%
None 7
 
0.3%
Number Forms 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 247
 
9.8%
합 244
 
9.7%
조 242
 
9.6%
노 240
 
9.6%
주 63
 
2.5%
전 60
 
2.4%
시 42
 
1.7%
산 41
 
1.6%
원 38
 
1.5%
업 31
 
1.2%
Other values (236) 1260
50.2%
ASCII
ValueCountFrequency (%)
158
58.5%
) 40
 
14.8%
( 40
 
14.8%
[ 5
 
1.9%
] 5
 
1.9%
S 4
 
1.5%
C 4
 
1.5%
G 2
 
0.7%
K 1
 
0.4%
F 1
 
0.4%
Other values (10) 10
 
3.7%
None
ValueCountFrequency (%)
㈜ 7
100.0%
Number Forms
ValueCountFrequency (%)
Ⅱ 1
100.0%

사업장명
Text

MISSING 

Distinct205
Distinct (%)92.8%
Missing18
Missing (%)7.5%
Memory size2.0 KiB
2024-04-21T20:12:57.881200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length14
Mean length6.5791855
Min length2

Characters and Unicode

Total characters1454
Distinct characters251
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

Unique191 ?
Unique (%)86.4%

Sample

1st row전주김제완주축산업협동조합
2nd row임실치즈농협
3rd row대한안전관리공사 등
4th row대한고속 등
5th row전북특별자치도청 등
ValueCountFrequency (%)
등 4
 
1.7%
전주시청 3
 
1.2%
제일여객 3
 
1.2%
새만금개발공사 2
 
0.8%
유)호남고속 2
 
0.8%
안전여객 2
 
0.8%
김제시청 2
 
0.8%
㈜동화택시 2
 
0.8%
광전자(주 2
 
0.8%
스마일교통 2
 
0.8%
Other values (212) 217
90.0%
2024-04-21T20:12:58.875977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
) 59
 
4.1%
( 57
 
3.9%
주 54
 
3.7%
시 40
 
2.8%
산 36
 
2.5%
전 33
 
2.3%
원 28
 
1.9%
교 26
 
1.8%
동 26
 
1.8%
유 25
 
1.7%
Other values (241) 1070
73.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1278
87.9%
Close Punctuation 59
 
4.1%
Open Punctuation 57
 
3.9%
Space Separator 21
 
1.4%
Other Symbol 17
 
1.2%
Uppercase Letter 17
 
1.2%
Other Punctuation 2
 
0.1%
Decimal Number 2
 
0.1%
Lowercase Letter 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 54
 
4.2%
시 40
 
3.1%
산 36
 
2.8%
전 33
 
2.6%
원 28
 
2.2%
교 26
 
2.0%
동 26
 
2.0%
유 25
 
2.0%
대 25
 
2.0%
택 22
 
1.7%
Other values (220) 963
75.4%
Uppercase Letter
ValueCountFrequency (%)
C 3
17.6%
G 2
11.8%
S 2
11.8%
B 1
 
5.9%
N 1
 
5.9%
K 1
 
5.9%
Q 1
 
5.9%
L 1
 
5.9%
I 1
 
5.9%
F 1
 
5.9%
Other values (3) 3
17.6%
Decimal Number
ValueCountFrequency (%)
1 1
50.0%
5 1
50.0%
Close Punctuation
ValueCountFrequency (%)
) 59
100.0%
Open Punctuation
ValueCountFrequency (%)
( 57
100.0%
Space Separator
ValueCountFrequency (%)
21
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 17
100.0%
Other Punctuation
ValueCountFrequency (%)
. 2
100.0%
Lowercase Letter
ValueCountFrequency (%)
n 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1295
89.1%
Common 141
 
9.7%
Latin 18
 
1.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 54
 
4.2%
시 40
 
3.1%
산 36
 
2.8%
전 33
 
2.5%
원 28
 
2.2%
교 26
 
2.0%
동 26
 
2.0%
유 25
 
1.9%
대 25
 
1.9%
택 22
 
1.7%
Other values (221) 980
75.7%
Latin
ValueCountFrequency (%)
C 3
16.7%
G 2
11.1%
S 2
11.1%
B 1
 
5.6%
N 1
 
5.6%
K 1
 
5.6%
Q 1
 
5.6%
L 1
 
5.6%
I 1
 
5.6%
n 1
 
5.6%
Other values (4) 4
22.2%
Common
ValueCountFrequency (%)
) 59
41.8%
( 57
40.4%
21
 
14.9%
. 2
 
1.4%
1 1
 
0.7%
5 1
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1278
87.9%
ASCII 159
 
10.9%
None 17
 
1.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
) 59
37.1%
( 57
35.8%
21
 
13.2%
C 3
 
1.9%
G 2
 
1.3%
. 2
 
1.3%
S 2
 
1.3%
B 1
 
0.6%
N 1
 
0.6%
K 1
 
0.6%
Other values (10) 10
 
6.3%
Hangul
ValueCountFrequency (%)
주 54
 
4.2%
시 40
 
3.1%
산 36
 
2.8%
전 33
 
2.6%
원 28
 
2.2%
교 26
 
2.0%
동 26
 
2.0%
유 25
 
2.0%
대 25
 
2.0%
택 22
 
1.7%
Other values (220) 963
75.4%
None
ValueCountFrequency (%)
㈜ 17
100.0%

설립일
Date

Distinct227
Distinct (%)95.0%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
Minimum1953-03-28 00:00:00
Maximum2022-01-25 00:00:00
2024-04-21T20:12:59.112036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:12:59.348211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

조합원수
Real number (ℝ)

ZEROS 

Distinct112
Distinct (%)46.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean79.740586
Minimum0
Maximum2282
Zeros3
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2024-04-21T20:12:59.580760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q110
median25
Q375
95-th percentile294
Maximum2282
Range2282
Interquartile range (IQR)65

Descriptive statistics

Standard deviation189.75807
Coefficient of variation (CV)2.3796925
Kurtosis80.346576
Mean79.740586
Median Absolute Deviation (MAD)20
Skewness7.8143838
Sum19058
Variance36008.126
MonotonicityNot monotonic
2024-04-21T20:13:00.052325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 9
 
3.8%
7 9
 
3.8%
2 8
 
3.3%
4 7
 
2.9%
8 7
 
2.9%
5 6
 
2.5%
15 6
 
2.5%
3 6
 
2.5%
23 6
 
2.5%
18 5
 
2.1%
Other values (102) 170
71.1%
ValueCountFrequency (%)
0 3
 
1.3%
1 2
 
0.8%
2 8
3.3%
3 6
2.5%
4 7
2.9%
5 6
2.5%
6 1
 
0.4%
7 9
3.8%
8 7
2.9%
9 5
2.1%
ValueCountFrequency (%)
2282 1
0.4%
1082 1
0.4%
801 1
0.4%
750 1
0.4%
512 1
0.4%
410 1
0.4%
379 1
0.4%
363 1
0.4%
345 1
0.4%
336 1
0.4%

상급단체
Text

MISSING 

Distinct54
Distinct (%)23.2%
Missing6
Missing (%)2.5%
Memory size2.0 KiB
2024-04-21T20:13:00.735157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length16
Mean length5.2274678
Min length3

Characters and Unicode

Total characters1218
Distinct characters61
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique35 ?
Unique (%)15.0%

Sample

1st row한)항운노련
2nd row한)자동차노련
3rd row민)민주일반연맹
4th row한국노총
5th row한국노총
ValueCountFrequency (%)
미가맹 99
36.7%
미가입 26
 
9.6%
상급단체 25
 
9.3%
한국노총 23
 
8.5%
한)화학노련 16
 
5.9%
한 6
 
2.2%
한)연합노련 4
 
1.5%
한)공공노련 4
 
1.5%
한)공공연맹 3
 
1.1%
연합노련 3
 
1.1%
Other values (45) 61
22.6%
2024-04-21T20:13:01.878336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
미 126
 
10.3%
가 126
 
10.3%
맹 122
 
10.0%
노 98
 
8.0%
한 82
 
6.7%
) 68
 
5.6%
련 49
 
4.0%
37
 
3.0%
국 36
 
3.0%
연 34
 
2.8%
Other values (51) 440
36.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1111
91.2%
Close Punctuation 68
 
5.6%
Space Separator 37
 
3.0%
Other Punctuation 2
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
미 126
 
11.3%
가 126
 
11.3%
맹 122
 
11.0%
노 98
 
8.8%
한 82
 
7.4%
련 49
 
4.4%
국 36
 
3.2%
연 34
 
3.1%
총 29
 
2.6%
합 28
 
2.5%
Other values (48) 381
34.3%
Close Punctuation
ValueCountFrequency (%)
) 68
100.0%
Space Separator
ValueCountFrequency (%)
37
100.0%
Other Punctuation
ValueCountFrequency (%)
· 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1111
91.2%
Common 107
 
8.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
미 126
 
11.3%
가 126
 
11.3%
맹 122
 
11.0%
노 98
 
8.8%
한 82
 
7.4%
련 49
 
4.4%
국 36
 
3.2%
연 34
 
3.1%
총 29
 
2.6%
합 28
 
2.5%
Other values (48) 381
34.3%
Common
ValueCountFrequency (%)
) 68
63.6%
37
34.6%
· 2
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1111
91.2%
ASCII 105
 
8.6%
None 2
 
0.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
미 126
 
11.3%
가 126
 
11.3%
맹 122
 
11.0%
노 98
 
8.8%
한 82
 
7.4%
련 49
 
4.4%
국 36
 
3.2%
연 34
 
3.1%
총 29
 
2.6%
합 28
 
2.5%
Other values (48) 381
34.3%
ASCII
ValueCountFrequency (%)
) 68
64.8%
37
35.2%
None
ValueCountFrequency (%)
· 2
100.0%

Interactions

2024-04-21T20:12:54.741745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-21T20:13:02.133733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정관청명조합원수상급단체
행정관청명1.0000.5440.915
조합원수0.5441.0000.960
상급단체0.9150.9601.000
2024-04-21T20:13:02.368281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
조합원수행정관청명
조합원수1.0000.301
행정관청명0.3011.000

Missing values

2024-04-21T20:12:54.916161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-21T20:12:55.115294image/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.
2024-04-21T20:12:55.269075image/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

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