Overview

Dataset statistics

Number of variables14
Number of observations1947
Missing cells2251
Missing cells (%)8.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory228.3 KiB
Average record size in memory120.1 B

Variable types

Categorical2
Text4
Numeric8

Alerts

영업상태명 has constant value ""Constant
노동조합원수(명) is highly overall correlated with 조합원남성수(명) and 1 other fieldsHigh correlation
조합원남성수(명) is highly overall correlated with 노동조합원수(명)High correlation
조합원여성수(명) is highly overall correlated with 노동조합원수(명)High correlation
WGS84위도 is highly overall correlated with 시군명High correlation
WGS84경도 is highly overall correlated with 시군명High correlation
시군명 is highly overall correlated with WGS84위도 and 1 other fieldsHigh correlation
소재지도로명주소 has 190 (9.8%) missing valuesMissing
폐업일자 has 1151 (59.1%) missing valuesMissing
소속연합단체명 has 148 (7.6%) missing valuesMissing
조합원남성수(명) has 31 (1.6%) missing valuesMissing
조합원여성수(명) has 340 (17.5%) missing valuesMissing
WGS84위도 has 184 (9.5%) missing valuesMissing
WGS84경도 has 184 (9.5%) missing valuesMissing
노동조합원수(명) is highly skewed (γ1 = 20.73863311)Skewed
조합원남성수(명) is highly skewed (γ1 = 24.48519105)Skewed
조합원여성수(명) is highly skewed (γ1 = 27.10305301)Skewed
노동조합원수(명) has 34 (1.7%) zerosZeros
조합원남성수(명) has 58 (3.0%) zerosZeros
조합원여성수(명) has 579 (29.7%) zerosZeros

Reproduction

Analysis started2023-12-10 21:33:55.877686
Analysis finished2023-12-10 21:34:05.430550
Duration9.55 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군명
Categorical

HIGH CORRELATION 

Distinct31
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size15.3 KiB
안산시
199 
성남시
179 
수원시
178 
화성시
151 
평택시
142 
Other values (26)
1098 

Length

Max length4
Median length3
Mean length3.0570108
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
안산시 199
 
10.2%
성남시 179
 
9.2%
수원시 178
 
9.1%
화성시 151
 
7.8%
평택시 142
 
7.3%
부천시 103
 
5.3%
고양시 102
 
5.2%
파주시 93
 
4.8%
용인시 87
 
4.5%
안양시 72
 
3.7%
Other values (21) 641
32.9%

Length

2023-12-11T06:34:05.518609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
안산시 199
 
10.2%
성남시 179
 
9.2%
수원시 178
 
9.1%
화성시 151
 
7.8%
평택시 142
 
7.3%
부천시 103
 
5.3%
고양시 102
 
5.2%
파주시 93
 
4.8%
용인시 87
 
4.5%
안양시 72
 
3.7%
Other values (21) 641
32.9%

사업장명
Text

Distinct1907
Distinct (%)97.9%
Missing0
Missing (%)0.0%
Memory size15.3 KiB
2023-12-11T06:34:05.875926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length35
Median length31
Mean length10.942989
Min length1

Characters and Unicode

Total characters21306
Distinct characters542
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1868 ?
Unique (%)95.9%

Sample

1st row우리병원노동조합
2nd row(주)청송노동조합
3rd row동운택시(주) 노동조합 제1노조
4th row팜스코(주) 제1노동조합
5th row청송노동조합
ValueCountFrequency (%)
노동조합 666
 
22.4%
전국연합노동조합연맹 15
 
0.5%
전국자동차노동조합연맹 13
 
0.4%
주식회사 11
 
0.4%
경기지역자동차노동조합 9
 
0.3%
경기도지역버스노동조합 7
 
0.2%
부천지역노동조합 7
 
0.2%
전국화학노동조합연맹 6
 
0.2%
지부 6
 
0.2%
전자노련 5
 
0.2%
Other values (2073) 2228
74.9%
2023-12-11T06:34:06.417381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 1753
 
8.2%
합 1668
 
7.8%
노 1622
 
7.6%
조 1618
 
7.6%
1026
 
4.8%
주 562
 
2.6%
) 531
 
2.5%
( 525
 
2.5%
지 309
 
1.5%
경 274
 
1.3%
Other values (532) 11418
53.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 18939
88.9%
Space Separator 1026
 
4.8%
Close Punctuation 541
 
2.5%
Open Punctuation 535
 
2.5%
Uppercase Letter 179
 
0.8%
Decimal Number 49
 
0.2%
Other Punctuation 17
 
0.1%
Lowercase Letter 13
 
0.1%
Other Symbol 5
 
< 0.1%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 1753
 
9.3%
합 1668
 
8.8%
노 1622
 
8.6%
조 1618
 
8.5%
주 562
 
3.0%
지 309
 
1.6%
경 274
 
1.4%
시 263
 
1.4%
기 253
 
1.3%
원 246
 
1.3%
Other values (480) 10371
54.8%
Uppercase Letter
ValueCountFrequency (%)
S 38
21.2%
C 24
13.4%
K 21
11.7%
T 16
8.9%
G 14
 
7.8%
P 8
 
4.5%
M 8
 
4.5%
D 6
 
3.4%
F 5
 
2.8%
L 4
 
2.2%
Other values (13) 35
19.6%
Decimal Number
ValueCountFrequency (%)
1 18
36.7%
8 9
18.4%
0 6
 
12.2%
3 3
 
6.1%
5 3
 
6.1%
4 2
 
4.1%
7 2
 
4.1%
2 2
 
4.1%
6 2
 
4.1%
9 2
 
4.1%
Lowercase Letter
ValueCountFrequency (%)
c 6
46.2%
s 1
 
7.7%
k 1
 
7.7%
g 1
 
7.7%
n 1
 
7.7%
e 1
 
7.7%
y 1
 
7.7%
h 1
 
7.7%
Other Punctuation
ValueCountFrequency (%)
. 8
47.1%
& 5
29.4%
, 4
23.5%
Close Punctuation
ValueCountFrequency (%)
) 531
98.2%
] 10
 
1.8%
Open Punctuation
ValueCountFrequency (%)
( 525
98.1%
[ 10
 
1.9%
Space Separator
ValueCountFrequency (%)
1026
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 5
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 18944
88.9%
Common 2170
 
10.2%
Latin 192
 
0.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 1753
 
9.3%
합 1668
 
8.8%
노 1622
 
8.6%
조 1618
 
8.5%
주 562
 
3.0%
지 309
 
1.6%
경 274
 
1.4%
시 263
 
1.4%
기 253
 
1.3%
원 246
 
1.3%
Other values (481) 10376
54.8%
Latin
ValueCountFrequency (%)
S 38
19.8%
C 24
12.5%
K 21
10.9%
T 16
 
8.3%
G 14
 
7.3%
P 8
 
4.2%
M 8
 
4.2%
D 6
 
3.1%
c 6
 
3.1%
F 5
 
2.6%
Other values (21) 46
24.0%
Common
ValueCountFrequency (%)
1026
47.3%
) 531
24.5%
( 525
24.2%
1 18
 
0.8%
[ 10
 
0.5%
] 10
 
0.5%
8 9
 
0.4%
. 8
 
0.4%
0 6
 
0.3%
& 5
 
0.2%
Other values (10) 22
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 18938
88.9%
ASCII 2362
 
11.1%
None 5
 
< 0.1%
Compat Jamo 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 1753
 
9.3%
합 1668
 
8.8%
노 1622
 
8.6%
조 1618
 
8.5%
주 562
 
3.0%
지 309
 
1.6%
경 274
 
1.4%
시 263
 
1.4%
기 253
 
1.3%
원 246
 
1.3%
Other values (479) 10370
54.8%
ASCII
ValueCountFrequency (%)
1026
43.4%
) 531
22.5%
( 525
22.2%
S 38
 
1.6%
C 24
 
1.0%
K 21
 
0.9%
1 18
 
0.8%
T 16
 
0.7%
G 14
 
0.6%
[ 10
 
0.4%
Other values (41) 139
 
5.9%
None
ValueCountFrequency (%)
㈜ 5
100.0%
Compat Jamo
ValueCountFrequency (%)
ㆍ 1
100.0%

소재지우편번호
Real number (ℝ)

Distinct1091
Distinct (%)56.3%
Missing8
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean324428.19
Minimum10013
Maximum609321
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.2 KiB
2023-12-11T06:34:06.609698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10013
5-th percentile11161
Q117979
median429450
Q3451821.5
95-th percentile480711.1
Maximum609321
Range599308
Interquartile range (IQR)433842.5

Descriptive statistics

Standard deviation193938.36
Coefficient of variation (CV)0.59778517
Kurtosis-1.0457332
Mean324428.19
Median Absolute Deviation (MAD)27393
Skewness-0.95432401
Sum6.2906626 × 108
Variance3.7612088 × 1010
MonotonicityNot monotonic
2023-12-11T06:34:06.776466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
425110 34
 
1.7%
425100 29
 
1.5%
462736 25
 
1.3%
425090 25
 
1.3%
425080 16
 
0.8%
429862 15
 
0.8%
413902 14
 
0.7%
451821 13
 
0.7%
441350 12
 
0.6%
445813 11
 
0.6%
Other values (1081) 1745
89.6%
ValueCountFrequency (%)
10013 1
0.1%
10022 2
0.1%
10023 1
0.1%
10030 2
0.1%
10057 1
0.1%
10064 1
0.1%
10106 1
0.1%
10109 2
0.1%
10205 1
0.1%
10212 1
0.1%
ValueCountFrequency (%)
609321 1
 
0.1%
487933 1
 
0.1%
487914 4
0.2%
487913 1
 
0.1%
487911 1
 
0.1%
487882 1
 
0.1%
487881 1
 
0.1%
487878 1
 
0.1%
487873 2
0.1%
487871 1
 
0.1%

소재지도로명주소
Text

MISSING 

Distinct1524
Distinct (%)86.7%
Missing190
Missing (%)9.8%
Memory size15.3 KiB
2023-12-11T06:34:07.133061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length51
Median length46
Mean length25.744451
Min length13

Characters and Unicode

Total characters45233
Distinct characters444
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

Unique1343 ?
Unique (%)76.4%

Sample

1st row경기도 가평군 청평면 경춘로 791-11
2nd row경기도 가평군 설악면 유명로 961-34
3rd row경기도 가평군 가평읍 가화로 159-1 (동운택시)
4th row경기도 가평군 설악면 미사리로 156-22
5th row경기도 가평군 설악면 유명로 961-34
ValueCountFrequency (%)
경기도 1757
 
17.8%
안산시 193
 
2.0%
단원구 176
 
1.8%
성남시 171
 
1.7%
수원시 155
 
1.6%
평택시 131
 
1.3%
화성시 115
 
1.2%
부천시 89
 
0.9%
고양시 89
 
0.9%
용인시 84
 
0.9%
Other values (2437) 6911
70.0%
2023-12-11T06:34:07.659104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
8122
 
18.0%
시 1895
 
4.2%
경 1886
 
4.2%
기 1877
 
4.1%
도 1821
 
4.0%
로 1612
 
3.6%
동 1500
 
3.3%
1 1369
 
3.0%
) 1302
 
2.9%
( 1302
 
2.9%
Other values (434) 22547
49.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 27184
60.1%
Space Separator 8122
 
18.0%
Decimal Number 6613
 
14.6%
Close Punctuation 1302
 
2.9%
Open Punctuation 1302
 
2.9%
Other Punctuation 347
 
0.8%
Dash Punctuation 306
 
0.7%
Uppercase Letter 42
 
0.1%
Lowercase Letter 13
 
< 0.1%
Other Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 1895
 
7.0%
경 1886
 
6.9%
기 1877
 
6.9%
도 1821
 
6.7%
로 1612
 
5.9%
동 1500
 
5.5%
구 828
 
3.0%
원 684
 
2.5%
길 657
 
2.4%
안 543
 
2.0%
Other values (396) 13881
51.1%
Decimal Number
ValueCountFrequency (%)
1 1369
20.7%
2 939
14.2%
3 702
10.6%
4 598
9.0%
5 562
8.5%
0 552
8.3%
7 495
 
7.5%
6 486
 
7.3%
8 470
 
7.1%
9 440
 
6.7%
Uppercase Letter
ValueCountFrequency (%)
I 7
16.7%
A 6
14.3%
K 6
14.3%
T 6
14.3%
S 5
11.9%
B 5
11.9%
D 3
7.1%
C 2
 
4.8%
Y 1
 
2.4%
U 1
 
2.4%
Lowercase Letter
ValueCountFrequency (%)
k 3
23.1%
s 3
23.1%
r 1
 
7.7%
e 1
 
7.7%
w 1
 
7.7%
o 1
 
7.7%
t 1
 
7.7%
u 1
 
7.7%
d 1
 
7.7%
Other Punctuation
ValueCountFrequency (%)
, 344
99.1%
. 2
 
0.6%
/ 1
 
0.3%
Space Separator
ValueCountFrequency (%)
8122
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1302
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1302
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 306
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 1
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 27185
60.1%
Common 17993
39.8%
Latin 55
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 1895
 
7.0%
경 1886
 
6.9%
기 1877
 
6.9%
도 1821
 
6.7%
로 1612
 
5.9%
동 1500
 
5.5%
구 828
 
3.0%
원 684
 
2.5%
길 657
 
2.4%
안 543
 
2.0%
Other values (397) 13882
51.1%
Latin
ValueCountFrequency (%)
I 7
12.7%
A 6
10.9%
K 6
10.9%
T 6
10.9%
S 5
9.1%
B 5
9.1%
k 3
 
5.5%
D 3
 
5.5%
s 3
 
5.5%
C 2
 
3.6%
Other values (9) 9
16.4%
Common
ValueCountFrequency (%)
8122
45.1%
1 1369
 
7.6%
) 1302
 
7.2%
( 1302
 
7.2%
2 939
 
5.2%
3 702
 
3.9%
4 598
 
3.3%
5 562
 
3.1%
0 552
 
3.1%
7 495
 
2.8%
Other values (8) 2050
 
11.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 27184
60.1%
ASCII 18048
39.9%
None 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8122
45.0%
1 1369
 
7.6%
) 1302
 
7.2%
( 1302
 
7.2%
2 939
 
5.2%
3 702
 
3.9%
4 598
 
3.3%
5 562
 
3.1%
0 552
 
3.1%
7 495
 
2.7%
Other values (27) 2105
 
11.7%
Hangul
ValueCountFrequency (%)
시 1895
 
7.0%
경 1886
 
6.9%
기 1877
 
6.9%
도 1821
 
6.7%
로 1612
 
5.9%
동 1500
 
5.5%
구 828
 
3.0%
원 684
 
2.5%
길 657
 
2.4%
안 543
 
2.0%
Other values (396) 13881
51.1%
None
ValueCountFrequency (%)
㈜ 1
100.0%

소재지지번주소
Text

Distinct1768
Distinct (%)91.5%
Missing14
Missing (%)0.7%
Memory size15.3 KiB
2023-12-11T06:34:07.971983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length49
Median length43
Mean length24.005173
Min length10

Characters and Unicode

Total characters46402
Distinct characters403
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1642 ?
Unique (%)84.9%

Sample

1st row경기도 가평군 청평면 청평리 442번지 1호
2nd row경기도 가평군 설악면 방일리 산 90번지 2호
3rd row경기도 가평군 가평읍 읍내리 625-7번지 (동운택시)
4th row경기도 가평군 설악면 송산리 675번지
5th row경기도 가평군 설악면 방일리 산 90번지 2호
ValueCountFrequency (%)
경기도 1932
 
18.3%
호 265
 
2.5%
1호 221
 
2.1%
화성시 149
 
1.4%
평택시 142
 
1.3%
안산시단원구 135
 
1.3%
2호 108
 
1.0%
부천시 97
 
0.9%
수원시 93
 
0.9%
성남시 91
 
0.9%
Other values (2289) 7303
69.3%
2023-12-11T06:34:08.459231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9766
21.0%
시 2043
 
4.4%
기 2002
 
4.3%
지 2001
 
4.3%
도 1994
 
4.3%
경 1949
 
4.2%
번 1897
 
4.1%
동 1645
 
3.5%
1 1560
 
3.4%
호 1176
 
2.5%
Other values (393) 20369
43.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 28504
61.4%
Space Separator 9766
 
21.0%
Decimal Number 7714
 
16.6%
Dash Punctuation 281
 
0.6%
Uppercase Letter 51
 
0.1%
Close Punctuation 32
 
0.1%
Open Punctuation 32
 
0.1%
Other Punctuation 12
 
< 0.1%
Lowercase Letter 10
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 2043
 
7.2%
기 2002
 
7.0%
지 2001
 
7.0%
도 1994
 
7.0%
경 1949
 
6.8%
번 1897
 
6.7%
동 1645
 
5.8%
호 1176
 
4.1%
구 875
 
3.1%
원 638
 
2.2%
Other values (358) 12284
43.1%
Uppercase Letter
ValueCountFrequency (%)
K 8
15.7%
B 8
15.7%
A 8
15.7%
I 7
13.7%
T 6
11.8%
S 5
9.8%
D 3
 
5.9%
L 2
 
3.9%
C 2
 
3.9%
U 1
 
2.0%
Decimal Number
ValueCountFrequency (%)
1 1560
20.2%
2 1045
13.5%
3 850
11.0%
4 773
10.0%
6 692
9.0%
5 656
8.5%
7 630
8.2%
0 589
 
7.6%
8 494
 
6.4%
9 425
 
5.5%
Lowercase Letter
ValueCountFrequency (%)
k 2
20.0%
s 2
20.0%
r 1
10.0%
e 1
10.0%
w 1
10.0%
o 1
10.0%
t 1
10.0%
u 1
10.0%
Other Punctuation
ValueCountFrequency (%)
, 10
83.3%
/ 2
 
16.7%
Space Separator
ValueCountFrequency (%)
9766
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 281
100.0%
Close Punctuation
ValueCountFrequency (%)
) 32
100.0%
Open Punctuation
ValueCountFrequency (%)
( 32
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 28504
61.4%
Common 17837
38.4%
Latin 61
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 2043
 
7.2%
기 2002
 
7.0%
지 2001
 
7.0%
도 1994
 
7.0%
경 1949
 
6.8%
번 1897
 
6.7%
동 1645
 
5.8%
호 1176
 
4.1%
구 875
 
3.1%
원 638
 
2.2%
Other values (358) 12284
43.1%
Latin
ValueCountFrequency (%)
K 8
13.1%
B 8
13.1%
A 8
13.1%
I 7
11.5%
T 6
9.8%
S 5
8.2%
D 3
 
4.9%
k 2
 
3.3%
L 2
 
3.3%
C 2
 
3.3%
Other values (9) 10
16.4%
Common
ValueCountFrequency (%)
9766
54.8%
1 1560
 
8.7%
2 1045
 
5.9%
3 850
 
4.8%
4 773
 
4.3%
6 692
 
3.9%
5 656
 
3.7%
7 630
 
3.5%
0 589
 
3.3%
8 494
 
2.8%
Other values (6) 782
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 28504
61.4%
ASCII 17898
38.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9766
54.6%
1 1560
 
8.7%
2 1045
 
5.8%
3 850
 
4.7%
4 773
 
4.3%
6 692
 
3.9%
5 656
 
3.7%
7 630
 
3.5%
0 589
 
3.3%
8 494
 
2.8%
Other values (25) 843
 
4.7%
Hangul
ValueCountFrequency (%)
시 2043
 
7.2%
기 2002
 
7.0%
지 2001
 
7.0%
도 1994
 
7.0%
경 1949
 
6.8%
번 1897
 
6.7%
동 1645
 
5.8%
호 1176
 
4.1%
구 875
 
3.1%
원 638
 
2.2%
Other values (358) 12284
43.1%

인허가일자
Real number (ℝ)

Distinct1531
Distinct (%)78.7%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean20034767
Minimum19530531
Maximum20180820
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.2 KiB
2023-12-11T06:34:08.607237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19530531
5-th percentile19840704
Q119980321
median20061028
Q320121014
95-th percentile20171016
Maximum20180820
Range650289
Interquartile range (IQR)140693

Descriptive statistics

Standard deviation112458.49
Coefficient of variation (CV)0.0056131671
Kurtosis0.18794826
Mean20034767
Median Absolute Deviation (MAD)69852
Skewness-0.86095073
Sum3.8987656 × 1010
Variance1.2646913 × 1010
MonotonicityNot monotonic
2023-12-11T06:34:08.747714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20081016 11
 
0.6%
19801004 9
 
0.5%
20081021 9
 
0.5%
20110701 9
 
0.5%
20081027 8
 
0.4%
20010327 6
 
0.3%
19870824 6
 
0.3%
20180608 5
 
0.3%
19870827 5
 
0.3%
19890217 4
 
0.2%
Other values (1521) 1874
96.3%
ValueCountFrequency (%)
19530531 1
0.1%
19600627 1
0.1%
19621004 2
0.1%
19621127 1
0.1%
19630710 1
0.1%
19650727 1
0.1%
19651217 1
0.1%
19671103 1
0.1%
19681010 1
0.1%
19681026 1
0.1%
ValueCountFrequency (%)
20180820 2
0.1%
20180810 2
0.1%
20180809 1
0.1%
20180806 1
0.1%
20180803 1
0.1%
20180718 1
0.1%
20180716 1
0.1%
20180713 1
0.1%
20180703 1
0.1%
20180702 2
0.1%

영업상태명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size15.3 KiB
운영중
1947 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
운영중 1947
100.0%

Length

2023-12-11T06:34:08.863086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T06:34:08.958702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
운영중 1947
100.0%

폐업일자
Real number (ℝ)

MISSING 

Distinct533
Distinct (%)67.0%
Missing1151
Missing (%)59.1%
Infinite0
Infinite (%)0.0%
Mean20125025
Minimum19980227
Maximum20180823
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.2 KiB
2023-12-11T06:34:09.058177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19980227
5-th percentile20071228
Q120090888
median20130204
Q320151211
95-th percentile20180201
Maximum20180823
Range200596
Interquartile range (IQR)60323

Descriptive statistics

Standard deviation35340.218
Coefficient of variation (CV)0.0017560335
Kurtosis-0.50682075
Mean20125025
Median Absolute Deviation (MAD)30086.5
Skewness-0.20663765
Sum1.601952 × 1010
Variance1.248931 × 109
MonotonicityNot monotonic
2023-12-11T06:34:09.203436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20080229 17
 
0.9%
20090309 10
 
0.5%
20120221 10
 
0.5%
20120110 10
 
0.5%
20090313 8
 
0.4%
20071228 8
 
0.4%
20140310 8
 
0.4%
20140521 7
 
0.4%
20100422 7
 
0.4%
20070530 6
 
0.3%
Other values (523) 705
36.2%
(Missing) 1151
59.1%
ValueCountFrequency (%)
19980227 1
0.1%
20000504 1
0.1%
20010416 1
0.1%
20010707 1
0.1%
20020329 1
0.1%
20030718 1
0.1%
20040107 1
0.1%
20061223 1
0.1%
20070109 1
0.1%
20070206 1
0.1%
ValueCountFrequency (%)
20180823 1
 
0.1%
20180718 1
 
0.1%
20180704 1
 
0.1%
20180703 1
 
0.1%
20180622 1
 
0.1%
20180611 1
 
0.1%
20180608 3
0.2%
20180604 1
 
0.1%
20180524 1
 
0.1%
20180510 1
 
0.1%

소속연합단체명
Text

MISSING 

Distinct372
Distinct (%)20.7%
Missing148
Missing (%)7.6%
Memory size15.3 KiB
2023-12-11T06:34:09.450659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length38
Median length27
Mean length6.5386326
Min length1

Characters and Unicode

Total characters11763
Distinct characters202
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique261 ?
Unique (%)14.5%

Sample

1st row민주노총
2nd row(주)청송노동조합
3rd row해당없음
4th row없음
5th row청송노동조합
ValueCountFrequency (%)
미가입 293
 
14.3%
없음 257
 
12.5%
한국노총 192
 
9.4%
전국화학노동조합연맹 67
 
3.3%
전국금속노동조합연맹 63
 
3.1%
해당없음 62
 
3.0%
전국연합노동조합연맹 52
 
2.5%
민주노총 51
 
2.5%
전국자동차노동조합연맹 49
 
2.4%
금속노련 44
 
2.1%
Other values (347) 921
44.9%
2023-12-11T06:34:09.867955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
노 1174
 
10.0%
국 768
 
6.5%
합 628
 
5.3%
동 625
 
5.3%
연 616
 
5.2%
조 545
 
4.6%
맹 520
 
4.4%
전 488
 
4.1%
총 375
 
3.2%
한 363
 
3.1%
Other values (192) 5661
48.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 11223
95.4%
Space Separator 252
 
2.1%
Close Punctuation 155
 
1.3%
Open Punctuation 82
 
0.7%
Other Punctuation 23
 
0.2%
Dash Punctuation 10
 
0.1%
Decimal Number 10
 
0.1%
Uppercase Letter 8
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
노 1174
 
10.5%
국 768
 
6.8%
합 628
 
5.6%
동 625
 
5.6%
연 616
 
5.5%
조 545
 
4.9%
맹 520
 
4.6%
전 488
 
4.3%
총 375
 
3.3%
한 363
 
3.2%
Other values (177) 5121
45.6%
Other Punctuation
ValueCountFrequency (%)
/ 12
52.2%
. 7
30.4%
, 3
 
13.0%
· 1
 
4.3%
Uppercase Letter
ValueCountFrequency (%)
T 3
37.5%
I 3
37.5%
K 1
 
12.5%
S 1
 
12.5%
Decimal Number
ValueCountFrequency (%)
0 7
70.0%
8 2
 
20.0%
1 1
 
10.0%
Space Separator
ValueCountFrequency (%)
252
100.0%
Close Punctuation
ValueCountFrequency (%)
) 155
100.0%
Open Punctuation
ValueCountFrequency (%)
( 82
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 11223
95.4%
Common 532
 
4.5%
Latin 8
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
노 1174
 
10.5%
국 768
 
6.8%
합 628
 
5.6%
동 625
 
5.6%
연 616
 
5.5%
조 545
 
4.9%
맹 520
 
4.6%
전 488
 
4.3%
총 375
 
3.3%
한 363
 
3.2%
Other values (177) 5121
45.6%
Common
ValueCountFrequency (%)
252
47.4%
) 155
29.1%
( 82
 
15.4%
/ 12
 
2.3%
- 10
 
1.9%
. 7
 
1.3%
0 7
 
1.3%
, 3
 
0.6%
8 2
 
0.4%
1 1
 
0.2%
Latin
ValueCountFrequency (%)
T 3
37.5%
I 3
37.5%
K 1
 
12.5%
S 1
 
12.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 11220
95.4%
ASCII 539
 
4.6%
Compat Jamo 3
 
< 0.1%
None 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
노 1174
 
10.5%
국 768
 
6.8%
합 628
 
5.6%
동 625
 
5.6%
연 616
 
5.5%
조 545
 
4.9%
맹 520
 
4.6%
전 488
 
4.3%
총 375
 
3.3%
한 363
 
3.2%
Other values (176) 5118
45.6%
ASCII
ValueCountFrequency (%)
252
46.8%
) 155
28.8%
( 82
 
15.2%
/ 12
 
2.2%
- 10
 
1.9%
. 7
 
1.3%
0 7
 
1.3%
, 3
 
0.6%
T 3
 
0.6%
I 3
 
0.6%
Other values (4) 5
 
0.9%
Compat Jamo
ValueCountFrequency (%)
ㄹ 3
100.0%
None
ValueCountFrequency (%)
· 1
100.0%

노동조합원수(명)
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct297
Distinct (%)15.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean78.372368
Minimum0
Maximum10352
Zeros34
Zeros (%)1.7%
Negative0
Negative (%)0.0%
Memory size17.2 KiB
2023-12-11T06:34:10.022559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q17
median22
Q367
95-th percentile271.4
Maximum10352
Range10352
Interquartile range (IQR)60

Descriptive statistics

Standard deviation343.47555
Coefficient of variation (CV)4.3826104
Kurtosis532.34373
Mean78.372368
Median Absolute Deviation (MAD)18
Skewness20.738633
Sum152591
Variance117975.45
MonotonicityNot monotonic
2023-12-11T06:34:10.168856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 110
 
5.6%
3 95
 
4.9%
5 95
 
4.9%
4 78
 
4.0%
7 66
 
3.4%
10 61
 
3.1%
6 59
 
3.0%
8 45
 
2.3%
20 43
 
2.2%
15 38
 
2.0%
Other values (287) 1257
64.6%
ValueCountFrequency (%)
0 34
 
1.7%
1 12
 
0.6%
2 110
5.6%
3 95
4.9%
4 78
4.0%
5 95
4.9%
6 59
3.0%
7 66
3.4%
8 45
2.3%
9 32
 
1.6%
ValueCountFrequency (%)
10352 1
0.1%
7000 1
0.1%
5623 1
0.1%
2238 1
0.1%
2000 1
0.1%
1929 1
0.1%
1580 1
0.1%
1538 1
0.1%
1500 1
0.1%
1492 1
0.1%

조합원남성수(명)
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct260
Distinct (%)13.6%
Missing31
Missing (%)1.6%
Infinite0
Infinite (%)0.0%
Mean63.98643
Minimum0
Maximum10060
Zeros58
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size17.2 KiB
2023-12-11T06:34:10.297559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q15
median18
Q357
95-th percentile214.25
Maximum10060
Range10060
Interquartile range (IQR)52

Descriptive statistics

Standard deviation295.23427
Coefficient of variation (CV)4.6140138
Kurtosis751.02492
Mean63.98643
Median Absolute Deviation (MAD)15
Skewness24.485191
Sum122598
Variance87163.276
MonotonicityNot monotonic
2023-12-11T06:34:10.688602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 130
 
6.7%
3 111
 
5.7%
4 98
 
5.0%
5 83
 
4.3%
6 71
 
3.6%
0 58
 
3.0%
8 52
 
2.7%
7 50
 
2.6%
10 41
 
2.1%
1 41
 
2.1%
Other values (250) 1181
60.7%
ValueCountFrequency (%)
0 58
3.0%
1 41
 
2.1%
2 130
6.7%
3 111
5.7%
4 98
5.0%
5 83
4.3%
6 71
3.6%
7 50
 
2.6%
8 52
 
2.7%
9 37
 
1.9%
ValueCountFrequency (%)
10060 1
0.1%
5486 1
0.1%
2500 1
0.1%
2177 1
0.1%
1898 1
0.1%
1853 1
0.1%
1444 1
0.1%
1334 1
0.1%
1247 1
0.1%
952 1
0.1%

조합원여성수(명)
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct123
Distinct (%)7.7%
Missing340
Missing (%)17.5%
Infinite0
Infinite (%)0.0%
Mean18.518979
Minimum0
Maximum4500
Zeros579
Zeros (%)29.7%
Negative0
Negative (%)0.0%
Memory size17.2 KiB
2023-12-11T06:34:10.815989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q39
95-th percentile60
Maximum4500
Range4500
Interquartile range (IQR)9

Descriptive statistics

Standard deviation130.0542
Coefficient of variation (CV)7.0227518
Kurtosis890.85828
Mean18.518979
Median Absolute Deviation (MAD)2
Skewness27.103053
Sum29760
Variance16914.094
MonotonicityNot monotonic
2023-12-11T06:34:10.956401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 579
29.7%
1 206
 
10.6%
2 126
 
6.5%
3 85
 
4.4%
4 57
 
2.9%
5 41
 
2.1%
7 36
 
1.8%
8 33
 
1.7%
10 30
 
1.5%
6 27
 
1.4%
Other values (113) 387
19.9%
(Missing) 340
17.5%
ValueCountFrequency (%)
0 579
29.7%
1 206
 
10.6%
2 126
 
6.5%
3 85
 
4.4%
4 57
 
2.9%
5 41
 
2.1%
6 27
 
1.4%
7 36
 
1.8%
8 33
 
1.7%
9 24
 
1.2%
ValueCountFrequency (%)
4500 1
0.1%
1306 1
0.1%
1106 1
0.1%
1012 1
0.1%
731 1
0.1%
660 1
0.1%
650 1
0.1%
420 1
0.1%
375 1
0.1%
344 1
0.1%

WGS84위도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct1441
Distinct (%)81.7%
Missing184
Missing (%)9.5%
Infinite0
Infinite (%)0.0%
Mean37.397631
Minimum36.941655
Maximum38.116688
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.2 KiB
2023-12-11T06:34:11.107767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.941655
5-th percentile37.012021
Q137.256303
median37.348506
Q337.525719
95-th percentile37.847047
Maximum38.116688
Range1.1750329
Interquartile range (IQR)0.26941633

Descriptive statistics

Standard deviation0.23743785
Coefficient of variation (CV)0.0063490078
Kurtosis-0.26500085
Mean37.397631
Median Absolute Deviation (MAD)0.11745872
Skewness0.45979878
Sum65932.023
Variance0.056376732
MonotonicityNot monotonic
2023-12-11T06:34:11.279461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.2529797883 12
 
0.6%
37.3303008024 9
 
0.5%
37.6103315074 6
 
0.3%
37.3149515253 5
 
0.3%
37.2549459243 5
 
0.3%
37.408906697 5
 
0.3%
37.6159259498 5
 
0.3%
37.7129807108 5
 
0.3%
37.3007084543 4
 
0.2%
37.3659481725 4
 
0.2%
Other values (1431) 1703
87.5%
(Missing) 184
 
9.5%
ValueCountFrequency (%)
36.9416547881 1
0.1%
36.9458724708 1
0.1%
36.9519013346 1
0.1%
36.9523591039 1
0.1%
36.9544884568 1
0.1%
36.9558508975 1
0.1%
36.9571548417 1
0.1%
36.9575511166 1
0.1%
36.9582198411 1
0.1%
36.9588612711 1
0.1%
ValueCountFrequency (%)
38.1166876633 1
 
0.1%
38.1005066857 1
 
0.1%
38.0982091102 1
 
0.1%
38.090977411 3
0.2%
38.0574726424 1
 
0.1%
38.0331469075 1
 
0.1%
38.032088631 1
 
0.1%
38.0312196531 1
 
0.1%
37.9987008254 1
 
0.1%
37.9738190292 1
 
0.1%

WGS84경도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct1441
Distinct (%)81.7%
Missing184
Missing (%)9.5%
Infinite0
Infinite (%)0.0%
Mean126.98793
Minimum126.54602
Maximum127.72659
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.2 KiB
2023-12-11T06:34:11.521362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.54602
5-th percentile126.72812
Q1126.7975
median126.98911
Q3127.12006
95-th percentile127.40541
Maximum127.72659
Range1.1805753
Interquartile range (IQR)0.32255645

Descriptive statistics

Standard deviation0.20373726
Coefficient of variation (CV)0.0016043829
Kurtosis0.39015323
Mean126.98793
Median Absolute Deviation (MAD)0.15565982
Skewness0.61267531
Sum223879.72
Variance0.041508873
MonotonicityNot monotonic
2023-12-11T06:34:11.733125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.4806834787 12
 
0.6%
126.9357157649 9
 
0.5%
127.1454453107 6
 
0.3%
126.9916827768 5
 
0.3%
126.9895715467 5
 
0.3%
127.1200571369 5
 
0.3%
127.144478278 5
 
0.3%
126.7984034509 5
 
0.3%
126.8114134814 4
 
0.2%
127.106631596 4
 
0.2%
Other values (1431) 1703
87.5%
(Missing) 184
 
9.5%
ValueCountFrequency (%)
126.5460177219 1
0.1%
126.54660312 1
0.1%
126.563726962 1
0.1%
126.5671177405 1
0.1%
126.5703057208 1
0.1%
126.5709885656 1
0.1%
126.5737154185 1
0.1%
126.5773156667 1
0.1%
126.5788256572 2
0.1%
126.5793165003 1
0.1%
ValueCountFrequency (%)
127.7265930476 1
0.1%
127.6981639156 1
0.1%
127.6691950314 1
0.1%
127.6668669441 1
0.1%
127.6577094782 1
0.1%
127.6511403478 1
0.1%
127.6483936454 1
0.1%
127.6428516364 2
0.1%
127.6425308451 1
0.1%
127.6246042734 1
0.1%

Interactions

2023-12-11T06:34:03.499563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:33:57.201938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:33:58.428009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:33:59.419114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:34:00.322435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:34:01.036705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:34:01.838715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:34:02.610541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:34:03.595708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:33:57.312237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:33:58.551599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:33:59.547970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:34:00.424170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:34:01.126439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:34:01.950932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:34:02.721795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:34:03.738804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:33:57.674393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:33:58.678762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:33:59.651743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:34:00.516892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:34:01.246569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:34:02.051438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:34:02.853120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:34:04.159483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:33:57.802744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:33:58.826921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:33:59.784054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:34:00.624587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:34:01.382466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:34:02.164226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:34:02.964037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:34:04.285894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:33:57.928550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:33:58.980554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:33:59.896324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:34:00.707434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:34:01.469568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:34:02.252359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:34:03.063403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:34:04.407598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:33:58.073737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:33:59.090605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:34:00.022577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:34:00.790837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:34:01.557437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:34:02.353077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:34:03.190287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:34:04.520398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:33:58.185767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:33:59.214292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:34:00.129263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:34:00.870434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:34:01.648282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:34:02.434773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:34:03.308431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:34:04.629979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:33:58.302484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:33:59.317603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:34:00.225748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:34:00.956194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:34:01.752205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:34:02.526668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:34:03.403632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T06:34:11.855748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명소재지우편번호인허가일자폐업일자노동조합원수(명)조합원남성수(명)조합원여성수(명)WGS84위도WGS84경도
시군명1.0000.7530.3120.5070.1810.1650.0000.9630.939
소재지우편번호0.7531.0000.5080.5310.0000.0000.0000.3710.596
인허가일자0.3120.5081.0000.4430.1210.0690.0820.1360.061
폐업일자0.5070.5310.4431.0000.0000.0000.0000.2550.211
노동조합원수(명)0.1810.0000.1210.0001.0000.9580.7870.0000.197
조합원남성수(명)0.1650.0000.0690.0000.9581.0000.4760.0000.218
조합원여성수(명)0.0000.0000.0820.0000.7870.4761.0000.0000.159
WGS84위도0.9630.3710.1360.2550.0000.0000.0001.0000.649
WGS84경도0.9390.5960.0610.2110.1970.2180.1590.6491.000
2023-12-11T06:34:12.001030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
소재지우편번호인허가일자폐업일자노동조합원수(명)조합원남성수(명)조합원여성수(명)WGS84위도WGS84경도시군명
소재지우편번호1.000-0.392-0.2360.0730.0720.035-0.1650.4590.496
인허가일자-0.3921.0000.458-0.408-0.377-0.1360.094-0.0000.114
폐업일자-0.2360.4581.0000.0340.076-0.0410.0640.0020.208
노동조합원수(명)0.073-0.4080.0341.0000.9320.512-0.1010.0520.080
조합원남성수(명)0.072-0.3770.0760.9321.0000.293-0.0940.0570.078
조합원여성수(명)0.035-0.136-0.0410.5120.2931.000-0.0530.0170.000
WGS84위도-0.1650.0940.064-0.101-0.094-0.0531.000-0.1600.779
WGS84경도0.459-0.0000.0020.0520.0570.017-0.1601.0000.693
시군명0.4960.1140.2080.0800.0780.0000.7790.6931.000

Missing values

2023-12-11T06:34:04.800840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T06:34:05.033820image/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.
2023-12-11T06:34:05.285251image/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

시군명사업장명소재지우편번호소재지도로명주소소재지지번주소인허가일자영업상태명폐업일자소속연합단체명노동조합원수(명)조합원남성수(명)조합원여성수(명)WGS84위도WGS84경도
0가평군우리병원노동조합477815경기도 가평군 청평면 경춘로 791-11경기도 가평군 청평면 청평리 442번지 1호20060525운영중<NA>민주노총32137.737202127.41531
1가평군(주)청송노동조합477851경기도 가평군 설악면 유명로 961-34경기도 가평군 설악면 방일리 산 90번지 2호20110824운영중<NA>(주)청송노동조합33<NA>37.627304127.490107
2가평군동운택시(주) 노동조합 제1노조12413경기도 가평군 가평읍 가화로 159-1 (동운택시)경기도 가평군 가평읍 읍내리 625-7번지 (동운택시)20170501운영중<NA>해당없음1110137.833376127.51108
3가평군팜스코(주) 제1노동조합12461경기도 가평군 설악면 미사리로 156-22경기도 가평군 설악면 송산리 675번지20170614운영중20170801없음33<NA>37.685414127.510025
4가평군청송노동조합477851경기도 가평군 설악면 유명로 961-34경기도 가평군 설악면 방일리 산 90번지 2호20080603운영중20110131청송노동조합22<NA>37.620183127.482277
5가평군(주)진흥고속노동조합477815경기도 가평군 청평면 청평중앙로 54경기도 가평군 청평면 청평리 432번지 24호19800929운영중<NA>전국자동차노동조합연맹181181037.738179127.420814
6가평군동운택시(주)노동조합477805경기도 가평군 가평읍 가화로 159-1경기도 가평군 읍내리 625번지 7호19861015운영중<NA>한)택시노련6964537.833376127.51108
7고양시효누림노동조합10258경기도 고양시 일산동구 성현로377번길 128 (문봉동)경기도 고양시 일산동구 문봉동 26-3번지20140516운영중<NA>없음3052537.701749126.832269
8고양시해누리요양센터노동조합10342경기도 고양시 일산서구 탄중로471번길 56 (일산동)경기도 고양시 일산서구 일산동 592-4번지20140602운영중<NA>없음41337.685912126.772721
9고양시서울고속도로톨게이트노동조합10454경기도 고양시 덕양구 독곶이길 201 (주교동)경기도 고양시 덕양구 주교동 924번지20140630운영중20151221한국노총20<NA>2037.656977126.822342
시군명사업장명소재지우편번호소재지도로명주소소재지지번주소인허가일자영업상태명폐업일자소속연합단체명노동조합원수(명)조합원남성수(명)조합원여성수(명)WGS84위도WGS84경도
1937화성시운수협동조합 단위노동조합18325경기도 화성시 안녕길 27 (안녕동)경기도 화성시 안녕동 188-408번지20160530운영중<NA>해당없음1919<NA>37.204873126.989616
1938화성시수원과학대학교 직원노동조합18516경기도 화성시 정남면 세자로 288 (수원과학대학)경기도 화성시 정남면 보통리 141-44번지 수원과학대학20160929운영중<NA>해당없음1212<NA>37.192551126.983423
1939화성시한국노총전국금속노동조합연맹핸즈식스노동조합18542경기도 화성시 마도면 마도공단로6길 12경기도 화성시 마도면 쌍송리 682번지20170720운영중<NA>한국노총 전국금속노동조합연맹398390837.178722126.785204
1940화성시서부네트워크 노동조합18315경기도 화성시 봉담읍 샘마을길 34, 2층경기도 화성시 봉담읍 상리 20번지 4호 2층20171010운영중<NA>서부네트워크 노동조합2323<NA>37.217255126.950524
1941화성시동양매직서비스445760경기도 화성시 봉담읍 효행로 250경기도 화성시 봉담읍 동화리 100번지 2 호20050601운영중20180212전국민간서비스산업 노동조합연맹1711472437.225339126.970011
1942화성시리한노동조합445861경기도 화성시 마도면 마도공단로1길 25경기도 화성시 마도면 쌍송리 664번지 마도산단 2 B - 1 L20070328운영중<NA>전국금속노동조합연맹110852537.184364126.792598
1943화성시성혜원445944경기도 화성시 장안면 포승장안로 1194-24경기도 화성시 장안면 독정리 803번지 8호20030927운영중<NA>없음158737.07239126.847457
1944화성시성안기계445861경기도 화성시 마도면 마도공단로6길 33경기도 화성시 마도면 쌍송리 677번지 마도산단 10동 1호19890515운영중<NA>전국금속 노동조합연맹4545037.179862126.788979
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