Overview

Dataset statistics

Number of variables8
Number of observations22
Missing cells30
Missing cells (%)17.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.7 KiB
Average record size in memory77.0 B

Variable types

Text1
Numeric7

Dataset

Description전라북도 군산시 광업제조업 현황(산업별, 사업체수_개, 종사자수_명, 급여액_백만원, 출하액_백만원, 주요생산비_백만원, 부가가치_백만원, 연말잔액_백만원)
Author전라북도
URLhttps://www.bigdatahub.go.kr/index.jeonbuk?startPage=1&menuCd=DOM_000000103007001000&pListTypeStr=&pId=3046082

Alerts

사업체수(개) is highly overall correlated with 종사자수(명) and 5 other fieldsHigh correlation
종사자수(명) is highly overall correlated with 사업체수(개) and 5 other fieldsHigh correlation
급여액(백만원) is highly overall correlated with 사업체수(개) and 5 other fieldsHigh correlation
출하액(백만원) is highly overall correlated with 사업체수(개) and 5 other fieldsHigh correlation
주요생산비(백만원) is highly overall correlated with 사업체수(개) and 5 other fieldsHigh correlation
부가가치(백만원) is highly overall correlated with 사업체수(개) and 5 other fieldsHigh correlation
유형자산연말잔액(백만원) is highly overall correlated with 사업체수(개) and 5 other fieldsHigh correlation
종사자수(명) has 5 (22.7%) missing valuesMissing
급여액(백만원) has 5 (22.7%) missing valuesMissing
출하액(백만원) has 5 (22.7%) missing valuesMissing
주요생산비(백만원) has 5 (22.7%) missing valuesMissing
부가가치(백만원) has 5 (22.7%) missing valuesMissing
유형자산연말잔액(백만원) has 5 (22.7%) missing valuesMissing
산업별 has unique valuesUnique
사업체수(개) has 1 (4.5%) zerosZeros
종사자수(명) has 1 (4.5%) zerosZeros
급여액(백만원) has 1 (4.5%) zerosZeros
출하액(백만원) has 1 (4.5%) zerosZeros
주요생산비(백만원) has 1 (4.5%) zerosZeros
부가가치(백만원) has 1 (4.5%) zerosZeros
유형자산연말잔액(백만원) has 1 (4.5%) zerosZeros

Reproduction

Analysis started2024-03-14 00:40:14.179537
Analysis finished2024-03-14 00:40:18.554935
Duration4.38 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

산업별
Text

UNIQUE 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size308.0 B
2024-03-14T09:40:18.672128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length29
Median length19.5
Mean length15.590909
Min length6

Characters and Unicode

Total characters343
Distinct characters82
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique22 ?
Unique (%)100.0%

Sample

1st row비금속광물 광업; 연료용 제외
2nd row식료품 제조업
3rd row음료 제조업
4th row섬유제품 제조업; 의복 제외
5th row의복, 의복 액세서리 및 모피제품 제조업
ValueCountFrequency (%)
제조업 20
20.0%
및 13
 
13.0%
제외 5
 
5.0%
기계 3
 
3.0%
의복 3
 
3.0%
기타 3
 
3.0%
의약품 2
 
2.0%
물질 2
 
2.0%
금속 2
 
2.0%
가구 2
 
2.0%
Other values (44) 45
45.0%
2024-03-14T09:40:18.934642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
78
22.7%
제 35
 
10.2%
업 23
 
6.7%
조 20
 
5.8%
품 14
 
4.1%
및 13
 
3.8%
기 9
 
2.6%
, 8
 
2.3%
비 7
 
2.0%
의 7
 
2.0%
Other values (72) 129
37.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 251
73.2%
Space Separator 78
 
22.7%
Other Punctuation 13
 
3.8%
Decimal Number 1
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
제 35
 
13.9%
업 23
 
9.2%
조 20
 
8.0%
품 14
 
5.6%
및 13
 
5.2%
기 9
 
3.6%
비 7
 
2.8%
의 7
 
2.8%
료 5
 
2.0%
외 5
 
2.0%
Other values (68) 113
45.0%
Other Punctuation
ValueCountFrequency (%)
, 8
61.5%
; 5
38.5%
Space Separator
ValueCountFrequency (%)
78
100.0%
Decimal Number
ValueCountFrequency (%)
1 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 251
73.2%
Common 92
 
26.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
제 35
 
13.9%
업 23
 
9.2%
조 20
 
8.0%
품 14
 
5.6%
및 13
 
5.2%
기 9
 
3.6%
비 7
 
2.8%
의 7
 
2.8%
료 5
 
2.0%
외 5
 
2.0%
Other values (68) 113
45.0%
Common
ValueCountFrequency (%)
78
84.8%
, 8
 
8.7%
; 5
 
5.4%
1 1
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 251
73.2%
ASCII 92
 
26.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
78
84.8%
, 8
 
8.7%
; 5
 
5.4%
1 1
 
1.1%
Hangul
ValueCountFrequency (%)
제 35
 
13.9%
업 23
 
9.2%
조 20
 
8.0%
품 14
 
5.6%
및 13
 
5.2%
기 9
 
3.6%
비 7
 
2.8%
의 7
 
2.8%
료 5
 
2.0%
외 5
 
2.0%
Other values (68) 113
45.0%

사업체수(개)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct17
Distinct (%)77.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.681818
Minimum0
Maximum63
Zeros1
Zeros (%)4.5%
Negative0
Negative (%)0.0%
Memory size330.0 B
2024-03-14T09:40:19.028311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12.5
median8
Q334.5
95-th percentile43.95
Maximum63
Range63
Interquartile range (IQR)32

Descriptive statistics

Standard deviation18.69654
Coefficient of variation (CV)1.0573879
Kurtosis-0.18681973
Mean17.681818
Median Absolute Deviation (MAD)7
Skewness0.99032431
Sum389
Variance349.56061
MonotonicityNot monotonic
2024-03-14T09:40:19.121308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
1 3
13.6%
2 2
 
9.1%
7 2
 
9.1%
8 2
 
9.1%
41 1
 
4.5%
37 1
 
4.5%
44 1
 
4.5%
12 1
 
4.5%
0 1
 
4.5%
63 1
 
4.5%
Other values (7) 7
31.8%
ValueCountFrequency (%)
0 1
 
4.5%
1 3
13.6%
2 2
9.1%
4 1
 
4.5%
6 1
 
4.5%
7 2
9.1%
8 2
9.1%
12 1
 
4.5%
13 1
 
4.5%
22 1
 
4.5%
ValueCountFrequency (%)
63 1
4.5%
44 1
4.5%
43 1
4.5%
41 1
4.5%
40 1
4.5%
37 1
4.5%
27 1
4.5%
22 1
4.5%
13 1
4.5%
12 1
4.5%

종사자수(명)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct17
Distinct (%)100.0%
Missing5
Missing (%)22.7%
Infinite0
Infinite (%)0.0%
Mean1164.4706
Minimum0
Maximum4882
Zeros1
Zeros (%)4.5%
Negative0
Negative (%)0.0%
Memory size330.0 B
2024-03-14T09:40:19.249947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile120.8
Q1250
median575
Q31665
95-th percentile3002
Maximum4882
Range4882
Interquartile range (IQR)1415

Descriptive statistics

Standard deviation1294.5481
Coefficient of variation (CV)1.1117053
Kurtosis3.0259728
Mean1164.4706
Median Absolute Deviation (MAD)424
Skewness1.6567352
Sum19796
Variance1675854.8
MonotonicityNot monotonic
2024-03-14T09:40:19.352381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
250 1
 
4.5%
173 1
 
4.5%
230 1
 
4.5%
2529 1
 
4.5%
1665 1
 
4.5%
382 1
 
4.5%
575 1
 
4.5%
0 1
 
4.5%
2532 1
 
4.5%
4882 1
 
4.5%
Other values (7) 7
31.8%
(Missing) 5
22.7%
ValueCountFrequency (%)
0 1
4.5%
151 1
4.5%
173 1
4.5%
230 1
4.5%
250 1
4.5%
333 1
4.5%
373 1
4.5%
382 1
4.5%
575 1
4.5%
589 1
4.5%
ValueCountFrequency (%)
4882 1
4.5%
2532 1
4.5%
2529 1
4.5%
2220 1
4.5%
1665 1
4.5%
1602 1
4.5%
1310 1
4.5%
589 1
4.5%
575 1
4.5%
382 1
4.5%

급여액(백만원)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct17
Distinct (%)100.0%
Missing5
Missing (%)22.7%
Infinite0
Infinite (%)0.0%
Mean53452.941
Minimum0
Maximum215798
Zeros1
Zeros (%)4.5%
Negative0
Negative (%)0.0%
Memory size330.0 B
2024-03-14T09:40:19.442157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4241.6
Q110493
median22205
Q370396
95-th percentile153133.2
Maximum215798
Range215798
Interquartile range (IQR)59903

Descriptive statistics

Standard deviation60752.885
Coefficient of variation (CV)1.1365677
Kurtosis1.7561419
Mean53452.941
Median Absolute Deviation (MAD)16903
Skewness1.4605781
Sum908700
Variance3.690913 × 109
MonotonicityNot monotonic
2024-03-14T09:40:19.521110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
17305 1
 
4.5%
7605 1
 
4.5%
6451 1
 
4.5%
137467 1
 
4.5%
69444 1
 
4.5%
10493 1
 
4.5%
17079 1
 
4.5%
0 1
 
4.5%
111558 1
 
4.5%
215798 1
 
4.5%
Other values (7) 7
31.8%
(Missing) 5
22.7%
ValueCountFrequency (%)
0 1
4.5%
5302 1
4.5%
6451 1
4.5%
7605 1
4.5%
10493 1
4.5%
13295 1
4.5%
17079 1
4.5%
17305 1
4.5%
22205 1
4.5%
27264 1
4.5%
ValueCountFrequency (%)
215798 1
4.5%
137467 1
4.5%
120338 1
4.5%
111558 1
4.5%
70396 1
4.5%
69444 1
4.5%
56700 1
4.5%
27264 1
4.5%
22205 1
4.5%
17305 1
4.5%

출하액(백만원)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct17
Distinct (%)100.0%
Missing5
Missing (%)22.7%
Infinite0
Infinite (%)0.0%
Mean805601.41
Minimum0
Maximum4625384
Zeros1
Zeros (%)4.5%
Negative0
Negative (%)0.0%
Memory size330.0 B
2024-03-14T09:40:19.605496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile12698.4
Q164577
median225635
Q31011000
95-th percentile2861825.6
Maximum4625384
Range4625384
Interquartile range (IQR)946423

Descriptive statistics

Standard deviation1230170.3
Coefficient of variation (CV)1.527021
Kurtosis5.290343
Mean805601.41
Median Absolute Deviation (MAD)202759
Skewness2.2295014
Sum13695224
Variance1.513319 × 1012
MonotonicityNot monotonic
2024-03-14T09:40:19.691041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
225635 1
 
4.5%
15873 1
 
4.5%
52335 1
 
4.5%
1011000 1
 
4.5%
1345870 1
 
4.5%
85581 1
 
4.5%
150589 1
 
4.5%
0 1
 
4.5%
2420936 1
 
4.5%
4625384 1
 
4.5%
Other values (7) 7
31.8%
(Missing) 5
22.7%
ValueCountFrequency (%)
0 1
4.5%
15873 1
4.5%
22876 1
4.5%
52335 1
4.5%
64577 1
4.5%
85581 1
4.5%
150589 1
4.5%
188722 1
4.5%
225635 1
4.5%
329885 1
4.5%
ValueCountFrequency (%)
4625384 1
4.5%
2420936 1
4.5%
2078017 1
4.5%
1345870 1
4.5%
1011000 1
4.5%
651282 1
4.5%
426662 1
4.5%
329885 1
4.5%
225635 1
4.5%
188722 1
4.5%

주요생산비(백만원)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct17
Distinct (%)100.0%
Missing5
Missing (%)22.7%
Infinite0
Infinite (%)0.0%
Mean571591.71
Minimum0
Maximum3784580
Zeros1
Zeros (%)4.5%
Negative0
Negative (%)0.0%
Memory size330.0 B
2024-03-14T09:40:19.773344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2361.6
Q139366
median122684
Q3692132
95-th percentile2146172
Maximum3784580
Range3784580
Interquartile range (IQR)652766

Descriptive statistics

Standard deviation971108.7
Coefficient of variation (CV)1.6989552
Kurtosis7.5539912
Mean571591.71
Median Absolute Deviation (MAD)121899
Skewness2.6190315
Sum9717059
Variance9.4305211 × 1011
MonotonicityNot monotonic
2024-03-14T09:40:19.909761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
105906 1
 
4.5%
2952 1
 
4.5%
39366 1
 
4.5%
692132 1
 
4.5%
864763 1
 
4.5%
55906 1
 
4.5%
88760 1
 
4.5%
0 1
 
4.5%
1736570 1
 
4.5%
3784580 1
 
4.5%
Other values (7) 7
31.8%
(Missing) 5
22.7%
ValueCountFrequency (%)
0 1
4.5%
2952 1
4.5%
12386 1
4.5%
35944 1
4.5%
39366 1
4.5%
55906 1
4.5%
88760 1
4.5%
105906 1
4.5%
122684 1
4.5%
244583 1
4.5%
ValueCountFrequency (%)
3784580 1
4.5%
1736570 1
4.5%
1341695 1
4.5%
864763 1
4.5%
692132 1
4.5%
338224 1
4.5%
250608 1
4.5%
244583 1
4.5%
122684 1
4.5%
105906 1
4.5%

부가가치(백만원)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct17
Distinct (%)100.0%
Missing5
Missing (%)22.7%
Infinite0
Infinite (%)0.0%
Mean250912.88
Minimum0
Maximum1012003
Zeros1
Zeros (%)4.5%
Negative0
Negative (%)0.0%
Memory size330.0 B
2024-03-14T09:40:20.050793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile9063.2
Q125898
median85481
Q3339091
95-th percentile822317.4
Maximum1012003
Range1012003
Interquartile range (IQR)313193

Descriptive statistics

Standard deviation314185.88
Coefficient of variation (CV)1.2521712
Kurtosis0.77786033
Mean250912.88
Median Absolute Deviation (MAD)74152
Skewness1.3453874
Sum4265519
Variance9.8712765 × 1010
MonotonicityNot monotonic
2024-03-14T09:40:20.199129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
125052 1
 
4.5%
12921 1
 
4.5%
12498 1
 
4.5%
339091 1
 
4.5%
516241 1
 
4.5%
29034 1
 
4.5%
62787 1
 
4.5%
0 1
 
4.5%
687766 1
 
4.5%
1012003 1
 
4.5%
Other values (7) 7
31.8%
(Missing) 5
22.7%
ValueCountFrequency (%)
0 1
4.5%
11329 1
4.5%
12498 1
4.5%
12921 1
4.5%
25898 1
4.5%
29034 1
4.5%
62787 1
4.5%
65624 1
4.5%
85481 1
4.5%
125052 1
4.5%
ValueCountFrequency (%)
1012003 1
4.5%
774896 1
4.5%
687766 1
4.5%
516241 1
4.5%
339091 1
4.5%
322928 1
4.5%
181970 1
4.5%
125052 1
4.5%
85481 1
4.5%
65624 1
4.5%

유형자산연말잔액(백만원)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct17
Distinct (%)100.0%
Missing5
Missing (%)22.7%
Infinite0
Infinite (%)0.0%
Mean309574.12
Minimum0
Maximum1708456
Zeros1
Zeros (%)4.5%
Negative0
Negative (%)0.0%
Memory size330.0 B
2024-03-14T09:40:20.317896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1788.8
Q136517
median132051
Q3488870
95-th percentile1142638.4
Maximum1708456
Range1708456
Interquartile range (IQR)452353

Descriptive statistics

Standard deviation449906.15
Coefficient of variation (CV)1.4533067
Kurtosis5.4891665
Mean309574.12
Median Absolute Deviation (MAD)124897
Skewness2.2566931
Sum5262760
Variance2.0241554 × 1011
MonotonicityNot monotonic
2024-03-14T09:40:20.421781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
62730 1
 
4.5%
2236 1
 
4.5%
31191 1
 
4.5%
494623 1
 
4.5%
519946 1
 
4.5%
36517 1
 
4.5%
46393 1
 
4.5%
0 1
 
4.5%
488870 1
 
4.5%
1001184 1
 
4.5%
Other values (7) 7
31.8%
(Missing) 5
22.7%
ValueCountFrequency (%)
0 1
4.5%
2236 1
4.5%
7154 1
4.5%
31191 1
4.5%
36517 1
4.5%
46393 1
4.5%
50119 1
4.5%
62730 1
4.5%
132051 1
4.5%
181096 1
4.5%
ValueCountFrequency (%)
1708456 1
4.5%
1001184 1
4.5%
519946 1
4.5%
494623 1
4.5%
488870 1
4.5%
306621 1
4.5%
193573 1
4.5%
181096 1
4.5%
132051 1
4.5%
62730 1
4.5%

Interactions

2024-03-14T09:40:17.668343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:40:14.403072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:40:14.895653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:40:15.401546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:40:15.979684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:40:16.447177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:40:17.168826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:40:17.762221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:40:14.466632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:40:14.967364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:40:15.488377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:40:16.046827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:40:16.520353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:40:17.248613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:40:17.831618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:40:14.534361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:40:15.060948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:40:15.570661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:40:16.113331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:40:16.589910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:40:17.325474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:40:17.907583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:40:14.602122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:40:15.127776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:40:15.643619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:40:16.186268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:40:16.652823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:40:17.383708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:40:17.995544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:40:14.665274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:40:15.189624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:40:15.730293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:40:16.244381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:40:16.716166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:40:17.445052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:40:18.122247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:40:14.737321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:40:15.260746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:40:15.820553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:40:16.311699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:40:16.789065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:40:17.513272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:40:18.194752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:40:14.812618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:40:15.323985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:40:15.895481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:40:16.372086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:40:16.867143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:40:17.575743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T09:40:20.506797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
산업별사업체수(개)종사자수(명)급여액(백만원)출하액(백만원)주요생산비(백만원)부가가치(백만원)유형자산연말잔액(백만원)
산업별1.0001.0001.0001.0001.0001.0001.0001.000
사업체수(개)1.0001.0000.6990.8260.5280.0000.3340.000
종사자수(명)1.0000.6991.0000.9390.9130.8590.9590.913
급여액(백만원)1.0000.8260.9391.0000.8290.8590.9440.846
출하액(백만원)1.0000.5280.9130.8291.0000.9900.9390.975
주요생산비(백만원)1.0000.0000.8590.8590.9901.0000.9550.990
부가가치(백만원)1.0000.3340.9590.9440.9390.9551.0000.915
유형자산연말잔액(백만원)1.0000.0000.9130.8460.9750.9900.9151.000
2024-03-14T09:40:20.604408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
사업체수(개)종사자수(명)급여액(백만원)출하액(백만원)주요생산비(백만원)부가가치(백만원)유형자산연말잔액(백만원)
사업체수(개)1.0000.8900.8210.8310.8310.8200.854
종사자수(명)0.8901.0000.9510.9560.9560.9390.919
급여액(백만원)0.8210.9511.0000.9660.9610.9710.963
출하액(백만원)0.8310.9560.9661.0000.9950.9880.966
주요생산비(백만원)0.8310.9560.9610.9951.0000.9780.963
부가가치(백만원)0.8200.9390.9710.9880.9781.0000.966
유형자산연말잔액(백만원)0.8540.9190.9630.9660.9630.9661.000

Missing values

2024-03-14T09:40:18.304823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T09:40:18.400941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-03-14T09:40:18.493573image/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

산업별사업체수(개)종사자수(명)급여액(백만원)출하액(백만원)주요생산비(백만원)부가가치(백만원)유형자산연말잔액(백만원)
0비금속광물 광업; 연료용 제외2<NA><NA><NA><NA><NA><NA>
1식료품 제조업40253211155824209361736570687766488870
2음료 제조업42501730522563510590612505262730
3섬유제품 제조업; 의복 제외615153022287612386113297154
4의복, 의복 액세서리 및 모피제품 제조업1<NA><NA><NA><NA><NA><NA>
5목재 및 나무제품 제조업; 가구 제외225892726432988524458385481132051
6펄프, 종이 및 종이제품 제조업83732220518872212268465624181096
7코크스, 연탄 및 석유정제품 제조업1<NA><NA><NA><NA><NA><NA>
8화학 물질 및 화학제품 제조업; 의약품 제외432220120338207801713416957748961708456
9의료용 물질 및 의약품 제조업1<NA><NA><NA><NA><NA><NA>
산업별사업체수(개)종사자수(명)급여액(백만원)출하액(백만원)주요생산비(백만원)부가가치(백만원)유형자산연말잔액(백만원)
121차 금속 제조업4148822157984625384378458010120031001184
13금속 가공제품 제조업; 기계 및 가구 제외63160256700426662250608181970193573
14전자 부품, 컴퓨터, 영상, 음향 및 통신장비 제조업0000000
15의료, 정밀, 광학 기기 및 시계 제조업857517079150589887606278746393
16전기장비 제조업123821049385581559062903436517
17기타 기계 및 장비 제조업441665694441345870864763516241519946
18자동차 및 트레일러 제조업3725291374671011000692132339091494623
19기타 운송장비 제조업7230645152335393661249831191
20기타 제품 제조업2<NA><NA><NA><NA><NA><NA>
21산업용 기계 및 장비 수리업71737605158732952129212236