Overview

Dataset statistics

Number of variables12
Number of observations199
Missing cells30
Missing cells (%)1.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory20.3 KiB
Average record size in memory104.7 B

Variable types

Categorical4
Numeric8

Dataset

Description- 제조업 중 부품과 소재의 품목을 선정하여 부품소재 12대 업종으로 분류
Author산업통상자원부
URLhttps://www.data.go.kr/data/3040005/fileData.do

Alerts

구분 is highly overall correlated with 지역코드 and 2 other fieldsHigh correlation
지역 is highly overall correlated with 지역코드 and 2 other fieldsHigh correlation
비고 is highly overall correlated with 지역코드 and 5 other fieldsHigh correlation
소재부품코드명 is highly overall correlated with 소재부품코드 and 1 other fieldsHigh correlation
지역코드 is highly overall correlated with 구분 and 2 other fieldsHigh correlation
소재부품코드 is highly overall correlated with 소재부품코드명 and 1 other fieldsHigh correlation
사업체수 is highly overall correlated with 종업원수 and 5 other fieldsHigh correlation
종업원수 is highly overall correlated with 사업체수 and 4 other fieldsHigh correlation
부가가치(백만원) is highly overall correlated with 사업체수 and 4 other fieldsHigh correlation
생산(백만원) is highly overall correlated with 사업체수 and 4 other fieldsHigh correlation
출하(백만원) is highly overall correlated with 사업체수 and 4 other fieldsHigh correlation
재고(백만원) is highly overall correlated with 사업체수 and 4 other fieldsHigh correlation
비고 is highly imbalanced (80.5%)Imbalance
종업원수 has 6 (3.0%) missing valuesMissing
부가가치(백만원) has 6 (3.0%) missing valuesMissing
생산(백만원) has 6 (3.0%) missing valuesMissing
출하(백만원) has 6 (3.0%) missing valuesMissing
재고(백만원) has 6 (3.0%) missing valuesMissing
소재부품코드 has 17 (8.5%) zerosZeros

Reproduction

Analysis started2023-12-12 02:46:24.162573
Analysis finished2023-12-12 02:46:31.685144
Duration7.52 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
비수도권
163 
수도권
36 

Length

Max length4
Median length4
Mean length3.8190955
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
비수도권 163
81.9%
수도권 36
 
18.1%

Length

2023-12-12T11:46:31.771248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:46:31.894166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
비수도권 163
81.9%
수도권 36
 
18.1%

지역코드
Real number (ℝ)

HIGH CORRELATION 

Distinct17
Distinct (%)8.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.929648
Minimum11
Maximum39
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-12T11:46:32.001185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile11
Q124
median31
Q335
95-th percentile38
Maximum39
Range28
Interquartile range (IQR)11

Descriptive statistics

Standard deviation7.2415102
Coefficient of variation (CV)0.25031449
Kurtosis-0.0097493994
Mean28.929648
Median Absolute Deviation (MAD)6
Skewness-0.69792309
Sum5757
Variance52.43947
MonotonicityIncreasing
2023-12-12T11:46:32.126645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
11 12
 
6.0%
21 12
 
6.0%
38 12
 
6.0%
37 12
 
6.0%
36 12
 
6.0%
35 12
 
6.0%
34 12
 
6.0%
33 12
 
6.0%
32 12
 
6.0%
31 12
 
6.0%
Other values (7) 79
39.7%
ValueCountFrequency (%)
11 12
6.0%
21 12
6.0%
22 12
6.0%
23 12
6.0%
24 12
6.0%
25 12
6.0%
26 12
6.0%
29 12
6.0%
31 12
6.0%
32 12
6.0%
ValueCountFrequency (%)
39 7
3.5%
38 12
6.0%
37 12
6.0%
36 12
6.0%
35 12
6.0%
34 12
6.0%
33 12
6.0%
32 12
6.0%
31 12
6.0%
29 12
6.0%

지역
Categorical

HIGH CORRELATION 

Distinct17
Distinct (%)8.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
서울
 
12
부산
 
12
대구
 
12
인천
 
12
광주
 
12
Other values (12)
139 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
서울 12
 
6.0%
부산 12
 
6.0%
대구 12
 
6.0%
인천 12
 
6.0%
광주 12
 
6.0%
대전 12
 
6.0%
울산 12
 
6.0%
세종 12
 
6.0%
경기 12
 
6.0%
강원 12
 
6.0%
Other values (7) 79
39.7%

Length

2023-12-12T11:46:32.281678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
서울 12
 
6.0%
강원 12
 
6.0%
경남 12
 
6.0%
경북 12
 
6.0%
전남 12
 
6.0%
전북 12
 
6.0%
충남 12
 
6.0%
충북 12
 
6.0%
경기 12
 
6.0%
부산 12
 
6.0%
Other values (7) 79
39.7%

소재부품코드
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct12
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17437.186
Minimum0
Maximum27000
Zeros17
Zeros (%)8.5%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-12T11:46:32.391006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q112500
median15000
Q324000
95-th percentile27000
Maximum27000
Range27000
Interquartile range (IQR)11500

Descriptive statistics

Standard deviation7740.4111
Coefficient of variation (CV)0.44390254
Kurtosis-0.23192568
Mean17437.186
Median Absolute Deviation (MAD)7000
Skewness-0.67110753
Sum3470000
Variance59913964
MonotonicityNot monotonic
2023-12-12T11:46:32.507843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 17
8.5%
12000 17
8.5%
13000 17
8.5%
14000 17
8.5%
22000 17
8.5%
24000 17
8.5%
25000 17
8.5%
11000 16
8.0%
15000 16
8.0%
21000 16
8.0%
Other values (2) 32
16.1%
ValueCountFrequency (%)
0 17
8.5%
11000 16
8.0%
12000 17
8.5%
13000 17
8.5%
14000 17
8.5%
15000 16
8.0%
21000 16
8.0%
22000 17
8.5%
24000 17
8.5%
25000 17
8.5%
ValueCountFrequency (%)
27000 16
8.0%
26000 16
8.0%
25000 17
8.5%
24000 17
8.5%
22000 17
8.5%
21000 16
8.0%
15000 16
8.0%
14000 17
8.5%
13000 17
8.5%
12000 17
8.5%

소재부품코드명
Categorical

HIGH CORRELATION 

Distinct12
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
소재부품
17 
화학물질 및 화학제품
17 
고무 및 플라스틱제품
17 
비금속 광물제품
17 
일반기계부품
17 
Other values (7)
114 

Length

Max length11
Median length8
Mean length6.6030151
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row소재부품
2nd row섬유제품
3rd row화학물질 및 화학제품
4th row고무 및 플라스틱제품
5th row비금속 광물제품

Common Values

ValueCountFrequency (%)
소재부품 17
8.5%
화학물질 및 화학제품 17
8.5%
고무 및 플라스틱제품 17
8.5%
비금속 광물제품 17
8.5%
일반기계부품 17
8.5%
전기장비부품 17
8.5%
전자부품 17
8.5%
섬유제품 16
8.0%
1차 금속제품 16
8.0%
금속가공제품 16
8.0%
Other values (2) 32
16.1%

Length

2023-12-12T11:46:32.653351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
및 34
 
11.3%
소재부품 17
 
5.7%
광물제품 17
 
5.7%
전자부품 17
 
5.7%
전기장비부품 17
 
5.7%
화학물질 17
 
5.7%
일반기계부품 17
 
5.7%
비금속 17
 
5.7%
플라스틱제품 17
 
5.7%
고무 17
 
5.7%
Other values (7) 113
37.7%

사업체수
Real number (ℝ)

HIGH CORRELATION 

Distinct140
Distinct (%)70.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean258.75377
Minimum1
Maximum9072
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-12T11:46:32.822450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q127
median71
Q3196
95-th percentile1073.7
Maximum9072
Range9071
Interquartile range (IQR)169

Descriptive statistics

Standard deviation750.26646
Coefficient of variation (CV)2.8995383
Kurtosis97.751825
Mean258.75377
Median Absolute Deviation (MAD)61
Skewness8.8379079
Sum51492
Variance562899.76
MonotonicityNot monotonic
2023-12-12T11:46:32.969098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 6
 
3.0%
8 5
 
2.5%
15 4
 
2.0%
36 4
 
2.0%
29 4
 
2.0%
54 4
 
2.0%
1 3
 
1.5%
5 3
 
1.5%
2 3
 
1.5%
19 3
 
1.5%
Other values (130) 160
80.4%
ValueCountFrequency (%)
1 3
1.5%
2 3
1.5%
3 1
 
0.5%
4 6
3.0%
5 3
1.5%
6 2
 
1.0%
7 1
 
0.5%
8 5
2.5%
9 2
 
1.0%
10 2
 
1.0%
ValueCountFrequency (%)
9072 1
0.5%
3051 1
0.5%
2221 1
0.5%
1897 1
0.5%
1848 1
0.5%
1713 1
0.5%
1617 1
0.5%
1327 1
0.5%
1276 1
0.5%
1251 1
0.5%

종업원수
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct190
Distinct (%)98.4%
Missing6
Missing (%)3.0%
Infinite0
Infinite (%)0.0%
Mean13658.611
Minimum35
Maximum416933
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-12T11:46:33.132717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35
5-th percentile193
Q11045
median3681
Q311978
95-th percentile53625.2
Maximum416933
Range416898
Interquartile range (IQR)10933

Descriptive statistics

Standard deviation37211.385
Coefficient of variation (CV)2.72439
Kurtosis74.134451
Mean13658.611
Median Absolute Deviation (MAD)3169
Skewness7.613613
Sum2636112
Variance1.3846872 × 109
MonotonicityNot monotonic
2023-12-12T11:46:33.314325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
489 2
 
1.0%
4965 2
 
1.0%
193 2
 
1.0%
13159 1
 
0.5%
21900 1
 
0.5%
2057 1
 
0.5%
8792 1
 
0.5%
139341 1
 
0.5%
1044 1
 
0.5%
9542 1
 
0.5%
Other values (180) 180
90.5%
(Missing) 6
 
3.0%
ValueCountFrequency (%)
35 1
0.5%
38 1
0.5%
62 1
0.5%
64 1
0.5%
86 1
0.5%
122 1
0.5%
134 1
0.5%
156 1
0.5%
192 1
0.5%
193 2
1.0%
ValueCountFrequency (%)
416933 1
0.5%
145188 1
0.5%
139505 1
0.5%
139341 1
0.5%
137523 1
0.5%
79073 1
0.5%
76601 1
0.5%
64952 1
0.5%
56788 1
0.5%
56666 1
0.5%

부가가치(백만원)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct193
Distinct (%)100.0%
Missing6
Missing (%)3.0%
Infinite0
Infinite (%)0.0%
Mean3083910
Minimum2394
Maximum1.2429884 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-12T11:46:33.481524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2394
5-th percentile18239.8
Q1145439
median486215
Q31923290
95-th percentile9336301.4
Maximum1.2429884 × 108
Range1.2429645 × 108
Interquartile range (IQR)1777851

Descriptive statistics

Standard deviation11703912
Coefficient of variation (CV)3.7951535
Kurtosis75.621041
Mean3083910
Median Absolute Deviation (MAD)430461
Skewness8.221385
Sum5.9519462 × 108
Variance1.3698155 × 1014
MonotonicityNot monotonic
2023-12-12T11:46:34.008020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
312979 1
 
0.5%
731494 1
 
0.5%
677271 1
 
0.5%
2444487 1
 
0.5%
9698759 1
 
0.5%
252725 1
 
0.5%
1330059 1
 
0.5%
38762568 1
 
0.5%
122177 1
 
0.5%
4440710 1
 
0.5%
Other values (183) 183
92.0%
(Missing) 6
 
3.0%
ValueCountFrequency (%)
2394 1
0.5%
3211 1
0.5%
5556 1
0.5%
6594 1
0.5%
10305 1
0.5%
10742 1
0.5%
12266 1
0.5%
17008 1
0.5%
17151 1
0.5%
18079 1
0.5%
ValueCountFrequency (%)
124298841 1
0.5%
89855938 1
0.5%
38762568 1
0.5%
25883624 1
0.5%
19417598 1
0.5%
18501980 1
0.5%
17254830 1
0.5%
15658778 1
0.5%
15325641 1
0.5%
9698759 1
0.5%

생산(백만원)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct193
Distinct (%)100.0%
Missing6
Missing (%)3.0%
Infinite0
Infinite (%)0.0%
Mean8051295.8
Minimum6203
Maximum2.3977476 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-12T11:46:34.195216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6203
5-th percentile44081.8
Q1410783
median1480137
Q35074044
95-th percentile28991504
Maximum2.3977476 × 108
Range2.3976856 × 108
Interquartile range (IQR)4663261

Descriptive statistics

Standard deviation23675311
Coefficient of variation (CV)2.9405591
Kurtosis54.314453
Mean8051295.8
Median Absolute Deviation (MAD)1303120
Skewness6.6402334
Sum1.5539001 × 109
Variance5.6052037 × 1014
MonotonicityNot monotonic
2023-12-12T11:46:34.376703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
708220 1
 
0.5%
1543336 1
 
0.5%
1607657 1
 
0.5%
10242285 1
 
0.5%
17806116 1
 
0.5%
615606 1
 
0.5%
5425262 1
 
0.5%
109793457 1
 
0.5%
372692 1
 
0.5%
14791062 1
 
0.5%
Other values (183) 183
92.0%
(Missing) 6
 
3.0%
ValueCountFrequency (%)
6203 1
0.5%
6992 1
0.5%
9794 1
0.5%
11672 1
0.5%
18932 1
0.5%
31433 1
0.5%
32911 1
0.5%
33574 1
0.5%
38325 1
0.5%
39199 1
0.5%
ValueCountFrequency (%)
239774764 1
0.5%
139832761 1
0.5%
109793457 1
0.5%
84925358 1
0.5%
67198108 1
0.5%
58388251 1
0.5%
52080992 1
0.5%
48143420 1
0.5%
36866352 1
0.5%
29217231 1
0.5%

출하(백만원)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct193
Distinct (%)100.0%
Missing6
Missing (%)3.0%
Infinite0
Infinite (%)0.0%
Mean8022417.7
Minimum6216
Maximum2.3920792 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-12T11:46:34.561244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6216
5-th percentile41999.4
Q1411709
median1477603
Q35030555
95-th percentile28782797
Maximum2.3920792 × 108
Range2.392017 × 108
Interquartile range (IQR)4618846

Descriptive statistics

Standard deviation23606790
Coefficient of variation (CV)2.9426029
Kurtosis54.428059
Mean8022417.7
Median Absolute Deviation (MAD)1302984
Skewness6.6476603
Sum1.5483266 × 109
Variance5.5728051 × 1014
MonotonicityNot monotonic
2023-12-12T11:46:34.752612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
686451 1
 
0.5%
1506156 1
 
0.5%
1592205 1
 
0.5%
10206132 1
 
0.5%
17515195 1
 
0.5%
611657 1
 
0.5%
5430714 1
 
0.5%
109475807 1
 
0.5%
364214 1
 
0.5%
14691076 1
 
0.5%
Other values (183) 183
92.0%
(Missing) 6
 
3.0%
ValueCountFrequency (%)
6216 1
0.5%
6988 1
0.5%
9797 1
0.5%
12276 1
0.5%
19103 1
0.5%
30103 1
0.5%
31829 1
0.5%
33682 1
0.5%
38387 1
0.5%
39120 1
0.5%
ValueCountFrequency (%)
239207918 1
0.5%
139478838 1
0.5%
109475807 1
0.5%
84393555 1
0.5%
67090847 1
0.5%
58216059 1
0.5%
51860397 1
0.5%
47702300 1
0.5%
36918810 1
0.5%
28941216 1
0.5%

재고(백만원)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct193
Distinct (%)100.0%
Missing6
Missing (%)3.0%
Infinite0
Infinite (%)0.0%
Mean322469.97
Minimum0
Maximum8975531
Zeros1
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-12T11:46:34.915152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2436.4
Q119195
median63165
Q3241587
95-th percentile1350132
Maximum8975531
Range8975531
Interquartile range (IQR)222392

Descriptive statistics

Standard deviation880147.79
Coefficient of variation (CV)2.7293946
Kurtosis53.175549
Mean322469.97
Median Absolute Deviation (MAD)55997
Skewness6.4601908
Sum62236704
Variance7.7466013 × 1011
MonotonicityNot monotonic
2023-12-12T11:46:35.084251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
85243 1
 
0.5%
195695 1
 
0.5%
110011 1
 
0.5%
334093 1
 
0.5%
704322 1
 
0.5%
38179 1
 
0.5%
183912 1
 
0.5%
3058424 1
 
0.5%
35333 1
 
0.5%
681919 1
 
0.5%
Other values (183) 183
92.0%
(Missing) 6
 
3.0%
ValueCountFrequency (%)
0 1
0.5%
241 1
0.5%
246 1
0.5%
301 1
0.5%
356 1
0.5%
804 1
0.5%
925 1
0.5%
1512 1
0.5%
1546 1
0.5%
2185 1
0.5%
ValueCountFrequency (%)
8975531 1
0.5%
4647558 1
0.5%
4030742 1
0.5%
3058424 1
0.5%
2706361 1
0.5%
2461796 1
0.5%
2199329 1
0.5%
1667836 1
0.5%
1631215 1
0.5%
1395459 1
0.5%

비고
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
<NA>
193 
* 사업체수 2인이하로 비공개
 
6

Length

Max length16
Median length4
Mean length4.361809
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 193
97.0%
* 사업체수 2인이하로 비공개 6
 
3.0%

Length

2023-12-12T11:46:35.242415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:46:35.358862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 193
88.9%
6
 
2.8%
사업체수 6
 
2.8%
2인이하로 6
 
2.8%
비공개 6
 
2.8%

Interactions

2023-12-12T11:46:30.298643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:46:24.801364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:46:25.691014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:46:26.416395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:46:27.161088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:46:28.266109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:46:29.042546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:46:29.663935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:46:30.393393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:46:24.910885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:46:25.814551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:46:26.498253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:46:27.260163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:46:28.375915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:46:29.138848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:46:29.730106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:46:30.581573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:46:25.011662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:46:25.900349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:46:26.580690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:46:27.358317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:46:28.489269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:46:29.224855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:46:29.802114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:46:30.668940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:46:25.125677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:46:25.993695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:46:26.661288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:46:27.474221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:46:28.581681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:46:29.308364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:46:29.866521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:46:30.743911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:46:25.236342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:46:26.069215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:46:26.767321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:46:27.565536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:46:28.677762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:46:29.381474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:46:29.934574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:46:30.851163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:46:25.357435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:46:26.159814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:46:26.875357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:46:27.664150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:46:28.790026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:46:29.455018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:46:30.008400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:46:30.960073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:46:25.476108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:46:26.241199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:46:26.956976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:46:27.749627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:46:28.869967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:46:29.519745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:46:30.111490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:46:31.054173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:46:25.581418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:46:26.332357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:46:27.062598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:46:27.837156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:46:28.956122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:46:29.586510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:46:30.194529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T11:46:35.440901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분지역코드지역소재부품코드소재부품코드명사업체수종업원수부가가치(백만원)생산(백만원)출하(백만원)재고(백만원)
구분1.0000.8881.0000.0000.0000.2010.2910.2340.1380.1340.138
지역코드0.8881.0001.0000.0000.0000.0510.0000.0000.0000.0000.000
지역1.0001.0001.0000.0000.0000.2890.0900.0000.0000.0000.000
소재부품코드0.0000.0000.0001.0001.0000.3180.4520.5310.3750.3510.436
소재부품코드명0.0000.0000.0001.0001.0000.2790.5220.3850.3400.2960.408
사업체수0.2010.0510.2890.3180.2791.0000.8170.7770.7980.8060.829
종업원수0.2910.0000.0900.4520.5220.8171.0000.9030.8650.8640.915
부가가치(백만원)0.2340.0000.0000.5310.3850.7770.9031.0000.9840.9790.946
생산(백만원)0.1380.0000.0000.3750.3400.7980.8650.9841.0001.0000.993
출하(백만원)0.1340.0000.0000.3510.2960.8060.8640.9791.0001.0000.990
재고(백만원)0.1380.0000.0000.4360.4080.8290.9150.9460.9930.9901.000
2023-12-12T11:46:35.566419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분지역비고소재부품코드명
구분1.0000.9611.0000.000
지역0.9611.0001.0000.000
비고1.0001.0001.0001.000
소재부품코드명0.0000.0001.0001.000
2023-12-12T11:46:35.694151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지역코드소재부품코드사업체수종업원수부가가치(백만원)생산(백만원)출하(백만원)재고(백만원)구분지역소재부품코드명비고
지역코드1.000-0.0130.0130.1120.1740.1830.1810.1740.6510.9760.0001.000
소재부품코드-0.0131.000-0.092-0.049-0.074-0.074-0.074-0.1960.0000.0000.9841.000
사업체수0.013-0.0921.0000.9390.8810.8760.8760.8600.2440.1470.1541.000
종업원수0.112-0.0490.9391.0000.9750.9680.9680.9280.1920.0430.2590.000
부가가치(백만원)0.174-0.0740.8810.9751.0000.9890.9890.9440.1660.0000.1580.000
생산(백만원)0.183-0.0740.8760.9680.9891.0001.0000.9510.1460.0000.1710.000
출하(백만원)0.181-0.0740.8760.9680.9891.0001.0000.9500.1420.0000.1470.000
재고(백만원)0.174-0.1960.8600.9280.9440.9510.9501.0000.1450.0000.2080.000
구분0.6510.0000.2440.1920.1660.1460.1420.1451.0000.9610.0001.000
지역0.9760.0000.1470.0430.0000.0000.0000.0000.9611.0000.0001.000
소재부품코드명0.0000.9840.1540.2590.1580.1710.1470.2080.0000.0001.0001.000
비고1.0001.0001.0000.0000.0000.0000.0000.0001.0001.0001.0001.000

Missing values

2023-12-12T11:46:31.202749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T11:46:31.389736image/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.
2023-12-12T11:46:31.571609image/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

구분지역코드지역소재부품코드소재부품코드명사업체수종업원수부가가치(백만원)생산(백만원)출하(백만원)재고(백만원)비고
0수도권11서울0소재부품70719154196453548281804802680233202<NA>
1수도권11서울11000섬유제품5498113541453807553692713402<NA>
2수도권11서울12000화학물질 및 화학제품3196813819428575827559431533<NA>
3수도권11서울13000고무 및 플라스틱제품271018531291188571183596869<NA>
4수도권11서울14000비금속 광물제품8134107423919939120804<NA>
5수도권11서울150001차 금속제품14553557541362801350724687<NA>
6수도권11서울21000금속가공제품458479207417701717461913895<NA>
7수도권11서울22000일반기계부품143378637567285254584441531629<NA>
8수도권11서울24000전기장비부품150368134085886738586229744224<NA>
9수도권11서울25000전자부품15249655343721320716132212161952<NA>
구분지역코드지역소재부품코드소재부품코드명사업체수종업원수부가가치(백만원)생산(백만원)출하(백만원)재고(백만원)비고
189비수도권38경남25000전자부품6146224862151533261152862246739<NA>
190비수도권38경남26000정밀기기부품49247825591279127078571923498<NA>
191비수도권38경남27000수송기계부품6384034561793521696077716959375567578<NA>
192비수도권39제주0소재부품203856453720034419708611442<NA>
193비수도권39제주12000화학물질 및 화학제품1<NA><NA><NA><NA><NA>* 사업체수 2인이하로 비공개
194비수도권39제주13000고무 및 플라스틱제품335239462036216356<NA>
195비수도권39제주14000비금속 광물제품10156171513357433682241<NA>
196비수도권39제주22000일반기계부품1<NA><NA><NA><NA><NA>* 사업체수 2인이하로 비공개
197비수도권39제주24000전기장비부품464659411672122760<NA>
198비수도권39제주25000전자부품1<NA><NA><NA><NA><NA>* 사업체수 2인이하로 비공개