Overview

Dataset statistics

Number of variables11
Number of observations307
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory28.9 KiB
Average record size in memory96.4 B

Variable types

Categorical3
Numeric8

Dataset

Description소재부품장비 지역별 생산통계에 관한 데이터로, 17개 광역단체별 사업체수, 종업원수, 생산액, 출하액 등을 제공합니다.
Author산업통상자원부
URLhttps://www.data.go.kr/data/15093972/fileData.do

Alerts

구분 is highly overall correlated with 지역코드 and 1 other fieldsHigh correlation
지역코드명 is highly overall correlated with 지역코드 and 1 other fieldsHigh correlation
지역코드 is highly overall correlated with 구분 and 1 other fieldsHigh correlation
소재부품장비코드 is highly overall correlated with 소재부품장비코드명High correlation
사업체수 is highly overall correlated with 종업원수 and 4 other fieldsHigh correlation
종업원수 is highly overall correlated with 사업체수 and 4 other fieldsHigh correlation
부가가치세금액_백만원 is highly overall correlated with 사업체수 and 4 other fieldsHigh correlation
생산금액_백만원 is highly overall correlated with 사업체수 and 4 other fieldsHigh correlation
출하금액_백만원 is highly overall correlated with 사업체수 and 4 other fieldsHigh correlation
재고금액_백만원 is highly overall correlated with 사업체수 and 4 other fieldsHigh correlation
소재부품장비코드명 is highly overall correlated with 소재부품장비코드High correlation
부가가치세금액_백만원 has unique valuesUnique
생산금액_백만원 has unique valuesUnique
출하금액_백만원 has unique valuesUnique
재고금액_백만원 has unique valuesUnique
소재부품장비코드 has 17 (5.5%) zerosZeros

Reproduction

Analysis started2024-03-14 18:00:20.619648
Analysis finished2024-03-14 18:00:41.417915
Duration20.8 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
비수도권
248 
수도권
59 

Length

Max length4
Median length4
Mean length3.8078176
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
비수도권 248
80.8%
수도권 59
 
19.2%

Length

2024-03-15T03:00:41.813872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T03:00:42.130451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
비수도권 248
80.8%
수도권 59
 
19.2%

지역코드
Real number (ℝ)

HIGH CORRELATION 

Distinct17
Distinct (%)5.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.736156
Minimum11
Maximum39
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2024-03-15T03:00:42.499044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile11
Q123
median31
Q335
95-th percentile38
Maximum39
Range28
Interquartile range (IQR)12

Descriptive statistics

Standard deviation7.2696946
Coefficient of variation (CV)0.25298076
Kurtosis-0.1107496
Mean28.736156
Median Absolute Deviation (MAD)6
Skewness-0.66377621
Sum8822
Variance52.84846
MonotonicityIncreasing
2024-03-15T03:00:42.795488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
37 20
 
6.5%
22 20
 
6.5%
23 20
 
6.5%
25 20
 
6.5%
38 20
 
6.5%
31 20
 
6.5%
21 20
 
6.5%
33 20
 
6.5%
34 20
 
6.5%
11 19
 
6.2%
Other values (7) 108
35.2%
ValueCountFrequency (%)
11 19
6.2%
21 20
6.5%
22 20
6.5%
23 20
6.5%
24 18
5.9%
25 20
6.5%
26 18
5.9%
29 14
4.6%
31 20
6.5%
32 16
5.2%
ValueCountFrequency (%)
39 7
 
2.3%
38 20
6.5%
37 20
6.5%
36 17
5.5%
35 18
5.9%
34 20
6.5%
33 20
6.5%
32 16
5.2%
31 20
6.5%
29 14
4.6%

지역코드명
Categorical

HIGH CORRELATION 

Distinct17
Distinct (%)5.5%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
경기
 
20
대전
 
20
충북
 
20
충남
 
20
경북
 
20
Other values (12)
207 

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 (%)
경기 20
 
6.5%
대전 20
 
6.5%
충북 20
 
6.5%
충남 20
 
6.5%
경북 20
 
6.5%
부산 20
 
6.5%
경남 20
 
6.5%
인천 20
 
6.5%
대구 20
 
6.5%
서울 19
 
6.2%
Other values (7) 108
35.2%

Length

2024-03-15T03:00:43.199566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기 20
 
6.5%
부산 20
 
6.5%
대전 20
 
6.5%
인천 20
 
6.5%
경남 20
 
6.5%
대구 20
 
6.5%
경북 20
 
6.5%
충남 20
 
6.5%
충북 20
 
6.5%
서울 19
 
6.2%
Other values (7) 108
35.2%

소재부품장비코드
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct20
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21071.661
Minimum0
Maximum35000
Zeros17
Zeros (%)5.5%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2024-03-15T03:00:43.537493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q113000
median22000
Q330000
95-th percentile34000
Maximum35000
Range35000
Interquartile range (IQR)17000

Descriptive statistics

Standard deviation9320.0928
Coefficient of variation (CV)0.4423046
Kurtosis-0.60744042
Mean21071.661
Median Absolute Deviation (MAD)8000
Skewness-0.40121577
Sum6469000
Variance86864129
MonotonicityNot monotonic
2024-03-15T03:00:43.929551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0 17
 
5.5%
13000 17
 
5.5%
14000 17
 
5.5%
20000 17
 
5.5%
22000 17
 
5.5%
24000 17
 
5.5%
10000 17
 
5.5%
26000 16
 
5.2%
32000 16
 
5.2%
30000 16
 
5.2%
Other values (10) 140
45.6%
ValueCountFrequency (%)
0 17
5.5%
10000 17
5.5%
11000 14
4.6%
12000 16
5.2%
13000 17
5.5%
14000 17
5.5%
15000 15
4.9%
20000 17
5.5%
21000 15
4.9%
22000 17
5.5%
ValueCountFrequency (%)
35000 12
3.9%
34000 12
3.9%
33000 10
3.3%
32000 16
5.2%
31000 14
4.6%
30000 16
5.2%
27000 16
5.2%
26000 16
5.2%
25000 16
5.2%
24000 17
5.5%

소재부품장비코드명
Categorical

HIGH CORRELATION 

Distinct20
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
소재부품장비
 
17
고무 및 플라스틱제품
 
17
비금속 광물제품
 
17
부품
 
17
일반기계부품
 
17
Other values (15)
222 

Length

Max length11
Median length10
Mean length6.0912052
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row소재부품장비
2nd row소재
3rd row섬유제품
4th row화학물질 및 화학제품
5th row고무 및 플라스틱제품

Common Values

ValueCountFrequency (%)
소재부품장비 17
 
5.5%
고무 및 플라스틱제품 17
 
5.5%
비금속 광물제품 17
 
5.5%
부품 17
 
5.5%
일반기계부품 17
 
5.5%
전기장비부품 17
 
5.5%
소재 17
 
5.5%
화학물질 및 화학제품 16
 
5.2%
전자부품 16
 
5.2%
수송기계부품 16
 
5.2%
Other values (10) 140
45.6%

Length

2024-03-15T03:00:44.277500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
및 33
 
7.9%
소재부품장비 17
 
4.1%
고무 17
 
4.1%
플라스틱제품 17
 
4.1%
비금속 17
 
4.1%
광물제품 17
 
4.1%
부품 17
 
4.1%
일반기계부품 17
 
4.1%
전기장비부품 17
 
4.1%
소재 17
 
4.1%
Other values (16) 231
55.4%

사업체수
Real number (ℝ)

HIGH CORRELATION 

Distinct184
Distinct (%)59.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean294.10749
Minimum3
Maximum11123
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2024-03-15T03:00:44.576479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile6
Q120
median73
Q3214
95-th percentile1306.2
Maximum11123
Range11120
Interquartile range (IQR)194

Descriptive statistics

Standard deviation853.13904
Coefficient of variation (CV)2.9007729
Kurtosis94.815904
Mean294.10749
Median Absolute Deviation (MAD)61
Skewness8.5666006
Sum90291
Variance727846.21
MonotonicityNot monotonic
2024-03-15T03:00:44.980152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8 11
 
3.6%
6 8
 
2.6%
13 6
 
2.0%
3 6
 
2.0%
16 6
 
2.0%
31 5
 
1.6%
12 5
 
1.6%
9 5
 
1.6%
32 4
 
1.3%
7 4
 
1.3%
Other values (174) 247
80.5%
ValueCountFrequency (%)
3 6
2.0%
4 3
 
1.0%
5 3
 
1.0%
6 8
2.6%
7 4
 
1.3%
8 11
3.6%
9 5
1.6%
10 4
 
1.3%
11 1
 
0.3%
12 5
1.6%
ValueCountFrequency (%)
11123 1
0.3%
6595 1
0.3%
3357 1
0.3%
2852 1
0.3%
2498 1
0.3%
2382 1
0.3%
2214 1
0.3%
2086 1
0.3%
1961 1
0.3%
1776 1
0.3%

종업원수
Real number (ℝ)

HIGH CORRELATION 

Distinct304
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14108.922
Minimum32
Maximum484929
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2024-03-15T03:00:45.383605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum32
5-th percentile117
Q1659.5
median2983
Q310567
95-th percentile59817.9
Maximum484929
Range484897
Interquartile range (IQR)9907.5

Descriptive statistics

Standard deviation40131.386
Coefficient of variation (CV)2.8443978
Kurtosis76.375299
Mean14108.922
Median Absolute Deviation (MAD)2693
Skewness7.7213777
Sum4331439
Variance1.6105282 × 109
MonotonicityNot monotonic
2024-03-15T03:00:45.833548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4938 2
 
0.7%
494 2
 
0.7%
114 2
 
0.7%
3608 1
 
0.3%
13869 1
 
0.3%
790 1
 
0.3%
45349 1
 
0.3%
162733 1
 
0.3%
130 1
 
0.3%
73 1
 
0.3%
Other values (294) 294
95.8%
ValueCountFrequency (%)
32 1
0.3%
33 1
0.3%
35 1
0.3%
38 1
0.3%
56 1
0.3%
63 1
0.3%
73 1
0.3%
81 1
0.3%
84 1
0.3%
85 1
0.3%
ValueCountFrequency (%)
484929 1
0.3%
338175 1
0.3%
162733 1
0.3%
151698 1
0.3%
144711 1
0.3%
136922 1
0.3%
104021 1
0.3%
101444 1
0.3%
87998 1
0.3%
85764 1
0.3%

부가가치세금액_백만원
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct307
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3777242.6
Minimum2643
Maximum1.6270219 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2024-03-15T03:00:46.237773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2643
5-th percentile12975
Q198162
median481651
Q31964048
95-th percentile15440890
Maximum1.6270219 × 108
Range1.6269955 × 108
Interquartile range (IQR)1865886

Descriptive statistics

Standard deviation14564525
Coefficient of variation (CV)3.8558618
Kurtosis79.331611
Mean3777242.6
Median Absolute Deviation (MAD)439685
Skewness8.4286476
Sum1.1596135 × 109
Variance2.121254 × 1014
MonotonicityNot monotonic
2024-03-15T03:00:46.709586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3376473 1
 
0.3%
7548 1
 
0.3%
1408895 1
 
0.3%
1943907 1
 
0.3%
7205834 1
 
0.3%
101482 1
 
0.3%
14549161 1
 
0.3%
47878491 1
 
0.3%
20501 1
 
0.3%
198545 1
 
0.3%
Other values (297) 297
96.7%
ValueCountFrequency (%)
2643 1
0.3%
3668 1
0.3%
3938 1
0.3%
4255 1
0.3%
5155 1
0.3%
6606 1
0.3%
7548 1
0.3%
7811 1
0.3%
9303 1
0.3%
9803 1
0.3%
ValueCountFrequency (%)
162702190 1
0.3%
138108126 1
0.3%
113035181 1
0.3%
47878491 1
0.3%
34358761 1
0.3%
29614262 1
0.3%
24480483 1
0.3%
22486100 1
0.3%
22437256 1
0.3%
22325284 1
0.3%

생산금액_백만원
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct307
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10021189
Minimum5693
Maximum3.1404336 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2024-03-15T03:00:47.144370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5693
5-th percentile25923.1
Q1248000.5
median1230350
Q36228166.5
95-th percentile47734455
Maximum3.1404336 × 108
Range3.1403767 × 108
Interquartile range (IQR)5980166

Descriptive statistics

Standard deviation29756251
Coefficient of variation (CV)2.9693334
Kurtosis51.811212
Mean10021189
Median Absolute Deviation (MAD)1154648
Skewness6.4360402
Sum3.076505 × 109
Variance8.8543448 × 1014
MonotonicityNot monotonic
2024-03-15T03:00:47.552141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7455543 1
 
0.3%
16826 1
 
0.3%
2253421 1
 
0.3%
5595239 1
 
0.3%
29156395 1
 
0.3%
247934 1
 
0.3%
59279310 1
 
0.3%
148054955 1
 
0.3%
37559 1
 
0.3%
442304 1
 
0.3%
Other values (297) 297
96.7%
ValueCountFrequency (%)
5693 1
0.3%
6346 1
0.3%
11573 1
0.3%
12316 1
0.3%
13145 1
0.3%
13868 1
0.3%
15786 1
0.3%
16057 1
0.3%
16826 1
0.3%
17676 1
0.3%
ValueCountFrequency (%)
314043361 1
0.3%
243558117 1
0.3%
175608823 1
0.3%
148054955 1
0.3%
108110536 1
0.3%
96202735 1
0.3%
78775270 1
0.3%
77797916 1
0.3%
73537481 1
0.3%
70261870 1
0.3%

출하금액_백만원
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct307
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9948409.2
Minimum5583
Maximum3.1275746 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2024-03-15T03:00:47.915586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5583
5-th percentile25787.2
Q1247159.5
median1234966
Q36200044
95-th percentile47384347
Maximum3.1275746 × 108
Range3.1275188 × 108
Interquartile range (IQR)5952884.5

Descriptive statistics

Standard deviation29588862
Coefficient of variation (CV)2.9742305
Kurtosis52.129669
Mean9948409.2
Median Absolute Deviation (MAD)1158772
Skewness6.4573303
Sum3.0541616 × 109
Variance8.7550076 × 1014
MonotonicityNot monotonic
2024-03-15T03:00:48.366367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7418346 1
 
0.3%
16945 1
 
0.3%
2251750 1
 
0.3%
5609144 1
 
0.3%
28559654 1
 
0.3%
246745 1
 
0.3%
58208357 1
 
0.3%
146764439 1
 
0.3%
37691 1
 
0.3%
436620 1
 
0.3%
Other values (297) 297
96.7%
ValueCountFrequency (%)
5583 1
0.3%
6156 1
0.3%
11809 1
0.3%
12255 1
0.3%
13911 1
0.3%
14050 1
0.3%
15013 1
0.3%
15418 1
0.3%
16852 1
0.3%
16945 1
0.3%
ValueCountFrequency (%)
312757460 1
0.3%
242600167 1
0.3%
174835790 1
0.3%
146764439 1
0.3%
107214098 1
0.3%
95477494 1
0.3%
78602632 1
0.3%
76848330 1
0.3%
72689265 1
0.3%
69579521 1
0.3%

재고금액_백만원
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct307
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean405761.12
Minimum0
Maximum10152450
Zeros1
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2024-03-15T03:00:48.812928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1229.3
Q111623.5
median58995
Q3263672
95-th percentile2155248.4
Maximum10152450
Range10152450
Interquartile range (IQR)252048.5

Descriptive statistics

Standard deviation1024295.9
Coefficient of variation (CV)2.5243816
Kurtosis34.901047
Mean405761.12
Median Absolute Deviation (MAD)55998
Skewness5.1219775
Sum1.2456866 × 108
Variance1.0491821 × 1012
MonotonicityNot monotonic
2024-03-15T03:00:49.173388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
353222 1
 
0.3%
14 1
 
0.3%
44695 1
 
0.3%
264838 1
 
0.3%
1666722 1
 
0.3%
19823 1
 
0.3%
3398868 1
 
0.3%
5219503 1
 
0.3%
371 1
 
0.3%
35721 1
 
0.3%
Other values (297) 297
96.7%
ValueCountFrequency (%)
0 1
0.3%
2 1
0.3%
14 1
0.3%
237 1
0.3%
245 1
0.3%
270 1
0.3%
371 1
0.3%
445 1
0.3%
488 1
0.3%
650 1
0.3%
ValueCountFrequency (%)
10152450 1
0.3%
6852244 1
0.3%
5219503 1
0.3%
5018402 1
0.3%
4175788 1
0.3%
3398868 1
0.3%
3388856 1
0.3%
3317669 1
0.3%
3313670 1
0.3%
3234859 1
0.3%

Interactions

2024-03-15T03:00:38.467509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:00:22.404549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:00:25.706072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:00:27.587873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:00:29.871558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:00:31.990029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:00:34.043811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:00:36.552526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:00:38.725735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:00:22.782250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:00:25.978867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:00:28.062864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:00:30.177467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:00:32.334957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:00:34.333494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:00:36.871381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:00:38.989033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:00:23.468945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:00:26.245975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:00:28.323071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:00:30.445000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:00:32.564591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:00:34.708910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:00:37.181728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:00:39.244521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:00:23.873025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:00:26.442492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:00:28.564584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:00:30.689338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:00:32.739949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:00:35.017447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:00:37.331782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:00:39.498443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:00:24.239753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:00:26.599982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:00:28.814281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:00:30.935688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:00:32.905494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:00:35.329889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:00:37.488298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:00:39.768848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:00:24.581590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:00:26.785522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:00:29.094091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:00:31.215455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:00:33.092786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:00:35.684370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:00:37.663994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:00:40.025047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:00:25.036201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:00:27.056990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:00:29.357531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:00:31.479407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:00:33.299391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:00:36.013242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:00:37.936461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:00:40.334606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:00:25.451515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:00:27.323339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:00:29.613135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:00:31.737217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:00:33.723487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:00:36.284996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:00:38.201096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-15T03:00:49.406187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분지역코드지역코드명소재부품장비코드소재부품장비코드명사업체수종업원수부가가치세금액_백만원생산금액_백만원출하금액_백만원재고금액_백만원
구분1.0000.8951.0000.0000.0000.2620.2510.2560.2140.2140.162
지역코드0.8951.0001.0000.0000.0000.0000.0000.0000.0000.0000.000
지역코드명1.0001.0001.0000.0000.0000.0690.0780.0000.0000.0000.000
소재부품장비코드0.0000.0000.0001.0001.0000.3910.3960.3910.3840.3840.435
소재부품장비코드명0.0000.0000.0001.0001.0000.2830.3530.3430.3480.3480.437
사업체수0.2620.0000.0690.3910.2831.0000.9780.9540.8650.8650.868
종업원수0.2510.0000.0780.3960.3530.9781.0000.9800.9310.9310.913
부가가치세금액_백만원0.2560.0000.0000.3910.3430.9540.9801.0000.9760.9760.943
생산금액_백만원0.2140.0000.0000.3840.3480.8650.9310.9761.0001.0000.989
출하금액_백만원0.2140.0000.0000.3840.3480.8650.9310.9761.0001.0000.989
재고금액_백만원0.1620.0000.0000.4350.4370.8680.9130.9430.9890.9891.000
2024-03-15T03:00:49.729656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분소재부품장비코드명지역코드명
구분1.0000.0000.975
소재부품장비코드명0.0001.0000.000
지역코드명0.9750.0001.000
2024-03-15T03:00:49.986942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지역코드소재부품장비코드사업체수종업원수부가가치세금액_백만원생산금액_백만원출하금액_백만원재고금액_백만원구분지역코드명소재부품장비코드명
지역코드1.000-0.0480.0340.0750.1040.1070.1070.1060.6590.9850.000
소재부품장비코드-0.0481.000-0.363-0.386-0.420-0.420-0.420-0.4880.0000.0000.981
사업체수0.034-0.3631.0000.9540.9070.9050.9050.8950.1870.0290.131
종업원수0.075-0.3860.9541.0000.9800.9770.9770.9520.1790.0330.167
부가가치세금액_백만원0.104-0.4200.9070.9801.0000.9930.9930.9660.1830.0000.163
생산금액_백만원0.107-0.4200.9050.9770.9931.0001.0000.9700.1590.0000.143
출하금액_백만원0.107-0.4200.9050.9770.9931.0001.0000.9690.1590.0000.143
재고금액_백만원0.106-0.4880.8950.9520.9660.9700.9691.0000.1200.0000.186
구분0.6590.0000.1870.1790.1830.1590.1590.1201.0000.9750.000
지역코드명0.9850.0000.0290.0330.0000.0000.0000.0000.9751.0000.000
소재부품장비코드명0.0000.9810.1310.1670.1630.1430.1430.1860.0000.0001.000

Missing values

2024-03-15T03:00:40.699505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T03:00:41.208281image/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.

Sample

구분지역코드지역코드명소재부품장비코드소재부품장비코드명사업체수종업원수부가가치세금액_백만원생산금액_백만원출하금액_백만원재고금액_백만원
0수도권11서울0소재부품장비78921153337647374555437418346353222
1수도권11서울10000소재128302611340642210916220589786470
2수도권11서울11000섬유제품68121215868976051475682325897
3수도권11서울12000화학물질 및 화학제품3211127773451059285105952924183
4수도권11서울13000고무 및 플라스틱제품1655818364935469835310035277
5수도권11서울14000비금속 광물제품784102642561225678807
6수도권11서울20000부품54314977184341143951914365366224741
7수도권11서울21000금속가공제품356759530918072817948015275
8수도권11서울22000일반기계부품133351946464591619191319732533
9수도권11서울24000전기장비부품15239204210741111051110652459754
구분지역코드지역코드명소재부품장비코드소재부품장비코드명사업체수종업원수부가가치세금액_백만원생산금액_백만원출하금액_백만원재고금액_백만원
297비수도권38경남33000반도체·디스플레이장비853425451362077460625635199
298비수도권38경남34000제조로봇 자동화장비122592610268213678711476
299비수도권38경남35000계측장비122162129047230452983649
300비수도권39제주0소재부품장비274749649123888323948712834
301비수도권39제주10000소재142072042232837326891237
302비수도권39제주13000고무 및 플라스틱제품332264363466156697
303비수도권39제주14000비금속 광물제품8135145492057420648488
304비수도권39제주20000부품132677606920604620679811597
305비수도권39제주22000일반기계부품333393813145140505785
306비수도권39제주24000전기장비부품81241044226649268562