Overview

Dataset statistics

Number of variables9
Number of observations316
Missing cells30
Missing cells (%)1.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory24.5 KiB
Average record size in memory79.4 B

Variable types

Numeric7
Text1
Categorical1

Dataset

Description- 제조업 중 부품과 소재의 품목을 선정하여 부품소재 12대 업종으로 분류
Author산업통상자원부
URLhttps://www.data.go.kr/data/3040003/fileData.do

Alerts

소재부품코드 is highly overall correlated with 비고High 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 overall correlated with 소재부품코드 and 1 other fieldsHigh correlation
비고 is highly imbalanced (86.4%)Imbalance
종업원수 has 6 (1.9%) missing valuesMissing
부가가치(백만원) has 6 (1.9%) missing valuesMissing
생산(백만원) has 6 (1.9%) missing valuesMissing
출하(백만원) has 6 (1.9%) missing valuesMissing
재고(백만원) has 6 (1.9%) missing valuesMissing
소재부품코드 has unique valuesUnique

Reproduction

Analysis started2023-12-12 10:21:54.980245
Analysis finished2023-12-12 10:22:01.971507
Duration6.99 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

소재부품코드
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct316
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19955.867
Minimum0
Maximum27111
Zeros1
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2023-12-12T19:22:02.068311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile11031.75
Q114050.75
median22071.5
Q325020.25
95-th percentile27050.25
Maximum27111
Range27111
Interquartile range (IQR)10969.5

Descriptive statistics

Standard deviation5634.2309
Coefficient of variation (CV)0.28233456
Kurtosis-1.0273524
Mean19955.867
Median Absolute Deviation (MAD)3949
Skewness-0.5089895
Sum6306054
Variance31744558
MonotonicityStrictly increasing
2023-12-12T19:22:02.233443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
0.3%
24030 1
 
0.3%
24051 1
 
0.3%
24050 1
 
0.3%
24042 1
 
0.3%
24041 1
 
0.3%
24040 1
 
0.3%
24032 1
 
0.3%
24031 1
 
0.3%
24023 1
 
0.3%
Other values (306) 306
96.8%
ValueCountFrequency (%)
0 1
0.3%
11000 1
0.3%
11010 1
0.3%
11011 1
0.3%
11012 1
0.3%
11013 1
0.3%
11014 1
0.3%
11019 1
0.3%
11020 1
0.3%
11021 1
0.3%
ValueCountFrequency (%)
27111 1
0.3%
27110 1
0.3%
27101 1
0.3%
27100 1
0.3%
27091 1
0.3%
27090 1
0.3%
27082 1
0.3%
27081 1
0.3%
27080 1
0.3%
27071 1
0.3%

소재부품코드명
Text

Distinct283
Distinct (%)89.6%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
2023-12-12T19:22:02.520781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length44
Median length20
Mean length9.6012658
Min length2

Characters and Unicode

Total characters3034
Distinct characters256
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique250 ?
Unique (%)79.1%

Sample

1st row소재부품
2nd row섬유제품
3rd row제사 및 방적
4th row면방적
5th row모방적
ValueCountFrequency (%)
및 110
 
14.0%
기타 50
 
6.3%
부품 36
 
4.6%
산업용 10
 
1.3%
기계 8
 
1.0%
자동차 6
 
0.8%
비철금속 6
 
0.8%
금속 6
 
0.8%
전자부품 5
 
0.6%
연신제품 5
 
0.6%
Other values (386) 546
69.3%
2023-12-12T19:22:03.030648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
472
 
15.6%
기 233
 
7.7%
및 125
 
4.1%
품 125
 
4.1%
부 85
 
2.8%
제 69
 
2.3%
타 60
 
2.0%
용 58
 
1.9%
전 52
 
1.7%
금 49
 
1.6%
Other values (246) 1706
56.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2512
82.8%
Space Separator 472
 
15.6%
Other Punctuation 45
 
1.5%
Decimal Number 3
 
0.1%
Close Punctuation 1
 
< 0.1%
Open Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
기 233
 
9.3%
및 125
 
5.0%
품 125
 
5.0%
부 85
 
3.4%
제 69
 
2.7%
타 60
 
2.4%
용 58
 
2.3%
전 52
 
2.1%
금 49
 
2.0%
장 46
 
1.8%
Other values (240) 1610
64.1%
Other Punctuation
ValueCountFrequency (%)
, 34
75.6%
. 11
 
24.4%
Space Separator
ValueCountFrequency (%)
472
100.0%
Decimal Number
ValueCountFrequency (%)
1 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2512
82.8%
Common 522
 
17.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
기 233
 
9.3%
및 125
 
5.0%
품 125
 
5.0%
부 85
 
3.4%
제 69
 
2.7%
타 60
 
2.4%
용 58
 
2.3%
전 52
 
2.1%
금 49
 
2.0%
장 46
 
1.8%
Other values (240) 1610
64.1%
Common
ValueCountFrequency (%)
472
90.4%
, 34
 
6.5%
. 11
 
2.1%
1 3
 
0.6%
) 1
 
0.2%
( 1
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2512
82.8%
ASCII 522
 
17.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
472
90.4%
, 34
 
6.5%
. 11
 
2.1%
1 3
 
0.6%
) 1
 
0.2%
( 1
 
0.2%
Hangul
ValueCountFrequency (%)
기 233
 
9.3%
및 125
 
5.0%
품 125
 
5.0%
부 85
 
3.4%
제 69
 
2.7%
타 60
 
2.4%
용 58
 
2.3%
전 52
 
2.1%
금 49
 
2.0%
장 46
 
1.8%
Other values (240) 1610
64.1%

사업체수
Real number (ℝ)

HIGH CORRELATION 

Distinct189
Distinct (%)59.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean325.89873
Minimum0
Maximum25746
Zeros1
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2023-12-12T19:22:03.221309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q122.75
median75
Q3205.75
95-th percentile1146
Maximum25746
Range25746
Interquartile range (IQR)183

Descriptive statistics

Standard deviation1534.639
Coefficient of variation (CV)4.7089445
Kurtosis241.09137
Mean325.89873
Median Absolute Deviation (MAD)60
Skewness14.7285
Sum102984
Variance2355117
MonotonicityNot monotonic
2023-12-12T19:22:03.410538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8 7
 
2.2%
5 7
 
2.2%
7 6
 
1.9%
27 6
 
1.9%
15 6
 
1.9%
9 6
 
1.9%
21 6
 
1.9%
25 5
 
1.6%
6 4
 
1.3%
16 4
 
1.3%
Other values (179) 259
82.0%
ValueCountFrequency (%)
0 1
 
0.3%
1 3
0.9%
2 3
0.9%
3 1
 
0.3%
4 2
 
0.6%
5 7
2.2%
6 4
1.3%
7 6
1.9%
8 7
2.2%
9 6
1.9%
ValueCountFrequency (%)
25746 1
0.3%
5334 1
0.3%
3441 1
0.3%
3372 1
0.3%
2956 1
0.3%
2433 1
0.3%
2298 1
0.3%
1909 1
0.3%
1863 1
0.3%
1779 1
0.3%

종업원수
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct275
Distinct (%)88.7%
Missing6
Missing (%)1.9%
Infinite0
Infinite (%)0.0%
Mean17007.91
Minimum0
Maximum1318251
Zeros1
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2023-12-12T19:22:03.582502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile178.45
Q1926.25
median3584
Q310944
95-th percentile51457.35
Maximum1318251
Range1318251
Interquartile range (IQR)10017.75

Descriptive statistics

Standard deviation79591.77
Coefficient of variation (CV)4.6796915
Kurtosis233.31242
Mean17007.91
Median Absolute Deviation (MAD)3134
Skewness14.46359
Sum5272452
Variance6.3348498 × 109
MonotonicityNot monotonic
2023-12-12T19:22:03.774879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2456 3
 
0.9%
1630 3
 
0.9%
7054 2
 
0.6%
4669 2
 
0.6%
4808 2
 
0.6%
857 2
 
0.6%
36857 2
 
0.6%
2043 2
 
0.6%
550 2
 
0.6%
81 2
 
0.6%
Other values (265) 288
91.1%
(Missing) 6
 
1.9%
ValueCountFrequency (%)
0 1
0.3%
69 1
0.3%
81 2
0.6%
89 1
0.3%
90 2
0.6%
96 1
0.3%
99 1
0.3%
104 2
0.6%
126 1
0.3%
136 1
0.3%
ValueCountFrequency (%)
1318251 1
0.3%
269384 1
0.3%
225953 1
0.3%
181047 1
0.3%
134208 1
0.3%
115410 1
0.3%
109696 1
0.3%
108116 1
0.3%
105257 1
0.3%
94781 1
0.3%

부가가치(백만원)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct279
Distinct (%)90.0%
Missing6
Missing (%)1.9%
Infinite0
Infinite (%)0.0%
Mean3840076.7
Minimum0
Maximum2.9762706 × 108
Zeros1
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2023-12-12T19:22:03.923887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile13588.75
Q1145547.75
median546081
Q31850031
95-th percentile10876257
Maximum2.9762706 × 108
Range2.9762706 × 108
Interquartile range (IQR)1704483.2

Descriptive statistics

Standard deviation19715158
Coefficient of variation (CV)5.1340532
Kurtosis166.37654
Mean3840076.7
Median Absolute Deviation (MAD)498592
Skewness12.002743
Sum1.1904238 × 109
Variance3.8868744 × 1014
MonotonicityNot monotonic
2023-12-12T19:22:04.106230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
562730 2
 
0.6%
546081 2
 
0.6%
351916 2
 
0.6%
3988979 2
 
0.6%
250021 2
 
0.6%
154116 2
 
0.6%
11341 2
 
0.6%
4093391 2
 
0.6%
404068 2
 
0.6%
3075768 2
 
0.6%
Other values (269) 290
91.8%
(Missing) 6
 
1.9%
ValueCountFrequency (%)
0 1
0.3%
6101 1
0.3%
7040 1
0.3%
7928 2
0.6%
9285 1
0.3%
9504 1
0.3%
9591 1
0.3%
9877 1
0.3%
11148 1
0.3%
11341 2
0.6%
ValueCountFrequency (%)
297627059 1
0.3%
127244470 1
0.3%
87055910 1
0.3%
74519704 1
0.3%
32050038 1
0.3%
30636669 1
0.3%
26409822 1
0.3%
25931944 1
0.3%
22372712 1
0.3%
19275690 1
0.3%

생산(백만원)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct279
Distinct (%)90.0%
Missing6
Missing (%)1.9%
Infinite0
Infinite (%)0.0%
Mean10025541
Minimum0
Maximum7.7705989 × 108
Zeros1
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2023-12-12T19:22:04.304954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile30134.5
Q1387800.25
median1552650.5
Q35534696
95-th percentile36046931
Maximum7.7705989 × 108
Range7.7705989 × 108
Interquartile range (IQR)5146895.8

Descriptive statistics

Standard deviation48386428
Coefficient of variation (CV)4.8263161
Kurtosis206.95408
Mean10025541
Median Absolute Deviation (MAD)1457451
Skewness13.431225
Sum3.1079176 × 109
Variance2.3412465 × 1015
MonotonicityNot monotonic
2023-12-12T19:22:04.491858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1334970 2
 
0.6%
1288447 2
 
0.6%
742869 2
 
0.6%
11096477 2
 
0.6%
522015 2
 
0.6%
395502 2
 
0.6%
22168 2
 
0.6%
9119473 2
 
0.6%
2439346 2
 
0.6%
14058771 2
 
0.6%
Other values (269) 290
91.8%
(Missing) 6
 
1.9%
ValueCountFrequency (%)
0 1
0.3%
10482 1
0.3%
14047 1
0.3%
14867 1
0.3%
17622 1
0.3%
22168 2
0.6%
22866 2
0.6%
23324 1
0.3%
24260 1
0.3%
24517 2
0.6%
ValueCountFrequency (%)
777059891 1
0.3%
222338580 1
0.3%
128549529 1
0.3%
121388433 1
0.3%
108474312 1
0.3%
108340234 1
0.3%
103198225 1
0.3%
60278666 1
0.3%
60268124 1
0.3%
57105676 1
0.3%

출하(백만원)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct279
Distinct (%)90.0%
Missing6
Missing (%)1.9%
Infinite0
Infinite (%)0.0%
Mean9989545
Minimum0
Maximum7.7427116 × 108
Zeros1
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2023-12-12T19:22:04.676887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile30168.95
Q1385683
median1543312.5
Q35491184
95-th percentile35909415
Maximum7.7427116 × 108
Range7.7427116 × 108
Interquartile range (IQR)5105501

Descriptive statistics

Standard deviation48213648
Coefficient of variation (CV)4.8264108
Kurtosis206.94142
Mean9989545
Median Absolute Deviation (MAD)1447653
Skewness13.430798
Sum3.0967589 × 109
Variance2.3245558 × 1015
MonotonicityNot monotonic
2023-12-12T19:22:04.869164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1324260 2
 
0.6%
1281050 2
 
0.6%
703118 2
 
0.6%
11057089 2
 
0.6%
525959 2
 
0.6%
387798 2
 
0.6%
22727 2
 
0.6%
9085112 2
 
0.6%
2379915 2
 
0.6%
14008151 2
 
0.6%
Other values (269) 290
91.8%
(Missing) 6
 
1.9%
ValueCountFrequency (%)
0 1
0.3%
10320 1
0.3%
13899 1
0.3%
14874 1
0.3%
17002 1
0.3%
22727 2
0.6%
22817 2
0.6%
23327 1
0.3%
24197 1
0.3%
24349 2
0.6%
ValueCountFrequency (%)
774271164 1
0.3%
221686056 1
0.3%
128023055 1
0.3%
120931641 1
0.3%
107937799 1
0.3%
107469543 1
0.3%
103228981 1
0.3%
60239011 1
0.3%
60078864 1
0.3%
56934375 1
0.3%

재고(백만원)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct279
Distinct (%)90.0%
Missing6
Missing (%)1.9%
Infinite0
Infinite (%)0.0%
Mean401556.54
Minimum0
Maximum31124437
Zeros1
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2023-12-12T19:22:05.419833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1355.3
Q115809.25
median74319
Q3270828.5
95-th percentile1294949
Maximum31124437
Range31124437
Interquartile range (IQR)255019.25

Descriptive statistics

Standard deviation1903643.8
Coefficient of variation (CV)4.7406619
Kurtosis221.66071
Mean401556.54
Median Absolute Deviation (MAD)71060
Skewness13.995974
Sum1.2448253 × 108
Variance3.6238596 × 1012
MonotonicityNot monotonic
2023-12-12T19:22:05.606230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
388885 2
 
0.6%
71835 2
 
0.6%
137838 2
 
0.6%
408179 2
 
0.6%
35066 2
 
0.6%
42498 2
 
0.6%
1075 2
 
0.6%
378151 2
 
0.6%
176230 2
 
0.6%
393067 2
 
0.6%
Other values (269) 290
91.8%
(Missing) 6
 
1.9%
ValueCountFrequency (%)
0 1
0.3%
271 2
0.6%
346 1
0.3%
592 1
0.3%
677 1
0.3%
834 1
0.3%
949 1
0.3%
986 2
0.6%
1017 1
0.3%
1075 2
0.6%
ValueCountFrequency (%)
31124437 1
0.3%
6394975 1
0.3%
6010994 1
0.3%
5455615 1
0.3%
3928071 1
0.3%
3423728 1
0.3%
3117747 1
0.3%
2465531 1
0.3%
2392375 1
0.3%
2229762 1
0.3%

비고
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
<NA>
310 
* 사업체수 2인이하로 비공개
 
6

Length

Max length16
Median length4
Mean length4.2278481
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> 310
98.1%
* 사업체수 2인이하로 비공개 6
 
1.9%

Length

2023-12-12T19:22:05.772400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T19:22:05.919771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 310
92.8%
6
 
1.8%
사업체수 6
 
1.8%
2인이하로 6
 
1.8%
비공개 6
 
1.8%

Interactions

2023-12-12T19:22:00.798925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:21:55.345620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:21:55.980948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:21:56.801127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:21:57.686610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:21:58.513259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:21:59.873659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:22:00.906762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:21:55.432019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:21:56.076874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:21:56.926844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:21:57.807960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:21:58.617356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:21:59.991717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:22:01.003520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:21:55.511417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:21:56.170077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:21:57.031651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:21:57.916702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:21:58.731442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:22:00.141971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:22:01.106063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:21:55.598997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:21:56.291171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:21:57.167592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:21:58.030432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:21:58.848625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:22:00.290884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:22:01.203616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:21:55.688163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:21:56.413577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:21:57.275948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:21:58.159865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:21:58.958528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:22:00.416682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:22:01.301063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:21:55.786990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:21:56.539762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:21:57.402769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:21:58.290952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:21:59.470320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:22:00.544697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:22:01.410549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:21:55.883067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:21:56.676637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:21:57.546231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:21:58.411325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:21:59.741463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:22:00.672419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T19:22:06.000048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
소재부품코드사업체수종업원수부가가치(백만원)생산(백만원)출하(백만원)재고(백만원)
소재부품코드1.0000.7380.7380.6370.7390.7390.739
사업체수0.7381.0000.9700.6820.9020.9020.902
종업원수0.7380.9701.0000.8630.9840.9840.984
부가가치(백만원)0.6370.6820.8631.0000.9440.9440.944
생산(백만원)0.7390.9020.9840.9441.0001.0001.000
출하(백만원)0.7390.9020.9840.9441.0001.0001.000
재고(백만원)0.7390.9020.9840.9441.0001.0001.000
2023-12-12T19:22:06.144415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
소재부품코드사업체수종업원수부가가치(백만원)생산(백만원)출하(백만원)재고(백만원)비고
소재부품코드1.000-0.099-0.031-0.116-0.133-0.132-0.2421.000
사업체수-0.0991.0000.9120.8180.8060.8060.7801.000
종업원수-0.0310.9121.0000.9590.9430.9430.9060.000
부가가치(백만원)-0.1160.8180.9591.0000.9880.9880.9520.000
생산(백만원)-0.1330.8060.9430.9881.0001.0000.9590.000
출하(백만원)-0.1320.8060.9430.9881.0001.0000.9580.000
재고(백만원)-0.2420.7800.9060.9520.9590.9581.0000.000
비고1.0001.0000.0000.0000.0000.0000.0001.000

Missing values

2023-12-12T19:22:01.557545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T19:22:01.732916image/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.
2023-12-12T19:22:01.879480image/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

소재부품코드소재부품코드명사업체수종업원수부가가치(백만원)생산(백만원)출하(백만원)재고(백만원)비고
00소재부품25746131825129762705977705989177427116431124437<NA>
111000섬유제품111533521337203997483289663510863051<NA>
211010제사 및 방적225735364215321889572152149253273<NA>
311011면방적27245618399580064479054285643<NA>
411012모방적309818314222540321604650885<NA>
511013화학섬유 방적77197420229765983365324763503<NA>
611014연사 및 가공사89192317159350037048962752381<NA>
711019기타 방적2<NA><NA><NA><NA><NA>* 사업체수 2인이하로 비공개
811020직물직조40010080103855932308253202210367998<NA>
911021면직물 직조67160718457257720757544853249<NA>
소재부품코드소재부품코드명사업체수종업원수부가가치(백만원)생산(백만원)출하(백만원)재고(백만원)비고
30627071철도차량부품 및 관련장치68272425521862062260958944853<NA>
30727080항공기용 엔진 및 부품8310947174839436524833647297103007<NA>
30827081항공기용 엔진1<NA><NA><NA><NA><NA>* 사업체수 2인이하로 비공개
30927082항공기용 부품821049216705293344914333594288727<NA>
31027090모터사이클91791435935016347081578<NA>
31127091모터사이클91791435935016347081578<NA>
31227100자전거 및 환자용 차량2<NA><NA><NA><NA><NA>* 사업체수 2인이하로 비공개
31327101자전거 및 환자용 차량2<NA><NA><NA><NA><NA>* 사업체수 2인이하로 비공개
31427110운송장비용 의자7531203729601489776148852517883<NA>
31527111운송장비용 의자7531203729601489776148852517883<NA>