Overview

Dataset statistics

Number of variables6
Number of observations10000
Missing cells13768
Missing cells (%)22.9%
Duplicate rows3
Duplicate rows (%)< 0.1%
Total size in memory585.9 KiB
Average record size in memory60.0 B

Variable types

Numeric4
Text2

Dataset

Description한국세라믹기술원 세라믹소재정보은행의 물성 제조공정 정보입니다. 금속/화학/세라믹 통합사이트 주소: http://www.matcenter.org 담당자: 김경훈 수석
Author한국세라믹기술원
URLhttps://www.data.go.kr/data/15072082/fileData.do

Alerts

Dataset has 3 (< 0.1%) duplicate rowsDuplicates
제조공정유형 is highly overall correlated with 공정명High correlation
공정명 is highly overall correlated with 제조공정유형High correlation
제조공정유형 has 102 (1.0%) missing valuesMissing
제조공정시간 has 194 (1.9%) missing valuesMissing
장비 has 7011 (70.1%) missing valuesMissing
공정명 has 6461 (64.6%) missing valuesMissing
제조공정시간 is highly skewed (γ1 = 70.47573647)Skewed

Reproduction

Analysis started2023-12-12 10:56:08.162638
Analysis finished2023-12-12 10:56:12.753637
Duration4.59 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

트리거순번
Real number (ℝ)

Distinct22
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.0527
Minimum1
Maximum22
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T19:56:12.885943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q36
95-th percentile10
Maximum22
Range21
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.8981999
Coefficient of variation (CV)0.71512816
Kurtosis2.113508
Mean4.0527
Median Absolute Deviation (MAD)2
Skewness1.2692523
Sum40527
Variance8.3995627
MonotonicityNot monotonic
2023-12-12T19:56:13.077042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
1 2040
20.4%
2 1788
17.9%
3 1336
13.4%
4 1248
12.5%
5 967
9.7%
6 762
 
7.6%
7 561
 
5.6%
8 472
 
4.7%
9 305
 
3.0%
10 237
 
2.4%
Other values (12) 284
 
2.8%
ValueCountFrequency (%)
1 2040
20.4%
2 1788
17.9%
3 1336
13.4%
4 1248
12.5%
5 967
9.7%
6 762
 
7.6%
7 561
 
5.6%
8 472
 
4.7%
9 305
 
3.0%
10 237
 
2.4%
ValueCountFrequency (%)
22 2
 
< 0.1%
21 1
 
< 0.1%
20 5
 
0.1%
19 4
 
< 0.1%
18 2
 
< 0.1%
17 12
 
0.1%
16 8
 
0.1%
15 21
0.2%
14 34
0.3%
13 47
0.5%

제조공정유형
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct131
Distinct (%)1.3%
Missing102
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean452910.8
Minimum105002
Maximum999003
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T19:56:13.314984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum105002
5-th percentile201003
Q1306004
median501018
Q3509003
95-th percentile822006
Maximum999003
Range894001
Interquartile range (IQR)202999

Descriptive statistics

Standard deviation199961.59
Coefficient of variation (CV)0.44150324
Kurtosis-0.57313709
Mean452910.8
Median Absolute Deviation (MAD)192012
Skewness0.56756056
Sum4.4829111 × 109
Variance3.9984636 × 1010
MonotonicityNot monotonic
2023-12-12T19:56:13.576504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
306004 1064
 
10.6%
502001 801
 
8.0%
201003 761
 
7.6%
502013 732
 
7.3%
401015 649
 
6.5%
501018 450
 
4.5%
309007 413
 
4.1%
309006 394
 
3.9%
201001 358
 
3.6%
822006 352
 
3.5%
Other values (121) 3924
39.2%
ValueCountFrequency (%)
105002 51
 
0.5%
105003 43
 
0.4%
201001 358
3.6%
201003 761
7.6%
202019 208
 
2.1%
202037 13
 
0.1%
202039 3
 
< 0.1%
202040 107
 
1.1%
203001 26
 
0.3%
203002 17
 
0.2%
ValueCountFrequency (%)
999003 1
 
< 0.1%
999001 5
 
0.1%
902003 5
 
0.1%
899006 1
 
< 0.1%
899002 2
 
< 0.1%
899001 1
 
< 0.1%
822008 347
3.5%
822006 352
3.5%
822001 349
3.5%
819002 1
 
< 0.1%

제조공정시간
Real number (ℝ)

MISSING  SKEWED 

Distinct363
Distinct (%)3.7%
Missing194
Missing (%)1.9%
Infinite0
Infinite (%)0.0%
Mean19847336
Minimum-100
Maximum1.03 × 1011
Zeros10
Zeros (%)0.1%
Negative2
Negative (%)< 0.1%
Memory size166.0 KiB
2023-12-12T19:56:13.822946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-100
5-th percentile0.52
Q14
median30
Q3820
95-th percentile1500
Maximum1.03 × 1011
Range1.03 × 1011
Interquartile range (IQR)816

Descriptive statistics

Standard deviation1.381314 × 109
Coefficient of variation (CV)69.596947
Kurtosis4987.9053
Mean19847336
Median Absolute Deviation (MAD)29.8
Skewness70.475736
Sum1.9462298 × 1011
Variance1.9080283 × 1018
MonotonicityNot monotonic
2023-12-12T19:56:14.083901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.0 1046
 
10.5%
24.0 673
 
6.7%
1.0 396
 
4.0%
3.0 372
 
3.7%
4.0 314
 
3.1%
100.0 235
 
2.4%
5.0 226
 
2.3%
1100.0 197
 
2.0%
30.0 195
 
1.9%
900.0 190
 
1.9%
Other values (353) 5962
59.6%
(Missing) 194
 
1.9%
ValueCountFrequency (%)
-100.0 1
 
< 0.1%
-50.0 1
 
< 0.1%
0.0 10
0.1%
1e-09 1
 
< 0.1%
1.5e-05 4
 
< 0.1%
0.001 1
 
< 0.1%
0.0015 4
 
< 0.1%
0.002 1
 
< 0.1%
0.0027 1
 
< 0.1%
0.00278 1
 
< 0.1%
ValueCountFrequency (%)
103000000000.0 1
 
< 0.1%
90010001050.0 1
 
< 0.1%
800011000.0 2
 
< 0.1%
7935000.0 1
 
< 0.1%
800900.0 1
 
< 0.1%
24500.0 15
0.1%
10000.0 4
 
< 0.1%
9000.0 1
 
< 0.1%
8000.0 3
 
< 0.1%
6000.0 1
 
< 0.1%

제조공정일련번호
Text

Distinct7547
Distinct (%)75.5%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T19:56:14.650367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters70000
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5586 ?
Unique (%)55.9%

Sample

1st rowM113569
2nd rowM113584
3rd rowM110134
4th rowM119323
5th rowM112380
ValueCountFrequency (%)
m121728 5
 
< 0.1%
m114578 5
 
< 0.1%
m104356 5
 
< 0.1%
m122235 5
 
< 0.1%
m112578 5
 
< 0.1%
m106504 5
 
< 0.1%
m121688 5
 
< 0.1%
m113888 5
 
< 0.1%
m113020 4
 
< 0.1%
m122094 4
 
< 0.1%
Other values (7537) 9952
99.5%
2023-12-12T19:56:15.392233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 18540
26.5%
M 10000
14.3%
0 8460
12.1%
2 5864
 
8.4%
6 4200
 
6.0%
4 4138
 
5.9%
3 3951
 
5.6%
5 3885
 
5.5%
8 3775
 
5.4%
7 3633
 
5.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 60000
85.7%
Uppercase Letter 10000
 
14.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 18540
30.9%
0 8460
14.1%
2 5864
 
9.8%
6 4200
 
7.0%
4 4138
 
6.9%
3 3951
 
6.6%
5 3885
 
6.5%
8 3775
 
6.3%
7 3633
 
6.1%
9 3554
 
5.9%
Uppercase Letter
ValueCountFrequency (%)
M 10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 60000
85.7%
Latin 10000
 
14.3%

Most frequent character per script

Common
ValueCountFrequency (%)
1 18540
30.9%
0 8460
14.1%
2 5864
 
9.8%
6 4200
 
7.0%
4 4138
 
6.9%
3 3951
 
6.6%
5 3885
 
6.5%
8 3775
 
6.3%
7 3633
 
6.1%
9 3554
 
5.9%
Latin
ValueCountFrequency (%)
M 10000
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 70000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 18540
26.5%
M 10000
14.3%
0 8460
12.1%
2 5864
 
8.4%
6 4200
 
6.0%
4 4138
 
5.9%
3 3951
 
5.6%
5 3885
 
5.5%
8 3775
 
5.4%
7 3633
 
5.2%

장비
Text

MISSING 

Distinct556
Distinct (%)18.6%
Missing7011
Missing (%)70.1%
Memory size156.2 KiB
2023-12-12T19:56:15.910979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length176
Median length104
Mean length19.62362
Min length1

Characters and Unicode

Total characters58655
Distinct characters159
Distinct categories14 ?
Distinct scripts5 ?
Distinct blocks7 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique226 ?
Unique (%)7.6%

Sample

1st rowin argon
2nd rowin N2 atmosphere
3rd rowball milling
4th rowhot plate
5th rowhot plate
ValueCountFrequency (%)
in 723
 
7.6%
furnace 490
 
5.1%
ball 323
 
3.4%
of 180
 
1.9%
a 178
 
1.9%
with 164
 
1.7%
argon 164
 
1.7%
atmosphere 162
 
1.7%
hot 160
 
1.7%
crucible 157
 
1.6%
Other values (819) 6866
71.8%
2023-12-12T19:56:16.848949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6743
 
11.5%
a 4665
 
8.0%
i 4661
 
7.9%
e 4569
 
7.8%
n 4356
 
7.4%
l 3775
 
6.4%
r 3648
 
6.2%
t 2974
 
5.1%
o 2319
 
4.0%
s 1971
 
3.4%
Other values (149) 18974
32.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 46439
79.2%
Space Separator 6743
 
11.5%
Uppercase Letter 2288
 
3.9%
Decimal Number 1693
 
2.9%
Other Punctuation 380
 
0.6%
Other Letter 329
 
0.6%
Connector Punctuation 327
 
0.6%
Dash Punctuation 149
 
0.3%
Open Punctuation 104
 
0.2%
Close Punctuation 103
 
0.2%
Other values (4) 100
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
볼 45
 
13.7%
밀 41
 
12.5%
기 9
 
2.7%
철 9
 
2.7%
제 9
 
2.7%
식 9
 
2.7%
음 8
 
2.4%
로 8
 
2.4%
초 8
 
2.4%
른 8
 
2.4%
Other values (62) 175
53.2%
Lowercase Letter
ValueCountFrequency (%)
a 4665
 
10.0%
i 4661
 
10.0%
e 4569
 
9.8%
n 4356
 
9.4%
l 3775
 
8.1%
r 3648
 
7.9%
t 2974
 
6.4%
o 2319
 
5.0%
s 1971
 
4.2%
c 1754
 
3.8%
Other values (20) 11747
25.3%
Uppercase Letter
ValueCountFrequency (%)
S 302
13.2%
A 277
12.1%
C 234
10.2%
F 191
 
8.3%
P 188
 
8.2%
M 135
 
5.9%
B 115
 
5.0%
N 100
 
4.4%
T 90
 
3.9%
O 87
 
3.8%
Other values (15) 569
24.9%
Decimal Number
ValueCountFrequency (%)
0 404
23.9%
1 355
21.0%
2 343
20.3%
5 230
13.6%
4 90
 
5.3%
3 89
 
5.3%
6 67
 
4.0%
9 48
 
2.8%
8 35
 
2.1%
7 32
 
1.9%
Other Punctuation
ValueCountFrequency (%)
% 136
35.8%
. 124
32.6%
/ 100
26.3%
: 11
 
2.9%
* 7
 
1.8%
· 2
 
0.5%
Math Symbol
ValueCountFrequency (%)
× 28
70.0%
~ 5
 
12.5%
+ 5
 
12.5%
< 1
 
2.5%
∼ 1
 
2.5%
Open Punctuation
ValueCountFrequency (%)
( 103
99.0%
[ 1
 
1.0%
Close Punctuation
ValueCountFrequency (%)
) 102
99.0%
] 1
 
1.0%
Other Symbol
ValueCountFrequency (%)
° 33
61.1%
℃ 21
38.9%
Space Separator
ValueCountFrequency (%)
6743
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 327
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 149
100.0%
Modifier Symbol
ValueCountFrequency (%)
^ 5
100.0%
Final Punctuation
ValueCountFrequency (%)
’ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 48690
83.0%
Common 9599
 
16.4%
Hangul 329
 
0.6%
Greek 35
 
0.1%
Cyrillic 2
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
볼 45
 
13.7%
밀 41
 
12.5%
기 9
 
2.7%
철 9
 
2.7%
제 9
 
2.7%
식 9
 
2.7%
음 8
 
2.4%
로 8
 
2.4%
초 8
 
2.4%
른 8
 
2.4%
Other values (62) 175
53.2%
Latin
ValueCountFrequency (%)
a 4665
 
9.6%
i 4661
 
9.6%
e 4569
 
9.4%
n 4356
 
8.9%
l 3775
 
7.8%
r 3648
 
7.5%
t 2974
 
6.1%
o 2319
 
4.8%
s 1971
 
4.0%
c 1754
 
3.6%
Other values (40) 13998
28.7%
Common
ValueCountFrequency (%)
6743
70.2%
0 404
 
4.2%
1 355
 
3.7%
2 343
 
3.6%
_ 327
 
3.4%
5 230
 
2.4%
- 149
 
1.6%
% 136
 
1.4%
. 124
 
1.3%
( 103
 
1.1%
Other values (22) 685
 
7.1%
Greek
ValueCountFrequency (%)
Φ 23
65.7%
α 9
 
25.7%
μ 2
 
5.7%
β 1
 
2.9%
Cyrillic
ValueCountFrequency (%)
ф 2
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 58203
99.2%
Hangul 329
 
0.6%
None 98
 
0.2%
Letterlike Symbols 21
 
< 0.1%
Cyrillic 2
 
< 0.1%
Punctuation 1
 
< 0.1%
Math Operators 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6743
 
11.6%
a 4665
 
8.0%
i 4661
 
8.0%
e 4569
 
7.9%
n 4356
 
7.5%
l 3775
 
6.5%
r 3648
 
6.3%
t 2974
 
5.1%
o 2319
 
4.0%
s 1971
 
3.4%
Other values (66) 18522
31.8%
Hangul
ValueCountFrequency (%)
볼 45
 
13.7%
밀 41
 
12.5%
기 9
 
2.7%
철 9
 
2.7%
제 9
 
2.7%
식 9
 
2.7%
음 8
 
2.4%
로 8
 
2.4%
초 8
 
2.4%
른 8
 
2.4%
Other values (62) 175
53.2%
None
ValueCountFrequency (%)
° 33
33.7%
× 28
28.6%
Φ 23
23.5%
α 9
 
9.2%
μ 2
 
2.0%
· 2
 
2.0%
β 1
 
1.0%
Letterlike Symbols
ValueCountFrequency (%)
℃ 21
100.0%
Cyrillic
ValueCountFrequency (%)
ф 2
100.0%
Punctuation
ValueCountFrequency (%)
’ 1
100.0%
Math Operators
ValueCountFrequency (%)
∼ 1
100.0%

공정명
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct132
Distinct (%)3.7%
Missing6461
Missing (%)64.6%
Infinite0
Infinite (%)0.0%
Mean418664.89
Minimum20801
Maximum999001
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T19:56:17.146753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20801
5-th percentile200002
Q1306004
median401015
Q3502013
95-th percentile822006
Maximum999001
Range978200
Interquartile range (IQR)196009

Descriptive statistics

Standard deviation200526.15
Coefficient of variation (CV)0.47896577
Kurtosis-0.3172655
Mean418664.89
Median Absolute Deviation (MAD)100998
Skewness0.54878205
Sum1.481655 × 109
Variance4.0210737 × 1010
MonotonicityNot monotonic
2023-12-12T19:56:17.429205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
502001 347
 
3.5%
200004 272
 
2.7%
502013 268
 
2.7%
309006 199
 
2.0%
309007 181
 
1.8%
401015 141
 
1.4%
306004 125
 
1.2%
822008 118
 
1.2%
202038 116
 
1.2%
201003 100
 
1.0%
Other values (122) 1672
 
16.7%
(Missing) 6461
64.6%
ValueCountFrequency (%)
20801 5
 
0.1%
20802 9
 
0.1%
39901 13
 
0.1%
39902 9
 
0.1%
105002 91
 
0.9%
105003 35
 
0.4%
105004 3
 
< 0.1%
105007 5
 
0.1%
200002 15
 
0.1%
200004 272
2.7%
ValueCountFrequency (%)
999001 1
 
< 0.1%
902003 3
 
< 0.1%
899002 2
 
< 0.1%
899001 1
 
< 0.1%
822008 118
1.2%
822006 64
0.6%
822003 67
0.7%
822001 47
 
0.5%
816008 1
 
< 0.1%
816007 18
 
0.2%

Interactions

2023-12-12T19:56:11.386837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:56:09.106623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:56:09.916781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:56:10.676665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:56:11.564936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:56:09.254246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:56:10.106340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2023-12-12T19:56:11.773118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:56:09.441646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:56:10.298058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:56:11.039982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:56:11.948738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:56:09.663800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:56:10.473610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:56:11.204089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T19:56:17.615121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
트리거순번제조공정유형제조공정시간공정명
트리거순번1.0000.6080.0000.509
제조공정유형0.6081.0000.0000.927
제조공정시간0.0000.0001.0000.000
공정명0.5090.9270.0001.000
2023-12-12T19:56:17.769231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
트리거순번제조공정유형제조공정시간공정명
트리거순번1.0000.481-0.1080.392
제조공정유형0.4811.000-0.0820.783
제조공정시간-0.108-0.0821.000-0.214
공정명0.3920.783-0.2141.000

Missing values

2023-12-12T19:56:12.185479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T19:56:12.402290image/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.
2023-12-12T19:56:12.613978image/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

트리거순번제조공정유형제조공정시간제조공정일련번호장비공정명
50246982200115.0M113569<NA><NA>
6906722010012.0M113584<NA><NA>
3507465200011350.0M110134<NA><NA>
93527781600210.0M119323in argon<NA>
52965108220060.28M112380<NA><NA>
43412350900325.0M111385in N2 atmosphere<NA>
8267133090073.0M117703<NA><NA>
8803588220061.0M121673<NA><NA>
464092309006900.0M113231<NA><NA>
572166822008130.0M112679<NA><NA>
트리거순번제조공정유형제조공정시간제조공정일련번호장비공정명
10017816006850.0M106646<NA>816006
2073115816006100.0M104357<NA>816006
9138432010012.0M119454<NA><NA>
78621130600424.0M120008in methanol using Si3N4 grinding balls<NA>
51935260000324.0M115730oven<NA>
57919330200324.0M112747<NA><NA>
56201401015100.0M106290press401015
98891103090075.0M121818<NA><NA>
97807430600424.0M121731ball milling<NA>
623476501022200.0M116781<NA><NA>

Duplicate rows

Most frequently occurring

트리거순번제조공정유형제조공정시간제조공정일련번호장비공정명# duplicates
023080020.166667M006973Dry oven3080052
1281600324.0M115518<NA><NA>2
228160072.0M110684in argon<NA>2