Overview

Dataset statistics

Number of variables15
Number of observations10000
Missing cells74459
Missing cells (%)49.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.3 MiB
Average record size in memory135.0 B

Variable types

Text6
Categorical2
Numeric6
Unsupported1

Alerts

인화점 is highly overall correlated with 증기압_25 and 3 other fieldsHigh correlation
증기압_25 is highly overall correlated with 인화점 and 3 other fieldsHigh correlation
증기압_20 is highly overall correlated with 인화점 and 3 other fieldsHigh correlation
끓는점 is highly overall correlated with 인화점 and 3 other fieldsHigh correlation
분자량 is highly overall correlated with 인화점 and 5 other fieldsHigh correlation
물질상태 is highly overall correlated with 분자량High correlation
끝점농도기준 is highly overall correlated with 분자량High correlation
끝점농도기준 is highly imbalanced (62.8%)Imbalance
국문 has 7282 (72.8%) missing valuesMissing
폭발상한범위 has 9630 (96.3%) missing valuesMissing
폭발하한범위 has 9619 (96.2%) missing valuesMissing
인화점 has 3082 (30.8%) missing valuesMissing
발화점 has 9441 (94.4%) missing valuesMissing
증기압_25 has 3995 (40.0%) missing valuesMissing
증기압_20 has 9599 (96.0%) missing valuesMissing
끓는점 has 2815 (28.1%) missing valuesMissing
분자량 has 161 (1.6%) missing valuesMissing
끝점농도 has 8835 (88.3%) missing valuesMissing
Unnamed: 14 has 10000 (100.0%) missing valuesMissing
증기압_25 is highly skewed (γ1 = 48.55885166)Skewed
분자량 is highly skewed (γ1 = 99.06518337)Skewed
고유(CAS)번호 has unique valuesUnique
Unnamed: 14 is an unsupported type, check if it needs cleaning or further analysisUnsupported
증기압_25 has 551 (5.5%) zerosZeros

Reproduction

Analysis started2024-01-09 22:59:20.823475
Analysis finished2024-01-09 22:59:26.178095
Duration5.35 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

고유(CAS)번호
Text

UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-01-10T07:59:26.419350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length11
Mean length10.3467
Min length8

Characters and Unicode

Total characters103467
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10000 ?
Unique (%)100.0%

Sample

1st row 58048-89-8
2nd row 69641-79-8
3rd row 2998-04-1
4th row 102-60-3
5th row 2429-84-7
ValueCountFrequency (%)
58048-89-8 1
 
< 0.1%
106899-94-9 1
 
< 0.1%
113221-69-5 1
 
< 0.1%
15283-48-4 1
 
< 0.1%
2554-05-4 1
 
< 0.1%
22460-59-9 1
 
< 0.1%
17011-51-7 1
 
< 0.1%
26335-33-1 1
 
< 0.1%
1121-86-4 1
 
< 0.1%
10124-36-4 1
 
< 0.1%
Other values (9990) 9990
99.9%
2024-01-10T07:59:26.903903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 20000
19.3%
10000
9.7%
1 9742
9.4%
2 7839
 
7.6%
3 7417
 
7.2%
5 7254
 
7.0%
6 7215
 
7.0%
0 7183
 
6.9%
8 6870
 
6.6%
7 6737
 
6.5%
Other values (2) 13210
12.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 73467
71.0%
Dash Punctuation 20000
 
19.3%
Space Separator 10000
 
9.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 9742
13.3%
2 7839
10.7%
3 7417
10.1%
5 7254
9.9%
6 7215
9.8%
0 7183
9.8%
8 6870
9.4%
7 6737
9.2%
4 6650
9.1%
9 6560
8.9%
Dash Punctuation
ValueCountFrequency (%)
- 20000
100.0%
Space Separator
ValueCountFrequency (%)
10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 103467
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 20000
19.3%
10000
9.7%
1 9742
9.4%
2 7839
 
7.6%
3 7417
 
7.2%
5 7254
 
7.0%
6 7215
 
7.0%
0 7183
 
6.9%
8 6870
 
6.6%
7 6737
 
6.5%
Other values (2) 13210
12.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 103467
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 20000
19.3%
10000
9.7%
1 9742
9.4%
2 7839
 
7.6%
3 7417
 
7.2%
5 7254
 
7.0%
6 7215
 
7.0%
0 7183
 
6.9%
8 6870
 
6.6%
7 6737
 
6.5%
Other values (2) 13210
12.8%

영문
Text

Distinct9993
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-01-10T07:59:27.122800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length429
Median length221
Mean length43.2294
Min length3

Characters and Unicode

Total characters432294
Distinct characters101
Distinct categories12 ?
Distinct scripts3 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9987 ?
Unique (%)99.9%

Sample

1st rowStyrene/methacrylic,acid/butyl,methacrylate/butyl,acrylate
2nd rown-Dodecyl 5-N,N-diethylamino-2-phenylsulfonyl-4-pentadienoate
3rd rowDiallyl adipate
4th row1,1',1'',1'''-(1,2-Ethanediyldinitrilo)tetrakis-2-propanol; Quadrol
5th rowC.I. direct red 001
ValueCountFrequency (%)
acid 2015
 
7.5%
with 997
 
3.7%
polymer 687
 
2.6%
ester 591
 
2.2%
salt 587
 
2.2%
and 523
 
2.0%
sodium 327
 
1.2%
c.i 277
 
1.0%
methyl 179
 
0.7%
acetate 143
 
0.5%
Other values (10329) 20380
76.3%
2024-01-10T07:59:27.483231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 36317
 
8.4%
o 28793
 
6.7%
i 26912
 
6.2%
- 25792
 
6.0%
l 23424
 
5.4%
t 22696
 
5.3%
a 22057
 
5.1%
n 22038
 
5.1%
y 19594
 
4.5%
h 17685
 
4.1%
Other values (91) 186986
43.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 318649
73.7%
Decimal Number 26066
 
6.0%
Dash Punctuation 25792
 
6.0%
Space Separator 16714
 
3.9%
Uppercase Letter 15386
 
3.6%
Other Punctuation 14898
 
3.4%
Open Punctuation 7200
 
1.7%
Close Punctuation 7192
 
1.7%
Math Symbol 348
 
0.1%
Modifier Symbol 45
 
< 0.1%
Other values (2) 4
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 36317
11.4%
o 28793
 
9.0%
i 26912
 
8.4%
l 23424
 
7.4%
t 22696
 
7.1%
a 22057
 
6.9%
n 22038
 
6.9%
y 19594
 
6.1%
h 17685
 
5.5%
r 16154
 
5.1%
Other values (24) 82979
26.0%
Uppercase Letter
ValueCountFrequency (%)
D 1809
11.8%
N 1504
9.8%
C 1388
 
9.0%
H 1270
 
8.3%
M 1142
 
7.4%
T 1082
 
7.0%
B 1044
 
6.8%
P 950
 
6.2%
I 790
 
5.1%
O 762
 
5.0%
Other values (16) 3645
23.7%
Other Punctuation
ValueCountFrequency (%)
, 11425
76.7%
' 1195
 
8.0%
. 933
 
6.3%
; 901
 
6.0%
: 341
 
2.3%
/ 31
 
0.2%
* 26
 
0.2%
26
 
0.2%
" 13
 
0.1%
% 3
 
< 0.1%
Other values (4) 4
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
2 7331
28.1%
1 6336
24.3%
3 3652
14.0%
4 3595
13.8%
5 1756
 
6.7%
6 1275
 
4.9%
7 637
 
2.4%
0 578
 
2.2%
9 472
 
1.8%
8 434
 
1.7%
Math Symbol
ValueCountFrequency (%)
= 171
49.1%
+ 105
30.2%
43
 
12.4%
± 23
 
6.6%
> 5
 
1.4%
~ 1
 
0.3%
Close Punctuation
ValueCountFrequency (%)
) 4795
66.7%
] 2394
33.3%
} 3
 
< 0.1%
Open Punctuation
ValueCountFrequency (%)
( 4786
66.5%
[ 2412
33.5%
{ 2
 
< 0.1%
Dash Punctuation
ValueCountFrequency (%)
- 25792
100.0%
Space Separator
ValueCountFrequency (%)
16714
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 45
100.0%
Final Punctuation
ValueCountFrequency (%)
3
100.0%
Letter Number
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 333485
77.1%
Common 98258
 
22.7%
Greek 551
 
0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 36317
 
10.9%
o 28793
 
8.6%
i 26912
 
8.1%
l 23424
 
7.0%
t 22696
 
6.8%
a 22057
 
6.6%
n 22038
 
6.6%
y 19594
 
5.9%
h 17685
 
5.3%
r 16154
 
4.8%
Other values (43) 97815
29.3%
Common
ValueCountFrequency (%)
- 25792
26.2%
16714
17.0%
, 11425
11.6%
2 7331
 
7.5%
1 6336
 
6.4%
) 4795
 
4.9%
( 4786
 
4.9%
3 3652
 
3.7%
4 3595
 
3.7%
[ 2412
 
2.5%
Other values (30) 11420
11.6%
Greek
ValueCountFrequency (%)
α 288
52.3%
ω 107
 
19.4%
β 102
 
18.5%
μ 20
 
3.6%
κ 16
 
2.9%
γ 14
 
2.5%
δ 2
 
0.4%
η 2
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 431645
99.8%
None 575
 
0.1%
Arrows 43
 
< 0.1%
Punctuation 30
 
< 0.1%
Number Forms 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 36317
 
8.4%
o 28793
 
6.7%
i 26912
 
6.2%
- 25792
 
6.0%
l 23424
 
5.4%
t 22696
 
5.3%
a 22057
 
5.1%
n 22038
 
5.1%
y 19594
 
4.5%
h 17685
 
4.1%
Other values (76) 186337
43.2%
None
ValueCountFrequency (%)
α 288
50.1%
ω 107
 
18.6%
β 102
 
17.7%
± 23
 
4.0%
μ 20
 
3.5%
κ 16
 
2.8%
γ 14
 
2.4%
δ 2
 
0.3%
η 2
 
0.3%
· 1
 
0.2%
Arrows
ValueCountFrequency (%)
43
100.0%
Punctuation
ValueCountFrequency (%)
26
86.7%
3
 
10.0%
1
 
3.3%
Number Forms
ValueCountFrequency (%)
1
100.0%

국문
Text

MISSING 

Distinct2713
Distinct (%)99.8%
Missing7282
Missing (%)72.8%
Memory size156.2 KiB
2024-01-10T07:59:27.729506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length144
Median length98
Mean length14.446652
Min length1

Characters and Unicode

Total characters39266
Distinct characters426
Distinct categories13 ?
Distinct scripts4 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2708 ?
Unique (%)99.6%

Sample

1st row스타이렌/메타크릴산/뷰틸,메타크릴산염/뷰틸,아크릴산염
2nd row트라이메틸클로로실레인
3rd row다이페닐아세트산
4th row3,5-다이브로모벤조산
5th row1-헥실-4-메틸피리디늄과 1,1,1-트리플루오로-N-[(트리플루오로메틸)술포닐]메탄술폰아미드의 염 (1:1)
ValueCountFrequency (%)
14
 
0.5%
나트륨 8
 
0.3%
c.i 8
 
0.3%
1:1 6
 
0.2%
디스퍼스 5
 
0.2%
1,1,1-트리플루오로-n-[(트리플루오로메틸)술포닐]메탄술폰아미드의 5
 
0.2%
클로라이드 4
 
0.1%
칼슘 4
 
0.1%
에스터 4
 
0.1%
탄산 3
 
0.1%
Other values (2840) 2886
97.9%
2024-01-10T07:59:28.456581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 3155
 
8.0%
, 2682
 
6.8%
2598
 
6.6%
1449
 
3.7%
1152
 
2.9%
1029
 
2.6%
1020
 
2.6%
2 903
 
2.3%
736
 
1.9%
735
 
1.9%
Other values (416) 23807
60.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 27577
70.2%
Decimal Number 3188
 
8.1%
Dash Punctuation 3155
 
8.0%
Other Punctuation 2964
 
7.5%
Close Punctuation 682
 
1.7%
Open Punctuation 680
 
1.7%
Uppercase Letter 551
 
1.4%
Space Separator 230
 
0.6%
Lowercase Letter 203
 
0.5%
Math Symbol 30
 
0.1%
Other values (3) 6
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
2598
 
9.4%
1449
 
5.3%
1152
 
4.2%
1029
 
3.7%
1020
 
3.7%
736
 
2.7%
735
 
2.7%
709
 
2.6%
658
 
2.4%
576
 
2.1%
Other values (336) 16915
61.3%
Lowercase Letter
ValueCountFrequency (%)
t 41
20.2%
e 29
14.3%
r 25
12.3%
a 17
8.4%
m 10
 
4.9%
p 10
 
4.9%
o 9
 
4.4%
n 9
 
4.4%
c 8
 
3.9%
i 7
 
3.4%
Other values (13) 38
18.7%
Uppercase Letter
ValueCountFrequency (%)
N 170
30.9%
I 105
19.1%
C 40
 
7.3%
H 39
 
7.1%
O 31
 
5.6%
S 29
 
5.3%
R 22
 
4.0%
D 20
 
3.6%
L 13
 
2.4%
T 13
 
2.4%
Other values (9) 69
12.5%
Other Punctuation
ValueCountFrequency (%)
, 2682
90.5%
' 123
 
4.1%
. 53
 
1.8%
: 41
 
1.4%
/ 31
 
1.0%
15
 
0.5%
; 7
 
0.2%
· 3
 
0.1%
% 3
 
0.1%
" 2
 
0.1%
Other values (3) 4
 
0.1%
Decimal Number
ValueCountFrequency (%)
2 903
28.3%
1 729
22.9%
4 506
15.9%
3 499
15.7%
5 222
 
7.0%
6 150
 
4.7%
9 54
 
1.7%
7 53
 
1.7%
8 40
 
1.3%
0 32
 
1.0%
Math Symbol
ValueCountFrequency (%)
+ 26
86.7%
= 2
 
6.7%
± 1
 
3.3%
> 1
 
3.3%
Close Punctuation
ValueCountFrequency (%)
) 504
73.9%
] 176
 
25.8%
} 2
 
0.3%
Open Punctuation
ValueCountFrequency (%)
( 499
73.4%
[ 180
 
26.5%
{ 1
 
0.1%
Dash Punctuation
ValueCountFrequency (%)
- 3155
100.0%
Space Separator
ValueCountFrequency (%)
230
100.0%
Final Punctuation
ValueCountFrequency (%)
3
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 2
100.0%
Initial Punctuation
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 27577
70.2%
Common 10935
 
27.8%
Latin 739
 
1.9%
Greek 15
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
2598
 
9.4%
1449
 
5.3%
1152
 
4.2%
1029
 
3.7%
1020
 
3.7%
736
 
2.7%
735
 
2.7%
709
 
2.6%
658
 
2.4%
576
 
2.1%
Other values (336) 16915
61.3%
Common
ValueCountFrequency (%)
- 3155
28.9%
, 2682
24.5%
2 903
 
8.3%
1 729
 
6.7%
4 506
 
4.6%
) 504
 
4.6%
3 499
 
4.6%
( 499
 
4.6%
230
 
2.1%
5 222
 
2.0%
Other values (28) 1006
 
9.2%
Latin
ValueCountFrequency (%)
N 170
23.0%
I 105
14.2%
t 41
 
5.5%
C 40
 
5.4%
H 39
 
5.3%
O 31
 
4.2%
S 29
 
3.9%
e 29
 
3.9%
r 25
 
3.4%
R 22
 
3.0%
Other values (28) 208
28.1%
Greek
ValueCountFrequency (%)
α 7
46.7%
κ 4
26.7%
ω 2
 
13.3%
μ 2
 
13.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 27577
70.2%
ASCII 11651
29.7%
Punctuation 19
 
< 0.1%
None 19
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 3155
27.1%
, 2682
23.0%
2 903
 
7.8%
1 729
 
6.3%
4 506
 
4.3%
) 504
 
4.3%
3 499
 
4.3%
( 499
 
4.3%
230
 
2.0%
5 222
 
1.9%
Other values (61) 1722
14.8%
Hangul
ValueCountFrequency (%)
2598
 
9.4%
1449
 
5.3%
1152
 
4.2%
1029
 
3.7%
1020
 
3.7%
736
 
2.7%
735
 
2.7%
709
 
2.6%
658
 
2.4%
576
 
2.1%
Other values (336) 16915
61.3%
Punctuation
ValueCountFrequency (%)
15
78.9%
3
 
15.8%
1
 
5.3%
None
ValueCountFrequency (%)
α 7
36.8%
κ 4
21.1%
· 3
15.8%
ω 2
 
10.5%
μ 2
 
10.5%
± 1
 
5.3%

물질상태
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
5501 
액체
2322 
고체
2145 
기체
 
32

Length

Max length4
Median length4
Mean length3.1002
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 5501
55.0%
액체 2322
23.2%
고체 2145
 
21.4%
기체 32
 
0.3%

Length

2024-01-10T07:59:28.596011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T07:59:28.704128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 5501
55.0%
액체 2322
23.2%
고체 2145
 
21.4%
기체 32
 
0.3%

폭발상한범위
Text

MISSING 

Distinct150
Distinct (%)40.5%
Missing9630
Missing (%)96.3%
Memory size156.2 KiB
2024-01-10T07:59:29.016155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length5
Mean length5.4081081
Min length5

Characters and Unicode

Total characters2001
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique65 ?
Unique (%)17.6%

Sample

1st row39.00%
2nd row1.40%
3rd row31.00%
4th row12.00%
5th row100.00%
ValueCountFrequency (%)
9.50 10
 
2.7%
8.00 8
 
2.2%
6.50 7
 
1.9%
8.40 7
 
1.9%
6.10 7
 
1.9%
6.40 7
 
1.9%
0.80 6
 
1.6%
6.30 6
 
1.6%
1.40 6
 
1.6%
7.80 6
 
1.6%
Other values (140) 300
81.1%
2024-01-10T07:59:29.473541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 504
25.2%
. 370
18.5%
% 370
18.5%
1 173
 
8.6%
6 96
 
4.8%
5 89
 
4.4%
8 77
 
3.8%
7 71
 
3.5%
9 63
 
3.1%
4 63
 
3.1%
Other values (2) 125
 
6.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1261
63.0%
Other Punctuation 740
37.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 504
40.0%
1 173
 
13.7%
6 96
 
7.6%
5 89
 
7.1%
8 77
 
6.1%
7 71
 
5.6%
9 63
 
5.0%
4 63
 
5.0%
3 63
 
5.0%
2 62
 
4.9%
Other Punctuation
ValueCountFrequency (%)
. 370
50.0%
% 370
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2001
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 504
25.2%
. 370
18.5%
% 370
18.5%
1 173
 
8.6%
6 96
 
4.8%
5 89
 
4.4%
8 77
 
3.8%
7 71
 
3.5%
9 63
 
3.1%
4 63
 
3.1%
Other values (2) 125
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2001
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 504
25.2%
. 370
18.5%
% 370
18.5%
1 173
 
8.6%
6 96
 
4.8%
5 89
 
4.4%
8 77
 
3.8%
7 71
 
3.5%
9 63
 
3.1%
4 63
 
3.1%
Other values (2) 125
 
6.2%

폭발하한범위
Text

MISSING 

Distinct62
Distinct (%)16.3%
Missing9619
Missing (%)96.2%
Memory size156.2 KiB
2024-01-10T07:59:29.689660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length5.0131234
Min length5

Characters and Unicode

Total characters1910
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique25 ?
Unique (%)6.6%

Sample

1st row1.80%
2nd row1.10%
3rd row0.40%
4th row2.80%
5th row2.00%
ValueCountFrequency (%)
0.80 29
 
7.6%
1.10 29
 
7.6%
1.30 27
 
7.1%
1.20 27
 
7.1%
1.00 27
 
7.1%
0.90 23
 
6.0%
2.00 18
 
4.7%
1.40 18
 
4.7%
1.80 17
 
4.5%
1.60 16
 
4.2%
Other values (52) 150
39.4%
2024-01-10T07:59:30.014656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 531
27.8%
. 381
19.9%
% 381
19.9%
1 233
12.2%
2 97
 
5.1%
3 54
 
2.8%
8 53
 
2.8%
4 43
 
2.3%
9 38
 
2.0%
6 35
 
1.8%
Other values (2) 64
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1148
60.1%
Other Punctuation 762
39.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 531
46.3%
1 233
20.3%
2 97
 
8.4%
3 54
 
4.7%
8 53
 
4.6%
4 43
 
3.7%
9 38
 
3.3%
6 35
 
3.0%
5 33
 
2.9%
7 31
 
2.7%
Other Punctuation
ValueCountFrequency (%)
. 381
50.0%
% 381
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1910
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 531
27.8%
. 381
19.9%
% 381
19.9%
1 233
12.2%
2 97
 
5.1%
3 54
 
2.8%
8 53
 
2.8%
4 43
 
2.3%
9 38
 
2.0%
6 35
 
1.8%
Other values (2) 64
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1910
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 531
27.8%
. 381
19.9%
% 381
19.9%
1 233
12.2%
2 97
 
5.1%
3 54
 
2.8%
8 53
 
2.8%
4 43
 
2.3%
9 38
 
2.0%
6 35
 
1.8%
Other values (2) 64
 
3.4%

인화점
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct2967
Distinct (%)42.9%
Missing3082
Missing (%)30.8%
Infinite0
Infinite (%)0.0%
Mean155.07747
Minimum-107.77778
Maximum5000
Zeros6
Zeros (%)0.1%
Negative191
Negative (%)1.9%
Memory size166.0 KiB
2024-01-10T07:59:30.156049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-107.77778
5-th percentile13
Q178.425
median127.7035
Q3199.25
95-th percentile350.7
Maximum5000
Range5107.7778
Interquartile range (IQR)120.825

Descriptive statistics

Standard deviation168.0115
Coefficient of variation (CV)1.0834036
Kurtosis200.47314
Mean155.07747
Median Absolute Deviation (MAD)57.7035
Skewness10.15475
Sum1072825.9
Variance28227.863
MonotonicityNot monotonic
2024-01-10T07:59:30.287196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
110.0 72
 
0.7%
40.0 28
 
0.3%
169.8 27
 
0.3%
85.0 25
 
0.2%
217.3 25
 
0.2%
162.4 25
 
0.2%
31.1 24
 
0.2%
172.7 24
 
0.2%
110.5 24
 
0.2%
39.4 22
 
0.2%
Other values (2957) 6622
66.2%
(Missing) 3082
30.8%
ValueCountFrequency (%)
-107.7777778 1
 
< 0.1%
-104.4444444 3
< 0.1%
-104.0 1
 
< 0.1%
-100.6 1
 
< 0.1%
-97.0 1
 
< 0.1%
-90.0 1
 
< 0.1%
-84.6 1
 
< 0.1%
-78.0 1
 
< 0.1%
-76.1 1
 
< 0.1%
-74.0 1
 
< 0.1%
ValueCountFrequency (%)
5000.0 1
< 0.1%
4300.0 1
< 0.1%
3000.0 1
< 0.1%
2500.0 2
< 0.1%
2280.0 1
< 0.1%
2200.0 1
< 0.1%
2072.0 1
< 0.1%
1890.0 1
< 0.1%
1860.0 1
< 0.1%
1800.0 1
< 0.1%

발화점
Real number (ℝ)

MISSING 

Distinct260
Distinct (%)46.5%
Missing9441
Missing (%)94.4%
Infinite0
Infinite (%)0.0%
Mean369.50801
Minimum-440
Maximum1010
Zeros0
Zeros (%)0.0%
Negative2
Negative (%)< 0.1%
Memory size166.0 KiB
2024-01-10T07:59:30.411245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-440
5-th percentile164.5
Q1258.89
median380
Q3460
95-th percentile600
Maximum1010
Range1450
Interquartile range (IQR)201.11

Descriptive statistics

Standard deviation146.42542
Coefficient of variation (CV)0.39627129
Kurtosis2.5598834
Mean369.50801
Median Absolute Deviation (MAD)100
Skewness-0.17315976
Sum206554.98
Variance21440.402
MonotonicityNot monotonic
2024-01-10T07:59:30.542732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
450.0 20
 
0.2%
300.0 12
 
0.1%
400.0 12
 
0.1%
250.0 10
 
0.1%
425.0 10
 
0.1%
240.0 9
 
0.1%
320.0 8
 
0.1%
600.0 8
 
0.1%
500.0 7
 
0.1%
225.0 7
 
0.1%
Other values (250) 456
 
4.6%
(Missing) 9441
94.4%
ValueCountFrequency (%)
-440.0 1
 
< 0.1%
-317.0 1
 
< 0.1%
4.0 1
 
< 0.1%
30.0 3
< 0.1%
40.0 1
 
< 0.1%
75.0 1
 
< 0.1%
77.0 1
 
< 0.1%
80.0 1
 
< 0.1%
90.0 1
 
< 0.1%
100.0 5
0.1%
ValueCountFrequency (%)
1010.0 1
 
< 0.1%
969.8 1
 
< 0.1%
715.0 1
 
< 0.1%
700.0 1
 
< 0.1%
688.0 1
 
< 0.1%
685.0 1
 
< 0.1%
680.0 2
< 0.1%
675.0 1
 
< 0.1%
662.22 1
 
< 0.1%
650.0 4
< 0.1%

증기압_25
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct3454
Distinct (%)57.5%
Missing3995
Missing (%)40.0%
Infinite0
Infinite (%)0.0%
Mean855.97278
Minimum0
Maximum1240000
Zeros551
Zeros (%)5.5%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T07:59:30.675073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13.64 × 10-8
median0.00113
Q30.373
95-th percentile82.76
Maximum1240000
Range1240000
Interquartile range (IQR)0.37299996

Descriptive statistics

Standard deviation19392.113
Coefficient of variation (CV)22.655057
Kurtosis2871.6043
Mean855.97278
Median Absolute Deviation (MAD)0.00113
Skewness48.558852
Sum5140116.6
Variance3.7605403 × 108
MonotonicityNot monotonic
2024-01-10T07:59:30.829452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 551
 
5.5%
0.1 35
 
0.4%
24.5 33
 
0.3%
1.41 31
 
0.3%
3.35e-05 26
 
0.3%
922.0 23
 
0.2%
1.81e-05 22
 
0.2%
6.21 22
 
0.2%
1.55e-07 22
 
0.2%
4.75 21
 
0.2%
Other values (3444) 5219
52.2%
(Missing) 3995
40.0%
ValueCountFrequency (%)
0.0 551
5.5%
1.8700000000000001e-35 1
 
< 0.1%
4.83e-35 1
 
< 0.1%
6.5e-35 2
 
< 0.1%
1.5299999999999999e-34 1
 
< 0.1%
1.5399999999999999e-34 1
 
< 0.1%
1.87e-34 1
 
< 0.1%
2.08e-34 1
 
< 0.1%
2.4299999999999998e-34 1
 
< 0.1%
1.03e-33 1
 
< 0.1%
ValueCountFrequency (%)
1240000.0 1
 
< 0.1%
466000.0 1
 
< 0.1%
343000.0 1
 
< 0.1%
322000.0 1
 
< 0.1%
205000.0 5
0.1%
154000.0 1
 
< 0.1%
110000.0 1
 
< 0.1%
75800.0 1
 
< 0.1%
54300.0 1
 
< 0.1%
48300.0 1
 
< 0.1%

증기압_20
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct278
Distinct (%)69.3%
Missing9599
Missing (%)96.0%
Infinite0
Infinite (%)0.0%
Mean130.18252
Minimum0
Maximum9800
Zeros5
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T07:59:31.005366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.3 × 10-8
Q10.05
median2.03
Q338
95-th percentile530
Maximum9800
Range9800
Interquartile range (IQR)37.95

Descriptive statistics

Standard deviation591.31596
Coefficient of variation (CV)4.5422071
Kurtosis183.66574
Mean130.18252
Median Absolute Deviation (MAD)2.03
Skewness12.093488
Sum52203.19
Variance349654.57
MonotonicityNot monotonic
2024-01-10T07:59:31.173615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1 14
 
0.1%
0.01 13
 
0.1%
1.0 10
 
0.1%
3.0 7
 
0.1%
21.0 5
 
0.1%
0.0 5
 
0.1%
17.0 5
 
0.1%
0.4 5
 
0.1%
0.05 5
 
0.1%
0.02 5
 
0.1%
Other values (268) 327
 
3.3%
(Missing) 9599
96.0%
ValueCountFrequency (%)
0.0 5
0.1%
7e-16 1
 
< 0.1%
3e-12 1
 
< 0.1%
6.8e-11 1
 
< 0.1%
7.21e-11 1
 
< 0.1%
7.35e-11 1
 
< 0.1%
1.5e-10 1
 
< 0.1%
1.88e-10 1
 
< 0.1%
9.98e-10 1
 
< 0.1%
2.03e-09 1
 
< 0.1%
ValueCountFrequency (%)
9800.0 1
< 0.1%
3690.0 1
< 0.1%
2580.0 1
< 0.1%
2430.0 1
< 0.1%
1840.0 1
< 0.1%
1680.0 1
< 0.1%
1530.0 1
< 0.1%
1400.0 1
< 0.1%
1280.0 1
< 0.1%
1070.0 1
< 0.1%

끓는점
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct3567
Distinct (%)49.6%
Missing2815
Missing (%)28.1%
Infinite0
Infinite (%)0.0%
Mean335.00624
Minimum-268.9
Maximum6000
Zeros0
Zeros (%)0.0%
Negative63
Negative (%)0.6%
Memory size166.0 KiB
2024-01-10T07:59:31.326926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-268.9
5-th percentile76
Q1180
median282.7
Q3406.7
95-th percentile701.9482
Maximum6000
Range6268.9
Interquartile range (IQR)226.7

Descriptive statistics

Standard deviation319.63678
Coefficient of variation (CV)0.95412188
Kurtosis93.292935
Mean335.00624
Median Absolute Deviation (MAD)112.3
Skewness7.433174
Sum2407019.8
Variance102167.67
MonotonicityNot monotonic
2024-01-10T07:59:31.490973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100.0 66
 
0.7%
158.0 52
 
0.5%
330.0 51
 
0.5%
19.5 28
 
0.3%
83.0 27
 
0.3%
333.6 26
 
0.3%
160.0 26
 
0.3%
412.3 25
 
0.2%
359.4 25
 
0.2%
338.5 24
 
0.2%
Other values (3557) 6835
68.3%
(Missing) 2815
28.1%
ValueCountFrequency (%)
-268.9 1
< 0.1%
-252.8 1
< 0.1%
-250.0 1
< 0.1%
-245.9 1
< 0.1%
-185.86 1
< 0.1%
-182.96 1
< 0.1%
-162.0 1
< 0.1%
-153.0 1
< 0.1%
-129.0 1
< 0.1%
-111.9 1
< 0.1%
ValueCountFrequency (%)
6000.0 1
< 0.1%
5900.0 1
< 0.1%
5596.0 1
< 0.1%
5560.0 1
< 0.1%
5100.0 1
< 0.1%
5000.0 1
< 0.1%
4820.0 1
< 0.1%
4300.0 2
< 0.1%
4200.0 1
< 0.1%
4000.0 2
< 0.1%

분자량
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct6571
Distinct (%)66.8%
Missing161
Missing (%)1.6%
Infinite0
Infinite (%)0.0%
Mean392.09504
Minimum0
Maximum840000
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T07:59:31.660686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile95.058
Q1159.355
median238.45
Q3379.4
95-th percentile726.694
Maximum840000
Range840000
Interquartile range (IQR)220.045

Descriptive statistics

Standard deviation8468.9616
Coefficient of variation (CV)21.599257
Kurtosis9822.2458
Mean392.09504
Median Absolute Deviation (MAD)96.21
Skewness99.065183
Sum3857823.1
Variance71723311
MonotonicityNot monotonic
2024-01-10T07:59:31.799839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
154.25 36
 
0.4%
156.27 30
 
0.3%
142.2 29
 
0.3%
158.24 25
 
0.2%
170.25 25
 
0.2%
198.3 24
 
0.2%
172.27 20
 
0.2%
184.28 20
 
0.2%
186.29 19
 
0.2%
114.14 19
 
0.2%
Other values (6561) 9592
95.9%
(Missing) 161
 
1.6%
ValueCountFrequency (%)
0.0 2
< 0.1%
2.02 1
< 0.1%
4.0 1
< 0.1%
4.03 1
< 0.1%
10.81 1
< 0.1%
12.01 2
< 0.1%
14.01 1
< 0.1%
16.04 1
< 0.1%
20.01 1
< 0.1%
20.18 1
< 0.1%
ValueCountFrequency (%)
840000.0 1
< 0.1%
6691.86 1
< 0.1%
6511.83 1
< 0.1%
2965.0 1
< 0.1%
2916.49 1
< 0.1%
2896.19 1
< 0.1%
2895.07 1
< 0.1%
2880.05 1
< 0.1%
2681.99 1
< 0.1%
2373.21 1
< 0.1%

끝점농도
Text

MISSING 

Distinct687
Distinct (%)59.0%
Missing8835
Missing (%)88.3%
Memory size156.2 KiB
2024-01-10T07:59:32.117347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length10
Mean length7.7218884
Min length5

Characters and Unicode

Total characters8996
Distinct characters14
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique537 ?
Unique (%)46.1%

Sample

1st row30.94 ppm
2nd row22 ppm
3rd row40 ppm
4th row0.13 ppm
5th row67.11 ppm
ValueCountFrequency (%)
ppm 1165
50.0%
0 62
 
2.7%
0.01 22
 
0.9%
1 15
 
0.6%
0.17 12
 
0.5%
0.02 12
 
0.5%
0.13 12
 
0.5%
2 12
 
0.5%
100 12
 
0.5%
40 11
 
0.5%
Other values (678) 995
42.7%
2024-01-10T07:59:32.577033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
p 2330
25.9%
1165
13.0%
m 1165
13.0%
. 844
 
9.4%
0 781
 
8.7%
1 548
 
6.1%
2 437
 
4.9%
3 327
 
3.6%
5 294
 
3.3%
4 271
 
3.0%
Other values (4) 834
 
9.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3495
38.9%
Decimal Number 3492
38.8%
Space Separator 1165
 
13.0%
Other Punctuation 844
 
9.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 781
22.4%
1 548
15.7%
2 437
12.5%
3 327
9.4%
5 294
 
8.4%
4 271
 
7.8%
7 238
 
6.8%
6 229
 
6.6%
8 190
 
5.4%
9 177
 
5.1%
Lowercase Letter
ValueCountFrequency (%)
p 2330
66.7%
m 1165
33.3%
Space Separator
ValueCountFrequency (%)
1165
100.0%
Other Punctuation
ValueCountFrequency (%)
. 844
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5501
61.1%
Latin 3495
38.9%

Most frequent character per script

Common
ValueCountFrequency (%)
1165
21.2%
. 844
15.3%
0 781
14.2%
1 548
10.0%
2 437
 
7.9%
3 327
 
5.9%
5 294
 
5.3%
4 271
 
4.9%
7 238
 
4.3%
6 229
 
4.2%
Other values (2) 367
 
6.7%
Latin
ValueCountFrequency (%)
p 2330
66.7%
m 1165
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8996
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
p 2330
25.9%
1165
13.0%
m 1165
13.0%
. 844
 
9.4%
0 781
 
8.7%
1 548
 
6.1%
2 437
 
4.9%
3 327
 
3.6%
5 294
 
3.3%
4 271
 
3.0%
Other values (4) 834
 
9.3%

끝점농도기준
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
8828 
PAC-2
1008 
IDLH*0.1
 
164

Length

Max length8
Median length4
Mean length4.1664
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 8828
88.3%
PAC-2 1008
 
10.1%
IDLH*0.1 164
 
1.6%

Length

2024-01-10T07:59:32.723207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T07:59:32.817253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 8828
88.3%
pac-2 1008
 
10.1%
idlh*0.1 164
 
1.6%

Unnamed: 14
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size166.0 KiB

Interactions

2024-01-10T07:59:24.874931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:59:22.276454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:59:22.780892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:59:23.319521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:59:23.854313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:59:24.342885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:59:24.967066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:59:22.355345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:59:22.869667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:59:23.398759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:59:23.930274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:59:24.433481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:59:25.102539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:59:22.446404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:59:22.959644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:59:23.493264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:59:24.012065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:59:24.525585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:59:25.214579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:59:22.531898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:59:23.046906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:59:23.579668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:59:24.088443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:59:24.615350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:59:25.324841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:59:22.611261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:59:23.134557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:59:23.660476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:59:24.171418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:59:24.692478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:59:25.434304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:59:22.695989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:59:23.220687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:59:23.748679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:59:24.252744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:59:24.783213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-10T07:59:32.884382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
물질상태폭발하한범위인화점발화점증기압_25증기압_20끓는점분자량끝점농도기준
물질상태1.0000.4530.1740.2750.1890.5150.361NaN0.060
폭발하한범위0.4531.0000.0000.0000.9560.9770.931NaN0.416
인화점0.1740.0001.0000.0000.0000.0000.890NaN0.076
발화점0.2750.0000.0001.0000.0000.1600.052NaN0.031
증기압_250.1890.9560.0000.0001.000NaN0.000NaN0.000
증기압_200.5150.9770.0000.160NaN1.0000.000NaN0.000
끓는점0.3610.9310.8900.0520.0000.0001.000NaN0.000
분자량NaNNaNNaNNaNNaNNaNNaN1.000NaN
끝점농도기준0.0600.4160.0760.0310.0000.0000.000NaN1.000
2024-01-10T07:59:32.996057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
물질상태끝점농도기준
물질상태1.0000.100
끝점농도기준0.1001.000
2024-01-10T07:59:33.075235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
인화점발화점증기압_25증기압_20끓는점분자량물질상태끝점농도기준
인화점1.0000.110-0.862-0.7790.8640.6250.1110.057
발화점0.1101.000-0.033-0.0440.050-0.0420.1920.038
증기압_25-0.862-0.0331.0000.845-0.869-0.6310.1440.000
증기압_20-0.779-0.0440.8451.000-0.756-0.5830.4510.000
끓는점0.8640.050-0.869-0.7561.0000.6150.2320.000
분자량0.625-0.042-0.631-0.5830.6151.0001.0001.000
물질상태0.1110.1920.1440.4510.2321.0001.0000.100
끝점농도기준0.0570.0380.0000.0000.0001.0000.1001.000

Missing values

2024-01-10T07:59:25.594297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-10T07:59:25.807169image/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.
2024-01-10T07:59:26.033797image/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

고유(CAS)번호영문국문물질상태폭발상한범위폭발하한범위인화점발화점증기압_25증기압_20끓는점분자량끝점농도끝점농도기준Unnamed: 14
611258048-89-8Styrene/methacrylic,acid/butyl,methacrylate/butyl,acrylate스타이렌/메타크릴산/뷰틸,메타크릴산염/뷰틸,아크릴산염고체<NA><NA>50.6<NA>2.44<NA>160.0460.6<NA><NA><NA>
1016469641-79-8n-Dodecyl 5-N,N-diethylamino-2-phenylsulfonyl-4-pentadienoate<NA><NA><NA><NA><NA><NA><NA><NA><NA>477.7<NA><NA><NA>
175122998-04-1Diallyl adipate<NA><NA><NA><NA>133.8<NA>0.00234<NA>288.4226.27<NA><NA><NA>
25055102-60-31,1',1'',1'''-(1,2-Ethanediyldinitrilo)tetrakis-2-propanol; Quadrol<NA>액체<NA><NA>145.7<NA>0.000001<NA>369.1292.4230.94 ppmPAC-2<NA>
191542429-84-7C.I. direct red 001<NA><NA><NA><NA><NA><NA><NA><NA><NA>627.54<NA><NA><NA>
681794088-84-3Hexadecyl 6-methylheptanoate<NA><NA><NA><NA>203.7<NA>0.000001<NA>407.2368.64<NA><NA><NA>
225521326-41-6C.I. sulphur brown 008<NA>액체<NA><NA>170.2<NA>0.0<NA>331.5182.13<NA><NA><NA>
480875-77-4Trimethylchlorosilane트라이메틸클로로실레인액체39.00%1.80%-17.78395.0234.0188.057.0108.6422 ppmPAC-2<NA>
1522650293-39-5[8,20-Dihydro-8,20-diphenyl-5,24:12,17-diimino-7,10:22,19-dinitrilodibenz[f,p][1,2,4,9,11,12,14,19]octaazacycloeicosinato(2-)-N25,N26,N27,N28]iron<NA><NA><NA><NA><NA><NA><NA><NA><NA>626.41<NA><NA><NA>
23177123-25-12-Butanedioic acid diethyl ester; Diethyl succinate<NA>액체<NA><NA>90.6<NA>0.126<NA>218.4174.1940 ppmPAC-2<NA>
고유(CAS)번호영문국문물질상태폭발상한범위폭발하한범위인화점발화점증기압_25증기압_20끓는점분자량끝점농도끝점농도기준Unnamed: 14
1113368332-90-1Hexanedioic acid mixed 2-butoxyethyl and 2-ethylhexyl esters<NA><NA><NA><NA>181.7<NA>0.0<NA>430.0358.51<NA><NA><NA>
24945103-96-8N,N'-Bis(1-methylheptyl)-1,4-benzenediamine; N,N'-Bis(1-methylheptyl)-p-phenylenediamine<NA>액체<NA><NA>257.5<NA>0.0<NA>456.4332.57<NA><NA><NA>
921675-68-31-Chloro-1,1-difluoroethane1-클로로-1,1-다이플루오로에탄액체<NA><NA>-64.4632.02470.0<NA>-9.3100.515000 ppmPAC-2<NA>
2480010476-95-62-Methyl-2-propene-1,1-diol diacetate<NA>액체<NA><NA>83.3<NA>0.526<NA>191.0172.180.28 ppmPAC-2<NA>
89007774-29-0Mercury diiodide<NA>고체<NA><NA>350.0<NA><NA><NA>353.9454.40.01 ppmPAC-2<NA>
34111000-90-4Zinc,isopropylxanthate아연,아이소프로필잔테인고체<NA><NA>37.2<NA><NA><NA>138.0335.82<NA><NA><NA>
1479952270-44-7Neodecanoic acid, cobalt(2+) salt<NA><NA><NA><NA>114.1<NA><NA><NA>256.2401.45<NA><NA><NA>
1862525646-71-3N-[2-[(4-Amino-3-methylphenyl)ethylamino]ethyl]methanesulfonamide sulfate (2:3)<NA>액체<NA><NA>329.2<NA>0.0<NA>620.7836.99<NA><NA><NA>
49458065-48-3Systox사이스톡스액체5.30%1.00%45.0463.890.002110.00034134.0258.340.19 ppmPAC-2<NA>
72449072-61-1α,α',α''-1,2,6-Hexanetriyltris[ω-hydroxypoly[oxy(methyl-1,2-ethanediyl)]]; Polypropylene glycol 1,2,6-hexanetriol triether<NA><NA><NA><NA>232.5<NA>0.0<NA>460.8308.41<NA><NA><NA>