Overview

Dataset statistics

Number of variables36
Number of observations85
Missing cells608
Missing cells (%)19.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory25.2 KiB
Average record size in memory303.6 B

Variable types

Text8
Categorical21
Numeric7

Dataset

Description한국세라믹기술원 세라믹소재정보은행의 측정장비 데이터 정보입니다. (순번, 시료명, 실험일, 관련소재 등) 금속/화학/세라믹 통합사이트 주소: http://www.matcenter.org 담당자: 김경훈 수석
Author한국세라믹기술원
URLhttps://www.data.go.kr/data/15072084/fileData.do

Alerts

팁 is highly imbalanced (57.0%)Imbalance
마모도2 is highly imbalanced (57.0%)Imbalance
수정일 is highly imbalanced (52.7%)Imbalance
스펙1 has 16 (18.8%) missing valuesMissing
밀림거리 has 17 (20.0%) missing valuesMissing
온도 has 28 (32.9%) missing valuesMissing
습도 has 18 (21.2%) missing valuesMissing
노트 has 32 (37.6%) missing valuesMissing
히트 has 68 (80.0%) missing valuesMissing
전도도 has 68 (80.0%) missing valuesMissing
이알 has 68 (80.0%) missing valuesMissing
경도 has 68 (80.0%) missing valuesMissing
마모계수1 has 36 (42.4%) missing valuesMissing
마모도1 has 65 (76.5%) missing valuesMissing
매스1 has 62 (72.9%) missing valuesMissing
매스2 has 62 (72.9%) missing valuesMissing
순번 has unique valuesUnique
온도 has 1 (1.2%) zerosZeros

Reproduction

Analysis started2023-12-12 22:12:57.024257
Analysis finished2023-12-12 22:12:57.676301
Duration0.65 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

순번
Text

UNIQUE 

Distinct85
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size812.0 B
2023-12-13T07:12:57.911668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length6
Mean length6.5647059
Min length6

Characters and Unicode

Total characters558
Distinct characters14
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique85 ?
Unique (%)100.0%

Sample

1st rowE00036
2nd rowE00037
3rd rowE00040
4th rowE00043
5th rowE00044
ValueCountFrequency (%)
e00036 1
 
1.2%
e00063 1
 
1.2%
e00074 1
 
1.2%
e00082 1
 
1.2%
e00073 1
 
1.2%
e00072 1
 
1.2%
e00071 1
 
1.2%
e00067 1
 
1.2%
e00033 1
 
1.2%
e00032 1
 
1.2%
Other values (75) 75
88.2%
2023-12-13T07:12:58.374616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 301
53.9%
E 85
 
15.2%
6 19
 
3.4%
7 19
 
3.4%
5 19
 
3.4%
4 18
 
3.2%
3 17
 
3.0%
8 16
 
2.9%
2 16
 
2.9%
1 15
 
2.7%
Other values (4) 33
 
5.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 455
81.5%
Uppercase Letter 97
 
17.4%
Connector Punctuation 6
 
1.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 301
66.2%
6 19
 
4.2%
7 19
 
4.2%
5 19
 
4.2%
4 18
 
4.0%
3 17
 
3.7%
8 16
 
3.5%
2 16
 
3.5%
1 15
 
3.3%
9 15
 
3.3%
Uppercase Letter
ValueCountFrequency (%)
E 85
87.6%
Q 6
 
6.2%
P 6
 
6.2%
Connector Punctuation
ValueCountFrequency (%)
_ 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 461
82.6%
Latin 97
 
17.4%

Most frequent character per script

Common
ValueCountFrequency (%)
0 301
65.3%
6 19
 
4.1%
7 19
 
4.1%
5 19
 
4.1%
4 18
 
3.9%
3 17
 
3.7%
8 16
 
3.5%
2 16
 
3.5%
1 15
 
3.3%
9 15
 
3.3%
Latin
ValueCountFrequency (%)
E 85
87.6%
Q 6
 
6.2%
P 6
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 558
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 301
53.9%
E 85
 
15.2%
6 19
 
3.4%
7 19
 
3.4%
5 19
 
3.4%
4 18
 
3.2%
3 17
 
3.0%
8 16
 
2.9%
2 16
 
2.9%
1 15
 
2.7%
Other values (4) 33
 
5.9%

시료명
Text

Distinct72
Distinct (%)84.7%
Missing0
Missing (%)0.0%
Memory size812.0 B
2023-12-13T07:12:58.648999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length47
Median length32
Mean length14.529412
Min length2

Characters and Unicode

Total characters1235
Distinct characters66
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique66 ?
Unique (%)77.6%

Sample

1st rowCMN #28
2nd rowCMN #29
3rd rowCMN #31
4th rowSiTiN #11
5th rowCrN #9
ValueCountFrequency (%)
28
 
11.2%
cmn 13
 
5.2%
tin 10
 
4.0%
sitin 9
 
3.6%
1set 9
 
3.6%
hss_disk 8
 
3.2%
pin 8
 
3.2%
산질화된 8
 
3.2%
hss 7
 
2.8%
scm420 6
 
2.4%
Other values (89) 145
57.8%
2023-12-13T07:12:59.035443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
174
 
14.1%
i 72
 
5.8%
N 66
 
5.3%
S 65
 
5.3%
C 52
 
4.2%
_ 42
 
3.4%
T 35
 
2.8%
l 34
 
2.8%
1 34
 
2.8%
0 34
 
2.8%
Other values (56) 627
50.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 357
28.9%
Lowercase Letter 342
27.7%
Decimal Number 182
14.7%
Space Separator 174
14.1%
Other Letter 68
 
5.5%
Other Punctuation 62
 
5.0%
Connector Punctuation 42
 
3.4%
Math Symbol 4
 
0.3%
Other Symbol 4
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 72
21.1%
l 34
9.9%
r 33
9.6%
e 28
 
8.2%
s 26
 
7.6%
n 26
 
7.6%
a 20
 
5.8%
p 19
 
5.6%
d 17
 
5.0%
b 13
 
3.8%
Other values (9) 54
15.8%
Uppercase Letter
ValueCountFrequency (%)
N 66
18.5%
S 65
18.2%
C 52
14.6%
T 35
9.8%
A 29
8.1%
M 21
 
5.9%
O 19
 
5.3%
G 16
 
4.5%
H 15
 
4.2%
B 13
 
3.6%
Other values (9) 26
 
7.3%
Decimal Number
ValueCountFrequency (%)
1 34
18.7%
0 34
18.7%
6 31
17.0%
2 27
14.8%
7 14
7.7%
4 12
 
6.6%
8 9
 
4.9%
3 9
 
4.9%
5 8
 
4.4%
9 4
 
2.2%
Other Letter
ValueCountFrequency (%)
화 15
22.1%
질 15
22.1%
산 10
14.7%
된 8
11.8%
크 4
 
5.9%
벌 4
 
5.9%
프 3
 
4.4%
레 3
 
4.4%
이 3
 
4.4%
스 3
 
4.4%
Other Punctuation
ValueCountFrequency (%)
# 28
45.2%
& 21
33.9%
/ 9
 
14.5%
. 4
 
6.5%
Space Separator
ValueCountFrequency (%)
174
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 42
100.0%
Math Symbol
ValueCountFrequency (%)
+ 4
100.0%
Other Symbol
ValueCountFrequency (%)
℃ 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 699
56.6%
Common 468
37.9%
Hangul 68
 
5.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 72
 
10.3%
N 66
 
9.4%
S 65
 
9.3%
C 52
 
7.4%
T 35
 
5.0%
l 34
 
4.9%
r 33
 
4.7%
A 29
 
4.1%
e 28
 
4.0%
s 26
 
3.7%
Other values (28) 259
37.1%
Common
ValueCountFrequency (%)
174
37.2%
_ 42
 
9.0%
1 34
 
7.3%
0 34
 
7.3%
6 31
 
6.6%
# 28
 
6.0%
2 27
 
5.8%
& 21
 
4.5%
7 14
 
3.0%
4 12
 
2.6%
Other values (8) 51
 
10.9%
Hangul
ValueCountFrequency (%)
화 15
22.1%
질 15
22.1%
산 10
14.7%
된 8
11.8%
크 4
 
5.9%
벌 4
 
5.9%
프 3
 
4.4%
레 3
 
4.4%
이 3
 
4.4%
스 3
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1163
94.2%
Hangul 68
 
5.5%
Letterlike Symbols 4
 
0.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
174
 
15.0%
i 72
 
6.2%
N 66
 
5.7%
S 65
 
5.6%
C 52
 
4.5%
_ 42
 
3.6%
T 35
 
3.0%
l 34
 
2.9%
1 34
 
2.9%
0 34
 
2.9%
Other values (45) 555
47.7%
Hangul
ValueCountFrequency (%)
화 15
22.1%
질 15
22.1%
산 10
14.7%
된 8
11.8%
크 4
 
5.9%
벌 4
 
5.9%
프 3
 
4.4%
레 3
 
4.4%
이 3
 
4.4%
스 3
 
4.4%
Letterlike Symbols
ValueCountFrequency (%)
℃ 4
100.0%

실험일
Categorical

Distinct41
Distinct (%)48.2%
Missing0
Missing (%)0.0%
Memory size812.0 B
2011-06-10
10 
2010-05-25
8 
2010-05-20
6 
2010-04-14
6 
2010-07-01
 
4
Other values (36)
51 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique24 ?
Unique (%)28.2%

Sample

1st row2010-09-10
2nd row2010-10-04
3rd row2010-05-05
4th row2010-03-11
5th row2010-04-14

Common Values

ValueCountFrequency (%)
2011-06-10 10
 
11.8%
2010-05-25 8
 
9.4%
2010-05-20 6
 
7.1%
2010-04-14 6
 
7.1%
2010-07-01 4
 
4.7%
2012-01-31 4
 
4.7%
2010-03-11 3
 
3.5%
2010-09-10 2
 
2.4%
2010-07-20 2
 
2.4%
2010-08-06 2
 
2.4%
Other values (31) 38
44.7%

Length

2023-12-13T07:12:59.199347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2011-06-10 10
 
11.8%
2010-05-25 8
 
9.4%
2010-05-20 6
 
7.1%
2010-04-14 6
 
7.1%
2010-07-01 4
 
4.7%
2012-01-31 4
 
4.7%
2010-03-11 3
 
3.5%
2010-07-02 2
 
2.4%
2010-10-04 2
 
2.4%
2011-06-23 2
 
2.4%
Other values (31) 38
44.7%

구분
Categorical

Distinct2
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size812.0 B
2
68 
1
17 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 68
80.0%
1 17
 
20.0%

Length

2023-12-13T07:12:59.313331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:12:59.423298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 68
80.0%
1 17
 
20.0%

견본타입1
Categorical

Distinct30
Distinct (%)35.3%
Missing0
Missing (%)0.0%
Memory size812.0 B
disk
26 
산질화된 HSSsquare disk
14 
Disk type
11 
DISK
4 
DISK TYPE
3 
Other values (25)
27 

Length

Max length25
Median length23
Mean length10.341176
Min length4

Unique

Unique23 ?
Unique (%)27.1%

Sample

1st rowCMN #28 disk
2nd rowCMN #29 disk
3rd rowdisk
4th rowdisk
5th rowdisk

Common Values

ValueCountFrequency (%)
disk 26
30.6%
산질화된 HSSsquare disk 14
16.5%
Disk type 11
12.9%
DISK 4
 
4.7%
DISK TYPE 3
 
3.5%
square disk 2
 
2.4%
Disk type of glass 2
 
2.4%
CMN #30 disk 1
 
1.2%
CMN #24 disk 1
 
1.2%
CMN #19 disk 1
 
1.2%
Other values (20) 20
23.5%

Length

2023-12-13T07:12:59.554408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
disk 83
46.6%
type 17
 
9.6%
hsssquare 14
 
7.9%
산질화된 14
 
7.9%
cmn 11
 
6.2%
ticn 5
 
2.8%
square 3
 
1.7%
of 2
 
1.1%
glass 2
 
1.1%
poresquare 2
 
1.1%
Other values (25) 25
 
14.0%

견본타입2
Categorical

Distinct15
Distinct (%)17.6%
Missing0
Missing (%)0.0%
Memory size812.0 B
ball
17 
<NA>
17 
Suj2 ball
13 
초경볼
7 
Al Bar_round tip
7 
Other values (10)
24 

Length

Max length20
Median length4
Mean length7.7294118
Min length3

Unique

Unique4 ?
Unique (%)4.7%

Sample

1st rowSuj2 ball
2nd rowSuj2 ball
3rd rowSuj2 ball
4th rowball
5th rowball

Common Values

ValueCountFrequency (%)
ball 17
20.0%
<NA> 17
20.0%
Suj2 ball 13
15.3%
초경볼 7
8.2%
Al Bar_round tip 7
8.2%
AL6082Bar _round tip 7
8.2%
SiC ball 4
 
4.7%
초경ball 3
 
3.5%
BALL 2
 
2.4%
초경 ball 2
 
2.4%
Other values (5) 6
 
7.1%

Length

2023-12-13T07:12:59.696480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ball 39
28.7%
na 17
12.5%
tip 16
11.8%
suj2 13
 
9.6%
초경볼 8
 
5.9%
round 8
 
5.9%
al 7
 
5.1%
bar_round 7
 
5.1%
al6082bar 7
 
5.1%
초경ball 5
 
3.7%
Other values (5) 9
 
6.6%

시험방법
Categorical

Distinct13
Distinct (%)15.3%
Missing0
Missing (%)0.0%
Memory size812.0 B
KS L 1606 _수정적용
30 
KSL 1606:2003
19 
Nano indentation_ Berkovich tip. oliver&pharr method
12 
KS L 1606
9 
ball on disk
6 
Other values (8)
9 

Length

Max length56
Median length53
Mean length21.082353
Min length9

Unique

Unique7 ?
Unique (%)8.2%

Sample

1st rowKS L 1606 _수정적용
2nd rowKS L 1606 _수정적용
3rd rowKS L 1606 _수정적용
4th rowKS L 1606 _수정적용
5th rowKS L 1606 _수정적용

Common Values

ValueCountFrequency (%)
KS L 1606 _수정적용 30
35.3%
KSL 1606:2003 19
22.4%
Nano indentation_ Berkovich tip. oliver&pharr method 12
 
14.1%
KS L 1606 9
 
10.6%
ball on disk 6
 
7.1%
Nano indentation _ Berkovich tip. Oliver&Pharr method 2
 
2.4%
KSL 1606:2033 1
 
1.2%
ball on disk 1
 
1.2%
KS L 1606 수정적용 1
 
1.2%
Nano Indentation _Berkovich tip. Oliver&Pharr method 1
 
1.2%
Other values (3) 3
 
3.5%

Length

2023-12-13T07:12:59.835932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ks 40
12.5%
1606 40
12.5%
l 40
12.5%
수정적용 31
9.7%
ksl 21
 
6.6%
1606:2003 19
 
5.9%
tip 17
 
5.3%
nano 17
 
5.3%
method 17
 
5.3%
oliver&pharr 16
 
5.0%
Other values (11) 62
19.4%

크기1
Categorical

Distinct25
Distinct (%)29.4%
Missing0
Missing (%)0.0%
Memory size812.0 B
20*20*3
38 
<NA>
22 
1.5*1.5*1
 
2
27*27*_
 
2
W_32.01*L_31.596*T_3.55
 
1
Other values (20)
20 

Length

Max length26
Median length25
Mean length10.105882
Min length4

Unique

Unique21 ?
Unique (%)24.7%

Sample

1st row20*20*3
2nd row20*20*3
3rd row20*20*3
4th row20*20*3
5th row20*20*3

Common Values

ValueCountFrequency (%)
20*20*3 38
44.7%
<NA> 22
25.9%
1.5*1.5*1 2
 
2.4%
27*27*_ 2
 
2.4%
W_32.01*L_31.596*T_3.55 1
 
1.2%
W_20.02*L_20.04*T_5 1
 
1.2%
W_20.97*L_21.18*T_5.716 1
 
1.2%
W_31.69* L_31.63 * T_3.54 1
 
1.2%
W_32.05* L_31.63 * T_3.52 1
 
1.2%
W_32.05* L_32.09 * T_3.53 1
 
1.2%
Other values (15) 15
 
17.6%

Length

2023-12-13T07:12:59.955278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
20*20*3 38
33.0%
na 22
19.1%
12
 
10.4%
1.5*1.5*1 2
 
1.7%
27*27 2
 
1.7%
l_31.63 2
 
1.7%
t_3.54 2
 
1.7%
w_32.05 2
 
1.7%
t_3.53 2
 
1.7%
h3.53 1
 
0.9%
Other values (30) 30
26.1%

크기2
Categorical

Distinct20
Distinct (%)23.5%
Missing0
Missing (%)0.0%
Memory size812.0 B
12.7
41 
<NA>
23 
dia_12.7
 
4
dia_5.01*L_28.06. tip dia _6
 
1
D_5.00 * L_28.08 * Pin dia_6
 
1
Other values (15)
15 

Length

Max length28
Median length4
Mean length8.3529412
Min length4

Unique

Unique17 ?
Unique (%)20.0%

Sample

1st row12.7
2nd row12.7
3rd row12.7
4th row12.7
5th row12.7

Common Values

ValueCountFrequency (%)
12.7 41
48.2%
<NA> 23
27.1%
dia_12.7 4
 
4.7%
dia_5.01*L_28.06. tip dia _6 1
 
1.2%
D_5.00 * L_28.08 * Pin dia_6 1
 
1.2%
D_5.03 * L_28.05 * Pin dia_6 1
 
1.2%
D_4.95 * L_28.02 * Pin dia_6 1
 
1.2%
D_4.95 * L_28.01 * Pin dia_6 1
 
1.2%
D_5.00 * L_28.05 * Pin dia_6 1
 
1.2%
12.7 dia 1
 
1.2%
Other values (10) 10
 
11.8%

Length

2023-12-13T07:13:00.161075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
12.7 42
29.0%
na 23
15.9%
16
 
11.0%
tip 8
 
5.5%
dia 8
 
5.5%
6 7
 
4.8%
pin 5
 
3.4%
dia_6 5
 
3.4%
dia_12.7 4
 
2.8%
d_5.00 2
 
1.4%
Other values (23) 25
17.2%

표면거침1
Categorical

Distinct12
Distinct (%)14.1%
Missing0
Missing (%)0.0%
Memory size812.0 B
<NA>
54 
<0.1
13 
0.09
6 
0.11
 
3
0.12
 
2
Other values (7)
7 

Length

Max length9
Median length4
Mean length4.1058824
Min length4

Unique

Unique7 ?
Unique (%)8.2%

Sample

1st row<0.1
2nd row<0.1
3rd row<0.1
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 54
63.5%
<0.1 13
 
15.3%
0.09 6
 
7.1%
0.11 3
 
3.5%
0.12 2
 
2.4%
0.576 1
 
1.2%
0.613 1
 
1.2%
0.1~0.2um 1
 
1.2%
0.116 1
 
1.2%
0.103 1
 
1.2%
Other values (2) 2
 
2.4%

Length

2023-12-13T07:13:00.356520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 54
63.5%
0.1 13
 
15.3%
0.09 6
 
7.1%
0.11 3
 
3.5%
0.12 2
 
2.4%
0.576 1
 
1.2%
0.613 1
 
1.2%
0.1~0.2um 1
 
1.2%
0.116 1
 
1.2%
0.103 1
 
1.2%
Other values (2) 2
 
2.4%

표면거침2
Categorical

Distinct2
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size812.0 B
<NA>
62 
<0.1
23 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<0.1
2nd row<0.1
3rd row<0.1
4th row<0.1
5th row<0.1

Common Values

ValueCountFrequency (%)
<NA> 62
72.9%
<0.1 23
 
27.1%

Length

2023-12-13T07:13:00.567867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:13:00.767542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 62
72.9%
0.1 23
 
27.1%

팁
Categorical

IMBALANCE 

Distinct5
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Memory size812.0 B
<NA>
68 
Berkovich type. area function Ap = 24.5h^2+C1h+C2h^1/2+C2h^1/4+C3h^1/8+C4h^1/16 C1= _9.02E004. C2=2.89E007 . C3=_9.23E008 . C4= 6.58E009
11 
Berkovich type. area function Ap = 24.5h^2+C1h+C2h^1/2+C2h^1/4+C3h^1/8+C4h^1/16 C1= _9.02E004. C2=2.89E007 . C3=_9.23E008 . C4= 6.58E009
 
4
Berkovich type. area function Ap = 24.5h^2+C1h+C2h^1/2+C2h^1/4+C3h^1/8+C4h^1/16 C1=_9.02E004 . C2=2.89E007 . C3=_9.23E008 . C4=6.58E009
 
1
Berkovich type. area function Ap = 24.5h^2+C1h+C2h^1/2+C2h^1/4+C3h^1/8+C4h^1/16 C1= _9.02E004. C2=2.89E007. =_9.23E008 . C4= 6.58E009
 
1

Length

Max length137
Median length4
Mean length30.411765
Min length4

Unique

Unique2 ?
Unique (%)2.4%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 68
80.0%
Berkovich type. area function Ap = 24.5h^2+C1h+C2h^1/2+C2h^1/4+C3h^1/8+C4h^1/16 C1= _9.02E004. C2=2.89E007 . C3=_9.23E008 . C4= 6.58E009 11
 
12.9%
Berkovich type. area function Ap = 24.5h^2+C1h+C2h^1/2+C2h^1/4+C3h^1/8+C4h^1/16 C1= _9.02E004. C2=2.89E007 . C3=_9.23E008 . C4= 6.58E009 4
 
4.7%
Berkovich type. area function Ap = 24.5h^2+C1h+C2h^1/2+C2h^1/4+C3h^1/8+C4h^1/16 C1=_9.02E004 . C2=2.89E007 . C3=_9.23E008 . C4=6.58E009 1
 
1.2%
Berkovich type. area function Ap = 24.5h^2+C1h+C2h^1/2+C2h^1/4+C3h^1/8+C4h^1/16 C1= _9.02E004. C2=2.89E007. =_9.23E008 . C4= 6.58E009 1
 
1.2%

Length

2023-12-13T07:13:00.957551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:13:01.198025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 68
21.2%
51
15.9%
type 17
 
5.3%
area 17
 
5.3%
function 17
 
5.3%
ap 17
 
5.3%
24.5h^2+c1h+c2h^1/2+c2h^1/4+c3h^1/8+c4h^1/16 17
 
5.3%
berkovich 17
 
5.3%
c2=2.89e007 17
 
5.3%
c4 16
 
5.0%
Other values (7) 67
20.9%

스펙1
Text

MISSING 

Distinct44
Distinct (%)63.8%
Missing16
Missing (%)18.8%
Memory size812.0 B
2023-12-13T07:13:01.675302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length90
Median length46
Mean length33.782609
Min length8

Characters and Unicode

Total characters2331
Distinct characters95
Distinct categories7 ?
Distinct scripts4 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique38 ?
Unique (%)55.1%

Sample

1st rowCr_Mo_N 3um coated/Fe base sample. PVD coating
2nd rowCr_Mo_N 3um coated/Fe base sample. PVD coating
3rd rowCr_Mo_N 3um coated/Fe base sample. PVD coating
4th rowSiTiN 700nm coated/Fe substrate/PVD
5th rowCrN 1.6μm coated/Fe substrate/PVD
ValueCountFrequency (%)
sample 37
 
9.5%
coated/fe 23
 
5.9%
metal 17
 
4.4%
coated 16
 
4.1%
coating 15
 
3.9%
3um 15
 
3.9%
cr_mo_n 13
 
3.4%
base 13
 
3.4%
pvd 13
 
3.4%
질화 12
 
3.1%
Other values (91) 214
55.2%
2023-12-13T07:13:02.136172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
323
 
13.9%
e 195
 
8.4%
a 138
 
5.9%
t 110
 
4.7%
s 109
 
4.7%
o 87
 
3.7%
i 83
 
3.6%
l 73
 
3.1%
m 69
 
3.0%
N 61
 
2.6%
Other values (85) 1083
46.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1248
53.5%
Uppercase Letter 389
 
16.7%
Space Separator 323
 
13.9%
Other Letter 152
 
6.5%
Decimal Number 108
 
4.6%
Other Punctuation 74
 
3.2%
Connector Punctuation 37
 
1.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
화 15
 
9.9%
질 15
 
9.9%
면 10
 
6.6%
표 10
 
6.6%
된 8
 
5.3%
처 8
 
5.3%
리 8
 
5.3%
구 6
 
3.9%
조 4
 
2.6%
용 4
 
2.6%
Other values (30) 64
42.1%
Lowercase Letter
ValueCountFrequency (%)
e 195
15.6%
a 138
11.1%
t 110
8.8%
s 109
8.7%
o 87
 
7.0%
i 83
 
6.7%
l 73
 
5.8%
m 69
 
5.5%
c 61
 
4.9%
d 59
 
4.7%
Other values (12) 264
21.2%
Uppercase Letter
ValueCountFrequency (%)
N 61
15.7%
S 43
11.1%
M 37
9.5%
P 36
9.3%
T 32
8.2%
D 26
 
6.7%
C 25
 
6.4%
V 23
 
5.9%
F 23
 
5.9%
B 15
 
3.9%
Other values (9) 68
17.5%
Decimal Number
ValueCountFrequency (%)
0 23
21.3%
3 19
17.6%
1 18
16.7%
6 15
13.9%
2 13
12.0%
4 8
 
7.4%
7 5
 
4.6%
5 4
 
3.7%
8 3
 
2.8%
Other Punctuation
ValueCountFrequency (%)
/ 37
50.0%
. 31
41.9%
# 6
 
8.1%
Space Separator
ValueCountFrequency (%)
323
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 37
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1631
70.0%
Common 542
 
23.3%
Hangul 152
 
6.5%
Greek 6
 
0.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 195
 
12.0%
a 138
 
8.5%
t 110
 
6.7%
s 109
 
6.7%
o 87
 
5.3%
i 83
 
5.1%
l 73
 
4.5%
m 69
 
4.2%
N 61
 
3.7%
c 61
 
3.7%
Other values (30) 645
39.5%
Hangul
ValueCountFrequency (%)
화 15
 
9.9%
질 15
 
9.9%
면 10
 
6.6%
표 10
 
6.6%
된 8
 
5.3%
처 8
 
5.3%
리 8
 
5.3%
구 6
 
3.9%
조 4
 
2.6%
용 4
 
2.6%
Other values (30) 64
42.1%
Common
ValueCountFrequency (%)
323
59.6%
/ 37
 
6.8%
_ 37
 
6.8%
. 31
 
5.7%
0 23
 
4.2%
3 19
 
3.5%
1 18
 
3.3%
6 15
 
2.8%
2 13
 
2.4%
4 8
 
1.5%
Other values (4) 18
 
3.3%
Greek
ValueCountFrequency (%)
μ 6
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2173
93.2%
Hangul 152
 
6.5%
None 6
 
0.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
323
 
14.9%
e 195
 
9.0%
a 138
 
6.4%
t 110
 
5.1%
s 109
 
5.0%
o 87
 
4.0%
i 83
 
3.8%
l 73
 
3.4%
m 69
 
3.2%
N 61
 
2.8%
Other values (44) 925
42.6%
Hangul
ValueCountFrequency (%)
화 15
 
9.9%
질 15
 
9.9%
면 10
 
6.6%
표 10
 
6.6%
된 8
 
5.3%
처 8
 
5.3%
리 8
 
5.3%
구 6
 
3.9%
조 4
 
2.6%
용 4
 
2.6%
Other values (30) 64
42.1%
None
ValueCountFrequency (%)
μ 6
100.0%

스펙2
Categorical

Distinct17
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Memory size812.0 B
<NA>
25 
SUJ2
15 
Ball type. SUS
13 
Ball type/초경/Suj2
10 
Al6080 bar. 한쪽 tip 끝부분은 지름 6mm로 가공 예측
5 
Other values (12)
17 

Length

Max length75
Median length37
Mean length11.941176
Min length4

Unique

Unique9 ?
Unique (%)10.6%

Sample

1st rowBall type. SUS
2nd rowBall type. SUS
3rd rowBall type. SUS
4th rowBall type/초경/Suj2
5th rowBall type/초경/Suj2

Common Values

ValueCountFrequency (%)
<NA> 25
29.4%
SUJ2 15
17.6%
Ball type. SUS 13
15.3%
Ball type/초경/Suj2 10
 
11.8%
Al6080 bar. 한쪽 tip 끝부분은 지름 6mm로 가공 예측 5
 
5.9%
SiC 12.7 dia ball 임. 표면은 거울면임 3
 
3.5%
Al6061 스프레이 PIN 3
 
3.5%
Al7075 벌크 PIN 2
 
2.4%
Al7075 벌크 pin 1
 
1.2%
Al6082 벌크 PIN 1
 
1.2%
Other values (7) 7
 
8.2%

Length

2023-12-13T07:13:02.331960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ball 27
 
12.6%
na 25
 
11.6%
suj2 16
 
7.4%
sus 15
 
7.0%
type 13
 
6.0%
type/초경/suj2 10
 
4.7%
bar 8
 
3.7%
pin 8
 
3.7%
한쪽 5
 
2.3%
끝부분은 5
 
2.3%
Other values (36) 83
38.6%

획득률
Categorical

Distinct2
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size812.0 B
<NA>
68 
10
17 

Length

Max length4
Median length4
Mean length3.6
Min length2

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> 68
80.0%
10 17
 
20.0%

Length

2023-12-13T07:13:02.506329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:13:02.640703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 68
80.0%
10 17
 
20.0%

최대하중
Categorical

Distinct3
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Memory size812.0 B
<NA>
68 
5
12 
50
 
5

Length

Max length4
Median length4
Mean length3.4588235
Min length1

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> 68
80.0%
5 12
 
14.1%
50 5
 
5.9%

Length

2023-12-13T07:13:02.782539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:13:02.915734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 68
80.0%
5 12
 
14.1%
50 5
 
5.9%

재하율
Categorical

Distinct3
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Memory size812.0 B
<NA>
68 
10
12 
100
 
5

Length

Max length4
Median length4
Mean length3.6588235
Min length2

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> 68
80.0%
10 12
 
14.1%
100 5
 
5.9%

Length

2023-12-13T07:13:03.052615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:13:03.202433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 68
80.0%
10 12
 
14.1%
100 5
 
5.9%

재재하율
Categorical

Distinct3
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Memory size812.0 B
<NA>
68 
10
12 
100
 
5

Length

Max length4
Median length4
Mean length3.6588235
Min length2

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> 68
80.0%
10 12
 
14.1%
100 5
 
5.9%

Length

2023-12-13T07:13:03.345356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:13:03.502270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 68
80.0%
10 12
 
14.1%
100 5
 
5.9%

밀림거리
Real number (ℝ)

MISSING 

Distinct13
Distinct (%)19.1%
Missing17
Missing (%)20.0%
Infinite0
Infinite (%)0.0%
Mean1455.5602
Minimum600.88
Maximum3001.4961
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size897.0 B
2023-12-13T07:13:03.614326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum600.88
5-th percentile962.76961
Q11000.47
median1000.47
Q31253.5298
95-th percentile3001.4961
Maximum3001.4961
Range2400.6161
Interquartile range (IQR)253.05985

Descriptive statistics

Standard deviation842.42847
Coefficient of variation (CV)0.5787658
Kurtosis-0.33122042
Mean1455.5602
Median Absolute Deviation (MAD)0.0037
Skewness1.2511792
Sum98978.094
Variance709685.72
MonotonicityNot monotonic
2023-12-13T07:13:03.738993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
1000.47 33
38.8%
3001.4961 13
 
15.3%
1000.4737 5
 
5.9%
1000.5237 3
 
3.5%
1000.474 3
 
3.5%
1004.374 2
 
2.4%
942.4694 2
 
2.4%
2000.9974 2
 
2.4%
1000.524 1
 
1.2%
2810.307 1
 
1.2%
Other values (3) 3
 
3.5%
(Missing) 17
20.0%
ValueCountFrequency (%)
600.88 1
 
1.2%
630.44 1
 
1.2%
942.4694 2
 
2.4%
1000.47 33
38.8%
1000.4737 5
 
5.9%
1000.474 3
 
3.5%
1000.5237 3
 
3.5%
1000.524 1
 
1.2%
1004.374 2
 
2.4%
2000.9974 2
 
2.4%
ValueCountFrequency (%)
3001.4961 13
 
15.3%
2999.9403 1
 
1.2%
2810.307 1
 
1.2%
2000.9974 2
 
2.4%
1004.374 2
 
2.4%
1000.524 1
 
1.2%
1000.5237 3
 
3.5%
1000.474 3
 
3.5%
1000.4737 5
 
5.9%
1000.47 33
38.8%

속도
Categorical

Distinct3
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Memory size812.0 B
0.1
53 
<NA>
17 
0.15
15 

Length

Max length4
Median length3
Mean length3.3764706
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.1
2nd row0.1
3rd row0.1
4th row0.1
5th row0.1

Common Values

ValueCountFrequency (%)
0.1 53
62.4%
<NA> 17
 
20.0%
0.15 15
 
17.6%

Length

2023-12-13T07:13:03.910233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:13:04.010293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.1 53
62.4%
na 17
 
20.0%
0.15 15
 
17.6%

온도
Real number (ℝ)

MISSING  ZEROS 

Distinct36
Distinct (%)63.2%
Missing28
Missing (%)32.9%
Infinite0
Infinite (%)0.0%
Mean24.539789
Minimum0
Maximum29.4
Zeros1
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size897.0 B
2023-12-13T07:13:04.136243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile21.3
Q122.9
median24.77
Q327
95-th percentile28.276
Maximum29.4
Range29.4
Interquartile range (IQR)4.1

Descriptive statistics

Standard deviation4.0969817
Coefficient of variation (CV)0.1669526
Kurtosis22.669787
Mean24.539789
Median Absolute Deviation (MAD)2.23
Skewness-3.8828161
Sum1398.768
Variance16.785259
MonotonicityNot monotonic
2023-12-13T07:13:04.309420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
21.4 6
 
7.1%
22.9 3
 
3.5%
27.0 3
 
3.5%
27.7 2
 
2.4%
20.9 2
 
2.4%
23.0 2
 
2.4%
26.824 2
 
2.4%
26.0 2
 
2.4%
24.5 2
 
2.4%
25.4 2
 
2.4%
Other values (26) 31
36.5%
(Missing) 28
32.9%
ValueCountFrequency (%)
0.0 1
 
1.2%
20.9 2
 
2.4%
21.4 6
7.1%
22.1 2
 
2.4%
22.4 2
 
2.4%
22.9 3
3.5%
23.0 2
 
2.4%
23.1 1
 
1.2%
23.6 2
 
2.4%
24.1 1
 
1.2%
ValueCountFrequency (%)
29.4 1
1.2%
28.79 1
1.2%
28.3 1
1.2%
28.27 1
1.2%
28.12 1
1.2%
28.0 1
1.2%
27.95 1
1.2%
27.71 1
1.2%
27.7 2
2.4%
27.6 1
1.2%

습도
Real number (ℝ)

MISSING 

Distinct47
Distinct (%)70.1%
Missing18
Missing (%)21.2%
Infinite0
Infinite (%)0.0%
Mean34.92251
Minimum7.77
Maximum66.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size897.0 B
2023-12-13T07:13:04.478699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7.77
5-th percentile7.77
Q112.5455
median41.517
Q349.6625
95-th percentile57.284
Maximum66.9
Range59.13
Interquartile range (IQR)37.117

Descriptive statistics

Standard deviation18.585184
Coefficient of variation (CV)0.53218351
Kurtosis-1.4312136
Mean34.92251
Median Absolute Deviation (MAD)11.593
Skewness-0.31871049
Sum2339.8082
Variance345.40907
MonotonicityNot monotonic
2023-12-13T07:13:04.946646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
10.44 6
 
7.1%
7.77 5
 
5.9%
42.114 2
 
2.4%
12.04 2
 
2.4%
10.0426 2
 
2.4%
39.0 2
 
2.4%
66.9 2
 
2.4%
55.5 2
 
2.4%
57.5 2
 
2.4%
49.845 2
 
2.4%
Other values (37) 40
47.1%
(Missing) 18
21.2%
ValueCountFrequency (%)
7.77 5
5.9%
10.0426 2
 
2.4%
10.44 6
7.1%
11.1 1
 
1.2%
12.04 2
 
2.4%
12.5 1
 
1.2%
12.591 1
 
1.2%
13.4 1
 
1.2%
14.0 1
 
1.2%
14.4 1
 
1.2%
ValueCountFrequency (%)
66.9 2
2.4%
57.5 2
2.4%
56.78 1
1.2%
55.5 2
2.4%
55.26 1
1.2%
53.72 1
1.2%
53.11 1
1.2%
52.9 1
1.2%
52.08 1
1.2%
51.6 1
1.2%

하중
Categorical

Distinct3
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Memory size812.0 B
9.8
67 
<NA>
17 
10.0
 
1

Length

Max length4
Median length3
Mean length3.2117647
Min length3

Unique

Unique1 ?
Unique (%)1.2%

Sample

1st row9.8
2nd row9.8
3rd row9.8
4th row9.8
5th row9.8

Common Values

ValueCountFrequency (%)
9.8 67
78.8%
<NA> 17
 
20.0%
10.0 1
 
1.2%

Length

2023-12-13T07:13:05.106126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:13:05.231941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
9.8 67
78.8%
na 17
 
20.0%
10.0 1
 
1.2%

노트
Text

MISSING 

Distinct32
Distinct (%)60.4%
Missing32
Missing (%)37.6%
Memory size812.0 B
2023-12-13T07:13:05.520480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length426
Median length341
Mean length165.0566
Min length17

Characters and Unicode

Total characters8748
Distinct characters266
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26 ?
Unique (%)49.1%

Sample

1st rowAir atmosphere. 시간제약에 따른 sliding distance조정. 2차 Disc & ball 아세톤 cleaning & 110℃ dry 30min 시험 전 시편의 하단과 옆면을 다듬질 후 시험. 마모후 분말 수집됨. 마모 시험후 ball & disc를 아세톤 cleaning 및 110℃ dry 진행 .시편 비마모량 미측정
2nd rowAir atmosphere. 시간제약에 따른 sliding distance조정. 1차 Disc & ball 아세톤 cleaning & 110℃ dry 30min 시험 전 시편의 하단과 옆면을 다듬질 후 시험. 마모후 분말 수집됨. 마모 시험후 ball & disc를 아세톤 cleaning 및 110℃ dry 진행. 시편 비마모량 미측정
3rd rowAir atmosphere. 시간제약에 따른 sliding distance조정. 1차 Disc & ball 아세톤 cleaning & 110℃ dry 30min 시험 전 시편의 하단과 옆면을 다듬질 후 시험. 마모후 분말 수집됨. 마모 시험후 ball & disc를 아세톤 cleaning 및 110℃ dry 진행. 시편 비마모량 미측정
4th rowAir atmosphere. 시간제약에 따른 sliding distance조정. 1차 Disc & ball 아세톤 cleaning & 110℃ dry 30min
5th rowAir atmosphere. 시간제약에 따른 sliding distance조정. 1차 Disc & ball 아세톤 cleaning & 110℃ dry 30min
ValueCountFrequency (%)
60
 
3.8%
따른 52
 
3.3%
ball 38
 
2.4%
110℃ 36
 
2.3%
dry 36
 
2.3%
cleaning 36
 
2.3%
아세톤 36
 
2.3%
특이사항 35
 
2.2%
air 32
 
2.0%
atmosphere 32
 
2.0%
Other values (295) 1197
75.3%
2023-12-13T07:13:06.017264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1572
 
18.0%
_ 1147
 
13.1%
i 251
 
2.9%
a 188
 
2.1%
n 175
 
2.0%
e 168
 
1.9%
l 161
 
1.8%
s 160
 
1.8%
. 156
 
1.8%
이 150
 
1.7%
Other values (256) 4620
52.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3239
37.0%
Lowercase Letter 2081
23.8%
Space Separator 1572
18.0%
Connector Punctuation 1147
 
13.1%
Decimal Number 322
 
3.7%
Other Punctuation 216
 
2.5%
Uppercase Letter 132
 
1.5%
Other Symbol 36
 
0.4%
Math Symbol 3
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
이 150
 
4.6%
시 149
 
4.6%
험 98
 
3.0%
에 72
 
2.2%
정 70
 
2.2%
후 69
 
2.1%
가 61
 
1.9%
마 58
 
1.8%
실 58
 
1.8%
를 57
 
1.8%
Other values (208) 2397
74.0%
Lowercase Letter
ValueCountFrequency (%)
i 251
12.1%
a 188
 
9.0%
n 175
 
8.4%
e 168
 
8.1%
l 161
 
7.7%
s 160
 
7.7%
d 137
 
6.6%
m 129
 
6.2%
r 115
 
5.5%
c 108
 
5.2%
Other values (10) 489
23.5%
Uppercase Letter
ValueCountFrequency (%)
K 38
28.8%
A 32
24.2%
D 28
21.2%
N 19
14.4%
R 7
 
5.3%
L 2
 
1.5%
I 1
 
0.8%
W 1
 
0.8%
J 1
 
0.8%
G 1
 
0.8%
Other values (2) 2
 
1.5%
Decimal Number
ValueCountFrequency (%)
1 136
42.2%
0 90
28.0%
3 59
18.3%
2 27
 
8.4%
5 3
 
0.9%
4 2
 
0.6%
7 2
 
0.6%
6 2
 
0.6%
8 1
 
0.3%
Other Punctuation
ValueCountFrequency (%)
. 156
72.2%
& 59
 
27.3%
: 1
 
0.5%
Space Separator
ValueCountFrequency (%)
1572
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1147
100.0%
Other Symbol
ValueCountFrequency (%)
℃ 36
100.0%
Math Symbol
ValueCountFrequency (%)
> 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3296
37.7%
Hangul 3239
37.0%
Latin 2213
25.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
이 150
 
4.6%
시 149
 
4.6%
험 98
 
3.0%
에 72
 
2.2%
정 70
 
2.2%
후 69
 
2.1%
가 61
 
1.9%
마 58
 
1.8%
실 58
 
1.8%
를 57
 
1.8%
Other values (208) 2397
74.0%
Latin
ValueCountFrequency (%)
i 251
11.3%
a 188
 
8.5%
n 175
 
7.9%
e 168
 
7.6%
l 161
 
7.3%
s 160
 
7.2%
d 137
 
6.2%
m 129
 
5.8%
r 115
 
5.2%
c 108
 
4.9%
Other values (22) 621
28.1%
Common
ValueCountFrequency (%)
1572
47.7%
_ 1147
34.8%
. 156
 
4.7%
1 136
 
4.1%
0 90
 
2.7%
3 59
 
1.8%
& 59
 
1.8%
℃ 36
 
1.1%
2 27
 
0.8%
> 3
 
0.1%
Other values (6) 11
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5473
62.6%
Hangul 3239
37.0%
Letterlike Symbols 36
 
0.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1572
28.7%
_ 1147
21.0%
i 251
 
4.6%
a 188
 
3.4%
n 175
 
3.2%
e 168
 
3.1%
l 161
 
2.9%
s 160
 
2.9%
. 156
 
2.9%
d 137
 
2.5%
Other values (37) 1358
24.8%
Hangul
ValueCountFrequency (%)
이 150
 
4.6%
시 149
 
4.6%
험 98
 
3.0%
에 72
 
2.2%
정 70
 
2.2%
후 69
 
2.1%
가 61
 
1.9%
마 58
 
1.8%
실 58
 
1.8%
를 57
 
1.8%
Other values (208) 2397
74.0%
Letterlike Symbols
ValueCountFrequency (%)
℃ 36
100.0%

히트
Real number (ℝ)

MISSING 

Distinct17
Distinct (%)100.0%
Missing68
Missing (%)80.0%
Infinite0
Infinite (%)0.0%
Mean11329.175
Minimum3017.959
Maximum20567.328
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size897.0 B
2023-12-13T07:13:06.146235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3017.959
5-th percentile3131.1678
Q13656.528
median12290.079
Q318098.333
95-th percentile19829.946
Maximum20567.328
Range17549.369
Interquartile range (IQR)14441.805

Descriptive statistics

Standard deviation6600.7166
Coefficient of variation (CV)0.58262992
Kurtosis-1.6505195
Mean11329.175
Median Absolute Deviation (MAD)6288.847
Skewness-0.036356345
Sum192595.98
Variance43569459
MonotonicityNot monotonic
2023-12-13T07:13:06.259380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
6919.98 1
 
1.2%
3332.027 1
 
1.2%
3466.88 1
 
1.2%
3159.47 1
 
1.2%
3656.528 1
 
1.2%
3017.959 1
 
1.2%
18578.926 1
 
1.2%
18678.233 1
 
1.2%
7004.166667 1
 
1.2%
13177.149 1
 
1.2%
Other values (7) 7
 
8.2%
(Missing) 68
80.0%
ValueCountFrequency (%)
3017.959 1
1.2%
3159.47 1
1.2%
3332.027 1
1.2%
3466.88 1
1.2%
3656.528 1
1.2%
6919.98 1
1.2%
7004.166667 1
1.2%
12108.484 1
1.2%
12290.079 1
1.2%
12547.856 1
1.2%
ValueCountFrequency (%)
20567.328 1
1.2%
19645.6 1
1.2%
18678.233 1
1.2%
18578.926 1
1.2%
18098.3333 1
1.2%
16346.983 1
1.2%
13177.149 1
1.2%
12547.856 1
1.2%
12290.079 1
1.2%
12108.484 1
1.2%

전도도
Real number (ℝ)

MISSING 

Distinct17
Distinct (%)100.0%
Missing68
Missing (%)80.0%
Infinite0
Infinite (%)0.0%
Mean185.75449
Minimum64.9606
Maximum302.296
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size897.0 B
2023-12-13T07:13:06.378338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum64.9606
5-th percentile68.62236
Q1128.179
median197.731
Q3239.464
95-th percentile285.1208
Maximum302.296
Range237.3354
Interquartile range (IQR)111.285

Descriptive statistics

Standard deviation71.780606
Coefficient of variation (CV)0.38642729
Kurtosis-1.0072319
Mean185.75449
Median Absolute Deviation (MAD)65.845
Skewness-0.16040569
Sum3157.8264
Variance5152.4554
MonotonicityNot monotonic
2023-12-13T07:13:06.476834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
64.9606 1
 
1.2%
136.771 1
 
1.2%
128.179 1
 
1.2%
129.464 1
 
1.2%
127.898 1
 
1.2%
126.238 1
 
1.2%
243.081 1
 
1.2%
280.827 1
 
1.2%
69.5378 1
 
1.2%
212.321 1
 
1.2%
Other values (7) 7
 
8.2%
(Missing) 68
80.0%
ValueCountFrequency (%)
64.9606 1
1.2%
69.5378 1
1.2%
126.238 1
1.2%
127.898 1
1.2%
128.179 1
1.2%
129.464 1
1.2%
136.771 1
1.2%
188.29 1
1.2%
197.731 1
1.2%
212.321 1
1.2%
ValueCountFrequency (%)
302.296 1
1.2%
280.827 1
1.2%
263.576 1
1.2%
243.081 1
1.2%
239.464 1
1.2%
224.529 1
1.2%
222.663 1
1.2%
212.321 1
1.2%
197.731 1
1.2%
188.29 1
1.2%

이알
Real number (ℝ)

MISSING 

Distinct17
Distinct (%)100.0%
Missing68
Missing (%)80.0%
Infinite0
Infinite (%)0.0%
Mean169.87616
Minimum62.575
Maximum257.549
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size897.0 B
2023-12-13T07:13:06.580320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum62.575
5-th percentile66.638733
Q1125.579
median183.351
Q3213.549
95-th percentile246.9306
Maximum257.549
Range194.974
Interquartile range (IQR)87.97

Descriptive statistics

Standard deviation58.846547
Coefficient of variation (CV)0.34640851
Kurtosis-0.80783163
Mean169.87616
Median Absolute Deviation (MAD)50.252
Skewness-0.38451941
Sum2887.8947
Variance3462.9161
MonotonicityNot monotonic
2023-12-13T07:13:06.694239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
62.575 1
 
1.2%
133.099 1
 
1.2%
125.579 1
 
1.2%
126.863 1
 
1.2%
125.495 1
 
1.2%
123.541 1
 
1.2%
217.138 1
 
1.2%
244.276 1
 
1.2%
67.65466667 1
 
1.2%
194.548 1
 
1.2%
Other values (7) 7
 
8.2%
(Missing) 68
80.0%
ValueCountFrequency (%)
62.575 1
1.2%
67.65466667 1
1.2%
123.541 1
1.2%
125.495 1
1.2%
125.579 1
1.2%
126.863 1
1.2%
133.099 1
1.2%
175.88 1
1.2%
183.351 1
1.2%
194.548 1
1.2%
ValueCountFrequency (%)
257.549 1
1.2%
244.276 1
1.2%
231.922 1
1.2%
217.138 1
1.2%
213.549 1
1.2%
203.657 1
1.2%
201.218 1
1.2%
194.548 1
1.2%
183.351 1
1.2%
175.88 1
1.2%

경도
Real number (ℝ)

MISSING 

Distinct17
Distinct (%)100.0%
Missing68
Missing (%)80.0%
Infinite0
Infinite (%)0.0%
Mean1049.1768
Minimum279.496
Maximum1904.758
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size897.0 B
2023-12-13T07:13:06.812236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum279.496
5-th percentile289.98
Q1338.634
median1138.195
Q31676.089
95-th percentile1836.4684
Maximum1904.758
Range1625.262
Interquartile range (IQR)1337.455

Descriptive statistics

Standard deviation611.26518
Coefficient of variation (CV)0.5826141
Kurtosis-1.6504839
Mean1049.1768
Median Absolute Deviation (MAD)582.416
Skewness-0.036410398
Sum17836.005
Variance373645.12
MonotonicityNot monotonic
2023-12-13T07:13:06.920118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
640.864 1
 
1.2%
308.582 1
 
1.2%
321.071 1
 
1.2%
292.601 1
 
1.2%
338.634 1
 
1.2%
279.496 1
 
1.2%
1720.611 1
 
1.2%
1729.345 1
 
1.2%
648.662 1
 
1.2%
1220.347 1
 
1.2%
Other values (7) 7
 
8.2%
(Missing) 68
80.0%
ValueCountFrequency (%)
279.496 1
1.2%
292.601 1
1.2%
308.582 1
1.2%
321.071 1
1.2%
338.634 1
1.2%
640.864 1
1.2%
648.662 1
1.2%
1121.377 1
1.2%
1138.195 1
1.2%
1162.068 1
1.2%
ValueCountFrequency (%)
1904.758 1
1.2%
1819.396 1
1.2%
1729.345 1
1.2%
1720.611 1
1.2%
1676.089 1
1.2%
1513.909 1
1.2%
1220.347 1
1.2%
1162.068 1
1.2%
1138.195 1
1.2%
1121.377 1
1.2%

마모계수1
Text

MISSING 

Distinct27
Distinct (%)55.1%
Missing36
Missing (%)42.4%
Memory size812.0 B
2023-12-13T07:13:07.092403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length5
Mean length6.1428571
Min length5

Characters and Unicode

Total characters301
Distinct characters16
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19 ?
Unique (%)38.8%

Sample

1st row0.000458
2nd row0.000543
3rd row0.00000002
4th row0.00000186
5th row3E-11
ValueCountFrequency (%)
2e-14 9
18.4%
1e-14 8
16.3%
1e-16 3
 
6.1%
0.00000002 2
 
4.1%
2e-15 2
 
4.1%
1e-11 2
 
4.1%
5e-13 2
 
4.1%
0.000458 2
 
4.1%
9e-15 1
 
2.0%
8e-15 1
 
2.0%
Other values (17) 17
34.7%
2023-12-13T07:13:07.434576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 71
23.6%
1 60
19.9%
E 37
12.3%
- 37
12.3%
4 24
 
8.0%
2 17
 
5.6%
5 15
 
5.0%
6 12
 
4.0%
. 12
 
4.0%
8 6
 
2.0%
Other values (6) 10
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 212
70.4%
Uppercase Letter 37
 
12.3%
Dash Punctuation 37
 
12.3%
Other Punctuation 13
 
4.3%
Modifier Symbol 1
 
0.3%
Connector Punctuation 1
 
0.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 71
33.5%
1 60
28.3%
4 24
 
11.3%
2 17
 
8.0%
5 15
 
7.1%
6 12
 
5.7%
8 6
 
2.8%
3 4
 
1.9%
7 2
 
0.9%
9 1
 
0.5%
Other Punctuation
ValueCountFrequency (%)
. 12
92.3%
* 1
 
7.7%
Uppercase Letter
ValueCountFrequency (%)
E 37
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 37
100.0%
Modifier Symbol
ValueCountFrequency (%)
^ 1
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 264
87.7%
Latin 37
 
12.3%

Most frequent character per script

Common
ValueCountFrequency (%)
0 71
26.9%
1 60
22.7%
- 37
14.0%
4 24
 
9.1%
2 17
 
6.4%
5 15
 
5.7%
6 12
 
4.5%
. 12
 
4.5%
8 6
 
2.3%
3 4
 
1.5%
Other values (5) 6
 
2.3%
Latin
ValueCountFrequency (%)
E 37
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 301
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 71
23.6%
1 60
19.9%
E 37
12.3%
- 37
12.3%
4 24
 
8.0%
2 17
 
5.6%
5 15
 
5.0%
6 12
 
4.0%
. 12
 
4.0%
8 6
 
2.0%
Other values (6) 10
 
3.3%

마모계수2
Categorical

Distinct14
Distinct (%)16.5%
Missing0
Missing (%)0.0%
Memory size812.0 B
<NA>
56 
7E-14
6 
0
 
4
1E-11
 
3
0.00002
 
3
Other values (9)
13 

Length

Max length14
Median length4
Mean length4.3764706
Min length1

Unique

Unique5 ?
Unique (%)5.9%

Sample

1st row1E-11
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row0.00002

Common Values

ValueCountFrequency (%)
<NA> 56
65.9%
7E-14 6
 
7.1%
0 4
 
4.7%
1E-11 3
 
3.5%
0.00002 3
 
3.5%
8E-14 2
 
2.4%
4E-14 2
 
2.4%
3E-14 2
 
2.4%
2E-14 2
 
2.4%
5E-16 1
 
1.2%
Other values (4) 4
 
4.7%

Length

2023-12-13T07:13:07.588507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 56
65.9%
7e-14 6
 
7.1%
0 4
 
4.7%
1e-11 3
 
3.5%
0.00002 3
 
3.5%
8e-14 2
 
2.4%
4e-14 2
 
2.4%
3e-14 2
 
2.4%
2e-14 2
 
2.4%
5e-16 1
 
1.2%
Other values (4) 4
 
4.7%

마모도1
Text

MISSING 

Distinct12
Distinct (%)60.0%
Missing65
Missing (%)76.5%
Memory size812.0 B
2023-12-13T07:13:07.766488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length27
Median length5
Mean length5.9
Min length1

Characters and Unicode

Total characters118
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

Unique7 ?
Unique (%)35.0%

Sample

1st row3E-16
2nd row1E-16
3rd row1E-16
4th row5E-19
5th row6E-19
ValueCountFrequency (%)
5e-19 4
20.0%
4e-19 3
15.0%
1e-16 2
10.0%
6e-19 2
10.0%
3e-19 2
10.0%
3e-16 1
 
5.0%
2e-19 1
 
5.0%
4e-17 1
 
5.0%
2e-20 1
 
5.0%
7e-19 1
 
5.0%
Other values (2) 2
10.0%
2023-12-13T07:13:08.079132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 21
17.8%
E 18
15.3%
- 18
15.3%
9 14
11.9%
4 10
8.5%
3 7
 
5.9%
5 5
 
4.2%
6 5
 
4.2%
2 5
 
4.2%
7 4
 
3.4%
Other values (4) 11
9.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 75
63.6%
Uppercase Letter 18
 
15.3%
Dash Punctuation 18
 
15.3%
Other Punctuation 7
 
5.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 21
28.0%
9 14
18.7%
4 10
13.3%
3 7
 
9.3%
5 5
 
6.7%
6 5
 
6.7%
2 5
 
6.7%
7 4
 
5.3%
0 3
 
4.0%
8 1
 
1.3%
Other Punctuation
ValueCountFrequency (%)
. 4
57.1%
/ 3
42.9%
Uppercase Letter
ValueCountFrequency (%)
E 18
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 18
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 100
84.7%
Latin 18
 
15.3%

Most frequent character per script

Common
ValueCountFrequency (%)
1 21
21.0%
- 18
18.0%
9 14
14.0%
4 10
10.0%
3 7
 
7.0%
5 5
 
5.0%
6 5
 
5.0%
2 5
 
5.0%
7 4
 
4.0%
. 4
 
4.0%
Other values (3) 7
 
7.0%
Latin
ValueCountFrequency (%)
E 18
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 118
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 21
17.8%
E 18
15.3%
- 18
15.3%
9 14
11.9%
4 10
8.5%
3 7
 
5.9%
5 5
 
4.2%
6 5
 
4.2%
2 5
 
4.2%
7 4
 
3.4%
Other values (4) 11
9.3%

마모도2
Categorical

IMBALANCE 

Distinct9
Distinct (%)10.6%
Missing0
Missing (%)0.0%
Memory size812.0 B
<NA>
65 
2E-18
8 
0
 
5
1E-18
 
2
9E-19
 
1
Other values (4)
 
4

Length

Max length13
Median length4
Mean length4.0941176
Min length1

Unique

Unique5 ?
Unique (%)5.9%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 65
76.5%
2E-18 8
 
9.4%
0 5
 
5.9%
1E-18 2
 
2.4%
9E-19 1
 
1.2%
3E-18 1
 
1.2%
8E-19 1
 
1.2%
5E-10 1
 
1.2%
3.3883/3.3883 1
 
1.2%

Length

2023-12-13T07:13:08.235584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:13:08.390995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 65
76.5%
2e-18 8
 
9.4%
0 5
 
5.9%
1e-18 2
 
2.4%
9e-19 1
 
1.2%
3e-18 1
 
1.2%
8e-19 1
 
1.2%
5e-10 1
 
1.2%
3.3883/3.3883 1
 
1.2%

매스1
Text

MISSING 

Distinct23
Distinct (%)100.0%
Missing62
Missing (%)72.9%
Memory size812.0 B
2023-12-13T07:13:08.598308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length16
Mean length15.478261
Min length13

Characters and Unicode

Total characters356
Distinct characters13
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

Unique23 ?
Unique (%)100.0%

Sample

1st row12.686/12.686
2nd row6.0574/5.09565
3rd row4.8379/4.48715
4th row18.0893/18.0892
5th row5.8233 / 5.57135
ValueCountFrequency (%)
7
 
17.9%
12.686/12.686 1
 
2.6%
27.6574 1
 
2.6%
26.9621/26.9598 1
 
2.6%
25.4384/25.2861 1
 
2.6%
29.3390/29.3348 1
 
2.6%
26.6379 1
 
2.6%
26.6345 1
 
2.6%
27.6596 1
 
2.6%
27.2613/27.2600 1
 
2.6%
Other values (23) 23
59.0%
2023-12-13T07:13:08.955630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 52
14.6%
. 46
12.9%
6 34
9.6%
8 34
9.6%
7 31
8.7%
9 25
7.0%
5 24
6.7%
/ 23
6.5%
3 20
 
5.6%
1 18
 
5.1%
Other values (3) 49
13.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 271
76.1%
Other Punctuation 69
 
19.4%
Space Separator 16
 
4.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 52
19.2%
6 34
12.5%
8 34
12.5%
7 31
11.4%
9 25
9.2%
5 24
8.9%
3 20
 
7.4%
1 18
 
6.6%
4 18
 
6.6%
0 15
 
5.5%
Other Punctuation
ValueCountFrequency (%)
. 46
66.7%
/ 23
33.3%
Space Separator
ValueCountFrequency (%)
16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 356
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 52
14.6%
. 46
12.9%
6 34
9.6%
8 34
9.6%
7 31
8.7%
9 25
7.0%
5 24
6.7%
/ 23
6.5%
3 20
 
5.6%
1 18
 
5.1%
Other values (3) 49
13.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 356
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 52
14.6%
. 46
12.9%
6 34
9.6%
8 34
9.6%
7 31
8.7%
9 25
7.0%
5 24
6.7%
/ 23
6.5%
3 20
 
5.6%
1 18
 
5.1%
Other values (3) 49
13.8%

매스2
Text

MISSING 

Distinct23
Distinct (%)100.0%
Missing62
Missing (%)72.9%
Memory size812.0 B
2023-12-13T07:13:09.165338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length15
Mean length14
Min length13

Characters and Unicode

Total characters322
Distinct characters13
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

Unique23 ?
Unique (%)100.0%

Sample

1st row8.3497/8.3488
2nd row3.37105/3.3711
3rd row3.3757/3.3756
4th row8.3498/8.3497
5th row3.3695 / 3.37005
ValueCountFrequency (%)
9
 
22.0%
8.3497/8.3488 1
 
2.4%
1.4709 1
 
2.4%
1.4642/1.45805 1
 
2.4%
1.4703/1.4647 1
 
2.4%
1.2508/1.2406 1
 
2.4%
1.3094 1
 
2.4%
1.3036 1
 
2.4%
1.4761 1
 
2.4%
1.4729/1.4706 1
 
2.4%
Other values (23) 23
56.1%
2023-12-13T07:13:09.469198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 48
14.9%
. 46
14.3%
3 36
11.2%
4 28
8.7%
5 24
7.5%
/ 23
7.1%
0 21
6.5%
7 20
6.2%
8 19
 
5.9%
6 18
 
5.6%
Other values (3) 39
12.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 235
73.0%
Other Punctuation 69
 
21.4%
Space Separator 18
 
5.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 48
20.4%
3 36
15.3%
4 28
11.9%
5 24
10.2%
0 21
8.9%
7 20
8.5%
8 19
 
8.1%
6 18
 
7.7%
9 13
 
5.5%
2 8
 
3.4%
Other Punctuation
ValueCountFrequency (%)
. 46
66.7%
/ 23
33.3%
Space Separator
ValueCountFrequency (%)
18
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 322
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 48
14.9%
. 46
14.3%
3 36
11.2%
4 28
8.7%
5 24
7.5%
/ 23
7.1%
0 21
6.5%
7 20
6.2%
8 19
 
5.9%
6 18
 
5.6%
Other values (3) 39
12.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 322
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 48
14.9%
. 46
14.3%
3 36
11.2%
4 28
8.7%
5 24
7.5%
/ 23
7.1%
0 21
6.5%
7 20
6.2%
8 19
 
5.9%
6 18
 
5.6%
Other values (3) 39
12.1%

생성일
Categorical

Distinct17
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Memory size812.0 B
2011-08-05
13 
2011-09-22
10 
2011-08-04
10 
2011-08-08
10 
2011-09-20
10 
Other values (12)
32 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique4 ?
Unique (%)4.7%

Sample

1st row2011-08-05
2nd row2011-08-05
3rd row2011-08-05
4th row2011-08-08
5th row2011-08-08

Common Values

ValueCountFrequency (%)
2011-08-05 13
15.3%
2011-09-22 10
11.8%
2011-08-04 10
11.8%
2011-08-08 10
11.8%
2011-09-20 10
11.8%
2011-08-16 7
8.2%
2011-07-14 5
 
5.9%
2011-08-02 4
 
4.7%
2011-08-17 4
 
4.7%
2012-02-06 2
 
2.4%
Other values (7) 10
11.8%

Length

2023-12-13T07:13:09.609223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2011-08-05 13
15.3%
2011-09-22 10
11.8%
2011-08-04 10
11.8%
2011-08-08 10
11.8%
2011-09-20 10
11.8%
2011-08-16 7
8.2%
2011-07-14 5
 
5.9%
2011-08-17 4
 
4.7%
2011-08-02 4
 
4.7%
2012-02-06 2
 
2.4%
Other values (7) 10
11.8%

수정일
Categorical

IMBALANCE 

Distinct11
Distinct (%)12.9%
Missing0
Missing (%)0.0%
Memory size812.0 B
<NA>
63 
2011-07-14
 
5
2011-08-02
 
3
2011-09-20
 
3
2011-08-08
 
2
Other values (6)
9 

Length

Max length10
Median length4
Mean length5.5529412
Min length4

Unique

Unique3 ?
Unique (%)3.5%

Sample

1st row<NA>
2nd row<NA>
3rd row2011-08-05
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 63
74.1%
2011-07-14 5
 
5.9%
2011-08-02 3
 
3.5%
2011-09-20 3
 
3.5%
2011-08-08 2
 
2.4%
2012-01-30 2
 
2.4%
2012-01-31 2
 
2.4%
2012-02-06 2
 
2.4%
2011-08-05 1
 
1.2%
2011-08-16 1
 
1.2%

Length

2023-12-13T07:13:09.721044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 63
74.1%
2011-07-14 5
 
5.9%
2011-08-02 3
 
3.5%
2011-09-20 3
 
3.5%
2011-08-08 2
 
2.4%
2012-01-30 2
 
2.4%
2012-01-31 2
 
2.4%
2012-02-06 2
 
2.4%
2011-08-05 1
 
1.2%
2011-08-16 1
 
1.2%

Sample

순번시료명실험일구분견본타입1견본타입2시험방법크기1크기2표면거침1표면거침2팁스펙1스펙2획득률최대하중재하율재재하율밀림거리속도온도습도하중노트히트전도도이알경도마모계수1마모계수2마모도1마모도2매스1매스2생성일수정일
0E00036CMN #282010-09-102CMN #28 diskSuj2 ballKS L 1606 _수정적용20*20*312.7<0.1<0.1<NA>Cr_Mo_N 3um coated/Fe base sample. PVD coatingBall type. SUS<NA><NA><NA><NA>1000.470.127.9555.269.8Air atmosphere. 시간제약에 따른 sliding distance조정. 2차 Disc & ball 아세톤 cleaning & 110℃ dry 30min 시험 전 시편의 하단과 옆면을 다듬질 후 시험. 마모후 분말 수집됨. 마모 시험후 ball & disc를 아세톤 cleaning 및 110℃ dry 진행 .시편 비마모량 미측정<NA><NA><NA><NA><NA>1E-11<NA><NA>12.686/12.6868.3497/8.34882011-08-05<NA>
1E00037CMN #292010-10-042CMN #29 diskSuj2 ballKS L 1606 _수정적용20*20*312.7<0.1<0.1<NA>Cr_Mo_N 3um coated/Fe base sample. PVD coatingBall type. SUS<NA><NA><NA><NA>1000.470.124.5949.929.8Air atmosphere. 시간제약에 따른 sliding distance조정. 1차 Disc & ball 아세톤 cleaning & 110℃ dry 30min 시험 전 시편의 하단과 옆면을 다듬질 후 시험. 마모후 분말 수집됨. 마모 시험후 ball & disc를 아세톤 cleaning 및 110℃ dry 진행. 시편 비마모량 미측정<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>2011-08-05<NA>
2E00040CMN #312010-05-052diskSuj2 ballKS L 1606 _수정적용20*20*312.7<0.1<0.1<NA>Cr_Mo_N 3um coated/Fe base sample. PVD coatingBall type. SUS<NA><NA><NA><NA>1000.470.124.6348.679.8Air atmosphere. 시간제약에 따른 sliding distance조정. 1차 Disc & ball 아세톤 cleaning & 110℃ dry 30min 시험 전 시편의 하단과 옆면을 다듬질 후 시험. 마모후 분말 수집됨. 마모 시험후 ball & disc를 아세톤 cleaning 및 110℃ dry 진행. 시편 비마모량 미측정<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>2011-08-052011-08-05
3E00043SiTiN #112010-03-112diskballKS L 1606 _수정적용20*20*312.7<NA><0.1<NA>SiTiN 700nm coated/Fe substrate/PVDBall type/초경/Suj2<NA><NA><NA><NA>1000.470.121.410.449.8Air atmosphere. 시간제약에 따른 sliding distance조정. 1차 Disc & ball 아세톤 cleaning & 110℃ dry 30min<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>2011-08-08<NA>
4E00044CrN #92010-04-142diskballKS L 1606 _수정적용20*20*312.7<NA><0.1<NA>CrN 1.6μm coated/Fe substrate/PVDBall type/초경/Suj2<NA><NA><NA><NA>1000.470.121.410.449.8Air atmosphere. 시간제약에 따른 sliding distance조정. 1차 Disc & ball 아세톤 cleaning & 110℃ dry 30min<NA><NA><NA><NA>0.0004580.00002<NA><NA><NA><NA>2011-08-08<NA>
5E00045CrN #162010-04-142diskballKS L 1606 _수정적용20*20*312.7<NA><0.1<NA>CrN 2μm coated/Fe substrate/PVDBall type/초경/Suj2<NA><NA><NA><NA>1000.470.121.410.449.8Air atmosphere. 시간제약에 따른 sliding distance조정. 1차 Disc & ball 아세톤 cleaning & 110℃ dry 30min<NA><NA><NA><NA>0.0005430.00002<NA><NA><NA><NA>2011-08-08<NA>
6E00048TiN C2010-05-252diskballKS L 1606 _수정적용20*20*312.7<NA><0.1<NA>TiN 300nm coated/Fe substrate/PVDBall type/초경/Suj2<NA><NA><NA><NA>1000.470.1<NA>42.39.8Air atmosphere. 시간제약에 따른 sliding distance조정. 1차 Disc & ball 아세톤 cleaning & 110℃ dry 30min<NA><NA><NA><NA>0.00000002<NA><NA><NA><NA><NA>2011-08-08<NA>
7E00049TiN D2010-05-252diskballKS L 1606 _수정적용20*20*312.7<NA><0.1<NA>TiN 300nm coated/Fe substrate/PVDBall type/초경/Suj2<NA><NA><NA><NA>1000.470.126.538.69.8Air atmosphere. 시간제약에 따른 sliding distance조정. 1차 Disc & ball 아세톤 cleaning & 110℃ dry 30min<NA><NA><NA><NA>0.00000186<NA><NA><NA><NA><NA>2011-08-082011-08-08
8E00053ZrO2/BN pore sintered ceramic & SiC ball 1set2011-03-112ZrO2/BN poresquare diskSiC ballKSL 1606:2003W_19.975*L_20.115*T_4.99dia_12.70.576<NA><NA>Pore sintered sample이라 가공후 표면 거칠기 spec out구조용 bulk ZrO2/BN pore sampleSiC 12.7 dia ball 임. 표면은 거울면임<NA><NA><NA><NA>1004.3740.124.413.49.8_일부 수정. 마모량 증가에 따른 섭동거리 감소 1Kg하중이며 이를 10N으로 계산함. 섭동거리를 1Km으로 근사함 온 습도는 평균값임 .________________실험시작시 특이사항________________ 마모가 상당히 진행이 되어 실험 거리를 2km__>1km로 변경 실험시작시 weight bar가 살짝 덜컹거리면서 data도 같이 튀었음 실험 종료시까지 거의 동일하게 진행이됨 ________________실험종료후 특이사항________________ 실험후 ball에 길게 긇힌 흔적이 있었음 마모량이 커서 probe식 depth측정 불가 마모가 심하여 depth를 3D광학 현미경으로 관찰<NA><NA><NA><NA>3E-1103E-1606.0574/5.095653.37105/3.37112011-08-162011-08-16
9E00054SiAlON/BN pore sintered ceramic & SiC ball 1set2011-03-152SiAlON/BN poresquare diskSiC ballKSL 1606:2003W_20.02*L_20.04*T_5dia_12.70.613<NA><NA>Pore sintered sample이라 가공후 표면 거칠기 spec out구조용 bulk SiAlON/BN pore sampleSiC 12.7 dia ball 임. 표면은 거울면임<NA><NA><NA><NA>1004.3740.126.211.19.8_일부 수정. 마모량 증가에 따른 섭동거리 감소 1Kg하중이며 이를 10N으로 계산함. 섭동거리를 1Km으로 근사함 온 습도는 평균값임 _______________실험시작시 특이사항________________ 마모가 상당히 진행이 되어 실험 거리를 2km__>1km로 변경 weight bar가 튀는 현상이 발생함. 그러면서 data도 같이 튀는 현상이 발생 실험 종료시까지 거의 동일하게 진행이됨 ________________실험종료후 특이사항________________ 실험후 ball쪽에 길게 긇힌 흔적이 있음 disk 시편이 직광에 sample형상이 올바르게 보이지 않음 전자식 형상 측정기 사용 불가. 마모량이 커서 probe식 depth측정 불가. 마모가 심하여 depth를 3D광학 현미경으로 관찰<NA><NA><NA><NA>1E-1101E-1604.8379/4.487153.3757/3.37562011-08-16<NA>
순번시료명실험일구분견본타입1견본타입2시험방법크기1크기2표면거침1표면거침2팁스펙1스펙2획득률최대하중재하율재재하율밀림거리속도온도습도하중노트히트전도도이알경도마모계수1마모계수2마모도1마모도2매스1매스2생성일수정일
75E00084nACo_under2011-06-101Disk type<NA>Nano indentation_ Berkovich tip. oliver&pharr method<NA><NA><NA><NA>Berkovich type. area function Ap = 24.5h^2+C1h+C2h^1/2+C2h^1/4+C3h^1/8+C4h^1/16 C1= _9.02E004. C2=2.89E007 . C3=_9.23E008 . C4= 6.58E009<NA><NA>1051010<NA><NA><NA><NA><NA><NA>18678.233280.827244.2761729.345<NA><NA><NA><NA><NA><NA>2011-09-22<NA>
76E00085nACo_upper2011-06-101Disk type<NA>Nano indentation_ Berkovich tip. oliver&pharr method<NA><NA><NA><NA>Berkovich type. area function Ap = 24.5h^2+C1h+C2h^1/2+C2h^1/4+C3h^1/8+C4h^1/16 C1= _9.02E004. C2=2.89E007 . C3=_9.23E008 . C4= 6.58E009<NA><NA>1051010<NA><NA><NA><NA><NA><NA>18578.926243.081217.1381720.611<NA><NA><NA><NA><NA><NA>2011-09-22<NA>
77E00069SiTiN #112010-03-112DISK초경BALLKS L 1606 수정적용20*20*312.7<NA><NA><NA>SiTiN#11SUJ2<NA><NA><NA><NA>1000.470.121.410.449.8시간 제약에 따른 섭동거리 조정<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>2011-09-202011-09-20
78E00070CrN#92010-04-142DISK초경BALLKS L 160620*20*312.7<NA><NA><NA>METAL COATED SAMPLESUJ2<NA><NA><NA><NA>1000.470.121.410.449.8Air atmosphere . 시간제약에 따른 sliding distance조정<NA><NA><NA><NA>5E-132E-14<NA><NA><NA><NA>2011-09-202011-09-20
79EQP_0000000094TiO2 300℃2012-01-311disk type<NA>Nano Indentation _Berkovich tip. Oliver&Pharr method<NA><NA><NA><NA>Berkovich type. area function Ap = 24.5h^2+C1h+C2h^1/2+C2h^1/4+C3h^1/8+C4h^1/16 C1= _9.02E004. C2=2.89E007 . C3=_9.23E008 . C4= 6.58E009<NA><NA>1051010<NA><NA><NA><NA><NA><NA>3017.959126.238123.541279.496<NA><NA><NA><NA><NA><NA>2012-01-312012-01-31
80EQP_0000000096TiO2 400℃2012-01-311DISK TYPE<NA>Nano indentation _ Berkovich tip. Oliver&Pharr method<NA><NA><NA><NA>Berkovich type. area function Ap = 24.5h^2+C1h+C2h^1/2+C2h^1/4+C3h^1/8+C4h^1/16 C1= _9.02E004. C2=2.89E007 . C3=_9.23E008 . C4= 6.58E009<NA><NA>1051010<NA><NA><NA><NA><NA><NA>3656.528127.898125.495338.634<NA><NA><NA><NA><NA><NA>2012-02-062012-02-06
81EQP_0000000097nACro Disk & 7075 Bulk Pin2012-02-132가스질화된 std61 square diskalbar_round tipKSL 1606;200331.41*31.93*3.525.00*28.08*60.19<NA><NA>nacro 마모시편70 5 bulk pin<NA><NA><NA><NA>2000.99740.1524.412.5919.8<NA><NA><NA><NA><NA>6E-127E-144.3311/4.4459/2.7344/3.08273.3883/3.388326.9374/26.44801.5118/1.50882012-03-262012-03-26
82EQP_0000000092Ti2012-01-301Disk type<NA>Nano indentation_Berkovich tip. oliver&pharr method<NA><NA><NA><NA>Berkovich type. area function Ap = 24.5h^2+C1h+C2h^1/2+C2h^1/4+C3h^1/8+C4h^1/16 C1= _9.02E004. C2=2.89E007 . C3=_9.23E008 . C4= 6.58E009<NA><NA>1050100100<NA><NA><NA><NA><NA><NA>3159.47129.464126.863292.601<NA><NA><NA><NA><NA><NA>2012-01-302012-01-30
83EQP_0000000093TiO2 300℃2012-01-311DISK TYPE<NA>Nano indentation _ Berkovich tip. Oliver&Pharr method<NA><NA><NA><NA>Berkovich type. area function Ap = 24.5h^2+C1h+C2h^1/2+C2h^1/4+C3h^1/8+C4h^1/16 C1= _9.02E004. C2=2.89E007 . C3=_9.23E008 . C4= 6.58E009<NA><NA>1050100100<NA><NA><NA><NA><NA><NA>3466.88128.179125.579321.071<NA><NA><NA><NA><NA><NA>2012-01-312012-01-31
84EQP_0000000095TiO2 400℃2012-01-311DISK TYPE<NA>Nano indentation _ Berkovich tip. Oliver & Pharr method<NA><NA><NA><NA>Berkovich type. area function Ap = 24.5h^2+C1h+C2h^1/2+C2h^1/4+C3h^1/8+C4h^1/16 C1= _9.02E004. C2=2.89E007. =_9.23E008 . C4= 6.58E009<NA><NA>1050100100<NA><NA><NA><NA><NA><NA>3332.027136.771133.099308.582<NA><NA><NA><NA><NA><NA>2012-02-062012-02-06