Overview

Dataset statistics

Number of variables3
Number of observations10000
Missing cells78
Missing cells (%)0.3%
Duplicate rows1
Duplicate rows (%)< 0.1%
Total size in memory322.3 KiB
Average record size in memory33.0 B

Variable types

Numeric1
Text2

Dataset

Description기술원에서 제공하는 환경 및 무역 관련 용어명과 그 해설을 제공(번호, 용어의 명칭, 명칭에 대한 해설과 정의, 사례 등)
URLhttps://www.data.go.kr/data/15052612/fileData.do

Alerts

Dataset has 1 (< 0.1%) duplicate rowsDuplicates

Reproduction

Analysis started2023-12-12 06:37:26.900686
Analysis finished2023-12-12 06:37:30.105950
Duration3.21 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

번호
Real number (ℝ)

Distinct9980
Distinct (%)100.0%
Missing20
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean15535.385
Minimum2
Maximum31119
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T15:37:30.195432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile1566.85
Q17651
median15478.5
Q323395.25
95-th percentile29542.2
Maximum31119
Range31117
Interquartile range (IQR)15744.25

Descriptive statistics

Standard deviation8991.3562
Coefficient of variation (CV)0.57876623
Kurtosis-1.2086787
Mean15535.385
Median Absolute Deviation (MAD)7877
Skewness0.0059001373
Sum1.5504314 × 108
Variance80844487
MonotonicityNot monotonic
2023-12-12T15:37:30.363515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15110 1
 
< 0.1%
24344 1
 
< 0.1%
28898 1
 
< 0.1%
29560 1
 
< 0.1%
18115 1
 
< 0.1%
15662 1
 
< 0.1%
6342 1
 
< 0.1%
19780 1
 
< 0.1%
15261 1
 
< 0.1%
16597 1
 
< 0.1%
Other values (9970) 9970
99.7%
(Missing) 20
 
0.2%
ValueCountFrequency (%)
2 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
13 1
< 0.1%
14 1
< 0.1%
17 1
< 0.1%
18 1
< 0.1%
19 1
< 0.1%
21 1
< 0.1%
23 1
< 0.1%
ValueCountFrequency (%)
31119 1
< 0.1%
31116 1
< 0.1%
31110 1
< 0.1%
31109 1
< 0.1%
31108 1
< 0.1%
31096 1
< 0.1%
31092 1
< 0.1%
31091 1
< 0.1%
31084 1
< 0.1%
31081 1
< 0.1%

용어
Text

Distinct9951
Distinct (%)99.6%
Missing13
Missing (%)0.1%
Memory size156.2 KiB
2023-12-12T15:37:30.690328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length1024
Median length97
Mean length18.027436
Min length2

Characters and Unicode

Total characters180040
Distinct characters1792
Distinct categories15 ?
Distinct scripts5 ?
Distinct blocks12 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9915 ?
Unique (%)99.3%

Sample

1st rowliquid cooling
2nd rowresolution of a crystal structure
3rd rownuclear magneton
4th row자동배수탱크[自動排水탱크, dosing tank]
5th rowBecquerel rays
ValueCountFrequency (%)
386
 
1.6%
of 212
 
0.9%
국명 120
 
0.5%
water 111
 
0.5%
and 83
 
0.3%
method 74
 
0.3%
system 74
 
0.3%
gas 73
 
0.3%
filter 71
 
0.3%
air 61
 
0.2%
Other values (13007) 23377
94.9%
2023-12-12T15:37:31.204314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
14943
 
8.3%
e 11612
 
6.4%
i 9239
 
5.1%
a 8449
 
4.7%
t 8403
 
4.7%
o 8167
 
4.5%
r 8114
 
4.5%
n 7746
 
4.3%
l 5524
 
3.1%
s 5279
 
2.9%
Other values (1782) 92564
51.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 102313
56.8%
Other Letter 31647
 
17.6%
Uppercase Letter 21136
 
11.7%
Space Separator 14960
 
8.3%
Open Punctuation 3153
 
1.8%
Close Punctuation 3140
 
1.7%
Other Punctuation 1982
 
1.1%
Dash Punctuation 1266
 
0.7%
Decimal Number 412
 
0.2%
Math Symbol 11
 
< 0.1%
Other values (5) 20
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
538
 
1.7%
470
 
1.5%
433
 
1.4%
368
 
1.2%
360
 
1.1%
344
 
1.1%
331
 
1.0%
325
 
1.0%
323
 
1.0%
314
 
1.0%
Other values (1679) 27841
88.0%
Lowercase Letter
ValueCountFrequency (%)
e 11612
11.3%
i 9239
 
9.0%
a 8449
 
8.3%
t 8403
 
8.2%
o 8167
 
8.0%
r 8114
 
7.9%
n 7746
 
7.6%
l 5524
 
5.4%
s 5279
 
5.2%
c 5250
 
5.1%
Other values (18) 24530
24.0%
Uppercase Letter
ValueCountFrequency (%)
E 2182
 
10.3%
A 1739
 
8.2%
I 1576
 
7.5%
T 1554
 
7.4%
R 1497
 
7.1%
C 1466
 
6.9%
S 1460
 
6.9%
O 1323
 
6.3%
N 1232
 
5.8%
L 1002
 
4.7%
Other values (16) 6105
28.9%
Other Punctuation
ValueCountFrequency (%)
, 1476
74.5%
: 158
 
8.0%
· 84
 
4.2%
. 84
 
4.2%
' 66
 
3.3%
/ 42
 
2.1%
? 30
 
1.5%
; 11
 
0.6%
% 8
 
0.4%
7
 
0.4%
Other values (4) 16
 
0.8%
Decimal Number
ValueCountFrequency (%)
0 89
21.6%
1 84
20.4%
2 63
15.3%
3 45
10.9%
4 33
 
8.0%
5 32
 
7.8%
9 23
 
5.6%
8 20
 
4.9%
7 14
 
3.4%
6 9
 
2.2%
Open Punctuation
ValueCountFrequency (%)
[ 1930
61.2%
( 1167
37.0%
55
 
1.7%
{ 1
 
< 0.1%
Close Punctuation
ValueCountFrequency (%)
] 1927
61.4%
) 1159
36.9%
53
 
1.7%
} 1
 
< 0.1%
Math Symbol
ValueCountFrequency (%)
= 4
36.4%
~ 3
27.3%
2
18.2%
2
18.2%
Letter Number
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%
Space Separator
ValueCountFrequency (%)
14943
99.9%
  17
 
0.1%
Dash Punctuation
ValueCountFrequency (%)
- 1263
99.8%
3
 
0.2%
Other Symbol
ValueCountFrequency (%)
8
88.9%
1
 
11.1%
Other Number
ValueCountFrequency (%)
² 2
50.0%
2
50.0%
Final Punctuation
ValueCountFrequency (%)
3
100.0%
Modifier Symbol
ValueCountFrequency (%)
´ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 123447
68.6%
Common 24941
 
13.9%
Hangul 24636
 
13.7%
Han 7011
 
3.9%
Greek 5
 
< 0.1%

Most frequent character per script

Han
ValueCountFrequency (%)
178
 
2.5%
156
 
2.2%
151
 
2.2%
121
 
1.7%
95
 
1.4%
93
 
1.3%
86
 
1.2%
73
 
1.0%
70
 
1.0%
67
 
1.0%
Other values (921) 5921
84.5%
Hangul
ValueCountFrequency (%)
538
 
2.2%
470
 
1.9%
433
 
1.8%
368
 
1.5%
360
 
1.5%
344
 
1.4%
331
 
1.3%
325
 
1.3%
323
 
1.3%
314
 
1.3%
Other values (748) 20830
84.6%
Latin
ValueCountFrequency (%)
e 11612
 
9.4%
i 9239
 
7.5%
a 8449
 
6.8%
t 8403
 
6.8%
o 8167
 
6.6%
r 8114
 
6.6%
n 7746
 
6.3%
l 5524
 
4.5%
s 5279
 
4.3%
c 5250
 
4.3%
Other values (45) 45664
37.0%
Common
ValueCountFrequency (%)
14943
59.9%
[ 1930
 
7.7%
] 1927
 
7.7%
, 1476
 
5.9%
- 1263
 
5.1%
( 1167
 
4.7%
) 1159
 
4.6%
: 158
 
0.6%
0 89
 
0.4%
· 84
 
0.3%
Other values (36) 745
 
3.0%
Greek
ValueCountFrequency (%)
β 4
80.0%
α 1
 
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 148143
82.3%
Hangul 24630
 
13.7%
CJK 6869
 
3.8%
None 226
 
0.1%
CJK Compat Ideographs 142
 
0.1%
Geometric Shapes 8
 
< 0.1%
Punctuation 8
 
< 0.1%
Compat Jamo 6
 
< 0.1%
Number Forms 3
 
< 0.1%
Math Operators 2
 
< 0.1%
Other values (2) 3
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
14943
 
10.1%
e 11612
 
7.8%
i 9239
 
6.2%
a 8449
 
5.7%
t 8403
 
5.7%
o 8167
 
5.5%
r 8114
 
5.5%
n 7746
 
5.2%
l 5524
 
3.7%
s 5279
 
3.6%
Other values (73) 60667
41.0%
Hangul
ValueCountFrequency (%)
538
 
2.2%
470
 
1.9%
433
 
1.8%
368
 
1.5%
360
 
1.5%
344
 
1.4%
331
 
1.3%
325
 
1.3%
323
 
1.3%
314
 
1.3%
Other values (747) 20824
84.5%
CJK
ValueCountFrequency (%)
178
 
2.6%
156
 
2.3%
151
 
2.2%
121
 
1.8%
95
 
1.4%
93
 
1.4%
86
 
1.3%
73
 
1.1%
70
 
1.0%
67
 
1.0%
Other values (885) 5779
84.1%
None
ValueCountFrequency (%)
· 84
37.2%
55
24.3%
53
23.5%
  17
 
7.5%
7
 
3.1%
β 4
 
1.8%
² 2
 
0.9%
2
 
0.9%
α 1
 
0.4%
´ 1
 
0.4%
CJK Compat Ideographs
ValueCountFrequency (%)
24
16.9%
19
13.4%
13
 
9.2%
9
 
6.3%
9
 
6.3%
8
 
5.6%
6
 
4.2%
5
 
3.5%
5
 
3.5%
4
 
2.8%
Other values (26) 40
28.2%
Geometric Shapes
ValueCountFrequency (%)
8
100.0%
Compat Jamo
ValueCountFrequency (%)
6
100.0%
Punctuation
ValueCountFrequency (%)
3
37.5%
3
37.5%
2
25.0%
Math Operators
ValueCountFrequency (%)
2
100.0%
Arrows
ValueCountFrequency (%)
2
100.0%
Number Forms
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%
Letterlike Symbols
ValueCountFrequency (%)
1
100.0%

해설
Text

Distinct9925
Distinct (%)99.7%
Missing45
Missing (%)0.4%
Memory size156.2 KiB
2023-12-12T15:37:31.714707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length1024
Median length754
Mean length181.71863
Min length2

Characters and Unicode

Total characters1809009
Distinct characters2809
Distinct categories18 ?
Distinct scripts5 ?
Distinct blocks16 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9905 ?
Unique (%)99.5%

Sample

1st rowthe use of a circulating liquid to cool equipment and hermetically sealed components.
2nd rowthe process of distinguishing individual parts of an object when examining it with radiation. Most X-ray structures of small molecules are determined to a resolution of 0.8-ㆍ0 A. At this resolution each atom is fairly distinct. For macromolecules, the resolution is not as high.
3rd rowa unit of measure of the strength of the magnetic moment of baryons.
4th row하수를 일정량씩 간헐적으로 배수하는 탱크, 보통 표준살수여상에 장치된다.
5th rowa term formerly used to identify all rays emitted by radioactive substances, now designated by the more specific terms alpha rays, beta rays, and gamma rays.
ValueCountFrequency (%)
a 7063
 
2.0%
the 7013
 
2.0%
of 5495
 
1.6%
in 2888
 
0.8%
to 2837
 
0.8%
있다 2600
 
0.7%
and 2186
 
0.6%
that 2182
 
0.6%
or 2035
 
0.6%
1674
 
0.5%
Other values (99241) 312890
89.7%
2023-12-12T15:37:32.806301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
343153
 
19.0%
e 69595
 
3.8%
t 54959
 
3.0%
a 51498
 
2.8%
o 46457
 
2.6%
i 46324
 
2.6%
n 42871
 
2.4%
r 39968
 
2.2%
s 35797
 
2.0%
c 25997
 
1.4%
Other values (2799) 1052390
58.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 751194
41.5%
Lowercase Letter 588747
32.5%
Space Separator 343223
19.0%
Other Punctuation 53882
 
3.0%
Decimal Number 26842
 
1.5%
Uppercase Letter 20887
 
1.2%
Close Punctuation 8724
 
0.5%
Open Punctuation 8575
 
0.5%
Dash Punctuation 2747
 
0.2%
Math Symbol 1811
 
0.1%
Other values (8) 2377
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
22774
 
3.0%
20193
 
2.7%
18607
 
2.5%
18417
 
2.5%
15958
 
2.1%
15514
 
2.1%
13356
 
1.8%
12046
 
1.6%
11250
 
1.5%
10659
 
1.4%
Other values (2524) 592420
78.9%
Other Symbol
ValueCountFrequency (%)
591
48.4%
° 76
 
6.2%
71
 
5.8%
66
 
5.4%
55
 
4.5%
42
 
3.4%
33
 
2.7%
28
 
2.3%
23
 
1.9%
23
 
1.9%
Other values (46) 214
 
17.5%
Lowercase Letter
ValueCountFrequency (%)
e 69595
11.8%
t 54959
 
9.3%
a 51498
 
8.7%
o 46457
 
7.9%
i 46324
 
7.9%
n 42871
 
7.3%
r 39968
 
6.8%
s 35797
 
6.1%
c 25997
 
4.4%
l 24985
 
4.2%
Other values (45) 150296
25.5%
Uppercase Letter
ValueCountFrequency (%)
A 2072
 
9.9%
C 1941
 
9.3%
O 1519
 
7.3%
E 1438
 
6.9%
S 1374
 
6.6%
T 1291
 
6.2%
P 1259
 
6.0%
N 1060
 
5.1%
I 1060
 
5.1%
D 980
 
4.7%
Other values (29) 6893
33.0%
Other Punctuation
ValueCountFrequency (%)
. 25511
47.3%
, 21366
39.7%
: 1407
 
2.6%
; 1400
 
2.6%
· 1380
 
2.6%
' 802
 
1.5%
/ 672
 
1.2%
% 605
 
1.1%
" 289
 
0.5%
* 130
 
0.2%
Other values (14) 320
 
0.6%
Other Number
ValueCountFrequency (%)
195
27.2%
87
12.1%
81
11.3%
79
11.0%
61
 
8.5%
³ 43
 
6.0%
40
 
5.6%
37
 
5.2%
22
 
3.1%
² 14
 
1.9%
Other values (14) 59
 
8.2%
Math Symbol
ValueCountFrequency (%)
~ 562
31.0%
405
22.4%
= 242
13.4%
209
 
11.5%
+ 132
 
7.3%
× 80
 
4.4%
66
 
3.6%
33
 
1.8%
11
 
0.6%
± 11
 
0.6%
Other values (12) 60
 
3.3%
Close Punctuation
ValueCountFrequency (%)
) 8424
96.6%
] 164
 
1.9%
75
 
0.9%
33
 
0.4%
14
 
0.2%
5
 
0.1%
5
 
0.1%
} 2
 
< 0.1%
1
 
< 0.1%
1
 
< 0.1%
Open Punctuation
ValueCountFrequency (%)
( 8274
96.5%
[ 163
 
1.9%
75
 
0.9%
33
 
0.4%
14
 
0.2%
7
 
0.1%
5
 
0.1%
{ 2
 
< 0.1%
1
 
< 0.1%
1
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
0 5751
21.4%
1 5374
20.0%
2 3508
13.1%
5 2258
 
8.4%
3 2218
 
8.3%
9 2111
 
7.9%
4 1600
 
6.0%
6 1384
 
5.2%
8 1358
 
5.1%
7 1280
 
4.8%
Letter Number
ValueCountFrequency (%)
54
32.5%
47
28.3%
28
16.9%
25
15.1%
7
 
4.2%
2
 
1.2%
1
 
0.6%
1
 
0.6%
1
 
0.6%
Modifier Symbol
ValueCountFrequency (%)
` 28
53.8%
˚ 20
38.5%
˝ 2
 
3.8%
´ 2
 
3.8%
Dash Punctuation
ValueCountFrequency (%)
- 2704
98.4%
40
 
1.5%
3
 
0.1%
Space Separator
ValueCountFrequency (%)
343153
> 99.9%
  70
 
< 0.1%
Initial Punctuation
ValueCountFrequency (%)
95
88.8%
12
 
11.2%
Final Punctuation
ValueCountFrequency (%)
92
87.6%
13
 
12.4%
Currency Symbol
ValueCountFrequency (%)
4
66.7%
$ 2
33.3%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 740165
40.9%
Latin 609397
33.7%
Common 448041
24.8%
Han 11073
 
0.6%
Greek 333
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
22774
 
3.1%
20193
 
2.7%
18607
 
2.5%
18417
 
2.5%
15958
 
2.2%
15514
 
2.1%
13356
 
1.8%
12046
 
1.6%
11250
 
1.5%
10659
 
1.4%
Other values (1312) 581391
78.5%
Han
ValueCountFrequency (%)
228
 
2.1%
205
 
1.9%
199
 
1.8%
166
 
1.5%
131
 
1.2%
122
 
1.1%
119
 
1.1%
109
 
1.0%
106
 
1.0%
102
 
0.9%
Other values (1221) 9586
86.6%
Common
ValueCountFrequency (%)
343153
76.6%
. 25511
 
5.7%
, 21366
 
4.8%
) 8424
 
1.9%
( 8274
 
1.8%
0 5751
 
1.3%
1 5374
 
1.2%
2 3508
 
0.8%
- 2704
 
0.6%
5 2258
 
0.5%
Other values (144) 21718
 
4.8%
Latin
ValueCountFrequency (%)
e 69595
11.4%
t 54959
 
9.0%
a 51498
 
8.5%
o 46457
 
7.6%
i 46324
 
7.6%
n 42871
 
7.0%
r 39968
 
6.6%
s 35797
 
5.9%
c 25997
 
4.3%
l 24985
 
4.1%
Other values (68) 170946
28.1%
Greek
ValueCountFrequency (%)
μ 91
27.3%
α 68
20.4%
β 41
12.3%
γ 33
 
9.9%
π 15
 
4.5%
χ 13
 
3.9%
ω 13
 
3.9%
Ω 9
 
2.7%
ρ 7
 
2.1%
δ 6
 
1.8%
Other values (14) 37
11.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1052122
58.2%
Hangul 739345
40.9%
CJK 10707
 
0.6%
None 2907
 
0.2%
Compat Jamo 776
 
< 0.1%
Letterlike Symbols 776
 
< 0.1%
Math Operators 419
 
< 0.1%
CJK Compat 418
 
< 0.1%
CJK Compat Ideographs 366
 
< 0.1%
Enclosed Alphanum 332
 
< 0.1%
Other values (6) 841
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
343153
32.6%
e 69595
 
6.6%
t 54959
 
5.2%
a 51498
 
4.9%
o 46457
 
4.4%
i 46324
 
4.4%
n 42871
 
4.1%
r 39968
 
3.8%
s 35797
 
3.4%
c 25997
 
2.5%
Other values (81) 295503
28.1%
Hangul
ValueCountFrequency (%)
22774
 
3.1%
20193
 
2.7%
18607
 
2.5%
18417
 
2.5%
15958
 
2.2%
15514
 
2.1%
13356
 
1.8%
12046
 
1.6%
11250
 
1.5%
10659
 
1.4%
Other values (1281) 580571
78.5%
None
ValueCountFrequency (%)
· 1380
47.5%
195
 
6.7%
μ 91
 
3.1%
87
 
3.0%
× 80
 
2.8%
° 76
 
2.6%
75
 
2.6%
75
 
2.6%
  70
 
2.4%
α 68
 
2.3%
Other values (90) 710
24.4%
Compat Jamo
ValueCountFrequency (%)
745
96.0%
11
 
1.4%
6
 
0.8%
5
 
0.6%
2
 
0.3%
1
 
0.1%
1
 
0.1%
1
 
0.1%
1
 
0.1%
1
 
0.1%
Other values (2) 2
 
0.3%
Letterlike Symbols
ValueCountFrequency (%)
591
76.2%
115
 
14.8%
70
 
9.0%
Math Operators
ValueCountFrequency (%)
405
96.7%
7
 
1.7%
3
 
0.7%
3
 
0.7%
1
 
0.2%
CJK
ValueCountFrequency (%)
228
 
2.1%
205
 
1.9%
199
 
1.9%
166
 
1.6%
131
 
1.2%
122
 
1.1%
119
 
1.1%
109
 
1.0%
106
 
1.0%
102
 
1.0%
Other values (1159) 9220
86.1%
Arrows
ValueCountFrequency (%)
209
75.7%
66
 
23.9%
1
 
0.4%
Punctuation
ValueCountFrequency (%)
95
34.2%
92
33.1%
40
14.4%
16
 
5.8%
13
 
4.7%
12
 
4.3%
6
 
2.2%
3
 
1.1%
1
 
0.4%
Enclosed Alphanum
ValueCountFrequency (%)
81
24.4%
79
23.8%
61
18.4%
37
11.1%
22
 
6.6%
13
 
3.9%
10
 
3.0%
6
 
1.8%
4
 
1.2%
4
 
1.2%
Other values (9) 15
 
4.5%
CJK Compat
ValueCountFrequency (%)
71
17.0%
66
15.8%
55
13.2%
33
7.9%
28
 
6.7%
23
 
5.5%
23
 
5.5%
23
 
5.5%
21
 
5.0%
18
 
4.3%
Other values (12) 57
13.6%
Number Forms
ValueCountFrequency (%)
54
32.5%
47
28.3%
28
16.9%
25
15.1%
7
 
4.2%
2
 
1.2%
1
 
0.6%
1
 
0.6%
1
 
0.6%
CJK Compat Ideographs
ValueCountFrequency (%)
50
 
13.7%
48
 
13.1%
24
 
6.6%
19
 
5.2%
17
 
4.6%
15
 
4.1%
13
 
3.6%
13
 
3.6%
13
 
3.6%
10
 
2.7%
Other values (52) 144
39.3%
Geometric Shapes
ValueCountFrequency (%)
42
44.2%
15
 
15.8%
11
 
11.6%
7
 
7.4%
4
 
4.2%
4
 
4.2%
4
 
4.2%
3
 
3.2%
2
 
2.1%
2
 
2.1%
Modifier Letters
ValueCountFrequency (%)
˚ 20
90.9%
˝ 2
 
9.1%
Box Drawing
ValueCountFrequency (%)
4
100.0%

Interactions

2023-12-12T15:37:29.749494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Missing values

2023-12-12T15:37:29.864352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T15:37:29.944619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-12-12T15:37:30.036810image/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

번호용어해설
1413114132liquid coolingthe use of a circulating liquid to cool equipment and hermetically sealed components.
1658616587resolution of a crystal structurethe process of distinguishing individual parts of an object when examining it with radiation. Most X-ray structures of small molecules are determined to a resolution of 0.8-ㆍ0 A. At this resolution each atom is fairly distinct. For macromolecules, the resolution is not as high.
1499414995nuclear magnetona unit of measure of the strength of the magnetic moment of baryons.
2746027421자동배수탱크[自動排水탱크, dosing tank]하수를 일정량씩 간헐적으로 배수하는 탱크, 보통 표준살수여상에 장치된다.
928929Becquerel raysa term formerly used to identify all rays emitted by radioactive substances, now designated by the more specific terms alpha rays, beta rays, and gamma rays.
86458646agroecosystemthe ecological relationships of agriculture in general or of a particular agricultural locale. Similarly, agroenvironment. Also, AGROSYSTEM.
66276628SEEDING식종, 파종
2534725328씨엔지(CNG)버스천연가스를 가압하여 압축한 가스(압축천연가스, CNG : Compressed Natural Gas)를 연료로 사용하여 운행하는 저공해버스를 말한다.
1138411385double distributionthe distribution of product that results from counter double-current extraction, an arrangement in which the two liquid phases are transferred continuously and simultaneously in opposite directions through a series of contact vessels.
2086920867그린 소프트웨어 (Green Software)저탄소 녹색성장 이끄는 핵심엔진으로 기업이 경제활동에 필요한 기반시설들을 저전력 시스템으로 구현해주고 업무과정을 지능화, 자동화함으로써 에너지를 절약하고 유해물질 배출을 줄이는데 기여하는 소프트웨어, 저탄소 에너지 절감에 기여하는 효율적인 애플리케이션과 관련 서비스를 포괄하는 IT 자산과 운영의 효율화를 지원하거나 유해물질이나 환경규제를 관리하는 솔루션 등이 대표적인데, 전문가들은 이를 잘 이용하면 서버나 스토리지, 네트워크 장비 등 하드웨어의 전력 사용량이나 발열량을 줄이는 것보다 더 직접적인 효과를 볼 수 있다. 그린 소프트웨어의 활용도는 빌딩 내 온도습도를 자동으로 제어하면서 에너지 효율을 높이는 지능형빌딩시스템(IBS)이나 운송망 관리, 에코 드라이빙, 재고 감소 등을 지원하는 운송 네트워크 설계 시스템 등 적용 범위와 폭이 점점 넓어지고 있습니다. 유해물질 사용제한, 전기전자제품 폐기물처리 등 국제 환경 규제에 대응하기 위한 체계적인 관리 소프트웨어는 가장 직접적인 그린 소프트웨어로 꼽힙니다.
번호용어해설
37163717HalogenA type of incandescent lamp with higher energy-efficiency that standard ones.
2383923825사도일반개요 행정구역: 경상남도 남해군 미조면 미조리 산 391 외 1 면적: 30,744㎡ 토지소유현황: 사유 자연환경 지형·지질·경관 북서쪽에서 남동남으로 길게 뻗어있으며 섬전체가 둥근 형태를 띠는 종순형으로 사면은 완만하고 높이는 45m이다. 지질은 중성화산암류로 이루어져 있으며, 곳에 따라 괴상을 나타낸다. 섬주위는 높이 3~20m, 경사 45~80。의 해식애로 되어 있으나, 해식애는 북서북단에서 규모가 크며(높이 20m, 경사 45~70。) 남동남단으로 갈수록 작아진다. 해식애의 기저부에는 너비 2~3m, 높이 3~5m의 비교적 소규모의 해식동굴이 곳곳에 형성되어 있다. 해식동굴의 주향 및 경사는 N 35~40。 E, 75~80。 NW이다. 사도는 떼섬과 마찬가지로 해식애 부분을 제외하면 울창한 해송, 사스레피나무, 광나무 숲으로 덮혀 있다. 지형경관은 V등급에 속한다. 육지식물·식생 개요 사도는 북위 34。41', 동경 128。 02'에 위치하며, 육지와 가깝게 인접해 있는 섬이다. 동서로 길게 늘어져 있어 남쪽사면은 높은 절벽으로 식생이 거의 없고, 북서와 북동사면은 다소 완만하며, 섬의 동쪽에는 경작지가 있었던 흔적이 있었다. 섬 전체는 울창한 해송림(흉고직경 평균 18~20cm, 수고 12~16m)으로 드물게 굴피나무와 벚나무도 출현하였다. 북사면의 낮은 지역에는 해안선을 따라 동백나무와 돈나무, 광나무 등 상록활엽수가 길게 군락을 형성하고 있었으며, 정상능선부에 나출된 지역에 넓은 면적의 칡군락이 있었다. 초본층은 참억새와 개솔새, 고사리가 높은 밀도로 출현하였으며, 술패랭이와 좁인잎계요동, 빗살서덜취도 드물게 분포하였다. 해송은 많은 개체가 솔껍질깍지벌레의 피해로 고사되어 있었다. 출현종 및 주요종 43과 85속 92종 15변종으로 총 107종류가 조사되었다. 섬 전체는 울창한 해송림으로 능선부의 중층에는 굴피나무와 벚나무, 소사나무가, 북사면의 낮은 지역엔 상록활엽수인 동백나무와 돈나무, 광나무 등이 군락을 형성하고 있으며, 하층에는 해송과 다정큼나무, 후박나무, 광나무, 보리밥나무,
10521053Brine MudWaste material, oftnen associated with well-drilling or mining, composed of mineral salts or other inorganic compounds.
23922393EGS환경재와 서비스 environmental goods and services
92679268baromila unit of measure in the CGS system used in a graduated mercury barometer.
3093330891환경제국주의선진국들은 자신들의 환경문제를 해결하기 위하여 공해산업을 후진국에 대한 배려라는 미명하에 후진국으로 이전하고 있는데 이는 제국주의의 한 단면으로 이해할 수 있다. 제철, 섬유, 석유, 화학, 고무산업 등이 그 예다. 우리나라는 다국적 기업에 의해 환경피해를 입은 대표적인 나라지만 다시 이러한 산업을 후진국에 수출에 국제적인 비난을 받고 있다. 우리나라의 환경문제는 1960년대 이후 일방적인 경제성장에서 비롯되었다. 그 과정에서 공해 산업의 무분별한 수입이 급속한 환경악화를 가져온 것이다. 최근 국내기업의 외국 진출과 함께 한국의 공해산업을 외국으로 수출하는 환경 제국주의적 성향이 나타나고 있는데, 이는 또다른 국가 이기주의에 빠지는 꼴이다. 우리가 다국적 기업의 공해확산을 반대하며 전지구적으로 환경운동을 펼치는 것은 결국 인류가 하나의 환경에 살고 있기 때문이다.
23112312E layera layer in the earth's atmosphere, occurring at 100 to 120 km above sea level, in which short-wave radio waves are reflected and temperature increases with increasing altitude.
36583659HUMAN RESPONSE TO GLOBAL CHANGE PROGRAM지구변화에 대한 인류의 대응 프로그램
1519915200overarman adjustable support at the end opposite the spindle of a milling cutter.
2955929517토지피복지도란?토지피복지도(Land Cover Map)는 위성자료를 이용하여 지표면의 자연상태를 분류하는 것으로 유럽 등 선진국에서는 이미 ‘80년대부터 국토의 환경관리를 위한 기본도로 활용하고 있다. 토지피복지도는 국토공간의 피복상태의 시계열 분석이 가능하며 국토의 피복상태의 변화를 신속, 정확하게 파악할 수 있어 지역, 유역, 권역별 오염부하량과 환경용량 산출 등에 활용된다.

Duplicate rows

Most frequently occurring

번호용어해설# duplicates
0<NA><NA><NA>13