Overview

Dataset statistics

Number of variables5
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.0 KiB
Average record size in memory41.3 B

Variable types

Text2
Categorical3

Dataset

DescriptionSample
Author한국인터넷진흥원
URLhttps://www.bigdata-telecom.kr/invoke/SOKBP2603/?goodsCode=KIS00000000000000011

Alerts

백신탐지명3 is highly overall correlated with 백신탐지명1 and 1 other fieldsHigh correlation
백신탐지명2 is highly overall correlated with 백신탐지명1 and 1 other fieldsHigh correlation
백신탐지명1 is highly overall correlated with 백신탐지명2 and 1 other fieldsHigh correlation
해시코드값 has unique valuesUnique
분석보고서명 has unique valuesUnique

Reproduction

Analysis started2023-12-10 06:57:40.912506
Analysis finished2023-12-10 06:57:41.335598
Duration0.42 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

해시코드값
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T15:57:41.528770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length64
Median length64
Mean length64
Min length64

Characters and Unicode

Total characters6400
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique100 ?
Unique (%)100.0%

Sample

1st rowf9af5d8af79f39ca76665696b77eb1a3929e5142eb5f7d125f2f079cf47f8989
2nd rowefd0e89459190db55c64d1613e5cf912c56a212ba3a602c86fa33504b3a16505
3rd row00aaa48083a22327a6e27cb5db607f5ce1e01f8ea25db1c5dd90cc921800295d
4th row43fd4f243a1b438108cfacb972ea20879ef27b9058a123c7ed7e1d64ab0ddec7
5th row37cfb600c95137447974612664353d5871e793cc8fefb48cb027bd8cbb96f604
ValueCountFrequency (%)
f9af5d8af79f39ca76665696b77eb1a3929e5142eb5f7d125f2f079cf47f8989 1
 
1.0%
f99aaba7b57d0a0ac889ca72d4fa549a1c2f5ebd1560520cba1001929838cfca 1
 
1.0%
2f86f74c8082ffc73ef8f09f732d5b2f368a4f722d8af646dde4fa72c185d672 1
 
1.0%
f3a3e7810049f5ff1cccdca59bef79e243392aa880090fafeec8916a48864f28 1
 
1.0%
9bdbaa9f16b3ec2d0c147f1630d0ff010983406f8275d1cb0aa92235f2fbf5c8 1
 
1.0%
12fc3f50a871fd7fc9f79ecc4f05c419cd5a232d985ea1c3bfb86f380ba0d248 1
 
1.0%
ab92e79c287556d6bd78dfbc47db49f75dbfb8b74281e433b9c14247583f2f2e 1
 
1.0%
8ac1f81601eec7649770a8abc9f362359ce226174f26851bf0f79ef1ba1b6097 1
 
1.0%
691ade0667fc7c53b44eefa9c4dbfc40a85c201f908e3fb0d60291703d2c24df 1
 
1.0%
9d677905fad87e576cbce3fc7812b24e04502fd7b765faf6fcb0c091ae8c87c5 1
 
1.0%
Other values (90) 90
90.0%
2023-12-10T15:57:41.920158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
c 453
 
7.1%
8 427
 
6.7%
0 424
 
6.6%
a 414
 
6.5%
e 409
 
6.4%
2 409
 
6.4%
f 407
 
6.4%
d 406
 
6.3%
7 403
 
6.3%
b 397
 
6.2%
Other values (6) 2251
35.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3914
61.2%
Lowercase Letter 2486
38.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 427
10.9%
0 424
10.8%
2 409
10.4%
7 403
10.3%
1 390
10.0%
9 389
9.9%
3 389
9.9%
4 367
9.4%
6 361
9.2%
5 355
9.1%
Lowercase Letter
ValueCountFrequency (%)
c 453
18.2%
a 414
16.7%
e 409
16.5%
f 407
16.4%
d 406
16.3%
b 397
16.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3914
61.2%
Latin 2486
38.8%

Most frequent character per script

Common
ValueCountFrequency (%)
8 427
10.9%
0 424
10.8%
2 409
10.4%
7 403
10.3%
1 390
10.0%
9 389
9.9%
3 389
9.9%
4 367
9.4%
6 361
9.2%
5 355
9.1%
Latin
ValueCountFrequency (%)
c 453
18.2%
a 414
16.7%
e 409
16.5%
f 407
16.4%
d 406
16.3%
b 397
16.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6400
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
c 453
 
7.1%
8 427
 
6.7%
0 424
 
6.6%
a 414
 
6.5%
e 409
 
6.4%
2 409
 
6.4%
f 407
 
6.4%
d 406
 
6.3%
7 403
 
6.3%
b 397
 
6.2%
Other values (6) 2251
35.2%

백신탐지명1
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
W32.Wapomi.C!inf
30 
W32.Wapomi!inf
28 
W32.Xpiro.D
19 
W32.Wapomi.B!inf
13 
W32.Zorg
5 

Length

Max length16
Median length14
Mean length13.84
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowW32.Zorg
2nd rowW32.Zorg
3rd rowW32.Zorg
4th rowW32.Zorg
5th rowW32.Zorg

Common Values

ValueCountFrequency (%)
W32.Wapomi.C!inf 30
30.0%
W32.Wapomi!inf 28
28.0%
W32.Xpiro.D 19
19.0%
W32.Wapomi.B!inf 13
13.0%
W32.Zorg 5
 
5.0%
W32.Xpiro.F 5
 
5.0%

Length

2023-12-10T15:57:42.068718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:57:42.172776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
w32.wapomi.c!inf 30
30.0%
w32.wapomi!inf 28
28.0%
w32.xpiro.d 19
19.0%
w32.wapomi.b!inf 13
13.0%
w32.zorg 5
 
5.0%
w32.xpiro.f 5
 
5.0%

백신탐지명2
Categorical

HIGH CORRELATION 

Distinct12
Distinct (%)12.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
Win32/Dellboy.BG
13 
Win32/Viking.DQ
11 
Win32/Wapomi
11 
Win32/Jadtre.E
11 
Win32/Expiro2.Gen
10 
Other values (7)
44 

Length

Max length17
Median length16
Mean length14.96
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWin32/Zorg
2nd rowWin32/Zorg
3rd rowWin32/Zorg
4th rowWin32/Zorg
5th rowWin32/Zorg

Common Values

ValueCountFrequency (%)
Win32/Dellboy.BG 13
13.0%
Win32/Viking.DQ 11
11.0%
Win32/Wapomi 11
11.0%
Win32/Jadtre.E 11
11.0%
Win32/Expiro2.Gen 10
10.0%
Win32/Expiro4.Gen 9
9.0%
Win32/Viking.DR 9
9.0%
Win32/VJadtre.Gen 6
6.0%
Win32/Viking.DP 6
6.0%
Win32/Zorg 5
 
5.0%
Other values (2) 9
9.0%

Length

2023-12-10T15:57:42.290716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
win32/dellboy.bg 13
13.0%
win32/viking.dq 11
11.0%
win32/wapomi 11
11.0%
win32/jadtre.e 11
11.0%
win32/expiro2.gen 10
10.0%
win32/expiro4.gen 9
9.0%
win32/viking.dr 9
9.0%
win32/vjadtre.gen 6
6.0%
win32/viking.dp 6
6.0%
win32/zorg 5
 
5.0%
Other values (2) 9
9.0%

백신탐지명3
Categorical

HIGH CORRELATION 

Distinct12
Distinct (%)12.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
Virus.Win32.Otwycal.a
24 
Virus.Win32.Qvod.b
13 
Virus.Win32.Otwycal.b
11 
Virus.Win32.Qvod.f
11 
Virus.Win32.Expiro.w
10 
Other values (7)
31 

Length

Max length21
Median length20
Mean length19.91
Min length18

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowVirus.Win32.Zorg.a
2nd rowVirus.Win32.Zorg.a
3rd rowVirus.Win32.Zorg.a
4th rowVirus.Win32.Zorg.a
5th rowVirus.Win32.Zorg.a

Common Values

ValueCountFrequency (%)
Virus.Win32.Otwycal.a 24
24.0%
Virus.Win32.Qvod.b 13
13.0%
Virus.Win32.Otwycal.b 11
11.0%
Virus.Win32.Qvod.f 11
11.0%
Virus.Win32.Expiro.w 10
10.0%
Virus.Win32.Nimnul.f 6
 
6.0%
Virus.Win32.Nimnul.d 6
 
6.0%
Virus.Win32.Zorg.a 5
 
5.0%
Virus.Win32.Expiro.nr 5
 
5.0%
Virus.Win32.Expiro.ao 4
 
4.0%
Other values (2) 5
 
5.0%

Length

2023-12-10T15:57:42.414465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
virus.win32.otwycal.a 24
24.0%
virus.win32.qvod.b 13
13.0%
virus.win32.otwycal.b 11
11.0%
virus.win32.qvod.f 11
11.0%
virus.win32.expiro.w 10
10.0%
virus.win32.nimnul.f 6
 
6.0%
virus.win32.nimnul.d 6
 
6.0%
virus.win32.zorg.a 5
 
5.0%
virus.win32.expiro.nr 5
 
5.0%
virus.win32.expiro.ao 4
 
4.0%
Other values (2) 5
 
5.0%

분석보고서명
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T15:57:42.667147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length69
Median length69
Mean length69
Min length69

Characters and Unicode

Total characters6900
Distinct characters20
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

Unique100 ?
Unique (%)100.0%

Sample

1st rowf9af5d8af79f39ca76665696b77eb1a3929e5142eb5f7d125f2f079cf47f8989.xlsx
2nd rowefd0e89459190db55c64d1613e5cf912c56a212ba3a602c86fa33504b3a16505.xlsx
3rd row00aaa48083a22327a6e27cb5db607f5ce1e01f8ea25db1c5dd90cc921800295d.xlsx
4th row43fd4f243a1b438108cfacb972ea20879ef27b9058a123c7ed7e1d64ab0ddec7.xlsx
5th row37cfb600c95137447974612664353d5871e793cc8fefb48cb027bd8cbb96f604.xlsx
ValueCountFrequency (%)
f9af5d8af79f39ca76665696b77eb1a3929e5142eb5f7d125f2f079cf47f8989.xlsx 1
 
1.0%
f99aaba7b57d0a0ac889ca72d4fa549a1c2f5ebd1560520cba1001929838cfca.xlsx 1
 
1.0%
2f86f74c8082ffc73ef8f09f732d5b2f368a4f722d8af646dde4fa72c185d672.xlsx 1
 
1.0%
f3a3e7810049f5ff1cccdca59bef79e243392aa880090fafeec8916a48864f28.xlsx 1
 
1.0%
9bdbaa9f16b3ec2d0c147f1630d0ff010983406f8275d1cb0aa92235f2fbf5c8.xlsx 1
 
1.0%
12fc3f50a871fd7fc9f79ecc4f05c419cd5a232d985ea1c3bfb86f380ba0d248.xlsx 1
 
1.0%
ab92e79c287556d6bd78dfbc47db49f75dbfb8b74281e433b9c14247583f2f2e.xlsx 1
 
1.0%
8ac1f81601eec7649770a8abc9f362359ce226174f26851bf0f79ef1ba1b6097.xlsx 1
 
1.0%
691ade0667fc7c53b44eefa9c4dbfc40a85c201f908e3fb0d60291703d2c24df.xlsx 1
 
1.0%
9d677905fad87e576cbce3fc7812b24e04502fd7b765faf6fcb0c091ae8c87c5.xlsx 1
 
1.0%
Other values (90) 90
90.0%
2023-12-10T15:57:43.045358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
c 453
 
6.6%
8 427
 
6.2%
0 424
 
6.1%
a 414
 
6.0%
e 409
 
5.9%
2 409
 
5.9%
f 407
 
5.9%
d 406
 
5.9%
7 403
 
5.8%
b 397
 
5.8%
Other values (10) 2751
39.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3914
56.7%
Lowercase Letter 2886
41.8%
Other Punctuation 100
 
1.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 427
10.9%
0 424
10.8%
2 409
10.4%
7 403
10.3%
1 390
10.0%
9 389
9.9%
3 389
9.9%
4 367
9.4%
6 361
9.2%
5 355
9.1%
Lowercase Letter
ValueCountFrequency (%)
c 453
15.7%
a 414
14.3%
e 409
14.2%
f 407
14.1%
d 406
14.1%
b 397
13.8%
x 200
6.9%
l 100
 
3.5%
s 100
 
3.5%
Other Punctuation
ValueCountFrequency (%)
. 100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4014
58.2%
Latin 2886
41.8%

Most frequent character per script

Common
ValueCountFrequency (%)
8 427
10.6%
0 424
10.6%
2 409
10.2%
7 403
10.0%
1 390
9.7%
9 389
9.7%
3 389
9.7%
4 367
9.1%
6 361
9.0%
5 355
8.8%
Latin
ValueCountFrequency (%)
c 453
15.7%
a 414
14.3%
e 409
14.2%
f 407
14.1%
d 406
14.1%
b 397
13.8%
x 200
6.9%
l 100
 
3.5%
s 100
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6900
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
c 453
 
6.6%
8 427
 
6.2%
0 424
 
6.1%
a 414
 
6.0%
e 409
 
5.9%
2 409
 
5.9%
f 407
 
5.9%
d 406
 
5.9%
7 403
 
5.8%
b 397
 
5.8%
Other values (10) 2751
39.9%

Correlations

2023-12-10T15:57:43.146094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
해시코드값백신탐지명1백신탐지명2백신탐지명3분석보고서명
해시코드값1.0001.0001.0001.0001.000
백신탐지명11.0001.0001.0001.0001.000
백신탐지명21.0001.0001.0000.9971.000
백신탐지명31.0001.0000.9971.0001.000
분석보고서명1.0001.0001.0001.0001.000
2023-12-10T15:57:43.252780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
백신탐지명3백신탐지명2백신탐지명1
백신탐지명31.0000.8920.968
백신탐지명20.8921.0000.968
백신탐지명10.9680.9681.000
2023-12-10T15:57:43.339413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
백신탐지명1백신탐지명2백신탐지명3
백신탐지명11.0000.9680.968
백신탐지명20.9681.0000.892
백신탐지명30.9680.8921.000

Missing values

2023-12-10T15:57:41.208245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T15:57:41.297365image/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.

Sample

해시코드값백신탐지명1백신탐지명2백신탐지명3분석보고서명
0f9af5d8af79f39ca76665696b77eb1a3929e5142eb5f7d125f2f079cf47f8989W32.ZorgWin32/ZorgVirus.Win32.Zorg.af9af5d8af79f39ca76665696b77eb1a3929e5142eb5f7d125f2f079cf47f8989.xlsx
1efd0e89459190db55c64d1613e5cf912c56a212ba3a602c86fa33504b3a16505W32.ZorgWin32/ZorgVirus.Win32.Zorg.aefd0e89459190db55c64d1613e5cf912c56a212ba3a602c86fa33504b3a16505.xlsx
200aaa48083a22327a6e27cb5db607f5ce1e01f8ea25db1c5dd90cc921800295dW32.ZorgWin32/ZorgVirus.Win32.Zorg.a00aaa48083a22327a6e27cb5db607f5ce1e01f8ea25db1c5dd90cc921800295d.xlsx
343fd4f243a1b438108cfacb972ea20879ef27b9058a123c7ed7e1d64ab0ddec7W32.ZorgWin32/ZorgVirus.Win32.Zorg.a43fd4f243a1b438108cfacb972ea20879ef27b9058a123c7ed7e1d64ab0ddec7.xlsx
437cfb600c95137447974612664353d5871e793cc8fefb48cb027bd8cbb96f604W32.ZorgWin32/ZorgVirus.Win32.Zorg.a37cfb600c95137447974612664353d5871e793cc8fefb48cb027bd8cbb96f604.xlsx
5d965270ca42dbe91d8f000639abaa89695b90c17c1d0163cd224a0ba11f8e2e7W32.Xpiro.FWin32/Expiro5.GenVirus.Win32.Expiro.nrd965270ca42dbe91d8f000639abaa89695b90c17c1d0163cd224a0ba11f8e2e7.xlsx
61a6ac0c6e85b95cd0529cc5467b0e336454c0a429c60dfc6ffdea9264bada430W32.Xpiro.FWin32/Expiro5.GenVirus.Win32.Expiro.nr1a6ac0c6e85b95cd0529cc5467b0e336454c0a429c60dfc6ffdea9264bada430.xlsx
79ba42e49291e6cae1572f7ea785dc61e170e37c2f8bbcb5b46c2cf5652a0f342W32.Xpiro.FWin32/Expiro5.GenVirus.Win32.Expiro.nr9ba42e49291e6cae1572f7ea785dc61e170e37c2f8bbcb5b46c2cf5652a0f342.xlsx
892c3ff323860168dced330b0a955472ed4f445cd321a07c75f90ab4487ed51c6W32.Xpiro.FWin32/Expiro5.GenVirus.Win32.Expiro.nr92c3ff323860168dced330b0a955472ed4f445cd321a07c75f90ab4487ed51c6.xlsx
9c467dfd70c680f2a13c847028945a56d2888837ef11734d2ed0efc59fd57bd75W32.Xpiro.FWin32/Expiro5.GenVirus.Win32.Expiro.nrc467dfd70c680f2a13c847028945a56d2888837ef11734d2ed0efc59fd57bd75.xlsx
해시코드값백신탐지명1백신탐지명2백신탐지명3분석보고서명
90c9baa34545cb59af5f4c46b156d150aa6fc6ce483edc06ae7fcc2b310212d606W32.Wapomi!infWin32/Jadtre.EVirus.Win32.Qvod.fc9baa34545cb59af5f4c46b156d150aa6fc6ce483edc06ae7fcc2b310212d606.xlsx
91cbe9dea3cdaad59e7f64d8ce51e013a75ae5a8e73390c3e7605cc8d8ad24cc4eW32.Wapomi!infWin32/Jadtre.EVirus.Win32.Qvod.fcbe9dea3cdaad59e7f64d8ce51e013a75ae5a8e73390c3e7605cc8d8ad24cc4e.xlsx
9279bb8c9ca3ee3801316c9adcf43839c918ee64e8013157e2e8681770fac63aebW32.Wapomi!infWin32/Jadtre.EVirus.Win32.Qvod.f79bb8c9ca3ee3801316c9adcf43839c918ee64e8013157e2e8681770fac63aeb.xlsx
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