Overview

Dataset statistics

Number of variables5
Number of observations98
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.1 KiB
Average record size in memory43.3 B

Variable types

Categorical2
Text2
Numeric1

Dataset

DescriptionSample
Author한국인터넷진흥원
URLhttps://www.bigdata-telecom.kr/invoke/SOKBP2603/?goodsCode=KIS0000027

Alerts

EA2AD22333C4EBDD8FBE446D232B99112FC502972C13CC9C3CA7536642EDF0F1 has unique valuesUnique
CWE_77_C_1.zip has unique valuesUnique

Reproduction

Analysis started2023-12-10 06:24:30.030506
Analysis finished2023-12-10 06:24:30.856337
Duration0.83 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

2019
Categorical

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size916.0 B
2019
59 
2018
39 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2019
2nd row2019
3rd row2019
4th row2019
5th row2019

Common Values

ValueCountFrequency (%)
2019 59
60.2%
2018 39
39.8%

Length

2023-12-10T15:24:30.974863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:24:31.128604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2019 59
60.2%
2018 39
39.8%

EA2AD22333C4EBDD8FBE446D232B99112FC502972C13CC9C3CA7536642EDF0F1
Text

UNIQUE 

Distinct98
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size916.0 B
2023-12-10T15:24:31.555793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length64
Median length64
Mean length64
Min length64

Characters and Unicode

Total characters6272
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

Unique98 ?
Unique (%)100.0%

Sample

1st row65BF7BF13DD650E520703C189A765EC109CB55B8368FF5B9792E1380DA85E680
2nd row759733931FFEDEA9315F44EAAFC6E483467900067FFDE82BD5E998F3A5D7646D
3rd row4DA44C4E0E3F6371D8F806A79EA55D630B336B791F713235701995051AEB7AF7
4th rowDCCC0AE14380275CEC9DBB37D1630FB4B4001629921C3858130B00635D8AE779
5th rowD01CFB24E88F8482EA5440FC5F034C0818A937BBC89F9C04334F25A47ABC8617
ValueCountFrequency (%)
65bf7bf13dd650e520703c189a765ec109cb55b8368ff5b9792e1380da85e680 1
 
1.0%
f9419d6b131d19ba52853b9ab46ed34ea6dc002f22caf37e45b5f3e6f79791f8 1
 
1.0%
b4050ee554e752aa1d2b8bce6dae41852c87e2c01cea59e3e59c97380c55a20e 1
 
1.0%
72f4a88a5f78a42b2f4bf6a31e9f018e9e6ab4cb7d59afb24057062ddcdab235 1
 
1.0%
93ff85e64c817754e497910b77d80aa0f8f1f42c849e65caf0260f61ffce28f4 1
 
1.0%
7015d5b2b92697c2e81b53e906442f64a53314ce11f5c1dd01feefdb6a5a1752 1
 
1.0%
f3690d1cb71f4d72141a5ac3cfd5ad2667765a3f54236bf5cbc8d0a819cdaede 1
 
1.0%
8c94654f7fad2724f2081163b9c27a4029444bfe7bc2fac661b867b1af0bd9fa 1
 
1.0%
06174b3ecc97edc4632271f220a21e890ada2b9c87796c89db60d415793e05e0 1
 
1.0%
9b54d0b48980acf5828c3f715262c298e505b12a33f6600c30ecebda115ba128 1
 
1.0%
Other values (88) 88
89.8%
2023-12-10T15:24:32.319333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 418
 
6.7%
9 415
 
6.6%
B 407
 
6.5%
0 407
 
6.5%
4 403
 
6.4%
A 399
 
6.4%
8 398
 
6.3%
6 393
 
6.3%
1 393
 
6.3%
5 390
 
6.2%
Other values (6) 2249
35.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3990
63.6%
Uppercase Letter 2282
36.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 418
10.5%
9 415
10.4%
0 407
10.2%
4 403
10.1%
8 398
10.0%
6 393
9.8%
1 393
9.8%
5 390
9.8%
7 388
9.7%
3 385
9.6%
Uppercase Letter
ValueCountFrequency (%)
B 407
17.8%
A 399
17.5%
C 387
17.0%
F 374
16.4%
E 374
16.4%
D 341
14.9%

Most occurring scripts

ValueCountFrequency (%)
Common 3990
63.6%
Latin 2282
36.4%

Most frequent character per script

Common
ValueCountFrequency (%)
2 418
10.5%
9 415
10.4%
0 407
10.2%
4 403
10.1%
8 398
10.0%
6 393
9.8%
1 393
9.8%
5 390
9.8%
7 388
9.7%
3 385
9.6%
Latin
ValueCountFrequency (%)
B 407
17.8%
A 399
17.5%
C 387
17.0%
F 374
16.4%
E 374
16.4%
D 341
14.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6272
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 418
 
6.7%
9 415
 
6.6%
B 407
 
6.5%
0 407
 
6.5%
4 403
 
6.4%
A 399
 
6.4%
8 398
 
6.3%
6 393
 
6.3%
1 393
 
6.3%
5 390
 
6.2%
Other values (6) 2249
35.9%

CWE_77_C_1.zip
Text

UNIQUE 

Distinct98
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size916.0 B
2023-12-10T15:24:32.748924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length15
Mean length14.846939
Min length13

Characters and Unicode

Total characters1455
Distinct characters26
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique98 ?
Unique (%)100.0%

Sample

1st rowCWE_77_B_2.zip
2nd rowCWE_77_B_4.zip
3rd rowCWE_77_A_1.zip
4th rowCWE_77_A_3.zip
5th rowCWE_77_A_2.zip
ValueCountFrequency (%)
cwe_195_c_2.zip 2
 
2.0%
cwe_121_a_1.zip 2
 
2.0%
cwe_562_a_2.zip 2
 
2.0%
cwe_195_a_2.zip 2
 
2.0%
cwe_416_a_2.zip 2
 
2.0%
cwe_195_b_1.zip 2
 
2.0%
cwe_416_a_1.zip 2
 
2.0%
cwe_562_b_2.zip 2
 
2.0%
cwe_121_a_2.zip 2
 
2.0%
cwe_562_a_1.zip 2
 
2.0%
Other values (71) 78
79.6%
2023-12-10T15:24:33.414992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
_ 291
20.0%
1 127
8.7%
C 103
 
7.1%
E 98
 
6.7%
W 98
 
6.7%
. 98
 
6.7%
z 98
 
6.7%
i 98
 
6.7%
p 98
 
6.7%
2 82
 
5.6%
Other values (16) 264
18.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 380
26.1%
Uppercase Letter 353
24.3%
Lowercase Letter 333
22.9%
Connector Punctuation 291
20.0%
Other Punctuation 98
 
6.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 127
33.4%
2 82
21.6%
5 36
 
9.5%
6 33
 
8.7%
3 26
 
6.8%
9 25
 
6.6%
4 23
 
6.1%
7 18
 
4.7%
0 8
 
2.1%
8 2
 
0.5%
Lowercase Letter
ValueCountFrequency (%)
z 98
29.4%
i 98
29.4%
p 98
29.4%
c 10
 
3.0%
a 10
 
3.0%
b 8
 
2.4%
d 5
 
1.5%
e 4
 
1.2%
f 2
 
0.6%
Uppercase Letter
ValueCountFrequency (%)
C 103
29.2%
E 98
27.8%
W 98
27.8%
A 30
 
8.5%
B 24
 
6.8%
Connector Punctuation
ValueCountFrequency (%)
_ 291
100.0%
Other Punctuation
ValueCountFrequency (%)
. 98
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 769
52.9%
Latin 686
47.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
C 103
15.0%
E 98
14.3%
W 98
14.3%
z 98
14.3%
i 98
14.3%
p 98
14.3%
A 30
 
4.4%
B 24
 
3.5%
c 10
 
1.5%
a 10
 
1.5%
Other values (4) 19
 
2.8%
Common
ValueCountFrequency (%)
_ 291
37.8%
1 127
16.5%
. 98
 
12.7%
2 82
 
10.7%
5 36
 
4.7%
6 33
 
4.3%
3 26
 
3.4%
9 25
 
3.3%
4 23
 
3.0%
7 18
 
2.3%
Other values (2) 10
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1455
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
_ 291
20.0%
1 127
8.7%
C 103
 
7.1%
E 98
 
6.7%
W 98
 
6.7%
. 98
 
6.7%
z 98
 
6.7%
i 98
 
6.7%
p 98
 
6.7%
2 82
 
5.6%
Other values (16) 264
18.1%

CWE-77
Categorical

Distinct13
Distinct (%)13.3%
Missing0
Missing (%)0.0%
Memory size916.0 B
CWE-562
19 
CWE-121
15 
CWE-195
15 
CWE-416
10 
CWE-77
9 
Other values (8)
30 

Length

Max length7
Median length7
Mean length6.9081633
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCWE-77
2nd rowCWE-77
3rd rowCWE-77
4th rowCWE-77
5th rowCWE-77

Common Values

ValueCountFrequency (%)
CWE-562 19
19.4%
CWE-121 15
15.3%
CWE-195 15
15.3%
CWE-416 10
10.2%
CWE-77 9
9.2%
CWE-134 9
9.2%
CWE-122 5
 
5.1%
CWE-190 4
 
4.1%
CWE-193 4
 
4.1%
CWE-680 2
 
2.0%
Other values (3) 6
 
6.1%

Length

2023-12-10T15:24:33.628465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
cwe-562 19
19.4%
cwe-121 15
15.3%
cwe-195 15
15.3%
cwe-416 10
10.2%
cwe-77 9
9.2%
cwe-134 9
9.2%
cwe-122 5
 
5.1%
cwe-190 4
 
4.1%
cwe-193 4
 
4.1%
cwe-680 2
 
2.0%
Other values (3) 6
 
6.1%

40
Real number (ℝ)

Distinct6
Distinct (%)6.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.510204
Minimum20
Maximum70
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1014.0 B
2023-12-10T15:24:33.796031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile20
Q120
median30
Q340
95-th percentile60
Maximum70
Range50
Interquartile range (IQR)20

Descriptive statistics

Standard deviation12.130939
Coefficient of variation (CV)0.39760269
Kurtosis1.5742278
Mean30.510204
Median Absolute Deviation (MAD)10
Skewness1.3508199
Sum2990
Variance147.15969
MonotonicityNot monotonic
2023-12-10T15:24:33.957681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
20 40
40.8%
30 32
32.7%
40 15
 
15.3%
50 5
 
5.1%
60 4
 
4.1%
70 2
 
2.0%
ValueCountFrequency (%)
20 40
40.8%
30 32
32.7%
40 15
 
15.3%
50 5
 
5.1%
60 4
 
4.1%
70 2
 
2.0%
ValueCountFrequency (%)
70 2
 
2.0%
60 4
 
4.1%
50 5
 
5.1%
40 15
 
15.3%
30 32
32.7%
20 40
40.8%

Interactions

2023-12-10T15:24:30.438377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T15:24:34.102754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2019EA2AD22333C4EBDD8FBE446D232B99112FC502972C13CC9C3CA7536642EDF0F1CWE_77_C_1.zipCWE-7740
20191.0001.0001.0000.5540.675
EA2AD22333C4EBDD8FBE446D232B99112FC502972C13CC9C3CA7536642EDF0F11.0001.0001.0001.0001.000
CWE_77_C_1.zip1.0001.0001.0001.0001.000
CWE-770.5541.0001.0001.0000.000
400.6751.0001.0000.0001.000
2023-12-10T15:24:34.275755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
CWE-772019
CWE-771.0000.488
20190.4881.000
2023-12-10T15:24:34.463436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
402019CWE-77
401.0000.4840.000
20190.4841.0000.488
CWE-770.0000.4881.000

Missing values

2023-12-10T15:24:30.610692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T15:24:30.779732image/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

2019EA2AD22333C4EBDD8FBE446D232B99112FC502972C13CC9C3CA7536642EDF0F1CWE_77_C_1.zipCWE-7740
0201965BF7BF13DD650E520703C189A765EC109CB55B8368FF5B9792E1380DA85E680CWE_77_B_2.zipCWE-7730
12019759733931FFEDEA9315F44EAAFC6E483467900067FFDE82BD5E998F3A5D7646DCWE_77_B_4.zipCWE-7730
220194DA44C4E0E3F6371D8F806A79EA55D630B336B791F713235701995051AEB7AF7CWE_77_A_1.zipCWE-7720
32019DCCC0AE14380275CEC9DBB37D1630FB4B4001629921C3858130B00635D8AE779CWE_77_A_3.zipCWE-7720
42019D01CFB24E88F8482EA5440FC5F034C0818A937BBC89F9C04334F25A47ABC8617CWE_77_A_2.zipCWE-7720
52019BB12B62AE69EAA2E2FF9A5B578D950681DD22718E52833502A4999359AFFB9DCCWE_77_B_3.zipCWE-7730
620199CA0169BEFA8644D38C0D8AAAA93CB4F99E9304DB0EA0D031C320EE866626306CWE_77_B_6.zipCWE-7730
72019E7E13C8FE0C3BFAA4EE2B4FFF434C9A8FC96E4C7585B90AB1F67BB980E206710CWE_77_B_1.zipCWE-7730
820195FEA16C1C85E30FD16BEB4A81157233F491A8F307FE516D6DBFD5DDEE89F5E09CWE_77_B_5.zipCWE-7730
9201933888D92BE7CBF0F279F213502E627686704E85537C84BD0899FEFB2341A9969CWE_121_B_2.zipCWE-12130
2019EA2AD22333C4EBDD8FBE446D232B99112FC502972C13CC9C3CA7536642EDF0F1CWE_77_C_1.zipCWE-7740
882018B91E3B97FF28DF283DC38A987D10F55928D59A30928B2523EDF53387F998BB85CWE_562_e_1.zipCWE-56260
89201841F492A0CCD7D25D288E3756B190C278C707BFC79E6C54D7F541FCF307057D78CWE_562_d_2.zipCWE-56250
902018BFCEDC35B2C7242216EAD3D81E31338D3C365DAFD4762E3BC3EF8591C144E3F7CWE_562_d_1.zipCWE-56250
912018A0B6677D804F81DE1805F3E9BB8144288C0296638126134F7E884A822B2B7F06CWE_562_c_2.zipCWE-56240
92201899B24C72831C030DB6095ED5957185B3852EA5403158F63F7A84E0809ACCD791CWE_416_c_1.zipCWE-41640
932018DD03CC6A2392997EE189ED8B02BD723806D17B5D4F3719257A992A2386D532ABCWE_416_a_2.zipCWE-41620
94201889631154123B563567E6B8B6D766C830F3B2ABC2A804F9351EA52D7599E87325CWE_416_d_2.zipCWE-41650
9520182A2177A0BA0933E04782EEA3C5838BD9C4FBF140D77EC995489EA87583006500CWE_416_c_2.zipCWE-41640
962018BCAFA3626403421B3475E5380C72E1C4C8F59633ADA76A4E8E65769D4F57B258CWE_416_a_1.zipCWE-41620
972018B7C3073484251FF519458A790D4FF942CE2A8793399EDD1471079D6BEA1DA64CCWE_416_b_1.zipCWE-41630