Overview

Dataset statistics

Number of variables6
Number of observations151
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.8 KiB
Average record size in memory52.9 B

Variable types

Categorical4
Numeric1
Text1

Dataset

DescriptionSample
Author한국인터넷진흥원
URLhttps://www.bigdata-telecom.kr/invoke/SOKBP2603/?goodsCode=KIS00000000000000007

Alerts

생성년도 has constant value ""Constant
생성월 has constant value ""Constant
생성일 is highly overall correlated with 생성시분초 and 1 other fieldsHigh correlation
URL is highly overall correlated with 생성일High correlation
생성시분초 is highly overall correlated with 생성일High correlation
URL is highly imbalanced (67.8%)Imbalance

Reproduction

Analysis started2023-12-10 06:46:35.595498
Analysis finished2023-12-10 06:46:36.130906
Duration0.54 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

생성년도
Categorical

CONSTANT 

Distinct1
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
2019
151 

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 151
100.0%

Length

2023-12-10T15:46:36.236842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:46:36.363131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2019 151
100.0%

생성월
Categorical

CONSTANT 

Distinct1
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
1
151 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 151
100.0%

Length

2023-12-10T15:46:36.524207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:46:36.641446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 151
100.0%

생성일
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
22
124 
10
27 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row10
2nd row10
3rd row10
4th row10
5th row10

Common Values

ValueCountFrequency (%)
22 124
82.1%
10 27
 
17.9%

Length

2023-12-10T15:46:36.791400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:46:36.953640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
22 124
82.1%
10 27
 
17.9%

생성시분초
Real number (ℝ)

HIGH CORRELATION 

Distinct8
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean111922.05
Minimum43000
Maximum160334
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-12-10T15:46:37.096056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum43000
5-th percentile43000
Q143000
median114000
Q3155000
95-th percentile160334
Maximum160334
Range117334
Interquartile range (IQR)112000

Descriptive statistics

Standard deviation47053.905
Coefficient of variation (CV)0.42041675
Kurtosis-1.3496502
Mean111922.05
Median Absolute Deviation (MAD)41000
Skewness-0.52356331
Sum16900230
Variance2.21407 × 109
MonotonicityNot monotonic
2023-12-10T15:46:37.252903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
43000 42
27.8%
155000 40
26.5%
114000 38
25.2%
160334 21
13.9%
84000 4
 
2.6%
143038 2
 
1.3%
143344 2
 
1.3%
143226 2
 
1.3%
ValueCountFrequency (%)
43000 42
27.8%
84000 4
 
2.6%
114000 38
25.2%
143038 2
 
1.3%
143226 2
 
1.3%
143344 2
 
1.3%
155000 40
26.5%
160334 21
13.9%
ValueCountFrequency (%)
160334 21
13.9%
155000 40
26.5%
143344 2
 
1.3%
143226 2
 
1.3%
143038 2
 
1.3%
114000 38
25.2%
84000 4
 
2.6%
43000 42
27.8%

IP주소
Text

Distinct127
Distinct (%)84.1%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
2023-12-10T15:46:37.575152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length9.7417219
Min length1

Characters and Unicode

Total characters1471
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

Unique125 ?
Unique (%)82.8%

Sample

1st row-
2nd row-
3rd row-
4th row-
5th row-
ValueCountFrequency (%)
23
 
15.2%
185.*.170.16 3
 
2.0%
36.*.10.174 1
 
0.7%
114.*.64.27 1
 
0.7%
36.*.35.108 1
 
0.7%
61.*.133.98 1
 
0.7%
61.*.41.163 1
 
0.7%
61.*.133.71 1
 
0.7%
220.*.10.125 1
 
0.7%
1.*.225.217 1
 
0.7%
Other values (117) 117
77.5%
2023-12-10T15:46:38.093516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 384
26.1%
1 281
19.1%
2 131
 
8.9%
* 128
 
8.7%
3 89
 
6.1%
6 84
 
5.7%
8 70
 
4.8%
4 65
 
4.4%
0 59
 
4.0%
9 58
 
3.9%
Other values (3) 122
 
8.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 936
63.6%
Other Punctuation 512
34.8%
Dash Punctuation 23
 
1.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 281
30.0%
2 131
14.0%
3 89
 
9.5%
6 84
 
9.0%
8 70
 
7.5%
4 65
 
6.9%
0 59
 
6.3%
9 58
 
6.2%
5 57
 
6.1%
7 42
 
4.5%
Other Punctuation
ValueCountFrequency (%)
. 384
75.0%
* 128
 
25.0%
Dash Punctuation
ValueCountFrequency (%)
- 23
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1471
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 384
26.1%
1 281
19.1%
2 131
 
8.9%
* 128
 
8.7%
3 89
 
6.1%
6 84
 
5.7%
8 70
 
4.8%
4 65
 
4.4%
0 59
 
4.0%
9 58
 
3.9%
Other values (3) 122
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1471
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 384
26.1%
1 281
19.1%
2 131
 
8.9%
* 128
 
8.7%
3 89
 
6.1%
6 84
 
5.7%
8 70
 
4.8%
4 65
 
4.4%
0 59
 
4.0%
9 58
 
3.9%
Other values (3) 122
 
8.3%

URL
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct24
Distinct (%)15.9%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
-
124 
hxxp://185.136.170.16/2018ë…„ë„%20ì—°ë§ì •ì‚°ì•ˆë‚´_190109.doc
 
2
hxxp://www.cinetfox.com/www/index.html
 
2
hxxp://185.136.170.16/2018ë…„%20ì—°ë§ì •ì‚°ì•ˆë‚´_190109.doc
 
2
hxxp://185.136.170.16/ì—°ë§ì •ì‚°ì•ˆë‚´(2018ë…„ë„).doc
 
2
Other values (19)
19 

Length

Max length62
Median length1
Mean length8.3443709
Min length1

Unique

Unique19 ?
Unique (%)12.6%

Sample

1st rowhxxp://www.cinetfox.com/www/CnGqMp.html
2nd rowhxxp://www.cinetfox.com/www/CfVbKr.jar
3rd rowhxxp://www.cinetfox.com/www/ww.swf
4th rowhxxp://www.cinetfox.com/www/MdTzGt.html
5th rowhxxp://185.136.170.16/ì—°ë§ì •ì‚°ì•ˆë‚´(2018ë…„ë„).doc

Common Values

ValueCountFrequency (%)
- 124
82.1%
hxxp://185.136.170.16/2018ë…„ë„%20ì—°ë§ì •ì‚°ì•ˆë‚´_190109.doc 2
 
1.3%
hxxp://www.cinetfox.com/www/index.html 2
 
1.3%
hxxp://185.136.170.16/2018ë…„%20ì—°ë§ì •ì‚°ì•ˆë‚´_190109.doc 2
 
1.3%
hxxp://185.136.170.16/ì—°ë§ì •ì‚°ì•ˆë‚´(2018ë…„ë„).doc 2
 
1.3%
hxxp://www.cinetfox.com/www/Long.js 1
 
0.7%
hxxp://www.cinetfox.com/www/ww.swf 1
 
0.7%
hxxp://www.cinetfox.com/www/MdTzGt.html 1
 
0.7%
hxxp://www.cinetfox.com/www/logo.swf 1
 
0.7%
hxxp://www.cinetfox.com/www/FxBvXp.html 1
 
0.7%
Other values (14) 14
 
9.3%

Length

2023-12-10T15:46:38.278227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
124
79.0%
•ì‚°ì•ˆë‚´_190109.doc 4
 
2.5%
hxxp://www.cinetfox.com/www/index.html 2
 
1.3%
hxxp://185.136.170.16/2018ë…„%20ì—°ë§ì 2
 
1.3%
hxxp://185.136.170.16/ì—°ë§ì 2
 
1.3%
•ì‚°ì•ˆë‚´(2018ë…„ë„).doc 2
 
1.3%
hxxp://185.136.170.16/2018ë…„ë„%20ì—°ë§ì 2
 
1.3%
hxxp://www.cinetfox.com/www/ww.js 1
 
0.6%
hxxp://www.cinetfox.com/www/bin_do.swf 1
 
0.6%
hxxp://www.cinetfox.com/www/swfobject.js 1
 
0.6%
Other values (16) 16
 
10.2%

Interactions

2023-12-10T15:46:35.765482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T15:46:38.395052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
생성일생성시분초URL
생성일1.0000.7581.000
생성시분초0.7581.0000.774
URL1.0000.7741.000
2023-12-10T15:46:38.514983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
생성일URL
생성일1.0000.923
URL0.9231.000
2023-12-10T15:46:38.627158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
생성시분초생성일URL
생성시분초1.0000.5960.399
생성일0.5961.0000.923
URL0.3990.9231.000

Missing values

2023-12-10T15:46:35.935399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T15:46:36.071092image/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

생성년도생성월생성일생성시분초IP주소URL
02019110160334-hxxp://www.cinetfox.com/www/CnGqMp.html
12019110160334-hxxp://www.cinetfox.com/www/CfVbKr.jar
22019110160334-hxxp://www.cinetfox.com/www/ww.swf
32019110160334-hxxp://www.cinetfox.com/www/MdTzGt.html
42019110143038-hxxp://185.136.170.16/ì—°ë§ì •ì‚°ì•ˆë‚´(2018ë…„ë„).doc
52019110143344-hxxp://185.136.170.16/2018ë…„ë„%20ì—°ë§ì •ì‚°ì•ˆë‚´_190109.doc
62019110160334-hxxp://www.cinetfox.com/www/logo.swf
72019110160334-hxxp://www.cinetfox.com/www/index.html
82019110160334-hxxp://www.cinetfox.com/www/FxBvXp.html
92019110160334-hxxp://www.cinetfox.com/www/jquery.js
생성년도생성월생성일생성시분초IP주소URL
141201912211400061.*.41.39-
14220191224300036.*.40.124-
143201912243000220.*.19.138-
14420191221140001.*.98.47-
1452019122114000114.*.180.225-
146201912215500036.*.104.244-
14720191224300036.*.159.142-
1482019122430001.*.254.42-
149201912243000114.*.181.192-
1502019122155000114.*.8.156-