Overview

Dataset statistics

Number of variables4
Number of observations45
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.7 KiB
Average record size in memory37.9 B

Variable types

Categorical1
Numeric2
Text1

Dataset

DescriptionSample
Author한국인터넷진흥원
URLhttps://www.bigdata-telecom.kr/invoke/SOKBP2603/?goodsCode=KIS0000031

Alerts

생성년도 has constant value ""Constant
파일명 has unique valuesUnique

Reproduction

Analysis started2023-12-10 06:31:48.908615
Analysis finished2023-12-10 06:31:49.984155
Duration1.08 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

생성년도
Categorical

CONSTANT 

Distinct1
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size492.0 B
2020
45 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020
2nd row2020
3rd row2020
4th row2020
5th row2020

Common Values

ValueCountFrequency (%)
2020 45
100.0%

Length

2023-12-10T15:31:50.093490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:31:50.271686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020 45
100.0%

생성월
Real number (ℝ)

Distinct7
Distinct (%)15.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.5333333
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size537.0 B
2023-12-10T15:31:50.437258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q17
median7
Q38
95-th percentile9
Maximum9
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.3413283
Coefficient of variation (CV)0.35836658
Kurtosis0.1223187
Mean6.5333333
Median Absolute Deviation (MAD)1
Skewness-1.1503204
Sum294
Variance5.4818182
MonotonicityIncreasing
2023-12-10T15:31:50.618078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
7 18
40.0%
8 8
17.8%
9 8
17.8%
2 5
 
11.1%
3 3
 
6.7%
6 2
 
4.4%
1 1
 
2.2%
ValueCountFrequency (%)
1 1
 
2.2%
2 5
 
11.1%
3 3
 
6.7%
6 2
 
4.4%
7 18
40.0%
8 8
17.8%
9 8
17.8%
ValueCountFrequency (%)
9 8
17.8%
8 8
17.8%
7 18
40.0%
6 2
 
4.4%
3 3
 
6.7%
2 5
 
11.1%
1 1
 
2.2%

생성일
Real number (ℝ)

Distinct23
Distinct (%)51.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.755556
Minimum2
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size537.0 B
2023-12-10T15:31:50.830374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2
Q17
median15
Q322
95-th percentile28.8
Maximum30
Range28
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.8962596
Coefficient of variation (CV)0.60290916
Kurtosis-1.1879609
Mean14.755556
Median Absolute Deviation (MAD)7
Skewness0.11976459
Sum664
Variance79.143434
MonotonicityNot monotonic
2023-12-10T15:31:51.082864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
2 4
 
8.9%
16 4
 
8.9%
28 3
 
6.7%
11 3
 
6.7%
15 3
 
6.7%
3 3
 
6.7%
21 3
 
6.7%
9 2
 
4.4%
4 2
 
4.4%
22 2
 
4.4%
Other values (13) 16
35.6%
ValueCountFrequency (%)
2 4
8.9%
3 3
6.7%
4 2
4.4%
6 2
4.4%
7 1
 
2.2%
9 2
4.4%
10 2
4.4%
11 3
6.7%
12 1
 
2.2%
14 1
 
2.2%
ValueCountFrequency (%)
30 1
 
2.2%
29 2
4.4%
28 3
6.7%
26 1
 
2.2%
25 1
 
2.2%
24 1
 
2.2%
23 1
 
2.2%
22 2
4.4%
21 3
6.7%
20 1
 
2.2%

파일명
Text

UNIQUE 

Distinct45
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size492.0 B
2023-12-10T15:31:51.494867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length39
Median length39
Mean length39
Min length39

Characters and Unicode

Total characters1755
Distinct characters26
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

Unique45 ?
Unique (%)100.0%

Sample

1st row2020-0102-171721-744_securityNotice.xml
2nd row2020-0221-100128-474_securityNotice.xml
3rd row2020-0225-162520-287_securityNotice.xml
4th row2020-0226-153602-030_securityNotice.xml
5th row2020-0228-160510-638_securityNotice.xml
ValueCountFrequency (%)
2020-0102-171721-744_securitynotice.xml 1
 
2.2%
2020-0716-114859-436_securitynotice.xml 1
 
2.2%
2020-0717-170052-968_securitynotice.xml 1
 
2.2%
2020-0722-180329-918_securitynotice.xml 1
 
2.2%
2020-0724-103310-156_securitynotice.xml 1
 
2.2%
2020-0729-105747-899_securitynotice.xml 1
 
2.2%
2020-0803-142322-300_securitynotice.xml 1
 
2.2%
2020-0804-174145-957_securitynotice.xml 1
 
2.2%
2020-0806-141956-999_securitynotice.xml 1
 
2.2%
2020-0811-114221-740_securitynotice.xml 1
 
2.2%
Other values (35) 35
77.8%
2023-12-10T15:31:52.088457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 219
 
12.5%
2 159
 
9.1%
- 135
 
7.7%
1 102
 
5.8%
e 90
 
5.1%
i 90
 
5.1%
c 90
 
5.1%
t 90
 
5.1%
7 51
 
2.9%
4 47
 
2.7%
Other values (16) 682
38.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 765
43.6%
Lowercase Letter 720
41.0%
Dash Punctuation 135
 
7.7%
Connector Punctuation 45
 
2.6%
Other Punctuation 45
 
2.6%
Uppercase Letter 45
 
2.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 90
12.5%
i 90
12.5%
c 90
12.5%
t 90
12.5%
s 45
6.2%
l 45
6.2%
m 45
6.2%
x 45
6.2%
o 45
6.2%
y 45
6.2%
Other values (2) 90
12.5%
Decimal Number
ValueCountFrequency (%)
0 219
28.6%
2 159
20.8%
1 102
13.3%
7 51
 
6.7%
4 47
 
6.1%
3 45
 
5.9%
9 42
 
5.5%
6 35
 
4.6%
5 34
 
4.4%
8 31
 
4.1%
Dash Punctuation
ValueCountFrequency (%)
- 135
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 45
100.0%
Other Punctuation
ValueCountFrequency (%)
. 45
100.0%
Uppercase Letter
ValueCountFrequency (%)
N 45
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 990
56.4%
Latin 765
43.6%

Most frequent character per script

Common
ValueCountFrequency (%)
0 219
22.1%
2 159
16.1%
- 135
13.6%
1 102
10.3%
7 51
 
5.2%
4 47
 
4.7%
_ 45
 
4.5%
3 45
 
4.5%
. 45
 
4.5%
9 42
 
4.2%
Other values (3) 100
10.1%
Latin
ValueCountFrequency (%)
e 90
11.8%
i 90
11.8%
c 90
11.8%
t 90
11.8%
s 45
 
5.9%
l 45
 
5.9%
m 45
 
5.9%
x 45
 
5.9%
o 45
 
5.9%
N 45
 
5.9%
Other values (3) 135
17.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1755
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 219
 
12.5%
2 159
 
9.1%
- 135
 
7.7%
1 102
 
5.8%
e 90
 
5.1%
i 90
 
5.1%
c 90
 
5.1%
t 90
 
5.1%
7 51
 
2.9%
4 47
 
2.7%
Other values (16) 682
38.9%

Interactions

2023-12-10T15:31:49.422293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:31:49.105249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:31:49.584850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:31:49.270565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T15:31:52.366598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
생성월생성일파일명
생성월1.0000.5621.000
생성일0.5621.0001.000
파일명1.0001.0001.000
2023-12-10T15:31:52.500011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
생성월생성일
생성월1.000-0.187
생성일-0.1871.000

Missing values

2023-12-10T15:31:49.760954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T15:31:49.922224image/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

생성년도생성월생성일파일명
02020122020-0102-171721-744_securityNotice.xml
120202212020-0221-100128-474_securityNotice.xml
220202252020-0225-162520-287_securityNotice.xml
320202262020-0226-153602-030_securityNotice.xml
420202282020-0228-160510-638_securityNotice.xml
520202282020-0228-174535-420_securityNotice.xml
62020322020-0302-112021-865_securityNotice.xml
72020322020-0302-153302-904_securityNotice.xml
820203112020-0311-104036-473_securityNotice.xml
920206292020-0629-101243-830_securityNotice.xml
생성년도생성월생성일파일명
3520208212020-0821-153021-623_securityNotice.xml
3620208282020-0828-143857-223_securityNotice.xml
372020932020-0903-134956-630_securityNotice.xml
382020942020-0904-092330-244_securityNotice.xml
392020992020-0909-140024-600_securityNotice.xml
4020209102020-0910-113946-531_securityNotice.xml
4120209112020-0911-181145-307_securityNotice.xml
4220209212020-0921-132659-060_securityNotice.xml
4320209222020-0922-171036-073_securityNotice.xml
4420209232020-0923-100911-747_securityNotice.xml