Overview

Dataset statistics

Number of variables6
Number of observations158
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.2 KiB
Average record size in memory52.8 B

Variable types

Categorical4
Numeric1
Text1

Dataset

DescriptionSample
Author한국인터넷진흥원
URLhttps://www.bigdata-telecom.kr/invoke/SOKBP2603/?goodsCode=KIS00000000000000008

Alerts

생성년도 has constant value ""Constant
URL has constant value ""Constant
생성월 is highly overall correlated with 생성일High correlation
생성일 is highly overall correlated with 생성월High correlation
IP주소 has unique valuesUnique

Reproduction

Analysis started2023-12-10 06:23:03.961879
Analysis finished2023-12-10 06:23:04.624765
Duration0.66 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

생성년도
Categorical

CONSTANT 

Distinct1
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
2019
158 

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 158
100.0%

Length

2023-12-10T15:23:04.729880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:23:04.898068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2019 158
100.0%

생성월
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
3
109 
2
30 
1
19 

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 (%)
3 109
69.0%
2 30
 
19.0%
1 19
 
12.0%

Length

2023-12-10T15:23:05.072397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:23:05.258344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3 109
69.0%
2 30
 
19.0%
1 19
 
12.0%

생성일
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
13
48 
4
36 
25
30 
14
25 
22
19 

Length

Max length2
Median length2
Mean length1.7721519
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row22
2nd row22
3rd row22
4th row22
5th row22

Common Values

ValueCountFrequency (%)
13 48
30.4%
4 36
22.8%
25 30
19.0%
14 25
15.8%
22 19
 
12.0%

Length

2023-12-10T15:23:05.455196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:23:05.683486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
13 48
30.4%
4 36
22.8%
25 30
19.0%
14 25
15.8%
22 19
 
12.0%

생성시분초
Real number (ℝ)

Distinct17
Distinct (%)10.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean139550.63
Minimum12000
Maximum230000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-12-10T15:23:05.964945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum12000
5-th percentile43000
Q1113000
median154000
Q3161000
95-th percentile223000
Maximum230000
Range218000
Interquartile range (IQR)48000

Descriptive statistics

Standard deviation47255.579
Coefficient of variation (CV)0.33862676
Kurtosis0.76974848
Mean139550.63
Median Absolute Deviation (MAD)19000
Skewness-0.68456474
Sum22049000
Variance2.2330898 × 109
MonotonicityNot monotonic
2023-12-10T15:23:06.177438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
154000 34
21.5%
113000 21
13.3%
155000 21
13.3%
161000 20
12.7%
43000 8
 
5.1%
223000 8
 
5.1%
112000 6
 
3.8%
173000 6
 
3.8%
84000 6
 
3.8%
174000 5
 
3.2%
Other values (7) 23
14.6%
ValueCountFrequency (%)
12000 4
 
2.5%
43000 8
 
5.1%
45000 4
 
2.5%
84000 6
 
3.8%
112000 6
 
3.8%
113000 21
13.3%
114000 3
 
1.9%
131000 5
 
3.2%
154000 34
21.5%
155000 21
13.3%
ValueCountFrequency (%)
230000 4
 
2.5%
223000 8
 
5.1%
213000 2
 
1.3%
182000 1
 
0.6%
174000 5
 
3.2%
173000 6
 
3.8%
161000 20
12.7%
155000 21
13.3%
154000 34
21.5%
131000 5
 
3.2%

IP주소
Text

UNIQUE 

Distinct158
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
2023-12-10T15:23:06.700583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length11.544304
Min length8

Characters and Unicode

Total characters1824
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique158 ?
Unique (%)100.0%

Sample

1st row118.*.85.243
2nd row111.*.214.45
3rd row61.*.132.94
4th row61.*.187.107
5th row61.*.130.28
ValueCountFrequency (%)
118.*.85.243 1
 
0.6%
103.*.251.13 1
 
0.6%
111.*.24.59 1
 
0.6%
1.*.21.23 1
 
0.6%
220.*.13.174 1
 
0.6%
1.*.153.120 1
 
0.6%
118.*.81.139 1
 
0.6%
111.*.227.239 1
 
0.6%
36.*.147.83 1
 
0.6%
36.*.10.154 1
 
0.6%
Other values (148) 148
93.7%
2023-12-10T15:23:07.462227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 474
26.0%
1 424
23.2%
2 178
 
9.8%
* 158
 
8.7%
6 106
 
5.8%
3 94
 
5.2%
4 78
 
4.3%
8 76
 
4.2%
7 64
 
3.5%
5 62
 
3.4%
Other values (2) 110
 
6.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1192
65.4%
Other Punctuation 632
34.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 424
35.6%
2 178
14.9%
6 106
 
8.9%
3 94
 
7.9%
4 78
 
6.5%
8 76
 
6.4%
7 64
 
5.4%
5 62
 
5.2%
9 58
 
4.9%
0 52
 
4.4%
Other Punctuation
ValueCountFrequency (%)
. 474
75.0%
* 158
 
25.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1824
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 474
26.0%
1 424
23.2%
2 178
 
9.8%
* 158
 
8.7%
6 106
 
5.8%
3 94
 
5.2%
4 78
 
4.3%
8 76
 
4.2%
7 64
 
3.5%
5 62
 
3.4%
Other values (2) 110
 
6.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1824
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 474
26.0%
1 424
23.2%
2 178
 
9.8%
* 158
 
8.7%
6 106
 
5.8%
3 94
 
5.2%
4 78
 
4.3%
8 76
 
4.2%
7 64
 
3.5%
5 62
 
3.4%
Other values (2) 110
 
6.0%

URL
Categorical

CONSTANT 

Distinct1
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
-
158 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-
2nd row-
3rd row-
4th row-
5th row-

Common Values

ValueCountFrequency (%)
- 158
100.0%

Length

2023-12-10T15:23:07.784398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:23:08.026489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
158
100.0%

Interactions

2023-12-10T15:23:04.210485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T15:23:08.196908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
생성월생성일생성시분초
생성월1.0001.0000.314
생성일1.0001.0000.536
생성시분초0.3140.5361.000
2023-12-10T15:23:08.361740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
생성월생성일
생성월1.0000.994
생성일0.9941.000
2023-12-10T15:23:08.528142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
생성시분초생성월생성일
생성시분초1.0000.3050.404
생성월0.3051.0000.994
생성일0.4040.9941.000

Missing values

2023-12-10T15:23:04.394375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T15:23:04.560965image/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
0201912243000118.*.85.243-
12019122155000111.*.214.45-
2201912215500061.*.132.94-
3201912211400061.*.187.107-
4201912215500061.*.130.28-
5201912215500061.*.135.13-
62019122114000220.*.199.166-
72019122155000114.*.6.179-
8201912243000111.*.227.8-
9201912243000118.*.32.231-
생성년도생성월생성일생성시분초IP주소URL
1482019314155000118.*.3.20-
149201931445000114.*.168.31-
15020193141550001.*.7.217-
1512019314131000111.*.209.14-
152201931415500061.*.189.73-
1532019314230000114.*.183.251-
154201931445000114.*.13.135-
1552019314155000118.*.129.218-
1562019314155000111.*.32.76-
15720193142300001.*.36.73-