Overview

Dataset statistics

Number of variables9
Number of observations104
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.3 KiB
Average record size in memory81.3 B

Variable types

Categorical2
Numeric6
Text1

Dataset

DescriptionSample
Author한국인터넷진흥원
URLhttps://www.bigdata-telecom.kr/invoke/SOKBP2603/?goodsCode=KIS0000039

Alerts

2020 has constant value ""Constant
2020.1 has constant value ""Constant
01 is highly overall correlated with 01.1 and 2 other fieldsHigh correlation
01.1 is highly overall correlated with 01 and 2 other fieldsHigh correlation
101136 is highly overall correlated with 101322High correlation
01.2 is highly overall correlated with 01 and 2 other fieldsHigh correlation
01.3 is highly overall correlated with 01 and 2 other fieldsHigh correlation
101322 is highly overall correlated with 101136High correlation

Reproduction

Analysis started2023-12-10 06:44:19.025205
Analysis finished2023-12-10 06:44:24.734423
Duration5.71 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

2020
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size964.0 B
2020
104 

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 104
100.0%

Length

2023-12-10T15:44:24.834667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:44:24.967180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020 104
100.0%

01
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)5.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.9711538
Minimum1
Maximum11
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T15:44:25.135360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median6
Q37
95-th percentile7
Maximum11
Range10
Interquartile range (IQR)6

Descriptive statistics

Standard deviation2.7179973
Coefficient of variation (CV)0.54675381
Kurtosis-0.63552403
Mean4.9711538
Median Absolute Deviation (MAD)1
Skewness-0.34172411
Sum517
Variance7.3875093
MonotonicityIncreasing
2023-12-10T15:44:25.297104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
6 41
39.4%
1 29
27.9%
7 26
25.0%
11 4
 
3.8%
3 2
 
1.9%
5 2
 
1.9%
ValueCountFrequency (%)
1 29
27.9%
3 2
 
1.9%
5 2
 
1.9%
6 41
39.4%
7 26
25.0%
11 4
 
3.8%
ValueCountFrequency (%)
11 4
 
3.8%
7 26
25.0%
6 41
39.4%
5 2
 
1.9%
3 2
 
1.9%
1 29
27.9%

01.1
Real number (ℝ)

HIGH CORRELATION 

Distinct15
Distinct (%)14.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.048077
Minimum1
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T15:44:25.470555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median6
Q318
95-th percentile30
Maximum30
Range29
Interquartile range (IQR)17

Descriptive statistics

Standard deviation9.6756925
Coefficient of variation (CV)0.8757807
Kurtosis-0.98058119
Mean11.048077
Median Absolute Deviation (MAD)5
Skewness0.55078497
Sum1149
Variance93.619025
MonotonicityNot monotonic
2023-12-10T15:44:25.642358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
1 29
27.9%
6 12
11.5%
17 12
11.5%
30 9
 
8.7%
21 8
 
7.7%
19 4
 
3.8%
5 4
 
3.8%
18 4
 
3.8%
2 4
 
3.8%
4 4
 
3.8%
Other values (5) 14
13.5%
ValueCountFrequency (%)
1 29
27.9%
2 4
 
3.8%
4 4
 
3.8%
5 4
 
3.8%
6 12
11.5%
9 4
 
3.8%
12 3
 
2.9%
14 3
 
2.9%
17 12
11.5%
18 4
 
3.8%
ValueCountFrequency (%)
30 9
8.7%
27 2
 
1.9%
23 2
 
1.9%
21 8
7.7%
19 4
 
3.8%
18 4
 
3.8%
17 12
11.5%
14 3
 
2.9%
12 3
 
2.9%
9 4
 
3.8%

101136
Real number (ℝ)

HIGH CORRELATION 

Distinct94
Distinct (%)90.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean149226.68
Minimum83730
Maximum221832
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T15:44:25.861295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum83730
5-th percentile111393.45
Q1122389.5
median143236
Q3173303.25
95-th percentile194312.7
Maximum221832
Range138102
Interquartile range (IQR)50913.75

Descriptive statistics

Standard deviation31502.904
Coefficient of variation (CV)0.21110771
Kurtosis-0.89262492
Mean149226.68
Median Absolute Deviation (MAD)27796
Skewness0.06674132
Sum15519575
Variance9.9243295 × 108
MonotonicityNot monotonic
2023-12-10T15:44:26.091999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
112746 3
 
2.9%
172540 2
 
1.9%
142929 2
 
1.9%
142918 2
 
1.9%
202654 2
 
1.9%
170456 2
 
1.9%
124248 2
 
1.9%
130157 2
 
1.9%
185834 2
 
1.9%
112542 1
 
1.0%
Other values (84) 84
80.8%
ValueCountFrequency (%)
83730 1
1.0%
83731 1
1.0%
83732 1
1.0%
101210 1
1.0%
103935 1
1.0%
111321 1
1.0%
111804 1
1.0%
112359 1
1.0%
112542 1
1.0%
112738 1
1.0%
ValueCountFrequency (%)
221832 1
1.0%
210447 1
1.0%
202654 2
1.9%
202653 1
1.0%
194523 1
1.0%
193121 1
1.0%
193015 1
1.0%
192419 1
1.0%
190240 1
1.0%
185845 1
1.0%

2020.1
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size964.0 B
2020
104 

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 104
100.0%

Length

2023-12-10T15:44:26.316058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:44:26.463869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020 104
100.0%

01.2
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)5.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.9711538
Minimum1
Maximum11
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T15:44:26.587701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median6
Q37
95-th percentile7
Maximum11
Range10
Interquartile range (IQR)6

Descriptive statistics

Standard deviation2.7179973
Coefficient of variation (CV)0.54675381
Kurtosis-0.63552403
Mean4.9711538
Median Absolute Deviation (MAD)1
Skewness-0.34172411
Sum517
Variance7.3875093
MonotonicityIncreasing
2023-12-10T15:44:26.778453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
6 41
39.4%
1 29
27.9%
7 26
25.0%
11 4
 
3.8%
3 2
 
1.9%
5 2
 
1.9%
ValueCountFrequency (%)
1 29
27.9%
3 2
 
1.9%
5 2
 
1.9%
6 41
39.4%
7 26
25.0%
11 4
 
3.8%
ValueCountFrequency (%)
11 4
 
3.8%
7 26
25.0%
6 41
39.4%
5 2
 
1.9%
3 2
 
1.9%
1 29
27.9%

01.3
Real number (ℝ)

HIGH CORRELATION 

Distinct15
Distinct (%)14.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.048077
Minimum1
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T15:44:26.943934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median6
Q318
95-th percentile30
Maximum30
Range29
Interquartile range (IQR)17

Descriptive statistics

Standard deviation9.6756925
Coefficient of variation (CV)0.8757807
Kurtosis-0.98058119
Mean11.048077
Median Absolute Deviation (MAD)5
Skewness0.55078497
Sum1149
Variance93.619025
MonotonicityNot monotonic
2023-12-10T15:44:27.106840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
1 29
27.9%
6 12
11.5%
17 12
11.5%
30 9
 
8.7%
21 8
 
7.7%
19 4
 
3.8%
5 4
 
3.8%
18 4
 
3.8%
2 4
 
3.8%
4 4
 
3.8%
Other values (5) 14
13.5%
ValueCountFrequency (%)
1 29
27.9%
2 4
 
3.8%
4 4
 
3.8%
5 4
 
3.8%
6 12
11.5%
9 4
 
3.8%
12 3
 
2.9%
14 3
 
2.9%
17 12
11.5%
18 4
 
3.8%
ValueCountFrequency (%)
30 9
8.7%
27 2
 
1.9%
23 2
 
1.9%
21 8
7.7%
19 4
 
3.8%
18 4
 
3.8%
17 12
11.5%
14 3
 
2.9%
12 3
 
2.9%
9 4
 
3.8%

101322
Real number (ℝ)

HIGH CORRELATION 

Distinct103
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean161712.95
Minimum83743
Maximum232947
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T15:44:27.629584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum83743
5-th percentile113442.9
Q1140962.5
median162914
Q3185848.75
95-th percentile213467.1
Maximum232947
Range149204
Interquartile range (IQR)44886.25

Descriptive statistics

Standard deviation32809.427
Coefficient of variation (CV)0.20288682
Kurtosis-0.41476808
Mean161712.95
Median Absolute Deviation (MAD)22904
Skewness-0.13579911
Sum16818147
Variance1.0764585 × 109
MonotonicityNot monotonic
2023-12-10T15:44:27.833293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
185818 2
 
1.9%
132919 1
 
1.0%
112813 1
 
1.0%
164525 1
 
1.0%
143617 1
 
1.0%
143620 1
 
1.0%
145818 1
 
1.0%
113801 1
 
1.0%
122112 1
 
1.0%
114333 1
 
1.0%
Other values (93) 93
89.4%
ValueCountFrequency (%)
83743 1
1.0%
85220 1
1.0%
85715 1
1.0%
111840 1
1.0%
112813 1
1.0%
113430 1
1.0%
113516 1
1.0%
113801 1
1.0%
114333 1
1.0%
114830 1
1.0%
ValueCountFrequency (%)
232947 1
1.0%
231945 1
1.0%
223156 1
1.0%
221832 1
1.0%
214620 1
1.0%
213648 1
1.0%
212442 1
1.0%
210551 1
1.0%
205528 1
1.0%
204830 1
1.0%

hxxps://bit.ly/2F3ZKuZ
Text

Distinct67
Distinct (%)64.4%
Missing0
Missing (%)0.0%
Memory size964.0 B
2023-12-10T15:44:28.277537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length44
Median length33
Mean length21.990385
Min length16

Characters and Unicode

Total characters2287
Distinct characters67
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 (%)43.3%

Sample

1st rowhxxps://iii.im/8BkK
2nd rowhxxps://tuney.kr/mk1
3rd rowhxxps://iii.im/8BkK
4th rowhxxp://band-us.io/589/7
5th rowhxxp://xco.kr/nDV
ValueCountFrequency (%)
hxxps://bit.ly/2aikkek 5
 
4.8%
hxxps://bit.ly/2n4wgt9 5
 
4.8%
hxxp://band-us.tv/kakao/123 5
 
4.8%
hxxp://bit.ly/3gy8mf9 4
 
3.8%
hxxps://bit.ly/39txyj4 4
 
3.8%
hxxps://bit.ly/2y8lstc 3
 
2.9%
hxxps://open.kakao.com/o/sjqtgyqb 3
 
2.9%
hxxps://tr.im/rhrxps 2
 
1.9%
hxxps://vo.la/jzzj 2
 
1.9%
hxxp://lco.jp/asz3 2
 
1.9%
Other values (57) 69
66.3%
2023-12-10T15:44:29.047231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 327
 
14.3%
x 221
 
9.7%
h 120
 
5.2%
p 117
 
5.1%
. 112
 
4.9%
: 105
 
4.6%
s 87
 
3.8%
i 81
 
3.5%
l 81
 
3.5%
t 75
 
3.3%
Other values (57) 961
42.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1353
59.2%
Other Punctuation 544
23.8%
Uppercase Letter 204
 
8.9%
Decimal Number 174
 
7.6%
Dash Punctuation 9
 
0.4%
Connector Punctuation 3
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
x 221
16.3%
h 120
 
8.9%
p 117
 
8.6%
s 87
 
6.4%
i 81
 
6.0%
l 81
 
6.0%
t 75
 
5.5%
k 65
 
4.8%
b 61
 
4.5%
y 50
 
3.7%
Other values (16) 395
29.2%
Uppercase Letter
ValueCountFrequency (%)
Y 21
 
10.3%
L 14
 
6.9%
G 13
 
6.4%
K 13
 
6.4%
N 11
 
5.4%
S 9
 
4.4%
F 9
 
4.4%
A 9
 
4.4%
W 8
 
3.9%
E 8
 
3.9%
Other values (16) 89
43.6%
Decimal Number
ValueCountFrequency (%)
2 32
18.4%
3 27
15.5%
9 24
13.8%
1 18
10.3%
5 17
9.8%
8 13
7.5%
4 13
7.5%
7 12
 
6.9%
0 11
 
6.3%
6 7
 
4.0%
Other Punctuation
ValueCountFrequency (%)
/ 327
60.1%
. 112
 
20.6%
: 105
 
19.3%
Dash Punctuation
ValueCountFrequency (%)
- 9
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1557
68.1%
Common 730
31.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
x 221
 
14.2%
h 120
 
7.7%
p 117
 
7.5%
s 87
 
5.6%
i 81
 
5.2%
l 81
 
5.2%
t 75
 
4.8%
k 65
 
4.2%
b 61
 
3.9%
y 50
 
3.2%
Other values (42) 599
38.5%
Common
ValueCountFrequency (%)
/ 327
44.8%
. 112
 
15.3%
: 105
 
14.4%
2 32
 
4.4%
3 27
 
3.7%
9 24
 
3.3%
1 18
 
2.5%
5 17
 
2.3%
8 13
 
1.8%
4 13
 
1.8%
Other values (5) 42
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2287
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/ 327
 
14.3%
x 221
 
9.7%
h 120
 
5.2%
p 117
 
5.1%
. 112
 
4.9%
: 105
 
4.6%
s 87
 
3.8%
i 81
 
3.5%
l 81
 
3.5%
t 75
 
3.3%
Other values (57) 961
42.0%

Interactions

2023-12-10T15:44:23.512456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:44:19.423915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:44:20.187479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:44:21.075654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:44:21.936723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:44:22.696452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:44:23.647944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:44:19.542893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:44:20.327789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:44:21.203957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:44:22.042042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:44:22.831133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:44:23.812111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:44:19.658040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:44:20.470866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:44:21.353792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:44:22.178919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:44:22.963737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:44:23.962202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:44:19.804600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:44:20.658359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:44:21.523112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:44:22.304509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:44:23.113733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:44:24.086011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:44:19.934248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:44:20.769933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:44:21.671785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:44:22.424434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:44:23.230033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:44:24.238506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:44:20.057313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:44:20.901039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:44:21.818297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:44:22.558347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:44:23.362805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T15:44:29.180831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
0101.110113601.201.3101322hxxps://bit.ly/2F3ZKuZ
011.0000.9100.6481.0000.9100.3611.000
01.10.9101.0000.9520.9101.0000.8280.994
1011360.6480.9521.0000.6480.9520.9250.991
01.21.0000.9100.6481.0000.9100.3611.000
01.30.9101.0000.9520.9101.0000.8280.994
1013220.3610.8280.9250.3610.8281.0000.867
hxxps://bit.ly/2F3ZKuZ1.0000.9940.9911.0000.9940.8671.000
2023-12-10T15:44:29.361054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
0101.110113601.201.3101322
011.0000.5970.3121.0000.5970.199
01.10.5971.0000.1500.5971.0000.071
1011360.3120.1501.0000.3120.1500.816
01.21.0000.5970.3121.0000.5970.199
01.30.5971.0000.1500.5971.0000.071
1013220.1990.0710.8160.1990.0711.000

Missing values

2023-12-10T15:44:24.438539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T15:44:24.648044image/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

20200101.11011362020.101.201.3101322hxxps://bit.ly/2F3ZKuZ
0202011101210202011132919hxxps://iii.im/8BkK
1202011111804202011111840hxxps://tuney.kr/mk1
2202011112359202011185818hxxps://iii.im/8BkK
3202011114716202011163343hxxp://band-us.io/589/7
4202011115214202011115239hxxp://xco.kr/nDV
5202011115717202011120032hxxps://vvd.bz/dIG
6202011120134202011184125hxxps://vvd.bz/dIG
7202011120648202011120719hxxp://na.to/nDh
8202011121805202011153827hxxps://iii.im/nq7K
9202011124146202011140151hxxp://pf.kakao.com/_DXYIxb/friend
20200101.11011362020.101.201.3101322hxxps://bit.ly/2F3ZKuZ
94202072183730202072185220hxxps://url.kr/UVP24c
95202072183731202072183743hxxps://url.kr/okvfgO
96202072183732202072185715hxxps://bit.ly/2LgVCL5hxxps://bit.ly/2LgVCL5
9720207211833142020721183402hxxp://bit.ly/2E5o9TB
9820207211833152020721184553hxxp://click.gl/ZbunRb
9920207211833202020721190550hxxp://bit.ly/2E5o9TB
1002020112118253720201121190300hxxps://vo.la/hfxlP
1012020112118295120201121184320hxxp://sum-kakaoplus.com/13
1022020112321044720201123214620hxxps://vo.la/Y6iSo
1032020112322183220201123221832hxxp://click.gl/eAGEnL