Overview

Dataset statistics

Number of variables12
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.9 KiB
Average record size in memory101.3 B

Variable types

Numeric4
Text3
Categorical4
DateTime1

Alerts

CHNNEL_CL_NM has constant value ""Constant
CHNNEL_NM has constant value ""Constant
UPPER_CTGRY_NM has constant value ""Constant
LWPRT_CTGRY_NM has constant value ""Constant
DPI_VALUE is highly overall correlated with VIEWS_COHigh correlation
VIEWS_CO is highly overall correlated with DPI_VALUEHigh correlation
SEQ_NO has unique valuesUnique
CNTNTS_ID has unique valuesUnique
CNTNTS_URL has unique valuesUnique
COMMENT_CO has 74 (74.0%) zerosZeros

Reproduction

Analysis started2023-12-10 10:08:03.852950
Analysis finished2023-12-10 10:08:09.744477
Duration5.89 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

SEQ_NO
Real number (ℝ)

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10662.55
Minimum9612
Maximum11663
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:08:09.929752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9612
5-th percentile9626.8
Q19688.75
median10276
Q311572.75
95-th percentile11653.05
Maximum11663
Range2051
Interquartile range (IQR)1884

Descriptive statistics

Standard deviation901.67152
Coefficient of variation (CV)0.084564341
Kurtosis-1.9305484
Mean10662.55
Median Absolute Deviation (MAD)662.5
Skewness-0.023578346
Sum1066255
Variance813011.52
MonotonicityNot monotonic
2023-12-10T19:08:10.377254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11663 1
 
1.0%
11557 1
 
1.0%
9612 1
 
1.0%
9614 1
 
1.0%
11550 1
 
1.0%
9613 1
 
1.0%
11552 1
 
1.0%
11563 1
 
1.0%
10356 1
 
1.0%
9623 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
9612 1
1.0%
9613 1
1.0%
9614 1
1.0%
9621 1
1.0%
9623 1
1.0%
9627 1
1.0%
9630 1
1.0%
9634 1
1.0%
9639 1
1.0%
9642 1
1.0%
ValueCountFrequency (%)
11663 1
1.0%
11661 1
1.0%
11659 1
1.0%
11656 1
1.0%
11654 1
1.0%
11653 1
1.0%
11640 1
1.0%
11639 1
1.0%
11635 1
1.0%
11633 1
1.0%

CNTNTS_ID
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:08:10.896562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length32
Median length32
Mean length32
Min length32

Characters and Unicode

Total characters3200
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

Unique100 ?
Unique (%)100.0%

Sample

1st row31287513f43d11eba9b2002b67f7b0e1
2nd row31287511f43d11ebb1fd002b67f7b0e1
3rd rowd3adc5a859fd11ebac3fc0b6f9fde92b
4th rowcdeb123459fd11eb8a54c0b6f9fde92b
5th row3128750af43d11ebb37a002b67f7b0e1
ValueCountFrequency (%)
31287513f43d11eba9b2002b67f7b0e1 1
 
1.0%
31284e66f43d11ebb573002b67f7b0e1 1
 
1.0%
923692c659fd11eb8a6cc0b6f9fde92b 1
 
1.0%
31284e56f43d11ebb26d002b67f7b0e1 1
 
1.0%
91c1d96c59fd11eb9787c0b6f9fde92b 1
 
1.0%
31284e58f43d11eb912d002b67f7b0e1 1
 
1.0%
31284e63f43d11ebbb6b002b67f7b0e1 1
 
1.0%
31276428f43d11ebbd2f002b67f7b0e1 1
 
1.0%
972a664059fd11eb8082c0b6f9fde92b 1
 
1.0%
9b06fbf059fd11eb9a7dc0b6f9fde92b 1
 
1.0%
Other values (90) 90
90.0%
2023-12-10T19:08:11.644897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
b 388
12.1%
1 355
11.1%
e 286
 
8.9%
f 274
 
8.6%
0 260
 
8.1%
2 209
 
6.5%
9 202
 
6.3%
d 176
 
5.5%
6 176
 
5.5%
7 158
 
4.9%
Other values (6) 716
22.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1885
58.9%
Lowercase Letter 1315
41.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 355
18.8%
0 260
13.8%
2 209
11.1%
9 202
10.7%
6 176
9.3%
7 158
8.4%
4 157
8.3%
3 150
8.0%
8 140
 
7.4%
5 78
 
4.1%
Lowercase Letter
ValueCountFrequency (%)
b 388
29.5%
e 286
21.7%
f 274
20.8%
d 176
13.4%
c 106
 
8.1%
a 85
 
6.5%

Most occurring scripts

ValueCountFrequency (%)
Common 1885
58.9%
Latin 1315
41.1%

Most frequent character per script

Common
ValueCountFrequency (%)
1 355
18.8%
0 260
13.8%
2 209
11.1%
9 202
10.7%
6 176
9.3%
7 158
8.4%
4 157
8.3%
3 150
8.0%
8 140
 
7.4%
5 78
 
4.1%
Latin
ValueCountFrequency (%)
b 388
29.5%
e 286
21.7%
f 274
20.8%
d 176
13.4%
c 106
 
8.1%
a 85
 
6.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3200
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
b 388
12.1%
1 355
11.1%
e 286
 
8.9%
f 274
 
8.6%
0 260
 
8.1%
2 209
 
6.5%
9 202
 
6.3%
d 176
 
5.5%
6 176
 
5.5%
7 158
 
4.9%
Other values (6) 716
22.4%

CHNNEL_CL_NM
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
커뮤니티
100 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
커뮤니티 100
100.0%

Length

2023-12-10T19:08:12.001470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:08:12.173455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
커뮤니티 100
100.0%

CHNNEL_NM
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
ppomppu
100 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
ppomppu 100
100.0%

Length

2023-12-10T19:08:12.457835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:08:12.645484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
ppomppu 100
100.0%

TITLE_NM
Text

Distinct85
Distinct (%)85.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:08:13.126343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length39
Median length27
Mean length15.4
Min length2

Characters and Unicode

Total characters1540
Distinct characters364
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique71 ?
Unique (%)71.0%

Sample

1st row손흥민 걱정되네요
2nd row영국과 유럽 확산세
3rd row네이마르 부상
4th row당분간 라멜라, 레길론, 로셀소 못 나올 듯 합니다
5th row손흥민 100호골
ValueCountFrequency (%)
손흥민 23
 
5.8%
골 9
 
2.3%
축구 8
 
2.0%
토트넘 7
 
1.8%
이강인 5
 
1.3%
4
 
1.0%
맨유 4
 
1.0%
100호골 4
 
1.0%
첼시 4
 
1.0%
공식 3
 
0.8%
Other values (273) 323
82.0%
2023-12-10T19:08:14.099533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
295
 
19.2%
이 33
 
2.1%
흥 27
 
1.8%
손 24
 
1.6%
민 24
 
1.6%
0 22
 
1.4%
골 21
 
1.4%
스 21
 
1.4%
요 17
 
1.1%
리 16
 
1.0%
Other values (354) 1040
67.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1079
70.1%
Space Separator 295
 
19.2%
Decimal Number 51
 
3.3%
Other Punctuation 33
 
2.1%
Uppercase Letter 31
 
2.0%
Lowercase Letter 21
 
1.4%
Math Symbol 9
 
0.6%
Close Punctuation 8
 
0.5%
Open Punctuation 8
 
0.5%
Dash Punctuation 3
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
이 33
 
3.1%
흥 27
 
2.5%
손 24
 
2.2%
민 24
 
2.2%
골 21
 
1.9%
스 21
 
1.9%
요 17
 
1.6%
리 16
 
1.5%
인 14
 
1.3%
의 13
 
1.2%
Other values (303) 869
80.5%
Lowercase Letter
ValueCountFrequency (%)
s 2
 
9.5%
o 2
 
9.5%
t 2
 
9.5%
e 2
 
9.5%
p 2
 
9.5%
r 2
 
9.5%
m 1
 
4.8%
v 1
 
4.8%
d 1
 
4.8%
a 1
 
4.8%
Other values (5) 5
23.8%
Uppercase Letter
ValueCountFrequency (%)
P 6
19.4%
L 4
12.9%
E 4
12.9%
A 3
9.7%
O 3
9.7%
F 2
 
6.5%
T 2
 
6.5%
S 2
 
6.5%
K 1
 
3.2%
C 1
 
3.2%
Other values (3) 3
9.7%
Decimal Number
ValueCountFrequency (%)
0 22
43.1%
1 15
29.4%
2 8
 
15.7%
6 3
 
5.9%
4 1
 
2.0%
3 1
 
2.0%
8 1
 
2.0%
Other Punctuation
ValueCountFrequency (%)
? 12
36.4%
, 7
21.2%
. 7
21.2%
% 3
 
9.1%
! 2
 
6.1%
& 1
 
3.0%
: 1
 
3.0%
Math Symbol
ValueCountFrequency (%)
~ 8
88.9%
> 1
 
11.1%
Close Punctuation
ValueCountFrequency (%)
] 5
62.5%
) 3
37.5%
Open Punctuation
ValueCountFrequency (%)
[ 5
62.5%
( 3
37.5%
Space Separator
ValueCountFrequency (%)
295
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%
Modifier Symbol
ValueCountFrequency (%)
^ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1079
70.1%
Common 409
 
26.6%
Latin 52
 
3.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
이 33
 
3.1%
흥 27
 
2.5%
손 24
 
2.2%
민 24
 
2.2%
골 21
 
1.9%
스 21
 
1.9%
요 17
 
1.6%
리 16
 
1.5%
인 14
 
1.3%
의 13
 
1.2%
Other values (303) 869
80.5%
Latin
ValueCountFrequency (%)
P 6
 
11.5%
L 4
 
7.7%
E 4
 
7.7%
A 3
 
5.8%
O 3
 
5.8%
F 2
 
3.8%
T 2
 
3.8%
S 2
 
3.8%
s 2
 
3.8%
o 2
 
3.8%
Other values (18) 22
42.3%
Common
ValueCountFrequency (%)
295
72.1%
0 22
 
5.4%
1 15
 
3.7%
? 12
 
2.9%
~ 8
 
2.0%
2 8
 
2.0%
, 7
 
1.7%
. 7
 
1.7%
] 5
 
1.2%
[ 5
 
1.2%
Other values (13) 25
 
6.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1071
69.5%
ASCII 461
29.9%
Compat Jamo 8
 
0.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
295
64.0%
0 22
 
4.8%
1 15
 
3.3%
? 12
 
2.6%
~ 8
 
1.7%
2 8
 
1.7%
, 7
 
1.5%
. 7
 
1.5%
P 6
 
1.3%
] 5
 
1.1%
Other values (41) 76
 
16.5%
Hangul
ValueCountFrequency (%)
이 33
 
3.1%
흥 27
 
2.5%
손 24
 
2.2%
민 24
 
2.2%
골 21
 
2.0%
스 21
 
2.0%
요 17
 
1.6%
리 16
 
1.5%
인 14
 
1.3%
의 13
 
1.2%
Other values (300) 861
80.4%
Compat Jamo
ValueCountFrequency (%)
ㅋ 4
50.0%
ㅜ 2
25.0%
ㅠ 2
25.0%

NTCE_DE
Date

Distinct27
Distinct (%)27.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
Minimum2021-01-01 00:00:00
Maximum2021-01-30 00:00:00
2023-12-10T19:08:14.359154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:14.649805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)

DPI_VALUE
Real number (ℝ)

HIGH CORRELATION 

Distinct88
Distinct (%)88.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.87
Minimum5.4
Maximum144.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:08:14.942583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5.4
5-th percentile18.93
Q131.75
median41
Q364.2
95-th percentile89.01
Maximum144.6
Range139.2
Interquartile range (IQR)32.45

Descriptive statistics

Standard deviation24.87253
Coefficient of variation (CV)0.51958491
Kurtosis1.7763933
Mean47.87
Median Absolute Deviation (MAD)14.9
Skewness1.1345939
Sum4787
Variance618.64273
MonotonicityNot monotonic
2023-12-10T19:08:15.229180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
41.4 2
 
2.0%
57.8 2
 
2.0%
35.4 2
 
2.0%
27.6 2
 
2.0%
31.8 2
 
2.0%
23.4 2
 
2.0%
41.0 2
 
2.0%
32.4 2
 
2.0%
38.4 2
 
2.0%
34.8 2
 
2.0%
Other values (78) 80
80.0%
ValueCountFrequency (%)
5.4 1
1.0%
8.4 1
1.0%
9.8 1
1.0%
12.8 1
1.0%
17.6 1
1.0%
19.0 1
1.0%
20.8 1
1.0%
21.4 1
1.0%
23.0 1
1.0%
23.2 1
1.0%
ValueCountFrequency (%)
144.6 1
1.0%
117.8 1
1.0%
116.6 1
1.0%
94.0 1
1.0%
89.2 1
1.0%
89.0 1
1.0%
84.2 1
1.0%
84.0 1
1.0%
83.8 1
1.0%
83.2 1
1.0%

VIEWS_CO
Real number (ℝ)

HIGH CORRELATION 

Distinct88
Distinct (%)88.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean237.65
Minimum27
Maximum707
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:08:15.491732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum27
5-th percentile94.65
Q1155.75
median205
Q3321
95-th percentile445.05
Maximum707
Range680
Interquartile range (IQR)165.25

Descriptive statistics

Standard deviation122.90108
Coefficient of variation (CV)0.51715159
Kurtosis1.6338617
Mean237.65
Median Absolute Deviation (MAD)74.5
Skewness1.1068945
Sum23765
Variance15104.674
MonotonicityNot monotonic
2023-12-10T19:08:15.744169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
324 3
 
3.0%
138 3
 
3.0%
177 2
 
2.0%
130 2
 
2.0%
346 2
 
2.0%
207 2
 
2.0%
178 2
 
2.0%
205 2
 
2.0%
116 2
 
2.0%
162 2
 
2.0%
Other values (78) 78
78.0%
ValueCountFrequency (%)
27 1
1.0%
42 1
1.0%
49 1
1.0%
64 1
1.0%
88 1
1.0%
95 1
1.0%
100 1
1.0%
107 1
1.0%
115 1
1.0%
116 2
2.0%
ValueCountFrequency (%)
707 1
1.0%
585 1
1.0%
575 1
1.0%
470 1
1.0%
446 1
1.0%
445 1
1.0%
421 1
1.0%
420 1
1.0%
419 1
1.0%
412 1
1.0%

COMMENT_CO
Real number (ℝ)

ZEROS 

Distinct8
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.66
Minimum0
Maximum8
Zeros74
Zeros (%)74.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:08:15.973092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile4
Maximum8
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.4370284
Coefficient of variation (CV)2.1773157
Kurtosis8.6552615
Mean0.66
Median Absolute Deviation (MAD)0
Skewness2.7871842
Sum66
Variance2.0650505
MonotonicityNot monotonic
2023-12-10T19:08:16.353498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 74
74.0%
1 9
 
9.0%
2 8
 
8.0%
4 4
 
4.0%
3 2
 
2.0%
8 1
 
1.0%
6 1
 
1.0%
5 1
 
1.0%
ValueCountFrequency (%)
0 74
74.0%
1 9
 
9.0%
2 8
 
8.0%
3 2
 
2.0%
4 4
 
4.0%
5 1
 
1.0%
6 1
 
1.0%
8 1
 
1.0%
ValueCountFrequency (%)
8 1
 
1.0%
6 1
 
1.0%
5 1
 
1.0%
4 4
 
4.0%
3 2
 
2.0%
2 8
 
8.0%
1 9
 
9.0%
0 74
74.0%

CNTNTS_URL
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:08:17.077633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length74
Median length68
Mean length70.89
Min length68

Characters and Unicode

Total characters7089
Distinct characters37
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

Unique100 ?
Unique (%)100.0%

Sample

1st rowhttps://m.ppomppu.co.kr/new/bbs_view.php?id=soccer&no=158531&page=100
2nd rowhttps://m.ppomppu.co.kr/new/bbs_view.php?id=soccer&no=158533&page=99
3rd rowhttp://ppomppu.co.kr/zboard/view.php?id=soccer&page=5&divpage=29&no=158532
4th rowhttp://ppomppu.co.kr/zboard/view.php?id=soccer&page=5&divpage=29&no=158544
5th rowhttps://m.ppomppu.co.kr/new/bbs_view.php?id=soccer&no=158541&page=99
ValueCountFrequency (%)
https://m.ppomppu.co.kr/new/bbs_view.php?id=soccer&no=158531&page=100 1
 
1.0%
https://m.ppomppu.co.kr/new/bbs_view.php?id=soccer&no=158634&page=93 1
 
1.0%
http://ppomppu.co.kr/zboard/view.php?id=soccer&page=1&divpage=29&no=158653 1
 
1.0%
https://m.ppomppu.co.kr/new/bbs_view.php?id=soccer&no=158652&page=92 1
 
1.0%
http://ppomppu.co.kr/zboard/view.php?id=soccer&page=1&divpage=29&no=158654 1
 
1.0%
https://m.ppomppu.co.kr/new/bbs_view.php?id=soccer&no=158650&page=92 1
 
1.0%
https://m.ppomppu.co.kr/new/bbs_view.php?id=soccer&no=158639&page=93 1
 
1.0%
https://m.ppomppu.co.kr/new/bbs_view.php?id=soccer&no=158637&page=93 1
 
1.0%
http://ppomppu.co.kr/zboard/view.php?id=soccer&page=1&divpage=29&no=158644 1
 
1.0%
http://ppomppu.co.kr/zboard/view.php?id=soccer&page=2&divpage=29&no=158637 1
 
1.0%
Other values (90) 90
90.0%
2023-12-10T19:08:17.810069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
p 848
 
12.0%
o 448
 
6.3%
e 400
 
5.6%
/ 400
 
5.6%
. 352
 
5.0%
= 348
 
4.9%
c 300
 
4.2%
r 248
 
3.5%
& 248
 
3.5%
i 248
 
3.5%
Other values (27) 3249
45.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4640
65.5%
Other Punctuation 1200
 
16.9%
Decimal Number 849
 
12.0%
Math Symbol 348
 
4.9%
Connector Punctuation 52
 
0.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
p 848
18.3%
o 448
 
9.7%
e 400
 
8.6%
c 300
 
6.5%
r 248
 
5.3%
i 248
 
5.3%
s 204
 
4.4%
t 200
 
4.3%
h 200
 
4.3%
a 196
 
4.2%
Other values (10) 1348
29.1%
Decimal Number
ValueCountFrequency (%)
5 179
21.1%
8 136
16.0%
1 133
15.7%
9 114
13.4%
2 83
9.8%
6 71
 
8.4%
3 38
 
4.5%
4 37
 
4.4%
7 37
 
4.4%
0 21
 
2.5%
Other Punctuation
ValueCountFrequency (%)
/ 400
33.3%
. 352
29.3%
& 248
20.7%
? 100
 
8.3%
: 100
 
8.3%
Math Symbol
ValueCountFrequency (%)
= 348
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 52
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4640
65.5%
Common 2449
34.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
p 848
18.3%
o 448
 
9.7%
e 400
 
8.6%
c 300
 
6.5%
r 248
 
5.3%
i 248
 
5.3%
s 204
 
4.4%
t 200
 
4.3%
h 200
 
4.3%
a 196
 
4.2%
Other values (10) 1348
29.1%
Common
ValueCountFrequency (%)
/ 400
16.3%
. 352
14.4%
= 348
14.2%
& 248
10.1%
5 179
7.3%
8 136
 
5.6%
1 133
 
5.4%
9 114
 
4.7%
? 100
 
4.1%
: 100
 
4.1%
Other values (7) 339
13.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7089
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
p 848
 
12.0%
o 448
 
6.3%
e 400
 
5.6%
/ 400
 
5.6%
. 352
 
5.0%
= 348
 
4.9%
c 300
 
4.2%
r 248
 
3.5%
& 248
 
3.5%
i 248
 
3.5%
Other values (27) 3249
45.8%

UPPER_CTGRY_NM
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
체육
100 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
체육 100
100.0%

Length

2023-12-10T19:08:18.102708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:08:18.297767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
체육 100
100.0%

LWPRT_CTGRY_NM
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
프로 스포츠
100 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row프로 스포츠
2nd row프로 스포츠
3rd row프로 스포츠
4th row프로 스포츠
5th row프로 스포츠

Common Values

ValueCountFrequency (%)
프로 스포츠 100
100.0%

Length

2023-12-10T19:08:18.477662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:08:18.804165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
프로 100
50.0%
스포츠 100
50.0%

Interactions

2023-12-10T19:08:07.752393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:04.964550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:05.709196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:06.652486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:07.930061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:05.113318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:05.944437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:06.860280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:08.564925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:05.318675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:06.177485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:07.175547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:08.764934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:05.485466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:06.387680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:07.488131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:08:18.917150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
SEQ_NOCNTNTS_IDTITLE_NMNTCE_DEDPI_VALUEVIEWS_COCOMMENT_COCNTNTS_URL
SEQ_NO1.0001.0000.0000.1770.0000.1440.0001.000
CNTNTS_ID1.0001.0001.0001.0001.0001.0001.0001.000
TITLE_NM0.0001.0001.0000.9960.9540.9201.0001.000
NTCE_DE0.1771.0000.9961.0000.5230.5820.2571.000
DPI_VALUE0.0001.0000.9540.5231.0000.9840.5671.000
VIEWS_CO0.1441.0000.9200.5820.9841.0000.4191.000
COMMENT_CO0.0001.0001.0000.2570.5670.4191.0001.000
CNTNTS_URL1.0001.0001.0001.0001.0001.0001.0001.000
2023-12-10T19:08:19.143796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
SEQ_NODPI_VALUEVIEWS_COCOMMENT_CO
SEQ_NO1.0000.3130.3100.087
DPI_VALUE0.3131.0000.9990.261
VIEWS_CO0.3100.9991.0000.240
COMMENT_CO0.0870.2610.2401.000

Missing values

2023-12-10T19:08:09.113985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:08:09.507370image/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

SEQ_NOCNTNTS_IDCHNNEL_CL_NMCHNNEL_NMTITLE_NMNTCE_DEDPI_VALUEVIEWS_COCOMMENT_COCNTNTS_URLUPPER_CTGRY_NMLWPRT_CTGRY_NM
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SEQ_NOCNTNTS_IDCHNNEL_CL_NMCHNNEL_NMTITLE_NMNTCE_DEDPI_VALUEVIEWS_COCOMMENT_COCNTNTS_URLUPPER_CTGRY_NMLWPRT_CTGRY_NM
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