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_CO and 1 other fieldsHigh correlation
VIEWS_CO is highly overall correlated with DPI_VALUE and 1 other fieldsHigh correlation
COMMENT_CO is highly overall correlated with DPI_VALUE and 1 other fieldsHigh correlation
SEQ_NO has unique valuesUnique
CNTNTS_ID has unique valuesUnique
TITLE_NM has unique valuesUnique
DPI_VALUE has unique valuesUnique
CNTNTS_URL has unique valuesUnique
COMMENT_CO has 14 (14.0%) zerosZeros

Reproduction

Analysis started2023-12-10 10:13:36.774890
Analysis finished2023-12-10 10:13:40.374203
Duration3.6 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%
Mean224533.58
Minimum112945
Maximum310590
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:13:40.496055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum112945
5-th percentile113940.55
Q1117584.25
median282341
Q3303313
95-th percentile308069.2
Maximum310590
Range197645
Interquartile range (IQR)185728.75

Descriptive statistics

Standard deviation88554.533
Coefficient of variation (CV)0.39439327
Kurtosis-1.8737102
Mean224533.58
Median Absolute Deviation (MAD)24548.5
Skewness-0.35337159
Sum22453358
Variance7.8419053 × 109
MonotonicityNot monotonic
2023-12-10T19:13:40.727916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
118437 1
 
1.0%
303472 1
 
1.0%
302902 1
 
1.0%
129330 1
 
1.0%
303637 1
 
1.0%
303666 1
 
1.0%
129754 1
 
1.0%
303260 1
 
1.0%
303252 1
 
1.0%
280058 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
112945 1
1.0%
113202 1
1.0%
113207 1
1.0%
113460 1
1.0%
113875 1
1.0%
113944 1
1.0%
114106 1
1.0%
114236 1
1.0%
114382 1
1.0%
114558 1
1.0%
ValueCountFrequency (%)
310590 1
1.0%
309526 1
1.0%
308916 1
1.0%
308618 1
1.0%
308244 1
1.0%
308060 1
1.0%
307877 1
1.0%
307733 1
1.0%
306966 1
1.0%
306813 1
1.0%

CNTNTS_ID
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:13:41.092636image/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 rowce9a2e5a5a1111eb8480c0b6f9fde92b
2nd rowaa7c6c36f43c11eb83ee002b67f7b0e1
3rd row1432ab3a5a1311eb9557c0b6f9fde92b
4th row8ee6e8985a0f11ebbd98c0b6f9fde92b
5th row0477ebc65a0f11eb8b95c0b6f9fde92b
ValueCountFrequency (%)
ce9a2e5a5a1111eb8480c0b6f9fde92b 1
 
1.0%
aa774f4ef43c11eb8c56002b67f7b0e1 1
 
1.0%
31207c36614d11ebbcafc0b6f9fde92b 1
 
1.0%
aa774f30f43c11eb8441002b67f7b0e1 1
 
1.0%
aa774f4df43c11eb8144002b67f7b0e1 1
 
1.0%
97d4828c614e11eb87aec0b6f9fde92b 1
 
1.0%
aa772787f43c11eb876f002b67f7b0e1 1
 
1.0%
aa77277ff43c11eb8d90002b67f7b0e1 1
 
1.0%
aa659824f43c11eb9dc3002b67f7b0e1 1
 
1.0%
9972efd8614f11ebafc3c0b6f9fde92b 1
 
1.0%
Other values (90) 90
90.0%
2023-12-10T19:13:41.632769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
b 388
12.1%
1 328
10.2%
0 292
9.1%
e 265
 
8.3%
f 259
 
8.1%
a 222
 
6.9%
7 222
 
6.9%
6 193
 
6.0%
9 169
 
5.3%
c 165
 
5.2%
Other values (6) 697
21.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1811
56.6%
Lowercase Letter 1389
43.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 328
18.1%
0 292
16.1%
7 222
12.3%
6 193
10.7%
9 169
9.3%
4 151
8.3%
2 149
8.2%
3 119
 
6.6%
5 95
 
5.2%
8 93
 
5.1%
Lowercase Letter
ValueCountFrequency (%)
b 388
27.9%
e 265
19.1%
f 259
18.6%
a 222
16.0%
c 165
11.9%
d 90
 
6.5%

Most occurring scripts

ValueCountFrequency (%)
Common 1811
56.6%
Latin 1389
43.4%

Most frequent character per script

Common
ValueCountFrequency (%)
1 328
18.1%
0 292
16.1%
7 222
12.3%
6 193
10.7%
9 169
9.3%
4 151
8.3%
2 149
8.2%
3 119
 
6.6%
5 95
 
5.2%
8 93
 
5.1%
Latin
ValueCountFrequency (%)
b 388
27.9%
e 265
19.1%
f 259
18.6%
a 222
16.0%
c 165
11.9%
d 90
 
6.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3200
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
b 388
12.1%
1 328
10.2%
0 292
9.1%
e 265
 
8.3%
f 259
 
8.1%
a 222
 
6.9%
7 222
 
6.9%
6 193
 
6.0%
9 169
 
5.3%
c 165
 
5.2%
Other values (6) 697
21.8%

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

2023-12-10T19:13:41.835279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:13:41.988026image/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
NBA매니아
100 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
NBA매니아 100
100.0%

Length

2023-12-10T19:13:42.192965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:13:42.324102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
nba매니아 100
100.0%

TITLE_NM
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:13:42.769007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length44
Median length31
Mean length19.53
Min length4

Characters and Unicode

Total characters1953
Distinct characters413
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks5 ?
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 row스털링 브라운은 로테이션에 들수있을까요?10
2nd rowLA 레이커스 vs 샌안토니오 스퍼스
3rd row1분8초 사이에 3점 4개 던져서 4메이드
4th row45득점째 커리 득점 커리어하이 갑시다!!!9
5th row계시판 왜 터지나 했는데.....
ValueCountFrequency (%)
vs 5
 
1.1%
오늘 5
 
1.1%
커리 4
 
0.9%
다 3
 
0.7%
워싱턴 3
 
0.7%
경기 3
 
0.7%
하든 3
 
0.7%
쿠즈마 2
 
0.4%
더 2
 
0.4%
같네요 2
 
0.4%
Other values (396) 417
92.9%
2023-12-10T19:13:43.542783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
349
 
17.9%
. 69
 
3.5%
요 50
 
2.6%
이 49
 
2.5%
스 33
 
1.7%
네 31
 
1.6%
가 30
 
1.5%
하 28
 
1.4%
는 27
 
1.4%
리 23
 
1.2%
Other values (403) 1264
64.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1399
71.6%
Space Separator 349
 
17.9%
Other Punctuation 100
 
5.1%
Decimal Number 74
 
3.8%
Lowercase Letter 16
 
0.8%
Uppercase Letter 10
 
0.5%
Math Symbol 3
 
0.2%
Dash Punctuation 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
요 50
 
3.6%
이 49
 
3.5%
스 33
 
2.4%
네 31
 
2.2%
가 30
 
2.1%
하 28
 
2.0%
는 27
 
1.9%
리 23
 
1.6%
다 20
 
1.4%
드 17
 
1.2%
Other values (365) 1091
78.0%
Decimal Number
ValueCountFrequency (%)
2 17
23.0%
1 16
21.6%
4 10
13.5%
5 7
9.5%
3 5
 
6.8%
0 5
 
6.8%
6 4
 
5.4%
8 4
 
5.4%
9 3
 
4.1%
7 3
 
4.1%
Uppercase Letter
ValueCountFrequency (%)
L 1
10.0%
A 1
10.0%
K 1
10.0%
C 1
10.0%
P 1
10.0%
R 1
10.0%
J 1
10.0%
S 1
10.0%
V 1
10.0%
W 1
10.0%
Lowercase Letter
ValueCountFrequency (%)
v 5
31.2%
s 4
25.0%
t 2
 
12.5%
p 1
 
6.2%
m 1
 
6.2%
x 1
 
6.2%
o 1
 
6.2%
j 1
 
6.2%
Other Punctuation
ValueCountFrequency (%)
. 69
69.0%
? 13
 
13.0%
! 11
 
11.0%
… 5
 
5.0%
, 2
 
2.0%
Math Symbol
ValueCountFrequency (%)
~ 1
33.3%
< 1
33.3%
> 1
33.3%
Space Separator
ValueCountFrequency (%)
349
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1399
71.6%
Common 528
 
27.0%
Latin 26
 
1.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
요 50
 
3.6%
이 49
 
3.5%
스 33
 
2.4%
네 31
 
2.2%
가 30
 
2.1%
하 28
 
2.0%
는 27
 
1.9%
리 23
 
1.6%
다 20
 
1.4%
드 17
 
1.2%
Other values (365) 1091
78.0%
Common
ValueCountFrequency (%)
349
66.1%
. 69
 
13.1%
2 17
 
3.2%
1 16
 
3.0%
? 13
 
2.5%
! 11
 
2.1%
4 10
 
1.9%
5 7
 
1.3%
3 5
 
0.9%
… 5
 
0.9%
Other values (10) 26
 
4.9%
Latin
ValueCountFrequency (%)
v 5
19.2%
s 4
15.4%
t 2
 
7.7%
L 1
 
3.8%
A 1
 
3.8%
K 1
 
3.8%
p 1
 
3.8%
C 1
 
3.8%
P 1
 
3.8%
m 1
 
3.8%
Other values (8) 8
30.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1394
71.4%
ASCII 549
 
28.1%
Punctuation 5
 
0.3%
Compat Jamo 4
 
0.2%
Jamo 1
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
349
63.6%
. 69
 
12.6%
2 17
 
3.1%
1 16
 
2.9%
? 13
 
2.4%
! 11
 
2.0%
4 10
 
1.8%
5 7
 
1.3%
3 5
 
0.9%
v 5
 
0.9%
Other values (27) 47
 
8.6%
Hangul
ValueCountFrequency (%)
요 50
 
3.6%
이 49
 
3.5%
스 33
 
2.4%
네 31
 
2.2%
가 30
 
2.2%
하 28
 
2.0%
는 27
 
1.9%
리 23
 
1.6%
다 20
 
1.4%
드 17
 
1.2%
Other values (362) 1086
77.9%
Punctuation
ValueCountFrequency (%)
… 5
100.0%
Compat Jamo
ValueCountFrequency (%)
ㅡ 2
50.0%
ㅠ 2
50.0%
Jamo
ValueCountFrequency (%)
ᆢ 1
100.0%

NTCE_DE
Date

Distinct27
Distinct (%)27.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
Minimum2021-01-02 00:00:00
Maximum2021-01-31 00:00:00
2023-12-10T19:13:43.726680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:13:43.930596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)

DPI_VALUE
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean389.958
Minimum8.2
Maximum2403.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:13:44.160698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8.2
5-th percentile52.99
Q1125.65
median265.5
Q3504.2
95-th percentile1155.76
Maximum2403.2
Range2395
Interquartile range (IQR)378.55

Descriptive statistics

Standard deviation397.08796
Coefficient of variation (CV)1.0182839
Kurtosis7.9094675
Mean389.958
Median Absolute Deviation (MAD)165.9
Skewness2.4673969
Sum38995.8
Variance157678.84
MonotonicityNot monotonic
2023-12-10T19:13:44.750683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
93.6 1
 
1.0%
287.6 1
 
1.0%
252.0 1
 
1.0%
668.6 1
 
1.0%
666.6 1
 
1.0%
99.8 1
 
1.0%
8.2 1
 
1.0%
366.0 1
 
1.0%
87.6 1
 
1.0%
94.6 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
8.2 1
1.0%
40.4 1
1.0%
43.4 1
1.0%
48.8 1
1.0%
52.8 1
1.0%
53.0 1
1.0%
55.8 1
1.0%
74.4 1
1.0%
74.8 1
1.0%
78.2 1
1.0%
ValueCountFrequency (%)
2403.2 1
1.0%
1839.2 1
1.0%
1517.2 1
1.0%
1417.2 1
1.0%
1360.2 1
1.0%
1145.0 1
1.0%
1019.4 1
1.0%
907.8 1
1.0%
874.2 1
1.0%
828.2 1
1.0%

VIEWS_CO
Real number (ℝ)

HIGH CORRELATION 

Distinct99
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1931.28
Minimum41
Maximum11912
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:13:44.980146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum41
5-th percentile264.9
Q1619.25
median1314.5
Q32505.25
95-th percentile5733.8
Maximum11912
Range11871
Interquartile range (IQR)1886

Descriptive statistics

Standard deviation1971.0585
Coefficient of variation (CV)1.020597
Kurtosis7.9598191
Mean1931.28
Median Absolute Deviation (MAD)821.5
Skewness2.4793715
Sum193128
Variance3885071.6
MonotonicityNot monotonic
2023-12-10T19:13:45.279933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
372 2
 
2.0%
458 1
 
1.0%
440 1
 
1.0%
3261 1
 
1.0%
491 1
 
1.0%
41 1
 
1.0%
1822 1
 
1.0%
438 1
 
1.0%
465 1
 
1.0%
1213 1
 
1.0%
Other values (89) 89
89.0%
ValueCountFrequency (%)
41 1
1.0%
202 1
1.0%
217 1
1.0%
244 1
1.0%
263 1
1.0%
265 1
1.0%
275 1
1.0%
372 2
2.0%
390 1
1.0%
415 1
1.0%
ValueCountFrequency (%)
11912 1
1.0%
9182 1
1.0%
7569 1
1.0%
7062 1
1.0%
6737 1
1.0%
5681 1
1.0%
5081 1
1.0%
4519 1
1.0%
4331 1
1.0%
4004 1
1.0%

COMMENT_CO
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct22
Distinct (%)22.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.51
Minimum0
Maximum61
Zeros14
Zeros (%)14.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:13:45.617089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median4
Q39
95-th percentile18
Maximum61
Range61
Interquartile range (IQR)7

Descriptive statistics

Standard deviation8.0132403
Coefficient of variation (CV)1.2309125
Kurtosis20.925289
Mean6.51
Median Absolute Deviation (MAD)3
Skewness3.6043851
Sum651
Variance64.21202
MonotonicityNot monotonic
2023-12-10T19:13:45.842406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
2 15
15.0%
0 14
14.0%
1 10
10.0%
3 8
 
8.0%
5 7
 
7.0%
6 6
 
6.0%
9 5
 
5.0%
12 4
 
4.0%
4 4
 
4.0%
8 4
 
4.0%
Other values (12) 23
23.0%
ValueCountFrequency (%)
0 14
14.0%
1 10
10.0%
2 15
15.0%
3 8
8.0%
4 4
 
4.0%
5 7
7.0%
6 6
 
6.0%
7 3
 
3.0%
8 4
 
4.0%
9 5
 
5.0%
ValueCountFrequency (%)
61 1
 
1.0%
26 1
 
1.0%
24 1
 
1.0%
20 1
 
1.0%
18 3
3.0%
17 1
 
1.0%
16 2
2.0%
14 3
3.0%
13 2
2.0%
12 4
4.0%

CNTNTS_URL
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:13:46.321463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length82
Median length74
Mean length76.84
Min length74

Characters and Unicode

Total characters7684
Distinct characters34
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://mania.kr/g2/bbs/board.php?bo_table=nbatalk&wr_id=7819971&page=214&page=214
2nd rowhttps://mania.kr/g2/bbs/board.php?bo_table=nbatalk&wr_id=7817762&page=2366
3rd rowhttps://mania.kr/g2/bbs/board.php?bo_table=nbatalk&wr_id=7817179&page=227&page=227
4th rowhttps://mania.kr/g2/bbs/board.php?bo_table=nbatalk&wr_id=7824953&page=192&page=192
5th rowhttps://mania.kr/g2/bbs/board.php?bo_table=nbatalk&wr_id=7826170&page=187&page=187
ValueCountFrequency (%)
https://mania.kr/g2/bbs/board.php?bo_table=nbatalk&wr_id=7819971&page=214&page=214 1
 
1.0%
https://mania.kr/g2/bbs/board.php?bo_table=nbatalk&wr_id=7875752&page=2130 1
 
1.0%
https://mania.kr/g2/bbs/board.php?bo_table=nbatalk&wr_id=7879838&page=88&page=88 1
 
1.0%
https://mania.kr/g2/bbs/board.php?bo_table=nbatalk&wr_id=7875889&page=2129 1
 
1.0%
https://mania.kr/g2/bbs/board.php?bo_table=nbatalk&wr_id=7875754&page=2130 1
 
1.0%
https://mania.kr/g2/bbs/board.php?bo_table=nbatalk&wr_id=7876726&page=103&page=103 1
 
1.0%
https://mania.kr/g2/bbs/board.php?bo_table=nbatalk&wr_id=7877753&page=2116 1
 
1.0%
https://mania.kr/g2/bbs/board.php?bo_table=nbatalk&wr_id=7877783&page=2115 1
 
1.0%
https://mania.kr/g2/bbs/board.php?bo_table=nbatalk&wr_id=7875848&page=2128 1
 
1.0%
https://mania.kr/g2/bbs/board.php?bo_table=nbatalk&wr_id=7875540&page=113&page=113 1
 
1.0%
Other values (90) 90
90.0%
2023-12-10T19:13:47.145060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 741
 
9.6%
b 600
 
7.8%
/ 500
 
6.5%
p 441
 
5.7%
t 400
 
5.2%
= 341
 
4.4%
r 300
 
3.9%
2 253
 
3.3%
& 241
 
3.1%
e 241
 
3.1%
Other values (24) 3626
47.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4764
62.0%
Decimal Number 1238
 
16.1%
Other Punctuation 1141
 
14.8%
Math Symbol 341
 
4.4%
Connector Punctuation 200
 
2.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 741
15.6%
b 600
12.6%
p 441
 
9.3%
t 400
 
8.4%
r 300
 
6.3%
e 241
 
5.1%
g 241
 
5.1%
h 200
 
4.2%
l 200
 
4.2%
o 200
 
4.2%
Other values (7) 1200
25.2%
Decimal Number
ValueCountFrequency (%)
2 253
20.4%
7 193
15.6%
8 189
15.3%
1 123
9.9%
3 99
 
8.0%
0 90
 
7.3%
5 77
 
6.2%
4 73
 
5.9%
9 71
 
5.7%
6 70
 
5.7%
Other Punctuation
ValueCountFrequency (%)
/ 500
43.8%
& 241
21.1%
. 200
 
17.5%
: 100
 
8.8%
? 100
 
8.8%
Math Symbol
ValueCountFrequency (%)
= 341
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 200
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4764
62.0%
Common 2920
38.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 741
15.6%
b 600
12.6%
p 441
 
9.3%
t 400
 
8.4%
r 300
 
6.3%
e 241
 
5.1%
g 241
 
5.1%
h 200
 
4.2%
l 200
 
4.2%
o 200
 
4.2%
Other values (7) 1200
25.2%
Common
ValueCountFrequency (%)
/ 500
17.1%
= 341
11.7%
2 253
8.7%
& 241
8.3%
_ 200
 
6.8%
. 200
 
6.8%
7 193
 
6.6%
8 189
 
6.5%
1 123
 
4.2%
: 100
 
3.4%
Other values (7) 580
19.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7684
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 741
 
9.6%
b 600
 
7.8%
/ 500
 
6.5%
p 441
 
5.7%
t 400
 
5.2%
= 341
 
4.4%
r 300
 
3.9%
2 253
 
3.3%
& 241
 
3.1%
e 241
 
3.1%
Other values (24) 3626
47.2%

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

2023-12-10T19:13:47.433954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:13:47.615509image/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

2023-12-10T19:13:47.807050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:13:48.022034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
프로 100
50.0%
스포츠 100
50.0%

Interactions

2023-12-10T19:13:39.408442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:13:37.629233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:13:38.296569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:13:38.866298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:13:39.561275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:13:37.804164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:13:38.448447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:13:39.004191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:13:39.708112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:13:37.961420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:13:38.570921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:13:39.141285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:13:39.829191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:13:38.131977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:13:38.717655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:13:39.282149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:13:48.134944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
SEQ_NOCNTNTS_IDTITLE_NMNTCE_DEDPI_VALUEVIEWS_COCOMMENT_COCNTNTS_URL
SEQ_NO1.0001.0001.0000.2740.0000.0000.0001.000
CNTNTS_ID1.0001.0001.0001.0001.0001.0001.0001.000
TITLE_NM1.0001.0001.0001.0001.0001.0001.0001.000
NTCE_DE0.2741.0001.0001.0000.0000.0000.4221.000
DPI_VALUE0.0001.0001.0000.0001.0001.0000.8421.000
VIEWS_CO0.0001.0001.0000.0001.0001.0000.8421.000
COMMENT_CO0.0001.0001.0000.4220.8420.8421.0001.000
CNTNTS_URL1.0001.0001.0001.0001.0001.0001.0001.000
2023-12-10T19:13:48.351881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
SEQ_NODPI_VALUEVIEWS_COCOMMENT_CO
SEQ_NO1.0000.1090.1050.112
DPI_VALUE0.1091.0001.0000.743
VIEWS_CO0.1051.0001.0000.739
COMMENT_CO0.1120.7430.7391.000

Missing values

2023-12-10T19:13:40.006313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:13:40.264312image/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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