Overview

Dataset statistics

Number of variables13
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory10.8 KiB
Average record size in memory110.3 B

Variable types

Numeric4
Text3
Categorical6

Alerts

CHNNEL_CL_NM has constant value ""Constant
UPPER_CTGRY_NM has constant value ""Constant
LWPRT_CTGRY_NM has constant value ""Constant
SEQ_NO is highly overall correlated with NTCE_DTHigh correlation
DPI_VALUE is highly overall correlated with RSPN_CO and 1 other fieldsHigh correlation
RSPN_CO is highly overall correlated with DPI_VALUE and 2 other fieldsHigh correlation
COMMENT_CO is highly overall correlated with DPI_VALUE and 1 other fieldsHigh correlation
CHNNEL_NM is highly overall correlated with RSPN_COHigh correlation
NTCE_DT is highly overall correlated with SEQ_NO and 1 other fieldsHigh correlation
RECOMEND_CO is highly overall correlated with NTCE_DTHigh correlation
RECOMEND_CO is highly imbalanced (80.6%)Imbalance
SEQ_NO has unique valuesUnique
CNTNTS_ID has unique valuesUnique
CNTNTS_URL has unique valuesUnique
DPI_VALUE has 32 (32.0%) zerosZeros
RSPN_CO has 36 (36.0%) zerosZeros
COMMENT_CO has 62 (62.0%) zerosZeros

Reproduction

Analysis started2023-12-10 09:50:15.853252
Analysis finished2023-12-10 09:50:21.440575
Duration5.59 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

SEQ_NO
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean59574.3
Minimum58459
Maximum60666
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:50:21.611611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum58459
5-th percentile58582.75
Q159017
median59572
Q360083
95-th percentile60556.6
Maximum60666
Range2207
Interquartile range (IQR)1066

Descriptive statistics

Standard deviation646.75117
Coefficient of variation (CV)0.010856211
Kurtosis-1.1626905
Mean59574.3
Median Absolute Deviation (MAD)559
Skewness-0.049311479
Sum5957430
Variance418287.08
MonotonicityNot monotonic
2023-12-10T18:50:21.897198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
58741 1
 
1.0%
59967 1
 
1.0%
60207 1
 
1.0%
60224 1
 
1.0%
60164 1
 
1.0%
59987 1
 
1.0%
59924 1
 
1.0%
59922 1
 
1.0%
60009 1
 
1.0%
60056 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
58459 1
1.0%
58462 1
1.0%
58497 1
1.0%
58537 1
1.0%
58559 1
1.0%
58584 1
1.0%
58589 1
1.0%
58596 1
1.0%
58621 1
1.0%
58630 1
1.0%
ValueCountFrequency (%)
60666 1
1.0%
60647 1
1.0%
60608 1
1.0%
60601 1
1.0%
60587 1
1.0%
60555 1
1.0%
60554 1
1.0%
60512 1
1.0%
60466 1
1.0%
60461 1
1.0%

CNTNTS_ID
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T18:50:22.325873image/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 rowf26b5facf43611eba7b3002b67f7b0e1
2nd rowf26b5fdbf43611eba11a002b67f7b0e1
3rd rowf26b8675f43611eb9e02002b67f7b0e1
4th rowf26b5fd2f43611ebbd4b002b67f7b0e1
5th rowf26b5fd6f43611ebb253002b67f7b0e1
ValueCountFrequency (%)
f26b5facf43611eba7b3002b67f7b0e1 1
 
1.0%
f26c4a20f43611eb869d002b67f7b0e1 1
 
1.0%
f26c97fef43611eb9518002b67f7b0e1 1
 
1.0%
f26c717bf43611ebb1cf002b67f7b0e1 1
 
1.0%
f26c4a97f43611eb9db8002b67f7b0e1 1
 
1.0%
f26c4a58f43611ebb440002b67f7b0e1 1
 
1.0%
f26c4a56f43611eb95f0002b67f7b0e1 1
 
1.0%
f26c70e0f43611eb860f002b67f7b0e1 1
 
1.0%
f26c710ff43611eba196002b67f7b0e1 1
 
1.0%
f26c4a87f43611ebabe8002b67f7b0e1 1
 
1.0%
Other values (90) 90
90.0%
2023-12-10T18:50:23.039422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
b 396
12.4%
0 343
10.7%
f 330
10.3%
1 330
10.3%
6 328
10.2%
2 251
7.8%
e 246
7.7%
7 229
7.2%
4 155
 
4.8%
3 133
 
4.2%
Other values (6) 459
14.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2025
63.3%
Lowercase Letter 1175
36.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 343
16.9%
1 330
16.3%
6 328
16.2%
2 251
12.4%
7 229
11.3%
4 155
7.7%
3 133
 
6.6%
9 94
 
4.6%
5 85
 
4.2%
8 77
 
3.8%
Lowercase Letter
ValueCountFrequency (%)
b 396
33.7%
f 330
28.1%
e 246
20.9%
c 92
 
7.8%
a 64
 
5.4%
d 47
 
4.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2025
63.3%
Latin 1175
36.7%

Most frequent character per script

Common
ValueCountFrequency (%)
0 343
16.9%
1 330
16.3%
6 328
16.2%
2 251
12.4%
7 229
11.3%
4 155
7.7%
3 133
 
6.6%
9 94
 
4.6%
5 85
 
4.2%
8 77
 
3.8%
Latin
ValueCountFrequency (%)
b 396
33.7%
f 330
28.1%
e 246
20.9%
c 92
 
7.8%
a 64
 
5.4%
d 47
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3200
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
b 396
12.4%
0 343
10.7%
f 330
10.3%
1 330
10.3%
6 328
10.2%
2 251
7.8%
e 246
7.7%
7 229
7.2%
4 155
 
4.8%
3 133
 
4.2%
Other values (6) 459
14.3%

CHNNEL_CL_NM
Categorical

CONSTANT 

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

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
news 100
100.0%

Length

2023-12-10T18:50:23.356914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:50:23.537570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
news 100
100.0%

CHNNEL_NM
Categorical

HIGH CORRELATION 

Distinct26
Distinct (%)26.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
연합뉴스
12 
뉴스1
9 
매일경제
9 
뉴시스
8 
중앙SUNDAY
6 
Other values (21)
56 

Length

Max length8
Median length4
Mean length4.16
Min length3

Unique

Unique8 ?
Unique (%)8.0%

Sample

1st row동아일보
2nd row머니투데이
3rd row매일경제
4th row경향신문
5th row매일경제

Common Values

ValueCountFrequency (%)
연합뉴스 12
 
12.0%
뉴스1 9
 
9.0%
매일경제 9
 
9.0%
뉴시스 8
 
8.0%
중앙SUNDAY 6
 
6.0%
서울경제 6
 
6.0%
동아일보 5
 
5.0%
경향신문 5
 
5.0%
오마이뉴스 5
 
5.0%
한겨레 5
 
5.0%
Other values (16) 30
30.0%

Length

2023-12-10T18:50:23.771535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
연합뉴스 12
 
12.0%
매일경제 9
 
9.0%
뉴스1 9
 
9.0%
뉴시스 8
 
8.0%
중앙sunday 6
 
6.0%
서울경제 6
 
6.0%
동아일보 5
 
5.0%
경향신문 5
 
5.0%
오마이뉴스 5
 
5.0%
한겨레 5
 
5.0%
Other values (16) 30
30.0%

TITLE_NM
Text

Distinct99
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T18:50:24.401537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length47
Median length37
Mean length27.89
Min length12

Characters and Unicode

Total characters2789
Distinct characters463
Distinct categories12 ?
Distinct scripts4 ?
Distinct blocks6 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique98 ?
Unique (%)98.0%

Sample

1st row[신춘문예 2021/동화 가작]니들이 사춘기를 알아
2nd row“코로나 완치자인데 여전히 아프다”…후유증 외면하는 ‘불안한 시스템’
3rd row이주의 새책 (1월 9일자)
4th row바삭바삭 구운 바닥 위에 바람 한 꼬집…세상에서 가장 맛있는 낮잠 레시피 [그림 책]
5th row코로나 발생 76일…우한에선 무슨 일이 있었나
ValueCountFrequency (%)
신간 9
 
1.4%
책 6
 
0.9%
책꽂이 5
 
0.8%
코로나 4
 
0.6%
세계 3
 
0.5%
발간 3
 
0.5%
새책 3
 
0.5%
미래 3
 
0.5%
1월 3
 
0.5%
이승우 3
 
0.5%
Other values (551) 607
93.5%
2023-12-10T18:50:25.251175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
550
 
19.7%
이 57
 
2.0%
' 50
 
1.8%
의 49
 
1.8%
는 39
 
1.4%
가 38
 
1.4%
, 37
 
1.3%
] 35
 
1.3%
[ 35
 
1.3%
다 34
 
1.2%
Other values (453) 1865
66.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1880
67.4%
Space Separator 550
 
19.7%
Other Punctuation 163
 
5.8%
Decimal Number 63
 
2.3%
Close Punctuation 38
 
1.4%
Open Punctuation 38
 
1.4%
Final Punctuation 21
 
0.8%
Initial Punctuation 20
 
0.7%
Uppercase Letter 9
 
0.3%
Math Symbol 5
 
0.2%
Other values (2) 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
이 57
 
3.0%
의 49
 
2.6%
는 39
 
2.1%
가 38
 
2.0%
다 34
 
1.8%
한 32
 
1.7%
문 27
 
1.4%
책 27
 
1.4%
시 25
 
1.3%
로 25
 
1.3%
Other values (411) 1527
81.2%
Other Punctuation
ValueCountFrequency (%)
' 50
30.7%
, 37
22.7%
· 28
17.2%
… 21
12.9%
. 9
 
5.5%
" 9
 
5.5%
? 4
 
2.5%
! 2
 
1.2%
/ 2
 
1.2%
: 1
 
0.6%
Decimal Number
ValueCountFrequency (%)
1 16
25.4%
2 15
23.8%
0 14
22.2%
6 4
 
6.3%
3 3
 
4.8%
8 3
 
4.8%
5 3
 
4.8%
9 2
 
3.2%
7 2
 
3.2%
4 1
 
1.6%
Uppercase Letter
ValueCountFrequency (%)
D 2
22.2%
B 1
11.1%
T 1
11.1%
S 1
11.1%
K 1
11.1%
P 1
11.1%
A 1
11.1%
I 1
11.1%
Math Symbol
ValueCountFrequency (%)
< 2
40.0%
> 2
40.0%
| 1
20.0%
Close Punctuation
ValueCountFrequency (%)
] 35
92.1%
) 3
 
7.9%
Open Punctuation
ValueCountFrequency (%)
[ 35
92.1%
( 3
 
7.9%
Initial Punctuation
ValueCountFrequency (%)
‘ 15
75.0%
“ 5
 
25.0%
Final Punctuation
ValueCountFrequency (%)
’ 15
71.4%
” 6
 
28.6%
Space Separator
ValueCountFrequency (%)
550
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%
Other Number
ValueCountFrequency (%)
① 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1877
67.3%
Common 900
32.3%
Latin 9
 
0.3%
Han 3
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
이 57
 
3.0%
의 49
 
2.6%
는 39
 
2.1%
가 38
 
2.0%
다 34
 
1.8%
한 32
 
1.7%
문 27
 
1.4%
책 27
 
1.4%
시 25
 
1.3%
로 25
 
1.3%
Other values (409) 1524
81.2%
Common
ValueCountFrequency (%)
550
61.1%
' 50
 
5.6%
, 37
 
4.1%
] 35
 
3.9%
[ 35
 
3.9%
· 28
 
3.1%
… 21
 
2.3%
1 16
 
1.8%
‘ 15
 
1.7%
’ 15
 
1.7%
Other values (24) 98
 
10.9%
Latin
ValueCountFrequency (%)
D 2
22.2%
B 1
11.1%
T 1
11.1%
S 1
11.1%
K 1
11.1%
P 1
11.1%
A 1
11.1%
I 1
11.1%
Han
ValueCountFrequency (%)
外 2
66.7%
美 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1877
67.3%
ASCII 818
29.3%
Punctuation 62
 
2.2%
None 28
 
1.0%
CJK 3
 
0.1%
Enclosed Alphanum 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
550
67.2%
' 50
 
6.1%
, 37
 
4.5%
] 35
 
4.3%
[ 35
 
4.3%
1 16
 
2.0%
2 15
 
1.8%
0 14
 
1.7%
. 9
 
1.1%
" 9
 
1.1%
Other values (25) 48
 
5.9%
Hangul
ValueCountFrequency (%)
이 57
 
3.0%
의 49
 
2.6%
는 39
 
2.1%
가 38
 
2.0%
다 34
 
1.8%
한 32
 
1.7%
문 27
 
1.4%
책 27
 
1.4%
시 25
 
1.3%
로 25
 
1.3%
Other values (409) 1524
81.2%
None
ValueCountFrequency (%)
· 28
100.0%
Punctuation
ValueCountFrequency (%)
… 21
33.9%
‘ 15
24.2%
’ 15
24.2%
” 6
 
9.7%
“ 5
 
8.1%
CJK
ValueCountFrequency (%)
外 2
66.7%
美 1
33.3%
Enclosed Alphanum
ValueCountFrequency (%)
① 1
100.0%

NTCE_DT
Categorical

HIGH CORRELATION 

Distinct39
Distinct (%)39.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2021-01-29 00:00:00
8 
2021-01-23 00:00:00
 
6
2021-01-14 00:00:00
 
6
2021-01-01 00:00:00
 
6
2021-01-26 00:00:00
 
5
Other values (34)
69 

Length

Max length19
Median length19
Mean length19
Min length19

Unique

Unique18 ?
Unique (%)18.0%

Sample

1st row2021-01-01 00:00:00
2nd row2021-01-01 00:00:00
3rd row2021-01-01 00:00:00
4th row2021-01-01 00:00:00
5th row2021-01-01 00:00:00

Common Values

ValueCountFrequency (%)
2021-01-29 00:00:00 8
 
8.0%
2021-01-23 00:00:00 6
 
6.0%
2021-01-14 00:00:00 6
 
6.0%
2021-01-01 00:00:00 6
 
6.0%
2021-01-26 00:00:00 5
 
5.0%
2021-01-21 00:00:00 5
 
5.0%
2021-01-19 00:00:00 5
 
5.0%
2021-01-11 00:00:00 5
 
5.0%
2021-01-20 00:00:00 4
 
4.0%
2021-01-09 00:00:00 4
 
4.0%
Other values (29) 46
46.0%

Length

2023-12-10T18:50:25.492891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
00:00:00 89
44.5%
2021-01-29 8
 
4.0%
2021-01-01 8
 
4.0%
2021-01-23 6
 
3.0%
2021-01-14 6
 
3.0%
2021-01-05 6
 
3.0%
2021-01-26 5
 
2.5%
2021-01-21 5
 
2.5%
2021-01-19 5
 
2.5%
2021-01-11 5
 
2.5%
Other values (30) 57
28.5%

DPI_VALUE
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct25
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.402
Minimum0
Maximum66.2
Zeros32
Zeros (%)32.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:50:25.679074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.4
Q31.4
95-th percentile14.02
Maximum66.2
Range66.2
Interquartile range (IQR)1.4

Descriptive statistics

Standard deviation7.6527935
Coefficient of variation (CV)3.186009
Kurtosis49.968565
Mean2.402
Median Absolute Deviation (MAD)0.4
Skewness6.4782191
Sum240.2
Variance58.565248
MonotonicityNot monotonic
2023-12-10T18:50:25.872308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
0.0 32
32.0%
0.2 15
15.0%
0.6 10
 
10.0%
0.8 7
 
7.0%
0.4 6
 
6.0%
3.2 3
 
3.0%
1.0 3
 
3.0%
2.0 3
 
3.0%
4.8 2
 
2.0%
2.4 2
 
2.0%
Other values (15) 17
17.0%
ValueCountFrequency (%)
0.0 32
32.0%
0.2 15
15.0%
0.4 6
 
6.0%
0.6 10
 
10.0%
0.8 7
 
7.0%
1.0 3
 
3.0%
1.2 1
 
1.0%
1.4 2
 
2.0%
1.6 2
 
2.0%
2.0 3
 
3.0%
ValueCountFrequency (%)
66.2 1
1.0%
21.8 1
1.0%
20.0 1
1.0%
18.8 1
1.0%
18.2 1
1.0%
13.8 1
1.0%
7.6 1
1.0%
5.2 1
1.0%
4.8 2
2.0%
3.8 1
1.0%

RSPN_CO
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct19
Distinct (%)19.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.77
Minimum0
Maximum203
Zeros36
Zeros (%)36.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:50:26.088126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q34
95-th percentile35.2
Maximum203
Range203
Interquartile range (IQR)4

Descriptive statistics

Standard deviation22.435927
Coefficient of variation (CV)3.3140217
Kurtosis60.413708
Mean6.77
Median Absolute Deviation (MAD)1
Skewness7.2161554
Sum677
Variance503.37081
MonotonicityNot monotonic
2023-12-10T18:50:26.290293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0 36
36.0%
1 21
21.0%
4 7
 
7.0%
3 7
 
7.0%
2 7
 
7.0%
10 3
 
3.0%
12 3
 
3.0%
8 3
 
3.0%
7 2
 
2.0%
11 2
 
2.0%
Other values (9) 9
 
9.0%
ValueCountFrequency (%)
0 36
36.0%
1 21
21.0%
2 7
 
7.0%
3 7
 
7.0%
4 7
 
7.0%
5 1
 
1.0%
6 1
 
1.0%
7 2
 
2.0%
8 3
 
3.0%
10 3
 
3.0%
ValueCountFrequency (%)
203 1
 
1.0%
59 1
 
1.0%
54 1
 
1.0%
46 1
 
1.0%
39 1
 
1.0%
35 1
 
1.0%
20 1
 
1.0%
12 3
3.0%
11 2
2.0%
10 3
3.0%

COMMENT_CO
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct15
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.59
Minimum0
Maximum64
Zeros62
Zeros (%)62.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:50:26.498239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile17.3
Maximum64
Range64
Interquartile range (IQR)1

Descriptive statistics

Standard deviation8.0917654
Coefficient of variation (CV)3.1242337
Kurtosis34.975482
Mean2.59
Median Absolute Deviation (MAD)0
Skewness5.3774936
Sum259
Variance65.476667
MonotonicityNot monotonic
2023-12-10T18:50:26.743938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0 62
62.0%
1 18
 
18.0%
3 6
 
6.0%
2 2
 
2.0%
6 2
 
2.0%
64 1
 
1.0%
7 1
 
1.0%
9 1
 
1.0%
25 1
 
1.0%
4 1
 
1.0%
Other values (5) 5
 
5.0%
ValueCountFrequency (%)
0 62
62.0%
1 18
 
18.0%
2 2
 
2.0%
3 6
 
6.0%
4 1
 
1.0%
6 2
 
2.0%
7 1
 
1.0%
8 1
 
1.0%
9 1
 
1.0%
17 1
 
1.0%
ValueCountFrequency (%)
64 1
1.0%
26 1
1.0%
25 1
1.0%
24 1
1.0%
23 1
1.0%
17 1
1.0%
9 1
1.0%
8 1
1.0%
7 1
1.0%
6 2
2.0%

RECOMEND_CO
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
0
97 
1
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 97
97.0%
1 3
 
3.0%

Length

2023-12-10T18:50:26.966084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:50:27.137895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 97
97.0%
1 3
 
3.0%

CNTNTS_URL
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T18:50:27.882893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length97
Median length97
Mean length96.78
Min length95

Characters and Unicode

Total characters9678
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://news.naver.com/main/read.naver?mode=LS2D&mid=shm&sid1=103&sid2=243&oid=020&aid=0003329728
2nd rowhttps://news.naver.com/main/read.naver?mode=LS2D&mid=shm&sid1=103&sid2=243&oid=008&aid=0004522420
3rd rowhttps://news.naver.com/main/read.naver?mode=LS2D&mid=shm&sid1=103&sid2=243&oid=009&aid=0004726650
4th rowhttps://news.naver.com/main/read.naver?mode=LS2D&mid=shm&sid1=103&sid2=243&oid=032&aid=0003051941
5th rowhttps://news.naver.com/main/read.naver?mode=LS2D&mid=shm&sid1=103&sid2=243&oid=009&aid=0004726641
ValueCountFrequency (%)
https://news.naver.com/main/read.naver?mode=ls2d&mid=shm&sid1=103&sid2=243&oid=020&aid=0003329728 1
 
1.0%
https://news.naver.com/main/read.naver?mode=ls2d&mid=shm&sid1=103&sid2=243&oid=001&aid=0012149257 1
 
1.0%
https://news.naver.com/main/read.naver?mode=ls2d&mid=shm&sid1=103&sid2=243&oid=020&aid=0003334269 1
 
1.0%
https://news.naver.com/main/read.naver?mode=ls2d&mid=shm&sid1=103&sid2=243&oid=020&aid=0003333971 1
 
1.0%
https://news.naver.com/main/read.naver?mode=ls2d&mid=shm&sid1=103&sid2=243&oid=015&aid=0004487606 1
 
1.0%
https://news.naver.com/main/read.naver?mode=ls2d&mid=shm&sid1=103&sid2=243&oid=469&aid=0000573806 1
 
1.0%
https://news.naver.com/main/read.naver?mode=ls2d&mid=shm&sid1=103&sid2=243&oid=015&aid=0004487620 1
 
1.0%
https://news.naver.com/main/read.naver?mode=ls2d&mid=shm&sid1=103&sid2=243&oid=015&aid=0004487603 1
 
1.0%
https://news.naver.com/main/read.naver?mode=ls2d&mid=shm&sid1=103&sid2=243&oid=016&aid=0001782294 1
 
1.0%
https://news.naver.com/main/read.naver?mode=ls2d&mid=shm&sid1=103&sid2=243&oid=001&aid=0012150815 1
 
1.0%
Other values (90) 90
90.0%
2023-12-10T18:50:28.887275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
d 700
 
7.2%
= 600
 
6.2%
i 600
 
6.2%
0 584
 
6.0%
s 500
 
5.2%
& 500
 
5.2%
m 500
 
5.2%
e 489
 
5.1%
a 489
 
5.1%
2 429
 
4.4%
Other values (24) 4287
44.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5178
53.5%
Decimal Number 2200
22.7%
Other Punctuation 1400
 
14.5%
Math Symbol 600
 
6.2%
Uppercase Letter 300
 
3.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
d 700
13.5%
i 600
11.6%
s 500
9.7%
m 500
9.7%
e 489
9.4%
a 489
9.4%
n 411
7.9%
o 300
5.8%
r 289
5.6%
h 211
 
4.1%
Other values (5) 689
13.3%
Decimal Number
ValueCountFrequency (%)
0 584
26.5%
2 429
19.5%
1 327
14.9%
3 313
14.2%
4 180
 
8.2%
5 85
 
3.9%
7 76
 
3.5%
6 73
 
3.3%
8 69
 
3.1%
9 64
 
2.9%
Other Punctuation
ValueCountFrequency (%)
& 500
35.7%
/ 400
28.6%
. 300
21.4%
? 100
 
7.1%
: 100
 
7.1%
Uppercase Letter
ValueCountFrequency (%)
L 100
33.3%
S 100
33.3%
D 100
33.3%
Math Symbol
ValueCountFrequency (%)
= 600
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 5478
56.6%
Common 4200
43.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
d 700
12.8%
i 600
11.0%
s 500
9.1%
m 500
9.1%
e 489
8.9%
a 489
8.9%
n 411
7.5%
o 300
 
5.5%
r 289
 
5.3%
h 211
 
3.9%
Other values (8) 989
18.1%
Common
ValueCountFrequency (%)
= 600
14.3%
0 584
13.9%
& 500
11.9%
2 429
10.2%
/ 400
9.5%
1 327
7.8%
3 313
7.5%
. 300
7.1%
4 180
 
4.3%
? 100
 
2.4%
Other values (6) 467
11.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9678
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
d 700
 
7.2%
= 600
 
6.2%
i 600
 
6.2%
0 584
 
6.0%
s 500
 
5.2%
& 500
 
5.2%
m 500
 
5.2%
e 489
 
5.1%
a 489
 
5.1%
2 429
 
4.4%
Other values (24) 4287
44.3%

UPPER_CTGRY_NM
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
생활/문화
100 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row생활/문화
2nd row생활/문화
3rd row생활/문화
4th row생활/문화
5th row생활/문화

Common Values

ValueCountFrequency (%)
생활/문화 100
100.0%

Length

2023-12-10T18:50:29.168459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:50:29.441650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
생활/문화 100
100.0%

LWPRT_CTGRY_NM
Categorical

CONSTANT 

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

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
책 100
100.0%

Length

2023-12-10T18:50:29.620772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:50:29.819908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
책 100
100.0%

Interactions

2023-12-10T18:50:19.741071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:17.458703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:18.153493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:19.028958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:19.895556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:17.622561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:18.444407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:19.191034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:20.155274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:17.781447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:18.617257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:19.365131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:20.347712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:17.978161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:18.815629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:19.544286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T18:50:29.970104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
SEQ_NOCNTNTS_IDCHNNEL_NMTITLE_NMNTCE_DTDPI_VALUERSPN_COCOMMENT_CORECOMEND_COCNTNTS_URL
SEQ_NO1.0001.0000.4600.9410.9800.0000.2370.3090.5871.000
CNTNTS_ID1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
CHNNEL_NM0.4601.0001.0001.0000.8530.7920.8470.7390.0001.000
TITLE_NM0.9411.0001.0001.0000.9621.0001.0001.0001.0001.000
NTCE_DT0.9801.0000.8530.9621.0000.0000.0000.0001.0001.000
DPI_VALUE0.0001.0000.7921.0000.0001.0000.8970.8880.0001.000
RSPN_CO0.2371.0000.8471.0000.0000.8971.0001.0000.0001.000
COMMENT_CO0.3091.0000.7391.0000.0000.8881.0001.0000.0001.000
RECOMEND_CO0.5871.0000.0001.0001.0000.0000.0000.0001.0001.000
CNTNTS_URL1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
2023-12-10T18:50:30.258532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
NTCE_DTRECOMEND_COCHNNEL_NM
NTCE_DT1.0000.7890.302
RECOMEND_CO0.7891.0000.000
CHNNEL_NM0.3020.0001.000
2023-12-10T18:50:30.890672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
SEQ_NODPI_VALUERSPN_COCOMMENT_COCHNNEL_NMNTCE_DTRECOMEND_CO
SEQ_NO1.000-0.121-0.028-0.1340.1560.7030.401
DPI_VALUE-0.1211.0000.9370.8150.4640.0000.000
RSPN_CO-0.0280.9371.0000.6450.5500.0000.000
COMMENT_CO-0.1340.8150.6451.0000.3920.0000.000
CHNNEL_NM0.1560.4640.5500.3921.0000.3020.000
NTCE_DT0.7030.0000.0000.0000.3021.0000.789
RECOMEND_CO0.4010.0000.0000.0000.0000.7891.000

Missing values

2023-12-10T18:50:20.687219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T18:50:21.193803image/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_DTDPI_VALUERSPN_COCOMMENT_CORECOMEND_COCNTNTS_URLUPPER_CTGRY_NMLWPRT_CTGRY_NM
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SEQ_NOCNTNTS_IDCHNNEL_CL_NMCHNNEL_NMTITLE_NMNTCE_DTDPI_VALUERSPN_COCOMMENT_CORECOMEND_COCNTNTS_URLUPPER_CTGRY_NMLWPRT_CTGRY_NM
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