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 2 other fieldsHigh correlation
RSPN_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
NTCE_DT is highly overall correlated with SEQ_NO and 1 other fieldsHigh correlation
RECOMEND_CO is highly overall correlated with DPI_VALUE and 1 other fieldsHigh correlation
RECOMEND_CO is highly imbalanced (84.9%)Imbalance
SEQ_NO has unique valuesUnique
CNTNTS_ID has unique valuesUnique
TITLE_NM has unique valuesUnique
CNTNTS_URL has unique valuesUnique
DPI_VALUE has 29 (29.0%) zerosZeros
RSPN_CO has 35 (35.0%) zerosZeros
COMMENT_CO has 57 (57.0%) zerosZeros

Reproduction

Analysis started2023-12-10 09:47:59.355235
Analysis finished2023-12-10 09:48:04.467558
Duration5.11 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%
Mean344195.67
Minimum339122
Maximum349585
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:48:04.637351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum339122
5-th percentile339336.25
Q1341763
median344019
Q3346799.25
95-th percentile348956.3
Maximum349585
Range10463
Interquartile range (IQR)5036.25

Descriptive statistics

Standard deviation3079.1259
Coefficient of variation (CV)0.008945859
Kurtosis-1.1608818
Mean344195.67
Median Absolute Deviation (MAD)2586
Skewness-0.0034572934
Sum34419567
Variance9481016.5
MonotonicityNot monotonic
2023-12-10T18:48:04.929572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
340794 1
 
1.0%
346124 1
 
1.0%
346679 1
 
1.0%
346836 1
 
1.0%
346298 1
 
1.0%
346627 1
 
1.0%
346196 1
 
1.0%
346482 1
 
1.0%
346583 1
 
1.0%
346275 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
339122 1
1.0%
339147 1
1.0%
339177 1
1.0%
339202 1
1.0%
339303 1
1.0%
339338 1
1.0%
339397 1
1.0%
339416 1
1.0%
339447 1
1.0%
339579 1
1.0%
ValueCountFrequency (%)
349585 1
1.0%
349477 1
1.0%
349250 1
1.0%
349128 1
1.0%
349000 1
1.0%
348954 1
1.0%
348893 1
1.0%
348774 1
1.0%
348492 1
1.0%
348486 1
1.0%

CNTNTS_ID
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T18:48:05.405933image/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 row8230ae14f43711eb9925002b67f7b0e1
2nd row8230d529f43711eb86c9002b67f7b0e1
3rd row076dcf82565211ebacaa70c94e625020
4th rowfbca4109565111eb927e70c94e625020
5th rowee2b558c565111ebb98870c94e625020
ValueCountFrequency (%)
8230ae14f43711eb9925002b67f7b0e1 1
 
1.0%
82347ed3f43711eba63b002b67f7b0e1 1
 
1.0%
82358fcef43711ebb1d4002b67f7b0e1 1
 
1.0%
82351ad0f43711ebbecb002b67f7b0e1 1
 
1.0%
82356898f43711ebb464002b67f7b0e1 1
 
1.0%
8234f3eff43711eb8d5f002b67f7b0e1 1
 
1.0%
8235419cf43711ebbc86002b67f7b0e1 1
 
1.0%
82354201f43711eba76b002b67f7b0e1 1
 
1.0%
82351ab9f43711ebb681002b67f7b0e1 1
 
1.0%
823541b2f43711ebb3fb002b67f7b0e1 1
 
1.0%
Other values (90) 90
90.0%
2023-12-10T18:48:06.054523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 359
11.2%
b 356
11.1%
0 346
10.8%
7 322
10.1%
2 271
8.5%
e 241
7.5%
3 224
7.0%
f 222
6.9%
6 177
 
5.5%
8 162
 
5.1%
Other values (6) 520
16.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2192
68.5%
Lowercase Letter 1008
31.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 359
16.4%
0 346
15.8%
7 322
14.7%
2 271
12.4%
3 224
10.2%
6 177
8.1%
8 162
7.4%
4 162
7.4%
5 92
 
4.2%
9 77
 
3.5%
Lowercase Letter
ValueCountFrequency (%)
b 356
35.3%
e 241
23.9%
f 222
22.0%
a 78
 
7.7%
c 62
 
6.2%
d 49
 
4.9%

Most occurring scripts

ValueCountFrequency (%)
Common 2192
68.5%
Latin 1008
31.5%

Most frequent character per script

Common
ValueCountFrequency (%)
1 359
16.4%
0 346
15.8%
7 322
14.7%
2 271
12.4%
3 224
10.2%
6 177
8.1%
8 162
7.4%
4 162
7.4%
5 92
 
4.2%
9 77
 
3.5%
Latin
ValueCountFrequency (%)
b 356
35.3%
e 241
23.9%
f 222
22.0%
a 78
 
7.7%
c 62
 
6.2%
d 49
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3200
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 359
11.2%
b 356
11.1%
0 346
10.8%
7 322
10.1%
2 271
8.5%
e 241
7.5%
3 224
7.0%
f 222
6.9%
6 177
 
5.5%
8 162
 
5.1%
Other values (6) 520
16.2%

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

2023-12-10T18:48:06.284180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:48:06.453707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
news 100
100.0%

CHNNEL_NM
Categorical

Distinct36
Distinct (%)36.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
연합뉴스
21 
뉴시스
15 
동아일보
7 
한국일보
5 
오마이뉴스
 
3
Other values (31)
49 

Length

Max length7
Median length4
Mean length3.95
Min length3

Unique

Unique16 ?
Unique (%)16.0%

Sample

1st row오마이뉴스
2nd row조선일보
3rd row한국일보
4th row머니투데이
5th row서울경제

Common Values

ValueCountFrequency (%)
연합뉴스 21
21.0%
뉴시스 15
15.0%
동아일보 7
 
7.0%
한국일보 5
 
5.0%
오마이뉴스 3
 
3.0%
KBS 3
 
3.0%
더팩트 3
 
3.0%
SBS 3
 
3.0%
SBS Biz 2
 
2.0%
경향신문 2
 
2.0%
Other values (26) 36
36.0%

Length

2023-12-10T18:48:06.776466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
연합뉴스 21
20.6%
뉴시스 15
14.7%
동아일보 7
 
6.9%
한국일보 5
 
4.9%
sbs 5
 
4.9%
오마이뉴스 3
 
2.9%
kbs 3
 
2.9%
더팩트 3
 
2.9%
조선일보 2
 
2.0%
중앙일보 2
 
2.0%
Other values (26) 36
35.3%

TITLE_NM
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T18:48:07.311726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length59
Median length35.5
Mean length28.56
Min length10

Characters and Unicode

Total characters2856
Distinct characters538
Distinct categories13 ?
Distinct scripts4 ?
Distinct blocks7 ?
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 row사람들 배불리 먹이고 싶어서 13년째 김밥 천 원에 팝니다
2nd row[2021 신춘문예] 조선일보 2021 신춘문예 당선자들
3rd row[2021 한국일보 신춘문예] 동시 당선작 '검은 고양이'
4th row'집콕'이지만 덜 지루하게 새해를 맞이하는 법
5th row연말 ‘한탕’ 욕심에...‘숙박업 객실 제한’ 해돋이 명소에선 나몰라라
ValueCountFrequency (%)
날씨 4
 
0.6%
2021 4
 
0.6%
신춘문예 3
 
0.5%
출시 3
 
0.5%
관악구 2
 
0.3%
주말 2
 
0.3%
눈 2
 
0.3%
열풍 2
 
0.3%
사업 2
 
0.3%
첫 2
 
0.3%
Other values (598) 621
96.0%
2023-12-10T18:48:08.256559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
547
 
19.2%
' 58
 
2.0%
이 37
 
1.3%
, 36
 
1.3%
… 30
 
1.1%
한 30
 
1.1%
2 27
 
0.9%
0 27
 
0.9%
[ 24
 
0.8%
지 24
 
0.8%
Other values (528) 2016
70.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1829
64.0%
Space Separator 547
 
19.2%
Other Punctuation 168
 
5.9%
Decimal Number 117
 
4.1%
Uppercase Letter 73
 
2.6%
Open Punctuation 29
 
1.0%
Close Punctuation 29
 
1.0%
Lowercase Letter 22
 
0.8%
Initial Punctuation 16
 
0.6%
Final Punctuation 15
 
0.5%
Other values (3) 11
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
이 37
 
2.0%
한 30
 
1.6%
지 24
 
1.3%
사 23
 
1.3%
가 23
 
1.3%
다 22
 
1.2%
에 22
 
1.2%
는 21
 
1.1%
의 21
 
1.1%
전 19
 
1.0%
Other values (456) 1587
86.8%
Uppercase Letter
ValueCountFrequency (%)
T 9
12.3%
S 8
 
11.0%
J 5
 
6.8%
N 5
 
6.8%
C 5
 
6.8%
M 5
 
6.8%
G 5
 
6.8%
E 4
 
5.5%
F 4
 
5.5%
P 3
 
4.1%
Other values (11) 20
27.4%
Lowercase Letter
ValueCountFrequency (%)
i 3
13.6%
e 3
13.6%
s 2
9.1%
h 2
9.1%
o 2
9.1%
p 2
9.1%
t 2
9.1%
w 1
 
4.5%
n 1
 
4.5%
g 1
 
4.5%
Other values (3) 3
13.6%
Other Punctuation
ValueCountFrequency (%)
' 58
34.5%
, 36
21.4%
… 30
17.9%
· 17
 
10.1%
. 16
 
9.5%
" 5
 
3.0%
? 3
 
1.8%
: 1
 
0.6%
! 1
 
0.6%
% 1
 
0.6%
Decimal Number
ValueCountFrequency (%)
2 27
23.1%
0 27
23.1%
1 22
18.8%
5 11
9.4%
3 10
 
8.5%
4 5
 
4.3%
9 5
 
4.3%
6 4
 
3.4%
7 3
 
2.6%
8 3
 
2.6%
Math Symbol
ValueCountFrequency (%)
× 1
16.7%
~ 1
16.7%
∙ 1
16.7%
+ 1
16.7%
< 1
16.7%
> 1
16.7%
Open Punctuation
ValueCountFrequency (%)
[ 24
82.8%
( 5
 
17.2%
Close Punctuation
ValueCountFrequency (%)
] 24
82.8%
) 5
 
17.2%
Final Punctuation
ValueCountFrequency (%)
’ 10
66.7%
” 5
33.3%
Initial Punctuation
ValueCountFrequency (%)
‘ 10
62.5%
“ 6
37.5%
Other Symbol
ValueCountFrequency (%)
㎏ 1
50.0%
㎝ 1
50.0%
Space Separator
ValueCountFrequency (%)
547
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1825
63.9%
Common 932
32.6%
Latin 95
 
3.3%
Han 4
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
이 37
 
2.0%
한 30
 
1.6%
지 24
 
1.3%
사 23
 
1.3%
가 23
 
1.3%
다 22
 
1.2%
에 22
 
1.2%
는 21
 
1.2%
의 21
 
1.2%
전 19
 
1.0%
Other values (452) 1583
86.7%
Common
ValueCountFrequency (%)
547
58.7%
' 58
 
6.2%
, 36
 
3.9%
… 30
 
3.2%
2 27
 
2.9%
0 27
 
2.9%
[ 24
 
2.6%
] 24
 
2.6%
1 22
 
2.4%
· 17
 
1.8%
Other values (28) 120
 
12.9%
Latin
ValueCountFrequency (%)
T 9
 
9.5%
S 8
 
8.4%
J 5
 
5.3%
N 5
 
5.3%
C 5
 
5.3%
M 5
 
5.3%
G 5
 
5.3%
E 4
 
4.2%
F 4
 
4.2%
P 3
 
3.2%
Other values (24) 42
44.2%
Han
ValueCountFrequency (%)
斷 1
25.0%
想 1
25.0%
必 1
25.0%
詩 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1825
63.9%
ASCII 945
33.1%
Punctuation 61
 
2.1%
None 18
 
0.6%
CJK 4
 
0.1%
CJK Compat 2
 
0.1%
Math Operators 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
547
57.9%
' 58
 
6.1%
, 36
 
3.8%
2 27
 
2.9%
0 27
 
2.9%
[ 24
 
2.5%
] 24
 
2.5%
1 22
 
2.3%
. 16
 
1.7%
5 11
 
1.2%
Other values (52) 153
 
16.2%
Hangul
ValueCountFrequency (%)
이 37
 
2.0%
한 30
 
1.6%
지 24
 
1.3%
사 23
 
1.3%
가 23
 
1.3%
다 22
 
1.2%
에 22
 
1.2%
는 21
 
1.2%
의 21
 
1.2%
전 19
 
1.0%
Other values (452) 1583
86.7%
Punctuation
ValueCountFrequency (%)
… 30
49.2%
’ 10
 
16.4%
‘ 10
 
16.4%
“ 6
 
9.8%
” 5
 
8.2%
None
ValueCountFrequency (%)
· 17
94.4%
× 1
 
5.6%
CJK
ValueCountFrequency (%)
斷 1
25.0%
想 1
25.0%
必 1
25.0%
詩 1
25.0%
Math Operators
ValueCountFrequency (%)
∙ 1
100.0%
CJK Compat
ValueCountFrequency (%)
㎏ 1
50.0%
㎝ 1
50.0%

NTCE_DT
Categorical

HIGH CORRELATION 

Distinct40
Distinct (%)40.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2021-01-21 00:00:00
8 
2021-01-11 00:00:00
7 
2021-01-07 00:00:00
 
6
2021-01-28 00:00:00
 
6
2021-01-14 00:00:00
 
6
Other values (35)
67 

Length

Max length19
Median length19
Mean length19
Min length19

Unique

Unique22 ?
Unique (%)22.0%

Sample

1st row2021-01-01 00:00:00
2nd row2021-01-01 00:00:00
3rd row2021-01-01 04:31:00
4th row2021-01-01 07:00:00
5th row2021-01-01 10:01:00

Common Values

ValueCountFrequency (%)
2021-01-21 00:00:00 8
 
8.0%
2021-01-11 00:00:00 7
 
7.0%
2021-01-07 00:00:00 6
 
6.0%
2021-01-28 00:00:00 6
 
6.0%
2021-01-14 00:00:00 6
 
6.0%
2021-01-26 00:00:00 5
 
5.0%
2021-01-22 00:00:00 5
 
5.0%
2021-01-04 00:00:00 5
 
5.0%
2021-01-06 00:00:00 4
 
4.0%
2021-01-27 00:00:00 4
 
4.0%
Other values (30) 44
44.0%

Length

2023-12-10T18:48:08.533206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
00:00:00 86
43.0%
2021-01-04 9
 
4.5%
2021-01-21 8
 
4.0%
2021-01-11 7
 
3.5%
2021-01-07 6
 
3.0%
2021-01-28 6
 
3.0%
2021-01-14 6
 
3.0%
2021-01-06 6
 
3.0%
2021-01-01 6
 
3.0%
2021-01-26 5
 
2.5%
Other values (31) 55
27.5%

DPI_VALUE
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct33
Distinct (%)33.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.602
Minimum0
Maximum311.2
Zeros29
Zeros (%)29.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:48:08.823685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.4
Q32.95
95-th percentile70.55
Maximum311.2
Range311.2
Interquartile range (IQR)2.95

Descriptive statistics

Standard deviation48.27171
Coefficient of variation (CV)3.5488685
Kurtosis25.214569
Mean13.602
Median Absolute Deviation (MAD)0.4
Skewness4.8983597
Sum1360.2
Variance2330.158
MonotonicityNot monotonic
2023-12-10T18:48:09.062576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
0.0 29
29.0%
0.2 13
13.0%
0.4 13
13.0%
1.0 7
 
7.0%
0.6 5
 
5.0%
4.6 3
 
3.0%
14.6 2
 
2.0%
0.8 2
 
2.0%
1.4 2
 
2.0%
165.8 1
 
1.0%
Other values (23) 23
23.0%
ValueCountFrequency (%)
0.0 29
29.0%
0.2 13
13.0%
0.4 13
13.0%
0.6 5
 
5.0%
0.8 2
 
2.0%
1.0 7
 
7.0%
1.2 1
 
1.0%
1.4 2
 
2.0%
1.8 1
 
1.0%
2.4 1
 
1.0%
ValueCountFrequency (%)
311.2 1
1.0%
285.6 1
1.0%
165.8 1
1.0%
155.8 1
1.0%
96.2 1
1.0%
69.2 1
1.0%
37.2 1
1.0%
27.0 1
1.0%
26.2 1
1.0%
23.8 1
1.0%

RSPN_CO
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct31
Distinct (%)31.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.53
Minimum0
Maximum948
Zeros35
Zeros (%)35.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:48:09.309269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q37
95-th percentile133.3
Maximum948
Range948
Interquartile range (IQR)7

Descriptive statistics

Standard deviation152.80029
Coefficient of variation (CV)3.770054
Kurtosis25.925864
Mean40.53
Median Absolute Deviation (MAD)1
Skewness5.0870998
Sum4053
Variance23347.928
MonotonicityNot monotonic
2023-12-10T18:48:09.567821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
0 35
35.0%
1 19
19.0%
2 7
 
7.0%
3 6
 
6.0%
4 4
 
4.0%
5 2
 
2.0%
11 2
 
2.0%
7 2
 
2.0%
51 1
 
1.0%
16 1
 
1.0%
Other values (21) 21
21.0%
ValueCountFrequency (%)
0 35
35.0%
1 19
19.0%
2 7
 
7.0%
3 6
 
6.0%
4 4
 
4.0%
5 2
 
2.0%
6 1
 
1.0%
7 2
 
2.0%
8 1
 
1.0%
11 2
 
2.0%
ValueCountFrequency (%)
948 1
1.0%
838 1
1.0%
829 1
1.0%
295 1
1.0%
177 1
1.0%
131 1
1.0%
130 1
1.0%
105 1
1.0%
96 1
1.0%
53 1
1.0%

COMMENT_CO
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct21
Distinct (%)21.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.52
Minimum0
Maximum359
Zeros57
Zeros (%)57.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:48:09.870385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile37.7
Maximum359
Range359
Interquartile range (IQR)2

Descriptive statistics

Standard deviation51.863745
Coefficient of variation (CV)3.8360758
Kurtosis26.650803
Mean13.52
Median Absolute Deviation (MAD)0
Skewness5.0326084
Sum1352
Variance2689.8481
MonotonicityNot monotonic
2023-12-10T18:48:10.197307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0 57
57.0%
1 14
 
14.0%
2 7
 
7.0%
8 2
 
2.0%
7 2
 
2.0%
5 2
 
2.0%
3 2
 
2.0%
6 1
 
1.0%
29 1
 
1.0%
34 1
 
1.0%
Other values (11) 11
 
11.0%
ValueCountFrequency (%)
0 57
57.0%
1 14
 
14.0%
2 7
 
7.0%
3 2
 
2.0%
5 2
 
2.0%
6 1
 
1.0%
7 2
 
2.0%
8 2
 
2.0%
9 1
 
1.0%
10 1
 
1.0%
ValueCountFrequency (%)
359 1
1.0%
242 1
1.0%
240 1
1.0%
152 1
1.0%
108 1
1.0%
34 1
1.0%
32 1
1.0%
29 1
1.0%
27 1
1.0%
17 1
1.0%

RECOMEND_CO
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
0
96 
2
 
2
5
 
1
13
 
1

Length

Max length2
Median length1
Mean length1.01
Min length1

Unique

Unique2 ?
Unique (%)2.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row5

Common Values

ValueCountFrequency (%)
0 96
96.0%
2 2
 
2.0%
5 1
 
1.0%
13 1
 
1.0%

Length

2023-12-10T18:48:10.662769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:48:10.914873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 96
96.0%
2 2
 
2.0%
5 1
 
1.0%
13 1
 
1.0%

CNTNTS_URL
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T18:48:11.474941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length97
Median length97
Mean length96.72
Min length95

Characters and Unicode

Total characters9672
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=245&oid=047&aid=0002297434
2nd rowhttps://news.naver.com/main/read.naver?mode=LS2D&mid=shm&sid1=103&sid2=245&oid=023&aid=0003587130
3rd rowhttps://news.naver.com/main/read.nhn?mode=LS2D&mid=shm&sid1=103&sid2=245&oid=469&aid=0000567922
4th rowhttps://news.naver.com/main/read.nhn?mode=LS2D&mid=shm&sid1=103&sid2=245&oid=008&aid=0004522315
5th rowhttps://news.naver.com/main/read.nhn?mode=LS2D&mid=shm&sid1=103&sid2=245&oid=011&aid=0003850216
ValueCountFrequency (%)
https://news.naver.com/main/read.naver?mode=ls2d&mid=shm&sid1=103&sid2=245&oid=047&aid=0002297434 1
 
1.0%
https://news.naver.com/main/read.naver?mode=ls2d&mid=shm&sid1=103&sid2=245&oid=020&aid=0003332852 1
 
1.0%
https://news.naver.com/main/read.naver?mode=ls2d&mid=shm&sid1=103&sid2=245&oid=003&aid=0010308075 1
 
1.0%
https://news.naver.com/main/read.naver?mode=ls2d&mid=shm&sid1=103&sid2=245&oid=025&aid=0003071594 1
 
1.0%
https://news.naver.com/main/read.naver?mode=ls2d&mid=shm&sid1=103&sid2=245&oid=020&aid=0003333743 1
 
1.0%
https://news.naver.com/main/read.naver?mode=ls2d&mid=shm&sid1=103&sid2=245&oid=003&aid=0010307067 1
 
1.0%
https://news.naver.com/main/read.naver?mode=ls2d&mid=shm&sid1=103&sid2=245&oid=047&aid=0002299614 1
 
1.0%
https://news.naver.com/main/read.naver?mode=ls2d&mid=shm&sid1=103&sid2=245&oid=001&aid=0012153297 1
 
1.0%
https://news.naver.com/main/read.naver?mode=ls2d&mid=shm&sid1=103&sid2=245&oid=032&aid=0003055227 1
 
1.0%
https://news.naver.com/main/read.naver?mode=ls2d&mid=shm&sid1=103&sid2=245&oid=023&aid=0003591176 1
 
1.0%
Other values (90) 90
90.0%
2023-12-10T18:48:12.341417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
d 700
 
7.2%
= 600
 
6.2%
i 600
 
6.2%
0 585
 
6.0%
s 500
 
5.2%
& 500
 
5.2%
m 500
 
5.2%
e 486
 
5.0%
a 486
 
5.0%
2 430
 
4.4%
Other values (24) 4285
44.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5172
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 486
9.4%
a 486
9.4%
n 414
8.0%
o 300
5.8%
r 286
5.5%
h 214
 
4.1%
Other values (5) 686
13.3%
Decimal Number
ValueCountFrequency (%)
0 585
26.6%
2 430
19.5%
1 347
15.8%
3 220
 
10.0%
5 192
 
8.7%
4 165
 
7.5%
7 72
 
3.3%
6 69
 
3.1%
8 64
 
2.9%
9 56
 
2.5%
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 5472
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 486
8.9%
a 486
8.9%
n 414
7.6%
o 300
 
5.5%
r 286
 
5.2%
h 214
 
3.9%
Other values (8) 986
18.0%
Common
ValueCountFrequency (%)
= 600
14.3%
0 585
13.9%
& 500
11.9%
2 430
10.2%
/ 400
9.5%
1 347
8.3%
. 300
7.1%
3 220
 
5.2%
5 192
 
4.6%
4 165
 
3.9%
Other values (6) 461
11.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9672
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
d 700
 
7.2%
= 600
 
6.2%
i 600
 
6.2%
0 585
 
6.0%
s 500
 
5.2%
& 500
 
5.2%
m 500
 
5.2%
e 486
 
5.0%
a 486
 
5.0%
2 430
 
4.4%
Other values (24) 4285
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

2023-12-10T18:48:12.597682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:48:12.752987image/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
생활문화일반
100 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
생활문화일반 100
100.0%

Length

2023-12-10T18:48:12.914710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:48:13.080980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
생활문화일반 100
100.0%

Interactions

2023-12-10T18:48:03.230244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:48:00.673022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:48:01.398607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:48:02.632259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:48:03.375612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:48:00.926627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:48:02.054022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:48:02.796414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:48:03.536988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:48:01.083817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:48:02.301286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:48:02.950495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:48:03.677016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:48:01.217912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:48:02.449084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:48:03.092366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T18:48:13.213051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
SEQ_NOCNTNTS_IDCHNNEL_NMTITLE_NMNTCE_DTDPI_VALUERSPN_COCOMMENT_CORECOMEND_COCNTNTS_URL
SEQ_NO1.0001.0000.4791.0000.9940.4600.4450.5340.0771.000
CNTNTS_ID1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
CHNNEL_NM0.4791.0001.0001.0000.8320.6620.0000.0000.7881.000
TITLE_NM1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
NTCE_DT0.9941.0000.8321.0001.0000.8030.3620.6141.0001.000
DPI_VALUE0.4601.0000.6621.0000.8031.0000.8120.9080.7141.000
RSPN_CO0.4451.0000.0001.0000.3620.8121.0000.9450.1961.000
COMMENT_CO0.5341.0000.0001.0000.6140.9080.9451.0000.0001.000
RECOMEND_CO0.0771.0000.7881.0001.0000.7140.1960.0001.0001.000
CNTNTS_URL1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
2023-12-10T18:48:13.536516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
NTCE_DTRECOMEND_COCHNNEL_NM
NTCE_DT1.0000.7910.274
RECOMEND_CO0.7911.0000.403
CHNNEL_NM0.2740.4031.000
2023-12-10T18:48:13.725049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
SEQ_NODPI_VALUERSPN_COCOMMENT_COCHNNEL_NMNTCE_DTRECOMEND_CO
SEQ_NO1.000-0.096-0.051-0.1260.1350.7670.000
DPI_VALUE-0.0961.0000.9350.8390.2690.4000.541
RSPN_CO-0.0510.9351.0000.6790.0000.1150.159
COMMENT_CO-0.1260.8390.6791.0000.0000.2440.000
CHNNEL_NM0.1350.2690.0000.0001.0000.2740.403
NTCE_DT0.7670.4000.1150.2440.2741.0000.791
RECOMEND_CO0.0000.5410.1590.0000.4030.7911.000

Missing values

2023-12-10T18:48:03.938567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T18:48:04.293190image/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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