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 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 NTCE_DTHigh correlation
RECOMEND_CO is highly imbalanced (89.8%)Imbalance
SEQ_NO has unique valuesUnique
CNTNTS_ID has unique valuesUnique
TITLE_NM has unique valuesUnique
CNTNTS_URL has unique valuesUnique
DPI_VALUE has 31 (31.0%) zerosZeros
RSPN_CO has 35 (35.0%) zerosZeros
COMMENT_CO has 70 (70.0%) zerosZeros

Reproduction

Analysis started2023-12-10 10:10:57.927823
Analysis finished2023-12-10 10:11:02.848134
Duration4.92 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%
Mean137845.46
Minimum136700
Maximum138842
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:11:02.977401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum136700
5-th percentile136834.35
Q1137348.25
median137824
Q3138328
95-th percentile138775.15
Maximum138842
Range2142
Interquartile range (IQR)979.75

Descriptive statistics

Standard deviation604.20701
Coefficient of variation (CV)0.0043832202
Kurtosis-1.0786611
Mean137845.46
Median Absolute Deviation (MAD)493
Skewness-0.079522818
Sum13784546
Variance365066.11
MonotonicityNot monotonic
2023-12-10T19:11:03.226467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
137028 1
 
1.0%
138181 1
 
1.0%
138324 1
 
1.0%
138316 1
 
1.0%
138247 1
 
1.0%
138298 1
 
1.0%
138243 1
 
1.0%
138264 1
 
1.0%
138230 1
 
1.0%
138178 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
136700 1
1.0%
136745 1
1.0%
136780 1
1.0%
136796 1
1.0%
136803 1
1.0%
136836 1
1.0%
136846 1
1.0%
136851 1
1.0%
136890 1
1.0%
137028 1
1.0%
ValueCountFrequency (%)
138842 1
1.0%
138814 1
1.0%
138794 1
1.0%
138787 1
1.0%
138778 1
1.0%
138775 1
1.0%
138754 1
1.0%
138726 1
1.0%
138687 1
1.0%
138668 1
1.0%

CNTNTS_ID
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:11:03.644570image/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 row533db426f43711ebbe3b002b67f7b0e1
2nd row74b641f8565811eb8aec70c94e625020
3rd row533db432f43711eb916d002b67f7b0e1
4th row533ddadaf43711eba387002b67f7b0e1
5th row533ddb19f43711eba3a9002b67f7b0e1
ValueCountFrequency (%)
533db426f43711ebbe3b002b67f7b0e1 1
 
1.0%
533e9e7df43711ebb902002b67f7b0e1 1
 
1.0%
533ec567f43711eb9157002b67f7b0e1 1
 
1.0%
533e9ee0f43711eba04f002b67f7b0e1 1
 
1.0%
533ec555f43711eb8c9f002b67f7b0e1 1
 
1.0%
533e9edcf43711eb9d9b002b67f7b0e1 1
 
1.0%
533ec533f43711eba89e002b67f7b0e1 1
 
1.0%
533e9ecff43711eb89e8002b67f7b0e1 1
 
1.0%
533e9e9bf43711ebbd7b002b67f7b0e1 1
 
1.0%
533e9eb8f43711eb9e2f002b67f7b0e1 1
 
1.0%
Other values (90) 90
90.0%
2023-12-10T19:11:04.405577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 357
11.2%
b 354
11.1%
7 346
10.8%
1 335
10.5%
e 325
10.2%
3 318
9.9%
f 225
7.0%
5 169
 
5.3%
2 161
 
5.0%
6 138
 
4.3%
Other values (6) 472
14.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2115
66.1%
Lowercase Letter 1085
33.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 357
16.9%
7 346
16.4%
1 335
15.8%
3 318
15.0%
5 169
8.0%
2 161
7.6%
6 138
 
6.5%
4 130
 
6.1%
9 90
 
4.3%
8 71
 
3.4%
Lowercase Letter
ValueCountFrequency (%)
b 354
32.6%
e 325
30.0%
f 225
20.7%
a 66
 
6.1%
c 62
 
5.7%
d 53
 
4.9%

Most occurring scripts

ValueCountFrequency (%)
Common 2115
66.1%
Latin 1085
33.9%

Most frequent character per script

Common
ValueCountFrequency (%)
0 357
16.9%
7 346
16.4%
1 335
15.8%
3 318
15.0%
5 169
8.0%
2 161
7.6%
6 138
 
6.5%
4 130
 
6.1%
9 90
 
4.3%
8 71
 
3.4%
Latin
ValueCountFrequency (%)
b 354
32.6%
e 325
30.0%
f 225
20.7%
a 66
 
6.1%
c 62
 
5.7%
d 53
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3200
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 357
11.2%
b 354
11.1%
7 346
10.8%
1 335
10.5%
e 325
10.2%
3 318
9.9%
f 225
7.0%
5 169
 
5.3%
2 161
 
5.0%
6 138
 
4.3%
Other values (6) 472
14.8%

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

2023-12-10T19:11:04.718052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:11:04.936918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
news 100
100.0%

CHNNEL_NM
Categorical

Distinct22
Distinct (%)22.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
뉴시스
18 
이데일리
13 
매일경제
12 
연합뉴스
10 
뉴스1
6 
Other values (17)
41 

Length

Max length6
Median length4
Mean length3.97
Min length3

Unique

Unique4 ?
Unique (%)4.0%

Sample

1st row조선일보
2nd row스포츠서울
3rd row이데일리
4th row뉴시스
5th row연합뉴스

Common Values

ValueCountFrequency (%)
뉴시스 18
18.0%
이데일리 13
13.0%
매일경제 12
12.0%
연합뉴스 10
10.0%
뉴스1 6
 
6.0%
스포츠서울 6
 
6.0%
데일리안 4
 
4.0%
파이낸셜뉴스 4
 
4.0%
국민일보 3
 
3.0%
조선일보 3
 
3.0%
Other values (12) 21
21.0%

Length

2023-12-10T19:11:05.248942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
뉴시스 18
18.0%
이데일리 13
13.0%
매일경제 12
12.0%
연합뉴스 10
10.0%
뉴스1 6
 
6.0%
스포츠서울 6
 
6.0%
데일리안 4
 
4.0%
파이낸셜뉴스 4
 
4.0%
국민일보 3
 
3.0%
조선일보 3
 
3.0%
Other values (12) 21
21.0%

TITLE_NM
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:11:05.914383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length68
Median length40
Mean length31.96
Min length14

Characters and Unicode

Total characters3196
Distinct characters536
Distinct categories16 ?
Distinct scripts4 ?
Distinct blocks9 ?
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 row이방원과 정도전의 격돌, 그 두번째 이야기
2nd row'이헌정의 도자, 만들지 않고 태어난'전 한달 연장
3rd row[웰컴 소극장]고도·외경·투키스
4th row새해, '제2 조성진' 탄생할까…쇼팽 등 국내외 콩쿠르 잇따라
5th row새 미술품 경매 커먼옥션 출범…단체전 형식 1차 경매
ValueCountFrequency (%)
공연 9
 
1.3%
모차르트 5
 
0.7%
첫 5
 
0.7%
코로나 5
 
0.7%
뮤지컬 5
 
0.7%
시대 3
 
0.4%
연극 3
 
0.4%
2020 3
 
0.4%
스테이지 3
 
0.4%
공개 3
 
0.4%
Other values (596) 645
93.6%
2023-12-10T19:11:06.915249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
591
 
18.5%
' 80
 
2.5%
, 43
 
1.3%
이 41
 
1.3%
연 39
 
1.2%
지 33
 
1.0%
2 32
 
1.0%
의 31
 
1.0%
0 28
 
0.9%
대 27
 
0.8%
Other values (526) 2251
70.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2076
65.0%
Space Separator 591
 
18.5%
Other Punctuation 214
 
6.7%
Decimal Number 117
 
3.7%
Lowercase Letter 55
 
1.7%
Uppercase Letter 35
 
1.1%
Close Punctuation 23
 
0.7%
Open Punctuation 23
 
0.7%
Final Punctuation 21
 
0.7%
Initial Punctuation 21
 
0.7%
Other values (6) 20
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
이 41
 
2.0%
연 39
 
1.9%
지 33
 
1.6%
의 31
 
1.5%
대 27
 
1.3%
스 26
 
1.3%
다 25
 
1.2%
리 25
 
1.2%
화 23
 
1.1%
국 22
 
1.1%
Other values (448) 1784
85.9%
Lowercase Letter
ValueCountFrequency (%)
e 7
12.7%
r 6
10.9%
a 4
 
7.3%
s 4
 
7.3%
u 4
 
7.3%
o 4
 
7.3%
t 3
 
5.5%
i 3
 
5.5%
l 3
 
5.5%
y 3
 
5.5%
Other values (11) 14
25.5%
Uppercase Letter
ValueCountFrequency (%)
P 4
11.4%
C 4
11.4%
E 3
 
8.6%
N 3
 
8.6%
L 3
 
8.6%
T 3
 
8.6%
D 2
 
5.7%
G 2
 
5.7%
S 2
 
5.7%
I 1
 
2.9%
Other values (8) 8
22.9%
Other Punctuation
ValueCountFrequency (%)
' 80
37.4%
, 43
20.1%
… 25
 
11.7%
· 22
 
10.3%
" 20
 
9.3%
. 15
 
7.0%
! 4
 
1.9%
% 2
 
0.9%
? 2
 
0.9%
: 1
 
0.5%
Decimal Number
ValueCountFrequency (%)
2 32
27.4%
0 28
23.9%
1 13
11.1%
5 9
 
7.7%
9 8
 
6.8%
3 6
 
5.1%
7 6
 
5.1%
6 6
 
5.1%
4 5
 
4.3%
8 4
 
3.4%
Math Symbol
ValueCountFrequency (%)
+ 1
25.0%
~ 1
25.0%
∼ 1
25.0%
→ 1
25.0%
Close Punctuation
ValueCountFrequency (%)
] 20
87.0%
) 3
 
13.0%
Open Punctuation
ValueCountFrequency (%)
[ 20
87.0%
( 3
 
13.0%
Final Punctuation
ValueCountFrequency (%)
’ 15
71.4%
” 6
 
28.6%
Initial Punctuation
ValueCountFrequency (%)
‘ 15
71.4%
“ 6
 
28.6%
Other Number
ValueCountFrequency (%)
② 1
50.0%
③ 1
50.0%
Space Separator
ValueCountFrequency (%)
591
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 8
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 4
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%
Other Symbol
ValueCountFrequency (%)
㎞ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2074
64.9%
Common 1030
32.2%
Latin 90
 
2.8%
Han 2
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
이 41
 
2.0%
연 39
 
1.9%
지 33
 
1.6%
의 31
 
1.5%
대 27
 
1.3%
스 26
 
1.3%
다 25
 
1.2%
리 25
 
1.2%
화 23
 
1.1%
국 22
 
1.1%
Other values (446) 1782
85.9%
Common
ValueCountFrequency (%)
591
57.4%
' 80
 
7.8%
, 43
 
4.2%
2 32
 
3.1%
0 28
 
2.7%
… 25
 
2.4%
· 22
 
2.1%
" 20
 
1.9%
] 20
 
1.9%
[ 20
 
1.9%
Other values (29) 149
 
14.5%
Latin
ValueCountFrequency (%)
e 7
 
7.8%
r 6
 
6.7%
P 4
 
4.4%
a 4
 
4.4%
s 4
 
4.4%
u 4
 
4.4%
C 4
 
4.4%
o 4
 
4.4%
E 3
 
3.3%
N 3
 
3.3%
Other values (29) 47
52.2%
Han
ValueCountFrequency (%)
神 1
50.0%
亞 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2074
64.9%
ASCII 1026
32.1%
Punctuation 67
 
2.1%
None 22
 
0.7%
CJK 2
 
0.1%
Enclosed Alphanum 2
 
0.1%
CJK Compat 1
 
< 0.1%
Math Operators 1
 
< 0.1%
Arrows 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
591
57.6%
' 80
 
7.8%
, 43
 
4.2%
2 32
 
3.1%
0 28
 
2.7%
" 20
 
1.9%
] 20
 
1.9%
[ 20
 
1.9%
. 15
 
1.5%
1 13
 
1.3%
Other values (57) 164
 
16.0%
Hangul
ValueCountFrequency (%)
이 41
 
2.0%
연 39
 
1.9%
지 33
 
1.6%
의 31
 
1.5%
대 27
 
1.3%
스 26
 
1.3%
다 25
 
1.2%
리 25
 
1.2%
화 23
 
1.1%
국 22
 
1.1%
Other values (446) 1782
85.9%
Punctuation
ValueCountFrequency (%)
… 25
37.3%
’ 15
22.4%
‘ 15
22.4%
“ 6
 
9.0%
” 6
 
9.0%
None
ValueCountFrequency (%)
· 22
100.0%
CJK
ValueCountFrequency (%)
神 1
50.0%
亞 1
50.0%
Enclosed Alphanum
ValueCountFrequency (%)
② 1
50.0%
③ 1
50.0%
CJK Compat
ValueCountFrequency (%)
㎞ 1
100.0%
Math Operators
ValueCountFrequency (%)
∼ 1
100.0%
Arrows
ValueCountFrequency (%)
→ 1
100.0%

NTCE_DT
Categorical

HIGH CORRELATION 

Distinct40
Distinct (%)40.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2021-01-15 00:00:00
7 
2021-01-05 00:00:00
7 
2021-01-27 00:00:00
7 
2021-01-21 00:00:00
 
6
2021-01-22 00:00:00
 
5
Other values (35)
68 

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 17:44:00
3rd row2021-01-02 00:00:00
4th row2021-01-03 00:00:00
5th row2021-01-04 00:00:00

Common Values

ValueCountFrequency (%)
2021-01-15 00:00:00 7
 
7.0%
2021-01-05 00:00:00 7
 
7.0%
2021-01-27 00:00:00 7
 
7.0%
2021-01-21 00:00:00 6
 
6.0%
2021-01-22 00:00:00 5
 
5.0%
2021-01-29 00:00:00 5
 
5.0%
2021-01-18 00:00:00 4
 
4.0%
2021-01-28 00:00:00 4
 
4.0%
2021-01-07 00:00:00 4
 
4.0%
2021-01-13 00:00:00 4
 
4.0%
Other values (30) 47
47.0%

Length

2023-12-10T19:11:07.156435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
00:00:00 91
45.5%
2021-01-05 10
 
5.0%
2021-01-15 7
 
3.5%
2021-01-27 7
 
3.5%
2021-01-21 6
 
3.0%
2021-01-06 6
 
3.0%
2021-01-07 5
 
2.5%
2021-01-29 5
 
2.5%
2021-01-22 5
 
2.5%
2021-01-18 4
 
2.0%
Other values (31) 54
27.0%

DPI_VALUE
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct25
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.112
Minimum0
Maximum38.8
Zeros31
Zeros (%)31.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:11:07.352067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.4
Q31.4
95-th percentile8.03
Maximum38.8
Range38.8
Interquartile range (IQR)1.4

Descriptive statistics

Standard deviation5.9395827
Coefficient of variation (CV)2.8123024
Kurtosis28.290681
Mean2.112
Median Absolute Deviation (MAD)0.4
Skewness5.0952286
Sum211.2
Variance35.278642
MonotonicityNot monotonic
2023-12-10T19:11:07.568770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
0.0 31
31.0%
0.2 14
14.0%
0.4 12
 
12.0%
0.8 6
 
6.0%
0.6 4
 
4.0%
1.6 3
 
3.0%
1.2 3
 
3.0%
1.0 3
 
3.0%
1.4 3
 
3.0%
2.0 3
 
3.0%
Other values (15) 18
18.0%
ValueCountFrequency (%)
0.0 31
31.0%
0.2 14
14.0%
0.4 12
 
12.0%
0.6 4
 
4.0%
0.8 6
 
6.0%
1.0 3
 
3.0%
1.2 3
 
3.0%
1.4 3
 
3.0%
1.6 3
 
3.0%
1.8 2
 
2.0%
ValueCountFrequency (%)
38.8 1
1.0%
38.4 1
1.0%
18.0 1
1.0%
13.4 1
1.0%
12.4 1
1.0%
7.8 1
1.0%
5.8 1
1.0%
5.4 2
2.0%
5.2 1
1.0%
4.8 1
1.0%

RSPN_CO
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct23
Distinct (%)23.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.88
Minimum0
Maximum138
Zeros35
Zeros (%)35.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:11:07.803940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1.5
Q35.25
95-th percentile27.1
Maximum138
Range138
Interquartile range (IQR)5.25

Descriptive statistics

Standard deviation21.167099
Coefficient of variation (CV)2.68618
Kurtosis23.672334
Mean7.88
Median Absolute Deviation (MAD)1.5
Skewness4.6687548
Sum788
Variance448.04606
MonotonicityNot monotonic
2023-12-10T19:11:08.056205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0 35
35.0%
1 15
15.0%
2 11
 
11.0%
4 6
 
6.0%
3 5
 
5.0%
8 3
 
3.0%
5 3
 
3.0%
7 3
 
3.0%
6 3
 
3.0%
21 2
 
2.0%
Other values (13) 14
 
14.0%
ValueCountFrequency (%)
0 35
35.0%
1 15
15.0%
2 11
 
11.0%
3 5
 
5.0%
4 6
 
6.0%
5 3
 
3.0%
6 3
 
3.0%
7 3
 
3.0%
8 3
 
3.0%
9 1
 
1.0%
ValueCountFrequency (%)
138 1
1.0%
124 1
1.0%
72 1
1.0%
67 1
1.0%
48 1
1.0%
26 1
1.0%
25 1
1.0%
21 2
2.0%
18 1
1.0%
16 1
1.0%

COMMENT_CO
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct10
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3
Minimum0
Maximum35
Zeros70
Zeros (%)70.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:11:08.269879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile6.05
Maximum35
Range35
Interquartile range (IQR)1

Descriptive statistics

Standard deviation4.5891836
Coefficient of variation (CV)3.5301412
Kurtosis38.826223
Mean1.3
Median Absolute Deviation (MAD)0
Skewness5.979986
Sum130
Variance21.060606
MonotonicityNot monotonic
2023-12-10T19:11:08.463187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 70
70.0%
1 15
 
15.0%
2 5
 
5.0%
3 2
 
2.0%
4 2
 
2.0%
7 2
 
2.0%
35 1
 
1.0%
6 1
 
1.0%
27 1
 
1.0%
9 1
 
1.0%
ValueCountFrequency (%)
0 70
70.0%
1 15
 
15.0%
2 5
 
5.0%
3 2
 
2.0%
4 2
 
2.0%
6 1
 
1.0%
7 2
 
2.0%
9 1
 
1.0%
27 1
 
1.0%
35 1
 
1.0%
ValueCountFrequency (%)
35 1
 
1.0%
27 1
 
1.0%
9 1
 
1.0%
7 2
 
2.0%
6 1
 
1.0%
4 2
 
2.0%
3 2
 
2.0%
2 5
 
5.0%
1 15
 
15.0%
0 70
70.0%

RECOMEND_CO
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
0
98 
1
 
1
3
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique2 ?
Unique (%)2.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 98
98.0%
1 1
 
1.0%
3 1
 
1.0%

Length

2023-12-10T19:11:08.684328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:11:08.876283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 98
98.0%
1 1
 
1.0%
3 1
 
1.0%

CNTNTS_URL
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:11:09.417797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length97
Median length97
Mean length96.82
Min length95

Characters and Unicode

Total characters9682
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=242&oid=023&aid=0003587194
2nd rowhttps://news.naver.com/main/read.nhn?mode=LS2D&mid=shm&sid1=103&sid2=242&oid=468&aid=0000729541
3rd rowhttps://news.naver.com/main/read.naver?mode=LS2D&mid=shm&sid1=103&sid2=242&oid=018&aid=0004819447
4th rowhttps://news.naver.com/main/read.naver?mode=LS2D&mid=shm&sid1=103&sid2=242&oid=003&aid=0010273244
5th rowhttps://news.naver.com/main/read.naver?mode=LS2D&mid=shm&sid1=103&sid2=242&oid=001&aid=0012117521
ValueCountFrequency (%)
https://news.naver.com/main/read.naver?mode=ls2d&mid=shm&sid1=103&sid2=242&oid=023&aid=0003587194 1
 
1.0%
https://news.naver.com/main/read.naver?mode=ls2d&mid=shm&sid1=103&sid2=242&oid=088&aid=0000683795 1
 
1.0%
https://news.naver.com/main/read.naver?mode=ls2d&mid=shm&sid1=103&sid2=242&oid=009&aid=0004737941 1
 
1.0%
https://news.naver.com/main/read.naver?mode=ls2d&mid=shm&sid1=103&sid2=242&oid=001&aid=0012157186 1
 
1.0%
https://news.naver.com/main/read.naver?mode=ls2d&mid=shm&sid1=103&sid2=242&oid=001&aid=0012155985 1
 
1.0%
https://news.naver.com/main/read.naver?mode=ls2d&mid=shm&sid1=103&sid2=242&oid=468&aid=0000735836 1
 
1.0%
https://news.naver.com/main/read.naver?mode=ls2d&mid=shm&sid1=103&sid2=242&oid=003&aid=0010308226 1
 
1.0%
https://news.naver.com/main/read.naver?mode=ls2d&mid=shm&sid1=103&sid2=242&oid=001&aid=0012157365 1
 
1.0%
https://news.naver.com/main/read.naver?mode=ls2d&mid=shm&sid1=103&sid2=242&oid=015&aid=0004487234 1
 
1.0%
https://news.naver.com/main/read.naver?mode=ls2d&mid=shm&sid1=103&sid2=242&oid=087&aid=0000829582 1
 
1.0%
Other values (90) 90
90.0%
2023-12-10T19:11:10.368804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
d 700
 
7.2%
i 600
 
6.2%
= 600
 
6.2%
0 580
 
6.0%
s 500
 
5.2%
& 500
 
5.2%
m 500
 
5.2%
e 491
 
5.1%
a 491
 
5.1%
2 490
 
5.1%
Other values (24) 4230
43.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5182
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
9.6%
m 500
9.6%
e 491
9.5%
a 491
9.5%
n 409
7.9%
o 300
5.8%
r 291
5.6%
h 209
 
4.0%
Other values (5) 691
13.3%
Decimal Number
ValueCountFrequency (%)
0 580
26.4%
2 490
22.3%
1 345
15.7%
4 206
 
9.4%
3 202
 
9.2%
8 93
 
4.2%
7 88
 
4.0%
5 80
 
3.6%
9 68
 
3.1%
6 48
 
2.2%
Other Punctuation
ValueCountFrequency (%)
& 500
35.7%
/ 400
28.6%
. 300
21.4%
? 100
 
7.1%
: 100
 
7.1%
Uppercase Letter
ValueCountFrequency (%)
D 100
33.3%
S 100
33.3%
L 100
33.3%
Math Symbol
ValueCountFrequency (%)
= 600
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 5482
56.6%
Common 4200
43.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
d 700
12.8%
i 600
10.9%
s 500
9.1%
m 500
9.1%
e 491
9.0%
a 491
9.0%
n 409
7.5%
o 300
 
5.5%
r 291
 
5.3%
h 209
 
3.8%
Other values (8) 991
18.1%
Common
ValueCountFrequency (%)
= 600
14.3%
0 580
13.8%
& 500
11.9%
2 490
11.7%
/ 400
9.5%
1 345
8.2%
. 300
7.1%
4 206
 
4.9%
3 202
 
4.8%
? 100
 
2.4%
Other values (6) 477
11.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9682
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
d 700
 
7.2%
i 600
 
6.2%
= 600
 
6.2%
0 580
 
6.0%
s 500
 
5.2%
& 500
 
5.2%
m 500
 
5.2%
e 491
 
5.1%
a 491
 
5.1%
2 490
 
5.1%
Other values (24) 4230
43.7%

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

2023-12-10T19:11:10.737479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:11:10.965104image/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
공연/전시
100 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row공연/전시
2nd row공연/전시
3rd row공연/전시
4th row공연/전시
5th row공연/전시

Common Values

ValueCountFrequency (%)
공연/전시 100
100.0%

Length

2023-12-10T19:11:11.205977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:11:11.359629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
공연/전시 100
100.0%

Interactions

2023-12-10T19:11:01.464540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:10:59.124187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:10:59.798821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:00.533400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:01.597828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:10:59.282335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:10:59.952701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:01.011641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:01.764930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:10:59.473646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:00.154892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:01.174738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:02.026371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:10:59.643359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:00.328188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:01.318976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:11:11.531951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
SEQ_NOCNTNTS_IDCHNNEL_NMTITLE_NMNTCE_DTDPI_VALUERSPN_COCOMMENT_CORECOMEND_COCNTNTS_URL
SEQ_NO1.0001.0000.2391.0000.9920.0000.0000.0000.2741.000
CNTNTS_ID1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
CHNNEL_NM0.2391.0001.0001.0000.3070.5810.5760.4110.5051.000
TITLE_NM1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
NTCE_DT0.9921.0000.3071.0001.0000.6690.4780.6291.0001.000
DPI_VALUE0.0001.0000.5811.0000.6691.0000.9200.8400.3931.000
RSPN_CO0.0001.0000.5761.0000.4780.9201.0000.8840.0001.000
COMMENT_CO0.0001.0000.4111.0000.6290.8400.8841.0000.4281.000
RECOMEND_CO0.2741.0000.5051.0001.0000.3930.0000.4281.0001.000
CNTNTS_URL1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
2023-12-10T19:11:11.801276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
RECOMEND_COCHNNEL_NMNTCE_DT
RECOMEND_CO1.0000.2720.786
CHNNEL_NM0.2721.0000.000
NTCE_DT0.7860.0001.000
2023-12-10T19:11:12.125191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
SEQ_NODPI_VALUERSPN_COCOMMENT_COCHNNEL_NMNTCE_DTRECOMEND_CO
SEQ_NO1.0000.1340.0740.2360.0830.7540.112
DPI_VALUE0.1341.0000.9600.6820.2800.2840.173
RSPN_CO0.0740.9601.0000.5260.2610.1620.000
COMMENT_CO0.2360.6820.5261.0000.2780.3970.239
CHNNEL_NM0.0830.2800.2610.2781.0000.0000.272
NTCE_DT0.7540.2840.1620.3970.0001.0000.786
RECOMEND_CO0.1120.1730.0000.2390.2720.7861.000

Missing values

2023-12-10T19:11:02.333519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:11:02.719052image/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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