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
Categorical5

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
SEQ_NO is highly overall correlated with COMMENT_COHigh correlation
DPI_VALUE is highly overall correlated with VIEWS_COHigh correlation
VIEWS_CO is highly overall correlated with DPI_VALUEHigh correlation
COMMENT_CO is highly overall correlated with SEQ_NOHigh correlation
SEQ_NO has unique valuesUnique
CNTNTS_ID has unique valuesUnique
CNTNTS_URL has unique valuesUnique
COMMENT_CO has 70 (70.0%) zerosZeros

Reproduction

Analysis started2023-12-10 09:50:10.923752
Analysis finished2023-12-10 09:50:15.232714
Duration4.31 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%
Mean99019.55
Minimum75373
Maximum125767
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:50:15.371118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum75373
5-th percentile75574.45
Q181908.5
median84919.5
Q3124547.75
95-th percentile125585.1
Maximum125767
Range50394
Interquartile range (IQR)42639.25

Descriptive statistics

Standard deviation21307.493
Coefficient of variation (CV)0.21518471
Kurtosis-1.8298153
Mean99019.55
Median Absolute Deviation (MAD)8829.5
Skewness0.36727571
Sum9901955
Variance4.5400926 × 108
MonotonicityNot monotonic
2023-12-10T18:50:15.640968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
82380 1
 
1.0%
85113 1
 
1.0%
124165 1
 
1.0%
84920 1
 
1.0%
84888 1
 
1.0%
84884 1
 
1.0%
124531 1
 
1.0%
124546 1
 
1.0%
124233 1
 
1.0%
84919 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
75373 1
1.0%
75391 1
1.0%
75484 1
1.0%
75507 1
1.0%
75545 1
1.0%
75576 1
1.0%
75679 1
1.0%
75843 1
1.0%
75870 1
1.0%
76065 1
1.0%
ValueCountFrequency (%)
125767 1
1.0%
125732 1
1.0%
125728 1
1.0%
125589 1
1.0%
125587 1
1.0%
125585 1
1.0%
125548 1
1.0%
125536 1
1.0%
125430 1
1.0%
125409 1
1.0%

CNTNTS_ID
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T18:50:16.147211image/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 row608b88b45a3311eb9921c0b6f9fde92b
2nd row69b94b525a3311ebbd7ec0b6f9fde92b
3rd row73005e645a3311ebbbc3c0b6f9fde92b
4th rowe29eb8c2570511eb8ff8c0b6f9fde92b
5th row41dae9405a3311eb827cc0b6f9fde92b
ValueCountFrequency (%)
608b88b45a3311eb9921c0b6f9fde92b 1
 
1.0%
3ac8e2e4614911eba33ec0b6f9fde92b 1
 
1.0%
c6489064614711ebbafbc0b6f9fde92b 1
 
1.0%
9df1ba0a614711eb9d72c0b6f9fde92b 1
 
1.0%
9a489f5e614711eba2aac0b6f9fde92b 1
 
1.0%
d5b6098bf43d11ebb713002b67f7b0e1 1
 
1.0%
d5b6099af43d11ebb621002b67f7b0e1 1
 
1.0%
d5b5bbfdf43d11ebb2b4002b67f7b0e1 1
 
1.0%
c4f915cc614711ebaae7c0b6f9fde92b 1
 
1.0%
6be85c86614711eba27cc0b6f9fde92b 1
 
1.0%
Other values (90) 90
90.0%
2023-12-10T18:50:17.427794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
b 428
13.4%
1 304
9.5%
e 273
 
8.5%
f 255
 
8.0%
0 254
 
7.9%
9 222
 
6.9%
6 199
 
6.2%
d 196
 
6.1%
2 157
 
4.9%
7 157
 
4.9%
Other values (6) 755
23.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1807
56.5%
Lowercase Letter 1393
43.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 304
16.8%
0 254
14.1%
9 222
12.3%
6 199
11.0%
2 157
8.7%
7 157
8.7%
5 147
8.1%
4 141
7.8%
3 138
7.6%
8 88
 
4.9%
Lowercase Letter
ValueCountFrequency (%)
b 428
30.7%
e 273
19.6%
f 255
18.3%
d 196
14.1%
c 122
 
8.8%
a 119
 
8.5%

Most occurring scripts

ValueCountFrequency (%)
Common 1807
56.5%
Latin 1393
43.5%

Most frequent character per script

Common
ValueCountFrequency (%)
1 304
16.8%
0 254
14.1%
9 222
12.3%
6 199
11.0%
2 157
8.7%
7 157
8.7%
5 147
8.1%
4 141
7.8%
3 138
7.6%
8 88
 
4.9%
Latin
ValueCountFrequency (%)
b 428
30.7%
e 273
19.6%
f 255
18.3%
d 196
14.1%
c 122
 
8.8%
a 119
 
8.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3200
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
b 428
13.4%
1 304
9.5%
e 273
 
8.5%
f 255
 
8.0%
0 254
 
7.9%
9 222
 
6.9%
6 199
 
6.2%
d 196
 
6.1%
2 157
 
4.9%
7 157
 
4.9%
Other values (6) 755
23.6%

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

2023-12-10T18:50:17.727924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:50:17.922077image/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
MLB파크
100 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
MLB파크 100
100.0%

Length

2023-12-10T18:50:18.124003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:50:18.378103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
mlb파크 100
100.0%

TITLE_NM
Text

Distinct99
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T18:50:19.024978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length43
Median length31
Mean length23.67
Min length8

Characters and Unicode

Total characters2367
Distinct characters426
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks3 ?
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 rowKBO엔씨 소프트 제2 사옥에 거의 2조원을 붇는군요...
2nd rowKBO(재업) 김하성 진출기념 연도별 외국인투수 상대 전적
3rd rowKBO새해인사 올려도 되죠??
4th rowKBO황재균 스톡킹봤는데 미국에서 자기가못한걸 변명만 늘어놓네요
5th rowKBO2000~2009 3루수 타격 부문별 1위... .jpg
ValueCountFrequency (%)
jpg 6
 
1.1%
타격 4
 
0.8%
최근 3
 
0.6%
누가 3
 
0.6%
핵폭탄 3
 
0.6%
있나요 3
 
0.6%
썰 3
 
0.6%
순위 3
 
0.6%
war 3
 
0.6%
팀별 3
 
0.6%
Other values (450) 488
93.5%
2023-12-10T18:50:20.178442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
422
 
17.8%
K 62
 
2.6%
O 62
 
2.6%
B 62
 
2.6%
이 59
 
2.5%
. 55
 
2.3%
요 50
 
2.1%
가 33
 
1.4%
? 32
 
1.4%
는 30
 
1.3%
Other values (416) 1500
63.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1517
64.1%
Space Separator 422
 
17.8%
Uppercase Letter 211
 
8.9%
Other Punctuation 93
 
3.9%
Decimal Number 73
 
3.1%
Lowercase Letter 42
 
1.8%
Math Symbol 5
 
0.2%
Close Punctuation 2
 
0.1%
Open Punctuation 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
이 59
 
3.9%
요 50
 
3.3%
가 33
 
2.2%
는 30
 
2.0%
에 25
 
1.6%
수 23
 
1.5%
구 17
 
1.1%
나 17
 
1.1%
타 17
 
1.1%
도 16
 
1.1%
Other values (370) 1230
81.1%
Uppercase Letter
ValueCountFrequency (%)
K 62
29.4%
O 62
29.4%
B 62
29.4%
S 5
 
2.4%
R 3
 
1.4%
A 3
 
1.4%
W 3
 
1.4%
F 2
 
0.9%
G 2
 
0.9%
L 2
 
0.9%
Other values (5) 5
 
2.4%
Lowercase Letter
ValueCountFrequency (%)
j 8
19.0%
p 8
19.0%
g 8
19.0%
t 4
9.5%
x 3
 
7.1%
s 2
 
4.8%
k 2
 
4.8%
a 2
 
4.8%
w 1
 
2.4%
n 1
 
2.4%
Other values (3) 3
 
7.1%
Decimal Number
ValueCountFrequency (%)
0 26
35.6%
2 20
27.4%
1 13
17.8%
3 5
 
6.8%
9 3
 
4.1%
5 3
 
4.1%
4 2
 
2.7%
8 1
 
1.4%
Other Punctuation
ValueCountFrequency (%)
. 55
59.1%
? 32
34.4%
/ 3
 
3.2%
, 2
 
2.2%
! 1
 
1.1%
Math Symbol
ValueCountFrequency (%)
~ 4
80.0%
+ 1
 
20.0%
Space Separator
ValueCountFrequency (%)
422
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1517
64.1%
Common 597
 
25.2%
Latin 253
 
10.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
이 59
 
3.9%
요 50
 
3.3%
가 33
 
2.2%
는 30
 
2.0%
에 25
 
1.6%
수 23
 
1.5%
구 17
 
1.1%
나 17
 
1.1%
타 17
 
1.1%
도 16
 
1.1%
Other values (370) 1230
81.1%
Latin
ValueCountFrequency (%)
K 62
24.5%
O 62
24.5%
B 62
24.5%
j 8
 
3.2%
p 8
 
3.2%
g 8
 
3.2%
S 5
 
2.0%
t 4
 
1.6%
x 3
 
1.2%
R 3
 
1.2%
Other values (18) 28
11.1%
Common
ValueCountFrequency (%)
422
70.7%
. 55
 
9.2%
? 32
 
5.4%
0 26
 
4.4%
2 20
 
3.4%
1 13
 
2.2%
3 5
 
0.8%
~ 4
 
0.7%
9 3
 
0.5%
5 3
 
0.5%
Other values (8) 14
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1511
63.8%
ASCII 850
35.9%
Compat Jamo 6
 
0.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
422
49.6%
K 62
 
7.3%
O 62
 
7.3%
B 62
 
7.3%
. 55
 
6.5%
? 32
 
3.8%
0 26
 
3.1%
2 20
 
2.4%
1 13
 
1.5%
j 8
 
0.9%
Other values (36) 88
 
10.4%
Hangul
ValueCountFrequency (%)
이 59
 
3.9%
요 50
 
3.3%
가 33
 
2.2%
는 30
 
2.0%
에 25
 
1.7%
수 23
 
1.5%
구 17
 
1.1%
나 17
 
1.1%
타 17
 
1.1%
도 16
 
1.1%
Other values (368) 1224
81.0%
Compat Jamo
ValueCountFrequency (%)
ㅋ 4
66.7%
ㄷ 2
33.3%

NTCE_DE
Categorical

Distinct24
Distinct (%)24.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2021-01-25
21 
2021-01-23
8 
2021-01-06
7 
2021-01-01
6 
2021-01-26
6 
Other values (19)
52 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique2 ?
Unique (%)2.0%

Sample

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

Common Values

ValueCountFrequency (%)
2021-01-25 21
21.0%
2021-01-23 8
 
8.0%
2021-01-06 7
 
7.0%
2021-01-01 6
 
6.0%
2021-01-26 6
 
6.0%
2021-01-09 5
 
5.0%
2021-01-02 4
 
4.0%
2021-01-14 4
 
4.0%
2021-01-28 4
 
4.0%
2021-01-27 3
 
3.0%
Other values (14) 32
32.0%

Length

2023-12-10T18:50:20.482539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2021-01-25 21
21.0%
2021-01-23 8
 
8.0%
2021-01-06 7
 
7.0%
2021-01-01 6
 
6.0%
2021-01-26 6
 
6.0%
2021-01-09 5
 
5.0%
2021-01-02 4
 
4.0%
2021-01-14 4
 
4.0%
2021-01-28 4
 
4.0%
2021-01-10 3
 
3.0%
Other values (14) 32
32.0%

DPI_VALUE
Real number (ℝ)

HIGH CORRELATION 

Distinct98
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean432.88
Minimum17.4
Maximum6379.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:50:20.732443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum17.4
5-th percentile27.5
Q1100.75
median245.2
Q3446.9
95-th percentile1392.12
Maximum6379.4
Range6362
Interquartile range (IQR)346.15

Descriptive statistics

Standard deviation770.70855
Coefficient of variation (CV)1.7804208
Kurtosis37.56532
Mean432.88
Median Absolute Deviation (MAD)151.1
Skewness5.4957965
Sum43288
Variance593991.66
MonotonicityNot monotonic
2023-12-10T18:50:21.019157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
235.8 2
 
2.0%
120.8 2
 
2.0%
543.0 1
 
1.0%
800.0 1
 
1.0%
46.8 1
 
1.0%
70.2 1
 
1.0%
291.0 1
 
1.0%
753.8 1
 
1.0%
104.6 1
 
1.0%
293.8 1
 
1.0%
Other values (88) 88
88.0%
ValueCountFrequency (%)
17.4 1
1.0%
20.8 1
1.0%
21.2 1
1.0%
23.4 1
1.0%
25.6 1
1.0%
27.6 1
1.0%
29.6 1
1.0%
31.0 1
1.0%
33.0 1
1.0%
41.0 1
1.0%
ValueCountFrequency (%)
6379.4 1
1.0%
3271.8 1
1.0%
2371.8 1
1.0%
1854.8 1
1.0%
1481.8 1
1.0%
1387.4 1
1.0%
1065.2 1
1.0%
920.4 1
1.0%
914.8 1
1.0%
800.0 1
1.0%

VIEWS_CO
Real number (ℝ)

HIGH CORRELATION 

Distinct97
Distinct (%)97.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2156.85
Minimum87
Maximum31821
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:50:21.320651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum87
5-th percentile137.5
Q1502.75
median1226
Q32228.5
95-th percentile6959.2
Maximum31821
Range31734
Interquartile range (IQR)1725.75

Descriptive statistics

Standard deviation3845.6547
Coefficient of variation (CV)1.7829959
Kurtosis37.540027
Mean2156.85
Median Absolute Deviation (MAD)765.5
Skewness5.4945329
Sum215685
Variance14789060
MonotonicityNot monotonic
2023-12-10T18:50:21.573691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
493 2
 
2.0%
1054 2
 
2.0%
604 2
 
2.0%
624 1
 
1.0%
938 1
 
1.0%
1810 1
 
1.0%
205 1
 
1.0%
4000 1
 
1.0%
234 1
 
1.0%
351 1
 
1.0%
Other values (87) 87
87.0%
ValueCountFrequency (%)
87 1
1.0%
104 1
1.0%
106 1
1.0%
117 1
1.0%
128 1
1.0%
138 1
1.0%
148 1
1.0%
155 1
1.0%
165 1
1.0%
205 1
1.0%
ValueCountFrequency (%)
31821 1
1.0%
16357 1
1.0%
11791 1
1.0%
9274 1
1.0%
7381 1
1.0%
6937 1
1.0%
5270 1
1.0%
4602 1
1.0%
4558 1
1.0%
4000 1
1.0%

COMMENT_CO
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct15
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.86
Minimum0
Maximum19
Zeros70
Zeros (%)70.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:50:21.827099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile13.05
Maximum19
Range19
Interquartile range (IQR)1

Descriptive statistics

Standard deviation4.2188131
Coefficient of variation (CV)2.2681791
Kurtosis6.3211889
Mean1.86
Median Absolute Deviation (MAD)0
Skewness2.644642
Sum186
Variance17.798384
MonotonicityNot monotonic
2023-12-10T18:50:22.070315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0 70
70.0%
1 8
 
8.0%
2 4
 
4.0%
7 3
 
3.0%
17 2
 
2.0%
5 2
 
2.0%
4 2
 
2.0%
3 2
 
2.0%
8 1
 
1.0%
15 1
 
1.0%
Other values (5) 5
 
5.0%
ValueCountFrequency (%)
0 70
70.0%
1 8
 
8.0%
2 4
 
4.0%
3 2
 
2.0%
4 2
 
2.0%
5 2
 
2.0%
7 3
 
3.0%
8 1
 
1.0%
10 1
 
1.0%
12 1
 
1.0%
ValueCountFrequency (%)
19 1
 
1.0%
17 2
2.0%
15 1
 
1.0%
14 1
 
1.0%
13 1
 
1.0%
12 1
 
1.0%
10 1
 
1.0%
8 1
 
1.0%
7 3
3.0%
5 2
2.0%

CNTNTS_URL
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T18:50:22.662127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length125
Median length122
Mean length120.97
Min length119

Characters and Unicode

Total characters12097
Distinct characters42
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 rowhttp://mlbpark.donga.com/mp/b.php?id=202101010050986814&p=1111&b=kbotown&m=view&select=spf&query=KBO&user=&site=naver.com
2nd rowhttp://mlbpark.donga.com/mp/b.php?id=202101010050984222&p=1141&b=kbotown&m=view&select=spf&query=KBO&user=&site=donga.com
3rd rowhttp://mlbpark.donga.com/mp/b.php?id=202101010050975390&p=1141&b=kbotown&m=view&select=spf&query=KBO&user=&site=donga.com
4th rowhttp://mlbpark.donga.com/mp/b.php?id=202101010050997434&p=841&b=kbotown&m=view&select=spf&query=KBO&user=&site=donga.com
5th rowhttp://mlbpark.donga.com/mp/b.php?id=202101010050993315&p=1081&b=kbotown&m=view&select=spf&query=KBO&user=&site=donga.com
ValueCountFrequency (%)
http://mlbpark.donga.com/mp/b.php?id=202101010050986814&p=1111&b=kbotown&m=view&select=spf&query=kbo&user=&site=naver.com 1
 
1.0%
http://mlbpark.donga.com/mp/b.php?id=202101230051460898&p=1321&b=kbotown&m=view&select=spf&query=kbo&user=&site=donga.com 1
 
1.0%
http://mlbpark.donga.com/mp/b.php?id=202101250051514043&p=961&b=kbotown&m=view&select=spf&query=kbo&user=&site=donga.com 1
 
1.0%
http://mlbpark.donga.com/mp/b.php?id=202101250051514661&p=931&b=kbotown&m=view&select=spf&query=kbo&user=&site=donga.com 1
 
1.0%
http://mlbpark.donga.com/mp/b.php?id=202101250051514703&p=901&b=kbotown&m=view&select=spf&query=kbo&user=&site=donga.com 1
 
1.0%
http://mlbpark.donga.com/mp/b.php?id=202101250051511982&p=55651&b=kbotown&m=view&select=spf&query=kbo&user=&site=donga.com 1
 
1.0%
http://mlbpark.donga.com/mp/b.php?id=202101250051511307&p=55651&b=kbotown&m=view&select=spf&query=kbo&user=&site=naver.com 1
 
1.0%
http://mlbpark.donga.com/mp/b.php?id=202101250051521045&p=55351&b=kbotown&m=view&select=spf&query=kbo&user=&site=donga.com 1
 
1.0%
http://mlbpark.donga.com/mp/b.php?id=202101250051514046&p=961&b=kbotown&m=view&select=spf&query=kbo&user=&site=donga.com 1
 
1.0%
http://mlbpark.donga.com/mp/b.php?id=202101250051515870&p=871&b=kbotown&m=view&select=spf&query=kbo&user=&site=donga.com 1
 
1.0%
Other values (90) 90
90.0%
2023-12-10T18:50:23.675176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
= 800
 
6.6%
p 700
 
5.8%
& 700
 
5.8%
e 608
 
5.0%
o 596
 
4.9%
1 545
 
4.5%
0 524
 
4.3%
m 500
 
4.1%
t 500
 
4.1%
b 402
 
3.3%
Other values (32) 6222
51.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 7106
58.7%
Decimal Number 2191
 
18.1%
Other Punctuation 1700
 
14.1%
Math Symbol 800
 
6.6%
Uppercase Letter 300
 
2.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
p 700
 
9.9%
e 608
 
8.6%
o 596
 
8.4%
m 500
 
7.0%
t 500
 
7.0%
b 402
 
5.7%
s 400
 
5.6%
r 306
 
4.3%
c 302
 
4.2%
a 300
 
4.2%
Other values (13) 2492
35.1%
Decimal Number
ValueCountFrequency (%)
1 545
24.9%
0 524
23.9%
2 329
15.0%
5 285
13.0%
6 110
 
5.0%
4 101
 
4.6%
3 84
 
3.8%
8 74
 
3.4%
7 72
 
3.3%
9 67
 
3.1%
Other Punctuation
ValueCountFrequency (%)
& 700
41.2%
/ 400
23.5%
. 400
23.5%
? 100
 
5.9%
: 100
 
5.9%
Uppercase Letter
ValueCountFrequency (%)
B 100
33.3%
O 100
33.3%
K 100
33.3%
Math Symbol
ValueCountFrequency (%)
= 800
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 7406
61.2%
Common 4691
38.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
p 700
 
9.5%
e 608
 
8.2%
o 596
 
8.0%
m 500
 
6.8%
t 500
 
6.8%
b 402
 
5.4%
s 400
 
5.4%
r 306
 
4.1%
c 302
 
4.1%
a 300
 
4.1%
Other values (16) 2792
37.7%
Common
ValueCountFrequency (%)
= 800
17.1%
& 700
14.9%
1 545
11.6%
0 524
11.2%
/ 400
8.5%
. 400
8.5%
2 329
7.0%
5 285
 
6.1%
6 110
 
2.3%
4 101
 
2.2%
Other values (6) 497
10.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12097
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
= 800
 
6.6%
p 700
 
5.8%
& 700
 
5.8%
e 608
 
5.0%
o 596
 
4.9%
1 545
 
4.5%
0 524
 
4.3%
m 500
 
4.1%
t 500
 
4.1%
b 402
 
3.3%
Other values (32) 6222
51.4%

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

2023-12-10T18:50:24.012493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:50:24.188016image/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

2023-12-10T18:50:24.386625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:50:24.576415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
프로 100
50.0%
스포츠 100
50.0%

Interactions

2023-12-10T18:50:13.823533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:11.765643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:12.419302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:13.082748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:14.001867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:11.906661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:12.573923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:13.241552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:14.184182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:12.066991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:12.748263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:13.401967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:14.475120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:12.223737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:12.912319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:13.613897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T18:50:24.746991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
SEQ_NOCNTNTS_IDTITLE_NMNTCE_DEDPI_VALUEVIEWS_COCOMMENT_COCNTNTS_URL
SEQ_NO1.0001.0000.5100.7070.0000.0000.3761.000
CNTNTS_ID1.0001.0001.0001.0001.0001.0001.0001.000
TITLE_NM0.5101.0001.0001.0001.0001.0001.0001.000
NTCE_DE0.7071.0001.0001.0000.0000.0000.4331.000
DPI_VALUE0.0001.0001.0000.0001.0001.0000.7431.000
VIEWS_CO0.0001.0001.0000.0001.0001.0000.7431.000
COMMENT_CO0.3761.0001.0000.4330.7430.7431.0001.000
CNTNTS_URL1.0001.0001.0001.0001.0001.0001.0001.000
2023-12-10T18:50:24.976582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
SEQ_NODPI_VALUEVIEWS_COCOMMENT_CONTCE_DE
SEQ_NO1.000-0.103-0.1100.7050.375
DPI_VALUE-0.1031.0001.0000.1800.000
VIEWS_CO-0.1101.0001.0000.1720.000
COMMENT_CO0.7050.1800.1721.0000.152
NTCE_DE0.3750.0000.0000.1521.000

Missing values

2023-12-10T18:50:14.749019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T18:50:15.083761image/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
082380608b88b45a3311eb9921c0b6f9fde92b커뮤니티MLB파크KBO엔씨 소프트 제2 사옥에 거의 2조원을 붇는군요...2021-01-01655.632780http://mlbpark.donga.com/mp/b.php?id=202101010050986814&p=1111&b=kbotown&m=view&select=spf&query=KBO&user=&site=naver.com체육프로 스포츠
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28240273005e645a3311ebbbc3c0b6f9fde92b커뮤니티MLB파크KBO새해인사 올려도 되죠??2021-01-0125.61280http://mlbpark.donga.com/mp/b.php?id=202101010050975390&p=1141&b=kbotown&m=view&select=spf&query=KBO&user=&site=donga.com체육프로 스포츠
376132e29eb8c2570511eb8ff8c0b6f9fde92b커뮤니티MLB파크KBO황재균 스톡킹봤는데 미국에서 자기가못한걸 변명만 늘어놓네요2021-01-011387.469370http://mlbpark.donga.com/mp/b.php?id=202101010050997434&p=841&b=kbotown&m=view&select=spf&query=KBO&user=&site=donga.com체육프로 스포츠
48235141dae9405a3311eb827cc0b6f9fde92b커뮤니티MLB파크KBO2000~2009 3루수 타격 부문별 1위... .jpg2021-01-01254.612730http://mlbpark.donga.com/mp/b.php?id=202101010050993315&p=1081&b=kbotown&m=view&select=spf&query=KBO&user=&site=donga.com체육프로 스포츠
58238362f604b05a3311eb8c60c0b6f9fde92b커뮤니티MLB파크KBO2010~2020 3루수 타격 부문별 1위... .jpg2021-01-01258.612930http://mlbpark.donga.com/mp/b.php?id=202101010050986379&p=1111&b=kbotown&m=view&select=spf&query=KBO&user=&site=donga.com체육프로 스포츠
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876115d350b3ec570511eb9360c0b6f9fde92b커뮤니티MLB파크KBO1020팬은 두산이 원탑 같네요2021-01-02660.033000http://mlbpark.donga.com/mp/b.php?id=202101020051001913&p=811&b=kbotown&m=view&select=spf&query=KBO&user=&site=donga.com체육프로 스포츠
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SEQ_NOCNTNTS_IDCHNNEL_CL_NMCHNNEL_NMTITLE_NMNTCE_DEDPI_VALUEVIEWS_COCOMMENT_COCNTNTS_URLUPPER_CTGRY_NMLWPRT_CTGRY_NM
9084227e1b8455e614411ebbef3c0b6f9fde92b커뮤니티MLB파크KBO럭키금성의 LG처럼 신세계도 SSG로 갈것 같네요2021-01-26316.415820http://mlbpark.donga.com/mp/b.php?id=202101260051546074&p=241&b=kbotown&m=view&select=spf&query=KBO&user=&site=donga.com체육프로 스포츠
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