Overview

Dataset statistics

Number of variables14
Number of observations101
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory11.8 KiB
Average record size in memory119.3 B

Variable types

Categorical7
Text3
Numeric4

Alerts

BRDCST_DE has constant value ""Constant
ADVRTS_END_DE has constant value ""Constant
CHNNEL_NM has constant value ""Constant
ADVRTS_BEGIN_TIME is highly overall correlated with WTCHNG_RT and 1 other fieldsHigh correlation
ADVRTS_TIME is highly overall correlated with ADVRTS_UNTPC_COHigh correlation
ADVRTS_UNTPC_CO is highly overall correlated with ADVRTS_TIMEHigh correlation
WTCHNG_RT is highly overall correlated with ADVRTS_BEGIN_TIME and 1 other fieldsHigh correlation
ADVRTS_TY_NM is highly overall correlated with ADVRTS_BEGIN_TIME and 1 other fieldsHigh correlation
INDUTY_LCLAS_NM is highly overall correlated with INDUTY_MLSFC_NM and 1 other fieldsHigh correlation
INDUTY_MLSFC_NM is highly overall correlated with INDUTY_LCLAS_NM and 1 other fieldsHigh correlation
INDUTY_SCLAS_NM is highly overall correlated with INDUTY_LCLAS_NM and 1 other fieldsHigh correlation
ADVRTS_BEGIN_TIME has unique valuesUnique

Reproduction

Analysis started2023-12-10 10:08:26.585465
Analysis finished2023-12-10 10:08:32.278014
Duration5.69 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

BRDCST_DE
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size940.0 B
20210701
101 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20210701
2nd row20210701
3rd row20210701
4th row20210701
5th row20210701

Common Values

ValueCountFrequency (%)
20210701 101
100.0%

Length

2023-12-10T19:08:32.429698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:08:32.641278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20210701 101
100.0%

ADVRTS_END_DE
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size940.0 B
20210701
101 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20210701
2nd row20210701
3rd row20210701
4th row20210701
5th row20210701

Common Values

ValueCountFrequency (%)
20210701 101
100.0%

Length

2023-12-10T19:08:32.803714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:08:32.952242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20210701 101
100.0%

CHNNEL_NM
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size940.0 B
채널A
101 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
채널A 101
100.0%

Length

2023-12-10T19:08:33.187824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:08:33.379263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
채널a 101
100.0%

ADVRTS_MATR_NM
Text

Distinct72
Distinct (%)71.3%
Missing0
Missing (%)0.0%
Memory size940.0 B
2023-12-10T19:08:33.669388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length39
Median length30
Mean length27.326733
Min length15

Characters and Unicode

Total characters2760
Distinct characters442
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique51 ?
Unique (%)50.5%

Sample

1st row인포웍스흥국생명무배당다사랑플러스암보험 (제품설명)
2nd row큐원상쾌환 (혜리/삐진사람1도없이/먹고들어간다/성시경)
3rd row비요뜨 (춤추는김세정/하늘에서떨어지는과자들/비요뜨를꺾어봐)
4th row아로나민씨플러스 (비타민C1200MG/피로는풀고/항산화관리)
5th row더뉴렉스턴스포츠 (운전하는이시영/모래바람을일으키는자동차)
ValueCountFrequency (%)
제품설명 10
 
5.0%
인사돌플러스 4
 
2.0%
최불암/꼭꼭씹는행복 4
 
2.0%
인생시계/유동근/김지호/이선균/약국에서만 3
 
1.5%
노란우산공제 3
 
1.5%
명인이가탄f 3
 
1.5%
둘코소프트 3
 
1.5%
egf10ppm/브랑떼화장품/네이버 3
 
1.5%
희망은노란우산을타고/김영철/송가인 3
 
1.5%
브랑떼프리미엄egf크림 3
 
1.5%
Other values (125) 163
80.7%
2023-12-10T19:08:34.635782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 193
 
7.0%
) 101
 
3.7%
101
 
3.7%
( 101
 
3.7%
이 71
 
2.6%
스 43
 
1.6%
는 36
 
1.3%
리 29
 
1.1%
인 28
 
1.0%
에 28
 
1.0%
Other values (432) 2029
73.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2084
75.5%
Other Punctuation 195
 
7.1%
Uppercase Letter 135
 
4.9%
Close Punctuation 101
 
3.7%
Space Separator 101
 
3.7%
Open Punctuation 101
 
3.7%
Decimal Number 42
 
1.5%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
이 71
 
3.4%
스 43
 
2.1%
는 36
 
1.7%
리 29
 
1.4%
인 28
 
1.3%
에 28
 
1.3%
트 27
 
1.3%
지 26
 
1.2%
하 25
 
1.2%
한 24
 
1.2%
Other values (397) 1747
83.8%
Uppercase Letter
ValueCountFrequency (%)
E 16
11.9%
G 15
11.1%
I 13
9.6%
F 11
 
8.1%
M 11
 
8.1%
P 8
 
5.9%
S 8
 
5.9%
L 8
 
5.9%
T 7
 
5.2%
A 6
 
4.4%
Other values (11) 32
23.7%
Decimal Number
ValueCountFrequency (%)
1 11
26.2%
0 9
21.4%
2 7
16.7%
3 5
11.9%
5 4
 
9.5%
4 3
 
7.1%
9 2
 
4.8%
8 1
 
2.4%
Other Punctuation
ValueCountFrequency (%)
/ 193
99.0%
. 2
 
1.0%
Close Punctuation
ValueCountFrequency (%)
) 101
100.0%
Space Separator
ValueCountFrequency (%)
101
100.0%
Open Punctuation
ValueCountFrequency (%)
( 101
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2084
75.5%
Common 541
 
19.6%
Latin 135
 
4.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
이 71
 
3.4%
스 43
 
2.1%
는 36
 
1.7%
리 29
 
1.4%
인 28
 
1.3%
에 28
 
1.3%
트 27
 
1.3%
지 26
 
1.2%
하 25
 
1.2%
한 24
 
1.2%
Other values (397) 1747
83.8%
Latin
ValueCountFrequency (%)
E 16
11.9%
G 15
11.1%
I 13
9.6%
F 11
 
8.1%
M 11
 
8.1%
P 8
 
5.9%
S 8
 
5.9%
L 8
 
5.9%
T 7
 
5.2%
A 6
 
4.4%
Other values (11) 32
23.7%
Common
ValueCountFrequency (%)
/ 193
35.7%
) 101
18.7%
101
18.7%
( 101
18.7%
1 11
 
2.0%
0 9
 
1.7%
2 7
 
1.3%
3 5
 
0.9%
5 4
 
0.7%
4 3
 
0.6%
Other values (4) 6
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2084
75.5%
ASCII 676
 
24.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/ 193
28.6%
) 101
14.9%
101
14.9%
( 101
14.9%
E 16
 
2.4%
G 15
 
2.2%
I 13
 
1.9%
1 11
 
1.6%
F 11
 
1.6%
M 11
 
1.6%
Other values (25) 103
15.2%
Hangul
ValueCountFrequency (%)
이 71
 
3.4%
스 43
 
2.1%
는 36
 
1.7%
리 29
 
1.4%
인 28
 
1.3%
에 28
 
1.3%
트 27
 
1.3%
지 26
 
1.2%
하 25
 
1.2%
한 24
 
1.2%
Other values (397) 1747
83.8%

ADVRTS_BEGIN_TIME
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct101
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean71515.782
Minimum20336
Maximum103327
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:08:34.918425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20336
5-th percentile21724
Q154851
median84103
Q385649
95-th percentile103102
Maximum103327
Range82991
Interquartile range (IQR)30798

Descriptive statistics

Standard deviation27111.852
Coefficient of variation (CV)0.37910307
Kurtosis-0.73884481
Mean71515.782
Median Absolute Deviation (MAD)18328
Skewness-0.64958119
Sum7223094
Variance7.3505254 × 108
MonotonicityStrictly increasing
2023-12-10T19:08:35.244850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20336 1
 
1.0%
85248 1
 
1.0%
85634 1
 
1.0%
85619 1
 
1.0%
85604 1
 
1.0%
85548 1
 
1.0%
85518 1
 
1.0%
85504 1
 
1.0%
85448 1
 
1.0%
85418 1
 
1.0%
Other values (91) 91
90.1%
ValueCountFrequency (%)
20336 1
1.0%
21624 1
1.0%
21639 1
1.0%
21654 1
1.0%
21709 1
1.0%
21724 1
1.0%
21754 1
1.0%
21809 1
1.0%
21824 1
1.0%
21839 1
1.0%
ValueCountFrequency (%)
103327 1
1.0%
103312 1
1.0%
103212 1
1.0%
103157 1
1.0%
103142 1
1.0%
103102 1
1.0%
102546 1
1.0%
102531 1
1.0%
102516 1
1.0%
102501 1
1.0%

ADVRTS_TIME
Real number (ℝ)

HIGH CORRELATION 

Distinct7
Distinct (%)6.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52.029703
Minimum15
Maximum400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:08:35.620428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile15
Q115
median15
Q330
95-th percentile400
Maximum400
Range385
Interquartile range (IQR)15

Descriptive statistics

Standard deviation101.37105
Coefficient of variation (CV)1.9483303
Kurtosis7.3881265
Mean52.029703
Median Absolute Deviation (MAD)0
Skewness2.9488673
Sum5255
Variance10276.089
MonotonicityNot monotonic
2023-12-10T19:08:35.852550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
15 73
72.3%
30 10
 
9.9%
400 7
 
6.9%
100 3
 
3.0%
200 3
 
3.0%
40 3
 
3.0%
20 2
 
2.0%
ValueCountFrequency (%)
15 73
72.3%
20 2
 
2.0%
30 10
 
9.9%
40 3
 
3.0%
100 3
 
3.0%
200 3
 
3.0%
400 7
 
6.9%
ValueCountFrequency (%)
400 7
 
6.9%
200 3
 
3.0%
100 3
 
3.0%
40 3
 
3.0%
30 10
 
9.9%
20 2
 
2.0%
15 73
72.3%

ADVRTS_UNTPC_CO
Real number (ℝ)

HIGH CORRELATION 

Distinct18
Distinct (%)17.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2540.104
Minimum499.5
Maximum40008
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:08:36.065811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum499.5
5-th percentile499.5
Q1749.2
median1300.5
Q32500.5
95-th percentile7992
Maximum40008
Range39508.5
Interquartile range (IQR)1751.3

Descriptive statistics

Standard deviation4432.7889
Coefficient of variation (CV)1.745121
Kurtosis51.572796
Mean2540.104
Median Absolute Deviation (MAD)650.2
Skewness6.4160573
Sum256550.5
Variance19649617
MonotonicityNot monotonic
2023-12-10T19:08:36.297873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
1300.5 32
31.7%
499.5 18
17.8%
749.2 12
 
11.9%
2601.0 8
 
7.9%
1950.7 8
 
7.9%
7992.0 6
 
5.9%
2500.5 3
 
3.0%
3468.0 2
 
2.0%
10404.0 2
 
2.0%
1734.0 2
 
2.0%
Other values (8) 8
 
7.9%
ValueCountFrequency (%)
499.5 18
17.8%
749.2 12
 
11.9%
999.0 1
 
1.0%
1300.5 32
31.7%
1734.0 2
 
2.0%
1950.7 8
 
7.9%
1998.0 1
 
1.0%
2500.5 3
 
3.0%
2601.0 8
 
7.9%
3468.0 2
 
2.0%
ValueCountFrequency (%)
40008.0 1
 
1.0%
10404.0 2
 
2.0%
10002.0 1
 
1.0%
7992.0 6
5.9%
6668.0 1
 
1.0%
5202.0 1
 
1.0%
5001.0 1
 
1.0%
3996.0 1
 
1.0%
3468.0 2
 
2.0%
2601.0 8
7.9%

ADVRTS_TY_NM
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size940.0 B
프로 B(정상요금)
55 
프로 C(정상요금)
39 
프로 A(정상요금)
7 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row프로 A(정상요금)
2nd row프로 C(정상요금)
3rd row프로 C(정상요금)
4th row프로 C(정상요금)
5th row프로 C(정상요금)

Common Values

ValueCountFrequency (%)
프로 B(정상요금) 55
54.5%
프로 C(정상요금) 39
38.6%
프로 A(정상요금) 7
 
6.9%

Length

2023-12-10T19:08:36.540105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:08:36.736206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
프로 101
50.0%
b(정상요금 55
27.2%
c(정상요금 39
 
19.3%
a(정상요금 7
 
3.5%

ADVRTSR_NM
Text

Distinct62
Distinct (%)61.4%
Missing0
Missing (%)0.0%
Memory size940.0 B
2023-12-10T19:08:37.143926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length10
Mean length6.4950495
Min length3

Characters and Unicode

Total characters656
Distinct characters167
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique38 ?
Unique (%)37.6%

Sample

1st row인포웍스마케팅
2nd row삼양사
3rd row서울우유
4th row일동제약
5th row쌍용자동차
ValueCountFrequency (%)
동국제약 5
 
5.0%
헨켈홈케어코리아 5
 
5.0%
글락소스미스클라인컨슈머헬스케어코리아 5
 
5.0%
인포웍스마케팅 3
 
3.0%
브랑떼이지에프코스메틱 3
 
3.0%
명인제약 3
 
3.0%
사노피아벤티스코리아 3
 
3.0%
글로벌브랜드그룹코리아 3
 
3.0%
중소기업중앙회 3
 
3.0%
한국코카콜라 2
 
2.0%
Other values (52) 66
65.3%
2023-12-10T19:08:37.848898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
스 38
 
5.8%
아 25
 
3.8%
코 25
 
3.8%
리 22
 
3.4%
이 21
 
3.2%
인 18
 
2.7%
라 14
 
2.1%
케 13
 
2.0%
어 13
 
2.0%
국 11
 
1.7%
Other values (157) 456
69.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 648
98.8%
Uppercase Letter 8
 
1.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
스 38
 
5.9%
아 25
 
3.9%
코 25
 
3.9%
리 22
 
3.4%
이 21
 
3.2%
인 18
 
2.8%
라 14
 
2.2%
케 13
 
2.0%
어 13
 
2.0%
국 11
 
1.7%
Other values (150) 448
69.1%
Uppercase Letter
ValueCountFrequency (%)
K 2
25.0%
T 1
12.5%
L 1
12.5%
G 1
12.5%
S 1
12.5%
F 1
12.5%
C 1
12.5%

Most occurring scripts

ValueCountFrequency (%)
Hangul 648
98.8%
Latin 8
 
1.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
스 38
 
5.9%
아 25
 
3.9%
코 25
 
3.9%
리 22
 
3.4%
이 21
 
3.2%
인 18
 
2.8%
라 14
 
2.2%
케 13
 
2.0%
어 13
 
2.0%
국 11
 
1.7%
Other values (150) 448
69.1%
Latin
ValueCountFrequency (%)
K 2
25.0%
T 1
12.5%
L 1
12.5%
G 1
12.5%
S 1
12.5%
F 1
12.5%
C 1
12.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 648
98.8%
ASCII 8
 
1.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
스 38
 
5.9%
아 25
 
3.9%
코 25
 
3.9%
리 22
 
3.4%
이 21
 
3.2%
인 18
 
2.8%
라 14
 
2.2%
케 13
 
2.0%
어 13
 
2.0%
국 11
 
1.7%
Other values (150) 448
69.1%
ASCII
ValueCountFrequency (%)
K 2
25.0%
T 1
12.5%
L 1
12.5%
G 1
12.5%
S 1
12.5%
F 1
12.5%
C 1
12.5%

BRAND_NM
Text

Distinct69
Distinct (%)68.3%
Missing0
Missing (%)0.0%
Memory size940.0 B
2023-12-10T19:08:38.436283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length19
Mean length6.990099
Min length2

Characters and Unicode

Total characters706
Distinct characters220
Distinct categories3 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique45 ?
Unique (%)44.6%

Sample

1st row인포웍스흥국생명무배당다사랑플러스암보험
2nd row큐원상쾌환
3rd row비요뜨
4th row아로나민씨플러스
5th row더뉴렉스턴스포츠
ValueCountFrequency (%)
인사돌플러스 4
 
4.0%
둘코소프트 3
 
3.0%
명인이가탄f 3
 
3.0%
스파이더 3
 
3.0%
노란우산공제 3
 
3.0%
브랑떼프리미엄egf크림 3
 
3.0%
라미실원스 3
 
3.0%
애드크로스쏘팔코사놀 2
 
2.0%
원스토어 2
 
2.0%
데이즈온프리바이오틱스에프오에스트리플 2
 
2.0%
Other values (59) 73
72.3%
2023-12-10T19:08:39.196542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
스 33
 
4.7%
이 28
 
4.0%
프 20
 
2.8%
트 17
 
2.4%
인 17
 
2.4%
리 17
 
2.4%
라 13
 
1.8%
플 12
 
1.7%
원 10
 
1.4%
보 10
 
1.4%
Other values (210) 529
74.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 658
93.2%
Uppercase Letter 38
 
5.4%
Decimal Number 10
 
1.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
스 33
 
5.0%
이 28
 
4.3%
프 20
 
3.0%
트 17
 
2.6%
인 17
 
2.6%
리 17
 
2.6%
라 13
 
2.0%
플 12
 
1.8%
원 10
 
1.5%
보 10
 
1.5%
Other values (192) 481
73.1%
Uppercase Letter
ValueCountFrequency (%)
G 8
21.1%
F 6
15.8%
E 5
13.2%
A 4
10.5%
I 4
10.5%
T 2
 
5.3%
V 2
 
5.3%
K 2
 
5.3%
L 1
 
2.6%
S 1
 
2.6%
Other values (3) 3
 
7.9%
Decimal Number
ValueCountFrequency (%)
0 2
20.0%
1 2
20.0%
2 2
20.0%
4 2
20.0%
3 2
20.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 658
93.2%
Latin 38
 
5.4%
Common 10
 
1.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
스 33
 
5.0%
이 28
 
4.3%
프 20
 
3.0%
트 17
 
2.6%
인 17
 
2.6%
리 17
 
2.6%
라 13
 
2.0%
플 12
 
1.8%
원 10
 
1.5%
보 10
 
1.5%
Other values (192) 481
73.1%
Latin
ValueCountFrequency (%)
G 8
21.1%
F 6
15.8%
E 5
13.2%
A 4
10.5%
I 4
10.5%
T 2
 
5.3%
V 2
 
5.3%
K 2
 
5.3%
L 1
 
2.6%
S 1
 
2.6%
Other values (3) 3
 
7.9%
Common
ValueCountFrequency (%)
0 2
20.0%
1 2
20.0%
2 2
20.0%
4 2
20.0%
3 2
20.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 658
93.2%
ASCII 48
 
6.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
스 33
 
5.0%
이 28
 
4.3%
프 20
 
3.0%
트 17
 
2.6%
인 17
 
2.6%
리 17
 
2.6%
라 13
 
2.0%
플 12
 
1.8%
원 10
 
1.5%
보 10
 
1.5%
Other values (192) 481
73.1%
ASCII
ValueCountFrequency (%)
G 8
16.7%
F 6
12.5%
E 5
10.4%
A 4
 
8.3%
I 4
 
8.3%
T 2
 
4.2%
V 2
 
4.2%
K 2
 
4.2%
0 2
 
4.2%
1 2
 
4.2%
Other values (8) 11
22.9%

INDUTY_LCLAS_NM
Categorical

HIGH CORRELATION 

Distinct18
Distinct (%)17.8%
Missing0
Missing (%)0.0%
Memory size940.0 B
제약 및 의료
21 
식품
18 
금융 보험 및 증권
12 
화장품 및 보건용품
11 
음료 및 기호품
6 
Other values (13)
33 

Length

Max length11
Median length10
Mean length6.4554455
Min length2

Unique

Unique6 ?
Unique (%)5.9%

Sample

1st row금융 보험 및 증권
2nd row식품
3rd row음료 및 기호품
4th row제약 및 의료
5th row수송기기

Common Values

ValueCountFrequency (%)
제약 및 의료 21
20.8%
식품 18
17.8%
금융 보험 및 증권 12
11.9%
화장품 및 보건용품 11
10.9%
음료 및 기호품 6
 
5.9%
컴퓨터 및 정보통신 6
 
5.9%
패션 4
 
4.0%
출판 4
 
4.0%
서비스 4
 
4.0%
관공서 및 단체 4
 
4.0%
Other values (8) 11
10.9%

Length

2023-12-10T19:08:39.530736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
및 64
26.1%
제약 21
 
8.6%
의료 21
 
8.6%
식품 18
 
7.3%
금융 12
 
4.9%
보험 12
 
4.9%
증권 12
 
4.9%
화장품 11
 
4.5%
보건용품 11
 
4.5%
음료 6
 
2.4%
Other values (20) 57
23.3%

INDUTY_MLSFC_NM
Categorical

HIGH CORRELATION 

Distinct36
Distinct (%)35.6%
Missing0
Missing (%)0.0%
Memory size940.0 B
건강식품
17 
금융 및 보험
12 
이비인후 치과 및 안과용제
7 
신문
 
4
통신정보서비스
 
4
Other values (31)
57 

Length

Max length14
Median length11
Mean length6.4059406
Min length2

Unique

Unique15 ?
Unique (%)14.9%

Sample

1st row금융 및 보험
2nd row건강식품
3rd row비알콜음료
4th row대사성의약
5th row승용자동차

Common Values

ValueCountFrequency (%)
건강식품 17
16.8%
금융 및 보험 12
 
11.9%
이비인후 치과 및 안과용제 7
 
6.9%
신문 4
 
4.0%
통신정보서비스 4
 
4.0%
두피 및 피부용제 4
 
4.0%
비알콜음료 4
 
4.0%
대사성의약 3
 
3.0%
부동산 임대 및 매매 3
 
3.0%
여성기초화장품 3
 
3.0%
Other values (26) 40
39.6%

Length

2023-12-10T19:08:39.783553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
및 38
19.6%
건강식품 17
 
8.8%
보험 12
 
6.2%
금융 12
 
6.2%
이비인후 7
 
3.6%
치과 7
 
3.6%
안과용제 7
 
3.6%
피부용제 4
 
2.1%
비알콜음료 4
 
2.1%
관공서 4
 
2.1%
Other values (44) 82
42.3%

INDUTY_SCLAS_NM
Categorical

HIGH CORRELATION 

Distinct50
Distinct (%)49.5%
Missing0
Missing (%)0.0%
Memory size940.0 B
건강보조식품
14 
구내염용제
7 
생명보험
 
6
지방신문
 
4
성인종합영양제
 
3
Other values (45)
67 

Length

Max length18
Median length13
Mean length6.2376238
Min length2

Unique

Unique30 ?
Unique (%)29.7%

Sample

1st row생명보험
2nd row건강식품 기타
3rd row호상요구르트
4th row성인종합영양제
5th row다목적승용차

Common Values

ValueCountFrequency (%)
건강보조식품 14
 
13.9%
구내염용제 7
 
6.9%
생명보험 6
 
5.9%
지방신문 4
 
4.0%
성인종합영양제 3
 
3.0%
아파트 임대 및 매매 3
 
3.0%
여성일반기초화장품 3
 
3.0%
무좀치료제 3
 
3.0%
인터넷서비스 3
 
3.0%
손해보험 3
 
3.0%
Other values (40) 52
51.5%

Length

2023-12-10T19:08:40.024120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
및 17
 
11.5%
건강보조식품 14
 
9.5%
구내염용제 7
 
4.7%
생명보험 6
 
4.1%
기타 6
 
4.1%
지방신문 4
 
2.7%
인터넷서비스 3
 
2.0%
스포츠웨어 3
 
2.0%
변비치료제 3
 
2.0%
손해보험 3
 
2.0%
Other values (58) 82
55.4%

WTCHNG_RT
Real number (ℝ)

HIGH CORRELATION 

Distinct31
Distinct (%)30.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4158203
Minimum0
Maximum2.62746
Zeros1
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:08:40.484158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.02157
Q10.09814
median0.26703
Q30.38736
95-th percentile1.29307
Maximum2.62746
Range2.62746
Interquartile range (IQR)0.28922

Descriptive statistics

Standard deviation0.56493708
Coefficient of variation (CV)1.3586087
Kurtosis5.7609845
Mean0.4158203
Median Absolute Deviation (MAD)0.159
Skewness2.3445852
Sum41.99785
Variance0.31915391
MonotonicityNot monotonic
2023-12-10T19:08:40.899716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
0.10803 12
 
11.9%
0.02813 9
 
8.9%
0.26703 7
 
6.9%
0.29075 7
 
6.9%
0.03394 4
 
4.0%
0.27261 4
 
4.0%
1.00952 4
 
4.0%
0.07054 4
 
4.0%
0.02157 4
 
4.0%
1.10383 4
 
4.0%
Other values (21) 42
41.6%
ValueCountFrequency (%)
0.0 1
 
1.0%
0.01352 1
 
1.0%
0.02157 4
 
4.0%
0.02235 1
 
1.0%
0.02813 9
8.9%
0.03394 4
 
4.0%
0.04906 1
 
1.0%
0.07054 4
 
4.0%
0.09814 1
 
1.0%
0.10803 12
11.9%
ValueCountFrequency (%)
2.62746 3
3.0%
1.85465 2
2.0%
1.29307 2
2.0%
1.22187 3
3.0%
1.10383 4
4.0%
1.00952 4
4.0%
0.61498 3
3.0%
0.55206 3
3.0%
0.40723 1
 
1.0%
0.38736 1
 
1.0%

Interactions

2023-12-10T19:08:31.128869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:29.051627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:29.701127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:30.399202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:31.272087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:29.192970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:29.860861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:30.597634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:31.428797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:29.352771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:30.035292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:30.769639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:31.580686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:29.525633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:30.216407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:30.944470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:08:41.410323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ADVRTS_MATR_NMADVRTS_BEGIN_TIMEADVRTS_TIMEADVRTS_UNTPC_COADVRTS_TY_NMADVRTSR_NMBRAND_NMINDUTY_LCLAS_NMINDUTY_MLSFC_NMINDUTY_SCLAS_NMWTCHNG_RT
ADVRTS_MATR_NM1.0000.0000.9810.7440.0001.0001.0001.0001.0001.0000.000
ADVRTS_BEGIN_TIME0.0001.0000.5010.6890.9460.0000.0000.0000.0000.0000.749
ADVRTS_TIME0.9810.5011.0000.9230.1960.9820.9770.4960.0000.0000.276
ADVRTS_UNTPC_CO0.7440.6890.9231.0000.3850.0000.8020.0000.0000.0000.301
ADVRTS_TY_NM0.0000.9460.1960.3851.0000.0000.0000.1530.0000.0000.875
ADVRTSR_NM1.0000.0000.9820.0000.0001.0001.0000.9990.9980.9970.000
BRAND_NM1.0000.0000.9770.8020.0001.0001.0001.0001.0001.0000.000
INDUTY_LCLAS_NM1.0000.0000.4960.0000.1530.9991.0001.0001.0001.0000.000
INDUTY_MLSFC_NM1.0000.0000.0000.0000.0000.9981.0001.0001.0001.0000.000
INDUTY_SCLAS_NM1.0000.0000.0000.0000.0000.9971.0001.0001.0001.0000.000
WTCHNG_RT0.0000.7490.2760.3010.8750.0000.0000.0000.0000.0001.000
2023-12-10T19:08:41.675275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
INDUTY_LCLAS_NMINDUTY_SCLAS_NMADVRTS_TY_NMINDUTY_MLSFC_NM
INDUTY_LCLAS_NM1.0000.7840.0490.885
INDUTY_SCLAS_NM0.7841.0000.0000.886
ADVRTS_TY_NM0.0490.0001.0000.000
INDUTY_MLSFC_NM0.8850.8860.0001.000
2023-12-10T19:08:41.867600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ADVRTS_BEGIN_TIMEADVRTS_TIMEADVRTS_UNTPC_COWTCHNG_RTADVRTS_TY_NMINDUTY_LCLAS_NMINDUTY_MLSFC_NMINDUTY_SCLAS_NM
ADVRTS_BEGIN_TIME1.000-0.0500.4430.7980.6850.0000.0000.000
ADVRTS_TIME-0.0501.0000.758-0.2150.1840.2680.0000.000
ADVRTS_UNTPC_CO0.4430.7581.0000.2280.3300.0000.0000.000
WTCHNG_RT0.798-0.2150.2281.0000.8460.0000.0000.000
ADVRTS_TY_NM0.6850.1840.3300.8461.0000.0490.0000.000
INDUTY_LCLAS_NM0.0000.2680.0000.0000.0491.0000.8850.784
INDUTY_MLSFC_NM0.0000.0000.0000.0000.0000.8851.0000.886
INDUTY_SCLAS_NM0.0000.0000.0000.0000.0000.7840.8861.000

Missing values

2023-12-10T19:08:31.793940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:08:32.146202image/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

BRDCST_DEADVRTS_END_DECHNNEL_NMADVRTS_MATR_NMADVRTS_BEGIN_TIMEADVRTS_TIMEADVRTS_UNTPC_COADVRTS_TY_NMADVRTSR_NMBRAND_NMINDUTY_LCLAS_NMINDUTY_MLSFC_NMINDUTY_SCLAS_NMWTCHNG_RT
02021070120210701채널A인포웍스흥국생명무배당다사랑플러스암보험 (제품설명)2033640040008.0프로 A(정상요금)인포웍스마케팅인포웍스흥국생명무배당다사랑플러스암보험금융 보험 및 증권금융 및 보험생명보험0.12155
12021070120210701채널A큐원상쾌환 (혜리/삐진사람1도없이/먹고들어간다/성시경)2162415499.5프로 C(정상요금)삼양사큐원상쾌환식품건강식품건강식품 기타0.10803
22021070120210701채널A비요뜨 (춤추는김세정/하늘에서떨어지는과자들/비요뜨를꺾어봐)2163915499.5프로 C(정상요금)서울우유비요뜨음료 및 기호품비알콜음료호상요구르트0.10803
32021070120210701채널A아로나민씨플러스 (비타민C1200MG/피로는풀고/항산화관리)2165415499.5프로 C(정상요금)일동제약아로나민씨플러스제약 및 의료대사성의약성인종합영양제0.10803
42021070120210701채널A더뉴렉스턴스포츠 (운전하는이시영/모래바람을일으키는자동차)2170915499.5프로 C(정상요금)쌍용자동차더뉴렉스턴스포츠수송기기승용자동차다목적승용차0.10803
52021070120210701채널ASK매직보더리스인덕션 (커스텀FIT라이팅/국내최고화력)2172430999.0프로 C(정상요금)SK매직SK매직보더리스인덕션가정용품주방용품가스렌지 및 전기렌지0.10803
62021070120210701채널A동부센트레빌 (정우성/산다는건/더몰입하고/더함께할수있다는것)2175415499.5프로 C(정상요금)동부건설동부센트레빌건설 건재 및 부동산부동산 임대 및 매매아파트 임대 및 매매0.10803
72021070120210701채널A맥도날드 (탱글한슈림프에/풍미의빅뱅/슈니언버거)2180915499.5프로 C(정상요금)한국맥도날드맥도날드서비스음식 및 숙박패스트푸드점0.10803
82021070120210701채널A해피클린 (화장실앞여/예전엔/요즘엔/장건강엔)2182415499.5프로 C(정상요금)해피런해피클린식품건강식품건강보조식품0.10803
92021070120210701채널A브랑떼프리미엄EGF크림 (EGF10PPM/브랑떼화장품/네이버)2183915499.5프로 C(정상요금)브랑떼이지에프코스메틱브랑떼프리미엄EGF크림화장품 및 보건용품여성기초화장품여성일반기초화장품0.10803
BRDCST_DEADVRTS_END_DECHNNEL_NMADVRTS_MATR_NMADVRTS_BEGIN_TIMEADVRTS_TIMEADVRTS_UNTPC_COADVRTS_TY_NMADVRTSR_NMBRAND_NMINDUTY_LCLAS_NMINDUTY_MLSFC_NMINDUTY_SCLAS_NMWTCHNG_RT
912021070120210701채널A전자랜드 (나태주/에어에어컨/가격도좋아/쇼핑몰도좋아)102501151300.5프로 B(정상요금)에스와이에스리테일전자랜드유통대형유통전자테마점1.00952
922021070120210701채널A둘코소프트 (약건네는이하늬/장에는순하게/변비는부드럽게)102516151300.5프로 B(정상요금)사노피아벤티스코리아둘코소프트제약 및 의료소화위장약변비치료제1.00952
932021070120210701채널A푸르지오 (PRUGIOFORMEN/남성모델)102531151300.5프로 B(정상요금)대우건설푸르지오건설 건재 및 부동산부동산 임대 및 매매아파트 임대 및 매매1.00952
942021070120210701채널A인사돌플러스 (최불암/꼭꼭씹는행복)102546151300.5프로 B(정상요금)동국제약인사돌플러스제약 및 의료이비인후 치과 및 안과용제구내염용제1.00952
952021070120210701채널A노란우산공제 (희망은노란우산을타고/김영철/송가인)103102406668.0프로 A(정상요금)중소기업중앙회노란우산공제금융 보험 및 증권금융 및 보험생명보험0.61498
962021070120210701채널A산업단지닷컴 (경제의원동력으로/4차산업의선두)103142152500.5프로 A(정상요금)산업단지닷컴산업단지닷컴컴퓨터 및 정보통신통신정보서비스인터넷서비스0.61498
972021070120210701채널A우체국 (섬/작은나비/함께웃어요/우체국예금/우체국보험)103157152500.5프로 A(정상요금)우정사업본부우체국관공서 및 단체관공서 및 단체 기타관공서 및 단체기업 PR0.61498
982021070120210701채널A호관원프리미엄 (관절건강/뼈건강까지/고객감사이벤트)10321210010002.0프로 A(정상요금)자연내림호관원프리미엄식품건강식품건강보조식품0.55206
992021070120210701채널A라쉬반 (바다배경군인들/그무엇도뜨거운나를/강철남자)103312152500.5프로 A(정상요금)라쉬반코리아라쉬반패션내의류남녀기초의류0.55206
1002021070120210701채널A원스토어 (아이템할인/무료배송/쏠쏠한앱생활)103327305001.0프로 A(정상요금)원스토어원스토어컴퓨터 및 정보통신통신정보서비스인터넷서비스0.55206