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

Categorical10
Text1
Numeric3

Alerts

BRDCST_DE has constant value ""Constant
ADVRTS_END_DE has constant value ""Constant
CHNNEL_NM has constant value ""Constant
INDUTY_SCLAS_NM is highly overall correlated with ADVRTS_TIME and 4 other fieldsHigh correlation
ADVRTSR_NM is highly overall correlated with ADVRTS_TIME and 4 other fieldsHigh correlation
INDUTY_MLSFC_NM is highly overall correlated with ADVRTS_TIME and 4 other fieldsHigh correlation
BRAND_NM is highly overall correlated with ADVRTS_TIME and 4 other fieldsHigh correlation
INDUTY_LCLAS_NM is highly overall correlated with ADVRTS_TIME and 4 other fieldsHigh correlation
ADVRTS_BEGIN_TIME is highly overall correlated with ADVRTS_UNTPC_CO and 2 other fieldsHigh correlation
ADVRTS_UNTPC_CO is highly overall correlated with ADVRTS_BEGIN_TIME and 2 other fieldsHigh correlation
WTCHNG_RT is highly overall correlated with ADVRTS_BEGIN_TIME and 2 other fieldsHigh correlation
ADVRTS_TIME is highly overall correlated with ADVRTSR_NM and 4 other fieldsHigh correlation
ADVRTS_TY_NM is highly overall correlated with ADVRTS_BEGIN_TIME and 2 other fieldsHigh correlation
ADVRTS_TIME is highly imbalanced (70.7%)Imbalance
ADVRTS_BEGIN_TIME has unique valuesUnique

Reproduction

Analysis started2023-12-10 09:51:50.625953
Analysis finished2023-12-10 09:51:54.480507
Duration3.85 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

2023-12-10T18:51:54.614392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:51:54.783203image/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

2023-12-10T18:51:55.020927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:51:55.206120image/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
tvN
101 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
tvN 101
100.0%

Length

2023-12-10T18:51:55.461514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:51:55.613442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
tvn 101
100.0%

ADVRTS_MATR_NM
Text

Distinct61
Distinct (%)60.4%
Missing0
Missing (%)0.0%
Memory size940.0 B
2023-12-10T18:51:55.997981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length39
Median length31
Mean length28.376238
Min length19

Characters and Unicode

Total characters2866
Distinct characters408
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

Unique39 ?
Unique (%)38.6%

Sample

1st row파울라너 (파도치는맥주/GERMANYSNO.1)
2nd row맥도날드 (탱글한슈림프에/풍미의빅뱅/슈니언버거)
3rd row우르오스스킨워시 (화장실급한여자/샤워하는남자/이세상개운함)
4th row하림펫푸드더리얼 (그런데왜/사람이못먹는걸/휴먼그레이드)
5th row올뉴카스 (윤여정/누군가랑/SSAC/진짜가되는시간)
ValueCountFrequency (%)
야놀자 8
 
4.0%
테크놀로지/버스/리조트/여행의모든것한번에쉽게 8
 
4.0%
우르오스스킨워시 6
 
3.0%
화장실급한여자/샤워하는남자/이세상개운함 6
 
3.0%
아로마티카알로에하이엑티브세럼 4
 
2.0%
소희/메마른나를촉촉하게 4
 
2.0%
쥬비스 4
 
2.0%
이승연/-9kg/인생을바꾸다 4
 
2.0%
랭킹닭컴 4
 
2.0%
유한젠 3
 
1.5%
Other values (100) 151
74.8%
2023-12-10T18:51:56.776094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 232
 
8.1%
101
 
3.5%
( 101
 
3.5%
) 101
 
3.5%
이 88
 
3.1%
스 54
 
1.9%
리 39
 
1.4%
지 37
 
1.3%
하 36
 
1.3%
한 36
 
1.3%
Other values (398) 2041
71.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2128
74.2%
Other Punctuation 237
 
8.3%
Uppercase Letter 147
 
5.1%
Space Separator 101
 
3.5%
Open Punctuation 101
 
3.5%
Close Punctuation 101
 
3.5%
Decimal Number 47
 
1.6%
Dash Punctuation 4
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
이 88
 
4.1%
스 54
 
2.5%
리 39
 
1.8%
지 37
 
1.7%
하 36
 
1.7%
한 36
 
1.7%
는 35
 
1.6%
자 34
 
1.6%
의 34
 
1.6%
에 29
 
1.4%
Other values (361) 1706
80.2%
Uppercase Letter
ValueCountFrequency (%)
E 16
 
10.9%
S 16
 
10.9%
K 11
 
7.5%
N 11
 
7.5%
A 10
 
6.8%
G 10
 
6.8%
O 8
 
5.4%
T 8
 
5.4%
R 7
 
4.8%
C 6
 
4.1%
Other values (13) 44
29.9%
Decimal Number
ValueCountFrequency (%)
0 10
21.3%
1 8
17.0%
2 8
17.0%
9 6
12.8%
8 6
12.8%
6 4
 
8.5%
3 3
 
6.4%
4 2
 
4.3%
Other Punctuation
ValueCountFrequency (%)
/ 232
97.9%
. 5
 
2.1%
Space Separator
ValueCountFrequency (%)
101
100.0%
Open Punctuation
ValueCountFrequency (%)
( 101
100.0%
Close Punctuation
ValueCountFrequency (%)
) 101
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2128
74.2%
Common 591
 
20.6%
Latin 147
 
5.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
이 88
 
4.1%
스 54
 
2.5%
리 39
 
1.8%
지 37
 
1.7%
하 36
 
1.7%
한 36
 
1.7%
는 35
 
1.6%
자 34
 
1.6%
의 34
 
1.6%
에 29
 
1.4%
Other values (361) 1706
80.2%
Latin
ValueCountFrequency (%)
E 16
 
10.9%
S 16
 
10.9%
K 11
 
7.5%
N 11
 
7.5%
A 10
 
6.8%
G 10
 
6.8%
O 8
 
5.4%
T 8
 
5.4%
R 7
 
4.8%
C 6
 
4.1%
Other values (13) 44
29.9%
Common
ValueCountFrequency (%)
/ 232
39.3%
101
17.1%
( 101
17.1%
) 101
17.1%
0 10
 
1.7%
1 8
 
1.4%
2 8
 
1.4%
9 6
 
1.0%
8 6
 
1.0%
. 5
 
0.8%
Other values (4) 13
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2128
74.2%
ASCII 738
 
25.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/ 232
31.4%
101
13.7%
( 101
13.7%
) 101
13.7%
E 16
 
2.2%
S 16
 
2.2%
K 11
 
1.5%
N 11
 
1.5%
A 10
 
1.4%
0 10
 
1.4%
Other values (27) 129
17.5%
Hangul
ValueCountFrequency (%)
이 88
 
4.1%
스 54
 
2.5%
리 39
 
1.8%
지 37
 
1.7%
하 36
 
1.7%
한 36
 
1.7%
는 35
 
1.6%
자 34
 
1.6%
의 34
 
1.6%
에 29
 
1.4%
Other values (361) 1706
80.2%

ADVRTS_BEGIN_TIME
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct101
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45366.891
Minimum20241
Maximum75322
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:51:57.055459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20241
5-th percentile20357
Q124322
median41904
Q364954
95-th percentile75055
Maximum75322
Range55081
Interquartile range (IQR)40632

Descriptive statistics

Standard deviation18571.896
Coefficient of variation (CV)0.40937113
Kurtosis-1.3877749
Mean45366.891
Median Absolute Deviation (MAD)20359
Skewness0.014640746
Sum4582056
Variance3.4491531 × 108
MonotonicityStrictly increasing
2023-12-10T18:51:57.313308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20241 1
 
1.0%
54842 1
 
1.0%
64940 1
 
1.0%
64924 1
 
1.0%
64909 1
 
1.0%
55531 1
 
1.0%
55516 1
 
1.0%
55501 1
 
1.0%
55446 1
 
1.0%
55432 1
 
1.0%
Other values (91) 91
90.1%
ValueCountFrequency (%)
20241 1
1.0%
20257 1
1.0%
20312 1
1.0%
20327 1
1.0%
20342 1
1.0%
20357 1
1.0%
20412 1
1.0%
20427 1
1.0%
20442 1
1.0%
20457 1
1.0%
ValueCountFrequency (%)
75322 1
1.0%
75307 1
1.0%
75252 1
1.0%
75237 1
1.0%
75110 1
1.0%
75055 1
1.0%
75040 1
1.0%
75025 1
1.0%
65925 1
1.0%
65910 1
1.0%

ADVRTS_TIME
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size940.0 B
15
93 
30
 
6
20
 
2

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row15
2nd row15
3rd row15
4th row15
5th row15

Common Values

ValueCountFrequency (%)
15 93
92.1%
30 6
 
5.9%
20 2
 
2.0%

Length

2023-12-10T18:51:57.574625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:51:57.833846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
15 93
92.1%
30 6
 
5.9%
20 2
 
2.0%

ADVRTS_UNTPC_CO
Real number (ℝ)

HIGH CORRELATION 

Distinct12
Distinct (%)11.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean437.17822
Minimum79.5
Maximum1500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:51:58.026120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum79.5
5-th percentile79.5
Q179.5
median198.7
Q3600
95-th percentile1500
Maximum1500
Range1420.5
Interquartile range (IQR)520.5

Descriptive statistics

Standard deviation451.43287
Coefficient of variation (CV)1.032606
Kurtosis1.0666518
Mean437.17822
Median Absolute Deviation (MAD)119.2
Skewness1.4594875
Sum44155
Variance203791.64
MonotonicityNot monotonic
2023-12-10T18:51:58.273485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
79.5 27
26.7%
600.0 26
25.7%
150.0 14
13.9%
1500.0 12
11.9%
198.7 10
 
9.9%
375.0 4
 
4.0%
159.0 2
 
2.0%
300.0 2
 
2.0%
1200.0 1
 
1.0%
397.5 1
 
1.0%
Other values (2) 2
 
2.0%
ValueCountFrequency (%)
79.5 27
26.7%
106.0 1
 
1.0%
150.0 14
13.9%
159.0 2
 
2.0%
198.7 10
 
9.9%
200.0 1
 
1.0%
300.0 2
 
2.0%
375.0 4
 
4.0%
397.5 1
 
1.0%
600.0 26
25.7%
ValueCountFrequency (%)
1500.0 12
11.9%
1200.0 1
 
1.0%
600.0 26
25.7%
397.5 1
 
1.0%
375.0 4
 
4.0%
300.0 2
 
2.0%
200.0 1
 
1.0%
198.7 10
 
9.9%
159.0 2
 
2.0%
150.0 14
13.9%

ADVRTS_TY_NM
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size940.0 B
프로 B(정상요금)
41 
프로 SSA
39 
프로 A(정상요금)
21 

Length

Max length10
Median length10
Mean length8.4554455
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row프로 SSA
2nd row프로 SSA
3rd row프로 SSA
4th row프로 SSA
5th row프로 SSA

Common Values

ValueCountFrequency (%)
프로 B(정상요금) 41
40.6%
프로 SSA 39
38.6%
프로 A(정상요금) 21
20.8%

Length

2023-12-10T18:51:58.553163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:51:58.769381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
프로 101
50.0%
b(정상요금 41
20.3%
ssa 39
 
19.3%
a(정상요금 21
 
10.4%

ADVRTSR_NM
Categorical

HIGH CORRELATION 

Distinct41
Distinct (%)40.6%
Missing0
Missing (%)0.0%
Memory size940.0 B
야놀자
8 
롯데칠성음료
7 
한국오츠카제약
 
6
하이트진로
 
6
유한크로락스
 
5
Other values (36)
69 

Length

Max length10
Median length7
Mean length4.960396
Min length2

Unique

Unique14 ?
Unique (%)13.9%

Sample

1st row비엘인터내셔날
2nd row한국맥도날드
3rd row한국오츠카제약
4th row하림펫푸드
5th rowOB맥주

Common Values

ValueCountFrequency (%)
야놀자 8
 
7.9%
롯데칠성음료 7
 
6.9%
한국오츠카제약 6
 
5.9%
하이트진로 6
 
5.9%
유한크로락스 5
 
5.0%
푸드나무 4
 
4.0%
기아자동차 4
 
4.0%
쥬비스 4
 
4.0%
아로마티카 4
 
4.0%
종근당 3
 
3.0%
Other values (31) 50
49.5%

Length

2023-12-10T18:51:59.030272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
야놀자 8
 
7.9%
롯데칠성음료 7
 
6.9%
한국오츠카제약 6
 
5.9%
하이트진로 6
 
5.9%
유한크로락스 5
 
5.0%
푸드나무 4
 
4.0%
기아자동차 4
 
4.0%
쥬비스 4
 
4.0%
아로마티카 4
 
4.0%
농심 3
 
3.0%
Other values (31) 50
49.5%

BRAND_NM
Categorical

HIGH CORRELATION 

Distinct49
Distinct (%)48.5%
Missing0
Missing (%)0.0%
Memory size940.0 B
야놀자
8 
우르오스스킨워시
 
6
아로마티카알로에하이엑티브세럼
 
4
쥬비스
 
4
랭킹닭컴
 
4
Other values (44)
75 

Length

Max length15
Median length11
Mean length5.5445545
Min length2

Unique

Unique20 ?
Unique (%)19.8%

Sample

1st row파울라너
2nd row맥도날드
3rd row우르오스스킨워시
4th row하림펫푸드더리얼
5th row올뉴카스

Common Values

ValueCountFrequency (%)
야놀자 8
 
7.9%
우르오스스킨워시 6
 
5.9%
아로마티카알로에하이엑티브세럼 4
 
4.0%
쥬비스 4
 
4.0%
랭킹닭컴 4
 
4.0%
칠성사이다 3
 
3.0%
아이시스8.0 3
 
3.0%
더뉴K3 3
 
3.0%
유한젠 3
 
3.0%
벤포벨정B 3
 
3.0%
Other values (39) 60
59.4%

Length

2023-12-10T18:51:59.326482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
야놀자 8
 
7.9%
우르오스스킨워시 6
 
5.9%
아로마티카알로에하이엑티브세럼 4
 
4.0%
쥬비스 4
 
4.0%
랭킹닭컴 4
 
4.0%
유한젠 3
 
3.0%
테라 3
 
3.0%
벤포벨정b 3
 
3.0%
스포티파이 3
 
3.0%
더뉴k3 3
 
3.0%
Other values (39) 60
59.4%

INDUTY_LCLAS_NM
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)13.9%
Missing0
Missing (%)0.0%
Memory size940.0 B
음료 및 기호품
23 
화장품 및 보건용품
18 
컴퓨터 및 정보통신
17 
서비스
12 
식품
7 
Other values (9)
24 

Length

Max length11
Median length10
Mean length7.1584158
Min length2

Unique

Unique2 ?
Unique (%)2.0%

Sample

1st row음료 및 기호품
2nd row서비스
3rd row화장품 및 보건용품
4th row기초재
5th row음료 및 기호품

Common Values

ValueCountFrequency (%)
음료 및 기호품 23
22.8%
화장품 및 보건용품 18
17.8%
컴퓨터 및 정보통신 17
16.8%
서비스 12
11.9%
식품 7
 
6.9%
수송기기 4
 
4.0%
제약 및 의료 4
 
4.0%
가정용품 4
 
4.0%
건설 건재 및 부동산 3
 
3.0%
화학공업 3
 
3.0%
Other values (4) 6
 
5.9%

Length

2023-12-10T18:51:59.572049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
및 66
27.6%
음료 23
 
9.6%
기호품 23
 
9.6%
화장품 18
 
7.5%
보건용품 18
 
7.5%
컴퓨터 17
 
7.1%
정보통신 17
 
7.1%
서비스 12
 
5.0%
식품 7
 
2.9%
의료 4
 
1.7%
Other values (14) 34
14.2%

INDUTY_MLSFC_NM
Categorical

HIGH CORRELATION 

Distinct26
Distinct (%)25.7%
Missing0
Missing (%)0.0%
Memory size940.0 B
통신정보서비스
13 
알콜음료
12 
비알콜음료
11 
남성화장품
6 
세제류
6 
Other values (21)
53 

Length

Max length14
Median length12
Mean length5.5544554
Min length2

Unique

Unique6 ?
Unique (%)5.9%

Sample

1st row알콜음료
2nd row음식 및 숙박
3rd row남성화장품
4th row농축산기초재
5th row알콜음료

Common Values

ValueCountFrequency (%)
통신정보서비스 13
12.9%
알콜음료 12
 
11.9%
비알콜음료 11
 
10.9%
남성화장품 6
 
5.9%
세제류 6
 
5.9%
개인서비스 6
 
5.9%
승용자동차 4
 
4.0%
컴퓨터게임 4
 
4.0%
축산품 4
 
4.0%
모발 및 목욕용제 4
 
4.0%
Other values (16) 31
30.7%

Length

2023-12-10T18:51:59.827247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
및 15
 
10.6%
통신정보서비스 13
 
9.2%
알콜음료 12
 
8.5%
비알콜음료 11
 
7.8%
남성화장품 6
 
4.3%
세제류 6
 
4.3%
개인서비스 6
 
4.3%
승용자동차 4
 
2.8%
컴퓨터게임 4
 
2.8%
축산품 4
 
2.8%
Other values (29) 60
42.6%

INDUTY_SCLAS_NM
Categorical

HIGH CORRELATION 

Distinct38
Distinct (%)37.6%
Missing0
Missing (%)0.0%
Memory size940.0 B
모바일컨텐츠서비스
11 
맥주
10 
남성기초화장품
 
6
표백제
 
5
생수
 
5
Other values (33)
64 

Length

Max length16
Median length11
Mean length5.7425743
Min length2

Unique

Unique14 ?
Unique (%)13.9%

Sample

1st row맥주
2nd row패스트푸드점
3rd row남성기초화장품
4th row사료
5th row맥주

Common Values

ValueCountFrequency (%)
모바일컨텐츠서비스 11
 
10.9%
맥주 10
 
9.9%
남성기초화장품 6
 
5.9%
표백제 5
 
5.0%
생수 5
 
5.0%
치킨가공품 4
 
4.0%
샴푸 린스 4
 
4.0%
이용 및 미용 4
 
4.0%
준중형승용차 3
 
3.0%
비타민제 3
 
3.0%
Other values (28) 46
45.5%

Length

2023-12-10T18:52:00.104420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
모바일컨텐츠서비스 11
 
8.0%
맥주 10
 
7.2%
및 9
 
6.5%
남성기초화장품 6
 
4.3%
제품종합 5
 
3.6%
생수 5
 
3.6%
표백제 5
 
3.6%
치킨가공품 4
 
2.9%
샴푸 4
 
2.9%
린스 4
 
2.9%
Other values (42) 75
54.3%

WTCHNG_RT
Real number (ℝ)

HIGH CORRELATION 

Distinct22
Distinct (%)21.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.22897554
Minimum0.05571
Maximum0.62152
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:52:00.317246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.05571
5-th percentile0.05571
Q10.12022
median0.1396
Q30.24041
95-th percentile0.59894
Maximum0.62152
Range0.56581
Interquartile range (IQR)0.12019

Descriptive statistics

Standard deviation0.18838847
Coefficient of variation (CV)0.82274492
Kurtosis0.042930957
Mean0.22897554
Median Absolute Deviation (MAD)0.05647
Skewness1.2836188
Sum23.12653
Variance0.035490214
MonotonicityNot monotonic
2023-12-10T18:52:00.527597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
0.1396 26
25.7%
0.59894 12
11.9%
0.05571 10
 
9.9%
0.21999 5
 
5.0%
0.24042 4
 
4.0%
0.54997 4
 
4.0%
0.12708 4
 
4.0%
0.08313 4
 
4.0%
0.62152 3
 
3.0%
0.17346 3
 
3.0%
Other values (12) 26
25.7%
ValueCountFrequency (%)
0.05571 10
9.9%
0.05722 3
 
3.0%
0.08313 4
 
4.0%
0.08587 2
 
2.0%
0.10767 2
 
2.0%
0.1097 2
 
2.0%
0.11404 2
 
2.0%
0.12022 3
 
3.0%
0.12708 4
 
4.0%
0.13484 1
 
1.0%
ValueCountFrequency (%)
0.62152 3
 
3.0%
0.59894 12
11.9%
0.5806 1
 
1.0%
0.54997 4
 
4.0%
0.24042 4
 
4.0%
0.24041 3
 
3.0%
0.23554 2
 
2.0%
0.21999 5
5.0%
0.20583 3
 
3.0%
0.18657 2
 
2.0%

Interactions

2023-12-10T18:51:53.340834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:51:52.259187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:51:52.744312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:51:53.481899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:51:52.409583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:51:52.967031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:51:53.650923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:51:52.581995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:51:53.197453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T18:52:00.716281image/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_NM1.0000.3561.0000.6120.7101.0001.0001.0001.0001.0000.625
ADVRTS_BEGIN_TIME0.3561.0000.0000.6660.8620.6600.6910.3620.6160.6640.782
ADVRTS_TIME1.0000.0001.0000.7970.0001.0001.0000.8840.9470.9880.158
ADVRTS_UNTPC_CO0.6120.6660.7971.0000.9560.8110.8020.3410.7820.7870.778
ADVRTS_TY_NM0.7100.8620.0000.9561.0000.7650.7660.1960.7150.7640.918
ADVRTSR_NM1.0000.6601.0000.8110.7651.0001.0000.9990.9990.9970.528
BRAND_NM1.0000.6911.0000.8020.7661.0001.0001.0001.0001.0000.481
INDUTY_LCLAS_NM1.0000.3620.8840.3410.1960.9991.0001.0001.0001.0000.000
INDUTY_MLSFC_NM1.0000.6160.9470.7820.7150.9991.0001.0001.0001.0000.417
INDUTY_SCLAS_NM1.0000.6640.9880.7870.7640.9971.0001.0001.0001.0000.380
WTCHNG_RT0.6250.7820.1580.7780.9180.5280.4810.0000.4170.3801.000
2023-12-10T18:52:00.986210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ADVRTS_TY_NMINDUTY_SCLAS_NMADVRTSR_NMINDUTY_MLSFC_NMBRAND_NMADVRTS_TIMEINDUTY_LCLAS_NM
ADVRTS_TY_NM1.0000.4300.4180.4340.3850.0000.097
INDUTY_SCLAS_NM0.4301.0000.8720.9170.9090.7630.851
ADVRTSR_NM0.4180.8721.0000.8720.9310.7820.824
INDUTY_MLSFC_NM0.4340.9170.8721.0000.8330.7430.928
BRAND_NM0.3850.9090.9310.8331.0000.7280.773
ADVRTS_TIME0.0000.7630.7820.7430.7281.0000.734
INDUTY_LCLAS_NM0.0970.8510.8240.9280.7730.7341.000
2023-12-10T18:52:01.565038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ADVRTS_BEGIN_TIMEADVRTS_UNTPC_COWTCHNG_RTADVRTS_TIMEADVRTS_TY_NMADVRTSR_NMBRAND_NMINDUTY_LCLAS_NMINDUTY_MLSFC_NMINDUTY_SCLAS_NM
ADVRTS_BEGIN_TIME1.000-0.648-0.7180.0000.8280.2710.2550.1370.2860.284
ADVRTS_UNTPC_CO-0.6481.0000.5270.4700.7340.4030.3620.1630.4370.392
WTCHNG_RT-0.7180.5271.0000.0610.6450.1950.1530.0000.1720.129
ADVRTS_TIME0.0000.4700.0611.0000.0000.7820.7280.7340.7430.763
ADVRTS_TY_NM0.8280.7340.6450.0001.0000.4180.3850.0970.4340.430
ADVRTSR_NM0.2710.4030.1950.7820.4181.0000.9310.8240.8720.872
BRAND_NM0.2550.3620.1530.7280.3850.9311.0000.7730.8330.909
INDUTY_LCLAS_NM0.1370.1630.0000.7340.0970.8240.7731.0000.9280.851
INDUTY_MLSFC_NM0.2860.4370.1720.7430.4340.8720.8330.9281.0000.917
INDUTY_SCLAS_NM0.2840.3920.1290.7630.4300.8720.9090.8510.9171.000

Missing values

2023-12-10T18:51:53.878793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T18:51:54.317981image/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
02021070120210701tvN파울라너 (파도치는맥주/GERMANYSNO.1)2024115600.0프로 SSA비엘인터내셔날파울라너음료 및 기호품알콜음료맥주0.11404
12021070120210701tvN맥도날드 (탱글한슈림프에/풍미의빅뱅/슈니언버거)2025715600.0프로 SSA한국맥도날드맥도날드서비스음식 및 숙박패스트푸드점0.11404
22021070120210701tvN우르오스스킨워시 (화장실급한여자/샤워하는남자/이세상개운함)2031215600.0프로 SSA한국오츠카제약우르오스스킨워시화장품 및 보건용품남성화장품남성기초화장품0.54997
32021070120210701tvN하림펫푸드더리얼 (그런데왜/사람이못먹는걸/휴먼그레이드)2032715600.0프로 SSA하림펫푸드하림펫푸드더리얼기초재농축산기초재사료0.54997
42021070120210701tvN올뉴카스 (윤여정/누군가랑/SSAC/진짜가되는시간)2034215600.0프로 SSAOB맥주올뉴카스음료 및 기호품알콜음료맥주0.54997
52021070120210701tvN파파존스 (헨리/미나/깊고진한/아메리칸쏘울피자)2035715600.0프로 SSA한국파파존스파파존스서비스음식 및 숙박패스트푸드점0.54997
62021070120210701tvN듀오락 (강하늘/한국인의장을/사람마다/NGS분석/유산균의힘)2041215600.0프로 SSA쎌바이오텍듀오락식품건강식품건강보조식품0.59894
72021070120210701tvN더뉴K3 (달리는차/임시완/미생2021/나만의완생을향해)2042715600.0프로 SSA기아자동차더뉴K3수송기기승용자동차준중형승용차0.59894
82021070120210701tvN테라 (물피해달리는공유/강력한리얼탄산/이맛이청정라거다)2044215600.0프로 SSA하이트진로테라음료 및 기호품알콜음료맥주0.59894
92021070120210701tvN벤포벨정B (이성민/약발오래가지/체내지속력이길어)2045715600.0프로 SSA종근당벤포벨정B제약 및 의료대사성의약비타민제0.59894
BRDCST_DEADVRTS_END_DECHNNEL_NMADVRTS_MATR_NMADVRTS_BEGIN_TIMEADVRTS_TIMEADVRTS_UNTPC_COADVRTS_TY_NMADVRTSR_NMBRAND_NMINDUTY_LCLAS_NMINDUTY_MLSFC_NMINDUTY_SCLAS_NMWTCHNG_RT
912021070120210701tvN퍼실딥클린플러스 (생일파티하는가족/김남주/8중효소)6591015150.0프로 A(정상요금)헨켈홈케어코리아퍼실딥클린플러스화장품 및 보건용품세제류세탁세제0.05571
922021070120210701tvN야놀자 (테크놀로지/버스/리조트/여행의모든것한번에쉽게)6592515150.0프로 A(정상요금)야놀자야놀자컴퓨터 및 정보통신통신정보서비스모바일컨텐츠서비스0.05571
932021070120210701tvN야놀자 (테크놀로지/버스/리조트/여행의모든것한번에쉽게)7502515375.0프로 A(정상요금)야놀자야놀자컴퓨터 및 정보통신통신정보서비스모바일컨텐츠서비스0.20583
942021070120210701tvN아로마티카알로에하이엑티브세럼 (소희/메마른나를촉촉하게)7504015375.0프로 A(정상요금)아로마티카아로마티카알로에하이엑티브세럼화장품 및 보건용품모발 및 목욕용제샴푸 린스0.20583
952021070120210701tvN유한락스주방청소용 (기름때/찌든때/칙/싹/간편하게끝)7505515375.0프로 A(정상요금)유한크로락스유한락스주방청소용화장품 및 보건용품세제류표백제0.20583
962021070120210701tvN칠성사이다 (이준기/박은빈/함께맛있게/함께청량하게)7511015375.0프로 A(정상요금)롯데칠성음료칠성사이다음료 및 기호품비알콜음료사이다0.13484
972021070120210701tvN아이시스8.0 (이승기/플로깅/핑크에너지)7523715150.0프로 A(정상요금)롯데칠성음료아이시스8.0음료 및 기호품비알콜음료생수0.08587
982021070120210701tvN여명808 (음주전후/해외수출/금탑산업훈장/여명1004)7525215150.0프로 A(정상요금)그래미여명808음료 및 기호품비알콜음료기능성건강 음료0.08587
992021070120210701tvN칸타타콘트라베이스 (두사람/오늘은유셰프/내일은유러너)7530715150.0프로 A(정상요금)롯데칠성음료칸타타콘트라베이스음료 및 기호품비알콜음료커피,코코아음료0.10767
1002021070120210701tvN좋은느낌 (박신혜/너무당연해진/유기농/천연린넨을입다)7532215150.0프로 A(정상요금)유한킴벌리좋은느낌화장품 및 보건용품가정 및 보건용제지생리대0.10767