Overview

Dataset statistics

Number of variables15
Number of observations595
Missing cells119
Missing cells (%)1.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory74.5 KiB
Average record size in memory128.2 B

Variable types

Categorical5
Text3
Boolean1
Numeric6

Alerts

BASE_YM has constant value ""Constant
GENRE_LCLAS_NM is highly overall correlated with BRDCST_TY_NM and 1 other fieldsHigh correlation
AVRG_VOD_PRCHS_PRICE is highly overall correlated with AVRG_WTCHNG_CO and 9 other fieldsHigh correlation
HLDY_AT is highly overall correlated with AVRG_VOD_PRCHS_PRICEHigh correlation
CTPRVN_NM is highly overall correlated with AVRG_VOD_PRCHS_PRICEHigh correlation
BRDCST_TY_NM is highly overall correlated with DAWN_AVRG_WTCHNG_TIME_CO and 4 other fieldsHigh correlation
AVRG_WTCHNG_CO is highly overall correlated with AVRG_WTCHNG_TIME_CO and 5 other fieldsHigh correlation
AVRG_WTCHNG_TIME_CO is highly overall correlated with AVRG_WTCHNG_CO and 5 other fieldsHigh correlation
DAWN_AVRG_WTCHNG_TIME_CO is highly overall correlated with AVRG_WTCHNG_CO and 6 other fieldsHigh correlation
AM_AVRG_WTCHNG_TIME_CO is highly overall correlated with AVRG_WTCHNG_CO and 6 other fieldsHigh correlation
PM_AVRG_WTCHNG_TIME_CO is highly overall correlated with AVRG_WTCHNG_CO and 6 other fieldsHigh correlation
EVENING_AVRG_WTCHNG_TIME_CO is highly overall correlated with AVRG_WTCHNG_CO and 5 other fieldsHigh correlation
AVRG_VOD_PRCHS_PRICE is highly imbalanced (97.3%)Imbalance
DAWN_AVRG_WTCHNG_TIME_CO has 51 (8.6%) missing valuesMissing
AM_AVRG_WTCHNG_TIME_CO has 39 (6.6%) missing valuesMissing
PM_AVRG_WTCHNG_TIME_CO has 14 (2.4%) missing valuesMissing
EVENING_AVRG_WTCHNG_TIME_CO has 11 (1.8%) missing valuesMissing
DAWN_AVRG_WTCHNG_TIME_CO has 31 (5.2%) zerosZeros
AM_AVRG_WTCHNG_TIME_CO has 21 (3.5%) zerosZeros
PM_AVRG_WTCHNG_TIME_CO has 19 (3.2%) zerosZeros
EVENING_AVRG_WTCHNG_TIME_CO has 23 (3.9%) zerosZeros

Reproduction

Analysis started2023-12-10 10:14:53.327045
Analysis finished2023-12-10 10:15:03.812476
Duration10.49 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

BASE_YM
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.8 KiB
202011
595 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row202011
2nd row202011
3rd row202011
4th row202011
5th row202011

Common Values

ValueCountFrequency (%)
202011 595
100.0%

Length

2023-12-10T19:15:04.022681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:15:04.199114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
202011 595
100.0%

CTPRVN_NM
Categorical

HIGH CORRELATION 

Distinct17
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size4.8 KiB
경기
151 
서울
100 
부산
42 
경북
41 
인천
39 
Other values (12)
222 

Length

Max length4
Median length2
Mean length2.010084
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row강원
2nd row강원
3rd row서울
4th row경기
5th row인천

Common Values

ValueCountFrequency (%)
경기 151
25.4%
서울 100
16.8%
부산 42
 
7.1%
경북 41
 
6.9%
인천 39
 
6.6%
경남 32
 
5.4%
대구 29
 
4.9%
광주 26
 
4.4%
충남 22
 
3.7%
강원 21
 
3.5%
Other values (7) 92
15.5%

Length

2023-12-10T19:15:04.396380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기 151
25.4%
서울 100
16.8%
부산 42
 
7.1%
경북 41
 
6.9%
인천 39
 
6.6%
경남 32
 
5.4%
대구 29
 
4.9%
광주 26
 
4.4%
충남 22
 
3.7%
강원 21
 
3.5%
Other values (7) 92
15.5%

SIGNGU_NM
Text

Distinct166
Distinct (%)27.9%
Missing0
Missing (%)0.0%
Memory size4.8 KiB
2023-12-10T19:15:04.959993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length3
Mean length3.5882353
Min length2

Characters and Unicode

Total characters2135
Distinct characters128
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

Unique47 ?
Unique (%)7.9%

Sample

1st row원주시
2nd row삼척시
3rd row종로구
4th row군포시
5th row부평구
ValueCountFrequency (%)
서구 27
 
3.9%
북구 23
 
3.3%
남구 21
 
3.0%
동구 17
 
2.5%
성남시 16
 
2.3%
고양시 14
 
2.0%
중구 14
 
2.0%
분당구 12
 
1.7%
안산시 11
 
1.6%
수원시 11
 
1.6%
Other values (165) 526
76.0%
2023-12-10T19:15:05.918392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
구 365
 
17.1%
시 298
 
14.0%
97
 
4.5%
주 69
 
3.2%
산 67
 
3.1%
남 62
 
2.9%
서 58
 
2.7%
동 57
 
2.7%
군 57
 
2.7%
성 56
 
2.6%
Other values (118) 949
44.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2038
95.5%
Space Separator 97
 
4.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 365
17.9%
시 298
 
14.6%
주 69
 
3.4%
산 67
 
3.3%
남 62
 
3.0%
서 58
 
2.8%
동 57
 
2.8%
군 57
 
2.8%
성 56
 
2.7%
천 47
 
2.3%
Other values (117) 902
44.3%
Space Separator
ValueCountFrequency (%)
97
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2038
95.5%
Common 97
 
4.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 365
17.9%
시 298
 
14.6%
주 69
 
3.4%
산 67
 
3.3%
남 62
 
3.0%
서 58
 
2.8%
동 57
 
2.8%
군 57
 
2.8%
성 56
 
2.7%
천 47
 
2.3%
Other values (117) 902
44.3%
Common
ValueCountFrequency (%)
97
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2038
95.5%
ASCII 97
 
4.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
구 365
17.9%
시 298
 
14.6%
주 69
 
3.4%
산 67
 
3.3%
남 62
 
3.0%
서 58
 
2.8%
동 57
 
2.8%
군 57
 
2.8%
성 56
 
2.7%
천 47
 
2.3%
Other values (117) 902
44.3%
ASCII
ValueCountFrequency (%)
97
100.0%

ADSTRD_NM
Text

Distinct526
Distinct (%)88.4%
Missing0
Missing (%)0.0%
Memory size4.8 KiB
2023-12-10T19:15:06.439600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length3
Mean length3.502521
Min length2

Characters and Unicode

Total characters2084
Distinct characters229
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique466 ?
Unique (%)78.3%

Sample

1st row중앙동
2nd row도계읍
3rd row이화동
4th row군포1동
5th row삼산2동
ValueCountFrequency (%)
중앙동 8
 
1.3%
신정3동 3
 
0.5%
호원1동 3
 
0.5%
중계1동 3
 
0.5%
반월동 2
 
0.3%
구미동 2
 
0.3%
상2동 2
 
0.3%
장수서창동 2
 
0.3%
검단동 2
 
0.3%
사1동 2
 
0.3%
Other values (516) 566
95.1%
2023-12-10T19:15:07.513892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 505
24.2%
1 103
 
4.9%
2 82
 
3.9%
면 76
 
3.6%
산 47
 
2.3%
3 37
 
1.8%
읍 36
 
1.7%
신 29
 
1.4%
정 28
 
1.3%
4 27
 
1.3%
Other values (219) 1114
53.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1809
86.8%
Decimal Number 267
 
12.8%
Other Punctuation 8
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 505
27.9%
면 76
 
4.2%
산 47
 
2.6%
읍 36
 
2.0%
신 29
 
1.6%
정 28
 
1.5%
곡 22
 
1.2%
서 22
 
1.2%
천 19
 
1.1%
현 19
 
1.1%
Other values (210) 1006
55.6%
Decimal Number
ValueCountFrequency (%)
1 103
38.6%
2 82
30.7%
3 37
 
13.9%
4 27
 
10.1%
5 9
 
3.4%
6 6
 
2.2%
7 2
 
0.7%
8 1
 
0.4%
Other Punctuation
ValueCountFrequency (%)
. 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1809
86.8%
Common 275
 
13.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 505
27.9%
면 76
 
4.2%
산 47
 
2.6%
읍 36
 
2.0%
신 29
 
1.6%
정 28
 
1.5%
곡 22
 
1.2%
서 22
 
1.2%
천 19
 
1.1%
현 19
 
1.1%
Other values (210) 1006
55.6%
Common
ValueCountFrequency (%)
1 103
37.5%
2 82
29.8%
3 37
 
13.5%
4 27
 
9.8%
5 9
 
3.3%
. 8
 
2.9%
6 6
 
2.2%
7 2
 
0.7%
8 1
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1809
86.8%
ASCII 275
 
13.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 505
27.9%
면 76
 
4.2%
산 47
 
2.6%
읍 36
 
2.0%
신 29
 
1.6%
정 28
 
1.5%
곡 22
 
1.2%
서 22
 
1.2%
천 19
 
1.1%
현 19
 
1.1%
Other values (210) 1006
55.6%
ASCII
ValueCountFrequency (%)
1 103
37.5%
2 82
29.8%
3 37
 
13.5%
4 27
 
9.8%
5 9
 
3.3%
. 8
 
2.9%
6 6
 
2.2%
7 2
 
0.7%
8 1
 
0.4%

BRDCST_TY_NM
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size4.8 KiB
LINEAR_TV
485 
VOD
110 

Length

Max length9
Median length9
Mean length7.8907563
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowVOD
2nd rowLINEAR_TV
3rd rowLINEAR_TV
4th rowLINEAR_TV
5th rowVOD

Common Values

ValueCountFrequency (%)
LINEAR_TV 485
81.5%
VOD 110
 
18.5%

Length

2023-12-10T19:15:07.986121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:15:08.158361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
linear_tv 485
81.5%
vod 110
 
18.5%

GENRE_LCLAS_NM
Categorical

HIGH CORRELATION 

Distinct19
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size4.8 KiB
영화
108 
홈쇼핑
91 
스포츠
86 
라이프
38 
TV 애니메이션
35 
Other values (14)
237 

Length

Max length8
Median length7
Mean length3.3563025
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row방송
2nd row스포츠
3rd row스포츠
4th row영화
5th row애니

Common Values

ValueCountFrequency (%)
영화 108
18.2%
홈쇼핑 91
15.3%
스포츠 86
14.5%
라이프 38
 
6.4%
TV 애니메이션 35
 
5.9%
공연/음악 31
 
5.2%
키즈 30
 
5.0%
시사/교양 28
 
4.7%
다큐 24
 
4.0%
TV 드라마 21
 
3.5%
Other values (9) 103
17.3%

Length

2023-12-10T19:15:08.313462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
영화 108
16.5%
홈쇼핑 91
13.9%
스포츠 86
13.1%
tv 56
8.5%
라이프 38
 
5.8%
애니메이션 35
 
5.3%
공연/음악 31
 
4.7%
키즈 30
 
4.6%
시사/교양 28
 
4.3%
다큐 24
 
3.7%
Other values (11) 129
19.7%

GENRE_MLSFC_NM
Text

Distinct101
Distinct (%)17.0%
Missing0
Missing (%)0.0%
Memory size4.8 KiB
2023-12-10T19:15:08.743900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length8
Mean length3.292437
Min length1

Characters and Unicode

Total characters1959
Distinct characters165
Distinct categories4 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)0.8%

Sample

1st row드라마
2nd row레저
3rd row스포츠
4th row기타
5th row미스터리/공포
ValueCountFrequency (%)
기타 79
 
13.0%
드라마 26
 
4.3%
수산물 12
 
2.0%
여행 11
 
1.8%
정보 11
 
1.8%
동물 11
 
1.8%
스포츠 10
 
1.6%
야구 10
 
1.6%
요리 10
 
1.6%
시리즈 10
 
1.6%
Other values (92) 419
68.8%
2023-12-10T19:15:09.406763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 127
 
6.5%
타 88
 
4.5%
기 85
 
4.3%
리 51
 
2.6%
구 42
 
2.1%
스 41
 
2.1%
포 36
 
1.8%
션 35
 
1.8%
드 33
 
1.7%
동 31
 
1.6%
Other values (155) 1390
71.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1763
90.0%
Other Punctuation 127
 
6.5%
Uppercase Letter 55
 
2.8%
Space Separator 14
 
0.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
타 88
 
5.0%
기 85
 
4.8%
리 51
 
2.9%
구 42
 
2.4%
스 41
 
2.3%
포 36
 
2.0%
션 35
 
2.0%
드 33
 
1.9%
동 31
 
1.8%
미 29
 
1.6%
Other values (146) 1292
73.3%
Uppercase Letter
ValueCountFrequency (%)
S 13
23.6%
V 13
23.6%
F 13
23.6%
T 13
23.6%
M 1
 
1.8%
C 1
 
1.8%
N 1
 
1.8%
Other Punctuation
ValueCountFrequency (%)
/ 127
100.0%
Space Separator
ValueCountFrequency (%)
14
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1763
90.0%
Common 141
 
7.2%
Latin 55
 
2.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
타 88
 
5.0%
기 85
 
4.8%
리 51
 
2.9%
구 42
 
2.4%
스 41
 
2.3%
포 36
 
2.0%
션 35
 
2.0%
드 33
 
1.9%
동 31
 
1.8%
미 29
 
1.6%
Other values (146) 1292
73.3%
Latin
ValueCountFrequency (%)
S 13
23.6%
V 13
23.6%
F 13
23.6%
T 13
23.6%
M 1
 
1.8%
C 1
 
1.8%
N 1
 
1.8%
Common
ValueCountFrequency (%)
/ 127
90.1%
14
 
9.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1763
90.0%
ASCII 196
 
10.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/ 127
64.8%
14
 
7.1%
S 13
 
6.6%
V 13
 
6.6%
F 13
 
6.6%
T 13
 
6.6%
M 1
 
0.5%
C 1
 
0.5%
N 1
 
0.5%
Hangul
ValueCountFrequency (%)
타 88
 
5.0%
기 85
 
4.8%
리 51
 
2.9%
구 42
 
2.4%
스 41
 
2.3%
포 36
 
2.0%
션 35
 
2.0%
드 33
 
1.9%
동 31
 
1.8%
미 29
 
1.6%
Other values (146) 1292
73.3%

HLDY_AT
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size727.0 B
True
300 
False
295 
ValueCountFrequency (%)
True 300
50.4%
False 295
49.6%
2023-12-10T19:15:09.673073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

AVRG_WTCHNG_CO
Real number (ℝ)

HIGH CORRELATION 

Distinct64
Distinct (%)10.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.9663866
Minimum1
Maximum205
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.4 KiB
2023-12-10T19:15:09.850764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median4
Q38
95-th percentile44.6
Maximum205
Range204
Interquartile range (IQR)5

Descriptive statistics

Standard deviation16.975092
Coefficient of variation (CV)1.7032344
Kurtosis35.280944
Mean9.9663866
Median Absolute Deviation (MAD)2
Skewness4.7770254
Sum5930
Variance288.15375
MonotonicityNot monotonic
2023-12-10T19:15:10.174548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 144
24.2%
3 113
19.0%
4 51
 
8.6%
5 45
 
7.6%
7 37
 
6.2%
6 33
 
5.5%
8 22
 
3.7%
11 12
 
2.0%
9 12
 
2.0%
10 10
 
1.7%
Other values (54) 116
19.5%
ValueCountFrequency (%)
1 4
 
0.7%
2 144
24.2%
3 113
19.0%
4 51
 
8.6%
5 45
 
7.6%
6 33
 
5.5%
7 37
 
6.2%
8 22
 
3.7%
9 12
 
2.0%
10 10
 
1.7%
ValueCountFrequency (%)
205 1
 
0.2%
96 1
 
0.2%
95 1
 
0.2%
92 1
 
0.2%
82 1
 
0.2%
81 2
0.3%
75 1
 
0.2%
73 3
0.5%
72 1
 
0.2%
71 1
 
0.2%

AVRG_WTCHNG_TIME_CO
Real number (ℝ)

HIGH CORRELATION 

Distinct207
Distinct (%)35.0%
Missing4
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean89.36379
Minimum0
Maximum960
Zeros5
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size5.4 KiB
2023-12-10T19:15:10.452918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q112
median30
Q392.5
95-th percentile409
Maximum960
Range960
Interquartile range (IQR)80.5

Descriptive statistics

Standard deviation158.80393
Coefficient of variation (CV)1.7770501
Kurtosis11.076264
Mean89.36379
Median Absolute Deviation (MAD)23
Skewness3.2364384
Sum52814
Variance25218.689
MonotonicityNot monotonic
2023-12-10T19:15:10.742773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 22
 
3.7%
4 19
 
3.2%
13 18
 
3.0%
12 16
 
2.7%
2 15
 
2.5%
8 15
 
2.5%
15 15
 
2.5%
7 12
 
2.0%
11 11
 
1.8%
20 11
 
1.8%
Other values (197) 437
73.4%
ValueCountFrequency (%)
0 5
 
0.8%
1 5
 
0.8%
2 15
2.5%
3 22
3.7%
4 19
3.2%
5 4
 
0.7%
6 9
1.5%
7 12
2.0%
8 15
2.5%
9 9
1.5%
ValueCountFrequency (%)
960 1
0.2%
927 1
0.2%
926 1
0.2%
857 1
0.2%
852 1
0.2%
847 1
0.2%
822 1
0.2%
803 1
0.2%
792 1
0.2%
763 1
0.2%

DAWN_AVRG_WTCHNG_TIME_CO
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct136
Distinct (%)25.0%
Missing51
Missing (%)8.6%
Infinite0
Infinite (%)0.0%
Mean42.277574
Minimum0
Maximum810
Zeros31
Zeros (%)5.2%
Negative0
Negative (%)0.0%
Memory size5.4 KiB
2023-12-10T19:15:11.023696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q17
median15
Q343.25
95-th percentile169.85
Maximum810
Range810
Interquartile range (IQR)36.25

Descriptive statistics

Standard deviation77.713729
Coefficient of variation (CV)1.8381786
Kurtosis30.509013
Mean42.277574
Median Absolute Deviation (MAD)11
Skewness4.5894618
Sum22999
Variance6039.4237
MonotonicityNot monotonic
2023-12-10T19:15:11.375488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 31
 
5.2%
2 24
 
4.0%
3 20
 
3.4%
9 20
 
3.4%
12 19
 
3.2%
8 18
 
3.0%
1 18
 
3.0%
16 18
 
3.0%
10 18
 
3.0%
7 16
 
2.7%
Other values (126) 342
57.5%
(Missing) 51
 
8.6%
ValueCountFrequency (%)
0 31
5.2%
1 18
3.0%
2 24
4.0%
3 20
3.4%
4 12
 
2.0%
5 15
2.5%
6 13
2.2%
7 16
2.7%
8 18
3.0%
9 20
3.4%
ValueCountFrequency (%)
810 1
0.2%
689 1
0.2%
480 1
0.2%
407 1
0.2%
380 1
0.2%
362 1
0.2%
341 1
0.2%
334 1
0.2%
307 1
0.2%
299 1
0.2%

AM_AVRG_WTCHNG_TIME_CO
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct153
Distinct (%)27.5%
Missing39
Missing (%)6.6%
Infinite0
Infinite (%)0.0%
Mean48.082734
Minimum0
Maximum706
Zeros21
Zeros (%)3.5%
Negative0
Negative (%)0.0%
Memory size5.4 KiB
2023-12-10T19:15:11.650259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q16.75
median16
Q347
95-th percentile207.5
Maximum706
Range706
Interquartile range (IQR)40.25

Descriptive statistics

Standard deviation82.809433
Coefficient of variation (CV)1.722228
Kurtosis18.454794
Mean48.082734
Median Absolute Deviation (MAD)12
Skewness3.667714
Sum26734
Variance6857.4022
MonotonicityNot monotonic
2023-12-10T19:15:11.898759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 30
 
5.0%
5 21
 
3.5%
0 21
 
3.5%
1 20
 
3.4%
2 18
 
3.0%
7 17
 
2.9%
15 17
 
2.9%
8 16
 
2.7%
6 15
 
2.5%
13 15
 
2.5%
Other values (143) 366
61.5%
(Missing) 39
 
6.6%
ValueCountFrequency (%)
0 21
3.5%
1 20
3.4%
2 18
3.0%
3 30
5.0%
4 14
2.4%
5 21
3.5%
6 15
2.5%
7 17
2.9%
8 16
2.7%
9 14
2.4%
ValueCountFrequency (%)
706 1
0.2%
630 2
0.3%
450 1
0.2%
445 1
0.2%
360 1
0.2%
358 2
0.3%
338 1
0.2%
326 1
0.2%
324 1
0.2%
320 1
0.2%

PM_AVRG_WTCHNG_TIME_CO
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct156
Distinct (%)26.9%
Missing14
Missing (%)2.4%
Infinite0
Infinite (%)0.0%
Mean48.748709
Minimum0
Maximum478
Zeros19
Zeros (%)3.2%
Negative0
Negative (%)0.0%
Memory size5.4 KiB
2023-12-10T19:15:12.137313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q18
median21
Q356
95-th percentile194
Maximum478
Range478
Interquartile range (IQR)48

Descriptive statistics

Standard deviation73.825392
Coefficient of variation (CV)1.5144071
Kurtosis10.26426
Mean48.748709
Median Absolute Deviation (MAD)17
Skewness2.9548203
Sum28323
Variance5450.1885
MonotonicityNot monotonic
2023-12-10T19:15:12.359926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 25
 
4.2%
1 23
 
3.9%
4 22
 
3.7%
0 19
 
3.2%
12 18
 
3.0%
5 16
 
2.7%
6 16
 
2.7%
18 14
 
2.4%
10 14
 
2.4%
11 13
 
2.2%
Other values (146) 401
67.4%
(Missing) 14
 
2.4%
ValueCountFrequency (%)
0 19
3.2%
1 23
3.9%
2 25
4.2%
3 12
2.0%
4 22
3.7%
5 16
2.7%
6 16
2.7%
7 12
2.0%
8 12
2.0%
9 13
2.2%
ValueCountFrequency (%)
478 1
0.2%
462 1
0.2%
442 1
0.2%
441 1
0.2%
399 1
0.2%
391 1
0.2%
385 1
0.2%
377 1
0.2%
358 2
0.3%
330 1
0.2%

EVENING_AVRG_WTCHNG_TIME_CO
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct164
Distinct (%)28.1%
Missing11
Missing (%)1.8%
Infinite0
Infinite (%)0.0%
Mean56.160959
Minimum0
Maximum669
Zeros23
Zeros (%)3.9%
Negative0
Negative (%)0.0%
Memory size5.4 KiB
2023-12-10T19:15:12.602540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q17
median20
Q359
95-th percentile259
Maximum669
Range669
Interquartile range (IQR)52

Descriptive statistics

Standard deviation95.216904
Coefficient of variation (CV)1.6954287
Kurtosis11.192474
Mean56.160959
Median Absolute Deviation (MAD)17
Skewness3.1603052
Sum32798
Variance9066.2588
MonotonicityNot monotonic
2023-12-10T19:15:12.854359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 38
 
6.4%
3 25
 
4.2%
0 23
 
3.9%
4 19
 
3.2%
10 18
 
3.0%
11 18
 
3.0%
12 14
 
2.4%
1 13
 
2.2%
9 13
 
2.2%
5 13
 
2.2%
Other values (154) 390
65.5%
ValueCountFrequency (%)
0 23
3.9%
1 13
 
2.2%
2 38
6.4%
3 25
4.2%
4 19
3.2%
5 13
 
2.2%
6 13
 
2.2%
7 8
 
1.3%
8 8
 
1.3%
9 13
 
2.2%
ValueCountFrequency (%)
669 1
0.2%
587 1
0.2%
549 1
0.2%
515 1
0.2%
505 1
0.2%
471 1
0.2%
467 1
0.2%
463 1
0.2%
459 1
0.2%
427 1
0.2%

AVRG_VOD_PRCHS_PRICE
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size4.8 KiB
<NA>
592 
966
 
1
700
 
1
500
 
1

Length

Max length4
Median length4
Mean length3.994958
Min length3

Unique

Unique3 ?
Unique (%)0.5%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 592
99.5%
966 1
 
0.2%
700 1
 
0.2%
500 1
 
0.2%

Length

2023-12-10T19:15:13.084376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:15:13.230680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 592
99.5%
966 1
 
0.2%
700 1
 
0.2%
500 1
 
0.2%

Interactions

2023-12-10T19:15:01.599906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:14:55.282843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:14:56.454192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:14:57.604953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:14:58.624860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:14:59.615430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:15:01.827134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:14:55.479112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:14:56.798061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:14:57.813025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:14:58.802223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:14:59.769596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:15:01.994783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:14:55.642612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:14:56.956295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:14:57.969590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:14:59.015399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:15:00.018425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:15:02.260736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:14:55.830729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:14:57.114941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:14:58.156856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:14:59.197707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:15:00.611578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:15:02.445716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:14:56.081679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:14:57.293027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:14:58.324877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:14:59.330533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:15:01.189305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:15:02.622261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:14:56.269422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:14:57.433244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:14:58.471364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:14:59.473857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:15:01.375144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:15:13.334419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
CTPRVN_NMBRDCST_TY_NMGENRE_LCLAS_NMHLDY_ATAVRG_WTCHNG_COAVRG_WTCHNG_TIME_CODAWN_AVRG_WTCHNG_TIME_COAM_AVRG_WTCHNG_TIME_COPM_AVRG_WTCHNG_TIME_COEVENING_AVRG_WTCHNG_TIME_COAVRG_VOD_PRCHS_PRICE
CTPRVN_NM1.0000.0000.0000.0680.2200.1540.0000.1570.1600.0001.000
BRDCST_TY_NM0.0001.0000.8610.0000.1270.6190.7910.5320.7190.599NaN
GENRE_LCLAS_NM0.0000.8611.0000.0000.5120.6490.6290.6290.6230.6171.000
HLDY_AT0.0680.0000.0001.0000.1410.1580.1280.1660.1260.1991.000
AVRG_WTCHNG_CO0.2200.1270.5120.1411.0000.7790.5030.7160.6140.6481.000
AVRG_WTCHNG_TIME_CO0.1540.6190.6490.1580.7791.0000.6580.7690.8900.9341.000
DAWN_AVRG_WTCHNG_TIME_CO0.0000.7910.6290.1280.5030.6581.0000.7570.7020.647NaN
AM_AVRG_WTCHNG_TIME_CO0.1570.5320.6290.1660.7160.7690.7571.0000.7700.7251.000
PM_AVRG_WTCHNG_TIME_CO0.1600.7190.6230.1260.6140.8900.7020.7701.0000.8791.000
EVENING_AVRG_WTCHNG_TIME_CO0.0000.5990.6170.1990.6480.9340.6470.7250.8791.0001.000
AVRG_VOD_PRCHS_PRICE1.000NaN1.0001.0001.0001.000NaN1.0001.0001.0001.000
2023-12-10T19:15:13.527519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
GENRE_LCLAS_NMAVRG_VOD_PRCHS_PRICEHLDY_ATCTPRVN_NMBRDCST_TY_NM
GENRE_LCLAS_NM1.0001.0000.0000.0000.795
AVRG_VOD_PRCHS_PRICE1.0001.0001.0001.0001.000
HLDY_AT0.0001.0001.0000.0530.000
CTPRVN_NM0.0001.0000.0531.0000.000
BRDCST_TY_NM0.7951.0000.0000.0001.000
2023-12-10T19:15:13.661947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
AVRG_WTCHNG_COAVRG_WTCHNG_TIME_CODAWN_AVRG_WTCHNG_TIME_COAM_AVRG_WTCHNG_TIME_COPM_AVRG_WTCHNG_TIME_COEVENING_AVRG_WTCHNG_TIME_COCTPRVN_NMBRDCST_TY_NMGENRE_LCLAS_NMHLDY_ATAVRG_VOD_PRCHS_PRICE
AVRG_WTCHNG_CO1.0000.7620.5590.6450.6520.6760.1060.0910.2630.1011.000
AVRG_WTCHNG_TIME_CO0.7621.0000.8140.8760.9270.9310.0600.4790.3050.1231.000
DAWN_AVRG_WTCHNG_TIME_CO0.5590.8141.0000.7180.7390.7760.0000.6090.3210.0951.000
AM_AVRG_WTCHNG_TIME_CO0.6450.8760.7181.0000.8080.7920.0640.5310.3020.1641.000
PM_AVRG_WTCHNG_TIME_CO0.6520.9270.7390.8081.0000.8380.0630.5590.2860.0961.000
EVENING_AVRG_WTCHNG_TIME_CO0.6760.9310.7760.7920.8381.0000.0000.4610.2820.1511.000
CTPRVN_NM0.1060.0600.0000.0640.0630.0001.0000.0000.0000.0531.000
BRDCST_TY_NM0.0910.4790.6090.5310.5590.4610.0001.0000.7950.0001.000
GENRE_LCLAS_NM0.2630.3050.3210.3020.2860.2820.0000.7951.0000.0001.000
HLDY_AT0.1010.1230.0950.1640.0960.1510.0530.0000.0001.0001.000
AVRG_VOD_PRCHS_PRICE1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000

Missing values

2023-12-10T19:15:02.887702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:15:03.297819image/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.
2023-12-10T19:15:03.603894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

BASE_YMCTPRVN_NMSIGNGU_NMADSTRD_NMBRDCST_TY_NMGENRE_LCLAS_NMGENRE_MLSFC_NMHLDY_ATAVRG_WTCHNG_COAVRG_WTCHNG_TIME_CODAWN_AVRG_WTCHNG_TIME_COAM_AVRG_WTCHNG_TIME_COPM_AVRG_WTCHNG_TIME_COEVENING_AVRG_WTCHNG_TIME_COAVRG_VOD_PRCHS_PRICE
0202011강원원주시중앙동VOD방송드라마Y13567254158391349<NA>
1202011강원삼척시도계읍LINEAR_TV스포츠레저N107341393432<NA>
2202011서울종로구이화동LINEAR_TV스포츠스포츠Y220241<NA>
3202011경기군포시군포1동LINEAR_TV영화기타N42220121313<NA>
4202011인천부평구삼산2동VOD애니미스터리/공포Y71974672139205<NA>
5202011경기의정부시송산1동LINEAR_TV공연/음악기타Y87414183349<NA>
6202011전북정읍시내장상동LINEAR_TVTV 애니메이션호러/공포N87215222771<NA>
7202011서울은평구신사1동LINEAR_TV홈쇼핑가전N5105456<NA>
8202011서울동작구사당1동LINEAR_TV영화서부Y3251142023<NA>
9202011경남밀양시삼랑진읍LINEAR_TV영화기타Y32931271217<NA>
BASE_YMCTPRVN_NMSIGNGU_NMADSTRD_NMBRDCST_TY_NMGENRE_LCLAS_NMGENRE_MLSFC_NMHLDY_ATAVRG_WTCHNG_COAVRG_WTCHNG_TIME_CODAWN_AVRG_WTCHNG_TIME_COAM_AVRG_WTCHNG_TIME_COPM_AVRG_WTCHNG_TIME_COEVENING_AVRG_WTCHNG_TIME_COAVRG_VOD_PRCHS_PRICE
585202011전북김제시교월동LINEAR_TV스포츠농구Y730113218<NA>
586202011충북청주시 서원구분평동LINEAR_TV라이프요리N213121573<NA>
587202011경남창원시 마산합포구구산면LINEAR_TV홈쇼핑식품Y71331183<NA>
588202011전북무주군설천면LINEAR_TV영화드라마Y1210589703415<NA>
589202011부산부산진구연지동LINEAR_TV홈쇼핑종합N61316743<NA>
590202011서울송파구잠실6동LINEAR_TV공연/음악클래식Y3191218715<NA>
591202011경북구미시산동면LINEAR_TV홈쇼핑가구N232323<NA>
592202011광주북구중흥1동LINEAR_TV게임오락N3221382412<NA>
593202011울산울주군삼남읍LINEAR_TV스포츠농구N64013161142<NA>
594202011경기화성시동탄4동LINEAR_TViTV등 기타기타Y11138571027582<NA>