Overview

Dataset statistics

Number of variables9
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.6 KiB
Average record size in memory77.3 B

Variable types

Categorical6
Text1
Numeric2

Dataset

DescriptionSample
Author써머스플랫폼
URLhttps://www.bigdata-telecom.kr/invoke/SOKBP2603/?goodsCode=SMPPRDCTGNDER

Alerts

PRDUCT_LCLAS_NM has constant value ""Constant
PRDUCT_MLSFC_NM has constant value ""Constant
YM has constant value ""Constant
QU_SE_VALUE has constant value ""Constant
SEXDSTN_RATE is highly overall correlated with SEXDSTN_CODEHigh correlation
TOTAL_PRDUCT_RATE is highly overall correlated with PRDUCT_BRAND_NMHigh correlation
SEXDSTN_CODE is highly overall correlated with SEXDSTN_RATEHigh correlation
PRDUCT_BRAND_NM is highly overall correlated with TOTAL_PRDUCT_RATEHigh correlation

Reproduction

Analysis started2023-12-10 06:32:04.010139
Analysis finished2023-12-10 06:32:05.482547
Duration1.47 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

PRDUCT_LCLAS_NM
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
컴퓨터
100 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
컴퓨터 100
100.0%

Length

2023-12-10T15:32:05.620757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:32:05.848334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
컴퓨터 100
100.0%

PRDUCT_MLSFC_NM
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
노트북
100 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
노트북 100
100.0%

Length

2023-12-10T15:32:06.026716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:32:06.193944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
노트북 100
100.0%

YM
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
202008
100 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row202008
2nd row202008
3rd row202008
4th row202008
5th row202008

Common Values

ValueCountFrequency (%)
202008 100
100.0%

Length

2023-12-10T15:32:06.362265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:32:06.565595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
202008 100
100.0%

QU_SE_VALUE
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
20203Q
100 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20203Q
2nd row20203Q
3rd row20203Q
4th row20203Q
5th row20203Q

Common Values

ValueCountFrequency (%)
20203Q 100
100.0%

Length

2023-12-10T15:32:06.745789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:32:06.909362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20203q 100
100.0%

SEXDSTN_CODE
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
1
60 
2
40 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row1
4th row2
5th row1

Common Values

ValueCountFrequency (%)
1 60
60.0%
2 40
40.0%

Length

2023-12-10T15:32:07.083959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:32:07.298285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 60
60.0%
2 40
40.0%

PRDUCT_BRAND_NM
Categorical

HIGH CORRELATION 

Distinct29
Distinct (%)29.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2020 그램17
12 
아이디어패드
9 
2020 울트라PC
8 
2020 그램15
8 
갤럭시북 플렉스
 
5
Other values (24)
58 

Length

Max length11
Median length9
Mean length6.94
Min length2

Unique

Unique6 ?
Unique (%)6.0%

Sample

1st row씽크패드
2nd row씽크패드
3rd row2019 맥북프로
4th row2019 맥북프로
5th row아이디어패드

Common Values

ValueCountFrequency (%)
2020 그램17 12
 
12.0%
아이디어패드 9
 
9.0%
2020 울트라PC 8
 
8.0%
2020 그램15 8
 
8.0%
갤럭시북 플렉스 5
 
5.0%
갤럭시북 플렉스 알파 5
 
5.0%
인스피론 5
 
5.0%
갤럭시북 이온 4
 
4.0%
씽크패드 4
 
4.0%
GP시리즈 3
 
3.0%
Other values (19) 37
37.0%

Length

2023-12-10T15:32:07.500068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2020 34
21.2%
갤럭시북 14
 
8.8%
그램17 12
 
7.5%
플렉스 10
 
6.2%
아이디어패드 9
 
5.6%
울트라pc 8
 
5.0%
그램15 8
 
5.0%
맥북프로 5
 
3.1%
rog 5
 
3.1%
인스피론 5
 
3.1%
Other values (22) 50
31.2%

PRDNM
Text

Distinct60
Distinct (%)60.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T15:32:08.004058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length27
Median length21
Mean length13.59
Min length6

Characters and Unicode

Total characters1359
Distinct characters59
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique20 ?
Unique (%)20.0%

Sample

1st rowE15-S036
2nd rowE15-S036
3rd row16 MVVK2KH/A
4th row16 MVVK2KH/A
5th rowSlim3 15ARE R5
ValueCountFrequency (%)
15 6
 
3.8%
i5 5
 
3.1%
legend 5
 
3.1%
r5 5
 
3.1%
15are 4
 
2.5%
13 3
 
1.9%
gp75 3
 
1.9%
제피러스 3
 
1.9%
g 3
 
1.9%
pro 3
 
1.9%
Other values (71) 119
74.8%
2023-12-10T15:32:08.777581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 127
 
9.3%
0 103
 
7.6%
1 91
 
6.7%
- 77
 
5.7%
A 60
 
4.4%
59
 
4.3%
7 56
 
4.1%
N 51
 
3.8%
K 46
 
3.4%
R 42
 
3.1%
Other values (49) 647
47.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 589
43.3%
Decimal Number 536
39.4%
Lowercase Letter 81
 
6.0%
Dash Punctuation 77
 
5.7%
Space Separator 59
 
4.3%
Other Letter 12
 
0.9%
Other Punctuation 5
 
0.4%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 60
 
10.2%
N 51
 
8.7%
K 46
 
7.8%
R 42
 
7.1%
X 41
 
7.0%
G 40
 
6.8%
D 33
 
5.6%
T 30
 
5.1%
V 29
 
4.9%
Z 27
 
4.6%
Other values (15) 190
32.3%
Lowercase Letter
ValueCountFrequency (%)
i 16
19.8%
e 11
13.6%
l 10
12.3%
m 8
9.9%
a 5
 
6.2%
t 4
 
4.9%
r 4
 
4.9%
o 4
 
4.9%
n 3
 
3.7%
d 3
 
3.7%
Other values (7) 13
16.0%
Decimal Number
ValueCountFrequency (%)
5 127
23.7%
0 103
19.2%
1 91
17.0%
7 56
10.4%
9 41
 
7.6%
6 32
 
6.0%
3 31
 
5.8%
4 30
 
5.6%
2 17
 
3.2%
8 8
 
1.5%
Other Letter
ValueCountFrequency (%)
러 3
25.0%
제 3
25.0%
피 3
25.0%
스 3
25.0%
Dash Punctuation
ValueCountFrequency (%)
- 77
100.0%
Space Separator
ValueCountFrequency (%)
59
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 677
49.8%
Latin 670
49.3%
Hangul 12
 
0.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 60
 
9.0%
N 51
 
7.6%
K 46
 
6.9%
R 42
 
6.3%
X 41
 
6.1%
G 40
 
6.0%
D 33
 
4.9%
T 30
 
4.5%
V 29
 
4.3%
Z 27
 
4.0%
Other values (32) 271
40.4%
Common
ValueCountFrequency (%)
5 127
18.8%
0 103
15.2%
1 91
13.4%
- 77
11.4%
59
8.7%
7 56
8.3%
9 41
 
6.1%
6 32
 
4.7%
3 31
 
4.6%
4 30
 
4.4%
Other values (3) 30
 
4.4%
Hangul
ValueCountFrequency (%)
러 3
25.0%
제 3
25.0%
피 3
25.0%
스 3
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1347
99.1%
Hangul 12
 
0.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 127
 
9.4%
0 103
 
7.6%
1 91
 
6.8%
- 77
 
5.7%
A 60
 
4.5%
59
 
4.4%
7 56
 
4.2%
N 51
 
3.8%
K 46
 
3.4%
R 42
 
3.1%
Other values (45) 635
47.1%
Hangul
ValueCountFrequency (%)
러 3
25.0%
제 3
25.0%
피 3
25.0%
스 3
25.0%

SEXDSTN_RATE
Real number (ℝ)

HIGH CORRELATION 

Distinct80
Distinct (%)80.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean60
Minimum6.12
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T15:32:09.048556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6.12
5-th percentile10.8975
Q131.3025
median60.53
Q389.2725
95-th percentile100
Maximum100
Range93.88
Interquartile range (IQR)57.97

Descriptive statistics

Standard deviation31.828777
Coefficient of variation (CV)0.53047961
Kurtosis-1.3807246
Mean60
Median Absolute Deviation (MAD)28.955
Skewness-0.19461149
Sum6000
Variance1013.071
MonotonicityNot monotonic
2023-12-10T15:32:09.283975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100.0 20
 
20.0%
50.0 2
 
2.0%
89.06 1
 
1.0%
22.28 1
 
1.0%
52.99 1
 
1.0%
47.01 1
 
1.0%
14.69 1
 
1.0%
85.31 1
 
1.0%
23.75 1
 
1.0%
76.25 1
 
1.0%
Other values (70) 70
70.0%
ValueCountFrequency (%)
6.12 1
1.0%
8.57 1
1.0%
8.59 1
1.0%
9.57 1
1.0%
10.09 1
1.0%
10.94 1
1.0%
11.68 1
1.0%
12.09 1
1.0%
12.18 1
1.0%
13.51 1
1.0%
ValueCountFrequency (%)
100.0 20
20.0%
93.88 1
 
1.0%
91.43 1
 
1.0%
91.41 1
 
1.0%
90.43 1
 
1.0%
89.91 1
 
1.0%
89.06 1
 
1.0%
88.32 1
 
1.0%
87.91 1
 
1.0%
87.82 1
 
1.0%

TOTAL_PRDUCT_RATE
Real number (ℝ)

HIGH CORRELATION 

Distinct44
Distinct (%)44.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.9109
Minimum0.4
Maximum3.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T15:32:09.503424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.4
5-th percentile0.41
Q10.4775
median0.67
Q31.0925
95-th percentile2.46
Maximum3.2
Range2.8
Interquartile range (IQR)0.615

Descriptive statistics

Standard deviation0.62462449
Coefficient of variation (CV)0.68572235
Kurtosis3.9372734
Mean0.9109
Median Absolute Deviation (MAD)0.23
Skewness1.9677279
Sum91.09
Variance0.39015575
MonotonicityDecreasing
2023-12-10T15:32:09.770497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
0.41 9
 
9.0%
0.47 5
 
5.0%
0.67 4
 
4.0%
0.56 4
 
4.0%
0.6 3
 
3.0%
0.4 3
 
3.0%
0.48 3
 
3.0%
0.81 3
 
3.0%
0.52 3
 
3.0%
0.55 3
 
3.0%
Other values (34) 60
60.0%
ValueCountFrequency (%)
0.4 3
 
3.0%
0.41 9
9.0%
0.42 2
 
2.0%
0.43 2
 
2.0%
0.44 2
 
2.0%
0.46 2
 
2.0%
0.47 5
5.0%
0.48 3
 
3.0%
0.5 2
 
2.0%
0.52 3
 
3.0%
ValueCountFrequency (%)
3.2 2
2.0%
2.85 2
2.0%
2.46 2
2.0%
1.77 2
2.0%
1.68 2
2.0%
1.66 2
2.0%
1.52 2
2.0%
1.5 2
2.0%
1.48 2
2.0%
1.28 2
2.0%

Interactions

2023-12-10T15:32:04.768739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:32:04.460046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:32:04.929029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:32:04.597596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T15:32:09.939257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
SEXDSTN_CODEPRDUCT_BRAND_NMPRDNMSEXDSTN_RATETOTAL_PRDUCT_RATE
SEXDSTN_CODE1.0000.0000.0000.9130.000
PRDUCT_BRAND_NM0.0001.0001.0000.0000.878
PRDNM0.0001.0001.0000.0001.000
SEXDSTN_RATE0.9130.0000.0001.0000.000
TOTAL_PRDUCT_RATE0.0000.8781.0000.0001.000
2023-12-10T15:32:10.113749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
SEXDSTN_CODEPRDUCT_BRAND_NM
SEXDSTN_CODE1.0000.000
PRDUCT_BRAND_NM0.0001.000
2023-12-10T15:32:10.634384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
SEXDSTN_RATETOTAL_PRDUCT_RATESEXDSTN_CODEPRDUCT_BRAND_NM
SEXDSTN_RATE1.000-0.0950.7220.000
TOTAL_PRDUCT_RATE-0.0951.0000.0000.517
SEXDSTN_CODE0.7220.0001.0000.000
PRDUCT_BRAND_NM0.0000.5170.0001.000

Missing values

2023-12-10T15:32:05.130458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T15:32:05.368234image/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

PRDUCT_LCLAS_NMPRDUCT_MLSFC_NMYMQU_SE_VALUESEXDSTN_CODEPRDUCT_BRAND_NMPRDNMSEXDSTN_RATETOTAL_PRDUCT_RATE
0컴퓨터노트북20200820203Q1씽크패드E15-S03689.063.2
1컴퓨터노트북20200820203Q2씽크패드E15-S03610.943.2
2컴퓨터노트북20200820203Q12019 맥북프로16 MVVK2KH/A91.412.85
3컴퓨터노트북20200820203Q22019 맥북프로16 MVVK2KH/A8.592.85
4컴퓨터노트북20200820203Q1아이디어패드Slim3 15ARE R581.412.46
5컴퓨터노트북20200820203Q2아이디어패드Slim3 15ARE R518.592.46
6컴퓨터노트북20200820203Q12020 그램1717ZD90N-VX50K26.71.77
7컴퓨터노트북20200820203Q22020 그램1717ZD90N-VX50K73.31.77
8컴퓨터노트북20200820203Q12020 그램1717ZD90N-VX70K75.931.68
9컴퓨터노트북20200820203Q22020 그램1717ZD90N-VX70K24.071.68
PRDUCT_LCLAS_NMPRDUCT_MLSFC_NMYMQU_SE_VALUESEXDSTN_CODEPRDUCT_BRAND_NMPRDNMSEXDSTN_RATETOTAL_PRDUCT_RATE
90컴퓨터노트북20200820203Q1아이디어패드Slim3-15IIL 7D2100.00.41
91컴퓨터노트북20200820203Q12020 울트라PC15U40N-GR36K86.430.41
92컴퓨터노트북20200820203Q22020 울트라PC15U40N-GR36K13.570.41
93컴퓨터노트북20200820203Q1갤럭시북 플렉스NT950QCG-X71SA50.00.41
94컴퓨터노트북20200820203Q2갤럭시북 플렉스NT950QCG-X71SA50.00.41
95컴퓨터노트북20200820203Q1갤럭시북 플렉스 알파NT730QCR-A38A50.50.41
96컴퓨터노트북20200820203Q2갤럭시북 플렉스 알파NT730QCR-A38A49.50.41
97컴퓨터노트북20200820203Q1HP15s-eq0139AU100.00.4
98컴퓨터노트북20200820203Q12020 그램1717ZD90N-VX7BK59.890.4
99컴퓨터노트북20200820203Q22020 그램1717ZD90N-VX7BK40.110.4