Overview

Dataset statistics

Number of variables8
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.8 KiB
Average record size in memory69.3 B

Variable types

Categorical5
Text1
Numeric2

Dataset

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

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
SEXDSTN_CODE is highly overall correlated with SEXDSTN_RATEHigh correlation

Reproduction

Analysis started2023-12-10 06:20:25.076188
Analysis finished2023-12-10 06:20:26.478193
Duration1.4 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

2023-12-10T15:20:26.607102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:20:26.762102image/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
모니터
100 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
모니터 100
100.0%

Length

2023-12-10T15:20:26.931523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:20:27.075307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
모니터 100
100.0%

YM
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
202007
100 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row202007
2nd row202007
3rd row202007
4th row202007
5th row202007

Common Values

ValueCountFrequency (%)
202007 100
100.0%

Length

2023-12-10T15:20:27.205280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:20:27.357434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
202007 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

2023-12-10T15:20:27.544290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:20:27.700012image/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
61 
2
39 

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 61
61.0%
2 39
39.0%

Length

2023-12-10T15:20:27.905687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:20:28.080884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 61
61.0%
2 39
39.0%

PRDUCT_BRAND_NM
Text

Distinct64
Distinct (%)64.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T15:20:28.506106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length10
Mean length4.66
Min length2

Characters and Unicode

Total characters466
Distinct characters135
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

Unique28 ?
Unique (%)28.0%

Sample

1st rowLG전자
2nd rowLG전자
3rd row삼성전자
4th row삼성전자
5th row울트라기어
ValueCountFrequency (%)
lg전자 2
 
1.9%
울트라샤프 2
 
1.9%
tv 2
 
1.9%
msi 2
 
1.9%
글로벌전자 2
 
1.9%
유디아 2
 
1.9%
asus 2
 
1.9%
어드밴스원디앤티 2
 
1.9%
지아이엘 2
 
1.9%
레노버 2
 
1.9%
Other values (56) 84
80.8%
2023-12-10T15:20:29.203511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
스 19
 
4.1%
이 18
 
3.9%
아 17
 
3.6%
트 13
 
2.8%
A 12
 
2.6%
디 10
 
2.1%
리 9
 
1.9%
I 9
 
1.9%
S 9
 
1.9%
크 8
 
1.7%
Other values (125) 342
73.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 343
73.6%
Uppercase Letter 95
 
20.4%
Lowercase Letter 24
 
5.2%
Space Separator 4
 
0.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
스 19
 
5.5%
이 18
 
5.2%
아 17
 
5.0%
트 13
 
3.8%
디 10
 
2.9%
리 9
 
2.6%
크 8
 
2.3%
앤 8
 
2.3%
마 7
 
2.0%
드 7
 
2.0%
Other values (87) 227
66.2%
Uppercase Letter
ValueCountFrequency (%)
A 12
12.6%
I 9
 
9.5%
S 9
 
9.5%
O 6
 
6.3%
V 6
 
6.3%
L 5
 
5.3%
E 5
 
5.3%
M 5
 
5.3%
G 5
 
5.3%
T 5
 
5.3%
Other values (13) 28
29.5%
Lowercase Letter
ValueCountFrequency (%)
y 3
12.5%
s 2
8.3%
a 2
8.3%
n 2
8.3%
i 2
8.3%
e 2
8.3%
d 2
8.3%
r 2
8.3%
t 2
8.3%
g 1
 
4.2%
Other values (4) 4
16.7%
Space Separator
ValueCountFrequency (%)
4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 343
73.6%
Latin 119
 
25.5%
Common 4
 
0.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
스 19
 
5.5%
이 18
 
5.2%
아 17
 
5.0%
트 13
 
3.8%
디 10
 
2.9%
리 9
 
2.6%
크 8
 
2.3%
앤 8
 
2.3%
마 7
 
2.0%
드 7
 
2.0%
Other values (87) 227
66.2%
Latin
ValueCountFrequency (%)
A 12
 
10.1%
I 9
 
7.6%
S 9
 
7.6%
O 6
 
5.0%
V 6
 
5.0%
L 5
 
4.2%
E 5
 
4.2%
M 5
 
4.2%
G 5
 
4.2%
T 5
 
4.2%
Other values (27) 52
43.7%
Common
ValueCountFrequency (%)
4
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 343
73.6%
ASCII 123
 
26.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
스 19
 
5.5%
이 18
 
5.2%
아 17
 
5.0%
트 13
 
3.8%
디 10
 
2.9%
리 9
 
2.6%
크 8
 
2.3%
앤 8
 
2.3%
마 7
 
2.0%
드 7
 
2.0%
Other values (87) 227
66.2%
ASCII
ValueCountFrequency (%)
A 12
 
9.8%
I 9
 
7.3%
S 9
 
7.3%
O 6
 
4.9%
V 6
 
4.9%
L 5
 
4.1%
E 5
 
4.1%
M 5
 
4.1%
G 5
 
4.1%
T 5
 
4.1%
Other values (28) 56
45.5%

SEXDSTN_RATE
Real number (ℝ)

HIGH CORRELATION 

Distinct73
Distinct (%)73.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64
Minimum1.76
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T15:20:29.459552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.76
5-th percentile7.2295
Q124.6575
median78.885
Q3100
95-th percentile100
Maximum100
Range98.24
Interquartile range (IQR)75.3425

Descriptive statistics

Standard deviation35.860703
Coefficient of variation (CV)0.56032349
Kurtosis-1.4241063
Mean64
Median Absolute Deviation (MAD)21.115
Skewness-0.4741709
Sum6400
Variance1285.99
MonotonicityNot monotonic
2023-12-10T15:20:29.693417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100.0 28
28.0%
81.38 1
 
1.0%
32.4 1
 
1.0%
63.49 1
 
1.0%
36.51 1
 
1.0%
37.96 1
 
1.0%
62.04 1
 
1.0%
7.25 1
 
1.0%
92.75 1
 
1.0%
67.6 1
 
1.0%
Other values (63) 63
63.0%
ValueCountFrequency (%)
1.76 1
1.0%
3.17 1
1.0%
5.76 1
1.0%
6.32 1
1.0%
6.84 1
1.0%
7.25 1
1.0%
7.53 1
1.0%
8.24 1
1.0%
9.11 1
1.0%
9.17 1
1.0%
ValueCountFrequency (%)
100.0 28
28.0%
98.24 1
 
1.0%
96.83 1
 
1.0%
94.24 1
 
1.0%
93.68 1
 
1.0%
93.16 1
 
1.0%
92.75 1
 
1.0%
92.47 1
 
1.0%
91.76 1
 
1.0%
90.89 1
 
1.0%

TOTAL_BRAND_RATE
Real number (ℝ)

Distinct48
Distinct (%)48.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8675
Minimum0.08
Maximum18.94
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T15:20:29.957887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.08
5-th percentile0.09
Q10.2075
median0.655
Q31.4425
95-th percentile11.22
Maximum18.94
Range18.86
Interquartile range (IQR)1.235

Descriptive statistics

Standard deviation3.6385715
Coefficient of variation (CV)1.9483649
Kurtosis10.790841
Mean1.8675
Median Absolute Deviation (MAD)0.53
Skewness3.274955
Sum186.75
Variance13.239203
MonotonicityDecreasing
2023-12-10T15:20:30.276286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
0.09 8
 
8.0%
0.84 4
 
4.0%
1.4 4
 
4.0%
0.1 4
 
4.0%
0.13 4
 
4.0%
0.69 3
 
3.0%
0.31 3
 
3.0%
0.11 3
 
3.0%
0.24 2
 
2.0%
0.28 2
 
2.0%
Other values (38) 63
63.0%
ValueCountFrequency (%)
0.08 1
 
1.0%
0.09 8
8.0%
0.1 4
4.0%
0.11 3
 
3.0%
0.12 1
 
1.0%
0.13 4
4.0%
0.14 1
 
1.0%
0.15 1
 
1.0%
0.16 1
 
1.0%
0.2 1
 
1.0%
ValueCountFrequency (%)
18.94 2
2.0%
13.2 2
2.0%
11.22 2
2.0%
9.85 2
2.0%
3.96 2
2.0%
3.09 2
2.0%
3.06 2
2.0%
2.71 2
2.0%
2.64 1
1.0%
2.06 2
2.0%

Interactions

2023-12-10T15:20:25.726017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:20:25.368111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:20:25.882623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:20:25.545508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T15:20:30.461305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
SEXDSTN_CODEPRDUCT_BRAND_NMSEXDSTN_RATETOTAL_BRAND_RATE
SEXDSTN_CODE1.0000.0000.9660.000
PRDUCT_BRAND_NM0.0001.0000.0001.000
SEXDSTN_RATE0.9660.0001.0000.234
TOTAL_BRAND_RATE0.0001.0000.2341.000
2023-12-10T15:20:30.630236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
SEXDSTN_RATETOTAL_BRAND_RATESEXDSTN_CODE
SEXDSTN_RATE1.000-0.3380.806
TOTAL_BRAND_RATE-0.3381.0000.000
SEXDSTN_CODE0.8060.0001.000

Missing values

2023-12-10T15:20:26.121978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T15:20:26.376171image/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_NMSEXDSTN_RATETOTAL_BRAND_RATE
0컴퓨터모니터20200720203Q1LG전자81.3818.94
1컴퓨터모니터20200720203Q2LG전자18.6218.94
2컴퓨터모니터20200720203Q1삼성전자78.1813.2
3컴퓨터모니터20200720203Q2삼성전자21.8213.2
4컴퓨터모니터20200720203Q1울트라기어82.5511.22
5컴퓨터모니터20200720203Q2울트라기어17.4511.22
6컴퓨터모니터20200720203Q1한성컴퓨터91.769.85
7컴퓨터모니터20200720203Q2한성컴퓨터8.249.85
8컴퓨터모니터20200720203Q1크로스오버75.563.96
9컴퓨터모니터20200720203Q2크로스오버24.443.96
PRDUCT_LCLAS_NMPRDUCT_MLSFC_NMYMQU_SE_VALUESEXDSTN_CODEPRDUCT_BRAND_NMSEXDSTN_RATETOTAL_BRAND_RATE
90컴퓨터모니터20200720203Q2아이존아이앤디9.170.1
91컴퓨터모니터20200720203Q1ENM100.00.09
92컴퓨터모니터20200720203Q1KXG100.00.09
93컴퓨터모니터20200720203Q1태경글로벌43.130.09
94컴퓨터모니터20200720203Q2태경글로벌56.870.09
95컴퓨터모니터20200720203Q1좋은디에스아이49.090.09
96컴퓨터모니터20200720203Q2좋은디에스아이50.910.09
97컴퓨터모니터20200720203Q1VAVA66.710.09
98컴퓨터모니터20200720203Q2VAVA33.290.09
99컴퓨터모니터20200720203Q1하이드림LCD100.00.08