Overview

Dataset statistics

Number of variables9
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.5 KiB
Average record size in memory76.3 B

Variable types

Categorical7
Numeric2

Dataset

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

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
TOTAL_PRDUCT_RATE is highly overall correlated with PRDUCT_BRAND_NM and 1 other fieldsHigh correlation
PRDUCT_BRAND_NM is highly overall correlated with TOTAL_PRDUCT_RATE and 1 other fieldsHigh correlation
PRDNM is highly overall correlated with TOTAL_PRDUCT_RATE and 1 other fieldsHigh correlation

Reproduction

Analysis started2023-12-10 06:44:59.558029
Analysis finished2023-12-10 06:45:00.828368
Duration1.27 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
생활가전
100 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
생활가전 100
100.0%

Length

2023-12-10T15:45:00.939371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:45:01.089087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
생활가전 100
100.0%

PRDUCT_MLSFC_NM
Categorical

CONSTANT 

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

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
청소기 100
100.0%

Length

2023-12-10T15:45:01.244442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:45:01.375201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
청소기 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

2023-12-10T15:45:01.526692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:45:01.652746image/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

2023-12-10T15:45:01.777267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:45:01.931738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20203q 100
100.0%

EMPLYR_AGE
Categorical

Distinct11
Distinct (%)11.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
35~39세
18 
40~44세
16 
30~34세
15 
25~29세
13 
50~54세
13 
Other values (6)
25 

Length

Max length6
Median length6
Mean length5.97
Min length5

Unique

Unique2 ?
Unique (%)2.0%

Sample

1st row25~29세
2nd row30~34세
3rd row35~39세
4th row40~44세
5th row45~49세

Common Values

ValueCountFrequency (%)
35~39세 18
18.0%
40~44세 16
16.0%
30~34세 15
15.0%
25~29세 13
13.0%
50~54세 13
13.0%
45~49세 10
10.0%
20~24세 9
9.0%
65세이상 2
 
2.0%
55~59세 2
 
2.0%
60~64세 1
 
1.0%

Length

2023-12-10T15:45:02.112241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
35~39세 18
18.0%
40~44세 16
16.0%
30~34세 15
15.0%
25~29세 13
13.0%
50~54세 13
13.0%
45~49세 10
10.0%
20~24세 9
9.0%
65세이상 2
 
2.0%
55~59세 2
 
2.0%
60~64세 1
 
1.0%

PRDUCT_BRAND_NM
Categorical

HIGH CORRELATION 

Distinct9
Distinct (%)9.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
다이슨
30 
샤오미
18 
코드제로
17 
제트
15 
에코백스
6 
Other values (4)
14 

Length

Max length5
Median length3
Mean length3.21
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
다이슨 30
30.0%
샤오미 18
18.0%
코드제로 17
17.0%
제트 15
15.0%
에코백스 6
 
6.0%
에브리봇 5
 
5.0%
치후360 4
 
4.0%
디베아 3
 
3.0%
제로홈 2
 
2.0%

Length

2023-12-10T15:45:02.323797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:45:02.529756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
다이슨 30
30.0%
샤오미 18
18.0%
코드제로 17
17.0%
제트 15
15.0%
에코백스 6
 
6.0%
에브리봇 5
 
5.0%
치후360 4
 
4.0%
디베아 3
 
3.0%
제로홈 2
 
2.0%

PRDNM
Categorical

HIGH CORRELATION 

Distinct19
Distinct (%)19.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
V8 플러피
9 
로보락 S6 MaxV
7 
V11 컴플리트
7 
VS20R9078S3
 
6
S9
 
6
Other values (14)
65 

Length

Max length16
Median length10
Mean length9.09
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA9S A9700
2nd rowA9S A9700
3rd rowA9S A9700
4th rowA9S A9700
5th rowA9S A9700

Common Values

ValueCountFrequency (%)
V8 플러피 9
 
9.0%
로보락 S6 MaxV 7
 
7.0%
V11 컴플리트 7
 
7.0%
VS20R9078S3 6
 
6.0%
S9 6
 
6.0%
드리미 무선청소기 V11 6
 
6.0%
A9S A9700 6
 
6.0%
V8 앱솔루트 6
 
6.0%
디봇 오즈모 930 6
 
6.0%
3i 5
 
5.0%
Other values (9) 36
36.0%

Length

2023-12-10T15:45:02.728738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
v11 18
 
8.8%
v8 15
 
7.3%
a9s 14
 
6.8%
로보락 12
 
5.9%
플러피 9
 
4.4%
s6 7
 
3.4%
maxv 7
 
3.4%
컴플리트 7
 
3.4%
a9700 6
 
2.9%
오즈모 6
 
2.9%
Other values (22) 104
50.7%

AGE_RATE
Real number (ℝ)

Distinct96
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.5004
Minimum2.42
Maximum60.37
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T15:45:02.918400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.42
5-th percentile2.9185
Q17.99
median15.03
Q327.795
95-th percentile37.7795
Maximum60.37
Range57.95
Interquartile range (IQR)19.805

Descriptive statistics

Standard deviation12.706454
Coefficient of variation (CV)0.68682048
Kurtosis0.42496658
Mean18.5004
Median Absolute Deviation (MAD)8.565
Skewness0.87691241
Sum1850.04
Variance161.45397
MonotonicityNot monotonic
2023-12-10T15:45:03.099359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.48 2
 
2.0%
7.9 2
 
2.0%
8.3 2
 
2.0%
2.69 2
 
2.0%
11.2 1
 
1.0%
11.16 1
 
1.0%
25.68 1
 
1.0%
27.73 1
 
1.0%
23.61 1
 
1.0%
11.82 1
 
1.0%
Other values (86) 86
86.0%
ValueCountFrequency (%)
2.42 1
1.0%
2.67 1
1.0%
2.69 2
2.0%
2.7 1
1.0%
2.93 1
1.0%
3.33 1
1.0%
4.3 1
1.0%
4.73 1
1.0%
5.18 1
1.0%
5.31 1
1.0%
ValueCountFrequency (%)
60.37 1
1.0%
54.23 1
1.0%
53.34 1
1.0%
39.31 1
1.0%
37.96 1
1.0%
37.77 1
1.0%
37.42 1
1.0%
37.0 1
1.0%
35.7 1
1.0%
35.6 1
1.0%

TOTAL_PRDUCT_RATE
Real number (ℝ)

HIGH CORRELATION 

Distinct19
Distinct (%)19.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8426
Minimum0.94
Maximum6.15
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T15:45:03.311972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.94
5-th percentile0.99
Q11.36
median2.55
Q33.21
95-th percentile6.15
Maximum6.15
Range5.21
Interquartile range (IQR)1.85

Descriptive statistics

Standard deviation1.5927377
Coefficient of variation (CV)0.56031017
Kurtosis-0.34917428
Mean2.8426
Median Absolute Deviation (MAD)1.06
Skewness0.85502053
Sum284.26
Variance2.5368134
MonotonicityDecreasing
2023-12-10T15:45:03.488988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
3.21 9
 
9.0%
2.09 7
 
7.0%
5.94 7
 
7.0%
6.15 6
 
6.0%
4.55 6
 
6.0%
3.19 6
 
6.0%
1.23 6
 
6.0%
2.55 6
 
6.0%
1.27 6
 
6.0%
1.34 5
 
5.0%
Other values (9) 36
36.0%
ValueCountFrequency (%)
0.94 3
3.0%
0.99 3
3.0%
1.23 6
6.0%
1.27 6
6.0%
1.34 5
5.0%
1.36 3
3.0%
1.62 5
5.0%
1.87 4
4.0%
1.89 4
4.0%
2.09 7
7.0%
ValueCountFrequency (%)
6.15 6
6.0%
5.94 7
7.0%
4.55 6
6.0%
4.25 5
5.0%
3.21 9
9.0%
3.19 6
6.0%
2.8 5
5.0%
2.64 4
4.0%
2.55 6
6.0%
2.09 7
7.0%

Interactions

2023-12-10T15:45:00.210137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:44:59.917903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:45:00.349675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:45:00.060391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T15:45:03.604947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
EMPLYR_AGEPRDUCT_BRAND_NMPRDNMAGE_RATETOTAL_PRDUCT_RATE
EMPLYR_AGE1.0000.0000.0000.2370.000
PRDUCT_BRAND_NM0.0001.0000.9860.3090.780
PRDNM0.0000.9861.0000.5561.000
AGE_RATE0.2370.3090.5561.0000.241
TOTAL_PRDUCT_RATE0.0000.7801.0000.2411.000
2023-12-10T15:45:03.993829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
PRDNMEMPLYR_AGEPRDUCT_BRAND_NM
PRDNM1.0000.0000.868
EMPLYR_AGE0.0001.0000.000
PRDUCT_BRAND_NM0.8680.0001.000
2023-12-10T15:45:04.139387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
AGE_RATETOTAL_PRDUCT_RATEEMPLYR_AGEPRDUCT_BRAND_NMPRDNM
AGE_RATE1.000-0.1700.1030.0990.236
TOTAL_PRDUCT_RATE-0.1701.0000.0000.5590.933
EMPLYR_AGE0.1030.0001.0000.0000.000
PRDUCT_BRAND_NM0.0990.5590.0001.0000.868
PRDNM0.2360.9330.0000.8681.000

Missing values

2023-12-10T15:45:00.539598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T15:45:00.744089image/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_VALUEEMPLYR_AGEPRDUCT_BRAND_NMPRDNMAGE_RATETOTAL_PRDUCT_RATE
0생활가전청소기20200720203Q25~29세코드제로A9S A970011.26.15
1생활가전청소기20200720203Q30~34세코드제로A9S A97007.96.15
2생활가전청소기20200720203Q35~39세코드제로A9S A970018.766.15
3생활가전청소기20200720203Q40~44세코드제로A9S A970035.66.15
4생활가전청소기20200720203Q45~49세코드제로A9S A970010.386.15
5생활가전청소기20200720203Q50~54세코드제로A9S A970016.166.15
6생활가전청소기20200720203Q20~24세샤오미로보락 S6 MaxV2.425.94
7생활가전청소기20200720203Q25~29세샤오미로보락 S6 MaxV6.615.94
8생활가전청소기20200720203Q30~34세샤오미로보락 S6 MaxV20.145.94
9생활가전청소기20200720203Q35~39세샤오미로보락 S6 MaxV35.75.94
PRDUCT_LCLAS_NMPRDUCT_MLSFC_NMYMQU_SE_VALUEEMPLYR_AGEPRDUCT_BRAND_NMPRDNMAGE_RATETOTAL_PRDUCT_RATE
90생활가전청소기20200720203Q35~39세제로홈S92.71.23
91생활가전청소기20200720203Q40~44세제로홈S92.931.23
92생활가전청소기20200720203Q40~44세치후360S928.211.23
93생활가전청소기20200720203Q50~54세치후360S99.451.23
94생활가전청소기20200720203Q35~39세다이슨V10 카본파이버54.230.99
95생활가전청소기20200720203Q40~44세다이슨V10 카본파이버28.150.99
96생활가전청소기20200720203Q50~54세다이슨V10 카본파이버17.620.99
97생활가전청소기20200720203Q20~24세디베아ALLNEW220008.30.94
98생활가전청소기20200720203Q25~29세디베아ALLNEW220008.30.94
99생활가전청소기20200720203Q30~34세디베아ALLNEW2200033.40.94