Overview

Dataset statistics

Number of variables11
Number of observations99
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.3 KiB
Average record size in memory96.3 B

Variable types

Text1
Categorical5
Numeric5

Dataset

DescriptionSample
Author(주)제로투원파트너스
URLhttps://www.bigdata-telecom.kr/invoke/SOKBP2603/?goodsCode=ZTO00000000000000005

Alerts

2020 has constant value ""Constant
2020H2 has constant value ""Constant
2020Q3 has constant value ""Constant
202007 has constant value ""Constant
0.02 is highly overall correlated with 0.20 and 3 other fieldsHigh correlation
0.20 is highly overall correlated with 0.02 and 3 other fieldsHigh correlation
0.57 is highly overall correlated with 0.02 and 3 other fieldsHigh correlation
0.10 is highly overall correlated with 0.02 and 3 other fieldsHigh correlation
0.11 is highly overall correlated with 0.02 and 3 other fieldsHigh correlation
0.02 has 1 (1.0%) zerosZeros
0.10 has 1 (1.0%) zerosZeros
0.11 has 1 (1.0%) zerosZeros

Reproduction

Analysis started2023-12-10 06:17:31.803489
Analysis finished2023-12-10 06:17:36.732540
Duration4.93 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

맥주
Text

Distinct51
Distinct (%)51.5%
Missing0
Missing (%)0.0%
Memory size924.0 B
2023-12-10T15:17:37.059749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length9
Mean length3.8585859
Min length2

Characters and Unicode

Total characters382
Distinct characters105
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)3.0%

Sample

1st row소주
2nd row전통주
3rd row와인
4th row양주
5th row커피
ValueCountFrequency (%)
소주 2
 
1.9%
컴퓨터 2
 
1.9%
즉석식품(레토르트식품 2
 
1.9%
tv 2
 
1.9%
완구 2
 
1.9%
서적 2
 
1.9%
애완동물용품 2
 
1.9%
스포츠/레저용품 2
 
1.9%
세탁세제 2
 
1.9%
헤어용품 2
 
1.9%
Other values (43) 83
80.6%
2023-12-10T15:17:37.715603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
기 23
 
6.0%
유 16
 
4.2%
품 16
 
4.2%
식 14
 
3.7%
고 9
 
2.4%
주 9
 
2.4%
세 8
 
2.1%
( 8
 
2.1%
류 8
 
2.1%
용 8
 
2.1%
Other values (95) 263
68.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 352
92.1%
Open Punctuation 8
 
2.1%
Close Punctuation 8
 
2.1%
Other Punctuation 6
 
1.6%
Space Separator 4
 
1.0%
Uppercase Letter 4
 
1.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
기 23
 
6.5%
유 16
 
4.5%
품 16
 
4.5%
식 14
 
4.0%
고 9
 
2.6%
주 9
 
2.6%
세 8
 
2.3%
류 8
 
2.3%
용 8
 
2.3%
음 8
 
2.3%
Other values (89) 233
66.2%
Uppercase Letter
ValueCountFrequency (%)
V 2
50.0%
T 2
50.0%
Open Punctuation
ValueCountFrequency (%)
( 8
100.0%
Close Punctuation
ValueCountFrequency (%)
) 8
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 6
100.0%
Space Separator
ValueCountFrequency (%)
4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 352
92.1%
Common 26
 
6.8%
Latin 4
 
1.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
기 23
 
6.5%
유 16
 
4.5%
품 16
 
4.5%
식 14
 
4.0%
고 9
 
2.6%
주 9
 
2.6%
세 8
 
2.3%
류 8
 
2.3%
용 8
 
2.3%
음 8
 
2.3%
Other values (89) 233
66.2%
Common
ValueCountFrequency (%)
( 8
30.8%
) 8
30.8%
/ 6
23.1%
4
15.4%
Latin
ValueCountFrequency (%)
V 2
50.0%
T 2
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 352
92.1%
ASCII 30
 
7.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
기 23
 
6.5%
유 16
 
4.5%
품 16
 
4.5%
식 14
 
4.0%
고 9
 
2.6%
주 9
 
2.6%
세 8
 
2.3%
류 8
 
2.3%
용 8
 
2.3%
음 8
 
2.3%
Other values (89) 233
66.2%
ASCII
ValueCountFrequency (%)
( 8
26.7%
) 8
26.7%
/ 6
20.0%
4
13.3%
V 2
 
6.7%
T 2
 
6.7%

2020
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size924.0 B
2020
99 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020
2nd row2020
3rd row2020
4th row2020
5th row2020

Common Values

ValueCountFrequency (%)
2020 99
100.0%

Length

2023-12-10T15:17:37.945314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:17:38.114053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020 99
100.0%

0.02
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)8.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.025353535
Minimum0
Maximum0.07
Zeros1
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size1023.0 B
2023-12-10T15:17:38.266208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.01
Q10.02
median0.02
Q30.03
95-th percentile0.05
Maximum0.07
Range0.07
Interquartile range (IQR)0.01

Descriptive statistics

Standard deviation0.01223187
Coefficient of variation (CV)0.48245223
Kurtosis1.4212559
Mean0.025353535
Median Absolute Deviation (MAD)0.01
Skewness1.0602709
Sum2.51
Variance0.00014961864
MonotonicityNot monotonic
2023-12-10T15:17:38.429617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0.02 47
47.5%
0.03 19
19.2%
0.01 13
 
13.1%
0.04 11
 
11.1%
0.05 6
 
6.1%
0.06 1
 
1.0%
0.07 1
 
1.0%
0.0 1
 
1.0%
ValueCountFrequency (%)
0.0 1
 
1.0%
0.01 13
 
13.1%
0.02 47
47.5%
0.03 19
19.2%
0.04 11
 
11.1%
0.05 6
 
6.1%
0.06 1
 
1.0%
0.07 1
 
1.0%
ValueCountFrequency (%)
0.07 1
 
1.0%
0.06 1
 
1.0%
0.05 6
 
6.1%
0.04 11
 
11.1%
0.03 19
19.2%
0.02 47
47.5%
0.01 13
 
13.1%
0.0 1
 
1.0%

2020H2
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size924.0 B
2020H2
99 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020H2
2nd row2020H2
3rd row2020H2
4th row2020H2
5th row2020H2

Common Values

ValueCountFrequency (%)
2020H2 99
100.0%

Length

2023-12-10T15:17:38.628435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:17:38.766273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020h2 99
100.0%

2020Q3
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size924.0 B
2020Q3
99 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020Q3
2nd row2020Q3
3rd row2020Q3
4th row2020Q3
5th row2020Q3

Common Values

ValueCountFrequency (%)
2020Q3 99
100.0%

Length

2023-12-10T15:17:38.930077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:17:39.091596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020q3 99
100.0%

202007
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size924.0 B
202007
99 

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 99
100.0%

Length

2023-12-10T15:17:39.263075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:17:39.461105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
202007 99
100.0%

W1
Categorical

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size924.0 B
W1
50 
W2
49 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowW1
2nd rowW1
3rd rowW1
4th rowW1
5th rowW1

Common Values

ValueCountFrequency (%)
W1 50
50.5%
W2 49
49.5%

Length

2023-12-10T15:17:39.683849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:17:39.841789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
w1 50
50.5%
w2 49
49.5%

0.20
Real number (ℝ)

HIGH CORRELATION 

Distinct12
Distinct (%)12.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1840404
Minimum0.09
Maximum0.26
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1023.0 B
2023-12-10T15:17:39.986316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.09
5-th percentile0.13
Q10.17
median0.19
Q30.2
95-th percentile0.21
Maximum0.26
Range0.17
Interquartile range (IQR)0.03

Descriptive statistics

Standard deviation0.027289797
Coefficient of variation (CV)0.14828156
Kurtosis0.9503099
Mean0.1840404
Median Absolute Deviation (MAD)0.02
Skewness-0.69136463
Sum18.22
Variance0.00074473304
MonotonicityNot monotonic
2023-12-10T15:17:40.178991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0.21 20
20.2%
0.2 18
18.2%
0.19 15
15.2%
0.17 12
12.1%
0.18 11
11.1%
0.13 7
 
7.1%
0.15 5
 
5.1%
0.16 5
 
5.1%
0.23 2
 
2.0%
0.14 2
 
2.0%
Other values (2) 2
 
2.0%
ValueCountFrequency (%)
0.09 1
 
1.0%
0.13 7
 
7.1%
0.14 2
 
2.0%
0.15 5
 
5.1%
0.16 5
 
5.1%
0.17 12
12.1%
0.18 11
11.1%
0.19 15
15.2%
0.2 18
18.2%
0.21 20
20.2%
ValueCountFrequency (%)
0.26 1
 
1.0%
0.23 2
 
2.0%
0.21 20
20.2%
0.2 18
18.2%
0.19 15
15.2%
0.18 11
11.1%
0.17 12
12.1%
0.16 5
 
5.1%
0.15 5
 
5.1%
0.14 2
 
2.0%

0.57
Real number (ℝ)

HIGH CORRELATION 

Distinct28
Distinct (%)28.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.52040404
Minimum0.26
Maximum0.74
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1023.0 B
2023-12-10T15:17:40.396194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.26
5-th percentile0.369
Q10.485
median0.54
Q30.58
95-th percentile0.6
Maximum0.74
Range0.48
Interquartile range (IQR)0.095

Descriptive statistics

Standard deviation0.077366332
Coefficient of variation (CV)0.14866589
Kurtosis1.0085702
Mean0.52040404
Median Absolute Deviation (MAD)0.04
Skewness-0.71109844
Sum51.52
Variance0.0059855494
MonotonicityNot monotonic
2023-12-10T15:17:40.617030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0.58 10
 
10.1%
0.59 9
 
9.1%
0.55 9
 
9.1%
0.57 7
 
7.1%
0.49 7
 
7.1%
0.56 6
 
6.1%
0.54 5
 
5.1%
0.48 5
 
5.1%
0.51 5
 
5.1%
0.5 4
 
4.0%
Other values (18) 32
32.3%
ValueCountFrequency (%)
0.26 1
1.0%
0.35 2
2.0%
0.36 2
2.0%
0.37 1
1.0%
0.38 2
2.0%
0.4 2
2.0%
0.41 2
2.0%
0.42 1
1.0%
0.43 2
2.0%
0.44 1
1.0%
ValueCountFrequency (%)
0.74 1
 
1.0%
0.66 1
 
1.0%
0.65 1
 
1.0%
0.6 4
 
4.0%
0.59 9
9.1%
0.58 10
10.1%
0.57 7
7.1%
0.56 6
6.1%
0.55 9
9.1%
0.54 5
5.1%

0.10
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct18
Distinct (%)18.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1279798
Minimum0
Maximum0.32
Zeros1
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size1023.0 B
2023-12-10T15:17:40.940341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.08
Q10.11
median0.12
Q30.14
95-th percentile0.181
Maximum0.32
Range0.32
Interquartile range (IQR)0.03

Descriptive statistics

Standard deviation0.040228424
Coefficient of variation (CV)0.31433418
Kurtosis5.3126195
Mean0.1279798
Median Absolute Deviation (MAD)0.02
Skewness0.91566843
Sum12.67
Variance0.0016183261
MonotonicityNot monotonic
2023-12-10T15:17:41.184747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
0.12 19
19.2%
0.14 13
13.1%
0.13 10
10.1%
0.09 9
9.1%
0.11 9
9.1%
0.1 7
 
7.1%
0.16 5
 
5.1%
0.17 5
 
5.1%
0.18 5
 
5.1%
0.08 4
 
4.0%
Other values (8) 13
13.1%
ValueCountFrequency (%)
0.0 1
 
1.0%
0.03 1
 
1.0%
0.06 1
 
1.0%
0.07 1
 
1.0%
0.08 4
 
4.0%
0.09 9
9.1%
0.1 7
 
7.1%
0.11 9
9.1%
0.12 19
19.2%
0.13 10
10.1%
ValueCountFrequency (%)
0.32 1
 
1.0%
0.22 2
 
2.0%
0.19 2
 
2.0%
0.18 5
 
5.1%
0.17 5
 
5.1%
0.16 5
 
5.1%
0.15 4
 
4.0%
0.14 13
13.1%
0.13 10
10.1%
0.12 19
19.2%

0.11
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct26
Distinct (%)26.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.14171717
Minimum0
Maximum0.41
Zeros1
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size1023.0 B
2023-12-10T15:17:41.499501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.069
Q10.1
median0.12
Q30.17
95-th percentile0.28
Maximum0.41
Range0.41
Interquartile range (IQR)0.07

Descriptive statistics

Standard deviation0.067869028
Coefficient of variation (CV)0.47890476
Kurtosis1.9481577
Mean0.14171717
Median Absolute Deviation (MAD)0.03
Skewness1.2063235
Sum14.03
Variance0.0046062049
MonotonicityNot monotonic
2023-12-10T15:17:41.708696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0.1 11
 
11.1%
0.12 10
 
10.1%
0.09 8
 
8.1%
0.08 7
 
7.1%
0.14 7
 
7.1%
0.11 6
 
6.1%
0.13 6
 
6.1%
0.17 5
 
5.1%
0.07 4
 
4.0%
0.16 3
 
3.0%
Other values (16) 32
32.3%
ValueCountFrequency (%)
0.0 1
 
1.0%
0.04 1
 
1.0%
0.06 3
 
3.0%
0.07 4
 
4.0%
0.08 7
7.1%
0.09 8
8.1%
0.1 11
11.1%
0.11 6
6.1%
0.12 10
10.1%
0.13 6
6.1%
ValueCountFrequency (%)
0.41 1
 
1.0%
0.32 1
 
1.0%
0.29 2
2.0%
0.28 3
3.0%
0.25 1
 
1.0%
0.24 2
2.0%
0.23 3
3.0%
0.22 1
 
1.0%
0.21 3
3.0%
0.2 2
2.0%

Interactions

2023-12-10T15:17:35.472558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:17:32.308738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:17:33.111719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:17:34.001373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:17:34.741409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:17:35.641765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:17:32.513434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:17:33.257377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:17:34.159863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:17:34.900241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:17:35.784655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:17:32.667566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:17:33.427644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:17:34.314427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:17:35.046749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:17:35.920849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:17:32.813187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:17:33.696178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:17:34.465817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:17:35.195865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:17:36.091960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:17:32.962306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:17:33.859855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:17:34.613793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:17:35.349659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T15:17:41.853290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
맥주0.02W10.200.570.100.11
맥주1.0000.9250.0000.9270.9660.9210.952
0.020.9251.0000.0000.8780.9020.7670.941
W10.0000.0001.0000.0000.0000.0000.000
0.200.9270.8780.0001.0000.9620.7880.954
0.570.9660.9020.0000.9621.0000.7890.916
0.100.9210.7670.0000.7880.7891.0000.620
0.110.9520.9410.0000.9540.9160.6201.000
2023-12-10T15:17:42.398207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
0.020.200.570.100.11W1
0.021.000-0.871-0.8780.5820.9370.000
0.20-0.8711.0000.987-0.807-0.9110.000
0.57-0.8780.9871.000-0.819-0.9200.000
0.100.582-0.807-0.8191.0000.5870.000
0.110.937-0.911-0.9200.5871.0000.000
W10.0000.0000.0000.0000.0001.000

Missing values

2023-12-10T15:17:36.291231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T15:17:36.562420image/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

맥주20200.022020H22020Q3202007W10.200.570.100.11
0소주20200.022020H22020Q3202007W10.210.580.090.1
1전통주20200.032020H22020Q3202007W10.180.510.120.16
2와인20200.012020H22020Q3202007W10.210.590.10.08
3양주20200.062020H22020Q3202007W10.130.360.140.32
4커피20200.022020H22020Q3202007W10.210.590.080.1
5탄산음료20200.032020H22020Q3202007W10.170.480.120.19
6주스20200.022020H22020Q3202007W10.20.570.120.1
7생수(먹는 샘물)20200.052020H22020Q3202007W10.150.420.140.25
8차음료20200.022020H22020Q3202007W10.210.590.090.09
9두유20200.032020H22020Q3202007W10.170.490.130.17
맥주20200.022020H22020Q3202007W10.200.570.100.11
89냉동식품20200.022020H22020Q3202007W20.210.580.110.09
90병/통조림20200.042020H22020Q3202007W20.140.40.180.24
91제과류20200.022020H22020Q3202007W20.210.580.090.11
92양곡20200.022020H22020Q3202007W20.190.520.130.14
93김치/절임식품20200.022020H22020Q3202007W20.190.550.110.13
94장류20200.042020H22020Q3202007W20.160.450.130.22
95식용유20200.022020H22020Q3202007W20.210.580.120.09
96돼지고기20200.012020H22020Q3202007W20.210.590.10.08
97소고기20200.052020H22020Q3202007W20.130.350.190.28
98닭고기20200.022020H22020Q3202007W20.190.530.120.13