Overview

Dataset statistics

Number of variables10
Number of observations39
Missing cells8
Missing cells (%)2.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.4 KiB
Average record size in memory89.4 B

Variable types

Categorical2
Text2
Numeric6

Dataset

Description농산물유통정보(https://www.kamis.or.kr/)를 통해 제공되는 설 성수기 주요 농축수산물의 가격동향 정보로 재료별 가격정보 제공
URLhttps://www.data.go.kr/data/15072476/fileData.do

Alerts

전통시장(1차조사) is highly overall correlated with 대형유통(1차조사) and 5 other fieldsHigh correlation
대형유통(1차조사) is highly overall correlated with 전통시장(1차조사) and 4 other fieldsHigh correlation
전통시장(2차조사) is highly overall correlated with 전통시장(1차조사) and 5 other fieldsHigh correlation
대형유통(2차조사) is highly overall correlated with 전통시장(1차조사) and 4 other fieldsHigh correlation
전통시장(3차조사) is highly overall correlated with 전통시장(1차조사) and 5 other fieldsHigh correlation
대형유통(3차조사) is highly overall correlated with 전통시장(1차조사) and 4 other fieldsHigh correlation
분류 is highly overall correlated with 구입단위High correlation
구입단위 is highly overall correlated with 전통시장(1차조사) and 3 other fieldsHigh correlation
음식명 has 1 (2.6%) missing valuesMissing
주재료 has 1 (2.6%) missing valuesMissing
전통시장(1차조사) has 1 (2.6%) missing valuesMissing
대형유통(1차조사) has 1 (2.6%) missing valuesMissing
전통시장(2차조사) has 1 (2.6%) missing valuesMissing
대형유통(2차조사) has 1 (2.6%) missing valuesMissing
전통시장(3차조사) has 1 (2.6%) missing valuesMissing
대형유통(3차조사) has 1 (2.6%) missing valuesMissing

Reproduction

Analysis started2023-12-12 19:10:43.595986
Analysis finished2023-12-12 19:10:49.354493
Duration5.76 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

분류
Categorical

HIGH CORRELATION 

Distinct9
Distinct (%)23.1%
Missing0
Missing (%)0.0%
Memory size444.0 B
탕류
9 
기타
8 
과일류
5 
적류
4 
과자류
4 
Other values (4)
9 

Length

Max length4
Median length2
Mean length2.4358974
Min length2

Unique

Unique1 ?
Unique (%)2.6%

Sample

1st row떡국
2nd row떡국
3rd row적류
4th row적류
5th row적류

Common Values

ValueCountFrequency (%)
탕류 9
23.1%
기타 8
20.5%
과일류 5
12.8%
적류 4
10.3%
과자류 4
10.3%
나물류 3
 
7.7%
부재료 3
 
7.7%
떡국 2
 
5.1%
<NA> 1
 
2.6%

Length

2023-12-13T04:10:49.459734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:10:49.620054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
탕류 9
23.1%
기타 8
20.5%
과일류 5
12.8%
적류 4
10.3%
과자류 4
10.3%
나물류 3
 
7.7%
부재료 3
 
7.7%
떡국 2
 
5.1%
na 1
 
2.6%

음식명
Text

MISSING 

Distinct25
Distinct (%)65.8%
Missing1
Missing (%)2.6%
Memory size444.0 B
2023-12-13T04:10:49.875617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length2
Mean length2.1842105
Min length1

Characters and Unicode

Total characters83
Distinct characters44
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)42.1%

Sample

1st row떡국
2nd row떡국
3rd row육적
4th row소적
5th row어적
ValueCountFrequency (%)
육탕 3
 
7.9%
나물 3
 
7.9%
소탕 3
 
7.9%
어탕 3
 
7.9%
어적 2
 
5.3%
녹두편 2
 
5.3%
나박김치 2
 
5.3%
식혜 2
 
5.3%
떡국 2
 
5.3%
밀가루 1
 
2.6%
Other values (15) 15
39.5%
2023-12-13T04:10:50.270857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
탕 9
 
10.8%
어 5
 
6.0%
나 5
 
6.0%
육 4
 
4.8%
소 4
 
4.8%
적 4
 
4.8%
물 3
 
3.6%
식 3
 
3.6%
치 2
 
2.4%
과 2
 
2.4%
Other values (34) 42
50.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 83
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
탕 9
 
10.8%
어 5
 
6.0%
나 5
 
6.0%
육 4
 
4.8%
소 4
 
4.8%
적 4
 
4.8%
물 3
 
3.6%
식 3
 
3.6%
치 2
 
2.4%
과 2
 
2.4%
Other values (34) 42
50.6%

Most occurring scripts

ValueCountFrequency (%)
Hangul 83
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
탕 9
 
10.8%
어 5
 
6.0%
나 5
 
6.0%
육 4
 
4.8%
소 4
 
4.8%
적 4
 
4.8%
물 3
 
3.6%
식 3
 
3.6%
치 2
 
2.4%
과 2
 
2.4%
Other values (34) 42
50.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 83
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
탕 9
 
10.8%
어 5
 
6.0%
나 5
 
6.0%
육 4
 
4.8%
소 4
 
4.8%
적 4
 
4.8%
물 3
 
3.6%
식 3
 
3.6%
치 2
 
2.4%
과 2
 
2.4%
Other values (34) 42
50.6%

주재료
Text

MISSING 

Distinct31
Distinct (%)81.6%
Missing1
Missing (%)2.6%
Memory size444.0 B
2023-12-13T04:10:50.503614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length3
Mean length2.7105263
Min length1

Characters and Unicode

Total characters103
Distinct characters59
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

Unique27 ?
Unique (%)71.1%

Sample

1st row흰떡
2nd row쇠고기(양지)
3rd row쇠고기(우둔)
4th row두부(부침용)
5th row동태살
ValueCountFrequency (%)
무 4
 
10.5%
다시마 3
 
7.9%
쇠고기(양지 2
 
5.3%
쌀 2
 
5.3%
엿기름 1
 
2.6%
게맛살 1
 
2.6%
밀가루 1
 
2.6%
산자 1
 
2.6%
약과 1
 
2.6%
사과 1
 
2.6%
Other values (21) 21
55.3%
2023-12-13T04:10:50.849194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
) 5
 
4.9%
( 5
 
4.9%
무 4
 
3.9%
시 4
 
3.9%
부 4
 
3.9%
고 4
 
3.9%
기 4
 
3.9%
다 4
 
3.9%
두 3
 
2.9%
지 3
 
2.9%
Other values (49) 63
61.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 93
90.3%
Close Punctuation 5
 
4.9%
Open Punctuation 5
 
4.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
무 4
 
4.3%
시 4
 
4.3%
부 4
 
4.3%
고 4
 
4.3%
기 4
 
4.3%
다 4
 
4.3%
두 3
 
3.2%
지 3
 
3.2%
쇠 3
 
3.2%
마 3
 
3.2%
Other values (47) 57
61.3%
Close Punctuation
ValueCountFrequency (%)
) 5
100.0%
Open Punctuation
ValueCountFrequency (%)
( 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 93
90.3%
Common 10
 
9.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
무 4
 
4.3%
시 4
 
4.3%
부 4
 
4.3%
고 4
 
4.3%
기 4
 
4.3%
다 4
 
4.3%
두 3
 
3.2%
지 3
 
3.2%
쇠 3
 
3.2%
마 3
 
3.2%
Other values (47) 57
61.3%
Common
ValueCountFrequency (%)
) 5
50.0%
( 5
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 93
90.3%
ASCII 10
 
9.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
) 5
50.0%
( 5
50.0%
Hangul
ValueCountFrequency (%)
무 4
 
4.3%
시 4
 
4.3%
부 4
 
4.3%
고 4
 
4.3%
기 4
 
4.3%
다 4
 
4.3%
두 3
 
3.2%
지 3
 
3.2%
쇠 3
 
3.2%
마 3
 
3.2%
Other values (47) 57
61.3%

구입단위
Categorical

HIGH CORRELATION 

Distinct18
Distinct (%)46.2%
Missing0
Missing (%)0.0%
Memory size444.0 B
1kg
5 
150g
4 
400g
4 
300g
4 
100g
3 
Other values (13)
19 

Length

Max length5
Median length4
Mean length3.4871795
Min length2

Unique

Unique9 ?
Unique (%)23.1%

Sample

1st row1kg
2nd row300g
3rd row1.8kg
4th row4모
5th row1kg

Common Values

ValueCountFrequency (%)
1kg 5
12.8%
150g 4
10.3%
400g 4
10.3%
300g 4
10.3%
100g 3
 
7.7%
10g 3
 
7.7%
1마리 3
 
7.7%
200g 2
 
5.1%
5개 2
 
5.1%
500g 1
 
2.6%
Other values (8) 8
20.5%

Length

2023-12-13T04:10:50.993684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1kg 5
12.8%
400g 4
10.3%
300g 4
10.3%
150g 4
10.3%
100g 3
 
7.7%
10g 3
 
7.7%
1마리 3
 
7.7%
200g 2
 
5.1%
5개 2
 
5.1%
10개 1
 
2.6%
Other values (8) 8
20.5%

전통시장(1차조사)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct32
Distinct (%)84.2%
Missing1
Missing (%)2.6%
Infinite0
Infinite (%)0.0%
Mean6928.5
Minimum125
Maximum78040
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size483.0 B
2023-12-13T04:10:51.113512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum125
5-th percentile125
Q11192.75
median3358
Q37904.25
95-th percentile19060.85
Maximum78040
Range77915
Interquartile range (IQR)6711.5

Descriptive statistics

Standard deviation13064.311
Coefficient of variation (CV)1.8855902
Kurtosis24.757026
Mean6928.5
Median Absolute Deviation (MAD)2476
Skewness4.6284801
Sum263283
Variance1.7067623 × 108
MonotonicityNot monotonic
2023-12-13T04:10:51.251915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
125 3
 
7.7%
155 3
 
7.7%
1491 2
 
5.1%
13215 2
 
5.1%
16338 1
 
2.6%
1010 1
 
2.6%
5827 1
 
2.6%
8592 1
 
2.6%
8775 1
 
2.6%
21661 1
 
2.6%
Other values (22) 22
56.4%
ValueCountFrequency (%)
125 3
7.7%
155 3
7.7%
250 1
 
2.6%
275 1
 
2.6%
1010 1
 
2.6%
1130 1
 
2.6%
1381 1
 
2.6%
1491 2
5.1%
2112 1
 
2.6%
2137 1
 
2.6%
ValueCountFrequency (%)
78040 1
2.6%
21661 1
2.6%
18602 1
2.6%
16338 1
2.6%
13215 2
5.1%
10730 1
2.6%
10668 1
2.6%
8775 1
2.6%
8592 1
2.6%
5841 1
2.6%

대형유통(1차조사)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct33
Distinct (%)86.8%
Missing1
Missing (%)2.6%
Infinite0
Infinite (%)0.0%
Mean9544.2105
Minimum96
Maximum96741
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size483.0 B
2023-12-13T04:10:51.395450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum96
5-th percentile96
Q11563.25
median4187.5
Q312039.5
95-th percentile26133.9
Maximum96741
Range96645
Interquartile range (IQR)10476.25

Descriptive statistics

Standard deviation16712.645
Coefficient of variation (CV)1.7510767
Kurtosis20.593036
Mean9544.2105
Median Absolute Deviation (MAD)3831.5
Skewness4.1549151
Sum362680
Variance2.7931251 × 108
MonotonicityNot monotonic
2023-12-13T04:10:51.528172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
96 3
 
7.7%
356 3
 
7.7%
22752 2
 
5.1%
5836 1
 
2.6%
1960 1
 
2.6%
7843 1
 
2.6%
8203 1
 
2.6%
9357 1
 
2.6%
24309 1
 
2.6%
14642 1
 
2.6%
Other values (23) 23
59.0%
ValueCountFrequency (%)
96 3
7.7%
190 1
 
2.6%
192 1
 
2.6%
356 3
7.7%
1207 1
 
2.6%
1505 1
 
2.6%
1738 1
 
2.6%
1960 1
 
2.6%
2015 1
 
2.6%
2182 1
 
2.6%
ValueCountFrequency (%)
96741 1
2.6%
36475 1
2.6%
24309 1
2.6%
22752 2
5.1%
15294 1
2.6%
14642 1
2.6%
13043 1
2.6%
12871 1
2.6%
12728 1
2.6%
9974 1
2.6%

전통시장(2차조사)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct33
Distinct (%)86.8%
Missing1
Missing (%)2.6%
Infinite0
Infinite (%)0.0%
Mean6970.3684
Minimum123
Maximum78040
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size483.0 B
2023-12-13T04:10:51.668692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum123
5-th percentile123
Q11196.5
median3358
Q37946.75
95-th percentile19666.95
Maximum78040
Range77917
Interquartile range (IQR)6750.25

Descriptive statistics

Standard deviation13093.813
Coefficient of variation (CV)1.8784965
Kurtosis24.449681
Mean6970.3684
Median Absolute Deviation (MAD)2505.5
Skewness4.5943052
Sum264874
Variance1.7144793 × 108
MonotonicityNot monotonic
2023-12-13T04:10:51.813986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
123 3
 
7.7%
155 3
 
7.7%
13215 2
 
5.1%
3176 1
 
2.6%
1010 1
 
2.6%
5827 1
 
2.6%
8629 1
 
2.6%
8906 1
 
2.6%
21905 1
 
2.6%
16717 1
 
2.6%
Other values (23) 23
59.0%
ValueCountFrequency (%)
123 3
7.7%
155 3
7.7%
246 1
 
2.6%
277 1
 
2.6%
1010 1
 
2.6%
1132 1
 
2.6%
1390 1
 
2.6%
1491 1
 
2.6%
1494 1
 
2.6%
2070 1
 
2.6%
ValueCountFrequency (%)
78040 1
2.6%
21905 1
2.6%
19272 1
2.6%
16717 1
2.6%
13215 2
5.1%
10668 1
2.6%
10613 1
2.6%
8906 1
2.6%
8629 1
2.6%
5900 1
2.6%

대형유통(2차조사)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct33
Distinct (%)86.8%
Missing1
Missing (%)2.6%
Infinite0
Infinite (%)0.0%
Mean9564.0526
Minimum88
Maximum94120
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size483.0 B
2023-12-13T04:10:51.961662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum88
5-th percentile88
Q11591.75
median4326.5
Q312188.5
95-th percentile27082.6
Maximum94120
Range94032
Interquartile range (IQR)10596.75

Descriptive statistics

Standard deviation16380.093
Coefficient of variation (CV)1.7126729
Kurtosis19.636011
Mean9564.0526
Median Absolute Deviation (MAD)3970.5
Skewness4.0371118
Sum363434
Variance2.6830746 × 108
MonotonicityNot monotonic
2023-12-13T04:10:52.100961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
88 3
 
7.7%
356 3
 
7.7%
22706 2
 
5.1%
5836 1
 
2.6%
1901 1
 
2.6%
7872 1
 
2.6%
8203 1
 
2.6%
9422 1
 
2.6%
25456 1
 
2.6%
16917 1
 
2.6%
Other values (23) 23
59.0%
ValueCountFrequency (%)
88 3
7.7%
176 1
 
2.6%
219 1
 
2.6%
356 3
7.7%
1203 1
 
2.6%
1546 1
 
2.6%
1729 1
 
2.6%
1901 1
 
2.6%
1965 1
 
2.6%
2418 1
 
2.6%
ValueCountFrequency (%)
94120 1
2.6%
36300 1
2.6%
25456 1
2.6%
22706 2
5.1%
16917 1
2.6%
14638 1
2.6%
13269 1
2.6%
12960 1
2.6%
12923 1
2.6%
9985 1
2.6%

전통시장(3차조사)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct33
Distinct (%)86.8%
Missing1
Missing (%)2.6%
Infinite0
Infinite (%)0.0%
Mean7036.6316
Minimum122
Maximum78196
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size483.0 B
2023-12-13T04:10:52.281757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum122
5-th percentile122
Q11196.5
median3358
Q38049.75
95-th percentile19999
Maximum78196
Range78074
Interquartile range (IQR)6853.25

Descriptive statistics

Standard deviation13162.733
Coefficient of variation (CV)1.8706014
Kurtosis24.027584
Mean7036.6316
Median Absolute Deviation (MAD)2505.5
Skewness4.5477141
Sum267392
Variance1.7325754 × 108
MonotonicityNot monotonic
2023-12-13T04:10:52.418676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
122 3
 
7.7%
155 3
 
7.7%
13380 2
 
5.1%
3176 1
 
2.6%
1010 1
 
2.6%
5856 1
 
2.6%
8776 1
 
2.6%
9210 1
 
2.6%
22838 1
 
2.6%
17050 1
 
2.6%
Other values (23) 23
59.0%
ValueCountFrequency (%)
122 3
7.7%
155 3
7.7%
244 1
 
2.6%
302 1
 
2.6%
1010 1
 
2.6%
1132 1
 
2.6%
1390 1
 
2.6%
1491 1
 
2.6%
1494 1
 
2.6%
2081 1
 
2.6%
ValueCountFrequency (%)
78196 1
2.6%
22838 1
2.6%
19498 1
2.6%
17050 1
2.6%
13380 2
5.1%
10613 1
2.6%
10600 1
2.6%
9210 1
2.6%
8776 1
2.6%
5871 1
2.6%

대형유통(3차조사)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct33
Distinct (%)86.8%
Missing1
Missing (%)2.6%
Infinite0
Infinite (%)0.0%
Mean9851.8421
Minimum88
Maximum96780
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size483.0 B
2023-12-13T04:10:52.540560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum88
5-th percentile88
Q11567.75
median4262
Q311926
95-th percentile27779.5
Maximum96780
Range96692
Interquartile range (IQR)10358.25

Descriptive statistics

Standard deviation16989.515
Coefficient of variation (CV)1.7245013
Kurtosis18.946332
Mean9851.8421
Median Absolute Deviation (MAD)3906
Skewness3.9715126
Sum374370
Variance2.8864361 × 108
MonotonicityNot monotonic
2023-12-13T04:10:52.660461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
88 3
 
7.7%
356 3
 
7.7%
23085 2
 
5.1%
6330 1
 
2.6%
1901 1
 
2.6%
7950 1
 
2.6%
9058 1
 
2.6%
9340 1
 
2.6%
25570 1
 
2.6%
19930 1
 
2.6%
Other values (23) 23
59.0%
ValueCountFrequency (%)
88 3
7.7%
176 1
 
2.6%
231 1
 
2.6%
356 3
7.7%
1203 1
 
2.6%
1511 1
 
2.6%
1738 1
 
2.6%
1901 1
 
2.6%
1965 1
 
2.6%
2183 1
 
2.6%
ValueCountFrequency (%)
96780 1
2.6%
40300 1
2.6%
25570 1
2.6%
23085 2
5.1%
19930 1
2.6%
13986 1
2.6%
13510 1
2.6%
13128 1
2.6%
12573 1
2.6%
9985 1
2.6%

Interactions

2023-12-13T04:10:47.572370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:10:44.129326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:10:44.919780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:10:45.557840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:10:46.206427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:10:46.854701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:10:48.058239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:10:44.268843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:10:45.043825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:10:45.678294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:10:46.341843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:10:47.012612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:10:48.185662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:10:44.394861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:10:45.151319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:10:45.771778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:10:46.441539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:10:47.122088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:10:48.330346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:10:44.539265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:10:45.269247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:10:45.887630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:10:46.533424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:10:47.234625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:10:48.437273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:10:44.658775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:10:45.363977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:10:45.996952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:10:46.627553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:10:47.340054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:10:48.565559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:10:44.787705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:10:45.470775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:10:46.108174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:10:46.753022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:10:47.474020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T04:10:52.746316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
분류음식명주재료구입단위전통시장(1차조사)대형유통(1차조사)전통시장(2차조사)대형유통(2차조사)전통시장(3차조사)대형유통(3차조사)
분류1.0001.0000.9670.8780.5360.3670.5360.3670.5360.471
음식명1.0001.0000.9710.8280.9020.7600.9020.7600.9020.798
주재료0.9670.9711.0000.9651.0001.0001.0001.0001.0001.000
구입단위0.8780.8280.9651.0000.9080.7160.9080.7160.9080.807
전통시장(1차조사)0.5360.9021.0000.9081.0000.7761.0000.7761.0000.803
대형유통(1차조사)0.3670.7601.0000.7160.7761.0000.7761.0000.7760.999
전통시장(2차조사)0.5360.9021.0000.9081.0000.7761.0000.7761.0000.803
대형유통(2차조사)0.3670.7601.0000.7160.7761.0000.7761.0000.7760.999
전통시장(3차조사)0.5360.9021.0000.9081.0000.7761.0000.7761.0000.803
대형유통(3차조사)0.4710.7981.0000.8070.8030.9990.8030.9990.8031.000
2023-12-13T04:10:52.884576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
분류구입단위
분류1.0000.512
구입단위0.5121.000
2023-12-13T04:10:52.987182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
전통시장(1차조사)대형유통(1차조사)전통시장(2차조사)대형유통(2차조사)전통시장(3차조사)대형유통(3차조사)분류구입단위
전통시장(1차조사)1.0000.9620.9990.9611.0000.9640.2370.600
대형유통(1차조사)0.9621.0000.9590.9990.9600.9980.2120.362
전통시장(2차조사)0.9990.9591.0000.9581.0000.9610.2370.600
대형유통(2차조사)0.9610.9990.9581.0000.9590.9990.2120.362
전통시장(3차조사)1.0000.9601.0000.9591.0000.9620.2370.600
대형유통(3차조사)0.9640.9980.9610.9990.9621.0000.2890.456
분류0.2370.2120.2370.2120.2370.2891.0000.512
구입단위0.6000.3620.6000.3620.6000.4560.5121.000

Missing values

2023-12-13T04:10:48.736269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T04:10:48.941509image/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.
2023-12-13T04:10:49.164269image/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

분류음식명주재료구입단위전통시장(1차조사)대형유통(1차조사)전통시장(2차조사)대형유통(2차조사)전통시장(3차조사)대형유통(3차조사)
0떡국떡국흰떡1kg584151915900536558715332
1떡국떡국쇠고기(양지)300g132152275213215227061338023085
2적류육적쇠고기(우둔)1.8kg780409674178040941207819696780
3적류소적두부(부침용)4모452212728453112960453112573
4적류어적동태살1kg107301529410613146381061313986
5적류어적계란10개219321822417241825212183
6탕류육탕쇠고기(양지)300g132152275213215227061338023085
7탕류육탕무100g125961238812288
8탕류육탕다시마10g155356155356155356
9탕류소탕두부(찌개용)1모113030751132313311323097
분류음식명주재료구입단위전통시장(1차조사)대형유통(1차조사)전통시장(2차조사)대형유통(2차조사)전통시장(3차조사)대형유통(3차조사)
29과일류배배5개216612430921905254562283825570
30과일류사과사과5개163381464216717169171705019930
31과자류다식다식150g317658363176583631766330
32과자류강정강정150g306861263068556030905003
33과자류약과약과150g149117381491172914911738
34과자류산자산자150g360644763614447636144489
35부재료밀가루밀가루1kg138112071390120313901203
36부재료게맛살게맛살300g211220152112196521121965
37부재료청주청주1.8L106689974106689985106009985
38<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>