Overview

Dataset statistics

Number of variables13
Number of observations107
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory12.1 KiB
Average record size in memory116.2 B

Variable types

Categorical2
Numeric10
Text1

Dataset

Description원료별국산원료사용비중
Author농림축산식품부
URLhttps://data.mafra.go.kr/opendata/data/indexOpenDataDetail.do?data_id=20220216000000002023

Alerts

2013 has constant value ""Constant
1 is highly overall correlated with 곡류 및 곡분High correlation
1083 is highly overall correlated with 663 and 5 other fieldsHigh correlation
663 is highly overall correlated with 1083 and 5 other fieldsHigh correlation
482511 is highly overall correlated with 1083 and 6 other fieldsHigh correlation
334349 is highly overall correlated with 1083 and 7 other fieldsHigh correlation
148162 is highly overall correlated with 663 and 5 other fieldsHigh correlation
69.3 is highly overall correlated with 334349 and 3 other fieldsHigh correlation
540660 is highly overall correlated with 1083 and 6 other fieldsHigh correlation
440841 is highly overall correlated with 1083 and 6 other fieldsHigh correlation
81.5 is highly overall correlated with 334349 and 3 other fieldsHigh correlation
곡류 및 곡분 is highly overall correlated with 1 and 7 other fieldsHigh correlation
1 has unique valuesUnique
쌀 has unique valuesUnique
482511 has unique valuesUnique
540660 has unique valuesUnique
334349 has 13 (12.1%) zerosZeros
148162 has 17 (15.9%) zerosZeros
69.3 has 13 (12.1%) zerosZeros
440841 has 13 (12.1%) zerosZeros
81.5 has 13 (12.1%) zerosZeros

Reproduction

Analysis started2023-12-11 03:06:40.756151
Analysis finished2023-12-11 03:06:50.027602
Duration9.27 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

2013
Categorical

CONSTANT 

Distinct1
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size988.0 B
2013
107 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2013
2nd row2013
3rd row2013
4th row2013
5th row2013

Common Values

ValueCountFrequency (%)
2013 107
100.0%

Length

2023-12-11T12:06:50.092977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T12:06:50.171624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2013 107
100.0%

1
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct107
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean55
Minimum2
Maximum108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-11T12:06:50.281897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile7.3
Q128.5
median55
Q381.5
95-th percentile102.7
Maximum108
Range106
Interquartile range (IQR)53

Descriptive statistics

Standard deviation31.032241
Coefficient of variation (CV)0.56422257
Kurtosis-1.2
Mean55
Median Absolute Deviation (MAD)27
Skewness0
Sum5885
Variance963
MonotonicityStrictly increasing
2023-12-11T12:06:50.428103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 1
 
0.9%
70 1
 
0.9%
81 1
 
0.9%
80 1
 
0.9%
79 1
 
0.9%
78 1
 
0.9%
77 1
 
0.9%
76 1
 
0.9%
75 1
 
0.9%
74 1
 
0.9%
Other values (97) 97
90.7%
ValueCountFrequency (%)
2 1
0.9%
3 1
0.9%
4 1
0.9%
5 1
0.9%
6 1
0.9%
7 1
0.9%
8 1
0.9%
9 1
0.9%
10 1
0.9%
11 1
0.9%
ValueCountFrequency (%)
108 1
0.9%
107 1
0.9%
106 1
0.9%
105 1
0.9%
104 1
0.9%
103 1
0.9%
102 1
0.9%
101 1
0.9%
100 1
0.9%
99 1
0.9%

곡류 및 곡분
Categorical

HIGH CORRELATION 

Distinct17
Distinct (%)15.9%
Missing0
Missing (%)0.0%
Memory size988.0 B
과일.채소류 및 과일 채소류 유래식품 소재
15 
양념 채소류
10 
곡류 및 곡분
9 
두류 및 서류(주정포함)
8 
식용유지류
8 
Other values (12)
57 

Length

Max length23
Median length9
Mean length9.7663551
Min length2

Unique

Unique2 ?
Unique (%)1.9%

Sample

1st row곡류 및 곡분
2nd row곡류 및 곡분
3rd row곡류 및 곡분
4th row곡류 및 곡분
5th row곡류 및 곡분

Common Values

ValueCountFrequency (%)
과일.채소류 및 과일 채소류 유래식품 소재 15
14.0%
양념 채소류 10
9.3%
곡류 및 곡분 9
8.4%
두류 및 서류(주정포함) 8
 
7.5%
식용유지류 8
 
7.5%
당류 8
 
7.5%
수산물 및 수산물 유래식품 8
 
7.5%
특용작물 7
 
6.5%
축산물 및 축산물 유래식품 7
 
6.5%
우유 및 유가공품 7
 
6.5%
Other values (7) 20
18.7%

Length

2023-12-11T12:06:50.577742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
및 57
18.9%
유래식품 30
 
9.9%
채소류 25
 
8.3%
소재 18
 
6.0%
수산물 16
 
5.3%
과일.채소류 15
 
5.0%
과일 15
 
5.0%
축산물 14
 
4.6%
양념 10
 
3.3%
곡류 9
 
3.0%
Other values (19) 93
30.8%

쌀
Text

UNIQUE 

Distinct107
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size988.0 B
2023-12-11T12:06:50.859359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length9
Mean length3.728972
Min length1

Characters and Unicode

Total characters399
Distinct characters149
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique107 ?
Unique (%)100.0%

Sample

1st row보리
2nd row옥수수
3rd row소맥(밀)
4th row메밀
5th row쌀가루
ValueCountFrequency (%)
보리 1
 
0.9%
명태(동태황태 1
 
0.9%
변성전분 1
 
0.9%
기타전분 1
 
0.9%
옥수수전분 1
 
0.9%
고구마전분 1
 
0.9%
감자전분 1
 
0.9%
어류부산물 1
 
0.9%
어육살 1
 
0.9%
정제소금 1
 
0.9%
Other values (98) 98
90.7%
2023-12-11T12:06:51.281469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
분 20
 
5.0%
기 18
 
4.5%
고 13
 
3.3%
유 12
 
3.0%
말 11
 
2.8%
( 8
 
2.0%
코 8
 
2.0%
) 8
 
2.0%
스 8
 
2.0%
마 8
 
2.0%
Other values (139) 285
71.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 375
94.0%
Open Punctuation 8
 
2.0%
Close Punctuation 8
 
2.0%
Other Punctuation 7
 
1.8%
Space Separator 1
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
분 20
 
5.3%
기 18
 
4.8%
고 13
 
3.5%
유 12
 
3.2%
말 11
 
2.9%
코 8
 
2.1%
스 8
 
2.1%
마 8
 
2.1%
전 7
 
1.9%
수 7
 
1.9%
Other values (134) 263
70.1%
Other Punctuation
ValueCountFrequency (%)
/ 6
85.7%
? 1
 
14.3%
Open Punctuation
ValueCountFrequency (%)
( 8
100.0%
Close Punctuation
ValueCountFrequency (%)
) 8
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 375
94.0%
Common 24
 
6.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
분 20
 
5.3%
기 18
 
4.8%
고 13
 
3.5%
유 12
 
3.2%
말 11
 
2.9%
코 8
 
2.1%
스 8
 
2.1%
마 8
 
2.1%
전 7
 
1.9%
수 7
 
1.9%
Other values (134) 263
70.1%
Common
ValueCountFrequency (%)
( 8
33.3%
) 8
33.3%
/ 6
25.0%
? 1
 
4.2%
1
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 375
94.0%
ASCII 24
 
6.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
분 20
 
5.3%
기 18
 
4.8%
고 13
 
3.5%
유 12
 
3.2%
말 11
 
2.9%
코 8
 
2.1%
스 8
 
2.1%
마 8
 
2.1%
전 7
 
1.9%
수 7
 
1.9%
Other values (134) 263
70.1%
ASCII
ValueCountFrequency (%)
( 8
33.3%
) 8
33.3%
/ 6
25.0%
? 1
 
4.2%
1
 
4.2%

1083
Real number (ℝ)

HIGH CORRELATION 

Distinct95
Distinct (%)88.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean493.04673
Minimum2
Maximum26920
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-11T12:06:51.423440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile14.3
Q147
median120
Q3277.5
95-th percentile980
Maximum26920
Range26918
Interquartile range (IQR)230.5

Descriptive statistics

Standard deviation2601.5659
Coefficient of variation (CV)5.2765097
Kurtosis103.21429
Mean493.04673
Median Absolute Deviation (MAD)82
Skewness10.077471
Sum52756
Variance6768145
MonotonicityNot monotonic
2023-12-11T12:06:51.567888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26 3
 
2.8%
4 2
 
1.9%
34 2
 
1.9%
108 2
 
1.9%
127 2
 
1.9%
646 2
 
1.9%
24 2
 
1.9%
182 2
 
1.9%
47 2
 
1.9%
31 2
 
1.9%
Other values (85) 86
80.4%
ValueCountFrequency (%)
2 1
0.9%
4 2
1.9%
6 1
0.9%
11 1
0.9%
14 1
0.9%
15 1
0.9%
19 1
0.9%
20 1
0.9%
24 2
1.9%
25 1
0.9%
ValueCountFrequency (%)
26920 1
0.9%
2010 1
0.9%
1732 1
0.9%
1306 1
0.9%
1236 1
0.9%
1028 1
0.9%
868 1
0.9%
847 1
0.9%
730 1
0.9%
715 1
0.9%

663
Real number (ℝ)

HIGH CORRELATION 

Distinct90
Distinct (%)84.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean310.73832
Minimum1
Maximum16956
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-11T12:06:51.692498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9.3
Q130.5
median76
Q3170.5
95-th percentile609.7
Maximum16956
Range16955
Interquartile range (IQR)140

Descriptive statistics

Standard deviation1638.3912
Coefficient of variation (CV)5.2725755
Kurtosis103.27376
Mean310.73832
Median Absolute Deviation (MAD)53
Skewness10.08172
Sum33249
Variance2684325.9
MonotonicityNot monotonic
2023-12-11T12:06:51.839463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18 3
 
2.8%
16 2
 
1.9%
241 2
 
1.9%
23 2
 
1.9%
61 2
 
1.9%
28 2
 
1.9%
62 2
 
1.9%
15 2
 
1.9%
112 2
 
1.9%
27 2
 
1.9%
Other values (80) 86
80.4%
ValueCountFrequency (%)
1 1
0.9%
2 1
0.9%
3 1
0.9%
4 1
0.9%
8 1
0.9%
9 1
0.9%
10 1
0.9%
13 1
0.9%
15 2
1.9%
16 2
1.9%
ValueCountFrequency (%)
16956 1
0.9%
1258 1
0.9%
1104 1
0.9%
800 1
0.9%
793 1
0.9%
643 1
0.9%
532 1
0.9%
526 1
0.9%
464 1
0.9%
445 1
0.9%

482511
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct107
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean314405.79
Minimum6
Maximum23361967
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-11T12:06:51.998150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile164.9
Q12233.5
median12563
Q356965.5
95-th percentile458608.9
Maximum23361967
Range23361961
Interquartile range (IQR)54732

Descriptive statistics

Standard deviation2267554.8
Coefficient of variation (CV)7.2121918
Kurtosis103.46799
Mean314405.79
Median Absolute Deviation (MAD)11771
Skewness10.100196
Sum33641419
Variance5.1418049 × 1012
MonotonicityNot monotonic
2023-12-11T12:06:52.124336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
125559 1
 
0.9%
15182 1
 
0.9%
9916 1
 
0.9%
51669 1
 
0.9%
61110 1
 
0.9%
13129 1
 
0.9%
4950 1
 
0.9%
29325 1
 
0.9%
80071 1
 
0.9%
90055 1
 
0.9%
Other values (97) 97
90.7%
ValueCountFrequency (%)
6 1
0.9%
14 1
0.9%
18 1
0.9%
19 1
0.9%
44 1
0.9%
131 1
0.9%
244 1
0.9%
407 1
0.9%
429 1
0.9%
495 1
0.9%
ValueCountFrequency (%)
23361967 1
0.9%
2227916 1
0.9%
1513705 1
0.9%
952685 1
0.9%
833856 1
0.9%
499003 1
0.9%
364356 1
0.9%
358740 1
0.9%
250311 1
0.9%
227898 1
0.9%

334349
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct95
Distinct (%)88.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean82526.29
Minimum0
Maximum4582331
Zeros13
Zeros (%)12.1%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-11T12:06:52.240249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1247
median2096
Q312082.5
95-th percentile107485.1
Maximum4582331
Range4582331
Interquartile range (IQR)11835.5

Descriptive statistics

Standard deviation490679.75
Coefficient of variation (CV)5.9457386
Kurtosis71.172561
Mean82526.29
Median Absolute Deviation (MAD)2096
Skewness8.2164714
Sum8830313
Variance2.4076662 × 1011
MonotonicityNot monotonic
2023-12-11T12:06:52.652877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 13
 
12.1%
34718 1
 
0.9%
65748 1
 
0.9%
69484 1
 
0.9%
90613 1
 
0.9%
1292 1
 
0.9%
63628 1
 
0.9%
3039 1
 
0.9%
41 1
 
0.9%
1800 1
 
0.9%
Other values (85) 85
79.4%
ValueCountFrequency (%)
0 13
12.1%
6 1
 
0.9%
10 1
 
0.9%
11 1
 
0.9%
13 1
 
0.9%
18 1
 
0.9%
27 1
 
0.9%
41 1
 
0.9%
78 1
 
0.9%
119 1
 
0.9%
ValueCountFrequency (%)
4582331 1
0.9%
2227916 1
0.9%
357978 1
0.9%
244559 1
0.9%
173825 1
0.9%
114716 1
0.9%
90613 1
0.9%
90526 1
0.9%
79441 1
0.9%
77165 1
0.9%

148162
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct90
Distinct (%)84.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean307580.48
Minimum0
Maximum18779636
Zeros17
Zeros (%)15.9%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-11T12:06:52.847035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1264.5
median4642
Q316834.5
95-th percentile323064.9
Maximum18779636
Range18779636
Interquartile range (IQR)16570

Descriptive statistics

Standard deviation2005852.4
Coefficient of variation (CV)6.5213905
Kurtosis72.195906
Mean307580.48
Median Absolute Deviation (MAD)4642
Skewness8.2825671
Sum32911111
Variance4.0234438 × 1012
MonotonicityNot monotonic
2023-12-11T12:06:53.077065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 17
 
15.9%
1 2
 
1.9%
4263 1
 
0.9%
9957 1
 
0.9%
5799 1
 
0.9%
364356 1
 
0.9%
1513705 1
 
0.9%
9074 1
 
0.9%
51420 1
 
0.9%
57454 1
 
0.9%
Other values (80) 80
74.8%
ValueCountFrequency (%)
0 17
15.9%
1 2
 
1.9%
2 1
 
0.9%
17 1
 
0.9%
19 1
 
0.9%
44 1
 
0.9%
82 1
 
0.9%
162 1
 
0.9%
166 1
 
0.9%
186 1
 
0.9%
ValueCountFrequency (%)
18779636 1
0.9%
8973245 1
0.9%
1513705 1
0.9%
719140 1
0.9%
490899 1
0.9%
364356 1
0.9%
226719 1
0.9%
212160 1
0.9%
168201 1
0.9%
160785 1
0.9%

69.3
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct71
Distinct (%)66.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.847196
Minimum0
Maximum100
Zeros13
Zeros (%)12.1%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-11T12:06:53.263757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q18.55
median27.7
Q385.1
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)76.55

Descriptive statistics

Standard deviation39.406096
Coefficient of variation (CV)0.87867469
Kurtosis-1.6133959
Mean44.847196
Median Absolute Deviation (MAD)27.7
Skewness0.27641149
Sum4798.65
Variance1552.8404
MonotonicityNot monotonic
2023-12-11T12:06:53.427988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100.0 17
 
15.9%
0.0 13
 
12.1%
0.5 2
 
1.9%
8.5 2
 
1.9%
69.4 2
 
1.9%
9.6 2
 
1.9%
99.8 2
 
1.9%
12.8 2
 
1.9%
22.4 2
 
1.9%
6.0 2
 
1.9%
Other values (61) 61
57.0%
ValueCountFrequency (%)
0.0 13
12.1%
0.05 1
 
0.9%
0.1 1
 
0.9%
0.2 1
 
0.9%
0.5 2
 
1.9%
0.8 1
 
0.9%
0.9 1
 
0.9%
1.6 1
 
0.9%
3.3 1
 
0.9%
6.0 2
 
1.9%
ValueCountFrequency (%)
100.0 17
15.9%
99.8 2
 
1.9%
98.8 1
 
0.9%
98.4 1
 
0.9%
98.3 1
 
0.9%
97.7 1
 
0.9%
91.6 1
 
0.9%
91.4 1
 
0.9%
85.9 1
 
0.9%
85.3 1
 
0.9%

540660
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct107
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean388274.47
Minimum8
Maximum21493014
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-11T12:06:53.608521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile986.3
Q19126.5
median29578
Q3123702
95-th percentile875161.8
Maximum21493014
Range21493006
Interquartile range (IQR)114575.5

Descriptive statistics

Standard deviation2131726.8
Coefficient of variation (CV)5.4902575
Kurtosis92.980864
Mean388274.47
Median Absolute Deviation (MAD)27203
Skewness9.4090584
Sum41545368
Variance4.5442592 × 1012
MonotonicityNot monotonic
2023-12-11T12:06:53.799174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
121310 1
 
0.9%
33027 1
 
0.9%
13296 1
 
0.9%
68846 1
 
0.9%
76280 1
 
0.9%
24833 1
 
0.9%
7406 1
 
0.9%
44692 1
 
0.9%
171829 1
 
0.9%
54297 1
 
0.9%
Other values (97) 97
90.7%
ValueCountFrequency (%)
8 1
0.9%
29 1
0.9%
90 1
0.9%
154 1
0.9%
158 1
0.9%
767 1
0.9%
1498 1
0.9%
1508 1
0.9%
1564 1
0.9%
2339 1
0.9%
ValueCountFrequency (%)
21493014 1
0.9%
4545088 1
0.9%
2202152 1
0.9%
2183704 1
0.9%
1310409 1
0.9%
878277 1
0.9%
867893 1
0.9%
720041 1
0.9%
582139 1
0.9%
510094 1
0.9%

440841
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct94
Distinct (%)87.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean136861.75
Minimum0
Maximum7542524
Zeros13
Zeros (%)12.1%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-11T12:06:53.980592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11094
median5669
Q342707.5
95-th percentile269902.8
Maximum7542524
Range7542524
Interquartile range (IQR)41613.5

Descriptive statistics

Standard deviation760184.47
Coefficient of variation (CV)5.5543969
Kurtosis87.38445
Mean136861.75
Median Absolute Deviation (MAD)5669
Skewness9.0852539
Sum14644207
Variance5.7788042 × 1011
MonotonicityNot monotonic
2023-12-11T12:06:54.174998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 13
 
12.1%
97 2
 
1.9%
31399 1
 
0.9%
4587 1
 
0.9%
66358 1
 
0.9%
214177 1
 
0.9%
4082 1
 
0.9%
39010 1
 
0.9%
8520 1
 
0.9%
6031 1
 
0.9%
Other values (84) 84
78.5%
ValueCountFrequency (%)
0 13
12.1%
8 1
 
0.9%
22 1
 
0.9%
27 1
 
0.9%
29 1
 
0.9%
84 1
 
0.9%
97 2
 
1.9%
357 1
 
0.9%
385 1
 
0.9%
452 1
 
0.9%
ValueCountFrequency (%)
7542524 1
0.9%
2202152 1
0.9%
720041 1
0.9%
682713 1
0.9%
279451 1
0.9%
278082 1
0.9%
250818 1
0.9%
235915 1
0.9%
214177 1
0.9%
197464 1
0.9%

81.5
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct74
Distinct (%)69.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.045794
Minimum0
Maximum100
Zeros13
Zeros (%)12.1%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-11T12:06:54.359354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q19.55
median40.5
Q387.35
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)77.8

Descriptive statistics

Standard deviation39.308565
Coefficient of variation (CV)0.83553834
Kurtosis-1.6328383
Mean47.045794
Median Absolute Deviation (MAD)39.5
Skewness0.16582699
Sum5033.9
Variance1545.1633
MonotonicityNot monotonic
2023-12-11T12:06:54.546662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100.0 17
 
15.9%
0.0 13
 
12.1%
25.9 2
 
1.9%
0.1 2
 
1.9%
40.5 2
 
1.9%
60.3 2
 
1.9%
24.3 2
 
1.9%
16.9 1
 
0.9%
99.1 1
 
0.9%
0.9 1
 
0.9%
Other values (64) 64
59.8%
ValueCountFrequency (%)
0.0 13
12.1%
0.1 2
 
1.9%
0.4 1
 
0.9%
0.6 1
 
0.9%
0.7 1
 
0.9%
0.9 1
 
0.9%
1.5 1
 
0.9%
2.1 1
 
0.9%
2.8 1
 
0.9%
3.4 1
 
0.9%
ValueCountFrequency (%)
100.0 17
15.9%
99.9 1
 
0.9%
99.7 1
 
0.9%
99.1 1
 
0.9%
98.9 1
 
0.9%
98.4 1
 
0.9%
97.8 1
 
0.9%
93.7 1
 
0.9%
92.9 1
 
0.9%
88.7 1
 
0.9%

Interactions

2023-12-11T12:06:48.975641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:06:41.156202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:06:41.966301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:06:43.121282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:06:44.009493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:06:44.758528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:06:45.584725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:06:46.429435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:06:47.219982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:06:48.108438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:06:49.052290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:06:41.231937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:06:42.043819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:06:43.212696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:06:44.083742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:06:44.860008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:06:45.670467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:06:46.503389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:06:47.283813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:06:48.184715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:06:49.128179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:06:41.302216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:06:42.125567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:06:43.319757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:06:44.157032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:06:44.940401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:06:45.753643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:06:46.579741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:06:47.349180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:06:48.261247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:06:49.207502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:06:41.377434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:06:42.209344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:06:43.411162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:06:44.239410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:06:45.024785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:06:45.850200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:06:46.681186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:06:47.631221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:06:48.382792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:06:49.278713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:06:41.451348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:06:42.579913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:06:43.488569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:06:44.315004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:06:45.107954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:06:45.929876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:06:46.754370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:06:47.693688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:06:48.460805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:06:49.351720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:06:41.530221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:06:42.664460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:06:43.571108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:06:44.384209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:06:45.186416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:06:46.000580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:06:46.844056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:06:47.758648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:06:48.535777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:06:49.434752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:06:41.613218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:06:42.771029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:06:43.661059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:06:44.463435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:06:45.278675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:06:46.082074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:06:46.927420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:06:47.831185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:06:48.624037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:06:49.508985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:06:41.689763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:06:42.883436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:06:43.743810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:06:44.536021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:06:45.354094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:06:46.168467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:06:46.992004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:06:47.898144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:06:48.701837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:06:49.577648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:06:41.762650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:06:42.960140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:06:43.843905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:06:44.608496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:06:45.426929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:06:46.251481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:06:47.075974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:06:47.966041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:06:48.793467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:06:49.659982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:06:41.890399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:06:43.042115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:06:43.933764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:06:44.683761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:06:45.504148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:06:46.341029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:06:47.153397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:06:48.042099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:06:48.895173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T12:06:54.668559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
1곡류 및 곡분108366348251133434914816269.354066044084181.5
11.0000.9690.0000.0000.0000.0920.0000.6590.0000.0920.548
곡류 및 곡분0.9691.0001.0001.0001.0000.8480.8390.6270.7670.8480.671
10830.0001.0001.0000.6940.6941.0001.0000.0001.0001.0000.662
6630.0001.0000.6941.0000.6941.0001.0000.0001.0001.0000.662
4825110.0001.0000.6940.6941.0001.0001.0000.0001.0001.0000.662
3343490.0920.8481.0001.0001.0001.0000.9400.0000.7991.0000.471
1481620.0000.8391.0001.0001.0000.9401.0000.0001.0000.9400.467
69.30.6590.6270.0000.0000.0000.0000.0001.0000.0000.0000.962
5406600.0000.7671.0001.0001.0000.7991.0000.0001.0000.7990.340
4408410.0920.8481.0001.0001.0001.0000.9400.0000.7991.0000.471
81.50.5480.6710.6620.6620.6620.4710.4670.9620.3400.4711.000
2023-12-11T12:06:54.830902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
1108366348251133434914816269.354066044084181.5곡류 및 곡분
11.000-0.105-0.092-0.104-0.3170.040-0.341-0.122-0.345-0.3380.823
1083-0.1051.0000.9970.6250.5300.498-0.0390.5950.502-0.0040.926
663-0.0920.9971.0000.6320.5130.521-0.0720.6030.484-0.0370.926
482511-0.1040.6250.6321.0000.6360.703-0.1450.8830.512-0.1330.926
334349-0.3170.5300.5130.6361.0000.1470.5230.5810.9100.5270.651
1481620.0400.4980.5210.7030.1471.000-0.6510.6290.070-0.6420.639
69.3-0.341-0.039-0.072-0.1450.523-0.6511.000-0.1060.5560.9860.288
540660-0.1220.5950.6030.8830.5810.629-0.1061.0000.634-0.0930.511
440841-0.3450.5020.4840.5120.9100.0700.5560.6341.0000.5770.651
81.5-0.338-0.004-0.037-0.1330.527-0.6420.986-0.0930.5771.0000.322
곡류 및 곡분0.8230.9260.9260.9260.6510.6390.2880.5110.6510.3221.000

Missing values

2023-12-11T12:06:49.778798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T12:06:49.951265image/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

20131곡류 및 곡분쌀108366348251133434914816269.354066044084181.5
020132곡류 및 곡분보리13987125559347189084127.71213103139925.9
120133곡류 및 곡분옥수수2811799526857944189732450.945450881921304.2
220134곡류 및 곡분소맥(밀)1259049900381044908990.2218370477920.4
320135곡류 및 곡분메밀39241392350104225.14383153535.0
420136곡류 및 곡분쌀가루192130113947911348469.4166121341780.8
520137곡류 및 곡분보리가루(분말)463288152735459.81564112571.9
620138곡류 및 곡분옥수수가루(분말)211134394273785356429.63882611052.8
720139곡류 및 곡분소맥분(밀가루)12368001266024621261400.051288017670.1
8201310곡류 및 곡분메밀가루57412497320217812.86483189429.2
9201311두류 및 서류(주정포함)대두5613472034723527116820117.358213910846818.6
20131곡류 및 곡분쌀108366348251133434914816269.354066044084181.5
97201399커피 및 커피류 식품 소재볶은커피201592309230.0904200.0
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