Overview

Dataset statistics

Number of variables15
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.3 MiB
Average record size in memory136.0 B

Variable types

Numeric5
Categorical8
Text2

Dataset

Description전국 도소매시장(도매시장, 전통시장, 대형마트)의 농수축산물 가격을 조사하여 제공
Author한국농수산식품유통공사
URLhttps://data.mafra.go.kr/opendata/data/indexOpenDataDetail.do?data_id=20220214000000001881

Alerts

GRAD_NM is highly overall correlated with GRAD_CDHigh correlation
AREA_CD is highly overall correlated with MRKT_CD and 2 other fieldsHigh correlation
GRAD_CD is highly overall correlated with GRAD_NMHigh correlation
FRMPRD_CATGORY_NM is highly overall correlated with PRDLST_CD and 2 other fieldsHigh correlation
FRMPRD_CATGORY_CD is highly overall correlated with PRDLST_CD and 2 other fieldsHigh correlation
AREA_NM is highly overall correlated with MRKT_CD and 2 other fieldsHigh correlation
MRKT_NM is highly overall correlated with MRKT_CD and 2 other fieldsHigh correlation
PRDLST_CD is highly overall correlated with FRMPRD_CATGORY_NM and 2 other fieldsHigh correlation
MRKT_CD is highly overall correlated with AREA_CD and 2 other fieldsHigh correlation
EXAMIN_UNIT is highly overall correlated with PRDLST_CD and 2 other fieldsHigh correlation
SPCIES_CD has 4298 (43.0%) zerosZeros

Reproduction

Analysis started2023-12-11 03:30:33.324619
Analysis finished2023-12-11 03:30:38.439072
Duration5.11 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

EXAMIN_DE
Real number (ℝ)

Distinct101
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20220317
Minimum20220103
Maximum20220531
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T12:30:38.514590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20220103
5-th percentile20220107
Q120220209
median20220318
Q320220422
95-th percentile20220524
Maximum20220531
Range428
Interquartile range (IQR)213

Descriptive statistics

Standard deviation142.22687
Coefficient of variation (CV)7.0338595 × 10-6
Kurtosis-1.2879035
Mean20220317
Median Absolute Deviation (MAD)108
Skewness-0.029286244
Sum2.0220317 × 1011
Variance20228.482
MonotonicityNot monotonic
2023-12-11T12:30:38.663266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20220125 127
 
1.3%
20220412 121
 
1.2%
20220316 117
 
1.2%
20220303 116
 
1.2%
20220127 115
 
1.1%
20220228 113
 
1.1%
20220203 113
 
1.1%
20220411 112
 
1.1%
20220110 111
 
1.1%
20220121 111
 
1.1%
Other values (91) 8844
88.4%
ValueCountFrequency (%)
20220103 106
1.1%
20220104 100
1.0%
20220105 96
1.0%
20220106 110
1.1%
20220107 100
1.0%
20220110 111
1.1%
20220111 91
0.9%
20220112 108
1.1%
20220113 108
1.1%
20220114 90
0.9%
ValueCountFrequency (%)
20220531 97
1.0%
20220530 93
0.9%
20220527 91
0.9%
20220526 88
0.9%
20220525 105
1.1%
20220524 95
0.9%
20220523 89
0.9%
20220520 105
1.1%
20220519 103
1.0%
20220518 94
0.9%

FRMPRD_CATGORY_NM
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
채소류
5413 
과일류
1272 
수산물
1149 
식량작물
1089 
특용작물
1077 

Length

Max length4
Median length3
Mean length3.2166
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row식량작물
2nd row채소류
3rd row채소류
4th row채소류
5th row특용작물

Common Values

ValueCountFrequency (%)
채소류 5413
54.1%
과일류 1272
 
12.7%
수산물 1149
 
11.5%
식량작물 1089
 
10.9%
특용작물 1077
 
10.8%

Length

2023-12-11T12:30:38.791937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T12:30:38.889616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
채소류 5413
54.1%
과일류 1272
 
12.7%
수산물 1149
 
11.5%
식량작물 1089
 
10.9%
특용작물 1077
 
10.8%

FRMPRD_CATGORY_CD
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
200
5413 
400
1272 
600
1149 
100
1089 
300
1077 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row100
2nd row200
3rd row200
4th row200
5th row300

Common Values

ValueCountFrequency (%)
200 5413
54.1%
400 1272
 
12.7%
600 1149
 
11.5%
100 1089
 
10.9%
300 1077
 
10.8%

Length

2023-12-11T12:30:39.010786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T12:30:39.117415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
200 5413
54.1%
400 1272
 
12.7%
600 1149
 
11.5%
100 1089
 
10.9%
300 1077
 
10.8%

PRDLST_CD
Real number (ℝ)

HIGH CORRELATION 

Distinct65
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean304.603
Minimum111
Maximum654
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T12:30:39.239302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum111
5-th percentile142
Q1224
median246
Q3411
95-th percentile638
Maximum654
Range543
Interquartile range (IQR)187

Descriptive statistics

Standard deviation140.33886
Coefficient of variation (CV)0.4607271
Kurtosis0.64791161
Mean304.603
Median Absolute Deviation (MAD)34
Skewness1.2350602
Sum3046030
Variance19694.995
MonotonicityNot monotonic
2023-12-11T12:30:39.372249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
242 504
 
5.0%
246 324
 
3.2%
241 323
 
3.2%
315 307
 
3.1%
214 297
 
3.0%
224 269
 
2.7%
312 247
 
2.5%
223 247
 
2.5%
244 236
 
2.4%
611 223
 
2.2%
Other values (55) 7023
70.2%
ValueCountFrequency (%)
111 162
1.6%
112 154
1.5%
141 174
1.7%
142 155
1.6%
143 114
1.1%
151 139
1.4%
152 191
1.9%
211 126
1.3%
212 139
1.4%
213 166
1.7%
ValueCountFrequency (%)
654 70
0.7%
653 72
0.7%
644 58
 
0.6%
642 71
0.7%
641 77
0.8%
640 70
0.7%
639 74
0.7%
638 60
 
0.6%
619 158
1.6%
615 77
0.8%

PRDLST_NM
Text

Distinct65
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-11T12:30:39.606036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length5
Mean length2.6098
Min length1

Characters and Unicode

Total characters26098
Distinct characters101
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

Unique0 ?
Unique (%)0.0%

Sample

1st row쌀
2nd row딸기
3rd row수박
4th row파
5th row땅콩
ValueCountFrequency (%)
풋고추 504
 
5.0%
파 324
 
3.2%
건고추 323
 
3.2%
느타리버섯 307
 
3.1%
상추 297
 
3.0%
호박 269
 
2.7%
참깨 247
 
2.5%
오이 247
 
2.5%
피마늘 236
 
2.4%
고등어 223
 
2.2%
Other values (55) 7023
70.2%
2023-12-11T12:30:39.962102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
추 1697
 
6.5%
고 1495
 
5.7%
마 853
 
3.3%
파 814
 
3.1%
이 680
 
2.6%
리 608
 
2.3%
토 592
 
2.3%
버 589
 
2.3%
섯 589
 
2.3%
배 555
 
2.1%
Other values (91) 17626
67.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 25734
98.6%
Close Punctuation 182
 
0.7%
Open Punctuation 182
 
0.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
추 1697
 
6.6%
고 1495
 
5.8%
마 853
 
3.3%
파 814
 
3.2%
이 680
 
2.6%
리 608
 
2.4%
토 592
 
2.3%
버 589
 
2.3%
섯 589
 
2.3%
배 555
 
2.2%
Other values (89) 17262
67.1%
Close Punctuation
ValueCountFrequency (%)
) 182
100.0%
Open Punctuation
ValueCountFrequency (%)
( 182
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 25734
98.6%
Common 364
 
1.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
추 1697
 
6.6%
고 1495
 
5.8%
마 853
 
3.3%
파 814
 
3.2%
이 680
 
2.6%
리 608
 
2.4%
토 592
 
2.3%
버 589
 
2.3%
섯 589
 
2.3%
배 555
 
2.2%
Other values (89) 17262
67.1%
Common
ValueCountFrequency (%)
) 182
50.0%
( 182
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 25734
98.6%
ASCII 364
 
1.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
추 1697
 
6.6%
고 1495
 
5.8%
마 853
 
3.3%
파 814
 
3.2%
이 680
 
2.6%
리 608
 
2.4%
토 592
 
2.3%
버 589
 
2.3%
섯 589
 
2.3%
배 555
 
2.2%
Other values (89) 17262
67.1%
ASCII
ValueCountFrequency (%)
) 182
50.0%
( 182
50.0%

SPCIES_CD
Real number (ℝ)

ZEROS 

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5482
Minimum0
Maximum24
Zeros4298
Zeros (%)43.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T12:30:40.076856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile6
Maximum24
Range24
Interquartile range (IQR)2

Descriptive statistics

Standard deviation3.0520171
Coefficient of variation (CV)1.9713326
Kurtosis30.060471
Mean1.5482
Median Absolute Deviation (MAD)1
Skewness5.0137409
Sum15482
Variance9.3148082
MonotonicityNot monotonic
2023-12-11T12:30:40.200212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 4298
43.0%
1 2372
23.7%
2 1807
18.1%
3 674
 
6.7%
6 244
 
2.4%
5 190
 
1.9%
4 151
 
1.5%
22 128
 
1.3%
10 86
 
0.9%
9 28
 
0.3%
ValueCountFrequency (%)
0 4298
43.0%
1 2372
23.7%
2 1807
18.1%
3 674
 
6.7%
4 151
 
1.5%
5 190
 
1.9%
6 244
 
2.4%
9 28
 
0.3%
10 86
 
0.9%
22 128
 
1.3%
ValueCountFrequency (%)
24 22
 
0.2%
22 128
 
1.3%
10 86
 
0.9%
9 28
 
0.3%
6 244
 
2.4%
5 190
 
1.9%
4 151
 
1.5%
3 674
 
6.7%
2 1807
18.1%
1 2372
23.7%

SPCIES_NM
Text

Distinct76
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-11T12:30:40.441827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length7
Mean length3.1472
Min length1

Characters and Unicode

Total characters31472
Distinct characters124
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row일반계
2nd row딸기
3rd row수박
4th row대파
5th row국산
ValueCountFrequency (%)
수입 801
 
7.6%
국산 474
 
4.5%
일반계 316
 
3.0%
냉동 310
 
3.0%
청 291
 
2.8%
월동 244
 
2.3%
생선 200
 
1.9%
흰 174
 
1.7%
대파 174
 
1.7%
애느타리버섯 169
 
1.6%
Other values (71) 7323
69.9%
2023-12-11T12:30:40.843968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
수 1485
 
4.7%
입 1196
 
3.8%
추 1085
 
3.4%
( 1012
 
3.2%
) 1012
 
3.2%
국 906
 
2.9%
고 800
 
2.5%
리 797
 
2.5%
산 759
 
2.4%
파 663
 
2.1%
Other values (114) 21757
69.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 28972
92.1%
Open Punctuation 1012
 
3.2%
Close Punctuation 1012
 
3.2%
Space Separator 476
 
1.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
수 1485
 
5.1%
입 1196
 
4.1%
추 1085
 
3.7%
국 906
 
3.1%
고 800
 
2.8%
리 797
 
2.8%
산 759
 
2.6%
파 663
 
2.3%
동 641
 
2.2%
양 631
 
2.2%
Other values (111) 20009
69.1%
Open Punctuation
ValueCountFrequency (%)
( 1012
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1012
100.0%
Space Separator
ValueCountFrequency (%)
476
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 28972
92.1%
Common 2500
 
7.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
수 1485
 
5.1%
입 1196
 
4.1%
추 1085
 
3.7%
국 906
 
3.1%
고 800
 
2.8%
리 797
 
2.8%
산 759
 
2.6%
파 663
 
2.3%
동 641
 
2.2%
양 631
 
2.2%
Other values (111) 20009
69.1%
Common
ValueCountFrequency (%)
( 1012
40.5%
) 1012
40.5%
476
19.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 28972
92.1%
ASCII 2500
 
7.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
수 1485
 
5.1%
입 1196
 
4.1%
추 1085
 
3.7%
국 906
 
3.1%
고 800
 
2.8%
리 797
 
2.8%
산 759
 
2.6%
파 663
 
2.3%
동 641
 
2.2%
양 631
 
2.2%
Other values (111) 20009
69.1%
ASCII
ValueCountFrequency (%)
( 1012
40.5%
) 1012
40.5%
476
19.0%

GRAD_CD
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
5
5572 
4
4360 
14
 
41
13
 
27

Length

Max length2
Median length1
Mean length1.0068
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row5
4th row5
5th row4

Common Values

ValueCountFrequency (%)
5 5572
55.7%
4 4360
43.6%
14 41
 
0.4%
13 27
 
0.3%

Length

2023-12-11T12:30:41.014289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T12:30:41.193007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
5 5572
55.7%
4 4360
43.6%
14 41
 
0.4%
13 27
 
0.3%

GRAD_NM
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
중품
5572 
상품
4360 
M과
 
41
S과
 
27

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row상품
2nd row상품
3rd row중품
4th row중품
5th row상품

Common Values

ValueCountFrequency (%)
중품 5572
55.7%
상품 4360
43.6%
M과 41
 
0.4%
S과 27
 
0.3%

Length

2023-12-11T12:30:41.336944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T12:30:41.465912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
중품 5572
55.7%
상품 4360
43.6%
m과 41
 
0.4%
s과 27
 
0.3%

EXAMIN_UNIT
Categorical

HIGH CORRELATION 

Distinct26
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
10kg
2227 
20kg
1162 
1kg
956 
4kg
896 
2kg
806 
Other values (21)
3953 

Length

Max length6
Median length4
Mean length3.5921
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20kg
2nd row2kg
3rd row1개
4th row1kg
5th row30kg

Common Values

ValueCountFrequency (%)
10kg 2227
22.3%
20kg 1162
11.6%
1kg 956
9.6%
4kg 896
9.0%
2kg 806
 
8.1%
30kg 675
 
6.8%
5kg 621
 
6.2%
40kg 462
 
4.6%
8kg 320
 
3.2%
15kg 277
 
2.8%
Other values (16) 1598
16.0%

Length

2023-12-11T12:30:41.616153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
10kg 2227
22.3%
20kg 1162
11.6%
1kg 956
9.6%
4kg 896
9.0%
2kg 806
 
8.1%
30kg 675
 
6.8%
5kg 621
 
6.2%
40kg 462
 
4.6%
8kg 320
 
3.2%
15kg 277
 
2.8%
Other values (16) 1598
16.0%

AREA_CD
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1101
2086 
2200
2041 
2501
1984 
2401
1948 
2100
1941 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2200
2nd row2100
3rd row2100
4th row2401
5th row2501

Common Values

ValueCountFrequency (%)
1101 2086
20.9%
2200 2041
20.4%
2501 1984
19.8%
2401 1948
19.5%
2100 1941
19.4%

Length

2023-12-11T12:30:41.994901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T12:30:42.107946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1101 2086
20.9%
2200 2041
20.4%
2501 1984
19.8%
2401 1948
19.5%
2100 1941
19.4%

AREA_NM
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
서울
2086 
대구
2041 
대전
1984 
광주
1948 
부산
1941 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row대구
2nd row부산
3rd row부산
4th row광주
5th row대전

Common Values

ValueCountFrequency (%)
서울 2086
20.9%
대구 2041
20.4%
대전 1984
19.8%
광주 1948
19.5%
부산 1941
19.4%

Length

2023-12-11T12:30:42.224078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T12:30:42.338051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서울 2086
20.9%
대구 2041
20.4%
대전 1984
19.8%
광주 1948
19.5%
부산 1941
19.4%

MRKT_CD
Real number (ℝ)

HIGH CORRELATION 

Distinct16
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean205063.94
Minimum110211
Maximum250116
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T12:30:42.452457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum110211
5-th percentile110211
Q1210031
median220021
Q3240123
95-th percentile250113
Maximum250116
Range139905
Interquartile range (IQR)30092

Descriptive statistics

Standard deviation50689.323
Coefficient of variation (CV)0.24718789
Kurtosis-0.27184446
Mean205063.94
Median Absolute Deviation (MAD)20102
Skewness-1.1578461
Sum2.0506394 × 109
Variance2.5694075 × 109
MonotonicityNot monotonic
2023-12-11T12:30:42.568741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
110211 1865
18.6%
240123 1633
16.3%
250113 1578
15.8%
220021 1467
14.7%
210042 1376
13.8%
210022 357
 
3.6%
220024 329
 
3.3%
240121 315
 
3.1%
220023 245
 
2.5%
250114 219
 
2.2%
Other values (6) 616
 
6.2%
ValueCountFrequency (%)
110211 1865
18.6%
110253 210
 
2.1%
110254 11
 
0.1%
210022 357
 
3.6%
210031 134
 
1.3%
210032 74
 
0.7%
210042 1376
13.8%
220021 1467
14.7%
220023 245
 
2.5%
220024 329
 
3.3%
ValueCountFrequency (%)
250116 67
 
0.7%
250114 219
 
2.2%
250113 1578
15.8%
250112 120
 
1.2%
240123 1633
16.3%
240121 315
 
3.1%
220024 329
 
3.3%
220023 245
 
2.5%
220021 1467
14.7%
210042 1376
13.8%

MRKT_NM
Categorical

HIGH CORRELATION 

Distinct15
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
가락도매
1865 
서부도매
1633 
오정도매
1578 
북부도매
1467 
엄궁도매
1376 
Other values (10)
2081 

Length

Max length6
Median length4
Mean length3.6688
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row서문
2nd row엄궁도매
3rd row엄궁도매
4th row서부도매
5th row역전

Common Values

ValueCountFrequency (%)
가락도매 1865
18.6%
서부도매 1633
16.3%
오정도매 1578
15.8%
북부도매 1467
14.7%
엄궁도매 1376
13.8%
부전 357
 
3.6%
칠성 329
 
3.3%
양동 315
 
3.1%
서문 245
 
2.5%
인동 219
 
2.2%
Other values (5) 616
 
6.2%

Length

2023-12-11T12:30:42.698124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
가락도매 1865
18.6%
서부도매 1633
16.3%
오정도매 1578
15.8%
북부도매 1467
14.7%
엄궁도매 1376
13.8%
부전 357
 
3.6%
칠성 329
 
3.3%
양동 315
 
3.1%
서문 245
 
2.5%
인동 219
 
2.2%
Other values (5) 616
 
6.2%

AMT
Real number (ℝ)

Distinct864
Distinct (%)8.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean86383.01
Minimum950
Maximum885000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T12:30:42.838958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum950
5-th percentile6000
Q113000
median34000
Q365000
95-th percentile483000
Maximum885000
Range884050
Interquartile range (IQR)52000

Descriptive statistics

Standard deviation156717.31
Coefficient of variation (CV)1.8142146
Kurtosis9.3223816
Mean86383.01
Median Absolute Deviation (MAD)22300
Skewness3.1042085
Sum8.638301 × 108
Variance2.4560317 × 1010
MonotonicityNot monotonic
2023-12-11T12:30:43.024858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13000 169
 
1.7%
12000 160
 
1.6%
11000 142
 
1.4%
9000 139
 
1.4%
10000 136
 
1.4%
8000 113
 
1.1%
7000 113
 
1.1%
14000 108
 
1.1%
6000 105
 
1.1%
50000 105
 
1.1%
Other values (854) 8710
87.1%
ValueCountFrequency (%)
950 2
 
< 0.1%
1000 2
 
< 0.1%
1020 1
 
< 0.1%
1070 1
 
< 0.1%
1080 2
 
< 0.1%
1100 2
 
< 0.1%
1130 1
 
< 0.1%
1150 4
< 0.1%
1170 1
 
< 0.1%
1200 9
0.1%
ValueCountFrequency (%)
885000 6
 
0.1%
880000 5
 
0.1%
873000 1
 
< 0.1%
870000 1
 
< 0.1%
866000 6
 
0.1%
856000 5
 
0.1%
850000 2
 
< 0.1%
837000 1
 
< 0.1%
831000 5
 
0.1%
830000 16
0.2%

Interactions

2023-12-11T12:30:37.649411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:30:35.653618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:30:36.147488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:30:36.630565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:30:37.162425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:30:37.738206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:30:35.740692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:30:36.255136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:30:36.731535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:30:37.260945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:30:37.828989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:30:35.834561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:30:36.337159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:30:36.832898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:30:37.361420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:30:37.923883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:30:35.953625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:30:36.427715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:30:36.932581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:30:37.453578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:30:38.017022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:30:36.057255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:30:36.533337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:30:37.052840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:30:37.550307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T12:30:43.138700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
EXAMIN_DEFRMPRD_CATGORY_NMFRMPRD_CATGORY_CDPRDLST_CDPRDLST_NMSPCIES_CDSPCIES_NMGRAD_CDGRAD_NMEXAMIN_UNITAREA_CDAREA_NMMRKT_CDMRKT_NMAMT
EXAMIN_DE1.0000.0480.0480.0440.1800.0330.2370.0680.0680.1330.0000.0000.0000.0000.060
FRMPRD_CATGORY_NM0.0481.0001.0000.9761.0000.2040.9940.2250.2250.9040.0590.0590.2730.7880.645
FRMPRD_CATGORY_CD0.0481.0001.0000.9761.0000.2040.9940.2250.2250.9040.0590.0590.2730.7880.645
PRDLST_CD0.0440.9760.9761.0001.0000.3810.9930.2750.2750.9040.0340.0340.4590.6960.506
PRDLST_NM0.1801.0001.0001.0001.0000.9690.9990.8530.8530.9980.1390.1390.4970.8210.906
SPCIES_CD0.0330.2040.2040.3810.9691.0001.0000.0870.0870.6190.0860.0860.1390.2220.340
SPCIES_NM0.2370.9940.9940.9930.9991.0001.0000.8760.8760.9940.2730.2730.4880.8110.937
GRAD_CD0.0680.2250.2250.2750.8530.0870.8761.0001.0000.3820.0000.0000.0000.1350.151
GRAD_NM0.0680.2250.2250.2750.8530.0870.8761.0001.0000.3820.0000.0000.0000.1350.151
EXAMIN_UNIT0.1330.9040.9040.9040.9980.6190.9940.3820.3821.0000.1210.1210.4250.7180.789
AREA_CD0.0000.0590.0590.0340.1390.0860.2730.0000.0000.1211.0001.0001.0001.0000.168
AREA_NM0.0000.0590.0590.0340.1390.0860.2730.0000.0000.1211.0001.0001.0001.0000.168
MRKT_CD0.0000.2730.2730.4590.4970.1390.4880.0000.0000.4251.0001.0001.0000.9860.378
MRKT_NM0.0000.7880.7880.6960.8210.2220.8110.1350.1350.7181.0001.0000.9861.0000.626
AMT0.0600.6450.6450.5060.9060.3400.9370.1510.1510.7890.1680.1680.3780.6261.000
2023-12-11T12:30:43.302109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
GRAD_NMEXAMIN_UNITAREA_CDGRAD_CDFRMPRD_CATGORY_NMFRMPRD_CATGORY_CDAREA_NMMRKT_NM
GRAD_NM1.0000.2090.0001.0000.1850.1850.0000.077
EXAMIN_UNIT0.2091.0000.0580.2090.7050.7050.0580.304
AREA_CD0.0000.0581.0000.0000.0220.0221.0000.998
GRAD_CD1.0000.2090.0001.0000.1850.1850.0000.077
FRMPRD_CATGORY_NM0.1850.7050.0220.1851.0001.0000.0220.460
FRMPRD_CATGORY_CD0.1850.7050.0220.1851.0001.0000.0220.460
AREA_NM0.0000.0581.0000.0000.0220.0221.0000.998
MRKT_NM0.0770.3040.9980.0770.4600.4600.9981.000
2023-12-11T12:30:43.459606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
EXAMIN_DEPRDLST_CDSPCIES_CDMRKT_CDAMTFRMPRD_CATGORY_NMFRMPRD_CATGORY_CDGRAD_CDGRAD_NMEXAMIN_UNITAREA_CDAREA_NMMRKT_NM
EXAMIN_DE1.000-0.015-0.0050.001-0.0340.0310.0310.0480.0480.0570.0000.0000.000
PRDLST_CD-0.0151.0000.0130.005-0.0580.9830.9830.1800.1800.6890.0230.0230.413
SPCIES_CD-0.0050.0131.000-0.0150.1610.1390.1390.0560.0560.3340.0580.0580.105
MRKT_CD0.0010.005-0.0151.0000.0020.0300.0300.0000.0000.0601.0001.0000.998
AMT-0.034-0.0580.1610.0021.0000.3220.3220.0910.0910.4290.0710.0710.289
FRMPRD_CATGORY_NM0.0310.9830.1390.0300.3221.0001.0000.1850.1850.7050.0220.0220.460
FRMPRD_CATGORY_CD0.0310.9830.1390.0300.3221.0001.0000.1850.1850.7050.0220.0220.460
GRAD_CD0.0480.1800.0560.0000.0910.1850.1851.0001.0000.2090.0000.0000.077
GRAD_NM0.0480.1800.0560.0000.0910.1850.1851.0001.0000.2090.0000.0000.077
EXAMIN_UNIT0.0570.6890.3340.0600.4290.7050.7050.2090.2091.0000.0580.0580.304
AREA_CD0.0000.0230.0581.0000.0710.0220.0220.0000.0000.0581.0001.0000.998
AREA_NM0.0000.0230.0581.0000.0710.0220.0220.0000.0000.0581.0001.0000.998
MRKT_NM0.0000.4130.1050.9980.2890.4600.4600.0770.0770.3040.9980.9981.000

Missing values

2023-12-11T12:30:38.171157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T12:30:38.357241image/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

EXAMIN_DEFRMPRD_CATGORY_NMFRMPRD_CATGORY_CDPRDLST_CDPRDLST_NMSPCIES_CDSPCIES_NMGRAD_CDGRAD_NMEXAMIN_UNITAREA_CDAREA_NMMRKT_CDMRKT_NMAMT
2760020220304식량작물100111쌀1일반계4상품20kg2200대구220023서문53600
107520220104채소류200226딸기0딸기4상품2kg2100부산210042엄궁도매40000
478620220112채소류200221수박0수박5중품1개2100부산210042엄궁도매15000
5426520220429채소류200246파0대파5중품1kg2401광주240123서부도매1330
5910120220512특용작물300314땅콩1국산4상품30kg2501대전250112역전420000
742320220117채소류200252미나리0미나리5중품15kg2200대구220021북부도매100000
3766520220328채소류200223오이3취청5중품50개1101서울110211가락도매52500
3897920220330채소류200241건고추0화건5중품30kg2501대전250112역전433000
646620220114채소류200258깐마늘(국산)5깐마늘(남도)5중품20kg1101서울110211가락도매160000
4628620220413채소류200241건고추2양건4상품30kg2100부산210022부전725000
EXAMIN_DEFRMPRD_CATGORY_NMFRMPRD_CATGORY_CDPRDLST_CDPRDLST_NMSPCIES_CDSPCIES_NMGRAD_CDGRAD_NMEXAMIN_UNITAREA_CDAREA_NMMRKT_CDMRKT_NMAMT
3797320220328채소류200246파0대파5중품1kg2401광주240123서부도매1430
5301920220427채소류200224호박2쥬키니5중품10kg2200대구220021북부도매7000
3343520220317채소류200253깻잎0깻잎4상품2kg2200대구220021북부도매22000
5578720220504채소류200244피마늘22난지(대서)5중품10kg1101서울110211가락도매55000
289020220107수산물600611고등어1생선5중품10kg2501대전250113오정도매53300
3562720220323과일류400418바나나2수입5중품13kg2501대전250113오정도매28000
1881220220214채소류200213시금치0시금치4상품4kg2501대전250113오정도매18000
3680520220324채소류200247생강0국산5중품20kg1101서울110211가락도매55000
3832020220329채소류200222참외0참외5중품10kg2401광주240123서부도매75200
5131420220425채소류200224호박2쥬키니5중품10kg2200대구220021북부도매8000