Overview

Dataset statistics

Number of variables10
Number of observations8330
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory707.9 KiB
Average record size in memory87.0 B

Variable types

Categorical3
Numeric7

Dataset

Description품목,등급명통일,거래수량,규격명통일,최저가,최고가,평균가,전7일평균,전일대비등락률,해당일자
Author서울시농수산식품공사
URLhttps://data.seoul.go.kr/dataList/OA-13450/S/1/datasetView.do

Alerts

규격명통일 is highly overall correlated with 거래수량 and 4 other fieldsHigh correlation
품목 is highly overall correlated with 거래수량 and 6 other fieldsHigh correlation
거래수량 is highly overall correlated with 품목 and 1 other fieldsHigh correlation
최저가 is highly overall correlated with 최고가 and 4 other fieldsHigh correlation
최고가 is highly overall correlated with 최저가 and 3 other fieldsHigh correlation
평균가 is highly overall correlated with 최저가 and 4 other fieldsHigh correlation
전7일평균 is highly overall correlated with 최저가 and 4 other fieldsHigh correlation
등급명통일 is highly overall correlated with 품목High correlation
규격명통일 is highly imbalanced (50.6%)Imbalance
전일대비등락률 is highly skewed (γ1 = 55.10230747)Skewed
전7일평균 has 176 (2.1%) zerosZeros
전일대비등락률 has 1913 (23.0%) zerosZeros

Reproduction

Analysis started2024-05-17 22:48:01.443351
Analysis finished2024-05-17 22:48:25.267105
Duration23.82 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

품목
Categorical

HIGH CORRELATION 

Distinct42
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size65.2 KiB
바나나 수입
844 
양상추 수입
628 
망고 수입
556 
냉동 가자미 수입
556 
당근 수입
 
508
Other values (37)
5238 

Length

Max length9
Median length8
Mean length6.2552221
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row부세 수입
2nd row대파 수입
3rd row양파 수입
4th row양파 수입
5th row양파 수입

Common Values

ValueCountFrequency (%)
바나나 수입 844
 
10.1%
양상추 수입 628
 
7.5%
망고 수입 556
 
6.7%
냉동 가자미 수입 556
 
6.7%
당근 수입 508
 
6.1%
양파 수입 481
 
5.8%
브로콜리 수입 429
 
5.2%
마늘 쫑 수입 420
 
5.0%
단호박 수입 333
 
4.0%
생표고 수입　 312
 
3.7%
Other values (32) 3263
39.2%

Length

2024-05-18T07:48:25.552299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
수입 8106
45.2%
바나나 844
 
4.7%
냉동 804
 
4.5%
양상추 628
 
3.5%
생표고 572
 
3.2%
망고 556
 
3.1%
가자미 556
 
3.1%
당근 508
 
2.8%
양파 481
 
2.7%
브로콜리 429
 
2.4%
Other values (35) 4464
24.9%

등급명통일
Categorical

HIGH CORRELATION 

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size65.2 KiB
상
2059 
하
1546 
보통
1518 
상품
1136 
특
917 
Other values (4)
1154 

Length

Max length2
Median length1
Mean length1.4444178
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row상품
2nd row상
3rd row하
4th row특
5th row상

Common Values

ValueCountFrequency (%)
상 2059
24.7%
하 1546
18.6%
보통 1518
18.2%
상품 1136
13.6%
특 917
11.0%
중품 770
 
9.2%
특품 139
 
1.7%
하품 139
 
1.7%
대 106
 
1.3%

Length

2024-05-18T07:48:26.069731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T07:48:26.593344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
상 2059
24.7%
하 1546
18.6%
보통 1518
18.2%
상품 1136
13.6%
특 917
11.0%
중품 770
 
9.2%
특품 139
 
1.7%
하품 139
 
1.7%
대 106
 
1.3%

거래수량
Real number (ℝ)

HIGH CORRELATION 

Distinct20
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.386194
Minimum1
Maximum80
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size73.3 KiB
2024-05-18T07:48:27.276526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q15
median8
Q310
95-th percentile60
Maximum80
Range79
Interquartile range (IQR)5

Descriptive statistics

Standard deviation15.766705
Coefficient of variation (CV)1.3847212
Kurtosis11.038965
Mean11.386194
Median Absolute Deviation (MAD)3
Skewness3.4419649
Sum94847
Variance248.58898
MonotonicityNot monotonic
2024-05-18T07:48:27.772025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
10.0 1555
18.7%
8.0 1351
16.2%
5.0 1237
14.8%
1.0 725
8.7%
13.0 704
8.5%
4.0 674
8.1%
3.0 419
 
5.0%
7.5 292
 
3.5%
12.0 280
 
3.4%
15.0 144
 
1.7%
Other values (10) 949
11.4%
ValueCountFrequency (%)
1.0 725
8.7%
3.0 419
 
5.0%
4.0 674
8.1%
5.0 1237
14.8%
6.0 129
 
1.5%
7.0 140
 
1.7%
7.5 292
 
3.5%
8.0 1351
16.2%
10.0 1555
18.7%
12.0 280
 
3.4%
ValueCountFrequency (%)
80.0 130
 
1.6%
78.0 65
 
0.8%
75.0 65
 
0.8%
70.0 130
 
1.6%
60.0 65
 
0.8%
45.0 65
 
0.8%
18.0 20
 
0.2%
17.0 140
 
1.7%
15.0 144
 
1.7%
13.0 704
8.5%

규격명통일
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size65.2 KiB
kg상자
6752 
kg
725 
kgPP대
 
520
Kg그물망
 
333

Length

Max length5
Median length4
Mean length3.9283313
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowkg상자
2nd rowkg상자
3rd rowkg
4th rowkg
5th rowkg

Common Values

ValueCountFrequency (%)
kg상자 6752
81.1%
kg 725
 
8.7%
kgPP대 520
 
6.2%
Kg그물망 333
 
4.0%

Length

2024-05-18T07:48:28.252806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T07:48:28.715767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
kg상자 6752
81.1%
kg 725
 
8.7%
kgpp대 520
 
6.2%
kg그물망 333
 
4.0%

최저가
Real number (ℝ)

HIGH CORRELATION 

Distinct621
Distinct (%)7.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41363.264
Minimum24
Maximum585000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size73.3 KiB
2024-05-18T07:48:29.198514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum24
5-th percentile1207
Q112000
median22500
Q332000
95-th percentile210000
Maximum585000
Range584976
Interquartile range (IQR)20000

Descriptive statistics

Standard deviation77610.96
Coefficient of variation (CV)1.8763258
Kurtosis24.209833
Mean41363.264
Median Absolute Deviation (MAD)10000
Skewness4.6054932
Sum3.4455599 × 108
Variance6.023461 × 109
MonotonicityNot monotonic
2024-05-18T07:48:29.697500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30000 361
 
4.3%
5000 223
 
2.7%
20000 195
 
2.3%
16000 189
 
2.3%
31000 186
 
2.2%
18000 186
 
2.2%
26000 182
 
2.2%
28000 180
 
2.2%
10000 157
 
1.9%
27000 157
 
1.9%
Other values (611) 6314
75.8%
ValueCountFrequency (%)
24 1
< 0.1%
27 1
< 0.1%
35 1
< 0.1%
50 1
< 0.1%
58 1
< 0.1%
147 1
< 0.1%
173 1
< 0.1%
200 2
< 0.1%
347 1
< 0.1%
373 1
< 0.1%
ValueCountFrequency (%)
585000 59
0.7%
480000 65
0.8%
376000 6
 
0.1%
350000 2
 
< 0.1%
306000 15
 
0.2%
280000 50
0.6%
240000 48
0.6%
230000 76
0.9%
220000 1
 
< 0.1%
215000 1
 
< 0.1%

최고가
Real number (ℝ)

HIGH CORRELATION 

Distinct616
Distinct (%)7.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54022.787
Minimum27
Maximum2800000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size73.3 KiB
2024-05-18T07:48:30.370154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum27
5-th percentile1276.15
Q116150
median26500
Q338000
95-th percentile252000
Maximum2800000
Range2799973
Interquartile range (IQR)21850

Descriptive statistics

Standard deviation120560.43
Coefficient of variation (CV)2.2316589
Kurtosis199.65666
Mean54022.787
Median Absolute Deviation (MAD)10500
Skewness10.533442
Sum4.5000982 × 108
Variance1.4534818 × 1010
MonotonicityNot monotonic
2024-05-18T07:48:30.850705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30000 347
 
4.2%
20000 234
 
2.8%
26000 196
 
2.4%
31000 187
 
2.2%
30500 174
 
2.1%
27000 161
 
1.9%
18000 159
 
1.9%
23000 151
 
1.8%
24000 141
 
1.7%
28000 136
 
1.6%
Other values (606) 6444
77.4%
ValueCountFrequency (%)
27 1
< 0.1%
58 1
< 0.1%
71 1
< 0.1%
433 1
< 0.1%
467 1
< 0.1%
507 1
< 0.1%
553 1
< 0.1%
573 2
< 0.1%
593 1
< 0.1%
613 2
< 0.1%
ValueCountFrequency (%)
2800000 6
 
0.1%
1100000 1
 
< 0.1%
833333 1
 
< 0.1%
640000 65
0.8%
584000 5
 
0.1%
544000 60
0.7%
424000 5
 
0.1%
420000 45
0.5%
415000 9
 
0.1%
410000 1
 
< 0.1%

평균가
Real number (ℝ)

HIGH CORRELATION 

Distinct4471
Distinct (%)53.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46110.938
Minimum26
Maximum740139
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size73.3 KiB
2024-05-18T07:48:31.502924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum26
5-th percentile1250.45
Q115000
median24280
Q335318
95-th percentile215000
Maximum740139
Range740113
Interquartile range (IQR)20318

Descriptive statistics

Standard deviation85175.842
Coefficient of variation (CV)1.8471939
Kurtosis21.542735
Mean46110.938
Median Absolute Deviation (MAD)9995.5
Skewness4.3796053
Sum3.8410412 × 108
Variance7.2549241 × 109
MonotonicityNot monotonic
2024-05-18T07:48:31.937887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30000 173
 
2.1%
31000 152
 
1.8%
26000 109
 
1.3%
30500 81
 
1.0%
28720 81
 
1.0%
30250 80
 
1.0%
215000 72
 
0.9%
250000 65
 
0.8%
241000 65
 
0.8%
210000 65
 
0.8%
Other values (4461) 7387
88.7%
ValueCountFrequency (%)
26 1
< 0.1%
44 1
< 0.1%
51 1
< 0.1%
55 1
< 0.1%
64 1
< 0.1%
403 1
< 0.1%
448 1
< 0.1%
464 1
< 0.1%
490 1
< 0.1%
500 1
< 0.1%
ValueCountFrequency (%)
740139 1
 
< 0.1%
612500 59
0.7%
532000 5
 
0.1%
512000 60
0.7%
508000 6
 
0.1%
365000 5
 
0.1%
360500 9
 
0.1%
358000 1
 
< 0.1%
350000 47
0.6%
340000 5
 
0.1%

전7일평균
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6098
Distinct (%)73.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43881.165
Minimum0
Maximum612500
Zeros176
Zeros (%)2.1%
Negative0
Negative (%)0.0%
Memory size73.3 KiB
2024-05-18T07:48:32.431642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1027.225
Q114365.2
median23738.9
Q334147.1
95-th percentile215000
Maximum612500
Range612500
Interquartile range (IQR)19781.9

Descriptive statistics

Standard deviation82292.533
Coefficient of variation (CV)1.8753498
Kurtosis23.311815
Mean43881.165
Median Absolute Deviation (MAD)9780.05
Skewness4.5435585
Sum3.6553011 × 108
Variance6.772061 × 109
MonotonicityNot monotonic
2024-05-18T07:48:32.986408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 176
 
2.1%
30000.0 141
 
1.7%
31000.0 135
 
1.6%
30250.0 80
 
1.0%
28720.0 80
 
1.0%
30500.0 80
 
1.0%
26000.0 75
 
0.9%
215000.0 63
 
0.8%
210000.0 60
 
0.7%
250000.0 60
 
0.7%
Other values (6088) 7380
88.6%
ValueCountFrequency (%)
0.0 176
2.1%
548.5 2
 
< 0.1%
588.8 1
 
< 0.1%
596.0 1
 
< 0.1%
646.5 1
 
< 0.1%
652.5 1
 
< 0.1%
665.8 1
 
< 0.1%
675.8 1
 
< 0.1%
677.8 1
 
< 0.1%
682.2 1
 
< 0.1%
ValueCountFrequency (%)
612500.0 57
0.7%
532000.0 3
 
< 0.1%
512000.0 57
0.7%
508000.0 3
 
< 0.1%
365000.0 3
 
< 0.1%
360500.0 7
 
0.1%
358000.0 1
 
< 0.1%
354166.7 1
 
< 0.1%
353333.3 1
 
< 0.1%
352500.0 1
 
< 0.1%

전일대비등락률
Real number (ℝ)

SKEWED  ZEROS 

Distinct167
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean112.94622
Minimum0
Maximum118043
Zeros1913
Zeros (%)23.0%
Negative0
Negative (%)0.0%
Memory size73.3 KiB
2024-05-18T07:48:33.497538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q176
median100
Q3100
95-th percentile119
Maximum118043
Range118043
Interquartile range (IQR)24

Descriptive statistics

Standard deviation1875.2683
Coefficient of variation (CV)16.603197
Kurtosis3112.5501
Mean112.94622
Median Absolute Deviation (MAD)5
Skewness55.102307
Sum940842
Variance3516631.2
MonotonicityNot monotonic
2024-05-18T07:48:34.042964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 2569
30.8%
0 1913
23.0%
99 249
 
3.0%
101 236
 
2.8%
98 201
 
2.4%
102 175
 
2.1%
97 169
 
2.0%
96 150
 
1.8%
95 138
 
1.7%
103 128
 
1.5%
Other values (157) 2402
28.8%
ValueCountFrequency (%)
0 1913
23.0%
29 1
 
< 0.1%
36 1
 
< 0.1%
42 2
 
< 0.1%
43 1
 
< 0.1%
45 1
 
< 0.1%
46 2
 
< 0.1%
48 2
 
< 0.1%
49 4
 
< 0.1%
50 2
 
< 0.1%
ValueCountFrequency (%)
118043 1
< 0.1%
99319 1
< 0.1%
74365 1
< 0.1%
595 1
< 0.1%
449 1
< 0.1%
415 1
< 0.1%
324 1
< 0.1%
274 1
< 0.1%
265 1
< 0.1%
247 2
< 0.1%

해당일자
Real number (ℝ)

Distinct148
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20232691
Minimum20230516
Maximum20240511
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size73.3 KiB
2024-05-18T07:48:34.889154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20230516
5-th percentile20230522
Q120230620
median20230802
Q320231111
95-th percentile20240325
Maximum20240511
Range9995
Interquartile range (IQR)491

Descriptive statistics

Standard deviation3834.5586
Coefficient of variation (CV)0.00018952292
Kurtosis0.14099339
Mean20232691
Median Absolute Deviation (MAD)195
Skewness1.4589193
Sum1.6853831 × 1011
Variance14703839
MonotonicityDecreasing
2024-05-18T07:48:35.492815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20230823 93
 
1.1%
20230718 91
 
1.1%
20240213 88
 
1.1%
20240325 87
 
1.0%
20230707 87
 
1.0%
20230516 82
 
1.0%
20230517 82
 
1.0%
20230628 82
 
1.0%
20230518 81
 
1.0%
20230519 81
 
1.0%
Other values (138) 7476
89.7%
ValueCountFrequency (%)
20230516 82
1.0%
20230517 82
1.0%
20230518 81
1.0%
20230519 81
1.0%
20230520 81
1.0%
20230522 81
1.0%
20230523 79
0.9%
20230524 68
0.8%
20230525 79
0.9%
20230526 78
0.9%
ValueCountFrequency (%)
20240511 51
0.6%
20240506 52
0.6%
20240429 44
0.5%
20240419 19
 
0.2%
20240413 51
0.6%
20240412 46
0.6%
20240408 19
 
0.2%
20240328 19
 
0.2%
20240327 57
0.7%
20240325 87
1.0%

Interactions

2024-05-18T07:48:21.724187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T07:48:05.941509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T07:48:08.874507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T07:48:11.020628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T07:48:12.903806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T07:48:15.493692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T07:48:18.947283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T07:48:22.055797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T07:48:06.339600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T07:48:09.259469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T07:48:11.312912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T07:48:13.200125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T07:48:15.796548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T07:48:19.342221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T07:48:22.357240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T07:48:06.858745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T07:48:09.655531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T07:48:11.587457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T07:48:13.483690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T07:48:16.200973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T07:48:19.758337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T07:48:22.675683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T07:48:07.236987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T07:48:09.945379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T07:48:11.818950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T07:48:14.021121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T07:48:16.735929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T07:48:20.280824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T07:48:23.040087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T07:48:07.696257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T07:48:10.243764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T07:48:12.098991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T07:48:14.344928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T07:48:17.422035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T07:48:20.675224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T07:48:23.341248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T07:48:08.053849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T07:48:10.518818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T07:48:12.367497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T07:48:14.750994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T07:48:17.885005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T07:48:21.027003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T07:48:23.743815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T07:48:08.509435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T07:48:10.745124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T07:48:12.641849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T07:48:15.165647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T07:48:18.672757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T07:48:21.412359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-18T07:48:35.797925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
품목등급명통일거래수량규격명통일최저가최고가평균가전7일평균전일대비등락률해당일자
품목1.0000.9021.0001.0000.9560.8860.9660.9750.0000.348
등급명통일0.9021.0000.3630.5100.5350.2680.5070.4000.0000.000
거래수량1.0000.3631.0000.7390.8110.5940.8540.8300.0000.058
규격명통일1.0000.5100.7391.0000.7340.4750.7150.8620.0000.079
최저가0.9560.5350.8110.7341.0000.8030.9720.9220.0000.101
최고가0.8860.2680.5940.4750.8031.0000.8100.7770.0000.048
평균가0.9660.5070.8540.7150.9720.8101.0000.9270.0000.081
전7일평균0.9750.4000.8300.8620.9220.7770.9271.0000.0000.110
전일대비등락률0.0000.0000.0000.0000.0000.0000.0000.0001.0000.000
해당일자0.3480.0000.0580.0790.1010.0480.0810.1100.0001.000
2024-05-18T07:48:36.115237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
등급명통일규격명통일품목
등급명통일1.0000.3510.606
규격명통일0.3511.0000.998
품목0.6060.9981.000
2024-05-18T07:48:36.412881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
거래수량최저가최고가평균가전7일평균전일대비등락률해당일자품목등급명통일규격명통일
거래수량1.0000.3500.4000.3890.332-0.090-0.1440.9900.2010.615
최저가0.3501.0000.9360.9710.8850.008-0.0440.7570.1970.574
최고가0.4000.9361.0000.9840.8980.001-0.1060.6540.1580.405
평균가0.3890.9710.9841.0000.9080.008-0.0820.7950.1830.551
전7일평균0.3320.8850.8980.9081.0000.003-0.1180.8430.2090.541
전일대비등락률-0.0900.0080.0010.0080.0031.000-0.0560.0000.0000.000
해당일자-0.144-0.044-0.106-0.082-0.118-0.0561.0000.2770.0000.052
품목0.9900.7570.6540.7950.8430.0000.2771.0000.6060.998
등급명통일0.2010.1970.1580.1830.2090.0000.0000.6061.0000.351
규격명통일0.6150.5740.4050.5510.5410.0000.0520.9980.3511.000

Missing values

2024-05-18T07:48:24.356630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-18T07:48:25.047080image/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

품목등급명통일거래수량규격명통일최저가최고가평균가전7일평균전일대비등락률해당일자
0부세 수입상품6.0kg상자37765441184009841131.710020240511
1대파 수입상10.0kg상자65001060087567744.09820240511
2양파 수입하1.0kg1353139013801701.59920240511
3양파 수입특1.0kg1453148014661766.010120240511
4양파 수입상1.0kg1407145314291748.310020240511
5양파 수입보통1.0kg1390140714001725.79920240511
6당근 수입하10.0kg상자4000500045676099.78820240511
7브로콜리 수입하8.0kg상자14000270002410221966.010820240511
8당근 수입특10.0kg상자6800680068008126.89420240511
9당근 수입상10.0kg상자6400680066077751.89720240511
품목등급명통일거래수량규격명통일최저가최고가평균가전7일평균전일대비등락률해당일자
8320바나나 수입특13.0kg상자28000280002800032000.77720230516
8321바나나 수입상13.0kg상자27000280002725628336.810120230516
8322바나나 수입보통13.0kg상자21000270002276519977.710820230516
8323마늘 쫑 수입하8.0kg상자28000280002800030000.010020230516
8324마늘 쫑 수입상8.0kg상자28000280002800030000.010020230516
8325마늘 쫑 수입보통8.0kg상자28000280002800030000.010020230516
8326주꾸미 수입중품3.0kg상자19500255002292930059.89220230516
8327주꾸미 수입상품3.0kg상자25500300002765231490.810620230516
8328냉동 낙지 수입상품5.0kg상자35000421573765639403.010020230516
8329냉동 조기 수입중품4.0kg상자1666735000249620.0020230516