Overview

Dataset statistics

Number of variables9
Number of observations2991
Missing cells360
Missing cells (%)1.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory219.2 KiB
Average record size in memory75.0 B

Variable types

DateTime1
Categorical3
Text2
Numeric3

Dataset

Description해외 생산지 중 대한민국에 영향이 많은 중국지역에서 유통되고 있는 주요 농산물에 대한 가격정보를 다롄, 칭다오 지역을 중점으로 조사한 1회성 데이터
URLhttps://www.data.go.kr/data/15117820/fileData.do

Alerts

품목명 is highly overall correlated with 품종명High correlation
품종명 is highly overall correlated with 최저가격 and 3 other fieldsHigh correlation
최저가격 is highly overall correlated with 최대가격 and 3 other fieldsHigh correlation
최대가격 is highly overall correlated with 최저가격 and 3 other fieldsHigh correlation
평균거래가격 is highly overall correlated with 최저가격 and 3 other fieldsHigh correlation
화폐단위명 is highly overall correlated with 최저가격 and 2 other fieldsHigh correlation
화폐단위명 is highly imbalanced (58.3%)Imbalance
생산지역명 has 252 (8.4%) missing valuesMissing
최저가격 has 108 (3.6%) missing valuesMissing

Reproduction

Analysis started2023-12-11 23:45:53.191271
Analysis finished2023-12-11 23:45:55.734640
Duration2.54 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기준일자
Date

Distinct77
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size23.5 KiB
Minimum2019-01-04 00:00:00
Maximum2020-08-17 00:00:00
2023-12-12T08:45:55.825446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:45:55.984957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

품목명
Categorical

HIGH CORRELATION 

Distinct13
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size23.5 KiB
콩
582 
건고추
470 
마늘
404 
참깨
317 
생강
221 
Other values (8)
997 

Length

Max length3
Median length2
Mean length1.847877
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row건고추
2nd row건고추
3rd row배추
4th row무
5th row건고추

Common Values

ValueCountFrequency (%)
콩 582
19.5%
건고추 470
15.7%
마늘 404
13.5%
참깨 317
10.6%
생강 221
 
7.4%
쌀 210
 
7.0%
녹두 188
 
6.3%
땅콩 171
 
5.7%
양파 120
 
4.0%
메밀 108
 
3.6%
Other values (3) 200
 
6.7%

Length

2023-12-12T08:45:56.147378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
콩 582
19.5%
건고추 470
15.7%
마늘 404
13.5%
참깨 317
10.6%
생강 221
 
7.4%
쌀 210
 
7.0%
녹두 188
 
6.3%
땅콩 171
 
5.7%
양파 120
 
4.0%
메밀 108
 
3.6%
Other values (3) 200
 
6.7%

품종명
Categorical

HIGH CORRELATION 

Distinct30
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size23.5 KiB
대두
258 
쌀
210 
익도홍
200 
녹두
 
188
땅콩
 
171
Other values (25)
1964 

Length

Max length14
Median length11
Mean length5.0939485
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row금탑
2nd row익도홍(청)
3rd row배추
4th row무
5th row금탑

Common Values

ValueCountFrequency (%)
대두 258
 
8.6%
쌀 210
 
7.0%
익도홍 200
 
6.7%
녹두 188
 
6.3%
땅콩 171
 
5.7%
마늘(5.0cm↑) 144
 
4.8%
마늘(6.0cm↑) 120
 
4.0%
마늘(5.5cm↑) 120
 
4.0%
참깨(착유용) 119
 
4.0%
참깨(순백색) 118
 
3.9%
Other values (20) 1343
44.9%

Length

2023-12-12T08:45:56.289671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
대두 258
 
8.5%
쌀 210
 
6.9%
익도홍 200
 
6.6%
녹두 188
 
6.2%
땅콩 171
 
5.6%
금탑 158
 
5.2%
마늘(5.0cm↑ 144
 
4.8%
마늘(6.0cm↑ 120
 
4.0%
마늘(5.5cm↑ 120
 
4.0%
참깨(착유용 119
 
3.9%
Other values (20) 1343
44.3%

생산지역명
Text

MISSING 

Distinct51
Distinct (%)1.9%
Missing252
Missing (%)8.4%
Memory size23.5 KiB
2023-12-12T08:45:56.538008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length10
Mean length6.4067178
Min length2

Characters and Unicode

Total characters17548
Distinct characters103
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)0.1%

Sample

1st row신강
2nd row산동
3rd row산동성 수광
4th row하북성 장북
5th row길림
ValueCountFrequency (%)
산동성 501
 
11.2%
stem 404
 
9.1%
하남성 199
 
4.5%
내몽고 188
 
4.2%
길림 172
 
3.9%
대련 144
 
3.2%
강소성 133
 
3.0%
산동 123
 
2.8%
기현/중무(soft 120
 
2.7%
비주(soft 120
 
2.7%
Other values (51) 2356
52.8%
2023-12-12T08:45:56.930673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1721
 
9.8%
성 1227
 
7.0%
s 764
 
4.4%
t 764
 
4.4%
산 700
 
4.0%
동 644
 
3.7%
( 593
 
3.4%
) 593
 
3.4%
강 439
 
2.5%
m 404
 
2.3%
Other values (93) 9699
55.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 11143
63.5%
Lowercase Letter 3232
 
18.4%
Space Separator 1721
 
9.8%
Open Punctuation 593
 
3.4%
Close Punctuation 593
 
3.4%
Other Punctuation 146
 
0.8%
Decimal Number 96
 
0.5%
Math Symbol 24
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
성 1227
 
11.0%
산 700
 
6.3%
동 644
 
5.8%
강 439
 
3.9%
하 326
 
2.9%
주 280
 
2.5%
수 279
 
2.5%
도 267
 
2.4%
무 266
 
2.4%
길 244
 
2.2%
Other values (76) 6471
58.1%
Lowercase Letter
ValueCountFrequency (%)
s 764
23.6%
t 764
23.6%
m 404
12.5%
e 404
12.5%
f 360
11.1%
o 360
11.1%
h 44
 
1.4%
a 44
 
1.4%
r 44
 
1.4%
d 44
 
1.4%
Decimal Number
ValueCountFrequency (%)
1 72
75.0%
0 24
 
25.0%
Space Separator
ValueCountFrequency (%)
1721
100.0%
Open Punctuation
ValueCountFrequency (%)
( 593
100.0%
Close Punctuation
ValueCountFrequency (%)
) 593
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 146
100.0%
Math Symbol
ValueCountFrequency (%)
~ 24
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 11143
63.5%
Latin 3232
 
18.4%
Common 3173
 
18.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
성 1227
 
11.0%
산 700
 
6.3%
동 644
 
5.8%
강 439
 
3.9%
하 326
 
2.9%
주 280
 
2.5%
수 279
 
2.5%
도 267
 
2.4%
무 266
 
2.4%
길 244
 
2.2%
Other values (76) 6471
58.1%
Latin
ValueCountFrequency (%)
s 764
23.6%
t 764
23.6%
m 404
12.5%
e 404
12.5%
f 360
11.1%
o 360
11.1%
h 44
 
1.4%
a 44
 
1.4%
r 44
 
1.4%
d 44
 
1.4%
Common
ValueCountFrequency (%)
1721
54.2%
( 593
 
18.7%
) 593
 
18.7%
/ 146
 
4.6%
1 72
 
2.3%
~ 24
 
0.8%
0 24
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 11143
63.5%
ASCII 6405
36.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1721
26.9%
s 764
11.9%
t 764
11.9%
( 593
 
9.3%
) 593
 
9.3%
m 404
 
6.3%
e 404
 
6.3%
f 360
 
5.6%
o 360
 
5.6%
/ 146
 
2.3%
Other values (7) 296
 
4.6%
Hangul
ValueCountFrequency (%)
성 1227
 
11.0%
산 700
 
6.3%
동 644
 
5.8%
강 439
 
3.9%
하 326
 
2.9%
주 280
 
2.5%
수 279
 
2.5%
도 267
 
2.4%
무 266
 
2.4%
길 244
 
2.2%
Other values (76) 6471
58.1%

화폐단위명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size23.5 KiB
위안화
2739 
달러화
 
252

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
위안화 2739
91.6%
달러화 252
 
8.4%

Length

2023-12-12T08:45:57.058672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:45:57.148984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
위안화 2739
91.6%
달러화 252
 
8.4%

최저가격
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct389
Distinct (%)13.5%
Missing108
Missing (%)3.6%
Infinite0
Infinite (%)0.0%
Mean6503.8092
Minimum1
Maximum17000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.4 KiB
2023-12-12T08:45:57.253751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q12600
median6300
Q310700
95-th percentile14000
Maximum17000
Range16999
Interquartile range (IQR)8100

Descriptive statistics

Standard deviation4578.6098
Coefficient of variation (CV)0.70398895
Kurtosis-1.1823557
Mean6503.8092
Median Absolute Deviation (MAD)3900
Skewness0.1977259
Sum18750482
Variance20963668
MonotonicityNot monotonic
2023-12-12T08:45:57.393641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 65
 
2.2%
11500 51
 
1.7%
10000 48
 
1.6%
15000 46
 
1.5%
7800 43
 
1.4%
4 42
 
1.4%
12000 42
 
1.4%
8200 40
 
1.3%
5700 39
 
1.3%
6 38
 
1.3%
Other values (379) 2429
81.2%
(Missing) 108
 
3.6%
ValueCountFrequency (%)
1 65
2.2%
2 9
 
0.3%
3 7
 
0.2%
4 42
1.4%
5 25
 
0.8%
6 38
1.3%
7 9
 
0.3%
8 8
 
0.3%
9 9
 
0.3%
10 7
 
0.2%
ValueCountFrequency (%)
17000 3
 
0.1%
15500 28
0.9%
15300 6
 
0.2%
15000 46
1.5%
14800 12
 
0.4%
14700 2
 
0.1%
14600 1
 
< 0.1%
14500 32
1.1%
14400 1
 
< 0.1%
14300 5
 
0.2%

최대가격
Real number (ℝ)

HIGH CORRELATION 

Distinct385
Distinct (%)12.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6604.119
Minimum1
Maximum18000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.4 KiB
2023-12-12T08:45:57.547730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q12860
median6000
Q311000
95-th percentile14500
Maximum18000
Range17999
Interquartile range (IQR)8140

Descriptive statistics

Standard deviation4664.459
Coefficient of variation (CV)0.70629542
Kurtosis-1.1641223
Mean6604.119
Median Absolute Deviation (MAD)4000
Skewness0.24601297
Sum19752920
Variance21757178
MonotonicityNot monotonic
2023-12-12T08:45:57.696603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12000 98
 
3.3%
1 65
 
2.2%
13500 57
 
1.9%
5800 49
 
1.6%
13000 45
 
1.5%
14500 42
 
1.4%
4 42
 
1.4%
11000 39
 
1.3%
6 38
 
1.3%
10000 37
 
1.2%
Other values (375) 2479
82.9%
ValueCountFrequency (%)
1 65
2.2%
2 9
 
0.3%
3 7
 
0.2%
4 42
1.4%
5 25
 
0.8%
6 38
1.3%
7 9
 
0.3%
8 8
 
0.3%
9 9
 
0.3%
10 7
 
0.2%
ValueCountFrequency (%)
18000 3
 
0.1%
16000 23
0.8%
15800 14
0.5%
15600 3
 
0.1%
15500 32
1.1%
15400 3
 
0.1%
15300 16
0.5%
15200 2
 
0.1%
15000 12
 
0.4%
14800 5
 
0.2%

평균거래가격
Real number (ℝ)

HIGH CORRELATION 

Distinct543
Distinct (%)18.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6494.6546
Minimum1
Maximum17500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.4 KiB
2023-12-12T08:45:57.853198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q12785
median5900
Q310850
95-th percentile14150
Maximum17500
Range17499
Interquartile range (IQR)8065

Descriptive statistics

Standard deviation4598.9946
Coefficient of variation (CV)0.70811997
Kurtosis-1.1590558
Mean6494.6546
Median Absolute Deviation (MAD)3850
Skewness0.25176231
Sum19425512
Variance21150752
MonotonicityNot monotonic
2023-12-12T08:45:57.986252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 65
 
2.2%
4 42
 
1.4%
11750 38
 
1.3%
6 38
 
1.3%
11000 38
 
1.3%
5750 33
 
1.1%
15500 31
 
1.0%
10500 31
 
1.0%
11900 29
 
1.0%
4750 29
 
1.0%
Other values (533) 2617
87.5%
ValueCountFrequency (%)
1 65
2.2%
2 9
 
0.3%
3 7
 
0.2%
4 42
1.4%
5 25
 
0.8%
6 38
1.3%
7 9
 
0.3%
8 8
 
0.3%
9 9
 
0.3%
10 7
 
0.2%
ValueCountFrequency (%)
17500 3
 
0.1%
15750 6
 
0.2%
15650 8
 
0.3%
15500 31
1.0%
15450 3
 
0.1%
15400 6
 
0.2%
15350 3
 
0.1%
15250 13
0.4%
15150 10
 
0.3%
15050 8
 
0.3%

비고
Text

Distinct104
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Memory size23.5 KiB
2023-12-12T08:45:58.211363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length38
Median length33
Mean length18.715814
Min length3

Characters and Unicode

Total characters55979
Distinct characters148
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique62 ?
Unique (%)2.1%

Sample

1st row<청도 지역 꼭지제거 전 거래가격>
2nd row<청도 지역 꼭지제거 후 공급자 매도호가>
3rd row<중국 도매시장별 평균거래가격>,산동성 수광농산물물류원
4th row<산지별 무 거래가격>
5th row<청도 지역 꼭지제거 전 거래가격>
ValueCountFrequency (%)
거래가격 1122
 
9.6%
지역별 564
 
4.8%
지역 466
 
4.0%
수매가격 461
 
4.0%
산지 447
 
3.8%
청도 430
 
3.7%
꼭지제거 430
 
3.7%
통마늘 404
 
3.5%
산지수매가격 404
 
3.5%
전 316
 
2.7%
Other values (133) 6593
56.7%
2023-12-12T08:45:58.645201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
8646
 
15.4%
가 3029
 
5.4%
< 2754
 
4.9%
> 2754
 
4.9%
지 2687
 
4.8%
격 2479
 
4.4%
거 1653
 
3.0%
산 1586
 
2.8%
수 1444
 
2.6%
매 1432
 
2.6%
Other values (138) 27515
49.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 35612
63.6%
Space Separator 8646
 
15.4%
Math Symbol 5750
 
10.3%
Other Punctuation 1798
 
3.2%
Uppercase Letter 1506
 
2.7%
Decimal Number 1029
 
1.8%
Lowercase Letter 882
 
1.6%
Close Punctuation 252
 
0.5%
Currency Symbol 252
 
0.5%
Open Punctuation 252
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
가 3029
 
8.5%
지 2687
 
7.5%
격 2479
 
7.0%
거 1653
 
4.6%
산 1586
 
4.5%
수 1444
 
4.1%
매 1432
 
4.0%
별 1326
 
3.7%
래 1223
 
3.4%
역 1030
 
2.9%
Other values (104) 17723
49.8%
Decimal Number
ValueCountFrequency (%)
0 235
22.8%
5 173
16.8%
3 164
15.9%
4 156
15.2%
6 103
10.0%
8 77
 
7.5%
2 68
 
6.6%
7 33
 
3.2%
1 14
 
1.4%
9 6
 
0.6%
Uppercase Letter
ValueCountFrequency (%)
F 292
19.4%
S 252
16.7%
C 252
16.7%
R 252
16.7%
U 252
16.7%
J 126
8.4%
B 40
 
2.7%
O 40
 
2.7%
Lowercase Letter
ValueCountFrequency (%)
a 252
28.6%
c 126
14.3%
i 126
14.3%
n 126
14.3%
o 126
14.3%
p 126
14.3%
Math Symbol
ValueCountFrequency (%)
< 2754
47.9%
> 2754
47.9%
~ 242
 
4.2%
Other Punctuation
ValueCountFrequency (%)
, 1184
65.9%
/ 374
 
20.8%
. 240
 
13.3%
Space Separator
ValueCountFrequency (%)
8646
100.0%
Close Punctuation
ValueCountFrequency (%)
) 252
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 252
100.0%
Open Punctuation
ValueCountFrequency (%)
( 252
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 35612
63.6%
Common 17979
32.1%
Latin 2388
 
4.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
가 3029
 
8.5%
지 2687
 
7.5%
격 2479
 
7.0%
거 1653
 
4.6%
산 1586
 
4.5%
수 1444
 
4.1%
매 1432
 
4.0%
별 1326
 
3.7%
래 1223
 
3.4%
역 1030
 
2.9%
Other values (104) 17723
49.8%
Common
ValueCountFrequency (%)
8646
48.1%
< 2754
 
15.3%
> 2754
 
15.3%
, 1184
 
6.6%
/ 374
 
2.1%
) 252
 
1.4%
$ 252
 
1.4%
( 252
 
1.4%
~ 242
 
1.3%
. 240
 
1.3%
Other values (10) 1029
 
5.7%
Latin
ValueCountFrequency (%)
F 292
12.2%
S 252
10.6%
C 252
10.6%
R 252
10.6%
U 252
10.6%
a 252
10.6%
c 126
 
5.3%
i 126
 
5.3%
n 126
 
5.3%
o 126
 
5.3%
Other values (4) 332
13.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 35612
63.6%
ASCII 20367
36.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8646
42.5%
< 2754
 
13.5%
> 2754
 
13.5%
, 1184
 
5.8%
/ 374
 
1.8%
F 292
 
1.4%
S 252
 
1.2%
C 252
 
1.2%
) 252
 
1.2%
R 252
 
1.2%
Other values (24) 3355
 
16.5%
Hangul
ValueCountFrequency (%)
가 3029
 
8.5%
지 2687
 
7.5%
격 2479
 
7.0%
거 1653
 
4.6%
산 1586
 
4.5%
수 1444
 
4.1%
매 1432
 
4.0%
별 1326
 
3.7%
래 1223
 
3.4%
역 1030
 
2.9%
Other values (104) 17723
49.8%

Interactions

2023-12-12T08:45:54.669541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:45:53.819083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:45:54.247717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:45:54.774342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:45:53.933789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:45:54.381500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:45:54.886875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:45:54.129095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:45:54.531115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T08:45:58.734272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준일자품목명품종명생산지역명화폐단위명최저가격최대가격평균거래가격
기준일자1.0000.6860.5490.0000.1120.4990.4790.473
품목명0.6861.0001.0000.9910.5010.7710.7670.767
품종명0.5491.0001.0000.9870.6070.9000.8990.899
생산지역명0.0000.9910.9871.000NaN0.8960.8910.897
화폐단위명0.1120.5010.607NaN1.0000.7400.7430.742
최저가격0.4990.7710.9000.8960.7401.0000.9910.996
최대가격0.4790.7670.8990.8910.7430.9911.0000.999
평균거래가격0.4730.7670.8990.8970.7420.9960.9991.000
2023-12-12T08:45:58.835120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
화폐단위명품목명품종명
화폐단위명1.0000.4670.486
품목명0.4671.0000.997
품종명0.4860.9971.000
2023-12-12T08:45:58.912332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
최저가격최대가격평균거래가격품목명품종명화폐단위명
최저가격1.0000.9991.0000.4570.5390.580
최대가격0.9991.0001.0000.4520.5370.582
평균거래가격1.0001.0001.0000.4530.5380.582
품목명0.4570.4520.4531.0000.9970.467
품종명0.5390.5370.5380.9971.0000.486
화폐단위명0.5800.5820.5820.4670.4861.000

Missing values

2023-12-12T08:45:55.063966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T08:45:55.246546image/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.
2023-12-12T08:45:55.676787image/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

기준일자품목명품종명생산지역명화폐단위명최저가격최대가격평균거래가격비고
02020-08-17건고추금탑신강위안화115001250012000<청도 지역 꼭지제거 전 거래가격>
12020-08-17건고추익도홍(청)산동위안화128001280012800<청도 지역 꼭지제거 후 공급자 매도호가>
22020-08-17배추배추산동성 수광위안화222<중국 도매시장별 평균거래가격>,산동성 수광농산물물류원
32020-08-17무무하북성 장북위안화180019001850<산지별 무 거래가격>
42020-08-17건고추금탑길림위안화120001250012250<청도 지역 꼭지제거 전 거래가격>
52020-08-17건고추금탑신강위안화144001530014850<청도 지역 꼭지제거 후 공급자 매도호가>
62020-08-17건고추금탑(신강 금탑)산동성위안화310032003150<절편진공포장 고추 FOB>, 신강 금탑
72020-08-17건고추익도홍내몽고위안화126001350013050<청도 지역 꼭지제거 후 공급자 매도호가>
82020-08-17배추배추하북성 장북위안화120013001250<주산지 한국품종 배추 거래가격>
92020-08-17마늘마늘(5.0cm↑)강소성 비주(soft stem)위안화420043004250<지역별 통마늘 산지수매가격>
기준일자품목명품종명생산지역명화폐단위명최저가격최대가격평균거래가격비고
29812019-01-04생강생강북경 순흠석문위안화777<중국 도매시장별 평균거래가격>, 북경 순흠석문농부산물도매시장
29822019-01-04생강생강(대강(250g))산동성 안구위안화340036003500<지역별 산지 수매가격>
29832019-01-04생강생강(재강(30g))산동성위안화200024002200<지역별 산지 수매가격>
29842019-01-04건고추익도홍산동 청도위안화98001050010150<청도 지역 꼭지제거 전 거래가격>
29852019-01-04건고추익도홍(도)산동위안화130001300013000<청도 지역 꼭지제거 후 공급자 매도호가>
29862019-01-04생강생강(대강(250g))산동성 래무위안화380040003900<지역별 산지 수매가격>
29872019-01-04건고추익도홍길림위안화128001350013150<청도 지역 꼭지제거 후 공급자 매도호가>
29882019-01-04건고추익도홍(청)산동위안화120001200012000<청도 지역 꼭지제거 후 공급자 매도호가>
29892019-01-04건고추금탑신강위안화115001200011750<청도 지역 꼭지제거 전 거래가격>
29902019-01-04건고추익도홍내몽고위안화128001350013150<청도 지역 꼭지제거 후 공급자 매도호가>