Overview

Dataset statistics

Number of variables13
Number of observations6327
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory698.3 KiB
Average record size in memory113.0 B

Variable types

Numeric8
Categorical4
Text1

Dataset

Description서울 강서 도매시장에서 거래되는 품목을 매일 조사(약 300개 품종), 품목별 등급(특,상,중,하), 가격(최고가, 최저가, 평균가) 등의 정보를 서비스하며, 일부품목은 농수산물 표준코드와 상이할 수 있음 금주 토요일정보는 매주 월요일에 갱신됨
Author농림수산식품교육문화정보원
URLhttps://data.mafra.go.kr/opendata/data/indexOpenDataDetail.do?data_id=20220209000000001733

Alerts

GRAD is highly overall correlated with GRAD_CDHigh correlation
GRAD_CD is highly overall correlated with GRADHigh correlation
PRDLST_CD is highly overall correlated with SPCIES_CD and 1 other fieldsHigh correlation
SPCIES_CD is highly overall correlated with PRDLST_CD and 1 other fieldsHigh correlation
DELNG_BUNDLE_QY is highly overall correlated with PRDLST_NM and 1 other fieldsHigh correlation
STNDRD_CD is highly overall correlated with PRDLST_NM and 1 other fieldsHigh correlation
MUMM_AMT is highly overall correlated with AVRG_AMT and 1 other fieldsHigh correlation
AVRG_AMT is highly overall correlated with MUMM_AMT and 1 other fieldsHigh correlation
MXMM_AMT is highly overall correlated with MUMM_AMT and 1 other fieldsHigh correlation
PRDLST_NM is highly overall correlated with PRDLST_CD and 4 other fieldsHigh correlation
STNDRD is highly overall correlated with DELNG_BUNDLE_QY and 2 other fieldsHigh correlation
STNDRD is highly imbalanced (67.4%)Imbalance

Reproduction

Analysis started2023-12-11 03:51:53.775478
Analysis finished2023-12-11 03:52:06.028555
Duration12.25 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

EXAMIN_DE
Real number (ℝ)

Distinct21
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20220516
Minimum20220502
Maximum20220531
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size55.7 KiB
2023-12-11T12:52:06.096965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20220502
5-th percentile20220503
Q120220510
median20220517
Q320220524
95-th percentile20220530
Maximum20220531
Range29
Interquartile range (IQR)14

Descriptive statistics

Standard deviation8.6855624
Coefficient of variation (CV)4.2954206 × 10-7
Kurtosis-1.1390255
Mean20220516
Median Absolute Deviation (MAD)7
Skewness-0.020189895
Sum1.2793521 × 1011
Variance75.438994
MonotonicityIncreasing
2023-12-11T12:52:06.252893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
20220503 311
 
4.9%
20220520 309
 
4.9%
20220510 308
 
4.9%
20220519 308
 
4.9%
20220509 307
 
4.9%
20220505 306
 
4.8%
20220531 304
 
4.8%
20220513 304
 
4.8%
20220502 302
 
4.8%
20220523 301
 
4.8%
Other values (11) 3267
51.6%
ValueCountFrequency (%)
20220502 302
4.8%
20220503 311
4.9%
20220505 306
4.8%
20220506 294
4.6%
20220509 307
4.9%
20220510 308
4.9%
20220511 300
4.7%
20220512 301
4.8%
20220513 304
4.8%
20220516 300
4.7%
ValueCountFrequency (%)
20220531 304
4.8%
20220530 297
4.7%
20220527 300
4.7%
20220526 295
4.7%
20220525 295
4.7%
20220524 297
4.7%
20220523 301
4.8%
20220520 309
4.9%
20220519 308
4.9%
20220518 294
4.6%

PRDLST_NM
Categorical

HIGH CORRELATION 

Distinct50
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size49.6 KiB
복숭아
924 
사과
521 
수박
 
336
오이
 
262
상추
 
252
Other values (45)
4032 

Length

Max length12
Median length2
Mean length2.6941679
Min length1

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row만감
2nd row만감
3rd row망고
4th row망고
5th row망고

Common Values

ValueCountFrequency (%)
복숭아 924
 
14.6%
사과 521
 
8.2%
수박 336
 
5.3%
오이 262
 
4.1%
상추 252
 
4.0%
호박 251
 
4.0%
포도 224
 
3.5%
감귤 168
 
2.7%
고구마 168
 
2.7%
피망(단고추) 168
 
2.7%
Other values (40) 3053
48.3%

Length

2023-12-11T12:52:06.405088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
복숭아 924
 
14.6%
사과 521
 
8.2%
수박 336
 
5.3%
오이 262
 
4.1%
상추 252
 
4.0%
호박 251
 
4.0%
포도 224
 
3.5%
감귤 168
 
2.7%
고구마 168
 
2.7%
피망(단고추 168
 
2.7%
Other values (40) 3053
48.3%

PRDLST_CD
Real number (ℝ)

HIGH CORRELATION 

Distinct50
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean875.68468
Minimum501
Maximum1711
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size55.7 KiB
2023-12-11T12:52:06.579272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum501
5-th percentile601
Q1604
median805
Q31011
95-th percentile1306
Maximum1711
Range1210
Interquartile range (IQR)407

Descriptive statistics

Standard deviation282.83627
Coefficient of variation (CV)0.32298872
Kurtosis0.061763036
Mean875.68468
Median Absolute Deviation (MAD)201
Skewness0.76125363
Sum5540457
Variance79996.357
MonotonicityNot monotonic
2023-12-11T12:52:06.749159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
604 924
 
14.6%
601 521
 
8.2%
801 336
 
5.3%
901 262
 
4.1%
1005 252
 
4.0%
902 251
 
4.0%
603 224
 
3.5%
614 168
 
2.7%
502 168
 
2.7%
1302 168
 
2.7%
Other values (40) 3053
48.3%
ValueCountFrequency (%)
501 84
 
1.3%
502 168
 
2.7%
601 521
8.2%
602 84
 
1.3%
603 224
 
3.5%
604 924
14.6%
605 84
 
1.3%
614 168
 
2.7%
615 92
 
1.5%
625 16
 
0.3%
ValueCountFrequency (%)
1711 84
1.3%
1704 84
1.3%
1406 1
 
< 0.1%
1403 21
 
0.3%
1326 84
1.3%
1306 63
 
1.0%
1305 30
 
0.5%
1303 63
 
1.0%
1302 168
2.7%
1301 63
 
1.0%
Distinct77
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size49.6 KiB
2023-12-11T12:52:07.095693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length7
Mean length4.4338549
Min length2

Characters and Unicode

Total characters28053
Distinct characters130
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

Unique1 ?
Unique (%)< 0.1%

Sample

1st row한라봉
2nd row한라봉
3rd row망고(수입)
4th row망고(수입)
5th row망고(수입)
ValueCountFrequency (%)
수박(일반 336
 
5.3%
홍로 252
 
4.0%
시금치(일반 168
 
2.7%
유명 168
 
2.7%
황도 168
 
2.7%
하우스감귤 168
 
2.7%
천도 168
 
2.7%
토마토(일반 147
 
2.3%
취청 134
 
2.1%
깻잎(일반 126
 
2.0%
Other values (67) 4492
71.0%
2023-12-11T12:52:07.570974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
( 2447
 
8.7%
) 2447
 
8.7%
2216
 
7.9%
2216
 
7.9%
735
 
2.6%
720
 
2.6%
671
 
2.4%
588
 
2.1%
567
 
2.0%
525
 
1.9%
Other values (120) 14921
53.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 23159
82.6%
Open Punctuation 2447
 
8.7%
Close Punctuation 2447
 
8.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
2216
 
9.6%
2216
 
9.6%
735
 
3.2%
720
 
3.1%
671
 
2.9%
588
 
2.5%
567
 
2.4%
525
 
2.3%
504
 
2.2%
498
 
2.2%
Other values (118) 13919
60.1%
Open Punctuation
ValueCountFrequency (%)
( 2447
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2447
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 23159
82.6%
Common 4894
 
17.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
2216
 
9.6%
2216
 
9.6%
735
 
3.2%
720
 
3.1%
671
 
2.9%
588
 
2.5%
567
 
2.4%
525
 
2.3%
504
 
2.2%
498
 
2.2%
Other values (118) 13919
60.1%
Common
ValueCountFrequency (%)
( 2447
50.0%
) 2447
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 23159
82.6%
ASCII 4894
 
17.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
( 2447
50.0%
) 2447
50.0%
Hangul
ValueCountFrequency (%)
2216
 
9.6%
2216
 
9.6%
735
 
3.2%
720
 
3.1%
671
 
2.9%
588
 
2.5%
567
 
2.4%
525
 
2.3%
504
 
2.2%
498
 
2.2%
Other values (118) 13919
60.1%

SPCIES_CD
Real number (ℝ)

HIGH CORRELATION 

Distinct77
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean87575.588
Minimum50101
Maximum171101
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size55.7 KiB
2023-12-11T12:52:07.781827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum50101
5-th percentile60103
Q160419
median80501
Q3101101
95-th percentile130601
Maximum171101
Range121000
Interquartile range (IQR)40682

Descriptive statistics

Standard deviation28278.991
Coefficient of variation (CV)0.32290952
Kurtosis0.062573699
Mean87575.588
Median Absolute Deviation (MAD)20093
Skewness0.7615479
Sum5.5409074 × 108
Variance7.9970136 × 108
MonotonicityNot monotonic
2023-12-11T12:52:08.278531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
80101 336
 
5.3%
60117 252
 
4.0%
60447 168
 
2.7%
60408 168
 
2.7%
60410 168
 
2.7%
100801 168
 
2.7%
61405 168
 
2.7%
80301 147
 
2.3%
90101 134
 
2.1%
101101 126
 
2.0%
Other values (67) 4492
71.0%
ValueCountFrequency (%)
50101 84
 
1.3%
50201 84
 
1.3%
50204 84
 
1.3%
60103 101
1.6%
60104 84
 
1.3%
60114 84
 
1.3%
60117 252
4.0%
60201 84
 
1.3%
60301 84
 
1.3%
60302 84
 
1.3%
ValueCountFrequency (%)
171101 84
1.3%
170401 84
1.3%
140601 1
 
< 0.1%
140302 21
 
0.3%
132603 63
1.0%
132602 21
 
0.3%
130601 63
1.0%
130501 30
 
0.5%
130301 63
1.0%
130202 84
1.3%

GRAD
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size49.6 KiB
1746 
보통
1704 
4등
1704 
1173 

Length

Max length2
Median length2
Mean length1.5386439
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row보통
2nd row4등
3rd row
4th row
5th row보통

Common Values

ValueCountFrequency (%)
1746
27.6%
보통 1704
26.9%
4등 1704
26.9%
1173
18.5%

Length

2023-12-11T12:52:08.422775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T12:52:08.536153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1746
27.6%
보통 1704
26.9%
4등 1704
26.9%
1173
18.5%

GRAD_CD
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size49.6 KiB
12
1746 
13
1704 
14
1704 
11
1173 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row13
2nd row14
3rd row11
4th row12
5th row13

Common Values

ValueCountFrequency (%)
12 1746
27.6%
13 1704
26.9%
14 1704
26.9%
11 1173
18.5%

Length

2023-12-11T12:52:08.675070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T12:52:08.797111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
12 1746
27.6%
13 1704
26.9%
14 1704
26.9%
11 1173
18.5%

DELNG_BUNDLE_QY
Real number (ℝ)

HIGH CORRELATION 

Distinct14
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.158527
Minimum1
Maximum500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size55.7 KiB
2023-12-11T12:52:08.910787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q14
median8
Q310
95-th percentile20
Maximum500
Range499
Interquartile range (IQR)6

Descriptive statistics

Standard deviation75.674427
Coefficient of variation (CV)3.7539661
Kurtosis35.384885
Mean20.158527
Median Absolute Deviation (MAD)3
Skewness6.0569623
Sum127543
Variance5726.6189
MonotonicityNot monotonic
2023-12-11T12:52:09.018634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
10.0 2082
32.9%
4.0 870
13.8%
5.0 720
 
11.4%
2.0 538
 
8.5%
8.0 420
 
6.6%
4.5 420
 
6.6%
1.0 255
 
4.0%
3.0 231
 
3.7%
15.0 223
 
3.5%
20.0 217
 
3.4%
Other values (4) 351
 
5.5%
ValueCountFrequency (%)
1.0 255
 
4.0%
2.0 538
 
8.5%
3.0 231
 
3.7%
4.0 870
13.8%
4.5 420
 
6.6%
5.0 720
 
11.4%
6.0 84
 
1.3%
8.0 420
 
6.6%
10.0 2082
32.9%
15.0 223
 
3.5%
ValueCountFrequency (%)
500.0 150
 
2.4%
100.0 87
 
1.4%
20.0 217
 
3.4%
16.0 30
 
0.5%
15.0 223
 
3.5%
10.0 2082
32.9%
8.0 420
 
6.6%
6.0 84
 
1.3%
5.0 720
 
11.4%
4.5 420
 
6.6%

STNDRD
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size49.6 KiB
kg 상자
5424 
kg 단
 
183
kg 개
 
168
 
167
g 단
 
153
Other values (3)
 
232

Length

Max length7
Median length6
Mean length5.7739845
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
kg 상자 5424
85.7%
kg 단 183
 
2.9%
kg 개 168
 
2.7%
167
 
2.6%
g 단 153
 
2.4%
kg 85
 
1.3%
kg 그물망 84
 
1.3%
kg PP대 63
 
1.0%

Length

2023-12-11T12:52:09.169736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T12:52:09.296108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
kg 6007
48.4%
상자 5424
43.7%
336
 
2.7%
335
 
2.7%
g 153
 
1.2%
그물망 84
 
0.7%
pp대 63
 
0.5%

STNDRD_CD
Real number (ℝ)

HIGH CORRELATION 

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean119448.59
Minimum101200
Maximum121200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size55.7 KiB
2023-12-11T12:52:09.415046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum101200
5-th percentile111100
Q1120100
median120100
Q3120100
95-th percentile121100
Maximum121200
Range20000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3320.9712
Coefficient of variation (CV)0.027802516
Kurtosis22.061789
Mean119448.59
Median Absolute Deviation (MAD)0
Skewness-4.751159
Sum7.557512 × 108
Variance11028850
MonotonicityNot monotonic
2023-12-11T12:52:09.513460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
120100 5424
85.7%
121100 183
 
2.9%
121200 168
 
2.7%
101200 167
 
2.6%
111100 153
 
2.4%
120000 85
 
1.3%
120500 84
 
1.3%
120400 63
 
1.0%
ValueCountFrequency (%)
101200 167
 
2.6%
111100 153
 
2.4%
120000 85
 
1.3%
120100 5424
85.7%
120400 63
 
1.0%
120500 84
 
1.3%
121100 183
 
2.9%
121200 168
 
2.7%
ValueCountFrequency (%)
121200 168
 
2.7%
121100 183
 
2.9%
120500 84
 
1.3%
120400 63
 
1.0%
120100 5424
85.7%
120000 85
 
1.3%
111100 153
 
2.4%
101200 167
 
2.6%

MUMM_AMT
Real number (ℝ)

HIGH CORRELATION 

Distinct366
Distinct (%)5.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16565.357
Minimum300
Maximum123000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size55.7 KiB
2023-12-11T12:52:09.668250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum300
5-th percentile1500
Q16000
median12000
Q321000
95-th percentile47000
Maximum123000
Range122700
Interquartile range (IQR)15000

Descriptive statistics

Standard deviation16745.129
Coefficient of variation (CV)1.0108523
Kurtosis9.8274541
Mean16565.357
Median Absolute Deviation (MAD)7000
Skewness2.7057604
Sum1.0480901 × 108
Variance2.8039935 × 108
MonotonicityNot monotonic
2023-12-11T12:52:09.814658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11000 245
 
3.9%
8000 235
 
3.7%
5000 227
 
3.6%
10000 218
 
3.4%
20000 204
 
3.2%
6000 197
 
3.1%
13000 188
 
3.0%
12000 178
 
2.8%
4000 166
 
2.6%
3000 165
 
2.6%
Other values (356) 4304
68.0%
ValueCountFrequency (%)
300 1
 
< 0.1%
400 1
 
< 0.1%
450 1
 
< 0.1%
500 25
0.4%
550 2
 
< 0.1%
560 3
 
< 0.1%
570 1
 
< 0.1%
600 7
 
0.1%
625 1
 
< 0.1%
650 3
 
< 0.1%
ValueCountFrequency (%)
123000 1
 
< 0.1%
122000 1
 
< 0.1%
121000 1
 
< 0.1%
117000 1
 
< 0.1%
114800 19
0.3%
113000 1
 
< 0.1%
110000 2
 
< 0.1%
103000 1
 
< 0.1%
100450 19
0.3%
100000 7
 
0.1%

AVRG_AMT
Real number (ℝ)

HIGH CORRELATION 

Distinct2938
Distinct (%)46.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18541.012
Minimum529
Maximum123000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size55.7 KiB
2023-12-11T12:52:09.987438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum529
5-th percentile1943.6
Q17500
median14111
Q323497
95-th percentile48596.5
Maximum123000
Range122471
Interquartile range (IQR)15997

Descriptive statistics

Standard deviation17250.889
Coefficient of variation (CV)0.93041785
Kurtosis8.8425193
Mean18541.012
Median Absolute Deviation (MAD)7368
Skewness2.5797071
Sum1.1730898 × 108
Variance2.9759317 × 108
MonotonicityNot monotonic
2023-12-11T12:52:10.183169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20000 78
 
1.2%
13000 70
 
1.1%
11000 69
 
1.1%
21000 69
 
1.1%
28000 64
 
1.0%
12000 62
 
1.0%
32000 58
 
0.9%
13500 55
 
0.9%
24000 52
 
0.8%
8000 49
 
0.8%
Other values (2928) 5701
90.1%
ValueCountFrequency (%)
529 1
< 0.1%
600 1
< 0.1%
626 1
< 0.1%
643 1
< 0.1%
657 1
< 0.1%
677 1
< 0.1%
720 1
< 0.1%
725 1
< 0.1%
765 1
< 0.1%
781 1
< 0.1%
ValueCountFrequency (%)
123000 1
 
< 0.1%
122500 1
 
< 0.1%
121500 1
 
< 0.1%
117500 1
 
< 0.1%
114800 19
0.3%
113231 1
 
< 0.1%
110000 2
 
< 0.1%
108800 1
 
< 0.1%
104294 19
0.3%
103000 1
 
< 0.1%

MXMM_AMT
Real number (ℝ)

HIGH CORRELATION 

Distinct393
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20256.251
Minimum570
Maximum123000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size55.7 KiB
2023-12-11T12:52:10.327077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum570
5-th percentile2365.7
Q19000
median15500
Q325000
95-th percentile53000
Maximum123000
Range122430
Interquartile range (IQR)16000

Descriptive statistics

Standard deviation18295.795
Coefficient of variation (CV)0.90321725
Kurtosis8.1372699
Mean20256.251
Median Absolute Deviation (MAD)8200
Skewness2.4813322
Sum1.281613 × 108
Variance3.3473611 × 108
MonotonicityNot monotonic
2023-12-11T12:52:10.481262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11000 241
 
3.8%
20000 226
 
3.6%
13000 204
 
3.2%
14000 187
 
3.0%
12000 179
 
2.8%
8000 178
 
2.8%
28000 178
 
2.8%
16000 164
 
2.6%
9000 150
 
2.4%
5000 146
 
2.3%
Other values (383) 4474
70.7%
ValueCountFrequency (%)
570 1
 
< 0.1%
600 1
 
< 0.1%
750 2
 
< 0.1%
800 2
 
< 0.1%
850 2
 
< 0.1%
890 1
 
< 0.1%
900 3
 
< 0.1%
950 3
 
< 0.1%
960 1
 
< 0.1%
1000 10
0.2%
ValueCountFrequency (%)
123000 2
 
< 0.1%
122000 1
 
< 0.1%
121000 2
 
< 0.1%
118000 1
 
< 0.1%
117000 1
 
< 0.1%
114800 38
0.6%
113000 1
 
< 0.1%
110000 3
 
< 0.1%
103000 2
 
< 0.1%
102000 1
 
< 0.1%

Interactions

2023-12-11T12:52:04.537803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:51:56.031716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:51:57.129955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:51:58.320786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:51:59.439282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:52:00.998687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:52:02.302619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:52:03.486036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:52:04.667168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:51:56.151771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:51:57.285880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:51:58.464531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:51:59.903459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:52:01.204581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:52:02.437288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:52:03.612909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:52:04.806365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:51:56.268734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:51:57.437848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:51:58.600376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:52:00.036591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:52:01.374733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:52:02.569159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:52:03.742877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:52:04.960965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:51:56.420407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:51:57.580806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:51:58.738955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:52:00.178550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:52:01.557277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:52:02.711147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:52:03.861264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:52:05.108414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:51:56.551298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:51:57.733872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:51:58.881229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:52:00.315016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:52:01.694311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:52:02.845586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:52:03.986525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:52:05.280832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:51:56.682275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:51:57.889437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:51:59.032430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:52:00.469160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:52:01.858192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:52:03.040937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:52:04.130728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:52:05.414890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:51:56.808091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:51:58.033700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:51:59.168806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:52:00.635930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:52:02.014797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:52:03.206850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:52:04.278004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:52:05.551344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:51:56.949792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:51:58.177873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:51:59.308554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:52:00.818180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:52:02.176366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:52:03.358260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:52:04.405722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T12:52:10.606110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
EXAMIN_DEPRDLST_NMPRDLST_CDSPCIES_NMSPCIES_CDGRADGRAD_CDDELNG_BUNDLE_QYSTNDRDSTNDRD_CDMUMM_AMTAVRG_AMTMXMM_AMT
EXAMIN_DE1.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0070.0670.087
PRDLST_NM0.0001.0001.0001.0001.0000.2880.2880.8870.9640.9510.7690.7920.823
PRDLST_CD0.0001.0001.0001.0001.0000.1660.1660.6190.6390.3470.4330.4410.474
SPCIES_NM0.0001.0001.0001.0001.0000.2750.2750.9860.9860.9360.8260.8380.873
SPCIES_CD0.0001.0001.0001.0001.0000.1660.1660.6190.6390.3470.4330.4410.474
GRAD0.0000.2880.1660.2750.1661.0001.0000.0000.0620.0000.4160.3370.282
GRAD_CD0.0000.2880.1660.2750.1661.0001.0000.0000.0620.0000.4160.3370.282
DELNG_BUNDLE_QY0.0000.8870.6190.9860.6190.0000.0001.0000.8821.0000.2010.2390.260
STNDRD0.0000.9640.6390.9860.6390.0620.0620.8821.0001.0000.2740.3050.315
STNDRD_CD0.0000.9510.3470.9360.3470.0000.0001.0001.0001.0000.1950.2250.255
MUMM_AMT0.0070.7690.4330.8260.4330.4160.4160.2010.2740.1951.0000.9890.962
AVRG_AMT0.0670.7920.4410.8380.4410.3370.3370.2390.3050.2250.9891.0000.981
MXMM_AMT0.0870.8230.4740.8730.4740.2820.2820.2600.3150.2550.9620.9811.000
2023-12-11T12:52:10.779403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
PRDLST_NMGRADGRAD_CDSTNDRD
PRDLST_NM1.0000.1470.1470.792
GRAD0.1471.0001.0000.028
GRAD_CD0.1471.0001.0000.028
STNDRD0.7920.0280.0281.000
2023-12-11T12:52:10.916721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
EXAMIN_DEPRDLST_CDSPCIES_CDDELNG_BUNDLE_QYSTNDRD_CDMUMM_AMTAVRG_AMTMXMM_AMTPRDLST_NMGRADGRAD_CDSTNDRD
EXAMIN_DE1.000-0.013-0.0130.016-0.004-0.018-0.020-0.0190.0000.0000.0000.000
PRDLST_CD-0.0131.0000.998-0.2270.059-0.073-0.085-0.0830.9970.1070.1070.381
SPCIES_CD-0.0130.9981.000-0.2270.059-0.074-0.086-0.0850.9970.1070.1070.381
DELNG_BUNDLE_QY0.016-0.227-0.2271.000-0.2480.3300.3680.3690.7000.0000.0000.854
STNDRD_CD-0.0040.0590.059-0.2481.0000.1230.1000.0920.7110.0060.0061.000
MUMM_AMT-0.018-0.073-0.0740.3300.1231.0000.9610.9270.3610.2610.2610.134
AVRG_AMT-0.020-0.085-0.0860.3680.1000.9611.0000.9890.3850.2070.2070.151
MXMM_AMT-0.019-0.083-0.0850.3690.0920.9270.9891.0000.4220.1720.1720.156
PRDLST_NM0.0000.9970.9970.7000.7110.3610.3850.4221.0000.1470.1470.792
GRAD0.0000.1070.1070.0000.0060.2610.2070.1720.1471.0001.0000.028
GRAD_CD0.0000.1070.1070.0000.0060.2610.2070.1720.1471.0001.0000.028
STNDRD0.0000.3810.3810.8541.0000.1340.1510.1560.7920.0280.0281.000

Missing values

2023-12-11T12:52:05.720671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T12:52:05.939318image/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_DEPRDLST_NMPRDLST_CDSPCIES_NMSPCIES_CDGRADGRAD_CDDELNG_BUNDLE_QYSTNDRDSTNDRD_CDMUMM_AMTAVRG_AMTMXMM_AMT
020220502만감615한라봉61504보통133.0kg 상자120100125001255413000
120220502만감615한라봉615044등143.0kg 상자12010080001091012500
220220502망고636망고(수입)63698115.0kg 상자120100390003900039000
320220502망고636망고(수입)63698125.0kg 상자120100390003900039000
420220502망고636망고(수입)63698보통135.0kg 상자120100380003876539000
520220502망고636망고(수입)636984등145.0kg 상자120100365003700038000
620220502수박801수박(일반)80101111.0kg120000312035823833
720220502수박801수박(일반)80101121.0kg120000262529583120
820220502수박801수박(일반)80101보통131.0kg120000140020742625
920220502수박801수박(일반)801014등141.0kg1200007509161400
EXAMIN_DEPRDLST_NMPRDLST_CDSPCIES_NMSPCIES_CDGRADGRAD_CDDELNG_BUNDLE_QYSTNDRDSTNDRD_CDMUMM_AMTAVRG_AMTMXMM_AMT
631720220531감귤614하우스감귤614054등145.0kg 상자120100130001333313500
631820220531만감615한라봉61504113.0kg 상자120100130001300013000
631920220531만감615한라봉61504123.0kg 상자120100130001300013000
632020220531만감615한라봉61504보통133.0kg 상자120100125001255413000
632120220531만감615한라봉615044등143.0kg 상자12010080001091012500
632220220531매실625매실(일반)625011110.0kg 상자120100280003338542000
632320220531매실625매실(일반)625011210.0kg 상자120100220002223728000
632420220531매실625매실(일반)62501보통1310.0kg 상자120100135001510022000
632520220531매실625매실(일반)625014등1410.0kg 상자1201003000966713500
632620220531망고636망고(수입)63698115.0kg 상자120100525005250052500