Overview

Dataset statistics

Number of variables11
Number of observations10000
Missing cells42194
Missing cells (%)38.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.0 MiB
Average record size in memory105.0 B

Variable types

Categorical1
Text1
Numeric9

Dataset

Description분류,품종구분명,품목코드,품목명,서울명과,농협,중앙청과,동화청과,한국청과,계,날짜
Author서울시농수산식품공사
URLhttps://data.seoul.go.kr/dataList/OA-13419/S/1/datasetView.do

Alerts

품목코드 is highly overall correlated with 품목명 and 5 other fieldsHigh correlation
품목명 is highly overall correlated with 품목코드 and 5 other fieldsHigh correlation
서울명과 is highly overall correlated with 품목코드 and 5 other fieldsHigh correlation
농협 is highly overall correlated with 품목코드 and 4 other fieldsHigh correlation
중앙청과 is highly overall correlated with 품목코드 and 6 other fieldsHigh correlation
동화청과 is highly overall correlated with 품목코드 and 4 other fieldsHigh correlation
한국청과 is highly overall correlated with 중앙청과 and 1 other fieldsHigh correlation
계 is highly overall correlated with 품목코드 and 6 other fieldsHigh correlation
품목코드 has 5691 (56.9%) missing valuesMissing
품목명 has 6670 (66.7%) missing valuesMissing
서울명과 has 5483 (54.8%) missing valuesMissing
농협 has 5282 (52.8%) missing valuesMissing
중앙청과 has 5693 (56.9%) missing valuesMissing
동화청과 has 9374 (93.7%) missing valuesMissing
한국청과 has 4000 (40.0%) missing valuesMissing

Reproduction

Analysis started2024-05-18 06:18:05.435337
Analysis finished2024-05-18 06:18:36.249743
Duration30.81 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

분류
Categorical

Distinct16
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
기타채소
2204 
과일류
1395 
양채류
1176 
<전체>
857 
엽채류
843 
Other values (11)
3525 

Length

Max length4
Median length3
Mean length3.3088
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row패류
2nd row<전체>
3rd row근채류
4th row과일류
5th row근채류

Common Values

ValueCountFrequency (%)
기타채소 2204
22.0%
과일류 1395
14.0%
양채류 1176
11.8%
<전체> 857
 
8.6%
엽채류 843
 
8.4%
선어류 672
 
6.7%
과채류 458
 
4.6%
연체류 444
 
4.4%
근채류 422
 
4.2%
패류 385
 
3.9%
Other values (6) 1144
11.4%

Length

2024-05-18T15:18:36.606755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
기타채소 2204
22.0%
과일류 1395
14.0%
양채류 1176
11.8%
전체 857
 
8.6%
엽채류 843
 
8.4%
선어류 672
 
6.7%
과채류 458
 
4.6%
연체류 444
 
4.4%
근채류 422
 
4.2%
패류 385
 
3.9%
Other values (6) 1144
11.4%

품종구분명
Text

Distinct327
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-18T15:18:37.287871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length8
Mean length3.1665
Min length1

Characters and Unicode

Total characters31665
Distinct characters275
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

Unique18 ?
Unique (%)0.2%

Sample

1st row기타 가공어류
2nd row합계
3rd row총각무(일반)
4th row유자
5th row당근
ValueCountFrequency (%)
기타 505
 
4.5%
가공 407
 
3.7%
무 117
 
1.0%
양채류 104
 
0.9%
패류 99
 
0.9%
엽채류 96
 
0.9%
낙지 95
 
0.9%
과일류 93
 
0.8%
건어류 89
 
0.8%
주꾸미 85
 
0.8%
Other values (316) 9455
84.8%
2024-05-18T15:18:38.874246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
류 1779
 
5.6%
1145
 
3.6%
기 828
 
2.6%
어 752
 
2.4%
리 715
 
2.3%
추 680
 
2.1%
타 656
 
2.1%
채 652
 
2.1%
가 621
 
2.0%
고 595
 
1.9%
Other values (265) 23242
73.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 29978
94.7%
Space Separator 1145
 
3.6%
Close Punctuation 271
 
0.9%
Open Punctuation 271
 
0.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
류 1779
 
5.9%
기 828
 
2.8%
어 752
 
2.5%
리 715
 
2.4%
추 680
 
2.3%
타 656
 
2.2%
채 652
 
2.2%
가 621
 
2.1%
고 595
 
2.0%
나 495
 
1.7%
Other values (262) 22205
74.1%
Space Separator
ValueCountFrequency (%)
1145
100.0%
Close Punctuation
ValueCountFrequency (%)
) 271
100.0%
Open Punctuation
ValueCountFrequency (%)
( 271
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 29978
94.7%
Common 1687
 
5.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
류 1779
 
5.9%
기 828
 
2.8%
어 752
 
2.5%
리 715
 
2.4%
추 680
 
2.3%
타 656
 
2.2%
채 652
 
2.2%
가 621
 
2.1%
고 595
 
2.0%
나 495
 
1.7%
Other values (262) 22205
74.1%
Common
ValueCountFrequency (%)
1145
67.9%
) 271
 
16.1%
( 271
 
16.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 29978
94.7%
ASCII 1687
 
5.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
류 1779
 
5.9%
기 828
 
2.8%
어 752
 
2.5%
리 715
 
2.4%
추 680
 
2.3%
타 656
 
2.2%
채 652
 
2.2%
가 621
 
2.1%
고 595
 
2.0%
나 495
 
1.7%
Other values (262) 22205
74.1%
ASCII
ValueCountFrequency (%)
1145
67.9%
) 271
 
16.1%
( 271
 
16.1%

품목코드
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct3549
Distinct (%)82.4%
Missing5691
Missing (%)56.9%
Infinite0
Infinite (%)0.0%
Mean42.643195
Minimum0.001
Maximum1811.8702
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T15:18:39.345877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.001
5-th percentile0.05816
Q10.764
median7
Q332.99
95-th percentile159.24884
Maximum1811.8702
Range1811.8692
Interquartile range (IQR)32.226

Descriptive statistics

Standard deviation137.27788
Coefficient of variation (CV)3.2192213
Kurtosis60.030648
Mean42.643195
Median Absolute Deviation (MAD)6.847
Skewness7.2302677
Sum183749.53
Variance18845.217
MonotonicityNot monotonic
2024-05-18T15:18:39.867638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.01 26
 
0.3%
0.1 22
 
0.2%
0.02 22
 
0.2%
0.04 18
 
0.2%
0.08 18
 
0.2%
0.12 15
 
0.1%
0.05 15
 
0.1%
0.2 14
 
0.1%
0.28 12
 
0.1%
0.24 11
 
0.1%
Other values (3539) 4136
41.4%
(Missing) 5691
56.9%
ValueCountFrequency (%)
0.001 1
 
< 0.1%
0.003 4
< 0.1%
0.0035 1
 
< 0.1%
0.004 3
< 0.1%
0.005 4
< 0.1%
0.006 2
< 0.1%
0.007 1
 
< 0.1%
0.008 4
< 0.1%
0.0084 1
 
< 0.1%
0.0085 1
 
< 0.1%
ValueCountFrequency (%)
1811.87025 1
< 0.1%
1706.16615 1
< 0.1%
1595.80765 1
< 0.1%
1470.63368 1
< 0.1%
1456.69185 1
< 0.1%
1422.5036 1
< 0.1%
1403.3643 1
< 0.1%
1299.0446 1
< 0.1%
1299.0013 1
< 0.1%
1293.8241 1
< 0.1%

품목명
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct2462
Distinct (%)73.9%
Missing6670
Missing (%)66.7%
Infinite0
Infinite (%)0.0%
Mean23.762497
Minimum0.002
Maximum919.0541
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T15:18:40.397165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.002
5-th percentile0.04256
Q10.48825
median2.456
Q314.4907
95-th percentile103.81392
Maximum919.0541
Range919.0521
Interquartile range (IQR)14.00245

Descriptive statistics

Standard deviation74.376834
Coefficient of variation (CV)3.1300092
Kurtosis44.706902
Mean23.762497
Median Absolute Deviation (MAD)2.3641
Skewness6.1361531
Sum79129.115
Variance5531.9135
MonotonicityNot monotonic
2024-05-18T15:18:41.002114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.02 29
 
0.3%
0.12 21
 
0.2%
0.08 21
 
0.2%
0.04 20
 
0.2%
0.2 18
 
0.2%
0.1 15
 
0.1%
0.32 13
 
0.1%
0.15 12
 
0.1%
0.24 12
 
0.1%
0.06 12
 
0.1%
Other values (2452) 3157
31.6%
(Missing) 6670
66.7%
ValueCountFrequency (%)
0.002 4
< 0.1%
0.0025 1
 
< 0.1%
0.003 3
 
< 0.1%
0.004 9
0.1%
0.005 2
 
< 0.1%
0.006 1
 
< 0.1%
0.0065 1
 
< 0.1%
0.007 1
 
< 0.1%
0.0071 1
 
< 0.1%
0.008 2
 
< 0.1%
ValueCountFrequency (%)
919.0541 1
< 0.1%
749.7981 1
< 0.1%
749.3484 1
< 0.1%
735.8387 1
< 0.1%
717.18772 1
< 0.1%
701.3768 1
< 0.1%
700.9943 1
< 0.1%
690.6655 1
< 0.1%
680.041 1
< 0.1%
674.5299 1
< 0.1%

서울명과
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct3461
Distinct (%)76.6%
Missing5483
Missing (%)54.8%
Infinite0
Infinite (%)0.0%
Mean37.80402
Minimum-519.7018
Maximum1691.9645
Zeros18
Zeros (%)0.2%
Negative6
Negative (%)0.1%
Memory size166.0 KiB
2024-05-18T15:18:41.720653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-519.7018
5-th percentile0.05
Q10.615
median4.208
Q320.052
95-th percentile169.5228
Maximum1691.9645
Range2211.6663
Interquartile range (IQR)19.437

Descriptive statistics

Standard deviation128.73329
Coefficient of variation (CV)3.4052804
Kurtosis57.881249
Mean37.80402
Median Absolute Deviation (MAD)4.078
Skewness6.9633339
Sum170760.76
Variance16572.259
MonotonicityNot monotonic
2024-05-18T15:18:42.166770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.02 22
 
0.2%
0.0 18
 
0.2%
0.12 17
 
0.2%
0.15 16
 
0.2%
0.3 15
 
0.1%
0.008 14
 
0.1%
0.04 14
 
0.1%
0.08 13
 
0.1%
0.05 13
 
0.1%
0.24 13
 
0.1%
Other values (3451) 4362
43.6%
(Missing) 5483
54.8%
ValueCountFrequency (%)
-519.7018 1
 
< 0.1%
-0.547 1
 
< 0.1%
-0.085 1
 
< 0.1%
-0.06 1
 
< 0.1%
-0.03 1
 
< 0.1%
-0.024 1
 
< 0.1%
0.0 18
0.2%
0.0015 1
 
< 0.1%
0.002 5
 
0.1%
0.004 9
0.1%
ValueCountFrequency (%)
1691.9645 1
< 0.1%
1651.7392 1
< 0.1%
1598.4377 1
< 0.1%
1457.8962 1
< 0.1%
1316.2655 1
< 0.1%
1288.9936 1
< 0.1%
1281.6537 1
< 0.1%
1279.0739 1
< 0.1%
1222.8566 1
< 0.1%
1218.1165 1
< 0.1%

농협
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct3722
Distinct (%)78.9%
Missing5282
Missing (%)52.8%
Infinite0
Infinite (%)0.0%
Mean41.980863
Minimum-0.972
Maximum1835.235
Zeros7
Zeros (%)0.1%
Negative1
Negative (%)< 0.1%
Memory size166.0 KiB
2024-05-18T15:18:42.579408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-0.972
5-th percentile0.06185
Q10.891
median5.8805
Q328.32525
95-th percentile184.4867
Maximum1835.235
Range1836.207
Interquartile range (IQR)27.43425

Descriptive statistics

Standard deviation142.4089
Coefficient of variation (CV)3.3922337
Kurtosis62.519635
Mean41.980863
Median Absolute Deviation (MAD)5.669
Skewness7.393577
Sum198065.71
Variance20280.294
MonotonicityNot monotonic
2024-05-18T15:18:43.020585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1 14
 
0.1%
0.05 12
 
0.1%
0.016 11
 
0.1%
0.01 11
 
0.1%
0.008 10
 
0.1%
0.06 9
 
0.1%
0.56 9
 
0.1%
0.006 9
 
0.1%
0.096 8
 
0.1%
0.092 8
 
0.1%
Other values (3712) 4617
46.2%
(Missing) 5282
52.8%
ValueCountFrequency (%)
-0.972 1
 
< 0.1%
0.0 7
0.1%
0.001 1
 
< 0.1%
0.002 3
 
< 0.1%
0.004 6
0.1%
0.005 7
0.1%
0.006 9
0.1%
0.007 2
 
< 0.1%
0.008 10
0.1%
0.009 3
 
< 0.1%
ValueCountFrequency (%)
1835.235 1
< 0.1%
1832.7 1
< 0.1%
1649.467 1
< 0.1%
1566.03 1
< 0.1%
1538.35 1
< 0.1%
1458.341 1
< 0.1%
1449.368 1
< 0.1%
1429.147 1
< 0.1%
1370.913 1
< 0.1%
1359.309 1
< 0.1%

중앙청과
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct3298
Distinct (%)76.6%
Missing5693
Missing (%)56.9%
Infinite0
Infinite (%)0.0%
Mean48.27328
Minimum0.0001
Maximum2062.3548
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T15:18:43.549502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.0001
5-th percentile0.05
Q10.42
median4.7
Q326.90075
95-th percentile211.70956
Maximum2062.3548
Range2062.3547
Interquartile range (IQR)26.48075

Descriptive statistics

Standard deviation160.73761
Coefficient of variation (CV)3.329743
Kurtosis52.678844
Mean48.27328
Median Absolute Deviation (MAD)4.604
Skewness6.7169841
Sum207913.02
Variance25836.581
MonotonicityNot monotonic
2024-05-18T15:18:43.986604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.2 27
 
0.3%
0.016 24
 
0.2%
0.04 20
 
0.2%
0.1 18
 
0.2%
0.12 17
 
0.2%
0.15 16
 
0.2%
0.02 15
 
0.1%
0.3 14
 
0.1%
0.06 13
 
0.1%
0.08 12
 
0.1%
Other values (3288) 4131
41.3%
(Missing) 5693
56.9%
ValueCountFrequency (%)
0.0001 1
 
< 0.1%
0.0005 1
 
< 0.1%
0.001 3
 
< 0.1%
0.0018 1
 
< 0.1%
0.002 2
 
< 0.1%
0.003 1
 
< 0.1%
0.0035 1
 
< 0.1%
0.0036 1
 
< 0.1%
0.004 10
0.1%
0.005 3
 
< 0.1%
ValueCountFrequency (%)
2062.35476 1
< 0.1%
2007.81926 1
< 0.1%
1749.93973 1
< 0.1%
1741.78671 1
< 0.1%
1663.78253 1
< 0.1%
1553.68552 1
< 0.1%
1504.3266 1
< 0.1%
1500.91797 1
< 0.1%
1467.42407 1
< 0.1%
1466.71105 1
< 0.1%

동화청과
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct617
Distinct (%)98.6%
Missing9374
Missing (%)93.7%
Infinite0
Infinite (%)0.0%
Mean450.1041
Minimum0.384
Maximum2883.47
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T15:18:44.691435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.384
5-th percentile6.79975
Q139.9025
median251.706
Q3639.209
95-th percentile1641.533
Maximum2883.47
Range2883.086
Interquartile range (IQR)599.3065

Descriptive statistics

Standard deviation525.1561
Coefficient of variation (CV)1.1667436
Kurtosis2.82118
Mean450.1041
Median Absolute Deviation (MAD)237.495
Skewness1.7102961
Sum281765.17
Variance275788.93
MonotonicityNot monotonic
2024-05-18T15:18:45.289253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
462.44 2
 
< 0.1%
3.024 2
 
< 0.1%
740.3 2
 
< 0.1%
36.32 2
 
< 0.1%
15.0 2
 
< 0.1%
17.808 2
 
< 0.1%
724.4 2
 
< 0.1%
4.344 2
 
< 0.1%
9.22 2
 
< 0.1%
641.392 1
 
< 0.1%
Other values (607) 607
 
6.1%
(Missing) 9374
93.7%
ValueCountFrequency (%)
0.384 1
< 0.1%
1.448 1
< 0.1%
2.576 1
< 0.1%
3.024 2
< 0.1%
3.2 1
< 0.1%
3.706 1
< 0.1%
3.732 1
< 0.1%
3.798 1
< 0.1%
4.344 2
< 0.1%
4.526 1
< 0.1%
ValueCountFrequency (%)
2883.47 1
< 0.1%
2644.295 1
< 0.1%
2518.505 1
< 0.1%
2489.906 1
< 0.1%
2419.624 1
< 0.1%
2380.737 1
< 0.1%
2177.092 1
< 0.1%
2155.645 1
< 0.1%
2054.607 1
< 0.1%
2002.79 1
< 0.1%

한국청과
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct3387
Distinct (%)56.5%
Missing4000
Missing (%)40.0%
Infinite0
Infinite (%)0.0%
Mean14.577789
Minimum0.0005
Maximum987.73647
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T15:18:46.090517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.0005
5-th percentile0.02397
Q10.225
median1.1867
Q35.92185
95-th percentile53.956625
Maximum987.73647
Range987.73597
Interquartile range (IQR)5.69685

Descriptive statistics

Standard deviation64.163145
Coefficient of variation (CV)4.4014318
Kurtosis94.854379
Mean14.577789
Median Absolute Deviation (MAD)1.1167
Skewness9.1292901
Sum87466.736
Variance4116.9092
MonotonicityNot monotonic
2024-05-18T15:18:47.024060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1 95
 
0.9%
0.2 71
 
0.7%
0.02 70
 
0.7%
0.08 66
 
0.7%
0.05 55
 
0.5%
0.04 54
 
0.5%
0.4 52
 
0.5%
0.3 52
 
0.5%
0.06 51
 
0.5%
0.12 48
 
0.5%
Other values (3377) 5386
53.9%
(Missing) 4000
40.0%
ValueCountFrequency (%)
0.0005 1
 
< 0.1%
0.001 2
 
< 0.1%
0.0018 3
 
< 0.1%
0.002 13
0.1%
0.0024 1
 
< 0.1%
0.003 3
 
< 0.1%
0.0036 1
 
< 0.1%
0.004 10
0.1%
0.0045 2
 
< 0.1%
0.0048 2
 
< 0.1%
ValueCountFrequency (%)
987.73647 1
< 0.1%
911.85099 1
< 0.1%
885.91517 1
< 0.1%
880.75375 1
< 0.1%
811.35235 1
< 0.1%
799.99748 1
< 0.1%
760.31272 1
< 0.1%
736.48861 1
< 0.1%
732.18854 1
< 0.1%
728.9659 1
< 0.1%

계
Real number (ℝ)

HIGH CORRELATION 

Distinct6925
Distinct (%)69.3%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean120.89709
Minimum-78.3591
Maximum9240.2841
Zeros5
Zeros (%)< 0.1%
Negative2
Negative (%)< 0.1%
Memory size166.0 KiB
2024-05-18T15:18:47.532869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-78.3591
5-th percentile0.045
Q10.51
median3.508
Q334.200115
95-th percentile509.32425
Maximum9240.2841
Range9318.6431
Interquartile range (IQR)33.690115

Descriptive statistics

Standard deviation595.65447
Coefficient of variation (CV)4.9269544
Kurtosis129.27662
Mean120.89709
Median Absolute Deviation (MAD)3.436
Skewness10.663067
Sum1208850
Variance354804.24
MonotonicityNot monotonic
2024-05-18T15:18:48.008740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1 80
 
0.8%
0.02 71
 
0.7%
0.2 59
 
0.6%
0.05 58
 
0.6%
0.08 58
 
0.6%
0.04 52
 
0.5%
0.3 46
 
0.5%
0.4 45
 
0.4%
0.01 42
 
0.4%
0.12 42
 
0.4%
Other values (6915) 9446
94.5%
ValueCountFrequency (%)
-78.3591 1
 
< 0.1%
-0.024 1
 
< 0.1%
0.0 5
0.1%
0.001 3
 
< 0.1%
0.0018 3
 
< 0.1%
0.002 12
0.1%
0.0024 1
 
< 0.1%
0.0025 1
 
< 0.1%
0.003 3
 
< 0.1%
0.0036 1
 
< 0.1%
ValueCountFrequency (%)
9240.28405 1
< 0.1%
8944.3754 1
< 0.1%
8925.5129 1
< 0.1%
8859.0877 1
< 0.1%
8852.45851 1
< 0.1%
8655.59629 1
< 0.1%
8550.07837 1
< 0.1%
8507.68078 1
< 0.1%
8505.09162 1
< 0.1%
8324.80152 1
< 0.1%

날짜
Real number (ℝ)

Distinct345
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20234303
Minimum20230516
Maximum20240517
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T15:18:48.571945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20230516
5-th percentile20230601
Q120230816
median20231115
Q320240216
95-th percentile20240501
Maximum20240517
Range10001
Interquartile range (IQR)9400

Descriptive statistics

Standard deviation4527.2887
Coefficient of variation (CV)0.00022374325
Kurtosis-1.6688115
Mean20234303
Median Absolute Deviation (MAD)411
Skewness0.56889433
Sum2.0234303 × 1011
Variance20496343
MonotonicityNot monotonic
2024-05-18T15:18:48.980399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20240305 48
 
0.5%
20231209 47
 
0.5%
20230602 47
 
0.5%
20231024 47
 
0.5%
20231216 46
 
0.5%
20240412 45
 
0.4%
20231013 45
 
0.4%
20231020 44
 
0.4%
20240502 44
 
0.4%
20230912 44
 
0.4%
Other values (335) 9543
95.4%
ValueCountFrequency (%)
20230516 42
0.4%
20230517 30
0.3%
20230518 31
0.3%
20230519 36
0.4%
20230520 30
0.3%
20230521 2
 
< 0.1%
20230522 27
0.3%
20230523 33
0.3%
20230524 29
0.3%
20230525 43
0.4%
ValueCountFrequency (%)
20240517 39
0.4%
20240516 28
0.3%
20240515 35
0.4%
20240514 35
0.4%
20240513 23
0.2%
20240511 32
0.3%
20240510 29
0.3%
20240509 39
0.4%
20240508 43
0.4%
20240507 30
0.3%

Interactions

2024-05-18T15:18:32.265075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:18:08.255387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:18:11.386352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:18:14.236681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:18:16.895771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:18:19.628700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:18:22.474544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:18:25.479078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:18:28.986901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:18:32.579381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:18:08.762483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:18:11.787425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:18:14.509041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:18:17.184387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:18:19.936586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:18:22.752448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:18:25.844638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:18:29.495217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:18:32.900249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:18:09.098412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:18:12.081654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:18:14.767860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:18:17.452392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:18:20.225034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:18:23.027358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:18:26.151522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:18:29.791761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:18:33.256580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:18:09.350668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:18:12.342582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:18:15.024042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:18:17.791191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:18:20.500577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:18:23.403164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:18:26.552411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:18:30.200357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:18:33.558449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:18:09.698132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:18:12.640870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:18:15.328027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:18:18.113595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:18:20.801913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:18:23.739595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:18:26.846358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:18:30.573671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:18:33.823658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:18:10.003060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:18:12.993101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:18:15.584947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:18:18.397797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:18:21.068614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:18:23.999721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:18:27.190790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:18:30.820332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:18:34.096606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:18:10.331690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:18:13.310010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:18:15.919813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:18:18.701812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:18:21.431356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:18:24.357092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:18:27.544605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:18:31.089948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:18:34.374428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:18:10.760403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:18:13.623042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:18:16.328186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:18:19.039247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:18:21.708534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:18:24.749682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:18:28.175071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:18:31.496067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:18:34.630324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:18:11.112713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:18:13.884991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:18:16.594390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:18:19.325568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:18:22.201777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:18:25.111601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:18:28.569119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:18:31.928486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-18T15:18:49.198627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
분류품목코드품목명서울명과농협중앙청과동화청과한국청과계날짜
분류1.0000.4050.3920.4390.4270.3820.6920.3150.4050.047
품목코드0.4051.0000.9030.9040.8990.9020.6620.7750.8390.057
품목명0.3920.9031.0000.7760.8460.8540.8310.7810.7650.083
서울명과0.4390.9040.7761.0000.8020.8240.6680.6810.8120.000
농협0.4270.8990.8460.8021.0000.9400.8000.8310.8370.087
중앙청과0.3820.9020.8540.8240.9401.0000.7670.8190.8450.061
동화청과0.6920.6620.8310.6680.8000.7671.0000.8050.7410.157
한국청과0.3150.7750.7810.6810.8310.8190.8051.0000.6780.000
계0.4050.8390.7650.8120.8370.8450.7410.6781.0000.026
날짜0.0470.0570.0830.0000.0870.0610.1570.0000.0261.000
2024-05-18T15:18:49.504454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
품목코드품목명서울명과농협중앙청과동화청과한국청과계날짜분류
품목코드1.0000.7720.7520.7430.7470.7390.4830.8750.0130.197
품목명0.7721.0000.7440.5420.5620.6850.4100.747-0.0360.190
서울명과0.7520.7441.0000.7890.7830.6280.4540.860-0.0310.218
농협0.7430.5420.7891.0000.8790.4710.4580.8760.0140.210
중앙청과0.7470.5620.7830.8791.0000.5070.5400.907-0.0230.184
동화청과0.7390.6850.6280.4710.5071.0000.2580.8490.1010.356
한국청과0.4830.4100.4540.4580.5400.2581.0000.807-0.0040.122
계0.8750.7470.8600.8760.9070.8490.8071.0000.0090.179
날짜0.013-0.036-0.0310.014-0.0230.101-0.0040.0091.0000.037
분류0.1970.1900.2180.2100.1840.3560.1220.1790.0371.000

Missing values

2024-05-18T15:18:35.020981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-18T15:18:35.548155image/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.
2024-05-18T15:18:35.969007image/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

분류품종구분명품목코드품목명서울명과농협중앙청과동화청과한국청과계날짜
4569패류기타 가공어류<NA><NA><NA><NA><NA><NA>0.680.6820240419
36786<전체>합계<NA><NA><NA><NA><NA><NA>0.36550.365520230924
27973근채류총각무(일반)<NA><NA><NA><NA><NA>272.08<NA>272.0820231120
25590과일류유자<NA>0.35<NA><NA><NA><NA><NA>0.3520231205
35941근채류당근71.713.928.6852.7898.25<NA><NA>265.3120231002
2515기타채소산채<NA>0.171<NA><NA><NA><NA><NA>0.17120240502
54075양채류파프리카56.5371.715<NA>3.4755.045<NA><NA>66.77220230606
22686기타채소달래<NA>2.6030.004<NA><NA><NA>2.8555.46220231222
46002선어류대구<NA><NA><NA><NA><NA><NA>0.180.1820230728
45595서류감자49.80311.0912.74862.1876.83498<NA><NA>212.6559820230731
분류품종구분명품목코드품목명서울명과농협중앙청과동화청과한국청과계날짜
14875과일류밤<NA><NA>0.03<NA><NA><NA>3.683.7120240215
2986과일류레몬9.181.89<NA><NA>0.918<NA>5.58617.57420240430
19036기타채소유채<NA>0.784<NA><NA>0.348<NA>7.028.15220240116
15211기타채소고수0.064<NA>1.4121.030.428<NA><NA>2.93420240213
35449연체류주꾸미<NA><NA><NA><NA><NA><NA>1.651.6520231005
16795기타채소신선초<NA><NA><NA><NA><NA><NA>0.010.0120240130
49397근채류더덕<NA><NA><NA><NA><NA><NA>2.922.9220230705
18893패류전복류<NA><NA><NA><NA><NA><NA>3.043.0420240117
43434엽채류쑥갓0.1<NA>3.463.0523.056<NA><NA>9.66820230815
12720과일류아 몬 드<NA><NA><NA><NA><NA><NA>0.50.520240229