Overview

Dataset statistics

Number of variables12
Number of observations10000
Missing cells378
Missing cells (%)0.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.1 MiB
Average record size in memory113.0 B

Variable types

Categorical1
Numeric9
Text2

Dataset

Description분류,정렬구분값,품목코드,품목명,서울청과,농협,중앙청과,동화청과,한국청과,대아청과,계,해당일자
Author서울시농수산식품공사
URLhttps://data.seoul.go.kr/dataList/OA-13420/S/1/datasetView.do

Alerts

정렬구분값 is highly overall correlated with 분류High correlation
서울청과 is highly overall correlated with 농협 and 4 other fieldsHigh correlation
농협 is highly overall correlated with 서울청과 and 3 other fieldsHigh correlation
중앙청과 is highly overall correlated with 서울청과 and 4 other fieldsHigh correlation
동화청과 is highly overall correlated with 서울청과 and 3 other fieldsHigh correlation
한국청과 is highly overall correlated with 서울청과 and 4 other fieldsHigh correlation
is highly overall correlated with 서울청과 and 4 other fieldsHigh correlation
분류 is highly overall correlated with 정렬구분값High correlation
정렬구분값 has 102 (1.0%) zerosZeros
서울청과 has 3423 (34.2%) zerosZeros
농협 has 4181 (41.8%) zerosZeros
중앙청과 has 3212 (32.1%) zerosZeros
동화청과 has 2708 (27.1%) zerosZeros
한국청과 has 3478 (34.8%) zerosZeros
대아청과 has 9058 (90.6%) zerosZeros

Reproduction

Analysis started2024-05-17 22:46:23.067182
Analysis finished2024-05-17 22:47:02.495878
Duration39.43 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

분류
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<일반채소류>
6780 
<과일류>
2014 
<과일과채류>
809 
< 전체 >
 
397

Length

Max length7
Median length7
Mean length6.5575
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<과일류>
2nd row<일반채소류>
3rd row<일반채소류>
4th row<일반채소류>
5th row<일반채소류>

Common Values

ValueCountFrequency (%)
<일반채소류> 6780
67.8%
<과일류> 2014
 
20.1%
<과일과채류> 809
 
8.1%
< 전체 > 397
 
4.0%

Length

2024-05-18T07:47:02.692755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T07:47:03.051491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
일반채소류 6780
62.8%
과일류 2014
 
18.7%
과일과채류 809
 
7.5%
794
 
7.4%
전체 397
 
3.7%

정렬구분값
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.9272
Minimum0
Maximum32
Zeros102
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T07:47:03.406493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile12
Q122
median32
Q332
95-th percentile32
Maximum32
Range32
Interquartile range (IQR)10

Descriptive statistics

Standard deviation9.448758
Coefficient of variation (CV)0.36443419
Kurtosis-0.12050966
Mean25.9272
Median Absolute Deviation (MAD)0
Skewness-1.1693901
Sum259272
Variance89.279028
MonotonicityNot monotonic
2024-05-18T07:47:03.806814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
32 6693
66.9%
12 1937
 
19.4%
22 727
 
7.3%
0 102
 
1.0%
2 99
 
1.0%
3 99
 
1.0%
1 97
 
1.0%
31 87
 
0.9%
21 82
 
0.8%
11 77
 
0.8%
ValueCountFrequency (%)
0 102
 
1.0%
1 97
 
1.0%
2 99
 
1.0%
3 99
 
1.0%
11 77
 
0.8%
12 1937
 
19.4%
21 82
 
0.8%
22 727
 
7.3%
31 87
 
0.9%
32 6693
66.9%
ValueCountFrequency (%)
32 6693
66.9%
31 87
 
0.9%
22 727
 
7.3%
21 82
 
0.8%
12 1937
 
19.4%
11 77
 
0.8%
3 99
 
1.0%
2 99
 
1.0%
1 97
 
1.0%
0 102
 
1.0%
Distinct174
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-18T07:47:04.512542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length4.8811
Min length1

Characters and Unicode

Total characters48811
Distinct characters18
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)< 0.1%

Sample

1st row42900
2nd row21109
3rd row23110
4th row26813
5th row26802
ValueCountFrequency (%)
246
 
2.5%
합계 102
 
1.0%
26809 100
 
1.0%
과일과채류계 99
 
1.0%
일반채소류계 99
 
1.0%
45120 98
 
1.0%
과일류계 97
 
1.0%
26839 95
 
0.9%
25400 94
 
0.9%
24500 93
 
0.9%
Other values (164) 8877
88.8%
2024-05-18T07:47:05.936914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 12940
26.5%
2 10490
21.5%
1 4613
 
9.5%
4 3931
 
8.1%
6 3570
 
7.3%
5 3100
 
6.4%
8 2714
 
5.6%
3 2564
 
5.3%
9 2022
 
4.1%
7 841
 
1.7%
Other values (8) 2026
 
4.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 46785
95.8%
Other Letter 2026
 
4.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 12940
27.7%
2 10490
22.4%
1 4613
 
9.9%
4 3931
 
8.4%
6 3570
 
7.6%
5 3100
 
6.6%
8 2714
 
5.8%
3 2564
 
5.5%
9 2022
 
4.3%
7 841
 
1.8%
Other Letter
ValueCountFrequency (%)
643
31.7%
295
14.6%
295
14.6%
295
14.6%
198
 
9.8%
102
 
5.0%
99
 
4.9%
99
 
4.9%

Most occurring scripts

ValueCountFrequency (%)
Common 46785
95.8%
Hangul 2026
 
4.2%

Most frequent character per script

Common
ValueCountFrequency (%)
0 12940
27.7%
2 10490
22.4%
1 4613
 
9.9%
4 3931
 
8.4%
6 3570
 
7.6%
5 3100
 
6.6%
8 2714
 
5.8%
3 2564
 
5.5%
9 2022
 
4.3%
7 841
 
1.8%
Hangul
ValueCountFrequency (%)
643
31.7%
295
14.6%
295
14.6%
295
14.6%
198
 
9.8%
102
 
5.0%
99
 
4.9%
99
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 46785
95.8%
Hangul 2026
 
4.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 12940
27.7%
2 10490
22.4%
1 4613
 
9.9%
4 3931
 
8.4%
6 3570
 
7.6%
5 3100
 
6.6%
8 2714
 
5.8%
3 2564
 
5.5%
9 2022
 
4.3%
7 841
 
1.8%
Hangul
ValueCountFrequency (%)
643
31.7%
295
14.6%
295
14.6%
295
14.6%
198
 
9.8%
102
 
5.0%
99
 
4.9%
99
 
4.9%
Distinct180
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-18T07:47:06.648011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length7
Mean length2.8864
Min length1

Characters and Unicode

Total characters28864
Distinct characters205
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

Unique7 ?
Unique (%)0.1%

Sample

1st row체리
2nd row배추얼갈이
3rd row다발무
4th row비름
5th row냉이
ValueCountFrequency (%)
기타 360
 
3.4%
246
 
2.4%
162
 
1.6%
합계 102
 
1.0%
돗나물 100
 
1.0%
일반채소류계 99
 
0.9%
과일과채류계 99
 
0.9%
블루베리 98
 
0.9%
과일류계 97
 
0.9%
베이비 95
 
0.9%
Other values (174) 8979
86.0%
2024-05-18T07:47:07.759861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
965
 
3.3%
856
 
3.0%
808
 
2.8%
695
 
2.4%
643
 
2.2%
640
 
2.2%
626
 
2.2%
616
 
2.1%
570
 
2.0%
565
 
2.0%
Other values (195) 21880
75.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 28221
97.8%
Space Separator 437
 
1.5%
Open Punctuation 103
 
0.4%
Close Punctuation 103
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
965
 
3.4%
856
 
3.0%
808
 
2.9%
695
 
2.5%
643
 
2.3%
640
 
2.3%
626
 
2.2%
616
 
2.2%
570
 
2.0%
565
 
2.0%
Other values (192) 21237
75.3%
Space Separator
ValueCountFrequency (%)
437
100.0%
Open Punctuation
ValueCountFrequency (%)
( 103
100.0%
Close Punctuation
ValueCountFrequency (%)
) 103
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 28221
97.8%
Common 643
 
2.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
965
 
3.4%
856
 
3.0%
808
 
2.9%
695
 
2.5%
643
 
2.3%
640
 
2.3%
626
 
2.2%
616
 
2.2%
570
 
2.0%
565
 
2.0%
Other values (192) 21237
75.3%
Common
ValueCountFrequency (%)
437
68.0%
( 103
 
16.0%
) 103
 
16.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 28221
97.8%
ASCII 643
 
2.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
965
 
3.4%
856
 
3.0%
808
 
2.9%
695
 
2.5%
643
 
2.3%
640
 
2.3%
626
 
2.2%
616
 
2.2%
570
 
2.0%
565
 
2.0%
Other values (192) 21237
75.3%
ASCII
ValueCountFrequency (%)
437
68.0%
( 103
 
16.0%
) 103
 
16.0%

서울청과
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct4971
Distinct (%)50.0%
Missing54
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean36.110771
Minimum0
Maximum1698.3212
Zeros3423
Zeros (%)34.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T07:47:08.474258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.5598
Q311.8365
95-th percentile164.03759
Maximum1698.3212
Range1698.3212
Interquartile range (IQR)11.8365

Descriptive statistics

Standard deviation134.6656
Coefficient of variation (CV)3.7292362
Kurtosis42.68578
Mean36.110771
Median Absolute Deviation (MAD)0.5598
Skewness6.0780982
Sum359157.73
Variance18134.823
MonotonicityNot monotonic
2024-05-18T07:47:09.129324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 3423
34.2%
0.01 44
 
0.4%
0.05 31
 
0.3%
0.06 27
 
0.3%
0.12 27
 
0.3%
0.24 26
 
0.3%
0.02 26
 
0.3%
0.2 21
 
0.2%
0.16 21
 
0.2%
0.03 20
 
0.2%
Other values (4961) 6280
62.8%
(Missing) 54
 
0.5%
ValueCountFrequency (%)
0.0 3423
34.2%
0.003 2
 
< 0.1%
0.0035 1
 
< 0.1%
0.004 3
 
< 0.1%
0.0045 1
 
< 0.1%
0.005 4
 
< 0.1%
0.006 3
 
< 0.1%
0.008 7
 
0.1%
0.0088 1
 
< 0.1%
0.009 1
 
< 0.1%
ValueCountFrequency (%)
1698.32125 1
< 0.1%
1576.0643 1
< 0.1%
1471.7596 1
< 0.1%
1455.92637 1
< 0.1%
1426.00645 1
< 0.1%
1415.7423 1
< 0.1%
1376.1367 1
< 0.1%
1375.83055 1
< 0.1%
1371.81535 1
< 0.1%
1287.737 1
< 0.1%

농협
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct3858
Distinct (%)38.8%
Missing54
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean19.088387
Minimum0
Maximum997.7314
Zeros4181
Zeros (%)41.8%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T07:47:09.626554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.1685
Q33.8905
95-th percentile122.77545
Maximum997.7314
Range997.7314
Interquartile range (IQR)3.8905

Descriptive statistics

Standard deviation71.976542
Coefficient of variation (CV)3.770698
Kurtosis51.421446
Mean19.088387
Median Absolute Deviation (MAD)0.1685
Skewness6.4417423
Sum189853.09
Variance5180.6227
MonotonicityNot monotonic
2024-05-18T07:47:10.231315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 4181
41.8%
0.02 44
 
0.4%
0.08 37
 
0.4%
0.04 32
 
0.3%
0.06 29
 
0.3%
0.2 29
 
0.3%
0.1 27
 
0.3%
0.12 26
 
0.3%
0.16 24
 
0.2%
0.24 20
 
0.2%
Other values (3848) 5497
55.0%
(Missing) 54
 
0.5%
ValueCountFrequency (%)
0.0 4181
41.8%
0.002 5
 
0.1%
0.0024 1
 
< 0.1%
0.003 4
 
< 0.1%
0.004 10
 
0.1%
0.005 2
 
< 0.1%
0.0055 1
 
< 0.1%
0.0056 1
 
< 0.1%
0.006 4
 
< 0.1%
0.008 17
 
0.2%
ValueCountFrequency (%)
997.7314 1
< 0.1%
946.68091 1
< 0.1%
910.3981 1
< 0.1%
905.8468 1
< 0.1%
871.9596 1
< 0.1%
834.5561 1
< 0.1%
818.8205 1
< 0.1%
786.6585 1
< 0.1%
786.6325 1
< 0.1%
782.559 1
< 0.1%

중앙청과
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct4745
Distinct (%)47.7%
Missing54
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean35.190485
Minimum0
Maximum1694.1935
Zeros3212
Zeros (%)32.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T07:47:10.731271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.688
Q39.903
95-th percentile190.105
Maximum1694.1935
Range1694.1935
Interquartile range (IQR)9.903

Descriptive statistics

Standard deviation130.52442
Coefficient of variation (CV)3.7090828
Kurtosis44.360845
Mean35.190485
Median Absolute Deviation (MAD)0.688
Skewness6.0909133
Sum350004.56
Variance17036.625
MonotonicityNot monotonic
2024-05-18T07:47:11.182731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 3212
32.1%
0.1 28
 
0.3%
0.08 25
 
0.2%
0.02 24
 
0.2%
0.008 23
 
0.2%
0.06 22
 
0.2%
0.4 21
 
0.2%
0.04 20
 
0.2%
0.15 20
 
0.2%
0.2 20
 
0.2%
Other values (4735) 6531
65.3%
(Missing) 54
 
0.5%
ValueCountFrequency (%)
0.0 3212
32.1%
0.001 1
 
< 0.1%
0.0018 1
 
< 0.1%
0.002 5
 
0.1%
0.003 1
 
< 0.1%
0.004 15
 
0.1%
0.0048 2
 
< 0.1%
0.005 1
 
< 0.1%
0.006 6
 
0.1%
0.0065 1
 
< 0.1%
ValueCountFrequency (%)
1694.1935 1
< 0.1%
1657.3427 1
< 0.1%
1472.9836 1
< 0.1%
1466.6215 1
< 0.1%
1425.0315 1
< 0.1%
1396.6118 1
< 0.1%
1324.1215 1
< 0.1%
1321.2431 1
< 0.1%
1308.1602 1
< 0.1%
1300.6024 1
< 0.1%

동화청과
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct5459
Distinct (%)54.9%
Missing54
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean41.346515
Minimum0
Maximum1810.0189
Zeros2708
Zeros (%)27.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T07:47:11.605849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1.3869
Q313.019
95-th percentile144.76
Maximum1810.0189
Range1810.0189
Interquartile range (IQR)13.019

Descriptive statistics

Standard deviation168.32258
Coefficient of variation (CV)4.0710222
Kurtosis38.331274
Mean41.346515
Median Absolute Deviation (MAD)1.3869
Skewness6.04973
Sum411232.44
Variance28332.491
MonotonicityNot monotonic
2024-05-18T07:47:12.110311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 2708
 
27.1%
0.1 26
 
0.3%
0.012 17
 
0.2%
0.004 16
 
0.2%
0.02 16
 
0.2%
0.008 15
 
0.1%
0.01 14
 
0.1%
0.05 14
 
0.1%
0.15 14
 
0.1%
0.04 13
 
0.1%
Other values (5449) 7093
70.9%
(Missing) 54
 
0.5%
ValueCountFrequency (%)
0.0 2708
27.1%
0.001 1
 
< 0.1%
0.002 2
 
< 0.1%
0.003 3
 
< 0.1%
0.004 16
 
0.2%
0.005 9
 
0.1%
0.006 8
 
0.1%
0.007 3
 
< 0.1%
0.008 15
 
0.1%
0.009 2
 
< 0.1%
ValueCountFrequency (%)
1810.0189 1
< 0.1%
1799.0769 1
< 0.1%
1723.3485 1
< 0.1%
1670.2185 1
< 0.1%
1666.9005 1
< 0.1%
1572.6671 1
< 0.1%
1528.1074 1
< 0.1%
1520.7986 1
< 0.1%
1520.38205 1
< 0.1%
1496.0407 1
< 0.1%

한국청과
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct4626
Distinct (%)46.5%
Missing54
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean41.494816
Minimum0
Maximum2070.9311
Zeros3478
Zeros (%)34.8%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T07:47:12.498463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.366
Q39.94
95-th percentile137.22237
Maximum2070.9311
Range2070.9311
Interquartile range (IQR)9.94

Descriptive statistics

Standard deviation173.49663
Coefficient of variation (CV)4.1811641
Kurtosis38.401844
Mean41.494816
Median Absolute Deviation (MAD)0.366
Skewness6.0018069
Sum412707.44
Variance30101.082
MonotonicityNot monotonic
2024-05-18T07:47:12.907682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 3478
34.8%
0.04 39
 
0.4%
0.2 35
 
0.4%
0.06 28
 
0.3%
0.08 27
 
0.3%
0.16 23
 
0.2%
0.016 21
 
0.2%
0.12 21
 
0.2%
0.02 19
 
0.2%
0.18 19
 
0.2%
Other values (4616) 6236
62.4%
(Missing) 54
 
0.5%
ValueCountFrequency (%)
0.0 3478
34.8%
0.0005 2
 
< 0.1%
0.001 1
 
< 0.1%
0.0016 1
 
< 0.1%
0.0018 1
 
< 0.1%
0.003 1
 
< 0.1%
0.0035 1
 
< 0.1%
0.0036 1
 
< 0.1%
0.004 7
 
0.1%
0.005 1
 
< 0.1%
ValueCountFrequency (%)
2070.93109 1
< 0.1%
2006.03016 1
< 0.1%
1820.66354 1
< 0.1%
1809.48548 1
< 0.1%
1741.40202 1
< 0.1%
1628.53977 1
< 0.1%
1617.94441 1
< 0.1%
1601.09253 1
< 0.1%
1594.682 1
< 0.1%
1545.225 2
< 0.1%

대아청과
Real number (ℝ)

ZEROS 

Distinct809
Distinct (%)8.1%
Missing54
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean51.769172
Minimum0
Maximum3381.023
Zeros9058
Zeros (%)90.6%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T07:47:13.175789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile247.03
Maximum3381.023
Range3381.023
Interquartile range (IQR)0

Descriptive statistics

Standard deviation255.25142
Coefficient of variation (CV)4.9305678
Kurtosis41.775272
Mean51.769172
Median Absolute Deviation (MAD)0
Skewness6.159083
Sum514896.19
Variance65153.285
MonotonicityNot monotonic
2024-05-18T07:47:13.551556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 9058
90.6%
10.0 5
 
0.1%
1920.092 3
 
< 0.1%
1441.412 3
 
< 0.1%
1573.508 3
 
< 0.1%
1102.467 3
 
< 0.1%
8.02 3
 
< 0.1%
1467.999 3
 
< 0.1%
1272.576 3
 
< 0.1%
1303.686 3
 
< 0.1%
Other values (799) 859
 
8.6%
(Missing) 54
 
0.5%
ValueCountFrequency (%)
0.0 9058
90.6%
1.232 1
 
< 0.1%
2.256 1
 
< 0.1%
2.312 1
 
< 0.1%
2.576 1
 
< 0.1%
2.747 1
 
< 0.1%
3.32 1
 
< 0.1%
3.374 1
 
< 0.1%
3.833 1
 
< 0.1%
3.84 1
 
< 0.1%
ValueCountFrequency (%)
3381.023 1
< 0.1%
2945.526 2
< 0.1%
2684.702 1
< 0.1%
2557.806 1
< 0.1%
2545.405 2
< 0.1%
2529.51 1
< 0.1%
2527.977 1
< 0.1%
2475.636 2
< 0.1%
2421.764 2
< 0.1%
2359.247 1
< 0.1%


Real number (ℝ)

HIGH CORRELATION 

Distinct7601
Distinct (%)76.4%
Missing54
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean225.00015
Minimum0.001
Maximum9440.7087
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T07:47:13.869777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.001
5-th percentile0.053
Q10.806
median8.501
Q374.34925
95-th percentile794.86002
Maximum9440.7087
Range9440.7077
Interquartile range (IQR)73.54325

Descriptive statistics

Standard deviation888.7986
Coefficient of variation (CV)3.9502134
Kurtosis39.355785
Mean225.00015
Median Absolute Deviation (MAD)8.381
Skewness6.0669027
Sum2237851.5
Variance789962.95
MonotonicityNot monotonic
2024-05-18T07:47:14.121397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.01 45
 
0.4%
0.02 37
 
0.4%
0.1 27
 
0.3%
0.06 26
 
0.3%
0.05 25
 
0.2%
0.016 24
 
0.2%
0.008 23
 
0.2%
0.2 21
 
0.2%
0.16 21
 
0.2%
0.012 20
 
0.2%
Other values (7591) 9677
96.8%
(Missing) 54
 
0.5%
ValueCountFrequency (%)
0.001 1
 
< 0.1%
0.002 2
 
< 0.1%
0.0024 1
 
< 0.1%
0.003 3
 
< 0.1%
0.004 11
0.1%
0.005 6
0.1%
0.006 6
0.1%
0.0065 1
 
< 0.1%
0.007 6
0.1%
0.0072 1
 
< 0.1%
ValueCountFrequency (%)
9440.70869 1
< 0.1%
9407.46381 1
< 0.1%
9366.58519 1
< 0.1%
9239.53687 1
< 0.1%
8928.02217 1
< 0.1%
8813.62684 1
< 0.1%
8549.36024 1
< 0.1%
8353.17864 1
< 0.1%
8236.28037 1
< 0.1%
8222.26592 1
< 0.1%

해당일자
Real number (ℝ)

Distinct341
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20234432
Minimum20230516
Maximum20240517
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T07:47:14.384205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20230516
5-th percentile20230603
Q120230818
median20231115
Q320240221
95-th percentile20240501
Maximum20240517
Range10001
Interquartile range (IQR)9403

Descriptive statistics

Standard deviation4564.3103
Coefficient of variation (CV)0.00022557145
Kurtosis-1.7355609
Mean20234432
Median Absolute Deviation (MAD)485
Skewness0.50742904
Sum2.0234432 × 1011
Variance20832929
MonotonicityNot monotonic
2024-05-18T07:47:14.722537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20240327 47
 
0.5%
20240517 47
 
0.5%
20230912 44
 
0.4%
20231206 43
 
0.4%
20230915 43
 
0.4%
20240322 43
 
0.4%
20230907 43
 
0.4%
20231010 43
 
0.4%
20240119 43
 
0.4%
20240422 42
 
0.4%
Other values (331) 9562
95.6%
ValueCountFrequency (%)
20230516 39
0.4%
20230517 18
0.2%
20230518 31
0.3%
20230519 19
0.2%
20230520 27
0.3%
20230521 1
 
< 0.1%
20230522 35
0.4%
20230523 29
0.3%
20230524 42
0.4%
20230525 39
0.4%
ValueCountFrequency (%)
20240517 47
0.5%
20240516 38
0.4%
20240515 39
0.4%
20240514 33
0.3%
20240513 35
0.4%
20240512 1
 
< 0.1%
20240511 35
0.4%
20240510 31
0.3%
20240509 23
0.2%
20240508 27
0.3%

Interactions

2024-05-18T07:46:58.770804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T07:46:30.029250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T07:46:33.293450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T07:46:37.949727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T07:46:41.184910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T07:46:44.893365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T07:46:48.448363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T07:46:51.898824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T07:46:55.648478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T07:46:59.063297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T07:46:30.339058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T07:46:33.703715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T07:46:38.231709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T07:46:41.587621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T07:46:45.421343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T07:46:48.937759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T07:46:52.285833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T07:46:55.929965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T07:46:59.349493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T07:46:30.565320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T07:46:34.258406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T07:46:38.633722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T07:46:41.890539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T07:46:45.823001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T07:46:49.297159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T07:46:52.674954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T07:46:56.347091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T07:46:59.624282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T07:46:30.889973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T07:46:34.924728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T07:46:38.942996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T07:46:42.247172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T07:46:46.247822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T07:46:49.686336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T07:46:53.093724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T07:46:56.662876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T07:46:59.904676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T07:46:31.329298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T07:46:35.560413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T07:46:39.332378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T07:46:42.581800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T07:46:46.692046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T07:46:50.080409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T07:46:53.482725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T07:46:56.995452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T07:47:00.217966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T07:46:31.620556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T07:46:36.014292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T07:46:39.746171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T07:46:42.938414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T07:46:47.020486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T07:46:50.424278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T07:46:53.922767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T07:46:57.398526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T07:47:00.502892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T07:46:32.022115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T07:46:36.326837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T07:46:40.082178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T07:46:43.312706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T07:46:47.311793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T07:46:50.778052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T07:46:54.320002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T07:46:57.802671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T07:47:00.793868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T07:46:32.504786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T07:46:36.791504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T07:46:40.444175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T07:46:43.752533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T07:46:47.633945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T07:46:51.196040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T07:46:54.732870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T07:46:58.146256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T07:47:01.067205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T07:46:32.867711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T07:46:37.299007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T07:46:40.804039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T07:46:44.143837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T07:46:48.035109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T07:46:51.550515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T07:46:55.167062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T07:46:58.409846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-18T07:47:14.999902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
분류정렬구분값서울청과농협중앙청과동화청과한국청과대아청과해당일자
분류1.0001.0000.6190.6220.6160.5510.5450.5010.5860.012
정렬구분값1.0001.0000.6190.6220.6160.5510.5450.5010.5860.012
서울청과0.6190.6191.0000.9430.9690.9270.9310.7830.9510.055
농협0.6220.6220.9431.0000.9390.9020.8990.6770.9220.040
중앙청과0.6160.6160.9690.9391.0000.9230.9110.7530.9310.064
동화청과0.5510.5510.9270.9020.9231.0000.9620.7950.9480.032
한국청과0.5450.5450.9310.8990.9110.9621.0000.7840.9470.035
대아청과0.5010.5010.7830.6770.7530.7950.7841.0000.8340.073
0.5860.5860.9510.9220.9310.9480.9470.8341.0000.043
해당일자0.0120.0120.0550.0400.0640.0320.0350.0730.0431.000
2024-05-18T07:47:15.328205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
정렬구분값서울청과농협중앙청과동화청과한국청과대아청과해당일자분류
정렬구분값1.000-0.378-0.357-0.365-0.219-0.125-0.003-0.2340.0111.000
서울청과-0.3781.0000.5800.6990.7660.7590.2460.840-0.0270.421
농협-0.3570.5801.0000.6550.4630.5530.0530.617-0.0220.424
중앙청과-0.3650.6990.6551.0000.7620.7650.1280.809-0.0350.419
동화청과-0.2190.7660.4630.7621.0000.8040.1260.818-0.0280.362
한국청과-0.1250.7590.5530.7650.8041.0000.2790.858-0.0370.357
대아청과-0.0030.2460.0530.1280.1260.2791.0000.392-0.0090.323
-0.2340.8400.6170.8090.8180.8580.3921.000-0.0380.392
해당일자0.011-0.027-0.022-0.035-0.028-0.037-0.009-0.0381.0000.008
분류1.0000.4210.4240.4190.3620.3570.3230.3920.0081.000

Missing values

2024-05-18T07:47:01.460464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-18T07:47:01.936564image/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.
2024-05-18T07:47:02.286823image/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

분류정렬구분값품목코드품목명서울청과농협중앙청과동화청과한국청과대아청과해당일자
14315<과일류>1242900체리0.590.00.320.2320.00.01.14220231228
15201<일반채소류>3221109배추얼갈이1.1760.05.32813.81213.0520.033.36820231219
17989<일반채소류>3223110다발무52.880.040.027.523.51515.0658.8920231122
1577<일반채소류>3226813비름0.0750.00.00.01.440.01.51520240503
18340<일반채소류>3226802냉이0.0080.49990.52150.00.0160.01.045420231118
22080<일반채소류>3224503쪽파8.610.63751.15411.8449.857.64239.737520231014
36404<일반채소류>3224400양파97.21539.765208.802133.306300.210.0779.29820230525
21176<일반채소류>3226831뉴그린0.00.00.00.10.00.00.120231023
115<일반채소류>3215200감자80.0811.8792.74157.6668.830.0411.1820240517
28035<일반채소류>3226824참나물0.060.02.4084.8683.4040.010.7420230817
분류정렬구분값품목코드품목명서울청과농협중앙청과동화청과한국청과대아청과해당일자
21058<일반채소류>3226810고추잎0.00.4830.33370.00.1540.00.970720231024
13854<일반채소류>3225903콜라비24.7253.720.011.762.280.042.48520240104
34983<과일류>1243300무화과0.00.00.00.350.00.00.3520230608
32366<일반채소류>3226824참나물0.320.05.6445.5766.5760.018.11620230703
32109<일반채소류>3221200양배추6.520.00.320.012.704299.744319.28820230705
30430<일반채소류>3225700칼리플라워0.5480.00.00.2720.00.00.8220230722
11773<일반채소류>3226903무말랭이0.00.1070.10.00.00.00.20720240124
13514<과일류>1241400포도19.72811.890827.10612.1663.66680.074.557620240106
14645<일반채소류>3225500비트1.660.480.01.580.260.03.9820231225
26906<일반채소류>3221400상추7.8641.34823.025.71218.0960.076.0220230828