Overview

Dataset statistics

Number of variables11
Number of observations10000
Missing cells469
Missing cells (%)0.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.0 MiB
Average record size in memory105.0 B

Variable types

Numeric9
Text2

Dataset

Description정렬구분값,품목코드,품목명,서울청과,농협,중앙청과,동화청과,한국청과,대아청과,해당품목의해당일반입물량합계(톤),조회기준년월일
Author서울시농수산식품공사
URLhttps://data.seoul.go.kr/dataList/OA-12832/S/1/datasetView.do

Alerts

서울청과 is highly overall correlated with 농협 and 4 other fieldsHigh correlation
농협 is highly overall correlated with 서울청과 and 2 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 3 other fieldsHigh correlation
해당품목의해당일반입물량합계(톤) is highly overall correlated with 서울청과 and 4 other fieldsHigh correlation
정렬구분값 has 108 (1.1%) zerosZeros
서울청과 has 3510 (35.1%) zerosZeros
농협 has 4610 (46.1%) zerosZeros
중앙청과 has 3297 (33.0%) zerosZeros
동화청과 has 2758 (27.6%) zerosZeros
한국청과 has 3542 (35.4%) zerosZeros
대아청과 has 9105 (91.0%) zerosZeros

Reproduction

Analysis started2024-05-18 09:32:05.885105
Analysis finished2024-05-18 09:32:39.676764
Duration33.79 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

정렬구분값
Real number (ℝ)

ZEROS 

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.8775
Minimum0
Maximum32
Zeros108
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T18:32:39.854276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile12
Q121
median32
Q332
95-th percentile32
Maximum32
Range32
Interquartile range (IQR)11

Descriptive statistics

Standard deviation9.4545244
Coefficient of variation (CV)0.36535695
Kurtosis-0.14656365
Mean25.8775
Median Absolute Deviation (MAD)0
Skewness-1.1558244
Sum258775
Variance89.388033
MonotonicityNot monotonic
2024-05-18T18:32:40.292949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
32 6662
66.6%
12 1937
 
19.4%
22 753
 
7.5%
0 108
 
1.1%
1 102
 
1.0%
11 96
 
1.0%
2 91
 
0.9%
3 90
 
0.9%
21 82
 
0.8%
31 79
 
0.8%
ValueCountFrequency (%)
0 108
 
1.1%
1 102
 
1.0%
2 91
 
0.9%
3 90
 
0.9%
11 96
 
1.0%
12 1937
 
19.4%
21 82
 
0.8%
22 753
 
7.5%
31 79
 
0.8%
32 6662
66.6%
ValueCountFrequency (%)
32 6662
66.6%
31 79
 
0.8%
22 753
 
7.5%
21 82
 
0.8%
12 1937
 
19.4%
11 96
 
1.0%
3 90
 
0.9%
2 91
 
0.9%
1 102
 
1.0%
0 108
 
1.1%
Distinct171
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-18T18:32:41.100852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length4.8727
Min length1

Characters and Unicode

Total characters48727
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

Unique2 ?
Unique (%)< 0.1%

Sample

1st row26810
2nd row39999
3rd row42900
4th row과일류계
5th row21300
ValueCountFrequency (%)
257
 
2.6%
합계 108
 
1.1%
22700 103
 
1.0%
과일류계 102
 
1.0%
15100 99
 
1.0%
26826 96
 
1.0%
26300 94
 
0.9%
25121 93
 
0.9%
과일과채류계 91
 
0.9%
26824 91
 
0.9%
Other values (161) 8866
88.7%
2024-05-18T18:32:42.455530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 13007
26.7%
2 10480
21.5%
1 4608
 
9.5%
4 3939
 
8.1%
6 3514
 
7.2%
5 3208
 
6.6%
8 2573
 
5.3%
3 2540
 
5.2%
9 2026
 
4.2%
7 865
 
1.8%
Other values (8) 1967
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 46760
96.0%
Other Letter 1967
 
4.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 13007
27.8%
2 10480
22.4%
1 4608
 
9.9%
4 3939
 
8.4%
6 3514
 
7.5%
5 3208
 
6.9%
8 2573
 
5.5%
3 2540
 
5.4%
9 2026
 
4.3%
7 865
 
1.8%
Other Letter
ValueCountFrequency (%)
648
32.9%
284
14.4%
283
14.4%
283
14.4%
181
 
9.2%
108
 
5.5%
90
 
4.6%
90
 
4.6%

Most occurring scripts

ValueCountFrequency (%)
Common 46760
96.0%
Hangul 1967
 
4.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 13007
27.8%
2 10480
22.4%
1 4608
 
9.9%
4 3939
 
8.4%
6 3514
 
7.5%
5 3208
 
6.9%
8 2573
 
5.5%
3 2540
 
5.4%
9 2026
 
4.3%
7 865
 
1.8%
Hangul
ValueCountFrequency (%)
648
32.9%
284
14.4%
283
14.4%
283
14.4%
181
 
9.2%
108
 
5.5%
90
 
4.6%
90
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 46760
96.0%
Hangul 1967
 
4.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 13007
27.8%
2 10480
22.4%
1 4608
 
9.9%
4 3939
 
8.4%
6 3514
 
7.5%
5 3208
 
6.9%
8 2573
 
5.5%
3 2540
 
5.4%
9 2026
 
4.3%
7 865
 
1.8%
Hangul
ValueCountFrequency (%)
648
32.9%
284
14.4%
283
14.4%
283
14.4%
181
 
9.2%
108
 
5.5%
90
 
4.6%
90
 
4.6%
Distinct177
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-18T18:32:43.296655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length7
Mean length2.9309
Min length1

Characters and Unicode

Total characters29309
Distinct characters201
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

Unique4 ?
Unique (%)< 0.1%

Sample

1st row고추잎
2nd row채소류 기타
3rd row체리
4th row과일류계
5th row시금치
ValueCountFrequency (%)
기타 404
 
3.8%
257
 
2.4%
162
 
1.5%
합계 108
 
1.0%
과일류계 102
 
1.0%
멜론 100
 
1.0%
고구마 99
 
0.9%
겨자잎 96
 
0.9%
취나물 94
 
0.9%
파프리카 93
 
0.9%
Other values (171) 8979
85.6%
2024-05-18T18:32:44.950711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
882
 
3.0%
870
 
3.0%
778
 
2.7%
720
 
2.5%
679
 
2.3%
648
 
2.2%
635
 
2.2%
615
 
2.1%
579
 
2.0%
576
 
2.0%
Other values (191) 22327
76.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 28491
97.2%
Space Separator 494
 
1.7%
Close Punctuation 162
 
0.6%
Open Punctuation 162
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
882
 
3.1%
870
 
3.1%
778
 
2.7%
720
 
2.5%
679
 
2.4%
648
 
2.3%
635
 
2.2%
615
 
2.2%
579
 
2.0%
576
 
2.0%
Other values (188) 21509
75.5%
Space Separator
ValueCountFrequency (%)
494
100.0%
Close Punctuation
ValueCountFrequency (%)
) 162
100.0%
Open Punctuation
ValueCountFrequency (%)
( 162
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 28491
97.2%
Common 818
 
2.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
882
 
3.1%
870
 
3.1%
778
 
2.7%
720
 
2.5%
679
 
2.4%
648
 
2.3%
635
 
2.2%
615
 
2.2%
579
 
2.0%
576
 
2.0%
Other values (188) 21509
75.5%
Common
ValueCountFrequency (%)
494
60.4%
) 162
 
19.8%
( 162
 
19.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 28491
97.2%
ASCII 818
 
2.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
882
 
3.1%
870
 
3.1%
778
 
2.7%
720
 
2.5%
679
 
2.4%
648
 
2.3%
635
 
2.2%
615
 
2.2%
579
 
2.0%
576
 
2.0%
Other values (188) 21509
75.5%
ASCII
ValueCountFrequency (%)
494
60.4%
) 162
 
19.8%
( 162
 
19.8%

서울청과
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct4927
Distinct (%)49.6%
Missing67
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean34.600525
Minimum-0.03
Maximum1693.4126
Zeros3510
Zeros (%)35.1%
Negative1
Negative (%)< 0.1%
Memory size166.0 KiB
2024-05-18T18:32:45.425090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-0.03
5-th percentile0
Q10
median0.532
Q311.655
95-th percentile156.86238
Maximum1693.4126
Range1693.4425
Interquartile range (IQR)11.655

Descriptive statistics

Standard deviation130.82957
Coefficient of variation (CV)3.781144
Kurtosis44.675481
Mean34.600525
Median Absolute Deviation (MAD)0.532
Skewness6.2121603
Sum343687.01
Variance17116.376
MonotonicityNot monotonic
2024-05-18T18:32:45.855587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 3510
35.1%
0.01 36
 
0.4%
0.04 34
 
0.3%
0.05 34
 
0.3%
0.08 27
 
0.3%
0.02 25
 
0.2%
0.1 24
 
0.2%
0.12 24
 
0.2%
0.2 22
 
0.2%
0.06 22
 
0.2%
Other values (4917) 6175
61.8%
(Missing) 67
 
0.7%
ValueCountFrequency (%)
-0.03 1
 
< 0.1%
0.0 3510
35.1%
0.002 2
 
< 0.1%
0.003 3
 
< 0.1%
0.004 6
 
0.1%
0.0045 1
 
< 0.1%
0.005 6
 
0.1%
0.0054 1
 
< 0.1%
0.006 2
 
< 0.1%
0.007 1
 
< 0.1%
ValueCountFrequency (%)
1693.41255 1
< 0.1%
1455.7706 1
< 0.1%
1447.61447 1
< 0.1%
1430.47035 1
< 0.1%
1430.11095 1
< 0.1%
1360.73535 1
< 0.1%
1352.63385 1
< 0.1%
1281.74395 1
< 0.1%
1267.70786 1
< 0.1%
1255.2772 1
< 0.1%

농협
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct3560
Distinct (%)35.8%
Missing67
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean17.199064
Minimum-0.284
Maximum986.463
Zeros4610
Zeros (%)46.1%
Negative2
Negative (%)< 0.1%
Memory size166.0 KiB
2024-05-18T18:32:46.378745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-0.284
5-th percentile0
Q10
median0.065
Q32.695
95-th percentile103.83884
Maximum986.463
Range986.747
Interquartile range (IQR)2.695

Descriptive statistics

Standard deviation68.653298
Coefficient of variation (CV)3.991688
Kurtosis57.722162
Mean17.199064
Median Absolute Deviation (MAD)0.065
Skewness6.8418983
Sum170838.31
Variance4713.2754
MonotonicityNot monotonic
2024-05-18T18:32:46.875578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 4610
46.1%
0.02 38
 
0.4%
0.08 34
 
0.3%
0.2 34
 
0.3%
0.04 30
 
0.3%
0.06 30
 
0.3%
0.1 29
 
0.3%
0.12 29
 
0.3%
0.24 22
 
0.2%
0.008 21
 
0.2%
Other values (3550) 5056
50.6%
(Missing) 67
 
0.7%
ValueCountFrequency (%)
-0.284 1
 
< 0.1%
-0.024 1
 
< 0.1%
0.0 4610
46.1%
0.001 1
 
< 0.1%
0.002 2
 
< 0.1%
0.0025 1
 
< 0.1%
0.003 4
 
< 0.1%
0.004 10
 
0.1%
0.005 4
 
< 0.1%
0.006 5
 
0.1%
ValueCountFrequency (%)
986.463 1
< 0.1%
901.9773 1
< 0.1%
881.9785 1
< 0.1%
870.8876 1
< 0.1%
822.3448 1
< 0.1%
806.7428 1
< 0.1%
792.94385 1
< 0.1%
782.039 1
< 0.1%
775.6092 1
< 0.1%
772.3962 1
< 0.1%

중앙청과
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct4677
Distinct (%)47.1%
Missing67
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean32.977932
Minimum-220.4178
Maximum2032.6337
Zeros3297
Zeros (%)33.0%
Negative15
Negative (%)0.1%
Memory size166.0 KiB
2024-05-18T18:32:47.384124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-220.4178
5-th percentile0
Q10
median0.568
Q39.168
95-th percentile174.05552
Maximum2032.6337
Range2253.0515
Interquartile range (IQR)9.168

Descriptive statistics

Standard deviation127.18849
Coefficient of variation (CV)3.856776
Kurtosis50.576472
Mean32.977932
Median Absolute Deviation (MAD)0.568
Skewness6.421062
Sum327569.79
Variance16176.913
MonotonicityNot monotonic
2024-05-18T18:32:47.854393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 3297
33.0%
0.08 35
 
0.4%
0.04 31
 
0.3%
0.02 31
 
0.3%
0.12 26
 
0.3%
0.1 23
 
0.2%
0.008 23
 
0.2%
0.15 22
 
0.2%
0.012 19
 
0.2%
0.004 19
 
0.2%
Other values (4667) 6407
64.1%
(Missing) 67
 
0.7%
ValueCountFrequency (%)
-220.4178 1
< 0.1%
-1.677 1
< 0.1%
-1.2 1
< 0.1%
-0.584 1
< 0.1%
-0.547 1
< 0.1%
-0.525 1
< 0.1%
-0.18 1
< 0.1%
-0.113 1
< 0.1%
-0.11 1
< 0.1%
-0.108 1
< 0.1%
ValueCountFrequency (%)
2032.6337 1
< 0.1%
1688.7905 1
< 0.1%
1649.1212 1
< 0.1%
1456.9681 1
< 0.1%
1386.1127 1
< 0.1%
1375.7255 1
< 0.1%
1305.453 1
< 0.1%
1259.5679 1
< 0.1%
1255.6878 1
< 0.1%
1243.9579 1
< 0.1%

동화청과
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct5211
Distinct (%)52.5%
Missing67
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean39.24867
Minimum-592
Maximum1798.468
Zeros2758
Zeros (%)27.6%
Negative1
Negative (%)< 0.1%
Memory size166.0 KiB
2024-05-18T18:32:48.350492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-592
5-th percentile0
Q10
median1.294
Q312.364
95-th percentile126.662
Maximum1798.468
Range2390.468
Interquartile range (IQR)12.364

Descriptive statistics

Standard deviation165.28639
Coefficient of variation (CV)4.2112609
Kurtosis40.255739
Mean39.24867
Median Absolute Deviation (MAD)1.294
Skewness6.2083231
Sum389857.04
Variance27319.591
MonotonicityNot monotonic
2024-05-18T18:32:48.828361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 2758
 
27.6%
0.1 27
 
0.3%
0.02 26
 
0.3%
0.05 21
 
0.2%
0.04 17
 
0.2%
0.008 16
 
0.2%
0.004 15
 
0.1%
0.06 14
 
0.1%
0.15 14
 
0.1%
0.012 13
 
0.1%
Other values (5201) 7012
70.1%
(Missing) 67
 
0.7%
ValueCountFrequency (%)
-592.0 1
 
< 0.1%
0.0 2758
27.6%
0.001 2
 
< 0.1%
0.002 3
 
< 0.1%
0.003 2
 
< 0.1%
0.004 15
 
0.1%
0.005 11
 
0.1%
0.006 7
 
0.1%
0.007 1
 
< 0.1%
0.008 16
 
0.2%
ValueCountFrequency (%)
1798.468 1
< 0.1%
1665.905 1
< 0.1%
1624.581 1
< 0.1%
1597.271 1
< 0.1%
1580.846 1
< 0.1%
1565.254 1
< 0.1%
1561.914 1
< 0.1%
1551.667 1
< 0.1%
1512.838 1
< 0.1%
1489.257 1
< 0.1%

한국청과
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct4572
Distinct (%)46.0%
Missing67
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean38.693323
Minimum-3.042
Maximum2004.5553
Zeros3542
Zeros (%)35.4%
Negative3
Negative (%)< 0.1%
Memory size166.0 KiB
2024-05-18T18:32:49.343824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-3.042
5-th percentile0
Q10
median0.345
Q39.189
95-th percentile107.90884
Maximum2004.5553
Range2007.5973
Interquartile range (IQR)9.189

Descriptive statistics

Standard deviation167.51261
Coefficient of variation (CV)4.3292381
Kurtosis39.334166
Mean38.693323
Median Absolute Deviation (MAD)0.345
Skewness6.1344958
Sum384340.78
Variance28060.473
MonotonicityNot monotonic
2024-05-18T18:32:49.826101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 3542
35.4%
0.04 35
 
0.4%
0.2 33
 
0.3%
0.02 31
 
0.3%
0.08 26
 
0.3%
0.1 24
 
0.2%
0.16 23
 
0.2%
0.12 22
 
0.2%
0.05 21
 
0.2%
0.18 21
 
0.2%
Other values (4562) 6155
61.6%
(Missing) 67
 
0.7%
ValueCountFrequency (%)
-3.042 1
 
< 0.1%
-1.134 1
 
< 0.1%
-0.468 1
 
< 0.1%
0.0 3542
35.4%
0.0005 1
 
< 0.1%
0.001 2
 
< 0.1%
0.0018 3
 
< 0.1%
0.002 3
 
< 0.1%
0.0036 1
 
< 0.1%
0.004 11
 
0.1%
ValueCountFrequency (%)
2004.55526 1
< 0.1%
1745.73273 1
< 0.1%
1688.75773 1
< 0.1%
1624.94329 1
< 0.1%
1587.30341 1
< 0.1%
1562.21082 1
< 0.1%
1529.53267 1
< 0.1%
1488.02859 1
< 0.1%
1471.03234 1
< 0.1%
1470.04033 1
< 0.1%

대아청과
Real number (ℝ)

ZEROS 

Distinct761
Distinct (%)7.7%
Missing67
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean47.273419
Minimum0
Maximum2943.286
Zeros9105
Zeros (%)91.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T18:32:50.298098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile184.908
Maximum2943.286
Range2943.286
Interquartile range (IQR)0

Descriptive statistics

Standard deviation239.54793
Coefficient of variation (CV)5.0672859
Kurtosis40.453947
Mean47.273419
Median Absolute Deviation (MAD)0
Skewness6.1331853
Sum469566.87
Variance57383.211
MonotonicityNot monotonic
2024-05-18T18:32:50.757438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 9105
91.0%
10.0 4
 
< 0.1%
1354.587 3
 
< 0.1%
1398.756 3
 
< 0.1%
1505.05 3
 
< 0.1%
1427.729 3
 
< 0.1%
1097.038 3
 
< 0.1%
2009.586 3
 
< 0.1%
1184.409 3
 
< 0.1%
1325.872 3
 
< 0.1%
Other values (751) 800
 
8.0%
(Missing) 67
 
0.7%
ValueCountFrequency (%)
0.0 9105
91.0%
0.624 1
 
< 0.1%
2.678 1
 
< 0.1%
2.956 1
 
< 0.1%
3.024 1
 
< 0.1%
3.628 1
 
< 0.1%
3.706 1
 
< 0.1%
3.833 1
 
< 0.1%
4.35 1
 
< 0.1%
4.491 1
 
< 0.1%
ValueCountFrequency (%)
2943.286 1
< 0.1%
2540.575 2
< 0.1%
2537.346 2
< 0.1%
2476.36 1
< 0.1%
2435.361 1
< 0.1%
2432.607 1
< 0.1%
2386.411 1
< 0.1%
2327.236 1
< 0.1%
2128.064 1
< 0.1%
2110.257 1
< 0.1%

해당품목의해당일반입물량합계(톤)
Real number (ℝ)

HIGH CORRELATION 

Distinct7521
Distinct (%)75.7%
Missing67
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean209.99293
Minimum-57.95
Maximum9398.7714
Zeros7
Zeros (%)0.1%
Negative7
Negative (%)0.1%
Memory size166.0 KiB
2024-05-18T18:32:51.439232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-57.95
5-th percentile0.05
Q10.712
median7.9431
Q369.2333
95-th percentile689.46782
Maximum9398.7714
Range9456.7214
Interquartile range (IQR)68.5213

Descriptive statistics

Standard deviation850.93705
Coefficient of variation (CV)4.0522176
Kurtosis40.611548
Mean209.99293
Median Absolute Deviation (MAD)7.8431
Skewness6.1742879
Sum2085859.8
Variance724093.87
MonotonicityNot monotonic
2024-05-18T18:32:52.147579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.05 36
 
0.4%
0.01 35
 
0.4%
0.02 35
 
0.4%
0.04 32
 
0.3%
0.08 31
 
0.3%
0.06 28
 
0.3%
0.008 23
 
0.2%
0.03 22
 
0.2%
0.016 21
 
0.2%
0.012 21
 
0.2%
Other values (7511) 9649
96.5%
(Missing) 67
 
0.7%
ValueCountFrequency (%)
-57.95 1
 
< 0.1%
-1.1 1
 
< 0.1%
-0.584 1
 
< 0.1%
-0.113 1
 
< 0.1%
-0.03 1
 
< 0.1%
-0.024 2
 
< 0.1%
0.0 7
0.1%
0.0006 1
 
< 0.1%
0.001 2
 
< 0.1%
0.002 3
< 0.1%
ValueCountFrequency (%)
9398.77141 1
< 0.1%
9207.21348 1
< 0.1%
8873.43898 1
< 0.1%
8703.82856 1
< 0.1%
8351.28431 1
< 0.1%
8197.36554 1
< 0.1%
8115.96233 1
< 0.1%
7765.88104 1
< 0.1%
7758.56843 1
< 0.1%
7692.41112 1
< 0.1%

조회기준년월일
Real number (ℝ)

Distinct344
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20234409
Minimum20230516
Maximum20240517
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T18:32:53.020315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20230516
5-th percentile20230602
Q120230816
median20231115
Q320240219
95-th percentile20240430
Maximum20240517
Range10001
Interquartile range (IQR)9403

Descriptive statistics

Standard deviation4560.2856
Coefficient of variation (CV)0.00022537281
Kurtosis-1.7260353
Mean20234409
Median Absolute Deviation (MAD)487
Skewness0.51657136
Sum2.0234409 × 1011
Variance20796205
MonotonicityNot monotonic
2024-05-18T18:32:53.648695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20231011 47
 
0.5%
20240511 45
 
0.4%
20230817 45
 
0.4%
20231031 44
 
0.4%
20240416 43
 
0.4%
20230824 43
 
0.4%
20230815 43
 
0.4%
20231102 42
 
0.4%
20230825 42
 
0.4%
20230621 42
 
0.4%
Other values (334) 9564
95.6%
ValueCountFrequency (%)
20230516 35
0.4%
20230517 29
0.3%
20230518 35
0.4%
20230519 32
0.3%
20230520 35
0.4%
20230521 1
 
< 0.1%
20230522 23
0.2%
20230523 27
0.3%
20230524 29
0.3%
20230525 30
0.3%
ValueCountFrequency (%)
20240517 33
0.3%
20240516 34
0.3%
20240515 38
0.4%
20240514 36
0.4%
20240513 34
0.3%
20240512 4
 
< 0.1%
20240511 45
0.4%
20240510 27
0.3%
20240509 33
0.3%
20240508 33
0.3%

Interactions

2024-05-18T18:32:35.598525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:32:10.915745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:32:13.714910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:32:16.676500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:32:19.893809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:32:23.341262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:32:26.111305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:32:28.816550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:32:32.197501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:32:35.909569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:32:11.283691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:32:14.039001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:32:17.055561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:32:20.479315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:32:23.613013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:32:26.422023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:32:29.163689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:32:32.559274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:32:36.208020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:32:11.624743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:32:14.422801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:32:17.370398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:32:20.880845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:32:23.955658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:32:26.752687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:32:29.510900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:32:32.953300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:32:36.495183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:32:11.907652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:32:14.707329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:32:17.782551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:32:21.211208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:32:24.237823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:32:27.015753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:32:29.907744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:32:33.318218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:32:36.792303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:32:12.222817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:32:15.062233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:32:18.111624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:32:21.551992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:32:24.513515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:32:27.316206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:32:30.207197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:32:33.694887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:32:37.059759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:32:12.531179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:32:15.350644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:32:18.397752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:32:21.808690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:32:24.894433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:32:27.598140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:32:30.612650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:32:34.097444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:32:37.371833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:32:12.845755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:32:15.663901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:32:18.716487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:32:22.154277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:32:25.286809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:32:27.884884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:32:31.040119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:32:34.560042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:32:37.653345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:32:13.156228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:32:16.034794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:32:19.095325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:32:22.765203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:32:25.563012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:32:28.216334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:32:31.425919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:32:34.935411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:32:37.939522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:32:13.442156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:32:16.372365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:32:19.550129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:32:23.069585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:32:25.849573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:32:28.497899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:32:31.840311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:32:35.284966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-18T18:32:54.099899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
정렬구분값서울청과농협중앙청과동화청과한국청과대아청과해당품목의해당일반입물량합계(톤)조회기준년월일
정렬구분값1.0000.6110.5980.6480.7420.5790.5120.5880.048
서울청과0.6111.0000.9270.9560.8290.9200.7910.9480.037
농협0.5980.9271.0000.9350.7540.8840.6740.9120.046
중앙청과0.6480.9560.9351.0000.8180.9140.7610.9360.000
동화청과0.7420.8290.7540.8181.0000.8550.6770.8500.039
한국청과0.5790.9200.8840.9140.8551.0000.7980.9410.000
대아청과0.5120.7910.6740.7610.6770.7981.0000.8390.087
해당품목의해당일반입물량합계(톤)0.5880.9480.9120.9360.8500.9410.8391.0000.053
조회기준년월일0.0480.0370.0460.0000.0390.0000.0870.0531.000
2024-05-18T18:32:54.606120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
정렬구분값서울청과농협중앙청과동화청과한국청과대아청과해당품목의해당일반입물량합계(톤)조회기준년월일
정렬구분값1.000-0.386-0.340-0.371-0.219-0.140-0.009-0.2480.009
서울청과-0.3861.0000.5170.6730.7460.7460.2450.838-0.000
농협-0.3400.5171.0000.5680.3910.4870.0470.560-0.099
중앙청과-0.3710.6730.5681.0000.7320.7390.1300.790-0.009
동화청과-0.2190.7460.3910.7321.0000.7920.1250.818-0.001
한국청과-0.1400.7460.4870.7390.7921.0000.2730.8540.003
대아청과-0.0090.2450.0470.1300.1250.2731.0000.376-0.000
해당품목의해당일반입물량합계(톤)-0.2480.8380.5600.7900.8180.8540.3761.000-0.010
조회기준년월일0.009-0.000-0.099-0.009-0.0010.003-0.000-0.0101.000

Missing values

2024-05-18T18:32:38.360604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-18T18:32:38.999332image/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.
2024-05-18T18:32:39.428949image/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

정렬구분값품목코드품목명서울청과농협중앙청과동화청과한국청과대아청과해당품목의해당일반입물량합계(톤)조회기준년월일
255483226810고추잎0.00.1510.0080.00.00.00.15920240119
352763239999채소류 기타0.00.00.27440.00.00.00.274420240430
88181242900체리0.00.01.24.40.00.05.620230810
42451과일류계과일류계177.733195.6594232.6783138.91951.2670.0696.256820230624
65373221300시금치0.9240.741.1486.5044.9640.014.2820230718
124913232200콩류0.01.14950.4120.00.0760.01.637520230914
368903226700두릅0.8521.1630.6560.0040.2920.02.96720240515
202873225600셀러리7.550.00.06.20.00.013.7520231127
34762222100수박144.1605119.7444195.616864.74230.4880.0554.751720230616
3322931546.8692210.1156532.11121040.5751036.87471363.1964729.741720240410
정렬구분값품목코드품목명서울청과농협중앙청과동화청과한국청과대아청과해당품목의해당일반입물량합계(톤)조회기준년월일
3130212417000.00.00.00.00.1680.00.16820240322
273473221500깻잎6.18470.73413.924513.54910.65810.045.050320240206
313973226700두릅0.01.3020.1070.0090.00.01.41820240323
266870합계합계1171.90705539.533751068.60261270.6581254.688191695.387000.7695920240131
26161245101망고스틴0.00.00.250.00.00.00.2520230608
176881249050수입과일 기타0.00.6650.00.00.00.00.66520231102
22823226838새싹0.0710.00.00.0660.0750.00.21220230606
370051244900아보카도0.00.00.00.00.840.00.8420240516
94653일반채소류계일반채소류계570.2777153.0233462.3352874.366989.136671455.7794504.9178720230817
2258411208.42885153.22414284.8973137.83757.31080.0841.6980920231220