Overview

Dataset statistics

Number of variables10
Number of observations407
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory34.7 KiB
Average record size in memory87.3 B

Variable types

Numeric7
Categorical3

Dataset

Description산지조합에서 생산하는 과실류(사과, 배, 단감, 감귤)의 약정 및 출하물량 정보
Author농림축산식품부
URLhttps://data.mafra.go.kr/opendata/data/indexOpenDataDetail.do?data_id=20220215000000001920

Alerts

BSNS_CPTAL is highly overall correlated with CNTRCT_VOLM_TON and 3 other fieldsHigh correlation
CNTRCT_VOLM_TON is highly overall correlated with BSNS_CPTAL and 3 other fieldsHigh correlation
SHIPMNT_VOLM_TON is highly overall correlated with BSNS_CPTAL and 3 other fieldsHigh correlation
PARTCPTN_MXTR_CO is highly overall correlated with BSNS_CPTAL and 3 other fieldsHigh correlation
PARTCPTN_FRMHS_CO is highly overall correlated with BSNS_CPTAL and 3 other fieldsHigh correlation
AREA_HDQRTRS_NM is highly overall correlated with PRDLST_NMHigh correlation
PRDLST_NM is highly overall correlated with AREA_HDQRTRS_NMHigh correlation
BSNS_CPTAL has 28 (6.9%) zerosZeros
CNTRCT_VOLM_TON has 28 (6.9%) zerosZeros
SHIPMNT_VOLM_TON has 33 (8.1%) zerosZeros
SHIPMNT_VOLM_PT has 33 (8.1%) zerosZeros
PARTCPTN_FRMHS_CO has 28 (6.9%) zerosZeros

Reproduction

Analysis started2023-12-11 03:39:27.950784
Analysis finished2023-12-11 03:39:35.355750
Duration7.4 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

BSNS_YEAR
Real number (ℝ)

Distinct14
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2009.5971
Minimum2003
Maximum2016
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2023-12-11T12:39:35.459675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2003
5-th percentile2003
Q12006.5
median2010
Q32013
95-th percentile2016
Maximum2016
Range13
Interquartile range (IQR)6.5

Descriptive statistics

Standard deviation3.9172212
Coefficient of variation (CV)0.001949257
Kurtosis-1.0614631
Mean2009.5971
Median Absolute Deviation (MAD)3
Skewness0.0010555461
Sum817906
Variance15.344622
MonotonicityNot monotonic
2023-12-11T12:39:35.642206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
2008 44
10.8%
2011 37
 
9.1%
2010 36
 
8.8%
2016 33
 
8.1%
2009 32
 
7.9%
2004 28
 
6.9%
2014 27
 
6.6%
2003 26
 
6.4%
2015 26
 
6.4%
2005 24
 
5.9%
Other values (4) 94
23.1%
ValueCountFrequency (%)
2003 26
6.4%
2004 28
6.9%
2005 24
5.9%
2006 24
5.9%
2007 23
5.7%
2008 44
10.8%
2009 32
7.9%
2010 36
8.8%
2011 37
9.1%
2012 23
5.7%
ValueCountFrequency (%)
2016 33
8.1%
2015 26
6.4%
2014 27
6.6%
2013 24
5.9%
2012 23
5.7%
2011 37
9.1%
2010 36
8.8%
2009 32
7.9%
2008 44
10.8%
2007 23
5.7%

AREA_HDQRTRS_NM
Categorical

HIGH CORRELATION 

Distinct20
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Memory size3.3 KiB
경남지역본부
54 
경북지역본부
47 
충남지역본부
46 
전남지역본부
44 
충북지역본부
39 
Other values (15)
177 

Length

Max length10
Median length6
Mean length6.7764128
Min length6

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st row충북지역본부
2nd row충남지역본부
3rd row울산지역본부
4th row경남지역본부
5th row경북지역본부

Common Values

ValueCountFrequency (%)
경남지역본부 54
13.3%
경북지역본부 47
11.5%
충남지역본부 46
11.3%
전남지역본부 44
10.8%
충북지역본부 39
9.6%
전북지역본부 33
8.1%
경기지역본부 20
 
4.9%
전북지역본부(대표) 19
 
4.7%
울산지역본부 18
 
4.4%
경북지역본부(대표) 16
 
3.9%
Other values (10) 71
17.4%

Length

2023-12-11T12:39:35.836574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경남지역본부 54
13.3%
경북지역본부 47
11.5%
충남지역본부 46
11.3%
전남지역본부 44
10.8%
충북지역본부 39
9.6%
전북지역본부 33
8.1%
경기지역본부 20
 
4.9%
전북지역본부(대표 19
 
4.7%
울산지역본부 18
 
4.4%
경북지역본부(대표 16
 
3.9%
Other values (10) 71
17.4%

BSNS_MTHD
Categorical

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.3 KiB
수탁
290 
매취
117 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
수탁 290
71.3%
매취 117
28.7%

Length

2023-12-11T12:39:36.001441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T12:39:36.158032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
수탁 290
71.3%
매취 117
28.7%

PRDLST_NM
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size3.3 KiB
배
189 
사과
159 
단감
38 
감귤
21 

Length

Max length2
Median length2
Mean length1.5356265
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row배
2nd row사과
3rd row배
4th row단감
5th row사과

Common Values

ValueCountFrequency (%)
배 189
46.4%
사과 159
39.1%
단감 38
 
9.3%
감귤 21
 
5.2%

Length

2023-12-11T12:39:36.309014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T12:39:36.454001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
배 189
46.4%
사과 159
39.1%
단감 38
 
9.3%
감귤 21
 
5.2%

BSNS_CPTAL
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct377
Distinct (%)92.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3198369.1
Minimum0
Maximum19239056
Zeros28
Zeros (%)6.9%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2023-12-11T12:39:36.615587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1243835
median959265
Q35045477
95-th percentile12264333
Maximum19239056
Range19239056
Interquartile range (IQR)4801642

Descriptive statistics

Standard deviation4190401.1
Coefficient of variation (CV)1.3101681
Kurtosis2.0663526
Mean3198369.1
Median Absolute Deviation (MAD)911165
Skewness1.6124433
Sum1.3017362 × 109
Variance1.7559461 × 1013
MonotonicityNot monotonic
2023-12-11T12:39:36.867749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 28
 
6.9%
309000 2
 
0.5%
758000 2
 
0.5%
212000 2
 
0.5%
62179 1
 
0.2%
239269 1
 
0.2%
4028733 1
 
0.2%
48100 1
 
0.2%
261410 1
 
0.2%
794286 1
 
0.2%
Other values (367) 367
90.2%
ValueCountFrequency (%)
0 28
6.9%
1800 1
 
0.2%
13500 1
 
0.2%
25000 1
 
0.2%
27100 1
 
0.2%
30000 1
 
0.2%
42000 1
 
0.2%
42100 1
 
0.2%
48100 1
 
0.2%
50550 1
 
0.2%
ValueCountFrequency (%)
19239056 1
0.2%
18641584 1
0.2%
18557621 1
0.2%
18253100 1
0.2%
17739588 1
0.2%
16691616 1
0.2%
16311500 1
0.2%
15633500 1
0.2%
15388000 1
0.2%
15213056 1
0.2%

CNTRCT_VOLM_TON
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct369
Distinct (%)90.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5162.0541
Minimum0
Maximum81705
Zeros28
Zeros (%)6.9%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2023-12-11T12:39:37.092587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1397.5
median1326
Q35940
95-th percentile17594.7
Maximum81705
Range81705
Interquartile range (IQR)5542.5

Descriptive statistics

Standard deviation10118.361
Coefficient of variation (CV)1.9601424
Kurtosis26.379495
Mean5162.0541
Median Absolute Deviation (MAD)1248
Skewness4.6652987
Sum2100956
Variance1.0238123 × 108
MonotonicityNot monotonic
2023-12-11T12:39:37.279799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 28
 
6.9%
1104 2
 
0.5%
398 2
 
0.5%
402 2
 
0.5%
467 2
 
0.5%
266 2
 
0.5%
972 2
 
0.5%
540 2
 
0.5%
287 2
 
0.5%
935 2
 
0.5%
Other values (359) 361
88.7%
ValueCountFrequency (%)
0 28
6.9%
5 1
 
0.2%
15 1
 
0.2%
33 1
 
0.2%
46 1
 
0.2%
50 1
 
0.2%
68 1
 
0.2%
75 1
 
0.2%
76 1
 
0.2%
78 1
 
0.2%
ValueCountFrequency (%)
81705 1
0.2%
74131 1
0.2%
69660 1
0.2%
68582 1
0.2%
67290 1
0.2%
55104 1
0.2%
53806 1
0.2%
46959 1
0.2%
42808 1
0.2%
25575 1
0.2%

SHIPMNT_VOLM_TON
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct362
Distinct (%)88.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4453.543
Minimum0
Maximum77067
Zeros33
Zeros (%)8.1%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2023-12-11T12:39:37.767198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1299
median1093
Q35009.5
95-th percentile14938.1
Maximum77067
Range77067
Interquartile range (IQR)4710.5

Descriptive statistics

Standard deviation9112.9742
Coefficient of variation (CV)2.0462302
Kurtosis28.731637
Mean4453.543
Median Absolute Deviation (MAD)1068
Skewness4.8486146
Sum1812592
Variance83046298
MonotonicityNot monotonic
2023-12-11T12:39:37.945164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 33
 
8.1%
228 3
 
0.7%
289 3
 
0.7%
806 2
 
0.5%
411 2
 
0.5%
427 2
 
0.5%
467 2
 
0.5%
306 2
 
0.5%
382 2
 
0.5%
181 2
 
0.5%
Other values (352) 354
87.0%
ValueCountFrequency (%)
0 33
8.1%
8 1
 
0.2%
14 1
 
0.2%
17 1
 
0.2%
19 1
 
0.2%
20 1
 
0.2%
24 1
 
0.2%
25 1
 
0.2%
33 1
 
0.2%
34 1
 
0.2%
ValueCountFrequency (%)
77067 1
0.2%
73704 1
0.2%
59753 1
0.2%
59103 1
0.2%
57405 1
0.2%
46378 1
0.2%
45707 1
0.2%
43647 1
0.2%
39122 1
0.2%
25909 1
0.2%

SHIPMNT_VOLM_PT
Real number (ℝ)

ZEROS 

Distinct85
Distinct (%)20.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean77.007371
Minimum0
Maximum117
Zeros33
Zeros (%)8.1%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2023-12-11T12:39:38.129095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q167
median88
Q3100
95-th percentile107
Maximum117
Range117
Interquartile range (IQR)33

Descriptive statistics

Standard deviation31.876261
Coefficient of variation (CV)0.41393779
Kurtosis0.66984456
Mean77.007371
Median Absolute Deviation (MAD)13
Skewness-1.3231928
Sum31342
Variance1016.096
MonotonicityNot monotonic
2023-12-11T12:39:38.343579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 33
 
8.1%
101 20
 
4.9%
100 19
 
4.7%
102 14
 
3.4%
103 13
 
3.2%
88 13
 
3.2%
98 12
 
2.9%
99 11
 
2.7%
97 10
 
2.5%
105 9
 
2.2%
Other values (75) 253
62.2%
ValueCountFrequency (%)
0 33
8.1%
1 1
 
0.2%
7 2
 
0.5%
9 2
 
0.5%
11 1
 
0.2%
13 1
 
0.2%
14 1
 
0.2%
18 1
 
0.2%
21 1
 
0.2%
23 2
 
0.5%
ValueCountFrequency (%)
117 1
 
0.2%
113 1
 
0.2%
112 2
 
0.5%
111 3
 
0.7%
110 2
 
0.5%
109 5
1.2%
108 5
1.2%
107 5
1.2%
106 7
1.7%
105 9
2.2%

PARTCPTN_MXTR_CO
Real number (ℝ)

HIGH CORRELATION 

Distinct43
Distinct (%)10.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.97543
Minimum1
Maximum62
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2023-12-11T12:39:38.545958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median3
Q311
95-th percentile26.7
Maximum62
Range61
Interquartile range (IQR)10

Descriptive statistics

Standard deviation10.442636
Coefficient of variation (CV)1.3093509
Kurtosis6.7117372
Mean7.97543
Median Absolute Deviation (MAD)2
Skewness2.4166949
Sum3246
Variance109.04866
MonotonicityNot monotonic
2023-12-11T12:39:38.745499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
1 114
28.0%
2 66
16.2%
3 33
 
8.1%
8 24
 
5.9%
7 18
 
4.4%
5 15
 
3.7%
4 14
 
3.4%
15 11
 
2.7%
13 11
 
2.7%
14 9
 
2.2%
Other values (33) 92
22.6%
ValueCountFrequency (%)
1 114
28.0%
2 66
16.2%
3 33
 
8.1%
4 14
 
3.4%
5 15
 
3.7%
6 8
 
2.0%
7 18
 
4.4%
8 24
 
5.9%
9 8
 
2.0%
10 3
 
0.7%
ValueCountFrequency (%)
62 1
0.2%
55 1
0.2%
54 1
0.2%
53 1
0.2%
51 2
0.5%
50 1
0.2%
49 1
0.2%
48 1
0.2%
42 1
0.2%
39 2
0.5%

PARTCPTN_FRMHS_CO
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct273
Distinct (%)67.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean366.90418
Minimum0
Maximum3787
Zeros28
Zeros (%)6.9%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2023-12-11T12:39:38.965320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q128
median132
Q3512
95-th percentile1463.8
Maximum3787
Range3787
Interquartile range (IQR)484

Descriptive statistics

Standard deviation568.29226
Coefficient of variation (CV)1.5488847
Kurtosis12.529775
Mean366.90418
Median Absolute Deviation (MAD)121
Skewness3.1067596
Sum149330
Variance322956.09
MonotonicityNot monotonic
2023-12-11T12:39:39.127986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 28
 
6.9%
24 7
 
1.7%
10 7
 
1.7%
17 6
 
1.5%
14 5
 
1.2%
26 4
 
1.0%
34 4
 
1.0%
5 4
 
1.0%
29 4
 
1.0%
21 4
 
1.0%
Other values (263) 334
82.1%
ValueCountFrequency (%)
0 28
6.9%
1 1
 
0.2%
2 1
 
0.2%
3 1
 
0.2%
5 4
 
1.0%
6 1
 
0.2%
7 2
 
0.5%
9 3
 
0.7%
10 7
 
1.7%
11 1
 
0.2%
ValueCountFrequency (%)
3787 1
0.2%
3698 1
0.2%
3617 1
0.2%
3576 1
0.2%
2739 1
0.2%
2726 1
0.2%
2721 1
0.2%
2438 1
0.2%
2422 1
0.2%
1838 1
0.2%

Interactions

2023-12-11T12:39:34.075955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:39:28.427941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:39:29.285662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:39:30.151688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:39:31.315908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:39:32.162142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:39:33.232804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:39:34.199283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:39:28.544550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:39:29.416451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:39:30.277192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:39:31.437727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:39:32.315129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:39:33.360168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:39:34.327794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:39:28.696309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:39:29.540610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:39:30.391177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:39:31.558429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:39:32.467804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:39:33.494062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:39:34.453903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:39:28.812893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:39:29.652275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:39:30.812413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:39:31.673084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:39:32.633131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:39:33.619125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:39:34.569805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:39:28.928362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:39:29.777567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:39:30.936816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:39:31.792352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:39:32.798100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:39:33.762548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:39:34.728272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:39:29.047413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:39:29.903594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:39:31.061718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:39:31.899670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:39:32.944765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:39:33.872967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:39:34.875618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:39:29.150451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:39:30.028724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:39:31.187347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:39:32.013660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:39:33.092435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:39:33.970946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T12:39:39.245344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
BSNS_YEARAREA_HDQRTRS_NMBSNS_MTHDPRDLST_NMBSNS_CPTALCNTRCT_VOLM_TONSHIPMNT_VOLM_TONSHIPMNT_VOLM_PTPARTCPTN_MXTR_COPARTCPTN_FRMHS_CO
BSNS_YEAR1.0000.0000.0000.0830.0000.0000.0000.3440.0000.000
AREA_HDQRTRS_NM0.0001.0000.1590.9340.5700.5870.5800.5860.7310.674
BSNS_MTHD0.0000.1591.0000.0000.4570.4080.3720.4500.2500.251
PRDLST_NM0.0830.9340.0001.0000.3850.6790.6790.2910.6340.743
BSNS_CPTAL0.0000.5700.4570.3851.0000.7890.7740.1010.8000.756
CNTRCT_VOLM_TON0.0000.5870.4080.6790.7891.0000.9960.0000.7840.967
SHIPMNT_VOLM_TON0.0000.5800.3720.6790.7740.9961.0000.0000.8050.938
SHIPMNT_VOLM_PT0.3440.5860.4500.2910.1010.0000.0001.0000.2200.172
PARTCPTN_MXTR_CO0.0000.7310.2500.6340.8000.7840.8050.2201.0000.802
PARTCPTN_FRMHS_CO0.0000.6740.2510.7430.7560.9670.9380.1720.8021.000
2023-12-11T12:39:39.401081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
BSNS_MTHDAREA_HDQRTRS_NMPRDLST_NM
BSNS_MTHD1.0000.1220.000
AREA_HDQRTRS_NM0.1221.0000.692
PRDLST_NM0.0000.6921.000
2023-12-11T12:39:39.510939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
BSNS_YEARBSNS_CPTALCNTRCT_VOLM_TONSHIPMNT_VOLM_TONSHIPMNT_VOLM_PTPARTCPTN_MXTR_COPARTCPTN_FRMHS_COAREA_HDQRTRS_NMBSNS_MTHDPRDLST_NM
BSNS_YEAR1.0000.1190.0040.0290.172-0.149-0.0120.0000.0000.053
BSNS_CPTAL0.1191.0000.9660.9580.2850.7390.9150.2120.3480.237
CNTRCT_VOLM_TON0.0040.9661.0000.9760.1940.7770.9480.2740.3040.357
SHIPMNT_VOLM_TON0.0290.9580.9761.0000.3380.7630.9190.2690.2780.357
SHIPMNT_VOLM_PT0.1720.2850.1940.3381.0000.0530.1240.2210.3430.176
PARTCPTN_MXTR_CO-0.1490.7390.7770.7630.0531.0000.7890.3170.1900.432
PARTCPTN_FRMHS_CO-0.0120.9150.9480.9190.1240.7891.0000.3390.1870.410
AREA_HDQRTRS_NM0.0000.2120.2740.2690.2210.3170.3391.0000.1220.692
BSNS_MTHD0.0000.3480.3040.2780.3430.1900.1870.1221.0000.000
PRDLST_NM0.0530.2370.3570.3570.1760.4320.4100.6920.0001.000

Missing values

2023-12-11T12:39:35.073910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T12:39:35.258910image/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

BSNS_YEARAREA_HDQRTRS_NMBSNS_MTHDPRDLST_NMBSNS_CPTALCNTRCT_VOLM_TONSHIPMNT_VOLM_TONSHIPMNT_VOLM_PTPARTCPTN_MXTR_COPARTCPTN_FRMHS_CO
02003충북지역본부매취배6217916200124
12003충남지역본부매취사과27896265323135294
22003울산지역본부매취배326245806811119
32003경남지역본부매취단감366477993869368
42003경북지역본부매취사과2252833459724205320724
52003충남지역본부매취배7313621122566503188
62003경북지역본부수탁배9135402201872407468
72003경기지역본부수탁배45022551046053195111665
82003충북지역본부수탁배337562110489781783
92003충남지역본부수탁사과313400489522107229
BSNS_YEARAREA_HDQRTRS_NMBSNS_MTHDPRDLST_NMBSNS_CPTALCNTRCT_VOLM_TONSHIPMNT_VOLM_TONSHIPMNT_VOLM_PTPARTCPTN_MXTR_COPARTCPTN_FRMHS_CO
3972016경북지역본부수탁사과4804005598612362121636
3982016충남지역본부(대표)수탁사과903000110884376134
3992016충북지역본부수탁배748000438467107424
4002016제주지역본부(대표)수탁감귤248838867115063755468
4012016충북지역본부수탁사과13728900877872788315867
4022016경남지역본부수탁사과8309000826349296017513
4032016경북지역본부수탁배67530091064371289
4042016전남지역본부수탁단감14000068203015
4052016충남지역본부수탁사과39200002058002117
4062016전북지역본부(대표)수탁배22809040232681120