Overview

Dataset statistics

Number of variables13
Number of observations8811
Missing cells5
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory972.4 KiB
Average record size in memory113.0 B

Variable types

Categorical3
Text2
Numeric8

Dataset

Description유기질비료 자자체(읍면동)/비종별 공급현황 자료로 사업년도,시도,시군구,읍면동,비료종류,신청물량(톤),신청금액(백만),선정물량(톤),선정금액(백만),포기물량(톤),포기금액(백만),실지원물량(톤),실지원금액(백만) 등을 제공합니다.
URLhttps://www.data.go.kr/data/15090527/fileData.do

Alerts

사업년도 has constant value ""Constant
신청물량(톤) is highly overall correlated with 신청금액(백만) and 4 other fieldsHigh correlation
신청금액(백만) is highly overall correlated with 신청물량(톤) and 4 other fieldsHigh correlation
선정물량(톤) is highly overall correlated with 신청물량(톤) and 4 other fieldsHigh correlation
선정금액(백만) is highly overall correlated with 신청물량(톤) and 4 other fieldsHigh correlation
포기물량(톤) is highly overall correlated with 포기금액(백만)High correlation
포기금액(백만) is highly overall correlated with 포기물량(톤)High correlation
실지원물량(톤) is highly overall correlated with 신청물량(톤) and 4 other fieldsHigh correlation
실지원금액(백만) is highly overall correlated with 신청물량(톤) and 4 other fieldsHigh correlation
포기물량(톤) is highly skewed (γ1 = 26.81726337)Skewed
포기금액(백만) is highly skewed (γ1 = 27.67283787)Skewed
선정물량(톤) has 118 (1.3%) zerosZeros
선정금액(백만) has 118 (1.3%) zerosZeros
포기물량(톤) has 7002 (79.5%) zerosZeros
포기금액(백만) has 7002 (79.5%) zerosZeros
실지원물량(톤) has 142 (1.6%) zerosZeros
실지원금액(백만) has 142 (1.6%) zerosZeros

Reproduction

Analysis started2023-12-12 20:57:18.571714
Analysis finished2023-12-12 20:57:28.184441
Duration9.61 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

사업년도
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size69.0 KiB
2020
8811 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020
2nd row2020
3rd row2020
4th row2020
5th row2020

Common Values

ValueCountFrequency (%)
2020 8811
100.0%

Length

2023-12-13T05:57:28.247766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:57:28.359428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020 8811
100.0%

시도
Categorical

Distinct17
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size69.0 KiB
경상북도
1327 
경기도
1276 
경상남도
1071 
전라남도
1040 
전라북도
818 
Other values (12)
3279 

Length

Max length7
Median length4
Mean length3.9642492
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경상북도 1327
15.1%
경기도 1276
14.5%
경상남도 1071
12.2%
전라남도 1040
11.8%
전라북도 818
9.3%
충청남도 781
8.9%
강원도 674
7.6%
충청북도 595
6.8%
인천광역시 178
 
2.0%
광주광역시 171
 
1.9%
Other values (7) 880
10.0%

Length

2023-12-13T05:57:28.503990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경상북도 1327
15.1%
경기도 1276
14.5%
경상남도 1071
12.2%
전라남도 1040
11.8%
전라북도 818
9.3%
충청남도 781
8.9%
강원도 674
7.6%
충청북도 595
6.8%
인천광역시 178
 
2.0%
광주광역시 171
 
1.9%
Other values (7) 880
10.0%
Distinct193
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size69.0 KiB
2023-12-13T05:57:28.884192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length2.9807059
Min length2

Characters and Unicode

Total characters26263
Distinct characters128
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row강남구
2nd row강남구
3rd row강남구
4th row강남구
5th row강남구
ValueCountFrequency (%)
창원시 153
 
1.7%
청주시 128
 
1.5%
북구 119
 
1.4%
상주시 110
 
1.2%
충주시 103
 
1.2%
익산시 100
 
1.1%
김천시 99
 
1.1%
안동시 95
 
1.1%
천안시 94
 
1.1%
용인시 93
 
1.1%
Other values (183) 7717
87.6%
2023-12-13T05:57:29.329651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4698
17.9%
3422
 
13.0%
1380
 
5.3%
1004
 
3.8%
983
 
3.7%
773
 
2.9%
754
 
2.9%
653
 
2.5%
562
 
2.1%
459
 
1.7%
Other values (118) 11575
44.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 26263
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
4698
17.9%
3422
 
13.0%
1380
 
5.3%
1004
 
3.8%
983
 
3.7%
773
 
2.9%
754
 
2.9%
653
 
2.5%
562
 
2.1%
459
 
1.7%
Other values (118) 11575
44.1%

Most occurring scripts

ValueCountFrequency (%)
Hangul 26263
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
4698
17.9%
3422
 
13.0%
1380
 
5.3%
1004
 
3.8%
983
 
3.7%
773
 
2.9%
754
 
2.9%
653
 
2.5%
562
 
2.1%
459
 
1.7%
Other values (118) 11575
44.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 26263
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
4698
17.9%
3422
 
13.0%
1380
 
5.3%
1004
 
3.8%
983
 
3.7%
773
 
2.9%
754
 
2.9%
653
 
2.5%
562
 
2.1%
459
 
1.7%
Other values (118) 11575
44.1%
Distinct2151
Distinct (%)24.4%
Missing5
Missing (%)0.1%
Memory size69.0 KiB
2023-12-13T05:57:29.661716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length3
Mean length3.1263911
Min length2

Characters and Unicode

Total characters27531
Distinct characters316
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique141 ?
Unique (%)1.6%

Sample

1st row개포1동
2nd row개포1동
3rd row세곡동
4th row세곡동
5th row세곡동
ValueCountFrequency (%)
남면 48
 
0.5%
중앙동 38
 
0.4%
서면 35
 
0.4%
북면 35
 
0.4%
금성면 20
 
0.2%
성산면 18
 
0.2%
대산면 17
 
0.2%
봉산면 17
 
0.2%
옥산면 17
 
0.2%
산내면 16
 
0.2%
Other values (2141) 8545
97.0%
2023-12-13T05:57:30.112764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4827
 
17.5%
3396
 
12.3%
1020
 
3.7%
796
 
2.9%
448
 
1.6%
437
 
1.6%
435
 
1.6%
1 393
 
1.4%
325
 
1.2%
318
 
1.2%
Other values (306) 15136
55.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 26655
96.8%
Decimal Number 852
 
3.1%
Other Punctuation 24
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
4827
 
18.1%
3396
 
12.7%
1020
 
3.8%
796
 
3.0%
448
 
1.7%
437
 
1.6%
435
 
1.6%
325
 
1.2%
318
 
1.2%
308
 
1.2%
Other values (295) 14345
53.8%
Decimal Number
ValueCountFrequency (%)
1 393
46.1%
2 278
32.6%
3 113
 
13.3%
4 33
 
3.9%
6 15
 
1.8%
5 14
 
1.6%
7 4
 
0.5%
8 1
 
0.1%
9 1
 
0.1%
Other Punctuation
ValueCountFrequency (%)
. 23
95.8%
, 1
 
4.2%

Most occurring scripts

ValueCountFrequency (%)
Hangul 26655
96.8%
Common 876
 
3.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
4827
 
18.1%
3396
 
12.7%
1020
 
3.8%
796
 
3.0%
448
 
1.7%
437
 
1.6%
435
 
1.6%
325
 
1.2%
318
 
1.2%
308
 
1.2%
Other values (295) 14345
53.8%
Common
ValueCountFrequency (%)
1 393
44.9%
2 278
31.7%
3 113
 
12.9%
4 33
 
3.8%
. 23
 
2.6%
6 15
 
1.7%
5 14
 
1.6%
7 4
 
0.5%
, 1
 
0.1%
8 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 26655
96.8%
ASCII 876
 
3.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
4827
 
18.1%
3396
 
12.7%
1020
 
3.8%
796
 
3.0%
448
 
1.7%
437
 
1.6%
435
 
1.6%
325
 
1.2%
318
 
1.2%
308
 
1.2%
Other values (295) 14345
53.8%
ASCII
ValueCountFrequency (%)
1 393
44.9%
2 278
31.7%
3 113
 
12.9%
4 33
 
3.8%
. 23
 
2.6%
6 15
 
1.7%
5 14
 
1.6%
7 4
 
0.5%
, 1
 
0.1%
8 1
 
0.1%

비료종류
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size69.0 KiB
가축분퇴비
2361 
혼합유박
2152 
혼합유기질
1872 
퇴비
1416 
유기질복합비료
1010 

Length

Max length7
Median length5
Mean length4.5028941
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row혼합유기질
2nd row가축분퇴비
3rd row혼합유박
4th row혼합유기질
5th row가축분퇴비

Common Values

ValueCountFrequency (%)
가축분퇴비 2361
26.8%
혼합유박 2152
24.4%
혼합유기질 1872
21.2%
퇴비 1416
16.1%
유기질복합비료 1010
11.5%

Length

2023-12-13T05:57:30.251744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:57:30.352037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
가축분퇴비 2361
26.8%
혼합유박 2152
24.4%
혼합유기질 1872
21.2%
퇴비 1416
16.1%
유기질복합비료 1010
11.5%

신청물량(톤)
Real number (ℝ)

HIGH CORRELATION 

Distinct5737
Distinct (%)65.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean497.41622
Minimum0.015
Maximum26866.42
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size77.6 KiB
2023-12-13T05:57:30.499232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.015
5-th percentile0.6
Q18.17
median62.4
Q3364.9575
95-th percentile2313.26
Maximum26866.42
Range26866.405
Interquartile range (IQR)356.7875

Descriptive statistics

Standard deviation1338.7704
Coefficient of variation (CV)2.691449
Kurtosis91.488907
Mean497.41622
Median Absolute Deviation (MAD)60.92
Skewness7.6320113
Sum4382734.3
Variance1792306.1
MonotonicityNot monotonic
2023-12-13T05:57:30.649462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.0 131
 
1.5%
2.0 130
 
1.5%
0.6 82
 
0.9%
0.4 82
 
0.9%
0.2 78
 
0.9%
4.0 77
 
0.9%
6.0 66
 
0.7%
3.0 58
 
0.7%
5.0 43
 
0.5%
0.1 42
 
0.5%
Other values (5727) 8022
91.0%
ValueCountFrequency (%)
0.015 1
 
< 0.1%
0.02 7
 
0.1%
0.04 13
 
0.1%
0.045 2
 
< 0.1%
0.06 18
0.2%
0.075 13
 
0.1%
0.08 11
 
0.1%
0.09 1
 
< 0.1%
0.1 42
0.5%
0.12 9
 
0.1%
ValueCountFrequency (%)
26866.42 1
< 0.1%
25730.78 1
< 0.1%
23929.44 1
< 0.1%
21426.78 1
< 0.1%
19441.92 1
< 0.1%
19428.62 1
< 0.1%
18392.46 1
< 0.1%
17899.58 1
< 0.1%
17417.94 1
< 0.1%
16666.6 1
< 0.1%

신청금액(백만)
Real number (ℝ)

HIGH CORRELATION 

Distinct6163
Distinct (%)69.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.395815
Minimum0.000825
Maximum1343.321
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size77.6 KiB
2023-12-13T05:57:30.788684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.000825
5-th percentile0.0319
Q10.44
median3.3187
Q319.5294
95-th percentile116.98479
Maximum1343.321
Range1343.3202
Interquartile range (IQR)19.0894

Descriptive statistics

Standard deviation67.520995
Coefficient of variation (CV)2.658745
Kurtosis90.670031
Mean25.395815
Median Absolute Deviation (MAD)3.2417
Skewness7.5910597
Sum223762.52
Variance4559.0848
MonotonicityNot monotonic
2023-12-13T05:57:30.944444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.055 96
 
1.1%
0.11 74
 
0.8%
0.022 73
 
0.8%
0.011 72
 
0.8%
0.033 68
 
0.8%
0.1 60
 
0.7%
0.0055 42
 
0.5%
0.2 41
 
0.5%
0.22 40
 
0.5%
0.165 37
 
0.4%
Other values (6153) 8208
93.2%
ValueCountFrequency (%)
0.000825 1
 
< 0.1%
0.0011 7
 
0.1%
0.0022 13
 
0.1%
0.002475 2
 
< 0.1%
0.003 2
 
< 0.1%
0.0033 16
 
0.2%
0.004125 13
 
0.1%
0.0044 11
 
0.1%
0.00495 1
 
< 0.1%
0.0055 42
0.5%
ValueCountFrequency (%)
1343.321 1
< 0.1%
1317.9102 1
< 0.1%
1196.472 1
< 0.1%
1071.389 1
< 0.1%
972.096 1
< 0.1%
972.051 1
< 0.1%
919.623 1
< 0.1%
894.979 1
< 0.1%
870.897 1
< 0.1%
833.49 1
< 0.1%

선정물량(톤)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct5886
Distinct (%)66.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean298.86256
Minimum0
Maximum11893.7
Zeros118
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size77.6 KiB
2023-12-13T05:57:31.358351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.24
Q14.82
median39.92
Q3230.4
95-th percentile1502.98
Maximum11893.7
Range11893.7
Interquartile range (IQR)225.58

Descriptive statistics

Standard deviation698.10184
Coefficient of variation (CV)2.3358624
Kurtosis49.791368
Mean298.86256
Median Absolute Deviation (MAD)39.14
Skewness5.4951712
Sum2633278.1
Variance487346.17
MonotonicityNot monotonic
2023-12-13T05:57:31.514541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 118
 
1.3%
0.2 67
 
0.8%
1.0 62
 
0.7%
2.0 51
 
0.6%
0.6 43
 
0.5%
0.1 42
 
0.5%
0.4 41
 
0.5%
4.0 27
 
0.3%
0.3 26
 
0.3%
0.06 24
 
0.3%
Other values (5876) 8310
94.3%
ValueCountFrequency (%)
0.0 118
1.3%
0.015 1
 
< 0.1%
0.02 13
 
0.1%
0.03 2
 
< 0.1%
0.035 1
 
< 0.1%
0.04 23
 
0.3%
0.045 4
 
< 0.1%
0.06 24
 
0.3%
0.07 1
 
< 0.1%
0.075 7
 
0.1%
ValueCountFrequency (%)
11893.7 1
< 0.1%
11577.5 1
< 0.1%
11138.5 1
< 0.1%
10422.86 1
< 0.1%
8835.12 1
< 0.1%
7854.16 1
< 0.1%
7634.2 1
< 0.1%
7359.36 1
< 0.1%
6678.58 1
< 0.1%
6592.46 1
< 0.1%

선정금액(백만)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6501
Distinct (%)73.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.267714
Minimum0
Maximum608.8997
Zeros118
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size77.6 KiB
2023-12-13T05:57:31.683604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0132
Q10.2574
median2.1061
Q312.3145
95-th percentile76.1167
Maximum608.8997
Range608.8997
Interquartile range (IQR)12.0571

Descriptive statistics

Standard deviation35.258291
Coefficient of variation (CV)2.3093367
Kurtosis49.511485
Mean15.267714
Median Absolute Deviation (MAD)2.0651
Skewness5.4761992
Sum134523.82
Variance1243.1471
MonotonicityNot monotonic
2023-12-13T05:57:31.861531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 118
 
1.3%
0.011 61
 
0.7%
0.055 42
 
0.5%
0.0055 41
 
0.5%
0.022 36
 
0.4%
0.1 34
 
0.4%
0.033 34
 
0.4%
0.0044 24
 
0.3%
0.0165 24
 
0.3%
0.0022 23
 
0.3%
Other values (6491) 8374
95.0%
ValueCountFrequency (%)
0.0 118
1.3%
0.000825 1
 
< 0.1%
0.0011 13
 
0.1%
0.00165 2
 
< 0.1%
0.001925 1
 
< 0.1%
0.0022 23
 
0.3%
0.002475 4
 
< 0.1%
0.003 2
 
< 0.1%
0.0033 22
 
0.2%
0.00385 1
 
< 0.1%
ValueCountFrequency (%)
608.8997 1
< 0.1%
578.875 1
< 0.1%
556.925 1
< 0.1%
521.1616 1
< 0.1%
441.756 1
< 0.1%
395.2895 1
< 0.1%
382.9396 1
< 0.1%
367.968 1
< 0.1%
367.3219 1
< 0.1%
340.237 1
< 0.1%

포기물량(톤)
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct747
Distinct (%)8.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.1603384
Minimum0
Maximum1413.32
Zeros7002
Zeros (%)79.5%
Negative0
Negative (%)0.0%
Memory size77.6 KiB
2023-12-13T05:57:32.035300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile8.465
Maximum1413.32
Range1413.32
Interquartile range (IQR)0

Descriptive statistics

Standard deviation30.353259
Coefficient of variation (CV)9.6044331
Kurtosis937.08608
Mean3.1603384
Median Absolute Deviation (MAD)0
Skewness26.817263
Sum27845.742
Variance921.32034
MonotonicityNot monotonic
2023-12-13T05:57:32.203039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 7002
79.5%
1.0 58
 
0.7%
2.0 47
 
0.5%
0.2 43
 
0.5%
0.4 35
 
0.4%
0.1 32
 
0.4%
0.6 26
 
0.3%
0.06 22
 
0.2%
3.0 21
 
0.2%
1.2 20
 
0.2%
Other values (737) 1505
 
17.1%
ValueCountFrequency (%)
0.0 7002
79.5%
0.02 19
 
0.2%
0.026 1
 
< 0.1%
0.04 18
 
0.2%
0.05 1
 
< 0.1%
0.052 1
 
< 0.1%
0.06 22
 
0.2%
0.08 16
 
0.2%
0.1 32
 
0.4%
0.12 10
 
0.1%
ValueCountFrequency (%)
1413.32 1
< 0.1%
1076.44 1
< 0.1%
913.32 1
< 0.1%
804.86 1
< 0.1%
709.68 1
< 0.1%
611.98 1
< 0.1%
485.78 1
< 0.1%
463.68 1
< 0.1%
454.82 1
< 0.1%
400.12 1
< 0.1%

포기금액(백만)
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct941
Distinct (%)10.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.16432504
Minimum0
Maximum77.7326
Zeros7002
Zeros (%)79.5%
Negative0
Negative (%)0.0%
Memory size77.6 KiB
2023-12-13T05:57:32.366539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.4474
Maximum77.7326
Range77.7326
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.5869462
Coefficient of variation (CV)9.6573608
Kurtosis1013.7978
Mean0.16432504
Median Absolute Deviation (MAD)0
Skewness27.672838
Sum1447.8679
Variance2.5183982
MonotonicityNot monotonic
2023-12-13T05:57:32.548913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 7002
79.5%
0.055 40
 
0.5%
0.011 38
 
0.4%
0.0055 31
 
0.4%
0.11 28
 
0.3%
0.022 25
 
0.3%
0.1 24
 
0.3%
0.05 19
 
0.2%
0.0033 19
 
0.2%
0.0011 16
 
0.2%
Other values (931) 1569
 
17.8%
ValueCountFrequency (%)
0.0 7002
79.5%
0.001 3
 
< 0.1%
0.0011 16
 
0.2%
0.00143 1
 
< 0.1%
0.002 2
 
< 0.1%
0.0022 16
 
0.2%
0.00275 1
 
< 0.1%
0.00286 1
 
< 0.1%
0.003 3
 
< 0.1%
0.0033 19
 
0.2%
ValueCountFrequency (%)
77.7326 1
< 0.1%
53.822 1
< 0.1%
45.666 1
< 0.1%
40.7134 1
< 0.1%
39.0324 1
< 0.1%
33.6589 1
< 0.1%
24.9274 1
< 0.1%
24.289 1
< 0.1%
22.7559 1
< 0.1%
20.006 1
< 0.1%

실지원물량(톤)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct5896
Distinct (%)66.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean295.70223
Minimum-17.9
Maximum11575.9
Zeros142
Zeros (%)1.6%
Negative20
Negative (%)0.2%
Memory size77.6 KiB
2023-12-13T05:57:32.744509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-17.9
5-th percentile0.2
Q14.69
median38.68
Q3228.81
95-th percentile1486.02
Maximum11575.9
Range11593.8
Interquartile range (IQR)224.12

Descriptive statistics

Standard deviation690.39903
Coefficient of variation (CV)2.3347779
Kurtosis50.00985
Mean295.70223
Median Absolute Deviation (MAD)37.96
Skewness5.5034688
Sum2605432.3
Variance476650.82
MonotonicityNot monotonic
2023-12-13T05:57:32.925133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 142
 
1.6%
0.2 65
 
0.7%
1.0 65
 
0.7%
2.0 49
 
0.6%
0.6 44
 
0.5%
0.1 42
 
0.5%
0.4 40
 
0.5%
0.3 26
 
0.3%
0.08 26
 
0.3%
4.0 26
 
0.3%
Other values (5886) 8286
94.0%
ValueCountFrequency (%)
-17.9 1
< 0.1%
-10.68 1
< 0.1%
-5.4 1
< 0.1%
-5.12 1
< 0.1%
-2.0 1
< 0.1%
-1.835 1
< 0.1%
-1.0 2
< 0.1%
-0.64 1
< 0.1%
-0.38 1
< 0.1%
-0.35 1
< 0.1%
ValueCountFrequency (%)
11575.9 1
< 0.1%
11430.02 1
< 0.1%
11138.5 1
< 0.1%
10422.86 1
< 0.1%
8835.12 1
< 0.1%
7854.16 1
< 0.1%
7634.2 1
< 0.1%
7359.36 1
< 0.1%
6592.46 1
< 0.1%
6464.44 1
< 0.1%

실지원금액(백만)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6492
Distinct (%)73.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.103389
Minimum-0.9845
Maximum583.9723
Zeros142
Zeros (%)1.6%
Negative20
Negative (%)0.2%
Memory size77.6 KiB
2023-12-13T05:57:33.099905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-0.9845
5-th percentile0.011
Q10.2502625
median2.068
Q312.1849
95-th percentile75.580363
Maximum583.9723
Range584.9568
Interquartile range (IQR)11.934638

Descriptive statistics

Standard deviation34.852772
Coefficient of variation (CV)2.3076127
Kurtosis49.598242
Mean15.103389
Median Absolute Deviation (MAD)2.0284
Skewness5.478967
Sum133075.96
Variance1214.7157
MonotonicityNot monotonic
2023-12-13T05:57:33.277225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 142
 
1.6%
0.011 59
 
0.7%
0.055 45
 
0.5%
0.0055 41
 
0.5%
0.022 35
 
0.4%
0.033 34
 
0.4%
0.1 31
 
0.4%
0.0044 26
 
0.3%
0.0165 24
 
0.3%
0.0022 21
 
0.2%
Other values (6482) 8353
94.8%
ValueCountFrequency (%)
-0.9845 1
< 0.1%
-0.534 1
< 0.1%
-0.297 1
< 0.1%
-0.256 1
< 0.1%
-0.11 1
< 0.1%
-0.100925 1
< 0.1%
-0.089 1
< 0.1%
-0.055 1
< 0.1%
-0.05 1
< 0.1%
-0.0352 1
< 0.1%
ValueCountFrequency (%)
583.9723 1
< 0.1%
578.795 1
< 0.1%
556.925 1
< 0.1%
521.1616 1
< 0.1%
441.756 1
< 0.1%
395.2895 1
< 0.1%
382.9396 1
< 0.1%
367.968 1
< 0.1%
355.5442 1
< 0.1%
340.061 1
< 0.1%

Interactions

2023-12-13T05:57:26.957574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:57:20.704694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:57:21.504778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:57:22.254386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:57:23.076036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:57:23.994048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:57:25.210351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:57:26.066659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:57:27.070953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:57:20.819386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:57:21.623749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:57:22.337121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:57:23.161089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:57:24.387494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:57:25.316972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:57:26.177646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:57:27.189733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:57:20.916406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:57:21.711210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:57:22.448403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:57:23.278902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:57:24.519195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:57:25.418622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:57:26.276236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:57:27.300746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:57:21.026034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:57:21.798110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:57:22.579179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:57:23.401261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:57:24.634763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:57:25.528196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:57:26.378078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:57:27.413402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:57:21.118901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:57:21.886163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:57:22.679353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:57:23.498824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:57:24.765851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:57:25.626543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:57:26.487000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:57:27.529363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:57:21.209366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:57:21.972610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:57:22.781282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:57:23.601293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:57:24.878024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:57:25.731460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:57:26.597432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:57:27.636355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:57:21.298612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:57:22.061203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:57:22.881688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:57:23.755986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:57:24.993038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:57:25.848340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:57:26.715152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:57:27.734936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:57:21.393944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:57:22.156663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:57:22.970989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:57:23.866757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:57:25.096451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:57:25.953767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:57:26.832344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T05:57:33.384311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시도비료종류신청물량(톤)신청금액(백만)선정물량(톤)선정금액(백만)포기물량(톤)포기금액(백만)실지원물량(톤)실지원금액(백만)
시도1.0000.1000.0820.0820.0930.0970.1480.1390.0830.091
비료종류0.1001.0000.3540.3560.4650.4600.0390.0320.3650.480
신청물량(톤)0.0820.3541.0001.0000.9270.9310.4300.4750.8240.930
신청금액(백만)0.0820.3561.0001.0000.9280.9270.4290.4740.8240.929
선정물량(톤)0.0930.4650.9270.9281.0000.9990.3880.4190.9840.998
선정금액(백만)0.0970.4600.9310.9270.9991.0000.3930.4240.9710.998
포기물량(톤)0.1480.0390.4300.4290.3880.3931.0000.9820.4340.347
포기금액(백만)0.1390.0320.4750.4740.4190.4240.9821.0000.3380.381
실지원물량(톤)0.0830.3650.8240.8240.9840.9710.4340.3381.0000.990
실지원금액(백만)0.0910.4800.9300.9290.9980.9980.3470.3810.9901.000
2023-12-13T05:57:33.512930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시도비료종류
시도1.0000.051
비료종류0.0511.000
2023-12-13T05:57:33.609882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
신청물량(톤)신청금액(백만)선정물량(톤)선정금액(백만)포기물량(톤)포기금액(백만)실지원물량(톤)실지원금액(백만)시도비료종류
신청물량(톤)1.0001.0000.9680.9680.3230.3230.9660.9660.0320.154
신청금액(백만)1.0001.0000.9670.9680.3250.3240.9660.9660.0320.155
선정물량(톤)0.9680.9671.0001.0000.3250.3240.9990.9980.0360.211
선정금액(백만)0.9680.9681.0001.0000.3260.3250.9980.9990.0380.208
포기물량(톤)0.3230.3250.3250.3261.0001.0000.3070.3080.0590.023
포기금액(백만)0.3230.3240.3240.3251.0001.0000.3060.3070.0590.020
실지원물량(톤)0.9660.9660.9990.9980.3070.3061.0001.0000.0330.220
실지원금액(백만)0.9660.9660.9980.9990.3080.3071.0001.0000.0350.219
시도0.0320.0320.0360.0380.0590.0590.0330.0351.0000.051
비료종류0.1540.1550.2110.2080.0230.0200.2200.2190.0511.000

Missing values

2023-12-13T05:57:27.881623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T05:57:28.093083image/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

사업년도시도시군구읍면동비료종류신청물량(톤)신청금액(백만)선정물량(톤)선정금액(백만)포기물량(톤)포기금액(백만)실지원물량(톤)실지원금액(백만)
02020서울특별시강남구개포1동혼합유기질3.540.19472.80.1540.00.02.80.154
12020서울특별시강남구개포1동가축분퇴비3.60.1980.00.00.00.00.00.0
22020서울특별시강남구세곡동혼합유박63.943.516728.781.58290.00.028.781.5829
32020서울특별시강남구세곡동혼합유기질207.7811.427998.85.4340.00.098.85.434
42020서울특별시강남구세곡동가축분퇴비48.02.6335.181.92810.00.035.181.9281
52020서울특별시강남구세곡동퇴비59.763.286838.022.09110.00.038.022.0911
62020서울특별시강남구수서동혼합유박0.40.0220.40.0220.00.00.40.022
72020서울특별시강남구수서동혼합유기질4.00.223.90.21450.00.03.90.2145
82020서울특별시강남구수서동가축분퇴비1.80.0991.420.07810.00.01.420.0781
92020서울특별시강남구수서동퇴비7.80.4295.580.30690.00.05.580.3069
사업년도시도시군구읍면동비료종류신청물량(톤)신청금액(백만)선정물량(톤)선정금액(백만)포기물량(톤)포기금액(백만)실지원물량(톤)실지원금액(백만)
88012020세종특별자치시세종시전동면유기질복합비료0.20.0110.20.0110.00.00.20.011
88022020세종특별자치시세종시전동면가축분퇴비756.938.3829712.5236.13998.920.446703.635.6939
88032020세종특별자치시세종시전동면퇴비14.00.7314.00.730.00.014.00.73
88042020세종특별자치시세종시전의면혼합유박72.944.011772.623.99410.00.072.623.9941
88052020세종특별자치시세종시전의면혼합유기질152.48.382150.968.30280.00.0150.968.3028
88062020세종특별자치시세종시전의면가축분퇴비530.7426.726530.7426.7260.00.0530.7426.726
88072020세종특별자치시세종시조치원읍혼합유박50.72.788547.062.58833.940.216743.122.3716
88082020세종특별자치시세종시조치원읍혼합유기질72.623.994165.843.62122.40.13263.443.4892
88092020세종특별자치시세종시조치원읍가축분퇴비392.420.3941361.5618.78219.70.5335351.8618.2486
88102020세종특별자치시세종시조치원읍퇴비1.00.051.00.050.00.01.00.05