Overview

Dataset statistics

Number of variables19
Number of observations486
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory79.9 KiB
Average record size in memory168.3 B

Variable types

Numeric14
Categorical3
Text2

Dataset

Description법인체의 경종농가에 대한 분뇨 살포 현황
Author농림축산식품부
URLhttps://data.mafra.go.kr/opendata/data/indexOpenDataDetail.do?data_id=20220215000000001901

Alerts

ADMINIST_ATPT_CD is highly overall correlated with ADMINIST_SIGNGU_CD and 2 other fieldsHigh correlation
ADMINIST_SIGNGU_CD is highly overall correlated with ADMINIST_ATPT_CD and 2 other fieldsHigh correlation
ADMINIST_EMD_CD is highly overall correlated with ADMINIST_ATPT_CD and 2 other fieldsHigh correlation
CPR_CO is highly overall correlated with LVSTCK_SOIL_TKAWY_TON_DE and 3 other fieldsHigh correlation
LVSTCK_SOIL_TKAWY_TON_DE is highly overall correlated with CPR_CO and 2 other fieldsHigh correlation
LVSTCK_SOIL_TKAWY_VOLM is highly overall correlated with CPR_CO and 2 other fieldsHigh correlation
CAP_SM is highly overall correlated with CAP_TURCOMP and 3 other fieldsHigh correlation
CAP_TURCOMP is highly overall correlated with CAP_SM and 2 other fieldsHigh correlation
CAP_LIQMACOMP is highly overall correlated with CAP_SM and 3 other fieldsHigh correlation
LIFER_TKAWY_SOIL_TON_DE is highly overall correlated with LIFER_TKAWY_VOLM_TONHigh correlation
LIFER_TKAWY_VOLM_TON is highly overall correlated with LIFER_TKAWY_SOIL_TON_DEHigh correlation
LIFER_SPRAY_AR is highly overall correlated with CPR_CO and 1 other fieldsHigh correlation
LIFER_SPRAY_QY is highly overall correlated with CPR_CO and 3 other fieldsHigh correlation
ADMINIST_ATPT_NM is highly overall correlated with ADMINIST_ATPT_CD and 2 other fieldsHigh correlation
CAP_PRFCTN is highly overall correlated with CAP_SM and 3 other fieldsHigh correlation
CAP_ETC is highly overall correlated with CAP_SM and 2 other fieldsHigh correlation
CAP_PRFCTN is highly imbalanced (92.2%)Imbalance
CAP_ETC is highly imbalanced (91.5%)Imbalance
LVSTCK_SOIL_TKAWY_TON_DE is highly skewed (γ1 = 20.7838975)Skewed
LVSTCK_SOIL_TKAWY_VOLM is highly skewed (γ1 = 20.78389381)Skewed
LIFER_TKAWY_SOIL_TON_DE is highly skewed (γ1 = 21.93093328)Skewed
LIFER_TKAWY_VOLM_TON is highly skewed (γ1 = 21.93091861)Skewed
LVSTCK_SOIL_TKAWY_TON_DE has 30 (6.2%) zerosZeros
CAP_SM has 429 (88.3%) zerosZeros
CAP_TURCOMP has 463 (95.3%) zerosZeros
CAP_LIQMACOMP has 434 (89.3%) zerosZeros
LIFER_TKAWY_SOIL_TON_DE has 228 (46.9%) zerosZeros
LIFER_TKAWY_VOLM_TON has 200 (41.2%) zerosZeros
LIFER_SPRAY_AR has 10 (2.1%) zerosZeros
LIFER_SPRAY_QY has 14 (2.9%) zerosZeros

Reproduction

Analysis started2023-12-11 03:34:51.929380
Analysis finished2023-12-11 03:35:20.084379
Duration28.15 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

EXAMIN_YEAR
Real number (ℝ)

Distinct15
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1992.6955
Minimum14
Maximum2016
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.4 KiB
2023-12-11T12:35:20.153426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile2012
Q12013
median2014
Q32015
95-th percentile2016
Maximum2016
Range2002
Interquartile range (IQR)2

Descriptive statistics

Standard deviation174.54919
Coefficient of variation (CV)0.087594514
Kurtosis103.76922
Mean1992.6955
Median Absolute Deviation (MAD)1
Skewness-9.822052
Sum968450
Variance30467.42
MonotonicityNot monotonic
2023-12-11T12:35:20.315581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
2014 153
31.5%
2015 145
29.8%
2013 109
22.4%
2016 46
 
9.5%
2012 15
 
3.1%
2011 5
 
1.0%
2010 3
 
0.6%
15 2
 
0.4%
1413 2
 
0.4%
14 1
 
0.2%
Other values (5) 5
 
1.0%
ValueCountFrequency (%)
14 1
 
0.2%
15 2
 
0.4%
1014 1
 
0.2%
1275 1
 
0.2%
1413 2
 
0.4%
1514 1
 
0.2%
1515 1
 
0.2%
1527 1
 
0.2%
2010 3
0.6%
2011 5
1.0%
ValueCountFrequency (%)
2016 46
 
9.5%
2015 145
29.8%
2014 153
31.5%
2013 109
22.4%
2012 15
 
3.1%
2011 5
 
1.0%
2010 3
 
0.6%
1527 1
 
0.2%
1515 1
 
0.2%
1514 1
 
0.2%

ADMINIST_ATPT_CD
Real number (ℝ)

HIGH CORRELATION 

Distinct11
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6449917.7
Minimum5690000
Maximum6500000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.4 KiB
2023-12-11T12:35:20.473811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5690000
5-th percentile6410000
Q16440000
median6460000
Q36470000
95-th percentile6500000
Maximum6500000
Range810000
Interquartile range (IQR)30000

Descriptive statistics

Standard deviation65575.906
Coefficient of variation (CV)0.010166937
Kurtosis110.05865
Mean6449917.7
Median Absolute Deviation (MAD)20000
Skewness-9.679405
Sum3.13466 × 109
Variance4.3001994 × 109
MonotonicityNot monotonic
2023-12-11T12:35:20.624826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
6480000 78
16.0%
6460000 77
15.8%
6450000 74
15.2%
6440000 59
12.1%
6470000 59
12.1%
6410000 46
9.5%
6500000 39
8.0%
6420000 31
 
6.4%
6430000 18
 
3.7%
5690000 3
 
0.6%
ValueCountFrequency (%)
5690000 3
 
0.6%
6310000 2
 
0.4%
6410000 46
9.5%
6420000 31
 
6.4%
6430000 18
 
3.7%
6440000 59
12.1%
6450000 74
15.2%
6460000 77
15.8%
6470000 59
12.1%
6480000 78
16.0%
ValueCountFrequency (%)
6500000 39
8.0%
6480000 78
16.0%
6470000 59
12.1%
6460000 77
15.8%
6450000 74
15.2%
6440000 59
12.1%
6430000 18
 
3.7%
6420000 31
 
6.4%
6410000 46
9.5%
6310000 2
 
0.4%

ADMINIST_ATPT_NM
Categorical

HIGH CORRELATION 

Distinct11
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size3.9 KiB
경상남도
78 
전라남도
77 
전라북도
74 
충청남도
59 
경상북도
59 
Other values (6)
139 

Length

Max length7
Median length4
Mean length4.1049383
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row제주특별자치도
2nd row세종특별자치시
3rd row울산광역시
4th row경기도
5th row경기도

Common Values

ValueCountFrequency (%)
경상남도 78
16.0%
전라남도 77
15.8%
전라북도 74
15.2%
충청남도 59
12.1%
경상북도 59
12.1%
경기도 46
9.5%
제주특별자치도 39
8.0%
강원도 31
 
6.4%
충청북도 18
 
3.7%
세종특별자치시 3
 
0.6%

Length

2023-12-11T12:35:20.826037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경상남도 78
16.0%
전라남도 77
15.8%
전라북도 74
15.2%
충청남도 59
12.1%
경상북도 59
12.1%
경기도 46
9.5%
제주특별자치도 39
8.0%
강원도 31
 
6.4%
충청북도 18
 
3.7%
세종특별자치시 3
 
0.6%

ADMINIST_SIGNGU_CD
Real number (ℝ)

HIGH CORRELATION 

Distinct93
Distinct (%)19.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5053923.9
Minimum3730000
Maximum9999010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.4 KiB
2023-12-11T12:35:21.002424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3730000
5-th percentile4152500
Q14610000
median4910000
Q35390000
95-th percentile6510000
Maximum9999010
Range6269010
Interquartile range (IQR)780000

Descriptive statistics

Standard deviation724445.07
Coefficient of variation (CV)0.14334309
Kurtosis12.338052
Mean5053923.9
Median Absolute Deviation (MAD)370000
Skewness2.385284
Sum2.456207 × 109
Variance5.2482065 × 1011
MonotonicityNot monotonic
2023-12-11T12:35:21.205242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4690000 22
 
4.5%
6520000 21
 
4.3%
6510000 18
 
3.7%
5480000 12
 
2.5%
5600000 12
 
2.5%
4300000 11
 
2.3%
4680000 11
 
2.3%
4500000 11
 
2.3%
5420000 10
 
2.1%
4700000 9
 
1.9%
Other values (83) 349
71.8%
ValueCountFrequency (%)
3730000 2
 
0.4%
3940000 4
0.8%
4050000 3
 
0.6%
4060000 4
0.8%
4070000 4
0.8%
4080000 2
 
0.4%
4090000 2
 
0.4%
4140000 4
0.8%
4190000 3
 
0.6%
4200000 8
1.6%
ValueCountFrequency (%)
9999010 3
 
0.6%
6520000 21
4.3%
6510000 18
3.7%
5710000 1
 
0.2%
5700000 4
 
0.8%
5680000 8
 
1.6%
5670000 4
 
0.8%
5600000 12
2.5%
5590000 3
 
0.6%
5530000 4
 
0.8%
Distinct93
Distinct (%)19.1%
Missing0
Missing (%)0.0%
Memory size3.9 KiB
2023-12-11T12:35:21.537753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.0432099
Min length3

Characters and Unicode

Total characters1479
Distinct characters81
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

Unique6 ?
Unique (%)1.2%

Sample

1st row서귀포시
2nd row세종시
3rd row울주군
4th row고양시
5th row용인시
ValueCountFrequency (%)
정읍시 22
 
4.5%
서귀포시 21
 
4.3%
제주시 18
 
3.7%
포천시 12
 
2.5%
합천군 12
 
2.5%
익산시 11
 
2.3%
공주시 11
 
2.3%
철원군 11
 
2.3%
고성군 10
 
2.1%
김해시 9
 
1.9%
Other values (83) 349
71.8%
2023-12-11T12:35:22.114914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
262
17.7%
231
 
15.6%
75
 
5.1%
68
 
4.6%
52
 
3.5%
41
 
2.8%
38
 
2.6%
37
 
2.5%
29
 
2.0%
29
 
2.0%
Other values (71) 617
41.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1479
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
262
17.7%
231
 
15.6%
75
 
5.1%
68
 
4.6%
52
 
3.5%
41
 
2.8%
38
 
2.6%
37
 
2.5%
29
 
2.0%
29
 
2.0%
Other values (71) 617
41.7%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1479
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
262
17.7%
231
 
15.6%
75
 
5.1%
68
 
4.6%
52
 
3.5%
41
 
2.8%
38
 
2.6%
37
 
2.5%
29
 
2.0%
29
 
2.0%
Other values (71) 617
41.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1479
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
262
17.7%
231
 
15.6%
75
 
5.1%
68
 
4.6%
52
 
3.5%
41
 
2.8%
38
 
2.6%
37
 
2.5%
29
 
2.0%
29
 
2.0%
Other values (71) 617
41.7%

ADMINIST_EMD_CD
Real number (ℝ)

HIGH CORRELATION 

Distinct164
Distinct (%)33.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5037259.6
Minimum3730020
Maximum6520043
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.4 KiB
2023-12-11T12:35:22.309561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3730020
5-th percentile4192559.2
Q14670055
median4930033
Q35400025
95-th percentile6510040
Maximum6520043
Range2790023
Interquartile range (IQR)729970

Descriptive statistics

Standard deviation609432.41
Coefficient of variation (CV)0.12098491
Kurtosis0.54440454
Mean5037259.6
Median Absolute Deviation (MAD)380029
Skewness0.81240779
Sum2.4481081 × 109
Variance3.7140786 × 1011
MonotonicityNot monotonic
2023-12-11T12:35:22.503845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4500234 6
 
1.2%
5350051 6
 
1.2%
6520043 5
 
1.0%
5600015 5
 
1.0%
4540045 5
 
1.0%
5480043 5
 
1.0%
5070050 5
 
1.0%
6510037 5
 
1.0%
6520029 5
 
1.0%
4300022 5
 
1.0%
Other values (154) 434
89.3%
ValueCountFrequency (%)
3730020 2
0.4%
3950019 4
0.8%
4060050 4
0.8%
4070038 2
0.4%
4070041 2
0.4%
4080036 2
0.4%
4090146 2
0.4%
4140030 4
0.8%
4190062 3
0.6%
4200051 3
0.6%
ValueCountFrequency (%)
6520043 5
1.0%
6520032 4
0.8%
6520031 4
0.8%
6520029 5
1.0%
6520028 3
0.6%
6510041 2
 
0.4%
6510040 3
0.6%
6510039 4
0.8%
6510038 4
0.8%
6510037 5
1.0%
Distinct164
Distinct (%)33.7%
Missing0
Missing (%)0.0%
Memory size3.9 KiB
2023-12-11T12:35:22.906856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length3.0390947
Min length2

Characters and Unicode

Total characters1477
Distinct characters143
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

Unique19 ?
Unique (%)3.9%

Sample

1st row중문동
2nd row연서면
3rd row언양읍
4th row성사1동
5th row원삼면
ValueCountFrequency (%)
월송동 6
 
1.2%
한림면 6
 
1.2%
중문동 5
 
1.0%
영중면 5
 
1.0%
광석면 5
 
1.0%
남원읍 5
 
1.0%
남후면 5
 
1.0%
한림읍 5
 
1.0%
율곡면 5
 
1.0%
철원읍 5
 
1.0%
Other values (154) 434
89.3%
2023-12-11T12:35:23.415718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
231
 
15.6%
170
 
11.5%
113
 
7.7%
38
 
2.6%
36
 
2.4%
30
 
2.0%
22
 
1.5%
21
 
1.4%
20
 
1.4%
20
 
1.4%
Other values (133) 776
52.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1463
99.1%
Decimal Number 14
 
0.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
231
 
15.8%
170
 
11.6%
113
 
7.7%
38
 
2.6%
36
 
2.5%
30
 
2.1%
22
 
1.5%
21
 
1.4%
20
 
1.4%
20
 
1.4%
Other values (131) 762
52.1%
Decimal Number
ValueCountFrequency (%)
1 11
78.6%
2 3
 
21.4%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1463
99.1%
Common 14
 
0.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
231
 
15.8%
170
 
11.6%
113
 
7.7%
38
 
2.6%
36
 
2.5%
30
 
2.1%
22
 
1.5%
21
 
1.4%
20
 
1.4%
20
 
1.4%
Other values (131) 762
52.1%
Common
ValueCountFrequency (%)
1 11
78.6%
2 3
 
21.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1463
99.1%
ASCII 14
 
0.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
231
 
15.8%
170
 
11.6%
113
 
7.7%
38
 
2.6%
36
 
2.5%
30
 
2.1%
22
 
1.5%
21
 
1.4%
20
 
1.4%
20
 
1.4%
Other values (131) 762
52.1%
ASCII
ValueCountFrequency (%)
1 11
78.6%
2 3
 
21.4%

CPR_CO
Real number (ℝ)

HIGH CORRELATION 

Distinct21
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.1440329
Minimum1
Maximum33
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.4 KiB
2023-12-11T12:35:23.550340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median4
Q36
95-th percentile13
Maximum33
Range32
Interquartile range (IQR)3

Descriptive statistics

Standard deviation4.0992626
Coefficient of variation (CV)0.79689665
Kurtosis13.38553
Mean5.1440329
Median Absolute Deviation (MAD)1
Skewness3.0997101
Sum2500
Variance16.803954
MonotonicityNot monotonic
2023-12-11T12:35:23.739876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
4 129
26.5%
3 104
21.4%
2 64
13.2%
6 41
 
8.4%
5 31
 
6.4%
8 30
 
6.2%
7 18
 
3.7%
1 16
 
3.3%
10 12
 
2.5%
12 8
 
1.6%
Other values (11) 33
 
6.8%
ValueCountFrequency (%)
1 16
 
3.3%
2 64
13.2%
3 104
21.4%
4 129
26.5%
5 31
 
6.4%
6 41
 
8.4%
7 18
 
3.7%
8 30
 
6.2%
9 7
 
1.4%
10 12
 
2.5%
ValueCountFrequency (%)
33 2
 
0.4%
27 1
 
0.2%
26 1
 
0.2%
24 2
 
0.4%
20 1
 
0.2%
18 4
0.8%
16 7
1.4%
15 1
 
0.2%
14 5
1.0%
13 2
 
0.4%

LVSTCK_SOIL_TKAWY_TON_DE
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct352
Distinct (%)72.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1465.4136
Minimum0
Maximum298742
Zeros30
Zeros (%)6.2%
Negative0
Negative (%)0.0%
Memory size4.4 KiB
2023-12-11T12:35:23.914556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q160
median225.5
Q3692.25
95-th percentile2282.75
Maximum298742
Range298742
Interquartile range (IQR)632.25

Descriptive statistics

Standard deviation13795.127
Coefficient of variation (CV)9.4138117
Kurtosis447.29444
Mean1465.4136
Median Absolute Deviation (MAD)211
Skewness20.783897
Sum712191
Variance1.9030554 × 108
MonotonicityNot monotonic
2023-12-11T12:35:24.073189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 30
 
6.2%
1 9
 
1.9%
18 6
 
1.2%
3 6
 
1.2%
70 4
 
0.8%
13 4
 
0.8%
9 4
 
0.8%
64 4
 
0.8%
246 4
 
0.8%
157 3
 
0.6%
Other values (342) 412
84.8%
ValueCountFrequency (%)
0 30
6.2%
1 9
 
1.9%
2 2
 
0.4%
3 6
 
1.2%
4 3
 
0.6%
5 2
 
0.4%
6 2
 
0.4%
7 1
 
0.2%
8 2
 
0.4%
9 4
 
0.8%
ValueCountFrequency (%)
298742 1
0.2%
39420 1
0.2%
20471 1
0.2%
20201 1
0.2%
19985 1
0.2%
18552 1
0.2%
15608 1
0.2%
8666 1
0.2%
8350 1
0.2%
7699 1
0.2%

LVSTCK_SOIL_TKAWY_VOLM
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct477
Distinct (%)98.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean221349.91
Minimum0
Maximum45110144
Zeros3
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size4.4 KiB
2023-12-11T12:35:24.247970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile85.92
Q19072.86
median34155.33
Q3104580
95-th percentile344733.3
Maximum45110144
Range45110144
Interquartile range (IQR)95507.14

Descriptive statistics

Standard deviation2083065.8
Coefficient of variation (CV)9.4107367
Kurtosis447.29433
Mean221349.91
Median Absolute Deviation (MAD)31886.33
Skewness20.783894
Sum1.0757606 × 108
Variance4.3391629 × 1012
MonotonicityNot monotonic
2023-12-11T12:35:24.437795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
84.0 3
 
0.6%
0.0 3
 
0.6%
17100.0 2
 
0.4%
48.0 2
 
0.4%
2400.0 2
 
0.4%
480.0 2
 
0.4%
108000.0 2
 
0.4%
523176.0 1
 
0.2%
62306.4 1
 
0.2%
19560.0 1
 
0.2%
Other values (467) 467
96.1%
ValueCountFrequency (%)
0.0 3
0.6%
10.0 1
 
0.2%
16.0 1
 
0.2%
19.35 1
 
0.2%
20.0 1
 
0.2%
30.0 1
 
0.2%
30.54 1
 
0.2%
30.66 1
 
0.2%
36.74 1
 
0.2%
40.4 1
 
0.2%
ValueCountFrequency (%)
45110144.0 1
0.2%
5952512.0 1
0.2%
3091200.0 1
0.2%
3050400.0 1
0.2%
3017800.0 1
0.2%
2801444.42 1
0.2%
2356880.7 1
0.2%
1308582.0 1
0.2%
1260992.0 1
0.2%
1162550.24 1
0.2%

CAP_SM
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct19
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean350846.45
Minimum-82496.24
Maximum15216166
Zeros429
Zeros (%)88.3%
Negative6
Negative (%)1.2%
Memory size4.4 KiB
2023-12-11T12:35:24.579632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-82496.24
5-th percentile0
Q10
median0
Q30
95-th percentile958321.38
Maximum15216166
Range15298662
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2001090.1
Coefficient of variation (CV)5.7036064
Kurtosis43.367926
Mean350846.45
Median Absolute Deviation (MAD)0
Skewness6.5737595
Sum1.7051137 × 108
Variance4.0043615 × 1012
MonotonicityNot monotonic
2023-12-11T12:35:24.710498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0.0 429
88.3%
71961.0 5
 
1.0%
2055234.36 4
 
0.8%
-82496.24 4
 
0.8%
15216166.14 4
 
0.8%
1195957.6 4
 
0.8%
14225905.36 4
 
0.8%
81739.0 4
 
0.8%
2912680.0 3
 
0.6%
1476452.0 3
 
0.6%
Other values (9) 22
 
4.5%
ValueCountFrequency (%)
-82496.24 4
 
0.8%
-70242.0 2
 
0.4%
0.0 429
88.3%
386.0 3
 
0.6%
1178.0 1
 
0.2%
18054.36 2
 
0.4%
18827.5 3
 
0.6%
27881.6 3
 
0.6%
71961.0 5
 
1.0%
81739.0 4
 
0.8%
ValueCountFrequency (%)
15216166.14 4
0.8%
14225905.36 4
0.8%
8394258.0 3
0.6%
2912680.0 3
0.6%
2055234.36 4
0.8%
1476452.0 3
0.6%
1195957.6 4
0.8%
245412.72 3
0.6%
128524.06 2
0.4%
81739.0 4
0.8%

CAP_TURCOMP
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3040.0807
Minimum0
Maximum182468.8
Zeros463
Zeros (%)95.3%
Negative0
Negative (%)0.0%
Memory size4.4 KiB
2023-12-11T12:35:24.843928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum182468.8
Range182468.8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation22286.644
Coefficient of variation (CV)7.3309382
Kurtosis56.015517
Mean3040.0807
Median Absolute Deviation (MAD)0
Skewness7.5701708
Sum1477479.2
Variance4.966945 × 108
MonotonicityNot monotonic
2023-12-11T12:35:24.966245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0.0 463
95.3%
182468.8 4
 
0.8%
166132.56 4
 
0.8%
376.0 3
 
0.6%
2000.0 3
 
0.6%
63.0 3
 
0.6%
24452.0 3
 
0.6%
1180.4 2
 
0.4%
40.0 1
 
0.2%
ValueCountFrequency (%)
0.0 463
95.3%
40.0 1
 
0.2%
63.0 3
 
0.6%
376.0 3
 
0.6%
1180.4 2
 
0.4%
2000.0 3
 
0.6%
24452.0 3
 
0.6%
166132.56 4
 
0.8%
182468.8 4
 
0.8%
ValueCountFrequency (%)
182468.8 4
 
0.8%
166132.56 4
 
0.8%
24452.0 3
 
0.6%
2000.0 3
 
0.6%
1180.4 2
 
0.4%
376.0 3
 
0.6%
63.0 3
 
0.6%
40.0 1
 
0.2%
0.0 463
95.3%

CAP_LIQMACOMP
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct17
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean259053.31
Minimum-70242
Maximum14966475
Zeros434
Zeros (%)89.3%
Negative2
Negative (%)0.4%
Memory size4.4 KiB
2023-12-11T12:35:25.113432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-70242
5-th percentile0
Q10
median0
Q30
95-th percentile697043.51
Maximum14966475
Range15036717
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1560943.5
Coefficient of variation (CV)6.0255687
Kurtosis67.684792
Mean259053.31
Median Absolute Deviation (MAD)0
Skewness7.9405588
Sum1.2589991 × 108
Variance2.4365446 × 1012
MonotonicityNot monotonic
2023-12-11T12:35:25.239145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0.0 434
89.3%
71961.0 5
 
1.0%
1931255.82 4
 
0.8%
14966474.7 4
 
0.8%
851098.2 4
 
0.8%
3817055.2 4
 
0.8%
81739.0 4
 
0.8%
2870680.0 3
 
0.6%
1442100.0 3
 
0.6%
8387718.0 3
 
0.6%
Other values (7) 18
 
3.7%
ValueCountFrequency (%)
-70242.0 2
 
0.4%
0.0 434
89.3%
5.0 3
 
0.6%
16385.96 2
 
0.4%
16827.5 3
 
0.6%
27881.6 3
 
0.6%
58524.06 2
 
0.4%
71961.0 5
 
1.0%
81739.0 4
 
0.8%
234879.42 3
 
0.6%
ValueCountFrequency (%)
14966474.7 4
0.8%
8387718.0 3
0.6%
3817055.2 4
0.8%
2870680.0 3
0.6%
1931255.82 4
0.8%
1442100.0 3
0.6%
851098.2 4
0.8%
234879.42 3
0.6%
81739.0 4
0.8%
71961.0 5
1.0%

CAP_PRFCTN
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size3.9 KiB
0.0
479 
121.6
 
4
52.0
 
3

Length

Max length5
Median length3
Mean length3.0226337
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 479
98.6%
121.6 4
 
0.8%
52.0 3
 
0.6%

Length

2023-12-11T12:35:25.402351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T12:35:25.529578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 479
98.6%
121.6 4
 
0.8%
52.0 3
 
0.6%

CAP_ETC
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size3.9 KiB
0.0
475 
10190468.4
 
4
101573.04
 
4
196.0
 
2
1138.0
 
1

Length

Max length10
Median length3
Mean length3.1213992
Min length3

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 475
97.7%
10190468.4 4
 
0.8%
101573.04 4
 
0.8%
196.0 2
 
0.4%
1138.0 1
 
0.2%

Length

2023-12-11T12:35:25.672641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T12:35:25.800203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 475
97.7%
10190468.4 4
 
0.8%
101573.04 4
 
0.8%
196.0 2
 
0.4%
1138.0 1
 
0.2%

LIFER_TKAWY_SOIL_TON_DE
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct200
Distinct (%)41.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean968.26543
Minimum0
Maximum317793
Zeros228
Zeros (%)46.9%
Negative0
Negative (%)0.0%
Memory size4.4 KiB
2023-12-11T12:35:26.248605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2.5
Q3257.5
95-th percentile1645.5
Maximum317793
Range317793
Interquartile range (IQR)257.5

Descriptive statistics

Standard deviation14426.244
Coefficient of variation (CV)14.899059
Kurtosis482.59905
Mean968.26543
Median Absolute Deviation (MAD)2.5
Skewness21.930933
Sum470577
Variance2.0811651 × 108
MonotonicityNot monotonic
2023-12-11T12:35:26.415446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 228
46.9%
1 11
 
2.3%
3 8
 
1.6%
9 5
 
1.0%
4 4
 
0.8%
2 4
 
0.8%
15 3
 
0.6%
16 3
 
0.6%
77 3
 
0.6%
5 3
 
0.6%
Other values (190) 214
44.0%
ValueCountFrequency (%)
0 228
46.9%
1 11
 
2.3%
2 4
 
0.8%
3 8
 
1.6%
4 4
 
0.8%
5 3
 
0.6%
6 3
 
0.6%
7 1
 
0.2%
8 1
 
0.2%
9 5
 
1.0%
ValueCountFrequency (%)
317793 1
0.2%
7891 1
0.2%
6874 1
0.2%
6642 1
0.2%
5894 1
0.2%
5473 1
0.2%
4748 1
0.2%
4625 1
0.2%
3905 1
0.2%
3338 1
0.2%

LIFER_TKAWY_VOLM_TON
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct281
Distinct (%)57.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean146251.96
Minimum0
Maximum47986844
Zeros200
Zeros (%)41.2%
Negative0
Negative (%)0.0%
Memory size4.4 KiB
2023-12-11T12:35:26.565506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median448
Q339011.615
95-th percentile248492.91
Maximum47986844
Range47986844
Interquartile range (IQR)39011.615

Descriptive statistics

Standard deviation2178365.9
Coefficient of variation (CV)14.89461
Kurtosis482.59862
Mean146251.96
Median Absolute Deviation (MAD)448
Skewness21.930919
Sum71078453
Variance4.7452781 × 1012
MonotonicityNot monotonic
2023-12-11T12:35:26.745101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 200
41.2%
200.0 3
 
0.6%
504.0 2
 
0.4%
84.0 2
 
0.4%
120.0 2
 
0.4%
64.0 2
 
0.4%
23740.0 1
 
0.2%
320825.4 1
 
0.2%
2255.6 1
 
0.2%
330156.0 1
 
0.2%
Other values (271) 271
55.8%
ValueCountFrequency (%)
0.0 200
41.2%
16.75 1
 
0.2%
19.35 1
 
0.2%
20.0 1
 
0.2%
30.0 1
 
0.2%
30.54 1
 
0.2%
30.66 1
 
0.2%
41.2 1
 
0.2%
48.0 1
 
0.2%
54.0 1
 
0.2%
ValueCountFrequency (%)
47986844.0 1
0.2%
1191552.0 1
0.2%
1037980.0 1
0.2%
1003017.92 1
0.2%
890064.0 1
0.2%
826469.6 1
0.2%
716954.0 1
0.2%
698488.0 1
0.2%
589736.0 1
0.2%
504102.0 1
0.2%

LIFER_SPRAY_AR
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct182
Distinct (%)37.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34643699
Minimum0
Maximum1.7462054 × 109
Zeros10
Zeros (%)2.1%
Negative0
Negative (%)0.0%
Memory size4.4 KiB
2023-12-11T12:35:26.900973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile341005.29
Q13745725.2
median9123962
Q319312479
95-th percentile80235031
Maximum1.7462054 × 109
Range1.7462054 × 109
Interquartile range (IQR)15566754

Descriptive statistics

Standard deviation1.5897254 × 108
Coefficient of variation (CV)4.5887865
Kurtosis94.629719
Mean34643699
Median Absolute Deviation (MAD)6761973.4
Skewness9.4646206
Sum1.6836838 × 1010
Variance2.5272268 × 1016
MonotonicityNot monotonic
2023-12-11T12:35:27.104186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 10
 
2.1%
727576.0 6
 
1.2%
52170171.41 5
 
1.0%
9762956.8 5
 
1.0%
1876978.66 5
 
1.0%
831837.0 5
 
1.0%
5847311.8 5
 
1.0%
13643687.3 5
 
1.0%
20081002.0 4
 
0.8%
7860188.7 4
 
0.8%
Other values (172) 432
88.9%
ValueCountFrequency (%)
0.0 10
2.1%
32917.0 2
 
0.4%
58146.0 1
 
0.2%
94780.78 1
 
0.2%
119551.9 1
 
0.2%
125040.9 3
 
0.6%
168869.0 1
 
0.2%
199888.0 2
 
0.4%
292833.0 2
 
0.4%
336067.2 2
 
0.4%
ValueCountFrequency (%)
1746205371.0 3
0.6%
1428267016.0 1
 
0.2%
651469435.17 2
0.4%
537617036.0 1
 
0.2%
280785443.0 1
 
0.2%
156398216.0 3
0.6%
143142500.63 4
0.8%
127668578.0 3
0.6%
105232634.2 3
0.6%
81524765.62 4
0.8%

LIFER_SPRAY_QY
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct180
Distinct (%)37.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean87169.158
Minimum0
Maximum4363622.7
Zeros14
Zeros (%)2.9%
Negative0
Negative (%)0.0%
Memory size4.4 KiB
2023-12-11T12:35:27.297089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile686.386
Q112811.68
median32540.6
Q365611.519
95-th percentile176634
Maximum4363622.7
Range4363622.7
Interquartile range (IQR)52799.839

Descriptive statistics

Standard deviation329147.49
Coefficient of variation (CV)3.7759627
Kurtosis121.94682
Mean87169.158
Median Absolute Deviation (MAD)23194.23
Skewness10.319962
Sum42364211
Variance1.0833807 × 1011
MonotonicityNot monotonic
2023-12-11T12:35:27.522513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 14
 
2.9%
686.386 6
 
1.2%
46169.0 5
 
1.0%
19887.919 5
 
1.0%
22287.0 5
 
1.0%
1693352.498 5
 
1.0%
148815.248 5
 
1.0%
39496.3 5
 
1.0%
4764.0 4
 
0.8%
59335.7 4
 
0.8%
Other values (170) 428
88.1%
ValueCountFrequency (%)
0.0 14
2.9%
20.0 3
 
0.6%
94.0 2
 
0.4%
230.0 1
 
0.2%
481.0 1
 
0.2%
515.5 1
 
0.2%
686.386 6
1.2%
730.0 2
 
0.4%
794.2 1
 
0.2%
813.83 3
 
0.6%
ValueCountFrequency (%)
4363622.69 2
 
0.4%
1693352.498 5
1.0%
743410.7 2
 
0.4%
566164.7 1
 
0.2%
237213.82 4
0.8%
233234.0 3
0.6%
226060.3 3
0.6%
195964.3 1
 
0.2%
186916.0 3
0.6%
176634.0 4
0.8%

Interactions

2023-12-11T12:35:17.412204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:53.074018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:55.234293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:57.050752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:58.684834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:35:00.456925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:35:02.689534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:35:04.516879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:35:06.425233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:35:08.425474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:35:10.318621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:35:12.473084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:35:14.305071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:35:15.892674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:35:17.593240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:53.189650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:55.396055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:57.210071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:58.807047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:35:00.605478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:35:02.818367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:35:04.648604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:35:06.588089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:35:08.576582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:35:10.788786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:35:12.601326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:35:14.435383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:35:15.993679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:35:17.735971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:53.368092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:55.523294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:57.355682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:58.926361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:35:00.763557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:35:02.961680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:35:04.800153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:35:06.756366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:35:08.752240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:35:10.932557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:35:12.751231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:35:14.565548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:35:16.092422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:35:18.202494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:53.509436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:55.641570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:57.462760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:59.105243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:35:00.952769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:35:03.083690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:35:04.960882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:35:06.915228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:35:08.897139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:35:11.049207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:35:12.882376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:35:14.676501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:35:16.182346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:35:18.332058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:53.646454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:55.754061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:57.567648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:59.218326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:35:01.085013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:35:03.241242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:35:05.097439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:35:07.077635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:35:09.059972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:35:11.173686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:35:13.034354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:35:14.790515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:35:16.273253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:35:18.451047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:53.754684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:55.882187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:57.703051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:59.329188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:35:01.229670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:35:03.371300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:35:05.236946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:35:07.211229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:35:09.220796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:35:11.316747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:35:13.190974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:35:14.947529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:35:16.389123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:35:18.574721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:53.886907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:56.003379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:57.835340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:59.440949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:35:01.361124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:35:03.508838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:35:05.361204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:35:07.350373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:35:09.383603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:35:11.452645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:35:13.312697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:35:15.063197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:35:16.506886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:35:18.702815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:53.996289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:56.101065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:57.950908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:59.574638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:35:01.469544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:35:03.636712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:35:05.473182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:35:07.484695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:35:09.489143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:35:11.571606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:35:13.421833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:35:15.165701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:35:16.591577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:35:18.807204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:54.147049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:56.236672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:58.046527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:59.696815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:35:01.621492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:35:03.796671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:35:05.606588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:35:07.637562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:35:09.597937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:35:11.706458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:35:13.542738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:35:15.276595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:35:16.689281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:35:18.922607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:54.281307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:56.369011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:58.145128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:59.794190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:35:01.759596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:35:03.950777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:35:05.736542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:35:07.777935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:35:09.748330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:35:11.835036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:35:13.668960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:35:15.393925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:35:16.786887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:35:19.078905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:54.423288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:56.513671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:58.251518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:59.913292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:35:01.877494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:35:04.072063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:35:05.868757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:35:07.921247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:35:09.886277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:35:11.958574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:35:13.778677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:35:15.493370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:35:16.882783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:35:19.206343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:54.541276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:56.644502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:58.356859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:35:00.067336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:35:02.010100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:35:04.173395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:35:06.008698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:35:08.054014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:35:10.005455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:35:12.083438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:35:13.918337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:35:15.600121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:35:16.989992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:35:19.342019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:54.653269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:56.781201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:58.463136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:35:00.232134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:35:02.134863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:35:04.285981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:35:06.141011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:35:08.175896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:35:10.110861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:35:12.207871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:35:14.065225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:35:15.694409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:35:17.081289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:35:19.463588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:55.109702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:56.923644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:34:58.587133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:35:00.351098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:35:02.270649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:35:04.409764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:35:06.303236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:35:08.313564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:35:10.224668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:35:12.342409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:35:14.195998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:35:15.789189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:35:17.196050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T12:35:27.711829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
EXAMIN_YEARADMINIST_ATPT_CDADMINIST_ATPT_NMADMINIST_SIGNGU_CDADMINIST_SIGNGU_NMADMINIST_EMD_CDCPR_COLVSTCK_SOIL_TKAWY_TON_DELVSTCK_SOIL_TKAWY_VOLMCAP_SMCAP_TURCOMPCAP_LIQMACOMPCAP_PRFCTNCAP_ETCLIFER_TKAWY_SOIL_TON_DELIFER_TKAWY_VOLM_TONLIFER_SPRAY_ARLIFER_SPRAY_QY
EXAMIN_YEAR1.0000.0880.0700.0000.0000.1020.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
ADMINIST_ATPT_CD0.0881.0001.0000.3691.0000.6270.1500.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
ADMINIST_ATPT_NM0.0701.0001.0000.9481.0000.8990.3640.0000.0000.2220.2370.2790.2400.2340.0000.0000.2960.278
ADMINIST_SIGNGU_CD0.0000.3690.9481.0001.0000.9360.3080.0000.0000.0000.0000.0810.0000.0680.0000.0000.2820.239
ADMINIST_SIGNGU_NM0.0001.0001.0001.0001.0001.0000.9050.7830.7830.8980.8950.9450.8950.9790.5120.5120.0000.960
ADMINIST_EMD_CD0.1020.6270.8990.9361.0001.0000.4370.0000.0000.1710.2280.2270.1830.2250.0000.0000.3010.312
CPR_CO0.0000.1500.3640.3080.9050.4371.0000.0000.0000.7730.8390.7350.7600.6010.0000.0000.7050.734
LVSTCK_SOIL_TKAWY_TON_DE0.0000.0000.0000.0000.7830.0000.0001.0001.0000.0000.0000.0000.0000.0001.0001.0000.0000.496
LVSTCK_SOIL_TKAWY_VOLM0.0000.0000.0000.0000.7830.0000.0001.0001.0000.0000.0000.0000.0000.0001.0001.0000.0000.496
CAP_SM0.0000.0000.2220.0000.8980.1710.7730.0000.0001.0000.7531.0000.6100.6120.0000.0000.3610.651
CAP_TURCOMP0.0000.0000.2370.0000.8950.2280.8390.0000.0000.7531.0000.7880.9880.5490.0000.0000.0000.000
CAP_LIQMACOMP0.0000.0000.2790.0810.9450.2270.7350.0000.0001.0000.7881.0000.7880.9090.0000.0000.5940.361
CAP_PRFCTN0.0000.0000.2400.0000.8950.1830.7600.0000.0000.6100.9880.7881.0000.7160.0000.0000.0000.000
CAP_ETC0.0000.0000.2340.0680.9790.2250.6010.0000.0000.6120.5490.9090.7161.0000.0000.0000.0000.000
LIFER_TKAWY_SOIL_TON_DE0.0000.0000.0000.0000.5120.0000.0001.0001.0000.0000.0000.0000.0000.0001.0000.7040.0000.000
LIFER_TKAWY_VOLM_TON0.0000.0000.0000.0000.5120.0000.0001.0001.0000.0000.0000.0000.0000.0000.7041.0000.0000.000
LIFER_SPRAY_AR0.0000.0000.2960.2820.0000.3010.7050.0000.0000.3610.0000.5940.0000.0000.0000.0001.0000.643
LIFER_SPRAY_QY0.0000.0000.2780.2390.9600.3120.7340.4960.4960.6510.0000.3610.0000.0000.0000.0000.6431.000
2023-12-11T12:35:27.925151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
CAP_PRFCTNCAP_ETCADMINIST_ATPT_NM
CAP_PRFCTN1.0000.7030.142
CAP_ETC0.7031.0000.130
ADMINIST_ATPT_NM0.1420.1301.000
2023-12-11T12:35:28.054283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
EXAMIN_YEARADMINIST_ATPT_CDADMINIST_SIGNGU_CDADMINIST_EMD_CDCPR_COLVSTCK_SOIL_TKAWY_TON_DELVSTCK_SOIL_TKAWY_VOLMCAP_SMCAP_TURCOMPCAP_LIQMACOMPLIFER_TKAWY_SOIL_TON_DELIFER_TKAWY_VOLM_TONLIFER_SPRAY_ARLIFER_SPRAY_QYADMINIST_ATPT_NMCAP_PRFCTNCAP_ETC
EXAMIN_YEAR1.000-0.0080.011-0.0080.0220.0460.0450.000-0.0080.0020.0900.063-0.015-0.0480.0380.0000.000
ADMINIST_ATPT_CD-0.0081.0000.6980.6770.0900.0450.046-0.069-0.079-0.0920.001-0.0050.1010.0980.9930.0000.000
ADMINIST_SIGNGU_CD0.0110.6981.0000.9720.0230.0170.019-0.038-0.130-0.0610.0510.0530.1770.1270.8600.0000.044
ADMINIST_EMD_CD-0.0080.6770.9721.000-0.0060.0040.005-0.045-0.133-0.0690.0380.0380.1580.1050.7240.0810.131
CPR_CO0.0220.0900.023-0.0061.0000.5010.5020.2230.1130.2730.2450.2240.5800.5640.1730.4610.400
LVSTCK_SOIL_TKAWY_TON_DE0.0460.0450.0170.0040.5011.0001.0000.1150.0470.1310.4700.4420.4760.5890.0000.0000.000
LVSTCK_SOIL_TKAWY_VOLM0.0450.0460.0190.0050.5021.0001.0000.1150.0470.1310.4690.4410.4780.5900.0000.0000.000
CAP_SM0.000-0.069-0.038-0.0450.2230.1150.1151.0000.6250.9590.3070.3060.0870.0510.1350.6250.539
CAP_TURCOMP-0.008-0.079-0.130-0.1330.1130.0470.0470.6251.0000.6240.1980.1940.013-0.0400.1400.8640.491
CAP_LIQMACOMP0.002-0.092-0.061-0.0690.2730.1310.1310.9590.6241.0000.3440.3420.1310.1010.1550.8010.586
LIFER_TKAWY_SOIL_TON_DE0.0900.0010.0510.0380.2450.4700.4690.3070.1980.3441.0000.9820.3310.2940.0000.0000.000
LIFER_TKAWY_VOLM_TON0.063-0.0050.0530.0380.2240.4420.4410.3060.1940.3420.9821.0000.3340.2900.0000.0000.000
LIFER_SPRAY_AR-0.0150.1010.1770.1580.5800.4760.4780.0870.0130.1310.3310.3341.0000.8050.1660.0000.000
LIFER_SPRAY_QY-0.0480.0980.1270.1050.5640.5890.5900.051-0.0400.1010.2940.2900.8051.0000.1690.0000.000
ADMINIST_ATPT_NM0.0380.9930.8600.7240.1730.0000.0000.1350.1400.1550.0000.0000.1660.1691.0000.1420.130
CAP_PRFCTN0.0000.0000.0000.0810.4610.0000.0000.6250.8640.8010.0000.0000.0000.0000.1421.0000.703
CAP_ETC0.0000.0000.0440.1310.4000.0000.0000.5390.4910.5860.0000.0000.0000.0000.1300.7031.000

Missing values

2023-12-11T12:35:19.665229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T12:35:19.949950image/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

EXAMIN_YEARADMINIST_ATPT_CDADMINIST_ATPT_NMADMINIST_SIGNGU_CDADMINIST_SIGNGU_NMADMINIST_EMD_CDADMINIST_EMD_NMCPR_COLVSTCK_SOIL_TKAWY_TON_DELVSTCK_SOIL_TKAWY_VOLMCAP_SMCAP_TURCOMPCAP_LIQMACOMPCAP_PRFCTNCAP_ETCLIFER_TKAWY_SOIL_TON_DELIFER_TKAWY_VOLM_TONLIFER_SPRAY_ARLIFER_SPRAY_QY
020136500000제주특별자치도6520000서귀포시6520043중문동752679463.20.00.00.00.00.000.0143142500.6355634.2
120145690000세종특별자치시9999010세종시5690072연서면102318350145.10.00.00.00.00.02318350145.113552407.472103.8
220146310000울산광역시3730000울주군3730020언양읍3699105551.70.00.00.00.00.000.018010494.948879.351
320146410000경기도3940000고양시3950019성사1동414421866.00.00.00.00.00.014421866.02530736.05095.0
420146410000경기도4050000용인시5620012원삼면221432434.30.00.00.00.00.000.02168253.38106.5
520146410000경기도4060000파주시4060050금촌1동4699105608.00.00.00.00.00.000.07853321.035717.0
620146410000경기도4070000이천시4070038설성면2416200.00.00.00.00.00.000.0199888.01267.0
720146410000경기도4070000이천시4070041중리동423034800.00.00.00.00.00.000.01790273.212809.0
820146410000경기도4080000안성시4080036보개면100.00.00.00.00.00.017927176.00.00.0
920146410000경기도4090000김포시4090146통진읍49814932.00.00.00.00.00.018628188.01372481.64356.0
EXAMIN_YEARADMINIST_ATPT_CDADMINIST_ATPT_NMADMINIST_SIGNGU_CDADMINIST_SIGNGU_NMADMINIST_EMD_CDADMINIST_EMD_NMCPR_COLVSTCK_SOIL_TKAWY_TON_DELVSTCK_SOIL_TKAWY_VOLMCAP_SMCAP_TURCOMPCAP_LIQMACOMPCAP_PRFCTNCAP_ETCLIFER_TKAWY_SOIL_TON_DELIFER_TKAWY_VOLM_TONLIFER_SPRAY_ARLIFER_SPRAY_QY
47620136480000경상남도5480000합천군5480042야로면81535231848.00.00.00.00.00.01535231848.04799588.040222.112
47720136480000경상남도5480000합천군5480043율곡면411517422.560.00.00.00.00.011417355.889762956.839496.3
47820136500000제주특별자치도6510000제주시6510037한림읍273984601608.02912680.00.02870680.00.00.01931291598.0537617036.0566164.7
47920136500000제주특별자치도6510000제주시6510038애월읍639058968.00.00.00.00.00.030746458.020081002.043100.0
48020136500000제주특별자치도6510000제주시6510040조천읍2192900.00.00.00.00.00.0152330.05024562.06475.0
48120136500000제주특별자치도6510000제주시6510041한경면29314090.00.00.00.00.00.0294468.03025697.011791.0
48220136500000제주특별자치도6520000서귀포시6520028대정읍4972146884.00.00.00.00.00.0972146884.020247073.028855.63
48320136500000제주특별자치도6520000서귀포시6520029남원읍8923139476.00.00.00.00.00.0780117792.0156398216.062675.0
48420136500000제주특별자치도6520000서귀포시6520031안덕면331147016.00.00.00.00.00.031147016.029022785.039732.0
48520136500000제주특별자치도6520000서귀포시6520032표선면102290345824.00.00.00.00.00.01830276408.01746205371.0226060.3