Overview

Dataset statistics

Number of variables12
Number of observations692
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory71.8 KiB
Average record size in memory106.2 B

Variable types

Numeric7
Categorical4
Text1

Dataset

Description과수거점산지유통센터건립지원사업에 대한 보조금 집행 정보
Author농림축산식품부
URLhttps://data.mafra.go.kr/opendata/data/indexOpenDataDetail.do?data_id=20220215000000001908

Alerts

CTRD_CODE is highly overall correlated with SIGNGU_CODE and 1 other fieldsHigh correlation
SIGNGU_CODE is highly overall correlated with CTRD_CODE and 1 other fieldsHigh correlation
DELVRY_AM is highly overall correlated with BUDGET_AM and 3 other fieldsHigh correlation
BUDGET_AM is highly overall correlated with DELVRY_AM and 3 other fieldsHigh correlation
EXCUT_AM is highly overall correlated with DELVRY_AM and 2 other fieldsHigh correlation
RL_EXCUT_RT is highly overall correlated with DELVRY_AM and 2 other fieldsHigh correlation
CTRD_NM is highly overall correlated with CTRD_CODE and 1 other fieldsHigh correlation
PRVYYDO_CYFD_AM is highly overall correlated with DELVRY_AM and 1 other fieldsHigh correlation
CYFD_AM is highly overall correlated with DISUSE_AMHigh correlation
DISUSE_AM is highly overall correlated with CYFD_AMHigh correlation
PRVYYDO_CYFD_AM is highly imbalanced (98.4%)Imbalance
CYFD_AM is highly imbalanced (98.0%)Imbalance
DISUSE_AM is highly imbalanced (97.3%)Imbalance
DELVRY_AM has 635 (91.8%) zerosZeros
BUDGET_AM has 635 (91.8%) zerosZeros
EXCUT_AM has 673 (97.3%) zerosZeros
RL_EXCUT_RT has 673 (97.3%) zerosZeros

Reproduction

Analysis started2023-12-11 03:14:35.843980
Analysis finished2023-12-11 03:14:41.212084
Duration5.37 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

YEAR
Real number (ℝ)

Distinct12
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2009.25
Minimum2004
Maximum2015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.2 KiB
2023-12-11T12:14:41.262548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2004
5-th percentile2005
Q12006
median2008
Q32012
95-th percentile2015
Maximum2015
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.6468074
Coefficient of variation (CV)0.0018150093
Kurtosis-1.3258394
Mean2009.25
Median Absolute Deviation (MAD)3
Skewness0.27351474
Sum1390401
Variance13.299204
MonotonicityNot monotonic
2023-12-11T12:14:41.358316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
2015 104
15.0%
2005 98
14.2%
2007 91
13.2%
2006 89
12.9%
2011 76
11.0%
2012 54
7.8%
2008 47
6.8%
2010 46
6.6%
2004 32
 
4.6%
2013 28
 
4.0%
Other values (2) 27
 
3.9%
ValueCountFrequency (%)
2004 32
 
4.6%
2005 98
14.2%
2006 89
12.9%
2007 91
13.2%
2008 47
6.8%
2009 2
 
0.3%
2010 46
6.6%
2011 76
11.0%
2012 54
7.8%
2013 28
 
4.0%
ValueCountFrequency (%)
2015 104
15.0%
2014 25
 
3.6%
2013 28
 
4.0%
2012 54
7.8%
2011 76
11.0%
2010 46
6.6%
2009 2
 
0.3%
2008 47
6.8%
2007 91
13.2%
2006 89
12.9%

CTRD_NM
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size5.5 KiB
경상북도
161 
경상남도
108 
충청남도
105 
경기도
91 
전라남도
88 
Other values (3)
139 

Length

Max length7
Median length4
Mean length3.9552023
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경상북도 161
23.3%
경상남도 108
15.6%
충청남도 105
15.2%
경기도 91
13.2%
전라남도 88
12.7%
충청북도 77
11.1%
전라북도 42
 
6.1%
제주특별자치도 20
 
2.9%

Length

2023-12-11T12:14:41.479096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T12:14:41.609498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경상북도 161
23.3%
경상남도 108
15.6%
충청남도 105
15.2%
경기도 91
13.2%
전라남도 88
12.7%
충청북도 77
11.1%
전라북도 42
 
6.1%
제주특별자치도 20
 
2.9%

CTRD_CODE
Real number (ℝ)

HIGH CORRELATION 

Distinct8
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6453049.1
Minimum6410000
Maximum6500000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.2 KiB
2023-12-11T12:14:41.728098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6410000
5-th percentile6410000
Q16440000
median6460000
Q36470000
95-th percentile6480000
Maximum6500000
Range90000
Interquartile range (IQR)30000

Descriptive statistics

Standard deviation24085.783
Coefficient of variation (CV)0.0037324654
Kurtosis-0.82267178
Mean6453049.1
Median Absolute Deviation (MAD)20000
Skewness-0.35726637
Sum4.46551 × 109
Variance5.8012493 × 108
MonotonicityNot monotonic
2023-12-11T12:14:41.845546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
6470000 161
23.3%
6480000 108
15.6%
6440000 105
15.2%
6410000 91
13.2%
6460000 88
12.7%
6430000 77
11.1%
6450000 42
 
6.1%
6500000 20
 
2.9%
ValueCountFrequency (%)
6410000 91
13.2%
6430000 77
11.1%
6440000 105
15.2%
6450000 42
 
6.1%
6460000 88
12.7%
6470000 161
23.3%
6480000 108
15.6%
6500000 20
 
2.9%
ValueCountFrequency (%)
6500000 20
 
2.9%
6480000 108
15.6%
6470000 161
23.3%
6460000 88
12.7%
6450000 42
 
6.1%
6440000 105
15.2%
6430000 77
11.1%
6410000 91
13.2%

SIGNGU_NM
Text

Distinct136
Distinct (%)19.7%
Missing0
Missing (%)0.0%
Memory size5.5 KiB
2023-12-11T12:14:42.224040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.0274566
Min length3

Characters and Unicode

Total characters2095
Distinct characters102
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 row성남시
2nd row의정부시
3rd row안양시
4th row부천시
5th row광명시
ValueCountFrequency (%)
서귀포시 10
 
1.4%
제주시 10
 
1.4%
태안군 7
 
1.0%
예산군 7
 
1.0%
홍성군 7
 
1.0%
괴산군 7
 
1.0%
금산군 7
 
1.0%
아산시 7
 
1.0%
보령시 7
 
1.0%
공주시 7
 
1.0%
Other values (126) 616
89.0%
2023-12-11T12:14:42.719640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
군 370
17.7%
시 338
 
16.1%
주 91
 
4.3%
천 90
 
4.3%
산 73
 
3.5%
양 62
 
3.0%
성 55
 
2.6%
안 50
 
2.4%
영 49
 
2.3%
진 38
 
1.8%
Other values (92) 879
42.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2095
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
군 370
17.7%
시 338
 
16.1%
주 91
 
4.3%
천 90
 
4.3%
산 73
 
3.5%
양 62
 
3.0%
성 55
 
2.6%
안 50
 
2.4%
영 49
 
2.3%
진 38
 
1.8%
Other values (92) 879
42.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2095
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
군 370
17.7%
시 338
 
16.1%
주 91
 
4.3%
천 90
 
4.3%
산 73
 
3.5%
양 62
 
3.0%
성 55
 
2.6%
안 50
 
2.4%
영 49
 
2.3%
진 38
 
1.8%
Other values (92) 879
42.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2095
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
군 370
17.7%
시 338
 
16.1%
주 91
 
4.3%
천 90
 
4.3%
산 73
 
3.5%
양 62
 
3.0%
성 55
 
2.6%
안 50
 
2.4%
영 49
 
2.3%
진 38
 
1.8%
Other values (92) 879
42.0%

SIGNGU_CODE
Real number (ℝ)

HIGH CORRELATION 

Distinct136
Distinct (%)19.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4926127.2
Minimum3740000
Maximum6520000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.2 KiB
2023-12-11T12:14:42.874747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3740000
5-th percentile3980000
Q14530000
median4975000
Q35260000
95-th percentile5674500
Maximum6520000
Range2780000
Interquartile range (IQR)730000

Descriptive statistics

Standard deviation546097.62
Coefficient of variation (CV)0.11085739
Kurtosis0.57429647
Mean4926127.2
Median Absolute Deviation (MAD)385000
Skewness0.254054
Sum3.40888 × 109
Variance2.9822261 × 1011
MonotonicityNot monotonic
2023-12-11T12:14:43.011540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6510000 10
 
1.4%
6520000 10
 
1.4%
5260000 7
 
1.0%
4590000 7
 
1.0%
4550000 7
 
1.0%
4520000 7
 
1.0%
4510000 7
 
1.0%
4500000 7
 
1.0%
4490000 7
 
1.0%
5680000 7
 
1.0%
Other values (126) 616
89.0%
ValueCountFrequency (%)
3740000 3
0.4%
3780000 3
0.4%
3820000 3
0.4%
3830000 3
0.4%
3860000 3
0.4%
3900000 3
0.4%
3910000 3
0.4%
3920000 3
0.4%
3930000 3
0.4%
3940000 3
0.4%
ValueCountFrequency (%)
6520000 10
1.4%
6510000 10
1.4%
5710000 7
1.0%
5700000 1
 
0.1%
5680000 7
1.0%
5670000 6
0.9%
5600000 3
 
0.4%
5590000 3
 
0.4%
5580000 7
1.0%
5570000 7
1.0%

DELVRY_AM
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct49
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.19979559
Minimum0
Maximum6.3
Zeros635
Zeros (%)91.8%
Negative0
Negative (%)0.0%
Memory size6.2 KiB
2023-12-11T12:14:43.141881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1.432
Maximum6.3
Range6.3
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.84812615
Coefficient of variation (CV)4.2449693
Kurtosis24.894143
Mean0.19979559
Median Absolute Deviation (MAD)0
Skewness4.9207851
Sum138.25855
Variance0.71931797
MonotonicityNot monotonic
2023-12-11T12:14:43.277042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
0.0 635
91.8%
1.432 2
 
0.3%
2.7 2
 
0.3%
1.8 2
 
0.3%
0.8 2
 
0.3%
1.246 2
 
0.3%
2.829 2
 
0.3%
1.56 2
 
0.3%
0.116 2
 
0.3%
1.112 2
 
0.3%
Other values (39) 39
 
5.6%
ValueCountFrequency (%)
0.0 635
91.8%
0.008 1
 
0.1%
0.018 1
 
0.1%
0.0272 1
 
0.1%
0.116 2
 
0.3%
0.15 1
 
0.1%
0.3483 1
 
0.1%
0.6 1
 
0.1%
0.624 1
 
0.1%
0.72 1
 
0.1%
ValueCountFrequency (%)
6.3 1
0.1%
5.76 1
0.1%
5.744 1
0.1%
5.6196 1
0.1%
5.241 1
0.1%
5.224 1
0.1%
5.123 1
0.1%
5.05 1
0.1%
5.026 1
0.1%
4.938 1
0.1%

PRVYYDO_CYFD_AM
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.5 KiB
0.0
691 
5.255
 
1

Length

Max length5
Median length3
Mean length3.0028902
Min length3

Unique

Unique1 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
0.0 691
99.9%
5.255 1
 
0.1%

Length

2023-12-11T12:14:43.438360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T12:14:43.544864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 691
99.9%
5.255 1
 
0.1%

BUDGET_AM
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct49
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.20738952
Minimum0
Maximum8.837
Zeros635
Zeros (%)91.8%
Negative0
Negative (%)0.0%
Memory size6.2 KiB
2023-12-11T12:14:43.645533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1.432
Maximum8.837
Range8.837
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.9003704
Coefficient of variation (CV)4.3414459
Kurtosis30.850798
Mean0.20738952
Median Absolute Deviation (MAD)0
Skewness5.2918808
Sum143.51355
Variance0.81066686
MonotonicityNot monotonic
2023-12-11T12:14:43.791210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
0.0 635
91.8%
1.432 2
 
0.3%
2.7 2
 
0.3%
1.8 2
 
0.3%
0.8 2
 
0.3%
1.246 2
 
0.3%
2.829 2
 
0.3%
1.56 2
 
0.3%
0.116 2
 
0.3%
1.112 2
 
0.3%
Other values (39) 39
 
5.6%
ValueCountFrequency (%)
0.0 635
91.8%
0.008 1
 
0.1%
0.018 1
 
0.1%
0.0272 1
 
0.1%
0.116 2
 
0.3%
0.15 1
 
0.1%
0.3483 1
 
0.1%
0.6 1
 
0.1%
0.624 1
 
0.1%
0.72 1
 
0.1%
ValueCountFrequency (%)
8.837 1
0.1%
6.3 1
0.1%
5.76 1
0.1%
5.744 1
0.1%
5.6196 1
0.1%
5.241 1
0.1%
5.224 1
0.1%
5.123 1
0.1%
5.05 1
0.1%
5.026 1
0.1%

EXCUT_AM
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct20
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.067300522
Minimum0
Maximum8.895
Zeros673
Zeros (%)97.3%
Negative0
Negative (%)0.0%
Memory size6.2 KiB
2023-12-11T12:14:43.910677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum8.895
Range8.895
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.62140483
Coefficient of variation (CV)9.233284
Kurtosis150.98708
Mean0.067300522
Median Absolute Deviation (MAD)0
Skewness11.826417
Sum46.571961
Variance0.38614396
MonotonicityNot monotonic
2023-12-11T12:14:44.017783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0.0 673
97.3%
2.801 1
 
0.1%
0.16 1
 
0.1%
1.964 1
 
0.1%
0.75 1
 
0.1%
7.56 1
 
0.1%
8.895 1
 
0.1%
8.773 1
 
0.1%
0.008 1
 
0.1%
0.0038 1
 
0.1%
Other values (10) 10
 
1.4%
ValueCountFrequency (%)
0.0 673
97.3%
0.0038 1
 
0.1%
0.008 1
 
0.1%
0.116 1
 
0.1%
0.121451 1
 
0.1%
0.16 1
 
0.1%
0.72 1
 
0.1%
0.75 1
 
0.1%
0.8 1
 
0.1%
0.933003 1
 
0.1%
ValueCountFrequency (%)
8.895 1
0.1%
8.773 1
0.1%
7.56 1
0.1%
5.026 1
0.1%
2.829 1
0.1%
2.801 1
0.1%
2.206 1
0.1%
1.964 1
0.1%
1.551707 1
0.1%
1.354 1
0.1%

CYFD_AM
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size5.5 KiB
0.0
690 
0.0232
 
1
5.255
 
1

Length

Max length6
Median length3
Mean length3.0072254
Min length3

Unique

Unique2 ?
Unique (%)0.3%

Sample

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

Common Values

ValueCountFrequency (%)
0.0 690
99.7%
0.0232 1
 
0.1%
5.255 1
 
0.1%

Length

2023-12-11T12:14:44.168713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T12:14:44.283868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 690
99.7%
0.0232 1
 
0.1%
5.255 1
 
0.1%

DISUSE_AM
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size5.5 KiB
0.0
688 
0.178997
 
1
0.008292
 
1
0.028549
 
1
0.0002
 
1

Length

Max length8
Median length3
Mean length3.0260116
Min length3

Unique

Unique4 ?
Unique (%)0.6%

Sample

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

Common Values

ValueCountFrequency (%)
0.0 688
99.4%
0.178997 1
 
0.1%
0.008292 1
 
0.1%
0.028549 1
 
0.1%
0.0002 1
 
0.1%

Length

2023-12-11T12:14:44.389594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T12:14:44.493995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 688
99.4%
0.178997 1
 
0.1%
0.008292 1
 
0.1%
0.028549 1
 
0.1%
0.0002 1
 
0.1%

RL_EXCUT_RT
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct13
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7505996
Minimum0
Maximum240.35616
Zeros673
Zeros (%)97.3%
Negative0
Negative (%)0.0%
Memory size6.2 KiB
2023-12-11T12:14:44.590528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum240.35616
Range240.35616
Interquartile range (IQR)0

Descriptive statistics

Standard deviation18.666472
Coefficient of variation (CV)6.7863285
Kurtosis79.674547
Mean2.7505996
Median Absolute Deviation (MAD)0
Skewness8.2484188
Sum1903.4149
Variance348.43719
MonotonicityNot monotonic
2023-12-11T12:14:44.709234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0.0 673
97.3%
100.0 8
 
1.2%
83.9031474820144 1
 
0.1%
99.46839743589744 1
 
0.1%
94.55307262569832 1
 
0.1%
80.96733333333333 1
 
0.1%
53.44399923678687 1
 
0.1%
13.970588235294116 1
 
0.1%
240.35616438356163 1
 
0.1%
224.90518331226292 1
 
0.1%
Other values (3) 3
 
0.4%
ValueCountFrequency (%)
0.0 673
97.3%
11.1731843575419 1
 
0.1%
13.970588235294116 1
 
0.1%
53.44399923678687 1
 
0.1%
69.42382467302933 1
 
0.1%
80.96733333333333 1
 
0.1%
83.9031474820144 1
 
0.1%
94.55307262569832 1
 
0.1%
99.46839743589744 1
 
0.1%
100.0 8
 
1.2%
ValueCountFrequency (%)
240.35616438356163 1
 
0.1%
224.90518331226292 1
 
0.1%
131.25 1
 
0.1%
100.0 8
1.2%
99.46839743589744 1
 
0.1%
94.55307262569832 1
 
0.1%
83.9031474820144 1
 
0.1%
80.96733333333333 1
 
0.1%
69.42382467302933 1
 
0.1%
53.44399923678687 1
 
0.1%

Interactions

2023-12-11T12:14:40.398242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:14:36.431905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:14:37.047939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:14:37.622588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:14:38.219850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:14:38.888148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:14:39.738580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:14:40.488199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:14:36.530535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:14:37.135999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:14:37.705485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:14:38.304220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:14:38.986405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:14:39.853232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:14:40.575537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:14:36.621807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:14:37.218130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:14:37.794395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:14:38.390740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:14:39.075471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:14:39.937092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:14:40.656884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:14:36.711115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:14:37.296434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:14:37.878899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:14:38.481047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:14:39.157256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:14:40.026859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:14:40.735716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:14:36.805018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:14:37.377952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:14:37.966641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:14:38.584473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:14:39.238960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:14:40.115804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:14:40.813468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:14:36.880035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:14:37.446997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:14:38.048938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:14:38.683384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:14:39.572667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:14:40.205844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:14:40.892681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:14:36.959960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:14:37.538424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:14:38.124406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:14:38.785342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:14:39.648888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:14:40.306389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T12:14:44.794339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
YEARCTRD_NMCTRD_CODESIGNGU_CODEDELVRY_AMPRVYYDO_CYFD_AMBUDGET_AMEXCUT_AMCYFD_AMDISUSE_AMRL_EXCUT_RT
YEAR1.0000.6130.5910.4700.2510.0000.0530.1340.0000.0000.230
CTRD_NM0.6131.0001.0000.8970.3400.0000.3290.2950.0000.0930.298
CTRD_CODE0.5911.0001.0000.9590.3530.0000.3320.4300.0000.0880.277
SIGNGU_CODE0.4700.8970.9591.0000.3100.0000.4330.2800.0000.0000.307
DELVRY_AM0.2510.3400.3530.3101.0000.7260.9430.7320.5500.4320.692
PRVYYDO_CYFD_AM0.0000.0000.0000.0000.7261.0001.0000.0000.0000.0000.000
BUDGET_AM0.0530.3290.3320.4330.9431.0001.0000.7180.6160.2760.726
EXCUT_AM0.1340.2950.4300.2800.7320.0000.7181.0000.6000.5560.901
CYFD_AM0.0000.0000.0000.0000.5500.0000.6160.6001.0000.7170.811
DISUSE_AM0.0000.0930.0880.0000.4320.0000.2760.5560.7171.0000.573
RL_EXCUT_RT0.2300.2980.2770.3070.6920.0000.7260.9010.8110.5731.000
2023-12-11T12:14:44.910909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
DISUSE_AMCTRD_NMCYFD_AMPRVYYDO_CYFD_AM
DISUSE_AM1.0000.0570.7040.000
CTRD_NM0.0571.0000.0000.000
CYFD_AM0.7040.0001.0000.000
PRVYYDO_CYFD_AM0.0000.0000.0001.000
2023-12-11T12:14:45.027363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
YEARCTRD_CODESIGNGU_CODEDELVRY_AMBUDGET_AMEXCUT_AMRL_EXCUT_RTCTRD_NMPRVYYDO_CYFD_AMCYFD_AMDISUSE_AM
YEAR1.000-0.175-0.1230.0050.0050.0670.0670.3520.0000.0000.000
CTRD_CODE-0.1751.0000.7710.1080.1080.0820.0811.0000.0000.0000.057
SIGNGU_CODE-0.1230.7711.0000.0620.0620.0600.0590.7220.0000.0000.000
DELVRY_AM0.0050.1080.0621.0001.0000.5550.5540.1690.5660.3920.193
BUDGET_AM0.0050.1080.0621.0001.0000.5540.5540.1680.9950.3370.163
EXCUT_AM0.0670.0820.0600.5550.5541.0001.0000.1620.0000.4920.398
RL_EXCUT_RT0.0670.0810.0590.5540.5541.0001.0000.1700.0000.4930.433
CTRD_NM0.3521.0000.7220.1690.1680.1620.1701.0000.0000.0000.057
PRVYYDO_CYFD_AM0.0000.0000.0000.5660.9950.0000.0000.0001.0000.0000.000
CYFD_AM0.0000.0000.0000.3920.3370.4920.4930.0000.0001.0000.704
DISUSE_AM0.0000.0570.0000.1930.1630.3980.4330.0570.0000.7041.000

Missing values

2023-12-11T12:14:40.999895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T12:14:41.151317image/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

YEARCTRD_NMCTRD_CODESIGNGU_NMSIGNGU_CODEDELVRY_AMPRVYYDO_CYFD_AMBUDGET_AMEXCUT_AMCYFD_AMDISUSE_AMRL_EXCUT_RT
02011경기도6410000성남시37800000.00.00.00.00.00.00.0
12011경기도6410000의정부시38200000.00.00.00.00.00.00.0
22011경기도6410000안양시38300000.00.00.00.00.00.00.0
32011경기도6410000부천시38600000.00.00.00.00.00.00.0
42011경기도6410000광명시39000000.00.00.00.00.00.00.0
52011경기도6410000평택시39100000.00.00.00.00.00.00.0
62011경기도6410000동두천시39200000.00.00.00.00.00.00.0
72011경기도6410000안산시39300000.00.00.00.00.00.00.0
82011경기도6410000고양시39400000.00.00.00.00.00.00.0
92011경기도6410000과천시39700000.00.00.00.00.00.00.0
YEARCTRD_NMCTRD_CODESIGNGU_NMSIGNGU_CODEDELVRY_AMPRVYYDO_CYFD_AMBUDGET_AMEXCUT_AMCYFD_AMDISUSE_AMRL_EXCUT_RT
6822011경기도6410000안성시40800000.00.00.00.00.00.00.0
6832011경기도6410000김포시40900000.00.00.00.00.00.00.0
6842011경기도6410000연천군41400000.00.00.00.00.00.00.0
6852011경기도6410000가평군41600000.00.00.00.00.00.00.0
6862011경기도6410000양평군41700000.00.00.00.00.00.00.0
6872011경기도6410000화성시55300000.00.00.00.00.00.00.0
6882011경기도6410000광주시55400000.00.00.00.00.00.00.0
6892011경기도6410000양주시55900000.00.00.00.00.00.00.0
6902011경기도6410000포천시56000000.00.00.00.00.00.00.0
6912011경기도6410000수원시37400000.00.00.00.00.00.00.0