Overview

Dataset statistics

Number of variables9
Number of observations60
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.8 KiB
Average record size in memory81.2 B

Variable types

Categorical2
Numeric7

Dataset

Description산림자원통합관리시스템을 활용하여 조림, 숲가꾸기 실적 정보산림자원통합관리시스템: 조림, 숲가꾸기, 입목 매각 등 산림정보화 시스템
Author산림청
URLhttps://www.data.go.kr/data/15093797/fileData.do

Alerts

사업진행건수 is highly overall correlated with 사업진행면적 and 2 other fieldsHigh correlation
사업진행면적 is highly overall correlated with 사업진행건수 and 3 other fieldsHigh correlation
사업진행재적 is highly overall correlated with 사업진행건수 and 2 other fieldsHigh correlation
사업준공건수 is highly overall correlated with 사업준공면적 and 1 other fieldsHigh correlation
사업준공면적 is highly overall correlated with 사업준공건수 and 1 other fieldsHigh correlation
사업준공재적 is highly overall correlated with 사업진행면적 and 2 other fieldsHigh correlation
연간계획재적 is highly overall correlated with 사업진행건수 and 3 other fieldsHigh correlation
사업종명 is highly overall correlated with 연간계획재적High correlation
사업진행건수 has 36 (60.0%) zerosZeros
사업진행면적 has 37 (61.7%) zerosZeros
사업진행재적 has 50 (83.3%) zerosZeros
사업준공건수 has 45 (75.0%) zerosZeros
사업준공면적 has 45 (75.0%) zerosZeros
사업준공재적 has 50 (83.3%) zerosZeros
연간계획재적 has 44 (73.3%) zerosZeros

Reproduction

Analysis started2023-12-11 23:58:39.930652
Analysis finished2023-12-11 23:58:45.462872
Duration5.53 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

산림청기관명
Categorical

Distinct20
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Memory size612.0 B
춘천국유림관리소
 
3
양구국유림관리소
 
3
인제국유림관리소
 
3
홍천국유림관리소
 
3
수원국유림관리소
 
3
Other values (15)
45 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row춘천국유림관리소
2nd row춘천국유림관리소
3rd row춘천국유림관리소
4th row양구국유림관리소
5th row양구국유림관리소

Common Values

ValueCountFrequency (%)
춘천국유림관리소 3
 
5.0%
양구국유림관리소 3
 
5.0%
인제국유림관리소 3
 
5.0%
홍천국유림관리소 3
 
5.0%
수원국유림관리소 3
 
5.0%
평창국유림관리소 3
 
5.0%
영월국유림관리소 3
 
5.0%
정선국유림관리소 3
 
5.0%
삼척국유림관리소 3
 
5.0%
영주국유림관리소 3
 
5.0%
Other values (10) 30
50.0%

Length

2023-12-12T08:58:45.545835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
춘천국유림관리소 3
 
5.0%
양구국유림관리소 3
 
5.0%
양양국유림관리소 3
 
5.0%
영암국유림관리소 3
 
5.0%
부여국유림관리소 3
 
5.0%
단양국유림관리소 3
 
5.0%
보은국유림관리소 3
 
5.0%
충주국유림관리소 3
 
5.0%
양산국유림관리소 3
 
5.0%
구미국유림관리소 3
 
5.0%
Other values (10) 30
50.0%

사업종명
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size612.0 B
조림
20 
숲가꾸기
20 
벌채
20 

Length

Max length4
Median length2
Mean length2.6666667
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row조림
2nd row숲가꾸기
3rd row벌채
4th row조림
5th row숲가꾸기

Common Values

ValueCountFrequency (%)
조림 20
33.3%
숲가꾸기 20
33.3%
벌채 20
33.3%

Length

2023-12-12T08:58:45.736311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:58:45.846160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
조림 20
33.3%
숲가꾸기 20
33.3%
벌채 20
33.3%

사업진행건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct13
Distinct (%)21.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9
Minimum0
Maximum15
Zeros36
Zeros (%)60.0%
Negative0
Negative (%)0.0%
Memory size672.0 B
2023-12-12T08:58:45.966836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33
95-th percentile9.05
Maximum15
Range15
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.4232505
Coefficient of variation (CV)1.8017108
Kurtosis5.1721673
Mean1.9
Median Absolute Deviation (MAD)0
Skewness2.2778036
Sum114
Variance11.718644
MonotonicityNot monotonic
2023-12-12T08:58:46.413484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0 36
60.0%
1 6
 
10.0%
3 6
 
10.0%
4 2
 
3.3%
6 2
 
3.3%
14 1
 
1.7%
8 1
 
1.7%
10 1
 
1.7%
7 1
 
1.7%
5 1
 
1.7%
Other values (3) 3
 
5.0%
ValueCountFrequency (%)
0 36
60.0%
1 6
 
10.0%
2 1
 
1.7%
3 6
 
10.0%
4 2
 
3.3%
5 1
 
1.7%
6 2
 
3.3%
7 1
 
1.7%
8 1
 
1.7%
9 1
 
1.7%
ValueCountFrequency (%)
15 1
 
1.7%
14 1
 
1.7%
10 1
 
1.7%
9 1
 
1.7%
8 1
 
1.7%
7 1
 
1.7%
6 2
 
3.3%
5 1
 
1.7%
4 2
 
3.3%
3 6
10.0%

사업진행면적
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct24
Distinct (%)40.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.6175
Minimum0
Maximum682.8
Zeros37
Zeros (%)61.7%
Negative0
Negative (%)0.0%
Memory size672.0 B
2023-12-12T08:58:46.541260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q368.25
95-th percentile341.58
Maximum682.8
Range682.8
Interquartile range (IQR)68.25

Descriptive statistics

Standard deviation138.66366
Coefficient of variation (CV)2.145915
Kurtosis9.2631387
Mean64.6175
Median Absolute Deviation (MAD)0
Skewness2.9340544
Sum3877.05
Variance19227.611
MonotonicityNot monotonic
2023-12-12T08:58:46.686323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0.0 37
61.7%
2.7 1
 
1.7%
97.5 1
 
1.7%
15.0 1
 
1.7%
101.0 1
 
1.7%
125.0 1
 
1.7%
58.5 1
 
1.7%
341.4 1
 
1.7%
13.0 1
 
1.7%
25.2 1
 
1.7%
Other values (14) 14
 
23.3%
ValueCountFrequency (%)
0.0 37
61.7%
2.7 1
 
1.7%
13.0 1
 
1.7%
13.3 1
 
1.7%
15.0 1
 
1.7%
21.0 1
 
1.7%
25.2 1
 
1.7%
31.8 1
 
1.7%
58.5 1
 
1.7%
97.5 1
 
1.7%
ValueCountFrequency (%)
682.8 1
1.7%
596.3 1
1.7%
345.0 1
1.7%
341.4 1
1.7%
330.3 1
1.7%
224.0 1
1.7%
183.65 1
1.7%
164.7 1
1.7%
157.9 1
1.7%
140.7 1
1.7%

사업진행재적
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)18.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean335.06483
Minimum0
Maximum6162.29
Zeros50
Zeros (%)83.3%
Negative0
Negative (%)0.0%
Memory size672.0 B
2023-12-12T08:58:46.833172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2254.3815
Maximum6162.29
Range6162.29
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1165.1895
Coefficient of variation (CV)3.4775046
Kurtosis17.15097
Mean335.06483
Median Absolute Deviation (MAD)0
Skewness4.132887
Sum20103.89
Variance1357666.6
MonotonicityNot monotonic
2023-12-12T08:58:46.986365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0.0 50
83.3%
2196.77 1
 
1.7%
3349.0 1
 
1.7%
5562.6 1
 
1.7%
6162.29 1
 
1.7%
446.8 1
 
1.7%
573.91 1
 
1.7%
761.62 1
 
1.7%
155.31 1
 
1.7%
31.7 1
 
1.7%
ValueCountFrequency (%)
0.0 50
83.3%
31.7 1
 
1.7%
155.31 1
 
1.7%
446.8 1
 
1.7%
573.91 1
 
1.7%
761.62 1
 
1.7%
863.89 1
 
1.7%
2196.77 1
 
1.7%
3349.0 1
 
1.7%
5562.6 1
 
1.7%
ValueCountFrequency (%)
6162.29 1
1.7%
5562.6 1
1.7%
3349.0 1
1.7%
2196.77 1
1.7%
863.89 1
1.7%
761.62 1
1.7%
573.91 1
1.7%
446.8 1
1.7%
155.31 1
1.7%
31.7 1
1.7%

사업준공건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct13
Distinct (%)21.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0166667
Minimum0
Maximum36
Zeros45
Zeros (%)75.0%
Negative0
Negative (%)0.0%
Memory size672.0 B
2023-12-12T08:58:47.137956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.25
95-th percentile20.5
Maximum36
Range36
Interquartile range (IQR)0.25

Descriptive statistics

Standard deviation7.8491966
Coefficient of variation (CV)2.6019436
Kurtosis9.3408227
Mean3.0166667
Median Absolute Deviation (MAD)0
Skewness3.1123732
Sum181
Variance61.609887
MonotonicityNot monotonic
2023-12-12T08:58:47.249876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0 45
75.0%
1 3
 
5.0%
7 2
 
3.3%
8 1
 
1.7%
36 1
 
1.7%
14 1
 
1.7%
2 1
 
1.7%
12 1
 
1.7%
32 1
 
1.7%
6 1
 
1.7%
Other values (3) 3
 
5.0%
ValueCountFrequency (%)
0 45
75.0%
1 3
 
5.0%
2 1
 
1.7%
4 1
 
1.7%
6 1
 
1.7%
7 2
 
3.3%
8 1
 
1.7%
12 1
 
1.7%
14 1
 
1.7%
20 1
 
1.7%
ValueCountFrequency (%)
36 1
1.7%
32 1
1.7%
30 1
1.7%
20 1
1.7%
14 1
1.7%
12 1
1.7%
8 1
1.7%
7 2
3.3%
6 1
1.7%
4 1
1.7%

사업준공면적
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct16
Distinct (%)26.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean106.66333
Minimum0
Maximum1372
Zeros45
Zeros (%)75.0%
Negative0
Negative (%)0.0%
Memory size672.0 B
2023-12-12T08:58:47.383006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.5
95-th percentile1081.945
Maximum1372
Range1372
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation315.55288
Coefficient of variation (CV)2.9584007
Kurtosis10.513947
Mean106.66333
Median Absolute Deviation (MAD)0
Skewness3.3918207
Sum6399.8
Variance99573.62
MonotonicityNot monotonic
2023-12-12T08:58:47.534213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0.0 45
75.0%
115.5 1
 
1.7%
126.9 1
 
1.7%
118.4 1
 
1.7%
5.0 1
 
1.7%
1372.0 1
 
1.7%
271.1 1
 
1.7%
37.0 1
 
1.7%
199.5 1
 
1.7%
1365.5 1
 
1.7%
Other values (6) 6
 
10.0%
ValueCountFrequency (%)
0.0 45
75.0%
2.0 1
 
1.7%
5.0 1
 
1.7%
37.0 1
 
1.7%
57.0 1
 
1.7%
115.5 1
 
1.7%
118.4 1
 
1.7%
126.9 1
 
1.7%
163.8 1
 
1.7%
199.5 1
 
1.7%
ValueCountFrequency (%)
1372.0 1
1.7%
1365.5 1
1.7%
1164.5 1
1.7%
1077.6 1
1.7%
324.0 1
1.7%
271.1 1
1.7%
199.5 1
1.7%
163.8 1
1.7%
126.9 1
1.7%
118.4 1
1.7%

사업준공재적
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)18.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean433.05335
Minimum0
Maximum9244.52
Zeros50
Zeros (%)83.3%
Negative0
Negative (%)0.0%
Memory size672.0 B
2023-12-12T08:58:47.677811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1524.7125
Maximum9244.52
Range9244.52
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1583.2828
Coefficient of variation (CV)3.6560918
Kurtosis21.51071
Mean433.05335
Median Absolute Deviation (MAD)0
Skewness4.5648995
Sum25983.201
Variance2506784.5
MonotonicityNot monotonic
2023-12-12T08:58:47.796629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0.0 50
83.3%
9244.52 1
 
1.7%
3943.65 1
 
1.7%
114.4 1
 
1.7%
1086.201 1
 
1.7%
7184.51 1
 
1.7%
55.66 1
 
1.7%
1231.89 1
 
1.7%
1397.4 1
 
1.7%
433.75 1
 
1.7%
ValueCountFrequency (%)
0.0 50
83.3%
55.66 1
 
1.7%
114.4 1
 
1.7%
433.75 1
 
1.7%
1086.201 1
 
1.7%
1231.89 1
 
1.7%
1291.22 1
 
1.7%
1397.4 1
 
1.7%
3943.65 1
 
1.7%
7184.51 1
 
1.7%
ValueCountFrequency (%)
9244.52 1
1.7%
7184.51 1
1.7%
3943.65 1
1.7%
1397.4 1
1.7%
1291.22 1
1.7%
1231.89 1
1.7%
1086.201 1
1.7%
433.75 1
1.7%
114.4 1
1.7%
55.66 1
1.7%

연간계획재적
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct17
Distinct (%)28.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1648.2333
Minimum0
Maximum19500
Zeros44
Zeros (%)73.3%
Negative0
Negative (%)0.0%
Memory size672.0 B
2023-12-12T08:58:47.922812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32182.5
95-th percentile8525
Maximum19500
Range19500
Interquartile range (IQR)2182.5

Descriptive statistics

Standard deviation3515.3153
Coefficient of variation (CV)2.1327777
Kurtosis11.015919
Mean1648.2333
Median Absolute Deviation (MAD)0
Skewness2.9747131
Sum98894
Variance12357442
MonotonicityNot monotonic
2023-12-12T08:58:48.070712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0 44
73.3%
9000 1
 
1.7%
3300 1
 
1.7%
4830 1
 
1.7%
2730 1
 
1.7%
19500 1
 
1.7%
3360 1
 
1.7%
3150 1
 
1.7%
2000 1
 
1.7%
4998 1
 
1.7%
Other values (7) 7
 
11.7%
ValueCountFrequency (%)
0 44
73.3%
2000 1
 
1.7%
2730 1
 
1.7%
3000 1
 
1.7%
3150 1
 
1.7%
3300 1
 
1.7%
3360 1
 
1.7%
4830 1
 
1.7%
4998 1
 
1.7%
5124 1
 
1.7%
ValueCountFrequency (%)
19500 1
1.7%
10600 1
1.7%
9000 1
1.7%
8500 1
1.7%
7000 1
1.7%
6384 1
1.7%
5418 1
1.7%
5124 1
1.7%
4998 1
1.7%
4830 1
1.7%

Interactions

2023-12-12T08:58:44.372243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:58:40.502161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:58:41.150528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:58:41.786095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:58:42.378890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:58:42.986045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:58:43.699380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:58:44.510835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:58:40.596204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:58:41.236288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:58:41.860467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:58:42.467070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:58:43.076972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:58:43.784003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:58:44.636534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:58:40.682360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:58:41.338069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:58:41.938655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:58:42.551392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:58:43.177469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:58:43.877211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:58:44.746975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:58:40.776781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:58:41.433592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:58:42.016652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:58:42.644685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:58:43.305384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:58:43.972551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:58:44.840088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:58:40.867655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:58:41.533804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:58:42.109881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:58:42.728340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:58:43.399964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:58:44.076064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:58:44.935909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:58:40.967203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:58:41.627457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:58:42.199354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:58:42.822049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:58:43.508702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:58:44.170924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:58:45.031864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:58:41.061798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:58:41.700875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:58:42.277356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:58:42.892548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:58:43.591316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:58:44.267062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T08:58:48.163990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
산림청기관명사업종명사업진행건수사업진행면적사업진행재적사업준공건수사업준공면적사업준공재적연간계획재적
산림청기관명1.0000.0000.0000.4860.0000.4500.1610.0000.000
사업종명0.0001.0000.4810.4250.2780.2120.3560.2020.626
사업진행건수0.0000.4811.0000.9780.8470.4230.5940.5110.789
사업진행면적0.4860.4250.9781.0000.8940.7020.7460.7420.785
사업진행재적0.0000.2780.8470.8941.0000.6430.6370.9030.851
사업준공건수0.4500.2120.4230.7020.6431.0000.9200.8510.476
사업준공면적0.1610.3560.5940.7460.6370.9201.0000.9000.436
사업준공재적0.0000.2020.5110.7420.9030.8510.9001.0000.572
연간계획재적0.0000.6260.7890.7850.8510.4760.4360.5721.000
2023-12-12T08:58:48.312268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
산림청기관명사업종명
산림청기관명1.0000.000
사업종명0.0001.000
2023-12-12T08:58:48.426740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
사업진행건수사업진행면적사업진행재적사업준공건수사업준공면적사업준공재적연간계획재적산림청기관명사업종명
사업진행건수1.0000.9720.6210.4080.4170.4690.5880.0000.343
사업진행면적0.9721.0000.5520.4590.4690.5170.5900.1740.283
사업진행재적0.6210.5521.0000.2710.2640.2790.6050.0000.212
사업준공건수0.4080.4590.2711.0000.9980.7960.4030.1680.133
사업준공면적0.4170.4690.2640.9981.0000.8140.4050.0000.149
사업준공재적0.4690.5170.2790.7960.8141.0000.4510.0000.148
연간계획재적0.5880.5900.6050.4030.4050.4511.0000.0000.503
산림청기관명0.0000.1740.0000.1680.0000.0000.0001.0000.000
사업종명0.3430.2830.2120.1330.1490.1480.5030.0001.000

Missing values

2023-12-12T08:58:45.212855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T08:58:45.394686image/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

산림청기관명사업종명사업진행건수사업진행면적사업진행재적사업준공건수사업준공면적사업준공재적연간계획재적
0춘천국유림관리소조림00.00.08115.50.00
1춘천국유림관리소숲가꾸기14596.32196.77361164.59244.529000
2춘천국유림관리소벌채00.00.000.00.00
3양구국유림관리소조림121.00.07163.80.00
4양구국유림관리소숲가꾸기4140.70.0141077.63943.653000
5양구국유림관리소벌채00.00.000.00.00
6인제국유림관리소조림00.00.000.00.00
7인제국유림관리소숲가꾸기8345.03349.000.00.08500
8인제국유림관리소벌채00.00.000.00.00
9홍천국유림관리소조림00.00.000.00.00
산림청기관명사업종명사업진행건수사업진행면적사업진행재적사업준공건수사업준공면적사업준공재적연간계획재적
50부여국유림관리소벌채00.00.000.00.00
51영암국유림관리소조림115.00.000.00.00
52영암국유림관리소숲가꾸기00.00.000.00.00
53영암국유림관리소벌채00.00.000.00.00
54양양국유림관리소조림00.00.000.00.00
55양양국유림관리소숲가꾸기497.5863.8900.00.04830
56양양국유림관리소벌채00.00.000.00.00
57순천국유림관리소조림00.00.000.00.00
58순천국유림관리소숲가꾸기00.00.000.00.03300
59순천국유림관리소벌채00.00.000.00.00