Overview

Dataset statistics

Number of variables11
Number of observations5187
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory476.3 KiB
Average record size in memory94.0 B

Variable types

Text1
Numeric6
Categorical3
Boolean1

Dataset

Description국유림 경영계획 수립된 "춘천경영계획구"에 대하여
Author산림청
URLhttps://www.data.go.kr/data/15071096/fileData.do

Alerts

사업자료유형구분코드 has constant value ""Constant
사용여부 is highly overall correlated with 숲가꾸기종구분코드High correlation
숲가꾸기종구분코드 is highly overall correlated with 사용여부High correlation
숲가꾸기면적 is highly overall correlated with 숲가꾸기누적면적 and 1 other fieldsHigh correlation
숲가꾸기누적면적 is highly overall correlated with 숲가꾸기면적 and 1 other fieldsHigh correlation
숲가꾸기노동력 is highly overall correlated with 숲가꾸기면적 and 1 other fieldsHigh correlation
사용여부 is highly imbalanced (51.8%)Imbalance

Reproduction

Analysis started2023-12-12 09:55:09.475980
Analysis finished2023-12-12 09:55:15.556925
Duration6.08 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

경영계획부번호
Text

Distinct620
Distinct (%)12.0%
Missing0
Missing (%)0.0%
Memory size40.7 KiB
2023-12-12T18:55:15.780400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length26
Mean length26
Min length26

Characters and Unicode

Total characters134862
Distinct characters19
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row춘천 8차기 001-000임반 001-000소반
2nd row춘천 8차기 001-000임반 001-000소반
3rd row춘천 8차기 001-000임반 001-000소반
4th row춘천 8차기 001-000임반 001-000소반
5th row춘천 8차기 001-000임반 001-000소반
ValueCountFrequency (%)
춘천 5187
25.0%
8차기 5187
25.0%
002-000소반 631
 
3.0%
001-000소반 628
 
3.0%
004-000소반 551
 
2.7%
005-000소반 539
 
2.6%
003-000소반 368
 
1.8%
006-000소반 310
 
1.5%
008-000소반 224
 
1.1%
007-000소반 218
 
1.1%
Other values (150) 6905
33.3%
2023-12-12T18:55:16.214917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 45982
34.1%
15561
 
11.5%
- 10374
 
7.7%
반 10374
 
7.7%
8 6890
 
5.1%
천 5187
 
3.8%
소 5187
 
3.8%
임 5187
 
3.8%
춘 5187
 
3.8%
기 5187
 
3.8%
Other values (9) 19746
14.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 67431
50.0%
Other Letter 41496
30.8%
Space Separator 15561
 
11.5%
Dash Punctuation 10374
 
7.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 45982
68.2%
8 6890
 
10.2%
1 4348
 
6.4%
2 2081
 
3.1%
5 2014
 
3.0%
3 1666
 
2.5%
4 1373
 
2.0%
6 1182
 
1.8%
7 993
 
1.5%
9 902
 
1.3%
Other Letter
ValueCountFrequency (%)
반 10374
25.0%
천 5187
12.5%
소 5187
12.5%
임 5187
12.5%
춘 5187
12.5%
기 5187
12.5%
차 5187
12.5%
Space Separator
ValueCountFrequency (%)
15561
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 10374
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 93366
69.2%
Hangul 41496
30.8%

Most frequent character per script

Common
ValueCountFrequency (%)
0 45982
49.2%
15561
 
16.7%
- 10374
 
11.1%
8 6890
 
7.4%
1 4348
 
4.7%
2 2081
 
2.2%
5 2014
 
2.2%
3 1666
 
1.8%
4 1373
 
1.5%
6 1182
 
1.3%
Other values (2) 1895
 
2.0%
Hangul
ValueCountFrequency (%)
반 10374
25.0%
천 5187
12.5%
소 5187
12.5%
임 5187
12.5%
춘 5187
12.5%
기 5187
12.5%
차 5187
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 93366
69.2%
Hangul 41496
30.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 45982
49.2%
15561
 
16.7%
- 10374
 
11.1%
8 6890
 
7.4%
1 4348
 
4.7%
2 2081
 
2.2%
5 2014
 
2.2%
3 1666
 
1.8%
4 1373
 
1.5%
6 1182
 
1.3%
Other values (2) 1895
 
2.0%
Hangul
ValueCountFrequency (%)
반 10374
25.0%
천 5187
12.5%
소 5187
12.5%
임 5187
12.5%
춘 5187
12.5%
기 5187
12.5%
차 5187
12.5%

이력관리번호
Real number (ℝ)

Distinct13
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3531907
Minimum1
Maximum15
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size45.7 KiB
2023-12-12T18:55:16.350150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile6
Maximum15
Range14
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.6682509
Coefficient of variation (CV)0.70893146
Kurtosis5.8666351
Mean2.3531907
Median Absolute Deviation (MAD)1
Skewness2.0390429
Sum12206
Variance2.7830611
MonotonicityNot monotonic
2023-12-12T18:55:16.460129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
2 1854
35.7%
1 1826
35.2%
3 605
 
11.7%
4 327
 
6.3%
5 240
 
4.6%
6 164
 
3.2%
7 95
 
1.8%
8 32
 
0.6%
9 25
 
0.5%
10 11
 
0.2%
Other values (3) 8
 
0.2%
ValueCountFrequency (%)
1 1826
35.2%
2 1854
35.7%
3 605
 
11.7%
4 327
 
6.3%
5 240
 
4.6%
6 164
 
3.2%
7 95
 
1.8%
8 32
 
0.6%
9 25
 
0.5%
10 11
 
0.2%
ValueCountFrequency (%)
15 3
 
0.1%
14 3
 
0.1%
11 2
 
< 0.1%
10 11
 
0.2%
9 25
 
0.5%
8 32
 
0.6%
7 95
 
1.8%
6 164
3.2%
5 240
4.6%
4 327
6.3%

사업자료유형구분코드
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size40.7 KiB
경영계획
5187 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경영계획 5187
100.0%

Length

2023-12-12T18:55:16.578017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:55:16.661490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경영계획 5187
100.0%

숲가꾸기번호
Real number (ℝ)

Distinct27
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.2639291
Minimum1
Maximum27
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size45.7 KiB
2023-12-12T18:55:16.755717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median3
Q36
95-th percentile13
Maximum27
Range26
Interquartile range (IQR)5

Descriptive statistics

Standard deviation4.1230982
Coefficient of variation (CV)0.96697157
Kurtosis4.6470714
Mean4.2639291
Median Absolute Deviation (MAD)2
Skewness1.969342
Sum22117
Variance16.999939
MonotonicityNot monotonic
2023-12-12T18:55:16.882602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
1 1562
30.1%
2 910
17.5%
3 507
 
9.8%
4 416
 
8.0%
5 381
 
7.3%
6 325
 
6.3%
7 264
 
5.1%
8 187
 
3.6%
9 117
 
2.3%
10 82
 
1.6%
Other values (17) 436
 
8.4%
ValueCountFrequency (%)
1 1562
30.1%
2 910
17.5%
3 507
 
9.8%
4 416
 
8.0%
5 381
 
7.3%
6 325
 
6.3%
7 264
 
5.1%
8 187
 
3.6%
9 117
 
2.3%
10 82
 
1.6%
ValueCountFrequency (%)
27 4
 
0.1%
26 4
 
0.1%
25 6
 
0.1%
24 6
 
0.1%
23 6
 
0.1%
22 6
 
0.1%
21 9
 
0.2%
20 9
 
0.2%
19 9
 
0.2%
18 27
0.5%

시급성코드
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size40.7 KiB
중
2863 
하
1481 
상
843 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
중 2863
55.2%
하 1481
28.6%
상 843
 
16.3%

Length

2023-12-12T18:55:16.996654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:55:17.090099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
중 2863
55.2%
하 1481
28.6%
상 843
 
16.3%

숲가꾸기종구분코드
Categorical

HIGH CORRELATION 

Distinct9
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size40.7 KiB
풀베기
1994 
덩굴류제거
1098 
무육솎아베기
663 
보식
494 
가지치기
380 
Other values (4)
558 

Length

Max length7
Median length6
Mean length4.0360517
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row무육솎아베기
2nd row무육솎아베기
3rd row무육솎아베기
4th row천연림개량
5th row무육솎아베기

Common Values

ValueCountFrequency (%)
풀베기 1994
38.4%
덩굴류제거 1098
21.2%
무육솎아베기 663
 
12.8%
보식 494
 
9.5%
가지치기 380
 
7.3%
천연림보육 218
 
4.2%
천연림개량 176
 
3.4%
어린나무가꾸기 117
 
2.3%
비료주기 47
 
0.9%

Length

2023-12-12T18:55:17.190327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:55:17.291090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
풀베기 1994
38.4%
덩굴류제거 1098
21.2%
무육솎아베기 663
 
12.8%
보식 494
 
9.5%
가지치기 380
 
7.3%
천연림보육 218
 
4.2%
천연림개량 176
 
3.4%
어린나무가꾸기 117
 
2.3%
비료주기 47
 
0.9%

숲가꾸기면적
Real number (ℝ)

HIGH CORRELATION 

Distinct141
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.00347
Minimum0.1
Maximum156
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size45.7 KiB
2023-12-12T18:55:17.415360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile1
Q14
median10
Q320
95-th percentile40
Maximum156
Range155.9
Interquartile range (IQR)16

Descriptive statistics

Standard deviation16.180186
Coefficient of variation (CV)1.0784296
Kurtosis14.307732
Mean15.00347
Median Absolute Deviation (MAD)8
Skewness2.9541387
Sum77823
Variance261.79843
MonotonicityNot monotonic
2023-12-12T18:55:17.545506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20.0 791
15.2%
10.0 517
 
10.0%
2.0 370
 
7.1%
30.0 320
 
6.2%
1.0 297
 
5.7%
4.0 285
 
5.5%
5.0 274
 
5.3%
6.0 231
 
4.5%
3.0 222
 
4.3%
15.0 164
 
3.2%
Other values (131) 1716
33.1%
ValueCountFrequency (%)
0.1 14
 
0.3%
0.3 13
 
0.3%
0.4 4
 
0.1%
0.5 3
 
0.1%
0.7 4
 
0.1%
0.8 5
 
0.1%
0.9 24
 
0.5%
1.0 297
5.7%
1.1 6
 
0.1%
1.2 30
 
0.6%
ValueCountFrequency (%)
156.0 2
 
< 0.1%
152.0 2
 
< 0.1%
136.0 2
 
< 0.1%
130.0 4
0.1%
118.0 2
 
< 0.1%
117.0 4
0.1%
112.0 4
0.1%
110.0 8
0.2%
107.0 2
 
< 0.1%
101.0 2
 
< 0.1%

숲가꾸기횟수
Real number (ℝ)

Distinct9
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3711201
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size45.7 KiB
2023-12-12T18:55:17.660185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile3
Maximum10
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.0626893
Coefficient of variation (CV)0.77505192
Kurtosis29.973054
Mean1.3711201
Median Absolute Deviation (MAD)0
Skewness4.8683036
Sum7112
Variance1.1293085
MonotonicityNot monotonic
2023-12-12T18:55:17.749459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1 4128
79.6%
2 763
 
14.7%
5 126
 
2.4%
3 86
 
1.7%
4 38
 
0.7%
10 32
 
0.6%
6 5
 
0.1%
8 5
 
0.1%
7 4
 
0.1%
ValueCountFrequency (%)
1 4128
79.6%
2 763
 
14.7%
3 86
 
1.7%
4 38
 
0.7%
5 126
 
2.4%
6 5
 
0.1%
7 4
 
0.1%
8 5
 
0.1%
10 32
 
0.6%
ValueCountFrequency (%)
10 32
 
0.6%
8 5
 
0.1%
7 4
 
0.1%
6 5
 
0.1%
5 126
 
2.4%
4 38
 
0.7%
3 86
 
1.7%
2 763
 
14.7%
1 4128
79.6%

숲가꾸기누적면적
Real number (ℝ)

HIGH CORRELATION 

Distinct170
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.126084
Minimum0.3
Maximum350
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size45.7 KiB
2023-12-12T18:55:17.896416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.3
5-th percentile1
Q14
median12
Q325
95-th percentile64.7
Maximum350
Range349.7
Interquartile range (IQR)21

Descriptive statistics

Standard deviation26.281833
Coefficient of variation (CV)1.3058592
Kurtosis31.158015
Mean20.126084
Median Absolute Deviation (MAD)8
Skewness4.2137054
Sum104394
Variance690.73474
MonotonicityNot monotonic
2023-12-12T18:55:18.037663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20.0 704
 
13.6%
10.0 430
 
8.3%
2.0 350
 
6.7%
4.0 308
 
5.9%
40.0 306
 
5.9%
30.0 267
 
5.1%
1.0 264
 
5.1%
5.0 219
 
4.2%
6.0 218
 
4.2%
3.0 191
 
3.7%
Other values (160) 1930
37.2%
ValueCountFrequency (%)
0.3 11
 
0.2%
0.4 4
 
0.1%
0.5 17
 
0.3%
0.8 5
 
0.1%
0.9 12
 
0.2%
1.0 264
5.1%
1.1 6
 
0.1%
1.2 30
 
0.6%
1.4 13
 
0.3%
1.5 10
 
0.2%
ValueCountFrequency (%)
350.0 3
0.1%
280.0 3
0.1%
250.0 4
0.1%
234.0 3
0.1%
200.0 5
0.1%
160.0 4
0.1%
156.0 2
 
< 0.1%
152.0 2
 
< 0.1%
150.0 1
 
< 0.1%
140.0 6
0.1%

숲가꾸기노동력
Real number (ℝ)

HIGH CORRELATION 

Distinct344
Distinct (%)6.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean184.70222
Minimum1.9
Maximum2901.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size45.7 KiB
2023-12-12T18:55:18.185397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.9
5-th percentile11.1
Q147.9
median102.6
Q3222.3
95-th percentile616
Maximum2901.6
Range2899.7
Interquartile range (IQR)174.4

Descriptive statistics

Standard deviation279.29567
Coefficient of variation (CV)1.5121403
Kurtosis29.354538
Mean184.70222
Median Absolute Deviation (MAD)67.9
Skewness4.6993719
Sum958050.4
Variance78006.07
MonotonicityNot monotonic
2023-12-12T18:55:18.370069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
154.0 417
 
8.0%
128.0 279
 
5.4%
308.0 265
 
5.1%
77.0 222
 
4.3%
64.0 174
 
3.4%
66.0 172
 
3.3%
231.0 164
 
3.2%
192.0 108
 
2.1%
34.2 94
 
1.8%
462.0 90
 
1.7%
Other values (334) 3202
61.7%
ValueCountFrequency (%)
1.9 2
 
< 0.1%
2.0 3
 
0.1%
3.2 7
 
0.1%
3.6 5
 
0.1%
3.7 46
0.9%
3.9 7
 
0.1%
4.0 2
 
< 0.1%
4.8 5
 
0.1%
4.9 2
 
< 0.1%
5.0 5
 
0.1%
ValueCountFrequency (%)
2901.6 2
 
< 0.1%
2827.2 2
 
< 0.1%
2695.0 3
 
0.1%
2529.6 2
 
< 0.1%
2418.0 4
0.1%
2194.8 2
 
< 0.1%
2176.2 4
0.1%
2156.0 3
 
0.1%
2046.0 8
0.2%
1990.2 2
 
< 0.1%

사용여부
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.2 KiB
True
4646 
False
541 
ValueCountFrequency (%)
True 4646
89.6%
False 541
 
10.4%
2023-12-12T18:55:18.513071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Interactions

2023-12-12T18:55:14.520034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:55:10.624942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:55:11.366083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:55:12.059628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:55:13.203573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:55:13.841327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:55:14.644366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:55:10.738276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:55:11.463615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:55:12.197813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:55:13.297440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:55:13.964048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:55:14.779241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:55:10.858178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:55:11.593663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:55:12.359003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:55:13.396146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:55:14.064139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:55:14.911722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:55:10.998632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:55:11.710990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:55:12.789209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:55:13.511758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:55:14.172333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:55:15.021418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:55:11.132685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:55:11.803041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:55:12.895982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:55:13.610329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:55:14.268197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:55:15.152756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:55:11.246417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:55:11.934923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:55:13.057651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:55:13.730145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:55:14.395509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T18:55:18.607703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
이력관리번호숲가꾸기번호시급성코드숲가꾸기종구분코드숲가꾸기면적숲가꾸기횟수숲가꾸기누적면적숲가꾸기노동력사용여부
이력관리번호1.0000.1320.2490.2250.1690.3440.3000.1820.024
숲가꾸기번호0.1321.0000.4000.4960.4370.1880.2030.2190.239
시급성코드0.2490.4001.0000.4610.2140.3300.2430.1270.023
숲가꾸기종구분코드0.2250.4960.4611.0000.3970.5310.4000.3961.000
숲가꾸기면적0.1690.4370.2140.3971.0000.1280.7620.9680.330
숲가꾸기횟수0.3440.1880.3300.5310.1281.0000.8060.4610.166
숲가꾸기누적면적0.3000.2030.2430.4000.7620.8061.0000.8540.149
숲가꾸기노동력0.1820.2190.1270.3960.9680.4610.8541.0000.193
사용여부0.0240.2390.0231.0000.3300.1660.1490.1931.000
2023-12-12T18:55:18.794527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
사용여부숲가꾸기종구분코드시급성코드
사용여부1.0000.9990.038
숲가꾸기종구분코드0.9991.0000.229
시급성코드0.0380.2291.000
2023-12-12T18:55:19.236264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
이력관리번호숲가꾸기번호숲가꾸기면적숲가꾸기횟수숲가꾸기누적면적숲가꾸기노동력시급성코드숲가꾸기종구분코드사용여부
이력관리번호1.0000.0100.0460.1410.0940.1080.1100.0730.026
숲가꾸기번호0.0101.0000.3860.1280.3730.1900.2620.2510.183
숲가꾸기면적0.0460.3861.0000.0760.9370.8720.1300.1930.253
숲가꾸기횟수0.1410.1280.0761.0000.3820.3280.1540.1950.165
숲가꾸기누적면적0.0940.3730.9370.3821.0000.9230.1100.1380.148
숲가꾸기노동력0.1080.1900.8720.3280.9231.0000.0760.1920.148
시급성코드0.1100.2620.1300.1540.1100.0761.0000.2290.038
숲가꾸기종구분코드0.0730.2510.1930.1950.1380.1920.2291.0000.999
사용여부0.0260.1830.2530.1650.1480.1480.0380.9991.000

Missing values

2023-12-12T18:55:15.310231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T18:55:15.467239image/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

경영계획부번호이력관리번호사업자료유형구분코드숲가꾸기번호시급성코드숲가꾸기종구분코드숲가꾸기면적숲가꾸기횟수숲가꾸기누적면적숲가꾸기노동력사용여부
0춘천 8차기 001-000임반 001-000소반3경영계획1중무육솎아베기1.611.627.4Y
1춘천 8차기 001-000임반 001-000소반4경영계획1중무육솎아베기1.611.627.4Y
2춘천 8차기 001-000임반 001-000소반5경영계획1중무육솎아베기2.012.034.2Y
3춘천 8차기 001-000임반 001-000소반5경영계획2중천연림개량1.211.222.3Y
4춘천 8차기 001-000임반 001-000소반6경영계획1중무육솎아베기2.012.034.2Y
5춘천 8차기 001-000임반 001-000소반6경영계획2중천연림개량1.211.222.3Y
6춘천 8차기 001-000임반 001-000소반7경영계획1중무육솎아베기1.611.627.4Y
7춘천 8차기 001-000임반 001-000소반7경영계획2중천연림개량1.211.222.3Y
8춘천 8차기 001-000임반 001-000소반8경영계획1중무육솎아베기1.611.627.4Y
9춘천 8차기 001-000임반 001-000소반8경영계획2중천연림개량1.211.222.3Y
경영계획부번호이력관리번호사업자료유형구분코드숲가꾸기번호시급성코드숲가꾸기종구분코드숲가꾸기면적숲가꾸기횟수숲가꾸기누적면적숲가꾸기노동력사용여부
5177춘천 8차기 112-000임반 011-000소반1경영계획5중덩굴류제거10.0110.064.0Y
5178춘천 8차기 112-000임반 011-000소반1경영계획6하덩굴류제거10.0110.064.0Y
5179춘천 8차기 112-000임반 011-000소반1경영계획7하덩굴류제거10.0110.064.0Y
5180춘천 8차기 112-000임반 011-000소반2경영계획1중보식2.012.033.0N
5181춘천 8차기 112-000임반 011-000소반2경영계획2중풀베기10.0110.077.0Y
5182춘천 8차기 112-000임반 011-000소반2경영계획3중풀베기10.0220.0154.0Y
5183춘천 8차기 112-000임반 011-000소반2경영계획4중풀베기10.0110.077.0Y
5184춘천 8차기 112-000임반 011-000소반2경영계획5중덩굴류제거10.0110.064.0Y
5185춘천 8차기 112-000임반 011-000소반2경영계획6하덩굴류제거10.0110.064.0Y
5186춘천 8차기 112-000임반 011-000소반2경영계획7하덩굴류제거10.0110.064.0Y