Overview

Dataset statistics

Number of variables10
Number of observations10000
Missing cells8475
Missing cells (%)8.5%
Duplicate rows150
Duplicate rows (%)1.5%
Total size in memory908.2 KiB
Average record size in memory93.0 B

Variable types

Numeric5
Text2
Categorical2
Unsupported1

Dataset

Description시범지자체의 산림자원조성사업 수종정보 이며 활착본수, 기관코드 사유림산림 사업번호, 용역사업계약, 임산물종류, 본수, 조성면적 등 정보로 구성
Author산림청
URLhttps://www.data.go.kr/data/15093779/fileData.do

Alerts

Dataset has 150 (1.5%) duplicate rowsDuplicates
활착본수 is highly overall correlated with 용역사업계약구분High correlation
계획본수 is highly overall correlated with 용역사업계약구분High correlation
본수 is highly overall correlated with 조성면적(제곱미터) and 1 other fieldsHigh correlation
조성면적(제곱미터) is highly overall correlated with 본수 and 1 other fieldsHigh correlation
용역사업계약구분 is highly overall correlated with 활착본수 and 3 other fieldsHigh correlation
용역사업계약구분 is highly imbalanced (98.7%)Imbalance
산림자원조성관리사업구분 is highly imbalanced (82.4%)Imbalance
활착본수 has 3362 (33.6%) missing valuesMissing
계획본수 has 5075 (50.7%) missing valuesMissing
계획본수 is highly skewed (γ1 = 39.29486618)Skewed
조성면적(제곱미터) is highly skewed (γ1 = 38.51304641)Skewed
필지고유번호(PNU) is an unsupported type, check if it needs cleaning or further analysisUnsupported
활착본수 has 3792 (37.9%) zerosZeros
계획본수 has 4913 (49.1%) zerosZeros
본수 has 238 (2.4%) zerosZeros
조성면적(제곱미터) has 137 (1.4%) zerosZeros

Reproduction

Analysis started2023-12-12 10:39:17.793904
Analysis finished2023-12-12 10:39:23.538294
Duration5.74 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

활착본수
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct1143
Distinct (%)17.2%
Missing3362
Missing (%)33.6%
Infinite0
Infinite (%)0.0%
Mean1416.9652
Minimum0
Maximum120000
Zeros3792
Zeros (%)37.9%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T19:39:23.624239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31000
95-th percentile7616.6
Maximum120000
Range120000
Interquartile range (IQR)1000

Descriptive statistics

Standard deviation4157.422
Coefficient of variation (CV)2.9340325
Kurtosis175.07794
Mean1416.9652
Median Absolute Deviation (MAD)0
Skewness9.5978761
Sum9405815
Variance17284158
MonotonicityNot monotonic
2023-12-12T19:39:23.807543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3792
37.9%
3000 74
 
0.7%
1500 58
 
0.6%
6000 52
 
0.5%
1 34
 
0.3%
600 33
 
0.3%
2400 25
 
0.2%
2 24
 
0.2%
9000 23
 
0.2%
4200 22
 
0.2%
Other values (1133) 2501
25.0%
(Missing) 3362
33.6%
ValueCountFrequency (%)
0 3792
37.9%
1 34
 
0.3%
2 24
 
0.2%
3 22
 
0.2%
4 10
 
0.1%
5 15
 
0.1%
6 9
 
0.1%
7 13
 
0.1%
8 5
 
0.1%
9 5
 
0.1%
ValueCountFrequency (%)
120000 1
< 0.1%
93000 1
< 0.1%
71000 1
< 0.1%
60000 1
< 0.1%
51000 1
< 0.1%
49200 1
< 0.1%
49000 1
< 0.1%
48400 1
< 0.1%
39000 1
< 0.1%
38250 1
< 0.1%

계획본수
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct12
Distinct (%)0.2%
Missing5075
Missing (%)50.7%
Infinite0
Infinite (%)0.0%
Mean5.688731
Minimum0
Maximum8400
Zeros4913
Zeros (%)49.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T19:39:23.951535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum8400
Range8400
Interquartile range (IQR)0

Descriptive statistics

Standard deviation156.30007
Coefficient of variation (CV)27.475385
Kurtosis1855.0688
Mean5.688731
Median Absolute Deviation (MAD)0
Skewness39.294866
Sum28017
Variance24429.713
MonotonicityNot monotonic
2023-12-12T19:39:24.089557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 4913
49.1%
1500 2
 
< 0.1%
2610 1
 
< 0.1%
1230 1
 
< 0.1%
2190 1
 
< 0.1%
3000 1
 
< 0.1%
8400 1
 
< 0.1%
4200 1
 
< 0.1%
70 1
 
< 0.1%
1350 1
 
< 0.1%
Other values (2) 2
 
< 0.1%
(Missing) 5075
50.7%
ValueCountFrequency (%)
0 4913
49.1%
17 1
 
< 0.1%
70 1
 
< 0.1%
1230 1
 
< 0.1%
1350 1
 
< 0.1%
1500 2
 
< 0.1%
1950 1
 
< 0.1%
2190 1
 
< 0.1%
2610 1
 
< 0.1%
3000 1
 
< 0.1%
ValueCountFrequency (%)
8400 1
< 0.1%
4200 1
< 0.1%
3000 1
< 0.1%
2610 1
< 0.1%
2190 1
< 0.1%
1950 1
< 0.1%
1500 2
< 0.1%
1350 1
< 0.1%
1230 1
< 0.1%
70 1
< 0.1%

기관명
Text

Distinct219
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T19:39:24.537172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length8
Mean length8.3186
Min length3

Characters and Unicode

Total characters83186
Distinct characters135
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

Unique16 ?
Unique (%)0.2%

Sample

1st row충청남도 금산군
2nd row서울특별시 강동구
3rd row경기도 연천군
4th row강원특별자치도 정선군
5th row충청남도 보령시
ValueCountFrequency (%)
전라남도 1807
 
9.1%
전라북도 1717
 
8.6%
충청남도 1396
 
7.0%
강원특별자치도 1177
 
5.9%
경상남도 1015
 
5.1%
경상북도 924
 
4.6%
충청북도 744
 
3.7%
경기도 699
 
3.5%
군산시 272
 
1.4%
진안군 232
 
1.2%
Other values (204) 9928
49.9%
2023-12-12T19:39:25.178042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9911
 
11.9%
도 9764
 
11.7%
군 6195
 
7.4%
남 4508
 
5.4%
시 4199
 
5.0%
전 3582
 
4.3%
라 3524
 
4.2%
북 3420
 
4.1%
경 2729
 
3.3%
청 2445
 
2.9%
Other values (125) 32909
39.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 73275
88.1%
Space Separator 9911
 
11.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 9764
 
13.3%
군 6195
 
8.5%
남 4508
 
6.2%
시 4199
 
5.7%
전 3582
 
4.9%
라 3524
 
4.8%
북 3420
 
4.7%
경 2729
 
3.7%
청 2445
 
3.3%
충 2221
 
3.0%
Other values (124) 30688
41.9%
Space Separator
ValueCountFrequency (%)
9911
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 73275
88.1%
Common 9911
 
11.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 9764
 
13.3%
군 6195
 
8.5%
남 4508
 
6.2%
시 4199
 
5.7%
전 3582
 
4.9%
라 3524
 
4.8%
북 3420
 
4.7%
경 2729
 
3.7%
청 2445
 
3.3%
충 2221
 
3.0%
Other values (124) 30688
41.9%
Common
ValueCountFrequency (%)
9911
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 73275
88.1%
ASCII 9911
 
11.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9911
100.0%
Hangul
ValueCountFrequency (%)
도 9764
 
13.3%
군 6195
 
8.5%
남 4508
 
6.2%
시 4199
 
5.7%
전 3582
 
4.9%
라 3524
 
4.8%
북 3420
 
4.7%
경 2729
 
3.7%
청 2445
 
3.3%
충 2221
 
3.0%
Other values (124) 30688
41.9%

사유림산림사업번호
Real number (ℝ)

Distinct3895
Distinct (%)39.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.7602151 × 1017
Minimum3.0000831 × 1017
Maximum6.5201841 × 1017
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T19:39:25.414152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.0000831 × 1017
5-th percentile4.0504261 × 1017
Q14.4501001 × 1017
median4.7401081 × 1017
Q35.0300511 × 1017
95-th percentile5.5902041 × 1017
Maximum6.5201841 × 1017
Range3.520101 × 1017
Interquartile range (IQR)5.79951 × 1016

Descriptive statistics

Standard deviation5.1408898 × 1016
Coefficient of variation (CV)0.10799701
Kurtosis1.3576602
Mean4.7602151 × 1017
Median Absolute Deviation (MAD)2.89943 × 1016
Skewness0.13402444
Sum9.551275 × 1017
Variance2.6428748 × 1033
MonotonicityNot monotonic
2023-12-12T19:39:25.607259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
509018508201900013 92
 
0.9%
467025408201900003 45
 
0.4%
425006108201800001 43
 
0.4%
475007308202000002 39
 
0.4%
482018208201400001 36
 
0.4%
467025408201900002 34
 
0.3%
425014608201900001 32
 
0.3%
373015008201500002 31
 
0.3%
429009408201900001 25
 
0.2%
467021708201800002 25
 
0.2%
Other values (3885) 9598
96.0%
ValueCountFrequency (%)
300008308201400003 1
< 0.1%
300008308201400004 1
< 0.1%
300008308201800001 2
< 0.1%
301009108201400001 2
< 0.1%
301017308202000003 1
< 0.1%
302007908201400002 2
< 0.1%
303008108201400006 2
< 0.1%
303008108201900001 1
< 0.1%
304014308201300001 1
< 0.1%
306011808201400002 1
< 0.1%
ValueCountFrequency (%)
652018408201900005 1
 
< 0.1%
652018408201900003 2
< 0.1%
652018408201900002 2
< 0.1%
652018408201900001 1
 
< 0.1%
652014508201800004 1
 
< 0.1%
652014508201800002 1
 
< 0.1%
652002408202000002 2
< 0.1%
652002408201600005 1
 
< 0.1%
652002408201600004 1
 
< 0.1%
652002408201500012 3
< 0.1%

용역사업계약구분
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
시공용역
9988 
설계용역
 
12

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
시공용역 9988
99.9%
설계용역 12
 
0.1%

Length

2023-12-12T19:39:25.808422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T19:39:25.940021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
시공용역 9988
99.9%
설계용역 12
 
0.1%

필지고유번호(PNU)
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size156.2 KiB

산림자원조성관리사업구분
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
조림/갱신
9544 
보완조림
 
437
<NA>
 
19

Length

Max length5
Median length5
Mean length4.9544
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row조림/갱신
2nd row조림/갱신
3rd row조림/갱신
4th row조림/갱신
5th row조림/갱신

Common Values

ValueCountFrequency (%)
조림/갱신 9544
95.4%
보완조림 437
 
4.4%
<NA> 19
 
0.2%

Length

2023-12-12T19:39:26.076913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T19:39:26.199913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
조림/갱신 9544
95.4%
보완조림 437
 
4.4%
na 19
 
0.2%

임산물종류명
Text

Distinct64
Distinct (%)0.6%
Missing13
Missing (%)0.1%
Memory size156.2 KiB
2023-12-12T19:39:26.435974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length7
Mean length3.4569941
Min length2

Characters and Unicode

Total characters34525
Distinct characters104
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)0.1%

Sample

1st row헛개나무
2nd row복자기나무
3rd row물푸레나무
4th row음나무
5th row편백
ValueCountFrequency (%)
편백 2619
26.2%
소나무 1165
11.7%
백합나무 890
 
8.9%
낙엽송 758
 
7.6%
활엽수기타 573
 
5.7%
상수리나무 556
 
5.6%
자작나무 458
 
4.6%
황칠나무 280
 
2.8%
헛개나무 279
 
2.8%
벚나무(산벚 228
 
2.3%
Other values (54) 2181
21.8%
2023-12-12T19:39:26.846540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
나 5561
16.1%
무 5561
16.1%
백 3651
 
10.6%
편 2619
 
7.6%
엽 1400
 
4.1%
수 1300
 
3.8%
소 1176
 
3.4%
합 890
 
2.6%
송 883
 
2.6%
낙 758
 
2.2%
Other values (94) 10726
31.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 33785
97.9%
Open Punctuation 370
 
1.1%
Close Punctuation 370
 
1.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
나 5561
16.5%
무 5561
16.5%
백 3651
 
10.8%
편 2619
 
7.8%
엽 1400
 
4.1%
수 1300
 
3.8%
소 1176
 
3.5%
합 890
 
2.6%
송 883
 
2.6%
낙 758
 
2.2%
Other values (92) 9986
29.6%
Open Punctuation
ValueCountFrequency (%)
( 370
100.0%
Close Punctuation
ValueCountFrequency (%)
) 370
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 33785
97.9%
Common 740
 
2.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
나 5561
16.5%
무 5561
16.5%
백 3651
 
10.8%
편 2619
 
7.8%
엽 1400
 
4.1%
수 1300
 
3.8%
소 1176
 
3.5%
합 890
 
2.6%
송 883
 
2.6%
낙 758
 
2.2%
Other values (92) 9986
29.6%
Common
ValueCountFrequency (%)
( 370
50.0%
) 370
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 33785
97.9%
ASCII 740
 
2.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
나 5561
16.5%
무 5561
16.5%
백 3651
 
10.8%
편 2619
 
7.8%
엽 1400
 
4.1%
수 1300
 
3.8%
소 1176
 
3.5%
합 890
 
2.6%
송 883
 
2.6%
낙 758
 
2.2%
Other values (92) 9986
29.6%
ASCII
ValueCountFrequency (%)
( 370
50.0%
) 370
50.0%

본수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1515
Distinct (%)15.2%
Missing12
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean3427.3573
Minimum0
Maximum330000
Zeros238
Zeros (%)2.4%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T19:39:27.070923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q1285
median1500
Q34200
95-th percentile12000
Maximum330000
Range330000
Interquartile range (IQR)3915

Descriptive statistics

Standard deviation7267.6337
Coefficient of variation (CV)2.1204774
Kurtosis596.58902
Mean3427.3573
Median Absolute Deviation (MAD)1440
Skewness17.495196
Sum34232445
Variance52818499
MonotonicityNot monotonic
2023-12-12T19:39:27.293101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3000 489
 
4.9%
1500 317
 
3.2%
6000 260
 
2.6%
0 238
 
2.4%
1200 172
 
1.7%
600 144
 
1.4%
1800 142
 
1.4%
4500 131
 
1.3%
9000 129
 
1.3%
900 128
 
1.3%
Other values (1505) 7838
78.4%
ValueCountFrequency (%)
0 238
2.4%
1 91
 
0.9%
2 56
 
0.6%
3 49
 
0.5%
4 30
 
0.3%
5 28
 
0.3%
6 18
 
0.2%
7 23
 
0.2%
8 26
 
0.3%
9 11
 
0.1%
ValueCountFrequency (%)
330000 1
< 0.1%
247000 1
< 0.1%
172896 1
< 0.1%
123900 1
< 0.1%
120000 1
< 0.1%
108360 1
< 0.1%
104000 1
< 0.1%
93000 1
< 0.1%
76600 1
< 0.1%
72000 1
< 0.1%

조성면적(제곱미터)
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct1281
Distinct (%)12.8%
Missing13
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean817564.55
Minimum0
Maximum1.1 × 109
Zeros137
Zeros (%)1.4%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T19:39:27.495681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile177.6
Q12900
median9600
Q320000
95-th percentile50000
Maximum1.1 × 109
Range1.1 × 109
Interquartile range (IQR)17100

Descriptive statistics

Standard deviation19154432
Coefficient of variation (CV)23.428648
Kurtosis1791.3229
Mean817564.55
Median Absolute Deviation (MAD)7600
Skewness38.513046
Sum8.1650172 × 109
Variance3.6689227 × 1014
MonotonicityNot monotonic
2023-12-12T19:39:27.696441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10000 726
 
7.3%
20000 437
 
4.4%
5000 367
 
3.7%
3000 242
 
2.4%
15000 225
 
2.2%
30000 220
 
2.2%
2000 220
 
2.2%
6000 211
 
2.1%
4000 200
 
2.0%
1000 200
 
2.0%
Other values (1271) 6939
69.4%
ValueCountFrequency (%)
0 137
1.4%
1 1
 
< 0.1%
2 2
 
< 0.1%
3 3
 
< 0.1%
4 2
 
< 0.1%
5 6
 
0.1%
6 1
 
< 0.1%
7 1
 
< 0.1%
8 6
 
0.1%
10 17
 
0.2%
ValueCountFrequency (%)
1100000000 1
< 0.1%
910000000 1
< 0.1%
500000000 2
< 0.1%
450000000 2
< 0.1%
430000000 1
< 0.1%
300000000 1
< 0.1%
285000000 1
< 0.1%
280000000 1
< 0.1%
229000000 1
< 0.1%
200000000 2
< 0.1%

Interactions

2023-12-12T19:39:22.174735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:39:18.949233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:39:20.067727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:39:20.770874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:39:21.442431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:39:22.305078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:39:19.077421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:39:20.206905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:39:20.900453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:39:21.566904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:39:22.466036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:39:19.243146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:39:20.327990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:39:21.025388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:39:21.690702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:39:22.640565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:39:19.414477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:39:20.474167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:39:21.163791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:39:21.859372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:39:22.818245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:39:19.917883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:39:20.612107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:39:21.302057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:39:22.039175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T19:39:28.181376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
활착본수계획본수사유림산림사업번호용역사업계약구분산림자원조성관리사업구분임산물종류명본수조성면적(제곱미터)
활착본수1.000NaN0.052NaN0.0000.0000.7010.000
계획본수NaN1.0000.0280.9900.0000.135NaNNaN
사유림산림사업번호0.0520.0281.0000.0720.1270.6460.0440.038
용역사업계약구분NaN0.9900.0721.0000.0000.000NaNNaN
산림자원조성관리사업구분0.0000.0000.1270.0001.0000.1240.0000.000
임산물종류명0.0000.1350.6460.0000.1241.0000.0000.000
본수0.701NaN0.044NaN0.0000.0001.0000.000
조성면적(제곱미터)0.000NaN0.038NaN0.0000.0000.0001.000
2023-12-12T19:39:28.357224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
산림자원조성관리사업구분용역사업계약구분
산림자원조성관리사업구분1.0000.000
용역사업계약구분0.0001.000
2023-12-12T19:39:28.485452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
활착본수계획본수사유림산림사업번호본수조성면적(제곱미터)용역사업계약구분산림자원조성관리사업구분
활착본수1.000NaN-0.1020.2330.1841.0000.000
계획본수NaN1.0000.016NaNNaN0.9120.000
사유림산림사업번호-0.1020.0161.000-0.071-0.0430.0550.098
본수0.233NaN-0.0711.0000.8631.0000.000
조성면적(제곱미터)0.184NaN-0.0430.8631.0001.0000.000
용역사업계약구분1.0000.9120.0551.0001.0001.0000.000
산림자원조성관리사업구분0.0000.0000.0980.0000.0000.0001.000

Missing values

2023-12-12T19:39:23.005903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T19:39:23.229778image/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.
2023-12-12T19:39:23.430832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

활착본수계획본수기관명사유림산림사업번호용역사업계약구분필지고유번호(PNU)산림자원조성관리사업구분임산물종류명본수조성면적(제곱미터)
991600충청남도 금산군455006708201400001시공용역4471036028200860002조림/갱신헛개나무28506000
65328380서울특별시 강동구324024108201900001시공용역1174010100200600003조림/갱신복자기나무383800
178759000<NA>경기도 연천군414006108201700003시공용역4180025024200380000조림/갱신물푸레나무900030000
7107312250강원특별자치도 정선군429009408201900001시공용역5177000000205379584.0조림/갱신음나무13509000
44201<NA><NA>충청남도 보령시451023708202000011시공용역4418041022200250021조림/갱신편백2551700
60829<NA><NA>전라남도 진도군500008308201800011시공용역4690033026110250007조림/갱신동백나무301000
85681500<NA>충청북도 단양군448008108201700003시공용역4380031040200450000조림/갱신소나무150010000
63292<NA><NA>충청북도 청주시571021508201800014시공용역4311332027200200000조림/갱신소나무495016500
3840<NA><NA>경기도 평택시391028408201800001시공용역4122025621103920001조림/갱신백합나무20008000
66613108000경기도 안성시408017808201900004시공용역4155042027200940032.0조림/갱신물푸레나무1080036000
활착본수계획본수기관명사유림산림사업번호용역사업계약구분필지고유번호(PNU)산림자원조성관리사업구분임산물종류명본수조성면적(제곱미터)
1177800충청남도 공주시450024708201500012시공용역4415031035200540000조림/갱신소나무30004000
5560800경기도 화성시553030808201500001시공용역4159035021201230002조림/갱신밤나무08000
34287<NA><NA>경상남도 김해시535028008201900001시공용역4825036021202130000조림/갱신편백30100
8501000전라남도 영광군497008608201800007시공용역4687025024200460288.0조림/갱신편백504000
3742600전라남도 순천시482018208201300001시공용역4615037023200210000조림/갱신편백18006000
8044000전라남도 장흥군491010108201900003시공용역4680034022200160256.0조림/갱신편백180600
3072400전라남도 순천시482018208201400001시공용역4615036027201300000조림/갱신가시나무12004000
49809<NA><NA>충청북도 옥천군443010308201800004시공용역4373035025200310003조림/갱신백합나무400020000
121551035<NA>전라남도 무안군495007808201700002시공용역4684025023200320000조림/갱신편백10356900
42954<NA><NA>경상북도 포항시503005108202100014시공용역4711131025206520003조림/갱신벚나무(산벚)45300

Duplicate rows

Most frequently occurring

활착본수계획본수기관명사유림산림사업번호용역사업계약구분산림자원조성관리사업구분임산물종류명본수조성면적(제곱미터)# duplicates
2100경상북도 칠곡군522012708201900002시공용역조림/갱신벚나무(산벚)1109
710<NA>경기도 안산시393026808201600001시공용역조림/갱신잣나무006
4900전라남도 장흥군491000808201400003시공용역조림/갱신편백3000100004
810<NA>전라남도 함평군496008908201600008시공용역조림/갱신가시나무004
1066000강원특별자치도 원주시419021208201900002시공용역조림/갱신옻나무60020004
500경기도 안산시393026808201500001시공용역조림/갱신잣나무003
2500전라남도 강진군492011408201400014시공용역조림/갱신동백나무41003
3800전라남도 순천시482018208201400001시공용역조림/갱신백합나무2000100003
820<NA>전라북도 익산시468031308201500004시공용역조림/갱신편백3000200003
9110제주특별자치도 제주시651012108201900002시공용역조림/갱신활엽수기타11003