Overview

Dataset statistics

Number of variables12
Number of observations1710
Missing cells1711
Missing cells (%)8.3%
Duplicate rows35
Duplicate rows (%)2.0%
Total size in memory175.5 KiB
Average record size in memory105.1 B

Variable types

Numeric6
Categorical5
Text1

Dataset

Description충청북도 단양군_산림정밀지도DB 추출 데이터로 숲가꾸기_지적면적, 사업종코드, 시행시작일, 시행종료일, 사업회수코드, 사업면적, 군유지 면적, 읍면동명, 리명, 군유림 여부, 면적, 데이터 기준일자 등의 데이터 포함
Author충청북도 단양군
URLhttps://www.data.go.kr/data/15089383/fileData.do

Alerts

데이터 기준일자 has constant value ""Constant
Dataset has 35 (2.0%) duplicate rowsDuplicates
숲가꾸기_사업종코드 is highly overall correlated with 숲가꾸기_시행시작일 and 2 other fieldsHigh correlation
숲가꾸기_사업회수코드 is highly overall correlated with 숲가꾸기_사업종코드High correlation
숲가꾸기_지적면적 is highly overall correlated with 숲가꾸기_사업면적 and 2 other fieldsHigh correlation
숲가꾸기_시행시작일 is highly overall correlated with 숲가꾸기_시행종료일 and 1 other fieldsHigh correlation
숲가꾸기_시행종료일 is highly overall correlated with 숲가꾸기_시행시작일 and 1 other fieldsHigh correlation
숲가꾸기_사업면적 is highly overall correlated with 숲가꾸기_지적면적 and 1 other fieldsHigh correlation
군유지_면적 is highly overall correlated with 숲가꾸기_지적면적 and 2 other fieldsHigh correlation
면적 is highly overall correlated with 숲가꾸기_지적면적 and 2 other fieldsHigh correlation
군유림여부 is highly overall correlated with 군유지_면적High correlation
숲가꾸기_사업종코드 is highly imbalanced (55.8%)Imbalance
숲가꾸기_지적면적 has 279 (16.3%) missing valuesMissing
군유지_면적 has 1429 (83.6%) missing valuesMissing

Reproduction

Analysis started2023-12-12 19:18:28.310444
Analysis finished2023-12-12 19:18:34.124843
Duration5.81 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

숲가꾸기_지적면적
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct362
Distinct (%)25.3%
Missing279
Missing (%)16.3%
Infinite0
Infinite (%)0.0%
Mean93800.159
Minimum1000
Maximum2565000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.2 KiB
2023-12-13T04:18:34.195967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1000
5-th percentile4702
Q117653
median39671
Q383000
95-th percentile307835
Maximum2565000
Range2564000
Interquartile range (IQR)65347

Descriptive statistics

Standard deviation213455.7
Coefficient of variation (CV)2.2756433
Kurtosis50.393243
Mean93800.159
Median Absolute Deviation (MAD)27327
Skewness6.4211187
Sum1.3422803 × 108
Variance4.5563338 × 1010
MonotonicityNot monotonic
2023-12-13T04:18:34.335405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11000 30
 
1.8%
20000 23
 
1.3%
8000 21
 
1.2%
17000 21
 
1.2%
4000 21
 
1.2%
5000 19
 
1.1%
10000 19
 
1.1%
12000 19
 
1.1%
30000 19
 
1.1%
21000 19
 
1.1%
Other values (352) 1220
71.3%
(Missing) 279
 
16.3%
ValueCountFrequency (%)
1000 8
0.5%
1124 2
 
0.1%
1494 2
 
0.1%
2000 10
0.6%
2921 1
 
0.1%
2975 1
 
0.1%
3000 12
0.7%
3174 2
 
0.1%
3233 1
 
0.1%
3875 2
 
0.1%
ValueCountFrequency (%)
2565000 1
 
0.1%
2470000 1
 
0.1%
1989389 1
 
0.1%
1954000 1
 
0.1%
1662347 7
0.4%
1569000 1
 
0.1%
1442631 1
 
0.1%
1232132 4
0.2%
1040000 1
 
0.1%
1030000 2
 
0.1%

숲가꾸기_사업종코드
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size13.5 KiB
5
1275 
3
419 
7
 
12
4
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5
2nd row5
3rd row5
4th row3
5th row3

Common Values

ValueCountFrequency (%)
5 1275
74.6%
3 419
 
24.5%
7 12
 
0.7%
4 4
 
0.2%

Length

2023-12-13T04:18:34.464592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:18:34.558018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
5 1275
74.6%
3 419
 
24.5%
7 12
 
0.7%
4 4
 
0.2%

숲가꾸기_시행시작일
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20018772
Minimum20000000
Maximum20080101
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.2 KiB
2023-12-13T04:18:34.668173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20000000
5-th percentile20000000
Q120010000
median20020000
Q320030421
95-th percentile20030421
Maximum20080101
Range80101
Interquartile range (IQR)20421

Descriptive statistics

Standard deviation11233.746
Coefficient of variation (CV)0.00056116063
Kurtosis0.92815759
Mean20018772
Median Absolute Deviation (MAD)10000
Skewness0.070059092
Sum3.42321 × 1010
Variance1.2619706 × 108
MonotonicityNot monotonic
2023-12-13T04:18:34.775996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
20020000 571
33.4%
20030421 521
30.5%
20010000 332
19.4%
20000000 253
14.8%
20041028 29
 
1.7%
20080101 4
 
0.2%
ValueCountFrequency (%)
20000000 253
14.8%
20010000 332
19.4%
20020000 571
33.4%
20030421 521
30.5%
20041028 29
 
1.7%
20080101 4
 
0.2%
ValueCountFrequency (%)
20080101 4
 
0.2%
20041028 29
 
1.7%
20030421 521
30.5%
20020000 571
33.4%
20010000 332
19.4%
20000000 253
14.8%

숲가꾸기_시행종료일
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20019025
Minimum20000000
Maximum20081231
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.2 KiB
2023-12-13T04:18:34.887285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20000000
5-th percentile20000000
Q120010000
median20020000
Q320031231
95-th percentile20031231
Maximum20081231
Range81231
Interquartile range (IQR)21231

Descriptive statistics

Standard deviation11513.807
Coefficient of variation (CV)0.00057514326
Kurtosis0.79903808
Mean20019025
Median Absolute Deviation (MAD)10000
Skewness0.093052552
Sum3.4232532 × 1010
Variance1.3256775 × 108
MonotonicityNot monotonic
2023-12-13T04:18:35.034489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
20020000 571
33.4%
20031231 521
30.5%
20010000 332
19.4%
20000000 253
14.8%
20041231 29
 
1.7%
20081231 4
 
0.2%
ValueCountFrequency (%)
20000000 253
14.8%
20010000 332
19.4%
20020000 571
33.4%
20031231 521
30.5%
20041231 29
 
1.7%
20081231 4
 
0.2%
ValueCountFrequency (%)
20081231 4
 
0.2%
20041231 29
 
1.7%
20031231 521
30.5%
20020000 571
33.4%
20010000 332
19.4%
20000000 253
14.8%

숲가꾸기_사업회수코드
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size13.5 KiB
1
1078 
<NA>
597 
2
 
35

Length

Max length4
Median length1
Mean length2.0473684
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
1 1078
63.0%
<NA> 597
34.9%
2 35
 
2.0%

Length

2023-12-13T04:18:35.184204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:18:35.295537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 1078
63.0%
na 597
34.9%
2 35
 
2.0%

숲가꾸기_사업면적
Real number (ℝ)

HIGH CORRELATION 

Distinct160
Distinct (%)9.4%
Missing3
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean3.5947276
Minimum0.05
Maximum195.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.2 KiB
2023-12-13T04:18:35.421656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.05
5-th percentile0.3
Q10.9
median2
Q33.9
95-th percentile10.87
Maximum195.4
Range195.35
Interquartile range (IQR)3

Descriptive statistics

Standard deviation8.1570265
Coefficient of variation (CV)2.269164
Kurtosis238.27813
Mean3.5947276
Median Absolute Deviation (MAD)1.3
Skewness12.717322
Sum6136.2
Variance66.537081
MonotonicityNot monotonic
2023-12-13T04:18:35.570745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.0 170
 
9.9%
0.5 119
 
7.0%
2.0 112
 
6.5%
3.0 59
 
3.5%
1.5 57
 
3.3%
0.4 52
 
3.0%
0.3 51
 
3.0%
2.5 49
 
2.9%
0.2 43
 
2.5%
1.2 42
 
2.5%
Other values (150) 953
55.7%
ValueCountFrequency (%)
0.05 4
 
0.2%
0.1 23
 
1.3%
0.2 43
 
2.5%
0.3 51
3.0%
0.36 1
 
0.1%
0.4 52
3.0%
0.5 119
7.0%
0.6 35
 
2.0%
0.7 38
 
2.2%
0.8 41
 
2.4%
ValueCountFrequency (%)
195.4 1
0.1%
136.0 1
0.1%
101.0 1
0.1%
69.8 1
0.1%
57.2 1
0.1%
52.5 1
0.1%
50.0 2
0.1%
45.0 2
0.1%
44.2 1
0.1%
43.9 1
0.1%

군유지_면적
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct155
Distinct (%)55.2%
Missing1429
Missing (%)83.6%
Infinite0
Infinite (%)0.0%
Mean175339.22
Minimum1587
Maximum1954909
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.2 KiB
2023-12-13T04:18:35.720329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1587
5-th percentile7537
Q142446
median113674
Q3198138
95-th percentile664755
Maximum1954909
Range1953322
Interquartile range (IQR)155692

Descriptive statistics

Standard deviation233385.16
Coefficient of variation (CV)1.3310494
Kurtosis17.603793
Mean175339.22
Median Absolute Deviation (MAD)74712
Skewness3.4868988
Sum49270320
Variance5.4468635 × 1010
MonotonicityNot monotonic
2023-12-13T04:18:35.866234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
169388 9
 
0.5%
188386 6
 
0.4%
126576 5
 
0.3%
161455 5
 
0.3%
152137 5
 
0.3%
58785 5
 
0.3%
134479 4
 
0.2%
42446 4
 
0.2%
214977 4
 
0.2%
126149 4
 
0.2%
Other values (145) 230
 
13.5%
(Missing) 1429
83.6%
ValueCountFrequency (%)
1587 1
0.1%
2083 1
0.1%
2390 1
0.1%
3787 2
0.1%
3875 2
0.1%
3967 1
0.1%
4364 1
0.1%
5554 1
0.1%
5752 1
0.1%
6347 1
0.1%
ValueCountFrequency (%)
1954909 1
 
0.1%
1569818 1
 
0.1%
1186215 1
 
0.1%
993143 1
 
0.1%
833851 2
0.1%
736996 3
0.2%
733033 2
0.1%
699586 2
0.1%
664755 3
0.2%
629157 1
 
0.1%

읍면동명
Categorical

Distinct8
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size13.5 KiB
영춘면
333 
적성면
316 
어상천면
256 
매포읍
250 
단양읍
193 
Other values (3)
362 

Length

Max length4
Median length3
Mean length3.1497076
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
영춘면 333
19.5%
적성면 316
18.5%
어상천면 256
15.0%
매포읍 250
14.6%
단양읍 193
11.3%
대강면 143
8.4%
가곡면 122
 
7.1%
단성면 97
 
5.7%

Length

2023-12-13T04:18:36.012337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:18:36.147682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
영춘면 333
19.5%
적성면 316
18.5%
어상천면 256
15.0%
매포읍 250
14.6%
단양읍 193
11.3%
대강면 143
8.4%
가곡면 122
 
7.1%
단성면 97
 
5.7%

리명
Text

Distinct86
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size13.5 KiB
2023-12-13T04:18:36.471640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.1438596
Min length2

Characters and Unicode

Total characters5376
Distinct characters82
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

Unique5 ?
Unique (%)0.3%

Sample

1st row현천리
2nd row현천리
3rd row현천리
4th row덕상리
5th row덕상리
ValueCountFrequency (%)
상원곡리 93
 
5.4%
연곡리 90
 
5.3%
만종리 75
 
4.4%
도곡리 66
 
3.9%
사지원리 65
 
3.8%
애곡리 63
 
3.7%
용진리 61
 
3.6%
노동리 57
 
3.3%
우덕리 56
 
3.3%
가평리 53
 
3.1%
Other values (76) 1031
60.3%
2023-12-13T04:18:36.921936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
리 1710
31.8%
곡 489
 
9.1%
대 169
 
3.1%
원 164
 
3.1%
상 144
 
2.7%
동 123
 
2.3%
가 120
 
2.2%
사 120
 
2.2%
덕 107
 
2.0%
방 106
 
2.0%
Other values (72) 2124
39.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5376
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 1710
31.8%
곡 489
 
9.1%
대 169
 
3.1%
원 164
 
3.1%
상 144
 
2.7%
동 123
 
2.3%
가 120
 
2.2%
사 120
 
2.2%
덕 107
 
2.0%
방 106
 
2.0%
Other values (72) 2124
39.5%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5376
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 1710
31.8%
곡 489
 
9.1%
대 169
 
3.1%
원 164
 
3.1%
상 144
 
2.7%
동 123
 
2.3%
가 120
 
2.2%
사 120
 
2.2%
덕 107
 
2.0%
방 106
 
2.0%
Other values (72) 2124
39.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5376
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
리 1710
31.8%
곡 489
 
9.1%
대 169
 
3.1%
원 164
 
3.1%
상 144
 
2.7%
동 123
 
2.3%
가 120
 
2.2%
사 120
 
2.2%
덕 107
 
2.0%
방 106
 
2.0%
Other values (72) 2124
39.5%

군유림여부
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size13.5 KiB
0
1429 
1
281 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 1429
83.6%
1 281
 
16.4%

Length

2023-12-13T04:18:37.106212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:18:37.239022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 1429
83.6%
1 281
 
16.4%

면적
Real number (ℝ)

HIGH CORRELATION 

Distinct889
Distinct (%)52.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean91127.323
Minimum50
Maximum2448309
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.2 KiB
2023-12-13T04:18:37.391742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile4364
Q117851
median39669
Q381421
95-th percentile299602.9
Maximum2448309
Range2448259
Interquartile range (IQR)63570

Descriptive statistics

Standard deviation196939.3
Coefficient of variation (CV)2.1611443
Kurtosis45.221034
Mean91127.323
Median Absolute Deviation (MAD)26975
Skewness6.0873695
Sum1.5582772 × 108
Variance3.8785086 × 1010
MonotonicityNot monotonic
2023-12-13T04:18:37.573820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11901.0 12
 
0.7%
13884.0 10
 
0.6%
17851.0 10
 
0.6%
169388.0 9
 
0.5%
27769.0 9
 
0.5%
36496.0 9
 
0.5%
4760.0 8
 
0.5%
19835.0 8
 
0.5%
53554.0 8
 
0.5%
1662347.0 7
 
0.4%
Other values (879) 1620
94.7%
ValueCountFrequency (%)
50.0 1
0.1%
151.0 1
0.1%
198.0 1
0.1%
278.0 1
0.1%
461.0 2
0.1%
499.0 1
0.1%
615.0 1
0.1%
706.0 1
0.1%
787.0 1
0.1%
830.0 1
0.1%
ValueCountFrequency (%)
2448309.0 1
 
0.1%
1954909.0 1
 
0.1%
1747439.0 1
 
0.1%
1662347.0 7
0.4%
1569818.0 1
 
0.1%
1442631.0 1
 
0.1%
1335130.0 3
0.2%
1232132.0 4
0.2%
1186215.0 1
 
0.1%
1040529.0 1
 
0.1%

데이터 기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size13.5 KiB
2022-09-23
1710 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022-09-23
2nd row2022-09-23
3rd row2022-09-23
4th row2022-09-23
5th row2022-09-23

Common Values

ValueCountFrequency (%)
2022-09-23 1710
100.0%

Length

2023-12-13T04:18:37.780155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:18:38.235296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022-09-23 1710
100.0%

Interactions

2023-12-13T04:18:33.061305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:18:28.988599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:18:29.693442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:18:30.493390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:18:31.291716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:18:32.377442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:18:33.169005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:18:29.087289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:18:29.812365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:18:30.621159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:18:31.711475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:18:32.497894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:18:33.288767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:18:29.201194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:18:29.956765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:18:30.776315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:18:31.833401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:18:32.594702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:18:33.403156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:18:29.321328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:18:30.113532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:18:30.913311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:18:31.988866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:18:32.697063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:18:33.514499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:18:29.437442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:18:30.253457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:18:31.057954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:18:32.118311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:18:32.814094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:18:33.615759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:18:29.592840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:18:30.361197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:18:31.176631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:18:32.251920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:18:32.922982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T04:18:38.324685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
숲가꾸기_지적면적숲가꾸기_사업종코드숲가꾸기_시행시작일숲가꾸기_시행종료일숲가꾸기_사업회수코드숲가꾸기_사업면적군유지_면적읍면동명리명군유림여부면적
숲가꾸기_지적면적1.0000.0000.0890.0890.1600.7500.9640.2340.7390.2460.988
숲가꾸기_사업종코드0.0001.0000.7930.793NaN0.0000.0320.2660.5820.1090.000
숲가꾸기_시행시작일0.0890.7931.0001.0000.2210.0000.0000.3260.8000.1250.090
숲가꾸기_시행종료일0.0890.7931.0001.0000.2210.0000.0000.3260.8000.1250.090
숲가꾸기_사업회수코드0.160NaN0.2210.2211.0000.0000.0000.1700.5130.0870.155
숲가꾸기_사업면적0.7500.0000.0000.0000.0001.0000.8980.0000.2230.1340.764
군유지_면적0.9640.0320.0000.0000.0000.8981.0000.4060.921NaN0.943
읍면동명0.2340.2660.3260.3260.1700.0000.4061.0001.0000.2610.206
리명0.7390.5820.8000.8000.5130.2230.9211.0001.0000.4500.755
군유림여부0.2460.1090.1250.1250.0870.134NaN0.2610.4501.0000.249
면적0.9880.0000.0900.0900.1550.7640.9430.2060.7550.2491.000
2023-12-13T04:18:38.486477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
군유림여부숲가꾸기_사업종코드숲가꾸기_사업회수코드읍면동명
군유림여부1.0000.0720.0550.196
숲가꾸기_사업종코드0.0721.0001.0000.121
숲가꾸기_사업회수코드0.0551.0001.0000.127
읍면동명0.1960.1210.1271.000
2023-12-13T04:18:38.591997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
숲가꾸기_지적면적숲가꾸기_시행시작일숲가꾸기_시행종료일숲가꾸기_사업면적군유지_면적면적숲가꾸기_사업종코드숲가꾸기_사업회수코드읍면동명군유림여부
숲가꾸기_지적면적1.000-0.040-0.0400.5890.9270.9650.0000.1600.1160.245
숲가꾸기_시행시작일-0.0401.0001.0000.233-0.071-0.0030.7540.1720.1810.140
숲가꾸기_시행종료일-0.0401.0001.0000.233-0.071-0.0030.7540.1720.1810.140
숲가꾸기_사업면적0.5890.2330.2331.0000.3440.5430.0000.0000.0000.143
군유지_면적0.927-0.071-0.0710.3441.0001.0000.0300.0000.2121.000
면적0.965-0.003-0.0030.5431.0001.0000.0000.1540.1020.248
숲가꾸기_사업종코드0.0000.7540.7540.0000.0300.0001.0001.0000.1210.072
숲가꾸기_사업회수코드0.1600.1720.1720.0000.0000.1541.0001.0000.1270.055
읍면동명0.1160.1810.1810.0000.2120.1020.1210.1271.0000.196
군유림여부0.2450.1400.1400.1431.0000.2480.0720.0550.1961.000

Missing values

2023-12-13T04:18:33.749143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T04:18:33.920618image/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.
2023-12-13T04:18:34.055515image/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

숲가꾸기_지적면적숲가꾸기_사업종코드숲가꾸기_시행시작일숲가꾸기_시행종료일숲가꾸기_사업회수코드숲가꾸기_사업면적군유지_면적읍면동명리명군유림여부면적데이터 기준일자
0230955200100002001000010.5<NA>단양읍현천리023095.02022-09-23
11122045200100002001000016.0<NA>단양읍현천리0112204.02022-09-23
2<NA>52001000020010000<NA>1.0<NA>단양읍현천리013091.02022-09-23
31800032003042120031231<NA>1.8<NA>단양읍덕상리018842.02022-09-23
4800032003042120031231<NA>0.8<NA>단양읍덕상리08032.02022-09-23
55200032003042120031231<NA>5.2<NA>단양읍덕상리052760.02022-09-23
61900032003042120031231<NA>1.9<NA>단양읍덕상리019835.02022-09-23
7900052003042120031231<NA>0.9<NA>단양읍덕상리09917.02022-09-23
810700032003042120031231<NA>10.7<NA>단양읍덕상리0107306.02022-09-23
9800032003042120031231<NA>0.8<NA>단양읍덕상리08727.02022-09-23
숲가꾸기_지적면적숲가꾸기_사업종코드숲가꾸기_시행시작일숲가꾸기_시행종료일숲가꾸기_사업회수코드숲가꾸기_사업면적군유지_면적읍면동명리명군유림여부면적데이터 기준일자
1700580005200200002002000015.8<NA>단성면대잠리058711.02022-09-23
1701680005200200002002000016.8<NA>단성면대잠리068865.02022-09-23
170213200052002000020020000112.9<NA>단성면대잠리0126070.02022-09-23
170310800052002000020020000110.8<NA>단성면대잠리0108694.02022-09-23
170460005200200002002000010.66347단성면대잠리16347.02022-09-23
1705960005200200002002000018.2<NA>단성면대잠리096871.02022-09-23
1706200005200200002002000011.5<NA>단성면대잠리020868.02022-09-23
17071438745200000002000000011.0<NA>단성면대잠리0143874.02022-09-23
17081438745200100002001000011.0<NA>단성면대잠리0143874.02022-09-23
17091438745200200002002000011.0<NA>단성면대잠리0143874.02022-09-23

Duplicate rows

Most frequently occurring

숲가꾸기_지적면적숲가꾸기_사업종코드숲가꾸기_시행시작일숲가꾸기_시행종료일숲가꾸기_사업회수코드숲가꾸기_사업면적군유지_면적읍면동명리명군유림여부면적데이터 기준일자# duplicates
055545200200002002000010.5<NA>영춘면용진리05554.02022-09-233
1900032003042120031231<NA>0.9<NA>적성면애곡리09928.02022-09-232
2900052003042120031231<NA>0.9<NA>단양읍덕상리09917.02022-09-232
3110005200100002001000011.0<NA>적성면대가리011504.02022-09-232
4110005200200002002000011.1<NA>대강면직티리011901.02022-09-232
5122985200200002002000011.0<NA>어상천면대전리012298.02022-09-232
6150005200200002002000011.5<NA>적성면상원곡리015868.02022-09-232
71600032003042120031231<NA>1.6<NA>적성면애곡리016661.02022-09-232
8170005200200002002000011.7<NA>적성면상원곡리017851.02022-09-232
9201535200200002002000011.5<NA>영춘면용진리020153.02022-09-232