Overview

Dataset statistics

Number of variables16
Number of observations1176
Missing cells2662
Missing cells (%)14.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory158.6 KiB
Average record size in memory138.1 B

Variable types

Categorical6
Numeric8
DateTime1
Text1

Dataset

Description충청북도 단양군 산림정밀지도DB 추출 데이터로 지적코드, 지적면적, 지정면적, 지정일, 고사번호, 해제면적, 해제일, 해제고시번호, 잔여면적, 직요, 군유지 면적, 읍면동명, 리명, 군유림 여부, 면적, 데이터 기준일자 등의 데이터 포함
Author충청북도 단양군
URLhttps://www.data.go.kr/data/15089366/fileData.do

Alerts

지적코드 has constant value ""Constant
데이터 기준일자 has constant value ""Constant
사방지지정_지적면적 is highly overall correlated with 군유지_면적 and 1 other fieldsHigh correlation
사방지지정_지정면적 is highly overall correlated with 사방지지정_해제면적 and 1 other fieldsHigh correlation
사방지지정_고사번호 is highly overall correlated with 사방지지정_해제일High correlation
사방지지정_해제면적 is highly overall correlated with 사방지지정_지정면적High correlation
사방지지정_해제고시번호 is highly overall correlated with 사방지지정_지정면적 and 1 other fieldsHigh correlation
군유지_면적 is highly overall correlated with 사방지지정_지적면적 and 2 other fieldsHigh correlation
면적 is highly overall correlated with 사방지지정_지적면적 and 1 other fieldsHigh correlation
사방지지정_해제일 is highly overall correlated with 사방지지정_고사번호 and 2 other fieldsHigh correlation
사방지지정_적요 is highly overall correlated with 사방지지정_해제일High correlation
군유림여부 is highly overall correlated with 군유지_면적High correlation
사방지지정_지정면적 has 95 (8.1%) missing valuesMissing
사방지지정_지정일 has 95 (8.1%) missing valuesMissing
사방지지정_고사번호 has 174 (14.8%) missing valuesMissing
사방지지정_해제면적 has 416 (35.4%) missing valuesMissing
사방지지정_해제고시번호 has 468 (39.8%) missing valuesMissing
사방지지정_잔여면적 has 407 (34.6%) missing valuesMissing
군유지_면적 has 1007 (85.6%) missing valuesMissing
사방지지정_지정면적 is highly skewed (γ1 = 28.03179094)Skewed
사방지지정_해제면적 is highly skewed (γ1 = 24.0337999)Skewed
사방지지정_잔여면적 has 563 (47.9%) zerosZeros

Reproduction

Analysis started2023-12-12 20:55:33.127536
Analysis finished2023-12-12 20:55:42.078108
Duration8.95 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

지적코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size9.3 KiB
4380000
1176 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4380000
2nd row4380000
3rd row4380000
4th row4380000
5th row4380000

Common Values

ValueCountFrequency (%)
4380000 1176
100.0%

Length

2023-12-13T05:55:42.138829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:55:42.229989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
4380000 1176
100.0%

사방지지정_지적면적
Real number (ℝ)

HIGH CORRELATION 

Distinct710
Distinct (%)60.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean262454.59
Minimum56
Maximum9074777
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.5 KiB
2023-12-13T05:55:42.347719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum56
5-th percentile496.25
Q113488
median46461.5
Q3123624.25
95-th percentile1263471
Maximum9074777
Range9074721
Interquartile range (IQR)110136.25

Descriptive statistics

Standard deviation846874.03
Coefficient of variation (CV)3.226745
Kurtosis43.564013
Mean262454.59
Median Absolute Deviation (MAD)39562
Skewness5.9983565
Sum3.086466 × 108
Variance7.1719562 × 1011
MonotonicityNot monotonic
2023-12-13T05:55:42.520490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
48000 10
 
0.9%
11901 10
 
0.9%
43636 8
 
0.7%
21818 8
 
0.7%
31339 8
 
0.7%
19835 7
 
0.6%
59504 7
 
0.6%
15868 7
 
0.6%
16661 7
 
0.6%
25785 7
 
0.6%
Other values (700) 1097
93.3%
ValueCountFrequency (%)
56 1
 
0.1%
83 1
 
0.1%
86 2
0.2%
99 3
0.3%
109 1
 
0.1%
132 1
 
0.1%
142 1
 
0.1%
152 2
0.2%
162 1
 
0.1%
165 1
 
0.1%
ValueCountFrequency (%)
9074777 3
0.3%
5866223 2
0.2%
5715071 1
 
0.1%
4973355 2
0.2%
4969042 4
0.3%
4862083 2
0.2%
4640430 2
0.2%
4629231 3
0.3%
4490837 1
 
0.1%
4216264 1
 
0.1%

사방지지정_지정면적
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct396
Distinct (%)36.6%
Missing95
Missing (%)8.1%
Infinite0
Infinite (%)0.0%
Mean5201.6031
Minimum1
Maximum1718678
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.5 KiB
2023-12-13T05:55:42.663867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile21
Q1178
median515
Q32000
95-th percentile10000
Maximum1718678
Range1718677
Interquartile range (IQR)1822

Descriptive statistics

Standard deviation55380.944
Coefficient of variation (CV)10.646899
Kurtosis853.93707
Mean5201.6031
Median Absolute Deviation (MAD)465
Skewness28.031791
Sum5622933
Variance3.067049 × 109
MonotonicityNot monotonic
2023-12-13T05:55:42.829615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 58
 
4.9%
200 56
 
4.8%
300 47
 
4.0%
500 46
 
3.9%
1000 43
 
3.7%
2000 38
 
3.2%
50 34
 
2.9%
400 27
 
2.3%
3000 26
 
2.2%
150 21
 
1.8%
Other values (386) 685
58.2%
(Missing) 95
 
8.1%
ValueCountFrequency (%)
1 2
 
0.2%
2 3
 
0.3%
3 10
0.9%
4 2
 
0.2%
5 1
 
0.1%
6 2
 
0.2%
7 4
 
0.3%
9 1
 
0.1%
10 8
0.7%
11 3
 
0.3%
ValueCountFrequency (%)
1718678 1
0.1%
435967 1
0.1%
186050 1
0.1%
170579 1
0.1%
146975 1
0.1%
145091 1
0.1%
137383 1
0.1%
108298 1
0.1%
106612 1
0.1%
72893 1
0.1%

사방지지정_지정일
Date

MISSING 

Distinct92
Distinct (%)8.5%
Missing95
Missing (%)8.1%
Memory size9.3 KiB
Minimum1960-05-30 00:00:00
Maximum2006-06-27 00:00:00
2023-12-13T05:55:42.987097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:55:43.158768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

사방지지정_고사번호
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct34
Distinct (%)3.4%
Missing174
Missing (%)14.8%
Infinite0
Infinite (%)0.0%
Mean26.374251
Minimum2
Maximum137
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.5 KiB
2023-12-13T05:55:43.329473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q16
median23
Q333
95-th percentile88
Maximum137
Range135
Interquartile range (IQR)27

Descriptive statistics

Standard deviation24.910001
Coefficient of variation (CV)0.94448182
Kurtosis5.6666027
Mean26.374251
Median Absolute Deviation (MAD)16
Skewness2.038409
Sum26427
Variance620.50815
MonotonicityNot monotonic
2023-12-13T05:55:43.471553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
4 169
14.4%
33 108
9.2%
6 85
7.2%
29 83
7.1%
38 72
 
6.1%
44 71
 
6.0%
21 64
 
5.4%
23 54
 
4.6%
88 50
 
4.3%
3 42
 
3.6%
Other values (24) 204
17.3%
(Missing) 174
14.8%
ValueCountFrequency (%)
2 13
 
1.1%
3 42
 
3.6%
4 169
14.4%
5 21
 
1.8%
6 85
7.2%
9 2
 
0.2%
10 4
 
0.3%
11 32
 
2.7%
12 6
 
0.5%
14 7
 
0.6%
ValueCountFrequency (%)
137 10
 
0.9%
136 7
 
0.6%
88 50
4.3%
49 10
 
0.9%
45 4
 
0.3%
44 71
6.0%
42 13
 
1.1%
41 4
 
0.3%
40 1
 
0.1%
39 2
 
0.2%

사방지지정_해제면적
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct234
Distinct (%)30.8%
Missing416
Missing (%)35.4%
Infinite0
Infinite (%)0.0%
Mean6033.0842
Minimum1
Maximum1716678
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.5 KiB
2023-12-13T05:55:43.605446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile32.95
Q1158.75
median500
Q31977.5
95-th percentile10820
Maximum1716678
Range1716677
Interquartile range (IQR)1818.75

Descriptive statistics

Standard deviation65444.678
Coefficient of variation (CV)10.847632
Kurtosis619.50303
Mean6033.0842
Median Absolute Deviation (MAD)415.5
Skewness24.0338
Sum4585144
Variance4.2830059 × 109
MonotonicityNot monotonic
2023-12-13T05:55:43.761384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 69
 
5.9%
200 50
 
4.3%
300 41
 
3.5%
500 37
 
3.1%
50 34
 
2.9%
1000 33
 
2.8%
2000 27
 
2.3%
400 25
 
2.1%
150 19
 
1.6%
600 16
 
1.4%
Other values (224) 409
34.8%
(Missing) 416
35.4%
ValueCountFrequency (%)
1 2
 
0.2%
2 2
 
0.2%
3 6
0.5%
4 1
 
0.1%
5 1
 
0.1%
6 1
 
0.1%
7 2
 
0.2%
9 1
 
0.1%
10 4
0.3%
11 1
 
0.1%
ValueCountFrequency (%)
1716678 1
0.1%
434967 1
0.1%
171250 1
0.1%
160879 1
0.1%
123883 1
0.1%
123175 1
0.1%
114891 1
0.1%
105612 1
0.1%
100798 1
0.1%
52818 1
0.1%

사방지지정_해제일
Categorical

HIGH CORRELATION 

Distinct31
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size9.3 KiB
<NA>
420 
2002-09-06
295 
1983-03-26
173 
1995-03-20
155 
2005-12-26
 
42
Other values (26)
91 

Length

Max length10
Median length10
Mean length7.8571429
Min length4

Unique

Unique13 ?
Unique (%)1.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 420
35.7%
2002-09-06 295
25.1%
1983-03-26 173
14.7%
1995-03-20 155
 
13.2%
2005-12-26 42
 
3.6%
1985-12-13 30
 
2.6%
1993-03-20 15
 
1.3%
1991-05-09 6
 
0.5%
1994-12-27 6
 
0.5%
1995-03-21 3
 
0.3%
Other values (21) 31
 
2.6%

Length

2023-12-13T05:55:43.914537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 420
35.7%
2002-09-06 295
25.1%
1983-03-26 173
14.7%
1995-03-20 155
 
13.2%
2005-12-26 42
 
3.6%
1985-12-13 30
 
2.6%
1993-03-20 15
 
1.3%
1991-05-09 6
 
0.5%
1994-12-27 6
 
0.5%
1995-03-21 3
 
0.3%
Other values (21) 31
 
2.6%

사방지지정_해제고시번호
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct8
Distinct (%)1.1%
Missing468
Missing (%)39.8%
Infinite0
Infinite (%)0.0%
Mean55.235876
Minimum0
Maximum3179
Zeros4
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size10.5 KiB
2023-12-13T05:55:44.050776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q14
median29
Q335
95-th percentile90.9
Maximum3179
Range3179
Interquartile range (IQR)31

Descriptive statistics

Standard deviation289.9421
Coefficient of variation (CV)5.2491627
Kurtosis112.30251
Mean55.235876
Median Absolute Deviation (MAD)6
Skewness10.641565
Sum39107
Variance84066.421
MonotonicityNot monotonic
2023-12-13T05:55:44.190091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
35 302
25.7%
4 187
 
15.9%
29 174
 
14.8%
121 30
 
2.6%
3179 6
 
0.5%
7 4
 
0.3%
0 4
 
0.3%
11 1
 
0.1%
(Missing) 468
39.8%
ValueCountFrequency (%)
0 4
 
0.3%
4 187
15.9%
7 4
 
0.3%
11 1
 
0.1%
29 174
14.8%
35 302
25.7%
121 30
 
2.6%
3179 6
 
0.5%
ValueCountFrequency (%)
3179 6
 
0.5%
121 30
 
2.6%
35 302
25.7%
29 174
14.8%
11 1
 
0.1%
7 4
 
0.3%
4 187
15.9%
0 4
 
0.3%

사방지지정_잔여면적
Real number (ℝ)

MISSING  ZEROS 

Distinct137
Distinct (%)17.8%
Missing407
Missing (%)34.6%
Infinite0
Infinite (%)0.0%
Mean583.08322
Minimum0
Maximum65455
Zeros563
Zeros (%)47.9%
Negative0
Negative (%)0.0%
Memory size10.5 KiB
2023-12-13T05:55:44.334994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q316
95-th percentile3000
Maximum65455
Range65455
Interquartile range (IQR)16

Descriptive statistics

Standard deviation3021.8592
Coefficient of variation (CV)5.1825521
Kurtosis283.00942
Mean583.08322
Median Absolute Deviation (MAD)0
Skewness14.423741
Sum448391
Variance9131633
MonotonicityNot monotonic
2023-12-13T05:55:44.479498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 563
47.9%
1000 9
 
0.8%
100 7
 
0.6%
300 7
 
0.6%
200 7
 
0.6%
2000 6
 
0.5%
3000 5
 
0.4%
1500 5
 
0.4%
400 5
 
0.4%
3 4
 
0.3%
Other values (127) 151
 
12.8%
(Missing) 407
34.6%
ValueCountFrequency (%)
0 563
47.9%
2 1
 
0.1%
3 4
 
0.3%
4 1
 
0.1%
6 1
 
0.1%
7 2
 
0.2%
11 1
 
0.1%
12 1
 
0.1%
13 1
 
0.1%
14 1
 
0.1%
ValueCountFrequency (%)
65455 1
0.1%
19400 1
0.1%
19100 1
0.1%
17000 1
0.1%
14400 1
0.1%
12000 1
0.1%
11500 1
0.1%
11350 1
0.1%
11200 1
0.1%
10800 1
0.1%

사방지지정_적요
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size9.3 KiB
목적달성
561 
<NA>
455 
비시설지
150 
변경고시
 
4
도로확장
 
4
Other values (2)
 
2

Length

Max length10
Median length4
Mean length4.005102
Min length4

Unique

Unique2 ?
Unique (%)0.2%

Sample

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

Common Values

ValueCountFrequency (%)
목적달성 561
47.7%
<NA> 455
38.7%
비시설지 150
 
12.8%
변경고시 4
 
0.3%
도로확장 4
 
0.3%
목적달성''87야계 1
 
0.1%
87야계 1
 
0.1%

Length

2023-12-13T05:55:44.611251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:55:44.773709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
목적달성 561
47.7%
na 455
38.7%
비시설지 150
 
12.8%
변경고시 4
 
0.3%
도로확장 4
 
0.3%
목적달성''87야계 1
 
0.1%
87야계 1
 
0.1%

군유지_면적
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct113
Distinct (%)66.9%
Missing1007
Missing (%)85.6%
Infinite0
Infinite (%)0.0%
Mean213566.05
Minimum99
Maximum2765794
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.5 KiB
2023-12-13T05:55:44.926682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum99
5-th percentile1513.8
Q124496
median102853
Q3199636
95-th percentile783401
Maximum2765794
Range2765695
Interquartile range (IQR)175140

Descriptive statistics

Standard deviation398551.42
Coefficient of variation (CV)1.866174
Kurtosis25.815151
Mean213566.05
Median Absolute Deviation (MAD)80625
Skewness4.6278852
Sum36092663
Variance1.5884324 × 1011
MonotonicityNot monotonic
2023-12-13T05:55:45.113733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
783401 5
 
0.4%
316165 5
 
0.4%
141265 4
 
0.3%
510688 4
 
0.3%
152137 4
 
0.3%
21818 4
 
0.3%
110050 4
 
0.3%
43636 4
 
0.3%
736996 3
 
0.3%
40762 3
 
0.3%
Other values (103) 129
 
11.0%
(Missing) 1007
85.6%
ValueCountFrequency (%)
99 1
0.1%
212 1
0.1%
721 1
0.1%
750 1
0.1%
783 1
0.1%
860 1
0.1%
868 1
0.1%
988 1
0.1%
1223 1
0.1%
1950 1
0.1%
ValueCountFrequency (%)
2765794 2
 
0.2%
2585157 1
 
0.1%
950380 1
 
0.1%
893038 1
 
0.1%
783401 5
0.4%
736996 3
0.3%
699586 2
 
0.2%
664755 1
 
0.1%
568059 1
 
0.1%
536033 1
 
0.1%

읍면동명
Categorical

Distinct8
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size9.3 KiB
대강면
409 
단성면
311 
영춘면
217 
어상천면
95 
매포읍
49 
Other values (3)
95 

Length

Max length4
Median length3
Mean length3.0807823
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
대강면 409
34.8%
단성면 311
26.4%
영춘면 217
18.5%
어상천면 95
 
8.1%
매포읍 49
 
4.2%
가곡면 47
 
4.0%
적성면 30
 
2.6%
단양읍 18
 
1.5%

Length

2023-12-13T05:55:45.258295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:55:45.392092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
대강면 409
34.8%
단성면 311
26.4%
영춘면 217
18.5%
어상천면 95
 
8.1%
매포읍 49
 
4.2%
가곡면 47
 
4.0%
적성면 30
 
2.6%
단양읍 18
 
1.5%

리명
Text

Distinct78
Distinct (%)6.6%
Missing0
Missing (%)0.0%
Memory size9.3 KiB
2023-12-13T05:55:45.685079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.0153061
Min length2

Characters and Unicode

Total characters3546
Distinct characters83
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

Unique9 ?
Unique (%)0.8%

Sample

1st row덕상리
2nd row덕상리
3rd row덕상리
4th row덕상리
5th row덕상리
ValueCountFrequency (%)
가산리 109
 
9.3%
미노리 75
 
6.4%
방곡리 73
 
6.2%
황정리 66
 
5.6%
벌천리 64
 
5.4%
직티리 62
 
5.3%
만종리 54
 
4.6%
상리 39
 
3.3%
남천리 38
 
3.2%
회산리 32
 
2.7%
Other values (68) 564
48.0%
2023-12-13T05:55:46.084747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
리 1176
33.2%
곡 191
 
5.4%
산 174
 
4.9%
가 132
 
3.7%
방 123
 
3.5%
천 108
 
3.0%
노 78
 
2.2%
평 78
 
2.2%
미 75
 
2.1%
정 69
 
1.9%
Other values (73) 1342
37.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3546
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 1176
33.2%
곡 191
 
5.4%
산 174
 
4.9%
가 132
 
3.7%
방 123
 
3.5%
천 108
 
3.0%
노 78
 
2.2%
평 78
 
2.2%
미 75
 
2.1%
정 69
 
1.9%
Other values (73) 1342
37.8%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3546
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 1176
33.2%
곡 191
 
5.4%
산 174
 
4.9%
가 132
 
3.7%
방 123
 
3.5%
천 108
 
3.0%
노 78
 
2.2%
평 78
 
2.2%
미 75
 
2.1%
정 69
 
1.9%
Other values (73) 1342
37.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3546
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
리 1176
33.2%
곡 191
 
5.4%
산 174
 
4.9%
가 132
 
3.7%
방 123
 
3.5%
천 108
 
3.0%
노 78
 
2.2%
평 78
 
2.2%
미 75
 
2.1%
정 69
 
1.9%
Other values (73) 1342
37.8%

군유림여부
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size9.3 KiB
0
1007 
1
169 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 1007
85.6%
1 169
 
14.4%

Length

2023-12-13T05:55:46.219355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:55:46.311951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 1007
85.6%
1 169
 
14.4%

면적
Real number (ℝ)

HIGH CORRELATION 

Distinct726
Distinct (%)61.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean240705.63
Minimum53
Maximum5866223
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.5 KiB
2023-12-13T05:55:46.410152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum53
5-th percentile400
Q112694
median43059.5
Q3109190.25
95-th percentile1253014
Maximum5866223
Range5866170
Interquartile range (IQR)96496.25

Descriptive statistics

Standard deviation766824.68
Coefficient of variation (CV)3.1857365
Kurtosis30.810042
Mean240705.63
Median Absolute Deviation (MAD)37874.5
Skewness5.3608084
Sum2.8306982 × 108
Variance5.880201 × 1011
MonotonicityNot monotonic
2023-12-13T05:55:46.535738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31339.0 9
 
0.8%
25785.0 8
 
0.7%
21818.0 8
 
0.7%
1747439.0 8
 
0.7%
7537.0 7
 
0.6%
1707028.0 7
 
0.6%
19835.0 7
 
0.6%
15868.0 7
 
0.6%
43636.0 7
 
0.6%
279179.0 6
 
0.5%
Other values (716) 1102
93.7%
ValueCountFrequency (%)
53.0 1
 
0.1%
56.0 1
 
0.1%
73.0 1
 
0.1%
84.0 2
0.2%
86.0 3
0.3%
99.0 2
0.2%
109.0 1
 
0.1%
119.0 1
 
0.1%
132.0 1
 
0.1%
142.0 1
 
0.1%
ValueCountFrequency (%)
5866223.0 5
0.4%
5715071.0 1
 
0.1%
5235749.0 6
0.5%
4862083.0 2
 
0.2%
4629231.0 5
0.4%
4490837.0 1
 
0.1%
4216264.0 1
 
0.1%
3779306.0 2
 
0.2%
3129611.0 1
 
0.1%
2739395.0 2
 
0.2%

데이터 기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size9.3 KiB
2022-10-14
1176 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022-10-14
2nd row2022-10-14
3rd row2022-10-14
4th row2022-10-14
5th row2022-10-14

Common Values

ValueCountFrequency (%)
2022-10-14 1176
100.0%

Length

2023-12-13T05:55:46.724854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:55:46.802985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022-10-14 1176
100.0%

Interactions

2023-12-13T05:55:40.487525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:55:34.349070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:55:35.199580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:55:36.515641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:55:37.442709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:55:38.271515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:55:39.077742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:55:39.810347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:55:40.560240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:55:34.446266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:55:35.317660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:55:36.629097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:55:37.538682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:55:38.373989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:55:39.177324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:55:39.888444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:55:40.653192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:55:34.546108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:55:35.470525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:55:36.763637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:55:37.652347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:55:38.462176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:55:39.268153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:55:39.968471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:55:40.740860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:55:34.643038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:55:35.613102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:55:36.878352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:55:37.757437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:55:38.547534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:55:39.356610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:55:40.060809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:55:40.836151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:55:34.751885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:55:35.763982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:55:37.020007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:55:37.868774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:55:38.648427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:55:39.454406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:55:40.176834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:55:40.932033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:55:34.854348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:55:36.205061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:55:37.121630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:55:37.960907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:55:38.787929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:55:39.543899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:55:40.261459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:55:41.030123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:55:34.984915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:55:36.303335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:55:37.240309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:55:38.069987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:55:38.888947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:55:39.632936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:55:40.342595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:55:41.110048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:55:35.093685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:55:36.400054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:55:37.343615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:55:38.173850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:55:38.986454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:55:39.716977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:55:40.418370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T05:55:46.865422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
사방지지정_지적면적사방지지정_지정면적사방지지정_지정일사방지지정_고사번호사방지지정_해제면적사방지지정_해제일사방지지정_해제고시번호사방지지정_잔여면적사방지지정_적요군유지_면적읍면동명리명군유림여부면적
사방지지정_지적면적1.0000.1290.0000.0000.3650.0000.0000.4220.1580.7520.2300.5870.1170.914
사방지지정_지정면적0.1291.0000.7130.0001.0000.6110.0000.0000.000NaN0.0000.0000.0000.091
사방지지정_지정일0.0000.7131.0001.0000.7640.9100.8410.8450.8780.0000.9130.9700.3120.000
사방지지정_고사번호0.0000.0001.0001.0000.0000.9140.9330.0000.3230.0000.5550.8630.1440.000
사방지지정_해제면적0.3651.0000.7640.0001.0000.7350.0000.0000.000NaN0.0000.0000.0000.389
사방지지정_해제일0.0000.6110.9100.9140.7351.0001.0000.0000.8700.0000.7600.9070.0790.000
사방지지정_해제고시번호0.0000.0000.8410.9330.0001.0001.0000.0000.0000.0000.1040.0000.0000.000
사방지지정_잔여면적0.4220.0000.8450.0000.0000.0000.0001.0000.3290.5450.1260.2180.1310.403
사방지지정_적요0.1580.0000.8780.3230.0000.8700.0000.3291.0000.1660.3480.7550.2600.210
군유지_면적0.752NaN0.0000.000NaN0.0000.0000.5450.1661.0000.0000.878NaN0.932
읍면동명0.2300.0000.9130.5550.0000.7600.1040.1260.3480.0001.0000.9990.0540.212
리명0.5870.0000.9700.8630.0000.9070.0000.2180.7550.8780.9991.0000.3630.657
군유림여부0.1170.0000.3120.1440.0000.0790.0000.1310.260NaN0.0540.3631.0000.242
면적0.9140.0910.0000.0000.3890.0000.0000.4030.2100.9320.2120.6570.2421.000
2023-12-13T05:55:47.003249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
군유림여부사방지지정_해제일사방지지정_적요읍면동명
군유림여부1.0000.0610.1860.041
사방지지정_해제일0.0611.0000.6650.417
사방지지정_적요0.1860.6651.0000.201
읍면동명0.0410.4170.2011.000
2023-12-13T05:55:47.114383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
사방지지정_지적면적사방지지정_지정면적사방지지정_고사번호사방지지정_해제면적사방지지정_해제고시번호사방지지정_잔여면적군유지_면적면적사방지지정_해제일사방지지정_적요읍면동명군유림여부
사방지지정_지적면적1.0000.429-0.1960.329-0.067-0.0120.9190.9400.0000.0580.0790.088
사방지지정_지정면적0.4291.000-0.1000.970-0.5180.2040.1280.4070.3560.0000.0000.000
사방지지정_고사번호-0.196-0.1001.000-0.1690.129-0.124-0.112-0.1770.6330.1130.3440.105
사방지지정_해제면적0.3290.970-0.1691.000-0.3840.235-0.0100.2890.4630.0000.0000.000
사방지지정_해제고시번호-0.067-0.5180.129-0.3841.000-0.1240.114-0.0320.9860.0000.1110.000
사방지지정_잔여면적-0.0120.204-0.1240.235-0.1241.000-0.063-0.0270.0000.1440.0870.087
군유지_면적0.9190.128-0.112-0.0100.114-0.0631.0001.0000.0000.1230.0001.000
면적0.9400.407-0.1770.289-0.032-0.0271.0001.0000.0000.1050.1020.185
사방지지정_해제일0.0000.3560.6330.4630.9860.0000.0000.0001.0000.6650.4170.061
사방지지정_적요0.0580.0000.1130.0000.0000.1440.1230.1050.6651.0000.2010.186
읍면동명0.0790.0000.3440.0000.1110.0870.0000.1020.4170.2011.0000.041
군유림여부0.0880.0000.1050.0000.0000.0871.0000.1850.0610.1860.0411.000

Missing values

2023-12-13T05:55:41.251095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T05:55:41.457935image/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.
2023-12-13T05:55:41.967898image/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

지적코드사방지지정_지적면적사방지지정_지정면적사방지지정_지정일사방지지정_고사번호사방지지정_해제면적사방지지정_해제일사방지지정_해제고시번호사방지지정_잔여면적사방지지정_적요군유지_면적읍면동명리명군유림여부면적데이터 기준일자
0438000068762482003-10-2229<NA><NA><NA><NA><NA><NA>단양읍덕상리0271.02022-10-14
1438000014032682003-10-2229<NA><NA><NA><NA><NA><NA>단양읍덕상리01403.02022-10-14
243800001142481342003-10-2229<NA><NA><NA><NA><NA><NA>단양읍덕상리0114248.02022-10-14
34380000757691342003-10-2229<NA><NA><NA><NA><NA>75769단양읍덕상리175769.02022-10-14
44380000202401212003-10-2229<NA><NA><NA><NA><NA><NA>단양읍덕상리0202401.02022-10-14
5438000013800030001994-03-253930002005-12-26<NA><NA>목적달성<NA>단양읍덕상리0137568.02022-10-14
6438000010790130002003-12-1232<NA><NA><NA><NA><NA><NA>단양읍심곡리0107901.02022-10-14
74380000327131202000-01-063<NA><NA><NA><NA><NA><NA>단양읍별곡리032713.02022-10-14
84380000324185302000-01-063<NA><NA><NA><NA><NA><NA>단양읍별곡리032418.02022-10-14
9438000088283602000-01-063<NA><NA><NA><NA><NA><NA>단양읍별곡리06989.02022-10-14
지적코드사방지지정_지적면적사방지지정_지정면적사방지지정_지정일사방지지정_고사번호사방지지정_해제면적사방지지정_해제일사방지지정_해제고시번호사방지지정_잔여면적사방지지정_적요군유지_면적읍면동명리명군유림여부면적데이터 기준일자
11664380000180893<NA><NA><NA>2001995-03-204500목적달성<NA>단성면대잠리0180893.02022-10-14
11674380000180893<NA><NA><NA>5002005-12-26<NA>0<NA><NA>단성면대잠리0180893.02022-10-14
11684380000313393001979-03-05332001983-03-2629100비시설지<NA>단성면대잠리031339.02022-10-14
1169438000031339<NA><NA><NA>1001995-03-2040목적달성<NA>단성면대잠리031339.02022-10-14
11704380000186452001979-03-05332001995-03-204<NA>목적달성<NA>단성면대잠리018645.02022-10-14
117143800001100503002004-04-17<NA><NA><NA><NA>300<NA>110050단성면대잠리1110050.02022-10-14
1172438000011005015002004-05-089<NA><NA><NA>1500<NA>110050단성면대잠리1110050.02022-10-14
117343800001100507202004-11-2432<NA><NA><NA>720<NA>110050단성면대잠리1110050.02022-10-14
1174438000011005050002005-09-05<NA><NA><NA><NA>5000<NA>110050단성면대잠리1110050.02022-10-14
11754380000347202652001-03-215<NA><NA><NA>265<NA><NA>단성면대잠리0157.02022-10-14