Overview

Dataset statistics

Number of variables12
Number of observations1275
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory130.9 KiB
Average record size in memory105.1 B

Variable types

Categorical3
Text1
Numeric8

Dataset

Description환경공간정보서비스에서 제공하는 년도별 대분류 토지피복정보 통계 현황 데이터 입니다.(1980년대~2004년)
Author환경부
URLhttps://www.data.go.kr/data/15124207/fileData.do

Alerts

시도 is highly overall correlated with 국가구분High correlation
국가구분 is highly overall correlated with 시도High correlation
농업지역 is highly overall correlated with 나지 and 2 other fieldsHigh correlation
산림지역 is highly overall correlated with 합계High correlation
초지 is highly overall correlated with 합계High correlation
나지 is highly overall correlated with 농업지역 and 1 other fieldsHigh correlation
수역 is highly overall correlated with 농업지역 and 1 other fieldsHigh correlation
합계 is highly overall correlated with 농업지역 and 2 other fieldsHigh correlation
농업지역 has 55 (4.3%) zerosZeros
초지 has 50 (3.9%) zerosZeros
습지 has 134 (10.5%) zerosZeros
수역 has 21 (1.6%) zerosZeros

Reproduction

Analysis started2023-12-12 22:36:36.281496
Analysis finished2023-12-12 22:36:44.435830
Duration8.15 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

자료년도
Categorical

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size10.1 KiB
1980
425 
1990
425 
2000
425 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1980
2nd row1980
3rd row1980
4th row1980
5th row1980

Common Values

ValueCountFrequency (%)
1980 425
33.3%
1990 425
33.3%
2000 425
33.3%

Length

2023-12-13T07:36:44.489640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:36:44.581911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1980 425
33.3%
1990 425
33.3%
2000 425
33.3%

국가구분
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size10.1 KiB
남한
753 
북한
522 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
남한 753
59.1%
북한 522
40.9%

Length

2023-12-13T07:36:44.673057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:36:44.755152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
남한 753
59.1%
북한 522
40.9%

시도
Categorical

HIGH CORRELATION 

Distinct28
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size10.1 KiB
경기도
132 
강원도
105 
평안북도
 
75
서울특별시
 
75
경상북도
 
72
Other values (23)
816 

Length

Max length7
Median length4
Mean length3.9576471
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경기도 132
 
10.4%
강원도 105
 
8.2%
평안북도 75
 
5.9%
서울특별시 75
 
5.9%
경상북도 72
 
5.6%
전라남도 66
 
5.2%
경상남도 66
 
5.2%
황해남도 60
 
4.7%
평안남도 57
 
4.5%
황해북도 57
 
4.5%
Other values (18) 510
40.0%

Length

2023-12-13T07:36:44.866732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기도 132
 
10.4%
강원도 105
 
8.2%
평안북도 75
 
5.9%
서울특별시 75
 
5.9%
경상북도 72
 
5.6%
전라남도 66
 
5.2%
경상남도 66
 
5.2%
황해남도 60
 
4.7%
평안남도 57
 
4.5%
황해북도 57
 
4.5%
Other values (18) 510
40.0%

시군구
Text

Distinct394
Distinct (%)30.9%
Missing0
Missing (%)0.0%
Memory size10.1 KiB
2023-12-13T07:36:45.168580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length3
Mean length3.2211765
Min length2

Characters and Unicode

Total characters4107
Distinct characters185
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

Unique0 ?
Unique (%)0.0%

Sample

1st row강릉시
2nd row고성군
3rd row동해시
4th row삼척시
5th row속초시
ValueCountFrequency (%)
중구 18
 
1.4%
동구 18
 
1.4%
남구 15
 
1.2%
서구 15
 
1.2%
북구 12
 
0.9%
고성군 9
 
0.7%
대동군 6
 
0.5%
영광군 6
 
0.5%
진천군 6
 
0.5%
옹진군 6
 
0.5%
Other values (384) 1164
91.3%
2023-12-13T07:36:45.611886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
군 714
 
17.4%
시 369
 
9.0%
구 330
 
8.0%
천 156
 
3.8%
산 123
 
3.0%
성 102
 
2.5%
주 96
 
2.3%
원 93
 
2.3%
동 84
 
2.0%
양 75
 
1.8%
Other values (175) 1965
47.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4107
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
군 714
 
17.4%
시 369
 
9.0%
구 330
 
8.0%
천 156
 
3.8%
산 123
 
3.0%
성 102
 
2.5%
주 96
 
2.3%
원 93
 
2.3%
동 84
 
2.0%
양 75
 
1.8%
Other values (175) 1965
47.8%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4107
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
군 714
 
17.4%
시 369
 
9.0%
구 330
 
8.0%
천 156
 
3.8%
산 123
 
3.0%
성 102
 
2.5%
주 96
 
2.3%
원 93
 
2.3%
동 84
 
2.0%
양 75
 
1.8%
Other values (175) 1965
47.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4107
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
군 714
 
17.4%
시 369
 
9.0%
구 330
 
8.0%
천 156
 
3.8%
산 123
 
3.0%
성 102
 
2.5%
주 96
 
2.3%
원 93
 
2.3%
동 84
 
2.0%
양 75
 
1.8%
Other values (175) 1965
47.8%

시가화건조지역
Real number (ℝ)

Distinct1246
Distinct (%)97.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.914117
Minimum0.0216
Maximum173.7819
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.3 KiB
2023-12-13T07:36:45.753528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.0216
5-th percentile1.00449
Q14.39785
median9.2655
Q315.03945
95-th percentile31.19364
Maximum173.7819
Range173.7603
Interquartile range (IQR)10.6416

Descriptive statistics

Standard deviation12.448774
Coefficient of variation (CV)1.0448759
Kurtosis48.329939
Mean11.914117
Median Absolute Deviation (MAD)5.1705
Skewness4.9967446
Sum15190.5
Variance154.97198
MonotonicityNot monotonic
2023-12-13T07:36:45.885047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.0323 2
 
0.2%
15.5421 2
 
0.2%
3.2544 2
 
0.2%
3.2355 2
 
0.2%
17.8092 2
 
0.2%
7.8966 2
 
0.2%
0.7614 2
 
0.2%
10.9863 2
 
0.2%
9.2718 2
 
0.2%
11.3625 2
 
0.2%
Other values (1236) 1255
98.4%
ValueCountFrequency (%)
0.0216 1
0.1%
0.0459 1
0.1%
0.0801 1
0.1%
0.0918 1
0.1%
0.1071 1
0.1%
0.1143 1
0.1%
0.1503 1
0.1%
0.1512 1
0.1%
0.1584 1
0.1%
0.1611 1
0.1%
ValueCountFrequency (%)
173.7819 1
0.1%
162.585 1
0.1%
135.5427 1
0.1%
73.1673 1
0.1%
72.2583 1
0.1%
69.93 1
0.1%
69.3783 1
0.1%
63.2556 1
0.1%
58.9491 1
0.1%
58.8465 1
0.1%

농업지역
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1211
Distinct (%)95.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean112.1178
Minimum0
Maximum618.3054
Zeros55
Zeros (%)4.3%
Negative0
Negative (%)0.0%
Memory size11.3 KiB
2023-12-13T07:36:46.031147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.00513
Q128.5813
median95.625
Q3169.1739
95-th percentile292.01814
Maximum618.3054
Range618.3054
Interquartile range (IQR)140.5926

Descriptive statistics

Standard deviation95.640729
Coefficient of variation (CV)0.85303789
Kurtosis0.90702398
Mean112.1178
Median Absolute Deviation (MAD)70.0767
Skewness0.9456341
Sum142950.19
Variance9147.149
MonotonicityNot monotonic
2023-12-13T07:36:46.162444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 55
 
4.3%
0.0009 3
 
0.2%
0.0144 2
 
0.2%
1.0746 2
 
0.2%
0.0018 2
 
0.2%
0.1098 2
 
0.2%
5.6403 2
 
0.2%
0.0036 2
 
0.2%
22.6305 2
 
0.2%
0.0054 2
 
0.2%
Other values (1201) 1201
94.2%
ValueCountFrequency (%)
0.0 55
4.3%
0.0009 3
 
0.2%
0.0018 2
 
0.2%
0.0027 1
 
0.1%
0.0036 2
 
0.2%
0.0045 1
 
0.1%
0.0054 2
 
0.2%
0.0099 1
 
0.1%
0.0117 1
 
0.1%
0.0126 1
 
0.1%
ValueCountFrequency (%)
618.3054 1
0.1%
512.9568 1
0.1%
467.4699 1
0.1%
455.8815 1
0.1%
444.5037 1
0.1%
441.3861 1
0.1%
440.0127 1
0.1%
419.2452 1
0.1%
400.5612 1
0.1%
397.5417 1
0.1%

산림지역
Real number (ℝ)

HIGH CORRELATION 

Distinct1273
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean366.36918
Minimum0.0549
Maximum1968.4917
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.3 KiB
2023-12-13T07:36:46.281431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.0549
5-th percentile3.16764
Q149.09455
median261.6408
Q3564.60105
95-th percentile1131.0238
Maximum1968.4917
Range1968.4368
Interquartile range (IQR)515.5065

Descriptive statistics

Standard deviation375.69394
Coefficient of variation (CV)1.0254518
Kurtosis1.8010868
Mean366.36918
Median Absolute Deviation (MAD)232.6176
Skewness1.3708134
Sum467120.7
Variance141145.93
MonotonicityNot monotonic
2023-12-13T07:36:46.433563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.1663 2
 
0.2%
251.2404 2
 
0.2%
878.436 1
 
0.1%
244.3464 1
 
0.1%
77.9634 1
 
0.1%
1112.9157 1
 
0.1%
146.9277 1
 
0.1%
572.9166 1
 
0.1%
893.3562 1
 
0.1%
155.4003 1
 
0.1%
Other values (1263) 1263
99.1%
ValueCountFrequency (%)
0.0549 1
0.1%
0.0576 1
0.1%
0.0585 1
0.1%
0.0738 1
0.1%
0.0855 1
0.1%
0.1062 1
0.1%
0.2295 1
0.1%
0.2538 1
0.1%
0.3177 1
0.1%
0.4824 1
0.1%
ValueCountFrequency (%)
1968.4917 1
0.1%
1958.0535 1
0.1%
1931.4063 1
0.1%
1882.5516 1
0.1%
1846.7424 1
0.1%
1743.8958 1
0.1%
1734.2712 1
0.1%
1645.3197 1
0.1%
1637.8137 1
0.1%
1636.6203 1
0.1%

초지
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1202
Distinct (%)94.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.007058
Minimum0
Maximum248.418
Zeros50
Zeros (%)3.9%
Negative0
Negative (%)0.0%
Memory size11.3 KiB
2023-12-13T07:36:46.571573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.02799
Q12.6334
median10.5543
Q323.7096
95-th percentile58.33206
Maximum248.418
Range248.418
Interquartile range (IQR)21.0762

Descriptive statistics

Standard deviation25.021711
Coefficient of variation (CV)1.3895502
Kurtosis20.029325
Mean18.007058
Median Absolute Deviation (MAD)8.9235
Skewness3.6544772
Sum22958.999
Variance626.08602
MonotonicityNot monotonic
2023-12-13T07:36:46.697564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 50
 
3.9%
16.0137 2
 
0.2%
1.0665 2
 
0.2%
1.4445 2
 
0.2%
25.3953 2
 
0.2%
1.5174 2
 
0.2%
0.8019 2
 
0.2%
9.4545 2
 
0.2%
2.6613 2
 
0.2%
7.5141 2
 
0.2%
Other values (1192) 1207
94.7%
ValueCountFrequency (%)
0.0 50
3.9%
0.0027 2
 
0.2%
0.0036 1
 
0.1%
0.0099 1
 
0.1%
0.0108 1
 
0.1%
0.0126 2
 
0.2%
0.0135 1
 
0.1%
0.0153 1
 
0.1%
0.018 1
 
0.1%
0.0225 1
 
0.1%
ValueCountFrequency (%)
248.418 1
0.1%
218.1348 1
0.1%
208.1745 1
0.1%
199.5111 1
0.1%
187.1091 1
0.1%
168.2856 1
0.1%
156.303 1
0.1%
153.9243 1
0.1%
149.5566 1
0.1%
147.4047 1
0.1%

습지
Real number (ℝ)

ZEROS 

Distinct636
Distinct (%)49.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1294762
Minimum0
Maximum202.131
Zeros134
Zeros (%)10.5%
Negative0
Negative (%)0.0%
Memory size11.3 KiB
2023-12-13T07:36:46.837951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.00945
median0.0828
Q30.71775
95-th percentile10.29834
Maximum202.131
Range202.131
Interquartile range (IQR)0.7083

Descriptive statistics

Standard deviation8.6570015
Coefficient of variation (CV)4.0653196
Kurtosis244.26469
Mean2.1294762
Median Absolute Deviation (MAD)0.0828
Skewness12.728898
Sum2715.0822
Variance74.943675
MonotonicityNot monotonic
2023-12-13T07:36:46.980040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 134
 
10.5%
0.0009 29
 
2.3%
0.0027 29
 
2.3%
0.0045 23
 
1.8%
0.0036 21
 
1.6%
0.0018 19
 
1.5%
0.0072 15
 
1.2%
0.0063 14
 
1.1%
0.018 14
 
1.1%
0.0135 14
 
1.1%
Other values (626) 963
75.5%
ValueCountFrequency (%)
0.0 134
10.5%
0.0009 29
 
2.3%
0.0018 19
 
1.5%
0.0027 29
 
2.3%
0.0036 21
 
1.6%
0.0045 23
 
1.8%
0.0054 8
 
0.6%
0.0063 14
 
1.1%
0.0072 15
 
1.2%
0.0081 14
 
1.1%
ValueCountFrequency (%)
202.131 1
0.1%
96.57 1
0.1%
73.9332 1
0.1%
55.7667 1
0.1%
54.5121 1
0.1%
54.2502 1
0.1%
52.4313 1
0.1%
51.5592 1
0.1%
48.2436 1
0.1%
47.6037 1
0.1%

나지
Real number (ℝ)

HIGH CORRELATION 

Distinct1226
Distinct (%)96.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.8462449
Minimum0
Maximum55.7757
Zeros2
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size11.3 KiB
2023-12-13T07:36:47.409160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.44064
Q11.9935
median4.6809
Q39.6939
95-th percentile19.32948
Maximum55.7757
Range55.7757
Interquartile range (IQR)7.7004

Descriptive statistics

Standard deviation7.0574377
Coefficient of variation (CV)1.030848
Kurtosis8.9672882
Mean6.8462449
Median Absolute Deviation (MAD)3.2733
Skewness2.3984071
Sum8728.9623
Variance49.807428
MonotonicityNot monotonic
2023-12-13T07:36:47.563144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.252 4
 
0.3%
5.4774 3
 
0.2%
1.3788 3
 
0.2%
8.4528 2
 
0.2%
1.4076 2
 
0.2%
3.1896 2
 
0.2%
0.9549 2
 
0.2%
3.3282 2
 
0.2%
6.246 2
 
0.2%
11.3634 2
 
0.2%
Other values (1216) 1251
98.1%
ValueCountFrequency (%)
0.0 2
0.2%
0.0081 1
0.1%
0.0126 1
0.1%
0.0387 1
0.1%
0.0396 1
0.1%
0.045 1
0.1%
0.0504 1
0.1%
0.0558 1
0.1%
0.0711 1
0.1%
0.0729 1
0.1%
ValueCountFrequency (%)
55.7757 1
0.1%
54.7722 1
0.1%
54.441 1
0.1%
46.2573 1
0.1%
45.2457 1
0.1%
42.1254 1
0.1%
41.6592 1
0.1%
41.2434 1
0.1%
40.1409 1
0.1%
39.3165 1
0.1%

수역
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1187
Distinct (%)93.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.8263275
Minimum0
Maximum175.9257
Zeros21
Zeros (%)1.6%
Negative0
Negative (%)0.0%
Memory size11.3 KiB
2023-12-13T07:36:47.720067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.02745
Q11.65555
median4.8195
Q310.3032
95-th percentile30.45114
Maximum175.9257
Range175.9257
Interquartile range (IQR)8.64765

Descriptive statistics

Standard deviation13.252402
Coefficient of variation (CV)1.5014627
Kurtosis45.649141
Mean8.8263275
Median Absolute Deviation (MAD)3.8124
Skewness5.0949654
Sum11253.568
Variance175.62615
MonotonicityNot monotonic
2023-12-13T07:36:47.873286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 21
 
1.6%
0.0009 7
 
0.5%
0.0027 5
 
0.4%
0.0054 3
 
0.2%
1.2825 3
 
0.2%
0.0018 3
 
0.2%
0.0387 3
 
0.2%
5.0391 2
 
0.2%
0.0117 2
 
0.2%
15.8868 2
 
0.2%
Other values (1177) 1224
96.0%
ValueCountFrequency (%)
0.0 21
1.6%
0.0009 7
 
0.5%
0.0018 3
 
0.2%
0.0027 5
 
0.4%
0.0036 2
 
0.2%
0.0045 1
 
0.1%
0.0054 3
 
0.2%
0.0063 2
 
0.2%
0.0072 1
 
0.1%
0.0081 1
 
0.1%
ValueCountFrequency (%)
175.9257 1
0.1%
174.3813 1
0.1%
104.7303 1
0.1%
97.2963 1
0.1%
96.3387 1
0.1%
71.9109 1
0.1%
70.8948 1
0.1%
65.8107 1
0.1%
64.1439 1
0.1%
63.7344 1
0.1%

합계
Real number (ℝ)

HIGH CORRELATION 

Distinct1054
Distinct (%)82.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean526.2102
Minimum3.0069
Maximum2086.1586
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.3 KiB
2023-12-13T07:36:48.045432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.0069
5-th percentile17.93925
Q1145.7055
median486.3888
Q3756.76275
95-th percentile1283.2457
Maximum2086.1586
Range2083.1517
Interquartile range (IQR)611.05725

Descriptive statistics

Standard deviation418.83917
Coefficient of variation (CV)0.79595411
Kurtosis0.92772916
Mean526.2102
Median Absolute Deviation (MAD)304.2909
Skewness0.90885117
Sum670918
Variance175426.25
MonotonicityNot monotonic
2023-12-13T07:36:48.204863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17.8371 3
 
0.2%
24.6474 3
 
0.2%
16.5564 3
 
0.2%
197.1018 3
 
0.2%
650.0826 3
 
0.2%
32.265 3
 
0.2%
21.6126 3
 
0.2%
65.2077 3
 
0.2%
95.8302 3
 
0.2%
535.6953 3
 
0.2%
Other values (1044) 1245
97.6%
ValueCountFrequency (%)
3.0069 3
0.2%
7.0416 1
 
0.1%
7.0425 2
0.2%
7.0713 3
0.2%
9.531 3
0.2%
10.044 3
0.2%
10.4841 3
0.2%
12.1401 3
0.2%
12.5649 1
 
0.1%
12.5658 2
0.2%
ValueCountFrequency (%)
2086.1586 1
0.1%
2086.1379 1
0.1%
2085.7311 1
0.1%
2049.264 2
0.2%
2049.2631 1
0.1%
2022.2118 1
0.1%
2022.1965 1
0.1%
2022.1578 1
0.1%
1885.8042 1
0.1%
1885.8006 1
0.1%

Interactions

2023-12-13T07:36:43.485021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:37.045737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:37.895115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:38.860457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:39.706990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:40.508152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:41.365198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:42.554751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:43.582417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:37.140052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:38.033005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:38.967232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:39.815532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:40.624670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:41.463496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:42.679910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:43.673457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:37.259152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:38.132073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:39.085537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:39.921534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:40.756241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:41.587907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:42.785704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:43.776923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:37.381895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:38.260579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:39.212419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:40.054788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:40.858135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:41.731147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:42.922961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:43.862838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:37.486674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:38.353037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:39.308736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:40.149513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:40.967020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:42.160257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:43.035600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:43.955316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:37.582375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:38.474168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:39.405871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:40.250762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:41.078734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:42.277238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:43.143234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:44.036145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:37.686815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:38.608552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:39.500458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:40.339295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:41.177349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:42.368385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:43.265091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:44.123240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:37.787857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:38.725374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:39.599319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:40.435092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:41.274350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:42.459678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:43.381871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T07:36:48.302363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
자료년도국가구분시도시가화건조지역농업지역산림지역초지습지나지수역합계
자료년도1.0000.0000.0000.2160.1260.0000.3180.1050.1930.0000.000
국가구분0.0001.0000.9970.1290.4350.3810.2750.0000.1230.0570.593
시도0.0000.9971.0000.6670.6680.6870.6490.1000.4310.6880.734
시가화건조지역0.2160.1290.6671.0000.7670.0620.2310.0000.3700.5440.163
농업지역0.1260.4350.6680.7671.0000.5770.4230.3560.5510.2830.694
산림지역0.0000.3810.6870.0620.5771.0000.5070.0000.3750.1820.957
초지0.3180.2750.6490.2310.4230.5071.0000.0000.5130.0000.561
습지0.1050.0000.1000.0000.3560.0000.0001.0000.3350.6610.082
나지0.1930.1230.4310.3700.5510.3750.5130.3351.0000.3280.472
수역0.0000.0570.6880.5440.2830.1820.0000.6610.3281.0000.247
합계0.0000.5930.7340.1630.6940.9570.5610.0820.4720.2471.000
2023-12-13T07:36:48.433114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
자료년도시도국가구분
자료년도1.0000.0000.000
시도0.0001.0000.946
국가구분0.0000.9461.000
2023-12-13T07:36:48.535862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시가화건조지역농업지역산림지역초지습지나지수역합계자료년도국가구분시도
시가화건조지역1.0000.108-0.3080.0560.1780.2180.107-0.2050.1480.1370.315
농업지역0.1081.0000.4580.4200.4170.6210.5970.6010.0750.3330.308
산림지역-0.3080.4581.0000.477-0.0060.3740.4050.9630.0000.2920.323
초지0.0560.4200.4771.0000.2360.4210.3460.5170.2000.2100.294
습지0.1780.417-0.0060.2361.0000.3560.4870.0840.0430.0000.044
나지0.2180.6210.3740.4210.3561.0000.5010.4610.1170.0940.168
수역0.1070.5970.4050.3460.4870.5011.0000.4910.0000.0610.332
합계-0.2050.6010.9630.5170.0840.4610.4911.0000.0000.4570.364
자료년도0.1480.0750.0000.2000.0430.1170.0000.0001.0000.0000.000
국가구분0.1370.3330.2920.2100.0000.0940.0610.4570.0001.0000.946
시도0.3150.3080.3230.2940.0440.1680.3320.3640.0000.9461.000

Missing values

2023-12-13T07:36:44.238954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T07:36:44.381161image/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

자료년도국가구분시도시군구시가화건조지역농업지역산림지역초지습지나지수역합계
01980남한강원도강릉시11.0331120.6045878.43617.19630.11259.10266.62671043.1117
11980남한강원도고성군3.813378.4143576.49596.80940.09.749719.0809694.3635
21980남한강원도동해시8.811920.6127138.90698.18640.01264.89780.4752181.9035
31980남한강원도삼척시8.253968.50531072.117829.11320.33577.57623.23011189.1322
41980남한강원도속초시2.354414.963483.57041.29330.00992.33462.3454106.8714
51980남한강원도양구군1.136775.123630.2346.82381.76318.97035.1444729.1953
61980남한강원도양양군1.973756.7207556.73558.77140.46625.54764.9905635.2056
71980남한강원도영월군5.355141.2658945.720913.81950.14048.96316.88051122.1452
81980남한강원도원주시9.0306141.5034692.93529.89640.01815.58445.1921874.1601
91980남한강원도인제군2.692893.11491507.068912.02310.391510.010713.04371638.3456
자료년도국가구분시도시군구시가화건조지역농업지역산림지역초지습지나지수역합계
12652000북한황해북도신계군4.0563397.5417309.93120.65340.1266.17587.083725.5674
12662000북한황해북도신평군2.6316127.9287936.14672.74680.78393.18966.60781080.0351
12672000북한황해북도연산군3.1941230.166288.51660.12420.02799.70386.0741537.8067
12682000북한황해북도연탄군2.2653212.2758336.62970.00990.061211.06466.5943568.9008
12692000북한황해북도은파군4.8096256.2894119.57760.00.03427.614912.1095400.4352
12702000북한황해북도인산군3.5244211.6593273.41280.00.122412.35433.4839504.5571
12712000북한황해북도중화군4.1994113.565693.47940.00.02881.81982.5101215.6031
12722000북한황해북도토산군6.2379190.3698216.60840.04770.25743.44073.8736420.8355
12732000북한황해북도평산군11.1537288.4824211.85820.37260.42395.21918.8767526.3866
12742000북한황해북도황주군10.0899383.6943100.30050.00.052210.37169.8235514.332