Overview

Dataset statistics

Number of variables11
Number of observations108
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory10.5 KiB
Average record size in memory99.2 B

Variable types

Numeric10
Categorical1

Dataset

Description지역별 이재민 임시주거시설 수, 면적, 수용능력 통계정보를 제공하는 서비스
Author충청남도
URLhttps://alldam.chungnam.go.kr/bigdata/collect/view.chungnam?menuCd=DOM_000000201001001000&apiIdx=2997

Alerts

이재민 임시주거시설 개소(개소) is highly overall correlated with 이재민 임시주거시설 면적(㎡) and 5 other fieldsHigh correlation
이재민 임시주거시설 면적(㎡) is highly overall correlated with 이재민 임시주거시설 개소(개소) and 4 other fieldsHigh correlation
이재민 임시주거시설 수용능력(명) is highly overall correlated with 이재민 임시주거시설 개소(개소) and 5 other fieldsHigh correlation
지진겸용 임시주거시설 개소(개소) is highly overall correlated with 이재민 임시주거시설 개소(개소) and 4 other fieldsHigh correlation
지진겸용 임시주거시설 면적(㎡) is highly overall correlated with 이재민 임시주거시설 개소(개소) and 4 other fieldsHigh correlation
지진겸용 임시주거시설 수용능력(명) is highly overall correlated with 이재민 임시주거시설 개소(개소) and 4 other fieldsHigh correlation
마을회관 개소(개소) is highly overall correlated with 마을회관 면적(㎡) and 1 other fieldsHigh correlation
마을회관 면적(㎡) is highly overall correlated with 마을회관 개소(개소) and 1 other fieldsHigh correlation
마을회관 수용능력(명) is highly overall correlated with 마을회관 개소(개소) and 1 other fieldsHigh correlation
지역 is highly overall correlated with 이재민 임시주거시설 개소(개소) and 1 other fieldsHigh correlation
지진겸용 임시주거시설 면적(㎡) has unique valuesUnique
마을회관 개소(개소) has 73 (67.6%) zerosZeros
마을회관 면적(㎡) has 73 (67.6%) zerosZeros
마을회관 수용능력(명) has 73 (67.6%) zerosZeros

Reproduction

Analysis started2024-01-09 20:03:53.753879
Analysis finished2024-01-09 20:04:07.166609
Duration13.41 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기준년도
Real number (ℝ)

Distinct6
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2018.5
Minimum2016
Maximum2021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-01-10T05:04:07.236890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2016
5-th percentile2016
Q12017
median2018.5
Q32020
95-th percentile2021
Maximum2021
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.7157871
Coefficient of variation (CV)0.00085003075
Kurtosis-1.2716396
Mean2018.5
Median Absolute Deviation (MAD)1.5
Skewness0
Sum217998
Variance2.9439252
MonotonicityIncreasing
2024-01-10T05:04:07.389983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2016 18
16.7%
2017 18
16.7%
2018 18
16.7%
2019 18
16.7%
2020 18
16.7%
2021 18
16.7%
ValueCountFrequency (%)
2016 18
16.7%
2017 18
16.7%
2018 18
16.7%
2019 18
16.7%
2020 18
16.7%
2021 18
16.7%
ValueCountFrequency (%)
2021 18
16.7%
2020 18
16.7%
2019 18
16.7%
2018 18
16.7%
2017 18
16.7%
2016 18
16.7%

지역
Categorical

HIGH CORRELATION 

Distinct18
Distinct (%)16.7%
Missing0
Missing (%)0.0%
Memory size996.0 B
합계
 
6
서울
 
6
부산
 
6
대구
 
6
인천
 
6
Other values (13)
78 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row합계
2nd row서울
3rd row부산
4th row대구
5th row인천

Common Values

ValueCountFrequency (%)
합계 6
 
5.6%
서울 6
 
5.6%
부산 6
 
5.6%
대구 6
 
5.6%
인천 6
 
5.6%
광주 6
 
5.6%
대전 6
 
5.6%
울산 6
 
5.6%
세종 6
 
5.6%
경기 6
 
5.6%
Other values (8) 48
44.4%

Length

2024-01-10T05:04:07.581362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
합계 6
 
5.6%
서울 6
 
5.6%
경남 6
 
5.6%
경북 6
 
5.6%
전남 6
 
5.6%
전북 6
 
5.6%
충남 6
 
5.6%
충북 6
 
5.6%
강원 6
 
5.6%
경기 6
 
5.6%
Other values (8) 48
44.4%

이재민 임시주거시설 개소(개소)
Real number (ℝ)

HIGH CORRELATION 

Distinct96
Distinct (%)88.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1563.2963
Minimum116
Maximum14555
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-01-10T05:04:07.771832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum116
5-th percentile151
Q1257.5
median624.5
Q31376.75
95-th percentile9818.45
Maximum14555
Range14439
Interquartile range (IQR)1119.25

Descriptive statistics

Standard deviation3125.1518
Coefficient of variation (CV)1.9990784
Kurtosis12.202471
Mean1563.2963
Median Absolute Deviation (MAD)455.5
Skewness3.6326429
Sum168836
Variance9766573.9
MonotonicityNot monotonic
2024-01-10T05:04:07.966936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
169 4
 
3.7%
174 3
 
2.8%
151 3
 
2.8%
1244 2
 
1.9%
817 2
 
1.9%
256 2
 
1.9%
1003 2
 
1.9%
285 2
 
1.9%
205 1
 
0.9%
265 1
 
0.9%
Other values (86) 86
79.6%
ValueCountFrequency (%)
116 1
 
0.9%
145 1
 
0.9%
148 1
 
0.9%
149 1
 
0.9%
150 1
 
0.9%
151 3
2.8%
152 1
 
0.9%
153 1
 
0.9%
157 1
 
0.9%
160 1
 
0.9%
ValueCountFrequency (%)
14555 1
0.9%
14386 1
0.9%
14106 1
0.9%
13882 1
0.9%
13872 1
0.9%
13617 1
0.9%
2764 1
0.9%
2664 1
0.9%
2590 1
0.9%
2578 1
0.9%

이재민 임시주거시설 면적(㎡)
Real number (ℝ)

HIGH CORRELATION 

Distinct107
Distinct (%)99.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1968899
Minimum54613
Maximum21031135
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-01-10T05:04:08.175089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum54613
5-th percentile83181.05
Q1272128.5
median772874
Q31897248.7
95-th percentile11309335
Maximum21031135
Range20976522
Interquartile range (IQR)1625120.2

Descriptive statistics

Standard deviation3978341.9
Coefficient of variation (CV)2.0205922
Kurtosis13.418423
Mean1968899
Median Absolute Deviation (MAD)603307.43
Skewness3.7348121
Sum2.1264109 × 108
Variance1.5827205 × 1013
MonotonicityNot monotonic
2024-01-10T05:04:08.358011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
74415.78 2
 
1.9%
20543210.0 1
 
0.9%
1498198.55 1
 
0.9%
173747.21 1
 
0.9%
174723.63 1
 
0.9%
192295.35 1
 
0.9%
498182.98 1
 
0.9%
403293.23 1
 
0.9%
572375.99 1
 
0.9%
1989512.99 1
 
0.9%
Other values (97) 97
89.8%
ValueCountFrequency (%)
54613.0 1
0.9%
74165.0 1
0.9%
74275.0 1
0.9%
74415.78 2
1.9%
79428.0 1
0.9%
90151.0 1
0.9%
92159.0 1
0.9%
93781.6 1
0.9%
93782.0 1
0.9%
93883.0 1
0.9%
ValueCountFrequency (%)
21031135.0 1
0.9%
20543210.0 1
0.9%
17508567.0 1
0.9%
16177313.0 1
0.9%
15622148.71 1
0.9%
15438171.32 1
0.9%
3641495.0 1
0.9%
3456490.0 1
0.9%
3024423.0 1
0.9%
2998197.26 1
0.9%

이재민 임시주거시설 수용능력(명)
Real number (ℝ)

HIGH CORRELATION 

Distinct105
Distinct (%)97.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean586275.63
Minimum12826
Maximum5871618
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-01-10T05:04:08.540909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum12826
5-th percentile28408.05
Q180041
median203741.5
Q3550033
95-th percentile3396915.5
Maximum5871618
Range5858792
Interquartile range (IQR)469992

Descriptive statistics

Standard deviation1178616.2
Coefficient of variation (CV)2.0103448
Kurtosis12.467179
Mean586275.63
Median Absolute Deviation (MAD)150562.5
Skewness3.6446182
Sum63317768
Variance1.3891361 × 1012
MonotonicityNot monotonic
2024-01-10T05:04:08.722601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
73824 2
 
1.9%
28555 2
 
1.9%
29295 2
 
1.9%
4606960 1
 
0.9%
239523 1
 
0.9%
170397 1
 
0.9%
138246 1
 
0.9%
202736 1
 
0.9%
725427 1
 
0.9%
5523566 1
 
0.9%
Other values (95) 95
88.0%
ValueCountFrequency (%)
12826 1
0.9%
20681 1
0.9%
20981 1
0.9%
22405 1
0.9%
23354 1
0.9%
28358 1
0.9%
28501 1
0.9%
28555 2
1.9%
29295 2
1.9%
35989 1
0.9%
ValueCountFrequency (%)
5871618 1
0.9%
5523566 1
0.9%
5490864 1
0.9%
5118197 1
0.9%
5047679 1
0.9%
4606960 1
0.9%
1149690 1
0.9%
1122558 1
0.9%
1118771 1
0.9%
1083249 1
0.9%

지진겸용 임시주거시설 개소(개소)
Real number (ℝ)

HIGH CORRELATION 

Distinct92
Distinct (%)85.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean519.22222
Minimum9
Maximum5657
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-01-10T05:04:09.344342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile33.4
Q1131.5
median246
Q3362
95-th percentile2363.4
Maximum5657
Range5648
Interquartile range (IQR)230.5

Descriptive statistics

Standard deviation1051.3866
Coefficient of variation (CV)2.024926
Kurtosis14.40653
Mean519.22222
Median Absolute Deviation (MAD)117
Skewness3.8821918
Sum56076
Variance1105413.7
MonotonicityNot monotonic
2024-01-10T05:04:09.513440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
88 5
 
4.6%
89 3
 
2.8%
206 2
 
1.9%
193 2
 
1.9%
32 2
 
1.9%
301 2
 
1.9%
191 2
 
1.9%
281 2
 
1.9%
285 2
 
1.9%
178 2
 
1.9%
Other values (82) 84
77.8%
ValueCountFrequency (%)
9 1
0.9%
14 1
0.9%
27 1
0.9%
28 1
0.9%
32 2
1.9%
36 2
1.9%
43 1
0.9%
72 1
0.9%
75 1
0.9%
84 1
0.9%
ValueCountFrequency (%)
5657 1
0.9%
5087 1
0.9%
5048 1
0.9%
4883 1
0.9%
4294 1
0.9%
3069 1
0.9%
1053 1
0.9%
916 1
0.9%
890 1
0.9%
870 1
0.9%

지진겸용 임시주거시설 면적(㎡)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct108
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1195795
Minimum21515
Maximum17179061
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-01-10T05:04:09.717395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum21515
5-th percentile39593.85
Q1170179.93
median496696.5
Q31023725.3
95-th percentile6502381.5
Maximum17179061
Range17157546
Interquartile range (IQR)853545.33

Descriptive statistics

Standard deviation2496241.3
Coefficient of variation (CV)2.0875161
Kurtosis19.683224
Mean1195795
Median Absolute Deviation (MAD)372895.54
Skewness4.228103
Sum1.2914586 × 108
Variance6.2312208 × 1012
MonotonicityNot monotonic
2024-01-10T05:04:09.956722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17179061.0 1
 
0.9%
924609.77 1
 
0.9%
67010.64 1
 
0.9%
135571.72 1
 
0.9%
120704.25 1
 
0.9%
117338.73 1
 
0.9%
353260.36 1
 
0.9%
391069.03 1
 
0.9%
456825.63 1
 
0.9%
1489550.7 1
 
0.9%
Other values (98) 98
90.7%
ValueCountFrequency (%)
21515.0 1
0.9%
22917.0 1
0.9%
23964.0 1
0.9%
30668.0 1
0.9%
36656.0 1
0.9%
39051.0 1
0.9%
40602.0 1
0.9%
44563.92 1
0.9%
44564.0 1
0.9%
60325.0 1
0.9%
ValueCountFrequency (%)
17179061.0 1
0.9%
10146515.0 1
0.9%
9842460.0 1
0.9%
9606531.18 1
0.9%
9174761.98 1
0.9%
8623601.0 1
0.9%
2562974.0 1
0.9%
2278998.0 1
0.9%
2142666.0 1
0.9%
1937910.0 1
0.9%

지진겸용 임시주거시설 수용능력(명)
Real number (ℝ)

HIGH CORRELATION 

Distinct104
Distinct (%)96.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean355762.31
Minimum3790
Maximum3808407
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-01-10T05:04:10.153891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3790
5-th percentile12605.05
Q152003.5
median152175.5
Q3308509.5
95-th percentile1613410.3
Maximum3808407
Range3804617
Interquartile range (IQR)256506

Descriptive statistics

Standard deviation724590.47
Coefficient of variation (CV)2.0367263
Kurtosis13.742458
Mean355762.31
Median Absolute Deviation (MAD)106502.5
Skewness3.7729906
Sum38422330
Variance5.2503135 × 1011
MonotonicityNot monotonic
2024-01-10T05:04:10.347754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
45041 2
 
1.9%
25733 2
 
1.9%
161748 2
 
1.9%
14037 2
 
1.9%
179352 1
 
0.9%
120606 1
 
0.9%
133781 1
 
0.9%
558493 1
 
0.9%
3357083 1
 
0.9%
15603 1
 
0.9%
Other values (94) 94
87.0%
ValueCountFrequency (%)
3790 1
0.9%
4600 1
0.9%
6933 1
0.9%
9295 1
0.9%
11058 1
0.9%
11834 1
0.9%
14037 2
1.9%
15603 1
0.9%
17564 1
0.9%
20113 1
0.9%
ValueCountFrequency (%)
3808407 1
0.9%
3592857 1
0.9%
3357083 1
0.9%
3339321 1
0.9%
2961576 1
0.9%
2151921 1
0.9%
613319 1
0.9%
613264 1
0.9%
604801 1
0.9%
602626 1
0.9%

마을회관 개소(개소)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct27
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean96.833333
Minimum0
Maximum3028
Zeros73
Zeros (%)67.6%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-01-10T05:04:10.517619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q36.75
95-th percentile505.7
Maximum3028
Range3028
Interquartile range (IQR)6.75

Descriptive statistics

Standard deviation377.47787
Coefficient of variation (CV)3.8982224
Kurtosis42.554082
Mean96.833333
Median Absolute Deviation (MAD)0
Skewness6.2087092
Sum10458
Variance142489.54
MonotonicityNot monotonic
2024-01-10T05:04:10.667396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0 73
67.6%
1 5
 
4.6%
25 2
 
1.9%
40 2
 
1.9%
22 2
 
1.9%
3 2
 
1.9%
62 2
 
1.9%
3028 1
 
0.9%
6 1
 
0.9%
75 1
 
0.9%
Other values (17) 17
 
15.7%
ValueCountFrequency (%)
0 73
67.6%
1 5
 
4.6%
3 2
 
1.9%
6 1
 
0.9%
9 1
 
0.9%
22 2
 
1.9%
25 2
 
1.9%
39 1
 
0.9%
40 2
 
1.9%
41 1
 
0.9%
ValueCountFrequency (%)
3028 1
0.9%
2201 1
0.9%
711 1
0.9%
596 1
0.9%
524 1
0.9%
519 1
0.9%
481 1
0.9%
422 1
0.9%
417 1
0.9%
302 1
0.9%

마을회관 면적(㎡)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct35
Distinct (%)32.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14785.129
Minimum0
Maximum461123
Zeros73
Zeros (%)67.6%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-01-10T05:04:10.833482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31532
95-th percentile74096.984
Maximum461123
Range461123
Interquartile range (IQR)1532

Descriptive statistics

Standard deviation57358.836
Coefficient of variation (CV)3.8794952
Kurtosis43.159935
Mean14785.129
Median Absolute Deviation (MAD)0
Skewness6.2646279
Sum1596793.9
Variance3.2900361 × 109
MonotonicityNot monotonic
2024-01-10T05:04:10.981357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
0.0 73
67.6%
115.0 2
 
1.9%
461123.0 1
 
0.9%
14435.95 1
 
0.9%
4612.2 1
 
0.9%
6885.0 1
 
0.9%
663.0 1
 
0.9%
551.0 1
 
0.9%
1979.14 1
 
0.9%
84994.17 1
 
0.9%
Other values (25) 25
 
23.1%
ValueCountFrequency (%)
0.0 73
67.6%
115.0 2
 
1.9%
551.0 1
 
0.9%
663.0 1
 
0.9%
664.0 1
 
0.9%
872.0 1
 
0.9%
1409.0 1
 
0.9%
1527.0 1
 
0.9%
1547.0 1
 
0.9%
1979.14 1
 
0.9%
ValueCountFrequency (%)
461123.0 1
0.9%
337273.96 1
0.9%
99196.0 1
0.9%
84994.17 1
0.9%
81664.0 1
0.9%
74711.36 1
0.9%
72956.0 1
0.9%
67601.0 1
0.9%
58635.0 1
0.9%
55782.51 1
0.9%

마을회관 수용능력(명)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct36
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4507.3704
Minimum0
Maximum123539
Zeros73
Zeros (%)67.6%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-01-10T05:04:11.134485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3486.25
95-th percentile22996.95
Maximum123539
Range123539
Interquartile range (IQR)486.25

Descriptive statistics

Standard deviation17307.058
Coefficient of variation (CV)3.839724
Kurtosis38.688013
Mean4507.3704
Median Absolute Deviation (MAD)0
Skewness6.0128451
Sum486796
Variance2.9953426 × 108
MonotonicityNot monotonic
2024-01-10T05:04:11.320278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
0 73
67.6%
119859 1
 
0.9%
5498 1
 
0.9%
1762 1
 
0.9%
2625 1
 
0.9%
44 1
 
0.9%
200 1
 
0.9%
211 1
 
0.9%
751 1
 
0.9%
32363 1
 
0.9%
Other values (26) 26
 
24.1%
ValueCountFrequency (%)
0 73
67.6%
44 1
 
0.9%
99 1
 
0.9%
130 1
 
0.9%
200 1
 
0.9%
211 1
 
0.9%
305 1
 
0.9%
332 1
 
0.9%
468 1
 
0.9%
541 1
 
0.9%
ValueCountFrequency (%)
123539 1
0.9%
119859 1
0.9%
32363 1
0.9%
26546 1
0.9%
25331 1
0.9%
23978 1
0.9%
21175 1
0.9%
20363 1
0.9%
16255 1
0.9%
11224 1
0.9%

Interactions

2024-01-10T05:04:05.632747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:03:54.618871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:03:55.777952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:03:56.975583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:03:58.134693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:03:59.313751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:04:00.444401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:04:01.697888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:04:03.320966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:04:04.545011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:04:05.746949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:03:54.726703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:03:55.890619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:03:57.103581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:03:58.259679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:03:59.425878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:04:00.582900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:04:01.809975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:04:03.428899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:04:04.644601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:04:05.869025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:03:54.836989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:03:56.008930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:03:57.223148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:03:58.383471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:03:59.545885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:04:00.707197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:04:02.331297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:04:03.560161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:04:04.747321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:04:05.978237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:03:54.939275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:03:56.133501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:03:57.317237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:03:58.493803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:03:59.652312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:04:00.818515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:04:02.442374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:04:03.668549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:04:04.845681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:04:06.116943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:03:55.065614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:03:56.247866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:03:57.427784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:03:58.598855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:03:59.765222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:04:00.936012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:04:02.557719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:04:03.791811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:04:04.957663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:04:06.226508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:03:55.177822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:03:56.359601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:03:57.535550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:03:58.698025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:03:59.883385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:04:01.057676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:04:02.677472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:04:03.900948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:04:05.066447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:04:06.356497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:03:55.314609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:03:56.494637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:03:57.656032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:03:58.837133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:03:59.999275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:04:01.190186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:04:02.820672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:04:04.066384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:04:05.200943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:04:06.483998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:03:55.420662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:03:56.620339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:03:57.782226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:03:58.958876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:04:00.120137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:04:01.320433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:04:02.952419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:04:04.200188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:04:05.324980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:04:06.601316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:03:55.557124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:03:56.732955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:03:57.899235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:03:59.073839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:04:00.222857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:04:01.458283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:04:03.081353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:04:04.338108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:04:05.422938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:04:06.717902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:03:55.662219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:03:56.855668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:03:58.002467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:03:59.193805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:04:00.326977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:04:01.579358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:04:03.200047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:04:04.436564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:04:05.517622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-10T05:04:11.446578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준년도지역이재민 임시주거시설 개소(개소)이재민 임시주거시설 면적(㎡)이재민 임시주거시설 수용능력(명)지진겸용 임시주거시설 개소(개소)지진겸용 임시주거시설 면적(㎡)지진겸용 임시주거시설 수용능력(명)마을회관 개소(개소)마을회관 면적(㎡)마을회관 수용능력(명)
기준년도1.0000.0000.0000.0000.0000.0000.0000.0000.2870.2870.165
지역0.0001.0001.0000.7700.8050.8000.7620.8070.3030.2580.584
이재민 임시주거시설 개소(개소)0.0001.0001.0000.7800.7680.9510.7020.9590.5080.5270.468
이재민 임시주거시설 면적(㎡)0.0000.7700.7801.0000.9850.9290.7820.9170.8240.8330.436
이재민 임시주거시설 수용능력(명)0.0000.8050.7680.9851.0000.9490.8450.9060.8980.8990.531
지진겸용 임시주거시설 개소(개소)0.0000.8000.9510.9290.9491.0000.9400.9930.7050.7100.643
지진겸용 임시주거시설 면적(㎡)0.0000.7620.7020.7820.8450.9401.0000.8570.7480.7720.851
지진겸용 임시주거시설 수용능력(명)0.0000.8070.9590.9170.9060.9930.8571.0000.6260.6350.554
마을회관 개소(개소)0.2870.3030.5080.8240.8980.7050.7480.6261.0001.0000.868
마을회관 면적(㎡)0.2870.2580.5270.8330.8990.7100.7720.6351.0001.0000.849
마을회관 수용능력(명)0.1650.5840.4680.4360.5310.6430.8510.5540.8680.8491.000
2024-01-10T05:04:11.632482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준년도이재민 임시주거시설 개소(개소)이재민 임시주거시설 면적(㎡)이재민 임시주거시설 수용능력(명)지진겸용 임시주거시설 개소(개소)지진겸용 임시주거시설 면적(㎡)지진겸용 임시주거시설 수용능력(명)마을회관 개소(개소)마을회관 면적(㎡)마을회관 수용능력(명)지역
기준년도1.0000.077-0.1210.0250.094-0.1070.033-0.189-0.191-0.1770.000
이재민 임시주거시설 개소(개소)0.0771.0000.9070.9170.8510.8310.8500.1040.1000.1020.903
이재민 임시주거시설 면적(㎡)-0.1210.9071.0000.9750.8690.9470.9270.1060.1030.1030.495
이재민 임시주거시설 수용능력(명)0.0250.9170.9751.0000.9000.9280.9510.0780.0750.0780.539
지진겸용 임시주거시설 개소(개소)0.0940.8510.8690.9001.0000.9220.9510.2110.2070.2080.428
지진겸용 임시주거시설 면적(㎡)-0.1070.8310.9470.9280.9221.0000.9690.1820.1790.1780.496
지진겸용 임시주거시설 수용능력(명)0.0330.8500.9270.9510.9510.9691.0000.1660.1620.1630.438
마을회관 개소(개소)-0.1890.1040.1060.0780.2110.1820.1661.0000.9990.9990.144
마을회관 면적(㎡)-0.1910.1000.1030.0750.2070.1790.1620.9991.0001.0000.119
마을회관 수용능력(명)-0.1770.1020.1030.0780.2080.1780.1630.9991.0001.0000.332
지역0.0000.9030.4950.5390.4280.4960.4380.1440.1190.3321.000

Missing values

2024-01-10T05:04:06.875025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-10T05:04:07.081531image/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

기준년도지역이재민 임시주거시설 개소(개소)이재민 임시주거시설 면적(㎡)이재민 임시주거시설 수용능력(명)지진겸용 임시주거시설 개소(개소)지진겸용 임시주거시설 면적(㎡)지진겸용 임시주거시설 수용능력(명)마을회관 개소(개소)마을회관 면적(㎡)마을회관 수용능력(명)
02016합계1361720543210.04606960565717179061.038084073028461123.0119859
12016서울10352138353.05302925091673874.042332831547.0468
22016부산367746769.0141018231677557.01298736011469.02635
32016대구153636765.0188476106486659.0146369254612.01397
42016인천443731267.0200371248639721.0168858407172.02166
52016광주149509472.085866134478697.0764941115.099
62016대전145257253.07189988231051.0662751664.0130
72016울산266941501.0154137148822317.012943100.00
82016세종11654613.0128264330668.09295222028.0546
92016경기24633024423.064249710532562974.053258171199196.026546
기준년도지역이재민 임시주거시설 개소(개소)이재민 임시주거시설 면적(㎡)이재민 임시주거시설 수용능력(명)지진겸용 임시주거시설 개소(개소)지진겸용 임시주거시설 면적(㎡)지진겸용 임시주거시설 수용능력(명)마을회관 개소(개소)마을회관 면적(㎡)마을회관 수용능력(명)
982021세종15074275.0285018967011.02573300.00
992021경기26642969873.011187719161575474.060262600.00
1002021강원763536553.0196164193240010.08906600.00
1012021충북817516607.0187195193238126.08584700.00
1022021충남19812260589.07104653361277145.042798700.00
1032021전북549851466.0319490286637193.023960300.00
1042021전남17591597619.0549834310618458.023397400.00
1052021경북13361507324.0535792458864647.030771900.00
1062021경남13381747169.03806853581201648.020105100.00
1072021제주16993883.0292953244564.01403700.00