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 구호물자(응급구호세트, 취사구호세트) 비축 통계정보를 제공하는 서비스
Author충청남도
URLhttps://alldam.chungnam.go.kr/bigdata/collect/view.chungnam?menuCd=DOM_000000201001001000&apiIdx=3005

Alerts

합계 기준(세트) is highly overall correlated with 합계 비축량(세트) and 5 other fieldsHigh correlation
합계 비축량(세트) is highly overall correlated with 합계 기준(세트) and 4 other fieldsHigh correlation
합계 비축률(%) is highly overall correlated with 응급구호세트 비축률(%) and 1 other fieldsHigh correlation
응급구호세트 기준(세트) is highly overall correlated with 합계 기준(세트) and 5 other fieldsHigh correlation
응급구호세트 비축량(세트) is highly overall correlated with 합계 기준(세트) and 4 other fieldsHigh correlation
응급구호세트 비축률(%) is highly overall correlated with 합계 비축률(%)High correlation
취사구호세트 기준(세트) is highly overall correlated with 합계 기준(세트) and 5 other fieldsHigh correlation
취사구호세트 비축량(세트) is highly overall correlated with 합계 기준(세트) and 5 other fieldsHigh correlation
취사구호세트 비축률(%) is highly overall correlated with 합계 비축률(%) and 1 other fieldsHigh correlation
지역 is highly overall correlated with 합계 기준(세트) and 2 other fieldsHigh correlation

Reproduction

Analysis started2024-01-09 22:27:16.161879
Analysis finished2024-01-09 22:27:24.443135
Duration8.28 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
2024-01-10T07:27:24.486018image/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
2024-01-10T07:27:24.578163image/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

2024-01-10T07:27:24.682331image/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%

합계 기준(세트)
Real number (ℝ)

HIGH CORRELATION 

Distinct35
Distinct (%)32.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5133.6667
Minimum111
Maximum53453
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-01-10T07:27:24.775767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum111
5-th percentile222
Q1686
median2616
Q34831
95-th percentile31966
Maximum53453
Range53342
Interquartile range (IQR)4145

Descriptive statistics

Standard deviation10324.199
Coefficient of variation (CV)2.011077
Kurtosis13.261602
Mean5133.6667
Median Absolute Deviation (MAD)1977
Skewness3.7390431
Sum554436
Variance1.0658908 × 108
MonotonicityNot monotonic
2024-01-10T07:27:24.884347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
686 6
 
5.6%
42578 4
 
3.7%
3517 4
 
3.7%
1657 4
 
3.7%
639 4
 
3.7%
555 4
 
3.7%
943 4
 
3.7%
111 4
 
3.7%
5937 4
 
3.7%
2616 4
 
3.7%
Other values (25) 66
61.1%
ValueCountFrequency (%)
111 4
3.7%
222 4
3.7%
312 2
 
1.9%
352 2
 
1.9%
420 2
 
1.9%
555 4
3.7%
582 2
 
1.9%
639 4
3.7%
686 6
5.6%
943 4
3.7%
ValueCountFrequency (%)
53453 2
1.9%
42578 4
3.7%
12258 2
1.9%
6642 2
1.9%
5937 4
3.7%
4974 2
1.9%
4965 4
3.7%
4957 2
1.9%
4875 2
1.9%
4831 4
3.7%

합계 비축량(세트)
Real number (ℝ)

HIGH CORRELATION 

Distinct99
Distinct (%)91.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7568.3148
Minimum119
Maximum84851
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-01-10T07:27:24.997321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum119
5-th percentile388.45
Q1982.5
median3279.5
Q35817
95-th percentile43626.35
Maximum84851
Range84732
Interquartile range (IQR)4834.5

Descriptive statistics

Standard deviation15710.648
Coefficient of variation (CV)2.075845
Kurtosis13.863295
Mean7568.3148
Median Absolute Deviation (MAD)2386
Skewness3.7369492
Sum817378
Variance2.4682447 × 108
MonotonicityNot monotonic
2024-01-10T07:27:25.141815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
670 3
 
2.8%
3527 2
 
1.9%
783 2
 
1.9%
5823 2
 
1.9%
7923 2
 
1.9%
1962 2
 
1.9%
119 2
 
1.9%
3244 2
 
1.9%
787 1
 
0.9%
1644 1
 
0.9%
Other values (89) 89
82.4%
ValueCountFrequency (%)
119 2
1.9%
168 1
0.9%
196 1
0.9%
377 1
0.9%
379 1
0.9%
406 1
0.9%
503 1
0.9%
612 1
0.9%
618 1
0.9%
645 1
0.9%
ValueCountFrequency (%)
84851 1
0.9%
83974 1
0.9%
72588 1
0.9%
59405 1
0.9%
54907 1
0.9%
52964 1
0.9%
26285 1
0.9%
24704 1
0.9%
22040 1
0.9%
14043 1
0.9%

합계 비축률(%)
Real number (ℝ)

HIGH CORRELATION 

Distinct101
Distinct (%)93.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean160.6437
Minimum63.54
Maximum775.64
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-01-10T07:27:25.278382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum63.54
5-th percentile85.3915
Q1107.21
median127.675
Q3151.64
95-th percentile343.776
Maximum775.64
Range712.1
Interquartile range (IQR)44.43

Descriptive statistics

Standard deviation130.0385
Coefficient of variation (CV)0.80948398
Kurtosis15.720537
Mean160.6437
Median Absolute Deviation (MAD)21.575
Skewness3.9335619
Sum17349.52
Variance16910.013
MonotonicityNot monotonic
2024-01-10T07:27:25.427317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
113.39 2
 
1.9%
119.29 2
 
1.9%
83.63 2
 
1.9%
134.54 2
 
1.9%
159.52 2
 
1.9%
107.21 2
 
1.9%
119.45 2
 
1.9%
157.1 1
 
0.9%
63.54 1
 
0.9%
94.36 1
 
0.9%
Other values (91) 91
84.3%
ValueCountFrequency (%)
63.54 1
0.9%
79.23 1
0.9%
83.46 1
0.9%
83.63 2
1.9%
83.96 1
0.9%
88.05 1
0.9%
90.63 1
0.9%
94.28 1
0.9%
94.36 1
0.9%
94.97 1
0.9%
ValueCountFrequency (%)
775.64 1
0.9%
774.32 1
0.9%
768.59 1
0.9%
740.54 1
0.9%
443.69 1
0.9%
371.23 1
0.9%
292.79 1
0.9%
236.53 1
0.9%
233.7 1
0.9%
219.5 1
0.9%

응급구호세트 기준(세트)
Real number (ℝ)

HIGH CORRELATION 

Distinct35
Distinct (%)32.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1256.4074
Minimum26
Maximum16017
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-01-10T07:27:25.571045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum26
5-th percentile52
Q1189
median587
Q3967
95-th percentile7105.35
Maximum16017
Range15991
Interquartile range (IQR)778

Descriptive statistics

Standard deviation2644.7349
Coefficient of variation (CV)2.1049979
Kurtosis18.959016
Mean1256.4074
Median Absolute Deviation (MAD)390.5
Skewness4.2321065
Sum135692
Variance6994622.9
MonotonicityNot monotonic
2024-01-10T07:27:25.704030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
204 6
 
5.6%
8953 4
 
3.7%
732 4
 
3.7%
403 4
 
3.7%
149 4
 
3.7%
130 4
 
3.7%
189 4
 
3.7%
26 4
 
3.7%
1299 4
 
3.7%
483 4
 
3.7%
Other values (25) 66
61.1%
ValueCountFrequency (%)
26 4
3.7%
52 4
3.7%
93 2
 
1.9%
106 2
 
1.9%
125 2
 
1.9%
130 4
3.7%
149 4
3.7%
174 2
 
1.9%
189 4
3.7%
204 6
5.6%
ValueCountFrequency (%)
16017 2
1.9%
8953 4
3.7%
3674 2
1.9%
1993 2
1.9%
1494 2
1.9%
1487 2
1.9%
1461 2
1.9%
1299 4
3.7%
1120 2
1.9%
1006 4
3.7%

응급구호세트 비축량(세트)
Real number (ℝ)

HIGH CORRELATION 

Distinct99
Distinct (%)91.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5198.7593
Minimum86
Maximum55318
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-01-10T07:27:25.825383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum86
5-th percentile273
Q1800.5
median2201.5
Q33812.5
95-th percentile30656.1
Maximum55318
Range55232
Interquartile range (IQR)3012

Descriptive statistics

Standard deviation10688.354
Coefficient of variation (CV)2.0559432
Kurtosis12.731201
Mean5198.7593
Median Absolute Deviation (MAD)1506
Skewness3.6307668
Sum561466
Variance1.142409 × 108
MonotonicityNot monotonic
2024-01-10T07:27:25.933146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2264 2
 
1.9%
1344 2
 
1.9%
488 2
 
1.9%
545 2
 
1.9%
4163 2
 
1.9%
273 2
 
1.9%
3227 2
 
1.9%
88 2
 
1.9%
2299 2
 
1.9%
55318 1
 
0.9%
Other values (89) 89
82.4%
ValueCountFrequency (%)
86 1
0.9%
88 2
1.9%
114 1
0.9%
263 1
0.9%
273 2
1.9%
333 1
0.9%
408 1
0.9%
470 1
0.9%
471 1
0.9%
488 2
1.9%
ValueCountFrequency (%)
55318 1
0.9%
54883 1
0.9%
47669 1
0.9%
45464 1
0.9%
39535 1
0.9%
37864 1
0.9%
17270 1
0.9%
17136 1
0.9%
14080 1
0.9%
11072 1
0.9%

응급구호세트 비축률(%)
Real number (ℝ)

HIGH CORRELATION 

Distinct98
Distinct (%)90.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean152.13269
Minimum53.67
Maximum893.53
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-01-10T07:27:26.047590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum53.67
5-th percentile78.4515
Q1100.59
median117.025
Q3139.925
95-th percentile294.0915
Maximum893.53
Range839.86
Interquartile range (IQR)39.335

Descriptive statistics

Standard deviation137.5011
Coefficient of variation (CV)0.90382353
Kurtosis17.610535
Mean152.13269
Median Absolute Deviation (MAD)18.54
Skewness4.1330771
Sum16430.33
Variance18906.553
MonotonicityNot monotonic
2024-01-10T07:27:26.150301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
103.53 3
 
2.8%
94.52 2
 
1.9%
112.92 2
 
1.9%
81.75 2
 
1.9%
119.61 2
 
1.9%
184.75 2
 
1.9%
110.98 2
 
1.9%
89.55 2
 
1.9%
103.37 2
 
1.9%
116.47 1
 
0.9%
Other values (88) 88
81.5%
ValueCountFrequency (%)
53.67 1
0.9%
72.24 1
0.9%
74.48 1
0.9%
77.22 1
0.9%
77.85 1
0.9%
78.35 1
0.9%
78.64 1
0.9%
81.75 2
1.9%
82.63 1
0.9%
83.27 1
0.9%
ValueCountFrequency (%)
893.53 1
0.9%
852.35 1
0.9%
714.61 1
0.9%
705.02 1
0.9%
473.53 1
0.9%
303.58 1
0.9%
276.47 1
0.9%
253.33 1
0.9%
241.21 1
0.9%
238.72 1
0.9%

취사구호세트 기준(세트)
Real number (ℝ)

HIGH CORRELATION 

Distinct35
Distinct (%)32.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3877.2593
Minimum85
Maximum37436
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-01-10T07:27:26.248396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum85
5-th percentile170
Q1490
median1940
Q33480
95-th percentile24860.65
Maximum37436
Range37351
Interquartile range (IQR)2990

Descriptive statistics

Standard deviation7746.9616
Coefficient of variation (CV)1.998051
Kurtosis12.491338
Mean3877.2593
Median Absolute Deviation (MAD)1458
Skewness3.6712592
Sum418744
Variance60015414
MonotonicityNot monotonic
2024-01-10T07:27:26.348849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
482 6
 
5.6%
33625 4
 
3.7%
2785 4
 
3.7%
1254 4
 
3.7%
490 4
 
3.7%
425 4
 
3.7%
754 4
 
3.7%
85 4
 
3.7%
4638 4
 
3.7%
2133 4
 
3.7%
Other values (25) 66
61.1%
ValueCountFrequency (%)
85 4
3.7%
170 4
3.7%
219 2
 
1.9%
246 2
 
1.9%
295 2
 
1.9%
408 2
 
1.9%
425 4
3.7%
482 6
5.6%
490 4
3.7%
667 2
 
1.9%
ValueCountFrequency (%)
37436 2
1.9%
33625 4
3.7%
8584 2
1.9%
4649 2
1.9%
4638 4
3.7%
3998 4
3.7%
3825 4
3.7%
3730 4
3.7%
3480 2
1.9%
3470 2
1.9%

취사구호세트 비축량(세트)
Real number (ℝ)

HIGH CORRELATION 

Distinct91
Distinct (%)84.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2369.5556
Minimum31
Maximum29968
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-01-10T07:27:26.455730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum31
5-th percentile111.95
Q1254.75
median890.5
Q31682.75
95-th percentile11884.1
Maximum29968
Range29937
Interquartile range (IQR)1428

Descriptive statistics

Standard deviation5119.8429
Coefficient of variation (CV)2.1606764
Kurtosis17.056396
Mean2369.5556
Median Absolute Deviation (MAD)680.5
Skewness4.033388
Sum255912
Variance26212791
MonotonicityNot monotonic
2024-01-10T07:27:26.560834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
180 3
 
2.8%
207 3
 
2.8%
2596 2
 
1.9%
82 2
 
1.9%
31 2
 
1.9%
200 2
 
1.9%
3760 2
 
1.9%
1373 2
 
1.9%
1980 2
 
1.9%
297 2
 
1.9%
Other values (81) 86
79.6%
ValueCountFrequency (%)
31 2
1.9%
82 2
1.9%
104 1
0.9%
106 1
0.9%
123 1
0.9%
125 2
1.9%
130 1
0.9%
143 1
0.9%
155 1
0.9%
170 1
0.9%
ValueCountFrequency (%)
29968 1
0.9%
28656 1
0.9%
24919 1
0.9%
17043 1
0.9%
13941 1
0.9%
13429 1
0.9%
9015 1
0.9%
7960 1
0.9%
7568 1
0.9%
5095 1
0.9%

취사구호세트 비축률(%)
Real number (ℝ)

HIGH CORRELATION 

Distinct90
Distinct (%)83.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean188.40065
Minimum88.03
Maximum919.35
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-01-10T07:27:26.666619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum88.03
5-th percentile97.9085
Q1111.785
median152.185
Q3201.325
95-th percentile372.2245
Maximum919.35
Range831.32
Interquartile range (IQR)89.54

Descriptive statistics

Standard deviation132.82502
Coefficient of variation (CV)0.70501363
Kurtosis16.349402
Mean188.40065
Median Absolute Deviation (MAD)42.805
Skewness3.5801566
Sum20347.27
Variance17642.487
MonotonicityNot monotonic
2024-01-10T07:27:26.763685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100.0 4
 
3.7%
101.47 3
 
2.8%
177.69 2
 
1.9%
346.15 2
 
1.9%
136.91 2
 
1.9%
119.23 2
 
1.9%
315.38 2
 
1.9%
133.15 2
 
1.9%
171.41 2
 
1.9%
188.66 2
 
1.9%
Other values (80) 85
78.7%
ValueCountFrequency (%)
88.03 2
1.9%
94.02 1
 
0.9%
94.62 1
 
0.9%
95.97 1
 
0.9%
97.8 1
 
0.9%
98.11 1
 
0.9%
98.63 1
 
0.9%
99.47 1
 
0.9%
99.59 1
 
0.9%
100.0 4
3.7%
ValueCountFrequency (%)
919.35 1
0.9%
918.28 1
0.9%
612.78 1
0.9%
392.22 1
0.9%
384.62 1
0.9%
375.0 1
0.9%
367.07 1
0.9%
346.15 2
1.9%
324.64 1
0.9%
319.26 1
0.9%

Interactions

2024-01-10T07:27:23.249970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:27:16.449868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:27:17.116815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:27:17.859679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:27:18.582184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:27:19.534176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:27:20.313355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:27:21.068697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:27:21.761124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:27:22.484760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:27:23.316307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:27:16.514708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:27:17.181356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:27:17.923700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:27:18.655463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:27:19.610347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:27:20.386185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:27:21.136473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:27:21.837222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:27:22.570668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:27:23.381261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:27:16.576264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:27:17.270994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:27:17.987818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:27:18.725409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:27:19.691793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:27:20.471294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:27:21.210447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:27:21.906106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:27:22.649180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:27:23.444132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:27:16.642563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:27:17.367446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:27:18.044826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:27:18.797993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:27:19.762799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:27:20.544464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:27:21.291680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:27:21.987855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:27:22.716681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:27:23.523395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:27:16.717772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:27:17.464456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:27:18.120218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:27:19.099486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:27:19.843825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:27:20.645498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:27:21.370720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:27:22.064726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:27:22.793628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:27:23.604329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:27:16.790355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:27:17.539537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:27:18.214155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:27:19.177522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:27:19.924634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:27:20.734386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:27:21.446310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:27:22.142682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:27:22.894709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:27:23.674002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:27:16.853788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:27:17.602683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:27:18.298000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:27:19.246878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:27:19.997267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:27:20.805940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:27:21.514042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:27:22.209860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:27:22.980399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:27:23.745145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:27:16.911892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:27:17.663580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:27:18.372453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:27:19.307178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:27:20.069372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:27:20.870247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:27:21.569365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:27:22.273368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:27:23.045216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:27:24.100266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:27:16.982658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:27:17.732682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:27:18.453307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:27:19.387254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:27:20.152649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:27:20.939814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:27:21.640358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:27:22.347599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:27:23.115725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:27:24.165768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:27:17.050691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:27:17.800545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:27:18.522073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:27:19.464462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:27:20.232759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:27:21.009589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:27:21.704154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:27:22.417977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:27:23.187918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-10T07:27:26.834440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준년도지역합계 기준(세트)합계 비축량(세트)합계 비축률(%)응급구호세트 기준(세트)응급구호세트 비축량(세트)응급구호세트 비축률(%)취사구호세트 기준(세트)취사구호세트 비축량(세트)취사구호세트 비축률(%)
기준년도1.0000.0000.0000.0000.1120.0000.0000.0000.0000.0000.154
지역0.0001.0000.8270.7330.6960.7840.6860.7520.8640.6380.774
합계 기준(세트)0.0000.8271.0000.9150.3000.9960.9470.2500.9970.9390.364
합계 비축량(세트)0.0000.7330.9151.0000.8100.8840.9470.4430.8950.9640.503
합계 비축률(%)0.1120.6960.3000.8101.0000.0000.7070.8890.1430.4740.832
응급구호세트 기준(세트)0.0000.7840.9960.8840.0001.0000.9150.0630.9930.9450.162
응급구호세트 비축량(세트)0.0000.6860.9470.9470.7070.9151.0000.4940.9270.9840.672
응급구호세트 비축률(%)0.0000.7520.2500.4430.8890.0630.4941.0000.1540.0000.908
취사구호세트 기준(세트)0.0000.8640.9970.8950.1430.9930.9270.1541.0000.9120.210
취사구호세트 비축량(세트)0.0000.6380.9390.9640.4740.9450.9840.0000.9121.0000.535
취사구호세트 비축률(%)0.1540.7740.3640.5030.8320.1620.6720.9080.2100.5351.000
2024-01-10T07:27:26.944018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준년도합계 기준(세트)합계 비축량(세트)합계 비축률(%)응급구호세트 기준(세트)응급구호세트 비축량(세트)응급구호세트 비축률(%)취사구호세트 기준(세트)취사구호세트 비축량(세트)취사구호세트 비축률(%)지역
기준년도1.000-0.056-0.093-0.205-0.157-0.025-0.1170.010-0.199-0.1310.000
합계 기준(세트)-0.0561.0000.9520.0950.9830.950-0.0160.9920.9240.2370.567
합계 비축량(세트)-0.0930.9521.0000.3430.9360.9860.2020.9430.9710.4550.413
합계 비축률(%)-0.2050.0950.3431.0000.0910.3130.8780.0720.3470.7780.378
응급구호세트 기준(세트)-0.1570.9830.9360.0911.0000.923-0.0190.9610.9260.2130.511
응급구호세트 비축량(세트)-0.0250.9500.9860.3130.9231.0000.2270.9470.9280.3790.351
응급구호세트 비축률(%)-0.117-0.0160.2020.878-0.0190.2271.000-0.0420.1560.4650.380
취사구호세트 기준(세트)0.0100.9920.9430.0720.9610.947-0.0421.0000.9080.2350.622
취사구호세트 비축량(세트)-0.1990.9240.9710.3470.9260.9280.1560.9081.0000.5140.312
취사구호세트 비축률(%)-0.1310.2370.4550.7780.2130.3790.4650.2350.5141.0000.401
지역0.0000.5670.4130.3780.5110.3510.3800.6220.3120.4011.000

Missing values

2024-01-10T07:27:24.255701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-10T07:27:24.389316image/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

기준년도지역합계 기준(세트)합계 비축량(세트)합계 비축률(%)응급구호세트 기준(세트)응급구호세트 비축량(세트)응급구호세트 비축률(%)취사구호세트 기준(세트)취사구호세트 비축량(세트)취사구호세트 비축률(%)
02016합계5345383974157.11601755318147.773743628656178.91
12016서울497411624233.714948816253.3334802808187.95
22016부산28613244113.398562264112.922005980114.49
32016대구686754109.91204499103.53482255125.0
42016인천2346196283.63702134481.75164461888.03
52016광주582783134.54174488119.61408295169.54
62016대전420670159.52125545184.75295125100.0
72016울산9531264132.63286967144.98667297103.85
82016세종352379107.67106273110.98246106100.0
92016경기1225824704201.53367417136199.6385847568205.99
기준년도지역합계 기준(세트)합계 비축량(세트)합계 비축률(%)응급구호세트 기준(세트)응급구호세트 비축량(세트)응급구호세트 비축률(%)취사구호세트 기준(세트)취사구호세트 비축량(세트)취사구호세트 비축률(%)
982021세종111119107.212688103.538531119.23
992021경기593714043236.53129911072238.7246382971228.71
1002021강원35173527100.287322805100.72278572298.63
1012021충북26163487133.34832713127.192133774160.25
1022021충남34324111119.786623191115.22770920138.97
1032021전북18522703145.954321785125.71420918212.5
1042021전남35564869136.927533841137.0328031028136.52
1052021경북46846649141.959544983133.5937301666174.63
1062021경남49656040121.659674483112.1339981557161.01
1072021제주222985443.6952805473.53170180346.15