Overview

Dataset statistics

Number of variables18
Number of observations108
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory17.1 KiB
Average record size in memory162.2 B

Variable types

Numeric17
Categorical1

Dataset

Description지역 및 실내외 장소별 폭염 인명피해 통계정보를 제공하는 서비스
Author충청남도
URLhttps://alldam.chungnam.go.kr/bigdata/collect/view.chungnam?menuCd=DOM_000000201001001000&apiIdx=3002

Alerts

합계(명) is highly overall correlated with 실외 소계(명) and 14 other fieldsHigh correlation
실외 소계(명) is highly overall correlated with 합계(명) and 14 other fieldsHigh correlation
실외 실외작업장(명) is highly overall correlated with 합계(명) and 13 other fieldsHigh correlation
실외 운동장(명) is highly overall correlated with 합계(명) and 13 other fieldsHigh correlation
실외 논밭(명) is highly overall correlated with 합계(명) and 14 other fieldsHigh correlation
실외 산(명) is highly overall correlated with 합계(명) and 14 other fieldsHigh correlation
실외 강가해(명)변 is highly overall correlated with 합계(명) and 7 other fieldsHigh correlation
실외 길가(명) is highly overall correlated with 합계(명) and 12 other fieldsHigh correlation
실외 주거지주(명)변 is highly overall correlated with 합계(명) and 12 other fieldsHigh correlation
실외 기타(명) is highly overall correlated with 합계(명) and 14 other fieldsHigh correlation
실내 소계(명) is highly overall correlated with 합계(명) and 13 other fieldsHigh correlation
실내 집(명) is highly overall correlated with 합계(명) and 12 other fieldsHigh correlation
실내 건물(명) is highly overall correlated with 합계(명) and 12 other fieldsHigh correlation
실내 작업장(명) is highly overall correlated with 합계(명) and 12 other fieldsHigh correlation
실내 비닐하우스(명) is highly overall correlated with 합계(명) and 4 other fieldsHigh correlation
실내 기타(명) is highly overall correlated with 합계(명) and 12 other fieldsHigh correlation
실외 운동장(명) has 3 (2.8%) zerosZeros
실외 논밭(명) has 7 (6.5%) zerosZeros
실외 산(명) has 18 (16.7%) zerosZeros
실외 강가해(명)변 has 34 (31.5%) zerosZeros
실외 길가(명) has 5 (4.6%) zerosZeros
실외 주거지주(명)변 has 10 (9.3%) zerosZeros
실외 기타(명) has 10 (9.3%) zerosZeros
실내 소계(명) has 2 (1.9%) zerosZeros
실내 집(명) has 10 (9.3%) zerosZeros
실내 건물(명) has 23 (21.3%) zerosZeros
실내 작업장(명) has 8 (7.4%) zerosZeros
실내 비닐하우스(명) has 42 (38.9%) zerosZeros
실내 기타(명) has 25 (23.1%) zerosZeros

Reproduction

Analysis started2024-01-09 22:50:31.992687
Analysis finished2024-01-09 22:50:56.394019
Duration24.4 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
2024-01-10T07:50:56.442444image/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
2024-01-10T07:50:56.538358image/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

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

2024-01-10T07:50:56.637198image/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(명)
Real number (ℝ)

HIGH CORRELATION 

Distinct91
Distinct (%)84.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean231.85185
Minimum2
Maximum4526
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-01-10T07:50:56.739607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile15.4
Q152.75
median103
Q3178.25
95-th percentile1028.65
Maximum4526
Range4524
Interquartile range (IQR)125.5

Descriptive statistics

Standard deviation540.73947
Coefficient of variation (CV)2.3322629
Kurtosis39.158275
Mean231.85185
Median Absolute Deviation (MAD)55
Skewness5.7035067
Sum25040
Variance292399.17
MonotonicityNot monotonic
2024-01-10T07:50:56.853808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
32 5
 
4.6%
54 3
 
2.8%
203 2
 
1.9%
26 2
 
1.9%
45 2
 
1.9%
126 2
 
1.9%
27 2
 
1.9%
92 2
 
1.9%
121 2
 
1.9%
64 2
 
1.9%
Other values (81) 84
77.8%
ValueCountFrequency (%)
2 1
0.9%
4 1
0.9%
11 1
0.9%
12 1
0.9%
13 1
0.9%
14 1
0.9%
18 1
0.9%
26 2
1.9%
27 2
1.9%
28 1
0.9%
ValueCountFrequency (%)
4526 1
0.9%
2125 1
0.9%
1841 1
0.9%
1574 1
0.9%
1376 1
0.9%
1078 1
0.9%
937 1
0.9%
616 1
0.9%
436 1
0.9%
357 1
0.9%

실외 소계(명)
Real number (ℝ)

HIGH CORRELATION 

Distinct84
Distinct (%)77.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean180.33333
Minimum2
Maximum3324
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-01-10T07:50:56.962961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile12.4
Q142.75
median79.5
Q3143.75
95-th percentile825.1
Maximum3324
Range3322
Interquartile range (IQR)101

Descriptive statistics

Standard deviation409.05914
Coefficient of variation (CV)2.2683502
Kurtosis35.004738
Mean180.33333
Median Absolute Deviation (MAD)39.5
Skewness5.418597
Sum19476
Variance167329.38
MonotonicityNot monotonic
2024-01-10T07:50:57.082853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11 3
 
2.8%
47 3
 
2.8%
65 3
 
2.8%
100 3
 
2.8%
18 2
 
1.9%
146 2
 
1.9%
43 2
 
1.9%
97 2
 
1.9%
56 2
 
1.9%
25 2
 
1.9%
Other values (74) 84
77.8%
ValueCountFrequency (%)
2 1
 
0.9%
4 1
 
0.9%
10 1
 
0.9%
11 3
2.8%
15 1
 
0.9%
17 1
 
0.9%
18 2
1.9%
22 1
 
0.9%
24 2
1.9%
25 2
1.9%
ValueCountFrequency (%)
3324 1
0.9%
1674 1
0.9%
1476 1
0.9%
1261 1
0.9%
1096 1
0.9%
907 1
0.9%
673 1
0.9%
362 1
0.9%
324 1
0.9%
276 1
0.9%

실외 실외작업장(명)
Real number (ℝ)

HIGH CORRELATION 

Distinct64
Distinct (%)59.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean72.166667
Minimum1
Maximum1274
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-01-10T07:50:57.197411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q117.75
median30
Q347
95-th percentile349.65
Maximum1274
Range1273
Interquartile range (IQR)29.25

Descriptive statistics

Standard deviation160.98137
Coefficient of variation (CV)2.2306887
Kurtosis31.327842
Mean72.166667
Median Absolute Deviation (MAD)13.5
Skewness5.1252037
Sum7794
Variance25915
MonotonicityNot monotonic
2024-01-10T07:50:57.310864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21 5
 
4.6%
16 5
 
4.6%
36 4
 
3.7%
31 4
 
3.7%
30 4
 
3.7%
26 4
 
3.7%
22 4
 
3.7%
17 4
 
3.7%
43 3
 
2.8%
19 3
 
2.8%
Other values (54) 68
63.0%
ValueCountFrequency (%)
1 1
 
0.9%
2 1
 
0.9%
3 1
 
0.9%
4 2
1.9%
5 2
1.9%
6 2
1.9%
8 3
2.8%
10 1
 
0.9%
11 1
 
0.9%
12 1
 
0.9%
ValueCountFrequency (%)
1274 1
0.9%
603 1
0.9%
596 1
0.9%
555 1
0.9%
491 1
0.9%
378 1
0.9%
297 1
0.9%
147 1
0.9%
143 1
0.9%
129 1
0.9%

실외 운동장(명)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct26
Distinct (%)24.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.074074
Minimum0
Maximum204
Zeros3
Zeros (%)2.8%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-01-10T07:50:57.416937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median5
Q39.25
95-th percentile50.9
Maximum204
Range204
Interquartile range (IQR)7.25

Descriptive statistics

Standard deviation27.605263
Coefficient of variation (CV)2.2863255
Kurtosis26.801349
Mean12.074074
Median Absolute Deviation (MAD)3
Skewness4.8807398
Sum1304
Variance762.05054
MonotonicityNot monotonic
2024-01-10T07:50:57.530084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
1 17
15.7%
3 12
11.1%
2 12
11.1%
4 9
 
8.3%
5 8
 
7.4%
7 6
 
5.6%
14 5
 
4.6%
6 5
 
4.6%
9 5
 
4.6%
11 4
 
3.7%
Other values (16) 25
23.1%
ValueCountFrequency (%)
0 3
 
2.8%
1 17
15.7%
2 12
11.1%
3 12
11.1%
4 9
8.3%
5 8
7.4%
6 5
 
4.6%
7 6
 
5.6%
8 4
 
3.7%
9 5
 
4.6%
ValueCountFrequency (%)
204 1
 
0.9%
127 1
 
0.9%
126 1
 
0.9%
95 1
 
0.9%
56 1
 
0.9%
53 1
 
0.9%
47 1
 
0.9%
31 1
 
0.9%
26 1
 
0.9%
16 3
2.8%

실외 논밭(명)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct51
Distinct (%)47.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.240741
Minimum0
Maximum506
Zeros7
Zeros (%)6.5%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-01-10T07:50:57.649465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median13
Q328.25
95-th percentile129.6
Maximum506
Range506
Interquartile range (IQR)25.25

Descriptive statistics

Standard deviation70.170403
Coefficient of variation (CV)2.1764513
Kurtosis23.756263
Mean32.240741
Median Absolute Deviation (MAD)12
Skewness4.5653503
Sum3482
Variance4923.8854
MonotonicityNot monotonic
2024-01-10T07:50:57.765955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 12
 
11.1%
2 7
 
6.5%
0 7
 
6.5%
3 6
 
5.6%
26 4
 
3.7%
22 4
 
3.7%
6 4
 
3.7%
25 4
 
3.7%
10 3
 
2.8%
4 3
 
2.8%
Other values (41) 54
50.0%
ValueCountFrequency (%)
0 7
6.5%
1 12
11.1%
2 7
6.5%
3 6
5.6%
4 3
 
2.8%
5 3
 
2.8%
6 4
 
3.7%
7 2
 
1.9%
8 2
 
1.9%
9 1
 
0.9%
ValueCountFrequency (%)
506 1
0.9%
333 1
0.9%
269 1
0.9%
262 1
0.9%
212 1
0.9%
159 1
0.9%
75 1
0.9%
69 1
0.9%
59 1
0.9%
58 1
0.9%

실외 산(명)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct18
Distinct (%)16.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.1481481
Minimum0
Maximum87
Zeros18
Zeros (%)16.7%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-01-10T07:50:57.864278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q36
95-th percentile25.95
Maximum87
Range87
Interquartile range (IQR)5

Descriptive statistics

Standard deviation12.972159
Coefficient of variation (CV)2.1099295
Kurtosis19.930067
Mean6.1481481
Median Absolute Deviation (MAD)2
Skewness4.3127603
Sum664
Variance168.27691
MonotonicityNot monotonic
2024-01-10T07:50:57.956182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
1 20
18.5%
0 18
16.7%
4 13
12.0%
2 11
10.2%
3 11
10.2%
6 8
 
7.4%
5 7
 
6.5%
9 4
 
3.7%
10 3
 
2.8%
7 3
 
2.8%
Other values (8) 10
9.3%
ValueCountFrequency (%)
0 18
16.7%
1 20
18.5%
2 11
10.2%
3 11
10.2%
4 13
12.0%
5 7
 
6.5%
6 8
 
7.4%
7 3
 
2.8%
8 3
 
2.8%
9 4
 
3.7%
ValueCountFrequency (%)
87 1
 
0.9%
61 1
 
0.9%
58 1
 
0.9%
54 1
 
0.9%
38 1
 
0.9%
34 1
 
0.9%
11 1
 
0.9%
10 3
2.8%
9 4
3.7%
8 3
2.8%

실외 강가해(명)변
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct16
Distinct (%)14.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.5
Minimum0
Maximum65
Zeros34
Zeros (%)31.5%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-01-10T07:50:58.041439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile11.3
Maximum65
Range65
Interquartile range (IQR)3

Descriptive statistics

Standard deviation8.5396273
Coefficient of variation (CV)2.4398935
Kurtosis33.054734
Mean3.5
Median Absolute Deviation (MAD)1
Skewness5.3993888
Sum378
Variance72.925234
MonotonicityNot monotonic
2024-01-10T07:50:58.128773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 34
31.5%
1 26
24.1%
2 17
15.7%
3 8
 
7.4%
5 8
 
7.4%
9 3
 
2.8%
4 2
 
1.9%
6 2
 
1.9%
50 1
 
0.9%
28 1
 
0.9%
Other values (6) 6
 
5.6%
ValueCountFrequency (%)
0 34
31.5%
1 26
24.1%
2 17
15.7%
3 8
 
7.4%
4 2
 
1.9%
5 8
 
7.4%
6 2
 
1.9%
8 1
 
0.9%
9 3
 
2.8%
10 1
 
0.9%
ValueCountFrequency (%)
65 1
 
0.9%
50 1
 
0.9%
28 1
 
0.9%
20 1
 
0.9%
14 1
 
0.9%
12 1
 
0.9%
10 1
 
0.9%
9 3
2.8%
8 1
 
0.9%
6 2
1.9%

실외 길가(명)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct40
Distinct (%)37.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.907407
Minimum0
Maximum606
Zeros5
Zeros (%)4.6%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-01-10T07:50:58.230687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q15
median10
Q319.5
95-th percentile132
Maximum606
Range606
Interquartile range (IQR)14.5

Descriptive statistics

Standard deviation68.162332
Coefficient of variation (CV)2.5332181
Kurtosis49.88209
Mean26.907407
Median Absolute Deviation (MAD)6
Skewness6.4087987
Sum2906
Variance4646.1035
MonotonicityNot monotonic
2024-01-10T07:50:58.368835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
8 7
 
6.5%
7 7
 
6.5%
5 6
 
5.6%
10 6
 
5.6%
4 6
 
5.6%
6 6
 
5.6%
0 5
 
4.6%
1 5
 
4.6%
12 5
 
4.6%
2 4
 
3.7%
Other values (30) 51
47.2%
ValueCountFrequency (%)
0 5
4.6%
1 5
4.6%
2 4
3.7%
3 4
3.7%
4 6
5.6%
5 6
5.6%
6 6
5.6%
7 7
6.5%
8 7
6.5%
9 2
 
1.9%
ValueCountFrequency (%)
606 1
0.9%
227 1
0.9%
198 1
0.9%
153 1
0.9%
137 1
0.9%
132 2
1.9%
125 1
0.9%
42 1
0.9%
40 2
1.9%
38 1
0.9%

실외 주거지주(명)변
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct27
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.5
Minimum0
Maximum230
Zeros10
Zeros (%)9.3%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-01-10T07:50:58.510917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median4
Q38
95-th percentile48.25
Maximum230
Range230
Interquartile range (IQR)6

Descriptive statistics

Standard deviation26.10636
Coefficient of variation (CV)2.48632
Kurtosis48.058743
Mean10.5
Median Absolute Deviation (MAD)2
Skewness6.2901623
Sum1134
Variance681.54206
MonotonicityNot monotonic
2024-01-10T07:50:58.639364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
3 16
14.8%
4 15
13.9%
5 12
11.1%
1 12
11.1%
0 10
9.3%
2 8
7.4%
6 5
 
4.6%
8 5
 
4.6%
12 3
 
2.8%
10 3
 
2.8%
Other values (17) 19
17.6%
ValueCountFrequency (%)
0 10
9.3%
1 12
11.1%
2 8
7.4%
3 16
14.8%
4 15
13.9%
5 12
11.1%
6 5
 
4.6%
7 2
 
1.9%
8 5
 
4.6%
9 1
 
0.9%
ValueCountFrequency (%)
230 1
0.9%
93 1
0.9%
75 1
0.9%
67 1
0.9%
52 1
0.9%
50 1
0.9%
45 1
0.9%
42 1
0.9%
22 1
0.9%
19 1
0.9%

실외 기타(명)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct32
Distinct (%)29.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.796296
Minimum0
Maximum352
Zeros10
Zeros (%)9.3%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-01-10T07:50:58.744487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median7
Q312.25
95-th percentile56.6
Maximum352
Range352
Interquartile range (IQR)10.25

Descriptive statistics

Standard deviation41.528002
Coefficient of variation (CV)2.4724499
Kurtosis42.190377
Mean16.796296
Median Absolute Deviation (MAD)5
Skewness5.9507227
Sum1814
Variance1724.5749
MonotonicityNot monotonic
2024-01-10T07:50:58.853323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
1 12
 
11.1%
0 10
 
9.3%
11 9
 
8.3%
7 8
 
7.4%
4 8
 
7.4%
2 6
 
5.6%
6 6
 
5.6%
3 6
 
5.6%
8 5
 
4.6%
9 5
 
4.6%
Other values (22) 33
30.6%
ValueCountFrequency (%)
0 10
9.3%
1 12
11.1%
2 6
5.6%
3 6
5.6%
4 8
7.4%
5 4
 
3.7%
6 6
5.6%
7 8
7.4%
8 5
4.6%
9 5
4.6%
ValueCountFrequency (%)
352 1
0.9%
180 1
0.9%
126 1
0.9%
119 1
0.9%
90 1
0.9%
58 1
0.9%
54 1
0.9%
40 1
0.9%
39 1
0.9%
35 2
1.9%

실내 소계(명)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct52
Distinct (%)48.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51.518519
Minimum0
Maximum1202
Zeros2
Zeros (%)1.9%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-01-10T07:50:58.980437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q18.75
median17.5
Q336.5
95-th percentile260.5
Maximum1202
Range1202
Interquartile range (IQR)27.75

Descriptive statistics

Standard deviation134.31519
Coefficient of variation (CV)2.6071244
Kurtosis51.541122
Mean51.518519
Median Absolute Deviation (MAD)10.5
Skewness6.5321369
Sum5564
Variance18040.57
MonotonicityNot monotonic
2024-01-10T07:50:59.387722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7 8
 
7.4%
13 7
 
6.5%
19 5
 
4.6%
5 4
 
3.7%
8 4
 
3.7%
18 4
 
3.7%
3 3
 
2.8%
6 3
 
2.8%
17 3
 
2.8%
24 3
 
2.8%
Other values (42) 64
59.3%
ValueCountFrequency (%)
0 2
 
1.9%
1 2
 
1.9%
2 1
 
0.9%
3 3
 
2.8%
5 4
3.7%
6 3
 
2.8%
7 8
7.4%
8 4
3.7%
9 3
 
2.8%
10 2
 
1.9%
ValueCountFrequency (%)
1202 1
0.9%
451 1
0.9%
365 1
0.9%
313 1
0.9%
280 1
0.9%
264 1
0.9%
254 1
0.9%
171 1
0.9%
112 1
0.9%
99 1
0.9%

실내 집(명)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct35
Distinct (%)32.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.537037
Minimum0
Maximum624
Zeros10
Zeros (%)9.3%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-01-10T07:50:59.498427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median6.5
Q315
95-th percentile111.95
Maximum624
Range624
Interquartile range (IQR)12

Descriptive statistics

Standard deviation67.572657
Coefficient of variation (CV)2.9982938
Kurtosis60.02892
Mean22.537037
Median Absolute Deviation (MAD)4.5
Skewness7.1433154
Sum2434
Variance4566.064
MonotonicityNot monotonic
2024-01-10T07:50:59.599149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
4 12
 
11.1%
0 10
 
9.3%
5 9
 
8.3%
1 7
 
6.5%
7 7
 
6.5%
8 7
 
6.5%
3 6
 
5.6%
2 6
 
5.6%
18 4
 
3.7%
6 4
 
3.7%
Other values (25) 36
33.3%
ValueCountFrequency (%)
0 10
9.3%
1 7
6.5%
2 6
5.6%
3 6
5.6%
4 12
11.1%
5 9
8.3%
6 4
 
3.7%
7 7
6.5%
8 7
6.5%
9 3
 
2.8%
ValueCountFrequency (%)
624 1
0.9%
199 1
0.9%
198 1
0.9%
123 1
0.9%
121 1
0.9%
113 1
0.9%
110 1
0.9%
63 1
0.9%
50 1
0.9%
38 1
0.9%

실내 건물(명)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct20
Distinct (%)18.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.7592593
Minimum0
Maximum119
Zeros23
Zeros (%)21.3%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-01-10T07:50:59.698132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q35
95-th percentile24.95
Maximum119
Range119
Interquartile range (IQR)4

Descriptive statistics

Standard deviation14.015259
Coefficient of variation (CV)2.4335176
Kurtosis41.541156
Mean5.7592593
Median Absolute Deviation (MAD)2
Skewness5.8371245
Sum622
Variance196.42748
MonotonicityNot monotonic
2024-01-10T07:50:59.793033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0 23
21.3%
1 22
20.4%
2 15
13.9%
5 9
 
8.3%
4 9
 
8.3%
3 8
 
7.4%
6 5
 
4.6%
8 3
 
2.8%
7 2
 
1.9%
9 2
 
1.9%
Other values (10) 10
9.3%
ValueCountFrequency (%)
0 23
21.3%
1 22
20.4%
2 15
13.9%
3 8
 
7.4%
4 9
 
8.3%
5 9
 
8.3%
6 5
 
4.6%
7 2
 
1.9%
8 3
 
2.8%
9 2
 
1.9%
ValueCountFrequency (%)
119 1
0.9%
52 1
0.9%
48 1
0.9%
40 1
0.9%
29 1
0.9%
26 1
0.9%
23 1
0.9%
19 1
0.9%
13 1
0.9%
10 1
0.9%

실내 작업장(명)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct32
Distinct (%)29.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.833333
Minimum0
Maximum273
Zeros8
Zeros (%)7.4%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-01-10T07:50:59.895991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median4
Q310.25
95-th percentile76.95
Maximum273
Range273
Interquartile range (IQR)8.25

Descriptive statistics

Standard deviation33.150605
Coefficient of variation (CV)2.3964293
Kurtosis36.473126
Mean13.833333
Median Absolute Deviation (MAD)3
Skewness5.4450717
Sum1494
Variance1098.9626
MonotonicityNot monotonic
2024-01-10T07:50:59.992849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
2 14
13.0%
3 13
12.0%
4 13
12.0%
1 9
 
8.3%
5 8
 
7.4%
0 8
 
7.4%
9 5
 
4.6%
8 5
 
4.6%
11 4
 
3.7%
6 3
 
2.8%
Other values (22) 26
24.1%
ValueCountFrequency (%)
0 8
7.4%
1 9
8.3%
2 14
13.0%
3 13
12.0%
4 13
12.0%
5 8
7.4%
6 3
 
2.8%
7 2
 
1.9%
8 5
 
4.6%
9 5
 
4.6%
ValueCountFrequency (%)
273 1
0.9%
125 1
0.9%
99 1
0.9%
98 1
0.9%
90 1
0.9%
85 1
0.9%
62 1
0.9%
34 1
0.9%
32 1
0.9%
30 1
0.9%

실내 비닐하우스(명)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct14
Distinct (%)13.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.9074074
Minimum0
Maximum38
Zeros42
Zeros (%)38.9%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-01-10T07:51:00.086099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile14.85
Maximum38
Range38
Interquartile range (IQR)3

Descriptive statistics

Standard deviation6.140926
Coefficient of variation (CV)2.1121656
Kurtosis15.259315
Mean2.9074074
Median Absolute Deviation (MAD)1
Skewness3.7884913
Sum314
Variance37.710973
MonotonicityNot monotonic
2024-01-10T07:51:00.179365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0 42
38.9%
1 20
18.5%
2 17
15.7%
4 8
 
7.4%
3 7
 
6.5%
5 3
 
2.8%
6 2
 
1.9%
27 2
 
1.9%
7 2
 
1.9%
26 1
 
0.9%
Other values (4) 4
 
3.7%
ValueCountFrequency (%)
0 42
38.9%
1 20
18.5%
2 17
15.7%
3 7
 
6.5%
4 8
 
7.4%
5 3
 
2.8%
6 2
 
1.9%
7 2
 
1.9%
9 1
 
0.9%
18 1
 
0.9%
ValueCountFrequency (%)
38 1
 
0.9%
27 2
 
1.9%
26 1
 
0.9%
21 1
 
0.9%
18 1
 
0.9%
9 1
 
0.9%
7 2
 
1.9%
6 2
 
1.9%
5 3
 
2.8%
4 8
7.4%

실내 기타(명)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct20
Distinct (%)18.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.4814815
Minimum0
Maximum148
Zeros25
Zeros (%)23.1%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-01-10T07:51:00.274602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q35
95-th percentile26
Maximum148
Range148
Interquartile range (IQR)4

Descriptive statistics

Standard deviation17.174466
Coefficient of variation (CV)2.6497747
Kurtosis45.514425
Mean6.4814815
Median Absolute Deviation (MAD)2
Skewness6.1719661
Sum700
Variance294.96227
MonotonicityNot monotonic
2024-01-10T07:51:00.369191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0 25
23.1%
1 19
17.6%
2 17
15.7%
3 10
 
9.3%
4 9
 
8.3%
8 5
 
4.6%
6 4
 
3.7%
5 3
 
2.8%
9 3
 
2.8%
26 2
 
1.9%
Other values (10) 11
10.2%
ValueCountFrequency (%)
0 25
23.1%
1 19
17.6%
2 17
15.7%
3 10
 
9.3%
4 9
 
8.3%
5 3
 
2.8%
6 4
 
3.7%
7 2
 
1.9%
8 5
 
4.6%
9 3
 
2.8%
ValueCountFrequency (%)
148 1
0.9%
75 1
0.9%
44 1
0.9%
43 1
0.9%
31 1
0.9%
26 2
1.9%
25 1
0.9%
17 1
0.9%
16 1
0.9%
10 1
0.9%

Interactions

2024-01-10T07:50:54.470051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:50:32.537985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:50:33.816319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:50:35.339761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:50:36.747769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:50:38.115770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:50:39.452005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:50:40.927225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:50:42.151131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:50:43.517772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:50:44.825155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:50:46.353173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:50:47.707765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:50:49.053306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:50:50.569156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:50:51.814779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:50:53.135732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:50:54.543865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:50:32.607609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:50:33.894977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:50:35.420625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:50:36.819063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:50:38.196564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:50:39.522135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:50:40.995918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:50:42.230600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:50:43.587017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:50:44.895734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:50:46.428055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:50:47.782397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:50:49.127954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:50:50.640147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:50:51.900554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:50:53.207545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:50:54.617672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:50:32.671629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:50:33.965914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:50:35.501326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:50:36.888779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:50:38.277085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:50:39.584708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:50:41.062340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:50:42.311733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:50:43.654858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:50:44.964552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:50:46.498148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:50:47.862195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:50:49.198712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:50:50.707127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:50:51.976810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:50:53.276596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:50:54.691545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:50:32.743670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:50:34.035045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:50:35.584325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:50:36.971903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:50:38.363410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:50:39.657013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2024-01-10T07:50:34.885868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:50:36.593215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:50:37.945682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:50:39.289521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:50:40.774445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:50:41.994299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:50:43.370282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:50:44.673148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:50:46.197773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:50:47.531079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:50:48.897557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:50:50.154466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:50:51.659149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:50:52.982733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:50:54.300667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:50:55.981717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:50:33.736480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:50:35.263101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:50:36.671869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:50:38.030139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:50:39.371884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:50:40.848571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:50:42.074697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:50:43.442597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:50:44.751581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:50:46.273405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:50:47.621971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:50:48.972459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:50:50.495930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:50:51.733937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:50:53.064217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:50:54.389747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-10T07:51:00.457994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준년도지역합계(명)실외 소계(명)실외 실외작업장(명)실외 운동장(명)실외 논밭(명)실외 산(명)실외 강가해(명)변실외 길가(명)실외 주거지주(명)변실외 기타(명)실내 소계(명)실내 집(명)실내 건물(명)실내 작업장(명)실내 비닐하우스(명)실내 기타(명)
기준년도1.0000.0000.0000.0000.0000.0000.2290.2210.2350.0000.2100.2790.0000.0000.0710.0000.0510.194
지역0.0001.0000.6140.4190.6680.5750.5590.4490.1120.6170.6140.4790.5010.4740.4790.6030.4800.613
합계(명)0.0000.6141.0001.0000.9840.9930.8840.8880.9031.0000.9890.9740.9390.9650.9950.9660.8920.874
실외 소계(명)0.0000.4191.0001.0000.9040.9350.9810.9820.9901.0000.9720.9300.9360.9480.9440.9070.9090.929
실외 실외작업장(명)0.0000.6680.9840.9041.0000.9890.8730.8680.9060.9630.9570.9720.8740.8710.9930.9700.8840.857
실외 운동장(명)0.0000.5750.9930.9350.9891.0000.8760.8760.9180.9990.9800.9720.9120.9250.9940.9630.8860.834
실외 논밭(명)0.2290.5590.8840.9810.8730.8761.0000.9970.9900.9010.9070.9690.9180.8820.8920.8920.9660.912
실외 산(명)0.2210.4490.8880.9820.8680.8760.9971.0000.9900.9010.9070.9570.9180.8870.8970.8900.9680.912
실외 강가해(명)변0.2350.1120.9030.9900.9060.9180.9900.9901.0000.9290.9320.9620.8790.8830.9270.8780.9310.915
실외 길가(명)0.0000.6171.0001.0000.9630.9990.9010.9010.9291.0001.0000.9521.0000.9700.9990.9560.9890.847
실외 주거지주(명)변0.2100.6140.9890.9720.9570.9800.9070.9070.9321.0001.0000.9800.9230.9610.9750.9760.9150.922
실외 기타(명)0.2790.4790.9740.9300.9720.9720.9690.9570.9620.9520.9801.0000.8530.9370.9790.9550.9710.924
실내 소계(명)0.0000.5010.9390.9360.8740.9120.9180.9180.8791.0000.9230.8531.0000.8230.9260.9240.9240.963
실내 집(명)0.0000.4740.9650.9480.8710.9250.8820.8870.8830.9700.9610.9370.8231.0000.8960.8790.9830.868
실내 건물(명)0.0710.4790.9950.9440.9930.9940.8920.8970.9270.9990.9750.9790.9260.8961.0000.9620.8980.858
실내 작업장(명)0.0000.6030.9660.9070.9700.9630.8920.8900.8780.9560.9760.9550.9240.8790.9621.0000.9080.801
실내 비닐하우스(명)0.0510.4800.8920.9090.8840.8860.9660.9680.9310.9890.9150.9710.9240.9830.8980.9081.0000.917
실내 기타(명)0.1940.6130.8740.9290.8570.8340.9120.9120.9150.8470.9220.9240.9630.8680.8580.8010.9171.000
2024-01-10T07:51:00.628754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준년도합계(명)실외 소계(명)실외 실외작업장(명)실외 운동장(명)실외 논밭(명)실외 산(명)실외 강가해(명)변실외 길가(명)실외 주거지주(명)변실외 기타(명)실내 소계(명)실내 집(명)실내 건물(명)실내 작업장(명)실내 비닐하우스(명)실내 기타(명)지역
기준년도1.000-0.236-0.235-0.127-0.343-0.205-0.171-0.295-0.183-0.230-0.300-0.259-0.297-0.271-0.019-0.111-0.3220.000
합계(명)-0.2361.0000.9900.9200.7920.8200.8040.6390.8480.8580.9070.9460.8680.8150.7580.5500.7810.273
실외 소계(명)-0.2350.9901.0000.9230.7910.8540.8140.6510.8210.8650.9110.9060.8280.8040.7200.5630.7500.186
실외 실외작업장(명)-0.1270.9200.9231.0000.6810.7250.7300.5840.7570.7650.7990.8560.7610.7560.7640.4740.7110.311
실외 운동장(명)-0.3430.7920.7910.6811.0000.6050.6400.5370.6880.7390.7390.7570.7430.6040.5450.4020.6790.249
실외 논밭(명)-0.2050.8200.8540.7250.6051.0000.7230.6480.5540.6920.7780.6790.5870.6380.5580.7420.5570.270
실외 산(명)-0.1710.8040.8140.7300.6400.7231.0000.5630.6970.6920.7850.7430.7010.6650.5720.5510.6230.203
실외 강가해(명)변-0.2950.6390.6510.5840.5370.6480.5631.0000.4670.4980.6020.5690.4890.4780.4610.4640.4860.026
실외 길가(명)-0.1830.8480.8210.7570.6880.5540.6970.4671.0000.7880.7370.8640.8490.7490.6640.3420.7210.358
실외 주거지주(명)변-0.2300.8580.8650.7650.7390.6920.6920.4980.7881.0000.7940.8160.7910.6690.6460.4580.6280.273
실외 기타(명)-0.3000.9070.9110.7990.7390.7780.7850.6020.7370.7941.0000.8660.8220.7910.6190.5050.7860.195
실내 소계(명)-0.2590.9460.9060.8560.7570.6790.7430.5690.8640.8160.8661.0000.9360.8090.8080.4560.8250.262
실내 집(명)-0.2970.8680.8280.7610.7430.5870.7010.4890.8490.7910.8220.9361.0000.7350.6280.3870.7630.255
실내 건물(명)-0.2710.8150.8040.7560.6040.6380.6650.4780.7490.6690.7910.8090.7351.0000.6490.3960.6470.195
실내 작업장(명)-0.0190.7580.7200.7640.5450.5580.5720.4610.6640.6460.6190.8080.6280.6491.0000.3410.5900.266
실내 비닐하우스(명)-0.1110.5500.5630.4740.4020.7420.5510.4640.3420.4580.5050.4560.3870.3960.3411.0000.3610.209
실내 기타(명)-0.3220.7810.7500.7110.6790.5570.6230.4860.7210.6280.7860.8250.7630.6470.5900.3611.0000.343
지역0.0000.2730.1860.3110.2490.2700.2030.0260.3580.2730.1950.2620.2550.1950.2660.2090.3431.000

Missing values

2024-01-10T07:50:56.105570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-10T07:50:56.305234image/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(명)실외 소계(명)실외 실외작업장(명)실외 운동장(명)실외 논밭(명)실외 산(명)실외 강가해(명)변실외 길가(명)실외 주거지주(명)변실외 기타(명)실내 소계(명)실내 집(명)실내 건물(명)실내 작업장(명)실내 비닐하우스(명)실내 기타(명)
02016합계2125167460312733361502279318045119952992675
12016서울170109289211421016613483016
22016부산112713133241738411951403
32016대구382958421522940203
42016인천1028341671118541971515
52016광주917526822412481682222
62016대전64502028017661491211
72016울산53422623035211134301
82016세종111062100100100100
92016경기357268129163262408358938725217
기준년도지역합계(명)실외 소계(명)실외 실외작업장(명)실외 운동장(명)실외 논밭(명)실외 산(명)실외 강가해(명)변실외 길가(명)실외 주거지주(명)변실외 기타(명)실내 소계(명)실내 집(명)실내 건물(명)실내 작업장(명)실내 비닐하우스(명)실내 기타(명)
982021세종1411100100000300300
992021경기27121611414263030524551891828
1002021강원55471711051517840112
1012021충북54472151210134720221
1022021충남837035195012441341512
1032021전북937536214108591862433
1042021전남1109746226434571340333
1052021경북1241014832043115723621302
1062021경남1261004541942153826741230
1072021제주65582841312154700430