Overview

Dataset statistics

Number of variables10
Number of observations36
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.2 KiB
Average record size in memory91.7 B

Variable types

Text1
Categorical2
Numeric7

Dataset

Description경남도내 18개 시·군의 신고방법별 환경오염신고 현황을 제공합니다.(전화, 모사전송, 컴퓨터통신, 엽서·편지, 직접방문 등)
Author경상남도
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15047242

Alerts

유독물 has constant value ""Constant
계 is highly overall correlated with 대 기 and 4 other fieldsHigh correlation
대 기 is highly overall correlated with 계 and 4 other fieldsHigh correlation
자동차매연 is highly overall correlated with 계 and 3 other fieldsHigh correlation
수 질 is highly overall correlated with 계 and 4 other fieldsHigh correlation
사업장폐기물 is highly overall correlated with 계 and 3 other fieldsHigh correlation
생활폐기물 is highly overall correlated with 계 and 4 other fieldsHigh correlation
대 기 has 2 (5.6%) zerosZeros
자동차매연 has 8 (22.2%) zerosZeros
수 질 has 3 (8.3%) zerosZeros
사업장폐기물 has 2 (5.6%) zerosZeros
생활폐기물 has 2 (5.6%) zerosZeros
기 타 has 5 (13.9%) zerosZeros

Reproduction

Analysis started2023-12-10 23:32:05.628019
Analysis finished2023-12-10 23:32:10.622947
Duration4.99 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군별
Text

Distinct18
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size420.0 B
2023-12-11T08:32:10.727860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters108
Distinct characters29
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 row창원시
2nd row창원시
3rd row진주시
4th row진주시
5th row통영시
ValueCountFrequency (%)
창원시 2
 
5.6%
진주시 2
 
5.6%
거창군 2
 
5.6%
함양군 2
 
5.6%
산청군 2
 
5.6%
하동군 2
 
5.6%
남해군 2
 
5.6%
고성군 2
 
5.6%
창녕군 2
 
5.6%
함안군 2
 
5.6%
Other values (8) 16
44.4%
2023-12-11T08:32:10.997129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
군 20
18.5%
시 16
14.8%
창 6
 
5.6%
양 6
 
5.6%
산 4
 
3.7%
거 4
 
3.7%
해 4
 
3.7%
천 4
 
3.7%
함 4
 
3.7%
성 2
 
1.9%
Other values (19) 38
35.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 108
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
군 20
18.5%
시 16
14.8%
창 6
 
5.6%
양 6
 
5.6%
산 4
 
3.7%
거 4
 
3.7%
해 4
 
3.7%
천 4
 
3.7%
함 4
 
3.7%
성 2
 
1.9%
Other values (19) 38
35.2%

Most occurring scripts

ValueCountFrequency (%)
Hangul 108
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
군 20
18.5%
시 16
14.8%
창 6
 
5.6%
양 6
 
5.6%
산 4
 
3.7%
거 4
 
3.7%
해 4
 
3.7%
천 4
 
3.7%
함 4
 
3.7%
성 2
 
1.9%
Other values (19) 38
35.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 108
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
군 20
18.5%
시 16
14.8%
창 6
 
5.6%
양 6
 
5.6%
산 4
 
3.7%
거 4
 
3.7%
해 4
 
3.7%
천 4
 
3.7%
함 4
 
3.7%
성 2
 
1.9%
Other values (19) 38
35.2%

상하반기구별
Categorical

Distinct2
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Memory size420.0 B
2018 상반기
18 
2017 하반기
18 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2018 상반기
2nd row2017 하반기
3rd row2018 상반기
4th row2017 하반기
5th row2018 상반기

Common Values

ValueCountFrequency (%)
2018 상반기 18
50.0%
2017 하반기 18
50.0%

Length

2023-12-11T08:32:11.111622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:32:11.201969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2018 18
25.0%
상반기 18
25.0%
2017 18
25.0%
하반기 18
25.0%

계
Real number (ℝ)

HIGH CORRELATION 

Distinct35
Distinct (%)97.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean470.63889
Minimum1
Maximum3745
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-11T08:32:11.528735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile14.75
Q159.25
median120.5
Q3339.25
95-th percentile2414.25
Maximum3745
Range3744
Interquartile range (IQR)280

Descriptive statistics

Standard deviation848.63982
Coefficient of variation (CV)1.8031655
Kurtosis6.9346952
Mean470.63889
Median Absolute Deviation (MAD)84.5
Skewness2.6738788
Sum16943
Variance720189.55
MonotonicityNot monotonic
2023-12-11T08:32:11.641155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
178 2
 
5.6%
1846 1
 
2.8%
2314 1
 
2.8%
92 1
 
2.8%
66 1
 
2.8%
14 1
 
2.8%
24 1
 
2.8%
103 1
 
2.8%
60 1
 
2.8%
37 1
 
2.8%
Other values (25) 25
69.4%
ValueCountFrequency (%)
1 1
2.8%
14 1
2.8%
15 1
2.8%
24 1
2.8%
35 1
2.8%
37 1
2.8%
39 1
2.8%
56 1
2.8%
57 1
2.8%
60 1
2.8%
ValueCountFrequency (%)
3745 1
2.8%
2715 1
2.8%
2314 1
2.8%
1846 1
2.8%
965 1
2.8%
887 1
2.8%
673 1
2.8%
400 1
2.8%
340 1
2.8%
339 1
2.8%

대 기
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct32
Distinct (%)88.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean145.77778
Minimum0
Maximum1607
Zeros2
Zeros (%)5.6%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-11T08:32:11.758263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.5
Q111.5
median25.5
Q398.75
95-th percentile568
Maximum1607
Range1607
Interquartile range (IQR)87.25

Descriptive statistics

Standard deviation323.95142
Coefficient of variation (CV)2.2222277
Kurtosis13.490394
Mean145.77778
Median Absolute Deviation (MAD)21
Skewness3.5678456
Sum5248
Variance104944.52
MonotonicityNot monotonic
2023-12-11T08:32:11.874368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
0 2
 
5.6%
16 2
 
5.6%
19 2
 
5.6%
3 2
 
5.6%
17 1
 
2.8%
8 1
 
2.8%
15 1
 
2.8%
12 1
 
2.8%
6 1
 
2.8%
9 1
 
2.8%
Other values (22) 22
61.1%
ValueCountFrequency (%)
0 2
5.6%
2 1
2.8%
3 2
5.6%
6 1
2.8%
8 1
2.8%
9 1
2.8%
10 1
2.8%
12 1
2.8%
15 1
2.8%
16 2
5.6%
ValueCountFrequency (%)
1607 1
2.8%
1111 1
2.8%
387 1
2.8%
386 1
2.8%
366 1
2.8%
315 1
2.8%
159 1
2.8%
142 1
2.8%
107 1
2.8%
96 1
2.8%

자동차매연
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct25
Distinct (%)69.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65.055556
Minimum0
Maximum705
Zeros8
Zeros (%)22.2%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-11T08:32:12.009942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median12
Q334.25
95-th percentile403.75
Maximum705
Range705
Interquartile range (IQR)33.25

Descriptive statistics

Standard deviation148.41485
Coefficient of variation (CV)2.2813555
Kurtosis10.915788
Mean65.055556
Median Absolute Deviation (MAD)11.5
Skewness3.2633729
Sum2342
Variance22026.968
MonotonicityNot monotonic
2023-12-11T08:32:12.140167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
0 8
22.2%
4 2
 
5.6%
15 2
 
5.6%
12 2
 
5.6%
1 2
 
5.6%
460 1
 
2.8%
385 1
 
2.8%
2 1
 
2.8%
10 1
 
2.8%
9 1
 
2.8%
Other values (15) 15
41.7%
ValueCountFrequency (%)
0 8
22.2%
1 2
 
5.6%
2 1
 
2.8%
4 2
 
5.6%
5 1
 
2.8%
8 1
 
2.8%
9 1
 
2.8%
10 1
 
2.8%
12 2
 
5.6%
15 2
 
5.6%
ValueCountFrequency (%)
705 1
2.8%
460 1
2.8%
385 1
2.8%
177 1
2.8%
94 1
2.8%
93 1
2.8%
80 1
2.8%
65 1
2.8%
56 1
2.8%
27 1
2.8%

수 질
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct21
Distinct (%)58.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19
Minimum0
Maximum108
Zeros3
Zeros (%)8.3%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-11T08:32:12.260137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q322.5
95-th percentile86
Maximum108
Range108
Interquartile range (IQR)20.5

Descriptive statistics

Standard deviation27.875232
Coefficient of variation (CV)1.4671175
Kurtosis3.5711809
Mean19
Median Absolute Deviation (MAD)4.5
Skewness2.0109153
Sum684
Variance777.02857
MonotonicityNot monotonic
2023-12-11T08:32:12.391846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
1 5
13.9%
5 4
 
11.1%
2 4
 
11.1%
0 3
 
8.3%
3 3
 
8.3%
18 2
 
5.6%
108 1
 
2.8%
4 1
 
2.8%
6 1
 
2.8%
10 1
 
2.8%
Other values (11) 11
30.6%
ValueCountFrequency (%)
0 3
8.3%
1 5
13.9%
2 4
11.1%
3 3
8.3%
4 1
 
2.8%
5 4
11.1%
6 1
 
2.8%
10 1
 
2.8%
18 2
 
5.6%
19 1
 
2.8%
ValueCountFrequency (%)
108 1
2.8%
98 1
2.8%
82 1
2.8%
54 1
2.8%
47 1
2.8%
46 1
2.8%
36 1
2.8%
28 1
2.8%
27 1
2.8%
21 1
2.8%

사업장폐기물
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct22
Distinct (%)61.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.25
Minimum0
Maximum59
Zeros2
Zeros (%)5.6%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-11T08:32:12.505421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.75
Q12
median5
Q325.5
95-th percentile46.25
Maximum59
Range59
Interquartile range (IQR)23.5

Descriptive statistics

Standard deviation16.624208
Coefficient of variation (CV)1.1666111
Kurtosis0.80642526
Mean14.25
Median Absolute Deviation (MAD)4.5
Skewness1.316455
Sum513
Variance276.36429
MonotonicityNot monotonic
2023-12-11T08:32:12.611810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
2 5
13.9%
1 4
 
11.1%
4 4
 
11.1%
5 2
 
5.6%
3 2
 
5.6%
0 2
 
5.6%
27 2
 
5.6%
38 1
 
2.8%
35 1
 
2.8%
12 1
 
2.8%
Other values (12) 12
33.3%
ValueCountFrequency (%)
0 2
 
5.6%
1 4
11.1%
2 5
13.9%
3 2
 
5.6%
4 4
11.1%
5 2
 
5.6%
6 1
 
2.8%
10 1
 
2.8%
12 1
 
2.8%
13 1
 
2.8%
ValueCountFrequency (%)
59 1
2.8%
56 1
2.8%
43 1
2.8%
40 1
2.8%
38 1
2.8%
35 1
2.8%
28 1
2.8%
27 2
5.6%
25 1
2.8%
18 1
2.8%

생활폐기물
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct31
Distinct (%)86.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean154.22222
Minimum0
Maximum1947
Zeros2
Zeros (%)5.6%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-11T08:32:12.720621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.75
Q17.75
median27.5
Q3118.75
95-th percentile615.25
Maximum1947
Range1947
Interquartile range (IQR)111

Descriptive statistics

Standard deviation359.50197
Coefficient of variation (CV)2.3310646
Kurtosis18.676994
Mean154.22222
Median Absolute Deviation (MAD)26.5
Skewness4.1049601
Sum5552
Variance129241.66
MonotonicityNot monotonic
2023-12-11T08:32:12.832085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
0 2
 
5.6%
17 2
 
5.6%
58 2
 
5.6%
3 2
 
5.6%
1 2
 
5.6%
77 1
 
2.8%
78 1
 
2.8%
20 1
 
2.8%
19 1
 
2.8%
28 1
 
2.8%
Other values (21) 21
58.3%
ValueCountFrequency (%)
0 2
5.6%
1 2
5.6%
2 1
2.8%
3 2
5.6%
6 1
2.8%
7 1
2.8%
8 1
2.8%
9 1
2.8%
13 1
2.8%
17 2
5.6%
ValueCountFrequency (%)
1947 1
2.8%
976 1
2.8%
495 1
2.8%
360 1
2.8%
266 1
2.8%
227 1
2.8%
195 1
2.8%
182 1
2.8%
160 1
2.8%
105 1
2.8%

유독물
Categorical

CONSTANT 

Distinct1
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Memory size420.0 B
0
36 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 36
100.0%

Length

2023-12-11T08:32:12.980134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:32:13.079656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 36
100.0%

기 타
Real number (ℝ)

ZEROS 

Distinct30
Distinct (%)83.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean137.38889
Minimum0
Maximum1556
Zeros5
Zeros (%)13.9%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-11T08:32:13.162987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q19
median25.5
Q382.5
95-th percentile608
Maximum1556
Range1556
Interquartile range (IQR)73.5

Descriptive statistics

Standard deviation333.52132
Coefficient of variation (CV)2.4275713
Kurtosis13.197973
Mean137.38889
Median Absolute Deviation (MAD)23
Skewness3.6533186
Sum4946
Variance111236.47
MonotonicityNot monotonic
2023-12-11T08:32:13.275935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
0 5
 
13.9%
9 2
 
5.6%
32 2
 
5.6%
323 1
 
2.8%
12 1
 
2.8%
56 1
 
2.8%
39 1
 
2.8%
17 1
 
2.8%
14 1
 
2.8%
22 1
 
2.8%
Other values (20) 20
55.6%
ValueCountFrequency (%)
0 5
13.9%
1 1
 
2.8%
7 1
 
2.8%
8 1
 
2.8%
9 2
 
5.6%
12 1
 
2.8%
13 1
 
2.8%
14 1
 
2.8%
17 1
 
2.8%
19 1
 
2.8%
ValueCountFrequency (%)
1556 1
2.8%
1319 1
2.8%
371 1
2.8%
323 1
2.8%
263 1
2.8%
176 1
2.8%
173 1
2.8%
92 1
2.8%
87 1
2.8%
81 1
2.8%

Interactions

2023-12-11T08:32:09.858337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:32:05.887461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:32:06.898111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:32:07.494956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:32:08.083173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:32:08.673190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:32:09.266602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:32:09.936007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:32:05.960495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:32:06.980506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:32:07.571783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:32:08.179744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:32:08.756973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:32:09.353964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:32:10.039940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:32:06.035384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:32:07.078229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:32:07.664178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:32:08.263309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:32:08.859124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:32:09.458403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:32:10.122804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:32:06.113939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:32:07.156497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:32:07.758338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:32:08.341546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:32:08.937426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:32:09.547284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:32:10.199262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:32:06.188649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:32:07.231149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:32:07.841474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:32:08.408238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:32:09.011888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:32:09.620304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:32:10.269351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:32:06.266993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:32:07.312855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:32:07.920547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:32:08.486157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:32:09.088471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:32:09.698342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:32:10.348350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:32:06.741196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:32:07.414604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:32:08.003857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:32:08.576241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:32:09.185285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:32:09.776003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T08:32:13.375963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군별상하반기구별계대 기자동차매연수 질사업장폐기물생활폐기물기 타
시군별1.0000.0000.4670.3590.4850.7860.7900.6290.861
상하반기구별0.0001.0000.0000.0000.0000.0000.0000.0800.000
계0.4670.0001.0000.9400.9370.8550.6490.8910.921
대 기0.3590.0000.9401.0000.9080.7380.5110.7640.872
자동차매연0.4850.0000.9370.9081.0000.6470.5030.7460.718
수 질0.7860.0000.8550.7380.6471.0000.7360.8130.935
사업장폐기물0.7900.0000.6490.5110.5030.7361.0000.4880.668
생활폐기물0.6290.0800.8910.7640.7460.8130.4881.0000.967
기 타0.8610.0000.9210.8720.7180.9350.6680.9671.000
2023-12-11T08:32:13.501887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
계대 기자동차매연수 질사업장폐기물생활폐기물기 타상하반기구별
계1.0000.9340.8410.7500.6410.8990.4840.000
대 기0.9341.0000.8910.7360.5530.8560.3560.000
자동차매연0.8410.8911.0000.6940.4600.7330.3960.000
수 질0.7500.7360.6941.0000.5220.6330.4510.000
사업장폐기물0.6410.5530.4600.5221.0000.7210.3750.000
생활폐기물0.8990.8560.7330.6330.7211.0000.2770.075
기 타0.4840.3560.3960.4510.3750.2771.0000.000
상하반기구별0.0000.0000.0000.0000.0000.0750.0001.000

Missing values

2023-12-11T08:32:10.450537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T08:32:10.574094image/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

시군별상하반기구별계대 기자동차매연수 질사업장폐기물생활폐기물유독물기 타
0창원시2018 상반기18461111460108382660323
1창원시2017 하반기2314160770598561820371
2진주시2018 상반기8873871770549500
3진주시2017 하반기400315934732609
4통영시2018 상반기154301931858045
5통영시2017 하반기1343315536087
6사천시2018 상반기298841231619500
7사천시2017 하반기340142172125105047
8김해시2018 상반기3745366805459194701319
9김해시2017 하반기271510765364097601556
시군별상하반기구별계대 기자동차매연수 질사업장폐기물생활폐기물유독물기 타
26하동군2018 상반기103190101428032
27하동군2017 하반기609021213024
28산청군2018 상반기3716122809
29산청군2017 하반기9319106451013
30함양군2018 상반기156011007
31함양군2017 하반기39124401022
32거창군2018 상반기35154123014
33거창군2017 하반기5716125217017
34합천군2018 상반기5680342039
35합천군2017 하반기7032227056