Overview

Dataset statistics

Number of variables11
Number of observations36
Missing cells39
Missing cells (%)9.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.5 KiB
Average record size in memory100.7 B

Variable types

Text1
Categorical1
Numeric9

Dataset

Description경남도내 18개 시·군 환경오염신고 내용조사결과 현황을 제공합니다.(행정처분, 개선권고, 자동차매연, 위반사실 미발견, 개인이해 및 허위신고 등)
Author경상남도
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15047241

Alerts

is highly overall correlated with 행정처분 and 3 other fieldsHigh correlation
행정처분 is highly overall correlated with and 2 other fieldsHigh correlation
개선권고개수 is highly overall correlated with and 2 other fieldsHigh correlation
자동차매연 is highly overall correlated with and 1 other fieldsHigh correlation
위반사실미발견 is highly overall correlated with and 2 other fieldsHigh correlation
병과 is highly overall correlated with 행정처분High correlation
행정처분 has 1 (2.8%) missing valuesMissing
개선권고개수 has 4 (11.1%) missing valuesMissing
자동차매연 has 5 (13.9%) missing valuesMissing
개인이해 has 4 (11.1%) missing valuesMissing
허위신고 has 10 (27.8%) missing valuesMissing
순수고발 has 8 (22.2%) missing valuesMissing
병과 has 7 (19.4%) missing valuesMissing
개선권고개수 has 3 (8.3%) zerosZeros
자동차매연 has 5 (13.9%) zerosZeros
개인이해 has 6 (16.7%) zerosZeros
허위신고 has 20 (55.6%) zerosZeros
순수고발 has 5 (13.9%) zerosZeros
병과 has 6 (16.7%) zerosZeros

Reproduction

Analysis started2023-12-10 23:28:29.103448
Analysis finished2023-12-10 23:28:38.204389
Duration9.1 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Text

Distinct18
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size420.0 B
2023-12-11T08:28:38.314890image/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%
2023-12-11T08:28:38.634840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
20
18.5%
16
14.8%
6
 
5.6%
6
 
5.6%
4
 
3.7%
4
 
3.7%
4
 
3.7%
4
 
3.7%
4
 
3.7%
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 (%)
20
18.5%
16
14.8%
6
 
5.6%
6
 
5.6%
4
 
3.7%
4
 
3.7%
4
 
3.7%
4
 
3.7%
4
 
3.7%
2
 
1.9%
Other values (19) 38
35.2%

Most occurring scripts

ValueCountFrequency (%)
Hangul 108
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
20
18.5%
16
14.8%
6
 
5.6%
6
 
5.6%
4
 
3.7%
4
 
3.7%
4
 
3.7%
4
 
3.7%
4
 
3.7%
2
 
1.9%
Other values (19) 38
35.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 108
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
20
18.5%
16
14.8%
6
 
5.6%
6
 
5.6%
4
 
3.7%
4
 
3.7%
4
 
3.7%
4
 
3.7%
4
 
3.7%
2
 
1.9%
Other values (19) 38
35.2%

연도
Categorical

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

Length

Max length9
Median length9
Mean length9
Min length9

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2018년 상반기
2nd row2017년 하반기
3rd row2018년 상반기
4th row2017년 하반기
5th row2018년 상반기

Common Values

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

Length

2023-12-11T08:28:38.767930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:28:38.867163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2018년 18
25.0%
상반기 18
25.0%
2017년 18
25.0%
하반기 18
25.0%


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
2023-12-11T08:28:38.974517image/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
2023-12-11T08:28:39.122237image/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%

행정처분
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct27
Distinct (%)77.1%
Missing1
Missing (%)2.8%
Infinite0
Infinite (%)0.0%
Mean37.942857
Minimum1
Maximum222
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-11T08:28:39.271991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q18
median19
Q349
95-th percentile138.4
Maximum222
Range221
Interquartile range (IQR)41

Descriptive statistics

Standard deviation51.909391
Coefficient of variation (CV)1.3680939
Kurtosis7.8680967
Mean37.942857
Median Absolute Deviation (MAD)14
Skewness2.7337565
Sum1328
Variance2694.5849
MonotonicityNot monotonic
2023-12-11T08:28:39.396595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
7 3
 
8.3%
1 3
 
8.3%
52 2
 
5.6%
19 2
 
5.6%
22 2
 
5.6%
18 2
 
5.6%
46 1
 
2.8%
36 1
 
2.8%
16 1
 
2.8%
4 1
 
2.8%
Other values (17) 17
47.2%
ValueCountFrequency (%)
1 3
8.3%
2 1
 
2.8%
4 1
 
2.8%
5 1
 
2.8%
7 3
8.3%
9 1
 
2.8%
10 1
 
2.8%
12 1
 
2.8%
14 1
 
2.8%
16 1
 
2.8%
ValueCountFrequency (%)
222 1
2.8%
221 1
2.8%
103 1
2.8%
76 1
2.8%
64 1
2.8%
63 1
2.8%
60 1
2.8%
52 2
5.6%
46 1
2.8%
40 1
2.8%

개선권고개수
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct23
Distinct (%)71.9%
Missing4
Missing (%)11.1%
Infinite0
Infinite (%)0.0%
Mean109.09375
Minimum0
Maximum1208
Zeros3
Zeros (%)8.3%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-11T08:28:39.514005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15.75
median15.5
Q363.5
95-th percentile625.3
Maximum1208
Range1208
Interquartile range (IQR)57.75

Descriptive statistics

Standard deviation255.964
Coefficient of variation (CV)2.3462756
Kurtosis11.693028
Mean109.09375
Median Absolute Deviation (MAD)11.5
Skewness3.34081
Sum3491
Variance65517.572
MonotonicityNot monotonic
2023-12-11T08:28:39.650153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
10 4
 
11.1%
4 3
 
8.3%
0 3
 
8.3%
15 2
 
5.6%
16 2
 
5.6%
3 1
 
2.8%
7 1
 
2.8%
5 1
 
2.8%
27 1
 
2.8%
538 1
 
2.8%
Other values (13) 13
36.1%
(Missing) 4
 
11.1%
ValueCountFrequency (%)
0 3
8.3%
3 1
 
2.8%
4 3
8.3%
5 1
 
2.8%
6 1
 
2.8%
7 1
 
2.8%
10 4
11.1%
15 2
5.6%
16 2
5.6%
17 1
 
2.8%
ValueCountFrequency (%)
1208 1
2.8%
732 1
2.8%
538 1
2.8%
227 1
2.8%
215 1
2.8%
98 1
2.8%
83 1
2.8%
65 1
2.8%
63 1
2.8%
33 1
2.8%

자동차매연
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct23
Distinct (%)74.2%
Missing5
Missing (%)13.9%
Infinite0
Infinite (%)0.0%
Mean69.741935
Minimum0
Maximum705
Zeros5
Zeros (%)13.9%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-11T08:28:39.767193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12.5
median15
Q346
95-th percentile375
Maximum705
Range705
Interquartile range (IQR)43.5

Descriptive statistics

Standard deviation151.50731
Coefficient of variation (CV)2.172399
Kurtosis10.671415
Mean69.741935
Median Absolute Deviation (MAD)14
Skewness3.1738424
Sum2162
Variance22954.465
MonotonicityNot monotonic
2023-12-11T08:28:39.879250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0 5
 
13.9%
1 3
 
8.3%
4 2
 
5.6%
15 2
 
5.6%
78 1
 
2.8%
12 1
 
2.8%
10 1
 
2.8%
9 1
 
2.8%
8 1
 
2.8%
5 1
 
2.8%
Other values (13) 13
36.1%
(Missing) 5
 
13.9%
ValueCountFrequency (%)
0 5
13.9%
1 3
8.3%
4 2
 
5.6%
5 1
 
2.8%
8 1
 
2.8%
9 1
 
2.8%
10 1
 
2.8%
12 1
 
2.8%
15 2
 
5.6%
17 1
 
2.8%
ValueCountFrequency (%)
705 1
2.8%
394 1
2.8%
356 1
2.8%
177 1
2.8%
93 1
2.8%
80 1
2.8%
78 1
2.8%
65 1
2.8%
27 1
2.8%
23 1
2.8%

위반사실미발견
Real number (ℝ)

HIGH CORRELATION 

Distinct30
Distinct (%)83.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean274.11111
Minimum1
Maximum3257
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-11T08:28:39.996035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.75
Q117.25
median49
Q3178.25
95-th percentile1303.75
Maximum3257
Range3256
Interquartile range (IQR)161

Descriptive statistics

Standard deviation669.38598
Coefficient of variation (CV)2.4420242
Kurtosis13.768824
Mean274.11111
Median Absolute Deviation (MAD)41.5
Skewness3.6811516
Sum9868
Variance448077.59
MonotonicityNot monotonic
2023-12-11T08:28:40.123173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
5 3
 
8.3%
60 2
 
5.6%
10 2
 
5.6%
32 2
 
5.6%
49 2
 
5.6%
724 1
 
2.8%
87 1
 
2.8%
103 1
 
2.8%
2 1
 
2.8%
43 1
 
2.8%
Other values (20) 20
55.6%
ValueCountFrequency (%)
1 1
 
2.8%
2 1
 
2.8%
3 1
 
2.8%
5 3
8.3%
10 2
5.6%
12 1
 
2.8%
19 1
 
2.8%
20 1
 
2.8%
23 1
 
2.8%
26 1
 
2.8%
ValueCountFrequency (%)
3257 1
2.8%
2425 1
2.8%
930 1
2.8%
724 1
2.8%
360 1
2.8%
355 1
2.8%
269 1
2.8%
221 1
2.8%
197 1
2.8%
172 1
2.8%

개인이해
Real number (ℝ)

MISSING  ZEROS 

Distinct21
Distinct (%)65.6%
Missing4
Missing (%)11.1%
Infinite0
Infinite (%)0.0%
Mean61.3125
Minimum0
Maximum605
Zeros6
Zeros (%)16.7%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-11T08:28:40.235582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median16
Q353
95-th percentile300
Maximum605
Range605
Interquartile range (IQR)50

Descriptive statistics

Standard deviation124.68087
Coefficient of variation (CV)2.033531
Kurtosis12.085713
Mean61.3125
Median Absolute Deviation (MAD)16
Skewness3.3208885
Sum1962
Variance15545.319
MonotonicityNot monotonic
2023-12-11T08:28:40.370290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0 6
16.7%
4 3
 
8.3%
3 3
 
8.3%
20 2
 
5.6%
53 2
 
5.6%
79 1
 
2.8%
38 1
 
2.8%
7 1
 
2.8%
9 1
 
2.8%
12 1
 
2.8%
Other values (11) 11
30.6%
(Missing) 4
 
11.1%
ValueCountFrequency (%)
0 6
16.7%
1 1
 
2.8%
3 3
8.3%
4 3
8.3%
7 1
 
2.8%
9 1
 
2.8%
12 1
 
2.8%
20 2
 
5.6%
24 1
 
2.8%
27 1
 
2.8%
ValueCountFrequency (%)
605 1
2.8%
333 1
2.8%
273 1
2.8%
118 1
2.8%
97 1
2.8%
92 1
2.8%
79 1
2.8%
53 2
5.6%
41 1
2.8%
39 1
2.8%

허위신고
Real number (ℝ)

MISSING  ZEROS 

Distinct6
Distinct (%)23.1%
Missing10
Missing (%)27.8%
Infinite0
Infinite (%)0.0%
Mean1.5
Minimum0
Maximum15
Zeros20
Zeros (%)55.6%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-11T08:28:40.489774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile10
Maximum15
Range15
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.7656341
Coefficient of variation (CV)2.5104227
Kurtosis7.3340444
Mean1.5
Median Absolute Deviation (MAD)0
Skewness2.7851913
Sum39
Variance14.18
MonotonicityNot monotonic
2023-12-11T08:28:40.583162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 20
55.6%
1 2
 
5.6%
11 1
 
2.8%
15 1
 
2.8%
7 1
 
2.8%
4 1
 
2.8%
(Missing) 10
27.8%
ValueCountFrequency (%)
0 20
55.6%
1 2
 
5.6%
4 1
 
2.8%
7 1
 
2.8%
11 1
 
2.8%
15 1
 
2.8%
ValueCountFrequency (%)
15 1
 
2.8%
11 1
 
2.8%
7 1
 
2.8%
4 1
 
2.8%
1 2
 
5.6%
0 20
55.6%

순수고발
Real number (ℝ)

MISSING  ZEROS 

Distinct13
Distinct (%)46.4%
Missing8
Missing (%)22.2%
Infinite0
Infinite (%)0.0%
Mean10.642857
Minimum0
Maximum180
Zeros5
Zeros (%)13.9%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-11T08:28:40.710423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q36.25
95-th percentile17.3
Maximum180
Range180
Interquartile range (IQR)5.25

Descriptive statistics

Standard deviation33.603587
Coefficient of variation (CV)3.157384
Kurtosis26.491513
Mean10.642857
Median Absolute Deviation (MAD)2
Skewness5.089811
Sum298
Variance1129.2011
MonotonicityNot monotonic
2023-12-11T08:28:40.843418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
1 7
19.4%
0 5
13.9%
2 3
 
8.3%
6 2
 
5.6%
3 2
 
5.6%
4 2
 
5.6%
11 1
 
2.8%
18 1
 
2.8%
180 1
 
2.8%
16 1
 
2.8%
Other values (3) 3
 
8.3%
(Missing) 8
22.2%
ValueCountFrequency (%)
0 5
13.9%
1 7
19.4%
2 3
8.3%
3 2
 
5.6%
4 2
 
5.6%
6 2
 
5.6%
7 1
 
2.8%
11 1
 
2.8%
12 1
 
2.8%
15 1
 
2.8%
ValueCountFrequency (%)
180 1
2.8%
18 1
2.8%
16 1
2.8%
15 1
2.8%
12 1
2.8%
11 1
2.8%
7 1
2.8%
6 2
5.6%
4 2
5.6%
3 2
5.6%

병과
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct11
Distinct (%)37.9%
Missing7
Missing (%)19.4%
Infinite0
Infinite (%)0.0%
Mean5.4482759
Minimum0
Maximum41
Zeros6
Zeros (%)16.7%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-11T08:28:40.955340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q34
95-th percentile20.8
Maximum41
Range41
Interquartile range (IQR)3

Descriptive statistics

Standard deviation9.0655794
Coefficient of variation (CV)1.6639355
Kurtosis8.0613941
Mean5.4482759
Median Absolute Deviation (MAD)2
Skewness2.7011487
Sum158
Variance82.184729
MonotonicityNot monotonic
2023-12-11T08:28:41.050950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 6
16.7%
1 6
16.7%
2 6
16.7%
4 4
11.1%
5 1
 
2.8%
19 1
 
2.8%
12 1
 
2.8%
41 1
 
2.8%
22 1
 
2.8%
18 1
 
2.8%
(Missing) 7
19.4%
ValueCountFrequency (%)
0 6
16.7%
1 6
16.7%
2 6
16.7%
4 4
11.1%
5 1
 
2.8%
7 1
 
2.8%
12 1
 
2.8%
18 1
 
2.8%
19 1
 
2.8%
22 1
 
2.8%
ValueCountFrequency (%)
41 1
 
2.8%
22 1
 
2.8%
19 1
 
2.8%
18 1
 
2.8%
12 1
 
2.8%
7 1
 
2.8%
5 1
 
2.8%
4 4
11.1%
2 6
16.7%
1 6
16.7%

Interactions

2023-12-11T08:28:36.819014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:29.497571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:30.472626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:31.281254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:32.401057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:33.454721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:34.435267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:35.274678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:36.075045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:36.888690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:29.586364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:30.575741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:31.368528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:32.515218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:33.549316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:34.554366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:35.377942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:36.164345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:36.955550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:29.697244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:30.669852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:31.449207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:32.638497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:33.650502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:34.648619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:35.483281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:36.233014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:37.032163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:29.799429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:30.770096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:31.527779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:32.798778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:33.752305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:34.739968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:35.569580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:36.322284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:37.109748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:29.918495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:30.863370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:31.613451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:32.903130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:33.869130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:34.851273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:35.653782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:36.424832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:37.448864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:30.033305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:30.957070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:31.714209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:33.021585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:33.966650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:34.938314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:35.736448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:36.507712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:37.521874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:30.141397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:31.042459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:32.088603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:33.125739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:34.084092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:35.025327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:35.831655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:36.587995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:37.623076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:30.250807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:31.124983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:32.173482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:33.241502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:34.215200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:35.109433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:35.908977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:36.669305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:37.717640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:30.379972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:31.210488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:32.275875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:33.358515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:34.332298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:35.195735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:35.997655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:36.749460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T08:28:41.132517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분연도행정처분개선권고개수자동차매연위반사실미발견개인이해허위신고순수고발병과
구분1.0000.0000.4670.8570.0000.0000.8950.0000.0000.0000.000
연도0.0001.0000.0000.0000.1900.0000.0000.2520.2150.0530.000
0.4670.0001.0000.7420.8900.9160.9380.8610.4021.0000.843
행정처분0.8570.0000.7421.0000.5010.5080.7120.0000.5990.5500.935
개선권고개수0.0000.1900.8900.5011.0000.9770.9130.9540.7190.6400.413
자동차매연0.0000.0000.9160.5080.9771.0000.8760.9500.7231.0000.680
위반사실미발견0.8950.0000.9380.7120.9130.8761.0000.4570.1420.8110.832
개인이해0.0000.2520.8610.0000.9540.9500.4571.0000.0000.5060.653
허위신고0.0000.2150.4020.5990.7190.7230.1420.0001.0001.0000.696
순수고발0.0000.0531.0000.5500.6401.0000.8110.5061.0001.0000.979
병과0.0000.0000.8430.9350.4130.6800.8320.6530.6960.9791.000
2023-12-11T08:28:41.268646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정처분개선권고개수자동차매연위반사실미발견개인이해허위신고순수고발병과연도
1.0000.7470.7800.7470.7550.4070.3410.4040.3870.000
행정처분0.7471.0000.4380.4800.603-0.0770.2580.4340.5060.000
개선권고개수0.7800.4381.0000.8790.5160.3210.1340.3550.3400.211
자동차매연0.7470.4800.8791.0000.4900.1930.0330.4330.3500.000
위반사실미발견0.7550.6030.5160.4901.0000.0180.4480.3590.4550.000
개인이해0.407-0.0770.3210.1930.0181.0000.2090.008-0.1280.285
허위신고0.3410.2580.1340.0330.4480.2091.000-0.2890.2570.233
순수고발0.4040.4340.3550.4330.3590.008-0.2891.0000.2770.000
병과0.3870.5060.3400.3500.455-0.1280.2570.2771.0000.000
연도0.0000.0000.2110.0000.0000.2850.2330.0000.0001.000

Missing values

2023-12-11T08:28:37.813407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T08:28:37.954151image/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.
2023-12-11T08:28:38.111573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

구분연도행정처분개선권고개수자동차매연위반사실미발견개인이해허위신고순수고발병과
0창원시2018년 상반기1846467323947243330115
1창원시2017년 하반기2314401208705930118111819
2진주시2018년 상반기8875222717736050<NA><NA>
3진주시2017년 하반기400199893102730<NA><NA>
4통영시2018년 상반기154729196353110
5통영시2017년 하반기134133151979021
6사천시2018년 상반기2985260172531504
7사천시2017년 하반기34023171719797061
8김해시2018년 상반기3745221838032574<NA>18012
9김해시2017년 하반기27152226565242501341
구분연도행정처분개선권고개수자동차매연위반사실미발견개인이해허위신고순수고발병과
26하동군2018년 상반기1031816<NA>5712<NA><NA>2
27하동군2017년 하반기601240430010
28산청군2018년 상반기37916112<NA><NA><NA>4
29산청군2017년 하반기93631010103070
30함양군2018년 상반기151<NA><NA>59<NA><NA><NA>
31함양군2017년 하반기39144267011
32거창군2018년 상반기35104420<NA><NA>12
33거창군2017년 하반기57220122300127
34합천군2018년 상반기564<NA><NA>3220<NA><NA><NA>
35합천군2017년 하반기701631443042