Overview

Dataset statistics

Number of variables9
Number of observations36
Missing cells1
Missing cells (%)0.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.9 KiB
Average record size in memory82.7 B

Variable types

Text1
Categorical1
Numeric7

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 2 other fieldsHigh correlation
행정처분 is highly overall correlated with and 3 other fieldsHigh correlation
개선권고개수 is highly overall correlated with and 2 other fieldsHigh correlation
위반사실미발견 is highly overall correlated with and 2 other fieldsHigh correlation
순수고발 is highly overall correlated with 행정처분High correlation
행정처분 has 1 (2.8%) missing valuesMissing
개선권고개수 has 1 (2.8%) zerosZeros
위반사실미발견 has 2 (5.6%) zerosZeros
개인이해 has 11 (30.6%) zerosZeros
허위신고 has 28 (77.8%) zerosZeros
순수고발 has 9 (25.0%) zerosZeros

Reproduction

Analysis started2023-12-10 23:28:11.071546
Analysis finished2023-12-10 23:28:15.728862
Duration4.66 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
2023-12-11T08:28:15.867311image/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%
2023-12-11T08:28:16.218571image/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
2020년도 상반기
18 
2020년도 하반기
18 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020년도 상반기
2nd row2020년도 하반기
3rd row2020년도 상반기
4th row2020년도 하반기
5th row2020년도 상반기

Common Values

ValueCountFrequency (%)
2020년도 상반기 18
50.0%
2020년도 하반기 18
50.0%

Length

2023-12-11T08:28:16.372913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:28:16.472408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020년도 36
50.0%
상반기 18
25.0%
하반기 18
25.0%


Real number (ℝ)

HIGH CORRELATION 

Distinct35
Distinct (%)97.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean639.77778
Minimum4
Maximum4201
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-11T08:28:16.846299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile33.5
Q184
median213
Q3542.25
95-th percentile3020.25
Maximum4201
Range4197
Interquartile range (IQR)458.25

Descriptive statistics

Standard deviation995.71947
Coefficient of variation (CV)1.5563521
Kurtosis4.9501936
Mean639.77778
Median Absolute Deviation (MAD)170
Skewness2.3216494
Sum23032
Variance991457.26
MonotonicityNot monotonic
2023-12-11T08:28:16.976953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
43 2
 
5.6%
2089 1
 
2.8%
40 1
 
2.8%
200 1
 
2.8%
193 1
 
2.8%
165 1
 
2.8%
302 1
 
2.8%
4 1
 
2.8%
81 1
 
2.8%
48 1
 
2.8%
Other values (25) 25
69.4%
ValueCountFrequency (%)
4 1
2.8%
20 1
2.8%
38 1
2.8%
40 1
2.8%
43 2
5.6%
48 1
2.8%
80 1
2.8%
81 1
2.8%
85 1
2.8%
132 1
2.8%
ValueCountFrequency (%)
4201 1
2.8%
3114 1
2.8%
2989 1
2.8%
2089 1
2.8%
2000 1
2.8%
1049 1
2.8%
929 1
2.8%
926 1
2.8%
717 1
2.8%
484 1
2.8%

행정처분
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct27
Distinct (%)77.1%
Missing1
Missing (%)2.8%
Infinite0
Infinite (%)0.0%
Mean69.885714
Minimum2
Maximum424
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-11T08:28:17.110512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3.4
Q117
median39
Q376
95-th percentile242.2
Maximum424
Range422
Interquartile range (IQR)59

Descriptive statistics

Standard deviation93.196116
Coefficient of variation (CV)1.3335503
Kurtosis6.6837613
Mean69.885714
Median Absolute Deviation (MAD)32
Skewness2.4758461
Sum2446
Variance8685.516
MonotonicityNot monotonic
2023-12-11T08:28:17.231044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
17 4
 
11.1%
4 3
 
8.3%
71 3
 
8.3%
2 2
 
5.6%
118 1
 
2.8%
158 1
 
2.8%
23 1
 
2.8%
21 1
 
2.8%
10 1
 
2.8%
14 1
 
2.8%
Other values (17) 17
47.2%
ValueCountFrequency (%)
2 2
5.6%
4 3
8.3%
5 1
 
2.8%
10 1
 
2.8%
14 1
 
2.8%
17 4
11.1%
20 1
 
2.8%
21 1
 
2.8%
23 1
 
2.8%
25 1
 
2.8%
ValueCountFrequency (%)
424 1
 
2.8%
336 1
 
2.8%
202 1
 
2.8%
174 1
 
2.8%
158 1
 
2.8%
118 1
 
2.8%
108 1
 
2.8%
102 1
 
2.8%
81 1
 
2.8%
71 3
8.3%

개선권고개수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct32
Distinct (%)88.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean198.66667
Minimum0
Maximum2158
Zeros1
Zeros (%)2.8%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-11T08:28:17.339302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.75
Q19.75
median46.5
Q396.25
95-th percentile1049.25
Maximum2158
Range2158
Interquartile range (IQR)86.5

Descriptive statistics

Standard deviation451.91238
Coefficient of variation (CV)2.2747267
Kurtosis11.390484
Mean198.66667
Median Absolute Deviation (MAD)38
Skewness3.3209573
Sum7152
Variance204224.8
MonotonicityNot monotonic
2023-12-11T08:28:17.469024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
10 3
 
8.3%
6 2
 
5.6%
56 2
 
5.6%
1509 1
 
2.8%
14 1
 
2.8%
37 1
 
2.8%
26 1
 
2.8%
62 1
 
2.8%
8 1
 
2.8%
9 1
 
2.8%
Other values (22) 22
61.1%
ValueCountFrequency (%)
0 1
 
2.8%
2 1
 
2.8%
3 1
 
2.8%
5 1
 
2.8%
6 2
5.6%
7 1
 
2.8%
8 1
 
2.8%
9 1
 
2.8%
10 3
8.3%
11 1
 
2.8%
ValueCountFrequency (%)
2158 1
2.8%
1509 1
2.8%
896 1
2.8%
740 1
2.8%
320 1
2.8%
254 1
2.8%
180 1
2.8%
151 1
2.8%
118 1
2.8%
89 1
2.8%

위반사실미발견
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct34
Distinct (%)94.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean357.38889
Minimum0
Maximum2760
Zeros2
Zeros (%)5.6%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-11T08:28:17.599137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8.25
Q137.25
median107
Q3238
95-th percentile1970
Maximum2760
Range2760
Interquartile range (IQR)200.75

Descriptive statistics

Standard deviation673.89691
Coefficient of variation (CV)1.8856124
Kurtosis7.4096911
Mean357.38889
Median Absolute Deviation (MAD)88.5
Skewness2.7941716
Sum12866
Variance454137.04
MonotonicityNot monotonic
2023-12-11T08:28:17.719997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
38 2
 
5.6%
0 2
 
5.6%
987 1
 
2.8%
17 1
 
2.8%
104 1
 
2.8%
35 1
 
2.8%
113 1
 
2.8%
118 1
 
2.8%
15 1
 
2.8%
18 1
 
2.8%
Other values (24) 24
66.7%
ValueCountFrequency (%)
0 2
5.6%
11 1
2.8%
15 1
2.8%
17 1
2.8%
18 1
2.8%
19 1
2.8%
27 1
2.8%
35 1
2.8%
38 2
5.6%
44 1
2.8%
ValueCountFrequency (%)
2760 1
2.8%
2648 1
2.8%
1744 1
2.8%
987 1
2.8%
797 1
2.8%
713 1
2.8%
515 1
2.8%
293 1
2.8%
253 1
2.8%
233 1
2.8%

개인이해
Real number (ℝ)

ZEROS 

Distinct23
Distinct (%)63.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean61.138889
Minimum0
Maximum755
Zeros11
Zeros (%)30.6%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-11T08:28:17.830418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median11
Q353
95-th percentile237
Maximum755
Range755
Interquartile range (IQR)53

Descriptive statistics

Standard deviation142.03021
Coefficient of variation (CV)2.3230747
Kurtosis17.556617
Mean61.138889
Median Absolute Deviation (MAD)11
Skewness4.0157996
Sum2201
Variance20172.58
MonotonicityNot monotonic
2023-12-11T08:28:17.960016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0 11
30.6%
4 2
 
5.6%
32 2
 
5.6%
10 2
 
5.6%
71 1
 
2.8%
25 1
 
2.8%
46 1
 
2.8%
62 1
 
2.8%
2 1
 
2.8%
30 1
 
2.8%
Other values (13) 13
36.1%
ValueCountFrequency (%)
0 11
30.6%
1 1
 
2.8%
2 1
 
2.8%
4 2
 
5.6%
7 1
 
2.8%
10 2
 
5.6%
12 1
 
2.8%
20 1
 
2.8%
25 1
 
2.8%
30 1
 
2.8%
ValueCountFrequency (%)
755 1
2.8%
426 1
2.8%
174 1
2.8%
121 1
2.8%
108 1
2.8%
86 1
2.8%
77 1
2.8%
71 1
2.8%
62 1
2.8%
50 1
2.8%

허위신고
Real number (ℝ)

ZEROS 

Distinct8
Distinct (%)22.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4722222
Minimum0
Maximum40
Zeros28
Zeros (%)77.8%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-11T08:28:18.092603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile15.25
Maximum40
Range40
Interquartile range (IQR)0

Descriptive statistics

Standard deviation7.7182015
Coefficient of variation (CV)3.1219692
Kurtosis17.200367
Mean2.4722222
Median Absolute Deviation (MAD)0
Skewness4.0172788
Sum89
Variance59.570635
MonotonicityNot monotonic
2023-12-11T08:28:18.216713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 28
77.8%
1 2
 
5.6%
13 1
 
2.8%
22 1
 
2.8%
40 1
 
2.8%
4 1
 
2.8%
5 1
 
2.8%
3 1
 
2.8%
ValueCountFrequency (%)
0 28
77.8%
1 2
 
5.6%
3 1
 
2.8%
4 1
 
2.8%
5 1
 
2.8%
13 1
 
2.8%
22 1
 
2.8%
40 1
 
2.8%
ValueCountFrequency (%)
40 1
 
2.8%
22 1
 
2.8%
13 1
 
2.8%
5 1
 
2.8%
4 1
 
2.8%
3 1
 
2.8%
1 2
 
5.6%
0 28
77.8%

순수고발
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct17
Distinct (%)47.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.5
Minimum0
Maximum103
Zeros9
Zeros (%)25.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-11T08:28:18.365820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.75
median4
Q311
95-th percentile26.5
Maximum103
Range103
Interquartile range (IQR)10.25

Descriptive statistics

Standard deviation17.86377
Coefficient of variation (CV)1.8803969
Kurtosis22.315546
Mean9.5
Median Absolute Deviation (MAD)4
Skewness4.36388
Sum342
Variance319.11429
MonotonicityNot monotonic
2023-12-11T08:28:18.488180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0 9
25.0%
3 4
11.1%
4 3
 
8.3%
1 3
 
8.3%
16 2
 
5.6%
10 2
 
5.6%
5 2
 
5.6%
11 2
 
5.6%
7 1
 
2.8%
31 1
 
2.8%
Other values (7) 7
19.4%
ValueCountFrequency (%)
0 9
25.0%
1 3
 
8.3%
2 1
 
2.8%
3 4
11.1%
4 3
 
8.3%
5 2
 
5.6%
7 1
 
2.8%
9 1
 
2.8%
10 2
 
5.6%
11 2
 
5.6%
ValueCountFrequency (%)
103 1
2.8%
31 1
2.8%
25 1
2.8%
21 1
2.8%
19 1
2.8%
16 2
5.6%
14 1
2.8%
11 2
5.6%
10 2
5.6%
9 1
2.8%

Interactions

2023-12-11T08:28:14.901816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:11.372822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:11.946506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:12.453182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:12.971232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:13.561334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:14.220007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:14.995449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:11.441842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:12.022725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:12.517197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:13.067517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:13.675964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:14.299811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:15.080452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:11.511168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:12.095239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:12.582879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:13.153082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:13.769431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:14.399612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:15.154973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:11.584064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:12.161113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:12.645698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:13.226550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:13.857833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:14.521281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:15.225103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:11.665958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:12.228227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:12.725646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:13.306283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:13.949112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:14.614710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:15.319162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:11.759541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:12.302682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:12.804257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:13.381907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:14.049279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:14.709821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:15.401935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:11.857941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:12.385935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:12.884847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:13.463361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:14.141660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:14.807271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T08:28:18.596622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분연도행정처분개선권고개수위반사실미발견개인이해허위신고순수고발
구분1.0000.0000.8670.5780.8180.9240.6030.7710.688
연도0.0001.0000.0000.0000.0000.0000.0800.0000.000
0.8670.0001.0000.8570.9640.9500.7390.6670.633
행정처분0.5780.0000.8571.0000.7630.8190.5680.7050.676
개선권고개수0.8180.0000.9640.7631.0000.8970.7730.5000.610
위반사실미발견0.9240.0000.9500.8190.8971.0000.4910.5000.487
개인이해0.6030.0800.7390.5680.7730.4911.0000.7830.824
허위신고0.7710.0000.6670.7050.5000.5000.7831.0000.925
순수고발0.6880.0000.6330.6760.6100.4870.8240.9251.000
2023-12-11T08:28:18.751520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정처분개선권고개수위반사실미발견개인이해허위신고순수고발연도
1.0000.8640.8210.8890.3380.2800.4890.000
행정처분0.8641.0000.6840.7710.0990.2760.5230.000
개선권고개수0.8210.6841.0000.5760.4040.1490.3640.000
위반사실미발견0.8890.7710.5761.0000.1610.2830.4400.000
개인이해0.3380.0990.4040.1611.0000.3880.4980.075
허위신고0.2800.2760.1490.2830.3881.0000.4780.000
순수고발0.4890.5230.3640.4400.4980.4781.0000.000
연도0.0000.0000.0000.0000.0750.0000.0001.000

Missing values

2023-12-11T08:28:15.533454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T08:28:15.674561image/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

구분연도행정처분개선권고개수위반사실미발견개인이해허위신고순수고발
0창원시2020년도 상반기2089118150998750014
1창원시2020년도 하반기3114712158515755011
2진주시2020년도 상반기48445320160100
3진주시2020년도 하반기71742425465709
4통영시2020년도 상반기2045118388603
5통영시2020년도 하반기231178944121010
6사천시2020년도 상반기2224430110321310
7사천시2020년도 하반기321632253005
8김해시2020년도 상반기29892021512648202225
9김해시2020년도 하반기4201336740276042640103
구분연도행정처분개선권고개수위반사실미발견개인이해허위신고순수고발
26하동군2020년도 상반기4014917005
27하동군2020년도 하반기484638000
28산청군2020년도 상반기201730000
29산청군2020년도 하반기38101018200
30함양군2020년도 상반기4341227000
31함양군2020년도 하반기8025453200
32거창군2020년도 상반기4321619000
33거창군2020년도 하반기85410116234
34합천군2020년도 상반기151237724617
35합천군2020년도 하반기1491711952503