Overview

Dataset statistics

Number of variables8
Number of observations36
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.6 KiB
Average record size in memory73.7 B

Variable types

Text1
Categorical1
Numeric6

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

행정처분 is highly overall correlated with 개선권고개수 and 1 other fieldsHigh correlation
개선권고개수 is highly overall correlated with 행정처분 and 1 other fieldsHigh correlation
위반사실미발견 is highly overall correlated with 행정처분 and 2 other fieldsHigh correlation
순수고발 is highly overall correlated with 위반사실미발견High correlation
행정처분 has 2 (5.6%) zerosZeros
개선권고개수 has 1 (2.8%) zerosZeros
위반사실미발견 has 6 (16.7%) zerosZeros
개인이해 has 8 (22.2%) zerosZeros
허위신고 has 30 (83.3%) zerosZeros
순수고발 has 21 (58.3%) zerosZeros

Reproduction

Analysis started2023-12-10 23:28:20.941996
Analysis finished2023-12-10 23:28:25.027765
Duration4.09 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
2023-12-11T08:28:25.150702image/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%
2023-12-11T08:28:25.485810image/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%

연도
Categorical

Distinct2
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Memory size420.0 B
2022년도 상반기
18 
2022년도 하반기
18 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022년도 상반기
2nd row2022년도 하반기
3rd row2022년도 상반기
4th row2022년도 하반기
5th row2022년도 상반기

Common Values

ValueCountFrequency (%)
2022년도 상반기 18
50.0%
2022년도 하반기 18
50.0%

Length

2023-12-11T08:28:25.628447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:28:25.742929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022년도 36
50.0%
상반기 18
25.0%
하반기 18
25.0%

행정처분
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct31
Distinct (%)86.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean63.694444
Minimum0
Maximum409
Zeros2
Zeros (%)5.6%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-11T08:28:25.856371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.75
Q17.75
median27
Q370.25
95-th percentile216.75
Maximum409
Range409
Interquartile range (IQR)62.5

Descriptive statistics

Standard deviation90.089739
Coefficient of variation (CV)1.414405
Kurtosis5.2974426
Mean63.694444
Median Absolute Deviation (MAD)21.5
Skewness2.1875891
Sum2293
Variance8116.1611
MonotonicityNot monotonic
2023-12-11T08:28:26.041984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
9 3
 
8.3%
0 2
 
5.6%
6 2
 
5.6%
32 2
 
5.6%
7 1
 
2.8%
15 1
 
2.8%
8 1
 
2.8%
45 1
 
2.8%
3 1
 
2.8%
1 1
 
2.8%
Other values (21) 21
58.3%
ValueCountFrequency (%)
0 2
5.6%
1 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 3
8.3%
12 1
 
2.8%
ValueCountFrequency (%)
409 1
2.8%
246 1
2.8%
207 1
2.8%
206 1
2.8%
172 1
2.8%
168 1
2.8%
150 1
2.8%
92 1
2.8%
77 1
2.8%
68 1
2.8%

개선권고개수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct33
Distinct (%)91.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean167.47222
Minimum0
Maximum1358
Zeros1
Zeros (%)2.8%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-11T08:28:26.190371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q17.75
median43
Q3157.25
95-th percentile776
Maximum1358
Range1358
Interquartile range (IQR)149.5

Descriptive statistics

Standard deviation309.7843
Coefficient of variation (CV)1.8497653
Kurtosis8.1123451
Mean167.47222
Median Absolute Deviation (MAD)40.5
Skewness2.8548723
Sum6029
Variance95966.313
MonotonicityNot monotonic
2023-12-11T08:28:26.350147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
7 2
 
5.6%
5 2
 
5.6%
2 2
 
5.6%
1358 1
 
2.8%
4 1
 
2.8%
8 1
 
2.8%
55 1
 
2.8%
45 1
 
2.8%
3 1
 
2.8%
20 1
 
2.8%
Other values (23) 23
63.9%
ValueCountFrequency (%)
0 1
2.8%
2 2
5.6%
3 1
2.8%
4 1
2.8%
5 2
5.6%
7 2
5.6%
8 1
2.8%
9 1
2.8%
10 1
2.8%
14 1
2.8%
ValueCountFrequency (%)
1358 1
2.8%
1160 1
2.8%
648 1
2.8%
635 1
2.8%
246 1
2.8%
244 1
2.8%
224 1
2.8%
213 1
2.8%
167 1
2.8%
154 1
2.8%

위반사실미발견
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct30
Distinct (%)83.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean292.91667
Minimum0
Maximum2284
Zeros6
Zeros (%)16.7%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-11T08:28:26.521622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q126.5
median122
Q3335
95-th percentile1197.25
Maximum2284
Range2284
Interquartile range (IQR)308.5

Descriptive statistics

Standard deviation479.92597
Coefficient of variation (CV)1.6384386
Kurtosis8.2877166
Mean292.91667
Median Absolute Deviation (MAD)98.5
Skewness2.7162775
Sum10545
Variance230328.94
MonotonicityNot monotonic
2023-12-11T08:28:26.679351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
0 6
 
16.7%
136 2
 
5.6%
740 1
 
2.8%
23 1
 
2.8%
63 1
 
2.8%
66 1
 
2.8%
19 1
 
2.8%
29 1
 
2.8%
25 1
 
2.8%
27 1
 
2.8%
Other values (20) 20
55.6%
ValueCountFrequency (%)
0 6
16.7%
19 1
 
2.8%
23 1
 
2.8%
25 1
 
2.8%
27 1
 
2.8%
29 1
 
2.8%
35 1
 
2.8%
47 1
 
2.8%
63 1
 
2.8%
65 1
 
2.8%
ValueCountFrequency (%)
2284 1
2.8%
1411 1
2.8%
1126 1
2.8%
941 1
2.8%
740 1
2.8%
642 1
2.8%
426 1
2.8%
395 1
2.8%
341 1
2.8%
333 1
2.8%

개인이해
Real number (ℝ)

ZEROS 

Distinct26
Distinct (%)72.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean158.13889
Minimum0
Maximum1738
Zeros8
Zeros (%)22.2%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-11T08:28:26.820839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12.75
median24
Q3107.5
95-th percentile652.75
Maximum1738
Range1738
Interquartile range (IQR)104.75

Descriptive statistics

Standard deviation394.51411
Coefficient of variation (CV)2.4947317
Kurtosis13.605938
Mean158.13889
Median Absolute Deviation (MAD)24
Skewness3.7546526
Sum5693
Variance155641.38
MonotonicityNot monotonic
2023-12-11T08:28:26.952354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0 8
22.2%
3 3
 
8.3%
5 2
 
5.6%
100 1
 
2.8%
78 1
 
2.8%
103 1
 
2.8%
161 1
 
2.8%
84 1
 
2.8%
96 1
 
2.8%
2 1
 
2.8%
Other values (16) 16
44.4%
ValueCountFrequency (%)
0 8
22.2%
2 1
 
2.8%
3 3
 
8.3%
4 1
 
2.8%
5 2
 
5.6%
8 1
 
2.8%
11 1
 
2.8%
15 1
 
2.8%
33 1
 
2.8%
38 1
 
2.8%
ValueCountFrequency (%)
1738 1
2.8%
1711 1
2.8%
300 1
2.8%
273 1
2.8%
270 1
2.8%
183 1
2.8%
161 1
2.8%
155 1
2.8%
121 1
2.8%
103 1
2.8%

허위신고
Real number (ℝ)

ZEROS 

Distinct7
Distinct (%)19.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.97222222
Minimum0
Maximum13
Zeros30
Zeros (%)83.3%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-11T08:28:27.081256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile7
Maximum13
Range13
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.8534301
Coefficient of variation (CV)2.9349567
Kurtosis11.181251
Mean0.97222222
Median Absolute Deviation (MAD)0
Skewness3.3656744
Sum35
Variance8.1420635
MonotonicityNot monotonic
2023-12-11T08:28:27.221406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 30
83.3%
3 1
 
2.8%
1 1
 
2.8%
13 1
 
2.8%
10 1
 
2.8%
2 1
 
2.8%
6 1
 
2.8%
ValueCountFrequency (%)
0 30
83.3%
1 1
 
2.8%
2 1
 
2.8%
3 1
 
2.8%
6 1
 
2.8%
10 1
 
2.8%
13 1
 
2.8%
ValueCountFrequency (%)
13 1
 
2.8%
10 1
 
2.8%
6 1
 
2.8%
3 1
 
2.8%
2 1
 
2.8%
1 1
 
2.8%
0 30
83.3%

순수고발
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)22.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7222222
Minimum0
Maximum11
Zeros21
Zeros (%)58.3%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-11T08:28:27.347102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32.25
95-th percentile8.75
Maximum11
Range11
Interquartile range (IQR)2.25

Descriptive statistics

Standard deviation3.0200389
Coefficient of variation (CV)1.753571
Kurtosis3.8175277
Mean1.7222222
Median Absolute Deviation (MAD)0
Skewness2.1008026
Sum62
Variance9.1206349
MonotonicityNot monotonic
2023-12-11T08:28:27.468780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 21
58.3%
1 4
 
11.1%
3 3
 
8.3%
4 2
 
5.6%
11 2
 
5.6%
2 2
 
5.6%
8 1
 
2.8%
7 1
 
2.8%
ValueCountFrequency (%)
0 21
58.3%
1 4
 
11.1%
2 2
 
5.6%
3 3
 
8.3%
4 2
 
5.6%
7 1
 
2.8%
8 1
 
2.8%
11 2
 
5.6%
ValueCountFrequency (%)
11 2
 
5.6%
8 1
 
2.8%
7 1
 
2.8%
4 2
 
5.6%
3 3
 
8.3%
2 2
 
5.6%
1 4
 
11.1%
0 21
58.3%

Interactions

2023-12-11T08:28:24.053609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:21.250280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:21.826589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:22.364856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:22.897774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:23.486342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:24.137234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:21.348423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:21.922115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:22.456040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:23.004071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:23.581453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:24.485583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:21.465967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:22.019001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:22.540797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:23.111861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:23.681704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:24.556393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:21.561263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:22.110602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:22.618237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:23.210559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:23.759227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:24.637204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:21.663399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:22.204355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:22.703324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:23.299463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:23.875552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:24.720061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:21.747582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:22.282802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:22.822891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:23.404822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:28:23.963433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T08:28:27.565000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분연도행정처분개선권고개수위반사실미발견개인이해허위신고순수고발
구분1.0000.0000.8130.8650.8560.9840.4290.313
연도0.0001.0000.0000.0000.0000.0000.1350.383
행정처분0.8130.0001.0000.8700.8720.8300.0000.760
개선권고개수0.8650.0000.8701.0000.9090.8120.0000.698
위반사실미발견0.8560.0000.8720.9091.0000.8510.0000.803
개인이해0.9840.0000.8300.8120.8511.0000.0000.520
허위신고0.4290.1350.0000.0000.0000.0001.0000.000
순수고발0.3130.3830.7600.6980.8030.5200.0001.000
2023-12-11T08:28:27.715401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정처분개선권고개수위반사실미발견개인이해허위신고순수고발연도
행정처분1.0000.6540.6960.3380.0150.4670.000
개선권고개수0.6541.0000.6190.375-0.1530.3390.000
위반사실미발견0.6960.6191.0000.252-0.0650.5260.000
개인이해0.3380.3750.2521.0000.2130.1880.000
허위신고0.015-0.153-0.0650.2131.0000.0330.065
순수고발0.4670.3390.5260.1880.0331.0000.373
연도0.0000.0000.0000.0000.0650.3731.000

Missing values

2023-12-11T08:28:24.832581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T08:28:24.969631image/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
0창원시2022년도 상반기206135874010004
1창원시2022년도 하반기1501160642273011
2진주시2022년도 상반기207244219008
3진주시2022년도 하반기44224220000
4통영시2022년도 상반기3921316615500
5통영시2022년도 하반기922461679300
6사천시2022년도 상반기210395501
7사천시2022년도 하반기62341001
8김해시2022년도 상반기246635941173804
9김해시2022년도 하반기40964811261711011
구분연도행정처분개선권고개수위반사실미발견개인이해허위신고순수고발
26하동군2022년도 상반기32429002
27하동군2022년도 하반기3019000
28산청군2022년도 상반기0200200
29산청군2022년도 하반기0220300
30함양군2022년도 상반기64166001
31함양군2022년도 하반기451963000
32거창군2022년도 상반기8509600
33거창군2022년도 하반기9508400
34합천군2022년도 상반기152016100
35합천군2022년도 하반기329010360