Overview

Dataset statistics

Number of variables9
Number of observations36
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.9 KiB
Average record size in memory82.7 B

Variable types

Text1
Categorical3
Numeric5

Dataset

Description경남도내 18개 시·군의 신고방법별 환경오염신고 현황을 제공합니다.(전화, 모사전송, 컴퓨터통신, 엽서·편지, 직접방문 등)
Author경상남도
URLhttps://www.data.go.kr/data/15047242/fileData.do

Alerts

계 is highly overall correlated with 전화 and 4 other fieldsHigh correlation
전화 is highly overall correlated with 계 and 4 other fieldsHigh correlation
컴퓨터통신 is highly overall correlated with 계 and 3 other fieldsHigh correlation
직접방문 is highly overall correlated with 계 and 4 other fieldsHigh correlation
기타 is highly overall correlated with 계 and 4 other fieldsHigh correlation
모사전송 is highly overall correlated with 직접방문High correlation
엽서 편지 is highly overall correlated with 계 and 2 other fieldsHigh correlation
모사전송 is highly imbalanced (81.7%)Imbalance
엽서 편지 is highly imbalanced (55.3%)Imbalance
전화 has unique valuesUnique
직접방문 has 6 (16.7%) zerosZeros
기타 has 25 (69.4%) zerosZeros

Reproduction

Analysis started2024-03-30 09:28:56.132385
Analysis finished2024-03-30 09:29:02.777682
Duration6.65 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
2024-03-30T09:29:02.978065image/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%
2024-03-30T09:29:03.567859image/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%

신고시기
Categorical

Distinct2
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Memory size420.0 B
2023년 상반기
18 
2023년 하반기
18 

Length

Max length9
Median length9
Mean length9
Min length9

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023년 상반기
2nd row2023년 하반기
3rd row2023년 상반기
4th row2023년 하반기
5th row2023년 상반기

Common Values

ValueCountFrequency (%)
2023년 상반기 18
50.0%
2023년 하반기 18
50.0%

Length

2024-03-30T09:29:03.801739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-30T09:29:04.140765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023년 36
50.0%
상반기 18
25.0%
하반기 18
25.0%

계
Real number (ℝ)

HIGH CORRELATION 

Distinct34
Distinct (%)94.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean566.52778
Minimum28
Maximum3070
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2024-03-30T09:29:04.412169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum28
5-th percentile38.25
Q1103.25
median157.5
Q3578.25
95-th percentile2524.5
Maximum3070
Range3042
Interquartile range (IQR)475

Descriptive statistics

Standard deviation825.46826
Coefficient of variation (CV)1.4570658
Kurtosis2.8705835
Mean566.52778
Median Absolute Deviation (MAD)107.5
Skewness1.9897928
Sum20395
Variance681397.86
MonotonicityNot monotonic
2024-03-30T09:29:04.902113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
117 2
 
5.6%
188 2
 
5.6%
2241 1
 
2.8%
41 1
 
2.8%
239 1
 
2.8%
125 1
 
2.8%
143 1
 
2.8%
30 1
 
2.8%
46 1
 
2.8%
28 1
 
2.8%
Other values (24) 24
66.7%
ValueCountFrequency (%)
28 1
2.8%
30 1
2.8%
41 1
2.8%
46 1
2.8%
54 1
2.8%
67 1
2.8%
79 1
2.8%
91 1
2.8%
101 1
2.8%
104 1
2.8%
ValueCountFrequency (%)
3070 1
2.8%
2658 1
2.8%
2480 1
2.8%
2241 1
2.8%
1922 1
2.8%
1053 1
2.8%
697 1
2.8%
685 1
2.8%
594 1
2.8%
573 1
2.8%

전화
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct36
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean374.66667
Minimum9
Maximum2070
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2024-03-30T09:29:05.221614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile18.25
Q149.5
median116.5
Q3354.25
95-th percentile1682.75
Maximum2070
Range2061
Interquartile range (IQR)304.75

Descriptive statistics

Standard deviation570.50113
Coefficient of variation (CV)1.5226899
Kurtosis2.8727572
Mean374.66667
Median Absolute Deviation (MAD)92.5
Skewness2.0002919
Sum13488
Variance325471.54
MonotonicityNot monotonic
2024-03-30T09:29:05.637723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
1602 1
 
2.8%
394 1
 
2.8%
119 1
 
2.8%
110 1
 
2.8%
128 1
 
2.8%
20 1
 
2.8%
26 1
 
2.8%
21 1
 
2.8%
27 1
 
2.8%
30 1
 
2.8%
Other values (26) 26
72.2%
ValueCountFrequency (%)
9 1
2.8%
13 1
2.8%
20 1
2.8%
21 1
2.8%
25 1
2.8%
26 1
2.8%
27 1
2.8%
30 1
2.8%
33 1
2.8%
55 1
2.8%
ValueCountFrequency (%)
2070 1
2.8%
1925 1
2.8%
1602 1
2.8%
1538 1
2.8%
1326 1
2.8%
776 1
2.8%
543 1
2.8%
418 1
2.8%
394 1
2.8%
341 1
2.8%

모사전송
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Memory size420.0 B
0
35 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)2.8%

Sample

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

Common Values

ValueCountFrequency (%)
0 35
97.2%
1 1
 
2.8%

Length

2024-03-30T09:29:05.936781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-30T09:29:06.251939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 35
97.2%
1 1
 
2.8%

컴퓨터통신
Real number (ℝ)

HIGH CORRELATION 

Distinct34
Distinct (%)94.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean171.97222
Minimum5
Maximum921
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2024-03-30T09:29:06.578568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile8.5
Q125.5
median55.5
Q3171.25
95-th percentile720
Maximum921
Range916
Interquartile range (IQR)145.75

Descriptive statistics

Standard deviation246.25289
Coefficient of variation (CV)1.4319341
Kurtosis3.0463687
Mean171.97222
Median Absolute Deviation (MAD)47.5
Skewness1.9667269
Sum6191
Variance60640.485
MonotonicityNot monotonic
2024-03-30T09:29:06.869594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
9 2
 
5.6%
30 2
 
5.6%
599 1
 
2.8%
5 1
 
2.8%
57 1
 
2.8%
69 1
 
2.8%
10 1
 
2.8%
12 1
 
2.8%
7 1
 
2.8%
42 1
 
2.8%
Other values (24) 24
66.7%
ValueCountFrequency (%)
5 1
2.8%
7 1
2.8%
9 2
5.6%
10 1
2.8%
11 1
2.8%
12 1
2.8%
13 1
2.8%
21 1
2.8%
27 1
2.8%
28 1
2.8%
ValueCountFrequency (%)
921 1
2.8%
870 1
2.8%
670 1
2.8%
599 1
2.8%
581 1
2.8%
328 1
2.8%
263 1
2.8%
255 1
2.8%
178 1
2.8%
169 1
2.8%

엽서 편지
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)11.1%
Missing0
Missing (%)0.0%
Memory size420.0 B
0
30 
1
 
3
2
 
2
5
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)2.8%

Sample

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

Common Values

ValueCountFrequency (%)
0 30
83.3%
1 3
 
8.3%
2 2
 
5.6%
5 1
 
2.8%

Length

2024-03-30T09:29:07.109148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-30T09:29:07.435010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 30
83.3%
1 3
 
8.3%
2 2
 
5.6%
5 1
 
2.8%

직접방문
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct18
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.5
Minimum0
Maximum40
Zeros6
Zeros (%)16.7%
Negative0
Negative (%)0.0%
Memory size456.0 B
2024-03-30T09:29:07.788899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median5.5
Q317
95-th percentile34.25
Maximum40
Range40
Interquartile range (IQR)16

Descriptive statistics

Standard deviation12.060561
Coefficient of variation (CV)1.1486249
Kurtosis0.31599864
Mean10.5
Median Absolute Deviation (MAD)4.5
Skewness1.2567687
Sum378
Variance145.45714
MonotonicityNot monotonic
2024-03-30T09:29:08.102412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
5 6
16.7%
0 6
16.7%
6 4
11.1%
1 4
11.1%
21 2
 
5.6%
32 2
 
5.6%
2 1
 
2.8%
8 1
 
2.8%
4 1
 
2.8%
20 1
 
2.8%
Other values (8) 8
22.2%
ValueCountFrequency (%)
0 6
16.7%
1 4
11.1%
2 1
 
2.8%
4 1
 
2.8%
5 6
16.7%
6 4
11.1%
7 1
 
2.8%
8 1
 
2.8%
9 1
 
2.8%
10 1
 
2.8%
ValueCountFrequency (%)
40 1
2.8%
38 1
2.8%
33 1
2.8%
32 2
5.6%
27 1
2.8%
21 2
5.6%
20 1
2.8%
16 1
2.8%
10 1
2.8%
9 1
2.8%

기타
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)30.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.0277778
Minimum0
Maximum175
Zeros25
Zeros (%)69.4%
Negative0
Negative (%)0.0%
Memory size456.0 B
2024-03-30T09:29:08.427230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33.25
95-th percentile38.25
Maximum175
Range175
Interquartile range (IQR)3.25

Descriptive statistics

Standard deviation30.207365
Coefficient of variation (CV)3.3460466
Kurtosis27.713569
Mean9.0277778
Median Absolute Deviation (MAD)0
Skewness5.0630726
Sum325
Variance912.48492
MonotonicityNot monotonic
2024-03-30T09:29:08.677852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 25
69.4%
4 2
 
5.6%
13 1
 
2.8%
39 1
 
2.8%
11 1
 
2.8%
1 1
 
2.8%
38 1
 
2.8%
30 1
 
2.8%
175 1
 
2.8%
7 1
 
2.8%
ValueCountFrequency (%)
0 25
69.4%
1 1
 
2.8%
3 1
 
2.8%
4 2
 
5.6%
7 1
 
2.8%
11 1
 
2.8%
13 1
 
2.8%
30 1
 
2.8%
38 1
 
2.8%
39 1
 
2.8%
ValueCountFrequency (%)
175 1
2.8%
39 1
2.8%
38 1
2.8%
30 1
2.8%
13 1
2.8%
11 1
2.8%
7 1
2.8%
4 2
5.6%
3 1
2.8%
1 1
2.8%

Interactions

2024-03-30T09:29:01.191314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T09:28:56.680158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T09:28:57.720857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T09:28:58.910156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T09:28:59.962188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T09:29:01.374875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T09:28:56.920100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T09:28:57.959532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T09:28:59.113272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T09:29:00.184627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T09:29:01.515961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T09:28:57.207698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T09:28:58.186837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T09:28:59.338137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T09:29:00.413014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T09:29:01.751225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T09:28:57.350797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T09:28:58.411128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T09:28:59.554132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T09:29:00.653373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T09:29:01.938265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T09:28:57.517513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T09:28:58.664664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T09:28:59.809112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T09:29:00.926006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-30T09:29:08.961936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군별신고시기계전화모사전송컴퓨터통신엽서 편지직접방문기타
시군별1.0000.0000.8470.8960.2030.7510.7260.7310.323
신고시기0.0001.0000.0000.0000.0000.0000.0000.0000.000
계0.8470.0001.0000.9450.0000.8670.9720.9410.944
전화0.8960.0000.9451.0000.0000.9400.8570.6880.640
모사전송0.2030.0000.0000.0001.0000.0000.0000.5120.000
컴퓨터통신0.7510.0000.8670.9400.0001.0000.6470.7310.866
엽서 편지0.7260.0000.9720.8570.0000.6471.0000.4750.850
직접방문0.7310.0000.9410.6880.5120.7310.4751.0000.761
기타0.3230.0000.9440.6400.0000.8660.8500.7611.000
2024-03-30T09:29:09.250168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
엽서 편지신고시기모사전송
엽서 편지1.0000.0000.000
신고시기0.0001.0000.000
모사전송0.0000.0001.000
2024-03-30T09:29:09.424702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
계전화컴퓨터통신직접방문기타신고시기모사전송엽서 편지
계1.0000.8910.8800.6990.5510.0000.0000.725
전화0.8911.0000.6720.7420.5490.0000.0000.747
컴퓨터통신0.8800.6721.0000.6110.5110.0000.0000.479
직접방문0.6990.7420.6111.0000.6050.0000.8740.307
기타0.5510.5490.5110.6051.0000.0000.0000.503
신고시기0.0000.0000.0000.0000.0001.0000.0000.000
모사전송0.0000.0000.0000.8740.0000.0001.0000.000
엽서 편지0.7250.7470.4790.3070.5030.0000.0001.000

Missing values

2024-03-30T09:29:02.263324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-30T09:29:02.679311image/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

시군별신고시기계전화모사전송컴퓨터통신엽서 편지직접방문기타
0창원시2023년 상반기22411602059902713
1창원시2023년 하반기24801538087003339
2진주시2023년 상반기594223032803211
3진주시2023년 하반기50321002550380
4통영시2023년 상반기4322900136060
5통영시2023년 하반기4752980169071
6사천시2023년 상반기162130149000
7사천시2023년 하반기18890178010
8김해시2023년 상반기30702070092114038
9김해시2023년 하반기26581925067013230
시군별신고시기계전화모사전송컴퓨터통신엽서 편지직접방문기타
26하동군2023년 상반기282107000
27하동군2023년 하반기412705054
28산청군2023년 상반기7930042250
29산청군2023년 하반기6733030040
30함양군2023년 상반기12689031060
31함양군2023년 하반기11755054080
32거창군2023년 상반기10169030020
33거창군2023년 하반기5425029000
34합천군2023년 상반기130114011050
35합천군2023년 하반기11796021000