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 memory73.6 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://www.data.go.kr/data/15047241/fileData.do

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 1 other fieldsHigh correlation
개선권고개수 has 3 (8.3%) zerosZeros
위반사실미발견 has 1 (2.8%) zerosZeros
개인이해 has 12 (33.3%) zerosZeros
허위신고 has 26 (72.2%) zerosZeros
순수고발 has 16 (44.4%) zerosZeros

Reproduction

Analysis started2024-03-15 02:16:27.736018
Analysis finished2024-03-15 02:16:38.074551
Duration10.34 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Text

Distinct18
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size416.0 B
2024-03-15T11:16:38.625782image/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%
2024-03-15T11:16:39.793993image/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 size416.0 B
2023년도 상반기
18 
2023년도 하반기
18 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023년도 상반기
2nd row2023년도 하반기
3rd row2023년도 상반기
4th row2023년도 하반기
5th row2023년도 상반기

Common Values

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

Length

2024-03-15T11:16:40.207428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T11:16:40.511338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023년도 36
50.0%
상반기 18
25.0%
하반기 18
25.0%

행정처분
Real number (ℝ)

HIGH CORRELATION 

Distinct28
Distinct (%)77.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean61.166667
Minimum2
Maximum371
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size452.0 B
2024-03-15T11:16:40.703604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2.75
Q17.75
median27
Q384.5
95-th percentile207.75
Maximum371
Range369
Interquartile range (IQR)76.75

Descriptive statistics

Standard deviation81.661846
Coefficient of variation (CV)1.3350711
Kurtosis5.03208
Mean61.166667
Median Absolute Deviation (MAD)22
Skewness2.0982169
Sum2202
Variance6668.6571
MonotonicityNot monotonic
2024-03-15T11:16:41.121703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
27 3
 
8.3%
5 3
 
8.3%
9 2
 
5.6%
83 2
 
5.6%
30 2
 
5.6%
2 2
 
5.6%
105 1
 
2.8%
3 1
 
2.8%
7 1
 
2.8%
16 1
 
2.8%
Other values (18) 18
50.0%
ValueCountFrequency (%)
2 2
5.6%
3 1
 
2.8%
4 1
 
2.8%
5 3
8.3%
6 1
 
2.8%
7 1
 
2.8%
8 1
 
2.8%
9 2
5.6%
12 1
 
2.8%
13 1
 
2.8%
ValueCountFrequency (%)
371 1
2.8%
234 1
2.8%
199 1
2.8%
169 1
2.8%
159 1
2.8%
134 1
2.8%
127 1
2.8%
105 1
2.8%
89 1
2.8%
83 2
5.6%

개선권고개수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct29
Distinct (%)80.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean131.86111
Minimum0
Maximum1554
Zeros3
Zeros (%)8.3%
Negative0
Negative (%)0.0%
Memory size452.0 B
2024-03-15T11:16:41.507141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q17.75
median16.5
Q370.25
95-th percentile714.25
Maximum1554
Range1554
Interquartile range (IQR)62.5

Descriptive statistics

Standard deviation333.18679
Coefficient of variation (CV)2.526801
Kurtosis12.879733
Mean131.86111
Median Absolute Deviation (MAD)14
Skewness3.6133326
Sum4747
Variance111013.44
MonotonicityNot monotonic
2024-03-15T11:16:41.887337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
0 3
 
8.3%
12 2
 
5.6%
4 2
 
5.6%
35 2
 
5.6%
7 2
 
5.6%
16 2
 
5.6%
1282 1
 
2.8%
28 1
 
2.8%
14 1
 
2.8%
10 1
 
2.8%
Other values (19) 19
52.8%
ValueCountFrequency (%)
0 3
8.3%
2 1
 
2.8%
3 1
 
2.8%
4 2
5.6%
7 2
5.6%
8 1
 
2.8%
9 1
 
2.8%
10 1
 
2.8%
12 2
5.6%
14 1
 
2.8%
ValueCountFrequency (%)
1554 1
2.8%
1282 1
2.8%
525 1
2.8%
271 1
2.8%
160 1
2.8%
146 1
2.8%
134 1
2.8%
132 1
2.8%
71 1
2.8%
70 1
2.8%

위반사실미발견
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct34
Distinct (%)94.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean317.97222
Minimum0
Maximum2245
Zeros1
Zeros (%)2.8%
Negative0
Negative (%)0.0%
Memory size452.0 B
2024-03-15T11:16:42.296897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile12.25
Q134.5
median83.5
Q3279.5
95-th percentile1726.75
Maximum2245
Range2245
Interquartile range (IQR)245

Descriptive statistics

Standard deviation556.71026
Coefficient of variation (CV)1.7508141
Kurtosis6.0061101
Mean317.97222
Median Absolute Deviation (MAD)59.5
Skewness2.5566178
Sum11447
Variance309926.31
MonotonicityNot monotonic
2024-03-15T11:16:42.706888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
36 2
 
5.6%
24 2
 
5.6%
746 1
 
2.8%
59 1
 
2.8%
75 1
 
2.8%
27 1
 
2.8%
20 1
 
2.8%
25 1
 
2.8%
50 1
 
2.8%
423 1
 
2.8%
Other values (24) 24
66.7%
ValueCountFrequency (%)
0 1
2.8%
1 1
2.8%
16 1
2.8%
20 1
2.8%
24 2
5.6%
25 1
2.8%
27 1
2.8%
30 1
2.8%
36 2
5.6%
50 1
2.8%
ValueCountFrequency (%)
2245 1
2.8%
2068 1
2.8%
1613 1
2.8%
901 1
2.8%
746 1
2.8%
729 1
2.8%
423 1
2.8%
349 1
2.8%
323 1
2.8%
265 1
2.8%

개인이해
Real number (ℝ)

ZEROS 

Distinct24
Distinct (%)66.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51.805556
Minimum0
Maximum342
Zeros12
Zeros (%)33.3%
Negative0
Negative (%)0.0%
Memory size452.0 B
2024-03-15T11:16:43.081371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median32.5
Q362.75
95-th percentile181.75
Maximum342
Range342
Interquartile range (IQR)62.75

Descriptive statistics

Standard deviation79.29072
Coefficient of variation (CV)1.5305447
Kurtosis8.17443
Mean51.805556
Median Absolute Deviation (MAD)32.5
Skewness2.7471636
Sum1865
Variance6287.0183
MonotonicityNot monotonic
2024-03-15T11:16:43.486898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0 12
33.3%
37 2
 
5.6%
70 1
 
2.8%
35 1
 
2.8%
90 1
 
2.8%
8 1
 
2.8%
10 1
 
2.8%
4 1
 
2.8%
68 1
 
2.8%
61 1
 
2.8%
Other values (14) 14
38.9%
ValueCountFrequency (%)
0 12
33.3%
4 1
 
2.8%
8 1
 
2.8%
10 1
 
2.8%
19 1
 
2.8%
28 1
 
2.8%
30 1
 
2.8%
35 1
 
2.8%
37 2
 
5.6%
45 1
 
2.8%
ValueCountFrequency (%)
342 1
2.8%
331 1
2.8%
132 1
2.8%
122 1
2.8%
101 1
2.8%
90 1
2.8%
81 1
2.8%
70 1
2.8%
68 1
2.8%
61 1
2.8%

허위신고
Real number (ℝ)

ZEROS 

Distinct6
Distinct (%)16.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5
Minimum0
Maximum19
Zeros26
Zeros (%)72.2%
Negative0
Negative (%)0.0%
Memory size452.0 B
2024-03-15T11:16:43.846273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile7.5
Maximum19
Range19
Interquartile range (IQR)2

Descriptive statistics

Standard deviation4.0107
Coefficient of variation (CV)2.6738
Kurtosis13.410942
Mean1.5
Median Absolute Deviation (MAD)0
Skewness3.6476972
Sum54
Variance16.085714
MonotonicityNot monotonic
2024-03-15T11:16:44.237619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 26
72.2%
2 6
 
16.7%
5 1
 
2.8%
3 1
 
2.8%
15 1
 
2.8%
19 1
 
2.8%
ValueCountFrequency (%)
0 26
72.2%
2 6
 
16.7%
3 1
 
2.8%
5 1
 
2.8%
15 1
 
2.8%
19 1
 
2.8%
ValueCountFrequency (%)
19 1
 
2.8%
15 1
 
2.8%
5 1
 
2.8%
3 1
 
2.8%
2 6
 
16.7%
0 26
72.2%

순수고발
Real number (ℝ)

ZEROS 

Distinct9
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2222222
Minimum0
Maximum17
Zeros16
Zeros (%)44.4%
Negative0
Negative (%)0.0%
Memory size452.0 B
2024-03-15T11:16:44.585416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile9.25
Maximum17
Range17
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.7425057
Coefficient of variation (CV)1.6841276
Kurtosis7.6612457
Mean2.2222222
Median Absolute Deviation (MAD)1
Skewness2.6621456
Sum80
Variance14.006349
MonotonicityNot monotonic
2024-03-15T11:16:44.896697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 16
44.4%
1 6
 
16.7%
2 4
 
11.1%
3 3
 
8.3%
4 3
 
8.3%
7 1
 
2.8%
8 1
 
2.8%
13 1
 
2.8%
17 1
 
2.8%
ValueCountFrequency (%)
0 16
44.4%
1 6
 
16.7%
2 4
 
11.1%
3 3
 
8.3%
4 3
 
8.3%
7 1
 
2.8%
8 1
 
2.8%
13 1
 
2.8%
17 1
 
2.8%
ValueCountFrequency (%)
17 1
 
2.8%
13 1
 
2.8%
8 1
 
2.8%
7 1
 
2.8%
4 3
 
8.3%
3 3
 
8.3%
2 4
 
11.1%
1 6
 
16.7%
0 16
44.4%

Interactions

2024-03-15T11:16:35.343288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:16:28.138263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:16:29.664970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:16:30.842950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:16:32.546849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:16:33.784930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:16:35.691664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:16:28.345917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:16:29.878941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:16:31.094789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:16:32.819367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:16:34.048813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:16:35.989013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:16:28.624739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:16:30.049485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:16:31.351358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:16:33.018087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:16:34.316794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:16:36.307245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:16:28.867989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:16:30.195380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:16:31.582044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:16:33.207800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:16:34.555092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:16:36.617876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:16:29.130570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:16:30.364805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:16:31.838093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:16:33.367387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:16:34.819409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:16:36.969082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:16:29.392382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:16:30.567986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:16:32.294271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:16:33.527402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:16:35.078009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-15T11:16:45.332592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분연도행정처분개선권고개수위반사실미발견개인이해허위신고순수고발
구분1.0000.0000.7090.7710.9770.8050.6610.798
연도0.0001.0000.0000.0000.0000.0000.0000.000
행정처분0.7090.0001.0000.8740.7750.4430.0000.845
개선권고개수0.7710.0000.8741.0000.6630.0000.0000.775
위반사실미발견0.9770.0000.7750.6631.0000.2680.3360.909
개인이해0.8050.0000.4430.0000.2681.0000.3150.408
허위신고0.6610.0000.0000.0000.3360.3151.0000.000
순수고발0.7980.0000.8450.7750.9090.4080.0001.000
2024-03-15T11:16:45.636229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정처분개선권고개수위반사실미발견개인이해허위신고순수고발연도
행정처분1.0000.6900.6960.2770.1380.4000.000
개선권고개수0.6901.0000.7050.2660.0290.3830.000
위반사실미발견0.6960.7051.0000.1590.1560.3120.000
개인이해0.2770.2660.1591.0000.1160.4630.000
허위신고0.1380.0290.1560.1161.0000.1950.000
순수고발0.4000.3830.3120.4630.1951.0000.000
연도0.0000.0000.0000.0000.0000.0001.000

Missing values

2024-03-15T11:16:37.356938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T11:16:37.823614image/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
0창원시2023년도 상반기13412827467027
1창원시2023년도 하반기15915547292828
2진주시2023년도 상반기169271152020
3진주시2023년도 하반기199160143001
4통영시2023년도 상반기27293235102
5통영시2023년도 하반기29373496000
6사천시2023년도 상반기8312303700
7사천시2023년도 하반기798713000
8김해시2023년도 상반기234525224553013
9김해시2023년도 하반기371702068132017
구분연도행정처분개선권고개수위반사실미발견개인이해허위신고순수고발
26하동군2023년도 상반기4024000
27하동군2023년도 하반기5036000
28산청군2023년도 상반기52591021
29산청군2023년도 하반기2350822
30함양군2023년도 상반기351572004
31함양군2023년도 하반기161088003
32거창군2023년도 상반기7409000
33거창군2023년도 하반기9713700
34합천군2023년도 상반기514111000
35합천군2023년도 하반기33579000