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
Categorical2
Numeric5

Dataset

Description경남도내 18개 시·군의 신고방법별 환경오염신고 현황을 제공합니다.(전화, 모사전송, 컴퓨터통신, 엽서·편지, 직접방문 등)
Author경상남도
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15047242

Alerts

전화 is highly overall correlated with 컴퓨터통신 and 2 other fieldsHigh correlation
컴퓨터통신 is highly overall correlated with 전화 and 3 other fieldsHigh correlation
엽서 또는 편지 is highly overall correlated with 전화 and 2 other fieldsHigh correlation
직접방문 is highly overall correlated with 전화 and 3 other fieldsHigh correlation
모사전송 is highly overall correlated with 컴퓨터통신 and 1 other fieldsHigh correlation
모사전송 is highly imbalanced (77.0%)Imbalance
컴퓨터통신 has 1 (2.8%) zerosZeros
엽서 또는 편지 has 22 (61.1%) zerosZeros
직접방문 has 2 (5.6%) zerosZeros
기타 has 22 (61.1%) zerosZeros

Reproduction

Analysis started2023-12-10 23:31:54.224504
Analysis finished2023-12-10 23:31:56.312016
Duration2.09 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
2023-12-11T08:31:56.420170image/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%
2023-12-11T08:31:56.713740image/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%

상하반기구별
Categorical

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

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row상반기
2nd row하반기
3rd row상반기
4th row하반기
5th row상반기

Common Values

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

Length

2023-12-11T08:31:56.829453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:31:56.923687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
상반기 18
50.0%
하반기 18
50.0%

전화
Real number (ℝ)

HIGH CORRELATION 

Distinct34
Distinct (%)94.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean475.88889
Minimum10
Maximum2595
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-11T08:31:57.015315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile17.5
Q172.25
median183.5
Q3397
95-th percentile2066.25
Maximum2595
Range2585
Interquartile range (IQR)324.75

Descriptive statistics

Standard deviation713.25354
Coefficient of variation (CV)1.4987817
Kurtosis3.1131221
Mean475.88889
Median Absolute Deviation (MAD)158.5
Skewness2.0526616
Sum17132
Variance508730.62
MonotonicityNot monotonic
2023-12-11T08:31:57.136960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
115 2
 
5.6%
397 2
 
5.6%
1849 1
 
2.8%
10 1
 
2.8%
135 1
 
2.8%
22 1
 
2.8%
24 1
 
2.8%
49 1
 
2.8%
19 1
 
2.8%
13 1
 
2.8%
Other values (24) 24
66.7%
ValueCountFrequency (%)
10 1
2.8%
13 1
2.8%
19 1
2.8%
22 1
2.8%
24 1
2.8%
49 1
2.8%
51 1
2.8%
67 1
2.8%
70 1
2.8%
73 1
2.8%
ValueCountFrequency (%)
2595 1
2.8%
2532 1
2.8%
1911 1
2.8%
1849 1
2.8%
1693 1
2.8%
1154 1
2.8%
410 1
2.8%
408 1
2.8%
397 2
5.6%
389 1
2.8%

모사전송
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Memory size420.0 B
0
34 
2
 
1
3
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique2 ?
Unique (%)5.6%

Sample

1st row0
2nd row0
3rd row0
4th row2
5th row0

Common Values

ValueCountFrequency (%)
0 34
94.4%
2 1
 
2.8%
3 1
 
2.8%

Length

2023-12-11T08:31:57.253540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:31:57.340045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 34
94.4%
2 1
 
2.8%
3 1
 
2.8%

컴퓨터통신
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct35
Distinct (%)97.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean179.05556
Minimum0
Maximum1056
Zeros1
Zeros (%)2.8%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-11T08:31:57.433678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6.5
Q122
median73.5
Q3168.5
95-th percentile844
Maximum1056
Range1056
Interquartile range (IQR)146.5

Descriptive statistics

Standard deviation267.82286
Coefficient of variation (CV)1.4957528
Kurtosis3.9193761
Mean179.05556
Median Absolute Deviation (MAD)63
Skewness2.1482558
Sum6446
Variance71729.083
MonotonicityNot monotonic
2023-12-11T08:31:57.553305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
10 2
 
5.6%
533 1
 
2.8%
514 1
 
2.8%
41 1
 
2.8%
44 1
 
2.8%
72 1
 
2.8%
11 1
 
2.8%
14 1
 
2.8%
0 1
 
2.8%
2 1
 
2.8%
Other values (25) 25
69.4%
ValueCountFrequency (%)
0 1
2.8%
2 1
2.8%
8 1
2.8%
10 2
5.6%
11 1
2.8%
13 1
2.8%
14 1
2.8%
19 1
2.8%
23 1
2.8%
28 1
2.8%
ValueCountFrequency (%)
1056 1
2.8%
907 1
2.8%
823 1
2.8%
533 1
2.8%
514 1
2.8%
476 1
2.8%
225 1
2.8%
187 1
2.8%
170 1
2.8%
168 1
2.8%

엽서 또는 편지
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)19.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4166667
Minimum0
Maximum12
Zeros22
Zeros (%)61.1%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-11T08:31:57.658439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile6.75
Maximum12
Range12
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.9019698
Coefficient of variation (CV)2.0484493
Kurtosis8.8737126
Mean1.4166667
Median Absolute Deviation (MAD)0
Skewness2.9460916
Sum51
Variance8.4214286
MonotonicityNot monotonic
2023-12-11T08:31:57.773578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 22
61.1%
1 4
 
11.1%
2 4
 
11.1%
3 2
 
5.6%
12 2
 
5.6%
4 1
 
2.8%
5 1
 
2.8%
ValueCountFrequency (%)
0 22
61.1%
1 4
 
11.1%
2 4
 
11.1%
3 2
 
5.6%
4 1
 
2.8%
5 1
 
2.8%
12 2
 
5.6%
ValueCountFrequency (%)
12 2
 
5.6%
5 1
 
2.8%
4 1
 
2.8%
3 2
 
5.6%
2 4
 
11.1%
1 4
 
11.1%
0 22
61.1%

직접방문
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct25
Distinct (%)69.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.75
Minimum0
Maximum158
Zeros2
Zeros (%)5.6%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-11T08:31:57.864350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.75
Q13.75
median7
Q320.25
95-th percentile51.75
Maximum158
Range158
Interquartile range (IQR)16.5

Descriptive statistics

Standard deviation28.035055
Coefficient of variation (CV)1.6737346
Kurtosis18.993977
Mean16.75
Median Absolute Deviation (MAD)6
Skewness4.010758
Sum603
Variance785.96429
MonotonicityNot monotonic
2023-12-11T08:31:57.986550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
4 4
 
11.1%
2 3
 
8.3%
1 3
 
8.3%
6 2
 
5.6%
0 2
 
5.6%
5 2
 
5.6%
7 2
 
5.6%
22 1
 
2.8%
29 1
 
2.8%
3 1
 
2.8%
Other values (15) 15
41.7%
ValueCountFrequency (%)
0 2
5.6%
1 3
8.3%
2 3
8.3%
3 1
 
2.8%
4 4
11.1%
5 2
5.6%
6 2
5.6%
7 2
5.6%
9 1
 
2.8%
10 1
 
2.8%
ValueCountFrequency (%)
158 1
2.8%
66 1
2.8%
47 1
2.8%
30 1
2.8%
29 1
2.8%
28 1
2.8%
24 1
2.8%
22 1
2.8%
21 1
2.8%
20 1
2.8%

기타
Real number (ℝ)

ZEROS 

Distinct11
Distinct (%)30.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.666667
Minimum0
Maximum285
Zeros22
Zeros (%)61.1%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-11T08:31:58.089091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33.25
95-th percentile30.5
Maximum285
Range285
Interquartile range (IQR)3.25

Descriptive statistics

Standard deviation47.621124
Coefficient of variation (CV)4.0818106
Kurtosis33.545483
Mean11.666667
Median Absolute Deviation (MAD)0
Skewness5.7156023
Sum420
Variance2267.7714
MonotonicityNot monotonic
2023-12-11T08:31:58.178006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 22
61.1%
5 2
 
5.6%
3 2
 
5.6%
18 2
 
5.6%
1 2
 
5.6%
9 1
 
2.8%
285 1
 
2.8%
38 1
 
2.8%
2 1
 
2.8%
4 1
 
2.8%
ValueCountFrequency (%)
0 22
61.1%
1 2
 
5.6%
2 1
 
2.8%
3 2
 
5.6%
4 1
 
2.8%
5 2
 
5.6%
9 1
 
2.8%
18 2
 
5.6%
28 1
 
2.8%
38 1
 
2.8%
ValueCountFrequency (%)
285 1
2.8%
38 1
2.8%
28 1
2.8%
18 2
5.6%
9 1
2.8%
5 2
5.6%
4 1
2.8%
3 2
5.6%
2 1
2.8%
1 2
5.6%

Interactions

2023-12-11T08:31:55.791814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:31:54.474135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:31:54.812967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:31:55.134082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:31:55.452867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:31:55.852469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:31:54.533525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:31:54.873298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:31:55.195621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:31:55.518315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:31:55.913115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:31:54.595967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:31:54.931492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:31:55.256215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:31:55.585610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:31:55.982621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:31:54.659059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:31:55.005346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:31:55.320978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:31:55.654037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:31:56.054827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:31:54.745702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:31:55.072914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:31:55.389409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:31:55.727767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T08:31:58.500841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군별상하반기구별전화모사전송컴퓨터통신엽서 또는 편지직접방문기타
시군별1.0000.0000.9290.3250.6520.7890.6870.325
상하반기구별0.0001.0000.0000.0000.0000.2940.0000.000
전화0.9290.0001.0000.7440.9040.8870.5430.744
모사전송0.3250.0000.7441.0000.7290.6681.0000.000
컴퓨터통신0.6520.0000.9040.7291.0000.8490.6820.764
엽서 또는 편지0.7890.2940.8870.6680.8491.0000.4910.642
직접방문0.6870.0000.5431.0000.6820.4911.0000.000
기타0.3250.0000.7440.0000.7640.6420.0001.000
2023-12-11T08:31:58.623577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
상하반기구별모사전송
상하반기구별1.0000.000
모사전송0.0001.000
2023-12-11T08:31:58.696478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
전화컴퓨터통신엽서 또는 편지직접방문기타상하반기구별모사전송
전화1.0000.9300.7130.7260.3660.0000.398
컴퓨터통신0.9301.0000.6780.6830.2330.0000.572
엽서 또는 편지0.7130.6781.0000.5980.3460.1880.334
직접방문0.7260.6830.5981.0000.2010.0000.969
기타0.3660.2330.3460.2011.0000.0000.000
상하반기구별0.0000.0000.1880.0000.0001.0000.000
모사전송0.3980.5720.3340.9690.0000.0001.000

Missing values

2023-12-11T08:31:56.147447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T08:31:56.260225image/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

시군별상하반기구별전화모사전송컴퓨터통신엽서 또는 편지직접방문기타
0창원시상반기184905334220
1창원시하반기169305141280
2진주시상반기389012421585
3진주시하반기3412751663
4통영시상반기3970168260
5통영시하반기3690225040
6사천시상반기24401535110
7사천시하반기2110139000
8김해시상반기259539073479
9김해시하반기253201056230285
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