Overview

Dataset statistics

Number of variables9
Number of observations36
Missing cells87
Missing cells (%)26.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.8 KiB
Average record size in memory79.7 B

Variable types

Text4
Categorical1
Unsupported1
Numeric3

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 has 1 (2.8%) missing valuesMissing
모사전송 has 36 (100.0%) missing valuesMissing
컴퓨터통신 has 2 (5.6%) missing valuesMissing
엽서, 편지 has 22 (61.1%) missing valuesMissing
직접방문 has 6 (16.7%) missing valuesMissing
기타 has 20 (55.6%) missing valuesMissing
모사전송 is an unsupported type, check if it needs cleaning or further analysisUnsupported
엽서, 편지 has 3 (8.3%) zerosZeros
직접방문 has 1 (2.8%) zerosZeros
기타 has 4 (11.1%) zerosZeros

Reproduction

Analysis started2023-12-10 23:31:59.692262
Analysis finished2023-12-10 23:32:01.459218
Duration1.77 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군별
Text

Distinct18
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size420.0 B
2023-12-11T08:32:01.573740image/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%
2023-12-11T08:32:01.887601image/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

2023-12-11T08:32:02.012863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:32:02.135247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
상반기 18
50.0%
하반기 18
50.0%

계
Text

Distinct35
Distinct (%)97.2%
Missing0
Missing (%)0.0%
Memory size420.0 B
2023-12-11T08:32:02.319056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length4
Mean length4.0277778
Min length2

Characters and Unicode

Total characters145
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique34 ?
Unique (%)94.4%

Sample

1st row2,089
2nd row3,114
3rd row484
4th row717
5th row204
ValueCountFrequency (%)
43 2
 
5.6%
48 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%
340 1
 
2.8%
2,089 1
 
2.8%
Other values (25) 25
69.4%
2023-12-11T08:32:02.647614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
36
24.8%
2 17
11.7%
1 16
11.0%
4 14
 
9.7%
0 14
 
9.7%
3 13
 
9.0%
9 11
 
7.6%
8 9
 
6.2%
, 6
 
4.1%
7 3
 
2.1%
Other values (2) 6
 
4.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 103
71.0%
Space Separator 36
 
24.8%
Other Punctuation 6
 
4.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 17
16.5%
1 16
15.5%
4 14
13.6%
0 14
13.6%
3 13
12.6%
9 11
10.7%
8 9
8.7%
7 3
 
2.9%
6 3
 
2.9%
5 3
 
2.9%
Space Separator
ValueCountFrequency (%)
36
100.0%
Other Punctuation
ValueCountFrequency (%)
, 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 145
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
36
24.8%
2 17
11.7%
1 16
11.0%
4 14
 
9.7%
0 14
 
9.7%
3 13
 
9.0%
9 11
 
7.6%
8 9
 
6.2%
, 6
 
4.1%
7 3
 
2.1%
Other values (2) 6
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 145
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
36
24.8%
2 17
11.7%
1 16
11.0%
4 14
 
9.7%
0 14
 
9.7%
3 13
 
9.0%
9 11
 
7.6%
8 9
 
6.2%
, 6
 
4.1%
7 3
 
2.1%
Other values (2) 6
 
4.1%

전화
Text

MISSING 

Distinct34
Distinct (%)97.1%
Missing1
Missing (%)2.8%
Memory size420.0 B
2023-12-11T08:32:02.846833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length4
Mean length4.0285714
Min length3

Characters and Unicode

Total characters141
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique33 ?
Unique (%)94.3%

Sample

1st row1,459
2nd row2,236
3rd row372
4th row296
5th row124
ValueCountFrequency (%)
39 2
 
5.7%
332 1
 
2.9%
46 1
 
2.9%
143 1
 
2.9%
122 1
 
2.9%
119 1
 
2.9%
237 1
 
2.9%
24 1
 
2.9%
261 1
 
2.9%
2,913 1
 
2.9%
Other values (24) 24
68.6%
2023-12-11T08:32:03.158097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
35
24.8%
1 22
15.6%
2 20
14.2%
6 14
 
9.9%
3 12
 
8.5%
9 8
 
5.7%
4 7
 
5.0%
5 7
 
5.0%
, 5
 
3.5%
7 5
 
3.5%
Other values (2) 6
 
4.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 101
71.6%
Space Separator 35
 
24.8%
Other Punctuation 5
 
3.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 22
21.8%
2 20
19.8%
6 14
13.9%
3 12
11.9%
9 8
 
7.9%
4 7
 
6.9%
5 7
 
6.9%
7 5
 
5.0%
8 5
 
5.0%
0 1
 
1.0%
Space Separator
ValueCountFrequency (%)
35
100.0%
Other Punctuation
ValueCountFrequency (%)
, 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 141
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
35
24.8%
1 22
15.6%
2 20
14.2%
6 14
 
9.9%
3 12
 
8.5%
9 8
 
5.7%
4 7
 
5.0%
5 7
 
5.0%
, 5
 
3.5%
7 5
 
3.5%
Other values (2) 6
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 141
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
35
24.8%
1 22
15.6%
2 20
14.2%
6 14
 
9.9%
3 12
 
8.5%
9 8
 
5.7%
4 7
 
5.0%
5 7
 
5.0%
, 5
 
3.5%
7 5
 
3.5%
Other values (2) 6
 
4.3%

모사전송
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing36
Missing (%)100.0%
Memory size456.0 B

컴퓨터통신
Text

MISSING 

Distinct29
Distinct (%)85.3%
Missing2
Missing (%)5.6%
Memory size420.0 B
2023-12-11T08:32:03.366021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length3
Mean length3.2352941
Min length2

Characters and Unicode

Total characters110
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique24 ?
Unique (%)70.6%

Sample

1st row538
2nd row793
3rd row111
4th row40
5th row66
ValueCountFrequency (%)
40 2
 
5.9%
3 2
 
5.9%
61 2
 
5.9%
24 2
 
5.9%
36 2
 
5.9%
4 1
 
2.9%
538 1
 
2.9%
58 1
 
2.9%
9 1
 
2.9%
14 1
 
2.9%
Other values (19) 19
55.9%
2023-12-11T08:32:03.670630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
34
30.9%
1 14
12.7%
3 11
 
10.0%
6 10
 
9.1%
4 8
 
7.3%
8 8
 
7.3%
2 7
 
6.4%
5 6
 
5.5%
7 4
 
3.6%
0 3
 
2.7%
Other values (2) 5
 
4.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 74
67.3%
Space Separator 34
30.9%
Other Punctuation 2
 
1.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 14
18.9%
3 11
14.9%
6 10
13.5%
4 8
10.8%
8 8
10.8%
2 7
9.5%
5 6
8.1%
7 4
 
5.4%
0 3
 
4.1%
9 3
 
4.1%
Space Separator
ValueCountFrequency (%)
34
100.0%
Other Punctuation
ValueCountFrequency (%)
, 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 110
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
34
30.9%
1 14
12.7%
3 11
 
10.0%
6 10
 
9.1%
4 8
 
7.3%
8 8
 
7.3%
2 7
 
6.4%
5 6
 
5.5%
7 4
 
3.6%
0 3
 
2.7%
Other values (2) 5
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 110
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
34
30.9%
1 14
12.7%
3 11
 
10.0%
6 10
 
9.1%
4 8
 
7.3%
8 8
 
7.3%
2 7
 
6.4%
5 6
 
5.5%
7 4
 
3.6%
0 3
 
2.7%
Other values (2) 5
 
4.5%

엽서, 편지
Real number (ℝ)

MISSING  ZEROS 

Distinct7
Distinct (%)50.0%
Missing22
Missing (%)61.1%
Infinite0
Infinite (%)0.0%
Mean2.6428571
Minimum0
Maximum12
Zeros3
Zeros (%)8.3%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-11T08:32:03.773926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q32.75
95-th percentile8.75
Maximum12
Range12
Interquartile range (IQR)1.75

Descriptive statistics

Standard deviation3.2724289
Coefficient of variation (CV)1.2382164
Kurtosis4.9083485
Mean2.6428571
Median Absolute Deviation (MAD)1
Skewness2.1378296
Sum37
Variance10.708791
MonotonicityNot monotonic
2023-12-11T08:32:03.866831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2 4
 
11.1%
1 3
 
8.3%
0 3
 
8.3%
12 1
 
2.8%
7 1
 
2.8%
4 1
 
2.8%
3 1
 
2.8%
(Missing) 22
61.1%
ValueCountFrequency (%)
0 3
8.3%
1 3
8.3%
2 4
11.1%
3 1
 
2.8%
4 1
 
2.8%
7 1
 
2.8%
12 1
 
2.8%
ValueCountFrequency (%)
12 1
 
2.8%
7 1
 
2.8%
4 1
 
2.8%
3 1
 
2.8%
2 4
11.1%
1 3
8.3%
0 3
8.3%

직접방문
Real number (ℝ)

MISSING  ZEROS 

Distinct17
Distinct (%)56.7%
Missing6
Missing (%)16.7%
Infinite0
Infinite (%)0.0%
Mean24.866667
Minimum0
Maximum381
Zeros1
Zeros (%)2.8%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-11T08:32:03.963308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q14
median10
Q319.25
95-th percentile52.2
Maximum381
Range381
Interquartile range (IQR)15.25

Descriptive statistics

Standard deviation68.720691
Coefficient of variation (CV)2.7635667
Kurtosis27.274484
Mean24.866667
Median Absolute Deviation (MAD)6.5
Skewness5.13572
Sum746
Variance4722.5333
MonotonicityNot monotonic
2023-12-11T08:32:04.065608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
4 5
13.9%
10 4
11.1%
1 3
8.3%
8 2
 
5.6%
2 2
 
5.6%
27 2
 
5.6%
12 2
 
5.6%
24 1
 
2.8%
22 1
 
2.8%
72 1
 
2.8%
Other values (7) 7
19.4%
(Missing) 6
16.7%
ValueCountFrequency (%)
0 1
 
2.8%
1 3
8.3%
2 2
 
5.6%
4 5
13.9%
6 1
 
2.8%
8 2
 
5.6%
10 4
11.1%
12 2
 
5.6%
15 1
 
2.8%
17 1
 
2.8%
ValueCountFrequency (%)
381 1
2.8%
72 1
2.8%
28 1
2.8%
27 2
5.6%
24 1
2.8%
22 1
2.8%
20 1
2.8%
17 1
2.8%
15 1
2.8%
12 2
5.6%

기타
Real number (ℝ)

MISSING  ZEROS 

Distinct9
Distinct (%)56.2%
Missing20
Missing (%)55.6%
Infinite0
Infinite (%)0.0%
Mean10.125
Minimum0
Maximum63
Zeros4
Zeros (%)11.1%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-11T08:32:04.164438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.75
median1.5
Q39.25
95-th percentile40.5
Maximum63
Range63
Interquartile range (IQR)8.5

Descriptive statistics

Standard deviation17.666824
Coefficient of variation (CV)1.7448715
Kurtosis4.7504929
Mean10.125
Median Absolute Deviation (MAD)1.5
Skewness2.1788647
Sum162
Variance312.11667
MonotonicityNot monotonic
2023-12-11T08:32:04.266288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1 4
 
11.1%
0 4
 
11.1%
3 2
 
5.6%
63 1
 
2.8%
2 1
 
2.8%
22 1
 
2.8%
33 1
 
2.8%
27 1
 
2.8%
5 1
 
2.8%
(Missing) 20
55.6%
ValueCountFrequency (%)
0 4
11.1%
1 4
11.1%
2 1
 
2.8%
3 2
5.6%
5 1
 
2.8%
22 1
 
2.8%
27 1
 
2.8%
33 1
 
2.8%
63 1
 
2.8%
ValueCountFrequency (%)
63 1
 
2.8%
33 1
 
2.8%
27 1
 
2.8%
22 1
 
2.8%
5 1
 
2.8%
3 2
5.6%
2 1
 
2.8%
1 4
11.1%
0 4
11.1%

Interactions

2023-12-11T08:32:00.462077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:31:59.977906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:32:00.210478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:32:00.690867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:32:00.056995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:32:00.291427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:32:00.865186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:32:00.139336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:32:00.371932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T08:32:04.343510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군별상하반기구별계전화컴퓨터통신엽서, 편지직접방문기타
시군별1.0000.0000.9630.9640.8440.0000.0000.000
상하반기구별0.0001.0001.0001.0000.6620.6610.0430.000
계0.9631.0001.0000.9970.9831.0001.0001.000
전화0.9641.0000.9971.0001.0001.0001.0001.000
컴퓨터통신0.8440.6620.9831.0001.0001.0000.0001.000
엽서, 편지0.0000.6611.0001.0001.0001.0001.0000.000
직접방문0.0000.0431.0001.0000.0001.0001.0000.000
기타0.0000.0001.0001.0001.0000.0000.0001.000
2023-12-11T08:32:04.466544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
엽서, 편지직접방문기타상하반기구별
엽서, 편지1.0000.4510.4290.365
직접방문0.4511.0000.4730.051
기타0.4290.4731.0000.000
상하반기구별0.3650.0510.0001.000

Missing values

2023-12-11T08:32:01.010214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T08:32:01.228249image/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.
2023-12-11T08:32:01.370724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

시군별상하반기구별계전화모사전송컴퓨터통신엽서, 편지직접방문기타
0창원시상반기2,0891,459<NA>53822763
1창원시하반기3,1142,236<NA>79312721
2진주시상반기484372<NA>111<NA>1<NA>
3진주시하반기717296<NA>40<NA>381<NA>
4통영시상반기204124<NA>66743
5통영시하반기231150<NA>754<NA>2
6사천시상반기222186<NA>24<NA>12<NA>
7사천시하반기321235<NA>80<NA>6<NA>
8김해시상반기2,9891,566<NA>1,37522422
9김해시하반기4,2012,913<NA>1,263<NA>223
시군별상하반기구별계전화모사전송컴퓨터통신엽서, 편지직접방문기타
26하동군상반기4039<NA><NA><NA>1<NA>
27하동군하반기4846<NA>2<NA><NA><NA>
28산청군상반기2016<NA><NA><NA>4<NA>
29산청군하반기3827<NA>11<NA><NA><NA>
30함양군상반기4326<NA>17<NA><NA><NA>
31함양군하반기8064<NA>14<NA>11
32거창군상반기4339<NA>31<NA><NA>
33거창군하반기8559<NA>24<NA>2<NA>
34합천군상반기151127<NA>9<NA>15<NA>
35합천군하반기149113<NA>23112<NA>