Overview

Dataset statistics

Number of variables5
Number of observations642
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory27.1 KiB
Average record size in memory43.2 B

Variable types

Categorical2
Text1
Numeric2

Dataset

Description김해시에서 통계기반 도시현황 파악을 위해 개발한 통계지수 중 하나로서, 통계연도, 시도명, 시군구명, 미세먼지(㎍/㎥), 초미세먼지(㎍/㎥)로 구성되어 있습니다. 김해시 중심의 통계지수로서, 데이터 수집, 가공 등의 어려움으로 김해시 외 지역의 정보는 누락될 수 있습니다.
Author경상남도 김해시
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15110174

Alerts

미세먼지(마이크로그램 퍼 세제곱미터) is highly overall correlated with 초미세먼지(마이크로그램 퍼 세제곱미터)High correlation
초미세먼지(마이크로그램 퍼 세제곱미터) is highly overall correlated with 미세먼지(마이크로그램 퍼 세제곱미터)High correlation

Reproduction

Analysis started2023-12-10 22:40:03.808874
Analysis finished2023-12-10 22:40:04.435934
Duration0.63 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

통계연도
Categorical

Distinct5
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size5.1 KiB
2020
153 
2021
153 
2019
144 
2018
105 
2017
87 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2017
2nd row2017
3rd row2017
4th row2017
5th row2017

Common Values

ValueCountFrequency (%)
2020 153
23.8%
2021 153
23.8%
2019 144
22.4%
2018 105
16.4%
2017 87
13.6%

Length

2023-12-11T07:40:04.507213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T07:40:04.612177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020 153
23.8%
2021 153
23.8%
2019 144
22.4%
2018 105
16.4%
2017 87
13.6%

시도명
Categorical

Distinct9
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size5.1 KiB
경기도
155 
전라남도
81 
경상북도
78 
충청남도
73 
경상남도
72 
Other values (4)
183 

Length

Max length7
Median length4
Mean length3.6775701
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경기도 155
24.1%
전라남도 81
12.6%
경상북도 78
12.1%
충청남도 73
11.4%
경상남도 72
11.2%
강원도 67
10.4%
전라북도 64
10.0%
충청북도 47
 
7.3%
제주특별자치도 5
 
0.8%

Length

2023-12-11T07:40:04.734859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T07:40:04.843822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경기도 155
24.1%
전라남도 81
12.6%
경상북도 78
12.1%
충청남도 73
11.4%
경상남도 72
11.2%
강원도 67
10.4%
전라북도 64
10.0%
충청북도 47
 
7.3%
제주특별자치도 5
 
0.8%

시군구명
Text

Distinct152
Distinct (%)23.7%
Missing0
Missing (%)0.0%
Memory size5.1 KiB
2023-12-11T07:40:05.163693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length2
Mean length2.0358255
Min length2

Characters and Unicode

Total characters1307
Distinct characters112
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 row수원
2nd row안양
3rd row성남
4th row의정부
5th row광명
ValueCountFrequency (%)
고성 7
 
1.1%
안성 5
 
0.8%
포항 5
 
0.8%
전주 5
 
0.8%
여수 5
 
0.8%
김제 5
 
0.8%
부안 5
 
0.8%
고창 5
 
0.8%
정읍 5
 
0.8%
남원 5
 
0.8%
Other values (142) 590
91.9%
2023-12-11T07:40:05.608155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
천 88
 
6.7%
주 83
 
6.4%
양 68
 
5.2%
산 61
 
4.7%
성 48
 
3.7%
안 48
 
3.7%
영 33
 
2.5%
포 30
 
2.3%
진 28
 
2.1%
남 27
 
2.1%
Other values (102) 793
60.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1307
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
천 88
 
6.7%
주 83
 
6.4%
양 68
 
5.2%
산 61
 
4.7%
성 48
 
3.7%
안 48
 
3.7%
영 33
 
2.5%
포 30
 
2.3%
진 28
 
2.1%
남 27
 
2.1%
Other values (102) 793
60.7%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1307
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
천 88
 
6.7%
주 83
 
6.4%
양 68
 
5.2%
산 61
 
4.7%
성 48
 
3.7%
안 48
 
3.7%
영 33
 
2.5%
포 30
 
2.3%
진 28
 
2.1%
남 27
 
2.1%
Other values (102) 793
60.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1307
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
천 88
 
6.7%
주 83
 
6.4%
양 68
 
5.2%
산 61
 
4.7%
성 48
 
3.7%
안 48
 
3.7%
영 33
 
2.5%
포 30
 
2.3%
진 28
 
2.1%
남 27
 
2.1%
Other values (102) 793
60.7%

미세먼지(마이크로그램 퍼 세제곱미터)
Real number (ℝ)

HIGH CORRELATION 

Distinct41
Distinct (%)6.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.749221
Minimum20
Maximum94
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2023-12-11T07:40:05.731499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile26
Q132
median37
Q343
95-th percentile50
Maximum94
Range74
Interquartile range (IQR)11

Descriptive statistics

Standard deviation7.7113107
Coefficient of variation (CV)0.20427735
Kurtosis3.8412762
Mean37.749221
Median Absolute Deviation (MAD)5
Skewness0.85221219
Sum24235
Variance59.464313
MonotonicityNot monotonic
2023-12-11T07:40:05.846280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
39 37
 
5.8%
36 34
 
5.3%
35 33
 
5.1%
34 32
 
5.0%
37 31
 
4.8%
41 31
 
4.8%
45 31
 
4.8%
32 30
 
4.7%
40 29
 
4.5%
43 27
 
4.2%
Other values (31) 327
50.9%
ValueCountFrequency (%)
20 1
 
0.2%
21 1
 
0.2%
23 2
 
0.3%
24 7
 
1.1%
25 13
2.0%
26 10
1.6%
27 18
2.8%
28 24
3.7%
29 18
2.8%
30 23
3.6%
ValueCountFrequency (%)
94 1
 
0.2%
63 1
 
0.2%
62 1
 
0.2%
58 1
 
0.2%
57 2
 
0.3%
56 5
0.8%
55 3
0.5%
54 4
0.6%
53 4
0.6%
52 5
0.8%

초미세먼지(마이크로그램 퍼 세제곱미터)
Real number (ℝ)

HIGH CORRELATION 

Distinct26
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.538941
Minimum0
Maximum36
Zeros5
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2023-12-11T07:40:05.952676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile13
Q117
median20
Q324
95-th percentile29
Maximum36
Range36
Interquartile range (IQR)7

Descriptive statistics

Standard deviation5.0627153
Coefficient of variation (CV)0.2464935
Kurtosis1.1038415
Mean20.538941
Median Absolute Deviation (MAD)4
Skewness-0.26442858
Sum13186
Variance25.631087
MonotonicityNot monotonic
2023-12-11T07:40:06.055215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
20 60
 
9.3%
18 53
 
8.3%
22 45
 
7.0%
19 44
 
6.9%
25 43
 
6.7%
21 42
 
6.5%
24 42
 
6.5%
16 40
 
6.2%
17 40
 
6.2%
23 32
 
5.0%
Other values (16) 201
31.3%
ValueCountFrequency (%)
0 5
 
0.8%
10 1
 
0.2%
11 7
 
1.1%
12 8
 
1.2%
13 25
3.9%
14 19
 
3.0%
15 31
4.8%
16 40
6.2%
17 40
6.2%
18 53
8.3%
ValueCountFrequency (%)
36 1
 
0.2%
33 3
 
0.5%
32 6
 
0.9%
31 3
 
0.5%
30 5
 
0.8%
29 16
 
2.5%
28 20
3.1%
27 26
4.0%
26 25
3.9%
25 43
6.7%

Interactions

2023-12-11T07:40:04.132513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:40:03.972320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:40:04.217357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:40:04.050099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T07:40:06.132402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
통계연도시도명미세먼지(마이크로그램 퍼 세제곱미터)초미세먼지(마이크로그램 퍼 세제곱미터)
통계연도1.0000.0000.4540.494
시도명0.0001.0000.4260.565
미세먼지(마이크로그램 퍼 세제곱미터)0.4540.4261.0000.696
초미세먼지(마이크로그램 퍼 세제곱미터)0.4940.5650.6961.000
2023-12-11T07:40:06.214227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
통계연도시도명
통계연도1.0000.000
시도명0.0001.000
2023-12-11T07:40:06.291548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
미세먼지(마이크로그램 퍼 세제곱미터)초미세먼지(마이크로그램 퍼 세제곱미터)통계연도시도명
미세먼지(마이크로그램 퍼 세제곱미터)1.0000.8250.3110.241
초미세먼지(마이크로그램 퍼 세제곱미터)0.8251.0000.3130.210
통계연도0.3110.3131.0000.000
시도명0.2410.2100.0001.000

Missing values

2023-12-11T07:40:04.318347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T07:40:04.399963image/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

통계연도시도명시군구명미세먼지(마이크로그램 퍼 세제곱미터)초미세먼지(마이크로그램 퍼 세제곱미터)
02017경기도수원4826
12017경기도안양4926
22017경기도성남4627
32017경기도의정부5427
42017경기도광명5028
52017경기도안산4827
62017경기도평택6233
72017경기도과천4625
82017경기도구리5830
92017경기도의왕4925
통계연도시도명시군구명미세먼지(마이크로그램 퍼 세제곱미터)초미세먼지(마이크로그램 퍼 세제곱미터)
6322021경상남도고성3216
6332021경상남도거창3117
6342021경상남도함안3118
6352021경상남도함양2816
6362021경상남도남해3015
6372021경상남도산청2715
6382021경상남도의령2714
6392021경상남도창녕3018
6402021경상남도합천3015
6412021제주특별자치도서귀포3113