Overview

Dataset statistics

Number of variables12
Number of observations204
Missing cells408
Missing cells (%)16.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory20.6 KiB
Average record size in memory103.6 B

Variable types

Categorical6
Numeric6

Dataset

Description미세먼지, 초미세먼지, 오존 농도를 ppm 단위로 나타낸 데이터이며 진주시 관내 다양한 장소에서 측정한 값입니다.
Author경상남도 진주시
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15117722

Alerts

데이터기준일자 has constant value ""Constant
관리부서 is highly overall correlated with 오존(O3) and 6 other fieldsHigh correlation
주소 is highly overall correlated with 위도 and 4 other fieldsHigh correlation
측정소명 is highly overall correlated with 위도 and 4 other fieldsHigh correlation
전화번호 is highly overall correlated with 오존(O3) and 6 other fieldsHigh correlation
월 is highly overall correlated with 미세먼지(PM10) and 1 other fieldsHigh correlation
오존(O3) is highly overall correlated with 관리부서 and 1 other fieldsHigh correlation
미세먼지(PM10) is highly overall correlated with 월 and 3 other fieldsHigh correlation
초미세먼지(PM2점5) is highly overall correlated with 관리부서 and 1 other fieldsHigh correlation
위도 is highly overall correlated with 측정소명 and 3 other fieldsHigh correlation
경도 is highly overall correlated with 측정소명 and 1 other fieldsHigh correlation
연도 is highly overall correlated with 월 and 1 other fieldsHigh correlation
오존(O3) has 136 (66.7%) missing valuesMissing
미세먼지(PM10) has 172 (84.3%) missing valuesMissing
초미세먼지(PM2점5) has 100 (49.0%) missing valuesMissing

Reproduction

Analysis started2023-12-11 00:01:06.569342
Analysis finished2023-12-11 00:01:11.793630
Duration5.22 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연도
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2022
136 
2023
68 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022
2nd row2022
3rd row2022
4th row2022
5th row2022

Common Values

ValueCountFrequency (%)
2022 136
66.7%
2023 68
33.3%

Length

2023-12-11T09:01:11.867201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:01:12.009248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022 136
66.7%
2023 68
33.3%

월
Real number (ℝ)

HIGH CORRELATION 

Distinct12
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.2941176
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-11T09:01:12.141681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median6
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.1904668
Coefficient of variation (CV)0.5068966
Kurtosis-0.96386808
Mean6.2941176
Median Absolute Deviation (MAD)3
Skewness0.023216478
Sum1284
Variance10.179079
MonotonicityNot monotonic
2023-12-11T09:01:12.279805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
6 28
13.7%
7 20
9.8%
8 20
9.8%
9 20
9.8%
1 16
7.8%
2 16
7.8%
3 16
7.8%
4 16
7.8%
5 16
7.8%
10 12
5.9%
Other values (2) 24
11.8%
ValueCountFrequency (%)
1 16
7.8%
2 16
7.8%
3 16
7.8%
4 16
7.8%
5 16
7.8%
6 28
13.7%
7 20
9.8%
8 20
9.8%
9 20
9.8%
10 12
5.9%
ValueCountFrequency (%)
12 12
5.9%
11 12
5.9%
10 12
5.9%
9 20
9.8%
8 20
9.8%
7 20
9.8%
6 28
13.7%
5 16
7.8%
4 16
7.8%
3 16
7.8%

측정소명
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
상대동
34 
정촌면
34 
대안동
17 
상봉동
17 
칠암동
17 
Other values (5)
85 

Length

Max length4
Median length3
Mean length3.0833333
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row대안동
2nd row상대동
3rd row상봉동
4th row정촌면
5th row대안동

Common Values

ValueCountFrequency (%)
상대동 34
16.7%
정촌면 34
16.7%
대안동 17
8.3%
상봉동 17
8.3%
칠암동 17
8.3%
망경동 17
8.3%
신안동 17
8.3%
충무공동 17
8.3%
가좌동 17
8.3%
사봉면 17
8.3%

Length

2023-12-11T09:01:12.435272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:01:12.622466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
상대동 34
16.7%
정촌면 34
16.7%
대안동 17
8.3%
상봉동 17
8.3%
칠암동 17
8.3%
망경동 17
8.3%
신안동 17
8.3%
충무공동 17
8.3%
가좌동 17
8.3%
사봉면 17
8.3%

오존(O3)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct36
Distinct (%)52.9%
Missing136
Missing (%)66.7%
Infinite0
Infinite (%)0.0%
Mean0.032630882
Minimum0
Maximum0.058
Zeros2
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-11T09:01:12.816818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.019
Q10.02475
median0.0335
Q30.04
95-th percentile0.04825
Maximum0.058
Range0.058
Interquartile range (IQR)0.01525

Descriptive statistics

Standard deviation0.010825898
Coefficient of variation (CV)0.33176849
Kurtosis1.1636339
Mean0.032630882
Median Absolute Deviation (MAD)0.0075
Skewness-0.44662614
Sum2.2189
Variance0.00011720008
MonotonicityNot monotonic
2023-12-11T09:01:12.982129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
0.023 6
 
2.9%
0.03 4
 
2.0%
0.029 4
 
2.0%
0.04 4
 
2.0%
0.021 4
 
2.0%
0.036 3
 
1.5%
0.043 3
 
1.5%
0.031 3
 
1.5%
0.034 3
 
1.5%
0.025 2
 
1.0%
Other values (26) 32
 
15.7%
(Missing) 136
66.7%
ValueCountFrequency (%)
0.0 2
 
1.0%
0.018 1
 
0.5%
0.019 2
 
1.0%
0.02 1
 
0.5%
0.021 4
2.0%
0.023 6
2.9%
0.024 1
 
0.5%
0.025 2
 
1.0%
0.027 1
 
0.5%
0.028 1
 
0.5%
ValueCountFrequency (%)
0.058 1
 
0.5%
0.054 2
1.0%
0.05 1
 
0.5%
0.045 1
 
0.5%
0.0448 1
 
0.5%
0.0433 1
 
0.5%
0.043 3
1.5%
0.042 2
1.0%
0.0418 1
 
0.5%
0.0413 1
 
0.5%

미세먼지(PM10)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct32
Distinct (%)100.0%
Missing172
Missing (%)84.3%
Infinite0
Infinite (%)0.0%
Mean24.971787
Minimum0.94235589
Maximum45.389222
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-11T09:01:13.143253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.94235589
5-th percentile2.177996
Q122.401413
median27.636888
Q330.188822
95-th percentile33.193105
Maximum45.389222
Range44.446866
Interquartile range (IQR)7.7874084

Descriptive statistics

Standard deviation9.3282657
Coefficient of variation (CV)0.37355219
Kurtosis2.3167566
Mean24.971787
Median Absolute Deviation (MAD)3.4345951
Skewness-1.172872
Sum799.09718
Variance87.016541
MonotonicityNot monotonic
2023-12-11T09:01:13.278140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
26.80645161 1
 
0.5%
20.93233083 1
 
0.5%
25.03095559 1
 
0.5%
29.18413978 1
 
0.5%
28.86826347 1
 
0.5%
30.15288221 1
 
0.5%
31.81830417 1
 
0.5%
34.76478495 1
 
0.5%
30.58383234 1
 
0.5%
0.94235589 1
 
0.5%
Other values (22) 22
 
10.8%
(Missing) 172
84.3%
ValueCountFrequency (%)
0.94235589 1
0.5%
1.286675639 1
0.5%
2.907258065 1
0.5%
16.98746867 1
0.5%
17.61654135 1
0.5%
18.82706767 1
0.5%
20.93233083 1
0.5%
21.98654105 1
0.5%
22.5397039 1
0.5%
23.30686406 1
0.5%
ValueCountFrequency (%)
45.38922156 1
0.5%
34.76478495 1
0.5%
31.90718563 1
0.5%
31.84408602 1
0.5%
31.81830417 1
0.5%
30.61077844 1
0.5%
30.58383234 1
0.5%
30.28822055 1
0.5%
30.15568862 1
0.5%
30.15288221 1
0.5%

초미세먼지(PM2점5)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct100
Distinct (%)96.2%
Missing100
Missing (%)49.0%
Infinite0
Infinite (%)0.0%
Mean17.721606
Minimum0.005012531
Maximum36.869421
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-11T09:01:13.418578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.005012531
5-th percentile5.8592559
Q113.289827
median17.639364
Q321.382639
95-th percentile30.168552
Maximum36.869421
Range36.864409
Interquartile range (IQR)8.0928123

Descriptive statistics

Standard deviation7.080875
Coefficient of variation (CV)0.39956169
Kurtosis0.29860633
Mean17.721606
Median Absolute Deviation (MAD)4.3044355
Skewness0.017427526
Sum1843.047
Variance50.13879
MonotonicityNot monotonic
2023-12-11T09:01:13.583849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12.0 3
 
1.5%
8.0 2
 
1.0%
13.29324547 2
 
1.0%
13.4602961 1
 
0.5%
1.495967742 1
 
0.5%
23.71727019 1
 
0.5%
30.48710991 1
 
0.5%
27.23558484 1
 
0.5%
23.71639785 1
 
0.5%
20.65053763 1
 
0.5%
Other values (90) 90
44.1%
(Missing) 100
49.0%
ValueCountFrequency (%)
0.005012531 1
0.5%
0.286675639 1
0.5%
1.495967742 1
0.5%
3.514049587 1
0.5%
4.586021505 1
0.5%
5.767473118 1
0.5%
6.379358438 1
0.5%
8.0 2
1.0%
9.804511278 1
0.5%
10.44110276 1
0.5%
ValueCountFrequency (%)
36.86942149 1
0.5%
33.00539084 1
0.5%
31.7125 1
0.5%
31.0 1
0.5%
30.72155689 1
0.5%
30.48710991 1
0.5%
28.36339166 1
0.5%
28.30487805 1
0.5%
28.1903172 1
0.5%
27.60565276 1
0.5%

주소
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
<NA>
119 
경상남도 진주시 산단중앙로 70
34 
경상남도 진주시 진주대로 1052(대안동)
17 
경상남도 진주시 북장대로64번길 14(상봉동)
17 
경상남도 진주시 사봉면 사곡리 1802-2
17 

Length

Max length25
Median length4
Mean length11.083333
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row경상남도 진주시 진주대로 1052(대안동)
2nd row<NA>
3rd row경상남도 진주시 북장대로64번길 14(상봉동)
4th row경상남도 진주시 산단중앙로 70
5th row경상남도 진주시 진주대로 1052(대안동)

Common Values

ValueCountFrequency (%)
<NA> 119
58.3%
경상남도 진주시 산단중앙로 70 34
 
16.7%
경상남도 진주시 진주대로 1052(대안동) 17
 
8.3%
경상남도 진주시 북장대로64번길 14(상봉동) 17
 
8.3%
경상남도 진주시 사봉면 사곡리 1802-2 17
 
8.3%

Length

2023-12-11T09:01:13.758507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:01:13.866979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 119
25.0%
경상남도 85
17.9%
진주시 85
17.9%
산단중앙로 34
 
7.1%
70 34
 
7.1%
진주대로 17
 
3.6%
1052(대안동 17
 
3.6%
북장대로64번길 17
 
3.6%
14(상봉동 17
 
3.6%
사봉면 17
 
3.6%
Other values (2) 34
 
7.1%

위도
Real number (ℝ)

HIGH CORRELATION 

Distinct10
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.171569
Minimum35.122945
Maximum35.195894
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-11T09:01:13.974008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.122945
5-th percentile35.122945
Q135.16435
median35.179732
Q335.187712
95-th percentile35.195894
Maximum35.195894
Range0.07294862
Interquartile range (IQR)0.023362032

Descriptive statistics

Standard deviation0.023993285
Coefficient of variation (CV)0.00068217841
Kurtosis0.072479827
Mean35.171569
Median Absolute Deviation (MAD)0.011394965
Skewness-1.1887136
Sum7175.0001
Variance0.00057567772
MonotonicityNot monotonic
2023-12-11T09:01:14.102120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
35.17973197 34
16.7%
35.12294549 34
16.7%
35.19354372 17
8.3%
35.19589411 17
8.3%
35.18495403 17
8.3%
35.187358 17
8.3%
35.17751681 17
8.3%
35.1659834 17
8.3%
35.159449 17
8.3%
35.18877333 17
8.3%
ValueCountFrequency (%)
35.12294549 34
16.7%
35.159449 17
8.3%
35.1659834 17
8.3%
35.17751681 17
8.3%
35.17973197 34
16.7%
35.18495403 17
8.3%
35.187358 17
8.3%
35.18877333 17
8.3%
35.19354372 17
8.3%
35.19589411 17
8.3%
ValueCountFrequency (%)
35.19589411 17
8.3%
35.19354372 17
8.3%
35.18877333 17
8.3%
35.187358 17
8.3%
35.18495403 17
8.3%
35.17973197 34
16.7%
35.17751681 17
8.3%
35.1659834 17
8.3%
35.159449 17
8.3%
35.12294549 34
16.7%

경도
Real number (ℝ)

HIGH CORRELATION 

Distinct10
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128.11122
Minimum128.07159
Maximum128.27355
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-11T09:01:14.223661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum128.07159
5-th percentile128.07159
Q1128.08997
median128.10367
Q3128.10852
95-th percentile128.27355
Maximum128.27355
Range0.2019558
Interquartile range (IQR)0.01855565

Descriptive statistics

Standard deviation0.0507528
Coefficient of variation (CV)0.00039616201
Kurtosis6.0460012
Mean128.11122
Median Absolute Deviation (MAD)0.00801
Skewness2.6743385
Sum26134.689
Variance0.0025758467
MonotonicityNot monotonic
2023-12-11T09:01:14.353472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
128.1084624 34
16.7%
128.1036659 34
16.7%
128.0845176 17
8.3%
128.0745707 17
8.3%
128.0948407 17
8.3%
128.091784 17
8.3%
128.0715917 17
8.3%
128.1108607 17
8.3%
128.108705 17
8.3%
128.2735475 17
8.3%
ValueCountFrequency (%)
128.0715917 17
8.3%
128.0745707 17
8.3%
128.0845176 17
8.3%
128.091784 17
8.3%
128.0948407 17
8.3%
128.1036659 34
16.7%
128.1084624 34
16.7%
128.108705 17
8.3%
128.1108607 17
8.3%
128.2735475 17
8.3%
ValueCountFrequency (%)
128.2735475 17
8.3%
128.1108607 17
8.3%
128.108705 17
8.3%
128.1084624 34
16.7%
128.1036659 34
16.7%
128.0948407 17
8.3%
128.091784 17
8.3%
128.0845176 17
8.3%
128.0745707 17
8.3%
128.0715917 17
8.3%

관리부서
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
진주시청 환경관리과
136 
경상남도 보건환경연구원
68 

Length

Max length12
Median length10
Mean length10.666667
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row경상남도 보건환경연구원
2nd row경상남도 보건환경연구원
3rd row경상남도 보건환경연구원
4th row경상남도 보건환경연구원
5th row경상남도 보건환경연구원

Common Values

ValueCountFrequency (%)
진주시청 환경관리과 136
66.7%
경상남도 보건환경연구원 68
33.3%

Length

2023-12-11T09:01:14.532215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:01:14.669534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
진주시청 136
33.3%
환경관리과 136
33.3%
경상남도 68
16.7%
보건환경연구원 68
16.7%

전화번호
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
055-749-5332
136 
055-254-2366
68 

Length

Max length12
Median length12
Mean length12
Min length12

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row055-254-2366
2nd row055-254-2366
3rd row055-254-2366
4th row055-254-2366
5th row055-254-2366

Common Values

ValueCountFrequency (%)
055-749-5332 136
66.7%
055-254-2366 68
33.3%

Length

2023-12-11T09:01:14.784544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:01:14.890913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
055-749-5332 136
66.7%
055-254-2366 68
33.3%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-08-07
204 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-08-07
2nd row2023-08-07
3rd row2023-08-07
4th row2023-08-07
5th row2023-08-07

Common Values

ValueCountFrequency (%)
2023-08-07 204
100.0%

Length

2023-12-11T09:01:15.007617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:01:15.110464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-08-07 204
100.0%

Interactions

2023-12-11T09:01:10.151036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:01:07.201427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:01:07.760538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:01:08.331470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:01:08.869331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:01:09.464663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:01:10.579006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:01:07.304412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:01:07.847929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:01:08.428385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:01:08.964887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:01:09.583099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:01:10.759925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:01:07.391045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:01:07.951210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:01:08.519767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:01:09.056580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:01:09.701841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:01:10.899265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:01:07.471206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:01:08.038213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:01:08.598940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:01:09.148407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:01:09.803037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:01:11.054674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:01:07.570168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:01:08.136859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:01:08.689616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:01:09.240309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:01:09.921825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:01:11.193449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:01:07.663770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:01:08.243098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:01:08.774328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:01:09.334543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:01:10.048214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T09:01:15.185190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도월측정소명오존(O3)미세먼지(PM10)초미세먼지(PM2점5)주소위도경도관리부서전화번호
연도1.0000.8870.0000.000NaN0.3310.0000.0000.0000.0000.000
월0.8871.0000.0000.7420.3110.7080.0000.0000.0000.3010.301
측정소명0.0000.0001.0000.0000.4570.2511.0001.0001.0000.9310.931
오존(O3)0.0000.7420.0001.000NaNNaN0.0000.0000.000NaNNaN
미세먼지(PM10)NaN0.3110.457NaN1.000NaN0.3000.1300.546NaNNaN
초미세먼지(PM2점5)0.3310.7080.251NaNNaN1.0000.0000.0000.230NaNNaN
주소0.0000.0001.0000.0000.3000.0001.0001.0001.0000.9260.926
위도0.0000.0001.0000.0000.1300.0001.0001.0000.6770.4490.449
경도0.0000.0001.0000.0000.5460.2301.0000.6771.0000.2520.252
관리부서0.0000.3010.931NaNNaNNaN0.9260.4490.2521.0001.000
전화번호0.0000.3010.931NaNNaNNaN0.9260.4490.2521.0001.000
2023-12-11T09:01:15.315234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도관리부서주소측정소명전화번호
연도1.0000.0000.0000.0000.000
관리부서0.0001.0000.7440.7640.989
주소0.0000.7441.0001.0000.744
측정소명0.0000.7641.0001.0000.764
전화번호0.0000.9890.7440.7641.000
2023-12-11T09:01:15.440217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
월오존(O3)미세먼지(PM10)초미세먼지(PM2점5)위도경도연도측정소명주소관리부서전화번호
월1.000-0.129-0.572-0.278-0.0490.0590.7080.0000.0000.2260.226
오존(O3)-0.1291.000NaNNaN-0.0340.0480.0000.0000.0001.0001.000
미세먼지(PM10)-0.572NaN1.000NaN0.2250.2661.0000.2440.0001.0001.000
초미세먼지(PM2점5)-0.278NaNNaN1.0000.1380.0720.2420.1170.0001.0001.000
위도-0.049-0.0340.2250.1381.000-0.3170.0000.9870.9880.5420.542
경도0.0590.0480.2660.072-0.3171.0000.0000.9820.9940.2830.283
연도0.7080.0001.0000.2420.0000.0001.0000.0000.0000.0000.000
측정소명0.0000.0000.2440.1170.9870.9820.0001.0001.0000.7640.764
주소0.0000.0000.0000.0000.9880.9940.0001.0001.0000.7440.744
관리부서0.2261.0001.0001.0000.5420.2830.0000.7640.7441.0000.989
전화번호0.2261.0001.0001.0000.5420.2830.0000.7640.7440.9891.000

Missing values

2023-12-11T09:01:11.369894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T09:01:11.556530image/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.
2023-12-11T09:01:11.709450image/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

연도월측정소명오존(O3)미세먼지(PM10)초미세먼지(PM2점5)주소위도경도관리부서전화번호데이터기준일자
020221대안동0.023<NA><NA>경상남도 진주시 진주대로 1052(대안동)35.193544128.084518경상남도 보건환경연구원055-254-23662023-08-07
120221상대동0.021<NA><NA><NA>35.179732128.108462경상남도 보건환경연구원055-254-23662023-08-07
220221상봉동0.023<NA><NA>경상남도 진주시 북장대로64번길 14(상봉동)35.195894128.074571경상남도 보건환경연구원055-254-23662023-08-07
320221정촌면0.018<NA><NA>경상남도 진주시 산단중앙로 7035.122945128.103666경상남도 보건환경연구원055-254-23662023-08-07
420222대안동0.036<NA><NA>경상남도 진주시 진주대로 1052(대안동)35.193544128.084518경상남도 보건환경연구원055-254-23662023-08-07
520222상대동0.034<NA><NA><NA>35.179732128.108462경상남도 보건환경연구원055-254-23662023-08-07
620222상봉동0.034<NA><NA>경상남도 진주시 북장대로64번길 14(상봉동)35.195894128.074571경상남도 보건환경연구원055-254-23662023-08-07
720222정촌면0.031<NA><NA>경상남도 진주시 산단중앙로 7035.122945128.103666경상남도 보건환경연구원055-254-23662023-08-07
820223대안동0.038<NA><NA>경상남도 진주시 진주대로 1052(대안동)35.193544128.084518경상남도 보건환경연구원055-254-23662023-08-07
920223상대동0.037<NA><NA><NA>35.179732128.108462경상남도 보건환경연구원055-254-23662023-08-07
연도월측정소명오존(O3)미세먼지(PM10)초미세먼지(PM2점5)주소위도경도관리부서전화번호데이터기준일자
19420229정촌면<NA><NA>12.390977경상남도 진주시 산단중앙로 7035.122945128.103666진주시청 환경관리과055-749-53322023-08-07
195202210정촌면<NA><NA>13.27957경상남도 진주시 산단중앙로 7035.122945128.103666진주시청 환경관리과055-749-53322023-08-07
196202211정촌면<NA><NA>23.2875경상남도 진주시 산단중앙로 7035.122945128.103666진주시청 환경관리과055-749-53322023-08-07
197202212정촌면<NA><NA>18.099462경상남도 진주시 산단중앙로 7035.122945128.103666진주시청 환경관리과055-749-53322023-08-07
19820231정촌면<NA><NA>19.46371경상남도 진주시 산단중앙로 7035.122945128.103666진주시청 환경관리과055-749-53322023-08-07
19920232정촌면<NA><NA>20.737542경상남도 진주시 산단중앙로 7035.122945128.103666진주시청 환경관리과055-749-53322023-08-07
20020233정촌면<NA><NA>24.345895경상남도 진주시 산단중앙로 7035.122945128.103666진주시청 환경관리과055-749-53322023-08-07
20120234정촌면<NA><NA>17.353268경상남도 진주시 산단중앙로 7035.122945128.103666진주시청 환경관리과055-749-53322023-08-07
20220235정촌면<NA><NA>8.0경상남도 진주시 산단중앙로 7035.122945128.103666진주시청 환경관리과055-749-53322023-08-07
20320236정촌면<NA><NA>13.899174경상남도 진주시 산단중앙로 7035.122945128.103666진주시청 환경관리과055-749-53322023-08-07