Overview

Dataset statistics

Number of variables12
Number of observations25
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.7 KiB
Average record size in memory109.3 B

Variable types

Categorical6
Text1
Numeric5

Dataset

Description측정일시,권역명,측정소명,미세먼지(㎍/㎥),초미세먼지농도(㎍/㎥),오존(ppm),이산화질소농도(ppm),일산화탄소농도(ppm),아황산가스농도(ppm),통합대기환경등급,통합대기환경지수,지수결정물질
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-2219/S/1/datasetView.do

Alerts

측정일시 has constant value ""Constant
지수결정물질 has constant value ""Constant
미세먼지(㎍/㎥) is highly overall correlated with 초미세먼지농도(㎍/㎥)High correlation
초미세먼지농도(㎍/㎥) is highly overall correlated with 미세먼지(㎍/㎥)High correlation
오존(ppm) is highly overall correlated with 통합대기환경지수High correlation
통합대기환경지수 is highly overall correlated with 오존(ppm) and 1 other fieldsHigh correlation
통합대기환경등급 is highly overall correlated with 통합대기환경지수High correlation
통합대기환경등급 is highly imbalanced (75.8%)Imbalance
측정소명 has unique valuesUnique

Reproduction

Analysis started2024-05-11 07:57:52.995367
Analysis finished2024-05-11 07:58:03.083742
Duration10.09 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

측정일시
Categorical

CONSTANT 

Distinct1
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
202405111600
25 

Length

Max length12
Median length12
Mean length12
Min length12

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row202405111600
2nd row202405111600
3rd row202405111600
4th row202405111600
5th row202405111600

Common Values

ValueCountFrequency (%)
202405111600 25
100.0%

Length

2024-05-11T07:58:03.407666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T07:58:03.729209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
202405111600 25
100.0%

권역명
Categorical

Distinct5
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
동북권
8 
서남권
7 
동남권
4 
도심권
3 
서북권
3 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row도심권
2nd row도심권
3rd row도심권
4th row서북권
5th row서북권

Common Values

ValueCountFrequency (%)
동북권 8
32.0%
서남권 7
28.0%
동남권 4
16.0%
도심권 3
 
12.0%
서북권 3
 
12.0%

Length

2024-05-11T07:58:04.032452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T07:58:04.345572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
동북권 8
32.0%
서남권 7
28.0%
동남권 4
16.0%
도심권 3
 
12.0%
서북권 3
 
12.0%

측정소명
Text

UNIQUE 

Distinct25
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
2024-05-11T07:58:04.960878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.08
Min length2

Characters and Unicode

Total characters77
Distinct characters36
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

Unique25 ?
Unique (%)100.0%

Sample

1st row중구
2nd row종로구
3rd row용산구
4th row은평구
5th row서대문구
ValueCountFrequency (%)
중구 1
 
4.0%
노원구 1
 
4.0%
송파구 1
 
4.0%
서초구 1
 
4.0%
강남구 1
 
4.0%
양천구 1
 
4.0%
금천구 1
 
4.0%
관악구 1
 
4.0%
동작구 1
 
4.0%
영등포구 1
 
4.0%
Other values (15) 15
60.0%
2024-05-11T07:58:06.196593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
구 26
33.8%
강 4
 
5.2%
동 4
 
5.2%
서 3
 
3.9%
중 2
 
2.6%
문 2
 
2.6%
천 2
 
2.6%
성 2
 
2.6%
포 2
 
2.6%
북 2
 
2.6%
Other values (26) 28
36.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 77
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 26
33.8%
강 4
 
5.2%
동 4
 
5.2%
서 3
 
3.9%
중 2
 
2.6%
문 2
 
2.6%
천 2
 
2.6%
성 2
 
2.6%
포 2
 
2.6%
북 2
 
2.6%
Other values (26) 28
36.4%

Most occurring scripts

ValueCountFrequency (%)
Hangul 77
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 26
33.8%
강 4
 
5.2%
동 4
 
5.2%
서 3
 
3.9%
중 2
 
2.6%
문 2
 
2.6%
천 2
 
2.6%
성 2
 
2.6%
포 2
 
2.6%
북 2
 
2.6%
Other values (26) 28
36.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 77
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
구 26
33.8%
강 4
 
5.2%
동 4
 
5.2%
서 3
 
3.9%
중 2
 
2.6%
문 2
 
2.6%
천 2
 
2.6%
성 2
 
2.6%
포 2
 
2.6%
북 2
 
2.6%
Other values (26) 28
36.4%

미세먼지(㎍/㎥)
Real number (ℝ)

HIGH CORRELATION 

Distinct14
Distinct (%)56.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.2
Minimum9
Maximum47
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2024-05-11T07:58:06.682841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile11
Q116
median21
Q323
95-th percentile27.2
Maximum47
Range38
Interquartile range (IQR)7

Descriptive statistics

Standard deviation7.3200638
Coefficient of variation (CV)0.36237939
Kurtosis6.9396586
Mean20.2
Median Absolute Deviation (MAD)3
Skewness1.8706973
Sum505
Variance53.583333
MonotonicityNot monotonic
2024-05-11T07:58:07.072812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
21 7
28.0%
24 4
16.0%
16 2
 
8.0%
11 2
 
8.0%
17 1
 
4.0%
18 1
 
4.0%
47 1
 
4.0%
19 1
 
4.0%
14 1
 
4.0%
28 1
 
4.0%
Other values (4) 4
16.0%
ValueCountFrequency (%)
9 1
 
4.0%
11 2
 
8.0%
13 1
 
4.0%
14 1
 
4.0%
16 2
 
8.0%
17 1
 
4.0%
18 1
 
4.0%
19 1
 
4.0%
20 1
 
4.0%
21 7
28.0%
ValueCountFrequency (%)
47 1
 
4.0%
28 1
 
4.0%
24 4
16.0%
23 1
 
4.0%
21 7
28.0%
20 1
 
4.0%
19 1
 
4.0%
18 1
 
4.0%
17 1
 
4.0%
16 2
 
8.0%

초미세먼지농도(㎍/㎥)
Real number (ℝ)

HIGH CORRELATION 

Distinct11
Distinct (%)44.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.56
Minimum2
Maximum13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2024-05-11T07:58:07.552181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2.4
Q16
median8
Q310
95-th percentile11.8
Maximum13
Range11
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.8589042
Coefficient of variation (CV)0.37816193
Kurtosis-0.36640751
Mean7.56
Median Absolute Deviation (MAD)2
Skewness-0.16656871
Sum189
Variance8.1733333
MonotonicityNot monotonic
2024-05-11T07:58:07.905466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
10 5
20.0%
8 4
16.0%
5 3
12.0%
6 3
12.0%
7 3
12.0%
2 2
 
8.0%
12 1
 
4.0%
11 1
 
4.0%
13 1
 
4.0%
9 1
 
4.0%
ValueCountFrequency (%)
2 2
 
8.0%
4 1
 
4.0%
5 3
12.0%
6 3
12.0%
7 3
12.0%
8 4
16.0%
9 1
 
4.0%
10 5
20.0%
11 1
 
4.0%
12 1
 
4.0%
ValueCountFrequency (%)
13 1
 
4.0%
12 1
 
4.0%
11 1
 
4.0%
10 5
20.0%
9 1
 
4.0%
8 4
16.0%
7 3
12.0%
6 3
12.0%
5 3
12.0%
4 1
 
4.0%

오존(ppm)
Real number (ℝ)

HIGH CORRELATION 

Distinct14
Distinct (%)56.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.03896
Minimum0.03
Maximum0.049
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2024-05-11T07:58:08.394620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.03
5-th percentile0.0312
Q10.034
median0.039
Q30.043
95-th percentile0.048
Maximum0.049
Range0.019
Interquartile range (IQR)0.009

Descriptive statistics

Standard deviation0.0059124163
Coefficient of variation (CV)0.15175607
Kurtosis-1.1459057
Mean0.03896
Median Absolute Deviation (MAD)0.005
Skewness0.2755973
Sum0.974
Variance3.4956667 × 10-5
MonotonicityNot monotonic
2024-05-11T07:58:09.088001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0.048 3
12.0%
0.04 3
12.0%
0.035 3
12.0%
0.032 2
8.0%
0.045 2
8.0%
0.034 2
8.0%
0.043 2
8.0%
0.037 2
8.0%
0.03 1
 
4.0%
0.033 1
 
4.0%
Other values (4) 4
16.0%
ValueCountFrequency (%)
0.03 1
 
4.0%
0.031 1
 
4.0%
0.032 2
8.0%
0.033 1
 
4.0%
0.034 2
8.0%
0.035 3
12.0%
0.037 2
8.0%
0.039 1
 
4.0%
0.04 3
12.0%
0.041 1
 
4.0%
ValueCountFrequency (%)
0.049 1
 
4.0%
0.048 3
12.0%
0.045 2
8.0%
0.043 2
8.0%
0.041 1
 
4.0%
0.04 3
12.0%
0.039 1
 
4.0%
0.037 2
8.0%
0.035 3
12.0%
0.034 2
8.0%

이산화질소농도(ppm)
Real number (ℝ)

Distinct10
Distinct (%)40.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0092
Minimum0.005
Maximum0.017
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2024-05-11T07:58:09.658989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.005
5-th percentile0.0052
Q10.008
median0.009
Q30.01
95-th percentile0.0128
Maximum0.017
Range0.012
Interquartile range (IQR)0.002

Descriptive statistics

Standard deviation0.0025819889
Coefficient of variation (CV)0.28065097
Kurtosis2.3756324
Mean0.0092
Median Absolute Deviation (MAD)0.001
Skewness0.92825307
Sum0.23
Variance6.6666667 × 10-6
MonotonicityNot monotonic
2024-05-11T07:58:10.471976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0.009 6
24.0%
0.01 4
16.0%
0.011 3
12.0%
0.008 3
12.0%
0.007 3
12.0%
0.005 2
 
8.0%
0.013 1
 
4.0%
0.012 1
 
4.0%
0.006 1
 
4.0%
0.017 1
 
4.0%
ValueCountFrequency (%)
0.005 2
 
8.0%
0.006 1
 
4.0%
0.007 3
12.0%
0.008 3
12.0%
0.009 6
24.0%
0.01 4
16.0%
0.011 3
12.0%
0.012 1
 
4.0%
0.013 1
 
4.0%
0.017 1
 
4.0%
ValueCountFrequency (%)
0.017 1
 
4.0%
0.013 1
 
4.0%
0.012 1
 
4.0%
0.011 3
12.0%
0.01 4
16.0%
0.009 6
24.0%
0.008 3
12.0%
0.007 3
12.0%
0.006 1
 
4.0%
0.005 2
 
8.0%

일산화탄소농도(ppm)
Categorical

Distinct3
Distinct (%)12.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
0.2
12 
0.3
7 
0.4
6 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0.2 12
48.0%
0.3 7
28.0%
0.4 6
24.0%

Length

2024-05-11T07:58:11.082164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T07:58:11.372005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.2 12
48.0%
0.3 7
28.0%
0.4 6
24.0%

아황산가스농도(ppm)
Categorical

Distinct2
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
0.002
19 
0.003
6 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.003
2nd row0.002
3rd row0.002
4th row0.003
5th row0.002

Common Values

ValueCountFrequency (%)
0.002 19
76.0%
0.003 6
 
24.0%

Length

2024-05-11T07:58:11.705560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T07:58:12.043495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.002 19
76.0%
0.003 6
 
24.0%

통합대기환경등급
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
보통
24 
좋음
 
1

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique1 ?
Unique (%)4.0%

Sample

1st row보통
2nd row좋음
3rd row보통
4th row보통
5th row보통

Common Values

ValueCountFrequency (%)
보통 24
96.0%
좋음 1
 
4.0%

Length

2024-05-11T07:58:12.470397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T07:58:12.954300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
보통 24
96.0%
좋음 1
 
4.0%

통합대기환경지수
Real number (ℝ)

HIGH CORRELATION 

Distinct13
Distinct (%)52.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean57.36
Minimum49
Maximum65
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2024-05-11T07:58:13.422905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum49
5-th percentile51.2
Q154
median57
Q361
95-th percentile65
Maximum65
Range16
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.9149432
Coefficient of variation (CV)0.0856859
Kurtosis-1.137486
Mean57.36
Median Absolute Deviation (MAD)4
Skewness0.20586952
Sum1434
Variance24.156667
MonotonicityNot monotonic
2024-05-11T07:58:13.924438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
65 4
16.0%
54 4
16.0%
52 3
12.0%
59 3
12.0%
62 2
8.0%
61 2
8.0%
49 1
 
4.0%
58 1
 
4.0%
51 1
 
4.0%
56 1
 
4.0%
Other values (3) 3
12.0%
ValueCountFrequency (%)
49 1
 
4.0%
51 1
 
4.0%
52 3
12.0%
53 1
 
4.0%
54 4
16.0%
55 1
 
4.0%
56 1
 
4.0%
57 1
 
4.0%
58 1
 
4.0%
59 3
12.0%
ValueCountFrequency (%)
65 4
16.0%
62 2
8.0%
61 2
8.0%
59 3
12.0%
58 1
 
4.0%
57 1
 
4.0%
56 1
 
4.0%
55 1
 
4.0%
54 4
16.0%
53 1
 
4.0%

지수결정물질
Categorical

CONSTANT 

Distinct1
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
O3
25 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowO3
2nd rowO3
3rd rowO3
4th rowO3
5th rowO3

Common Values

ValueCountFrequency (%)
O3 25
100.0%

Length

2024-05-11T07:58:14.476198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T07:58:15.219515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
o3 25
100.0%

Interactions

2024-05-11T07:57:59.994731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T07:57:53.939819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T07:57:55.414510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T07:57:56.777556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T07:57:58.314470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T07:58:00.286458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T07:57:54.245520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T07:57:55.677706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T07:57:57.055292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T07:57:58.617245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T07:58:00.587747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T07:57:54.518030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T07:57:55.915899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T07:57:57.318750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T07:57:58.873988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T07:58:00.930594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T07:57:54.798409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T07:57:56.221275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T07:57:57.612576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T07:57:59.245513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T07:58:01.265923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T07:57:55.059725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T07:57:56.503042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T07:57:57.915862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T07:57:59.701448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-11T07:58:15.491742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
권역명측정소명미세먼지(㎍/㎥)초미세먼지농도(㎍/㎥)오존(ppm)이산화질소농도(ppm)일산화탄소농도(ppm)아황산가스농도(ppm)통합대기환경등급통합대기환경지수
권역명1.0001.0000.0000.5460.0000.0000.4480.0000.3410.411
측정소명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
미세먼지(㎍/㎥)0.0001.0001.0000.3200.5220.0000.0000.3500.0000.000
초미세먼지농도(㎍/㎥)0.5461.0000.3201.0000.1420.0000.0000.0000.0000.000
오존(ppm)0.0001.0000.5220.1421.0000.0000.0000.0000.4830.937
이산화질소농도(ppm)0.0001.0000.0000.0000.0001.0000.0000.7580.0000.000
일산화탄소농도(ppm)0.4481.0000.0000.0000.0000.0001.0000.1220.0000.000
아황산가스농도(ppm)0.0001.0000.3500.0000.0000.7580.1221.0000.0000.000
통합대기환경등급0.3411.0000.0000.0000.4830.0000.0000.0001.0001.000
통합대기환경지수0.4111.0000.0000.0000.9370.0000.0000.0001.0001.000
2024-05-11T07:58:16.064775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일산화탄소농도(ppm)권역명아황산가스농도(ppm)통합대기환경등급
일산화탄소농도(ppm)1.0000.3530.1890.000
권역명0.3531.0000.0000.381
아황산가스농도(ppm)0.1890.0001.0000.000
통합대기환경등급0.0000.3810.0001.000
2024-05-11T07:58:16.486503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
미세먼지(㎍/㎥)초미세먼지농도(㎍/㎥)오존(ppm)이산화질소농도(ppm)통합대기환경지수권역명일산화탄소농도(ppm)아황산가스농도(ppm)통합대기환경등급
미세먼지(㎍/㎥)1.0000.679-0.2480.330-0.2600.0000.0000.2100.000
초미세먼지농도(㎍/㎥)0.6791.000-0.1380.209-0.1400.1810.0000.0000.000
오존(ppm)-0.248-0.1381.000-0.4540.9950.0000.0000.0000.390
이산화질소농도(ppm)0.3300.209-0.4541.000-0.4410.0000.0000.4410.000
통합대기환경지수-0.260-0.1400.995-0.4411.0000.0880.0000.0000.808
권역명0.0000.1810.0000.0000.0881.0000.3530.0000.381
일산화탄소농도(ppm)0.0000.0000.0000.0000.0000.3531.0000.1890.000
아황산가스농도(ppm)0.2100.0000.0000.4410.0000.0000.1891.0000.000
통합대기환경등급0.0000.0000.3900.0000.8080.3810.0000.0001.000

Missing values

2024-05-11T07:58:01.936188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-11T07:58:02.712785image/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

측정일시권역명측정소명미세먼지(㎍/㎥)초미세먼지농도(㎍/㎥)오존(ppm)이산화질소농도(ppm)일산화탄소농도(ppm)아황산가스농도(ppm)통합대기환경등급통합대기환경지수지수결정물질
0202405111600도심권중구24120.0320.0110.20.003보통52O3
1202405111600도심권종로구21100.030.0090.20.002좋음49O3
2202405111600도심권용산구1650.0320.010.30.002보통52O3
3202405111600서북권은평구2160.0330.010.40.003보통52O3
4202405111600서북권서대문구1780.0480.0080.40.002보통65O3
5202405111600서북권마포구1880.040.010.20.002보통58O3
6202405111600동북권광진구47100.0450.0110.40.003보통62O3
7202405111600동북권성동구2170.0340.0130.20.003보통54O3
8202405111600동북권중랑구19110.0310.0120.30.002보통51O3
9202405111600동북권동대문구24100.0430.0080.20.002보통61O3
측정일시권역명측정소명미세먼지(㎍/㎥)초미세먼지농도(㎍/㎥)오존(ppm)이산화질소농도(ppm)일산화탄소농도(ppm)아황산가스농도(ppm)통합대기환경등급통합대기환경지수지수결정물질
15202405111600서남권구로구1150.0480.0050.20.002보통65O3
16202405111600서남권영등포구2160.0350.0080.20.002보통54O3
17202405111600서남권동작구28100.0370.0070.20.002보통55O3
18202405111600서남권관악구2390.0410.0090.20.002보통59O3
19202405111600서남권금천구2160.0350.0170.40.002보통54O3
20202405111600서남권양천구2470.0390.010.20.002보통57O3
21202405111600동남권강남구21100.0430.0110.30.003보통61O3
22202405111600동남권서초구1320.0450.0070.30.002보통62O3
23202405111600동남권송파구2040.0350.0090.30.002보통54O3
24202405111600동남권강동구920.0480.0090.40.002보통65O3