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 memory110.3 B

Variable types

Categorical5
Numeric6
Text1

Dataset

Description측정날짜,측정소 행정코드,측정소명,통합대기환경지수 등급,통합대기환경지수,지수결정물질,이산화질소 농도(단위:ppm),오존 농도(단위:ppm),일산화탄소 농도(단위:ppm),아황산가스 농도(단위:ppm),미세먼지 농도(단위:㎍/㎥),초미세먼지 농도(단위:㎍/㎥)
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-1200/S/1/datasetView.do

Alerts

측정날짜 has constant value ""Constant
지수결정물질 has constant value ""Constant
통합대기환경지수 is highly overall correlated with 오존 농도(단위:ppm) and 1 other fieldsHigh correlation
오존 농도(단위:ppm) is highly overall correlated with 통합대기환경지수High correlation
미세먼지 농도(단위:㎍/㎥) is highly overall correlated with 초미세먼지 농도(단위:㎍/㎥)High correlation
초미세먼지 농도(단위:㎍/㎥) is highly overall correlated with 미세먼지 농도(단위:㎍/㎥)High correlation
통합대기환경지수 등급 is highly overall correlated with 통합대기환경지수High correlation
통합대기환경지수 등급 is highly imbalanced (75.8%)Imbalance
측정소 행정코드 has unique valuesUnique
측정소명 has unique valuesUnique

Reproduction

Analysis started2024-05-11 07:45:34.907691
Analysis finished2024-05-11 07:45:55.891642
Duration20.98 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

측정날짜
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

2024-05-11T07:45:56.100480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T07:45:56.361481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
202405111600 25
100.0%

측정소 행정코드
Real number (ℝ)

UNIQUE 

Distinct25
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean111211.04
Minimum111121
Maximum111311
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2024-05-11T07:45:56.684389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum111121
5-th percentile111124.6
Q1111152
median111212
Q3111262
95-th percentile111299
Maximum111311
Range190
Interquartile range (IQR)110

Descriptive statistics

Standard deviation61.169491
Coefficient of variation (CV)0.00055003075
Kurtosis-1.4051597
Mean111211.04
Median Absolute Deviation (MAD)60
Skewness0.035398473
Sum2780276
Variance3741.7067
MonotonicityNot monotonic
2024-05-11T07:45:57.118482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
111123 1
 
4.0%
111121 1
 
4.0%
111274 1
 
4.0%
111273 1
 
4.0%
111261 1
 
4.0%
111262 1
 
4.0%
111251 1
 
4.0%
111241 1
 
4.0%
111231 1
 
4.0%
111281 1
 
4.0%
Other values (15) 15
60.0%
ValueCountFrequency (%)
111121 1
4.0%
111123 1
4.0%
111131 1
4.0%
111141 1
4.0%
111142 1
4.0%
111151 1
4.0%
111152 1
4.0%
111161 1
4.0%
111171 1
4.0%
111181 1
4.0%
ValueCountFrequency (%)
111311 1
4.0%
111301 1
4.0%
111291 1
4.0%
111281 1
4.0%
111274 1
4.0%
111273 1
4.0%
111262 1
4.0%
111261 1
4.0%
111251 1
4.0%
111241 1
4.0%

측정소명
Text

UNIQUE 

Distinct25
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
2024-05-11T07:45:57.562967image/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 row종로구
2nd row중구
3rd row용산구
4th row성동구
5th row광진구
ValueCountFrequency (%)
종로구 1
 
4.0%
마포구 1
 
4.0%
송파구 1
 
4.0%
강남구 1
 
4.0%
서초구 1
 
4.0%
관악구 1
 
4.0%
동작구 1
 
4.0%
영등포구 1
 
4.0%
금천구 1
 
4.0%
구로구 1
 
4.0%
Other values (15) 15
60.0%
2024-05-11T07:45:58.639215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

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

통합대기환경지수 등급
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 row좋음
2nd row보통
3rd row보통
4th row보통
5th row보통

Common Values

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

Length

2024-05-11T07:45:59.129337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T07:45:59.386097image/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
2024-05-11T07:45:59.632589image/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
2024-05-11T07:45:59.923480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
54 4
16.0%
65 4
16.0%
52 3
12.0%
59 3
12.0%
62 2
8.0%
61 2
8.0%
49 1
 
4.0%
51 1
 
4.0%
56 1
 
4.0%
58 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

2024-05-11T07:46:00.424813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T07:46:00.755420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
o3 25
100.0%

이산화질소 농도(단위: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
2024-05-11T07:46:01.059808image/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
2024-05-11T07:46:01.518771image/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%

오존 농도(단위: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
2024-05-11T07:46:01.957518image/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
2024-05-11T07:46:02.429153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0.04 3
12.0%
0.048 3
12.0%
0.035 3
12.0%
0.032 2
8.0%
0.034 2
8.0%
0.045 2
8.0%
0.043 2
8.0%
0.037 2
8.0%
0.03 1
 
4.0%
0.031 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%

일산화탄소 농도(단위: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 row0.2
5th row0.4

Common Values

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

Length

2024-05-11T07:46:02.949732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T07:46:03.321152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.2 12
48.0%
0.3 7
28.0%
0.4 6
24.0%

아황산가스 농도(단위: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 row0.002
2nd row0.003
3rd row0.002
4th row0.003
5th row0.003

Common Values

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

Length

2024-05-11T07:46:03.741262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T07:46:04.089218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.002 19
76.0%
0.003 6
 
24.0%

미세먼지 농도(단위:㎍/㎥)
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
2024-05-11T07:46:04.566273image/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
2024-05-11T07:46:04.935862image/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%
47 1
 
4.0%
19 1
 
4.0%
14 1
 
4.0%
17 1
 
4.0%
18 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%

초미세먼지 농도(단위:㎍/㎥)
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
2024-05-11T07:46:05.343766image/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
2024-05-11T07:46:05.845468image/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%
7 3
12.0%
6 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%

Interactions

2024-05-11T07:45:52.771124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T07:45:40.254600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T07:45:42.699391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T07:45:45.189382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T07:45:47.305967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T07:45:50.072999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T07:45:53.127517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T07:45:40.697652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T07:45:43.114429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T07:45:45.461820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T07:45:47.715522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T07:45:50.799481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T07:45:53.549380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T07:45:41.151813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T07:45:43.660319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T07:45:45.749541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T07:45:48.168492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T07:45:51.197763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T07:45:53.873449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T07:45:41.520230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T07:45:44.137045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T07:45:46.000204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T07:45:48.656117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T07:45:51.669212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T07:45:54.312733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T07:45:41.878810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T07:45:44.459719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T07:45:46.353605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T07:45:49.237084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T07:45:52.043961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T07:45:54.584820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T07:45:42.293274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T07:45:44.841219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T07:45:46.918487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T07:45:49.721910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T07:45:52.434245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-11T07:46:06.126876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
측정소 행정코드측정소명통합대기환경지수 등급통합대기환경지수이산화질소 농도(단위:ppm)오존 농도(단위:ppm)일산화탄소 농도(단위:ppm)아황산가스 농도(단위:ppm)미세먼지 농도(단위:㎍/㎥)초미세먼지 농도(단위:㎍/㎥)
측정소 행정코드1.0001.0000.5270.3870.0000.0000.6870.0000.0000.360
측정소명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
통합대기환경지수 등급0.5271.0001.0001.0000.0000.4830.0000.0000.0000.000
통합대기환경지수0.3871.0001.0001.0000.0000.9370.0000.0000.0000.000
이산화질소 농도(단위:ppm)0.0001.0000.0000.0001.0000.0000.0000.7580.0000.000
오존 농도(단위:ppm)0.0001.0000.4830.9370.0001.0000.0000.0000.5220.142
일산화탄소 농도(단위:ppm)0.6871.0000.0000.0000.0000.0001.0000.1220.0000.000
아황산가스 농도(단위:ppm)0.0001.0000.0000.0000.7580.0000.1221.0000.3500.000
미세먼지 농도(단위:㎍/㎥)0.0001.0000.0000.0000.0000.5220.0000.3501.0000.320
초미세먼지 농도(단위:㎍/㎥)0.3601.0000.0000.0000.0000.1420.0000.0000.3201.000
2024-05-11T07:46:06.573431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
아황산가스 농도(단위:ppm)일산화탄소 농도(단위:ppm)통합대기환경지수 등급
아황산가스 농도(단위:ppm)1.0000.1890.000
일산화탄소 농도(단위:ppm)0.1891.0000.000
통합대기환경지수 등급0.0000.0001.000
2024-05-11T07:46:06.847739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
측정소 행정코드통합대기환경지수이산화질소 농도(단위:ppm)오존 농도(단위:ppm)미세먼지 농도(단위:㎍/㎥)초미세먼지 농도(단위:㎍/㎥)통합대기환경지수 등급일산화탄소 농도(단위:ppm)아황산가스 농도(단위:ppm)
측정소 행정코드1.0000.444-0.2380.476-0.059-0.2440.0000.4360.000
통합대기환경지수0.4441.000-0.4410.995-0.260-0.1400.8080.0000.000
이산화질소 농도(단위:ppm)-0.238-0.4411.000-0.4540.3300.2090.0000.0000.441
오존 농도(단위:ppm)0.4760.995-0.4541.000-0.248-0.1380.3900.0000.000
미세먼지 농도(단위:㎍/㎥)-0.059-0.2600.330-0.2481.0000.6790.0000.0000.210
초미세먼지 농도(단위:㎍/㎥)-0.244-0.1400.209-0.1380.6791.0000.0000.0000.000
통합대기환경지수 등급0.0000.8080.0000.3900.0000.0001.0000.0000.000
일산화탄소 농도(단위:ppm)0.4360.0000.0000.0000.0000.0000.0001.0000.189
아황산가스 농도(단위:ppm)0.0000.0000.4410.0000.2100.0000.0000.1891.000

Missing values

2024-05-11T07:45:55.031991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-11T07:45:55.622346image/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

측정날짜측정소 행정코드측정소명통합대기환경지수 등급통합대기환경지수지수결정물질이산화질소 농도(단위:ppm)오존 농도(단위:ppm)일산화탄소 농도(단위:ppm)아황산가스 농도(단위:ppm)미세먼지 농도(단위:㎍/㎥)초미세먼지 농도(단위:㎍/㎥)
0202405111600111123종로구좋음49O30.0090.030.20.0022110
1202405111600111121중구보통52O30.0110.0320.20.0032412
2202405111600111131용산구보통52O30.010.0320.30.002165
3202405111600111142성동구보통54O30.0130.0340.20.003217
4202405111600111141광진구보통62O30.0110.0450.40.0034710
5202405111600111152동대문구보통61O30.0080.0430.20.0022410
6202405111600111151중랑구보통51O30.0120.0310.30.0021911
7202405111600111161성북구보통56O30.0090.0370.30.002115
8202405111600111291강북구보통65O30.0060.0490.20.002168
9202405111600111171도봉구보통59O30.0050.040.40.003147
측정날짜측정소 행정코드측정소명통합대기환경지수 등급통합대기환경지수지수결정물질이산화질소 농도(단위:ppm)오존 농도(단위:ppm)일산화탄소 농도(단위:ppm)아황산가스 농도(단위:ppm)미세먼지 농도(단위:㎍/㎥)초미세먼지 농도(단위:㎍/㎥)
15202405111600111212강서구보통53O30.0070.0340.20.002218
16202405111600111221구로구보통65O30.0050.0480.20.002115
17202405111600111281금천구보통54O30.0170.0350.40.002216
18202405111600111231영등포구보통54O30.0080.0350.20.002216
19202405111600111241동작구보통55O30.0070.0370.20.0022810
20202405111600111251관악구보통59O30.0090.0410.20.002239
21202405111600111262서초구보통62O30.0070.0450.30.002132
22202405111600111261강남구보통61O30.0110.0430.30.0032110
23202405111600111273송파구보통54O30.0090.0350.30.002204
24202405111600111274강동구보통65O30.0090.0480.40.00292