Overview

Dataset statistics

Number of variables8
Number of observations383
Missing cells55
Missing cells (%)1.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory26.3 KiB
Average record size in memory70.3 B

Variable types

Categorical2
Numeric5
DateTime1

Dataset

Description전북특별자치도 대기오염 측정망 운영 결과 데이터입니다. 측정항목, 측정위치(전주시, 군산시, 익산시 등) 대기정보를 제공합니다.
Author전북특별자치도
URLhttps://www.data.go.kr/data/3081364/fileData.do

Alerts

이산화질소(NO2) is highly overall correlated with 미세먼지(PM10) and 1 other fieldsHigh correlation
미세먼지(PM10) is highly overall correlated with 이산화질소(NO2) and 1 other fieldsHigh correlation
미세먼지(PM2_5) is highly overall correlated with 이산화질소(NO2) and 1 other fieldsHigh correlation
오존(O3) has 15 (3.9%) missing valuesMissing
이산화질소(NO2) has 7 (1.8%) missing valuesMissing
이산화황(SO2) has 6 (1.6%) missing valuesMissing
미세먼지(PM10) has 13 (3.4%) missing valuesMissing
미세먼지(PM2_5) has 14 (3.7%) missing valuesMissing

Reproduction

Analysis started2024-03-14 10:45:27.873879
Analysis finished2024-03-14 10:45:33.280507
Duration5.41 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

측정지역
Categorical

Distinct44
Distinct (%)11.5%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
전주시 노송동
 
9
정읍시 신태인
 
9
군산시 신풍동
 
9
전주시 송천동
 
9
전주시 팔복동
 
9
Other values (39)
338 

Length

Max length8
Median length7
Mean length6.8015666
Min length6

Unique

Unique1 ?
Unique (%)0.3%

Sample

1st row전주시 노송동
2nd row전주시 삼천동
3rd row전주시 송천동
4th row전주시 팔복동
5th row전주시 혁신동

Common Values

ValueCountFrequency (%)
전주시 노송동 9
 
2.3%
정읍시 신태인 9
 
2.3%
군산시 신풍동 9
 
2.3%
전주시 송천동 9
 
2.3%
전주시 팔복동 9
 
2.3%
전주시 혁신동 9
 
2.3%
전주시 서신동 9
 
2.3%
전주시 평균 9
 
2.3%
군산시 개정동 9
 
2.3%
군산시 비응도 9
 
2.3%
Other values (34) 293
76.5%

Length

2024-03-14T19:45:33.455571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
평균 72
 
9.5%
전주시 63
 
8.3%
군산시 59
 
7.8%
익산시 55
 
7.3%
완주군 36
 
4.8%
정읍시 35
 
4.6%
임실군 27
 
3.6%
부안군 27
 
3.6%
고창군 18
 
2.4%
무주군 9
 
1.2%
Other values (41) 356
47.0%

오존(O3)
Real number (ℝ)

MISSING 

Distinct37
Distinct (%)10.1%
Missing15
Missing (%)3.9%
Infinite0
Infinite (%)0.0%
Mean0.035915761
Minimum0.017
Maximum0.054
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2024-03-14T19:45:33.737055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.017
5-th percentile0.023
Q10.02975
median0.034
Q30.04325
95-th percentile0.05
Maximum0.054
Range0.037
Interquartile range (IQR)0.0135

Descriptive statistics

Standard deviation0.0085234711
Coefficient of variation (CV)0.23731841
Kurtosis-0.88869795
Mean0.035915761
Median Absolute Deviation (MAD)0.0065
Skewness0.22524131
Sum13.217
Variance7.264956 × 10-5
MonotonicityNot monotonic
2024-03-14T19:45:33.991985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
0.031 24
 
6.3%
0.029 21
 
5.5%
0.045 20
 
5.2%
0.034 20
 
5.2%
0.03 19
 
5.0%
0.036 18
 
4.7%
0.035 17
 
4.4%
0.033 16
 
4.2%
0.027 15
 
3.9%
0.032 14
 
3.7%
Other values (27) 184
48.0%
(Missing) 15
 
3.9%
ValueCountFrequency (%)
0.017 1
 
0.3%
0.019 1
 
0.3%
0.02 2
 
0.5%
0.021 7
1.8%
0.022 6
 
1.6%
0.023 7
1.8%
0.024 5
 
1.3%
0.025 6
 
1.6%
0.026 10
2.6%
0.027 15
3.9%
ValueCountFrequency (%)
0.054 4
 
1.0%
0.053 4
 
1.0%
0.052 2
 
0.5%
0.051 6
 
1.6%
0.05 8
 
2.1%
0.049 8
 
2.1%
0.048 11
2.9%
0.047 10
2.6%
0.046 6
 
1.6%
0.045 20
5.2%

이산화질소(NO2)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct24
Distinct (%)6.4%
Missing7
Missing (%)1.8%
Infinite0
Infinite (%)0.0%
Mean0.0099468085
Minimum0.002
Maximum0.029
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2024-03-14T19:45:34.388718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.002
5-th percentile0.004
Q10.007
median0.009
Q30.012
95-th percentile0.018
Maximum0.029
Range0.027
Interquartile range (IQR)0.005

Descriptive statistics

Standard deviation0.0044896729
Coefficient of variation (CV)0.45136819
Kurtosis0.93090001
Mean0.0099468085
Median Absolute Deviation (MAD)0.003
Skewness0.9602933
Sum3.74
Variance2.0157163 × 10-5
MonotonicityNot monotonic
2024-03-14T19:45:34.790750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0.006 43
11.2%
0.007 42
11.0%
0.008 40
10.4%
0.01 33
8.6%
0.009 30
 
7.8%
0.012 30
 
7.8%
0.011 23
 
6.0%
0.005 19
 
5.0%
0.004 18
 
4.7%
0.015 16
 
4.2%
Other values (14) 82
21.4%
ValueCountFrequency (%)
0.002 1
 
0.3%
0.003 8
 
2.1%
0.004 18
4.7%
0.005 19
5.0%
0.006 43
11.2%
0.007 42
11.0%
0.008 40
10.4%
0.009 30
7.8%
0.01 33
8.6%
0.011 23
6.0%
ValueCountFrequency (%)
0.029 1
 
0.3%
0.025 1
 
0.3%
0.023 2
 
0.5%
0.022 4
 
1.0%
0.021 4
 
1.0%
0.02 3
 
0.8%
0.019 2
 
0.5%
0.018 11
2.9%
0.017 9
2.3%
0.016 6
1.6%

이산화황(SO2)
Real number (ℝ)

MISSING 

Distinct6
Distinct (%)1.6%
Missing6
Missing (%)1.6%
Infinite0
Infinite (%)0.0%
Mean0.0026923077
Minimum0.001
Maximum0.006
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2024-03-14T19:45:35.145914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.001
5-th percentile0.002
Q10.002
median0.003
Q30.003
95-th percentile0.004
Maximum0.006
Range0.005
Interquartile range (IQR)0.001

Descriptive statistics

Standard deviation0.00084788174
Coefficient of variation (CV)0.3149275
Kurtosis1.8466966
Mean0.0026923077
Median Absolute Deviation (MAD)0.001
Skewness1.2918061
Sum1.015
Variance7.1890344 × 10-7
MonotonicityNot monotonic
2024-03-14T19:45:35.509690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0.002 186
48.6%
0.003 136
35.5%
0.004 40
 
10.4%
0.005 10
 
2.6%
0.006 4
 
1.0%
0.001 1
 
0.3%
(Missing) 6
 
1.6%
ValueCountFrequency (%)
0.001 1
 
0.3%
0.002 186
48.6%
0.003 136
35.5%
0.004 40
 
10.4%
0.005 10
 
2.6%
0.006 4
 
1.0%
ValueCountFrequency (%)
0.006 4
 
1.0%
0.005 10
 
2.6%
0.004 40
 
10.4%
0.003 136
35.5%
0.002 186
48.6%
0.001 1
 
0.3%

일산화탄소(CO)
Categorical

Distinct6
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
0.3
188 
0.4
113 
0.5
40 
0.2
29 
0.6
 
7

Length

Max length4
Median length3
Mean length3.0156658
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.5
2nd row0.5
3rd row0.4
4th row0.6
5th row0.5

Common Values

ValueCountFrequency (%)
0.3 188
49.1%
0.4 113
29.5%
0.5 40
 
10.4%
0.2 29
 
7.6%
0.6 7
 
1.8%
<NA> 6
 
1.6%

Length

2024-03-14T19:45:35.911341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T19:45:36.260839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.3 188
49.1%
0.4 113
29.5%
0.5 40
 
10.4%
0.2 29
 
7.6%
0.6 7
 
1.8%
na 6
 
1.6%

미세먼지(PM10)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct72
Distinct (%)19.5%
Missing13
Missing (%)3.4%
Infinite0
Infinite (%)0.0%
Mean38.527027
Minimum12
Maximum96
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2024-03-14T19:45:36.857842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile16.45
Q121.25
median38
Q347
95-th percentile75.55
Maximum96
Range84
Interquartile range (IQR)25.75

Descriptive statistics

Standard deviation18.378454
Coefficient of variation (CV)0.47702756
Kurtosis0.19350774
Mean38.527027
Median Absolute Deviation (MAD)15
Skewness0.8122934
Sum14255
Variance337.76756
MonotonicityNot monotonic
2024-03-14T19:45:37.202785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19 21
 
5.5%
20 17
 
4.4%
22 15
 
3.9%
18 15
 
3.9%
41 14
 
3.7%
43 13
 
3.4%
16 12
 
3.1%
37 12
 
3.1%
46 12
 
3.1%
17 11
 
2.9%
Other values (62) 228
59.5%
(Missing) 13
 
3.4%
ValueCountFrequency (%)
12 1
 
0.3%
14 2
 
0.5%
15 4
 
1.0%
16 12
3.1%
17 11
2.9%
18 15
3.9%
19 21
5.5%
20 17
4.4%
21 10
2.6%
22 15
3.9%
ValueCountFrequency (%)
96 1
0.3%
93 1
0.3%
89 2
0.5%
88 1
0.3%
87 1
0.3%
86 1
0.3%
85 2
0.5%
84 1
0.3%
82 1
0.3%
80 2
0.5%

미세먼지(PM2_5)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct33
Distinct (%)8.9%
Missing14
Missing (%)3.7%
Infinite0
Infinite (%)0.0%
Mean18.943089
Minimum5
Maximum37
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2024-03-14T19:45:37.422751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile8
Q112
median19
Q324
95-th percentile31
Maximum37
Range32
Interquartile range (IQR)12

Descriptive statistics

Standard deviation7.4600029
Coefficient of variation (CV)0.39381131
Kurtosis-0.89306105
Mean18.943089
Median Absolute Deviation (MAD)6
Skewness0.10081926
Sum6990
Variance55.651644
MonotonicityNot monotonic
2024-03-14T19:45:37.643859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
24 26
 
6.8%
21 23
 
6.0%
10 22
 
5.7%
19 21
 
5.5%
9 20
 
5.2%
20 19
 
5.0%
23 18
 
4.7%
12 16
 
4.2%
26 16
 
4.2%
22 15
 
3.9%
Other values (23) 173
45.2%
ValueCountFrequency (%)
5 1
 
0.3%
6 7
 
1.8%
7 5
 
1.3%
8 15
3.9%
9 20
5.2%
10 22
5.7%
11 15
3.9%
12 16
4.2%
13 5
 
1.3%
14 11
2.9%
ValueCountFrequency (%)
37 1
 
0.3%
36 1
 
0.3%
35 2
 
0.5%
34 5
1.3%
33 4
 
1.0%
32 3
 
0.8%
31 7
1.8%
30 9
2.3%
29 11
2.9%
28 9
2.3%

측정기준일
Date

Distinct10
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
Minimum2017-08-31 00:00:00
Maximum2021-09-30 00:00:00
2024-03-14T19:45:37.983545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:45:38.351108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)

Interactions

2024-03-14T19:45:31.768023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:45:28.311215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:45:29.296619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:45:30.155362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:45:30.933977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:45:31.935692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:45:28.583516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:45:29.458071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:45:30.323520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:45:31.181993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:45:32.092496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:45:28.821574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:45:29.611174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:45:30.477394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:45:31.332383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:45:32.308410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:45:28.982541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:45:29.763588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:45:30.631132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:45:31.479843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:45:32.479868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:45:29.132097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:45:29.997257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:45:30.774379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:45:31.614238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T19:45:38.595743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
측정지역오존(O3)이산화질소(NO2)이산화황(SO2)일산화탄소(CO)미세먼지(PM10)미세먼지(PM2_5)측정기준일
측정지역1.0000.3750.4890.8060.5390.0000.0000.000
오존(O3)0.3751.0000.4080.1660.5110.6460.6110.827
이산화질소(NO2)0.4890.4081.0000.1110.7240.5750.6180.652
이산화황(SO2)0.8060.1660.1111.0000.1160.2110.0000.000
일산화탄소(CO)0.5390.5110.7240.1161.0000.5660.6230.703
미세먼지(PM10)0.0000.6460.5750.2110.5661.0000.8750.908
미세먼지(PM2_5)0.0000.6110.6180.0000.6230.8751.0000.856
측정기준일0.0000.8270.6520.0000.7030.9080.8561.000
2024-03-14T19:45:38.886298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일산화탄소(CO)측정지역
일산화탄소(CO)1.0000.265
측정지역0.2651.000
2024-03-14T19:45:39.133636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
오존(O3)이산화질소(NO2)이산화황(SO2)미세먼지(PM10)미세먼지(PM2_5)측정지역일산화탄소(CO)
오존(O3)1.000-0.1330.0730.3380.2160.1310.234
이산화질소(NO2)-0.1331.000-0.0780.5640.6120.1800.381
이산화황(SO2)0.073-0.0781.0000.1360.0930.4780.078
미세먼지(PM10)0.3380.5640.1361.0000.8480.0000.267
미세먼지(PM2_5)0.2160.6120.0930.8481.0000.0000.299
측정지역0.1310.1800.4780.0000.0001.0000.265
일산화탄소(CO)0.2340.3810.0780.2670.2990.2651.000

Missing values

2024-03-14T19:45:32.706669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T19:45:32.930655image/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.
2024-03-14T19:45:33.141429image/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

측정지역오존(O3)이산화질소(NO2)이산화황(SO2)일산화탄소(CO)미세먼지(PM10)미세먼지(PM2_5)측정기준일
0전주시 노송동0.0220.0220.0030.543192021-01-31
1전주시 삼천동0.0230.0230.0040.5<NA>232021-01-31
2전주시 송천동0.0190.0180.0030.441212021-01-31
3전주시 팔복동0.0230.0250.0020.642252021-01-31
4전주시 혁신동0.020.0210.0020.543242021-01-31
5전주시 서신동<NA>0.0290.0030.542192021-01-31
6전주시 평균0.0210.0220.0030.542222021-01-31
7군산시 개정동0.0260.0120.0040.447212021-01-31
8군산시 비응도0.0250.0150.0050.542202021-01-31
9군산시 소룡동0.0260.0150.0050.445232021-01-31
측정지역오존(O3)이산화질소(NO2)이산화황(SO2)일산화탄소(CO)미세먼지(PM10)미세먼지(PM2_5)측정기준일
373임실군 임실읍0.030.0050.0040.31462021-09-30
374임실군 관촌면0.0290.0070.0030.31862021-09-30
375임실군 평균0.030.0060.0030.31662021-09-30
376순창군 순창읍0.0270.0040.0050.31872021-09-30
377고창군고창읍0.0340.0040.0030.316102021-09-30
378고창군 심원면0.0360.0030.0030.31882021-09-30
379고창군 평균0.0350.0030.0030.31792021-09-30
380부안군 부안읍0.0360.010.0020.318112021-09-30
381부안군 계화면0.0330.0060.0020.324142021-09-30
382부안군 평균0.0340.0080.0020.321122021-09-30