Overview

Dataset statistics

Number of variables8
Number of observations362
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory24.9 KiB
Average record size in memory70.4 B

Variable types

DateTime1
Categorical1
Numeric6

Dataset

Description경기도 포천시에서 제공하는 월별, 일별(2021년 1월 ~ 6월), 초미세먼지, 미세먼지, 오존, 이산화질소, 일산화탄소, 아황산가스 현황입니다.
Author경기도 포천시
URLhttps://www.data.go.kr/data/15090304/fileData.do

Alerts

초미세먼지 is highly overall correlated with 미세먼지 and 3 other fieldsHigh correlation
미세먼지 is highly overall correlated with 초미세먼지 and 3 other fieldsHigh correlation
오존 is highly overall correlated with 일산화탄소High correlation
이산화질소 is highly overall correlated with 초미세먼지 and 3 other fieldsHigh correlation
일산화탄소 is highly overall correlated with 초미세먼지 and 4 other fieldsHigh correlation
아황산가스 is highly overall correlated with 초미세먼지 and 3 other fieldsHigh correlation

Reproduction

Analysis started2023-12-12 02:45:57.553893
Analysis finished2023-12-12 02:46:02.404260
Duration4.85 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

측정일자
Date

Distinct181
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
Minimum2021-01-01 00:00:00
Maximum2021-06-30 00:00:00
2023-12-12T11:46:02.501405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:46:02.708239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

측정장소
Categorical

Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
일동면
212 
선단동
150 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
일동면 212
58.6%
선단동 150
41.4%

Length

2023-12-12T11:46:02.889881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:46:03.017890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
일동면 212
58.6%
선단동 150
41.4%

초미세먼지
Real number (ℝ)

HIGH CORRELATION 

Distinct62
Distinct (%)17.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.149171
Minimum0
Maximum95
Zeros1
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size3.3 KiB
2023-12-12T11:46:03.164723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6.05
Q112
median18
Q328
95-th percentile50
Maximum95
Range95
Interquartile range (IQR)16

Descriptive statistics

Standard deviation14.600492
Coefficient of variation (CV)0.65918908
Kurtosis2.9796253
Mean22.149171
Median Absolute Deviation (MAD)7
Skewness1.5206209
Sum8018
Variance213.17436
MonotonicityNot monotonic
2023-12-12T11:46:03.376760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12 19
 
5.2%
16 18
 
5.0%
11 17
 
4.7%
13 16
 
4.4%
20 16
 
4.4%
19 15
 
4.1%
9 13
 
3.6%
18 12
 
3.3%
22 12
 
3.3%
15 12
 
3.3%
Other values (52) 212
58.6%
ValueCountFrequency (%)
0 1
 
0.3%
2 2
 
0.6%
3 3
 
0.8%
4 7
1.9%
5 3
 
0.8%
6 3
 
0.8%
7 12
3.3%
8 8
2.2%
9 13
3.6%
10 12
3.3%
ValueCountFrequency (%)
95 1
0.3%
87 1
0.3%
71 1
0.3%
70 1
0.3%
67 1
0.3%
64 2
0.6%
61 2
0.6%
60 1
0.3%
58 2
0.6%
56 1
0.3%

미세먼지
Real number (ℝ)

HIGH CORRELATION 

Distinct96
Distinct (%)26.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41.502762
Minimum3
Maximum411
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.3 KiB
2023-12-12T11:46:03.567752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile11
Q122
median34
Q352
95-th percentile85.95
Maximum411
Range408
Interquartile range (IQR)30

Descriptive statistics

Standard deviation38.593
Coefficient of variation (CV)0.92988991
Kurtosis42.117012
Mean41.502762
Median Absolute Deviation (MAD)13
Skewness5.3879387
Sum15024
Variance1489.4197
MonotonicityNot monotonic
2023-12-12T11:46:03.757008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35 15
 
4.1%
34 13
 
3.6%
30 13
 
3.6%
27 12
 
3.3%
22 12
 
3.3%
29 11
 
3.0%
24 9
 
2.5%
23 9
 
2.5%
45 8
 
2.2%
16 8
 
2.2%
Other values (86) 252
69.6%
ValueCountFrequency (%)
3 1
 
0.3%
4 1
 
0.3%
5 3
 
0.8%
6 1
 
0.3%
7 5
1.4%
8 4
1.1%
9 1
 
0.3%
10 2
 
0.6%
11 7
1.9%
12 8
2.2%
ValueCountFrequency (%)
411 1
0.3%
374 1
0.3%
260 1
0.3%
244 1
0.3%
175 1
0.3%
159 1
0.3%
116 1
0.3%
104 1
0.3%
101 1
0.3%
97 1
0.3%

오존
Real number (ℝ)

HIGH CORRELATION 

Distinct63
Distinct (%)17.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.032491713
Minimum0
Maximum0.077
Zeros1
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size3.3 KiB
2023-12-12T11:46:03.939129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.013
Q10.025
median0.031
Q30.04
95-th percentile0.05395
Maximum0.077
Range0.077
Interquartile range (IQR)0.015

Descriptive statistics

Standard deviation0.012643906
Coefficient of variation (CV)0.38914248
Kurtosis0.29608876
Mean0.032491713
Median Absolute Deviation (MAD)0.008
Skewness0.32031093
Sum11.762
Variance0.00015986835
MonotonicityNot monotonic
2023-12-12T11:46:04.121175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.025 21
 
5.8%
0.026 20
 
5.5%
0.04 17
 
4.7%
0.033 15
 
4.1%
0.028 14
 
3.9%
0.037 13
 
3.6%
0.043 12
 
3.3%
0.031 11
 
3.0%
0.023 11
 
3.0%
0.018 10
 
2.8%
Other values (53) 218
60.2%
ValueCountFrequency (%)
0.0 1
 
0.3%
0.002 1
 
0.3%
0.003 1
 
0.3%
0.005 3
0.8%
0.006 1
 
0.3%
0.007 1
 
0.3%
0.008 1
 
0.3%
0.01 3
0.8%
0.012 6
1.7%
0.013 2
 
0.6%
ValueCountFrequency (%)
0.077 1
0.3%
0.068 2
0.6%
0.066 1
0.3%
0.065 2
0.6%
0.063 1
0.3%
0.062 2
0.6%
0.061 1
0.3%
0.06 1
0.3%
0.059 1
0.3%
0.057 1
0.3%

이산화질소
Real number (ℝ)

HIGH CORRELATION 

Distinct36
Distinct (%)9.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.012151934
Minimum0
Maximum0.05
Zeros3
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size3.3 KiB
2023-12-12T11:46:04.306965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.004
Q10.007
median0.01
Q30.015
95-th percentile0.02695
Maximum0.05
Range0.05
Interquartile range (IQR)0.008

Descriptive statistics

Standard deviation0.0073391606
Coefficient of variation (CV)0.60395002
Kurtosis3.2322079
Mean0.012151934
Median Absolute Deviation (MAD)0.004
Skewness1.5597415
Sum4.399
Variance5.3863279 × 10-5
MonotonicityNot monotonic
2023-12-12T11:46:04.480510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
0.007 37
 
10.2%
0.006 33
 
9.1%
0.009 29
 
8.0%
0.011 27
 
7.5%
0.01 25
 
6.9%
0.008 21
 
5.8%
0.005 20
 
5.5%
0.012 20
 
5.5%
0.013 18
 
5.0%
0.014 18
 
5.0%
Other values (26) 114
31.5%
ValueCountFrequency (%)
0.0 3
 
0.8%
0.003 5
 
1.4%
0.004 13
 
3.6%
0.005 20
5.5%
0.006 33
9.1%
0.007 37
10.2%
0.008 21
5.8%
0.009 29
8.0%
0.01 25
6.9%
0.011 27
7.5%
ValueCountFrequency (%)
0.05 1
 
0.3%
0.042 1
 
0.3%
0.04 1
 
0.3%
0.038 1
 
0.3%
0.035 1
 
0.3%
0.032 3
0.8%
0.031 4
1.1%
0.03 1
 
0.3%
0.029 3
0.8%
0.028 1
 
0.3%

일산화탄소
Real number (ℝ)

HIGH CORRELATION 

Distinct13
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.43756906
Minimum0.2
Maximum1.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.3 KiB
2023-12-12T11:46:04.655305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile0.3
Q10.3
median0.4
Q30.5
95-th percentile0.795
Maximum1.4
Range1.2
Interquartile range (IQR)0.2

Descriptive statistics

Standard deviation0.17319757
Coefficient of variation (CV)0.39581768
Kurtosis5.9239023
Mean0.43756906
Median Absolute Deviation (MAD)0.1
Skewness1.9817082
Sum158.4
Variance0.029997398
MonotonicityNot monotonic
2023-12-12T11:46:04.823243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0.3 115
31.8%
0.4 108
29.8%
0.5 59
16.3%
0.6 38
 
10.5%
0.2 12
 
3.3%
0.7 11
 
3.0%
0.8 7
 
1.9%
1.0 5
 
1.4%
0.9 3
 
0.8%
1.2 1
 
0.3%
Other values (3) 3
 
0.8%
ValueCountFrequency (%)
0.2 12
 
3.3%
0.3 115
31.8%
0.4 108
29.8%
0.5 59
16.3%
0.6 38
 
10.5%
0.7 11
 
3.0%
0.8 7
 
1.9%
0.9 3
 
0.8%
1.0 5
 
1.4%
1.1 1
 
0.3%
ValueCountFrequency (%)
1.4 1
 
0.3%
1.3 1
 
0.3%
1.2 1
 
0.3%
1.1 1
 
0.3%
1.0 5
 
1.4%
0.9 3
 
0.8%
0.8 7
 
1.9%
0.7 11
 
3.0%
0.6 38
10.5%
0.5 59
16.3%

아황산가스
Real number (ℝ)

HIGH CORRELATION 

Distinct7
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0034447514
Minimum0
Maximum0.007
Zeros2
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size3.3 KiB
2023-12-12T11:46:04.951309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.003
Q10.003
median0.003
Q30.004
95-th percentile0.005
Maximum0.007
Range0.007
Interquartile range (IQR)0.001

Descriptive statistics

Standard deviation0.00075795875
Coefficient of variation (CV)0.22003293
Kurtosis3.7298548
Mean0.0034447514
Median Absolute Deviation (MAD)0
Skewness0.62967397
Sum1.247
Variance5.7450146 × 10-7
MonotonicityNot monotonic
2023-12-12T11:46:05.090171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0.003 215
59.4%
0.004 111
30.7%
0.005 23
 
6.4%
0.002 6
 
1.7%
0.006 4
 
1.1%
0.0 2
 
0.6%
0.007 1
 
0.3%
ValueCountFrequency (%)
0.0 2
 
0.6%
0.002 6
 
1.7%
0.003 215
59.4%
0.004 111
30.7%
0.005 23
 
6.4%
0.006 4
 
1.1%
0.007 1
 
0.3%
ValueCountFrequency (%)
0.007 1
 
0.3%
0.006 4
 
1.1%
0.005 23
 
6.4%
0.004 111
30.7%
0.003 215
59.4%
0.002 6
 
1.7%
0.0 2
 
0.6%

Interactions

2023-12-12T11:46:01.087533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:45:57.835332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:45:58.551142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:45:59.300261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:45:59.936827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:46:00.482436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:46:01.178564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:45:57.918730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:45:58.668224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:45:59.399151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:46:00.015795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:46:00.577879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:46:01.326124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:45:58.028028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:45:58.806697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:45:59.502704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:46:00.105195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:46:00.690687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:46:01.451568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:45:58.218080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:45:58.933790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:45:59.625415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:46:00.219927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:46:00.798715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:46:01.572183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:45:58.328068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:45:59.049913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:45:59.729744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:46:00.299159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:46:00.892945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:46:01.708960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:45:58.440683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:45:59.180002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:45:59.823612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:46:00.384842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:46:00.985398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T11:46:05.196511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
측정장소초미세먼지미세먼지오존이산화질소일산화탄소아황산가스
측정장소1.0000.0000.0000.3900.3060.3600.205
초미세먼지0.0001.0000.6960.3540.6710.6750.601
미세먼지0.0000.6961.0000.1710.4030.4510.423
오존0.3900.3540.1711.0000.6980.7860.472
이산화질소0.3060.6710.4030.6981.0000.8930.754
일산화탄소0.3600.6750.4510.7860.8931.0000.711
아황산가스0.2050.6010.4230.4720.7540.7111.000
2023-12-12T11:46:05.315010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
초미세먼지미세먼지오존이산화질소일산화탄소아황산가스측정장소
초미세먼지1.0000.855-0.2020.6850.7260.5540.000
미세먼지0.8551.000-0.1800.6070.6490.5420.000
오존-0.202-0.1801.000-0.417-0.574-0.4240.296
이산화질소0.6850.607-0.4171.0000.6630.5920.260
일산화탄소0.7260.649-0.5740.6631.0000.7020.274
아황산가스0.5540.542-0.4240.5920.7021.0000.217
측정장소0.0000.0000.2960.2600.2740.2171.000

Missing values

2023-12-12T11:46:01.868185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T11:46:02.331599image/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

측정일자측정장소초미세먼지미세먼지오존이산화질소일산화탄소아황산가스
02021-01-01선단동18360.0170.00.60.004
12021-01-02선단동8370.0260.00.40.004
22021-01-03선단동17430.0180.00.60.004
32021-01-04선단동30560.010.0210.80.004
42021-01-05선단동14300.0230.0120.50.004
52021-01-06선단동20420.0140.0230.70.004
62021-01-07선단동10420.0240.0060.40.004
72021-01-08선단동7250.0250.0070.50.005
82021-01-09선단동18370.0190.0130.60.005
92021-01-10선단동25470.0130.020.80.004
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3522021-06-21일동면25350.0450.0050.40.003
3532021-06-22일동면7110.0330.0050.30.003
3542021-06-23일동면490.0190.0030.20.002
3552021-06-24일동면16220.0610.0050.30.003
3562021-06-25일동면20270.0550.0050.40.003
3572021-06-26일동면15200.0240.0050.30.002
3582021-06-27일동면9130.0280.0040.30.002
3592021-06-28일동면12170.0330.0040.30.002
3602021-06-29일동면7140.0180.0030.30.002
3612021-06-30일동면20230.0370.0040.40.003