Overview

Dataset statistics

Number of variables7
Number of observations10000
Missing cells3929
Missing cells (%)5.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory673.8 KiB
Average record size in memory69.0 B

Variable types

Categorical1
DateTime1
Numeric5

Dataset

Description대기오염정보관리시스템의 기간별 대기오염도 현황입니다.(전남도내 측정소별, 날짜별 대기오염도 수치를 조회하실 수 있습니다.)
Author전라남도
URLhttps://www.data.go.kr/data/15123886/fileData.do

Alerts

미세먼지(PM10) is highly overall correlated with 미세먼지(PM2.5)High correlation
미세먼지(PM2.5) is highly overall correlated with 미세먼지(PM10)High correlation
이산화질소(NO2) is highly overall correlated with 아황산가스(SO2)High correlation
아황산가스(SO2) is highly overall correlated with 이산화질소(NO2)High correlation
미세먼지(PM10) has 329 (3.3%) missing valuesMissing
미세먼지(PM2.5) has 2933 (29.3%) missing valuesMissing
오존(O3) has 217 (2.2%) missing valuesMissing
이산화질소(NO2) has 226 (2.3%) missing valuesMissing
아황산가스(SO2) has 224 (2.2%) missing valuesMissing

Reproduction

Analysis started2023-12-12 15:50:57.846871
Analysis finished2023-12-12 15:51:03.075716
Duration5.23 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

측정소
Categorical

Distinct38
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
여천동
 
594
문수동
 
572
용당동
 
564
서강동
 
558
월내동
 
549
Other values (33)
7163 

Length

Max length4
Median length3
Mean length2.9037
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row여천동
2nd row빛가람동
3rd row중동
4th row용당동
5th row신지면

Common Values

ValueCountFrequency (%)
여천동 594
 
5.9%
문수동 572
 
5.7%
용당동 564
 
5.6%
서강동 558
 
5.6%
월내동 549
 
5.5%
중동 524
 
5.2%
장천동 514
 
5.1%
태인동 493
 
4.9%
광양읍 491
 
4.9%
연향동 478
 
4.8%
Other values (28) 4663
46.6%

Length

2023-12-13T00:51:03.152774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
여천동 594
 
5.9%
문수동 572
 
5.7%
용당동 564
 
5.6%
서강동 558
 
5.6%
월내동 549
 
5.5%
중동 524
 
5.2%
장천동 514
 
5.1%
태인동 493
 
4.9%
광양읍 491
 
4.9%
연향동 478
 
4.8%
Other values (28) 4663
46.6%

날짜
Date

Distinct4365
Distinct (%)43.6%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2008-01-02 00:00:00
Maximum2022-12-30 00:00:00
2023-12-13T00:51:03.293936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:51:03.451998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

미세먼지(PM10)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct172
Distinct (%)1.8%
Missing329
Missing (%)3.3%
Infinite0
Infinite (%)0.0%
Mean34.817496
Minimum1
Maximum701
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T00:51:03.604100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile13
Q121
median30
Q342
95-th percentile71
Maximum701
Range700
Interquartile range (IQR)21

Descriptive statistics

Standard deviation25.7962
Coefficient of variation (CV)0.74089763
Kurtosis150.01288
Mean34.817496
Median Absolute Deviation (MAD)10
Skewness8.4282976
Sum336720
Variance665.44394
MonotonicityNot monotonic
2023-12-13T00:51:03.761614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26 331
 
3.3%
24 306
 
3.1%
22 305
 
3.0%
20 295
 
2.9%
27 294
 
2.9%
23 290
 
2.9%
28 280
 
2.8%
21 278
 
2.8%
30 272
 
2.7%
25 271
 
2.7%
Other values (162) 6749
67.5%
(Missing) 329
 
3.3%
ValueCountFrequency (%)
1 1
 
< 0.1%
2 2
 
< 0.1%
3 5
 
0.1%
4 6
 
0.1%
5 9
 
0.1%
6 22
 
0.2%
7 29
 
0.3%
8 43
0.4%
9 53
0.5%
10 79
0.8%
ValueCountFrequency (%)
701 1
< 0.1%
656 1
< 0.1%
554 1
< 0.1%
525 1
< 0.1%
441 2
< 0.1%
437 1
< 0.1%
422 1
< 0.1%
409 1
< 0.1%
343 1
< 0.1%
332 1
< 0.1%

미세먼지(PM2.5)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct92
Distinct (%)1.3%
Missing2933
Missing (%)29.3%
Infinite0
Infinite (%)0.0%
Mean18.249611
Minimum1
Maximum126
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T00:51:03.903372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q110
median16
Q323
95-th percentile40
Maximum126
Range125
Interquartile range (IQR)13

Descriptive statistics

Standard deviation11.4166
Coefficient of variation (CV)0.62558048
Kurtosis6.9193221
Mean18.249611
Median Absolute Deviation (MAD)6
Skewness1.9086712
Sum128970
Variance130.33876
MonotonicityNot monotonic
2023-12-13T00:51:04.049647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12 368
 
3.7%
10 353
 
3.5%
14 331
 
3.3%
11 329
 
3.3%
13 321
 
3.2%
9 314
 
3.1%
15 311
 
3.1%
8 311
 
3.1%
16 298
 
3.0%
17 287
 
2.9%
Other values (82) 3844
38.4%
(Missing) 2933
29.3%
ValueCountFrequency (%)
1 11
 
0.1%
2 30
 
0.3%
3 52
 
0.5%
4 102
 
1.0%
5 149
1.5%
6 218
2.2%
7 275
2.8%
8 311
3.1%
9 314
3.1%
10 353
3.5%
ValueCountFrequency (%)
126 1
< 0.1%
113 1
< 0.1%
110 1
< 0.1%
101 1
< 0.1%
94 1
< 0.1%
93 1
< 0.1%
92 1
< 0.1%
91 2
< 0.1%
90 1
< 0.1%
87 1
< 0.1%

오존(O3)
Real number (ℝ)

MISSING 

Distinct85
Distinct (%)0.9%
Missing217
Missing (%)2.2%
Infinite0
Infinite (%)0.0%
Mean0.031263007
Minimum0.002
Maximum0.125
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T00:51:04.205388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.002
5-th percentile0.012
Q10.022
median0.03
Q30.039
95-th percentile0.053
Maximum0.125
Range0.123
Interquartile range (IQR)0.017

Descriptive statistics

Standard deviation0.01252373
Coefficient of variation (CV)0.40059263
Kurtosis0.74371581
Mean0.031263007
Median Absolute Deviation (MAD)0.008
Skewness0.54039789
Sum305.846
Variance0.00015684382
MonotonicityNot monotonic
2023-12-13T00:51:04.362273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.029 363
 
3.6%
0.035 354
 
3.5%
0.025 341
 
3.4%
0.024 318
 
3.2%
0.03 316
 
3.2%
0.026 307
 
3.1%
0.028 302
 
3.0%
0.032 293
 
2.9%
0.021 288
 
2.9%
0.027 288
 
2.9%
Other values (75) 6613
66.1%
ValueCountFrequency (%)
0.002 6
 
0.1%
0.003 6
 
0.1%
0.004 13
 
0.1%
0.005 17
 
0.2%
0.006 28
 
0.3%
0.007 30
 
0.3%
0.008 49
0.5%
0.009 71
0.7%
0.01 73
0.7%
0.011 118
1.2%
ValueCountFrequency (%)
0.125 1
 
< 0.1%
0.11 1
 
< 0.1%
0.096 1
 
< 0.1%
0.091 1
 
< 0.1%
0.089 3
< 0.1%
0.085 2
< 0.1%
0.083 1
 
< 0.1%
0.079 1
 
< 0.1%
0.078 2
< 0.1%
0.077 2
< 0.1%

이산화질소(NO2)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct62
Distinct (%)0.6%
Missing226
Missing (%)2.3%
Infinite0
Infinite (%)0.0%
Mean0.013578269
Minimum0.001
Maximum0.073
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T00:51:04.522174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.001
5-th percentile0.004
Q10.007
median0.012
Q30.018
95-th percentile0.03
Maximum0.073
Range0.072
Interquartile range (IQR)0.011

Descriptive statistics

Standard deviation0.0085947784
Coefficient of variation (CV)0.63298043
Kurtosis2.9042415
Mean0.013578269
Median Absolute Deviation (MAD)0.005
Skewness1.3874642
Sum132.714
Variance7.3870216 × 10-5
MonotonicityNot monotonic
2023-12-13T00:51:04.695366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.009 651
 
6.5%
0.006 618
 
6.2%
0.007 601
 
6.0%
0.011 582
 
5.8%
0.005 552
 
5.5%
0.013 513
 
5.1%
0.008 498
 
5.0%
0.004 476
 
4.8%
0.01 465
 
4.7%
0.015 433
 
4.3%
Other values (52) 4385
43.9%
ValueCountFrequency (%)
0.001 18
 
0.2%
0.002 99
 
1.0%
0.003 297
3.0%
0.004 476
4.8%
0.005 552
5.5%
0.006 618
6.2%
0.007 601
6.0%
0.008 498
5.0%
0.009 651
6.5%
0.01 465
4.7%
ValueCountFrequency (%)
0.073 2
< 0.1%
0.07 1
 
< 0.1%
0.068 1
 
< 0.1%
0.066 1
 
< 0.1%
0.062 2
< 0.1%
0.06 2
< 0.1%
0.059 2
< 0.1%
0.057 1
 
< 0.1%
0.056 4
< 0.1%
0.055 2
< 0.1%

아황산가스(SO2)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct39
Distinct (%)0.4%
Missing224
Missing (%)2.2%
Infinite0
Infinite (%)0.0%
Mean0.0048591448
Minimum0.001
Maximum0.052
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T00:51:04.848550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.001
5-th percentile0.002
Q10.003
median0.004
Q30.006
95-th percentile0.012
Maximum0.052
Range0.051
Interquartile range (IQR)0.003

Descriptive statistics

Standard deviation0.0038193081
Coefficient of variation (CV)0.78600417
Kurtosis16.382198
Mean0.0048591448
Median Absolute Deviation (MAD)0.001
Skewness3.1762293
Sum47.503
Variance1.4587114 × 10-5
MonotonicityNot monotonic
2023-12-13T00:51:04.984590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
0.003 2498
25.0%
0.004 1842
18.4%
0.002 1768
17.7%
0.005 927
 
9.3%
0.006 611
 
6.1%
0.007 443
 
4.4%
0.008 287
 
2.9%
0.009 279
 
2.8%
0.001 239
 
2.4%
0.011 176
 
1.8%
Other values (29) 706
 
7.1%
(Missing) 224
 
2.2%
ValueCountFrequency (%)
0.001 239
 
2.4%
0.002 1768
17.7%
0.003 2498
25.0%
0.004 1842
18.4%
0.005 927
 
9.3%
0.006 611
 
6.1%
0.007 443
 
4.4%
0.008 287
 
2.9%
0.009 279
 
2.8%
0.01 154
 
1.5%
ValueCountFrequency (%)
0.052 1
 
< 0.1%
0.049 1
 
< 0.1%
0.04 2
< 0.1%
0.038 1
 
< 0.1%
0.036 4
< 0.1%
0.035 1
 
< 0.1%
0.033 1
 
< 0.1%
0.032 4
< 0.1%
0.031 4
< 0.1%
0.03 4
< 0.1%

Interactions

2023-12-13T00:51:02.151292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:50:58.716345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:50:59.904785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:51:00.621766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:51:01.432915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:51:02.254114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:50:58.870855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:51:00.065871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:51:00.780567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:51:01.616166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:51:02.355867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:50:59.047723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:51:00.197961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:51:00.939083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:51:01.755198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:51:02.456519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:50:59.578527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:51:00.341147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:51:01.095559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:51:01.911256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:51:02.569075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:50:59.759834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:51:00.482109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:51:01.272207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:51:02.036041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T00:51:05.090067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
측정소미세먼지(PM10)미세먼지(PM2.5)오존(O3)이산화질소(NO2)아황산가스(SO2)
측정소1.0000.1450.2520.2900.4700.441
미세먼지(PM10)0.1451.0000.6010.0630.1510.198
미세먼지(PM2.5)0.2520.6011.0000.3060.4020.248
오존(O3)0.2900.0630.3061.0000.3150.077
이산화질소(NO2)0.4700.1510.4020.3151.0000.436
아황산가스(SO2)0.4410.1980.2480.0770.4361.000
2023-12-13T00:51:05.196127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
미세먼지(PM10)미세먼지(PM2.5)오존(O3)이산화질소(NO2)아황산가스(SO2)측정소
미세먼지(PM10)1.0000.8460.1470.4360.3540.053
미세먼지(PM2.5)0.8461.0000.0960.4930.3700.090
오존(O3)0.1470.0961.000-0.248-0.1070.105
이산화질소(NO2)0.4360.493-0.2481.0000.6060.182
아황산가스(SO2)0.3540.370-0.1070.6061.0000.177
측정소0.0530.0900.1050.1820.1771.000

Missing values

2023-12-13T00:51:02.717253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T00:51:02.853196image/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.
2023-12-13T00:51:02.974483image/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

측정소날짜미세먼지(PM10)미세먼지(PM2.5)오존(O3)이산화질소(NO2)아황산가스(SO2)
59362여천동2019-07-0130180.0340.0080.002
88666빛가람동2021-06-2418100.0410.0080.002
63222중동2019-01-27<NA><NA><NA><NA><NA>
42용당동2008-02-1288<NA>0.0420.0050.002
69122신지면2019-12-2624180.0180.0120.004
24516여천동2013-11-1544<NA>0.0250.020.006
35350여천동2015-07-2226<NA>0.0090.010.005
26742중동2013-12-2133230.0330.010.005
84903여천동2021-03-031790.0220.0130.003
78879장흥읍2020-02-232080.0470.0060.003
측정소날짜미세먼지(PM10)미세먼지(PM2.5)오존(O3)이산화질소(NO2)아황산가스(SO2)
57009장성읍2018-02-2757400.0210.0280.003
99761화양면2022-11-1721130.0270.0090.002
75815중동2020-10-082340.040.0040.002
96522신안군2021-01-0223130.0370.0050.003
66225장흥읍2019-07-251970.0360.0040.003
6015연향동2009-01-1122150.030.0110.009
63894태인동2019-11-301590.0260.0150.005
26413중동2013-01-2615140.0290.0060.004
17086광양읍2011-09-1026170.0260.0070.006
3350광양읍2008-09-2432<NA>0.0140.0120.01