Overview

Dataset statistics

Number of variables10
Number of observations124
Missing cells264
Missing cells (%)21.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory10.9 KiB
Average record size in memory90.1 B

Variable types

Numeric9
Categorical1

Dataset

Description광주교통공사 공기질 측정에 대한 데이터로 유지기준, 권고기준에 따른 PM10, CO2, HCHO, CO 등 역별 공기질 측정결과 정보를 제공합니다.
Author광주교통공사
URLhttps://www.data.go.kr/data/15046051/fileData.do

Alerts

연도 is highly overall correlated with 유지기준(PM10_100) and 1 other fieldsHigh correlation
유지기준(PM10_100) is highly overall correlated with 연도 and 1 other fieldsHigh correlation
유지기준(PM2_5_50) is highly overall correlated with 유지기준(PM10_100)High correlation
유지기준(CO2_1000) is highly overall correlated with 권고기준(VOC_500)High correlation
권고기준(Rn_148) is highly overall correlated with 연도High correlation
권고기준(VOC_500) is highly overall correlated with 유지기준(CO2_1000)High correlation
유지기준(PM2_5_50) has 54 (43.5%) missing valuesMissing
권고기준(NO2_0_1) has 70 (56.5%) missing valuesMissing
권고기준(Rn_148) has 70 (56.5%) missing valuesMissing
권고기준(VOC_500) has 70 (56.5%) missing valuesMissing

Reproduction

Analysis started2024-04-06 08:35:54.729448
Analysis finished2024-04-06 08:36:12.234549
Duration17.51 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연도
Real number (ℝ)

HIGH CORRELATION 

Distinct7
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2019.9516
Minimum2017
Maximum2023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-04-06T17:36:12.310877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2017
5-th percentile2017
Q12018
median2020
Q32022
95-th percentile2023
Maximum2023
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation1.9871737
Coefficient of variation (CV)0.0009837729
Kurtosis-1.2338789
Mean2019.9516
Median Absolute Deviation (MAD)2
Skewness0.017396704
Sum250474
Variance3.9488592
MonotonicityIncreasing
2024-04-06T17:36:12.517617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2017 18
14.5%
2018 18
14.5%
2019 18
14.5%
2020 18
14.5%
2021 18
14.5%
2022 18
14.5%
2023 16
12.9%
ValueCountFrequency (%)
2017 18
14.5%
2018 18
14.5%
2019 18
14.5%
2020 18
14.5%
2021 18
14.5%
2022 18
14.5%
2023 16
12.9%
ValueCountFrequency (%)
2023 16
12.9%
2022 18
14.5%
2021 18
14.5%
2020 18
14.5%
2019 18
14.5%
2018 18
14.5%
2017 18
14.5%

역명
Categorical

Distinct19
Distinct (%)15.3%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
소태
 
7
화정
 
7
남광주
 
7
문화전당
 
7
금남로4가
 
7
Other values (14)
89 

Length

Max length5
Median length2
Mean length2.7983871
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row소태
2nd row증심사
3rd row남광주
4th row문화전당
5th row금남로4가

Common Values

ValueCountFrequency (%)
소태 7
 
5.6%
화정 7
 
5.6%
남광주 7
 
5.6%
문화전당 7
 
5.6%
금남로4가 7
 
5.6%
금남로5가 7
 
5.6%
양동 7
 
5.6%
증심사 7
 
5.6%
농성 7
 
5.6%
쌍촌 7
 
5.6%
Other values (9) 54
43.5%

Length

2024-04-06T17:36:12.896844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
소태 7
 
5.6%
농성 7
 
5.6%
도산 7
 
5.6%
송정공원 7
 
5.6%
마륵 7
 
5.6%
상무 7
 
5.6%
운천 7
 
5.6%
화정 7
 
5.6%
쌍촌 7
 
5.6%
증심사 7
 
5.6%
Other values (9) 54
43.5%

유지기준(PM10_100)
Real number (ℝ)

HIGH CORRELATION 

Distinct112
Distinct (%)90.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.997581
Minimum9.3
Maximum79.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-04-06T17:36:13.294799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9.3
5-th percentile17.13
Q127.425
median35.75
Q354.575
95-th percentile68.98
Maximum79.3
Range70
Interquartile range (IQR)27.15

Descriptive statistics

Standard deviation16.733334
Coefficient of variation (CV)0.41835865
Kurtosis-0.9056744
Mean39.997581
Median Absolute Deviation (MAD)11.2
Skewness0.39673525
Sum4959.7
Variance280.00447
MonotonicityNot monotonic
2024-04-06T17:36:13.561612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
28.1 2
 
1.6%
42.3 2
 
1.6%
36.8 2
 
1.6%
40.0 2
 
1.6%
51.9 2
 
1.6%
21.5 2
 
1.6%
25.5 2
 
1.6%
31.8 2
 
1.6%
60.9 2
 
1.6%
43.6 2
 
1.6%
Other values (102) 104
83.9%
ValueCountFrequency (%)
9.3 1
0.8%
11.7 1
0.8%
13.5 1
0.8%
14.1 1
0.8%
15.2 1
0.8%
15.5 1
0.8%
17.1 1
0.8%
17.3 1
0.8%
17.9 1
0.8%
20.1 1
0.8%
ValueCountFrequency (%)
79.3 1
0.8%
72.1 1
0.8%
71.0 1
0.8%
69.9 1
0.8%
69.2 2
1.6%
69.1 1
0.8%
68.3 1
0.8%
67.9 1
0.8%
67.6 1
0.8%
67.4 1
0.8%

유지기준(PM2_5_50)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct54
Distinct (%)77.1%
Missing54
Missing (%)43.5%
Infinite0
Infinite (%)0.0%
Mean15.451429
Minimum7
Maximum30.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-04-06T17:36:13.924357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile8.36
Q111.825
median15.2
Q317.9
95-th percentile23.63
Maximum30.1
Range23.1
Interquartile range (IQR)6.075

Descriptive statistics

Standard deviation4.8061176
Coefficient of variation (CV)0.31104681
Kurtosis0.43752845
Mean15.451429
Median Absolute Deviation (MAD)3.15
Skewness0.56689111
Sum1081.6
Variance23.098766
MonotonicityNot monotonic
2024-04-06T17:36:14.187581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17.5 3
 
2.4%
15.1 3
 
2.4%
12.6 2
 
1.6%
18.9 2
 
1.6%
17.4 2
 
1.6%
16.3 2
 
1.6%
10.2 2
 
1.6%
17.9 2
 
1.6%
11.3 2
 
1.6%
14.4 2
 
1.6%
Other values (44) 48
38.7%
(Missing) 54
43.5%
ValueCountFrequency (%)
7.0 1
0.8%
7.3 1
0.8%
7.7 1
0.8%
8.0 1
0.8%
8.8 1
0.8%
9.0 1
0.8%
9.6 1
0.8%
9.7 1
0.8%
10.2 2
1.6%
10.3 1
0.8%
ValueCountFrequency (%)
30.1 1
0.8%
26.6 1
0.8%
26.5 1
0.8%
24.8 1
0.8%
22.2 1
0.8%
21.9 1
0.8%
21.7 1
0.8%
20.9 1
0.8%
20.5 1
0.8%
20.2 1
0.8%

유지기준(CO2_1000)
Real number (ℝ)

HIGH CORRELATION 

Distinct104
Distinct (%)83.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean450.90726
Minimum232
Maximum634
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-04-06T17:36:14.931834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum232
5-th percentile295.75
Q1410.5
median445
Q3510.625
95-th percentile575.25
Maximum634
Range402
Interquartile range (IQR)100.125

Descriptive statistics

Standard deviation83.611386
Coefficient of variation (CV)0.18542923
Kurtosis0.069410826
Mean450.90726
Median Absolute Deviation (MAD)52
Skewness-0.28099583
Sum55912.5
Variance6990.8639
MonotonicityNot monotonic
2024-04-06T17:36:15.240568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
445.0 3
 
2.4%
422.0 3
 
2.4%
393.0 2
 
1.6%
428.0 2
 
1.6%
402.0 2
 
1.6%
413.0 2
 
1.6%
411.0 2
 
1.6%
460.0 2
 
1.6%
415.0 2
 
1.6%
433.0 2
 
1.6%
Other values (94) 102
82.3%
ValueCountFrequency (%)
232.0 1
0.8%
246.0 1
0.8%
252.0 2
1.6%
290.0 1
0.8%
295.0 2
1.6%
300.0 1
0.8%
303.0 1
0.8%
319.0 2
1.6%
331.0 1
0.8%
334.0 1
0.8%
ValueCountFrequency (%)
634.0 1
0.8%
612.0 1
0.8%
609.0 1
0.8%
607.0 1
0.8%
603.3 1
0.8%
589.5 1
0.8%
576.0 1
0.8%
571.0 1
0.8%
570.0 1
0.8%
567.5 2
1.6%

유지기준(HCHO_100)
Real number (ℝ)

Distinct74
Distinct (%)59.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.8717742
Minimum1.3
Maximum49.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-04-06T17:36:15.507211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.3
5-th percentile2
Q12.975
median4.4
Q37.025
95-th percentile23.365
Maximum49.1
Range47.8
Interquartile range (IQR)4.05

Descriptive statistics

Standard deviation7.5485804
Coefficient of variation (CV)1.0984908
Kurtosis10.832224
Mean6.8717742
Median Absolute Deviation (MAD)1.9
Skewness3.0965182
Sum852.1
Variance56.981067
MonotonicityNot monotonic
2024-04-06T17:36:15.807671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.0 4
 
3.2%
2.8 4
 
3.2%
3.0 4
 
3.2%
3.6 4
 
3.2%
4.4 4
 
3.2%
2.2 3
 
2.4%
3.2 3
 
2.4%
5.3 3
 
2.4%
3.7 3
 
2.4%
4.2 3
 
2.4%
Other values (64) 89
71.8%
ValueCountFrequency (%)
1.3 1
 
0.8%
1.6 1
 
0.8%
1.7 1
 
0.8%
1.8 3
2.4%
2.0 4
3.2%
2.1 1
 
0.8%
2.2 3
2.4%
2.3 2
1.6%
2.4 3
2.4%
2.5 2
1.6%
ValueCountFrequency (%)
49.1 1
0.8%
36.3 1
0.8%
31.2 1
0.8%
31.0 1
0.8%
28.9 1
0.8%
26.4 1
0.8%
23.5 1
0.8%
22.6 1
0.8%
22.2 1
0.8%
21.5 1
0.8%

유지기준(CO_10)
Real number (ℝ)

Distinct28
Distinct (%)22.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1523387
Minimum0.15
Maximum2.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-04-06T17:36:16.060457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.15
5-th percentile0.6
Q10.9
median1.2
Q31.4525
95-th percentile1.6
Maximum2.1
Range1.95
Interquartile range (IQR)0.5525

Descriptive statistics

Standard deviation0.35820943
Coefficient of variation (CV)0.31085429
Kurtosis-0.12389925
Mean1.1523387
Median Absolute Deviation (MAD)0.3
Skewness-0.45737663
Sum142.89
Variance0.128314
MonotonicityNot monotonic
2024-04-06T17:36:16.309989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
1.5 15
12.1%
1.2 12
9.7%
1.0 11
8.9%
0.8 11
8.9%
1.3 10
 
8.1%
1.4 9
 
7.3%
1.6 9
 
7.3%
1.1 9
 
7.3%
0.6 8
 
6.5%
0.9 5
 
4.0%
Other values (18) 25
20.2%
ValueCountFrequency (%)
0.15 1
 
0.8%
0.22 1
 
0.8%
0.34 1
 
0.8%
0.4 1
 
0.8%
0.41 1
 
0.8%
0.6 8
6.5%
0.7 5
4.0%
0.8 11
8.9%
0.9 5
4.0%
1.0 11
8.9%
ValueCountFrequency (%)
2.1 1
 
0.8%
1.8 1
 
0.8%
1.6 9
7.3%
1.55 1
 
0.8%
1.54 1
 
0.8%
1.53 2
 
1.6%
1.5 15
12.1%
1.49 1
 
0.8%
1.44 1
 
0.8%
1.4 9
7.3%

권고기준(NO2_0_1)
Real number (ℝ)

MISSING 

Distinct24
Distinct (%)44.4%
Missing70
Missing (%)56.5%
Infinite0
Infinite (%)0.0%
Mean0.019314815
Minimum0.008
Maximum0.039
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-04-06T17:36:16.548344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.008
5-th percentile0.00965
Q10.014
median0.018
Q30.02475
95-th percentile0.035
Maximum0.039
Range0.031
Interquartile range (IQR)0.01075

Descriptive statistics

Standard deviation0.0076252413
Coefficient of variation (CV)0.39478718
Kurtosis-0.056593203
Mean0.019314815
Median Absolute Deviation (MAD)0.0055
Skewness0.69961409
Sum1.043
Variance5.8144305 × 10-5
MonotonicityNot monotonic
2024-04-06T17:36:16.808361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0.019 5
 
4.0%
0.016 5
 
4.0%
0.018 4
 
3.2%
0.025 4
 
3.2%
0.01 4
 
3.2%
0.014 3
 
2.4%
0.035 3
 
2.4%
0.022 3
 
2.4%
0.012 3
 
2.4%
0.009 2
 
1.6%
Other values (14) 18
 
14.5%
(Missing) 70
56.5%
ValueCountFrequency (%)
0.008 1
 
0.8%
0.009 2
 
1.6%
0.01 4
3.2%
0.011 2
 
1.6%
0.012 3
2.4%
0.013 1
 
0.8%
0.014 3
2.4%
0.015 2
 
1.6%
0.016 5
4.0%
0.017 1
 
0.8%
ValueCountFrequency (%)
0.039 1
 
0.8%
0.035 3
2.4%
0.034 1
 
0.8%
0.029 1
 
0.8%
0.028 1
 
0.8%
0.027 2
1.6%
0.026 1
 
0.8%
0.025 4
3.2%
0.024 1
 
0.8%
0.023 1
 
0.8%

권고기준(Rn_148)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct37
Distinct (%)68.5%
Missing70
Missing (%)56.5%
Infinite0
Infinite (%)0.0%
Mean20.661111
Minimum4
Maximum47.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-04-06T17:36:17.182112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile6.5
Q112
median18.5
Q329
95-th percentile40.35
Maximum47.5
Range43.5
Interquartile range (IQR)17

Descriptive statistics

Standard deviation11.827014
Coefficient of variation (CV)0.57242877
Kurtosis-0.70890283
Mean20.661111
Median Absolute Deviation (MAD)9.25
Skewness0.62238805
Sum1115.7
Variance139.87827
MonotonicityNot monotonic
2024-04-06T17:36:17.409746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
12.0 5
 
4.0%
40.0 3
 
2.4%
29.0 3
 
2.4%
20.0 3
 
2.4%
34.5 3
 
2.4%
18.5 3
 
2.4%
6.5 2
 
1.6%
8.0 2
 
1.6%
7.0 2
 
1.6%
23.0 1
 
0.8%
Other values (27) 27
 
21.8%
(Missing) 70
56.5%
ValueCountFrequency (%)
4.0 1
0.8%
5.5 1
0.8%
6.5 2
1.6%
7.0 2
1.6%
8.0 2
1.6%
8.3 1
0.8%
9.0 1
0.8%
9.5 1
0.8%
9.7 1
0.8%
10.5 1
0.8%
ValueCountFrequency (%)
47.5 1
 
0.8%
46.0 1
 
0.8%
41.0 1
 
0.8%
40.0 3
2.4%
38.7 1
 
0.8%
36.0 1
 
0.8%
34.5 3
2.4%
32.0 1
 
0.8%
29.3 1
 
0.8%
29.0 3
2.4%

권고기준(VOC_500)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct52
Distinct (%)96.3%
Missing70
Missing (%)56.5%
Infinite0
Infinite (%)0.0%
Mean108.45
Minimum3.7
Maximum264.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-04-06T17:36:17.674930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.7
5-th percentile16.255
Q136.075
median82.25
Q3190.85
95-th percentile228.44
Maximum264.6
Range260.9
Interquartile range (IQR)154.775

Descriptive statistics

Standard deviation80.393161
Coefficient of variation (CV)0.7412924
Kurtosis-1.4442099
Mean108.45
Median Absolute Deviation (MAD)56.55
Skewness0.3905258
Sum5856.3
Variance6463.0603
MonotonicityNot monotonic
2024-04-06T17:36:17.954612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36.3 2
 
1.6%
182.3 2
 
1.6%
49.5 1
 
0.8%
225.2 1
 
0.8%
236.4 1
 
0.8%
231.3 1
 
0.8%
224.9 1
 
0.8%
199.1 1
 
0.8%
264.6 1
 
0.8%
99.8 1
 
0.8%
Other values (42) 42
33.9%
(Missing) 70
56.5%
ValueCountFrequency (%)
3.7 1
0.8%
7.8 1
0.8%
9.3 1
0.8%
20.0 1
0.8%
23.2 1
0.8%
24.9 1
0.8%
26.5 1
0.8%
28.4 1
0.8%
29.9 1
0.8%
30.1 1
0.8%
ValueCountFrequency (%)
264.6 1
0.8%
236.4 1
0.8%
231.3 1
0.8%
226.9 1
0.8%
225.2 1
0.8%
224.9 1
0.8%
223.4 1
0.8%
221.0 1
0.8%
201.0 1
0.8%
200.8 1
0.8%

Interactions

2024-04-06T17:36:09.835745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:35:55.398078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:35:57.421164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:35:59.122371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:00.736942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:02.374871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:04.235924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:06.235445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:08.075325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:10.031185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:35:55.637373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:35:57.607431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:35:59.315020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:00.992850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:02.535323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:04.426788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:06.504130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:08.314570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:10.240788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:35:55.848712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:35:57.790272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:35:59.492364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:01.182240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:02.727695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:04.630275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:06.760150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:08.528852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:10.428200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:35:56.044861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:35:57.964548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:35:59.640396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:01.322425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:02.873926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:04.805997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:07.002120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:08.760498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:10.591066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:35:56.254744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:35:58.186513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:35:59.822555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:01.517077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:03.067136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:05.010507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:07.176498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:08.988825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:10.748958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:35:56.494910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:35:58.360674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:00.020417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:01.680670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:03.224372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:05.214978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:07.335021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:09.130121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:10.915869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:35:56.691260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:35:58.552013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:00.238180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:01.897439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:03.785335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:05.485067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:07.519464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:09.330085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:11.070890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:35:56.957853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:35:58.748484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:00.396777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:02.083910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:03.952013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:05.739291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:07.720045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:09.512701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:11.220434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:35:57.207141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:35:58.951672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:00.588443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:02.232221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:04.088249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:05.980974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:07.893031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:36:09.663352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-06T17:36:18.200551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도역명유지기준(PM10_100)유지기준(PM2_5_50)유지기준(CO2_1000)유지기준(HCHO_100)유지기준(CO_10)권고기준(NO2_0_1)권고기준(Rn_148)권고기준(VOC_500)
연도1.0000.0000.7750.5610.8130.4950.4660.6010.6630.728
역명0.0001.0000.0000.0000.0000.0000.1400.0000.0000.000
유지기준(PM10_100)0.7750.0001.0000.6060.5180.2860.4420.6210.0000.578
유지기준(PM2_5_50)0.5610.0000.6061.0000.6270.0000.3000.0000.2960.351
유지기준(CO2_1000)0.8130.0000.5180.6271.0000.0000.4220.2970.3060.623
유지기준(HCHO_100)0.4950.0000.2860.0000.0001.0000.4460.0000.0000.806
유지기준(CO_10)0.4660.1400.4420.3000.4220.4461.0000.3960.0000.743
권고기준(NO2_0_1)0.6010.0000.6210.0000.2970.0000.3961.0000.0800.218
권고기준(Rn_148)0.6630.0000.0000.2960.3060.0000.0000.0801.0000.000
권고기준(VOC_500)0.7280.0000.5780.3510.6230.8060.7430.2180.0001.000
2024-04-06T17:36:18.545694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도유지기준(PM10_100)유지기준(PM2_5_50)유지기준(CO2_1000)유지기준(HCHO_100)유지기준(CO_10)권고기준(NO2_0_1)권고기준(Rn_148)권고기준(VOC_500)역명
연도1.000-0.6340.442-0.3750.1090.150-0.1610.727-0.2400.000
유지기준(PM10_100)-0.6341.0000.6570.474-0.107-0.062-0.189-0.331-0.4680.000
유지기준(PM2_5_50)0.4420.6571.000-0.033-0.174-0.041-0.2550.194-0.2240.000
유지기준(CO2_1000)-0.3750.474-0.0331.0000.153-0.040-0.166-0.217-0.5630.000
유지기준(HCHO_100)0.109-0.107-0.1740.1531.000-0.0530.0430.029-0.0090.000
유지기준(CO_10)0.150-0.062-0.041-0.040-0.0531.0000.0120.056-0.0490.036
권고기준(NO2_0_1)-0.161-0.189-0.255-0.1660.0430.0121.000-0.3710.3930.000
권고기준(Rn_148)0.727-0.3310.194-0.2170.0290.056-0.3711.000-0.2680.000
권고기준(VOC_500)-0.240-0.468-0.224-0.563-0.009-0.0490.393-0.2681.0000.000
역명0.0000.0000.0000.0000.0000.0360.0000.0000.0001.000

Missing values

2024-04-06T17:36:11.451933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-06T17:36:11.760890image/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.
2024-04-06T17:36:12.115492image/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

연도역명유지기준(PM10_100)유지기준(PM2_5_50)유지기준(CO2_1000)유지기준(HCHO_100)유지기준(CO_10)권고기준(NO2_0_1)권고기준(Rn_148)권고기준(VOC_500)
02017소태50.3<NA>436.02.00.9<NA><NA><NA>
12017증심사65.8<NA>428.02.31.1<NA><NA><NA>
22017남광주56.9<NA>457.02.51.3<NA><NA><NA>
32017문화전당61.2<NA>464.02.11.3<NA><NA><NA>
42017금남로4가79.3<NA>576.02.21.0<NA><NA><NA>
52017금남로5가61.0<NA>470.02.71.2<NA><NA><NA>
62017양동68.3<NA>558.03.81.5<NA><NA><NA>
72017돌고개54.5<NA>446.02.81.2<NA><NA><NA>
82017농성71.0<NA>472.02.51.3<NA><NA><NA>
92017화정72.1<NA>452.02.40.9<NA><NA><NA>
연도역명유지기준(PM10_100)유지기준(PM2_5_50)유지기준(CO2_1000)유지기준(HCHO_100)유지기준(CO_10)권고기준(NO2_0_1)권고기준(Rn_148)권고기준(VOC_500)
1142023양동39.321.7319.05.61.6<NA><NA><NA>
1152023농성26.417.9348.04.41.2<NA><NA><NA>
1162023화정23.318.4348.05.21.5<NA><NA><NA>
1172023쌍촌34.726.6290.03.31.5<NA><NA><NA>
1182023운천31.820.5295.04.41.4<NA><NA><NA>
1192023상무27.220.2334.03.31.5<NA><NA><NA>
1202023마륵28.817.4295.01.71.1<NA><NA><NA>
1212023송정공원36.818.9252.02.91.2<NA><NA><NA>
1222023광주송정36.818.9252.02.91.2<NA><NA><NA>
1232023도산21.514.0232.02.21.2<NA><NA><NA>