Overview

Dataset statistics

Number of variables12
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.1 MiB
Average record size in memory114.0 B

Variable types

Numeric9
Categorical3

Dataset

Description파일 다운로드
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-2221/S/1/datasetView.do

Alerts

측정소명 is highly overall correlated with 측정소코드 and 2 other fieldsHigh correlation
권역명 is highly overall correlated with 측정소코드 and 2 other fieldsHigh correlation
권역코드 is highly overall correlated with 측정소코드 and 2 other fieldsHigh correlation
측정소코드 is highly overall correlated with 권역코드 and 2 other fieldsHigh correlation
미세먼지 1시간(㎍/㎥) is highly overall correlated with 미세먼지 24시간(㎍/㎥) and 1 other fieldsHigh correlation
미세먼지 24시간(㎍/㎥) is highly overall correlated with 미세먼지 1시간(㎍/㎥) and 1 other fieldsHigh correlation
초미세먼지(㎍/㎥) is highly overall correlated with 미세먼지 1시간(㎍/㎥) and 1 other fieldsHigh correlation
미세먼지 1시간(㎍/㎥) has 537 (5.4%) zerosZeros
초미세먼지(㎍/㎥) has 225 (2.2%) zerosZeros

Reproduction

Analysis started2024-05-11 05:14:18.143738
Analysis finished2024-05-11 05:14:57.739758
Duration39.6 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

측정일시
Real number (ℝ)

Distinct720
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0190616 × 1011
Minimum2.0190601 × 1011
Maximum2.019063 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T05:14:58.038714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0190601 × 1011
5-th percentile2.0190602 × 1011
Q12.0190608 × 1011
median2.0190616 × 1011
Q32.0190623 × 1011
95-th percentile2.0190629 × 1011
Maximum2.019063 × 1011
Range292300
Interquartile range (IQR)150200

Descriptive statistics

Standard deviation86792.386
Coefficient of variation (CV)4.2986498 × 10-7
Kurtosis-1.2098629
Mean2.0190616 × 1011
Median Absolute Deviation (MAD)77900
Skewness-0.00041164792
Sum2.0190616 × 1015
Variance7.5329184 × 109
MonotonicityNot monotonic
2024-05-11T05:14:58.756021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
201906050900 21
 
0.2%
201906111200 20
 
0.2%
201906280100 20
 
0.2%
201906131600 20
 
0.2%
201906290600 20
 
0.2%
201906131800 19
 
0.2%
201906260500 19
 
0.2%
201906010600 19
 
0.2%
201906280600 19
 
0.2%
201906301600 19
 
0.2%
Other values (710) 9804
98.0%
ValueCountFrequency (%)
201906010000 13
0.1%
201906010100 15
0.1%
201906010200 12
0.1%
201906010300 8
0.1%
201906010400 12
0.1%
201906010500 14
0.1%
201906010600 19
0.2%
201906010700 13
0.1%
201906010800 15
0.1%
201906010900 11
0.1%
ValueCountFrequency (%)
201906302300 15
0.1%
201906302200 13
0.1%
201906302100 12
0.1%
201906302000 14
0.1%
201906301900 10
0.1%
201906301800 14
0.1%
201906301700 11
0.1%
201906301600 19
0.2%
201906301500 16
0.2%
201906301400 13
0.1%

권역코드
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
102
3183 
103
2842 
104
1577 
100
1214 
101
1184 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row103
2nd row102
3rd row100
4th row102
5th row102

Common Values

ValueCountFrequency (%)
102 3183
31.8%
103 2842
28.4%
104 1577
15.8%
100 1214
 
12.1%
101 1184
 
11.8%

Length

2024-05-11T05:14:59.184889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T05:14:59.519839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
102 3183
31.8%
103 2842
28.4%
104 1577
15.8%
100 1214
 
12.1%
101 1184
 
11.8%

권역명
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
동북권
3183 
서남권
2842 
동남권
1577 
도심권
1214 
서북권
1184 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row서남권
2nd row동북권
3rd row도심권
4th row동북권
5th row동북권

Common Values

ValueCountFrequency (%)
동북권 3183
31.8%
서남권 2842
28.4%
동남권 1577
15.8%
도심권 1214
 
12.1%
서북권 1184
 
11.8%

Length

2024-05-11T05:15:00.071297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T05:15:00.542316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
동북권 3183
31.8%
서남권 2842
28.4%
동남권 1577
15.8%
도심권 1214
 
12.1%
서북권 1184
 
11.8%

측정소코드
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean111210.83
Minimum111121
Maximum111311
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T05:15:01.022261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum111121
5-th percentile111123
Q1111152
median111212
Q3111262
95-th percentile111301
Maximum111311
Range190
Interquartile range (IQR)110

Descriptive statistics

Standard deviation59.886848
Coefficient of variation (CV)0.00053849833
Kurtosis-1.3684259
Mean111210.83
Median Absolute Deviation (MAD)60
Skewness0.035695857
Sum1.1121083 × 109
Variance3586.4346
MonotonicityNot monotonic
2024-05-11T05:15:01.478614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
111131 426
 
4.3%
111201 418
 
4.2%
111281 418
 
4.2%
111241 417
 
4.2%
111152 413
 
4.1%
111212 411
 
4.1%
111261 408
 
4.1%
111221 407
 
4.1%
111301 404
 
4.0%
111262 403
 
4.0%
Other values (15) 5875
58.8%
ValueCountFrequency (%)
111121 397
4.0%
111123 391
3.9%
111131 426
4.3%
111141 403
4.0%
111142 401
4.0%
111151 393
3.9%
111152 413
4.1%
111161 397
4.0%
111171 393
3.9%
111181 383
3.8%
ValueCountFrequency (%)
111311 392
3.9%
111301 404
4.0%
111291 391
3.9%
111281 418
4.2%
111274 384
3.8%
111273 382
3.8%
111262 403
4.0%
111261 408
4.1%
111251 383
3.8%
111241 417
4.2%

측정소명
Categorical

HIGH CORRELATION 

Distinct25
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
용산구
 
426
금천구
 
418
마포구
 
418
동작구
 
417
동대문구
 
413
Other values (20)
7908 

Length

Max length4
Median length3
Mean length3.0801
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row관악구
2nd row노원구
3rd row종로구
4th row강북구
5th row성동구

Common Values

ValueCountFrequency (%)
용산구 426
 
4.3%
금천구 418
 
4.2%
마포구 418
 
4.2%
동작구 417
 
4.2%
동대문구 413
 
4.1%
강서구 411
 
4.1%
강남구 408
 
4.1%
구로구 407
 
4.1%
양천구 404
 
4.0%
서초구 403
 
4.0%
Other values (15) 5875
58.8%

Length

2024-05-11T05:15:02.057107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
용산구 426
 
4.3%
금천구 418
 
4.2%
마포구 418
 
4.2%
동작구 417
 
4.2%
동대문구 413
 
4.1%
강서구 411
 
4.1%
강남구 408
 
4.1%
구로구 407
 
4.1%
양천구 404
 
4.0%
서초구 403
 
4.0%
Other values (15) 5875
58.8%

미세먼지 1시간(㎍/㎥)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct111
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.1402
Minimum0
Maximum117
Zeros537
Zeros (%)5.4%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T05:15:02.648254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q116
median26
Q336
95-th percentile56
Maximum117
Range117
Interquartile range (IQR)20

Descriptive statistics

Standard deviation16.643991
Coefficient of variation (CV)0.61325971
Kurtosis2.1090683
Mean27.1402
Median Absolute Deviation (MAD)10
Skewness0.97796079
Sum271402
Variance277.02245
MonotonicityNot monotonic
2024-05-11T05:15:03.271608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 537
 
5.4%
27 314
 
3.1%
25 299
 
3.0%
29 285
 
2.9%
24 273
 
2.7%
23 265
 
2.6%
22 263
 
2.6%
30 263
 
2.6%
31 258
 
2.6%
28 249
 
2.5%
Other values (101) 6994
69.9%
ValueCountFrequency (%)
0 537
5.4%
3 57
 
0.6%
4 52
 
0.5%
5 64
 
0.6%
6 98
 
1.0%
7 116
 
1.2%
8 194
 
1.9%
9 217
2.2%
10 174
 
1.7%
11 211
 
2.1%
ValueCountFrequency (%)
117 2
< 0.1%
115 1
< 0.1%
113 1
< 0.1%
111 1
< 0.1%
110 2
< 0.1%
109 1
< 0.1%
106 1
< 0.1%
105 2
< 0.1%
104 2
< 0.1%
103 2
< 0.1%

미세먼지 24시간(㎍/㎥)
Real number (ℝ)

HIGH CORRELATION 

Distinct89
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.166
Minimum0
Maximum100
Zeros63
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T05:15:03.894309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile9
Q119
median28
Q336
95-th percentile51
Maximum100
Range100
Interquartile range (IQR)17

Descriptive statistics

Standard deviation13.323866
Coefficient of variation (CV)0.47304785
Kurtosis1.0702805
Mean28.166
Median Absolute Deviation (MAD)8
Skewness0.68654391
Sum281660
Variance177.5254
MonotonicityNot monotonic
2024-05-11T05:15:04.505080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30 359
 
3.6%
29 352
 
3.5%
31 351
 
3.5%
25 341
 
3.4%
28 338
 
3.4%
27 334
 
3.3%
24 296
 
3.0%
32 292
 
2.9%
26 281
 
2.8%
33 278
 
2.8%
Other values (79) 6778
67.8%
ValueCountFrequency (%)
0 63
 
0.6%
3 6
 
0.1%
4 35
 
0.4%
5 52
 
0.5%
6 60
 
0.6%
7 103
1.0%
8 138
1.4%
9 183
1.8%
10 193
1.9%
11 226
2.3%
ValueCountFrequency (%)
100 1
 
< 0.1%
91 1
 
< 0.1%
90 1
 
< 0.1%
88 1
 
< 0.1%
87 1
 
< 0.1%
86 1
 
< 0.1%
84 3
 
< 0.1%
83 4
< 0.1%
82 8
0.1%
81 2
 
< 0.1%

초미세먼지(㎍/㎥)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct84
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.3037
Minimum0
Maximum93
Zeros225
Zeros (%)2.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T05:15:05.237719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q111
median18
Q325
95-th percentile40
Maximum93
Range93
Interquartile range (IQR)14

Descriptive statistics

Standard deviation11.890484
Coefficient of variation (CV)0.61596916
Kurtosis2.6635022
Mean19.3037
Median Absolute Deviation (MAD)7
Skewness1.1764281
Sum193037
Variance141.3836
MonotonicityNot monotonic
2024-05-11T05:15:05.668392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17 425
 
4.2%
16 418
 
4.2%
18 405
 
4.0%
19 395
 
4.0%
20 374
 
3.7%
21 359
 
3.6%
15 355
 
3.5%
12 332
 
3.3%
14 331
 
3.3%
13 330
 
3.3%
Other values (74) 6276
62.8%
ValueCountFrequency (%)
0 225
2.2%
1 73
 
0.7%
2 92
 
0.9%
3 148
1.5%
4 200
2.0%
5 207
2.1%
6 279
2.8%
7 255
2.5%
8 259
2.6%
9 294
2.9%
ValueCountFrequency (%)
93 1
 
< 0.1%
87 1
 
< 0.1%
81 1
 
< 0.1%
80 1
 
< 0.1%
79 1
 
< 0.1%
78 1
 
< 0.1%
77 2
< 0.1%
76 2
< 0.1%
75 4
< 0.1%
74 3
< 0.1%

오존(ppm)
Real number (ℝ)

Distinct114
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0380592
Minimum0
Maximum0.125
Zeros51
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T05:15:06.203215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.011
Q10.026
median0.037
Q30.049
95-th percentile0.069
Maximum0.125
Range0.125
Interquartile range (IQR)0.023

Descriptive statistics

Standard deviation0.017515547
Coefficient of variation (CV)0.46021847
Kurtosis0.5425397
Mean0.0380592
Median Absolute Deviation (MAD)0.011
Skewness0.54054247
Sum380.592
Variance0.00030679437
MonotonicityNot monotonic
2024-05-11T05:15:06.935828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.032 263
 
2.6%
0.034 255
 
2.5%
0.038 253
 
2.5%
0.029 243
 
2.4%
0.03 239
 
2.4%
0.04 236
 
2.4%
0.033 236
 
2.4%
0.027 233
 
2.3%
0.035 230
 
2.3%
0.037 227
 
2.3%
Other values (104) 7585
75.8%
ValueCountFrequency (%)
0.0 51
0.5%
0.001 1
 
< 0.1%
0.002 12
 
0.1%
0.003 20
 
0.2%
0.004 27
0.3%
0.005 37
0.4%
0.006 39
0.4%
0.007 49
0.5%
0.008 49
0.5%
0.009 60
0.6%
ValueCountFrequency (%)
0.125 1
 
< 0.1%
0.124 1
 
< 0.1%
0.121 1
 
< 0.1%
0.12 1
 
< 0.1%
0.112 2
< 0.1%
0.111 1
 
< 0.1%
0.109 3
< 0.1%
0.108 3
< 0.1%
0.107 1
 
< 0.1%
0.106 1
 
< 0.1%
Distinct72
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0204669
Minimum0
Maximum0.074
Zeros51
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T05:15:08.399831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.007
Q10.012
median0.018
Q30.027
95-th percentile0.041
Maximum0.074
Range0.074
Interquartile range (IQR)0.015

Descriptive statistics

Standard deviation0.010917189
Coefficient of variation (CV)0.53340708
Kurtosis0.96321521
Mean0.0204669
Median Absolute Deviation (MAD)0.007
Skewness0.96372133
Sum204.669
Variance0.00011918502
MonotonicityNot monotonic
2024-05-11T05:15:09.263545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.015 464
 
4.6%
0.013 459
 
4.6%
0.011 417
 
4.2%
0.014 416
 
4.2%
0.012 415
 
4.2%
0.017 403
 
4.0%
0.018 399
 
4.0%
0.01 389
 
3.9%
0.016 387
 
3.9%
0.019 379
 
3.8%
Other values (62) 5872
58.7%
ValueCountFrequency (%)
0.0 51
 
0.5%
0.001 3
 
< 0.1%
0.002 16
 
0.2%
0.003 21
 
0.2%
0.004 67
 
0.7%
0.005 123
 
1.2%
0.006 174
1.7%
0.007 237
2.4%
0.008 304
3.0%
0.009 333
3.3%
ValueCountFrequency (%)
0.074 2
 
< 0.1%
0.071 1
 
< 0.1%
0.07 1
 
< 0.1%
0.069 2
 
< 0.1%
0.068 3
< 0.1%
0.067 1
 
< 0.1%
0.065 5
0.1%
0.064 4
< 0.1%
0.063 2
 
< 0.1%
0.062 2
 
< 0.1%
Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.38534
Minimum0
Maximum1.1
Zeros54
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T05:15:10.115571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.2
Q10.3
median0.4
Q30.4
95-th percentile0.6
Maximum1.1
Range1.1
Interquartile range (IQR)0.1

Descriptive statistics

Standard deviation0.13606225
Coefficient of variation (CV)0.35309661
Kurtosis1.6240186
Mean0.38534
Median Absolute Deviation (MAD)0.1
Skewness0.81888021
Sum3853.4
Variance0.018512936
MonotonicityNot monotonic
2024-05-11T05:15:10.714694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0.3 3327
33.3%
0.4 3051
30.5%
0.5 1344
13.4%
0.2 986
 
9.9%
0.6 720
 
7.2%
0.7 290
 
2.9%
0.8 96
 
1.0%
0.1 84
 
0.8%
0.0 54
 
0.5%
0.9 31
 
0.3%
Other values (2) 17
 
0.2%
ValueCountFrequency (%)
0.0 54
 
0.5%
0.1 84
 
0.8%
0.2 986
 
9.9%
0.3 3327
33.3%
0.4 3051
30.5%
0.5 1344
13.4%
0.6 720
 
7.2%
0.7 290
 
2.9%
0.8 96
 
1.0%
0.9 31
 
0.3%
ValueCountFrequency (%)
1.1 6
 
0.1%
1.0 11
 
0.1%
0.9 31
 
0.3%
0.8 96
 
1.0%
0.7 290
 
2.9%
0.6 720
 
7.2%
0.5 1344
13.4%
0.4 3051
30.5%
0.3 3327
33.3%
0.2 986
 
9.9%
Distinct14
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0035871
Minimum0
Maximum0.013
Zeros59
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T05:15:11.443962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.002
Q10.003
median0.003
Q30.004
95-th percentile0.006
Maximum0.013
Range0.013
Interquartile range (IQR)0.001

Descriptive statistics

Standard deviation0.0012294571
Coefficient of variation (CV)0.34274403
Kurtosis2.5711709
Mean0.0035871
Median Absolute Deviation (MAD)0.001
Skewness0.89086038
Sum35.871
Variance1.5115647 × 10-6
MonotonicityNot monotonic
2024-05-11T05:15:11.875866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0.003 3520
35.2%
0.004 2964
29.6%
0.002 1602
16.0%
0.005 1180
 
11.8%
0.006 458
 
4.6%
0.007 149
 
1.5%
0.0 59
 
0.6%
0.008 37
 
0.4%
0.009 12
 
0.1%
0.01 7
 
0.1%
Other values (4) 12
 
0.1%
ValueCountFrequency (%)
0.0 59
 
0.6%
0.001 5
 
0.1%
0.002 1602
16.0%
0.003 3520
35.2%
0.004 2964
29.6%
0.005 1180
 
11.8%
0.006 458
 
4.6%
0.007 149
 
1.5%
0.008 37
 
0.4%
0.009 12
 
0.1%
ValueCountFrequency (%)
0.013 1
 
< 0.1%
0.012 2
 
< 0.1%
0.011 4
 
< 0.1%
0.01 7
 
0.1%
0.009 12
 
0.1%
0.008 37
 
0.4%
0.007 149
 
1.5%
0.006 458
 
4.6%
0.005 1180
 
11.8%
0.004 2964
29.6%

Interactions

2024-05-11T05:14:52.093950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:14:26.046794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:14:28.629693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:14:31.126400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:14:34.362476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:14:38.240241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:14:41.653711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:14:45.123106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:14:48.860620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:14:52.401655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:14:26.272972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:14:28.880225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:14:31.403311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:14:34.702078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:14:38.611217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:14:42.075248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:14:45.434475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:14:49.196568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:14:52.800044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:14:26.533708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:14:29.099182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:14:31.710283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:14:35.196637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:14:38.938985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:14:42.489700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:14:45.831391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:14:49.664340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:14:53.150882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:14:26.829836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:14:29.408050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:14:32.003129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:14:35.669261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:14:39.303314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:14:42.903822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:14:46.338566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:14:49.981972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:14:53.660209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:14:27.115932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:14:29.665416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:14:32.371385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:14:36.116609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:14:39.701325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:14:43.318745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:14:46.633084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:14:50.313920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:14:54.098201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:14:27.409660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:14:29.885870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:14:32.656868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:14:36.640480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:14:40.032223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:14:43.700253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:14:47.234066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:14:50.616307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:14:54.607061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:14:27.724283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:14:30.167108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:14:32.951940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:14:37.019823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:14:40.407125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:14:44.046402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:14:47.663373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:14:50.936132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:14:55.183629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:14:28.045372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:14:30.508835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:14:33.249324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:14:37.429409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:14:40.839037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:14:44.474397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:14:48.149285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:14:51.339102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:14:55.630723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:14:28.337106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:14:30.820201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:14:33.613082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:14:37.774947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:14:41.233536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:14:44.803451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:14:48.521742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:14:51.769858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-11T05:15:12.243504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
측정일시권역코드권역명측정소코드측정소명미세먼지 1시간(㎍/㎥)미세먼지 24시간(㎍/㎥)초미세먼지(㎍/㎥)오존(ppm)이산화질소농도(ppm)일산화탄소농도(ppm)아황산가스농도(ppm)
측정일시1.0000.0000.0000.0000.0000.6220.6900.6490.4260.4060.4520.325
권역코드0.0001.0001.0000.9961.0000.2240.2790.1870.1350.2620.3890.303
권역명0.0001.0001.0000.9961.0000.2240.2790.1870.1350.2620.3890.303
측정소코드0.0000.9960.9961.0001.0000.2580.3130.2280.1730.2160.5220.406
측정소명0.0001.0001.0001.0001.0000.3110.3780.2780.2320.3610.6380.615
미세먼지 1시간(㎍/㎥)0.6220.2240.2240.2580.3111.0000.8600.8990.3270.5000.5140.280
미세먼지 24시간(㎍/㎥)0.6900.2790.2790.3130.3780.8601.0000.8190.2620.4250.5010.273
초미세먼지(㎍/㎥)0.6490.1870.1870.2280.2780.8990.8191.0000.2710.4650.5610.337
오존(ppm)0.4260.1350.1350.1730.2320.3270.2620.2711.0000.4700.3070.311
이산화질소농도(ppm)0.4060.2620.2620.2160.3610.5000.4250.4650.4701.0000.5650.324
일산화탄소농도(ppm)0.4520.3890.3890.5220.6380.5140.5010.5610.3070.5651.0000.523
아황산가스농도(ppm)0.3250.3030.3030.4060.6150.2800.2730.3370.3110.3240.5231.000
2024-05-11T05:15:12.752677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
측정소명권역명권역코드
측정소명1.0000.9990.999
권역명0.9991.0001.000
권역코드0.9991.0001.000
2024-05-11T05:15:13.056329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
측정일시측정소코드미세먼지 1시간(㎍/㎥)미세먼지 24시간(㎍/㎥)초미세먼지(㎍/㎥)오존(ppm)이산화질소농도(ppm)일산화탄소농도(ppm)아황산가스농도(ppm)권역코드권역명측정소명
측정일시1.000-0.014-0.158-0.177-0.057-0.034-0.093-0.108-0.1270.0000.0000.000
측정소코드-0.0141.0000.1090.1020.031-0.064-0.048-0.1680.0100.9050.9050.999
미세먼지 1시간(㎍/㎥)-0.1580.1091.0000.8130.7780.1490.3750.3660.2380.0950.0950.115
미세먼지 24시간(㎍/㎥)-0.1770.1020.8131.0000.7660.1520.3150.3390.2280.1180.1180.140
초미세먼지(㎍/㎥)-0.0570.0310.7780.7661.0000.1210.3400.3850.2850.0790.0790.101
오존(ppm)-0.034-0.0640.1490.1520.1211.000-0.326-0.1870.0920.0560.0560.083
이산화질소농도(ppm)-0.093-0.0480.3750.3150.340-0.3261.0000.4870.2270.1120.1120.136
일산화탄소농도(ppm)-0.108-0.1680.3660.3390.385-0.1870.4871.0000.0840.1710.1710.283
아황산가스농도(ppm)-0.1270.0100.2380.2280.2850.0920.2270.0841.0000.1300.1300.267
권역코드0.0000.9050.0950.1180.0790.0560.1120.1710.1301.0001.0000.999
권역명0.0000.9050.0950.1180.0790.0560.1120.1710.1301.0001.0000.999
측정소명0.0000.9990.1150.1400.1010.0830.1360.2830.2670.9990.9991.000

Missing values

2024-05-11T05:14:56.484751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-11T05:14:57.443329image/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

측정일시권역코드권역명측정소코드측정소명미세먼지 1시간(㎍/㎥)미세먼지 24시간(㎍/㎥)초미세먼지(㎍/㎥)오존(ppm)이산화질소농도(ppm)일산화탄소농도(ppm)아황산가스농도(ppm)
17739201906011000103서남권111251관악구5142370.0530.0160.20.004
14759201906060900102동북권111311노원구5452320.0190.030.60.005
15026201906052200100도심권111123종로구5655350.0280.0490.60.005
4860201906222100102동북권111291강북구141370.030.010.30.002
7706201906180300102동북권111142성동구4124290.0320.0190.50.003
7500201906181100100도심권111123종로구1920110.0210.0380.60.004
16120201906040300103서남권111221구로구2437220.0220.0270.30.004
15309201906051100102동북권111291강북구3432160.0210.0210.40.002
10398201906131600104동남권111273송파구4037270.0710.0310.40.004
10390201906131600103서남권111231영등포구3343250.0520.0240.30.004
측정일시권역코드권역명측정소코드측정소명미세먼지 1시간(㎍/㎥)미세먼지 24시간(㎍/㎥)초미세먼지(㎍/㎥)오존(ppm)이산화질소농도(ppm)일산화탄소농도(ppm)아황산가스농도(ppm)
6440201906200600103서남권111231영등포구3337240.0430.0170.30.002
7122201906190300104동남권111273송파구3636270.0340.0140.40.004
1126201906290200100도심권111121중구2225160.0240.030.60.004
14900201906060300100도심권111123종로구1835100.0230.0150.40.003
16683201906030400102동북권111291강북구3132170.0430.0090.40.002
12586201906100000102동북권111141광진구030170.0350.010.50.005
6358201906200900102동북권111161성북구403100.0380.0210.60.002
2768201906260900103서남권111231영등포구413400.0240.050.40.003
3551201906250100100도심권111131용산구1818150.040.0210.30.002
2139201906271000103서남권111241동작구171990.0140.0250.30.002