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

권역명 is highly overall correlated with 측정소코드 and 2 other fieldsHigh correlation
측정소명 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 3 other fieldsHigh correlation
오존(ppm) is highly overall correlated with 이산화질소농도(ppm) and 1 other fieldsHigh correlation
이산화질소농도(ppm) is highly overall correlated with 초미세먼지(㎍/㎥) and 2 other fieldsHigh correlation
일산화탄소농도(ppm) is highly overall correlated with 초미세먼지(㎍/㎥) and 2 other fieldsHigh correlation
초미세먼지(㎍/㎥) has 115 (1.1%) zerosZeros

Reproduction

Analysis started2024-05-04 00:13:05.652488
Analysis finished2024-05-04 00:13:39.112195
Duration33.46 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

측정일시
Real number (ℝ)

Distinct744
Distinct (%)7.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0190116 × 1011
Minimum2.0190101 × 1011
Maximum2.0190131 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T00:13:39.306127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0190101 × 1011
5-th percentile2.0190102 × 1011
Q12.0190108 × 1011
median2.0190116 × 1011
Q32.0190124 × 1011
95-th percentile2.019013 × 1011
Maximum2.0190131 × 1011
Range302300
Interquartile range (IQR)159000

Descriptive statistics

Standard deviation89631.477
Coefficient of variation (CV)4.4393741 × 10-7
Kurtosis-1.2138768
Mean2.0190116 × 1011
Median Absolute Deviation (MAD)79500
Skewness0.005693464
Sum2.0190116 × 1015
Variance8.0338017 × 109
MonotonicityNot monotonic
2024-05-04T00:13:39.757837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
201901170100 20
 
0.2%
201901261600 20
 
0.2%
201901181000 20
 
0.2%
201901191700 20
 
0.2%
201901150300 19
 
0.2%
201901021400 19
 
0.2%
201901070300 19
 
0.2%
201901042200 19
 
0.2%
201901051300 19
 
0.2%
201901251600 18
 
0.2%
Other values (734) 9807
98.1%
ValueCountFrequency (%)
201901010000 17
0.2%
201901010100 11
0.1%
201901010200 14
0.1%
201901010300 13
0.1%
201901010400 11
0.1%
201901010500 14
0.1%
201901010600 15
0.1%
201901010700 14
0.1%
201901010800 13
0.1%
201901010900 11
0.1%
ValueCountFrequency (%)
201901312300 12
0.1%
201901312200 15
0.1%
201901312100 15
0.1%
201901312000 7
 
0.1%
201901311900 16
0.2%
201901311800 13
0.1%
201901311700 16
0.2%
201901311600 18
0.2%
201901311500 11
0.1%
201901311400 14
0.1%

권역코드
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
102
3218 
103
2830 
104
1576 
101
1199 
100
1177 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
102 3218
32.2%
103 2830
28.3%
104 1576
15.8%
101 1199
 
12.0%
100 1177
 
11.8%

Length

2024-05-04T00:13:40.190934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T00:13:40.512743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
102 3218
32.2%
103 2830
28.3%
104 1576
15.8%
101 1199
 
12.0%
100 1177
 
11.8%

권역명
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
동북권
3218 
서남권
2830 
동남권
1576 
서북권
1199 
도심권
1177 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
동북권 3218
32.2%
서남권 2830
28.3%
동남권 1576
15.8%
서북권 1199
 
12.0%
도심권 1177
 
11.8%

Length

2024-05-04T00:13:40.983092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T00:13:41.352746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
동북권 3218
32.2%
서남권 2830
28.3%
동남권 1576
15.8%
서북권 1199
 
12.0%
도심권 1177
 
11.8%

측정소코드
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean111211.24
Minimum111121
Maximum111311
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T00:13:41.704272image/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 deviation59.831328
Coefficient of variation (CV)0.00053799715
Kurtosis-1.3655035
Mean111211.24
Median Absolute Deviation (MAD)60
Skewness0.031255582
Sum1.1121124 × 109
Variance3579.7878
MonotonicityNot monotonic
2024-05-04T00:13:42.124591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
111241 427
 
4.3%
111141 415
 
4.2%
111171 415
 
4.2%
111221 414
 
4.1%
111281 409
 
4.1%
111291 408
 
4.1%
111262 408
 
4.1%
111152 403
 
4.0%
111311 403
 
4.0%
111191 403
 
4.0%
Other values (15) 5895
59.0%
ValueCountFrequency (%)
111121 389
3.9%
111123 398
4.0%
111131 390
3.9%
111141 415
4.2%
111142 396
4.0%
111151 384
3.8%
111152 403
4.0%
111161 394
3.9%
111171 415
4.2%
111181 401
4.0%
ValueCountFrequency (%)
111311 403
4.0%
111301 397
4.0%
111291 408
4.1%
111281 409
4.1%
111274 392
3.9%
111273 393
3.9%
111262 408
4.1%
111261 383
3.8%
111251 387
3.9%
111241 427
4.3%

측정소명
Categorical

HIGH CORRELATION 

Distinct25
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
동작구
 
427
도봉구
 
415
광진구
 
415
구로구
 
414
금천구
 
409
Other values (20)
7920 

Length

Max length4
Median length3
Mean length3.0816
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row도봉구
2nd row중구
3rd row양천구
4th row영등포구
5th row성동구

Common Values

ValueCountFrequency (%)
동작구 427
 
4.3%
도봉구 415
 
4.2%
광진구 415
 
4.2%
구로구 414
 
4.1%
금천구 409
 
4.1%
서초구 408
 
4.1%
강북구 408
 
4.1%
서대문구 403
 
4.0%
동대문구 403
 
4.0%
노원구 403
 
4.0%
Other values (15) 5895
59.0%

Length

2024-05-04T00:13:42.660420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
동작구 427
 
4.3%
도봉구 415
 
4.2%
광진구 415
 
4.2%
구로구 414
 
4.1%
금천구 409
 
4.1%
서초구 408
 
4.1%
강북구 408
 
4.1%
서대문구 403
 
4.0%
동대문구 403
 
4.0%
노원구 403
 
4.0%
Other values (15) 5895
59.0%

미세먼지 1시간(㎍/㎥)
Real number (ℝ)

HIGH CORRELATION 

Distinct222
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65.1783
Minimum0
Maximum262
Zeros92
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T00:13:43.081378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile26
Q140
median56
Q380
95-th percentile139
Maximum262
Range262
Interquartile range (IQR)40

Descriptive statistics

Standard deviation36.47003
Coefficient of variation (CV)0.55954252
Kurtosis2.8025962
Mean65.1783
Median Absolute Deviation (MAD)19
Skewness1.4996105
Sum651783
Variance1330.0631
MonotonicityNot monotonic
2024-05-04T00:13:43.780660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
48 195
 
1.9%
37 190
 
1.9%
43 183
 
1.8%
42 175
 
1.8%
39 174
 
1.7%
41 174
 
1.7%
38 169
 
1.7%
44 169
 
1.7%
51 167
 
1.7%
40 166
 
1.7%
Other values (212) 8238
82.4%
ValueCountFrequency (%)
0 92
0.9%
10 1
 
< 0.1%
11 2
 
< 0.1%
12 3
 
< 0.1%
13 5
 
0.1%
14 8
 
0.1%
15 6
 
0.1%
16 6
 
0.1%
17 15
 
0.1%
18 11
 
0.1%
ValueCountFrequency (%)
262 1
< 0.1%
259 1
< 0.1%
249 1
< 0.1%
247 1
< 0.1%
239 2
< 0.1%
232 2
< 0.1%
231 1
< 0.1%
230 1
< 0.1%
227 2
< 0.1%
226 1
< 0.1%

미세먼지 24시간(㎍/㎥)
Real number (ℝ)

HIGH CORRELATION 

Distinct184
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean61.4541
Minimum0
Maximum205
Zeros13
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T00:13:44.261049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile30
Q141
median56
Q372
95-th percentile118
Maximum205
Range205
Interquartile range (IQR)31

Descriptive statistics

Standard deviation28.55209
Coefficient of variation (CV)0.46460837
Kurtosis3.4423828
Mean61.4541
Median Absolute Deviation (MAD)15
Skewness1.6292736
Sum614541
Variance815.22182
MonotonicityNot monotonic
2024-05-04T00:13:44.726616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40 228
 
2.3%
61 227
 
2.3%
62 213
 
2.1%
37 213
 
2.1%
63 209
 
2.1%
43 208
 
2.1%
39 201
 
2.0%
64 200
 
2.0%
42 194
 
1.9%
45 194
 
1.9%
Other values (174) 7913
79.1%
ValueCountFrequency (%)
0 13
 
0.1%
17 1
 
< 0.1%
18 4
 
< 0.1%
19 6
 
0.1%
20 5
 
0.1%
21 12
 
0.1%
22 23
0.2%
23 33
0.3%
24 42
0.4%
25 41
0.4%
ValueCountFrequency (%)
205 2
< 0.1%
201 1
 
< 0.1%
200 1
 
< 0.1%
198 1
 
< 0.1%
197 1
 
< 0.1%
196 2
< 0.1%
195 3
< 0.1%
193 1
 
< 0.1%
192 4
< 0.1%
191 1
 
< 0.1%

초미세먼지(㎍/㎥)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct182
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.1835
Minimum0
Maximum188
Zeros115
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T00:13:45.133489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile11
Q118
median26
Q346
95-th percentile102
Maximum188
Range188
Interquartile range (IQR)28

Descriptive statistics

Standard deviation30.075656
Coefficient of variation (CV)0.80884414
Kurtosis3.9275356
Mean37.1835
Median Absolute Deviation (MAD)10
Skewness1.9313665
Sum371835
Variance904.54508
MonotonicityNot monotonic
2024-05-04T00:13:45.644028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17 384
 
3.8%
18 368
 
3.7%
16 358
 
3.6%
20 355
 
3.5%
19 349
 
3.5%
22 337
 
3.4%
21 313
 
3.1%
23 302
 
3.0%
15 292
 
2.9%
14 292
 
2.9%
Other values (172) 6650
66.5%
ValueCountFrequency (%)
0 115
1.1%
1 2
 
< 0.1%
2 4
 
< 0.1%
3 7
 
0.1%
4 12
 
0.1%
5 17
 
0.2%
6 44
 
0.4%
7 32
 
0.3%
8 48
0.5%
9 60
0.6%
ValueCountFrequency (%)
188 1
< 0.1%
187 1
< 0.1%
185 1
< 0.1%
181 1
< 0.1%
180 1
< 0.1%
179 1
< 0.1%
177 2
< 0.1%
176 1
< 0.1%
175 2
< 0.1%
174 1
< 0.1%

오존(ppm)
Real number (ℝ)

HIGH CORRELATION 

Distinct51
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0120451
Minimum0
Maximum0.056
Zeros60
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T00:13:46.065497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.002
Q10.003
median0.009
Q30.02
95-th percentile0.031
Maximum0.056
Range0.056
Interquartile range (IQR)0.017

Descriptive statistics

Standard deviation0.01011083
Coefficient of variation (CV)0.83941439
Kurtosis-0.45835265
Mean0.0120451
Median Absolute Deviation (MAD)0.007
Skewness0.75482388
Sum120.451
Variance0.00010222889
MonotonicityNot monotonic
2024-05-04T00:13:46.654745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.002 1723
 
17.2%
0.003 815
 
8.2%
0.004 499
 
5.0%
0.001 421
 
4.2%
0.005 405
 
4.0%
0.006 378
 
3.8%
0.007 322
 
3.2%
0.009 271
 
2.7%
0.02 266
 
2.7%
0.008 266
 
2.7%
Other values (41) 4634
46.3%
ValueCountFrequency (%)
0.0 60
 
0.6%
0.001 421
 
4.2%
0.002 1723
17.2%
0.003 815
8.2%
0.004 499
 
5.0%
0.005 405
 
4.0%
0.006 378
 
3.8%
0.007 322
 
3.2%
0.008 266
 
2.7%
0.009 271
 
2.7%
ValueCountFrequency (%)
0.056 1
 
< 0.1%
0.051 2
 
< 0.1%
0.05 1
 
< 0.1%
0.049 1
 
< 0.1%
0.048 2
 
< 0.1%
0.047 3
< 0.1%
0.044 5
0.1%
0.043 3
< 0.1%
0.042 5
0.1%
0.041 4
< 0.1%

이산화질소농도(ppm)
Real number (ℝ)

HIGH CORRELATION 

Distinct96
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0389196
Minimum0
Maximum0.11
Zeros63
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T00:13:47.215263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.013
Q10.024
median0.039
Q30.052
95-th percentile0.069
Maximum0.11
Range0.11
Interquartile range (IQR)0.028

Descriptive statistics

Standard deviation0.017664635
Coefficient of variation (CV)0.45387505
Kurtosis-0.58665814
Mean0.0389196
Median Absolute Deviation (MAD)0.014
Skewness0.20395248
Sum389.196
Variance0.00031203934
MonotonicityNot monotonic
2024-05-04T00:13:47.717714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.044 212
 
2.1%
0.043 207
 
2.1%
0.049 206
 
2.1%
0.046 203
 
2.0%
0.04 203
 
2.0%
0.02 202
 
2.0%
0.037 198
 
2.0%
0.022 197
 
2.0%
0.041 195
 
1.9%
0.045 194
 
1.9%
Other values (86) 7983
79.8%
ValueCountFrequency (%)
0.0 63
0.6%
0.001 2
 
< 0.1%
0.003 2
 
< 0.1%
0.004 4
 
< 0.1%
0.005 7
 
0.1%
0.006 20
 
0.2%
0.007 27
0.3%
0.008 53
0.5%
0.009 60
0.6%
0.01 67
0.7%
ValueCountFrequency (%)
0.11 1
 
< 0.1%
0.096 1
 
< 0.1%
0.095 2
< 0.1%
0.094 1
 
< 0.1%
0.093 2
< 0.1%
0.092 3
< 0.1%
0.091 2
< 0.1%
0.09 2
< 0.1%
0.088 2
< 0.1%
0.087 2
< 0.1%

일산화탄소농도(ppm)
Real number (ℝ)

HIGH CORRELATION 

Distinct27
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.75414
Minimum0
Maximum2.7
Zeros68
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T00:13:48.163649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.3
Q10.5
median0.7
Q31
95-th percentile1.4
Maximum2.7
Range2.7
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.35183126
Coefficient of variation (CV)0.46653309
Kurtosis0.98767776
Mean0.75414
Median Absolute Deviation (MAD)0.2
Skewness0.93439518
Sum7541.4
Variance0.12378524
MonotonicityNot monotonic
2024-05-04T00:13:48.638400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0.5 1386
13.9%
0.6 1325
13.2%
0.4 1296
13.0%
0.7 1101
11.0%
0.8 955
9.6%
0.9 775
7.8%
1.0 635
6.3%
1.1 513
 
5.1%
0.3 440
 
4.4%
1.2 408
 
4.1%
Other values (17) 1166
11.7%
ValueCountFrequency (%)
0.0 68
 
0.7%
0.1 14
 
0.1%
0.2 109
 
1.1%
0.3 440
 
4.4%
0.4 1296
13.0%
0.5 1386
13.9%
0.6 1325
13.2%
0.7 1101
11.0%
0.8 955
9.6%
0.9 775
7.8%
ValueCountFrequency (%)
2.7 1
 
< 0.1%
2.5 1
 
< 0.1%
2.4 1
 
< 0.1%
2.3 2
 
< 0.1%
2.2 11
 
0.1%
2.1 13
 
0.1%
2.0 17
 
0.2%
1.9 34
0.3%
1.8 40
0.4%
1.7 76
0.8%
Distinct16
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0051827
Minimum0
Maximum0.018
Zeros54
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T00:13:48.974298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.003
Q10.004
median0.005
Q30.006
95-th percentile0.008
Maximum0.018
Range0.018
Interquartile range (IQR)0.002

Descriptive statistics

Standard deviation0.0016936964
Coefficient of variation (CV)0.32679808
Kurtosis1.4277026
Mean0.0051827
Median Absolute Deviation (MAD)0.001
Skewness0.71464044
Sum51.827
Variance2.8686076 × 10-6
MonotonicityNot monotonic
2024-05-04T00:13:49.341300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0.004 2649
26.5%
0.005 2579
25.8%
0.006 1536
15.4%
0.003 1075
10.8%
0.007 977
 
9.8%
0.008 618
 
6.2%
0.009 271
 
2.7%
0.002 100
 
1.0%
0.01 99
 
1.0%
0.0 54
 
0.5%
Other values (6) 42
 
0.4%
ValueCountFrequency (%)
0.0 54
 
0.5%
0.001 1
 
< 0.1%
0.002 100
 
1.0%
0.003 1075
10.8%
0.004 2649
26.5%
0.005 2579
25.8%
0.006 1536
15.4%
0.007 977
 
9.8%
0.008 618
 
6.2%
0.009 271
 
2.7%
ValueCountFrequency (%)
0.018 2
 
< 0.1%
0.016 1
 
< 0.1%
0.013 1
 
< 0.1%
0.012 10
 
0.1%
0.011 27
 
0.3%
0.01 99
 
1.0%
0.009 271
 
2.7%
0.008 618
6.2%
0.007 977
9.8%
0.006 1536
15.4%

Interactions

2024-05-04T00:13:35.407796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:13:11.785151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:13:14.746762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:13:17.417099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:13:20.288960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:13:23.353296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:13:26.513576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:13:29.052998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:13:31.934605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:13:35.728338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:13:12.235107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:13:15.070987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:13:17.732541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:13:20.650980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:13:23.653663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:13:26.807625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:13:29.337919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:13:32.314845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:13:36.017620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:13:12.638249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:13:15.390901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:13:18.018425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:13:20.944080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:13:24.293566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:13:27.099964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:13:29.670950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:13:32.689591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:13:36.320448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:13:12.984856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:13:15.619369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:13:18.308774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:13:21.199033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:13:24.591187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:13:27.386870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:13:29.935319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:13:33.095028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:13:36.591654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:13:13.270164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:13:15.890181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:13:18.628505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:13:21.539478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:13:24.908884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:13:27.649933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:13:30.231202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:13:33.510064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:13:36.891045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:13:13.570203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:13:16.226537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:13:18.969119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:13:21.896173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:13:25.299639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:13:27.952543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:13:30.536639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:13:33.822452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:13:37.234156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:13:13.868957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:13:16.524258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:13:19.275343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:13:22.330023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:13:25.602564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:13:28.206541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:13:30.903648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:13:34.176903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:13:37.554920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:13:14.158029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:13:16.799710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:13:19.578660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:13:22.697446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:13:25.894749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:13:28.482519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:13:31.172446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:13:34.627240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:13:37.839244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:13:14.438761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:13:17.080910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:13:19.915305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:13:22.988801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:13:26.208534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:13:28.754901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:13:31.547323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:13:35.009511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-04T00:13:49.642211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
측정일시권역코드권역명측정소코드측정소명미세먼지 1시간(㎍/㎥)미세먼지 24시간(㎍/㎥)초미세먼지(㎍/㎥)오존(ppm)이산화질소농도(ppm)일산화탄소농도(ppm)아황산가스농도(ppm)
측정일시1.0000.0000.0000.0000.0000.7270.7560.7160.4190.4360.4740.210
권역코드0.0001.0001.0000.9961.0000.1480.1780.1240.1570.2650.3290.343
권역명0.0001.0001.0000.9961.0000.1480.1780.1240.1570.2650.3290.343
측정소코드0.0000.9960.9961.0001.0000.1830.2140.1880.2200.2960.3840.452
측정소명0.0001.0001.0001.0001.0000.2100.2750.2460.3100.3830.4340.662
미세먼지 1시간(㎍/㎥)0.7270.1480.1480.1830.2101.0000.9030.9130.1860.4660.5230.351
미세먼지 24시간(㎍/㎥)0.7560.1780.1780.2140.2750.9031.0000.8320.1160.3510.4330.307
초미세먼지(㎍/㎥)0.7160.1240.1240.1880.2460.9130.8321.0000.3730.5660.6460.365
오존(ppm)0.4190.1570.1570.2200.3100.1860.1160.3731.0000.7640.5390.156
이산화질소농도(ppm)0.4360.2650.2650.2960.3830.4660.3510.5660.7641.0000.7080.467
일산화탄소농도(ppm)0.4740.3290.3290.3840.4340.5230.4330.6460.5390.7081.0000.544
아황산가스농도(ppm)0.2100.3430.3430.4520.6620.3510.3070.3650.1560.4670.5441.000
2024-05-04T00:13:50.013849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
권역명측정소명권역코드
권역명1.0000.9991.000
측정소명0.9991.0000.999
권역코드1.0000.9991.000
2024-05-04T00:13:50.293199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
측정일시측정소코드미세먼지 1시간(㎍/㎥)미세먼지 24시간(㎍/㎥)초미세먼지(㎍/㎥)오존(ppm)이산화질소농도(ppm)일산화탄소농도(ppm)아황산가스농도(ppm)권역코드권역명측정소명
측정일시1.000-0.0020.1460.172-0.1560.182-0.157-0.207-0.0540.0000.0000.000
측정소코드-0.0021.0000.0710.0840.065-0.0190.026-0.1340.0360.9060.9060.999
미세먼지 1시간(㎍/㎥)0.1460.0711.0000.8910.700-0.0890.2830.4490.2030.0620.0620.075
미세먼지 24시간(㎍/㎥)0.1720.0840.8911.0000.5780.0020.1550.3110.1450.0750.0750.099
초미세먼지(㎍/㎥)-0.1560.0650.7000.5781.000-0.3300.5440.6930.3410.0510.0510.089
오존(ppm)0.182-0.019-0.0890.002-0.3301.000-0.803-0.552-0.0810.0670.0670.114
이산화질소농도(ppm)-0.1570.0260.2830.1550.544-0.8031.0000.6790.2530.1160.1160.143
일산화탄소농도(ppm)-0.207-0.1340.4490.3110.693-0.5520.6791.0000.2750.1430.1430.167
아황산가스농도(ppm)-0.0540.0360.2030.1450.341-0.0810.2530.2751.0000.1490.1490.299
권역코드0.0000.9060.0620.0750.0510.0670.1160.1430.1491.0001.0000.999
권역명0.0000.9060.0620.0750.0510.0670.1160.1430.1491.0001.0000.999
측정소명0.0000.9990.0750.0990.0890.1140.1430.1670.2990.9990.9991.000

Missing values

2024-05-04T00:13:38.283671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-04T00:13:38.855139image/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)
8910201901170300102동북권111171도봉구3428190.0020.0360.80.005
3127201901261800100도심권111121중구3739180.0330.0190.40.003
6819201901201500103서남권111301양천구6164140.0330.0150.40.003
16714201901040300103서남권111231영등포구5849380.0050.0491.70.005
18106201901011900102동북권111142성동구3443270.0190.030.40.003
12811201901101500102동북권111141광진구7167370.0060.0520.90.005
4737201901240200102동북권111291강북구3645150.0290.0130.50.003
6140201901211800103서남권111251관악구6955340.0160.0370.50.005
7515201901191100103서남권111281금천구9189640.0170.0450.80.006
5003201901231500101서북권111191서대문구5679230.0360.0120.90.003
측정일시권역코드권역명측정소코드측정소명미세먼지 1시간(㎍/㎥)미세먼지 24시간(㎍/㎥)초미세먼지(㎍/㎥)오존(ppm)이산화질소농도(ppm)일산화탄소농도(ppm)아황산가스농도(ppm)
18090201901012000103서남권111231영등포구3736240.0180.0220.60.005
553201901310100101서북권111191서대문구7660440.0170.0240.70.004
5957201901220100102동북권111291강북구6162200.0080.0330.70.003
8639201901171400103서남권111241동작구5251250.0320.0190.40.005
7842201901182200103서남권111281금천구9474640.0010.0731.30.006
1064201901300500103서남권111251관악구5965310.0040.0560.50.006
15918201901051100103서남권111251관악구5470310.0160.0320.50.006
16149201901050200104동남권111261강남구10665960.0050.0390.90.005
5474201901222100104동남권111273송파구7964500.0020.0640.90.008
13632201901090600102동북권111141광진구3033100.0170.0120.60.003