Overview

Dataset statistics

Number of variables9
Number of observations9125
Missing cells1271
Missing cells (%)1.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory704.1 KiB
Average record size in memory79.0 B

Variable types

Numeric7
Categorical2

Dataset

Description파일 다운로드
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-2220/S/1/datasetView.do

Alerts

측정소명 is highly overall correlated with 권역명High correlation
권역명 is highly overall correlated with 측정소명High correlation
이산화질소농도(ppm) is highly overall correlated with 일산화탄소농도(ppm) and 2 other fieldsHigh correlation
일산화탄소농도(ppm) is highly overall correlated with 이산화질소농도(ppm) and 2 other fieldsHigh correlation
미세먼지농도(㎍/㎥) is highly overall correlated with 이산화질소농도(ppm) and 2 other fieldsHigh correlation
초미세먼지농도(㎍/㎥) is highly overall correlated with 이산화질소농도(ppm) and 2 other fieldsHigh correlation
이산화질소농도(ppm) has 206 (2.3%) missing valuesMissing
오존농도(ppm) has 197 (2.2%) missing valuesMissing
일산화탄소농도(ppm) has 259 (2.8%) missing valuesMissing
아황산가스농도(ppm) has 193 (2.1%) missing valuesMissing
미세먼지농도(㎍/㎥) has 213 (2.3%) missing valuesMissing
초미세먼지농도(㎍/㎥) has 203 (2.2%) missing valuesMissing

Reproduction

Analysis started2024-05-03 21:41:57.299073
Analysis finished2024-05-03 21:42:14.488626
Duration17.19 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

측정일시
Real number (ℝ)

Distinct365
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20190668
Minimum20190101
Maximum20191231
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size80.3 KiB
2024-05-03T21:42:14.732521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20190101
5-th percentile20190119
Q120190402
median20190702
Q320191001
95-th percentile20191213
Maximum20191231
Range1130
Interquartile range (IQR)599

Descriptive statistics

Standard deviation345.02079
Coefficient of variation (CV)1.7088131 × 10-5
Kurtosis-1.2057171
Mean20190668
Median Absolute Deviation (MAD)300
Skewness-0.010696166
Sum1.8423985 × 1011
Variance119039.35
MonotonicityIncreasing
2024-05-03T21:42:15.176959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20190101 25
 
0.3%
20190909 25
 
0.3%
20190907 25
 
0.3%
20190906 25
 
0.3%
20190905 25
 
0.3%
20190904 25
 
0.3%
20190903 25
 
0.3%
20190902 25
 
0.3%
20190901 25
 
0.3%
20190831 25
 
0.3%
Other values (355) 8875
97.3%
ValueCountFrequency (%)
20190101 25
0.3%
20190102 25
0.3%
20190103 25
0.3%
20190104 25
0.3%
20190105 25
0.3%
20190106 25
0.3%
20190107 25
0.3%
20190108 25
0.3%
20190109 25
0.3%
20190110 25
0.3%
ValueCountFrequency (%)
20191231 25
0.3%
20191230 25
0.3%
20191229 25
0.3%
20191228 25
0.3%
20191227 25
0.3%
20191226 25
0.3%
20191225 25
0.3%
20191224 25
0.3%
20191223 25
0.3%
20191222 25
0.3%

권역명
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size71.4 KiB
동북권
2920 
서남권
2555 
동남권
1460 
서북권
1095 
도심권
1095 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
동북권 2920
32.0%
서남권 2555
28.0%
동남권 1460
16.0%
서북권 1095
 
12.0%
도심권 1095
 
12.0%

Length

2024-05-03T21:42:15.583677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-03T21:42:15.895507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
동북권 2920
32.0%
서남권 2555
28.0%
동남권 1460
16.0%
서북권 1095
 
12.0%
도심권 1095
 
12.0%

측정소명
Categorical

HIGH CORRELATION 

Distinct25
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size71.4 KiB
강남구
 
365
강동구
 
365
강북구
 
365
강서구
 
365
관악구
 
365
Other values (20)
7300 

Length

Max length4
Median length3
Mean length3.08
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row강남구
2nd row강동구
3rd row강북구
4th row강서구
5th row관악구

Common Values

ValueCountFrequency (%)
강남구 365
 
4.0%
강동구 365
 
4.0%
강북구 365
 
4.0%
강서구 365
 
4.0%
관악구 365
 
4.0%
광진구 365
 
4.0%
구로구 365
 
4.0%
금천구 365
 
4.0%
노원구 365
 
4.0%
도봉구 365
 
4.0%
Other values (15) 5475
60.0%

Length

2024-05-03T21:42:16.413498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
강남구 365
 
4.0%
서대문구 365
 
4.0%
중구 365
 
4.0%
종로구 365
 
4.0%
은평구 365
 
4.0%
용산구 365
 
4.0%
영등포구 365
 
4.0%
양천구 365
 
4.0%
송파구 365
 
4.0%
성북구 365
 
4.0%
Other values (15) 5475
60.0%

이산화질소농도(ppm)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct78
Distinct (%)0.9%
Missing206
Missing (%)2.3%
Infinite0
Infinite (%)0.0%
Mean0.028073887
Minimum0.001
Maximum0.089
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size80.3 KiB
2024-05-03T21:42:16.861477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.001
5-th percentile0.01
Q10.018
median0.026
Q30.036
95-th percentile0.052
Maximum0.089
Range0.088
Interquartile range (IQR)0.018

Descriptive statistics

Standard deviation0.012786305
Coefficient of variation (CV)0.45545191
Kurtosis-0.043405731
Mean0.028073887
Median Absolute Deviation (MAD)0.009
Skewness0.63407081
Sum250.391
Variance0.00016348961
MonotonicityNot monotonic
2024-05-03T21:42:17.312865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.022 310
 
3.4%
0.019 305
 
3.3%
0.023 301
 
3.3%
0.02 296
 
3.2%
0.018 290
 
3.2%
0.024 287
 
3.1%
0.021 280
 
3.1%
0.025 279
 
3.1%
0.016 276
 
3.0%
0.017 264
 
2.9%
Other values (68) 6031
66.1%
ValueCountFrequency (%)
0.001 2
 
< 0.1%
0.002 3
 
< 0.1%
0.003 5
 
0.1%
0.004 10
 
0.1%
0.005 16
 
0.2%
0.006 34
 
0.4%
0.007 46
 
0.5%
0.008 77
0.8%
0.009 124
1.4%
0.01 133
1.5%
ValueCountFrequency (%)
0.089 1
 
< 0.1%
0.078 1
 
< 0.1%
0.077 1
 
< 0.1%
0.076 2
< 0.1%
0.074 2
< 0.1%
0.073 2
< 0.1%
0.072 2
< 0.1%
0.071 3
< 0.1%
0.07 4
< 0.1%
0.069 1
 
< 0.1%

오존농도(ppm)
Real number (ℝ)

MISSING 

Distinct78
Distinct (%)0.9%
Missing197
Missing (%)2.2%
Infinite0
Infinite (%)0.0%
Mean0.024771281
Minimum0.001
Maximum0.088
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size80.3 KiB
2024-05-03T21:42:17.745377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.001
5-th percentile0.006
Q10.014
median0.024
Q30.034
95-th percentile0.048
Maximum0.088
Range0.087
Interquartile range (IQR)0.02

Descriptive statistics

Standard deviation0.013230365
Coefficient of variation (CV)0.53410096
Kurtosis-0.11719388
Mean0.024771281
Median Absolute Deviation (MAD)0.01
Skewness0.49380189
Sum221.158
Variance0.00017504256
MonotonicityNot monotonic
2024-05-03T21:42:18.182251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.024 254
 
2.8%
0.025 251
 
2.8%
0.012 247
 
2.7%
0.018 245
 
2.7%
0.022 241
 
2.6%
0.014 241
 
2.6%
0.021 238
 
2.6%
0.027 237
 
2.6%
0.023 236
 
2.6%
0.016 232
 
2.5%
Other values (68) 6506
71.3%
ValueCountFrequency (%)
0.001 2
 
< 0.1%
0.002 33
 
0.4%
0.003 99
1.1%
0.004 136
1.5%
0.005 142
1.6%
0.006 184
2.0%
0.007 195
2.1%
0.008 180
2.0%
0.009 206
2.3%
0.01 218
2.4%
ValueCountFrequency (%)
0.088 1
 
< 0.1%
0.085 1
 
< 0.1%
0.079 3
< 0.1%
0.077 2
 
< 0.1%
0.075 2
 
< 0.1%
0.073 1
 
< 0.1%
0.072 1
 
< 0.1%
0.071 6
0.1%
0.07 1
 
< 0.1%
0.069 1
 
< 0.1%

일산화탄소농도(ppm)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct20
Distinct (%)0.2%
Missing259
Missing (%)2.8%
Infinite0
Infinite (%)0.0%
Mean0.53050981
Minimum0.1
Maximum2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size80.3 KiB
2024-05-03T21:42:18.562985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.3
Q10.4
median0.5
Q30.6
95-th percentile0.9
Maximum2
Range1.9
Interquartile range (IQR)0.2

Descriptive statistics

Standard deviation0.22012074
Coefficient of variation (CV)0.41492304
Kurtosis2.3844054
Mean0.53050981
Median Absolute Deviation (MAD)0.1
Skewness1.1892465
Sum4703.5
Variance0.048453141
MonotonicityNot monotonic
2024-05-03T21:42:18.950246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0.4 2032
22.3%
0.5 1718
18.8%
0.3 1293
14.2%
0.6 1272
13.9%
0.7 880
9.6%
0.8 505
 
5.5%
0.2 376
 
4.1%
0.9 336
 
3.7%
1.0 197
 
2.2%
1.1 102
 
1.1%
Other values (10) 155
 
1.7%
(Missing) 259
 
2.8%
ValueCountFrequency (%)
0.1 28
 
0.3%
0.2 376
 
4.1%
0.3 1293
14.2%
0.4 2032
22.3%
0.5 1718
18.8%
0.6 1272
13.9%
0.7 880
9.6%
0.8 505
 
5.5%
0.9 336
 
3.7%
1.0 197
 
2.2%
ValueCountFrequency (%)
2.0 1
 
< 0.1%
1.9 1
 
< 0.1%
1.8 1
 
< 0.1%
1.7 2
 
< 0.1%
1.6 10
 
0.1%
1.5 16
 
0.2%
1.4 18
 
0.2%
1.3 31
 
0.3%
1.2 47
0.5%
1.1 102
1.1%

아황산가스농도(ppm)
Real number (ℝ)

MISSING 

Distinct10
Distinct (%)0.1%
Missing193
Missing (%)2.1%
Infinite0
Infinite (%)0.0%
Mean0.0039514107
Minimum0.001
Maximum0.01
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size80.3 KiB
2024-05-03T21:42:19.445341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.001
5-th percentile0.002
Q10.003
median0.004
Q30.005
95-th percentile0.006
Maximum0.01
Range0.009
Interquartile range (IQR)0.002

Descriptive statistics

Standard deviation0.0013153067
Coefficient of variation (CV)0.33287015
Kurtosis1.2118772
Mean0.0039514107
Median Absolute Deviation (MAD)0.001
Skewness0.93292379
Sum35.294
Variance1.7300316 × 10-6
MonotonicityNot monotonic
2024-05-03T21:42:19.768425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0.003 2877
31.5%
0.004 2751
30.1%
0.005 1420
15.6%
0.002 826
 
9.1%
0.006 632
 
6.9%
0.007 266
 
2.9%
0.008 107
 
1.2%
0.009 34
 
0.4%
0.001 11
 
0.1%
0.01 8
 
0.1%
(Missing) 193
 
2.1%
ValueCountFrequency (%)
0.001 11
 
0.1%
0.002 826
 
9.1%
0.003 2877
31.5%
0.004 2751
30.1%
0.005 1420
15.6%
0.006 632
 
6.9%
0.007 266
 
2.9%
0.008 107
 
1.2%
0.009 34
 
0.4%
0.01 8
 
0.1%
ValueCountFrequency (%)
0.01 8
 
0.1%
0.009 34
 
0.4%
0.008 107
 
1.2%
0.007 266
 
2.9%
0.006 632
 
6.9%
0.005 1420
15.6%
0.004 2751
30.1%
0.003 2877
31.5%
0.002 826
 
9.1%
0.001 11
 
0.1%

미세먼지농도(㎍/㎥)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct194
Distinct (%)2.2%
Missing213
Missing (%)2.3%
Infinite0
Infinite (%)0.0%
Mean41.764026
Minimum3
Maximum228
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size80.3 KiB
2024-05-03T21:42:20.133894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile11
Q124
median36
Q352
95-th percentile91.45
Maximum228
Range225
Interquartile range (IQR)28

Descriptive statistics

Standard deviation26.785521
Coefficient of variation (CV)0.64135389
Kurtosis5.9150485
Mean41.764026
Median Absolute Deviation (MAD)13
Skewness1.9188493
Sum372201
Variance717.46412
MonotonicityNot monotonic
2024-05-03T21:42:20.485652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
27 212
 
2.3%
32 206
 
2.3%
35 205
 
2.2%
33 202
 
2.2%
36 198
 
2.2%
26 196
 
2.1%
25 195
 
2.1%
34 192
 
2.1%
22 191
 
2.1%
31 188
 
2.1%
Other values (184) 6927
75.9%
(Missing) 213
 
2.3%
ValueCountFrequency (%)
3 6
 
0.1%
4 21
 
0.2%
5 30
 
0.3%
6 44
0.5%
7 54
0.6%
8 62
0.7%
9 69
0.8%
10 92
1.0%
11 86
0.9%
12 83
0.9%
ValueCountFrequency (%)
228 1
< 0.1%
225 1
< 0.1%
224 1
< 0.1%
213 1
< 0.1%
212 1
< 0.1%
201 1
< 0.1%
200 1
< 0.1%
199 1
< 0.1%
198 1
< 0.1%
197 1
< 0.1%

초미세먼지농도(㎍/㎥)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct140
Distinct (%)1.6%
Missing203
Missing (%)2.2%
Infinite0
Infinite (%)0.0%
Mean24.934656
Minimum1
Maximum153
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size80.3 KiB
2024-05-03T21:42:20.736681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q114
median21
Q330
95-th percentile59
Maximum153
Range152
Interquartile range (IQR)16

Descriptive statistics

Standard deviation18.444453
Coefficient of variation (CV)0.73971154
Kurtosis9.3338625
Mean24.934656
Median Absolute Deviation (MAD)8
Skewness2.4728751
Sum222467
Variance340.19784
MonotonicityNot monotonic
2024-05-03T21:42:21.034811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21 346
 
3.8%
20 345
 
3.8%
18 333
 
3.6%
19 311
 
3.4%
17 310
 
3.4%
16 307
 
3.4%
14 294
 
3.2%
15 292
 
3.2%
13 291
 
3.2%
22 290
 
3.2%
Other values (130) 5803
63.6%
ValueCountFrequency (%)
1 19
 
0.2%
2 43
 
0.5%
3 78
 
0.9%
4 93
 
1.0%
5 161
1.8%
6 157
1.7%
7 147
1.6%
8 197
2.2%
9 235
2.6%
10 247
2.7%
ValueCountFrequency (%)
153 1
 
< 0.1%
148 1
 
< 0.1%
147 2
< 0.1%
146 1
 
< 0.1%
144 1
 
< 0.1%
143 1
 
< 0.1%
141 4
< 0.1%
140 1
 
< 0.1%
139 4
< 0.1%
138 4
< 0.1%

Interactions

2024-05-03T21:42:11.036246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:41:59.918514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:42:01.761133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:42:03.604535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:42:05.447340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:42:07.375165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:42:09.203863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:42:11.319335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:42:00.142229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:42:02.025690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:42:03.871654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:42:05.725886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:42:07.639816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:42:09.467114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:42:11.882721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:42:00.411294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:42:02.278031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:42:04.123469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:42:05.994497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:42:07.899312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:42:09.719776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:42:12.188694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:42:00.583797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:42:02.531722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:42:04.374949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:42:06.259577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:42:08.152555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:42:09.975021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:42:12.479386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:42:00.848860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:42:02.816109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:42:04.657579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:42:06.547512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:42:08.429658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:42:10.254498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:42:12.750444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:42:01.107832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:42:03.070720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:42:04.912776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:42:06.819170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:42:08.681957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:42:10.507987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:42:13.018980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:42:01.429131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:42:03.324749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:42:05.168308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:42:07.084135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:42:08.941351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:42:10.756900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-03T21:42:21.325795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
측정일시권역명측정소명이산화질소농도(ppm)오존농도(ppm)일산화탄소농도(ppm)아황산가스농도(ppm)미세먼지농도(㎍/㎥)초미세먼지농도(㎍/㎥)
측정일시1.0000.0000.0000.5730.7050.5630.5180.6010.571
권역명0.0001.0001.0000.2350.0920.3230.2930.1000.045
측정소명0.0001.0001.0000.3070.1820.4230.5660.1150.063
이산화질소농도(ppm)0.5730.2350.3071.0000.5330.8020.4190.6120.612
오존농도(ppm)0.7050.0920.1820.5331.0000.4480.2430.2730.298
일산화탄소농도(ppm)0.5630.3230.4230.8020.4481.0000.3890.6340.675
아황산가스농도(ppm)0.5180.2930.5660.4190.2430.3891.0000.4470.461
미세먼지농도(㎍/㎥)0.6010.1000.1150.6120.2730.6340.4471.0000.934
초미세먼지농도(㎍/㎥)0.5710.0450.0630.6120.2980.6750.4610.9341.000
2024-05-03T21:42:21.634512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
측정소명권역명
측정소명1.0000.999
권역명0.9991.000
2024-05-03T21:42:22.020840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
측정일시이산화질소농도(ppm)오존농도(ppm)일산화탄소농도(ppm)아황산가스농도(ppm)미세먼지농도(㎍/㎥)초미세먼지농도(㎍/㎥)권역명측정소명
측정일시1.000-0.173-0.172-0.147-0.424-0.419-0.3610.0000.000
이산화질소농도(ppm)-0.1731.000-0.4700.6570.3210.6100.5930.0990.113
오존농도(ppm)-0.172-0.4701.000-0.398-0.011-0.073-0.0470.0380.065
일산화탄소농도(ppm)-0.1470.657-0.3981.0000.3040.5960.5980.1400.162
아황산가스농도(ppm)-0.4240.321-0.0110.3041.0000.4360.4310.1260.237
미세먼지농도(㎍/㎥)-0.4190.610-0.0730.5960.4361.0000.8820.0400.041
초미세먼지농도(㎍/㎥)-0.3610.593-0.0470.5980.4310.8821.0000.0190.022
권역명0.0000.0990.0380.1400.1260.0400.0191.0000.999
측정소명0.0000.1130.0650.1620.2370.0410.0220.9991.000

Missing values

2024-05-03T21:42:13.414267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-03T21:42:13.885976image/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.
2024-05-03T21:42:14.272472image/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

측정일시권역명측정소명이산화질소농도(ppm)오존농도(ppm)일산화탄소농도(ppm)아황산가스농도(ppm)미세먼지농도(㎍/㎥)초미세먼지농도(㎍/㎥)
020190101동남권강남구0.0270.0130.60.0053530
120190101동남권강동구0.0370.0120.70.0054030
220190101동북권강북구0.0240.020.60.0034428
320190101서남권강서구0.0290.0170.50.0054624
420190101서남권관악구0.0350.0160.50.0053928
520190101동북권광진구0.0340.0111.10.0043524
620190101서남권구로구0.0270.0170.40.0063316
720190101서남권금천구0.0330.0150.60.0043324
820190101동북권노원구0.0310.0140.70.0054230
920190101동북권도봉구0.0240.0191.20.0043423
측정일시권역명측정소명이산화질소농도(ppm)오존농도(ppm)일산화탄소농도(ppm)아황산가스농도(ppm)미세먼지농도(㎍/㎥)초미세먼지농도(㎍/㎥)
911520191231동북권성동구0.020.0210.30.0032516
911620191231동북권성북구0.0180.0230.60.0032612
911720191231동남권송파구0.0170.0190.40.0032618
911820191231서남권양천구0.0190.0180.40.0042917
911920191231서남권영등포구0.0180.0190.50.0022214
912020191231도심권용산구0.0110.0220.30.0033718
912120191231서북권은평구0.0110.0230.40.0032413
912220191231도심권종로구0.0160.020.40.0032719
912320191231도심권중구0.0140.0220.40.0022618
912420191231동북권중랑구0.0180.0190.40.0032312