Overview

Dataset statistics

Number of variables9
Number of observations9125
Missing cells724
Missing cells (%)0.9%
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
미세먼지(㎍/㎥) is highly overall correlated with 이산화질소농도(ppm) and 2 other fieldsHigh correlation
초미세먼지(㎍/㎥) is highly overall correlated with 이산화질소농도(ppm) and 2 other fieldsHigh correlation
일산화탄소농도(ppm) has 260 (2.8%) missing valuesMissing
아황산가스농도(ppm) has 153 (1.7%) missing valuesMissing

Reproduction

Analysis started2024-05-03 21:52:13.681980
Analysis finished2024-05-03 21:52:33.923630
Duration20.24 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%
Mean20230668
Minimum20230101
Maximum20231231
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size80.3 KiB
2024-05-03T21:52:34.167575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20230101
5-th percentile20230119
Q120230402
median20230702
Q320231001
95-th percentile20231213
Maximum20231231
Range1130
Interquartile range (IQR)599

Descriptive statistics

Standard deviation345.02079
Coefficient of variation (CV)1.7054345 × 10-5
Kurtosis-1.2057171
Mean20230668
Median Absolute Deviation (MAD)300
Skewness-0.010696166
Sum1.8460485 × 1011
Variance119039.35
MonotonicityIncreasing
2024-05-03T21:52:34.747529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20230101 25
 
0.3%
20230909 25
 
0.3%
20230907 25
 
0.3%
20230906 25
 
0.3%
20230905 25
 
0.3%
20230904 25
 
0.3%
20230903 25
 
0.3%
20230902 25
 
0.3%
20230901 25
 
0.3%
20230831 25
 
0.3%
Other values (355) 8875
97.3%
ValueCountFrequency (%)
20230101 25
0.3%
20230102 25
0.3%
20230103 25
0.3%
20230104 25
0.3%
20230105 25
0.3%
20230106 25
0.3%
20230107 25
0.3%
20230108 25
0.3%
20230109 25
0.3%
20230110 25
0.3%
ValueCountFrequency (%)
20231231 25
0.3%
20231230 25
0.3%
20231229 25
0.3%
20231228 25
0.3%
20231227 25
0.3%
20231226 25
0.3%
20231225 25
0.3%
20231224 25
0.3%
20231223 25
0.3%
20231222 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

2024-05-03T21:52:35.430128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-03T21:52:35.753192image/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

2024-05-03T21:52:36.189797image/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 

Distinct562
Distinct (%)6.2%
Missing77
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean0.020383963
Minimum0.0019
Maximum0.0696
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size80.3 KiB
2024-05-03T21:52:36.781934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.0019
5-th percentile0.0077
Q10.0123
median0.0174
Q30.0259
95-th percentile0.0434
Maximum0.0696
Range0.0677
Interquartile range (IQR)0.0136

Descriptive statistics

Standard deviation0.011042318
Coefficient of variation (CV)0.54171594
Kurtosis1.1358772
Mean0.020383963
Median Absolute Deviation (MAD)0.0062
Skewness1.1694211
Sum184.4341
Variance0.00012193278
MonotonicityNot monotonic
2024-05-03T21:52:37.271213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.015 61
 
0.7%
0.0108 59
 
0.6%
0.0121 58
 
0.6%
0.016 58
 
0.6%
0.0118 55
 
0.6%
0.01 55
 
0.6%
0.0152 55
 
0.6%
0.0125 54
 
0.6%
0.0128 54
 
0.6%
0.011 54
 
0.6%
Other values (552) 8485
93.0%
(Missing) 77
 
0.8%
ValueCountFrequency (%)
0.0019 1
 
< 0.1%
0.0028 1
 
< 0.1%
0.0033 2
< 0.1%
0.0035 2
< 0.1%
0.0036 1
 
< 0.1%
0.0037 1
 
< 0.1%
0.0038 2
< 0.1%
0.0039 4
< 0.1%
0.004 2
< 0.1%
0.0042 4
< 0.1%
ValueCountFrequency (%)
0.0696 1
< 0.1%
0.0684 1
< 0.1%
0.068 2
< 0.1%
0.0673 1
< 0.1%
0.0672 1
< 0.1%
0.0668 1
< 0.1%
0.0664 2
< 0.1%
0.0658 1
< 0.1%
0.0652 1
< 0.1%
0.0651 1
< 0.1%

오존농도(ppm)
Real number (ℝ)

Distinct705
Distinct (%)7.8%
Missing71
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean0.030583422
Minimum0.002
Maximum0.0951
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size80.3 KiB
2024-05-03T21:52:37.862853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.002
5-th percentile0.0092
Q10.0207
median0.0293
Q30.0395
95-th percentile0.0548
Maximum0.0951
Range0.0931
Interquartile range (IQR)0.0188

Descriptive statistics

Standard deviation0.013846683
Coefficient of variation (CV)0.45275127
Kurtosis0.16537272
Mean0.030583422
Median Absolute Deviation (MAD)0.0093
Skewness0.4792767
Sum276.9023
Variance0.00019173063
MonotonicityNot monotonic
2024-05-03T21:52:38.444725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0243 45
 
0.5%
0.0318 36
 
0.4%
0.027 35
 
0.4%
0.0266 35
 
0.4%
0.0204 34
 
0.4%
0.024 34
 
0.4%
0.021 34
 
0.4%
0.0215 33
 
0.4%
0.0274 33
 
0.4%
0.0283 33
 
0.4%
Other values (695) 8702
95.4%
(Missing) 71
 
0.8%
ValueCountFrequency (%)
0.002 2
< 0.1%
0.0022 1
 
< 0.1%
0.0023 1
 
< 0.1%
0.0024 1
 
< 0.1%
0.0025 3
< 0.1%
0.0028 1
 
< 0.1%
0.0029 2
< 0.1%
0.003 3
< 0.1%
0.0032 2
< 0.1%
0.0033 2
< 0.1%
ValueCountFrequency (%)
0.0951 1
< 0.1%
0.0876 1
< 0.1%
0.087 1
< 0.1%
0.086 1
< 0.1%
0.0838 1
< 0.1%
0.0834 1
< 0.1%
0.0827 1
< 0.1%
0.0817 2
< 0.1%
0.0815 1
< 0.1%
0.0812 1
< 0.1%

일산화탄소농도(ppm)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct115
Distinct (%)1.3%
Missing260
Missing (%)2.8%
Infinite0
Infinite (%)0.0%
Mean0.45122279
Minimum0.09
Maximum1.41
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size80.3 KiB
2024-05-03T21:52:39.045413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.09
5-th percentile0.26
Q10.35
median0.41
Q30.52
95-th percentile0.77
Maximum1.41
Range1.32
Interquartile range (IQR)0.17

Descriptive statistics

Standard deviation0.15829955
Coefficient of variation (CV)0.35082349
Kurtosis2.5882251
Mean0.45122279
Median Absolute Deviation (MAD)0.08
Skewness1.3494107
Sum4000.09
Variance0.025058748
MonotonicityNot monotonic
2024-05-03T21:52:39.509379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.35 398
 
4.4%
0.36 344
 
3.8%
0.4 339
 
3.7%
0.37 333
 
3.6%
0.39 326
 
3.6%
0.38 320
 
3.5%
0.41 286
 
3.1%
0.42 275
 
3.0%
0.33 265
 
2.9%
0.34 264
 
2.9%
Other values (105) 5715
62.6%
(Missing) 260
 
2.8%
ValueCountFrequency (%)
0.09 1
 
< 0.1%
0.1 3
 
< 0.1%
0.11 1
 
< 0.1%
0.13 2
 
< 0.1%
0.14 4
 
< 0.1%
0.15 1
 
< 0.1%
0.16 1
 
< 0.1%
0.17 10
 
0.1%
0.18 15
0.2%
0.19 26
0.3%
ValueCountFrequency (%)
1.41 1
 
< 0.1%
1.38 1
 
< 0.1%
1.34 3
< 0.1%
1.25 1
 
< 0.1%
1.22 2
< 0.1%
1.2 1
 
< 0.1%
1.19 2
< 0.1%
1.18 1
 
< 0.1%
1.17 2
< 0.1%
1.16 2
< 0.1%

아황산가스농도(ppm)
Real number (ℝ)

MISSING 

Distinct55
Distinct (%)0.6%
Missing153
Missing (%)1.7%
Infinite0
Infinite (%)0.0%
Mean0.0028125279
Minimum0.0012
Maximum0.0079
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size80.3 KiB
2024-05-03T21:52:40.148455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.0012
5-th percentile0.002
Q10.0024
median0.0028
Q30.0031
95-th percentile0.0038
Maximum0.0079
Range0.0067
Interquartile range (IQR)0.0007

Descriptive statistics

Standard deviation0.00056714684
Coefficient of variation (CV)0.20165021
Kurtosis3.9557954
Mean0.0028125279
Median Absolute Deviation (MAD)0.0004
Skewness1.0773645
Sum25.234
Variance3.2165554 × 10-7
MonotonicityNot monotonic
2024-05-03T21:52:40.812345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.003 754
 
8.3%
0.0026 663
 
7.3%
0.0027 650
 
7.1%
0.0029 624
 
6.8%
0.0024 618
 
6.8%
0.0028 616
 
6.8%
0.0025 590
 
6.5%
0.0023 545
 
6.0%
0.0031 459
 
5.0%
0.0022 450
 
4.9%
Other values (45) 3003
32.9%
ValueCountFrequency (%)
0.0012 1
 
< 0.1%
0.0013 3
 
< 0.1%
0.0014 3
 
< 0.1%
0.0015 5
 
0.1%
0.0016 19
 
0.2%
0.0017 32
 
0.4%
0.0018 60
 
0.7%
0.0019 136
1.5%
0.002 309
3.4%
0.0021 314
3.4%
ValueCountFrequency (%)
0.0079 1
 
< 0.1%
0.0074 1
 
< 0.1%
0.0072 1
 
< 0.1%
0.0068 1
 
< 0.1%
0.0067 1
 
< 0.1%
0.0065 3
< 0.1%
0.0063 1
 
< 0.1%
0.0061 2
< 0.1%
0.006 3
< 0.1%
0.0057 1
 
< 0.1%

미세먼지(㎍/㎥)
Real number (ℝ)

HIGH CORRELATION 

Distinct190
Distinct (%)2.1%
Missing89
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean37.734949
Minimum3
Maximum319
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size80.3 KiB
2024-05-03T21:52:41.596859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile10
Q121
median32
Q345
95-th percentile87
Maximum319
Range316
Interquartile range (IQR)24

Descriptive statistics

Standard deviation26.9441
Coefficient of variation (CV)0.71403568
Kurtosis17.22586
Mean37.734949
Median Absolute Deviation (MAD)12
Skewness3.0175362
Sum340973
Variance725.98453
MonotonicityNot monotonic
2024-05-03T21:52:42.185438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21 239
 
2.6%
18 236
 
2.6%
31 235
 
2.6%
20 231
 
2.5%
22 230
 
2.5%
29 229
 
2.5%
30 223
 
2.4%
23 221
 
2.4%
19 220
 
2.4%
27 220
 
2.4%
Other values (180) 6752
74.0%
ValueCountFrequency (%)
3 9
 
0.1%
4 44
0.5%
5 48
0.5%
6 52
0.6%
7 60
0.7%
8 68
0.7%
9 82
0.9%
10 109
1.2%
11 104
1.1%
12 107
1.2%
ValueCountFrequency (%)
319 1
< 0.1%
309 1
< 0.1%
306 1
< 0.1%
291 1
< 0.1%
288 1
< 0.1%
286 1
< 0.1%
282 1
< 0.1%
273 1
< 0.1%
269 1
< 0.1%
267 2
< 0.1%

초미세먼지(㎍/㎥)
Real number (ℝ)

HIGH CORRELATION 

Distinct93
Distinct (%)1.0%
Missing74
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean19.660259
Minimum1
Maximum116
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size80.3 KiB
2024-05-03T21:52:42.824583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q111
median17
Q325
95-th percentile47
Maximum116
Range115
Interquartile range (IQR)14

Descriptive statistics

Standard deviation13.013382
Coefficient of variation (CV)0.66191304
Kurtosis4.163792
Mean19.660259
Median Absolute Deviation (MAD)7
Skewness1.6784699
Sum177945
Variance169.3481
MonotonicityNot monotonic
2024-05-03T21:52:43.523562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14 452
 
5.0%
16 436
 
4.8%
10 403
 
4.4%
15 393
 
4.3%
17 377
 
4.1%
13 375
 
4.1%
11 373
 
4.1%
18 371
 
4.1%
12 357
 
3.9%
19 329
 
3.6%
Other values (83) 5185
56.8%
ValueCountFrequency (%)
1 61
 
0.7%
2 102
 
1.1%
3 132
 
1.4%
4 141
 
1.5%
5 184
2.0%
6 196
2.1%
7 268
2.9%
8 275
3.0%
9 327
3.6%
10 403
4.4%
ValueCountFrequency (%)
116 1
 
< 0.1%
101 1
 
< 0.1%
98 3
< 0.1%
96 2
< 0.1%
95 2
< 0.1%
94 2
< 0.1%
92 1
 
< 0.1%
91 1
 
< 0.1%
90 1
 
< 0.1%
89 2
< 0.1%

Interactions

2024-05-03T21:52:29.752192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:52:16.679926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:52:18.530210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:52:20.370345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:52:22.256639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:52:24.236809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:52:26.882974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:52:29.991683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:52:16.950774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:52:18.818742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:52:20.621145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:52:22.536182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:52:24.534802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:52:27.296157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:52:30.703049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:52:17.193486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:52:19.101793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:52:20.879657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:52:22.823089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:52:25.013540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:52:27.864330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:52:31.075543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:52:17.360613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:52:19.371315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:52:21.130519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:52:23.086197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:52:25.361313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:52:28.338696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:52:31.454201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:52:17.676015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:52:19.602408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:52:21.416774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:52:23.371445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:52:25.708458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:52:28.688769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:52:31.882272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:52:17.980526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:52:19.851070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:52:21.704789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:52:23.657063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:52:26.138333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:52:29.013524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:52:32.226213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:52:18.262842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:52:20.062744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:52:21.974737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:52:23.940205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:52:26.501791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:52:29.304214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-03T21:52:44.099235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
측정일시권역명측정소명이산화질소농도(ppm)오존농도(ppm)일산화탄소농도(ppm)아황산가스농도(ppm)미세먼지(㎍/㎥)초미세먼지(㎍/㎥)
측정일시1.0000.0000.0000.5760.6900.5680.3440.5730.501
권역명0.0001.0001.0000.2110.0830.1880.3080.0430.014
측정소명0.0001.0001.0000.2990.1220.2910.4300.0000.054
이산화질소농도(ppm)0.5760.2110.2991.0000.6340.7800.5690.5040.610
오존농도(ppm)0.6900.0830.1220.6341.0000.5330.2440.2470.388
일산화탄소농도(ppm)0.5680.1880.2910.7800.5331.0000.6930.5380.715
아황산가스농도(ppm)0.3440.3080.4300.5690.2440.6931.0000.4160.359
미세먼지(㎍/㎥)0.5730.0430.0000.5040.2470.5380.4161.0000.753
초미세먼지(㎍/㎥)0.5010.0140.0540.6100.3880.7150.3590.7531.000
2024-05-03T21:52:44.569907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
측정소명권역명
측정소명1.0000.999
권역명0.9991.000
2024-05-03T21:52:44.990503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
측정일시이산화질소농도(ppm)오존농도(ppm)일산화탄소농도(ppm)아황산가스농도(ppm)미세먼지(㎍/㎥)초미세먼지(㎍/㎥)권역명측정소명
측정일시1.000-0.172-0.100-0.137-0.255-0.423-0.2920.0000.000
이산화질소농도(ppm)-0.1721.000-0.4670.7290.4780.5300.5810.0890.109
오존농도(ppm)-0.100-0.4671.000-0.295-0.0350.0750.0320.0350.043
일산화탄소농도(ppm)-0.1370.729-0.2951.0000.4260.5770.6820.0790.106
아황산가스농도(ppm)-0.2550.478-0.0350.4261.0000.4630.4670.1330.166
미세먼지(㎍/㎥)-0.4230.5300.0750.5770.4631.0000.8510.0180.000
초미세먼지(㎍/㎥)-0.2920.5810.0320.6820.4670.8511.0000.0070.020
권역명0.0000.0890.0350.0790.1330.0180.0071.0000.999
측정소명0.0000.1090.0430.1060.1660.0000.0200.9991.000

Missing values

2024-05-03T21:52:32.648987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-03T21:52:33.157628image/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.
2024-05-03T21:52:33.691376image/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)미세먼지(㎍/㎥)초미세먼지(㎍/㎥)
020230101동남권강남구0.0240.0240.60.0045236
120230101동남권강동구0.0270.0170.70.0035643
220230101동북권강북구<NA>0.0260.70.0045134
320230101서남권강서구0.0210.0250.60.0046238
420230101서남권관악구0.0250.020.60.0035435
520230101동북권광진구0.01750.01760.790.00264836
620230101서남권구로구0.0190.0210.40.0035335
720230101서남권금천구0.0230.0240.60.0044738
820230101동북권노원구0.0310.0220.70.0045139
920230101동북권도봉구0.020.0290.70.0034332
측정일시권역명측정소명이산화질소농도(ppm)오존농도(ppm)일산화탄소농도(ppm)아황산가스농도(ppm)미세먼지(㎍/㎥)초미세먼지(㎍/㎥)
911520231231동북권성동구0.02810.01360.650.00222417
911620231231동북권성북구0.02680.01450.840.00212118
911720231231동남권송파구0.02920.0140.70.00252116
911820231231서남권양천구0.03150.01640.730.0032722
911920231231서남권영등포구0.02660.01820.680.00272318
912020231231도심권용산구0.02860.01460.780.00252215
912120231231서북권은평구0.01720.01781.120.00252118
912220231231도심권종로구0.02380.0180.730.00292218
912320231231도심권중구0.02610.01790.670.00282018
912420231231동북권중랑구0.03160.0130.680.00242016