Overview

Dataset statistics

Number of variables10
Number of observations9074
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory779.9 KiB
Average record size in memory88.0 B

Variable types

Numeric8
Categorical2

Dataset

Description파일 다운로드
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-2224/S/1/datasetView.do

Alerts

msrste_nm is highly overall correlated with msrrgn_cd and 2 other fieldsHigh correlation
msrrgn_nm is highly overall correlated with msrrgn_cd and 2 other fieldsHigh correlation
msrrgn_cd is highly overall correlated with msradm and 2 other fieldsHigh correlation
msradm is highly overall correlated with msrrgn_cd and 2 other fieldsHigh correlation
pm10 is highly overall correlated with no2High correlation
no2 is highly overall correlated with pm10 and 1 other fieldsHigh correlation
co is highly overall correlated with no2High correlation
pm10 has 620 (6.8%) zerosZeros
o3 has 369 (4.1%) zerosZeros
no2 has 378 (4.2%) zerosZeros
co has 378 (4.2%) zerosZeros
so2 has 372 (4.1%) zerosZeros

Reproduction

Analysis started2024-05-11 06:42:49.125054
Analysis finished2024-05-11 06:43:17.993988
Duration28.87 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

msrdt_de
Real number (ℝ)

Distinct364
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20200671
Minimum20200101
Maximum20201231
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size79.9 KiB
2024-05-11T06:43:18.213480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20200101
5-th percentile20200120
Q120200403
median20200703
Q320201002
95-th percentile20201213
Maximum20201231
Range1130
Interquartile range (IQR)599

Descriptive statistics

Standard deviation343.83321
Coefficient of variation (CV)1.7020881 × 10-5
Kurtosis-1.1995848
Mean20200671
Median Absolute Deviation (MAD)299
Skewness-0.016878685
Sum1.8330089 × 1011
Variance118221.28
MonotonicityDecreasing
2024-05-11T06:43:18.682091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20201231 25
 
0.3%
20200520 25
 
0.3%
20200430 25
 
0.3%
20200501 25
 
0.3%
20200502 25
 
0.3%
20200503 25
 
0.3%
20200504 25
 
0.3%
20200505 25
 
0.3%
20200506 25
 
0.3%
20200507 25
 
0.3%
Other values (354) 8824
97.2%
ValueCountFrequency (%)
20200101 20
0.2%
20200102 20
0.2%
20200103 20
0.2%
20200104 20
0.2%
20200105 20
0.2%
20200106 24
0.3%
20200107 25
0.3%
20200108 25
0.3%
20200109 25
0.3%
20200110 25
0.3%
ValueCountFrequency (%)
20201231 25
0.3%
20201230 25
0.3%
20201229 25
0.3%
20201228 25
0.3%
20201227 25
0.3%
20201226 25
0.3%
20201225 25
0.3%
20201224 25
0.3%
20201223 25
0.3%
20201222 25
0.3%

msrrgn_cd
Real number (ℝ)

HIGH CORRELATION 

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean318.38131
Minimum100
Maximum999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size79.9 KiB
2024-05-11T06:43:19.087912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile100
Q1102
median104
Q3108
95-th percentile999
Maximum999
Range899
Interquartile range (IQR)6

Descriptive statistics

Standard deviation382.53054
Coefficient of variation (CV)1.2014855
Kurtosis-0.51769466
Mean318.38131
Median Absolute Deviation (MAD)4
Skewness1.2174639
Sum2888992
Variance146329.61
MonotonicityNot monotonic
2024-05-11T06:43:19.459191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
999 2178
24.0%
103 1456
16.0%
108 1451
16.0%
102 1451
16.0%
100 1092
12.0%
104 1082
11.9%
101 364
 
4.0%
ValueCountFrequency (%)
100 1092
12.0%
101 364
 
4.0%
102 1451
16.0%
103 1456
16.0%
104 1082
11.9%
108 1451
16.0%
999 2178
24.0%
ValueCountFrequency (%)
999 2178
24.0%
108 1451
16.0%
104 1082
11.9%
103 1456
16.0%
102 1451
16.0%
101 364
 
4.0%
100 1092
12.0%

msrrgn_nm
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size71.0 KiB
기타
2178 
서남권
1456 
입체
1451 
동북권
1451 
도심권
1092 
Other values (2)
1446 

Length

Max length3
Median length3
Mean length2.6000661
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row서남권
2nd row기타
3rd row입체
4th row기타
5th row동북권

Common Values

ValueCountFrequency (%)
기타 2178
24.0%
서남권 1456
16.0%
입체 1451
16.0%
동북권 1451
16.0%
도심권 1092
12.0%
동남권 1082
11.9%
서북권 364
 
4.0%

Length

2024-05-11T06:43:19.886141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T06:43:20.319517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
기타 2178
24.0%
서남권 1456
16.0%
입체 1451
16.0%
동북권 1451
16.0%
도심권 1092
12.0%
동남권 1082
11.9%
서북권 364
 
4.0%

msradm
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean217872.8
Minimum111122
Maximum555556
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size79.9 KiB
2024-05-11T06:43:20.708032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum111122
5-th percentile111124
Q1111162
median111242
Q3111545
95-th percentile555555
Maximum555556
Range444434
Interquartile range (IQR)383

Descriptive statistics

Standard deviation189784.32
Coefficient of variation (CV)0.87107851
Kurtosis-0.51758176
Mean217872.8
Median Absolute Deviation (MAD)86
Skewness1.2175925
Sum1.9769778 × 109
Variance3.6018086 × 1010
MonotonicityNot monotonic
2024-05-11T06:43:21.090027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
111232 364
 
4.0%
111162 364
 
4.0%
555556 364
 
4.0%
111124 364
 
4.0%
555555 364
 
4.0%
111143 364
 
4.0%
111272 364
 
4.0%
111545 364
 
4.0%
111122 364
 
4.0%
111155 364
 
4.0%
Other values (15) 5434
59.9%
ValueCountFrequency (%)
111122 364
4.0%
111124 364
4.0%
111143 364
4.0%
111154 364
4.0%
111155 364
4.0%
111156 364
4.0%
111162 364
4.0%
111192 364
4.0%
111200 359
4.0%
111202 364
4.0%
ValueCountFrequency (%)
555556 364
4.0%
555555 364
4.0%
555554 364
4.0%
555553 364
4.0%
555552 364
4.0%
555550 358
3.9%
111545 364
4.0%
111278 359
4.0%
111277 364
4.0%
111276 359
4.0%

msrste_nm
Categorical

HIGH CORRELATION 

Distinct25
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size71.0 KiB
영등포로
 
364
정릉로
 
364
세곡
 
364
관악산
 
364
홍릉로
 
364
Other values (20)
7254 

Length

Max length6
Median length5
Mean length3.5188451
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row영등포로
2nd row북한산
3rd row마포아트센터
4th row관악산
5th row홍릉로

Common Values

ValueCountFrequency (%)
영등포로 364
 
4.0%
정릉로 364
 
4.0%
세곡 364
 
4.0%
관악산 364
 
4.0%
홍릉로 364
 
4.0%
남산 364
 
4.0%
공항대로 364
 
4.0%
화랑로 364
 
4.0%
동작대로 364
 
4.0%
북한산 364
 
4.0%
Other values (15) 5434
59.9%

Length

2024-05-11T06:43:21.562331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
영등포로 364
 
4.0%
신촌로 364
 
4.0%
청계천로 364
 
4.0%
행주 364
 
4.0%
서울숲 364
 
4.0%
올림픽공원 364
 
4.0%
시흥대로 364
 
4.0%
한강대로 364
 
4.0%
종로 364
 
4.0%
정릉로 364
 
4.0%
Other values (15) 5434
59.9%

pm10
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct123
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.782896
Minimum0
Maximum167
Zeros620
Zeros (%)6.8%
Negative0
Negative (%)0.0%
Memory size79.9 KiB
2024-05-11T06:43:21.987378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q121
median33
Q348
95-th percentile70
Maximum167
Range167
Interquartile range (IQR)27

Descriptive statistics

Standard deviation20.533532
Coefficient of variation (CV)0.59033418
Kurtosis1.0268396
Mean34.782896
Median Absolute Deviation (MAD)13
Skewness0.59994923
Sum315620
Variance421.62596
MonotonicityNot monotonic
2024-05-11T06:43:22.482517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 620
 
6.8%
30 226
 
2.5%
28 215
 
2.4%
25 212
 
2.3%
26 200
 
2.2%
24 195
 
2.1%
29 189
 
2.1%
27 188
 
2.1%
31 187
 
2.1%
32 186
 
2.0%
Other values (113) 6656
73.4%
ValueCountFrequency (%)
0 620
6.8%
3 11
 
0.1%
4 16
 
0.2%
5 35
 
0.4%
6 51
 
0.6%
7 46
 
0.5%
8 64
 
0.7%
9 82
 
0.9%
10 81
 
0.9%
11 92
 
1.0%
ValueCountFrequency (%)
167 2
< 0.1%
166 1
< 0.1%
153 1
< 0.1%
146 1
< 0.1%
132 1
< 0.1%
131 1
< 0.1%
127 2
< 0.1%
126 1
< 0.1%
118 1
< 0.1%
117 2
< 0.1%

o3
Real number (ℝ)

ZEROS 

Distinct82
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.02218812
Minimum0
Maximum0.097
Zeros369
Zeros (%)4.1%
Negative0
Negative (%)0.0%
Memory size79.9 KiB
2024-05-11T06:43:23.028009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.003
Q10.013
median0.021
Q30.03
95-th percentile0.046
Maximum0.097
Range0.097
Interquartile range (IQR)0.017

Descriptive statistics

Standard deviation0.013160079
Coefficient of variation (CV)0.59311376
Kurtosis0.680673
Mean0.02218812
Median Absolute Deviation (MAD)0.009
Skewness0.67757593
Sum201.335
Variance0.00017318769
MonotonicityNot monotonic
2024-05-11T06:43:23.465527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 369
 
4.1%
0.017 305
 
3.4%
0.014 286
 
3.2%
0.018 286
 
3.2%
0.021 284
 
3.1%
0.019 282
 
3.1%
0.015 281
 
3.1%
0.026 273
 
3.0%
0.016 270
 
3.0%
0.013 267
 
2.9%
Other values (72) 6171
68.0%
ValueCountFrequency (%)
0.0 369
4.1%
0.002 9
 
0.1%
0.003 84
 
0.9%
0.004 128
 
1.4%
0.005 188
2.1%
0.006 166
1.8%
0.007 179
2.0%
0.008 194
2.1%
0.009 223
2.5%
0.01 244
2.7%
ValueCountFrequency (%)
0.097 1
 
< 0.1%
0.094 1
 
< 0.1%
0.086 1
 
< 0.1%
0.084 1
 
< 0.1%
0.081 2
< 0.1%
0.08 1
 
< 0.1%
0.079 1
 
< 0.1%
0.077 1
 
< 0.1%
0.074 3
< 0.1%
0.073 1
 
< 0.1%

no2
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct86
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.026707296
Minimum0
Maximum0.127
Zeros378
Zeros (%)4.2%
Negative0
Negative (%)0.0%
Memory size79.9 KiB
2024-05-11T06:43:23.916768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.005
Q10.016
median0.025
Q30.037
95-th percentile0.053
Maximum0.127
Range0.127
Interquartile range (IQR)0.021

Descriptive statistics

Standard deviation0.014824119
Coefficient of variation (CV)0.55505878
Kurtosis0.10474236
Mean0.026707296
Median Absolute Deviation (MAD)0.01
Skewness0.48240878
Sum242.342
Variance0.0002197545
MonotonicityNot monotonic
2024-05-11T06:43:24.513960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 378
 
4.2%
0.022 268
 
3.0%
0.017 261
 
2.9%
0.016 241
 
2.7%
0.018 241
 
2.7%
0.023 240
 
2.6%
0.019 234
 
2.6%
0.029 232
 
2.6%
0.028 231
 
2.5%
0.024 229
 
2.5%
Other values (76) 6519
71.8%
ValueCountFrequency (%)
0.0 378
4.2%
0.001 11
 
0.1%
0.002 3
 
< 0.1%
0.003 5
 
0.1%
0.004 20
 
0.2%
0.005 39
 
0.4%
0.006 70
 
0.8%
0.007 102
 
1.1%
0.008 138
 
1.5%
0.009 177
2.0%
ValueCountFrequency (%)
0.127 1
 
< 0.1%
0.095 1
 
< 0.1%
0.09 1
 
< 0.1%
0.085 1
 
< 0.1%
0.083 1
 
< 0.1%
0.08 2
< 0.1%
0.079 2
< 0.1%
0.078 2
< 0.1%
0.077 3
< 0.1%
0.076 2
< 0.1%

co
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct19
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.52013445
Minimum0
Maximum8
Zeros378
Zeros (%)4.2%
Negative0
Negative (%)0.0%
Memory size79.9 KiB
2024-05-11T06:43:25.006286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.1
Q10.4
median0.5
Q30.7
95-th percentile1
Maximum8
Range8
Interquartile range (IQR)0.3

Descriptive statistics

Standard deviation0.2590182
Coefficient of variation (CV)0.49798316
Kurtosis76.785839
Mean0.52013445
Median Absolute Deviation (MAD)0.1
Skewness3.0311672
Sum4719.7
Variance0.067090426
MonotonicityNot monotonic
2024-05-11T06:43:25.519921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0.5 1778
19.6%
0.4 1678
18.5%
0.6 1323
14.6%
0.3 1011
11.1%
0.7 856
9.4%
0.8 563
 
6.2%
0.2 518
 
5.7%
0.9 382
 
4.2%
0.0 378
 
4.2%
1.0 260
 
2.9%
Other values (9) 327
 
3.6%
ValueCountFrequency (%)
0.0 378
 
4.2%
0.1 84
 
0.9%
0.2 518
 
5.7%
0.3 1011
11.1%
0.4 1678
18.5%
0.5 1778
19.6%
0.6 1323
14.6%
0.7 856
9.4%
0.8 563
 
6.2%
0.9 382
 
4.2%
ValueCountFrequency (%)
8.0 1
 
< 0.1%
1.7 1
 
< 0.1%
1.6 4
 
< 0.1%
1.5 6
 
0.1%
1.4 15
 
0.2%
1.3 31
 
0.3%
1.2 71
 
0.8%
1.1 114
 
1.3%
1.0 260
2.9%
0.9 382
4.2%

so2
Real number (ℝ)

ZEROS 

Distinct14
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0034059952
Minimum0
Maximum0.058
Zeros372
Zeros (%)4.1%
Negative0
Negative (%)0.0%
Memory size79.9 KiB
2024-05-11T06:43:26.136277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.001
Q10.003
median0.003
Q30.004
95-th percentile0.006
Maximum0.058
Range0.058
Interquartile range (IQR)0.001

Descriptive statistics

Standard deviation0.0015207994
Coefficient of variation (CV)0.44650662
Kurtosis276.00498
Mean0.0034059952
Median Absolute Deviation (MAD)0.001
Skewness8.3059305
Sum30.906
Variance2.3128308 × 10-6
MonotonicityNot monotonic
2024-05-11T06:43:26.904448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0.003 3436
37.9%
0.004 2545
28.0%
0.002 1112
 
12.3%
0.005 977
 
10.8%
0.0 372
 
4.1%
0.006 362
 
4.0%
0.001 147
 
1.6%
0.007 106
 
1.2%
0.008 12
 
0.1%
0.009 1
 
< 0.1%
Other values (4) 4
 
< 0.1%
ValueCountFrequency (%)
0.0 372
 
4.1%
0.001 147
 
1.6%
0.002 1112
 
12.3%
0.003 3436
37.9%
0.004 2545
28.0%
0.005 977
 
10.8%
0.006 362
 
4.0%
0.007 106
 
1.2%
0.008 12
 
0.1%
0.009 1
 
< 0.1%
ValueCountFrequency (%)
0.058 1
 
< 0.1%
0.049 1
 
< 0.1%
0.025 1
 
< 0.1%
0.011 1
 
< 0.1%
0.009 1
 
< 0.1%
0.008 12
 
0.1%
0.007 106
 
1.2%
0.006 362
 
4.0%
0.005 977
 
10.8%
0.004 2545
28.0%

Interactions

2024-05-11T06:43:13.751551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:42:54.608672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:42:58.160779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:43:01.277211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:43:04.204426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:43:06.891285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:43:08.885046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:43:11.233322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:43:14.135378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:42:55.126885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:42:58.483490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:43:01.627857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:43:04.761417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:43:07.107629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:43:09.179083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:43:11.527888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:43:14.459462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:42:55.568411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:42:59.180212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:43:02.062201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:43:05.069865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:43:07.312484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:43:09.442724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:43:11.805284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:43:14.802786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:42:56.130082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:42:59.532094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:43:02.465310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:43:05.420110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:43:07.525769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:43:09.725673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:43:12.099369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:43:15.217494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:42:56.651023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:42:59.885569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:43:02.774007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:43:05.708468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:43:07.883341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:43:10.027322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:43:12.399660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:43:15.733727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:42:57.024682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:43:00.224688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:43:03.142616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:43:05.990492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:43:08.063533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:43:10.311900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:43:12.694447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:43:16.058571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:42:57.341360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:43:00.572615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:43:03.536342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:43:06.278476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:43:08.291416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:43:10.625864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:43:12.999191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:43:16.399527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:42:57.856811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:43:00.942821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:43:03.860501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:43:06.603741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:43:08.602252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:43:10.945396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:43:13.311484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-11T06:43:27.283845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
msrdt_demsrrgn_cdmsrrgn_nmmsradmmsrste_nmpm10o3no2coso2
msrdt_de1.0000.0000.0000.0000.0000.5520.6160.3030.3830.137
msrrgn_cd0.0001.0001.0001.0001.0000.0780.2390.3120.0170.019
msrrgn_nm0.0001.0001.0001.0001.0000.1520.1960.3790.1080.155
msradm0.0001.0001.0001.0001.0000.0680.2390.3090.0140.024
msrste_nm0.0001.0001.0001.0001.0000.2500.3430.5220.2700.532
pm100.5520.0780.1520.0680.2501.0000.3370.4240.3920.109
o30.6160.2390.1960.2390.3430.3371.0000.3790.3260.165
no20.3030.3120.3790.3090.5220.4240.3791.0000.4010.119
co0.3830.0170.1080.0140.2700.3920.3260.4011.0000.648
so20.1370.0190.1550.0240.5320.1090.1650.1190.6481.000
2024-05-11T06:43:28.005366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
msrste_nmmsrrgn_nm
msrste_nm1.0000.999
msrrgn_nm0.9991.000
2024-05-11T06:43:28.458317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
msrdt_demsrrgn_cdmsradmpm10o3no2coso2msrrgn_nmmsrste_nm
msrdt_de1.0000.0020.002-0.301-0.192-0.096-0.081-0.2100.0000.000
msrrgn_cd0.0021.0000.684-0.1180.171-0.356-0.109-0.1261.0000.999
msradm0.0020.6841.000-0.0650.088-0.1600.036-0.1471.0000.999
pm10-0.301-0.118-0.0651.0000.0990.5120.4890.4480.0770.090
o3-0.1920.1710.0880.0991.000-0.313-0.255-0.0360.1000.128
no2-0.096-0.356-0.1600.512-0.3131.0000.6510.3690.2110.229
co-0.081-0.1090.0360.489-0.2550.6511.0000.3720.0610.133
so2-0.210-0.126-0.1470.448-0.0360.3690.3721.0000.0990.259
msrrgn_nm0.0001.0001.0000.0770.1000.2110.0610.0991.0000.999
msrste_nm0.0000.9990.9990.0900.1280.2290.1330.2590.9991.000

Missing values

2024-05-11T06:43:16.881922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-11T06:43:17.627982image/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

msrdt_demsrrgn_cdmsrrgn_nmmsradmmsrste_nmpm10o3no2coso2
020201231103서남권111232영등포로280.0130.0310.50.004
120201231999기타555553북한산200.0260.0090.40.002
220201231108입체111200마포아트센터250.0220.020.70.005
320201231999기타555554관악산270.0240.0140.20.003
420201231102동북권111154홍릉로50.0140.0270.50.004
520201231999기타555552남산210.0220.0130.30.003
620201231102동북권111276강변북로280.0160.0260.90.004
720201231103서남권111213공항대로330.0160.0250.40.003
820201231999기타555550궁동260.0160.020.40.003
920201231102동북권111277화랑로300.010.0270.30.003
msrdt_demsrrgn_cdmsrrgn_nmmsradmmsrste_nmpm10o3no2coso2
906420200101100도심권111122한강대로390.0040.0390.70.003
906520200101999기타555553북한산330.0040.0320.80.004
906620200101999기타555552남산360.0040.0450.90.004
906720200101102동북권111277화랑로370.0070.030.40.004
906820200101104동남권111156천호대로370.0040.0370.60.003
906920200101108입체111192자연사박물관300.00.00.00.0
907020200101108입체111143서울숲320.0030.0420.60.004
907120200101102동북권111162정릉로390.0030.0330.60.004
907220200101103서남권111545시흥대로320.0030.0440.70.004
907320200101999기타555554관악산240.0170.0160.60.004