Overview

Dataset statistics

Number of variables9
Number of observations9125
Missing cells711
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
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 117 (1.3%) missing valuesMissing
오존농도(ppm) has 123 (1.3%) missing valuesMissing
일산화탄소농도(ppm) has 140 (1.5%) missing valuesMissing
아황산가스농도(ppm) has 136 (1.5%) missing valuesMissing
미세먼지농도(㎍/㎥) has 97 (1.1%) missing valuesMissing
초미세먼지농도(㎍/㎥) has 98 (1.1%) missing valuesMissing

Reproduction

Analysis started2024-05-03 21:42:25.649715
Analysis finished2024-05-03 21:42:42.681925
Duration17.03 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%
Mean20180668
Minimum20180101
Maximum20181231
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size80.3 KiB
2024-05-03T21:42:42.942723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20180101
5-th percentile20180119
Q120180402
median20180702
Q320181001
95-th percentile20181213
Maximum20181231
Range1130
Interquartile range (IQR)599

Descriptive statistics

Standard deviation345.02079
Coefficient of variation (CV)1.7096599 × 10-5
Kurtosis-1.2057171
Mean20180668
Median Absolute Deviation (MAD)300
Skewness-0.010696166
Sum1.841486 × 1011
Variance119039.35
MonotonicityIncreasing
2024-05-03T21:42:43.490697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20180101 25
 
0.3%
20180909 25
 
0.3%
20180907 25
 
0.3%
20180906 25
 
0.3%
20180905 25
 
0.3%
20180904 25
 
0.3%
20180903 25
 
0.3%
20180902 25
 
0.3%
20180901 25
 
0.3%
20180831 25
 
0.3%
Other values (355) 8875
97.3%
ValueCountFrequency (%)
20180101 25
0.3%
20180102 25
0.3%
20180103 25
0.3%
20180104 25
0.3%
20180105 25
0.3%
20180106 25
0.3%
20180107 25
0.3%
20180108 25
0.3%
20180109 25
0.3%
20180110 25
0.3%
ValueCountFrequency (%)
20181231 25
0.3%
20181230 25
0.3%
20181229 25
0.3%
20181228 25
0.3%
20181227 25
0.3%
20181226 25
0.3%
20181225 25
0.3%
20181224 25
0.3%
20181223 25
0.3%
20181222 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

2024-05-03T21:42:43.923512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-03T21:42:44.243671image/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

2024-05-03T21:42:44.700880image/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 

Distinct82
Distinct (%)0.9%
Missing117
Missing (%)1.3%
Infinite0
Infinite (%)0.0%
Mean0.028154529
Minimum0.001
Maximum0.089
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size80.3 KiB
2024-05-03T21:42:45.417283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.001
5-th percentile0.011
Q10.018
median0.026
Q30.036
95-th percentile0.053
Maximum0.089
Range0.088
Interquartile range (IQR)0.018

Descriptive statistics

Standard deviation0.013112747
Coefficient of variation (CV)0.46574202
Kurtosis0.51354674
Mean0.028154529
Median Absolute Deviation (MAD)0.009
Skewness0.83963934
Sum253.616
Variance0.00017194414
MonotonicityNot monotonic
2024-05-03T21:42:45.915972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.018 333
 
3.6%
0.017 331
 
3.6%
0.02 330
 
3.6%
0.022 324
 
3.6%
0.021 314
 
3.4%
0.019 306
 
3.4%
0.016 299
 
3.3%
0.015 293
 
3.2%
0.023 286
 
3.1%
0.025 270
 
3.0%
Other values (72) 5922
64.9%
ValueCountFrequency (%)
0.001 2
 
< 0.1%
0.003 1
 
< 0.1%
0.005 6
 
0.1%
0.006 21
 
0.2%
0.007 25
 
0.3%
0.008 54
 
0.6%
0.009 111
1.2%
0.01 113
1.2%
0.011 175
1.9%
0.012 181
2.0%
ValueCountFrequency (%)
0.089 1
 
< 0.1%
0.087 1
 
< 0.1%
0.085 3
< 0.1%
0.081 3
< 0.1%
0.08 2
 
< 0.1%
0.079 3
< 0.1%
0.078 2
 
< 0.1%
0.077 3
< 0.1%
0.076 6
0.1%
0.075 5
0.1%

오존농도(ppm)
Real number (ℝ)

MISSING 

Distinct72
Distinct (%)0.8%
Missing123
Missing (%)1.3%
Infinite0
Infinite (%)0.0%
Mean0.023333704
Minimum0
Maximum0.08
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size80.3 KiB
2024-05-03T21:42:46.373305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.005
Q10.014
median0.023
Q30.031
95-th percentile0.045
Maximum0.08
Range0.08
Interquartile range (IQR)0.017

Descriptive statistics

Standard deviation0.012203163
Coefficient of variation (CV)0.52298439
Kurtosis0.058565153
Mean0.023333704
Median Absolute Deviation (MAD)0.009
Skewness0.51288203
Sum210.05
Variance0.00014891718
MonotonicityNot monotonic
2024-05-03T21:42:46.897902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.024 286
 
3.1%
0.026 282
 
3.1%
0.021 280
 
3.1%
0.023 275
 
3.0%
0.028 274
 
3.0%
0.025 273
 
3.0%
0.022 262
 
2.9%
0.029 262
 
2.9%
0.019 256
 
2.8%
0.031 256
 
2.8%
Other values (62) 6296
69.0%
ValueCountFrequency (%)
0.0 2
 
< 0.1%
0.001 1
 
< 0.1%
0.002 30
 
0.3%
0.003 118
1.3%
0.004 134
1.5%
0.005 175
1.9%
0.006 152
1.7%
0.007 223
2.4%
0.008 229
2.5%
0.009 216
2.4%
ValueCountFrequency (%)
0.08 1
 
< 0.1%
0.074 1
 
< 0.1%
0.07 2
 
< 0.1%
0.069 4
< 0.1%
0.068 4
< 0.1%
0.066 5
0.1%
0.065 1
 
< 0.1%
0.064 8
0.1%
0.063 7
0.1%
0.062 6
0.1%

일산화탄소농도(ppm)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct15
Distinct (%)0.2%
Missing140
Missing (%)1.5%
Infinite0
Infinite (%)0.0%
Mean0.50570952
Minimum0.1
Maximum1.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size80.3 KiB
2024-05-03T21:42:47.294530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.2
Q10.4
median0.5
Q30.6
95-th percentile0.9
Maximum1.5
Range1.4
Interquartile range (IQR)0.2

Descriptive statistics

Standard deviation0.20210852
Coefficient of variation (CV)0.39965338
Kurtosis0.99436523
Mean0.50570952
Median Absolute Deviation (MAD)0.1
Skewness0.91445833
Sum4543.8
Variance0.040847852
MonotonicityNot monotonic
2024-05-03T21:42:47.678469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0.4 2040
22.4%
0.5 1759
19.3%
0.3 1615
17.7%
0.6 1212
13.3%
0.7 810
 
8.9%
0.8 500
 
5.5%
0.2 417
 
4.6%
0.9 297
 
3.3%
1.0 158
 
1.7%
1.1 79
 
0.9%
Other values (5) 98
 
1.1%
(Missing) 140
 
1.5%
ValueCountFrequency (%)
0.1 43
 
0.5%
0.2 417
 
4.6%
0.3 1615
17.7%
0.4 2040
22.4%
0.5 1759
19.3%
0.6 1212
13.3%
0.7 810
 
8.9%
0.8 500
 
5.5%
0.9 297
 
3.3%
1.0 158
 
1.7%
ValueCountFrequency (%)
1.5 4
 
< 0.1%
1.4 7
 
0.1%
1.3 11
 
0.1%
1.2 33
 
0.4%
1.1 79
 
0.9%
1.0 158
 
1.7%
0.9 297
 
3.3%
0.8 500
5.5%
0.7 810
8.9%
0.6 1212
13.3%

아황산가스농도(ppm)
Real number (ℝ)

MISSING 

Distinct15
Distinct (%)0.2%
Missing136
Missing (%)1.5%
Infinite0
Infinite (%)0.0%
Mean0.0043653354
Minimum0.001
Maximum0.016
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size80.3 KiB
2024-05-03T21:42:48.071728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.001
5-th percentile0.002
Q10.003
median0.004
Q30.005
95-th percentile0.007
Maximum0.016
Range0.015
Interquartile range (IQR)0.002

Descriptive statistics

Standard deviation0.0015795984
Coefficient of variation (CV)0.36185041
Kurtosis0.76720609
Mean0.0043653354
Median Absolute Deviation (MAD)0.001
Skewness0.63304798
Sum39.24
Variance2.4951311 × 10-6
MonotonicityNot monotonic
2024-05-03T21:42:48.443593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0.003 2094
22.9%
0.004 2039
22.3%
0.005 1977
21.7%
0.006 1125
12.3%
0.002 864
9.5%
0.007 550
 
6.0%
0.008 208
 
2.3%
0.009 67
 
0.7%
0.001 39
 
0.4%
0.01 16
 
0.2%
Other values (5) 10
 
0.1%
(Missing) 136
 
1.5%
ValueCountFrequency (%)
0.001 39
 
0.4%
0.002 864
9.5%
0.003 2094
22.9%
0.004 2039
22.3%
0.005 1977
21.7%
0.006 1125
12.3%
0.007 550
 
6.0%
0.008 208
 
2.3%
0.009 67
 
0.7%
0.01 16
 
0.2%
ValueCountFrequency (%)
0.016 1
 
< 0.1%
0.015 1
 
< 0.1%
0.013 1
 
< 0.1%
0.012 2
 
< 0.1%
0.011 5
 
0.1%
0.01 16
 
0.2%
0.009 67
 
0.7%
0.008 208
 
2.3%
0.007 550
6.0%
0.006 1125
12.3%

미세먼지농도(㎍/㎥)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct142
Distinct (%)1.6%
Missing97
Missing (%)1.1%
Infinite0
Infinite (%)0.0%
Mean39.664931
Minimum3
Maximum204
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size80.3 KiB
2024-05-03T21:42:48.911065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile10
Q122.75
median36
Q351
95-th percentile87
Maximum204
Range201
Interquartile range (IQR)28.25

Descriptive statistics

Standard deviation23.818312
Coefficient of variation (CV)0.60048792
Kurtosis2.0124823
Mean39.664931
Median Absolute Deviation (MAD)14
Skewness1.2271928
Sum358095
Variance567.312
MonotonicityNot monotonic
2024-05-03T21:42:49.480470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33 207
 
2.3%
26 190
 
2.1%
30 190
 
2.1%
25 189
 
2.1%
36 181
 
2.0%
32 181
 
2.0%
39 180
 
2.0%
22 180
 
2.0%
24 174
 
1.9%
37 173
 
1.9%
Other values (132) 7183
78.7%
ValueCountFrequency (%)
3 10
 
0.1%
4 21
 
0.2%
5 35
 
0.4%
6 60
0.7%
7 68
0.7%
8 89
1.0%
9 124
1.4%
10 119
1.3%
11 135
1.5%
12 118
1.3%
ValueCountFrequency (%)
204 1
 
< 0.1%
165 1
 
< 0.1%
148 1
 
< 0.1%
146 1
 
< 0.1%
143 1
 
< 0.1%
142 5
0.1%
141 1
 
< 0.1%
139 3
< 0.1%
138 1
 
< 0.1%
137 3
< 0.1%

초미세먼지농도(㎍/㎥)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct110
Distinct (%)1.2%
Missing98
Missing (%)1.1%
Infinite0
Infinite (%)0.0%
Mean22.74543
Minimum1
Maximum124
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size80.3 KiB
2024-05-03T21:42:50.302312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q111
median20
Q330
95-th percentile53
Maximum124
Range123
Interquartile range (IQR)19

Descriptive statistics

Standard deviation15.734794
Coefficient of variation (CV)0.69177827
Kurtosis3.8303244
Mean22.74543
Median Absolute Deviation (MAD)9
Skewness1.5835327
Sum205323
Variance247.58376
MonotonicityNot monotonic
2024-05-03T21:42:51.035352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18 316
 
3.5%
20 294
 
3.2%
6 293
 
3.2%
22 288
 
3.2%
17 286
 
3.1%
19 286
 
3.1%
8 281
 
3.1%
9 279
 
3.1%
7 276
 
3.0%
16 272
 
3.0%
Other values (100) 6156
67.5%
ValueCountFrequency (%)
1 22
 
0.2%
2 68
 
0.7%
3 128
1.4%
4 195
2.1%
5 237
2.6%
6 293
3.2%
7 276
3.0%
8 281
3.1%
9 279
3.1%
10 269
2.9%
ValueCountFrequency (%)
124 1
< 0.1%
119 1
< 0.1%
114 1
< 0.1%
113 1
< 0.1%
112 1
< 0.1%
108 2
< 0.1%
106 2
< 0.1%
105 1
< 0.1%
103 2
< 0.1%
101 2
< 0.1%

Interactions

2024-05-03T21:42:39.132551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:42:28.303145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:42:29.843404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:42:31.869324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:42:33.539064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:42:35.194496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:42:37.225253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:42:39.433665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:42:28.530122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:42:30.231836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:42:32.173883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:42:33.723953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:42:35.445684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:42:37.492579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:42:39.826842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:42:28.697952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:42:30.487036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:42:32.362724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:42:33.956208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:42:35.724492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:42:37.736621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:42:40.122143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:42:28.894599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:42:30.771555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:42:32.566993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:42:34.158035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:42:36.035952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:42:37.992773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:42:40.419853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:42:29.107102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:42:31.039665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:42:32.810493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:42:34.406054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:42:36.323679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:42:38.249582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:42:40.700909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:42:29.406598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:42:31.321908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:42:33.103543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:42:34.724987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:42:36.611874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:42:38.552935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:42:41.000655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:42:29.664934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:42:31.606343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:42:33.347431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:42:35.009266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:42:36.920753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:42:38.845343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-03T21:42:51.488272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
측정일시권역명측정소명이산화질소농도(ppm)오존농도(ppm)일산화탄소농도(ppm)아황산가스농도(ppm)미세먼지농도(㎍/㎥)초미세먼지농도(㎍/㎥)
측정일시1.0000.0000.0000.5420.6580.5400.4240.5480.531
권역명0.0001.0001.0000.2360.1400.1940.3380.0940.074
측정소명0.0001.0001.0000.2840.2110.4170.5390.1230.075
이산화질소농도(ppm)0.5420.2360.2841.0000.5890.7800.5350.6210.660
오존농도(ppm)0.6580.1400.2110.5891.0000.5090.2780.3150.340
일산화탄소농도(ppm)0.5400.1940.4170.7800.5091.0000.5130.5990.638
아황산가스농도(ppm)0.4240.3380.5390.5350.2780.5131.0000.4890.500
미세먼지농도(㎍/㎥)0.5480.0940.1230.6210.3150.5990.4891.0000.876
초미세먼지농도(㎍/㎥)0.5310.0740.0750.6600.3400.6380.5000.8761.000
2024-05-03T21:42:51.959188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
측정소명권역명
측정소명1.0000.999
권역명0.9991.000
2024-05-03T21:42:52.581926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
측정일시이산화질소농도(ppm)오존농도(ppm)일산화탄소농도(ppm)아황산가스농도(ppm)미세먼지농도(㎍/㎥)초미세먼지농도(㎍/㎥)권역명측정소명
측정일시1.000-0.065-0.201-0.062-0.199-0.254-0.2620.0000.000
이산화질소농도(ppm)-0.0651.000-0.4890.6930.3960.6600.6770.1000.104
오존농도(ppm)-0.201-0.4891.000-0.406-0.225-0.121-0.1170.0580.078
일산화탄소농도(ppm)-0.0620.693-0.4061.0000.4040.6210.6420.0820.160
아황산가스농도(ppm)-0.1990.396-0.2250.4041.0000.4500.4510.1540.241
미세먼지농도(㎍/㎥)-0.2540.660-0.1210.6210.4501.0000.9080.0390.043
초미세먼지농도(㎍/㎥)-0.2620.677-0.1170.6420.4510.9081.0000.0310.026
권역명0.0000.1000.0580.0820.1540.0390.0311.0000.999
측정소명0.0000.1040.0780.1600.2410.0430.0260.9991.000

Missing values

2024-05-03T21:42:41.402010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-03T21:42:41.867484image/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.
2024-05-03T21:42:42.439582image/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(㎍/㎥)
020180101동남권강남구0.0340.0090.60.0063422
120180101동남권강동구0.0390.010.70.0054924
220180101동북권강북구0.0260.0180.60.0043818
320180101서남권강서구0.0380.0110.70.004<NA>13
420180101서남권관악구0.0370.0080.50.0083824
520180101동북권광진구0.0380.0120.70.0054222
620180101서남권구로구0.0250.0080.60.0074828
720180101서남권금천구0.0370.010.60.0053921
820180101동북권노원구0.0340.0110.80.0074221
920180101동북권도봉구0.030.0160.60.0064522
측정일시권역명측정소명이산화질소농도(ppm)오존농도(ppm)일산화탄소농도(ppm)아황산가스농도(ppm)미세먼지농도(㎍/㎥)초미세먼지농도(㎍/㎥)
911520181231동북권성동구0.0450.0050.70.0045433
911620181231동북권성북구0.0510.0061.00.0044623
911720181231동남권송파구0.0480.0050.80.0044627
911820181231서남권양천구0.050.0070.90.0036236
911920181231서남권영등포구0.0330.0090.90.0054733
912020181231도심권용산구0.0470.0050.70.0034030
912120181231서북권은평구0.0350.011.00.0054023
912220181231도심권종로구0.0480.0060.80.0044529
912320181231도심권중구0.0490.0060.80.0034631
912420181231동북권중랑구0.0370.0070.70.0074430