Overview

Dataset statistics

Number of variables8
Number of observations4200
Missing cells171
Missing cells (%)0.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory291.3 KiB
Average record size in memory71.0 B

Variable types

Numeric7
Categorical1

Dataset

Description측정일시,측정소명,이산화질소농도(ppm),오존농도(ppm),일산화탄소농도(ppm),아황산가스(ppm),미세먼지(㎍/㎥),초미세먼지(㎍/㎥)
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-2275/S/1/datasetView.do

Alerts

일산화탄소농도(ppm) is highly overall correlated with 초미세먼지(㎍/㎥)High correlation
미세먼지(㎍/㎥) is highly overall correlated with 초미세먼지(㎍/㎥)High correlation
초미세먼지(㎍/㎥) is highly overall correlated with 일산화탄소농도(ppm) and 1 other fieldsHigh correlation
초미세먼지(㎍/㎥) has 48 (1.1%) missing valuesMissing

Reproduction

Analysis started2024-05-11 06:22:24.867909
Analysis finished2024-05-11 06:22:38.112557
Duration13.24 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

측정일시
Real number (ℝ)

Distinct168
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0240508 × 1011
Minimum2.0240504 × 1011
Maximum2.0240511 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size37.0 KiB
2024-05-11T15:22:38.259420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0240504 × 1011
5-th percentile2.0240504 × 1011
Q12.0240506 × 1011
median2.0240508 × 1011
Q32.0240509 × 1011
95-th percentile2.0240511 × 1011
Maximum2.0240511 × 1011
Range69900
Interquartile range (IQR)31150

Descriptive statistics

Standard deviation20454.615
Coefficient of variation (CV)1.0105782 × 10-7
Kurtosis-1.1462849
Mean2.0240508 × 1011
Median Absolute Deviation (MAD)19100
Skewness-0.0045823039
Sum8.5010133 × 1014
Variance4.1839128 × 108
MonotonicityDecreasing
2024-05-11T15:22:38.595804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
202405111400 25
 
0.6%
202405061800 25
 
0.6%
202405070200 25
 
0.6%
202405070100 25
 
0.6%
202405070000 25
 
0.6%
202405062300 25
 
0.6%
202405062200 25
 
0.6%
202405062100 25
 
0.6%
202405062000 25
 
0.6%
202405061900 25
 
0.6%
Other values (158) 3950
94.0%
ValueCountFrequency (%)
202405041500 25
0.6%
202405041600 25
0.6%
202405041700 25
0.6%
202405041800 25
0.6%
202405041900 25
0.6%
202405042000 25
0.6%
202405042100 25
0.6%
202405042200 25
0.6%
202405042300 25
0.6%
202405050000 25
0.6%
ValueCountFrequency (%)
202405111400 25
0.6%
202405111300 25
0.6%
202405111200 25
0.6%
202405111100 25
0.6%
202405111000 25
0.6%
202405110900 25
0.6%
202405110800 25
0.6%
202405110700 25
0.6%
202405110600 25
0.6%
202405110500 25
0.6%

측정소명
Categorical

Distinct25
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size32.9 KiB
강북구
 
168
서대문구
 
168
양천구
 
168
금천구
 
168
성북구
 
168
Other values (20)
3360 

Length

Max length4
Median length3
Mean length3.08
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row강북구
2nd row서대문구
3rd row양천구
4th row금천구
5th row성북구

Common Values

ValueCountFrequency (%)
강북구 168
 
4.0%
서대문구 168
 
4.0%
양천구 168
 
4.0%
금천구 168
 
4.0%
성북구 168
 
4.0%
관악구 168
 
4.0%
종로구 168
 
4.0%
노원구 168
 
4.0%
강동구 168
 
4.0%
광진구 168
 
4.0%
Other values (15) 2520
60.0%

Length

2024-05-11T15:22:39.039816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
강북구 168
 
4.0%
동대문구 168
 
4.0%
구로구 168
 
4.0%
서초구 168
 
4.0%
동작구 168
 
4.0%
중랑구 168
 
4.0%
강서구 168
 
4.0%
도봉구 168
 
4.0%
마포구 168
 
4.0%
은평구 168
 
4.0%
Other values (15) 2520
60.0%

이산화질소농도(ppm)
Real number (ℝ)

Distinct317
Distinct (%)7.6%
Missing18
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean0.013230727
Minimum0.0015
Maximum0.0405
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size37.0 KiB
2024-05-11T15:22:39.337152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.0015
5-th percentile0.006
Q10.009
median0.0118
Q30.0161
95-th percentile0.0253
Maximum0.0405
Range0.039
Interquartile range (IQR)0.0071

Descriptive statistics

Standard deviation0.0059694331
Coefficient of variation (CV)0.45117952
Kurtosis1.3797203
Mean0.013230727
Median Absolute Deviation (MAD)0.0033
Skewness1.1697631
Sum55.3309
Variance3.5634131 × 10-5
MonotonicityNot monotonic
2024-05-11T15:22:39.696783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0115 61
 
1.5%
0.0083 49
 
1.2%
0.0092 46
 
1.1%
0.0119 45
 
1.1%
0.0118 44
 
1.0%
0.0107 42
 
1.0%
0.0094 42
 
1.0%
0.0087 42
 
1.0%
0.0091 41
 
1.0%
0.011 41
 
1.0%
Other values (307) 3729
88.8%
ValueCountFrequency (%)
0.0015 1
 
< 0.1%
0.0021 1
 
< 0.1%
0.0027 1
 
< 0.1%
0.003 2
< 0.1%
0.0031 1
 
< 0.1%
0.0032 1
 
< 0.1%
0.0033 1
 
< 0.1%
0.0034 1
 
< 0.1%
0.0035 1
 
< 0.1%
0.0036 3
0.1%
ValueCountFrequency (%)
0.0405 1
< 0.1%
0.0398 2
< 0.1%
0.0387 1
< 0.1%
0.0383 1
< 0.1%
0.0377 1
< 0.1%
0.0371 1
< 0.1%
0.037 1
< 0.1%
0.0368 2
< 0.1%
0.0364 1
< 0.1%
0.0361 1
< 0.1%

오존농도(ppm)
Real number (ℝ)

Distinct873
Distinct (%)20.9%
Missing19
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean0.045200359
Minimum0.0028
Maximum0.1581
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size37.0 KiB
2024-05-11T15:22:40.006823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.0028
5-th percentile0.019
Q10.0312
median0.0423
Q30.0536
95-th percentile0.0804
Maximum0.1581
Range0.1553
Interquartile range (IQR)0.0224

Descriptive statistics

Standard deviation0.021102952
Coefficient of variation (CV)0.46687577
Kurtosis4.2594573
Mean0.045200359
Median Absolute Deviation (MAD)0.0112
Skewness1.5535991
Sum188.9827
Variance0.0004453346
MonotonicityNot monotonic
2024-05-11T15:22:40.810851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0457 18
 
0.4%
0.0441 17
 
0.4%
0.0318 17
 
0.4%
0.0351 17
 
0.4%
0.0337 17
 
0.4%
0.0415 16
 
0.4%
0.0489 16
 
0.4%
0.0483 16
 
0.4%
0.0328 16
 
0.4%
0.0486 16
 
0.4%
Other values (863) 4015
95.6%
(Missing) 19
 
0.5%
ValueCountFrequency (%)
0.0028 1
 
< 0.1%
0.0029 1
 
< 0.1%
0.0033 1
 
< 0.1%
0.0037 1
 
< 0.1%
0.0042 1
 
< 0.1%
0.0044 1
 
< 0.1%
0.0046 1
 
< 0.1%
0.0047 1
 
< 0.1%
0.0055 3
0.1%
0.0057 1
 
< 0.1%
ValueCountFrequency (%)
0.1581 1
< 0.1%
0.1554 1
< 0.1%
0.1537 1
< 0.1%
0.1511 1
< 0.1%
0.1499 1
< 0.1%
0.1476 1
< 0.1%
0.1472 1
< 0.1%
0.1459 1
< 0.1%
0.1444 1
< 0.1%
0.1439 1
< 0.1%

일산화탄소농도(ppm)
Real number (ℝ)

HIGH CORRELATION 

Distinct56
Distinct (%)1.3%
Missing22
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean0.34817377
Minimum0.15
Maximum0.92
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size37.0 KiB
2024-05-11T15:22:41.164317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.15
5-th percentile0.23
Q10.28
median0.33
Q30.4
95-th percentile0.53
Maximum0.92
Range0.77
Interquartile range (IQR)0.12

Descriptive statistics

Standard deviation0.094314007
Coefficient of variation (CV)0.270882
Kurtosis0.55744419
Mean0.34817377
Median Absolute Deviation (MAD)0.06
Skewness0.8350442
Sum1454.67
Variance0.0088951319
MonotonicityNot monotonic
2024-05-11T15:22:41.482724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.28 200
 
4.8%
0.27 199
 
4.7%
0.29 196
 
4.7%
0.3 191
 
4.5%
0.25 182
 
4.3%
0.26 179
 
4.3%
0.32 175
 
4.2%
0.34 173
 
4.1%
0.31 172
 
4.1%
0.33 167
 
4.0%
Other values (46) 2344
55.8%
ValueCountFrequency (%)
0.15 1
 
< 0.1%
0.16 6
 
0.1%
0.17 2
 
< 0.1%
0.18 7
 
0.2%
0.19 6
 
0.1%
0.2 23
 
0.5%
0.21 56
 
1.3%
0.22 92
2.2%
0.23 129
3.1%
0.24 150
3.6%
ValueCountFrequency (%)
0.92 1
 
< 0.1%
0.7 3
 
0.1%
0.69 1
 
< 0.1%
0.67 3
 
0.1%
0.66 5
 
0.1%
0.65 4
 
0.1%
0.64 2
 
< 0.1%
0.63 5
 
0.1%
0.62 11
0.3%
0.61 16
0.4%

아황산가스(ppm)
Real number (ℝ)

Distinct54
Distinct (%)1.3%
Missing30
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean0.0025539089
Minimum0.0014
Maximum0.007
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size37.0 KiB
2024-05-11T15:22:41.770618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.0014
5-th percentile0.0018
Q10.0021
median0.0024
Q30.0028
95-th percentile0.0038
Maximum0.007
Range0.0056
Interquartile range (IQR)0.0007

Descriptive statistics

Standard deviation0.00068089814
Coefficient of variation (CV)0.26661019
Kurtosis6.6573418
Mean0.0025539089
Median Absolute Deviation (MAD)0.0003
Skewness2.0442721
Sum10.6498
Variance4.6362228 × 10-7
MonotonicityNot monotonic
2024-05-11T15:22:42.084053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0022 391
 
9.3%
0.0021 345
 
8.2%
0.0024 343
 
8.2%
0.0023 337
 
8.0%
0.002 319
 
7.6%
0.0025 297
 
7.1%
0.0026 260
 
6.2%
0.0027 242
 
5.8%
0.0028 213
 
5.1%
0.0019 213
 
5.1%
Other values (44) 1210
28.8%
ValueCountFrequency (%)
0.0014 20
 
0.5%
0.0015 16
 
0.4%
0.0016 35
 
0.8%
0.0017 67
 
1.6%
0.0018 137
 
3.3%
0.0019 213
5.1%
0.002 319
7.6%
0.0021 345
8.2%
0.0022 391
9.3%
0.0023 337
8.0%
ValueCountFrequency (%)
0.007 1
 
< 0.1%
0.0068 2
 
< 0.1%
0.0066 1
 
< 0.1%
0.0065 2
 
< 0.1%
0.0063 1
 
< 0.1%
0.0062 2
 
< 0.1%
0.0061 2
 
< 0.1%
0.006 4
0.1%
0.0059 5
0.1%
0.0058 4
0.1%

미세먼지(㎍/㎥)
Real number (ℝ)

HIGH CORRELATION 

Distinct89
Distinct (%)2.1%
Missing34
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean23.969995
Minimum3
Maximum121
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size37.0 KiB
2024-05-11T15:22:42.384258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile4
Q114
median22
Q332
95-th percentile48
Maximum121
Range118
Interquartile range (IQR)18

Descriptive statistics

Standard deviation14.324311
Coefficient of variation (CV)0.59759342
Kurtosis2.1876137
Mean23.969995
Median Absolute Deviation (MAD)10
Skewness1.046798
Sum99859
Variance205.18589
MonotonicityNot monotonic
2024-05-11T15:22:42.703473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19 175
 
4.2%
20 149
 
3.5%
3 143
 
3.4%
22 143
 
3.4%
17 141
 
3.4%
18 136
 
3.2%
21 135
 
3.2%
23 121
 
2.9%
16 117
 
2.8%
6 116
 
2.8%
Other values (79) 2790
66.4%
ValueCountFrequency (%)
3 143
3.4%
4 67
1.6%
5 87
2.1%
6 116
2.8%
7 105
2.5%
8 114
2.7%
9 101
2.4%
10 96
2.3%
11 63
1.5%
12 62
1.5%
ValueCountFrequency (%)
121 1
< 0.1%
99 1
< 0.1%
93 2
< 0.1%
92 1
< 0.1%
91 1
< 0.1%
88 1
< 0.1%
86 1
< 0.1%
84 1
< 0.1%
83 2
< 0.1%
82 1
< 0.1%

초미세먼지(㎍/㎥)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct53
Distinct (%)1.3%
Missing48
Missing (%)1.1%
Infinite0
Infinite (%)0.0%
Mean12.881744
Minimum1
Maximum56
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size37.0 KiB
2024-05-11T15:22:43.038233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q16
median11
Q318
95-th percentile31
Maximum56
Range55
Interquartile range (IQR)12

Descriptive statistics

Standard deviation9.2803823
Coefficient of variation (CV)0.72042905
Kurtosis1.0972698
Mean12.881744
Median Absolute Deviation (MAD)6
Skewness1.0822191
Sum53485
Variance86.125497
MonotonicityNot monotonic
2024-05-11T15:22:43.336599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 291
 
6.9%
8 276
 
6.6%
9 260
 
6.2%
10 244
 
5.8%
11 231
 
5.5%
6 204
 
4.9%
7 179
 
4.3%
12 162
 
3.9%
5 156
 
3.7%
13 154
 
3.7%
Other values (43) 1995
47.5%
ValueCountFrequency (%)
1 291
6.9%
2 152
3.6%
3 140
3.3%
4 147
3.5%
5 156
3.7%
6 204
4.9%
7 179
4.3%
8 276
6.6%
9 260
6.2%
10 244
5.8%
ValueCountFrequency (%)
56 1
 
< 0.1%
55 1
 
< 0.1%
51 2
 
< 0.1%
50 1
 
< 0.1%
49 3
0.1%
48 2
 
< 0.1%
47 4
0.1%
46 2
 
< 0.1%
45 7
0.2%
44 7
0.2%

Interactions

2024-05-11T15:22:35.667061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:22:26.226195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:22:27.808947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:22:29.768800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:22:31.262087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:22:32.698611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:22:34.207673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:22:35.973298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:22:26.449860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:22:28.046072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:22:29.962692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:22:31.488306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:22:32.887638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:22:34.415421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:22:36.221111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:22:26.671020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:22:28.270693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:22:30.167663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:22:31.755483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:22:33.105719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:22:34.639015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:22:36.506896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:22:26.972607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:22:28.499121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:22:30.382838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:22:31.960715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:22:33.350586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:22:34.847439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:22:36.722476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:22:27.199219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:22:28.751578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:22:30.584294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:22:32.147834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:22:33.599740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:22:35.039979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:22:36.929095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:22:27.407944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:22:29.362979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:22:30.788211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:22:32.313075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:22:33.788619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:22:35.233797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:22:37.121618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:22:27.617577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:22:29.571034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:22:31.015886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:22:32.510921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:22:34.010264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:22:35.462841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-11T15:22:43.560767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
측정일시측정소명이산화질소농도(ppm)오존농도(ppm)일산화탄소농도(ppm)아황산가스(ppm)미세먼지(㎍/㎥)초미세먼지(㎍/㎥)
측정일시1.0000.0000.4300.6620.5100.5980.6570.656
측정소명0.0001.0000.3490.2180.4880.4600.1300.170
이산화질소농도(ppm)0.4300.3491.0000.5510.3510.4270.4530.467
오존농도(ppm)0.6620.2180.5511.0000.2970.7660.7840.759
일산화탄소농도(ppm)0.5100.4880.3510.2971.0000.3610.4250.499
아황산가스(ppm)0.5980.4600.4270.7660.3611.0000.7880.766
미세먼지(㎍/㎥)0.6570.1300.4530.7840.4250.7881.0000.916
초미세먼지(㎍/㎥)0.6560.1700.4670.7590.4990.7660.9161.000
2024-05-11T15:22:43.824094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
측정일시이산화질소농도(ppm)오존농도(ppm)일산화탄소농도(ppm)아황산가스(ppm)미세먼지(㎍/㎥)초미세먼지(㎍/㎥)측정소명
측정일시1.0000.1370.109-0.2330.1120.122-0.0430.000
이산화질소농도(ppm)0.1371.000-0.0920.3720.3610.3880.3960.129
오존농도(ppm)0.109-0.0921.0000.1340.3570.4680.3850.078
일산화탄소농도(ppm)-0.2330.3720.1341.0000.3390.4550.5370.209
아황산가스(ppm)0.1120.3610.3570.3391.0000.5000.4450.180
미세먼지(㎍/㎥)0.1220.3880.4680.4550.5001.0000.8880.045
초미세먼지(㎍/㎥)-0.0430.3960.3850.5370.4450.8881.0000.058
측정소명0.0000.1290.0780.2090.1800.0450.0581.000

Missing values

2024-05-11T15:22:37.387715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-11T15:22:37.680811image/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.
2024-05-11T15:22:37.952221image/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

측정일시측정소명이산화질소농도(ppm)오존농도(ppm)일산화탄소농도(ppm)아황산가스(ppm)미세먼지(㎍/㎥)초미세먼지(㎍/㎥)
0202405111400강북구0.00610.05220.220.0021268
1202405111400서대문구0.00720.05230.390.0024411
2202405111400양천구0.00570.04960.20.0022319
3202405111400금천구0.01060.04790.20.0024356
4202405111400성북구0.00670.04310.30.0023366
5202405111400관악구0.00610.05090.220.0024389
6202405111400종로구0.00630.04440.240.00263110
7202405111400노원구0.00860.0480.260.00243211
8202405111400강동구0.00590.05860.340.0021367
9202405111400광진구0.00870.05180.340.0031308
측정일시측정소명이산화질소농도(ppm)오존농도(ppm)일산화탄소농도(ppm)아황산가스(ppm)미세먼지(㎍/㎥)초미세먼지(㎍/㎥)
4190202405041500양천구0.01760.1210.52<NA>6441
4191202405041500강동구0.01770.15540.530.00346037
4192202405041500중랑구0.01780.11870.480.00335340
4193202405041500노원구0.01920.13440.490.00426338
4194202405041500금천구0.01830.12870.540.00566942
4195202405041500성동구0.01920.13240.540.00526738
4196202405041500마포구0.02620.15110.60.00627044
4197202405041500서대문구0.01830.14070.620.00548142
4198202405041500서초구0.01750.14590.590.00529348
4199202405041500관악구0.01650.13850.60.00577645