Overview

Dataset statistics

Number of variables12
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.1 MiB
Average record size in memory114.0 B

Variable types

Numeric9
Categorical3

Dataset

Description파일 다운로드
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-2221/S/1/datasetView.do

Alerts

권역명 is highly overall correlated with 측정소코드 and 2 other fieldsHigh correlation
측정소명 is highly overall correlated with 측정소코드 and 2 other fieldsHigh correlation
권역코드 is highly overall correlated with 측정소코드 and 2 other fieldsHigh correlation
측정일시 is highly overall correlated with 미세먼지 24시간(㎍/㎥) and 1 other fieldsHigh correlation
측정소코드 is highly overall correlated with 권역코드 and 2 other fieldsHigh correlation
미세먼지 1시간(㎍/㎥) is highly overall correlated with 미세먼지 24시간(㎍/㎥) and 2 other fieldsHigh correlation
미세먼지 24시간(㎍/㎥) is highly overall correlated with 측정일시 and 3 other fieldsHigh correlation
초미세먼지(㎍/㎥) is highly overall correlated with 미세먼지 1시간(㎍/㎥) and 2 other fieldsHigh correlation
오존(ppm) is highly overall correlated with 이산화질소농도(ppm)High correlation
이산화질소농도(ppm) is highly overall correlated with 오존(ppm) and 1 other fieldsHigh correlation
일산화탄소농도(ppm) is highly overall correlated with 측정일시 and 4 other fieldsHigh correlation
미세먼지 1시간(㎍/㎥) has 179 (1.8%) zerosZeros
초미세먼지(㎍/㎥) has 114 (1.1%) zerosZeros
오존(ppm) has 237 (2.4%) zerosZeros
이산화질소농도(ppm) has 198 (2.0%) zerosZeros
일산화탄소농도(ppm) has 177 (1.8%) zerosZeros
아황산가스농도(ppm) has 196 (2.0%) zerosZeros

Reproduction

Analysis started2024-05-04 00:10:38.659810
Analysis finished2024-05-04 00:11:15.970546
Duration37.31 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

측정일시
Real number (ℝ)

HIGH CORRELATION 

Distinct744
Distinct (%)7.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0190316 × 1011
Minimum2.0190301 × 1011
Maximum2.0190331 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T00:11:16.195037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0190301 × 1011
5-th percentile2.0190302 × 1011
Q12.0190308 × 1011
median2.0190316 × 1011
Q32.0190324 × 1011
95-th percentile2.019033 × 1011
Maximum2.0190331 × 1011
Range302300
Interquartile range (IQR)158800

Descriptive statistics

Standard deviation89309.355
Coefficient of variation (CV)4.4233758 × 10-7
Kurtosis-1.198862
Mean2.0190316 × 1011
Median Absolute Deviation (MAD)79400
Skewness-0.0012255737
Sum2.0190316 × 1015
Variance7.9761609 × 109
MonotonicityNot monotonic
2024-05-04T00:11:16.642946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
201903252300 21
 
0.2%
201903050800 20
 
0.2%
201903160200 20
 
0.2%
201903240800 20
 
0.2%
201903241300 20
 
0.2%
201903170600 19
 
0.2%
201903130200 19
 
0.2%
201903270600 19
 
0.2%
201903121000 19
 
0.2%
201903291300 19
 
0.2%
Other values (734) 9804
98.0%
ValueCountFrequency (%)
201903010000 13
0.1%
201903010100 15
0.1%
201903010200 13
0.1%
201903010300 12
0.1%
201903010400 15
0.1%
201903010500 16
0.2%
201903010600 12
0.1%
201903010700 12
0.1%
201903010800 16
0.2%
201903010900 12
0.1%
ValueCountFrequency (%)
201903312300 15
0.1%
201903312200 18
0.2%
201903312100 8
0.1%
201903312000 13
0.1%
201903311900 10
0.1%
201903311800 13
0.1%
201903311700 15
0.1%
201903311600 16
0.2%
201903311500 14
0.1%
201903311400 18
0.2%

권역코드
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
102
3241 
103
2785 
104
1591 
101
1203 
100
1180 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row104
2nd row104
3rd row102
4th row102
5th row102

Common Values

ValueCountFrequency (%)
102 3241
32.4%
103 2785
27.9%
104 1591
15.9%
101 1203
 
12.0%
100 1180
 
11.8%

Length

2024-05-04T00:11:17.057721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T00:11:17.501577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
102 3241
32.4%
103 2785
27.9%
104 1591
15.9%
101 1203
 
12.0%
100 1180
 
11.8%

권역명
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
동북권
3241 
서남권
2785 
동남권
1591 
서북권
1203 
도심권
1180 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
동북권 3241
32.4%
서남권 2785
27.9%
동남권 1591
15.9%
서북권 1203
 
12.0%
도심권 1180
 
11.8%

Length

2024-05-04T00:11:17.845411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T00:11:18.315558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
동북권 3241
32.4%
서남권 2785
27.9%
동남권 1591
15.9%
서북권 1203
 
12.0%
도심권 1180
 
11.8%

측정소코드
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean111211.51
Minimum111121
Maximum111311
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T00:11:18.713039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum111121
5-th percentile111123
Q1111152
median111212
Q3111262
95-th percentile111301
Maximum111311
Range190
Interquartile range (IQR)110

Descriptive statistics

Standard deviation60.104131
Coefficient of variation (CV)0.00054044886
Kurtosis-1.3715156
Mean111211.51
Median Absolute Deviation (MAD)60
Skewness0.028097263
Sum1.1121151 × 109
Variance3612.5066
MonotonicityNot monotonic
2024-05-04T00:11:19.068033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
111201 426
 
4.3%
111291 425
 
4.2%
111311 421
 
4.2%
111142 416
 
4.2%
111273 413
 
4.1%
111212 411
 
4.1%
111251 409
 
4.1%
111161 408
 
4.1%
111151 407
 
4.1%
111301 406
 
4.1%
Other values (15) 5858
58.6%
ValueCountFrequency (%)
111121 398
4.0%
111123 396
4.0%
111131 386
3.9%
111141 395
4.0%
111142 416
4.2%
111151 407
4.1%
111152 395
4.0%
111161 408
4.1%
111171 374
3.7%
111181 385
3.9%
ValueCountFrequency (%)
111311 421
4.2%
111301 406
4.1%
111291 425
4.2%
111281 384
3.8%
111274 386
3.9%
111273 413
4.1%
111262 404
4.0%
111261 388
3.9%
111251 409
4.1%
111241 389
3.9%

측정소명
Categorical

HIGH CORRELATION 

Distinct25
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
마포구
 
426
강북구
 
425
노원구
 
421
성동구
 
416
송파구
 
413
Other values (20)
7899 

Length

Max length4
Median length3
Mean length3.0784
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row강동구
2nd row강남구
3rd row광진구
4th row성동구
5th row동대문구

Common Values

ValueCountFrequency (%)
마포구 426
 
4.3%
강북구 425
 
4.2%
노원구 421
 
4.2%
성동구 416
 
4.2%
송파구 413
 
4.1%
강서구 411
 
4.1%
관악구 409
 
4.1%
성북구 408
 
4.1%
중랑구 407
 
4.1%
양천구 406
 
4.1%
Other values (15) 5858
58.6%

Length

2024-05-04T00:11:19.443393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
마포구 426
 
4.3%
강북구 425
 
4.2%
노원구 421
 
4.2%
성동구 416
 
4.2%
송파구 413
 
4.1%
강서구 411
 
4.1%
관악구 409
 
4.1%
성북구 408
 
4.1%
중랑구 407
 
4.1%
양천구 406
 
4.1%
Other values (15) 5858
58.6%

미세먼지 1시간(㎍/㎥)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct255
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean68.3528
Minimum0
Maximum293
Zeros179
Zeros (%)1.8%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T00:11:19.882855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile19
Q135
median52
Q393.25
95-th percentile173
Maximum293
Range293
Interquartile range (IQR)58.25

Descriptive statistics

Standard deviation47.311461
Coefficient of variation (CV)0.69216567
Kurtosis1.438777
Mean68.3528
Median Absolute Deviation (MAD)22
Skewness1.3148328
Sum683528
Variance2238.3744
MonotonicityNot monotonic
2024-05-04T00:11:20.338396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
34 198
 
2.0%
36 195
 
1.9%
35 193
 
1.9%
39 183
 
1.8%
0 179
 
1.8%
33 178
 
1.8%
29 177
 
1.8%
41 174
 
1.7%
38 169
 
1.7%
40 165
 
1.7%
Other values (245) 8189
81.9%
ValueCountFrequency (%)
0 179
1.8%
3 8
 
0.1%
4 2
 
< 0.1%
5 2
 
< 0.1%
6 3
 
< 0.1%
7 4
 
< 0.1%
8 7
 
0.1%
9 5
 
0.1%
10 5
 
0.1%
11 5
 
0.1%
ValueCountFrequency (%)
293 1
 
< 0.1%
266 1
 
< 0.1%
264 1
 
< 0.1%
263 1
 
< 0.1%
262 3
< 0.1%
261 1
 
< 0.1%
260 1
 
< 0.1%
258 1
 
< 0.1%
256 1
 
< 0.1%
255 1
 
< 0.1%

미세먼지 24시간(㎍/㎥)
Real number (ℝ)

HIGH CORRELATION 

Distinct207
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.8303
Minimum0
Maximum225
Zeros23
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T00:11:20.973040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile25
Q138
median52
Q384
95-th percentile151
Maximum225
Range225
Interquartile range (IQR)46

Descriptive statistics

Standard deviation38.381897
Coefficient of variation (CV)0.59203639
Kurtosis1.361632
Mean64.8303
Median Absolute Deviation (MAD)18
Skewness1.3344739
Sum648303
Variance1473.17
MonotonicityNot monotonic
2024-05-04T00:11:21.427037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
38 217
 
2.2%
40 214
 
2.1%
37 205
 
2.1%
41 205
 
2.1%
35 202
 
2.0%
36 200
 
2.0%
39 195
 
1.9%
42 191
 
1.9%
43 181
 
1.8%
34 179
 
1.8%
Other values (197) 8011
80.1%
ValueCountFrequency (%)
0 23
 
0.2%
13 1
 
< 0.1%
14 2
 
< 0.1%
15 6
 
0.1%
16 14
 
0.1%
17 12
 
0.1%
18 30
0.3%
19 46
0.5%
20 58
0.6%
21 60
0.6%
ValueCountFrequency (%)
225 1
 
< 0.1%
224 1
 
< 0.1%
223 1
 
< 0.1%
222 1
 
< 0.1%
221 2
< 0.1%
220 1
 
< 0.1%
219 3
< 0.1%
218 1
 
< 0.1%
217 1
 
< 0.1%
216 1
 
< 0.1%

초미세먼지(㎍/㎥)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct180
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.4007
Minimum0
Maximum195
Zeros114
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T00:11:22.074307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10
Q119
median31
Q363
95-th percentile123
Maximum195
Range195
Interquartile range (IQR)44

Descriptive statistics

Standard deviation35.220789
Coefficient of variation (CV)0.79324851
Kurtosis1.3113945
Mean44.4007
Median Absolute Deviation (MAD)15
Skewness1.3581545
Sum444007
Variance1240.504
MonotonicityNot monotonic
2024-05-04T00:11:22.490274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20 267
 
2.7%
16 256
 
2.6%
22 247
 
2.5%
13 244
 
2.4%
21 240
 
2.4%
17 237
 
2.4%
24 233
 
2.3%
23 226
 
2.3%
25 226
 
2.3%
18 217
 
2.2%
Other values (170) 7607
76.1%
ValueCountFrequency (%)
0 114
1.1%
1 6
 
0.1%
2 1
 
< 0.1%
3 2
 
< 0.1%
4 17
 
0.2%
5 19
 
0.2%
6 30
 
0.3%
7 65
0.7%
8 74
0.7%
9 113
1.1%
ValueCountFrequency (%)
195 1
 
< 0.1%
187 1
 
< 0.1%
179 1
 
< 0.1%
178 1
 
< 0.1%
177 1
 
< 0.1%
176 1
 
< 0.1%
174 3
< 0.1%
173 3
< 0.1%
172 2
< 0.1%
170 1
 
< 0.1%

오존(ppm)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct84
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0275969
Minimum0
Maximum0.09
Zeros237
Zeros (%)2.4%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T00:11:22.842207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.002
Q10.015
median0.029
Q30.039
95-th percentile0.053
Maximum0.09
Range0.09
Interquartile range (IQR)0.024

Descriptive statistics

Standard deviation0.016027093
Coefficient of variation (CV)0.58075699
Kurtosis-0.55097399
Mean0.0275969
Median Absolute Deviation (MAD)0.012
Skewness0.079779389
Sum275.969
Variance0.0002568677
MonotonicityNot monotonic
2024-05-04T00:11:23.226085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.002 378
 
3.8%
0.034 294
 
2.9%
0.032 280
 
2.8%
0.033 279
 
2.8%
0.036 260
 
2.6%
0.035 257
 
2.6%
0.037 256
 
2.6%
0.031 252
 
2.5%
0.003 251
 
2.5%
0.029 239
 
2.4%
Other values (74) 7254
72.5%
ValueCountFrequency (%)
0.0 237
2.4%
0.001 45
 
0.4%
0.002 378
3.8%
0.003 251
2.5%
0.004 180
1.8%
0.005 172
1.7%
0.006 137
 
1.4%
0.007 159
1.6%
0.008 150
 
1.5%
0.009 113
 
1.1%
ValueCountFrequency (%)
0.09 1
 
< 0.1%
0.084 1
 
< 0.1%
0.082 1
 
< 0.1%
0.081 1
 
< 0.1%
0.079 2
 
< 0.1%
0.078 3
< 0.1%
0.077 3
< 0.1%
0.076 3
< 0.1%
0.075 4
< 0.1%
0.074 5
0.1%

이산화질소농도(ppm)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct102
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0330026
Minimum0
Maximum0.107
Zeros198
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T00:11:23.618639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.01
Q10.019
median0.03
Q30.045
95-th percentile0.067
Maximum0.107
Range0.107
Interquartile range (IQR)0.026

Descriptive statistics

Standard deviation0.018245007
Coefficient of variation (CV)0.55283544
Kurtosis-0.12080244
Mean0.0330026
Median Absolute Deviation (MAD)0.013
Skewness0.63633207
Sum330.026
Variance0.00033288028
MonotonicityNot monotonic
2024-05-04T00:11:24.008481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.023 275
 
2.8%
0.021 265
 
2.6%
0.014 252
 
2.5%
0.016 251
 
2.5%
0.024 248
 
2.5%
0.02 247
 
2.5%
0.019 247
 
2.5%
0.018 240
 
2.4%
0.017 235
 
2.4%
0.022 235
 
2.4%
Other values (92) 7505
75.0%
ValueCountFrequency (%)
0.0 198
2.0%
0.003 1
 
< 0.1%
0.004 2
 
< 0.1%
0.005 19
 
0.2%
0.006 34
 
0.3%
0.007 46
 
0.5%
0.008 67
 
0.7%
0.009 104
1.0%
0.01 151
1.5%
0.011 193
1.9%
ValueCountFrequency (%)
0.107 1
 
< 0.1%
0.104 1
 
< 0.1%
0.103 1
 
< 0.1%
0.102 1
 
< 0.1%
0.1 1
 
< 0.1%
0.099 2
< 0.1%
0.098 1
 
< 0.1%
0.097 4
< 0.1%
0.096 1
 
< 0.1%
0.094 3
< 0.1%

일산화탄소농도(ppm)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct21
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.60522
Minimum0
Maximum2
Zeros177
Zeros (%)1.8%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T00:11:24.410312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.2
Q10.4
median0.5
Q30.8
95-th percentile1.2
Maximum2
Range2
Interquartile range (IQR)0.4

Descriptive statistics

Standard deviation0.2940976
Coefficient of variation (CV)0.48593504
Kurtosis0.82525667
Mean0.60522
Median Absolute Deviation (MAD)0.2
Skewness0.84681147
Sum6052.2
Variance0.086493401
MonotonicityNot monotonic
2024-05-04T00:11:24.804878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0.4 1884
18.8%
0.5 1555
15.6%
0.3 1173
11.7%
0.6 1159
11.6%
0.7 1047
10.5%
0.8 741
 
7.4%
0.9 615
 
6.2%
1.0 464
 
4.6%
0.2 328
 
3.3%
1.1 304
 
3.0%
Other values (11) 730
 
7.3%
ValueCountFrequency (%)
0.0 177
 
1.8%
0.1 4
 
< 0.1%
0.2 328
 
3.3%
0.3 1173
11.7%
0.4 1884
18.8%
0.5 1555
15.6%
0.6 1159
11.6%
0.7 1047
10.5%
0.8 741
 
7.4%
0.9 615
 
6.2%
ValueCountFrequency (%)
2.0 2
 
< 0.1%
1.9 5
 
0.1%
1.8 10
 
0.1%
1.7 11
 
0.1%
1.6 19
 
0.2%
1.5 41
 
0.4%
1.4 106
 
1.1%
1.3 137
1.4%
1.2 218
2.2%
1.1 304
3.0%

아황산가스농도(ppm)
Real number (ℝ)

ZEROS 

Distinct16
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0044097
Minimum0
Maximum0.017
Zeros196
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T00:11:25.171537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.002
Q10.003
median0.004
Q30.005
95-th percentile0.008
Maximum0.017
Range0.017
Interquartile range (IQR)0.002

Descriptive statistics

Standard deviation0.0017956526
Coefficient of variation (CV)0.40720517
Kurtosis1.7457013
Mean0.0044097
Median Absolute Deviation (MAD)0.001
Skewness0.69066695
Sum44.097
Variance3.2243683 × 10-6
MonotonicityNot monotonic
2024-05-04T00:11:25.464972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0.004 2545
25.4%
0.003 2214
22.1%
0.005 1906
19.1%
0.006 1168
11.7%
0.002 797
 
8.0%
0.007 666
 
6.7%
0.008 272
 
2.7%
0.0 196
 
2.0%
0.009 128
 
1.3%
0.01 48
 
0.5%
Other values (6) 60
 
0.6%
ValueCountFrequency (%)
0.0 196
 
2.0%
0.001 3
 
< 0.1%
0.002 797
 
8.0%
0.003 2214
22.1%
0.004 2545
25.4%
0.005 1906
19.1%
0.006 1168
11.7%
0.007 666
 
6.7%
0.008 272
 
2.7%
0.009 128
 
1.3%
ValueCountFrequency (%)
0.017 1
 
< 0.1%
0.014 4
 
< 0.1%
0.013 5
 
0.1%
0.012 15
 
0.1%
0.011 32
 
0.3%
0.01 48
 
0.5%
0.009 128
 
1.3%
0.008 272
 
2.7%
0.007 666
6.7%
0.006 1168
11.7%

Interactions

2024-05-04T00:11:11.519336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:10:43.480743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:10:46.970895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:10:50.857990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:10:54.400269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:10:57.880695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:11:01.322477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:11:04.645983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:11:08.232649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:11:11.927792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:10:43.797084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:10:47.519444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:10:51.257088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:10:54.769666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:10:58.312114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:11:01.687375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:11:05.008134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:11:08.637166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:11:12.362350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:10:44.161354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:10:47.859449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:10:51.703877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:10:55.171687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:10:58.874721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:11:02.103705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:11:05.392433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:11:08.987953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:11:12.742148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:10:44.487610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:10:48.323185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:10:52.077372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:10:55.488057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:10:59.171814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:11:02.441295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:11:05.871041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:11:09.298929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:11:13.028955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:10:44.791953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:10:48.722303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:10:52.441410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:10:55.784296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:10:59.518130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:11:02.736439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:11:06.148912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:11:09.595674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:11:13.469486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:10:45.337071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:10:49.078708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:10:52.838750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:10:56.193890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:10:59.884968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:11:03.060204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:11:06.670784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:11:09.916714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:11:13.815012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:10:45.711479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:10:49.495266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:10:53.161824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:10:56.577130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:11:00.234056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:11:03.326204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:11:07.065681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:11:10.208986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:11:14.115844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:10:46.074815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:10:49.881650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:10:53.513037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:10:56.936354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:11:00.622701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:11:03.584919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:11:07.356118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:11:10.684237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:11:14.469862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:10:46.559732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:10:50.451365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:10:53.937840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:10:57.377294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:11:01.014999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:11:04.239454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:11:07.791615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:11:11.131850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-04T00:11:25.713212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
측정일시권역코드권역명측정소코드측정소명미세먼지 1시간(㎍/㎥)미세먼지 24시간(㎍/㎥)초미세먼지(㎍/㎥)오존(ppm)이산화질소농도(ppm)일산화탄소농도(ppm)아황산가스농도(ppm)
측정일시1.0000.0000.0000.0000.0000.8050.8540.8110.5160.5500.6410.462
권역코드0.0001.0001.0000.9961.0000.1610.2010.0950.1130.2180.3760.245
권역명0.0001.0001.0000.9961.0000.1610.2010.0950.1130.2180.3760.245
측정소코드0.0000.9960.9961.0001.0000.1730.2460.1090.1800.1890.4760.338
측정소명0.0001.0001.0001.0001.0000.2080.2850.1490.2490.2930.5300.532
미세먼지 1시간(㎍/㎥)0.8050.1610.1610.1730.2081.0000.8970.9340.2490.4330.6210.378
미세먼지 24시간(㎍/㎥)0.8540.2010.2010.2460.2850.8971.0000.8620.2600.4000.5870.353
초미세먼지(㎍/㎥)0.8110.0950.0950.1090.1490.9340.8621.0000.3090.4670.6470.378
오존(ppm)0.5160.1130.1130.1800.2490.2490.2600.3091.0000.6870.4730.376
이산화질소농도(ppm)0.5500.2180.2180.1890.2930.4330.4000.4670.6871.0000.7070.579
일산화탄소농도(ppm)0.6410.3760.3760.4760.5300.6210.5870.6470.4730.7071.0000.757
아황산가스농도(ppm)0.4620.2450.2450.3380.5320.3780.3530.3780.3760.5790.7571.000
2024-05-04T00:11:26.028950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
권역명측정소명권역코드
권역명1.0000.9991.000
측정소명0.9991.0000.999
권역코드1.0000.9991.000
2024-05-04T00:11:26.328320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
측정일시측정소코드미세먼지 1시간(㎍/㎥)미세먼지 24시간(㎍/㎥)초미세먼지(㎍/㎥)오존(ppm)이산화질소농도(ppm)일산화탄소농도(ppm)아황산가스농도(ppm)권역코드권역명측정소명
측정일시1.000-0.001-0.495-0.533-0.4680.223-0.444-0.514-0.3480.0000.0000.000
측정소코드-0.0011.0000.0400.0520.015-0.019-0.026-0.1490.0500.9040.9040.999
미세먼지 1시간(㎍/㎥)-0.4950.0401.0000.9040.8890.0260.4070.6000.3770.0670.0670.074
미세먼지 24시간(㎍/㎥)-0.5330.0520.9041.0000.8220.0700.3440.5480.3500.0850.0850.104
초미세먼지(㎍/㎥)-0.4680.0150.8890.8221.000-0.0250.4450.6320.3780.0400.0400.052
오존(ppm)0.223-0.0190.0260.070-0.0251.000-0.610-0.252-0.0190.0460.0460.091
이산화질소농도(ppm)-0.444-0.0260.4070.3440.445-0.6101.0000.6540.3790.0920.0920.107
일산화탄소농도(ppm)-0.514-0.1490.6000.5480.632-0.2520.6541.0000.3590.1580.1580.208
아황산가스농도(ppm)-0.3480.0500.3770.3500.378-0.0190.3790.3591.0000.1040.1040.217
권역코드0.0000.9040.0670.0850.0400.0460.0920.1580.1041.0001.0000.999
권역명0.0000.9040.0670.0850.0400.0460.0920.1580.1041.0001.0000.999
측정소명0.0000.9990.0740.1040.0520.0910.1070.2080.2170.9990.9991.000

Missing values

2024-05-04T00:11:14.978815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-04T00:11:15.704931image/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

측정일시권역코드권역명측정소코드측정소명미세먼지 1시간(㎍/㎥)미세먼지 24시간(㎍/㎥)초미세먼지(㎍/㎥)오존(ppm)이산화질소농도(ppm)일산화탄소농도(ppm)아황산가스농도(ppm)
10547201903141000104동남권111274강동구5143210.0140.0480.50.004
7697201903190400104동남권111261강남구4542300.0020.0530.60.007
2663201903271300102동북권111141광진구9485560.0570.0190.70.007
15235201903061400102동북권111142성동구1921581420.0340.060.80.006
15262201903061300102동북권111152동대문구1741401200.0440.0490.80.007
4201201903242300100도심권111131용산구1919110.0320.0240.30.004
12789201903101600103서남권111301양천구3457220.0490.0170.40.005
4458201903241300102동북권111161성북구3130140.0380.0140.50.004
17487201903022000102동북권111291강북구127110820.0390.0420.80.004
10409201903141500102동북권111151중랑구2929140.0310.0220.30.006
측정일시권역코드권역명측정소코드측정소명미세먼지 1시간(㎍/㎥)미세먼지 24시간(㎍/㎥)초미세먼지(㎍/㎥)오존(ppm)이산화질소농도(ppm)일산화탄소농도(ppm)아황산가스농도(ppm)
10377201903141600100도심권111123종로구3633150.0310.0310.60.002
668201903302100103서남권111221구로구3835260.0370.0150.30.004
14436201903072200102동북권111151중랑구4447270.0160.0360.60.008
6843201903201400103서남권111231영등포구128108850.0040.1030.70.006
6418201903210700103서남권111231영등포구312900.0170.0270.30.003
5462201903222100102동북권111311노원구2237150.0320.0130.30.003
5503201903221900101서북권111191서대문구3149110.0350.0170.60.002
1270201903292100103서남권111281금천구4241260.020.0490.50.005
16980201903031600101서북권111181은평구9177550.0750.0140.70.006
9797201903151600104동남권111261강남구3755190.0230.030.30.006