Overview

Dataset statistics

Number of variables13
Number of observations6966
Missing cells0
Missing cells (%)0.0%
Duplicate rows18
Duplicate rows (%)0.3%
Total size in memory782.4 KiB
Average record size in memory115.0 B

Variable types

DateTime2
Numeric9
Categorical2

Dataset

Description송파구 관내의 CCTV 위치별 조도 등 환경 데이터 입니다. DATE(관측 연월일), TIME(관측 시간), LATITUDE(위도), LONGTITUDE(경도), ILLUMINATION(조도), BRIGHTNESS(휘도), LUNAR_PHASE(달 위상), TEMPERATURE(기온), HUMIDITY(습도), WIND_DIR(풍향), WIND_SPD(풍속), PM10(미세먼지), PM25(초미세먼지) 데이터 입니다. 1차 전반 측정 : 2021. 8. 25. 21:00 기준 1차 후반 측정: 2021. 8. 25. 23:00 기준 2차 전반 측정: 2021. 11. 7. 21:00 기준 2차 후반 측정: 2021. 11. 7. 23:00 기준 3차 전반 측정: 2021. 11.22. 21:00 기준 3차 후반 측정: 2021. 11.22. 23:00 기준
Author서울특별시 송파구
URLhttps://www.data.go.kr/data/15097749/fileData.do

Alerts

Dataset has 18 (0.3%) duplicate rowsDuplicates
초미세먼지(PM25) is highly overall correlated with 기온(TEMPERATURE) and 5 other fieldsHigh correlation
달위상(LUNAR_PHASE) is highly overall correlated with 기온(TEMPERATURE) and 5 other fieldsHigh correlation
위도(LATITUDE) is highly overall correlated with 경도(LONGITUDE)High correlation
경도(LONGITUDE) is highly overall correlated with 위도(LATITUDE)High correlation
조도(ILLUMINATION) is highly overall correlated with 휘도(BRIGHTNESS)High correlation
휘도(BRIGHTNESS) is highly overall correlated with 조도(ILLUMINATION)High correlation
기온(TEMPERATURE) is highly overall correlated with 습도(HUMIDITY) and 5 other fieldsHigh correlation
습도(HUMIDITY) is highly overall correlated with 기온(TEMPERATURE) and 3 other fieldsHigh correlation
풍향(WIND_DIR) is highly overall correlated with 기온(TEMPERATURE) and 4 other fieldsHigh correlation
풍속(WIND_SPD) is highly overall correlated with 기온(TEMPERATURE) and 4 other fieldsHigh correlation
미세먼지(PM10) is highly overall correlated with 기온(TEMPERATURE) and 5 other fieldsHigh correlation

Reproduction

Analysis started2023-12-12 05:42:34.432297
Analysis finished2023-12-12 05:42:46.083874
Duration11.65 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size54.6 KiB
Minimum2021-08-25 00:00:00
Maximum2021-11-22 00:00:00
2023-12-12T14:42:46.118418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:42:46.195506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=3)
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size54.6 KiB
Minimum2023-12-12 21:00:00
Maximum2023-12-12 23:00:00
2023-12-12T14:42:46.279107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:42:46.362900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=2)

위도(LATITUDE)
Real number (ℝ)

HIGH CORRELATION 

Distinct1148
Distinct (%)16.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.50347
Minimum37.4693
Maximum37.539936
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size61.4 KiB
2023-12-12T14:42:46.466722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.4693
5-th percentile37.481808
Q137.494429
median37.502281
Q337.509318
95-th percentile37.533775
Maximum37.539936
Range0.07063579
Interquartile range (IQR)0.01488943

Descriptive statistics

Standard deviation0.013461521
Coefficient of variation (CV)0.00035894067
Kurtosis0.56685355
Mean37.50347
Median Absolute Deviation (MAD)0.00730514
Skewness0.64103704
Sum261249.17
Variance0.00018121254
MonotonicityNot monotonic
2023-12-12T14:42:46.584708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.50633621 18
 
0.3%
37.50331497 12
 
0.2%
37.49263 12
 
0.2%
37.50440979 12
 
0.2%
37.49958038 12
 
0.2%
37.50095522 12
 
0.2%
37.52802277 12
 
0.2%
37.49923325 12
 
0.2%
37.50170135 12
 
0.2%
37.49938369 12
 
0.2%
Other values (1138) 6840
98.2%
ValueCountFrequency (%)
37.46930028 6
0.1%
37.4699123 6
0.1%
37.47334726 6
0.1%
37.47431183 6
0.1%
37.47451401 6
0.1%
37.47543 6
0.1%
37.4755 6
0.1%
37.47558212 6
0.1%
37.47609711 6
0.1%
37.47715378 6
0.1%
ValueCountFrequency (%)
37.53993607 6
0.1%
37.53941464 6
0.1%
37.53881073 6
0.1%
37.53842923 6
0.1%
37.53842649 6
0.1%
37.53836268 6
0.1%
37.53835678 6
0.1%
37.538307 6
0.1%
37.53825378 6
0.1%
37.53818582 6
0.1%

경도(LONGITUDE)
Real number (ℝ)

HIGH CORRELATION 

Distinct1148
Distinct (%)16.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.11984
Minimum127.07109
Maximum127.16097
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size61.4 KiB
2023-12-12T14:42:46.728527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum127.07109
5-th percentile127.08388
Q1127.10647
median127.11909
Q3127.1349
95-th percentile127.15227
Maximum127.16097
Range0.0898743
Interquartile range (IQR)0.0284358

Descriptive statistics

Standard deviation0.020394632
Coefficient of variation (CV)0.00016043626
Kurtosis-0.65307001
Mean127.11984
Median Absolute Deviation (MAD)0.0142212
Skewness-0.11760378
Sum885516.79
Variance0.000415941
MonotonicityNot monotonic
2023-12-12T14:42:46.867233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.1095789 12
 
0.2%
127.1224747 12
 
0.2%
127.1199036 12
 
0.2%
127.0965424 12
 
0.2%
127.1303406 12
 
0.2%
127.1353989 12
 
0.2%
127.1045898 12
 
0.2%
127.110527 12
 
0.2%
127.1157303 12
 
0.2%
127.1251984 12
 
0.2%
Other values (1138) 6846
98.3%
ValueCountFrequency (%)
127.0710907 6
0.1%
127.0713272 6
0.1%
127.0716095 6
0.1%
127.0735525 6
0.1%
127.0737839 6
0.1%
127.073928 6
0.1%
127.0740738 6
0.1%
127.0741864 6
0.1%
127.0749735 6
0.1%
127.0750885 6
0.1%
ValueCountFrequency (%)
127.160965 6
0.1%
127.1607628 6
0.1%
127.1597978 6
0.1%
127.1594805 6
0.1%
127.1593037 6
0.1%
127.1591266 6
0.1%
127.159073 6
0.1%
127.1588955 6
0.1%
127.1587226 6
0.1%
127.1583557 6
0.1%

조도(ILLUMINATION)
Real number (ℝ)

HIGH CORRELATION 

Distinct2437
Distinct (%)35.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.693196
Minimum3.11
Maximum69.69
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size61.4 KiB
2023-12-12T14:42:46.990800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.11
5-th percentile7.9
Q111.9725
median16
Q321.5475
95-th percentile33.12
Maximum69.69
Range66.58
Interquartile range (IQR)9.575

Descriptive statistics

Standard deviation8.0712099
Coefficient of variation (CV)0.45617593
Kurtosis2.8868123
Mean17.693196
Median Absolute Deviation (MAD)4.55
Skewness1.3771716
Sum123250.8
Variance65.144429
MonotonicityNot monotonic
2023-12-12T14:42:47.337468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15.92 18
 
0.3%
16.38 17
 
0.2%
18.42 16
 
0.2%
12.75 16
 
0.2%
18.38 16
 
0.2%
11.43 14
 
0.2%
14.85 14
 
0.2%
16.09 13
 
0.2%
17.27 13
 
0.2%
14.63 13
 
0.2%
Other values (2427) 6816
97.8%
ValueCountFrequency (%)
3.11 1
< 0.1%
3.24 1
< 0.1%
3.49 1
< 0.1%
3.92 2
< 0.1%
4.18 1
< 0.1%
4.31 1
< 0.1%
4.36 1
< 0.1%
4.52 1
< 0.1%
4.68 1
< 0.1%
4.72 1
< 0.1%
ValueCountFrequency (%)
69.69 1
< 0.1%
67.19 1
< 0.1%
65.54 1
< 0.1%
64.91 1
< 0.1%
60.66 1
< 0.1%
59.9 1
< 0.1%
58.0 1
< 0.1%
57.63 1
< 0.1%
57.29 1
< 0.1%
57.04 1
< 0.1%

휘도(BRIGHTNESS)
Real number (ℝ)

HIGH CORRELATION 

Distinct258
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.91709015
Minimum0.19
Maximum5.74
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size61.4 KiB
2023-12-12T14:42:47.449114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.19
5-th percentile0.44
Q10.63
median0.82
Q31.09
95-th percentile1.7
Maximum5.74
Range5.55
Interquartile range (IQR)0.46

Descriptive statistics

Standard deviation0.43015634
Coefficient of variation (CV)0.46904477
Kurtosis11.28828
Mean0.91709015
Median Absolute Deviation (MAD)0.22
Skewness2.278783
Sum6388.45
Variance0.18503447
MonotonicityNot monotonic
2023-12-12T14:42:47.574638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.62 120
 
1.7%
0.6 115
 
1.7%
0.72 107
 
1.5%
0.69 105
 
1.5%
0.65 101
 
1.4%
0.71 99
 
1.4%
0.8 97
 
1.4%
0.68 97
 
1.4%
0.64 95
 
1.4%
0.74 95
 
1.4%
Other values (248) 5935
85.2%
ValueCountFrequency (%)
0.19 2
 
< 0.1%
0.2 1
 
< 0.1%
0.24 6
0.1%
0.25 2
 
< 0.1%
0.26 1
 
< 0.1%
0.27 3
 
< 0.1%
0.28 4
 
0.1%
0.29 7
0.1%
0.3 3
 
< 0.1%
0.31 10
0.1%
ValueCountFrequency (%)
5.74 1
 
< 0.1%
5.72 1
 
< 0.1%
4.85 1
 
< 0.1%
4.63 1
 
< 0.1%
4.02 1
 
< 0.1%
3.8 2
< 0.1%
3.65 4
0.1%
3.64 1
 
< 0.1%
3.63 1
 
< 0.1%
3.58 1
 
< 0.1%

달위상(LUNAR_PHASE)
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size54.6 KiB
90.8
2322 
1.2
2322 
92.2
2322 

Length

Max length4
Median length4
Mean length3.6666667
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row90.8
2nd row90.8
3rd row90.8
4th row90.8
5th row90.8

Common Values

ValueCountFrequency (%)
90.8 2322
33.3%
1.2 2322
33.3%
92.2 2322
33.3%

Length

2023-12-12T14:42:47.679682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T14:42:47.760692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
90.8 2322
33.3%
1.2 2322
33.3%
92.2 2322
33.3%

기온(TEMPERATURE)
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.216667
Minimum0.7
Maximum24.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size61.4 KiB
2023-12-12T14:42:47.851010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.7
5-th percentile0.7
Q11.6
median20
Q324.2
95-th percentile24.8
Maximum24.8
Range24.1
Interquartile range (IQR)22.6

Descriptive statistics

Standard deviation10.121895
Coefficient of variation (CV)0.66518479
Kurtosis-1.4944335
Mean15.216667
Median Absolute Deviation (MAD)4.5
Skewness-0.60412418
Sum105999.3
Variance102.45276
MonotonicityDecreasing
2023-12-12T14:42:47.958447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
24.8 1161
16.7%
24.2 1161
16.7%
20.3 1161
16.7%
19.7 1161
16.7%
1.6 1161
16.7%
0.7 1161
16.7%
ValueCountFrequency (%)
0.7 1161
16.7%
1.6 1161
16.7%
19.7 1161
16.7%
20.3 1161
16.7%
24.2 1161
16.7%
24.8 1161
16.7%
ValueCountFrequency (%)
24.8 1161
16.7%
24.2 1161
16.7%
20.3 1161
16.7%
19.7 1161
16.7%
1.6 1161
16.7%
0.7 1161
16.7%

습도(HUMIDITY)
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean75
Minimum43
Maximum96
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size61.4 KiB
2023-12-12T14:42:48.041822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum43
5-th percentile43
Q151
median85
Q390
95-th percentile96
Maximum96
Range53
Interquartile range (IQR)39

Descriptive statistics

Standard deviation20.28281
Coefficient of variation (CV)0.27043747
Kurtosis-1.3780754
Mean75
Median Absolute Deviation (MAD)8
Skewness-0.63736417
Sum522450
Variance411.39239
MonotonicityNot monotonic
2023-12-12T14:42:48.136688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
96 1161
16.7%
86 1161
16.7%
90 1161
16.7%
84 1161
16.7%
51 1161
16.7%
43 1161
16.7%
ValueCountFrequency (%)
43 1161
16.7%
51 1161
16.7%
84 1161
16.7%
86 1161
16.7%
90 1161
16.7%
96 1161
16.7%
ValueCountFrequency (%)
96 1161
16.7%
90 1161
16.7%
86 1161
16.7%
84 1161
16.7%
51 1161
16.7%
43 1161
16.7%

풍향(WIND_DIR)
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean225.03333
Minimum164.6
Maximum314.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size61.4 KiB
2023-12-12T14:42:48.280337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum164.6
5-th percentile164.6
Q1170.5
median203.5
Q3293.2
95-th percentile314.9
Maximum314.9
Range150.3
Interquartile range (IQR)122.7

Descriptive statistics

Standard deviation60.728425
Coefficient of variation (CV)0.26986413
Kurtosis-1.5749964
Mean225.03333
Median Absolute Deviation (MAD)35.95
Skewness0.38732464
Sum1567582.2
Variance3687.9416
MonotonicityNot monotonic
2023-12-12T14:42:48.404989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
173.1 1161
16.7%
164.6 1161
16.7%
233.9 1161
16.7%
170.5 1161
16.7%
314.9 1161
16.7%
293.2 1161
16.7%
ValueCountFrequency (%)
164.6 1161
16.7%
170.5 1161
16.7%
173.1 1161
16.7%
233.9 1161
16.7%
293.2 1161
16.7%
314.9 1161
16.7%
ValueCountFrequency (%)
314.9 1161
16.7%
293.2 1161
16.7%
233.9 1161
16.7%
173.1 1161
16.7%
170.5 1161
16.7%
164.6 1161
16.7%

풍속(WIND_SPD)
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.05
Minimum0.9
Maximum3.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size61.4 KiB
2023-12-12T14:42:48.558636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.9
5-th percentile0.9
Q11.2
median1.75
Q33.1
95-th percentile3.6
Maximum3.6
Range2.7
Interquartile range (IQR)1.9

Descriptive statistics

Standard deviation0.98791368
Coefficient of variation (CV)0.48190911
Kurtosis-1.363637
Mean2.05
Median Absolute Deviation (MAD)0.7
Skewness0.4461681
Sum14280.3
Variance0.97597344
MonotonicityNot monotonic
2023-12-12T14:42:48.689665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2.0 1161
16.7%
0.9 1161
16.7%
1.5 1161
16.7%
1.2 1161
16.7%
3.1 1161
16.7%
3.6 1161
16.7%
ValueCountFrequency (%)
0.9 1161
16.7%
1.2 1161
16.7%
1.5 1161
16.7%
2.0 1161
16.7%
3.1 1161
16.7%
3.6 1161
16.7%
ValueCountFrequency (%)
3.6 1161
16.7%
3.1 1161
16.7%
2.0 1161
16.7%
1.5 1161
16.7%
1.2 1161
16.7%
0.9 1161
16.7%

미세먼지(PM10)
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.666667
Minimum14
Maximum44
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size61.4 KiB
2023-12-12T14:42:48.812713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile14
Q117
median20.5
Q338
95-th percentile44
Maximum44
Range30
Interquartile range (IQR)21

Descriptive statistics

Standard deviation11.205962
Coefficient of variation (CV)0.43659592
Kurtosis-1.2896296
Mean25.666667
Median Absolute Deviation (MAD)5
Skewness0.65354823
Sum178794
Variance125.57358
MonotonicityNot monotonic
2023-12-12T14:42:48.932448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
14 1161
16.7%
17 1161
16.7%
21 1161
16.7%
20 1161
16.7%
44 1161
16.7%
38 1161
16.7%
ValueCountFrequency (%)
14 1161
16.7%
17 1161
16.7%
20 1161
16.7%
21 1161
16.7%
38 1161
16.7%
44 1161
16.7%
ValueCountFrequency (%)
44 1161
16.7%
38 1161
16.7%
21 1161
16.7%
20 1161
16.7%
17 1161
16.7%
14 1161
16.7%

초미세먼지(PM25)
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size54.6 KiB
9
2322 
4
1161 
5
1161 
21
1161 
19
1161 

Length

Max length2
Median length1
Mean length1.3333333
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row4
4th row4
5th row4

Common Values

ValueCountFrequency (%)
9 2322
33.3%
4 1161
16.7%
5 1161
16.7%
21 1161
16.7%
19 1161
16.7%

Length

2023-12-12T14:42:49.158843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T14:42:49.389994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
9 2322
33.3%
4 1161
16.7%
5 1161
16.7%
21 1161
16.7%
19 1161
16.7%

Interactions

2023-12-12T14:42:45.052758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:42:36.459648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:42:37.672281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:42:38.569935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:42:39.465542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:42:40.654518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:42:42.068934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:42:43.057242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:42:44.103960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:42:45.147723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:42:36.622004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:42:37.789130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:42:38.686116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:42:39.592784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:42:40.793635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:42:42.204511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:42:43.187730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:42:44.222495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:42:45.227385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:42:36.735914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:42:37.890113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:42:38.774153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:42:39.737860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:42:40.910810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:42:42.301760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:42:43.277098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:42:44.325024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:42:45.325957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:42:36.889615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:42:37.989417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:42:38.868120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:42:39.837279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:42:41.044131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:42:42.402509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:42:43.379682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:42:44.442281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:42:45.403825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:42:37.033397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:42:38.087682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:42:38.972699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:42:39.954174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:42:41.149048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:42:42.513755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:42:43.494775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:42:44.547642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:42:45.476395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:42:37.156167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:42:38.201918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:42:39.066663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:42:40.143250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:42:41.571157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:42:42.629548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:42:43.609837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:42:44.655041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:42:45.545803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:42:37.278145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:42:38.301610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:42:39.162796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:42:40.274046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:42:41.682981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:42:42.734634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:42:43.737206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:42:44.735828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:42:45.626387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:42:37.414422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:42:38.395461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:42:39.282410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:42:40.402765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:42:41.820531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:42:42.858947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:42:43.880237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:42:44.832858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:42:45.704161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:42:37.538456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:42:38.477837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:42:39.371225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:42:40.507748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:42:41.939501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:42:42.955982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:42:43.993104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:42:44.942591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T14:42:49.549384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관측연월일(DATE)관측시간(TIME)위도(LATITUDE)경도(LONGITUDE)조도(ILLUMINATION)휘도(BRIGHTNESS)달위상(LUNAR_PHASE)기온(TEMPERATURE)습도(HUMIDITY)풍향(WIND_DIR)풍속(WIND_SPD)미세먼지(PM10)초미세먼지(PM25)
관측연월일(DATE)1.0000.0000.0000.0000.2710.3281.0001.0000.8330.7431.0001.0001.000
관측시간(TIME)0.0001.0000.0000.0000.2460.1440.0000.7850.6830.9321.0000.6830.683
위도(LATITUDE)0.0000.0001.0000.7470.2600.2300.0000.0000.0000.0000.0000.0000.000
경도(LONGITUDE)0.0000.0000.7471.0000.3540.2590.0000.0000.0000.0000.0000.0000.000
조도(ILLUMINATION)0.2710.2460.2600.3541.0000.5740.2710.2480.3130.2550.2620.3410.341
휘도(BRIGHTNESS)0.3280.1440.2300.2590.5741.0000.3280.2190.1630.1920.2320.2020.202
달위상(LUNAR_PHASE)1.0000.0000.0000.0000.2710.3281.0001.0000.8330.7431.0001.0001.000
기온(TEMPERATURE)1.0000.7850.0000.0000.2480.2191.0001.0000.8760.9821.0000.8410.841
습도(HUMIDITY)0.8330.6830.0000.0000.3130.1630.8330.8761.0001.0001.0000.9950.995
풍향(WIND_DIR)0.7430.9320.0000.0000.2550.1920.7430.9821.0001.0001.0000.9900.990
풍속(WIND_SPD)1.0001.0000.0000.0000.2620.2321.0001.0001.0001.0001.0001.0001.000
미세먼지(PM10)1.0000.6830.0000.0000.3410.2021.0000.8410.9950.9901.0001.0001.000
초미세먼지(PM25)1.0000.6830.0000.0000.3410.2021.0000.8410.9950.9901.0001.0001.000
2023-12-12T14:42:49.722745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
초미세먼지(PM25)달위상(LUNAR_PHASE)
초미세먼지(PM25)1.0001.000
달위상(LUNAR_PHASE)1.0001.000
2023-12-12T14:42:49.844707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위도(LATITUDE)경도(LONGITUDE)조도(ILLUMINATION)휘도(BRIGHTNESS)기온(TEMPERATURE)습도(HUMIDITY)풍향(WIND_DIR)풍속(WIND_SPD)미세먼지(PM10)달위상(LUNAR_PHASE)초미세먼지(PM25)
위도(LATITUDE)1.000-0.5300.061-0.0080.0000.0000.0000.0000.0000.0000.000
경도(LONGITUDE)-0.5301.000-0.141-0.1910.0000.0000.0000.0000.0000.0000.000
조도(ILLUMINATION)0.061-0.1411.0000.684-0.168-0.1110.3000.2360.2490.1680.148
휘도(BRIGHTNESS)-0.008-0.1910.6841.000-0.175-0.1070.2770.2150.2410.1530.117
기온(TEMPERATURE)0.0000.000-0.168-0.1751.0000.943-0.657-0.600-0.8861.0000.816
습도(HUMIDITY)0.0000.000-0.111-0.1070.9431.000-0.486-0.486-0.7710.8660.901
풍향(WIND_DIR)0.0000.0000.3000.277-0.657-0.4861.0000.8860.8290.8160.882
풍속(WIND_SPD)0.0000.0000.2360.215-0.600-0.4860.8861.0000.6001.0001.000
미세먼지(PM10)0.0000.0000.2490.241-0.886-0.7710.8290.6001.0001.0000.866
달위상(LUNAR_PHASE)0.0000.0000.1680.1531.0000.8660.8161.0001.0001.0001.000
초미세먼지(PM25)0.0000.0000.1480.1170.8160.9010.8821.0000.8661.0001.000

Missing values

2023-12-12T14:42:45.829721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T14:42:46.002836image/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

관측연월일(DATE)관측시간(TIME)위도(LATITUDE)경도(LONGITUDE)조도(ILLUMINATION)휘도(BRIGHTNESS)달위상(LUNAR_PHASE)기온(TEMPERATURE)습도(HUMIDITY)풍향(WIND_DIR)풍속(WIND_SPD)미세먼지(PM10)초미세먼지(PM25)
02021-08-2521:0037.478458127.13914515.950.8990.824.896173.12.0144
12021-08-2521:0037.47847127.14204415.551.5690.824.896173.12.0144
22021-08-2521:0037.47999127.1416812.641.2390.824.896173.12.0144
32021-08-2521:0037.480915127.12665612.350.6590.824.896173.12.0144
42021-08-2521:0037.48148127.1313938.170.3890.824.896173.12.0144
52021-08-2521:0037.481808127.13095111.50.5890.824.896173.12.0144
62021-08-2521:0037.482105127.1270229.480.5490.824.896173.12.0144
72021-08-2521:0037.482693127.1310357.220.3890.824.896173.12.0144
82021-08-2521:0037.4828127.1295479.660.5390.824.896173.12.0144
92021-08-2521:0037.483255127.1246311.930.790.824.896173.12.0144
관측연월일(DATE)관측시간(TIME)위도(LATITUDE)경도(LONGITUDE)조도(ILLUMINATION)휘도(BRIGHTNESS)달위상(LUNAR_PHASE)기온(TEMPERATURE)습도(HUMIDITY)풍향(WIND_DIR)풍속(WIND_SPD)미세먼지(PM10)초미세먼지(PM25)
69562021-11-2223:0037.52394127.106626.291.0392.20.743293.23.63819
69572021-11-2223:0037.524788127.10290519.281.0192.20.743293.23.63819
69582021-11-2223:0037.510052127.12092620.381.3992.20.743293.23.63819
69592021-11-2223:0037.525181127.10646818.610.9592.20.743293.23.63819
69602021-11-2223:0037.533772127.11865216.691.3692.20.743293.23.63819
69612021-11-2223:0037.495457127.10137912.080.6892.20.743293.23.63819
69622021-11-2223:0037.511162127.1250310.460.7292.20.743293.23.63819
69632021-11-2223:0037.481197127.13466613.010.9592.20.743293.23.63819
69642021-11-2223:0037.478291127.14407415.881.6692.20.743293.23.63819
69652021-11-2223:0037.477352127.13912213.190.9892.20.743293.23.63819

Duplicate rows

Most frequently occurring

관측연월일(DATE)관측시간(TIME)위도(LATITUDE)경도(LONGITUDE)조도(ILLUMINATION)휘도(BRIGHTNESS)달위상(LUNAR_PHASE)기온(TEMPERATURE)습도(HUMIDITY)풍향(WIND_DIR)풍속(WIND_SPD)미세먼지(PM10)초미세먼지(PM25)# duplicates
02021-08-2521:0037.499233127.11990412.480.5890.824.896173.12.01442
12021-08-2521:0037.499384127.1045917.030.9990.824.896173.12.01442
22021-08-2521:0037.500955127.10957915.040.8590.824.896173.12.01442
32021-08-2523:0037.499233127.11990421.350.9490.824.286164.60.91752
42021-08-2523:0037.499384127.1045917.921.1590.824.286164.60.91752
52021-08-2523:0037.500955127.10957911.850.7790.824.286164.60.91752
62021-10-0721:0037.499233127.11990416.820.711.220.390233.91.52192
72021-10-0721:0037.499384127.1045930.321.651.220.390233.91.52192
82021-10-0721:0037.500955127.10957920.91.11.220.390233.91.52192
92021-10-0723:0037.499233127.11990416.190.611.219.784170.51.22092