Overview

Dataset statistics

Number of variables9
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows5
Duplicate rows (%)0.1%
Total size in memory859.4 KiB
Average record size in memory88.0 B

Variable types

Numeric8
DateTime1

Dataset

Description서울특별시 서대문구 계절별(가을) 도로 주도, 휘도 데이터입니다. 날짜, 시간, 위도, 경도, 조도, 휘도, 달 위상, 기온 등 데이터를 제공합니다.
Author서울특별시 서대문구
URLhttps://www.data.go.kr/data/15109199/fileData.do

Alerts

Dataset has 5 (0.1%) duplicate rowsDuplicates
날짜 is highly overall correlated with 위도High correlation
위도 is highly overall correlated with 날짜High correlation
조도 is highly overall correlated with 휘도High correlation
휘도 is highly overall correlated with 조도High correlation

Reproduction

Analysis started2023-12-12 21:01:08.157373
Analysis finished2023-12-12 21:01:18.536212
Duration10.38 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

날짜
Real number (ℝ)

HIGH CORRELATION 

Distinct15
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20221109
Minimum20221031
Maximum20221118
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T06:01:18.612602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20221031
5-th percentile20221102
Q120221104
median20221109
Q320221114
95-th percentile20221117
Maximum20221118
Range87
Interquartile range (IQR)10

Descriptive statistics

Standard deviation10.572662
Coefficient of variation (CV)5.2285273 × 10-7
Kurtosis39.055233
Mean20221109
Median Absolute Deviation (MAD)5
Skewness-5.6463581
Sum2.0221109 × 1011
Variance111.78118
MonotonicityNot monotonic
2023-12-13T06:01:18.755989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
20221109 1198
12.0%
20221111 1128
11.3%
20221108 926
9.3%
20221104 849
8.5%
20221103 839
8.4%
20221114 801
8.0%
20221116 801
8.0%
20221110 743
7.4%
20221117 670
6.7%
20221115 528
 
5.3%
Other values (5) 1517
15.2%
ValueCountFrequency (%)
20221031 144
 
1.4%
20221101 232
 
2.3%
20221102 463
 
4.6%
20221103 839
8.4%
20221104 849
8.5%
20221107 370
 
3.7%
20221108 926
9.3%
20221109 1198
12.0%
20221110 743
7.4%
20221111 1128
11.3%
ValueCountFrequency (%)
20221118 308
 
3.1%
20221117 670
6.7%
20221116 801
8.0%
20221115 528
5.3%
20221114 801
8.0%
20221111 1128
11.3%
20221110 743
7.4%
20221109 1198
12.0%
20221108 926
9.3%
20221107 370
 
3.7%

시간
Date

Distinct241
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2023-12-13 17:49:00
Maximum2023-12-13 21:50:00
2023-12-13T06:01:18.907438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:01:19.071291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

위도
Real number (ℝ)

HIGH CORRELATION 

Distinct9564
Distinct (%)95.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.577014
Minimum37.555532
Maximum37.604909
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T06:01:19.246034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.555532
5-th percentile37.559027
Q137.568953
median37.577304
Q337.58408
95-th percentile37.596435
Maximum37.604909
Range0.04937698
Interquartile range (IQR)0.015127838

Descriptive statistics

Standard deviation0.01091427
Coefficient of variation (CV)0.00029045068
Kurtosis-0.59878459
Mean37.577014
Median Absolute Deviation (MAD)0.00767912
Skewness0.14655453
Sum375770.14
Variance0.00011912128
MonotonicityNot monotonic
2023-12-13T06:01:19.423271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.588729 5
 
0.1%
37.58589122 5
 
0.1%
37.584261 4
 
< 0.1%
37.58194311 4
 
< 0.1%
37.59974374 4
 
< 0.1%
37.591754 4
 
< 0.1%
37.58234012 3
 
< 0.1%
37.600242 3
 
< 0.1%
37.58194669 3
 
< 0.1%
37.58284801 3
 
< 0.1%
Other values (9554) 9962
99.6%
ValueCountFrequency (%)
37.55553186 1
< 0.1%
37.555593 1
< 0.1%
37.555684 1
< 0.1%
37.555725 1
< 0.1%
37.55579491 1
< 0.1%
37.55587738 1
< 0.1%
37.55592793 1
< 0.1%
37.55593982 1
< 0.1%
37.55594136 1
< 0.1%
37.5559499 1
< 0.1%
ValueCountFrequency (%)
37.60490884 1
< 0.1%
37.60487231 1
< 0.1%
37.60485461 1
< 0.1%
37.60484594 1
< 0.1%
37.60467676 1
< 0.1%
37.60443461 1
< 0.1%
37.60433744 1
< 0.1%
37.603977 2
< 0.1%
37.60389258 1
< 0.1%
37.603641 1
< 0.1%

경도
Real number (ℝ)

Distinct9572
Distinct (%)95.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.93428
Minimum126.90383
Maximum126.96858
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T06:01:19.589561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.90383
5-th percentile126.91229
Q1126.92449
median126.93302
Q3126.94529
95-th percentile126.95969
Maximum126.96858
Range0.0647493
Interquartile range (IQR)0.0208009

Descriptive statistics

Standard deviation0.014075283
Coefficient of variation (CV)0.00011088638
Kurtosis-0.61690728
Mean126.93428
Median Absolute Deviation (MAD)0.00999305
Skewness0.23003705
Sum1269342.8
Variance0.0001981136
MonotonicityNot monotonic
2023-12-13T06:01:19.767074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.93565 5
 
0.1%
126.93491 4
 
< 0.1%
126.945087 4
 
< 0.1%
126.9142844 4
 
< 0.1%
126.933878 3
 
< 0.1%
126.9496 3
 
< 0.1%
126.959718 3
 
< 0.1%
126.94388 3
 
< 0.1%
126.950491 3
 
< 0.1%
126.923335 3
 
< 0.1%
Other values (9562) 9965
99.7%
ValueCountFrequency (%)
126.9038287 1
< 0.1%
126.9039355 1
< 0.1%
126.9040036 1
< 0.1%
126.9040559 1
< 0.1%
126.9041314 1
< 0.1%
126.9042853 1
< 0.1%
126.9043642 1
< 0.1%
126.904388 1
< 0.1%
126.9043971 1
< 0.1%
126.9045147 1
< 0.1%
ValueCountFrequency (%)
126.968578 1
< 0.1%
126.9682183 1
< 0.1%
126.9680209 1
< 0.1%
126.968012 1
< 0.1%
126.9679935 1
< 0.1%
126.9678554 1
< 0.1%
126.9678362 1
< 0.1%
126.9677555 1
< 0.1%
126.9677173 1
< 0.1%
126.9677077 1
< 0.1%

조도
Real number (ℝ)

HIGH CORRELATION 

Distinct1391
Distinct (%)13.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.307998
Minimum0.05
Maximum200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T06:01:19.948983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.05
5-th percentile0.6
Q19.9
median28.2
Q352.2
95-th percentile107.01
Maximum200
Range199.95
Interquartile range (IQR)42.3

Descriptive statistics

Standard deviation37.080402
Coefficient of variation (CV)0.99389954
Kurtosis4.0753475
Mean37.307998
Median Absolute Deviation (MAD)20.5
Skewness1.7858723
Sum373079.98
Variance1374.9562
MonotonicityNot monotonic
2023-12-13T06:01:20.117789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.4 116
 
1.2%
0.5 99
 
1.0%
0.3 92
 
0.9%
0.6 88
 
0.9%
0.2 84
 
0.8%
0.7 80
 
0.8%
200.0 75
 
0.8%
1.2 68
 
0.7%
0.8 55
 
0.5%
1.5 51
 
0.5%
Other values (1381) 9192
91.9%
ValueCountFrequency (%)
0.05 1
 
< 0.1%
0.07 1
 
< 0.1%
0.1 17
 
0.2%
0.2 84
0.8%
0.23 1
 
< 0.1%
0.27 1
 
< 0.1%
0.28 1
 
< 0.1%
0.3 92
0.9%
0.36 1
 
< 0.1%
0.4 116
1.2%
ValueCountFrequency (%)
200.0 75
0.8%
199.9 1
 
< 0.1%
199.8 1
 
< 0.1%
198.9 1
 
< 0.1%
198.7 1
 
< 0.1%
198.3 1
 
< 0.1%
198.2 1
 
< 0.1%
197.8 2
 
< 0.1%
196.0 1
 
< 0.1%
193.7 1
 
< 0.1%

휘도
Real number (ℝ)

HIGH CORRELATION 

Distinct637
Distinct (%)6.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.204392
Minimum0.01
Maximum31.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T06:01:20.292699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.05
Q10.29
median0.75
Q31.52
95-th percentile3.99
Maximum31.6
Range31.59
Interquartile range (IQR)1.23

Descriptive statistics

Standard deviation1.5042676
Coefficient of variation (CV)1.248985
Kurtosis27.472703
Mean1.204392
Median Absolute Deviation (MAD)0.54
Skewness3.6378547
Sum12043.92
Variance2.262821
MonotonicityNot monotonic
2023-12-13T06:01:20.490841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.04 145
 
1.5%
0.05 135
 
1.4%
0.06 125
 
1.2%
0.12 123
 
1.2%
0.03 111
 
1.1%
0.14 108
 
1.1%
0.02 106
 
1.1%
0.07 101
 
1.0%
0.17 95
 
0.9%
0.08 92
 
0.9%
Other values (627) 8859
88.6%
ValueCountFrequency (%)
0.01 70
0.7%
0.02 106
1.1%
0.03 111
1.1%
0.04 145
1.5%
0.05 135
1.4%
0.06 125
1.2%
0.07 101
1.0%
0.08 92
0.9%
0.09 75
0.8%
0.1 85
0.9%
ValueCountFrequency (%)
31.6 1
 
< 0.1%
10.0 71
0.7%
9.95 1
 
< 0.1%
9.84 1
 
< 0.1%
9.8 1
 
< 0.1%
9.71 1
 
< 0.1%
9.62 2
 
< 0.1%
9.61 1
 
< 0.1%
9.55 1
 
< 0.1%
9.5 1
 
< 0.1%

달 위상
Real number (ℝ)

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.72500667
Minimum0.4
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T06:01:20.647476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.4
5-th percentile0.46666667
Q10.53333333
median0.66666667
Q30.93333333
95-th percentile1
Maximum1
Range0.6
Interquartile range (IQR)0.4

Descriptive statistics

Standard deviation0.19351649
Coefficient of variation (CV)0.26691685
Kurtosis-1.4512106
Mean0.72500667
Median Absolute Deviation (MAD)0.2
Skewness0.017853362
Sum7250.0667
Variance0.037448634
MonotonicityNot monotonic
2023-12-13T06:01:20.776201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0.933333333 1669
16.7%
0.666666667 1650
16.5%
0.866666667 1498
15.0%
0.6 1367
13.7%
0.533333333 1264
12.6%
1.0 1198
12.0%
0.466666667 902
9.0%
0.4 452
 
4.5%
ValueCountFrequency (%)
0.4 452
 
4.5%
0.466666667 902
9.0%
0.533333333 1264
12.6%
0.6 1367
13.7%
0.666666667 1650
16.5%
0.866666667 1498
15.0%
0.933333333 1669
16.7%
1.0 1198
12.0%
ValueCountFrequency (%)
1.0 1198
12.0%
0.933333333 1669
16.7%
0.866666667 1498
15.0%
0.666666667 1650
16.5%
0.6 1367
13.7%
0.533333333 1264
12.6%
0.466666667 902
9.0%
0.4 452
 
4.5%

기온
Real number (ℝ)

Distinct1811
Distinct (%)18.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.496683
Minimum4.6
Maximum18.28
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T06:01:20.929238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.6
5-th percentile5.9295
Q19.1166667
median11.596667
Q313.22
95-th percentile17.161667
Maximum18.28
Range13.68
Interquartile range (IQR)4.1033333

Descriptive statistics

Standard deviation3.0975511
Coefficient of variation (CV)0.26942998
Kurtosis-0.46505534
Mean11.496683
Median Absolute Deviation (MAD)2.2366667
Skewness0.092225876
Sum114966.83
Variance9.5948229
MonotonicityNot monotonic
2023-12-13T06:01:21.070888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.42 26
 
0.3%
9.0 25
 
0.2%
12.18 24
 
0.2%
9.48 24
 
0.2%
9.45 24
 
0.2%
8.84 24
 
0.2%
12.84 23
 
0.2%
9.49 23
 
0.2%
12.06 22
 
0.2%
8.6 21
 
0.2%
Other values (1801) 9764
97.6%
ValueCountFrequency (%)
4.6 3
< 0.1%
4.61 5
0.1%
4.62 1
 
< 0.1%
4.63 4
< 0.1%
4.64 1
 
< 0.1%
4.65 3
< 0.1%
4.66 2
 
< 0.1%
4.67 3
< 0.1%
4.68 4
< 0.1%
4.69 1
 
< 0.1%
ValueCountFrequency (%)
18.28 1
 
< 0.1%
18.26166667 6
0.1%
18.24333333 7
0.1%
18.225 9
0.1%
18.20666667 10
0.1%
18.18833333 11
0.1%
18.17 8
0.1%
18.15166667 7
0.1%
18.13333333 3
 
< 0.1%
18.115 4
 
< 0.1%

습도
Real number (ℝ)

Distinct1331
Distinct (%)13.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean62.591597
Minimum29
Maximum96.49881
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T06:01:21.241712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum29
5-th percentile37.331667
Q156.4
median63.583333
Q370.266667
95-th percentile77.883333
Maximum96.49881
Range67.49881
Interquartile range (IQR)13.866667

Descriptive statistics

Standard deviation11.725272
Coefficient of variation (CV)0.18732982
Kurtosis0.88466149
Mean62.591597
Median Absolute Deviation (MAD)6.9833333
Skewness-0.45895612
Sum625915.97
Variance137.48201
MonotonicityNot monotonic
2023-12-13T06:01:21.442563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
64.0 32
 
0.3%
58.8 31
 
0.3%
60.0 30
 
0.3%
49.13333333 29
 
0.3%
66.5 28
 
0.3%
69.0 28
 
0.3%
55.5 27
 
0.3%
59.8 26
 
0.3%
62.75 26
 
0.3%
64.5 26
 
0.3%
Other values (1321) 9717
97.2%
ValueCountFrequency (%)
29.0 2
 
< 0.1%
29.08333333 4
< 0.1%
29.16666667 3
< 0.1%
29.25 2
 
< 0.1%
29.33333333 2
 
< 0.1%
29.41666667 1
 
< 0.1%
29.5 3
< 0.1%
29.58333333 6
0.1%
29.66666667 2
 
< 0.1%
29.75 2
 
< 0.1%
ValueCountFrequency (%)
96.49880952 1
< 0.1%
96.49761905 2
< 0.1%
96.49642857 1
< 0.1%
96.4952381 1
< 0.1%
96.49404762 2
< 0.1%
96.49285714 1
< 0.1%
96.49166667 1
< 0.1%
96.49047619 1
< 0.1%
96.48809524 2
< 0.1%
96.48690476 1
< 0.1%

Interactions

2023-12-13T06:01:17.337597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:01:10.294690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:01:11.163761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:01:12.064135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:01:13.056671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:01:14.097622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:01:15.066301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:01:16.084593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:01:17.456392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:01:10.401535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:01:11.243841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:01:12.206902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:01:13.171539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:01:14.218345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:01:15.169053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:01:16.197144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:01:17.569273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:01:10.514245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:01:11.325759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:01:12.312179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:01:13.300141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:01:14.351066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:01:15.302289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:01:16.323318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:01:17.681403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:01:10.638474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:01:11.424852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:01:12.435489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:01:13.444143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:01:14.475950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:01:15.409211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:01:16.443022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:01:17.791852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:01:10.786102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:01:11.542037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:01:12.580086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:01:13.574463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:01:14.617657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:01:15.559228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:01:16.578236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:01:17.902026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:01:10.890083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:01:11.647813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:01:12.703264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:01:13.690627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:01:14.734339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:01:15.679835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:01:16.989726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:01:18.007634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:01:10.981486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:01:11.809034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:01:12.817641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:01:13.820493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:01:14.856924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:01:15.810930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:01:17.099517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:01:18.155039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:01:11.071787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:01:11.922881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:01:12.942765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:01:13.968201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:01:14.959600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:01:15.947788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:01:17.216081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T06:01:21.543475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
날짜위도경도조도휘도달 위상기온습도
날짜1.0000.5930.7520.1410.0480.6340.8700.431
위도0.5931.0000.7550.2610.0930.6550.7370.670
경도0.7520.7551.0000.1940.0950.7070.8160.692
조도0.1410.2610.1941.0000.5610.0980.1460.125
휘도0.0480.0930.0950.5611.0000.0810.1240.095
달 위상0.6340.6550.7070.0980.0811.0000.8130.779
기온0.8700.7370.8160.1460.1240.8131.0000.933
습도0.4310.6700.6920.1250.0950.7790.9331.000
2023-12-13T06:01:21.994588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
날짜위도경도조도휘도달 위상기온습도
날짜1.0000.544-0.224-0.0250.019-0.2040.0550.109
위도0.5441.000-0.029-0.050-0.065-0.098-0.1390.036
경도-0.224-0.0291.0000.098-0.044-0.1080.100-0.091
조도-0.025-0.0500.0981.0000.6720.013-0.024-0.034
휘도0.019-0.065-0.0440.6721.000-0.034-0.033-0.001
달 위상-0.204-0.098-0.1080.013-0.0341.0000.4170.346
기온0.055-0.1390.100-0.024-0.0330.4171.0000.219
습도0.1090.036-0.091-0.034-0.0010.3460.2191.000

Missing values

2023-12-13T06:01:18.326695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T06:01:18.462805image/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

날짜시간위도경도조도휘도달 위상기온습도
111992022111718:5437.576064126.90509810.80.680.46666711.8262.2
118502022111720:1137.602715126.94787329.20.250.46666710.75333369.366667
86892022111419:2537.577415126.922758.30.510.6666678.73333365.083333
79872022111120:2337.578083126.9280397.60.20.86666715.95560.916667
21482022110418:2237.566882126.9518775.40.130.6666676.8730.833333
90812022111420:5737.571382126.91541351.83.410.6666678.3169.9
72462022111118:4237.571108126.92156731.21.370.86666717.7353.7
96162022111519:0837.583181126.9235322.01.390.69.4274.933333
81562022111121:0437.577445126.9261752.30.150.86666715.3664.066667
12022103118:3837.562084126.9622564.10.260.410.42071496.028571
날짜시간위도경도조도휘도달 위상기온습도
442022103119:1037.560805126.96444623.90.330.410.43928696.488095
47262022110918:1537.570924126.93147718.00.071.013.22570.5
56062022110921:1137.576789126.93307834.51.141.011.60833378.366667
100972022111518:5837.591374126.9479241.20.850.69.53333373.9
60502022110920:5337.585578126.92290751.04.311.011.7777.883333
91002022111421:0137.570672126.91336928.01.760.6666678.28833369.816667
77022022111119:0737.57891126.93012192.10.440.86666717.27166754.583333
40262022110820:0837.568123126.91836612.60.940.93333312.1867.533333
69822022111018:3837.569603126.96219243.51.730.93333315.5368.8
60702022110921:0037.585369126.9249513.60.861.011.778.0

Duplicate rows

Most frequently occurring

날짜시간위도경도조도휘도달 위상기온습도# duplicates
02022110418:5137.570506126.9459563.00.050.6666676.43533.252
12022110818:3637.568182126.932566.80.20.93333313.6861.82
22022110819:5537.56758126.92357451.91.70.93333312.37566.6666672
32022110820:0237.567197126.92369432.80.120.93333312.2767.1333332
42022110918:2837.573541126.93501244.30.971.013.07333370.9333332