Overview

Dataset statistics

Number of variables9
Number of observations6541
Missing cells48
Missing cells (%)0.1%
Duplicate rows2
Duplicate rows (%)< 0.1%
Total size in memory504.8 KiB
Average record size in memory79.0 B

Variable types

Categorical1
DateTime1
Numeric7

Dataset

Description서울특별시 서대문구 계절별(여름) 도로 주도, 휘도 데이터입니다. 날짜, 시간, 위도, 경도, 조도, 휘도, 달 위상, 기온 등 데이터를 제공합니다.
Author서울특별시 서대문구
URLhttps://www.data.go.kr/data/15109198/fileData.do

Alerts

Dataset has 2 (< 0.1%) duplicate rowsDuplicates
위도 is highly overall correlated with 달 위상High correlation
조도 is highly overall correlated with 휘도High correlation
휘도 is highly overall correlated with 조도High correlation
달 위상 is highly overall correlated with 위도 and 1 other fieldsHigh correlation
기온 is highly overall correlated with 습도 and 1 other fieldsHigh correlation
습도 is highly overall correlated with 기온 and 1 other fieldsHigh correlation
날짜 is highly overall correlated with 달 위상 and 2 other fieldsHigh correlation
휘도 is highly skewed (γ1 = 28.95628705)Skewed

Reproduction

Analysis started2023-12-12 06:46:44.422807
Analysis finished2023-12-12 06:46:52.217677
Duration7.79 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

날짜
Categorical

HIGH CORRELATION 

Distinct12
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size51.2 KiB
2022-07-18
1265 
2022-07-19
1030 
2022-07-21
927 
2022-07-15
871 
2022-07-12
770 
Other values (7)
1678 

Length

Max length10
Median length10
Mean length9.9981654
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022-07-11
2nd row2022-07-11
3rd row2022-07-11
4th row2022-07-11
5th row2022-07-11

Common Values

ValueCountFrequency (%)
2022-07-18 1265
19.3%
2022-07-19 1030
15.7%
2022-07-21 927
14.2%
2022-07-15 871
13.3%
2022-07-12 770
11.8%
2022-07-14 534
8.2%
2022-07-11 516
7.9%
2022-07-20 331
 
5.1%
2022-07-22 203
 
3.1%
2022-07-28 56
 
0.9%
Other values (2) 38
 
0.6%

Length

2023-12-12T15:46:52.285655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2022-07-18 1265
19.3%
2022-07-19 1030
15.7%
2022-07-21 927
14.2%
2022-07-15 871
13.3%
2022-07-12 770
11.8%
2022-07-14 534
8.2%
2022-07-11 516
7.9%
2022-07-20 331
 
5.1%
2022-07-22 203
 
3.1%
2022-07-28 56
 
0.9%
Other values (2) 38
 
0.6%

시간
Date

Distinct257
Distinct (%)3.9%
Missing2
Missing (%)< 0.1%
Memory size51.2 KiB
Minimum2023-12-12 00:00:00
Maximum2023-12-12 23:18:00
2023-12-12T15:46:52.469968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:46:52.588613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

위도
Real number (ℝ)

HIGH CORRELATION 

Distinct6361
Distinct (%)97.3%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean37.576754
Minimum37.5555
Maximum37.606026
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.6 KiB
2023-12-12T15:46:52.715096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.5555
5-th percentile37.558662
Q137.566577
median37.576723
Q337.585916
95-th percentile37.59707
Maximum37.606026
Range0.05052605
Interquartile range (IQR)0.01933876

Descriptive statistics

Standard deviation0.011929714
Coefficient of variation (CV)0.0003174759
Kurtosis-0.93741599
Mean37.576754
Median Absolute Deviation (MAD)0.00966661
Skewness0.18670785
Sum245714.39
Variance0.00014231807
MonotonicityNot monotonic
2023-12-12T15:46:52.874243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.584676 4
 
0.1%
37.56399 3
 
< 0.1%
37.566048 3
 
< 0.1%
37.57684042 3
 
< 0.1%
37.584021 3
 
< 0.1%
37.559507 3
 
< 0.1%
37.558946 3
 
< 0.1%
37.56419495 3
 
< 0.1%
37.5693048 2
 
< 0.1%
37.56957141 2
 
< 0.1%
Other values (6351) 6510
99.5%
ValueCountFrequency (%)
37.5555 1
< 0.1%
37.555547 1
< 0.1%
37.55576222 1
< 0.1%
37.55577158 1
< 0.1%
37.55584244 1
< 0.1%
37.555902 1
< 0.1%
37.55593646 1
< 0.1%
37.555962 1
< 0.1%
37.555966 1
< 0.1%
37.555996 1
< 0.1%
ValueCountFrequency (%)
37.60602605 1
< 0.1%
37.60587656 1
< 0.1%
37.60554864 1
< 0.1%
37.60553802 1
< 0.1%
37.60548745 1
< 0.1%
37.60536435 1
< 0.1%
37.60530975 1
< 0.1%
37.60529656 1
< 0.1%
37.60521266 1
< 0.1%
37.60515074 1
< 0.1%

경도
Real number (ℝ)

Distinct6379
Distinct (%)97.6%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean126.93847
Minimum126.90285
Maximum126.96924
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.6 KiB
2023-12-12T15:46:53.077128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.90285
5-th percentile126.91269
Q1126.9279
median126.9379
Q3126.9496
95-th percentile126.96161
Maximum126.96924
Range0.0663834
Interquartile range (IQR)0.0217046

Descriptive statistics

Standard deviation0.014981307
Coefficient of variation (CV)0.00011802022
Kurtosis-0.76498929
Mean126.93847
Median Absolute Deviation (MAD)0.0109434
Skewness-0.13557662
Sum830050.67
Variance0.00022443956
MonotonicityNot monotonic
2023-12-12T15:46:53.265764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.941711 3
 
< 0.1%
126.947526 3
 
< 0.1%
126.9151194 3
 
< 0.1%
126.946217 3
 
< 0.1%
126.934294 3
 
< 0.1%
126.951736 2
 
< 0.1%
126.955256 2
 
< 0.1%
126.9251313 2
 
< 0.1%
126.937987 2
 
< 0.1%
126.937887 2
 
< 0.1%
Other values (6369) 6514
99.6%
ValueCountFrequency (%)
126.9028544 1
< 0.1%
126.9032416 1
< 0.1%
126.9035721 1
< 0.1%
126.9037623 1
< 0.1%
126.9038508 1
< 0.1%
126.904066 1
< 0.1%
126.9041462 1
< 0.1%
126.9042413 1
< 0.1%
126.9043498 1
< 0.1%
126.9043815 1
< 0.1%
ValueCountFrequency (%)
126.9692378 1
< 0.1%
126.969083 1
< 0.1%
126.9690008 1
< 0.1%
126.9688339 1
< 0.1%
126.9687839 1
< 0.1%
126.9687 1
< 0.1%
126.968555 1
< 0.1%
126.9685335 1
< 0.1%
126.9684901 1
< 0.1%
126.9684033 1
< 0.1%

조도
Real number (ℝ)

HIGH CORRELATION 

Distinct1435
Distinct (%)22.0%
Missing31
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean37.608395
Minimum0.01
Maximum309
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.6 KiB
2023-12-12T15:46:53.435507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.23
Q110.7
median27.3
Q353.6
95-th percentile113.1
Maximum309
Range308.99
Interquartile range (IQR)42.9

Descriptive statistics

Standard deviation37.060561
Coefficient of variation (CV)0.98543318
Kurtosis2.8819744
Mean37.608395
Median Absolute Deviation (MAD)20.7
Skewness1.552524
Sum244830.65
Variance1373.4851
MonotonicityNot monotonic
2023-12-12T15:46:53.609661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.2 34
 
0.5%
0.1 31
 
0.5%
0.5 29
 
0.4%
0.4 29
 
0.4%
0.24 25
 
0.4%
0.02 24
 
0.4%
0.3 23
 
0.4%
113.1 23
 
0.4%
1.6 22
 
0.3%
0.04 22
 
0.3%
Other values (1425) 6248
95.5%
(Missing) 31
 
0.5%
ValueCountFrequency (%)
0.01 10
 
0.2%
0.02 24
0.4%
0.03 14
0.2%
0.04 22
0.3%
0.05 14
0.2%
0.06 18
0.3%
0.07 15
0.2%
0.08 8
 
0.1%
0.09 7
 
0.1%
0.1 31
0.5%
ValueCountFrequency (%)
309.0 1
 
< 0.1%
233.9 1
 
< 0.1%
200.0 6
0.1%
199.9 1
 
< 0.1%
197.6 1
 
< 0.1%
196.5 1
 
< 0.1%
196.1 1
 
< 0.1%
195.1 1
 
< 0.1%
194.6 2
 
< 0.1%
194.3 1
 
< 0.1%

휘도
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct643
Distinct (%)9.8%
Missing5
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean1.2719691
Minimum0.01
Maximum161.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.6 KiB
2023-12-12T15:46:53.746009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.05
Q10.28
median0.69
Q31.46
95-th percentile4.11
Maximum161.3
Range161.29
Interquartile range (IQR)1.18

Descriptive statistics

Standard deviation3.0275241
Coefficient of variation (CV)2.3801868
Kurtosis1327.6073
Mean1.2719691
Median Absolute Deviation (MAD)0.5
Skewness28.956287
Sum8313.59
Variance9.1659022
MonotonicityNot monotonic
2023-12-12T15:46:53.914151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.04 97
 
1.5%
0.03 87
 
1.3%
0.05 77
 
1.2%
0.02 77
 
1.2%
0.07 70
 
1.1%
0.12 66
 
1.0%
0.16 65
 
1.0%
0.08 62
 
0.9%
0.06 61
 
0.9%
0.19 61
 
0.9%
Other values (633) 5813
88.9%
ValueCountFrequency (%)
0.01 49
0.7%
0.02 77
1.2%
0.03 87
1.3%
0.04 97
1.5%
0.05 77
1.2%
0.06 61
0.9%
0.07 70
1.1%
0.08 62
0.9%
0.09 53
0.8%
0.1 58
0.9%
ValueCountFrequency (%)
161.3 1
< 0.1%
87.3 1
< 0.1%
54.3 1
< 0.1%
50.5 1
< 0.1%
35.9 1
< 0.1%
34.0 1
< 0.1%
28.92 1
< 0.1%
27.65 1
< 0.1%
25.0 1
< 0.1%
22.95 1
< 0.1%

달 위상
Real number (ℝ)

HIGH CORRELATION 

Distinct11
Distinct (%)0.2%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean72.366009
Minimum4
Maximum97.333333
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.6 KiB
2023-12-12T15:46:54.065360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile50.666667
Q164
median70.666667
Q389.333333
95-th percentile97.333333
Maximum97.333333
Range93.333333
Interquartile range (IQR)25.333333

Descriptive statistics

Standard deviation17.710606
Coefficient of variation (CV)0.24473652
Kurtosis0.85657819
Mean72.366009
Median Absolute Deviation (MAD)18.666667
Skewness-0.66635554
Sum473201.33
Variance313.66555
MonotonicityNot monotonic
2023-12-12T15:46:54.179713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
70.66666667 1265
19.3%
64.0 1030
15.7%
50.66666667 927
14.2%
90.66666667 869
13.3%
89.33333333 770
11.8%
97.33333333 536
8.2%
82.66666667 516
7.9%
57.33333333 331
 
5.1%
44.0 203
 
3.1%
4.0 56
 
0.9%
ValueCountFrequency (%)
4.0 56
 
0.9%
10.66666667 36
 
0.6%
44.0 203
 
3.1%
50.66666667 927
14.2%
57.33333333 331
 
5.1%
64.0 1030
15.7%
70.66666667 1265
19.3%
82.66666667 516
7.9%
89.33333333 770
11.8%
90.66666667 869
13.3%
ValueCountFrequency (%)
97.33333333 536
8.2%
90.66666667 869
13.3%
89.33333333 770
11.8%
82.66666667 516
7.9%
70.66666667 1265
19.3%
64.0 1030
15.7%
57.33333333 331
 
5.1%
50.66666667 927
14.2%
44.0 203
 
3.1%
10.66666667 36
 
0.6%

기온
Real number (ℝ)

HIGH CORRELATION 

Distinct1483
Distinct (%)22.7%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean24.525657
Minimum20.10119
Maximum28.44119
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.6 KiB
2023-12-12T15:46:54.299087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20.10119
5-th percentile20.182857
Q123.741726
median24.306786
Q325.385238
95-th percentile27.707381
Maximum28.44119
Range8.3400006
Interquartile range (IQR)1.643512

Descriptive statistics

Standard deviation1.7402244
Coefficient of variation (CV)0.070955264
Kurtosis0.66309913
Mean24.525657
Median Absolute Deviation (MAD)0.78892854
Skewness-0.13785137
Sum160373.27
Variance3.0283811
MonotonicityNot monotonic
2023-12-12T15:46:54.440320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24.40285706 13
 
0.2%
24.63892871 12
 
0.2%
25.78047624 12
 
0.2%
24.03809528 11
 
0.2%
27.99452363 11
 
0.2%
20.12142807 11
 
0.2%
26.39047619 11
 
0.2%
28.4092855 10
 
0.2%
22.35119079 10
 
0.2%
22.22738146 10
 
0.2%
Other values (1473) 6428
98.3%
ValueCountFrequency (%)
20.10118967 2
 
< 0.1%
20.10238017 3
< 0.1%
20.10357066 6
0.1%
20.10476115 6
0.1%
20.10595165 6
0.1%
20.10714214 4
0.1%
20.10833264 5
0.1%
20.10952313 5
0.1%
20.11071363 7
0.1%
20.11190412 6
0.1%
ValueCountFrequency (%)
28.44119026 6
0.1%
28.42523788 8
0.1%
28.4092855 10
0.2%
28.39333312 7
0.1%
28.37738074 5
0.1%
28.36142836 8
0.1%
28.34547598 9
0.1%
28.3295236 6
0.1%
28.31357123 6
0.1%
28.29761885 8
0.1%

습도
Real number (ℝ)

HIGH CORRELATION 

Distinct1421
Distinct (%)21.7%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean83.763431
Minimum63.755952
Maximum97.357143
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.6 KiB
2023-12-12T15:46:54.591784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum63.755952
5-th percentile68.047619
Q180.4125
median85.585714
Q388.927381
95-th percentile96.67619
Maximum97.357143
Range33.60119
Interquartile range (IQR)8.514881

Descriptive statistics

Standard deviation8.0692077
Coefficient of variation (CV)0.096333299
Kurtosis-0.28318533
Mean83.763431
Median Absolute Deviation (MAD)3.8940476
Skewness-0.69896218
Sum547729.08
Variance65.112112
MonotonicityNot monotonic
2023-12-12T15:46:54.761955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
87.57619048 18
 
0.3%
84.45238095 15
 
0.2%
84.11904762 14
 
0.2%
89.1 14
 
0.2%
89.21428571 14
 
0.2%
85.69047619 14
 
0.2%
88.375 13
 
0.2%
87.92857143 13
 
0.2%
70.9047619 13
 
0.2%
85.16666667 13
 
0.2%
Other values (1411) 6398
97.8%
ValueCountFrequency (%)
63.75595238 6
0.1%
63.86904762 8
0.1%
63.98214286 10
0.2%
64.0952381 7
0.1%
64.20833333 5
0.1%
64.32142857 8
0.1%
64.43452381 9
0.1%
64.54761905 6
0.1%
64.66071429 6
0.1%
64.77380952 8
0.1%
ValueCountFrequency (%)
97.35714286 2
 
< 0.1%
97.35357143 3
< 0.1%
97.35 4
0.1%
97.34642857 2
 
< 0.1%
97.34404762 2
 
< 0.1%
97.34285714 3
< 0.1%
97.33928571 2
 
< 0.1%
97.33095238 7
0.1%
97.31785714 4
0.1%
97.3047619 3
< 0.1%

Interactions

2023-12-12T15:46:50.732394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:46:45.857363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:46:46.549929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:46:47.355092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:46:48.245803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:46:49.153094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:46:49.975755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:46:50.820161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:46:45.946970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:46:46.669889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:46:47.455147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:46:48.369235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:46:49.268810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:46:50.096114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:46:50.943367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:46:46.048656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:46:46.801008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:46:47.634327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:46:48.504488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:46:49.384119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:46:50.240835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:46:51.045303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:46:46.149311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:46:46.900852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:46:47.752555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:46:48.628029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:46:49.495707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:46:50.337973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:46:51.187917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:46:46.265952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:46:47.052737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:46:47.894840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:46:48.766772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:46:49.642888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:46:50.445594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:46:51.296024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:46:46.363215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:46:47.160046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:46:48.012256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:46:48.879491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:46:49.752188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:46:50.530493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:46:51.693056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:46:46.453838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:46:47.246889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:46:48.120790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:46:49.005670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:46:49.862816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:46:50.641991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T15:46:54.874537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
날짜위도경도조도휘도달 위상기온습도
날짜1.0000.7190.7070.1740.0001.0000.8040.832
위도0.7191.0000.7670.1380.0820.6810.5810.727
경도0.7070.7671.0000.1420.0000.5900.5770.728
조도0.1740.1380.1421.0000.1330.1350.1350.120
휘도0.0000.0820.0000.1331.0000.0000.0000.000
달 위상1.0000.6810.5900.1350.0001.0000.6970.728
기온0.8040.5810.5770.1350.0000.6971.0000.866
습도0.8320.7270.7280.1200.0000.7280.8661.000
2023-12-12T15:46:55.001381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위도경도조도휘도달 위상기온습도날짜
위도1.000-0.0930.031-0.018-0.6150.0040.1840.409
경도-0.0931.0000.0660.049-0.0700.113-0.0720.397
조도0.0310.0661.0000.6280.029-0.0720.0960.079
휘도-0.0180.0490.6281.0000.070-0.0380.0670.000
달 위상-0.615-0.0700.0290.0701.0000.058-0.1251.000
기온0.0040.113-0.072-0.0380.0581.000-0.7830.536
습도0.184-0.0720.0960.067-0.125-0.7831.0000.550
날짜0.4090.3970.0790.0001.0000.5360.5501.000

Missing values

2023-12-12T15:46:51.821819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T15:46:51.977906image/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.
2023-12-12T15:46:52.118363image/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

날짜시간위도경도조도휘도달 위상기온습도
02022-07-1120:4837.565272126.9668527.60.6182.66666723.73714372.485714
12022-07-1120:5037.56495126.96629942.65.1782.66666723.7572.452381
22022-07-1120:5637.565276126.96655458.83.4982.66666723.78857172.352381
32022-07-1120:5937.565909126.9661150.390.1482.66666723.80785772.302381
42022-07-1121:0037.565486126.966120.740.6482.66666723.81428672.285714
52022-07-1121:0337.565627126.96606214.50.5582.66666723.87392972.214286
62022-07-1121:0337.565674126.96576172.21.382.66666723.87392972.214286
72022-07-1121:0437.565845126.96551171.54.582.66666723.89380972.190476
82022-07-1121:0437.565654126.96548313.40.2182.66666723.89380972.190476
92022-07-1121:0537.565609126.96594816.10.7882.66666723.9136972.166667
날짜시간위도경도조도휘도달 위상기온습도
65312022-07-2820:5937.561475126.96061311.590.694.027.17059578.028571
65322022-07-2821:0137.561552126.9611951.310.074.027.20047677.919048
65332022-07-2821:0237.561143126.960747128.20.114.027.22238177.838095
65342022-07-2821:0237.56151126.9613079.050.34.027.22238177.838095
65352022-07-2821:0337.561281126.96137129.290.764.027.24428677.757143
65362022-07-2821:0337.561221126.96124324.510.464.027.24428677.757143
65372022-07-2821:0437.561339126.9609520.610.14.027.2661977.67619
65382022-07-2821:0537.561469126.96168113.841.144.027.28809577.595238
6539<NA><NA><NA><NA><NA><NA><NA><NA><NA>
6540<NA><NA><NA><NA><NA><NA><NA><NA><NA>

Duplicate rows

Most frequently occurring

날짜시간위도경도조도휘도달 위상기온습도# duplicates
02022-07-1821:2137.579271126.90753319.31.4970.66666724.82285785.5857142
1<NA><NA><NA><NA><NA><NA><NA><NA><NA>2