Overview

Brought to you by YData

Dataset statistics

Number of variables21
Number of observations728
Missing cells1933
Missing cells (%)12.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory111.0 KiB
Average record size in memory156.2 B

Variable types

Numeric16
DateTime1
Categorical2
Unsupported2

Alerts

snow has constant value "10.0"Constant
Global_active_power is highly overall correlated with Global_intensityHigh correlation
Global_intensity is highly overall correlated with Global_active_powerHigh correlation
tavg is highly overall correlated with tmax and 1 other fieldsHigh correlation
tmax is highly overall correlated with tavg and 1 other fieldsHigh correlation
tmin is highly overall correlated with tavg and 1 other fieldsHigh correlation
snow has 477 (65.5%) missing valuesMissing
wpgt has 728 (100.0%) missing valuesMissing
tsun has 728 (100.0%) missing valuesMissing
Datetime has unique valuesUnique
wpgt is an unsupported type, check if it needs cleaning or further analysisUnsupported
tsun is an unsupported type, check if it needs cleaning or further analysisUnsupported
Global_reactive_power has 181 (24.9%) zerosZeros
Sub_metering_1 has 691 (94.9%) zerosZeros
Sub_metering_2 has 522 (71.7%) zerosZeros
Sub_metering_3 has 468 (64.3%) zerosZeros
prcp has 411 (56.5%) zerosZeros

Reproduction

Analysis started2024-10-19 04:23:45.138971
Analysis finished2024-10-19 04:24:35.429113
Duration50.29 seconds
Software versionydata-profiling vv4.9.0
Download configurationconfig.json

Variables

Global_active_power
Real number (ℝ)

HIGH CORRELATION 

Distinct406
Distinct (%)55.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.80824725
Minimum0.08
Maximum5.544
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2024-10-19T09:54:35.584036image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0.08
5-th percentile0.1587
Q10.264
median0.394
Q31.241
95-th percentile2.7808
Maximum5.544
Range5.464
Interquartile range (IQR)0.977

Descriptive statistics

Standard deviation0.90083816
Coefficient of variation (CV)1.1145576
Kurtosis4.9245651
Mean0.80824725
Median Absolute Deviation (MAD)0.169
Skewness2.154245
Sum588.404
Variance0.81150939
MonotonicityNot monotonic
2024-10-19T09:54:35.707615image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.326 8
 
1.1%
0.25 7
 
1.0%
0.324 7
 
1.0%
0.29 6
 
0.8%
0.292 6
 
0.8%
0.354 6
 
0.8%
0.258 6
 
0.8%
0.314 6
 
0.8%
0.218 6
 
0.8%
0.404 5
 
0.7%
Other values (396) 665
91.3%
ValueCountFrequency (%)
0.08 3
0.4%
0.082 3
0.4%
0.084 1
 
0.1%
0.086 1
 
0.1%
0.102 3
0.4%
0.104 1
 
0.1%
0.108 1
 
0.1%
0.114 3
0.4%
0.116 2
0.3%
0.118 1
 
0.1%
ValueCountFrequency (%)
5.544 1
0.1%
5.376 1
0.1%
4.786 1
0.1%
4.718 1
0.1%
4.646 1
0.1%
4.36 1
0.1%
4.286 1
0.1%
4.094 1
0.1%
3.966 1
0.1%
3.902 1
0.1%

Global_reactive_power
Real number (ℝ)

ZEROS 

Distinct148
Distinct (%)20.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.11026374
Minimum0
Maximum0.588
Zeros181
Zeros (%)24.9%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2024-10-19T09:54:35.851512image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.046
median0.094
Q30.154
95-th percentile0.2986
Maximum0.588
Range0.588
Interquartile range (IQR)0.108

Descriptive statistics

Standard deviation0.097748107
Coefficient of variation (CV)0.88649369
Kurtosis1.3053862
Mean0.11026374
Median Absolute Deviation (MAD)0.06
Skewness1.032959
Sum80.272
Variance0.0095546924
MonotonicityNot monotonic
2024-10-19T09:54:36.003799image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 181
24.9%
0.09 12
 
1.6%
0.058 12
 
1.6%
0.082 11
 
1.5%
0.102 11
 
1.5%
0.104 11
 
1.5%
0.088 11
 
1.5%
0.086 10
 
1.4%
0.094 10
 
1.4%
0.12 10
 
1.4%
Other values (138) 449
61.7%
ValueCountFrequency (%)
0 181
24.9%
0.046 7
 
1.0%
0.048 5
 
0.7%
0.05 8
 
1.1%
0.052 6
 
0.8%
0.054 4
 
0.5%
0.056 6
 
0.8%
0.058 12
 
1.6%
0.06 6
 
0.8%
0.062 8
 
1.1%
ValueCountFrequency (%)
0.588 1
 
0.1%
0.564 1
 
0.1%
0.44 1
 
0.1%
0.404 1
 
0.1%
0.396 1
 
0.1%
0.392 1
 
0.1%
0.382 3
0.4%
0.38 1
 
0.1%
0.378 1
 
0.1%
0.368 1
 
0.1%

Voltage
Real number (ℝ)

Distinct505
Distinct (%)69.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean241.88342
Minimum229.4
Maximum248.61
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2024-10-19T09:54:36.158298image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum229.4
5-th percentile236.5415
Q1240.455
median242.33
Q3243.7125
95-th percentile245.853
Maximum248.61
Range19.21
Interquartile range (IQR)3.2575

Descriptive statistics

Standard deviation2.8415497
Coefficient of variation (CV)0.0117476
Kurtosis1.2009578
Mean241.88342
Median Absolute Deviation (MAD)1.59
Skewness-0.83414206
Sum176091.13
Variance8.0744049
MonotonicityNot monotonic
2024-10-19T09:54:36.318148image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
243.03 6
 
0.8%
241.45 5
 
0.7%
243.38 5
 
0.7%
242.56 5
 
0.7%
241.25 5
 
0.7%
241.55 4
 
0.5%
241.95 4
 
0.5%
242.8 4
 
0.5%
241.94 4
 
0.5%
243.92 4
 
0.5%
Other values (495) 682
93.7%
ValueCountFrequency (%)
229.4 1
 
0.1%
231.82 1
 
0.1%
232.21 1
 
0.1%
232.73 1
 
0.1%
232.83 1
 
0.1%
232.88 1
 
0.1%
233.05 4
0.5%
233.18 1
 
0.1%
233.77 1
 
0.1%
233.8 1
 
0.1%
ValueCountFrequency (%)
248.61 1
0.1%
248.58 1
0.1%
248.44 1
0.1%
247.29 1
0.1%
247.27 1
0.1%
247.23 1
0.1%
247.16 1
0.1%
247.1 1
0.1%
247.07 1
0.1%
246.97 1
0.1%

Global_intensity
Real number (ℝ)

HIGH CORRELATION 

Distinct81
Distinct (%)11.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4211538
Minimum0.2
Maximum23.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2024-10-19T09:54:36.485963image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile0.67
Q11.2
median1.8
Q35.2
95-th percentile11.73
Maximum23.2
Range23
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.7321523
Coefficient of variation (CV)1.0909045
Kurtosis5.1451087
Mean3.4211538
Median Absolute Deviation (MAD)0.8
Skewness2.1853675
Sum2490.6
Variance13.92896
MonotonicityNot monotonic
2024-10-19T09:54:36.630199image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.4 81
 
11.1%
1 79
 
10.9%
1.2 69
 
9.5%
1.6 45
 
6.2%
1.8 45
 
6.2%
0.8 42
 
5.8%
2 27
 
3.7%
2.2 26
 
3.6%
0.6 24
 
3.3%
2.4 22
 
3.0%
Other values (71) 268
36.8%
ValueCountFrequency (%)
0.2 8
 
1.1%
0.4 5
 
0.7%
0.6 24
 
3.3%
0.8 42
5.8%
1 79
10.9%
1.2 69
9.5%
1.4 81
11.1%
1.6 45
6.2%
1.8 45
6.2%
2 27
 
3.7%
ValueCountFrequency (%)
23.2 1
0.1%
22.6 1
0.1%
20 1
0.1%
19.8 2
0.3%
18 1
0.1%
17.8 1
0.1%
17 2
0.3%
16.4 1
0.1%
16 1
0.1%
15.8 1
0.1%

Sub_metering_1
Real number (ℝ)

ZEROS 

Distinct8
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.62774725
Minimum0
Maximum40
Zeros691
Zeros (%)94.9%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2024-10-19T09:54:36.748240image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.65
Maximum40
Range40
Interquartile range (IQR)0

Descriptive statistics

Standard deviation4.6807457
Coefficient of variation (CV)7.4564177
Kurtosis61.206129
Mean0.62774725
Median Absolute Deviation (MAD)0
Skewness7.918295
Sum457
Variance21.90938
MonotonicityNot monotonic
2024-10-19T09:54:36.888154image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 691
94.9%
1 21
 
2.9%
38 5
 
0.7%
2 4
 
0.5%
39 4
 
0.5%
35 1
 
0.1%
40 1
 
0.1%
7 1
 
0.1%
ValueCountFrequency (%)
0 691
94.9%
1 21
 
2.9%
2 4
 
0.5%
7 1
 
0.1%
35 1
 
0.1%
38 5
 
0.7%
39 4
 
0.5%
40 1
 
0.1%
ValueCountFrequency (%)
40 1
 
0.1%
39 4
 
0.5%
38 5
 
0.7%
35 1
 
0.1%
7 1
 
0.1%
2 4
 
0.5%
1 21
 
2.9%
0 691
94.9%

Sub_metering_2
Real number (ℝ)

ZEROS 

Distinct12
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.76510989
Minimum0
Maximum38
Zeros522
Zeros (%)71.7%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2024-10-19T09:54:37.015330image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum38
Range38
Interquartile range (IQR)1

Descriptive statistics

Standard deviation3.5237129
Coefficient of variation (CV)4.6054991
Kurtosis82.406366
Mean0.76510989
Median Absolute Deviation (MAD)0
Skewness8.8701191
Sum557
Variance12.416553
MonotonicityNot monotonic
2024-10-19T09:54:37.142795image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 522
71.7%
1 126
 
17.3%
2 65
 
8.9%
4 6
 
0.8%
36 2
 
0.3%
21 1
 
0.1%
37 1
 
0.1%
30 1
 
0.1%
18 1
 
0.1%
26 1
 
0.1%
Other values (2) 2
 
0.3%
ValueCountFrequency (%)
0 522
71.7%
1 126
 
17.3%
2 65
 
8.9%
4 6
 
0.8%
18 1
 
0.1%
21 1
 
0.1%
26 1
 
0.1%
30 1
 
0.1%
35 1
 
0.1%
36 2
 
0.3%
ValueCountFrequency (%)
38 1
 
0.1%
37 1
 
0.1%
36 2
 
0.3%
35 1
 
0.1%
30 1
 
0.1%
26 1
 
0.1%
21 1
 
0.1%
18 1
 
0.1%
4 6
 
0.8%
2 65
8.9%

Sub_metering_3
Real number (ℝ)

ZEROS 

Distinct12
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4574176
Minimum0
Maximum19
Zeros468
Zeros (%)64.3%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2024-10-19T09:54:37.259174image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile18
Maximum19
Range19
Interquartile range (IQR)1

Descriptive statistics

Standard deviation6.8399546
Coefficient of variation (CV)1.9783421
Kurtosis0.71756129
Mean3.4574176
Median Absolute Deviation (MAD)0
Skewness1.6350579
Sum2517
Variance46.784979
MonotonicityNot monotonic
2024-10-19T09:54:37.379696image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 468
64.3%
1 123
 
16.9%
18 89
 
12.2%
17 24
 
3.3%
19 17
 
2.3%
2 1
 
0.1%
16 1
 
0.1%
10 1
 
0.1%
7 1
 
0.1%
12 1
 
0.1%
Other values (2) 2
 
0.3%
ValueCountFrequency (%)
0 468
64.3%
1 123
 
16.9%
2 1
 
0.1%
3 1
 
0.1%
7 1
 
0.1%
10 1
 
0.1%
11 1
 
0.1%
12 1
 
0.1%
16 1
 
0.1%
17 24
 
3.3%
ValueCountFrequency (%)
19 17
 
2.3%
18 89
12.2%
17 24
 
3.3%
16 1
 
0.1%
12 1
 
0.1%
11 1
 
0.1%
10 1
 
0.1%
7 1
 
0.1%
3 1
 
0.1%
2 1
 
0.1%

Datetime
Date

UNIQUE 

Distinct728
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size5.8 KiB
Minimum2006-12-17 00:00:00
Maximum2008-12-13 00:00:00
2024-10-19T09:54:37.528443image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-10-19T09:54:37.674255image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

tavg
Real number (ℝ)

HIGH CORRELATION 

Distinct216
Distinct (%)29.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.488049
Minimum-1.6
Maximum26.2
Zeros0
Zeros (%)0.0%
Negative8
Negative (%)1.1%
Memory size5.8 KiB
2024-10-19T09:54:37.805558image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum-1.6
5-th percentile2.77
Q18.3
median12.5
Q317.7
95-th percentile20.965
Maximum26.2
Range27.8
Interquartile range (IQR)9.4

Descriptive statistics

Standard deviation5.8174435
Coefficient of variation (CV)0.46584085
Kurtosis-0.79642261
Mean12.488049
Median Absolute Deviation (MAD)4.6
Skewness-0.1317714
Sum9091.3
Variance33.842649
MonotonicityNot monotonic
2024-10-19T09:54:37.937716image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18.2 11
 
1.5%
10.2 11
 
1.5%
10.3 11
 
1.5%
9.5 10
 
1.4%
10.7 10
 
1.4%
9.1 9
 
1.2%
18.7 9
 
1.2%
17.9 9
 
1.2%
17.8 8
 
1.1%
8.7 8
 
1.1%
Other values (206) 632
86.8%
ValueCountFrequency (%)
-1.6 1
0.1%
-1.1 1
0.1%
-0.9 2
0.3%
-0.8 1
0.1%
-0.6 1
0.1%
-0.2 2
0.3%
0.3 2
0.3%
0.5 2
0.3%
0.7 1
0.1%
1.1 2
0.3%
ValueCountFrequency (%)
26.2 1
0.1%
25.6 1
0.1%
25.5 1
0.1%
25.3 1
0.1%
25.1 1
0.1%
24.6 1
0.1%
24.4 1
0.1%
24.2 1
0.1%
23.8 1
0.1%
23.4 1
0.1%

tmin
Real number (ℝ)

HIGH CORRELATION 

Distinct204
Distinct (%)28.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.1200549
Minimum-3.6
Maximum20.4
Zeros3
Zeros (%)0.4%
Negative29
Negative (%)4.0%
Memory size5.8 KiB
2024-10-19T09:54:38.088688image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum-3.6
5-th percentile0.2
Q15.4
median9.1
Q313.625
95-th percentile16.765
Maximum20.4
Range24
Interquartile range (IQR)8.225

Descriptive statistics

Standard deviation5.2176701
Coefficient of variation (CV)0.5721095
Kurtosis-0.87096483
Mean9.1200549
Median Absolute Deviation (MAD)4.2
Skewness-0.20312558
Sum6639.4
Variance27.224081
MonotonicityNot monotonic
2024-10-19T09:54:38.238954image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14.4 9
 
1.2%
15.8 9
 
1.2%
15.1 8
 
1.1%
12 8
 
1.1%
11.8 8
 
1.1%
5.4 8
 
1.1%
10.2 8
 
1.1%
12.8 8
 
1.1%
6.3 8
 
1.1%
6.9 8
 
1.1%
Other values (194) 646
88.7%
ValueCountFrequency (%)
-3.6 1
0.1%
-3.1 1
0.1%
-2.8 2
0.3%
-2.4 1
0.1%
-2.3 2
0.3%
-2.2 2
0.3%
-2.1 1
0.1%
-1.7 2
0.3%
-1.6 1
0.1%
-1.4 1
0.1%
ValueCountFrequency (%)
20.4 1
 
0.1%
19.7 1
 
0.1%
19.4 1
 
0.1%
19.3 1
 
0.1%
18.7 1
 
0.1%
18.6 1
 
0.1%
18.5 1
 
0.1%
18.4 1
 
0.1%
18.1 1
 
0.1%
18 3
0.4%

tmax
Real number (ℝ)

HIGH CORRELATION 

Distinct248
Distinct (%)34.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.555357
Minimum0.7
Maximum34
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2024-10-19T09:54:38.373008image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0.7
5-th percentile5.805
Q111.275
median16.55
Q322.1
95-th percentile27.3
Maximum34
Range33.3
Interquartile range (IQR)10.825

Descriptive statistics

Standard deviation6.9032794
Coefficient of variation (CV)0.41698161
Kurtosis-0.74517443
Mean16.555357
Median Absolute Deviation (MAD)5.45
Skewness0.02555871
Sum12052.3
Variance47.655267
MonotonicityNot monotonic
2024-10-19T09:54:38.507498image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11.8 11
 
1.5%
11.7 10
 
1.4%
12.9 9
 
1.2%
21.4 8
 
1.1%
8.5 8
 
1.1%
22.8 7
 
1.0%
20.7 7
 
1.0%
10.6 7
 
1.0%
22.6 7
 
1.0%
13.2 7
 
1.0%
Other values (238) 647
88.9%
ValueCountFrequency (%)
0.7 1
0.1%
0.8 1
0.1%
0.9 2
0.3%
1 1
0.1%
1.1 1
0.1%
1.8 1
0.1%
2.4 1
0.1%
2.6 1
0.1%
2.7 2
0.3%
2.8 2
0.3%
ValueCountFrequency (%)
34 1
0.1%
33.3 2
0.3%
32.4 1
0.1%
31.8 2
0.3%
31.7 1
0.1%
31.6 1
0.1%
31.5 1
0.1%
31.3 2
0.3%
30.8 1
0.1%
30.5 1
0.1%

prcp
Real number (ℝ)

ZEROS 

Distinct62
Distinct (%)8.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7192308
Minimum0
Maximum44.7
Zeros411
Zeros (%)56.5%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2024-10-19T09:54:38.641724image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31.5
95-th percentile8.225
Maximum44.7
Range44.7
Interquartile range (IQR)1.5

Descriptive statistics

Standard deviation4.1770498
Coefficient of variation (CV)2.4296039
Kurtosis30.735513
Mean1.7192308
Median Absolute Deviation (MAD)0
Skewness4.7737746
Sum1251.6
Variance17.447745
MonotonicityNot monotonic
2024-10-19T09:54:38.756142image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 411
56.5%
0.5 43
 
5.9%
0.3 34
 
4.7%
0.8 23
 
3.2%
1.5 16
 
2.2%
2.3 15
 
2.1%
1.3 14
 
1.9%
1 14
 
1.9%
3.3 10
 
1.4%
2 9
 
1.2%
Other values (52) 139
 
19.1%
ValueCountFrequency (%)
0 411
56.5%
0.2 2
 
0.3%
0.3 34
 
4.7%
0.5 43
 
5.9%
0.8 23
 
3.2%
1 14
 
1.9%
1.3 14
 
1.9%
1.5 16
 
2.2%
1.8 6
 
0.8%
2 9
 
1.2%
ValueCountFrequency (%)
44.7 1
0.1%
31.5 1
0.1%
30.5 2
0.3%
27.4 1
0.1%
24.9 1
0.1%
24.4 1
0.1%
21.1 2
0.3%
19.6 1
0.1%
18.5 1
0.1%
17.5 1
0.1%

snow
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)0.4%
Missing477
Missing (%)65.5%
Memory size44.9 KiB
10.0
251 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters1004
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row10.0
2nd row10.0
3rd row10.0
4th row10.0
5th row10.0

Common Values

ValueCountFrequency (%)
10.0 251
34.5%
(Missing) 477
65.5%

Length

2024-10-19T09:54:38.902735image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-19T09:54:39.007058image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
10.0 251
100.0%

Most occurring characters

ValueCountFrequency (%)
0 502
50.0%
1 251
25.0%
. 251
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1004
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 502
50.0%
1 251
25.0%
. 251
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1004
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 502
50.0%
1 251
25.0%
. 251
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1004
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 502
50.0%
1 251
25.0%
. 251
25.0%

wdir
Real number (ℝ)

Distinct290
Distinct (%)39.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean187.57143
Minimum0
Maximum359
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2024-10-19T09:54:39.120201image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile13
Q1112.5
median208
Q3260
95-th percentile340.3
Maximum359
Range359
Interquartile range (IQR)147.5

Descriptive statistics

Standard deviation100.08765
Coefficient of variation (CV)0.53359754
Kurtosis-0.8845513
Mean187.57143
Median Absolute Deviation (MAD)67
Skewness-0.35689492
Sum136552
Variance10017.538
MonotonicityNot monotonic
2024-10-19T09:54:39.273246image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
214 10
 
1.4%
216 9
 
1.2%
210 7
 
1.0%
8 7
 
1.0%
219 7
 
1.0%
261 6
 
0.8%
201 6
 
0.8%
20 6
 
0.8%
306 6
 
0.8%
213 6
 
0.8%
Other values (280) 658
90.4%
ValueCountFrequency (%)
0 1
 
0.1%
2 3
0.4%
3 1
 
0.1%
4 2
 
0.3%
5 3
0.4%
6 1
 
0.1%
7 5
0.7%
8 7
1.0%
9 2
 
0.3%
10 2
 
0.3%
ValueCountFrequency (%)
359 1
 
0.1%
358 2
0.3%
356 3
0.4%
355 2
0.3%
354 4
0.5%
353 3
0.4%
352 2
0.3%
350 2
0.3%
349 4
0.5%
348 1
 
0.1%

wspd
Real number (ℝ)

Distinct167
Distinct (%)22.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.53489
Minimum3.1
Maximum36.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2024-10-19T09:54:39.390950image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum3.1
5-th percentile6
Q18.5
median10.9
Q313.9
95-th percentile18.8
Maximum36.4
Range33.3
Interquartile range (IQR)5.4

Descriptive statistics

Standard deviation4.1463618
Coefficient of variation (CV)0.35946262
Kurtosis2.3670798
Mean11.53489
Median Absolute Deviation (MAD)2.6
Skewness1.0418591
Sum8397.4
Variance17.192316
MonotonicityNot monotonic
2024-10-19T09:54:39.521752image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.8 16
 
2.2%
13 15
 
2.1%
9.7 14
 
1.9%
10 13
 
1.8%
9.5 12
 
1.6%
8.8 12
 
1.6%
9.8 12
 
1.6%
7.5 11
 
1.5%
8.6 11
 
1.5%
8.5 10
 
1.4%
Other values (157) 602
82.7%
ValueCountFrequency (%)
3.1 1
 
0.1%
4.2 1
 
0.1%
4.3 2
0.3%
4.4 1
 
0.1%
4.5 1
 
0.1%
4.6 3
0.4%
4.8 4
0.5%
4.9 2
0.3%
5 1
 
0.1%
5.1 2
0.3%
ValueCountFrequency (%)
36.4 1
0.1%
29.1 1
0.1%
27.8 1
0.1%
26.2 1
0.1%
25.6 1
0.1%
25 1
0.1%
24.8 1
0.1%
23.5 2
0.3%
23.2 2
0.3%
22.5 1
0.1%

wpgt
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing728
Missing (%)100.0%
Memory size5.8 KiB

pres
Real number (ℝ)

Distinct316
Distinct (%)43.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1017.3728
Minimum989
Maximum1041.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2024-10-19T09:54:39.652963image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum989
5-th percentile1001.07
Q11011.6
median1017
Q31023
95-th percentile1032.9
Maximum1041.4
Range52.4
Interquartile range (IQR)11.4

Descriptive statistics

Standard deviation9.2739657
Coefficient of variation (CV)0.0091156021
Kurtosis0.043953189
Mean1017.3728
Median Absolute Deviation (MAD)5.7
Skewness-0.10968287
Sum740647.4
Variance86.006439
MonotonicityNot monotonic
2024-10-19T09:54:39.793545image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1021.5 9
 
1.2%
1022.7 9
 
1.2%
1016.8 8
 
1.1%
1015.3 8
 
1.1%
1028.3 8
 
1.1%
1016.4 8
 
1.1%
1011 8
 
1.1%
1013.8 8
 
1.1%
1021.4 7
 
1.0%
1020.5 7
 
1.0%
Other values (306) 648
89.0%
ValueCountFrequency (%)
989 1
0.1%
989.9 1
0.1%
991.4 1
0.1%
992.3 1
0.1%
992.9 2
0.3%
993.8 1
0.1%
993.9 1
0.1%
994.8 1
0.1%
995 1
0.1%
995.8 2
0.3%
ValueCountFrequency (%)
1041.4 1
0.1%
1039.7 1
0.1%
1039.5 1
0.1%
1039.4 1
0.1%
1039.2 1
0.1%
1038.7 2
0.3%
1038.4 1
0.1%
1038.2 1
0.1%
1037.5 2
0.3%
1036.9 1
0.1%

tsun
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing728
Missing (%)100.0%
Memory size5.8 KiB

Year
Categorical

Distinct3
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size43.5 KiB
2007
365 
2008
348 
2006
 
15

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters2912
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2006
2nd row2006
3rd row2006
4th row2006
5th row2006

Common Values

ValueCountFrequency (%)
2007 365
50.1%
2008 348
47.8%
2006 15
 
2.1%

Length

2024-10-19T09:54:39.924824image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-19T09:54:40.023984image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
2007 365
50.1%
2008 348
47.8%
2006 15
 
2.1%

Most occurring characters

ValueCountFrequency (%)
0 1456
50.0%
2 728
25.0%
7 365
 
12.5%
8 348
 
12.0%
6 15
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2912
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1456
50.0%
2 728
25.0%
7 365
 
12.5%
8 348
 
12.0%
6 15
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2912
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1456
50.0%
2 728
25.0%
7 365
 
12.5%
8 348
 
12.0%
6 15
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2912
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1456
50.0%
2 728
25.0%
7 365
 
12.5%
8 348
 
12.0%
6 15
 
0.5%

Month
Real number (ℝ)

Distinct12
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.4972527
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2024-10-19T09:54:40.111687image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.440993
Coefficient of variation (CV)0.52960737
Kurtosis-1.2057319
Mean6.4972527
Median Absolute Deviation (MAD)3
Skewness-0.0052322921
Sum4730
Variance11.840433
MonotonicityNot monotonic
2024-10-19T09:54:40.222391image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1 62
8.5%
3 62
8.5%
5 62
8.5%
7 62
8.5%
8 62
8.5%
10 62
8.5%
4 60
8.2%
6 60
8.2%
9 60
8.2%
11 60
8.2%
Other values (2) 116
15.9%
ValueCountFrequency (%)
1 62
8.5%
2 57
7.8%
3 62
8.5%
4 60
8.2%
5 62
8.5%
6 60
8.2%
7 62
8.5%
8 62
8.5%
9 60
8.2%
10 62
8.5%
ValueCountFrequency (%)
12 59
8.1%
11 60
8.2%
10 62
8.5%
9 60
8.2%
8 62
8.5%
7 62
8.5%
6 60
8.2%
5 62
8.5%
4 60
8.2%
3 62
8.5%

Day
Real number (ℝ)

Distinct31
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.741758
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2024-10-19T09:54:40.325371image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.8278235
Coefficient of variation (CV)0.56079019
Kurtosis-1.2020996
Mean15.741758
Median Absolute Deviation (MAD)8
Skewness0.0049701175
Sum11460
Variance77.930468
MonotonicityNot monotonic
2024-10-19T09:54:40.442228image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
17 24
 
3.3%
18 24
 
3.3%
13 24
 
3.3%
12 24
 
3.3%
11 24
 
3.3%
10 24
 
3.3%
9 24
 
3.3%
8 24
 
3.3%
7 24
 
3.3%
6 24
 
3.3%
Other values (21) 488
67.0%
ValueCountFrequency (%)
1 24
3.3%
2 24
3.3%
3 24
3.3%
4 24
3.3%
5 24
3.3%
6 24
3.3%
7 24
3.3%
8 24
3.3%
9 24
3.3%
10 24
3.3%
ValueCountFrequency (%)
31 14
1.9%
30 22
3.0%
29 23
3.2%
28 24
3.3%
27 24
3.3%
26 24
3.3%
25 24
3.3%
24 24
3.3%
23 24
3.3%
22 24
3.3%

Interactions

2024-10-19T09:54:11.089810image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-10-19T09:53:46.938218image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-10-19T09:53:48.490718image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-10-19T09:53:50.218373image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-10-19T09:53:51.884344image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-10-19T09:53:53.306261image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-10-19T09:53:54.883216image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-10-19T09:53:56.597547image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-10-19T09:53:58.128407image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-10-19T09:53:59.772080image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-10-19T09:54:01.212008image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-10-19T09:54:02.724716image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-10-19T09:54:04.300220image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-10-19T09:54:06.288410image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-10-19T09:54:07.827574image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-10-19T09:54:09.405123image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-10-19T09:54:11.208852image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-10-19T09:53:47.064268image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-10-19T09:53:48.592347image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-10-19T09:53:50.305740image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-10-19T09:53:51.959158image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-10-19T09:53:53.406341image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-10-19T09:53:54.983677image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-10-19T09:53:56.686557image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-10-19T09:53:58.223132image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-10-19T09:53:59.873771image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-10-19T09:54:01.306350image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-10-19T09:54:02.810427image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-10-19T09:54:04.405049image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-10-19T09:54:06.374768image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-10-19T09:54:07.921817image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-10-19T09:54:09.500666image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-10-19T09:54:11.329618image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-10-19T09:53:47.166344image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-10-19T09:53:48.684119image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-10-19T09:53:50.413993image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-10-19T09:53:52.058688image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-10-19T09:53:53.505589image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-10-19T09:53:55.259642image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-10-19T09:53:56.789087image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-10-19T09:53:58.324099image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-10-19T09:53:59.962652image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-10-19T09:54:01.397340image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-10-19T09:54:02.898824image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-10-19T09:54:04.523463image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-10-19T09:54:06.458929image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-10-19T09:54:08.039009image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-10-19T09:54:09.606208image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-10-19T09:54:11.451051image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-10-19T09:53:47.279536image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-10-19T09:53:48.992169image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-10-19T09:53:50.514056image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-10-19T09:53:52.153285image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-10-19T09:53:53.628179image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-10-19T09:53:55.362836image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-10-19T09:53:56.890416image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-10-19T09:53:58.422211image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-10-19T09:54:00.062759image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-10-19T09:54:01.507665image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
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2024-10-19T09:53:51.185814image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-10-19T09:53:52.774986image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-10-19T09:53:54.306930image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-10-19T09:53:56.041412image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-10-19T09:53:57.563187image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-10-19T09:53:59.061255image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
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2024-10-19T09:53:54.403495image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-10-19T09:53:56.120651image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-10-19T09:53:57.658034image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-10-19T09:53:59.140945image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-10-19T09:54:00.778922image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-10-19T09:54:02.259498image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-10-19T09:54:03.760426image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
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2024-10-19T09:54:08.931361image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-10-19T09:54:10.531103image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-10-19T09:54:12.901835image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
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2024-10-19T09:53:49.842958image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-10-19T09:53:51.369267image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-10-19T09:53:52.951195image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
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2024-10-19T09:54:07.408350image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-10-19T09:54:09.024791image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-10-19T09:54:10.646456image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-10-19T09:54:12.992437image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-10-19T09:53:48.225929image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-10-19T09:53:49.941124image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-10-19T09:53:51.471904image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-10-19T09:53:53.040840image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-10-19T09:53:54.592083image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-10-19T09:53:56.322395image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
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2024-10-19T09:53:59.525485image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
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2024-10-19T09:54:03.967818image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-10-19T09:54:06.000087image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-10-19T09:54:07.511945image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-10-19T09:54:09.121153image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-10-19T09:54:10.763645image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-10-19T09:54:13.075137image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-10-19T09:53:48.323894image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-10-19T09:53:50.023086image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
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2024-10-19T09:53:54.784634image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
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2024-10-19T09:53:58.023358image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-10-19T09:53:59.698582image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-10-19T09:54:01.125958image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-10-19T09:54:02.623661image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-10-19T09:54:04.172223image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-10-19T09:54:06.191414image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-10-19T09:54:07.704906image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-10-19T09:54:09.310605image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-10-19T09:54:10.980576image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Correlations

2024-10-19T09:54:40.553827image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
DayGlobal_active_powerGlobal_intensityGlobal_reactive_powerMonthSub_metering_1Sub_metering_2Sub_metering_3VoltageYearprcpprestavgtmaxtminwdirwspd
Day1.000-0.110-0.103-0.0580.010-0.0570.000-0.0590.0620.000-0.0600.084-0.067-0.062-0.069-0.044-0.163
Global_active_power-0.1101.0000.9910.278-0.0870.2470.2030.421-0.1420.157-0.0130.065-0.247-0.236-0.251-0.0250.006
Global_intensity-0.1030.9911.0000.322-0.0890.2450.2220.428-0.1720.175-0.0080.054-0.211-0.202-0.215-0.0220.005
Global_reactive_power-0.0580.2780.3221.000-0.0010.0600.4770.041-0.0530.0000.044-0.0970.1400.1370.1270.0350.010
Month0.010-0.087-0.089-0.0011.000-0.001-0.0550.002-0.0040.208-0.0680.1410.0400.0150.070-0.035-0.121
Sub_metering_1-0.0570.2470.2450.060-0.0011.0000.0310.131-0.1310.0000.067-0.0200.0190.0320.0140.0890.027
Sub_metering_20.0000.2030.2220.477-0.0550.0311.000-0.020-0.0610.0000.005-0.0090.0490.0480.0350.003-0.008
Sub_metering_3-0.0590.4210.4280.0410.0020.131-0.0201.000-0.1780.093-0.051-0.0130.1140.1190.098-0.033-0.038
Voltage0.062-0.142-0.172-0.053-0.004-0.131-0.061-0.1781.0000.219-0.0400.110-0.438-0.436-0.433-0.0960.026
Year0.0000.1570.1750.0000.2080.0000.0000.0930.2191.0000.0000.2520.2100.2350.1860.1540.053
prcp-0.060-0.013-0.0080.044-0.0680.0670.005-0.051-0.0400.0001.000-0.397-0.060-0.0920.0180.3300.338
pres0.0840.0650.054-0.0970.141-0.020-0.009-0.0130.1100.252-0.3971.000-0.180-0.174-0.217-0.101-0.351
tavg-0.067-0.247-0.2110.1400.0400.0190.0490.114-0.4380.210-0.060-0.1801.0000.9800.9600.029-0.123
tmax-0.062-0.236-0.2020.1370.0150.0320.0480.119-0.4360.235-0.092-0.1740.9801.0000.909-0.009-0.152
tmin-0.069-0.251-0.2150.1270.0700.0140.0350.098-0.4330.1860.018-0.2170.9600.9091.0000.093-0.060
wdir-0.044-0.025-0.0220.035-0.0350.0890.003-0.033-0.0960.1540.330-0.1010.029-0.0090.0931.0000.128
wspd-0.1630.0060.0050.010-0.1210.027-0.008-0.0380.0260.0530.338-0.351-0.123-0.152-0.0600.1281.000

Missing values

2024-10-19T09:54:15.221744image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
A simple visualization of nullity by column.
2024-10-19T09:54:16.076749image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Global_active_powerGlobal_reactive_powerVoltageGlobal_intensitySub_metering_1Sub_metering_2Sub_metering_3DatetimetavgtmintmaxprcpsnowwdirwspdwpgtprestsunYearMonthDay
01.0440.152242.734.40.02.00.02006-12-174.41.17.93.3NaN217.04.6NaN1029.5NaN20061217
10.2780.126246.171.20.02.00.02006-12-184.63.46.00.0NaN125.06.5NaN1029.0NaN20061218
20.4140.242241.192.00.01.00.02006-12-192.60.55.20.0NaN22.014.8NaN1034.1NaN20061219
30.8240.058245.573.40.00.00.02006-12-202.90.55.70.0NaN21.013.5NaN1037.5NaN20061220
41.8140.148243.517.60.00.018.02006-12-215.21.78.50.0NaN23.014.5NaN1039.4NaN20061221
50.2060.000245.700.80.00.00.02006-12-225.13.86.80.0NaN30.015.0NaN1039.5NaN20061222
62.3280.000241.259.60.00.00.02006-12-232.71.85.60.0NaN34.010.4NaN1038.2NaN20061223
75.3760.046237.5722.60.00.018.02006-12-242.41.62.80.0NaN65.07.4NaN1036.4NaN20061224
80.5860.260243.652.60.01.00.02006-12-251.40.22.80.0NaN27.07.2NaN1035.5NaN20061225
92.4900.102245.4410.00.01.00.02006-12-260.3-0.51.00.0NaN115.06.6NaN1034.2NaN20061226
Global_active_powerGlobal_reactive_powerVoltageGlobal_intensitySub_metering_1Sub_metering_2Sub_metering_3DatetimetavgtmintmaxprcpsnowwdirwspdwpgtprestsunYearMonthDay
7180.3460.062245.431.40.01.00.02008-12-044.80.210.30.010.0198.010.6NaN994.8NaN2008124
7191.0240.180245.444.40.00.00.02008-12-056.95.98.35.610.0225.015.1NaN991.4NaN2008125
7201.5600.074244.076.40.00.018.02008-12-067.05.88.92.310.0283.011.7NaN1010.8NaN2008126
7211.4520.142246.235.80.00.00.02008-12-074.02.97.80.010.028.06.5NaN1027.2NaN2008127
7221.6000.148242.056.60.01.018.02008-12-082.01.13.50.010.0135.06.9NaN1025.6NaN2008128
7230.2180.048246.580.80.00.00.02008-12-092.20.63.90.010.0277.08.9NaN1018.1NaN2008129
7240.2120.052244.620.80.00.00.02008-12-103.42.34.83.310.0322.011.0NaN1017.2NaN20081210
7250.3360.086244.491.40.00.00.02008-12-111.5-0.54.70.010.0216.06.2NaN1013.7NaN20081211
7260.3720.062242.861.60.00.00.02008-12-12-0.9-1.62.70.010.0177.09.6NaN1018.5NaN20081212
7270.2120.048244.870.80.00.00.02008-12-130.3-2.13.50.010.0150.015.9NaN1001.4NaN20081213