diff --git "a/reports/weather_data_ts.html" "b/reports/weather_data_ts.html" new file mode 100644--- /dev/null +++ "b/reports/weather_data_ts.html" @@ -0,0 +1,143066 @@ +Weather Data Report

Overview

Dataset statistics

Number of variables19
Number of observations19203
Missing cells44422
Missing cells (%)12.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.9 MiB
Average record size in memory160.0 B

Variable types

Numeric6
TimeSeries9
Categorical3
DateTime1

Timeseries statistics

Number of series9
Time series length19203
Starting point2010-01-01 00:00:00
Ending point2018-11-12 00:00:00
Period4 hours, 2 minutes and 45.97 seconds
2024-04-22T01:30:25.636362image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-22T01:30:26.160175image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Alerts

SnowIce is highly imbalanced (77.3%)Imbalance
DewPoint has 8691 (45.3%) missing valuesMissing
WindSpeed has 1208 (6.3%) missing valuesMissing
MaxSustainedWind has 4248 (22.1%) missing valuesMissing
Gust has 11876 (61.8%) missing valuesMissing
SnowDepth has 18368 (95.7%) missing valuesMissing
MeanTemp is non stationaryNon stationary
MinTemp is non stationaryNon stationary
MaxTemp is non stationaryNon stationary
DewPoint is non stationaryNon stationary
Year is non stationaryNon stationary
Month is non stationaryNon stationary
Day is non stationaryNon stationary
Day_Of_Year is non stationaryNon stationary
Week is non stationaryNon stationary
MeanTemp is seasonalSeasonal
MinTemp is seasonalSeasonal
MaxTemp is seasonalSeasonal
DewPoint is seasonalSeasonal
Year is seasonalSeasonal
Month is seasonalSeasonal
Day is seasonalSeasonal
Day_Of_Year is seasonalSeasonal
Week is seasonalSeasonal
Percipitation is highly skewed (γ1 = 50.5842843)Skewed
Unnamed: 0 has unique valuesUnique
Percipitation has 14943 (77.8%) zerosZeros
WindSpeed has 3041 (15.8%) zerosZeros

Reproduction

Analysis started2024-04-22 05:30:04.129634
Analysis finished2024-04-22 05:30:25.188388
Duration21.06 seconds
Software versionydata-profiling vv4.7.0
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ)

UNIQUE 

Distinct19203
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69918.278
Minimum7680
Maximum151061
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size300.0 KiB
2024-04-22T01:30:26.393248image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum7680
5-th percentile8640.1
Q121310.5
median69973
Q374773.5
95-th percentile150100.9
Maximum151061
Range143381
Interquartile range (IQR)53463

Descriptive statistics

Standard deviation43872.267
Coefficient of variation (CV)0.62747922
Kurtosis-0.57717589
Mean69918.278
Median Absolute Deviation (MAD)23907
Skewness0.37414852
Sum1.3426407 × 109
Variance1.9247758 × 109
MonotonicityNot monotonic
2024-04-22T01:30:26.503067image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7680 1
 
< 0.1%
9794 1
 
< 0.1%
20712 1
 
< 0.1%
20711 1
 
< 0.1%
67811 1
 
< 0.1%
74055 1
 
< 0.1%
150258 1
 
< 0.1%
9793 1
 
< 0.1%
67808 1
 
< 0.1%
9790 1
 
< 0.1%
Other values (19193) 19193
99.9%
ValueCountFrequency (%)
7680 1
< 0.1%
7681 1
< 0.1%
7682 1
< 0.1%
7683 1
< 0.1%
7684 1
< 0.1%
7685 1
< 0.1%
7686 1
< 0.1%
7687 1
< 0.1%
7688 1
< 0.1%
7689 1
< 0.1%
ValueCountFrequency (%)
151061 1
< 0.1%
151060 1
< 0.1%
151059 1
< 0.1%
151058 1
< 0.1%
151057 1
< 0.1%
151056 1
< 0.1%
151055 1
< 0.1%
151054 1
< 0.1%
151053 1
< 0.1%
151052 1
< 0.1%

MeanTemp
Numeric time series

NON STATIONARY  SEASONAL 

Distinct800
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean55.782503
Minimum6.9
Maximum92.7
Zeros0
Zeros (%)0.0%
Memory size300.0 KiB
2024-04-22T01:30:26.689245image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum6.9
5-th percentile27.9
Q142.1
median56.4
Q371.3
95-th percentile80.19
Maximum92.7
Range85.8
Interquartile range (IQR)29.2

Descriptive statistics

Standard deviation17.053824
Coefficient of variation (CV)0.30571994
Kurtosis-0.94028378
Mean55.782503
Median Absolute Deviation (MAD)14.6
Skewness-0.20797317
Sum1071191.4
Variance290.8329
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.0001746132044
2024-04-22T01:30:26.840214image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2024-04-22T01:30:27.305537image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Gap statistics

number of gaps0
min0
max0
mean0
std0
2024-04-22T01:30:27.438761image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
73.5 70
 
0.4%
73.8 69
 
0.4%
73.9 64
 
0.3%
76 62
 
0.3%
73.3 59
 
0.3%
72.9 59
 
0.3%
72.4 58
 
0.3%
72.8 58
 
0.3%
74.8 56
 
0.3%
77.5 55
 
0.3%
Other values (790) 18593
96.8%
ValueCountFrequency (%)
6.9 1
< 0.1%
7.2 1
< 0.1%
7.4 1
< 0.1%
8.1 1
< 0.1%
8.3 1
< 0.1%
8.5 1
< 0.1%
8.8 1
< 0.1%
8.9 1
< 0.1%
9.3 1
< 0.1%
9.4 2
< 0.1%
ValueCountFrequency (%)
92.7 1
< 0.1%
92.6 1
< 0.1%
92.5 1
< 0.1%
91.3 1
< 0.1%
91.1 1
< 0.1%
90.9 1
< 0.1%
90.8 1
< 0.1%
90.7 1
< 0.1%
90.3 1
< 0.1%
90.1 1
< 0.1%
2024-04-22T01:30:27.056160image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ACF and PACF

MinTemp
Numeric time series

NON STATIONARY  SEASONAL 

Distinct441
Distinct (%)2.3%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean49.126919
Minimum-0.9
Maximum84.9
Zeros0
Zeros (%)0.0%
Memory size300.0 KiB
2024-04-22T01:30:27.666895image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-0.9
5-th percentile21.2
Q136
median49.3
Q364
95-th percentile73.4
Maximum84.9
Range85.8
Interquartile range (IQR)28

Descriptive statistics

Standard deviation16.810057
Coefficient of variation (CV)0.34217609
Kurtosis-0.90129211
Mean49.126919
Median Absolute Deviation (MAD)14.2
Skewness-0.19561575
Sum943335.1
Variance282.57801
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.0002353460267
2024-04-22T01:30:27.824715image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2024-04-22T01:30:28.417710image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Gap statistics

number of gaps0
min0
max0
mean0
std0
2024-04-22T01:30:28.555247image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
68 318
 
1.7%
71.1 272
 
1.4%
64 271
 
1.4%
69.1 262
 
1.4%
37 254
 
1.3%
66 246
 
1.3%
64.9 246
 
1.3%
39 245
 
1.3%
50 244
 
1.3%
70 239
 
1.2%
Other values (431) 16605
86.5%
ValueCountFrequency (%)
-0.9 1
 
< 0.1%
0.1 1
 
< 0.1%
0.3 1
 
< 0.1%
1 4
 
< 0.1%
1.9 2
 
< 0.1%
2.1 1
 
< 0.1%
2.5 2
 
< 0.1%
3 7
< 0.1%
3.4 1
 
< 0.1%
3.9 10
0.1%
ValueCountFrequency (%)
84.9 1
 
< 0.1%
84.7 1
 
< 0.1%
84.2 1
 
< 0.1%
84 1
 
< 0.1%
83.8 1
 
< 0.1%
83.3 1
 
< 0.1%
82.9 3
< 0.1%
82.8 2
< 0.1%
82.6 1
 
< 0.1%
82.4 2
< 0.1%
2024-04-22T01:30:28.034822image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ACF and PACF

MaxTemp
Numeric time series

NON STATIONARY  SEASONAL 

Distinct451
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.114123
Minimum15.3
Maximum104
Zeros0
Zeros (%)0.0%
Memory size300.0 KiB
2024-04-22T01:30:28.784701image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum15.3
5-th percentile35.1
Q150
median64.9
Q379.5
95-th percentile89.98
Maximum104
Range88.7
Interquartile range (IQR)29.5

Descriptive statistics

Standard deviation17.731912
Coefficient of variation (CV)0.27656797
Kurtosis-0.94015326
Mean64.114123
Median Absolute Deviation (MAD)14.9
Skewness-0.22276131
Sum1231183.5
Variance314.42072
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value5.207019276 × 10-5
2024-04-22T01:30:28.940997image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2024-04-22T01:30:29.406791image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Gap statistics

number of gaps0
min0
max0
mean0
std0
2024-04-22T01:30:29.547342image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
82 294
 
1.5%
84.9 288
 
1.5%
84 272
 
1.4%
82.9 270
 
1.4%
86 261
 
1.4%
87.1 258
 
1.3%
80.1 239
 
1.2%
81 229
 
1.2%
75.9 221
 
1.2%
79 220
 
1.1%
Other values (441) 16651
86.7%
ValueCountFrequency (%)
15.3 3
< 0.1%
15.4 1
 
< 0.1%
15.6 1
 
< 0.1%
15.8 1
 
< 0.1%
16.5 1
 
< 0.1%
16.7 1
 
< 0.1%
17.1 1
 
< 0.1%
17.2 1
 
< 0.1%
17.6 2
< 0.1%
17.8 1
 
< 0.1%
ValueCountFrequency (%)
104 2
 
< 0.1%
102.9 6
 
< 0.1%
102 2
 
< 0.1%
100.9 6
 
< 0.1%
100.4 1
 
< 0.1%
100.2 1
 
< 0.1%
100 7
 
< 0.1%
99 7
 
< 0.1%
98.1 26
0.1%
97.7 1
 
< 0.1%
2024-04-22T01:30:29.156506image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ACF and PACF

DewPoint
Numeric time series

MISSING  NON STATIONARY  SEASONAL 

Distinct819
Distinct (%)7.8%
Missing8691
Missing (%)45.3%
Infinite0
Infinite (%)0.0%
Mean42.663975
Minimum-16.3
Maximum77
Zeros1
Zeros (%)< 0.1%
Memory size300.0 KiB
2024-04-22T01:30:29.774465image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-16.3
5-th percentile9.7
Q127.8
median44.2
Q358.825
95-th percentile69.3
Maximum77
Range93.3
Interquartile range (IQR)31.025

Descriptive statistics

Standard deviation18.884629
Coefficient of variation (CV)0.44263643
Kurtosis-0.84084314
Mean42.663975
Median Absolute Deviation (MAD)15.5
Skewness-0.32259967
Sum448483.7
Variance356.62923
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.000664761102
2024-04-22T01:30:29.936254image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2024-04-22T01:30:30.498991image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Gap statistics

number of gaps0
min0
max0
mean0
std0
2024-04-22T01:30:30.626542image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
62.7 34
 
0.2%
52.8 33
 
0.2%
65.2 32
 
0.2%
58.5 32
 
0.2%
57.9 32
 
0.2%
59.4 30
 
0.2%
67 30
 
0.2%
60.5 29
 
0.2%
53.9 29
 
0.2%
58.1 29
 
0.2%
Other values (809) 10202
53.1%
(Missing) 8691
45.3%
ValueCountFrequency (%)
-16.3 1
< 0.1%
-16.1 1
< 0.1%
-12.1 1
< 0.1%
-11.8 1
< 0.1%
-11.1 1
< 0.1%
-10.5 1
< 0.1%
-10.2 1
< 0.1%
-9.8 1
< 0.1%
-9.4 1
< 0.1%
-8.7 1
< 0.1%
ValueCountFrequency (%)
77 1
 
< 0.1%
76.8 1
 
< 0.1%
76.6 2
< 0.1%
76.3 1
 
< 0.1%
76.2 1
 
< 0.1%
76 1
 
< 0.1%
75.8 4
< 0.1%
75.6 1
 
< 0.1%
75.4 3
< 0.1%
75.3 3
< 0.1%
2024-04-22T01:30:30.132787image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ACF and PACF

Percipitation
Real number (ℝ)

SKEWED  ZEROS 

Distinct248
Distinct (%)1.3%
Missing30
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean0.10995567
Minimum0
Maximum99.99
Zeros14943
Zeros (%)77.8%
Negative0
Negative (%)0.0%
Memory size300.0 KiB
2024-04-22T01:30:30.786106image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.47
Maximum99.99
Range99.99
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.9307131
Coefficient of variation (CV)17.559014
Kurtosis2612.9128
Mean0.10995567
Median Absolute Deviation (MAD)0
Skewness50.584284
Sum2108.18
Variance3.7276532
MonotonicityNot monotonic
2024-04-22T01:30:30.894840image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 14943
77.8%
0.01 523
 
2.7%
0.02 330
 
1.7%
0.03 219
 
1.1%
0.04 182
 
0.9%
0.05 167
 
0.9%
0.06 139
 
0.7%
0.07 122
 
0.6%
0.08 92
 
0.5%
0.09 85
 
0.4%
Other values (238) 2371
 
12.3%
ValueCountFrequency (%)
0 14943
77.8%
0.01 523
 
2.7%
0.02 330
 
1.7%
0.03 219
 
1.1%
0.04 182
 
0.9%
0.05 167
 
0.9%
0.06 139
 
0.7%
0.07 122
 
0.6%
0.08 92
 
0.5%
0.09 85
 
0.4%
ValueCountFrequency (%)
99.99 7
< 0.1%
9.99 1
 
< 0.1%
6.31 1
 
< 0.1%
5.61 1
 
< 0.1%
5.37 1
 
< 0.1%
5.02 1
 
< 0.1%
5.01 1
 
< 0.1%
5 1
 
< 0.1%
4.91 1
 
< 0.1%
4.88 1
 
< 0.1%

WindSpeed
Real number (ℝ)

MISSING  ZEROS 

Distinct240
Distinct (%)1.3%
Missing1208
Missing (%)6.3%
Infinite0
Infinite (%)0.0%
Mean6.2767213
Minimum0
Maximum28.3
Zeros3041
Zeros (%)15.8%
Negative0
Negative (%)0.0%
Memory size300.0 KiB
2024-04-22T01:30:30.998278image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13.35
median5.9
Q39
95-th percentile14.3
Maximum28.3
Range28.3
Interquartile range (IQR)5.65

Descriptive statistics

Standard deviation4.4550893
Coefficient of variation (CV)0.70977968
Kurtosis0.24795782
Mean6.2767213
Median Absolute Deviation (MAD)2.8
Skewness0.54589655
Sum112949.6
Variance19.84782
MonotonicityNot monotonic
2024-04-22T01:30:31.101274image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3041
 
15.8%
4.6 208
 
1.1%
5.3 196
 
1.0%
3.7 196
 
1.0%
4.3 195
 
1.0%
5.8 191
 
1.0%
5.5 190
 
1.0%
4.8 189
 
1.0%
5.4 186
 
1.0%
6.2 183
 
1.0%
Other values (230) 13220
68.8%
(Missing) 1208
 
6.3%
ValueCountFrequency (%)
0 3041
15.8%
0.2 2
 
< 0.1%
0.4 1
 
< 0.1%
0.5 4
 
< 0.1%
0.6 4
 
< 0.1%
0.7 2
 
< 0.1%
0.8 5
 
< 0.1%
0.9 5
 
< 0.1%
1 7
 
< 0.1%
1.1 7
 
< 0.1%
ValueCountFrequency (%)
28.3 1
< 0.1%
27.2 1
< 0.1%
26.7 1
< 0.1%
25.9 1
< 0.1%
25.8 1
< 0.1%
25.7 1
< 0.1%
25.6 1
< 0.1%
25.5 1
< 0.1%
25.3 1
< 0.1%
24.7 1
< 0.1%

MaxSustainedWind
Real number (ℝ)

MISSING 

Distinct48
Distinct (%)0.3%
Missing4248
Missing (%)22.1%
Infinite0
Infinite (%)0.0%
Mean14.01219
Minimum2.9
Maximum49
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size300.0 KiB
2024-04-22T01:30:31.209193image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum2.9
5-th percentile7
Q19.9
median13
Q317.1
95-th percentile24.1
Maximum49
Range46.1
Interquartile range (IQR)7.2

Descriptive statistics

Standard deviation5.4065791
Coefficient of variation (CV)0.38584826
Kurtosis0.77435371
Mean14.01219
Median Absolute Deviation (MAD)3.9
Skewness0.76555545
Sum209552.3
Variance29.231097
MonotonicityNot monotonic
2024-04-22T01:30:31.315368image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
12 1193
 
6.2%
9.9 1091
 
5.7%
13 1064
 
5.5%
11.1 1057
 
5.5%
8.9 1042
 
5.4%
14 1041
 
5.4%
15 988
 
5.1%
15.9 900
 
4.7%
8 874
 
4.6%
18.1 714
 
3.7%
Other values (38) 4991
26.0%
(Missing) 4248
22.1%
ValueCountFrequency (%)
2.9 5
 
< 0.1%
4.1 46
 
0.2%
5.1 232
 
1.2%
6 425
 
2.2%
7 644
3.4%
8 874
4.6%
8.9 1042
5.4%
9.9 1091
5.7%
11.1 1057
5.5%
12 1193
6.2%
ValueCountFrequency (%)
49 1
 
< 0.1%
46 1
 
< 0.1%
45.8 1
 
< 0.1%
42 2
 
< 0.1%
41 1
 
< 0.1%
40 2
 
< 0.1%
39 2
 
< 0.1%
38.1 1
 
< 0.1%
36.9 4
< 0.1%
35.9 7
< 0.1%

Gust
Real number (ℝ)

MISSING 

Distinct49
Distinct (%)0.7%
Missing11876
Missing (%)61.8%
Infinite0
Infinite (%)0.0%
Mean23.870848
Minimum14
Maximum69
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size300.0 KiB
2024-04-22T01:30:31.417499image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile15
Q119
median22
Q328
95-th percentile38.1
Maximum69
Range55
Interquartile range (IQR)9

Descriptive statistics

Standard deviation7.034577
Coefficient of variation (CV)0.29469322
Kurtosis1.6520109
Mean23.870848
Median Absolute Deviation (MAD)4
Skewness1.1455969
Sum174901.7
Variance49.485273
MonotonicityNot monotonic
2024-04-22T01:30:31.518187image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
21 510
 
2.7%
20 508
 
2.6%
22 494
 
2.6%
19 481
 
2.5%
18.1 453
 
2.4%
22.9 432
 
2.2%
17.1 430
 
2.2%
15.9 397
 
2.1%
24.1 391
 
2.0%
25.1 335
 
1.7%
Other values (39) 2896
 
15.1%
(Missing) 11876
61.8%
ValueCountFrequency (%)
14 169
 
0.9%
15 298
1.6%
15.9 397
2.1%
16.9 3
 
< 0.1%
17.1 430
2.2%
18.1 453
2.4%
19 481
2.5%
20 508
2.6%
21 510
2.7%
22 494
2.6%
ValueCountFrequency (%)
69 1
 
< 0.1%
63.9 1
 
< 0.1%
62 1
 
< 0.1%
59.1 3
 
< 0.1%
55.9 3
 
< 0.1%
55 1
 
< 0.1%
54 3
 
< 0.1%
52.1 4
< 0.1%
51.1 9
< 0.1%
49.9 5
< 0.1%

Rain
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size300.0 KiB
0
15364 
1
3839 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters19203
Distinct characters2
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 row0
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 15364
80.0%
1 3839
 
20.0%

Length

2024-04-22T01:30:31.611159image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-22T01:30:31.688445image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 15364
80.0%
1 3839
 
20.0%

Most occurring characters

ValueCountFrequency (%)
0 15364
80.0%
1 3839
 
20.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 19203
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15364
80.0%
1 3839
 
20.0%

Most occurring scripts

ValueCountFrequency (%)
Common 19203
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15364
80.0%
1 3839
 
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 19203
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15364
80.0%
1 3839
 
20.0%

SnowDepth
Real number (ℝ)

MISSING 

Distinct25
Distinct (%)3.0%
Missing18368
Missing (%)95.7%
Infinite0
Infinite (%)0.0%
Mean4.8766467
Minimum1.2
Maximum27.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size300.0 KiB
2024-04-22T01:30:31.767102image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1.2
5-th percentile1.2
Q11.2
median3.9
Q37.1
95-th percentile14.2
Maximum27.2
Range26
Interquartile range (IQR)5.9

Descriptive statistics

Standard deviation4.386246
Coefficient of variation (CV)0.89943896
Kurtosis3.6310588
Mean4.8766467
Median Absolute Deviation (MAD)2.7
Skewness1.7261871
Sum4072
Variance19.239154
MonotonicityNot monotonic
2024-04-22T01:30:31.860969image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
1.2 259
 
1.3%
2 94
 
0.5%
5.9 73
 
0.4%
3.1 64
 
0.3%
5.1 64
 
0.3%
3.9 58
 
0.3%
7.9 49
 
0.3%
7.1 39
 
0.2%
9.1 31
 
0.2%
9.8 24
 
0.1%
Other values (15) 80
 
0.4%
(Missing) 18368
95.7%
ValueCountFrequency (%)
1.2 259
1.3%
2 94
 
0.5%
3.1 64
 
0.3%
3.9 58
 
0.3%
5.1 64
 
0.3%
5.9 73
 
0.4%
7.1 39
 
0.2%
7.9 49
 
0.3%
9.1 31
 
0.2%
9.8 24
 
0.1%
ValueCountFrequency (%)
27.2 2
 
< 0.1%
25.2 1
 
< 0.1%
22.8 2
 
< 0.1%
22 1
 
< 0.1%
20.9 1
 
< 0.1%
20.1 3
 
< 0.1%
18.9 3
 
< 0.1%
18.1 8
< 0.1%
16.9 7
< 0.1%
16.1 4
< 0.1%

SnowIce
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size300.0 KiB
0
18498 
1
 
705

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters19203
Distinct characters2
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 row0
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 18498
96.3%
1 705
 
3.7%

Length

2024-04-22T01:30:31.951259image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-22T01:30:32.021579image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 18498
96.3%
1 705
 
3.7%

Most occurring characters

ValueCountFrequency (%)
0 18498
96.3%
1 705
 
3.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 19203
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 18498
96.3%
1 705
 
3.7%

Most occurring scripts

ValueCountFrequency (%)
Common 19203
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 18498
96.3%
1 705
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 19203
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 18498
96.3%
1 705
 
3.7%

Year
Numeric time series

NON STATIONARY  SEASONAL 

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2013.9829
Minimum2010
Maximum2018
Zeros0
Zeros (%)0.0%
Memory size300.0 KiB
2024-04-22T01:30:32.186347image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum2010
5-th percentile2010
Q12012
median2014
Q32016
95-th percentile2018
Maximum2018
Range8
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.5143728
Coefficient of variation (CV)0.0012484578
Kurtosis-1.2231803
Mean2013.9829
Median Absolute Deviation (MAD)2
Skewness-0.030901661
Sum38674514
Variance6.3220705
MonotonicityIncreasing
Augmented Dickey-Fuller test p-value0.8833221686
2024-04-22T01:30:32.332483image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
2024-04-22T01:30:32.920956image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Gap statistics

number of gaps0
min0
max0
mean0
std0
2024-04-22T01:30:33.048096image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
2017 2521
13.1%
2016 2327
12.1%
2011 2190
11.4%
2013 2176
11.3%
2012 2175
11.3%
2015 2163
11.3%
2014 2110
11.0%
2010 1972
10.3%
2018 1569
8.2%
ValueCountFrequency (%)
2010 1972
10.3%
2011 2190
11.4%
2012 2175
11.3%
2013 2176
11.3%
2014 2110
11.0%
2015 2163
11.3%
2016 2327
12.1%
2017 2521
13.1%
2018 1569
8.2%
ValueCountFrequency (%)
2018 1569
8.2%
2017 2521
13.1%
2016 2327
12.1%
2015 2163
11.3%
2014 2110
11.0%
2013 2176
11.3%
2012 2175
11.3%
2011 2190
11.4%
2010 1972
10.3%
2024-04-22T01:30:32.542853image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ACF and PACF

Month
Numeric time series

NON STATIONARY  SEASONAL 

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.4456595
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Memory size300.0 KiB
2024-04-22T01:30:33.266558image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.4400095
Coefficient of variation (CV)0.53369395
Kurtosis-1.2123065
Mean6.4456595
Median Absolute Deviation (MAD)3
Skewness0.013498815
Sum123776
Variance11.833666
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value5.731670111 × 10-5
2024-04-22T01:30:33.407154image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
2024-04-22T01:30:33.860509image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Gap statistics

number of gaps0
min0
max0
mean0
std0
2024-04-22T01:30:34.095605image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
3 1705
8.9%
8 1664
8.7%
1 1649
8.6%
10 1641
8.5%
4 1631
8.5%
5 1603
8.3%
7 1603
8.3%
9 1594
8.3%
2 1548
8.1%
12 1538
8.0%
Other values (2) 3027
15.8%
ValueCountFrequency (%)
1 1649
8.6%
2 1548
8.1%
3 1705
8.9%
4 1631
8.5%
5 1603
8.3%
6 1533
8.0%
7 1603
8.3%
8 1664
8.7%
9 1594
8.3%
10 1641
8.5%
ValueCountFrequency (%)
12 1538
8.0%
11 1494
7.8%
10 1641
8.5%
9 1594
8.3%
8 1664
8.7%
7 1603
8.3%
6 1533
8.0%
5 1603
8.3%
4 1631
8.5%
3 1705
8.9%
2024-04-22T01:30:33.612886image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ACF and PACF

Day
Numeric time series

NON STATIONARY  SEASONAL 

Distinct31
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.692027
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Memory size300.0 KiB
2024-04-22T01:30:34.311543image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

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

Descriptive statistics

Standard deviation8.7929423
Coefficient of variation (CV)0.56034457
Kurtosis-1.1924172
Mean15.692027
Median Absolute Deviation (MAD)8
Skewness0.012005159
Sum301334
Variance77.315834
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0
2024-04-22T01:30:34.464413image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
2024-04-22T01:30:34.936653image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Gap statistics

number of gaps0
min0
max0
mean0
std0
2024-04-22T01:30:35.062527image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
2 636
 
3.3%
10 636
 
3.3%
3 635
 
3.3%
8 635
 
3.3%
12 635
 
3.3%
16 634
 
3.3%
13 634
 
3.3%
7 633
 
3.3%
9 633
 
3.3%
14 633
 
3.3%
Other values (21) 12859
67.0%
ValueCountFrequency (%)
1 631
3.3%
2 636
3.3%
3 635
3.3%
4 632
3.3%
5 632
3.3%
6 632
3.3%
7 633
3.3%
8 635
3.3%
9 633
3.3%
10 636
3.3%
ValueCountFrequency (%)
31 366
1.9%
30 570
3.0%
29 582
3.0%
28 628
3.3%
27 629
3.3%
26 629
3.3%
25 628
3.3%
24 630
3.3%
23 630
3.3%
22 630
3.3%
2024-04-22T01:30:34.685338image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ACF and PACF
Distinct3238
Distinct (%)16.9%
Missing0
Missing (%)0.0%
Memory size300.0 KiB
Minimum2010-01-01 00:00:00
Maximum2018-11-12 00:00:00
2024-04-22T01:30:35.208384image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-22T01:30:35.315526image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Day_Of_Year
Numeric time series

NON STATIONARY  SEASONAL 

Distinct366
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean180.72452
Minimum1
Maximum366
Zeros0
Zeros (%)0.0%
Memory size300.0 KiB
2024-04-22T01:30:35.498518image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile19
Q189
median180
Q3271
95-th percentile346
Maximum366
Range365
Interquartile range (IQR)182

Descriptive statistics

Standard deviation105.15282
Coefficient of variation (CV)0.58184035
Kurtosis-1.2041157
Mean180.72452
Median Absolute Deviation (MAD)91
Skewness0.023035262
Sum3470453
Variance11057.116
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value2.498243102 × 10-5
2024-04-22T01:30:35.771228image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2024-04-22T01:30:36.240301image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Gap statistics

number of gaps0
min0
max0
mean0
std0
2024-04-22T01:30:36.364385image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
99 55
 
0.3%
87 55
 
0.3%
85 55
 
0.3%
84 55
 
0.3%
83 55
 
0.3%
82 55
 
0.3%
81 55
 
0.3%
80 55
 
0.3%
79 55
 
0.3%
78 55
 
0.3%
Other values (356) 18653
97.1%
ValueCountFrequency (%)
1 50
0.3%
2 53
0.3%
3 53
0.3%
4 50
0.3%
5 53
0.3%
6 53
0.3%
7 53
0.3%
8 54
0.3%
9 54
0.3%
10 54
0.3%
ValueCountFrequency (%)
366 13
 
0.1%
365 48
0.2%
364 49
0.3%
363 49
0.3%
362 49
0.3%
361 49
0.3%
360 49
0.3%
359 49
0.3%
358 49
0.3%
357 50
0.3%
2024-04-22T01:30:35.987807image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ACF and PACF

Week
Numeric time series

NON STATIONARY  SEASONAL 

Distinct53
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.229391
Minimum1
Maximum53
Zeros0
Zeros (%)0.0%
Memory size300.0 KiB
2024-04-22T01:30:36.580892image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q113
median26
Q339
95-th percentile50
Maximum53
Range52
Interquartile range (IQR)26

Descriptive statistics

Standard deviation15.014437
Coefficient of variation (CV)0.57242796
Kurtosis-1.2037286
Mean26.229391
Median Absolute Deviation (MAD)13
Skewness0.023307223
Sum503683
Variance225.43332
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value7.278748818 × 10-6
2024-04-22T01:30:36.742986image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2024-04-22T01:30:37.204966image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Gap statistics

number of gaps0
min0
max0
mean0
std0
2024-04-22T01:30:37.441478image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
15 385
 
2.0%
16 385
 
2.0%
6 385
 
2.0%
7 385
 
2.0%
9 385
 
2.0%
10 385
 
2.0%
11 385
 
2.0%
12 385
 
2.0%
13 385
 
2.0%
14 385
 
2.0%
Other values (43) 15353
80.0%
ValueCountFrequency (%)
1 371
1.9%
2 377
2.0%
3 374
1.9%
4 368
1.9%
5 382
2.0%
6 385
2.0%
7 385
2.0%
8 382
2.0%
9 385
2.0%
10 385
2.0%
ValueCountFrequency (%)
53 53
 
0.3%
52 341
1.8%
51 350
1.8%
50 350
1.8%
49 347
1.8%
48 349
1.8%
47 348
1.8%
46 340
1.8%
45 353
1.8%
44 358
1.9%
2024-04-22T01:30:36.957544image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ACF and PACF

Season
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size300.0 KiB
Spring
4922 
Summer
4882 
Winter
4784 
Autumn
4615 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters115218
Distinct characters12
Distinct categories2 ?
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 rowWinter
2nd rowWinter
3rd rowWinter
4th rowWinter
5th rowWinter

Common Values

ValueCountFrequency (%)
Spring 4922
25.6%
Summer 4882
25.4%
Winter 4784
24.9%
Autumn 4615
24.0%

Length

2024-04-22T01:30:37.582283image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-22T01:30:37.660678image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
spring 4922
25.6%
summer 4882
25.4%
winter 4784
24.9%
autumn 4615
24.0%

Most occurring characters

ValueCountFrequency (%)
r 14588
12.7%
m 14379
12.5%
n 14321
12.4%
u 14112
12.2%
S 9804
8.5%
i 9706
8.4%
e 9666
8.4%
t 9399
8.2%
p 4922
 
4.3%
g 4922
 
4.3%
Other values (2) 9399
8.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 96015
83.3%
Uppercase Letter 19203
 
16.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 14588
15.2%
m 14379
15.0%
n 14321
14.9%
u 14112
14.7%
i 9706
10.1%
e 9666
10.1%
t 9399
9.8%
p 4922
 
5.1%
g 4922
 
5.1%
Uppercase Letter
ValueCountFrequency (%)
S 9804
51.1%
W 4784
24.9%
A 4615
24.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 115218
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 14588
12.7%
m 14379
12.5%
n 14321
12.4%
u 14112
12.2%
S 9804
8.5%
i 9706
8.4%
e 9666
8.4%
t 9399
8.2%
p 4922
 
4.3%
g 4922
 
4.3%
Other values (2) 9399
8.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 115218
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 14588
12.7%
m 14379
12.5%
n 14321
12.4%
u 14112
12.2%
S 9804
8.5%
i 9706
8.4%
e 9666
8.4%
t 9399
8.2%
p 4922
 
4.3%
g 4922
 
4.3%
Other values (2) 9399
8.2%
\ No newline at end of file