Overview

Dataset statistics

Number of variables13
Number of observations52
Missing cells252
Missing cells (%)37.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.5 KiB
Average record size in memory107.4 B

Variable types

Categorical7
Numeric5
DateTime1

Dataset

DescriptionAEMET - Estado de la meteorología
URLhttps://opendata.aemet.es/dist/index.html

Variable descriptions

PKIdentificador único (Primary Key) del dataset, compuesto por <datetime>_TODO
datetimeFecha y hora de la petición a la API
estadoCielo_valueEstado del cielo
precipitacion_valuePrecipitación
probPrecipitacion_valueProbabilidad de precipitación
probTormenta_valueProbabilidad de tormenta
nieve_valueNieve
probNieve_valueProbabilidad de nieve
temperatura_valueTemperatura
sensTermica_valueSensación térmica
humedadRelativa_valueHumedad relativa
direccionDirección del viento
velocidadVelocidad del viento
vientoAndRachaMax_valueViento racha máxima

Alerts

precipitacion_value has constant value ""Constant
probPrecipitacion_value has constant value ""Constant
probTormenta_value has constant value ""Constant
nieve_value has constant value ""Constant
probNieve_value has constant value ""Constant
estadoCielo_value has 4 (7.7%) missing valuesMissing
precipitacion_value has 4 (7.7%) missing valuesMissing
probPrecipitacion_value has 48 (92.3%) missing valuesMissing
probTormenta_value has 48 (92.3%) missing valuesMissing
nieve_value has 4 (7.7%) missing valuesMissing
probNieve_value has 48 (92.3%) missing valuesMissing
temperatura_value has 4 (7.7%) missing valuesMissing
sensTermica_value has 4 (7.7%) missing valuesMissing
humedadRelativa_value has 4 (7.7%) missing valuesMissing
direccion has 28 (53.8%) missing valuesMissing
velocidad has 28 (53.8%) missing valuesMissing
vientoAndRachaMax_value has 28 (53.8%) missing valuesMissing

Reproduction

Analysis started2024-03-21 12:07:59.936656
Analysis finished2024-03-21 12:08:02.075690
Duration2.14 seconds
Software versionydata-profiling vv4.7.0
Download configurationconfig.json

Variables

estadoCielo_value
Categorical

MISSING 

Estado del cielo

Distinct3
Distinct (%)6.2%
Missing4
Missing (%)7.7%
Memory size494.0 B
Nubes altas
28 
Despejado
14 
Poco nuboso
6 

Length

Max length11
Median length11
Mean length10.416667
Min length9

Characters and Unicode

Total characters500
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
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 rowNubes altas
2nd rowNubes altas
3rd rowNubes altas
4th rowNubes altas
5th rowNubes altas

Common Values

ValueCountFrequency (%)
Nubes altas 28
53.8%
Despejado 14
26.9%
Poco nuboso 6
 
11.5%
(Missing) 4
 
7.7%

Length

2024-03-21T13:08:02.264421image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-21T13:08:02.508671image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
nubes 28
34.1%
altas 28
34.1%
despejado 14
17.1%
poco 6
 
7.3%
nuboso 6
 
7.3%

Most occurring characters

ValueCountFrequency (%)
s 76
15.2%
a 70
14.0%
e 56
11.2%
o 38
7.6%
u 34
6.8%
b 34
6.8%
34
6.8%
N 28
 
5.6%
t 28
 
5.6%
l 28
 
5.6%
Other values (7) 74
14.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 418
83.6%
Uppercase Letter 48
 
9.6%
Space Separator 34
 
6.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 76
18.2%
a 70
16.7%
e 56
13.4%
o 38
9.1%
u 34
8.1%
b 34
8.1%
t 28
 
6.7%
l 28
 
6.7%
p 14
 
3.3%
j 14
 
3.3%
Other values (3) 26
 
6.2%
Uppercase Letter
ValueCountFrequency (%)
N 28
58.3%
D 14
29.2%
P 6
 
12.5%
Space Separator
ValueCountFrequency (%)
34
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 466
93.2%
Common 34
 
6.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 76
16.3%
a 70
15.0%
e 56
12.0%
o 38
8.2%
u 34
7.3%
b 34
7.3%
N 28
 
6.0%
t 28
 
6.0%
l 28
 
6.0%
D 14
 
3.0%
Other values (6) 60
12.9%
Common
ValueCountFrequency (%)
34
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 76
15.2%
a 70
14.0%
e 56
11.2%
o 38
7.6%
u 34
6.8%
b 34
6.8%
34
6.8%
N 28
 
5.6%
t 28
 
5.6%
l 28
 
5.6%
Other values (7) 74
14.8%

precipitacion_value
Categorical

CONSTANT  MISSING 

Precipitación

Distinct1
Distinct (%)2.1%
Missing4
Missing (%)7.7%
Memory size3.2 KiB
0.0
48 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters144
Distinct characters2
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 row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 48
92.3%
(Missing) 4
 
7.7%

Length

2024-03-21T13:08:02.733876image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-21T13:08:02.956976image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 48
100.0%

Most occurring characters

ValueCountFrequency (%)
0 96
66.7%
. 48
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 96
66.7%
Other Punctuation 48
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 96
100.0%
Other Punctuation
ValueCountFrequency (%)
. 48
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 144
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 96
66.7%
. 48
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 144
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 96
66.7%
. 48
33.3%

probPrecipitacion_value
Categorical

CONSTANT  MISSING 

Probabilidad de precipitación

Distinct1
Distinct (%)25.0%
Missing48
Missing (%)92.3%
Memory size3.4 KiB
0.0
4 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters12
Distinct characters2
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 row0.0
2nd row0.0
3rd row0.0
4th row0.0

Common Values

ValueCountFrequency (%)
0.0 4
 
7.7%
(Missing) 48
92.3%

Length

2024-03-21T13:08:03.151750image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-21T13:08:03.536785image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 4
100.0%

Most occurring characters

ValueCountFrequency (%)
0 8
66.7%
. 4
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8
66.7%
Other Punctuation 4
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 8
100.0%
Other Punctuation
ValueCountFrequency (%)
. 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 12
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 8
66.7%
. 4
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 8
66.7%
. 4
33.3%

probTormenta_value
Categorical

CONSTANT  MISSING 

Probabilidad de tormenta

Distinct1
Distinct (%)25.0%
Missing48
Missing (%)92.3%
Memory size3.4 KiB
0.0
4 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters12
Distinct characters2
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 row0.0
2nd row0.0
3rd row0.0
4th row0.0

Common Values

ValueCountFrequency (%)
0.0 4
 
7.7%
(Missing) 48
92.3%

Length

2024-03-21T13:08:03.917862image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-21T13:08:04.168494image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 4
100.0%

Most occurring characters

ValueCountFrequency (%)
0 8
66.7%
. 4
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8
66.7%
Other Punctuation 4
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 8
100.0%
Other Punctuation
ValueCountFrequency (%)
. 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 12
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 8
66.7%
. 4
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 8
66.7%
. 4
33.3%

nieve_value
Categorical

CONSTANT  MISSING 

Nieve

Distinct1
Distinct (%)2.1%
Missing4
Missing (%)7.7%
Memory size3.2 KiB
0.0
48 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters144
Distinct characters2
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 row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 48
92.3%
(Missing) 4
 
7.7%

Length

2024-03-21T13:08:04.489574image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-21T13:08:04.709134image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 48
100.0%

Most occurring characters

ValueCountFrequency (%)
0 96
66.7%
. 48
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 96
66.7%
Other Punctuation 48
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 96
100.0%
Other Punctuation
ValueCountFrequency (%)
. 48
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 144
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 96
66.7%
. 48
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 144
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 96
66.7%
. 48
33.3%

probNieve_value
Categorical

CONSTANT  MISSING 

Probabilidad de nieve

Distinct1
Distinct (%)25.0%
Missing48
Missing (%)92.3%
Memory size3.4 KiB
0.0
4 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters12
Distinct characters2
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 row0.0
2nd row0.0
3rd row0.0
4th row0.0

Common Values

ValueCountFrequency (%)
0.0 4
 
7.7%
(Missing) 48
92.3%

Length

2024-03-21T13:08:04.909846image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-21T13:08:05.148212image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 4
100.0%

Most occurring characters

ValueCountFrequency (%)
0 8
66.7%
. 4
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8
66.7%
Other Punctuation 4
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 8
100.0%
Other Punctuation
ValueCountFrequency (%)
. 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 12
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 8
66.7%
. 4
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 8
66.7%
. 4
33.3%

temperatura_value
Real number (ℝ)

MISSING 

Temperatura

Distinct13
Distinct (%)27.1%
Missing4
Missing (%)7.7%
Infinite0
Infinite (%)0.0%
Mean11.083333
Minimum4
Maximum18
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size548.0 B
2024-03-21T13:08:05.331827image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile5
Q16
median11.5
Q316
95-th percentile18
Maximum18
Range14
Interquartile range (IQR)10

Descriptive statistics

Standard deviation4.8458508
Coefficient of variation (CV)0.43721962
Kurtosis-1.5773425
Mean11.083333
Median Absolute Deviation (MAD)5.5
Skewness0.035910742
Sum532
Variance23.48227
MonotonicityNot monotonic
2024-03-21T13:08:05.582039image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
6 10
19.2%
17 6
11.5%
8 4
 
7.7%
5 4
 
7.7%
12 4
 
7.7%
16 4
 
7.7%
18 4
 
7.7%
4 2
 
3.8%
10 2
 
3.8%
14 2
 
3.8%
Other values (3) 6
11.5%
(Missing) 4
 
7.7%
ValueCountFrequency (%)
4 2
 
3.8%
5 4
 
7.7%
6 10
19.2%
8 4
 
7.7%
10 2
 
3.8%
11 2
 
3.8%
12 4
 
7.7%
13 2
 
3.8%
14 2
 
3.8%
15 2
 
3.8%
ValueCountFrequency (%)
18 4
7.7%
17 6
11.5%
16 4
7.7%
15 2
 
3.8%
14 2
 
3.8%
13 2
 
3.8%
12 4
7.7%
11 2
 
3.8%
10 2
 
3.8%
8 4
7.7%

sensTermica_value
Real number (ℝ)

MISSING 

Sensación térmica

Distinct14
Distinct (%)29.2%
Missing4
Missing (%)7.7%
Infinite0
Infinite (%)0.0%
Mean10.041667
Minimum1
Maximum18
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size548.0 B
2024-03-21T13:08:05.841005image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13.75
median11.5
Q316
95-th percentile18
Maximum18
Range17
Interquartile range (IQR)12.25

Descriptive statistics

Standard deviation6.0175571
Coefficient of variation (CV)0.59925879
Kurtosis-1.6368301
Mean10.041667
Median Absolute Deviation (MAD)5.5
Skewness-0.11439453
Sum482
Variance36.210993
MonotonicityNot monotonic
2024-03-21T13:08:06.068857image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
3 8
15.4%
17 6
11.5%
6 4
7.7%
4 4
7.7%
12 4
7.7%
16 4
7.7%
18 4
7.7%
2 2
 
3.8%
1 2
 
3.8%
10 2
 
3.8%
Other values (4) 8
15.4%
(Missing) 4
7.7%
ValueCountFrequency (%)
1 2
 
3.8%
2 2
 
3.8%
3 8
15.4%
4 4
7.7%
6 4
7.7%
10 2
 
3.8%
11 2
 
3.8%
12 4
7.7%
13 2
 
3.8%
14 2
 
3.8%
ValueCountFrequency (%)
18 4
7.7%
17 6
11.5%
16 4
7.7%
15 2
 
3.8%
14 2
 
3.8%
13 2
 
3.8%
12 4
7.7%
11 2
 
3.8%
10 2
 
3.8%
6 4
7.7%

humedadRelativa_value
Real number (ℝ)

MISSING 

Humedad relativa

Distinct22
Distinct (%)45.8%
Missing4
Missing (%)7.7%
Infinite0
Infinite (%)0.0%
Mean56.375
Minimum34
Maximum80
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size548.0 B
2024-03-21T13:08:06.333127image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum34
5-th percentile35
Q141.5
median57
Q373.25
95-th percentile78
Maximum80
Range46
Interquartile range (IQR)31.75

Descriptive statistics

Standard deviation15.964888
Coefficient of variation (CV)0.28319092
Kurtosis-1.5793783
Mean56.375
Median Absolute Deviation (MAD)16.5
Skewness0.035502275
Sum2706
Variance254.87766
MonotonicityNot monotonic
2024-03-21T13:08:06.782985image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
42 4
 
7.7%
75 4
 
7.7%
68 2
 
3.8%
58 2
 
3.8%
49 2
 
3.8%
39 2
 
3.8%
36 2
 
3.8%
35 2
 
3.8%
34 2
 
3.8%
37 2
 
3.8%
Other values (12) 24
46.2%
(Missing) 4
 
7.7%
ValueCountFrequency (%)
34 2
3.8%
35 2
3.8%
36 2
3.8%
37 2
3.8%
39 2
3.8%
40 2
3.8%
42 4
7.7%
45 2
3.8%
49 2
3.8%
50 2
3.8%
ValueCountFrequency (%)
80 2
3.8%
78 2
3.8%
76 2
3.8%
75 4
7.7%
74 2
3.8%
73 2
3.8%
69 2
3.8%
68 2
3.8%
62 2
3.8%
60 2
3.8%

direccion
Categorical

MISSING 

Dirección del viento

Distinct5
Distinct (%)20.8%
Missing28
Missing (%)53.8%
Memory size649.0 B
NE
10 
E
5 
S
5 
NO
3 
SE
 
1

Length

Max length2
Median length2
Mean length1.5833333
Min length1

Characters and Unicode

Total characters38
Distinct characters4
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

Unique1 ?
Unique (%)4.2%

Sample

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

Common Values

ValueCountFrequency (%)
NE 10
 
19.2%
E 5
 
9.6%
S 5
 
9.6%
NO 3
 
5.8%
SE 1
 
1.9%
(Missing) 28
53.8%

Length

2024-03-21T13:08:07.209581image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-21T13:08:07.607404image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
ne 10
41.7%
e 5
20.8%
s 5
20.8%
no 3
 
12.5%
se 1
 
4.2%

Most occurring characters

ValueCountFrequency (%)
E 16
42.1%
N 13
34.2%
S 6
 
15.8%
O 3
 
7.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 38
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 16
42.1%
N 13
34.2%
S 6
 
15.8%
O 3
 
7.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 38
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 16
42.1%
N 13
34.2%
S 6
 
15.8%
O 3
 
7.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 38
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 16
42.1%
N 13
34.2%
S 6
 
15.8%
O 3
 
7.9%

velocidad
Real number (ℝ)

MISSING 

Velocidad del viento

Distinct9
Distinct (%)37.5%
Missing28
Missing (%)53.8%
Infinite0
Infinite (%)0.0%
Mean8.7083333
Minimum4
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size548.0 B
2024-03-21T13:08:07.886236image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile5.15
Q17
median8.5
Q310.25
95-th percentile12
Maximum12
Range8
Interquartile range (IQR)3.25

Descriptive statistics

Standard deviation2.2550266
Coefficient of variation (CV)0.25895042
Kurtosis-0.68142141
Mean8.7083333
Median Absolute Deviation (MAD)1.5
Skewness-0.24844727
Sum209
Variance5.0851449
MonotonicityNot monotonic
2024-03-21T13:08:08.147681image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
8 5
 
9.6%
10 4
 
7.7%
12 3
 
5.8%
11 3
 
5.8%
7 3
 
5.8%
6 2
 
3.8%
9 2
 
3.8%
5 1
 
1.9%
4 1
 
1.9%
(Missing) 28
53.8%
ValueCountFrequency (%)
4 1
 
1.9%
5 1
 
1.9%
6 2
 
3.8%
7 3
5.8%
8 5
9.6%
9 2
 
3.8%
10 4
7.7%
11 3
5.8%
12 3
5.8%
ValueCountFrequency (%)
12 3
5.8%
11 3
5.8%
10 4
7.7%
9 2
 
3.8%
8 5
9.6%
7 3
5.8%
6 2
 
3.8%
5 1
 
1.9%
4 1
 
1.9%

vientoAndRachaMax_value
Real number (ℝ)

MISSING 

Viento racha máxima

Distinct11
Distinct (%)45.8%
Missing28
Missing (%)53.8%
Infinite0
Infinite (%)0.0%
Mean14.833333
Minimum5
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size548.0 B
2024-03-21T13:08:08.393317image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile7.3
Q112.25
median16.5
Q318
95-th percentile19
Maximum20
Range15
Interquartile range (IQR)5.75

Descriptive statistics

Standard deviation4.1979981
Coefficient of variation (CV)0.28301111
Kurtosis-0.095811489
Mean14.833333
Median Absolute Deviation (MAD)1.5
Skewness-1.0227163
Sum356
Variance17.623188
MonotonicityNot monotonic
2024-03-21T13:08:08.630518image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
17 5
 
9.6%
18 4
 
7.7%
16 3
 
5.8%
15 2
 
3.8%
19 2
 
3.8%
9 2
 
3.8%
10 2
 
3.8%
13 1
 
1.9%
20 1
 
1.9%
7 1
 
1.9%
(Missing) 28
53.8%
ValueCountFrequency (%)
5 1
 
1.9%
7 1
 
1.9%
9 2
 
3.8%
10 2
 
3.8%
13 1
 
1.9%
15 2
 
3.8%
16 3
5.8%
17 5
9.6%
18 4
7.7%
19 2
 
3.8%
ValueCountFrequency (%)
20 1
 
1.9%
19 2
 
3.8%
18 4
7.7%
17 5
9.6%
16 3
5.8%
15 2
 
3.8%
13 1
 
1.9%
10 2
 
3.8%
9 2
 
3.8%
7 1
 
1.9%

datetime
Date

Fecha y hora de la petición a la API

Distinct28
Distinct (%)53.8%
Missing0
Missing (%)0.0%
Memory size548.0 B
Minimum2024-03-13 00:00:00
Maximum2024-03-13 23:00:00
2024-03-21T13:08:08.886065image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T13:08:09.112455image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=28)

Missing values

2024-03-21T13:08:00.984552image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-21T13:08:01.616984image/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

estadoCielo_valueprecipitacion_valueprobPrecipitacion_valueprobTormenta_valuenieve_valueprobNieve_valuetemperatura_valuesensTermica_valuehumedadRelativa_valuedireccionvelocidadvientoAndRachaMax_valuedatetime
0Nubes altas0.0NaNNaN0.0NaN8.06.068.0NE10.0NaN2024-03-13 00:00:00
1Nubes altas0.0NaNNaN0.0NaN8.06.068.0NaNNaN18.02024-03-13 00:00:00
2Nubes altas0.0NaNNaN0.0NaN6.03.075.0NE12.0NaN2024-03-13 01:00:00
3Nubes altas0.0NaNNaN0.0NaN6.03.075.0NaNNaN17.02024-03-13 01:00:00
4NaNNaN0.00.0NaN0.0NaNNaNNaNNaNNaNNaN2024-03-13 01:07:00
5Nubes altas0.0NaNNaN0.0NaN6.04.073.0NE11.0NaN2024-03-13 02:00:00
6Nubes altas0.0NaNNaN0.0NaN6.04.073.0NaNNaN16.02024-03-13 02:00:00
7Poco nuboso0.0NaNNaN0.0NaN6.04.074.0NE11.0NaN2024-03-13 03:00:00
8Poco nuboso0.0NaNNaN0.0NaN6.04.074.0NaNNaN17.02024-03-13 03:00:00
9Poco nuboso0.0NaNNaN0.0NaN6.03.075.0NE12.0NaN2024-03-13 04:00:00
estadoCielo_valueprecipitacion_valueprobPrecipitacion_valueprobTormenta_valuenieve_valueprobNieve_valuetemperatura_valuesensTermica_valuehumedadRelativa_valuedireccionvelocidadvientoAndRachaMax_valuedatetime
42Despejado0.0NaNNaN0.0NaN17.017.039.0NaNNaN7.02024-03-13 19:00:00
43NaNNaN0.00.0NaN0.0NaNNaNNaNNaNNaNNaN2024-03-13 19:01:00
44Despejado0.0NaNNaN0.0NaN15.015.042.0S6.0NaN2024-03-13 20:00:00
45Despejado0.0NaNNaN0.0NaN15.015.042.0NaNNaN5.02024-03-13 20:00:00
46Despejado0.0NaNNaN0.0NaN13.013.049.0NO4.0NaN2024-03-13 21:00:00
47Despejado0.0NaNNaN0.0NaN13.013.049.0NaNNaN9.02024-03-13 21:00:00
48Despejado0.0NaNNaN0.0NaN11.011.058.0NO7.0NaN2024-03-13 22:00:00
49Despejado0.0NaNNaN0.0NaN11.011.058.0NaNNaN10.02024-03-13 22:00:00
50Despejado0.0NaNNaN0.0NaN12.012.060.0NO8.0NaN2024-03-13 23:00:00
51Despejado0.0NaNNaN0.0NaN12.012.060.0NaNNaN10.02024-03-13 23:00:00