Overview

Dataset statistics

Number of variables14
Number of observations801383
Missing cells86815
Missing cells (%)0.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory526.2 MiB
Average record size in memory688.6 B

Variable types

Numeric7
Text3
Categorical2
DateTime2

Dataset

DescriptionINFORMO - Estado del tráfico
URLhttps://informo.madrid.es/informo/tmadrid/pm.xml

Variable descriptions

PKIdentificador único (Primary Key) del dataset, compuesto por <datetime>_<idelem>
dateFecha de la petición a la API
datetimeFecha y hora de la petición a la API
idelemIdentificador del punto de medida. Permite su posicionamiento sobre plano e identificación del vial y sentido de la circulación
descripcionDenominación del punto de medida
accesoAsociadoCódigo de control relacionado con el control semafórico para la modificación de los tiempos
intensidadIntensidad de número de vehículos por hora. Un valor negativo implica la ausencia de datos
ocupacionPorcentaje de tiempo que está un detector de tráfico ocupado por un vehículo
cargaParámetro de carga del vial. Representa una estimación del grado
nivelServicioParámetro calculado en función de la velocidad y la ocupación
intensidadSatIntensidad de saturación de la vía en veh/hora
errorCódigo de control de la validez de los datos del punto de medida
subareaIdentificador de la subárea de explotación de tráfico a la que pertenece el punto de medida
st_xCoordenada X UTM del centroide que representa al punto de medida en el fichero georreferenciado
st_yCoordenada Y UTM del centroide que representa al punto de medida en el fichero georreferenciado
velocidadVelocidad medida

Alerts

date has constant value ""Constant
error is highly imbalanced (67.7%)Imbalance
accesoAsociado has 86815 (10.8%) missing valuesMissing
intensidad is highly skewed (γ1 = 48.6574727)Skewed
accesoAsociado has 69273 (8.6%) zerosZeros
intensidad has 50878 (6.3%) zerosZeros
ocupacion has 131520 (16.4%) zerosZeros
carga has 38330 (4.8%) zerosZeros
subarea has 29177 (3.6%) zerosZeros

Reproduction

Analysis started2024-04-26 07:12:58.930370
Analysis finished2024-04-26 07:14:24.030806
Duration1 minute and 25.1 seconds
Software versionydata-profiling vv4.7.0
Download configurationconfig.json

Variables

idelem
Real number (ℝ)

Identificador del punto de medida. Permite su posicionamiento sobre plano e identificación del vial y sentido de la circulación

Distinct4477
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6791.2826
Minimum3395
Maximum11300
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size91.3 MiB
2024-04-26T09:14:24.728400image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum3395
5-th percentile3670
Q14680
median5861
Q310094
95-th percentile11068
Maximum11300
Range7905
Interquartile range (IQR)5414

Descriptive statistics

Standard deviation2640.0992
Coefficient of variation (CV)0.38874824
Kurtosis-1.25859
Mean6791.2826
Median Absolute Deviation (MAD)1431
Skewness0.57965966
Sum5.4424184 × 109
Variance6970123.5
MonotonicityNot monotonic
2024-04-26T09:14:26.278141image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10112 179
 
< 0.1%
9973 179
 
< 0.1%
9912 179
 
< 0.1%
9910 179
 
< 0.1%
3737 179
 
< 0.1%
10897 179
 
< 0.1%
9898 179
 
< 0.1%
9976 179
 
< 0.1%
9974 179
 
< 0.1%
5103 179
 
< 0.1%
Other values (4467) 799593
99.8%
ValueCountFrequency (%)
3395 179
< 0.1%
3396 179
< 0.1%
3397 179
< 0.1%
3398 179
< 0.1%
3399 179
< 0.1%
3400 179
< 0.1%
3401 179
< 0.1%
3402 179
< 0.1%
3403 179
< 0.1%
3404 179
< 0.1%
ValueCountFrequency (%)
11300 179
< 0.1%
11299 179
< 0.1%
11298 179
< 0.1%
11297 179
< 0.1%
11296 179
< 0.1%
11295 179
< 0.1%
11294 179
< 0.1%
11293 179
< 0.1%
11292 179
< 0.1%
11291 179
< 0.1%

descripcion
Text

Denominación del punto de medida

Distinct4336
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size186.8 MiB
2024-04-26T09:14:28.170881image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length119
Median length90
Mean length47.439133
Min length1

Characters and Unicode

Total characters38016915
Distinct characters91
Distinct categories13 ?
Distinct scripts2 ?
Distinct blocks2 ?
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 rowArturo Soria - Pablo Vidal - Vicente Muzas
2nd rowTorrelaguna - Arturo Baldasano-José Silva
3rd rowTorrelaguna - Av. Ramón y Cajal-Acceso M30
4th rowTorrelaguna - Sorzano-Acceso M30
5th rowAv. Ramón y Cajal - Puente M30-Torrelaguna
ValueCountFrequency (%)
501916
 
9.6%
de 221244
 
4.2%
av 168081
 
3.2%
s-n 74106
 
1.4%
n-s 70526
 
1.3%
e-o 68378
 
1.3%
o-e 66946
 
1.3%
del 54774
 
1.0%
san 46003
 
0.9%
la 42960
 
0.8%
Other values (6279) 3940685
75.0%
2024-04-26T09:14:30.659578image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4507757
 
11.9%
A 2545917
 
6.7%
a 2159098
 
5.7%
- 1725918
 
4.5%
E 1566787
 
4.1%
O 1479256
 
3.9%
e 1432000
 
3.8%
r 1279671
 
3.4%
S 1238322
 
3.3%
o 1224360
 
3.2%
Other values (81) 18857829
49.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 16540495
43.5%
Lowercase Letter 13235439
34.8%
Space Separator 4507757
 
11.9%
Dash Punctuation 1725918
 
4.5%
Other Punctuation 615402
 
1.6%
Open Punctuation 495830
 
1.3%
Close Punctuation 494398
 
1.3%
Decimal Number 358895
 
0.9%
Other Letter 33473
 
0.1%
Modifier Symbol 6086
 
< 0.1%
Other values (3) 3222
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 2545917
15.4%
E 1566787
 
9.5%
O 1479256
 
8.9%
S 1238322
 
7.5%
R 1209682
 
7.3%
N 1038737
 
6.3%
I 942793
 
5.7%
L 903234
 
5.5%
C 835393
 
5.1%
T 648338
 
3.9%
Other values (24) 4132036
25.0%
Lowercase Letter
ValueCountFrequency (%)
a 2159098
16.3%
e 1432000
10.8%
r 1279671
9.7%
o 1224360
9.3%
l 1011529
 
7.6%
n 924356
 
7.0%
i 878711
 
6.6%
s 640641
 
4.8%
d 506033
 
3.8%
t 476498
 
3.6%
Other values (23) 2702542
20.4%
Decimal Number
ValueCountFrequency (%)
1 60144
16.8%
0 56922
15.9%
3 51373
14.3%
4 44213
12.3%
2 43318
12.1%
5 30788
8.6%
6 25418
7.1%
8 18616
 
5.2%
7 17005
 
4.7%
9 11098
 
3.1%
Other Punctuation
ValueCountFrequency (%)
. 563134
91.5%
, 51731
 
8.4%
/ 537
 
0.1%
Other Letter
ValueCountFrequency (%)
º 27745
82.9%
ª 5728
 
17.1%
Modifier Symbol
ValueCountFrequency (%)
´ 5370
88.2%
` 716
 
11.8%
Space Separator
ValueCountFrequency (%)
4507757
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1725918
100.0%
Open Punctuation
ValueCountFrequency (%)
( 495830
100.0%
Close Punctuation
ValueCountFrequency (%)
) 494398
100.0%
Math Symbol
ValueCountFrequency (%)
+ 1611
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1432
100.0%
Control
ValueCountFrequency (%)
 179
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 29809407
78.4%
Common 8207508
 
21.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 2545917
 
8.5%
a 2159098
 
7.2%
E 1566787
 
5.3%
O 1479256
 
5.0%
e 1432000
 
4.8%
r 1279671
 
4.3%
S 1238322
 
4.2%
o 1224360
 
4.1%
R 1209682
 
4.1%
N 1038737
 
3.5%
Other values (59) 14635577
49.1%
Common
ValueCountFrequency (%)
4507757
54.9%
- 1725918
 
21.0%
. 563134
 
6.9%
( 495830
 
6.0%
) 494398
 
6.0%
1 60144
 
0.7%
0 56922
 
0.7%
, 51731
 
0.6%
3 51373
 
0.6%
4 44213
 
0.5%
Other values (12) 156088
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 37583556
98.9%
None 433359
 
1.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4507757
 
12.0%
A 2545917
 
6.8%
a 2159098
 
5.7%
- 1725918
 
4.6%
E 1566787
 
4.2%
O 1479256
 
3.9%
e 1432000
 
3.8%
r 1279671
 
3.4%
S 1238322
 
3.3%
o 1224360
 
3.3%
Other values (62) 18424470
49.0%
None
ValueCountFrequency (%)
á 90395
20.9%
í 80908
18.7%
é 59965
13.8%
ó 52089
12.0%
ñ 38485
8.9%
Ø 29177
 
6.7%
º 27745
 
6.4%
Á 11456
 
2.6%
Ñ 9845
 
2.3%
ú 7876
 
1.8%
Other values (9) 25418
 
5.9%

accesoAsociado
Real number (ℝ)

MISSING  ZEROS 

Código de control relacionado con el control semafórico para la modificación de los tiempos

Distinct3598
Distinct (%)0.5%
Missing86815
Missing (%)10.8%
Infinite0
Infinite (%)0.0%
Mean2573555.6
Minimum0
Maximum9905004
Zeros69273
Zeros (%)8.6%
Negative0
Negative (%)0.0%
Memory size91.3 MiB
2024-04-26T09:14:31.679481image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1311804.75
median842403.5
Q35508002.2
95-th percentile7403001
Maximum9905004
Range9905004
Interquartile range (IQR)5196197.5

Descriptive statistics

Standard deviation2854913.4
Coefficient of variation (CV)1.1093265
Kurtosis-0.93212117
Mean2573555.6
Median Absolute Deviation (MAD)821600
Skewness0.76269509
Sum1.8389805 × 1012
Variance8.1505307 × 1012
MonotonicityNot monotonic
2024-04-26T09:14:32.757240image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 69273
 
8.6%
229002 358
 
< 0.1%
501001 358
 
< 0.1%
501002 358
 
< 0.1%
801001 358
 
< 0.1%
301002 358
 
< 0.1%
501003 358
 
< 0.1%
512003 358
 
< 0.1%
501004 358
 
< 0.1%
341201 179
 
< 0.1%
Other values (3588) 642252
80.1%
(Missing) 86815
 
10.8%
ValueCountFrequency (%)
0 69273
8.6%
20101 179
 
< 0.1%
20102 179
 
< 0.1%
20103 179
 
< 0.1%
20104 179
 
< 0.1%
20201 179
 
< 0.1%
20202 179
 
< 0.1%
20203 179
 
< 0.1%
20204 179
 
< 0.1%
20301 179
 
< 0.1%
ValueCountFrequency (%)
9905004 179
< 0.1%
9905003 179
< 0.1%
9905002 179
< 0.1%
9905001 179
< 0.1%
9734024 179
< 0.1%
9734004 179
< 0.1%
9734003 179
< 0.1%
9734002 179
< 0.1%
9734001 179
< 0.1%
9730102 179
< 0.1%

intensidad
Real number (ℝ)

SKEWED  ZEROS 

Intensidad de número de vehículos por hora. Un valor negativo implica la ausencia de datos

Distinct2423
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean422.03289
Minimum-1
Maximum83980
Zeros50878
Zeros (%)6.3%
Negative206
Negative (%)< 0.1%
Memory size91.3 MiB
2024-04-26T09:14:33.955624image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile0
Q1120
median270
Q3560
95-th percentile1320
Maximum83980
Range83981
Interquartile range (IQR)440

Descriptive statistics

Standard deviation630.36634
Coefficient of variation (CV)1.4936427
Kurtosis5158.8414
Mean422.03289
Median Absolute Deviation (MAD)190
Skewness48.657473
Sum3.3820998 × 108
Variance397361.72
MonotonicityNot monotonic
2024-04-26T09:14:34.972526image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 50878
 
6.3%
120 32985
 
4.1%
180 32071
 
4.0%
60 31868
 
4.0%
240 26965
 
3.4%
360 22347
 
2.8%
300 20784
 
2.6%
40 17425
 
2.2%
80 17202
 
2.1%
160 15451
 
1.9%
Other values (2413) 533407
66.6%
ValueCountFrequency (%)
-1 206
 
< 0.1%
0 50878
6.3%
4 4
 
< 0.1%
5 135
 
< 0.1%
6 90
 
< 0.1%
7 79
 
< 0.1%
8 127
 
< 0.1%
9 126
 
< 0.1%
10 77
 
< 0.1%
11 71
 
< 0.1%
ValueCountFrequency (%)
83980 1
< 0.1%
78020 1
< 0.1%
76840 1
< 0.1%
76500 1
< 0.1%
75940 1
< 0.1%
75640 1
< 0.1%
73680 1
< 0.1%
73440 1
< 0.1%
72900 1
< 0.1%
72640 1
< 0.1%

ocupacion
Real number (ℝ)

ZEROS 

Porcentaje de tiempo que está un detector de tráfico ocupado por un vehículo

Distinct102
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.7846835
Minimum-1
Maximum100
Zeros131520
Zeros (%)16.4%
Negative206
Negative (%)< 0.1%
Memory size91.3 MiB
2024-04-26T09:14:35.772353image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile0
Q11
median3
Q38
95-th percentile44
Maximum100
Range101
Interquartile range (IQR)7

Descriptive statistics

Standard deviation15.52426
Coefficient of variation (CV)1.7671962
Kurtosis11.471715
Mean8.7846835
Median Absolute Deviation (MAD)3
Skewness3.2355163
Sum7039896
Variance241.00264
MonotonicityNot monotonic
2024-04-26T09:14:36.898743image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 131520
16.4%
1 104429
13.0%
2 91680
11.4%
3 85778
10.7%
5 57509
 
7.2%
4 51354
 
6.4%
6 36877
 
4.6%
7 31306
 
3.9%
8 26016
 
3.2%
10 16343
 
2.0%
Other values (92) 168571
21.0%
ValueCountFrequency (%)
-1 206
 
< 0.1%
0 131520
16.4%
1 104429
13.0%
2 91680
11.4%
3 85778
10.7%
4 51354
 
6.4%
5 57509
7.2%
6 36877
 
4.6%
7 31306
 
3.9%
8 26016
 
3.2%
ValueCountFrequency (%)
100 2186
0.3%
99 315
 
< 0.1%
98 450
 
0.1%
97 241
 
< 0.1%
96 157
 
< 0.1%
95 233
 
< 0.1%
94 128
 
< 0.1%
93 265
 
< 0.1%
92 290
 
< 0.1%
91 228
 
< 0.1%

carga
Real number (ℝ)

ZEROS 

Parámetro de carga del vial. Representa una estimación del grado

Distinct134
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.030563
Minimum-1
Maximum272
Zeros38330
Zeros (%)4.8%
Negative206
Negative (%)< 0.1%
Memory size91.3 MiB
2024-04-26T09:14:38.106512image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile1
Q110
median21
Q336
95-th percentile60
Maximum272
Range273
Interquartile range (IQR)26

Descriptive statistics

Standard deviation18.841804
Coefficient of variation (CV)0.7527519
Kurtosis0.94812658
Mean25.030563
Median Absolute Deviation (MAD)12
Skewness0.97979297
Sum20059068
Variance355.01359
MonotonicityNot monotonic
2024-04-26T09:14:39.160168image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 38330
 
4.8%
6 20456
 
2.6%
9 20279
 
2.5%
7 20229
 
2.5%
3 20132
 
2.5%
15 19537
 
2.4%
10 19387
 
2.4%
8 19154
 
2.4%
13 18955
 
2.4%
12 18799
 
2.3%
Other values (124) 586125
73.1%
ValueCountFrequency (%)
-1 206
 
< 0.1%
0 38330
4.8%
1 5444
 
0.7%
2 10337
 
1.3%
3 20132
2.5%
4 15801
2.0%
5 16455
2.1%
6 20456
2.6%
7 20229
2.5%
8 19154
2.4%
ValueCountFrequency (%)
272 1
< 0.1%
214 1
< 0.1%
175 1
< 0.1%
161 1
< 0.1%
151 1
< 0.1%
142 1
< 0.1%
136 2
< 0.1%
127 2
< 0.1%
125 1
< 0.1%
123 2
< 0.1%

nivelServicio
Categorical

Parámetro calculado en función de la velocidad y la ocupación

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size131.0 MiB
0.0
464031 
1.0
255500 
2.0
68319 
3.0
 
13179
nan
 
354

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2404149
Distinct characters7
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 row1.0
2nd row1.0
3rd row0.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 464031
57.9%
1.0 255500
31.9%
2.0 68319
 
8.5%
3.0 13179
 
1.6%
nan 354
 
< 0.1%

Length

2024-04-26T09:14:39.913619image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-26T09:14:40.695845image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 464031
57.9%
1.0 255500
31.9%
2.0 68319
 
8.5%
3.0 13179
 
1.6%
nan 354
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 1265060
52.6%
. 801029
33.3%
1 255500
 
10.6%
2 68319
 
2.8%
3 13179
 
0.5%
n 708
 
< 0.1%
a 354
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1602058
66.6%
Other Punctuation 801029
33.3%
Lowercase Letter 1062
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1265060
79.0%
1 255500
 
15.9%
2 68319
 
4.3%
3 13179
 
0.8%
Lowercase Letter
ValueCountFrequency (%)
n 708
66.7%
a 354
33.3%
Other Punctuation
ValueCountFrequency (%)
. 801029
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2403087
> 99.9%
Latin 1062
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1265060
52.6%
. 801029
33.3%
1 255500
 
10.6%
2 68319
 
2.8%
3 13179
 
0.5%
Latin
ValueCountFrequency (%)
n 708
66.7%
a 354
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2404149
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1265060
52.6%
. 801029
33.3%
1 255500
 
10.6%
2 68319
 
2.8%
3 13179
 
0.5%
n 708
 
< 0.1%
a 354
 
< 0.1%

intensidadSat
Real number (ℝ)

Intensidad de saturación de la vía en veh/hora

Distinct745
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1972.3244
Minimum50
Maximum9000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size91.3 MiB
2024-04-26T09:14:41.747572image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile461
Q11200
median1500
Q33000
95-th percentile4464
Maximum9000
Range8950
Interquartile range (IQR)1800

Descriptive statistics

Standard deviation1174.8725
Coefficient of variation (CV)0.59567912
Kurtosis1.2962698
Mean1972.3244
Median Absolute Deviation (MAD)670
Skewness1.0229247
Sum1.5805872 × 109
Variance1380325.3
MonotonicityNot monotonic
2024-04-26T09:14:42.763303image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1500 147496
 
18.4%
3000 96302
 
12.0%
1800 31146
 
3.9%
3600 26671
 
3.3%
1200 22733
 
2.8%
1000 20227
 
2.5%
4500 17721
 
2.2%
900 11635
 
1.5%
1400 11456
 
1.4%
2400 10561
 
1.3%
Other values (735) 405435
50.6%
ValueCountFrequency (%)
50 2506
0.3%
65 179
 
< 0.1%
75 358
 
< 0.1%
80 716
 
0.1%
86 179
 
< 0.1%
90 358
 
< 0.1%
100 537
 
0.1%
106 179
 
< 0.1%
110 179
 
< 0.1%
115 179
 
< 0.1%
ValueCountFrequency (%)
9000 179
 
< 0.1%
7500 179
 
< 0.1%
7200 716
0.1%
7100 179
 
< 0.1%
6800 179
 
< 0.1%
6720 179
 
< 0.1%
6550 358
< 0.1%
6500 179
 
< 0.1%
6400 716
0.1%
6290 179
 
< 0.1%

error
Categorical

IMBALANCE 

Código de control de la validez de los datos del punto de medida

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size129.5 MiB
N
754248 
S
 
47135

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters801383
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 rowN
2nd rowN
3rd rowN
4th rowN
5th rowN

Common Values

ValueCountFrequency (%)
N 754248
94.1%
S 47135
 
5.9%

Length

2024-04-26T09:14:43.578780image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-26T09:14:44.188618image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
n 754248
94.1%
s 47135
 
5.9%

Most occurring characters

ValueCountFrequency (%)
N 754248
94.1%
S 47135
 
5.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 801383
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 754248
94.1%
S 47135
 
5.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 801383
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 754248
94.1%
S 47135
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 801383
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 754248
94.1%
S 47135
 
5.9%

subarea
Real number (ℝ)

ZEROS 

Identificador de la subárea de explotación de tráfico a la que pertenece el punto de medida

Distinct302
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20776.925
Minimum0
Maximum130043
Zeros29177
Zeros (%)3.6%
Negative0
Negative (%)0.0%
Memory size91.3 MiB
2024-04-26T09:14:45.180848image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile101
Q11712
median3108
Q33802
95-th percentile130012
Maximum130043
Range130043
Interquartile range (IQR)2090

Descriptive statistics

Standard deviation42531.529
Coefficient of variation (CV)2.047056
Kurtosis1.5352825
Mean20776.925
Median Absolute Deviation (MAD)1367
Skewness1.8613314
Sum1.6650275 × 1010
Variance1.808931 × 109
MonotonicityNot monotonic
2024-04-26T09:14:46.562655image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
101 48867
 
6.1%
0 29177
 
3.6%
3202 16289
 
2.0%
4013 15215
 
1.9%
105 14857
 
1.9%
1773 14027
 
1.8%
303 13425
 
1.7%
302 13067
 
1.6%
3203 12530
 
1.6%
304 12530
 
1.6%
Other values (292) 611399
76.3%
ValueCountFrequency (%)
0 29177
3.6%
101 48867
6.1%
104 1432
 
0.2%
105 14857
 
1.9%
108 895
 
0.1%
112 1790
 
0.2%
114 3222
 
0.4%
124 1074
 
0.1%
129 1611
 
0.2%
143 1074
 
0.1%
ValueCountFrequency (%)
130043 537
 
0.1%
130041 179
 
< 0.1%
130039 716
 
0.1%
130038 10024
1.3%
130037 358
 
< 0.1%
130036 537
 
0.1%
130035 1074
 
0.1%
130033 1432
 
0.2%
130032 1611
 
0.2%
130031 895
 
0.1%

st_x
Text

Coordenada X UTM del centroide que representa al punto de medida en el fichero georreferenciado

Distinct4474
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size140.8 MiB
2024-04-26T09:14:47.888624image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length16
Median length16
Mean length15.880277
Min length12

Characters and Unicode

Total characters12726184
Distinct characters11
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 row443972,01821329
2nd row443981,955537857
3rd row443984,139107713
4th row444079,304201131
5th row443837,934167538
ValueCountFrequency (%)
448422,339605214 358
 
< 0.1%
448445,26557761 358
 
< 0.1%
448422,261023287 358
 
< 0.1%
439999,634124171 179
 
< 0.1%
444095,612944315 179
 
< 0.1%
443837,934167538 179
 
< 0.1%
444106,480195963 179
 
< 0.1%
445569,618362281 179
 
< 0.1%
443883,357564368 179
 
< 0.1%
444549,556272109 179
 
< 0.1%
Other values (4464) 799056
99.7%
2024-04-26T09:14:50.418494image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4 2380342
18.7%
3 1287010
10.1%
9 1079191
8.5%
1 1062186
8.3%
2 1038737
8.2%
8 1031398
8.1%
5 1027997
8.1%
7 1027460
8.1%
6 1026923
8.1%
0 963557
7.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 11924801
93.7%
Other Punctuation 801383
 
6.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 2380342
20.0%
3 1287010
10.8%
9 1079191
9.0%
1 1062186
8.9%
2 1038737
8.7%
8 1031398
8.6%
5 1027997
8.6%
7 1027460
8.6%
6 1026923
8.6%
0 963557
8.1%
Other Punctuation
ValueCountFrequency (%)
, 801383
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 12726184
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 2380342
18.7%
3 1287010
10.1%
9 1079191
8.5%
1 1062186
8.3%
2 1038737
8.2%
8 1031398
8.1%
5 1027997
8.1%
7 1027460
8.1%
6 1026923
8.1%
0 963557
7.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12726184
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 2380342
18.7%
3 1287010
10.1%
9 1079191
8.5%
1 1062186
8.3%
2 1038737
8.2%
8 1031398
8.1%
5 1027997
8.1%
7 1027460
8.1%
6 1026923
8.1%
0 963557
7.6%

st_y
Text

Coordenada Y UTM del centroide que representa al punto de medida en el fichero georreferenciado

Distinct4475
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size140.8 MiB
2024-04-26T09:14:51.775041image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length16
Median length16
Mean length15.888095
Min length13

Characters and Unicode

Total characters12732449
Distinct characters11
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 row4478986,47739102
2nd row4478451,45254494
3rd row4478277,30226478
4th row4478026,60397703
5th row4478283,27889757
ValueCountFrequency (%)
4482932,91159346 358
 
< 0.1%
4481475,92192701 358
 
< 0.1%
4466903,24471986 179
 
< 0.1%
4479073,14520214 179
 
< 0.1%
4478283,27889757 179
 
< 0.1%
4478409,94175933 179
 
< 0.1%
4476917,95736366 179
 
< 0.1%
4478111,26725855 179
 
< 0.1%
4478442,62607625 179
 
< 0.1%
4478182,57819929 179
 
< 0.1%
Other values (4465) 799235
99.7%
2024-04-26T09:14:54.292477image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4 2547349
20.0%
7 1521321
11.9%
8 1128595
8.9%
6 1041601
8.2%
9 982710
 
7.7%
2 965526
 
7.6%
5 965347
 
7.6%
1 961588
 
7.6%
3 947984
 
7.4%
0 869045
 
6.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 11931066
93.7%
Other Punctuation 801383
 
6.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 2547349
21.4%
7 1521321
12.8%
8 1128595
9.5%
6 1041601
8.7%
9 982710
 
8.2%
2 965526
 
8.1%
5 965347
 
8.1%
1 961588
 
8.1%
3 947984
 
7.9%
0 869045
 
7.3%
Other Punctuation
ValueCountFrequency (%)
, 801383
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 12732449
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 2547349
20.0%
7 1521321
11.9%
8 1128595
8.9%
6 1041601
8.2%
9 982710
 
7.7%
2 965526
 
7.6%
5 965347
 
7.6%
1 961588
 
7.6%
3 947984
 
7.4%
0 869045
 
6.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12732449
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 2547349
20.0%
7 1521321
11.9%
8 1128595
8.9%
6 1041601
8.2%
9 982710
 
7.7%
2 965526
 
7.6%
5 965347
 
7.6%
1 961588
 
7.6%
3 947984
 
7.4%
0 869045
 
6.8%

datetime
Date

Fecha y hora de la petición a la API

Distinct179
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size91.3 MiB
Minimum2024-03-13 07:55:08
Maximum2024-03-13 22:50:11
2024-04-26T09:14:55.557623image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-26T09:14:56.709072image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

date
Date

CONSTANT 

Fecha de la petición a la API

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size91.3 MiB
Minimum2024-03-13 00:00:00
Maximum2024-03-13 00:00:00
2024-04-26T09:14:57.758650image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-26T09:14:58.675260image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Missing values

2024-04-26T09:14:11.900058image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-26T09:14:16.636924image/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

idelemdescripcionaccesoAsociadointensidadocupacioncarganivelServiciointensidadSaterrorsubareast_xst_ydatetimedate
PK
2024-03-13 07:55:08_I10112_S3203.010112Arturo Soria - Pablo Vidal - Vicente Muzas4604002.07404291.02438.0N3203.0443972,018213294478986,477391022024-03-13 07:55:082024-03-13
2024-03-13 07:55:08_I6038_S3246.06038Torrelaguna - Arturo Baldasano-José Silva4627002.03802281.01390.0N3246.0443981,9555378574478451,452544942024-03-13 07:55:082024-03-13
2024-03-13 07:55:08_I6039_S3215.06039Torrelaguna - Av. Ramón y Cajal-Acceso M304628002.08406240.03000.0N3215.0443984,1391077134478277,302264782024-03-13 07:55:082024-03-13
2024-03-13 07:55:08_I6040_S3215.06040Torrelaguna - Sorzano-Acceso M304628001.05205311.02000.0N3215.0444079,3042011314478026,603977032024-03-13 07:55:082024-03-13
2024-03-13 07:55:08_I6041_S3215.06041Av. Ramón y Cajal - Puente M30-Torrelaguna4627003.014004341.03380.0N3215.0443837,9341675384478283,278897572024-03-13 07:55:082024-03-13
2024-03-13 07:55:08_I6042_S3203.06042José Silva - Agastia-Torrelaguna4627004.06006240.02112.0N3203.0444106,4801959634478409,941759332024-03-13 07:55:082024-03-13
2024-03-13 07:55:08_I6037_S3203.06037José del Hierro - José del Hierro-Arturo Soria4618003.01402170.0926.0N3203.0445569,6183622814476917,957363662024-03-13 07:55:082024-03-13
2024-03-13 07:55:08_I6043_S3215.06043Normas - M30-Torrelaguna4628003.080013672.01600.0N3215.0443883,3575643684478111,267258552024-03-13 07:55:082024-03-13
2024-03-13 07:55:08_I6044_S3203.06044Arturo Soria - Lorenzo Solano Tendero-Estrecho de Mesina4609002.010408311.02300.0N3203.0444578,8896499534478182,578199292024-03-13 07:55:082024-03-13
2024-03-13 07:55:08_I6045_S3203.06045Arturo Soria - Estrecho de Mesina-Lorenzo Solano Tendero4608001.094023642.01877.0N3203.0444623,0801173134478128,928468932024-03-13 07:55:082024-03-13
idelemdescripcionaccesoAsociadointensidadocupacioncarganivelServiciointensidadSaterrorsubareast_xst_ydatetimedate
PK
2024-03-13 22:50:11_I5440_S314.05440(TACTICO) Ramón Castroviejo O-E - Pto.Maspalomas-Av.BetanzosNaN1002100.0900.0N314.0439539,9946104984481251,98250262024-03-13 22:50:112024-03-13
2024-03-13 22:50:11_I5441_S314.05441Pedro Rico O-E - Ginzo Limia-Arzobispo Morcillo4304031.0601100.0450.0N314.0440639,1949898264481625,610191892024-03-13 22:50:112024-03-13
2024-03-13 22:50:11_I5442_S314.05442(TACTICO)Santiago de Compostela E-O - Ginzo de Limia-Centro CívicoNaN0010.0700.0S314.0439905,1500926974481438,817802232024-03-13 22:50:112024-03-13
2024-03-13 22:50:11_I5443_S314.05443(TACTICO) Pº Vaguada O-E - Centro Cívico-Ginzo de LimiaNaN401550.02700.0S314.0440178,5466522224481433,551317912024-03-13 22:50:112024-03-13
2024-03-13 22:50:11_I5444_S314.05444(TACTICO) Santiago de Compostela E-O(Entrada Cruce) - (TACTICO) Santiago de Compostela E-O(Entrada Cruce)NaN0000.0450.0S314.0440293,9747538484481548,746812962024-03-13 22:50:112024-03-13
2024-03-13 22:50:11_I10031_S314.010031Narcís Monturiol N-S - Sangenjo-Av. Ilustración4304002.020020.0775.0N314.0440656,7563117054481793,239724782024-03-13 22:50:112024-03-13
2024-03-13 22:50:11_I10463_S314.010463Av. Ilustración O-E (Azobispo Morcillo - Nudo Norte) - Azobispo Morcillo - Nudo NorteNaN7001140.04420.0N314.0440789,2294428774481734,19124372024-03-13 22:50:112024-03-13
2024-03-13 22:50:11_I3421_S304.03421Bravo Murillo E-O - Pl.Castilla-Conde Serrallo6201004.04801160.02900.0N304.0441453,9700354794479675,623063072024-03-13 22:50:112024-03-13
2024-03-13 22:50:11_I3423_S301.03423Lateral Pº Castellana N-S - Pl.Castilla-Rosario Pino6003012.0280280.03200.0N301.0441493,7615599934479352,765088332024-03-13 22:50:112024-03-13
2024-03-13 22:50:11_I10899_S3008.010899Av. Entrevías - Mejorana-Sierra de Albarracín6805002.02201170.01800.0N3008.0443058,8945281194470747,452195092024-03-13 22:50:112024-03-13