Imputación de datos temporales

Cómo tratar datos faltantes en Python

Suraj Donthi

Deep Learning & Computer Vision Consultant

Conjunto de datos Airquality

import pandas as pd
airquality = pd.read_csv('air-quality.csv', parse_dates='Date', 
                                index_col='Date')

airquality.head()

             Ozone    Solar    Wind    Temp
Date                
1976-05-01    41.0    190.0     7.4    67
1976-05-02    36.0    118.0     8.0    72
1976-05-03    12.0    149.0    12.6    74
1976-05-04    18.0    313.0    11.5    62
1976-05-05     NaN      NaN    14.3    56

Conjunto de datos Airquality

airquality.isnull().sum()

Ozone    37
Solar     7
Wind      0
Temp      0
dtype: int64

airquality.isnull.mean() * 100

Ozone    24.183007
Solar     4.575163
Wind      0.000000
Temp      0.000000
dtype: float64

El método .fillna()

El atributo method en .fillna() puede configurarse como

'ffill' o 'pad'
'bfill' o 'backwardfill'

Método ffill

Sustituye NaN por el último valor observado
pad es igual que 'ffill'

airquality.fillna(method='ffill', inplace=True)



airquality['Ozone'][30:40]

Date         Ozone        
1976-05-31    37.0
1976-06-01     NaN
1976-06-02     NaN
1976-06-03     NaN
1976-06-04     NaN
1976-06-05     NaN
1976-06-06     NaN
1976-06-07    29.0
1976-06-08     NaN
1976-06-09    71.0

airquality.fillna(method='ffill', 
                         inplace=True)
airquality['Ozone'][30:40]

Date         Ozone        
1976-05-31    37.0
1976-06-01    37.0
1976-06-02    37.0
1976-06-03    37.0
1976-06-04    37.0
1976-06-05    37.0
1976-06-06    37.0
1976-06-07    29.0
1976-06-08    29.0
1976-06-09    71.0

Método bfill

Sustituye NaN por el siguiente valor observado
backfill es igual que 'bfill'

df.fillna(method='bfill', inplace=True)



airquality['Ozone'][30:40]

Date         Ozone        
1976-05-31    37.0
1976-06-01     NaN
1976-06-02     NaN
1976-06-03     NaN
1976-06-04     NaN
1976-06-05     NaN
1976-06-06     NaN
1976-06-07    29.0
1976-06-08     NaN
1976-06-09    71.0

airquality.fillna(method='bfill', 
                         inplace=True)
airquality['Ozone'][30:40]

Date         Ozone        
1976-05-31    37.0
1976-06-01    29.0
1976-06-02    29.0
1976-06-03    29.0
1976-06-04    29.0
1976-06-05    29.0
1976-06-06    29.0
1976-06-07    29.0
1976-06-08    71.0
1976-06-09    71.0

El método .interpolate()

El método .interpolate() extiende la secuencia de valores a los ausentes

El atributo method en .interpolate() puede configurarse como

'linear'
'quadratic'
'nearest'

Interpolación lineal

Imputa linealmente o con valores equidistantes

df.interpolate(method='linear', inplace=True)

fragmento de interpolación lineal



airquality['Ozone'][30:40]

Date         Ozone        
1976-05-31    37.0
1976-06-01     NaN
1976-06-02     NaN
1976-06-03     NaN
1976-06-04     NaN
1976-06-05     NaN
1976-06-06     NaN
1976-06-07    29.0
1976-06-08     NaN
1976-06-09    71.0

airquality.interpolate(
          method='linear', inplace=True)
airquality['Ozone'][30:40]

Date         Ozone        
1976-05-31    37.0
1976-06-01    35.9
1976-06-02    34.7
1976-06-03    33.6
1976-06-04    32.4
1976-06-05    31.3
1976-06-06    30.1
1976-06-07    29.0
1976-06-08    50.0
1976-06-09    71.0

Interpolación cuadrática

Imputa los valores de forma cuadrática

df.interpolate(method='quadratic', inplace=True)

fragmento de interpolación cuadrática



airquality['Ozone'][30:39]

             Ozone
Date                
1976-05-31    37.0
1976-06-01     NaN
1976-06-02     NaN
1976-06-03     NaN
1976-06-04     NaN
1976-06-05     NaN
1976-06-06     NaN
1976-06-07    29.0
1976-06-08     NaN

airquality.interpolate(
  method='quadratic', inplace=True)
airquality['Ozone'][30:39]

             Ozone
Date                
1976-05-31    37.0
1976-06-01   -38.4
1976-06-02   -79.4
1976-06-03   -85.9
1976-06-04   -62.4
1976-06-06    -2.8
1976-06-07    29.0
1976-06-08    62.2

Imputación por valor más cercano

Imputa con el valor observable más cercano

df.interpolate(method='nearest', inplace=True)

fragmento de interpolación por vecino más cercano



airquality['Ozone'][30:39]

Date         Ozone        
1976-05-31    37.0
1976-06-01     NaN
1976-06-02     NaN
1976-06-03     NaN
1976-06-04     NaN
1976-06-05     NaN
1976-06-06     NaN
1976-06-07    29.0
1976-06-08     NaN

airquality.interpolate(
  method='nearest', inplace=True)
airquality['Ozone'][30:39]

Date         Ozone        
1976-05-31    37.0
1976-06-01    37.0
1976-06-02    37.0
1976-06-03    37.0
1976-06-04    29.0
1976-06-05    29.0
1976-06-06    29.0
1976-06-07    29.0
1976-06-08    29.0

¡Vamos a practicar!

Cómo tratar datos faltantes en Python