你如何使用 numpy/scipy 处理丢失的数据？

Question

One of the things I deal with most in data cleaning is missing values. R deals with this well using its "NA" missing data label. In python, it appears that I'll have to deal with masked arrays which seem to be a major pain to set up and don't seem to be well documented. Any suggestions on making this process easier in Python? This is becoming a deal-breaker in moving into Python for data analysis. Thanks

Update It's obviously been a while since I've looked at the methods in the numpy.ma module. It appears that at least the basic analysis functions are available for masked arrays, and the examples provided helped me understand how to create masked arrays (thanks to the authors). I would like to see if some of the newer statistical methods in Python (being developed in this year's GSoC) incorporates this aspect, and at least does the complete case analysis.

Answer 1

If you are willing to consider a library, pandas (http://pandas.pydata.org/) is a library built on top of numpy which amongst many other things provides:

Intelligent data alignment and integrated handling of missing data: gain automatic label-based alignment in computations and easily manipulate messy data into an orderly form

I've been using it for almost one year in the financial industry where missing and badly aligned data is the norm and it really made my life easier.

Answer 2

I also question the problem with masked arrays. Here are a couple of examples:

import numpy as np
data = np.ma.masked_array(np.arange(10))
data[5] = np.ma.masked # Mask a specific value

data[data>6] = np.ma.masked # Mask any value greater than 6

# Same thing done at initialization time
init_data = np.arange(10)
data = np.ma.masked_array(init_data, mask=(init_data > 6))

Answer 3

Masked arrays are the anwswer, as DpplerShift describes. For quick and dirty use, you can use fancy indexing with boolean arrays:

>>> import numpy as np
>>> data = np.arange(10)
>>> valid_idx = data % 2 == 0 #pretend that even elements are missing

>>> # Get non-missing data
>>> data[valid_idx]
array([0, 2, 4, 6, 8])

You can now use valid_idx as a quick mask on other data as well

>>> comparison = np.arange(10) + 10
>>> comparison[valid_idx]
array([10, 12, 14, 16, 18])

Answer 4

See sklearn.preprocessing.Imputer

import numpy as np
from sklearn.preprocessing import Imputer
imp = Imputer(missing_values='NaN', strategy='mean', axis=0)
imp.fit([[1, 2], [np.nan, 3], [7, 6]])
X = [[np.nan, 2], [6, np.nan], [7, 6]]
print(imp.transform(X))  

Example from http://scikit-learn.org/