Data imputations library to preprocess datasets with missing data

>>> n = 5
>>> arr = np.random.uniform(high=6, size=(n, n))
>>> for _ in range(3):
>>>    arr[np.random.randint(n), np.random.randint(n)] = np.nan
>>> print(arr)
array([[0.25288643, 1.8149261 , 4.79943748, 0.54464834, np.nan],
       [4.44798362, 0.93518716, 3.24430922, 2.50915032, 5.75956805],
       [0.79802036, np.nan, 0.51729349, 5.06533123, 3.70669172],
       [1.30848217, 2.08386584, 2.29894541, np.nan, 3.38661392],
       [2.70989501, 3.13116687, 0.25851597, 4.24064355, 1.99607231]])
>>> import impyute as impy
>>> print(impy.mean(arr))
array([[0.25288643, 1.8149261 , 4.79943748, 0.54464834, 3.7122365],
       [4.44798362, 0.93518716, 3.24430922, 2.50915032, 5.75956805],
       [0.79802036, 1.99128649, 0.51729349, 5.06533123, 3.70669172],
       [1.30848217, 2.08386584, 2.29894541, 3.08994336, 3.38661392],
       [2.70989501, 3.13116687, 0.25851597, 4.24064355, 1.99607231]])

$ pip install impyute

$ git clone https://github.com/eltonlaw/impyute
$ cd impyute
$ python setup.py install