import numpy as np
from sklearn.datasets import load_iris
iris = load_iris()
X, y = iris.data, iris.target
# some values missing only
rng = np.random.RandomState(0)
X_some_missing = X.copy()
mask = np.abs(X[:, 2] - rng.normal(loc=5.5, scale=.7, size=X.shape[0])) < .6
X_some_missing[mask, 3] = np.NaN
# different random numbers
mask2 = np.abs(X[:, 2] - rng.normal(loc=5.5, scale=.7, size=X.shape[0])) < .6
X_some_missing[mask2, 2] = np.NaN

from fancyimpute import KNN
res = KNN().complete(X_some_missing)

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 150000 entries, 0 to 149999
Data columns (total 12 columns):
Unnamed: 0                              150000 non-null int64
SeriousDlqin2yrs                        150000 non-null int64
RevolvingUtilizationOfUnsecuredLines    150000 non-null float64
age                                     150000 non-null int64
NumberOfTime30-59DaysPastDueNotWorse    150000 non-null int64
DebtRatio                               150000 non-null float64
MonthlyIncome                           120269 non-null float64
NumberOfOpenCreditLinesAndLoans         150000 non-null int64
NumberOfTimes90DaysLate                 150000 non-null int64
NumberRealEstateLoansOrLines            150000 non-null int64
NumberOfTime60-89DaysPastDueNotWorse    150000 non-null int64
NumberOfDependents                      146076 non-null float64
dtypes: float64(4), int64(8)
memory usage: 13.7 MB

from knnimpute import knn_impute_reference
X_imputed =knn_impute_reference(test_data.iloc[:,2:].values, np.isnan(test_data.iloc[:,2:].values), k=3)

---------------------------------------------------------------------------
MemoryError                               Traceback (most recent call last)
<ipython-input-39-d0cef60a839b> in <module>()
      1 from knnimpute import knn_impute_reference
----> 2 X_imputed =knn_impute_reference(test_data.iloc[:,2:].values, np.isnan(test_data.iloc[:,2:].values), k=3)

d:\Anaconda3\lib\site-packages\knnimpute\reference.py in knn_impute_reference(X, missing_mask, k, verbose, print_interval)
     29     n_rows, n_cols = X.shape
     30     X_result, D, effective_infinity = \
---> 31         knn_initialize(X, missing_mask, verbose=verbose)
     32 
     33     for i in range(n_rows):

d:\Anaconda3\lib\site-packages\knnimpute\common.py in knn_initialize(X, missing_mask, verbose, min_dist, max_dist_multiplier)
     37         # to put NaN's back in the data matrix for the distances function
     38         X_row_major[missing_mask] = np.nan
---> 39     D = all_pairs_normalized_distances(X_row_major)
     40     D_finite_flat = D[np.isfinite(D)]
     41     if len(D_finite_flat) > 0:

d:\Anaconda3\lib\site-packages\knnimpute\normalized_distance.py in all_pairs_normalized_distances(X)
     36 
     37     # matrix of mean squared difference between between samples
---> 38     D = np.ones((n_rows, n_rows), dtype="float32", order="C") * np.inf
     39 
     40     # we can cheaply determine the number of columns that two rows share

d:\Anaconda3\lib\site-packages\numpy\core\numeric.py in ones(shape, dtype, order)
    190 
    191     """
--> 192     a = empty(shape, dtype, order)
    193     multiarray.copyto(a, 1, casting='unsafe')
    194     return a

MemoryError: 

X = array([[ nan,   1.],
       [  1.,  nan]])