#NOTES:
#A] IF WE RUN ONLY #1 THEN CODE PASSES INFINITE TIMES
#b] IF WE RUN #1 and #2 THEN CODE PASSES for the first time, but second time crashes, if run it for the third time it passes again and fourth time crashes and so on

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-4-ad8aef2aa149> in <module>
      6 
      7 #1 Calc PCA
----> 8 x_pca = PCA(8).fit_transform(DATA)
      9 
     10 

~\anaconda3\lib\site-packages\sklearn\decomposition\_pca.py in fit_transform(self, X, y)
    374         C-ordered array, use 'np.ascontiguousarray'.
    375         """
--> 376         U, S, Vt = self._fit(X)
    377         U = U[:, :self.n_components_]
    378 

~\anaconda3\lib\site-packages\sklearn\decomposition\_pca.py in _fit(self, X)
    423             return self._fit_full(X, n_components)
    424         elif self._fit_svd_solver in ['arpack', 'randomized']:
--> 425             return self._fit_truncated(X, n_components, self._fit_svd_solver)
    426         else:
    427             raise ValueError("Unrecognized svd_solver='{0}'"

~\anaconda3\lib\site-packages\sklearn\decomposition\_pca.py in _fit_truncated(self, X, n_components, svd_solver)
    539         elif svd_solver == 'randomized':
    540             # sign flipping is done inside
--> 541             U, S, Vt = randomized_svd(X, n_components=n_components,
    542                                       n_iter=self.iterated_power,
    543                                       flip_sign=True,

~\anaconda3\lib\site-packages\sklearn\utils\validation.py in inner_f(*args, **kwargs)
     61             extra_args = len(args) - len(all_args)
     62             if extra_args <= 0:
---> 63                 return f(*args, **kwargs)
     64 
     65             # extra_args > 0

~\anaconda3\lib\site-packages\sklearn\utils\extmath.py in randomized_svd(M, n_components, n_oversamples, n_iter, power_iteration_normalizer, transpose, flip_sign, random_state)
    355 
    356     # compute the SVD on the thin matrix: (k + p) wide
--> 357     Uhat, s, Vt = linalg.svd(B, full_matrices=False)
    358 
    359     del B

~\anaconda3\lib\site-packages\scipy\linalg\decomp_svd.py in svd(a, full_matrices, compute_uv, overwrite_a, check_finite, lapack_driver)
    104 
    105     """
--> 106     a1 = _asarray_validated(a, check_finite=check_finite)
    107     if len(a1.shape) != 2:
    108         raise ValueError('expected matrix')

~\anaconda3\lib\site-packages\scipy\_lib\_util.py in _asarray_validated(a, check_finite, sparse_ok, objects_ok, mask_ok, as_inexact)
    260             raise ValueError('masked arrays are not supported')
    261     toarray = np.asarray_chkfinite if check_finite else np.asarray
--> 262     a = toarray(a)
    263     if not objects_ok:
    264         if a.dtype is np.dtype('O'):

~\anaconda3\lib\site-packages\numpy\lib\function_base.py in asarray_chkfinite(a, dtype, order)
    483     a = asarray(a, dtype=dtype, order=order)
    484     if a.dtype.char in typecodes['AllFloat'] and not np.isfinite(a).all():
--> 485         raise ValueError(
    486             "array must not contain infs or NaNs")
    487     return a

ValueError: array must not contain infs or NaNs



#### Versions
 pandas 1.2.1 
 matplotlib 3.3.3 
 sklearn 0.24.1 
numpy 1.19.5