To remove NaN values from a NumPy array x:

x = x[~numpy.isnan(x)]

Explanation

The inner function numpy.isnan returns a boolean/logical array which has the value True everywhere that x is not-a-number. Since we want the opposite, we use the logical-not operator ~ to get an array with Trues everywhere that x is a valid number.

Lastly, we use this logical array to index into the original array x, in order to retrieve just the non-NaN values.

>>> a = np.array([[1,2,3], [4,5,np.nan], [7,8,9]])
array([[  1.,   2.,   3.],
       [  4.,   5.,  nan],
       [  7.,   8.,   9.]])

>>> a[~np.isnan(a).any(axis=1)]
array([[ 1.,  2.,  3.],
       [ 7.,  8.,  9.]])

and reassign this to a.

Explanation: np.isnan(a) returns a similar array with True where NaN, False elsewhere. .any(axis=1) reduces an m*n array to n with an logical or operation on the whole rows, ~ inverts True/False and a[ ] chooses just the rows from the original array, which have True within the brackets.

import numpy as np

a = np.array([
    [1, 0, 0],
    [0, np.nan, 0],
    [0, 0, 0],
    [np.nan, np.nan, np.nan],
    [2, 3, 4]
])

mask = np.all(np.isnan(a) | np.equal(a, 0), axis=1)
a[~mask]

If x is the original array, the following puts the valid rows into y:

y = x[~np.isnan(x[:, 0])]

Dataframe:

   values_1  values_2
0     700.0       NaN
1       NaN     150.0
2     500.0     350.0
3       NaN     400.0
4    1200.0    5000.0

You can try this:

df = df.dropna()
df = df.reset_index(drop=True)

Outputs:

   values_1  values_2
0     500.0     350.0
1    1200.0    5000.0

You could use boolean indexing to select only those columns which do not contain nan:

>>> x[:, ~np.isnan(x).any(axis=0)]
array([[ 1.,  2.,  4.],
       [ 2.,  4.,  8.],
       [ 3.,  6.,  0.]])

(This is nearly identical to the answer you've linked to; only the axes have been switched.)