dropping dataframe rows based on multiple conditions

Normally, a column is checked for nullness with the isnull method:

df.drop(df[df['column1'].isnull() 
          & df['column2'].isnull() 
          & df['column3'].isnull() 
          & df['column4'].isnull()].index)

However, there are more compact and idiomatic ways for that:

df.dropna(subset=['column1', 'column2', 'column3', 'column4'], how='all')

A demo:

prng = np.random.RandomState(0)
df = pd.DataFrame(prng.randn(100, 6), columns=['column{}'.format(i) for i in range(1, 7)])

df.head()
Out: 
    column1   column2   column3   column4   column5   column6
0  1.764052  0.400157  0.978738  2.240893  1.867558 -0.977278
1  0.950088 -0.151357 -0.103219  0.410599  0.144044  1.454274
2  0.761038  0.121675  0.443863  0.333674  1.494079 -0.205158
3  0.313068 -0.854096 -2.552990  0.653619  0.864436 -0.742165
4  2.269755 -1.454366  0.045759 -0.187184  1.532779  1.469359

df = df.mask(prng.binomial(1, 0.5, df.shape).astype('bool'), np.nan)

df.head()
Out: 
    column1   column2   column3   column4   column5   column6
0       NaN  0.400157       NaN  2.240893       NaN       NaN
1  0.950088 -0.151357 -0.103219  0.410599  0.144044       NaN
2  0.761038  0.121675       NaN       NaN       NaN -0.205158
3       NaN       NaN -2.552990       NaN  0.864436       NaN
4  2.269755 -1.454366  0.045759 -0.187184       NaN       NaN

The following drops rows only if columns 1, 3, 5 and 6 are null:

df.dropna(subset=['column1', 'column3', 'column5', 'column6'], how='all').head()
Out: 
    column1   column2   column3   column4   column5   column6
1  0.950088 -0.151357 -0.103219  0.410599  0.144044       NaN
2  0.761038  0.121675       NaN       NaN       NaN -0.205158
3       NaN       NaN -2.552990       NaN  0.864436       NaN
4  2.269755 -1.454366  0.045759 -0.187184       NaN       NaN
5  0.154947  0.378163 -0.887786 -1.980796 -0.347912       NaN