def makeMissingCustomDataframe(nrows, ncols, density=<NUM_LIT>, random_state=None,<EOL>c_idx_names=True, r_idx_names=True,<EOL>c_idx_nlevels=<NUM_LIT:1>, r_idx_nlevels=<NUM_LIT:1>,<EOL>data_gen_f=None,<EOL>c_ndupe_l=None, r_ndupe_l=None, dtype=None,<EOL>c_idx_type=None, r_idx_type=None):
df = makeCustomDataframe(nrows, ncols, c_idx_names=c_idx_names,<EOL>r_idx_names=r_idx_names,<EOL>c_idx_nlevels=c_idx_nlevels,<EOL>r_idx_nlevels=r_idx_nlevels,<EOL>data_gen_f=data_gen_f,<EOL>c_ndupe_l=c_ndupe_l, r_ndupe_l=r_ndupe_l,<EOL>dtype=dtype, c_idx_type=c_idx_type,<EOL>r_idx_type=r_idx_type)<EOL>i, j = _create_missing_idx(nrows, ncols, density, random_state)<EOL>df.values[i, j] = np.nan<EOL>return df<EOL>
Parameters
----------
Density : float, optional
    Float in (0, 1) that gives the percentage of non-missing numbers in
    the DataFrame.
random_state : {np.random.RandomState, int}, optional
    Random number generator or random seed.

See makeCustomDataframe for descriptions of the rest of the parameters.