list_cols= ['col01', 'col02', 'col03', 'col04', 'col05','col06', 'col07', 'col08', 'col09', 'col10','col11', 'col12', 'col13', 'col14', 'col15', 'col16']

X_full = pd.DataFrame(np.random.uniform(low=1.0, high=100.0, size=(5,16)), columns=list(list_cols))

# Add a single nan value to each row
rng = np.random.RandomState(0)
n_samples, n_features = X_full.shape
X_missing = X_full.copy()
missing_samples = np.arange(n_samples)
missing_features = rng.choice(n_features, n_samples, replace=True)
X_missing[missing_samples, missing_features] = np.nan

for row, column in zip(missing_samples, missing_features):
    X_missing.iat[row, column] = np.nan