vif error

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
Cell In[58], line 6
      4 vif=pd.DataFrame()
      5 vif["feature"]=df.columns
----> 6 vif["value"]=[variance_inflation_factor(df.values,i) for i in range(len(df.columns))]

File ~\AppData\Roaming\Python\Python312\site-packages\statsmodels\stats\outliers_influence.py:196, in variance_inflation_factor(exog, exog_idx)
    194 mask = np.arange(k_vars) != exog_idx
    195 x_noti = exog[:, mask]
--> 196 r_squared_i = OLS(x_i, x_noti).fit().rsquared
    197 vif = 1. / (1. - r_squared_i)
    198 return vif

File ~\AppData\Roaming\Python\Python312\site-packages\statsmodels\regression\linear_model.py:924, in OLS.__init__(self, endog, exog, missing, hasconst, **kwargs)
    921     msg = ("Weights are not supported in OLS and will be ignored"
    922            "An exception will be raised in the next version.")
    923     warnings.warn(msg, ValueWarning)
--> 924 super().__init__(endog, exog, missing=missing,
    925                           hasconst=hasconst, **kwargs)
    926 if "weights" in self._init_keys:
    927     self._init_keys.remove("weights")

File ~\AppData\Roaming\Python\Python312\site-packages\statsmodels\regression\linear_model.py:749, in WLS.__init__(self, endog, exog, weights, missing, hasconst, **kwargs)
    747 else:
    748     weights = weights.squeeze()
--> 749 super().__init__(endog, exog, missing=missing,
    750                           weights=weights, hasconst=hasconst, **kwargs)
    751 nobs = self.exog.shape[0]
    752 weights = self.weights

File ~\AppData\Roaming\Python\Python312\site-packages\statsmodels\regression\linear_model.py:203, in RegressionModel.__init__(self, endog, exog, **kwargs)
    202 def __init__(self, endog, exog, **kwargs):
--> 203     super().__init__(endog, exog, **kwargs)
    204     self.pinv_wexog: Float64Array | None = None
    205     self._data_attr.extend(['pinv_wexog', 'wendog', 'wexog', 'weights'])

File ~\AppData\Roaming\Python\Python312\site-packages\statsmodels\base\model.py:270, in LikelihoodModel.__init__(self, endog, exog, **kwargs)
    269 def __init__(self, endog, exog=None, **kwargs):
--> 270     super().__init__(endog, exog, **kwargs)
    271     self.initialize()

File ~\AppData\Roaming\Python\Python312\site-packages\statsmodels\base\model.py:95, in Model.__init__(self, endog, exog, **kwargs)
     93 missing = kwargs.pop('missing', 'none')
     94 hasconst = kwargs.pop('hasconst', None)
---> 95 self.data = self._handle_data(endog, exog, missing, hasconst,
     96                               **kwargs)
     97 self.k_constant = self.data.k_constant
     98 self.exog = self.data.exog

File ~\AppData\Roaming\Python\Python312\site-packages\statsmodels\base\model.py:135, in Model._handle_data(self, endog, exog, missing, hasconst, **kwargs)
    134 def _handle_data(self, endog, exog, missing, hasconst, **kwargs):
--> 135     data = handle_data(endog, exog, missing, hasconst, **kwargs)
    136     # kwargs arrays could have changed, easier to just attach here
    137     for key in kwargs:

File ~\AppData\Roaming\Python\Python312\site-packages\statsmodels\base\data.py:675, in handle_data(endog, exog, missing, hasconst, **kwargs)
    672     exog = np.asarray(exog)
    674 klass = handle_data_class_factory(endog, exog)
--> 675 return klass(endog, exog=exog, missing=missing, hasconst=hasconst,
    676              **kwargs)

File ~\AppData\Roaming\Python\Python312\site-packages\statsmodels\base\data.py:88, in ModelData.__init__(self, endog, exog, missing, hasconst, **kwargs)
     86 self.const_idx = None
     87 self.k_constant = 0
---> 88 self._handle_constant(hasconst)
     89 self._check_integrity()
     90 self._cache = {}

File ~\AppData\Roaming\Python\Python312\site-packages\statsmodels\base\data.py:133, in ModelData._handle_constant(self, hasconst)
    131 check_implicit = False
    132 exog_max = np.max(self.exog, axis=0)
--> 133 if not np.isfinite(exog_max).all():
    134     raise MissingDataError('exog contains inf or nans')
    135 exog_min = np.min(self.exog, axis=0)

TypeError: ufunc 'isfinite' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe''