# Error: "Normal has no finite default value to use" when using data with NaN

**URL:** <https://discourse.pymc.io/t/error-normal-has-no-finite-default-value-to-use-when-using-data-with-nan/8103>\
**Category:** Questions\
**Created:** [September 30, 2021, 10:23am UTC](https://discourse.pymc.io/t/error-normal-has-no-finite-default-value-to-use-when-using-data-with-nan/8103 "2021-09-30T10:23:54Z")\
**Posts on this page:** 1\
**Page:** 1

<div class="post-metadata">

**Author:** ![Sherpaman78](https://yyz2.discourse-cdn.com/flex036/user_avatar/discourse.pymc.io/sherpaman78/32/4455_2.png) [@Sherpaman78](https://discourse.pymc.io/u/Sherpaman78)\
**Post date:** [September 30, 2021, 10:23am UTC](https://discourse.pymc.io/t/error-normal-has-no-finite-default-value-to-use-when-using-data-with-nan/8103/1 "2021-09-30T10:23:54Z")

</div>

I am trying to fit a simple model using some missing value in my data:

```auto
with pm.Model(coords=coords) as h_model:

    mu = pm.TruncatedNormal(name="mu",
                            mu=90.,
                            sigma=20.,
                            lower=0.0)
    sigma = pm.HalfNormal(name="sigma", 
                          sigma=10.0)

    b = pm.Normal(name="b",
                  mu=mu,
                  sigma=sigma,
                  dims=tuple(coords)[1])

    s = pm.HalfNormal(name="s", sigma=30.0)
    a = pm.HalfNormal(name="a", sigma=s, dims=tuple(coords)[1])

    eps = pm.HalfCauchy(name="eps", beta=20.0)

    weight_est = pm.Deterministic('Linear_Model', a + b * data_x,
                                  dims=tuple(coords))

    _gaussian = pm.Normal(name='Gaussian',
                          mu=weight_est,
                          sigma=eps,
                          observed=data_y)

```

where data\_x and data\_y are pandas.DataFrames contaiing some NaN values :

data\_x (just a sub-set overall shape is 34 x 64):

```auto
  completed_at 27914 27915 27918 27919
0 2021-06-09 1.318627 0.940294 1.078431 0.543137
1 2021-06-11 1.062745 0.940294 1.093137 1.543137
2 2021-06-14 NaN NaN 1.008824 NaN
3 2021-06-16 1.837255 NaN 1.236275 NaN
4 2021-06-17 NaN NaN NaN NaN

```

data\_y:

```auto
  completed_at 27914 27915 27918 27919
0 2021-06-09 110.4 163.2 21.6 216.0
1 2021-06-11 96.0 192.0 136.8 192.0
2 2021-06-14 NaN NaN 182.4 NaN
3 2021-06-16 163.2 NaN 168.0 NaN
4 2021-06-17 NaN NaN NaN NaN

```

(as you can notice values are missing both on observed and predictor)

What I get is the following error:

```auto
/lib/python3.8/site-packages/pymc3/distributions/distribution.py in get_test_val(self, val, defaults)
    153 if np.all(np.isfinite(attr_val)):
    154 return attr_val
--> 155 raise AttributeError(
    156 "%s has no finite default value to use, "
    157 "checked: %s. Pass testval argument or "

AttributeError: [unnamed] ~ Normal has no finite default value to use, checked: ('median', 'mean', 'mode'). Pass testval argument or adjust so value is finite.

```

It seems like the NaN gives some problem.

I have also tried to use Data Containers:

```auto
with pm.Model(coords=coords) as h_model:
    x = pm.Data(name='data_x', value=data_x, dims=tuple(coords))
    y = pm.Data(name='data_y', value=data_y, dims=tuple(coords))
    mu = pm.TruncatedNormal(name="mu",
                            mu=90.,
                            sigma=20.,
                            lower=0.0)
    sigma = pm.HalfNormal(name="sigma", 
                          sigma=10.0)

    b = pm.Normal(name="b",
                  mu=mu,
                  sigma=sigma,
                  dims=tuple(coords)[1])

    s = pm.HalfNormal(name="s", sigma=30.0)
    a = pm.HalfNormal(name="a", sigma=s, dims=tuple(coords)[1])

    eps = pm.HalfCauchy(name="eps", beta=20.0)

    weight_est = pm.Deterministic('Linear_Model', a + b * x,
                                  dims=tuple(coords))

    _gaussian = pm.Normal(name='Gaussian',
                          mu=weight_est,
                          sigma=eps,
                          observed=y)

```

but when I try to sample the model i get a SamplingError:

```auto
with h_model:
    step = pm.Metropolis()
    h_trace = pm.sample(step=step, return_inferencedata=True)

SamplingError: Initial evaluation of model at starting point failed!
Starting values:
{'mu_lowerbound__': array(0.), 'sigma_log__': array(2.07679374), 'b': array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.]), 's_log__': array(3.17540603), 'a_log__': array([2.94961468, 2.94961468, 2.94961468, 2.94961468, 2.94961468,
       2.94961468, 2.94961468, 2.94961468, 2.94961468, 2.94961468,
       2.94961468, 2.94961468, 2.94961468, 2.94961468, 2.94961468,
       2.94961468, 2.94961468, 2.94961468, 2.94961468, 2.94961468,
       2.94961468, 2.94961468, 2.94961468, 2.94961468, 2.94961468,
       2.94961468, 2.94961468, 2.94961468, 2.94961468, 2.94961468,
       2.94961468, 2.94961468, 2.94961468, 2.94961468, 2.94961468,
       2.94961468, 2.94961468, 2.94961468, 2.94961468, 2.94961468,
       2.94961468, 2.94961468, 2.94961468, 2.94961468, 2.94961468,
       2.94961468, 2.94961468, 2.94961468, 2.94961468, 2.94961468,
       2.94961468, 2.94961468, 2.94961468, 2.94961468, 2.94961468,
       2.94961468, 2.94961468, 2.94961468, 2.94961468, 2.94961468,
       2.94961468, 2.94961468, 2.94961468]), 'eps_log__': array(2.99573227)}

Initial evaluation results:
mu_lowerbound__ -13.82
sigma_log__ -0.77
b -188.73
s_log__ -0.77
a_log__ -48.50
eps_log__ -1.14
Gaussian NaN
Name: Log-probability of test_point, dtype: float64

```

The problem is “solved” if I use data\_x.fillna(1e-4) and data\_x.fillna(1e-4) but I would prefer pymc3 to fill the missing value from the prior distribution.