Building multi-regression model throws an error: `Pandas data cast to numpy dtype of object. Check input data with np.asarray(data).`

712 Asked by CarolBower in Python , Asked on Jun 16, 2021

I have pandas data frame with some categorical predictors (i.e. variables) as 0 & 1 and some numeric variables. When I fit that to a stasmodel like:

est = sm.OLS(y, X).fit()

It throws:

Pandas datacast to numpy dtype of object. Check input data with np.asarray(data).

I converted all the dtypes of the DataFrame using

df.convert_objects(convert_numeric=True)

After this, all dtypes of data frame variables appear as int32 or int64. But in the end it still shows dtype: object, like this:

4516        int32
4523        int32
4525        int32
4531        int32
4533        int32
4542        int32
4562        int32
sex         int64
race        int64
dispstd     int64
age_days    int64
dtype: object

Here 4516, 4523 are variable labels.

Any idea? I need to build a multi-regression model on more than hundreds of variables. For that, I have concatenated 3 pandas DataFrames to come up with final DataFrame to be used in model building.

Answered by Gabrielle Dowd

If X is your data frame, then try using the .astype method to convert to float when running the model:

  est = sm.OLS(y, X.astype(float)).fit()

All categorical variables should be converted into dummy variables before sticking them in the model. This will revolve error: `Pandas data cast to numpy dtype of object. Check input data with np.asarray(data).`

Building multi-regression model throws an error: `Pandas data cast to numpy dtype of object. Check input data with np.asarray(data).`

Your Answer