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

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

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

if both y(dependent) and X are taken from a data frame then type cast both:-

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

As Mário and Daniel suggested, yes, the issue is due to categorical values not previously converted into dummy variables.

I faced this issue reviewing StatLearning book lab on linear regression for the "Carseats" dataset from statsmodels, where the columns 'ShelveLoc', 'US' and 'Urban' are categorical values, I assume the categorical values causing issues in your dataset are also strings like in this one. Considering the previous, I will use this as an example since you didn't provide dataframes for the question.

The columns we have at the beginning are the following, as stated before 'ShelveLoc', 'US' and 'Urban'are categorical:

Index(['Sales', 'CompPrice', 'Income', 'Advertising', 'Population', 'Price',       'ShelveLoc', 'Age', 'Education', 'Urban', 'US'],      dtype='object')

In a simple line for Python, I converted them to categorical values and dropped the ones that had "No" and "Bad" labels (as this is what was being requested from the lab in the book).

carseats = pd.get_dummies(carseats, columns=['ShelveLoc', 'US', 'Urban'], drop_first = True)

This will return a dataframe with the following columns:

Index(['Sales', 'CompPrice', 'Income', 'Advertising', 'Population', 'Price',       'Age', 'Education', 'ShelveLoc_Good', 'ShelveLoc_Medium', 'US_Yes',       'Urban_Yes'],      dtype='object')

And that's it, you have dummy variables ready for OLS. Hope this is useful.