Interview Bite

The error message you’re seeing:

pgsqlCopyEditValueError: Pandas data cast to numpy dtype of object. Check input data with np.asarray(data).

usually occurs when you pass non-numeric (e.g., strings or mixed types) data to a function that expects numeric input, such as in scikit-learn or statsmodels.

✅ Common Scenario (e.g., with statsmodels or sklearn)

You might encounter this in situations like:

pythonCopyEditimport statsmodels.api as sm

X = df[['col1', 'col2']]  # might contain strings or categories
y = df['target']

model = sm.OLS(y, X)
results = model.fit()

If any column in X is non-numeric (like strings or categories), it throws that ValueError.

🔍 How to Fix It

✅ Step 1: Check your Data Types

pythonCopyEditprint(df.dtypes)

Look for columns that are object or category.

✅ Step 2: Convert Non-Numeric Data

Use pd.get_dummies() to convert categorical data to numeric:

pythonCopyEditX = pd.get_dummies(df[['col1', 'col2']], drop_first=True)

Or convert manually:

pythonCopyEditdf['col1'] = df['col1'].astype(float)

✅ Step 3: Add Constant (if needed)

If you’re using statsmodels.OLS, don’t forget to add a constant:

pythonCopyEditX = sm.add_constant(X)

✅ Step 4: Verify Conversion

You can check the NumPy dtype like this:

pythonCopyEditimport numpy as np
print(np.asarray(X).dtype)

It should return a numeric dtype like float64.

🧪 Example Fix

pythonCopyEditimport pandas as pd
import numpy as np
import statsmodels.api as sm

# Sample DataFrame
df = pd.DataFrame({
    'col1': ['A', 'B', 'A', 'C'],
    'col2': [1, 2, 3, 4],
    'target': [10, 20, 30, 40]
})

# Convert categorical to dummy variables
X = pd.get_dummies(df[['col1', 'col2']], drop_first=True)

# Add constant
X = sm.add_constant(X)

y = df['target']

# Fit model
model = sm.OLS(y, X)
results = model.fit()

print(results.summary())

🧠 Final Tip

If you still get the error after conversion, try forcing the dtype:

pythonCopyEditX = X.astype(float)