One error you may encounter when using Python is:

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

This error occurs when you attempt to fit a regression model in Python and fail to convert categorical variables to dummy variables first before fitting the model.

The following example shows how to fix this error in practice.

**How to Reproduce the Error**

Suppose we have the following pandas DataFrame:

import pandas as pd #create DataFrame df = pd.DataFrame({'team': ['A', 'A', 'A', 'A', 'B', 'B', 'B', 'B'], 'assists': [5, 7, 7, 9, 12, 9, 9, 4], 'rebounds': [11, 8, 10, 6, 6, 5, 9, 12], 'points': [14, 19, 8, 12, 17, 19, 22, 25]}) #view DataFrame df team assists rebounds points 0 A 5 11 14 1 A 7 8 19 2 A 7 10 8 3 A 9 6 12 4 B 12 6 17 5 B 9 5 19 6 B 9 9 22 7 B 4 12 25

Now suppose we attempt to fit a multiple linear regression model using team, assists, and rebounds as predictor variables and points as the response variable:

import statsmodels.api as sm #define response variable y = df['points'] #define predictor variables x = df[['team', 'assists', 'rebounds']] #add constant to predictor variables x = sm.add_constant(x) #attempt to fit regression model model = sm.OLS(y, x).fit() ValueError: Pandas data cast to numpy dtype of object. Check input data with np.asarray(data).

We receive an error because the variable “team” is categorical and we did not convert it to a dummy variable before fitting the regression model.

**How to Fix the Error**

The easiest way to fix this error is to convert the “team” variable to a dummy variable using the pandas.get_dummies() function.

**Note**: Check out this tutorial for a quick refresher on dummy variables in regression models.

The following code shows how to convert “team” to a dummy variable:

import pandas as pd #create DataFrame df = pd.DataFrame({'team': ['A', 'A', 'A', 'A', 'B', 'B', 'B', 'B'], 'assists': [5, 7, 7, 9, 12, 9, 9, 4], 'rebounds': [11, 8, 10, 6, 6, 5, 9, 12], 'points': [14, 19, 8, 12, 17, 19, 22, 25]}) #convert "team" to dummy variable df = pd.get_dummies(df, columns=['team'], drop_first=True) #view updated DataFrame df assists rebounds points team_B 0 5 11 14 0 1 7 8 19 0 2 7 10 8 0 3 9 6 12 0 4 12 6 17 1 5 9 5 19 1 6 9 9 22 1 7 4 12 25 1

The values in the “team” column have been converted from “A” and “B” to 0 and 1.

We can now fit the multiple linear regression model using the new “team_B” variable:

import statsmodels.api as sm #define response variable y = df['points'] #define predictor variables x = df[['team_B', 'assists', 'rebounds']] #add constant to predictor variables x = sm.add_constant(x) #fit regression model model = sm.OLS(y, x).fit() #view summary of model fit print(model.summary()) OLS Regression Results ============================================================================== Dep. Variable: points R-squared: 0.701 Model: OLS Adj. R-squared: 0.476 Method: Least Squares F-statistic: 3.119 Date: Thu, 11 Nov 2021 Prob (F-statistic): 0.150 Time: 14:49:53 Log-Likelihood: -19.637 No. Observations: 8 AIC: 47.27 Df Residuals: 4 BIC: 47.59 Df Model: 3 Covariance Type: nonrobust ============================================================================== coef std err t P>|t| [0.025 0.975] ------------------------------------------------------------------------------ const 27.1891 17.058 1.594 0.186 -20.171 74.549 team_B 9.1288 3.032 3.010 0.040 0.709 17.548 assists -1.3445 1.148 -1.171 0.307 -4.532 1.843 rebounds -0.5174 1.099 -0.471 0.662 -3.569 2.534 ============================================================================== Omnibus: 0.691 Durbin-Watson: 3.075 Prob(Omnibus): 0.708 Jarque-Bera (JB): 0.145 Skew: 0.294 Prob(JB): 0.930 Kurtosis: 2.698 Cond. No. 140. ==============================================================================

Notice that we’re able to fit the regression model without any errors this time.

**Note**: You can find the complete documentation for the **ols()** function from the statsmodels library here.

**Additional Resources**

The following tutorials explain how to fix other common errors in Python:

How to Fix KeyError in Pandas

How to Fix: ValueError: cannot convert float NaN to integer

How to Fix: ValueError: operands could not be broadcast together with shapes

Thanks Zach,

that was very helful. I kept getting an error because after getting the dummies, there was a need to concat. Had to find it somewhere else but came back to continue with the remaining steps.

Appreciate your help and time on this.

df_neighborhood = pd.get_dummies(df[‘Neighborhood’])

df_new=pd.concat([df,df_neighborhood], axis=1)

x6=df_neighborhood

x=sm.add_constant(x6)

model= sm.OLS(y,x).fit()

print(model.summary())

Big thanks!