Fix: pandas data cast to numpy dtype of object? Check input data with np.asarray(data).

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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 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 using team, assists, and rebounds as predictor variables and points as the :

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 function.

Note: Check out 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 .

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