I want to use statsmodels OLS class to create a multiple regression model. Consider the following dataset:
import statsmodels.api as sm
import pandas as pd
import numpy as np
dict = {'industry': ['mining', 'transportation', 'hospitality', 'finance', 'entertainment'],
'debt_ratio':np.random.randn(5), 'cash_flow':np.random.randn(5) + 90}
df = pd.DataFrame.from_dict(dict)
x = data[['debt_ratio', 'industry']]
y = data['cash_flow']
def reg_sm(x, y):
x = np.array(x).T
x = sm.add_constant(x)
results = sm.OLS(endog = y, exog = x).fit()
return results
When I run the following code:
reg_sm(x, y)
I get the following error:
TypeError: '>=' not supported between instances of 'float' and 'str'
I've tried converting the industry variable to categorical, but I still get an error. I'm out of options.
You're on the right path with converting to a Categorical dtype. However, once you convert the DataFrame to a NumPy array, you get an object dtype (NumPy arrays are one uniform type as a whole). This means that the individual values are still underlying str which a regression definitely is not going to like.
What you might want to do is to dummify this feature. Instead of factorizing it, which would effectively treat the variable as continuous, you want to maintain some semblance of categorization:
>>> import statsmodels.api as sm
>>> import pandas as pd
>>> import numpy as np
>>> np.random.seed(444)
>>> data = {
... 'industry': ['mining', 'transportation', 'hospitality', 'finance', 'entertainment'],
... 'debt_ratio':np.random.randn(5),
... 'cash_flow':np.random.randn(5) + 90
... }
>>> data = pd.DataFrame.from_dict(data)
>>> data = pd.concat((
... data,
... pd.get_dummies(data['industry'], drop_first=True)), axis=1)
>>> # You could also use data.drop('industry', axis=1)
>>> # in the call to pd.concat()
>>> data
industry debt_ratio cash_flow finance hospitality mining transportation
0 mining 0.357440 88.856850 0 0 1 0
1 transportation 0.377538 89.457560 0 0 0 1
2 hospitality 1.382338 89.451292 0 1 0 0
3 finance 1.175549 90.208520 1 0 0 0
4 entertainment -0.939276 90.212690 0 0 0 0
Now you have dtypes that statsmodels can better work with. The purpose of drop_first is to avoid the dummy trap:
>>> y = data['cash_flow']
>>> x = data.drop(['cash_flow', 'industry'], axis=1)
>>> sm.OLS(y, x).fit()
<statsmodels.regression.linear_model.RegressionResultsWrapper object at 0x115b87cf8>
Lastly, just a small pointer: it helps to try to avoid naming references with names that shadow built-in object types, such as dict.