module 'statsmodels.formula.api' has no attribute 'OLS'

In some version of 'statsmodels' OLS is directly available in statsmodels.api. So we don't have to use 'statsmodels.formula.api', instead of it, we can use 'statsmodels.api'. So final syntax would be like-
Code:-
import statsmodels.formula.api as sm
model_ols = sm.OLS(endog=y, exog=X).fit()

Output:-

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-2-39f3885d5ce0> in <module>
----> 1 model_ols = sm.OLS(endog=y, exog=X).fit()

AttributeError: module 'statsmodels.formula.api' has no attribute 'OLS'

How we can fix:-

import statsmodels.api as sm

model_ols = sm.OLS(endog=y, exog=X).fit()
model_ols.summary()

Output:-

OLS Regression Results

Dep. Variable:
Profit
R-squared (uncentered):
0.988
Model:
OLS
Adj. R-squared (uncentered):
0.986
Method:
Least Squares
F-statistic:
727.1
Date:
Fri, 17 Apr 2020
Prob (F-statistic):
7.87e-42
Time:
15:19:25
Log-Likelihood:
-545.15
No. Observations:
50
AIC:
1100.
Df Residuals:
45
BIC:
1110.
Df Model:
5

Covariance Type:
nonrobust



coef
std err
t
P>|t|
[0.025
0.975]
R&D Spend
0.7182
0.066
10.916
0.000
0.586
0.851
Administration
0.3113
0.035
8.885
0.000
0.241
0.382
Marketing Spend
0.0786
0.023
3.429
0.001
0.032
0.125
State_Florida
3464.4536
4905.406
0.706
0.484
-6415.541
1.33e+04
State_New York
5067.8937
4668.238
1.086
0.283
-4334.419
1.45e+04


Omnibus:
1.355
Durbin-Watson:
1.288
Prob(Omnibus):
0.508
Jarque-Bera (JB):
1.241
Skew:
-0.237
Prob(JB):
0.538
Kurtosis:
2.391
Cond. No.
8.28e+05

Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[2] The condition number is large, 8.28e+05. This might indicate that there are
strong multicollinearity or other numerical problems.