[I started using Linux (Ubuntu) very recently to be able to run SageMath without problems - i.e. no idea!]
The Jupyter notebook in Ubuntu allows running SageMath, Rstats and Python all at the same time, and it works perfectly. Currently I'm using Ubuntu on WSL, and running python3. I have tried to implement the suggestions in multiple similar questions on this site and others without success.
When I use the command line I have no issues importing pandas and statsmodels and working with them (see example below).
However, if I try to run the same chunk of code from the Jupyter NB, I get the following message:
import statsmodels.api as sm
import statsmodels.stats.stattools as stools
import statsmodels.stats as stats
from statsmodels.graphics.regressionplots import *
from statsmodels.sandbox.regression.predstd import wls_prediction_std
from statsmodels.formula.api import ols
---------------------------------------------
ModuleNotFoundError Traceback (most recent call last)
/tmp/ipykernel_1044/2072660261.py in <module>
----> 1 import statsmodels.api as sm
2 import statsmodels.stats.stattools as stools
3 import statsmodels.stats as stats
4 from statsmodels.graphics.regressionplots import *
5 from statsmodels.sandbox.regression.predstd import wls_prediction_std
ModuleNotFoundError: No module named 'statsmodels'
I tried restarting the PC. Why doesn't Jupyter NB recognize statsmodels, and how can I fix this?
Example (from the command line in Ubuntu):
user@antoni:~$ python3
Python 3.10.6 (main, Mar 10 2023, 10:55:28) [GCC 11.3.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import numpy as np
t pandas as pd
import matplotlib.pyplot as plt
import statsmodels.api as sm
import statsmodels.stats.stattools as stools
import statsmodels.stats as stats
from statsmodels.graphics.regressionplots import *
from statsmodels.sandbox.regression.predstd import wls_prediction_std
from statsmodels.formula.api import ols
import io
import requests
url = "https://raw.githubusercontent.com/RInterested/datasets/gh-pages/mtcars.csv"
contents = requests.get(url).content
mtcars = pd.read_csv(io.StringIO(contents.decode('utf-8')))
mtcars['wt_square']=mtcars['wt']**2
model = ols('mpg ~ wt + wt_square', data=mtcars).fit()
print(model.summary())>>> import pandas as pd
>>> import matplotlib.pyplot as plt
>>> import statsmodels.api as sm
>>> import statsmodels.stats.stattools as stools
>>> import statsmodels.stats as stats
>>> from statsmodels.graphics.regressionplots import *
>>> from statsmodels.sandbox.regression.predstd import wls_prediction_std
>>> from statsmodels.formula.api import ols
>>> import io
>>> import requests
>>>
>>> url = "https://raw.githubusercontent.com/RInterested/datasets/gh-pages/mtcars.csv"
>>> contents = requests.get(url).content
>>> mtcars = pd.read_csv(io.StringIO(contents.decode('utf-8')))
>>> mtcars['wt_square']=mtcars['wt']**2
>>> model = ols('mpg ~ wt + wt_square', data=mtcars).fit()
>>> print(model.summary())
OLS Regression Results
==============================================================================
Dep. Variable: mpg R-squared: 0.819
Model: OLS Adj. R-squared: 0.807
Method: Least Squares F-statistic: 65.64
Date: Sat, 20 May 2023 Prob (F-statistic): 1.71e-11
Time: 13:46:59 Log-Likelihood: -75.024
No. Observations: 32 AIC: 156.0
Df Residuals: 29 BIC: 160.4
Df Model: 2
Covariance Type: nonrobust
==============================================================================
coef std err t P>|t| [0.025 0.975]
---------------------------------------------
Intercept 49.9308 4.211 11.856 0.000 41.318 58.544
wt -13.3803 2.514 -5.322 0.000 -18.522 -8.239
wt_square 1.1711 0.359 3.258 0.003 0.436 1.906
==============================================================================
Omnibus: 4.261 Durbin-Watson: 1.732
Prob(Omnibus): 0.119 Jarque-Bera (JB): 3.788
Skew: 0.832 Prob(JB): 0.150
Kurtosis: 2.731 Cond. No. 142.
==============================================================================
Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
