Python export problems
post two, write in org-mode, export to .md, jekyll produces .html
this is written in org-mode
it is exported with C-c C-e h k (not the pre- org 8.0 C-c C-e m m)
python is executed in org-babel blocks file:/Users/AbuDavid/personal/literateProg/blog/gapminder.csv
(setq org-babel-python-command "python3")
import sys
print(sys.version)
some modules not installed yet
pip3 install seaborn
Requirement already up-to-date: numpy in /usr/local/lib/python3.5/site-packages
Requirement already satisfied (use --upgrade to upgrade): numpy in /usr/local/lib/python3.5/site-packages
#+END~SRC~
Results append doesn’t work, because
the summary statistics are a print statement, not a return. So, use :results output
import pandas
import numpy
import statsmodels.api as sm
import statsmodels.formula.api as smf
import seaborn as sb
# bug fix for display formats to avoid run time errors
pandas.set_option('display.float_format', lambda x:'%.2f'%x)
data = pandas.read_csv('gapminder.csv')
# convert variables to numeric format using convert_objects function
data['internetuserate'] = pandas.to_numeric(data['internetuserate'], errors='coerce')
data['urbanrate'] = pandas.to_numeric(data['urbanrate'], errors='coerce')
############################################################################################
# BASIC LINEAR REGRESSION
############################################################################################
scat1 = sb.regplot(x="urbanrate", y="internetuserate", scatter=True, data=data)
# plt.xlabel('Urbanization Rate')
# plt.ylabel('Internet Use Rate')
# plt.title ('Scatterplot for the Association Between Urban Rate and Internet Use Rate')
print(scat1)
print ("OLS regression model for the association between urban rate and internet use rate")
reg1 = smf.ols('internetuserate ~ urbanrate', data=data).fit()
print (reg1.summary())
(pasted in, can’t figure out export)
begin_example
OLS regression model for the association between urban rate and internet use rate
| OLS Regression | Results | ||
| _________ | ________ | __________ | ____ |
| Dep. Variable: | internetuserate | R-squared: | 0.377 |
| Model: | OLS | Adj. R-squared: | 0.374 |
| Method: | Least Squares | F-statistic: | 113.7 |
| Date: | Thu, 13 Oct 2016 | Prob (F-statistic): | 4.56e-21 |
| Time: | 14:03:01 | Log-Likelihood: | -856.14 |
| No. Observations: | 190 | AIC: | 1716. |
| Df Residuals: | 188 | BIC: | 1723. |
| Df Model: | 1 | ||
| Covariance Type: | nonrobust |
| . | coef | std err | t | P>t | 95.0 % | Conf.Int |
| Intercept | -4.9037 | 4.115 | -1.192 | 0.235 | -13.021 | 3.213 |
| urbanrate | 0.7202 | 0.068 | 10.665 | 0.000 | 0.587 | 0.853 |
| . | |||
| ________ | _______ | ___________ | _______ |
| Omnibus: | 10.750 | Durbin-Watson: | 2.097 |
| Prob(Omnibus): | 0.005 | Jarque-Bera (JB): | 10.990 |
| Skew: | 0.574 | Prob(JB): | 0.00411 |
| Kurtosis: | 3.262 | Cond. No. | 157. |
Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
insert some concluding remarks
todo: checkout code on http://kotfic.github.io/org-mode-export-of-matplotlib-images-etc.html it may help to get plots working from python.