#+TITLE: CO2 concentration in the atmosphere since 1958
#+AUTHOR: LOAN TRUONG
#+DATE: 23 Mar
#+LANGUAGE: en
# #+PROPERTY: header-args :eval never-export
#+HTML_HEAD:
#+HTML_HEAD:
#+HTML_HEAD:
#+HTML_HEAD:
#+HTML_HEAD:
#+HTML_HEAD:
import pandas as pd
import plotly.offline as py
import plotly.graph_objs as go
import datetime as dt
py.init_notebook_mode(connected=True)
co2data = pd.read_csv("module3/exo3/monthly_in_situ_co2_mlo.csv")
co2data.head(11)
Finally, an example for graphical output:
#+begin_src python :results output file :session :var matplot_lib_filename="./cosxsx.png" :exports results
import matplotlib.pyplot as plt
plt.figure(figsize=(10,5))
plt.plot(x,numpy.cos(x)/x)
plt.tight_layout()
plt.savefig(matplot_lib_filename)
print(matplot_lib_filename)
#+end_src
#+RESULTS:
[[file:./cosxsx.png]]
Note the parameter ~:exports results~, which indicates that the code
will not appear in the exported document. We recommend that in the
context of this MOOC, you always leave this parameter setting as
~:exports both~, because we want your analyses to be perfectly
transparent and reproducible.
Watch out: the figure generated by the code block is /not/ stored in
the org document. It's a plain file, here named ~cosxsx.png~. You have
to commit it explicitly if you want your analysis to be legible and
understandable on GitLab.
Finally, don't forget that we provide in the resource section of this
MOOC a configuration with a few keyboard shortcuts that allow you to
quickly create code blocks in Python by typing ~