From 0be13543406721b27437afbba29153dd240fb7bc Mon Sep 17 00:00:00 2001 From: Jamal KHAN Date: Thu, 17 Sep 2020 01:19:30 +0200 Subject: [PATCH] Exercise 3 Part 2 --- module3/exo2/chickenpox_incidence.csv | 1556 ++++++++++++++++++ module3/exo2/chickepox_timeseries.png | Bin 0 -> 41884 bytes module3/exo2/chickepox_timeseries_short.png | Bin 0 -> 34299 bytes module3/exo2/chickepox_timeseries_yearly.png | Bin 0 -> 32636 bytes module3/exo2/exercice_python_en.org | 162 +- 5 files changed, 1648 insertions(+), 70 deletions(-) create mode 100644 module3/exo2/chickenpox_incidence.csv create mode 100644 module3/exo2/chickepox_timeseries.png create mode 100644 module3/exo2/chickepox_timeseries_short.png create mode 100644 module3/exo2/chickepox_timeseries_yearly.png diff --git a/module3/exo2/chickenpox_incidence.csv b/module3/exo2/chickenpox_incidence.csv new file mode 100644 index 0000000..da43a1d --- /dev/null +++ b/module3/exo2/chickenpox_incidence.csv @@ -0,0 +1,1556 @@ +# @source="réseau 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Once opened in -Emacs, this document can easily be exported to HTML, PDF, and Office -formats. For more information on org-mode, see -https://orgmode.org/guide/. +#+BEGIN_SRC python :session *python* :results output :var data_url=data-url +data_file = 'chickenpox_incidence.csv' -When you type the shortcut =C-c C-e h o=, this document will be -exported as HTML. All the code in it will be re-executed, and the -results will be retrieved and included into the exported document. If -you do not want to re-execute all code each time, you can delete the # -and the space before ~#+PROPERTY:~ in the header of this document. +import datetime +from urllib.request import urlretrieve +import os -Like we showed in the video, Python code is included as follows (and -is exxecuted by typing ~C-c C-c~): +if not os.path.exists(data_file): + urlretrieve(data_url, data_file) -#+begin_src python :results output :exports both -print("Hello world!") -#+end_src +print(f'Data is retrieved at {datetime.datetime.utcnow()} UTC') +#+END_SRC #+RESULTS: -: Hello world! +: +: Data is retrieved at 2020-09-16 22:07:31.650075 UTC -And now the same but in an Python session. With a session, Python's -state, i.e. the values of all the variables, remains persistent from -one code block to the next. The code is still executed using ~C-c -C-c~. +Now we extract the interesting part of the data. from the format of the file The week is column 0, incidence is column 4. We took Monday as the first day of the week so '%W' code in python/pandas. -#+begin_src python :results output :session :exports both -import numpy -x=numpy.linspace(-15,15) -print(x) -#+end_src +#+BEGIN_SRC python :session *python* :results outputs :export both +import pandas as pd +data = pd.read_csv(data_file, skiprows=2, header=None) +data = data.loc[:, [0, 2]].rename(columns={0:'Datetime', 2:'Incidence'}) +data.Datetime = pd.to_datetime(data.Datetime*10+1, format='%Y%W%w') +data = data.sort_values(by='Datetime') +data = data.set_index('Datetime') +data.describe() +#+END_SRC #+RESULTS: -#+begin_example -[-15. -14.3877551 -13.7755102 -13.16326531 -12.55102041 - -11.93877551 -11.32653061 -10.71428571 -10.10204082 -9.48979592 - -8.87755102 -8.26530612 -7.65306122 -7.04081633 -6.42857143 - -5.81632653 -5.20408163 -4.59183673 -3.97959184 -3.36734694 - -2.75510204 -2.14285714 -1.53061224 -0.91836735 -0.30612245 - 0.30612245 0.91836735 1.53061224 2.14285714 2.75510204 - 3.36734694 3.97959184 4.59183673 5.20408163 5.81632653 - 6.42857143 7.04081633 7.65306122 8.26530612 8.87755102 - 9.48979592 10.10204082 10.71428571 11.32653061 11.93877551 - 12.55102041 13.16326531 13.7755102 14.3877551 15. ] -#+end_example - -Finally, an example for graphical output: -#+begin_src python :results output file :session :var matplot_lib_filename="./cosxsx.png" :exports results +: Incidence +: count 1554.000000 +: mean 12647.119691 +: std 6657.542827 +: min 161.000000 +: 25% 7326.750000 +: 50% 12627.000000 +: 75% 17155.000000 +: max 36298.000000 + +Now check for missing data. Pandas automatically handles the missing data, so we will check for na value in the dataframe. +#+BEGIN_SRC python :session *python* :results outputs :export both +data.is_na() +#+END_SRC + +#+RESULTS: + +Looks ok. Now time to plot a timeseries of the chicken pox incidence. +#+BEGIN_SRC python :session *python* :results output file :var ts_plot="chickepox_timeseries.png" :export file +import matplotlib.pyplot as plt + +fig, ax = plt.subplots(figsize=(6, 3)) +data.plot(ax=ax) +plt.savefig(ts_plot) +print(ts_plot) +#+END_SRC + +#+RESULTS: +[[file:chickepox_timeseries.png]] + +Additionally, the data starts at the beginning of 1991 and ends at the beginning of the 2020. The monthly evolution is not clear from the long timeseries. Plotting a shorter version. + +#+BEGIN_SRC python :session *python* :results output file :var ts_plot="chickepox_timeseries_short.png" :export file +import matplotlib.pyplot as plt +fig, ax = plt.subplots(figsize=(6, 3)) +data['1991-01-01':'1994-01-01'].plot(ax=ax) +plt.savefig(ts_plot) +print(ts_plot) +#+END_SRC + +#+RESULTS: +[[file:chickepox_timeseries_short.png]] + +It appears that the dip is in november. So I need to group the yearly data starting from November. + +#+BEGIN_SRC python :session *python* :results output file :var ts_plot="chickepox_timeseries_yearly.png" :export both +data_yearly = data.groupby(data.index.shift(8, freq='m').year).sum() +data_yearly = data_yearly.iloc[1:-2] import matplotlib.pyplot as plt +fig, ax = plt.subplots(figsize=(6, 3)) +data_yearly.plot(ax=ax) +plt.savefig(ts_plot) +print(data_yearly.sort_values(by='Incidence')) +print(ts_plot) +#+END_SRC -plt.figure(figsize=(10,5)) -plt.plot(x,numpy.cos(x)/x) -plt.tight_layout() +#+RESULTS: -plt.savefig(matplot_lib_filename) -print(matplot_lib_filename) -#+end_src +Plot of the aggregated values. +#+BEGIN_SRC python :session *python* :results output file :var ts_plot="chickepox_timeseries_yearly.png" :export both +import matplotlib.pyplot as plt +fig, ax = plt.subplots(figsize=(6, 3)) +data_yearly.plot(ax=ax) +plt.savefig(ts_plot) +print(ts_plot) +#+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 ~