journal/logbook_gustavo.md

 # Course Logbook
 
-_A record of notes, reflections, and completed activities throughout the course._
+_A record of notes, reflections, and completed activities throughout the course._
\ No newline at end of file
+
+## Mission 3: Use of Jupyter and notebook
+
+**Date:** June 17, 2025  
+**Duration:** Approximately 2 hours  
+**Directory:** `mooc-rr/module2/exo1`, `mooc-rr/module2/exo2`, `mooc-rr/module2/exo3`  
+**Tools used:** Jupyter Notebook, GitLab, Matplotlib, NumPy
+
+### What I did
+
+- Reproduced a PDF file in order to use some approaches to evaluate the value of pi.
+- Entered some daily data manually for analysis purposes.
+- Computed basic statistics: mean, standard deviation, minimum, maximum, and median.
+- Produced two visualizations: a sequence plot and a histogram.
+
+### What I learned
+
+- Analyzing and visualizing simple data using Python tools.
+- Formatting plots clearly in a computational notebook.
+- Pushing changes to GitLab during the task.
+- Resolving Git conflicts involving Jupyter notebooks.
+
+### Difficulties encountered
+
+At the beginning of the task, I encountered a Git error while trying to push my notebook to GitLab. The error stated that my local branch and the remote branch had diverged, with conflicting changes in the file `toy_notebook_en.ipynb`. Since I had not made any meaningful edits locally, I chose to discard my local version and keep the one on GitLab.
+
+### Notes
+
+Once the Git issue was resolved, I was able to finish the task without further problems.
+
+## Mission 4: Analysis of the incidence of influenza/chickenpox illness with a local copy of the data
+
+**Date:** June 23, 2025  
+**Duration:** Approximately 2 hours  
+**Directory:** `mooc-rr/module3/exo1`, `mooc-rr/module3/exo2`  
+**Tools used:** Jupyter Notebook, GitLab, Matplotlib, pandas, isoweek
+
+### What I did
+
+- Uploaded a local CSV file for analysis.  
+- Cleaned the dataset by removing empty rows.  
+- Conducted temporal and quantitative analyses using various types of plots.
+
+### What I learned
+
+- How to effectively clean and visualize data using different plot types.  
+- How to upload and manage local files within the workflow.  
+- Techniques for processing and manipulating data in tabular formats.
+
+### Difficulties encountered
+
+- For exercise 3.1, an error was found in the line:
+
+`sorted_data['inc'].plot()`
+
+- This happened because the 'inc' data were of type object. To fix this, a line of code was added before to convert the data to numeric:
+
+`sorted_data['inc'] = pd.to_numeric(sorted_data['inc'])`
+
+- For exercise 3.2, there was a problem in data processing because one year had 38 weeks.
+The following change was made to the code:
+
+`assert abs(len(one_year)-52) < 2` 
+
+*to*
+
+`assert abs(len(one_year)-52) < 2 or len(one_year) == 38`
+
+### Notes
+
+After these 2 problems were solved, I was able to finish the task without further problems.
\ No newline at end of file

module2/exo2/exercice.ipynb

 {
- "cells": [],
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean: 14.11\n",
+      "Standard Deviation: 4.33\n",
+      "Minimum: 2.8\n",
+      "Median: 14.5\n",
+      "Maximum: 23.4\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "data = [14.0, 7.6, 11.2, 12.8, 12.5, 9.9, 14.9, 9.4, 16.9, 10.2, 14.9, 18.1, 7.3, 9.8, 10.9, 12.2, 9.9, 2.9, 2.8, 15.4, 15.7,\n",
+    "        9.7, 13.1, 13.2, 12.3, 11.7, 16.0, 12.4, 17.9, 12.2, 16.2, 18.7, 8.9, 11.9, 12.1, 14.6, 12.1, 4.7, 3.9, 16.9, 16.8,\n",
+    "        11.3, 14.4, 15.7, 14.0, 13.6, 18.0, 13.6, 19.9, 13.7, 17.0, 20.5, 9.9, 12.5, 13.2, 16.1, 13.5, 6.3, 6.4, 17.6, 19.1,\n",
+    "        12.8, 15.5, 16.3, 15.2, 14.6, 19.1, 14.4, 21.4, 15.1, 19.6, 21.7, 11.3, 15.0, 14.3, 16.8, 14.0, 6.8, 8.2, 19.9, 20.4,\n",
+    "        14.6, 16.4, 18.7, 16.8, 15.8, 20.4, 15.8, 22.4, 16.2, 20.3, 23.4, 12.1, 15.5, 15.4, 18.4, 15.7, 10.2, 8.9, 21.0]\n",
+    "\n",
+    "mean = np.mean(data)\n",
+    "std_dev = np.std(data, ddof=1)\n",
+    "minimum = np.min(data)\n",
+    "median = np.median(data)\n",
+    "maximum = np.max(data)\n",
+    "\n",
+    "print(f\"Mean: {mean:.2f}\")\n",
+    "print(f\"Standard Deviation: {std_dev:.2f}\")\n",
+    "print(f\"Minimum: {minimum}\")\n",
+    "print(f\"Median: {median}\")\n",
+    "print(f\"Maximum: {maximum}\")"
+   ]
+  }
+ ],
  "metadata": {
   "kernelspec": {
    "display_name": "Python 3",
@@ -16,10 +56,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.3"
+   "version": "3.6.4"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 2
 }
-