quizzes: teacher notebooks were still .py

Author: parmentelat

…eacher/QUIZ-1-numpy-basics-corrige-nb.py …eacher/QUIZ-1-numpy-basics-corrige-nb.mdnotebooks/quizzes/.teacher/QUIZ-1-numpy-basics-corrige-nb.py renamed to notebooks/quizzes/.teacher/QUIZ-1-numpy-basics-corrige-nb.md notebooks/quizzes/.teacher/QUIZ-1-numpy-basics-corrige-nb.py renamed to notebooks/quizzes/.teacher/QUIZ-1-numpy-basics-corrige-nb.md

@@ -1,41 +1,37 @@
-# -*- coding: utf-8 -*-
-# ---
-# jupyter:
-#   jupytext:
-#     cell_metadata_filter: all,-hidden,-heading_collapsed,-run_control,-trusted,-editable
-#     formats: py:percent
-#     notebook_metadata_filter: all, -jupytext.text_representation.jupytext_version,
-#       -jupytext.text_representation.format_version,-language_info.version, -language_info.codemirror_mode.version,
-#       -language_info.codemirror_mode,-language_info.file_extension, -language_info.mimetype,
-#       -toc, -rise, -version
-#     text_representation:
-#       extension: .py
-#       format_name: percent
-#   kernelspec:
-#     display_name: Python 3 (ipykernel)
-#     language: python
-#     name: python3
-#   language_info:
-#     name: python
-#     nbconvert_exporter: python
-#     pygments_lexer: ipython3
-#   nbhosting:
-#     title: basic numpy
-# ---
-
-# %%
+---
+jupytext:
+  text_representation:
+    extension: .md
+    format_name: myst
+  encoding: '# -*- coding: utf-8 -*-'
+kernelspec:
+  display_name: Python 3 (ipykernel)
+  language: python
+  name: python3
+language_info:
+  name: python
+  nbconvert_exporter: python
+  pygments_lexer: ipython3
+nbhosting:
+  title: basic numpy
+---
+
+```{code-cell} ipython3
 import numpy as np
 import pandas as pd
 import matplotlib.pyplot as plt
 import seaborn as sns
+```
 
-# %% [markdown]
-# # data loading
+# data loading
 
-# %% [markdown]
-# Let's load a dataset on rain precipitations on Seattle on 2014
++++
+
+Let's load a dataset on rain precipitations on Seattle on 2014
+
+```{code-cell} ipython3
+:lines_to_next_cell: 2
 
-# %%
 # we download the file from Internet and save it
 # easiest way, we can pass a URL to read_csv (or a local file)
 URL = "http://www-sop.inria.fr/members/Arnaud.Legout/formationPython/Exos/Seattle2014.csv"
@@ -54,79 +50,87 @@
 # rainfall is an array of precipitation per day 
 # for each day of 2014
 # rainfall = pd.read_csv('Seattle2014.csv')['PRCP'].to_numpy()
+```
 
+## Let's visualize
 
-# %% [markdown]
-# ## Let's visualize
++++
 
-# %% [markdown]
-# **[assignement]**: plot the amount of rain (in mm) over time; make sure you put a proper label on both axes, and on the global figure
-#
+**[assignement]**: plot the amount of rain (in mm) over time; make sure you put a proper label on both axes, and on the global figure
 
-# %%
+```{code-cell} ipython3
 # your code here
+```
+
+```{code-cell} ipython3
+:tags: []
 
-# %% tags=[]
 # prune-cell 
 
 plt.plot(rainfall)
 plt.xlabel("days")
 plt.ylabel("mm")
 plt.title("Rainy days in 2014 at Seattle")
+```
+
+## Let's answer the following questions
 
-# %% [markdown]
-# ## Let's answer the following questions
++++
 
-# %% [markdown]
-# **What is the shape and dype of the ndarray?**
+**What is the shape and dype of the ndarray?**
 
-# %%
+```{code-cell} ipython3
 # your code here
+```
 
-# %%
+```{code-cell} ipython3
 # prune-cell 
 print(f"{rainfall.shape=}\n{rainfall.dtype=}")
+```
 
-# %% [markdown]
-# **How many rainy days?**
+**How many rainy days?**
 
-# %%
+```{code-cell} ipython3
 # your code here
+```
 
-# %%
+```{code-cell} ipython3
 # prune-cell 
 rainy_days = np.sum(rainfall > 0)
 print(f"{rainy_days=} days")
+```
 
-# %% [markdown]
-# **Average precipitation on the year?**
+**Average precipitation on the year?**
 
-# %%
+```{code-cell} ipython3
 # your code here
+```
 
-# %%
+```{code-cell} ipython3
 # prune-cell 
 ave_precip_year = np.mean(rainfall)
 print(f"{ave_precip_year=:.2f}mm")
+```
 
-# %% [markdown]
-# **Average precipitation on the rainy days?**
+**Average precipitation on the rainy days?**
 
-# %%
+```{code-cell} ipython3
 # your code here
+```
 
-# %%
+```{code-cell} ipython3
 # prune-cell 
 ave_precip_rainy = np.mean(rainfall[rainfall > 0])
 print(f"{ave_precip_rainy=:.2f}mm")
+```
 
-# %% [markdown]
-# **Mean precipitation on January?**
+**Mean precipitation on January?**
 
-# %%
+```{code-cell} ipython3
 # your code here
+```
 
-# %%
+```{code-cell} ipython3
 # prune-cell 
 
 # let's build expressive masks
@@ -137,23 +141,24 @@
 # mean precipitation on January
 mean_jan = np.mean(rainfall[january])
 print(f"{mean_jan=:.2f}mm")
+```
 
-# %% [markdown]
-# **Mean precipitation on January on the rainy days?**
+**Mean precipitation on January on the rainy days?**
 
-# %%
+```{code-cell} ipython3
 # your code here
+```
 
-# %%
+```{code-cell} ipython3
 # prune-cell
 
 mean_jan_rainy = np.mean(rainfall[january & rainy])
 print(f"{mean_jan_rainy=:.2f}mm")
+```
 
-# %% [markdown]
-# # A transition to pandas
+# A transition to pandas
 
-# %%
+```{code-cell} ipython3
 # But in practice we don’t do that. Here is what we do…
 # We start to convert to a pandas Series
 s = pd.Series(rainfall)
@@ -164,11 +169,12 @@
 
 # possibly resample per month to get the total monthly rain
 s = s.resample('m').max()
+```
 
-# %%
+```{code-cell} ipython3
 # then plot
 
-# %matplotlib ipympl
+%matplotlib ipympl
 
 s.plot.bar()
 plt.xlabel('month')
@@ -177,6 +183,6 @@
 fig = plt.gcf()
 fig.autofmt_xdate()
 # plt.show() # if in a terminal
+```
 
-# %% [markdown]
-# ***
+***