From 31e82fb19651cb9ac72199b33123c46a9b48f48a Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Fran=C3=A7ois=20Laurent?= <francois.laurent@posteo.net>
Date: Wed, 29 Sep 2021 16:53:18 +0200
Subject: [PATCH] live corrections
---
notebooks/matplotlib_TP_solutions.ipynb | 28 +-
notebooks/pandas_TP_solution.ipynb | 6 +-
notebooks/scipy_TP_solutions.ipynb | 588 ++++++++++++++++++------
notebooks/scipy_cours.ipynb | 374 ++++++++-------
notebooks/seaborn_TP.ipynb | 6 +-
notebooks/seaborn_TP_solutions.ipynb | 6 +-
notebooks/seaborn_cours.ipynb | 6 +-
7 files changed, 696 insertions(+), 318 deletions(-)
diff --git a/notebooks/matplotlib_TP_solutions.ipynb b/notebooks/matplotlib_TP_solutions.ipynb
index d5b36b7..e84c45e 100644
--- a/notebooks/matplotlib_TP_solutions.ipynb
+++ b/notebooks/matplotlib_TP_solutions.ipynb
@@ -19,7 +19,7 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": 2,
"id": "8db20ce6-572a-4bd9-ab90-7e2b00b4c27c",
"metadata": {},
"outputs": [],
@@ -572,7 +572,7 @@
},
{
"cell_type": "code",
- "execution_count": 21,
+ "execution_count": 3,
"id": "7d937154-5e81-4ffc-a110-82755ca4efa4",
"metadata": {},
"outputs": [
@@ -580,8 +580,8 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "4.9112441705122984\n",
- "3.0821900958177255\n"
+ "5.000318501712216\n",
+ "2.9242256666061213\n"
]
}
],
@@ -601,7 +601,7 @@
},
{
"cell_type": "code",
- "execution_count": 22,
+ "execution_count": 4,
"id": "726c4bb3-b4e1-4cfd-ab6b-5c5297bafc17",
"metadata": {},
"outputs": [
@@ -609,8 +609,8 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "-8.881784197001253e-17\n",
- "0.9999999999999999\n"
+ "2.806643806252396e-16\n",
+ "1.0\n"
]
}
],
@@ -630,23 +630,23 @@
},
{
"cell_type": "code",
- "execution_count": 23,
+ "execution_count": 5,
"id": "2a3a5a0e-97a5-40df-935a-6e45a2326894",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "<matplotlib.legend.Legend at 0x7f48794fefa0>"
+ "<matplotlib.legend.Legend at 0x7f56337a6790>"
]
},
- "execution_count": 23,
+ "execution_count": 5,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
- "image/png": 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\n",
+ "image/png": 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\n",
"text/plain": [
"<Figure size 864x432 with 1 Axes>"
]
@@ -659,8 +659,8 @@
],
"source": [
"fig = plt.figure(figsize=(12,6))\n",
- "_ = plt.hist(data, color='blue', alpha=0.5, bins=75, ec='k', label='data')\n",
- "_ = plt.hist(d_std, color='orange', alpha=0.5, bins=75, ec='k', label='standardized data')\n",
+ "_, bins, _= plt.hist(data, color='blue', alpha=0.5, bins=75, ec='k', label='data')\n",
+ "_ = plt.hist(d_std, color='orange', alpha=0.5, bins=bins, ec='k', label='standardized data')\n",
"plt.legend()"
]
},
@@ -1049,7 +1049,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.8.12"
+ "version": "3.8.10"
}
},
"nbformat": 4,
diff --git a/notebooks/pandas_TP_solution.ipynb b/notebooks/pandas_TP_solution.ipynb
index 42fb09a..115f796 100644
--- a/notebooks/pandas_TP_solution.ipynb
+++ b/notebooks/pandas_TP_solution.ipynb
@@ -2911,9 +2911,9 @@
],
"metadata": {
"kernelspec": {
- "display_name": "dev",
+ "display_name": "Python 3 (ipykernel)",
"language": "python",
- "name": "dev"
+ "name": "python3"
},
"language_info": {
"codemirror_mode": {
@@ -2925,7 +2925,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.9.5"
+ "version": "3.8.10"
}
},
"nbformat": 4,
diff --git a/notebooks/scipy_TP_solutions.ipynb b/notebooks/scipy_TP_solutions.ipynb
index 9d54cb7..f1cd821 100644
--- a/notebooks/scipy_TP_solutions.ipynb
+++ b/notebooks/scipy_TP_solutions.ipynb
@@ -335,7 +335,7 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": 5,
"id": "2f0a8116",
"metadata": {
"hidden": true
@@ -547,35 +547,81 @@
" <td>40</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>4</th>\n",
- " <td>21.17</td>\n",
- " <td>No</td>\n",
- " <td>0.5</td>\n",
+ " <th>...</th>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>814</th>\n",
+ " <td>32.75</td>\n",
+ " <td>Yes</td>\n",
+ " <td>2.5</td>\n",
" <td>Female</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
- " <td>94.1796</td>\n",
- " <td>18.68</td>\n",
+ " <td>86.6744</td>\n",
+ " <td>20.07</td>\n",
" <td>No</td>\n",
- " <td>0.404886</td>\n",
+ " <td>0.252037</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
- " <td>2.0</td>\n",
- " <td>10.00</td>\n",
+ " <td>1.0</td>\n",
+ " <td>8.00</td>\n",
" <td>3</td>\n",
" <td>No</td>\n",
- " <td>No</td>\n",
+ " <td>Yes</td>\n",
" <td>Never</td>\n",
" <td>No</td>\n",
- " <td>PhD</td>\n",
+ " <td>Baccalaureat</td>\n",
" <td>No</td>\n",
- " <td>(3000-inf]</td>\n",
+ " <td>(1000-2000]</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
- " <td>Yes</td>\n",
" <td>No</td>\n",
+ " <td>Yes</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
@@ -585,122 +631,196 @@
" <td>No</td>\n",
" <td>Yes</td>\n",
" <td>No</td>\n",
- " <td>5</td>\n",
- " <td>1.0</td>\n",
- " <td>64</td>\n",
- " <td>36.0</td>\n",
- " <td>9.883</td>\n",
- " <td>40</td>\n",
+ " <td>5279</td>\n",
+ " <td>3.0</td>\n",
+ " <td>62</td>\n",
+ " <td>36.3</td>\n",
+ " <td>9.800</td>\n",
+ " <td>278</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>5</th>\n",
- " <td>26.17</td>\n",
+ " <th>815</th>\n",
+ " <td>39.17</td>\n",
+ " <td>No</td>\n",
+ " <td>4.0</td>\n",
+ " <td>Male</td>\n",
+ " <td>No</td>\n",
" <td>Yes</td>\n",
- " <td>1.5</td>\n",
- " <td>Female</td>\n",
+ " <td>Yes</td>\n",
+ " <td>98.9744</td>\n",
+ " <td>22.77</td>\n",
+ " <td>Yes</td>\n",
+ " <td>0.264940</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>7.50</td>\n",
+ " <td>0</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>Never</td>\n",
+ " <td>Yes</td>\n",
+ " <td>PhD</td>\n",
+ " <td>No</td>\n",
+ " <td>(2000-3000]</td>\n",
+ " <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>Yes</td>\n",
- " <td>105.1250</td>\n",
- " <td>29.01</td>\n",
" <td>No</td>\n",
- " <td>-0.303782</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>Yes</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>5303</td>\n",
+ " <td>0.0</td>\n",
+ " <td>39</td>\n",
+ " <td>36.3</td>\n",
+ " <td>9.583</td>\n",
+ " <td>277</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>816</th>\n",
+ " <td>59.00</td>\n",
+ " <td>No</td>\n",
+ " <td>0.0</td>\n",
+ " <td>Male</td>\n",
+ " <td>Yes</td>\n",
+ " <td>Yes</td>\n",
+ " <td>Yes</td>\n",
+ " <td>84.6433</td>\n",
+ " <td>27.39</td>\n",
+ " <td>Yes</td>\n",
+ " <td>0.281522</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
- " <td>1.0</td>\n",
- " <td>9.00</td>\n",
+ " <td>0.0</td>\n",
+ " <td>8.00</td>\n",
" <td>0</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>Never</td>\n",
- " <td>Yes</td>\n",
+ " <td>No</td>\n",
" <td>Baccalaureat</td>\n",
" <td>No</td>\n",
- " <td>[0-1000]</td>\n",
+ " <td>(2000-3000]</td>\n",
+ " <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
+ " <td>Yes</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
- " <td>Yes</td>\n",
" <td>No</td>\n",
- " <td>Yes</td>\n",
" <td>No</td>\n",
- " <td>8</td>\n",
+ " <td>5701</td>\n",
" <td>0.0</td>\n",
- " <td>67</td>\n",
- " <td>36.7</td>\n",
- " <td>8.550</td>\n",
- " <td>81</td>\n",
+ " <td>61</td>\n",
+ " <td>36.1</td>\n",
+ " <td>9.317</td>\n",
+ " <td>241</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
+ "<p>816 rows × 43 columns</p>\n",
"</div>"
],
"text/plain": [
- " Age OwnsHouse PhysicalActivity Sex LivesWithPartner LivesWithKids \\\n",
- "1 22.33 Yes 3.0 Female No No \n",
- "2 28.83 Yes 0.0 Female Yes No \n",
- "3 23.67 Yes 0.0 Female Yes No \n",
- "4 21.17 No 0.5 Female No No \n",
- "5 26.17 Yes 1.5 Female No No \n",
+ " Age OwnsHouse PhysicalActivity Sex LivesWithPartner LivesWithKids \\\n",
+ "1 22.33 Yes 3.0 Female No No \n",
+ "2 28.83 Yes 0.0 Female Yes No \n",
+ "3 23.67 Yes 0.0 Female Yes No \n",
+ ".. ... ... ... ... ... ... \n",
+ "814 32.75 Yes 2.5 Female No No \n",
+ "815 39.17 No 4.0 Male No Yes \n",
+ "816 59.00 No 0.0 Male Yes Yes \n",
"\n",
- " BornInCity Inbreeding BMI CMVPositiveSerology FluIgG MetabolicScore \\\n",
- "1 Yes 94.9627 20.13 No 0.464319 0 \n",
- "2 Yes 79.1024 21.33 Yes -0.049817 1 \n",
- "3 Yes 117.2540 22.18 No 0.332944 2 \n",
- "4 No 94.1796 18.68 No 0.404886 0 \n",
- "5 Yes 105.1250 29.01 No -0.303782 1 \n",
+ " BornInCity Inbreeding BMI CMVPositiveSerology FluIgG \\\n",
+ "1 Yes 94.9627 20.13 No 0.464319 \n",
+ "2 Yes 79.1024 21.33 Yes -0.049817 \n",
+ "3 Yes 117.2540 22.18 No 0.332944 \n",
+ ".. ... ... ... ... ... \n",
+ "814 No 86.6744 20.07 No 0.252037 \n",
+ "815 Yes 98.9744 22.77 Yes 0.264940 \n",
+ "816 Yes 84.6433 27.39 Yes 0.281522 \n",
"\n",
- " LowAppetite TroubleConcentrating TroubleSleeping HoursOfSleep Listless \\\n",
- "1 0 0 1.0 9.00 3 \n",
- "2 0 0 1.0 7.05 3 \n",
- "3 0 0 1.0 6.50 3 \n",
- "4 0 0 2.0 10.00 3 \n",
- "5 0 0 1.0 9.00 0 \n",
+ " MetabolicScore LowAppetite TroubleConcentrating TroubleSleeping \\\n",
+ "1 0 0 0 1.0 \n",
+ "2 1 0 0 1.0 \n",
+ "3 2 0 0 1.0 \n",
+ ".. ... ... ... ... \n",
+ "814 0 0 0 1.0 \n",
+ "815 0 0 0 2.0 \n",
+ "816 1 0 0 0.0 \n",
"\n",
- " UsesCannabis RecentPersonalCrisis Smoking Employed Education \\\n",
- "1 No No Never No PhD \n",
- "2 No No Active Yes Baccalaureat \n",
- "3 Yes No Active Yes Baccalaureat \n",
- "4 No No Never No PhD \n",
- "5 No No Never Yes Baccalaureat \n",
+ " HoursOfSleep Listless UsesCannabis RecentPersonalCrisis Smoking \\\n",
+ "1 9.00 3 No No Never \n",
+ "2 7.05 3 No No Active \n",
+ "3 6.50 3 Yes No Active \n",
+ ".. ... ... ... ... ... \n",
+ "814 8.00 3 No Yes Never \n",
+ "815 7.50 0 No No Never \n",
+ "816 8.00 0 No No Never \n",
"\n",
- " DustExposure Income HadMeasles HadRubella HadChickenPox HadMumps \\\n",
- "1 No (1000-2000] No No Yes No \n",
- "2 No (2000-3000] No No Yes No \n",
- "3 Current (2000-3000] No No Yes No \n",
- "4 No (3000-inf] No No Yes No \n",
- "5 No [0-1000] No No No No \n",
+ " Employed Education DustExposure Income HadMeasles HadRubella \\\n",
+ "1 No PhD No (1000-2000] No No \n",
+ "2 Yes Baccalaureat No (2000-3000] No No \n",
+ "3 Yes Baccalaureat Current (2000-3000] No No \n",
+ ".. ... ... ... ... ... ... \n",
+ "814 No Baccalaureat No (1000-2000] No No \n",
+ "815 Yes PhD No (2000-3000] No No \n",
+ "816 No Baccalaureat No (2000-3000] No No \n",
"\n",
- " HadTonsillectomy HadAppendicectomy VaccineHepA VaccineMMR VaccineTyphoid \\\n",
- "1 No No No No No \n",
- "2 No No No No No \n",
- "3 No No No No No \n",
- "4 No No No No No \n",
- "5 No No No No No \n",
+ " HadChickenPox HadMumps HadTonsillectomy HadAppendicectomy VaccineHepA \\\n",
+ "1 Yes No No No No \n",
+ "2 Yes No No No No \n",
+ "3 Yes No No No No \n",
+ ".. ... ... ... ... ... \n",
+ "814 No Yes No No No \n",
+ "815 No Yes No No No \n",
+ "816 No No Yes No No \n",
"\n",
- " VaccineWhoopingCough VaccineYellowFever VaccineHepB VaccineFlu SUBJID \\\n",
- "1 Yes No Yes No 2 \n",
- "2 Yes No Yes No 3 \n",
- "3 No No Yes No 4 \n",
- "4 No No Yes No 5 \n",
- "5 Yes No Yes No 8 \n",
+ " VaccineMMR VaccineTyphoid VaccineWhoopingCough VaccineYellowFever \\\n",
+ "1 No No Yes No \n",
+ "2 No No Yes No \n",
+ "3 No No No No \n",
+ ".. ... ... ... ... \n",
+ "814 No No No No \n",
+ "815 Yes No No No \n",
+ "816 No No No No \n",
"\n",
- " DepressionScore HeartRate Temperature HourOfSampling DayOfSampling \n",
- "1 0.0 66 36.8 8.883 40 \n",
- "2 0.0 66 37.4 9.350 40 \n",
- "3 0.0 62 36.9 8.667 40 \n",
- "4 1.0 64 36.0 9.883 40 \n",
- "5 0.0 67 36.7 8.550 81 "
+ " VaccineHepB VaccineFlu SUBJID DepressionScore HeartRate Temperature \\\n",
+ "1 Yes No 2 0.0 66 36.8 \n",
+ "2 Yes No 3 0.0 66 37.4 \n",
+ "3 Yes No 4 0.0 62 36.9 \n",
+ ".. ... ... ... ... ... ... \n",
+ "814 Yes No 5279 3.0 62 36.3 \n",
+ "815 No No 5303 0.0 39 36.3 \n",
+ "816 No No 5701 0.0 61 36.1 \n",
+ "\n",
+ " HourOfSampling DayOfSampling \n",
+ "1 8.883 40 \n",
+ "2 9.350 40 \n",
+ "3 8.667 40 \n",
+ ".. ... ... \n",
+ "814 9.800 278 \n",
+ "815 9.583 277 \n",
+ "816 9.317 241 \n",
+ "\n",
+ "[816 rows x 43 columns]"
]
},
- "execution_count": 4,
+ "execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
@@ -708,7 +828,8 @@
"source": [
"# in Jupyter-lab, pandas is set to display dataframes with a limited number of columns\n",
"pd.options.display.max_columns = None\n",
- "df.head()"
+ "pd.options.display.max_rows = 6\n",
+ "df"
]
},
{
@@ -1059,15 +1180,15 @@
"name": "stdout",
"output_type": "stream",
"text": [
- " _________________________________________\r\n",
- "/ Where on Earth ALL younger people own a \\\r\n",
- "\\ house while elder people do not? /\r\n",
- " -----------------------------------------\r\n",
- " \\ ^__^\r\n",
- " \\ (oo)\\_______\r\n",
- " (__)\\ )\\/\\\r\n",
- " ||----w |\r\n",
- " || ||\r\n"
+ " _________________________________________\n",
+ "/ Where on Earth ALL younger people own a \\\n",
+ "\\ house while elder people do not? /\n",
+ " -----------------------------------------\n",
+ " \\ ^__^\n",
+ " \\ (oo)\\_______\n",
+ " (__)\\ )\\/\\\n",
+ " ||----w |\n",
+ " || ||\n"
]
}
],
@@ -1099,7 +1220,7 @@
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": 7,
"id": "55d18f16",
"metadata": {
"hidden": true
@@ -1115,7 +1236,7 @@
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": 8,
"id": "3d1a44e6",
"metadata": {
"hidden": true
@@ -1150,7 +1271,7 @@
},
{
"cell_type": "code",
- "execution_count": 29,
+ "execution_count": 9,
"id": "1fca5a60",
"metadata": {
"hidden": true
@@ -1163,7 +1284,7 @@
},
{
"cell_type": "code",
- "execution_count": 31,
+ "execution_count": 10,
"id": "9f044f06",
"metadata": {
"hidden": true
@@ -1188,6 +1309,29 @@
" print(f'{group_name}: {m:.2f} ± {z_times_sem:.2f} years old on average')"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "id": "9613dc45-cdac-4932-b646-d36965d2d4e6",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(37.56221005578753, 42.072095499768025)"
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "mean = np.mean(house_owners_age)\n",
+ "sem = stats.sem(house_owners_age)\n",
+ "stats.norm(mean, sem).interval(.99)"
+ ]
+ },
{
"cell_type": "markdown",
"id": "ea79970d",
@@ -1212,7 +1356,30 @@
},
{
"cell_type": "code",
- "execution_count": 37,
+ "execution_count": 13,
+ "id": "6ec85793-d159-47ac-bf2e-d28778dbb865",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "stats.probplot(house_owners_age, fit=True, plot=plt);"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
"id": "ddf5d4b0",
"metadata": {
"hidden": true
@@ -1239,6 +1406,29 @@
"plt.ylabel('ordered observations (age)');"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "id": "bc84e35b-fb1b-456c-9ce5-82cd77efe4a0",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "3"
+ ]
+ },
+ "execution_count": 18,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "a = ((1, None, 3), 'odfh', dict())\n",
+ "(e, f, g), c, d = a\n",
+ "g"
+ ]
+ },
{
"cell_type": "markdown",
"id": "24b49c4c",
@@ -1266,7 +1456,7 @@
},
{
"cell_type": "code",
- "execution_count": 35,
+ "execution_count": 28,
"id": "0f888c53",
"metadata": {
"hidden": true
@@ -1292,6 +1482,50 @@
"plt.axline((0, a), slope=b, color='r');"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "id": "f976e7bb-9713-4e6c-8853-e226a3f8c5d0",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([-0.98673271, 0.98673271])"
+ ]
+ },
+ "execution_count": 26,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "i = np.flatnonzero(theoretical_quantiles>-1)[0]\n",
+ "j = np.flatnonzero(theoretical_quantiles<=1)[-1]\n",
+ "theoretical_quantiles[i:j+1][[0,-1]]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "id": "3e95412d-2d79-4c42-81d1-e6dd21499d0c",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([-0.98673271, 0.98673271])"
+ ]
+ },
+ "execution_count": 29,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "theoretical_quantiles[central_part][[0,-1]]"
+ ]
+ },
{
"cell_type": "markdown",
"id": "7981096d",
@@ -1604,7 +1838,7 @@
},
{
"cell_type": "code",
- "execution_count": 16,
+ "execution_count": 36,
"id": "81e31c08",
"metadata": {
"hidden": true
@@ -1620,7 +1854,7 @@
},
{
"cell_type": "code",
- "execution_count": 17,
+ "execution_count": 37,
"id": "2ae175e5",
"metadata": {
"hidden": true
@@ -1656,7 +1890,76 @@
},
{
"cell_type": "code",
- "execution_count": 18,
+ "execution_count": 34,
+ "id": "37b3e673-be8b-4f6d-92ca-ee13c5a7eca7",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "1"
+ ]
+ },
+ "execution_count": 34,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "from collections import defaultdict\n",
+ "a = defaultdict(lambda: 0)\n",
+ "a['Student'] += 1\n",
+ "a['Student']"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 39,
+ "id": "420b4aea-7d0f-4bb5-8e33-ddac0af11981",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "sample_sizes = []\n",
+ "test_types = []\n",
+ "rejection_rates = []\n",
+ "\n",
+ "rng = np.random.default_rng()\n",
+ "\n",
+ "for relative_sample_size in (1, .2, .1, .05, .025):\n",
+ " n = int(relative_sample_size * sample_size)\n",
+ " nreplicates = 100\n",
+ " nStudent = nWelch = 0\n",
+ " for _ in range(nreplicates):\n",
+ " subsample1 = rng.choice(sample1, n)\n",
+ " subsample2 = rng.choice(sample2, n)\n",
+ " \n",
+ " t, pv = stats.ttest_ind(subsample1, subsample2, equal_var=True)\n",
+ " if pv<=significance_level:\n",
+ " nStudent += 1\n",
+ " \n",
+ " t, pv = stats.ttest_ind(subsample1, subsample2, equal_var=False)\n",
+ " if pv<=significance_level:\n",
+ " nWelch += 1\n",
+ " \n",
+ " rejection_rates += [nStudent / nreplicates, nWelch / nreplicates]\n",
+ " sample_sizes += [n, n]\n",
+ " test_types += ['Student', 'Welch']"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 40,
+ "id": "dc4fc10a-efa4-46c3-8c86-410f46b290c6",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "\n",
+ "result = pd.DataFrame({'sample size': sample_sizes, 'test': test_types, 'power': rejection_rates})"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 42,
"id": "d15c23da",
"metadata": {
"hidden": true
@@ -1705,49 +2008,49 @@
" <th>2</th>\n",
" <td>57</td>\n",
" <td>Student</td>\n",
- " <td>1.00</td>\n",
+ " <td>0.97</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>57</td>\n",
" <td>Welch</td>\n",
- " <td>1.00</td>\n",
+ " <td>0.97</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>28</td>\n",
" <td>Student</td>\n",
- " <td>0.76</td>\n",
+ " <td>0.75</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>28</td>\n",
" <td>Welch</td>\n",
- " <td>0.76</td>\n",
+ " <td>0.75</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>14</td>\n",
" <td>Student</td>\n",
- " <td>0.51</td>\n",
+ " <td>0.46</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>14</td>\n",
" <td>Welch</td>\n",
- " <td>0.51</td>\n",
+ " <td>0.46</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>7</td>\n",
" <td>Student</td>\n",
- " <td>0.29</td>\n",
+ " <td>0.18</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>7</td>\n",
" <td>Welch</td>\n",
- " <td>0.29</td>\n",
+ " <td>0.16</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
@@ -1757,22 +2060,23 @@
" sample size test power\n",
"0 288 Student 1.00\n",
"1 288 Welch 1.00\n",
- "2 57 Student 1.00\n",
- "3 57 Welch 1.00\n",
- "4 28 Student 0.76\n",
- "5 28 Welch 0.76\n",
- "6 14 Student 0.51\n",
- "7 14 Welch 0.51\n",
- "8 7 Student 0.29\n",
- "9 7 Welch 0.29"
+ "2 57 Student 0.97\n",
+ "3 57 Welch 0.97\n",
+ "4 28 Student 0.75\n",
+ "5 28 Welch 0.75\n",
+ "6 14 Student 0.46\n",
+ "7 14 Welch 0.46\n",
+ "8 7 Student 0.18\n",
+ "9 7 Welch 0.16"
]
},
- "execution_count": 18,
+ "execution_count": 42,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
+ "pd.options.display.max_rows = 20\n",
"result"
]
},
@@ -1808,7 +2112,7 @@
},
{
"cell_type": "code",
- "execution_count": 19,
+ "execution_count": 44,
"id": "0aeaeee7",
"metadata": {},
"outputs": [
@@ -1854,7 +2158,7 @@
},
{
"cell_type": "code",
- "execution_count": 20,
+ "execution_count": 45,
"id": "e9c1cc3c",
"metadata": {
"hidden": true
@@ -1866,7 +2170,7 @@
"(47.758187772925766, 44.85779329608938, 16.298908849529322, 9.611832029475966)"
]
},
- "execution_count": 20,
+ "execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
@@ -1892,7 +2196,7 @@
},
{
"cell_type": "code",
- "execution_count": 21,
+ "execution_count": 46,
"id": "b6bfdb58",
"metadata": {
"hidden": true
@@ -1979,7 +2283,7 @@
},
{
"cell_type": "code",
- "execution_count": 22,
+ "execution_count": 47,
"id": "f57a8ff6",
"metadata": {
"hidden": true
@@ -1992,7 +2296,7 @@
" array([ 2, 12, 44, 67, 65, 53, 57, 34, 16, 8]))"
]
},
- "execution_count": 22,
+ "execution_count": 47,
"metadata": {},
"output_type": "execute_result"
}
@@ -2016,7 +2320,7 @@
},
{
"cell_type": "code",
- "execution_count": 23,
+ "execution_count": 48,
"id": "de19e80b",
"metadata": {
"hidden": true
@@ -2028,7 +2332,7 @@
"816"
]
},
- "execution_count": 23,
+ "execution_count": 48,
"metadata": {},
"output_type": "execute_result"
}
@@ -2071,6 +2375,28 @@
"print(f'χ²({dof}) = {chi2:.1f}, p-value = {pvalue:.3g}')"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": 51,
+ "id": "fbdbb32d-6595-40dd-88e4-8f6bb2947e71",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([[ 2, 12, 44, 67, 65, 53, 57, 34, 16, 8],\n",
+ " [75, 32, 26, 23, 22, 30, 39, 56, 94, 61]])"
+ ]
+ },
+ "execution_count": 51,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "np.stack((lives_with_kids_freqs, lives_without_kids_freqs), axis=0)"
+ ]
+ },
{
"cell_type": "markdown",
"id": "218a59ec",
diff --git a/notebooks/scipy_cours.ipynb b/notebooks/scipy_cours.ipynb
index 0f98bfd..fcc8fb5 100644
--- a/notebooks/scipy_cours.ipynb
+++ b/notebooks/scipy_cours.ipynb
@@ -5,9 +5,6 @@
"execution_count": 1,
"id": "8ccafd3d",
"metadata": {
- "jupyter": {
- "source_hidden": true
- },
"tags": []
},
"outputs": [
@@ -48,7 +45,7 @@
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": 138,
"id": "42342d74",
"metadata": {},
"outputs": [],
@@ -167,7 +164,8 @@
"cell_type": "markdown",
"id": "f1be90cf",
"metadata": {
- "heading_collapsed": true
+ "heading_collapsed": true,
+ "tags": []
},
"source": [
"## What Python can do -- What Python cannot"
@@ -203,7 +201,8 @@
"id": "23c92344",
"metadata": {
"heading_collapsed": true,
- "hidden": true
+ "hidden": true,
+ "tags": []
},
"source": [
"### Workflow"
@@ -291,7 +290,8 @@
"cell_type": "markdown",
"id": "d6b0c2ee-4f6b-49db-8c26-dd64aede72fb",
"metadata": {
- "heading_collapsed": true
+ "heading_collapsed": true,
+ "tags": []
},
"source": [
"## Data exploration"
@@ -1079,7 +1079,8 @@
"id": "700ff9a1-e4e9-4ef5-9559-f6d6740977ed",
"metadata": {
"heading_collapsed": true,
- "hidden": true
+ "hidden": true,
+ "tags": []
},
"source": [
"### Confidence intervals"
@@ -1099,10 +1100,14 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 139,
"id": "e60ba8f3",
"metadata": {
- "hidden": true
+ "hidden": true,
+ "jupyter": {
+ "source_hidden": true
+ },
+ "tags": []
},
"outputs": [
{
@@ -1183,7 +1188,7 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": 141,
"id": "7e3b5520",
"metadata": {
"hidden": true
@@ -1197,7 +1202,7 @@
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": 142,
"id": "d6bbb149",
"metadata": {
"hidden": true
@@ -1209,7 +1214,7 @@
"0.945200708300442"
]
},
- "execution_count": 8,
+ "execution_count": 142,
"metadata": {},
"output_type": "execute_result"
}
@@ -1221,7 +1226,7 @@
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": 143,
"id": "1341b01d",
"metadata": {
"hidden": true
@@ -1233,7 +1238,7 @@
"0.011092083467945555"
]
},
- "execution_count": 9,
+ "execution_count": 143,
"metadata": {},
"output_type": "execute_result"
}
@@ -1261,7 +1266,7 @@
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": 76,
"id": "62f75d44",
"metadata": {
"hidden": true
@@ -1273,7 +1278,7 @@
"1.9599639845400545"
]
},
- "execution_count": 10,
+ "execution_count": 76,
"metadata": {},
"output_type": "execute_result"
}
@@ -1298,7 +1303,7 @@
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": 77,
"id": "a2a23163",
"metadata": {
"hidden": true
@@ -1321,7 +1326,7 @@
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": 78,
"id": "99fe274d",
"metadata": {
"hidden": true
@@ -1351,7 +1356,7 @@
},
{
"cell_type": "code",
- "execution_count": 18,
+ "execution_count": 79,
"id": "a7d15497",
"metadata": {
"hidden": true
@@ -1363,7 +1368,7 @@
"(45.535126163333835, 47.43629540529361)"
]
},
- "execution_count": 18,
+ "execution_count": 79,
"metadata": {},
"output_type": "execute_result"
}
@@ -1377,7 +1382,8 @@
"id": "52848e98",
"metadata": {
"heading_collapsed": true,
- "hidden": true
+ "hidden": true,
+ "tags": []
},
"source": [
"### Outliers"
@@ -1395,10 +1401,11 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": 149,
"id": "69922361-f828-44ad-bb36-402f355dd0cb",
"metadata": {
- "hidden": true
+ "hidden": true,
+ "tags": []
},
"outputs": [
{
@@ -1433,15 +1440,16 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 81,
"id": "ac5a7020-b903-4196-8db6-31b90070d22b",
"metadata": {
- "hidden": true
+ "hidden": true,
+ "tags": []
},
"outputs": [
{
"data": {
- "image/png": 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\n",
+ "image/png": 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\n",
"text/plain": [
"<Figure size 957.6x295.2 with 2 Axes>"
]
@@ -1479,7 +1487,8 @@
"id": "91a55884-2ca8-4cb0-9190-6c42bea7047b",
"metadata": {
"heading_collapsed": true,
- "hidden": true
+ "hidden": true,
+ "tags": []
},
"source": [
"### Modes"
@@ -1498,10 +1507,14 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": 82,
"id": "578edd4a-bcb1-4f9e-a8ba-d3e63bbabb71",
"metadata": {
- "hidden": true
+ "hidden": true,
+ "jupyter": {
+ "source_hidden": true
+ },
+ "tags": []
},
"outputs": [
{
@@ -1533,7 +1546,8 @@
"id": "c101eeb7-5580-4a75-889d-0fd2e965c7d6",
"metadata": {
"heading_collapsed": true,
- "hidden": true
+ "hidden": true,
+ "tags": []
},
"source": [
"### Another example"
@@ -1541,7 +1555,7 @@
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": 153,
"id": "72a790bd",
"metadata": {
"hidden": true
@@ -1555,7 +1569,7 @@
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": 154,
"id": "d6ab1d43",
"metadata": {
"hidden": true
@@ -1599,14 +1613,14 @@
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": 155,
"id": "92b00d5d",
"metadata": {
"hidden": true
},
"outputs": [],
"source": [
- "normal_distribution = stats.norm(x.mean(), x.var())"
+ "normal_distribution = stats.norm(x.mean(), x.std())"
]
},
{
@@ -1621,7 +1635,7 @@
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": 156,
"id": "4f114ad2-9cd2-431a-8b3e-2283006ef970",
"metadata": {
"hidden": true
@@ -1643,7 +1657,7 @@
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": 157,
"id": "8e114e17-8edb-4674-9309-c6f566885c0a",
"metadata": {
"hidden": true
@@ -1672,16 +1686,19 @@
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": 88,
"id": "6eaa04b5",
"metadata": {
"hidden": true,
+ "jupyter": {
+ "source_hidden": true
+ },
"tags": []
},
"outputs": [
{
"data": {
- "image/png": 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\n",
+ "image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
@@ -1700,10 +1717,13 @@
},
{
"cell_type": "code",
- "execution_count": 14,
+ "execution_count": 89,
"id": "ddfe2b2f",
"metadata": {
"hidden": true,
+ "jupyter": {
+ "source_hidden": true
+ },
"tags": []
},
"outputs": [
@@ -1738,7 +1758,7 @@
},
{
"cell_type": "code",
- "execution_count": 15,
+ "execution_count": 90,
"id": "417bfc62",
"metadata": {
"hidden": true,
@@ -1751,7 +1771,7 @@
"NormaltestResult(statistic=17.050178832107033, pvalue=0.00019842697373015917)"
]
},
- "execution_count": 15,
+ "execution_count": 90,
"metadata": {},
"output_type": "execute_result"
}
@@ -1773,7 +1793,7 @@
},
{
"cell_type": "code",
- "execution_count": 16,
+ "execution_count": 91,
"id": "5b1b8872",
"metadata": {
"hidden": true,
@@ -1786,7 +1806,7 @@
"KstestResult(statistic=0.17414497571515752, pvalue=4.115192819897108e-22)"
]
},
- "execution_count": 16,
+ "execution_count": 91,
"metadata": {},
"output_type": "execute_result"
}
@@ -1799,7 +1819,8 @@
"cell_type": "markdown",
"id": "ddeacee0-2062-476d-b7ec-9e0f036d6c4b",
"metadata": {
- "heading_collapsed": true
+ "heading_collapsed": true,
+ "tags": []
},
"source": [
"## Statistical testing"
@@ -1839,7 +1860,7 @@
},
{
"cell_type": "code",
- "execution_count": 17,
+ "execution_count": 92,
"id": "8346ad8e",
"metadata": {
"hidden": true
@@ -1856,7 +1877,7 @@
},
{
"cell_type": "code",
- "execution_count": 18,
+ "execution_count": 93,
"id": "404476b6",
"metadata": {
"hidden": true,
@@ -1900,11 +1921,15 @@
},
{
"cell_type": "code",
- "execution_count": 19,
+ "execution_count": 94,
"id": "b162e92e",
"metadata": {
"hidden": true,
- "hide_input": true
+ "hide_input": true,
+ "jupyter": {
+ "source_hidden": true
+ },
+ "tags": []
},
"outputs": [
{
@@ -1929,7 +1954,8 @@
"cell_type": "markdown",
"id": "446ba63e-df67-46ca-8921-eca686462c93",
"metadata": {
- "heading_collapsed": true
+ "heading_collapsed": true,
+ "tags": []
},
"source": [
"## *t* tests"
@@ -1947,7 +1973,7 @@
},
{
"cell_type": "code",
- "execution_count": 20,
+ "execution_count": 95,
"id": "67d046f0-ca64-4a12-8e32-58b8e7e142a0",
"metadata": {
"hidden": true,
@@ -2005,7 +2031,8 @@
"id": "ca6bf548-cadf-4c75-8130-fe0c3ef8de9a",
"metadata": {
"heading_collapsed": true,
- "hidden": true
+ "hidden": true,
+ "tags": []
},
"source": [
"### One-sample *t* test"
@@ -2048,7 +2075,7 @@
},
{
"cell_type": "code",
- "execution_count": 21,
+ "execution_count": 96,
"id": "b471633d-c9ad-455e-84e1-d32af085b32a",
"metadata": {
"hidden": true
@@ -2060,7 +2087,7 @@
"Ttest_1sampResult(statistic=0.6024056396957578, pvalue=0.5658990587680466)"
]
},
- "execution_count": 21,
+ "execution_count": 96,
"metadata": {},
"output_type": "execute_result"
}
@@ -2087,7 +2114,7 @@
},
{
"cell_type": "code",
- "execution_count": 22,
+ "execution_count": 97,
"id": "b732c69f-1851-4ef2-bdc2-8f80b4c5281e",
"metadata": {
"hidden": true
@@ -2099,7 +2126,7 @@
"Ttest_1sampResult(statistic=0.6024056396957578, pvalue=0.2829495293840233)"
]
},
- "execution_count": 22,
+ "execution_count": 97,
"metadata": {},
"output_type": "execute_result"
}
@@ -2110,7 +2137,7 @@
},
{
"cell_type": "code",
- "execution_count": 23,
+ "execution_count": 98,
"id": "dc360e24-d676-4398-8e4b-dba71e9e68e8",
"metadata": {
"hidden": true
@@ -2122,7 +2149,7 @@
"(56.1713713175, 10.244544391411772)"
]
},
- "execution_count": 23,
+ "execution_count": 98,
"metadata": {},
"output_type": "execute_result"
}
@@ -2136,7 +2163,8 @@
"id": "2144869e-9e4a-4e63-ae58-81c5a6be7205",
"metadata": {
"heading_collapsed": true,
- "hidden": true
+ "hidden": true,
+ "tags": []
},
"source": [
"### *t* test for independent samples"
@@ -2168,7 +2196,7 @@
},
{
"cell_type": "code",
- "execution_count": 24,
+ "execution_count": 99,
"id": "6231e214-ac36-4c4f-8a16-e1551c8484b4",
"metadata": {
"hidden": true
@@ -2180,7 +2208,7 @@
"Ttest_indResult(statistic=-1.96174329619957, pvalue=0.06998888828308221)"
]
},
- "execution_count": 24,
+ "execution_count": 99,
"metadata": {},
"output_type": "execute_result"
}
@@ -2210,10 +2238,11 @@
},
{
"cell_type": "code",
- "execution_count": 25,
+ "execution_count": 100,
"id": "2ba983bc-ef4c-4a15-ac23-776fffd88afb",
"metadata": {
- "hidden": true
+ "hidden": true,
+ "tags": []
},
"outputs": [
{
@@ -2222,7 +2251,7 @@
"1.0485958993113402"
]
},
- "execution_count": 25,
+ "execution_count": 100,
"metadata": {},
"output_type": "execute_result"
}
@@ -2243,22 +2272,28 @@
},
{
"cell_type": "code",
- "execution_count": 26,
+ "execution_count": 101,
"id": "3beb1fbb-a1ac-40aa-b4ce-b97f151392f5",
"metadata": {
- "hidden": true
+ "hidden": true,
+ "jupyter": {
+ "source_hidden": true
+ },
+ "tags": []
},
"outputs": [
{
"data": {
- "image/png": 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\n",
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "20fb3ecb21fa409095ff731740632a74",
+ "version_major": 2,
+ "version_minor": 0
+ },
"text/plain": [
- "<Figure size 432x288 with 1 Axes>"
+ "interactive(children=(FloatSlider(value=0.5, description='cohen_d', max=4.0, step=0.5), Output()), _dom_classe…"
]
},
- "metadata": {
- "needs_background": "light"
- },
+ "metadata": {},
"output_type": "display_data"
}
],
@@ -2315,7 +2350,8 @@
"id": "1083c04c-1221-446f-84a0-7084413a7722",
"metadata": {
"heading_collapsed": true,
- "hidden": true
+ "hidden": true,
+ "tags": []
},
"source": [
"### *t* test for paired samples"
@@ -2359,7 +2395,7 @@
},
{
"cell_type": "code",
- "execution_count": 27,
+ "execution_count": 102,
"id": "25adcf4f-6611-434f-b619-27f341b3caad",
"metadata": {
"hidden": true,
@@ -2372,7 +2408,7 @@
"1.2657389481669015"
]
},
- "execution_count": 27,
+ "execution_count": 102,
"metadata": {},
"output_type": "execute_result"
}
@@ -2386,7 +2422,8 @@
"cell_type": "markdown",
"id": "6b0fd532-87d0-4747-b3a3-322d77c2bfbd",
"metadata": {
- "heading_collapsed": true
+ "heading_collapsed": true,
+ "tags": []
},
"source": [
"## Analysis of variance"
@@ -2425,7 +2462,7 @@
},
{
"cell_type": "code",
- "execution_count": 28,
+ "execution_count": 103,
"id": "2bf0aafa-d23f-44fe-b9e0-cf4bf13e7314",
"metadata": {
"hidden": true
@@ -2437,7 +2474,7 @@
"F_onewayResult(statistic=2.3575322551335636, pvalue=0.11384795345837218)"
]
},
- "execution_count": 28,
+ "execution_count": 103,
"metadata": {},
"output_type": "execute_result"
}
@@ -2518,7 +2555,8 @@
"cell_type": "markdown",
"id": "e38c759e-8807-4975-9620-9c2100b964a8",
"metadata": {
- "heading_collapsed": true
+ "heading_collapsed": true,
+ "tags": []
},
"source": [
"## Checking for common assumptions"
@@ -2564,10 +2602,11 @@
},
{
"cell_type": "code",
- "execution_count": 29,
+ "execution_count": 104,
"id": "6bcaa795-3a85-4b8c-aad9-d554e244a2ec",
"metadata": {
- "hidden": true
+ "hidden": true,
+ "tags": []
},
"outputs": [
{
@@ -2597,7 +2636,7 @@
},
{
"cell_type": "code",
- "execution_count": 30,
+ "execution_count": 105,
"id": "e1f2356b-dd02-47d5-8e2c-82c77a6a5c97",
"metadata": {
"hidden": true
@@ -2645,10 +2684,13 @@
},
{
"cell_type": "code",
- "execution_count": 31,
+ "execution_count": 106,
"id": "e964be07-0696-4ff8-844a-3e7d318a7f3a",
"metadata": {
"hidden": true,
+ "jupyter": {
+ "source_hidden": true
+ },
"tags": []
},
"outputs": [
@@ -2737,7 +2779,7 @@
},
{
"cell_type": "code",
- "execution_count": 32,
+ "execution_count": 107,
"id": "8a31b653-d891-4f07-ab8b-73562b2ed86d",
"metadata": {
"hidden": true
@@ -2783,7 +2825,7 @@
},
{
"cell_type": "code",
- "execution_count": 33,
+ "execution_count": 108,
"id": "280180fa-4f20-44a6-a463-2ce9b0ad75a4",
"metadata": {
"hidden": true
@@ -2795,7 +2837,7 @@
"BartlettResult(statistic=3.3024375753550457, pvalue=0.19181598314036113)"
]
},
- "execution_count": 33,
+ "execution_count": 108,
"metadata": {},
"output_type": "execute_result"
}
@@ -2825,10 +2867,13 @@
},
{
"cell_type": "code",
- "execution_count": 34,
+ "execution_count": 109,
"id": "b8123d48-7285-4fa3-8473-04f316dedffc",
"metadata": {
"hidden": true,
+ "jupyter": {
+ "source_hidden": true
+ },
"tags": []
},
"outputs": [
@@ -2930,7 +2975,7 @@
},
{
"cell_type": "code",
- "execution_count": 35,
+ "execution_count": 110,
"id": "84f4e9b3-a653-422c-8fc5-83b29869ba87",
"metadata": {
"hidden": true
@@ -2942,7 +2987,7 @@
"410"
]
},
- "execution_count": 35,
+ "execution_count": 110,
"metadata": {},
"output_type": "execute_result"
}
@@ -2955,7 +3000,7 @@
},
{
"cell_type": "code",
- "execution_count": 36,
+ "execution_count": 111,
"id": "8bb2916a-e9a4-4bbe-b04f-14818bb68723",
"metadata": {
"hidden": true
@@ -2967,7 +3012,7 @@
"array([98.4, 82. , 65.6, 57.4, 53.3, 53.3])"
]
},
- "execution_count": 36,
+ "execution_count": 111,
"metadata": {},
"output_type": "execute_result"
}
@@ -2998,7 +3043,7 @@
},
{
"cell_type": "code",
- "execution_count": 37,
+ "execution_count": 112,
"id": "a8898631-a5b5-49e8-a158-3e149345391a",
"metadata": {
"hidden": true
@@ -3010,7 +3055,7 @@
"8.566983829178941"
]
},
- "execution_count": 37,
+ "execution_count": 112,
"metadata": {},
"output_type": "execute_result"
}
@@ -3023,7 +3068,7 @@
},
{
"cell_type": "code",
- "execution_count": 38,
+ "execution_count": 113,
"id": "7a8af457-13dc-483a-90e3-50d3949c8edf",
"metadata": {
"hidden": true
@@ -3035,7 +3080,7 @@
"0.1276329790529603"
]
},
- "execution_count": 38,
+ "execution_count": 113,
"metadata": {},
"output_type": "execute_result"
}
@@ -3057,7 +3102,7 @@
},
{
"cell_type": "code",
- "execution_count": 39,
+ "execution_count": 114,
"id": "1e246915-e9be-4826-9677-d9f30348d0df",
"metadata": {
"hidden": true
@@ -3069,7 +3114,7 @@
"Power_divergenceResult(statistic=8.566983829178941, pvalue=0.1276329790529603)"
]
},
- "execution_count": 39,
+ "execution_count": 114,
"metadata": {},
"output_type": "execute_result"
}
@@ -3092,7 +3137,7 @@
},
{
"cell_type": "code",
- "execution_count": 40,
+ "execution_count": 115,
"id": "f2231df3-3ce3-40c4-ae75-f6159061e3d6",
"metadata": {
"hidden": true
@@ -3104,7 +3149,7 @@
"2.9269410361636843"
]
},
- "execution_count": 40,
+ "execution_count": 115,
"metadata": {},
"output_type": "execute_result"
}
@@ -3150,7 +3195,7 @@
},
{
"cell_type": "code",
- "execution_count": 41,
+ "execution_count": 116,
"id": "13145d41-7dc3-4eff-9f9c-38d0abed1e8d",
"metadata": {
"hidden": true
@@ -3166,7 +3211,7 @@
},
{
"cell_type": "code",
- "execution_count": 42,
+ "execution_count": 117,
"id": "fe4ce304-25ac-4734-9a0a-e20db59b742f",
"metadata": {
"hidden": true
@@ -3178,7 +3223,7 @@
"array([0.50963855, 0.29518072, 0.14939759, 0.04578313])"
]
},
- "execution_count": 42,
+ "execution_count": 117,
"metadata": {},
"output_type": "execute_result"
}
@@ -3206,7 +3251,7 @@
},
{
"cell_type": "code",
- "execution_count": 43,
+ "execution_count": 118,
"id": "087ea780-304c-459d-a7c0-066817f3d82f",
"metadata": {
"hidden": true
@@ -3220,7 +3265,7 @@
" [167.16144578, 96.81927711, 49.00240964, 15.01686747]])"
]
},
- "execution_count": 43,
+ "execution_count": 118,
"metadata": {},
"output_type": "execute_result"
}
@@ -3246,7 +3291,7 @@
},
{
"cell_type": "code",
- "execution_count": 44,
+ "execution_count": 119,
"id": "d9585b87-f5dd-4cf2-ad91-0a79dcc95c86",
"metadata": {
"hidden": true
@@ -3258,7 +3303,7 @@
"26.7075512595244"
]
},
- "execution_count": 44,
+ "execution_count": 119,
"metadata": {},
"output_type": "execute_result"
}
@@ -3272,7 +3317,7 @@
},
{
"cell_type": "code",
- "execution_count": 45,
+ "execution_count": 120,
"id": "a7cd985e-eb2b-4a72-a221-30e3e0fdf8ec",
"metadata": {
"hidden": true,
@@ -3285,7 +3330,7 @@
"0.00016426084515914902"
]
},
- "execution_count": 45,
+ "execution_count": 120,
"metadata": {},
"output_type": "execute_result"
}
@@ -3306,7 +3351,7 @@
},
{
"cell_type": "code",
- "execution_count": 46,
+ "execution_count": 121,
"id": "4cd7ea08-110a-4ff7-9521-0f12e4169d35",
"metadata": {
"hidden": true
@@ -3323,7 +3368,7 @@
" [167.16144578, 96.81927711, 49.00240964, 15.01686747]]))"
]
},
- "execution_count": 46,
+ "execution_count": 121,
"metadata": {},
"output_type": "execute_result"
}
@@ -3344,7 +3389,7 @@
},
{
"cell_type": "code",
- "execution_count": 47,
+ "execution_count": 122,
"id": "d7e7fd75-068c-4f37-b981-498890c4a0d7",
"metadata": {
"hidden": true
@@ -3362,7 +3407,7 @@
" [ 12.04096386, 10.94216867, 15.01686747]]))"
]
},
- "execution_count": 47,
+ "execution_count": 122,
"metadata": {},
"output_type": "execute_result"
}
@@ -3376,7 +3421,8 @@
"id": "ca862d48",
"metadata": {
"heading_collapsed": true,
- "hidden": true
+ "hidden": true,
+ "tags": []
},
"source": [
"### Two-sample goodness-of-fit"
@@ -3414,6 +3460,7 @@
"id": "6efe325b",
"metadata": {
"hidden": true,
+ "jp-MarkdownHeadingCollapsed": true,
"tags": []
},
"source": [
@@ -3463,7 +3510,7 @@
},
{
"cell_type": "code",
- "execution_count": 48,
+ "execution_count": 123,
"id": "f5a36568-d5b5-4388-b3f9-f38855c58c80",
"metadata": {
"hidden": true
@@ -3475,7 +3522,7 @@
"(0.3518680132574827, 0.05653920630309695)"
]
},
- "execution_count": 48,
+ "execution_count": 123,
"metadata": {},
"output_type": "execute_result"
}
@@ -3500,7 +3547,7 @@
},
{
"cell_type": "code",
- "execution_count": 49,
+ "execution_count": 124,
"id": "3581eeb0-f98d-4b2f-a0f1-bef073d86980",
"metadata": {
"hidden": true
@@ -3508,7 +3555,7 @@
"outputs": [
{
"data": {
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\n",
+ "image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
@@ -3526,7 +3573,7 @@
},
{
"cell_type": "code",
- "execution_count": 50,
+ "execution_count": 125,
"id": "730a1bd8-51b6-4c06-9431-9a0fce53c42c",
"metadata": {
"hidden": true
@@ -3538,7 +3585,7 @@
"(0.4132334074789644, 0.023224608467418917)"
]
},
- "execution_count": 50,
+ "execution_count": 125,
"metadata": {},
"output_type": "execute_result"
}
@@ -3552,7 +3599,7 @@
},
{
"cell_type": "code",
- "execution_count": 51,
+ "execution_count": 126,
"id": "bfd1c00e-20b3-48bd-9724-7342ae70f626",
"metadata": {
"hidden": true
@@ -3560,7 +3607,7 @@
"outputs": [
{
"data": {
- "image/png": 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\n",
+ "image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
@@ -3585,12 +3632,12 @@
"source": [
"Pearson $r$ assumes the observations are drawn from normal distributions.\n",
"\n",
- "If the data are not normally distributed, Spearman $r$ ([spearman](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.spearmanr.html)) is an interesting alternative, although Pearson $r$ works well enough in most cases:"
+ "If the data are not normally distributed, Spearman $r$ ([spearmanr](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.spearmanr.html)) is an interesting alternative, although Pearson $r$ works well enough in most cases:"
]
},
{
"cell_type": "code",
- "execution_count": 175,
+ "execution_count": 162,
"id": "39423438-a688-4afa-b258-e17076ba1fbd",
"metadata": {
"hidden": true
@@ -3599,10 +3646,10 @@
{
"data": {
"text/plain": [
- "(0.018346666276950804, 0.009331832682953218)"
+ "(0.40738888339548257, 0.16305184149013052)"
]
},
- "execution_count": 175,
+ "execution_count": 162,
"metadata": {},
"output_type": "execute_result"
}
@@ -3624,7 +3671,7 @@
},
{
"cell_type": "code",
- "execution_count": 176,
+ "execution_count": 163,
"id": "bca6551e-9ffb-4f60-8b96-9a085b2e2282",
"metadata": {
"hidden": true
@@ -3632,7 +3679,7 @@
"outputs": [
{
"data": {
- "image/png": "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\n",
+ "image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
@@ -3650,7 +3697,7 @@
},
{
"cell_type": "code",
- "execution_count": 177,
+ "execution_count": 129,
"id": "a2dee6dc-0f17-4b33-9431-c6dc2d2bd418",
"metadata": {
"hidden": true
@@ -3658,7 +3705,7 @@
"outputs": [
{
"data": {
- "image/png": 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\n",
+ "image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
@@ -3695,7 +3742,8 @@
"id": "37816425",
"metadata": {
"heading_collapsed": true,
- "hidden": true
+ "hidden": true,
+ "tags": []
},
"source": [
"### Linear regression"
@@ -3715,7 +3763,7 @@
},
{
"cell_type": "code",
- "execution_count": 180,
+ "execution_count": 130,
"id": "fb9d664f",
"metadata": {
"hidden": true
@@ -3724,10 +3772,10 @@
{
"data": {
"text/plain": [
- "(5.43327239488117, 0.5804387568555759, 0.01834666627695083)"
+ "(7.336186400380409, 0.30266286257727054, 0.22110436687994597)"
]
},
- "execution_count": 180,
+ "execution_count": 130,
"metadata": {},
"output_type": "execute_result"
}
@@ -3753,7 +3801,7 @@
},
{
"cell_type": "code",
- "execution_count": 182,
+ "execution_count": 131,
"id": "7f69b66e",
"metadata": {
"hidden": true
@@ -3762,10 +3810,10 @@
{
"data": {
"text/plain": [
- "8.335466179159049"
+ "8.849500713266762"
]
},
- "execution_count": 182,
+ "execution_count": 131,
"metadata": {},
"output_type": "execute_result"
}
@@ -3778,7 +3826,7 @@
},
{
"cell_type": "code",
- "execution_count": 185,
+ "execution_count": 132,
"id": "14836378",
"metadata": {
"hidden": true
@@ -3787,10 +3835,10 @@
{
"data": {
"text/plain": [
- "7.867716535433071"
+ "8.8012568735271"
]
},
- "execution_count": 185,
+ "execution_count": 132,
"metadata": {},
"output_type": "execute_result"
}
@@ -3803,15 +3851,19 @@
},
{
"cell_type": "code",
- "execution_count": 191,
+ "execution_count": 133,
"id": "51bff28b",
"metadata": {
- "hidden": true
+ "hidden": true,
+ "jupyter": {
+ "source_hidden": true
+ },
+ "tags": []
},
"outputs": [
{
"data": {
- "image/png": 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\n",
+ "image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
@@ -3837,7 +3889,8 @@
"cell_type": "markdown",
"id": "9f26feda-d0fd-4d9d-a748-b0b82d0f84b4",
"metadata": {
- "heading_collapsed": true
+ "heading_collapsed": true,
+ "tags": []
},
"source": [
"## Effect sizes and test power"
@@ -3857,13 +3910,10 @@
},
{
"cell_type": "code",
- "execution_count": 55,
+ "execution_count": 134,
"id": "78c838fa-6eff-459a-867c-b9e930b0cebd",
"metadata": {
"hidden": true,
- "jupyter": {
- "outputs_hidden": true
- },
"scrolled": true,
"tags": []
},
@@ -3874,28 +3924,28 @@
"text": [
"Requirement already satisfied: statsmodels in /home/flaurent/.local/lib/python3.8/site-packages (0.12.2)\n",
"Requirement already satisfied: pingouin in /home/flaurent/.local/lib/python3.8/site-packages (0.4.0)\n",
+ "Requirement already satisfied: numpy>=1.15 in /home/flaurent/.local/lib/python3.8/site-packages (from statsmodels) (1.21.1)\n",
"Requirement already satisfied: scipy>=1.1 in /home/flaurent/.local/lib/python3.8/site-packages (from statsmodels) (1.7.1)\n",
"Requirement already satisfied: patsy>=0.5 in /home/flaurent/.local/lib/python3.8/site-packages (from statsmodels) (0.5.1)\n",
- "Requirement already satisfied: numpy>=1.15 in /home/flaurent/.local/lib/python3.8/site-packages (from statsmodels) (1.21.1)\n",
"Requirement already satisfied: pandas>=0.21 in /home/flaurent/.local/lib/python3.8/site-packages (from statsmodels) (1.3.1)\n",
+ "Requirement already satisfied: outdated in /home/flaurent/.local/lib/python3.8/site-packages (from pingouin) (0.2.1)\n",
+ "Requirement already satisfied: scikit-learn in /home/flaurent/.local/lib/python3.8/site-packages (from pingouin) (0.24.2)\n",
+ "Requirement already satisfied: tabulate in /home/flaurent/.local/lib/python3.8/site-packages (from pingouin) (0.8.9)\n",
"Requirement already satisfied: matplotlib>=3.0.2 in /home/flaurent/.local/lib/python3.8/site-packages (from pingouin) (3.4.2)\n",
"Requirement already satisfied: pandas-flavor>=0.2.0 in /home/flaurent/.local/lib/python3.8/site-packages (from pingouin) (0.2.0)\n",
- "Requirement already satisfied: tabulate in /home/flaurent/.local/lib/python3.8/site-packages (from pingouin) (0.8.9)\n",
"Requirement already satisfied: seaborn>=0.9.0 in /home/flaurent/.local/lib/python3.8/site-packages (from pingouin) (0.11.1)\n",
- "Requirement already satisfied: scikit-learn in /home/flaurent/.local/lib/python3.8/site-packages (from pingouin) (0.24.2)\n",
- "Requirement already satisfied: outdated in /home/flaurent/.local/lib/python3.8/site-packages (from pingouin) (0.2.1)\n",
"Requirement already satisfied: six in /usr/lib/python3/dist-packages (from patsy>=0.5->statsmodels) (1.14.0)\n",
- "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/lib/python3/dist-packages (from pandas>=0.21->statsmodels) (2.7.3)\n",
"Requirement already satisfied: pytz>=2017.3 in /usr/lib/python3/dist-packages (from pandas>=0.21->statsmodels) (2019.3)\n",
+ "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/lib/python3/dist-packages (from pandas>=0.21->statsmodels) (2.7.3)\n",
+ "Requirement already satisfied: requests in /usr/lib/python3/dist-packages (from outdated->pingouin) (2.22.0)\n",
+ "Requirement already satisfied: littleutils in /home/flaurent/.local/lib/python3.8/site-packages (from outdated->pingouin) (0.2.2)\n",
+ "Requirement already satisfied: joblib>=0.11 in /home/flaurent/.local/lib/python3.8/site-packages (from scikit-learn->pingouin) (1.0.1)\n",
+ "Requirement already satisfied: threadpoolctl>=2.0.0 in /home/flaurent/.local/lib/python3.8/site-packages (from scikit-learn->pingouin) (2.2.0)\n",
"Requirement already satisfied: pillow>=6.2.0 in /home/flaurent/.local/lib/python3.8/site-packages (from matplotlib>=3.0.2->pingouin) (8.3.1)\n",
- "Requirement already satisfied: pyparsing>=2.2.1 in /home/flaurent/.local/lib/python3.8/site-packages (from matplotlib>=3.0.2->pingouin) (2.4.7)\n",
"Requirement already satisfied: kiwisolver>=1.0.1 in /home/flaurent/.local/lib/python3.8/site-packages (from matplotlib>=3.0.2->pingouin) (1.3.1)\n",
+ "Requirement already satisfied: pyparsing>=2.2.1 in /home/flaurent/.local/lib/python3.8/site-packages (from matplotlib>=3.0.2->pingouin) (2.4.7)\n",
"Requirement already satisfied: cycler>=0.10 in /home/flaurent/.local/lib/python3.8/site-packages (from matplotlib>=3.0.2->pingouin) (0.10.0)\n",
"Requirement already satisfied: xarray in /home/flaurent/.local/lib/python3.8/site-packages (from pandas-flavor>=0.2.0->pingouin) (0.19.0)\n",
- "Requirement already satisfied: threadpoolctl>=2.0.0 in /home/flaurent/.local/lib/python3.8/site-packages (from scikit-learn->pingouin) (2.2.0)\n",
- "Requirement already satisfied: joblib>=0.11 in /home/flaurent/.local/lib/python3.8/site-packages (from scikit-learn->pingouin) (1.0.1)\n",
- "Requirement already satisfied: requests in /usr/lib/python3/dist-packages (from outdated->pingouin) (2.22.0)\n",
- "Requirement already satisfied: littleutils in /home/flaurent/.local/lib/python3.8/site-packages (from outdated->pingouin) (0.2.2)\n",
"Requirement already satisfied: setuptools>=40.4 in /usr/lib/python3/dist-packages (from xarray->pandas-flavor>=0.2.0->pingouin) (45.2.0)\n"
]
}
@@ -3907,7 +3957,7 @@
},
{
"cell_type": "code",
- "execution_count": 56,
+ "execution_count": 135,
"id": "5eaf887e-9310-4b89-acca-ccde9dff5f02",
"metadata": {
"hidden": true
@@ -3923,7 +3973,8 @@
"id": "5532ddbe-202e-48f5-96fd-825c465a503f",
"metadata": {
"heading_collapsed": true,
- "hidden": true
+ "hidden": true,
+ "tags": []
},
"source": [
"### Effect sizes"
@@ -3959,7 +4010,7 @@
},
{
"cell_type": "code",
- "execution_count": 57,
+ "execution_count": 136,
"id": "d934a53e-f2b5-423a-a101-21c69e1a472b",
"metadata": {
"hidden": true
@@ -3988,7 +4039,8 @@
"id": "3b30414c-6575-4dff-9637-664cb0f29a5a",
"metadata": {
"heading_collapsed": true,
- "hidden": true
+ "hidden": true,
+ "tags": []
},
"source": [
"### Power analysis"
@@ -4018,7 +4070,7 @@
},
{
"cell_type": "code",
- "execution_count": 169,
+ "execution_count": 137,
"id": "de47ba11-f486-44e3-9f3b-9ddffacc95ee",
"metadata": {
"hidden": true
diff --git a/notebooks/seaborn_TP.ipynb b/notebooks/seaborn_TP.ipynb
index 7997bd1..0ef1a6f 100644
--- a/notebooks/seaborn_TP.ipynb
+++ b/notebooks/seaborn_TP.ipynb
@@ -118,9 +118,9 @@
],
"metadata": {
"kernelspec": {
- "display_name": "dev",
+ "display_name": "Python 3 (ipykernel)",
"language": "python",
- "name": "dev"
+ "name": "python3"
},
"language_info": {
"codemirror_mode": {
@@ -132,7 +132,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.9.5"
+ "version": "3.8.10"
}
},
"nbformat": 4,
diff --git a/notebooks/seaborn_TP_solutions.ipynb b/notebooks/seaborn_TP_solutions.ipynb
index c49006d..2afeaf7 100644
--- a/notebooks/seaborn_TP_solutions.ipynb
+++ b/notebooks/seaborn_TP_solutions.ipynb
@@ -473,9 +473,9 @@
],
"metadata": {
"kernelspec": {
- "display_name": "dev",
+ "display_name": "Python 3 (ipykernel)",
"language": "python",
- "name": "dev"
+ "name": "python3"
},
"language_info": {
"codemirror_mode": {
@@ -487,7 +487,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.9.5"
+ "version": "3.8.10"
}
},
"nbformat": 4,
diff --git a/notebooks/seaborn_cours.ipynb b/notebooks/seaborn_cours.ipynb
index 1cb28fb..f2c5ee5 100644
--- a/notebooks/seaborn_cours.ipynb
+++ b/notebooks/seaborn_cours.ipynb
@@ -602,9 +602,9 @@
],
"metadata": {
"kernelspec": {
- "display_name": "dev",
+ "display_name": "Python 3 (ipykernel)",
"language": "python",
- "name": "dev"
+ "name": "python3"
},
"language_info": {
"codemirror_mode": {
@@ -616,7 +616,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.9.5"
+ "version": "3.8.10"
}
},
"nbformat": 4,
--
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