Refactor code in numerical methods notebooks

- Updated import order in Point_Fixe.ipynb for consistency.
- Changed lambda functions to regular function definitions for clarity in Point_Fixe.ipynb.
- Added numpy import in TP1_EDO_EulerExp.ipynb, TP2_Lokta_Volterra.ipynb, and TP3_Convergence.ipynb for better readability.
- Modified for loops in TP1_EDO_EulerExp.ipynb and TP2_Lokta_Volterra.ipynb to include strict=False for compatibility with future Python versions.

M1/Statistical Learning/TP5_Naive_Bayes.ipynb

@@ -32,9 +32,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
-    "import matplotlib.pyplot as plt"
+    "import pandas as pd"
    ]
   },
   {
@@ -84,8 +84,7 @@
          "type": "unknown"
         }
        ],
-       "conversionMethod": "pd.DataFrame",
-       "ref": "37d5b76b-9fed-490f-9dd5-20a98409d9ca",
+       "ref": "5aa01ea5-52e9-47bd-bf16-02eda59eafb2",
        "rows": [
         [
          "0",
@@ -294,8 +293,7 @@
          "type": "unknown"
         }
        ],
-       "conversionMethod": "pd.DataFrame",
-       "ref": "7dc949eb-d6ef-4d5f-969f-c852d588c859",
+       "ref": "fdbea167-a638-4d3f-8877-210d50fec511",
        "rows": [
         [
          "0",
@@ -491,8 +489,7 @@
          "type": "integer"
         }
        ],
-       "conversionMethod": "pd.DataFrame",
-       "ref": "6f54a3b7-bd30-4a68-b39b-4220633acbfc",
+       "ref": "5b56b570-be86-4796-82f4-bd777c0f3302",
        "rows": [
         [
          "0",
@@ -728,8 +725,7 @@
          "type": "integer"
         }
        ],
-       "conversionMethod": "pd.DataFrame",
-       "ref": "dc527433-ad4a-4862-bddf-ed1bfa01897a",
+       "ref": "af40f60c-6cb1-4d32-bdfe-3ea091985909",
        "rows": [
         [
          "0",
@@ -930,8 +926,7 @@
          "type": "integer"
         }
        ],
-       "conversionMethod": "pd.DataFrame",
-       "ref": "01a11b3a-e783-4b35-8867-c7a57df85078",
+       "ref": "74c5f61c-9b82-4dc6-991b-6a173848eccb",
        "rows": [
         [
          "2",
@@ -1205,7 +1200,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -1245,7 +1240,7 @@
     "\n",
     "print(\n",
     "    \"The vocabulary arranged in alphabetical order :  \",\n",
-    "    sorted(list(vec.vocabulary_.keys())),\n",
+    "    sorted(vec.vocabulary_.keys()),\n",
     ")\n",
     "\n",
     "# 2. Displaying the vectors :\n",
@@ -1341,7 +1336,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [
     {
@@ -2190,7 +2185,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": ".venv",
+   "display_name": "studies",
    "language": "python",
    "name": "python3"
   },
@@ -2204,7 +2199,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.9"
+   "version": "3.13.3"
   }
  },
  "nbformat": 4,