commiting

data_zscore_new.txt

@@ -0,0 +1,183 @@
+
+
+   "source": [
+    "gw = pd.read_excel(\"WFA_girls.xlsx\")\n",
+    "gw['age']=gw['Month']\n",
+    "gw = gw.drop('Month',axis=1)\n",
+    "print(gw)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 383,
+   "id": "1474f562-9026-49d7-a324-a1db9b648edf",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "         age id  weight       L        M        S  SD4neg  SD3neg  SD2neg  \\\n",
+      "goutham   78  2      20 -0.3804  21.6810  0.13554   12.96  14.855  16.751   \n",
+      "john123   65  3      19 -0.2443  19.2132  0.13142   11.48  13.186  14.892   \n",
+      "\n",
+      "         SD1neg     SD0     SD1     SD2     SD3     SD4        WZ  \n",
+      "goutham  18.997  21.681  24.918  28.862  33.723  38.583 -0.604660  \n",
+      "john123  16.882  19.213  21.959  25.210  29.083  32.956 -0.085023  \n"
+     ]
+    }
+   ],
+   "source": [
+    "merged_gw = pd.DataFrame()\n",
+    "# print(merged_gw)\n",
+    "merged_gw = pd.merge(weight_df_girl, gw, left_on='age', right_on='age', how='left')\n",
+    "merged_gw.set_index(df_girl.index, inplace=True)\n",
+    "# print(merged_gw)\n",
+    "# Calculate L * M * S and create a new column in df_girl\n",
+    "merged_gw['WZ'] = ((merged_gw['weight']/merged_gw['M'])**merged_gw['L'] -1)/(merged_gw['L'] * merged_gw['S'])\n",
+    "# print(merged_df_girl)\n",
+    "# print(merged_df_girl['WZ'])\n",
+    "# Now you can use merged_df_girl to see the updated DataFrame\n",
+    "weight_df_girl = merged_gw\n",
+    "\n",
+    "# Show the updated df_girl with the new column\n",
+    "print(weight_df_girl)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 384,
+   "id": "a5e7c58a-de94-4dbe-ac31-1973e0f4ca09",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "          L        M        S  SD4neg  SD3neg  SD2neg  SD1neg     SD0     SD1  \\\n",
+      "0   -0.7387  15.2641  0.08390  11.204  12.118  13.031  14.071  15.264  16.645   \n",
+      "1   -0.7621  15.2616  0.08414  11.204  12.115  13.027  14.066  15.262  16.648   \n",
+      "2   -0.7856  15.2604  0.08439  11.204  12.114  13.024  14.063  15.260  16.653   \n",
+      "3   -0.8089  15.2605  0.08464  11.205  12.114  13.022  14.061  15.260  16.659   \n",
+      "4   -0.8322  15.2619  0.08490  11.207  12.114  13.021  14.060  15.262  16.667   \n",
+      "..      ...      ...      ...     ...     ...     ...     ...     ...     ...   \n",
+      "163 -0.9048  22.0374  0.12911  14.148  15.811  17.473  19.504  22.037  25.281   \n",
+      "164 -0.8892  22.0760  0.12920  14.151  15.823  17.495  19.535  22.076  25.324   \n",
+      "165 -0.8735  22.1140  0.12930  14.153  15.834  17.515  19.564  22.114  25.366   \n",
+      "166 -0.8578  22.1514  0.12939  14.155  15.845  17.535  19.594  22.151  25.408   \n",
+      "167 -0.8419  22.1883  0.12948  14.156  15.855  17.554  19.622  22.188  25.449   \n",
+      "\n",
+      "        SD2     SD3     SD4  age  \n",
+      "0    18.259  20.166  22.072   61  \n",
+      "1    18.273  20.200  22.127   62  \n",
+      "2    18.290  20.238  22.186   63  \n",
+      "3    18.308  20.277  22.247   64  \n",
+      "4    18.328  20.320  22.312   65  \n",
+      "..      ...     ...     ...  ...  \n",
+      "163  29.572  35.503  41.434  224  \n",
+      "164  29.609  35.507  41.406  225  \n",
+      "165  29.646  35.512  41.378  226  \n",
+      "166  29.681  35.515  41.348  227  \n",
+      "167  29.716  35.516  41.317  228  \n",
+      "\n",
+      "[168 rows x 13 columns]\n"
+     ]
+    }
+   ],
+   "source": [
+    "gg = pd.read_excel(\"BFA_girls.xlsx\")\n",
+    "gg['age']=gg['Month']\n",
+    "gg = gg.drop('Month',axis=1)\n",
+    "print(gg)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 385,
+   "id": "4b1155dd-0e94-4b2c-ae51-4778e3d4f26a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "         age id    BMI\n",
+      "goutham   78  2  16.53\n",
+      "john123   65  3  11.24\n"
+     ]
+    }
+   ],
+   "source": [
+    "bmi_df_girl = pd.DataFrame()\n",
+    "bmi_df_girl[['age','id','BMI']] = df_girl[['age','id','BMI']]\n",
+    "print(bmi_df_girl)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 386,
+   "id": "d1bd97d2-58fb-4301-b5e1-90331746918c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "         age id    BMI       L        M        S  SD4neg  SD3neg  SD2neg  \\\n",
+      "goutham   78  2  16.53 -1.1230  15.3825  0.08865  11.292  12.189  13.086   \n",
+      "john123   65  3  11.24 -0.8322  15.2619  0.08490  11.207  12.114  13.021   \n",
+      "\n",
+      "         SD1neg     SD0     SD1     SD2     SD3     SD4        BZ  \n",
+      "goutham  14.136  15.382  16.888  18.745  21.097  23.449  0.779658  \n",
+      "john123  14.060  15.262  16.667  18.328  20.320  22.312 -4.102910  \n"
+     ]
+    }
+   ],
+   "source": [
+    "merged_gg = pd.DataFrame()\n",
+    "# print(merged_gg)\n",
+    "merged_gg = pd.merge(bmi_df_girl, gg, left_on='age', right_on='age', how='left')\n",
+    "merged_gg.set_index(df_girl.index, inplace=True)\n",
+    "# print(merged_gg)\n",
+    "# Calculate L * M * S and create a new column in df_girl\n",
+    "merged_gg['BZ'] = ((merged_gg['BMI']/merged_gg['M'])**merged_gg['L'] -1)/(merged_gg['L'] * merged_gg['S'])\n",
+    "# print(merged_gg)\n",
+    "# print(merged_gg['BZ'])\n",
+    "# Now you can use merged_df_girl to see the updated DataFrame\n",
+    "bmi_df_girl = merged_gg\n",
+    "\n",
+    "# Show the updated df_girl with the new column\n",
+    "print(bmi_df_girl)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4bd7447d-99d9-4602-8918-bfaa2601f66a",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

extract.py

@@ -0,0 +1,12 @@
+import nbformat
+
+# Load the notebook
+with open("data_zscore.ipynb", "r", encoding="utf-8") as f:
+    notebook = nbformat.read(f, as_version=4)
+
+# Extract code cells
+code_cells = [cell["source"] for cell in notebook.cells if cell.cell_type == "code"]
+
+# Save to a Python file
+with open("extracted_code.py", "w", encoding="utf-8") as f:
+    f.write("\n\n".join(code_cells))