diff --git a/doc/source/notebooks/convert.ipynb b/doc/source/notebooks/convert.ipynb
index 2b77a5c8..2ccad6bc 100644
--- a/doc/source/notebooks/convert.ipynb
+++ b/doc/source/notebooks/convert.ipynb
@@ -20,7 +20,7 @@
     "- renaming the index names of results/extensions\n",
     "- adjusting the numerical values of the data,\n",
     "  e.g. for unit conversion or characterisation\n",
-    "- finding and extracting data based on indicies across a table or an mrio(-extension).\n",
+    "- finding and extracting data based on indices across a table or an mrio(-extension).\n",
     "  This can be system based on name and potentially constrained by sector/region\n",
     "  or any other specification.\n",
     "- Aggregation/Summation of satellite accounts\n",
@@ -103,7 +103,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 59,
    "id": "ae7e3af0",
    "metadata": {},
    "outputs": [],
@@ -137,10 +137,74 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 60,
    "id": "f72ae844",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>stressor</th>\n",
+       "      <th>compartment</th>\n",
+       "      <th>chem_stressor__stressor</th>\n",
+       "      <th>compartment__compartment</th>\n",
+       "      <th>factor</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Carbon Dioxide</td>\n",
+       "      <td>[A|a]ir</td>\n",
+       "      <td>CO2</td>\n",
+       "      <td>Air</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Methane</td>\n",
+       "      <td>[A|a]ir</td>\n",
+       "      <td>CH4</td>\n",
+       "      <td>Air</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         stressor compartment chem_stressor__stressor  \\\n",
+       "0  Carbon Dioxide     [A|a]ir                     CO2   \n",
+       "1         Methane     [A|a]ir                     CH4   \n",
+       "\n",
+       "  compartment__compartment  factor  \n",
+       "0                      Air     1.0  \n",
+       "1                      Air     1.0  "
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "ghg_map = pd.DataFrame(\n",
     "    columns=[\n",
@@ -160,10 +224,75 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 61,
    "id": "f73cd886",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>region</th>\n",
+       "      <th>Region1</th>\n",
+       "      <th>Region2</th>\n",
+       "      <th>Region3</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>chem_stressor</th>\n",
+       "      <th>compartment</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>CH4</th>\n",
+       "      <th>Air</th>\n",
+       "      <td>0.5</td>\n",
+       "      <td>0.6</td>\n",
+       "      <td>0.7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CO2</th>\n",
+       "      <th>Air</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>7.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "region                     Region1  Region2  Region3\n",
+       "chem_stressor compartment                           \n",
+       "CH4           Air              0.5      0.6      0.7\n",
+       "CO2           Air              5.0      6.0      7.0"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "ghg_new = pymrio.convert(ghg_result, ghg_map)\n",
     "ghg_new"
@@ -192,10 +321,67 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 62,
    "id": "48688b14",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>stressor</th>\n",
+       "      <th>compartment</th>\n",
+       "      <th>chem_stressor__stressor</th>\n",
+       "      <th>compartment__compartment</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Carbon Dioxide</td>\n",
+       "      <td>[A|a]ir</td>\n",
+       "      <td>CO2</td>\n",
+       "      <td>Air</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Methane</td>\n",
+       "      <td>[A|a]ir</td>\n",
+       "      <td>CH4</td>\n",
+       "      <td>Air</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         stressor compartment chem_stressor__stressor compartment__compartment\n",
+       "0  Carbon Dioxide     [A|a]ir                     CO2                      Air\n",
+       "1         Methane     [A|a]ir                     CH4                      Air"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "ghg_map_wo_factor = pd.DataFrame(\n",
     "    columns=[\n",
@@ -214,12 +400,77 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 63,
    "id": "826f558a",
    "metadata": {
     "lines_to_next_cell": 2
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>region</th>\n",
+       "      <th>Region1</th>\n",
+       "      <th>Region2</th>\n",
+       "      <th>Region3</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>chem_stressor</th>\n",
+       "      <th>compartment</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>CH4</th>\n",
+       "      <th>Air</th>\n",
+       "      <td>0.5</td>\n",
+       "      <td>0.6</td>\n",
+       "      <td>0.7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CO2</th>\n",
+       "      <th>Air</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>7.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "region                     Region1  Region2  Region3\n",
+       "chem_stressor compartment                           \n",
+       "CH4           Air              0.5      0.6      0.7\n",
+       "CO2           Air              5.0      6.0      7.0"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "ghg_new_wo_factor = pymrio.convert(ghg_result, ghg_map_wo_factor)\n",
     "ghg_new_wo_factor"
@@ -245,10 +496,75 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 64,
    "id": "e4fe084e",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>region</th>\n",
+       "      <th>Region1</th>\n",
+       "      <th>Region2</th>\n",
+       "      <th>Region3</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>stressor</th>\n",
+       "      <th>compartment</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Carbon Dioxide</th>\n",
+       "      <th>Air</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>7.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Methane</th>\n",
+       "      <th>air</th>\n",
+       "      <td>0.5</td>\n",
+       "      <td>0.6</td>\n",
+       "      <td>0.7</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "region                      Region1  Region2  Region3\n",
+       "stressor       compartment                           \n",
+       "Carbon Dioxide Air              5.0      6.0      7.0\n",
+       "Methane        air              0.5      0.6      0.7"
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "ghg_result_ton = pd.DataFrame(\n",
     "    columns=[\"Region1\", \"Region2\", \"Region3\"],\n",
@@ -270,9 +586,80 @@
    "id": "1ad41ad6",
    "metadata": {},
    "source": [
-    "We can get the data in kg by\n",
-    "\n",
-    "\n",
+    "We can get the data in kg by"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "id": "ae5eba1e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>stressor</th>\n",
+       "      <th>compartment</th>\n",
+       "      <th>chem_stressor__stressor</th>\n",
+       "      <th>compartment__compartment</th>\n",
+       "      <th>factor</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Carbon Dioxide</td>\n",
+       "      <td>[A|a]ir</td>\n",
+       "      <td>CO2</td>\n",
+       "      <td>Air</td>\n",
+       "      <td>1000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Methane</td>\n",
+       "      <td>[A|a]ir</td>\n",
+       "      <td>CH4</td>\n",
+       "      <td>Air</td>\n",
+       "      <td>1000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         stressor compartment chem_stressor__stressor  \\\n",
+       "0  Carbon Dioxide     [A|a]ir                     CO2   \n",
+       "1         Methane     [A|a]ir                     CH4   \n",
+       "\n",
+       "  compartment__compartment  factor  \n",
+       "0                      Air    1000  \n",
+       "1                      Air    1000  "
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
     "ghg_map_to_kg = pd.DataFrame(\n",
     "    columns=[\n",
     "        \"stressor\",\n",
@@ -286,8 +673,81 @@
     "        [\"Methane\", \"[A|a]ir\", \"CH4\", \"Air\", 1000],\n",
     "    ],\n",
     ")\n",
-    "ghg_map_to_kg\n",
-    "\n",
+    "ghg_map_to_kg"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "id": "77effc7a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>region</th>\n",
+       "      <th>Region1</th>\n",
+       "      <th>Region2</th>\n",
+       "      <th>Region3</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>chem_stressor</th>\n",
+       "      <th>compartment</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>CH4</th>\n",
+       "      <th>Air</th>\n",
+       "      <td>500.0</td>\n",
+       "      <td>600.0</td>\n",
+       "      <td>700.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CO2</th>\n",
+       "      <th>Air</th>\n",
+       "      <td>5000.0</td>\n",
+       "      <td>6000.0</td>\n",
+       "      <td>7000.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "region                     Region1  Region2  Region3\n",
+       "chem_stressor compartment                           \n",
+       "CH4           Air            500.0    600.0    700.0\n",
+       "CO2           Air           5000.0   6000.0   7000.0"
+      ]
+     },
+     "execution_count": 66,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
     "ghg_new_kg = pymrio.convert(ghg_result_ton, ghg_map_to_kg)\n",
     "ghg_new_kg"
    ]
@@ -340,12 +800,77 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 67,
    "id": "677f3872",
    "metadata": {
     "lines_to_next_cell": 2
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>region</th>\n",
+       "      <th>Region1</th>\n",
+       "      <th>Region2</th>\n",
+       "      <th>Region3</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>chem_stressor</th>\n",
+       "      <th>compartment</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>CH4</th>\n",
+       "      <th>Air</th>\n",
+       "      <td>500.0</td>\n",
+       "      <td>600.0</td>\n",
+       "      <td>700.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CO2</th>\n",
+       "      <th>Air</th>\n",
+       "      <td>5000.0</td>\n",
+       "      <td>6000.0</td>\n",
+       "      <td>7000.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "region                     Region1  Region2  Region3\n",
+       "chem_stressor compartment                           \n",
+       "CH4           Air            500.0    600.0    700.0\n",
+       "CO2           Air           5000.0   6000.0   7000.0"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "ghg_new_kg"
    ]
@@ -363,10 +888,113 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 68,
    "id": "289eae3b",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>chem_stressor</th>\n",
+       "      <th>GWP__chem_stressor</th>\n",
+       "      <th>factor</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>CO2</td>\n",
+       "      <td>GWP100</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>CH4</td>\n",
+       "      <td>GWP100</td>\n",
+       "      <td>29</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>NHx</td>\n",
+       "      <td>GWP100</td>\n",
+       "      <td>273</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>CO2</td>\n",
+       "      <td>GWP20</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>CH4</td>\n",
+       "      <td>GWP20</td>\n",
+       "      <td>80</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>NHx</td>\n",
+       "      <td>GWP20</td>\n",
+       "      <td>273</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>CO2</td>\n",
+       "      <td>GWP500</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>CH4</td>\n",
+       "      <td>GWP500</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>NHx</td>\n",
+       "      <td>GWP500</td>\n",
+       "      <td>130</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  chem_stressor GWP__chem_stressor  factor\n",
+       "0           CO2             GWP100       1\n",
+       "1           CH4             GWP100      29\n",
+       "2           NHx             GWP100     273\n",
+       "3           CO2              GWP20       1\n",
+       "4           CH4              GWP20      80\n",
+       "5           NHx              GWP20     273\n",
+       "6           CO2             GWP500       1\n",
+       "7           CH4             GWP500       8\n",
+       "8           NHx             GWP500     130"
+      ]
+     },
+     "execution_count": 68,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "GWP_characterization = pd.DataFrame(\n",
     "    columns=[\"chem_stressor\", \"GWP__chem_stressor\", \"factor\"],\n",
@@ -387,12 +1015,80 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 69,
    "id": "04df3552",
    "metadata": {
     "lines_to_next_cell": 2
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th>region</th>\n",
+       "      <th>Region1</th>\n",
+       "      <th>Region2</th>\n",
+       "      <th>Region3</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>GWP</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>GWP100</th>\n",
+       "      <td>19500.0</td>\n",
+       "      <td>23400.0</td>\n",
+       "      <td>27300.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>GWP20</th>\n",
+       "      <td>45000.0</td>\n",
+       "      <td>54000.0</td>\n",
+       "      <td>63000.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>GWP500</th>\n",
+       "      <td>9000.0</td>\n",
+       "      <td>10800.0</td>\n",
+       "      <td>12600.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "region  Region1  Region2  Region3\n",
+       "GWP                              \n",
+       "GWP100  19500.0  23400.0  27300.0\n",
+       "GWP20   45000.0  54000.0  63000.0\n",
+       "GWP500   9000.0  10800.0  12600.0"
+      ]
+     },
+     "execution_count": 69,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "GWP_result = pymrio.convert(ghg_new_kg, GWP_characterization)\n",
     "GWP_result"
@@ -413,13 +1109,86 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 70,
    "id": "ab77360e",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>region</th>\n",
+       "      <th>Region1</th>\n",
+       "      <th>Region2</th>\n",
+       "      <th>Region3</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>GWP</th>\n",
+       "      <th>compartment</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>GWP100</th>\n",
+       "      <th>Air</th>\n",
+       "      <td>19500.0</td>\n",
+       "      <td>23400.0</td>\n",
+       "      <td>27300.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>GWP20</th>\n",
+       "      <th>Air</th>\n",
+       "      <td>45000.0</td>\n",
+       "      <td>54000.0</td>\n",
+       "      <td>63000.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>GWP500</th>\n",
+       "      <th>Air</th>\n",
+       "      <td>9000.0</td>\n",
+       "      <td>10800.0</td>\n",
+       "      <td>12600.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "region              Region1  Region2  Region3\n",
+       "GWP    compartment                           \n",
+       "GWP100 Air          19500.0  23400.0  27300.0\n",
+       "GWP20  Air          45000.0  54000.0  63000.0\n",
+       "GWP500 Air           9000.0  10800.0  12600.0"
+      ]
+     },
+     "execution_count": 70,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "GWP_result_with_comp = pymrio.convert(\n",
-    "    ghg_new_kg, GWP_characterization, drop_not_bridged=False\n",
+    "    ghg_new_kg, GWP_characterization, drop_not_bridged_index=False\n",
     ")\n",
     "GWP_result_with_comp"
    ]
@@ -457,10 +1226,99 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 71,
    "id": "00156b6d",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th>region</th>\n",
+       "      <th>Region1</th>\n",
+       "      <th>Region2</th>\n",
+       "      <th>Region3</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>stressor</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Wheat</th>\n",
+       "      <td>3</td>\n",
+       "      <td>10</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Maize</th>\n",
+       "      <td>5</td>\n",
+       "      <td>20</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Rice</th>\n",
+       "      <td>0</td>\n",
+       "      <td>12</td>\n",
+       "      <td>34</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Pasture</th>\n",
+       "      <td>12</td>\n",
+       "      <td>34</td>\n",
+       "      <td>9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Forest extensive</th>\n",
+       "      <td>32</td>\n",
+       "      <td>27</td>\n",
+       "      <td>11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Forest intensive</th>\n",
+       "      <td>43</td>\n",
+       "      <td>17</td>\n",
+       "      <td>24</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "region            Region1  Region2  Region3\n",
+       "stressor                                   \n",
+       "Wheat                   3       10        1\n",
+       "Maize                   5       20        3\n",
+       "Rice                    0       12       34\n",
+       "Pasture                12       34        9\n",
+       "Forest extensive       32       27       11\n",
+       "Forest intensive       43       17       24"
+      ]
+     },
+     "execution_count": 71,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "land_use_result = pd.DataFrame(\n",
     "    columns=[\"Region1\", \"Region2\", \"Region3\"],\n",
@@ -500,11 +1358,142 @@
    ]
   },
   {
-   "cell_type": "markdown",
-   "id": "1f1b48ff",
+   "cell_type": "code",
+   "execution_count": 72,
+   "id": "e3ed4128",
    "metadata": {
     "lines_to_next_cell": 2
    },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>stressor</th>\n",
+       "      <th>BioDiv__stressor</th>\n",
+       "      <th>region</th>\n",
+       "      <th>factor</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Wheat|Maize</td>\n",
+       "      <td>BioImpact</td>\n",
+       "      <td>Region1</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Wheat</td>\n",
+       "      <td>BioImpact</td>\n",
+       "      <td>Region[2,3]</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Maize</td>\n",
+       "      <td>BioImpact</td>\n",
+       "      <td>Region[2,3]</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Rice</td>\n",
+       "      <td>BioImpact</td>\n",
+       "      <td>Region1</td>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Rice</td>\n",
+       "      <td>BioImpact</td>\n",
+       "      <td>Region2</td>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Rice</td>\n",
+       "      <td>BioImpact</td>\n",
+       "      <td>Region3</td>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>Pasture</td>\n",
+       "      <td>BioImpact</td>\n",
+       "      <td>Region[1,2,3]</td>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>Forest.*</td>\n",
+       "      <td>BioImpact</td>\n",
+       "      <td>Region1</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>Forest.*</td>\n",
+       "      <td>BioImpact</td>\n",
+       "      <td>Region2</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>Forest ext.*</td>\n",
+       "      <td>BioImpact</td>\n",
+       "      <td>Region3</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>Forest int.*</td>\n",
+       "      <td>BioImpact</td>\n",
+       "      <td>Region3</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        stressor BioDiv__stressor         region  factor\n",
+       "0    Wheat|Maize        BioImpact        Region1       3\n",
+       "1          Wheat        BioImpact    Region[2,3]       4\n",
+       "2          Maize        BioImpact    Region[2,3]       7\n",
+       "3           Rice        BioImpact        Region1      12\n",
+       "4           Rice        BioImpact        Region2      12\n",
+       "5           Rice        BioImpact        Region3      12\n",
+       "6        Pasture        BioImpact  Region[1,2,3]      12\n",
+       "7       Forest.*        BioImpact        Region1       2\n",
+       "8       Forest.*        BioImpact        Region2       3\n",
+       "9   Forest ext.*        BioImpact        Region3       1\n",
+       "10  Forest int.*        BioImpact        Region3       3"
+      ]
+     },
+     "execution_count": 72,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "landuse_characterization = pd.DataFrame(\n",
     "    columns=[\"stressor\", \"BioDiv__stressor\", \"region\", \"factor\"],\n",
@@ -559,10 +1548,64 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 73,
    "id": "8f2e3a37",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th>region</th>\n",
+       "      <th>Region1</th>\n",
+       "      <th>Region2</th>\n",
+       "      <th>Region3</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>BioDiv</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>BioImpact</th>\n",
+       "      <td>318</td>\n",
+       "      <td>864</td>\n",
+       "      <td>624</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "region     Region1  Region2  Region3\n",
+       "BioDiv                              \n",
+       "BioImpact      318      864      624"
+      ]
+     },
+     "execution_count": 73,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "biodiv_result = pymrio.convert(land_use_result, landuse_characterization)\n",
     "biodiv_result"
@@ -583,12 +1626,106 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "9d435a0a",
+   "execution_count": 74,
+   "id": "157dd099",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "stressor          region \n",
+       "Wheat             Region1     3\n",
+       "                  Region2    10\n",
+       "                  Region3     1\n",
+       "Maize             Region1     5\n",
+       "                  Region2    20\n",
+       "                  Region3     3\n",
+       "Rice              Region1     0\n",
+       "                  Region2    12\n",
+       "                  Region3    34\n",
+       "Pasture           Region1    12\n",
+       "                  Region2    34\n",
+       "                  Region3     9\n",
+       "Forest extensive  Region1    32\n",
+       "                  Region2    27\n",
+       "                  Region3    11\n",
+       "Forest intensive  Region1    43\n",
+       "                  Region2    17\n",
+       "                  Region3    24\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 74,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "land_use_result_stacked = land_use_result.stack(level=\"region\")\n",
+    "land_use_result_stacked"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "id": "9d435a0a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th>region</th>\n",
+       "      <th>Region1</th>\n",
+       "      <th>Region2</th>\n",
+       "      <th>Region3</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>BioDiv</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>BioImpact</th>\n",
+       "      <td>318</td>\n",
+       "      <td>864</td>\n",
+       "      <td>624</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "region     Region1  Region2  Region3\n",
+       "BioDiv                              \n",
+       "BioImpact      318      864      624"
+      ]
+     },
+     "execution_count": 75,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
     "biodiv_result_stacked = pymrio.convert(\n",
     "    land_use_result_stacked, landuse_characterization, drop_not_bridged_index=False\n",
     ")\n",
@@ -616,7 +1753,7 @@
    "metadata": {},
    "source": [
     "Irrespectively of the table or the mrio system, the convert function always follows the same pattern.\n",
-    "It requires a bridge table, which contains the mapping of the indicies of the source data to the indicies of the target data.\n",
+    "It requires a bridge table, which contains the mapping of the indices of the source data to the indices of the target data.\n",
     "This bridge table has to follow a specific format, depending on the table to be converted."
    ]
   }
@@ -637,7 +1774,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.19"
+   "version": "3.12.0"
   }
  },
  "nbformat": 4,
diff --git a/doc/source/notebooks/convert.py b/doc/source/notebooks/convert.py
index a14daeec..f499c6a7 100644
--- a/doc/source/notebooks/convert.py
+++ b/doc/source/notebooks/convert.py
@@ -5,7 +5,7 @@
 #       extension: .py
 #       format_name: percent
 #       format_version: '1.3'
-#       jupytext_version: 1.16.4
+#       jupytext_version: 1.15.2
 #   kernelspec:
 #     display_name: Python 3 (ipykernel)
 #     language: python
@@ -23,7 +23,7 @@
 # - renaming the index names of results/extensions
 # - adjusting the numerical values of the data,
 #   e.g. for unit conversion or characterisation
-# - finding and extracting data based on indicies across a table or an mrio(-extension).
+# - finding and extracting data based on indices across a table or an mrio(-extension).
 #   This can be system based on name and potentially constrained by sector/region
 #   or any other specification.
 # - Aggregation/Summation of satellite accounts
@@ -166,25 +166,26 @@
 
 # %% [markdown]
 # We can get the data in kg by
-#
-#
-# ghg_map_to_kg = pd.DataFrame(
-#     columns=[
-#         "stressor",
-#         "compartment",
-#         "chem_stressor__stressor",
-#         "compartment__compartment",
-#         "factor",
-#     ],
-#     data=[
-#         ["Carbon Dioxide", "[A|a]ir", "CO2", "Air", 1000],
-#         ["Methane", "[A|a]ir", "CH4", "Air", 1000],
-#     ],
-# )
-# ghg_map_to_kg
-#
-# ghg_new_kg = pymrio.convert(ghg_result_ton, ghg_map_to_kg)
-# ghg_new_kg
+
+# %%
+ghg_map_to_kg = pd.DataFrame(
+    columns=[
+        "stressor",
+        "compartment",
+        "chem_stressor__stressor",
+        "compartment__compartment",
+        "factor",
+    ],
+    data=[
+        ["Carbon Dioxide", "[A|a]ir", "CO2", "Air", 1000],
+        ["Methane", "[A|a]ir", "CH4", "Air", 1000],
+    ],
+)
+ghg_map_to_kg
+
+# %%
+ghg_new_kg = pymrio.convert(ghg_result_ton, ghg_map_to_kg)
+ghg_new_kg
 
 # %% [markdown]
 # In case of unit conversion of pymrio satellite accounts,
@@ -248,7 +249,7 @@
 
 # %%
 GWP_result_with_comp = pymrio.convert(
-    ghg_new_kg, GWP_characterization, drop_not_bridged=False
+    ghg_new_kg, GWP_characterization, drop_not_bridged_index=False
 )
 GWP_result_with_comp
 
@@ -299,24 +300,24 @@
 # any bridge column mapping it to a new name. Thus, the "region" can either be in the index
 # or in the columns of the source data - in the given case it is in the columns.
 
-# %% [markdown]
-# landuse_characterization = pd.DataFrame(
-#     columns=["stressor", "BioDiv__stressor", "region", "factor"],
-#     data=[
-#         ["Wheat|Maize", "BioImpact", "Region1", 3],
-#         ["Wheat", "BioImpact", "Region[2,3]", 4],
-#         ["Maize", "BioImpact", "Region[2,3]", 7],
-#         ["Rice", "BioImpact", "Region1", 12],
-#         ["Rice", "BioImpact", "Region2", 12],
-#         ["Rice", "BioImpact", "Region3", 12],
-#         ["Pasture", "BioImpact", "Region[1,2,3]", 12],
-#         ["Forest.*", "BioImpact", "Region1", 2],
-#         ["Forest.*", "BioImpact", "Region2", 3],
-#         ["Forest ext.*", "BioImpact", "Region3", 1],
-#         ["Forest int.*", "BioImpact", "Region3", 3],
-#     ],
-# )
-# landuse_characterization
+# %%
+landuse_characterization = pd.DataFrame(
+    columns=["stressor", "BioDiv__stressor", "region", "factor"],
+    data=[
+        ["Wheat|Maize", "BioImpact", "Region1", 3],
+        ["Wheat", "BioImpact", "Region[2,3]", 4],
+        ["Maize", "BioImpact", "Region[2,3]", 7],
+        ["Rice", "BioImpact", "Region1", 12],
+        ["Rice", "BioImpact", "Region2", 12],
+        ["Rice", "BioImpact", "Region3", 12],
+        ["Pasture", "BioImpact", "Region[1,2,3]", 12],
+        ["Forest.*", "BioImpact", "Region1", 2],
+        ["Forest.*", "BioImpact", "Region2", 3],
+        ["Forest ext.*", "BioImpact", "Region3", 1],
+        ["Forest int.*", "BioImpact", "Region3", 3],
+    ],
+)
+landuse_characterization
 
 
 # %% [markdown]
@@ -354,6 +355,9 @@
 
 # %%
 land_use_result_stacked = land_use_result.stack(level="region")
+land_use_result_stacked
+
+# %%
 biodiv_result_stacked = pymrio.convert(
     land_use_result_stacked, landuse_characterization, drop_not_bridged_index=False
 )
@@ -370,5 +374,5 @@
 
 # %% [markdown]
 # Irrespectively of the table or the mrio system, the convert function always follows the same pattern.
-# It requires a bridge table, which contains the mapping of the indicies of the source data to the indicies of the target data.
+# It requires a bridge table, which contains the mapping of the indices of the source data to the indices of the target data.
 # This bridge table has to follow a specific format, depending on the table to be converted.
diff --git a/doc/source/notebooks/extract_data.ipynb b/doc/source/notebooks/extract_data.ipynb
index 93512e89..609243c8 100644
--- a/doc/source/notebooks/extract_data.ipynb
+++ b/doc/source/notebooks/extract_data.ipynb
@@ -1242,7 +1242,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.19"
+   "version": "3.12.0"
   }
  },
  "nbformat": 4,
diff --git a/doc/source/notebooks/extract_data.py b/doc/source/notebooks/extract_data.py
index 397557fc..c6608581 100644
--- a/doc/source/notebooks/extract_data.py
+++ b/doc/source/notebooks/extract_data.py
@@ -5,7 +5,7 @@
 #       extension: .py
 #       format_name: percent
 #       format_version: '1.3'
-#       jupytext_version: 1.16.4
+#       jupytext_version: 1.15.2
 #   kernelspec:
 #     display_name: Python 3 (ipykernel)
 #     language: python
diff --git a/pymrio/core/mriosystem.py b/pymrio/core/mriosystem.py
index ee7189c6..e2128948 100644
--- a/pymrio/core/mriosystem.py
+++ b/pymrio/core/mriosystem.py
@@ -3246,6 +3246,11 @@ def remove_extension(self, ext):
 
         return self
 
+    def extension_convert():
+        raise NotImplementedError("TODO: extension convert as method")
+        # Can pass extensions a string of extensions in the IO System, get them as extensions,
+        # put it as new extension in pymrio, with a switch to remove converted extensions
+
 
 def extension_convert(
     *extensions,
@@ -3271,7 +3276,7 @@ def extension_convert(
 
     extensions : list of extensions
         Extensions to convert. All extensions passed must
-        have an index structure (index names)  ase described in df_map.
+        have an index structure (index names) as described in df_map.
 
     df_map : pd.DataFrame
         The DataFrame with the mapping of the old to the new classification.
@@ -3376,6 +3381,12 @@ def extension_convert(
     gather = []
 
     for ext in extensions:
+        if ext.name not in df_map[extension_col_name].unique():
+            warnings.warn(
+                f"Extension {ext.name} not found in df_map. Skipping extension."
+            )
+            # TODO: later go to logging
+            continue
         gather.append(
             ext.convert(
                 df_map=df_map[df_map[extension_col_name] == ext.name],
diff --git a/tests/test_core.py b/tests/test_core.py
index a4eebffe..de69fe09 100644
--- a/tests/test_core.py
+++ b/tests/test_core.py
@@ -679,13 +679,7 @@ def test_extension_convert_function(fix_testmrio):
         ],
     )
 
-    # CONT: Something wrong with setting the index to a multiindex when compartment is passed
-    # Next steps: run this in interprester (with autoreload) and set breakpoint in extension_convert
-    # Seems to be in gather, but after that in the aggregation or Concatenate we get a problem
-
-    # x = tt_pre.extension_convert(df_map, new_extension_name="emissions_new_pre_calc")
-
-    # Doing two time the same extension
+    # CASE 1: Doing two time the same extension, must double the results
     ext_double = pymrio.extension_convert(
         tt_pre.emissions,
         tt_pre.emissions,
@@ -693,58 +687,137 @@ def test_extension_convert_function(fix_testmrio):
         new_extension_name="emissions_new_pre_calc",
     )
 
-    # TODO: check return type and update test
-    # assert ext_double.unit.loc["total_sum_tonnes", "unit"] == "t"
-    # assert ext_double.unit.loc["water_emissions", "unit"] == "g"
-
-    # pdt.assert_series_equal(
-    #     ext_double.F.loc["total_sum_tonnes"],
-    #     tt_pre.emissions.F.sum(axis=0) * 1e-3 * 2,
-    #     check_names=False,
-    # )
-    #
-    # pdt.assert_series_equal(
-    #     ext_double.F.loc["water_emissions"],
-    #     tt_pre.emissions.F.loc["emission_type2", :].iloc[0, :] * 1000 * 2,
-    #     check_names=False,
-    # )
-    #
-    # tt_pre.emission_new = ext_double
-    #
-    # df_map_add_across = pd.DataFrame(
-    #     columns=[
-    #         "extension",
-    #         "stressor",
-    #         "compartment",
-    #         "total__stressor",
-    #         "factor",
-    #         "unit_orig",
-    #         "unit_new",
-    #     ],
-    #     data=[
-    #         ["Emissions", "emission_type2", ".*", "water", 1, "kg", "kg"],
-    #         [
-    #             "emission_new_pre_calc",
-    #             "water_emissions",
-    #             ".*",
-    #             "water",
-    #             1e-3,
-    #             "g",
-    #             "kg",
-    #         ],
-    #     ],
-    # )
-    #
-    # ext_across = pymrio.extension_convert(
-    #     tt_pre.emissions,
-    #     ext_double,
-    #     df_map=df_map_add_across,
-    #     new_extension_name="add_across",
-    # )
-
-    # CONT:
-    # make a second extensions are check running over 2
-    # cleanup docstrings and write docs
+    assert ext_double.unit.loc["total_sum_tonnes", "unit"].values == ["t"]
+    assert ext_double.unit.loc["water_emissions", "unit"].values == ["g"]
+
+    pdt.assert_series_equal(
+        ext_double.F.loc[("total_sum_tonnes", "total")],
+        tt_pre.emissions.F.sum(axis=0) * 1e-3 * 2,
+        check_names=False,
+    )
+
+    pdt.assert_series_equal(
+        ext_double.F.loc[("water_emissions", "water")],
+        tt_pre.emissions.F.loc["emission_type2", :].iloc[0, :] * 1000 * 2,
+        check_names=False,
+    )
+
+    # CASE 2: convert across 2 extensions
+    tt_pre.emission_new = ext_double
+
+    df_map_add_across = pd.DataFrame(
+        columns=[
+            "extension",
+            "stressor",
+            "compartment",
+            "total__stressor",
+            "factor",
+            "unit_orig",
+            "unit_new",
+        ],
+        data=[
+            ["Emissions", "emission_type2", ".*", "water", 1, "kg", "kg"],
+            [
+                "emissions_new_pre_calc",
+                "water_emissions",
+                ".*",
+                "water",
+                1e-3,
+                "g",
+                "kg",
+            ],
+        ],
+    )
+
+    df_map_add_across_wrong_name = df_map_add_across.copy()
+
+    df_map_add_across_wrong_name.loc[:, "extension"] = df_map_add_across_wrong_name.extension.str.replace("emissions_new_pre_calc", "foo")
+
+    ext_across_correct = pymrio.extension_convert(
+        tt_pre.emissions,
+        ext_double,
+        df_map=df_map_add_across,
+        new_extension_name="add_across",
+    )
+
+    ext_across_wrong = pymrio.extension_convert(
+        tt_pre.emissions,
+        ext_double,
+        df_map=df_map_add_across_wrong_name,
+        new_extension_name="add_across",
+    )
+
+    expected_df_correct_F = tt_pre.emissions.F.loc["emission_type2", :].iloc[0, :] + ext_double.F.loc[("water_emissions", "water")] * 1e-3
+    expected_df_wrong_F = tt_pre.emissions.F.loc["emission_type2", :].iloc[0, :]
+    expected_df_correct_F_Y = tt_pre.emissions.F_Y.loc["emission_type2", :].iloc[0, :] + ext_double.F_Y.loc[("water_emissions", "water")] * 1e-3
+    expected_df_wrong_F_Y = tt_pre.emissions.F_Y.loc["emission_type2", :].iloc[0, :]
+
+    pdt.assert_series_equal(
+        ext_across_correct.F.loc[("water",)],
+        expected_df_correct_F,
+        check_names=False,
+    )
+    pdt.assert_series_equal(
+        ext_across_correct.F_Y.loc[("water",)],
+        expected_df_correct_F_Y,
+        check_names=False,
+    )
+    pdt.assert_series_equal(
+        ext_across_wrong.F.loc[("water",)],
+        expected_df_wrong_F,
+        check_names=False,
+    )
+    pdt.assert_series_equal(
+        ext_across_wrong.F_Y.loc[("water",)],
+        expected_df_wrong_F_Y,
+        check_names=False,
+    )
+
+    # CASE 3: Test for full calculated system
+    tt_post = fix_testmrio.testmrio.copy().calc_all()
+
+    # when one extensions has less calculated parts then the other, these should
+    # silently set to None
+    ext_test_missing = pymrio.extension_convert(
+        tt_post.emissions,
+        ext_double,
+        df_map=df_map_add_across,
+        new_extension_name="add_across",
+    )
+
+    assert ext_test_missing.S is None
+    assert ext_test_missing.D_cba is None
+
+    tt_post.add_across = ext_double
+    tt_post.calc_all()
+
+    ext_test_all = pymrio.extension_convert(
+        tt_post.emissions,
+        tt_post.add_across,
+        df_map=df_map_add_across,
+        new_extension_name="add_across",
+    )
+
+    expected_df_D_cba = tt_post.emissions.D_cba.loc["emission_type2", :].iloc[0, :] + tt_post.add_across.D_cba.loc[("water_emissions", "water")] * 1e-3
+    expected_df_S = tt_post.emissions.S.loc["emission_type2", :].iloc[0, :] + tt_post.add_across.S.loc[("water_emissions", "water")] * 1e-3
+    expected_df_M = tt_post.emissions.M.loc["emission_type2", :].iloc[0, :] + tt_post.add_across.M.loc[("water_emissions", "water")] * 1e-3
+
+    pdt.assert_series_equal(
+        ext_test_all.D_cba.iloc[0],
+        expected_df_D_cba,
+        check_names=False,
+    )
+    pdt.assert_series_equal(
+        ext_test_all.S.iloc[0],
+        expected_df_S,
+        check_names=False,
+    )
+    pdt.assert_series_equal(
+        ext_test_all.M.iloc[0],
+        expected_df_M,
+        check_names=False,
+    )
+
 
 
 def test_extension_convert_test_unit_fail(fix_testmrio):