diff --git a/CHANGELOG.rst b/CHANGELOG.rst
index f8a6b4e2..44a0856e 100644
--- a/CHANGELOG.rst
+++ b/CHANGELOG.rst
@@ -2,6 +2,7 @@
 Changelog
 #########
 
+
 v0.6dev
 =======
 
diff --git a/doc/source/notebooks/convert.py b/doc/source/notebooks/convert.py
index f499c6a7..526b48bc 100644
--- a/doc/source/notebooks/convert.py
+++ b/doc/source/notebooks/convert.py
@@ -269,7 +269,7 @@
 # For that, we assume some land use results for different regions:
 
 # %%
-land_use_result = pd.DataFrame(
+land_use_data = pd.DataFrame(
     columns=["Region1", "Region2", "Region3"],
     index=[
         "Wheat",
@@ -288,9 +288,9 @@
         [43, 17, 24],
     ],
 )
-land_use_result.index.names = ["stressor"]
-land_use_result.columns.names = ["region"]
-land_use_result
+land_use_data.index.names = ["stressor"]
+land_use_data.columns.names = ["region"]
+land_use_data
 
 # %% [markdown]
 # Now we setup a pseudo characterization table for converting the land use data into
@@ -342,8 +342,8 @@
 # With that setup we can now characterize the land use data in land_use_result.
 
 # %%
-biodiv_result = pymrio.convert(land_use_result, landuse_characterization)
-biodiv_result
+biodiv_characterised = pymrio.convert(land_use_data, landuse_characterization)
+biodiv_characterised
 
 # %% [markdown]
 # Note, that in this example the region is not in the index
@@ -354,25 +354,93 @@
 # output. Thus the result is equivalent to
 
 # %%
-land_use_result_stacked = land_use_result.stack(level="region")
-land_use_result_stacked
+land_use_data_stacked = land_use_data.stack(level="region")
+land_use_data_stacked
 
 # %%
-biodiv_result_stacked = pymrio.convert(
-    land_use_result_stacked, landuse_characterization, drop_not_bridged_index=False
+biodiv_characterised_stacked = pymrio.convert(
+    land_use_data_stacked, landuse_characterization, drop_not_bridged_index=False
 )
-biodiv_result_stacked.unstack(level="region")[0]
+biodiv_characterised_stacked.unstack(level="region")[0]
 
 # %% [markdown]
 # In this case we have to specify to not drop the not bridged "region" index.
 # We then unstack the result again, and have to select the first element ([0]),
 # since there where not other columns left after stacking them before the
 # characterization.
-#
-# CONT: start working on convert for extensions/mrio method
 
 
 # %% [markdown]
+# TODO: section perhaps needed somewhere?
 # 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 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.
+
+# %% [markdown]
+## Converting pymrio Extensions
+
+# %% [markdown]
+# The same principles as for individual tables can be used for converting full pymrio type Extensions (aka satellite accounts).
+# In difference to the single tables, pymrio Extensions consist of several pandas DataFrames which can be converted in one go.
+# Almost the same bridge table structure as for single tables can be used. The main additional information needed is in regard to 
+# units. Since pymrio Extensions include a unit dataframe, information about the unit names need to be included.
+
+# %% [markdown]
+# Extensions can be converted either one at a time, but the main power of the method lies in collecting stressor data across different extensions 
+# and converting them in one go.
+
+# %% [markdown]
+# We start with a simple example for converting a single extension of a pymrio MRIO system.
+# To do so, we load the test MRIO system from pymrio.
+
+# %%
+mrio = pymrio.load_test()
+
+# %% [markdown]
+# Among others, this system has an extension "emissions" with industry and final demand emissions.
+
+
+# %%
+mrio.emissions.F
+
+# %%
+mrio.emissions.F_Y
+
+# %%
+mrio.emissions.unit
+
+# %% [markdown]
+# We now setup a bridge table for converting/characterizing these emission data 
+# to several other accounts.
+
+# %%
+emis_bridge = pd.DataFrame(
+    columns=[
+        "stressor",
+        "compartment",
+        "total__stressor",
+        "factor",
+        "unit_orig",
+        "unit_new",
+    ],
+    data=[
+        ["emis.*", "air|water", "total_sum_tonnes", 1e-3, "kg", "t"],
+        ["emission_type[1|2]", ".*", "total_sum", 1, "kg", "kg"],
+        ["emission_type1", ".*", "air_emissions", 1e-3, "kg", "t"],
+        ["emission_type2", ".*", "water_emissions", 1000, "kg", "g"],
+    ],
+)
+emis_bridge
+
+# %% [markdown]
+# This is a fully made up example showing various capabilities of the method.
+# In line
+#    - 0: find all stressors with emissions (emis.*) in either air or water (air|water) compartment, rename it to "total_sum_tonnes" (total__stressor) by multiplying with a factor 0.0001 which converts the original unit "kg" to tonnes.
+#    - 1: find emission_type1 and 2, over all compartments and sum them together without any multiplication
+#    - 2: convert emissions of type 1 to air emissions in tons
+#    - 3: convert emissions of type 2 to water emissions in g
+
+
+# %%
+mrio.emissions.convert(emis_bridge, new_extension_name="abc").F
+
diff --git a/pymrio/core/mriosystem.py b/pymrio/core/mriosystem.py
index e2128948..17b61ee0 100644
--- a/pymrio/core/mriosystem.py
+++ b/pymrio/core/mriosystem.py
@@ -2079,15 +2079,18 @@ def convert(
             unit = pd.DataFrame(columns=["unit"], index=new_extension.get_rows())
             bridge_columns = [col for col in df_map.columns if "__" in col]
             unique_new_index = (
-                df_map.loc[:, bridge_columns]
-                .drop_duplicates()
+                df_map
+                .drop_duplicates(subset=bridge_columns)
+                .loc[:, bridge_columns]
                 .set_index(bridge_columns)
                 .index
             )
             unique_new_index.names = [col.split("__")[0] for col in bridge_columns]
 
             unit.unit = (
-                df_map.set_index(bridge_columns)
+                df_map
+                .drop_duplicates(subset=bridge_columns)
+                .set_index(bridge_columns)
                 .loc[unique_new_index]
                 .loc[:, unit_column_new]
             )
diff --git a/tests/test_core.py b/tests/test_core.py
index de69fe09..34bc6241 100644
--- a/tests/test_core.py
+++ b/tests/test_core.py
@@ -556,6 +556,7 @@ def test_characterize_extension(fix_testmrio):
 def test_extension_convert(fix_testmrio):
     """Testing the convert function within extensions object"""
     tt_pre = fix_testmrio.testmrio.copy()
+
     df_map = pd.DataFrame(
         columns=[
             "stressor",
@@ -570,13 +571,19 @@ def test_extension_convert(fix_testmrio):
             ["emission_type[1|2]", ".*", "total_sum", 1, "kg", "kg"],
             ["emission_type1", ".*", "air_emissions", 1e-3, "kg", "t"],
             ["emission_type2", ".*", "water_emissions", 1000, "kg", "g"],
+            ["emission_type1", ".*", "char_emissions", 2, "kg", "kg_eq"],
+            ["emission_type2", ".*", "char_emissions", 10, "kg", "kg_eq"],
         ],
     )
+
     tt_pre.pre_calc = tt_pre.emissions.convert(
         df_map, new_extension_name="emissions_new_pre_calc"
     )
+
     tt_pre.calc_all()
 
+    # CONT: continue writing tests for characterized "char_emissions"
+
     pdt.assert_series_equal(
         tt_pre.emissions.D_cba.loc["emission_type1", "air"],
         tt_pre.pre_calc.D_cba.loc["air_emissions"] * 1000,
@@ -603,6 +610,7 @@ def test_extension_convert(fix_testmrio):
     assert tt_pre.pre_calc.unit.loc["total_sum", "unit"] == "kg"
     assert tt_pre.pre_calc.unit.loc["air_emissions", "unit"] == "t"
     assert tt_pre.pre_calc.unit.loc["water_emissions", "unit"] == "g"
+    assert tt_pre.pre_calc.unit.loc["char_emissions", "unit"] == "kg_eq"
 
     tt_post = fix_testmrio.testmrio.copy()
     tt_post.calc_all()