Repository: IAMconsortium/pyam

Issue:
Pandas 2.0 breaks some tests
As I was updating https://github.com/IAMconsortium/nomenclature to be pandas 2.0 compatible I checked if pandas 2.0 would break anything in pyam. 
Turns out it does:
```console
================================================================ short test summary info =================================================================
FAILED tests/test_core.py::test_init_df_with_duplicates_raises[test_df0] - AttributeError: 'DataFrame' object has no attribute 'append'
FAILED tests/test_core.py::test_init_df_with_duplicates_raises[test_df1] - AttributeError: 'DataFrame' object has no attribute 'append'
FAILED tests/test_core.py::test_init_df_with_duplicates_raises[test_df2] - AttributeError: 'DataFrame' object has no attribute 'append'
FAILED tests/test_core.py::test_init_df_with_duplicates_raises[test_df3] - AttributeError: 'DataFrame' object has no attribute 'append'
FAILED tests/test_core.py::test_interpolate_datetimes[test_df1] - TypeError: assert_series_equal() got an unexpected keyword argument 'check_less_precise'
FAILED tests/test_core.py::test_interpolate_datetimes[test_df2] - TypeError: assert_series_equal() got an unexpected keyword argument 'check_less_precise'
FAILED tests/test_core.py::test_interpolate_datetimes[test_df3] - TypeError: assert_series_equal() got an unexpected keyword argument 'check_less_precise'
FAILED tests/test_filter.py::test_filter_mixed_time_domain[arg_year0-arg_time0] - AttributeError: module 'pandas' has no attribute 'Int64Index'
FAILED tests/test_filter.py::test_filter_mixed_time_domain[arg_year1-arg_time1] - AttributeError: module 'pandas' has no attribute 'Int64Index'
FAILED tests/test_filter.py::test_filter_with_numpy_64_date_vals[test_df1] - AssertionError: assert False
FAILED tests/test_filter.py::test_filter_with_numpy_64_date_vals[test_df2] - AssertionError: assert False
FAILED tests/test_filter.py::test_filter_with_numpy_64_date_vals[test_df3] - AssertionError: assert False
FAILED tests/test_iiasa.py::test_query_with_meta_arg[kwargs0-meta0] - TypeError: Passing a set as an indexer is not supported. Use a list instead.
FAILED tests/test_iiasa.py::test_query_with_meta_arg[kwargs0-meta1] - TypeError: Passing a set as an indexer is not supported. Use a list instead.
FAILED tests/test_iiasa.py::test_query_with_meta_arg[kwargs1-meta0] - TypeError: Passing a set as an indexer is not supported. Use a list instead.
FAILED tests/test_iiasa.py::test_query_with_meta_arg[kwargs1-meta1] - TypeError: Passing a set as an indexer is not supported. Use a list instead.
FAILED tests/test_iiasa.py::test_query_with_meta_arg[kwargs2-meta0] - TypeError: Passing a set as an indexer is not supported. Use a list instead.
FAILED tests/test_iiasa.py::test_query_with_meta_arg[kwargs2-meta1] - TypeError: Passing a set as an indexer is not supported. Use a list instead.
FAILED tests/test_statistics.py::test_statistics - AttributeError: 'DataFrame' object has no attribute 'append'
FAILED tests/test_statistics.py::test_statistics_by_filter - AssertionError: MultiIndex level [1] are different
FAILED tests/test_statistics.py::test_statistics_with_rows - AssertionError: MultiIndex level [1] are different
FAILED tests/test_tutorials.py::test_iiasa_dbs - subprocess.CalledProcessError: Command '['jupyter', 'nbconvert', '--to', 'notebook', '--execute', '--E...
```

@danielhuppmann as this does not look like a 5 minute fix I would propose a version pin to `pandas<2` until it's resolved.

Discussion:
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  Files         59      59             
  Lines       6057    6006     -51     
=======================================
- Hits        5761    5712     -49     
+ Misses       296     294      -2     
```


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danielhuppmann: fyi @phackstock @gidden @coroa 

Patch:
diff --git a/RELEASE_NOTES.md b/RELEASE_NOTES.md
index 0687aa4..75f077e 100644
--- a/RELEASE_NOTES.md
+++ b/RELEASE_NOTES.md
@@ -1,5 +1,7 @@
 # Next Release
 
+- [#738](https://github.com/IAMconsortium/pyam/pull/738) Ensure compatibility with **pandas v2.0**
+
 # Release v1.8.0
 
 ## Highlights
diff --git a/docs/tutorials/quantiles.ipynb b/docs/tutorials/quantiles.ipynb
index d5cc822..6a441c4 100644
--- a/docs/tutorials/quantiles.ipynb
+++ b/docs/tutorials/quantiles.ipynb
@@ -11,7 +11,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -37,235 +37,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "pyam.core - INFO: Reading file tutorial_data.csv\n"
-     ]
-    },
-    {
-     "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></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th>2010</th>\n",
-       "      <th>2020</th>\n",
-       "      <th>2030</th>\n",
-       "      <th>2040</th>\n",
-       "      <th>2050</th>\n",
-       "      <th>2060</th>\n",
-       "      <th>2070</th>\n",
-       "      <th>2080</th>\n",
-       "      <th>2090</th>\n",
-       "      <th>2100</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>model</th>\n",
-       "      <th>scenario</th>\n",
-       "      <th>region</th>\n",
-       "      <th>variable</th>\n",
-       "      <th>unit</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"5\" valign=\"top\">AIM/CGE 2.1</th>\n",
-       "      <th rowspan=\"5\" valign=\"top\">CD-LINKS_INDCi</th>\n",
-       "      <th rowspan=\"5\" valign=\"top\">R5ASIA</th>\n",
-       "      <th>Emissions|CO2</th>\n",
-       "      <th>Mt CO2/yr</th>\n",
-       "      <td>11231.0880</td>\n",
-       "      <td>14359.2801</td>\n",
-       "      <td>14873.5967</td>\n",
-       "      <td>15238.9081</td>\n",
-       "      <td>15180.1854</td>\n",
-       "      <td>15513.1760</td>\n",
-       "      <td>16003.2060</td>\n",
-       "      <td>16343.3124</td>\n",
-       "      <td>17097.8681</td>\n",
-       "      <td>17722.1245</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Primary Energy</th>\n",
-       "      <th>EJ/yr</th>\n",
-       "      <td>145.7409</td>\n",
-       "      <td>191.0565</td>\n",
-       "      <td>216.2135</td>\n",
-       "      <td>234.2793</td>\n",
-       "      <td>245.9771</td>\n",
-       "      <td>258.3201</td>\n",
-       "      <td>268.7644</td>\n",
-       "      <td>275.0764</td>\n",
-       "      <td>283.1479</td>\n",
-       "      <td>288.6838</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Primary Energy|Biomass</th>\n",
-       "      <th>EJ/yr</th>\n",
-       "      <td>23.6647</td>\n",
-       "      <td>24.0751</td>\n",
-       "      <td>25.9262</td>\n",
-       "      <td>27.3646</td>\n",
-       "      <td>29.6938</td>\n",
-       "      <td>29.8102</td>\n",
-       "      <td>30.1178</td>\n",
-       "      <td>30.0109</td>\n",
-       "      <td>29.6166</td>\n",
-       "      <td>29.5846</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Primary Energy|Fossil</th>\n",
-       "      <th>EJ/yr</th>\n",
-       "      <td>116.1932</td>\n",
-       "      <td>155.0735</td>\n",
-       "      <td>168.2376</td>\n",
-       "      <td>179.0562</td>\n",
-       "      <td>185.2168</td>\n",
-       "      <td>195.6202</td>\n",
-       "      <td>203.4916</td>\n",
-       "      <td>207.3614</td>\n",
-       "      <td>214.7828</td>\n",
-       "      <td>217.7714</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Primary Energy|Non-Biomass Renewables</th>\n",
-       "      <th>EJ/yr</th>\n",
-       "      <td>4.5139</td>\n",
-       "      <td>9.2641</td>\n",
-       "      <td>17.0767</td>\n",
-       "      <td>22.0967</td>\n",
-       "      <td>25.3211</td>\n",
-       "      <td>26.6589</td>\n",
-       "      <td>27.9490</td>\n",
-       "      <td>29.3259</td>\n",
-       "      <td>29.3942</td>\n",
-       "      <td>30.6799</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                                                                         2010  \\\n",
-       "model       scenario       region variable                              unit                    \n",
-       "AIM/CGE 2.1 CD-LINKS_INDCi R5ASIA Emissions|CO2                         Mt CO2/yr  11231.0880   \n",
-       "                                  Primary Energy                        EJ/yr        145.7409   \n",
-       "                                  Primary Energy|Biomass                EJ/yr         23.6647   \n",
-       "                                  Primary Energy|Fossil                 EJ/yr        116.1932   \n",
-       "                                  Primary Energy|Non-Biomass Renewables EJ/yr          4.5139   \n",
-       "\n",
-       "                                                                                         2020  \\\n",
-       "model       scenario       region variable                              unit                    \n",
-       "AIM/CGE 2.1 CD-LINKS_INDCi R5ASIA Emissions|CO2                         Mt CO2/yr  14359.2801   \n",
-       "                                  Primary Energy                        EJ/yr        191.0565   \n",
-       "                                  Primary Energy|Biomass                EJ/yr         24.0751   \n",
-       "                                  Primary Energy|Fossil                 EJ/yr        155.0735   \n",
-       "                                  Primary Energy|Non-Biomass Renewables EJ/yr          9.2641   \n",
-       "\n",
-       "                                                                                         2030  \\\n",
-       "model       scenario       region variable                              unit                    \n",
-       "AIM/CGE 2.1 CD-LINKS_INDCi R5ASIA Emissions|CO2                         Mt CO2/yr  14873.5967   \n",
-       "                                  Primary Energy                        EJ/yr        216.2135   \n",
-       "                                  Primary Energy|Biomass                EJ/yr         25.9262   \n",
-       "                                  Primary Energy|Fossil                 EJ/yr        168.2376   \n",
-       "                                  Primary Energy|Non-Biomass Renewables EJ/yr         17.0767   \n",
-       "\n",
-       "                                                                                         2040  \\\n",
-       "model       scenario       region variable                              unit                    \n",
-       "AIM/CGE 2.1 CD-LINKS_INDCi R5ASIA Emissions|CO2                         Mt CO2/yr  15238.9081   \n",
-       "                                  Primary Energy                        EJ/yr        234.2793   \n",
-       "                                  Primary Energy|Biomass                EJ/yr         27.3646   \n",
-       "                                  Primary Energy|Fossil                 EJ/yr        179.0562   \n",
-       "                                  Primary Energy|Non-Biomass Renewables EJ/yr         22.0967   \n",
-       "\n",
-       "                                                                                         2050  \\\n",
-       "model       scenario       region variable                              unit                    \n",
-       "AIM/CGE 2.1 CD-LINKS_INDCi R5ASIA Emissions|CO2                         Mt CO2/yr  15180.1854   \n",
-       "                                  Primary Energy                        EJ/yr        245.9771   \n",
-       "                                  Primary Energy|Biomass                EJ/yr         29.6938   \n",
-       "                                  Primary Energy|Fossil                 EJ/yr        185.2168   \n",
-       "                                  Primary Energy|Non-Biomass Renewables EJ/yr         25.3211   \n",
-       "\n",
-       "                                                                                         2060  \\\n",
-       "model       scenario       region variable                              unit                    \n",
-       "AIM/CGE 2.1 CD-LINKS_INDCi R5ASIA Emissions|CO2                         Mt CO2/yr  15513.1760   \n",
-       "                                  Primary Energy                        EJ/yr        258.3201   \n",
-       "                                  Primary Energy|Biomass                EJ/yr         29.8102   \n",
-       "                                  Primary Energy|Fossil                 EJ/yr        195.6202   \n",
-       "                                  Primary Energy|Non-Biomass Renewables EJ/yr         26.6589   \n",
-       "\n",
-       "                                                                                         2070  \\\n",
-       "model       scenario       region variable                              unit                    \n",
-       "AIM/CGE 2.1 CD-LINKS_INDCi R5ASIA Emissions|CO2                         Mt CO2/yr  16003.2060   \n",
-       "                                  Primary Energy                        EJ/yr        268.7644   \n",
-       "                                  Primary Energy|Biomass                EJ/yr         30.1178   \n",
-       "                                  Primary Energy|Fossil                 EJ/yr        203.4916   \n",
-       "                                  Primary Energy|Non-Biomass Renewables EJ/yr         27.9490   \n",
-       "\n",
-       "                                                                                         2080  \\\n",
-       "model       scenario       region variable                              unit                    \n",
-       "AIM/CGE 2.1 CD-LINKS_INDCi R5ASIA Emissions|CO2                         Mt CO2/yr  16343.3124   \n",
-       "                                  Primary Energy                        EJ/yr        275.0764   \n",
-       "                                  Primary Energy|Biomass                EJ/yr         30.0109   \n",
-       "                                  Primary Energy|Fossil                 EJ/yr        207.3614   \n",
-       "                                  Primary Energy|Non-Biomass Renewables EJ/yr         29.3259   \n",
-       "\n",
-       "                                                                                         2090  \\\n",
-       "model       scenario       region variable                              unit                    \n",
-       "AIM/CGE 2.1 CD-LINKS_INDCi R5ASIA Emissions|CO2                         Mt CO2/yr  17097.8681   \n",
-       "                                  Primary Energy                        EJ/yr        283.1479   \n",
-       "                                  Primary Energy|Biomass                EJ/yr         29.6166   \n",
-       "                                  Primary Energy|Fossil                 EJ/yr        214.7828   \n",
-       "                                  Primary Energy|Non-Biomass Renewables EJ/yr         29.3942   \n",
-       "\n",
-       "                                                                                         2100  \n",
-       "model       scenario       region variable                              unit                   \n",
-       "AIM/CGE 2.1 CD-LINKS_INDCi R5ASIA Emissions|CO2                         Mt CO2/yr  17722.1245  \n",
-       "                                  Primary Energy                        EJ/yr        288.6838  \n",
-       "                                  Primary Energy|Biomass                EJ/yr         29.5846  \n",
-       "                                  Primary Energy|Fossil                 EJ/yr        217.7714  \n",
-       "                                  Primary Energy|Non-Biomass Renewables EJ/yr         30.6799  "
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "df = pyam.IamDataFrame(data='tutorial_data.csv')\n",
     "df.timeseries().head()"
@@ -280,27 +54,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['1.0',\n",
-       " 'CD-LINKS_INDCi',\n",
-       " 'CD-LINKS_NPi',\n",
-       " 'CD-LINKS_NPi2020_1000',\n",
-       " 'CD-LINKS_NPi2020_1600',\n",
-       " 'CD-LINKS_NPi2020_400',\n",
-       " 'CD-LINKS_NoPolicy',\n",
-       " 'Faster Transition Scenario']"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "df.filter(variable='Emissions|CO2', region='World').scenario"
    ]
@@ -316,32 +72,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<AxesSubplot:title={'center':'model: unweighted - scenario: quantile_0.5 - region: World - variable: Emissions|CO2'}, xlabel='Year', ylabel='Mt CO2/yr'>"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "fig, ax = plt.subplots()\n",
     "\n",
@@ -372,32 +105,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<AxesSubplot:title={'center':'model: unweighted - region: World - variable: Emissions|CO2'}, xlabel='Year', ylabel='Mt CO2/yr'>"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "fig, ax = plt.subplots()\n",
     "\n",
@@ -430,87 +140,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "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></th>\n",
-       "      <th>exclude</th>\n",
-       "      <th>weight</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>model</th>\n",
-       "      <th>scenario</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"5\" valign=\"top\">AIM/CGE 2.1</th>\n",
-       "      <th>CD-LINKS_INDCi</th>\n",
-       "      <td>False</td>\n",
-       "      <td>0.602473</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>CD-LINKS_NPi</th>\n",
-       "      <td>False</td>\n",
-       "      <td>0.787914</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>CD-LINKS_NPi2020_1000</th>\n",
-       "      <td>False</td>\n",
-       "      <td>0.400528</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>CD-LINKS_NPi2020_1600</th>\n",
-       "      <td>False</td>\n",
-       "      <td>0.927644</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>CD-LINKS_NPi2020_400</th>\n",
-       "      <td>False</td>\n",
-       "      <td>0.691232</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                   exclude    weight\n",
-       "model       scenario                                \n",
-       "AIM/CGE 2.1 CD-LINKS_INDCi           False  0.602473\n",
-       "            CD-LINKS_NPi             False  0.787914\n",
-       "            CD-LINKS_NPi2020_1000    False  0.400528\n",
-       "            CD-LINKS_NPi2020_1600    False  0.927644\n",
-       "            CD-LINKS_NPi2020_400     False  0.691232"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "weights = df.meta.assign(weight=np.random.rand(len(df.meta)))\n",
     "weights.head()"
@@ -518,32 +150,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<AxesSubplot:title={'center':'model: weighted - region: World - variable: Emissions|CO2'}, xlabel='Year', ylabel='Mt CO2/yr'>"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "fig, ax = plt.subplots()\n",
     "\n",
@@ -564,6 +173,13 @@
     "    .line(ax=ax)\n",
     ")"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -582,7 +198,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.13"
+   "version": "3.10.0"
   }
  },
  "nbformat": 4,
diff --git a/pyam/figures.py b/pyam/figures.py
index cd63692..3d31aa2 100755
--- a/pyam/figures.py
+++ b/pyam/figures.py
@@ -51,7 +51,10 @@ def sankey(df, mapping):
         mapping, orient="index", columns=["source", "target"]
     ).merge(df._data, how="left", left_index=True, right_on="variable")
     label_mapping = dict(
-        [(label, i) for i, label in enumerate(set(_df["source"].append(_df["target"])))]
+        [
+            (label, i)
+            for i, label in enumerate(set(pd.concat([_df["source"], _df["target"]])))
+        ]
     )
     _df.replace(label_mapping, inplace=True)
     region = get_index_levels(_df, "region")[0]
diff --git a/pyam/iiasa.py b/pyam/iiasa.py
index 46b9017..d5eeb69 100644
--- a/pyam/iiasa.py
+++ b/pyam/iiasa.py
@@ -526,7 +526,7 @@ class Connection(object):
             if islistable(meta):
                 # always merge 'version' (even if not requested explicitly)
                 # 'run_id' is required to determine `_args`, dropped later
-                _meta = _meta[set(meta).union(["version", "run_id"])]
+                _meta = _meta[list(set(meta).union(["version", "run_id"]))]
         else:
             _meta = self._query_index(default_only=default_only).set_index(META_IDX)
 
diff --git a/pyam/statistics.py b/pyam/statistics.py
index b82f623..d37cb55 100644
--- a/pyam/statistics.py
+++ b/pyam/statistics.py
@@ -198,10 +198,11 @@ class Statistics(object):
                 else (levels + [[row]], [[0]] * (self.idx_depth + 1))
             )
             _stats_f.index = pd.MultiIndex(levels=lvls, codes=lbls)
-            _stats = _stats_f if _stats is None else _stats.append(_stats_f)
+            _stats = _stats_f if _stats is None else pd.concat([_stats, _stats_f])
 
         # add header
         _stats = pd.concat([_stats], keys=[header], names=[""], axis=1)
+        _stats.index.names = [None] * len(_stats.index.names)
         subheader = _stats.columns.get_level_values(1).unique()
         self._add_to_header(header, subheader)
 
@@ -272,15 +273,14 @@ def format_rows(
         legend = "{} ({})".format(
             center, "max, min" if fullrange is True else "interquartile range"
         )
-        index = row.index.droplevel(2).drop_duplicates()
-        count_arg = dict(tuples=[("count", "")], names=[None, legend])
+
+        row_index = row.index.droplevel(2).drop_duplicates()
+        ret_index = pd.MultiIndex.from_tuples([("count", "")]).append(row_index)
+        ret_index.names = [None, legend]
     else:
-        msg = "displaying multiple range formats simultaneously not supported"
-        raise NotImplementedError(msg)
+        raise ValueError("Use either fullrange or interquartile range.")
 
-    ret = pd.Series(
-        index=pd.MultiIndex.from_tuples(**count_arg).append(index), dtype=float
-    )
+    ret = pd.Series(index=ret_index, dtype=float)
 
     row = row.sort_index()
     center = "50%" if center == "median" else center
@@ -295,7 +295,7 @@ def format_rows(
     upper, lower = ("max", "min") if fullrange is True else ("75%", "25%")
 
     # format `describe()` columns to string output
-    for i in index:
+    for i in row_index:
         x = row.loc[i]
         _count = x["count"]
         if np.isnan(_count) or _count == 0:
diff --git a/pyam/utils.py b/pyam/utils.py
index 7d46e2e..8d86677 100644
--- a/pyam/utils.py
+++ b/pyam/utils.py
@@ -401,7 +401,8 @@ def format_data(df, index, **kwargs):
             df = _format_from_legacy_database(df)
 
         # replace missing units by an empty string for user-friendly filtering
-        df = df.assign(unit=df["unit"].fillna(""))
+        if "unit" in df.columns:
+            df = df.assign(unit=df["unit"].fillna(""))
 
         df, time_col, extra_cols = _format_data_to_series(df, index)
 
diff --git a/setup.cfg b/setup.cfg
index b1d44be..9b34180 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -66,7 +66,7 @@ optional_io_formats =
 tutorials =
     pypandoc
     nbformat
-    nbconvert
+    nbconvert >= 7.3
     jupyter_client
     ipykernel
 docs =


Test patch:
diff --git a/tests/test_core.py b/tests/test_core.py
index c70b7d5..4f0b243 100644
--- a/tests/test_core.py
+++ b/tests/test_core.py
@@ -86,8 +86,8 @@ def test_init_df_with_float_cols_raises(test_pd_df):
 
 
 def test_init_df_with_duplicates_raises(test_df):
-    _df = test_df.timeseries()
-    _df = _df.append(_df.iloc[0]).reset_index()
+    _df = test_df.timeseries().reset_index()
+    _df = pd.concat([_df, _df.iloc[0].to_frame().T])
     match = "0  model_a   scen_a  World  Primary Energy  EJ/yr"
     with pytest.raises(ValueError, match=match):
         IamDataFrame(_df)
@@ -604,7 +604,7 @@ def test_interpolate_datetimes(test_df):
         test_df.interpolate(some_date, inplace=True)
         obs = test_df.filter(time=some_date).data["value"].reset_index(drop=True)
         exp = pd.Series([3, 1.5, 4], name="value")
-        pd.testing.assert_series_equal(obs, exp, check_less_precise=True)
+        pd.testing.assert_series_equal(obs, exp, rtol=0.01)
         # redo the interpolation and check that no duplicates are added
         test_df.interpolate(some_date, inplace=True)
         assert not test_df.filter()._data.index.duplicated().any()
diff --git a/tests/test_filter.py b/tests/test_filter.py
index c3cec96..7028b83 100644
--- a/tests/test_filter.py
+++ b/tests/test_filter.py
@@ -55,7 +55,8 @@ def test_filter_mixed_time_domain(test_df_mixed, arg_year, arg_time):
     assert obs.time_col == "year"
     assert obs.time_domain == "year"
     assert obs.year == [2005]
-    pdt.assert_index_equal(obs.time, pd.Int64Index([2005], name="time"))
+    pdt.assert_index_equal(obs.time, pd.Index([2005], name="time"))
+    assert obs.time.dtype == "int64"
 
 
 def test_filter_time_domain_raises(test_df_year):
@@ -106,10 +107,12 @@ def test_filter_day(test_df, test_day):
 
 def test_filter_with_numpy_64_date_vals(test_df):
     dates = test_df[test_df.time_col].unique()
-    key = "year" if test_df.time_col == "year" else "time"
-    res_0 = test_df.filter(**{key: dates[0]})
-    res = test_df.filter(**{key: dates})
-    assert np.equal(res_0.data[res_0.time_col].values, dates[0]).all()
+    res_0 = test_df.filter(**{test_df.time_col: dates[0]})
+    res = test_df.filter(**{test_df.time_col: dates})
+    if test_df.time_col == "year":
+        assert res_0.data[res_0.time_col].values[0] == dates[0]
+    else:
+        assert res_0.data[res_0.time_col].values[0] == np.datetime64(dates[0])
     assert res.equals(test_df)
 
 
diff --git a/tests/test_statistics.py b/tests/test_statistics.py
index 46e7959..5a7f687 100644
--- a/tests/test_statistics.py
+++ b/tests/test_statistics.py
@@ -9,6 +9,7 @@ def stats_add(stats, plot_df):
     # test describing as pd.DataFrame
     primary = plot_df.filter(variable="Primary Energy", year=2005).timeseries()
     stats.add(data=primary, header="primary")
+
     # test describing as unamed pd.Series with `subheader` arg
     coal = plot_df.filter(variable="Primary Energy|Coal").timeseries()[2010]
     coal.name = None
@@ -48,7 +49,6 @@ def test_statistics(plot_df):
     idx = pd.MultiIndex(
         levels=[["category", "scen"], ["b", "a", "test"]],
         codes=[[0, 0, 1], [0, 1, 2]],
-        names=["", ""],
     )
     cols = pd.MultiIndex(
         levels=[["count", "primary", "coal"], ["", 2005]],
diff --git a/tests/test_time.py b/tests/test_time.py
index ff7c26d..8cb50cb 100644
--- a/tests/test_time.py
+++ b/tests/test_time.py
@@ -39,7 +39,7 @@ OE_SUBANNUAL_FORMAT = lambda x: x.strftime("%m-%d %H:%M%z").replace("+0100", "+0
 @pytest.mark.parametrize(
     "time, domain, index",
     [
-        (TEST_YEARS, "year", pd.Int64Index([2005, 2010], name="time")),
+        (TEST_YEARS, "year", pd.Index([2005, 2010], name="time")),
         (TEST_DTS, "datetime", pd.DatetimeIndex(TEST_DTS, name="time")),
         (TEST_TIME_STR, "datetime", pd.DatetimeIndex(TEST_DTS, name="time")),
         (TEST_TIME_STR_HR, "datetime", pd.DatetimeIndex(TEST_TIME_STR_HR, name="time")),
diff --git a/tests/test_tutorials.py b/tests/test_tutorials.py
index 2ad9a4f..e8492f3 100644
--- a/tests/test_tutorials.py
+++ b/tests/test_tutorials.py
@@ -1,138 +1,46 @@
-import io
-import os
-import subprocess
-import sys
 import pytest
 
-from .conftest import here, IIASA_UNAVAILABLE
-
 try:
     import nbformat
-except:
+    from nbconvert.preprocessors import ExecutePreprocessor
+except ModuleNotFoundError:
     pytest.skip(
         "Missing Jupyter Notebook and related dependencies", allow_module_level=True
     )
 
-tut_path = os.path.join(here, "..", "docs", "tutorials")
-
-
-# taken from the excellent example here:
-# https://blog.thedataincubator.com/2016/06/testing-jupyter-notebooks/
-
-
-def _notebook_run(path, kernel=None, timeout=60, capsys=None):
-    """Execute a notebook via nbconvert and collect output.
-    :returns (parsed nb object, execution errors)
-    """
-    major_version = sys.version_info[0]
-    dirname, __ = os.path.split(path)
-    os.chdir(dirname)
-    fname = os.path.join(here, "test.ipynb")
-    args = [
-        "jupyter",
-        "nbconvert",
-        "--to",
-        "notebook",
-        "--execute",
-        "--ExecutePreprocessor.timeout={}".format(timeout),
-        "--output",
-        fname,
-        path,
-    ]
-    subprocess.check_call(args)
-
-    nb = nbformat.read(io.open(fname, encoding="utf-8"), nbformat.current_nbformat)
-
-    errors = [
-        output
-        for cell in nb.cells
-        if "outputs" in cell
-        for output in cell["outputs"]
-        if output.output_type == "error"
-    ]
-
-    # removing files fails on CI (GitHub Actions) on Windows & py3.8
-    try:
-        os.remove(fname)
-    except PermissionError:
-        pass
-
-    return nb, errors
-
-
-def test_pyam_first_steps(capsys):
-    fname = os.path.join(tut_path, "pyam_first_steps.ipynb")
-    nb, errors = _notebook_run(fname, capsys=capsys)
-    assert errors == []
-    assert os.path.exists(os.path.join(tut_path, "tutorial_export.xlsx"))
-
-    def has_log_output(cell):
-        return cell["cell_type"] == "code" and any(
-            "Running in a notebook" in output.get("text", "")
-            for output in cell["outputs"]
-        )
-
-    assert any(has_log_output(cell) for cell in nb["cells"])
-
-
-def test_data_table_formats():
-    fname = os.path.join(tut_path, "data_table_formats.ipynb")
-    nb, errors = _notebook_run(fname)
-    assert errors == []
-
-
-def test_unit_conversion():
-    fname = os.path.join(tut_path, "unit_conversion.ipynb")
-    nb, errors = _notebook_run(fname)
-    assert errors == []
-
-
-def test_aggregating_downscaling_consistency():
-    fname = os.path.join(tut_path, "aggregating_downscaling_consistency.ipynb")
-    nb, errors = _notebook_run(fname)
-    assert errors == []
-
-
-def test_subannual_time_resolution():
-    fname = os.path.join(tut_path, "subannual_time_resolution.ipynb")
-    nb, errors = _notebook_run(fname)
-    assert errors == []
-
-
-def test_pyam_logo():
-    fname = os.path.join(tut_path, "pyam_logo.ipynb")
-    nb, errors = _notebook_run(fname)
-    assert errors == []
+from .conftest import here, IIASA_UNAVAILABLE
 
+nb_path = here.parent / "docs" / "tutorials"
 
-def test_ipcc_colors():
-    fname = os.path.join(tut_path, "ipcc_colors.ipynb")
-    nb, errors = _notebook_run(fname)
-    assert errors == []
 
+def _run_notebook(file, timeout=30):
+    """Execute a notebook file"""
+    with open(nb_path / f"{file}.ipynb") as f:
+        nb = nbformat.read(f, as_version=4)
 
-def test_legends():
-    fname = os.path.join(tut_path, "legends.ipynb")
-    nb, errors = _notebook_run(fname)
-    assert errors == []
+    ep = ExecutePreprocessor(timeout=timeout)
+    ep.preprocess(nb, {"metadata": {"path": nb_path}})
 
 
-def test_ops():
-    fname = os.path.join(tut_path, "algebraic_operations.ipynb")
-    nb, errors = _notebook_run(fname)
-    assert errors == []
+@pytest.mark.parametrize(
+    "file",
+    [
+        "pyam_first_steps",
+        "data_table_formats",
+        "unit_conversion",
+        "aggregating_downscaling_consistency",
+        "subannual_time_resolution",
+        "pyam_logo",
+        "ipcc_colors",
+        "legends",
+        "algebraic_operations",
+        "aggregating_variables_and_plotting_with_negative_values",
+    ],
+)
+def test_tutorial_notebook(file):
+    _run_notebook(file)
 
 
 @pytest.mark.skipif(IIASA_UNAVAILABLE, reason="IIASA database API unavailable")
-def test_iiasa_dbs():
-    fname = os.path.join(tut_path, "iiasa_dbs.ipynb")
-    nb, errors = _notebook_run(fname, timeout=600)
-    assert errors == []
-
-
-def test_aggregating_variables_and_plotting_with_negative_values():
-    fname = os.path.join(
-        tut_path, "aggregating_variables_and_plotting_with_negative_values.ipynb"
-    )
-    nb, errors = _notebook_run(fname)
-    assert errors == []
+def test_tutorial_iiasa_dbs():
+    _run_notebook("iiasa_dbs")