using altair for italy notebooks

cfddccd6 · Rok Roškar · 449652dc · cfddccd6 · cfddccd6 · cfddccd6
Commit cfddccd6 authored 4 years ago by Rok Roškar
--- a/README.md
+++ b/README.md
@@ -58,8 +58,8 @@ The environment image allows you to work in Python or R in JupyterLab or RStudio
 <td><a href="https://github.com/pcm-dpc/COVID-19">Covid-19 data for Italy</a></td>
 <td><a href="https://renkulab.io/projects/covid-19/covid-19-public-data/datasets/286c58b1-dbbc-4caa-a23a-fcb001d5ac51/">covid-19-italy</a></td>
 <td><code>data/covid-19-italy</code></td>
-<td><a href="https://renkulab.io/projects/covid-19/covid-19-public-data/files/blob/notebooks/examples/italy-examples/italy-notebook-example.ipynb">notebook</a>, 
-    <a href="https://renkulab.io/projects/covid-19/covid-19-public-data/files/blob/notebooks/examples/italy-examples/italy-dashboard-example.ipynb">dashboard</a></td>
+<td><a href="https://renkulab.io/projects/covid-19/covid-19-public-data/files/blob/notebooks/examples/italy-examples/italy-notebook-example.ipynb">notebook</a>
+</td>
 </tr>
 <tr>
    <td><a href="https://github.com/itoledor/coronavirus.git">Covid-19 data for Chile</a></td>

--- a/notebooks/examples/italy-examples/italy-dashboard-example.ipynb
+++ b/notebooks/examples/italy-examples/italy-dashboard-example.ipynb
@@ -2,7 +2,7 @@
 "cells": [
  {
   "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
@@ -12,7 +12,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
@@ -25,7 +25,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
@@ -34,9 +34,30 @@
  },
  {
   "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
   "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "KeyError",
+     "evalue": "'note_it'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-4-98cd8ebec933>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      3\u001b[0m     \u001b[0;34m\"dpc-covid19-ita-province.csv\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0;34m\"dati-province-description.json\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m     \u001b[0muse_time_index\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m )\n\u001b[1;32m      7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/work/covid-19-public-data/notebooks/examples/italy-examples/italy_utils.py\u001b[0m in \u001b[0;36mprepare_dataframe\u001b[0;34m(data_folder, df_filename, description_filename, use_time_index)\u001b[0m\n\u001b[1;32m     29\u001b[0m     \u001b[0mdata_file_path\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata_folder\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdf_filename\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     30\u001b[0m     \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata_file_path\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 31\u001b[0;31m     \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtranslate_columns\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata_folder\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdescription_filename\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     32\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0muse_time_index\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     33\u001b[0m         \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mset_time_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/work/covid-19-public-data/notebooks/examples/italy-examples/italy_utils.py\u001b[0m in \u001b[0;36mtranslate_columns\u001b[0;34m(data_folder, df, description_filename)\u001b[0m\n\u001b[1;32m     12\u001b[0m         \u001b[0mdescriptions\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m \u001b[0mcolumn_dict\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Nome campo'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mcolumn_dict\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mcolumn_dict\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdescriptions\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m     \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrename\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mcol\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mdescriptions\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcol\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Field name'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minplace\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     15\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/pandas/util/_decorators.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    225\u001b[0m         \u001b[0;34m@\u001b[0m\u001b[0mwraps\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    226\u001b[0m         \u001b[0;32mdef\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mCallable\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m...\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mAny\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 227\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    228\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    229\u001b[0m         \u001b[0mkind\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minspect\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mParameter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mPOSITIONAL_OR_KEYWORD\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36mrename\u001b[0;34m(self, mapper, index, columns, axis, copy, inplace, level, errors)\u001b[0m\n\u001b[1;32m   4131\u001b[0m             \u001b[0minplace\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minplace\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4132\u001b[0m             \u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4133\u001b[0;31m             \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4134\u001b[0m         )\n\u001b[1;32m   4135\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36mrename\u001b[0;34m(self, mapper, index, columns, axis, copy, inplace, level, errors)\u001b[0m\n\u001b[1;32m   1098\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1099\u001b[0m             result._data = result._data.rename_axis(\n\u001b[0;32m-> 1100\u001b[0;31m                 \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbaxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1101\u001b[0m             )\n\u001b[1;32m   1102\u001b[0m             \u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_clear_item_cache\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/pandas/core/internals/managers.py\u001b[0m in \u001b[0;36mrename_axis\u001b[0;34m(self, mapper, axis, copy, level)\u001b[0m\n\u001b[1;32m    194\u001b[0m         \"\"\"\n\u001b[1;32m    195\u001b[0m         \u001b[0mobj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdeep\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 196\u001b[0;31m         \u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_transform_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmapper\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    197\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    198\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/pandas/core/internals/managers.py\u001b[0m in \u001b[0;36m_transform_index\u001b[0;34m(index, func, level)\u001b[0m\n\u001b[1;32m   1957\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mMultiIndex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrom_tuples\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnames\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnames\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1958\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1959\u001b[0;31m         \u001b[0mitems\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1960\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mIndex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtupleize_cols\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1961\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/pandas/core/internals/managers.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m   1957\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mMultiIndex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrom_tuples\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnames\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnames\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1958\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1959\u001b[0;31m         \u001b[0mitems\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1960\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mIndex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtupleize_cols\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1961\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/work/covid-19-public-data/notebooks/examples/italy-examples/italy_utils.py\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(col)\u001b[0m\n\u001b[1;32m     12\u001b[0m         \u001b[0mdescriptions\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m \u001b[0mcolumn_dict\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Nome campo'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mcolumn_dict\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mcolumn_dict\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdescriptions\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m     \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrename\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mcol\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mdescriptions\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcol\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Field name'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minplace\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     15\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mKeyError\u001b[0m: 'note_it'"
+     ]
+    }
+   ],
   "source": [
    "df_provinces = prepare_dataframe(\n",
    "    data_folder, \n",

 %% Cell type:code id: tags:

 ``` python
 %load_ext autoreload
 %autoreload 2
 ```

 %% Cell type:code id: tags:

 ``` python
 import ipywidgets as widgets
 import matplotlib.pyplot as plt
 import pandas as pd

 from italy_utils import *
 ```

 %% Cell type:code id: tags:

 ``` python
 data_folder = "../../../data/covid-19-italy/"
 ```

 %% Cell type:code id: tags:

 ``` python
 df_provinces = prepare_dataframe(
    data_folder,
    "dpc-covid19-ita-province.csv",
    "dati-province-description.json",
    use_time_index=True
 )

 province_dict = get_province_structure(df_provinces)
 ```

+%% Output
+
+    ---------------------------------------------------------------------------
+    KeyError                                  Traceback (most recent call last)
+    <ipython-input-4-98cd8ebec933> in <module>
+          3     "dpc-covid19-ita-province.csv",
+          4     "dati-province-description.json",
+    ----> 5     use_time_index=True
+          6 )
+          7
+    /work/covid-19-public-data/notebooks/examples/italy-examples/italy_utils.py in prepare_dataframe(data_folder, df_filename, description_filename, use_time_index)
+         29     data_file_path = os.path.join(data_folder, df_filename)
+         30     df = pd.read_csv(data_file_path)
+    ---> 31     df = translate_columns(data_folder, df, description_filename)
+         32     if use_time_index:
+         33         df = set_time_index(df)
+    /work/covid-19-public-data/notebooks/examples/italy-examples/italy_utils.py in translate_columns(data_folder, df, description_filename)
+         12         descriptions = { column_dict['Nome campo']: column_dict for column_dict in descriptions}
+         13
+    ---> 14     df.rename(columns=lambda col: descriptions[col]['Field name'], inplace=True)
+         15     return df
+         16
+    /opt/conda/lib/python3.7/site-packages/pandas/util/_decorators.py in wrapper(*args, **kwargs)
+        225         @wraps(func)
+        226         def wrapper(*args, **kwargs) -> Callable[..., Any]:
+    --> 227             return func(*args, **kwargs)
+        228
+        229         kind = inspect.Parameter.POSITIONAL_OR_KEYWORD
+    /opt/conda/lib/python3.7/site-packages/pandas/core/frame.py in rename(self, mapper, index, columns, axis, copy, inplace, level, errors)
+       4131             inplace=inplace,
+       4132             level=level,
+    -> 4133             errors=errors,
+       4134         )
+       4135
+    /opt/conda/lib/python3.7/site-packages/pandas/core/generic.py in rename(self, mapper, index, columns, axis, copy, inplace, level, errors)
+       1098
+       1099             result._data = result._data.rename_axis(
+    -> 1100                 f, axis=baxis, copy=copy, level=level
+       1101             )
+       1102             result._clear_item_cache()
+    /opt/conda/lib/python3.7/site-packages/pandas/core/internals/managers.py in rename_axis(self, mapper, axis, copy, level)
+        194         """
+        195         obj = self.copy(deep=copy)
+    --> 196         obj.set_axis(axis, _transform_index(self.axes[axis], mapper, level))
+        197         return obj
+        198
+    /opt/conda/lib/python3.7/site-packages/pandas/core/internals/managers.py in _transform_index(index, func, level)
+       1957         return MultiIndex.from_tuples(items, names=index.names)
+       1958     else:
+    -> 1959         items = [func(x) for x in index]
+       1960         return Index(items, name=index.name, tupleize_cols=False)
+       1961
+    /opt/conda/lib/python3.7/site-packages/pandas/core/internals/managers.py in <listcomp>(.0)
+       1957         return MultiIndex.from_tuples(items, names=index.names)
+       1958     else:
+    -> 1959         items = [func(x) for x in index]
+       1960         return Index(items, name=index.name, tupleize_cols=False)
+       1961
+    /work/covid-19-public-data/notebooks/examples/italy-examples/italy_utils.py in <lambda>(col)
+         12         descriptions = { column_dict['Nome campo']: column_dict for column_dict in descriptions}
+         13
+    ---> 14     df.rename(columns=lambda col: descriptions[col]['Field name'], inplace=True)
+         15     return df
+         16
+    KeyError: 'note_it'
+
 %% Cell type:code id: tags:

 ``` python
 def get_growth_factor_series(province, df, N_min=1000):
    ts = df.loc[
                (df['province'] == province) & \
                (df['total_cases'] >= N_min)
            ] \
            ['total_cases'] \
            .rolling('3d') \
            .mean() \
            .pct_change() \
            .add(1.0)
    return ts.iloc[1:]
 ```

 %% Cell type:code id: tags:

 ``` python
 def plot_growth_factors(provinces, df, N_min=1000):
    if len(provinces) == 0:
        return
    plt.figure(figsize=(9, 6))
    datemin = datemax = df.index[-1]
    for province in provinces:
        data = get_growth_factor_series(province, df, N_min=N_min)
        if len(data) >= 1:
            data.plot(label=province)
            datemin = min(data.index[0], datemin)

    ax = plt.gca()
    plt.legend(loc='upper left', bbox_to_anchor=(1.05, 1.0), frameon=False)
    plt.plot([datemin, datemax], [1,1], color='gray', alpha=0.2)
    plt.ylim(0.95, ax.get_ylim()[1])
    plt.title('Daily growth rate of total cases per province')
    plt.xlabel('');

 def plot_total_cases(provinces, df, N_min=500):
    if len(provinces) == 0:
        return
    plt.figure(figsize=(9, 6))
    for province in provinces:
        data = df.loc[
                (df['province'] == province) & \
                (df['total_cases'] >= N_min)
            ] \
            ['total_cases'] \
            .rolling('1d') \
            .mean() \
            .add(1.0)
        if len(data) >= 1:
            data.plot(label=province, logy=True)

    ax = plt.gca()
    plt.legend(loc='upper left', bbox_to_anchor=(1.05, 1.0), frameon=False)
    plt.title('Total cases per province')
    plt.xlabel('');

 def make_plots(provinces, df):
    plot_growth_factors(provinces, df)
    plot_total_cases(provinces, df)
 ```

 %% Cell type:code id: tags:

 ``` python
 def get_province_selector(region):
    return widgets.SelectMultiple(
        options=province_dict[region],
        value=[],
        description='Provinces:',
        disabled=region_selector.value is None
    )
 def get_interactive_widgets(region):
    widgets.interact(lambda prov: make_plots(list(prov), df_provinces), prov=get_province_selector(region_selector.value));
 ```

 %% Cell type:code id: tags:

 ``` python
 region_selector = widgets.Dropdown(
    options=[key for key in province_dict],
    value='Lombardia',
    description='Region:',
    disabled=False,
 )
 widgets.interact(lambda reg: get_interactive_widgets(reg), reg=region_selector);
 ```

--- a/notebooks/examples/italy-examples/italy-notebook-example.ipynb
+++ b/notebooks/examples/italy-examples/italy-notebook-example.ipynb
--- a/notebooks/examples/italy-examples/italy_utils.py
+++ b/notebooks/examples/italy-examples/italy_utils.py
@@ -7,10 +7,8 @@ def translate_columns(data_folder, df, description_filename):
    description_file_path = os.path.join(data_folder, description_filename)

    with open(description_file_path, 'r') as description_file:
-        decoded_data = description_file.read().encode().decode('utf-8-sig')
-        descriptions = json.loads(decoded_data)
-        descriptions = { column_dict['Nome campo']: column_dict for column_dict in descriptions}
-    
+        descriptions = json.loads(description_file.read().encode().decode('utf-8-sig'))
+    descriptions = { column_dict['Nome campo']: column_dict for column_dict in descriptions}
    df.rename(columns=lambda col: descriptions[col]['Field name'], inplace=True) 
    return df

@@ -28,6 +26,8 @@ def set_time_index(df, drop_hour=True):
 def prepare_dataframe(data_folder, df_filename, description_filename, use_time_index=False):
    data_file_path = os.path.join(data_folder, df_filename)
    df = pd.read_csv(data_file_path)
+    del df['note_it']
+    del df['note_en']
    df = translate_columns(data_folder, df, description_filename)
    if use_time_index:
        df = set_time_index(df)

--- a/requirements.txt
+++ b/requirements.txt
@@ -91,6 +91,7 @@ Send2Trash==1.5.0
 simpervisor==0.3
 six==1.14.0
 smmap==3.0.1
+SPARQLWrapper==1.8.5
 SQLAlchemy==1.3.13
 tenacity==6.1.0
 terminado==0.8.3