Merge ebf274d3c4 into d9405e0129

2-Working-With-Data/07-python/notebook-covidspread.ipynb

@@ -1599,8 +1599,8 @@
     }
    ],
    "source": [
-    "infected.loc['US'][2:].plot()\n",
-    "recovered.loc['US'][2:].plot()\n",
+    "infected.loc['US'][3:].plot()\n",
+    "recovered.loc['US'][3:].plot()\n",
     "plt.show()"
    ]
   },
@@ -1608,7 +1608,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "> **Note** how we use `[2:]` to remove first two elements of a sequence that contain geolocation of a country. We can also drop those two columns altogether:"
+    "> **Note** how we use `[3:]` to remove first two elements of a sequence that contain geolocation of a country. We can also drop those two columns altogether:"
    ]
   },
   {
@@ -1617,9 +1617,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "infected.drop(columns=['Lat','Long'],inplace=True)\n",
-    "recovered.drop(columns=['Lat','Long'],inplace=True)\n",
-    "deaths.drop(columns=['Lat','Long'],inplace=True)"
+    "infected.drop(columns=['Lat','Long','Province/State'],inplace=True)\n",
+    "recovered.drop(columns=['Lat','Long','Province/State'],inplace=True)\n",
+    "deaths.drop(columns=['Lat','Long','Province/State'],inplace=True)"
    ]
   },
   {
@@ -1758,7 +1758,7 @@
    ],
    "source": [
     "def mkframe(country):\n",
-    "    df = pd.DataFrame({ 'infected' : infected.loc[country] ,\n",
+    "    df = pd.DataFrame({ 'infected' : infected.loc[country],\n",
     "                        'recovered' : recovered.loc[country],\n",
     "                        'deaths' : deaths.loc[country]})\n",
     "    df.index = pd.to_datetime(df.index)\n",