Add a final summary to Chapter 4

Author: jvns

cookbook/Chapter 4 - Find out on which weekday people bike the most with groupby and aggregate.ipynb

@@ -556,6 +556,61 @@
       "So it looks like Montrealers are commuter cyclists -- they bike much more during the week. Neat!"
      ]
     },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "# 4.3 Putting it together"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Let's put all that together, to prove how easy it is. 6 lines of magical pandas!\n",
+      "\n",
+      "If you want to play around, try changing `sum` to `max`, `np.median`, or any other function you like."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "bikes = pd.read_csv('../data/bikes.csv', \n",
+      "                    sep=';', encoding='latin1', \n",
+      "                    parse_dates=['Date'], dayfirst=True, \n",
+      "                    index_col='Date')\n",
+      "# Add the weekday column\n",
+      "berri_bikes = bikes[['Berri 1']]\n",
+      "berri_bikes['weekday'] = berri_bikes.index.weekday\n",
+      "\n",
+      "# Add up the number of cyclists by weekday, and plot!\n",
+      "weekday_counts = berri_bikes.groupby('weekday').aggregate(sum)\n",
+      "weekday_counts.index = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']\n",
+      "weekday_counts.plot(kind='bar')"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 13,
+       "text": [
+        "<matplotlib.axes.AxesSubplot at 0x37d3850>"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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MVF1drebmZr3//vvx/dRxHC1atMjbwYAMwA1aACANDh8+rB/84Ac6\ne/asbrnlFj366KP69Kc/7fVYAADAMMoeAKTQc889p5KSEt188826+eabddNNN2nhwoW8ITcAAEg5\nyh4ApNDo6KjOnj2rs2fP6ty5c/rVr36l119/XY7j6MUXX/R6PAAAYBhlDwDSYN++ffrFL36hoqIi\nffazn9WyZct0/fXXez0WAAAwjBu0AECa+Xw+7hIHAABSjiN7AJBCw8PD8VM4L57G+bOf/Uzj4+P6\nl3/5F6/HAwAAhlH2ACCFvvGNb+jmm29WcXFx/CYtN998s+bPn8/RPQAAkFKUPQAAAAAwiP+tDAAA\nAAAGUfYAAAAAwCDKHgAAAAAYRNkDAAAAAIP+HyBxTzzw2gEQAAAAAElFTkSuQmCC\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x37d32d0>"
+       ]
+      }
+     ],
+     "prompt_number": 13
+    },
     {
      "cell_type": "markdown",
      "metadata": {},