Added method chaining example

Author: pybokeh

pandas/PandasCheatSheet.ipynb

@@ -55,7 +55,8 @@
     "- [Merging 2 dataframes using merge()](#merge)\n",
     "- [Finding rows containing data with missing values](#missing)\n",
     "- [Converting a data type of a column in a dataframe](#convert_type)\n",
-    "- [Plotting dataframes using MATPLOTLIB ver 1.5+](#matplotlib)\n",
+    "- [Plotting data frames using MATPLOTLIB ver 1.5+](#matplotlib)\n",
+    "- [Method chaining](#chaining)\n",
     "- [Sending Pandas dataframe to R using rpy2 IPython notebook extension](#rpy2)\n",
     "- [BONUS: A HUGE list of python and pandas snippets by Chris Albon](http://chrisalbon.com/)"
    ]
@@ -4996,13 +4997,6 @@
     "perc_of_columns"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<a id='index2column'></a>"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -5214,6 +5208,13 @@
     "df.dtypes"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a id=\"index2column\"></a>"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -7591,6 +7592,80 @@
     "plt.show()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a id=\"chaining\"></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Method Chaining"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[[back to top](#top)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "With method chaining, it eliminates the need for making intermediary variables.  You can process your data frame in a series of method calls when you enclose your data frame with parenthesis. Here's a contrived example:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYkAAAEMCAYAAAAxoErWAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fa40ee46438>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "\n",
+    "df = (pd.read_csv('/home/pybokeh/temp/vehicles.csv',\n",
+    "                 usecols=['year', 'make', 'model', 'comb08', 'fuelType', 'fuelType1', \n",
+    "                          'fuelType2', 'atvType', 'cylinders', 'VClass'])\n",
+    "      .rename(columns={'comb08':'combmpg'})\n",
+    "      .query(\"make in('Honda','Acura','Toyota','Lexus') \\\n",
+    "              & fuelType1 in('Regular Gasoline','Premium Gasoline','Midgrade Gasoline') \\\n",
+    "              & cylinders in(4, 6) \\\n",
+    "              & VClass in('Compact Cars','Subcompact Cars','Midsize Cars','Large Cars','Sport Utility','Minivan') \\\n",
+    "              & ~(fuelType2 in('E85','Electricity','Natural Gas','Propane'))\")\n",
+    "      ['combmpg'].plot.hist(alpha=0.5, label='Honda Motor Co')\n",
+    "     )\n",
+    "plt.title(\"Histogram of Combined Hwy+City MPG\", weight=\"bold\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Also check out [pipe() method](http://pandas.pydata.org/pandas-docs/stable/basics.html#tablewise-function-application).**"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},