diff --git a/pandas/pandas.ipynb b/pandas/pandas.ipynb
index 46f32ac..c0c9169 100644
--- a/pandas/pandas.ipynb
+++ b/pandas/pandas.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:a0f056a23acdf795f7de8660469dcb0323a659e710786e7d33a158a6b72c6c03"
+  "signature": "sha256:364642bdaf4f304a679c7bbde135fea5a7e31eb8da19fb9b24192091e3e34cb0"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -22,7 +22,8 @@
       "* Arithmetic and Data Alignment\n",
       "* Function Application and Mapping\n",
       "* Sorting and Ranking\n",
-      "* Axis Indices with Duplicate Values"
+      "* Axis Indices with Duplicate Values\n",
+      "* Summarizing and Computing Descriptive Statistics"
      ]
     },
     {
@@ -5354,6 +5355,238 @@
       }
      ],
      "prompt_number": 102
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "## Summarizing and Computing Descriptive Statistics"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Unlike NumPy arrays, Pandas descriptive statistics automatically exclude missing data.  NaN values are excluded unless the entire row or column is NA."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "df_6"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "html": [
+        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+        "<table border=\"1\" class=\"dataframe\">\n",
+        "  <thead>\n",
+        "    <tr style=\"text-align: right;\">\n",
+        "      <th></th>\n",
+        "      <th>state</th>\n",
+        "      <th>pop</th>\n",
+        "      <th>unempl</th>\n",
+        "      <th>year</th>\n",
+        "    </tr>\n",
+        "  </thead>\n",
+        "  <tbody>\n",
+        "    <tr>\n",
+        "      <th>0</th>\n",
+        "      <td>  VA</td>\n",
+        "      <td> 5.0</td>\n",
+        "      <td> NaN</td>\n",
+        "      <td> 2012</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>1</th>\n",
+        "      <td>  VA</td>\n",
+        "      <td> 5.1</td>\n",
+        "      <td> NaN</td>\n",
+        "      <td> 2013</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2</th>\n",
+        "      <td>  VA</td>\n",
+        "      <td> 5.2</td>\n",
+        "      <td> 6.0</td>\n",
+        "      <td> 2014</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>3</th>\n",
+        "      <td>  MD</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 6.0</td>\n",
+        "      <td> 2014</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>4</th>\n",
+        "      <td>  MD</td>\n",
+        "      <td> 4.1</td>\n",
+        "      <td> 6.1</td>\n",
+        "      <td> 2015</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>5</th>\n",
+        "      <td> NaN</td>\n",
+        "      <td> NaN</td>\n",
+        "      <td> NaN</td>\n",
+        "      <td>  NaN</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>6</th>\n",
+        "      <td> NaN</td>\n",
+        "      <td> NaN</td>\n",
+        "      <td> NaN</td>\n",
+        "      <td>  NaN</td>\n",
+        "    </tr>\n",
+        "  </tbody>\n",
+        "</table>\n",
+        "</div>"
+       ],
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 120,
+       "text": [
+        "  state  pop  unempl  year\n",
+        "0    VA  5.0     NaN  2012\n",
+        "1    VA  5.1     NaN  2013\n",
+        "2    VA  5.2     6.0  2014\n",
+        "3    MD  4.0     6.0  2014\n",
+        "4    MD  4.1     6.1  2015\n",
+        "5   NaN  NaN     NaN   NaN\n",
+        "6   NaN  NaN     NaN   NaN"
+       ]
+      }
+     ],
+     "prompt_number": 120
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "df_6.sum()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 112,
+       "text": [
+        "pop          23.4\n",
+        "unempl       18.1\n",
+        "year      10068.0\n",
+        "dtype: float64"
+       ]
+      }
+     ],
+     "prompt_number": 112
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Sum over the rows:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "df_6.sum(axis=1)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 113,
+       "text": [
+        "0    2017.0\n",
+        "1    2018.1\n",
+        "2    2025.2\n",
+        "3    2024.0\n",
+        "4    2025.2\n",
+        "5       NaN\n",
+        "6       NaN\n",
+        "dtype: float64"
+       ]
+      }
+     ],
+     "prompt_number": 113
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Account for NaNs:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "df_6.sum(axis=1, skipna=False)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 115,
+       "text": [
+        "0       NaN\n",
+        "1       NaN\n",
+        "2    2025.2\n",
+        "3    2024.0\n",
+        "4    2025.2\n",
+        "5       NaN\n",
+        "6       NaN\n",
+        "dtype: float64"
+       ]
+      }
+     ],
+     "prompt_number": 115
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "df_6.idxmax()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "ename": "ValueError",
+       "evalue": "could not convert string to float: MD",
+       "output_type": "pyerr",
+       "traceback": [
+        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+        "\u001b[0;32m<ipython-input-117-eca0089f6d98>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf_6\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0midxmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+        "\u001b[0;32m/Users/dmartin/anaconda/lib/python2.7/site-packages/pandas/core/frame.pyc\u001b[0m in \u001b[0;36midxmax\u001b[0;34m(self, axis, skipna)\u001b[0m\n\u001b[1;32m   4197\u001b[0m         \"\"\"\n\u001b[1;32m   4198\u001b[0m         \u001b[0maxis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_axis_number\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4199\u001b[0;31m         \u001b[0mindices\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnanops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnanargmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mskipna\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mskipna\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4200\u001b[0m         \u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4201\u001b[0m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mi\u001b[0m \u001b[0;34m>=\u001b[0m \u001b[0;36m0\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mNA\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mindices\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+        "\u001b[0;32m/Users/dmartin/anaconda/lib/python2.7/site-packages/pandas/core/nanops.pyc\u001b[0m in \u001b[0;36mnanargmax\u001b[0;34m(values, axis, skipna)\u001b[0m\n\u001b[1;32m    432\u001b[0m     \"\"\"\n\u001b[1;32m    433\u001b[0m     values, mask, dtype, _ = _get_values(values, skipna, fill_value_typ='-inf',\n\u001b[0;32m--> 434\u001b[0;31m                                          isfinite=True)\n\u001b[0m\u001b[1;32m    435\u001b[0m     \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    436\u001b[0m     \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_maybe_arg_null_out\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmask\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mskipna\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+        "\u001b[0;32m/Users/dmartin/anaconda/lib/python2.7/site-packages/pandas/core/nanops.pyc\u001b[0m in \u001b[0;36m_get_values\u001b[0;34m(values, skipna, fill_value, fill_value_typ, isfinite, copy)\u001b[0m\n\u001b[1;32m    157\u001b[0m     \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_values_from_object\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    158\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0misfinite\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 159\u001b[0;31m         \u001b[0mmask\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_isfinite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    160\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    161\u001b[0m         \u001b[0mmask\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0misnull\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+        "\u001b[0;32m/Users/dmartin/anaconda/lib/python2.7/site-packages/pandas/core/nanops.pyc\u001b[0m in \u001b[0;36m_isfinite\u001b[0;34m(values)\u001b[0m\n\u001b[1;32m    200\u001b[0m             is_integer_dtype(values) or is_bool_dtype(values)):\n\u001b[1;32m    201\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0;34m~\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misfinite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 202\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0;34m~\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misfinite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'float64'\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    203\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    204\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+        "\u001b[0;31mValueError\u001b[0m: could not convert string to float: MD"
+       ]
+      }
+     ],
+     "prompt_number": 117
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
     }
    ],
    "metadata": {}