additional snippets

Author: chrisalbon

.ipynb_checkpoints/pandas_example_dataframes-checkpoint.ipynb

@@ -1,24 +1,32 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:85afe0ddffbad3067a73cf28ef2d7844f0fae41bf2916d3837f4cc1e0850080a"
+  "signature": "sha256:f5859cf89f0ea0e405b8c2650886baef21a76064f0b680268a01ead40eff5014"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
  "worksheets": [
   {
    "cells": [
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "# Simple Example Dataframe\n",
+      "\n",
+      "This snippit was written by [Chris R. Albon](http://www.chrisralbon.com/) and is part of his collection of [well-documented Python snippits](https://github.com/chrisalbon/code_py). All code is written in Python 3 in iPython notebook and offered under the [Creative Commons Attribution-ShareAlike 4.0 International License](http://creativecommons.org/licenses/by-sa/4.0/\"><img alt=\"Creative Commons License)."
+     ]
+    },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "import pandas as pd\n",
-      "%matplotlib inline"
+      "import pandas as pd"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 3
+     "prompt_number": 2
     },
     {
      "cell_type": "code",
@@ -40,35 +48,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 14
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "plt.scatter(regiment.readiness, regiment.veterans, s=300, c=regiment.size)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 15,
-       "text": [
-        "<matplotlib.collections.PathCollection at 0x10a207e50>"
-       ]
-      },
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": 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Vu30BcENcHIn9+/PaO++E/WWp9T0CEbG9tLQ0vli5kpfT0rjW5eJrKr/zVRHwKnB+kya0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\nBnyHcEb8D0fpS/T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-       "text": [
-        "<matplotlib.figure.Figure at 0x10b1f11d0>"
-       ]
-      }
-     ],
-     "prompt_number": 15
+     "prompt_number": 3
     },
     {
      "cell_type": "code",