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nb/0_introduction.ipynb

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+    "# Table of Contents\n",
+    " <p><div class=\"lev1 toc-item\"><a href=\"#Pandas\" data-toc-modified-id=\"Pandas-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>Pandas</a></div><div class=\"lev2 toc-item\"><a href=\"#Why-Pandas?\" data-toc-modified-id=\"Why-Pandas?-11\"><span class=\"toc-item-num\">1.1&nbsp;&nbsp;</span>Why Pandas?</a></div><div class=\"lev2 toc-item\"><a href=\"#How-Pandas?\" data-toc-modified-id=\"How-Pandas?-12\"><span class=\"toc-item-num\">1.2&nbsp;&nbsp;</span>How Pandas?</a></div>"
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+    "** NAVIGATION **\n",
+    "\n",
+    "**Got Pandas? _Practical Data Wrangling with Pandas_**\n",
+    "\n",
+    "* **Introduction**\n",
+    "1. [Data Structures](./0_data_structures.ipynb)\n",
+    "2. [Importing Data](./1_importing_data.ipynb)\n",
+    "3. [Manipulating DataFrames](./2_dataframe_operations.ipynb)\n",
+    "4. [Wrap Up](3_wrapping_up.ipynb)\n",
+    "\n",
+    "---"
+   ]
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+    "**NOTEBOOK OBJECTIVES**\n",
+    "\n",
+    "In this notebook we'll:\n",
+    "\n",
+    "* explore the purpose of Pandas,\n",
+    "* understand where Pandas fits in the scientific data analysis ecosystem,\n",
+    "* understand installation options."
+   ]
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+    "# Pandas\n",
+    "\n",
+    "Pandas is a fantastic library, and if you don't _Got Pandas?_ ... perhaps it is time you do.\n",
+    "\n",
+    "Pandas is a fast and built on top of [NumPy](http://www.numpy.org/) with dependencies on [statsmodel](http://www.statsmodels.org/stable/index.html), so if you have familiarity with NumPy, Pandas might be what you've always wanted and never knew you did!\n",
+    "\n",
+    "For readers who are familiar with R and considering Python, Pandas may be the right tool to make the transition smoothly as the core DataFrame structure in Pandas is modeled after that of R's `data.frame`.\n",
+    "\n",
+    "\n",
+    "Pandas has many strengths but here are a few that might pique your interests:\n",
+    "\n",
+    "* flexible, consistent data import and export from a wide array of sources, including SQL, CSV, EXCEL, etc.\n",
+    "* tabular / matrix data representation with heterogeneous labeled or unlabeled columns\n",
+    "* intuitive handling of missing data \n",
+    "* import and conversion of data to / from NumPy\n",
+    "* sophisticated slicing, indexing and subsetting of data\n",
+    "* support for hierarchical labeling of data\n",
+    "* support for time series data, including time/date conversion, moving windows, etc.\n",
+    "* and much more ...\n",
+    "\n",
+    "\n"
+   ]
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+   "source": [
+    "```\n",
+    "picture\n",
+    "```"
+   ]
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+   "metadata": {},
+   "source": [
+    "## Why Pandas?\n",
+    "\n",
+    "Pandas has become known as the go-to library in the Python data science stack.  With its strong support for importing various data formats, it can be the _first tool_ you might use to work with, manipulate, convert, reorganize and prepare data for analysis.\n",
+    "\n",
+    "Pandas is not a replacement for NumPy, but rather a supplement to it.  With its sophisticated indexing, it becomes a more powerful way to access and prepare data for analysis in NumPy, and in many cases it will become a necessary compliment to the features already provided by NumPy.\n",
+    "\n",
+    "Pandas brings the fun back into data engineering, and once mastered is one of many tools that will be required for doing high quality data analysis in Python.\n",
+    "\n",
+    "Everything you'd every want to know about Python can be found :\n",
+    "\n",
+    "* [pandas.pydata.org](http://pandas.pydata.org): go here for complete, up-to-date documentation on the latest and greatest of Pandas\n",
+    "* [github.com/pandas-dev/pandas](http://github.com/pandas-dev/pandas): if you want to browse source code for the project\n",
+    "\n",
+    "There are also many great tutorials around the web and in the blogosphere."
+   ]
+  },
+  {
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+   "metadata": {},
+   "source": [
+    "## How Pandas?\n",
+    "\n",
+    "Pandas can be installed in Python 2 and Python 3, though it is recommended to use Python 3 as Python 2 will soon lose support and updates.\n",
+    "\n",
+    "Pandas can be installed from a variety of mechanisms.\n",
+    "\n",
+    "If you've installed [Anaconda](https://www.continuum.io/what-is-anaconda) then you need do nothing -- Pandas is installed by default in the conda stack.\n",
+    "\n",
+    "If you want, you can install Pandas via [binaries from Pypi](http://pypi.python.org/pypi/pandas) or you can install via `pip`:\n",
+    "\n",
+    "```bash\n",
+    "pip install pandas\n",
+    "```\n",
+    "\n",
+    "should get you going.\n",
+    "\n",
+    "For more about installation, please see:\n",
+    "\n",
+    "* [complete Pandas installation documentation on pydata.org](http://pandas.pydata.org/pandas-docs/stable/install.html)\n",
+    "&Xi;"
+   ]
+  }
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