4.datasci/notebooks/03.00-Introduction-to-Pandas.ipynb

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    "# Data Manipulation with Pandas"
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    "> Pandas, and in particular its ``Series`` and ``DataFrame`` objects, builds on the NumPy array structure and provides efficient access to these sorts of \"data munging\" tasks that occupy much of a data scientist's time."
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    "<!--BOOK_INFORMATION-->\n",
    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
    "\n",
    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
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    "<!--NAVIGATION-->\n",
    "< [Structured Data: NumPy's Structured Arrays](02.09-Structured-Data-NumPy.ipynb) | [Contents](Index.ipynb) | [Introducing Pandas Objects](03.01-Introducing-Pandas-Objects.ipynb) >"
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    "### [3. Data Manipulation with Pandas](03.00-Introduction-to-Pandas.ipynb)\n",
    "- [Introducing Pandas Objects](03.01-Introducing-Pandas-Objects.ipynb)\n",
    "- [Data Indexing and Selection](03.02-Data-Indexing-and-Selection.ipynb)\n",
    "- [Operating on Data in Pandas](03.03-Operations-in-Pandas.ipynb)\n",
    "- [Handling Missing Data](03.04-Missing-Values.ipynb)\n",
    "- [Hierarchical Indexing](03.05-Hierarchical-Indexing.ipynb)\n",
    "- [Combining Datasets: Concat and Append](03.06-Concat-And-Append.ipynb)\n",
    "- [Combining Datasets: Merge and Join](03.07-Merge-and-Join.ipynb)\n",
    "- [Aggregation and Grouping](03.08-Aggregation-and-Grouping.ipynb)\n",
    "- [Pivot Tables](03.09-Pivot-Tables.ipynb)\n",
    "- [Vectorized String Operations](03.10-Working-With-Strings.ipynb)\n",
    "- [Working with Time Series](03.11-Working-with-Time-Series.ipynb)\n",
    "- [High-Performance Pandas: eval() and query()](03.12-Performance-Eval-and-Query.ipynb)\n",
    "- [Further Resources](03.13-Further-Resources.ipynb)"
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    "NumPy and its ``ndarray`` object provide efficient storage and manipulation of dense typed arrays in Python.\n",
    "\n",
    "We need more flexibility\n",
    "- attaching labels to data, \n",
    "- working with missing data, etc.) \n",
    "- groupings, pivots, etc.), \n",
    "- analyzing the less structured data .\n"
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    "- Pandas is built on top of NumPy, and \n",
    "- Pandas provides an efficient implementation of a ``DataFrame``.\n",
    "\n",
    "``DataFrame``s are essentially multidimensional arrays with attached row and column labels, and often with heterogeneous types and/or missing data.\n"
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    "## Installing and Using Pandas\n",
    "\n",
    "Installation of Pandas on your system requires NumPy to be installed, and if building the library from source, requires the appropriate tools to compile the C and Cython sources on which Pandas is built.\n",
    "Details on this installation can be found in the [Pandas documentation](http://pandas.pydata.org/).\n",
    "If you followed the advice outlined in the [Preface](00.00-Preface.ipynb) and used the Anaconda stack, you already have Pandas installed.\n",
    "\n",
    "Once Pandas is installed, you can import it and check the version:"
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   "source": [
    "import pandas\n",
    "pandas.__version__"
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    "Just as we generally import NumPy under the alias ``np``, we will import Pandas under the alias ``pd``:"
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    "import pandas as pd"
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    "## Reminder about Built-In Documentation\n",
    "\n",
    "As you read through this chapter, don't forget that IPython gives you the ability to quickly explore the contents of a package (by using the tab-completion feature) as well as the documentation of various functions (using the ``?`` character). (Refer back to [Help and Documentation in IPython](01.01-Help-And-Documentation.ipynb) if you need a refresher on this.)\n",
    "\n",
    "More detailed documentation, along with tutorials and other resources, can be found at http://pandas.pydata.org/."
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    "For example, to display all the contents of the pandas namespace, you can type\n",
    "\n",
    "```ipython\n",
    "In [3]: pd.<TAB>\n",
    "```\n",
    "\n",
    "And to display Pandas's built-in documentation, you can use this:\n",
    "\n",
    "```ipython\n",
    "In [4]: pd?\n",
    "```\n",
    "\n",
    "More detailed documentation, along with tutorials and other resources, can be found at http://pandas.pydata.org/."
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    "pd.read_csv?"
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    "<!--NAVIGATION-->\n",
    "< [Structured Data: NumPy's Structured Arrays](02.09-Structured-Data-NumPy.ipynb) | [Contents](Index.ipynb) | [Introducing Pandas Objects](03.01-Introducing-Pandas-Objects.ipynb) >"
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