DSPD0101EN-Introduction-to-Pandas.ipynb

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    "In the previous chapter, \n",
    "* We done into detail on NumPy and its ``ndarray`` object, which provides efficient storage and manipulation of dense typed arrays in Python.\n",
    "\n",
    "* Here we'll build on this knowledge by looking in detail at the data structures provided by the Pandas library.\n",
    "* Pandas is a newer package built on top of NumPy, and provides an efficient implementation of a ``DataFrame``.\n",
    "``DataFrame``s are essentially multidimensional arrays with attached row and column labels, and often with heterogeneous types and/or missing data.\n",
    "* As well as offering a convenient storage interface for labeled data, Pandas implements a number of powerful data operations familiar to users of both database frameworks and spreadsheet programs.\n",
    "\n",
    "\n",
    "### Features of Pandas\n",
    "* DataFrame object for data manipulation with integrated indexing.\n",
    "* Tools for reading and writing data between in-memory data structures and different file formats.\n",
    "* Data alignment and integrated handling of missing data.\n",
    "* Reshaping and pivoting of data sets.\n",
    "* Label-based slicing, fancy indexing, and subsetting of large data sets.\n",
    "* Data structure column insertion and deletion.\n",
    "* Group by engine allowing split-apply-combine operations on data sets.\n",
    "* Data set merging and joining.\n",
    "* Hierarchical axis indexing to work with high-dimensional data in a lower-dimensional data structure.\n",
    "* Time series-functionality: Date range generation[4] and frequency conversion, moving window statistics, moving window linear regressions, date shifting and lagging.\n",
    "* Provides data filtration.\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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     "text": [
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   "source": [
    "!pip install pandas"
   ]
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     ]
    }
   ],
   "source": [
    "!pip install pandas-profiling[notebook,html]   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1.0.3'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas\n",
    "pandas.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Just as we generally import NumPy under the alias ``np``, we will import Pandas under the alias ``pd``:"
   ]
  },
  {
   "cell_type": "code",
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   "source": [
    "import pandas as pd"
   ]
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   "cell_type": "code",
   "execution_count": 5,
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1.0.3'"
      ]
     },
     "execution_count": 5,
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   ],
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    "pd.__version__"
   ]
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   "cell_type": "markdown",
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   "source": [
    "This import convention will be used throughout the remainder of this book."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas_profiling as pp  # Generating the Report "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "'2.8.0'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "pp.__version__"
   ]
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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd."
   ]
  },
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   "cell_type": "markdown",
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   "source": [
    "## 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",
    "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/.\n",
    "\n",
    "\n",
    "\n",
    "## Data Science Life Cycle "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src='https://lh3.googleusercontent.com/-aOkQ7qExbEY/XdtGI_BFQ8I/AAAAAAAAkk4/Nyo1dzF0ByEFzDS5CQaV4ODCmBHOYB78ACK8BGAsYHg/s0/2019-11-24.png'/>"
   ]
  }
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