{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "![Pandas Logo](http://pandas.pydata.org/_static/pandas_logo.png)\n",
    "\n",
    "# <span style=\"color:blue\">Week 08 - Introduction to Pandas </span>\n",
    "\n",
    "## <span style=\"color:red\">Today's Agenda </span>\n",
    "- Pandas: Introduction\n",
    "    - Series\n",
    "    - DataFrames\n",
    "    - Indexing, Selecting, Filtering\n",
    "    - Drop columns\n",
    "    - Handling missing Data\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# Importing modules\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "sns.set_context(\"notebook\")\n",
    "import matplotlib\n",
    "matplotlib.rc(\"text\", usetex=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Series\n",
    "A _Series_ is a one-dimensional array-like object containing an array of data and an associated array of __data labels__.\n",
    "One can use any NumPy data type to assign to the _Series_\n",
    "\n",
    "Creating a Series:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4.17022005e-01, 7.20324493e-01, 1.14374817e-04, 3.02332573e-01,\n",
       "       1.46755891e-01, 9.23385948e-02, 1.86260211e-01, 3.45560727e-01,\n",
       "       3.96767474e-01, 5.38816734e-01])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.seed(1)\n",
    "\n",
    "np.random.random(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.419195\n",
       "1    0.685220\n",
       "2    0.204452\n",
       "3    0.878117\n",
       "4    0.027388\n",
       "5    0.670468\n",
       "6    0.417305\n",
       "7    0.558690\n",
       "8    0.140387\n",
       "9    0.198101\n",
       "dtype: float64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "series_1 = pd.Series(np.random.random(10))\n",
    "series_1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "One can get a NumPy array from the Series, by typing:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.41919451, 0.6852195 , 0.20445225, 0.87811744, 0.02738759,\n",
       "       0.67046751, 0.4173048 , 0.55868983, 0.14038694, 0.19810149])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "series_1.values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Reindexing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "One can also get the indices of each element, by typing:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "series_1.index.values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "One can also have a custom set of indices:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# import string\n",
    "# alphabet = string.lowercase\n",
    "# alphabet = np.array([x for x in alphabet])[0:10]\n",
    "# alphabet\n",
    "\n",
    "alphabet = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j']\n",
    "alphabet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    0.800745\n",
       "b    0.968262\n",
       "c    0.313424\n",
       "d    0.692323\n",
       "e    0.876389\n",
       "f    0.894607\n",
       "g    0.085044\n",
       "h    0.039055\n",
       "i    0.169830\n",
       "j    0.878143\n",
       "dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "series_2 = pd.Series(np.random.random(len(alphabet)), index=alphabet)\n",
    "series_2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "One can select only a subsample of the _Series_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.419195\n",
       "1    0.685220\n",
       "2    0.204452\n",
       "dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "series_1[[0, 1, 2]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    0.685220\n",
       "3    0.878117\n",
       "4    0.027388\n",
       "dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "series_1[[1,3,4]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    0.800745\n",
       "d    0.692323\n",
       "j    0.878143\n",
       "dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "series_2[['a','d','j']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Arithmetic and function Mapping"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "You can also perform numerical expressions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.175724\n",
       "1    0.469526\n",
       "2    0.041801\n",
       "3    0.771090\n",
       "4    0.000750\n",
       "5    0.449527\n",
       "6    0.174143\n",
       "7    0.312134\n",
       "8    0.019708\n",
       "9    0.039244\n",
       "dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "series_1**2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4695257637239847"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "series_1[1]**2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Or find values greater than some value '__x__'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    0.685220\n",
       "5    0.670468\n",
       "7    0.558690\n",
       "dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = 0.5\n",
    "series_1[(series_1 >= x) & (series_1 < 0.8)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "You can apply functions to a column, and save it as a _new_ Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "import sys\n",
    "def exponentials(arr, basis=10.):\n",
    "    \"\"\"\n",
    "    Uses the array `arr` as the exponents for `basis`\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    arr: numpy array, list, pandas Series; shape (N,)\n",
    "        array to be used as exponents of `basis`\n",
    "    \n",
    "    power: int or float, optional (default = 10)\n",
    "        number used as the basis\n",
    "    \n",
    "    Returns\n",
    "    -------\n",
    "    exp_arr: numpy array or list, shape (N,)\n",
    "        array of values for `basis`**`arr`\n",
    "    \"\"\"\n",
    "    if isinstance(arr, list):\n",
    "        exp_arr = [basis**x for x in arr]\n",
    "        return exp_arr        \n",
    "    elif isinstance(arr, np.ndarray) or isinstance(arr, pd.core.series.Series):\n",
    "        exp_arr = basis**arr\n",
    "        return exp_arr\n",
    "    else:\n",
    "        cmd = \">>>> `arr` is not a list nor a numpy array\"\n",
    "        cmd +=\"\\n>>>> Please give the correct type of object\"\n",
    "        print(cmd)\n",
    "        sys.exit(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4.84417139, 7.55296438, 4.68238921])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exponentials(series_1[(series_1 >= x) & (series_1 > 0.6)]).values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "You can also __create__ a _Series_ using a _dictionary_ (we talked about these on __Week 4__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'foo': 100, 'bar': 200, 'baz': 300}"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels_arr = ['foo', 'bar', 'baz']\n",
    "data_arr   = [100, 200, 300]\n",
    "dict_1     = dict(zip(labels_arr, data_arr))\n",
    "dict_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "foo    100\n",
       "bar    200\n",
       "baz    300\n",
       "dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "series_3 = pd.Series(dict_1)\n",
    "series_3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Handling Missing Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "One of the most useful features of pandas is that it __can handle missing data__ quite easily:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "foo    100.0\n",
       "bar    200.0\n",
       "baz    300.0\n",
       "qux      NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index = ['foo', 'bar', 'baz', 'qux']\n",
    "series_4 = pd.Series(dict_1, index=index)\n",
    "series_4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "foo    False\n",
       "bar    False\n",
       "baz    False\n",
       "qux     True\n",
       "dtype: bool"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.isnull(series_4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "foo    100\n",
       "bar    200\n",
       "baz    300\n",
       "dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "series_3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "bar    400.0\n",
       "baz    600.0\n",
       "foo    200.0\n",
       "qux      NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "series_3 + series_4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "So using a Series is powerful, but __DataFrames__ are probably what gets used the most since it represents a _tabular data structure_ containing an ordered collection of __columns__ and __rows__."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# DataFrames"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "A DataFrame is a \"tabular data structure\" containing an _ordered collection of columns_. Each column can a have a __different__ data type.\n",
    "\n",
    "Row and column operations are treated roughly symmetrically.\n",
    "One can obtain a DataFrame from a normal dictionary, or by reading a file with columns and rows."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Creating a DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>state</th>\n",
       "      <th>year</th>\n",
       "      <th>popu</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>VA</td>\n",
       "      <td>2012</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>VA</td>\n",
       "      <td>2013</td>\n",
       "      <td>5.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>VA</td>\n",
       "      <td>2014</td>\n",
       "      <td>5.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>MD</td>\n",
       "      <td>2014</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>MD</td>\n",
       "      <td>2015</td>\n",
       "      <td>4.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  state  year  popu\n",
       "0    VA  2012   5.0\n",
       "1    VA  2013   5.1\n",
       "2    VA  2014   5.2\n",
       "3    MD  2014   4.0\n",
       "4    MD  2015   4.1"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_1 = {'state' : ['VA', 'VA', 'VA', 'MD', 'MD'],\n",
    "          'year' : [2012, 2013, 2014, 2014, 2015],\n",
    "          'popu' : [5.0, 5.1, 5.2, 4.0, 4.1]}\n",
    "df_1 = pd.DataFrame(data_1)\n",
    "df_1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "This DataFrame has 4 rows and 3 columns by the name \"_pop_\", \"_state_\", and \"_year_\".\n",
    "\n",
    "The way to __access__ a DataFrame is quite similar to that of accessing a _Series_.<br>\n",
    "To access a __column__, one writes the name of the `column`, as in the following example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    5.0\n",
       "1    5.1\n",
       "2    5.2\n",
       "3    4.0\n",
       "4    4.1\n",
       "Name: popu, dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_1['popu']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    5.0\n",
       "1    5.1\n",
       "2    5.2\n",
       "3    4.0\n",
       "4    4.1\n",
       "Name: popu, dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_1.popu"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "One can also handle __missing data__ with DataFrames.\n",
    "Like Series, columns that are not present in the data are NaNs:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>state</th>\n",
       "      <th>popu</th>\n",
       "      <th>unempl</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2012</td>\n",
       "      <td>VA</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013</td>\n",
       "      <td>VA</td>\n",
       "      <td>5.1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014</td>\n",
       "      <td>VA</td>\n",
       "      <td>5.2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014</td>\n",
       "      <td>MD</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015</td>\n",
       "      <td>MD</td>\n",
       "      <td>4.1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year state  popu unempl\n",
       "0  2012    VA   5.0    NaN\n",
       "1  2013    VA   5.1    NaN\n",
       "2  2014    VA   5.2    NaN\n",
       "3  2014    MD   4.0    NaN\n",
       "4  2015    MD   4.1    NaN"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2 = pd.DataFrame(data_1, columns=['year', 'state', 'popu', 'unempl'])\n",
    "df_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    VA\n",
       "1    VA\n",
       "2    VA\n",
       "3    MD\n",
       "4    MD\n",
       "Name: state, dtype: object"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2['state']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "One can __retrieve a row__ by:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>state</th>\n",
       "      <th>popu</th>\n",
       "      <th>unempl</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013</td>\n",
       "      <td>VA</td>\n",
       "      <td>5.1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014</td>\n",
       "      <td>VA</td>\n",
       "      <td>5.2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014</td>\n",
       "      <td>MD</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year state  popu unempl\n",
       "1  2013    VA   5.1    NaN\n",
       "2  2014    VA   5.2    NaN\n",
       "3  2014    MD   4.0    NaN"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2.iloc[1:4]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Editing a DataFrame is quite easy to do. One can _assign_ a Series to a column of the DataFrame. If the Series is a list or an array, __the length must match the DataFrame__."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1     1.0\n",
       "3     2.0\n",
       "5    10.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unempl = pd.Series([1.0, 2.0, 10.], index=[1,3,5])\n",
    "unempl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>state</th>\n",
       "      <th>popu</th>\n",
       "      <th>unempl</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2012</td>\n",
       "      <td>VA</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013</td>\n",
       "      <td>VA</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014</td>\n",
       "      <td>VA</td>\n",
       "      <td>5.2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014</td>\n",
       "      <td>MD</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015</td>\n",
       "      <td>MD</td>\n",
       "      <td>4.1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year state  popu  unempl\n",
       "0  2012    VA   5.0     NaN\n",
       "1  2013    VA   5.1     1.0\n",
       "2  2014    VA   5.2     NaN\n",
       "3  2014    MD   4.0     2.0\n",
       "4  2015    MD   4.1     NaN"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2['unempl'] = unempl\n",
    "df_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     True\n",
       "1    False\n",
       "2     True\n",
       "3    False\n",
       "4     True\n",
       "Name: unempl, dtype: bool"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2.unempl.isnull()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "You can also __transpose__ a DataFrame, i.e. switch rows by columns, and columns by rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <td>2012</td>\n",
       "      <td>2013</td>\n",
       "      <td>2014</td>\n",
       "      <td>2014</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>state</th>\n",
       "      <td>VA</td>\n",
       "      <td>VA</td>\n",
       "      <td>VA</td>\n",
       "      <td>MD</td>\n",
       "      <td>MD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>popu</th>\n",
       "      <td>5</td>\n",
       "      <td>5.1</td>\n",
       "      <td>5.2</td>\n",
       "      <td>4</td>\n",
       "      <td>4.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unempl</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           0     1     2     3     4\n",
       "year    2012  2013  2014  2014  2015\n",
       "state     VA    VA    VA    MD    MD\n",
       "popu       5   5.1   5.2     4   4.1\n",
       "unempl   NaN     1   NaN     2   NaN"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2.T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Now, let's say you want to show __only the 'year' and 'popu' columns__.\n",
    "You can do it by:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>state</th>\n",
       "      <th>popu</th>\n",
       "      <th>unempl</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2012</td>\n",
       "      <td>VA</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013</td>\n",
       "      <td>VA</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014</td>\n",
       "      <td>VA</td>\n",
       "      <td>5.2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014</td>\n",
       "      <td>MD</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015</td>\n",
       "      <td>MD</td>\n",
       "      <td>4.1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year state  popu  unempl\n",
       "0  2012    VA   5.0     NaN\n",
       "1  2013    VA   5.1     1.0\n",
       "2  2014    VA   5.2     NaN\n",
       "3  2014    MD   4.0     2.0\n",
       "4  2015    MD   4.1     NaN"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>unempl</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2012</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year  unempl\n",
       "0  2012     NaN\n",
       "1  2013     1.0\n",
       "2  2014     NaN\n",
       "3  2014     2.0\n",
       "4  2015     NaN"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2[['year', 'unempl']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Dropping Entries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Let's say you only need a subsample of the table that you have, and you need to __drop__ a column from the DataFrame.\n",
    "You can do that by using the '_drop_' option:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>state</th>\n",
       "      <th>popu</th>\n",
       "      <th>unempl</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2012</td>\n",
       "      <td>VA</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013</td>\n",
       "      <td>VA</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014</td>\n",
       "      <td>VA</td>\n",
       "      <td>5.2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014</td>\n",
       "      <td>MD</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015</td>\n",
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       "      <td>4.1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year state  popu  unempl\n",
       "0  2012    VA   5.0     NaN\n",
       "1  2013    VA   5.1     1.0\n",
       "2  2014    VA   5.2     NaN\n",
       "3  2014    MD   4.0     2.0\n",
       "4  2015    MD   4.1     NaN"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>state</th>\n",
       "      <th>popu</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2012</td>\n",
       "      <td>VA</td>\n",
       "      <td>5.0</td>\n",
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       "      <th>1</th>\n",
       "      <td>2013</td>\n",
       "      <td>VA</td>\n",
       "      <td>5.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014</td>\n",
       "      <td>VA</td>\n",
       "      <td>5.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014</td>\n",
       "      <td>MD</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015</td>\n",
       "      <td>MD</td>\n",
       "      <td>4.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year state  popu\n",
       "0  2012    VA   5.0\n",
       "1  2013    VA   5.1\n",
       "2  2014    VA   5.2\n",
       "3  2014    MD   4.0\n",
       "4  2015    MD   4.1"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_3 = df_2.drop('unempl', axis=1)\n",
    "df_3\n",
    "\n",
    "df_2.drop('unempl', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <th>0</th>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014</td>\n",
       "      <td>VA</td>\n",
       "      <td>5.2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014</td>\n",
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       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015</td>\n",
       "      <td>MD</td>\n",
       "      <td>4.1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year state  popu  unempl\n",
       "0  2012    VA   5.0     NaN\n",
       "1  2013    VA   5.1     1.0\n",
       "2  2014    VA   5.2     NaN\n",
       "3  2014    MD   4.0     2.0\n",
       "4  2015    MD   4.1     NaN"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "You can also __drop certain rows__:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>year</th>\n",
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       "      <th>popu</th>\n",
       "      <th>unempl</th>\n",
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       "      <th>0</th>\n",
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       "      <th>2</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014</td>\n",
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       "      <td>2015</td>\n",
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       "      <td>4.1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year state  popu  unempl\n",
       "0  2012    VA   5.0     NaN\n",
       "1  2013    VA   5.1     1.0\n",
       "2  2014    VA   5.2     NaN\n",
       "3  2014    MD   4.0     2.0\n",
       "4  2015    MD   4.1     NaN"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <th>unempl</th>\n",
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       "      <th>3</th>\n",
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       "      <td>2015</td>\n",
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       "      <td>NaN</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year state  popu  unempl\n",
       "0  2012    VA   5.0     NaN\n",
       "3  2014    MD   4.0     2.0\n",
       "4  2015    MD   4.1     NaN"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_4 = df_2.drop([1,2])\n",
    "df_4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "__Look at this carefully__! The DataFrame _preserved_ the same indices as for __df_2__.\n",
    "\n",
    "If you can to __reset__ the indices, you can do that by:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>year</th>\n",
       "      <th>state</th>\n",
       "      <th>popu</th>\n",
       "      <th>unempl</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2012</td>\n",
       "      <td>VA</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>2014</td>\n",
       "      <td>MD</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>2015</td>\n",
       "      <td>MD</td>\n",
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       "      <td>NaN</td>\n",
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       "  </tbody>\n",
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       "</div>"
      ],
      "text/plain": [
       "   index  year state  popu  unempl\n",
       "0      0  2012    VA   5.0     NaN\n",
       "1      3  2014    MD   4.0     2.0\n",
       "2      4  2015    MD   4.1     NaN"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_4.reset_index(inplace=True)\n",
    "df_4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Gaia Dataset \n",
    "![Gaia](http://pbs.twimg.com/profile_images/1266601315/gaiaLogo_reasonably_small.png)\n",
    "\n",
    "Pandas is great at reading Data tables and CSV files, and other kinds of documents.\n",
    "For the remainder of this notebook, we will be using the [Gaia](https://www.cosmos.esa.int/web/gaia)'s DR1 catalogue."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Path to online file\n",
    "url_path = 'http://cdn.gea.esac.esa.int/Gaia/gdr2/gaia_source/csv/GaiaSource_1000172165251650944_1000424567594791808.csv.gz'\n",
    "\n",
    "# Converting data to DataFrame\n",
    "gaia_df = pd.read_csv(url_path, compression='gzip')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
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       "      <th>ra</th>\n",
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       "      <td>1.311143</td>\n",
       "      <td>1.453106</td>\n",
       "      <td>1.937890</td>\n",
       "      <td>1.852440</td>\n",
       "      <td>2.023341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1635721458409799680</td>\n",
       "      <td>Gaia DR2 1000274106300491264</td>\n",
       "      <td>1000274106300491264</td>\n",
       "      <td>299262776</td>\n",
       "      <td>2015.5</td>\n",
       "      <td>103.424758</td>\n",
       "      <td>0.464608</td>\n",
       "      <td>56.450903</td>\n",
       "      <td>0.582490</td>\n",
       "      <td>0.314035</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1635721458409799680</td>\n",
       "      <td>Gaia DR2 1000396156385741312</td>\n",
       "      <td>1000396156385741312</td>\n",
       "      <td>1148557518</td>\n",
       "      <td>2015.5</td>\n",
       "      <td>105.049751</td>\n",
       "      <td>0.838232</td>\n",
       "      <td>56.508777</td>\n",
       "      <td>0.744511</td>\n",
       "      <td>1.939951</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1635721458409799680</td>\n",
       "      <td>Gaia DR2 1000250024419296000</td>\n",
       "      <td>1000250024419296000</td>\n",
       "      <td>574278759</td>\n",
       "      <td>2015.5</td>\n",
       "      <td>103.352525</td>\n",
       "      <td>0.023159</td>\n",
       "      <td>56.395144</td>\n",
       "      <td>0.022836</td>\n",
       "      <td>0.747108</td>\n",
       "      <td>...</td>\n",
       "      <td>0.2870</td>\n",
       "      <td>0.1196</td>\n",
       "      <td>0.4051</td>\n",
       "      <td>200111.0</td>\n",
       "      <td>1.507958</td>\n",
       "      <td>1.435618</td>\n",
       "      <td>1.540208</td>\n",
       "      <td>2.427377</td>\n",
       "      <td>2.152597</td>\n",
       "      <td>2.702158</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 94 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           solution_id                   designation            source_id  \\\n",
       "0  1635721458409799680  Gaia DR2 1000225938242805248  1000225938242805248   \n",
       "1  1635721458409799680  Gaia DR2 1000383512003001728  1000383512003001728   \n",
       "2  1635721458409799680  Gaia DR2 1000274106300491264  1000274106300491264   \n",
       "3  1635721458409799680  Gaia DR2 1000396156385741312  1000396156385741312   \n",
       "4  1635721458409799680  Gaia DR2 1000250024419296000  1000250024419296000   \n",
       "\n",
       "   random_index  ref_epoch          ra  ra_error        dec  dec_error  \\\n",
       "0    1197051105     2015.5  103.447529  0.041099  56.022025   0.045175   \n",
       "1     598525552     2015.5  105.187856  0.016978  56.267982   0.016904   \n",
       "2     299262776     2015.5  103.424758  0.464608  56.450903   0.582490   \n",
       "3    1148557518     2015.5  105.049751  0.838232  56.508777   0.744511   \n",
       "4     574278759     2015.5  103.352525  0.023159  56.395144   0.022836   \n",
       "\n",
       "   parallax  ...  e_bp_min_rp_val  e_bp_min_rp_percentile_lower  \\\n",
       "0  0.582790  ...           0.0595                        0.0080   \n",
       "1  1.385686  ...           0.2430                        0.0830   \n",
       "2  0.314035  ...              NaN                           NaN   \n",
       "3  1.939951  ...              NaN                           NaN   \n",
       "4  0.747108  ...           0.2870                        0.1196   \n",
       "\n",
       "   e_bp_min_rp_percentile_upper  flame_flags  radius_val  \\\n",
       "0                        0.1351     200111.0    1.024730   \n",
       "1                        0.4030     200111.0    1.388711   \n",
       "2                           NaN          NaN         NaN   \n",
       "3                           NaN          NaN         NaN   \n",
       "4                        0.4051     200111.0    1.507958   \n",
       "\n",
       "   radius_percentile_lower  radius_percentile_upper   lum_val  \\\n",
       "0                 1.017359                 1.038814  1.075774   \n",
       "1                 1.311143                 1.453106  1.937890   \n",
       "2                      NaN                      NaN       NaN   \n",
       "3                      NaN                      NaN       NaN   \n",
       "4                 1.435618                 1.540208  2.427377   \n",
       "\n",
       "   lum_percentile_lower  lum_percentile_upper  \n",
       "0              0.801798              1.349751  \n",
       "1              1.852440              2.023341  \n",
       "2                   NaN                   NaN  \n",
       "3                   NaN                   NaN  \n",
       "4              2.152597              2.702158  \n",
       "\n",
       "[5 rows x 94 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gaia_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Shape, Columns and Rows"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "You can get the shape of the \"gaia_df\" DataFrame by typing: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(14209, 94)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gaia_df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "That means there are __14209 rows__ and __94 columns__."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "To get an array of the columns available, one could write:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['a_g_percentile_lower', 'a_g_percentile_upper', 'a_g_val',\n",
       "       'astrometric_chi2_al', 'astrometric_excess_noise',\n",
       "       'astrometric_excess_noise_sig', 'astrometric_gof_al',\n",
       "       'astrometric_matched_observations', 'astrometric_n_bad_obs_al',\n",
       "       'astrometric_n_good_obs_al', 'astrometric_n_obs_ac',\n",
       "       'astrometric_n_obs_al', 'astrometric_params_solved',\n",
       "       'astrometric_primary_flag', 'astrometric_pseudo_colour',\n",
       "       'astrometric_pseudo_colour_error', 'astrometric_sigma5d_max',\n",
       "       'astrometric_weight_al', 'b', 'bp_g', 'bp_rp', 'dec', 'dec_error',\n",
       "       'dec_parallax_corr', 'dec_pmdec_corr', 'dec_pmra_corr',\n",
       "       'designation', 'duplicated_source', 'e_bp_min_rp_percentile_lower',\n",
       "       'e_bp_min_rp_percentile_upper', 'e_bp_min_rp_val', 'ecl_lat',\n",
       "       'ecl_lon', 'flame_flags', 'frame_rotator_object_type', 'g_rp', 'l',\n",
       "       'lum_percentile_lower', 'lum_percentile_upper', 'lum_val',\n",
       "       'matched_observations', 'mean_varpi_factor_al', 'parallax',\n",
       "       'parallax_error', 'parallax_over_error', 'parallax_pmdec_corr',\n",
       "       'parallax_pmra_corr', 'phot_bp_mean_flux',\n",
       "       'phot_bp_mean_flux_error', 'phot_bp_mean_flux_over_error',\n",
       "       'phot_bp_mean_mag', 'phot_bp_n_obs', 'phot_bp_rp_excess_factor',\n",
       "       'phot_g_mean_flux', 'phot_g_mean_flux_error',\n",
       "       'phot_g_mean_flux_over_error', 'phot_g_mean_mag', 'phot_g_n_obs',\n",
       "       'phot_proc_mode', 'phot_rp_mean_flux', 'phot_rp_mean_flux_error',\n",
       "       'phot_rp_mean_flux_over_error', 'phot_rp_mean_mag',\n",
       "       'phot_rp_n_obs', 'phot_variable_flag', 'pmdec', 'pmdec_error',\n",
       "       'pmra', 'pmra_error', 'pmra_pmdec_corr', 'priam_flags', 'ra',\n",
       "       'ra_dec_corr', 'ra_error', 'ra_parallax_corr', 'ra_pmdec_corr',\n",
       "       'ra_pmra_corr', 'radial_velocity', 'radial_velocity_error',\n",
       "       'radius_percentile_lower', 'radius_percentile_upper', 'radius_val',\n",
       "       'random_index', 'ref_epoch', 'rv_nb_transits', 'rv_template_fe_h',\n",
       "       'rv_template_logg', 'rv_template_teff', 'solution_id', 'source_id',\n",
       "       'teff_percentile_lower', 'teff_percentile_upper', 'teff_val',\n",
       "       'visibility_periods_used'], dtype=object)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gaia_df.columns.values.sort()\n",
    "gaia_df.columns.values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Let's say you only want a DataFrame with the the colums:\n",
    "- ra (right ascension)\n",
    "- dec (declination)\n",
    "- l (galactic longitude)\n",
    "- b (galactic latitude)\n",
    "\n",
    "You do this by using the __loc__ option for the DataFrame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ra</th>\n",
       "      <th>dec</th>\n",
       "      <th>l</th>\n",
       "      <th>b</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>103.447529</td>\n",
       "      <td>56.022025</td>\n",
       "      <td>160.163475</td>\n",
       "      <td>22.533932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>105.187856</td>\n",
       "      <td>56.267982</td>\n",
       "      <td>160.174346</td>\n",
       "      <td>23.534087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>103.424758</td>\n",
       "      <td>56.450903</td>\n",
       "      <td>159.712110</td>\n",
       "      <td>22.635989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>105.049751</td>\n",
       "      <td>56.508777</td>\n",
       "      <td>159.899324</td>\n",
       "      <td>23.518554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>103.352525</td>\n",
       "      <td>56.395144</td>\n",
       "      <td>159.758838</td>\n",
       "      <td>22.582657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>101.929791</td>\n",
       "      <td>55.973333</td>\n",
       "      <td>159.959619</td>\n",
       "      <td>21.705035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>101.853926</td>\n",
       "      <td>56.129320</td>\n",
       "      <td>159.785705</td>\n",
       "      <td>21.709303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>105.128850</td>\n",
       "      <td>56.285081</td>\n",
       "      <td>160.147553</td>\n",
       "      <td>23.506483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>103.396330</td>\n",
       "      <td>56.714410</td>\n",
       "      <td>159.432230</td>\n",
       "      <td>22.690559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>101.780437</td>\n",
       "      <td>55.945333</td>\n",
       "      <td>159.962507</td>\n",
       "      <td>21.616907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>103.500366</td>\n",
       "      <td>56.844629</td>\n",
       "      <td>159.312220</td>\n",
       "      <td>22.779748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>105.649481</td>\n",
       "      <td>56.632527</td>\n",
       "      <td>159.854367</td>\n",
       "      <td>23.868954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>103.189617</td>\n",
       "      <td>56.815154</td>\n",
       "      <td>159.294467</td>\n",
       "      <td>22.607972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>103.530144</td>\n",
       "      <td>55.988175</td>\n",
       "      <td>160.212164</td>\n",
       "      <td>22.569395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>105.938862</td>\n",
       "      <td>56.588687</td>\n",
       "      <td>159.941603</td>\n",
       "      <td>24.013615</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            ra        dec           l          b\n",
       "0   103.447529  56.022025  160.163475  22.533932\n",
       "1   105.187856  56.267982  160.174346  23.534087\n",
       "2   103.424758  56.450903  159.712110  22.635989\n",
       "3   105.049751  56.508777  159.899324  23.518554\n",
       "4   103.352525  56.395144  159.758838  22.582657\n",
       "5   101.929791  55.973333  159.959619  21.705035\n",
       "6   101.853926  56.129320  159.785705  21.709303\n",
       "7   105.128850  56.285081  160.147553  23.506483\n",
       "8   103.396330  56.714410  159.432230  22.690559\n",
       "9   101.780437  55.945333  159.962507  21.616907\n",
       "10  103.500366  56.844629  159.312220  22.779748\n",
       "11  105.649481  56.632527  159.854367  23.868954\n",
       "12  103.189617  56.815154  159.294467  22.607972\n",
       "13  103.530144  55.988175  160.212164  22.569395\n",
       "14  105.938862  56.588687  159.941603  24.013615"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gaia_df_2 = gaia_df.loc[:,['ra','dec','l','b']]\n",
    "\n",
    "# Displaying the first 15 lines\n",
    "gaia_df_2.head(15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "This selects all of the rows, and only the selected columns in the list."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "You can also select only a subsample of the rows as well, as in the following example.\n",
    "Let's say I just want a random subsample of __10%__ of the galaxies in the Gaia DR1 catalogue. I can do that by:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method Random.sample of <random.Random object at 0x7fb45d83e618>>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import random\n",
    "random.sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(14209, 4)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Decission indices to select from DataFrame\n",
    "import random\n",
    "\n",
    "# Number of rows\n",
    "nrows = len(gaia_df_2)\n",
    "\n",
    "# Randomly selecting `nrows` from `gaia_df_2`\n",
    "gaia_df_3 = gaia_df_2.sample(nrows)\n",
    "\n",
    "gaia_df_3.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "I'm __re-normalizing__ the indices of this DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ra</th>\n",
       "      <th>dec</th>\n",
       "      <th>l</th>\n",
       "      <th>b</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>102.023218</td>\n",
       "      <td>56.433992</td>\n",
       "      <td>159.500110</td>\n",
       "      <td>21.886331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>105.272250</td>\n",
       "      <td>56.502381</td>\n",
       "      <td>159.938521</td>\n",
       "      <td>23.636142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>105.087569</td>\n",
       "      <td>56.445058</td>\n",
       "      <td>159.972272</td>\n",
       "      <td>23.523299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>103.359287</td>\n",
       "      <td>56.193791</td>\n",
       "      <td>159.970039</td>\n",
       "      <td>22.532502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>103.240760</td>\n",
       "      <td>56.630222</td>\n",
       "      <td>159.495644</td>\n",
       "      <td>22.585832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>103.222515</td>\n",
       "      <td>56.549474</td>\n",
       "      <td>159.577022</td>\n",
       "      <td>22.554616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>103.361986</td>\n",
       "      <td>56.583872</td>\n",
       "      <td>159.563251</td>\n",
       "      <td>22.637842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>103.775851</td>\n",
       "      <td>56.168097</td>\n",
       "      <td>160.063471</td>\n",
       "      <td>22.749245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>101.836603</td>\n",
       "      <td>56.119531</td>\n",
       "      <td>159.792805</td>\n",
       "      <td>21.697240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>102.678893</td>\n",
       "      <td>56.936834</td>\n",
       "      <td>159.086470</td>\n",
       "      <td>22.371832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>105.667572</td>\n",
       "      <td>56.619706</td>\n",
       "      <td>159.870549</td>\n",
       "      <td>23.875620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>102.236439</td>\n",
       "      <td>55.945081</td>\n",
       "      <td>160.041579</td>\n",
       "      <td>21.861470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>102.433467</td>\n",
       "      <td>56.518693</td>\n",
       "      <td>159.480950</td>\n",
       "      <td>22.127565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>105.105611</td>\n",
       "      <td>56.486566</td>\n",
       "      <td>159.931001</td>\n",
       "      <td>23.543060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>105.362118</td>\n",
       "      <td>56.534916</td>\n",
       "      <td>159.917022</td>\n",
       "      <td>23.692076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>102.368405</td>\n",
       "      <td>56.455920</td>\n",
       "      <td>159.535245</td>\n",
       "      <td>22.075601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>103.676978</td>\n",
       "      <td>56.307551</td>\n",
       "      <td>159.901923</td>\n",
       "      <td>22.732851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>102.504898</td>\n",
       "      <td>57.212852</td>\n",
       "      <td>158.771444</td>\n",
       "      <td>22.355833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>101.804305</td>\n",
       "      <td>55.852613</td>\n",
       "      <td>160.062130</td>\n",
       "      <td>21.602850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>102.119267</td>\n",
       "      <td>56.169582</td>\n",
       "      <td>159.789621</td>\n",
       "      <td>21.862448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>102.071402</td>\n",
       "      <td>56.730196</td>\n",
       "      <td>159.201815</td>\n",
       "      <td>21.995346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>102.774558</td>\n",
       "      <td>56.830031</td>\n",
       "      <td>159.212977</td>\n",
       "      <td>22.393163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>102.137264</td>\n",
       "      <td>56.350726</td>\n",
       "      <td>159.605436</td>\n",
       "      <td>21.923322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>103.921963</td>\n",
       "      <td>56.655100</td>\n",
       "      <td>159.575937</td>\n",
       "      <td>22.953848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>102.932901</td>\n",
       "      <td>56.335646</td>\n",
       "      <td>159.753100</td>\n",
       "      <td>22.342795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>105.385988</td>\n",
       "      <td>56.643913</td>\n",
       "      <td>159.804840</td>\n",
       "      <td>23.730851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>105.542361</td>\n",
       "      <td>56.377615</td>\n",
       "      <td>160.109832</td>\n",
       "      <td>23.751272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>102.647940</td>\n",
       "      <td>56.268813</td>\n",
       "      <td>159.775683</td>\n",
       "      <td>22.172426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>103.711527</td>\n",
       "      <td>56.349395</td>\n",
       "      <td>159.863591</td>\n",
       "      <td>22.762315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>103.597854</td>\n",
       "      <td>56.158071</td>\n",
       "      <td>160.045628</td>\n",
       "      <td>22.650994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14179</th>\n",
       "      <td>102.489214</td>\n",
       "      <td>56.081397</td>\n",
       "      <td>159.943479</td>\n",
       "      <td>22.035450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14180</th>\n",
       "      <td>102.147983</td>\n",
       "      <td>56.711074</td>\n",
       "      <td>159.234323</td>\n",
       "      <td>22.030321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14181</th>\n",
       "      <td>103.716786</td>\n",
       "      <td>56.159878</td>\n",
       "      <td>160.062696</td>\n",
       "      <td>22.715352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14182</th>\n",
       "      <td>105.252391</td>\n",
       "      <td>56.393553</td>\n",
       "      <td>160.050906</td>\n",
       "      <td>23.599250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14183</th>\n",
       "      <td>102.596930</td>\n",
       "      <td>56.310072</td>\n",
       "      <td>159.724415</td>\n",
       "      <td>22.156631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14184</th>\n",
       "      <td>105.268657</td>\n",
       "      <td>56.207321</td>\n",
       "      <td>160.250440</td>\n",
       "      <td>23.562900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14185</th>\n",
       "      <td>102.044837</td>\n",
       "      <td>56.476426</td>\n",
       "      <td>159.459891</td>\n",
       "      <td>21.909801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14186</th>\n",
       "      <td>102.284147</td>\n",
       "      <td>56.917347</td>\n",
       "      <td>159.042894</td>\n",
       "      <td>22.159330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14187</th>\n",
       "      <td>102.602635</td>\n",
       "      <td>57.186376</td>\n",
       "      <td>158.814544</td>\n",
       "      <td>22.399603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14188</th>\n",
       "      <td>102.825593</td>\n",
       "      <td>56.441695</td>\n",
       "      <td>159.625292</td>\n",
       "      <td>22.314552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14189</th>\n",
       "      <td>101.982163</td>\n",
       "      <td>56.314866</td>\n",
       "      <td>159.616222</td>\n",
       "      <td>21.830699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14190</th>\n",
       "      <td>102.244534</td>\n",
       "      <td>56.641037</td>\n",
       "      <td>159.322869</td>\n",
       "      <td>22.061669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14191</th>\n",
       "      <td>102.608435</td>\n",
       "      <td>57.217545</td>\n",
       "      <td>158.783009</td>\n",
       "      <td>22.411067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14192</th>\n",
       "      <td>102.743681</td>\n",
       "      <td>57.158952</td>\n",
       "      <td>158.865450</td>\n",
       "      <td>22.465819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14193</th>\n",
       "      <td>102.777462</td>\n",
       "      <td>56.444267</td>\n",
       "      <td>159.614761</td>\n",
       "      <td>22.289662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14194</th>\n",
       "      <td>105.891445</td>\n",
       "      <td>56.646945</td>\n",
       "      <td>159.872951</td>\n",
       "      <td>24.001706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14195</th>\n",
       "      <td>102.788402</td>\n",
       "      <td>56.867407</td>\n",
       "      <td>159.176279</td>\n",
       "      <td>22.410572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14196</th>\n",
       "      <td>102.019136</td>\n",
       "      <td>56.186270</td>\n",
       "      <td>159.755308</td>\n",
       "      <td>21.813762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14197</th>\n",
       "      <td>103.381823</td>\n",
       "      <td>56.792650</td>\n",
       "      <td>159.348148</td>\n",
       "      <td>22.703505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14198</th>\n",
       "      <td>102.970067</td>\n",
       "      <td>56.429717</td>\n",
       "      <td>159.661250</td>\n",
       "      <td>22.388156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14199</th>\n",
       "      <td>101.940300</td>\n",
       "      <td>55.874246</td>\n",
       "      <td>160.063572</td>\n",
       "      <td>21.682161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14200</th>\n",
       "      <td>102.951998</td>\n",
       "      <td>56.286436</td>\n",
       "      <td>159.807412</td>\n",
       "      <td>22.339588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14201</th>\n",
       "      <td>103.637537</td>\n",
       "      <td>56.235559</td>\n",
       "      <td>159.970962</td>\n",
       "      <td>22.692768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14202</th>\n",
       "      <td>102.089903</td>\n",
       "      <td>55.881491</td>\n",
       "      <td>160.082031</td>\n",
       "      <td>21.764630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14203</th>\n",
       "      <td>103.215704</td>\n",
       "      <td>56.428451</td>\n",
       "      <td>159.702186</td>\n",
       "      <td>22.518617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14204</th>\n",
       "      <td>102.642189</td>\n",
       "      <td>56.958484</td>\n",
       "      <td>159.058057</td>\n",
       "      <td>22.358449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14205</th>\n",
       "      <td>102.233121</td>\n",
       "      <td>57.066272</td>\n",
       "      <td>158.880057</td>\n",
       "      <td>22.173822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14206</th>\n",
       "      <td>104.919145</td>\n",
       "      <td>56.263134</td>\n",
       "      <td>160.139589</td>\n",
       "      <td>23.388225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14207</th>\n",
       "      <td>103.295677</td>\n",
       "      <td>56.738090</td>\n",
       "      <td>159.391665</td>\n",
       "      <td>22.643558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14208</th>\n",
       "      <td>101.923237</td>\n",
       "      <td>56.098917</td>\n",
       "      <td>159.828998</td>\n",
       "      <td>21.737590</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>14209 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               ra        dec           l          b\n",
       "0      102.023218  56.433992  159.500110  21.886331\n",
       "1      105.272250  56.502381  159.938521  23.636142\n",
       "2      105.087569  56.445058  159.972272  23.523299\n",
       "3      103.359287  56.193791  159.970039  22.532502\n",
       "4      103.240760  56.630222  159.495644  22.585832\n",
       "5      103.222515  56.549474  159.577022  22.554616\n",
       "6      103.361986  56.583872  159.563251  22.637842\n",
       "7      103.775851  56.168097  160.063471  22.749245\n",
       "8      101.836603  56.119531  159.792805  21.697240\n",
       "9      102.678893  56.936834  159.086470  22.371832\n",
       "10     105.667572  56.619706  159.870549  23.875620\n",
       "11     102.236439  55.945081  160.041579  21.861470\n",
       "12     102.433467  56.518693  159.480950  22.127565\n",
       "13     105.105611  56.486566  159.931001  23.543060\n",
       "14     105.362118  56.534916  159.917022  23.692076\n",
       "15     102.368405  56.455920  159.535245  22.075601\n",
       "16     103.676978  56.307551  159.901923  22.732851\n",
       "17     102.504898  57.212852  158.771444  22.355833\n",
       "18     101.804305  55.852613  160.062130  21.602850\n",
       "19     102.119267  56.169582  159.789621  21.862448\n",
       "20     102.071402  56.730196  159.201815  21.995346\n",
       "21     102.774558  56.830031  159.212977  22.393163\n",
       "22     102.137264  56.350726  159.605436  21.923322\n",
       "23     103.921963  56.655100  159.575937  22.953848\n",
       "24     102.932901  56.335646  159.753100  22.342795\n",
       "25     105.385988  56.643913  159.804840  23.730851\n",
       "26     105.542361  56.377615  160.109832  23.751272\n",
       "27     102.647940  56.268813  159.775683  22.172426\n",
       "28     103.711527  56.349395  159.863591  22.762315\n",
       "29     103.597854  56.158071  160.045628  22.650994\n",
       "...           ...        ...         ...        ...\n",
       "14179  102.489214  56.081397  159.943479  22.035450\n",
       "14180  102.147983  56.711074  159.234323  22.030321\n",
       "14181  103.716786  56.159878  160.062696  22.715352\n",
       "14182  105.252391  56.393553  160.050906  23.599250\n",
       "14183  102.596930  56.310072  159.724415  22.156631\n",
       "14184  105.268657  56.207321  160.250440  23.562900\n",
       "14185  102.044837  56.476426  159.459891  21.909801\n",
       "14186  102.284147  56.917347  159.042894  22.159330\n",
       "14187  102.602635  57.186376  158.814544  22.399603\n",
       "14188  102.825593  56.441695  159.625292  22.314552\n",
       "14189  101.982163  56.314866  159.616222  21.830699\n",
       "14190  102.244534  56.641037  159.322869  22.061669\n",
       "14191  102.608435  57.217545  158.783009  22.411067\n",
       "14192  102.743681  57.158952  158.865450  22.465819\n",
       "14193  102.777462  56.444267  159.614761  22.289662\n",
       "14194  105.891445  56.646945  159.872951  24.001706\n",
       "14195  102.788402  56.867407  159.176279  22.410572\n",
       "14196  102.019136  56.186270  159.755308  21.813762\n",
       "14197  103.381823  56.792650  159.348148  22.703505\n",
       "14198  102.970067  56.429717  159.661250  22.388156\n",
       "14199  101.940300  55.874246  160.063572  21.682161\n",
       "14200  102.951998  56.286436  159.807412  22.339588\n",
       "14201  103.637537  56.235559  159.970962  22.692768\n",
       "14202  102.089903  55.881491  160.082031  21.764630\n",
       "14203  103.215704  56.428451  159.702186  22.518617\n",
       "14204  102.642189  56.958484  159.058057  22.358449\n",
       "14205  102.233121  57.066272  158.880057  22.173822\n",
       "14206  104.919145  56.263134  160.139589  23.388225\n",
       "14207  103.295677  56.738090  159.391665  22.643558\n",
       "14208  101.923237  56.098917  159.828998  21.737590\n",
       "\n",
       "[14209 rows x 4 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gaia_df_3.reset_index(inplace=True, drop=True)\n",
    "gaia_df_3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "You can produce __plots__ directly from the DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a16cd80f0>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "title_txt = 'Right Ascension and Declination for Gaia'\n",
    "\n",
    "gaia_df_3.plot('ra','dec',       # Columns to plot\n",
    "               kind='scatter',   # Kind of plot. In this case, it's `scatter`\n",
    "               label='Gaia',     # Label of the points\n",
    "               title=title_txt,  # Title of the figure\n",
    "               color='#4c72b0',  # Color of the points\n",
    "               figsize=(12,8))  # Size of the fiure"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Or even __Scatterplot Matrices__:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x1a1760b438>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(gaia_df_3, plot_kws={'color': '#4c72b0'}, diag_kws={'color': '#4c72b0'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x1a193999e8>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot(gaia_df_3['l'], gaia_df_3['b'], color='#3c8f40')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Indexing, Selecting, Filtering Data\n",
    "\n",
    "Now I want to filter the data based on __ra__ and __dec__:\n",
    "\n",
    "I want to select all the stars within:\n",
    "- 45 < RA < 50\n",
    "- 5 < Dec < 10\n",
    "\n",
    "Normally, you would could do in numpy using the __`np.where`__ function, like in the following example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "ra_arr = gaia_df.ra.values\n",
    "dec_arr = gaia_df.dec.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[103.44752895,  56.02202543],\n",
       "       [105.18785594,  56.2679821 ],\n",
       "       [103.42475813,  56.45090293],\n",
       "       [105.04975071,  56.50877738],\n",
       "       [103.35252488,  56.39514381],\n",
       "       [101.92979073,  55.97333308],\n",
       "       [101.85392576,  56.12931976],\n",
       "       [105.12884963,  56.28508092],\n",
       "       [103.39632957,  56.7144103 ],\n",
       "       [101.78043734,  55.94533326],\n",
       "       [103.50036565,  56.84462941],\n",
       "       [105.64948082,  56.63252739],\n",
       "       [103.18961712,  56.81515376],\n",
       "       [103.53014423,  55.98817459],\n",
       "       [105.93886175,  56.58868695],\n",
       "       [102.24453393,  56.64103702],\n",
       "       [102.02432422,  56.0158414 ],\n",
       "       [103.0848673 ,  56.25264172],\n",
       "       [102.30172541,  56.6658301 ],\n",
       "       [103.28806439,  56.2536194 ],\n",
       "       [103.05853189,  56.72134178],\n",
       "       [102.0578448 ,  56.39003547],\n",
       "       [103.71357369,  56.62167297],\n",
       "       [103.37050966,  56.11391562],\n",
       "       [103.44554963,  56.29543043]])"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Just showing the first 25 elements\n",
    "np.column_stack((ra_arr, dec_arr))[0:25]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[103.42475813  56.45090293]\n",
      " [102.24453393  56.64103702]\n",
      " [102.30172541  56.6658301 ]\n",
      " ...\n",
      " [103.81978156  56.62006303]\n",
      " [103.31712396  56.61945699]\n",
      " [103.57468884  56.4318757 ]]\n"
     ]
    }
   ],
   "source": [
    "## Numpy way of finding the stars that meet the criteria\n",
    "\n",
    "ra_min, ra_max = (102, 104)\n",
    "dec_min, dec_max = (56.4, 56.7)\n",
    "\n",
    "# RA critera\n",
    "ra_idx = np.where((ra_arr >= ra_min) & (ra_arr <= ra_max))[0]\n",
    "\n",
    "# Dec criteria\n",
    "dec_idx = np.where((dec_arr >= dec_min) & (dec_arr <= dec_max))[0]\n",
    "\n",
    "# Finding `intersecting' indices that meet both criteria\n",
    "radec_idx = np.intersect1d(ra_idx, dec_idx)\n",
    "\n",
    "# Selecting the values from only those indices\n",
    "ra_new = ra_arr[radec_idx]\n",
    "dec_new = dec_arr[radec_idx]\n",
    "\n",
    "# Printing out ra and dec for corresponding indices\n",
    "print(np.column_stack((ra_new, dec_new)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "This is rather convoluted and __long__, and one can easily make a mistake if s/he doesn't keep track of which arrays s/he is using!\n",
    "\n",
    "In Pandas, this is __much easier!!__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>astrometric_excess_noise_sig</th>\n",
       "      <th>astrometric_matched_observations</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>103.424758</td>\n",
       "      <td>56.450903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>102.244534</td>\n",
       "      <td>56.641037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>102.301725</td>\n",
       "      <td>56.665830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>103.713574</td>\n",
       "      <td>56.621673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>103.462653</td>\n",
       "      <td>56.532017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>103.360486</td>\n",
       "      <td>56.413851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>103.676326</td>\n",
       "      <td>56.623116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>103.085718</td>\n",
       "      <td>56.447884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>103.783947</td>\n",
       "      <td>56.521326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>102.206913</td>\n",
       "      <td>56.640966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>103.726192</td>\n",
       "      <td>56.673523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>102.069819</td>\n",
       "      <td>56.405446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>102.920433</td>\n",
       "      <td>56.430698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>102.975403</td>\n",
       "      <td>56.593006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>103.870145</td>\n",
       "      <td>56.684652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>103.201988</td>\n",
       "      <td>56.538079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>102.256910</td>\n",
       "      <td>56.592725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>103.819070</td>\n",
       "      <td>56.577742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>103.797238</td>\n",
       "      <td>56.403185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>102.342651</td>\n",
       "      <td>56.599794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>103.661779</td>\n",
       "      <td>56.492773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>103.637186</td>\n",
       "      <td>56.622849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>102.241080</td>\n",
       "      <td>56.684968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>102.843411</td>\n",
       "      <td>56.658409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>102.898776</td>\n",
       "      <td>56.470195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>103.480558</td>\n",
       "      <td>56.617093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>103.236574</td>\n",
       "      <td>56.509793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>103.633321</td>\n",
       "      <td>56.650588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>103.776568</td>\n",
       "      <td>56.622350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>102.260166</td>\n",
       "      <td>56.659839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14066</th>\n",
       "      <td>102.337580</td>\n",
       "      <td>56.565822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14068</th>\n",
       "      <td>103.495447</td>\n",
       "      <td>56.455223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14069</th>\n",
       "      <td>103.408320</td>\n",
       "      <td>56.628237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14071</th>\n",
       "      <td>103.436448</td>\n",
       "      <td>56.564892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14075</th>\n",
       "      <td>102.350965</td>\n",
       "      <td>56.622774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14078</th>\n",
       "      <td>103.267697</td>\n",
       "      <td>56.473831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14080</th>\n",
       "      <td>102.227288</td>\n",
       "      <td>56.665774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14081</th>\n",
       "      <td>102.962817</td>\n",
       "      <td>56.594834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14088</th>\n",
       "      <td>103.021430</td>\n",
       "      <td>56.620846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14090</th>\n",
       "      <td>103.863479</td>\n",
       "      <td>56.469286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14104</th>\n",
       "      <td>102.954900</td>\n",
       "      <td>56.594641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14114</th>\n",
       "      <td>103.442994</td>\n",
       "      <td>56.647008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14119</th>\n",
       "      <td>102.902805</td>\n",
       "      <td>56.627175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14130</th>\n",
       "      <td>102.974802</td>\n",
       "      <td>56.451924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14135</th>\n",
       "      <td>103.838059</td>\n",
       "      <td>56.442466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14138</th>\n",
       "      <td>102.619619</td>\n",
       "      <td>56.504853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14139</th>\n",
       "      <td>103.687508</td>\n",
       "      <td>56.489265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14141</th>\n",
       "      <td>103.495952</td>\n",
       "      <td>56.632908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14142</th>\n",
       "      <td>103.412026</td>\n",
       "      <td>56.620438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14143</th>\n",
       "      <td>102.714416</td>\n",
       "      <td>56.661466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14161</th>\n",
       "      <td>102.844313</td>\n",
       "      <td>56.595309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14167</th>\n",
       "      <td>103.724074</td>\n",
       "      <td>56.430243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14168</th>\n",
       "      <td>103.211444</td>\n",
       "      <td>56.645173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14173</th>\n",
       "      <td>102.584448</td>\n",
       "      <td>56.539904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14180</th>\n",
       "      <td>103.682558</td>\n",
       "      <td>56.585142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14195</th>\n",
       "      <td>102.307805</td>\n",
       "      <td>56.625449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14200</th>\n",
       "      <td>103.733646</td>\n",
       "      <td>56.568726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14201</th>\n",
       "      <td>103.819782</td>\n",
       "      <td>56.620063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14204</th>\n",
       "      <td>103.317124</td>\n",
       "      <td>56.619457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14207</th>\n",
       "      <td>103.574689</td>\n",
       "      <td>56.431876</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3156 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       astrometric_excess_noise_sig  astrometric_matched_observations\n",
       "2                        103.424758                         56.450903\n",
       "15                       102.244534                         56.641037\n",
       "18                       102.301725                         56.665830\n",
       "22                       103.713574                         56.621673\n",
       "32                       103.462653                         56.532017\n",
       "35                       103.360486                         56.413851\n",
       "36                       103.676326                         56.623116\n",
       "39                       103.085718                         56.447884\n",
       "40                       103.783947                         56.521326\n",
       "46                       102.206913                         56.640966\n",
       "54                       103.726192                         56.673523\n",
       "57                       102.069819                         56.405446\n",
       "63                       102.920433                         56.430698\n",
       "64                       102.975403                         56.593006\n",
       "69                       103.870145                         56.684652\n",
       "73                       103.201988                         56.538079\n",
       "74                       102.256910                         56.592725\n",
       "78                       103.819070                         56.577742\n",
       "79                       103.797238                         56.403185\n",
       "81                       102.342651                         56.599794\n",
       "84                       103.661779                         56.492773\n",
       "89                       103.637186                         56.622849\n",
       "92                       102.241080                         56.684968\n",
       "98                       102.843411                         56.658409\n",
       "103                      102.898776                         56.470195\n",
       "105                      103.480558                         56.617093\n",
       "110                      103.236574                         56.509793\n",
       "112                      103.633321                         56.650588\n",
       "113                      103.776568                         56.622350\n",
       "114                      102.260166                         56.659839\n",
       "...                             ...                               ...\n",
       "14066                    102.337580                         56.565822\n",
       "14068                    103.495447                         56.455223\n",
       "14069                    103.408320                         56.628237\n",
       "14071                    103.436448                         56.564892\n",
       "14075                    102.350965                         56.622774\n",
       "14078                    103.267697                         56.473831\n",
       "14080                    102.227288                         56.665774\n",
       "14081                    102.962817                         56.594834\n",
       "14088                    103.021430                         56.620846\n",
       "14090                    103.863479                         56.469286\n",
       "14104                    102.954900                         56.594641\n",
       "14114                    103.442994                         56.647008\n",
       "14119                    102.902805                         56.627175\n",
       "14130                    102.974802                         56.451924\n",
       "14135                    103.838059                         56.442466\n",
       "14138                    102.619619                         56.504853\n",
       "14139                    103.687508                         56.489265\n",
       "14141                    103.495952                         56.632908\n",
       "14142                    103.412026                         56.620438\n",
       "14143                    102.714416                         56.661466\n",
       "14161                    102.844313                         56.595309\n",
       "14167                    103.724074                         56.430243\n",
       "14168                    103.211444                         56.645173\n",
       "14173                    102.584448                         56.539904\n",
       "14180                    103.682558                         56.585142\n",
       "14195                    102.307805                         56.625449\n",
       "14200                    103.733646                         56.568726\n",
       "14201                    103.819782                         56.620063\n",
       "14204                    103.317124                         56.619457\n",
       "14207                    103.574689                         56.431876\n",
       "\n",
       "[3156 rows x 2 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gaia_df_4 = gaia_df.loc[(\n",
    "                (gaia_df.ra >= ra_min) & (gaia_df.ra <= ra_max) &\n",
    "                (gaia_df.dec >= dec_min) & (gaia_df.dec <= dec_max))]\n",
    "gaia_df_4[['ra','dec']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# <span style=\"color:red\">Future of Pandas </span>\n",
    "Pandas is a __great__ for handling data, especially comma-delimited or space-separated data. Pandas is also compatible with many other packages, like __seaborn__, __astropy__, NumPy, etc.\n",
    "\n",
    "We will have another lecture on Pandas that will cover much more advanced aspects of Pandas. __Make sure you keep checking the schedule!__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   "source": [
    "# <span style=\"color:blue\">Resources </span>\n",
    "- [12 Useful Pandas Techniques in Python for Data Manipulation](https://www.analyticsvidhya.com/blog/2016/01/12-pandas-techniques-python-data-manipulation/)\n",
    "- [Datacamp Pandas Tutorial](https://www.datacamp.com/community/tutorials/pandas-tutorial-dataframe-python)\n",
    "- [Top 8 resources for learning data analysis with pandas](http://www.dataschool.io/best-python-pandas-resources/)"
   ]
  }
 ],
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