Pandas_Summary.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pandas\n",
    "\n",
    "Juan G. Marinero Tarazona"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline\n",
    "plt.style.use('seaborn-whitegrid')\n",
    "from IPython.display import display_html\n",
    "\n",
    "# ------------------------------------------------------------------------------\n",
    "# *** private functions ***\n",
    "\n",
    "def make_df(cols, ind):\n",
    "    \"\"\"Quickly make a DataFrame\"\"\"\n",
    "    data = {c: [str(c) + str(i) for i in ind]\n",
    "            for c in cols} \n",
    "    # print(data) # if cols='AB' and ind=[0,1], then, data = {'A': ['A'+'0', 'A'+'1'], 'B': ['B'+'0', 'B'+'1']}\n",
    "    return pd.DataFrame(data, ind)\n",
    "\n",
    "# display() is used to print multiple DataFrames side by side (Van der Plas solution)\n",
    "class display(object):\n",
    "    \"\"\"Display HTML representation of multiple objects\"\"\"\n",
    "    template = \"\"\"<div style=\"float: left; padding: 10px;\">\n",
    "    <p style='font-family:\"Courier New\", Courier, monospace'>{0}</p>{1}\n",
    "    </div>\"\"\"\n",
    "    def __init__(self, *args): self.args = args        \n",
    "    def _repr_html_(self):\n",
    "        return '\\n'.join(self.template.format(a, eval(a)._repr_html_())\n",
    "                         for a in self.args)\n",
    "    def __repr__(self):\n",
    "        return '\\n\\n'.join(a + '\\n' + repr(eval(a))\n",
    "                           for a in self.args)\n",
    "\n",
    "df1 = make_df('AB', [1, 2])\n",
    "df2 = make_df('AB', [3, 4])\n",
    "#display('df1', 'df2', 'pd.concat([df1, df2])')\n",
    "\n",
    "# display_html() is used to print multiple DataFrames side by side ( pandas styling methods solution)\n",
    "# http://pandas.pydata.org/pandas-docs/stable/style.html\n",
    "# https://stackoverflow.com/questions/38783027/jupyter-notebook-display-two-pandas-tables-side-by-side/50899244#50899244\n",
    "from IPython.display import display_html\n",
    "\n",
    "def display_htmlFcn(*args, space=0, n=10):\n",
    "    \"\"\"Display HTML representation of multiple objects\"\"\"\n",
    "    html_str = ''\n",
    "    for index,k in enumerate(args):\n",
    "        aux = eval(k)\n",
    "        # debug: print(type(aux)); print(k); print(type(k));\n",
    "        caption = [k[i:i+n] for i in range(0, len(k), n)]\n",
    "        for index in range(len(caption)): caption[index] += '\\n' \n",
    "        html_str += aux.style.set_table_attributes(\"style='display:inline'\").set_caption(''.join(caption))._repr_html_()\n",
    "        html_str += int(space)*'&nbsp'\n",
    "    display_html(html_str, raw = True)\n",
    "    \n",
    "def color_negative_red(val):\n",
    "    \"\"\"\n",
    "    Takes a scalar and returns a string with\n",
    "    the css property `'color: red'` for negative\n",
    "    strings, black otherwise.\n",
    "    \"\"\"\n",
    "    color = 'red' if val < 0 else 'black'\n",
    "    return 'color: %s' % color"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style>  \n",
       "<table id=\"T_ac82e836_1ea3_11e9_8f1c_1cc63ca220e2\" style='display:inline'><caption>Caption 1</caption> \n",
       "<thead>    <tr> \n",
       "        <th class=\"blank level0\" ></th> \n",
       "        <th class=\"col_heading level0 col0\" >A</th> \n",
       "        <th class=\"col_heading level0 col1\" >B</th> \n",
       "    </tr></thead> \n",
       "<tbody>    <tr> \n",
       "        <th id=\"T_ac82e836_1ea3_11e9_8f1c_1cc63ca220e2level0_row0\" class=\"row_heading level0 row0\" >1</th> \n",
       "        <td id=\"T_ac82e836_1ea3_11e9_8f1c_1cc63ca220e2row0_col0\" class=\"data row0 col0\" >A1</td> \n",
       "        <td id=\"T_ac82e836_1ea3_11e9_8f1c_1cc63ca220e2row0_col1\" class=\"data row0 col1\" >B1</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_ac82e836_1ea3_11e9_8f1c_1cc63ca220e2level0_row1\" class=\"row_heading level0 row1\" >2</th> \n",
       "        <td id=\"T_ac82e836_1ea3_11e9_8f1c_1cc63ca220e2row1_col0\" class=\"data row1 col0\" >A2</td> \n",
       "        <td id=\"T_ac82e836_1ea3_11e9_8f1c_1cc63ca220e2row1_col1\" class=\"data row1 col1\" >B2</td> \n",
       "    </tr></tbody> \n",
       "</table> <style  type=\"text/css\" >\n",
       "</style>  \n",
       "<table id=\"T_ac8475ca_1ea3_11e9_8f1c_1cc63ca220e2\" style='display:inline'><caption>Caption 2</caption> \n",
       "<thead>    <tr> \n",
       "        <th class=\"blank level0\" ></th> \n",
       "        <th class=\"col_heading level0 col0\" >A</th> \n",
       "        <th class=\"col_heading level0 col1\" >B</th> \n",
       "    </tr></thead> \n",
       "<tbody>    <tr> \n",
       "        <th id=\"T_ac8475ca_1ea3_11e9_8f1c_1cc63ca220e2level0_row0\" class=\"row_heading level0 row0\" >3</th> \n",
       "        <td id=\"T_ac8475ca_1ea3_11e9_8f1c_1cc63ca220e2row0_col0\" class=\"data row0 col0\" >A3</td> \n",
       "        <td id=\"T_ac8475ca_1ea3_11e9_8f1c_1cc63ca220e2row0_col1\" class=\"data row0 col1\" >B3</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_ac8475ca_1ea3_11e9_8f1c_1cc63ca220e2level0_row1\" class=\"row_heading level0 row1\" >4</th> \n",
       "        <td id=\"T_ac8475ca_1ea3_11e9_8f1c_1cc63ca220e2row1_col0\" class=\"data row1 col0\" >A4</td> \n",
       "        <td id=\"T_ac8475ca_1ea3_11e9_8f1c_1cc63ca220e2row1_col1\" class=\"data row1 col1\" >B4</td> \n",
       "    </tr></tbody> \n",
       "</table> "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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       "<style  type=\"text/css\" >\n",
       "</style>  \n",
       "<table id=\"T_ac874b7e_1ea3_11e9_8f1c_1cc63ca220e2\" style='display:inline'><caption>make_df('A\n",
       "B', [1, 2]\n",
       ")\n",
       "</caption> \n",
       "<thead>    <tr> \n",
       "        <th class=\"blank level0\" ></th> \n",
       "        <th class=\"col_heading level0 col0\" >A</th> \n",
       "        <th class=\"col_heading level0 col1\" >B</th> \n",
       "    </tr></thead> \n",
       "<tbody>    <tr> \n",
       "        <th id=\"T_ac874b7e_1ea3_11e9_8f1c_1cc63ca220e2level0_row0\" class=\"row_heading level0 row0\" >1</th> \n",
       "        <td id=\"T_ac874b7e_1ea3_11e9_8f1c_1cc63ca220e2row0_col0\" class=\"data row0 col0\" >A1</td> \n",
       "        <td id=\"T_ac874b7e_1ea3_11e9_8f1c_1cc63ca220e2row0_col1\" class=\"data row0 col1\" >B1</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_ac874b7e_1ea3_11e9_8f1c_1cc63ca220e2level0_row1\" class=\"row_heading level0 row1\" >2</th> \n",
       "        <td id=\"T_ac874b7e_1ea3_11e9_8f1c_1cc63ca220e2row1_col0\" class=\"data row1 col0\" >A2</td> \n",
       "        <td id=\"T_ac874b7e_1ea3_11e9_8f1c_1cc63ca220e2row1_col1\" class=\"data row1 col1\" >B2</td> \n",
       "    </tr></tbody> \n",
       "</table> &nbsp&nbsp&nbsp&nbsp&nbsp<style  type=\"text/css\" >\n",
       "</style>  \n",
       "<table id=\"T_ac89ece4_1ea3_11e9_8f1c_1cc63ca220e2\" style='display:inline'><caption>make_df('A\n",
       "B', [3, 4]\n",
       ")\n",
       "</caption> \n",
       "<thead>    <tr> \n",
       "        <th class=\"blank level0\" ></th> \n",
       "        <th class=\"col_heading level0 col0\" >A</th> \n",
       "        <th class=\"col_heading level0 col1\" >B</th> \n",
       "    </tr></thead> \n",
       "<tbody>    <tr> \n",
       "        <th id=\"T_ac89ece4_1ea3_11e9_8f1c_1cc63ca220e2level0_row0\" class=\"row_heading level0 row0\" >3</th> \n",
       "        <td id=\"T_ac89ece4_1ea3_11e9_8f1c_1cc63ca220e2row0_col0\" class=\"data row0 col0\" >A3</td> \n",
       "        <td id=\"T_ac89ece4_1ea3_11e9_8f1c_1cc63ca220e2row0_col1\" class=\"data row0 col1\" >B3</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_ac89ece4_1ea3_11e9_8f1c_1cc63ca220e2level0_row1\" class=\"row_heading level0 row1\" >4</th> \n",
       "        <td id=\"T_ac89ece4_1ea3_11e9_8f1c_1cc63ca220e2row1_col0\" class=\"data row1 col0\" >A4</td> \n",
       "        <td id=\"T_ac89ece4_1ea3_11e9_8f1c_1cc63ca220e2row1_col1\" class=\"data row1 col1\" >B4</td> \n",
       "    </tr></tbody> \n",
       "</table> &nbsp&nbsp&nbsp&nbsp&nbsp"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Combine ``display_html()`` with df.style:\n",
      "\tdisplay_html(df.style.highlight_max(axis=0).applymap(color_negative_red))\n"
     ]
    },
    {
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       "            : ;\n",
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       "            background-color:  yellow;\n",
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       "            : ;\n",
       "            color:  red;\n",
       "        }</style>  \n",
       "<table id=\"T_ac949112_1ea3_11e9_8f1c_1cc63ca220e2\" > \n",
       "<thead>    <tr> \n",
       "        <th class=\"blank level0\" ></th> \n",
       "        <th class=\"col_heading level0 col0\" >A</th> \n",
       "        <th class=\"col_heading level0 col1\" >B</th> \n",
       "        <th class=\"col_heading level0 col2\" >C</th> \n",
       "        <th class=\"col_heading level0 col3\" >D</th> \n",
       "    </tr></thead> \n",
       "<tbody>    <tr> \n",
       "        <th id=\"T_ac949112_1ea3_11e9_8f1c_1cc63ca220e2level0_row0\" class=\"row_heading level0 row0\" >0</th> \n",
       "        <td id=\"T_ac949112_1ea3_11e9_8f1c_1cc63ca220e2row0_col0\" class=\"data row0 col0\" >44</td> \n",
       "        <td id=\"T_ac949112_1ea3_11e9_8f1c_1cc63ca220e2row0_col1\" class=\"data row0 col1\" >-48</td> \n",
       "        <td id=\"T_ac949112_1ea3_11e9_8f1c_1cc63ca220e2row0_col2\" class=\"data row0 col2\" >35</td> \n",
       "        <td id=\"T_ac949112_1ea3_11e9_8f1c_1cc63ca220e2row0_col3\" class=\"data row0 col3\" >-37</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_ac949112_1ea3_11e9_8f1c_1cc63ca220e2level0_row1\" class=\"row_heading level0 row1\" >1</th> \n",
       "        <td id=\"T_ac949112_1ea3_11e9_8f1c_1cc63ca220e2row1_col0\" class=\"data row1 col0\" >-20</td> \n",
       "        <td id=\"T_ac949112_1ea3_11e9_8f1c_1cc63ca220e2row1_col1\" class=\"data row1 col1\" >0</td> \n",
       "        <td id=\"T_ac949112_1ea3_11e9_8f1c_1cc63ca220e2row1_col2\" class=\"data row1 col2\" >-37</td> \n",
       "        <td id=\"T_ac949112_1ea3_11e9_8f1c_1cc63ca220e2row1_col3\" class=\"data row1 col3\" >29</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_ac949112_1ea3_11e9_8f1c_1cc63ca220e2level0_row2\" class=\"row_heading level0 row2\" >2</th> \n",
       "        <td id=\"T_ac949112_1ea3_11e9_8f1c_1cc63ca220e2row2_col0\" class=\"data row2 col0\" >-47</td> \n",
       "        <td id=\"T_ac949112_1ea3_11e9_8f1c_1cc63ca220e2row2_col1\" class=\"data row2 col1\" >-15</td> \n",
       "        <td id=\"T_ac949112_1ea3_11e9_8f1c_1cc63ca220e2row2_col2\" class=\"data row2 col2\" >25</td> \n",
       "        <td id=\"T_ac949112_1ea3_11e9_8f1c_1cc63ca220e2row2_col3\" class=\"data row2 col3\" >-18</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_ac949112_1ea3_11e9_8f1c_1cc63ca220e2level0_row3\" class=\"row_heading level0 row3\" >3</th> \n",
       "        <td id=\"T_ac949112_1ea3_11e9_8f1c_1cc63ca220e2row3_col0\" class=\"data row3 col0\" >15</td> \n",
       "        <td id=\"T_ac949112_1ea3_11e9_8f1c_1cc63ca220e2row3_col1\" class=\"data row3 col1\" >34</td> \n",
       "        <td id=\"T_ac949112_1ea3_11e9_8f1c_1cc63ca220e2row3_col2\" class=\"data row3 col2\" >-3</td> \n",
       "        <td id=\"T_ac949112_1ea3_11e9_8f1c_1cc63ca220e2row3_col3\" class=\"data row3 col3\" >46</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_ac949112_1ea3_11e9_8f1c_1cc63ca220e2level0_row4\" class=\"row_heading level0 row4\" >4</th> \n",
       "        <td id=\"T_ac949112_1ea3_11e9_8f1c_1cc63ca220e2row4_col0\" class=\"data row4 col0\" >25</td> \n",
       "        <td id=\"T_ac949112_1ea3_11e9_8f1c_1cc63ca220e2row4_col1\" class=\"data row4 col1\" >14</td> \n",
       "        <td id=\"T_ac949112_1ea3_11e9_8f1c_1cc63ca220e2row4_col2\" class=\"data row4 col2\" >33</td> \n",
       "        <td id=\"T_ac949112_1ea3_11e9_8f1c_1cc63ca220e2row4_col3\" class=\"data row4 col3\" >-9</td> \n",
       "    </tr></tbody> \n",
       "</table> "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hierrachical DataFrame (with MultiIndex) can YES use df.style:\n"
     ]
    },
    {
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       "            background-color:  #f7fcf5;\n",
       "            max-width:  50px;\n",
       "        }    #T_accb6d90_1ea3_11e9_8f1c_1cc63ca220e2row1_col5 {\n",
       "            background-color:  #bce4b5;\n",
       "            max-width:  50px;\n",
       "        }    #T_accb6d90_1ea3_11e9_8f1c_1cc63ca220e2row2_col0 {\n",
       "            width:  10em;\n",
       "             height:  80%;\n",
       "            background:  linear-gradient(90deg,#d65f5f 28.6%, transparent 0%);\n",
       "            max-width:  50px;\n",
       "        }    #T_accb6d90_1ea3_11e9_8f1c_1cc63ca220e2row2_col1 {\n",
       "            width:  10em;\n",
       "             height:  80%;\n",
       "            background:  linear-gradient(90deg,#d65f5f 50.0%, transparent 0%);\n",
       "            max-width:  50px;\n",
       "        }    #T_accb6d90_1ea3_11e9_8f1c_1cc63ca220e2row2_col2 {\n",
       "            : ;\n",
       "            max-width:  50px;\n",
       "        }    #T_accb6d90_1ea3_11e9_8f1c_1cc63ca220e2row2_col3 {\n",
       "            : ;\n",
       "            max-width:  50px;\n",
       "        }    #T_accb6d90_1ea3_11e9_8f1c_1cc63ca220e2row2_col4 {\n",
       "            background-color:  #00682a;\n",
       "            max-width:  50px;\n",
       "        }    #T_accb6d90_1ea3_11e9_8f1c_1cc63ca220e2row2_col5 {\n",
       "            background-color:  #f7fcf5;\n",
       "            max-width:  50px;\n",
       "        }    #T_accb6d90_1ea3_11e9_8f1c_1cc63ca220e2row3_col0 {\n",
       "            width:  10em;\n",
       "             height:  80%;\n",
       "            background:  linear-gradient(90deg,#d65f5f 28.6%, transparent 0%);\n",
       "            max-width:  50px;\n",
       "        }    #T_accb6d90_1ea3_11e9_8f1c_1cc63ca220e2row3_col1 {\n",
       "            width:  10em;\n",
       "             height:  80%;\n",
       "            background:  linear-gradient(90deg,#d65f5f 100.0%, transparent 0%);\n",
       "            max-width:  50px;\n",
       "        }    #T_accb6d90_1ea3_11e9_8f1c_1cc63ca220e2row3_col2 {\n",
       "            : ;\n",
       "            max-width:  50px;\n",
       "        }    #T_accb6d90_1ea3_11e9_8f1c_1cc63ca220e2row3_col3 {\n",
       "            : ;\n",
       "            max-width:  50px;\n",
       "        }    #T_accb6d90_1ea3_11e9_8f1c_1cc63ca220e2row3_col4 {\n",
       "            background-color:  #f7fcf5;\n",
       "            max-width:  50px;\n",
       "        }    #T_accb6d90_1ea3_11e9_8f1c_1cc63ca220e2row3_col5 {\n",
       "            background-color:  #bce4b5;\n",
       "            max-width:  50px;\n",
       "        }    #T_accb6d90_1ea3_11e9_8f1c_1cc63ca220e2row4_col0 {\n",
       "            width:  10em;\n",
       "             height:  80%;\n",
       "            max-width:  50px;\n",
       "        }    #T_accb6d90_1ea3_11e9_8f1c_1cc63ca220e2row4_col1 {\n",
       "            width:  10em;\n",
       "             height:  80%;\n",
       "            max-width:  50px;\n",
       "        }    #T_accb6d90_1ea3_11e9_8f1c_1cc63ca220e2row4_col2 {\n",
       "            : ;\n",
       "            max-width:  50px;\n",
       "        }    #T_accb6d90_1ea3_11e9_8f1c_1cc63ca220e2row4_col3 {\n",
       "            : ;\n",
       "            max-width:  50px;\n",
       "        }    #T_accb6d90_1ea3_11e9_8f1c_1cc63ca220e2row4_col4 {\n",
       "            background-color:  #00441b;\n",
       "            max-width:  50px;\n",
       "        }    #T_accb6d90_1ea3_11e9_8f1c_1cc63ca220e2row4_col5 {\n",
       "            background-color:  #00441b;\n",
       "            max-width:  50px;\n",
       "        }    #T_accb6d90_1ea3_11e9_8f1c_1cc63ca220e2row5_col0 {\n",
       "            width:  10em;\n",
       "             height:  80%;\n",
       "            background:  linear-gradient(90deg,#d65f5f 85.7%, transparent 0%);\n",
       "            max-width:  50px;\n",
       "        }    #T_accb6d90_1ea3_11e9_8f1c_1cc63ca220e2row5_col1 {\n",
       "            width:  10em;\n",
       "             height:  80%;\n",
       "            background:  linear-gradient(90deg,#d65f5f 33.3%, transparent 0%);\n",
       "            max-width:  50px;\n",
       "        }    #T_accb6d90_1ea3_11e9_8f1c_1cc63ca220e2row5_col2 {\n",
       "            background-color:  yellow;\n",
       "            max-width:  50px;\n",
       "        }    #T_accb6d90_1ea3_11e9_8f1c_1cc63ca220e2row5_col3 {\n",
       "            : ;\n",
       "            max-width:  50px;\n",
       "        }    #T_accb6d90_1ea3_11e9_8f1c_1cc63ca220e2row5_col4 {\n",
       "            background-color:  #88ce87;\n",
       "            max-width:  50px;\n",
       "        }    #T_accb6d90_1ea3_11e9_8f1c_1cc63ca220e2row5_col5 {\n",
       "            background-color:  #e1f3dc;\n",
       "            max-width:  50px;\n",
       "        }</style>  \n",
       "<table id=\"T_accb6d90_1ea3_11e9_8f1c_1cc63ca220e2\" > \n",
       "<thead>    <tr> \n",
       "        <th class=\"blank\" ></th> \n",
       "        <th class=\"index_name level0\" >subject</th> \n",
       "        <th class=\"col_heading level0 col0\" colspan=2>Bob</th> \n",
       "        <th class=\"col_heading level0 col2\" colspan=2>Guido</th> \n",
       "        <th class=\"col_heading level0 col4\" colspan=2>Sue</th> \n",
       "    </tr>    <tr> \n",
       "        <th class=\"blank\" ></th> \n",
       "        <th class=\"index_name level1\" >type</th> \n",
       "        <th class=\"col_heading level1 col0\" >HR</th> \n",
       "        <th class=\"col_heading level1 col1\" >Temp</th> \n",
       "        <th class=\"col_heading level1 col2\" >HR</th> \n",
       "        <th class=\"col_heading level1 col3\" >Temp</th> \n",
       "        <th class=\"col_heading level1 col4\" >HR</th> \n",
       "        <th class=\"col_heading level1 col5\" >Temp</th> \n",
       "    </tr>    <tr> \n",
       "        <th class=\"index_name level0\" >year</th> \n",
       "        <th class=\"index_name level1\" >visit</th> \n",
       "        <th class=\"blank\" ></th> \n",
       "        <th class=\"blank\" ></th> \n",
       "        <th class=\"blank\" ></th> \n",
       "        <th class=\"blank\" ></th> \n",
       "        <th class=\"blank\" ></th> \n",
       "        <th class=\"blank\" ></th> \n",
       "    </tr></thead> \n",
       "<tbody>    <tr> \n",
       "        <th id=\"T_accb6d90_1ea3_11e9_8f1c_1cc63ca220e2level0_row0\" class=\"row_heading level0 row0\" rowspan=3>2013</th> \n",
       "        <th id=\"T_accb6d90_1ea3_11e9_8f1c_1cc63ca220e2level1_row0\" class=\"row_heading level1 row0\" >1</th> \n",
       "        <td id=\"T_accb6d90_1ea3_11e9_8f1c_1cc63ca220e2row0_col0\" class=\"data row0 col0\" >8</td> \n",
       "        <td id=\"T_accb6d90_1ea3_11e9_8f1c_1cc63ca220e2row0_col1\" class=\"data row0 col1\" >7</td> \n",
       "        <td id=\"T_accb6d90_1ea3_11e9_8f1c_1cc63ca220e2row0_col2\" class=\"data row0 col2\" >7</td> \n",
       "        <td id=\"T_accb6d90_1ea3_11e9_8f1c_1cc63ca220e2row0_col3\" class=\"data row0 col3\" >2</td> \n",
       "        <td id=\"T_accb6d90_1ea3_11e9_8f1c_1cc63ca220e2row0_col4\" class=\"data row0 col4\" >5</td> \n",
       "        <td id=\"T_accb6d90_1ea3_11e9_8f1c_1cc63ca220e2row0_col5\" class=\"data row0 col5\" >7</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_accb6d90_1ea3_11e9_8f1c_1cc63ca220e2level1_row1\" class=\"row_heading level1 row1\" >2</th> \n",
       "        <td id=\"T_accb6d90_1ea3_11e9_8f1c_1cc63ca220e2row1_col0\" class=\"data row1 col0\" >3</td> \n",
       "        <td id=\"T_accb6d90_1ea3_11e9_8f1c_1cc63ca220e2row1_col1\" class=\"data row1 col1\" >5</td> \n",
       "        <td id=\"T_accb6d90_1ea3_11e9_8f1c_1cc63ca220e2row1_col2\" class=\"data row1 col2\" >2</td> \n",
       "        <td id=\"T_accb6d90_1ea3_11e9_8f1c_1cc63ca220e2row1_col3\" class=\"data row1 col3\" >2</td> \n",
       "        <td id=\"T_accb6d90_1ea3_11e9_8f1c_1cc63ca220e2row1_col4\" class=\"data row1 col4\" >0</td> \n",
       "        <td id=\"T_accb6d90_1ea3_11e9_8f1c_1cc63ca220e2row1_col5\" class=\"data row1 col5\" >2</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_accb6d90_1ea3_11e9_8f1c_1cc63ca220e2level1_row2\" class=\"row_heading level1 row2\" >3</th> \n",
       "        <td id=\"T_accb6d90_1ea3_11e9_8f1c_1cc63ca220e2row2_col0\" class=\"data row2 col0\" >3</td> \n",
       "        <td id=\"T_accb6d90_1ea3_11e9_8f1c_1cc63ca220e2row2_col1\" class=\"data row2 col1\" >5</td> \n",
       "        <td id=\"T_accb6d90_1ea3_11e9_8f1c_1cc63ca220e2row2_col2\" class=\"data row2 col2\" >7</td> \n",
       "        <td id=\"T_accb6d90_1ea3_11e9_8f1c_1cc63ca220e2row2_col3\" class=\"data row2 col3\" >5</td> \n",
       "        <td id=\"T_accb6d90_1ea3_11e9_8f1c_1cc63ca220e2row2_col4\" class=\"data row2 col4\" >8</td> \n",
       "        <td id=\"T_accb6d90_1ea3_11e9_8f1c_1cc63ca220e2row2_col5\" class=\"data row2 col5\" >0</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_accb6d90_1ea3_11e9_8f1c_1cc63ca220e2level0_row3\" class=\"row_heading level0 row3\" rowspan=3>2014</th> \n",
       "        <th id=\"T_accb6d90_1ea3_11e9_8f1c_1cc63ca220e2level1_row3\" class=\"row_heading level1 row3\" >1</th> \n",
       "        <td id=\"T_accb6d90_1ea3_11e9_8f1c_1cc63ca220e2row3_col0\" class=\"data row3 col0\" >3</td> \n",
       "        <td id=\"T_accb6d90_1ea3_11e9_8f1c_1cc63ca220e2row3_col1\" class=\"data row3 col1\" >8</td> \n",
       "        <td id=\"T_accb6d90_1ea3_11e9_8f1c_1cc63ca220e2row3_col2\" class=\"data row3 col2\" >6</td> \n",
       "        <td id=\"T_accb6d90_1ea3_11e9_8f1c_1cc63ca220e2row3_col3\" class=\"data row3 col3\" >9</td> \n",
       "        <td id=\"T_accb6d90_1ea3_11e9_8f1c_1cc63ca220e2row3_col4\" class=\"data row3 col4\" >0</td> \n",
       "        <td id=\"T_accb6d90_1ea3_11e9_8f1c_1cc63ca220e2row3_col5\" class=\"data row3 col5\" >2</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_accb6d90_1ea3_11e9_8f1c_1cc63ca220e2level1_row4\" class=\"row_heading level1 row4\" >2</th> \n",
       "        <td id=\"T_accb6d90_1ea3_11e9_8f1c_1cc63ca220e2row4_col0\" class=\"data row4 col0\" >1</td> \n",
       "        <td id=\"T_accb6d90_1ea3_11e9_8f1c_1cc63ca220e2row4_col1\" class=\"data row4 col1\" >2</td> \n",
       "        <td id=\"T_accb6d90_1ea3_11e9_8f1c_1cc63ca220e2row4_col2\" class=\"data row4 col2\" >8</td> \n",
       "        <td id=\"T_accb6d90_1ea3_11e9_8f1c_1cc63ca220e2row4_col3\" class=\"data row4 col3\" >6</td> \n",
       "        <td id=\"T_accb6d90_1ea3_11e9_8f1c_1cc63ca220e2row4_col4\" class=\"data row4 col4\" >9</td> \n",
       "        <td id=\"T_accb6d90_1ea3_11e9_8f1c_1cc63ca220e2row4_col5\" class=\"data row4 col5\" >7</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_accb6d90_1ea3_11e9_8f1c_1cc63ca220e2level1_row5\" class=\"row_heading level1 row5\" >3</th> \n",
       "        <td id=\"T_accb6d90_1ea3_11e9_8f1c_1cc63ca220e2row5_col0\" class=\"data row5 col0\" >7</td> \n",
       "        <td id=\"T_accb6d90_1ea3_11e9_8f1c_1cc63ca220e2row5_col1\" class=\"data row5 col1\" >4</td> \n",
       "        <td id=\"T_accb6d90_1ea3_11e9_8f1c_1cc63ca220e2row5_col2\" class=\"data row5 col2\" >2</td> \n",
       "        <td id=\"T_accb6d90_1ea3_11e9_8f1c_1cc63ca220e2row5_col3\" class=\"data row5 col3\" >6</td> \n",
       "        <td id=\"T_accb6d90_1ea3_11e9_8f1c_1cc63ca220e2row5_col4\" class=\"data row5 col4\" >4</td> \n",
       "        <td id=\"T_accb6d90_1ea3_11e9_8f1c_1cc63ca220e2row5_col5\" class=\"data row5 col5\" >1</td> \n",
       "    </tr></tbody> \n",
       "</table> "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Combine ``display_html()`` and df.style:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "    #T_ace592d8_1ea3_11e9_8f1c_1cc63ca220e2row0_col0 {\n",
       "            background-color:  yellow;\n",
       "        }    #T_ace592d8_1ea3_11e9_8f1c_1cc63ca220e2row0_col5 {\n",
       "            background-color:  yellow;\n",
       "        }    #T_ace592d8_1ea3_11e9_8f1c_1cc63ca220e2row3_col1 {\n",
       "            background-color:  yellow;\n",
       "        }    #T_ace592d8_1ea3_11e9_8f1c_1cc63ca220e2row3_col3 {\n",
       "            background-color:  yellow;\n",
       "        }    #T_ace592d8_1ea3_11e9_8f1c_1cc63ca220e2row4_col2 {\n",
       "            background-color:  yellow;\n",
       "        }    #T_ace592d8_1ea3_11e9_8f1c_1cc63ca220e2row4_col4 {\n",
       "            background-color:  yellow;\n",
       "        }    #T_ace592d8_1ea3_11e9_8f1c_1cc63ca220e2row4_col5 {\n",
       "            background-color:  yellow;\n",
       "        }</style>  \n",
       "<table id=\"T_ace592d8_1ea3_11e9_8f1c_1cc63ca220e2\" style='display:inline'><caption>df1</caption> \n",
       "<thead>    <tr> \n",
       "        <th class=\"blank\" ></th> \n",
       "        <th class=\"index_name level0\" >subject</th> \n",
       "        <th class=\"col_heading level0 col0\" colspan=2>Bob</th> \n",
       "        <th class=\"col_heading level0 col2\" colspan=2>Guido</th> \n",
       "        <th class=\"col_heading level0 col4\" colspan=2>Sue</th> \n",
       "    </tr>    <tr> \n",
       "        <th class=\"blank\" ></th> \n",
       "        <th class=\"index_name level1\" >type</th> \n",
       "        <th class=\"col_heading level1 col0\" >HR</th> \n",
       "        <th class=\"col_heading level1 col1\" >Temp</th> \n",
       "        <th class=\"col_heading level1 col2\" >HR</th> \n",
       "        <th class=\"col_heading level1 col3\" >Temp</th> \n",
       "        <th class=\"col_heading level1 col4\" >HR</th> \n",
       "        <th class=\"col_heading level1 col5\" >Temp</th> \n",
       "    </tr>    <tr> \n",
       "        <th class=\"index_name level0\" >year</th> \n",
       "        <th class=\"index_name level1\" >visit</th> \n",
       "        <th class=\"blank\" ></th> \n",
       "        <th class=\"blank\" ></th> \n",
       "        <th class=\"blank\" ></th> \n",
       "        <th class=\"blank\" ></th> \n",
       "        <th class=\"blank\" ></th> \n",
       "        <th class=\"blank\" ></th> \n",
       "    </tr></thead> \n",
       "<tbody>    <tr> \n",
       "        <th id=\"T_ace592d8_1ea3_11e9_8f1c_1cc63ca220e2level0_row0\" class=\"row_heading level0 row0\" rowspan=3>2013</th> \n",
       "        <th id=\"T_ace592d8_1ea3_11e9_8f1c_1cc63ca220e2level1_row0\" class=\"row_heading level1 row0\" >1</th> \n",
       "        <td id=\"T_ace592d8_1ea3_11e9_8f1c_1cc63ca220e2row0_col0\" class=\"data row0 col0\" >8</td> \n",
       "        <td id=\"T_ace592d8_1ea3_11e9_8f1c_1cc63ca220e2row0_col1\" class=\"data row0 col1\" >7</td> \n",
       "        <td id=\"T_ace592d8_1ea3_11e9_8f1c_1cc63ca220e2row0_col2\" class=\"data row0 col2\" >7</td> \n",
       "        <td id=\"T_ace592d8_1ea3_11e9_8f1c_1cc63ca220e2row0_col3\" class=\"data row0 col3\" >2</td> \n",
       "        <td id=\"T_ace592d8_1ea3_11e9_8f1c_1cc63ca220e2row0_col4\" class=\"data row0 col4\" >5</td> \n",
       "        <td id=\"T_ace592d8_1ea3_11e9_8f1c_1cc63ca220e2row0_col5\" class=\"data row0 col5\" >7</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_ace592d8_1ea3_11e9_8f1c_1cc63ca220e2level1_row1\" class=\"row_heading level1 row1\" >2</th> \n",
       "        <td id=\"T_ace592d8_1ea3_11e9_8f1c_1cc63ca220e2row1_col0\" class=\"data row1 col0\" >3</td> \n",
       "        <td id=\"T_ace592d8_1ea3_11e9_8f1c_1cc63ca220e2row1_col1\" class=\"data row1 col1\" >5</td> \n",
       "        <td id=\"T_ace592d8_1ea3_11e9_8f1c_1cc63ca220e2row1_col2\" class=\"data row1 col2\" >2</td> \n",
       "        <td id=\"T_ace592d8_1ea3_11e9_8f1c_1cc63ca220e2row1_col3\" class=\"data row1 col3\" >2</td> \n",
       "        <td id=\"T_ace592d8_1ea3_11e9_8f1c_1cc63ca220e2row1_col4\" class=\"data row1 col4\" >0</td> \n",
       "        <td id=\"T_ace592d8_1ea3_11e9_8f1c_1cc63ca220e2row1_col5\" class=\"data row1 col5\" >2</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_ace592d8_1ea3_11e9_8f1c_1cc63ca220e2level1_row2\" class=\"row_heading level1 row2\" >3</th> \n",
       "        <td id=\"T_ace592d8_1ea3_11e9_8f1c_1cc63ca220e2row2_col0\" class=\"data row2 col0\" >3</td> \n",
       "        <td id=\"T_ace592d8_1ea3_11e9_8f1c_1cc63ca220e2row2_col1\" class=\"data row2 col1\" >5</td> \n",
       "        <td id=\"T_ace592d8_1ea3_11e9_8f1c_1cc63ca220e2row2_col2\" class=\"data row2 col2\" >7</td> \n",
       "        <td id=\"T_ace592d8_1ea3_11e9_8f1c_1cc63ca220e2row2_col3\" class=\"data row2 col3\" >5</td> \n",
       "        <td id=\"T_ace592d8_1ea3_11e9_8f1c_1cc63ca220e2row2_col4\" class=\"data row2 col4\" >8</td> \n",
       "        <td id=\"T_ace592d8_1ea3_11e9_8f1c_1cc63ca220e2row2_col5\" class=\"data row2 col5\" >0</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_ace592d8_1ea3_11e9_8f1c_1cc63ca220e2level0_row3\" class=\"row_heading level0 row3\" rowspan=3>2014</th> \n",
       "        <th id=\"T_ace592d8_1ea3_11e9_8f1c_1cc63ca220e2level1_row3\" class=\"row_heading level1 row3\" >1</th> \n",
       "        <td id=\"T_ace592d8_1ea3_11e9_8f1c_1cc63ca220e2row3_col0\" class=\"data row3 col0\" >3</td> \n",
       "        <td id=\"T_ace592d8_1ea3_11e9_8f1c_1cc63ca220e2row3_col1\" class=\"data row3 col1\" >8</td> \n",
       "        <td id=\"T_ace592d8_1ea3_11e9_8f1c_1cc63ca220e2row3_col2\" class=\"data row3 col2\" >6</td> \n",
       "        <td id=\"T_ace592d8_1ea3_11e9_8f1c_1cc63ca220e2row3_col3\" class=\"data row3 col3\" >9</td> \n",
       "        <td id=\"T_ace592d8_1ea3_11e9_8f1c_1cc63ca220e2row3_col4\" class=\"data row3 col4\" >0</td> \n",
       "        <td id=\"T_ace592d8_1ea3_11e9_8f1c_1cc63ca220e2row3_col5\" class=\"data row3 col5\" >2</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_ace592d8_1ea3_11e9_8f1c_1cc63ca220e2level1_row4\" class=\"row_heading level1 row4\" >2</th> \n",
       "        <td id=\"T_ace592d8_1ea3_11e9_8f1c_1cc63ca220e2row4_col0\" class=\"data row4 col0\" >1</td> \n",
       "        <td id=\"T_ace592d8_1ea3_11e9_8f1c_1cc63ca220e2row4_col1\" class=\"data row4 col1\" >2</td> \n",
       "        <td id=\"T_ace592d8_1ea3_11e9_8f1c_1cc63ca220e2row4_col2\" class=\"data row4 col2\" >8</td> \n",
       "        <td id=\"T_ace592d8_1ea3_11e9_8f1c_1cc63ca220e2row4_col3\" class=\"data row4 col3\" >6</td> \n",
       "        <td id=\"T_ace592d8_1ea3_11e9_8f1c_1cc63ca220e2row4_col4\" class=\"data row4 col4\" >9</td> \n",
       "        <td id=\"T_ace592d8_1ea3_11e9_8f1c_1cc63ca220e2row4_col5\" class=\"data row4 col5\" >7</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_ace592d8_1ea3_11e9_8f1c_1cc63ca220e2level1_row5\" class=\"row_heading level1 row5\" >3</th> \n",
       "        <td id=\"T_ace592d8_1ea3_11e9_8f1c_1cc63ca220e2row5_col0\" class=\"data row5 col0\" >7</td> \n",
       "        <td id=\"T_ace592d8_1ea3_11e9_8f1c_1cc63ca220e2row5_col1\" class=\"data row5 col1\" >4</td> \n",
       "        <td id=\"T_ace592d8_1ea3_11e9_8f1c_1cc63ca220e2row5_col2\" class=\"data row5 col2\" >2</td> \n",
       "        <td id=\"T_ace592d8_1ea3_11e9_8f1c_1cc63ca220e2row5_col3\" class=\"data row5 col3\" >6</td> \n",
       "        <td id=\"T_ace592d8_1ea3_11e9_8f1c_1cc63ca220e2row5_col4\" class=\"data row5 col4\" >4</td> \n",
       "        <td id=\"T_ace592d8_1ea3_11e9_8f1c_1cc63ca220e2row5_col5\" class=\"data row5 col5\" >1</td> \n",
       "    </tr></tbody> \n",
       "</table> &nbsp&nbsp&nbsp&nbsp&nbsp&nbsp&nbsp&nbsp&nbsp&nbsp<style  type=\"text/css\" >\n",
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       "<table id=\"T_ace84ac8_1ea3_11e9_8f1c_1cc63ca220e2\" style='display:inline'><caption>df2</caption> \n",
       "<thead>    <tr> \n",
       "        <th class=\"blank level0\" ></th> \n",
       "        <th class=\"col_heading level0 col0\" >A</th> \n",
       "        <th class=\"col_heading level0 col1\" >B</th> \n",
       "        <th class=\"col_heading level0 col2\" >C</th> \n",
       "        <th class=\"col_heading level0 col3\" >D</th> \n",
       "    </tr></thead> \n",
       "<tbody>    <tr> \n",
       "        <th id=\"T_ace84ac8_1ea3_11e9_8f1c_1cc63ca220e2level0_row0\" class=\"row_heading level0 row0\" >0</th> \n",
       "        <td id=\"T_ace84ac8_1ea3_11e9_8f1c_1cc63ca220e2row0_col0\" class=\"data row0 col0\" >17</td> \n",
       "        <td id=\"T_ace84ac8_1ea3_11e9_8f1c_1cc63ca220e2row0_col1\" class=\"data row0 col1\" >24</td> \n",
       "        <td id=\"T_ace84ac8_1ea3_11e9_8f1c_1cc63ca220e2row0_col2\" class=\"data row0 col2\" >-10</td> \n",
       "        <td id=\"T_ace84ac8_1ea3_11e9_8f1c_1cc63ca220e2row0_col3\" class=\"data row0 col3\" >5</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_ace84ac8_1ea3_11e9_8f1c_1cc63ca220e2level0_row1\" class=\"row_heading level0 row1\" >1</th> \n",
       "        <td id=\"T_ace84ac8_1ea3_11e9_8f1c_1cc63ca220e2row1_col0\" class=\"data row1 col0\" >-20</td> \n",
       "        <td id=\"T_ace84ac8_1ea3_11e9_8f1c_1cc63ca220e2row1_col1\" class=\"data row1 col1\" >13</td> \n",
       "        <td id=\"T_ace84ac8_1ea3_11e9_8f1c_1cc63ca220e2row1_col2\" class=\"data row1 col2\" >-48</td> \n",
       "        <td id=\"T_ace84ac8_1ea3_11e9_8f1c_1cc63ca220e2row1_col3\" class=\"data row1 col3\" >-38</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_ace84ac8_1ea3_11e9_8f1c_1cc63ca220e2level0_row2\" class=\"row_heading level0 row2\" >2</th> \n",
       "        <td id=\"T_ace84ac8_1ea3_11e9_8f1c_1cc63ca220e2row2_col0\" class=\"data row2 col0\" >44</td> \n",
       "        <td id=\"T_ace84ac8_1ea3_11e9_8f1c_1cc63ca220e2row2_col1\" class=\"data row2 col1\" >-39</td> \n",
       "        <td id=\"T_ace84ac8_1ea3_11e9_8f1c_1cc63ca220e2row2_col2\" class=\"data row2 col2\" >-13</td> \n",
       "        <td id=\"T_ace84ac8_1ea3_11e9_8f1c_1cc63ca220e2row2_col3\" class=\"data row2 col3\" >45</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_ace84ac8_1ea3_11e9_8f1c_1cc63ca220e2level0_row3\" class=\"row_heading level0 row3\" >3</th> \n",
       "        <td id=\"T_ace84ac8_1ea3_11e9_8f1c_1cc63ca220e2row3_col0\" class=\"data row3 col0\" >-1</td> \n",
       "        <td id=\"T_ace84ac8_1ea3_11e9_8f1c_1cc63ca220e2row3_col1\" class=\"data row3 col1\" >43</td> \n",
       "        <td id=\"T_ace84ac8_1ea3_11e9_8f1c_1cc63ca220e2row3_col2\" class=\"data row3 col2\" >-39</td> \n",
       "        <td id=\"T_ace84ac8_1ea3_11e9_8f1c_1cc63ca220e2row3_col3\" class=\"data row3 col3\" >-50</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_ace84ac8_1ea3_11e9_8f1c_1cc63ca220e2level0_row4\" class=\"row_heading level0 row4\" >4</th> \n",
       "        <td id=\"T_ace84ac8_1ea3_11e9_8f1c_1cc63ca220e2row4_col0\" class=\"data row4 col0\" >36</td> \n",
       "        <td id=\"T_ace84ac8_1ea3_11e9_8f1c_1cc63ca220e2row4_col1\" class=\"data row4 col1\" >30</td> \n",
       "        <td id=\"T_ace84ac8_1ea3_11e9_8f1c_1cc63ca220e2row4_col2\" class=\"data row4 col2\" >39</td> \n",
       "        <td id=\"T_ace84ac8_1ea3_11e9_8f1c_1cc63ca220e2row4_col3\" class=\"data row4 col3\" >39</td> \n",
       "    </tr></tbody> \n",
       "</table> "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df1 = make_df('AB', [1, 2])\n",
    "df2 = make_df('AB', [3, 4])\n",
    "df1_styler = df1.style.set_table_attributes(\"style='display:inline'\").set_caption('Caption 1')\n",
    "df2_styler = df2.style.set_table_attributes(\"style='display:inline'\").set_caption('Caption 2')\n",
    "display_html(df1_styler._repr_html_()+df2_styler._repr_html_(), raw=True)\n",
    "\n",
    "display_htmlFcn(\"make_df('AB', [1, 2])\",\"make_df('AB', [3, 4])\",space=5)\n",
    "\n",
    "print('Combine ``display_html()`` with df.style:')\n",
    "print('\\tdisplay_html(df.style.highlight_max(axis=0).applymap(color_negative_red))')\n",
    "df = pd.DataFrame(np.random.randint(0,100,size=(5, 4))-50, columns=list('ABCD'))\n",
    "display_html(df.style.highlight_max(axis=0).applymap(color_negative_red))\n",
    "\n",
    "print('Hierrachical DataFrame (with MultiIndex) can YES use df.style:')\n",
    "hier_index = pd.MultiIndex.from_product([[2013, 2014], range(1,4)],                names=['year', 'visit'])\n",
    "hier_cols  = pd.MultiIndex.from_product([['Bob', 'Guido', 'Sue'], ['HR', 'Temp']], names=['subject', 'type'])\n",
    "df = pd.DataFrame(np.random.randint(10, size=(6,6)), index=hier_index, columns=hier_cols)\n",
    "s = df.style\\\n",
    ".background_gradient(subset=['Sue'],cmap='Greens')\\\n",
    ".bar(subset=['Bob'], color='#d65f5f')\\\n",
    ".highlight_min(subset=['Guido'],axis=0)\\\n",
    ".set_properties(**{'max-width': '50px'})\n",
    "display_html(s)\n",
    "\n",
    "print('Combine ``display_html()`` and df.style:')\n",
    "df1 = df;\n",
    "df2 = pd.DataFrame(np.random.randint(0,100,size=(5, 4))-50, columns=list('ABCD')); \n",
    "df1_styler = df1.style.set_table_attributes(\"style='display:inline'\").set_caption('df1').highlight_max(axis=0)\n",
    "df2_styler = df2.style.set_table_attributes(\"style='display:inline'\").set_caption('df2').applymap(color_negative_red)\n",
    "display_html(df1_styler._repr_html_() + 10*'&nbsp' + df2_styler._repr_html_(), raw=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "** Pandas Style **\n",
      "\n",
      "If it is displayed a normal DataFrame, then run this cell.\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/xopi/Desktop/IT_xopi/Python/Books/SciPyAndMore/venv/lib/python3.6/site-packages/matplotlib/colors.py:504: RuntimeWarning: invalid value encountered in less\n",
      "  xa[xa < 0] = -1\n"
     ]
    },
    {
     "data": {
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       "        }    #T_ad031330_1ea3_11e9_8f1c_1cc63ca220e2row3_col2 {\n",
       "            color:  red;\n",
       "            : ;\n",
       "            : ;\n",
       "            background-color:  ;\n",
       "            color:  ;\n",
       "            border-color:  red;\n",
       "            font-size:  110%;\n",
       "            text-align:  left;\n",
       "            max-width:  100px;\n",
       "            width:  10em;\n",
       "             height:  80%;\n",
       "            background:  linear-gradient(90deg, transparent 0%, transparent -0.0%, #d65f5f -0.0%, #d65f5f 3.9%, transparent 3.9%);\n",
       "        }    #T_ad031330_1ea3_11e9_8f1c_1cc63ca220e2row3_col3 {\n",
       "            color:  red;\n",
       "            : ;\n",
       "            background-color:  ;\n",
       "            color:  ;\n",
       "            border-color:  red;\n",
       "            font-size:  110%;\n",
       "            text-align:  left;\n",
       "            max-width:  100px;\n",
       "            width:  10em;\n",
       "             height:  80%;\n",
       "            background:  linear-gradient(90deg, transparent 0%, transparent 52.0%, #d65f5f 52.0%, #d65f5f 59.0%, transparent 59.0%);\n",
       "        }    #T_ad031330_1ea3_11e9_8f1c_1cc63ca220e2row4_col0 {\n",
       "            background-color:  #238b45;\n",
       "            color:  black;\n",
       "            : ;\n",
       "            background-color:  ;\n",
       "            color:  ;\n",
       "            border-color:  red;\n",
       "            font-size:  110%;\n",
       "            text-align:  left;\n",
       "            max-width:  100px;\n",
       "        }    #T_ad031330_1ea3_11e9_8f1c_1cc63ca220e2row4_col1 {\n",
       "            color:  red;\n",
       "            : ;\n",
       "            : ;\n",
       "            background-color:  ;\n",
       "            color:  ;\n",
       "            border-color:  red;\n",
       "            font-size:  110%;\n",
       "            text-align:  left;\n",
       "            max-width:  100px;\n",
       "        }    #T_ad031330_1ea3_11e9_8f1c_1cc63ca220e2row4_col2 {\n",
       "            color:  red;\n",
       "            : ;\n",
       "            : ;\n",
       "            background-color:  ;\n",
       "            color:  ;\n",
       "            border-color:  red;\n",
       "            font-size:  110%;\n",
       "            text-align:  left;\n",
       "            max-width:  100px;\n",
       "            width:  10em;\n",
       "             height:  80%;\n",
       "            background:  linear-gradient(90deg, transparent 0%, transparent 3.8%, #d65f5f 3.8%, #d65f5f 3.9%, transparent 3.9%);\n",
       "        }    #T_ad031330_1ea3_11e9_8f1c_1cc63ca220e2row4_col3 {\n",
       "            color:  black;\n",
       "            : ;\n",
       "            background-color:  ;\n",
       "            color:  ;\n",
       "            border-color:  red;\n",
       "            font-size:  110%;\n",
       "            text-align:  left;\n",
       "            max-width:  100px;\n",
       "            width:  10em;\n",
       "             height:  80%;\n",
       "            background:  linear-gradient(90deg, transparent 0%, transparent 59.0%, #5fba7d 59.0%, #5fba7d 75.2%, transparent 75.2%);\n",
       "        }    #T_ad031330_1ea3_11e9_8f1c_1cc63ca220e2row5_col0 {\n",
       "            background-color:  #00441b;\n",
       "            color:  black;\n",
       "            : ;\n",
       "            background-color:  ;\n",
       "            color:  ;\n",
       "            border-color:  red;\n",
       "            font-size:  110%;\n",
       "            text-align:  left;\n",
       "            max-width:  100px;\n",
       "        }    #T_ad031330_1ea3_11e9_8f1c_1cc63ca220e2row5_col1 {\n",
       "            color:  black;\n",
       "            background-color:  yellow;\n",
       "            : ;\n",
       "            background-color:  ;\n",
       "            color:  ;\n",
       "            border-color:  red;\n",
       "            font-size:  110%;\n",
       "            text-align:  left;\n",
       "            max-width:  100px;\n",
       "        }    #T_ad031330_1ea3_11e9_8f1c_1cc63ca220e2row5_col2 {\n",
       "            color:  red;\n",
       "            : ;\n",
       "            : ;\n",
       "            background-color:  ;\n",
       "            color:  ;\n",
       "            border-color:  red;\n",
       "            font-size:  110%;\n",
       "            text-align:  left;\n",
       "            max-width:  100px;\n",
       "            width:  10em;\n",
       "             height:  80%;\n",
       "            background:  linear-gradient(90deg, transparent 0%, transparent 3.1%, #d65f5f 3.1%, #d65f5f 3.9%, transparent 3.9%);\n",
       "        }    #T_ad031330_1ea3_11e9_8f1c_1cc63ca220e2row5_col3 {\n",
       "            color:  black;\n",
       "            : ;\n",
       "            background-color:  ;\n",
       "            color:  ;\n",
       "            border-color:  red;\n",
       "            font-size:  110%;\n",
       "            text-align:  left;\n",
       "            max-width:  100px;\n",
       "            width:  10em;\n",
       "             height:  80%;\n",
       "            background:  linear-gradient(90deg, transparent 0%, transparent 59.0%, #5fba7d 59.0%, #5fba7d 63.8%, transparent 63.8%);\n",
       "        }</style>  \n",
       "<table id=\"T_ad031330_1ea3_11e9_8f1c_1cc63ca220e2\" ><caption>Caption (not title), thus fontsize as every cell</caption> \n",
       "<thead>    <tr> \n",
       "        <th class=\"blank level0\" ></th> \n",
       "        <th class=\"col_heading level0 col0\" >A</th> \n",
       "        <th class=\"col_heading level0 col1\" >B</th> \n",
       "        <th class=\"col_heading level0 col2\" >C</th> \n",
       "        <th class=\"col_heading level0 col3\" >D</th> \n",
       "    </tr></thead> \n",
       "<tbody>    <tr> \n",
       "        <th id=\"T_ad031330_1ea3_11e9_8f1c_1cc63ca220e2level0_row0\" class=\"row_heading level0 row0\" >0</th> \n",
       "        <td id=\"T_ad031330_1ea3_11e9_8f1c_1cc63ca220e2row0_col0\" class=\"data row0 col0\" >nan</td> \n",
       "        <td id=\"T_ad031330_1ea3_11e9_8f1c_1cc63ca220e2row0_col1\" class=\"data row0 col1\" >-1.1</td> \n",
       "        <td id=\"T_ad031330_1ea3_11e9_8f1c_1cc63ca220e2row0_col2\" class=\"data row0 col2\" >2.3</td> \n",
       "        <td id=\"T_ad031330_1ea3_11e9_8f1c_1cc63ca220e2row0_col3\" class=\"data row0 col3\" >-1.1</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_ad031330_1ea3_11e9_8f1c_1cc63ca220e2level0_row1\" class=\"row_heading level0 row1\" >1</th> \n",
       "        <td id=\"T_ad031330_1ea3_11e9_8f1c_1cc63ca220e2row1_col0\" class=\"data row1 col0\" >2.8</td> \n",
       "        <td id=\"T_ad031330_1ea3_11e9_8f1c_1cc63ca220e2row1_col1\" class=\"data row1 col1\" >-1.0</td> \n",
       "        <td id=\"T_ad031330_1ea3_11e9_8f1c_1cc63ca220e2row1_col2\" class=\"data row1 col2\" >1.1</td> \n",
       "        <td id=\"T_ad031330_1ea3_11e9_8f1c_1cc63ca220e2row1_col3\" class=\"data row1 col3\" >-0.11</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_ad031330_1ea3_11e9_8f1c_1cc63ca220e2level0_row2\" class=\"row_heading level0 row2\" >2</th> \n",
       "        <td id=\"T_ad031330_1ea3_11e9_8f1c_1cc63ca220e2row2_col0\" class=\"data row2 col0\" >4.6</td> \n",
       "        <td id=\"T_ad031330_1ea3_11e9_8f1c_1cc63ca220e2row2_col1\" class=\"data row2 col1\" >0.2</td> \n",
       "        <td id=\"T_ad031330_1ea3_11e9_8f1c_1cc63ca220e2row2_col2\" class=\"data row2 col2\" >0.33</td> \n",
       "        <td id=\"T_ad031330_1ea3_11e9_8f1c_1cc63ca220e2row2_col3\" class=\"data row2 col3\" >0.77</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_ad031330_1ea3_11e9_8f1c_1cc63ca220e2level0_row3\" class=\"row_heading level0 row3\" >3</th> \n",
       "        <td id=\"T_ad031330_1ea3_11e9_8f1c_1cc63ca220e2row3_col0\" class=\"data row3 col0\" >6.4</td> \n",
       "        <td id=\"T_ad031330_1ea3_11e9_8f1c_1cc63ca220e2row3_col1\" class=\"data row3 col1\" >-0.084</td> \n",
       "        <td id=\"T_ad031330_1ea3_11e9_8f1c_1cc63ca220e2row3_col2\" class=\"data row3 col2\" >-0.092</td> \n",
       "        <td id=\"T_ad031330_1ea3_11e9_8f1c_1cc63ca220e2row3_col3\" class=\"data row3 col3\" >-0.13</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_ad031330_1ea3_11e9_8f1c_1cc63ca220e2level0_row4\" class=\"row_heading level0 row4\" >4</th> \n",
       "        <td id=\"T_ad031330_1ea3_11e9_8f1c_1cc63ca220e2row4_col0\" class=\"data row4 col0\" >8.2</td> \n",
       "        <td id=\"T_ad031330_1ea3_11e9_8f1c_1cc63ca220e2row4_col1\" class=\"data row4 col1\" >-0.29</td> \n",
       "        <td id=\"T_ad031330_1ea3_11e9_8f1c_1cc63ca220e2row4_col2\" class=\"data row4 col2\" >-0.0025</td> \n",
       "        <td id=\"T_ad031330_1ea3_11e9_8f1c_1cc63ca220e2row4_col3\" class=\"data row4 col3\" >0.3</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_ad031330_1ea3_11e9_8f1c_1cc63ca220e2level0_row5\" class=\"row_heading level0 row5\" >5</th> \n",
       "        <td id=\"T_ad031330_1ea3_11e9_8f1c_1cc63ca220e2row5_col0\" class=\"data row5 col0\" >1e+01</td> \n",
       "        <td id=\"T_ad031330_1ea3_11e9_8f1c_1cc63ca220e2row5_col1\" class=\"data row5 col1\" >0.25</td> \n",
       "        <td id=\"T_ad031330_1ea3_11e9_8f1c_1cc63ca220e2row5_col2\" class=\"data row5 col2\" >-0.018</td> \n",
       "        <td id=\"T_ad031330_1ea3_11e9_8f1c_1cc63ca220e2row5_col3\" class=\"data row5 col3\" >0.089</td> \n",
       "    </tr></tbody> \n",
       "</table> "
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f187441b128>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('** Pandas Style **\\n')\n",
    "# http://pandas.pydata.org/pandas-docs/stable/style.html\n",
    "\n",
    "print('If it is displayed a normal DataFrame, then run this cell.\\n')\n",
    "\n",
    "df = pd.DataFrame({'A': np.linspace(1, 10, 6)})\n",
    "df = pd.concat([df, pd.DataFrame(np.random.randn(6, 3), columns=list('BCD'))],\n",
    "           axis=1)\n",
    "df.iloc[0,0] = np.NaN\n",
    "\n",
    "def color_negative_red(val):\n",
    "    color = 'red' if val < 0 else 'black'\n",
    "    return 'color: %s' % color\n",
    "def highlight_max(s):\n",
    "    is_max = s == s.max()\n",
    "    return ['background-color: yellow' if v else '' for v in is_max]\n",
    "\n",
    "df.style\\\n",
    ".background_gradient(cmap='Greens',subset=['A'])\\\n",
    ".applymap(color_negative_red)\\\n",
    ".apply(highlight_max, subset=['B', 'C'], axis=0)\\\n",
    ".highlight_null(null_color='red')\\\n",
    ".set_properties(**{'background-color': '',\n",
    "                   'color': '',\n",
    "                   'border-color': 'red',\n",
    "                   \"font-size\": \"110%\",\n",
    "                   \"text-align\": \"left\",\n",
    "                   'max-width': '100px'})\\\n",
    ".set_table_styles([dict(selector=\"th\", props=[(\"font-size\", \"150%\"),\n",
    "                               (\"text-align\", \"center\")]),\n",
    "                  dict(selector=\"caption\", props=[(\"caption-side\", \"bottom\")])])\\\n",
    ".bar(subset=['C', 'D'], align='mid', color=['#d65f5f', '#5fba7d'])\\\n",
    ".set_caption('Caption (not title), thus fontsize as every cell')\\\n",
    ".format(\"{:.2}\")#df.style.format({'B': \"{:0<4.0f}\", 'D': '{:+.2f}'})\n",
    "# df.style.hide_index()\n",
    "#df.style.hide_columns(['C','D'])\n",
    "\n",
    "# .set_table_styles([dict(selector=\"th\", props=[(\"font-size\", \"150%\"),    changes fontsize header e index\n",
    "# df.style.hide_index()    hides index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Instead of output we can just print it: display_html(_)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/xopi/Desktop/IT_xopi/Python/Books/SciPyAndMore/venv/lib/python3.6/site-packages/matplotlib/colors.py:504: RuntimeWarning: invalid value encountered in less\n",
      "  xa[xa < 0] = -1\n"
     ]
    },
    {
     "data": {
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       "<style  type=\"text/css\" >\n",
       "    #T_ad16cf9c_1ea3_11e9_8f1c_1cc63ca220e2 th {\n",
       "          font-size: 150%;\n",
       "          text-align: center;\n",
       "    }    #T_ad16cf9c_1ea3_11e9_8f1c_1cc63ca220e2 caption {\n",
       "          caption-side: bottom;\n",
       "    }    #T_ad16cf9c_1ea3_11e9_8f1c_1cc63ca220e2row0_col0 {\n",
       "            background-color:  #f7fcf5;\n",
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       "            background-color:  ;\n",
       "            color:  ;\n",
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       "            font-size:  110%;\n",
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       "            border-color:  red;\n",
       "            font-size:  110%;\n",
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       "        }    #T_ad16cf9c_1ea3_11e9_8f1c_1cc63ca220e2row0_col1 {\n",
       "            color:  red;\n",
       "            : ;\n",
       "            : ;\n",
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       "            font-size:  110%;\n",
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       "            color:  black;\n",
       "            background-color:  yellow;\n",
       "            : ;\n",
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       "            text-align:  left;\n",
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       "            text-align:  left;\n",
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       "            background:  linear-gradient(90deg, transparent 0%, transparent 3.9%, #5fba7d 3.9%, #5fba7d 100.0%, transparent 100.0%);\n",
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       "            background:  linear-gradient(90deg, transparent 0%, transparent 0.0%, #d65f5f 0.0%, #d65f5f 59.0%, transparent 59.0%);\n",
       "        }    #T_ad16cf9c_1ea3_11e9_8f1c_1cc63ca220e2row1_col0 {\n",
       "            background-color:  #f7fcf5;\n",
       "            color:  black;\n",
       "            : ;\n",
       "            background-color:  ;\n",
       "            color:  ;\n",
       "            border-color:  red;\n",
       "            font-size:  110%;\n",
       "            text-align:  left;\n",
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       "            border-color:  red;\n",
       "            font-size:  110%;\n",
       "            text-align:  left;\n",
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       "        }    #T_ad16cf9c_1ea3_11e9_8f1c_1cc63ca220e2row1_col1 {\n",
       "            color:  red;\n",
       "            : ;\n",
       "            : ;\n",
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       "            color:  ;\n",
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       "            font-size:  110%;\n",
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       "            : ;\n",
       "            : ;\n",
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       "            border-color:  red;\n",
       "            font-size:  110%;\n",
       "            text-align:  left;\n",
       "            max-width:  100px;\n",
       "        }    #T_ad16cf9c_1ea3_11e9_8f1c_1cc63ca220e2row1_col2 {\n",
       "            color:  black;\n",
       "            : ;\n",
       "            : ;\n",
       "            background-color:  ;\n",
       "            color:  ;\n",
       "            border-color:  red;\n",
       "            font-size:  110%;\n",
       "            text-align:  left;\n",
       "            max-width:  100px;\n",
       "            width:  10em;\n",
       "             height:  80%;\n",
       "            background:  linear-gradient(90deg, transparent 0%, transparent 3.9%, #5fba7d 3.9%, #5fba7d 51.0%, transparent 51.0%);\n",
       "            color:  black;\n",
       "            : ;\n",
       "            : ;\n",
       "            background-color:  ;\n",
       "            color:  ;\n",
       "            border-color:  red;\n",
       "            font-size:  110%;\n",
       "            text-align:  left;\n",
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       "            background:  linear-gradient(90deg, transparent 0%, transparent 3.9%, #5fba7d 3.9%, #5fba7d 51.0%, transparent 51.0%);\n",
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       "            color:  red;\n",
       "            : ;\n",
       "            background-color:  ;\n",
       "            color:  ;\n",
       "            border-color:  red;\n",
       "            font-size:  110%;\n",
       "            text-align:  left;\n",
       "            max-width:  100px;\n",
       "            width:  10em;\n",
       "             height:  80%;\n",
       "            background:  linear-gradient(90deg, transparent 0%, transparent 53.1%, #d65f5f 53.1%, #d65f5f 59.0%, transparent 59.0%);\n",
       "            color:  red;\n",
       "            : ;\n",
       "            background-color:  ;\n",
       "            color:  ;\n",
       "            border-color:  red;\n",
       "            font-size:  110%;\n",
       "            text-align:  left;\n",
       "            max-width:  100px;\n",
       "            width:  10em;\n",
       "             height:  80%;\n",
       "            background:  linear-gradient(90deg, transparent 0%, transparent 53.1%, #d65f5f 53.1%, #d65f5f 59.0%, transparent 59.0%);\n",
       "        }    #T_ad16cf9c_1ea3_11e9_8f1c_1cc63ca220e2row2_col0 {\n",
       "            background-color:  #c8e9c1;\n",
       "            color:  black;\n",
       "            : ;\n",
       "            background-color:  ;\n",
       "            color:  ;\n",
       "            border-color:  red;\n",
       "            font-size:  110%;\n",
       "            text-align:  left;\n",
       "            max-width:  100px;\n",
       "            background-color:  #c8e9c1;\n",
       "            color:  black;\n",
       "            : ;\n",
       "            background-color:  ;\n",
       "            color:  ;\n",
       "            border-color:  red;\n",
       "            font-size:  110%;\n",
       "            text-align:  left;\n",
       "            max-width:  100px;\n",
       "        }    #T_ad16cf9c_1ea3_11e9_8f1c_1cc63ca220e2row2_col1 {\n",
       "            color:  black;\n",
       "            : ;\n",
       "            : ;\n",
       "            background-color:  ;\n",
       "            color:  ;\n",
       "            border-color:  red;\n",
       "            font-size:  110%;\n",
       "            text-align:  left;\n",
       "            max-width:  100px;\n",
       "            color:  black;\n",
       "            : ;\n",
       "            : ;\n",
       "            background-color:  ;\n",
       "            color:  ;\n",
       "            border-color:  red;\n",
       "            font-size:  110%;\n",
       "            text-align:  left;\n",
       "            max-width:  100px;\n",
       "        }    #T_ad16cf9c_1ea3_11e9_8f1c_1cc63ca220e2row2_col2 {\n",
       "            color:  black;\n",
       "            : ;\n",
       "            : ;\n",
       "            background-color:  ;\n",
       "            color:  ;\n",
       "            border-color:  red;\n",
       "            font-size:  110%;\n",
       "            text-align:  left;\n",
       "            max-width:  100px;\n",
       "            width:  10em;\n",
       "             height:  80%;\n",
       "            background:  linear-gradient(90deg, transparent 0%, transparent 3.9%, #5fba7d 3.9%, #5fba7d 18.1%, transparent 18.1%);\n",
       "            color:  black;\n",
       "            : ;\n",
       "            : ;\n",
       "            background-color:  ;\n",
       "            color:  ;\n",
       "            border-color:  red;\n",
       "            font-size:  110%;\n",
       "            text-align:  left;\n",
       "            max-width:  100px;\n",
       "            width:  10em;\n",
       "             height:  80%;\n",
       "            background:  linear-gradient(90deg, transparent 0%, transparent 3.9%, #5fba7d 3.9%, #5fba7d 18.1%, transparent 18.1%);\n",
       "        }    #T_ad16cf9c_1ea3_11e9_8f1c_1cc63ca220e2row2_col3 {\n",
       "            color:  black;\n",
       "            : ;\n",
       "            background-color:  ;\n",
       "            color:  ;\n",
       "            border-color:  red;\n",
       "            font-size:  110%;\n",
       "            text-align:  left;\n",
       "            max-width:  100px;\n",
       "            width:  10em;\n",
       "             height:  80%;\n",
       "            background:  linear-gradient(90deg, transparent 0%, transparent 59.0%, #5fba7d 59.0%, #5fba7d 100.0%, transparent 100.0%);\n",
       "            color:  black;\n",
       "            : ;\n",
       "            background-color:  ;\n",
       "            color:  ;\n",
       "            border-color:  red;\n",
       "            font-size:  110%;\n",
       "            text-align:  left;\n",
       "            max-width:  100px;\n",
       "            width:  10em;\n",
       "             height:  80%;\n",
       "            background:  linear-gradient(90deg, transparent 0%, transparent 59.0%, #5fba7d 59.0%, #5fba7d 100.0%, transparent 100.0%);\n",
       "        }    #T_ad16cf9c_1ea3_11e9_8f1c_1cc63ca220e2row3_col0 {\n",
       "            background-color:  #73c476;\n",
       "            color:  black;\n",
       "            : ;\n",
       "            background-color:  ;\n",
       "            color:  ;\n",
       "            border-color:  red;\n",
       "            font-size:  110%;\n",
       "            text-align:  left;\n",
       "            max-width:  100px;\n",
       "            background-color:  #73c476;\n",
       "            color:  black;\n",
       "            : ;\n",
       "            background-color:  ;\n",
       "            color:  ;\n",
       "            border-color:  red;\n",
       "            font-size:  110%;\n",
       "            text-align:  left;\n",
       "            max-width:  100px;\n",
       "        }    #T_ad16cf9c_1ea3_11e9_8f1c_1cc63ca220e2row3_col1 {\n",
       "            color:  red;\n",
       "            : ;\n",
       "            : ;\n",
       "            background-color:  ;\n",
       "            color:  ;\n",
       "            border-color:  red;\n",
       "            font-size:  110%;\n",
       "            text-align:  left;\n",
       "            max-width:  100px;\n",
       "            color:  red;\n",
       "            : ;\n",
       "            : ;\n",
       "            background-color:  ;\n",
       "            color:  ;\n",
       "            border-color:  red;\n",
       "            font-size:  110%;\n",
       "            text-align:  left;\n",
       "            max-width:  100px;\n",
       "        }    #T_ad16cf9c_1ea3_11e9_8f1c_1cc63ca220e2row3_col2 {\n",
       "            color:  red;\n",
       "            : ;\n",
       "            : ;\n",
       "            background-color:  ;\n",
       "            color:  ;\n",
       "            border-color:  red;\n",
       "            font-size:  110%;\n",
       "            text-align:  left;\n",
       "            max-width:  100px;\n",
       "            width:  10em;\n",
       "             height:  80%;\n",
       "            background:  linear-gradient(90deg, transparent 0%, transparent -0.0%, #d65f5f -0.0%, #d65f5f 3.9%, transparent 3.9%);\n",
       "            color:  red;\n",
       "            : ;\n",
       "            : ;\n",
       "            background-color:  ;\n",
       "            color:  ;\n",
       "            border-color:  red;\n",
       "            font-size:  110%;\n",
       "            text-align:  left;\n",
       "            max-width:  100px;\n",
       "            width:  10em;\n",
       "             height:  80%;\n",
       "            background:  linear-gradient(90deg, transparent 0%, transparent -0.0%, #d65f5f -0.0%, #d65f5f 3.9%, transparent 3.9%);\n",
       "        }    #T_ad16cf9c_1ea3_11e9_8f1c_1cc63ca220e2row3_col3 {\n",
       "            color:  red;\n",
       "            : ;\n",
       "            background-color:  ;\n",
       "            color:  ;\n",
       "            border-color:  red;\n",
       "            font-size:  110%;\n",
       "            text-align:  left;\n",
       "            max-width:  100px;\n",
       "            width:  10em;\n",
       "             height:  80%;\n",
       "            background:  linear-gradient(90deg, transparent 0%, transparent 52.0%, #d65f5f 52.0%, #d65f5f 59.0%, transparent 59.0%);\n",
       "            color:  red;\n",
       "            : ;\n",
       "            background-color:  ;\n",
       "            color:  ;\n",
       "            border-color:  red;\n",
       "            font-size:  110%;\n",
       "            text-align:  left;\n",
       "            max-width:  100px;\n",
       "            width:  10em;\n",
       "             height:  80%;\n",
       "            background:  linear-gradient(90deg, transparent 0%, transparent 52.0%, #d65f5f 52.0%, #d65f5f 59.0%, transparent 59.0%);\n",
       "        }    #T_ad16cf9c_1ea3_11e9_8f1c_1cc63ca220e2row4_col0 {\n",
       "            background-color:  #238b45;\n",
       "            color:  black;\n",
       "            : ;\n",
       "            background-color:  ;\n",
       "            color:  ;\n",
       "            border-color:  red;\n",
       "            font-size:  110%;\n",
       "            text-align:  left;\n",
       "            max-width:  100px;\n",
       "            background-color:  #238b45;\n",
       "            color:  black;\n",
       "            : ;\n",
       "            background-color:  ;\n",
       "            color:  ;\n",
       "            border-color:  red;\n",
       "            font-size:  110%;\n",
       "            text-align:  left;\n",
       "            max-width:  100px;\n",
       "        }    #T_ad16cf9c_1ea3_11e9_8f1c_1cc63ca220e2row4_col1 {\n",
       "            color:  red;\n",
       "            : ;\n",
       "            : ;\n",
       "            background-color:  ;\n",
       "            color:  ;\n",
       "            border-color:  red;\n",
       "            font-size:  110%;\n",
       "            text-align:  left;\n",
       "            max-width:  100px;\n",
       "            color:  red;\n",
       "            : ;\n",
       "            : ;\n",
       "            background-color:  ;\n",
       "            color:  ;\n",
       "            border-color:  red;\n",
       "            font-size:  110%;\n",
       "            text-align:  left;\n",
       "            max-width:  100px;\n",
       "        }    #T_ad16cf9c_1ea3_11e9_8f1c_1cc63ca220e2row4_col2 {\n",
       "            color:  red;\n",
       "            : ;\n",
       "            : ;\n",
       "            background-color:  ;\n",
       "            color:  ;\n",
       "            border-color:  red;\n",
       "            font-size:  110%;\n",
       "            text-align:  left;\n",
       "            max-width:  100px;\n",
       "            width:  10em;\n",
       "             height:  80%;\n",
       "            background:  linear-gradient(90deg, transparent 0%, transparent 3.8%, #d65f5f 3.8%, #d65f5f 3.9%, transparent 3.9%);\n",
       "            color:  red;\n",
       "            : ;\n",
       "            : ;\n",
       "            background-color:  ;\n",
       "            color:  ;\n",
       "            border-color:  red;\n",
       "            font-size:  110%;\n",
       "            text-align:  left;\n",
       "            max-width:  100px;\n",
       "            width:  10em;\n",
       "             height:  80%;\n",
       "            background:  linear-gradient(90deg, transparent 0%, transparent 3.8%, #d65f5f 3.8%, #d65f5f 3.9%, transparent 3.9%);\n",
       "        }    #T_ad16cf9c_1ea3_11e9_8f1c_1cc63ca220e2row4_col3 {\n",
       "            color:  black;\n",
       "            : ;\n",
       "            background-color:  ;\n",
       "            color:  ;\n",
       "            border-color:  red;\n",
       "            font-size:  110%;\n",
       "            text-align:  left;\n",
       "            max-width:  100px;\n",
       "            width:  10em;\n",
       "             height:  80%;\n",
       "            background:  linear-gradient(90deg, transparent 0%, transparent 59.0%, #5fba7d 59.0%, #5fba7d 75.2%, transparent 75.2%);\n",
       "            color:  black;\n",
       "            : ;\n",
       "            background-color:  ;\n",
       "            color:  ;\n",
       "            border-color:  red;\n",
       "            font-size:  110%;\n",
       "            text-align:  left;\n",
       "            max-width:  100px;\n",
       "            width:  10em;\n",
       "             height:  80%;\n",
       "            background:  linear-gradient(90deg, transparent 0%, transparent 59.0%, #5fba7d 59.0%, #5fba7d 75.2%, transparent 75.2%);\n",
       "        }    #T_ad16cf9c_1ea3_11e9_8f1c_1cc63ca220e2row5_col0 {\n",
       "            background-color:  #00441b;\n",
       "            color:  black;\n",
       "            : ;\n",
       "            background-color:  ;\n",
       "            color:  ;\n",
       "            border-color:  red;\n",
       "            font-size:  110%;\n",
       "            text-align:  left;\n",
       "            max-width:  100px;\n",
       "            background-color:  #00441b;\n",
       "            color:  black;\n",
       "            : ;\n",
       "            background-color:  ;\n",
       "            color:  ;\n",
       "            border-color:  red;\n",
       "            font-size:  110%;\n",
       "            text-align:  left;\n",
       "            max-width:  100px;\n",
       "        }    #T_ad16cf9c_1ea3_11e9_8f1c_1cc63ca220e2row5_col1 {\n",
       "            color:  black;\n",
       "            background-color:  yellow;\n",
       "            : ;\n",
       "            background-color:  ;\n",
       "            color:  ;\n",
       "            border-color:  red;\n",
       "            font-size:  110%;\n",
       "            text-align:  left;\n",
       "            max-width:  100px;\n",
       "            color:  black;\n",
       "            background-color:  yellow;\n",
       "            : ;\n",
       "            background-color:  ;\n",
       "            color:  ;\n",
       "            border-color:  red;\n",
       "            font-size:  110%;\n",
       "            text-align:  left;\n",
       "            max-width:  100px;\n",
       "        }    #T_ad16cf9c_1ea3_11e9_8f1c_1cc63ca220e2row5_col2 {\n",
       "            color:  red;\n",
       "            : ;\n",
       "            : ;\n",
       "            background-color:  ;\n",
       "            color:  ;\n",
       "            border-color:  red;\n",
       "            font-size:  110%;\n",
       "            text-align:  left;\n",
       "            max-width:  100px;\n",
       "            width:  10em;\n",
       "             height:  80%;\n",
       "            background:  linear-gradient(90deg, transparent 0%, transparent 3.1%, #d65f5f 3.1%, #d65f5f 3.9%, transparent 3.9%);\n",
       "            color:  red;\n",
       "            : ;\n",
       "            : ;\n",
       "            background-color:  ;\n",
       "            color:  ;\n",
       "            border-color:  red;\n",
       "            font-size:  110%;\n",
       "            text-align:  left;\n",
       "            max-width:  100px;\n",
       "            width:  10em;\n",
       "             height:  80%;\n",
       "            background:  linear-gradient(90deg, transparent 0%, transparent 3.1%, #d65f5f 3.1%, #d65f5f 3.9%, transparent 3.9%);\n",
       "        }    #T_ad16cf9c_1ea3_11e9_8f1c_1cc63ca220e2row5_col3 {\n",
       "            color:  black;\n",
       "            : ;\n",
       "            background-color:  ;\n",
       "            color:  ;\n",
       "            border-color:  red;\n",
       "            font-size:  110%;\n",
       "            text-align:  left;\n",
       "            max-width:  100px;\n",
       "            width:  10em;\n",
       "             height:  80%;\n",
       "            background:  linear-gradient(90deg, transparent 0%, transparent 59.0%, #5fba7d 59.0%, #5fba7d 63.8%, transparent 63.8%);\n",
       "            color:  black;\n",
       "            : ;\n",
       "            background-color:  ;\n",
       "            color:  ;\n",
       "            border-color:  red;\n",
       "            font-size:  110%;\n",
       "            text-align:  left;\n",
       "            max-width:  100px;\n",
       "            width:  10em;\n",
       "             height:  80%;\n",
       "            background:  linear-gradient(90deg, transparent 0%, transparent 59.0%, #5fba7d 59.0%, #5fba7d 63.8%, transparent 63.8%);\n",
       "        }</style>  \n",
       "<table id=\"T_ad16cf9c_1ea3_11e9_8f1c_1cc63ca220e2\" ><caption>Caption (not title), thus fontsize as every cell</caption> \n",
       "<thead>    <tr> \n",
       "        <th class=\"blank level0\" ></th> \n",
       "        <th class=\"col_heading level0 col0\" >A</th> \n",
       "        <th class=\"col_heading level0 col1\" >B</th> \n",
       "        <th class=\"col_heading level0 col2\" >C</th> \n",
       "        <th class=\"col_heading level0 col3\" >D</th> \n",
       "    </tr></thead> \n",
       "<tbody>    <tr> \n",
       "        <th id=\"T_ad16cf9c_1ea3_11e9_8f1c_1cc63ca220e2level0_row0\" class=\"row_heading level0 row0\" >0</th> \n",
       "        <td id=\"T_ad16cf9c_1ea3_11e9_8f1c_1cc63ca220e2row0_col0\" class=\"data row0 col0\" >nan</td> \n",
       "        <td id=\"T_ad16cf9c_1ea3_11e9_8f1c_1cc63ca220e2row0_col1\" class=\"data row0 col1\" >-1.1</td> \n",
       "        <td id=\"T_ad16cf9c_1ea3_11e9_8f1c_1cc63ca220e2row0_col2\" class=\"data row0 col2\" >2.3</td> \n",
       "        <td id=\"T_ad16cf9c_1ea3_11e9_8f1c_1cc63ca220e2row0_col3\" class=\"data row0 col3\" >-1.1</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_ad16cf9c_1ea3_11e9_8f1c_1cc63ca220e2level0_row1\" class=\"row_heading level0 row1\" >1</th> \n",
       "        <td id=\"T_ad16cf9c_1ea3_11e9_8f1c_1cc63ca220e2row1_col0\" class=\"data row1 col0\" >2.8</td> \n",
       "        <td id=\"T_ad16cf9c_1ea3_11e9_8f1c_1cc63ca220e2row1_col1\" class=\"data row1 col1\" >-1.0</td> \n",
       "        <td id=\"T_ad16cf9c_1ea3_11e9_8f1c_1cc63ca220e2row1_col2\" class=\"data row1 col2\" >1.1</td> \n",
       "        <td id=\"T_ad16cf9c_1ea3_11e9_8f1c_1cc63ca220e2row1_col3\" class=\"data row1 col3\" >-0.11</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_ad16cf9c_1ea3_11e9_8f1c_1cc63ca220e2level0_row2\" class=\"row_heading level0 row2\" >2</th> \n",
       "        <td id=\"T_ad16cf9c_1ea3_11e9_8f1c_1cc63ca220e2row2_col0\" class=\"data row2 col0\" >4.6</td> \n",
       "        <td id=\"T_ad16cf9c_1ea3_11e9_8f1c_1cc63ca220e2row2_col1\" class=\"data row2 col1\" >0.2</td> \n",
       "        <td id=\"T_ad16cf9c_1ea3_11e9_8f1c_1cc63ca220e2row2_col2\" class=\"data row2 col2\" >0.33</td> \n",
       "        <td id=\"T_ad16cf9c_1ea3_11e9_8f1c_1cc63ca220e2row2_col3\" class=\"data row2 col3\" >0.77</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_ad16cf9c_1ea3_11e9_8f1c_1cc63ca220e2level0_row3\" class=\"row_heading level0 row3\" >3</th> \n",
       "        <td id=\"T_ad16cf9c_1ea3_11e9_8f1c_1cc63ca220e2row3_col0\" class=\"data row3 col0\" >6.4</td> \n",
       "        <td id=\"T_ad16cf9c_1ea3_11e9_8f1c_1cc63ca220e2row3_col1\" class=\"data row3 col1\" >-0.084</td> \n",
       "        <td id=\"T_ad16cf9c_1ea3_11e9_8f1c_1cc63ca220e2row3_col2\" class=\"data row3 col2\" >-0.092</td> \n",
       "        <td id=\"T_ad16cf9c_1ea3_11e9_8f1c_1cc63ca220e2row3_col3\" class=\"data row3 col3\" >-0.13</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_ad16cf9c_1ea3_11e9_8f1c_1cc63ca220e2level0_row4\" class=\"row_heading level0 row4\" >4</th> \n",
       "        <td id=\"T_ad16cf9c_1ea3_11e9_8f1c_1cc63ca220e2row4_col0\" class=\"data row4 col0\" >8.2</td> \n",
       "        <td id=\"T_ad16cf9c_1ea3_11e9_8f1c_1cc63ca220e2row4_col1\" class=\"data row4 col1\" >-0.29</td> \n",
       "        <td id=\"T_ad16cf9c_1ea3_11e9_8f1c_1cc63ca220e2row4_col2\" class=\"data row4 col2\" >-0.0025</td> \n",
       "        <td id=\"T_ad16cf9c_1ea3_11e9_8f1c_1cc63ca220e2row4_col3\" class=\"data row4 col3\" >0.3</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_ad16cf9c_1ea3_11e9_8f1c_1cc63ca220e2level0_row5\" class=\"row_heading level0 row5\" >5</th> \n",
       "        <td id=\"T_ad16cf9c_1ea3_11e9_8f1c_1cc63ca220e2row5_col0\" class=\"data row5 col0\" >1e+01</td> \n",
       "        <td id=\"T_ad16cf9c_1ea3_11e9_8f1c_1cc63ca220e2row5_col1\" class=\"data row5 col1\" >0.25</td> \n",
       "        <td id=\"T_ad16cf9c_1ea3_11e9_8f1c_1cc63ca220e2row5_col2\" class=\"data row5 col2\" >-0.018</td> \n",
       "        <td id=\"T_ad16cf9c_1ea3_11e9_8f1c_1cc63ca220e2row5_col3\" class=\"data row5 col3\" >0.089</td> \n",
       "    </tr></tbody> \n",
       "</table> "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Instead of output we can just print it: display_html(_)\") \n",
    "# \"Underscore Shortcuts and Previous Outputs\" 00.00-01_Preface_Summary.ipynb\n",
    "display_html(_) # \"_\"  stand for last output (previous cell), read: 00.00-01_Preface_Summary.ipynb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Summary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Series\n",
    "Pandas ``Series`` is much more general and flexible than the 1D NumPy array that it emulates.\n",
    "\n",
    "From what we've seen so far, it may look like the ``Series`` object is basically interchangeable with a 1D NumPy array.\n",
    "The essential difference is the presence of the **index**: \n",
    "\n",
    "<ul  class=\"list-unstyled\">\n",
    "  <li>while the Numpy Array &nbsp;has an <font color='green'>implicitly defined</font> integer <font color='green'>index</font> used to access the values, </li>\n",
    "  <li>the Pandas \n",
    "      <kbd style=\"background:#EEEEEE; letter-spacing:0.1em; padding-left:0.5em; padding-right:0.4em;\">Series</kbd>\n",
    "       has an <font color='green'>explicitly defined</font> &emsp;&emsp;&emsp; <font color='green'>index</font> associated with the values</li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "** Series 1D**\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print('** Series 1D**\\n')\n",
    "if 0:    \n",
    "    print('A Pandas Series is a 1D array of indexed data.\\n')\n",
    "    x = pd.Series([0.25, 0.5, 0.75, 1.0]);\n",
    "    # Series wraps both a sequence of values and a sequence of indices. Access it with the values and index attributes\n",
    "    \n",
    "    print(f\"pd.Series([0.25, 0.5, 0.75, 1.0]): \\n{x}\\n\") # 0\\t 0.25\\n 1\\t 0.50\\n 2\\t 0.75\\n 3\\t 1.00\\n dtype: float64\n",
    "    print(f\"x.values: {x.values}\") # [0.25 0.5  0.75 1.  ] # values are simply a familiar NumPy array\n",
    "    print(f\"x.index : {x.index}\")  # RangeIndex(start=0, stop=4, step=1) # index is an array-like object of type pd.Index\n",
    "    \n",
    "    print('\\n',80*'-',\"\\nSeries has an explicitly defined INDEX associated with the values\")\n",
    "    x = pd.Series([0.25, 0.5, 0.75, 1.0],index=['z', 'b', 0, 'd']);\n",
    "    print(f\"pd.Series([0.25, 0.5, 0.75, 1.0],index=['z', 'b', 0, 'd']): \\\n",
    "          \\n{x}\") # z\\t 0.25\\n b\\t 0.50\\n 0\\t 0.75\\n d\\t 1.00\\n dtype: float64\n",
    "    \n",
    "    print('\\n',80*'-',\"\\nACCESS cell like with a 1D NumPy array via square-bracket notation\")\n",
    "    \n",
    "    print('\\n',80*'-',\"\\nACCESS one cell\")\n",
    "    print(f\"x['b']: {x['b']}\") # 0.5\n",
    "    print(f\"x[1]:   {x[1]}\")   # 0.5\n",
    "    print(f\"x[0]:      {x[0]}\")     # 0.75\n",
    "    print(f\"x.loc[0]:  {x.loc[0]}\") # 0.75\n",
    "    print(f\"x.iloc[0]: {x.iloc[0]}\") # 0.25\n",
    "    \n",
    "    print('\\n',80*'-',\"\\nACCESS multiple cellS\") \n",
    "    print(f\"·x[0:3]: \\n{x[0:3]}\")       # stop EXclusive # 0\\t 0.25\\n 3\\t 1.00\\n dtype: float64\n",
    "    print(f\"·x[[0, 3]]: \\n{x[[0, 3]]}\") # stop INclusive # \"\"\n",
    "    print(f'·x[:]: \\n{x[:]}') # 0\\t 0.25\\n 1\\t 0.50\\n 2\\t 0.75\\n 3\\t 1.00\\n dtype: float64\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "** Series MATRIX**\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print('** Series MATRIX**\\n')\n",
    "if 0:\n",
    "    print('\\n',80*'-',\"\\nSeries MATRIX\")\n",
    "    print(f'·pd.Series([[0.,1,2],[1,2.,3]]):  \\n{pd.Series([[0.,1,2],[1,2.,3]])}') # 0\\t [0.0, 1, 2]\\n 1\\t [1, 2.0, 3]\\n dtype: object\n",
    "    print(f'·pd.Series([[0.,1,2],[1,2.,3]])[0]: {pd.Series([[0.,1,2],[1,2.,3]])[0]}') # [0.0, 1, 2]\n",
    "    \n",
    "    print('\\n',80*'-','\\nIndices in a Serie must be of just one HASHABLE type, i.e. either:')\n",
    "    print(f\"# strings:  pd.Series([1,1,1], ['a','b','end'])\")\n",
    "    print(f\"# integers: pd.Series([1,1,1], [2,5,99])\")\n",
    "    print(f\"# tuple:    pd.Series([1,1,1], [(2),(5),(99)])\")\n",
    "    # or frozenset\n",
    "\n",
    "    print('\\n',80*'-','''\n",
    "    Think of a Pandas Series a bit like a specialization of a Python dictionary.\n",
    "       · Dictionary is a structure that maps arbitrary keys to a set of arbitrary values\n",
    "       · Series     is a structure that maps typed     keys to a set of typed     values\n",
    "    ''')\n",
    "\n",
    "    print('\\nThe Series-as-dictionary analogy can be made even more clear by constructing a Series object directly from a Python dictionary:')\n",
    "    x = {'z':33, 'a':7, 'b':99}\n",
    "    x = pd.Series(x)\n",
    "    print(f\"pd.Series({{'z':33, 'a':7, 'b':99}}): \\n{x}\") # z\\t 33\\n a\\t 7\\n b\\t 99\\n dtype: int64   \n",
    "   \n",
    "    print('\\n',80*'-','\\nRepeated Data:')\n",
    "    x = pd.Series(5, index=[100, 200, 300])\n",
    "    print(f\"pd.Series(5, index=[100, 200, 300]): \\n{x}\")\n",
    "    # error: x = pd.Series([5,25], index=[100, 200, 300]); print(f\"pd.Series([5,25], index=[100, 200, 300]): \\n{x}\")\n",
    "    # error: print('\\n',80*'-','\\nRepeated Index:'); x = pd.Series([5, 15, 25], index=[200]); print(f\"pd.Series([5, 15, 25], index=[200, 300]): \\n{x}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### DataFrame\n",
    "If a ``Series`` is an analog of a 1D array with flexible indices, a ``DataFrame`` is an analog of a 2D array with both flexible row indices and flexible column names.\n",
    "Just as you might think of a **2D array as an ordered sequence of aligned 1D columns**, you can think of a **``DataFrame`` as a sequence of aligned ``Series`` objects**.\n",
    "Here, by \"aligned\" we mean that they share the same index."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "** Quick DataFrame **\n",
      "\n",
      "DataFrames are essentially multidimensional arrays          \n",
      " with attached row and column labels,           \n",
      " and often with heterogeneous types           \n",
      " and/or missing data.\n",
      "\n",
      "pd.DataFrame(np.random.randint(0,100,size=(5, 4))-50, columns=list('ABCD')):\n"
     ]
    },
    {
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       "<thead>    <tr> \n",
       "        <th class=\"blank level0\" ></th> \n",
       "        <th class=\"col_heading level0 col0\" >A</th> \n",
       "        <th class=\"col_heading level0 col1\" >B</th> \n",
       "        <th class=\"col_heading level0 col2\" >C</th> \n",
       "        <th class=\"col_heading level0 col3\" >D</th> \n",
       "    </tr></thead> \n",
       "<tbody>    <tr> \n",
       "        <th id=\"T_ad49b8a8_1ea3_11e9_8f1c_1cc63ca220e2level0_row0\" class=\"row_heading level0 row0\" >0</th> \n",
       "        <td id=\"T_ad49b8a8_1ea3_11e9_8f1c_1cc63ca220e2row0_col0\" class=\"data row0 col0\" >2</td> \n",
       "        <td id=\"T_ad49b8a8_1ea3_11e9_8f1c_1cc63ca220e2row0_col1\" class=\"data row0 col1\" >29</td> \n",
       "        <td id=\"T_ad49b8a8_1ea3_11e9_8f1c_1cc63ca220e2row0_col2\" class=\"data row0 col2\" >5</td> \n",
       "        <td id=\"T_ad49b8a8_1ea3_11e9_8f1c_1cc63ca220e2row0_col3\" class=\"data row0 col3\" >-42</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_ad49b8a8_1ea3_11e9_8f1c_1cc63ca220e2level0_row1\" class=\"row_heading level0 row1\" >1</th> \n",
       "        <td id=\"T_ad49b8a8_1ea3_11e9_8f1c_1cc63ca220e2row1_col0\" class=\"data row1 col0\" >13</td> \n",
       "        <td id=\"T_ad49b8a8_1ea3_11e9_8f1c_1cc63ca220e2row1_col1\" class=\"data row1 col1\" >-16</td> \n",
       "        <td id=\"T_ad49b8a8_1ea3_11e9_8f1c_1cc63ca220e2row1_col2\" class=\"data row1 col2\" >43</td> \n",
       "        <td id=\"T_ad49b8a8_1ea3_11e9_8f1c_1cc63ca220e2row1_col3\" class=\"data row1 col3\" >-36</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_ad49b8a8_1ea3_11e9_8f1c_1cc63ca220e2level0_row2\" class=\"row_heading level0 row2\" >2</th> \n",
       "        <td id=\"T_ad49b8a8_1ea3_11e9_8f1c_1cc63ca220e2row2_col0\" class=\"data row2 col0\" >-37</td> \n",
       "        <td id=\"T_ad49b8a8_1ea3_11e9_8f1c_1cc63ca220e2row2_col1\" class=\"data row2 col1\" >4</td> \n",
       "        <td id=\"T_ad49b8a8_1ea3_11e9_8f1c_1cc63ca220e2row2_col2\" class=\"data row2 col2\" >-47</td> \n",
       "        <td id=\"T_ad49b8a8_1ea3_11e9_8f1c_1cc63ca220e2row2_col3\" class=\"data row2 col3\" >-30</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_ad49b8a8_1ea3_11e9_8f1c_1cc63ca220e2level0_row3\" class=\"row_heading level0 row3\" >3</th> \n",
       "        <td id=\"T_ad49b8a8_1ea3_11e9_8f1c_1cc63ca220e2row3_col0\" class=\"data row3 col0\" >34</td> \n",
       "        <td id=\"T_ad49b8a8_1ea3_11e9_8f1c_1cc63ca220e2row3_col1\" class=\"data row3 col1\" >11</td> \n",
       "        <td id=\"T_ad49b8a8_1ea3_11e9_8f1c_1cc63ca220e2row3_col2\" class=\"data row3 col2\" >21</td> \n",
       "        <td id=\"T_ad49b8a8_1ea3_11e9_8f1c_1cc63ca220e2row3_col3\" class=\"data row3 col3\" >-25</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_ad49b8a8_1ea3_11e9_8f1c_1cc63ca220e2level0_row4\" class=\"row_heading level0 row4\" >4</th> \n",
       "        <td id=\"T_ad49b8a8_1ea3_11e9_8f1c_1cc63ca220e2row4_col0\" class=\"data row4 col0\" >-21</td> \n",
       "        <td id=\"T_ad49b8a8_1ea3_11e9_8f1c_1cc63ca220e2row4_col1\" class=\"data row4 col1\" >21</td> \n",
       "        <td id=\"T_ad49b8a8_1ea3_11e9_8f1c_1cc63ca220e2row4_col2\" class=\"data row4 col2\" >-11</td> \n",
       "        <td id=\"T_ad49b8a8_1ea3_11e9_8f1c_1cc63ca220e2row4_col3\" class=\"data row4 col3\" >9</td> \n",
       "    </tr></tbody> \n",
       "</table> "
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     "output_type": "display_data"
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       "<table id=\"T_ad4a768a_1ea3_11e9_8f1c_1cc63ca220e2\" style='display:inline'><caption>pd.DataFrame(df.dtypes)\n",
       "</caption> \n",
       "<thead>    <tr> \n",
       "        <th class=\"blank level0\" ></th> \n",
       "        <th class=\"col_heading level0 col0\" >0</th> \n",
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       "<tbody>    <tr> \n",
       "        <th id=\"T_ad4a768a_1ea3_11e9_8f1c_1cc63ca220e2level0_row0\" class=\"row_heading level0 row0\" >A</th> \n",
       "        <td id=\"T_ad4a768a_1ea3_11e9_8f1c_1cc63ca220e2row0_col0\" class=\"data row0 col0\" >int64</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_ad4a768a_1ea3_11e9_8f1c_1cc63ca220e2level0_row1\" class=\"row_heading level0 row1\" >B</th> \n",
       "        <td id=\"T_ad4a768a_1ea3_11e9_8f1c_1cc63ca220e2row1_col0\" class=\"data row1 col0\" >int64</td> \n",
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       "        <th id=\"T_ad4a768a_1ea3_11e9_8f1c_1cc63ca220e2level0_row2\" class=\"row_heading level0 row2\" >C</th> \n",
       "        <td id=\"T_ad4a768a_1ea3_11e9_8f1c_1cc63ca220e2row2_col0\" class=\"data row2 col0\" >int64</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_ad4a768a_1ea3_11e9_8f1c_1cc63ca220e2level0_row3\" class=\"row_heading level0 row3\" >D</th> \n",
       "        <td id=\"T_ad4a768a_1ea3_11e9_8f1c_1cc63ca220e2row3_col0\" class=\"data row3 col0\" >int64</td> \n",
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       "</style>  \n",
       "<table id=\"T_ad4ae142_1ea3_11e9_8f1c_1cc63ca220e2\" style='display:inline'><caption>pd.DataFrame(df.values)\n",
       "</caption> \n",
       "<thead>    <tr> \n",
       "        <th class=\"blank level0\" ></th> \n",
       "        <th class=\"col_heading level0 col0\" >0</th> \n",
       "        <th class=\"col_heading level0 col1\" >1</th> \n",
       "        <th class=\"col_heading level0 col2\" >2</th> \n",
       "        <th class=\"col_heading level0 col3\" >3</th> \n",
       "    </tr></thead> \n",
       "<tbody>    <tr> \n",
       "        <th id=\"T_ad4ae142_1ea3_11e9_8f1c_1cc63ca220e2level0_row0\" class=\"row_heading level0 row0\" >0</th> \n",
       "        <td id=\"T_ad4ae142_1ea3_11e9_8f1c_1cc63ca220e2row0_col0\" class=\"data row0 col0\" >2</td> \n",
       "        <td id=\"T_ad4ae142_1ea3_11e9_8f1c_1cc63ca220e2row0_col1\" class=\"data row0 col1\" >29</td> \n",
       "        <td id=\"T_ad4ae142_1ea3_11e9_8f1c_1cc63ca220e2row0_col2\" class=\"data row0 col2\" >5</td> \n",
       "        <td id=\"T_ad4ae142_1ea3_11e9_8f1c_1cc63ca220e2row0_col3\" class=\"data row0 col3\" >-42</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_ad4ae142_1ea3_11e9_8f1c_1cc63ca220e2level0_row1\" class=\"row_heading level0 row1\" >1</th> \n",
       "        <td id=\"T_ad4ae142_1ea3_11e9_8f1c_1cc63ca220e2row1_col0\" class=\"data row1 col0\" >13</td> \n",
       "        <td id=\"T_ad4ae142_1ea3_11e9_8f1c_1cc63ca220e2row1_col1\" class=\"data row1 col1\" >-16</td> \n",
       "        <td id=\"T_ad4ae142_1ea3_11e9_8f1c_1cc63ca220e2row1_col2\" class=\"data row1 col2\" >43</td> \n",
       "        <td id=\"T_ad4ae142_1ea3_11e9_8f1c_1cc63ca220e2row1_col3\" class=\"data row1 col3\" >-36</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_ad4ae142_1ea3_11e9_8f1c_1cc63ca220e2level0_row2\" class=\"row_heading level0 row2\" >2</th> \n",
       "        <td id=\"T_ad4ae142_1ea3_11e9_8f1c_1cc63ca220e2row2_col0\" class=\"data row2 col0\" >-37</td> \n",
       "        <td id=\"T_ad4ae142_1ea3_11e9_8f1c_1cc63ca220e2row2_col1\" class=\"data row2 col1\" >4</td> \n",
       "        <td id=\"T_ad4ae142_1ea3_11e9_8f1c_1cc63ca220e2row2_col2\" class=\"data row2 col2\" >-47</td> \n",
       "        <td id=\"T_ad4ae142_1ea3_11e9_8f1c_1cc63ca220e2row2_col3\" class=\"data row2 col3\" >-30</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_ad4ae142_1ea3_11e9_8f1c_1cc63ca220e2level0_row3\" class=\"row_heading level0 row3\" >3</th> \n",
       "        <td id=\"T_ad4ae142_1ea3_11e9_8f1c_1cc63ca220e2row3_col0\" class=\"data row3 col0\" >34</td> \n",
       "        <td id=\"T_ad4ae142_1ea3_11e9_8f1c_1cc63ca220e2row3_col1\" class=\"data row3 col1\" >11</td> \n",
       "        <td id=\"T_ad4ae142_1ea3_11e9_8f1c_1cc63ca220e2row3_col2\" class=\"data row3 col2\" >21</td> \n",
       "        <td id=\"T_ad4ae142_1ea3_11e9_8f1c_1cc63ca220e2row3_col3\" class=\"data row3 col3\" >-25</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_ad4ae142_1ea3_11e9_8f1c_1cc63ca220e2level0_row4\" class=\"row_heading level0 row4\" >4</th> \n",
       "        <td id=\"T_ad4ae142_1ea3_11e9_8f1c_1cc63ca220e2row4_col0\" class=\"data row4 col0\" >-21</td> \n",
       "        <td id=\"T_ad4ae142_1ea3_11e9_8f1c_1cc63ca220e2row4_col1\" class=\"data row4 col1\" >21</td> \n",
       "        <td id=\"T_ad4ae142_1ea3_11e9_8f1c_1cc63ca220e2row4_col2\" class=\"data row4 col2\" >-11</td> \n",
       "        <td id=\"T_ad4ae142_1ea3_11e9_8f1c_1cc63ca220e2row4_col3\" class=\"data row4 col3\" >9</td> \n",
       "    </tr></tbody> \n",
       "</table> &nbsp&nbsp&nbsp&nbsp&nbsp&nbsp&nbsp&nbsp&nbsp&nbsp&nbsp&nbsp&nbsp&nbsp&nbsp&nbsp&nbsp&nbsp&nbsp&nbsp&nbsp&nbsp&nbsp&nbsp&nbsp<style  type=\"text/css\" >\n",
       "</style>  \n",
       "<table id=\"T_ad4e811c_1ea3_11e9_8f1c_1cc63ca220e2\" style='display:inline'><caption>df.describe()\n",
       "</caption> \n",
       "<thead>    <tr> \n",
       "        <th class=\"blank level0\" ></th> \n",
       "        <th class=\"col_heading level0 col0\" >A</th> \n",
       "        <th class=\"col_heading level0 col1\" >B</th> \n",
       "        <th class=\"col_heading level0 col2\" >C</th> \n",
       "        <th class=\"col_heading level0 col3\" >D</th> \n",
       "    </tr></thead> \n",
       "<tbody>    <tr> \n",
       "        <th id=\"T_ad4e811c_1ea3_11e9_8f1c_1cc63ca220e2level0_row0\" class=\"row_heading level0 row0\" >count</th> \n",
       "        <td id=\"T_ad4e811c_1ea3_11e9_8f1c_1cc63ca220e2row0_col0\" class=\"data row0 col0\" >5</td> \n",
       "        <td id=\"T_ad4e811c_1ea3_11e9_8f1c_1cc63ca220e2row0_col1\" class=\"data row0 col1\" >5</td> \n",
       "        <td id=\"T_ad4e811c_1ea3_11e9_8f1c_1cc63ca220e2row0_col2\" class=\"data row0 col2\" >5</td> \n",
       "        <td id=\"T_ad4e811c_1ea3_11e9_8f1c_1cc63ca220e2row0_col3\" class=\"data row0 col3\" >5</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_ad4e811c_1ea3_11e9_8f1c_1cc63ca220e2level0_row1\" class=\"row_heading level0 row1\" >mean</th> \n",
       "        <td id=\"T_ad4e811c_1ea3_11e9_8f1c_1cc63ca220e2row1_col0\" class=\"data row1 col0\" >-1.8</td> \n",
       "        <td id=\"T_ad4e811c_1ea3_11e9_8f1c_1cc63ca220e2row1_col1\" class=\"data row1 col1\" >9.8</td> \n",
       "        <td id=\"T_ad4e811c_1ea3_11e9_8f1c_1cc63ca220e2row1_col2\" class=\"data row1 col2\" >2.2</td> \n",
       "        <td id=\"T_ad4e811c_1ea3_11e9_8f1c_1cc63ca220e2row1_col3\" class=\"data row1 col3\" >-24.8</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_ad4e811c_1ea3_11e9_8f1c_1cc63ca220e2level0_row2\" class=\"row_heading level0 row2\" >std</th> \n",
       "        <td id=\"T_ad4e811c_1ea3_11e9_8f1c_1cc63ca220e2row2_col0\" class=\"data row2 col0\" >27.941</td> \n",
       "        <td id=\"T_ad4e811c_1ea3_11e9_8f1c_1cc63ca220e2row2_col1\" class=\"data row2 col1\" >17.2829</td> \n",
       "        <td id=\"T_ad4e811c_1ea3_11e9_8f1c_1cc63ca220e2row2_col2\" class=\"data row2 col2\" >33.9882</td> \n",
       "        <td id=\"T_ad4e811c_1ea3_11e9_8f1c_1cc63ca220e2row2_col3\" class=\"data row2 col3\" >19.9424</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_ad4e811c_1ea3_11e9_8f1c_1cc63ca220e2level0_row3\" class=\"row_heading level0 row3\" >min</th> \n",
       "        <td id=\"T_ad4e811c_1ea3_11e9_8f1c_1cc63ca220e2row3_col0\" class=\"data row3 col0\" >-37</td> \n",
       "        <td id=\"T_ad4e811c_1ea3_11e9_8f1c_1cc63ca220e2row3_col1\" class=\"data row3 col1\" >-16</td> \n",
       "        <td id=\"T_ad4e811c_1ea3_11e9_8f1c_1cc63ca220e2row3_col2\" class=\"data row3 col2\" >-47</td> \n",
       "        <td id=\"T_ad4e811c_1ea3_11e9_8f1c_1cc63ca220e2row3_col3\" class=\"data row3 col3\" >-42</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_ad4e811c_1ea3_11e9_8f1c_1cc63ca220e2level0_row4\" class=\"row_heading level0 row4\" >25%</th> \n",
       "        <td id=\"T_ad4e811c_1ea3_11e9_8f1c_1cc63ca220e2row4_col0\" class=\"data row4 col0\" >-21</td> \n",
       "        <td id=\"T_ad4e811c_1ea3_11e9_8f1c_1cc63ca220e2row4_col1\" class=\"data row4 col1\" >4</td> \n",
       "        <td id=\"T_ad4e811c_1ea3_11e9_8f1c_1cc63ca220e2row4_col2\" class=\"data row4 col2\" >-11</td> \n",
       "        <td id=\"T_ad4e811c_1ea3_11e9_8f1c_1cc63ca220e2row4_col3\" class=\"data row4 col3\" >-36</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_ad4e811c_1ea3_11e9_8f1c_1cc63ca220e2level0_row5\" class=\"row_heading level0 row5\" >50%</th> \n",
       "        <td id=\"T_ad4e811c_1ea3_11e9_8f1c_1cc63ca220e2row5_col0\" class=\"data row5 col0\" >2</td> \n",
       "        <td id=\"T_ad4e811c_1ea3_11e9_8f1c_1cc63ca220e2row5_col1\" class=\"data row5 col1\" >11</td> \n",
       "        <td id=\"T_ad4e811c_1ea3_11e9_8f1c_1cc63ca220e2row5_col2\" class=\"data row5 col2\" >5</td> \n",
       "        <td id=\"T_ad4e811c_1ea3_11e9_8f1c_1cc63ca220e2row5_col3\" class=\"data row5 col3\" >-30</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_ad4e811c_1ea3_11e9_8f1c_1cc63ca220e2level0_row6\" class=\"row_heading level0 row6\" >75%</th> \n",
       "        <td id=\"T_ad4e811c_1ea3_11e9_8f1c_1cc63ca220e2row6_col0\" class=\"data row6 col0\" >13</td> \n",
       "        <td id=\"T_ad4e811c_1ea3_11e9_8f1c_1cc63ca220e2row6_col1\" class=\"data row6 col1\" >21</td> \n",
       "        <td id=\"T_ad4e811c_1ea3_11e9_8f1c_1cc63ca220e2row6_col2\" class=\"data row6 col2\" >21</td> \n",
       "        <td id=\"T_ad4e811c_1ea3_11e9_8f1c_1cc63ca220e2row6_col3\" class=\"data row6 col3\" >-25</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_ad4e811c_1ea3_11e9_8f1c_1cc63ca220e2level0_row7\" class=\"row_heading level0 row7\" >max</th> \n",
       "        <td id=\"T_ad4e811c_1ea3_11e9_8f1c_1cc63ca220e2row7_col0\" class=\"data row7 col0\" >34</td> \n",
       "        <td id=\"T_ad4e811c_1ea3_11e9_8f1c_1cc63ca220e2row7_col1\" class=\"data row7 col1\" >29</td> \n",
       "        <td id=\"T_ad4e811c_1ea3_11e9_8f1c_1cc63ca220e2row7_col2\" class=\"data row7 col2\" >43</td> \n",
       "        <td id=\"T_ad4e811c_1ea3_11e9_8f1c_1cc63ca220e2row7_col3\" class=\"data row7 col3\" >9</td> \n",
       "    </tr></tbody> \n",
       "</table> &nbsp&nbsp&nbsp&nbsp&nbsp&nbsp&nbsp&nbsp&nbsp&nbsp&nbsp&nbsp&nbsp&nbsp&nbsp&nbsp&nbsp&nbsp&nbsp&nbsp&nbsp&nbsp&nbsp&nbsp&nbsp"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "df.index:        RangeIndex(start=0, stop=5, step=1)\n",
      "df.columns:      Index(['A', 'B', 'C', 'D'], dtype='object')\n",
      "type(df.values): <class 'numpy.ndarray'>\n",
      "df.shape:        (5, 4)\n",
      "\n",
      "Plot 1\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x216 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plot 2\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x216 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plot 3\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/xopi/Desktop/IT_xopi/Python/Books/SciPyAndMore/venv/lib/python3.6/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('** Quick DataFrame **\\n')\n",
    "if 1:\n",
    "    print('DataFrames are essentially multidimensional arrays\\\n",
    "          \\n with attached row and column labels, \\\n",
    "          \\n and often with heterogeneous types \\\n",
    "          \\n and/or missing data.\\n')\n",
    "    df = pd.DataFrame(np.random.randint(0,100,size=(5, 4))-50, columns=list('ABCD'))\n",
    "    print(\"pd.DataFrame(np.random.randint(0,100,size=(5, 4))-50, columns=list('ABCD')):\")\n",
    "    display_html(df.style.background_gradient(cmap='Greens').applymap(color_negative_red))\n",
    "    \n",
    "    display_htmlFcn(\"pd.DataFrame(df.dtypes)\",\"pd.DataFrame(df.values)\",\"df.describe()\",space=25,n=25)\n",
    "    \n",
    "    print(f\"df.index:        {df.index}\")\n",
    "    print(f\"df.columns:      {df.columns}\")\n",
    "    print(f\"type(df.values): {type(df.values)}\")\n",
    "    print(f\"df.shape:        {df.shape}\\n\")\n",
    "    \n",
    "    plt.style.use('seaborn-whitegrid')\n",
    "    \n",
    "    i=1; print(f'Plot {i}')\n",
    "    fig, ax = plt.subplots(1,2,figsize=(8, 3))\n",
    "    sns.heatmap(df,cmap='Greens',annot=True, ax=ax[0])\n",
    "    df.plot(ax=ax[1], grid=True);\n",
    "    #ax[1].legend(loc='upper center', bbox_to_anchor=(0.5, 1.15), ncol=4, fancybox=True, shadow=True)\n",
    "    ax[1].legend(loc='center left', bbox_to_anchor=(1, 0.5))\n",
    "    plt.show()\n",
    "    \n",
    "    i+=1; print(f'Plot {i}')\n",
    "    #First create the clustermap figure\n",
    "    g = sns.clustermap(df, cmap='Greens',annot=True, figsize=(8,3))\n",
    "    # set the gridspec to only cover half of the figure\n",
    "    g.gs.update(left=0.05, right=0.45)\n",
    "    #create new gridspec for the right part\n",
    "    import matplotlib.gridspec\n",
    "    gs2 = matplotlib.gridspec.GridSpec(1,1, left=0.6)\n",
    "    # create axes within this new gridspec\n",
    "    ax2 = g.fig.add_subplot(gs2[0])\n",
    "    # plot boxplot in the new axes\n",
    "    sns.boxplot(data=df, orient=\"h\", palette=\"Greens\", ax = ax2)\n",
    "    plt.show()\n",
    "    \n",
    "    i+=1; print(f'Plot {i}')\n",
    "    g = sns.PairGrid(df, height=3, aspect=2)\n",
    "    g.map_diag(sns.distplot,\n",
    "               rug=True, rug_kws={\"color\": \"g\"},\n",
    "               kde_kws={\"color\": \"k\", \"lw\": 3, \"label\": \"KDE\"},\n",
    "               hist_kws={\"histtype\": \"step\", \"linewidth\": 3, \"alpha\": 1, \"color\": \"g\"})\n",
    "    g.map_upper(sns.regplot)\n",
    "    g.map_lower(sns.kdeplot,n_levels=3 ,shade=True, shade_lowest=False,alpha=.8)\n",
    "    g.fig.set_size_inches(5,5)\n",
    "    plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "** DataFrame Construction Ways **\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print('** DataFrame Construction Ways **\\n')\n",
    "if 0:\n",
    "    print('\\n',80*'-','\\nFrom a 2D NumPy array')\n",
    "    df = pd.DataFrame(np.random.rand(3, 2),\n",
    "                 columns=['foo', 'bar'],\n",
    "                 index=['a', 'b', 'c'])\n",
    "    print(f\"pd.DataFrame(np.random.rand(3, 2), columns=['foo', 'bar'], index=['a', 'b', 'c']):\"); display_html(df)\n",
    "    \n",
    "    print('\\n',80*'-','\\nFrom Pandas Series')\n",
    "    population_dict = {'C': 3, 'T': 5, 'NY': 6}\n",
    "    population = pd.Series(population_dict)\n",
    "    area_dict = {'C': 2, 'T': 3, 'NY': 1}\n",
    "    area = pd.Series(area_dict)\n",
    "    df = pd.DataFrame({'population': population, 'area': area})\n",
    "    print(\"pd.DataFrame({'population': pd.Series({'C':3,'T':5,'NY':6}), 'area': pd.Series({'C':2,'T':3,'NY':1})}):\")\n",
    "    display_html(df)\n",
    "\n",
    "    print('\\nFrom a single Pandas Series')\n",
    "    df = pd.DataFrame(pd.Series(population_dict), columns=['population'])\n",
    "    print(f\"pd.DataFrame(pd.Series(population_dict), columns=['population']):\"); display_html(df)\n",
    "\n",
    "    print('\\n',80*'-','\\nFrom a list of dicts')\n",
    "    data = [{'a': i, 'b': 2 * i} for i in range(3)]\n",
    "    df = pd.DataFrame(data)\n",
    "    print(f\"pd.DataFrame([{{'a': i, 'b': 2 * i}} for i in range(3)]):\"); display_html(df)\n",
    "\n",
    "    print('\\n',80*'-','\\nFrom a NumPy structured array')\n",
    "    df = pd.DataFrame(np.zeros(3, dtype=[('A', 'i8'), ('B', 'f8')]))\n",
    "    print(f\"pd.DataFrame(np.zeros(3, dtype=[('A', 'i8'), ('B', 'f8')])):\"); display_html(df)   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice the potential confusion: \n",
    "\n",
    "<ul  class=\"list-unstyled\">\n",
    "  <li>\n",
    "    in a 2D NumPy array, \n",
    "    <kbd style=\"background:#EEEEEE; letter-spacing:0.1em; padding-left:0.5em; padding-right:0.4em;\">data[0]</kbd>\n",
    "      &emsp;&emsp;&emsp;will return the 1st row\n",
    "    </li>\n",
    "    <li>\n",
    "    in a DataFrame,&emsp;&emsp;\n",
    "    <kbd style=\"background:#EEEEEE; letter-spacing:0.1em; padding-left:0.5em; padding-right:0.4em;\">data['col0']</kbd>\n",
    "      will return the 1st column\n",
    "    </li>\n",
    "</ul>\n",
    "\n",
    "Because of this, it is probably better to think about ``DataFrame``s as generalized dictionaries rather than generalized arrays, though both ways of looking at the situation can be useful."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "** DataFrame CELL **\n"
     ]
    }
   ],
   "source": [
    "print('** DataFrame CELL **')\n",
    "\n",
    "df = pd.DataFrame(np.random.randint(10, size=(3,4)),columns='A B C D'.split(),index=['i' + str(x) for x in range(3)])\n",
    "if 0:\n",
    "    display_html(df.head().style.set_caption('df.head()'))\n",
    "    #errors: #df[[0][0]] #df[0;0] #df[[0];[0]] #df[0,0] #df[[0],[0]]\n",
    "    print(f\"df.iloc[0,0]:          {df.iloc[0,0]:.{3}f}\")      # same as df.iloc[0].iloc[0]\n",
    "    print(f\"df.iloc[0][0]:         {df.iloc[0][0]:.{3}f}\")     # same as \"\"\n",
    "    print(f\"df.iloc[0]['A']:       {df.iloc[0]['A']:.{3}f}\")   # same as df.iloc[0].loc['A']\n",
    "    # error: #df.iloc['i0'][0]\n",
    "    # error: #df.loc['i0',0]\n",
    "    print(f\"df.loc['i0','A']:      {df.loc['i0','A']:.{3}f}\")     # same as df.loc['i0'].loc['A'] \n",
    "    print(f\"df.loc['i0']['A']:     {df.loc['i0']['A']:.{3}f}\")    # same as \"\"\n",
    "    print(f\"df.loc['i0'][0]:       {df.loc['i0'][0]:.{3}f}\")      # same as df.loc['i0'].iloc[0]     \n",
    "    print(f\"df.loc['i0'].iloc[0]:  {df.loc['i0'].iloc[0]:.{3}f}\") # (the proof)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "** DataFrame ROW **\n"
     ]
    }
   ],
   "source": [
    "print('** DataFrame ROW **')\n",
    "\n",
    "df = pd.DataFrame(np.random.randint(10, size=(3,4)),columns='A B C D'.split(),index=['i' + str(x) for x in range(3)])\n",
    "if 0:\n",
    "    display_html(df.head().style.set_caption('df.head()'))\n",
    "\n",
    "    print('\\n\\n# ONE ROW:')\n",
    "    display_htmlFcn(\"pd.DataFrame(df.iloc[-1]).T\",\"pd.DataFrame(df.loc['i2']).T\",\"df.iloc[[-1]]\",\"df.loc[['i2']]\",space=15,n=12)\n",
    "    # error df[-1]\n",
    "    # df.iloc[-1]     # pandas Series\n",
    "    # df.loc['i2']    # pandas Series\n",
    "    # df.iloc[[-1]]   # pandas DataFrame\n",
    "    # df.loc[['i2']]  # pandas DataFrame  \n",
    "    \n",
    "    print('\\n\\n# whole ROWS: [], loc or iloc')\n",
    "    display_htmlFcn(\"df[0:2]\",\"df.iloc[0:2]\",\"df.loc['i0':'i1']\",\"df.iloc[[0,1,2]]\",\"df.reindex(['i0','i2'])\")\n",
    "    # print(f\"df[0:2]:       \\n{df[0:2]}\\n\")\n",
    "    # error df[[0,1,2]]\n",
    "    # error df[0:2,:]\n",
    "    # df.iloc[0:2]}      # stop EXclusive\n",
    "    # df.loc['i0':'i1']  # stop INclusive\n",
    "    # df.iloc[[0,1,2]]\n",
    "    # df.loc[['i0','i2']] # !!! FutureWarning: Passing list-likes to .loc or [] with any missing label will raise KeyError in the future, you can use .reindex() as an alternative.\n",
    "    # df.reindex(['i0','i2'])\n",
    "    \n",
    "    print('''\n",
    "    Gotchas to remember when using iloc in this manner. Note that .iloc returns: \n",
    "        a Pandas Series when one row is selected, and \n",
    "        a Pandas DataFrame when multiple rows are selected, or if any column in full is selected.\n",
    "    ''')\n",
    "    print(f\"type(df.iloc[0:2]): {type(df.iloc[0:2])}\")\n",
    "    print(f\"type(df.iloc[-1]):  {type(df.iloc[-1])}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "** DataFrame COLUMN **\n"
     ]
    }
   ],
   "source": [
    "print('** DataFrame COLUMN **')\n",
    "\n",
    "df = pd.DataFrame(np.random.randint(10, size=(3,4)),columns='A B C D'.split(),index=['i' + str(x) for x in range(3)])\n",
    "if 0: \n",
    "    display_html(df.head().style.set_caption('df.head()'))\n",
    "    \n",
    "    print('\\n# ONE COLUMN:')\n",
    "    display_htmlFcn(\"pd.DataFrame(df['A'])\",\"pd.DataFrame(df.iloc[:,-1])\",\\\n",
    "                    \"pd.DataFrame(df.loc[:,'C'])\",\"df.iloc[:,[-1]]\",\"df.loc[:,['C']]\", space=28, n=28)\n",
    "    #error: df[0] # no column label is 0\n",
    "    # Note a ROW in Numpy is: x = np.random.randint(10,size=(3, 4)); print(f'x[0]: {x[0]}')\n",
    "    # df['A']         # pandas Series\n",
    "    # df.iloc[:, -1]  # pandas Series\n",
    "    # df.loc[:, 'C']  # pandas Series\n",
    "    # df.iloc[:,[-1]] # pandas DataFrame\n",
    "    # df.loc[:,['C']] # pandas DataFrame\n",
    "        \n",
    "    print('\\n# whole COLUMNS: [[]], loc[:, ] or iloc[:, ]') \n",
    "    # https://stackoverflow.com/questions/11285613/selecting-multiple-columns-in-a-pandas-dataframe\n",
    "    display_htmlFcn(\"df[['A','C']]\",\"df.iloc[:,0:2]\",\"df.loc[:,'A':'C']\",\"df.iloc[:,[0,1,2]]\", n=50, space=3)\n",
    "    # df[['A','C']] # column names must be explicity (no ':' allowed) like: df[['A':'C']]\n",
    "    # df.iloc[:, 0:2]    # stop EXclusive\n",
    "    # df.loc[:, 'A':'C'] # stop INclusive\n",
    "    # df.iloc[:, [0,1,2]]\n",
    "    #error: #df[:, 'A':'C'] #df.loc[:, 0:2] #df[[0,2]] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "** DataFrame ROWS_slice && COLUMNS_slice **\n"
     ]
    }
   ],
   "source": [
    "print('** DataFrame ROWS_slice && COLUMNS_slice **')\n",
    "\n",
    "df = pd.DataFrame(np.random.randint(10, size=(3,4)),columns='A B C D'.split(),index=['i' + str(x) for x in range(3)])\n",
    "if 0:\n",
    "    display_html(df.head().style.set_caption('df.head()'))\n",
    "    \n",
    "    print('# ROWS_single && COLUMNS_single')\n",
    "    display_htmlFcn(\"df.iloc[[0,2],[1,3]]\",n=50)\n",
    "    \n",
    "    print('# ROWS_single && COLUMNS_slice')\n",
    "    display_htmlFcn(\"df.iloc[[0,2]]\",\"df.iloc[[0,2]].iloc[:,0:2]\",\"df.iloc[[0,2]].iloc[:,[0,2]]\",space=15,n=50)\n",
    "  \n",
    "    print('# ROWS_slice  && COLUMNS_single')  \n",
    "    display_htmlFcn(\"df.iloc[0:2,1:3]\",\"df.iloc[0:2].iloc[:,[0,2]]\",space=10,n=50)\n",
    "    \n",
    "    print(80*'-','\\nWith the default COLUMNs LABEL, we can access directly to the k-th column(s):')\n",
    "    df = pd.DataFrame(np.random.randint(10, size=(3,4)))\n",
    "    \n",
    "    display_htmlFcn(\"df\",\"pd.DataFrame(df[0])\",\"df[[0,2]]\",\"df.iloc[:, 0:2]\",\"df.loc[:,0:2]\",space=20,n=50) \n",
    "    # df[0] # Note a ROW in Numpy is: x = np.random.randint(10,size=(3, 4)); print(f'x[0]: {x[0]}')\n",
    "    # error: df[-1] # no column label is -1\n",
    "    # df[[0,2]] # column names must be explicity (no ':' allowed) like: df[[0:2]]\n",
    "    # df.iloc[:, 0:2]\n",
    "    # df.loc[:, 0:2]    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "** DataFrame boolean indexing **\n"
     ]
    }
   ],
   "source": [
    "print('** DataFrame boolean indexing **')\n",
    "\n",
    "df = pd.DataFrame(np.array(range(0,12)).reshape(3, 4),columns='A B C D'.split(),index=['i' + str(x) for x in range(3)])\n",
    "if 0:\n",
    "    display_html(df.head().style.set_caption('df.head()'))\n",
    "\n",
    "    print('# ROWS whose COLUMN values a scalar')\n",
    "    display_htmlFcn(\"df\",\"pd.DataFrame(df['C'])\",\"pd.DataFrame(df['C'] == 6)\",\"df.loc[df['C'] == 6]\",space=10)\n",
    "\n",
    "    print('# ROWS whose COLUMN values in an iterable')\n",
    "    display_htmlFcn(\"df\",\"pd.DataFrame(df['C'])\",\"pd.DataFrame(df['C'].isin(range(3,7)))\",\\\n",
    "                    \"pd.DataFrame(df.loc[df['C'].isin(range(3,7))])\",\\\n",
    "                    \"pd.DataFrame(df.loc[~df['C'].isin(range(3,7))])\",\"df.loc[df['C'].isin([1,2,7])]\",space=12,n=12)\n",
    "\n",
    "    print('# CELL values a scalar')\n",
    "    display_htmlFcn(\"df\", \"df == 7\", \"df[df == 7]\", \"df[df == 7].fillna(0)\",space=10, n=50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "DataFrame Modify<br>\n",
    "Warning: It is sometimes difficult to predict if an operation returns a copy or a view. For this reason the [docs recommend avoiding assignments with \"chained indexing\"](http://pandas.pydata.org/pandas-docs/stable/indexing.html#returning-a-view-versus-a-copy). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "** DataFrame Modify **\n"
     ]
    }
   ],
   "source": [
    "print('** DataFrame Modify **')\n",
    "if 0:\n",
    "    dates = pd.date_range(start='1/1/2018',periods=6, freq='D')\n",
    "    df = pd.DataFrame(np.random.randn(6,3),index=dates,columns=list('ABC'))\n",
    "\n",
    "    df_copy = df.copy()\n",
    "    df_copy['D'] = 0; #['one', 'one', 'two', 'three', 'four', 'three']\n",
    "\n",
    "    df2 = df # no copy, thus after modifing 'df2' then 'df' is modified too\n",
    "    df2.at[dates[0],'B'] = 0 # modify cell like df.loc[dates[0],'B'] = 0\n",
    "    df2.iat[-1,1] = 99       # modify cell like df.iloc[-1,1] = 99\n",
    "    df2.loc[:, 'A'] = np.array([5] * len(df))\n",
    "    #df2.iat[:,-1] = -1 # Error: iAt based indexing can only have integer indexers\n",
    "    df2.iloc[:,-1] = -1\n",
    "\n",
    "    display_htmlFcn(\"df\", \"df_copy\", \"df2\",space=10, n=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "** NaN **\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print('** NaN **\\n')\n",
    "\n",
    "if 0:\n",
    "    print(f\"np.array([1, np.nan, 3, 4]).dtype: {np.array([1, np.nan, 3, 4]).dtype}\")\n",
    "\n",
    "    print('\\nMissing Data in DataFrame ufuncs:')\n",
    "    df = pd.DataFrame(np.random.randint(10, size=(3, 4)), columns=list('QRST'))\n",
    "    df2 = df.iloc[0, ::2]\n",
    "    display_htmlFcn(\"df\", \"pd.DataFrame(df.iloc[0, ::2]).T\", \"df - df.iloc[0, ::2]\", space=10, n=20)\n",
    "\n",
    "    print('\\nRegardless of the operation, the result of arithmetic with NaN will be another NaN. E.g.:')\n",
    "    print(f\"\\t1 + np.nan: {1 + np.nan}\")\n",
    "    print(f\"\\t0 * np.nan: {0 *  np.nan}\")\n",
    "\n",
    "    print('\\nAggregations that will ignore NaN:') # np.nansum(df), np.nanmin(df), np.nanmax(df)\n",
    "    print(f\"·np.sum(df - df.iloc[0, ::2]):  \\n{np.sum(df - df.iloc[0, ::2])}\")\n",
    "    print(f\"·np.nansum(df - df.iloc[0, ::2]): {np.nansum(df - df.iloc[0, ::2])}\")\n",
    "\n",
    "    print('\\n',80*'-','\\nSeries: NaN')\n",
    "    x = pd.Series([1, np.nan, 'HELLO', None])\n",
    "    display_htmlFcn(\"pd.DataFrame(x)\", \"pd.DataFrame(x.isnull())\", \"pd.DataFrame(x[x.notnull()])\", \n",
    "                    \"pd.DataFrame(x.dropna())\", \"pd.DataFrame(x.fillna(99))\", space=25, n=28)\n",
    "\n",
    "    print('\\n',80*'-','\\nDataFrames: NaN')\n",
    "    df = pd.DataFrame([[1,np.nan,2],[2,3,5],[np.nan,4,6]])\n",
    "    display_htmlFcn(\"df\", \"df.dropna()\", \"df.dropna(axis=1)\", \"df.dropna(how='all')\", \"df.dropna(axis=1,how='all')\", \n",
    "                    \"df.dropna(thresh=3)\", space=15,n=50)\n",
    "    # df.dropna()                         # drop all ROWS with ANY NaN\n",
    "    # df.dropna(axis='columns')           # drop all COLS with ANY NaN\n",
    "    # df.dropna(how='all')                # drop all ROWS with ALL NaN\n",
    "    # df.dropna(axis='columns',how='all') # drop all COLS with ALL NaN\n",
    "    # df.dropna(thresh=3)                 # only ROWS with at least 3 no-NaN\n",
    "\n",
    "    print('\\n',80*'-','\\nSeries && DataFrames: replace NaN')\n",
    "    x = pd.Series([1, np.nan, 'hello', None])\n",
    "    df = pd.DataFrame([[1,2,np.nan],[0,np.nan,np.nan],[np.nan,4,6]])\n",
    "    display_htmlFcn(\"df\", \"df.fillna(99)\", \"df.fillna(method='ffill')\", \"df.fillna(method='bfill')\", \n",
    "                    \"df.fillna(method='ffill', axis=1)\", space=10, n=50)\n",
    "    # df.fillna(99)     # replace NaN with 99\n",
    "    # df.fillna(method='ffill')         # forward-fill to propagate the previous value forward\n",
    "    # df.fillna(method='bfill')         # back-fill to propagate the next values backward\n",
    "    # df.fillna(method='ffill', axis=1) # plus the axis option"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hierarchical Indexing\n",
    "*Hierarchical indexing* (also known as *multi-indexing*) to incorporate multiple index *levels* within a single index."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "** Hierarchical Indexing: Basic**\n",
      "\n",
      "type(df): <class 'pandas.core.frame.DataFrame'>\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style>  \n",
       "<table id=\"T_b2f9e764_1ea3_11e9_8f1c_1cc63ca220e2\" style='display:inline'><caption>df\n",
       "</caption> \n",
       "<thead>    <tr> \n",
       "        <th class=\"blank\" ></th> \n",
       "        <th class=\"blank level0\" ></th> \n",
       "        <th class=\"col_heading level0 col0\" >A</th> \n",
       "        <th class=\"col_heading level0 col1\" >B</th> \n",
       "    </tr>    <tr> \n",
       "        <th class=\"index_name level0\" >Group</th> \n",
       "        <th class=\"index_name level1\" >Num</th> \n",
       "        <th class=\"blank\" ></th> \n",
       "        <th class=\"blank\" ></th> \n",
       "    </tr></thead> \n",
       "<tbody>    <tr> \n",
       "        <th id=\"T_b2f9e764_1ea3_11e9_8f1c_1cc63ca220e2level0_row0\" class=\"row_heading level0 row0\" rowspan=3>G1</th> \n",
       "        <th id=\"T_b2f9e764_1ea3_11e9_8f1c_1cc63ca220e2level1_row0\" class=\"row_heading level1 row0\" >1</th> \n",
       "        <td id=\"T_b2f9e764_1ea3_11e9_8f1c_1cc63ca220e2row0_col0\" class=\"data row0 col0\" >0.372589</td> \n",
       "        <td id=\"T_b2f9e764_1ea3_11e9_8f1c_1cc63ca220e2row0_col1\" class=\"data row0 col1\" >-0.873417</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_b2f9e764_1ea3_11e9_8f1c_1cc63ca220e2level1_row1\" class=\"row_heading level1 row1\" >2</th> \n",
       "        <td id=\"T_b2f9e764_1ea3_11e9_8f1c_1cc63ca220e2row1_col0\" class=\"data row1 col0\" >-0.316024</td> \n",
       "        <td id=\"T_b2f9e764_1ea3_11e9_8f1c_1cc63ca220e2row1_col1\" class=\"data row1 col1\" >-1.22783</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_b2f9e764_1ea3_11e9_8f1c_1cc63ca220e2level1_row2\" class=\"row_heading level1 row2\" >3</th> \n",
       "        <td id=\"T_b2f9e764_1ea3_11e9_8f1c_1cc63ca220e2row2_col0\" class=\"data row2 col0\" >1.08985</td> \n",
       "        <td id=\"T_b2f9e764_1ea3_11e9_8f1c_1cc63ca220e2row2_col1\" class=\"data row2 col1\" >0.130695</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_b2f9e764_1ea3_11e9_8f1c_1cc63ca220e2level0_row3\" class=\"row_heading level0 row3\" rowspan=3>G2</th> \n",
       "        <th id=\"T_b2f9e764_1ea3_11e9_8f1c_1cc63ca220e2level1_row3\" class=\"row_heading level1 row3\" >1</th> \n",
       "        <td id=\"T_b2f9e764_1ea3_11e9_8f1c_1cc63ca220e2row3_col0\" class=\"data row3 col0\" >-1.43453</td> \n",
       "        <td id=\"T_b2f9e764_1ea3_11e9_8f1c_1cc63ca220e2row3_col1\" class=\"data row3 col1\" >0.447564</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_b2f9e764_1ea3_11e9_8f1c_1cc63ca220e2level1_row4\" class=\"row_heading level1 row4\" >2</th> \n",
       "        <td id=\"T_b2f9e764_1ea3_11e9_8f1c_1cc63ca220e2row4_col0\" class=\"data row4 col0\" >0.579852</td> \n",
       "        <td id=\"T_b2f9e764_1ea3_11e9_8f1c_1cc63ca220e2row4_col1\" class=\"data row4 col1\" >-1.01152</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_b2f9e764_1ea3_11e9_8f1c_1cc63ca220e2level1_row5\" class=\"row_heading level1 row5\" >3</th> \n",
       "        <td id=\"T_b2f9e764_1ea3_11e9_8f1c_1cc63ca220e2row5_col0\" class=\"data row5 col0\" >0.913472</td> \n",
       "        <td id=\"T_b2f9e764_1ea3_11e9_8f1c_1cc63ca220e2row5_col1\" class=\"data row5 col1\" >0.0342507</td> \n",
       "    </tr></tbody> \n",
       "</table> <style  type=\"text/css\" >\n",
       "</style>  \n",
       "<table id=\"T_b2fad61a_1ea3_11e9_8f1c_1cc63ca220e2\" style='display:inline'><caption>df.xs(1,le\n",
       "vel='Num')\n",
       "</caption> \n",
       "<thead>    <tr> \n",
       "        <th class=\"blank level0\" ></th> \n",
       "        <th class=\"col_heading level0 col0\" >A</th> \n",
       "        <th class=\"col_heading level0 col1\" >B</th> \n",
       "    </tr>    <tr> \n",
       "        <th class=\"index_name level0\" >Group</th> \n",
       "        <th class=\"blank\" ></th> \n",
       "        <th class=\"blank\" ></th> \n",
       "    </tr></thead> \n",
       "<tbody>    <tr> \n",
       "        <th id=\"T_b2fad61a_1ea3_11e9_8f1c_1cc63ca220e2level0_row0\" class=\"row_heading level0 row0\" >G1</th> \n",
       "        <td id=\"T_b2fad61a_1ea3_11e9_8f1c_1cc63ca220e2row0_col0\" class=\"data row0 col0\" >0.372589</td> \n",
       "        <td id=\"T_b2fad61a_1ea3_11e9_8f1c_1cc63ca220e2row0_col1\" class=\"data row0 col1\" >-0.873417</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_b2fad61a_1ea3_11e9_8f1c_1cc63ca220e2level0_row1\" class=\"row_heading level0 row1\" >G2</th> \n",
       "        <td id=\"T_b2fad61a_1ea3_11e9_8f1c_1cc63ca220e2row1_col0\" class=\"data row1 col0\" >-1.43453</td> \n",
       "        <td id=\"T_b2fad61a_1ea3_11e9_8f1c_1cc63ca220e2row1_col1\" class=\"data row1 col1\" >0.447564</td> \n",
       "    </tr></tbody> \n",
       "</table> "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('** Hierarchical Indexing: Basic**\\n')\n",
    "outside = ['G1','G1','G1','G2','G2','G2']\n",
    "inside = [1,2,3,1,2,3]\n",
    "hier_index = list(zip(outside, inside)) # hier_index comes from Index Hierarchy\n",
    "hier_index = pd.MultiIndex.from_tuples(hier_index)\n",
    "df = pd.DataFrame(np.random.randn(6,2),index=hier_index,columns=['A','B'])\n",
    "df.index.names = ['Group','Num']\n",
    "print(f\"type(df): {type(df)}\\n\") # <class 'pandas.core.frame.DataFrame'>\n",
    "display_htmlFcn(\"df\",\n",
    "        \"df.xs(1,level='Num')\") # we get all cells with Num = 1\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "** Hierarchical Indexing: Construction**\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print('** Hierarchical Indexing: Construction**\\n')\n",
    "if 0:\n",
    "    print('The BAD WAY (rest are good ways)')\n",
    "    index = [('C',2010),('C',2009), ('NY',2009),('NY',2010), ('T',2009),('T',2010)]\n",
    "    populations = np.random.randint(0,100, size=6)\n",
    "    df = pd.Series(populations, index=index)\n",
    "    print(f\"·index:       {index}\")\n",
    "    print(f\"·populations: {populations}\")\n",
    "    print(f\"·type(df): {type(df)}\\n\")\n",
    "    print(\"pd.DataFrame(index)\", \"pd.DataFrame(populations)\", \"pd.DataFrame(pd.Series(populations, index=index))\")\n",
    "    # display_htmlFcn('pd.DataFrame(index)') # ValueError: DataFrame constructor not properly called!\n",
    "    display_html(pd.DataFrame(index).style.set_table_attributes(\"style='display:inline'\").set_caption(\"pd.DataFrame (index)\")._repr_html_() + 10*'&nbsp' +\\\n",
    "                 pd.DataFrame(populations).style.set_table_attributes(\"style='display:inline'\").set_caption(\"pd.DataFrame (populations)\")._repr_html_() + 10*'&nbsp' +\\\n",
    "                 pd.DataFrame(populations, index=index).style.set_table_attributes(\"style='display:inline'\").set_caption(\"pd.DataFrame (populations, index=index)\")._repr_html_() \\\n",
    "                 , raw=True)\n",
    "    \n",
    "    \n",
    "    print('Series MultiIndex')\n",
    "    hier_index = pd.MultiIndex.from_tuples(index)\n",
    "    print(f\"·hier_index = pd.MultiIndex.from_tuples(index): \\n\\t{hier_index}\")\n",
    "    df = df.reindex(hier_index)\n",
    "    print(f\"·df = df.reindex(hier_index):\"); display_html(df)\n",
    "    \n",
    "    print('\\nAdd columns')\n",
    "    df = pd.DataFrame({'total': df, 'col2': np.random.randn(6)})\n",
    "    print(f\"·df = pd.DataFrame({{'total': df, 'col2': np.random.randn(6)}}):\"); display_html(df)\n",
    "    \n",
    "    print('\\nAtractive ufuncs')\n",
    "    df = (df['col2'] / df['total']).unstack()\n",
    "    print(f\"·df = (df['col2'] / df['total']).unstack():\");\n",
    "    display_html(df.style.highlight_max(axis=0))\n",
    "\n",
    "    \n",
    "    print('\\n',80*'-','\\nDataFrame MultiIndex');\n",
    "    outside = ['G1','G1','G1','G2','G2','G2'];\n",
    "    inside = [1,2,3,1,2,3];\n",
    "    hier_index = list(zip(outside, inside)); # hier_index comes from Index Hierarchy\n",
    "    print(f\"·hier_index = list(zip(['G1','G1','G1','G2','G2','G2'], [1,2,3,1,2,3])): \\n\\t{hier_index}\");\n",
    "    hier_index = pd.MultiIndex.from_tuples(hier_index);\n",
    "    print(f\"·hier_index = pd.MultiIndex.from_tuples(hier_index): \\n\\t{hier_index}\");\n",
    "    df = pd.DataFrame(np.random.randn(6,2),index=hier_index,columns=['A','B']);\n",
    "    # print(f\"·pd.DataFrame(np.random.randn(6,2),index=hier_index,columns=['A','B']): \\n{df}\")\n",
    "    print(f\"·type(df): {type(df)}\");\n",
    "    x = pd.DataFrame(np.random.randn(6,2),index=hier_index,columns=['A','B'])\n",
    "    print(\"pd.DataFrame(np.random.randn(6,2),index=hier_index,columns=['A','B']):\"); display_html(x)\n",
    "\n",
    "    \n",
    "    print('\\n',80*'-','\\nStraightforward DataFrame \\nThe work of creating the MultiIndex is done in the background.')\n",
    "    df = pd.DataFrame(np.random.rand(4, 2),\n",
    "                  index=[['a', 'a', 'b', 'b'], [1, 2, 1, 2]],\n",
    "                  columns=['data1', 'data2'])\n",
    "    print(f\"·df = pd.DataFrame(np.random.rand(4, 2), index=[['a', 'a', 'b', 'b'], [1, 2, 1, 2]], columns=['data1', 'data2']):\"); display_html(df)\n",
    "    x = {('C', 2000): 3,\n",
    "        ('C', 2010): 2,\n",
    "        ('T', 2000): 3,\n",
    "        ('T', 2010): 1,\n",
    "        ('N', 2000): 0,\n",
    "        ('N', 2010): 9}\n",
    "    df = pd.Series(x)\n",
    "    print(f\"·pd.Series({{('C', 2000): 3,('C', 2010): 2,('T', 2000): 3,('T', 2010): 1,('N', 2000): 0,('N', 2010): 9}}):\"); display_html(pd.DataFrame(df))  \n",
    "\n",
    "    \n",
    "    print('\\n',80*'-','\\nExplicit MultiIndex constructors')\n",
    "    hier_index = pd.MultiIndex.from_arrays([['a', 'a', 'b', 'b'], [1, 2, 1, 2]])\n",
    "    print(f\"·hier_index = pd.MultiIndex.from_arrays([['a', 'a', 'b', 'b'], [1, 2, 1, 2]]): \\n\\t{hier_index}\")\n",
    "    hier_index = pd.MultiIndex.from_tuples([('a', 1), ('a', 2), ('b', 1), ('b', 2)])\n",
    "    print(f\"·hier_index = pd.MultiIndex.from_tuples([('a', 1), ('a', 2), ('b', 1), ('b', 2)]): \\n\\t{hier_index}\")\n",
    "    hier_index = pd.MultiIndex.from_product([['a', 'b'], [1, 2]])\n",
    "    print(f\"·hier_index = pd.MultiIndex.from_product([['a', 'b'], [1, 2]]): \\n\\t{hier_index}\")\n",
    "    hier_index = pd.MultiIndex(levels=[['a', 'b'], [1, 2]], labels=[[0, 0, 1, 1], [0, 1, 0, 1]])\n",
    "    print(f\"·hier_index = pd.MultiIndex(levels=[['a', 'b'], [1, 2]], labels=[[0, 0, 1, 1], [0, 1, 0, 1]]): \\n\\t{hier_index}\")\n",
    "\n",
    "    \n",
    "    print('\\n',80*'-','\\nMultiIndex level names: to name the levels of the MultiIndex')\n",
    "    hier_index= pd.MultiIndex.from_product([['a', 'b'], [1, 2]])\n",
    "    hier_index.names = ['state', 'year']\n",
    "    df = pd.DataFrame(np.random.rand(4, 2),\n",
    "                  index= hier_index,\n",
    "                  columns=['data1', 'data2']) \n",
    "    print(f\"·hier_index.names = ['state', 'year']\")\n",
    "    print(f\"·df = pd.DataFrame(np.random.rand(4, 2), index=hier_index, columns=['data1', 'data2']):\")\n",
    "    display_html(df)  \n",
    "    print(f\"·df.index.names: {df.index.names}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "** MultiIndex access INDICES && COLUMNS **\n"
     ]
    }
   ],
   "source": [
    "print('** MultiIndex access INDICES && COLUMNS **')\n",
    "if 0:\n",
    "    hier_index = pd.MultiIndex.from_product([[2013, 2014], [1, 2]],                    names=['year', 'visit'])\n",
    "    print(f\"·hier_index = pd.MultiIndex.from_product([[2013, 2014], [1, 2]], names=['year', 'visit']): \\n{hier_index}\")\n",
    "    hier_cols  = pd.MultiIndex.from_product([['Bob', 'Guido', 'Sue'], ['HR', 'Temp']], names=['subject', 'type'])\n",
    "    print(\"·hier_cols\",\n",
    "          f\"= pd.MultiIndex.from_product([['Bob', 'Guido', 'Sue'], ['HR', 'Temp']], names=['subject', 'type']): \\n{hier_cols}\")\n",
    "    df = pd.DataFrame(np.random.randint(10, size=(4,6)), index=hier_index, columns=hier_cols)\n",
    "    print(\"pd.DataFrame(np.random.randint(10, size=(4,6)),index=hier_index,columns=hier_cols):\"); display_html(df) \n",
    "    \n",
    "    print('\\nMultiply indexed DataFrames\\n')\n",
    "    display_htmlFcn(\"df['Guido']\",\"pd.DataFrame(df['Guido','HR'])\",n=15)\n",
    "    \n",
    "    print('\\nloc, iloc and ix\\n')\n",
    "    idx = pd.IndexSlice\n",
    "    display_htmlFcn(\"df.iloc[::2, :4]\",\"pd.DataFrame(df.loc[:, ('Bob', 'HR')])\",\n",
    "                    \"df.loc[idx[:, 1], idx[:, 'HR']]\",space=5, n=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "** Rearranging MultiIndex**\n"
     ]
    }
   ],
   "source": [
    "print('** Rearranging MultiIndex**')\n",
    "if 0:\n",
    "    print('\\nstack && unstack\\n')\n",
    "    hier_index = pd.MultiIndex.from_product([['a', 'b'], [1, 2]])\n",
    "    print(f\"·hier_index = pd.MultiIndex.from_tuples(index): \\n\\t{hier_index}\")\n",
    "    df = pd.DataFrame(np.random.randint(10, size=(4,2)), index=hier_index);\n",
    "    display_htmlFcn(\"df\",\"df.unstack()\",\"df.unstack().stack()\",\"df.unstack().stack(level=0)\",\"df.unstack().stack(level=1)\")\n",
    "    \n",
    "    print('\\n',80*'-','\\nSorted and unsorted indices')\n",
    "    index = pd.MultiIndex.from_product([['a', 'c', 'b'], [1, 2]])\n",
    "    df = pd.Series(np.random.rand(6), index=index)\n",
    "    df.index.names = ['char', 'int']\n",
    "    print(f\"·df:\"); display_html(pd.DataFrame(df))\n",
    "    print('\\nIf we try to take a partial slice of this index, it will result in an error:')\n",
    "    try: print(f\"·df['a':'b']: \\n{df['a':'b']}\")\n",
    "    except Exception as e: print(f\"·Error on df['a':'b']:\\t {e}\")\n",
    "    df = df.sort_index()\n",
    "    print(f\"·df = df.sort_index():\"); display_htmlFcn(\"pd.DataFrame(df.sort_index())\")\n",
    "    print(\"No error in MultiIndex with sorted labels:\")\n",
    "    print(f\"·df['a':'b']: \"); display_html(pd.DataFrame(df['a':'b']))\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From \"Combining Datasets: Concat and Append\" ([link here](localhost:8888/notebooks/PythonDataScienceHandBook_Vanderplas/notebooks/00.00-03_Introduction-to-Pandas_Summary.ipynb#Combining-Datasets:-Concat-and-Append) or [link here](http://localhost:8888/notebooks/00.00-03_Introduction-to-Pandas_Summary.ipynb#Combining-Datasets:-Concat-and-Append)) section till end is NOT summarized yet.\n",
    "\n",
    "NOT summarized yet:\n",
    "- [Concat](https://jakevdp.github.io/PythonDataScienceHandbook/03.06-concat-and-append.html)\n",
    "- [Aggregation and Groupby](https://jakevdp.github.io/PythonDataScienceHandbook/03.08-aggregation-and-grouping.html)\n",
    "- [Pivot Tables](https://jakevdp.github.io/PythonDataScienceHandbook/03.09-pivot-tables.html)\n",
    "- [String Operations](https://jakevdp.github.io/PythonDataScienceHandbook/03.10-working-with-strings.html)\n",
    "- [Time Series](https://jakevdp.github.io/PythonDataScienceHandbook/03.11-working-with-time-series.html)\n",
    "- [eval() and query()](https://jakevdp.github.io/PythonDataScienceHandbook/03.12-performance-eval-and-query.html)"
   ]
  }
 ],
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