formations/algo/algofundoc/notebooks/Pandas.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The **import** keyword is used to import a library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6.123233995736766e-17\n"
     ]
    }
   ],
   "source": [
    "import math\n",
    "print(math.cos(math.pi / 2))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pandas\n",
    "======="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>col1</th>\n",
       "      <th>col2</th>\n",
       "      <th>col3</th>\n",
       "      <th>col4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>line1</th>\n",
       "      <td>-0.882125</td>\n",
       "      <td>2.176452</td>\n",
       "      <td>0.163955</td>\n",
       "      <td>-0.618232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>line2</th>\n",
       "      <td>-0.721538</td>\n",
       "      <td>0.035578</td>\n",
       "      <td>0.180072</td>\n",
       "      <td>1.015987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>line3</th>\n",
       "      <td>-1.162355</td>\n",
       "      <td>0.384632</td>\n",
       "      <td>-0.674092</td>\n",
       "      <td>0.162693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>line4</th>\n",
       "      <td>-1.399455</td>\n",
       "      <td>-0.698512</td>\n",
       "      <td>0.039420</td>\n",
       "      <td>0.898408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>line5</th>\n",
       "      <td>1.755342</td>\n",
       "      <td>-0.073242</td>\n",
       "      <td>-1.502503</td>\n",
       "      <td>-0.586194</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           col1      col2      col3      col4\n",
       "line1 -0.882125  2.176452  0.163955 -0.618232\n",
       "line2 -0.721538  0.035578  0.180072  1.015987\n",
       "line3 -1.162355  0.384632 -0.674092  0.162693\n",
       "line4 -1.399455 -0.698512  0.039420  0.898408\n",
       "line5  1.755342 -0.073242 -1.502503 -0.586194"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy\n",
    "import pandas\n",
    "rows = ['line1', 'line2', 'line3', 'line4', 'line5']\n",
    "cols = ['col1', 'col2', 'col3', 'col4']\n",
    "from IPython.display import display\n",
    "dataframe = pandas.DataFrame(numpy.random.randn(5,4), index=rows, columns=cols)\n",
    "display(dataframe)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "reorganise a **dataframe** from datas as a dictionary with tuples as keys\n",
    "-----------------------------------------------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Column 1</th>\n",
       "      <th>Column 2</th>\n",
       "      <th>Column 3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Cerise</td>\n",
       "      <td>Lanister</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Paul</td>\n",
       "      <td>Durand</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Pierre</td>\n",
       "      <td>Dupont</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>john</td>\n",
       "      <td>Snow</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Column 1  Column 2  Column 3\n",
       "0   Cerise  Lanister        14\n",
       "1     Paul    Durand        13\n",
       "2   Pierre    Dupont        16\n",
       "3     john      Snow        12"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dico = {('john', 'Snow') : 12, ('Paul', 'Durand') : 13, (\"Pierre\", \"Dupont\") : 16, (\"Cerise\", \"Lanister\") : 14}\n",
    "import pandas\n",
    "df = pandas.Series(dico).reset_index()\n",
    "df.columns = ['Column 1', 'Column 2', 'Column 3']\n",
    "from IPython.display import display\n",
    "display(df)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}