Examples/ExcelTest3.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Markdown cell Title\n",
    "\n",
    "With **some** text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test\n"
     ]
    }
   ],
   "source": [
    "print(\"Test\")"
   ]
  },
  {
   "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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>1.764052</td>\n",
       "      <td>0.400157</td>\n",
       "      <td>0.978738</td>\n",
       "      <td>2.240893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>1.867558</td>\n",
       "      <td>-0.977278</td>\n",
       "      <td>0.950088</td>\n",
       "      <td>-0.151357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>-0.103219</td>\n",
       "      <td>0.410599</td>\n",
       "      <td>0.144044</td>\n",
       "      <td>1.454274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>0.761038</td>\n",
       "      <td>0.121675</td>\n",
       "      <td>0.443863</td>\n",
       "      <td>0.333674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td>1.494079</td>\n",
       "      <td>-0.205158</td>\n",
       "      <td>0.313068</td>\n",
       "      <td>-0.854096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-06</th>\n",
       "      <td>-2.552990</td>\n",
       "      <td>0.653619</td>\n",
       "      <td>0.864436</td>\n",
       "      <td>-0.742165</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2013-01-01  1.764052  0.400157  0.978738  2.240893\n",
       "2013-01-02  1.867558 -0.977278  0.950088 -0.151357\n",
       "2013-01-03 -0.103219  0.410599  0.144044  1.454274\n",
       "2013-01-04  0.761038  0.121675  0.443863  0.333674\n",
       "2013-01-05  1.494079 -0.205158  0.313068 -0.854096\n",
       "2013-01-06 -2.552990  0.653619  0.864436 -0.742165"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "np.random.seed(0)\n",
    "\n",
    "dates = pd.date_range('20130101',periods=6)\n",
    "\n",
    "df = pd.DataFrame(np.random.randn(6,4),index=dates,columns=list('ABCD'))\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2013-01-01', '2013-01-02', '2013-01-03', '2013-01-04',\n",
       "               '2013-01-05', '2013-01-06'],\n",
       "              dtype='datetime64[ns]', freq='D')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "men_means, men_std = (20, 35, 30, 35, 27), (2, 3, 4, 1, 2)\n",
    "women_means, women_std = (25, 32, 34, 20, 25), (3, 5, 2, 3, 3)\n",
    "\n",
    "ind = np.arange(len(men_means))  # the x locations for the groups\n",
    "width = 0.35  # the width of the bars\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "rects1 = ax.bar(ind - width/2, men_means, width, yerr=men_std,\n",
    "                label='Men')\n",
    "rects2 = ax.bar(ind + width/2, women_means, width, yerr=women_std,\n",
    "                label='Women')\n",
    "\n",
    "# Add some text for labels, title and custom x-axis tick labels, etc.\n",
    "ax.set_ylabel('Scores')\n",
    "ax.set_title('Scores by group and gender')\n",
    "ax.set_xticks(ind)\n",
    "ax.set_xticklabels(('G1', 'G2', 'G3', 'G4', 'G5'))\n",
    "ax.legend()\n",
    "\n",
    "\n",
    "def autolabel(rects, xpos='center'):\n",
    "    \"\"\"\n",
    "    Attach a text label above each bar in *rects*, displaying its height.\n",
    "\n",
    "    *xpos* indicates which side to place the text w.r.t. the center of\n",
    "    the bar. It can be one of the following {'center', 'right', 'left'}.\n",
    "    \"\"\"\n",
    "\n",
    "    ha = {'center': 'center', 'right': 'left', 'left': 'right'}\n",
    "    offset = {'center': 0, 'right': 1, 'left': -1}\n",
    "\n",
    "    for rect in rects:\n",
    "        height = rect.get_height()\n",
    "        ax.annotate('{}'.format(height),\n",
    "                    xy=(rect.get_x() + rect.get_width() / 2, height),\n",
    "                    xytext=(offset[xpos]*3, 3),  # use 3 points offset\n",
    "                    textcoords=\"offset points\",  # in both directions\n",
    "                    ha=ha[xpos], va='bottom')\n",
    "\n",
    "\n",
    "autolabel(rects1, \"left\")\n",
    "autolabel(rects2, \"right\")\n",
    "\n",
    "fig.tight_layout()\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is the end of the notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}