sample.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "\n",
    "pd.options.display.max_rows = 10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$\n",
    "y = \\sin{x}\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x10be92908>]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = pyplot.subplots(figsize=(10,6))\n",
    "\n",
    "x = np.linspace(-np.pi, np.pi, 100)\n",
    "y = np.sin(x)\n",
    "\n",
    "ax.plot(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal_length  sepal_width  petal_length  petal_width    species\n",
       "0             5.1          3.5           1.4          0.2     setosa\n",
       "1             4.9          3.0           1.4          0.2     setosa\n",
       "2             4.7          3.2           1.3          0.2     setosa\n",
       "3             4.6          3.1           1.5          0.2     setosa\n",
       "4             5.0          3.6           1.4          0.2     setosa\n",
       "..            ...          ...           ...          ...        ...\n",
       "145           6.7          3.0           5.2          2.3  virginica\n",
       "146           6.3          2.5           5.0          1.9  virginica\n",
       "147           6.5          3.0           5.2          2.0  virginica\n",
       "148           6.2          3.4           5.4          2.3  virginica\n",
       "149           5.9          3.0           5.1          1.8  virginica\n",
       "\n",
       "[150 rows x 5 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sns.load_dataset('iris')"
   ]
  },
  {
   "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": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}