{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Week 7 Day 1: Structured data (AKA: Pandas DataFrames)\n",
    "\n",
    "## Objectives:\n",
    "\n",
    "* Learn to create and read in DataFrames\n",
    "* Learn to use Series\n",
    "* Learn some basic manipulation skills\n",
    "* Learn where to find more information about Pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's start with the following table of data:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      time          x          y\n",
      "\n",
      "       0.0   -0.01255   -0.04794\n",
      "    0.2222     0.1117     0.4421\n",
      "    0.4444     0.1565     0.7246\n",
      "    0.6667     0.2099     0.8601\n",
      "    0.8889     0.2323     0.9558\n",
      "     1.111     0.2989      0.956\n",
      "     1.333     0.3558     0.8693\n",
      "     1.556     0.5033     0.6938\n",
      "     1.778     0.5434     0.3883\n",
      "       2.0     0.6208   -0.02088\n"
     ]
    }
   ],
   "source": [
    "state = np.random.RandomState(42)\n",
    "time = np.linspace(0, 2, 10)\n",
    "xs = time * 0.3 + (state.rand(10) - 0.5) * 0.1\n",
    "ys = -(time ** 2) + 2 * time + (state.rand(10) - 0.5) * 0.1\n",
    "print(\"      time          x          y\\n\")\n",
    "for t, x, y in zip(time, xs, ys):\n",
    "    print(f\"{t:10.4} {x:10.4} {y:10.4}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We would like to view and work with this as a coherent single entity. If we chose to use a matrix, we'd have some issues:\n",
    "\n",
    "* Our axes are inherently different - operations along columns make sense, rows... Not so much.\n",
    "* One of the axis has labels (time, x, and y) that we'd lose, making our code harder to read\n",
    "* What happens if we have different data types?\n",
    "* Normal matrix operations (like multiplication) don't really make sense"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's just look at what a Pandas DataFrame would look like, then we'll talk a bit more about them. Note that there are a **ton** of ways to make a DataFrame. And we have some choices we will ignore for now."
   ]
  },
  {
   "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>time</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.012546</td>\n",
       "      <td>-0.047942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.111738</td>\n",
       "      <td>0.442053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.444444</td>\n",
       "      <td>0.156533</td>\n",
       "      <td>0.724602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.209866</td>\n",
       "      <td>0.860123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.888889</td>\n",
       "      <td>0.232269</td>\n",
       "      <td>0.955837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.111111</td>\n",
       "      <td>0.298933</td>\n",
       "      <td>0.955995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.333333</td>\n",
       "      <td>0.355808</td>\n",
       "      <td>0.869313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.555556</td>\n",
       "      <td>0.503284</td>\n",
       "      <td>0.693834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.777778</td>\n",
       "      <td>0.543445</td>\n",
       "      <td>0.388256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.620807</td>\n",
       "      <td>-0.020877</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       time         x         y\n",
       "0  0.000000 -0.012546 -0.047942\n",
       "1  0.222222  0.111738  0.442053\n",
       "2  0.444444  0.156533  0.724602\n",
       "3  0.666667  0.209866  0.860123\n",
       "4  0.888889  0.232269  0.955837\n",
       "5  1.111111  0.298933  0.955995\n",
       "6  1.333333  0.355808  0.869313\n",
       "7  1.555556  0.503284  0.693834\n",
       "8  1.777778  0.543445  0.388256\n",
       "9  2.000000  0.620807 -0.020877"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({\"time\": time, \"x\": xs, \"y\": ys})\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our time would make a better index than the default 0,1,2,...,9, so let's try that:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>x</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.000000</th>\n",
       "      <td>-0.012546</td>\n",
       "      <td>-0.047942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.222222</th>\n",
       "      <td>0.111738</td>\n",
       "      <td>0.442053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.444444</th>\n",
       "      <td>0.156533</td>\n",
       "      <td>0.724602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.666667</th>\n",
       "      <td>0.209866</td>\n",
       "      <td>0.860123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.888889</th>\n",
       "      <td>0.232269</td>\n",
       "      <td>0.955837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.111111</th>\n",
       "      <td>0.298933</td>\n",
       "      <td>0.955995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.333333</th>\n",
       "      <td>0.355808</td>\n",
       "      <td>0.869313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.555556</th>\n",
       "      <td>0.503284</td>\n",
       "      <td>0.693834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.777778</th>\n",
       "      <td>0.543445</td>\n",
       "      <td>0.388256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.000000</th>\n",
       "      <td>0.620807</td>\n",
       "      <td>-0.020877</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 x         y\n",
       "time                        \n",
       "0.000000 -0.012546 -0.047942\n",
       "0.222222  0.111738  0.442053\n",
       "0.444444  0.156533  0.724602\n",
       "0.666667  0.209866  0.860123\n",
       "0.888889  0.232269  0.955837\n",
       "1.111111  0.298933  0.955995\n",
       "1.333333  0.355808  0.869313\n",
       "1.555556  0.503284  0.693834\n",
       "1.777778  0.543445  0.388256\n",
       "2.000000  0.620807 -0.020877"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.set_index(\"time\", inplace=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df.index = pd.to_timedelta(df.index, unit='s')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fc3f8983910>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "df.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fc3f5df91d0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAEHCAYAAACjh0HiAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAS20lEQVR4nO3db4wcd33H8ff37MvZ4NBc7aOkPrtxG1Mw1LHgCJT0TygtjVNklzpSk6BGpEhWKKbtAxTnQZtWolVVS5UoSoLlplGUB62F6kBSasiDIojUkNZn5ByYEGRMiS+minM4EIN9uct+++DWZL1e+9bOzu1ufu+XdOJm5pfbj4bzffY3MzsTmYkkqVwD3Q4gSeoui0CSCmcRSFLhLAJJKpxFIEmFswgkqXCLq/rBEXEf8H7g2cx8a4vtAfwjcD3wE+BDmfm1+X7uihUr8oorruhwWkl6ddu/f/9zmTnSaltlRQDcD9wFPHCO7RuBtfWvdwKfrv/veV1xxRWMj493KKIklSEivneubZUdGsrMR4EfnGfIZuCBnPM4cFlEXF5VHklSa908R7ASONKwPFlfJ0laQN0sgmixruX9LiJia0SMR8T4sWPHKo4lSWXpZhFMAqsalkeBo60GZuauzBzLzLGRkZbnOiRJF6mbRfAwcEvMeRfww8z8fhfzSFKRqrx89F+Ba4EVETEJ/BUwCJCZO4G9zF06eoi5y0dvrSqLJOncKiuCzLxpnu0JfLSq15cktcdPFqvvTZ2Y5okjzzN1Yrqo15Y6pcoPlEmVe+jAM2zfM8HgwAAztRo7tqxn04aFuQq5m68tdZIzAvWtqRPTbN8zwamZGi9Mz3JqpsbteyYW5N15N19b6jSLQH1r8vhJBgfO/BUeHBhg8vjJV/VrS51mEahvjQ4vZaZWO2PdTK3G6PDSV/VrS51mEahvLV82xI4t61kyOMClQ4tZMjjAji3rWb5s6FX92lKnxdxVnP1jbGwsvfuoGk2dmGby+ElGh5cu+B/ibr62dCEiYn9mjrXa5lVD6nvLlw117Y9wN19b6hQPDUlS4SwCSSqcRSBJhbMIJKlwFoEkFc4ikKTCWQSSVDiLQJIKZxFIUuEsAqlP+BAcVcVbTEh9wIfgqErOCNSTfPf7Mh+Co6o5I1DP8d3vmU4/BOcULz//4PRDcLzhnTrBGYF6iu9+z+ZDcFQ1i0A9xUdAns2H4KhqHhpST/Hdb2ubNqzkmitX+BAcVcIZgXqK737PbfmyIa5adZn7Qh3njEA9x3e/0sKyCNSTfASktHA8NCRJhbMIJKlwFoEkFa7SIoiI6yLiqYg4FBF3tNj+MxHx7xHxREQcjIhbq8wjSTpbZUUQEYuAu4GNwDrgpohY1zTso8A3M/Mq4FrgHyLikqoySZLOVuWM4GrgUGYezswXgd3A5qYxCVwaEQEsA34AzFaYSZLUpMoiWAkcaVierK9rdBfwZuAo8HXgzzKz1jSGiNgaEeMRMX7s2LGq8kpSkaosgmixLpuWfxc4APw8sAG4KyJed9Z/lLkrM8cyc2xkZKTzSSWpYFUWwSSwqmF5lLl3/o1uBR7MOYeA7wJvqjCTJKlJlUWwD1gbEWvqJ4BvBB5uGvM08F6AiPg54JeBwxVmkiQ1qewWE5k5GxHbgEeARcB9mXkwIm6rb98JfAK4PyK+ztyhpO2Z+VxVmSRJZ6v0XkOZuRfY27RuZ8P3R4H3VZlBknR+frJYkgpnEUhS4SwCSSqcRSBJhbMIJKlwFoEkFc4i0AWbOjHNE0eeZ+rEdLejSOoAn1msC/LQgWfYvmeCwYEBZmo1dmxZz6YNzfcSlNRPnBGobVMnptm+Z4JTMzVemJ7l1EyN2/dMODOQ+pxFoLZNHj/J4MCZvzKDAwNMHj/ZpUSSOsEiUNtGh5cyUzvzcREztRqjw0u7lEhSJ1gEatvyZUPs2LKeJYMDXDq0mCWDA+zYsp7ly4a6HU3SK+DJYl2QTRtWcs2VK5g8fpLR4aWWgLpq6sS0v4sdYBHogi1fNuQ/OnWdV7B1joeGJPUdr2DrLItAUt/xCrbOsggk9R2vYOssi0BS3/EKts7yZLGkvuQVbJ1jEUjqW17B1hkeGpKkwlkEklQ4i0CSCmcRSFLhLAJJKpxFIEmFswgkqXAWgSQVziKQpMJVWgQRcV1EPBURhyLijnOMuTYiDkTEwYj4SpV5JElnq+wWExGxCLgb+B1gEtgXEQ9n5jcbxlwG3ANcl5lPR8Trq8ojSWqtyhnB1cChzDycmS8Cu4HNTWNuBh7MzKcBMvPZCvNIklqosghWAkcalifr6xq9ERiOiC9HxP6IuKXCPJKkFqq8+2i0WJctXv/twHuBpcBXI+LxzPz2GT8oYiuwFWD16tUVRJWkclU5I5gEVjUsjwJHW4z5Ymb+ODOfAx4Frmr+QZm5KzPHMnNsZGSkssCSVKIqi2AfsDYi1kTEJcCNwMNNYx4Cfj0iFkfEa4B3Ak9WmEmS1KSyQ0OZORsR24BHgEXAfZl5MCJuq2/fmZlPRsQXgQmgBtybmd+oKpMk6WyR2XzYvreNjY3l+Ph4t2NIUl+JiP2ZOdZqm58slqTCWQSSVDiLQJIKZxFIUuEsAkkqnEUgSYWzCCSpcBaBJBXOIpCkwlkEklQ4i0CSCmcRSFLhLAJJKpxFIEmFswgkqXAWgSQVziKQpMJZBJJUOItAkgpnEUhS4SwCSSrcvEUQEdsiYnghwkiSFl47M4I3APsi4jMRcV1ERNWh1DlTJ6Z54sjzTJ2Y7nYUST1q8XwDMvMvIuIvgfcBtwJ3RcRngH/OzO9UHVAX76EDz7B9zwSDAwPM1Grs2LKeTRtWdjuWpB7T1jmCzEzg/+pfs8Aw8G8RsaPCbHoFpk5Ms33PBKdmarwwPcupmRq375lwZiDpLO2cI/jTiNgP7AD+C/iVzPwI8HZgS8X5dJEmj59kcODM/3sHBwaYPH6yS4kk9ap5Dw0BK4A/yMzvNa7MzFpEvL+aWHqlRoeXMlOrnbFuplZjdHhplxJJ6lXzzggy887mEmjY9mTnI6kTli8bYseW9SwZHODSocUsGRxgx5b1LF821O1oknpMOzMC9alNG1ZyzZUrmDx+ktHhpZaApJYsgle55cuGLABJ51XpJ4vrnzt4KiIORcQd5xn3joh4KSJuqDKPJOlslRVBRCwC7gY2AuuAmyJi3TnG/T3wSFVZJEnnVuWM4GrgUGYezswXgd3A5hbjPgbsAZ6tMIsk6RyqLIKVwJGG5cn6up+KiJXAB4CdFeaQpHmVfDuWKk8Wt7onUTYtfxLYnpkvne8WRhGxFdgKsHr16o4FlCTwdixVzggmgVUNy6PA0aYxY8DuiPhf4Abgnoj4/eYflJm7MnMsM8dGRkaqyiupQN6OpdoZwT5gbUSsAZ4BbgRubhyQmWtOfx8R9wOfz8zPVZhJks5w+nYsp3j5k/inb8dSyqXXlRVBZs5GxDbmrgZaBNyXmQcj4rb6ds8LSOo6b8dS8QfKMnMvsLdpXcsCyMwPVZlFklo5fTuW25vOEZQyGwA/WSxJxd+OxSKQJMq+HYsPr5ekwlkEklQ4i0CSCmcRSFLhLAJJKpxFIEmFswgkqXAWgSQVziKQpMJZBJJUOItAkgpnEUhS4SwCSSqcRSBJhbMIJKlwFoEkFc4ikKTCWQSSVDiLQJIKZxFIUuEsAkkqnEUgSYWzCCSpcBaBJBXOIpCkwlkEklQ4i0CSCmcRSFLhKi2CiLguIp6KiEMRcUeL7R+MiIn612MRcVWVeSRJZ6usCCJiEXA3sBFYB9wUEeuahn0X+M3MXA98AthVVR5JUmtVzgiuBg5l5uHMfBHYDWxuHJCZj2Xm8fri48BohXkkSS1UWQQrgSMNy5P1defyYeALrTZExNaIGI+I8WPHjnUwoiSpyiKIFuuy5cCI9zBXBNtbbc/MXZk5lpljIyMjHYwoSVpc4c+eBFY1LI8CR5sHRcR64F5gY2ZOVZhHktRClTOCfcDaiFgTEZcANwIPNw6IiNXAg8AfZea3K8wiSTqHymYEmTkbEduAR4BFwH2ZeTAibqtv3wncCSwH7okIgNnMHKsqkyTpbJHZ8rB9zxobG8vx8fFux5CkvhIR+8/1RttPFktS4SwCSSqcRSBJhbMIJKlwFoEkFc4ikKTCWQSSVDiLQJIKZxFIUuEsAkkqnEUgSYWzCCSpcBaBJBXOIpCkwlkEklQ4i0CSCmcRSFLhLAJJKpxFIEmFswgkqXAWgSQVziKQpMJZBJJUOItAkgpnEUhS4SwCSSqcRSBJhbMIJKlwFoEkFa7SIoiI6yLiqYg4FBF3tNgeEfGp+vaJiHhblXkkSWerrAgiYhFwN7ARWAfcFBHrmoZtBNbWv7YCn64qjySptSpnBFcDhzLzcGa+COwGNjeN2Qw8kHMeBy6LiMsrzCRJalJlEawEjjQsT9bXXegYSVKFqiyCaLEuL2IMEbE1IsYjYvzYsWMdCSdJmlNlEUwCqxqWR4GjFzGGzNyVmWOZOTYyMtLxoJLUi6ZOTPPEkeeZOjFd6essrvBn7wPWRsQa4BngRuDmpjEPA9siYjfwTuCHmfn9CjNJUl946MAzbN8zweDAADO1Gju2rGfThmqOnFc2I8jMWWAb8AjwJPCZzDwYEbdFxG31YXuBw8Ah4J+AP6kqjyT1i6kT02zfM8GpmRovTM9yaqbG7XsmKpsZVDkjIDP3MvfHvnHdzobvE/holRkkqd9MHj/J4MAAp6j9dN3gwACTx0+yfNlQx1/PTxZLUo8ZHV7KTK12xrqZWo3R4aWVvF6xRbBQJ2Ek6UItXzbEji3rWTI4wKVDi1kyOMCOLesrmQ1AxYeGetVCnoSRpIuxacNKrrlyBZPHTzI6vLSyEoACi6DxJMzp42+375ngmitXVLqjJelCLV82tCB/l4o7NHT6JEyj0ydhJKlExRXBQp+EkaReV1wRLPRJGEnqdcWdI4CFPQkjSb2uyCKAhTsJI0m9rrhDQ5KkM1kEklQ4i0CSCmcRSFLhLAJJKpxFIEmFswgkqXAx92yY/hERx4DvVfgSK4DnKvz5Vevn/GbvDrN3x0Jn/4XMbPnQ974rgqpFxHhmjnU7x8Xq5/xm7w6zd0cvZffQkCQVziKQpMJZBGfb1e0Ar1A/5zd7d5i9O3omu+cIJKlwzggkqXDFFkFEXBcRT0XEoYi4o8X2iIhP1bdPRMTbupGzlTayvykivhoR0xHx8W5kPJc2sn+wvr8nIuKxiLiqGzlbaSP75nruAxExHhG/1o2crcyXvWHcOyLipYi4YSHznU8b+/3aiPhhfb8fiIg7u5GzlXb2ez3/gYg4GBFfWeiMAGRmcV/AIuA7wC8ClwBPAOuaxlwPfAEI4F3Af3c79wVkfz3wDuBvgY93O/MFZn83MFz/fmOf7fdlvHy4dT3wrW7nbjd7w7gvAXuBG7qd+wL2+7XA57ud9SKzXwZ8E1hdX359N7KWOiO4GjiUmYcz80VgN7C5acxm4IGc8zhwWURcvtBBW5g3e2Y+m5n7gJluBDyPdrI/lpnH64uPA6MLnPFc2sl+Iuv/moHXAr1yAq6d33eAjwF7gGcXMtw82s3ei9rJfjPwYGY+DXP/dhc4I1DuoaGVwJGG5cn6ugsd0w29mqsdF5r9w8zNynpBW9kj4gMR8S3gP4A/XqBs85k3e0SsBD4A7FzAXO1o93fmVyPiiYj4QkS8ZWGizaud7G8EhiPiyxGxPyJuWbB0DUp9VGW0WNf87q2dMd3Qq7na0Xb2iHgPc0XQK8fZ28qemZ8FPhsRvwF8AvjtqoO1oZ3snwS2Z+ZLEa2Gd0072b/G3O0TTkTE9cDngLWVJ5tfO9kXA28H3gssBb4aEY9n5rerDtccokSTwKqG5VHg6EWM6YZezdWOtrJHxHrgXmBjZk4tULb5XNB+z8xHI+KXImJFZnb7XjjtZB8DdtdLYAVwfUTMZubnFibiOc2bPTN/1PD93oi4p4/2+yTwXGb+GPhxRDwKXAUsaBF0/YRKN76YK8DDwBpePonzlqYxv8eZJ4v/p9u5283eMPav6a2Txe3s99XAIeDd3c57Edmv5OWTxW8Dnjm93OvZm8bfT++cLG5nv7+hYb9fDTzdL/sdeDPwn/WxrwG+Abx1obMWOSPIzNmI2AY8wtyZ/fsy82BE3FbfvpO5KyeuZ+6P0k+AW7uVt1E72SPiDcA48DqgFhF/ztzVCj865w9eAG3u9zuB5cA99Xens9kDN+ZqM/sW4JaImAFOAn+Y9X/t3dRm9p7UZvYbgI9ExCxz+/3GftnvmflkRHwRmABqwL2Z+Y2FzuoniyWpcKVeNSRJqrMIJKlwFoEkFc4ikKTCWQSSVDiLQJIKZxFIUuEsAukVqt/DfyIilkTEa+v3lX9rt3NJ7fIDZVIHRMTfAEuYu3HYZGb+XZcjSW2zCKQOiIhLgH3AKebuk/RSlyNJbfPQkNQZP8vcE8ouZW5mIPUNZwRSB0TEw8w9gWoNcHlmbutyJKltRd59VOqk+lOlZjPzXyJiEfBYRPxWZn6p29mkdjgjkKTCeY5AkgpnEUhS4SwCSSqcRSBJhbMIJKlwFoEkFc4ikKTCWQSSVLj/B0zYGKSH9CnOAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# df.plot('x', 'y', kind='scatter');\n",
    "df.plot.scatter(\"x\", \"y\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are two ways to access columns:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "time\n",
       "0.000000   -0.012546\n",
       "0.222222    0.111738\n",
       "0.444444    0.156533\n",
       "0.666667    0.209866\n",
       "0.888889    0.232269\n",
       "1.111111    0.298933\n",
       "1.333333    0.355808\n",
       "1.555556    0.503284\n",
       "1.777778    0.543445\n",
       "2.000000    0.620807\n",
       "Name: x, dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"x\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "time\n",
       "0.000000   -0.012546\n",
       "0.222222    0.111738\n",
       "0.444444    0.156533\n",
       "0.666667    0.209866\n",
       "0.888889    0.232269\n",
       "1.111111    0.298933\n",
       "1.333333    0.355808\n",
       "1.555556    0.503284\n",
       "1.777778    0.543445\n",
       "2.000000    0.620807\n",
       "Name: x, dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A column is a \"Series\": That's like a 1D array but with an index and possibly a name attached. The Series align on index instead of location - if you add two Series, matching indexed value will add."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `['x']` syntax is more general, but the `.x` syntax is shorter, and much nicer in a notebook. It doesn't work for setting brand new columns, or if the name of a column is not valid in Python or would overwrite an existing property or method.\n",
    "\n",
    "DataFrames and Series follow the array protocol, so numpy operation work on them too:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>x</th>\n",
       "      <th>y</th>\n",
       "      <th>r</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.000000</th>\n",
       "      <td>-0.012546</td>\n",
       "      <td>-0.047942</td>\n",
       "      <td>0.049556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.222222</th>\n",
       "      <td>0.111738</td>\n",
       "      <td>0.442053</td>\n",
       "      <td>0.455956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.444444</th>\n",
       "      <td>0.156533</td>\n",
       "      <td>0.724602</td>\n",
       "      <td>0.741317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.666667</th>\n",
       "      <td>0.209866</td>\n",
       "      <td>0.860123</td>\n",
       "      <td>0.885356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.888889</th>\n",
       "      <td>0.232269</td>\n",
       "      <td>0.955837</td>\n",
       "      <td>0.983653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.111111</th>\n",
       "      <td>0.298933</td>\n",
       "      <td>0.955995</td>\n",
       "      <td>1.001642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.333333</th>\n",
       "      <td>0.355808</td>\n",
       "      <td>0.869313</td>\n",
       "      <td>0.939311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.555556</th>\n",
       "      <td>0.503284</td>\n",
       "      <td>0.693834</td>\n",
       "      <td>0.857147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.777778</th>\n",
       "      <td>0.543445</td>\n",
       "      <td>0.388256</td>\n",
       "      <td>0.667889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.000000</th>\n",
       "      <td>0.620807</td>\n",
       "      <td>-0.020877</td>\n",
       "      <td>0.621158</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 x         y         r\n",
       "time                                  \n",
       "0.000000 -0.012546 -0.047942  0.049556\n",
       "0.222222  0.111738  0.442053  0.455956\n",
       "0.444444  0.156533  0.724602  0.741317\n",
       "0.666667  0.209866  0.860123  0.885356\n",
       "0.888889  0.232269  0.955837  0.983653\n",
       "1.111111  0.298933  0.955995  1.001642\n",
       "1.333333  0.355808  0.869313  0.939311\n",
       "1.555556  0.503284  0.693834  0.857147\n",
       "1.777778  0.543445  0.388256  0.667889\n",
       "2.000000  0.620807 -0.020877  0.621158"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"r\"] = np.sqrt(df.x ** 2 + df.y ** 2)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "DataFrames are designed to make it easy to add and operate on columns; you should not in general be adding new rows (this should tell you what the internal memory layout must be like)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that you can use `.apply` to apply a function to a DataFrame with a bit more control, or `.applymap` to apply a function element-wise to a DataFrame or Series (but the function is a Python function, so the loop must happen in Python so it is slower than `.apply`)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pandas features\n",
    "\n",
    "#### Pandas design:\n",
    "* Make everything as Pythonic as possible. Even if that means there are many ways of doing things.\n",
    "* Design around everyday usage rather than theory\n",
    "\n",
    "#### Features:\n",
    "\n",
    "* **Index** types: Several custom arrays with extra features for types of indexes\n",
    "    * Including hierarchical indexes, which allow multidimensional-like data to be used\n",
    "* **Series**: A 1D array with an attached index\n",
    "    * New types: Powerful datetime and timedelta features, including special calender support, periodic times, etc.\n",
    "    * Categorical support (a bit more powerful than sets)\n",
    "* **DataFrame**: a table of data with indexes and headers\n",
    "* Great input/output support for lots of data formats (`.csv`, Excel, many more)\n",
    "* Great output display, notebook support\n",
    "* Amazing data manipulation\n",
    "* Like Numpy, can be a standard for other statistical packages"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Constructing and writing a dataframe\n",
    "\n",
    "* See the DataFrame's help for a massive list of options.\n",
    "* Reading in a DataFrame is done with the `pd.read_*` functions.\n",
    "* Writing a DataFrame is done with the `df.to_*` methods.\n",
    "\n",
    "See **Table 5-1: Possible data inputs to DataFrame constructor** in *Python for Data Analysis, 2nd edition*, by Wes McKinney. Also Chapter 6 for reading/writing DataFrames."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Indexing\n",
    "\n",
    "`df[x]` is a special case - it behaves differently depending on the arguments - columns normally but some cases are rows (such as when using a boolean Series). When doing something specific, use the specialized accessors:\n",
    "\n",
    "* `df[column]`: Select a column\n",
    "* `df.loc[row, column]`: Indexing by names\n",
    "* `df.iloc[row, column]`: Indexing by number\n",
    "* `at` and `iat` are available for single values.\n",
    "\n",
    "Note that using a `list` is different than a `tuple` in an indexing expression in Pandas.\n",
    "\n",
    "#### See Table 5.4 for ways to index a DataFrame\n",
    "\n",
    "Indexing options with DataFrame in *Python for Data Analysis, 2nd edition*, by Wes McKinney."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Iris dataset\n",
    "Let's look at one of the seaborn datasets (seaborn is a plotting wrapper on MatPlotLib that helps with statistical visualization - but we are just using it's handy example datasets which load in Pandas. Internet required.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = sns.load_dataset(\"iris\")\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A few things of note:\n",
    "\n",
    "* No column makes sense as an index here - we'll just leave the numerical index.\n",
    "* We have a categorical column - but it didn't read in as a categorical datatype! That's easy to fix:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.species = df.species.astype(\"category\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can quickly get some information about the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.843333</td>\n",
       "      <td>3.057333</td>\n",
       "      <td>3.758000</td>\n",
       "      <td>1.199333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.828066</td>\n",
       "      <td>0.435866</td>\n",
       "      <td>1.765298</td>\n",
       "      <td>0.762238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.300000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.100000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>0.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.800000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.350000</td>\n",
       "      <td>1.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.400000</td>\n",
       "      <td>3.300000</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>1.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.900000</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>6.900000</td>\n",
       "      <td>2.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       sepal_length  sepal_width  petal_length  petal_width\n",
       "count    150.000000   150.000000    150.000000   150.000000\n",
       "mean       5.843333     3.057333      3.758000     1.199333\n",
       "std        0.828066     0.435866      1.765298     0.762238\n",
       "min        4.300000     2.000000      1.000000     0.100000\n",
       "25%        5.100000     2.800000      1.600000     0.300000\n",
       "50%        5.800000     3.000000      4.350000     1.300000\n",
       "75%        6.400000     3.300000      5.100000     1.800000\n",
       "max        7.900000     4.400000      6.900000     2.500000"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 150 entries, 0 to 149\n",
      "Data columns (total 5 columns):\n",
      " #   Column        Non-Null Count  Dtype   \n",
      "---  ------        --------------  -----   \n",
      " 0   sepal_length  150 non-null    float64 \n",
      " 1   sepal_width   150 non-null    float64 \n",
      " 2   petal_length  150 non-null    float64 \n",
      " 3   petal_width   150 non-null    float64 \n",
      " 4   species       150 non-null    category\n",
      "dtypes: category(1), float64(4)\n",
      "memory usage: 5.1 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sepal_length     float64\n",
       "sepal_width      float64\n",
       "petal_length     float64\n",
       "petal_width      float64\n",
       "species         category\n",
       "dtype: object"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Accessors\n",
    "\n",
    "You can use an \"accessor\" (Pandas terminology) to perform operations on series as a specific type:\n",
    "* `.str`: string methods that act on the series\n",
    "* `.cat`: Operations on categories\n",
    "* `.dt`: Datetime operations\n",
    "* `.plot`: Serves two purposes, either acts like a plot function or gives you access to other types of plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    Setosa\n",
       "1    Setosa\n",
       "2    Setosa\n",
       "3    Setosa\n",
       "4    Setosa\n",
       "Name: species, dtype: object"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.species.str.capitalize().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['setosa', 'versicolor', 'virginica'], dtype='object')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.species.cat.categories"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use a boolean Series to select rows from a DataFrame (or another Series):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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",
       "  </tbody>\n",
       "</table>\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"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.species == \"setosa\"].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's select just two rows of our table: (Note: this *must* be a list, not a tuple)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal_length  sepal_width\n",
       "0           5.1          3.5\n",
       "1           4.9          3.0\n",
       "2           4.7          3.2\n",
       "3           4.6          3.1\n",
       "4           5.0          3.6"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[[\"sepal_length\", \"sepal_width\"]].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can combine what we've learned:\n",
    "\n",
    "(notice the automatic x and y axis labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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": [
    "fig, ax = plt.subplots()\n",
    "for i, category in enumerate(df.species.cat.categories):\n",
    "    df[df.species == category].plot.scatter(\n",
    "        \"sepal_length\", \"sepal_width\", label=category, c=f\"C{i}\", ax=ax\n",
    "    )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "MatPlotLib now has better support for Pandas, which makes this a bit easier:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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": [
    "fig, ax = plt.subplots()\n",
    "for category in df.species.cat.categories:\n",
    "    ax.scatter(\n",
    "        \"sepal_length\", \"sepal_width\", data=df[df.species == category], label=category\n",
    "    )\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We could make this even nicer with a groupby, which returns a group you can iterate over to get the name and dataframe:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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": [
    "fig, ax = plt.subplots()\n",
    "for name, group in df.groupby(\"species\"):\n",
    "    ax.scatter(\"sepal_length\", \"sepal_width\", data=group, label=name)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pandas supports lots more, like database style merges and joins, etc.\n",
    "\n",
    "In general, the best thing to do with Pandas is search and look around to see if the functionality you want is there. Avoid loops and anything that looks ugly until you are sure it's the only what to do what you want."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pandas can be seen as similar to:\n",
    "\n",
    "* Excel\n",
    "* R\n",
    "* SQL\n",
    "* SAS\n",
    "* Stata\n",
    "* ROOT (some parts)\n",
    "\n",
    "Learn more at:\n",
    "\n",
    "* [Pandas website](https://pandas.pydata.org)\n",
    "* Our recommended book (available on the UC libraries online)\n",
    "* [10 minutes to Pandas](http://pandas.pydata.org/pandas-docs/stable/10min.html)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "compclass",
   "language": "python",
   "name": "compclass"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}