ScientificPythonPart1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "# numpy: multidimensional vectors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(range(10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l = list(range(10))\n",
    "l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create a numpy array from a list\n",
    "a = np.array(l)\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(list, numpy.ndarray)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# list and array's have different type!\n",
    "type(l), type(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "45"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(l)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "45"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### IMPORTANT! numpy arrays are way faster than lists"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "l = list(range(int(10e5)))\n",
    "a = np.array(l)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16.9 ms ± 131 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit sum(l)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.6 ms ± 18.2 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit np.sum(a)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Simple statistics with numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(499999.5, 288675.1345946685, 83333333333.25)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# numpy arrays provide handy functions\n",
    "a.mean(), a.std(), a.var()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multidim arrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]), (10,))"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# np.zeros creates an empty array of (10 in this case) zeros\n",
    "a = np.zeros(10)\n",
    "a, a.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]]), (1, 10))"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# this is a 2-dimensional array, shape is 1x10\n",
    "a = np.zeros((1, 10))\n",
    "a, a.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[0.],\n",
       "        [0.],\n",
       "        [0.],\n",
       "        [0.],\n",
       "        [0.],\n",
       "        [0.],\n",
       "        [0.],\n",
       "        [0.],\n",
       "        [0.],\n",
       "        [0.]]),\n",
       " (10, 1))"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# a so-called column vector: 10x1\n",
    "a = np.zeros((10, 1))\n",
    "a, a.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
       "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
       "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
       "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
       "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
       "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
       "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
       "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
       "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
       "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]]),\n",
       " (10, 10))"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# a square 2-dimensional matrix\n",
    "a = np.zeros((10, 10))\n",
    "a, a.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[[0., 0., 0., ..., 0., 0., 0.],\n",
       "         [0., 0., 0., ..., 0., 0., 0.],\n",
       "         [0., 0., 0., ..., 0., 0., 0.],\n",
       "         ...,\n",
       "         [0., 0., 0., ..., 0., 0., 0.],\n",
       "         [0., 0., 0., ..., 0., 0., 0.],\n",
       "         [0., 0., 0., ..., 0., 0., 0.]],\n",
       " \n",
       "        [[0., 0., 0., ..., 0., 0., 0.],\n",
       "         [0., 0., 0., ..., 0., 0., 0.],\n",
       "         [0., 0., 0., ..., 0., 0., 0.],\n",
       "         ...,\n",
       "         [0., 0., 0., ..., 0., 0., 0.],\n",
       "         [0., 0., 0., ..., 0., 0., 0.],\n",
       "         [0., 0., 0., ..., 0., 0., 0.]],\n",
       " \n",
       "        [[0., 0., 0., ..., 0., 0., 0.],\n",
       "         [0., 0., 0., ..., 0., 0., 0.],\n",
       "         [0., 0., 0., ..., 0., 0., 0.],\n",
       "         ...,\n",
       "         [0., 0., 0., ..., 0., 0., 0.],\n",
       "         [0., 0., 0., ..., 0., 0., 0.],\n",
       "         [0., 0., 0., ..., 0., 0., 0.]]]),\n",
       " (3, 1024, 768))"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# an array that could represent a 1024x768 color image!\n",
    "a = np.zeros((3, 1024, 768))\n",
    "a, a.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Question time!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Q: is there diag as in R?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function numpy.diag(v, k=0)>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# A: indeed! you can check the documentation with `np.diag?`\n",
    "np.diag"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Q: what's the difference between `shape` and `reshape`?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 1, 2],\n",
       "       [3, 4, 5],\n",
       "       [6, 7, 8]])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# A: `reshape` reshapes an array into a different... shape\n",
    "np.arange(9).reshape((3, 3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 3)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# `shape` gives you the shape of an array\n",
    "np.arange(9).reshape((3, 3)).shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Array indexing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# let's create a 4x4 matrix, with values from 0 to 15\n",
    "a = np.arange(16).reshape((4, 4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3,  4,  5,  6,  7],\n",
       "       [ 8,  9, 10, 11, 12, 13, 14, 15]])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# note, you can create also non-squared matrices\n",
    "np.arange(16).reshape((2, 8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5, 5)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# is there a difference?\n",
    "a[1][1], a[1, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get the first row\n",
    "first_row = a[0]\n",
    "first_row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  4,  8, 12])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get the first column\n",
    "# `:` stands for \"every element from the first dimension\"\n",
    "first_column = a[:, 0]\n",
    "first_column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2],\n",
       "       [ 4,  5,  6],\n",
       "       [ 8,  9, 10]])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# select a 3x3 matrix from a\n",
    "a[:3, :3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Array operations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]),\n",
       " array([ 3,  4,  5,  6,  7,  8,  9, 10, 11, 12]))"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.arange(10)\n",
    "a, a + 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]),\n",
       " array([10, 11, 12, 13, 14, 15, 16, 17, 18, 19]),\n",
       " array([10, 12, 14, 16, 18, 20, 22, 24, 26, 28]))"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.arange(10)\n",
    "b = np.arange(10, 20)\n",
    "\n",
    "a, b, a + b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we already now we can use `array.sum()`\n",
    "a = np.arange(6).reshape(3, 2)\n",
    "a.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's sum the values of a column\n",
    "a[:, 0].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 2)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([6, 9])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# summing arrays over given axis\n",
    "a.sum(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 5, 9])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.sum(axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Q: what if the 2 arrays have a different lengths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "operands could not be broadcast together with shapes (10,) (11,) ",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-33-19c3d8aa3291>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mones\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m11\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0ma\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m: operands could not be broadcast together with shapes (10,) (11,) "
     ]
    }
   ],
   "source": [
    "a = np.ones(10)\n",
    "b = np.ones(11)\n",
    "\n",
    "a + b"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Q: complexity of forloop vs numpy:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = np.ones(int(1e5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "def foo(a):\n",
    "    s = 0\n",
    "    for i in a:\n",
    "        s += i\n",
    "\n",
    "def bar(a):\n",
    "    np.sum(a)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "57.3 ms ± 2 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
      "\n",
      "127 µs ± 5.51 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit foo(a)\n",
    "print()\n",
    "%timeit bar(a)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plotting with MatPlotLib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# we're going to use is the matplotlib library: i.e. a library plotting\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'values')"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX4AAAEWCAYAAABhffzLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nO3dd2BV9fnH8ffD3nuPEPYMIIQhLhRUVKoiWrWuqpVqa2v9tQKKW6vgqKO2Wqw4KlUrQxBRcaC4BVQSCHvvEWQTMu7z++MGGygjhNycOz6vf7j3nHPP98kh+eTke899jrk7IiKSOEoFXYCIiJQsBb+ISIJR8IuIJBgFv4hIglHwi4gkGAW/iEiCUfCLAGZW0czeNrPtZvammV1hZtMCquWXZvZ5EGNLYlDwS8wxsxVmlm1mdQ5a/oOZuZklF2G3FwP1gdrufom7j3X3s4pY30tm9mBRXluEsT4xs1+VxFgSPxT8EquWA5fvf2JmKUDF49hfM2CRu+cebUMzK3Mc44gETsEvsepfwNUFnl8DvLL/iZn1MLONBUPazAab2Q8H78jM7gPuBi41s11mdv3B0y35f0n81swWA4st7Akz25Q/PZRmZp3MbAhwBTA0f19vH6r4/P393syWmdkWM3vUzA7582hmfcxsZv44M82sT/7yPwOnAM/kj/VM4Q+fJDIFv8Sqr4FqZtbezEoDlwKv7l/p7jOBTODMAq+5kvAvjAO4+z3AQ8Ab7l7F3V84zJgXAr2ADsBZwKlAG6BG/viZ7j4aGAs8kr+vnx3haxgEpALdgAuA6w7ewMxqAe8ATwO1gb8A75hZbXcfAXwG3Jw/1s1HGEvkJwp+iWX7z/rPBBYAaw9a/zLhsN8foGcD/z6O8R52963uvhfIAaoC7QBz9/nuvv4Y9zcqf3+rgCcpMHVVwHnAYnf/l7vnuvtrhL/WI/1CETkizVVKLPsXMANoToFpngJeBeabWRXg58BnRQjnglbvf+DuH+dPrfwNSDKzicCf3H1HUfYHrAQaHWKbRvnrOGjbxscwjsgBdMYvMcvdVxJ+k/dcYMIh1q8FviI8pXIVh5jmOdYhD9r/0+7eHehIeMrntkNtdwRNCzxOAtYdYpt1hN945qBt9/91o/a6cswU/BLrrgfOcPfdh1n/CjAUSAEmFteg+W8e9zKzssBuIAvIy1+9EWhRiN3cZmY1zawpcAvwxiG2mQq0MbNfmFkZM7uU8HsMU45xLJGfKPglprn7UnefdYRNJhI+Y554hF8ORVENeB74kfDUSybwWP66F4AOZrbNzN46wj4mAbOBHwi/gfs/byq7eyYwEPhj/hhDgYHuviV/k6eAi83sRzN7+ri/KkkIphuxSLwzs6XAr939w6Br2c/MHGjt7kuCrkUSj874Ja6Z2WDC8+AfB12LSLTQVT0St8zsE8Lz4Ve5eyjgckSihqZ6REQSjKZ6REQSTExM9dSpU8eTk5ODLkNEJKbMnj17i7vXPXh5TAR/cnIys2Yd6Yo9ERE5mJkd/KlvQFM9IiIJR8EvIpJgFPwiIglGwS8ikmAU/CIiCUbBLyKSYBT8IiIJRsEvIhKFftydzX1vz2NHVk6x7zsmPsAlIpIo3J2p6Ru4Z/Jctu3J4aSWdejfoX6xjqHgFxGJEht3ZHHXW3OZlrGRlMbV+df1vWjfsFqxj6PgFxEJmLvzn1mrefCd+WTnhrj9nHZcf3JzypSOzGy8gl9EJECrMvdw+8Q0vliSSc/mtRg1uDPN61SO6JgKfhGRAOSFnJe+XMFj7y+kdCnjwQs78YueSZQqZREfW8EvIlLCFm/cydDxaXy/ahtntKvHgxd2olGNiiU2voJfRKSEZOeGeO7TpTzz8RIqly/NU5d15fwujTCL/Fl+QQp+EZESMGf1NoaNT2PBhp38rEsj7v1ZB2pXKR9ILQp+EZEI2pudx5MfLuL5z5ZRt2p5nr86lTOL+br8Y6XgFxGJkK+XZTJ8fBorMvdwec+m3H5ue6pVKBt0WQp+EZHitjMrh5HvLmDsN6tIqlWJf/+qF31a1Qm6rJ8o+EVEitHHCzYyYuJcNu7I4oZTmvN/Z7alYrnSQZd1AAW/iEgxyNy1j/unZDDph3W0rV+VZ6/sTtemNYIu65AU/CIix8HdeTttPfdOnsfOrBz+0L81v+nbinJlorf5sYJfRKSINmzP4s630vlw/ia6NK3BI4M707ZB1aDLOioFv4jIMQqFnNdnrubhqfPJCYW487z2XHtSc0qXQLuF4qDgFxE5Biu27Gb4hDS+XraVE1vUZuTgFJrVjmxTteKm4BcRKYS8kDPm8+U8/sFCypYqxciLUri0R9MSb7dQHBT8IiJHsXDDToaOm8OcNdvp374eD16YQoPqFYIuq8giFvxmNgYYCGxy9075y7oCzwEVgFzgN+7+baRqEBE5Htm5If42fQl//2QJ1SqU5a+Xn8DAzg1j8iy/oEie8b8EPAO8UmDZI8B97v6umZ2b/7xvBGsQESmS71f9yLDxaSzauItBJzTmroEdqFW5XNBlFYuIBb+7zzCz5IMXA/tvIFkdWBep8UVEimJPdi6PT1vEmC+W06BaBcb8MpUz2gXbVK24lfQc/x+A983sMaAU0OdwG5rZEGAIQFJSUslUJyIJ7cslWxg+IZ1VW/dwRa8khp/TjqpR0FStuJV08N8E3Oru483s58ALQP9Dbejuo4HRAKmpqV5yJYpIotm+N4eHp87n9ZmrSa5dideH9KZ3i9pBlxUxJR381wC35D9+E/hnCY8vInKADzI2cudb6WzeuY9fn9aCW/u3oULZ6GqqVtxKOvjXAacBnwBnAItLeHwREQC27NrHvZPnMSVtPe0aVOX5q1Pp3CQ6m6oVt0hezvka4St26pjZGuAe4AbgKTMrA2SRP4cvIlJS3J1JP6zjvrfnsXtfHn88sw039m1J2dLR21StuEXyqp7LD7Oqe6TGFBE5knXb9jJiYjrTF27mhKRwU7XW9aO/qVpx0yd3RSTuhULO2G9XMerdBeSFnLsHduCaPskx01StuCn4RSSuLd+ym2Hj0/h2+VZOblWHhy9KoWmtSkGXFSgFv4jEpdy8EP/8fDlPfLCIcmVK8cjgzlyS2iTm2y0UBwW/iMSdjHU7GDY+jfS12zmrQ30euLAT9avFblO14qbgF5G4sS83j2c+XsKznyylRqWy/O0X3Tg3pYHO8g+i4BeRuDB7Zbip2pJNu7ioW2PuOq8DNeOkqVpxU/CLSEzbk53Lo+8v5KUvV9CoekVeurYHfdvWC7qsqKbgF5GY9fniLQyfkMaaH/dy9YnNGDqgHVXKK9aORkdIRGLO9j05/HlqBv+ZtYYWdSrzn1+fSM/mtYIuK2Yo+EUkprw3dwN3TZrL1t3Z3NS3Jbf0ax33TdWKm4JfRGLC5p3hpmrvpK+nQ8NqvPjLHnRqXD3osmKSgl9Eopq7M+G7tdw/JYO92XncdnZbhpzaIqGaqhU3Bb+IRK212/Zyx4R0Pl20me7NajJqcGda1asSdFkxT8EvIlEnFHJe/WYlo95dgAP3nd+Rq3o3o1SCNlUrbgp+EYkqSzfvYvj4NGau+JFTWtfhoUFqqlbcFPwiEhVy8kI8/9kynvxwMRXLluaxS7owuFtjtVuIAAW/iARu7trtDBufxrx1OxjQsQH3X9iRelXVVC1SFPwiEpisnDz++vFinvt0GTUrlePZK7pxTkrDoMuKewp+EQnErBVbGTo+jWWbd3NJ9yaMOK89NSqpqVpJUPCLSInavS/cVO3lr8JN1V65rientqkbdFkJRcEvIiXm00WbuWNCOuu27+WaE5O57ey2VFZTtRKnIy4iEbdtTzYPTJnP+O/W0LJuZd789YmkJqupWlAU/CISUe+mr+euSfPYtiebm09vxc1ntFJTtYAp+EUkIjbtyOLuSfN4b94GOjWuxsvX9aBjIzVViwYKfhEpVu7Om7PX8OCUDLJyQwwb0I4bTmlOGTVVixoKfhEpNqu37uGOiel8tngLPZNrMXJwCi3qqqlatFHwi8hxyws5r3y1gkffX4gBD1zQkSt6qalatFLwi8hxWbJpJ0PHpfHdqm30bVuXPw9KoXGNikGXJUeg4BeRIsnJC/GPT5fy9EdLqFS+NE9c2oULu6qpWixQ8IvIMUtfs53bxs1hwYadnNe5Ifed35E6VcoHXZYUkoJfRAotKyePJz9czPOfLaN25XL846runN2xQdBlyTFS8ItIoXyzLJPhE9JZvmU3l/Voyu3ntqd6xbJBlyVFoOAXkSPamZXDqPcW8OrXq2haqyJjf9WLk1rVCbosOQ4KfhE5rOkLNjFiYjrrd2Rx/cnN+eNZbahUTrER6/Q/KCL/Y+vubB6YksHE79fSul4Vxt/Uh25JNYMuS4pJxILfzMYAA4FN7t6pwPLfATcDucA77j40UjWIyLFxd6akrefeyfPYvjeH3/drzW9Pb0n5MmqqFk8iecb/EvAM8Mr+BWZ2OnAB0Nnd95lZvQiOLyLHYOOOLEZMnMuH8zfSuUl1xt7Qi3YNqgVdlkRAxILf3WeYWfJBi28CRrr7vvxtNkVqfBEpHHfnjZmr+fPU+WTnhhhxbnuuPSlZTdXiWEnP8bcBTjGzPwNZwJ/cfeahNjSzIcAQgKSkpJKrUCSBrMrcw/AJaXy5NJNezWsxanBnkutUDrosibCSDv4yQE2gN9AD+I+ZtXB3P3hDdx8NjAZITU39n/UiUnR5IefFL5bz2LSFlC1ViocGpXBZj6ZqqpYgSjr41wAT8oP+WzMLAXWAzSVch0jCWrQx3FTth9Xb6NeuHg8O6kTD6mqqlkhKOvjfAs4APjGzNkA5YEsJ1yCSkLJzQzz7yVKemb6YqhXK8tRlXTm/SyM1VUtAkbyc8zWgL1DHzNYA9wBjgDFmNhfIBq451DSPiBSvOau3MXRcGgs37uSCro24e2AHaqupWsKK5FU9lx9m1ZWRGlNEDrQ3O4+/fLCQFz5fTr2qFfjn1an071A/6LIkYPrkrkic+mppJsMnpLEycw+/6JXE8HPaUa2CmqqJgl8k7uzIyuHhqQt47dtVNKtdiX/f0Is+LdVUTf5LwS8SRz6av5ERE+eyaWcWQ05twa3921CxnNotyIEU/CJxIHPXPu57O4PJc9bRrkFV/nFVd7o0rRF0WRKlFPwiMczdmTxnHfe9ncHOrBxu7d+Gm/q2pFwZtVuQw1Pwi8So9dv3cufEuXy0YBNdm9bgkYs706Z+1aDLkhig4BeJMaGQ89rMVTw8dQG5oRB3nteea09qTmm1W5BCUvCLxJAVW3YzfEIaXy/bSp+WtRl5UWeSalcKuiyJMQp+kRiQmxdizBfLeXzaIsqVLsXIi1K4tEdTtVuQIlHwi0S5BRt2MGxcGnPWbKd/+/o8eGEnGlSvEHRZEsMU/CJRal9uHn+bvpS/T19C9Ypl+evlJzCwc0Od5ctxU/CLRKHvV/3IsPFpLNq4i0EnNOaugR2oVblc0GVJnFDwi0SRPdm5PD5tEWO+WE6DahV48Zc9OL2dbk0txeuYgt/MSgFV3H1HhOoRSVhfLtnC8AnprNq6hyt7JzFsQDuqqqmaRMBRg9/M/g3cCOQBs4HqZvYXd3800sWJJILte3N4eOp8Xp+5muZ1KvPGkN70alE76LIkjhXmjL+Du+8wsyuAqcAwwr8AFPwix+mDjI3c+VY6m3fu49enhZuqVSirpmoSWYUJ/rJmVha4EHjG3XPMTHfNEjkOW3bt497J85iStp52Dary/NWpdG6ipmpSMgoT/P8AVgBzgBlm1gzQHL9IEbg7b/2wlvvezmDPvjz+eGYbbuzbkrKl1VRNSs5Rg9/dnwaeLrBopZmdHrmSROLTum17GTExnekLN9MtqQajBnemtZqqSQAK8+ZufeAhoJG7n2NmHYATgRciXZxIPAiFnLHfrmLUuwvICzn3/KwDV5+YrKZqEpjCTPW8BLwIjMh/vgh4AwW/yFEt27yL4ePT+XbFVk5uVYeHL0qhaS01VZNgFSb467j7f8zsdgB3zzWzvAjXJRLTcvNC/PPz5TzxwSLKlynFIxd35pLuTdRuQaJCYYJ/t5nVBhzAzHoD2yNalUgMy1i3g6Hj5zB37Q7O7lifBy7oRL1qaqom0aMwwf9/wGSgpZl9AdQFLo5oVSIxaF9uHs98vIRnP1lKjUpl+fsV3TinUwOd5UvUKcxVPd+Z2WlAW8CAhe6eE/HKRGLI7JXhpmpLNu1icLcm3Hlee2qqqZpEqcJc1XP1QYu6mRnu/kqEahKJGbv35fLYtIW89OUKGlWvyMvX9eS0NnWDLkvkiAoz1dOjwOMKQD/gO0DBLwnts8WbuX1COmt+3Ms1JzbjtgHtqFJeDW8l+hVmqud3BZ+bWXXgXxGrSCTKbd+Tw4PvZPDm7DW0qFuZN288kR7JtYIuS6TQinJ6sgdoXdyFiMSC9+Zu4K5Jc9m6O5vf9G3J7/u1VlM1iTmFmeN/m/xLOYFSQAfgP5EsSiTabNqZxb2T5zE1fQMdGlbjxV/2oFPj6kGXJVIkhTnjf6zA41xgpbuviVA9IlHF3Znw3Vrun5LB3pw8bju7LUNObaGmahLTCjPH/2lJFCISbdb8uIc7Js5lxqLNpDarycjBnWlVr0rQZYkct8MGv5nt5L9TPAesAtzdq0WsKpEAhULOq9+sZNS7C3DgvvM7clXvZpRSUzWJE4cNfndXv1hJOEs372LYuDRmrfyRU9vU5aFBnWhSU03VJL4U+qoeM6tH+Dp+ANx9VUQqEglATl6I0TOW8dRHi6lYtjSPXdKFwd0aq92CxKXCXNVzPvA40AjYBDQD5gMdj/K6McBAYJO7dzpo3Z8I37O3rrtvKVrpIsVj7trtDBufxrx1Ozg3pQH3nt+RelXVVE3iV2HO+B8AegMfuvsJ+XffurwQr3sJeIaDPuFrZk2BMwH9xSCBysrJ4+mPFvOPGcuoVbkcz13ZjQGdGgZdlkjEFSb4c9w908xKmVkpd59uZqOO9iJ3n2FmyYdY9QQwFJh0bKWKFJ9ZK7YydHwayzbv5pLuTbjzvA5Ur1Q26LJESkRhgn+bmVUBPgPGmtkmwtfzH7P8aaO17j7naHOnZjYEGAKQlJRUlOFE/seufbk8+t4CXvl6JY1rVORf1/fklNZqqiaJpTDBPwOoAdwCXAlUB+4/1oHMrBLh2zeeVZjt3X00MBogNTX1UJeVihyTTxdt5o4J6azbvpdrTkzmtrPbUllN1SQBFea73oD3ga3A68Ab7p5ZhLFaAs2B/Wf7TYDvzKynu28owv5ECmXbnmzun5LBhO/W0rJuZcbdeCLdm6mpmiSuwnxy9z7gPjPrDFwKfGpma9y9/7EM5O7pQL39z81sBZCqq3okkqamr+fuSXPZtieH353Rit+e3kpN1SThHcvfuZuADUAmBQL8cMzsNaAvUMfM1gD3uPsLRSlS5Fht2pHF3ZPm8d68DaQ0rs4r1/WiQyN92FwECncd/02Ez/TrAuOAG9w942ivc/cjXvLp7smFrFGk0NydN2ev4cEpGezLDTH8nHb86uTmlFFTNZGfFOaMvxnwB3f/IdLFiByP1Vv3cMfEdD5bvIWeybUYOTiFFnXVVE3kYIWZ4x9eEoWIFFVeyHnlqxU8+v5CDHjgwk5c0TNJTdVEDkPXsklMW7JpJ0PHpfHdqm30bVuXPw9KoXGNikGXJRLVFPwSk3LyQvzj06U8/dESKpcvzROXduHCrmqqJlIYCn6JOelrtnPbuDks2LCTgZ0bcu/5HalTpXzQZYnEDAW/xIysnDye/HAxz3+2jNqVyzH6qu6c1bFB0GWJxBwFv8SEb5ZlMnxCOsu37OayHk25/dz2VK+opmoiRaHgl6i2MyuHUe8t4NWvV9G0VkXG/qoXJ7WqE3RZIjFNwS9Ra/qCTYyYmM76HVlcf3Jz/nhWGyqV07esyPHST5FEna27s3lgSgYTv19L63pVGH9TH7ol1Qy6LJG4oeCXqOHuvJO+nnsmzWP73hxu6dea35zekvJl1FRNpDgp+CUqbNyRxZ1vzeWDjI10blKdsTf0ol0DNVUTiQQFvwTK3Xlj5mr+PHU+2bkhRpzbnmtPSlZTNZEIUvBLYFZl7mH4hDS+XJpJr+a1GDW4M8l1KgddlkjcU/BLicsLOS9+sZzHpi2kTKlSPDQohct6NFVTNZESouCXErVoY7ip2g+rt9GvXT0eHNSJhtXVVE2kJCn4pURk54Z49pOlPDN9MVUrlOWpy7pyfpdGaqomEgAFv0TcnNXbGDoujYUbd3JB10bcPbADtdVUTSQwCn6JmL3Zefzlg4W88Ply6lWtwD+vTqV/h/pBlyWS8BT8EhFfLc1k+IQ0Vmbu4Re9khh+TjuqVVBTNZFooOCXYrUjK4eHpy7gtW9X0ax2Jf59Qy/6tFRTNZFoouCXYvNhxkZGvJXO5p37GHJqC27t34aK5dRuQSTaKPjluGXu2sd9b2cwec462jWoyuirUunStEbQZYnIYSj4pcjcnclz1nHv5Hns2pfLrf3bcFPflpQro3YLItFMwS9Fsn77Xu6cOJePFmyia9MaPHJxZ9rUrxp0WSJSCAp+OSahkPPazFU8PHUBuaEQd57XnmtPak5ptVsQiRkKfim0FVt2M3xCGl8v20qflrUZeVFnkmpXCrosETlGCn45qty8EGO+WM7j0xZRrkwpRg1O4eepTdVuQSRGKfjliOav38Gw8WmkrdlO//b1efDCTjSoXiHoskTkOCj45ZD25ebxt+lL+fv0JVSvWJZnfnEC56U01Fm+SBxQ8Mv/+G7Vjwwbl8biTbsYdEJj7h7YgZqVywVdlogUEwW//GRPdi6PT1vEmC+W06BaBV78ZQ9Ob1cv6LJEpJgp+AWAL5ZsYfiENFZv3ctVvZsxdEBbqqqpmkhcUvAnuO17c3h46nxen7ma5nUq88aQ3vRqUTvoskQkghT8CWzavA3c+dZcMndnc+NpLflD/9ZUKKumaiLxLmLBb2ZjgIHAJnfvlL/sUeBnQDawFLjW3bdFqgY5tM0793Hv2/N4J2097RtW44VrepDSpHrQZYlICYlkN62XgAEHLfsA6OTunYFFwO0RHF8O4u5M/H4NZz7xKR/M28ifzmrD5JtPUuiLJJiInfG7+wwzSz5o2bQCT78GLo7U+HKgtdv2MmJiOp8s3Ey3pHBTtVb11FRNJBEFOcd/HfDG4Vaa2RBgCEBSUlJJ1RR3QiFn7DcrGfnuAhy492cduOrEZDVVE0lggQS/mY0AcoGxh9vG3UcDowFSU1O9hEqLK8s272L4+HS+XbGVU1rX4aFBKTStpaZqIomuxIPfzK4h/KZvP3dXoEdAbl6I5z9bzhMfLqJCmVI8enFnLu7eRO0WRAQo4eA3swHAMOA0d99TkmMniox1Oxg6fg5z1+5gQMcG3H9BR+pVU1M1EfmvSF7O+RrQF6hjZmuAewhfxVMe+CD/7PNrd78xUjUkkqycPJ75eAnPfbqUGpXK8ewV3TgnpWHQZYlIFIrkVT2XH2LxC5EaL5HNXrmVoePSWLp5N4O7NeGuge2pUUlN1UTk0PTJ3Ri2e18uj76/kJe/WkGj6hV5+bqenNambtBliUiUU/DHqBmLNnP7hHTWbd/L1b2bcduAdlQpr/9OETk6JUWM2b4nhwfeyWDc7DW0qFuZN399IqnJtYIuS0RiiII/hrw3dz13TZrH1t3Z/Pb0lvzuDDVVE5Fjp+CPAZt2ZnHPpHm8O3cDHRtV46Vre9CxkfrriEjRKPijmLsz/ru1PDAlg705eQwd0JYbTmlB2dKR7K0nIvFOwR+lVm/dwx0T0/ls8RZ6JNdk5ODOtKxbJeiyRCQOKPijTCjkvPLVCh55fyEG3H9BR67s1YxSaqomIsVEwR9FlmzaxfDxacxa+SOntqnLQ4M60aSmmqqJSPFS8EeBnLwQo2cs46kPF1OxXGkev6QLF3VrrKZqIhIRCv6AzV27naHj0shYv4PzUhpy7/kdqVu1fNBliUgcU/AHJCsnj6c+WszoGcuoVbkcz13ZnQGdGgRdlogkAAV/AGau2MqwcWks27Kbn6c2YcS5HaheqWzQZYlIglDwl6Bd+3J55L0FvPLVSprUrMir1/fi5NZ1gi5LRBKMgr+EfLJwEyMmzmXd9r1ce1IyfzqrLZXVVE1EAqDkibAfd2fzwDsZTPhuLa3qVWHcjX3o3qxm0GWJSAJT8EeIuzM1fQP3TJ7Ltj053Hx6K37XrxXly6ipmogES8EfAZt2ZHHnW3OZlrGRlMbVeeW6XnRoVC3oskREAAV/sXJ33py1hgfeySA7N8Tt57Tj+pObU0ZN1UQkiij4i8nqrXu4fUI6ny/ZQs/mtRh5UQot1FRNRKKQgv845YWcl79cwaPvL6R0KePBCzvxi55JaqomIlFLwX8cFm/cydDxaXy/aht929bloUEpNKpRMeiyRESOSMFfBNm5IZ77dCnPfLyEyuVL8+SlXbmgayM1VRORmKDgP0Zpa7YxdFwaCzbsZGDncFO1OlXUVE1EYoeCv5CycvJ44oNFPP/ZMupWLc/oq7pzVkc1VROR2KPgL4Svl2UyfHwaKzL3cHnPpgw/pz3VK6qpmojEJgX/EezMymHkuwsY+80qkmpV4t+/6kWfVmqqJiKxTcF/GB8v2MiIiXPZuCOLX53cnP87qw2VyulwiUjsU5IdZOvubO5/ex5v/bCO1vWq8Peb+nBCkpqqiUj8UPDnc3feTlvPvZPnsWNvDrf0a81vTm+ppmoiEncU/MCG7eGmah/O30iXJtUZdUMv2jVQUzURiU8JHfzuzuszV/PQO/PJCYUYcW57rju5OaXVbkFE4ljCBv/KzN0MH5/OV8sy6d2iFiMv6kxyncpBlyUiEnEJF/x5IefFL5bz2LSFlC1ViocGpXBZj6ZqqiYiCSOhgn/hhnBTtTmrt9GvXT0eHNSJhtXVVE1EEkvEgt/MxgADgU3u3il/WS3gDSAZWAH83N1/jFQN+2Xnhvj7J0v42/QlVK1Qlqcu68r5XdRUTUQSUyRvDfUSMOCgZcOBj9y9NfBR/vOI+mH1Nn7218958sPFnJvSkA9uPZULujZW6PxE/AsAAAXtSURBVItIworYGb+7zzCz5IMWXwD0zX/8MvAJMCxSNfz1o8U88eEi6lWtwAvXpNKvff1IDSUiEjNKeo6/vruvB3D39WZW73AbmtkQYAhAUlJSkQZLql2Jy3omMfycdlSroKZqIiIA5u6R23n4jH9KgTn+be5eo8D6H939qP0QUlNTfdasWRGrU0QkHpnZbHdPPXh5JOf4D2WjmTXML6ghsKmExxcRSXglHfyTgWvyH18DTCrh8UVEEl7Egt/MXgO+Atqa2Rozux4YCZxpZouBM/Ofi4hICYrkVT2XH2ZVv0iNKSIiR1fSUz0iIhIwBb+ISIJR8IuIJBgFv4hIgonoB7iKi5ltBlYW8eV1gC3FWE6s0/H4Lx2LA+l4HCgejkczd6978MKYCP7jYWazDvXJtUSl4/FfOhYH0vE4UDwfD031iIgkGAW/iEiCSYTgHx10AVFGx+O/dCwOpONxoLg9HnE/xy8iIgdKhDN+EREpQMEvIpJg4jr4zWyAmS00syVmFvH7+0YrM2tqZtPNbL6ZzTOzW4KuKRqYWWkz+97MpgRdS9DMrIaZjTOzBfnfJycGXVNQzOzW/J+TuWb2mplVCLqm4ha3wW9mpYG/AecAHYDLzaxDsFUFJhf4o7u3B3oDv03gY1HQLcD8oIuIEk8B77l7O6ALCXpczKwx8HsgNf/OgaWBy4KtqvjFbfADPYEl7r7M3bOB1wnf7D3huPt6d/8u//FOwj/UjYOtKlhm1gQ4D/hn0LUEzcyqAacCLwC4e7a7bwu2qkCVASqaWRmgErAu4HqKXTwHf2NgdYHna0jwsIOf7oN8AvBNsJUE7klgKBAKupAo0ALYDLyYP/X1TzOrHHRRQXD3tcBjwCpgPbDd3acFW1Xxi+fgt0MsS+hrV82sCjAe+IO77wi6nqCY2UBgk7vPDrqWKFEG6AY86+4nALuBhHxPzMxqEp4ZaA40Aiqb2ZXBVlX84jn41wBNCzxvQhz+yVZYZlaWcOiPdfcJQdcTsJOA881sBeEpwDPM7NVgSwrUGmCNu+//K3Ac4V8Eiag/sNzdN7t7DjAB6BNwTcUunoN/JtDazJqbWTnCb9BMDrimQJiZEZ6/ne/ufwm6nqC5++3u3sTdkwl/X3zs7nF3VldY7r4BWG1mbfMX9QMyAiwpSKuA3mZWKf/nph9x+EZ3xO65GzR3zzWzm4H3Cb8zP8bd5wVcVlBOAq4C0s3sh/xld7j71ABrkujyO2Bs/knSMuDagOsJhLt/Y2bjgO8IXw33PXHYukEtG0REEkw8T/WIiMghKPhFRBKMgl9EJMEo+EVEEoyCX0QkwSj4JSGZ2ZfHuH1fdfGUeKHgl4Tk7nH3aUyRwlLwS0Iys135//Y1s08K9KIfm/+Jzf33c1hgZp8DFxV4bWUzG2NmM/Obml2Qv/xpM7s7//HZZjbDzEqZWXcz+9TMZpvZ+2bWMH+b35tZhpmlmdnrJX4QJGHpA1ySkMxsl7tXMbO+wCSgI+FeTl8AtwGzgMXAGcAS4A2gkrsPNLOHgAx3f9XMagDfEu546oRbhdwMPAecS7gFwKfABe6+2cwuBc529+vMbB3Q3N33mVmNBG+FLCUobls2iByDb919DUB+S4tkYBfhZl2L85e/CgzJ3/4swk3e/pT/vAKQ5O7zzewGYAZwq7svNbNOQCfgg/w/JEoTbvcLkEa4TcJbwFsR/hpFfqLgF4F9BR7n8d+fi8P9OWzAYHdfeIh1KUAm4Za++7ed5+6HupXheYRvgHI+cJeZdXT33GMtXuRYaY5f5NAWAM3NrGX+88sLrHsf+F2B9wJOyP+3GfBHwtM+55hZL2AhUHf/PWzNrKyZdTSzUkBTd59O+IYwNYAqJfB1iSj4RQ7F3bMIT+28k//m7soCqx8AygJpZjYXeKBA6+s/ufs64HrCt3UsBVwMjDKzOcAPhPu7lwZeNbN0wh0gn9Acv5QUvbkrIpJgdMYvIpJgFPwiIglGwS8ikmAU/CIiCUbBLyKSYBT8IiIJRsEvIpJg/h9kzdJjwmAI1wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(np.arange(10, 20))\n",
    "plt.title(\"My first plot\")\n",
    "plt.xlabel(\"indexes\")\n",
    "plt.ylabel(\"values\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  1,  4,  9, 16, 25, 36, 49, 64, 81])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.arange(10) ** 2\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f7efb643790>]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXAAAAD4CAYAAAD1jb0+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nO3deXyU5b3+8c+XJCxhSdjBhAgIiJSdACrVWkFr3ZcDakVQUVxqkR77U9pjbYunp9r29ChWPaJUEFdUULSt1RO11tqyhGBUXECBkBBJEBKWhKzf3x8ZLWCQCZnJM5Nc79fL18w8M5PncoCLh3ue577N3RERkfjTKugAIiJyZFTgIiJxSgUuIhKnVOAiInFKBS4iEqcSm3Jn3bp18759+zblLkVE4l52dvZ2d+9+8PYmLfC+ffuyevXqptyliEjcM7PN9W3XEIqISJxSgYuIxCkVuIhInFKBi4jEKRW4iEicUoGLiERT7hL4n6Hw89S629wlEfvRTXoaoYhIi5K7BF6cBVXldY9Lt9Q9Bhg+pdE/XkfgIiLRkjX3X+X9haryuu0REFaBm9kPzex9M3vPzJ40s7Zm1sXMXjWz9aHbzhFJJCLSTHhpfv1PHGp7Ax22wM0sDZgFZLr7UCABuASYA2S5+0AgK/RYRERC9rTpVf8TKekR+fnhDqEkAu3MLBFIBrYC5wGLQs8vAs6PSCIRkWbg9Q+LuG3PBVRYmwOfSGoHE2+PyD4OW+DuXgD8FsgDCoFSd38F6OnuhaHXFAI96nu/mc00s9Vmtrq4uDgioUVEYtmGoj3MejKHDT3PxM6ZByl9AKu7PWdeRL7AhDDOQgmNbZ8H9ANKgGfMbGq4O3D3+cB8gMzMTC3AKSLNWml5FTMfXU3rxFbMn5ZJ69STYPQlUdlXOEMok4CN7l7s7lXAUuBEYJuZ9QYI3RZFJaGISJyoqXVmPZnDlp1lPDB1DGmp7aK6v3AKPA843sySzcyAicAHwHJgeug104EXohNRRCQ+/PrlD/nrx8X84tyhjOvXJer7O+wQiruvMLNngTVANZBD3ZBIB2CJmc2gruQnRzOoiEgsez6ngAff/JTLjz+a743PaJJ9hnUlprv/DPjZQZsrqDsaFxFp0XLzS7j1uVzG9+vC7ecMabL96kpMEZFGKNq1j5mPZtOtQxvuv2w0SQlNV6uaC0VE5AhVVNdw3WPZlJZX8dz1J9K1Q5vDvymCVOAiIkfA3fnp8++xJq+E+y8bzZCjOjV5Bg2hiIgcgYVvb2LJ6nxmnTqAM4f1DiSDClxEpIH+vmE7//nHDzhtSE9mTxoUWA4VuIhIA2z+fC83PL6GY7q3538uHkmrVhZYFhW4iEiY9lRUc82jqzGDh6Zl0qFNsF8j6ktMEZEw1NY6P3x6LZ8U7+XRq8ZxdNf2QUfSEbiISDjuzlrPq+u28R9nHseEAd2CjgOowEVEDuvP7xYyL2s9k8ekc+WEvkHH+ZIKXETka6zbuot/X/IOozJS+c8LhlI3p19sUIGLiBzCjr2VXPPoalLaJfHg1DG0SUwIOtIB9CWmiEg9qmpqueHxbIr3VPDMtSfQo1PboCN9hY7ARUTqMffFdfzz0x3cddEwRvRJDTpOvVTgIiIHeWJFHov/uZmZJ/fnglGRWUE+Gg5b4GZ2rJmt3e+/XWY228y6mNmrZrY+dNu5KQKLiETTqk07+Nny9/jWoO7cesbgoON8rXBWpf/I3Ue6+0hgDFAGLAPmAFnuPhDICj0WEYlbBSXlXLc4m/TOycy7dBQJAV4mH46GDqFMBD5x983UrVS/KLR9EXB+JIOJiDSl8soaZj66msrqWh6alklKu6SgIx1WQwv8EuDJ0P2e7l4IELrtUd8bzGymma02s9XFxcVHnlREJErcnf/37DusK9zFvEtHMaBHh6AjhSXsAjez1sC5wDMN2YG7z3f3THfP7N69e0PziYhE3f1vfMJLuYXc8p3BfHtwvceiMakhR+DfBda4+7bQ421m1hsgdFsU6XAiItGW9cE2fvvKR5w74iiu+1b/oOM0SEMK/FL+NXwCsByYHro/HXghUqFERJrChqLd3PTUWr5xVCfuumh4TF0mH46wCtzMkoHTgKX7bb4TOM3M1oeeuzPy8UREoqO0rIqrF62mbVIr5l+eSbvWsXWZfDjCupTe3cuArgdt+5y6s1JEROJKdU0tNz65hoKScp685niOSm0XdKQjorlQRKTFuevlD/nb+u3ceeEwMvt2CTrOEdOl9CLSojyXnc9Df9vI9BOO5pJxGUHHaRQVuIi0GGu3lPDjZe9yQv+u3Hb2kKDjNJoKXERahKJd+7h28Wp6dGzDfZeNJikh/utPY+Ai0uztq6ph5uJsdu+rZukNJ9KlfeugI0WEClxEmjV35z+WvcfaLSX879TRDO7VKehIEaMCF5HmKXcJZM2F0nx+WNuVicNmccbQs4JOFVEqcBFpfnKXwIuzoKocA9JbbSdt068gtzcMnxJ0uoiJ/1F8EZGDZc2FqvIDNllVed32ZkQFLiLNjpfm1//EobbHKRW4iDQrxbsr2Gbd6n8yJXbXtzwSKnARaTZKy6uY9oeV/LbmYmoSDprfJKkdTLw9mGBRogIXkWahvLKGaxatZkPRbs6ZOpuE8+ZBSh/A6m7PmdesvsAEnYUiIs1AVU0tNz6xhlWbdzDvklF8a1B3YEqzK+yD6QhcROJaba1zy7O5ZH1YxB3nDeWcEUcFHanJhLugQ6qZPWtmH5rZB2Z2gpl1MbNXzWx96LZztMOKiOzP3Zn70jqW5RTwo9MHMfX4o4OO1KTCPQK/B3jZ3QcDI4APgDlAlrsPBLJCj0VEmsy9r21g4dubuGpCP77/7QFBx2lyhy1wM+sEnAwsAHD3SncvAc4DFoVetgg4P1ohRUQOtvgfm/jdqx9z4eg0bjvruLhbzzISwjkC7w8UA4+YWY6ZPWxm7YGe7l4IELrtUd+bzWymma02s9XFxcURCy4iLdcLawu4ffn7TDquB3ddNJxWrVpeeUN4BZ4IjAYecPdRwF4aMFzi7vPdPdPdM7t3736EMUVE6rzxURE3L3mHsX278PvvNY95vY9UOP/n+UC+u68IPX6WukLfZma9AUK3RdGJKCJSJ3vzDq57LJtje3Xk4emZtE2Kv5XkI+mwBe7unwFbzOzY0KaJwDpgOTA9tG068EJUEoqIAB9+tosrH1lF75R2LLpqHJ3aJgUdKXDhXsjzA+BxM2sNfApcSV35LzGzGUAeMDk6EUWkpcv7vIzLF6wkuXUii2eMo1uHNkFHiglhFbi7rwUy63lqYmTjiIgcqGj3PqYuWEFVTS1PXHsC6Z2Tg44UM1ru6L+IxLzS8iqmLVjJ9j0VPHLFWAb27Bh0pJiiAheRmFReWcOMhav4pHgPD14+hlEZutj7YJrMSkRiTlVNLTc8nk123k7u+95oThqoU5DroyNwEYkptbXOj555h9c/Kua/LhjGmcN6Bx0pZqnARSRmuDu/ePF9Xli7lVvOOJZLx2UEHSmmqcBFJGbck7WeRf/YzDUn9eP6bx0TdJyYpwIXkZiw8O8bufv/1jN5TDo/ObNlTk7VUCpwEQnc8zkF/PzFdZw+pCe/unCYyjtMKnARCdTrHxbxo2fe4fj+XZh36SgSW/DkVA2lT0pEArNqU93kVMf17sRD0zQ5VUOpwEUkEOu27uKqhatIS23HwivH0lGTUzWYClxEmtzmz/cy7Q8r6dAmkcVXj6erJqc6IipwEWlS23bVTU5VU1vL4hnjSEttF3SkuKUCF5EmU1pWNznVjj2VLLxyHAN6aHKqxtBcKCLSJMoqq7ly4Uo2bt/LI1eOZUSf1KAjxT0dgYtI1FVW13L9Y2tYu6WEeZeOZMKAbkFHahbCOgI3s03AbqAGqHb3TDPrAjwN9AU2AVPcfWd0YopIvKqpdW5+5h3++nExd100jDOGanKqSGnIEfi33X2ku3+xMs8cIMvdBwJZNGClehFpGdydny9/nxff2cqc7w7m4rGanCqSGjMGfh5wSuj+IuAN4NZG5hGReJe7BLLmQmk+u9v0pHT3hVz7rWlcp8mpIi7cAnfgFTNz4EF3nw/0dPdCAHcvNLMe9b3RzGYCMwEyMvS3r0izlrsEXpwFVeUAdKr4jN+2WUBS2ijguGCzNUPhDqFMcPfRwHeB75vZyeHuwN3nu3umu2d2765VNUSatay5X5b3F1p7BZY1N6BAzVtYBe7uW0O3RcAyYBywzcx6A4Rui6IVUkTiRGl+w7ZLoxy2wM2svZl1/OI+cDrwHrAcmB562XTghWiFFJH4sLtNr/qfSElv2iAtRDhH4D2Bt8zsHWAl8Ed3fxm4EzjNzNYDp4Uei0gL5O789ysf8R+7L6DCDprXJKkdTLw9mGDN3GG/xHT3T4ER9Wz/HJgYjVAiEj9qa525L61j4dubuGTspSQNGAWv1Z2FQkp6XXkPnxJ0zGZJl9KLyBGrrqnlludyWbqmgGtO6hdaCm04jFBhNwUVuIgckYrqGn7wRA6vrNvGzacN4sZTB2gptCamAheRBttbUc21i7N5a8N2fn7OEK6Y0C/oSC2SClxEGqS0rIorFq7knS0l/PfkEVw0RmeYBEUFLiJhK9q9j2kLVvJp8V7uv2wMZww9xGmD0iRU4CISlvydZUx9eAXbdlXwhyvG8s2BmhI2aCpwETmsDUV7uHzBCvZWVPPY1eMZc3TnoCMJKnAROYz3CkqZ/oeVmMFTM09gyFGdgo4kISpwETmkVZt2cNUjq+jULonHrh5Pv27tg44k+1GBi0i93vioiOsey+ao1HY8NmM8R2n1+JijAheRr/jTu4Xc9FQOA3t05NEZ4+jWoc3h3yRNTgUuIgdYsmoLc5bmMjqjMwuuGEtKu6SgI8khqMBF5EsL3trIHS+t46SB3Xjw8jEkt1ZFxDL96ogI7s7d/7eee7LW892hvbj7kpG0SUwIOpYchgpcpIWrrXXu+OM6Hvn7JiaPSedXFw4jMSHc1RYlSGH/KplZgpnlmNlLocddzOxVM1sfutWZ/SJx5ovpYB/5+yaumtCPuy4arvKOIw35lboJ+GC/x3OALHcfCGSFHotInKioruHGJ3J4NjufH04axE/PPo5WrTQdbDwJq8DNLB04C3h4v83nAYtC9xcB50c2mohES1llNVcvWs3L73/G7WcP4aZJAzWXdxwKdwz8buAWoON+23q6eyGAuxeaWY/63mhmM4GZABkZGY2IKiKRUFpexVULV5GTt5Pf/NtwJmf2CTqSHKFwVqU/Gyhy9+wj2YG7z3f3THfP7N69+5H8CBGJkOLdFVwy/5/k5pdw/2WjVd5xLpwj8AnAuWZ2JtAW6GRmjwHbzKx36Oi7N1AUzaAi0jgFJeVMfXgFn5XuY8H0sZw8SAdU8e6wR+Du/mN3T3f3vsAlwGvuPhVYDkwPvWw68ELUUopIo3xSvIfJD7zN9j0VLJ4xTuXdTDTmPPA7gSVmNgPIAyZHJpKIRNL7W0uZtmAlAE/NPJ5vHJUScCKJlAYVuLu/AbwRuv85MDHykUQkUrI37+CKR1bRsU0ii68ezzHdOwQdSSJIV2KKNFN/W1/MzEez6ZXSlseuHk+apoNtdlTgIs1F7hLImgul+ZQn92bZrgvp2/0MHr1qHN07ajrY5kgFLtIc5C6BF2dBVTkA7cq28l+JD1E7YSjJHU8KOJxEiyY9EGkOsuZ+Wd5faEsFyX/7ZUCBpCmowEWaAS/Nr/+JQ22XZkEFLhLn8j4vo8i61f9kSnrThpEmpQIXiWNvfFTEOb9/i//xS6lJaHvgk0ntYOLtwQSTJqECF4lD7s59r2/gyoWr6J3Slht+8GMSzrsXUvoAVnd7zjwYPiXoqBJFOgtFJM7sqajm5iVr+cv72zh3xFHcedGwurUru05RYbcwKnCROPJJ8R6uXZzNxu17ue2s45jxzX6ax7sFU4GLxIlX123j359eS1JiKxbPGMeJxxzii0tpMVTgIjGutta5+/8+Zt5rGxiensIDU8fosngBVOAiMa20vIrZT+Xw+kfFTB6Tzh3nD6VtUkLQsSRGqMBFYtRHn+3m2sWrKSgp547zhzJ1fIbGu+UAKnCRGPRS7lZueTaX9m0SefKa48ns2yXoSBKDVOAiMaS6ppbfvPIRD/71U8Yc3Zn7LxtNz05tD/9GaZEOW+Bm1hZ4E2gTev2z7v4zM+sCPA30BTYBU9x9Z/SiijRvO/ZWMuvJHN7asJ2px2dw+9nfoHWirrWTQwvnCLwCONXd95hZEvCWmf0ZuBDIcvc7zWwOMAe4NYpZRZqt9wpKuXZxNsW7K/j1RcOZMlarxcvhhbOosbv7ntDDpNB/DpwHLAptXwScH5WEIs3c0jX5XPTA29S688x1J6i8JWxhjYGbWQKQDQwA7nP3FWbW090LAdy90Mx6HOK9M4GZABkZGZFJLdIMVNXU8ss/fsDCtzcxvl8X7rtsNN06aOUcCV9YBe7uNcBIM0sFlpnZ0HB34O7zgfkAmZmZfkQpRZqZ4t0VfP+JNazcuIOrJvTjx2cOJilB493SMA1dlb7EzN4AzgC2mVnv0NF3b6AoGgFFmpucvJ1c/9gaSsorueeSkZw3Mi3oSBKnDvtXvpl1Dx15Y2btgEnAh8ByYHroZdOBF6IVUqS5eGplHhc/+E+SEo3nrj9R5S2NEs4ReG9gUWgcvBWwxN1fMrN/AEvMbAaQB0yOYk6RuFZRXcPPl6/jyZV5nDSwG/deOorU5NZBx5I4d9gCd/dcYFQ92z8HJkYjlEhz8lnpPq5/PJucvBKuP+UYfnT6sSS00iXx0ni6ElMkilZu3MENj6+hrLKaBy4bzXeH9Q46kjQjKnCRKHB3Hv3HZu54aR19uiTz5DXjGdizY9CxpJlRgYtE2L6qGn6y7F2Wrilg0nE9+N3FI+nUNinoWNIMqcBFGit3CWTNhdJ8qjumcW/txSz9fAyzJw1k1qkDaaXxbokSXTkg0hi5S+DFWVC6BXASd+dz4557eemUrcyeNEjlLVGlAhdpjKy5UFV+wKZ2VsnQD+4JKJC0JCpwkUbw0vz6nzjUdpEIUoGLHIHyyhr+86V1FHjX+l+Qkt60gaRFUoGLNNDbn2znO3e/ycNvbeTtjBvwxINWiE9qBxNvDyactCg6C0UkTLv2VfGrP33Akyu30LdrMk/NPJ7j+58FuX2+PAuFlPS68h4+Jei40gKowEXC8Oq6bdz2/LsU767g2pP7M3vSINq1Tqh7cvgUFbYEQgUu8jW276ng58vf56XcQgb36shD0zIZnp4adCwRQAUuUi9354W1W/nFi++zt6KGm08bxLXfOkaLDEtMUYGLHGRrSTm3Pf8er31YxKiMVH590XDNYyIxSQUuElJb6zyxMo87//whNbXO7WcPYfqJfTX1q8Sswxa4mfUBHgV6AbXAfHe/x8y6AE8DfYFNwBR33xm9qCLRs3H7XuY8l8uKjTuYMKArv7pgOBldk4OOJfK1wjkCrwZudvc1ZtYRyDazV4ErgCx3v9PM5gBzgFujF1Uk8qpralnw1kZ+9+rHtE5sxa8vGs7kzHTMdNQtsS+cFXkKgcLQ/d1m9gGQBpwHnBJ62SLgDVTgEkfWbd3Frc/l8m5BKacP6ckd5w+lZ6e2QccSCVuDxsDNrC91y6utAHqGyp3QyvQ9DvGemcBMgIyMjMZkFYmIiuoafv/aBh544xNSk5O473ujOXNYLx11S9wJu8DNrAPwHDDb3XeF+5vd3ecD8wEyMzP9SEKKREr25p3c+lwuG4r2cOHoNH561hA6t9fiwhKfwipwM0uirrwfd/eloc3bzKx36Oi7N1AUrZAijVVWWc1v/vIRC9/eRO9ObXnkyrF8+9h6/9EoEjfCOQvFgAXAB+7+u/2eWg5MB+4M3b4QlYQijfTW+u3MWZpL/s5ypp1wNLecMZgObXQGrcS/cH4XTwAuB941s7WhbT+hrriXmNkMIA+YHJ2IIkemtKyKX/5pHUtW59O/W3uWXHsC4/p1CTqWSMSEcxbKW8ChBrwnRjaOSGS8/N5n/PSF99ixt5LrTzmGmyYOpG1SQtCxRCJK/46UZqV4d93kU398t5AhvTvxyBVjGZqWEnQskahQgUv82m81eE9JZ2X/G5m5tj/llTX8v+8cy8yT+5OUoMmnpPlSgUt8+mI1+NCCwla6hWFrfsoVKTdxztTZDOjRIeCAItGnwxOJT/WsBp9slcy2p1Te0mKowCXu7N5XdcjV4E2rwUsLogKXuLGnoprfv7aeb971OgW1Wg1eRGPgEvP2VlSz6B+beOjNT9lZVsXEwT2o7ftT+PucA4dRtBq8tDAqcIlZZZXVPPqPzcx/81N27K3k28d2Z/akQYzokwqMhS7JWg1eWjQVuMSc8soaHvvnZh588xO276nk5EHdmT1pIKMzOh/4Qq0GLy2cClxixr6qGh5fkccDb3zC9j0VfHNAN3542kDGHK3L30XqowKXwO2rquGplXnc/8YnFO2u4MRjuvLA1NGM7aviFvk6KnAJTEV1DU+v2sL9r3/CZ7v2Mb5fF+ZdOorj+x/iDBMROYAKXJpcZXUtS1Zv4b7XN1BYuo+xfTvzu4tHcOIx3YKOJhJXVODSZCqra3k2O5/7Xt9AQUk5Y47uzG/+bQQTBnTVcmYiR0AFLlFXVVPL0jX53PvaBvJ3ljOyTyr/deEwTh7YTcUt0gjhrMjzB+BsoMjdh4a2dQGeBvoCm4Ap7r4zejElHlXX1LI0p4Dfv7aBvB1ljEhP4Y7zh3LKoO4qbpEICOcIfCHwe+DR/bbNAbLc/U4zmxN6fGvk40k8qq6p5YW1W7n3tfVs+ryMoWmdWDA9k1MH91Bxi0RQOCvyvGlmfQ/afB5wSuj+IuANVOAtXk2t8+I7W5mXtZ5Pt+9lSO9OPDQtk0nHqbhFouFIx8B7unshQGhVei3v3dIctJhC9oAfcOvHx/JJ8V4G9+rI/04dw+lDetKqlYpbJFqi/iWmmc0EZgJkZGREe3fSFOpZTGHI6tuYlHwjN182kzO+0UvFLdIEjnQ62W1m1hsgdFt0qBe6+3x3z3T3zO7dux/h7iSW1Lz6i3oXU5jT+hnOHNZb5S3SRI60wJcD00P3pwMvRCaOxKq9FdUsXZPP5QtWYLu0mIJILAjnNMInqfvCspuZ5QM/A+4ElpjZDCAPmBzNkBKM6ppa/v7J5yxbk89f3t9GeVUNaant2N2mFymVn331DVpMQaRJhXMWyqWHeGpihLNIDHB33t+6i6VrClj+zla276mgU9tEzh+VxgWj0sg8ujOt3rvjgDFwQIspiARAV2IKAPk7y3hh7VaW5RSwoWgPSQnGqYN7cMGoNE45tgdtkxL+9eIv5uDWYgoigVKBt2Cl5VX8+d1CluYUsHLjDgDG9u3MLy8YylnDepOa3PrQb9ZiCiKBU4G3MJXVtbzxURHLcgrI+rCIyupa+ndrz82nDeL8UWn06ZIcdEQRCZMKvAVwd9bk7WRZTgEv5RZSUlZF1/at+d64DC4Ylcbw9BRdKSkSh1TgzdjG7XtZllPA8zkF5O0oo21SK04f0osLRqXxzYHdSEo40rNIRSQWqMCbmc/3VPBSbiHLcgpYu6UEM5hwTDdmTRzIGUN70aGNfslFmgv9aY43+81B8sXZH/uOu4hX123j+ZwC/vpxMdW1znG9O/GTMwdz7og0eqW0DTq1iESBCjyeHDQHCaVbqFx2I7c/l8uSihPo1aktM07qxwWj0hjcq1OwWUUk6lTgccLdqXn1FyQeNAdJa6/gx62XcP602Yzv15UEzUMi0mKowGPU3opqcvNLydmyk7V5JazdUsI/K/Ohnn7uXFWkBYFFWiAVeAyorXU2FO9hbV4JOVt2kpNXwsfbdlPrdc/369aeCQO6UbaxNx32FX71B2gOEpEWSQUegO17Kr4s67VbSnhnSyl7KqoBSGmXxIg+qXznG70YmZHKyPRUOrcPXRGZO1dzkIjIl1TgUbavqoZ1hbvICQ2D5OTtJH9nXQEntjIG9+7IBaPSGNknlZEZqfTr2v7Q82lrDhIR2Y8KPFz1nL53cHG6O3k7yg4o63WFu6iqqRsLOSqlLaMyOjP9hL6MzEhl6FEptGudUN/eDk1zkIhIiAo8HPWcvseLsyirrGF1p0lflvU7+aXs2FsJQHLrBIalpTDjm/0Z2SeVURmp9Oyk87FFJHJU4IdRUV1D4qu/IOGg0/eoKmfH8tuYVtkJMxjQvQOTjuvByD6dGZWRysAeHUjUpeoiEkWNKnAzOwO4B0gAHnb3OyOSan9hDF2Eo6qmlpKyKkrLK9lZVkVJWRU7yyopDd2WlFdRUlYZ2l5FaVnd68qravi0Tf2n76W1+pzHrx7PsPQUOrVNisD/rIhI+I64wM0sAbgPOA3IB1aZ2XJ3XxepcPUNXfiLs9hbUc22vudSUrZ/6VZSWh4q41BBl5RXsnNvFaXlVV+e5VGfxFZGanISKe2S6JzcmrTUtnzjqE6ktkuic/vWlK2o//Q9S0lnwgCdfy0iwWjMEfg4YIO7fwpgZk8B5wGRK/CsuV9Z/dyqyil58adMrEz5ystbGV+WcEpyEj06tmVQj46kJrcmNTmJzslJpCS3rivm0LbU5CQ6tEn8+ulUu+r0PRGJPY0p8DRgy36P84HxB7/IzGYCMwEyMjIatodDrHKe1upz7r54ZKiAW9M5OYnUdq3p2Dbx0KfgNYZO3xORGNSYAq+vKf0rG9znA/MBMjMzv/L810pJrzvj4+Adp6Rz/qi0Bv2oRtPpeyISYxpzmkQ+0Ge/x+nA1sbFOcjE2+uGKvanoQsREaBxBb4KGGhm/cysNXAJsDwysUKGT4Fz5kFKH8Dqbs+ZpyNhEREaMYTi7tVmdiPwF+pOI/yDu78fsWRf0NCFiEi9GnUeuLv/CfhThLKIiEgD6FJBEZE4pQIXEYlTKnARkTilAhcRiVPm3rBraxq1M7NiYPMRvr0bsD2CceKdPo9/0WdxIH0eB2oOn8fR7t794I1NWuCNYWar3T0z6ByxQp/Hv+izOJA+jwM1589DQygiInFKBS4iEqfiqcDnBx0gxujz+Bd9FgfS53GgZvt5xM0YuNcUsCAAAAJfSURBVIiIHCiejsBFRGQ/KnARkTgVFwVuZmeY2UdmtsHM5gSdJyhm1sfMXjezD8zsfTO7KehMscDMEswsx8xeCjpL0Mws1cyeNbMPQ79PTgg6U1DM7IehPyfvmdmTZtY26EyRFvMFvt/iyd8FhgCXmtmQYFMFphq42d2PA44Hvt+CP4v93QR8EHSIGHEP8LK7DwZG0EI/FzNLA2YBme4+lLopry8JNlXkxXyBs9/iye5eCXyxeHKL4+6F7r4mdH83dX84m3htudhiZunAWcDDQWcJmpl1Ak4GFgC4e6W7lwSbKlCJQDszSwSSifSKYTEgHgq8vsWTW3RpAZhZX2AUsCLYJIG7G7gFqA06SAzoDxQDj4SGlB42s/ZBhwqCuxcAvwXygEKg1N1fCTZV5MVDgYe1eHJLYmYdgOeA2e6+K+g8QTGzs4Eid88OOkuMSARGAw+4+yhgL9AivzMys87U/Uu9H3AU0N7MpgabKvLiocCjv3hyHDGzJOrK+3F3Xxp0noBNAM41s03UDa2damaPBRspUPlAvrt/8a+yZ6kr9JZoErDR3YvdvQpYCpwYcKaIi4cCj/7iyXHCzIy68c0P3P13QecJmrv/2N3T3b0vdb8vXnP3ZneUFS53/wzYYmbHhjZNBNYFGClIecDxZpYc+nMzkWb4hW6j1sRsCk22eHJ8mABcDrxrZmtD234SWptUBOAHwOOhg51PgSsDzhMId19hZs8Ca6g7eyuHZnhJvS6lFxGJU/EwhCIiIvVQgYuIxCkVuIhInFKBi4jEKRW4iEicUoGLiMQpFbiISJz6/8/zOpyl/7VwAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "\n",
    "plt.plot(a)\n",
    "plt.plot(a, 'o')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# do not worry about the `sklearn` library, we just need a bunch of data\n",
    "from sklearn.datasets import load_iris"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150, 4)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we load the iris data with the `load_iris` function\n",
    "iris = load_iris()\n",
    "iris.data.shape  # 150 flower samples with 4 features each"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f7eca98b250>"
      ]
     },
     "execution_count": 43,
     "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": [
    "# create a scatter plot, plotting the first feature vs the second one\n",
    "plt.scatter(\n",
    "    iris.data[:, 0],\n",
    "    iris.data[:, 1]\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "        0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "        2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "        2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]),\n",
       " (150,))"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# `iris.target` is an array that contains the species of each flower\n",
    "iris.target, iris.target.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f7eca8da0d0>"
      ]
     },
     "execution_count": 45,
     "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": [
    "plt.scatter(\n",
    "    iris.data[:, 0],   # x-axis\n",
    "    iris.data[:, 1],   # y-axis\n",
    "    c=iris.target      # color each dot by its species\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f7eca8c5a10>"
      ]
     },
     "execution_count": 46,
     "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": [
    "plt.scatter(\n",
    "    iris.data[:, 2],   # x-axis\n",
    "    iris.data[:, 3],   # y-axis\n",
    "    c=iris.target      # color for each dot\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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": [
    "# create a boxplot, for mroe information check\n",
    "# for more information check out https://en.wikipedia.org/wiki/Box_plot\n",
    "_ = plt.boxplot(iris.data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# let's use seaborn, a library for statistical data viz\n",
    "# seaborn is built on top of matplotlib\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7eca811bd0>"
      ]
     },
     "execution_count": 49,
     "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": [
    "# plt.boxplot(iris.data)     # boxplot with matplotib\n",
    "sns.boxplot(data=iris.data)  # that's wayyyy too different"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x7f7ec89b12d0>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 804.75x720 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# let's reload the iris dataset, this time with seaborn\n",
    "df = sns.load_dataset(\"iris\")\n",
    "\n",
    "# and let's create a pairplot\n",
    "sns.pairplot(df, hue=\"species\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# in all this, what's `df`?\n",
    "type(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "# it's a `pandas` dataframe (i.e. table), similar to R dataframes\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "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": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['sepal_length', 'sepal_width', 'petal_length', 'petal_width',\n",
       "       'species'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get the \"list\" of columns\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      5.1\n",
       "1      4.9\n",
       "2      4.7\n",
       "3      4.6\n",
       "4      5.0\n",
       "      ... \n",
       "145    6.7\n",
       "146    6.3\n",
       "147    6.5\n",
       "148    6.2\n",
       "149    5.9\n",
       "Name: sepal_length, Length: 150, dtype: float64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# access the values of 1 column\n",
    "df.sepal_length"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      5.1\n",
       "1      4.9\n",
       "2      4.7\n",
       "3      4.6\n",
       "4      5.0\n",
       "      ... \n",
       "145    6.7\n",
       "146    6.3\n",
       "147    6.5\n",
       "148    6.2\n",
       "149    5.9\n",
       "Name: sepal_length, Length: 150, dtype: float64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# access the values of 1 column in a dict-like fashion\n",
    "df['sepal_length']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "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",
       "    <tr>\n",
       "      <th>...</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 2 columns</p>\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\n",
       "..            ...          ...\n",
       "145           6.7          3.0\n",
       "146           6.3          2.5\n",
       "147           6.5          3.0\n",
       "148           6.2          3.4\n",
       "149           5.9          3.0\n",
       "\n",
       "[150 rows x 2 columns]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# access the values of 2 columns\n",
    "df[['sepal_length', 'sepal_width']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sepal_length    5.843333\n",
       "sepal_width     3.057333\n",
       "dtype: float64"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# mean values of the 2 columns\n",
    "df[['sepal_length', 'sepal_width']].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sepal_length    0.685694\n",
       "sepal_width     0.189979\n",
       "dtype: float64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# var of 2 columns\n",
    "df[['sepal_length', 'sepal_width']].var()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "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",
       "    <tr>\n",
       "      <th>species</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>setosa</th>\n",
       "      <td>5.006</td>\n",
       "      <td>3.428</td>\n",
       "      <td>1.462</td>\n",
       "      <td>0.246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>versicolor</th>\n",
       "      <td>5.936</td>\n",
       "      <td>2.770</td>\n",
       "      <td>4.260</td>\n",
       "      <td>1.326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>virginica</th>\n",
       "      <td>6.588</td>\n",
       "      <td>2.974</td>\n",
       "      <td>5.552</td>\n",
       "      <td>2.026</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            sepal_length  sepal_width  petal_length  petal_width\n",
       "species                                                         \n",
       "setosa             5.006        3.428         1.462        0.246\n",
       "versicolor         5.936        2.770         4.260        1.326\n",
       "virginica          6.588        2.974         5.552        2.026"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# mean of all 4 columns, grouping by \"species\"\n",
    "df.groupby('species').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "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": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# the first 5 lines of the dataframe\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "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>140</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.4</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.1</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>5.8</td>\n",
       "      <td>2.7</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.9</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>6.8</td>\n",
       "      <td>3.2</td>\n",
       "      <td>5.9</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.3</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.5</td>\n",
       "      <td>virginica</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",
       "</div>"
      ],
      "text/plain": [
       "     sepal_length  sepal_width  petal_length  petal_width    species\n",
       "140           6.7          3.1           5.6          2.4  virginica\n",
       "141           6.9          3.1           5.1          2.3  virginica\n",
       "142           5.8          2.7           5.1          1.9  virginica\n",
       "143           6.8          3.2           5.9          2.3  virginica\n",
       "144           6.7          3.3           5.7          2.5  virginica\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"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# the last 10 lines of the dataframe\n",
    "df.tail(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "# back to numpy for a sec!\n",
    "a = np.arange(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False,  True,  True,  True,\n",
       "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True])"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we can compare every element of `a` with a constant\n",
    "# we obtain a \"binary mask\" (boolean vector)\n",
    "a > 50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67,\n",
       "       68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84,\n",
       "       85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99])"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# and we can use it to select only the values that satisfy the condition\n",
    "a[a > 50]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "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": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we can do the same with dataframes ;)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "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>5</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.4</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.3</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4.4</td>\n",
       "      <td>2.9</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.1</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.1</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>4.3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>5.8</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>5.7</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.4</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.4</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.3</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>5.7</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.3</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.3</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.4</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.3</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.5</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.4</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>5.2</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>5.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.4</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>5.2</td>\n",
       "      <td>4.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.1</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>5.5</td>\n",
       "      <td>4.2</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>4.9</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>35</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>5.5</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.1</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>4.4</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.3</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>4.5</td>\n",
       "      <td>2.3</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.3</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>4.4</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>43</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.6</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.4</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.3</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>5.3</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.3</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\n",
       "5            5.4          3.9           1.7          0.4  setosa\n",
       "6            4.6          3.4           1.4          0.3  setosa\n",
       "7            5.0          3.4           1.5          0.2  setosa\n",
       "8            4.4          2.9           1.4          0.2  setosa\n",
       "9            4.9          3.1           1.5          0.1  setosa\n",
       "10           5.4          3.7           1.5          0.2  setosa\n",
       "11           4.8          3.4           1.6          0.2  setosa\n",
       "12           4.8          3.0           1.4          0.1  setosa\n",
       "13           4.3          3.0           1.1          0.1  setosa\n",
       "14           5.8          4.0           1.2          0.2  setosa\n",
       "15           5.7          4.4           1.5          0.4  setosa\n",
       "16           5.4          3.9           1.3          0.4  setosa\n",
       "17           5.1          3.5           1.4          0.3  setosa\n",
       "18           5.7          3.8           1.7          0.3  setosa\n",
       "19           5.1          3.8           1.5          0.3  setosa\n",
       "20           5.4          3.4           1.7          0.2  setosa\n",
       "21           5.1          3.7           1.5          0.4  setosa\n",
       "22           4.6          3.6           1.0          0.2  setosa\n",
       "23           5.1          3.3           1.7          0.5  setosa\n",
       "24           4.8          3.4           1.9          0.2  setosa\n",
       "25           5.0          3.0           1.6          0.2  setosa\n",
       "26           5.0          3.4           1.6          0.4  setosa\n",
       "27           5.2          3.5           1.5          0.2  setosa\n",
       "28           5.2          3.4           1.4          0.2  setosa\n",
       "29           4.7          3.2           1.6          0.2  setosa\n",
       "30           4.8          3.1           1.6          0.2  setosa\n",
       "31           5.4          3.4           1.5          0.4  setosa\n",
       "32           5.2          4.1           1.5          0.1  setosa\n",
       "33           5.5          4.2           1.4          0.2  setosa\n",
       "34           4.9          3.1           1.5          0.2  setosa\n",
       "35           5.0          3.2           1.2          0.2  setosa\n",
       "36           5.5          3.5           1.3          0.2  setosa\n",
       "37           4.9          3.6           1.4          0.1  setosa\n",
       "38           4.4          3.0           1.3          0.2  setosa\n",
       "39           5.1          3.4           1.5          0.2  setosa\n",
       "40           5.0          3.5           1.3          0.3  setosa\n",
       "41           4.5          2.3           1.3          0.3  setosa\n",
       "42           4.4          3.2           1.3          0.2  setosa\n",
       "43           5.0          3.5           1.6          0.6  setosa\n",
       "44           5.1          3.8           1.9          0.4  setosa\n",
       "45           4.8          3.0           1.4          0.3  setosa\n",
       "46           5.1          3.8           1.6          0.2  setosa\n",
       "47           4.6          3.2           1.4          0.2  setosa\n",
       "48           5.3          3.7           1.5          0.2  setosa\n",
       "49           5.0          3.3           1.4          0.2  setosa"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# select only the rows for which `species` is equal to `setosa`\n",
    "df[df.species == 'setosa']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "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>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>5</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.4</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>5.8</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>5.7</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.4</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.4</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.3</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>5.7</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.3</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.3</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.4</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.3</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.5</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.4</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>5.2</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>5.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.4</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>5.2</td>\n",
       "      <td>4.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.1</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>5.5</td>\n",
       "      <td>4.2</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>5.5</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.3</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.6</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.4</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>5.3</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.3</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",
       "4            5.0          3.6           1.4          0.2  setosa\n",
       "5            5.4          3.9           1.7          0.4  setosa\n",
       "7            5.0          3.4           1.5          0.2  setosa\n",
       "10           5.4          3.7           1.5          0.2  setosa\n",
       "14           5.8          4.0           1.2          0.2  setosa\n",
       "15           5.7          4.4           1.5          0.4  setosa\n",
       "16           5.4          3.9           1.3          0.4  setosa\n",
       "17           5.1          3.5           1.4          0.3  setosa\n",
       "18           5.7          3.8           1.7          0.3  setosa\n",
       "19           5.1          3.8           1.5          0.3  setosa\n",
       "20           5.4          3.4           1.7          0.2  setosa\n",
       "21           5.1          3.7           1.5          0.4  setosa\n",
       "23           5.1          3.3           1.7          0.5  setosa\n",
       "25           5.0          3.0           1.6          0.2  setosa\n",
       "26           5.0          3.4           1.6          0.4  setosa\n",
       "27           5.2          3.5           1.5          0.2  setosa\n",
       "28           5.2          3.4           1.4          0.2  setosa\n",
       "31           5.4          3.4           1.5          0.4  setosa\n",
       "32           5.2          4.1           1.5          0.1  setosa\n",
       "33           5.5          4.2           1.4          0.2  setosa\n",
       "35           5.0          3.2           1.2          0.2  setosa\n",
       "36           5.5          3.5           1.3          0.2  setosa\n",
       "39           5.1          3.4           1.5          0.2  setosa\n",
       "40           5.0          3.5           1.3          0.3  setosa\n",
       "43           5.0          3.5           1.6          0.6  setosa\n",
       "44           5.1          3.8           1.9          0.4  setosa\n",
       "46           5.1          3.8           1.6          0.2  setosa\n",
       "48           5.3          3.7           1.5          0.2  setosa\n",
       "49           5.0          3.3           1.4          0.2  setosa"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# combine 2 conditions with the & (and) operator (can also use | for or conditions)\n",
    "df[\n",
    "    (df.species == 'setosa')\n",
    "    & (df.sepal_length >= 5)\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "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>petal_width</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>2.5</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>1.9</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>2.1</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>2.2</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>2.1</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>1.7</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>2.5</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>2.0</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>1.9</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>2.1</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>2.0</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>2.4</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>2.2</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>1.5</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>2.0</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>2.0</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>2.1</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>2.1</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>1.6</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>1.9</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>2.0</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>2.2</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>1.5</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>1.4</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>2.4</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>2.1</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>2.4</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>1.9</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>2.5</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>1.9</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>2.0</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     petal_width    species\n",
       "100          2.5  virginica\n",
       "101          1.9  virginica\n",
       "102          2.1  virginica\n",
       "103          1.8  virginica\n",
       "104          2.2  virginica\n",
       "105          2.1  virginica\n",
       "106          1.7  virginica\n",
       "107          1.8  virginica\n",
       "108          1.8  virginica\n",
       "109          2.5  virginica\n",
       "110          2.0  virginica\n",
       "111          1.9  virginica\n",
       "112          2.1  virginica\n",
       "113          2.0  virginica\n",
       "114          2.4  virginica\n",
       "115          2.3  virginica\n",
       "116          1.8  virginica\n",
       "117          2.2  virginica\n",
       "118          2.3  virginica\n",
       "119          1.5  virginica\n",
       "120          2.3  virginica\n",
       "121          2.0  virginica\n",
       "122          2.0  virginica\n",
       "123          1.8  virginica\n",
       "124          2.1  virginica\n",
       "125          1.8  virginica\n",
       "126          1.8  virginica\n",
       "127          1.8  virginica\n",
       "128          2.1  virginica\n",
       "129          1.6  virginica\n",
       "130          1.9  virginica\n",
       "131          2.0  virginica\n",
       "132          2.2  virginica\n",
       "133          1.5  virginica\n",
       "134          1.4  virginica\n",
       "135          2.3  virginica\n",
       "136          2.4  virginica\n",
       "137          1.8  virginica\n",
       "138          1.8  virginica\n",
       "139          2.1  virginica\n",
       "140          2.4  virginica\n",
       "141          2.3  virginica\n",
       "142          1.9  virginica\n",
       "143          2.3  virginica\n",
       "144          2.5  virginica\n",
       "145          2.3  virginica\n",
       "146          1.9  virginica\n",
       "147          2.0  virginica\n",
       "148          2.3  virginica\n",
       "149          1.8  virginica"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# use the `.loc` indexing operator to select both on rows and columns\n",
    "# here we select the virginica species, and only consider 2 columns\n",
    "df.loc[\n",
    "    df.species == 'virginica',\n",
    "    ['petal_width', 'species']\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "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>100</th>\n",
       "      <td>6.3</td>\n",
       "      <td>3.3</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>5.8</td>\n",
       "      <td>2.7</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.9</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>7.1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.9</td>\n",
       "      <td>2.1</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.9</td>\n",
       "      <td>5.6</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.8</td>\n",
       "      <td>2.2</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>7.6</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.6</td>\n",
       "      <td>2.1</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>4.9</td>\n",
       "      <td>2.5</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.7</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>7.3</td>\n",
       "      <td>2.9</td>\n",
       "      <td>6.3</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>6.7</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>7.2</td>\n",
       "      <td>3.6</td>\n",
       "      <td>6.1</td>\n",
       "      <td>2.5</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.2</td>\n",
       "      <td>5.1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>6.4</td>\n",
       "      <td>2.7</td>\n",
       "      <td>5.3</td>\n",
       "      <td>1.9</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>6.8</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.5</td>\n",
       "      <td>2.1</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>5.7</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>5.8</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.1</td>\n",
       "      <td>2.4</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>6.4</td>\n",
       "      <td>3.2</td>\n",
       "      <td>5.3</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.5</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>7.7</td>\n",
       "      <td>3.8</td>\n",
       "      <td>6.7</td>\n",
       "      <td>2.2</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>7.7</td>\n",
       "      <td>2.6</td>\n",
       "      <td>6.9</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>6.0</td>\n",
       "      <td>2.2</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.2</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>5.6</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.9</td>\n",
       "      <td>2.0</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>7.7</td>\n",
       "      <td>2.8</td>\n",
       "      <td>6.7</td>\n",
       "      <td>2.0</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.7</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.3</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.1</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>7.2</td>\n",
       "      <td>3.2</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>6.2</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>6.1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>6.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.1</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>7.2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.8</td>\n",
       "      <td>1.6</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>7.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>6.1</td>\n",
       "      <td>1.9</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>7.9</td>\n",
       "      <td>3.8</td>\n",
       "      <td>6.4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>6.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.2</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>6.1</td>\n",
       "      <td>2.6</td>\n",
       "      <td>5.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>7.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.1</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>6.3</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.4</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>6.4</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.5</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.1</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.4</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.1</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>5.8</td>\n",
       "      <td>2.7</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.9</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>6.8</td>\n",
       "      <td>3.2</td>\n",
       "      <td>5.9</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.3</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.5</td>\n",
       "      <td>virginica</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",
       "</div>"
      ],
      "text/plain": [
       "     sepal_length  sepal_width  petal_length  petal_width    species\n",
       "100           6.3          3.3           6.0          2.5  virginica\n",
       "101           5.8          2.7           5.1          1.9  virginica\n",
       "102           7.1          3.0           5.9          2.1  virginica\n",
       "103           6.3          2.9           5.6          1.8  virginica\n",
       "104           6.5          3.0           5.8          2.2  virginica\n",
       "105           7.6          3.0           6.6          2.1  virginica\n",
       "106           4.9          2.5           4.5          1.7  virginica\n",
       "107           7.3          2.9           6.3          1.8  virginica\n",
       "108           6.7          2.5           5.8          1.8  virginica\n",
       "109           7.2          3.6           6.1          2.5  virginica\n",
       "110           6.5          3.2           5.1          2.0  virginica\n",
       "111           6.4          2.7           5.3          1.9  virginica\n",
       "112           6.8          3.0           5.5          2.1  virginica\n",
       "113           5.7          2.5           5.0          2.0  virginica\n",
       "114           5.8          2.8           5.1          2.4  virginica\n",
       "115           6.4          3.2           5.3          2.3  virginica\n",
       "116           6.5          3.0           5.5          1.8  virginica\n",
       "117           7.7          3.8           6.7          2.2  virginica\n",
       "118           7.7          2.6           6.9          2.3  virginica\n",
       "119           6.0          2.2           5.0          1.5  virginica\n",
       "120           6.9          3.2           5.7          2.3  virginica\n",
       "121           5.6          2.8           4.9          2.0  virginica\n",
       "122           7.7          2.8           6.7          2.0  virginica\n",
       "123           6.3          2.7           4.9          1.8  virginica\n",
       "124           6.7          3.3           5.7          2.1  virginica\n",
       "125           7.2          3.2           6.0          1.8  virginica\n",
       "126           6.2          2.8           4.8          1.8  virginica\n",
       "127           6.1          3.0           4.9          1.8  virginica\n",
       "128           6.4          2.8           5.6          2.1  virginica\n",
       "129           7.2          3.0           5.8          1.6  virginica\n",
       "130           7.4          2.8           6.1          1.9  virginica\n",
       "131           7.9          3.8           6.4          2.0  virginica\n",
       "132           6.4          2.8           5.6          2.2  virginica\n",
       "133           6.3          2.8           5.1          1.5  virginica\n",
       "134           6.1          2.6           5.6          1.4  virginica\n",
       "135           7.7          3.0           6.1          2.3  virginica\n",
       "136           6.3          3.4           5.6          2.4  virginica\n",
       "137           6.4          3.1           5.5          1.8  virginica\n",
       "138           6.0          3.0           4.8          1.8  virginica\n",
       "139           6.9          3.1           5.4          2.1  virginica\n",
       "140           6.7          3.1           5.6          2.4  virginica\n",
       "141           6.9          3.1           5.1          2.3  virginica\n",
       "142           5.8          2.7           5.1          1.9  virginica\n",
       "143           6.8          3.2           5.9          2.3  virginica\n",
       "144           6.7          3.3           5.7          2.5  virginica\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"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# you can also simply select over the rows ;)\n",
    "df.loc[\n",
    "    df.species == 'virginica',\n",
    "]"
   ]
  },
  {
   "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": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}