00_python_warmup.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# python warm-up for statistics and machine learning in life sciences <a id='top'></a>\n",
    "\n",
    "The goal of this notebook is to provide a warm-up before the course, as well as provide a gentle yet functionnal intro to some of the libraries we will be using during the course.\n",
    "\n",
    "\n",
    "* [01. Basic python](#basic)\n",
    "* [02. numpy and vectorized operations](#numpy)\n",
    "* [03. basic plotting](#plot)\n",
    "* [Exercise 00.01](#ex01)\n",
    "* [04. generating random numbers](#random)\n",
    "* [05. statistical testing](#stats)\n",
    "* [06. bringing together numpy, numpy.random, and matplotlib](#together)\n",
    "* [07. the briefest intro to pandas](#pandas)\n",
    "* [Exercise 00.02 : tying everything together](#exo2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 01. basic python <a class=\"anchor\" id=\"basic\"></a>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 1, 4, 9, 16, 25, 36, 49, 64, 81]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "X = []\n",
    "\n",
    "for i in range(10):\n",
    "    X.append( i**2 )\n",
    "\n",
    "print(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "4\n",
      "9\n",
      "16\n",
      "25\n",
      "36\n",
      "49\n",
      "64\n",
      "81\n"
     ]
    }
   ],
   "source": [
    "\n",
    "for x in X:\n",
    "    print(x)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 is even\n",
      "1 is odd\n",
      "4 is even\n",
      "9 is odd\n",
      "16 is even\n",
      "25 is odd\n",
      "36 is even\n",
      "49 is odd\n",
      "64 is even\n",
      "81 is odd\n"
     ]
    }
   ],
   "source": [
    "for x in X:\n",
    "    if x%2 == 1:\n",
    "        print(x,'is odd')\n",
    "    else:\n",
    "        print(x,'is even')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X     [0, 1, 4, 9, 16, 25, 36, 49, 64, 81]\n",
      "Xeven [0, 4, 16, 36, 64]\n",
      "Xodd  [1, 9, 25, 49, 81]\n"
     ]
    }
   ],
   "source": [
    "# list comprehension is a very fine way of compressing all this\n",
    "\n",
    "X = [ i**2 for i in range(10) ]\n",
    "\n",
    "Xeven = [ x for x in X if x%2 == 0 ]\n",
    "Xodd = [ x for x in X if x%2 == 1 ]\n",
    "\n",
    "\n",
    "print( 'X    ', X )\n",
    "print( 'Xeven', Xeven )\n",
    "print( 'Xodd ', Xodd )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[back to the top](#top)\n",
    "\n",
    "## 02. numpy and vectorized operations <a class=\"anchor\" id=\"numpy\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0  1  4  9 16 25 36 49 64 81]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "X_array = np.array(X)\n",
    "\n",
    "print(X_array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.   0.5  2.   4.5  8.  12.5 18.  24.5 32.  40.5]\n"
     ]
    }
   ],
   "source": [
    "print(X_array / 2 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1.00000000e+00 2.71828183e+00 5.45981500e+01 8.10308393e+03\n",
      " 8.88611052e+06 7.20048993e+10 4.31123155e+15 1.90734657e+21\n",
      " 6.23514908e+27 1.50609731e+35]\n",
      "[      -inf 0.         1.38629436 2.19722458 2.77258872 3.21887582\n",
      " 3.58351894 3.8918203  4.15888308 4.39444915]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_490123/2855859755.py:2: RuntimeWarning: divide by zero encountered in log\n",
      "  print( np.log(X_array ) )\n"
     ]
    }
   ],
   "source": [
    "print( np.exp(X_array ) )\n",
    "print( np.log(X_array ) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape (10,)\n",
      "mean  28.5\n",
      "standard deviation 26.852374196707448\n"
     ]
    }
   ],
   "source": [
    "print( 'shape' , X_array.shape )\n",
    "print( 'mean ' , np.mean(X_array) )\n",
    "print( 'standard deviation' , np.std(X_array) )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### linspace and arange"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "linspace 0,2,9 :\t[0.   0.25 0.5  0.75 1.   1.25 1.5  1.75 2.  ]\n",
      "linspace -0.5,0.5,11 :\t[-0.5 -0.4 -0.3 -0.2 -0.1  0.   0.1  0.2  0.3  0.4  0.5]\n",
      "linspace 10,0,11 :\t[10.  9.  8.  7.  6.  5.  4.  3.  2.  1.  0.]\n"
     ]
    }
   ],
   "source": [
    "print( 'linspace 0,2,9 :' , np.linspace(0,2,9) , sep='\\t' )\n",
    "print( 'linspace -0.5,0.5,11 :' , np.linspace(-0.5,0.5,11) , sep='\\t' )\n",
    "print( 'linspace 10,0,11 :' , np.linspace(10,0,11) , sep='\\t' )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "arange 0,2,0.1 :\t[1.5 1.6 1.7 1.8 1.9]\n",
      "arange -1,1,0.125 :\t[-1.    -0.875 -0.75  -0.625 -0.5   -0.375 -0.25  -0.125  0.     0.125\n",
      "  0.25   0.375  0.5    0.625  0.75   0.875]\n",
      "arange 10,2 :\t[]\n"
     ]
    }
   ],
   "source": [
    "print( \"arange 0,2,0.1 :\", np.arange(1.5,2,0.1) , sep='\\t' )\n",
    "print( \"arange -1,1,0.125 :\", np.arange(-1,1,0.125) , sep='\\t' )\n",
    "print( \"arange 10,2 :\", np.arange(10,2,1) , sep='\\t' ) # reverse does not work!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[back to the top](#top)\n",
    "\n",
    "## 03. basic plotting <a class=\"anchor\" id=\"plot\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.plot( [0,1,2,3] , [10,5,7,0.2] )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Adding color, symbols, ...**\n",
    "\n",
    "`matplotlib` offers many options to customize the appearance of your plot.\n",
    "\n",
    "Here are the (some) common arguments to `plot()` (which can also be applied to many other graphical representations):\n",
    " * `color` : could be given as a (red,green,blue) tuple, a [name](https://matplotlib.org/3.1.0/gallery/color/named_colors.html), a hex code, ...  (see [here](https://matplotlib.org/tutorials/colors/colors.html) for all the options)\n",
    " * `marker` : symbols for the data point. `'.'` is a point, `'v'` a down triangle, ... see [here](https://matplotlib.org/3.3.3/api/markers_api.html#module-matplotlib.markers) for the list of possibilities.\n",
    " * `linestyle` : style of the line. `'-'` is solid, `'--'` is dashed, `''` for no line. See [here](https://matplotlib.org/3.3.3/gallery/lines_bars_and_markers/linestyles.html) for more options\n",
    " * `linewidth` : width of the lines\n",
    " * `markersize` : size of the markers\n",
    "\n",
    "You are invited to experiment and explore these options. Here are a few examples:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y1 = [1,2,3,10,5]\n",
    "y2 = [10,9,7,5.5,6]\n",
    "y3 = [4,3,1.5,1]\n",
    "\n",
    "# green, dashed line, with circle markers\n",
    "plt.plot( y1, color = 'green', marker = 'o', linestyle = '--', linewidth = 2, markersize = 8 )\n",
    "\n",
    "# blue triangle with no line\n",
    "plt.plot( y2, color = 'blue', marker = 'v', linestyle = '' , markersize = 16 )\n",
    "\n",
    "# solid orange line\n",
    "plt.plot(y3, color = 'orange', marker = '', linestyle = '-', linewidth = 4 )\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that:\n",
    " * you can call plot several time in a row to make several lines appear (only `plt.show()` causes the figure to appear)\n",
    " * the frame of the picture automatically adjust to what it needs to show"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**multiple subplots**\n",
    "\n",
    "Now would normally be when we show you how to add labels, titles and legends to figures. \n",
    "\n",
    "However, the way `matplotlib` is built, it is actually a bit more efficient to first learn how to create multiple subplots.\n",
    "\n",
    "\n",
    "Creating multiple plots is possible with the function `plt.subplots()`.\n",
    "Amon its many arguments, it takes:\n",
    " * `nrows` : number of subplot rows\n",
    " * `ncols` : number of subplot columns\n",
    " * `figsize` : tuple (width,height) of the figure\n",
    "\n",
    "This function creates a Figure and an Axes object.\n",
    "The Axes object can be either : \n",
    " * a simple Axe is there is 1 row and 1 columns\n",
    " * a list of Axe objects if there is 1 row and multiple columns, or 1 column and multiple rows\n",
    " * a list of lists of Axes objects if there is multiple rows and multiple columns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y1 = [1,2,3,10,5]\n",
    "y2 = [10,9,7,5.5,6]\n",
    "y3 = [4,3,1.5,1]\n",
    "\n",
    "\n",
    "# subplots returns a Figure and an Axes object\n",
    "fig, ax = plt.subplots(nrows=1, ncols=2) # 2 columns and 1 row\n",
    "\n",
    "# ax is a list with two objects. Each object correspond to 1 subplot\n",
    "\n",
    "# accessing to the first column ax[0]\n",
    "ax[0].plot( y1, color = 'green', marker = 'o', linestyle = '--', linewidth = 2, markersize = 8 )\n",
    "\n",
    "# accessing to the second column ax[1]\n",
    "ax[1].plot( y2, color = 'blue', marker = 'v', linestyle = '' , markersize = 16 )\n",
    "ax[1].plot( y3, color = 'orange', marker = '', linestyle = '-' )\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice how we call `ax[0].plot(...)` instead of `plt.plot(...)` to specify in which subplots we want to plot."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**multiple subplots - continued**\n",
    "\n",
    "Let's see the same thing with several lines and several columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x1200 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y1 = [1,2,3,10,5]\n",
    "y2 = [10,9,7,5.5,6]\n",
    "y3 = [4,3,1.5,1]\n",
    "y4 = [1,2,3,7,5]\n",
    "\n",
    "# 2 columns and 2 rows, and we also set the figure size\n",
    "fig, ax = plt.subplots(nrows=2, ncols=2 , figsize = (12,12))\n",
    "\n",
    "# ax is a list of two lists with two objects each.\n",
    "\n",
    "# accessing to the first row, first column : ax[0][0]\n",
    "ax[0][0].plot( y1, color = 'green', marker = 'o', linestyle = '--', linewidth = 2, markersize = 8 )\n",
    "\n",
    "# accessing to the first row, second column : ax[0][1]\n",
    "ax[0][1].plot( y2, color = 'blue', marker = 'v', linestyle = '' , markersize = 16 )\n",
    "\n",
    "# accessing to the second row, first column : ax[1][0]\n",
    "ax[1][0].plot( y3, color = 'orange', marker = 'x', linestyle = '-' )\n",
    "\n",
    "# accessing to the first row, second column : ax[1][1]\n",
    "ax[1][1].plot( y4, color = 'teal', linestyle = '-.' , linewidth=5 )\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**setting up labels**\n",
    "\n",
    "To set the labels at the x-axis, y-axis and title, we use the method of the Axe object:\n",
    " * `.set_xlabel(...)`\n",
    " * `.set_ylabel(...)`\n",
    " * `.set_title(...) `\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y1 = [1,2,3,10,5]\n",
    "y2 = [10,9,7,5.5,6]\n",
    "y3 = [4,3,1.5,1]\n",
    "\n",
    "# subplots returns a Figure and an Axes object\n",
    "fig, ax = plt.subplots(nrows=1, ncols=2 , figsize=(10,5)) # 2 columns and 1 row\n",
    "\n",
    "\n",
    "# accessing to the first column ax[0]\n",
    "ax[0].plot( y1, color = 'green', marker = 'o', linestyle = '--', linewidth = 2, markersize = 8 )\n",
    "ax[0].set_xlabel('x-axis label')\n",
    "ax[0].set_ylabel('y-axis label')\n",
    "ax[0].set_title('plot 1')\n",
    "\n",
    "\n",
    "# accessing to the second column ax[1]\n",
    "ax[1].plot( y2, color = 'blue', marker = 'v', linestyle = '' , markersize = 16 )\n",
    "ax[1].plot( y3, color = 'orange', marker = '', linestyle = '-' )\n",
    "ax[1].set_xlabel('x-axis label')\n",
    "ax[1].set_ylabel('y-axis label')\n",
    "ax[1].set_title('plot 2')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**setting up a legend** \n",
    "\n",
    "Each element we add to the figure using `plot()` can be given a label using the `label` argument.\n",
    "Then, a legend may be added to the figure using the `legend()` method.\n",
    "\n",
    "This `legend()` method can take a `loc` argument that specifies where it should be plotted. \n",
    "Possible values for this argument are: `'best' , 'upper right' , 'upper left' , 'lower left' , 'lower right' , 'right' , 'center left' , 'center right' , 'lower center' , 'upper center' , 'center'` (the default is `best`).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "fig, ax = plt.subplots(nrows=1, ncols=1 , figsize=(10,5)) # 2 columns and 1 row\n",
    "\n",
    "# NB : with 1 col and 1 row, ax is directly the sole subplot we have\n",
    "#      so to call it we just use ax.plot , ax.set_xlabel , ...\n",
    "\n",
    "ax.plot( y1, color = 'green', marker = 'o', linestyle = '--', linewidth = 2 , label = 'line A' )\n",
    "ax.plot( y2, color = 'blue', marker = 'v', linestyle = '' , markersize =  8 , label = 'line B' )\n",
    "ax.plot( y3, color = 'orange', marker = '', linestyle = '-' , linewidth = 2 , label = 'line C' )\n",
    "\n",
    "ax.set_xlabel('x-axis label')\n",
    "ax.set_ylabel('y-axis label')\n",
    "ax.set_title('plot with a legend')\n",
    "\n",
    "#adding a legend in the upper right\n",
    "ax.legend( loc='upper right')\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**additional : writing a figure to a file**\n",
    "\n",
    "Writing a matplotlib figure to a file can be achieved simply by replacing the call to `plt.show()` to `plt.savefig(...)`.\n",
    "\n",
    "`plt.savefig` takes a number of argument, the most commons are :\n",
    " * `fname` : name of the file to write the figure. The extension is used to determine the output format (.pdf,.png, .jpg , .svg ,  ...). Many formats are supported, you can get a list with this command : `plt.gcf().canvas.get_supported_filetypes()`\n",
    " * `dpi` : dots per inches , useful to set-up when saving to raster formats (ie., pixel-based such as png or jpeg). The actual size of the image is set using the argument `figsize` of `plt.subplots()`\n",
    "\n",
    "\n",
    "> Note : in a jupyter notebook the figure will still be shown, whereas in a standard .py script it will not appear on screen.\n",
    "\n",
    "\n",
    "Here is a demonstration. Apply in on your side and verify that the file `testPlot.png` was created:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "y1 = [1,2,3,10,5]\n",
    "y2 = [10,9,7,5.5,6]\n",
    "y3 = [4,3,1.5,1]\n",
    "\n",
    "\n",
    "# subplots returns a Figure and an Axes object\n",
    "fig, ax = plt.subplots(nrows=1, ncols=2 , figsize = (10,6) ) # 2 columns and 1 row\n",
    "\n",
    "# ax is a list with two objects. Each object correspond to 1 subplot\n",
    "\n",
    "# accessing to the first column ax[0]\n",
    "ax[0].plot( y1, color = 'green', marker = 'o', linestyle = '--', linewidth = 2, markersize = 8 )\n",
    "\n",
    "# accessing to the second column ax[1]\n",
    "ax[1].plot( y2, color = 'blue', marker = 'v', linestyle = '' , markersize = 16 )\n",
    "ax[1].plot( y3, color = 'orange', marker = '', linestyle = '-' )\n",
    "\n",
    "plt.savefig( 'testPlot.png' , dpi = 90  )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise 00.01 : bringing together numpy and matplotlib <a class=\"anchor\" id=\"ex01\"></a>\n",
    "\n",
    "Numpy arrays can be plotted as if they were lists.\n",
    "\n",
    "1. plot x and y, where:\n",
    "    * y = 1/(1+exp(-x))\n",
    "    * x varies between -5 and 5 (plotting around a 100 points should suffice).\n",
    "\n",
    "2. **Bonus :** plot multiples lines : y = 1/(1+exp(-x*b)) , for the following values of b: 0.5 , 1 , 2 , 4.\n",
    "    * x still varies between -5 and 5 (plotting around a 100 points should suffice).\n",
    "    * put a legend in your plot\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can load the solution directly in this notebook by uncommenting and running the following line:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# %load  -r -8 solutions/solution_00_01.py"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "bonus question solution:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# %load  -r 9- solutions/solution_00_01.py"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[back to the top](#top)\n",
    "\n",
    "## 04. generating random numbers <a class=\"anchor\" id=\"random\"></a>\n",
    "\n",
    "\n",
    "**the basics**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6696103730869407\n",
      "0.7426639266737763\n",
      "0.6767219223242785\n",
      "0.8602105555191791\n"
     ]
    }
   ],
   "source": [
    "import numpy.random as rd\n",
    "\n",
    "# random floats between 0 and 1\n",
    "for i in range(4):\n",
    "    print( rd.random() )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.37971723 0.80354745 0.4168427  0.70867247 0.17547126 0.43760884\n",
      " 0.75933345 0.06571168 0.45772397 0.67191214]\n"
     ]
    }
   ],
   "source": [
    "print( rd.random(size=10) ) # draw directly 10 numbers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**setting the seed : pseudorandomness and reproducibility**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1st draw [0.37454012 0.95071431 0.73199394 0.59865848 0.15601864]\n",
      "2nd draw [0.15599452 0.05808361 0.86617615 0.60111501 0.70807258]\n",
      "after resetting seed [0.37454012 0.95071431 0.73199394 0.59865848 0.15601864]\n"
     ]
    }
   ],
   "source": [
    "rd.seed(42) # setting the seed to 42\n",
    "print( '1st draw' , rd.random(size=5) )\n",
    "print( '2nd draw' , rd.random(size=5) )\n",
    "rd.seed(42)\n",
    "print( 'after resetting seed' , rd.random(size=5) )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**beyond the uniform distribution**\n",
    "\n",
    "numpy offers you quite a large [set of distributions you can draw from](https://docs.scipy.org/doc/numpy-1.15.0/reference/routines.random.html#distributions).\n",
    "\n",
    "Let's look at the normal distribution:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean  0.025354699638558926\n",
      "stdev 1.0003731428167348\n"
     ]
    }
   ],
   "source": [
    "\n",
    "normalDraw = rd.normal(size = 1000 )\n",
    "\n",
    "print( 'mean ' , np.mean( normalDraw ) )\n",
    "print( 'stdev' , np.std( normalDraw ) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean  -1.9773491637651965\n",
      "stdev 2.964622032924749\n"
     ]
    }
   ],
   "source": [
    "normalDraw2 = rd.normal( loc = -2 , scale = 3 , size = 300 ) # loc chnages the location (mean), and scale changes the standard deviation\n",
    "\n",
    "print( 'mean ' , np.mean( normalDraw2 ) )\n",
    "print( 'stdev' , np.std( normalDraw2 ) )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "of course, we could want to plot these drawn numbers:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist( normalDraw  , alpha = 0.5 , label='loc=0  , scale=1')\n",
    "plt.hist( normalDraw2 , alpha = 0.5 , label='loc=-2 , scale=3')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[back to the top](#top)\n",
    "\n",
    "## 05. statistical testing <a class=\"anchor\" id=\"stats\"></a>\n",
    "\n",
    "`numpy.random` let's you draw random numbers ;\n",
    "`scipy.stats` implements the probability density functions, and Percent point function, as well as the most statistical tests.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import scipy.stats as stats\n",
    "\n",
    "# plotting the probability density function for 1 of the random draw we just made:\n",
    "\n",
    "x = np.linspace(-10,10,1001)\n",
    "\n",
    "normPDF = stats.norm.pdf( x , loc = -2 , scale = 3 )\n",
    "\n",
    "plt.hist( normalDraw2 , alpha = 0.5 , label='random draw' , density = True) # don't forget density=True\n",
    "plt.plot(x,normPDF , label='PDF' )\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also get the expected quantiles of a distribution:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "95% quantile of a Chi-square distribution with 3 degrees of freedom: 7.814727903251179\n",
      "fraction of a Chi-square distribution with 3 degrees of freedom above of equal to 5 0.17179714429673354\n"
     ]
    }
   ],
   "source": [
    "print( '95% quantile of a Chi-square distribution with 3 degrees of freedom:', stats.chi2.ppf(0.95 , df=3))\n",
    "print( 'fraction of a Chi-square distribution with 3 degrees of freedom above of equal to 5' ,  \n",
    "      1 - stats.chi2.cdf( 5 , df=3 ) )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And you can apply some classical statistical tests:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "result of the t-test\n",
      "\tt: 0.26673986193074073\n",
      "\tp-value: 0.7901311339594405\n"
     ]
    }
   ],
   "source": [
    "# t-test of independance between two random samples:\n",
    "rd.seed(73)\n",
    "\n",
    "s1 = rd.normal(size=67)\n",
    "s2 = rd.normal(size=54 , loc = 0.2)\n",
    "\n",
    "testStat , pval = stats.ttest_ind(s1,s2 , equal_var=True)  # equal variance : Student's t-test ; unequal : Welch's\n",
    "#almost all of these stat functions return the same test-statistic , pvalue tuple\n",
    "\n",
    "print('result of the t-test')\n",
    "print('\\tt:',testStat)\n",
    "print('\\tp-value:',pval)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**What is our conclusion for these tests results? What do you think about this?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "result of the Kolmogorov-Smirnov test comparing our sample to a Chi-square distribution with 13 degrees of freedom\n",
      "\tK: 0.12249766392962913\n",
      "\tp-value: 0.5003109000967569\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Kolmogorov-smirnov test for a chi-square distribution\n",
    "\n",
    "sample = rd.chisquare(df=13 , size = 43)\n",
    "\n",
    "\n",
    "# kstest expect as second argument the cdf function of the reference distribution\n",
    "# this is how to handle the fact that me must set an argument (degree of freedom)\n",
    "refDistribution = stats.chi2(df=13).cdf\n",
    "\n",
    "testStat , pval = stats.kstest( sample , refDistribution )\n",
    "# alternative : \n",
    "# testStat , pval = stats.kstest( sample , lambda x : stats.chi2.cdf(x , df=13 ) )\n",
    "\n",
    "print('result of the Kolmogorov-Smirnov test comparing our sample to a Chi-square distribution with 13 degrees of freedom')\n",
    "print('\\tK:',testStat)\n",
    "print('\\tp-value:',pval)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you are interested, this [webpage](https://machinelearningmastery.com/statistical-hypothesis-tests-in-python-cheat-sheet/) references all implemented tests, with examples."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[back to the top](#top)\n",
    "\n",
    "## 06. bringing together numpy, numpy.random, and matplotlib <a class=\"anchor\" id=\"together\"></a>\n",
    "\n",
    "The random generation function return a numpy array, meaning it is fairly trivial to combine it with other arrays:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# combining \n",
    "\n",
    "x = np.sort( rd.normal(loc=170 , scale = 23 , size = 100) )\n",
    "\n",
    "y_theoretical = 0.75 * x + 100 # simple linear relationship : y = a * x + b\n",
    "\n",
    "measurement_noise = rd.normal(scale = 10 , size = 100) # some noise associated to the measure\n",
    "\n",
    "y_observed = y_theoretical + measurement_noise # observed = expected + noise\n",
    "\n",
    "fig,ax = plt.subplots(figsize=(8,8))\n",
    "plt.plot( x , y_theoretical , label = 'expected' )\n",
    "plt.plot( x , y_observed , marker = '.' , linestyle='' , alpha = 0.7 , label = 'observed')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[back to the top](#top)\n",
    "\n",
    "## 07. the briefest intro to pandas <a class=\"anchor\" id=\"pandas\"></a>\n",
    "\n",
    "`pandas` is a powerful library when doing data analysis, especially in the forms of table.\n",
    "\n",
    "Basically, it reimplements R data.frame as a DataFrame object and ties together neatly with the libraries we've just seen.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>dose</th>\n",
       "      <th>nexp</th>\n",
       "      <th>ndied</th>\n",
       "      <th>prop</th>\n",
       "      <th>nalive</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>49.1</td>\n",
       "      <td>59</td>\n",
       "      <td>6</td>\n",
       "      <td>0.102</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>53.0</td>\n",
       "      <td>60</td>\n",
       "      <td>13</td>\n",
       "      <td>0.217</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>56.9</td>\n",
       "      <td>62</td>\n",
       "      <td>18</td>\n",
       "      <td>0.290</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>60.8</td>\n",
       "      <td>56</td>\n",
       "      <td>28</td>\n",
       "      <td>0.500</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>64.8</td>\n",
       "      <td>63</td>\n",
       "      <td>52</td>\n",
       "      <td>0.825</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   dose  nexp  ndied   prop  nalive\n",
       "1  49.1    59      6  0.102      53\n",
       "2  53.0    60     13  0.217      47\n",
       "3  56.9    62     18  0.290      44\n",
       "4  60.8    56     28  0.500      28\n",
       "5  64.8    63     52  0.825      11"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " import pandas as pd\n",
    "    \n",
    "df = pd.read_table( 'data/beetle.csv' , sep=',' , index_col=0 ) # pandas automatically detects header.\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of rows: 8 number of columns: 5\n",
      "column names Index(['dose', 'nexp', 'ndied', 'prop', 'nalive'], dtype='object')\n"
     ]
    }
   ],
   "source": [
    "Nrows, Ncols = df.shape\n",
    "print( 'number of rows:',Nrows, 'number of columns:', Ncols )\n",
    "print( 'column names' , df.columns )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>dose</th>\n",
       "      <th>nexp</th>\n",
       "      <th>ndied</th>\n",
       "      <th>prop</th>\n",
       "      <th>nalive</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>62.800000</td>\n",
       "      <td>60.125000</td>\n",
       "      <td>36.375000</td>\n",
       "      <td>0.602000</td>\n",
       "      <td>23.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9.599702</td>\n",
       "      <td>2.232071</td>\n",
       "      <td>22.557466</td>\n",
       "      <td>0.367937</td>\n",
       "      <td>21.985385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>49.100000</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.102000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>55.925000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>16.750000</td>\n",
       "      <td>0.271750</td>\n",
       "      <td>4.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>62.800000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>0.662500</td>\n",
       "      <td>19.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>69.675000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>54.750000</td>\n",
       "      <td>0.919500</td>\n",
       "      <td>44.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>76.500000</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>61.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>53.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            dose       nexp      ndied      prop     nalive\n",
       "count   8.000000   8.000000   8.000000  8.000000   8.000000\n",
       "mean   62.800000  60.125000  36.375000  0.602000  23.750000\n",
       "std     9.599702   2.232071  22.557466  0.367937  21.985385\n",
       "min    49.100000  56.000000   6.000000  0.102000   0.000000\n",
       "25%    55.925000  59.000000  16.750000  0.271750   4.750000\n",
       "50%    62.800000  60.000000  40.000000  0.662500  19.500000\n",
       "75%    69.675000  62.000000  54.750000  0.919500  44.750000\n",
       "max    76.500000  63.000000  61.000000  1.000000  53.000000"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    49.1\n",
       "2    53.0\n",
       "3    56.9\n",
       "4    60.8\n",
       "5    64.8\n",
       "6    68.7\n",
       "7    72.6\n",
       "8    76.5\n",
       "Name: dose, dtype: float64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# select a single column:\n",
    "df['dose']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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>ndied</th>\n",
       "      <th>nalive</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>18</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>52</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>53</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>61</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ndied  nalive\n",
       "1      6      53\n",
       "2     13      47\n",
       "3     18      44\n",
       "4     28      28\n",
       "5     52      11\n",
       "6     53       6\n",
       "7     61       1\n",
       "8     60       0"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[ ['ndied','nalive'] ] # select several columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Plotting DataFrame Columns**\n",
    "\n",
    "Because `DataFrame` columns are iterable, they can seamlessly be given as argument to `plot()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# plotting the column dose along the x-axis and prop along the y-axis\n",
    "# I use the + marker, with a teal color.\n",
    "plt.plot(df['dose'] , df['prop'] , color = 'teal' , linestyle='' , marker = '+' , markersize=10 )\n",
    "plt.xlabel( 'dose' )\n",
    "plt.ylabel( 'proportion of dead' )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "DataFrame column can be manipulated like numpy array:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/wandrille/Installed_software/anaconda3/envs/introML2022/lib/python3.10/site-packages/pandas/core/arraylike.py:397: RuntimeWarning: divide by zero encountered in log\n",
      "  result = getattr(ufunc, method)(*inputs, **kwargs)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "## we can combine columns using normal operators\n",
    "Odds = df['nalive'] /df['ndied'] # the odds of being alive is nalive / ndead\n",
    "\n",
    "## adding a new column to the DataFrame is trivial:\n",
    "df['Odds'] = Odds\n",
    "\n",
    "\n",
    "## we can also apply numpy function to them\n",
    "df['logOdds'] = np.log( df['Odds'] )\n",
    "\n",
    "\n",
    "plt.plot(df['dose'] , df['logOdds'] , color = 'teal' , linestyle='' , marker = '+' , markersize=10 )\n",
    "plt.xlabel( 'dose' )\n",
    "plt.ylabel( 'log Odds' )\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[back to the top](#top)\n",
    "\n",
    "## Exercise 00.02 : tying everything together <a class=\"anchor\" id=\"exo2\"></a>\n",
    "\n",
    "1. Read the file `'data/kyphosis.csv'`.\n",
    "2. how many columns are there ?\n",
    "3. What is the maximum Age ? \n",
    "4. create a new column `Stop` , corresponding to the addition of columns `'Start'` and `'Number'`\n",
    "5. plot the relationship between `'Age'` and `'Number'` (bonus point : use colors to indicate the presence or absence of kyphosis ).\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Solutions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "# %load  -r -7 solutions/solution_00_02.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "# %load  -r 8-9 solutions/solution_00_02.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "# %load  -r 11-12 solutions/solution_00_02.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "# %load  -r 14-15 solutions/solution_00_02.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "# %load  -r 17-22 solutions/solution_00_02.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "# %load  -r 24- solutions/solution_00_02.py"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Environment (conda_introml2022)",
   "language": "python",
   "name": "conda_introml2022"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}