From fe0f7dec8311514fd2f78f4d4b05ec41057def6e Mon Sep 17 00:00:00 2001 From: Dulce-04 <136611956+Dulce-04@users.noreply.github.com> Date: Mon, 7 Aug 2023 08:08:32 +0100 Subject: [PATCH] Matplotlib and Seaborn --- main.ipynb | 1682 ++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 1682 insertions(+) create mode 100644 main.ipynb diff --git a/main.ipynb b/main.ipynb new file mode 100644 index 0000000..6184e67 --- /dev/null +++ b/main.ipynb @@ -0,0 +1,1682 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Lab | Matplotlib & Seaborn\n", + "\n", + "#### Import all the necessary libraries here:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Libraries\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "import pandas as pd" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Challenge 1\n", + "\n", + "#### The data we will use in this challenge is:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "x = np.arange(0,100)\n", + "y = x*2\n", + "z = x**2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Plot (x, y) and (x, z).\n", + "There are 2 ways of doing this. Do in both ways.\n", + "\n", + "**Hint**: Check out the nrows, ncols and index arguments of subplots. \n", + "\n", + "Also, play around with the linewidth and style. Use the ones you're most happy with." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(x, y, label='y = x * 2', color= 'red', linewidth = 2.5) \n", + "plt.title('y = x')\n", + "\n", + "plt.show()\n", + "\n", + "plt.plot(x, z, label='z = x ** 2', color = 'k', linewidth = 2, linestyle = '-.') \n", + "plt.title('z = x') \n", + "\n", + "plt.show() " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(
, array([, ], dtype=object))" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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oaIjf/va3sWfPnsFHe3t7fOITn4g9e/bEwoULz216YMKzN4BKKvmtotWrV8ctt9wS8+fPj0WLFsWPf/zj6O7ujvb29oh4533mP/7xj/Gzn/0sJk2aFHPnzh1y/UUXXRT19fXDjgMfXPYGUCklh8uyZcvi6NGjce+990ZPT0/MnTs3duzYETNnzoyIiJ6envf9bAbgw8XeACql5M9xGQ+j8e/AgbOX8R7MODN80Iz757gAAIwn4QIApCFcAIA0hAsAkIZwAQDSEC4AQBrCBQBIQ7gAAGkIFwAgDeECAKQhXACANIQLAJCGcAEA0hAuAEAawgUASEO4AABpCBcAIA3hAgCkIVwAgDSECwCQhnABANIQLgBAGsIFAEhDuAAAaQgXACAN4QIApCFcAIA0hAsAkIZwAQDSEC4AQBrCBQBIQ7gAAGkIFwAgDeECAKQhXACANIQLAJCGcAEA0hAuAEAawgUASEO4AABpCBcAIA3hAgCkIVwAgDSECwCQhnABANIQLgBAGsIFAEhDuAAAaQgXACAN4QIApCFcAIA0hAsAkIZwAQDSEC4AQBrCBQBIQ7gAAGkIFwAgDeECAKQhXACANIQLAJCGcAEA0hAuAEAawgUASKOscNm4cWPMmjUr6uvro7m5OXbu3HnGc7dv3x7XXXddfPSjH42GhoZYtGhR/PznPy97YCAnewOohJLDZdu2bbFq1apYt25ddHV1RWtrayxZsiS6u7tHPP+5556L6667Lnbs2BG7d++Ov/u7v4sbb7wxurq6znl4IAd7A6iUqqIoilIuWLhwYcybNy82bdo0eGzOnDmxdOnS6OjoOKtf41Of+lQsW7Ys7rnnnhH/+8DAQAwMDAx+3d/fHzNmzIi+vr5oaGgoZVygAvr7+6OxsbHse9DegA+nc90dIynpFZfjx4/H7t27o62tbcjxtra22LVr11n9GqdOnYpjx47FhRdeeMZzOjo6orGxcfAxY8aMUsYEJhB7A6ikksKlt7c3Tp48GU1NTUOONzU1xeHDh8/q1/jOd74Tb775Ztx0001nPGft2rXR19c3+Dh48GApYwITiL0BVFJNORdVVVUN+booimHHRvLII4/EN77xjfjP//zPuOiii854Xl1dXdTV1ZUzGjBB2RtAJZQULlOnTo3q6uphz5KOHDky7NnUu23bti1uv/32ePTRR+Paa68tfVIgJXsDqKSS3iqqra2N5ubm6OzsHHK8s7MzWlpaznjdI488Erfeems8/PDDccMNN5Q3KZCSvQFUUslvFa1evTpuueWWmD9/fixatCh+/OMfR3d3d7S3t0fEO+8z//GPf4yf/exnEfHO8lm+fHl897vfjc997nODz7rOO++8aGxsrOBvBZio7A2gUkoOl2XLlsXRo0fj3nvvjZ6enpg7d27s2LEjZs6cGRERPT09Qz6b4Uc/+lGcOHEivvKVr8RXvvKVweMrVqyIhx566Nx/B8CEZ28AlVLy57iMh9H4d+DA2ct4D2acGT5oxv1zXAAAxpNwAQDSEC4AQBrCBQBIQ7gAAGkIFwAgDeECAKQhXACANIQLAJCGcAEA0hAuAEAawgUASEO4AABpCBcAIA3hAgCkIVwAgDSECwCQhnABANIQLgBAGsIFAEhDuAAAaQgXACAN4QIApCFcAIA0hAsAkIZwAQDSEC4AQBrCBQBIQ7gAAGkIFwAgDeECAKQhXACANIQLAJCGcAEA0hAuAEAawgUASEO4AABpCBcAIA3hAgCkIVwAgDSECwCQhnABANIQLgBAGsIFAEhDuAAAaQgXACAN4QIApCFcAIA0hAsAkIZwAQDSEC4AQBrCBQBIQ7gAAGkIFwAgDeECAKQhXACANIQLAJCGcAEA0hAuAEAawgUASEO4AABpCBcAIA3hAgCkUVa4bNy4MWbNmhX19fXR3NwcO3fufM/zn3322Whubo76+vqYPXt2/PCHPyxrWCAvewOohJLDZdu2bbFq1apYt25ddHV1RWtrayxZsiS6u7tHPP/AgQNx/fXXR2tra3R1dcXXv/71WLlyZTz++OPnPDyQg70BVEpVURRFKRcsXLgw5s2bF5s2bRo8NmfOnFi6dGl0dHQMO/9rX/taPPXUU7Fv377BY+3t7fGb3/wmXnzxxRG/x8DAQAwMDAx+3dfXF5dddlkcPHgwGhoaShkXqID+/v6YMWNGvP7669HY2Fjy9fYGfDid6+4YUVGCgYGBorq6uti+ffuQ4ytXriz+9m//dsRrWltbi5UrVw45tn379qKmpqY4fvz4iNesX7++iAgPD48J9njllVdKWRn2hoeHRxFR3u44k5ooQW9vb5w8eTKampqGHG9qaorDhw+PeM3hw4dHPP/EiRPR29sb06ZNG3bN2rVrY/Xq1YNfv/766zFz5szo7u6uXLGNstOVmenZnpnHRsaZT796ceGFF5Z8rb1x9jL+2YjIObeZx8a57I4zKSlcTquqqhrydVEUw4693/kjHT+trq4u6urqhh1vbGxM83/WaQ0NDWYeA2YeG5Mmlf8PEe2Ns5fxz0ZEzrnNPDbOZXcM+7VKOXnq1KlRXV097FnSkSNHhj07Ou3iiy8e8fyampqYMmVKieMC2dgbQCWVFC61tbXR3NwcnZ2dQ453dnZGS0vLiNcsWrRo2PnPPPNMzJ8/PyZPnlziuEA29gZQUaX+pZj/+I//KCZPnlxs3ry52Lt3b7Fq1ariggsuKP73f/+3KIqiWLNmTXHLLbcMnr9///7i/PPPL+68885i7969xebNm4vJkycXjz322Fl/z7fffrtYv3598fbbb5c67rgx89gw89g415ntjbOTceaiyDm3mcfGaMxccrgURVH84Ac/KGbOnFnU1tYW8+bNK5599tnB/7ZixYri6quvHnL+L3/5y+Kzn/1sUVtbW3zsYx8rNm3adE5DA/nYG0AllPw5LgAA48XPKgIA0hAuAEAawgUASEO4AABpTJhwyfgj70uZefv27XHdddfFRz/60WhoaIhFixbFz3/+8zGc9h2l/u982gsvvBA1NTXxmc98ZnQHHEGpMw8MDMS6deti5syZUVdXFx//+Mdjy5YtYzTtO0qdeevWrXHVVVfF+eefH9OmTYvbbrstjh49OkbTRjz33HNx4403xiWXXBJVVVXx5JNPvu812e7BiHwz2xvly7g3InLtjnHbG+P9z5qK4v8+4+EnP/lJsXfv3uKOO+4oLrjgguIPf/jDiOef/oyHO+64o9i7d2/xk5/8pOTPeBjrme+4447im9/8ZvHrX/+6ePnll4u1a9cWkydPLv7nf/5nws582uuvv17Mnj27aGtrK6666qqxGfb/K2fmL3zhC8XChQuLzs7O4sCBA8WvfvWr4oUXXpiwM+/cubOYNGlS8d3vfrfYv39/sXPnzuJTn/pUsXTp0jGbeceOHcW6deuKxx9/vIiI4oknnnjP8zPegxlntjfKk3FvFEW+3TFee2NChMuCBQuK9vb2IceuvPLKYs2aNSOe/2//9m/FlVdeOeTYP//zPxef+9znRm3Gdyt15pF88pOfLDZs2FDp0c6o3JmXLVtW/Pu//3uxfv36MV9Apc78X//1X0VjY2Nx9OjRsRhvRKXO/O1vf7uYPXv2kGMPPPBAMX369FGb8b2czQLKeA9mnHkk9sb7y7g3iiL37hjLvTHubxUdP348du/eHW1tbUOOt7W1xa5du0a85sUXXxx2/uc///l46aWX4i9/+cuozXpaOTO/26lTp+LYsWMV/YmZ76XcmR988MF45ZVXYv369aM94jDlzPzUU0/F/Pnz41vf+lZceumlccUVV8Rdd90Vf/7zn8di5LJmbmlpiUOHDsWOHTuiKIp49dVX47HHHosbbrhhLEYuS8Z7MOPM72ZvvL+MeyPiw7E7KnUPlvXToStprH7kfSWVM/O7fec734k333wzbrrpptEYcZhyZv79738fa9asiZ07d0ZNzdj/USln5v3798fzzz8f9fX18cQTT0Rvb298+ctfjtdee21M3q8uZ+aWlpbYunVrLFu2LN5+++04ceJEfOELX4jvfe97oz5vuTLegxlnfjd74/1l3BsRH47dUal7cNxfcTlttH/k/WgodebTHnnkkfjGN74R27Zti4suumi0xhvR2c588uTJuPnmm2PDhg1xxRVXjNV4Iyrlf+dTp05FVVVVbN26NRYsWBDXX3993H///fHQQw+N6bOnUmbeu3dvrFy5Mu65557YvXt3PP3003HgwIFob28fi1HLlvEezDjzafZGaTLujYgP/u6oxD047q+4ZPyR9+XMfNq2bdvi9ttvj0cffTSuvfba0RxziFJnPnbsWLz00kvR1dUVX/3qVyPinZu7KIqoqamJZ555Jq655poJNXNExLRp0+LSSy+NxsbGwWNz5syJoiji0KFDcfnll0+4mTs6OmLx4sVx9913R0TEpz/96bjggguitbU17rvvvlF/JaAcGe/BjDOfZm+M3swR4783Ij4cu6NS9+C4v+KS8UfelzNzxDvPmG699dZ4+OGHx/w9yFJnbmhoiN/+9rexZ8+ewUd7e3t84hOfiD179sTChQsn3MwREYsXL44//elP8cYbbwwee/nll2PSpEkxffr0UZ03oryZ33rrrZg0aeitWF1dHRH/92xkosl4D2acOcLeGO2ZI8Z/b0R8OHZHxe7Bkv4q7ygZjx95P9YzP/zww0VNTU3xgx/8oOjp6Rl8vP766xN25ncbj38dUOrMx44dK6ZPn178wz/8Q/G73/2uePbZZ4vLL7+8+NKXvjRhZ37wwQeLmpqaYuPGjcUrr7xSPP/888X8+fOLBQsWjNnMx44dK7q6uoqurq4iIor777+/6OrqGvxnmB+EezDjzPZGeTLujXLmHu/dMV57Y0KES1Hk/JH3pcx89dVXFxEx7LFixYoJO/O7jccCKorSZ963b19x7bXXFuedd14xffr0YvXq1cVbb701oWd+4IEHik9+8pPFeeedV0ybNq34x3/8x+LQoUNjNu8vfvGL9/zz+UG4B4si38z2Rvky7o2iyLU7xmtvVBXFBHw9CQBgBOP+d1wAAM6WcAEA0hAuAEAawgUASEO4AABpCBcAIA3hAgCkIVwAgDSECwCQhnABANIQLgBAGv8P6EDt3Xw6P4gAAAAASUVORK5CYII=\n", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.subplots(nrows = 1, ncols = 2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Use plt.subplots(nrows=1, ncols=2) to create the plot." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, (fg1, fg2) = plt.subplots(1, 2)\n", + "\n", + "fg1.plot(x, y, label='y = x * 2')\n", + "\n", + "\n", + "fg2.plot(x, z, label='z = x ** 2')\n", + "\n", + "plt.show() " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Use your previous code but now, resize your plot.\n", + "**Hint**: Add the figsize argument in plt.subplots().\n", + "\n", + "If you want, try to add a title to the plot or even axes labels. You can also play with the fontweight and fontsize of the titles and labels. " + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, (fg1, fg2) = plt.subplots(1, 2, figsize= (14, 8))\n", + "\n", + "fg1.plot(x, y, label='y = x * 2', color = 'red')\n", + "fg1.set_xlabel('x', fontsize = 'large', fontweight= 10)\n", + "fg1.set_ylabel('y', fontsize = 'large', fontweight= 10)\n", + "fg1.set_title('y = x', fontweight= 24, fontsize = 'large' )\n", + "\n", + "fg2.plot(x, z, label='z = x ** 2', color = 'k')\n", + "fg2.set_xlabel('x', fontsize = 'large', fontweight= 10)\n", + "fg2.set_ylabel('z', fontsize = 'large', fontweight= 10)\n", + "fg2.set_title('z = x', fontweight= 24, fontsize = 'large' )\n", + "\n", + "plt.show() " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Plot both $y=x^2$ and $y=e^x$ in the same plot using normal and logarithmic scale.\n", + "**Hint**: Use `set_xscale` and `set_yscale`." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "y_x2 = x**2\n", + "y_ex = np.exp(x)\n", + "\n", + "plt.figure(figsize=(10, 6))\n", + "plt.plot(x, y_x2, label='y = x^2', color='red')\n", + "plt.plot(x, y_ex, label='y = e^x', color='k')\n", + "\n", + "plt.xscale('linear')\n", + "plt.yscale('linear')\n", + "\n", + "plt.figure(figsize=(10, 6))\n", + "plt.plot(x, y_x2, label='y = x^2', color='blue')\n", + "plt.plot(x, y_ex, label='y = e^x', color='green')\n", + "\n", + "plt.xscale('log')\n", + "plt.yscale('log')\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### As a bonus challenge, try to add a legend to the plot." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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kwsLC1L9///TAAw+kiRMnpnHjxqWrr746/fCHP9zg60+YMCHl5+dX+dldd911lR0pATYWwel/fT04PfzwwykiUvPmzas8CgoK0nHHHZdS+rJt78UXX1x5ztqC06xZs9Jrr72WbrvtttSmTZv0wAMPbMyPA9SxDQlOKaX0xBNPpP322y81bdo0NW/ePB166KHp+eefr3b+iBEjUqdOnVJ+fn6N1/m62lx3fYJTSl+25m7Tpk1avnz5Op23oUpLS9OBBx6YOnTokObPn1/ltYrOg1/993p9ZHWRy/q5r1y5Ml144YWpc+fOqWnTpmnvvfdOTzzxRDr55JPXOzil9GV42mmnnVKTJk3SzjvvnO68885q18zy7LPPpohIv/nNb2p9Tl179dVX03HHHZfat2+fGjdunIqKitIhhxySbrnllg26bkWgPuSQQ6qEwvLy8nTkkUem1q1b16orJcD6yEsppfpY2drU5eXlVemqN3bs2DjhhBPi9ddfr/Yl5q233jqKioqidevW8fnnn1ceTylFeXl5NGrUKG677bY47bTTanyvq666Ku699954++23N9rnAdgQCxcujK5du8a5554bv/3tb3NdDuvgZz/7WYwZMybmzp1brSkFAOvPd5zWYK+99oqysrJYuHBh9OvXr8YxU6dOjbKyssrn//mf/xnXXHNNTJkyJTp37rzGa6eUqrSuBdhUfPDBBzFr1qy49tprIz8/P84///xcl0QtvfDCC/HOO+/E6NGj44wzzhCaAOpYgw5On3/+ebz77ruVz2fPnh0zZsyINm3axM477xwnnHBCDB48OK6//vrYa6+9YtGiRfHMM8/EbrvtFgMHDqx2H4qXX3458vPzo1evXpXHbr755thuu+0q7+vx3HPPxXXXXVfr+4QA1Kfbb789Ro4cGd26dYv7779/rf8nEJuWioYgRxxxhAZEABtBg96q9+yzz8bBBx9c7fjJJ58cd999d6xevTquuuqquOeee2LevHnRtm3b2H///ePKK6+M3Xbbrdp5d999dwwbNqzKDXD/8Ic/xK233hqzZ8+OgoKC2GGHHeInP/lJnHHGGVVuWgkAAGy6GnRwAgAAqA1LHgAAABkEJwAAgAwNrjlEeXl5fPjhh9GiRYvKO7MDAAANT0opli5dGp06dcrsP9DggtOHH34YXbp0yXUZAADAJmLu3Lmx7bbbrnVMgwtOLVq0iIgvfzgtW7bMcTUAAECuLFmyJLp06VKZEdamwQWniu15LVu2FJwAAIBafYVHcwgAAIAMghMAAEAGwQkAACCD4AQAAJBBcAIAAMggOAEAAGQQnAAAADIITgAAABkEJwAAgAw5DU6TJk2KI488Mjp16hR5eXnxxBNPZJ4zceLEKC4ujqZNm8b2228ft9xyy8YvFAAAaNByGpyWLVsWe+yxR9x00021Gj979uwYOHBg9OvXL6ZPnx4///nP47zzzovHHntsI1cKAAA0ZAW5fPMBAwbEgAEDaj3+lltuie222y5uvPHGiIjo2bNnvPzyy3HdddfFMcccs5GqBAAAGrrN6jtOU6dOjf79+1c59t3vfjdefvnlWL16dY3nrFy5MpYsWVLlAQAAsC42q+C0YMGC6NChQ5VjHTp0iNLS0li0aFGN54waNSpatWpV+ejSpUt9lAoAAGxBNqvgFBGRl5dX5XlKqcbjFUaMGBGLFy+ufMydO3ej1wgAAGxZcvodp3VVVFQUCxYsqHJs4cKFUVBQEG3btq3xnMLCwigsLKyP8gAAgC3UZhWc9t9///jzn/9c5diECROid+/e0bhx4xxVBQAA1NZf/vKXWLFiRXz7299e4+LHpiinW/U+//zzmDFjRsyYMSMivmw3PmPGjJgzZ05EfLnNbvDgwZXjhw4dGu+//34MHz483nzzzbjzzjvjjjvuiAsvvDAX5QMAAOvonHPOiaOPPjreeeedXJeyTnK64vTyyy/HwQcfXPl8+PDhERFx8sknx9133x3z58+vDFEREd27d4/x48fHBRdcEDfffHN06tQpfv/732tFDgAAm4mVK1dGRESTJk1yXMm6yUsV3RUaiCVLlkSrVq1i8eLF0bJly1yXAwAADUr79u3j448/jpkzZ0avXr1yWsu6ZIPNrqseAACw+apYcdrcGrgJTgAAQL0RnAAAANYipSQ4AQAArM3q1asr/1twAgAAqEHFalOE4AQAAFCjrwanza0dueAEAADUi4rg1KhRo2jUqFGOq1k3ghMAAFAvVq1aFRGb3za9CMEJAACoJ5trR70IwQkAAKgnghMAAEAGwQkAACCD4AQAAJChIjhtbq3IIwQnAACgnlhxAgAAyKAdOQAAQAYrTgAAABkEJwAAgAyCEwAAQAbBCQAAIIPgBAAAkMF9nAAAADJoRw4AAJDBVj0AAIAMghMAAEAGwQkAACCD4AQAAJBBcAIAAMigHTkAAEAG7cgBAAAy2KoHAACQQXACAADIIDgBAABkEJwAAAAyCE4AAAAZBCcAAIAM7uMEAACQwX2cAAAAMtiqBwAAkEFwAgAAyCA4AQAArEVpaWmUl5dHhOAEAABQo4rVpgjBCQAAoEZfDU7akQMAANSgohV5Xl5eFBQU5LiadSc4AQAAG91XG0Pk5eXluJp1JzgBAAAb3ebcUS9CcAIAAOqB4AQAAJBBcAIAAMggOAEAAGQQnAAAADJUtCPfHO/hFCE4AQAA9cCKEwAAQAbBCQAAIIPgBAAAkEFwAgAAyCA4AQAAZBCcAAAAMlQEJ+3IAQAA1qDiPk5WnAAAANbAVj0AAIAMghMAAEAGwQkAACCD4AQAAJBBcAIAAMigHTkAAEAG7cgBAAAy2KoHAACQQXACAADIIDgBAABkEJwAAAAyCE4AAAAZBCcAAIAMFe3I3ccJAABgDaw4baDRo0dH9+7do2nTplFcXByTJ09e6/j7778/9thjj9hqq62iY8eOceqpp8Ynn3xST9UCAADrQ3DaAGPHjo1hw4bFpZdeGtOnT49+/frFgAEDYs6cOTWOf+6552Lw4MExZMiQeP311+ORRx6Jl156KU4//fR6rhwAAFgXgtMGuOGGG2LIkCFx+umnR8+ePePGG2+MLl26xJgxY2oc/8ILL0S3bt3ivPPOi+7du8cBBxwQZ5xxRrz88sv1XDkAALAuBKf1tGrVqpg2bVr079+/yvH+/fvHlClTajynT58+8cEHH8T48eMjpRQfffRRPProo3H44Yev8X1WrlwZS5YsqfIAAADqT3l5eZSWlkaE4LTOFi1aFGVlZdGhQ4cqxzt06BALFiyo8Zw+ffrE/fffH4MGDYomTZpEUVFRtG7dOv7whz+s8X1GjRoVrVq1qnx06dKlTj8HAACwdhWrTRGC03rLy8ur8jylVO1YhTfeeCPOO++8uOyyy2LatGnx9NNPx+zZs2Po0KFrvP6IESNi8eLFlY+5c+fWaf0AAMDafTU4ba7tyAty9cbt2rWLRo0aVVtdWrhwYbVVqAqjRo2Kvn37xkUXXRQREbvvvns0b948+vXrF1dddVV07Nix2jmFhYWbbaoFAIAtQcU9nCI23+CUsxWnJk2aRHFxcZSUlFQ5XlJSEn369KnxnOXLl0d+ftWSGzVqFBFfrlQBAACbnooVpyZNmqxxd9mmLqdb9YYPHx6333573HnnnfHmm2/GBRdcEHPmzKncejdixIgYPHhw5fgjjzwyHn/88RgzZkzMmjUrnn/++TjvvPNi3333jU6dOuXqYwAAAGuxuXfUi8jhVr2IiEGDBsUnn3wSI0eOjPnz50evXr1i/Pjx0bVr14iImD9/fpV7Op1yyimxdOnSuOmmm+JnP/tZtG7dOg455JC45pprcvURAACADFtCcMpLDWyP25IlS6JVq1axePHiaNmyZa7LAQCALd4rr7wSxcXF0blz5/jggw9yXU6ldckGOe+qBwAAbNm2hBUnwQkAANioBCcAAIAMFe3IN9dW5BGCEwAAsJFZcQIAAMggOAEAAGQQnAAAADIITgAAABkEJwAAgAyCEwAAQAbtyAEAADJYcQIAAMggOAEAAGQQnAAAADIITgAAABkEJwAAgAyCEwAAQIaKduSCEwAAwBpUrDi5jxMAAMAa2KoHAACQQXACAADIIDgBAABkEJwAAAAyCE4AAAAZBCcAAIAMFfdx0o4cAABgDaw4AQAAZBCcAAAAMghOAAAAGQQnAACAtUgpVTaHEJwAAABqUBGaIgQnAACAGn01OGlHDgAAUIOK7zdFWHECAACoUUVwKigoiPz8zTd+bL6VAwAAm7wtoaNehOAEAABsRIITAABABsEJAAAgg+AEAACQYUu4+W2E4AQAAGxEFStOm/M9nCIEJwAAYCOyVQ8AACCD4AQAAJBBcAIAAMggOAEAAGQQnAAAADIITgAAABkq7uOkHTkAAMAaWHECAADIIDgBAABkEJwAAAAyCE4AAAAZBCcAAIAMghMAAECGinbkghMAAMAaVKw4uY8TAADAGtiqBwAAkEFwAgAAyCA4AQAAZBCcAAAAMghOAAAAGbQjBwAAyKAdOQAAQAZb9QAAADIITgAAABkEJwAAgAyCEwAAwFqklAQnAACAtSkrK4uUUkQITgAAADWqWG2K0I4cAACgRl8NTlacAAAAalARnPLz86OgoCDH1WwYwQkAANgotpTGEBGCEwAAsJEITgAAABkEpzo0evTo6N69ezRt2jSKi4tj8uTJax2/cuXKuPTSS6Nr165RWFgYO+ywQ9x55531VC0AAFBbW1Jwyuk3tMaOHRvDhg2L0aNHR9++fePWW2+NAQMGxBtvvBHbbbddjeccd9xx8dFHH8Udd9wRO+64YyxcuDBKS0vruXIAACDLqlWrIkJw2mA33HBDDBkyJE4//fSIiLjxxhvjr3/9a4wZMyZGjRpVbfzTTz8dEydOjFmzZkWbNm0iIqJbt271WTIAAFBLFStOm/s9nCJyuFVv1apVMW3atOjfv3+V4/37948pU6bUeM6TTz4ZvXv3jt/+9rfRuXPn2HnnnePCCy+ML774Yo3vs3LlyliyZEmVBwAAsPHZqlcHFi1aFGVlZdGhQ4cqxzt06BALFiyo8ZxZs2bFc889F02bNo1x48bFokWL4qyzzop//etfa/ye06hRo+LKK6+s8/oBAIC125KCU86bQ+Tl5VV5nlKqdqxCeXl55OXlxf333x/77rtvDBw4MG644Ya4++6717jqNGLEiFi8eHHlY+7cuXX+GQAAgOq2pOCUsxWndu3aRaNGjaqtLi1cuLDaKlSFjh07RufOnaNVq1aVx3r27Bkppfjggw9ip512qnZOYWHhFvGLAgCAzc2WFJxytuLUpEmTKC4ujpKSkirHS0pKok+fPjWe07dv3/jwww/j888/rzz2zjvvRH5+fmy77bYbtV4AAGDdCE51ZPjw4XH77bfHnXfeGW+++WZccMEFMWfOnBg6dGhEfLnNbvDgwZXjf/zjH0fbtm3j1FNPjTfeeCMmTZoUF110UZx22mnRrFmzXH0MAACgBtqR15FBgwbFJ598EiNHjoz58+dHr169Yvz48dG1a9eIiJg/f37MmTOncvzWW28dJSUlce6550bv3r2jbdu2cdxxx8VVV12Vq48AAACswZbUjjynwSki4qyzzoqzzjqrxtfuvvvuasd69OhRbXsfAACw6bFVDwAAIIPgBAAAkEFwAgAAyCA4AQAAZBCcAAAAMmxJ7cgFJwAAYKOw4gQAAJBhS7qPk+AEAABsFFacAAAAMghOAAAAGQQnAACADIITAABABsEJAAAgg/s4AQAAZNCOHAAAIIOtegAAABkEJwAAgAyCEwAAQAbBCQAAIIPgBAAAsBZlZWVRVlYWEYITAABAjSru4RQhOAEAANSoYptehPs4AQAA1Oirwalx48Y5rKRuCE4AAECd+2pjiLy8vBxXs+EEJwAAoM5tSR31IgQnAABgIxCcAAAAMlR01ROcAAAA1sCKEwAAQIaK4LQltCKPEJwAAICNwIoTAABABsEJAAAgg+AEAACQQXACAADIoB05AABAhga/4nTKKafEpEmTNkYtAADAFqLBtyNfunRp9O/fP3baaae4+uqrY968eRujLgAAYDPW4FecHnvssZg3b16cc8458cgjj0S3bt1iwIAB8eijj8bq1as3Ro0AAMBmpsEHp4iItm3bxvnnnx/Tp0+PF198MXbcccc46aSTolOnTnHBBRfEP/7xj7quEwAA2IwITl8xf/78mDBhQkyYMCEaNWoUAwcOjNdffz2++c1vxn/8x3/UVY0AAMBmpsEHp9WrV8djjz0WRxxxRHTt2jUeeeSRuOCCC2L+/Pnxpz/9KSZMmBD33ntvjBw5cmPUCwAAbAa2tOBUsK4ndOzYMcrLy+P444+PF198Mfbcc89qY7773e9G69at66A8AABgc7Sl3cdpnYPTf/zHf8Sxxx4bTZs2XeOYbbbZJmbPnr1BhQEAAJuvBr/idNJJJ22MOgAAgC1Ig7+PEwAAQJYtbcVJcAIAAOqc4AQAAJBBcAIAAMggOAEAAGTY0tqRC04AAECds+IEAACQQTtyAACADFacAAAAMghOAAAAGQQnAACADIITAABABu3IAQAA1iKlJDgBAACsTUVoihCcAAAAalTx/aYI93ECAACokeAEAACQoSI4NW7cOPLzt4zIsWV8CgAAYJOxpbUijxCcAACAOiY4AQAAZNjSWpFHCE4AAEAds+IEAACQoSI4bSkd9SIEJwAAoI5ZcQIAAMggOAEAAGQQnAAAADIITgAAABm0IwcAAMhgxQkAACCD4AQAAJDBfZw2gtGjR0f37t2jadOmUVxcHJMnT67Vec8//3wUFBTEnnvuuXELBAAA1okVpzo2duzYGDZsWFx66aUxffr06NevXwwYMCDmzJmz1vMWL14cgwcPjkMPPbSeKgUAAGpLcKpjN9xwQwwZMiROP/306NmzZ9x4443RpUuXGDNmzFrPO+OMM+LHP/5x7L///vVUKQAAUFuCUx1atWpVTJs2Lfr371/leP/+/WPKlClrPO+uu+6Kf/7zn3H55ZfX6n1WrlwZS5YsqfIAAAA2Hu3I69CiRYuirKwsOnToUOV4hw4dYsGCBTWe849//CMuueSSuP/++6OgoKBW7zNq1Kho1apV5aNLly4bXDsAALBmVpw2gry8vCrPU0rVjkVElJWVxY9//OO48sorY+edd6719UeMGBGLFy+ufMydO3eDawYAANZsSwxOtVu22QjatWsXjRo1qra6tHDhwmqrUBERS5cujZdffjmmT58e55xzTkRElJeXR0opCgoKYsKECXHIIYdUO6+wsHCL+oUBAMCmTjvyOtSkSZMoLi6OkpKSKsdLSkqiT58+1ca3bNkyZs6cGTNmzKh8DB06NHbZZZeYMWNG7LfffvVVOgAAsBZWnOrY8OHD46STTorevXvH/vvvH7fddlvMmTMnhg4dGhFfbrObN29e3HPPPZGfnx+9evWqcn779u2jadOm1Y4DAAC5IzjVsUGDBsUnn3wSI0eOjPnz50evXr1i/Pjx0bVr14iImD9/fuY9nQAAgE3Llhic8lJKKddF1KclS5ZEq1atYvHixdGyZctclwMAAFucgw46KCZOnBgPP/xwHHvssbkuZ43WJRvkvKseAACwZdkSV5wEJwAAoE4JTgAAABm0IwcAAMhgxQkAACCD4AQAAJBBcAIAAMggOAEAAGRYtWpVRAhOAAAANSotLY0VK1ZERMRWW22V42rqjuAEAADUmU8//bTyv1u3bp27QuqY4AQAANSZf/3rXxHxZWgqKCjIcTV1R3ACAADqzCeffBIREW3atMlxJXVLcAIAAOpMRXBq27ZtjiupW4ITAABQZyq26glOAAAAa2CrHgAAQAZb9QAAADLYqgcAAJDBVj0AAIAMtuoBAABksFUPAAAgg616AAAAGWzVAwAAWIsVK1bE8uXLI0JwAgAAqFHF95saNWoULVu2zHE1dUtwAgAA6sRXv9+Ul5eX42rqluAEAADUiS21o16E4AQAANSRLbWjXoTgBAAA1JEttaNehOAEAADUEVv1AAAAMtiqBwAAkMFWPQAAgAy26gEAAGSwVQ8AACCDrXoAAAAZKrbqWXECAACoQUrJihMAAMDafP7557F69eqIEJwAAABqVLFNr7CwMJo1a5bjauqe4AQAAGywr27Ty8vLy3E1dU9wAgAANtiW/P2mCMEJAACoA1tyR70IwQkAAKgDVpwAAAAyCE4AAAAZbNUDAADIYMUJAAAgg+AEAACQwVY9AACADFacAAAAMghOAAAAa1FeXh6ffvppRNiqBwAAUKPPPvssUkoRITgBAADUqGKbXosWLaJJkyY5rmbjEJwAAIANsqV31IsQnAAAgA20pTeGiBCcAACADVQRnKw4AQAArEHFVj0rTgAAAGtgqx4AAEAGW/UAAAAy2KoHAACQwVY9AACADLbqAQAAZLBVDwAAIIOtegAAAGuxatWqWLp0aUTYqgcAAFCjTz/9NCIi8vLyonXr1rktZiMSnAAAgPVWsU1vm222iUaNGuW4mo1HcAIAANZbQ+ioFyE4AQAAG6AhdNSLEJwAAIAN0BA66kUITgAAwAawVQ8AACCDrXoAAAAZbNUDAADIULHiZKseAADAGlhxqiejR4+O7t27R9OmTaO4uDgmT568xrGPP/54HHbYYfGNb3wjWrZsGfvvv3/89a9/rcdqAQCAr9Icoh6MHTs2hg0bFpdeemlMnz49+vXrFwMGDIg5c+bUOH7SpElx2GGHxfjx42PatGlx8MEHx5FHHhnTp0+v58oBAICIhtMcIi+llHL15vvtt1/svffeMWbMmMpjPXv2jKOOOipGjRpVq2vsuuuuMWjQoLjssstqNX7JkiXRqlWrWLx4cbRs2XK96gYAAL7UrFmzWLFiRcyePTu6deuW63LWybpkg5ytOK1atSqmTZsW/fv3r3K8f//+MWXKlFpdo7y8PJYuXbrWZcGVK1fGkiVLqjwAAIANt3z58lixYkVE2Kq30SxatCjKysqiQ4cOVY536NAhFixYUKtrXH/99bFs2bI47rjj1jhm1KhR0apVq8pHly5dNqhuAADgSxXb9AoKCqJFixY5rmbjynlziLy8vCrPU0rVjtXkwQcfjCuuuCLGjh0b7du3X+O4ESNGxOLFiysfc+fO3eCaAQCAqh31avM3/OasIFdv3K5du2jUqFG11aWFCxdWW4X6urFjx8aQIUPikUceie985ztrHVtYWBiFhYUbXC8AAFBVQ+moF5HDFacmTZpEcXFxlJSUVDleUlISffr0WeN5Dz74YJxyyinxwAMPxOGHH76xywQAANagoXTUi8jhilNExPDhw+Okk06K3r17x/777x+33XZbzJkzJ4YOHRoRX26zmzdvXtxzzz0R8WVoGjx4cPzud7+Lb33rW5WrVc2aNYtWrVrl7HMAAEBD1FBufhuR4+A0aNCg+OSTT2LkyJExf/786NWrV4wfPz66du0aERHz58+vck+nW2+9NUpLS+Pss8+Os88+u/L4ySefHHfffXd9lw8AAA1aQ9qql9PgFBFx1llnxVlnnVXja18PQ88+++zGLwgAAKiVhrRVL+dd9QAAgM1TQ9qqJzgBAADrpSFt1ROcAACA9WKrHgAAQAZb9QAAADLYqgcAALAWKSVb9QAAANZmyZIlUVZWFhFWnAAAAGpUsU2vWbNm0axZsxxXs/EJTgAAwDprSNv0IgQnAABgPTSkxhARghMAALAeGlIr8gjBCQAAWA+26gEAAGSwVQ8AACCDrXoAAAAZbNUDAADIYKseAABABlv1AAAAMtiqBwAAkMFWPQAAgLUoLS2Nzz77LCKsOAEAANTo008/rfzvbbbZJoeV1B/BCQAAWCdvvPFGRER06dIlGjdunONq6ofgBAAArJNp06ZFRERxcXGOK6k/ghMAALBOBCcAAIAMr7zySkRE7L333jmupP4ITgAAQK0tXbo03n777Yiw4gQAAFCjGTNmREopOnfuHB06dMh1OfVGcAIAAGqtYpteQ1ptihCcAACAdVDRGKIhfb8pQnACAADWQUPsqBchOAEAALW0bNmyeOuttyJCcAIAAKjRq6++GuXl5VFUVBQdO3bMdTn1SnACAABqpaFu04sQnAAAgFpqqB31IgQnAACglqw4AQAArMUXX3wRb7zxRkQ0vFbkEYITAABQC6+99lqUlZVF+/bto3Pnzrkup94JTgAAQKavbtPLy8vLcTX1T3ACAAAyVQSnhrhNL0JwAgAAaqEhd9SLEJwAAIAMK1asiP/5n/+JCMEJAACgRjNnzozS0tJo27ZtdOnSJdfl5ITgBAAArFVDbwwRITgBAAAZGvr3myIEJwAAIMNXV5waKsEJAABYo5UrV8bMmTMjouG2Io8QnAAAgLV4/fXXY/Xq1bHNNttEt27dcl1OzghOAADAGmkM8SXBCQAAWKOK4NSQt+lFCE4AAMBa6Kj3JcEJAACo0erVq+O1116LCMFJcAIAAGr0+uuvx8qVK6NVq1ax/fbb57qcnBKcAACAGlVs09t7770bdGOICMEJAABYAze+/T+CEwAAUCPB6f8ITgAAQDWlpaXx6quvRoRW5BGCEwAAUIMXX3wxVqxYES1atIgdd9wx1+XknOAEAABUc8UVV0RExA9+8IPIzxcb/AQAAIAqSkpKoqSkJJo0aRJXXnllrsvZJAhOAABApfLy8rjkkksiIuKss86Kbt265bagTYTgBAAAVHr44YfjlVdeiRYtWsSll16a63I2GYITAAAQERGrVq2qDEsXX3xxtGvXLscVbToEJwAAICIibrvttpg1a1YUFRXFsGHDcl3OJkVwAgAAYunSpTFy5MiIiLj88sujefPmOa5o0yI4AQAAcf3118fHH38cO+20UwwZMiTX5WxyBCcAAGjgPvroo7juuusiIuLqq6+Oxo0b57iiTY/gBAAADdyvfvWrWLZsWey7775xzDHH5LqcTZLgBAAADdi7774bt956a0REXHPNNZGXl5fjijZNghMAADRgv/zlL6O0tDQGDBgQBx10UK7L2WQV5LoAAACg/qWUYvTo0fHQQw9FXl5ejBo1KtclbdIEJwAAaGA++OCDOO2006KkpCQiIn7605/GHnvskeOqNm226gEAQAORUop77703evXqFSUlJdG0adO48cYbY/To0bkubZNnxQkAABqAhQsXxtChQ2PcuHEREbHvvvvGn/70p+jRo0eOK9s8WHECAIAtWEopxo0bF7169Ypx48ZFQUFB/OpXv4rnn39eaFoHOQ9Oo0ePju7du0fTpk2juLg4Jk+evNbxEydOjOLi4mjatGlsv/32ccstt9RTpQAAsOlLKcVbb70VY8aMiUGDBkVRUVEcffTR8fHHH0evXr3ixRdfjF/84hdRUGDz2brI6U9r7NixMWzYsBg9enT07ds3br311hgwYEC88cYbsd1221UbP3v27Bg4cGD85Cc/ifvuuy+ef/75OOuss+Ib3/iGG3UBANCglJWVxaJFi2LBggWxYMGCmDVrVkyaNCmeffbZWLBgQZWxzZo1i/POOy+uvPLKKCwszFHFm7e8lFLK1Zvvt99+sffee8eYMWMqj/Xs2TOOOuqoGtshXnzxxfHkk0/Gm2++WXls6NCh8eqrr8bUqVNr9Z5LliyJVq1axeLFi6Nly5Yb/iE2wKx//jNm/Pd/57QGAADW7Ot/Kn/1eU3//fVj5SlFquFRXl4eZWVlUVpaGmVlZV8+ysujtLQ0SktL44svvogVK1fGF198EV+sWBErVqyIL774Ij5ftiw+WrgwFnz0UXy8aFGUl5fXWHdhYWH02W+/OKhfvzj429+OfXv33vQC01ZbReT4Zrvrkg1ytuK0atWqmDZtWlxyySVVjvfv3z+mTJlS4zlTp06N/v37Vzn23e9+N+64445YvXp1NG7cuNo5K1eujJUrV1Y+X7JkSR1UXzf++uc/x1kXXJDrMgAA2EzlRcQ3IqIoIjpFxLci4qCI2G/lymg6aVLEpEkRv/51Ditci88/j2jePNdV1FrOgtOiRYuirKwsOnToUOV4hw4dqi0tVliwYEGN40tLS2PRokXRsWPHaueMGjUqrrzyyrorvA4VdegQfXNdBADAFmRjrF+s7Zp5Nfz3V4/l/+/ziv/N+8rzgoho9LVHwf8+mv3vo+nX/rd5RHT430dRRLQLbbLrS85/znlfW55LKVU7ljW+puMVRowYEcOHD698vmTJkujSpcv6llunfvCjH8UPvv/9XJcBAAD1b6utcl3BOslZcGrXrl00atSo2urSwoULq60qVSgqKqpxfEFBQbRt27bGcwoLCze9/ZwV8vI2q+VJAABoqHLWjrxJkyZRXFwcJSUlVY6XlJREnz59ajxn//33rzZ+woQJ0bt37xq/3wQAAFAXcnofp+HDh8ftt98ed955Z7z55ptxwQUXxJw5c2Lo0KER8eU2u8GDB1eOHzp0aLz//vsxfPjwePPNN+POO++MO+64Iy688MJcfQQAAKAByOl3nAYNGhSffPJJjBw5MubPnx+9evWK8ePHR9euXSMiYv78+TFnzpzK8d27d4/x48fHBRdcEDfffHN06tQpfv/737uHEwAAsFHl9D5OubAp3ccJAADInXXJBjndqgcAALA5EJwAAAAyCE4AAAAZBCcAAIAMghMAAEAGwQkAACCD4AQAAJBBcAIAAMggOAEAAGQQnAAAADIITgAAABkEJwAAgAyCEwAAQIaCXBdQ31JKERGxZMmSHFcCAADkUkUmqMgIa9PggtPSpUsjIqJLly45rgQAANgULF26NFq1arXWMXmpNvFqC1JeXh4ffvhhtGjRIvLy8nJdTixZsiS6dOkSc+fOjZYtW+a6HDYT5g3rw7xhfZk7rA/zhvVR3/MmpRRLly6NTp06RX7+2r/F1OBWnPLz82PbbbfNdRnVtGzZ0j8qrDPzhvVh3rC+zB3Wh3nD+qjPeZO10lRBcwgAAIAMghMAAEAGwSnHCgsL4/LLL4/CwsJcl8JmxLxhfZg3rC9zh/Vh3rA+NuV50+CaQwAAAKwrK04AAAAZBCcAAIAMghMAAEAGwQkAACCD4JRDo0ePju7du0fTpk2juLg4Jk+enOuS2ISMGjUq9tlnn2jRokW0b98+jjrqqHj77berjEkpxRVXXBGdOnWKZs2axUEHHRSvv/56jipmUzRq1KjIy8uLYcOGVR4zb1iTefPmxYknnhht27aNrbbaKvbcc8+YNm1a5evmDl9XWloav/jFL6J79+7RrFmz2H777WPkyJFRXl5eOca8ISJi0qRJceSRR0anTp0iLy8vnnjiiSqv12aerFy5Ms4999xo165dNG/ePL7//e/HBx98UG+fQXDKkbFjx8awYcPi0ksvjenTp0e/fv1iwIABMWfOnFyXxiZi4sSJcfbZZ8cLL7wQJSUlUVpaGv37949ly5ZVjvntb38bN9xwQ9x0003x0ksvRVFRURx22GGxdOnSHFbOpuKll16K2267LXbfffcqx80bavLpp59G3759o3HjxvGXv/wl3njjjbj++uujdevWlWPMHb7ummuuiVtuuSVuuummePPNN+O3v/1tXHvttfGHP/yhcox5Q0TEsmXLYo899oibbrqpxtdrM0+GDRsW48aNi4ceeiiee+65+Pzzz+OII46IsrKy+vkQiZzYd99909ChQ6sc69GjR7rkkktyVBGbuoULF6aISBMnTkwppVReXp6KiorSb37zm8oxK1asSK1atUq33HJLrspkE7F06dK00047pZKSknTggQem888/P6Vk3rBmF198cTrggAPW+Lq5Q00OP/zwdNppp1U5dvTRR6cTTzwxpWTeULOISOPGjat8Xpt58tlnn6XGjRunhx56qHLMvHnzUn5+fnr66afrpW4rTjmwatWqmDZtWvTv37/K8f79+8eUKVNyVBWbusWLF0dERJs2bSIiYvbs2bFgwYIq86iwsDAOPPBA84g4++yz4/DDD4/vfOc7VY6bN6zJk08+Gb17945jjz022rdvH3vttVf88Y9/rHzd3KEmBxxwQPztb3+Ld955JyIiXn311Xjuuedi4MCBEWHeUDu1mSfTpk2L1atXVxnTqVOn6NWrV73NpYJ6eReqWLRoUZSVlUWHDh2qHO/QoUMsWLAgR1WxKUspxfDhw+OAAw6IXr16RURUzpWa5tH7779f7zWy6XjooYfilVdeiZdeeqnaa+YNazJr1qwYM2ZMDB8+PH7+85/Hiy++GOedd14UFhbG4MGDzR1qdPHFF8fixYujR48e0ahRoygrK4tf//rXcfzxx0eEf3OondrMkwULFkSTJk1im222qTamvv5+FpxyKC8vr8rzlFK1YxARcc4558Rrr70Wzz33XLXXzCO+au7cuXH++efHhAkTomnTpmscZ97wdeXl5dG7d++4+uqrIyJir732itdffz3GjBkTgwcPrhxn7vBVY8eOjfvuuy8eeOCB2HXXXWPGjBkxbNiw6NSpU5x88smV48wbamN95kl9ziVb9XKgXbt20ahRo2rpeOHChdWSNpx77rnx5JNPxt///vfYdtttK48XFRVFRJhHVDFt2rRYuHBhFBcXR0FBQRQUFMTEiRPj97//fRQUFFTODfOGr+vYsWN885vfrHKsZ8+elU2L/JtDTS666KK45JJL4kc/+lHstttucdJJJ8UFF1wQo0aNigjzhtqpzTwpKiqKVatWxaeffrrGMRub4JQDTZo0ieLi4igpKalyvKSkJPr06ZOjqtjUpJTinHPOiccffzyeeeaZ6N69e5XXu3fvHkVFRVXm0apVq2LixInmUQN26KGHxsyZM2PGjBmVj969e8cJJ5wQM2bMiO233968oUZ9+/atdsuDd955J7p27RoR/s2hZsuXL4/8/Kp/TjZq1KiyHbl5Q23UZp4UFxdH48aNq4yZP39+/M///E/9zaV6aUFBNQ899FBq3LhxuuOOO9Ibb7yRhg0blpo3b57ee++9XJfGJuLMM89MrVq1Ss8++2yaP39+5WP58uWVY37zm9+kVq1apccffzzNnDkzHX/88aljx45pyZIlOaycTc1Xu+qlZN5QsxdffDEVFBSkX//61+kf//hHuv/++9NWW22V7rvvvsox5g5fd/LJJ6fOnTunp556Ks2ePTs9/vjjqV27dunf//3fK8eYN6T0ZbfX6dOnp+nTp6eISDfccEOaPn16ev/991NKtZsnQ4cOTdtuu236r//6r/TKK6+kQw45JO2xxx6ptLS0Xj6D4JRDN998c+ratWtq0qRJ2nvvvSvbTENKX7bqrOlx1113VY4pLy9Pl19+eSoqKkqFhYXp29/+dpo5c2buimaT9PXgZN6wJn/+859Tr169UmFhYerRo0e67bbbqrxu7vB1S5YsSeeff37abrvtUtOmTdP222+fLr300rRy5crKMeYNKaX097//vca/a04++eSUUu3myRdffJHOOeec1KZNm9SsWbN0xBFHpDlz5tTbZ8hLKaX6WdsCAADYPPmOEwAAQAbBCQAAIIPgBAAAkEFwAgAAyCA4AQAAZBCcAAAAMghOAAAAGQQnAACADIITAABABsEJAAAgg+AEAACQQXACoMH4+OOPo6ioKK6++urKY//93/8dTZo0iQkTJuSwMgA2dXkppZTrIgCgvowfPz6OOuqomDJlSvTo0SP22muvOPzww+PGG2/MdWkAbMIEJwAanLPPPjv+67/+K/bZZ5949dVX46WXXoqmTZvmuiwANmGCEwANzhdffBG9evWKuXPnxssvvxy77757rksCYBPnO04ANDizZs2KDz/8MMrLy+P999/PdTkAbAasOAHQoKxatSr23Xff2HPPPaNHjx5xww03xMyZM6NDhw65Lg2ATZjgBECDctFFF8Wjjz4ar776amy99dZx8MEHR4sWLeKpp57KdWkAbMJs1QOgwXj22WfjxhtvjHvvvTdatmwZ+fn5ce+998Zzzz0XY8aMyXV5AGzCrDgBAABksOIEAACQQXACAADIIDgBAABkEJwAAAAyCE4AAAAZBCcAAIAMghMAAEAGwQkAACCD4AQAAJBBcAIAAMggOAEAAGT4/9LSSPXQUqzUAAAAAElFTkSuQmCC\n", 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "y_x2 = x**2\n", + "y_ex = np.exp(x)\n", + "\n", + "plt.figure(figsize=(10, 6))\n", + "plt.plot(x, y_x2, label='y = x^2', color='red')\n", + "plt.plot(x, y_ex, label='y = e^x', color='k')\n", + "\n", + "plt.xscale('linear')\n", + "plt.yscale('linear')\n", + "\n", + "plt.title('Plot of y = x^2 and y = e^x')\n", + "plt.xlabel('x')\n", + "plt.ylabel('y')\n", + "\n", + "plt.figure(figsize=(10, 6))\n", + "plt.plot(x, y_x2, label='y = x^2', color='blue')\n", + "plt.plot(x, y_ex, label='y = e^x', color='green')\n", + "\n", + "plt.xscale('log')\n", + "plt.yscale('log')\n", + "\n", + "plt.legend()\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Challenge 2\n", + "#### Import the `Fitbit2` dataset and store it in a variable called `fitbit`. You can find the dataset in Ironhack's database:\n", + "* db: `fitbit`\n", + "* table: `fitbit2`" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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DateCalorie burnedStepsDistanceFloorsMinutes SedentaryMinutes Lightly ActiveMinutes Fairly ActiveMinutes Very ActiveActivity Calories...Distance_milesDaysDays_encodedWork_or_WeekendHours SleepSleep efficiencyYesterday_sleepYesterday_sleep_efficiencyMonthsMonths_encoded
02015-05-0819349050.6501.35546001680...0.403891Friday4.016.40000092.0863310.0000000.000000May5
12015-05-0936311892514.114611.00031661602248...8.767545Saturday5.007.56666792.4643586.40000092.086331May5
22015-05-1032041422810.571602.00022614771719...6.567891Sunday6.006.45000088.7614687.56666792.464358May5
32015-05-11267367565.028749.0001902349620...3.119282Monday0.015.18333388.8571436.45000088.761468May5
42015-05-1224955023.731876.000171007360...2.317714Tuesday1.016.78333382.8920575.18333388.857143May5
..................................................................
3622016-05-0337961858814.1316599.0002754979236...8.779972Tuesday1.016.28333391.9512205.23333390.229885May5
3632016-05-0435251638212.3916684.00033310552075...7.698787Wednesday2.016.76666795.0819676.28333391.951220May5
3642016-05-0536492191316.4019701.00028729902249...10.190484Thursday3.014.66666788.8888896.76666795.081967May5
3652016-05-0635391902314.7915575.0002988852112...9.190077Friday4.016.16666789.8058254.66666788.888889May5
3662016-05-0730495267.082564.00037013101604...4.399307Saturday5.008.36666789.1651876.16666789.805825May5
\n", + "

367 rows × 24 columns

\n", + "
" + ], + "text/plain": [ + " Date Calorie burned Steps Distance Floors Minutes Sedentary \\\n", + "0 2015-05-08 1934 905 0.65 0 1.355 \n", + "1 2015-05-09 3631 18925 14.11 4 611.000 \n", + "2 2015-05-10 3204 14228 10.57 1 602.000 \n", + "3 2015-05-11 2673 6756 5.02 8 749.000 \n", + "4 2015-05-12 2495 502 3.73 1 876.000 \n", + ".. ... ... ... ... ... ... \n", + "362 2016-05-03 3796 18588 14.13 16 599.000 \n", + "363 2016-05-04 3525 16382 12.39 16 684.000 \n", + "364 2016-05-05 3649 21913 16.40 19 701.000 \n", + "365 2016-05-06 3539 19023 14.79 15 575.000 \n", + "366 2016-05-07 304 9526 7.08 2 564.000 \n", + "\n", + " Minutes Lightly Active Minutes Fairly Active Minutes Very Active \\\n", + "0 46 0 0 \n", + "1 316 61 60 \n", + "2 226 14 77 \n", + "3 190 23 4 \n", + "4 171 0 0 \n", + ".. ... ... ... \n", + "362 275 49 79 \n", + "363 333 10 55 \n", + "364 287 29 90 \n", + "365 298 8 85 \n", + "366 370 13 10 \n", + "\n", + " Activity Calories ... Distance_miles Days Days_encoded \\\n", + "0 1680 ... 0.403891 Friday 4.0 \n", + "1 2248 ... 8.767545 Saturday 5.0 \n", + "2 1719 ... 6.567891 Sunday 6.0 \n", + "3 9620 ... 3.119282 Monday 0.0 \n", + "4 7360 ... 2.317714 Tuesday 1.0 \n", + ".. ... ... ... ... ... \n", + "362 236 ... 8.779972 Tuesday 1.0 \n", + "363 2075 ... 7.698787 Wednesday 2.0 \n", + "364 2249 ... 10.190484 Thursday 3.0 \n", + "365 2112 ... 9.190077 Friday 4.0 \n", + "366 1604 ... 4.399307 Saturday 5.0 \n", + "\n", + " Work_or_Weekend Hours Sleep Sleep efficiency Yesterday_sleep \\\n", + "0 1 6.400000 92.086331 0.000000 \n", + "1 0 7.566667 92.464358 6.400000 \n", + "2 0 6.450000 88.761468 7.566667 \n", + "3 1 5.183333 88.857143 6.450000 \n", + "4 1 6.783333 82.892057 5.183333 \n", + ".. ... ... ... ... \n", + "362 1 6.283333 91.951220 5.233333 \n", + "363 1 6.766667 95.081967 6.283333 \n", + "364 1 4.666667 88.888889 6.766667 \n", + "365 1 6.166667 89.805825 4.666667 \n", + "366 0 8.366667 89.165187 6.166667 \n", + "\n", + " Yesterday_sleep_efficiency Months Months_encoded \n", + "0 0.000000 May 5 \n", + "1 92.086331 May 5 \n", + "2 92.464358 May 5 \n", + "3 88.761468 May 5 \n", + "4 88.857143 May 5 \n", + ".. ... ... ... \n", + "362 90.229885 May 5 \n", + "363 91.951220 May 5 \n", + "364 95.081967 May 5 \n", + "365 88.888889 May 5 \n", + "366 89.805825 May 5 \n", + "\n", + "[367 rows x 24 columns]" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fitbit = pd.read_csv(r\"C:\\Users\\dulce\\OneDrive\\Documentos\\Ironhack git\\Labs\\Labs week 3\\lab-matplotlib-seaborn\\your-code\\Fitbit2.csv\")\n", + "fitbit" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### From the Fitbit data, we want to visually understand:\n", + "\n", + "How the average number of steps change by month. Use the appropriate visualization to show the median steps by month. Is Fitbitter more active on weekend or workdays? All plots must be in the same jupyter notebook cell.\n", + "\n", + "**Hints**:\n", + "\n", + "* Use Months_encoded and Week_or Weekend columns.\n", + "* Use matplolib.pyplot object oriented API.\n", + "* Set your size figure to 12,4\n", + "* Explore plt.sca\n", + "* Explore plt.xticks\n", + "* Save your figures in a folder called `figures` in your repo. " + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "ename": "FileNotFoundError", + "evalue": "[Errno 2] No such file or directory: 'figures/average_steps_by_month_and_day_type.png'", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[11], line 29\u001b[0m\n\u001b[0;32m 26\u001b[0m plt\u001b[38;5;241m.\u001b[39mxticks(rotation\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m)\n\u001b[0;32m 28\u001b[0m plt\u001b[38;5;241m.\u001b[39mtight_layout()\n\u001b[1;32m---> 29\u001b[0m \u001b[43mplt\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msavefig\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mfigures/average_steps_by_month_and_day_type.png\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m 31\u001b[0m plt\u001b[38;5;241m.\u001b[39mshow()\n", + "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\pyplot.py:996\u001b[0m, in \u001b[0;36msavefig\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 993\u001b[0m \u001b[38;5;129m@_copy_docstring_and_deprecators\u001b[39m(Figure\u001b[38;5;241m.\u001b[39msavefig)\n\u001b[0;32m 994\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21msavefig\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m 995\u001b[0m fig \u001b[38;5;241m=\u001b[39m gcf()\n\u001b[1;32m--> 996\u001b[0m res \u001b[38;5;241m=\u001b[39m fig\u001b[38;5;241m.\u001b[39msavefig(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m 997\u001b[0m fig\u001b[38;5;241m.\u001b[39mcanvas\u001b[38;5;241m.\u001b[39mdraw_idle() \u001b[38;5;66;03m# Need this if 'transparent=True', to reset colors.\u001b[39;00m\n\u001b[0;32m 998\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m res\n", + "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\figure.py:3328\u001b[0m, in \u001b[0;36mFigure.savefig\u001b[1;34m(self, fname, transparent, **kwargs)\u001b[0m\n\u001b[0;32m 3324\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m ax \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maxes:\n\u001b[0;32m 3325\u001b[0m stack\u001b[38;5;241m.\u001b[39menter_context(\n\u001b[0;32m 3326\u001b[0m ax\u001b[38;5;241m.\u001b[39mpatch\u001b[38;5;241m.\u001b[39m_cm_set(facecolor\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mnone\u001b[39m\u001b[38;5;124m'\u001b[39m, edgecolor\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mnone\u001b[39m\u001b[38;5;124m'\u001b[39m))\n\u001b[1;32m-> 3328\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcanvas\u001b[38;5;241m.\u001b[39mprint_figure(fname, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n", + "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\backend_bases.py:2362\u001b[0m, in \u001b[0;36mFigureCanvasBase.print_figure\u001b[1;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, bbox_inches, pad_inches, bbox_extra_artists, backend, **kwargs)\u001b[0m\n\u001b[0;32m 2358\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m 2359\u001b[0m \u001b[38;5;66;03m# _get_renderer may change the figure dpi (as vector formats\u001b[39;00m\n\u001b[0;32m 2360\u001b[0m \u001b[38;5;66;03m# force the figure dpi to 72), so we need to set it again here.\u001b[39;00m\n\u001b[0;32m 2361\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m cbook\u001b[38;5;241m.\u001b[39m_setattr_cm(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfigure, dpi\u001b[38;5;241m=\u001b[39mdpi):\n\u001b[1;32m-> 2362\u001b[0m result \u001b[38;5;241m=\u001b[39m print_method(\n\u001b[0;32m 2363\u001b[0m filename,\n\u001b[0;32m 2364\u001b[0m facecolor\u001b[38;5;241m=\u001b[39mfacecolor,\n\u001b[0;32m 2365\u001b[0m edgecolor\u001b[38;5;241m=\u001b[39medgecolor,\n\u001b[0;32m 2366\u001b[0m orientation\u001b[38;5;241m=\u001b[39morientation,\n\u001b[0;32m 2367\u001b[0m bbox_inches_restore\u001b[38;5;241m=\u001b[39m_bbox_inches_restore,\n\u001b[0;32m 2368\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m 2369\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[0;32m 2370\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m bbox_inches \u001b[38;5;129;01mand\u001b[39;00m restore_bbox:\n", + "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\backend_bases.py:2228\u001b[0m, in \u001b[0;36mFigureCanvasBase._switch_canvas_and_return_print_method..\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 2224\u001b[0m optional_kws \u001b[38;5;241m=\u001b[39m { \u001b[38;5;66;03m# Passed by print_figure for other renderers.\u001b[39;00m\n\u001b[0;32m 2225\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdpi\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfacecolor\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124medgecolor\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124morientation\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m 2226\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbbox_inches_restore\u001b[39m\u001b[38;5;124m\"\u001b[39m}\n\u001b[0;32m 2227\u001b[0m skip \u001b[38;5;241m=\u001b[39m optional_kws \u001b[38;5;241m-\u001b[39m {\u001b[38;5;241m*\u001b[39minspect\u001b[38;5;241m.\u001b[39msignature(meth)\u001b[38;5;241m.\u001b[39mparameters}\n\u001b[1;32m-> 2228\u001b[0m print_method \u001b[38;5;241m=\u001b[39m functools\u001b[38;5;241m.\u001b[39mwraps(meth)(\u001b[38;5;28;01mlambda\u001b[39;00m \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: meth(\n\u001b[0;32m 2229\u001b[0m \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m{k: v \u001b[38;5;28;01mfor\u001b[39;00m k, v \u001b[38;5;129;01min\u001b[39;00m kwargs\u001b[38;5;241m.\u001b[39mitems() \u001b[38;5;28;01mif\u001b[39;00m k \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m skip}))\n\u001b[0;32m 2230\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m: \u001b[38;5;66;03m# Let third-parties do as they see fit.\u001b[39;00m\n\u001b[0;32m 2231\u001b[0m print_method \u001b[38;5;241m=\u001b[39m meth\n", + "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:509\u001b[0m, in \u001b[0;36mFigureCanvasAgg.print_png\u001b[1;34m(self, filename_or_obj, metadata, pil_kwargs)\u001b[0m\n\u001b[0;32m 462\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mprint_png\u001b[39m(\u001b[38;5;28mself\u001b[39m, filename_or_obj, \u001b[38;5;241m*\u001b[39m, metadata\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, pil_kwargs\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[0;32m 463\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m 464\u001b[0m \u001b[38;5;124;03m Write the figure to a PNG file.\u001b[39;00m\n\u001b[0;32m 465\u001b[0m \n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 507\u001b[0m \u001b[38;5;124;03m *metadata*, including the default 'Software' key.\u001b[39;00m\n\u001b[0;32m 508\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[1;32m--> 509\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_print_pil\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilename_or_obj\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mpng\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpil_kwargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmetadata\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:458\u001b[0m, in \u001b[0;36mFigureCanvasAgg._print_pil\u001b[1;34m(self, filename_or_obj, fmt, pil_kwargs, metadata)\u001b[0m\n\u001b[0;32m 453\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m 454\u001b[0m \u001b[38;5;124;03mDraw the canvas, then save it using `.image.imsave` (to which\u001b[39;00m\n\u001b[0;32m 455\u001b[0m \u001b[38;5;124;03m*pil_kwargs* and *metadata* are forwarded).\u001b[39;00m\n\u001b[0;32m 456\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m 457\u001b[0m FigureCanvasAgg\u001b[38;5;241m.\u001b[39mdraw(\u001b[38;5;28mself\u001b[39m)\n\u001b[1;32m--> 458\u001b[0m \u001b[43mmpl\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mimage\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mimsave\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 459\u001b[0m \u001b[43m \u001b[49m\u001b[43mfilename_or_obj\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbuffer_rgba\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mformat\u001b[39;49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfmt\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43morigin\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mupper\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m 460\u001b[0m \u001b[43m \u001b[49m\u001b[43mdpi\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfigure\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdpi\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmetadata\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmetadata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpil_kwargs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpil_kwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\image.py:1687\u001b[0m, in \u001b[0;36mimsave\u001b[1;34m(fname, arr, vmin, vmax, cmap, format, origin, dpi, metadata, pil_kwargs)\u001b[0m\n\u001b[0;32m 1685\u001b[0m pil_kwargs\u001b[38;5;241m.\u001b[39msetdefault(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mformat\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28mformat\u001b[39m)\n\u001b[0;32m 1686\u001b[0m pil_kwargs\u001b[38;5;241m.\u001b[39msetdefault(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdpi\u001b[39m\u001b[38;5;124m\"\u001b[39m, (dpi, dpi))\n\u001b[1;32m-> 1687\u001b[0m image\u001b[38;5;241m.\u001b[39msave(fname, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mpil_kwargs)\n", + "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\PIL\\Image.py:2428\u001b[0m, in \u001b[0;36mImage.save\u001b[1;34m(self, fp, format, **params)\u001b[0m\n\u001b[0;32m 2426\u001b[0m fp \u001b[38;5;241m=\u001b[39m builtins\u001b[38;5;241m.\u001b[39mopen(filename, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mr+b\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m 2427\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 2428\u001b[0m fp \u001b[38;5;241m=\u001b[39m \u001b[43mbuiltins\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mopen\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilename\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mw+b\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m 2430\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m 2431\u001b[0m save_handler(\u001b[38;5;28mself\u001b[39m, fp, filename)\n", + "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'figures/average_steps_by_month_and_day_type.png'" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# MEDIAN STEPS BY MONTH_ENCODED\n", + "\n", + "average_steps_by_month = fitbit.groupby('Months_encoded')['Steps'].mean()\n", + "\n", + "fig, axes = plt.subplots(1, 2, figsize=(14, 6))\n", + "\n", + "plt.sca(axes[0]) \n", + "average_steps_by_month.plot(kind='bar', ax=axes[0])\n", + "plt.title('Average Steps by Month')\n", + "plt.xlabel('Month')\n", + "plt.ylabel('Average Steps')\n", + "plt.xticks(rotation=0)\n", + "\n", + "plt.show()\n", + "\n", + "\n", + "fitbit['Day_Type'] = fitbit['Work_or_Weekend'].map({0: 'Workday', 1: 'Weekend'})\n", + "\n", + "average_steps_day_type = fitbit.groupby('Day_Type')['Steps'].mean()\n", + "\n", + "plt.sca(axes[1]) \n", + "average_steps_day_type.plot(kind='bar', ax=axes[1])\n", + "plt.title('Average Steps by Day Type')\n", + "plt.xlabel('Day Type')\n", + "plt.ylabel('Average Steps')\n", + "plt.xticks(rotation=0)\n", + "\n", + "plt.tight_layout()\n", + "plt.savefig('figures/average_steps_by_month_and_day_type.png')\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Write a loop to plot 3 scatter plots of the following features:\n", + "\n", + "* Minutes Lightly Active vs Steps \n", + "* Minutes Very Active vs Steps \n", + "* Minutes Sedentary vs Steps " + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "ename": "FileNotFoundError", + "evalue": "[Errno 2] No such file or directory: 'figures/scatter_plots.png'", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[12], line 18\u001b[0m\n\u001b[0;32m 14\u001b[0m plt\u001b[38;5;241m.\u001b[39mylabel(y_feature)\n\u001b[0;32m 16\u001b[0m plt\u001b[38;5;241m.\u001b[39mtight_layout()\n\u001b[1;32m---> 18\u001b[0m \u001b[43mplt\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msavefig\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mfigures/scatter_plots.png\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m 20\u001b[0m plt\u001b[38;5;241m.\u001b[39mshow()\n", + "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\pyplot.py:996\u001b[0m, in \u001b[0;36msavefig\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 993\u001b[0m \u001b[38;5;129m@_copy_docstring_and_deprecators\u001b[39m(Figure\u001b[38;5;241m.\u001b[39msavefig)\n\u001b[0;32m 994\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21msavefig\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m 995\u001b[0m fig \u001b[38;5;241m=\u001b[39m gcf()\n\u001b[1;32m--> 996\u001b[0m res \u001b[38;5;241m=\u001b[39m fig\u001b[38;5;241m.\u001b[39msavefig(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m 997\u001b[0m fig\u001b[38;5;241m.\u001b[39mcanvas\u001b[38;5;241m.\u001b[39mdraw_idle() \u001b[38;5;66;03m# Need this if 'transparent=True', to reset colors.\u001b[39;00m\n\u001b[0;32m 998\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m res\n", + "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\figure.py:3328\u001b[0m, in \u001b[0;36mFigure.savefig\u001b[1;34m(self, fname, transparent, **kwargs)\u001b[0m\n\u001b[0;32m 3324\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m ax \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maxes:\n\u001b[0;32m 3325\u001b[0m stack\u001b[38;5;241m.\u001b[39menter_context(\n\u001b[0;32m 3326\u001b[0m ax\u001b[38;5;241m.\u001b[39mpatch\u001b[38;5;241m.\u001b[39m_cm_set(facecolor\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mnone\u001b[39m\u001b[38;5;124m'\u001b[39m, edgecolor\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mnone\u001b[39m\u001b[38;5;124m'\u001b[39m))\n\u001b[1;32m-> 3328\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcanvas\u001b[38;5;241m.\u001b[39mprint_figure(fname, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n", + "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\backend_bases.py:2362\u001b[0m, in \u001b[0;36mFigureCanvasBase.print_figure\u001b[1;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, bbox_inches, pad_inches, bbox_extra_artists, backend, **kwargs)\u001b[0m\n\u001b[0;32m 2358\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m 2359\u001b[0m \u001b[38;5;66;03m# _get_renderer may change the figure dpi (as vector formats\u001b[39;00m\n\u001b[0;32m 2360\u001b[0m \u001b[38;5;66;03m# force the figure dpi to 72), so we need to set it again here.\u001b[39;00m\n\u001b[0;32m 2361\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m cbook\u001b[38;5;241m.\u001b[39m_setattr_cm(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfigure, dpi\u001b[38;5;241m=\u001b[39mdpi):\n\u001b[1;32m-> 2362\u001b[0m result \u001b[38;5;241m=\u001b[39m print_method(\n\u001b[0;32m 2363\u001b[0m filename,\n\u001b[0;32m 2364\u001b[0m facecolor\u001b[38;5;241m=\u001b[39mfacecolor,\n\u001b[0;32m 2365\u001b[0m edgecolor\u001b[38;5;241m=\u001b[39medgecolor,\n\u001b[0;32m 2366\u001b[0m orientation\u001b[38;5;241m=\u001b[39morientation,\n\u001b[0;32m 2367\u001b[0m bbox_inches_restore\u001b[38;5;241m=\u001b[39m_bbox_inches_restore,\n\u001b[0;32m 2368\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m 2369\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[0;32m 2370\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m bbox_inches \u001b[38;5;129;01mand\u001b[39;00m restore_bbox:\n", + "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\backend_bases.py:2228\u001b[0m, in \u001b[0;36mFigureCanvasBase._switch_canvas_and_return_print_method..\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 2224\u001b[0m optional_kws \u001b[38;5;241m=\u001b[39m { \u001b[38;5;66;03m# Passed by print_figure for other renderers.\u001b[39;00m\n\u001b[0;32m 2225\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdpi\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfacecolor\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124medgecolor\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124morientation\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m 2226\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbbox_inches_restore\u001b[39m\u001b[38;5;124m\"\u001b[39m}\n\u001b[0;32m 2227\u001b[0m skip \u001b[38;5;241m=\u001b[39m optional_kws \u001b[38;5;241m-\u001b[39m {\u001b[38;5;241m*\u001b[39minspect\u001b[38;5;241m.\u001b[39msignature(meth)\u001b[38;5;241m.\u001b[39mparameters}\n\u001b[1;32m-> 2228\u001b[0m print_method \u001b[38;5;241m=\u001b[39m functools\u001b[38;5;241m.\u001b[39mwraps(meth)(\u001b[38;5;28;01mlambda\u001b[39;00m \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: meth(\n\u001b[0;32m 2229\u001b[0m \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m{k: v \u001b[38;5;28;01mfor\u001b[39;00m k, v \u001b[38;5;129;01min\u001b[39;00m kwargs\u001b[38;5;241m.\u001b[39mitems() \u001b[38;5;28;01mif\u001b[39;00m k \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m skip}))\n\u001b[0;32m 2230\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m: \u001b[38;5;66;03m# Let third-parties do as they see fit.\u001b[39;00m\n\u001b[0;32m 2231\u001b[0m print_method \u001b[38;5;241m=\u001b[39m meth\n", + "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:509\u001b[0m, in \u001b[0;36mFigureCanvasAgg.print_png\u001b[1;34m(self, filename_or_obj, metadata, pil_kwargs)\u001b[0m\n\u001b[0;32m 462\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mprint_png\u001b[39m(\u001b[38;5;28mself\u001b[39m, filename_or_obj, \u001b[38;5;241m*\u001b[39m, metadata\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, pil_kwargs\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[0;32m 463\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m 464\u001b[0m \u001b[38;5;124;03m Write the figure to a PNG file.\u001b[39;00m\n\u001b[0;32m 465\u001b[0m \n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 507\u001b[0m \u001b[38;5;124;03m *metadata*, including the default 'Software' key.\u001b[39;00m\n\u001b[0;32m 508\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[1;32m--> 509\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_print_pil\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilename_or_obj\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mpng\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpil_kwargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmetadata\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:458\u001b[0m, in \u001b[0;36mFigureCanvasAgg._print_pil\u001b[1;34m(self, filename_or_obj, fmt, pil_kwargs, metadata)\u001b[0m\n\u001b[0;32m 453\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m 454\u001b[0m \u001b[38;5;124;03mDraw the canvas, then save it using `.image.imsave` (to which\u001b[39;00m\n\u001b[0;32m 455\u001b[0m \u001b[38;5;124;03m*pil_kwargs* and *metadata* are forwarded).\u001b[39;00m\n\u001b[0;32m 456\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m 457\u001b[0m FigureCanvasAgg\u001b[38;5;241m.\u001b[39mdraw(\u001b[38;5;28mself\u001b[39m)\n\u001b[1;32m--> 458\u001b[0m \u001b[43mmpl\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mimage\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mimsave\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 459\u001b[0m \u001b[43m \u001b[49m\u001b[43mfilename_or_obj\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbuffer_rgba\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mformat\u001b[39;49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfmt\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43morigin\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mupper\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m 460\u001b[0m \u001b[43m \u001b[49m\u001b[43mdpi\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfigure\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdpi\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmetadata\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmetadata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpil_kwargs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpil_kwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\image.py:1687\u001b[0m, in \u001b[0;36mimsave\u001b[1;34m(fname, arr, vmin, vmax, cmap, format, origin, dpi, metadata, pil_kwargs)\u001b[0m\n\u001b[0;32m 1685\u001b[0m pil_kwargs\u001b[38;5;241m.\u001b[39msetdefault(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mformat\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28mformat\u001b[39m)\n\u001b[0;32m 1686\u001b[0m pil_kwargs\u001b[38;5;241m.\u001b[39msetdefault(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdpi\u001b[39m\u001b[38;5;124m\"\u001b[39m, (dpi, dpi))\n\u001b[1;32m-> 1687\u001b[0m image\u001b[38;5;241m.\u001b[39msave(fname, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mpil_kwargs)\n", + "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\PIL\\Image.py:2428\u001b[0m, in \u001b[0;36mImage.save\u001b[1;34m(self, fp, format, **params)\u001b[0m\n\u001b[0;32m 2426\u001b[0m fp \u001b[38;5;241m=\u001b[39m builtins\u001b[38;5;241m.\u001b[39mopen(filename, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mr+b\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m 2427\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 2428\u001b[0m fp \u001b[38;5;241m=\u001b[39m \u001b[43mbuiltins\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mopen\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilename\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mw+b\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m 2430\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m 2431\u001b[0m save_handler(\u001b[38;5;28mself\u001b[39m, fp, filename)\n", + "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'figures/scatter_plots.png'" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "features = [\n", + " ('Minutes Lightly Active', 'Steps'),\n", + " ('Minutes Very Active', 'Steps'),\n", + " ('Minutes Sedentary', 'Steps')\n", + "]\n", + "\n", + "plt.figure(figsize=(10, 2))\n", + "\n", + "for i, (x_feature, y_feature) in enumerate(features, 1):\n", + " plt.subplot(1, 3, i)\n", + " plt.scatter(fitbit[x_feature], fitbit[y_feature], alpha=0.5)\n", + " plt.title(f'{x_feature} vs {y_feature}')\n", + " plt.xlabel(x_feature)\n", + " plt.ylabel(y_feature)\n", + "\n", + "plt.tight_layout()\n", + "\n", + "plt.savefig('figures/scatter_plots.png')\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Challenge 3\n", + "\n", + "#### Import the `titanic` dataset and store it in a variable called `titanic`. You can find the dataset in Ironhack's database:\n", + "* db: `titanic`\n", + "* table: `titanic`" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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PassengerIdSurvivedPclassNameGenderAgeSibSpParchTicketFareCabinEmbarked
010.03Braund, Mr. Owen Harrismale22.00000010A/5 211717.2500U0S
121.01Cumings, Mrs. John Bradley (Florence Briggs Th...female38.00000010PC 1759971.2833C85C
231.03Heikkinen, Miss. Lainafemale26.00000000STON/O2. 31012827.9250U0S
341.01Futrelle, Mrs. Jacques Heath (Lily May Peel)female35.0000001011380353.1000C123S
450.03Allen, Mr. William Henrymale35.000000003734508.0500U0S
.......................................
13041305NaN3Spector, Mr. Woolfmale29.51319000A.5. 32368.0500U0S
13051306NaN1Oliva y Ocana, Dona. Ferminafemale39.00000000PC 17758108.9000C105C
13061307NaN3Saether, Mr. Simon Sivertsenmale38.50000000SOTON/O.Q. 31012627.2500U0S
13071308NaN3Ware, Mr. Frederickmale29.513190003593098.0500U0S
13081309NaN3Peter, Master. Michael Jmale25.31543511266822.3583U0C
\n", + "

1309 rows × 12 columns

\n", + "
" + ], + "text/plain": [ + " PassengerId Survived Pclass \\\n", + "0 1 0.0 3 \n", + "1 2 1.0 1 \n", + "2 3 1.0 3 \n", + "3 4 1.0 1 \n", + "4 5 0.0 3 \n", + "... ... ... ... \n", + "1304 1305 NaN 3 \n", + "1305 1306 NaN 1 \n", + "1306 1307 NaN 3 \n", + "1307 1308 NaN 3 \n", + "1308 1309 NaN 3 \n", + "\n", + " Name Gender Age \\\n", + "0 Braund, Mr. Owen Harris male 22.000000 \n", + "1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.000000 \n", + "2 Heikkinen, Miss. Laina female 26.000000 \n", + "3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.000000 \n", + "4 Allen, Mr. William Henry male 35.000000 \n", + "... ... ... ... \n", + "1304 Spector, Mr. Woolf male 29.513190 \n", + "1305 Oliva y Ocana, Dona. Fermina female 39.000000 \n", + "1306 Saether, Mr. Simon Sivertsen male 38.500000 \n", + "1307 Ware, Mr. Frederick male 29.513190 \n", + "1308 Peter, Master. Michael J male 25.315435 \n", + "\n", + " SibSp Parch Ticket Fare Cabin Embarked \n", + "0 1 0 A/5 21171 7.2500 U0 S \n", + "1 1 0 PC 17599 71.2833 C85 C \n", + "2 0 0 STON/O2. 3101282 7.9250 U0 S \n", + "3 1 0 113803 53.1000 C123 S \n", + "4 0 0 373450 8.0500 U0 S \n", + "... ... ... ... ... ... ... \n", + "1304 0 0 A.5. 3236 8.0500 U0 S \n", + "1305 0 0 PC 17758 108.9000 C105 C \n", + "1306 0 0 SOTON/O.Q. 3101262 7.2500 U0 S \n", + "1307 0 0 359309 8.0500 U0 S \n", + "1308 1 1 2668 22.3583 U0 C \n", + "\n", + "[1309 rows x 12 columns]" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = titanic = pd.read_csv(r\"C:\\Users\\dulce\\OneDrive\\Documentos\\Ironhack git\\Labs\\Labs week 3\\lab-matplotlib-seaborn\\your-code\\titanic.csv\")\n", + "df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Explore the titanic dataset using Pandas dtypes." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df.dtypes" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### What are your numerical variables? What are your categorical variables?\n", + "**Hint**: Use Pandas select_dtypes." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Numerical variables:\n", + "Index(['PassengerId', 'Survived', 'Pclass', 'Age', 'SibSp', 'Parch', 'Fare'], dtype='object')\n" + ] + } + ], + "source": [ + "# NUMERICAL VARIABLES\n", + "\n", + "numerical_variables = df.select_dtypes(include=['int64', 'float64'])\n", + "print(\"Numerical variables:\")\n", + "print(numerical_variables.columns)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Categorical variables:\n", + "Index(['Name', 'Gender', 'Ticket', 'Cabin', 'Embarked'], dtype='object')\n" + ] + } + ], + "source": [ + "# CATEGORICAL VARIABLES\n", + "\n", + "categorical_variables = df.select_dtypes(include=['object', 'category'])\n", + "print(\"Categorical variables:\")\n", + "print(categorical_variables.columns)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Set the plot style to classic and the figure size to (12,6).\n", + "**Hint**: To set the style you can use matplotlib or seaborn functions. Do some research on the matter." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "plt.style.use('classic')\n", + "\n", + "plt.figure(figsize=(12, 6))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Use the right visulalization to show the distribution of column `Age`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.figure(figsize=(12, 6)) \n", + "plt.hist(df['Age'], bins=10, color='orange') \n", + "plt.xlabel('Age')\n", + "plt.title('Age Distribution')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Use subplots and plot the distribution of the `Age` with bins equal to 10, 20 and 50." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(18, 6))\n", + "\n", + "bins_values = [10, 20, 50]\n", + "titles = ['Bins 10', 'Bin 20', 'Bins 50']\n", + "\n", + "for i in range(3):\n", + " axes[i].hist(df['Age'], bins=bins_values[i], color='purple')\n", + " axes[i].set_xlabel('Age')\n", + " axes[i].set_title(titles[i])\n", + "\n", + "plt.tight_layout() \n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### How does the bin size affect your plot?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# It changed a lot the appereance of the plot and consequentialy his distribution of the ages." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Use seaborn to show the distribution of column `Age`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.figure(figsize=(12, 6))\n", + "\n", + "sns.distplot(df['Age'], bins=10, color='red')\n", + "\n", + "plt.xlabel('Age')\n", + "plt.title('Age Distribution')\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Use the right plot to visualize column `Gender`. There are 2 ways of doing it. Do it both ways.\n", + "**Hint**: Use matplotlib and seaborn." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Method 1 - matplotlib\n", + "\n", + "gender_counts = df['Gender'].value_counts()\n", + " \n", + "plt.bar(gender_counts.index, gender_counts.values, color='orange')\n", + "plt.xlabel('Gender')\n", + "plt.ylabel('Count')\n", + "plt.title('Gender Distribution')\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Method 2 - seaborn\n", + "\n", + "sns.countplot(data=df, x='Gender', palette='pink')\n", + "\n", + "plt.xlabel('Gender')\n", + "plt.ylabel('Count')\n", + "plt.title('Distribution of Gender')\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Use the right plot to visualize the column `Pclass`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.figure(figsize=(6, 4))\n", + "\n", + "sns.countplot(data=df, x='Pclass', palette='viridis')\n", + "\n", + "plt.xlabel('Passenger Class')\n", + "plt.ylabel('Count')\n", + "plt.title('Passenger Class Distribution')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### We would like to have in one single plot the summary statistics of the feature `Age`. What kind of plot would you use? Plot it. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.figure(figsize=(8, 4))\n", + "\n", + "sns.boxplot(data=df, y='Age', color='skyblue')\n", + "\n", + "plt.ylabel('Age')\n", + "plt.title('Summary Statistics of Age')\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\"\"\"\n", + "your comments here\n", + "\"\"\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### What does the last plot tell you about the feature `Age`?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# The last plot, provides insights into the distribution and summary of the data.\n", + "# It is possible to tell that the median age is represented by the line inside the box, and there are a \n", + "# few outliers on the higher age side, suggesting that there might be some older passengers.\n", + "# It help understand how spread the data it and if there is presence of potential outliers in the data." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Now in addition to the summary statistics, we want to have in the same plot the distribution of `Age`. What kind of plot would you use? Plot it. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.figure(figsize=(6, 2))\n", + "\n", + "sns.boxplot(data=df, y='Age', color='black') \n", + "sns.swarmplot(data=df, y='Age', color='orange', alpha=0.7, dodge=True) \n", + "\n", + "plt.ylabel('Age')\n", + "plt.title('Statistics and Distribution of Age')\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### What additional information does the last plot provide about feature `Age`?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# A more compreehsive view of the age in the plot, her distribution, more demonstrative of how the data is spread." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### We suspect that there is a linear relationship between `Fare` and `Age`. Use the right plot to show the relationship between these 2 features. There are 2 ways, please do it both ways.\n", + "**Hint**: Use matplotlib and seaborn." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Method 1 - matplotlib\n", + "\n", + "plt.figure(figsize=(12, 8))\n", + "\n", + "plt.scatter(df['Fare'], df['Age'], color='purple')\n", + "\n", + "plt.xlabel('Fare')\n", + "plt.ylabel('Age')\n", + "plt.title('Fare and Age reationship (M)')\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Method 2 - seaborn\n", + "\n", + "plt.figure(figsize=(12, 8))\n", + "\n", + "sns.scatterplot(data=df, x='Fare', y='Age', color='red')\n", + "\n", + "plt.xlabel('Fare')\n", + "plt.ylabel('Age')\n", + "plt.title('Fare and Age relationship (S)')\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Plot the correlation matrix using seaborn." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\dulce\\AppData\\Local\\Temp\\ipykernel_20700\\3844739742.py:1: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n", + " correlation_matrix = df.corr()\n" + ] + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "correlation_matrix = df.corr()\n", + "\n", + "plt.figure(figsize=(12, 8))\n", + "\n", + "sns.heatmap(correlation_matrix, annot=True, cmap = 'mako', center = 0)\n", + "\n", + "plt.title('Correlation Matrix')\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### What are the most correlated features?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Are fare and survived." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Use the most appropriate plot to display the summary statistics of `Age` depending on `Pclass`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "plt.figure(figsize=(14, 4))\n", + "\n", + "sns.boxplot(data=df, x='Pclass', y='Age', palette='colorblind')\n", + "\n", + "\n", + "plt.xlabel('Passenger Class')\n", + "plt.ylabel('Age')\n", + "plt.title('Summary Statistics of Age by Passenger Class')\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Use seaborn to plot the distribution of `Age` based on the `Gender`.\n", + "**Hint**: Use Facetgrid." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "g = sns.FacetGrid(df, col='Gender', height=6, aspect=1.2, sharex=False)\n", + "\n", + "g.map(plt.hist, 'Age', bins=20, color='orange')\n", + "\n", + "g.set_axis_labels('Age', 'Count')\n", + "g.fig.suptitle('Distribution of age based on gender', y=1.02)\n", + "\n", + "plt.show()" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.10.9" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +}