{ "cells": [ { "cell_type": "markdown", "id": "6deb8608-1631-4f47-bfcc-9b27461eea2a", "metadata": {}, "source": [ "# Visualizing Proportions\n", "\n", "![Visualizing Proportions](../images/data_visualization/visualizing_proportions.png)\n", "\n", "## Get to know your mushrooms 🍄\n", "\n", "Mushrooms are very interesting. Let's import a dataset to study them:" ] }, { "cell_type": "code", "execution_count": 1, "id": "ae3aa1a2-18f0-43d6-aa02-eb90f64211a2", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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classcap-shapecap-surfacecap-colorbruisesodorgill-attachmentgill-spacinggill-sizegill-color...stalk-surface-below-ringstalk-color-above-ringstalk-color-below-ringveil-typeveil-colorring-numberring-typespore-print-colorpopulationhabitat
0PoisonousConvexSmoothBrownBruisesPungentFreeCloseNarrowBlack...SmoothWhiteWhitePartialWhiteOnePendantBlackScatteredUrban
1EdibleConvexSmoothYellowBruisesAlmondFreeCloseBroadBlack...SmoothWhiteWhitePartialWhiteOnePendantBrownNumerousGrasses
2EdibleBellSmoothWhiteBruisesAniseFreeCloseBroadBrown...SmoothWhiteWhitePartialWhiteOnePendantBrownNumerousMeadows
3PoisonousConvexScalyWhiteBruisesPungentFreeCloseNarrowBrown...SmoothWhiteWhitePartialWhiteOnePendantBlackScatteredUrban
4EdibleConvexSmoothGreenNo BruisesNaNFreeCrowdedBroadBlack...SmoothWhiteWhitePartialWhiteOneEvanescentBrownAbundantGrasses
\n", "

5 rows × 23 columns

\n", "
" ], "text/plain": [ " class cap-shape cap-surface cap-color bruises odor \\\n", "0 Poisonous Convex Smooth Brown Bruises Pungent \n", "1 Edible Convex Smooth Yellow Bruises Almond \n", "2 Edible Bell Smooth White Bruises Anise \n", "3 Poisonous Convex Scaly White Bruises Pungent \n", "4 Edible Convex Smooth Green No Bruises NaN \n", "\n", " gill-attachment gill-spacing gill-size gill-color ... \\\n", "0 Free Close Narrow Black ... \n", "1 Free Close Broad Black ... \n", "2 Free Close Broad Brown ... \n", "3 Free Close Narrow Brown ... \n", "4 Free Crowded Broad Black ... \n", "\n", " stalk-surface-below-ring stalk-color-above-ring stalk-color-below-ring \\\n", "0 Smooth White White \n", "1 Smooth White White \n", "2 Smooth White White \n", "3 Smooth White White \n", "4 Smooth White White \n", "\n", " veil-type veil-color ring-number ring-type spore-print-color population \\\n", "0 Partial White One Pendant Black Scattered \n", "1 Partial White One Pendant Brown Numerous \n", "2 Partial White One Pendant Brown Numerous \n", "3 Partial White One Pendant Black Scattered \n", "4 Partial White One Evanescent Brown Abundant \n", "\n", " habitat \n", "0 Urban \n", "1 Grasses \n", "2 Meadows \n", "3 Urban \n", "4 Grasses \n", "\n", "[5 rows x 23 columns]" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "\n", "mushrooms = pd.read_csv(\"../../data/mushrooms.csv\")\n", "mushrooms.head()" ] }, { "cell_type": "markdown", "id": "a0a550de-62ce-4534-8af3-77d32be67100", "metadata": {}, "source": [ "Right away, you notice that all the data is textual. You will have to convert this data to be able to use it in a chart. Most of the data, in fact, is represented as an object:" ] }, { "cell_type": "code", "execution_count": 2, "id": "557ec8a9-2937-412b-b2a6-c793816473a8", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Index(['class', 'cap-shape', 'cap-surface', 'cap-color', 'bruises', 'odor',\n", " 'gill-attachment', 'gill-spacing', 'gill-size', 'gill-color',\n", " 'stalk-shape', 'stalk-root', 'stalk-surface-above-ring',\n", " 'stalk-surface-below-ring', 'stalk-color-above-ring',\n", " 'stalk-color-below-ring', 'veil-type', 'veil-color', 'ring-number',\n", " 'ring-type', 'spore-print-color', 'population', 'habitat'],\n", " dtype='object')\n" ] } ], "source": [ "print(mushrooms.select_dtypes([\"object\"]).columns)" ] }, { "cell_type": "markdown", "id": "67d16026-5dbc-4487-8fbd-9fb29eb6827f", "metadata": {}, "source": [ "Take this data and convert the 'class' column to a category:" ] }, { "cell_type": "code", "execution_count": 3, "id": "fb963e08-5ad4-4ad1-891f-4021dfbfbc46", "metadata": {}, "outputs": [], "source": [ "cols = mushrooms.select_dtypes([\"object\"]).columns\n", "mushrooms[cols] = mushrooms[cols].astype(\"category\")" ] }, { "cell_type": "code", "execution_count": 4, "id": "c3bf2ce5-8732-4840-a461-b3e1aee0e60d", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/var/folders/7w/fv5n0x414253d7dv5g2wwmb40000gn/T/ipykernel_93696/2186401341.py:1: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n", " edibleclass = mushrooms.groupby([\"class\"]).count()\n" ] }, { "data": { "text/html": [ "
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cap-shapecap-surfacecap-colorbruisesodorgill-attachmentgill-spacinggill-sizegill-colorstalk-shape...stalk-surface-below-ringstalk-color-above-ringstalk-color-below-ringveil-typeveil-colorring-numberring-typespore-print-colorpopulationhabitat
class
Edible420842084208420880042084208420842084208...4208420842084208420842084208420842084208
Poisonous3916391639163916379639163916391639163916...3916391639163916391638803880391639163916
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2 rows × 22 columns

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" ], "text/plain": [ " cap-shape cap-surface cap-color bruises odor gill-attachment \\\n", "class \n", "Edible 4208 4208 4208 4208 800 4208 \n", "Poisonous 3916 3916 3916 3916 3796 3916 \n", "\n", " gill-spacing gill-size gill-color stalk-shape ... \\\n", "class ... \n", "Edible 4208 4208 4208 4208 ... \n", "Poisonous 3916 3916 3916 3916 ... \n", "\n", " stalk-surface-below-ring stalk-color-above-ring \\\n", "class \n", "Edible 4208 4208 \n", "Poisonous 3916 3916 \n", "\n", " stalk-color-below-ring veil-type veil-color ring-number \\\n", "class \n", "Edible 4208 4208 4208 4208 \n", "Poisonous 3916 3916 3916 3880 \n", "\n", " ring-type spore-print-color population habitat \n", "class \n", "Edible 4208 4208 4208 4208 \n", "Poisonous 3880 3916 3916 3916 \n", "\n", "[2 rows x 22 columns]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "edibleclass = mushrooms.groupby([\"class\"]).count()\n", "edibleclass" ] }, { "cell_type": "markdown", "id": "592b3bdc-0a82-446d-9b64-79f58bdeeb18", "metadata": {}, "source": [ "Now, if you print out the mushrooms data, you can see that it has been grouped into categories according to the poisonous/edible class." ] }, { "cell_type": "markdown", "id": "622edb65-9809-4cc7-90e3-4f71d945230a", "metadata": {}, "source": [ "## Pie 🥧" ] }, { "cell_type": "code", "execution_count": 5, "id": "5f400920-c41c-4f17-973a-63d9f7308a7e", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "labels = [\"Edible\", \"Poisonous\"]\n", "plt.pie(edibleclass[\"population\"], labels=labels, autopct=\"%.1f %%\")\n", "plt.title(\"Edible?\")\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "0f7a9478-8690-47d4-80d8-a6391857f815", "metadata": {}, "source": [ "Voila, a pie chart showing the proportions of this data according to these two classes of mushrooms. It's quite important to get the order of the labels correct, especially here, so be sure to verify the order with which the label array is built!" ] }, { "cell_type": "markdown", "id": "a420ade9-a874-4a87-a049-6c5ee1f1efcb", "metadata": {}, "source": [ "## Donuts 🍩\n", "\n", "A somewhat more visually interesting pie chart is a donut chart, which is a pie chart with a hole in the middle. Let's look at our data using this method.\n", "\n", "Take a look at the various habitats where mushrooms grow:" ] }, { "cell_type": "code", "execution_count": 6, "id": "511ff0e7-759e-4ceb-8494-5a53f9970112", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/var/folders/7w/fv5n0x414253d7dv5g2wwmb40000gn/T/ipykernel_93696/3512452478.py:1: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n", " habitat = mushrooms.groupby([\"habitat\"]).count()\n" ] }, { "data": { "text/html": [ "
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classcap-shapecap-surfacecap-colorbruisesodorgill-attachmentgill-spacinggill-sizegill-color...stalk-surface-above-ringstalk-surface-below-ringstalk-color-above-ringstalk-color-below-ringveil-typeveil-colorring-numberring-typespore-print-colorpopulation
habitat
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Leaves832832832832832576832832832832...832832832832832832832832832832
Meadows292292292292292256292292292292...292292292292292292292292292292
Paths1144114411441144114411041144114411441144...1144114411441144114411441144114411441144
Urban368368368368368272368368368368...368368368368368368368368368368
Waste1921921921921920192192192192...192192192192192192192192192192
Wood3148314831483148314813323148314831483148...3148314831483148314831483112311231483148
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7 rows × 22 columns

\n", "
" ], "text/plain": [ " class cap-shape cap-surface cap-color bruises odor \\\n", "habitat \n", "Grasses 2148 2148 2148 2148 2148 1056 \n", "Leaves 832 832 832 832 832 576 \n", "Meadows 292 292 292 292 292 256 \n", "Paths 1144 1144 1144 1144 1144 1104 \n", "Urban 368 368 368 368 368 272 \n", "Waste 192 192 192 192 192 0 \n", "Wood 3148 3148 3148 3148 3148 1332 \n", "\n", " gill-attachment gill-spacing gill-size gill-color ... \\\n", "habitat ... \n", "Grasses 2148 2148 2148 2148 ... \n", "Leaves 832 832 832 832 ... \n", "Meadows 292 292 292 292 ... \n", "Paths 1144 1144 1144 1144 ... \n", "Urban 368 368 368 368 ... \n", "Waste 192 192 192 192 ... \n", "Wood 3148 3148 3148 3148 ... \n", "\n", " stalk-surface-above-ring stalk-surface-below-ring \\\n", "habitat \n", "Grasses 2148 2148 \n", "Leaves 832 832 \n", "Meadows 292 292 \n", "Paths 1144 1144 \n", "Urban 368 368 \n", "Waste 192 192 \n", "Wood 3148 3148 \n", "\n", " stalk-color-above-ring stalk-color-below-ring veil-type \\\n", "habitat \n", "Grasses 2148 2148 2148 \n", "Leaves 832 832 832 \n", "Meadows 292 292 292 \n", "Paths 1144 1144 1144 \n", "Urban 368 368 368 \n", "Waste 192 192 192 \n", "Wood 3148 3148 3148 \n", "\n", " veil-color ring-number ring-type spore-print-color population \n", "habitat \n", "Grasses 2148 2148 2148 2148 2148 \n", "Leaves 832 832 832 832 832 \n", "Meadows 292 292 292 292 292 \n", "Paths 1144 1144 1144 1144 1144 \n", "Urban 368 368 368 368 368 \n", "Waste 192 192 192 192 192 \n", "Wood 3148 3112 3112 3148 3148 \n", "\n", "[7 rows x 22 columns]" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "habitat = mushrooms.groupby([\"habitat\"]).count()\n", "habitat" ] }, { "cell_type": "markdown", "id": "13896a38-e46b-40ba-81d2-a29f36808b6b", "metadata": {}, "source": [ "Here, you are grouping your data by habitat. There are 7 listed, so use those as labels for your donut chart:" ] }, { "cell_type": "code", "execution_count": 7, "id": "fa5105ea-749d-463b-ba18-ec5d5fa975f8", "metadata": {}, "outputs": [ { "data": { "image/png": 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rPP7444qiKMqxY8cUjUajnDhxolo5l112mfLkk08qiqIoHTt2VKZNm3bJz08I0fRJjaSR2r59OytXrsTLy8v21aZNGwBb89WBAwe4+eabiYuLw8fHh9jYWADS0tIACA4O5oorrmDu3LkAHDlyhHXr1jFu3DgAdu7cicVioXXr1tXus2rVKts9pk6dygsvvED//v155pln2LFjR0O+DEIIF6B1dgDi3EpKShg1ahSvvvpqjcfCw8MBGDVqFDExMcyaNYuIiAisVisdOnTAaDTazh03bhxTp05l5syZzJs3j44dO9KxY0fbPTQaDZs3b0aj0VS7x+nmq7vvvpthw4bx888/s3TpUl5++WWmT5/Ov//9b0c9daewWKzklBrJLTGSV2okr8xIWaWZCpOFcpOVcpOl6v/Gqn9NFitqtQqtWoVGrUKtqvr/6WPuei2+7jp83Kr+9XXX4euhw8dNh7+HHne9pvaghHARkkgaqW7durFo0SJiY2PRamv+mHJzc0lNTWXWrFm2/o41a9bUOG/06NHcc889LFmyhHnz5nH77bfbHuvatSsWi4Xs7GxbGecSHR3Nfffdx3333ceTTz7JrFmzXC+RmMrh1D7I3gcFaVB8EooyoLjqq8Qrnt7HpjZYOL7uOiL83In0cyPCz/3v/1f9GxvoQaCXocFiEaK+JJE0AoWFhWzbtq3asXvuuYdZs2Zx880389hjjxEQEMDBgweZP38+n3zyCf7+/gQGBvLxxx8THh5OWloaTzzxRI2yPT09ueaaa3jqqafYu3cvN998s+2x1q1bM27cOG6//XamT59O165dOXXqFCtWrKBTp06MGDGCBx54gCuvvJLWrVuTn5/PypUradu2raNfkktnMUPeIcjaDdl7IXtP1b/5R0Cxnvcyd51PAwYJheUmCstN7M0oOufjgZ56WoV6kRjqTeswb1qHVn35uusaNE4h6kISSSOQkpJC165dqx276667WLt2LY8//jhXXHEFlZWVxMTEMHz4cNRqNSqVivnz5zN16lQ6dOhAYmIiM2bMYPDgwTXKHzduHFdddRWDBg2iRYsW1R777LPPeOGFF3j44Yc5ceIEQUFB9OnTh5EjRwJgsViYPHky6enp+Pj4MHz4cN566y2HvRYXxVwJx/+C9I1nEkbOAbBUXnRRuvKc2k9qQLmlRnIP57H+cF614+G+bnSK8qV7jD/dWvjTMcoXg1aayYRzqRTlrLGhQjRmFjOc3AJHVsGR1XB8I5jL7VZ8e/MXlJpd601Zr1HTLsLHllh6tQwg2FuaxUTDkkQiGi+rFTJ3VCWNI6shbR0YSxx2u2v0H7OtyLXnyKhU0DbMh0Gtg0lqHUyPWH90GhmcKRxLEoloXEqyYe+PcGglHF0DFQUNduuHfN/i26zQBrtfQ/AyaOkTF0hS6yAGJ4YQHeDh7JBEEyR9JML5yvNh70+waxEc+QMUi1PCiDGUOuW+jlRSaWb53iyW780CdtMu3IcrO4RxVadw4oNdu/YlGg9JJMI5jKWw7xfY9Q0c+h0sxtqvcbBIbbGzQ3C4PRlF7MkoYvqy/bQJ82ZU5whGdYqgRaDUVMSlk6Yt0XBMFXBgaVXN48BSMJU5O6JqVkdP5PYD559P05R1ifbj+u5RjO4SgbebDDEWF0cSiXC843/B5tlVzVeV55430Rjsib6Zqw6McnYYTuWh1zCiYzg3925Btxb+zg5HuAhJJMIxTBVVzVZ/zYKMbc6Opk5ORg6n36Hbaz+xmUgM9eamXtFc2zUKXw+ppYjzk0Qi7KvgOGycBVu+gPK82s9vRIpCe9Pp2P3ODqPRMWjVjOkayd0D40gIkQ56UZMkEmEfJ7fBnzNgzw9gNTs7mkti9EugdeZzzg6j0VKp4LI2odybFEfP2ABnhyMaEUkk4tIpSlWn+Z8z4egfzo6m3hSDLy0LP3B2GC6haws/7h0UxxXtwlCrVc4ORziZJBJxafb9DL+/ULXGVRPiisukOFPLIE8mJycwpmskGkkozZYkEnFxjq2D5c/A8Q3OjsQhmsIyKc4QH+zJQ5cnclXHMFQqSSjNjSQSUTdZe2DFs7B/ibMjcaiHfN/m26wQZ4fhstpH+PDIFYkkt5HXsDmRme3iwgqOw8qXYMf8C+7n0VS0MJQA8iZ4qXafLOKO2RvpEePPo8MS6R0X6OyQRAOQRCLOrSwP/pheNQ/kEvb3cFWR2sY7YdKVbDqWz40fr2d4+zD+b2RbovxlCZamTBKJqM5UAevehbUzoLLQ2dE0uFBN019vqyEt2Z1Jyv5s7kuK576keNx0MpChKZI+EnHGkT/gp6mQd9jZkTiNLJPiOFH+7vzfiLYM7xDu7FCEncmONwIqCuHHqfD5qGadRAB8rQXODqHJSs8v574vt3DrJxs4mO24DcpEw5NE0tzt+xne6w1bPgekcupldq1lXVzRmoM5XDXjD95beRCzxbkDODIzM7n//vtJSEjAzc2N0NBQ+vfvzwcffEBZWeNanboxkz6S5qokG355FPZ87+xIGhX3ylxnh9AsGM1WXv8tlSW7Mnn9hk60CfNp8BgOHz5M//798fPz46WXXqJjx44YDAZ27tzJxx9/TGRkJFdffXWN60wmEzqdLGJ5NqmRNEfb5sF7vSSJnIO2IsfZITQrO08UcvXMtcxYcaDBayeTJk1Cq9WyadMmxo4dS9u2bYmLi2P06NH8/PPPjBpV1VemUqn44IMPuPrqq/H09OTFF1/EYrFw11130bJlS9zd3UlMTOSdd96pVn5KSgq9evXC09MTPz8/+vfvz7FjxwDYvn07ycnJeHt74+PjQ/fu3dm0aZPt2jVr1jBw4EDc3d2Jjo5m6tSplJae2cHz/fffp1WrVrZa1PXXX98Ar9j5SSJpTvKPwRdj4PuJVdvbihrUFQV4apr+fJnGxGix8uay/Vz97lp2n2yYkYK5ubksXbqUyZMn4+npec5zzp6hP23aNMaMGcPOnTu58847sVqtREVFsXDhQvbs2cPTTz/Nf/7zH77++msAzGYz11xzDUlJSezYsYN169Zxzz332MocN24cUVFRbNy4kc2bN/PEE0/YajmHDh1i+PDhXHfddezYsYMFCxawZs0apkyZAsCmTZuYOnUqzz33HKmpqSxZsoRBgwY58uWqlYzaai62fgm/PAamprcvub1drf+YHbJMilPoNCoeHZbIvwbGOXSplQ0bNtCnTx++/fZbxowZYzseFBRERUUFAJMnT+bVV19FpVLxwAMP8NZbb12wzClTppCZmck333xDXl4egYGBpKSkkJSUVONcHx8fZs6cyfjx42s8dvfdd6PRaPjoo49sx9asWUNSUhKlpaX88ssv3HHHHaSnp+Pt7X2pL4FdSY2kqTOVw/eT4YfJl5xEVh8zM+qrMiKmF6N6tojv95mqPa4oCk+vrCB8ejHuLxYxdE4pB3ItFyzz5T8q6TmrBO+Xiwh5vZhr5peRmlP9mod+qyDg1SKi3ypm7o7q91y428SorxzTGRrvLp2szmKyKLz0yz7unL2RvFJjg9//r7/+Ytu2bbRv357KyjMTcXv06FHj3Pfee4/u3bsTHByMl5cXH3/8MWlpaQAEBAQwYcIEhg0bxqhRo3jnnXfIyMiwXfvQQw9x9913M3ToUF555RUOHTpke2z79u3Mnj0bLy8v29ewYcOwWq0cOXKEyy+/nJiYGOLi4rjtttuYO3eu0wcGSCJpynIPwSeXw7Yv61VMqVGhc6ia965yO+fjr601MmODkQ9HuLHhbk889SqGfVlGhfn8ld1Vx8xM7qln/V2eLLvNA5MVrviyjFJj1TU/pZqYt9PE0ts8eW2oG3f/VE5OWVWTU2GFwn9/rzxvPPUVY5Chqc62MvUUV73zB+sPO2bwQ0JCAiqVitTU1GrH4+LiSEhIwN3dvdrxfzZ/zZ8/n0ceeYS77rqLpUuXsm3bNu644w6MxjPJ77PPPmPdunX069ePBQsW0Lp1a9avXw9UNZXt3r2bESNG8Pvvv9OuXTu+++47AEpKSrj33nvZtm2b7Wv79u0cOHCA+Ph4vL292bJlC1999RXh4eE8/fTTdO7cmYKCAge8UnUjTVtN1Z4f4Icpdt8jXfVsEd/d6M41baracxVFIeLNEh7uq+eRfgag6o0+9I1iZl/jzk0d6ja65VSplZA3Slg1wYNBMVpeW1vJlgwL86+vWloj9I1iFt/sQc9IDff+VE6bIDUP9jXY9bmd9nXk4zx2qLNDyhYXR6NW8e8hCUwd0sru+54MGzaM3bt3k5qaWiNRDB48mC5duvD222+jUqn47rvvuOaaa2yP//vf/2bPnj2sWLHCdmzo0KHk5OSwbdu2c96vb9++9OzZkxkzZtR47Oabb6a0tJQff/yRcePGkZWVxfLly+v0PEpLS/Hz82PBggVce+21dbrG3qRG0tRYTLDkP/D17XZPIudypEAhs0RhaNyZkeS+bip6R2lYd/zCzVtnK/y7FSHAverNonOohk0nLeSXK2w+aaHcpJAQoGZNmpktmRam9tbb9XmcLVQt6201FharwtvLD3DLJ+s5VWzfNd/ef/99zGYzPXr0YMGCBezdu5fU1FS+/PJL9u3bh0Zz/uVcWrVqxaZNm/jtt9/Yv38/Tz31FBs3brQ9fuTIEZ588knWrVvHsWPHWLp0KQcOHKBt27aUl5czZcoUUlJSOHbsGGvXrmXjxo20bdsWgMcff5w///yTKVOmsG3bNg4cOMAPP/xg62xfvHgxM2bMYNu2bRw7dow5c+ZgtVpJTEy06+tzMWQeSVNSdBIWTmjQvUIyS6qam0I9q39aDPVUkVlat9FPVkXhgSUV9I/W0CGk6o93WIKWWzvp6DmrBHedis+vccdTDxN/rmD2aHc+2GRi5l9GgjxUfDzSjfYh9lvDKYgCu5Ul7GP94TxGv7uGj2/vQYdIX7uUGR8fz9atW3nppZd48sknSU9Px2Aw0K5dOx555BEmTZp03mvvvfdetm7dyo033ohKpeLmm29m0qRJ/PrrrwB4eHiwb98+Pv/8c3JzcwkPD2fy5Mnce++9mM1mcnNzuf3228nKyiIoKIhrr72WZ599FoBOnTqxatUq/vvf/zJw4EAURSE+Pp4bb7wRAD8/P7799lumTZtGRUUFrVq14quvvqJ9+/Z2eV0uhTRtNRWHfodF/4Iyx86D+GfT1p/HzfT/tIyTD3kR7n2mgjt2YRkqFSy4vvZVXycuLufXg2bW3OlJlM/5K8nPplRSUKFwR1cdV3xRxs6Jnizeb+bdjUY232O/UVYnIq+k/6Hb7FaesB93nYbXb+jEyE4Rzg5FnKXJN21NmDChWttmk7T6DfjyOocnkXMJ86r6Fcoqrf55JKtUIcyz9l+vKb+Us/iAmZXjL5xE9uVY+HKnieeHGEg5amZQjIZgTzVj2+vYkmGluNJ+n4e8zDLHprEqN1mYMm8rby5NRT4DNx4OTSQTJkxApVJx33331Xhs8uTJqFQqJkyY4MgQmjaLuapD/ffnnbbpVEs/FWFeKlYcNtuOFVUqbEi30Df6/M1NiqIw5Zdyvttn5vfbPWjpf/5fRUVRuHdxBW9eYcBLr8JiBdPfT/f0vxY7vqe4G2WZlMZuxu8Hue/LzZQZzbWfLBzO4TWS6Oho5s+fT3l5ue1YRUUF8+bNo0WLFo6+fdNVWQJf3Qhbv3D4rUqMCtsyLWzLrOo8P5JvZVumhbRCa9Vkrd56Xvijkh9TTezMsnD7d+VEeKu4ps2ZLrjL5pTy7l9nhkZO/qWCL3eYmHetO94GFZklVjJLrJSbamaET7aYCPZQMSqxqjmtfwstvx8xsz7dzFvrKmkXrMbPzX4jerTlp+xWlnCc33Znce37f5JRWF77ycKhHJ5IunXrRnR0NN9++63t2LfffkuLFi3o2rWr7ZjVauXll1+2rV3TuXNnvvnmG9vjdVnbxmKx8NBDD+Hn50dgYCCPPfZYjepvZWUlU6dOJSQkBDc3NwYMGFBttEWPHj144403bN9fc8016HQ6Skqq5hakp6ejUqk4ePAg4KQ1b4oz4bMr4WDdhgfW16aTFrp+VErXj6omND60tJKuH5Xy9MqqUTSP9dfz71567vmpgp6zSikxKiy51QM37Zk390N5Vts8EIAPNpkorITBn5cRPr3E9rVgd/WJh1klVl78o5IZV56ZM9IrUsPDfQ2MmFfO13vMfDa6+pj/+pJlUlzHvsxirn3/T/ZnyYZkztQgo7buvPNOPvvsM8aNGwfAp59+yh133EFKSortnJdffpkvv/ySDz/8kFatWrF69WpuvfVWgoODSUpKqra2TWBgIH/++Sf33HMP4eHhjB07FoDp06cze/ZsPv30U9q2bcv06dP57rvvGDJkiO0+jz32GIsWLeLzzz8nJiaG1157jWHDhnHw4EECAgJISkoiJSWFRx55BEVR+OOPP/Dz82PNmjUMHz6cVatWERkZSUJCgm3Nmy+++IJ+/fqRl5fHH3/84dDX8nDhYSw/PUCrzB0Ovc/ZBsdqUZ45/+qsKpWK55LdeC75/BMEjz5QfSmHC5V3tlAvdY1rAZ5OMvB0kmPmkQDEe5bJMikuIqOwghs+XMes23vQq2WAs8Nplhw6amvChAkUFBQwa9YsoqOjbbNI27Rpw/Hjx7n77rvx8/Pjo48+IiAggOXLl9O3b1/b9XfffTdlZWXMmzfvnOWfvbYNQEREBA8++CCPPvooULVwWsuWLenevTvff/89paWl+Pv7M3v2bG655Ragakno2NhYHnjgAR599FF++uknbrvtNnJzc9m1axfDhw/nxhtvxM3NjVdeeYV//etflJWVMXfuXL799tsGXfNmV84uJi2fhE6l4cvMU4TnH3f4PZurB3zf5vusEGeHIS6CQavmvVu6MbRdqLNDaXYaZNRWcHAwI0aMYPbs2Xz22WeMGDGCoKAg2+MHDx6krKyMyy+/vNr6MnPmzKm2Bs2F1rYpLCwkIyOD3r17287XarXV1sg5dOgQJpOJ/v37247pdDp69erF3r17ARg4cCDFxcVs3bqVVatWkZSUxODBg221p1WrVjF48GCABl3zZt3Jddz1213kV+aTXZHDvZFRFHr4O+ReQpZJcUWVZiv3fbmZ77amOzuUZqfBJiTeeeedtpmZ7733XrXHTvc//Pzzz0RGRlZ7zGCoar44vbbN9OnT6du3L97e3rz++uts2GDfyXd+fn507tyZlJQU1q1bx+WXX86gQYO48cYb2b9/PwcOHLCt5nl6zZuUlBSWLl3K008/zbRp09i4cSN+fn52iynleAoPpTyEyXqm/+BI6Qkmt+rCrL0bcDfKIoP2FqmVNndXZLYqPPT1doorzNzeN9bZ4TQbDTaPZPjw4RiNRkwmE8OGDav2WLt27TAYDKSlpZGQkFDtKzo6GoC1a9fSr18/Jk2aRNeuXUlISKhWW/H19SU8PLxaYjGbzWzevNn2fXx8PHq9nrVr19qOmUwmNm7cSLt27WzHkpKSWLlyJatXr2bw4MEEBATQtm1bXnzxRcLDw2ndurXtXK1Wy9ChQ3nttdfYsWMHR48e5ffff7fb67bq+KoaSeS07UWHeLT9QMxqWaDA3kI1skyKq1IUePqH3cxZd9TZoTQbDfYOpNFobM1H/1zDxtvbm0ceeYQHH3wQq9XKgAEDKCwsZO3atfj4+DB+/HhatWrFnDlz+O2332jZsiVffPEFGzdupGXLlrZy7r//fl555RVatWpFmzZtePPNN6utiOnp6cnEiRN59NFHCQgIoEWLFrz22muUlZVx11132c4bPHgwM2fOJDg4mDZt2tiOvfvuu9xwww228xYvXszhw4cZNGgQ/v7+/PLLL3Zd82Z1+urzJpHTVhXs5bkuw3huy892uaeoEkTDbLAkHOeZH3ejVqm4tU+Ms0Np8hr0o6yPz/lH6jz//PMEBwfz8ssvc/jwYfz8/OjWrRv/+c9/gNrXtgF4+OGHycjIYPz48ajVau68807GjBlDYeGZN4VXXnkFq9XKbbfdRnFxMT169OC3337D3/9Mf8PAgQOxWq3VNqQZPHgw77zzjq1/BBy75s2aE2t4cOWDGK2178nwXf5OgrqMYOo2SSb24qsUODsEUU+KAk/9sAuNWsXNvWTOmiPJWluN0J8n/mTqyqlUWi5utdMnvdpyy87fHBRV81IY2pvOx+53dhjCDlQqePW6ToztEe3sUJqsJr/WlqtZd3Id96+8/6KTCMCrpan8llhzW09x8dyNec4OQdiJosATi3awaLOM5nIUSSSNyLbsbUz9fSoVlopLut6qWPmP+QR/xfa0c2TNj7a84RfAFI5jVeDRb7azZFdG7SeLiyaJpJE4VHCIySsmX3ISOc1oNXK/voTUsHa1nyzOS1VRgLum7htzicbPqsD987ex8ajUNu1NEkkjkFmayX3L76PIaJ8hpyWmUib66zkRIB2Ml0qFQoKHLAbY1FSardz9+SYOZss8IXuSznYnKzYWc/uvt3Ow4KDdy471jGDOkQP4l8qy6JdClkmpXeG6rynbvw5TXjoqrR5DZFv8kyagC4yqdl7lib3kr/4CY0YqqNToQ+IIGfscal3t66UVrl9IwarP8e5+NQFD77Edz1sxi9JdK1Dp3PBLGo9X+2TbY6X71lC6awUh1z9zzjIj/dz5dlI/Qn3Ovz6cqDupkTiRyWriwZQHHZJEAI6WnmRyQkfK9J4OKb+pk2VSaldxfBfe3UYQdusbhN74PFjMZH39FFbjmSbayhN7yfr6GdxbdiXstjcJv/0tvLuNRKWq/e2nMmM/xduWoAuOrXa87OAGSveuImTs8/gPvoO8JTOxlFUN87dWllKweg4BV0w8b7knCsqZ8NlGiivOP0dL1J0kEid6bt1zbMhw7P7qO4sO81D7/pjUOofepymK0EoiqU3o2Ofw6jgUfXAM+pA4Akc8iKXoFMasMx+O8lZ8gk/3Ufj2uQF9cAy6wCg82w5Epb3w76TVWE7OT28QOPzfqN2qr8Rsyj2OW3RHDOGt8GyXhErvgbkwC4D8lZ/h3fUqtD4Xrk3uzSji3i82Y7LIlgH1JYnEST7Z+QnfH/y+Qe61tmAfz3S5AgX7bf7UHMgyKRfPWlm1Z83pN35LaQHGjFTUnn5kfvEIx2feSua8J6hI311rWXnLPsA9vifusV1qPKYPbokx8yCWihIqMw+imCvR+kdQkb4bY9YhvLuPqlO8fx7K5Zkfa49FXJgkEidYc2INM7fObNB7/pS/k7e6XtWg93R1QRQ4OwSXoihW8lfMwhDZDv3fTVHmgkwACtfMw6vzMELHPos+NJ6s+f/FlHfivGWV7lmFMfMQ/knjz/m4e1x3PNsPJvPzB8n9+S2CRjyIWmcg77f3CRg2meKtv3Bi1r1kfvkoxlPHLhj3vA1pzP8r7dKetAAkkTS49OJ0Hl/9OFYn7LH+WcFO5nQc3uD3dVV+skzKRclb+gHGU8cIuvox27HTY3m8ugzHq9Pl6EPjCbjsX+gCoijZueyc5ZiLTpG3YhZBox5BpdWf935+A8YRee8sIu56D4/W/ShctxC32C6o1BoK1y0gbNxreHW6gtyf36w19qd/2M2WtPyLfMbiNEkkDajCXMGDKQ/abZjvpXijZC8/t0mu/USBt0nmG9RV3rIPKD+0kdCbX0Lrc2avIY1X1Rp2uqDqQ9F1gdGYi06dsyxj5kGsZQVkzL6fY69dzbHXrqby+C6KN//EsdeuRrHWnN9jyj1O6Z6V+A28lYq0nbhFdUDj4YtHm4EYsw5hrbzwVgtGi5WJX24mu6h+87iaK1l/vAE9u+5Z9uXtc2oMCgr/ZzqGf8ve9Dvi2I5+V+cmy6TUSlEU8pd/SNn+dYTe/DI6v7Bqj2t9Q9F4BWDOrb48iSnvBO5x3c9ZpltMZ8LvfLfasdxf3kEXGIVP7+tQqauvHq4oCrm/vYf/kLtR691BsaJYzVUPnv63Di0AWUWVTJy7ha/+1Qe9Vj5jXwx5tRrIvL3zWHx4sbPDAMBsNfOgtpDdER2cHUqjpis/9ydmcUbesg8o2Z1C0KhHUes9sJTkYynJx2qqWitOpVLh0+s6ijb/ROm+NZjyT1Kw+gvMeel4dbrCVk7W/P9QtPknANQGD/TBsdW+VDoDajdvW9/L2Uq2/4bG3QePhKrdUQ2Rbak4toPKE/so2vgDusAWNUZ9nc/mY/lM+0k63y+W1EgawNbsrby+6XVnh1FNmbmMST7+fGFsSYucI84Op1E6vUxKuUVT+8nNVMnWXwDI+urJascDr3oAr45DAfDpORrFYiT/90+wVhSjD25JyI3Po/MPt51vys/EUH7xTb6W0nwK131N2K1n/r4MEYn49BpD9jfPovbwJWjEgxdV5rwNafSNC2RU54iLjqe5kpntDlZYWci1P15Ldln2ec/J/T2XvN/zMOVUTY4yRBoIGR2Cdyfv815jKbWQtSiLos1FWEot6AJ1hN8SjnfnqmsK/iwg85tMrBVW/Af6E37zmT9a4ykjR984Svy0eGICI/ni2FGCSs4fX3M2SjeLncUyobO58XHT8sv9A4ny93B2KC5BmrYcbNqf0y6YRAB0/jrCbggjflo88dPi8WrrRdo7aVScOHfHn9Vs5egbRzHmGImeEk2rl1sReUckWv+qCqa52MyJz04QfmM4sY/EUvBnAUXbznzaO/nFSUJvCEXjriG9LJNJcW0oNZw/aTVncR6lzg5BOEFRhZkHF2zDYpXP2XUhicSBFu1fxPK05bWe59PVB+/O3hjCDBjCDIReH4raTU3ZwXOPNClYXYC5xEzM1Bg8W3miD9bj2cYT9xbuQFWNQ+Ouwbe3Lx5xHni29aTyZFWbdcH6AlQaFb49fG3l7S0+ygPt+mDSnH+oZXPVwiCJpLnaeDSfmb8fcHYYLkESiYMYjx1j/4pFF32dYlUoWF+AtdKKR8K5q9VF24rwSPDg5Bcn2Tt1Lwf+e4Dsn7JR/v70ZAg1YDVaKT9WjrnETPmRctyi3bCUWsj+NpvwW8NrlLm+IJX/dh4qs9//IUorq8Q2ZzN/P8jmYzJ6rzbSR+IAisXCsXG3Ur59Oxkje/B4+51UqMwXvKbieAWHXziM1WRFbVATfV+0rb/jn/Y/sR9Tjgnfvr4EXhaIMcvIyTknCbw8kJBrqtYXKtpcRNZ3WShGBd++voSOCSX9f+m4RbnhHutOxtwMFItCyDUh+PY8Uzu51a8Tj29tHKPLGoOU6ElMODDA2WEIJ4ryd+eX+wfi4ybr1Z2P1EgcIHfWJ5Rv2waKQvhPG5nzTRj9Ky68X7Q+XE/8c/HEPx1PwJAA0j9JP28fCQpofbRE3hGJe6w7vr19CR4VTN7KM5+cfLr70OqFVrR+rTWhY0Ip3VdKZXolAYMDOP7BccJvCafFlBac+PQE5qIzSe7Lgh182ulKe7wMTUIQhc4OQThZen45z/+0x9lhNGqSSOysYt8+ct57r/rBg0d54L0TPHOsK6rz1P/UWjWGUAPuse6E3RCGW7QbucvOvY+I1k+LPkyPSn2mGcoQYcBcaMZqrjnxymqycnLOSSLGR2DMNqJYFDzbeGIIr+qTKTtUvS/m7eI9/Nj2sot74k2ULJMiABZuTmftQdl++XwkkdiRYrWS8dTTKKaaexwoRiPt521k7tLWJJqCznH1Py8AxXTurOPRygNjltHWJwJQmVmJ1k+L+hwzck/9eAqvjl64x7pXXXNWrlHM1b+vurXCM5VH+CO+b+1xNnHeJtkUTFR58tudVJhk++VzkURiR/nzvqJi584LnqPdsocXPi5j4qlOtmOZCzMpTS3FeMpIxfGKqu/3leLX1w+A9I/TyVyYaTs/IDkAS6mFjLkZVGZWUrytmFOLTxEwJKDG/SpOVFD4VyGh14YCYAg3gAryVuVRvK2YyoxK3OPca1xnVsw8rM5jZ1SnGo81J7JMijgtLa+MN5ftd3YYjZJ0ttuJKSubwyNGYC2p+2ZIxcldebLnEbbM3kfpnlLMhWbU7mrcot0IvioYrw5Vyzocfvkw+iA9Uf86s31p2cEyMuZlUJFWgdZfi/8gf4JHBFdr7lIUhSMvHSFoRBA+XXxsx4u2FZHxRQaKSSHkuhACkmomoNP89b7MySsj9tShi3k5mgyrewBx+e/WfqJoFjRqFd9N6kenKD9nh9KoSCKxk/QHHqR4yZKLvk4VFsJX1wfxrXfj/aQT6RHKF8ePE1yUWfvJTYyCijbGL6i0SuVdVGkb7sNPU/qj1cjvxGnySthByerVl5REAJTMbG56fx9v7+6Km9I4lz47UZbFfTEJFLv51n5yE6NCoZXnhZcgF83L3owiZv0h69OdTRJJPVkrKsh87vl6FmIl4seNzFlU+zBhZ9lfksbUNj0wagzODqXBxXtIIhHVvfv7AbKLZe+S0ySR1FPO+x9gSk+v/cS6OHB6mHC38w4TdqZNhQd4ovMQrKrm9Wsjy6SIfyo1Wnh9Saqzw2g0mtc7gp2ZTpwgb/Zsu5ZZNUz4L+YubU0rc6Bdy7aHZfm7ealr85qwGKmt+wAK0Xws2pLOrhMyYRUkkdRL9jvvoBiNDilbu2UPL31UXm2YcGOxIH8nH3W+ytlhNJhQjfO2RhaNl1WB52TGOyCJ5JJV7N1L0U+OXZNKKSoi+ZMt/G9dR0IsddvhraG8W7SLb9sNdXYYDSKIAmeHIBqpv47m8fOODGeH4XSSSC5R9uuvQwONnPZO2cp7c/SMKW7dIPerq+cqDpGS0PQXNPSXZVLEBbz8614qzc17xrskkktQ8scaSv9c16D3VDKzufn9fby1p/EME7YoFh4li23RXZ0dikN5mWV2uzi/9Pxy5vx5zNlhOJUkkoukWK1kT5/unJtbrUT+sJE5i8Lp10iGCVdYKpniYeJQSOOqLdmTe6UkEnFhH60+RJnxwltFNGWSSC5S0U8/Ublvn3ODOHCEB98/ydMXWE24IRUai7gvyJtMv0hnh+IQuopTzg5BNHI5JUbmrGu+tRJJJBdBsVg49d77zg4DAKWykg7zNvLlssYxTDiz/BQTo2MpdPdzdih2pyrPx6CuuTy/EGf7ePVhSiubZ61EEslFKPrlV0xpac4Ooxrd5qphwvfmdHR2KBwsOc7UxG5Uat2cHYpdqVBI8Cx3dhiikcsrNfL5uqPODsMpJJHUkaIo5M6a5ewwzkkpKuKyWVv5ZEMngqyeTo1lS+FBHu00GItK49Q47C3OXZZJEbWbtfowJc2wViKJpI5KVq6kcn/jXaEXwOf3LXwwx43RJa2cGsfK/D0833W4U2OwtxhZJkXUQX6Zic//POrsMBqcJJI6yvnoI2eHUCdKRhbj3kvlzT3dnDpMeFH+Tt7rMsJp97e3KF2xs0MQLuKTPw5Tbmxe80okkdRB6fr1VGzf4eww6s5qJeqHv5izKJy+FVG1n+8gHxbu5OsOVzjt/vYky6SIusovM/HNFjst5OoiJJHUgavURmo4cISH3s/gqTTnDRN+sWw/y1sPdM7N7UiWSREX49M1R7BaG8HY/AYiiaQWFXv2ULZuvbPDuGRKZSUd5/49TNjU8MOErYqVxy0ZbIrp3uD3tic/WSZFXIQjOaUs25vl7DAajGy1W4uMp56iYOE3zg7DLlS+PiwfG89HQTsb/N7eOi9mF1ppneXkyZx1FRAHoe3BJwK8wjCGdGS9qgshPgZ83XVo1Wp0GhUAZquCxapQVG4iu7iy6quoguziSrKKKtifVczB7BKa0QdUAfSJC2D+PX2dHUaDkERyAZbiYg4MSkIpb1pzCIqGdOOJnofIUTfsSKQQt0C+zMwhPP94g963Vn4tIKonRHSF8M4Q3gnc/Ox6izKjmb0Zxew8UciuE4VsTSvg0CnZ56SpW/LAQNqE+Tg7DIeTRHIBeV98SdaLLzo7DIdQRYTxxbX+/Oh9oEHv29IzkjmHU/Erc+L6VSoVRPaAxCurvkLaOSWMtNwyVuzLYtmeLDYcycMiVZYm58Ye0bx6fePbU8jeJJFcwKGRIzEePOTsMBxHrSZ9VHeebLuDSlXDDVfs5BPPJ3s34G5s4El+Mf2g883Qehh4hTbsvWtRWG5iVWo23209Scr+7IbaoUA4mJtOzcb/DsXbTefsUBxKEsl5lP71F2m3j3d2GA2jdUveGqGwzq3hhiwO8mvLO9tXoLU6eBaw3hM63QQ976rq83ABx/PKmLvhGAs2Hie/zOTscEQ9vTSmI7f0buHsMBxKEsl5pD/4IMW/LnF2GA1GZTCw/fpOvBi9FUXVMPcc7d+RF7b87JjCA+KgzyTofCMYXLONutJk4eedGfxvzRF2n5R5LK6qaws/vpvU39lhOJQkknMw5+RwIHkImJrfp0FTj/ZMG5zDAV1ug9zvbr+O3L/VjsnEOwySnoCut4KmaTQnWK0Kv+7K5I2lqRzJkaVaXNHyh5JICGlc22Xbk8wjOYein39ulkkEQLdpNy/NKudfuQ2zmvAnBTuZ23FY/Qty84Wh02DqVuhxR5NJIgBqtYoRncJZ+uAgXhrTkRBvg7NDEhdp4aZGNlLRzqRGcg5Hxt5IxQ4XWhLFQQov68aTPRw/TFitUvOqLobhqasu/mKVCnrdA4OfBHd/+wfXCJUbLcz64zDv/n4Qo0X2SXEFwd4G1j0xBK2maX52l0TyD8a0NA5dYYdPyE2EKiKML67z50cvxw4T1ql1fGANoPeRjXW/KCAORr9XNRqrGUrNLObRb7azI73Q2aGIOvjf+B5c1rZxjRa0l6aZHuuh6JdfnB1Co6KczOTW91KZvq8bBsVxe4yYrCYe0JWwL7wOczpUKugzESaubbZJBCAxzJtvJ/bjsWGJ6JvoJ92m5NstJ5wdgsNIjeQfDo8aReWBg84Oo1FSEuN4c4SFDQbH/UEEGQL4IjuPqLzz7ETp1wLGfNSsE8i5pGYW8+CCbezJkNFdjZWHXsOWpy7HTde0Nn0DqZFUU5G6X5LIBahSD/PI+1n897jjVhPOqczjvogI8jyDaj4YOwD+tVKSyDkkhnnzzcS+jOgY7uxQxHmUGS2s3n/K2WE4hCSSsxT97KA5DU2IUlFB5y838sWKROLNAQ65x7HSk0xKaE+Z4azhkj3ugtu+h3MlGAGAh17Le+O68cgVic4ORZzHkt2Zzg7BISSRnKXo11+dHYLL0G/czSuzKh02THh30REebNcXk9YdRrwJI99sUkN6HWnKkARm3d4dT33Ta0JxdSv2ZmNqgiPtJJH8rfLQIUzHm/ZYb3tTCgq5/OOtzNrYmUCrh93L31qSxrF7l1ctbyIuyuXtwlg0qR/BMuekUSksN7HuUMNM9m1Ikkj+VrJqtbNDcFm+yzfz4RceXF3Sym5leum8+Pjyj0kI7mC3MpubNmE+LLinD+G+bs4ORZylKTZvSSL5W8kfkkjqwzZMeG/9hwl76byYdcUsuoR0sU9wzVhcsBdf39uXCEkmjcbS3Vk0tcGykkgAa2kp5Zs2OzsM12e1Ev39X8z5LpJelZGXVIS71p0Ph35IhyCpidhLdIAHc//VR5ZWaSRySiqb3DBtSSRA6fr1KM10bS1HUKUe5tH3s/jP8a4XdZ1WreW9y96jc0hnB0XWfLUM8mTev/rg4651digCWHswx9kh2JUkEqR/xBGUigq6fLmRL5e3qfMw4af6PEXPsJ4Ojqz5Sgjx4r1buqFuoG0CxPmtOdi0OtwlkQAlf/zh7BCaLP3GXbwyq5K78i7cVDWu7TiubXVtA0XVfA1sFcxTI52ztbA4Y+ORPIzmpjMMuNknksrDhzFnZDg7jCZNKShk2Efb+Pg8w4T7hvflkR6POCGy5umO/i0Z2yPa2WE0a+UmC5uP5Ts7DLtp9omkbLN0sjcUv7+HCY8sSbAdi/aO5vWk19Gqpe2+IT1/TXu6xzSPZfcbq6bUT9LsE0n51m3ODqFZUU5mcvv7B3gjtRse6Hl90Ov4GnydHVazY9BqmHlzV7wNksCdZY0kkqajfMsWZ4fQ/FgstPj2L37S3E/7oPbOjqbZivBz5/+kv8Rpdp0opMJkcXYYduH0RBIbG8vbb7/tlHub8/MxHj3qlHs3d4bWrQgae5Ozw2j2buwZzeDWwc4Oo1kyWxV2n2wam5JdVCKZMGECKpUKlUqFXq8nISGB5557DrPZXOu1s2fPxs/P71LjdAhp1nISjYbwl15Crdc7OxIBvHRtR2nicpJtx5thIgEYPnw4GRkZHDhwgIcffphp06bx+uuvOyI2hyvfutXZITRLgXffhXsHmbneWEgTl/NsP17g7BDs4qITicFgICwsjJiYGCZOnMjQoUP58ccfefPNN+nYsSOenp5ER0czadIkSkpKAEhJSeGOO+6gsLDQVqOZNm2arcyysjLuvPNOvL29adGiBR9//LHtMaPRyJQpUwgPD8fNzY2YmBhefvnl+j9zoGyr9I80NG1wMEH33uvsMMQ/3NA9ivYRPs4Oo9nZnl7g7BDsot59JO7u7hiNRtRqNTNmzGD37t18/vnn/P777zz22GMA9OvXj7fffhsfHx8yMjLIyMjgkUfOzBuYPn06PXr0YOvWrUyaNImJEyeSmpoKwIwZM/jxxx/5+uuvSU1NZe7cucTGxtY3bBSLhYpdu+tdjrg4QZMnofaw/5Lzon7UahWPD2/j7DCanWO5ZRSUGZ0dRr1dciJRFIXly5fz22+/MWTIEB544AGSk5OJjY1lyJAhvPDCC3z99dcA6PV6fH19UalUhIWFERYWhpfXmd3vrrrqKiZNmkRCQgKPP/44QUFBrFy5EoC0tDRatWrFgAEDiImJYcCAAdx88831fNpgPHYMpaKi3uWIutPFxOB33XXODkOcx6DWwfSND3R2GM3OtibQvHXRiWTx4sV4eXnh5ubGlVdeyY033si0adNYvnw5l112GZGRkXh7e3PbbbeRm5tLWVlZrWV26tTJ9v/TySY7Oxuo6uDftm0biYmJTJ06laVLl15syOdU+XeNRzSckPvvR6WTXQ4bM6mVNLyd6a7f4X7RiSQ5OZlt27Zx4MABysvL+fzzzzl16hQjR46kU6dOLFq0iM2bN/Pee+8BVX0ctdH9481FpVJhtVatQ9OtWzeOHDnC888/T3l5OWPHjuX666+/2LBrqPg7kWwqK2NS+nGSDh6kXeo+lhcXn/eaaZmZtEvdx5y8vAuWXZcyP83LZcDBAww4eIDP8qov4La9vJzrjx7B3IT2LHBr3w7v4cOcHYaoRZdoP67sEObsMJqVA9klzg6h3i46kXh6epKQkECLFi3QaquGDG7evBmr1cr06dPp06cPrVu35uTJk9Wu0+v1WCyXNvnGx8eHG2+8kVmzZrFgwQIWLVpEXi1v5rWp3H8AgDKrlUSDG0+Fhl7w/OXFxWyvKCdEW/swydrKTK2o4N2cHN4Ij+D18Ahm5OSwv7Kqmc2sKDyblckzoWFoVU1nmdbAu+5GpXb6tCVRB/clxTs7hGbl0CnXTyR2GTyekJCAyWRi5syZjBo1irVr1/Lhhx9WOyc2NpaSkhJWrFhB586d8fDwwKMOna5vvvkm4eHhdO3aFbVazcKFCwkLC6v3nJTKQwcBGOTlxaCz+mvOJctk4sXsLD6OimZieu37utdW5mGjkdYGA308PQFobTD8fcyNT/Py6OHuQUd394t4No2bJigI76GXOTsMUUedo/3oFOXLjibQ5OIKjuSUoigKKhf+4GiXj4idO3fmzTff5NVXX6VDhw7MnTu3xhDdfv36cd9993HjjTcSHBzMa6+9Vqeyvb29ee211+jRowc9e/bk6NGj/PLLL6jr8enWajRiSj9Rt3MVhScyM7gzIIBWBvvsMNfaYOCo0chJk4kTJhPHjEZa6Q2kGY18V1jA/cFBdrlPY+F/ww2oZPKhS7mtT4yzQ2g2yowWTha69sAfldLUNg+ug4rU/RwZPbrG8Xap+5gREclQb2/bsY9zc/mrrJRZUdGoVCqGHjrI7f4B3B5Qt82azlUmwPyCfObkVS0jfXuAPzf5+XPn8TRu8fPHgsJ7OTloVSr+ExJKD1ceLqtWk7BiObrwcGdHIi5CudFCn5dXUFguO4c2hM/v7EWSCy9V0yzXRTAeOVKn83ZXVPBFfh6LYmPtXu28ya8qeZz2fWEhnmo1XdzdGXHkMAtiYskym3j45EmWxcWhd9H+Be8hQySJuCB3vYYbekTxyR91+1sR9XMou8SlE4lrvjvVk+kfAwHOZ3NZGXkWC5cdOkTH1H10TN3HSbOZ105lM/TvPhZ7yDebeT83h/+GhLKjopxYvZ5YvZ7eHp6YUThqct0JS77Xya6Hrko2v2o4rt7h3ixrJKbMuu2IeLWvL33/7hA/7V/px7nax4cxvvbbQ+OVU9nc7u9PmE7HrooKTGe1NloUBYuLNj6q3Nzw7NvX2WGIS9Q61JsWAR6k5dU+F0zUj6u/xs0ykZgzMm3/L7VaSTtrrssJk4m9FRX4ajRE6HT4aTTVrtUCQRotLfVnOt7vOJ7GUC9vxvn716nMs/1ZWspRo5GXw6qafzq4uXHEaGR1SQmZZjNqlYqWLtpR7dm/P2o3N2eHIerh8nah/G+NNG85WlaRa3e2N8tEYso8k0h2V5Qz4fiZIb2vnqqaUX+Njw8vhUfUqbzjRiP5ljNL6de1zAqrlReyspgeEYH67z6YMJ2O/4aE8t/MDPQqFS+HhePmsv0jyc4OQdTT0LaSSBpCpozacj37Bw7EcqrpbHPZKKlUtPpjNdqgpjWUubkxWax0f2EZReW17zkk6mff88Nx02lqP7ERcs2PuvWgGI1YcnJrP1HUi3vnzpJEmgCdRs3g1iHODqNZcOVaSbNLJKbsbGh+lbAG596tq7NDEHbSI9a/9pNEvWW6cD9Js0sk5qwsZ4fQLLi1b+/sEISddIy03whFcX6u3OHe7BKJpVDWD2oIkkiajrbhPmjUrrsOlKuQROJCrBdYJl7Yh9rTE32LFs4OQ9iJm05D69ALL2wq6s+VBzQ0u0RiKXHtGaSuwK19O1kyvonpIM1bDldSKYnEZViLJZE4miFRdtlratqF+zg7hCZPEokLsZZI05aj6cJlh72mJty36eyP01iVSiJxHRapkTicNth1VzEV5xbibZ+9eMT5SY3EhUhnu+NpQ2QCW1MT4iOJxNEkkbgQS6nUSBxNaiRNT7DUSBxOmrZcicl1f1iuQmokTY9Bq8HfQ1f7ieKSlRktzg7hkjW/RCIcS6tF4yVzDpqiAE/X3M7AVVitrrt0UzNMJK77w3IFKp18am2qtDI3yKFcOI80v0TSDFfNb1AqjWsugy1qp9XIMimOZHXh96bmt7GV6/6shHCqWf6BGE0yn8RhPFz37dh1IxeNkmK1OjsE4SCa3HK0p8qdHUaTpfF13T6oZte0JXuROJhZRsU1VYp8RnAsles2HTa/RCIcSjEaUYxGZ4chHECpdN3hqS7BhZfqb3aJRKV33eqjqzDn5Dg7BGFnilXBUiwfEBxJJYnEdag9PZ0dQpNnPnXK2SEIO7OWmV17fKorcOF3YxcO/dKovSSROJo5WxJJU2MprnR2CE2eyuC6Y5+aXSLRSI3E4aRG0vRYi6RZy9E0Ljz8t9klErWnLN/haKbsbGeHIOxM+kccT+3puqtCNMNEIjUSRzMeOezsEISdmbJl/oijqV14Uczml0hkQUGHq9i129khCDsznZDtFxxN7SlNWy5DaiSOZzpxAnNBgbPDEHZkPCmJxNGkRuJCtAH+zg6hWajYvcfZIQg7MeeWo5TLigWOJonEhWhDQ50dQrNQsVuat5oKozRrNQiNNG25Dl1YmLNDaBYqdu10dgjCTozHi50dQrMgNRIXovb0lA73BlC6bj2K0eTsMIQdVOzPd3YIzYIrD/913bpUPWhDQzGWSHXdESxBkZR0H0FeYDu8j5UQ0Ur6pFyZObccc1aZs8NoFtQuPCHRdSOvB11oKMZDh5wdRpOgaLRUdBtKQcu+nCKM3FNmlErgJHhvPSWJxMWV781zdgjNgtpLh0rjug1EzTKRaKWfpF7MUa0o7jKMXJ9EMvN1GMstkA1QfWTP0R05DBzb2ikxCvuokETSILTBrr3zZPNMJKEhzg7BpSgGd8q6D6MguhdZ5kAKcs1QQtUX59+joiingtwTJQRGSp+UK7KWm6g8UuDsMJoFXbCHs0Ool2aZSPRR0c4OodEzxnWiuONQcjziycpVYzZaIQv+WeuozYFNWZJIXFT5rlyQXREbhDZIaiQuxxAf5+wQGh2rly9lPa4iP7wrWRV+FOWboJCqr3q8m+xZc5KeI1qi0bpu+29zVbLupLNDaDa0IVIjcTn6hARnh9AoGNv2obBtMjn6FmSdUrBaFMgAsN+w3fJiE4e3nqJVT5kI6kqMaUWYTpY6O4xmQyc1Etej8fJCGxqKOSvL2aE0KEtAKKXdryIvuCNZJZ6UFpkhDxzdfrFzVbokEhdTsj7D2SE0HxoVmgA3Z0dRL80ykQAY4uObfCJR1BoqOw+mMKE/2eoIck5ZUEzASbjYvo76yDhYSE56CUFR0lfiCiylJsp2yOZkDUUb6O7S+7VDM04k+oR4Sv/809lh2J05NIbSbleS69+WzEI3KkrNcAouNLqqIexYeZwht7V1agyibkr/ygSz7M/eUFx96C8040RiiG8a/SRWrZ7KbpdT0LIv2dZgcnPMUE7VVwPWOmqzb10m3a6IwS/UtTsVmzpruYni1enODqNZ0UkicV2GhHhnh3DJzC3aUNR5GLnercjK02KssFzS0NyGpFgVNvx4mGH/6uDsUMQFFK9KlyXjG5jWxeeQQHNOJImJoFKB0vir8FY3T8p7Xkl+ZHeyjAEU5pmhmKovJzdZXYyDm7PpekURITE+zg5FnIOlqJKStTLkt6Hpwlx/s71mm0g0Xl7o4+MwHmyca24ZW3WnqP0Qctxbkp2jwmyyQiY05lpHXaz//jBX39/F2WGIcyhakYZikhmIDUml16ALl0Ti0ty7dGk0icTqHUBpz6vID+1CZpkPJYUmKAAKFKDx15rq6vjePNL25NGiXYCzQxFnMWWXUbox09lhNDv6Ft4uP2ILmnki8ejShcJvFjnl3opKRWWHARS3HkS2LopTDpoQ2BhtWbKR8PgkdAbXHjvfVChWhfxv9styKE6gbyLNvM06kbh36dKg97MEhlPafQS5Qe3JKvKgrMQMudBc/oJ1Bgu+gVs5/NcaVvkeYuhdk5wdkgBK1pzAmCa7IDqDIVYSicvTx8ej9vHBWlTkkPIVjZbKLkMoiOtHtiq8aq8OIw0+IbAxCI7MIPf4EtJ3V+22t33pL7Tu3Z8WHTo7ObLmzZRdRuHSo84Oo3lSg76FJBKXp1KpcO/YkdK1a+1WpjkijuKuV5Lnm0hmvp7KcsvfEwKbV+I4zcOnAr1uDcd37ajx2G8fzmD8G++id3P9cfSuyNakJZMPnUIX7oXaoHF2GHbRrBMJVDVv1SeRKHo3yrsPo6BFL7IsQeTnmKGUqi8XGpprbyqVQnDUAU7uW4bZWHnOc4pOZbFy9scMu+/+Bo5OABSnHJcmLScyNJH+EZBEgkeP7hd9jallB4o6Xk6uVwJZuWpMlZe2V0dT5RdSSGXRMtJ2pNV67q6VywhtGU+XYSMbIDJxWvneXIqWHXN2GM2avon0j4AkEty7d0fl5oZSUXHec6yePpT1uJL8iG5/79VhhiKqvppJR3ld6AwW/AK3kr5nDYpS99fl99kfExAZLf0lDcSUVUbe/NSmNKrcJTWVjnaQRIJar8ejRw9K16ypdryyTS+K2yZzyhBDdg5YzKeH5kqt41yCIzPITfuV45kFF32tYrWy/MP3uf3Fd9D6ypBgR7KWmciZsxulsvk2uzYGGn8DGh+Ds8Owm2afSAA8+/WjeOc+SnuOIC+kE5klXlV7deSDfGy7ME/fCrSaPzi+a+cllxES0pIh0ePI+XQ3IRM7o3aTX0tHUCwKufP2Yck9f+1bNAxDvJ+zQ7Ar+YsFNFdcw+pNsVjNSrMcmnspVCqF4OgDnNy7FLPReMnltIrvTXf3oSh5JsyYyJ2zh8AJ7VHrm8ZolsZCsSrkLUyl8mCBs0MRgHuHIGeHYFeykTbgF+WPp1/TqWY6ml9IIW6GRaRtX1yvJNKz89V00wxBKT2TuCsPF5L7xR5Z88mOFKtC/rcHKN8mm1U1Biq9BrcEP2eHYVeSSP4W0zHQ2SE0eno3M0ERm8jc/yn5GbWPyLqQoX3vJq64LZwjYVQeKCDniz1YjdKOX1+nk0jZpqa9G6grcUv0R6VtWm+9TevZ1ENMB0kkFxIUlYG5dA7pu1fXa+l9jUbP6AEPE5gZeMHup8r9+eR+vhurdApfMsWikP91qiSRRsa9fdN7r5E+kr9FtfFHq1djNkqTytlOd6an77z0zvTTvLz8uarjRFQn6pYcKg8VcurjHQTe3g6trzQ9XgxruZm8+fuoSM13dijibBoVbm2a3srXUiP5m1anoUW7pvdJ4VKpVAoh0fspPPkJWYfqn0TCQuMZ2XoyqsyLq2GYTpSQPXMrlcccsx5aU2Q6VUb2+9skiTRChjjfJjkqsek9o3po3SuUwxfZIbl061f8+NcnDO5wLdf3n3zOc9anLuHLlNerHdNqdLx99xLb98u3f83ybQsAuLzLjVzWeaztsaNZe1mw5h0eGfMeGrXjRzNdzMz0umid0JduhmSU/EtbHt9aYuLUxzvwv7YVnt1D7RJTU1WRmkfuV/tQKqRJsDFqis1aIImkmtiOQRg8tFSW1W3477Hsfazdu5jIgLhaz3XTe/L0jbPP+diJ3EP8vGk29w1/ERSFD5f8lzZRPYgMjMNitTD/j7e5edCDDk8iejczPgHbSN/zh922IO7ddQyxJYkoZfV8Y7Mo5C/cjymjBN/hLZtcZ2V9KVaFkjUnKPz1iEx9aqxU4N5EWz0kkZxFo1MT3zWYPWszaj230lTO7N9f4uZBD7Fky9xaz1cBPh7nbhvNKjhOZEAciZFdAYgIjKs6FhjH8u0LSAjvSExIm4t6LhcrOCqD3GO/kp5RYLcyh/W7B79Mf7slJYCSNSepPFSI/w2t0Ud42a1cV2bOqyB/0X4qDxU6OxRxAfoo7yY1m/1skkj+oXXvsDolkgVr3qFDiz60iepep0RSaSrnqbk3oygK0UGtuLrXXYQHxAIQEdCS7MJ08oqrRtdkF6QTERDLqcKTrE9dwuPXfliv53QhtpnpduhMP02r1TOqz1T0JxxTgzJllJL97jZ8hkTjPTi62dZOFKtC6V+ZFP5yGEUGiTR6bk20WQskkdQQ0coPrwADJXnnXvocYNPB3zmec5DHxrxfpzJDfKMZN/hRIgPiKDeWsmL710z/YSr/veF/+HsFE+Yfw6hed/Huz48BcHXvuwnzj2Hm4ke5pvc97E3fyC+b5qBRa7m+32QSIjrV+3mqVFaCow5ycl/9Zqb/k7d3EFe2v6fOI7MumVWhaHka5btzm2XtxJxfQf43B6g8VODsUERdqMGza4izo3AYSST/oFKpaN0zjC2/nXuJ7fySbBb9+R5TRryGTquvU5lxYe2JC2t/5vvQ9jz/9R2s3buYkT3vAGBgu1EMbDfKds761N8w6NxpGdqO5xdM4NFr36eg5BSfrniBZ2/5Ep2mbvc+F7+QQioKl5K24/gll3EukRGJDAy9ASWr4facr6qdbMWzRxg+l7VA08SHCVvLTRSnpFO89iSYpRbiKtwSA5r076YkknNI7H3+RJJ2aj/F5QW8uug+2zGrYuVQxg5W7/6et+9egrqWTnGNRkt0UAKnCk+c8/GS8kJ+3fwFD1z9Fkez9xHiG2X7slrNZBekExlYewf/P+ndzPj4byV97xq79lsAtG09kM7agSgFDZdEbKxQ+lcmpVuy8e4fgXdSFGoPXcPH4UBWo4XSP09StCodpVzWgnM1nr3CnB2CQ0kiOYeACE+Cor3IOV5S47HEyG7854ZPqh37MuV1Qv2iubzLTbUmEQCr1cLJvCO0i+51zscXrXuf5E7X4e8VTNqpVCzWM81EFqvlovb6OC048iQ5aUvs2pl+Wr9u1xNdlIBS7uQhp2YrxavSKfkrE+9BUXj1CUft7tq/4lajhbIt2RT9noa1yH5NkKLhaHwNuCU2vUmIZ3PtvzIHat0rjJzjB2scd9N7EBHQstoxvdYNT4OP7fic31/B1zOI0b3vBuDXzXOIDWlHsG8E5ZUlLN/+NXnFWfRre1WN8vembyK7MJ3bkh8HoEVwIlkFaexO20B+ySnUajUhftF1fh6evuVoNWvqtcz7hQzvNxHfDB8a05hTpdxM0W9HKf49DY8uIXj2DXe5PhRTTjmlGzIo3ZQlNRAX59kzFJVa5ewwHEoSyXkk9g5j/Q+HqpaWv0h5JdmoVGd+ccoqS5i3ejrFZfm4G7xoEdyah66ZQbh/bLXrjOZKFq6ZyZ1Dn0KtqhqJ5O8VzA39p/BlyutoNTpuG/w4em3tba0qtZXgSPt3pp+m07kxqvdUdCca7x+IYrJSujGT0o2Z6Ft449knHI+OQah0jXOJesVipSI1n5J1J6k8UODscIQ9qMGjZ9Nu1gJQKYqdG8ubkGWf7Wb/Btdb8O50Z3pBpn0700/z9QlheNt/QbbrfVJW6dQYWvnj3i6gqgPU+9IHLdiDtdxMxf58yvfkUrE/X2ofTYxbmwCCJrSv/UQXJzWSC+iUHO1SicSRnemnRUW2Y0DwtSjZTuhUtwPFZKViTy4Ve3JBBfpob9zaBKCP8UEf4eXwPhWr0YLpZAnG48VUpOZTebgQrPJZrqny7N30ayMgieSCQmN9CG3pQ9aRxr9goCM700/r0HYwHeiHUuiaSaQGBYxpxRjTim2HNIFu6CO90Ed6owvzQONrQO2tR+N5caPArOVmLMVGLEVGTFmlmE6UYEwvwXyqrDF1JwkHag6d7KdJIqlFp+Qolh3Z4+wwzsvRnemnDehxE5EFsSiWpr0YoCW3gvLcCsp35FR/QKNC46NH462vqrVoVH93oKpQrApYFawVZixFRqzFRtnhUeDRo+l3sp/WKBJJbGwsDzzwAA888ICzQ6khvnsI6747REn++We6O0NVZ/oBTu5b5pDO9DM3UnFVv0l4n/SiWX+UtihY8iuxNLLfA9FIaVV4NZNmLbDjfiSDBw8+ZyKYPXs2fn5+9rpNg9No1HRKrvtw24bgH1KIQfcNaTt+dmgS0es9uK7fY38nESFEXXn2CGuyCzSei1NXuzM68pO0HbUfGIHezflDRvVuZoLC/yJj/6cUZKY79F5+fuGM6fIg2pMOvY0QTY9GhffgKGdH0aAaNJFMmDCBa665hhdffJGIiAgSExNtjxUXF3PzzTfj6elJZGQk7733XrVr33zzTTp27IinpyfR0dFMmjSJkpIzM89P13x+++032rZti5eXF8OHDycjo/aVfGujd9fSfmBkvcupj+Cok5hK55C+x3Ejsk5rEdWB4TF3wikZiirExfLsForWz83ZYTSoBq+RrFixgtTUVJYtW8bixYttx19//XU6d+7M1q1beeKJJ7j//vtZtmzZmUDVambMmMHu3bv5/PPP+f3333nssceqlV1WVsYbb7zBF198werVq0lLS+ORRx6xS9ydhkSj1jZ8x5mnbzm+AUs5vnM+5UUFDr9fp3ZD6ed1NUqRJBEhLppahXcjawpvCA3e2e7p6cknn3yCXl99Ilj//v154oknAGjdujVr167lrbfe4vLLLweo1v8SGxvLCy+8wH333cf7759Zyt1kMvHhhx8SHx8PwJQpU3juuefsEreXv4H2AyLZmeLYJqXTGqwz/SyDet5CeF4LFGvTHpklhKN4dAlGG9C8aiPghBpJx44dayQRgL59+9b4fu/evbbvly9fzmWXXUZkZCTe3t7cdttt5ObmUlZWZjvHw8PDlkQAwsPDyc7OtlvsPa6KRWdwfF9JQ3Wmn6ZSqRnZ/9+E50TL5DghLpUavIe0cHYUTmG3ROLj40NhYc2tPgsKCvD19bV97+npedFlHz16lJEjR9KpUycWLVrE5s2bbX0oZ3fY63TVJ42pVCrsuQKMh4+ezpc5rtrakJ3ppxncPLmu32N4nvRokPsJ0VR5dApGF+Tu7DCcwm6JJDExkS1bttQ4vmXLFlq3bl3r9evXr6/xfdu2bQHYvHkzVquV6dOn06dPH1q3bs3Jk84ZTtTl8ha4XeQs57poyM700wICIhnT8UE0J6UWIkS9qJpvbQTs2EcyceJE3n33XaZOncrdd9+NwWDg559/5quvvuKnn36q9fq1a9fy2muvcc0117Bs2TIWLlzIzz//DEBCQgImk4mZM2cyatQo1q5dy4cfOm4f8wsxuGvpNiyGP7+tucT8hfy2dR7bj6whqyANncZAXFg7Rve+h5YxwejUqzm+c9cFr9+adpK567fSPiKUOwb0sB1P2XeIlamHAUhuE8/gxDMbXh3LzefbLbuYell/NOrqnxliW3Smj99IlJwmstyJEE7k3jEIXUjzrdXbrUYSFxfH6tWr2bdvH0OHDqV37958/fXXLFy4kOHDh9d6/cMPP8ymTZvo2rUrL7zwAm+++SbDhg0DoHPnzrz55pu8+uqrdOjQgblz5/Lyyy/bK/SL1jE5Ek+/i5tsdPDkDga1v5pHrnmXKSNfw2I188GSh8g+9iFZhy+cRPJKy1i8fS8tg6qv23OyoIjfdu/n1j5dubVPV5bsSiWjoGpdMIvVyqLNu7iue8caSaRLh2H0cb8KpVhGZglRb2oVPpc139oIyDLyl2z3HydImZt6Sdf6hxSSnfE9D34yh4nJfYgPDjzvuVarwvsr19GzZRRHcvIpN5psNZJtx0+yOvUIU4f2B+Cd5WsZnBhH5+hwVuw9SHFFJdd0rb6E9eBetxOaGw6yFJQQduHVPwK/UfG1n9iEOXVmuytr2z8Cv9CLq8qe3ZmedSINAI9zjGA727I9B/By09M7ruYnnnBfb06VlJJfWk5eaRk5xSWE+XqRU1LKxiPpDO9wZsKnWq1h1IAHCD0lSUQIe1F76vC5PMbZYThdo1i00RWp1Sp6jWrJ0k921+n8oKiT5B79lfSMQqyKwg/b9hAb5E+4r/d5rzlyKo+/jhznoSsGnvPxUB9vruyQyMerNwBwZcc2hPp481HKekZ2bkNq5imW7t6PTqvlzXEv4HGi+az9I0RD8B0ei9pN3kblFaiHhO4h7ExJJ+NgzWHPp3n4VqBTryb9rM7077bsIrOwmMlD+p73ugqTmXl/beP6Hh3xNJy/1tIvIYZ+CWc+EW08mo5BpyUm0J9Xf01h2g030NKrP1M+e4Y/712AQevcHQGFaCp0UV549Ah1dhiNgiSSelCpVCTdnMjXL27E+o+JfKdnpp/YuwyL6cxcl2+37GLPyWwmJffFz+P8Y85z/26y+mzNJtux091Zjy38hceuTCLIq/qcnNJKI8t272dScl/ScguICgrh1rb/RikxY7aYOZx/nLbBzbstVwi7UIHf1fGoVM1jv5HaSCKpp8BILzomR7F9xZn90f1DCyjPX0rajjOTChVF4butu9l1IpOJg/sS6HXh/pUQHy8eHjao2rElO1OpNJsZ3bU9fu41k9AP2/YwqHVL/Dzc0fnE89fBQpSSqpFZFqsFq1U6R4SwB49uoRha+Dg7jEZDEokd9BrVkoObszFVlOLjv4X0vWtrTCr8dssutqad5I7+PTBoNRSVVwDgrtOh01Ytu/LVhm34urtxVac26DSaGv0n7vqqiZDn6lfZn3mKU8Wl3NSrM5f1mYDpoIqXcj9n5aH1nCzORq3WEBfQvIcoCmEPKoMG3+Gxzg6jUZFEYgd6Ny2DboxgyXv/R3rGuftL1h2qGqX1QUr1Gfw39uxEz5ZVy67kl5VfUlXZZLbw3dbdjO/fkzEDH8LthB684fmhD/Dwr6+g1+h4a8R/cNdJZ7sQ9eUzNAaNt/Q1nk3mkdjR968/z6FNG5xybw8PX0Z0noQ6U5qvhHAUbYg7ofd3R6WRvpGzyTwSO7rsrono3Rt+mYSQkJZc3fbfkkSEcCQV+F/bSpLIOUgisSPvgCAG3jKhQe/ZKr43Q0JuQcmTNbOEcCSvAZEYYn1rP7EZkkRiZ50vv5LINu0a5F49O19NN80QlFJZM0sIR9KGeOA7LNbZYTRakkjsTKVSMey++9EZHLtL2tC+dxNX3BZM0pwlhEOpVQSMbY1KK2+X5yOvjAP4h0eSPOEeh5St0egZPeBhAjMDQYZJCOFw3oOj0EedfykjIYnEYToOuYJWvfvZtUwvL3+u6/UIbidk1LYQDUEf7Y3PZbIoY20kkTjQFfdMxSswyC5lhYUlMLL1ZFSZFruUJ4S4MJVBQ8BNiTJKqw4kkTiQm5cXV01+CJWqfi9zYkI/BgeORcmXkVlCNBS/q+PRBjbPPdgvliQSB4tu34k+1910ydf37jqGLqpBKGVSExGiobh3Dsazu6zsW1eSSBpA3+tuokXHLhd93bB+9xBb2BrM0qsuREPRBrrhPybB2WG4FEkkDUClVjPi34/g6R9Q+8mAVqtnzIBH8Mvwl5FZQjQglUFD4Pj2slnVRWpUieTDDz/E29sbs/nMBLuSkhJ0Oh2DBw+udm5KSgoqlYpDhw5d8v2OHj2KSqVi27Ztl1xGXXn4+jFi6qOo1Bd+yb29g7i2xyPoT2gcHpMQ4iwqCLi5DbqQhl/myNU1qkSSnJxMSUkJmzad2czpjz/+ICwsjA0bNlBRUWE7vnLlSlq0aEF8vOts1BTdriNJt9553scjIxIZkXAfqizpDxGiofkMi8W9Td1aDUR1jSqRJCYmEh4eTkpKiu1YSkoKo0ePpmXLlqxfv77a8eTkZL744gt69OiBt7c3YWFh3HLLLWRnZ9vOy8/PZ9y4cQQHB+Pu7k6rVq347LPPAGjZsiUAXbt2RaVSVav1fPLJJ7Rt2xY3NzfatGnD+++/b5fn2H3ENXRIvqLG8XaJgxjodx1KgYzMEqKhuXcOxmdwtLPDcFmNKpFAVa1k5cqVtu9XrlzJ4MGDSUpKsh0vLy9nw4YNJCcnYzKZeP7559m+fTvff/89R48eZcKECbbrn3rqKfbs2cOvv/7K3r17+eCDDwgKqprb8ddffwGwfPlyMjIy+PbbbwGYO3cuTz/9NC+++CJ79+7lpZde4qmnnuLzzz+3y3Mcevckotp2sH3fr9v1dLT0QymXmogQDU0X6UXA9a2cHYZLa3Q9SsnJyTzwwAOYzWbKy8vZunUrSUlJmEwmPvzwQwDWrVtHZWUlycnJtGhxZte/uLg4ZsyYQc+ePSkpKcHLy4u0tDS6du1Kjx49AIiNjbWdHxwcDEBgYCBhYWG248888wzTp0/n2muvBapqLnv27OGjjz5i/Pjx9X6OGq2WUQ89ybz/e5i+8WPwzfBBetWFaHhqLx2Bt7dDpZM+yfpodDWSwYMHU1paysaNG/njjz9o3bo1wcHBJCUl2fpJUlJSiIuLo0WLFmzevJlRo0bRokULvL29SUpKAiAtrWpHwokTJzJ//ny6dOnCY489xp9//nnB+5eWlnLo0CHuuusuvLy8bF8vvPBCvTr2/8nDx5dx/52OX760yQrhFBoVgbe1Q+srO4fWV6OrkSQkJBAVFcXKlSvJz8+3JYaIiAiio6P5888/WblyJUOGDKG0tJRhw4YxbNgw5s6dS3BwMGlpaQwbNgyj0QjAlVdeybFjx/jll19YtmwZl112GZMnT+aNN9445/1LSkoAmDVrFr179672mEZj308t7qG+BN7WlpxPd4FFaiRCNCT/axIwxPg4O4wmodHVSKCqeSslJYWUlJRqHeCDBg3i119/5a+//iI5OZl9+/aRm5vLK6+8wsCBA2nTpk21jvbTgoODGT9+PF9++SVvv/02H3/8MQB6fdW+yxbLmb6J0NBQIiIiOHz4MAkJCdW+TnfO25NbvB8BN7QGWc5HiAbjPSQaz55htZ8o6qTR1UigKpFMnjwZk8lkq5EAJCUlMWXKFIxGI8nJyWi1WvR6PTNnzuS+++5j165dPP/889XKevrpp+nevTvt27ensrKSxYsX07ZtWwBCQkJwd3dnyZIlREVF4ebmhq+vL88++yxTp07F19eX4cOHU1lZyaZNm8jPz+ehhx6y+/P16BKCOb+Sot+O2r1sIUR1XgMi8b0i1tlhNCmNtkZSXl5OQkICoaFn1rtJSkqiuLjYNkw4ODiY2bNns3DhQtq1a8crr7xSo8lKr9fz5JNP0qlTJwYNGoRGo2H+/PkAaLVaZsyYwUcffURERASjR48G4O677+aTTz7hs88+o2PHjiQlJTF79myH1EhO80mOxrNvuMPKF0KAZ68w/EbGOTuMJkelKIo0zjcSiqJQ8P1BSjdkOjsUIZocjy7B+I9NRKWWdmR7a5Q1kuZKpVLhd00Cnr2l7VYIe3JrH4j/DZJEHEUSSSNjSya9JJkIYQ+G1v4E3txGNqhyIEkkjZBKpcJvjCQTIepL39KHoNvaotLKW50jyavbSEkyEaJ+dNHeBE1oL7PWG0CjHP4rqpxOJihQulE64IWoK32MD0Hj26E2yFtcQ5BXuZFTqVT4XZuAoiiUbcpydjhCNHpubQMIvKWN1EQakAz/dREyNFiI2nn2DMNvTIKMzmpgkkhcTPGq4xQuOSqLBQvxD95DomXGupNIInFBZTtPkbdgP5itzg5FCOdTgd/oeLz6RDg7kmZLEomLqkwrInfOHqwlsqOiaMa0KgJvaoN7hyBnR9KsSSJxYea8CnI+24X5VLmzQxGiwancNATd3h5DnK+zQ2n2JJG4OGuZidwv91J5uNDZoQg7m7P1e77Y+j3phVUDLFoHteSBfuNJju8DQHZJLi+mfMAfRzdRYiwjPiCaf/e9jasSB5+3zL4fjCW9qOaAjdu7XsOLV1StbP3sindZuOtXPHRuPJl0L2PaX2E7b/G+lSza9RufXf+KHZ/pxdP46gm6owO6ME+nxiGqSCJpAhSLlfxFByjbUnMvFuG6lh1ci0alpqV/FAqwcNcSPtrwFb9O+B+JwS25ZcFDFFWW8PzQBwjw8OP7Pct4c81n/Dz+YzqEtj5nmbllBVisZ/bfSc05wi0LHuLrm9+hb4uuLDu4lseXvM5n173Ckfx0Hvn1Ff6a+A0BHn4UVZYw8vN7+Oqmt4j0CT1n+Q3BkOBHwE2JaLz0TotBVCcz25sAlUZNwNhEfIa2kA2ympDLE/ozJL4vLQOiiQuI5vFB/8JD787Wk7sB2HxiN3d0u46uEe2I8Yvg/n7j8TF4sTNz/3nLDPTwI8Qr0Pa14uCfxPhF0ie6CwAHc4/RJ7oLncPbcE27oXjrPUkrzADgpZUfcFvXa5yXRFTgnRxN0J0dJIk0MpJImhCfoTEETWiP2lPn7FCEnVmsFn7Ys4JyUwXdIjsA0D2yPT/t+5388iKsipUf9qyg0mKkT4sudSrTaDHx7Z5l3NjpKlSqqk8gbYMT2JGZSkFFMTsyU6kwVxLrH8Vf6TvYmXWAO7tf56ineEEqdy2Bt7fDd1iszBFphKRpqwmyFBnJW7CPykPSb+Lq9p46xDVfTKLSbMRT787MUU8xJL4vAIUVxUz6YRqrj25Eq9bgrnXjg2ueJallrzqV/dPe3/n3T8+zfuJCwrzPjHp6c82nfLt7GW5aPQ8PvIvL4vty1ey7eXPEf9hyYhefbfmWAHdfXhn2KInBjtvs7TRdpBeB49qiDXBz+L3EpZFE0kQpVoXilccpWnEMZLqJyzJaTJwoyqK4spRfUlP4avtiFt4yk9ZBsTy17G22Zezl8UH3EODhy2/7/+CTTQv5ZtxM2gbH11r2uAUPo9foau04f2vNZxRWlnBjx6sY9/XDLLtzNssP/snnW77llwmf2OupnpNnzzD8ro5HpZPGk8ZMfjpNlEqtwueyFgT/qxMaX2lPdlV6jY6W/lF0CkvkiaR7aReSwKebFnI0/wSzt3zLG1c+wYDY7rQLSeDBAXfQKSyROVu+q7Xc9MJM1hzbzE2dR1zwvIO5x/h2z1IeHXgX69K20juqM4Eefoxqk8zOrP2UVJbZ66lWo9Kp8b++Ff7XtZIk4gLkJ9TEGVr6EjK1G25tA5wdirADq2Kl0mKi3FwBgFpVvb9ArVJjrUMjw9c7fyHIw4/L/m4mOxdFUXjitzd4esgUPPUeWBQrJqsZwPavRbGc9/pLpQ12J3hiZzx7yBYKrkISSTOg8dQRNL49viPjQHaJcxmvrPqI9ce3cbwwg72nDvHKqo9Yl7aNMe0uJyEghlj/SJ747Q22ntzD0fwTfPTXfP44uolhrQbYyrhp/gPM3ryoWrlWxcrXO3/l+g7D0arPvwD4V9sXE+jux+UJ/QHoEdmBP49tYcuJ3XyycSGtA2PxdfO23xNWg/fgKELv74Y+wst+5QqHk2XkmxHvAZEYYn3I/2Y/pkzHNEkI+8kpzefBxS+RXZqLt8GTtsHxfDn2DQa17AnAnOtf4+VVH3HnoicpNZUT6xfJWyP+Y+uMBziWf5K88uqDLv44uokTRVnc2On8zVqnSvOYue4Lvrv1fduxrhHtuKfXjYz/5nGCPPx4c8R/7PZcdWEe+F/fGn2UHROTaDDS2d4MKRYrxX+coHhFGopJeuKFE2lUeA+OxmdINCqNNJC4KkkkzZg5t5z8Hw5RuT/f2aGIZkgX6VVVCwmXZU5cnSQSQdm2bAoWH5aVhEXD0FaNKPQeFI1K+uyaBEkkAqha/LFwydGqveHlN0I4iD7GB/9rE9CFSi2kKZFEIqqpPFpI/rcHMWdLZ7ywH02AG77DY/HoFOzsUIQDSCIRNSgWKyVrTlCUko5SbnZ2OMKFqdy1+AyJxqtvBCqtdKY3VZJIxHlZy80Ur06nZO0JFKOM7hIXQaPCq28EPkOiUXvIIqJNnSQSUStLsZHilccp2ZABFvl1ERfm3ikI32GxaAPdnR2KaCCSSESdmfMrKFqeRtnWLFkIUtSgj/HBd0RLDC18nB2KaGCSSMRFM2WXUbT0KOW7c2WEl0Df0gfvpGjc28h6bs2VJBJxyYzpxRQtT6MiNU8SSnOjAvf2gXgnRaOPlmVNmjtJJKLezDnllPx5ktLNWSiV9l8NVjQiWjWe3UPwHhiFNkj6QEQVSSTCbqyVZko3ZVH650nMuRXODkfYkdpDi2efcLz6Rch+6aIGSSTC7hSrQkVqHiV/nqTyQIGzwxH1oAlww7t/BB49w1DrNc4ORzRSkkiEQ5mySin58yRlW7JlpWFXoVXj3j4Qzx6hGBL8UKlkPSxxYZJIRIOwVpgp35VD2fZTVB4qBKv82jU2ukgvPHuE4tE5WCYRiosiiUQ0OEuJkfKdVUnFeKxIRnw5kSbADY/OwXh0DUEX4uHscISLkkQinMpcWEn5jlOUbT+FKb3E2eE0C2pvHe4dgvDoEoIhRiYPivqTRCIaDXNuOWU7TlG+MwdTRqnUVOxFrUIf441b6wDcEv3RhXtKv4ewK0kkolGylBipPFBAxf58Kg7mYy2WTbcuhsZHj6G1P26JAbi18kPtpnV2SKIJk0QiXIIxo5TKQwVUHi7EeLQQa5ksb1+NVo2hhTduiVXJQxcmG0eJhiOJRLgcRVEwZ5VRebiQymNFmDJKMOeUN5+FJDUqdGGe6CO90EV5oY/0RhfmgUoj+30I55BEIpoExWTFlF2GKaMUU0YJpsxSTJmlWEtdvOaiBl2IB7oob/Snk0a4p2wSJRoVSSSiSbMUGc8kluxyLIWVWIqMWIoqUSoax7pgKjcNWn83NP5uaP0Nf//rhsbfgDbIXWaUi0ZPEolotqxGC5YiI9ai08nFaEsylmJj1Ux8s4JisaJYFBSzFcxWlL+P1djkS6NCbdCg0mtQGTTV/3/WMbWXHm3AmYShdpeO8LoaPHgwXbp04e2333Z2KOIs8hssmi21XoM6yB0ucRVbRVFsiUalVTfZ5qYPP/yQRx99lPz8fLTaqreMkpIS/P396d+/PykpKbZzU1JSSE5O5uDBg8THxzspYtHQmuZvvhANQKVSodKpUbtpm2wSAUhOTqakpIRNmzbZjv3xxx+EhYWxYcMGKirOrPS8cuVKWrRoIUmkmWm6v/1CCLtITEwkPDy8Rs1j9OjRtGzZkvXr11c7npycTGVlJVOnTiUkJAQ3NzcGDBjAxo0bq5W7atUqevXqhcFgIDw8nCeeeAKz+czgiNLSUm6//Xa8vLwIDw9n+vTpDn+u4tJIIhFC1Co5OZmVK1favl+5ciWDBw8mKSnJdry8vJwNGzaQnJzMY489xqJFi/j888/ZsmULCQkJDBs2jLy8PABOnDjBVVddRc+ePdm+fTsffPAB//vf/3jhhRds93j00UdZtWoVP/zwA0uXLiUlJYUtW7Y07BMXdaMIIUQtZs2apXh6eiomk0kpKipStFqtkp2drcybN08ZNGiQoiiKsmLFCgVQjh49quh0OmXu3Lm2641GoxIREaG89tpriqIoyn/+8x8lMTFRsVqttnPee+89xcvLS7FYLEpxcbGi1+uVr7/+2vZ4bm6u4u7urtx///0N86RFnUlnuxCiVoMHD6a0tJSNGzeSn59P69atCQ4OJikpiTvuuIOKigpSUlKIi4ujsLAQk8lE//79bdfrdDp69erF3r17Adi7dy99+/attuZX//79KSkpIT09nfz8fIxGI71797Y9HhAQQGJiYsM9aVFnkkiEELVKSEggKiqKlStXkp+fT1JSEgARERFER0fz559/snLlSoYMGeLkSIUzSB+JEKJOkpOTSUlJISUlhcGDB9uODxo0iF9//ZW//vqL5ORk4uPj0ev1rF271naOyWRi48aNtGvXDoC2bduybt26qiHUf1u7di3e3t5ERUURHx+PTqdjw4YNtsfz8/PZv3+/45+ouHjOblsTQriGTz/9VHF3d1e0Wq2SmZlpO/75558r3t7eCqCcPHlSURRFuf/++5WIiAjl119/VXbv3q2MHz9e8ff3V/Ly8hRFUZT09HTFw8NDmTx5srJ3717l+++/V4KCgpRnnnn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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "labels = [\"Grasses\", \"Leaves\", \"Meadows\", \"Paths\", \"Urban\", \"Waste\", \"Wood\"]\n", "\n", "plt.pie(habitat[\"class\"], labels=labels, autopct=\"%1.1f%%\", pctdistance=0.85)\n", "\n", "center_circle = plt.Circle((0, 0), 0.40, fc=\"white\")\n", "fig = plt.gcf()\n", "\n", "fig.gca().add_artist(center_circle)\n", "\n", "plt.title(\"Mushroom Habitats\")\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "9bde4bd0-6cd1-440a-b39b-b1761f6ca906", "metadata": {}, "source": [ "This code draws a chart and a center circle, then adds that center circle in the chart. Edit the width of the center circle by changing `0.40` to another value.\n", "\n", "Donut charts can be tweaked in several ways to change the labels. The labels in particular can be highlighted for readability. Learn more in the [docs](https://matplotlib.org/stable/gallery/pie_and_polar_charts/pie_and_donut_labels.html?highlight=donut).\n", "\n", "Now that you know how to group your data and then display it as a pie or donut, you can explore other types of charts. Try a waffle chart, which is just a different way of exploring quantity.m" ] }, { "cell_type": "markdown", "id": "bbf85d4f-dbb8-486b-b1d9-6d0348335e88", "metadata": {}, "source": [ "## Waffles 🧇\n", "\n", "A 'waffle' type chart is a different way to visualize quantities as a 2D array of squares. Try visualizing the different quantities of mushroom cap colors in this dataset. To do this, you need to install a helper library called [PyWaffle](https://pypi.org/project/pywaffle/) and use Matplotlib:\n", "\n", "```bash\n", "pip install pywaffle\n", "```\n", "\n", "Select a segment of your data to group:" ] }, { "cell_type": "code", "execution_count": 8, "id": "8ab5a5be-cbc3-460f-9052-efc158d370eb", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/var/folders/7w/fv5n0x414253d7dv5g2wwmb40000gn/T/ipykernel_93696/1230659777.py:1: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n", " capcolor = mushrooms.groupby([\"cap-color\"]).count()\n" ] }, { "data": { "text/html": [ "
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classcap-shapecap-surfacebruisesodorgill-attachmentgill-spacinggill-sizegill-colorstalk-shape...stalk-surface-below-ringstalk-color-above-ringstalk-color-below-ringveil-typeveil-colorring-numberring-typespore-print-colorpopulationhabitat
cap-color
Brown2284228422842284110022842284228422842284...2284228422842284228422722272228422842284
Buff16816816816896168168168168168...168168168168168168168168168168
Cinnamon44444444124444444444...44444444443232444444
Green185618561856185680818561856185618561856...1856185618561856185618561856185618561856
Pink14414414414464144144144144144...144144144144144144144144144144
Purple1616161601616161616...16161616161616161616
Red150015001500150087615001500150015001500...1500150015001500150014881488150015001500
White104010401040104059210401040104010401040...1040104010401040104010401040104010401040
Yellow1072107210721072104810721072107210721072...1072107210721072107210721072107210721072
\n", "

9 rows × 22 columns

\n", "
" ], "text/plain": [ " class cap-shape cap-surface bruises odor gill-attachment \\\n", "cap-color \n", "Brown 2284 2284 2284 2284 1100 2284 \n", "Buff 168 168 168 168 96 168 \n", "Cinnamon 44 44 44 44 12 44 \n", "Green 1856 1856 1856 1856 808 1856 \n", "Pink 144 144 144 144 64 144 \n", "Purple 16 16 16 16 0 16 \n", "Red 1500 1500 1500 1500 876 1500 \n", "White 1040 1040 1040 1040 592 1040 \n", "Yellow 1072 1072 1072 1072 1048 1072 \n", "\n", " gill-spacing gill-size gill-color stalk-shape ... \\\n", "cap-color ... \n", "Brown 2284 2284 2284 2284 ... \n", "Buff 168 168 168 168 ... \n", "Cinnamon 44 44 44 44 ... \n", "Green 1856 1856 1856 1856 ... \n", "Pink 144 144 144 144 ... \n", "Purple 16 16 16 16 ... \n", "Red 1500 1500 1500 1500 ... \n", "White 1040 1040 1040 1040 ... \n", "Yellow 1072 1072 1072 1072 ... \n", "\n", " stalk-surface-below-ring stalk-color-above-ring \\\n", "cap-color \n", "Brown 2284 2284 \n", "Buff 168 168 \n", "Cinnamon 44 44 \n", "Green 1856 1856 \n", "Pink 144 144 \n", "Purple 16 16 \n", "Red 1500 1500 \n", "White 1040 1040 \n", "Yellow 1072 1072 \n", "\n", " stalk-color-below-ring veil-type veil-color ring-number \\\n", "cap-color \n", "Brown 2284 2284 2284 2272 \n", "Buff 168 168 168 168 \n", "Cinnamon 44 44 44 32 \n", "Green 1856 1856 1856 1856 \n", "Pink 144 144 144 144 \n", "Purple 16 16 16 16 \n", "Red 1500 1500 1500 1488 \n", "White 1040 1040 1040 1040 \n", "Yellow 1072 1072 1072 1072 \n", "\n", " ring-type spore-print-color population habitat \n", "cap-color \n", "Brown 2272 2284 2284 2284 \n", "Buff 168 168 168 168 \n", "Cinnamon 32 44 44 44 \n", "Green 1856 1856 1856 1856 \n", "Pink 144 144 144 144 \n", "Purple 16 16 16 16 \n", "Red 1488 1500 1500 1500 \n", "White 1040 1040 1040 1040 \n", "Yellow 1072 1072 1072 1072 \n", "\n", "[9 rows x 22 columns]" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "capcolor = mushrooms.groupby([\"cap-color\"]).count()\n", "capcolor" ] }, { "cell_type": "markdown", "id": "3e2270a2-263e-47bf-96f1-46520f190827", "metadata": {}, "source": [ "Create a waffle chart by creating labels and then grouping your data:" ] }, { "cell_type": "code", "execution_count": 9, "id": "80efb206-ac46-46a0-9211-b956f7451268", "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "from pywaffle import Waffle\n", "\n", "data = {\n", " \"color\": [\n", " \"brown\",\n", " \"buff\",\n", " \"cinnamon\",\n", " \"green\",\n", " \"pink\",\n", " \"purple\",\n", " \"red\",\n", " \"white\",\n", " \"yellow\",\n", " ],\n", " \"amount\": capcolor[\"class\"],\n", "}\n", "\n", "df = pd.DataFrame(data)\n", "\n", "fig = plt.figure(\n", " FigureClass=Waffle,\n", " rows=100,\n", " values=df.amount,\n", " labels=list(df.color),\n", " figsize=(30, 30),\n", " colors=[\n", " \"brown\",\n", " \"tan\",\n", " \"maroon\",\n", " \"green\",\n", " \"pink\",\n", " \"purple\",\n", " \"red\",\n", " \"whitesmoke\",\n", " \"yellow\",\n", " ],\n", ")" ] }, { "cell_type": "markdown", "id": "81a93838-a12c-4e89-8c87-822fedd4e40a", "metadata": {}, "source": [ "Using a waffle chart, you can plainly see the proportions of cap colors of this mushrooms dataset. Interestingly, there are many green-capped mushrooms!\n", "\n", "✅ Pywaffle supports icons within the charts that use any icon available in [Font Awesome](https://fontawesome.com/). Do some experiments to create an even more interesting waffle chart using icons instead of squares.\n", "\n", "In this lesson, you learned three ways to visualize proportions. First, you need to group your data into categories and then decide which is the best way to display the data - pie, donut, or waffle. All are delicious and gratify the user with an instant snapshot of a dataset." ] } ], "metadata": { "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": "3.12.4" } }, "nbformat": 4, "nbformat_minor": 5 }