{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Основы анализа данных в Python\n", "\n", "*Тамбовцева Алла, НИУ ВШЭ*\n", "\n", "## Семинар 3. Визуализация данных с `matplotlib`" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Часть 1: предварительная обработка датафрейма" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Информация о данных\n", "\n", "В рамках этого семинара используем данные с результатами опроса преподавателей двух каталонских универсистетов (Открытый университет Каталонии и университет Помпеу Фабра), посвященного использованию Википедии в методических и академических целях.\n", "\n", "* Codebook для набора данных: [ссылка](https://github.com/allatambov/PyDataAnalysis/blob/main/wiki_codebook.pdf).\n", "* Оригинальное описание на платформе data.world: [ссылка](https://data.world/uci/wiki4he)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Загрузка данных \n", "\n", "Импортируем библиотеку `pandas` с сокращенным названием `pd`:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import pandas as pd" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Загрузим данные из файла `\"wiki.csv\"` и сохраним их в датафрейм `wiki`, сообщив Python, что в качестве разделителя столбцов используется точка с запятой, а не запятая:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "wiki = pd.read_csv(\"wiki.csv\", sep = \";\") " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Если мы не добавим аргумент `sep` с разделителем, все данные склеятся в один столбец, так как по умолчанию функция `read_csv()` подразумевает разделение по запятой (ведь название формата CSV – это сокращение от *comma separated values*).\n", "\n", "**Примечание для Jupyter Notebook.** Удобно, если файл с данными при работе лежит в той же папке, что и текущий ipynb-файл, в котором мы запускаем код, так не придется полностью прописывать к нему путь, достаточно одного названия с расширением.\n", "\n", "**Примечание для Google Colab.** Загрузить файл с данными в облачное хранилище можно через кнопку *Files* (значок папки слева от рабочей области с ячейками), при нажатии на которую появляется возможность выбрать файл с компьютера (значок стрелки). После добавления файла его можно выбрать, кликнуть на три точки справа от названия, скопировать путь через *Copy path* и вставить его в функцию `read_csv()`. Например:\n", "\n", " wiki = pd.read_csv(\"/content/wiki.csv\", sep = \";\") \n", "\n", "Теперь запросим техническую информацию по датафрейму:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "wiki.info() # для компактности конспекта ячейка не запущена" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Итак, датафрейм довольно большой: 913 наблюдений (строк) и 53 переменных (столбцов). Какие-то столбцы содержат целые числа (`int` от `integer`), какие-то – текст (`object`, он же `string` за рамками `pandas`). Но, как это бывает, не всегда типы данных при загрузке совпадают с нашими ожиданиями, чуть позже разберемся, что в этом датафрейме надо поправить." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Выбор столбцов и строк\n", "\n", "Датафрейм большой, давайте отберем из него только те столбцы, которые нам понадобятся. Выбирать столбцы удобнее всего по названию, его мы указываем в квадратных скобках (да и вообще любой выбор далее будет осуществляться с помощью квадратных скобок). Выберем столбец со значениями возраста сотрудников университетов:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 40\n", "1 42\n", "2 37\n", "3 40\n", "4 51\n", " ..\n", "908 43\n", "909 53\n", "910 39\n", "911 40\n", "912 41\n", "Name: AGE, Length: 913, dtype: int64" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "wiki[\"AGE\"] " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Теперь выберем несколько столбцов – перечислим их названия в виде списка (одни квадратные скобки – для выбора, вторые – для создания списка) и сохраним их в датафрейм `small`:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "small = wiki[[\"AGE\", \"GENDER\", \"YEARSEXP\", \"UNIVERSITY\", \n", " \"UOC_POSITION\", \"Qu3\"]] " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Если названия столбцов переписывать, можно опечататься, удобнее будет их скопировать из перечня названий, который хранится в атрибуте `.columns`:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['AGE', 'GENDER', 'DOMAIN', 'PhD', 'YEARSEXP', 'UNIVERSITY',\n", " 'UOC_POSITION', 'OTHER_POSITION', 'OTHERSTATUS', 'USERWIKI', 'PU1',\n", " 'PU2', 'PU3', 'PEU1', 'PEU2', 'PEU3', 'ENJ1', 'ENJ2', 'Qu1', 'Qu2',\n", " 'Qu3', 'Qu4', 'Qu5', 'Vis1', 'Vis2', 'Vis3', 'Im1', 'Im2', 'Im3', 'SA1',\n", " 'SA2', 'SA3', 'Use1', 'Use2', 'Use3', 'Use4', 'Use5', 'Pf1', 'Pf2',\n", " 'Pf3', 'JR1', 'JR2', 'BI1', 'BI2', 'Inc1', 'Inc2', 'Inc3', 'Inc4',\n", " 'Exp1', 'Exp2', 'Exp3', 'Exp4', 'Exp5'],\n", " dtype='object')" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "wiki.columns" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "*Пояснение.* Атрибут – некоторая фиксированная характеристика объекта (число строк или столбцов в датафрейме, названия строк или столбцов, тип данных), метод – функция, которая выполняет некоторую операцию над объектом. И атрибуты, и методы вызываются через точку, но в конце метода всегда есть круглые скобки (например, `.info()`), они означают, что мы хотим применить метод, а не просто вызвать и посмотреть на него." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Теперь займемся строками. Давайте оставим в датафрейме `small` только строки, соответствующие сотрудникам Открытого университета Каталонии (закодированы значением 1 в столбце `UNIVERSITY`):" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "small = small[small[\"UNIVERSITY\"] == 1]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Как устроен код выше? В квадратных скобках, как обычно, указывается некоторый критерий отбора, здесь он представлен в виде условия (помним, что оператор `==` используется для проверки равенства, а оператор `=` для присваивания, второй нам здесь не подойдет). Выражение в квадратных скобках, само по себе, проверяет условие для каждой строки в `small` и возвращает набор из логических значений `True` (если условие выполнено) и `False` (если условие не выполнено):" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 True\n", "1 True\n", "2 True\n", "3 True\n", "4 True\n", " ... \n", "795 True\n", "796 True\n", "797 True\n", "798 True\n", "799 True\n", "Name: UNIVERSITY, Length: 800, dtype: bool" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "small[\"UNIVERSITY\"] == 1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Соответственно, строка кода в предыдущей ячейке отбирает те строки из `small`, где выражение в квадратных скобках вернуло `True`. \n", "\n", "Теперь отберем только сотрудников, имеющих должность *lecturer* или *assistant* (метки \"3\" или \"4\") и сохраним их в датафрейм `tiny`. Для этого нам понадобится оператор `|`, соответствующий объединению условий через ИЛИ (хотя бы одно из условий верно):" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "tiny = small[(small[\"UOC_POSITION\"] == '3') | \n", " (small[\"UOC_POSITION\"] == '4')]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Важно: не потеряйте круглые скобки вокруг каждого из условий. Оператор `|` в примере самый сильный, поэтому без скобок Python начнет «раскручивать» условие с него. И тогда он попробует через ИЛИ объединить значение `'3'` и столбец `small[\"UOC_POSITION\"]`, это у него, естественно, не получится, что приведет к ошибке `TypeError`. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "И напоследок удалим из `tiny` столбец с названием университета (у нас сотрудники только одного, у всех одно и то же значение) и столбец с должностями сотрудников. Для этого воспользуемся методом `.drop()`." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "tiny = tiny.drop(columns = [\"UNIVERSITY\", \"UOC_POSITION\"])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Метод `.drop()` по умолчанию работает со строками, поэтому в скобках мы указываем, что удалять нужно все-таки столбцы, а затем перечисляем их названия в виде списка. Этот метод работает осторожно, он не вносит изменения в датафрейм, он возвращает его измененную копию. Мы сохраняем эту копию с тем же названием `tiny`." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Часть 2: описание данных" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Посмотрим теперь на более содержательное описание данных. Запросим описательные статистики через метод `.describe()`:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " AGE GENDER\n", "count 68.000000 68.000000\n", "mean 40.382353 0.544118\n", "std 6.376331 0.501753\n", "min 29.000000 0.000000\n", "25% 35.000000 0.000000\n", "50% 40.000000 1.000000\n", "75% 45.250000 1.000000\n", "max 56.000000 1.000000" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tiny.describe() " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Метод `.describe()` по умолчанию выводит описательные статистики только для числовых столбцов (типы `integer` и `float`). Для столбца `AGE` все полученные значения имеют понятный смысл: \n", "\n", "* `count`: число заполненных ячеек 68;\n", "* `mean` : средний возраст сотрудников примерно 40 лет;\n", "* `std` : стандартное отклонение возраста примерно 6 лет;\n", "* `50%`: возраст половины сотрудников также не превышает 40 лет (медиана); \n", "* `25%`: 25% сотрудников не старше 35 лет (нижний квартиль);\n", "* `75%`: 75% сотрудников не старше 45 лет (верхний квартиль);\n", "* `min` и `max`: самому молодому сотруднику 29 лет, самому пожилому – 56.\n", "\n", "Если полученные значения кажутся немного заниженными, вспомните, что мы ранее отфильтровали сотрудников по должности, тут только преподаватели и ассистенты, а более возрастная категория – это все-таки профессора. \n", "\n", "Для столбца `GENDER` не все статистики стоит внимательно изучать, все-таки пол – показатель качественный, но на среднее стоит посмотреть – это просто доля «единиц» в нашей выборке, то есть доля мужчин среди опрошенных (примерно 54%)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Если мы хотим получить описание текстовых столбцов (если все нормально с типами данных, это показатели в качественной или порядковой шкале), в `.describe()` можно добавить опцию `include` и указать соответствующий тип:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " YEARSEXP Qu3\n", "count 68 68\n", "unique 20 6\n", "top 12 3\n", "freq 8 32" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tiny.describe(include = \"object\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Почему число лет опыта работы `YEARSEXP` оказалось текстовым, мы выясним чуть позже, а пока посмотрим на `Qu3`. В строке `unique` сохранено число уникальных значений, здесь из 6 (согласие/несогласие с утверждением по шкале Лайкерта и значение для кодирования пропусков). В строке `top` хранится мода, самое частое значение, здесь это 3 (*Neither disagree nor agree*, что ожидаемо). В строке `freq` находится частота, соответствующая моде, в данном случае ответ 3 выбрали 32 человека. \n", "\n", "Теперь посмотрим, что не так с `YEARSEXP`. Выведем уникальные значения:" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array(['13', '8', '17', '11', '12', '25', '10', '9', '5', '?', '18', '15',\n", " '19', '4', '7', '20', '3', '16', '6', '2'], dtype=object)" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tiny[\"YEARSEXP\"].unique() " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Ага! Проблема в знаке вопроса – так в этих данных были закодированы пропущенные ответы. Выкинем строки с пропущенными ответами – выберем только те строки, где значение в столбце `YEARSEXP` не `?`:" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "tiny = tiny[tiny[\"YEARSEXP\"] != \"?\"] " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Проверим:" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array(['13', '8', '17', '11', '12', '25', '10', '9', '5', '18', '15',\n", " '19', '4', '7', '20', '3', '16', '6', '2'], dtype=object)" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tiny[\"YEARSEXP\"].unique() " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Лишнее выкинулось, но небольшая проблема осталась. Значения по-прежнему текстовые! Первое, что хочется сделать – пройтись в цикле по всем элементам массива выше и преобразовать каждый в число. Но, к счастью, есть более быстрый и компактный способ – воспользоваться методом `.astype()`, он умеет изменять тип сразу всех элементов массива или столбца:" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "tiny[\"YEARSEXP\"] = tiny[\"YEARSEXP\"].astype(int) # переделываем в integer" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Напоследок давайте вспомним о том, как выводить описательные статистики по группам, и наконец перейдем к визуализации. Сгруппируем всех респондентов в `tiny` по полу и выведем среднее значение для числовых столбцов. Для этого воспользуемся методом `.groupby()` для группировки и методом `.agg()` для агрегирования (внутри указываем, какую функцию для каждой группы применить – здесь функцию для вычисления среднего):" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " AGE YEARSEXP\n", "GENDER \n", "0 41.689655 11.310345\n", "1 39.222222 10.333333" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tiny.groupby(\"GENDER\").agg(\"mean\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Итак, видно, что средние значения возраста сотрудников мужского и женского пола отличаются несильно. То же верно и для числа лет опыта работы. Посмотрим на медианные значения по группам:" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " AGE YEARSEXP\n", "GENDER \n", "0 41 10\n", "1 40 11" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tiny.groupby(\"GENDER\").agg(\"median\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Примерно та же история. Чисто технически, метод `.agg()` можно было опустить и написать код проще:" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " AGE YEARSEXP\n", "GENDER \n", "0 41 10\n", "1 40 11" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tiny.groupby(\"GENDER\").median()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Но этот метод все-таки более универсальный, он позволяет выводить сразу несколько характеристик для каждой группы. В таком случае соответствующие функции нужно записывать в виде списка:" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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meanstdmeanstd
GENDER
041.6896556.72470611.3103454.575583
139.2222225.83639410.3333335.466783
\n", "
" ], "text/plain": [ " AGE YEARSEXP \n", " mean std mean std\n", "GENDER \n", "0 41.689655 6.724706 11.310345 4.575583\n", "1 39.222222 5.836394 10.333333 5.466783" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tiny.groupby(\"GENDER\").agg([\"mean\", \"std\"]) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Итак, мы вывели среднее и стандартное отклонение – распространенные характеристики выборок." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Часть 3: визуализация" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Для создания разных типов графиков нам понадобится библиотека `matplotlib`, а точнее, основной ее модуль, под названием `pyplot`. Его часто импортируют с сокращенным названием `plt`, что мы и сделаем:" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "from matplotlib import pyplot as plt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Гистограмма\n", "\n", "Начнем знакомство с визуализацией с гистограммы – довольно простого в плане оформления графика. Почему простого? \n", "\n", "Во-первых, потому что для гистограммы достаточно выбрать один цвет (один показатель, не разные категории, не путать со столбиковой диаграммой). Во-вторых, потому что не нужно думать о том, как упорядочить столбцы на графике (они сами упорядочиваются естественным образом в силу логики построения). В-третьих, потому что не нужно переживать о том, как уместить названия на осях (по горизонтальной и вертикальной осям идут обычные числа, а они занимают мало места).\n", "\n", "Для начала построим самую простую гистограмму для визуализации распределения возраста. Для этого из модуля `plt` вызовем функцию `.hist()`:" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(array([ 6., 6., 9., 8., 13., 7., 8., 4., 2., 2.]),\n", " array([29. , 31.7, 34.4, 37.1, 39.8, 42.5, 45.2, 47.9, 50.6, 53.3, 56. ]),\n", " )" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.hist(tiny[\"AGE\"])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Если мы планируем оставлять этот график в ipynb-файле, в конце предыдущей строчки можно добавить точку с запятой, она сообщит Python, что нужен только сам график, без массивов, которые показывают, как функция разбила данные на группы для построения гистограммы:" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.hist(tiny[\"AGE\"]);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Гистограмма сейчас совсем несимпатичная. Изменим цвет заливки (`color`) и добавим цвет границ столбцов (`edgecolor`):" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.hist(tiny[\"AGE\"], color = \"#2d2966\", edgecolor = \"white\");" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Цвета (как в примере выше) можно добавлять в шестнадцатеричном формате, то есть можно выбрать цвет, например, в [палитре](https://g.co/kgs/yqtg6m) Google и указать его в коде. Подробнее о цветах в `matplotlib` можно почитать в соответствующей [документации](https://matplotlib.org/stable/tutorials/colors/colors.html). \n", "\n", "По умолчанию Python сам выбирает количество столбцов для гистограммы, но при желании это число можно изменить, например, если гистограмма получилась неудачной (с «дырками» или большим числом узких столбцов), добавив аргумент `bins`:" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# 8 столбцов\n", "\n", "plt.hist(tiny[\"AGE\"], color = \"#2d2966\", edgecolor = \"white\", bins = 8);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "В аргумент `bins` можно записывать не только числа, в нем можно указать алгоритм, который используется для выбора числа столбцов, про доступные опции можно почитать [здесь](https://numpy.org/doc/stable/reference/generated/numpy.histogram_bin_edges.html#numpy.histogram_bin_edges)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Теперь давайте приведем гистограмму в порядок. Как минимум, добавим подписи к осям и заголовок графика (если график в дальнейшим будет выгружен в файл и добавлен в текстовый документ, заголовок часто опускают, он будет в самом текстовом документе с соответствующим шрифтом, номером рисунка и проч). \n", "\n", "Если мы хотим получить красивые графики с возможностью регулировать их оформление с помощью `matplotlib`, нам придется более глубоко разобраться с логикой их построения. График `matplotlib` состоит из двух частей: \n", "\n", "* «рамка» для картинки (обычно сохраняется как `fig`); \n", "* поле с осями, внутри которого строится график (обычно сохраняется как `ax`).\n", "\n", "Так, мы можем воспользоваться функцией `subplots()` и сообщить Python, что мы хотим получить картинку размера 16 на 9 дюймов:" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(figsize =(16, 9)) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Неформально, создавая `fig` мы резервируем место под картинку соответствующего размера, а затем проводим различные манипуляции с `ax`, чтобы внутри этого поля для картинки построить график, настроить подписи, оси и прочее. Соответственно, к `ax` мы будем применять различные методы, а по завершении работы сможем выгрузить объект `fig` в файл PNG или JPEG.\n", "\n", "Вообще функция `subplots()`, как следует из ее названия, может использоваться для построения сразу нескольких графиков, но у нас пока внутри `fig` будет один. На список доступных опций, помимо размера, можно посмотреть в соответствующей [документации](https://matplotlib.org/stable/api/figure_api.html#matplotlib.figure.Figure).\n", "\n", "Построим гистограмму и настроим ее:" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(figsize =(16, 9)) \n", "\n", "# строим гистограмму, обратите внимание, перед hist \n", "# стоит ax, не plt\n", "\n", "ax.hist(tiny[\"AGE\"], color = \"#2d2966\", edgecolor = \"white\")\n", "\n", "# добавляем заголовок графика и выравниваем по левому краю\n", "# добавляем подписи по осям x и y\n", "\n", "ax.set_title('Histogram of age', loc ='left')\n", "ax.set_xlabel(\"Age\")\n", "ax.set_ylabel(\"Counts\")\n", "\n", "# выгружаем в файл, он появится в рабочей папке\n", "# рядом с текущим ipynb-файлом \n", "\n", "fig.savefig(\"my_hist.png\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Проблема: подписи на графике очень мелкие. Можно решить эту проблему глобально, один раз и для всех графиков в рамках данного ipynb-файла, обновив параметры модуля `plt`. На список доступных параметров можно посмотреть [здесь](https://matplotlib.org/stable/api/matplotlib_configuration_api.html#matplotlib.rcParams).\n", "\n", "Мы же обновим размер базового шрифта:" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [], "source": [ "plt.rcParams.update({'font.size': 16})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Теперь, если мы перезапустим код выше, все подписи на графике, включая числа, увеличатся (и это будет работать для всех последующих графиков):" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(figsize =(16, 9)) \n", "\n", "ax.hist(tiny[\"AGE\"], color = \"#2d2966\", edgecolor = \"white\")\n", "ax.set_title('Histogram of age', loc ='left')\n", "ax.set_xlabel(\"Age\")\n", "ax.set_ylabel(\"Counts\")\n", "\n", "fig.savefig(\"my_hist.png\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Небольшое дополнение для любителей эстетики (не обсуждали на занятии): как добавить координатную сетку, так называемый *grid*:" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(figsize =(16, 9)) \n", "\n", "ax.hist(tiny[\"AGE\"], color = \"#2d2966\", edgecolor = \"white\")\n", "ax.set_xlabel(\"Age\")\n", "ax.set_ylabel(\"Counts\")\n", "\n", "# отправляет сетку на задний план,\n", "# чтобы не перечеркивала график\n", "\n", "ax.set_axisbelow(True)\n", "\n", "# цвет сетки, тип линии, ширина, прозрачность\n", "\n", "ax.grid(color ='grey', linestyle = '-.', \n", " linewidth = 0.5, alpha = 0.4)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Документацию по функции `grid()` можно посмотреть [здесь](https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.grid.html), типы доступных линий – [здесь](https://matplotlib.org/stable/gallery/lines_bars_and_markers/linestyles.html)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Итак, будем считать, что красивую гистограмму построили (надо остановиться). Проинтерпретируем полученный график. Распределение возраста сотрудников университета в должности преподавателя или ассистента немного скошено вправо, значения выше 50 лет являются не совсем типичными. Возраст большинства преподавателей и ассистентов находится на интервале от 40 до 42 лет." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Диаграмма рассеивания" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Построим диаграмму рассеивания, которая покажет нам связь между двумя количественными показателями — возрастом и числом лет опыта работы. Для построения такой диаграммы используется простая функция `plot()`, но тут есть подвох:" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "image/png": 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yM6FaqSZJF5EfSCwkpaCS6gZlcW3MTQhwtSXCz4kAFx99RO7vYoO1edvz3LUqNcdSi5gzuP1syGdzyvjrdC6PTejZoeIO2gYhT93Umze3xvFzZAazL/o5XO20HvnbvzjI/O+O8ssDI3CyMW4N4Vqn/VZFBIJrGJVaQ1J+pT4y33bqPFkl1frj9fnyIf7OFyJzD3u90Excupe48+UG472drAlwtWFYQDe9iAe62uJmZ9EoSr0cTqSVUKPUMDLIpd2u+enORGwtTLl3pH+7XbMlFo4KYHd8Pq9uOcMQ/274u9gYHG/okV+8OpLV9wmPPAiBFwgaUVWnIu58uUFk3jBfbm6q0H8/qIcTL07qQ3B3e6zMmxeUW/p5MGWAJ4GuF+yGV0qADiYVYKKQGBrQlJO57ZzLLWfrqRyWjA3Cwbpjo/d6FAqJD2YNYOLSfTy+LpoNDwzH7KIF4sF+znwwcwCP/BjF/62P4ZM5wiMvBF7wr6aosk5nRyw18JdrLsqXzxvWg1AvbWR+JKWIlzZqK3PcNdSXcN/GG4cu5tEOWoQ0hgOJBfT3dmi3VMqnOxOxMjPhvlFXJnqvx8PBijen9WPJ2hN8sjOR/9zQq9GYKQM8yS6p5q0/4/B2tOK5f7lHXgi84F9Bfb68fqNQfWTeMF/u6WBJiKcDk/p76hdBvRytDNIl2SXVvPNnHAEuNiQXdP2iV+U1SmIyS3nwusB2uV5SfgVbTmZz/5jATslzT+rvwT9xXny6M4HrerkwqEfju5LFY7Qe+a/2JuPlZNUhFs6rBSHwgmuO+nz5mZxSfU2WMzlllFYrAW3+O0CXLw/xuOBkudiCdzGyLPPCb7GoNTKvTg1l7oqjV+LHuSyOphSh1siMCOrWLtf7bFcilqYmLBx9ZaP3hrx6ayjHUot4fF00Wx8djd1FdyZaj3wIOaXVvLL5NJ4OVlwf4t5Js+1chMALrmqq69ScPV8flWsj87jz5dQ29Jd3t+OWfh6E6KLyPq3ky5vjt6gsdsXn89/JIfg6Xx1WvAOJhViYKhhoRBqpNVILKtkUnc29I/1wsbVoh9ldGnaWZnw0K4xZXx3ilc1n+GDWgEZjTE0UfHxHOHOWH+aRH6P4afEwBvwLPfJC4AVXDcWVdQYVEk9nl5GcX6HPl9tbmhLq6cDcYT10Yu5AoKvNZe/WBMgrr+HVLWcY1MOJe0b4kVlcddnXvBIcSCxgsJ9zuyzofr47EVOFxKIxAa0P7mAi/Jx5eFwQH+9MZHywG5P6ezQaY21uyop7BjPt8wPct+oYvz00Ep+r5IO5vRACL+hyyLJMVkl1g41C2sg8u0G+3MPBklBPe21krtsB6u1k1a72wobzeWnjKaqVat65vb9BAa+uTH55LfG55UwN97zsa2UUVfHriSzuHtaj3XbCXi6PTOjJnoQCnv8tloE9HPFwsGo0pqFH/p6VR/n1wRFG19m5FhACL+h0NBqZradyiE4v0Ufm9flySYIAFxsi/Jz1tVhCPR1azZe3J1tjz/PX6VyemRhMkFvX6l1To1RzOruUgb5OjT7cDiYVADCqHfzvn+9OQiFJPNBOi7XtgZmJgqWzw5j08T6eXB/D6nuHNmmLDHKzZfncQcxdcZRF3/+7PPJC4AWdzqHkQh5eG4W5Pl/enRDdRqE+HnaXtHuzvSiqrOO/m07Rz8uBRZ24sNgUaYWVPLTmBKezyzj2wvW42hnmxQ8mFurTVpdDVkk1G45nMGewL90dukb0Xo+/iw3/nRzCs7/GsmJ/SrPpo6EB3Xh/1gAe/TGKJ9fH8PG/xCMvBF7Q6RxPK0aS4Njz11+xjTPG8uqW05TVKFkzc2i75PLbi79On+fJ9TGU16gY1MOpkbgDHEgqYHhgt8tOKX25OwmAB8Z2nei9IbN1DULe+0vbICTE077JcbfqPPJv/6mtI//czde+R77r/I8V/GuJSi+mp5ttlxP3HWdy2RSdzZJxQQR3b1o0rjRKtYY3/jjD/auPU6OrX9NUuYD0wioyi6svuzzB+dIa1h3LYMYgH7wcG+e4uwKSJPH27f1xsDbjsZ+i9L+Xprh/TAB3D/Plqz3JrD6cdgVn2TkIgRd0KrIsE5VR0mQbuc6ktFrJCxtjCe5ux0Njgzp7OoBWbO9Yfpiv96Uwb3gP+nk54OlgyU2hjT3eB3T59xGBlyfwX+5JQiPLPNRFo/d6nG3MeX/mABLyKnj7z7hmx0mSxCtTQpkQ7MbLm07xz9ncKzjLK48QeEGnklpYRUmVknDfruVRfvOPsxRU1PHejAGNWtR1BvsS8rnl432czSnjkzvCmT3YhxPpJdwzwq/J1NH+xALc7S0IdLVp4mrGkVdWw49H05k+0OuqsBde18uV+SP8+O5gKnvO5Tc7ztREwSd3hhPq6cDDa6M4mVlyBWd5Zen8/7mCfzVR6dp2vWFdSOD3JeSzLjKDxWMC6Od9eQuUl4taI7N0xznmfXsUF1tzNj8yiikDPFl5IBUrM5MmSwBrNDKHkgoZGehyWbbR5XuTUWlklozrGncwxvDszcH0crflyfUxFFbUNjvO2tyUFfMjcLYx597vjpFRdHXsa2grQuAFnUpUegk25ib0dLPr7KkAUFGr4tlfYglwteGxTiwQBtpG2/NXHmXpjgSmhXuxcclIAl1tyS+vZXN0NjMGeTe5bhF3vpyiyjpGXEb+vaCilh+OpDE1zJMe3S79LuBKY2lmwrI54ZRWKXn211haamjkZmfJqnsHU6fSMH/lUUqq6q7gTK8MQuAFnUp0RgkDfBy7zOahd7fFkV1azXsz+neqVzoytYhJH+/nSEoRb0/vxwczB+jtomuOpFGn1jB/pF+T59b730deRv2Zb/alUKfSXFXRez19POx5emJv/j6Ty0/HWu4WGuRmx9fzIsgoqmbx6uPUqppfoL0aEQIv6DSq69SczSnrMvn3tUfS+f5QGo5WZoR4dE5qJreshl4v/smMLw9hYabgt4dGMGeIrz7VUqtS88PhNMb1diXQtelNVwcSCwhwtWm0s7O8RskzG07q02LNUVRZx/eHUnX167vWxi5juXekPyODuvG/LWdIaaXq59CAbrw3sz9HU4p4cv1JNJqW25heTQiBF3Qap7JLUWnkLuGgqa5T8/xvsQAUVykZ/MYOnvv1JCfSi1u8zW8vEnLLeWD1cYa++Y++mciWR0Y12qS0JSaHgoo67m2mFrtSreFoShEjL3LPKNUaHlpzgnWRGfzf+hj9ezTFt/tTqFaqefgqjN7rUSgk3p+pXSB//KcolOrmf16AqWFePDMxmC0x2bz7V/wVmmXHIwRe0Gl0pQXWD/++8Ef9+V0DuSm0Oxujspn++UGu/3APX+1JIq+8poUrtB1ZljmSXMh93x3jho/2su30eUDbMSrpzVsaNeiQZZmVB1Lo6WbbbPmBmIwSKuvUBukZWZZ58bdT7Eso4PaB3iTnV/L9odQmzy+tUvLdwVRu6etBT/eusS5yqXg4WPHW9H7EZJby8T8JrY5/4LoA7hrqy5d7kvjhGvHIi52sgk4jKr0EX2frTi09C3AivZgV+1P0zwf7OXNLPw9euTWEP07msP54Jm/9Gce7f8UzrrcrMyN8Lit1odbI/HX6PF/tTSYmw9Cid+dQX96c1q/J846mFHE6u4y3pvdr1h2zP7EASYJhARcE/tOdiayLzODR8UE8cUMvCitrWbYjgalhXo12wH57IIWKWhUPj796o/eG3NLPgxmDvPlsVyJjerky2K/5toWSJPHqraHklNbw302n8HCwZEKfq7uOvIjgBZ1GVHpJp+ffa1Vqnt5wEnd7S56ZGGxwzM7SjDlDfPnlwRHs+M91LBztT0xmKfevPs71H+4BID63vKnLNkl1nZrVh1IZ/8FuHlpzgpwGTbsBvpo7qFlxB634OlmbMS3cq9kxBxML6evpoK+Y+MvxTD74+xzTw7144oZeSJLES5NDqFaqee8vww1BZTVKvj2Qwk2h7vTx6Bo7d9uDV24NxdvJmifWRVNWo2xxrKmJgk/uCCfE0/6a8MgLgRd0Cjml1ZwvqyG8k5swfPJPIol5Fbw5vR+2ls3f0Aa52fLczX049Ox4VtwToRfAr/YkM/XT/fxwOE1fAfNiCitq+ejvc4x8ZycvbTqNo7U5T97Yi7zyCz7tPU+N5abQ7s2+f3phFdvP5HLHEN9m3T1VdSqiMor13ZsOJBbwzC8nGRHYjbdv76+P+gNdbVkw0o/1xzMNBGzVgVTKa1Q8Mr5z7aHtja2FKR/NHkB2ibbDU2vYWJjy7fzBOo985FXtkRcCL+gUotK1wmJMw+qO4lRWKV/sSeL2gd6M6+1m1DmmJgom9HHny7sHAhDqaU+tSsOLG08x5I0dPPZTFAcSC9BoZFILKnlxYywj39nJsn8SCPdxZN3iYcwf0YP3t58DwNxEQewrN7bqNV91KBUTSWLu8B7NjjmaUoRSLTMqyIX489pF20BXW76cO6jRbtxHJvSkm405r2w+jSzLVNSq+GZ/Ctf3caOvV+du7uoIBvVw5uHxPfn1RBa/n8xudbybnSXfLRhMnUrN/JVHKa1qOfLvqogcvKBTiEovxtxU0WmpAKVaw9MbTuJsY85Lky+9quB9o/yZFu5FbFYp6yMz2RSdxaZoQwGZFeHNotEB+Haz5pkNJ9moOz6pnwfL5oS1WqWyvEbJumMZ3NLPo8mmFvUcTCrE3ESBr7M1dyw/jJW5CSsXDG60WAtgb2nG0xODeXrDSTZGZ5FTWkNptfKai94b8uj4IPaey+f5X2MZ6OuEZyvF03q627F8XgTzVhxl0epIVt83BAvTq6uOvIjgBZ1CVHoJ/bwcOq3Oy1d7kjiTU8brt/W9rA4/sqxdnOvr6cDY3q74N7H4mllczbZT5wl79W+9uP9vaiif3TXQqBLEG45nUlGratIaKcuyfgfmgcQCene348EfTlBareTb+YNbFLEZA70Z4O3AfzeeZumOBMb2dr2m+5aa6hqEqDQy//dzjFF+92ENPPJPXYUeeRHBC644dSoNsVmlzB3WfLqhIzmXW87H/yQyub9Hi3nv5jiWWsQzG04CcDi5ELVGZvm+ZBLzKvB0sOTFSX2YM8SX0molvxzP5MO/z3EwqVB//lM39Tb6Z1drZL47mMpAX0fCGoivLMvsTyzgw7/PcTKzlG2PjeZ0dhkAJgqJFfdEtJpqUSgkXr41lOmfHwS4pqP3evxcbHhlSihP/3KSr/clc78RHaqmhnmRVVLNu9vi8Xay4umLFuO7MkLgBVecuPNl1Ko0nZJ/V2tkntpwEltLU169NdTo82RZZs+5fD7flcTR1CL96+uPZ7L+eCZ9POy17eP6e2Cmi8otTRVU1qkaXeu9v+LZGJXFzAhvpoV7N9mso56dcXmkFVbx1E299a8dTCrgo7/PcSxVu4/Ax9mKuPMX3Dxv3NaXsUauKfRpUOe+2xVsg9iZzIzw5p+4XN7fHs+oni5Gdbx68LpAMoqq+Xx3El5OVtw1tHOCk7YiBF5wxbmwwHrl0wHf7k8hJqOEj+8Ip5sR/nuNzrP+2e5ETmWVNTlm9X1DGBVkWLkxt6yGJWtOEJmmFeGIHk6suGcwkgL+OJnDz5EZvLk1jne3xTMu2I2Zg7wZF+ym/3BoOF9PB0smhnbnaEoRH/19jkPJhXS3t9SXxr1jiC+P/BgFwINjA5kzpHGFyeb48Wi6/vvX/zjLN/dEGH3u1YokSbw9vT83Ld3LYz9F8/sjo1qtOyRJEq9NDeV8aTUvbTyFp4MV44KN+xDtTEQOXnDFiUovxt3eAo8r3N8zpaCS97fHc0OIO1P6e7Q4VqnWsD4yg+s/2sODa040Evfeul2eH84awOiergbifjCxgHHv79aL+51Dfflx8TAcrM2wtzTjjiG+/PbQSHb8Zwz3jfInKr2ExauPM/ytf3jjjzMk6Lz1Z7LLOJRcSF8vBxZ8d4xZXx0iMb+Cl6eEsPupsZgoJEwVEmaKC3/GTzeI9FujRqnmyz1JDA/oxjMTg9lxNrfFOurXEk425nwwawCJeRW8tfWsUeeYmij49M6BhHjas2TtCWIzSzt4lpePURG8JEnewDNABDAAsAL8ZVlOvWhccysQ4bIsR1/GPFdwgQYAACAASURBVAXXEPUdnC6nVnlb0WhkntlwEnNTBa/f1rfF9/7+UCq/nsgi66KNSPVsf2IMFqYKrntvd6P3+GxXIh/8fU7/2stTQpg/wq/J9wtys+O5W/rw5E292ROfz8+RGaw8kMrX+1II83EkWrfLdfuZXLrZmPPipD7cNbQHVuYm1CjVbDieib2VGW/oBOq5m4Pb9Dv9OTKDvPJals4JY1APJ9YdS+d/W06z7fExje4krkVG93Tl3pH+fHsghbHBbkZZZW0sTPn2nsFM+/wg9646xq8PjujSzVCM/VcMAmYBxcC+VsZ+Bwy/6OtcSycI/j0UVtSSVlh1xdMzPxxJ42hqES9NDsHdvvGdQ1mNUt9c+pOdiU2Ke4CLDbGv3EivJmq0FFXWseC7Y3pxV0iwcsFgFoz0b1V0zUwUXB/izvJ5ERx+fgLTwr304l7PO7f3596R/liZa1MJW2NzKK1WUlR5oYa5sXl30O7g/WJ3EoP9nBge0A0LUxNenBRCUn4lqw6mGn2dq52nJ/amt7sdT60/SUELDUIa4mav9cjXKNUs+O5Yl/bIGyvwe2VZdpdl+RZgfStjs2RZPnzR19W7FUzQrtQL15VcYM0oquLtP+MY3dOFmYO8DY4VVtTy/l/xjHx7ZyNRD3C14ea+WpdNf28HflsyErsmPOUn0ouZ9PE+fXrD28mKbY+PMXrzVD1nc8p44bdYfovK0r82wNsBO0tTFn4fyZj3drF0xzkyi6v4aIf2g8TVzoIhfs642FrQy934+jgbjmeSU1rDoxN66j+AJvRxY0wvV5btSDBa7K52LM1MWDonjLJqJc/+0nKDkIb0dLdj+dwI0gorWbw6ssvWkTdK4GVZbrnWpkBgJFHpJZgoJPpdod2Ssizz3K+xSGBQpCu7pJpXt5xm5Ds7+XRXIuU1F9wu/i42fD0vgpenhPJPXB6hnvasvncoDlaNxX3lgVRmfXmInFJtpcnBfk5sWjKyySi/ORJyy1my5gQ3L9vHwcRClowLxNJMwdjermx6eBTHXrieZXPC8Otmw7J/Ehj1zi4yirQfRl/ePZCUwkpGBHYzOj1Tp9Lw+a4kwn0dDapSSpLEf3V1at6/hkrmtkZ9g5AdZ3P58WjLDUIaMjywG+/PHMCRlCKe3nDyipSVbisd4aJ5UJKkpwA1cBh4WZbl1tI6gn8JURnF9PGw06caOpq1R9PZn1jAa7f1xdvJmpSCSr7cncSvUZko1Rf+ICVJu2kJ4Of7h5OQW86C744R6GrLD/cNbdQar/4DITbrwkLb7QO9eXN6X6N3OyblV/DxPwlsjsnG2syER8YHsXBUAP/E5VKj1HDvSO3GJkszE6aGeTE1zIu0wkqD3P+8FUcblQdujd+iMskqqeb1aY3XIoLcbJk/wo8VB1K4a2iPdu9JW1Wn4mBiIefyyrl3pH+nds1qyL0j/dkdn89rv59hWIAzAUZWC50a5kVmcTXv/aX1yD91U9fyyLe3wP8A/A5kAz2Ap4CdkiTdIMvy7qZOkCRpMbAYwNfXeHuX4OpDrZGJyShtsRpie7LheCYvbTzFqCAXBvo68vDaE2yNzaHhZkQLUwUzBnmzcHQA+xMLeGnjKY6kFPLk+hj8XWxYs3AoThf5w89klzHl0/0Grz17czD3jwkwKopOLajk450JbIzKwsLUhAeuC2TR6ACcbcyRZZkV+1MIcrNldE/Dmu+yLPNRgwXctQuHsv54JrFZpUZb9pRqDZ/uSqS/twNje7k2OebR63uyMTqLV7acZsMDwy9rMVyWZZLyK9gdn8/u+HyOphRRp9ZgZ2HKtHCvFksvXEkUCokPZg3gpqV7eXxdNL88OMLoheaHxgaSWVzFZ7uS8HK05s6hXUfH2lXgZVme2+DpPkmSNgGngNeBUc2csxxYDhAREdH17nEE7UZiXgUVtaorssC6+lAqL206jZWZCSqNhkkfGwqyo7UZ84b7MW94D309+v2J2l6mD6+NoqebLT8sHIrzReL+87EMXtp0ioZ341/NHWTUjtiMoio+2ZnALyeyMDORWDg6gMVjAgzq4R9LLeZ0dhlvTmtc8/3z3Un6Ugc/3z+cIf7ObW6svSk6m4yial6eHNqscNtbmvHUTb155pdYNkVnc1sbP5Dro/Td5/LYFZdvsLbhbm/B/BH+3DnEt8mG4Z2Ju70lb0/vxwM/nGDpjnNGR+Naj3xfsktqeElXR76reOQ7dKOTLMvlkiT9AdzXke8juDqo7+DU0QusX+1J4q0/tbXOq5VqDidf2Hnq42zFwlEBzIzw1jexrueEzrdub2nKmkVDDYS3uk7NS5tOseF4psE5C0f5tyruWSXVfLozkfWRGSgUEvOG9+DBsYG42TV283y7PwXHJmq+b4zK4j1dXjzQ1YbBfm3/HarUGj7blUiIhz0T+rQsQDMH+bDmSDpv/XmWG0LcsbFoXiqai9Ib0sfDnkWj/Znc37PT6g8Zw8S+Hswc5M3nu5O4rpcbQ/ybbxDSEFMTBZ/dNZDZXx1iydoT/Hz/8C5RlfNK7GSVABGZC4hKL8HR2gy/bh3jG9ZoZG5etq/JJhz9vR1YPCaAiaHdmyzwFZVerHevrF00zEB8k/IrWLLmBPG55ViYKqhVaXCyNqO4SkmIZ/PVMM+X1vD57kR+0i3c3TnUl4fGBtG9mQ1eGUVVbD9znvuvCzRYoziYVMBTG2KwtzSlrEbFXUN7XFLa5PeTOdo1iLsHtXq+QiHx8pRQbv/iIJ/tSmxUf6VhlL47Pp/MYm2Ubm6iMBD3cb1dWTQ6gOFtWATubF6+NZSjqUU8sS6aPx8f3WQ1zqaw1dWRn/75QRZ8d4zfHhqBt1PneuQ7VOAlSbIHJgFHOvJ9BFcHURnFhPs4tvsfulKtYVN0Nk+uj2l0bFxvVxaPCWRYgHOz73sys4R53x7VP2/ok//9ZDbPbDiJJEm42lqQV17LlAGePDwuiJuW7m3yennlNXyxO4k1R9LRaGRmDfZhybggvFopT7vqYCqSpI3w6zmXW879q4/j182G3t3t+PtMLrcP9G7hKk2j1sh8sjOB4O523BhiXBu6QT2cmBbuxTf7Upg92AelWmZ3fJ5BlG5tbsKIQBcG+jqRkFfB2ZwyzE0VTA/34r5R/ldlX1dtg5AwZn55iJc3neaj2WFGn+tub8nKBYO5/YuDLFh5jA0PjmjSfXWlMFrgJUmaoft2kO7xZkmS8oF8WZb3SJL0JNAb2MWFRdYnge7AXe03ZcHVSFmNkoS8Cib392y3a9Yo1ayPzOCL3Ulkl15oiC1JMD3cm8VjAujdvWWBOZVVyt3fHMHR2oy5w3rwuW6zU61KzZt/nGXVoTT8XWyoU2nIKqnm8et78tiEnqQ30eWnoKKWr/YksfpwGkq1zIyB3jw8PsionY4VtapGNd/zympYsPIYlmYmfHbXQKZ9doDJ/T0vKXf956kckvIr+ezOgSgUxn3AVtWpiPBz4reoLAPnTk83W+4Z0YNRPV3JK6vh+0NpxGaV4mxjzqMTejJ3WI8WC6hdDQz0deKR8UEs3ZHAuGA3bh1g/P/bXu52fDV3EPd8e5T7V0ey6t7OqyPflgj+4g1On+se9wBjgXhgmu7LASgDDgD3ybJ8FMG/mpMZpchy+xQYK69RsuZIOt/sS6agos7g2P1jAlgw0r/ZNEhDzmSXcfeKI9hZmvHjomHsitduVMoqqWbhqlPEZJYyppcr586XU1xVxyd3hDOliT/0oso6lu9NZtXBVGpVam4L9+LR8T3xc2m5S1NDNkRmUF6r4t6RfgBU1qq4d9Uxiqvq+Pn+4USmFlNZp74kh4ZGI/PJP4n0dLPVb9xqCm0uvZLd8XnsOZfPkWTDXPrE0O68OLkP9lZm/HQ0ned+OUl2aQ0Brja8Oa0f0wd6dRnbY3vw8Lgg9pzL54XfYhnUw6nVO7CGjAh04d0Z/XliXQzPbDjJR7PDOiVFZbTAy7Lc4uxkWd4CbLnsGQmuSaLSi5EkLquhRFFlHd8dSOHrfSlUKw13DjpYmbHvmXFG50vjzpdx1zeHsTYz4cdFwwxypbd9dgA7C1PuGOLDlpgcrMxNWHf/cIN67PV8sP0cL208RZVSza0DPHl0Qk8CjfRQ16PR1XwP93Uk3NcJlVrDw2tPcDannG/mRRDqac8zv5wkuLsdAy/hA3L7mfPE55azbE5Yo+i9qk7FoaRCdsfnsys+T59LD3KzZd7wHowLdqO/twOTP9nPnnP5uO+14JcTWVTUqhge0I3XbuvLuN5uRt8VXE3UNwi5Zdk+/rMumrWLhmHShp9zWrg3WcXVvL/9HN5O1jzZhkJw7YUoFyy4IkRllBDkamu0ADfkfGkNX+9LZsX+lCaPP39LMIvHtN64oZ6E3HLu+voI5qYK1i4ahm83a1RqDe9t0zpv3O0tmBjandWH0wjubs+K+REGfu2yGiXLdiQA2mh/Un8PHp/Q85Lzzbvi80gtrOL/buyNLMu8tOk0u+LzeXNaP8YFuxGTUcLp7DJem9q8tbE5ZFlm2T+JBLjYMLm/Z7NRen0u/YHrAhnb29XgAy86o4TcshpqlBpWHUrjtjBPFo4O6BIukY6mRzcbXr41lKc3aBuEPGBEg5CGLBkXRGZxNZ/uSsTLyYo72lDKuT0QAi/ocGRZJiq9mBuMXNyrJ7Wgkq/2JjXaPj7A24EYXanWt6f3a1P988S8Cu74+ggKhcSPi4bh52JDXlkNj/wYRZlud2pwd3tWHUrjxhB3ls4J09spK2pVfHcgheV7k/VjF48J4PlbLr2nK8C3B1LwcLBkYt/ufLEniR+PpvPQ2EB9OmbtkXSszEyYegkbxHaczeNsThmT+nnwyubT7D6Xpy9zUB+lj+3txmB/J4M8sVojs+NsLt/sS+ZYajF2lqbUKLXpmpcmhxhVS/9aYeYgb3bF5fHB9nhGBbm06YNNkiReu60vOaU1vLjxFN0dLNtco+hyEAIv6HDSCqsorlIa7X8/m1PGF7uT2Bxj2Lx6cn8P5gz25f3t8ZgoJD6cNYCpYcaLXkpBJXd+fRiAHxcNI8DVlkNJhTzyYxSVtSoCXW1Iyq9kz7l8HhwbyFM39kahkKisVfH9oTSW702iuErJ9X3cmD7Qm4fWnCC4lUXc1og7X8aBxEKemRjM1tgc3t0Wz9QwT568UXs7X1ajZHNMNrcO8DT67keWZZILKtkVl8frf2hLCf8Rm4OVmQkjg7px/5hAruvl2uTib1Wdig3HM/l2fwqphVV4OVrx0uQQZg/24XxpNROX7uP97fG8Nb3/Zf3cVxOSJPHmtH6cSC/msZ+i+P2R0W0qtWGm88jP+vIQS9ZcWY+8EHhBh1NfQbKpHHZDTqQX89nORP6JyzN4ff4IP+4bpS2VO3fFUZLyKvjiroHc2IZ+qmmFldyx/DBqjcyPi4cR4GKjrd2+PR5/Fxtev60vD/xwHNDWVb//ukCq69T8cDiNL/ckUVhZx9jerjxxfS8G+DiSVljZxt9C06zcn4qlmQJ/Fxse/TGKof7OvDujvz6nvTEqi2qlmruGtXyX0jCX3jBKB+3Grc/vGtQoSm9IXlkNqw6lsuZIOiVVSsJ8HPnspmBuCnXX7xsIcrPjnhF+fKurU/NvSNHU42Rjzgczw7h7xRHe3HqW127r26bzbS1MWblgMNM+O8CC746xccnINi3aXipC4AUdTlR6MdbmJk1WWJRlmQOJhXz8T4JBr1MzE4lHx/fk7mE9cLIx53xpDbO+OkR2STXf3BPBmGbqqDRFRlEVdyw/TK1KzY+Lh+Fqa8F9q46xKz6fWwd4Mrm/h4GHflJ/D77dn8Lnu5MoqKhldE8XHr++F4N6tO8O3MKKWn6LziLcx5GnN8Tg282a5XMj9CIsyzJrj6TT18ue/t6GH471Ubp292geR1KKqFNp9FH64jGBLNuRgIWpgt1PjW22rkrc+TK+3pvC5pgsVBqZm0K6s3C0P4N6NN2Q5dEJPdkYlcUrm0+z/jLr1FxtjOrpwn2j/FmxP4XxwW5tLkeg9cgPYcYXB1mw8ijrH+h4j7wQeEGHE5VRwgBvRwMHgkYj8/fZXD7YHs+53Ar96+72Fjw2oZeB5S6jqIo7vzlMcaWS7+8davT2cYDM4irmLD9MlVLN2oXDqFFqmPzJfvLLa3nttr4oJHhwzQkCXW24fZALKw+kct17u1FrZIYFOPP5XQPb9H5tYe2RdOpUGo6kFOFia8HK+YMNPO4n0kuIO1/Om9P6AdpyCYeSC9gVZxilB7raMG+YYS5977l8CipqeXNav0biLssyexMK+GZfMvsSCrAyM+HOIb7cO8qfHt1atnY6WGnr1Dz7ayybY7LblCK7Fnjqpt4cSNTuLN72+BiDchbG0Lu7ziO/8igPrD7OqnuHdGjpBiHwgg6lRqnmTHYZi8cEANp6KFtOZvP672cpbNCNqJ+XAw+PD+KGPu4GlrvEvAru/uYI1Uo1axYObZPNMrukmju+Pkx5jZK1i4ZxNKWQN7aexd3eknX3D2NzTDYrD6QyuqcLo3u68OZWrYvG19maN6b1ZURg2wp5tYU6lYav9iYDYGVmwsr5gxvlxNccSQO0kf7cFUeajNLHNpFL1zpnEvB0sOT2QRcEuFalZlN0Niv2pRCfW46bnQVPT+zNnUN8cbQ2LKrWEjMjdHVqtsZxQ4h7o5o+1zKWZiYsmxPOlE/388yGk3xzT0Sb72JGBLnwzu39+c/PMTzzy0k+nDWgw+6E/j3/MoJO4VRWKSqNTIinPT8cTuPFjacMjk8IduPBsYFE+DWOks9klzF3xREkCX5aPIw+Hs3XfbmY86U13PH1YUoqlXw1dxBf7E7ij9gcru/jxstTQnlp0yl2x+djZ2FKUl4F+xIK9Oeuu39Yk4XA2pNN0VovOcBnd4Xr667XR+mborPZpKsc+cHf5wh0tWHusB6M7e3KEH/nFndGHkoq5HhaMa9NDcXC1ITiyjp+OJzGqkNpFFTUEtzdjg9mDmDKgEsr/GWikHjl1hBu/+IQn+9K6hR/d2fSu7sdz04M5n+/n2HNkXTuHtaj9ZMuYvpArUf+g7/P4e1kxf/d2DG/Q6krdSGJiIiQIyMjO3sagmbYFZdnEHW3RGm1ktjMEo6lFjfZ39TMRGLR6IBmGytEZxTzw+F0AJaMC8TfxZZqpZqU/ErSCisJcLWhd/emBT+vvIZ3t2krLw72c+JYqrZKZP1Ozo93JjY6Z3J/DzKKq4nJKOHZm4NbvfVOL6zk452JjAzqxrTwttWGkWWZpzacBLQ7e0cEdmN3fD6ns8sajTU3VfDQ2MA2Fa2qX09YNNqfHw6nG2wKmxbu1abuT0P9nZsttfD4T1FsPXWeHU9ch28HFZDrqmg0MvesPMqx1CJ+f2Q0QW7Nb26TZZlalYaqOjVVdSrdo5qqWhWv/XGWszllvHN7P2YPvjSPvCRJx2VZjmjymBB4gbFM//wAJ9JLWh8ouGa4e5gvr9/Wr8lj50trGP/BbkYFubB8XpP6clWh0chUK9UXCXEDQa7/vlb7PLWwUl+B9Po+btSpZarrVFTWqqlWqqmsVVFdp6ayTmXQZKYp+ns7sPnhJltmtEpLAi9SNAKj+eLuQdSpWm7Pm1tWw4wvDzV57Mkbe3HrAC8kSdvyLrmgguT8SpLzK0guqCQ5v1KftmiIk7UZAa62BLjYEOBqi6+zNdEZxWyOySa3TNsc2tnGnJFBLmzReefNTCSUahkfZyuWzg7j2V9iSci7sJj7wi19uDHUHUWDSPaHw2l8tTeZXx8agWsrEXxaYRV3rzjCE9drF4RbQpYhpVC7e3TlgVSDY73cbRnX283Al34kpYgn18fw6PggZkb4tHjtelQama2xOfqa8aCN1ucO79Hqz1KrUrP9TC4bIjNJLtDaP4cHdGPWYG9u7uvR7HndHSxZMi6I9/6KZ39CAaN6dtyaRUNUag1VynqhbUKADcRY+1hZp9aKb51aL7oGjzpRbguWZhfSWzvO5hHm44i1uQmejuZYm5vovky1jxYmWJuZYG2hfW5jbopVg0dPx45JCQqBFxhNwzK6F5NWWMm72+L5Izan2THvbz/H+9vPNXpdIYGPs7WBuH9250AG+znhamfRZDphUn8PXpgUQlZJNb/HZLPqYKpe3AGUapm7hvoyPLAbt39x4QPnjWl9uWOwb5O1U7x1AuvjZN1qNUSN7s7Xx9mqyRRGdZ2aw8mF7NKV1724+mQvd1s2PzyqyeJc72+Px87SlAfHBrW6oaa8Rsm6YxmsPJCqT4UFuNjwx6Mtb8aRZZlTWWWsP57BpuhsSquVeDla8eiEnswc5G1UBUyA+0b5s+5YBq9uOc3Wx0brHTuyLFOn1mhFWKnWR7YtRceVtc2IbwNRrqpTtxpkNESSwNrMBCtzU2wsTLAyM8HGwhRbC1Pc7CwuCLCBGJtibWaiHW9uio25iV6M649bmZnoXWHPbDjJz8czeO7mYIYGGN8b90ogBF5wWcSfL+fhtScMomNjuWuoL/NH+OHbzZqtsTk8uf4k/b0d+G7BEKP9wV6OVswe7MPG6GyDksEAa46ks+aINo8vSRD32sQOK9sqyzIp9b70c/kcTi7UO15GBHZjfLAb3x1M1Y//bsGQJsW9qLKOP2PPc+dQ3xYFOqukmpX7U/jpWAYVtSqG+juTVVKNk7UZvz86qtlzCytq2RidzfrIDOLOl2NuqmBsL1cm9fcgzMeRaqWa/Ipa0gqrmoyIDSNerSjXf3j1fOFP3O0t9OeoW8tLNMBEIekj2wsRrylONuZ4OTUQ34sEuaFoNxTh+u8tzRQd7tX/75QQjqQU8p+fY9j62OhOrf9+MULgBUahVGtIK6zSp1N+O5HVZOekem4IcefvM7kArF00lODu9jjbmFOn0rDheCaf7kxgzZF0zuWW093Bit9PZjPMvxvf3BPRYnu4iymtUjL7q8MGc3GxtaCgotZgnLmJgsd/iubWAZ6MC3Zrt7K2u+LzickoYVeDKD3A1Ya7h/ZgXLArg/2cKatRMv3zg1ibm1BVp2Zyfw88m9nFuOF4BnVqTaOywGqNTFWdiiPJRXy8M4GTulo8jtZmPDQ2kIpaFUdStBvFvtyTbJCOKK9RsuNsXqP3Aq1dc/uZXLbr/q1aw9xUgc1FQjsswFnfFrGflyM+zlZNinHDx4ujY3OTjhfijsJG1yBkxpeH+O+mUyybE97ZU9IjBF6gR5ZlCirqGuTEdTnygkrSi6pajMgem9CTZf8k0N/bgd8eGomJQmLmlwdRa2QDP7m5qYI7h/py+yAvXQPr04DW5fLg2MC2iXu1kgkf7mkk5vXPLc0UfHH3IOwsTNkSk80fsTn8eeo8tham3BjqzpQBnowKcml2l2dTpOhqvHx/KBWALTHZWJopGBnowqLR/owIcsHFxkKfTojOKGHOcm39Gyvdh4qJQuKzXYmN0hCVtSp26so0LFlzgqo6bV64okbVqMdpPSVVSn2TEoDiKiUf/5Og/zBpij4e9oT5OOqE+kJeuGlBvhAdW5uZNNnuELQVOicu24ernQUvTwk1+vd5rRDu68Sj43vy0Y5zjA926zIbwITA/wupUWodAPoFzvxKknSCXl5zIQ9ubqrAv5sNvd3tkEC/AFfPI+ODWDg6AAcrMx5acxxzEwUfzhqAiUJCqdZwMrO0WY+wuYmC4iql/rmjtRnzvj3KqCAXnrih9bIA5TVKBry63eA1DwdLenSz5mhKEV5OVnx7z2B9Cd8IP2demhzCoeRCNus85r+e0DogBvs5MaGPu74p+KboLGwsTKmsVVFUWcfOuDzizjd/t2Jtbsqx1CL2JuSjVDf/IVi/iFfvb1dIXFhsszAlpcHvN8jNFkmCP0+dp6HRLcDVhgUj/XX5Y60QJ+dX8NSGk8wf4cdD4wL552weG45ncjytGBOFxLjerswY5MP4YLcO2zXZ092Oe4b7sfJgCncN9f1X1ampZ8m4QPYm5PPixlMM6uHU6f1YQdgkr1lkWSa3rJbk/Aq9eCfnV5KUX0FWSbWBaHS3tyTA1Ub75WJLgKsNga62uNtb8nNkRqPNSa/f1peZEd76fPafsTk8uOYET93UmyXjggCIzSxlyqf7+fTO8EZt+mRZ5u1tcXy1J5npA7149/b+KNUya46k8cXuC4W9HtPVWK+qU+mtaVV1KnLLalmy9oTBNeubYNczxM8ZCzNFg0W6CymLKiNsay3haG2GlZkJObqc/6wI7yaj3ze3nqWsRsWkfh4Eutny8T8JPDMxmNmDfbA2N8HC1DAtsWTNCQ4kFbDl4VH8HJnBD4fTKK5SMsDHkUWj/ZttGL74+0i2n8nlplB39p4roFqpJtDVhlkRPkwb6NXhm7bqKa1WMv793QS42vDz/f+uOjX1pBdWccvH+wjxtOfHNjYIuVSED/5fRE5pNQ+vjSIup4zKZm7R67EwVeDXzQZrC8N8dJ1K0+SmG9B2ZGr4f7akSqmPPPt5OWBqoj0YpfPLu9ha4ON8Id+skSEm44KXvp+XA7FZpcb/gJ1EiIc9Fg1scemFVfpNX021IYxqsF8g3NdR/7zh76ghBRW1BhUgGxLm40hzWpmcX0lptbLR682dY6ZQ8N8pIR0WYf94NJ3nfo1l2ZywLpOmaA80GpmKOhVl1UpKq5WUVasoq1FeeF6jPVZWo+RIchFZJdW8Ma0vdw1t+y7XtiJ88P8iTBSS7va97ZUPq+rUHE8rbvS6naVps6V+Gwq5YxPNoAsqaunjoU2TqDUyB5MKDY5fjri72lmg0cgGu2tHX4YXO6WgUt+y7mLO5JTh72KDt5P2w8rW0lT/vrYXrRs0LHswKsiFhLwL6Z2mfkf7Ewu4OM7ycbbCr5XCX6AtzlYv8BE9nFp03pRUKYlKLyIpv6LDBH5WhA8/HE7rcnVqZFm7ialemLUirdSJtMrguYGA1ygprVJSXqtq9G/UXH6FSwAAIABJREFUEEkCOwtT7K3McLAyw8fZCl8jraYdSdf47QvaDTc7S764e1CbzimpquOhNScMxD3A1YaV8we3WF1wZ1wu+xIKeHR8EP+5qJaGLMvMXXGU2KxSls0Jx9bClCfWRQPaRT6NRiY+t5yebrY8cUMvJoZ2b9KbXlaj5Jt9KXz8T4L+tfrm16eySlm4KhJrcxOWzg5rU314Y8grr+GPkzlsicnmRHoJKQWVOFqbMaW/J/28HZj55SE+nDWA6QMvlCo4mlLEkeQiwnwc+f4+rRXy2V9OsjE6i0PPTsDJRlvUq1alZnN0Niv2pxgIR9RLN+jHtDdrjqQR+1sp4T7tW/a4ISYKiVdvDWXGl4f4YndSu9ZYqVWpW4ycL46syy463tL6CIC1uQn2llqBtrcypbu9Jb3d7bC3MsPeUive2u+1xy+MNcPWwvSKpGPaihD4fzE5pdXM+uqQQWpgbG9XPpwVhnMrIlNWo+T5X0/Ry92WJeODGh2XJImXp4Qwcdk+3tx6lsKKWnbF5wPajk0BrjZ8fEc4k/p5tPiHIWvg9EVR/qM/RfHk+hhqVRo8HSxZ/8BwQj3bPyJ1s7NkwUh/Foz0J6Ooii0ns9kSk8P/fj+jH/PTsQzGB7vhaG1OYl4Fi76PxNvZiuXzBmFpZkJhRS2/RmUxY5A3TjbmlFTVseZIOt8dTCW/XFv4a8oAT7bEZPPxHeEdJu6gvdvqZmNukDLrCCL8nJka5slXe5OZFeGj3zSlUmsor1Hpo+aLI+nWUh/1LQObw9xEoRNhUxyszHC0Nse3mw32lqZ6IW4o4Fqh1j63szRtk5vqakEI/L+QhNxybvhor8Frc4f14Plb+hjdiuytrWfJK6/hy7kjm9081NPdjhkDvVkXeaGnao9u1jw2oSdTw7xajXhOZpbw0JoT5JbV8Oqtocwb3oPiKiUDX/ubWt1uxkA3W2yuQBrAx9mah8YG8dDYIBJyy/l0VyKborM5mlLE4Dd2EOJhT0xmKdbmJqxaMERffvfHo9qa7+N7u/HfTadYH5lJtVLNmF6ufDjLn1FBLty/+jjONubcFNq2nrVtJSq9mHBfx0te/NRoZCrrVAZCXHpRpFyqE+Ok/ArqVBpGv7sLTwdLympUTZahaIiJQroQKeuEuLuDpYEQXxxJOzQQ6vba23AtIQT+X8Th5EK9J7ueZ28OZtHogDbdXu5PKODHoxncPyag2dy8LMtsjT1vIO7v3t6f6QO9mvVSNzz3h8NpvPb7WVztLFj/wAjCfBypVal5/Q9t9DwisBuBrrb8HJnBhA/3cPtALx4Z39PoLfaXQ093O/5zQy82RWezYKQfSrVGX/myqk7N29viuHWAJyODXPSlGRatjsRMoWBqmCcLRwfQW9fLNbeshn/i8lg42r/DdtmCdkNYUn4lN/f1ILespunIubkctO55eY2yVfdRfR76/9k77/CoyvQN32cmbdJm0jPpjVSSEErovag0G6KAFQv2XXtbV1131d/ay7qiiA1QQVABdRGQ3iGQAAkJkN77pJeZOb8/piRDCgkEgnDu6+Ii00vgOd95vvd9XuczujnnDvU3r6xNguxsZ4XSvu2yg438iqy8uZBIAn8FsDa5kEe/PWxx3YfzEpgZp+71f6j6Zi3Prkkh2N2Bx6aGd3qf3afLefGnY5wus6ybt7ftulHGRF2zlufWHGVdciETjXaRi4MNFXXNLPrmEAdzqnhsSjiPTg5DEAQemRTGf7edZvm+XNYkFXDTUH8enhR2UeZdgqG6ZsPxYgAWjQuhvkXL2iOF/JJimcmzaFwoC8cEdShZ/P5AHjq9yLweRsW2aPXdrpw7W1nXNraaexg+2nKKj7Z0jEs2obCWWwixp5MdAzydOqysz7Q4nO2scbSz9KGbWnVMfXcbCms5z0+PPOvvXqLvkQT+MkUURT7Zlsn//e+ExfWr7h/JsE6Ga/SUNzekU1DdyMpFIzucEu/PquSdjenmtnWAT28bwuQoL2Z/tJPXfkljcqRXlzZQenEtDyw/RHZ5PU9dFcED40ORyQTSi2u5+6sDlNU2d6ir93S246VZMSwaF8rHW0/x7f5cVh/K55ZEfx6cEIa38sLWgL+yLpW6Zi3/uDaG6xN8+f5AHvY2VtQ0WdoRqw7mUd+sZUacmnAvJ+qatFQ2tPDORsMKf19WBb+nFnfpQWt66ENbywWLVbJSYY2/i8Is8A9PDEOtsuu4klZY42Rn3aeNUHbWcv42I5pF3xxi2d4c7hwd3GfPLdEzpDr4ywy9XuTFn4+ZQ7YArGQCvz82rsvhGj2hVadnXXIhj69M5s5RQbw8u60d/VBOFe9tyrAoD7SSCXxz93BGhhrS9fZnVTJ38R4enTyAxztZ+a9Jyuf5H4/iaGvNh/MSzI/bcqKUR749jMJGzme3D+3SEjJRUN3If7acYuWBPGQygQXDA3hwQthZ0yF7S05FPePf3AoYShW1OrHHw1C6QyZgUalhFuL2lzu73SjmZzZPmbhj6X5Kapr431/Hnfd77A2maqqU/Gq2PjXxrJv3Er1HqoO/AsirbGDVoXyLcsIQdwdW3T8Stx4MBtbpRYprmsirbCC/qrHt76oGCqoaLaYyPWUc0ZacV827mzLYml6Gm4MN84cH8NvRInR6ka8WJpIQ0FaOlxjsyux4HxZvO83coX7mNu6mVh2vrDvOt/vzGB7syofzEvB0tkMURZbuyuZfv6QSpXZmyR1DUSvPbrv4qhS8dn0s1w3yZeGXB/hiVzaZZfV8tTCxx99lT6hvbmsiM2XSm3C0tTJvKMb6KvFyNqyY5TLDgS67wjI6eHKkJ3eMCiIhQIWDjVWn5aLng14vciSvmumxfVtG2hPaV1O99Xu6eYC4xMVBEvg/MY0tOv53vIiVB/LZk1lh0bm49uHRxPm1rXb1epGyumbyqxrIq2w0/51XZRDywupGtO120AQBvJzs8HdVMNDX2Szwz14TSVZ5Pe9tymBTWiku9tY8e00kicGu3Pf1IWSCwPL7RhDt03Gc3nPTI9mYWsJrv6bx8YIh5FTU88CyJFKLanhwQiiPTw3HSi6jVafn7z8f49v9eVwV48W7Nw/qccPMieIaPt2WydrkQkRgVrwPf5ncsYzzXNHrRf44UcqLP7fFN4wIceXRyQOIVjvjaGtFcU0T4/69hXvHhfDcNVGdPs/xQg0zPtgJwB/ppWw+UUq02pnZg3yYFe/Tp3sIWRWGbtcLWf/eHQO8nLh9ZCBf7s5mwfCAC1LSKtE5ksD/yRBFw2ps5cF81icXUtusJcDVniemhjMx0pP1KUV8su00n+/MwsHWivyqRvIrG8ivbuwwKMEUIxDvr2JmnBo/F3v8XRX4udjjo7LD1kqOVqdnxOubzY85nFvFG7+dQKmw5qmrIrhjVBCZZXXcvnQ/tlYylt8zssv5lGqlggcnhPL2xgxeXnuc1YfykckElt45lEmRhhLB6oYWHliWxJ7MCh6cEMqT0yLOuqIVRZE9pytYvD2TbRllKKzl3DoikLvHBPdZVU1ji47VSfks3ZllEbr2yuwY7hgVZHHfb/bkIAgCt4+0vL49G44VIwiw/amJ2FjJWJ9SxNrkQt747QRv/HaCIYEuzI73YXqs+rztJVO38aBOIhUuFn+dYqg6emVtKt8vGiFVy1wkJA/+T0JZbTNf7s7iP1tOW1zvYCNHrVJQWN3YIR7Wxd7aLNr+Lvb4uSjwc7XH30WBr8r+rDXvoijyzOoUVh7MN1/nZGvF3WODWTgmGGc7aw5kV3LXFwdQ2Vuz4p4RZx2+XNesZeBLGwAY6OvMJ7cOMds1mWV13P3VQQqqGnnjxliLDtHO0Or0/HqsmE+3n+ZYQQ3ujjbcOSqIW0cEmuvQz5ey2ma+2ZPNN8bgrzg/JfeMDSFa7cyUd7Z16GStb9Yy8vXNjA334D/zB3f5vkf/3x9EqZ358i5L6yi3wtBQtfZIIekltcgEGBXqzux4H64a6H1OwyRe+PEoa48UkvzStD63f3rDin25PP/jUXMnskTfcN4evCAIfsAzwFAgHlAAwaIoZp9xPzvgVeBWQAUcAZ4RRdGyq0aiU+qatRYWSlZ5PV/vyeny/k62VmbBHjfAA39XBccKalidlM8P949k6HlUywA8/UMKqw61ifsjk8K4Z0wISmOeyo6TZdz79UF8VAqW3zP8rB65KQjNxHWDfM3ivutUOQ8sO4S1XMaKe4d3+94bWrR8fyCPz3dmkV/VSIi7A6/fEMv1Cb591uxysqSWJTuy+PFIAa06PZMjvbh3bDCJwa4IgkBORX2nj1uTlE9Nk5aF3VSMbD5RSklNM69e27E0MsDNnocmhvHQxDDSi2tZl1zI2uRCnl6dwt9+Osb4CA9mxfswJcqzx7bV4dxqQ0hcP7fS3zzMn+X7cnjt1zQm9+L9S5w7Pf2Gw4C5wCFgBzCti/t9DswAngIygYeADYIgjBRF8ch5vtc/PY0tOgqq23ngVY0Wgt4+7rYz5iX6Mz7cw7Aqd7E3C217/nesiNVJ+b0anHEm2eX1TH13m0V2x5kZKb8fL+bhFYcJ8XBg2T3DcT/LRu72jDL++v0Rmlt1fDgvge8O5PLhH6e4cbAfvxwt4qW1xwn1cODzO4Z1aauU1Tbz1W7DalrT2MqQQBdenBnN1CivPhEvURTZdaqCJTsz2Zpehp21jLlD/Vg4OrhHFUh6vcgXu7KJ91cxuBs7ZPm+XLyd7ZgU6dnt80V4OxHhHcET08JJztew9kgh61MK2ZhagsJaztRow9CS8eEeXZY3NrRoOVFcY45x7k/kMoGXZsUwd/EePtl6ukN+kUTf01MV2C6KoheAIAj30InAC4IQD8wHFoqi+IXxum3AceAfwOw+eceXMM1aHYXVTZYbme1EvMMYOSuZwTZxsSfY3YHDeVUWuTBXxXgxd6hB1C9Gk0heZQMf/nHSwpKBjuL+85ECHl+ZzEBfJV/dNaxbO0SnF3l/80k+/OMk4Z5OfHzrYEI9HInwdmLau9tJeHUjABMjPPhgXgJOdh0PWqfL6liyI5PVSYbV9NQoLxaND2FI4PmdoZho0RpKQJfszCKtqAZ3R1uemBrOghGBvSrr25ZRRmZ5Pe/fMqhLjzmvsoEdJ8t4dNKAHv9OBUFgkL+KQf4qXpgRxf6sStYmF/LbMYNv72xnxTUD1cwe5MOIEDeLZqOUfA16sfNI4/7AXE21PZOb2uXUSFwYeiTwoij2ZIz5bKAV+L7d47SCIHwHPCsIgq0ois1dPvpPgFanp0jTZKg86UTAS2qbLJIBrWQCPioF/q4KpkR54ueiwN/V4IX7u9jj5mjL3swKVh3M47djxTRr9UR6OzFniB/XJ/j2qLyxLyiobuSjP06x6mCeRSUNwH8XDLYQ9+8P5PLsmqMMC3Jl6Z3DOkTltqe8rpm/fneEnafKmTPEj1evHWj2/ds3II0KdWPJHcM6xCUcyqlk8bZMNqaVYC2XceNgP+4ZG0zoedTzt8cU/PXV7mxKa5uJ8HLi33PiuHaQzznFBizdlYWXsy3TY9Vd3ufb/bkIwC2J/uf0nuUygZGhbowMdeMf18aw82Q5a5MNK/vvD+bh7mjLzDg1s+J9GNwuh35QP1XQdEb7aqreJp9K9I5eb7IaV/CfcYYHbxTyBFEUI864/1wMoj9QFMXj3T33pbbJWlrTxLubMsw54UWaprNOirezlpk3NL2Vdsi6WMmtOpTfoaolzk/JQF8l52M2fH+go0hLGGrNTQeV3MoGi6YsMOSvjx3g0ePv3vQc9jZyrk/w5WRJHfuzDR28C4Z3HjvQqtObz466us+50tSqZ0t6KZVdNFvNHx5wXv+u+hpTI16Iu4O5qa2vmBDhydToCxvcdilxsRqdXDFNT7akst3tHRAE4T7gPoCAgL79R3++FNc0sT2jnGatoTrFpRPPuzOqGlqoamjpdpjFmeIOhtPp/KpGzsdOlsS9czYbh1l3hrujDY0tOn43Zsr0hPI6g5A2tOjYcLzYfFkuE8zZNF09BujyPueDTDB8lpombYd/XyvadTa7O/Z/N6mpGSyzvJ7KhhasOvlHX9+sM8+x7SnWcgEHW6srSuC7oy8FXgA6U5du5UoUxU+BT8Gwgu/D93PexPmp2PXspAv2/DkV9WxKK2VTagn7syvR6UVkgsCUKE+mRHkxOsy9x/G9Jv53rIj7lxnmlb44M5q7x5w9/0Or07Pom0NsSS9lZKgbu05V8O7N8Vyf4Icoiry5IZ2Pt57mukE+vHVTfJfesaaxlSdXJbMxtYRrBnrzzNWRbEorYdXBfNJLLIdWb35iPKEejlTUNTPkn5vM1w/0dea+caFMH9j5/NHeotXp+WxHVodMnumx3jw4IeycJxuZogremRvPxAhPRry+mRsG+/H6DV13ai5Yspfs8ga2Pz3xogyHKKhuZPQbfxDh5YTS3pr9WYa1llqpYFa8mplxPvhcpFC2M2nR6vlqdzb/+jWNIDcHpkZ7UVhtOEsurDY03p0p7oIAnk62qJUKfFR2+CgVqFUKfJR2+KgUqFV2uDvY9nu10KVEXwp8JdDZEtyl3e0S7Qh0c+DuMcHcPSYYTUMrWzNK2Zhawi8pRXx3IA87axljwjyYGu3JpEivXjW8BLja89qvacT4ODMipOtTYFEUeXndcTafKOW+cSF8vSebSZGeXDfIF71e5B/rU/lydzbzEgP413UDu/zPczRfw4MrDlFU3cTfZ0Zz1+ggBEHgnrEhLBwdzG1L97HrVNu4vslvb+vwHInBrnx/X980wdQ1a1l5II+luwyllIFu9tw1Kgi1SsH6lCI2HC/m16PFRKudmTvUj2sH+Z7zsI0V+3Np1upZODqoy/tkldez61QFT0wNv2iTfw7nGk6o37opnlg/JUWaRtYnGzZmX/v1BK/9eoLEIFdmDfJh+kDvPtvz0elFyuuaKahupKi6iSJNI4XVTUYBb6RQ00RZbdt23JG8ao7kVaOyt8ZHqcDPRcGwIFfUKjt8VQrUSgVqpR3eSrvLcijHhaQvBf44cL0gCPaiKLYP24gGWoCuM0olUNpbc+0gX64d5EuLVs/+rEo2pZWwMbWETWklCMJRBvmrmBLlxdRoLwZ4OnYrhG/PjeeZH1J4eEUS6x8Z22Wq4uLtmSzbm8uicSEk51djLZPxr+sHohfhuTWGJqe7xwTztxlRnb6eKIqs2J/LK2tTcXe0YeX9IxncLoOmvlnLX78/wq5TFdw6IoCZcT4dMundHW0or2thf5ZhWLGpNv5cKNI08uWubFbsz6W2ScuwIEMp5ZQoL7OwXhXjjaahlbXJBaw8mM/L61J5zThDdM5QP8YN8OixCGt1Il/vyWbsAHcGeDl1eb9v9+cilwncPOzcNlfPhcO51dhayYg0zsRVKxXcOy6Ee8eFkFVeb66xf/GnY7y89jijwwwNVdNivHDupJoJDL/v6oZWCjUG8S40ineR8XJBdSMlNU0drEJ7Gzlq40o70tsZtXEFXtes5R/rU7lhsC/vzB10wb+TK42+3GQdBBwG7hRF8SvjdVbAUeCUKIqzzvbcl9om66WAKIqkFdWyKc0g9Cn5Bl8/wNWeKVFeTIn2ZFiQq3llY7JofvvLWKxkAtf+ZxcR3k58f9/IDrXS65ILeeTbw8yMUzM82JUXfz7OGzfEcuMQPx77/gjrU4p4dFIYj00N71Tc65u1vPDjUX46Usi4cA/eu9ly1F9BdSP3fHWQE8U1TIzwpKlVx+7TFTjaWjF/eABXxXix61QFqw7lWZSH/vTQaOL9lL1ayR8r0LBkRybrU4rQiyLXxKq5d2zXA0nak1ZUw6qD+fx4OJ+qhla8ne24YbAvNw31J9i985m0JotmQoQHW9PL+OKuYUyM6LyuvVmrY8Rrmxke7MYnt128qpEbPt6FXCaw6v5RXd5HFEVOFNeyNrmQdcmF5qHjDjZyhga5Eql2orKuxWCdGEX8TOvEWi7grbQzWCdmu8Tws1qpwFelwFlh1eXv8+W1x/l6TzbrHxnbaYaRRPd0t8naY4EXBGGO8cfJwP3Ag0AZUCaK4jbjfb4DrsLQ6JQFPADMBEaJoph0tteQBP7sFGua2HyihE2pJew6XUGLVo9SYc3ECA+mRHvR0KLj6R9S+O0vY4lSO/NLShEPrUjithGBvHrdQPPz7M+q5NYl+xjkr+L/5sQx84MdDApQ8fkdw3h4RRKb0kp59ppI7h8f2un7OFlSywPLk8gsq+OxKeE8NDHMwr45nFvFXV8eoLpd85aXsy13jQ5m/vAAixWiXi+yN6uC+Z/tM18X7uXITUP8uS7Bt0trSq8X2ZJeymc7MtmbWYmDjZxbEgO4c1TQOdVXt2j1/HGihJUH89maXopehMQgV+YM9WNGrNqieax9XHCIhwObHhvfpX3185EC/vLdEb5emMi4cI9ev69zoVmrI/bl37lzVBDPTzcEnrVo9ZTUGD3uTlbehdWNHXLs2zMlyotAN3vUSqN1YhRxd8fz8701Da1MeGsLA7yc+syiu5LoK4Hv6o7bRFGcYLyPAvgXhoYnFZCMIapga09eQxL43lHfrGXHyXI2pZXwxwnLErl5if48NDEMPxd7Xv81jcXbM3nrpnjmDPHjVGkdN/53N26ONqx5YBR/+e4IB7Ir+emh0fxjXSo7T5Xz6rUx3NZFWNZPhwt4bs1RHGzlfHBLAqPC3C1uN2WOmAj3cuTesSFcO8i324ESTa06hr+2GU2jIfMlJV+DlUxgUqQnNw31Z0KEB9ZyGU2tOtYkFfD5zkxOl9WjVtpx1+ggbkkM6NJa6C0lNU2sSSpg1cE8MsvrsbeRMyNWzdxh/gwNdCG3ssEs8K9eN5DbRgR2+VxzF++hWNPE1icnXLANQFNaqGmj8rdjxaxLLgQg3l9FYbWh0e7M/+4qe+szVt4G68TT2Za8ygb2ZVWyOa0UTWMrKntrrhnozax4H4YHu/XpXsLyfTm88OOxDgNdJM5Onwj8xUAS+HNHpxc5nFvFk6uSLfLGo9TOTIzw4Os9ObTo9Cy+bQgv/nSMplYdPz44mn1ZlTy5Kpknp4WzNb2MpNwq/j3HcCA4k6ZWHf9Yn8qKfbkkBrny4fwEvJzbvP1iTZNF8mSElxPPXhPJhAiPHq/KfjtaxAPLk3j12hhGhrqx6mA+q5MKKK9rRiZgMRN0oK8z944NYXqs+oJtvomiSFJuFSsP5LM+pZD6Fh3B7g4kBrma582m/uOqLnNVTpXWMuWd7TxzdSQPTOj8bKgn70HT2Npho7Ko2rh5qTH43u2jJUw42MgZHOiCup1dolbZmStRepIH06LVsz2jjHXGmISGFh2eTrbMjPNhVryaQf7nPsjbhE4vMuvDnVQ3tLD5iQm9rh67kpEE/grC5MF/vGAwBVWNbEwr4WB2ZYdhyavuH0mgqz1T3tmGl7MdttYyThTV8v4tCcyI69iJmVvRwIMrDnGsoIb7x4fy5LRwcxljRkktH2w+yfp2c0jPNexMFEUWLNlHalENW5+cgMrehhPFNdy6ZL9F1MMtw/x5/YbYi3o6X9+s5bdjxaw8mGcuOfRwsuXAC1O6fMwr646zbG8Oe56b3GVeT0OLtoNdUqQxrMRNlShn+t5WMoPv7WMU6vaet49KwRv/O0FGcS17n5/cd1+A8b1uTitlXXIhW9PLaNHpCXC1Z1a8mtnxvuZh4ufC2aZ+SXSONNHpCiTY3YHpsWruHRdCVX0LG9NKePqHFPPtty7ZR7OxGaamqQ4bKxmLjfNTz+T348U8sSoZAfjs9qFMjfYyZ7B/uv00W9LLzPddMDyAf1438JyF1zABKIZr3t/OnE/24O+iYEt6GbZWMhYMD+BEcS2Hc6u4JTHgonu1DrZWzBnix5whfmzLKOOOpft5qJtVeVOrjpUH8ojxUXK6tI6dJ8vbqk+qjatwTaPFPgUY6r09HG1RqxREeDkxIdzTUPetUpgrUdwdbbu1SLLK6xgc2Pf5M/Y2VsyKNwwl0TS2suG4wQr679bT/GfLacK9HJltvD3QrfMN6q5IDHZlVidTvyTOHUngrwBU9tYcNVbfDPB05GRpnVncTbRo9ZwqrSPI3cGc9dKq0/PmhnQ+3Z5JrK+SjxcMRq20Y31KIZ9tzyQ537JT99PbhjAt5vzGwrVo9RwvNARknSqt41RpHY9PDefWEYGcKK5h+b5c7h0b3KPqmAtJkDH3vkmr50heNUXVjcaVd1vd95E8Qw7Mkbxqbm5XGqpUWJs3KocEqiwad3xUCryc7c5r+HVZbTN5lY3cPiLovD7j2VAqrJk71J+5Q/0pq202hJ8dKeSt3zN46/cM4v2U5oNBeyuvO567JpKNqcXmqV8S54ck8FcAn+3I5Ju9OSwaF8Jz06O49+uDbEwtMd8+doA7lfUtvP7bCV7/7QQh7g4M9FWy1rhJd9uIQJ6cFsHPyQV8tiOTvMpGgtzsGTvAnZ2nylE727HkjmHnVeKmaWhlxf5cvtydRUlNM55OtpTWNjM4QMUjk8JobNXx7OqjBLnZ8/jUCx8zK4oiNY1as13S5nkbfjZZNG/8Ztkha2ctw0elwKddNv4bN8QarjN63+cT5dwTTAeWi5kg6eFky+0jg7h9ZBD5VQ38YpxQ9c9f0vjXr2kMN67Opw9Ud9tQ5qNS8OCEMN7ZmMHu0+WMCnXv8r4SZ0cS+Muc9SmGrsUZcWqeuToSgNOldebbP5iXwGzjdJ3C6kY2p5Xw8rpUi7F03+zN4Zu9hsEjCQEqXpgexanSOt76PYNB/io+vX0Ink49W6GdSV5lA5/vzGLlwTwaWnSMCXPnjRvjmBDuwbJ9ubz40zF+O1bMwewqcisb+P6+EX2yAdfYojvDLmnfuGNYiZ85IctKJuDlbFh5D/JXcSSvmkH+Kh6eGGauPlHZWyMIAieKa7j6vR38bUYUtyRe3Iylw7lVWMmEc45hOF+iwoa9AAAgAElEQVT8XOxZND6UReNDOV1WZ26oeuHHY7z083HGDnBnVrwP02K8O00jvW9cCCsP5vGPdamsf2TMRYnKvlyRBP4yZn9WJY9/n8ywIBfevikemUzgnY0ZFuL94eaTTIr0xNHWCm9nOyrrW9F3s/GusJabs25mx/vw7zlx5zRF6VBOFUt2ZLLheDFymcCseB/uGRNicRYwPzGAFftyeXC54fVuHxnI8G5iF0y06vQUa5os7BJz9YlRxM/0vcGwCvVR2jHA04nxRt9brVSYW+bb+96mOvjbRwYypZNgqxX7crGxMkQcX2wO51YTpXbus+lW50OohyN/nRLOXyYP4HhhDeuMDVVb0pOxtTrK5ChPZsf7MCHC0/x+7azl/G1GFPcvS2LF/txuZ9tKdI8k8JcpmWX1PP/jUfxcFHx621DsrOX8caKEDzafBGDT4+MorWnm1s/38fQPybx67UD++v0RixhdG7mM6xN8WTgmmOqGFr4/kMeawwVtr1Fex+JtmUyJ9iRa7XzWTU+dXuT348V8tiOTpNxqlApr7h8fyh2jgjr1aOUygeeuieT2pfsBePrqSPR6kfL6ZsuNyup2FSeaRkprO9Z7O9tZGW0SBQkBKgvLxEepwEtpe04Z8J3R0KLlx6QCpg/0Pud8m3NFpxdJzq/utMy1PxEEwxnFQF8lz1wdSVJuFWuTC/n1aBG/Hi3G0daKaTFezI73YXSYO1fFeDMq1I23f89gVpzPRf8eLxckgb9MeWhFEm4ONnx5VyIuDjbsOlXOwi8NJaif3jaEME8nwjydeObqSF7/7QS/Hm2Lr3W2s+LWEYHcOSoIT6PwphfXsj+7ElsrGQ9MCMXWSs6mtBLe25zBu5sy8FHaMSXaiylRXowIcbPYJKxv1rLyoCH4K6+ykQBXe16ZHcOcIX4WfrTJ9y7UtK22//bTMfPt8a/8jlwQaNFZbhDbWcuMyYJ2jBvg0VYyqFLge5F87/asSy6ktlnLgm6any4UGSW1NLToLpkJTp0hkwkMDXJlaJArf58ZzZ7MCtYeKeR/x4tZk1SAq4MN1wz05pqB3uzJrOCdjRkWXdgSPUcS+H4iraiGXafKuXNUUJ96jO1948/vHEaAmz2b00q4+yuDuM8d6meudGlq1fHDIcvxfC/OjObmYf4W3uiWE6U88u1h7G3krFw0knhjBcsDE0Ipr2vmjxOGyONVB/P5ek8OjrZWjA/3YHy4B5nl9azYl0NNk5YYH2eevjqAGB8lJTVNLNmR1eZ/G1fi9S1d53/r9CL3jA+2SBj0USlwMfrelwor9uUywNORoYEXf4qSaYJTwiU0wak7rOQyxg7wYOwAD/55/UC2pZexNrmQ1Un5NLUaDuTL9+Uwf3gAUWopp6a3SALfTyz65hC5lQ3885c0AJ66KoL7xoWcV0emTi/y+MpkAJ6YGs4gf5U5iwYMqY3/uHYgmoZW/rvtNJ9sO23xeMNsz7aNL1EU+XxnFq/9mkaU2pkldwxFrbTMD3d3tGXuUH+uT/Alt7KBHw7l89+tp/nlaBG/HC2yuO/xwhqOF9Z0eLyPyo4wD0fGDnA3r8Q9HG1ZtOwQ1nIZmx4fz5e7snl3U4Yhe70HPnx/caxAQ3K+hpdmRffLQedwbhUu9tYEuv35ashtreRMi/FmWow39c1aNqWVsC65kG0ZZRzMrpQE/hyQBL6fWPPgKJ5fc5TfjeWKb25I580N6QA8OS2c+8aF9qoWWhRFXlnXNhFxSrQXqw7m8czqtuaml2fH8O//pbN0V5b5ugkRHiy9YxhZFfVc+9EuHlh2iO8XjUQuE/j7z8f4dn8e06K9+NuMaEprmknOq263admWMFhc09Tle5sY4WG0SxTmlnkflSHfuyvf+71NGVQ3tLLk9qEoFdYsGm+orHh57fFLurJi+b5cbK1k3JDQPx744bxqEgJcLqkzmnPBwdbKHJ/drNVhc4n+vi91JIHvJ9wdbfn0dkN3cWmNwWs2ib2pUQQMK/F7x4WctSJiyY4svt6TQ4CrPbmVDXy9J4dv9+diLRfQGzNK/vLdEYuZsj/cP5IhgS7UNGlp1emZFa/m2/15RL74P4vn3pJean5vJmytDPXebg42HcR9zhA/Hp8azlsb0tlwvJj35yX0KgTsRHENH/1ximsH+ZgrVOys5bwwI4oHlyfx7YG8bsO9+ou6Zi1rjxQwK94HZQ/HO/YlmsZWTpXWcW385RXW1Veb31ciksBfAng621mI/Ys/H2PDcYOgvr0xg7c3ton9PWNDOtSB/5JSxL9+TWNGrJoZcWqDCO43zOBsH0DVXtwH+jrz3JqjFJ7F954V79M2Hs3oefuoFOhFkWV7c/hmj6E+PsbHEPw1I64t+Ouu0cGsOVzAygN53DM2pEffhVan5+kfUlAqrHlpVozFbdcM9GZEiCtv/57OrDg1KvtLq7Li5yMF1LfomN/HA7V7SrK5wenP4b9LXHgkgb/E8HS2Y/FtbWL/95+P8z/jgOb2Yv/YlHDuHB3E1vRS/vLdEQDCvZzMNePd4epgg0wQCPFwYHSYuznnJKus3vz8AD8/NNq8oWriVGkdb25IZ01SPs1aPZMiPblnbDAjQ9w62AKxfkoSg1z5cnc2d40O7lG87JKdWaTka/jP/MEWw0PAUGr38uwYpr+/g3c3ZvDKtZdOZYUoiizfm0uktxMJ/RSjcDi3GkGAOP/+aXCSuPSQBP4SRRRFBEHgwYmhXJfgy+G8KhZvyzTf/u4mQ3lie868fCb/un4gNw7269TuWbY3h/c2nyTCy4m3born/mWHeGDZIdY9MgZXBxv2ZFbw+Y4sNp8oNXjMg/24e0wwYZ6O3b7mwjFB3L8siU1pJVx1lpya02V1vLMxg6tjvJke2/l9I72duXVEIMv25TJveACR3pfGxltyvobUohpePY+gtfPlcF4VAzwd+ywTX+LPjyTw/URNU2vHNvn2JYOaJlrOCASzsZLhq1IggEU36tkYGujCR/MHdzqXVavT889f0vhyt2HY9vu3DMLJzprFtw3h2v/sYsg/NxHp7cSJ4lrcHGz465QB3DYisMcDmqdGe+PnomDpzqxuBV6nF3n6hxQU1nL+cV1MtyL5+NRw1iYX8sraVFbcO/yS2FBcsS8Hexs51w3qH/9bFEUO51Zz9XmGvUlcXkgCfwFoatWZ67rPTBg0VZ/UNVuORpPLBLycDBGxcX4qro6xs/C81Uo7XB1sEASBxhYdif/aRG1z1+PVTNw42I83bozttPyypqmVh1ccZntGGXePCeb56VHIZQKaxlZ2nSo3e/Ynimt544ZYrkvw7XX7u1wmcOeoIP75SxrHCjRd5qN8vSebQzlVvDM3/qy5Nip7G56YGs6LPx/nf8eKuSa2Y379xUTT2Mra5EKuG+SLUz+tnrPK69E0tl7SDU4SFx9J4PuIFq2eu786QGphDRXtRueZcHe0Qa1UEOze5nubY2JVCjwcbc9a+mfKYJ/32d4Otz11VQSz4nwY9+YWi+tXJ+WzOimfhyeGcdvIQHMkQG5FAwu/OkB2eT2v3xDLvMQA8iobWLori5UH8qhv0TEq1I3cygbyqxpxVlifc7bJ3GH+vLsxgy92ZfP23PgOt+dWNPDv/6UzMcKD6xN8e/Sc8xIDWL4vl3/+ksbESM9+zV356XABTa16Fgzvv8oec4OTtMEq0Q5J4PsIQQBPJzvSZbUW1ysV1kyO9CQx2JU4PxXhXo69ruE2Zbgs3p5pjoK1s5YxwNOJowWGpprbRgQyf0nb0OoHJ4SSU2mIbQX4aMspPtpyCoBhQS4cyK5CqbDm67sTUVjLeWh5Er8dK0ImGIO/xgYT46OkRavn5k/38NSqZAZ4OjLAq/cTe5ztrLlpqD8r9uXyzDURFit0URR5ZnUKVjKB13oxoclKLuOlWTHM+2wvn27P5NHJA3r9vvoCUTR0rsb6Kon167/NzcN5VTjaWp11T0TiykIS+D7CWi4zr06LNU0k51eTkl9NSr6GTWkl5pAuO2sZMT5K4vyUDPJXEeenIsjNvlNha2rVsepQPp/vyLSYs3rdIB8KNU0cyK7k/26MZWKEJzEvbbAY4jEr3ocotTP/mW+YC/ruppNmsT+QXQUYrIX5nxkOCs52Vtw3LpQ7RwVZePU2VjL+u2AIMz/cwaJvDvHzw6PPyYa4Y1QQX+3JZvneXB5rN47t2/157Mms4PUbYjt0yZ6NkaFuzIhV8/HWU9w4xA9fVe8e3xccyq0ivaSW12+Iveiv3Z7DudXE+yv7dBC2xJ8fSeAvAN5KO7yV3uZNRVEUya5oICW/muQ8Dcn51Xy7P5cvdmUDBnGN81MR768kzk9FgKs9G44X8/WeHCrrW4j3UzIzTs36lCJGhbqRWV7P8cIa3rt5EB6OtiS+1jbo+uVZ0by8LtXi/YR5OvHBLQn4qRQs3p5JZ9Q0aRFFkYaWjr6+t9KOj+YPZsGSfTy5KplPbh3S643NYHcHJkV4snxfDg9MCMXOWk5hdSOv/ZrG6DA3bhnm36vnM/Hc9Eg2pZXw+q9pfDR/8Dk9x/mwYl8ujrZW5kz9/qChRcuJ4loeGH9uQ70lLl8kgb8ICIJAsLsDwe4OXDvI4DFrdXoySuoMop+vITmvmv9sOd3hsWMHuDM4wIX3N58kyM2eYk0T+VWNfLxgMBnFteYaeIDdz04iJb+6w3PUN2uZ/9leixF7gwNUjAp1J7O8zpwkuXh7pvkAcM+YYOYO82eApyOCIDAixI3np0fx6vpU/rvtNA9OCOv197BwTDALluxjXXIhc4b48fyPR9HpRd64Ie6cK2H8XOy5f3wo728+yW0jKnqUF9/XXJfgc1HTKs/kaL4GnV6UNlglOiAJfD9hJZcR7eNMtI8zUepqahpbSS1qC+KK8XGmsUXHjpPl5ox2k00zyF/FEyuTzZU4LvbWrHtkDD4qRQeBb58kCYZu0HvGhjCkXdKhKIqk5Gv4bEcm6402zpKdWSzZacisuWt0EHOG+LFwdBBH8qp5a0M6sb5Kxg7w6NVnHhXqRoSXE0t3ZSMTBLaml/HSrGj8Xc8vGOv+8aH8cCifl40TgC62TTE/sX9jEw4b92X6e06txKWHJPD9hF4vsjWjlMXbMtmXVYmTrRWLxodw16hgswdeXtfM1He2UXXG9CHTRquJKLUz2zLKiPdTmaMJtmWUcdMne8wHgRAPB768M5GATlIGBUEg3l/FR/MH88EtIofzqvhiV7ZZ7L/YlW22k+YO9UMvwiPfHmbdw2N6Jc6CILBwTBDPrD7KE6uSGRrowh19MK1HYSPn+elRPLQiie8O5F6Uapb2A0XOZxZtX3Akt5oAV/se9yZIXDlIAn+Radbq+PlIIZ9tz+RkaR1qpR0vTI/ilkR/i83LxhYd93x10CzuSoU1kyI9WZ9SaJEvE+hmT2pRDbtPV1i8Tvth0KvuH8mwINcevT+ZTGBIoCtDAl354BaRA9mVfL03x7xBu/KgIT++uqGVsf/ewvJ7hjMyxA1ZD1fN1w7y5ZnVRwH4vzlxPX7c2Zge683wYFfe2pDOzNgLH/ZlGro9KrR/o4tFUSQpt4qR/fw+JC5NJIG/SNQ0tbJiXy5f7MqipKaZSG8n3pkbz6x4nw5NSDq9yF++O2xREunmaMOPhwsIcXcgv7oRpcKa7+8bQYiHIxV1zbzx2wlWnTG8w8TCLw4Q62fYwI33UxLvr0KttDur7y2TCQwPcWN4iBsf3CKyL7OC5ftyLXLeFxhLM+clBjA73ofEYNduLZLNaaXmn6360Eox5dTM+GAH727K4OXZMWd/0Hmw3BjmNiOuf5ussisaKK1t7rf8G4lLG0ngLzCF1Y18sSuLb/fnUdesZXSYG/+eE8+4Ae5dCuw/f0m1iOdt1urJqWhgXqI/m9JKcbaz5tt7RyACz/94lNWHDMFfEyM8CPd2YvG2TG4Y7MvC0cGkFtYYSzY1fL4z07z6d3e0Jd4o+nH+SuL9VB3CvdojlwmMCnNnVJg77+n07DldYZ6VCvDt/lxzguXcoX7MivdhRIibxcGrsr6Fv/98DG9nO0pqm/hyd3aHxMjzIUrtzPzhAXyzN4d5iQFEePe+Zr8nVNQ1m5MbFRexwaq0psk4NEXD8cIaUotqyDHuywzt4RmaxJWFIJ45nbgfGTp0qHjw4MGz3/FPQFpRDZ9tz2RtciEiMCNWzX3jQrps1Tfx+c4sXl1vWebo6WTLU1dF8PbvGbTq9Px1ajjb0kvZlFaKjZWMGxJ8uXtMMAO8nNDpRdKKaojx6TgEu6lVR1pRDSn5GrPony6rM/vJ/q6KtlW+n4qBvspuq0P0epHbl+5n56lyvJxtKalp7nCfGwf7MTNOzegwd576IZlfjxax7pExLN6WycbUEvY8N6lP2/ur6luY8NZWBvo6s+zuC5NTs3jbaV43WmDvzI3nhsF9O9xDrxfJqWwwC/nxwhpSC2sor2v7fgPd7InxcSbGR0mCv4pRYe59+h4k/jwIgnBIFMWhnd4mCXzfIYoiu09XsHh7JtszylBYy7l5mD93jwnu0Wbkb0eLeOCMuN/RYW48OS2C+745RFltMzZyGS06Pa4ONtw2IpDbRgbifh6ba7VNrRwt0JCSrzHX6RdUNwIgEyDM09FYo28Q/khvZ4tJU5qGVmZ9tJNmrY41D47meIGG1Un55jz7Mxno68wP94/iZEkdsz7ayYszo7l7TPA5v//O+HpPNn//+Tif3DqYqwf2rYWi14tMensrDS06Smubz1vgm7U6TpbUkdpuZZ5WVGPO6LeSCQzwcjKKuUHQo9RO/ZZ5I3HpIQn8BUar0/PL0SI+25HJsYIa3B1tuHNUELeOCOzxUIpDOZXc+N895suCAI9MDOOqgd7M+GCn+foQDwfuGRPCDYN7H/zVU8rrms1ib+rGNeXr2MhlRKmdLES/Watnzie7ifdTseye4VjLZTS16thyopQ1hwvYmNpR7GfEqfklpQh3R1v2PT+5T0sbtTo9Mz/cSV2zlk2Pj+/T72nnyXJu/Xwff50ygPc2neyVwNc2tZJWVGuxMj9VWmu2zRxs5ESp24Q82seZAV6O0kQjiW65aAIvCMIEYEsnN2lEUTzrLtCfTeDrm7WsPJjHkh1ZFFQ3EuLuwL3jQri+l6mLWeX1THxrq/myi701T10Vya5T5eYNTSc7K967eRATIzz7rPKkp4iiSH5VY9sqP7+ao/ka8yrTwUZu/lmttGPlopH4uSjM9shDK5L4JaWoSxtHLhN4Z248kyI9+2xluvt0OfM/28cTU8N5pA9zah5cfojdpytYuWgk097d3qXAl9Y0cbyoxmJlntMubsLd0YZoH6XFyjzQ1f6i/24l/vx0J/AXapP1UeBAu8tnz7X9E1FW28xXu7P5Zm8OmsZWhgS68PdZ0UyN8ur1f9CKumYLcZfLDF2vz/941Hzda9fH9tsYODBUqPi72uPvam+uGtHpRTLL6kg2i76hG7dI08TYf2/B1cGGOD8lNY2tJOVWc/NQf/5vThy1Ta1sSivh5yOFbE0vMz+XqSN3SpQn1wxUMyXaC6Xi3MV+VKg702O9+XjraW4c4odPH+TUlNY28fvxEu4cFYSt0abSi4YDdHshP96FX37TED9ijKLu6dx9JLKERF9woVbwU0VR3NTbx1/qK/jTZXUs2ZHJ6qQCWnV6pkZ5sWh8CEMCz62CoalV12HA9ZmseXAUg/8kEbANLVoS/rGRZq2eeD8l5XUtZj8fwFelMOftxPkpCXRzYOEXB0gvqe30+caHezA91pup0d7dVvh0RX5VA5Pf3sa0GG8+nJdwzp/LxLsbM3h/80nuHx9KenENW4wHKBPt/fJoo9US5eMsTViSuKD0h0VzWQn8wexKFm/PZFNaCdZyGTcO9uPescGEeJx7NKtOLxL6/K8W1/m5KJgd78OPhwuoaWzl67sTz/ng0V+U1jQx88Od2NvIGRLoyuqkfJ65OhIrmcARY8JmXqVB9AXBUK5ZVmtY7c5L9OdUaZ057bI9o8PcuGagmqtivPFw6vmm8jsbM/hg80lWLhpJYnDPv0uTX55qXJUfLdBworjjgcjJzoq/zYgixkcp+eUS/UJ/CHwp4A5UAxuAZ0VRzD3b489V4Jfvy6GiroU4Y3mfyzms9jrjYHYlcz7Z0+F6X5UCpcIapcIalb01DrZW9NY5PbMpycFGztAgV7ZlGFaFQW72Pe4+vdQ4lFtFZlnbSMGbhlh61FUNLaTkayit7ejH94TEYFdemR1DlPrsEQGNLTomv70Vlb0N67rIqSmtbTKXIpqsluwz/PLyOsMm89Ux3jx9dQSCIDDxra0XpExSQqI3XEwPXgO8DWwDaoAE4HlgjyAICaIolp75AEEQ7gPuAwgIODefeWNqidnPBUM9d7yfinijFXC2eu6u+P5AXqfXFxhH8fUVaqUdVQ0tZnEHw4SoXafK++w1+guVvXWnn8NKJuBjzNwp1DT16jn3Z1Xy4k/HeGfuoE6zddqjsJHz/IwoHl5xmG/35zImzN3YJNTml5e1O9AEuBr88jlD/Ig2bn56Otly3zeHSMqp4oN5CdhYycip6PlMXAmJ/qJPBV4UxcPA4XZXbRMEYTuwH8PG6986ecynwKdgWMGfy+suvWMYvx0r5t1NGZwqrSOvspG8ykZzWJZMgAGeTsT5KYnrop67M+L8Vaw6lM+BF6bgZGdFdUMrmsZWqhtaqG5sRWO63NhCdUMr1Y2t1DS2Gn82XFfb1P3+sqOtFbVNWppaDcM6lAprpseqzWcHKuPfzgprVAobVPaGMwd7G/klMWy6r1j45QFS8qvZ9ewkbK3k6PUiWRX1Fhn6prF0AAdzqszjCRODXXl+epRFmmKLVs/J0lrzyhzgbz8dM99uJRMI83Rk3AAPg2du/NOZX16saeKPE6XcOzbkrP9mJCQuJS54VIEoikmCIGQAwy7Ua8hkAjPi1Fw90Jv1KYW8v+kkmeX1eDvbMSnKE3dHW1Lyq9l8otRsjdjIZUT5OJvb9eP9lIR6OHZZBWNnLcdbKbeYdtQTdHrRIPrGA4Om0XSQMPzJrWxgdVKbXePhZMvG1BI0jS0WoWJnYi0XULYTfJXCGqW94SBgPjgYbzNctkGlMBwoLsWpPwtHB3Pr5/tYl1zEnCF+yGQCoR6OhHo4cn2CwQJp1elJL64lOb+a344Ws9N4ZrA/q5Lr/rOry+e2t5FjbyOnoUWHvY2c7+8byQAvxx6Xsn5/IA+dXmRe4rkNJZGQ6C8uVhaNAFzwjiq5TODaQb7MiFWzNrmQ9zefZMW+XOL8lDw2NZwJ4R7meu7k/GqS86pZfSifr/fkAIbV9EBfZ6O1o6Kg6vxtGLlMwMXBxrgv4GBxW01TK7ct2YeNXMbi24YwMdLTfJsoijS26swHgurGlnZnB63mswmN8UyhSNPEieJaNI2t5ojgrnCyszKeHdi0OzuwNl+nNB8sDAcG0wHjQg62Hh3mRriXI0t3ZnHjYN9Oz06s5TK8nO3wVSkYGeqG0t6aQ9lVFNd0b/FMifJikPFsLK2oBltrWY8/i1an57sDuYwd4E6gm8PZHyAhcQlxwQVeEIShQDiw8kK/lgkruYwbBhsCr35MKuCDP05y1xcHGByg4vGpEUyP9e5Qz30kr9rcyPPFrmxadG3zTRd+eYBJkZ7GGarKPsndrm1q5Y6l+0ktquG/CyzFHQy15/Y2VtjbWPW6hrtVp7c4UzAdBDTtDgyms4nqxlYKqhrNP+v0XR+Hba1k7awjG5Ttzh5U9tYoTQeDMw4WTnZWZ+0PEASBhaODeXbNUfZlVTI82JXcygZzuFaq0S9vvzHr76pgkL/K0Cjk60y0Wkl9i5ble3NZuivLfL+1yYWsTS40X5727nbmJfqbD+TdDULfml5GkaaJl2ZF9/Trl5C4ZOjrKprlQBaQhKGCJgF4DmgABoui2O2u4YUqk2zR6vnhUD4f/XGSQk0TiUGuPDY1vMsM7WatjvTiWp5alUJ6SS2uDjZUNbSYQ7na13PH+6mI9VPi2ItN3LpmLXcs3W8Y07dgsHl2a38jiiJ1zdp2ZwdtZw8a455D+8vtDxqNrboun1cQaLORFIYDgeqMKqSy2mZOl9VZbJabkMsEBng6mjc9Y3yciVI7n7URKqOklvXJhaxPKSKzvOtN0e4GoS/88gBHCzTsfnaSRTJmTkU949+Uqmgk+p+LWSb5HDAPCATsgWLgN+AlURSLunssXPg6+GatjpUH8vhoyylKapoZGeLG49PCuyxH/GZvDi/+dIwDL0xBYSPnWEFb1+aZ9dyhHo4WAhGlduq0JrqhRcudSw9wKLeKj+YlcE1s/+aJ9xXNWl3bQaDd32fuOxRWN3Iwp2Ode3e4Otjg6WRrcfZgtpbsLTefTfdxtLUy2zyiKJJWVMv6lEJ+PlJoroDyVSkI9XTEy8mWrPJ6jhVqzJvdznZWuDvaklleT4SXE18tTLTYf5EEXuJSQQobO4OmVh0r9uXy8dbTlNc1M3aAO49NDe/QMdpe4DtrrqmsbzHE7ua1Cb+pRd1aLhDp7Wyx0vd1UXDPVwfYn1XJ+7ckMCve54J/1v6kvK7ZIr88rbCGrIp685mQq4MNUWonAlwd8HNR4KtS0KLV8/TqFGysZDw6KaztLMF80DAcMKoaWmnR6rt8bblMsNh8bn8GsfJgvsUZh41cxrhwD64Z6E2gmz2nSg0RDKZ8exOeTrbE+akY5K/Ew8mWZ1YflQReot/pjyyaSxo7azkLxwQzLzGAZXtz+GTbaW74eDcTIzx4bGo4cX49m47j6mDDxAhPJkYY/HNRFCnUNJGS17bK//lwIcv2dhQKgNyKBvxdFX/6ckdRFMmrbGyXxaIhtajGIljMz0VBjI8z1yX4msO1vJxtO/3su0+XsymtlDtGBXUbPtZk3ITuSelqRV0Lp8vqOrWTWnR6NqWVsCmtBGNpWtoAAA8BSURBVE8nW/Y8N5kbh/ixOa2EUA9HnrwqwpyqmZxXzaa0tnTMS7EiSULCxBUp8CYUNnLuHRfC/OEBfL0nh8XbTzP7o11MifLisam9TyAUBAFflWElarJe9HqRE8W1TP9gh/l+1Y2tPPKtoV3Axd7aXKZpmq7k6XTpBlG16vScKq3rsDKvNVbuyGUCYR6OjA51N3vm0T5n98vbc9foYH46UsgPh/K5a3TXWfHnU7qaWljDrI92EuhmzyuzY8wWktJYRrrxWAmltc386/pYhgS6MCSw7exO09jKsQINWeX1TI7y6tVrS0hcTK5ogTfhYGvFAxNCuXVEAF/tzubT7ZnM+KDzgRW9pVWv580Nhuk//54Tx9yh/rRo9WSU1JrtneT8aj7aUoapgEWttDNUeBhH6cX6KfslsKq+WcuJYkP1yvGCGo4XacgorjNXGCms5USqnbg2wce8+Rnu5XTe5ZTx/iqGBLrw5e5sbh8Z1OerZLlMINZPyaOTwvjgj1M42FoxIcKyimn5vhy8ne2YGOHR4fFKhTWjw9wZLU1RkrjEuSI9+LOhaWxl6c4s3t98EoDhwa7887qBDPDq3YzPFq2eB5cfYlNaKa/fEMu8xK6jGBpatBwvrCG5nb3TPj88xMPBHL0Q52coDezLuvQKs1/eVpbY3i93sbc2i7hpZR7s7nDBLIpfUop4aEUSn90+lKnRF2aV3NCiZfLb23B1sGHtw205NbkVDYx7cwt/nTKAv04JvyCvLSHRV0gefC9RKqx5bGo41nKBt37PYF9WJdPe287seB/+MnlAj1IkW3V6Hl6RxKa0Uv553cBuxR3A3saKYUGuFhU91cZQLpPo7zpVzo+HCwBDq32Et1PbDFV/FQM8u67nNmEa3tE+u/x4oaZTv/zaQb7mGnNvZ7uLuldwVYwXPko7lu7MumACb29jxXPTo3j028OsPJhn/h19eyAXmQA3D5M6VyX+3EgC3w1K47i93x8bx5qkAr7anc265EKuT/Dj0clhXXY2anV6/vLdYX5PLeGV2THcOiLwnF5fZW/DuHAPxoW32QTFmiZjU5Zh0++XlEJztYedtYyBPsaqHX8lMT5KWrR60opqLDY/Tfk4Z/rl0T7OxKiVKO37P7/cSi7jjlFBvP7bCVILa4j2OXty5LkwK07Nsj05vLkhnemxahTWclYdzGNSpBdq5fkPCZGQ6E8kge8BLvY2PHtNJPeMDWbxttN8vSeHn44UMGewHw9PCrMYqK3V6XlsZTK/Hi3mxZnR3DEqqE/fi7fSjquV3lw90NAcpdeLZFfUszezku8P5nEwp6rbOvNp0V5MjPQkWu1MhPf5++UXkluGBfDeppN8sSuLN2+KvyCvIQgCL82OZtaHO3lvUwZDAl0or2thwYj+m6AlIdFXSALfC9wdbXlhRjT3jg3h462nWbE/lzWH85k71J+HJobh5WzHk6uSWZdcyPPTI7l7TNcVIOeDyS9Pbbcyzypv88ud7KywkcvQ6kW0Oj2tetFcM/57agnJ+dXGem6jp++ruiRW7WeitLdmzhA/vj+QxzPXROLeBxERnRHjo+SWREMl1c6T5fiqFIwb0HFzVULiz4Yk8N3RxQa0p7MdL8+OYdH4ED7ecprvDuSy6mA+UWonkvM1PH11BPeNC+2Dl2/zy1PNfnmNRbiWr8rol8f7mjdA1cqOfnlji47UIg3JxqaslHwNG1PbKoWC3OyN1o7B04/xUaKw6f/V/Z2jg/hmbw7L9+bylyl9Nzz7TJ6cFsH65EJOltbx5LRwqb5d4rJAqqLpgn2ZFTzy7WEaW3Xsf35Kt2JXUN3IR3+cYvWhfB6dHMbDk3ovRFqdnlNldRwvMK3MDaJe084vD/VwsKhkiVY7o7I/9+lVmsZWjhqTNU2566aDhyn/xRS9EOenJMLbySKP5WJx1xf7OVpQw65nJ17QkXgr9uXy+m9pbH58vDQUW+JPgxRV0Av0epFPd2Ty5oZ0Alzt+XjB4B6NhgND5UxPBLChRWue92myWU4U15ptFDtrGZHezuaOzxifi+eXl9Y0mcs0TQmbmsZWwJAmGePjbN7EjfNTEezmcNakyPNlx8kybvt8P2/fFM+NQy5sLIBOL0qrd4k/FZLA9xBNQytPrDrCprRSZsSqeePG2G5b5XtCZX2LRUliaqGGzHZ+ucre2kLIY3ycCXZ3vGRERhRFcisbSDaWa6bkV3OsoMbc7u9kZ2WuzTd143ZmEZ3ve5j27nZsrGSsf2TMnz7aQUKiL5Hq4HtASn41Dy5PoqTGkP1956igXglJm19uEHHTJmiRxtIvj/ZxZlZ8W+dnX4thXyMIAoFuDgS6OTDbGI5mspNS8jQcMdo7n23PRGtsxfVwsm2LXuiDQeiCILBwTDDPrTnK/qxKhod0HvMsISFhyRW/ghdFkWV7c3h1fRrujjb8Z8FgEs5IlTwTrU7P6bJ6y3Ctdn65zBgf3H5lHu1zfn75pU5Tq460ohrjKt/g658ua8tgD3C1N4v9uQxCb2zRMfKNzQwPdmXxbZ0uViQkrkikFXwX1DdreXbNUdYlFzIhwoN35w7qsNJsbNGRVlxjsTJv75fbWsmIVDszM97HLOiRl3h9+YXAzlpOQoCLxcGxpqmVY/kas6d/OLe6y0Hog/xURHg7dTnUWmEjZ35iAP/ddprcigYC3Ow7vZ+EhEQbV+wKPqOklgeWHSKrvJ7Hp4bz4IQwNI2tFimJpvpyUwiYUmFt9slNK/Ngd4ezxgNItFFW22wxNCU5r5qqBsMmbvtB6PHGjdwQ97ZB6EWaRsb+3xbuGBXEizOlEXoSEiBtsnZgTVI+L/x4jMZWHYlBrjgrrDhe2NEvj1I7twm6rxKfS9wv/zNi2rtIbpe3frRAQ0OLYRO3/SD0eH8Vy/bmcDRfw57nJ/dqTKKExOWKZNG04/OdWby6PtV8+WBOJaEejiQGu5pX5tFq5/PaFJToOYIg4O9qj7+rPTPjDJu4Or3I6bI6Cz9/6a4sWnVti5GfjxSwYPi5ZfxISFwpXHECH+rhwILhAebVeaS38yXRsSnRhlwmEO7lRLiXEzcNNSQ6Nmt1nCiqJSW/mtSimh73JkhIXMlckRaNhISExOVCdxaNtDsoISEhcZkiCbyEhITEZYok8BISEhKXKZLAS0hISFymSAIvISEhcZkiCbyEhITEZYok8BISEhKXKZLAS0hISFymXFKNToIglAE5F+Gl3IHyi/A6/cXl/vng8v+Ml/vng8v/M16szxcoimKnU+IvKYG/WAiCcLCrzq/Lgcv988Hl/xkv988Hl/9nvBQ+n2TRSEhISFymSAIvISEhcZlypQr8p/39Bi4wl/vng8v/M17unw8u/8/Y75/vivTgJSQkJK4ErtQVvISEhMRljyTwEhISEpcpl53AC4Lw/+2dW2gcVRjHf3+L92qTvKhIQlpqo5GktmorVLBRwSfrc18URF8EFW9otT6ZEhD74oMgiE+9iHgjvohoi6C2RVq0mJbWatUGCWrTpLWV2JTPh3MWx83M7mSz2bnk/GDYnTPfwPff/5mzZ76zl/sk7ZI0JmlK0qikdyX1VsV1SnpP0qSk05I+kNSVVd6zIY1GSeslWcw2kWXujSDpE5/7YFV7u6S3JP0p6aykzyT1ZZXnXIjTKKk7wUOT1JZlvvVI2/+K6mEafXnwr4x/2dcB7AfeAP4AuoAXgL2S+szsF0lXALuAKeAhwIBBYLekfjM7m03qqamrMRL7BPBNZH+6ZVk2AUkbgZUx7QKGgaXA48ApYBPOw1vMbLSlic6BJI0RhnBao5yZv4yaSmL/K4mHaa6v7Pwzs9JvQA9uEH/G7z8JXACWR2KW4sx5Out8m6Rxvd+/N+vc5qCpDRgDNnotg5FjD/i2gUjbEmAceD3r3Juksdu3PZJ1ng3oqtv/iuxhSn2Z+1e6Ek0CJ/3jef+4AdhrZscqAWZ2HPgK1+mKSLXGMvAqMGJmO2OObQB+M7PdlQYzmwQ+plge1tJYdsriYW4p7QAvaZGkSyTdALyJmyW94w/fDHwfc9oI0BvTnkvqaKywXdIFSScl7SjQOsOdwIPAYwkhtTzskrR4vnJrFik0VhiSNO3Xi4aLUKOOUKv/Fd5D0l1fmflXxhp8hX3Arf75MeBuM/vd73fg6n3VjAPtLcitWdTSOAlsBb4ATgOrgBeBPZJWReJyh6SLcW9Yr5nZkYSwDuDnmPZx/9gO/NX87JpDSo1TPuZT3FrLjTgPv5a0xswOtyTZxkjT/4rsYRp92fuXdS1rHmtkNwFrcbXN/cAo0O2P/QMMxZyzBZjOOvdmaEyIX41bZxhsVY4N6toM/ARcHmmrrk//AOyMOfdRH9uZtY65akw4rxM3oGzLWkMDmv/X/4ruYT19efCvtCUaMztsZvvM1TbvARbjPmkCbvbeEXNaO/Ez+1xSR2Nc/AHgKHB7i1KcNf4W9yXgZeBSSW2Rj5RV9hfhZnlJHkKOfZyFxhmY2QngS3LsYRIx/a+wHsaR5vpqtX+lHeCjmNkEroSx3DeN4Op/1fQCh1qVVzOJ0ZiEcLOjvLIMuAzYhrvAKxvAs/55H7U9/NXM8nprD+k1JpF3D2sRzb3IHiaRxpuW+bcgBnhJ1+DqXz/6pmHgDknLIjHdwDpmfl61EMRojIu5DViBq93nlW+BgZgN3IA4gHsjGwaul3RX5URJVwP3k38P02qcgZ/9ryPfHsYS0/+K7OEM0lxfrfavdD82JulD4ABwEFfrWgE8BVwLrDGzo5KuBL4D/sbVQg14BbgK6M/7zCGlxu3AcR83gVsE2gScA1abWaH+SUeSAVvMbLPfvwh3q9sJPMd/X5LpB1b6W+FCEaNxK24Stge3SNeD07gEWGvJi7OZk6b/FdnDlPqy9y/rhYl5WOh4HrfgOOFf7CO4lezuqrgu4H3cAHkG+Kg6Jq9bGo2+Ix3ErfafB07gfr70uqzzb1DzjAVIXP32bVwt9xzwOW5gyDzfZmgEHsZ9S/IUbvFuDNgB9GSdawotqfpfUT1Moy8P/pVuBh8IBAIBx4KowQcCgcBCJAzwgUAgUFLCAB8IBAIlJQzwgUAgUFLCAB8IBAIlJQzwgUAgUFLCAB8IBAIlJQzwgUAgUFL+BRZEzN/pZ3mCAAAAAElFTkSuQmCC\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.plot(tiny[\"AGE\"], tiny[\"YEARSEXP\"]);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Подвох заключается в том, что по умолчанию функция `plot()` используется для построения графиков математических функций, то есть графиков, где точки соединены линией. Нам нужно добавить аргумент, который будет соответствовать только точкам:" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.plot(tiny[\"AGE\"], tiny[\"YEARSEXP\"], 'o')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Здесь `'o'` – это один из возможных маркеров для точек, перечень маркеров с обозначениями можно посмотреть [здесь](https://matplotlib.org/stable/api/markers_api.html).\n", "\n", "Давайте построим более красивый график, выполним все те же действия, что и с гистограммой (скорректируем размер, цвет, добавим подписи и сетку):" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(figsize =(16, 9))\n", "\n", "plt.plot(tiny[\"AGE\"], tiny[\"YEARSEXP\"], 'o', color = \"#2d2966\")\n", "ax.set_xlabel(\"Age\")\n", "ax.set_ylabel(\"Years of experience\")\n", "\n", "ax.set_axisbelow(True)\n", "ax.grid(b = True, color ='grey', linestyle = '-.', \n", " linewidth = 0.5, alpha = 0.4)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Итак, судя по графику, связь между показателями есть, и она положительна. У более старших сотрудников число лет опыта работы больше (что вполне логично)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Матрица диаграмм рассеивания\n", "\n", "Построим более специфический график — матрицу диаграмм рассеивания. На самом деле, это просто набор диаграмм рассеивания сразу для нескольких показателей, полезный в случае, если мы хотим посмотреть на связь «всего со всем». Давайте сначала построим матрицу диаграмм рассеивания, а потом разберемся, что есть что.\n", "\n", "Для этого нам понадобится функция `scatter_matrix()` из модуля для графики `plotting` внутри библиотеки `pandas`. Вызов функции будет выглядеть довольно длинно:" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# извлекаем столбцы AGE и YEARSEXP\n", "\n", "pd.plotting.scatter_matrix(tiny[[\"AGE\", \"YEARSEXP\"]]);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Что представляет собой этот график? Матрицу (сетку или таблицу), которая содержит симметричные диаграммы рассеивания (`AGE` на `YEARSEXP` в нижнем углу и `YEARSEXP` на `AGE` в верхнем углу) и гистограммы на главной диагонали. Логика простая: смотрим на названия переменных по строкам и столбцам таблицы и на пересечении находим соответствующую диаграмму рассеивания. Строить диаграмму рассеивания для связи переменной самой с собой бессмысленно, поэтому вместо таких диаграмм добавлены гистограммы. \n", "\n", "График довольно полезный, особенно, если показателей, между которыми мы хотим визуализировать связь, много. Разумно много, не забывайте, что визуализация должна быть читаемой и понятной. Например, если у нас есть пять показателей и мы хотим для каждой пары показателей построить диаграмму рассеивания, нам придется построить целых $\\frac{5 \\times 4}{2} = 10$ графиков (считаем, что диаграмма рассеивания $X$ на $Y$ и $Y$ на $X$ – это одно и то же, просто с перевернутыми осями). А так мы можем написать одну строчку кода для матрицы диаграмм рассеивания и бонусом получить еще и гистограммы.\n", "\n", "Еще одно удобство состоит в том, что необязательно самим отбирать столбцы для построения матрицы через `scatter_matrix()`. Эта функция умеет сама выбирать числовые столбцы из датафрейма:" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# здесь не самая удачная картинка, \n", "# пол в датафрейме закодирован числами 0 и 1,\n", "# поэтому мы видим такие «неприятные» диаграммы рассеивания\n", "# и гистограмму из двух столбиков, что тоже не супер\n", "\n", "pd.plotting.scatter_matrix(tiny);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Давайте сделаем график красивым. Названия аргументов будут аналогичны тем, что мы видели в примерах выше, только здесь уже не нужно использовать `fig` и `ax`, функция `scatter_matrix()` из библиотеки `pandas`, а не `matplotlib`, у нее своя логика работы." ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# figsize: размер матрицы в дюймах, логично делать ее квадратной\n", "# color и alpha: цвет и прозрачность точек\n", "# marker: тип маркера для точек\n", "# hist_kwds: от histogram keywords\n", "\n", "\n", "pd.plotting.scatter_matrix(tiny[[\"AGE\", \"YEARSEXP\"]], \n", " figsize = (10, 10),\n", " color = \"#8B0000\", \n", " alpha = 0.8, \n", " marker = \"o\",\n", " hist_kwds = {\"color\": \"#2d2966\", \n", " \"edgecolor\" : \"white\"});" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Перечень аргументов функции `scatter_matrix()` можно посмотреть [здесь](https://pandas.pydata.org/docs/reference/api/pandas.plotting.scatter_matrix.html). Более продвинутые вопросы вроде изменения подписей под каждым графиком пока рассматривать не будем, это долгая история с осями и циклами." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Столбиковая диаграмма" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Столбиковая диаграмма – один из самых простых видов графиков, однако в Python его построение сопряжено с небольшой сложностью. Для создания столбиковой диаграммы Python должен точно знать, какие категории будут идти по горизонтальной оси (или вертикальной, если мы поворачиваем график), и чему будут равны высоты соответствующих столбиков. В отличие от случая с гистограммой, здесь сам он посчитать частоты не может. \n", "\n", "Поэтому таблицу с частотами нужно подготовить самостоятельно. Для этого можно воспользоваться методом `.value_counts()`. Выведем таблицу частот для столбца `Qu3`, степени согласия с утверждением *Articles in Wikipedia are comprehensive*:" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "3 30\n", "2 16\n", "4 14\n", "5 2\n", "1 2\n", "? 1\n", "Name: Qu3, dtype: int64" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tiny[\"Qu3\"].value_counts()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Итак, в таблице записаны категории и соответствующие им частоты (30 ответов 3, 16 ответов 2, и так далее), причем категории упорядочены от самой частой до самой редкой.\n", "\n", "Полученный результат – это объект типа `pandas.Series`, то есть просто столбец с данными, где у каждого значения есть индекс (номер). Для тех, кто хорошо владеет Python: `pandas.Series` – это что-то среднее между массивом и словарем; от массива у него того, что все элементы одного типа, а от словаря – соответствие ключ-значение, то есть индекс-значение.\n", "\n", "Сохраним полученную таблицу в переменную `tab` и извлечем из нее названия категорий и частоты:" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [], "source": [ "tab = tiny[\"Qu3\"].value_counts()\n", "vals = tab.index # категории\n", "freqs = tab.values # частоты" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['3', '2', '4', '5', '1', '?'], dtype='object')" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "vals" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([30, 16, 14, 2, 2, 1])" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "freqs" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Сейчас у нас есть два массива (первое – не совсем массив, но суть та же), мы можем сообщить Python, какие значения располагать по горизонтальной и вертикальной осям. Для построения столбиковой диаграммы используется функция `bar()`:" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "data": { "image/png": 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b9/Yfc/0d8LcrOo0muR14LfB24AC6H/+/DLy/qvakPwu3WF+c9/Wl/e0mul+yW6327j9WBd9sTJIa5UlWSWqUgZekRhl4SWqUgZekRhl4SWqUgZekRhl4SWqUgZekRv0/OJ8cQjaCgmYAAAAASUVORK5CYII=\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.bar(vals, freqs);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Столбики упорядочены вполне разумным образом, от самого популярного ответа до наименее популярного. Но график точно нужно красиво оформить! Действуем по аналогии с гистограммой, только помним, что, в отличие от гистограммы, цвета столбиков у такой диаграммы могут быть разными. \n", "\n", "Есть готовые палитры цветов для графиков, более того, палитры контрастных цветов, я взяла набор цветов [отсюда](https://colorpalettes.net/color-palette-4284/), [ресурс](https://colorpalettes.net/category/contrasting-color/) для дизайнеров, не для аналитиков, но палитры вдохновляющие (вдохновляйтесь, но не забывайте, что у дизайнеров немного другие цели, брать слишком яркие и неестественные «кислотные» цвета плохо, равно как и блеклые цвета, которые будут плохо смотреться на белом фоне)." ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(figsize =(16, 9)) \n", "\n", "# tick_label: подписи для категорий-столбцов\n", "# \\n в подписях нужен для разбиения длинных строк,\n", "# это символ для перехода на новую строку\n", "\n", "ax.bar(vals, freqs, \n", " color = [\"#7e0f12\", \"#b71a3b\", \"#bc6d4c\", \n", " \"#6a7045\", \"#313c33\", \"black\"],\n", " tick_label = [\"Neither agree\\nnor disagree\", \n", " \"Disagree\", \n", " \"Agree\", \n", " \"Strongly\\ndisagree\",\n", " \"Strongly\\nagree\", \n", " \"No answer\"])\n", "\n", "# добавляем подписи\n", "\n", "ax.set_title(\"Articles in Wikipedia are comprehensive\", loc = 'left')\n", "ax.set_ylabel(\"Counts\")\n", "\n", "# добавляем сетку\n", "\n", "ax.set_axisbelow(True)\n", "ax.grid(b = True, color ='grey', linestyle = '-.', \n", " linewidth = 0.5, alpha = 0.4)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Этот же график можно было бы построить вертикально. Тот же код, только вместо функции `bar()` понадобится функция `barh()` (от *horizontal*): " ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(figsize =(16, 9)) \n", "\n", "ax.barh(vals, freqs, \n", " color = [\"#7e0f12\", \"#b71a3b\", \"#bc6d4c\", \n", " \"#6a7045\", \"#313c33\", \"black\"],\n", " tick_label = [\"Neither agree\\nnor disagree\", \n", " \"Disagree\", \n", " \"Agree\", \n", " \"Strongly\\ndisagree\",\n", " \"Strongly\\nagree\", \n", " \"No answer\"])\n", "# и Counts по оси x\n", "\n", "ax.set_title(\"Articles in Wikipedia are comprehensive\", loc = 'left')\n", "ax.set_xlabel(\"Counts\")\n", "\n", "ax.set_axisbelow(True)\n", "ax.grid(b = True, color ='grey', linestyle = '-.', \n", " linewidth = 0.5, alpha = 0.4)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Не самый лучший вариант, график будет более читаемым, если самый длинный прямоугольник будет сверху. При желании можно все категории упорядочить вручную и просто создать два списка, список с названиями категорий и список с частотами. Если значений немного, такой вариант приемлем.\n", "\n", "Напоследок рассмотрим пример, который требует сортировки названий столбцов. Предположим, что мы хотим построить столбиковую диаграмму, где столбцы упорядочены по ответам, от *Strongly disagree* (значение 1) до *Strongly agree* (значение 5), при этом категорию *No answer* мы исключаем. Как в таком случае поступить? Вспомним, как выглядит `tab`:" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "3 30\n", "2 16\n", "4 14\n", "5 2\n", "1 2\n", "? 1\n", "Name: Qu3, dtype: int64" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tab" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Отсортируем строки в `tab` по индексу, метод `.sort_index()` нам поможет:" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1 2\n", "2 16\n", "3 30\n", "4 14\n", "5 2\n", "? 1\n", "Name: Qu3, dtype: int64" ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tab_new = tab.sort_index()\n", "tab_new" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Отлично! Уберем последнюю строку с вопросительным знаком:" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1 2\n", "2 16\n", "3 30\n", "4 14\n", "5 2\n", "Name: Qu3, dtype: int64" ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# все строки, с 0 до -1, то есть последняя исключена\n", "\n", "tab_new = tab_new[:-1]\n", "tab_new" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Забираем, как и раньше, названия категорий и частоты:" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [], "source": [ "vals_new = tab_new.index\n", "freqs_new = tab_new.values" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "И подставляем их в код для графика:" ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(figsize =(16, 9)) \n", "\n", "ax.bar(vals_new, freqs_new, \n", " color = [\"#bc6d4c\", \"#b71a3b\", \"#7e0f12\", \n", " \"#6a7045\", \"#313c33\"],\n", " tick_label = [\"Strongly\\ndisagree\", \n", " \"Disagree\", \n", " \"Neither agree\\nnor disagree\",\n", " \"Agree\",\n", " \"Strongly\\nagree\"])\n", "\n", "ax.set_title(\"Articles in Wikipedia are comprehensive\", loc = 'left')\n", "ax.set_ylabel(\"Counts\")\n", "\n", "ax.set_axisbelow(True)\n", "ax.grid(b = True, color ='grey', linestyle = '-.', \n", " linewidth = 0.5, alpha = 0.4)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "На этом закончим знакомство с базовыми графиками в `matplotlib`, о более сложных случаях построения (добавление значений над столбцами, построение графиков по группам и проч) можно будет почитать позже в дополнительных материалах." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.4" } }, "nbformat": 4, "nbformat_minor": 2 }