Lecture5.ipynb

{"cells":[{"cell_type":"markdown","metadata":{"id":"q7EDGO1Vx3SL"},"source":["# Lecture 5\n","\n","In this lecture we will get to know and become experts in:\n","1. [Data Manipulation with pandas](#Data-Manipulation-with-pandas)\n","    * [Handling Files](#Handling-Files)\n","    * Counting and Summary Statistics\n","    * [Grouped Operations](#Grouped-Operations)\n","2. [Plotting](#Plotting)\n","    * matplotlib\n","    * pandas\n","\n","And if you want to delve deeper, look at the \n","[Advanced topics](#Advanced-topics)\n","\n","Relevant DataCamp lessons:\n","\n","* [Data manipulation with pandas](https://campus.datacamp.com/courses/data-manipulation-with-pandas), Chaps 2 and 4\n","* [Matplotlib](https://campus.datacamp.com/courses/intermediate-python), Chap 1"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"QaXZ0yqax3SP"},"outputs":[],"source":["import pandas as pd\n","import numpy as np"]},{"cell_type":"markdown","metadata":{"id":"Xv1Eq7I4x3SR"},"source":["### Data Manipulation with pandas\n","\n","While we have seen panda's ability to (i) mix data types (strings, numbers, categories, Boolean, ...) and (ii) refer to columns and rows by names, this library offers a lot more powerful tools for efficiently gaining insights from data, e.g.\n","\n","- summarize/aggregate data in efficient pivot style manners\n","- handling missing values\n","- visualize/plot data\n","\n","\n","\n","\n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Y4L3mNDgx3SS"},"outputs":[],"source":["!pip install gapminder\n","from gapminder import gapminder"]},{"cell_type":"markdown","metadata":{"id":"3XQ2J8Olx3SS"},"source":["### Handling Files\n","\n","Get to know your friends\n","\n","* `pd.read_csv`\n","* `pd.read_table`\n","* `pd.read_excel`\n","\n","But before that we need to connect to our Google drives ! (more instructions can be found [here](https://gist.github.com/kelly-sovacool/c33b64b70164be37d75bd40c9b56b498))"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"MOzuka_Ox3ST","executionInfo":{"status":"ok","timestamp":1683555782984,"user_tz":-120,"elapsed":28649,"user":{"displayName":"Markus Loecher","userId":"02488037228155275753"}},"outputId":"b9e77325-c671-45ef-a217-a89438c5b078"},"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/gdrive\n"]}],"source":["# mount Google Drive\n","#you will have to give permissions\n","from google.colab import drive\n","drive.mount('/content/gdrive')"]},{"cell_type":"code","source":["#note that you might want to create a folder on your drive\n","gdrive_path = '/content/gdrive/My Drive/'\n","# write a file\n","gapminder.to_csv(gdrive_path + \"gapminder.csv\""],"metadata":{"id":"FRDO1aMCyWzv"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["\"Sam\" + \" Altman\" "],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":35},"id":"Tx16PIJBv2QZ","executionInfo":{"status":"ok","timestamp":1683555854441,"user_tz":-120,"elapsed":4,"user":{"displayName":"Markus Loecher","userId":"02488037228155275753"}},"outputId":"2c49553e-941c-4125-c2c3-52e6b4c730c2"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["'Sam Altman'"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":5}]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"id":"0cwUXF51x3ST","executionInfo":{"status":"ok","timestamp":1683439223857,"user_tz":-120,"elapsed":226,"user":{"displayName":"Markus 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class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>country</th>\n","      <th>continent</th>\n","      <th>year</th>\n","      <th>lifeExp</th>\n","      <th>pop</th>\n","      <th>gdpPercap</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Afghanistan</td>\n","      <td>Asia</td>\n","      <td>1952</td>\n","      <td>28.801</td>\n","      <td>8425333</td>\n","      <td>779.445314</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>Afghanistan</td>\n","      <td>Asia</td>\n","      <td>1957</td>\n","      <td>30.332</td>\n","      <td>9240934</td>\n","      <td>820.853030</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>Afghanistan</td>\n","      <td>Asia</td>\n","      <td>1962</td>\n","      <td>31.997</td>\n","      <td>10267083</td>\n","      <td>853.100710</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>Afghanistan</td>\n","      <td>Asia</td>\n","      <td>1967</td>\n","      <td>34.020</td>\n","      <td>11537966</td>\n","      <td>836.197138</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>Afghanistan</td>\n","      <td>Asia</td>\n","      <td>1972</td>\n","      <td>36.088</td>\n","      <td>13079460</td>\n","      <td>739.981106</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8dacfd70-e62c-4d50-aa0b-f306254823a4')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 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index_col=0)\n","gapminder.head(3)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":143},"id":"CWB9wPIRzG7y","executionInfo":{"status":"ok","timestamp":1683439371535,"user_tz":-120,"elapsed":1079,"user":{"displayName":"Markus Loecher","userId":"02488037228155275753"}},"outputId":"80b7bf82-1c44-4c1d-bb77-03ec72319eee"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["       country continent  year  lifeExp       pop   gdpPercap\n","0  Afghanistan      Asia  1952   28.801   8425333  779.445314\n","1  Afghanistan      Asia  1957   30.332   9240934  820.853030\n","2  Afghanistan      Asia  1962   31.997  10267083  853.100710"],"text/html":["\n","  <div id=\"df-6f0384f6-9b5a-4ac0-b802-85fcdaa61167\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: 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<td>10267083</td>\n","      <td>853.100710</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6f0384f6-9b5a-4ac0-b802-85fcdaa61167')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 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Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "]},"metadata":{},"execution_count":8}]},{"cell_type":"markdown","source":["Counting and Summary Statistics"],"metadata":{"id":"e6q0tFoLzH4D"}},{"cell_type":"code","execution_count":null,"metadata":{"id":"gbxsGvY8x3SV","outputId":"af1ef667-f29d-4c92-86b8-2fd1c6ffe159"},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>country</th>\n","      <th>continent</th>\n","      <th>year</th>\n","      <th>lifeExp</th>\n","      <th>pop</th>\n","      <th>gdpPercap</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Afghanistan</td>\n","      <td>Asia</td>\n","      <td>1952</td>\n","      <td>28.801</td>\n","      <td>8425333</td>\n","      <td>779.445314</td>\n","    </tr>\n","    <tr>\n","      <th>528</th>\n","      <td>France</td>\n","      <td>Europe</td>\n","      <td>1952</td>\n","      <td>67.410</td>\n","      <td>42459667</td>\n","      <td>7029.809327</td>\n","    </tr>\n","    <tr>\n","      <th>540</th>\n","      <td>Gabon</td>\n","      <td>Africa</td>\n","      <td>1952</td>\n","      <td>37.003</td>\n","      <td>420702</td>\n","      <td>4293.476475</td>\n","    </tr>\n","    <tr>\n","      <th>1656</th>\n","      <td>West Bank and Gaza</td>\n","      <td>Asia</td>\n","      <td>1952</td>\n","      <td>43.160</td>\n","      <td>1030585</td>\n","      <td>1515.592329</td>\n","    </tr>\n","    <tr>\n","      <th>552</th>\n","      <td>Gambia</td>\n","      <td>Africa</td>\n","      <td>1952</td>\n","      <td>30.000</td>\n","      <td>284320</td>\n","      <td>485.230659</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["                 country continent  year  lifeExp       pop    gdpPercap\n","0            Afghanistan      Asia  1952   28.801   8425333   779.445314\n","528               France    Europe  1952   67.410  42459667  7029.809327\n","540                Gabon    Africa  1952   37.003    420702  4293.476475\n","1656  West Bank and Gaza      Asia  1952   43.160   1030585  1515.592329\n","552               Gambia    Africa  1952   30.000    284320   485.230659"]},"execution_count":71,"metadata":{},"output_type":"execute_result"}],"source":["gapminder.sort_values(by=\"year\").head()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"eJ_bM4pXx3SV","outputId":"2a36faee-6c99-4bc8-bc6a-0bb8785462e3","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1683556772006,"user_tz":-120,"elapsed":284,"user":{"displayName":"Markus Loecher","userId":"02488037228155275753"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["Afghanistan          12\n","Pakistan             12\n","New Zealand          12\n","Nicaragua            12\n","Niger                12\n","                     ..\n","Eritrea              12\n","Equatorial Guinea    12\n","El Salvador          12\n","Egypt                12\n","Zimbabwe             12\n","Name: country, Length: 142, dtype: int64"]},"metadata":{},"execution_count":10}],"source":["#How many countries?\n","CtryCts = gapminder[\"country\"].value_counts()\n","CtryCts\n","#note the similarity with np.unique(..., return_counts=True)"]},{"cell_type":"markdown","metadata":{"id":"1JAapRYlx3SX"},"source":["### Grouped Operations\n","\n","The gapminder data is a good example for wanting to apply functions to subsets to data that correspond to categories, e.g.\n","* by year\n","* by country\n","* by continent\n","\n","The powerful pandas `.groupby()` method enables exactly this goal rather elegantly and efficiently."]},{"cell_type":"markdown","source":["First, think how you could possibly compute the average GDP seprataley for each continent. The `numpy.mean(..., axis=...)` will not help you. \n","\n","Instead you will have to manually find all continents and then use Boolean logic:"],"metadata":{"id":"K3Lu_HPn0EdR"}},{"cell_type":"code","source":["continents =np.unique(gapminder[\"continent\"])\n","continents"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"VoObm79y0cNg","executionInfo":{"status":"ok","timestamp":1683439733094,"user_tz":-120,"elapsed":212,"user":{"displayName":"Markus Loecher","userId":"02488037228155275753"}},"outputId":"15481d0e-0491-467c-9721-db0329d72684"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array(['Africa', 'Americas', 'Asia', 'Europe', 'Oceania'], dtype=object)"]},"metadata":{},"execution_count":10}]},{"cell_type":"code","source":["AfricaRows = gapminder[\"continent\"]==\"Africa\"\n","gapminder[AfricaRows][\"gdpPercap\"].mean()\n"],"metadata":{"id":"DIN7SQyJxkxU"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["#you could use a for loop instead, of course\n","gapminder[gapminder[\"continent\"]==\"Africa\"][\"gdpPercap\"].mean()\n","gapminder[gapminder[\"continent\"]==\"Americas\"][\"gdpPercap\"].mean()\n","gapminder[gapminder[\"continent\"]==\"Asia\"][\"gdpPercap\"].mean()\n","gapminder[gapminder[\"continent\"]==\"Europe\"][\"gdpPercap\"].mean()\n","gapminder[gapminder[\"continent\"]==\"Oceania\"][\"gdpPercap\"].mean()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"hQWB1_AS0-ui","executionInfo":{"status":"ok","timestamp":1683556339119,"user_tz":-120,"elapsed":223,"user":{"displayName":"Markus Loecher","userId":"02488037228155275753"}},"outputId":"bc685147-f274-4a07-d91d-8db44044b594"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["18621.609223333333"]},"metadata":{},"execution_count":7}]},{"cell_type":"markdown","source":["Instead, we should embrace the concept of **grouping by a variable**"],"metadata":{"id":"q19LIaco1pAE"}},{"cell_type":"code","source":["gapminder.mean()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"rDi2iifXyKlN","executionInfo":{"status":"ok","timestamp":1683556453453,"user_tz":-120,"elapsed":4,"user":{"displayName":"Markus Loecher","userId":"02488037228155275753"}},"outputId":"e401809a-8936-441b-e045-f08629f010e2"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["<ipython-input-8-451a80012f7c>:1: FutureWarning: The default value of numeric_only in DataFrame.mean is deprecated. In a future version, it will default to False. In addition, specifying 'numeric_only=None' is deprecated. Select only valid columns or specify the value of numeric_only to silence this warning.\n","  gapminder.mean()\n"]},{"output_type":"execute_result","data":{"text/plain":["year         1.979500e+03\n","lifeExp      5.947444e+01\n","pop          2.960121e+07\n","gdpPercap    7.215327e+03\n","dtype: float64"]},"metadata":{},"execution_count":8}]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":292},"id":"8vlrYPfhx3SY","executionInfo":{"status":"ok","timestamp":1683556842613,"user_tz":-120,"elapsed":8,"user":{"displayName":"Markus Loecher","userId":"02488037228155275753"}},"outputId":"18a97f92-9784-4722-c2be-3c52c2f3913e"},"outputs":[{"output_type":"stream","name":"stderr","text":["<ipython-input-12-91beadf16788>:2: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n","  byContinent.mean()\n"]},{"output_type":"execute_result","data":{"text/plain":["             year    lifeExp           pop     gdpPercap\n","continent                                               \n","Africa     1979.5  48.865330  9.916003e+06   2193.754578\n","Americas   1979.5  64.658737  2.450479e+07   7136.110356\n","Asia       1979.5  60.064903  7.703872e+07   7902.150428\n","Europe     1979.5  71.903686  1.716976e+07  14469.475533\n","Oceania    1979.5  74.326208  8.874672e+06  18621.609223"],"text/html":["\n","  <div id=\"df-9e17e362-14cc-4068-a4ec-3ebcc0a6afcc\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>year</th>\n","      <th>lifeExp</th>\n","      <th>pop</th>\n","      <th>gdpPercap</th>\n","    </tr>\n","    <tr>\n","      <th>continent</th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>Africa</th>\n","      <td>1979.5</td>\n","      <td>48.865330</td>\n","      <td>9.916003e+06</td>\n","      <td>2193.754578</td>\n","    </tr>\n","    <tr>\n","      <th>Americas</th>\n","      <td>1979.5</td>\n","      <td>64.658737</td>\n","      <td>2.450479e+07</td>\n","      <td>7136.110356</td>\n","    </tr>\n","    <tr>\n","      <th>Asia</th>\n","      <td>1979.5</td>\n","      <td>60.064903</td>\n","      <td>7.703872e+07</td>\n","      <td>7902.150428</td>\n","    </tr>\n","    <tr>\n","      <th>Europe</th>\n","      <td>1979.5</td>\n","      <td>71.903686</td>\n","      <td>1.716976e+07</td>\n","      <td>14469.475533</td>\n","    </tr>\n","    <tr>\n","      <th>Oceania</th>\n","      <td>1979.5</td>\n","      <td>74.326208</td>\n","      <td>8.874672e+06</td>\n","      <td>18621.609223</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9e17e362-14cc-4068-a4ec-3ebcc0a6afcc')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","      <script>\n","        const buttonEl =\n","          document.querySelector('#df-9e17e362-14cc-4068-a4ec-3ebcc0a6afcc button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 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mean"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"PpyhCnklzFSh","executionInfo":{"status":"ok","timestamp":1683556960873,"user_tz":-120,"elapsed":4,"user":{"displayName":"Markus Loecher","userId":"02488037228155275753"}},"outputId":"1cc02fd3-3a03-4576-8f37-393660f66c95"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["76.442"]},"metadata":{},"execution_count":16}]},{"cell_type":"code","source":["byContinent[\"gdpPercap\"].agg([min,max, np.mean])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":237},"id":"TjokjsCI0bFz","executionInfo":{"status":"ok","timestamp":1683557058870,"user_tz":-120,"elapsed":947,"user":{"displayName":"Markus Loecher","userId":"02488037228155275753"}},"outputId":"bd3688a0-cbb4-4529-af96-d9fd89761ad9"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["                    min           max          mean\n","continent                                          \n","Africa       241.165876   21951.21176   2193.754578\n","Americas    1201.637154   42951.65309   7136.110356\n","Asia         331.000000  113523.13290   7902.150428\n","Europe       973.533195   49357.19017  14469.475533\n","Oceania    10039.595640   34435.36744  18621.609223"],"text/html":["\n","  <div id=\"df-faa90c88-6b88-43cc-9e80-c69e3f47eec9\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>min</th>\n","      <th>max</th>\n","      <th>mean</th>\n","    </tr>\n","    <tr>\n","      <th>continent</th>\n","      <th></th>\n","      <th></th>\n"," 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Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "]},"metadata":{},"execution_count":17}]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":238},"id":"6Plz9mFax3SY","executionInfo":{"status":"ok","timestamp":1683442764530,"user_tz":-120,"elapsed":320,"user":{"displayName":"Markus Loecher","userId":"02488037228155275753"}},"outputId":"1b15b924-c0f4-46ce-f5cc-884c324dfa60"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["                    min           max          mean\n","continent                                          \n","Africa       241.165876   21951.21176   2193.754578\n","Americas    1201.637154   42951.65309   7136.110356\n","Asia         331.000000  113523.13290   7902.150428\n","Europe       973.533195   49357.19017  14469.475533\n","Oceania    10039.595640   34435.36744  18621.609223"],"text/html":["\n","  <div id=\"df-2c5b6b6a-c411-4919-aa90-4a512fa77b69\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>min</th>\n","      <th>max</th>\n","      <th>mean</th>\n","    </tr>\n","    <tr>\n","      <th>continent</th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>Africa</th>\n","      <td>241.165876</td>\n","      <td>21951.21176</td>\n","      <td>2193.754578</td>\n","    </tr>\n","    <tr>\n","      <th>Americas</th>\n","      <td>1201.637154</td>\n","      <td>42951.65309</td>\n","      <td>7136.110356</td>\n","    </tr>\n","    <tr>\n","      <th>Asia</th>\n","      <td>331.000000</td>\n","      <td>113523.13290</td>\n","      <td>7902.150428</td>\n","    </tr>\n","    <tr>\n","      <th>Europe</th>\n","      <td>973.533195</td>\n","      <td>49357.19017</td>\n","      <td>14469.475533</td>\n","    </tr>\n","    <tr>\n","      <th>Oceania</th>\n","      <td>10039.595640</td>\n","      <td>34435.36744</td>\n","      <td>18621.609223</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2c5b6b6a-c411-4919-aa90-4a512fa77b69')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 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\"year\"])[\"gdpPercap\"]\n","byContinentYear.mean()"],"metadata":{"id":"3Fh0kYjq05qB"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["#multiple keys\n","gapminder[\"past1990\"] = gapminder[\"year\"] > 1990\n","byContinentYear = gapminder.groupby([\"continent\", \"past1990\"])[\"gdpPercap\"]\n","byContinentYear.mean()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"k9ZVk-GaBGuI","executionInfo":{"status":"ok","timestamp":1683443085773,"user_tz":-120,"elapsed":211,"user":{"displayName":"Markus Loecher","userId":"02488037228155275753"}},"outputId":"e3ea1161-2b2e-481e-eb94-77195c4ae250"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["continent  past1990\n","Africa     False        1997.008411\n","           True         2587.246913\n","Americas   False        6051.047533\n","           True         9306.236000\n","Asia       False        6713.113041\n","           True        10280.225202\n","Europe     False       11341.142807\n","           True        20726.140986\n","Oceania    False       15224.015414\n","           True        25416.796842\n","Name: gdpPercap, dtype: float64"]},"metadata":{},"execution_count":24}]},{"cell_type":"markdown","metadata":{"id":"un4ahfFVx3SZ"},"source":["---------------------------------------\n","\n","#### Titanic data"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":487},"id":"pfe5y6EXx3SZ","executionInfo":{"status":"ok","timestamp":1683557391047,"user_tz":-120,"elapsed":2159,"user":{"displayName":"Markus Loecher","userId":"02488037228155275753"}},"outputId":"15f8cf86-995e-4711-8ba0-63d32ea31ff8"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["     survived  pclass     sex   age  sibsp  parch     fare embarked   class  \\\n","0           0       3    male  22.0      1      0   7.2500        S   Third   \n","1           1       1  female  38.0      1      0  71.2833        C   First   \n","2           1       3  female  26.0      0      0   7.9250        S   Third   \n","3           1       1  female  35.0      1      0  53.1000        S   First   \n","4           0       3    male  35.0      0      0   8.0500        S   Third   \n","..        ...     ...     ...   ...    ...    ...      ...      ...     ...   \n","886         0       2    male  27.0      0      0  13.0000        S  Second   \n","887         1       1  female  19.0      0      0  30.0000        S   First   \n","888         0       3  female   NaN      1      2  23.4500        S   Third   \n","889         1       1    male  26.0      0      0  30.0000        C   First   \n","890         0       3    male  32.0      0      0   7.7500        Q   Third   \n","\n","       who  adult_male deck  embark_town alive  alone  3rdClass   male  \n","0      man        True  NaN  Southampton    no  False      True   True  \n","1    woman       False    C    Cherbourg   yes  False     False  False  \n","2    woman       False  NaN  Southampton   yes   True      True  False  \n","3    woman       False    C  Southampton   yes  False     False  False  \n","4      man        True  NaN  Southampton    no   True      True   True  \n","..     ...         ...  ...          ...   ...    ...       ...    ...  \n","886    man        True  NaN  Southampton    no   True     False   True  \n","887  woman       False    B  Southampton   yes   True     False  False  \n","888  woman       False  NaN  Southampton    no  False      True  False  \n","889    man        True    C    Cherbourg   yes   True     False   True  \n","890    man        True  NaN   Queenstown    no   True      True   True  \n","\n","[891 rows x 17 columns]"],"text/html":["\n","  <div id=\"df-332e1944-6cd0-434e-8184-a0c3998b8b8e\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe 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<td>man</td>\n","      <td>True</td>\n","      <td>NaN</td>\n","      <td>Southampton</td>\n","      <td>no</td>\n","      <td>False</td>\n","      <td>True</td>\n","      <td>True</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>female</td>\n","      <td>38.0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>71.2833</td>\n","      <td>C</td>\n","      <td>First</td>\n","      <td>woman</td>\n","      <td>False</td>\n","      <td>C</td>\n","      <td>Cherbourg</td>\n","      <td>yes</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>1</td>\n","      <td>3</td>\n","      <td>female</td>\n","      <td>26.0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>7.9250</td>\n","      <td>S</td>\n","      <td>Third</td>\n","      <td>woman</td>\n","      <td>False</td>\n","      <td>NaN</td>\n","      <td>Southampton</td>\n","      <td>yes</td>\n","      <td>True</td>\n","      <td>True</td>\n","      <td>False</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>female</td>\n","      <td>35.0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>53.1000</td>\n","      <td>S</td>\n","      <td>First</td>\n","      <td>woman</td>\n","      <td>False</td>\n","      <td>C</td>\n","      <td>Southampton</td>\n","      <td>yes</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>0</td>\n","      <td>3</td>\n","      <td>male</td>\n","      <td>35.0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>8.0500</td>\n","      <td>S</td>\n","      <td>Third</td>\n","      <td>man</td>\n","      <td>True</td>\n","      <td>NaN</td>\n","      <td>Southampton</td>\n","      <td>no</td>\n","      <td>True</td>\n","      <td>True</td>\n","      <td>True</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>886</th>\n","      <td>0</td>\n","      <td>2</td>\n","      <td>male</td>\n","      <td>27.0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>13.0000</td>\n","      <td>S</td>\n","      <td>Second</td>\n","      <td>man</td>\n","      <td>True</td>\n","      <td>NaN</td>\n","      <td>Southampton</td>\n","      <td>no</td>\n","      <td>True</td>\n","      <td>False</td>\n","      <td>True</td>\n","    </tr>\n","    <tr>\n","      <th>887</th>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>female</td>\n","      <td>19.0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>30.0000</td>\n","      <td>S</td>\n","      <td>First</td>\n","      <td>woman</td>\n","      <td>False</td>\n","      <td>B</td>\n","      <td>Southampton</td>\n","      <td>yes</td>\n","      <td>True</td>\n","      <td>False</td>\n","      <td>False</td>\n","    </tr>\n","    <tr>\n","      <th>888</th>\n","      <td>0</td>\n","      <td>3</td>\n","      <td>female</td>\n","      <td>NaN</td>\n","      <td>1</td>\n","      <td>2</td>\n","      <td>23.4500</td>\n","      <td>S</td>\n","      <td>Third</td>\n","      <td>woman</td>\n","      <td>False</td>\n","      <td>NaN</td>\n","      <td>Southampton</td>\n","      <td>no</td>\n","      <td>False</td>\n","      <td>True</td>\n","      <td>False</td>\n","    </tr>\n","    <tr>\n","      <th>889</th>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>male</td>\n","      <td>26.0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>30.0000</td>\n","      <td>C</td>\n","      <td>First</td>\n","      <td>man</td>\n","      <td>True</td>\n","      <td>C</td>\n","      <td>Cherbourg</td>\n","      <td>yes</td>\n","      <td>True</td>\n","      <td>False</td>\n","      <td>True</td>\n","    </tr>\n","    <tr>\n","      <th>890</th>\n","      <td>0</td>\n","      <td>3</td>\n","      <td>male</td>\n","      <td>32.0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>7.7500</td>\n","      <td>Q</td>\n","      <td>Third</td>\n","      <td>man</td>\n","      <td>True</td>\n","      <td>NaN</td>\n","      <td>Queenstown</td>\n","      <td>no</td>\n","      <td>True</td>\n","      <td>True</td>\n","      <td>True</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>891 rows × 17 columns</p>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-332e1944-6cd0-434e-8184-a0c3998b8b8e')\"\n","              title=\"Convert this dataframe to an interactive 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Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "]},"metadata":{},"execution_count":19}],"source":["# Since pandas does not have any built in data, I am going to \"cheat\" and \n","# make use of the `seaborn` library\n","import seaborn as sns \n","\n","titanic = sns. load_dataset('titanic')\n","titanic[\"3rdClass\"] = titanic[\"pclass\"]==3\n","titanic[\"male\"] = titanic[\"sex\"]==\"male\"\n","\n","titanic"]},{"cell_type":"code","source":["#overall survival rate\n","titanic.survived.mean()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"1k8msEJ72XRk","executionInfo":{"status":"ok","timestamp":1683557620846,"user_tz":-120,"elapsed":2110,"user":{"displayName":"Markus Loecher","userId":"02488037228155275753"}},"outputId":"6ac4b681-5e13-4afd-b0b0-998429f89681"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.3838383838383838"]},"metadata":{},"execution_count":20}]},{"cell_type":"markdown","source":["Tasks:\n","\n","- compute the proportion of survived separately for \n","  * male/female\n","  * the three classes\n","  * Pclass and sex \n","\n","- compute the mean age separately for male/female"],"metadata":{"id":"Lz92d-3KB2fW"}},{"cell_type":"code","source":["#I would like to compute the mean survical seprately for each group\n","bySex = titanic.groupby(\"sex\")\n","#here I am specifically asking for the mean\n","bySex[\"survived\"].mean()\n","#if you want multiple summaries, you can list them all inside the agg():\n","bySex[\"survived\"].agg([min, max, np.mean ])"],"metadata":{"id":"7SWUAlqZFYfh","colab":{"base_uri":"https://localhost:8080/","height":143},"executionInfo":{"status":"ok","timestamp":1683558598306,"user_tz":-120,"elapsed":8,"user":{"displayName":"Markus Loecher","userId":"02488037228155275753"}},"outputId":"124df0a5-7e0e-4c4e-b0cb-260c30ce820f"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["        min  max      mean\n","sex                       \n","female    0    1  0.742038\n","male      0    1  0.188908"],"text/html":["\n","  <div id=\"df-500239dc-c9a3-4300-8deb-6b909fa15d64\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: 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mean\n","bySexPclass[\"survived\"].mean()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"taUpimyF6q5J","executionInfo":{"status":"ok","timestamp":1683558808601,"user_tz":-120,"elapsed":3,"user":{"displayName":"Markus Loecher","userId":"02488037228155275753"}},"outputId":"b75bc315-0ee3-4982-f34b-293a22ca193b"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["pclass  sex   \n","1       female    0.968085\n","        male      0.368852\n","2       female    0.921053\n","        male      0.157407\n","3       female    0.500000\n","        male      0.135447\n","Name: survived, dtype: float64"]},"metadata":{},"execution_count":28}]},{"cell_type":"code","source":["bySex = titanic.groupby(\"sex\")\n","#here I am specifically asking for the mean\n","bySex[\"survived\"].mean()"],"metadata":{"id":"o6KdOyX-65By"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["### Plotting\n","\n","We will not spend much time with basic plots in matplotlib but instead move quickly toward the pandas versions of these functions."],"metadata":{"id":"__SHF7pYJLRY"}},{"cell_type":"code","source":["#%matplotlib inline\n","import matplotlib.pyplot as plt\n","\n","#plt.rcParams['figure.dpi'] = 800\n","year = [1950, 1970, 1990, 2010]\n","pop = [2.519, 3.692, 5.263, 6.972]\n","plt.plot(year, pop)\n","#plt.bar(year, pop)\n","#plt.scatter(year, pop)\n","plt.xlabel('Year')\n","plt.ylabel('Population')\n","plt.title('World Population')\n","x = 1\n","#plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":472},"id":"TVNwfSvnJNLW","executionInfo":{"status":"ok","timestamp":1683559052108,"user_tz":-120,"elapsed":287,"user":{"displayName":"Markus Loecher","userId":"02488037228155275753"}},"outputId":"88cfc1ab-221d-4b59-f6a7-6ff4c4e1dcb5"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"iVBORw0KGgoAAAANSUhEUgAAAioAAAHHCAYAAACRAnNyAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAABP5UlEQVR4nO3deVhU5fsG8HvYhh0EQURWEZBFFMEM3LLMXTNbTc01tTQ1s8xvi1ulZYtWZubPLbUsc88tzV1xAUEFFQFBEBEXZN9n3t8fo1MTSuxnZrg/18V1xZkzZ57zOsPcnXOe98iEEAJEREREWshA6gKIiIiIHoVBhYiIiLQWgwoRERFpLQYVIiIi0loMKkRERKS1GFSIiIhIazGoEBERkdZiUCEiIiKtxaBCREREWotBhYjUPDw8MHLkyP9cb/Xq1ZDJZEhJSan3mupbVfe5OkaOHAkPD4863SZRY8WgQqQFfvvtN8hkMmzZsqXCY23btoVMJsPBgwcrPObm5obw8PCGKLFGZs+eDZlMpv4xNzeHv78/PvjgA+Tm5kpdXq3cuHEDs2fPRkxMjNSlEOk1BhUiLdC5c2cAwLFjxzSW5+bmIjY2FkZGRjh+/LjGY2lpaUhLS1M/V5stXboUa9euxVdffYXWrVvjk08+Qe/evaHLtxq7ceMG5syZ89Cgsnz5csTHxzd8UUR6yEjqAogIcHZ2hqenZ4WgEhERASEEXnjhhQqPPfi9tkFFCIHi4mKYmZnVajuVef7559G0aVMAwIQJE/Dcc89h8+bNOHnyJMLCwurtdaVibGwsdQlEeoNHVIi0ROfOnREdHY2ioiL1suPHjyMgIAB9+vTByZMnoVQqNR6TyWTo1KkTAKC8vBzz5s2Dl5cX5HI5PDw88L///Q8lJSUar+Ph4YH+/ftj7969CA0NhZmZGZYtW/bIuuLi4vDkk0/CzMwMLi4u+PjjjzXqqIknn3wSAJCcnAwAKCgowNtvvw1XV1fI5XL4+vriiy++qHDERSaTYdKkSVi/fj18fX1hamqKkJAQHDlyRGO9R10j8uBUVGWysrIwffp0tGnTBpaWlrC2tkafPn1w7tw59TqHDh1Chw4dAACjRo1Sn9pavXr1I1+/uvu4detWBAYGQi6XIyAgAHv27Km0biJ9xSMqRFqic+fOWLt2LU6dOoUnnngCgCqMhIeHIzw8HDk5OYiNjUVQUJD6sdatW8Pe3h4AMHbsWKxZswbPP/883n77bZw6dQrz58/HpUuXKlz7Eh8fjyFDhmD8+PF47bXX4Ovr+9Cabt68ie7du6O8vBzvvfceLCws8OOPP9b66EtSUhIAwN7eHkIIDBw4EAcPHsSYMWPQrl077N27F++88w7S09Px9ddfazz38OHD+PXXXzF58mTI5XJ8//336N27N06fPo3AwMBa1QUAV69exdatW/HCCy/A09MTmZmZWLZsGbp164aLFy/C2dkZfn5+mDt3Lj766COMGzcOXbp0AYBHXi9U3X08duwYNm/ejDfeeANWVlb45ptv8NxzzyE1NVX9703UaAgi0gpxcXECgJg3b54QQoiysjJhYWEh1qxZI4QQolmzZmLJkiVCCCFyc3OFoaGheO2114QQQsTExAgAYuzYsRrbnD59ugAgDhw4oF7m7u4uAIg9e/ZUqMHd3V2MGDFC/fvUqVMFAHHq1Cn1slu3bgkbGxsBQCQnJ1e6T7NmzRIARHx8vLh9+7ZITk4Wy5YtE3K5XDRr1kwUFBSIrVu3CgDi448/1nju888/L2QymUhMTFQvAyAAiMjISPWya9euCVNTU/Hss8+ql40YMUK4u7s/sp7K9rm4uFgoFAqNdZKTk4VcLhdz585VLztz5owAIFatWlXhdf79+tXdRxMTE41l586dEwDEt99+W+G1iPQdT/0QaQk/Pz/Y29urrz05d+4cCgoK1P+XHh4err6gNiIiAgqFQn19yq5duwAA06ZN09jm22+/DQDYuXOnxnJPT0/06tXrP2vatWsXHn/8cTz22GPqZQ4ODhg6dGi19s3X1xcODg7w9PTE+PHj0apVK+zcuRPm5ubYtWsXDA0NMXny5Aq1CyGwe/dujeVhYWEICQlR/+7m5oZnnnkGe/fuhUKhqFZdDyOXy2FgoPrTqFAocPfuXVhaWsLX1xdnz56t0Taru489evSAl5eX+vegoCBYW1vj6tWrNXp9Il3GUz9EWkImkyE8PBxHjhyBUqnE8ePH4ejoiFatWgFQBZXvvvsOANSB5UFQuXbtGgwMDNTrPuDk5ARbW1tcu3ZNY7mnp2eVarp27Ro6duxYYfmjThU9yqZNm2BtbQ1jY2O4uLhofAlfu3YNzs7OsLKy0niOn5+f+vF/8vb2rrB9Hx8fFBYW4vbt23BycqpWbf+mVCqxePFifP/990hOTtYIPzU97VLdfXRzc6uwjSZNmuDevXs1en0iXcYjKkRapHPnzsjJycGFCxfU16c8EB4ejmvXriE9PR3Hjh2Ds7MzWrZsqfH8/7pQ9IH67PB5mK5du6JHjx7o1q2bRkipL48ah6occfn0008xbdo0dO3aFevWrcPevXuxb98+BAQE1Poi4qoyNDR86HKhw+3cRDXFoEKkRf45n8rx48fVHT0AEBISArlcjkOHDuHUqVMaj7m7u0OpVCIhIUFje5mZmcjOzoa7u3uN6nF3d6+wTQB1OkeIu7s7bty4gby8PI3lly9fVj/+Tw+r58qVKzA3N4eDgwMA1dGH7OzsCuv9+8jFw/z+++/o3r07VqxYgZdffhk9e/ZEjx49KmyvqqHwwT5UZx+J6G8MKkRaJDQ0FKampli/fj3S09M1jqjI5XK0b98eS5YsQUFBgcb8KX379gUALFq0SGN7X331FQCgX79+Naqnb9++OHnyJE6fPq1edvv2baxfv75G23vUaygUCvVprQe+/vpryGQy9OnTR2N5RESExrUiaWlp2LZtG3r27Kk+EuHl5YWcnBycP39evV5GRsZDZ/79N0NDwwpHLjZu3Ij09HSNZRYWFgDw0EBU230kor/xGhUiLWJiYoIOHTrg6NGjkMvlGheNAqrTP19++SUAzYne2rZtixEjRuDHH39EdnY2unXrhtOnT2PNmjUYNGgQunfvXqN63n33Xaxduxa9e/fGlClT1O3J7u7uGiGgNgYMGIDu3bvj/fffR0pKCtq2bYs///wT27Ztw9SpUyucKgoMDESvXr002pMBYM6cOep1Xn75ZcyYMQPPPvssJk+ejMLCQixduhQ+Pj7/eUFs//79MXfuXIwaNQrh4eG4cOEC1q9fX+E0m5eXF2xtbfHDDz/AysoKFhYW6Nix40Ov/6nuPhLRP0jbdERE/zZz5kwBQISHh1d4bPPmzQKAsLKyEuXl5RqPlZWViTlz5ghPT09hbGwsXF1dxcyZM0VxcbHGeu7u7qJfv34Pfe1/t+oKIcT58+dFt27dhKmpqWjRooWYN2+eWLFiRbXak2/fvl3penl5eeKtt94Szs7OwtjYWHh7e4uFCxcKpVKpsR4AMXHiRLFu3Trh7e0t5HK5CA4OFgcPHqywzT///FMEBgYKExMT4evrK9atW1fl9uS3335bNG/eXJiZmYlOnTqJiIgI0a1bN9GtWzeN527btk34+/sLIyMjjVblh7VHV3cf/+1h/zZEjYFMCF6dRUS6QSaTYeLEiRVOoRCR/uI1KkRERKS1GFSIiIhIazGoEBERkdZi1w8R6QxeUkfU+PCIChEREWktBhUiIiLSWjp96kepVOLGjRuwsrKq1nTWREREJB0hBPLy8uDs7Ky+W/mj6HRQuXHjBlxdXaUug4iIiGogLS0NLi4ula6j00HlwS3T09LSYG1tLXE1REREVBW5ublwdXVVf49XRqeDyoPTPdbW1gwqREREOqYql23wYloiIiLSWgwqREREpLUYVIiIiEhrMagQERGR1mJQISIiIq3FoEJERERai0GFiIiItBaDChEREWktSYOKh4cHZDJZhZ+JEydKWRYRERFpCUlnpj1z5gwUCoX699jYWDz99NN44YUXJKyKiIiItIWkQcXBwUHj9wULFsDLywvdunWTqCIiIiLSJlpzr5/S0lKsW7cO06ZNe+Tc/yUlJSgpKVH/npub21DlERERkQS05mLarVu3Ijs7GyNHjnzkOvPnz4eNjY36x9XVteEKJCIiamQOxt9CablS0hpkQgghaQX39erVCyYmJtixY8cj13nYERVXV1fk5OTw7slERER1RAiBr/cn4Ju/EvBCiAs+fz6oSnc6rqrc3FzY2NhU6ftbK079XLt2Dfv378fmzZsrXU8ul0MulzdQVURERI1PSbkC7/5+HttibgAA7C3lEAKow5xSLVoRVFatWgVHR0f069dP6lKIiIgarayCUoxfG4kzKfdgZCDDx4MC8fJjbpLWJHlQUSqVWLVqFUaMGAEjI8nLISIiapSu3s7H6NVnkHK3EFZyIywdFoLO3k2lLkv6oLJ//36kpqZi9OjRUpdCRETUKJ26ehfj10Uhu7AMLWzNsGpUB/g0s5K6LABaEFR69uwJLbmel4iIqNHZfPY6Zmw6jzKFQFtXW/zfq6FwsNKe60ElDypERETU8IQQWLQ/AYv/SgAA9Al0wlcvtoOZiaHElWliUCEiImpkSsoVeG/TBWyJTgcAjO/WEjN6tYaBgUStPZVgUCEiImpE7hWUYvzaKJxOyYLh/c6eIRJ39lSGQYWIiKiRSL5TgNGrzyD5TgGs5Eb4flh7dPF2+O8nSohBhYiIqBE4nZyFcWsjtbKzpzIMKkRERHpua3Q63v39PEoVSrR1scHyEaFwtDKVuqwqYVAhIiLSU0IILP4rAYv2qzp7egc44euXtK+zpzIMKkRERHqoQmdP15aY0Vs7O3sqw6BCRESkZ7ILSzFubRROJ6s6e+Y9E4hXOmpvZ09lGFSIiIj0SMqdAoy639ljKTfC90Pbo6uPdnf2VIZBhYiISE+cScnCuJ8ice9+Z8/KkR3g66T9nT2VYVAhIiLSA9ti0vHORlVnT5CLDf5Phzp7KsOgQkREpMOEEPj2QCK+2ncFANAroBkWvRSsU509lWFQISIi0lGl5Uq8t/k8Np9Vdfa81sUT7/Xxg6GOdfZUhkGFiIhIB2UXqu7Zc+p+Z8+cgQEY9ri71GXVOQYVIiIiHXPtbgFGrTqDq/c7e5YMbY9uOtzZUxkGFSIiIh0SmZKF1+539jjbmGLlqA5o7WQtdVn1hkGFiIhIR/yzs6dNCxusGBEKR2vd7+ypDIMKERGRlhNCYMnBRHzxp6qz52n/Zlj8cjuYm+j/17j+7yEREZEOKy1XYubmC9h09joAYGxnT8zsq1+dPZVhUCEiItJSOYVlGL8uEievZsFABsx5JhDD9bCzpzIMKkRERFoo9W4hRq4+jau3C2BhYojvhrZHd19HqctqcAwqREREWibqWhZe+ykKWQWlaG5jipUjO8Cvuf529lSGQYWIiEiL7Dh3A29vPIfSciUCW1hjxYgOaKbnnT2VYVAhIiLSAkIIfH8oCQv3xgMAevg1wzdDGkdnT2Ua994TERFpgdJyJf635QJ+j1J19ozp7In/NaLOnsowqBAREUkop7AME9ZFIeLqXVVnz8AADA/zkLosrcGgQkREJJHUu4UYtfo0kh509rzSHt1bN77OnsowqBAREUkg6to9jPspEncLSuFkrers8XdunJ09lWFQISIiamB/nL+Bab+pOnsCnFWdPU42jbezpzIMKkRERA2kYmePIxa/HAwLOb+OH4UjQ0RE1ADKFEq8v+UCfotUdfaM6uSBD/r5s7PnPzCoEBER1bOcojK8vi4KJ5JUnT2zBgRgRLiH1GXpBAYVIiKiepSWVYhRq88g8VY+zE0M8d0rwXiydTOpy9IZDCpERET15GzqPby25u/OnhUjQxHgbCN1WTqFQYWIiKge7DyfgWm/xaCkXAn/5tZYOZKdPTXBoEJERFSHhBD44fBVfLbnMgDgqdaO+GYIO3tqiqNGRERUR8oUSnywJRa/RqYBAEaGe+DD/uzsqQ0GFSIiojqQU1SGN9ZH4XiiqrPnw/7+GNXJU+qydB6DChERUS2lZRVi9OozSLjf2fPtkGA85cfOnrrAoEJERFQL0an38NpPkbiTX4pm1nKsGNEBgS3Y2VNXGFSIiIhqaPeFDEz9VdXZ49fcGitHhqK5jZnUZekVBhUiIqJqEkJg2ZGrWLBb1dnz5P3OHkt29tQ5jigREVE1lCmU+GhbLH45rersGRHmjg/7+8PI0EDiyvQTgwoREVEV5RaXYeL6sziacAcyGfARO3vqHYMKERFRFaRlFWLMmjO4kpkPM2NVZ08Pf3b21DcGFSIiov8Qk5aNsWsicSe/BI5Wcqwcyc6ehsKgQkREVIndFzLw1m8xKC5TorWTFVaO7ABnW3b2NBQGFSIioocQQuDHI1exYM9lCAF093XAt6+0Z2dPA+NoExER/YuqsycOv5xOBQC8GuaOj9jZIwkGFSIion/4d2fPh/38MaqTB2Qy3lhQCgwqRERE912/p7pnz4POnsUvt0PPACepy2rUGFSIiIgAnEvLxph/dPasGNEBbVzY2SM1BhUiImr09sTexNRfo9nZo4UYVIiIqNESQuD/jibj092XIATQzccB370SDCtTY6lLo/sYVIiIqFEqVyjx0fY4/HxK1dkz7HE3zB4QwM4eLcOgQkREjU5ecRkm/hyNI1duQyYD3u/rhzGdPdnZo4UYVIiIqFFJzy7CmNVncPlmHsyMDbHo5Xboxc4ercWgQkREjcb566rOntt5JXCwkmPFiFAEudhKXRZVgkGFiIgahb1xNzFlw9+dPStGdkALdvZoPQYVIiLSa0IIrDiWjE92qTp7uvo4YAk7e3QGgwoREemtcoUSs3fEYd1JVWfPKx3dMHcgO3t0CYMKERHppbziMkz6ORqH73f2/K+PH8Z2YWePrmFQISIivXMjuwij73f2mBobYNFLwegdyM4eXST5sa/09HQMGzYM9vb2MDMzQ5s2bRAZGSl1WUREpKMuXM/BoCXHcflmHhys5Ph1XBhDig6T9IjKvXv30KlTJ3Tv3h27d++Gg4MDEhIS0KRJEynLIiIiHbXvYiYm/xKNojIFfJtZYcXIULg0MZe6LKoFSYPKZ599BldXV6xatUq9zNPTU8KKiIhIFwkhsPJ4Cj7eeRFCAF28m2LJ0PawZmePzpP01M/27dsRGhqKF154AY6OjggODsby5csfuX5JSQlyc3M1foiIqHErVygxa3sc5v2hCimvdHTDypEdGFL0hKRB5erVq1i6dCm8vb2xd+9evP7665g8eTLWrFnz0PXnz58PGxsb9Y+rq2sDV0xERNokv6QcY3+KxE8R11SdPX1b45NBgTBm+7HekAkhhFQvbmJigtDQUJw4cUK9bPLkyThz5gwiIiIqrF9SUoKSkhL177m5uXB1dUVOTg6sra0bpGYiItIOGTlFGLXqn5097dA7sLnUZVEV5ObmwsbGpkrf35Jeo9K8eXP4+/trLPPz88OmTZseur5cLodcLm+I0oiISIvFpudgzJozyMwtQVNLOf5vRCjaudpKXRbVA0mDSqdOnRAfH6+x7MqVK3B3d5eoIiIi0nb7L2bizfudPT7NLLFyZAd29ugxSYPKW2+9hfDwcHz66ad48cUXcfr0afz444/48ccfpSyLiIi01KrjyZj7Bzt7GhNJr1EBgD/++AMzZ85EQkICPD09MW3aNLz22mtVem51znEREZHuKlcoMe+Pi1gTcQ0AMOQxV8x9hhfN6qrqfH9LHlRqg0GFiEj/5ZeU482fz+Jg/G0AwMw+rTGua0ves0eH6czFtERERJXJyCnC6NWRuJSRC7mRqrOnTxt29jQmDCpERKSVNDt7TLD81VAEu/EWK40NgwoREWmdvy6pOnsKSxXwdlR19rjasbOnMWJQISIirbLqeDLm/XERSgF0bqXq7LExY2dPY8WgQkREWkGhFJj3x0WsPpECAHgp1BUfP8vOnsaOQYWIiCRXUFKOyb9E46/LtwAAM3q3xoRu7OwhBhUiIpLYzZxijFlzBnE3VJ09X7/UDn3Z2UP3MagQEZFk4m7kYMzqSNzMLUZTSxP8+Goo2rOzh/6BQYWIiCRx4HImJv2s6uxp5WiJVezsoYdgUCEioga35kQK5uyIg1IA4V72WDoshJ099FAMKkRE1GAUSoGPd17EquMpAIAXQ13w8aA2MDFiZw89HIMKERE1iIKSckzZEI39l1SdPe/29sXr3bzY2UOVYlAhIqJ6l5lbjNGrVZ09JkYG+OrFtugf5Cx1WaQDGFSIiKheXbyRizFrziAjpxj2FqrOnhB3dvZQ1TCoEBFRvTl4+RYm/XwWBaUKeDlYYNXIx+Bmz84eqjoGFSIiqhdrI1Iwa/s/OnuGhsDGnJ09VD0MKkREVKcUSoFPdl7CyuPJAIAXQlzwybPs7KGaYVAhIqI6U1hajsm/xGD/pUwAwDu9fPHGE+zsoZpjUCEiojqRmau6Z09suqqz58sX2mJAW3b2UO0wqBARUa1dysjF6NWqzh47CxMsfzUEIe52UpdFeoBBhYiIauVQ/C1MXK/q7GnpYIFVIzvA3d5C6rJITzCoEBFRja09eQ2zt8dBoRQIa2mPH4axs4fqFoMKERFVm0IpMH/XJfzfMVVnz/MhLviUnT1UDxhUiIioWgpLyzFlQwz2XVR19kzv6YOJ3Vuxs4fqBYMKERFV2a3cYoxZE4kL6TkwMTLAFy+0xUB29lA9YlAhIqIquXwzF6NXncGN+509Pw4PQagHO3uofjGoEBHRfzp85TYmrj+L/JJydvZQg2JQISKiSq07eQ2z7nf2dPS0w7LhIbA1N5G6LGokGFSIiOihlEqB+bsvYflRVWfP4PYtsGBwEDt7qEExqBARUQVFpQpM/TUae+NUnT1vP+2DSU+ys4caHoMKERFpuJVXjLFrInH+eg5MDA2w8IUgPNOuhdRlUSPFoEJERGrxN/MwevUZpGcXoYm5MX58NRQd2NlDEmJQISIiAMCR+509eSXl8Gyq6uzxaMrOHpIWgwoREeHnU6n4cFssFEqBxzztsGxYCJpYsLOHpMegQkTUiCmVAp/tuYxlR64CAAYHt8D859pAbmQocWVEKgwqRESNVFGpAm/9GoM9cTcBAG/18MHkp9jZQ9qFQYWIqBG6lVeM19ZE4tz9zp7Pnw/CoGB29pD2YVAhImpkrmTmYdQqVWePrbkxfhweisc82dlD2olBhYioETmacBtvrPu7s2flyA7wZGcPaTEGFSKiRuKX06n4YOv9zh4P1T172NlD2o5BhYhIzymVAp/tvYxlh1WdPc8Gt8ACdvaQjmBQISLSY8Vlqs6e3bGqzp6pPbwx5SlvdvaQzmBQISLSU7fzSjD2p0icS8uGsaEMnz8fhGeDXaQui6haGFSIiPRQQmYeRv6js2fZsBB0bGkvdVlE1cagQkSkZ44l3MHr66OQV1wOD3tzrBzZAS0dLKUui6hGGFSIiPTIhvudPeVKgQ4eTbBseCjs2NlDOoxBhYhIDyiVAgv/jMfSQ0kAgEHtnPHZ80Hs7CGdx6BCRKTjissUePu3c9h5IQMAMOUpb0ztwc4e0g8MKkREOiw9uwivr4vC+es5MDaUYcHgIDwXws4e0h8MKkREOup44h28+Us0sgpKYWtujB+GheBxdvaQnmFQISLSMUII/HjkKj7bcxlKAQS2sMbSoSFwtTOXujSiOsegQkSkQ/JLyjHj9/Pq61GeD3HBx4MCYWrMi2ZJPzGoEBHpiKTb+ZiwNgoJt/JhbCjDRwMCMKyjGy+aJb3GoEJEpAP+jLuJt387h7yScjhaybF0WHuEuNtJXRZRvWNQISLSYgqlwKL9V/DtgUQAwGMedvhuaDAcrUwlroyoYTCoEBFpqezCUkzZEIPDV24DAEaGe+D9fn4wNjSQuDKihlOjd3tmZiaGDx8OZ2dnGBkZwdDQUOOHiIhqJ+5GDgZ8dwyHr9yGqbEBFr3UDrMHBjCkUKNToyMqI0eORGpqKj788EM0b96cF3IREdWhLdHX8d6mCygpV8LVzgzLhoXC39la6rKIJFGjoHLs2DEcPXoU7dq1q+NyiIgarzKFEp/svITVJ1IAAN18HLD45XawNedNBanxqlFQcXV1hRCirmshImq0buUWY+LPZ3Em5R4AYPKTrTClhw8MDXjEmhq3Gp3sXLRoEd577z2kpKTUcTlERI1P1LUs9P/2GM6k3IOV3Aj/92oopvX0ZUghQg2PqLz00ksoLCyEl5cXzM3NYWxsrPF4VlZWnRRHRKTPhBBYe/Ia5u64iHKlgE8zS/wwLAQtHSylLo1Ia9QoqCxatKiOyyAialyKyxT435YL2Hw2HQDQL6g5Pn8uCBZyzhpB9E81+kSMGDGirusgImo00rIKMWFdFOJu5MJABszs44exXTzZQUn0EDWO7gqFAlu3bsWlS5cAAAEBARg4cGC15lGZPXs25syZo7HM19cXly9frmlZRERa7ciV25i8IRrZhWWwszDBd0OCEd6qqdRlEWmtGgWVxMRE9O3bF+np6fD19QUAzJ8/H66urti5cye8vLyqvK2AgADs37//74KMeNiTiPSPEALfH0rCF3/GQwigrYsNlg4LgbOtmdSlEWm1GqWCyZMnw8vLCydPnoSdneqmWHfv3sWwYcMwefJk7Ny5s+oFGBnBycmpJmUQEemEvOIyTN94DnvjMgEAL3dwxeyBATA15kzeRP+lRkHl8OHDGiEFAOzt7bFgwQJ06tSpWttKSEiAs7MzTE1NERYWhvnz58PNza0mZRERaZ3EW3kYtzYKV28XwMTQAHOeCcCQx/g3jqiqahRU5HI58vLyKizPz8+HiUnVZ1Ds2LEjVq9eDV9fX2RkZGDOnDno0qULYmNjYWVlVWH9kpISlJSUqH/Pzc2tSflERA1iT2wG3v7tHApKFXCyNsXSYe0R7NZE6rKIdEqNJnzr378/xo0bh1OnTkEIASEETp48iQkTJmDgwIFV3k6fPn3wwgsvICgoCL169cKuXbuQnZ2N33777aHrz58/HzY2NuofV1fXmpRPRFSvFEqBz/ZcxoR1Z1FQqsDjLe3wx+TODClENVCjoPLNN9/Ay8sLYWFhMDU1hampKTp16oRWrVph8eLFNS7G1tYWPj4+SExMfOjjM2fORE5OjvonLS2txq9FRFQfsgpKMWLlaSw9lAQAeK2LJ9aN6YimlnKJKyPSTTU69WNra4tt27YhISFB3Urs5+eHVq1a1aqY/Px8JCUlYfjw4Q99XC6XQy7nh52ItNOF6zmYsC4K6dlFMDM2xOfPB2FAW2epyyLSabXqBfb29oa3t3eNnz99+nQMGDAA7u7uuHHjBmbNmgVDQ0MMGTKkNmURETW4jZFpeH9rLErLlfCwN8ey4aHwdap4rR0RVU+Vg8q0adMwb948WFhYYNq0aZWu+9VXX1Vpm9evX8eQIUNw9+5dODg4oHPnzjh58iQcHByqWhYRkaRKy5WY+0cc1p1MBQA81doRX73UDjZmxv/xTCKqiioHlejoaJSVlan/uy5s2LChTrZDRCSFmznFeGN9FM6mZkMmA6Y+5YM3n2wFA971mKjOyIQQQuoiaio3Nxc2NjbIycmBtbW11OUQUSNy6updTPw5GnfyS2BtaoTFLweje2tHqcsi0gnV+f6uUdfP6NGjHzqPSkFBAUaPHl2TTRIR6QQhBFYdT8bQ/zuFO/klaO1khR1vdmZIIaonNTqiYmhoiIyMDDg6an4w79y5AycnJ5SXl9dZgZXhERUiakhFpQq8t/k8tsXcAAAMbOuMBc+1gbkJ71FGVB3V+f6u1qcrNzdXPcFbXl4eTE1N1Y8pFArs2rWrQnghItIHqXcLMW5tJC7fzIOhgQz/6+uH0Z08IJPxehSi+lStoGJrawuZTAaZTAYfH58Kj8tkMsyZM6fOiiMi0gYH429hyi/RyC0uR1NLE3z3Sns83tJe6rKIGoVqBZWDBw9CCIEnn3wSmzZt0rgpoYmJCdzd3eHszMmNiEg/KJUC3x1MxNf7r0AIINjNFkuHhsDJxvS/n0xEdaJaQaVbt24AgOTkZLi6usLAoEbX4hIRab3c4jJM+zUG+y/dAgAM7eiGjwb4Q25kKHFlRI1Lja4Ac3d3BwAUFhYiNTUVpaWlGo8HBQXVvjIiIonE38zDhHVRSL5TABMjA3z8TCBe7MCboBJJoUZB5fbt2xg1ahR279790McVCkWtiiIiksof52/g3d/Po7BUgRa2Zlg6rD2CXGylLouo0arRuZupU6ciOzsbp06dgpmZGfbs2YM1a9bA29sb27dvr+saiYjqXblCiU93XcKkn6NRWKpAp1b22PFmZ4YUIonV6IjKgQMHsG3bNoSGhsLAwADu7u54+umnYW1tjfnz56Nfv351XScRUb25k1+CN3+ORsTVuwCACd28ML2nD4wMeR0ekdRqFFQKCgrU86U0adIEt2/fho+PD9q0aYOzZ8/WaYFERPUpJi0br6+LQkZOMSxMDLHwhbbo26a51GUR0X01+t8FX19fxMfHAwDatm2LZcuWIT09HT/88AOaN+cHnIh0w4bTqXjxhwhk5BSjZVMLbJ3YiSGFSMvU6IjKlClTkJGRAQCYNWsWevfujfXr18PExASrV6+uy/qIiOpcSbkCs7fH4ZfTaQCAp/2b4csX28La1Fjiyojo3+rk7smFhYW4fPky3Nzc0LRp07qoq0p4rx8iqq4b2UV4ff1ZnEvLhkwGTO/pi9e7ecHAgFPhEzWUervXz6OYm5ujffv2dbEpIqJ6cyLpDt78ORp3C0phY2aMb4YEo5uPg9RlEVElqhxUpk2bVuWNfvXVVzUqhoioPgghsOJYMubvvgyFUsC/uTWWDQ+Bq5251KUR0X+oclCJjo6u0nq8kygRaZOCknLM2HQef5xXXVc3OLgFPnm2DcxMOBU+kS6oclA5ePBgfdZBRFTnku8UYMLaKMRn5sHIQIYP+/vj1TB3/g8VkQ6pk2tUiIi0zV+XMjH11xjkFZfDwUqOpUPbI9TD7r+fSERapUZBpXv37pX+H8mBAwdqXBARUW0olQKL/krAN38lAABC3Zvg+6Ht4WhtKnFlRFQTNQoq7dq10/i9rKwMMTExiI2NxYgRI+qiLiKiasspLMPUX6NxMP42AGBEmDve7+cPEyNOhU+kq2oUVL7++uuHLp89ezby8/NrVRARUU1cysjF+LVRSM0qhNzIAJ8+2wbPhbhIXRYR1VKdTPj2QGJiIh577DFkZWXV1SYrxQnfiAgAtsWkY8am8yguU8KliRl+GBaCwBY2UpdFRI/Q4BO+PRAREQFTU54HJqKGUaZQYv6uy1h5PBkA0NXHAYtfaocmFiYSV0ZEdaVGQWXw4MEavwshkJGRgcjISHz44Yd1UhgRUWVu5RVj0s/ROJ2sOoI7qXsrvPW0Dww5FT6RXqlRULGx0TykamBgAF9fX8ydOxc9e/ask8KIiB4l6to9vLE+Cpm5JbCUG+HLF9uiV4CT1GURUT2oUVBZtWpVXddBRPSfhBBYfyoVc3bEoUwh0MrREsuGh8DLwVLq0oiontTqGpXIyEhcunQJAODv74+QkJA6KYqI6N+KyxT4cGssNkZdBwD0CXTCwhfawlLOeSuJ9FmNPuHXr1/HkCFDcPz4cdja2gIAsrOzER4ejg0bNsDFhS2BRFR3rt8rxOvrzuJCeg4MZMC7vVtjfNeWnAqfqBGo0SxIY8eORVlZGS5duoSsrCxkZWXh0qVLUCqVGDt2bF3XSESN2LGEOxjw7TFcSM9BE3Nj/DS6IyZ082JIIWokajSPipmZGU6cOIHg4GCN5VFRUejSpQsKCwvrrMDKcB4VIv0lhMCyI1fx+Z7LUAqgTQsbLB3WHi5NzKUujYhqqd7nUXF1dUVZWVmF5QqFAs7OzjXZJBGRWn5JOd7ZeA67Y28CAF4IccG8QYEwNTaUuDIiamg1OvWzcOFCvPnmm4iMjFQvi4yMxJQpU/DFF1/UWXFE1Pgk3c7HoCXHsTv2JowNZfh4UCA+fz6IIYWokarRqZ8mTZqgsLAQ5eXlMDJSHZR58N8WFhYa69bndPo89UOkX/bG3cTbv51Dfkk5mlnLsXRYCNq7NZG6LCKqY/V+6mfRokU1eRoR0UMplAJf7YvHkoNJAIDHPO2w5JX2cLCSS1wZEUmtRkFlxIgRdV0HETVS9wpKMXlDNI4m3AEAjO7kiZl9W8PYsEZnpolIz9R4piSFQoGtW7eqJ3wLCAjAwIEDYWjI88hEVDWx6TmYsC4K1+8VwdTYAJ89F4Rn2rWQuiwi0iI1CiqJiYno27cv0tPT4evrCwCYP38+XF1dsXPnTnh5edVpkUSkfzafvY6Zmy+gpFwJNztzLBseAr/mvNaMiDTV6GLavn37qu65sX497OzsAAB3797FsGHDYGBggJ07d9Z5oQ/Di2mJdE9puRKf7LyINRHXAADdfR2w6KVg2JgbS1wZETWUer+Y9vDhwzh58qQ6pACAvb09FixYgE6dOtVkk0TUCGTmFuON9WcRde0eAGDKU96Y8pQ3DAw4yywRPVyNgopcLkdeXl6F5fn5+TAxMal1UUSkf86kZOGN9WdxO68EVqZG+PrFdujh30zqsohIy9Xosvr+/ftj3LhxOHXqFIQQEELg5MmTmDBhAgYOHFjXNRKRDhNCYM2JFAz58SRu55XAt5kVtk/qzJBCRFVSoyMq33zzDUaOHInw8HCNCd8GDhyIxYsX12mBRKS7ikoVeH/LBWyOTgcA9A9qjs+eC4KFvMYNh0TUyFTrr4VSqcTChQuxfft2lJaWYtCgQRgxYgRkMhn8/PzQqlWr+qqTiHRMWlYhxq+NwsWMXBgayDCzT2uM6ezJux4TUbVUK6h88sknmD17Nnr06AEzMzPs2rULNjY2WLlyZX3VR0Q66PCV25j8SzRyispgb2GCb18JRrhXU6nLIiIdVK32ZG9vb0yfPh3jx48HAOzfvx/9+vVDUVERDAwafhZJticTaRelUmDp4SR88Wc8hADautpi6dD2cLY1k7o0ItIi9daenJqair59+6p/79GjB2QyGW7cuAEXF5eaVUtEeiGvuAxv/3YOf17MBAAMecwNswf6Q27E2aqJqOaqFVTKy8thamqqsczY2BhlZWV1WhQR6ZaEzDyMXxuFq3cKYGJogLnPBODlx9ykLouI9EC1gooQAiNHjoRc/vcdTYuLizFhwgRYWFiol23evLnuKiQirbb7QgambzyHglIFmtuYYumwELRztZW6LCLSE9UKKg+7a/KwYcPqrBgi0h3lCiUW/hmPZYevAgDCWtrj21eC0dRS/h/PJCKqumoFlVWrVtVXHUSkQ7IKSvHmL2dxPPEuAGBc15Z4t5cvjAwb/qJ6ItJvnHWJiKrl/PVsvL7uLNKzi2BuYojPnw9C/yBnqcsiIj3FoEJEVfZbZBo+2BqL0nIlPJtaYNnwEPg0s5K6LCLSYwwqRPSfSsoVmLvjItafSgUA9PBrhq9eagtrU2OJKyMifcegQkSVuplTjNfXRyE6NRsyGTCthw8mdm8FAwNOhU9E9Y9BhYge6eTVu5j081ncyS+FtakRFg8JRndfR6nLIqJGhEGFiCoQQmDl8RR8uusSFEqB1k5WWDY8BO72Fv/9ZCKiOsSgQkQaCkvLMXPzBWyLuQEAeKadMxYMDoKZCafCJ6KGx6BCRGrX7hZg/NooXL6ZByMDGd7v54eR4R6QyXg9ChFJg0GFiAAABy/fwpQN0cgtLkdTSzmWvBKMji3tpS6LiBo5BhWiRk6pFPj2QCIW/XUFQgDBbrZYOjQETjam//1kIqJ6xqBC1IjlFJVh2q8x+OvyLQDA8Mfd8WF/f5gYcSp8ItIODCpEjVT8zTyMXxuJlLuFMDEywCeDAvFCqKvUZRERaWBQIWqEdpy7gXd/P4+iMgVa2Jrhh2EhaONiI3VZREQVaM3x3QULFkAmk2Hq1KlSl0Kkt8oVSnz8x0W8+Us0isoU6NyqKXa82ZkhhYi0llYcUTlz5gyWLVuGoKAgqUsh0lt38ksw6eezOHk1CwDw+hNemN7TF4acCp+ItJjkR1Ty8/MxdOhQLF++HE2aNJG6HCK9FJ16DwO+PYaTV7NgYWKIH4a1x4zerRlSiEjrSR5UJk6ciH79+qFHjx7/uW5JSQlyc3M1foiocr+cTsVLy04iI6cYLR0ssG1SJ/QObC51WUREVSLpqZ8NGzbg7NmzOHPmTJXWnz9/PubMmVPPVRHph+IyBWZvj8OGM2kAgF4BzfDFC21hZWoscWVERFUn2RGVtLQ0TJkyBevXr4epadUmlpo5cyZycnLUP2lpafVcJZFuupFdhJeWRWDDmTTIZMA7vXzxw7AQhhQi0jkyIYSQ4oW3bt2KZ599FoaGf9/oTKFQQCaTwcDAACUlJRqPPUxubi5sbGyQk5MDa2vr+i6ZSCecSLqDST9HI6ugFLbmxvjm5WB09XGQuiwiIrXqfH9LdurnqaeewoULFzSWjRo1Cq1bt8aMGTP+M6QQkSYhBJYfvYoFuy9DKYAAZ2v8MCwErnbmUpdGRFRjkgUVKysrBAYGaiyzsLCAvb19heVEVLmCknK8u+k8dp7PAAAMbt8Cnz7bBqbGDPxEpNu0Yh4VIqq5q7fzMWFdFK5k5sPIQIZZA/wx7HF3yGRsPSYi3adVQeXQoUNSl0CkU/ZdzMS0X2OQV1IORys5lg5rjxB3O6nLIiKqM1oVVIioahRKgcX7r+CbA4kAgA4eTbDklfZwtK5aBx0Rka5gUCHSMdmFpZj6awwOxd8GAIwM98D7/fxgbCj5/I1ERHWOQYVIh1y8kYsJ66KQmlUIU2MDzB/cBs8Gu0hdFhFRvWFQIdIRW6PT8d7m8yguU8LVzgw/DAtBgDPvekxE+o1BhUjLlSmU+GTnJaw+kQIA6OrjgG9ebgdbcxNpCyMiagAMKkRa7FZeMSatj8bplCwAwJtPtsLUHj686zERNRoMKkRaKuraPbyxPgqZuSWwkhvhyxfbomeAk9RlERE1KAYVIi0jhMC6U6mYuyMOZQoBb0dL/DA8BF4OllKXRkTU4BhUiLRIcZkCH2yNxe9R1wEAfds44fPn28JSzo8qETVO/OtHpCXSsgrx+vooxKbnwkAGzOjdGuO6tuRU+ETUqDGoEGmBowm3MfmXaNwrLIOdhQm+HRKMTq2aSl0WEZHkGFSIJCSEwNLDSfhibzyUAghyscHSYSFoYWsmdWlERFqBQYVIInnFZXhn43nsibsJAHgx1AVznwmEqbGhxJUREWkPBhUiCSTeysf4tZFIul0AY0MZ5gwMxJDHXHk9ChHRvzCoEDWwPbE3MX3jOeSXlMPJ2hTfD2uP9m5NpC6LiEgrMagQNZCiUgUW7b+CZUeuAgA6etrhu1faw8FKLnFlRETai0GFqJ4JIbA79iY+2XkJ6dlFAICxnT0xo09rGBsaSFwdEZF2Y1AhqkdXMvMwe3scTiTdBQA425jiowEB6B3IqfCJiKqCQYWoHuQUlWHR/iv4KeIaFEoBEyMDTOjaEq8/0QpmJuzqISKqKgYVojqkVApsjErD53vicbegFADQ078ZPujnDzd7c4mrIyLSPQwqRHUkJi0bs7bF4tz1HABASwcLzB4QgK4+DhJXRkSkuxhUiGrpdl4JPt9zGRvv30jQwsQQU3p4Y2S4J0yMeLEsEVFtMKgQ1VCZQok1J1KweH8C8krKAQCD27fAe71bw9HaVOLqiIj0A4MKUQ0cT7yD2dvjkHArHwAQ2MIacwYGIMTdTuLKiIj0C4MKUTVcv1eIT3Zewu5Y1f157CxM8E4vX7wY6gpDA05/T0RU1xhUiKqguEyBHw4nYemhJJSUK2EgA4Y/7o5pT/vCxtxY6vKIiPQWgwpRJYQQ2BuXiY93XsT1e6pZZTt62mH2wAD4NbeWuDoiIv3HoEL0CIm38jB7+0UcS7wDAGhuY4r/9fVD/6DmvMsxEVEDYVAh+pe84jIs3p+A1SdSUK4UMDE0wLiuLfFGdy+Ym/AjQ0TUkPhXl+g+pVJg09nr+GxPPO7klwAAevg54sP+/nC3t5C4OiKixolBhQjA+evZmLU9DtGp2QAAz6YW+GiAP7r7OkpbGBFRI8egQo3a3fwSLNwbj18j0yCEalbZN5/yxuhOnFWWiEgbMKhQo1SuUGLtyWv4at8V5BWrZpV9NrgF3uvTGs04qywRkdZgUKFG50TSHczZfhHxmXkAAP/m1pj7TABCPTirLBGRtmFQoUYjPbsIn+68hJ0XMgAAtubGmN7TF0Mec+OsskREWopBhfRecZkCy49cxZJDiSguU80qO7SjO6Y97YMmFiZSl0dERJVgUCG9JYTAvouZmLfzItKyVLPKPuZhh1kD/RHgbCNxdUREVBUMKqSXkm7nY86Oizhy5TYAoJm1HP/r64eBbZ05qywRkQ5hUCG9kl9Sjm//SsDK48koUwgYG8owtktLTOreChZyvt2JiHQN/3KTXhBCYEt0OubvvozbeapZZbv7OuCjAQHwbMpZZYmIdBWDCum82PQczNoeh6hr9wAAHvbm+GiAP55s3UziyoiIqLYYVEhnZRWUYuHeeGw4kwohADNjQ0x6shXGdvGE3MhQ6vKIiKgOMKiQzilXKPHz6VR8+ecV5BSVAQAGtnXGzL6t0dzGTOLqiIioLjGokE45dfUuZm2Pw+WbqlllWztZYc7AAHRsaS9xZUREVB8YVEgnZOQU4dNdl7Hj3A0AgI2ZMab39MGQx9xgZMibBxIR6SsGFdJqJeUK/N/RZCw5mIjCUgVkMmDIY26Y3tMXdpxVlohI7zGokNb661Im5v5xEdfuFgIAQtybYM7AAAS24KyyRESNBYMKaZ3kOwWYuyMOB+NVs8o6Wskxs29rDGrXgrPKEhE1MgwqpDUKSsrx3cFErDiajFKFEsaGMozu5Ik3n/KGJWeVJSJqlPjXnyQnhMD2czfw6a5LyMxVzSrbzccBHw3wh5eDpcTVERGRlBhUSFIXb+Ri9vY4nE7JAgC42Znjw/7+6OHnyNM8RETEoELSyC4sxRd/xuPnU6lQCsDU2ACTurfC2C4tYWrMWWWJiEiFQYUalEIp8MvpVHzxZzyyC1WzyvYLao73+/rB2ZazyhIRkSYGFWowZ1KyMGtbHC5m5AIAfJtZYdZAf4R7NZW4MiIi0lYMKlTvMnOLMX/XJWyNUc0qa21qhGlP+2DY4+6cVZaIiCrFoEL1prRciZXHk/HtXwkouD+r7Euhrninly/sLeVSl0dERDqAQYXqxcH4W5i34yKu3ikAAAS72WLOwAAEudhKWxgREekUBhWqU9fuFmDeHxex/9ItAEBTSzne69Mag4NbwMCA7cZERFQ9DCpUJwpLy7HkYCKWH1HNKmtkIMOoTh548ylvWJsaS10eERHpKAYVqhUhBP44n4FPd11CRk4xAKCLd1PMGuCPVo5WEldHRES6jkGFauzyTdWssievqmaVdWlihg/7+6OnfzPOKktERHWCQYWqLaewDF/ti8fak9egFIDcyABvPNEK47txVlkiIqpbDCpUZQqlwG+RaVi4Nx5ZBaUAgD6BTni/nx9cmphLXB0REekjBhWqkqhr9zB7exwupOcAALwdLTF7YAA6teKsskREVH8knRZ06dKlCAoKgrW1NaytrREWFobdu3dLWRL9y628Ykz7LQbPLT2BC+k5sJIb4cP+/tg1pQtDChER1TtJj6i4uLhgwYIF8Pb2hhACa9aswTPPPIPo6GgEBARIWVqjV1quxJoTKVj8VwLyS8oBAC+GuuDd3q3RlLPKEhFRA5EJIYTURfyTnZ0dFi5ciDFjxvznurm5ubCxsUFOTg6sra0boLrG4ciV25izIw5Jt1WzyrZ1scGcZwLRztVW2sKIiEgvVOf7W2uuUVEoFNi4cSMKCgoQFhYmdTmNUlpWIeb9cRF/XswEANhbmGBG79Z4PsSFs8oSEZEkJA8qFy5cQFhYGIqLi2FpaYktW7bA39//oeuWlJSgpKRE/Xtubm5DlanXikoVWHooET8cuYrSciUMDWQYEeaBKT28YWPGWWWJiEg6kgcVX19fxMTEICcnB7///jtGjBiBw4cPPzSszJ8/H3PmzJGgSv0khMDu2Jv4ZOclpGcXAQDCvewxe2AAfJpxVlkiIpKe1l2j0qNHD3h5eWHZsmUVHnvYERVXV1deo1IDVzLzMHt7HE4k3QUAtLA1wwf9/NA70ImzyhIRUb3SyWtUHlAqlRph5J/kcjnkcnac1EZOURkW7b+CnyKuQaEUMDEywIRuXni9mxfMTDirLBERaRdJg8rMmTPRp08fuLm5IS8vDz///DMOHTqEvXv3SlmWXlIqBX6Puo7P9lzG3fuzyvYKaIYP+vnD1Y6zyhIRkXaSNKjcunULr776KjIyMmBjY4OgoCDs3bsXTz/9tJRl6Z2YtGzM2haLc9dVs8p6OVhg9sAAdPF2kLgyIiKiykkaVFasWCHly+u923kl+HzPZWyMug4AsJQbYcpT3hgR7gETI0knJSYiIqoSrbtGhWqvTKHETxHXsGjfFeTdn1X2ufYumNHHF45WphJXR0REVHUMKnrmeOIdzN4eh4Rb+QCANi1sMHtgAELcm0hcGRERUfUxqOiJ6/cK8cnOS9gdexMAYGdhgnd7+eLFUFfOKktERDqLQUXHFZcp8MPhJCw9lISS+7PKDn/cHW/18IGNOWeVJSIi3cagoqOEENgbl4mPd17E9XuqWWUfb2mH2QMD0NqJk98REZF+YFDRQYm38jBnx0UcTbgDAGhuY4r3+/mhX5vmnFWWiIj0CoOKDskrLsPi/QlYfSIF5UoBE0MDjOvaEm9094K5Cf8piYhI//DbTQcolQKbo9OxYPdl3MlX3V6gh18zfNjfD+72FhJXR0REVH8YVLTc+evZmLU9DtGp2QCAlk0t8NEAfzzh6yhtYURERA2AQUVL3c0vwcK98fg1Mg1CABYmhpj8lDdGdfLkrLJERNRoMKhomXKFEmtPXsNX+64gr1g1q+yzwS3wXp/WaGbNWWWJiKhxYVDRIhFJdzF7exziM/MAAAHO1pgzMAChHnYSV0ZERCQNBhUtcCO7CJ/suoSd5zMAAE3MjTG9ly9e7uAGQ84qS0REjRiDioSKyxRYfuQqlhxKRHGZEgYyYNjj7pj2tA9szU2kLo+IiEhyDCoSEEJg/6VbmPfHRaRmFQIAHvNQzSrr78xZZYmIiB5gUGlgSbfzMXfHRRy+chsA4GRtipl9W2NgW2fOKktERPQvDCoNJL+kHN/+lYCVx5NRplDNKju2iycmdm8FCzn/GYiIiB6G35D1TAiBLdHpmL/7Mm7nqWaVfaq1Iz7s7w+PppxVloiIqDIMKvUoNj0Hs7bHIeraPQCAh705Zg0IQPfWnFWWiIioKhhU6kFWQSm++DMev5xOhRCAuYkhJj3ZCmM6e0JuZCh1eURERDqDQaUOlSuU+Pl0Kr788wpyisoAAM+0c8bMPn5wsuGsskRERNXFoFJHTl29i1nb43D5pmpWWb/mqlllH/PkrLJEREQ1xaBSSxk5RZi/6zK2n7sBALAxM8b0nj54paM7Z5UlIiKqJQaVGiopV+D/jiZjycFEFJYqIJMBrzzmhuk9fdHEgrPKEhER1QUGlRr461Im5v5xEdfuqmaVDXVvgtkDAxDYwkbiyoiIiPQLg0o1JN8pwNwdcTgYr5pV1tFKjv/19cMz7TirLBERUX1gUKmCgpJyfHcwESuOJqNUoYSxoQyjO3vizSe9YclZZYmIiOoNv2UrIYTA9nM38OmuS8jMVc0q283HAR8N8IeXg6XE1REREek/BpVHuHgjF7O3x+F0ShYAwM3OHB/198dTfo48zUNERNRAGFQeYs2JFMzZEQelAMyM/55V1tSYs8oSERE1JAaVhwj1aAIA6B/UHP/r6wdnWzOJKyIiImqcGFQeIsDZBgfefoJ3NyYiIpKYgdQFaCuGFCIiIukxqBAREZHWYlAhIiIircWgQkRERFqLQYWIiIi0FoMKERERaS0GFSIiItJaDCpERESktRhUiIiISGsxqBAREZHWYlAhIiIircWgQkRERFqLQYWIiIi0FoMKERERaS0jqQuoDSEEACA3N1fiSoiIiKiqHnxvP/ger4xOB5W8vDwAgKurq8SVEBERUXXl5eXBxsam0nVkoipxRksplUrcuHEDVlZWkMlkdbrt3NxcuLq6Ii0tDdbW1nW6bX3Dsao6jlXVcayqjmNVdRyr6qmv8RJCIC8vD87OzjAwqPwqFJ0+omJgYAAXF5d6fQ1ra2u+mauIY1V1HKuq41hVHceq6jhW1VMf4/VfR1Ie4MW0REREpLUYVIiIiEhrMag8glwux6xZsyCXy6UuRetxrKqOY1V1HKuq41hVHceqerRhvHT6YloiIiLSbzyiQkRERFqLQYWIiIi0FoMKERERaS0GFSIiItJaeh1Ujhw5ggEDBsDZ2RkymQxbt27VeDwzMxMjR46Es7MzzM3N0bt3byQkJGis88QTT0Amk2n8TJgwQWOd1NRU9OvXD+bm5nB0dMQ777yD8vLy+t69OlUXYwUAERERePLJJ2FhYQFra2t07doVRUVF6sezsrIwdOhQWFtbw9bWFmPGjEF+fn59716dqu1YpaSkVHhPPfjZuHGjej2+r1Ru3ryJ4cOHw8nJCRYWFmjfvj02bdqksQ7fVypJSUl49tln4eDgAGtra7z44ovIzMzUWEcfxmr+/Pno0KEDrKys4OjoiEGDBiE+Pl5jneLiYkycOBH29vawtLTEc889V2EsqvIZO3ToENq3bw+5XI5WrVph9erV9b17daquxmry5MkICQmBXC5Hu3btHvpa58+fR5cuXWBqagpXV1d8/vnndbIPeh1UCgoK0LZtWyxZsqTCY0IIDBo0CFevXsW2bdsQHR0Nd3d39OjRAwUFBRrrvvbaa8jIyFD//HPwFQoF+vXrh9LSUpw4cQJr1qzB6tWr8dFHH9X7/tWluhiriIgI9O7dGz179sTp06dx5swZTJo0SWN65KFDhyIuLg779u3DH3/8gSNHjmDcuHENso91pbZj5erqqvF+ysjIwJw5c2BpaYk+ffoA4Pvqn++rV199FfHx8di+fTsuXLiAwYMH48UXX0R0dLR6Hb6vVM/v2bMnZDIZDhw4gOPHj6O0tBQDBgyAUqlUb0sfxurw4cOYOHEiTp48iX379qGsrAw9e/bUeN+89dZb2LFjBzZu3IjDhw/jxo0bGDx4sPrxqnzGkpOT0a9fP3Tv3h0xMTGYOnUqxo4di7179zbo/tZGXYzVA6NHj8ZLL7300NfJzc1Fz5494e7ujqioKCxcuBCzZ8/Gjz/+WPudEI0EALFlyxb17/Hx8QKAiI2NVS9TKBTCwcFBLF++XL2sW7duYsqUKY/c7q5du4SBgYG4efOmetnSpUuFtbW1KCkpqdN9aCg1HauOHTuKDz744JHbvXjxogAgzpw5o162e/duIZPJRHp6et3uRAOp6Vj9W7t27cTo0aPVv/N99fdYWVhYiJ9++kljW3Z2dup1+L5SjcPevXuFgYGByMnJUa+TnZ0tZDKZ2LdvnxBCP8dKCCFu3bolAIjDhw8LIVT7bWxsLDZu3Khe59KlSwKAiIiIEEJU7TP27rvvioCAAI3Xeumll0SvXr3qe5fqTU3G6p9mzZol2rZtW2H5999/L5o0aaLx92nGjBnC19e31jXr9RGVypSUlAAATE1N1csMDAwgl8tx7NgxjXXXr1+Ppk2bIjAwEDNnzkRhYaH6sYiICLRp0wbNmjVTL+vVqxdyc3MRFxdXz3vRMKoyVrdu3cKpU6fg6OiI8PBwNGvWDN26ddMYy4iICNja2iI0NFS9rEePHjAwMMCpU6caaG/qV3XeVw9ERUUhJiYGY8aMUS/j++rvsQoPD8evv/6KrKwsKJVKbNiwAcXFxXjiiScA8H31YKxKSkogk8k0JuYyNTWFgYGBeh19HaucnBwAgJ2dHQDVZ6qsrAw9evRQr9O6dWu4ubkhIiICQNU+YxERERrbeLDOg23oopqMVVVERESga9euMDExUS/r1asX4uPjce/evVrV3GiDyoN/iJkzZ+LevXsoLS3FZ599huvXryMjI0O93iuvvIJ169bh4MGDmDlzJtauXYthw4apH79586bGGx2A+vebN282zM7Us6qM1dWrVwEAs2fPxmuvvYY9e/agffv2eOqpp9Tn0W/evAlHR0eNbRsZGcHOzq5RjdW/rVixAn5+fggPD1cv4/vq77H67bffUFZWBnt7e8jlcowfPx5btmxBq1atAPB99WCsHn/8cVhYWGDGjBkoLCxEQUEBpk+fDoVCoV5HH8dKqVRi6tSp6NSpEwIDAwGo9tPExAS2trYa6zZr1ky9n1X5jD1qndzcXI1r73RFTceqKurzb1ajDSrGxsbYvHkzrly5Ajs7O5ibm+PgwYPo06ePxjUV48aNQ69evdCmTRsMHToUP/30E7Zs2YKkpCQJq29YVRmrB+fAx48fj1GjRiE4OBhff/01fH19sXLlSinLb1BVfV89UFRUhJ9//lnjaEpjUdWx+vDDD5GdnY39+/cjMjIS06ZNw4svvogLFy5IWH3DqspYOTg4YOPGjdixYwcsLS1hY2OD7OxstG/f/qHvPX0xceJExMbGYsOGDVKXovV0dayMpC5ASiEhIYiJiUFOTg5KS0vh4OCAjh07ahwW/beOHTsCABITE+Hl5QUnJyecPn1aY50HV0s7OTnVX/EN7L/Gqnnz5gAAf39/jef5+fkhNTUVgGo8bt26pfF4eXk5srKyGtVY/dPvv/+OwsJCvPrqqxrL+b5SjVVSUhK+++47xMbGIiAgAADQtm1bHD16FEuWLMEPP/zA99U/3lc9e/ZEUlIS7ty5AyMjI9ja2sLJyQktW7YEoH+fwUmTJqkvCHZxcVEvd3JyQmlpKbKzszWOFGRmZqr3syqfMScnpwrdL5mZmbC2toaZmVl97FK9qc1YVcWjxurBY7WhvzG7GmxsbODg4ICEhARERkbimWeeeeS6MTExAP7+Yg4LC8OFCxc0Pvz79u2DtbV1hS9tffCosfLw8ICzs3OFtrcrV67A3d0dgGqssrOzERUVpX78wIEDUCqV6gCoT6ryvlqxYgUGDhwIBwcHjeV8X6nG6sH1YP8+ImBoaKg+isf3VcX3VdOmTWFra4sDBw7g1q1bGDhwIAD9GSshBCZNmoQtW7bgwIED8PT01Hg8JCQExsbG+Ouvv9TL4uPjkZqairCwMABV+4yFhYVpbOPBOg+2oQvqYqyqIiwsDEeOHEFZWZl62b59++Dr64smTZrUeif0Vl5enoiOjhbR0dECgPjqq69EdHS0uHbtmhBCiN9++00cPHhQJCUlia1btwp3d3cxePBg9fMTExPF3LlzRWRkpEhOThbbtm0TLVu2FF27dlWvU15eLgIDA0XPnj1FTEyM2LNnj3BwcBAzZ85s8P2tjdqOlRBCfP3118La2lps3LhRJCQkiA8++ECYmpqKxMRE9Tq9e/cWwcHB4tSpU+LYsWPC29tbDBkypEH3tbbqYqyEECIhIUHIZDKxe/fuCo/xfaVSWloqWrVqJbp06SJOnTolEhMTxRdffCFkMpnYuXOnej2+r1RWrlwpIiIiRGJioli7dq2ws7MT06ZN01hHH8bq9ddfFzY2NuLQoUMiIyND/VNYWKheZ8KECcLNzU0cOHBAREZGirCwMBEWFqZ+vCqfsatXrwpzc3PxzjvviEuXLoklS5YIQ0NDsWfPngbd39qoi7ESQvX3Kjo6WowfP174+Pio36sPunyys7NFs2bNxPDhw0VsbKzYsGGDMDc3F8uWLav1Puh1UDl48KAAUOFnxIgRQgghFi9eLFxcXISxsbFwc3MTH3zwgUZrVWpqqujatauws7MTcrlctGrVSrzzzjsa7X9CCJGSkiL69OkjzMzMRNOmTcXbb78tysrKGnJXa622Y/XA/PnzhYuLizA3NxdhYWHi6NGjGo/fvXtXDBkyRFhaWgpra2sxatQokZeX1xC7WGfqaqxmzpwpXF1dhUKheOjr8H2lcuXKFTF48GDh6OgozM3NRVBQUIV2Zb6vVGbMmCGaNWsmjI2Nhbe3t/jyyy+FUqnUWEcfxuph4wRArFq1Sr1OUVGReOONN0STJk2Eubm5ePbZZ0VGRobGdqryGTt48KBo166dMDExES1bttR4DV1QV2PVrVu3h24nOTlZvc65c+dE586dhVwuFy1atBALFiyok32Q3d8RIiIiIq3Da1SIiIhIazGoEBERkdZiUCEiIiKtxaBCREREWotBhYiIiLQWgwoRERFpLQYVIiIi0loMKkRERKS1GFSIqF4JIdCjRw/06tWrwmPff/89bG1tcf36dQkqIyJdwKBCRPVKJpNh1apVOHXqFJYtW6ZenpycjHfffRfffvutxt1c68I/b4xGRLqNQYWI6p2rqysWL16M6dOnIzk5GUIIjBkzBj179kRwcDD69OkDS0tLNGvWDMOHD8edO3fUz92zZw86d+4MW1tb2Nvbo3///khKSlI/npKSAplMhl9//RXdunWDqakp1q9fL8VuElE94L1+iKjBDBo0CDk5ORg8eDDmzZuHuLg4BAQEYOzYsXj11VdRVFSEGTNmoLy8HAcOHAAAbNq0CTKZDEFBQcjPz8dHH32ElJQUxMTEwMDAACkpKfD09ISHhwe+/PJLBAcHw9TUFM2bN5d4b4moLjCoEFGDuXXrFgICApCVlYVNmzYhNjYWR48exd69e9XrXL9+Ha6uroiPj4ePj0+Fbdy5cwcODg64cOECAgMD1UFl0aJFmDJlSkPuDhE1AJ76IaIG4+joiPHjx8PPzw+DBg3CuXPncPDgQVhaWqp/WrduDQDq0zsJCQkYMmQIWrZsCWtra3h4eAAAUlNTNbYdGhraoPtCRA3DSOoCiKhxMTIygpGR6k9Pfn4+BgwYgM8++6zCeg9O3QwYMADu7u5Yvnw5nJ2doVQqERgYiNLSUo31LSws6r94ImpwDCpEJJn27dtj06ZN8PDwUIeXf7p79y7i4+OxfPlydOnSBQBw7Nixhi6TiCTEUz9EJJmJEyciKysLQ4YMwZkzZ5CUlIS9e/di1KhRUCgUaNKkCezt7fHjjz8iMTERBw4cwLRp06Qum4gaEIMKEUnG2dkZx48fh0KhQM+ePdGmTRtMnToVtra2MDAwgIGBATZs2ICoqCgEBgbirbfewsKFC6Uum4gaELt+iIiISGvxiAoRERFpLQYVIiIi0loMKkRERKS1GFSIiIhIazGoEBERkdZiUCEiIiKtxaBCREREWotBhYiIiLQWgwoRERFpLQYVIiIi0loMKkRERKS1GFSIiIhIa/0/y1eeD0MSPKIAAAAASUVORK5CYII=\n"},"metadata":{}}]},{"cell_type":"code","source":[],"metadata":{"id":"uVXEn1XsM9Y6"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["pandas offers plots directly from its objects"],"metadata":{"id":"fzYAeyDnNBwK"}},{"cell_type":"code","source":["titanic.age.hist()\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":430},"id":"EwbYXeUFLwKB","executionInfo":{"status":"ok","timestamp":1683445717582,"user_tz":-120,"elapsed":795,"user":{"displayName":"Markus Loecher","userId":"02488037228155275753"}},"outputId":"ec9586a0-ab4f-4032-994a-2a1fee7fbf6a"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"markdown","source":["And often the axis labels are taken care of"],"metadata":{"id":"y6dSN8MrPLmb"}},{"cell_type":"code","source":["#titanic.groupby(\"pclass\").survived.mean().plot.bar()\n","SurvByPclass = titanic.groupby(\"pclass\").survived.mean()\n","\n","SurvByPclass.plot(kind=\"bar\", title = \"Mean Survival\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":562},"id":"zIsL3vM5O3EW","executionInfo":{"status":"ok","timestamp":1683447104681,"user_tz":-120,"elapsed":2210,"user":{"displayName":"Markus Loecher","userId":"02488037228155275753"}},"outputId":"7c33029b-26ff-4fdf-93b7-428c217104c0"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<Axes: title={'center': 'Mean Survival'}, xlabel='pclass'>"]},"metadata":{},"execution_count":60},{"output_type":"display_data","data":{"text/plain":["<Figure size 5120x3840 with 1 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you can customize each plot as you wish:"],"metadata":{"id":"VdICGRyPP6aa"}},{"cell_type":"code","source":["SurvByPclass.plot(kind=\"bar\", x = \"Passenger Class\", y = \"Survived\", title = \"Mean Survival\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":562},"id":"vlFUldGdNHgA","executionInfo":{"status":"ok","timestamp":1683447141883,"user_tz":-120,"elapsed":2079,"user":{"displayName":"Markus Loecher","userId":"02488037228155275753"}},"outputId":"6153b612-c097-4c44-8e65-57a672b9b1af"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<Axes: title={'center': 'Mean Survival'}, xlabel='pclass'>"]},"metadata":{},"execution_count":62},{"output_type":"display_data","data":{"text/plain":["<Figure size 5120x3840 with 1 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Compute the avg. life expectancy in the gapminder data for each year\n","* Plot this as a line plot and give meaningful x and y labels and a title"],"metadata":{"id":"iZvK7HYbQG_J"}},{"cell_type":"code","source":["lifeExpbyYear = gapminder.groupby(\"year\")[\"lifeExp\"].mean()\n","\n","lifeExpbyYear.plot(y= \"avg. life Exp\", title = \"Average life Expectancvy per year\")"],"metadata":{"id":"ATrZbg6iQXPJ","colab":{"base_uri":"https://localhost:8080/","height":489},"executionInfo":{"status":"ok","timestamp":1683559646567,"user_tz":-120,"elapsed":1472,"user":{"displayName":"Markus Loecher","userId":"02488037228155275753"}},"outputId":"2542aceb-04b0-40ed-ea00-555f7774751e"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<Axes: title={'center': 'Average life Expectancvy per year'}, xlabel='year'>"]},"metadata":{},"execution_count":31},{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":[],"metadata":{"id":"bgcdyMLF9waH"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"VKphhWnLx3SZ"},"source":["_________________________"]},{"cell_type":"markdown","metadata":{"id":"zE6ICzhqx3Sa"},"source":["### Advanced topics\n","\n","#### Creating Dataframes\n","\n","1. Zip\n","2. From list of dicts\n","\n","#### Indexing:\n","\n","1. multilevel indexes \n","2. sorting \n","3. asking for ranges\n","\n","### Types of columns\n","\n","1. categorical\n","2. dates"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"KO7vSA8tx3Sa","outputId":"6c173660-0f22-4a64-e749-5de2438ec655"},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Name</th>\n","      <th>Age</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>tom</td>\n","      <td>25</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>krish</td>\n","      <td>30</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>nick</td>\n","      <td>26</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>juli</td>\n","      <td>22</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["    Name  Age\n","0    tom   25\n","1  krish   30\n","2   nick   26\n","3   juli   22"]},"execution_count":97,"metadata":{},"output_type":"execute_result"}],"source":["# Creating Dataframes\n","#using zip\n","# List1\n","Name = ['tom', 'krish', 'nick', 'juli']\n","  \n","# List2\n","Age = [25, 30, 26, 22]\n","  \n","# get the list of tuples from two lists.\n","# and merge them by using zip().\n","list_of_tuples = list(zip(Name, Age))\n","list_of_tuples = zip(Name, Age)\n","# Assign data to tuples.\n","#print(list_of_tuples)\n","  \n","  \n","# Converting lists of tuples into\n","# pandas Dataframe.\n","df = pd.DataFrame(list_of_tuples,\n","                  columns=['Name', 'Age'])\n","  \n","# Print data.\n","df"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"dUU_0D0Ex3Sa","outputId":"b5f577a1-56f4-4787-dc41-fcbd601eb79a"},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>a</th>\n","      <th>b</th>\n","      <th>c</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1</td>\n","      <td>2</td>\n","      <td>3</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>10</td>\n","      <td>20</td>\n","      <td>30</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["    a   b   c\n","0   1   2   3\n","1  10  20  30"]},"execution_count":98,"metadata":{},"output_type":"execute_result"}],"source":["#from list of dicts\n","data = [{'a': 1, 'b': 2, 'c': 3},\n","        {'a': 10, 'b': 20, 'c': 30}]\n","  \n","# Creates DataFrame.\n","df = pd.DataFrame(data)\n","  \n","df"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"nNWM5uPwx3Sb","outputId":"7c9ea75c-d99e-4d45-df40-aa876668244c"},"outputs":[{"name":"stdout","output_type":"stream","text":["             pop\n","year state      \n","2000 Ohio    1.5\n","2001 Ohio    1.7\n","2002 Ohio    3.6\n","2001 Nevada  2.4\n","2002 Nevada  2.9\n","2003 Nevada  3.2\n","             pop\n","year state      \n","2000 Ohio    1.5\n","2001 Nevada  2.4\n","     Ohio    1.7\n","2002 Nevada  2.9\n","     Ohio    3.6\n","2003 Nevada  3.2\n","    state  year\n","0    Ohio  2000\n","1    Ohio  2001\n","2    Ohio  2002\n","3  Nevada  2001\n","4  Nevada  2002\n","5  Nevada  2003\n"]}],"source":["# Indexing:\n","\n","advLesson = True\n","if advLesson:\n","    frame2 = frame.set_index([\"year\", \"state\"])\n","    print(frame2)\n","    frame3 = frame2.sort_index()\n","    print(frame3)\n","    print(frame.loc[:,\"state\":\"year\"])"]},{"cell_type":"markdown","metadata":{"id":"YkN34dftx3Sb"},"source":["#### Inplace\n","\n","Note that I reassigned the objects in the code above. That is because most operations, such as `set_index`, `sort_index`, `drop`, etc. do not operate **inplace** unless specified!"]}],"metadata":{"kernelspec":{"display_name":".venv","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":"3.9.0"},"orig_nbformat":4,"vscode":{"interpreter":{"hash":"cc4e3cb0b66e3cf40341812cc2bd10256c32822435ddcf03aec3c5d3b18cd7d2"}},"colab":{"provenance":[]}},"nbformat":4,"nbformat_minor":0}