{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "industrial-connection",
   "metadata": {},
   "source": [
    "# <center>Plotting Data with Pandas</center>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "amino-signal",
   "metadata": {},
   "source": [
    "<center>Dr. W.J.B. Mattingly</center>\n",
    "\n",
    "<center>Smithsonian Data Science Lab and United States Holocaust Memorial Museum</center>\n",
    "\n",
    "<center>August 2021</center>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "electoral-samuel",
   "metadata": {},
   "source": [
    "## Covered in this Chapter"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "familiar-dealer",
   "metadata": {},
   "source": [
    "1) Key Ways to Plot Data in Pandas<br>\n",
    "2) How to Create a Bar or Barh Graph<br>\n",
    "3) How to Create a Pie Chart<br>\n",
    "4) How to Plot Data in a Scatter Plot"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "frank-commerce",
   "metadata": {},
   "source": [
    "## Importing the DataFrame"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "diagnostic-crime",
   "metadata": {},
   "source": [
    "This notebook begins Part 3 of this textbook. Here, we will build upon our skills from Parts 1 and 2, and begin exploring how to visualize data in Pandas. Pandas sits on top of Matplotlib, one of the standard libraries used by data scientists for plotting data. As we will see in the next notebooks, you can also leverage other, more robust graphing libraries through Pandas. For now, though, let's start with the basics. In this notebook, we will explore how to create three types of graphs: bar (and barh), pie, and scatter. I will also introduce you to some of the more recent features of Pandas 1.3.0, that allow you to control the graph a bit more.\n",
    "\n",
    "Before we do any of that, however, let's import pandas and our data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "handmade-testimony",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ongoing-blast",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"data/titanic.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "handed-fighter",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
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       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>887</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Montvila, Rev. Juozas</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>211536</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>888</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Graham, Miss. Margaret Edith</td>\n",
       "      <td>female</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112053</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>B42</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>889</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>W./C. 6607</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>890</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Behr, Mr. Karl Howell</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>111369</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>C148</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>891</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Dooley, Mr. Patrick</td>\n",
       "      <td>male</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>370376</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass  \\\n",
       "0              1         0       3   \n",
       "1              2         1       1   \n",
       "2              3         1       3   \n",
       "3              4         1       1   \n",
       "4              5         0       3   \n",
       "..           ...       ...     ...   \n",
       "886          887         0       2   \n",
       "887          888         1       1   \n",
       "888          889         0       3   \n",
       "889          890         1       1   \n",
       "890          891         0       3   \n",
       "\n",
       "                                                  Name     Sex   Age  SibSp  \\\n",
       "0                              Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1    Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                               Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                             Allen, Mr. William Henry    male  35.0      0   \n",
       "..                                                 ...     ...   ...    ...   \n",
       "886                              Montvila, Rev. Juozas    male  27.0      0   \n",
       "887                       Graham, Miss. Margaret Edith  female  19.0      0   \n",
       "888           Johnston, Miss. Catherine Helen \"Carrie\"  female   NaN      1   \n",
       "889                              Behr, Mr. Karl Howell    male  26.0      0   \n",
       "890                                Dooley, Mr. Patrick    male  32.0      0   \n",
       "\n",
       "     Parch            Ticket     Fare Cabin Embarked  \n",
       "0        0         A/5 21171   7.2500   NaN        S  \n",
       "1        0          PC 17599  71.2833   C85        C  \n",
       "2        0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3        0            113803  53.1000  C123        S  \n",
       "4        0            373450   8.0500   NaN        S  \n",
       "..     ...               ...      ...   ...      ...  \n",
       "886      0            211536  13.0000   NaN        S  \n",
       "887      0            112053  30.0000   B42        S  \n",
       "888      2        W./C. 6607  23.4500   NaN        S  \n",
       "889      0            111369  30.0000  C148        C  \n",
       "890      0            370376   7.7500   NaN        Q  \n",
       "\n",
       "[891 rows x 12 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "composite-official",
   "metadata": {},
   "source": [
    "## Bar and Barh Charts with Pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "swedish-ordinary",
   "metadata": {},
   "source": [
    "With our data imported successfully, let's jump right in with bar charts. Bar charts a great way to visualize qualitative data quantitatively. To demonstrate what I mean by this, let's consider if we wanted to know how many male passengers were on the Titanic relative to female passengers. I could grab all the value counts and look at the numbers by calling .value_counts(), as in the example below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "academic-mills",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "male      577\n",
       "female    314\n",
       "Name: Sex, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Sex'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "invalid-light",
   "metadata": {},
   "source": [
    "This kind of raw numerical data is useful, but it is often difficult to present visually to audiences. For this reason, it is quite common to have the raw numerical data available, but to give the audience a quick sense of the numbers visually.  We can take that initial code we see above and append two other methods to it .plot.bar() and we get the following result."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "dominican-recipient",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Sex'].value_counts().plot.bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "hidden-season",
   "metadata": {},
   "source": [
    "Not bad, but this chart is quite staid. For one thing, we don't even have a title! Let's fix that. We can pass a keyword argument of title. This will take a string."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "alpine-dealer",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Passengers on the Titanic'}>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Sex'].value_counts().plot.bar(title=\"Passengers on the Titanic\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "electoral-stand",
   "metadata": {},
   "source": [
    "We have another serious issue, though. Both types of gender are represented with the same color. This can be difficult for audiences to decipher in some instances, so let's change that. We can pass the keyword argument of color which will take a list of colors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "industrial-reason",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Passengers on the Titanic'}>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Sex'].value_counts().plot.bar(title=\"Passengers on the Titanic\", color=[\"blue\", \"red\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "tired-payment",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Passengers on the Titanic'}>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Sex'].value_counts().plot.bar(title=\"Passengers on the Titanic\", color=[\"blue\", \"red\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "studied-sodium",
   "metadata": {},
   "source": [
    "We can do the same thing with a barh graph, or a bar-horizontal graph."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "fatty-amazon",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Passengers on the Titanic'}>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Sex'].value_counts().plot.barh(title=\"Passengers on the Titanic\", color=[\"blue\", \"red\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "interior-sector",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "curious-secretary",
   "metadata": {},
   "source": [
    "## Pie Charts with Pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "pleasant-venue",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Sex'>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Sex'].value_counts().plot.pie()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "protecting-friend",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Sex'>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Sex'].value_counts().plot.pie(figsize=(6, 6))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "opened-religion",
   "metadata": {},
   "source": [
    "Let's say I was interested in the title of the genders not being lowercase. I can add in some custom labels to the data as a keyword argument, labels, which takes a list."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "graphic-devon",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Sex'>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Sex'].value_counts().plot.pie(labels=[\"Male\", \"Female\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "homeless-privilege",
   "metadata": {},
   "source": [
    "Now that we have our labels as we want them, let's give thee audience a bit of a better experience. Let's allow them to easily see the percentage of each gender, not just visually, but quantitatively. To do this, we can pass the keyword argument, autopct, which will take a string. In this case, we can pass in the argument \"%.2f\" which is a formatted string. This argument will convert our data into a percentage."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "growing-geography",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Sex'>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Sex'].value_counts().plot.pie(labels=[\"Male\", \"Female\"], autopct=\"%.2f\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "assigned-plain",
   "metadata": {},
   "source": [
    "## Scatter Plots with Pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "coated-poultry",
   "metadata": {},
   "source": [
    "Scatter plots allow us to plot qualitative data quantitatively in relation to two numerical attributes. Let's imagine that we are interested in exploring all passengers, something qualitative. Now, we want to know how each passenger relates to other passengers on two numerical, or quantitative attributes, e.g. the age of the passenger and the fare that they paid. Both of these are quantitative. We can therefore represent each person as a point on the scatter plot and plot them in relation to their fare (vertical, or y axis) and age (horizontal, or x axis) on the graph.\n",
    "\n",
    "In Pandas we can do this by passing two keyword arguments, x and y and set them both equal to the DataFramee column we want, e.g. \"Age\" and \"Fare\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "running-martin",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Age', ylabel='Fare'>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.scatter(x=\"Age\", y=\"Fare\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "interracial-pursuit",
   "metadata": {},
   "source": [
    "That looks good, but we can do better. Let's try to color coordinate this data. Let's say we are interested in seeing not only the passenger's age and fare, but we're also interested in color-coordinating the graph so that their Pclass effects the color of each plot. We can do this by passing a few new keyword arguments.\n",
    "\n",
    "1) c=\"Pclass\" => c will be the column that affects the color<br>\n",
    "2) cmap=\"virdis\" => will be the color map we want to use (these are built into Pandas)<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "exciting-paste",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Age', ylabel='Fare'>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Gc3AcgQpARFoB25RSPhE5BegCjA1beSql2pSEUmoJZvnb0uMbKKcYlVLKC1xcXfJoNBrNIXPkJtN9C/QUkdaYzYt+AL4AztrfjrrAn0aj0RwAR2gynRGOGr0AeEMpNQLT2rNfdFkOjUajORCOzMilgIgMw/T7nhses0ezo15JaDQazQEgKrpXLeMazOjSUUqpjSLSAvg0mh2jXkmIyIlAG6XURyKSBsQppTYelLgajUZzJHKERi4ppVYAtwOISDIQr5SKqvRRVCsJERkJ3Ac8EB6yA58duKgajUZzJCOm4zqaVy1CRH4XkQQRSQEWAO+LSLnNjkoT7UriAsxIpQUASqkdezsjaWqWUChkPtkIrJm3Ab/XT9uerXDHuornKKUIBoLYHZEmyM0rtrJnZw4tOjWhMNdD+pZMmnZoTJ2GKYf5KjSHi4KcQtYu2EBcUiytu7dAl0c7CI7AlQSQqJTKE5HrMUNfR4rIkmh2jFZJ+JVSSsS0tIVb5mlqEMMweOv2Mfw0eipKQVxSDH5vAIvVgjvezeszR1G3SR1+eOtn3hvxKSF/kPZ92vDkhPtJSI3n7bs+YtLoX7E5bHg9PiwiONwOgoEQD35xB/2G9qrpS9RUMRuWbOaegSMxQgZGyKDH4C6M/N9/sVi0a/KAMGpagIPCJiINgEuAhw5kx2h/O74RkfcwS2r8B/gVeP/AZNRUJd+/8TNTPv6dUND8g8/bU4C30Icnr4jsXTm8euN7LP59Oe/f9xkBbwDDUKyZt56nL3uVZX+v5OcPfsNX5Kcw10MoECLgD1KY68Hn8fH0Za8R8Adq+hI1VcyoYa9SkF2IJ68Ib6GPBVOXMP3LGTUt1pHF3jyJI8zcBDwBTAHWKaXmikhLYG00O+5XSYTLdX+NWb77W6Ad8KhS6o2Dl1dzqMydvAifx1fuNiNksG3NTpb9vQq/d9/NPhgIsXzmanas3w2VmRmUIjcjr6pF1tQw6Vsiqyb7PH52rN9VQ9IcuVRFdJOINBGR6SKyItyU7Y5y5oiIvB5uxLZERHqU2DZcRNaGX8NL71sapdQ4pVQXpdTN4c8blFIXRnO9+zU3hc1Mk5RSnTHLfWtqAXWb1sFqsxIKhspsszlstD2+Jcn1k3C47Pg8/uJtCXXiadG5KcqoeM1sd9pJrpdUHWJrapBmHZuwdt56DMO8gzljHLTq2rxmhToSqRqfRBC4Rym1IOzfnS8iU8NRSHs5E2gTfvUB3gH6hJ3PI4GeYWnmi8gEpVR2RScTERdmY7eOQLHDUil17f4EjdbctEBEtJG6FjH88UtIrBOPK86JK9aJzW7D5rDhjHHQtH0jbn/nP5x25ck069AEd5wLV6wTZ4yTEWNuoU2Pllzz1DDsTnt4mwObw4Y7zkVsYgxPTXwAq81a05eoqWIe/uou6jRONX9fHDbOuv5U+g7tWdNiHZMopXYqpfYGAuVjFj9tVGraeZhOZqWUmoVp7m8ADAGmKqWywophKnDGfk75KVA/vO8fmBW486ORNVrHdR/gchHZDBRi1sFVSqkuUe6vqWJS6iczZuWrzP5pAaGQQa8zuhEMhAj4AtRrllbsjHxtxlPMmjifghwPXU4+joat6gNw4Z3nMOTqgeSk51K3WRrrFm5k45LNdDmlI03aNqzJSzviKCr0svKfNVisFjr2b1cmiqy2UL95Xcaue5Ndm9KJTYwhKS2xpkU6IjmARLk6IjKvxOfRSqnRZY4n0hwzenR2qU3FjdjC7G3SVtF4ZbRWSl0sIucppT4RkS+Av6K5iGiVxJAo52kOI7GJsQy67KRK59jsNk68oE+52+KSYolLiuXDBz9n/GuTsDlsBANB7v34Vk6+qG91iHzUsWdnNrf1eYDCPA9KKeo2qcNrM0cRmxBT06KVi9VmpVHrqEr2aMpDcSBlOTKVUpUu1UQkDtPXe6dSqjodgXudkzki0gnYBdSNZseozE1Kqc1Kqc1AEUdlxfRjl/WLNzH+9UnFkU4+j5/nhr+J36ejm6LhrdvHkLUrG09eEUX5Xnas38WnT4yrabE01UkV9ZMQETumgvhcKfVdOVOKG7GF2dukraLxyhgdzrR+BLN3zwrg+f1LGX3G9VARWQtsxLRnbQJ+jmZfTe1m18b0Mv4HAfIydXRTNGxbs4NQcF8QQMAXZOuq/f29ao5kqii6STB76KxUSlWU+TwBuCoc5XQCkKuU2okZynq6iCSHb/ynh8cqRCn1gVIqWyn1h1KqpVKqrlLq3WiuN1pz05PACcCvSqnuIjIQuCLKfTW1mBadmxIKREZIOdwOHd0UJR36tWX72p3FocbOGAcd+x1yK3dNbaZqbCj9gSuBpSKyKDz2INAUIHwDn4TZ72Ed4MEs0odSKktEngTmhvd7QimVVd5JROTuyoSoREEVE62SCCil9oiIRUQsSqnpIvJqlPtqajENW9Xnzvdu4JUb3kMsFhwuO09PelBHN0XJjS9cxZYV21k1dx3KUPQc0o1LRgytabE01UkVKAml1N/spxF2uH3zLRVsGwOMieJUh1w+KVolkRN2sPwJfC4i6ZhRTpqjgNOuGMCJ/zqB3Iw8UhsmY7PrNiPR4o5z89Lvj5O1KwerzaIjho5yamkZ8ApRSj1+qMeo1CchIk3Db8/DXO7cBUwG1rOvcYXmKMAV46ReszStIA4CESG1QbJWEMcKhkT3qkWIyCciklTic7KIRLMS2e9K4nugh1KqUES+Dadxf3LQkmqqlM0rtzHp/V8JBQ2GXH0KbXq0rGmRqp2cjFzGvz6J3Iw8+g7tRZ+zelQ6PxgIMuGdKWxcuoW2PVpy1g2nYbVqU5rm4DmSVhIl6KKUytn7QSmVLSLdo9lxf0qipDo8+u9ARxAbl27m9n4P4fX4QMHkMb/x7OSH6XTicTUtWrWRl5XPjd1GkJeZRzAQ4tfP/uLGF6/k3JvKT+MxDIOHzn6G5TNW4SvyM/3LGSyctoxHvrlbl8jWHDxHppKwiEjy3tId4dIeUZkN9hcCqyp4r6lhvnhmvFngL/xT8Xn8fPzo1zUrVDUz/csZFGQXEAxHY/k8Pj56+KsK529atpUV/6zGV+Qvnj970gJ2b86ocB+NplKiDH+thauNl4BZIvJkODJqJlHmSexPk3QVkTzMFYU7/B72leVIOFiJNYdGUX4RqtQvorew/KqwRws+jy8iJwEgUEnSn9fjK9MrwWK1FCsNjeagqH0KYL8opcaGS4QMCg/9q1QxwQqpdCWhlLIqpRKUUvFKKVv4/d7PWkHUIEOuHogzxlH82Rnj5IxrB9agRNVPn3OOx+bY91zjcDs46aITKpzfqmsz3AluLFbz19xqs5JSP4nGbXRZCs3BI0Z0r9qAiLhE5E4ReRM4CXhXKfVmtAoCoq8Cq6llnHThCdz21vU0bFWf+i3qcu2oYZx9w+CaFqtaaXZcY56e9CAtuzQjrUkqZ1w7kDvfvbHC+U63k9dnPEXXUzqQ2iiF4wd34ZU/n9A5IJpjiU8wS4ovxSw9/uKBHkBUaZvFEUTPnj3VvHnz9j/xCCQ3M4+lf63EGeOk+6BOtSI0dfXcdaxftIn6LevRfVCnYufvrk3prJm3nuR6SXQ6sX2tdgpvXLaFLSu306hNfVp3a1HT4mgOIyIyf38F9/aHq1ET1ezGSpOYi1kz8u5DPt+hIiJLw72AEBEbMEcpVXlIYClq/s6jKcOm5Vu586SHUYZCGYpGbRrw6t9P4nQ7a0ym/70ykY8f+RJBQGDgsBO5e/RNzJ28kMcvegmrzYIRMuh9Vg8e/uquWqkovnt9EmMe/Ly4WdO/7z2fKx+9uKbF0hxJ1E6ndGUUO+2UUsGD+bvU5qZayIvXvY0n12NWFi3wsmXlNia8XWn9rmqlMM/Dhw98js/jx+vx4S30Me2Lv1m3aCNPX/YaPo+vuG/ynJ8XMnfyohqTtSJyM/P44L7P8Hn8ePKK8Hn8fPXseHZu3F3TommONKqoCuxhoquI5IVf+UCXve9LBCJVil5J1EIytu6JiFzyewM12os4b08+VpuVoD9YPGazW8ncnkVhbmR1FmUYZGzNPNwi7pesndnYHLaIaCi708ae7Vk0aFGvBiXTHHHUHgWwX5RSh+yA0yuJWkiHvm0jonhcsU4612CSXFrjVGLiXRFjoWCItse3pHHbhmVMS217tjqc4kVF/Zb1KL3SDgUNmrTfX0MvjWYfwpEV3VQVVJuSEJEmIjJdRFaIyHIRuSM8niIiU0Vkbfj/5PC4iMjrIrJORJaIyAE5V44m7n7/Jtp0b4HNYcNqs3DWf05j4LATa0wem93G87+OpF7zNESEhNQ4npxwPyn1k3lq4gPUbVYHu9PssX3Ty1fXyvIg7lgXo356kLjkWLO3d7yLx74bQWIdHcmtOQCO3GS6g6baopvCDbsbKKUWiEg8MB84H7gayFJKPSsi9wPJSqn7ROQs4DbM+ul9gNeUUuX33QxzNEc3AeRnF+Bw2WvUYV2agD9QpoezUoq8PfnEJsbUiiisyjAMg7w9+cSnxOkaTscYVRHd5G7QRLW4NrroppVP13x0U1VQbSsJpdROpdSC8Pt8YCVms+7z2Fck8BNMxUF4fKwymQUkhRXNMUt8clytUhBAGQUBZhXUxDoJtV5BAFgsZjlvrSA0B82R5bg+ZA6LT0JEmgPdgdlAvXALPjCbce/1GjYCtpbYbVt4rPSxbhCReSIyLyND1+DRaDSHl2PN3FTtSiLcrOhb4E6lVETIVbjz0gF9nUqp0UqpnkqpnmlpaVUoqUaj0UTBMbaSqFb7gIjYMRXE50qp78LDu0WkgVJqZ9iclB4e3w40KbF74/CYRqPR1A5U1UUuhZv+nAOkK6U6lbN9BHB5+KMNOA5IC/e43gTkAyEgWJ2+j+qMbhLgQ2BlqWbbE4Dh4ffDgR9KjF8VjnI6AcgtYZbSaDSa2kHVrSQ+Bs6o8DRKvaCU6qaU6gY8APyhlMoqMWVgeHu1OsercyXRH7gSWCoii8JjDwLPAt+IyHXAZuCS8LZJmJFN6zBbpV5TjbJpNBrNQVFV/gal1J9hf200DAO+rJozHxjVpiSUUn8T2dmuJKeWM18Bt1SXPBqNRlMlRK8k6oR7OOxltFJq9IGeTkRiMFcct5aS4hcRUcB7B3PcaKn9MYsajUZTWzgwp3RmFZmCzgVmlDI1naiU2i4idYGpIrJKKfVnFZyrDLosh0aj0USJUCMhsJdSytSklNoe/j8dGA/0rtIzlkArCY1GozkADqeSEJFEYAD7AnwQkdhwFQtEJBY4HVhWNWcsizY3aTQazYFQdQrgS+AUTN/FNmAkYAdQSr0bnnYB8ItSqmS55XrA+HBhTRvwhVJqctVIVRatJDTVxs4Nu1k4bRmxCW76Du2Jw+XY/041xLpFG1k9Zx2pDVPofVZ3LBa9yNZUQNVFNw2LYs7HmKGyJcc2AF2rRor9o5WEplpY+tdKHjxrFEqBWIT6zdN4Y9YzuGJqVy0qgCkfT+eNWz4AAbFY6D6oE4+Pv7dWdtfT1DBHWcmNaNCPS5pq4aX/vIO30IfP48Nb4GXH+l38/MFvNS1WGUKhEK/d/D6+Ir/Zea/Ay8Jpy1j429KaFk1TW9FlOTSaQyc3I7Izor8oQNbO7BqSpmK8hT6MYGSdBRHI3p1bQxJpajtHU0OhaNArCU210OXkDthLdNdzxjjpOrBMeZqDQilFKBQqd1tF4xURmxBD/RZpiGWfackIGbTv0/qQZKwODvTaNNWDrgJ7FFOQU8jCaUtZt3AjwUCQFbPWsOTPFXg9vpoW7bCxYclmFvy2lLw9+RXO2blhNwt+XUL6IfSqvvfjW+jYvz0WiwW7y861o4bR8/RD97X9/OFvDE24kjOdw7h7wKPF1zF70gIuSL2aMx3D+E+Xu9m9Ofoy8s9OeYSm7RshIsQmxvDQl3fRqHXtaWWy5M8VXNLges50DGN4m9vYskrXvawxojU1HUVKoto60x0ODqQz3frFm/jvoMcwQgahoIHdaSPoD2KxWohNiOH1f0ZRp1Fq9QpcgyilePG6t/njm5nY7DaUUjwz+WE6nNA2Yt63r05kzENfYneY38+do2/ktMtPPujzhoIhLFZLlTiBl89czX2nP4HP4wfAZrfSZUBH7njnP9zQ9Z7icYtFaNi6Ph+tev2Ajh8MBGtd46Ts3TkMb3MbRQVewDSFJdVL4sst72K16cZJB0JVdKaLSWui2v8rus50C0frznRHFKOGvUJBdiGevCJ8Hh8F2YV4C3148orI2pXNa//3fk2LWK3MmbSAP8f9g8/jpzDXgyeviCcvfilizq5N6Yx58Av8ReYcX5GfV/7zHoW5hRUcdf9YbdYqixJa8scKAr5g8edgIMSyGatYNXstFuu+X2XDUOzckE5hnueAjl/bFATAukWbIq5NKSjKLzqkVZ7m4KmhjOsa5ZhREulbKv6jCgUNtq7ecRilOfzsWL+bUCkH7Z6d2ZRcSe7elIHdGdme1Gq3kLk9i9pAcr3EMvLFJ8eSVDexzPLeYhVcsbUv3PZASa6bWMYc6vcGSEiJqyGJNGKoqF5HC8eMkmjWoTEWS/lPtHaHjXa9Wh1miQ4vLbs0w2rb9+MWgUat60c85Tdq24BgIBi5o4K6zWpHB8BBl59E0/YNcce5cMY4cMY4uOfDm+k2qBNdB3bEFefC6TbHb3vz+qOij7XVbkWVc8MpubrQHEaOQZ9E7VtfVxOPfHMPdw8YSX5WPsFAiLQmqWRs3YPVZqFx24bc+sZ1NS1itdL1lI5cMuI8vnj6O2wOG+44F49/f1/EnDoNUxjx0S08f/Vbph8BeOy7EbhjXTUjdCkcTjuvzRzFjPFzyM8upOspHWna3myD/vj4e5k1cT57tmfRrndr2h5/dCj9zG17cMU68eQVFY85XHZy0vNwx7lrULJjl6PJlBQNx4zjGkwn6q5N6cQmxpCUlkjm9j0E/EHqNUs7Zsow7NqcTvqmDNr2bo3LXb45xpNfxJ4dWaQ1qXPYM6RzMnIJBQ1S6ieRn1WA3+sntWHKMZv9nLFtD9e0v73YKQ8QlxzLNzvfx+6wV7KnpjRV4biOrdNEdTj3rqjmzvv4nqPCcX3MrCTAdKKWDG08mqOZyuOr58bzychvsNosxKfE8cJvj9G4TdlQz5h4NzHtGh1W2YKBIE9d+gqzf1oAAnGJMRRkF2KxWmjaoTHP/fIICSnxFe6fvTuHMQ99ya6N6XTo24bcPQVsW72DTie25/KHL6zWG2peVj4Lf1uGxWqh55CuVbrySmucyoOf38nTl7+GYRi4Y52M+ulBrSBqkGNtJXFMKYljmaV/reSzJ78l6A8S9IO/KJuR5z/Hh8tfrWnRABj30gTmTVlE0G/6RHLSwxnbgRCblm3ltZtG88g395S7rye/iJt73Uf2rlxCwRCLfl+GiKAMxarZa1m3cCNP/fhAtci9c+NubuvzAH5fABTEp8bx9tznSKyTUGXn6HdeL77P/pi8PfkkpiUcFb6WI5pjTEkcGzYWDesWbsQI7YtuUkqxdfUOaou5cfnfqyNMKiUJ+oOsmruuwn3nT11CYa6HUDCckawodvb6ivzMn7qE7PTqKbPx9h0fkZ9dSFG+l6ICL1k7svn0iXFVfh6b3UZK/WStIGoaZZbliOZ1tKCVxDFC/RZ1I6KbAJLrJdUaW3+T4xphd5a/sLVYhIat6le8834Uncj+5xwsu7dkRCjfYCDEro3p1XIuTc2j8yQ0Ry0nnHM8fc/tiSvWSWxiDO44F498HZ0D7nBwxSMX0bBVfdzxLtzxLmx2K65YJzEJbhJS47l79E0V7tv91M64YpwRYaF7dZ/DZadT//ZmLkU10H1gJxzufX0ynDFOug/qXC3n0tQSlIrudZSgfRKHCcMwyM3IIy45tkKno1KKvD35uGKdOCuIPDpYRIQHPr+DNfM3kJuRR5seLUiul1Sl5zgUYhNieGfB86yYuYZQMETbXq1YNG0Zhbke+p3fi/ikipPH4pJieWvuc7z330/YtTGD4/q0IXdPPtvX7aRT//ZcO2pYta2YrnvmcnZuTDcd7sDAYf254I6zquVcmtrB0bRKiIZjKgS2pli/eBMPnPEUBbkeBLhnzM0MuvTEiDlZu7K57/Qn2b52J0ZIcekD53P145fWjMA1jGEY/Kfz3WxZaRayc8e5+GDZy9RtWjuS+srDV+RDLBYcTh11VFupihDYuJQmqvOQO6OaO+ur/x4VIbDa3FTNGIbBA2c8RfbuXALeAH5vgJevf4ft63ZGzHvm8tfYumoHAV+QUDDEty9PZNbE+TUkdc3y4rVvFysIgKICL3cNGFmDEu0fp9upFcQxQlU5rkVkjIiki8iyCrafIiK5IrIo/Hq0xLYzRGS1iKwTkfur7urKos1N1UxuZj4FOZGF5qw2K+sXbYrI2Vgzf8O+6BzMZjgrZ6/lhHOOrxa5Av4AUz/5gz07s+nYrx09TutS5edYM389c35eSGxCDIOvGkBcUmxU+y37e1WZscxte6pavCOGUCjEtC/+ZueG3bTu3oK+5/asNQEHxyJVGLn0MfAmMLaSOX8ppc6JOL+IFXgLGAxsA+aKyASl1Ioqk6wEWklUM/HJsRENbcAsKFi3aZ2IsdSGKXjy9j09u2Kc1CunZtL6xZuY8vF0RISzrj+VZh2aHLBMwUCQuweMZOPSLfiL/DjcDq5+8t9cdNe5B3ysipg1cT5PXfoyAW8Am8PGuJcmMHrxS1EpivrN09i5YXfEmDuubIJaYW4h370+iT3bs+g5pBsnXtCnyuTfH8tnrmb6l3/hjHFy7v8NoX7zutVyHqUUI89/nsW/L8fr8eGKcXLu/53ODc9fVS3n0+wHRZU5pZVSf4pI84PYtTewTim1AUBEvgLOA6pFSWhzUzVjs9sY8dHNOGMcxCS4ccY4GXL1KbTv3SZi3n1jbyMmwU1Mght3nIu2vVpx+vABEXNWzl7LrX0eYPxrk/ju1Z+4sdsIrmp9K9d2uJOJ7/0StUxzJy9i8/Kt+Dw+lFL4PD4+vP/zKu189ubtH+Lz+DEMhd8bICc9N+oe1w98cQcOVwnTjcDDX0VGYhUVFPF/Pe7li1Hf8dPoX3n2yjf48tnxVSZ/Zcz5eSH3DX6CH96awv9e+pGbuo9g58bd+9/xIFg9d52pIAp9oMwV5vjXfyY/u6BazqfZPwcQAltHROaVeN1wEKfrKyKLReRnEekYHmsEbC0xZ1t4rFrQK4nDwCmX9Kft8a1Yv2gTdZvWoV2vsq0x2/VsxcerX2fFP2uITYyh88nHlUmcevk/7xRnJINZi2rvE/e793yC1W7lzGtP3a88BTmFZZJGDUMR8AWxxhx8spbf6+fN28cw+6cF5JTqER3wBaO+sSXXTWJc+od8/8bPeAu9DLlmII1aRZYPmfH9XLLTc4u/D5/Hx2dPjOPS+86vdlPMB/d/hq/ITPwzDEVhnocbu/6XxLQELn/4Qs64ZlCVnasgx1Om4qvVZqUw10N8si4XXiNEv5DIPETH9QKgmVKqQETOAr4H2lS+S9WjlcRhomGr+pUnhGEmt/U/v3eF2ytryenz+Jn0/m9RKYkuJ3eIWDJb7VZad2t+yMX8Xrz2bWZ8Pwe/N1BmmzPGQe+zekR9rJg4N5c98K8Kt+9dBZUkGAihlKp2JeEr3e5Wmc71ogIvb942htjEWE76V9WYvtr2bBlhrrRYLaTUTyKtybFVd6y2sDeZ7nCglMor8X6SiLwtInWA7UBJO3Pj8Fi1UG3mpvI89yKSIiJTRWRt+P/k8LiIyOthT/0SEYn+bnIMUadx5TeGCBNNJdRrlsbTkx6iQct6uONcdB3QkacmHnptoxnfz41QEGIR7E4bKQ2SueeDm+nUv/0hn2Mvx5/eNUIZ2F12+pzd47BU8x181QCcFShUn8fHr5/+UWXnSkiJ58Vpj9H0uMa44ly069WaF6eN1OU5agoVXcOhqmg6JCL1JfxLLiK9Me/Xe4C5QBsRaSEiDuBSYMIhn7ACqnMl8TFlPff3A78ppZ4Nh23dD9wHnIm5jGoD9AHeCf+vKcFd797AiFMfL9NhDswn9atGXhL1sTqfdBxj171ZleJhd9nwe/fVX3I47dz40nDOven0Kj0PQP3mdXlx2mO8etNoctJz6TG4K7e9eXh6glz20IUopZg8Zjo5GXn4i/Zds4gQk1C1fR5ad2vBh8tfqdJjag6BKlpJiMiXwCmYvottwEjADqCUehe4CPg/EQkCRcClylw+B0XkVmAKYAXGKKWWV41U5chZncl0Yc/9RKVUp/Dn1cApSqmdItIA+F0p1U5E3gu//7L0vMqOf6Qk01Ulq+euY+LoqVitFrqf2pmFvy0lFDI46/rTOK7PYTdXRvDju1N4779j8Xn82Bw2kuom8sHSl4hNjC709Uhk2YxV3D/kKXweHyJmy9Q3Zj9Ds+Ma17RomlJURTJdfFJj1eOkO6Ka++fEe4+KZLrD7ZOoV+LGvwuoF35fkbe+jJIIRwjcANC0adPqk7SW0q5X6wjH94CL+9WgNJGcPvwU/v5uNsv+Xo0r1sGtr197VCsIgE792/Pq30/y62d/YrVaOOs/pxXnv0z+aBqfPj6OUDDE2TecxhWPXKzzG450FHAU9a+OhhpzXCullMiBu4CUUqOB0WCuJKpcsCOU79+YxNjHxxH0Bxl0+Unc+vq12OwH/uPdsGQzoy59hd2bM2jSvhGPfHP3fh3ue3nhmrdZ9vdq/F4/fq+fZ654nbfmPFMml2Pziq38Me4fbA4bg68cQFrY15K+NZNfP/uTUCDEgEv6FbcmLUlhnofJH04jLyufXkO60enE4w74GsHMP5jx/RxWz1tPgxb1GHL1KVht0dv5Q8EQO9bvwuFy0Kprc1p3axGxfeYPc3nztg+Ly59//fwEHG4n/x5x3kHJq6lFHGN3ncOtJHaLSIMS5qa9NZUPq7f+SMMwDHas343dYaNu0zplnkb/Hj+bDx74ojjq5tdP/yA2wc1/nrvygM5TmFvIPQNHUpBdCJiJe3efMpLPNrwVlcL5Z8K8CJ9EKBBkzs+LIpTEytlrGTHoMfxeP2Kx8PVz3/PuwhdQhuLmnvfhLfSiFHzzwg+8OO2xiFWTJ7+Im7qPIGtnNn5fgG9fmcid793IaZeffEDXCTD63k+Z+O4veAt9OGOc/P71DJ6d8nBUju+cjFzuHjCSjK2ZGCGDXmd045Fx90Q4k3/97M+I/hg+j4/fPv1TK4mjgGOtwN/hTqabAAwPvx8O/FBi/KpwlNMJQO7+/BHHCnlZ+fxfj3u5qfsIrj3uDh4Z+izBQDBizt/fzY4Iy/R5/Pw9fs4Bn2v94s3FzXrAbNzjyfWUyX6uCJsj8klcKVUmU/qduz7CV+RHKTBCBp68Ij5/6lu+GPUtnrwiQkEDI2TgLfTx/n2fRez72+d/kb0rx4ygUuZ1vnvXxwd8nYW5hXz/+iQzQQ3zBr5y1ppyy4GUx6s3vseO9bvwFvrwewPM+2UxE96eEjEnNtFdRpm7q9ihrakZDld0U22hOkNgvwT+AdqJyDYRuQ54FhgsImuB08KfASYBG4B1wPvAzdUl1/4IBoLM/GEuUz/9o9K8hMPFG7d+yJZV2/F5zBvSounL+PaViRFzkuomljGVJKREn2i1celm/u/4e3n0vOcoyi+K2BYMBIlNjCmzz9wpixj7+DdMHjOtWGm5SimEUMggoU5kX+qtq8ouEFfOXkteVkGZvIf8rMjku8JcD8FAZFZ43p58Lqp7LU9d+jKeUrJXRFGBF0up78tX5OeegSO5IPVqZk9aUOn+axduJFRCDp/Hz5q56yPmXHr/BbjjXMWJcM4YB9c/c3mlx10+czXXdbqLi+peyxOXvERhnqfS+ZoaQB3A6yih2sxNSqlhFWwqk+0VDuu6pbpkiRa/L8BdJz/C1nAFUqUUz/z80EHbvauCdQs2RGRZ+zx+Vs0xW3nu2pRuxuQLuONdZhmMkIHdaeP/Xrk6quPn7cnnrgGPUpjrMX+xxcxvEMDhdjDoshPZvTkTESnuP/HlM9/x+ajv8Bf5cbod/PLJ77wwbSR5mfkRx7ZYLOxYtytizFqO2crpdpBYSpkA2B021i7YQLOOTXA47fQ6oxufPv5NRCFEpcwiin+Pn4Mnr4inJz2032tOaZBMvWZ12LFuV3E48d4VVEF2IU9c9CLXPn0ZLTo3o/ugTmVWBE3bN2bP9qzifZ1uBy26RAZRNGrdgFGTHuSjh74kEAhw0d1D6XxS5O9RycS/XZvSuX/Ik8Wrm1k/zuOJi17kuV8epTI2LNnMpmVbaNSmQbFpzjAMvn/jZ+ZPXUz95nW5cuTFJKVVT9OlYw0zme4o0gBRoDOuS/DLx7+HaxrtsyW/cM3bfLL2jRqTqelxjdm5YXfxDcnhstOiSzO2rt7Orb0fKM48trvsDL15CMn1kug7tGex0/e3L/7ijVs/wF8UoNOJ7Xnqx/txuPZ1Ulvxzxp84bpAQLGiuPi/QzEMgx/enMzvX88k6A9xyb1DSamXxMePfl3cstPr8bFu4UYW/raMuk3rRCgFh8tO47ZmpI/f62fDks207NqMRdOWFd+UrTYrvc7oxobFm8tc+5r5G7hn4GMkpMbx6t9PkVwvEYut/MVvKBBi3i+LMQxjv34Fi8XCC789xjOXv8baBRvw5EWuQPzeAGMe/AKrzUrXgR15fPy9WCwWdm1KZ9H05fQ7ryfrF2+kMMeDUtC6R0suuP0slFKMf30S416cQCgYIjczDyNkXueoS18hedpIOp/UgS+e/pax4ainNt1b8sK0kSz8bWnE02fAF2TRtOWEgqEKHerfvfYTYx78AovNghFSXHD7mVz39OW8fvP7/PrZX/g8Pqx2K1M+no7T7SSpbgJ3vntjGWWlOUCOov7V0aCVRAn27Mwqrsmzl5yM3ApmHx5uf/s/3N5vIwVZhRhK0aJTE/49Yiiv3PAeRYXe4putz+Nn49It3PTS8OJ9F05fxrNXvL7v829LufuUkbw565niscwdewgGQoRcVkIJdmxZPixBxRnXDuLG7iPwh3tgAHz2xP9wuOwRPZ3BTCArzPXwyDd3M2LQ4yilCAaC9D+/N/3P70361kzuOOlhcggS8gSwO+3YbFaUUjQ9rhHDHvgX793zCVabtdQqQVGUX4S/yMcr/3mX40/vGmHmKY06ADtwaoNkXpz2GKFQiHPjriDgi/TzmNccYNH05cz6cT6JaQncP+TJYrn8RQFsDitisZC5fQ8+j59p3//NmIe+LFu2A9P/MmrYa9z08lV89PBXxeNrF2xgxKDHuGTEeWWMv1a7pUzdpr3kZeXz/n2fRawyv331J069/GR+/nBa8c8oFAgRCoTwefzk7cnnwTNH8c7CF2jcpkG5x9XsH72SOIbp1L89Trez+I/cZrfSoW+7GpUptUEyY1a8yrqFm7A5bLTu3hyr1UrunvwyN8W8UgX0xr30w96FQTGr566LmJNUJ5H8vmlk/KsFhEyHW8MP17BzUzo2uxV/KTN/eXWZlFJ07NeWOo1S+XTDW2xYspn4lDiad2yCiPD0ze+w8LJGBFKcKBESFmdxfWobBl92Mq27t8Bqs3LFoxcz4/u5FBUU4fcGIhRRKGiwaflWug3qXLkiOIgUBKvVym1vXs9bt4/BUAYBb6SyMEIGmduz+PDBz4tNQXvZq1j27Mhm3EsTWDFzTbkKYi95e/KZ8HbZar3rFm6i33m9+PSJcezamI7fG8AZ42D44/+uMK9iz47sCIVqyhNg54Zd5c4vvh7DYP4viw9ZSSil2LUpHW+hjybtGh5UuPURyVHmb4iGY+QnGx3HD+7K8McvYcxDX2KEDNoc34oHPru9psXC6XbSsV+ksgr0rocxzYLFb95MDYcFX6/Ingbrg4UomyDBfb/VIXek6cLVrg4ZFzRH2S1gN3//d17fjhZdm1X61G6xWrDaraQ1TuW+T26lTiMz1yEuKdYsIFiCv1oY+OvEQNhUlN85mfnp+dxaIrw1tUEyH654hZk/zGXOpIX89d2sCIXQuG0D+p3Xkw/uj4x42ovVZqHboM4HVbvpzOtOpWXX5qyes45vXvjBDG0Nn1sE2vVuTc7IvAr3D/qDpG/dQ2xi5dFLdRql4HCV/ZMTAYfLwZtznuWn96aSuSOb7oM60afSgoiqrMIMf+x3Xi/m/LwwolzIXixWS7m9OQ6EUCjEqEtfZfZP87HarCSmJfDKn08U/w4c3RxdkUvRoPtJlOLie4YysfAzJuSN5fWZo0hILetQrQ1kdEtiz9lNCMbbCcbZyBnUkN0nRjYpCp7dAiPWhmE1n0YNu4XCDkl8/tS3LJthhnum2wJIKV2g7EIw1sa9Y2/FGePAHV/2puJw2flsw1t8suYNgoEQnz/1LZPe/xW/r+xKw9cotlhBACinFW/TstFX8clxDLl6IB36lldeRFg9dz02e6SSs9qsxCXH0ndoLx755u5yv6toaNezFUNvHsJLvz9Og1b1sNmt2BxmAEC7nq3oMqADdmf5z1TOGCe9hnTjqsf+jSvWaTr+S60AbA4bj40fEV4dRO5/4r9OAMAd6+Kiu8/lphev2o+CgLikOKylQo7tLjupDVN44PM7GHrzEFp2aUazjo2LfVB2p42U+kmcdNEJB/LVlGHKmOnM+XkBfm+AogIvGVszeeGatw/pmEcUSkX3OkrQK4lysFqtWN21u8pmm5RUZg9szOYBptnAZrFwUkrkk1zHTi344r97SJi1G2t+kJhlWcQtzmbsgq+xP2vnikcvZlVGBqpe5LFVSKFyvJx8YV+6ndKJnRvTCfgCPHfVG+zelEF8SiyPfHMPKfWTmfjeL7x7zycEvAHsLgcT3/uF12aOwu7YV5H2uKYNmL97J4RLXltDikEnd67w2taVytcA2LZ2B7s2ppcJgbXaLIzf8/GBfn0VUq9ZGh+tep3CXA/uOFex0/iOD27k4sfeZFMdsPgN2s3Lxzd1IxaLcOFd5zDw0v6ICO/Mf55fPvkdpRT9zuvN5hXbCPqD9Dm7R3Fm+TOTH+bVm0bjLfRy4oUncOvr1x6wnGmNU+k/tDezfppvdhd02WnXuw2tu7fAYrFw4wv7OtfNnbyQeb8sJqVBMufcOBh37KGtJNYu2BAR3BEKGmxatuWQjnnEoKq0fekRgVYSRyh39OnHjK1b2JaXCwipbjcPnzwwYs6jAwbxy4Z1pA+2Ezcvk4RZ6VgCBgamo/vD+z/HEu8g8eS65A5oACEFFqHuZ+soOsMDjSEhNb54NfXp+rcI+APFCkApxTt3f1Js1vB5fGxds5N/Jszj5Iv6Fsvx8lnncOE3X+Dx+1FAp4b1GN6t4t7dLTs3xel2FAcRWKwWmh3XmDbHt8ThtOMN2/0tVgvNO1V9/a4tK7exfOYakuom0OfsHlitVl5dNIftbWMIBYOEgHXnxDF29C30aNgowsTljHGSlJaIUorUBknlFl08fnBXPl3/1iHL+eCXd/DTe7+yau5aWnRqyvm3nVmuua3XGd3pdUb3iDGlFFM+ns7UsX8QmxjDVY9dUqa0SEW06NwMZ4yjWFFYrBaalFNC5ajlKFolRINWEkcAhmGwa2M6NoeNtMapiAjxTicTLr2CJem7CBmKrvXq47RF/jhtFgszr7mBiWtXM2P7nyy1bCZQKn4vv4GLpN92Ejcnk1CSA3tGEYbLRlyLlHJlKblCMEIGgVLmJWUoCnIik8CaJCYyffh1LEvfjctmo1Pdelgr8R2cf9uZzP15IStnr8VisRCXHMvd799EnUapXHjPOXz97PdYbVaS6ycx8n/3RPUdlofX42P35gxSGyQX997+e/xsnr3ydUCwWIR2vVvz7JSH+WntGrzBEvkqwSC/btpIz8b7So5sW7uTW3vfb5YmUTD2sW94Y/YzZWpQbVy6mY8e+YqC7EIGXXYiZ98w+KAK/1mtVobePIShDDngfce/PikiEmvhb0t5a+5z5dbLKs3ZN5zGrJ/ms+SPFVhtFtzxbkZ8VONpToePY0tHVG+p8OrmWCgVnp9dwH8HPcb2tbtQhkHjdg1RSmGxWBh2/wUMuCS6KrCbV2zlll73lwnxLWqdwJ6zm5A2biO2HB++JnFkXtmG/w35Fy1b1cVt/wGCy8HaHom9AhE7SilU0XgIzOaes/ewam5BsRnIarfSsFV96jROoWm7Riycvoy4xBjueDmZ5i2ngsRA/ANYnBV34ANTMW5athW/10/LLs0icjsK8zwU5npIaZDE4unLyUnP47gT2kRdiBBgyZ8reGTosyhDEQqGuPXN6zjz2lO5IOVqCnIKi+e54lzc+/GtPJi7hO35+5zXVqDlggI6bAhy8X+HEvAF+e7ViayYtab4JiIi9B3ak8fH31u83/Z1O/m/HvcW16hyxji5/KF/MaySLnzVwWXNbiJj6559AwL/HnEe1z97RVT7K6XYFO6T3qJzU5zuQ+tqeDioilLhCXGN1Amdboxq7tTZI3WpcE3188atH7Jl5fbiePiSSWcvXPsWdpedfkN7EQqGWDl7LQFfgPa9W+OOi4y0adahCUNvPYNxL06IeBJyrc8Dpdh+VyeU3YIDC3F/7+Th55/ggbdWc/wpHqxWP+Bi94bpbNl+F/XqfkuTRj8CRYx838Wzt7Rm+dwYRIRgIMjWVdvZumo7C39dCkDfIbnUr7vZNGcBKusKlq95CmXtTPs+bYrDJ4OBICtnrSUUDNG+TxtadmkGmApjyZ8rKMgupMuADsQlxeKOc/Hoec+x+I8ViJirmke+uadch69hGBTkFBKfHIeIEPAHePS85yKS6N66bQydTzquTCkMI2Su4u4/6yRGTJ+CNxg0oz0KAvi/W8WygiDL/l6FM8ZhhsSW+G6VUuRmRkZFTfvi7+LaVWCa6L577afDriQO9WlYRGhRDaa+Wo9CJ9Npahdr56+PSJgqic/j58d3fuH4wV2455SRbFm5HbGYjW9en/k09Zrti3Zau2ADE96cvO8p1yKgwB3nou3nWzjxw2Fk24L89cQknHN2k9TcR5d+uVit5g6//+Dk5bsKsTlfIugr4t+3xXP5XUUkJHt5+suNSNIrnJv6QZmkNICBF2TjcO67KxkGrPrrdT55vgUNWtXj1b+eRCm4tsOdZO3IAszubh8sf4WktASuaXc7uzaZdbQsVgsv//E4uRn5LP5jBd4Cb/Fxn7vqDb7L/Cji3L99/ifPX/0WRsjAarPy6Lf30Lpr8zJFEq12K1tX7aB19xasX7SpOE/DX+Rn9IixMAK69mzC5rZuAlke4v7eha0gslxKaVwxTk660Iwk2rR8K18//z2r567DMErfZQ5/j4kL7z6Hjx75ysy2x5R18PBTDrscRxqC0sl0mtpFk/aNIspylMbhsjPupR/ZuHRLcaKbt9DHqze9xzM/P1w8b/mM1ZHZzIZCBF796wm8Hj+v3vgeuzamE1PkxwDsTgMjJIDC67Hw0l1N8Xst+Lw+wMLXb9TjpHNyadomnDymfFis5d/sXrqrCb98lcITYzditYHVCjZbiKICL1tX7eDzUd+xZeU29mzP2isdhbkeHjrraTr0bVesIMB8sn/w7Ke5/unLUaUyvwtyCiPKcqRvzeTZq94oVoyhYIjHLniBr7a9R+kbcygQomGrejzxw308fM4zbFhSNsIqc95WYqOwblqsFlwxTs69eQj/uuNstq7ezu19Hyw2MZVmb+mSw8m/7jibmAR3seN6+GP/PuhuejMnzOXtOz7CU1BE33N7csfb/4kwDx51HGNK4pjJk1BKYRR+jJHeHyO9H0bB22Wqjh7yOYJbMfZcgrG7F8aeS1BBMyxQGVkYWdeZ45nnoAIrypHPh5H7IMbuPhgZA1HeaQDc/tZ1pDawERNnYHcZETH2zhgnl95/AVtWbIvIhDZCBtvWRFZaN0KhMuGjFquV1IYpPHjmKDYu3UJRgbf4CXrrOhc5GTaCAcjOsGGxRH5XVrti91YHRgiCQcDRi4vvORdnTFnbtK/IytLZcUz9xnSGe4uE6d8nAWaW8JYV21g1e22Z/bat2cHqeevKjHvyimjXu3XEmMVimj9KRvfMnrQgwqyS1sjPi9+uJTY4iM8XZdLiOEVsYgwOt4PLHr6QZh2aUKdhCu8ueIFJRV+UOW80OGOc3PDClfyQO5brn7kcEeGn0b+aJVQq+HXbsnLbQZ3rUBARzrz2VF7+/Qme/OF+WnePLrKpNKvnruPpYa+ye3MG+XsK+P2rGbx60+gqlraWUUV5EiIyRkTSRWRZBdsvF5ElIrJURGaKSNcS2zaFxxeJSLU6Zo+ZlYQq+h7yX8HsJw4UvIeSeCT2wBrzVHh85UNlDQMjEzAgsASVdRmqzlTIugaCa4EgBHNRWVdAnV8Qa519++eNhKKfAB+EslE5d0LqZ6Qk/sUHvy9n3VKwORR+n5Ofvz4VsTfk/NvOol3PVqzq25aZE+btKyfisNG+d2TopVgtiEiEYlSGwfKZq8tVls6YGO4f1oFXfoLUhrvLlNb2e4WY+BDL58Xw7siWPD4hxBWPXEydxqnMGD+H2T9Fltv2FVnYuMpF5k4nbz3cmOVz4sLncdCxfzvSt2aSvTuyTlZsUizNOzVl7fwNEeOuGCdtj2/FLW9cyxu3fIARMmjYqj5PTrgvYl69pvu+X5vd4KXx60itH8BmA3vcRt7+LYnlq94irUnjMk5vm92GWKTCMiAWq4VGrevTsmtzctJzWfrXSgAGXXYiF9x+VsTcYCBYqQ/AViJi7Ehjzs8LIxIo/d4AM3+YW4MSVTNV65P4GHgTGFvB9o3AAKVUtoicidmRs0+J7QOVUplVJk0FHDNKAu8EihUEmO+LxqPcZzNtcxbP/f0XRcEA57fvwJ19+lUaogmmUsDIBksaIlYIrgNVyL7fIMP8HFhkbqOUrT6wEKyDS8g3FShZ98eP8v4B3kk43R469DJHRYroPKAIS9JtxTOH3jyEFf+sYcb42YjFQtP2jbj97esjTheXFEuobRLbz2lEKM5OzMocmvyyi9jEmLI3Qquw87HjSW7cAFqcjSsmllETV/Lwuc8SDITweXwEAxbuPs9URDHxblbOWkvdpmmcee2pnHntqVzb4c4yvSNa9H6E2FZ9ycp6CodrE0pBz9O7ctHd59L7zO7c1OPefTWbBP5973k0bd+I2T/NLy5DLiKM/Pa/AJx57amcPvwUfB4/MfGRjnql/PQc3JBGbdPYviaDhs39xCeF2BclrLBIgC79rIij/Kioi+85l29emFBm3GqzEJsUy4vTHyOlfjIAviIfYrHgcJa94Q++6hQmj5kW6bcwLXnhGk2XlHv+I4HYhBhsdltEKHTpviJHG1LGp3RwKKX+FJHmlWyfWeLjLMyOnYedY0dJSDyGAl/IhgLc1iASXE5o90kkFqayM/9MCoMO3p8/ly+XLsFps3FG6zbc3/9k7NbIp2jDMwHyHsJsvuCGlI9AYkGVrm8RAksKZR8jDTMUNAInENmPQSyx+ANuXrqtKX/+mITFqrjopnSGPxpZ0sJqtfLQF3eSnZ5LwBegTqOUMklVzQcfx+b0Nhh2c7ygZxr2/m3odGJ7WvdoyZp56/B5/CiHhdwT67PHGuSf3dsZ9u3XTLn8ajqdeBzf7PqAXRvTuaHL3RE+EkMp4kuVL3l+6iPcesKDZO/OAQWDrxrAmdcNQkR4feYoMrdnYbVZim+yLTo3Y8zKVxn/2k94PX7WzF/PJ49+jVgEp9vBza9fg9Vi5YShx1O38b4VgtVqJSbezarMDB747Rd2FRTQq56FJ7u+Q7w9yId/OJn45cUsn7UHh2s9EY+BKljOz2Ef1z97BYV5Xv4ePwt3nAsVMti9JZNQyODUy08qlh2oNAS0Xc9WjPrpQT5+5Cu8hT76X9CbnPRcCnM8nHxxX/qee3BRkhmeQu7/dQorMjJokZTE84PPoHHC4e0bMXj4AMa9PIHcjDwC/iBOl4P/K1GJ+OijxkpuXAf8HCkIv4iIAt5TSlWbje+YURLTtibx/bpTmbK9JQCDGmzm1b6/4rQG6JicziPdZ3D/3IH4DYMsr7ni+HLZEvzBEE8OOq34OCq4CfIeZu9T/+LMeG778X/sLoqjRcJlvNNvIi3i0wE3OAcitraomOvAMxZzJeMCWztw9ImQD0sihEquHA2UJY0xT7mZ8TOEgkIoKHw3Oo1G7Q2GlArVfmfubN6aN5ugYXB2m3Y8c+rpOMLKTakgs3ZvxO6y4wuZikw5LCyXAiwWC89PfYQpH01nwdL1TPDvZE8H84YfNAx25hewJS+XFknJOJx2mrZvxA0vXMkH938R7nVgoWPftnQb2DHyclLceJ47kS1rt6EcVuyDuhUnjIlIcYmKkjRq3YBb37ier54bz/Qv/45wxM+aMK/CBjwZnkL+/b+vKAhndP+yKUh6/kC+GPgjorycO2wcQ++YicoDvJNBFYV/Pv3A1haABb8tZeU/a0htmMxpV56MzW7j6+d/4NdP/8Dn8ZGbboayOt0hggELk97/jS4nd+DEC/qUK1Npug7oyCt/PlnpHL8vwK9j/yB7dy6dTmpP1wEdK5wbMgyGffs1W3JzCRoGGZ5CLvrmS6YNv44Y++EzX8UnxzF68Ut89+pPZO/OYdBlJ5Up8HhUoTgQJVGnlL9g9MHczEVkIKaSOLHE8IlKqe0iUheYKiKrlFJ/Huixo+GYURKj5jnZUtAKFY5q+Wt3Y15b1pN7u87BaTXokVq2j7M3GOTHtat4ctBpeIMBvl6+lN05S+mV2JQB9deQ7XMy/I9zKAg6AMX6XDuXTx/CH+d8jt0CxJiOS0m4B+XogvIvQGxNwH0RIqW+eqOcPtLB9cyZWoTfu28l4yuyMnvylggl8ePqVbw5dxZF4Yzgn9euIcnl4pGTB2IUfAgFL+HytMZCf0r+yO0W009hs9s4+4bBNEvvwtf/+xqCJZzgysBljZTV7w2Qf1wihY3dOPODePyBMn6Ne375mdVZmQSSzSiX0fPn0SmtHqe2bBUxr9DvZ9a2rSBwQqMmxDocbF65vawjfm3FLc9nb9uKoVTxes1v2JifWR9P0EaMLQjKB0YGkvAsOPqjAisRW2twn4+I8M2LExj72Df4vX6cLgeTP5rGS9Mf56tnxxf7eeISgzwxdiPtupsd/L54rR7LZqyOWknsj4A/wJ39H2LLqh0EvH7sLjs3vTScc248vdz5W/Jy2ZmfTzBs+jCUIie/kCvPeIhOiXW4850bijsJVidKKT56+Esmj5mG1WZl7uRFvPT749RvXnf/Ox+pRG9tyjzUZDoR6QJ8AJyplCrOflRKbQ//ny4i44HegFYSh0J+wFGsIAC8ITuz0s0SBIaCRrH53NlxLgv31OWhbv8Qaw/wy7YWjF7VnaIdvbhv7snc1G4mqU09zMusz0drOtIuMQdztadwWAz8hpWCgJ3thXE0j8+D7OtQdWcAIVTRdxBYhLI0QOw9wd42UkBLPVSooFhChRuLtTHJdUNs32BBqXBxPJtBar3I39JpG9fj9fiIXZWLBAyK2iby28YNPNzHCQWvA0GGNF7Pa8t7EPTGEjAsuG02bu8Tma3dIa0uXerVZ8GuHfhDIZxWK/1DiSwbv4Dstg3YvmYny/9ZzaebV5IzrAXKbkGCBr/tKuKDBz+n+ymdWT13HTO+n8MGXy4ytAk0MM05RcEA7382mRmZVhoN7USvzq1pEBfPeV9/Rr7PDygSnC5+uPQKjuvThr/+Nyuir0fDVvVZ9vdKWrTbgls9AyofrC0guBJXQQOUcRoQ+QRtK67EpsCSYq5k3EMR99DiOaFgiDEPfVFcFt3r8bFh8WbmT11CUYkcjHte2UqbLkXFPo1Lbs5gycLKezfsWL+Lnz/8jYA/yKmXnUSbHi3LzMnLymfC21NYMXM1m5ZvK7bt+zx+3rnr4+KSHTs37iZ9cyaN2zUktUEybpuNUCnFHAiGyFibzsxtG1k8bTlfbn/vkIv57Y+/vp3F1LF/EPAFCfiC+L0Bnh72Kq//83S1nXPnht0sn7mahNR4jj+9C1br4S3GebjyJESkKfAdcKVSak2J8VjAopTKD78/HXiiuuQ4ZpRE+7RGzNy2g73x8RYMGseZJgQBnFaDK1ov47p2Idw284ZxUYtV9K+3jVm7ExjV42di7eaT+qCGm5mfWZ9Eh5duKbt5+YRpJDh8pBfF8t2mttR3723+40XljIDgSjAygCAYWWYUVNpUxLKvPtJy339pqu4Iy6ZYl1+XDqnncMvT73HXufEYIbPvQGx8iGF377PJAyQrG01eWIolz2/6Qy1CwlOnmE7zsFkszh7gx9P/x/uruvLuqh40jE/gouP2mTN2FxSwqyCfVsnJzNuxDasIsZO3sO23nbxhs5n1iEQIGgbZz/eCcMc05bDirevm089n8O0rE01/vaGwA4025LL1vq4EU5yIP8SGqSvJ/Gc36sPfefG+rqQ0TiWjsLA4f8MXCvHcn7/z1DWnMf+PZcwaPxcsgtVlZ+WsNTx0zijs9kJe+SGTRi39EFwCwMAGW/h+8Ldc88eZbC9KxEGQK1otx2GLARWkwPUoG3dn0yQxkRT3Ph/E7EkLWDFzdZl8C2+hj8kfTYsIN+7YqzAiIdAVY9BzoLmC+uLp7/jxnSlYLBYue/hCzrhmINvW7OSOfg/hLfRiGIqJ7/7C05MeijDFFOYWclP3EWTvzi03YdLvM5svfffaT3z86NfY7GbnvvvG3s5J/+rD6a1a89uG9Xg9flAGzs2FOLabJUUK8zxMfGcKF959bqU9Nvy+AFab5aBvtBuXboloxmSEDDatqL6Q3vlTFzPyghewhCsK762tdVgVRRUpCRH5EjgF0yy1DRhJ+ElHKfUu8CiQCrwdNtUGwyuTesD48JgN+EIpNblKhCqHY0JJeAIBlmWYiVqCwioGiQ4f93f5B0NBwBDsokhy+iN+/m5biJYJuezxRT6NOa0GfdJ2MnjSxUw84zvTpAHUdxdy83ELI/sF+KeVkibc2so/H1z7optu+20TBd5L6Zaym/yAg2U5TXgguIphnRox8MNCJkysj80GV1++nqQGkSab1N93Y83yYdlb9kKg3rhNcGNPEGfYBg+JDj//brmKd1f1YHNuDv83aQLfXHQpz8/8i/fmzUHYt5K25vpJnLKNUFBRVCIyy3CUc8NRipAFVInmRoL5OXZJFrkD6iPeEAmz0s0yy94QsX/uZOe5dggqmjyzCHuWn1CslYWFMzmbjzCcFmJiA7gcBpvbJZN7ckMQIW5hJs/e5uONn9ZhmEVrEYFm8bmMO+0HXlvWkx6Ju3DNzSTQ/ynG/JrFV7dOwJY1jkBdF9zcnawkC64ig9yiIkKpdhx3diT1u424NxeibBZyTqrH5x19NJlkrpQAMnfZmTg2lenjk3HHhmjdxcvSOesoKriJbXWE3dc0RYKKnY+M5fWb3zfNbyV+l3wePx8+8DmvzRjF+wvm8fI/fxP0+EnoEUfyj3vKz7lWZq2njx7+0nxKDwfnPXvFa/TK/Ihb63Zg8/Bv8WQWYFgFCSmkxDlH3/sZo+/9jH/deTb/9/LVBANBPrj/c/4eP5uYeDcWm4WNS7YgFuGKhy/kikcuLk+KSmnYuj6uWGexohCBBi2qz9T03FVvRHT/WzV7HX+Om8XAS/tX2zkjUApCVRbdNGw/268Hri9nfAPQtewe1cMxoSTWZ2dhKLORpwJubL+Qa9otJclhhiQ6rYqN+Qm0iM8r0+4ToGV82T7XBkLjuAKCxr7Z0RfyVGWiarKKPBT43Uzb2dw8FiHSCwv5aONlfJC+kKLjzR/VIyubkNbyHE6Oh4U7d/DBwnmsmD4bW6jEDVrB+jXbwf0EFI2D4Gr8IYOAEeS/c8xy4kHDYMHOHSzcuZ33589CYYmIwbLmB1A2gWDkU5PVb2DfXUSgQUzxBSur4NqYj4gqNouFLwIVvosb8XZyT6hL0ozdiAKLN7w0skHmRS1oOHo11sJQ8Xdv9YVo2amI5/+3gZX56Vz9V2PyA05yT27AvHmQ49tKrN2PJXxXtArUcRUxqtdf5vW1gFW72jJu+CPYPUEEcOzwEHpuDp5Hu+OxCsTZQQR/41h2X9+epk8twuoNkfTXbgpOqEtB23jiFmUjwEOXtcRTYMVXZCrJNYtjAHPF6EgXbAPq4VqXjyXHV2FuRVGBl4lrVvHM33+YA04r2SfVwxCo8/3mcvdZM29Duf23t6/dyf1DnsSzx5TBEqz46fa7V3+i26DOzP5pHr9++mfZEiIh+Oq5H2jRuRn9z6+88GJpTr38JGaMn828X5Zgs1ux2qw8+MUdB3SMAyFvT2SL3mAgyJ4dWRXMriZ0xvXRR4rLTSAU4ob2C/jx9G8Y3mZZsYLYiwXFlvw49vjcBMI3/qKglWk7GpPs9CKi8IbMJa0naOON5T3I8MZitxzoU4XDtKU7zD/G3QUFTF2/jjYpqdhKaBmXzUbvRo0Zt2oHRaF9utwbsvLtqo0s2LmDy8d/w8/r1pLVOi7iCd+wCfmt4xFxICmfI0mvstx7Cxf8ejnzM/eVgIhzOPhr0wLc1iAN3AW0SdjD3sffQJqL0uoyvAbCiIl8thARlMPCGZftwekOR0+JaYoq7L7PpFZwvBnRJA64/Zo1fHjST/RK20lR+ySCsbZSZxPWLYvB4VS0T87iyePNm79yWsk9vi6b8hOwSeQfq8L0LwGIBcZNeQMx9nmiRIHFF8K2x2sqqL3ft0VQVsHX3AwtVhZwbikkZlVe8b7ZGfZiBbFXvr1YAoq4OZnErM7BEij/BuKKcXLalQP4YH6p5Fibhdy+FT95B/xlO/0BZGzbQ7CcOlkVlYH654e5TP9yZrk1psAsNLhwWrmJv5VisVgY+e0IXpvxFE/8cB9j179Jsw5N9r/jQdKmRwss1n0/B6vNQoe+bSvZoxrQnemOPhrExTH6xMncM7s/76/qTqqziHf6T6F7nXTAvOl/vaE976/uRorTy7BWK0hy+Ph1R3NW56Tw97mfYShhRkYfOtetw7PzCpm4xTT5jNvYngubrwbAbTMzayvPw1MQdyMiduZs38a1E77DIkLIMIhzOMn1ebFbrYzodxInNm2Gy1IUsbdgEGMt4oP50/GGbfn5fdJw7PSQ+JfpSC1qnUDWeWYFVREbyjGALk37keD+HzFFmYQMAxFh1MDB2IKzuaL1Mm7tuIBFe9L4z19nURSyo5xWdt7Ungbvr8ZSFMRwWvHXdWPxBAnF2yOWTRbDwN8slpuf2kG77h5mTErkH29Ttg9uSSh+Xw0fCRjY61u467FNnHp2NgC903Zx1R9nkxFnw1oYLHGPU7hjTAXstBp0KxF9lpzkBHtbDJWBNXLhggBBQ1icncaEjETqhEo5l0MKo5w+04ggAfN8oiCUYI/aQanAbPma7ERZ8os7l4kIzhgH8clxnH3jYC6+51zeGfth2QPYzZ4ZnryifcmEgDveRZseLbHarRH9xq12C806NilTpNDhdvDw13fx6NDnypyifsu6FbZfBbP1ab1mdSrcXhkiQquuzQ9q3wNl5HcjePDMUWxZuQ2xWLjppeF06Ntu/ztWFSWfRI4RjgklUZDzOrfOPJn8gJnwlOmL4dq/zmLqmV8SMPYqiO4ohD0+N2+u2Be1luTw4rQa2C0GpzbagiVtDJmhcbht2/EGQzy9qC+/bm9Go9h84m0+7u06Zz/LswDkjEDV6cAdkyfhCex7UhQRXh1yFmc3y4LQMoyCBYzqMZ4rpp9MXsCNoIixBbm25be8sKgRsbZUTqq/DYsYzPh3Y7IuqsvJaVuxOxX3dvkeI30CWFtBYBFW/Iw79Xh+2XMn6V4rHerUZdrGDbhDG7mr0zysFkXvtF10Tslg8Z66+AwbvuZxeF5sxy+DviLf4uK5xX3whGLZvlMiTFPOGMWzb69mTzCGMy/L5szLspm2w+D2f9oQDJvBYuwBxtz0O8fdv6c4AABMxTqi8yzev+84ttxj1jcSFBYrPPmZWY7DMGBrgZm74bLZuO/kwbRv1BlfzmxiLPuO5Q3ZsIrBHzub8OqyXrRt76HgpFj8fxciAQNlt5DfOw0jMay4lDKVnaGwZxRh31GI4bDgaZ9IUZsECvrWI37mbsRfyWpRzJyTvJPqY9itxKzOJcawYBEzCfDNuc+S1qQOlrBSPbFJczbnLo48hkVYMbILrT/ZiGVNdlg0xYiPbqV1txacee0gpn76R3GY8XXPXE69ZmkMf+JSPn38m+L8k7P+cxp9z+nJebeeyQ9v7su7SmmQxKX3nU9CShzv3vMJPo8fi9WCETJwxjqxiFC3aR3Ouan8cNvaRJ2GKYxe/BJFhV4cLvthj2wyk+mOrVrhx0TToWnL7uLWP+rhDe0LkYyz+RjV80++3tCebYUJbCk0M1WtYmCzhHBbQ8RYfbzY53d61y3xNOoaimHvx/jNbdies57O7vfI8wvbC+K4qcMi82k2YsnvJLLcBiBxEPdf2o3ZFRHGaLdY+G+3HVzXenLEPkVBK88v6YMIXNl6OYJiVU4ynVMy2emJw1BCi/gc5mfWZ0CDrbisoWIZlIIN+UnkBRy0TcgnNrYX/oR36Dn6bTzBIBe1WMn/tV/Iw/NPZlthAt1SdtEyIZcPVnchzubnu8HjqecuYvqOJtw0YwiGsrDXu2OunITTG2/khd7Tmbm7AV1TMzEUWEXx9Yb2PL+kDwl2P0/3+oPTGpXtgzxi1gAmbm0DAoNSNrB5tJfTGm/m4lsyaNTCX3wNf+1uxsT0u+mYVpcf1qwix1vE0z0m0yV5BUHDgs1i8PziXkze3poLmq3mjo7z8RtWsnwuhk08mz2+GKxBg5tPOxmFoqm8yR87ElmUVY92iXtYursOu4PxOAlyZQcP2aov6fnbWPnJclyLsjFibfg6JRGzqQhbjIMLzjuRgoU7cMY4yRpQjwl527CIcG7jVuyYsR5DKQraJTIvz1ytdkqry7iLh7E1N5czvvgk7COLxI7QLdPOtS070bJXS/7wZbAlN5c+jRrTaEeAXRszCLVMYNS6eewuLKBBfDwPN+4O2wpo3KY+nU48rvhY//w4l5k/zKN+y7oMu/+C4ginWRPnM2P8bBLSEjjlkn5sWr4Vd5yL3md2P7ort1I1TYcSHfVUv/qV+puLmbz1taOi6dAxoST+74eX+WVzCFXiGd9pCTL1zK9pEFOAN2Tjmj/Nomwv9/mVuu4iFGBFRZiu9+GGmCuR+HtQObeAf0ZxBFEZ3MOh6AugtG3Zwbm/DGV1bmrx6tVtDfLBSZPoU3df4phS8M7K7ny94ThsFoP/az+PFdlpnNZoI2+t7EHT2AKsFoM1uSm8d+IUkp2+iH3fXdmNt1b2wCZmwuA7J/6CoYSJW1vy+IITObfJWn7b0YKcgBNDWXBYgnRIymSXJ5ZMXwwOS4hUu4ft3gSMiDWSItbqp35MIa/3/ZV2SdmMmH0Kf+5qTJeUTHL8ThZn1aFhjIfCgJ2ikI1xp47nuKTs4iO8tOR43ll1PPsM6YoYa4AEh5/hbZZyfbslxd99yBDy7Ddywy9ZrM+LwWEJkumLoWtKOqnOIpZl1yHdG0vL+Bx+GPwtblsIX8jK4J//ze6iWELKAmH1tuQ/FxGTey5g5kGc/vMlbCpIxFDm9bmsQSZd3Jt/fTcLQyk6JGeS53eyIieFvW48l83GNxddypJdO3n0j2nl3vRL069xEy7q0JkHfvsFX6j8HiEOq5WlN93GWV+MZUN2FgqwiHBF567c0/dETvroffL8+37GiU4XM669ISLLusDv56FpU5mzfRt1Y2N5+uTGdEhYbpaIcV+ASGS0nqEUb82dxdfLl+KwWLm7b3/Oadu+0mv5dcM6np3xJ0WBIOe3P467T+i/33pnNU2VKYl6l0Y1d/K2148KJXFMmJv61t1Ic3cWY9d2BiCkhDaJWTSMNSMl3LYgD3abydaCeBrEeKKIUioCzxiIvwtJehO8P6F8v4P3x7JTnf3A3h7yHje9qaoI82bl553+k3l3ZXe6pmSS4XURa/NFKAiAxVl1GL+pDds98bisAZrGFXBBi3UoBSfUm1Ti9grZvsj6QSJwddulrMxNZVlWGm/2n4rTatq2z2+2lgubr+GPnU34eVur4htkwLDSOiGHsadMxGkJYSjYVRTH6ZMvpXRds8KQkw35doZNP4+pZ37Fz9ta0a/uNq5rt5jj6+zGWsKx7AsJznADo73302GtV7HNk8iPW9rQKCafy1st46ymG1BK+HJ9ez5d14Gr2phl1S2iSAy+y5cDBUHx3zkD+XFLGxZn1SshkWLC4HHF51mZk0qu3xlWEACCyxpg25bLaZtoKoj8gJ3NJRQEmKvJhdvn0zwui7Gn/IRNDEQU2wrisVoMCgJOnltyApPWruGLZYvLKIgeqTvZVhhPujeyxta8nTvoUq9BOQpC8WLvaQxsuIXCoJOfVzZhffa+iB1DKcYuWcSZrdtilKoDFlIGG7Oz6Fi3HsrIh9BmbvxxMfN3ZeAPhdhdWMCl32/llzPHUc8dBM/nkPot4EAVfgBF37Pbo5i3qRM78s3v8t5fp5DocnFS0+aUx7wd27l98k/FPb8/XvgPqmgiIwbciNjKJgwCqOAWVN5jENoKjl5I/EOIJbbcubWeI/jB+mA46pWE8s/l382mY7f4GdRwC2tyk7Gg2FUU+QfcMj6Xui5PBUcpDwMIIeIA97ngOhPl+wOzQFAYSUCcJ5nOY3snVGAx5D1SvLlxbAFPHv8XIvvM46XpnJzJ/077nnOmXMy17RbTJSXsrC29j4JUl6/M/m5biP92nsOHq7tS0pNgtyizx7I1VCITXfF2vyn0r78dd1iZWITiPJBIwuGvWDCUMDezAVe0XsqdHefhspW12TqtKuL6RKBhTCFP9/iDLsnpXNZ6BQ7Lvn4Zt3ZcyNi1ZrLf3usUKA55jS1XJnBY9p3HaQ1iqMgvVSG4rftWfTHWIFYxSigSU+GmyhQ+GrDTLAQZPkTz+Lzw+wLe7T+FCRm9KAp4MDte79u7YUwBOX4X6d7IgGqHxUrrlBTcNjtFJUqfNI3L44wmG3FZQyQ6/HyydiLQqcy1WQQCIS8lgxIDQS/JbjfKNwuVcxO+kI3ZO/4dofQUMCu9Puc1WwehbeCdjApthYL3gSLqOeHtfpv497TzWJFTB28wyPerVlaoJH5au7pYQQAUhax8vyGG/3a6BOpMQayRdbmUkYfaczGoXMCAop2o4BYk9bNyj1+7Oboil6Khdq8Pq4LAYmzhMNXj6+xmWKtVXNhiDbd0iOx3EGd3Uj+pb7ETsHKc4DzZVBBhRGxQ5zew9wFLmvl/nd+KazSJvR3ivhgkslrq3tNVdFqrBWJtAYa1Ws7xqbtx2UJl9q1sfzAVQVHIVuaGKQI96+yiSWw+DkuQ9olZ9K+/nRhbsNjMJgKpLi9nNN6Ay7r3xlY29NRtDXJXp/IVRGWyuh0hzq+zCqc1sqFSjC3IkEYbK7y2WzrMD2d27JVF0bvOjojIsnaJWXROycAVdm67rAH61d1G49h91XatFsVjPf7GZQ3gsgaIsfnpk7aTfvU2E2fzVyi30xrk7CYbaRxTMm5f4bYGufG4RYw6/s/isb08eNIAzm/fgYEtWuCy2YhzOEh1x/D+Sb/hsu77uXZKSi/nG4TOKQUMa7kKd1hWtzXA8LbLqO/KQOX8HygPVvLL/FEL5s/HFCcERh54vqZk6XyXNci5Tc0GTxbM8OiKiLU7sJb6obitQbOqrv/vsjv45wF+9qVq+iGwEGWUzT+q9SjMSIpoXkcJtWolISJnAK9hPpp9oJR69pAPaqmPYKdkPwebpfSTgBWSXkWcfVFZwyGwZK9EYG0KeMHWzXwKU5ngOAFJeJjSWKyJkPpphaKICCS9bv5BYzPrD0WBzWJGNW3IT+S4pEzs1uifZPwhCz9ubs2fOxuT29GJ2xbEUmIV4rAafHPq93ywqisiqkS9o0he7D2dz9Z34Lftzcjxu9iQl4TXsOO0BGkcm0/futtxWA/8D8MIweZVTpL7l10ZpLrK+nlWLXTz3shG5GVbuaLHr0yJ6UiRw0kbewZjzv0rYq5F4KOTf+KjNV1YnZtCp+QMhrdZVkbpXNJyNR2S9rA4qy713IUMargZy36eFUQsxIUm8N3gANf/NZD5mQ0Y2GAzI7rMoW2i6XcZN+h7Xlram4CtF//p3pPTW5v9N9444xw2ZGdREAjQNiUVZ/bYiKJxQxpvo8VKK5vyQsU+ics6dcFpC/BwjwWc2nA96/OTaJ2QzQn1cs3ikMr8/mwWxQ3tF/HRms4Uhew4LAb1YwoZ0GDrXsnB2RcKx0T+HBT4DQsWIMbh4Npux1d47Zd37srnSxdR4C8ipCy4rAFGdJkdXjSVU4FWyqtKq6hlt5/oOcZWErXGcS0iVmANMBjYBswFhimlyvb6DBON41qpECr7erPJD2L+McXdCo6TwPs9KB/iPh9xdC+ej3+OeQO3d0esaVV0hSVkCqVDcCWqcKx5rtLRT6UwFHy8phP5AQc3d1iI3bLPtl8yiqmi1cQOTwz5fhfN4nKKn/T3/tj3mroO5L1hwI9bWzM3owFNYvO4svVyYsJhreXJUXrfvSsUIwTeIgufvlyPGx8t1W41XHKj5PvtGxzcfHpbvJ695p29v7uCwxWi75A8Hnxny36/j+iwg6UhGOVlQgslVwiGgk/WdKKu28OZTTZEKhjnUCzJL1Z6JqNoIuQ+iPl7YAdLHbyJ4xm7dC1b83Lo3agJQ9u2B/yojCHhisEhwAbWhpA6ATL6Yza9Mq990rZ2/JM7nMYJdbmy2efEMts0fyY+hThPxvB8B3mPYTrvhZBy89aGB/Cq+lzeuRtNEhMrlXlHfh6fzn2bQu96zm6yml5pe8wGXHV+KuNrUMqPyjzP9EfgB9zgPgtL4jOVnqOqqRLHtT1N9Uu6MKq5kzPfOyoc17VJSfQFHlNKDQl/fgBAKVXhb1K00U1KGeCfDSoH7N0Q6+FvPF8eSvlQeaPA9wdYksA9NNzhLh4Cf0NoPSAg9UDtDcN1sjcq59BxYP7RRt70KsaOGaVlDb8vTw4L+x6L9x7XDsQAuezcbOfj5xpy3PEFBAPCr+NSufOFrbTvsT9/UDzjPujHx0/uJlhBVrPVqvh29VICfkEZQkJKDGJrD6EtoLJAkkFlszfKCWsbCO0Gax0IZQB7ncXJEPMviLsN8l80uwZaEiDmQgiuh6LJlG4Qha0DOAZDcDn4p5tjziFI0itRmTCVfx7K9xdiSQT3xYglvvx5oV2o3IfM3xNbOyRxFGJNQ/lmmJF2WEAFIP5OLLHXVX5O7zSUdwJIPBJ7PWJrtl85I/ZXCuX5yozuszZC4m5CLMnlzzUKUIXvQXAzOHojMZchcnit3VWiJGxpqm/SBVHNnbLn/aNCSdSm9V4jYGuJz9uI7OcKgIjcANwA0LRp06gOLGIxl9i1DBEnknhoFX6NUAbkPQ++H8rZagNbByTlA8SStN9jqcBSVNaVoEresJ1I2hTE2vCQ5NxLo/ow8Oq5THh7CnaHjdtHn89xJ6Sh8CEqAJY0VP7LUPQpEYrLWg9HwpmI9TMIlF+qIhQSzm/dBTB7UP9Y8FmZdqJKeSGUDtY0RNzlHSaSxEfNVwkM/0IIlVQSAvYeWBJuieYrKBdx9EQc+7+fiLU+klI2a1uc/SHtd/MmbK2HWMtvyRqxj2sQ4hp0MOKa+4sgscMgdv95A2KJQ+LvOehz1Sp0xnXtJtzZaTSYK4kaFqfGsVjTUPE3ovxTS4TXuiDxOcTZH7EkRH8wWwezU1tgJabpw2UqV0vVrrz6De1Fv6G9IsYinrVjL0d5/xdxPRJ/BwOHncDnT31LQTBktk+VcN0oY28+i2AYCmeMk9OHn1Juv2kRF9iie7ioCIm/wywBHzbVIG4k9opDOmZVIJYkcCTVtBhHP7XE+nK4qE1KYjtQsjJY4/CYZj+IrTWk/g9V+DEoL+L+F+Lst9/9yhxHrJAyFlXwPgTXgL0LEntNlBFfVYfYWkDqt6jCj0AVha+nP0kueHfh83z13PfkpOfR47TObF6xjcztWXQ9pQO7N2Wwa1MG3QZ2rLCjW5XI5xoCyXFmIylxm99RBfkBmqMMpY6qyKVoqE0+CRum4/pUTOUwF7hMKbW8on2i9UloNBpNlfgkrHVU39hzo5o7Jf9j7ZOoSpRSQRG5FZiC6RkdU5mC0Gg0msOPQoVC+592FFFrlASAUmoSMKmm5dBoNJpy0aXCNRqNRlMpx1ip8KO/LIdGo9FUEQpQhorqtT9EZIyIpItIuS0BxeR1EVknIktEpEeJbcNFZG34NbzqrrAsWkloNBpNtKhw06FoXvvnY+CMSrafCbQJv24A3gEQkRRgJGYeWW9gpIiUn8VYBWglodFoNAeACoWieu33OEr9yb40//I4DxirTGYBSSLSABgCTFVKZSmlsoGpVK5sDokj2icxf/78TBEpr7hOedQBMqtTnlqIvuZjh2Pxug/0mg+s7kg55JM95Vf1v2ibgbtEpGSM/uhwMnC0lFeFolEl49XCEa0klFJRV98TkXlHQ8zygaCv+djhWLzumrhmpVS1PbHXVrS5SaPRaGonFVWhOKzVKbSS0Gg0mtrJBOCqcJTTCUCuUmonZsLx6SKSHHZYnx4eqxaOaHPTAXIgtsCjBX3Nxw7H4nUf0dcsIl8CpwB1RGQbZsSSHUAp9S5mYvFZwDrAA1wT3pYlIk9ili4CeEIpVZkD/NDkrC21mzQajUZT+9DmJo1Go9FUiFYSGo1Go6kQrSQ0Go1GUyFaSWg0Go2mQrSS0Gg0Gk2FaCWh0Wg0mgrRSkKj0Wg0FfL/Xc98dRfEdEsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.scatter(x=\"Age\", y=\"Fare\", c=\"Pclass\",cmap=\"viridis\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "immediate-corpus",
   "metadata": {},
   "source": [
    "This is starting to look a lot better now. But let's say we didn't want to represent our data as a series of marginally changing numbers. When we pass a DataFrame column to c as a set of numbers, Pandas presumes that that number corresponds to a gradient change in color. But passenger class is not a gradient change, it is a integral change, meaning no one will be Pclass 1.2. They will be 1, 2, or 3. In order to fix this graph, we can make a few changes. First, we can use df.loc that we met in a previous notebook to grab all classes. Now, we know there are three. We can convert these from numerical representations of the class into string representations, e.g. First, Second, and Third.\n",
    "\n",
    "Next, we can convert that entire column from a string column into a Pandas Categorical Class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "interested-demand",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[(df.Pclass == 1),'Pclass']=\"First\"\n",
    "df.loc[(df.Pclass == 2),'Pclass']=\"Second\"\n",
    "df.loc[(df.Pclass == 3),'Pclass']=\"Third\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "helpful-queens",
   "metadata": {},
   "source": [
    "We can now see that our data has now been altered in the Pclass column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "improved-confirmation",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Third</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>First</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Third</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>First</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>Third</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>887</td>\n",
       "      <td>0</td>\n",
       "      <td>Second</td>\n",
       "      <td>Montvila, Rev. Juozas</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>211536</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>888</td>\n",
       "      <td>1</td>\n",
       "      <td>First</td>\n",
       "      <td>Graham, Miss. Margaret Edith</td>\n",
       "      <td>female</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112053</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>B42</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>889</td>\n",
       "      <td>0</td>\n",
       "      <td>Third</td>\n",
       "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>W./C. 6607</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>890</td>\n",
       "      <td>1</td>\n",
       "      <td>First</td>\n",
       "      <td>Behr, Mr. Karl Howell</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>111369</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>C148</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>891</td>\n",
       "      <td>0</td>\n",
       "      <td>Third</td>\n",
       "      <td>Dooley, Mr. Patrick</td>\n",
       "      <td>male</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>370376</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass  \\\n",
       "0              1         0   Third   \n",
       "1              2         1   First   \n",
       "2              3         1   Third   \n",
       "3              4         1   First   \n",
       "4              5         0   Third   \n",
       "..           ...       ...     ...   \n",
       "886          887         0  Second   \n",
       "887          888         1   First   \n",
       "888          889         0   Third   \n",
       "889          890         1   First   \n",
       "890          891         0   Third   \n",
       "\n",
       "                                                  Name     Sex   Age  SibSp  \\\n",
       "0                              Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1    Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                               Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                             Allen, Mr. William Henry    male  35.0      0   \n",
       "..                                                 ...     ...   ...    ...   \n",
       "886                              Montvila, Rev. Juozas    male  27.0      0   \n",
       "887                       Graham, Miss. Margaret Edith  female  19.0      0   \n",
       "888           Johnston, Miss. Catherine Helen \"Carrie\"  female   NaN      1   \n",
       "889                              Behr, Mr. Karl Howell    male  26.0      0   \n",
       "890                                Dooley, Mr. Patrick    male  32.0      0   \n",
       "\n",
       "     Parch            Ticket     Fare Cabin Embarked  \n",
       "0        0         A/5 21171   7.2500   NaN        S  \n",
       "1        0          PC 17599  71.2833   C85        C  \n",
       "2        0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3        0            113803  53.1000  C123        S  \n",
       "4        0            373450   8.0500   NaN        S  \n",
       "..     ...               ...      ...   ...      ...  \n",
       "886      0            211536  13.0000   NaN        S  \n",
       "887      0            112053  30.0000   B42        S  \n",
       "888      2        W./C. 6607  23.4500   NaN        S  \n",
       "889      0            111369  30.0000  C148        C  \n",
       "890      0            370376   7.7500   NaN        Q  \n",
       "\n",
       "[891 rows x 12 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "biblical-tulsa",
   "metadata": {},
   "source": [
    "Now that our data is successfully converted into a string, you might be thinking that we can run the same code as before and we should see the data divided between strings, rather than a gradient shift between floats. If we execute the cell below, however, we get a rather large and scary looking error. (Scroll down to see the solution)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "loose-thought",
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "'c' argument must be a color, a sequence of colors, or a sequence of numbers, not ['Third' 'First' 'Third' 'First' 'Third' 'Third' 'First' 'Third' 'Third'\n 'Second' 'Third' 'First' 'Third' 'Third' 'Third' 'Second' 'Third'\n 'Second' 'Third' 'Third' 'Second' 'Second' 'Third' 'First' 'Third'\n 'Third' 'Third' 'First' 'Third' 'Third' 'First' 'First' 'Third' 'Second'\n 'First' 'First' 'Third' 'Third' 'Third' 'Third' 'Third' 'Second' 'Third'\n 'Second' 'Third' 'Third' 'Third' 'Third' 'Third' 'Third' 'Third' 'Third'\n 'First' 'Second' 'First' 'First' 'Second' 'Third' 'Second' 'Third'\n 'Third' 'First' 'First' 'Third' 'First' 'Third' 'Second' 'Third' 'Third'\n 'Third' 'Second' 'Third' 'Second' 'Third' 'Third' 'Third' 'Third' 'Third'\n 'Second' 'Third' 'Third' 'Third' 'Third' 'First' 'Second' 'Third' 'Third'\n 'Third' 'First' 'Third' 'Third' 'Third' 'First' 'Third' 'Third' 'Third'\n 'First' 'First' 'Second' 'Second' 'Third' 'Third' 'First' 'Third' 'Third'\n 'Third' 'Third' 'Third' 'Third' 'Third' 'First' 'Third' 'Third' 'Third'\n 'Third' 'Third' 'Third' 'Second' 'First' 'Third' 'Second' 'Third'\n 'Second' 'Second' 'First' 'Third' 'Third' 'Third' 'Third' 'Third' 'Third'\n 'Third' 'Third' 'Second' 'Second' 'Second' 'First' 'First' 'Third'\n 'First' 'Third' 'Third' 'Third' 'Third' 'Second' 'Second' 'Third' 'Third'\n 'Second' 'Second' 'Second' 'First' 'Third' 'Third' 'Third' 'First'\n 'Third' 'Third' 'Third' 'Third' 'Third' 'Second' 'Third' 'Third' 'Third'\n 'Third' 'First' 'Third' 'First' 'Third' 'First' 'Third' 'Third' 'Third'\n 'First' 'Third' 'Third' 'First' 'Second' 'Third' 'Third' 'Second' 'Third'\n 'Second' 'Third' 'First' 'Third' 'First' 'Third' 'Third' 'Second'\n 'Second' 'Third' 'Second' 'First' 'First' 'Third' 'Third' 'Third'\n 'Second' 'Third' 'Third' 'Third' 'Third' 'Third' 'Third' 'Third' 'Third'\n 'Third' 'First' 'Third' 'Second' 'Third' 'Second' 'Third' 'First' 'Third'\n 'Second' 'First' 'Second' 'Third' 'Second' 'Third' 'Third' 'First'\n 'Third' 'Second' 'Third' 'Second' 'Third' 'First' 'Third' 'Second'\n 'Third' 'Second' 'Third' 'Second' 'Second' 'Second' 'Second' 'Third'\n 'Third' 'Second' 'Third' 'Third' 'First' 'Third' 'Second' 'First'\n 'Second' 'Third' 'Third' 'First' 'Third' 'Third' 'Third' 'First' 'First'\n 'First' 'Second' 'Third' 'Third' 'First' 'First' 'Third' 'Second' 'Third'\n 'Third' 'First' 'First' 'First' 'Third' 'Second' 'First' 'Third' 'First'\n 'Third' 'Second' 'Third' 'Third' 'Third' 'Third' 'Third' 'Third' 'First'\n 'Third' 'Third' 'Third' 'Second' 'Third' 'First' 'First' 'Second' 'Third'\n 'Third' 'First' 'Third' 'First' 'First' 'First' 'Third' 'Third' 'Third'\n 'Second' 'Third' 'First' 'First' 'First' 'Second' 'First' 'First' 'First'\n 'Second' 'Third' 'Second' 'Third' 'Second' 'Second' 'First' 'First'\n 'Third' 'Third' 'Second' 'Second' 'Third' 'First' 'Third' 'Second'\n 'Third' 'First' 'Third' 'First' 'First' 'Third' 'First' 'Third' 'First'\n 'First' 'Third' 'First' 'Second' 'First' 'Second' 'Second' 'Second'\n 'Second' 'Second' 'Third' 'Third' 'Third' 'Third' 'First' 'Third' 'Third'\n 'Third' 'Third' 'First' 'Second' 'Third' 'Third' 'Third' 'Second' 'Third'\n 'Third' 'Third' 'Third' 'First' 'Third' 'Third' 'First' 'First' 'Third'\n 'Third' 'First' 'Third' 'First' 'Third' 'First' 'Third' 'Third' 'First'\n 'Third' 'Third' 'First' 'Third' 'Second' 'Third' 'Second' 'Third'\n 'Second' 'First' 'Third' 'Third' 'First' 'Third' 'Third' 'Third' 'Second'\n 'Second' 'Second' 'Third' 'Third' 'Third' 'Third' 'Third' 'Second'\n 'Third' 'Second' 'Third' 'Third' 'Third' 'Third' 'First' 'Second' 'Third'\n 'Third' 'Second' 'Second' 'Second' 'Third' 'Third' 'Third' 'Third'\n 'Third' 'Third' 'Third' 'Second' 'Second' 'Third' 'Third' 'First' 'Third'\n 'Second' 'Third' 'First' 'First' 'Third' 'Second' 'First' 'Second'\n 'Second' 'Third' 'Third' 'Second' 'Third' 'First' 'Second' 'First'\n 'Third' 'First' 'Second' 'Third' 'First' 'First' 'Third' 'Third' 'First'\n 'First' 'Second' 'Third' 'First' 'Third' 'First' 'Second' 'Third' 'Third'\n 'Second' 'First' 'Third' 'Third' 'Third' 'Third' 'Second' 'Second'\n 'Third' 'First' 'Second' 'Third' 'Third' 'Third' 'Third' 'Second' 'Third'\n 'Third' 'First' 'Third' 'First' 'First' 'Third' 'Third' 'Third' 'Third'\n 'First' 'First' 'Third' 'Third' 'First' 'Third' 'First' 'Third' 'Third'\n 'Third' 'Third' 'Third' 'First' 'First' 'Second' 'First' 'Third' 'Third'\n 'Third' 'Third' 'First' 'First' 'Third' 'First' 'Second' 'Third' 'Second'\n 'Third' 'First' 'Third' 'Third' 'First' 'Third' 'Third' 'Second' 'First'\n 'Third' 'Second' 'Second' 'Third' 'Third' 'Third' 'Third' 'Second'\n 'First' 'First' 'Third' 'First' 'First' 'Third' 'Third' 'Second' 'First'\n 'First' 'Second' 'Second' 'Third' 'Second' 'First' 'Second' 'Third'\n 'Third' 'Third' 'First' 'First' 'First' 'First' 'Third' 'Third' 'Third'\n 'Second' 'Third' 'Third' 'Third' 'Third' 'Third' 'Third' 'Third' 'Second'\n 'First' 'First' 'Third' 'Third' 'Third' 'Second' 'First' 'Third' 'Third'\n 'Second' 'First' 'Second' 'First' 'Third' 'First' 'Second' 'First'\n 'Third' 'Third' 'Third' 'First' 'Third' 'Third' 'Second' 'Third' 'Second'\n 'Third' 'Third' 'First' 'Second' 'Third' 'First' 'Third' 'First' 'Third'\n 'Third' 'First' 'Second' 'First' 'Third' 'Third' 'Third' 'Third' 'Third'\n 'Second' 'Third' 'Third' 'Second' 'Second' 'Third' 'First' 'Third'\n 'Third' 'Third' 'First' 'Second' 'First' 'Third' 'Third' 'First' 'Third'\n 'First' 'First' 'Third' 'Second' 'Third' 'Second' 'Third' 'Third' 'Third'\n 'First' 'Third' 'Third' 'Third' 'First' 'Third' 'First' 'Third' 'Third'\n 'Third' 'Second' 'Third' 'Third' 'Third' 'Second' 'Third' 'Third'\n 'Second' 'First' 'First' 'Third' 'First' 'Third' 'Third' 'Second'\n 'Second' 'Third' 'Third' 'First' 'Second' 'First' 'Second' 'Second'\n 'Second' 'Third' 'Third' 'Third' 'Third' 'First' 'Third' 'First' 'Third'\n 'Third' 'Second' 'Second' 'Third' 'Third' 'Third' 'First' 'First' 'Third'\n 'Third' 'Third' 'First' 'Second' 'Third' 'Third' 'First' 'Third' 'First'\n 'First' 'Third' 'Third' 'Third' 'Second' 'Second' 'First' 'First' 'Third'\n 'First' 'First' 'First' 'Third' 'Second' 'Third' 'First' 'Second' 'Third'\n 'Third' 'Second' 'Third' 'Second' 'Second' 'First' 'Third' 'Second'\n 'Third' 'Second' 'Third' 'First' 'Third' 'Second' 'Second' 'Second'\n 'Third' 'Third' 'First' 'Third' 'Third' 'First' 'First' 'First' 'Third'\n 'Third' 'First' 'Third' 'Second' 'First' 'Third' 'Second' 'Third' 'Third'\n 'Third' 'Second' 'Second' 'Third' 'Second' 'Third' 'First' 'Third'\n 'Third' 'Third' 'First' 'Third' 'First' 'First' 'Third' 'Third' 'Third'\n 'Third' 'Third' 'Second' 'Third' 'Second' 'Third' 'Third' 'Third' 'Third'\n 'First' 'Third' 'First' 'First' 'Third' 'Third' 'Third' 'Third' 'Third'\n 'Third' 'First' 'Third' 'Second' 'Third' 'First' 'Third' 'Second' 'First'\n 'Third' 'Third' 'Third' 'Second' 'Second' 'First' 'Third' 'Third' 'Third'\n 'First' 'Third' 'Second' 'First' 'Third' 'Third' 'Second' 'Third' 'Third'\n 'First' 'Third' 'Second' 'Third' 'Third' 'First' 'Third' 'First' 'Third'\n 'Third' 'Third' 'Third' 'Second' 'Third' 'First' 'Third' 'Second' 'Third'\n 'Third' 'Third' 'First' 'Third' 'Third' 'Third' 'First' 'Third' 'Second'\n 'First' 'Third' 'Third' 'Third' 'Third' 'Third' 'Second' 'First' 'Third'\n 'Third' 'Third' 'First' 'Second' 'Third' 'First' 'First' 'Third' 'Third'\n 'Third' 'Second' 'First' 'Third' 'Second' 'Second' 'Second' 'First'\n 'Third' 'Third' 'Third' 'First' 'First' 'Third' 'Second' 'Third' 'Third'\n 'Third' 'Third' 'First' 'Second' 'Third' 'Third' 'Second' 'Third' 'Third'\n 'Second' 'First' 'Third' 'First' 'Third']",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32mc:\\users\\wma22\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\matplotlib\\axes\\_axes.py\u001b[0m in \u001b[0;36m_parse_scatter_color_args\u001b[1;34m(c, edgecolors, kwargs, xsize, get_next_color_func)\u001b[0m\n\u001b[0;32m   4290\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m  \u001b[1;31m# Is 'c' acceptable as PathCollection facecolors?\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 4291\u001b[1;33m                 \u001b[0mcolors\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmcolors\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto_rgba_array\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   4292\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mTypeError\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\wma22\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\matplotlib\\colors.py\u001b[0m in \u001b[0;36mto_rgba_array\u001b[1;34m(c, alpha)\u001b[0m\n\u001b[0;32m    340\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 341\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mto_rgba\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mcc\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mc\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    342\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\wma22\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\matplotlib\\colors.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m    340\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 341\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mto_rgba\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mcc\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mc\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    342\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\wma22\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\matplotlib\\colors.py\u001b[0m in \u001b[0;36mto_rgba\u001b[1;34m(c, alpha)\u001b[0m\n\u001b[0;32m    188\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mrgba\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m  \u001b[1;31m# Suppress exception chaining of cache lookup failure.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 189\u001b[1;33m         \u001b[0mrgba\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_to_rgba_no_colorcycle\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    190\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\wma22\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\matplotlib\\colors.py\u001b[0m in \u001b[0;36m_to_rgba_no_colorcycle\u001b[1;34m(c, alpha)\u001b[0m\n\u001b[0;32m    259\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0malpha\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0malpha\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m \u001b[1;32melse\u001b[0m \u001b[1;36m1.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 260\u001b[1;33m         \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mf\"Invalid RGBA argument: {orig_c!r}\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    261\u001b[0m     \u001b[1;31m# tuple color.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: Invalid RGBA argument: 'Third'",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-18-5db8b5e23a68>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mscatter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"Age\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"Fare\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mc\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"Pclass\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcmap\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"viridis\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0ms\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m50\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mc:\\users\\wma22\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\pandas\\plotting\\_core.py\u001b[0m in \u001b[0;36mscatter\u001b[1;34m(self, x, y, s, c, **kwargs)\u001b[0m\n\u001b[0;32m   1634\u001b[0m             ...                       colormap='viridis')\n\u001b[0;32m   1635\u001b[0m         \"\"\"\n\u001b[1;32m-> 1636\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkind\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"scatter\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0ms\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mc\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1637\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1638\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mhexbin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mC\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreduce_C_function\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mgridsize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\wma22\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\pandas\\plotting\\_core.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m    915\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mkind\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_dataframe_kinds\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    916\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mABCDataFrame\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 917\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mplot_backend\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mkind\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    918\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    919\u001b[0m                 \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mf\"plot kind {kind} can only be used for data frames\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\wma22\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\pandas\\plotting\\_matplotlib\\__init__.py\u001b[0m in \u001b[0;36mplot\u001b[1;34m(data, kind, **kwargs)\u001b[0m\n\u001b[0;32m     69\u001b[0m             \u001b[0mkwargs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"ax\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"left_ax\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     70\u001b[0m     \u001b[0mplot_obj\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mPLOT_CLASSES\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mkind\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 71\u001b[1;33m     \u001b[0mplot_obj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgenerate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     72\u001b[0m     \u001b[0mplot_obj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     73\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mplot_obj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresult\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\wma22\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\pandas\\plotting\\_matplotlib\\core.py\u001b[0m in \u001b[0;36mgenerate\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    286\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_compute_plot_data\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    287\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_setup_subplots\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 288\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_make_plot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    289\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_add_table\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    290\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_make_legend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\wma22\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\pandas\\plotting\\_matplotlib\\core.py\u001b[0m in \u001b[0;36m_make_plot\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   1068\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1069\u001b[0m             \u001b[0mlabel\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1070\u001b[1;33m         scatter = ax.scatter(\n\u001b[0m\u001b[0;32m   1071\u001b[0m             \u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1072\u001b[0m             \u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\wma22\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\matplotlib\\__init__.py\u001b[0m in \u001b[0;36minner\u001b[1;34m(ax, data, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1445\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0minner\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1446\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mdata\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1447\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0mmap\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msanitize_sequence\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1448\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1449\u001b[0m         \u001b[0mbound\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnew_sig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbind\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\wma22\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\matplotlib\\cbook\\deprecation.py\u001b[0m in \u001b[0;36mwrapper\u001b[1;34m(*inner_args, **inner_kwargs)\u001b[0m\n\u001b[0;32m    409\u001b[0m                          \u001b[1;32melse\u001b[0m \u001b[0mdeprecation_addendum\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    410\u001b[0m                 **kwargs)\n\u001b[1;32m--> 411\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0minner_args\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0minner_kwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    412\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    413\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\wma22\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\matplotlib\\axes\\_axes.py\u001b[0m in \u001b[0;36mscatter\u001b[1;34m(self, x, y, s, c, marker, cmap, norm, vmin, vmax, alpha, linewidths, verts, edgecolors, plotnonfinite, **kwargs)\u001b[0m\n\u001b[0;32m   4449\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4450\u001b[0m         \u001b[0mc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolors\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0medgecolors\u001b[0m \u001b[1;33m=\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 4451\u001b[1;33m             self._parse_scatter_color_args(\n\u001b[0m\u001b[0;32m   4452\u001b[0m                 \u001b[0mc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0medgecolors\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msize\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4453\u001b[0m                 get_next_color_func=self._get_patches_for_fill.get_next_color)\n",
      "\u001b[1;32mc:\\users\\wma22\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\matplotlib\\axes\\_axes.py\u001b[0m in \u001b[0;36m_parse_scatter_color_args\u001b[1;34m(c, edgecolors, kwargs, xsize, get_next_color_func)\u001b[0m\n\u001b[0;32m   4298\u001b[0m                     \u001b[1;31m# Both the mapping *and* the RGBA conversion failed: pretty\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4299\u001b[0m                     \u001b[1;31m# severe failure => one may appreciate a verbose feedback.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 4300\u001b[1;33m                     raise ValueError(\n\u001b[0m\u001b[0;32m   4301\u001b[0m                         \u001b[1;34mf\"'c' argument must be a color, a sequence of colors, \"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4302\u001b[0m                         f\"or a sequence of numbers, not {c}\") from err\n",
      "\u001b[1;31mValueError\u001b[0m: 'c' argument must be a color, a sequence of colors, or a sequence of numbers, not ['Third' 'First' 'Third' 'First' 'Third' 'Third' 'First' 'Third' 'Third'\n 'Second' 'Third' 'First' 'Third' 'Third' 'Third' 'Second' 'Third'\n 'Second' 'Third' 'Third' 'Second' 'Second' 'Third' 'First' 'Third'\n 'Third' 'Third' 'First' 'Third' 'Third' 'First' 'First' 'Third' 'Second'\n 'First' 'First' 'Third' 'Third' 'Third' 'Third' 'Third' 'Second' 'Third'\n 'Second' 'Third' 'Third' 'Third' 'Third' 'Third' 'Third' 'Third' 'Third'\n 'First' 'Second' 'First' 'First' 'Second' 'Third' 'Second' 'Third'\n 'Third' 'First' 'First' 'Third' 'First' 'Third' 'Second' 'Third' 'Third'\n 'Third' 'Second' 'Third' 'Second' 'Third' 'Third' 'Third' 'Third' 'Third'\n 'Second' 'Third' 'Third' 'Third' 'Third' 'First' 'Second' 'Third' 'Third'\n 'Third' 'First' 'Third' 'Third' 'Third' 'First' 'Third' 'Third' 'Third'\n 'First' 'First' 'Second' 'Second' 'Third' 'Third' 'First' 'Third' 'Third'\n 'Third' 'Third' 'Third' 'Third' 'Third' 'First' 'Third' 'Third' 'Third'\n 'Third' 'Third' 'Third' 'Second' 'First' 'Third' 'Second' 'Third'\n 'Second' 'Second' 'First' 'Third' 'Third' 'Third' 'Third' 'Third' 'Third'\n 'Third' 'Third' 'Second' 'Second' 'Second' 'First' 'First' 'Third'\n 'First' 'Third' 'Third' 'Third' 'Third' 'Second' 'Second' 'Third' 'Third'\n 'Second' 'Second' 'Second' 'First' 'Third' 'Third' 'Third' 'First'\n 'Third' 'Third' 'Third' 'Third' 'Third' 'Second' 'Third' 'Third' 'Third'\n 'Third' 'First' 'Third' 'First' 'Third' 'First' 'Third' 'Third' 'Third'\n 'First' 'Third' 'Third' 'First' 'Second' 'Third' 'Third' 'Second' 'Third'\n 'Second' 'Third' 'First' 'Third' 'First' 'Third' 'Third' 'Second'\n 'Second' 'Third' 'Second' 'First' 'First' 'Third' 'Third' 'Third'\n 'Second' 'Third' 'Third' 'Third' 'Third' 'Third' 'Third' 'Third' 'Third'\n 'Third' 'First' 'Third' 'Second' 'Third' 'Second' 'Third' 'First' 'Third'\n 'Second' 'First' 'Second' 'Third' 'Second' 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     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.scatter(x=\"Age\", y=\"Fare\", c=\"Pclass\", cmap=\"viridis\", s=50)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "medical-popularity",
   "metadata": {},
   "source": [
    "Keeping this massive error in the textbook is essential, despite its size being rather annoying. It tells us a lot of information about the problem. When we try and pass a keyword argument of c, Pandas is expecting a series of numbers (which will correspond to gradient shifts in the cmap), a list of colors, or a Pandas Categorical column. To change our data to a list of colors, let's convert our data into three different colors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "collectible-vector",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[(df.Pclass == \"First\"),'Pclass']=\"red\"\n",
    "df.loc[(df.Pclass == \"Second\"),'Pclass']=\"blue\"\n",
    "df.loc[(df.Pclass == \"Third\"),'Pclass']=\"green\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "controlled-bearing",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Age', ylabel='Fare'>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.scatter(x=\"Age\", y=\"Fare\", c=\"Pclass\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "listed-devices",
   "metadata": {},
   "source": [
    "Now, our plots are all color coordinated. But I don't like this. It doesn't have a nice ledger to read. Instead, we should convert this data into a Categorical Column. To do this, let's first get our data back into First, Second, and Third class format."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "sized-maple",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[(df.Pclass == \"red\"),'Pclass']=\"First\"\n",
    "df.loc[(df.Pclass == \"blue\"),'Pclass']=\"Second\"\n",
    "df.loc[(df.Pclass == \"green\"),'Pclass']=\"Third\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "renewable-senator",
   "metadata": {},
   "source": [
    "Now, let's try this again by first converting Pclass into a Categorical type."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "functioning-management",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Pclass'] = df.Pclass.astype('category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "fifty-situation",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Age', ylabel='Fare'>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.scatter(x=\"Age\", y=\"Fare\", c=\"Pclass\", cmap=\"viridis\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cooked-florist",
   "metadata": {},
   "source": [
    "Now, like magic, we have precisely what we want to see. But we can do even better! Let's say we don't like the size of the nodes (points) on the graph. We want to see smaller nodes to distinguish better between the points. We can pass another keyword argument, s, which stands for size. This expects an integer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "viral-sharp",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Age', ylabel='Fare'>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.scatter(x=\"Age\", y=\"Fare\", c=\"Pclass\", cmap=\"viridis\", s=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "printable-athletics",
   "metadata": {},
   "source": [
    "To make it a bit easier to read, let's also adjust the size a bit. We can do this by passing the keyword argument, figsize, that we saw above with pie chars."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "private-connecticut",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Age', ylabel='Fare'>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.scatter(x=\"Age\", y=\"Fare\", c=\"Pclass\", cmap=\"viridis\", s=5, figsize=(15,5))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "owned-cricket",
   "metadata": {},
   "source": [
    "By now, you should have a good sense of how to create simple bar, pie, and scatter charts. In the next few notebooks, we will be looking at other ways of leveraging Pandas to produce visualizations, such as using plotly and social networks with networkx."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "round-pakistan",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.9.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}