life_expectancy_gdp.ipynb

{
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   "source": [
    "# Introduction\n",
    "\n",
    "For this project, you will act as a data researcher for the World Health Organization. You will investigate if there is a strong correlation between the economic output of a country and the life expectancy of its citizens.  \n",
    "\n",
    "During this project, you will analyze, prepare, and plot data, and seek to answer questions in a meaningful way.\n",
    "\n",
    "After you perform analysis, you'll be creating an article with your visualizations to be featured in the fictional \"Time Magazine\".\n",
    "\n",
    "**Focusing Questions**: \n",
    "+ Has life expectancy increased over time in the six nations?\n",
    "+ Has GDP increased over time in the six nations?\n",
    "+ Is there a correlation between GDP and life expectancy of a country?\n",
    "+ What is the average life expactancy in these nations?\n",
    "+ What is the distribution of that life expectancy?\n",
    "\n",
    "GDP Source:[World Bank](https://data.worldbank.org/indicator/NY.GDP.MKTP.CD)national accounts data, and OECD National Accounts data files.\n",
    "\n",
    "Life expectancy Data Source: [World Health Organization](http://apps.who.int/gho/data/node.main.688)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 1. Import Python Modules"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import the modules that you'll be using in this project:\n",
    "- `from matplotlib import pyplot as plt`\n",
    "- `import pandas as pd`\n",
    "- `import seaborn as sns`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 2 Prep The Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To look for connections between GDP and life expectancy you will need to load the datasets into DataFrames so that they can be visualized.\n",
    "\n",
    "Load **all_data.csv** into a DataFrame called `df`. Then, quickly inspect the DataFrame using `.head()`.\n",
    "\n",
    "Hint: Use `pd.read_csv()`\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>Country</th>\n",
       "      <th>Year</th>\n",
       "      <th>Life expectancy at birth (years)</th>\n",
       "      <th>GDP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Chile</td>\n",
       "      <td>2000</td>\n",
       "      <td>77.3</td>\n",
       "      <td>7.786093e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Chile</td>\n",
       "      <td>2001</td>\n",
       "      <td>77.3</td>\n",
       "      <td>7.097992e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Chile</td>\n",
       "      <td>2002</td>\n",
       "      <td>77.8</td>\n",
       "      <td>6.973681e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Chile</td>\n",
       "      <td>2003</td>\n",
       "      <td>77.9</td>\n",
       "      <td>7.564346e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Chile</td>\n",
       "      <td>2004</td>\n",
       "      <td>78.0</td>\n",
       "      <td>9.921039e+10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Country  Year  Life expectancy at birth (years)           GDP\n",
       "0   Chile  2000                              77.3  7.786093e+10\n",
       "1   Chile  2001                              77.3  7.097992e+10\n",
       "2   Chile  2002                              77.8  6.973681e+10\n",
       "3   Chile  2003                              77.9  7.564346e+10\n",
       "4   Chile  2004                              78.0  9.921039e+10"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('all_data.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 3 Examine The Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The datasets are large and it may be easier to view the entire dataset locally on your computer. You can open the CSV files directly from the folder you downloaded for this project.\n",
    "\n",
    "Let's learn more about our data:\n",
    "- GDP stands for **G**ross **D**omestic **P**roduct. GDP is a monetary measure of the market value of all final goods and services produced in a time period. \n",
    "- The GDP values are in current US dollars."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What six countries are represented in the data?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Chile, China, Germany, Mexico, USA, Zimbabwe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What years are represented in the data?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "2000 - 2015"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 4 Tweak The DataFrame\n",
    "\n",
    "Look at the column names of the DataFrame `df` using `.head()`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>Country</th>\n",
       "      <th>Year</th>\n",
       "      <th>Life expectancy at birth (years)</th>\n",
       "      <th>GDP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Chile</td>\n",
       "      <td>2000</td>\n",
       "      <td>77.3</td>\n",
       "      <td>7.786093e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Chile</td>\n",
       "      <td>2001</td>\n",
       "      <td>77.3</td>\n",
       "      <td>7.097992e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Chile</td>\n",
       "      <td>2002</td>\n",
       "      <td>77.8</td>\n",
       "      <td>6.973681e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Chile</td>\n",
       "      <td>2003</td>\n",
       "      <td>77.9</td>\n",
       "      <td>7.564346e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Chile</td>\n",
       "      <td>2004</td>\n",
       "      <td>78.0</td>\n",
       "      <td>9.921039e+10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "  Country  Year  Life expectancy at birth (years)           GDP\n",
       "0   Chile  2000                              77.3  7.786093e+10\n",
       "1   Chile  2001                              77.3  7.097992e+10\n",
       "2   Chile  2002                              77.8  6.973681e+10\n",
       "3   Chile  2003                              77.9  7.564346e+10\n",
       "4   Chile  2004                              78.0  9.921039e+10"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What do you notice? The first two column names are one word each, and the third is five words long! `Life expectancy at birth (years)` is descriptive, which will be good for labeling the axis, but a little difficult to wrangle for coding the plot itself. \n",
    "\n",
    "**Revise The DataFrame Part A:** \n",
    "\n",
    "Use Pandas to change the name of the last column to `LEABY`.\n",
    "\n",
    "Hint: Use `.rename()`. [You can read the documentation here.](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.rename.html)). </font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.rename(columns={'Life expectancy at birth (years)': 'LEABY'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run `df.head()` again to check your new column name worked."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Year</th>\n",
       "      <th>LEABY</th>\n",
       "      <th>GDP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Chile</td>\n",
       "      <td>2000</td>\n",
       "      <td>77.3</td>\n",
       "      <td>7.786093e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Chile</td>\n",
       "      <td>2001</td>\n",
       "      <td>77.3</td>\n",
       "      <td>7.097992e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Chile</td>\n",
       "      <td>2002</td>\n",
       "      <td>77.8</td>\n",
       "      <td>6.973681e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Chile</td>\n",
       "      <td>2003</td>\n",
       "      <td>77.9</td>\n",
       "      <td>7.564346e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Chile</td>\n",
       "      <td>2004</td>\n",
       "      <td>78.0</td>\n",
       "      <td>9.921039e+10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "  Country  Year  LEABY           GDP\n",
       "0   Chile  2000   77.3  7.786093e+10\n",
       "1   Chile  2001   77.3  7.097992e+10\n",
       "2   Chile  2002   77.8  6.973681e+10\n",
       "3   Chile  2003   77.9  7.564346e+10\n",
       "4   Chile  2004   78.0  9.921039e+10"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 5 Bar Charts To Compare Average"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To take a first high level look at both datasets, create a bar chart for each DataFrame:\n",
    "\n",
    "A) Create a bar chart from the data in `df` using `Country` on the x-axis and `GDP` on the y-axis. \n",
    "Remember to `plt.show()` your chart!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(data=df, x='Country', y='GDP')\n",
    "plt.show()\n",
    "plt.savefig(\"gdp_per_country.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "B) Create a bar chart using the data in `df` with `Country` on the x-axis and `LEABY` on the y-axis.\n",
    "Remember to `plt.show()` your chart!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(data=df, x='Country', y='LEABY')\n",
    "plt.show()\n",
    "plt.savefig(\"live_expectancy_per_country.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What do you notice about the two bar charts? Do they look similar?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "No they do not look similar. It signifies that GDP doesn't really influence live expectancy at birth"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 6. Violin Plots To Compare Life Expectancy Distributions "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another way to compare two datasets is to visualize the distributions of each and to look for patterns in the shapes.\n",
    "\n",
    "We have added the code to instantiate a figure with the correct dimmensions to observe detail. \n",
    "1. Create an `sns.violinplot()` for the dataframe `df` and map `Country` and `LEABY` as its respective `x` and `y` axes. \n",
    "2. Be sure to show your plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.subplots(figsize=(15, 10)) \n",
    "sns.violinplot(data=df, x='Country', y='LEABY')\n",
    "plt.show()\n",
    "plt.savefig(\"live_expectancy_spreed_violin.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What do you notice about this distribution? Which country's life expactancy has changed the most?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    " Zimbabwe had the largest increase in life expectancy rate"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 7. Bar Plots Of GDP and Life Expectancy over time\n",
    "\n",
    "We want to compare the GDPs of the countries over time, in order to get a sense of the relationship between GDP and life expectancy. \n",
    "\n",
    "First, can plot the progession of GDP's over the years by country in a barplot using Seaborn.\n",
    "We have set up a figure with the correct dimensions for your plot. Under that declaration:\n",
    "1. Save `sns.barplot()` to a variable named `ax`\n",
    "2. Chart `Country` on the x axis, and `GDP` on the `Y` axis on the barplot. Hint: `ax = sns.barplot(x=\"Country\", y=\"GDP\")`\n",
    "3. Use the `Year` as a `hue` to differentiate the 15 years in our data. Hint: `ax = sns.barplot(x=\"Country\", y=\"GDP\", hue=\"Year\", data=df)`\n",
    "4. Since the names of the countries are long, let's rotate their label by 90 degrees so that they are legible. Use `plt.xticks(\"rotation=90\")`\n",
    "5. Since our GDP is in trillions of US dollars, make sure your Y label reflects that by changing it to `\"GDP in Trillions of U.S. Dollars\"`. Hint: `plt.ylabel(\"GDP in Trillions of U.S. Dollars\")`\n",
    "6. Be sure to show your plot.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(figsize=(10, 15)) \n",
    "ax = sns.barplot(x=\"Country\", y=\"GDP\", hue=\"Year\", data=df)\n",
    "plt.ylabel(\"GDP in Trillions of U.S. Dollars\")\n",
    "plt.show()\n",
    "plt.savefig(\"gdp_per_country_per_year.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have plotted a barplot that clusters GDP over time by Country, let's do the same for Life Expectancy.\n",
    "\n",
    "The code will essentially be the same as above! The beauty of Seaborn relies in its flexibility and extensibility. Paste the code from above in the cell bellow, and: \n",
    "1. Change your `y` value to `LEABY` in order to plot life expectancy instead of GDP. Hint: `ax = sns.barplot(x=\"Country\", y=\"LEABY\", hue=\"Year\", data=df)`\n",
    "2. Tweak the name of your `ylabel` to reflect this change, by making the label `\"Life expectancy at birth in years\"` Hint: `ax.set(ylabel=\"Life expectancy at birth in years\")`\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(figsize=(10, 15)) \n",
    "ax = sns.barplot(x=\"Country\", y=\"LEABY\", hue=\"Year\", data=df)\n",
    "#plt.ylabel(\"Life expectancy at birth in years\")\n",
    "ax.set(ylabel=\"Life expectancy at birth in years\")\n",
    "plt.show()\n",
    "plt.savefig(\"live_expectancy_per country_per_year.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What are your first impressions looking at the visualized data?\n",
    "\n",
    "- Which countries' bars changes the most?\n",
    "- What years are there the biggest changes in the data?\n",
    "- Which country has had the least change in GDP over time? \n",
    "- How do countries compare to one another?\n",
    "- Now that you can see the both bar charts, what do you think about the relationship between GDP and life expectancy?\n",
    "- Can you think of any reasons that the data looks like this for particular countries?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Chile\n",
    "2000-2015\n",
    "Chile\n",
    "*****\n",
    "GDP is not a true indictor of life expectancy\n",
    "*****"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note: You've mapped two bar plots showcasing a variable over time by country, however, bar charts are not traditionally used for this purpose. In fact, a great way to visualize a variable over time is by using a line plot. While the bar charts tell us some information, the data would be better illustrated on a line plot.  We will complete this in steps 9 and 10, for now let's switch gears and create another type of chart."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 8. Scatter Plots of GDP and Life Expectancy Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To create a visualization that will make it easier to see the possible correlation between GDP and life expectancy, you can plot each set of data on its own subplot, on a shared figure.\n",
    "\n",
    "To create multiple plots for comparison, Seaborn has a special (function)[https://seaborn.pydata.org/generated/seaborn.FacetGrid.html] called `FacetGrid`. A FacetGrid takes in a function and creates an individual graph for which you specify the arguments!\n",
    "    \n",
    "Since this may be the first time you've learned about FacetGrid, we have prepped a fill in the blank code snippet below. \n",
    "Here are the instructors to fill in the blanks from the commented word bank:\n",
    "\n",
    "1. In this graph, we want GDP on the X axis and Life Expectancy on the Y axis.\n",
    "2. We want the columns to be split up for every Year in the data\n",
    "3. We want the data points to be differentiated (hue) by Country.\n",
    "4. We want to use a Matplotlib scatter plot to visualize the different graphs\n",
    "\n",
    "\n",
    "Be sure to show your plot!\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\AKINTAYO\\anaconda3\\lib\\site-packages\\seaborn\\axisgrid.py:243: UserWarning: The `size` parameter has been renamed to `height`; please update your code.\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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Evg5sBa6qZMXMzMzMzCayobp2HBwRh0qqBzZExFvS8lsk3VPhupmZmZmZTVhDtUh3AkREN/BEn3k9FamRmZmZmVkVGKpFeh9JlwAqmib9e0ZFa2ZmZmZmNoENlUifUzTd2mde37/NzMzMzGrGoIl0RCztr1xSE/CeitTIzMzMzKwKDPlmwwJJdZKOl3Q18Efg5MpVy8zMzMxsYhuqaweSjgYWAO8GlgNvAl4VEe0VrpuZmZmZ2YQ11CvCNwDrgW8A50TEVkl/cBJtZmZmZrVuqK4d3yd5OsfJwHskTQai1JVL2l3SDZIelPSApCMlTZX0C0kPp+OXjaD+ZmZmZmbjYtBEOiI+BswCvgK8DXgImC7pA5J2KWH9FwO3RMRBwOuAB4DzgGURcQCwLP3bzMzMzKyqDHmzYSRui4jTSJLqBcCJwLrBlpO0G3A0cGW6ns6IeBY4ASg8DWRpui4zMzMzs6oyaCKdJsM7RURXRPwoIhYARw2x7v2ANuA/Ja2S9K20a8ieEfFkur4ngT0G+O7TJbVKam1rayt1eyyDHAtWzPFgBY4FMxtvQ7VI316YkLSsz7zvDrFsPXA48I2ImANsYxjdOCLi8oiYFxHzpk+fXupilkGOBSvmeLACx4KZjbehEmkVTU8dZF5/NgAbIuKu9O8bSBLrTZL2BkjHfyqxrmZmZmZmE8ZQiXQMMN3f371nRjwFPC7pNWnRscBa4GZgYVq2ELiptKqamZmZmU0cQ72QZQ9JnyRpfS5Mk/5dynW0M4HvSmoEHgP+hiR5v17SIpJnVL+/rJqbmZmZmY2joRLpK4Bd+5kG+NZQK4+I1cC8fmYdW1LtzMzMzMwmqEET6Yj43EDzJH189KtjZmZmZlYdhnyO9CA+OfRHzMzMzMyyaSSJ9FBP7TAzMzMzy6yRJNKDPrXDzMzMzCzLBu0jLWkr/SfMAporUiMzMzMzsyow1M2Guw4238zMzMysVo2ka4eZmZmZWc1yIm1mZmZmVgYn0mZmZmZmZXAibWZmZmZWBifSZmZmZmZlcCJtZmZmZlYGJ9JmZmZmZmVwIm1mZmZmVgYn0mZmZmZmZXAibWZmZmZWhkFfET5SktYBW4EeoDsi5kmaCnwPmAWsAz4QEc9Ush5mZmZmZqNtLFqk3xYRsyNiXvr3ecCyiDgAWJb+bWZmZmZWVcaja8cJwNJ0eilw4jjUwczMzMxsRCqdSAdwq6SVkk5Py/aMiCcB0vEe/S0o6XRJrZJa29raKlxNm8gcC1bM8WAFjgUzG2+VTqTfFBGHA8cD/yjp6FIXjIjLI2JeRMybPn165WpoE55jwYo5HqzAsWBm462iiXREPJGO/wTcCBwBbJK0N0A6/lMl62BmZmZmVgkVS6QlTZa0a2EaOA64H7gZWJh+bCFwU6XqYGZmZmZWKZV8/N2ewI2SCt9zTUTcImkFcL2kRcB64P0VrIOZmZmZWUVULJGOiMeA1/VTvhk4tlLfa2ZmZmY2FvxmQzMzMzOzMlR1Ih35PJ0d7USk43x+vKtkZmZmZjWioq8Ir6TI52l//jl+csmFbHxwLTMOOph3L15Cy25TUK6qzw/MzMzMrApUbcbZtWM7P7nkQh5fcx/5nh4eX3MfP7nkQrp2bB/vqtk48RUKMzMzG0tV2yLd0NTExgfX9irb+OBaGpqaxqlGNp58hcKKRT5P147tNDQ10bV9Ow2TmhwHZmY26qr2yNK1fTszDjq4V9mMgw6ma7tbpGuRr1BYQeGk6odf+jxf++D7+OGXPk/788/5CkUN89UqM6uUqk2kGyY18e7FS3jlIa8lV1fHKw95Le9evISGSW6RrkW+QmEFPqmyYj6xMrNKqtquHcrlaNltCiee82lfvrWdVygeX3PfzrLCFYrG5pZxrJmNNZ9UWbHiEytg54nVied82vsGMxuxqs46lcvR2NyClI6dRNcsX6GwAnf7smI+sTKzSqraFmmzYr5CYQWFk6q+N576pKo2+WqVmVWSE2nLjMIVCsAHyBrmkyor5hMrM6skJ9Jmljk+qbICn1iZWSU5kTYzs0zziZWZVYpPyc3MzMzMyuBE2szMzMysDE6kzczMzMzKoIgY7zoMSVIb8MdBPvJy4Okxqs54yOL2PR0R7xzuQiXEAmTz9yrI6rZVKh6y+nsVZHH7vG8oT1a3rax4MBsrVZFID0VSa0TMG+96VErWt2+0Zfn3yvK2VULWf6+sb99oy/LvleVtM5vI3LXDzMzMzKwMTqTNzMzMzMqQlUT68vGuQIVlfftGW5Z/ryxvWyVk/ffK+vaNtiz/XlneNrMJKxN9pM3MzMzMxlpWWqTNzMzMzMaUE2kzMzMzszI4kTYzMzMzK4MTaTMzMzOzMjiRNjMzMzMrgxNpMzMzM7MyOJE2MzMzMyuDE2kzMzMzszI4kTYzMzMzK4MT6T6U+I2k44vKPiDplnGs076Sbpe0VtIaSR8tmjdN0jJJD0v6uaQpabkkXSbpEUn3SJrdZ51TJD0p6WtjvT3VJOvxIKleUo+k1elw43ht10SX9VhI582S9Mt0fWslvXI8tqsaZD0eJL29aL+wWtIOSX8xXttmNmFFhIc+A3Ao8ADQBEwGHgZePcJ11o9g2VcAs9Pp3YBHgQPTv78CnJ1O/xPwr+n0e4EfpdNvBn7bZ53/DlwDfG28f++JPmQ5HoB64Nnx/o2rZchyLKR/3wEck07vAjSP928+kYesx0PReqcDm4Gm8f7NPXiYaINbpPsREfcDPwLOBT4LXB0Rj0paKGl5enZ+maQcgKTLJbWmLQCfKaxH0gZJn5b0W+B9I6jPExGxOp1+HngQmJHOPgFYmk4vBU4sKr86XeY3wF6Spqf1OgLYHbit3DrVkqzHg5Uuy7Eg6TCgJyJuS+e9EBEd5datFmQ5Hvqs+v3AjyNie7l1M8uq+vGuwAT2OeBuoBOYJ+lQkh3cGyOiW9LlwCkkrbrnRcQWSfXAryTdEBFr0/Vsi4g39V25pA8Dn+zne38fEScPVClJ+5G0gqxIi6ZFRFs6vRHYO52eATxetOgGYIakLcCXgFOBdw3+E1iRTMYD8AwwWdLKdNsuiIgfDfpLWFZjYX/geUk/BPYFbgX+b0TkB/sxLLPx0FZUdgpwwUDfZVbLnEgPICK2Sfoe8EJE7JD0dmA+0CoJoJkXdz6nSlpE8nu+AjgYKOwcvzfA+q8mbQUolaTdgO8DZ0bECwNVvfDxAeadCdwUEU+k22ElyHA89AD7pvGwP7BM0n0RsW44daklGY6FeuAoYA5JonUD8CFebMW0fmQ4Hgrr2gd4DfDL4dTBrFY4kR5cPh0g2dl8OyI+XfwBSQcAHwOOiIhnJf0XSX+5gm39rXi4rQySGoEfAFdFxM1FszZLmp62NMwAnkrLNwCvBH6X/r0P8ATwBuCNkhaT9IFslLQtIj7V7y9gxTIXDxERJHFBRDwi6Q5gNrCuv3raTpmLBWBXYGXhJCptmT4cJ9KlyGI8FJwMfD8iuvurn1mtcx/p0v0S+ICkl8POO6BnktzQsZXkkujewJ+XsrKIuDoiZvcz9LdjFHAVsDoiLu4z+2ZgYTq9ELipqPzD6fJvBjZFRFtEnBIRMyNiFnAeyQ7fSfTwZSIeJE2VNCktnw4cSXLzlJUuE7FAkkjtIWla+rljeLG11EqXlXgoOBW4tpS6mtUit0iXKCLuk/Q54JfpjSNdwN8DrSQHm/uBx4DfVuDr30KyM7tX0uq07NyI+DlJv7XrJX0E+ANJ6wEkN8AcL+lRkpaOhdioyVA8HAJcJilP0pL2+Yj4fQXqnFlZiYW0P+85JH13BSwHvl2BOmdaVuIBIO3utQfwmwrU1SwTlFzZNTMzMzOz4XDXDjMzMzOzMjiRNjMzMzMrgxNpMzMzM7MyOJE2MzMzMytDVSTS73znO4PkAfEesjOUxbGQ2aEsjodMDmVxLGR2MJvQqiKRfvrpp8e7CjZBOBasmOPBChwLZjYeqiKRNjMzMzObaJxIm5mZmZmVwYm0mZmZmVkZnEibmZmZmZXBibSZmZmZWRkqmkhL+oSkNZLul3StpCZJr5J0l6SHJX1PUmMl62BmZjZa8pFnW9e2XmMzq10VS6QlzQAWA/Mi4lCgDjgF+CLw1Yg4AHgGWFSpOpiZlcsJk/WVjzxbtm/hzNvOZO535nLmbWeyZfsWx4ZZDat01456oFlSPdACPAkcA9yQzl8KnFjhOgzIB0or5niwAidM1p+O7g6W/HoJK55aQXd0s+KpFSz59RI6ujvGu2pmNk4qlkhHxEbgy8B6kgT6OWAl8GxEdKcf2wDM6G95SadLapXU2tbWNur184GyelQ6FsDxUE1KjYd8PnhhRzf5SMf50l+S5oSpOgxn3zCSeChorm9m1aZVvcpWbVpFc33zsNdlZtlQya4dLwNOAF4FvAKYDBzfz0f73ZtFxOURMS8i5k2fPn3U6+cDZfUoNRacONWGUuIhnw82b+vktKWtHPipn3Ha0lY2b+ssOSacMFWH4ewbRhIPBR3dHczZc06vsjl7zvF+wqyGVbJrx9uBP0REW0R0AT8A3gjsnnb1ANgHeGIkX1Ju8uQDZbY4cbJi7V09LL52FXc+tpnufHDnY5tZfO0q2rt6Slq+FhOmyAed27uJSMdltNhOVCONh4Lm+mYuPPpC5u81n3rVM3+v+Vx49IWZ309kOTbMRqqSifR64A2SWiQJOBZYC/wKOCn9zELgpnK/YCTJU1YPlLW6w3PiVJpaiY+WxjpWrNvSq2zFui20NNaVtHytJUyRDzq2dvLTy+7lm/94Oz+97F46tnZmJj5GGg8FOeWY2jSVS4+5lJUfWsmlx1zK1Kap5JTdJ8lmPTbMRqqSfaTvIrmp8G7gvvS7LgfOBT4p6RFgGnBlud8xkuQpiwfKWt7hOXEaWi3FR3tnD/NnTe1VNn/WVNo7SzuxqrWEqauzh1uvXMPGh54lnw82PvQst165hq4Sf6+JbqTxUCynHJMbJvcaZ1nWY8NspOqH/kj5IuKzwGf7FD8GHDEa6x9J8lR8oGyub6aju4Pm+uaq3ikW7/CAnTu8d51xGI1NFf2nHneFA+Wdj23eWVY4UO4yaehtz2I89FVL8dHSUMclp85h8bWrWLFuC/NnTeWSU+fQ0lB6C2QhUQJ2jrOqYVIdTz7yXK+yJx95joZJw2uxnahGIx5qVdZjw2ykqvroORrJU5YOlLW8w3PiNLRaio9cTkyb3MgVC+fR0lhHe2cPLQ115HIa76pNSF07eth7/yk7T7IA9t5/Cl07ejJxkuV4KF/WY8NspKq6ua2QPB253zTqc+LI/abVdCtDYYdXrLDDy7riA+VD/3o8Vyycx7TJjT5QFqm1+MjlxC6T6skpHedE5PP0bNvWa2zQ0FjHcYsOYcaBu5PLiRkH7s5xiw6hYZh9iCeyvvEgwrFQglqIDbORqOrTyX5bGepzREc70dxMvqODXHMzylX1+ULJCju8W69cw5OPPMfe+0+pqR1e4UAJlHRFotbUenxEPk/Pli1sPOss2lfeTcvcw5lx0UXUTZ1aM/uIgSgnmndt5F1nHEbDpDq6dvTQ0FiHMnoi6lgoXa3FhtlwVX22UZw8TW7I1fTO0Tu8l4p8fucJVa2dWPVV6/GR7+hI9g13LQeg/a7lbDzrLPa57DLqJmevK89wKaedl+qzfsnesTA8tRQbZsOVqYyi186xu3vnzjHfka1HmA2msMOT0nGNJEn9KbQ6bTjjDB487HVsOOMMerZsqelLuLUcH7nmZtpX3t2rrH3l3eSas/NkFiuNY8HMRkumEmnvHK2YT6ysWL6jg5a5h/cqa5l7eP/xkM/Djhcg0nENn3xl0bBi4SULOzbM7EWZSqRHtHO0zBnyxMoHxJqSa25mxkUX0fL6I6C+npbXH8GMiy566Yl2Pg/tbXDtKfD56cm4vc3xkSElx0Jfjg0z6yNTifSgO0cnTTVn0BMrHxBrjnI56qZOZZ/LLuOge+9J+sP2d/9EVzvcsAjW3QH57mR8w6Kk3DKh5Fjoy7FhZn1kKpEecOcITppq0KAnVj4g1iTlctRNntxr/BKNLbD+zt5l6+9Myi0zSoqFvhwbZtZHphJpGGDn6KSpJg3a6uQDog2ks+t5eicAACAASURBVB1mHtm7bOaRSbnVNseGmfWRuUS6X06aataArU4+INpAGlrgpCth1lGQq0/GJ12ZlFttc2yYWR+18UDIQtK07o4XywpJ06Rdxq9eNn4KB8QbFiUnVTOP9AHRErkctEyHU69LTrY725O4qNHnj1sRx4aZ9VEbibSTJuvLB0QbTC734km2T7atmGPDzIrURiLtpMn64wOimZmZjUBtJNLgpMnMzMzMRlXFmmQlvUbS6qLheUkflzRV0i8kPZyOX1apOpiZmZmZVUrFEumI+H1EzI6I2cBcoB24ETgPWBYRBwDL0r/NzMzMzKrKWHUSPhZ4NCL+CJwALE3LlwInjlEdzMzMzMxGzVgl0qcA16bTe0bEkwDpeI8xqoOZmZmZ2aipeCItqRF4L/Dfw1zudEmtklrb2toqUzmrCo4FK+Z4sALHgpmNt7FokT4euDsiNqV/b5K0N0A6/lN/C0XE5RExLyLmTZ8+fQyqaROVY8GKOR6swLFgZuNtLBLpU3mxWwfAzcDCdHohcNMY1MHMzMzMbFRVNJGW1AK8A/hBUfEXgHdIejid94VK1sHMzMzMrBIGfSGLpDdExO/KXXlEtAPT+pRtJnmKh5mZmZlZ1RqqRfobkv5D0u5jUhszMzMzsyoxVCI9F3gAWC7pQ2NQHzMzMzOzqjBoIh0R+Yj4GslLU74uaWv6qu+tkp4fmyqamZmZmU08Q95sKGkRyZM1PgXsFhG7RcSuEbFbxWtnZmZmZjZBDXWz4f8C64CjIuKpMamRmZmZmVkVGDSRBj4bEb8Yk5qYmZmZmVWRobp23CFpoaT3KnGupB9LuljSy8ekhmZmZmZmE9BQifTVwHHA3wK3AzOBrwNbgasqWTEzMzMzs4lsqK4dB0fEoZLqgQ0R8Za0/BZJ91S4bmZmZmZmE9ZQLdKdABHRDTzRZ15PRWpkZmZmZlYFhmqR3kfSJYCKpkn/nlHRmpmZmZmZTWBDJdLnFE239pnX928zMzMzs5oxaCIdEUv7K5fUBLynIjUyMzMzM6sCQ77ZsEBSnaTjJV0N/BE4uXLVMjMzMzOb2Ibq2oGko4EFwLuB5cCbgFdFRHuF62ZmZmZmNmEN9YrwDcB64BvAORGxVdIfnESbmZmZWa0bqmvH90meznEy8B5Jk4EodeWSdpd0g6QHJT0g6UhJUyX9QtLD6fhlI6i/mZmZmdm4GDSRjoiPAbOArwBvAx4Cpkv6gKRdSlj/xcAtEXEQ8DrgAeA8YFlEHAAsS/82MzMzM6sqQ95sGInbIuI0kqR6AXAisG6w5STtBhwNXJmupzMingVOAApPA1marsvMzMzMrKoMmkinyfBOEdEVET+KiAXAUUOsez+gDfhPSaskfSvtGrJnRDyZru9JYI/yq29mZmZmNj6GapG+vTAhaVmfed8dYtl64HDgGxExB9jGMLpxSDpdUquk1ra2tlIXswxyLFgxx4MVOBbMbLwNlUiraHrqIPP6swHYEBF3pX/fQJJYb5K0N0A6/lN/C0fE5RExLyLmTZ8+fYivsixzLFgxx4MVOBbMbLwNlUjHANP9/d17ZsRTwOOSXpMWHQusBW4GFqZlC4GbSquqmZmZmdnEMdQLWfaQ9EmS1ufCNOnfpZz+nwl8V1Ij8BjwNyTJ+/WSFpE8o/r9ZdXczMzMzGwcDZVIXwHs2s80wLeGWnlErAbm9TPr2JJqZ2ZmZmY2QQ2aSEfE5waaJ+njo18dMzMzM7PqMORzpAfxyaE/YmZmZmaWTSNJpId6aoeZmZmZWWaNJJEe9KkdZmZmZmZZNmgfaUlb6T9hFtBckRqZmZmZmVWBoW423HWw+WZmZmZmtWokXTvMzMzMzGqWE2kzMzMzszI4kTYzMzMzK4MTaTMzMzOzMjiRNjMzMzMrgxNpMzMzM7MyOJE2MzMzMyuDE2kzMzMzszI4kTYzMzObwCTtJek6SY9KWivpp5IOHMX1v1XSG0drfbXEibSZmZnZBCVJwI3A7RHx6og4GDgf2HMUv+atQL+JtKRB34Jd6yqaSEtaJ+k+SasltaZlUyX9QtLD6fhllayDmZmZWRV7G9AVEd8sFETEauA3kr4k6f401zoZdrYu/7jwWUlfl/TX6fQ6SZ+TdHe6zEGSZgF/D3wizdeOknSVpK9I+hXwpTRnm56uIyfpEUkvH6sfYCIbixbpt0XE7IiYl/59HrAsIg4AlqV/m5mZmdlLHQqs7Kf8/wCzgdcBbydJePcuYX1PR8ThwDeAsyNiHfBN4KtpvnZH+rkDgbdHxCeA/wI+mJa/HbgnIp4ud4OyZDy6dpwALE2nlwInjkMdzMzMzKrZm4FrI6InIjYB/wPML2G5H6TjlcCsQT733xHRk05/G/hwOv23wH8Ov7rZVOlEOoBbJa2UdHpatmdEPAmQjvfob0FJp0tqldTa1tZW4WraROZYsGKOBytwLFiNWAPM7adcA3y+m975XVOf+TvScQ8wWP/nbYWJiHgc2CTpGOD1wM8Gq3AtqXQi/ab08sHxwD9KOrrUBSPi8oiYFxHzpk+fXrka2oTnWLBijgcrcCxYjbgNmCTptEKBpPnAM8DJkurS/stHA8uBPwIHS5okaQpwbAnfsRXYdYjPfIuki8f1RS3VNa+iiXREPJGO/0Ryx+kRJGc0ewOk4z9Vsg5mZmZm1SoiAngf8I708XdrgH8GrgHuBe4hSbaXRMRTaevx9em87wKrSviaHwHvK9xsOMBnbgZ2wd06eqnYI00kTQZyEbE1nT4O+BeSf4iFwBfS8U2VqoOZmZlZtUsbJj/Qz6xz0qHv55cAS/opn1U03Ury2Dsi4iHgsKKP3sFLvY7kJsMHh1H1zKvkswH3BG5MHn9IPXBNRNwiaQVwvaRFwHrg/RWsg5mZmZmNgKTzgH/gxSd3WKpiiXREPEZy9tK3fDOl9dcxMzMzs3EWEV8g6UlgffjNhmZmlmmRz9PZ0U5EOs7nx7tKZpYRfu2jmZllVuTztD//HD+55EI2PriWGQcdzLsXL6Fltyko57YkMxsZ70XMzCyzunZs5yeXXMjja+4j39PD42vu4yeXXEjXju3jXTUzywAn0maWOb6UbwUNTU1sfHBtr7KND66loanvOyrMzIbPibSZZUrhUv4Pv/R5vvbB9/HDL32e9uefczJdo7q2b2fGQQf3Kptx0MF0bXeLtJVP0l6Srkuf67xW0k/TN23+eIDPf0vSwen0OkkvH9saW6U4kbbMcCukgS/lW28Nk5p49+IlvPKQ15Krq+OVh7yWdy9eQsMkt0hbeZQ81/dG4PaIeHVEHAycT/LY335FxN9FxNqB5lv1quqbDSOfp2vHdhqamujavp2GSU2+eaRG+YYiK/ClfCumXI6W3aZw4jmf9rGiBnXn8ws6OnsumDypfua2Hd3rmxvrzq/P5a4Z4WrfBnRFxDcLBRGxWtLuwLGSbgAOBVYCfxURIel24Oz0JSg7SforYDHQCNwFnOHXb1eXqt2T+PKtFXMrpBX4Ur71pVyOxuYWpHTsJLomdOfzC7a80HnF6Vev3PfAT/1Mp1+9ct8tL3Re0Z3PLxjhqgtJcn/mAB8HDgb2A9400Eok/RlwMvCmiJgN9OAXnlSdqt2bOHGyYm6FtAJfyjczgI7Ongs+dt3qljsf20x3Prjzsc187LrVLR2dPRdU8GuXR8SGiMgDq4FZg3z2WGAusELS6vTv/SpYN6uAqu3a4cTJihVaIR9fc9/OskIrZGNzyzjWzMaaL+WbGcDkSfUzV6zb0qtsxbotTJ5UP3OEq14DnDTAvB1F0z0MnmcJWBoR/3eE9bFxVLVHFl++tWJuhbRivpRvZtt2dK+fP2tqr7L5s6aybUf3+hGu+jZgkqTTCgWS5gNvGeZ6lgEnSdojXcdUSfuOsG42xqr26OLEyYoVt0J+/Ls3cuI5n/aNhmZmNay5se78i0+Z3X7kftOoz4kj95vGxafMbm9urDt/JOuNiADeB7wjffzdGuCfgSeGuZ61wD8Bt0q6F/gFsPdI6mZjT0k8TGzz5s2L1tbWl5T7qR1VTeUsNFAsWNVzPFiBY8GKlRUPBRV6aofZTlXbRxpevHwLuB+smZmZ9VKfy12za1OSOO/a1DDe1bEMcvOtmZmZmVkZnEibmZmZmZWhKvpIS2oD/jjIR14OPD1G1RkPWdy+pyPincNdqIRYgGz+XgVZ3bZKxUNWf6+CLG6f9w3lyeq2lRUPZmOlKhLpoUhqjYh5412PSsn69o22LP9eWd62Ssj675X17RttWf69srxtZhOZu3aYmZmZmZXBibSZmZnZMEjaS9J16XOk10r6qaTTJf14gM9/S9LB/c2z6lbVj78rcvl4V6DCsr59oy3Lv1eWt60Ssv57ZX37RluWf68sb9uEIknAjSSv9z4lLZsNvGegZSLi78aoejbGMtFH2szMzOwl8t0L6Gy/gEm7zGTHC+tpbDmfXP2IXsgi6RjgnyPi6D7lbyV5w+HTwKHASuCvIiIk3Q6cHRGtkl4ALgb+AugAToiITZLeQ/Kmw0ZgM/DBiNg0krpa5blrh5mZmWVPvnsB256+gusW7Mvnp4vrFuzLtqevIN+9YIRrLiTJ/ZkDfBw4GNgPeFM/n5kM/C4iXgf8GjgtLf8N8IaImANcBywZYT1tDDiRNjMzs+zpbL+A7/9dC+vugHw3rLsDvv93LXS2X1DBb10eERsiIg+sBmb1VzOg0Jd6ZdFn9gF+Luk+4BzgkArW00aJE2kzMzPLnkm7zGT9nb3L1t+ZlI/MGmDuAPN2FE330P+9aF3xYr/a4s9cCnw9Il4LfARoGmE9bQw4kTYzM7Ps2fHCemYe2bts5pFJ+cjcBkySVOiSgaT5wFtGuN4pwMZ0euEI12VjxIm0mZmZZU9jy/n85bfamXUU5Oph1lHwl99qp7Hl/JGsNm1Nfh/wjvTxd2tIbjJ8YoQ1/mfgvyXdQTbfUplJfmqHmZmZZVMFntphVsyJtJmZmZlZGdy1w8zMzMysDE6kzczMzMzK4ES6DyV+I+n4orIPSLplHOu0r6TbJa2VtEbSR4vmTZO0TNLDkn4uaUpaLkmXSXpE0j3p60sLy1yUrucBSV9NX3dq/aiRePiypPvT4aTx2KZqkKFYOETSnZJ2SPp4n/W9S9Lv0zg5Z6y3p5rUSDwsldQmafVYb4tZ1YgID30GkrcWPUDyDMfJwMPAq0e4zvoRLPsKYHY6vRvwKHBg+vdXSF47CsmrRf81nX4v8KN0+s3Ab9Ppo0nepJQjeXblcuDN4/2bT+Qh4/FwAnALUAfsQvJygF3G+zefqENGYmFPYB7wBeDjRetqAB4D9gUmAfcV1uWh9uIhnfcW4Ahg9Xj/1h48TNTBLdL9iIj7gR8B5wKfBa6OiEclLZS0XNLqtHUvByDpckmtaQvAZwrrkbRB0qcl/ZbkUTnl1ueJiFidTj8PPAjMSGefACxNp5cCJxaVX50u8xtgL0nTgSDZ6TeSHCzrgT+VW7dakPF4OBi4PSJ6IuIF4H7guHLrlnVZiIWI2BQRrUB3n9W9AXggIv4YETuA69N12AAyHg9ExP8AW8qtj1kt6O+NO5b4HHA3yas850k6lGQH98aI6JZ0OXAKcA1wXkRskVQP/ErSDRGxNl3Ptoh4U9+VS/ow8Ml+vvf3EXHyQJWStB9JK8iKtGhaRLSl0xuBvdPpGcDjRYtuAGZExB2S/hd4ChDwtYh4aPCfwshoPAD3AOdJupikRfot6XbawKo9FgbSX4y8bohlLLvxYIOQtCfwVZIT0GdI/v0vjIgbx7ViNuacSA8gIrZJ+h7wQkTskPR2YD7QqqRLcTMvHnROlbSI5Pd8BUkrX2Hn+L0B1n81aQthqSTtBnwfODNtPex31YWP9zdP0muAV5McNOuAX0r6eUT873DqUmuyGg8R8VNJ84A7Sa5M3Ek/LVP2ogzEwoCrKWOZmpfheLABKPmH/SGwNCIWpGX7knShK2X5uojoqWAVbQw5kR5cPh0gOch8OyI+XfwBSQcAHwOOiIhnJf0XSdeJgm39rXi4rQySGoEfAFdFxM1FszZLmp62NMwgaWmGpDXplcDv0r/3IXnr0t8B/xsR29L13kJyRu1EemhZjAci4l+Af0nXez1JP08bXDXHwkAKMVKwM0ZsSFmMh0zozncv2N69/YKWhpaZ7V3t65vqm86vH/kLWY4BOiPim4WCiPgjcKmkOpL+5m8l6T757xHxH5LeStL950lgtqR3kdyf8huSY/A9wH+SXOHYA/hgRCyXdATwNZITsg7gbyLi95L+miRxbyFpHLsxIpakJ2qHRsQnAJS8xvzPIqK/GLJR4D7Spfsl8AFJL4edd0DPJLmhYyvwvKS9gT8vZWURcXVEzO5n6G/HKOAqkhs+Lu4z+2ZgYTq9ELipqPzD6fJvBjalO9D1wFsk1UtqILmU/0CJv4G9KBPxkMbB1LR8DvBnwLISfwNLVFssDOR3wMFKnvwwCfhAug4bnqzEQ9XrzncveGb7M1cs/tXifed+Z64W/2rxvs9sf+aK7nz3ghGu+hAG7gK3CHguIuaTXJk4TdKr0nlHAJ+KiIPTv/cHLgYOAw4CFpDcDH42UHiN+YPA0RExB/gMcEHRd80GTgZeC5ws6ZXAdcB70+M7wN+QJOhWIW6RLlFE3CfpcyRdIXJAF/D3QCvJpbn7Se54/20Fvv4twKnAvXrxMUTnRsTPSf5TXS/pI8AfSP5TQXIDzPGSHiVp6SjsQK8jOVO+N/37JxHxswrUOdMyFA+TgN+kl6CfI2kF8SXHYai2WJC0D0nSvBuQl3Q2yZMd2iUtBn5B0u3r8oj4fQXqnGkZi4f/JknspknaAPxTRFxVgXpXxPbu7Rece8e5LSueSrqJr3hqBefecW7LJW+75IJdGncZtdeES/p3kt+pE/gjcJhefJToFOCAdN7yiPhD0aJ/iIj70nWsAZZFREi6D5hVtPzS9IpGkDxdp2BZRDyXLr8W2DciHpd0G/AXkh4AGgrfYZXhV4SbmZlZ5uQjn5/7nbnqjhdv+6hXPSs/tDJyypV9RV7SscBnIuItRWUvJzlZWklyEvrzPsu8leTxg3+R/j0L+HFEHJr+fVX69w3F89LyuyPikrT89oiYlXbtmBcRH02X/zHw5Yi4XdLrSVq0HwT+GBGXlbutNjR37TAzM7PMae9qXz9nzzm9yubsOYf2rvb1I1z1bUCTpH8oKmtJxz8H/qHQtULSgZImj+C7ppA8ZQXgr0tZICLuIrnfYQFw7Qi+20rgRNrMzMwyp6m+6fwvHvXF9vl7zade9czfaz5fPOqL7U31TecPvfTAIrmUfyLJ/UZ/kLSc5Nnc5wLfIunCc7ek+4H/YGTdaC8E/k3JM8brhrHc9SQv3npmBN9tJXDXDjMzM8ukCj21Y8JLu3p8NSJ883iFOZE2MzMzywBJuwPLgXsi4v3jXZ9aUBVdO975zncGyd2qHrIzlMWxkNmhLI6HTA5lcSxkdrBhiIhnI+JAJ9FjpyoS6aeffnq8q2AThGPBijkerMCxYGbjoSoSaTMzMzOzicaJtJmZmZlZGZxIm5mZmZmVwYm0mZmZWYkkhaTvFP1dL6ktfeRcOet7r6TzRq+GNpZG8pBwMzOzmpSPPB3dHTTXN+8c5+S2qRqxDThUUnNEdADv4MW3Dw5bRNwM3DxalbOxVdH/9ZI+IWmNpPslXSupSdKrJN0l6WFJ35PUWMk6mJUjH3m2dW3rNbba5FiwvvKRZ8v2LZx525nM/c5czrztTLZs3+LYmICiu3tBz9at6yKfz/ds3bouursXjNKqfwa8O50+laJXcUuaLOnbklZIWiXphLT8k5K+nU6/Ns2NWiT9taSvp+V7SrpR0j3p8MaiZe9Ph4+P0jbYKKhYIi1pBrAYmBcRh5K82vIU4Iskb9s5AHgGWDSS78nngxd2dJOPdJwf2WMnfdA0HyStwLGQHaN5rOjo7mDJr5ew4qkVdEc3K55awZJfL6Gju2MUa2wjFd3dC3qeeeaKDR/96L4PHvY6bfjoR/fteeaZK0Ypmb4OOEVSE3AYcFfRvE8Bt0XEfOBtwJckTQa+Buwv6X3AfwIfiYj2Puu9BPifiHgdcDiwRtJc4G+A1wNvAE6TNGcUtsFGQaWvQ9UDzZLqgRbgSeAY4IZ0/lKS99WXJZ8PNm/r5LSlrRz4qZ9x2tJWNm/rLHsH6YNm9RuNg6UPktngWLCC0T5WNNc3s2rTql5lqzatorm+eTSqa6Mk39Fxwcazz25pv2s5dHfTftdyNp59dku+o+OCka47Iu4FZpG0Rv+0z+zjgPMkrQZuB5qAmRGRB/4a+A5JsvzbflZ9DPCN9Dt6IuI54M3AjRGxLSJeAH4AHDXSbbDRUbFEOiI2Al8G1pMk0M8BK4FnI6I7/dgGYEZ/y0s6XVKrpNa2trZ+v6O9q4fF167izsc2050P7nxsM4uvXUV7V09ZdfZBc2IqJRZg9A6WPkhObKXEg2NhdEU+6NzeTUQ6HuGVv9FS6r6hEseKOXv2bhCcs+ecmjtWTNS4KMhNnjyzfeXdvcraV95NbvLkmaP0FTeT5DnX9ikX8JcRMTsdZkbEA+m8A4AXgFcM43s08qpapVSya8fLgBOAV5EEzGTg+H4+2u//vIi4PCLmRcS86dOn9/sdLY11rFi3pVfZinVbaGmsK6vOPmhOTKXEAozewbJWD5IT/aBYUEo8OBZGT+SDjq2d/PSye/nmP97OTy+7l46tnRMiPkrdN1TiWHHh0Rcyf6/51Kue+XvN58KjL6ypY8VEjouC/LZt61vmHt6rrGXu4eS3bVs/Sl/xbeBfIuK+PuU/B86UJIBCNwxJU4CLgaOBaZJO6medy4B/SD9fJ2k34NfAiWl/6snA+4A7RmkbbIQq2bXj7cAfIqItIrpILkW8Edg97eoBsA/wRLlf0N7Zw/xZU3uVzZ81lfZOtzIMR7UkUEMZrYNlLR4kq+GgOByOhdHT1dnDrVeuYeNDz5LPBxsfepZbr1xDV5n72fEw2seKnHJMbZrKpcdcysoPreTSYy5latPUmnpqRzXERa65+fwZX/5ye8vrj4D6elpefwQzvvzl9lxz8/mjsf6I2BARF/cz6/NAA3CvpPvTvwG+ClwWEQ+R3B/2BUl79Fn2Y8DbJN1HchX/kIi4G7gKWE7SF/tbEbEKmxAq+fi79cAbJLUAHcCxQCvwK+Akko76C4Gbyv2CloY6Ljl1DouvXcWKdVuYP2sql5w6h5aGkbUyLPn1ElZtWsWcPedk/qBZSKBuvXINTz7yHHvvP4XjFh1C866NKFddV5MKB8s7H9u8s6xwsNxlUumhXnyQrJVHWxUfFIGdB8V3nXEYjU3V95RMx8LoaZhUx5OPPNer7MlHnqNhUnn72fEw2scKSGJjcsNkgJ3jWlINcaH6+mvqXvYy9vn61y/ITZ48M79t2/pcc/P5qq+/ZiTrjYhd+im7naQ/NOkj8T7Sz2f+tmj6cWD/9M+r0oGI2ERyNb/vsl8BvjKSeltlVOwIGRF3SboBuBvoBlYBlwM/Aa6T9P/SsivL/Y5cTkyb3MgVC+fR0lhHe2cPLQ115MpMAGvxoJmlBGo0D5a1dpCshoPicDgWRk/Xjh723n/Kzn0EwN77T6FrR0/V7CNG+1hh1RMXqq+/pm7XXa8BqNt11/GujmVQRaM9Ij4LfLZP8WPAEaP1HbmcdrYwDaelacD11dhBM0sJlA+W5auWg2KpHAujp6GxjuMWHfKSq1YNZfYvHi+jfayodVmJC7OR8t6kxmUxgfLBcviyeFB0LIwO5UTzro2864zDaJhUR9eOHhoa66qu65eNLseFWcJHlxqXxQTKhs8HRRuMctp5Yl2NJ9gDiXyefEcHuebmnWPlstuVb7RlNS7MhiOTke+dY+mynkA5FkpXCwdFx4MVRD5Pz5YtbDzrLNpX3k3L3MOZcdFF1E2d6pgws5Jlbm9R2DluOOMMHjzsdWw44wx6tmwh8n474UAKCZSUjjOURDsWrMDxYMXyHR1JEl381ruzziLfMY6PO83nYccLEOnYsWk24WUukZ6QO0cbFxWPBR/0qsq47BscIxNWrrmZft961zxOjzvN56G9Da49BT4/PRm3tzlmJhhJs9JnQxeX/bOks4dYbp6kS9Lpt0r/v727D5OkLu/9//7M7C67i4CuLgQRRI9GAj6ALCgqJIbogZgInuADmARPOPrzaCDGBOLPxKgxx2NQomKCBkVdk4hGVMCYGAlKIEqE5UFgATXB5UmEFZSnfZ65zx9djb3j7Mxsz/TMdO/7dV1zVde3q6rv6rmn6p5vf7sqz+vitdckedw47b+T5Pok1yW5IckxTftrkkx6B8WpLjcdSc5t4vv9bTz/7SRj7xA50zF8LMn+M73dgSuke3Jw9GTYl3p6ovSk13dmvXAyR+a10fXrGfeud3PV6bJ5HZx3Eqy5DEa3tKbnndRqV9+rqlVVdUoz+0u0blA3bUmeAPwx8IKqeibwXOC65unXMLVbkU91ua4k+TngeVX1zKp6/zjP/wKtevSI5s6NvYhhuKr+V1XdONPbHrhCesYPjp4M+1ZPT5Se9PrOrBdO5si8NrRkCXudcQZb3fXujDPmrkd60VK47fKt2267vNWuro2O1Akb129ZU1WjG9dvWTM6Uif08vWSXJLkL5JckeS7SQ5v2n8pyT8m2Rd4PfD7Sa5NcniS5Uk+n+TK5uf5zTqPTfLVJNck+RtgvHGXuwMPAg8BVNVDVfX95vbjK4C/b15nSZI/bbZ/Q5Kz0zLecgcn+bckVyX5lyR7NvGckuTGpmf5M+Ps++Ikn2h6x69J8sLmqa8Cu7f3d5x9OAH422a5l455L9+f5NIkNyU5JMkXknyvuRdJe7nfbN7va5P8TZLhpv2hJH+W5FvAYc32VjTPHZXk6qYn/OKm7dAk32xi/2aSp03hVXBSOwAAIABJREFUVz54hfSMHxw9Gfatnp4oPen1nVkvnMyReS1DQwwvW8YTzjqL/a77Nk8466y5/aLhpnWwz2Fbt+1zWKtdXRkdqRPWP7Tpo//84eue+JE3XpJ//vB1T1z/0KaP9rqYBhZU1aHAmxhzL42qWgN8BHh/VR1YVZcBH2zmDwF+A/hYs/jbgX+vqoOAC4F9xnmtbwN3A99vithfb17nPFp3k3518zrrgb+qqkOq6unAEuDXxi5H6wZ6HwKOq6qDgY8D/6d5rbcABzU9368fJ5Y3Nq/9DOB4YGWSxbSK4//q2N+xXgl8Fji3Wa/Tpqo6onnPLmhe4+nAa5p/NH6hWf/5TfwjwKubdXcGbqiq51TVv7c3mGQ58FHgN6rqWcDLm6duBo5o3u8/Bd49Tqw/Y+C+mt95cJyRb+Z7MuxbM54LndonvTUdx4T2SW+nn7l7rOaBnubDeMyReS9DQwzv3PokuT2dMwuXwnHntDpqbru8lSvHndNqV1c2bxp590XnrF7aeefei85ZvfTo//3Md++0pOvbhNcU2r/QTK8C9p3CNn8F2D95pMN51yS7AEcA/wOgqr6c5Mc/86JVI0mOAg4BjgTen+TgqnrHOK/zwiSnAUuBZcBq4EtjlnkarUL1oiaeYeCu5rnraPVcnw+cP872X0CrCKeqbk5yK/DzwAPb2vEkhwBrq+rWJHcAH0/ymKpq7+uFzfR6YHVV3dWsdwuwd/OaBwNXNvEuAe5p1hkBPj/Oyz4XuLSqvt/Eel/Tvhut4v+ptH6fC7cVd6eBK6Rhhg+Ongz7Ws9OlJ70+tKsFk7miLbH0BAsXQ7Hf6bVUbNpXStXvBRf1xYtHt5nvDv3Llo8PF7P7lTdCzxmTNsy4Psd8xub6QhTq7OGgMOaXuNHNIXhtgr3R1RVAVcAVyS5CPgE8I4x21oMnAWsqKrbk7wDWDzO5kKrYD1snOdeQqu4fynwtiQHVNWWMetur+OB/ZKsaeZ3Zete+fZ7OdrxuD2/oHnNlVX1/4+z7Q1VNTJOexj/fX0X8PWqelkzBOeSqeyAf6GTaZ8M9z0chha0pp4M1XnSe9va1nTpck96+ilzRNtraKjVQZNmaq5My6YNI7ft+ZTdtmrb8ym7sWnDyG3dbrOqHgLuSnIkQJJlwFHAv0+44tYeBHbpmP8q8LvtmSQHNg8vpRmmkORofraAJ8njk3R++eNA4NZxXqddNP8oyaOA47YRz3eA5UkOa7a/MMkBSYaAvavq68BpwKOBsb2JnfH+PK2hKN/ZxntAs82XA8+sqn2ral/gGH52eMdELgaOS7J7s81lSZ44yTqXA7+Y5EntdZr23YA7m8evmWoAA9kjPaPsJdC2tE964KcTGp85Is2ZhYuG3/qikw746EXnrF7avnPvi046YN3CRcNvneamfxv46yRnNPPvrKr/2o71vwScl9Zl6k4GTmm2dx2tuuxSWmOQ3wmcm+Rq4N+A8f4BWAi8L63L120A1vLT8cufBD6SZD1wGK1xwdcDa4ArO7YxdrnjgDOT7NbE8wHgu8DfNW2hNab7J2NiOavZzvW0xlq/pqo2dgxZGesI4M6qurOj7VJaw1z23NZKnarqxiR/Any1Kcw30xpHfesE66xN8jrgC8069wAvAk6nNbTjzcDXpvL6AGl9IjC/rVixolatWjXXYWhmdXXXF3NhYJkPajMX1GladwgbHakTNm8aefeixcP7bNowctvCRcNvHRpOt+OjpZ9hj7QkSRpIQ8P5dPuLhTstseTRzHN8giRJktQFC2lJkiSpCz0rpJM8rbnLTPvngSRvar5ReVFzZ5qLkvzMt1AlSZKk+a5nhXRVfae5i82BtC6WvQ74Iq0741xcVU+lddmSt/QqBkmSJKlXZmtox5G0bg95K61rBK5s2lcCx85SDJIkSdKMma1C+lW07qEOsEf7Fo/NdPfxVkjyuiSrkqxau3btLIWp+chcUCfzQW3mguZCkpeNGbp6bZLRJK9Oct52buuSJCu2Y/nXJPmr7Y9avdLzQjrJIlq3k/zc9qxXVWdX1YqqWrF8+fLeBKe+YC6ok/mgNnNBc6GqvtgeutoMXz0LuAw4t6qOm2R1DZjZ6JE+Gri6qu5u5u9u37Gmmd4zCzFIkqQdzOjIyAkb1z28pmp0dOO6h9eMjoycMJPbb26F/afAbwH7JLmhaX9NkvOTfCnJ95P8bpI3J7kmyX903JYa4DeTfDPJDUkObdY/tGm7ppk+rWP5vZN8Jcl3kry9Wf60JKc0j9+f5GvN4yOT/F3z+MVJLk9ydZLPNbcK1zTNRiF9PD8d1gFwIXBi8/hE4IJZiEGSJO1ARkdGTlj3wP0fveB9f/7ED7z6ZbngfX/+xHUP3P/RmSqmkywEPg38YVWNd/vupwMnAIcC/wdYV1UHAZfTus14285V9TzgDcDHm7abgSOa5f8UeHfH8ocCrwYOBF7eDA25FDi8eX4F8KgmvhcAlyV5HPAnwK9U1bOBVcCbp7P/aulpIZ1kKa37l3+ho/k9wIuSfK957j29jEGSJO14Nm/c8O5/+tB7l96++npGR0a4ffX1/NOH3rt088YN75587Sl5F7C6qj6zjee/XlUPVtVa4H7gS0379cC+HcudC1BVlwK7Jnk0sBvwuaaH+/3AAR3LX1RV91bVelr11QuAq4CDk+wCbKRVrK+gVVxfBjwX2B/4RpJraXVkPnE6O6+WCe+XmeS5VfUf3W68qtYBjx3Tdi+tq3hIkiT1xKIlS/a58+Ybt2q78+YbWbRkyT7T3XaSXwJ+A3j2BItt7Hg82jE/ytb1V41Zr2gV6V+vqpcl2Re4ZKLlq2pzkjXA/wS+CVwHvBD4b8BNzfSiqjp+gnjVhcl6pD+c5G+a/44kSZL6wqb162/ba7/9t2rba7/92bR+/XjDMKasuZHcJ4DfrqoHp7Otxiub7b4AuL+q7qfVI31n8/xrxiz/oubmdktoXUL4G037pcAfNtPLgNcD11ZVAf8BPD/JU5rXWtqM79Y0TVZIH0zrP5krkvzWLMQjSZI0bQt3WvzWXz351HV7H/AMhoaH2fuAZ/CrJ5+6buFOi986zU2/ntalez/ceQk8moK4Cz9O8k3gI8BJTdvpwP9N8g1geMzy/w78LXAt8PmqWtW0XwbsCVzeXOBhQ9NGM7zkNcC5Sa6jVVjv12W86pDWPyqTLJTsT2u8zRCtjxRC66OEXXsbXsuKFStq1apVky+ofpJuVjIXBpb5oDZzQZ26yoe20ZGREzZv3PDuRUuW7LNp/frbFu60+K1Dw8OfnqngpEm/bJjkJFpX1vhjYNeq2rWqdpmtIlqSJKkbQ8PDn95p6c77JkNDOy3deV+LaM20yb5s+E1gDXB4Vf1wViKSJEmS+sCEhTTw9qq6aFYikSRJkvrIZEM7LktyYpKXpuWPkvxjkg82F/eWJEmSdkiTFdKfAl4M/A6taxjuA/wV8CDwyV4GJkmSJM1nkw3t2L+qnp5kAXBHVf1i0/6VJN/ucWySJEnSvDVZj/QmgKraAvxgzHMjPYlIkiRJ6gOT9Ug/IcmZtK7j2H5MM79XTyOTJEmS5rHJCulTOx6PvdK9V76XJEnSDmvCQrqqVo7XnmQx8Os9iUiSJEnqA5Pe2bAtyXCSo5N8CriV7u8pL0mSJPW9yYZ2kOQI4ATgJcAVwPOBJ1XVuh7HJkmSJM1bk90i/A7gNuDDwKlV9WCS71tES5IkaUc32dCOz9O6OscrgV9PsjNQU914kkcnOS/JzUluSnJYkmVJLkryvWb6mGnEL0mSJM2JCQvpqvo9YF/gL4EXAt8Flid5RZJHTWH7HwS+UlX7Ac8CbgLeAlxcVU8FLm7mJUmSpL4y6ZcNq+VrVfVaWkX1CcCxwJqJ1kuyK3AEcE6znU1V9RPgGKB9NZCVzbYkSZKkvjJhId0Uw4+oqs1V9aWqOgE4fJJtPxlYC3wiyTVJPtYMDdmjqu5qtncXsPs2Xvt1SVYlWbV27dqp7o8GkLmgTuaD2swFSXNtsh7pS9oPklw85rm/n2TdBcCzgQ9X1UHAw2zHMI6qOruqVlTViuXLl091NQ0gc0GdzAe1mQuS5tpkhXQ6Hi+b4Lnx3AHcUVXfaubPo1VY351kT4Bmes8UY5UkSZLmjckK6drG4/Hmt36y6ofA7Ume1jQdCdwIXAic2LSdCFwwtVAlSZKk+WOyG7LsnuTNtHqf249p5qfyOdrJwN8nWQTcAvxPWsX7PyQ5idY1ql/eVeSSJEnSHJqskP4osMs4jwE+NtnGq+paYMU4Tx05pegkSZKkeWrCQrqq3rmt55K8aebDkSRJkvrDpNeRnsCbJ19EkiRJGkzTKaQnu2qHJEmSNLCmU0hPeNUOSZIkaZBNOEY6yYOMXzAHWNKTiCRJmkE1OsrmjRtYuHgxmzdsYOFOi8nQdPqRJKllsi8b7jLR85IkzWc1Osq6B+7ny2eezp0338he++3PS045jaW77mYxLWnaPIpIkgbW5o0b+PKZp3P76usZHRnh9tXX8+UzT2fzxg1zHZqkAWAhLUkaWAsXL+bOm2/cqu3Om29k4eLFcxSRpEFiIS1JGlibN2xgr/3236ptr/32Z/MGe6QlTZ+FtCRpYC3caTEvOeU09j7gGQwND7P3Ac/gJaecxsKd7JGWNH2T3SJckvqOV2lQW4aGWLrrbhx76tvMB0kzzkJa0kDxKg0aK0NDLFqyFOCRqSTNBM8qkgaKV2mQJM0WC2lJA8WrNEiSZouFtKSB4lUaJEmzxUJa0kDxKg2SpNnS0y8bJlkDPAiMAFuqakWSZcBngX2BNcArqurHvYxDOwav1CDwKg2SpNkzG2eWF1bVgVW1opl/C3BxVT0VuLiZl6alfaWG89/7Lj7w6pdx/nvfxboH7qdGR+c6NM2B9lUakmZqES1J6oG5OLscA6xsHq8Ejp2DGDRgvFKDJEmabb0upAv4apKrkryuadujqu4CaKa7j7diktclWZVk1dq1a3scpuazqeSCV2rYcXhsUJu5IGmu9bqQfn5VPRs4GnhjkiOmumJVnV1VK6pqxfLly8dfZnSUTevXUdVM/Rh/IE0lF7xSw45jKvmgHYO5IGmu9bSQrqofNNN7gC8ChwJ3J9kToJne09W2HROrDl6pQZIkzbaeXbUjyc7AUFU92Dx+MfBnwIXAicB7mukF3Wy/c0ws8MiY2GNPfZu3gN0BeaUGSZI023p5+bs9gC8mab/Op6vqK0muBP4hyUnAbcDLu9m4Y2I1VvtKDYD/TEmSpJ7rWSFdVbcAzxqn/V7gyOluvz0mtt0jDT8dE2sRJUmSpF7r28+9HRMrSZKkudTTOxv2kmNiJUmSNJf6tpAGx8RKkiRp7th9K0mSJHXBQlqSJEnqgoW0JEmS1AULaUmSJKkLFtKSJElSFyykJUmSpC5YSEuSJEldsJCWJEmSumAhLUmSJHXBQlqSJEnqgoW0JEmS1AULaUmSJKkLqaq5jmFSSdYCt06wyOOAH81SOHNhEPfvR1V11PauNIVcgMF8v9oGdd96lQ+D+n61DeL+eWzozqDuW1f5IM2WviikJ5NkVVWtmOs4emXQ92+mDfL7Ncj71guD/n4N+v7NtEF+vwZ536T5zKEdkiRJUhcspCVJkqQuDEohffZcB9Bjg75/M22Q369B3rdeGPT3a9D3b6YN8vs1yPsmzVsDMUZakiRJmm2D0iMtSZIkzSoLaUmSJKkLFtKSJElSFyykJUmSpC5YSEuSJEldsJCWJEmSumAhLUmSJHXBQlqSJEnqgoW0JEmS1AUL6THS8u9Jju5oe0WSr8xhTE9MckmSG5OsTvK7Hc89NsnFSb6X5F+S7Na0H5Dk8iQbk7xpKtvSz9oB8mHnJFckubbZ3p/OxT71g0HPhY71FiS5Lsn5s7kv/WZHyIckdyS5vjk+fGu290fqB94ifBxJng58DjgIGAauBY6qqv+axjYXVNWWLtd9PLB7VV2bZFfgGuDoqvpukr8EflBV70vyJ8CSqvrjJHsAewPHAT+sqg9Mtq1u923QDXg+DDXLPJxkIXA58PqqWtXtvg2yQc6Fjm2eBhwILK2qY7vdrx3BoOdDkjuAp1fVT7rdH2nQ2SM9jqq6AfgS8EfA24FPVdV/JTmxo/furKYIIcnZSVY1PQCP9Og1/82/Lck3gJdNI54fVNW1zeMHgJuBvZqnjwFWNo9XAsc2y93dFENbtmNbGseA58NoVT3czC4CFgL+d70Ng5wLTVxPBF4EfKLbmHYkg54Pkia3YK4DmMfeCVwNbAJWND0PLwOeV1VbkpwNvAr4NPCWqrovyQLg60nOq6obm+08XFXPH7vxJL8NvHmc1/1OVb1yW0EleTLwdODKpumxVbW2eXwnsOdUd3CcbWnbBjYfkiwCrgCeAnywqq6abJ0d3MDmAvAB4FTgcVNYVi2DnA8FfC1JAWdV1TlTWEfaoVhIb0PzUfdngYeqamOSXwEOAVYlAVgC3N4sfnySk2i9n48H9gfaB8fPbmP7nwI+tT0xNR/VfR44uaoe2lboM7gtNQY5H6pqE3BgkscAX0zyC1V10/bEsiMZ1FxIcixwezMs4Fe25/V3ZIOaD43nVNUPkvwccFGSm6rqm9sTizToLKQnNtr8AAT4eFW9rXOBJE8Ffg84tKp+kuTvgMUdizzMOLa3l6HpNfwC8MmqurDjqXuTLG96GvYCfjjZTk2wLU1sIPOhrap+nOQy4L8DFtITG8RceB7wP5K8tIlz1yQrq+rESdbTYOYDVfWDZvrDJBcAhwIW0lIHx0hP3b8Cr0jyOHjkG9D7ALsCDwIPJNmTVhEyqar6VFUdOM7PeAfGAJ8Erq2qD455+kKgfaI7EbhgotedZFuaukHJh93z02/vLwWOpDWuUlM3ELlQVadV1ROqal/gN4GvWkR3ZSDyIcmjkjyq/ZjW2PkbphKztCOxR3qKqur6JO8E/rX54shm4PXAKlofzd0A3AJ8owcv/4vA8cB1Sa5t2v6oqv4FeDfwD0n+P+D7wCsBkjwB+A9aB+/RJH8I/DytHoVtbUtTNED58Hjgk80JeBg4t6rm7PJd/WhQcqGq1vUgvh3OoOQDrTHU5zXDUxYAf1tV/9qDmKW+5uXvJEmSpC44tEOSJEnqgoW0JEmS1AULaUmSJKkLFtKSJElSFyykJUmSpC70RSF91FFHFa27MPkzOD9dMRcG9qcr5sNA/nTFXBjYH2le64tC+kc/+tFch6B5wlxQJ/NBbeaCpLnQF4W0JEmSNN9YSEuSJEldsJCWpCkarVEe3vzwVlPtOPz9SxrLQrqDB0mNZU6obbRGuW/DfZz8tZM5+G8P5uSvncx9G+4zJ/rE6Gjx0MYtjFYzHd2+77H5+5c0np4W0kl+P8nqJDckOTfJ4iRPSvKtJN9L8tkki3oZw1R5kBwM0z1ZbrUtc6LvzWQ+rN+yntMuPY0rf3glW2oLV/7wSk679DTWb1k/gxGrF0ZHi3sf3sRrV67i5//4n3ntylXc+/Cm7coHf/+SxtOzQjrJXsApwIqqejowDLwK+Avg/VX1VODHwEnTfa2ZOFl6kOx/M3Gy7GRO9LeZzoclC5Zwzd3XbNV2zd3XsGTBkpkIVz20bvMIp5x7DZffci9bRovLb7mXU869hnWbR6a8DX//ksbT66EdC4AlSRYAS4G7gF8GzmueXwkcO50XmKmTpQfJ/jcTJ8tO5kR/m+l8WL9lPQftcdBWbQftcZD/WPWBpYuGuXLNfVu1XbnmPpYuGp7yNvz9z5waLTZt2EJVM53GJ0XSXOtZIV1VdwLvA26jVUDfD1wF/KSqtjSL3QHsNZ3XmamTpQfJ/jcTJ8tO5sTE5vvJcKbzYcmCJZx+xOkc8nOHsCALOOTnDuH0I073H6ttmE/5sW7TCIfsu2yrtkP2Xca6TdvXI+3vf/pqtFj/4Cb+6azr+MgbL+GfzrqO9Q9umnfHD2mqejm04zHAMcCTgMcDOwNHj7PouH89SV6XZFWSVWvXrt3m68zUydKD5LbN9QlxqrkwEyfLTubEts3lyXCu8mEoQyxbvIwP/fKHuOq3ruJDv/whli1exlD8zvZYs5UfUz5PLBzmzOMP4rAnP5YFQ+GwJz+WM48/iKULp36e8Pc/MzZvGuGr56zmzu/+hNHR4s7v/oSvnrOazV3+XUpzLVW9OfEleTlwVFWd1Mz/NnAY8HLg56pqS5LDgHdU1X+faFsrVqyoVatWjfvcQxu38NqVq7j8lnsfaTvsyY/loyeu4FE7LdiumEdrlPVb1rNkwZJHpjv6QbJ9QvzqOau56z/vZ8+n7MaLTzqAJbssIkOZzqa7WnmiXGgP8znl3Gu4cs19HLLvMs48/iAeu/MihrqM1ZwY36YNW/ins67jzu/+5JG2vX7+0fzqG57JosXb93fX6It80NRMMz9mPBeglQ/rNo+wdNEw6zaNsHThsHkwB6qKj7zxkq2GXw4Nhdf/9S+RjPv78Jekea2rM94U3QY8N8lSYD1wJLAK+DpwHPAZ4ETggum8SLunYezJcnt6GtqGMsTOC3cGeGS6o+vsPQAe6T2YRsHUM0ND4bE7L+KjJ66YsZOlOTG+hTsNc9d/3r9V213/eT8Ld+pu2EQv9CIfNDXzMT+GhvJI58r2drJo5mzeOMKeT9ltq3+y9nzKbmzeODLvzinSVPQsa6vqW0nOA64GtgDXAGcDXwY+k+TPm7ZzpvM6nix7az6eECfiyXJ29MvJ0HyYG/2SH5p9CxcN8+KTDviZTzkXdvndBWmu9fSIVlVvB94+pvkW4NCZfB1Plr3jCVHj8WSoiZgf2pYMhSW7LOJX3/BMFu40zOaNIyxcNDzdoYLSnLES0oQG5YRYo6OMrl/P0JIlj0wz5FjnbvXzydBc6L1+yQ9zYW5kKI90xNgho343sBnsAXJm9MsJcSI1OsrIffdx5x/8AeuuupqlBz+bvc44g+Fly8yJaejHk6G5MHvme34MfC6MjsLmdbBoKWxaBwuXwiDslzTPDORfVfsAeccb3sDNz3wWd7zhDYzcdx816q2du9E+ISbNtI+KaIDR9etbJ8tvXQFbtrDuW1dw5x/8AaPrvR70jmbgcmF0FDY+BNVMPcZN2cDlQqfRUVi3Fs59FbxreWu6bq35IfXAQBbSfXOA9CQ4K4aWLGHdVVdv1bbuqqsZWtIH14M2R2ZUX+fCWBZL0zJQuTDW5nVw3kmw5jIY3dKanndSq13SjBrIQrovDpCeBGfN6Pr1LD342Vu1LT342fPvH6uxzJEZ17e5MB6LpWkZqFwYa9FSuO3yrdtuu7zVLmlGDWQh3RcHSE+Cs2ZoyRL2OuMMlj7nUFiwgKXPOZS9zjhjfv1jNR5zZMb1bS6Mx2JpWgYqF8batA72OWzrtn0Oa7VLmlHz7xsgM6B9gBz7JZJ5dYD0JDhrMjTE8LJlPOGss/rry6fmyIzr21wYT7tYWnPZT9vaxdJOj5q7uPrEQOXCWAuXwnHntP7xvu3yVl4cd06rXdKMGshCui8OkJ4EZ1WGhhjeuXVnwvZ03jNHeqIvc2E8FkvTNjC5MNbQECxdDsd/xqt2SD02sH9V7QNk53ReaZ8E9z0chha0pp4E1ckc0UQ6i6W3rW1Nly63WFLL0FDrH+40U/NC6omB7JHuC/YYaDLmiCbTLpbATykkaQ5YSM8lT4KajDkiSdK8ZdeWJEmS1AULaUmSJKkLFtKSJElSFyykJUmSpC5YSEuSJEld6FkhneRpSa7t+HkgyZuSLEtyUZLvNdPH9CoGSZIkqVd6VkhX1Xeq6sCqOhA4GFgHfBF4C3BxVT0VuLiZlyRJkvrKbA3tOBL4r6q6FTgGWNm0rwSOnaUYJEmSpBkzW4X0q4Bzm8d7VNVdAM1091mKQZIkSZoxPS+kkywCXgp8bjvXe12SVUlWrV27tjfBqS+YC+pkPqjNXJA012ajR/po4OqquruZvzvJngDN9J7xVqqqs6tqRVWtWL58+SyEqfnKXFAn80Ft5oKkuTYbhfTx/HRYB8CFwInN4xOBC2YhBkmSJGlG9bSQTrIUeBHwhY7m9wAvSvK95rn39DIGSZIkqRcWTPRkkudW1X90u/GqWgc8dkzbvbSu4iFJkiT1rcl6pD+c5G+SPHpWopEkSZL6xGSF9MHATcAVSX5rFuKRJEmS+sKEhXRVjVbVB2jdNOWvkjzY3Or7wSQPzE6IkiRJ0vwz6ZcNk5xE68oafwzsWlW7VtUuVbVrz6OTJEmS5qnJvmz4TWANcHhV/XBWIpIkSZL6wISFNPD2qrpoViKRJEmS+shkQzsuS3Jikpem5Y+S/GOSDyZ53KxEKEmSJM1DkxXSnwJeDPwOcAmwD/BXwIPAJ3sZmCRJkjSfTTa0Y/+qenqSBcAdVfWLTftXkny7x7FJkiRJ89ZkPdKbAKpqC/CDMc+N9CQiSZIkqQ9M1iP9hCRnAul4TDO/V08jkyRJkuaxyQrpUzserxrz3Nh5SZIkaYcxYSFdVSvHa0+yGPj1nkQkSZIk9YFJ72zYlmQ4ydFJPgXcCryyd2FJkiRJ89tkQztIcgRwAvAS4Arg+cCTqmpdj2OTJEmS5q0Je6ST3AG8B/gGrUvh/Qaw3iJaktQvanSUTevXUdVMR0fnOiRJA2KyoR2fp3V1jlcCv55kZ6CmuvEkj05yXpKbk9yU5LAky5JclOR7zfQx04hfkqRtqtFR1j1wP+e/91184NUv4/z3vot1D9xvMS1pRkxYSFfV7wH7An8JvBD4LrA8ySuSPGoK2/8g8JWq2g94FnAT8Bbg4qp6KnBxMy9J0ozbvHEDXz7zdG5ffT2jIyPcvvp6vnzm6WzeuGGuQ5M0ACb9smG1fK2qXkurqD4BOBZYM9F6SXYFjgDOabazqap+AhwDtK8GsrLZliTNGD/KV9vCxYu58+Ybt2q78+YbWbh48RxFJGmQTDZGetfAzrqJAAAJEElEQVTO+araXFVfqqoTgMMn2faTgbXAJ5Jck+RjzdCQParqrmZ7dwG7b+O1X5dkVZJVa9euner+aACZC+o0WT74Uf6OYyrHhs0bNrDXfvtv1bbXfvuzeYM90pKmb7Ie6UvaD5JcPOa5v59k3QXAs4EPV9VBwMNsxzCOqjq7qlZU1Yrly5dPdTUNIHNBnSbLBz/K33FM5diwcKfFvOSU09j7gGcwNDzM3gc8g5ecchoLd7JHWtL0TXb5u3Q8XjbBc+O5A7ijqr7VzJ9Hq5C+O8meVXVXkj2Be6YcrTSBGh1l88YNLFy8mM0bNrBwp8VkaMqXSteA8KN8dcrQEEt33Y1jT32bxwZJM26yI0lt4/F481s/WfVD4PYkT2uajgRuBC4ETmzaTgQumFqo0rb5cb7a/ChfY2VoiEVLlpI0U4toSTNksqPJ7knenOQPOh6356fyGfvJwN8nuQ44EHg3retSvyjJ94AXNfPStPhxvtr8KF+SNFsmG9rxUWCXcR4DfGyyjVfVtcCKcZ46ckrRSVPkx/lq86N8SdJsmbCQrqp3buu5JG+a+XC2j2Ni1db+OP/21dc/0tb+OH/RkqVzGJnmQvujfMDfvySpZ6ZTdb55xqLogmNi1cmP8yVJ0mybbGjHRCa7akdPdY6JBR4ZE3vsqW+zB2oH5Mf5kiRptk2nkJ7wqh295phYjeXH+ZIkaTZNdmfDB5M8MM7Pg8DjZynGcXmJK0mSJM2lCQvpqtqlqnYd52eXqppOb/a0OSZWkiRJc2lOi+HpcEysJEmS5lLfFtLgmFhJkiTNHbtvJUmSpC5YSEuSJEldsJCWJEmSumAhLUmSJHXBQlqSJEnqgoW0JEmS1AULaUmSJKkLFtKSJElSF3p6Q5Yka4AHgRFgS1WtSLIM+CywL7AGeEVV/biXcUiSJEkzbTZ6pF9YVQdW1Ypm/i3AxVX1VODiZl6SJEnqK3MxtOMYYGXzeCVw7BzEIEmSJE1LrwvpAr6a5Kokr2va9qiquwCa6e7jrZjkdUlWJVm1du3aHoep+cxcUCfzQW3mgqS51utC+vlV9WzgaOCNSY6Y6opVdXZVraiqFcuXL+9dhJr3zAV1Mh/UZi5Imms9LaSr6gfN9B7gi8ChwN1J9gRopvf0MgZJkiSpF3pWSCfZOcku7cfAi4EbgAuBE5vFTgQu6FUMkiRJUq/08vJ3ewBfTNJ+nU9X1VeSXAn8Q5KTgNuAl/cwBkmSJKknelZIV9UtwLPGab8XOLJXrytJkiTNBu9sKEmSJHXBQlqSJEnqgoW0JEmS1AULaUmSJKkLFtKSJElSFyykJUmSpC5YSEuSJEldsJCWJEmSumAhLUmSJHXBQlqSJEnqgoW0JEmS1AULaUmSJKkLFtKSJElSFyykJUmSpC5YSEuSJEldsJCWJEmSupCqmusYJpVkLXDrBIs8DvjRLIUzFwZx/35UVUdt70pTyAUYzPerbVD3rVf5MKjvV9sg7p/Hhu4M6r51lQ/SbOmLQnoySVZV1Yq5jqNXBn3/Ztogv1+DvG+9MOjv16Dv30wb5PdrkPdNms8c2iFJkiR1wUJakiRJ6sKgFNJnz3UAPTbo+zfTBvn9GuR964VBf78Gff9m2iC/X4O8b9K8NRBjpCVJkqTZNig90pIkSdKsspCWJEmSutD3hXSSo5J8J8l/JnnLXMczk5J8PMk9SW6Y61j6gbmgTuaD2swFSb3S14V0kmHgr4Gjgf2B45PsP7dRzahPAl6IfgrMBXUyH9RmLkjqpb4upIFDgf+sqluqahPwGeCYOY5pxlTVpcB9cx1HnzAX1Ml8UJu5IKln+r2Q3gu4vWP+jqZNOx5zQZ3MB7WZC5J6pt8L6YzT5vX8dkzmgjqZD2ozFyT1TL8X0ncAe3fMPwH4wRzForllLqiT+aA2c0FSz/R7IX0l8NQkT0qyCHgVcOEcx6S5YS6ok/mgNnNBUs/0dSFdVVuA3wX+BbgJ+IeqWj23Uc2cJOcClwNPS3JHkpPmOqb5ylxQJ/NBbeaCpF7yFuGSJElSF/q6R1qSJEmaKxbSkiRJUhcspCVJkqQuWEhLkiRJXbCQ1riSfDzJPUlumMKyRyS5OsmWJMd1tD8xyVVJrk2yOsnrexu1esV8UJu5oE7mg3Z0FtKzJMkeST6d5JbmgHF5kpcl+aUk9ye5Jsl3klya5Nc61ntHkjubA8wNSV46SyF/EjhqisveBrwG+PSY9ruA51XVgcBzgLckefxMBdjPzAfzoc1cMBc6mQ/mg/rLgrkOYEeQJMD5wMqqOqFpeyLwUuDHwGVV9WtN+4HA+UnWV9XFzSbeX1XvS/ILwGVJdq+q0V7GXFWXJtl3zH78N+CvgeXAOuC1VXVzVa1pnh8ds41NHbM74T9ugPnQMB8wFxrmQsN8AMwH9RmTdXb8MrCpqj7SbqiqW6vqQ2MXrKprgT+jdQOBsc/dBGwBHtfDWCdyNnByVR0M/CFw1mQrJNk7yXXA7cBfVJW35jUfzIefMhfMhU7mg/mgPmOP9Ow4ALh6O5a/Gjh1bGOS5wCjwNoZimvKkjwKeB7wuVanCdDqOZhQVd0OPLP5mO78JOdV1d29i7QvmA/mQ5u5YC50Mh/MB/UZC+k5kOSvgRcAmxjnIAhkzPzvJ/lN4EHglTU3t6McAn7SjGHbblX1gySrgcOB82Y0sj5nPpgPbeaCudDJfDAfNP85tGN2rAae3Z6pqjcCR9IaPzaeg4CbOubfX1UHVtXhVXVZ78Lctqp6APh+kpdDayxfkmdNtE6SJyRZ0jx+DPB84Ds9D3b+Mx/MhzZzwVzoZD6YD+ozFtKz42vA4iT/u6Nt6XgLJnkm8DZaX9SYM0nOBS4HnpbkjiQnAa8GTkrybVoH/GOaZQ9JcgfwcuBvmt4EgF8AvtUs/2/A+6rq+tnel3nIfDAf2swFc6GT+WA+qM9kbj752fEk2RN4P61L+6wFHgY+AtwNXADcQuuAeQ9welV9qVnvHcBDVfW+OQhbPWI+qM1cUCfzQeovFtKSJElSFxzaIUmSJHXBQlqSJEnqgoW0JEmS1AULaUmSJKkLFtKSJElSFyykJUmSpC5YSEuSJEld+H96kwu2ZK5KgwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 736.75x576 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# WORDBANK:\n",
    "# \"Year\"\n",
    "# \"Country\" \n",
    "# \"GDP\" \n",
    "# \"LEABY\" \n",
    "# plt.scatter\n",
    "\n",
    "\n",
    "# Uncomment the code below and fill in the blanks\n",
    "g = sns.FacetGrid(df, col='Year', hue='Country', col_wrap=4, size=2)\n",
    "g = (g.map(plt.scatter, 'GDP', 'LEABY', edgecolor=\"w\").add_legend())\n",
    "plt.show()\n",
    "plt.savefig(\"live_expectancy_scatterplot.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "+ Which country moves the most along the X axis over the years?\n",
    "+ Which country moves the most along the Y axis over the years?\n",
    "+ Is this surprising?\n",
    "+ Do you think these scatter plots are easy to read? Maybe there's a way to plot that! "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "*China\n",
    "*Zimbabwe\n",
    "*Yes it is, as Zimbabwe were able to incre there life expectancy without increasing its GDP, \n",
    " while China inspite of there GDP seems to have plateaued in life expectancy\n",
    "*No, possiblyusing a boxplot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 9. Line Plots for Life Expectancy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the scatter plot grid above, it was hard to isolate the change for GDP and Life expectancy over time. \n",
    "It would be better illustrated with a line graph for each GDP and Life Expectancy by country. \n",
    "\n",
    "FacetGrid also allows you to do that! Instead of passing in `plt.scatter` as your Matplotlib function, you would have to pass in `plt.plot` to see a line graph. A few other things have to change as well. So we have created a different codesnippets with fill in the blanks.  that makes use of a line chart, and we will make two seperate FacetGrids for both GDP and Life Expectancy separately.\n",
    "\n",
    "Here are the instructors to fill in the blanks from the commented word bank:\n",
    "\n",
    "1. In this graph, we want Years on the X axis and Life Expectancy on the Y axis.\n",
    "2. We want the columns to be split up by Country\n",
    "3. We want to use a Matplotlib line plot to visualize the different graphs\n",
    "\n",
    "\n",
    "Be sure to show your plot!\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 872x576 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# WORDBANK:\n",
    "# plt.plot\n",
    "# \"LEABY\"\n",
    "# \"Year\"\n",
    "# \"Country\"\n",
    "\n",
    "\n",
    "# Uncomment the code below and fill in the blanks\n",
    "# g3 = sns.FacetGrid(df, col=\"Country\", col_wrap=3, size=4)\n",
    "# g3 = (g3.map(plt.plot, \"Year\", \"LEABY\").add_legend())\n",
    "\n",
    "g3 = sns.FacetGrid(df, col='Country', col_wrap=3, size=4)\n",
    "g3 = (g3.map(plt.plot, 'Year', 'LEABY').add_legend())\n",
    "plt.show()\n",
    "plt.savefig(\"live_expectancy.png\")\n",
    "plt.savefig(\"gdp_scatter_plot.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What are your first impressions looking at the visualized data?\n",
    "\n",
    "- Which countries' line changes the most?\n",
    "- What years are there the biggest changes in the data?\n",
    "- Which country has had the least change in life expectancy over time? \n",
    "- Can you think of any reasons that the data looks like this for particular countries?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    " Zimbabwe\n",
    " 2010's\n",
    " Mexico\n",
    " Zimbabwe could possibly have had insurgency and war in the early 2000's."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 10. Line Plots for GDP"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's recreate the same FacetGrid for GDP now. Instead of Life Expectancy on the Y axis, we now we want GDP.\n",
    "\n",
    "Once you complete and successfully run the code above, copy and paste it into the cell below. Change the variable for the X axis. Change the color on your own! Be sure to show your plot.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\AKINTAYO\\anaconda3\\lib\\site-packages\\seaborn\\axisgrid.py:243: UserWarning: The `size` parameter has been renamed to `height`; please update your code.\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 872x576 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g3 = sns.FacetGrid(df, col='Country', col_wrap=3, size=4)\n",
    "g3 = (g3.map(plt.plot, 'Year', 'GDP', color='red').add_legend())\n",
    "plt.show()\n",
    "plt.savefig(\"gdpplot.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Which countries have the highest and lowest GDP?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "USA and Zimbabwe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Which countries have the highest and lowest life expectancy?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Germany and Zimbabwe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 11 Researching Data Context "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on the visualization, choose one part the data to research a little further so you can add some real world context to the visualization. You can choose anything you like, or use the example question below.\n",
    "\n",
    "What happened in China between in the past 10 years that increased the GDP so drastically?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Stricter governmental policy to drive the economy \n",
    "as well as higher increase of american companies into the economy due to cheaper labour"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 12 Create Blog Post"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use the content you have created in this Jupyter notebook to create a blog post reflecting on this data.\n",
    "Include the following visuals in your blogpost:\n",
    "\n",
    "1. The violin plot of the life expectancy distribution by country\n",
    "2. The facet grid of scatter graphs mapping GDP as a function Life Expectancy by country\n",
    "3. The facet grid of line graphs mapping GDP by country\n",
    "4. The facet grid of line graphs mapping Life Expectancy by country\n",
    "\n",
    "\n",
    "We encourage you to spend some time customizing the color and style of your plots! Remember to use `plt.savefig(\"filename.png\")` to save your figures as a `.png` file.\n",
    "\n",
    "When authoring your blog post, here are a few guiding questions to guide your research and writing:\n",
    "+ How do you think the histories and the cultural values of each country relate to its GDP and life expectancy?\n",
    "+ What would have helped make the project data more reliable? What were the limitations of the dataset?\n",
    "+ Which graphs better illustrate different relationships??"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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