BasicPandasPlotting.ipynb

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    "# Basic plotting with Pandas\n",
    "\n",
    "Pandas has some built-in methods for plotting, based in the background on Matplotlib, the very powerful (and for me, confusing) grand-daddy of the Python visualization modules.\n",
    "\n",
    "**[Pandas chart visualization documentation](https://pandas.pydata.org/pandas-docs/stable/user_guide/visualization.html)**\n",
    "has much more detail and more plot types than we'll cover here.\n",
    "\n",
    "We'll start, like usual, by importing Pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note: For some older versions of Jupyter notebooks we also need to use an\n",
    "[iPython magic command](https://ipython.readthedocs.io/en/stable/interactive/magics.html)\n",
    "to tell Matplotlib to render inline in the notebook. *Change the cell below to a code cell if you find you need to execute it.*"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "*To preserve the mystery, select from the notebook menus*\n",
    "\n",
    "`Edit -> Clear All Outputs`\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading the data from a CSV file\n",
    "\n",
    "This is the first time you're seeing data loading from a CSV (Commma-Separated Value) file. That's a very common file type for tablular (spreadsheet) data, where the values are stored in a plain text file, and the values are separated with commas as the \"delimeter\" (separating) character. Another common delimeter is a tab character.\n",
    "\n",
    "It's not uncommon to run into cases when you need to change the `read_csv()` defaults to do things like force column data types, but here we can get away with just supplying the file name. For a much more full example of options, see the beginning of the [Groupby_NCexploration.ipynb](Groupby_NCexploration.ipynb) notebook.\n",
    "\n",
    "*With most `read_csv()` and `to_csv()` commands you should get into the habit of including what's called the \"[character encoding](https://docs.python.org/3/library/codecs.html#standard-encodings)\" explicitly in the arguments you pass. There are many encoding schemes that allow text files to include characters beyond the first 256 ASCII characters, such as accented and Asian language characters. For example, the most common character encoding is called UTF-8.*\n",
    "\n",
    "So, I almost always would write the commands like this:\n",
    "\n",
    "```\n",
    "df = pd.read_csv('in_file.csv', encoding='utf-8')\n",
    "df.to_csv('out_file.csv', encoding='utf-8')\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('data/women_percent_deg_usa_subset.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Percent women in bachelor's degree categories data set\n",
    "\n",
    "The Department of Education Statistics releases a data set annually containing the percentage of bachelor's degrees granted to women. The data set is broken up into 17 categories of degrees, with each column as a separate category. Randal Olson has made it available on \n",
    "[his personal website](http://www.randalolson.com/wp-content/uploads/percent-bachelors-degrees-women-usa.csv), or you can get it from [Kaggle](https://www.kaggle.com/sureshsrinivas/bachelorsdegreewomenusa)\n",
    "\n",
    "**For this exercise we will look at a subset of five of the degrees.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### `.head()` and `.tail()`\n",
    "\n",
    "Often DataFrames are too large to view all at once. The Jupyter notebook default for the maximum number of rows to display is 60, but sometimes we want an even shorter view of the data, so we print out the first or last few rows using the `.head()` or `.tail()` methods."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Agriculture</th>\n",
       "      <th>Business</th>\n",
       "      <th>Engineering</th>\n",
       "      <th>Health</th>\n",
       "      <th>Psychology</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1970</td>\n",
       "      <td>4.229798</td>\n",
       "      <td>9.064439</td>\n",
       "      <td>0.8</td>\n",
       "      <td>77.1</td>\n",
       "      <td>44.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1971</td>\n",
       "      <td>5.452797</td>\n",
       "      <td>9.503187</td>\n",
       "      <td>1.0</td>\n",
       "      <td>75.5</td>\n",
       "      <td>46.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1972</td>\n",
       "      <td>7.420710</td>\n",
       "      <td>10.558962</td>\n",
       "      <td>1.2</td>\n",
       "      <td>76.9</td>\n",
       "      <td>47.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1973</td>\n",
       "      <td>9.653602</td>\n",
       "      <td>12.804602</td>\n",
       "      <td>1.6</td>\n",
       "      <td>77.4</td>\n",
       "      <td>50.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1974</td>\n",
       "      <td>14.074623</td>\n",
       "      <td>16.204850</td>\n",
       "      <td>2.2</td>\n",
       "      <td>77.9</td>\n",
       "      <td>52.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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      "text/plain": [
       "   Year  Agriculture   Business  Engineering  Health  Psychology\n",
       "0  1970     4.229798   9.064439          0.8    77.1        44.4\n",
       "1  1971     5.452797   9.503187          1.0    75.5        46.2\n",
       "2  1972     7.420710  10.558962          1.2    76.9        47.6\n",
       "3  1973     9.653602  12.804602          1.6    77.4        50.4\n",
       "4  1974    14.074623  16.204850          2.2    77.9        52.6"
      ]
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   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### You can put a number in the parentheses to control the line count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Agriculture</th>\n",
       "      <th>Business</th>\n",
       "      <th>Engineering</th>\n",
       "      <th>Health</th>\n",
       "      <th>Psychology</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>2009</td>\n",
       "      <td>48.667224</td>\n",
       "      <td>48.840474</td>\n",
       "      <td>16.8</td>\n",
       "      <td>85.1</td>\n",
       "      <td>77.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>2010</td>\n",
       "      <td>48.730042</td>\n",
       "      <td>48.757988</td>\n",
       "      <td>17.2</td>\n",
       "      <td>85.0</td>\n",
       "      <td>77.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>2011</td>\n",
       "      <td>50.037182</td>\n",
       "      <td>48.180418</td>\n",
       "      <td>17.5</td>\n",
       "      <td>84.8</td>\n",
       "      <td>76.7</td>\n",
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       "    Year  Agriculture   Business  Engineering  Health  Psychology\n",
       "39  2009    48.667224  48.840474         16.8    85.1        77.1\n",
       "40  2010    48.730042  48.757988         17.2    85.0        77.0\n",
       "41  2011    50.037182  48.180418         17.5    84.8        76.7"
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   "source": [
    "df.tail(3)"
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## Line plot\n",
    "\n",
    "We often print table values to see what our data looks like, but **data visualization is a marvelous way to explore our data!**\n",
    "\n",
    "Since this is a time-series, the natural starting place for visualization is a line plot. Pandas has a pretty simple syntax for its basic chart types.\n",
    "\n",
    "You can either use the plot method directly, and tell it which \"kind\" of plot you want, \n",
    "\n",
    "`df.plot(kind='line')`\n",
    "\n",
    "or you can follow the plot with a dot and the type of plot\n",
    "\n",
    "`df.plot.line()`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pandas plots expect \"wide\" data kind of like Excel does!\n",
    "\n",
    "Pandas is kind of like Excel for plotting, in that it wants the data in the sort of \"wide\" format our data is in right now, where our X-axis data is in one column, and each series that will go on the Y-axis are in separate columns. \n",
    "\n",
    "**This probably means that Pandas expects you to make a Pivot Table out of your data before plotting, just like Excel does!**\n",
    "\n",
    "*I show in other notebooks that packages like Seaborn and Altair want the data in \"tidy\" or \"tall\" format, as do the \"tidyverse\" R packages and some commercial programs like Tableau.*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### If you specify the x column, then Pandas will assume you want to use the rest for Y!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Year'>"
      ]
     },
     "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.plot.line(x='Year')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Or, you can specify which columns to plot with `y=[,,]`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Year'>"
      ]
     },
     "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.plot.line(x='Year', y=['Business','Health'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Index is default for X\n",
    "\n",
    "If you don't specify anything for X, Pandas will assume you want to use the DataFrame Index. In this case we have a default, sequential integer Index, and we see the Year gets plotted as one of our lines."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "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.plot.line()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Set Index from a column\n",
    "\n",
    "We can set the Year column to be the Index with `df.set_index()`\n",
    "\n",
    "**Note that this time we're using the `inplace=True` argument instead of reassigning the result.** A lot of Pandas DataFrame methods have this option."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>Agriculture</th>\n",
       "      <th>Business</th>\n",
       "      <th>Engineering</th>\n",
       "      <th>Health</th>\n",
       "      <th>Psychology</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1970</th>\n",
       "      <td>4.229798</td>\n",
       "      <td>9.064439</td>\n",
       "      <td>0.8</td>\n",
       "      <td>77.1</td>\n",
       "      <td>44.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1971</th>\n",
       "      <td>5.452797</td>\n",
       "      <td>9.503187</td>\n",
       "      <td>1.0</td>\n",
       "      <td>75.5</td>\n",
       "      <td>46.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1972</th>\n",
       "      <td>7.420710</td>\n",
       "      <td>10.558962</td>\n",
       "      <td>1.2</td>\n",
       "      <td>76.9</td>\n",
       "      <td>47.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1973</th>\n",
       "      <td>9.653602</td>\n",
       "      <td>12.804602</td>\n",
       "      <td>1.6</td>\n",
       "      <td>77.4</td>\n",
       "      <td>50.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1974</th>\n",
       "      <td>14.074623</td>\n",
       "      <td>16.204850</td>\n",
       "      <td>2.2</td>\n",
       "      <td>77.9</td>\n",
       "      <td>52.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Agriculture   Business  Engineering  Health  Psychology\n",
       "Year                                                         \n",
       "1970     4.229798   9.064439          0.8    77.1        44.4\n",
       "1971     5.452797   9.503187          1.0    75.5        46.2\n",
       "1972     7.420710  10.558962          1.2    76.9        47.6\n",
       "1973     9.653602  12.804602          1.6    77.4        50.4\n",
       "1974    14.074623  16.204850          2.2    77.9        52.6"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.set_index('Year', inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Now we don't have to specify X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Year'>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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fwBWlp3M5PeRnVlKYXUVlYR0VhXVUFtfhcR1L42ANMBHVL5jkUUlEJwUTnRyMX6DKE97TqQCuKN2MlJKKgjpy95WRu7eM/ANVeNwaQkBwpB+hMf4kDg0jLCaA0Bh/wqL9sQWafWLiidKxVABXej2PW6OisI7So3ZKj9RQerTmlLMPAX3ccPMJHc0mqJitxhYTW6x+5qbHZqux1cknHpeGy+Gm8FAVufvKqalwABAWG0DqjHj6DgsnbmAoJou66KgcowK40utUldRzeE8ZJbnVlB6toTy/Fs2jdxwbzQYi4gIIjfbnZO1ZKcHjORaA6+pbzgh0OTw4691nVDaLn4nElDDGXxFB4rBwgsJtZ/gpld5ABXClx5NSUnqkhqwdJWTvLKEsT19Q2C/ITJ/EIBJnhROZGEhkQhChUX4dMu1Z02TTdG9HvRun99bl8LRosR8/JTs4wtZp066VnkcFcKVH8rg08g9Wkr2zlOydJdRUOBACYgeGMmXuQJJHRRLSp/NGZRgMAqu/Gau/udPOoSgqgCs+T0pJdWkDRdn6uOfC7GpKj9jRPBKT2UDisHAmXNmfpJERamSG0qOoAK74HI9bo/iwnbzMCoqyqijKqabe7gLAZDEQ1S+Y0RclEjMglISUMMzqwp/SQ6kArnR7HpdG0eFq8jMrycusoPBQFW7vGOiwGH/6pUYQnRxCTP9gwmMDVB+y0muoAK50S7VVDg5tKyZ7Z2mLgB0RH8iwqXHEDQ4lblCo6hJRejUVwJVuo77GyaFtJRzcWkReZiVICI8LYNi0OOIHhxE3MBRboLooqCiNVABXOpWUes6OxnHWJ+4ABYeqOJhWxJGMCqQmCY32Z/zlSQwcF0147JnlT1aU3kAFcKVDSSkpL6ht6q/OP1DZdIHxVIIjbYy5uC+DxkcRER+opoUrShuoAK6cESklznp30+ojZXm15GdWkHegkoYaPWAHhllJHBZO7IDQU+adDo32J6pfkAraitJOKoArJ5BSUlftxF7WgL2sgeqyeuzlDuqqHE3rCtZVO/G4tRbHBYZb6ZcaQfzgUOIGhREcaVNBWVE6kQrgvZiexKm2KYFTRUEt1WUN2MsbWqQqBbAFmAkI1VfaDo0KbVpx2z/Ygl+whZBIP4Ij1bJcinIuqQDei5Tk2sk/UEnpETsl3oDdeHHRZDYQFhtARHwASSMjCY6wEdS4hduw2NR/FUXpbtRfZS9QVVLHhg8OcWhbCQD+wRYiEwPpNzyCyIRAIhMDCYnyx6CW2VIUn6ICeA/WUOsi7Yscdn97FINRMH5OMsOnxREQYu3qoimK0gFUAO+BPG6NPWvz2PJ5No46N0PPj2XiVf1V4FaUHkYF8B5ESkn2zlJ+WHGQquJ6ElLCmDJ3IJEJQV1dNEVROoEK4D1E8eFqvl92kPwDlYTF+HPF/SPplxqhhvEpSg+mAriPs5c3sPGjQ2RuKsIvyMwFNw1m2NQ4lZFPUXoBFcB9lLPBzbavDrNj5RGQMPaSfpx3aT8sfuqfVFF6C/XX7mM0j0b6DwVs+iSb+mong8ZHM+ma/gRHqEk0itLbqADuQwqzqljzzn7K8mqIHRjCFfeOJDo5uKuLpShKF1EB3Ac46lxs+DCLvd/lERhq5ZK7Uxkwto+6QKkovZwK4N2YlJIDaUWsX3qQBruTURcmMuHKZDWtXVEUQAXwbquqpI61/9vPkfQKovoFceUDo+jTV43nVhTlGBXAuxmPR2P7V7mkfZGDwSiYfuNghk+PV3lKFEU5QZsCuBAiFPgPkApIYBGwH3gPSAJygBuklBWdUcjeoqqkjm9e20dRdjUDxkYx7YZBBISq6e+KorSurS3w/wd8KaWcK4SwAP7AL4FVUsonhBCLgcXALzqpnD2alJL9GwtZ924mBqPgkrtTGXheVFcXS1GUbu60AVwIEQxMB24HkFI6AacQ4mpghne3N4E1qADebo46F2v+t5+DacXEDQrlojuGERRu6+piKYriA9rSAu8PlACvCyFGAVuBh4BoKWUBgJSyQAjRapNRCPEj4EcAffv27ZBC9xT5Byr45vV91FU6mXRNf8bM7qf6uhVFabO2JMwwAWOBF6WUY4Ba9O6SNpFSviylHCelHNenT58zLGbP4vFobPzoEB8+sx2j0cB1Pz+P8y5NUsFbUZR2aUsL/ChwVEq5yft4GXoALxJCxHpb37FAcWcVsicpPVrD6rfSKcm1M/T8WKbeMEiN61YU5YycNnJIKQuFEEeEEEOklPuBWcA+77YQeMJ7+1GnltTHeVwaaV/ksO3Lw1gDTFz6o1QGjFUXKhVFOXNtbfo9CLzjHYGSBdyB3v3yvhDiTiAXmNc5RfR9hdlVrH4rg4qCWoZMjGHqvEHYAs1dXSxFUXxcmwK4lHIHMK6Vl2Z1aGl6GJfTw6aPs9i16ggBoVauuH8kSSMiu7pYiqL0EKrztZPk7a9g9dsZVJfUM3x6POdfO0Dl6lYUpUOpiNLBNI/G5k+y2frVYYIj/bjm4THEDwnr6mIpitIDqQDegWoqGvj61b0UHKxi6JRYpt0wGLPV2NXFUhSlh1IBvIPk7C5l1RvpuN0aF90xjCETY7q6SIqi9HAqgJ8lj0dj04dZbP8ml4j4QC65ezhhMQFdXSxFUXoBFcDPgr28ga//s4fCrGqGT49n6tyBmCyqy0RRlHNDBfAzIKUkc3MR372XiaZJZt81nEHjoru6WIqi9DIqgLdTyRE7372XScHBKqKSgrl40TBCo/y7uliKovRCKoC3UUOti00f6QsLWwPMzLwthaGTYxEqAZWiKF1EBfDT0DTJvvX5bPzoEM56D6kzEpgwJxlbgJoKryhK11IB/BQKs6pYu2Q/pUdqiBsUyvQbBxMRH9jVxVIURQFUAG+V2+Vh08fZ7FiZS2Coldl3DWfgeVEIobpLFEXpPlQAP05Jrp2Vb+yjPL9Wz2Fy3QCVr1tRlG5JRSYvj0dj25eHSfssB78gM3MeHEW/4RFdXSxFUZSTUgEcKM+vZdWb+yg+bGfwhGimzR+sLlIqitLt9eoALjXJztVH2PhhFmarkUvuTmXgeWqVHEVpMynBUQ21pVBTDLUl3q0U6krBaAG/ULA1biHexyHgHwn+4aCuLZ2xXhvA3U4PK9/Yx6FtJSSNjGTGLUMICLF2dbEUpfvRNLDnQ9khKDsI5Vn6bdkhqMwFj6P146wh4HGCu/7k720OgNBECEmE0L76/dC++mNbCFgCwRKg3xp9M1yV1zpZse0oN07oS6C1Yz+Db9bIWaqvcfL5C7spzKri/OsGMvriRDXCROkdHHYo2Q/F+6A4Xb8tzwapeXcQzW4EIKGmpGUQNvlBxACIGgpDLoPAaAjoAwGR3ts+4B8BJou+v9sBDdXQUAkNVVBfqd+vKYaqI/qXQNURyEuD+oqTl91k0wO5NRCC4iB6uHdLhehheqDvJqSUbD1cwTubcvlsdwFOt0Z8qB+XjYjt0PP0ugBeWVzHp8/tpKbCobpMlJ6tthSOpsHRLVC4Ww/YVbnHXjf5QVQKJE7Quzqk9L7gvZVSvx/QB8L7Q8RAfQuKBYOh7eUwWSGwj76djsMOVUf1zVENjhpw1hy7bbxfmQs73wWn3XuggPBkPaD3SdG7Z5q6a0KPddvYQsHs12ndNtUNLj7cnsc7G3PZX2QnyGripvGJ3DyxH0Nigjr8fL0qgBccquLzF3YBcPXDY4gdENLFJVKUZjQPuOr1bgePCzTXsfsepx5QjRa9ZWu0trwPULTnWMDOS4OKHP15YYQ+Q/RAfd5CiBqmB+7QpPYF4nPBGqS37KOGnn5fKaHyMBTt9W57oHAPpH9K05dQq4QexE22E29NNjAYwWDybkYwGJHCRJVD4pAGPMKMR5hxN9tcwsQBu4WlucHscsUzMCGav10/gitHxeFv6bwwK6Q81QftWOPGjZNpaWnn7HzNHdxazMrX9xEYZmXOg6NUAiqla9WWQdFuPeA0Bp6SDD1on62gOEgY593GQ+xosPSi/+8et956r684sdumvhJcdfoXpbsBXA1695Db4X3OAdIDmhupuWlwOLDXNVDX4ACPG5PwYMWFGTcW3JhxYxaeE8sQltyse8fb1ROWfMZfmEKIrVLKExaW7/EtcCklO745wg8rDhLTP4TL7xuBX6Clq4ul9HQOO1QXQHUe2AugOl/fKg/rwbqm8Ni+gTEQkwoDZur9yEYLGM3eW+99g1n/2e9xgtupXzhsvPU49dZ7nyEQPw5C4rvuc5+GpkkOFNewKbuM3LI6jEaBySAwCoHRYMBkFBgN+nNBNhMhfhZC/Mz65q/fBliMp75mZTTpo1v8w9tdPikl6QV2Pt2Vz6e7Csgtr8NsFEwb1IcrRsSSEOaHyWjAbBSYDN5bITHhJlKW41ex/9ivgaK9kPEZTb8G5r8NQ688s4o7iR4dwEty7Wz76jAHtxYzYGwUF90+VC24oHQMR40ejCsO610Vld7bisN60HZUn3iMLVQfXdF/hh6wo1MhZoQetHsoTZNkFtvZlFXOxqwyNmWXU17rBMBmNiAleDSJW2t7T4DJIEiKDGDaoEimD+rDxP7hp+ymcLo1dudVsuGQfv7qehcGg8Ag9C8OgwGM3sd5lfVkldRiNAjOHxDBAzMHcsnwGEL82zIvJByiB0LKFc1OXqf/siraC4kT2/wZ26rHdaFoHo3snaXsXH2EgoNVmKxGxlyUyPgrklXq195GSv1ndFOAbRZsq456fy5LfQTG8VtzTa09763mOnG0hCUQQvtBWD89SAfHQnC8fsEvOE6/7SXdGB5Nsi6zhGXbjvLDwVIq6vRuofhQPyb1j2Bi/3Am948gIcyvRUta8wZyjyZxejRqHG6q6lxU1juprndR5d0q61zsza9mU3YZDS4Ni9HAef3CmD64D9MGRTIkJog9eVVsyCpjw6Ey0nIqqHfp3RwpMUHEhNjwaBJNSu+tfm6PlATZzMweFs1lqTFEBHafYcUn60LpMQHcUedi3/oCdq85ir28gaAIGyNnJjB0ShxWvx79Q8N3uR2Qt00Ppk47OGtbjjZw1uotGI+z5cW8Fhf2PN4g7B0x0RiQkd73O64l7BemB9rQvmD2B2HwbqLlbWOwbn4xrPFvxWDUg3NYP/1CYFiSmpAC5FXW8/6WIyxNO0J+VQMRARZmpkTpQTs5nMTwjv0Ca3B5SMup4LsDJaw7UEp6gf5vbRDQ2KAfHB3I5P4RTB4QwcTkCMICfLP7tMcG8NpKB1u/yCF9YyFuh4e4QaGMmpVI0shIDKrF3b046+DoZjj8g74d3aJfSDqe2b/ZBI6Aln3BLe6b9REWzYOuMBwbw2z2O9Yqbry1qZFHHcnl0ViVXsy7W3JZm1kCwNSBkdw0oS8XDY3GYjp3o1yK7Q18f7CUjEI7I+NDmdg/nMhu1Io+Gz3uIqbL4WH7N7ls//owmkcyeEI0Iy9MpE9ix4+1VM6AlHp3RcFOyN+uB+z8baC59SAbMxLG3wX9zoeIQfrkjMagbVDXKbqzwqoGNmaVsTGrjFUZxZTYHUQHW3lg5kBuGJfY4S3ttooKsnHtmIQuOXdX8bkArmmS/RsL2PRRFrVVTgaM7cPkawcQ0qd39C92S5oGpZl6sC7cpd8W7AJHlf66wQRxY+H8B6HfFP1iji24a8ustFlR9bGAvTGrnOzSWgCCbSbOHxDJ3PMSmDGkDyZjNxtT3gv4VAA/mlHO98sPUnqkhujkYC65O5XYgaFdXazey+OC3Uth3dNQfkh/zmTTR1eMuB5iR+lb1DB9Np7iUzIKq3lsxW6251YCEGQzMTE5glsm9mVS/wiGxgZjVN2UXconAnhFYS0/LD9Izu4ygsJtzL5zOAPHqRVyuozbCTuXwHd/10d0xIyAq57TxyBHDvbZpEOKzu3R+Pe6LJ5dmUmIn5nHLkthysBIFbC7IZ/4S9vxTS75ByqZfO0ARl6YgMms+ki7hNsB29+G9f/Qkw/FjYXL/gaDL+31IzB6ioPFdn62dBc7j1RyxchY/nh1KuE+OnKjN/CJAD7pmgFMumYAfkHqP1KXcDXAtrf0wG3Ph4QJMOdZGDhLBe4ewqNJXl2fxdNfZxJgMfL8zWOYMzKuq4ulnIZPBHAVuLuIqx62vgHrn9Wnfvc9H659EZIvUIG7B8kureXRpTtJO1zB7GHR/PnaEfQJUtcsfIFPBHDlHHPWQdpr8P3/g9piSJoG1/8Hkqd1dcmUDlJZ52TN/hJWphexMr0Ii9HAs/NHc/XoOHVtyYeoAK4c46yFLf+BH57Tl8VKvgAueAOSpnR1yZQOkF1ay6r0Ir7ZV0Ta4Qo8miQy0Mq1YxL4yUWDiA62dXURlXZSAVzR7V4GX/wc6sqg/0yYsRj6TurqUilnKae0luXbjvL57gIOlejjt1Nigrj3ggHMGhrFqIRQNWPZh6kA3ttpGnz7J31IYMIEuOldPfG/4rNqHW4+313A0q1H2ZxdjkHA+QMiWTA5iQtTorpspqTS8docwIUQRiANyJNSzhFChAPvAUlADnCDlPIUC9op3Y6jBj74P8j4FMYugMv/fmwdQ8WnNK7B+H7aET7bVUCt00NyZAA/v3QI141JICZEdY/0RO1pgT8EpAONc6AXA6uklE8IIRZ7H/+ig8undJaqo7DkRj1P8SV/hUn3qpElPkjTJJ/syuefqw5wqKQWf4uROSNjmTcukXH9wtQFyR6uTQFcCJEAXAH8Gfip9+mrgRne+28Ca1AB3Dcc2QLv3qwPE7z5fRh0cVeXSGknKSVrM0t48sv97CuoJiUmiKfmjuTyEbEEWFXPaG/R1n/pZ4GfA81T/UVLKQsApJQFQohWl3cXQvwI+BFA3759z7ykSsfY9T589IC+4MDCT/TFbRWfsi23gr99kcGm7HISw/34fzeO5sqRcepiZC902gAuhJgDFEsptwohZrT3BFLKl4GXQc8H3t7jlQ6ieWD1n2D9M9BvKtzwFgREdHWplHY4WGznyS/38/W+IiIDLfzh6uHcOL7vOc25rXQvbWmBTwGuEkJcDtiAYCHE20CRECLW2/qOBYo7s6DKWagtgxV3waHV6mKlD6p1uHnyywz+u/Ew/hYTP7t4MIumJquuEuX0AVxK+RjwGIC3Bf6IlPJWIcRTwELgCe/tR51XTOWM5W2F9xdCTRFc+f9g7EJ1sdKHfH+wlF8s30VeZT0LJvXjoYsGq+RSSpOz+Qp/AnhfCHEnkAvM65giKR1CStj6OnzxCwiMgUVfQfzYri6V0kb2Bhd/+TyDJZtz6R8ZwNL/m8y4pPCuLpbSzbQrgEsp16CPNkFKWQbM6vgiKWfNWQef/Qx2/g8GzNLzmPirP35fsTazhMeW76KwuoEfTe/PTy8ejE2lUFZaoTrRepryLHhvARTtgQsWwwU/V2tM+oiqehd/+nQfS7ceZVBUIMvvPZ8xfcO6ulhKN6YCeE+S/R28e4vex33z+zB4dleXSDkFTZPsL7KzMauMDYfK2JBVRp3Tw/0zB/DjWYOwmtQXr3JqKoD3FIdWw5KbIawf3PwehCV1dYmU40gpOVBcowfrQ2Vsyi6jos4FQGK4H5elxrBgchKp8SFdXFLFV6gA3hMc+EZveUcOggUfQUBkV5dI8ZJSsje/ms92F/DZrgJyy+sAiA/148KUaCYPiGBS/3ASwlSCKaX9VAD3dRmfw9KFEDUUbvtQXazsBqSUZBTa+WxXAZ/tLiC7tBajQXD+gAjunTGAqQMjVUZApUOoAO7L9n0EyxZB7Ci4dQX4hXZ1iXoMTZPtmpre2NL+em8hn3lzbxsETB4QwY+m9+eS4TFq/LbS4VQA91V7lsPyuyFhHNyyFGyq37QjbD1cwa8+2E1ueR3n9QtjQlI4E5LDGZUYesJQPo8m2ZJTzld7C/l6bxF5lfUYBExIDueOKclcmhpDZKBaW1LpPCqA+6Kd78GH90DfyfoFS2vQ6Y9RTqmqzsXfvsrgf5tyiQuxcc2YeLYdruCZlZlICRajgdGJoUxIDmdgVCA/HCplZXox5bVOLCYD0wZG8tCsQcwaGkWECtrKOaICuK/Z/raeTTB5mr56jiWgq0vk06SUfLKrgD98so/yWgd3TU3m4YsHN+UZqaxzkpZTweaccjZll/Pi2kN4NEmQ1cSFQ6OYPSyGC4b0IVDlJVG6gPpf50s2vwKfPwIDLoQb/wdmv64ukU/LLavj1x/tYV1mCSMTQnjjjvEnDOEL9bdw0bBoLhoWDeiJpbJLaxkcHaSyACpdTgVwX/H9P+Gb38CQy2HeG2Dq2J/pVfUujlbUkRITjLGH55UusTt4d3Muz397ELPRwONXDuO2yUlt+twBVpMap610GyqAd3dSwtonYc1fYPi1cN0rYDR32NvvPFLJO5sO8/HOfBpcGpGBFi4aGs3s4dGcPyCyx+TgKK918sUefSz2xqwyNAmXDo/h8auGq/UiFZ+lAnh3JiWs+j2s/weMuhmufr5D8prUOd18sjOftzfmsjuvCn+LkWvHJDC2byjrDpTy6a4C3t1yBH+LkQsG92H28GguHBJNiH/HfXGcC5V1Tr7aW8inuwr44VAZHk3Sv08AD1w4iDkjYxkcrS7+tsblcnH06FEaGhq6uii9js1mIyEhAbO5bX9rQspzt0jOuHHjZFpa2jk7n0/TNPjqMdj0EoxbpC/CYDjzPleH28P+QjsrtuWxfNtR7A1uBkcHcuukflwzJp5gm7nFvhuzyvl6byHf7Cui2O7AaBD0jwxgcHSQdwtkUHQQSRH+mIwty+XyaJTVOCmtcVBa46Cq3oXVZCTIZiLIZiLQaiLQZiLYZsZqMuDRJOW1TkqbHaNvTmodbmJDbCSE+ZMQ5kdCmD9RQdYWY7Q9miS3vI4DRXYOFNdwqLiGA8U1ZBRW4/JI+ob7M2dkLHNGxjE0Nkgt9Hsa2dnZBAUFERERoerqHJJSUlZWht1uJzk5ucVrQoitUspxxx+jAnh3pHng05/Atrdg0n1wyV/avAiDR5McKa8jo9BOZpGd/YV29hfZyS6txaNJzEbBZamx3DqpH+OTTr9quaZJduVVsTq9iH0Fdg4U28ktr6Pxv43FaKB/nwBC/MyU1eoBuNKb36MtTAaBR0pa+29oMRkIsBib8oU0MhsF8aF+xIX6UV7rJKu0Fqdba3o9LsTGgKhAUuNDuDw1ltT4YBWI2iE9PZ2UlBRVZ11ASklGRgZDhw5t8fzJArjqQuluPG746D7Y9R5M+xlc+JvTBu8Gl4c1+4v5eGc+32aUUO/yNL3WN9yfITFBXDo8hsExQZw/IKJdk0sMBsHoxFBGJ4Y2PVfv9HCwuIbMIjuZxXYyC+3UOjwMjg5kcn/9/SODLPptoJVQfzMNLg81DW5qHG7sDW7sDjf2Bhf2Bjdmo4E+gd79g6ze4ywEWk0IIah3esirrOdoRR1HK+o5WlFPXmU9eRV1xIX6ccHgPgyM0n8RDOgTQJDNt7p6uiMVvLtGe+tdBfDuRNPg4wf04D3z13DBoyfd1e3R2JBVxsc78vlyTyF2h5uIAAvXjY1nVEIog2OCGBQV2CnrJvpZjIxICGFEwrkZjeFnMTIwKpCBUYHn5HyK4itUAO8upITPfwY7l8CMX540eGcW2fnfplw+3VVAaY2DQKuJS4bHcPXoOM4fEHFCf7Si+KoPPviA6667rqlLpz1++9vfMn36dC666KJ2nzcpKYm0tDRMJhP/+9//uO+++9r9HueKCuDdgZTw9a8h7TWY8pC+is4Ju0iWbD7C4x/vBQGzUqK4alQcM1OiesxQP0VpbsmSJUydOpV3332Xxx9/vM3HeTwe/vCHP5z1+SsrK3nhhRfaHcA9Hg9G47n5m1QBvDtY81fY8DxM+BFc9PsT+rzrnR5+/eEelm87yrRBkTw7f7TKt6GcE7//ZC/78qs79D2HxQXzuyuHn3Kfmpoavv/+e7799luuuuoqHn/8cTRN44EHHmDt2rUkJyejaRqLFi1i7ty5JCUlsWjRIr7++mseeOABvvzyS+bMmcPcuXPZsmULDz30ELW1tVitVlatWsXy5ctJS0vj+eefB2DOnDk88sgjzJgxo6kMixcv5tChQ4wePZqLL76YK664gqeffppPP/0UgAceeIBx48Zx++23n3D+8PBwfve73+FwOBgwYACvv/46gYEd3wWoAnhXW/8srP0bjL4VLv3bCcE7u7SWe9/eyv4iOw/NGsSPZw3q8TMlFeXDDz/k0ksvZfDgwYSHh7Nt2zaysrLIyclh9+7dFBcXM3ToUBYtWtR0jM1mY/369QB8+eWXADidTubPn897773H+PHjqa6uxs+vbSkonnjiCfbs2cOOHTsAWLNmzSn3bzx/aWkp1113HStXriQgIIC//e1vPPPMM/z2t79tf0WchgrgXWnzK7Dyd5B6PVz1zxPGeX+1t5BH3t+J0Sh4/fbxzBgS1UUFVXqr07WUO8uSJUv4yU9+AsCNN97IkiVLcLlczJs3D4PBQExMDDNnzmxxzPz58094n/379xMbG8v48eMBCA4O7rQyN55/48aN7Nu3jylTpgD6l8jkyZM75ZwqgHeV7W/riamGXA7X/rvFDEu3R+Opr/fz77VZjEwI4YVbxqolt5Reo6ysjNWrV7Nnzx6EEHg8HoQQXHvttac8LiDgxMycUspWh+aZTCY07djcgbbMOj3dMY3nl1Jy8cUXs2TJktO+59lSQxa6wt4P4OMHof9MmPt6i9wmJXYHt766iX+vzeKWiX1Zes9kFbyVXmXZsmUsWLCAw4cPk5OTw5EjR0hOTiYyMpLly5ejaRpFRUWn7dIASElJIT8/ny1btgBgt9txu90kJSWxY8cONE3jyJEjbN68+YRjg4KCsNvtTY/79evHvn37cDgcVFVVsWrVqlbPOWnSJL7//nsOHjwIQF1dHZmZmWdQE6enWuDnWu5GWPF/kDDBmxL2WCKlrYfLue+dbVTVu/j7vFFcf15CFxZUUbrGkiVLWLx4cYvnrr/+etLT00lISCA1NZXBgwczceJEQkJOPRfBYrHw3nvv8eCDD1JfX4+fnx8rV65kypQpJCcnM2LECFJTUxk7duwJx0ZERDBlyhRSU1O57LLLeOqpp7jhhhsYOXIkgwYNYsyYMa2es0+fPrzxxhvcdNNNOBwOAP70pz8xePDgM6yRk1NT6c+lskPwn4vALwzuWtm0ALGUkjd/yOFPn6UTH+bHS7eex9DYzuurU5RTSU9PP2Eqd3dRU1NDYGAgZWVlTJgwge+//56YmJiuLlaHaq3+1VT6rlZXDu/M0+/fsrQpeNc53SxevpuPd+Zz0dBo/n7DKEL81FRwRWnNnDlzqKysxOl08pvf/KbHBe/2UgH8XHA74N1boOooLPwYIgYAkFVSwz1vb+VgcQ2PXjKEey8Y0K6V0BWlt2lLv3dvogJ4Z5MSProfcn+Aua9B30kAfLmnkEeW7sRiMvDWoolMHRTZxQVVFMXXqADe2b79C+xeCrN+C6nX4/JoPP3Vfv69LotRiaG8eMtY4kLV2paKorSfCuCdafs7sO5JGHMbTP0pR8rr+PG729meW8mtk/rymznDsJpUHhNFUc6MCuCdJWstfPJj6D8D5vyDL/cW8fNlO5ESXrhlLJePiO3qEiqK4uNUAO8MJfvhvdsgYhAN177BXz/dz5sbDjMqIYTnbhpL3wg1MUdRTsVoNDJixAiklBiNRp5//nnOP//8dr/PSy+9hL+/PwsWLOiEUnY9FcA7Wk0xvDMXTFaOXPYm97y+h7351dw1NZmfX5qCxaQmvyrK6fj5+TUlkfrqq6947LHHWLt2bbvf55577ungknUvKoB3JFc9LLkJakpYN/VN7n0jG7PJwH8WjOOiYdFdXTpFab8vFkPh7o59z5gRcNkTbd69urqasLAwQB9GeLKUrosXL+bjjz/GZDIxe/Zsnn76aR5//HECAwObUsVOnDiRb7/9lsrKSl599VWmTZuGx+Nh8eLFrFmzBofDwf3338///d//UVBQwPz586mursbtdvPiiy9y/vnnc+edd5KWloYQgkWLFvHwww93bP20gwrgHUXT4IP/g7ytbJ30TxZ86WZcvzD+edMYNcpEUdqpvr6e0aNH09DQQEFBAatXrz7l/uXl5XzwwQdkZGQghKCysrLV/dxuN5s3b+bzzz/n97//PStXruTVV18lJCSELVu24HA4mDJlCrNnz2bFihVccskl/OpXv8Lj8VBXV8eOHTvIy8tjz549ACc9z7miAnhHWfV72PcRhZN+w63fRzG2bxDv3D1RjTJRfFs7WsodqXkXyoYNG1iwYEFT0GxNcHAwNpuNu+66iyuuuII5c+a0ut91110HwHnnnUdOTg4AX3/9Nbt27WLZsmUAVFVVceDAAcaPH8+iRYtwuVxcc801jB49mv79+5OVlcWDDz7IFVdcwezZszvuQ5+B03bICiEShRDfCiHShRB7hRAPeZ8PF0J8I4Q44L0N6/zidlNb34Dvn6V+1O1cv30Mof5mXrrtPBW8FaUDTJ48mdLSUkpKSk6a0tVkMrF582auv/76psUgWmO16itZGY1G3G43oOcieu6559ixYwc7duwgOzub2bNnM336dNatW0d8fDy33XYbb731FmFhYezcuZMZM2bwr3/9i7vuuquTP/2pteWKmhv4mZRyKDAJuF8IMQxYDKySUg4CVnkf9z6HVsOnP0UbcBG3F86ltNbJy7eNIyrIdvpjFUU5rYyMDDweDxERESdN6VpTU0NVVRWXX345zz77bFPrvS0uueQSXnzxRVwuFwCZmZnU1tZy+PBhoqKiuPvuu7nzzjvZtm0bpaWlaJrG9ddfzx//+Ee2bdvWGR+5zU7bhSKlLAAKvPftQoh0IB64Gpjh3e1NYA3wi04pZXdVtA/eX4iMSuFx66Ns2lvGczeNYUTCqVNcKopyao194ODN1vnmmxiNRhITE1tN6Wq327n66qtpaGhASsk//vGPNp/rrrvuIicnh7FjxyKlpE+fPnz44YesWbOGp556CrPZTGBgIG+99RZ5eXnccccdTb8C/vrXv3b4Z2+PdqWTFUIkAeuAVCBXShna7LUKKeUpu1F6VDpZexH8ZxZobt4f/QY//6aMB2YO5JFLhnR1yRTlrHTndLK9QXvSybZ5ULIQIhBYDvxEStnmZaqFED8SQqQJIdJKSkraelj3Vl8Jb18PdeVsn/oSj60q5+Jh0fz04o5P2K4oinIybRqFIoQwowfvd6SUK7xPFwkhYqWUBUKIWKC4tWOllC8DL4PeAu+AMnctZx0suRFKMiic8xa3f+JkQJ8A/jF/tEoFq/RoUkpcmosGdwMNngYa3A24NTdGgxGTwYRJmPTbxk3o4UUi0aSGRCLlsfsGYcBmtGEymFpdt1I5vdMGcKHX7KtAupTymWYvfQwsBJ7w3n7UKSXsTjwuWHo75G6k/pr/cOvqAIRw8J8F4wm0qhGZim/QpMaBigOkFaWxpXALGeUZ2Iw2gixBBFmCuDXiVvJr8jEKI0aDEZfmwuF2UO+uR5PHRoBYjBYsRgtuzU2DuwGP5kHS/jaaURixmWz6ZtRvLUYLBqFmLZ9OW6LOFOA2YLcQYof3uV+iB+73hRB3ArnAvE4pYXehafDhfXDgK+ScZ3l4dzLZpUX8984JKreJ0q15NA8HKg+wpXALWwq3sLVoK9VOvRc0PjCeEZEj8EgP1c5qyhrKcGkuqp3VeDQPAAZhwGqyEmINaQqyVqMVo6HlMFkpJR7pwa259U3qw/QMGBBCIBAYxLH7mtRatObLG8ppvCYnEE0te7PBjMlgwiiMTa17m0kvQ2/XllEo64GT/b6Z1bHF6aakhC8Xw+73YdZveb1hBl/u3cevLh/K+QPUQgxK9+LRPOyv2M+Wwi2kFaWxtWgrdqe+unpiUCKz+s5ifMx4xkWPIzbwxKyY6enppISnNHV3NAbd0xFCNHWjtJW/+VjjR0qJ0+Ns0T3jlm5cmot6dz1uzd3iWJvJRog1hGBLMBajpc3nPBda+zILMAdgNnTsconqd39brP0bbP43TH6AHf0W8dd/b+CiodHcNS25q0um9HIuzUVFQwUFtQXsKN7BlsItbCvaht2lB+y+QX25uN/FjIsex/iY8cQEtH0NSSEERnHuJqMJIbCarE2t/eM1D4q1rlqqHFUU1RZRVFuEn9mPEIsezM3Gzl1TVkqJW9O/WFyaC6fH2XS/MWB7pIfjR/j1De6L2aIC+Lm16WVY81cYfSuVU3/L/c99T3Swjb/PG6UuvCidSkpJcV0x2dXZ5FTlkFeTR2l9adNWVl9GhaOixTFJwUnMTprd1MKODvDNJGqN6WQb3XjjjSxevLiphW8z2Yjwi8DpcVLlqKLaWU1hbSGFtYX4mfxa9J9fP/t6ln+9vOmxQRhOuNjaeN8gDC1azU33vVtjoD4+OK/9ai3Zmdk88LMHsJlsrV7Q7YwvFhXAT2XXUvjiUUiZg7zyWR55ewfF9gaW3XM+If5q5XilYzg8DnKrc8muytY3b8DOrsqmzl3XtJ/FYKGPfx8i/CLoG9SXsVFjifSLJMIvgki/SFIjU4nyj+rCT9JxmudCORWLUa+TPv59cLgdVDmrqHPVtbiYuuzrZS0eOzUnde66pj7+02neNWQz2QgyBGE2mLEYLZgNZoQmGH7L8HZ/xo6gAvjJZHyuZxdMmgbXv8or3+eyMr2Yx68cxqjE0K4uneKD6lx1pJenNwXqnGo9SOfV5LUY3RETEENycDLXDLyGpJAkkkOSSQ5OJso/6pz/6vvb5r+RUZ7Roe+ZEp7CLyac2aTtpKQkFi5cyCeffILL5WLp0qWkpKRQUlLCzTffTFlZGePHj+fLL79k69atREZGEhgYSE1NDWvWrOHxxx8nMjKSPXv2MPa8sbzx5ht48LAlbQuPPfoYNTU1REZG8vKrL5MQl8Dh7MP8+MEfU1JSgr+/P6+88gopKSncfvvthIeHs337dsaOHcuIESNIS0vj+eef5/bbbyc4OJi0tDQKCwt58sknmTt3Lpqm8cADD7B27VqSk5PRNI1FixYxd+7cM65LFcBbk7VGHy4YNxpuWkJaXh1/+3I/l4+IYeH5SV1bNsVn1Lnq9H7poi2kFaaxp3RP08gMq9FKUnASwyKGcUX/K0gOTiYpJImk4KQWF/Z6q+ZT6QEee+wx5s+fD0BkZCTbtm3jhRde4Omnn+Y///kPv//977nwwgt57LHH+PLLL3n55Zdbfd/t27ezd+9e4uLimDJlCps3bmbixIks/uliPvroI/r06cN7773Hnx//M6+99hr33nMvL730EoMGDWLTpk3cd999TaltMzMzWblyJUajkTfeeKPFeQoKCli/fj0ZGRlcddVVzJ07lxUrVpCTk8Pu3bspLi5m6NChLFq06KzqSQXw4+Vu0hdliBgItyyj3G3lgf9tJiHMjyeuH6n6vZVWVTurya/JJ8+ex+7S3Wwp2sK+0n24pRuTMDEschgLhy9kbPRYBoQOIDYg1ifGOZ9pS/lsnaoLpXlK2BUr9HmF69ev54MPPgDg0ksvbVoA4ngTJkwgISEBgNGjR5OTk0NoaCh79uzh4osvBsDj8RAbG0tNTQ0//PAD8+YdGyHtcDia7s+bNw+jsfWLvNdccw0Gg4Fhw4ZRVFTUVMZ58+ZhMBiIiYlh5syZba2Ok1IBvLmCXfDOPAiKhds+QLOF8fAbWyivc7Li3vMJtql+797Ko3nIr8lv6p/Or80nryaP/Jp88mvyqXHVNO1rEiZSI1O5I/UOxkWPY3TUaNWq7kAnSwnbnmObHy+lZPjw4WzYsKHFvtXV1YSGhp70iyQgIKBN52ksW3vyTrWVCuCNSjLhv9eCNQgWfARB0bz47UHWZpbwp2tSSY1XGQY7Q+OQMIfHgcPjwOlxtrh1a+6mGX9Wo7Xp1mq0YjaYcXqc2J12qp3V2J32Y5vLTr27/pTnNhvMJ7xn4327096inzq3Ohen5mw6NsAcQFxgHPEB8YyLHqffD4wnNjCW5OBkFbDPsalTp/L+++/zi1/8gq+//pqKiorTH+Q1ZMgQSkpK2LBhA5MnT8blcpGZmcnw4cNJTk5m6dKlzJs3Dyklu3btYtSoUWdcxjfffJOFCxdSUlLCmjVruPnmm8/ovRqpAA5QkQNvXQ3CAAs/htBE0nLKeeabTK4cFcctE/t2dQm7tWpnNdWOk+c3c2tuiuqK9C6GmjwKaguaWq9FdUUtLuB1JIE4aZeXlPK0076NwkhiUCJJIUlMi5+mX0wMSSYpOIkQa4jqTutEx/eBX3rppTzxxMlXB/rd737HTTfdxHvvvccFF1xAbGwsQUFBbTqXxWJh2bJl/PjHP6aqqgq3281PfvIThg8fzjvvvMO9997Ln/70J1wuFzfeeOMZB/Drr7+eVatWkZqayuDBg5k4cSIhIWfXMGxXOtmz1S3TyVYXwOuX6hkGb/8MYlKpqnNx+T+/w2gQfPbjqQT18q4Tt+bmUOUhjtqPnhCA82vymyaNtIVBGIjyjyIu4FiLNdQa2tQCtpqsWA3HWsMmg+lYq1xztmihN7gb9GFd3hwejVuwJZggS9Bpp1q7NTdOj/OEVr9Dc+Bn9CMxKLHTJ4V0R76YTtbhcGA0GjGZTGzYsIF77723XYs6nCs1NTUEBgZSVlbGhAkT+P7774mJaTm5qj3pZHt3C7wqD96+DmpLYcHHEJOKlJLFK3ZRVN3AsnvP75XBu7iumN0lu9lZupNdJbvYV7avRXeEv8m/qctgbPRY4gPj9RbpSTIuGISBaP9oYgNjifGP6TZBsXGSheru8H25ubnccMMNaJqGxWLhlVde6eoitWrOnDlUVlbidDr5zW9+c0Lwbq/eG8D3LIdPH9YzDN78PiScB8CSzUf4Yk8hiy9LYXQvGO/dPDPdtqJt7CrdRWFtIaAHuKHhQ7lu0HWMiBxBckgy8YHxBFuCVfeB0q0MGjSI7du3d3UxTmvNmjUd+n69L4DXV8Lnj8DupRA/Dq57GSIGAJBZZOf3n+xl2qBIfjStf9eWs5NoUiOzIlNPdFSYxtbirVQ5qgCIC4hjdJ/RjBw2kpF9RpISnqIyvilKN9a7AnjWWvjwXrAXwozHYNojYNSroMHl4cH/bSfQauLvN4zqUYszaFJjY/5Glh9YzsaCjU2pRBMCE7gw8ULGxYxrGkmhKIrv6B0B3NUAq/8IG56H8AFw5zdNXSaN/vTZPvYX2XnjjvE9ZkX5svoyPjz4Icsyl3G05ihh1jAu6nfRGWWmUxSl++nZAVzT4MhG+OxnULwPxt0Js/8IlpYD8L/cU8jbG3O5e1oyM4b4djIgKSVbCrewNHMpK3NX4tbcjIsex4NjHuSifhd1u7zJiqKcuZ4XwDUNjmyCfR9B+sdQnQcBUXDzUhg8+4Td8yvr+cXyXYyID+HRS1K6oMBnz+VxsaNkB9/nfc+q3FXkVOcQZAnixiE3Mm/wPPqH9sz+fKXnakxA1eiNN95oShbVXmvWrOHpp5/m008/Zc2aNVgsFs4//3wAbr/9dubMmXNWCaW6Us8I4JoHcjfoQXvfx1BTCEYrDJwFs34LQy4HW/AJh7k9Gj95dwduj8Y/bxqDxdT9c1M0Omo/yg/5P7A+bz2bCjZR567DJEyMjhrN3SPvZna/2dhMPaMrSFE6ypo1awgMDGwK4L7OdwK4lFBbAuXZ+szJimzv/WwozYT6CjDZYNDFMOwaGDS71aDdKLesjp8t3cGWnAr+Pm8UyZEnz2vQHbg1N9uLt7M6dzXr89aTU50D6CNHruh/BVPipzAxZiKBlsCuLajSoxT+5S840js2nax1aAoxv/zlGR9fUlLCPffcQ25uLgDPPvusnllw82Z+8pOfUF9fj5+fH6+//jpDhgxpOi4nJ4eXXnoJo9HI22+/zXPPPQfAunXreOaZZ1qkfvUVvhHAV/4eNr8MzppmTwoIjofwZEiZo7e2B14M1lMHMCkl76cd4Q+f7MMgBM/cMIrrxiZ0bvnPUJ2rjg35G1h9ZDVrj66lylGFxWBhfOx45g+Zz5T4KSQFJ6kx2UqPc/xU+vLycq666ioAHnroIR5++GGmTp1Kbm4ul1xyib6OZ0oK69atw2QysXLlSn75y1+yfPmxlXiSkpK45557CAwM5JFHHgHg1VdfbTX1q6/wjQAeNQzG3AphyXrADkuG0L5gbl8XQWmNg8XLd7MyvYhJ/cP5+w2jiQ/166RCnxmHx8EX2V+w6vAqNhRswOFxEGwJZnrCdC7seyFT4qaomYPKOXM2LeWzcXw62cY+cICVK1eyb9++pteqq6ux2+1UVVWxcOFCDhw4gBACl8vVpnO1lvrVV/hGAB85T9/Owjf7ili8fBd2h5tfXzGURVOSu9VY7wZ3A8syl/HantcoqS8hNiCWuYPnMjNxJmOjx3b4ataK4qs0TWPDhg34+bVsfD344IPMnDmTDz74gJycHGbMmNGm92st9auv8I0AfhZqHG7++Mk+3ks7wtDYYP43fzRDYtqWpexcaHA3sDRzKa/teY3S+lLGRY/jr9P+yoSYCaprRFFaMXv2bJ5//nkeffRRAHbs2MHo0aOpqqoiPj4e4IQVchoFBQVRXX3yzJm+xneGXbSTlJKv9hYy+5m1LN16hPtmDOCj+6d0m+Bd767nrb1vcenyS3lyy5P0D+nPa5e8xuuXvs7E2IkqeCvKSfzzn/8kLS2NkSNHMmzYMF566SUAfv7zn/PYY48xZcoUPJ7WFyy+8sor+eCDDxg9ejTffffduSx2p+iR6WSPlNfx+Md7WZVRzJDoIP5yXSrn9Qvv9PO2hZSSFQdW8Nz25yhrKGNizETuGXUP42JOyBSpKF3CF9PJ9iS9Np2s063xyndZPLf6AAYh+NXlQ7l9ShJmY/f4oVHZUMnvfvgdq4+sZmzUWJ6Z8Qxjo8d2dbEURfFRPSaA/3CwlN98tIdDJbVclhrDb+YMI64bjTDZkL+BX6//NeWOch4Z9wi3DbvNJxa1VRSl+/LpAC6lZNfRKl5dn83HO/PpG+7P63eMZ2Y3ymfi8rj45/Z/8sbeN0gOSeb5Wc8zNEL9PFUU5ez5ZAAvrXHw4fY83k87QmZRDVaTgR9fOJD7Zg7EZjZ2dfGaZFVlsXjdYtLL05k3eB6Pjn8UP1P3+VWgKIpv85kA7vJofJtRzNKtR/k2oxi3JhmdGMqfr01lzsg4Qvy6zzhpp8fJigMr+Hva37GZbDw781lm9Z3V1cVSFKWH8YkA/tr6bF5Yc5DSGieRgVYWTU1m3nkJDIruHkMCG+VW57IscxkfHvyQCkcFE2Mn8pepfyHKv/t06SiK0nP4RAAXAs7rF8a88xK5YEifbjOqBMClufg291uWZi5lY8FGjMLIjMQZzBs8j8lxk9WFSkU5A0ajkREjRuB2uxk6dChvvvkm/v5nn0Kiveljc3JymDNnDnv27Dnrc3cGnwjgd0xJ5o4pyV1djCZSSrKqsvgs6zM+OPgBpfWlxATE8MDoB7h20LWqxa0oZ6l5LpRbbrmFl156iZ/+9KddW6huyCcCeHfg0TzsLNnJt0e+ZXXuanLtuRiEgenx05k3ZB5T4qZgNHSfC6iK0hG+ez+T0iM1p9+xHSITA5l2w+A27z9t2jR27dpFQUEB8+fPp7q6GrfbzYsvvkhmZiZ79uzhH//4BwCvvPIK6enpPPPMM7z11ls8/fTTCCEYOXIk//3vf4HW08dKKfn5z3/OF198gRCCX//618yfP79FORoaGrj33ntJS0vDZDLxzDPPMHPmTOrq6rj99tvJyMhg6NCh5OTk8K9//YudO3eetGwdRQXwU2hwN7CxYCOrc/V0ruUN5ZgMJibGTGTh8IXMSJyhWtuK0oncbjdffPEFl156Kf/73/+45JJL+NWvfoXH46Guro6xY8cycuRInnzyScxmM6+//jr//ve/2bt3L3/+85/5/vvviYyMpLy8vOk9W0sfu2LFCnbs2MHOnTspLS1l/PjxTJ8+vUVZ/vWvfwGwe/duMjIymD17NpmZmbzwwguEhYWxa9cu9uzZ05QG98Ybb2y1bB1JBXCv8oZy9pfvZ3/5fjIqMthfvp/sqmw80kOgOZBp8dP0dK7xUwiydK+Lp4rSWdrTUu5IzfOBT5s2jTvvvJONGzeyaNEiXC4X11xzTdPrF154IZ9++ilDhw7F5XIxYsQInnvuOebOnUtkZCQA4eHHUmm0lj52/fr13HTTTRiNRqKjo7ngggvYsmULI0eObDpu/fr1PPjggwCkpKTQr18/MjMzWb9+PQ899BAAqampTccEBAS0WraO1GsCuJSSKkcVebV55NfkN2259lwyyzMpri9u2jfKP4qU8BRmJs5sWsHdbOw+wxQVpac7Ph84wPTp01m3bh2fffYZt912G48++igLFizgrrvu4i9/+QspKSnccccdgP73frKEcK2lj21LTqiT7XOqY1srW0fyuQAupaTeXY/dadc3l35b7aw+9pzz2HM1zhpK6kvIr8mnzl3X4r0CzAEkBCYwIXYCKeEpDAkfwpCwIYTZwrro0ymKcjKHDx8mPj6eu+++m9raWrZt28aCBQuYOHEiR44cYdu2bezatQuAWbNmce211/Lwww8TERFBeXl5i1b48aZPn86///1vFi5cSHl5OevWreOpp56ioaGhxT7vvPMOF154IZmZmeTm5jJkyBCmTp3K+++/z8yZM9m3bx+7d+9uOqa1snWkswrgQohLgf8HGIH/SCmf6JBSHeelnS/xyaFPmgKzW7pPub/VaCXIEtS0JQQlMCl2ErEBscQHxhMXGEdcYBzBlmCVtlVRfMSaNWt46qmnMJvNBAYG8tZbbzW9dsMNN7Bjxw7CwvTG1/Dhw/nVr37FBRdcgNFoZMyYMSfNEQ5w7bXXsmHDBkaNGoUQgieffJKYmBhycnKa9rnvvvu45557GDFiBCaTiTfeeAOr1cp9993HwoULGTlyJGPGjGHkyJGEhISctGwd6YzTyQohjEAmcDFwFNgC3CSl3HeyY840neyKAyvYVLCJIEsQwZZgAi2BTcE52Kw/DrYENz1nMVrO6DMpiuKb6WTnzJnDww8/zKxZ537Gs8fjweVyYbPZOHToELNmzSIzMxOLxXJGZTtX6WQnAAellFneE7wLXA2cNICfqesGXcd1g67r6LdVFMXHVVZWMmHCBEaNGtUlwRugrq6OmTNn4nK5kFLy4osvYrFYzknZziaAxwNHmj0+Ckw8fichxI+AHwH07dv3LE6nKIrSUmhoKJmZmV1ahqCgIFrrWTgXZTubed6tdR6f0B8jpXxZSjlOSjmuT58+Z3E6RVHOFV9b3LenaG+9n00APwokNnucAOSfxfspitIN2Gw2ysrKVBA/x6SUlJWVYbPZ2nzM2XShbAEGCSGSgTzgRuDms3g/RVG6gYSEBI4ePUpJSUlXF6XXsdlsJCQktHn/Mw7gUkq3EOIB4Cv0YYSvSSn3nun7KYrSPZjNZpKTu0/yOOXkzmocuJTyc+DzDiqLoiiK0g4qWbWiKIqPUgFcURTFR53xTMwzOpkQJcDhMzw8EijtwOL0VKqe2k7VVduoemqbzqynflLKE8Zhn9MAfjaEEGmtTSVVWlL11HaqrtpG1VPbdEU9qS4URVEUH6UCuKIoio/ypQD+clcXwEeoemo7VVdto+qpbc55PflMH7iiKIrSki+1wBVFUZRmVABXFEXxUV0awIUQrwkhioUQe5o9N0oIsUEIsVsI8YkQItj7/C1CiB3NNk0IMdr72nne/Q8KIf4petg6ae2sJ7MQ4k3v8+lCiMeaHaPq6Vg9WYQQr3uf3ymEmNHsmJ5eT4lCiG+9/z/2CiEe8j4fLoT4RghxwHsb1uyYx7z1sV8IcUmz53tsXbW3noQQEd79a4QQzx/3Xp1TT1LKLtuA6cBYYE+z57YAF3jvLwL+2MpxI4CsZo83A5PRc5R/AVzWlZ+rK+sJPSPku977/kAOkKTq6YR6uh943Xs/CtgKGHpJPcUCY733g9CXRhwGPAks9j6/GPib9/4wYCdgBZKBQ4Cxp9fVGdRTADAVuAd4/rj36pR66tIWuJRyHVB+3NNDgHXe+98A17dy6E3AEgAhRCwQLKXcIPWaegu4plMK3EXaWU8SCBBCmAA/wAlUq3oCWtbTMGCV97hioBIY10vqqUBKuc173w6ko6+wdTXwpne3Nzn2ua9GbxQ4pJTZwEFgQk+vq/bWk5SyVkq5Hmho/j6dWU/dsQ98D3CV9/48Wi4a0Wg+3gCOXqFHm7121PtcT3eyeloG1AIFQC7wtJSyHFVP0LKedgJXCyFM3pz253lf61X1JIRIAsYAm4BoKWUB6MEL/ZcJtL58Yjy9qK7aWE8n02n11B0D+CLgfiHEVvSfLc7mLwohJgJ1UsrGfs42Le3WA52sniYAHiAO/efuz4QQ/VH1dHw9vYb+h5QGPAv8ALjpRfUkhAgElgM/kVJWn2rXVp6Tp3i+R2lHPZ30LVp5rkPq6azygXcGKWUGMBtACDEYuOK4XW7kWOsb9D/C5ktY9Iql3U5RTzcDX0opXUCxEOJ7YBzwHaqemupJSukGHm7cTwjxA3AAqKAX1JMQwowelN6RUq7wPl0khIiVUhZ4f/YXe58/2fKJPf5vr531dDKdVk/drgUuhIjy3hqAXwMvNXvNgP4z+N3G57w/YexCiEneK7sLgI/OaaG7wCnqKRe4UOgCgElAhqqnlvUkhPD31g9CiIsBt5RyX2+oJ+/nehVIl1I+0+ylj4GF3vsLOfa5PwZuFEJYvd1Ng4DNPb2uzqCeWtWp9dTFV3mXoPfVutC/pe4EHkK/2psJPIF3tqh3/xnAxlbeZxx6X+ch4Pnmx/SErT31BAQCS4G9wD7gUVVPrdZTErAf/cLUSvR0nb2lnqai/4TfBezwbpcDEegXdg94b8ObHfMrb33sp9kIip5cV2dYTznoF9JrvP8Hh3VmPamp9IqiKD6q23WhKIqiKG2jAriiKIqPUgFcURTFR6kAriiK4qNUAFcURfFRKoArPZp3PPx6IcRlzZ67QQjxZVeWS1E6ghpGqPR4QohU9LHxYwAj+njeS6WUh87gvYxSSk/HllBRzowK4EqvIIR4Ej3JV4D3th96WmIT8LiU8iNvwqL/evcBeEBK+YM3V/jv0CcJjZZSDju3pVeU1qkArvQK3mnz29CTWX0K7JVSvi2ECEXP1TwGfdadJqVsEEIMApZIKcd5A/hnQKrU06kqSrfQ7ZJZKUpnkFLWCiHeQ5/ifANwpRDiEe/LNqAveoKh54W+0pMHGNzsLTar4K10NyqAK72J5t0EcL2Ucn/zF4UQjwNFwCj0C/zNE/PXnqMyKkqbqVEoSm/0FfBg47qEQogx3udDgAIppQbchn7BU1G6LRXAld7oj4AZ2CX0BZD/6H3+BWChEGIjeveJanUr3Zq6iKkoiuKjVAtcURTFR6kAriiK4qNUAFcURfFRKoAriqL4KBXAFUVRfJQK4IqiKD5KBXBFURQf9f8B+08jx100L8kAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.line()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get rid of annoying text output with ; (semi-colon)\n",
    "\n",
    "Some Python objects have a useful default output when the cell is executed. These plots do not – that's why you see the `<AxesSubplot:xlabel='Year'>` before the plot itself. I get tired of that extra text output inside <> charcters that also prints out with a Matplotlib plot.\n",
    "\n",
    "*It seems that the only way to get rid of that text is to follow your plot command by a semi-colon character `;`. Evidently that suppresses cell output, which here is that extra text.*"
   ]
  },
  {
   "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"
    }
   ],
   "source": [
    "df.plot.line();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Set the plot size\n",
    "\n",
    "We can also set the figure size with an easy argument, `figsize=[,]`. I have no idea what the units are.\n",
    "\n",
    "#### It's horrible that figsize is size [across,down]!\n",
    "\n",
    "This is especially hard for me to remember because many conventions in Pandas are [down,across], like `df.loc[row,column]`. It's probably an x,y convention sneaking its way in..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.line(y=['Business','Psychology'], figsize=[8,5]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Subplots for small-multiples\n",
    "\n",
    "Often plots will become confusing with too many lines overlapping. One solution is to split the plots up in a way that still allows comparison across categories.\n",
    "\n",
    "- We typically call these \"small multiples\" or \"faceted\" plots\n",
    "- Figsize is for whole composite figure (not each subplot)\n",
    "- **Note that the default is to have independent y-axis ranges!** (But shared X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 216x576 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.line(subplots=True, figsize=(3,8));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Set the subplot arrangement grid\n",
    "\n",
    "`layout=(rows, columns)` sets the grid for subplots. \n",
    "\n",
    "#### Notice that suplots are [rows,columns] like DataFrame access!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.line(subplots=True, layout=(2,3));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### You can put a `-1` in one layout entry to say \"as many as needed\"\n",
    "\n",
    "*Notice that even here where plots are side-by-side, the default is still not a shared y-axis range*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x144 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.line(subplots=True, layout=(1,-1), figsize=(12,2));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Force shared axes with `sharex=True` and/or `sharey=True`\n",
    "\n",
    "**Shared axes should really be the default so we can make realistic comparisons!** Notice how different your perception is of these degree comparisons with a shared Y axis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x144 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.line(subplots=True, layout=(1,-1), figsize=(12,2), sharey=True);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## Bar chart\n",
    "\n",
    "Trying to do a bar chart of our whole time-series won't be illuminating, so let's calculate the mean values in each field over the data set. *Notice that this output is a Series.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Agriculture    33.848165\n",
       "Business       40.653471\n",
       "Engineering    12.892857\n",
       "Health         82.983333\n",
       "Psychology     68.776190\n",
       "dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Vertical bar chart\n",
    "\n",
    "**You can *chain the plot commands* right onto the end of the mean calculation!** This is the same thing as if we did a sequence\n",
    "\n",
    "```\n",
    "deg_means = df.mean()\n",
    "deg_means.plot.bar()\n",
    "```\n",
    "\n",
    "but we don't need an intermediate variable. Again, that is called **chaining** methods when we link them together like a long chain. As you read left-to-right, the methods (functions) work on the output of the previous command."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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"
    }
   ],
   "source": [
    "df.mean().plot.bar();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Horizontal bar chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAaAAAAD4CAYAAACqnDJ3AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAAAUrUlEQVR4nO3df5TddX3n8eeLCQYiEMSknCmiozaIlEjEyMoP2RRZquKxRanItgfoYZvaum11q27c7rG1PW7T1aN0pWgpVSrbg/JLygaXH6sgBSyQQEgCgrQaFAoiFsOPIj/Ce/+43ynXcSaZTGbmcwPPxzn33Hu/Pz7f172Z5JXvj7k3VYUkSbNtp9YBJEnPTxaQJKkJC0iS1IQFJElqwgKSJDUxp3WAHcWCBQtqZGSkdQxJ2qGsWbPmwapaON48C2iSRkZGWL16desYkrRDSXL3RPM8BCdJasICkiQ1YQFJkpqwgCRJTVhAkqQmLCBJUhMWkCSpCQtIktSEBSRJasJPQpik9fduYmTFpa1jSBpQG1ce2zrCDsc9IElSExaQJKkJC0iS1IQFJElqwgKSJDVhAUmSmrCAJElNbHMBJdmcZG2SDUnOTzJvOoIkOTvJ8duw/EiSDdOxbUnS7JvKHtDjVbWkqg4EngTeM82ZJEnPA9t7CO7vgZ9LMpzkmr49ozcmOTXJp0YXTPIbST7ZPT4pyboktyY5p2+8I5Ncn+Tbo3tD6fl4N+76JCeMDZFklySf7+bfkuQXuunzkpzXbetLSW5IsnRL2SRJs2PKH8WTZA7wFuAy4D8Cl1fVx5IMAfOAm4F1ST5UVU8Bvw78ZpKfB/4AOLyqHkyyV9+ww8ARwP7AJcAFwDuAJcBBwALgpiTXjInzXoCqWpxkf+CKJPsBvw08VFWvSXIgsLZb/ovjZRvnNS4HlgMM7bFwiu+UJGk8U9kD2jXJWmA18F3gr4GbgF9P8kfA4qp6pKoeA74GvK0rhZ2raj1wFHBBVT0IUFX/0jf2xVX1TFXdDuzdTTsCOLeqNlfV94GvA68fk+kI4JxuvDuAu4H9uulf7KZvANZ1jyfK9hOq6syqWlpVS4fmzZ/CWyVJmshU9oAer6olY6Zdk+RI4FjgnCQfr6ovAGcB/w24A/h8t2yAmmDsJ/oeZ8z9lky0zJbWHS+bJGmWTMtl2EleBjxQVX9Fb4/oYICqugHYl94hunO7xb8KvCvJi7t19/rpEX/CNcAJSYaSLASOBG4cZ5lf7cbbD3gpcCdwLfCubvoBwOLRFSbIJkmaJdP1dQzLgA8meQp4FDipb955wJKqegigqm5L8jHg60k2A7cAp2xh7C8DhwK30ttz+lBV3Z9kpG+ZM4DPJlkPPA2cUlVPJDkD+Jsk67rtrAM2TZRNkjR7UjXR0bBp2kCyCvhUVX11Rjc0/raH6J3f+XGSV9Lb+9qvqp7c1mxzhxfV8MmnzWheSTsuvw9ofEnWVNXS8ebN2BfSJdmT3qGyW1uUT2cecFWSnemdD/qtqnpyQLJJ0vPajBVQVf2I3pVozVTVI8BPNe8gZJOk5zs/C06S1IQFJElqwgKSJDUxY+eAnmsW7zOf1V7lIknTxj0gSVITFpAkqQkLSJLUhAUkSWrCApIkNWEBSZKasIAkSU1YQJKkJiwgSVITFpAkqQkLSJLUhAUkSWrCApIkNWEBSZKasIAkSU1YQJKkJiwgSVITFpAkqQkLSJLUhAUkSWrCApIkNTGndYAdxfp7NzGy4tLWMSRpVm1ceeyMje0ekCSpCQtIktSEBSRJasICkiQ1YQFJkpqwgCRJTQxcASV5dMzzU5KcPsWxliVZ1ff4sL55Zyc5fvvSSpKmauAKaAYtAw7b2kKSpNmxQxVQkoVJLkxyU3c7vJt+SJLrk9zS3b9qzHojwHuA9ydZm+SN3awju+W/7d6QJM2uQfwkhF2TrO17vhdwSff4z4FPVdW1SV4KXA68GrgDOLKqnk5yNPA/gHeODlBVG5N8Fni0qj4BkORUYBg4Ati/28YFM/rKJEn/ZhAL6PGqWjL6JMkpwNLu6dHAAUlGZ++RZHdgPvA3SRYBBew8yW1dXFXPALcn2XvszCTLgeUAQ3ss3PZXIkma0CAW0JbsBBxaVY/3T0zyaeCqqjquO9x29STHe6J/mLEzq+pM4EyAucOLaiqBJUnj26HOAQFXAP959EmSJd3D+cC93eNTJlj3EWD3mQomSdo2O1oB/S6wNMm6JLfTu7AA4H8Cf5rkOmBognX/D3DcmIsQJEmNpMojS5Mxd3hRDZ98WusYkjSrtvfrGJKsqaql483b0faAJEnPERaQJKkJC0iS1IQFJElqwgKSJDWxo/0iajOL95nP6u28GkSS9Cz3gCRJTVhAkqQmLCBJUhMWkCSpCQtIktSEBSRJasICkiQ1YQFJkpqwgCRJTVhAkqQmLCBJUhMWkCSpCQtIktSEBSRJasICkiQ1YQFJkpqwgCRJTVhAkqQmLCBJUhMWkCSpCQtIktTEnNYBdhTr793EyIpLW8fYbhtXHts6giQB7gFJkhqxgCRJTVhAkqQmLCBJUhMWkCSpCQtIktSEBSRJamKrBZRkc5K1fbcVU91Ykuunuu42bOPt25NRkjQ7JvOLqI9X1ZLp2FhVHTYd40wkyZyqugS4ZCa3I0naflM+BJdkY5KPJrk5yfok+3fTFya5spv+l0nuTrKgm/dod78sydVJLkhyR5K/TZJu3uuSfD3JmiSXJxnupr8yyWXd9L/v297ZST6Z5Crgz5KckuT0vnn/K8n1Sb6d5Phu+k5JzkhyW5JVSb4yOk+SNDsmU0C7jjkEd0LfvAer6mDgM8AHuml/CHytm/5l4KUTjPta4H3AAcArgMOT7Ax8Gji+ql4HfA74WLf8mcDvdNM/AJzRN9Z+wNFV9fvjbGcYOAJ4G7Cym/YOYARYDPwn4NDxAiZZnmR1ktWb/3XTBC9DkjQV23sI7qLufg29f9Sh94/9cQBVdVmShyZY98aqugcgyVp6hfAj4EDgym6HaAi4L8luwGHA+d10gLl9Y51fVZsn2M7FVfUMcHuSvfsynt9Nv7/be/opVXUmveJj7vCimmB8SdIUbO+HkT7R3W/uGysTLDvRuv3rB7itqn5ijyTJHsCPtlCEj01yOxlzL0lqZCYuw74WeBdAkmOAF23DuncCC5Mc2q2/c5Kfr6qHge8k+ZVuepIctJ0Z39mdC9obWLYdY0mSpmAq54BWbmX5jwLHJLkZeAtwH/DIZMJU1ZPA8fQuJrgVWEvv0BvArwKndtNvA35pMmNO4ELgHmAD8JfADYAneSRpFqVqek9tJJkLbK6qp7s9mc9M12Xc0ynJblX1aJIXAzcCh1fV/RMtP3d4UQ2ffNqs5Zspfh+QpNmUZE1VLR1v3kx8Id1LgfOS7AQ8CfzGDGxjOqxKsifwAuBPtlQ+kqTpN+0FVFV30bvEeqBV1bLWGSTp+czPgpMkNWEBSZKasIAkSU3MxEUIz0mL95nPaq8gk6Rp4x6QJKkJC0iS1IQFJElqwgKSJDVhAUmSmrCAJElNWECSpCYsIElSExaQJKkJC0iS1IQFJElqwgKSJDVhAUmSmrCAJElNWECSpCYsIElSExaQJKkJC0iS1IQFJElqwgKSJDVhAUmSmpjTOsCOYv29mxhZcWnrGHoe2bjy2NYRpBnlHpAkqQkLSJLUhAUkSWrCApIkNWEBSZKasIAkSU3MeAEl2ZxkbZJbk9yc5LApjvOeJCdNdz5JUhuz8XtAj1fVEoAkvwj8KfDvt3WQqvrsNOeSJDU024fg9gAeAkiyLMmq0RlJTk9ySvd4ZZLbk6xL8olu2h8l+UD3+Ookf5bkxiTfSvLGbvpQko8nualb9ze76cNJrun2xDYkeWO37Nnd8/VJ3j+7b4UkPb/Nxh7QrknWArsAw8BRW1o4yV7AccD+VVVJ9pxg0TlVdUiStwJ/CBwNnApsqqrXJ5kLXJfkCuAdwOVV9bEkQ8A8YAmwT1Ud2G13ou1IkmbAbB+COxT4QpIDt7D8w8CPgbOSXAqsmmC5i7r7NcBI9/gY4DVJju+ezwcWATcBn0uyM3BxVa1N8m3gFUk+DVwKXDF2A0mWA8sBhvZYOImXKkmarFk9BFdV3wAWAAuBp8dsf5dumaeBQ4ALgV8GLptguCe6+808W6QBfqeqlnS3l1fVFVV1DXAkcC9wTpKTquoh4CDgauC9wFnj5D2zqpZW1dKhefOn+KolSeOZ1Q8jTbI/MAT8ELgbOKA7VLYL8Cbg2iS7AfOq6itJ/gH4x23YxOXAbyX5WlU9lWQ/eqWzALi3qv4qyQuBg5N8BXiyqi5M8k/A2dP1OiVJWzeb54Cgt4dyclVtBr6X5DxgHXAXcEu3zO7A3yXZpVt+Wy4OOIve4bibkwT4Ab29qGXAB5M8BTwKnATsA3w+yehe2Ien8uIkSVOTqmqdYYcwd3hRDZ98WusYeh7x6xj0XJBkTVUtHW+en4QgSWrCApIkNWEBSZKasIAkSU1YQJKkJmb194B2ZIv3mc9qr0qSpGnjHpAkqQkLSJLUhAUkSWrCApIkNWEBSZKasIAkSU1YQJKkJiwgSVITFpAkqQkLSJLUhAUkSWrCApIkNWEBSZKasIAkSU1YQJKkJiwgSVITFpAkqQkLSJLUhAUkSWrCApIkNWEBSZKamNM6wI5i/b2bGFlxaesYGgAbVx7bOoL0nOAekCSpCQtIktSEBSRJasICkiQ1YQFJkpqwgCRJTVhAkqQmplxASY5LUkn2n8K6f5zk6Clud2OSBUn2TPLbUxlDktTe9uwBnQhcC7x7W1ZKMlRVH6mq/7cd2wbYE9jmAkoytJ3blSRNgykVUJLdgMOBU+kKKMlOSc5IcluSVUm+kuT4bt7GJB9Jci3wK0nO7pv3+iTXJ7k1yY1Jdk9ySpLT+7a3KsmyMTFWAq9MsjbJx5MsS7Kqb53Tk5wywfaPSfKNJDcnOb97PZKkWTTVj+L5ZeCyqvpWkn9JcjDwCmAEWAz8DPBN4HN96/y4qo4ASPLm7v4FwJeAE6rqpiR7AI9PMsMK4MCqWtKNtWwry/+4qo5IsgC4CDi6qh5L8l+B/wL88dgVkiwHlgMM7bFwkrEkSZMx1QI6ETite/zF7vnOwPlV9Qxwf5KrxqzzpXHGeRVwX1XdBFBVDwMkmWKsLRrd/huAA4Druu28APjGeCtU1ZnAmQBzhxfVTISSpOerbS6gJC8GjgIOTFLAEFDAl7ey6mPjDdetO9bT/OThwV0mEW1r64xuP8CVVXXiJMaUJM2QqZwDOh74QlW9rKpGqmpf4DvAg8A7u3NBewPLJjHWHcDPJnk9QHf+Zw6wEVjSjbUvcMg46z4C7N73/G7ggCRzk8wH3jTBNv8BODzJz3XbnJdkv0lklSRNo6kcgjuR3gUA/S4EXg3cA2wAvgXcAGza0kBV9WSSE4BPJ9mV3vmfo4Hr6JXa+m68m8dZ94dJrkuyAfi/VfXBJOcB64C7gFsm2OYPuosTzk0yt5v837vMkqRZkqrpO7WRZLeqerQ7THcjcHhV3T9tG2ho7vCiGj75tNYxNAD8PiBp8pKsqaql482b7i+kW5VkT3on9v/kuVI+kqTpN60FVFXLpnM8SdJzl58FJ0lqwgKSJDVhAUmSmpjuixCesxbvM5/VXv0kSdPGPSBJUhMWkCSpCQtIktSEBSRJasICkiQ1YQFJkpqwgCRJTVhAkqQmLCBJUhMWkCSpiWn9QrrnsiSPAHe2zjGBBfS+En3QDGouGNxsg5oLBjebubbdbGZ7WVUtHG+GnwU3eXdO9K1+rSVZPYjZBjUXDG62Qc0Fg5vNXNtuULJ5CE6S1IQFJElqwgKavDNbB9iCQc02qLlgcLMNai4Y3Gzm2nYDkc2LECRJTbgHJElqwgKSJDVhAU1CkjcnuTPJPyZZ0TjL55I8kGRD37S9klyZ5K7u/kUNcu2b5Kok30xyW5LfG4RsSXZJcmOSW7tcHx2EXH35hpLckmTVgOXamGR9krVJVg9KtiR7JrkgyR3dz9qhA5LrVd17NXp7OMn7BiTb+7uf/Q1Jzu3+TjTPBRbQViUZAv4CeAtwAHBikgMaRjobePOYaSuAr1bVIuCr3fPZ9jTw+1X1auANwHu796l1tieAo6rqIGAJ8OYkbxiAXKN+D/hm3/NByQXwC1W1pO/3RQYh258Dl1XV/sBB9N675rmq6s7uvVoCvA74V+DLrbMl2Qf4XWBpVR0IDAHvbp3r31SVty3cgEOBy/uefxj4cONMI8CGvud3AsPd42F6vzTb+n37O+A/DFI2YB5wM/DvBiEX8BJ6f/mPAlYN0p8lsBFYMGZa02zAHsB36C6eGpRc4+Q8BrhuELIB+wDfA/ai98EDq7p8A/GeuQe0daN/gKPu6aYNkr2r6j6A7v5nWoZJMgK8FriBAcjWHeZaCzwAXFlVA5ELOA34EPBM37RByAVQwBVJ1iRZPiDZXgH8APh8d9jyrCQvHIBcY70bOLd73DRbVd0LfAL4LnAfsKmqrmida5QFtHUZZ5rXrk8gyW7AhcD7qurh1nkAqmpz9Q6NvAQ4JMmBjSOR5G3AA1W1pnWWCRxeVQfTO/T83iRHtg5E73/wBwOfqarXAo/R9hDlT0nyAuDtwPmtswB053Z+CXg58LPAC5P8WttUz7KAtu4eYN++5y8B/rlRlol8P8kwQHf/QIsQSXamVz5/W1UXDVI2gKr6EXA1vXNorXMdDrw9yUbgi8BRSf73AOQCoKr+ubt/gN65jEMGINs9wD3dHizABfQKqXWufm8Bbq6q73fPW2c7GvhOVf2gqp4CLgIOG4BcgAU0GTcBi5K8vPvfzbuBSxpnGusS4OTu8cn0zr/MqiQB/hr4ZlV9clCyJVmYZM/u8a70/kLe0TpXVX24ql5SVSP0fqa+VlW/1joXQJIXJtl99DG9cwYbWmerqvuB7yV5VTfpTcDtrXONcSLPHn6D9tm+C7whybzu7+ib6F240TpXT4sTTzvaDXgr8C3gn4A/aJzlXHrHcp+i9z/CU4EX0zuZfVd3v1eDXEfQOzS5Dljb3d7aOhvwGuCWLtcG4CPd9ObvWV/GZTx7EULzXPTOtdza3W4b/ZkfkGxLgNXdn+fFwIsGIVeXbR7wQ2B+37Tm2YCP0vtP1wbgHGDuIOSqKj+KR5LUhofgJElNWECSpCYsIElSExaQJKkJC0iS1IQFJElqwgKSJDXx/wGa/wAJR1Cp4wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.mean().plot.barh();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sorted bar charts\n",
    "\n",
    "**This visualization would more effectively show us the patterns in the data if the bars were sorted by length (descending)**. We can easliy do that with the `.sort_values()` method, which we can use for either a DataFrame or a Series. (Here, the mean over the columns is a Series.)\n",
    "\n",
    "**We will again chain the commands for convenience.** You can just read the sequence left to right.\n",
    "\n",
    "### Vertical bar charts go from left to right with the Series order\n",
    "\n",
    "For the vertical bars, the only complication is that ascending values is the default sort order, so **to get descending order we need to set `ascending=False` explicitly**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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"
    }
   ],
   "source": [
    "df.mean().sort_values(ascending=False).plot.bar();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Horizontal bars go from bottom to top in the Series order!\n",
    "\n",
    "But for the vertical bar chart, **like in Excel, the plot ordering goes from bottom to top, so we actually want an ascending sort if we want the bars decreasing from top to bottom!**\n",
    "\n",
    "*Think of the Y-axis being ordered like in a scatterplot, bottom to top, even though we read it top to bottom.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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"
    }
   ],
   "source": [
    "df.mean().sort_values().plot.barh();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## Histogram\n",
    "\n",
    "Another useful way to explore your data is to look at the distribution of values in each column. Pandas can make easy histograms.\n",
    "\n",
    "**Confusingly, there's a difference between calling `df.hist()` and `df.plot.hist()`!**\n",
    "\n",
    "- `df.hist()` calls matplotlib.pyplot.hist(), on each series in the DataFrame, resulting in one histogram per column.\n",
    "- `df.plot.hist()` groups the values of all given Series in the DataFrame into bins and draws all bins in one matplotlib.axes.Axes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### `df.hist()`\n",
    "\n",
    "The default is to have each set of axes independent, but that makes it harder to compare across the small multiples. Set `sharex=True` and `sharey=True` to make them the same. *I know this makes the default 10 bins for each column different widths, which is unfortunate, but see how your first impression is that the ranges are all similar when you don't force the same axes!*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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y4vkLwJj8fAfg6Kp/hneQfix2ADYAFlXM+zaptt2r8uKSoW5726rle5//uWLadOCOiv30PUDKq7csvwnYD/i4pF2p7zte63+GNbXoHYBtq8rzGUAXgKQ3SrpJ0pOSlgFfINXGh6K/8lyr00gd0b1KWcadwGsQEYsi4p6KSQ8Do4BNSDVpSM0cu9a5iceB71X9gIyOiBl53l+BsRXzNouIN1eGWOd2ARaRzmfu9Zb8eHHFtCNJ+0d+PKqB7VkbVZbliFhOSmLjae53/DjwaFV53jQi3pPnf4tUQZgQEZuRkntfF0lBY2W7T5K2Aw4DLqqYXMoy7gRen11Jh22rgY/kDphuYLM613c5cISkd+dDu40kTZG0XUQsAm4DzpW0maT1JL1B0lAPOftzDXCapPGStgC+lqdX/uN05TjIj9tgpSepm/SDfRfN/Y5/CyyT9FlJG+cyvZukvfP8TYFlpNsM7AJ8dIB1LQZ2aiCWvvwn8BnglYpppSzjTuBr/Ehrnwd+ffUbJPUepp0dEctIh25vA54mdUC+TKotD0lEPE6qAZwBPEWqwXyaNd/PB0mdnn8CngWuJTWvNMOFpB+IP5JqRQ+SDrNbfv66tU4uy9eR+laWNXPd+dqHI0jt0o+S7jNyEbB5fsunSB36y0nl7/sDrO5iYNfcFHNDo7FJOhxYEhFzGl1XESii6UcoHUnSBsBNwI8j4qt52lxgSkQskjSOdLbKeRFxZhtDrZukL5LObtmS9E+3GfADUqdU5X72RMTE9kVqjaixLHfkd5zL+AmkSspGlLyMuwZeg3za4MXAA70FPvst8Ml82tQ5pDbxG1ofYf3yIe578nnf55Pa988nnTHwk4j4AOly8al5kanAD9sSrDVsgLI8Ir7jiPhcRGwXEd10QBl3DbwGkt4B/By4lzXtZmcAWwCXkJo3XgI+ExG1XIJfGJI2AX4G7AK8SDof/TTSOeGfiojDJb2W1Fb+emA+cHQ+fctKZoCyfBcj7DtWuliu1GXcCdzMrKTchGJmVlKD3u9C0iWky7CXRMRuedpWpJ7jbmAecEy+im9AY8eOja233prRo0c3EnNHW7lypT+fQcyZM2dpRGw9+DvbbySW+ZFWhluxv/2W+YgY8A94J6k99L6KaV8Gpufn04EvDbaeiGCvvfaKn/70p2H98+czOGB21FDeivA3Esu897f5+ivzgzahRMSdQHVjfimvWjIz6yT13jJ0rauWJPV71ZIqBnjt6upixYoV9PT01LnZzteJn8+9Tzw/5GUmjd988DeZtUD39JsHnD9t0ipOrHrPvBmHDWdIrxr2ez5HxQCvkydPjjFjxjBlypTh3mxp9fT0dNznU124azHv+CnND8Ssw9R7FsrifLUS+XFJ80Iyaz9Jl0haIum+immlvOWoda56E3gpr1oyG4JLSQMGVCrlLUetcw2awCVdBfwamChpgaSTSDf9P0jSg8BBrBkEwKwjuPPeymDQNvCIOK6fWQc2ORazoqup836kd9x32v5OmzTwGOFdG6/7nlbtvweuNWuykd5x32kd8YN1wk+btIpz7107lbaqE96X0pvVzp33VihO4Ga1c+e9FYoTuFkf3HlvZeA2cLM+uPPeysAJfIQa7PJgMys+N6GYmZWUE7iZWUk5gZuZlZTbwM3MmqyePqZ6bkHrGriZWUk5gZuZlZQTuJlZSTmBm5mVlBO4mVlJOYGbmZWUE7iZWUk5gZuZlZQTuJlZSTV0JaakecByYDWwKiImNyMoMzMbXDMupT8gIpY2YT1mZsOmE2+h7CYUM7OSarQGHsBtkgL4dh6N26yjuenQiqLRBL5/RCyUtA0wS9KfI+LOyjdIOgU4BaCrq4sVK1bQ09OzzorufeL5IW980vjN6wq6yPr7fJpt2qRVw76NRrTiM2iQmw6t7RpK4BGxMD8ukXQ9sA9wZ9V7ZgIzASZPnhxjxoxhypQp66zrxHpuv3j8uuspu56enj4/n2ar5/NupU78bs2are4ELmk0sF5ELM/PDwY+37TIzIprwKbDWo86O9VQ97dVR9/DddTZtXFz1l1PGWmkBt4FXC+pdz1XRsStDazPrCwGbDqs9aizUw31KLJVR9/DddQ5bdIqzr238RP66tmnurcaEY8Ae9S7vFlZ1dJ0aNYKHlKtA3Ti+a1F5aZDKxIncLOhcdOhFYYTuNkQuOmwGHzUmfhKTDOzknICNzMrKSdwM7OScgI3MyspJ3Azs5LyWShmJVLP2RfzZhw2DJFYEbgGbmZWUk7gZmYl5QRuZlZSbgMfJvVeKTZt0qrC36vbzIrBCdzM+uQO0+JzE4qZWUk5gZuZlZQTuJlZSTmBm5mVVKk7MX1PYCuzVpXfVv6fdE+/2WdStZBr4GZmJdVQApd0iKS5kh6SNL1ZQZkVlcu8FUndCVzSKOAC4FBgV+A4Sbs2KzCzonGZt6JppAa+D/BQRDwSES8DVwNHNicss0JymbdCUUTUt6D0fuCQiDg5vz4B2DciTq163ynAKfnlROBpYGndEXe+sfjzGcwOEbF1qzfqMl+zkVaGW7G/fZb5Rs5CUR/T1vk1iIiZwMxXF5JmR8TkBrbb0fz5FJrLfA28v63TSBPKAmD7itfbAQsbC8es0FzmrVAaSeB3AxMk7ShpQ+BY4MbmhNXZJIWknetYrjsvW+rz90vMZd4Kpe4EHhGrgFOBHwMPANdExP01LDpz8Le0h6R5kl6UtELSYknfkTSmxWEU9vMZ6TqxzA8T72+L1N2J2YkkzQNOjojbJY0n/aPeFBFNPd9XUgATIuKhIS7XDTwKbJCTiZmNYL4Ssx8R8QTw38AkSedJWiLpeUl/lLSbpL1zLf3V5gxJ75P0+/x8lKQzJD0sabmkOZIq20/fJelBSc9KukCS8nLrSfo3SY/lbX5X0uZ9xShpW0k3SnomX1jy4Yp5G0u6LK//AUmfkbQgz/u0pOuq1vUNSf/ZpI/PzFrACbwfOdm+B9gKeCfwRmAL4B+BpyPibtLpYQdVLPYB4Hv5+b8Cx+V1bAb8M/BCxXsPB/YG9gCOAd6dp5+Y/w4AdgLGAOf3E+ZVpI61bYH3A1+QdGCedybQnddxUI6t1+XAIZK2yPu6ft6v72Fm5RERLfkDDgHmAg8B01u13SHGOA9YATwHPAZ8EzgM+AuwH7Be1fs/C1yRn29FStDj8uu5wJH9bCeAdwCXAEuA53s/E+BnpPbVB4FZpCT/N9Ipn9152fVJZ0OsBjatWO8XgUvz80eAd1fMOxlYUPH6v4EP5+eHA39q9+df9j/SOd+/r/hbBpwOnAU8UTH9Pe2OtYn7/EngfuA+UoVio/y/MKuiDG/Z7jiHeX/b9v22pAZeskuQj4qILSJih4j4WETcTKoBXwAsljRT0mb5vZcDR+SOzmOAn0fEojxve+DhAbbzJHAp6YftFVJNG2A34DcRMQG4g1QzXh/oqlp+W+CZiFheMe0xYHzF/Mcr5lU+B7iMNbXyyiMHq1NEzI2IPSNiT2Av0g/69Xn2eb3zIuKWtgXZRLmf6BPA5IjYDRhFOjNnOnBHRRnuiHvGDLC/0Kbvt1VNKKW+BDkivh4RewFvJjWlfDpPfwL4NfD3wAmsnQQfB94wyHrvBJ6pmrwRqfYOKcn+A7AKWFz1voXAVpI2rZj2elJNAGAR6TzlXpXt7wA3ALtL2o1UA79ioFhtyA4EHo6Ix9odyDBbH9g4N8NtQiqXR5LKLvnxqPaENiz62t+2aVUCH8/aNcAFrKkpFlrurNxX0gbASuAlUtNFr+8CnwEmsaa2BXAR8O+SJijZXdJra9kkcJKkHYHlpCT8/ag66yQiHgd+BXxR0kaSdgdOYk0ivgb4nKQtc83h1KrlXwKuBa4EfhsR82uIzWp3LOkQu9epuQP8EklbtiuoZsoVmK8A80kVhucj4jagq/dIND9u074om2eA/YU2fb+tSuA1XYJcUJsBFwLPkpooniZ9ib2uB3YAro+IlRXTv0pKoreR2kIvBjauYXsvk2ryd5JOGQzgX/p573GkdvGFOY4zI2JWnvd50g/lo8DtpGT916rlLyP98Lj5pInyRT7vBf4rT/oW6WhsT9I//rntiay5cqI6EtiR1GQ3WtIHBl6qvAbY37Z9v626oq8UlyBHRHcf0+4Adh9gmRckPUVVEoyI1cDZ+a96meoftCci4t/y88XAhRHxeUnjgJ6IeDYvN4+KH8OIWEBq/ugrrpWkZh0AJH2U9D1Umg+8CFyHNdOhwD0RsRig9xFA0oXATe0KrMneBTwaEU8BSPoB8HZSX9G4iFiUy/CSdgbZRH3ub0Rc3vuGVn+/raqBd+wlyJLeR6ol/6RJq7wRmJqfTwV+WGdc4yTtn88rnwhMo6KJR9J6pFMdr46IZQ3GbGs7jormk5zEev096QyGTjAf2E/SJvk6hgNJZ1A1pQwXUJ/7287vtyU18IhYJan3EuRRwCVR2yXIhSaph3RWzQkR8Uody18FTAHG5otszgRmANdIOolUYI6uM7wNgW+TDveeI3UcfzNvdzSppv8Y6SwYaxJJm5DOu/9fFZO/LGlP0g/9vKp5pRURd0m6FriH1NH+O9Jl5WNoThkulAH296J2fb++lN7MrKR8JaaZWUm19LakY8eOje7u7lZukpUrVzJ69OiWbrNWRY4NihvfnDlzlkYbRuQxK5qWJvDu7m5mz57dyk3S09PDlClTWrrNWhU5NihufJI6/eIYs5q4CcXMrKQ8sosNu+7pNw95mXkzDhuGSMw6i2vgZmYl5QRuZlZSTuBmZiXlBG5mVlJO4GZmJeUEbmZWUk7gZmYl5QRuZlZSTuBmZiXlBG5mVlJO4GZmJeUEbmZWUk7gZmYlNWgCl3SJpCWS7quYtpWkWZIezI9bDm+YZmZWrZYa+KWsO/DtdOCOiJgA3JFfm5lZCw2awCPiTuCZqslHApfl55cBRzU3LDMzG0xNo9JL6gZuiojd8uvnImKLivnPRkSfzSiSTgFOAejq6trr6quvbkLYtVuxYgVjxoxp6TZrVeTYoHnx3fvE80NeZtL4zfudd8ABB8yJiMmNxGTWCYZ9RJ6ImAnMBJg8eXK0eozFoo7rCMWODZoX34n1jMhzfOPbNet09Z6FsljSOID8uKR5IZmZWS3qTeA3AlPz86nAD5sTjpmZ1WrQJhRJVwFTgLGSFgBnAjOAaySdBMwHjh7OIK0Y6hmc2MyGz6AJPCKO62fWgU2OxczMhsBXYpqZlZQTuJlZSTmBm5mVlBO4mVlJOYGbmZWUE7iZWUk5gZuZlZQTuJlZSTmBm5mVlBO4mVlJDfvtZK2YarmvybRJq+q6FayZtUZDCVzSPGA5sBpY5Zvsm5m1TjNq4AdExNImrMfMzIbAbeBmZiVV05iY/S4sPQo8CwTw7Tx8WvV7PCZmP9oZWy3jVHZtDItfbEEwffCYmGaDazSBbxsRCyVtA8wC/iWPYt+nyZMnx+zZs+veXj2KPO5kO2OrtRPz3Hvb0889b8Zh/c6T5ARuRoNNKBGxMD8uAa4H9mlGUGZmNri6E7ik0ZI27X0OHAzc16zAzMxsYI0cH3cB10vqXc+VEXFrU6IyM7NB1Z3AI+IRYI8mxmJmZkPg0wjNzErKCdzMrKQKcy+UWk5rqzbQqWYjST2fnZmVn2vgZmYl5QRuZlZSTuBmZiXlBG5mVlKF6cQsslZ2sLpD0sxq5Rq4mVlJOYGbmZWUE7iZWUmVug28yAPzFjk2M+sMDdXAJR0iaa6khyRNb1ZQZmY2uEbuBz4KuAA4FNgVOE7Srs0KzMzMBtZIDXwf4KGIeCQiXgauBo5sTlhmZjaYusfElPR+4JCIODm/PgHYNyJOrXrfq4MaAxOBufWHW5exwNIWb7NWRY4NihvfDhGxdbuDMGu3Rjox1ce0dX4N8kj164xW3yqSZhd1ANwixwbFj89spGukCWUBsH3F6+2AhY2FY2ZmtWokgd8NTJC0o6QNgWOBG5sTlpmZDaaRMTFXSToV+DEwCrgkIu5vWmTN07bmmxoUOTYofnxmI1rdnZhmZtZevpTezKyknMDNzEqqoxK4pHmS7pX0e0mz87StJM2S9GB+3LKN8W0h6VpJf5b0gKS3FSE+SRPzZ9b7t0zS6UWIzcz611EJPDsgIvasOH95OnBHREwA7siv2+VrwK0RsQuwB/BAEeKLiLn5M9sT2At4Abi+CLGZWf86qhNT0jxgckQsrZg2F5gSEYskjQN6ImJiG2LbDPgDsFNUfOhFia8inoOBMyNi/6LFZmZr67QaeAC3SZqTL+EH6IqIRQD5cZs2xbYT8BTwHUm/k3SRpNEFiq/XscBV+XnRYjOzCp2WwPePiLeS7pD4cUnvbHdAFdYH3gp8KyLeAqykYE0S+YKs9wL/1e5YzGxwHZXAI2JhflxCasPdB1icD//Jj0vaFN4CYEFE3JVfX0tK6EWJD9IP3z0RsTi/LlJsZlalYxK4pNGSNu19DhwM3Ee6vH9qfttU4IftiC8ingQel9Tbhnwg8CcKEl92HGuaT6BYsZlZlY7pxJS0E6nWDam54sqIOEfSa4FrgNcD84GjI+KZNsW4J3ARsCHwCPAh0o9o2+OTtAnwOKmT9fk8rTCfnZmtq2MSuJnZSNMxTShmZiONE7iZWUk5gZuZlZQTuJlZSTmBm5mVlBO4mVlJOYGbmZXU/weWpJQD22utnAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.hist();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### We can change the layout of the small multiples\n",
    "\n",
    "**But notice that the bins are all still different sizes/ranges for each column!**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x144 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.hist(sharex=True, sharey=True, layout=(1,-1), figsize=(12,2));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### `df.plot.hist()`\n",
    "\n",
    "This results in a single set of axes, with all histograms overlapped in different colors. With this data set there are too many categories overlapping for this to be very useful. **At least it put them automatically on common scales and uses the same bins.**\n",
    "\n",
    "It helps to make the curves less opaque by setting the `alpha=` value. 1=opaque, 0=transparent."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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"
    }
   ],
   "source": [
    "df.plot.hist(alpha=0.5);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Can still use all the same small multiple tricks\n",
    "\n",
    "I like this form much better"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x360 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.hist(alpha=0.5, \n",
    "             subplots=True, \n",
    "             layout=(2,-1),\n",
    "             figsize=(9,5),\n",
    "             sharex=True, \n",
    "             sharey=True);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## Box plot\n",
    "\n",
    "A more compact summary of the distribution for comparison is a box (and whiskers) plot.\n",
    "\n",
    "If you're not familiar with this type of plot, the center line is the median value for that column, the top and bottom of the box are the upper and lower quartiles (75% and 25% percentile values), the \"whiskers\" extend by default at most to 1.5x the inner-quartile range, and any values outside this limit are shown as individual outlier circles."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXAAAAD4CAYAAAD1jb0+AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAAAZ80lEQVR4nO3dfXRc9X3n8fcHWYmNYwwsgsUxRWkKZFKz0ETJhqBk0RJoT9ITkhZC5M3GxNqyTlKVZhss00kh3mYOFummdckmlEUGp40VntLCQjbgdURSJVlAhCeTgbDLo2sHlCdiDATZ+e4f98oeyXoYSTMaXc3ndY7OzNyZe+93ru585nd/cx8UEZiZWfYcUusCzMxsehzgZmYZ5QA3M8soB7iZWUY5wM3MMmrBbM7sqKOOiubm5tmcpZlZ5t13330/iYim0cNnNcCbm5sZGBiYzVmamWWepKfHGu4uFDOzjHKAm5lllAPczCyjHOBmZhnlADczyygHuJlZRjnAzcwyygFuZpZRs3ogj5nVXvO62ysynac2vK8i07Hpc4Cb1ZnJgrd53e0O54xwF4qZWUa5BW42z5yy/k5eeHloRtOYaTfL0kWNPHjZ2TOahk3OAW42z7zw8lDNu0Aq1c9uE3MXiplZRjnAzcwyyl0oZvPMktw6Tt68rsY1AHhPlmpzgJvNM7uLG9wHXifchWJmllEOcDOzjHIXitk8VOsujKWLGms6/3pRVoBL+hTwn4AAHgY+BhwKXA80A08BH4qIn1elSjMr20z7v30ofXZM2oUi6fXAnwAtEbECaAA+DKwDtkXECcC29LGZmc2ScvvAFwCLJC0gaXnvBM4BNqfPbwY+UPHqzMxsXJMGeET8C/BXwDPALuCFiLgTOCYidqWv2QUcPdb4ki6UNCBpYHBwsHKVm5nVuXK6UI4gaW2/AVgGLJb0kXJnEBFXR0RLRLQ0NTVNv1IzMxuhnC6U9wBPRsRgRAwBXwfeCTwn6ViA9Pb56pVpZmajlRPgzwDvkHSoJAFnAkXgVmBV+ppVwC3VKdHMzMYy6W6EEXG3pJuAHwB7gfuBq4HXATdI6iAJ+fOqWaiZmY1U1n7gEXEZcNmowb8iaY2bmWVS1q8P6iMxzaxuZf36oIqIWZtZS0tLDAwMzNr8zOxgWW91TkUlLi83U5W4vJyk+yKiZfRwt8DN6sx4wdvb20uhUKBYLJLL5cjn87S3t89ydZU13y8v5wA3M3p7e8nn8/T09NDa2kp/fz8dHR0AmQ/x+cynkzUzCoUCPT09tLW10djYSFtbGz09PRQKhVqXZhNwgJsZxWKR1tbWEcNaW1spFos1qsjK4QA3M3K5HP39/SOG9ff3k8vlalSRlcMBbmbk83k6Ojro6+tjaGiIvr4+Ojo6yOfztS7NJuAfMc1s/w+VnZ2d+/dCKRQK/gFzjnOAmxmQhLgDO1vchWJmllFugZvZvLUkt46TN9f2ao9LcgDVOZjIAW5m89bu4oZ5fSSmu1DMzDLKAW5mllEOcDOzjHKAm5lllAPczCyjHOBmZhnlADczyygHuJlZRjnAzcwyygFuZpZRDnAzs4xygJuZZZQD3MwsoxzgZmYZ5QA3M8sonw/czOa1ap6PuxxLFzVWbdoOcDObt2Z6MYfmdbfX/IIQE3EXiplZRjnAzcwyygFuZpZRDnAzs4xygJuZZZQD3MwsoxzgZmYZVVaASzpc0k2SHpVUlHSapCMlbZX0eHp7RLWLNTOzA8ptgW8EvhkRbwJOAYrAOmBbRJwAbEsfm5nZLJk0wCUdBrwb6AGIiFcj4hfAOcDm9GWbgQ9Up0QzMxtLOS3w3wQGgWsl3S/pGkmLgWMiYhdAenv0WCNLulDSgKSBwcHBihVuZlbvygnwBcBbgC9HxO8Ae5hCd0lEXB0RLRHR0tTUNM0yzcxstHICfAewIyLuTh/fRBLoz0k6FiC9fb46JZqZ2VgmDfCI+DHwrKST0kFnAj8EbgVWpcNWAbdUpUIzMxtTuaeT7QS+Kuk1wBPAx0jC/wZJHcAzwHnVKdHMzMZSVoBHxANAyxhPnVnRaszMrGw+EtPMLKMc4GZmGeUANzPLKAe4mVlGOcDNzDLKAW5mllEOcDOzjHKAm5lllAPczCyjHOBmZhnlADczyygHuJlZRjnAzcwyygFuZpZRDnAzs4xygJuZZVS5V+SxDGped3tFpvPUhvdVZDpmVlmKiFmbWUtLSwwMDFR1Hg6t8jWvu70u3qfZeLKSF5Lui4iDroo271rgky1Ih5aZDct6FrgP3Mwso+ZdC7yenLL+Tl54eWhG05jpJuTSRY08eNnZM5qG2VzT29tLoVCgWCySy+XI5/O0t7fXuqyDOMAz7IWXh2q+CVipPkSzuaK3t5d8Pk9PTw+tra309/fT0dEBMOdC3F0oZmYlCoUCPT09tLW10djYSFtbGz09PRQKhVqXdhAHuJlZiWKxyI4dO1ixYgUNDQ2sWLGCHTt2UCwWa13aQdyFkmFLcus4efO6GtcAkO1f8s1KLVu2jLVr17Jly5b9XSgrV65k2bJltS7tIA7wDNtd3OA+cLMqkDTh47nCXShmZiV27txJd3c3nZ2dLFy4kM7OTrq7u9m5c2etSztI5lrg3nXOzKopl8vx2GOPjRj22GOPkcvlalTR+DIX4N51zsyqqa2tje7ubrq7u1mzZg1XXXUVXV1drFmzptalHcRdKGZmJfr6+ujq6mLTpk0sWbKETZs20dXVRV9fX61LO0jmWuBmZtVULBa5//77+dznPrd/2NDQEJdffnkNqxqbW+BmZiVyuRz9/f0jhvX398/JPnAHuJlZiXw+T0dHB319fQwNDdHX10dHRwf5fL7WpR3EXShmZiWGz3fS2dm5/2RWhUJhzp0HBRzgZmYHaW9vn5OBPZq7UMzMMsoBbmaWUWUHuKQGSfdLui19fKSkrZIeT2+PqF6ZZmY22lRa4BcBpedTXAdsi4gTgG3pYzMzmyVlBbik5STnDL2mZPA5wOb0/mbgAxWtzMzMJlRuC/xvgLXAr0uGHRMRuwDS26PHGlHShZIGJA0MDg7OpFYzMysxaYBL+n3g+Yi4bzoziIirI6IlIlqampqmMwkzMxtDOfuBnw68X9J7gYXAYZL+AXhO0rERsUvSscDz1SzUzMxGmrQFHhGXRMTyiGgGPgx8KyI+AtwKrEpftgq4pWpVmpnZQWayH/gG4CxJjwNnpY/NzGyWTOlQ+oi4C7grvf9T4MzKl2RmZuXwkZhmZhnlk1llXK0v77Z0UWNN529WzxzgGTbTa4M2r7u95tcXNbPpcxeKmVlGOcDNzDLKAW5mllEOcDOzjHKAm5lllAPczCyjHOBmZhmVuf3Al+TWcfLm2l78Z0kOkutbmJnVTuYCfHdxQ80PPqn10Y9mZuAuFDOzzHKAm5lllAPczCyjHOBmZhmVuR8xofY/IvoUqmY2F2QuwH0KVTOzhLtQzMwyygFuZpZRDnAzs4xygJuZZZQD3MwsoxzgZmYZ5QA3M8soB7iZWUY5wM3MMsoBbmaWUQ5wM7OMcoCbmWWUA9zMLKMc4GZmGeUANzPLqMydD9zKV86FL8p5jc+fbjY3OcDnMQev2fzmLhQzs4xygJuZZdSkAS7pOEl9koqSHpF0UTr8SElbJT2e3h5R/XLNzGxYOS3wvcCfRUQOeAfwSUlvBtYB2yLiBGBb+tgyoLe3lxUrVtDQ0MCKFSvo7e2tdUlmNg2T/ogZEbuAXen93ZKKwOuBc4Az0pdtBu4CuqpSpVVMb28v+Xyenp4eWltb6e/vp6OjA4D29vYaV2dmUzGlPnBJzcDvAHcDx6ThPhzyR1e8Oqu4QqFAT08PbW1tNDY20tbWRk9PD4VCodalmdkUlR3gkl4H3Az8aUT8cgrjXShpQNLA4ODgdGq0CioWi7S2to4Y1traSrFYrFFFZjZdZQW4pEaS8P5qRHw9HfycpGPT548Fnh9r3Ii4OiJaIqKlqampEjXbDORyOfr7+0cM6+/vJ5fL1agiM5uuSfvAJQnoAYoR8YWSp24FVgEb0ttbqlKhVVQ+n6ejo+OgPvD53oVSzhGn5fDBUTaXlHMk5unAfwQelvRAOuzPSYL7BkkdwDPAeVWp0Cpq+IfKzs5OisUiuVyOQqEw73/AnCx4m9fd7nC2zClnL5R+QOM8fWZly7HZ0N7ePu8D26we+EjMOuT9wM3mB5/Mqs54P3Cz+cMBXmdK9wMH9u8H3tnZmekAP2X9nbzw8tCMpjHTHzqXLmrkwcvOntE0zKbCAV5n5ut+4C+8PFTzHyErtaeLWbncB15nvB+42fzhAK8zw/uB9/X1MTQ0RF9fHx0dHeTz+VqXZmZT5C6UOlOv+4GbzUcO8Drk/cDN5gcHeB3q7e2lUCjsb4Hn8/nMB/qS3DpO3lzbU9IvyQH4aE6bPQ7wOjNf9wPfXdzgvVCs7vhHzDrj84GbzR8O8DozX/cDN6tHDvA64/3AzeYP94HXmXw+z/nnn8/ixYt5+umnOf7449mzZw8bN26sdWlmNkVugdex5FodZpZVDvA6UygUuP7663nyySfZt28fTz75JNdff71/xDTLIHeh1JlisciOHTtYsWLF/v3Au7q65sWPmLXejW/posaazt/qjwO8zixbtozOzk6OOOIIIoI9e/bQ2dnJsmXLal3ajMx0H3BfUs2yaN4FeDmtsHJeM18/zC+99BIvvvgil156KWvWrOGqq65i7dq1NDQ01Lo0M5uieRfg8zV4K+VnP/sZl1xyCZs2beLiiy8ml8uxdu1aLr/88lqXZmZT5B8x61BbWxvbt29n3759bN++ff/VecwsW+omwH0h38Ty5ctZtWrViPOBr1q1iuXLl9e6NDOboroI8OETOF155ZW88sorXHnlleTz+boM8SuuuIK9e/eyevVqFi5cyOrVq9m7dy9XXHFFrUszsymqiwD3CZwOaG9vZ+PGjSxevBiAxYsXs3HjxkyfidCsXikiZm1mLS0tMTAwMGvzG9bQ0MArr7xCY+OB/XSHhoZYuHAh+/btm/V6bO7xboQ2l0m6LyJaRg+vixZ4Lpdj/fr1I/rA169f7xM4mVmm1UWAt7W10d3dzerVq9m9ezerV6+mu7vbe1+YWabVRYD39fXR1dXFpk2bWLJkCZs2baKrq4u+vr5al2ZmNm11EeDFYpGTTjppxLCTTjppXpz/w8zq17w7EnMsy5YtY+3atWzZsmX/dSBXrlyZ+fN/mFl9q4sWOBx87mufC9vMsq4uAnznzp10d3fT2dnJwoUL6ezspLu7m507d9a6NDOzaauLLpRcLsfy5cvZvn37/mF9fX3ejdDMMq0uWuD5fJ6Ojo4R5//o6Oggn8/XujQzs2mrixb48GHinZ2d+69CUygUfPi4mWVaXbTAIQnx0lOoOrwNDpyl8ukr3l/XZ6m0bKqbADcbrbe3l4suuog9e/ZAenm5iy66yCFumVEXJ7MyG8txxx3H3r172bJlCxd845dc997DWLlyJQsWLODZZ5+tdXlm+413MqsZ9YFL+j1gI9AAXBMRG2YyPbNqGes6qDt27ODoD/0lH7vjJdSwgI/d8RK/fvcn2XHDX4x73VSfsdDmkmm3wCU1AD8CzgJ2APcC7RHxw/HGcQvc5hJJ3HnnnZx11ln7h23dupWzzz6b2dwyNZtMNU4n+3bg/0bEExHxKvA14JwZTM9sVvnycpZ1Mwnw1wOlHYU70mEjSLpQ0oCkgcHBwRnMzqyyfHk5y7qZBPhYJxM5aLszIq6OiJaIaGlqaprB7Mwqy5eXs6ybyY+YO4DjSh4vB3xyEcuU9vZ2B7Zl1kxa4PcCJ0h6g6TXAB8Gbq1MWWZmNplpt8AjYq+kPwbuINmNcFNEPFKxyszMbEIz2g88Ir4BfKNCtZiZ2RT4UHozs4xygJuZZdSsngtF0iDw9KzNcGxHAT+pcQ1zhZfFAV4WB3hZHDBXlsXxEXHQftizGuBzgaSBsQ5JrUdeFgd4WRzgZXHAXF8W7kIxM8soB7iZWUbVY4BfXesC5hAviwO8LA7wsjhgTi+LuusDNzObL+qxBW5mNi84wM3MMmpOBbikD0oKSW+axrj/VdJ7pjnfpyQdJelwSZ+YzjSqTdI+SQ9IelDSDyS9c5rTWSPpo5Wur5JK3uvw37oZTOt7laxtnHm8fyY1VoOkF0c9vkDSF6c5rTMk3VZy/50lz10n6dyZVTvleobXj+2SbpR0aIWmO6X3IqlZ0vZKzHu6ZnQulCpoB/pJzmz42XJHktQQEZdWYP6HA58AvjSVkdL576vA/CfyckScms7vd4HLgX831YlExFUVrqsa9r/XmYqIaX3RlUvSgoi4lfo5E+cZwItA1b8YJ1D6WfgqsAb4Qg3rqZk50wKX9DrgdKCDJMCRdIikL0l6RNJtkr4x/A2ZtpovldQPnFf67SnpbZK+l7ZW75G0ZHQLJJ3eGaPK2AC8Mf12/3xpyyMd54uSLhhn/mdL+n7aOr4xfT/Vchjw87SOiWrcIOmHkh6S9FfpsM9K+nR6/y5J3eky+pGkd6XDG9L3f2867n9Ohx8r6TslrZ93pa+9Ln38sKRPVetNp8t8fbqMHx7eUpPUJGlrOvzvJD0t6aj0uRdLltNdkm6S9Kikr0pS+txbJX1b0n2S7pB0bDr8jZK+mQ7/55L5XSfpC5L6gO7SdSt97m/T9e+JknVy3HV5tqXL6+b0/3uvpNPT4W9P674/vT1p1HjNJGH5qXQdeFf61LtHv99Z9M/Ab42zbnZI+uuS+v9I0hfS+x9N1+0HJf19yfQOei9KfL5kHT9/dBGSFkq6Nn3+fklt6fBDJd2Qzut6SXdLapmotimJiDnxB3wE6Envfw94C3AuydkODwH+NUlonZu+5ilgbcn416Wvfw3wBPC2dPhhJFsaFwBfLHn9bcAZJdM6CmgGtpe85gzgtpLHXwQuGD3/dNzvAIvTx13ApRVePvuAB4BHgReAt05UI3Ak8BgH9jQ6PL39LPDp9P5dwH9L778X+N/p/QuBz6T3XwsMAG8A/gzIp8MbgCXAW4GtJfM/vILvdfjv/JJl3pne/wRwTcl7viS9/3skV4Y6Kn38YslyeoHkwiOHAN8HWoFGkvWtKX3d+SSnRgbYBpyQ3v+3wLdK1rXbgIb08QWk61b63I3pPN5Mct1YmGBdrtLnafQyfKakxi1Aa3r/N4Bi6Wclvf8e4ObR61jp+jPR+63mX8n/dAFwC/DxcdbNxcD/AxrT4d8DTgZ+m+SzMbyOHDnJ/+4Pga3pdI9Jl+WxlORFOv9r0/tvSl+zEPg08Hfp8BXAXqBlvNqmuizmUhdKO/A36f2vpY8bgRsj4tfAj9MWT6nrx5jOScCuiLgXICJ+CckVyKtgeP7vIPmHfzedz2tIAqKSSjcbTwO+ImnFBK//JfAKcI2k20kCZyxfT2/vI1khAc4G/k1Ja2opcALJRTw2SWoE/ikiHpD0BPCbkq4EbgfunM6bG2WiLpTSev8gvd8KfBAgIr4p6efjjHtPROwAkPQAyfv9BckHa2v6v2sAdinZgnoncGPJuvPakmndGON3m/1Tus7+UNIxJTVOtC5X2ohlqGSrbPiQ8PcAby55X4dJWkLyf94s6QSSL8HGMuc11vutpkXp/w+SFngPyWdwxLoJIOlbwO9LKpKE5cOSOoGbIuInABHxs0neSyvQm/6/n5P0beBtwEMl47UCV6bTe1TS08CJ6fCN6fDtkh5K7+8Zq7apLog5EeCS/hXw74EVkoLkQxTAP04y6p6xJscY1+Yk+eYr7TJaWEZpk40zPH+RtEJn5dpcEfF9JV0ETYxTYyQX3Hg7cCZJl9Qfkyzj0X6V3u7jwPogkpbuHaNfLOndwPuAv5f0+Yj4iqRTgN8FPgl8CFg9w7c4kfHqncq4peMLeCQiTit9oaTDgF9M8EUy1ro31nw06nYuOAQ4LSJeLh2Yfgn3RcQH0+6Su8qc3ljvt5rG+oL/zljrJnAN8OckW67XltQ43gEw0/3fjfeaicYdq7YpmSt94OcCX4mI4yOiOSKOA54kOQvYH6b9h8eQbMpN5lFgmaS3ASjp/15Asvl9ajqt44C3jzHubpJNr2FPk7RUXitpKUkYjuX/AKdL+q10nodKOrGMWqdFSV9sA/DT8WpMW5BLI7noxp8Cp05hFncAH09bM0g6UdJiSccDz0fE/yBp9bwl/SI5JCJuBv6CpOtrtvWTfHEg6WzgiCmM+xjQlG7VIKlR0m+nW25PSjovHa70i2omNU51Xa6WO0m+0AGQdGp6dynwL+n9C8YZd/RnZE4Ya90EiIi7Sa7duxLoTV++DfhQ2nBE0pGTTP47wPlKfu9pAt4N3DPGa/5DOr0TSbqmHmPkuvlmki4cJqhtSuZEC5yku2TDqGE3AzmSiydvB34E3E3SjzmuiHg1/ZHhSkmLgJdJNhm/S/Kl8HA6vR+MMe5PJX1Xya5B/ysiLpZ0A8mm0uPA/ePMczDdRO2VNLyZ/Zm05kop3WwUsCrdpHt2nBqXALdIWpi+fio/Ll5D0r3wAyXb2YPAB0hC52JJQyR7InwUeD1wraThxsAl03lzo5S+V4BvRsREu+mtJ1n25wPfBnaRBM2k0vXlXOBv0y/ABSRdeY+QfCC/LOkzJN0JXwMenOJ7GXYzyZdr2etyFf0J8N/TzfkFJOGzBriCpAvlvwDfGmfc/wncJOkcoHM2ii3TGRy8bg67ATg1In4OEBGPSCoA35a0j+Qzc8EE0/5H4DSS/32Q/Pb143QrZdiXgKskPUyyVXxBRPxK0pdIlulD6XweYuT/fURtUzXnD6WX9LqIeDH9trwHOD0iflzrumzuSL8096XdRqcBX56g66NmvC7XhpK9tP46IrbVYN4NJP3br0h6I0nr/8SIeLUStc2VFvhEbpN0OMkPg3/pFd7G8BvADelWwKvAH9W4nvF4XZ5F6bK+B3iwFuGdOhToS7sjBXw83eqrSG1zvgVuZmZjmys/YpqZ2RQ5wM3MMsoBbmaWUQ5wM7OMcoCbmWXU/wefQ1+JeYax7gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.box();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sorting a box plot\n",
    "\n",
    "To sort the box plot, it's slightly less straightforward than the bar chart, because **the box plot needs to be computed and plotted with the whole original DataFrame** – we just want to change the order in which the columns are plotted. \n",
    "\n",
    "To do this we'll sort the `.median()` Series, which gives us the degree subject area names in the order we want. *We'll just use the degree names to specify the column ordering for plotting, which are stored in the Index of the Series. We won't actually use the median values – those get calculated by the box plot routine for chart creation.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Health         83.700000\n",
       "Psychology     72.750000\n",
       "Business       47.210123\n",
       "Agriculture    33.317552\n",
       "Engineering    14.100000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "median_series = df.median().sort_values(ascending=False)\n",
    "median_series"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Ordered degree names are in the Index\n",
    "\n",
    "Note that, just like the line plot, we need to give the degree names in the `y=` argument, since in the Pandas plotting routines **Y is always the columns that are being used**, not the Y-axis of the resulting plot!!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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"
    }
   ],
   "source": [
    "df.plot.box(y=median_series.index);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Horizontal box plot\n",
    "\n",
    "To make the boxplot horizontal, we need to feed in a `vert=False` argument. **This type of thing is why I don't tend to use the Pandas plotting routines – the syntax doesn't always make sense, and the arguments change based on the plot you're doing!**\n",
    "\n",
    "*Take my Altair workshop to learn about a Python plotting module that is more consistent in its syntax, even if it isn't as convenient because it's not built into Pandas.*\n",
    "\n",
    "Just like in the bar chart, left-to-right vertical boxplot translates into bottom-to-top in the horizontal version."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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"
    }
   ],
   "source": [
    "df.plot.box(y=df.median().sort_values(ascending=True).index, vert=False);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  },
  "toc-autonumbering": false
 },
 "nbformat": 4,
 "nbformat_minor": 4
}