{
    "cells": [
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "# Plotting with Pandas\n",
                "\n",
                "It might surprise you to be reading about pandas in a week about plotting, but when it comes to making quick exploratory plots, pandas actually has a lot to offer.\n",
                "\n",
                "Indeed, pandas not only comes with a built-in plotter for directly plotting pandas data in matplotlib (e.g., `df.plot()`), but with the `kind` argument (e.g., `df.plot(kind=\"...\")`) you can also get pandas to quickly generate many of the plots we've discussed here:\n",
                "\n",
                "- bar plots (`\"bar\"` or `\"hbar\"`), which can be stacked (`stacked=True`) or grouped\n",
                "- histograms (`\"hist\"`)\n",
                "- boxplots (`\"box\"`)\n",
                "- histogram/density plots (`\"density\"` or `\"kde\"`)\n",
                "- area plots (`\"area\"`)\n",
                "- scatter plots (`\"scatter\"`)\n",
                "- hexagonal bin plots (`\"hexbin\"`)\n",
                "- pie plots (`\"pie\"`)\n",
                "\n",
                "In all cases, the use of these methods will return a matplotlib object you can then modify further using standard matplotlib methods if you want!\n",
                "\n",
                "Our basic plotting structure remains the same:\n",
                "```\n",
                "fig, ax = plt.subplots()\n",
                "<plot things here using pandas methods to the axis 'ax'>\n",
                "<modify the plot components for customizations>\n",
                "```"
            ]
        },
        {
            "cell_type": "markdown",
            "id": "5490121c",
            "metadata": {},
            "source": [
                "## Example: Bar plots\n",
                "\n",
                "Let's look at a specific example using bar plots. We'll start with our farm production example from earlier which contains the following data:"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 1,
            "id": "c1676021",
            "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>Grain</th>\n",
                            "      <th>Wheat</th>\n",
                            "      <th>Barley</th>\n",
                            "    </tr>\n",
                            "  </thead>\n",
                            "  <tbody>\n",
                            "    <tr>\n",
                            "      <th>Farm A</th>\n",
                            "      <td>50</td>\n",
                            "      <td>75</td>\n",
                            "      <td>36</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>Farm B</th>\n",
                            "      <td>15</td>\n",
                            "      <td>98</td>\n",
                            "      <td>33</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>Farm C</th>\n",
                            "      <td>0</td>\n",
                            "      <td>20</td>\n",
                            "      <td>77</td>\n",
                            "    </tr>\n",
                            "  </tbody>\n",
                            "</table>\n",
                            "</div>"
                        ],
                        "text/plain": [
                            "        Grain  Wheat  Barley\n",
                            "Farm A     50     75      36\n",
                            "Farm B     15     98      33\n",
                            "Farm C      0     20      77"
                        ]
                    },
                    "execution_count": 1,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "%config InlineBackend.figure_format = 'retina'\n",
                "\n",
                "import pandas as pd\n",
                "import numpy as np\n",
                "import matplotlib.pyplot as plt\n",
                "\n",
                "data = {\n",
                "    \"Farm A\": [50, 75, 36],\n",
                "    \"Farm B\": [15, 98, 33],\n",
                "    \"Farm C\": [0, 20, 77],\n",
                "}\n",
                "columns = [\"Grain\", \"Wheat\", \"Barley\"]\n",
                "\n",
                "df = pd.DataFrame.from_dict(data, orient=\"index\", columns=columns)\n",
                "df"
            ]
        },
        {
            "cell_type": "markdown",
            "id": "1645e15c",
            "metadata": {},
            "source": [
                "Let's go ahead and make a simple bar plot from this DataFrame. We can do this with the pandas method `plot` and specify the keyword argument `kind` to be the type of plot we want and the `ax` to be the Axes object we want to plot it on."
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 7,
            "id": "4716ae7f",
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": [
                            "Text(0, 0.5, 'Tons')"
                        ]
                    },
                    "execution_count": 7,
                    "metadata": {},
                    "output_type": "execute_result"
                },
                {
                    "data": {
                        "image/png": 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",
                        "text/plain": [
                            "<Figure size 640x480 with 1 Axes>"
                        ]
                    },
                    "metadata": {
                        "image/png": {
                            "height": 447,
                            "width": 571
                        }
                    },
                    "output_type": "display_data"
                }
            ],
            "source": [
                "fig, ax = plt.subplots()\n",
                "df.plot(kind='bar', ax=ax)\n",
                "ax.set_ylabel('Tons')"
            ]
        },
        {
            "cell_type": "markdown",
            "id": "41bf079f",
            "metadata": {},
            "source": [
                "We can change it from a grouped plot to a stack plot by setting one simple keyword argument: `stacked = True`."
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 4,
            "id": "a35d93e6",
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": [
                            "Text(0, 0.5, 'Tons')"
                        ]
                    },
                    "execution_count": 4,
                    "metadata": {},
                    "output_type": "execute_result"
                },
                {
                    "data": {
                        "image/png": 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",
                        "text/plain": [
                            "<Figure size 640x480 with 1 Axes>"
                        ]
                    },
                    "metadata": {
                        "image/png": {
                            "height": 447,
                            "width": 571
                        }
                    },
                    "output_type": "display_data"
                }
            ],
            "source": [
                "fig, ax = plt.subplots()\n",
                "df.plot(kind='bar', ax=ax, stacked=True)\n",
                "ax.set_ylabel('Tons')"
            ]
        },
        {
            "cell_type": "markdown",
            "id": "bac8d88e",
            "metadata": {},
            "source": [
                "We can switch it to horizontal bars just as easily by switching the `kind` of plot from `bar` to `barh`:"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 5,
            "id": "4ebaf315",
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": [
                            "Text(0, 0.5, 'Tons')"
                        ]
                    },
                    "execution_count": 5,
                    "metadata": {},
                    "output_type": "execute_result"
                },
                {
                    "data": {
                        "image/png": 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",
                        "text/plain": [
                            "<Figure size 640x480 with 1 Axes>"
                        ]
                    },
                    "metadata": {
                        "image/png": {
                            "height": 413,
                            "width": 593
                        }
                    },
                    "output_type": "display_data"
                }
            ],
            "source": [
                "fig, ax = plt.subplots()\n",
                "df.plot(kind='barh', ax=ax, stacked=True)\n",
                "ax.set_xlabel('Tons')"
            ]
        },
        {
            "cell_type": "markdown",
            "id": "405401fb",
            "metadata": {},
            "source": [
                "We can even modify the properties of the objects as we would if we were plotting directly with the matplotlib `bar` plot method itself. For example, let's say we wanted to set the `edgecolor` property for the bar plot to 'white'. We add it into the pandas method instead:"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 8,
            "id": "8a55ab83",
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": [
                            "Text(0, 0.5, 'Tons')"
                        ]
                    },
                    "execution_count": 8,
                    "metadata": {},
                    "output_type": "execute_result"
                },
                {
                    "data": {
                        "image/png": 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979xyyy3p27fvYhdJP/fcc7Pxxhvn97//fV599dV88cUXmTlzZsaMGZMbbrghffv2zZlnnlnjsXPnzk2PHj1y/PHH5y9/+UvefvvtTJ8+vVzDfffdl4MPPjj/8R//kalTp9ZZx4rGmlEAAABAg/ryulEDBgxYaH916FQdQlX/t/oJe507dy6PnTFjRv75z38mSbbZZpu0bNmyxnM+8sgjeeaZZ7LJJpvkxBNPTJ8+fTJjxozcfffdufzyyzN9+vQcdthheeutt2qc44wzzsi5556bJPn2t7+do48+OhtttFFatGiRN954I3/4wx8yYsSInHPOOenSpUtOOOGEhY4vlUpJkp122im77757+vTpk65du2bKlCl59913c80112TEiBF59NFHc/zxx+fGG29c6PiXXnop//73v7PbbrslSf7nf/4ne++990JjunXrVuPX3tQJowAAAIAG1a9fv7Ro0SJz5syp8Wqn6te23377JMk3v/nNrLHGGhk/fnyGDRuWvfbaqzx25MiRmTNnTpK6b9EbOXJk9thjj9x9990LhU3bb799VltttZx++un54IMP8uCDD2bfffdd6Nhnn302v/nNb5Ikp59+ejmUqrblllvm4IMPzhFHHJFbbrklp512Wg477LB06tSpPKaysjJvvPFGvvnNby5S24ABA3LUUUflzDPPzDnnnJObb745p59+enr16lUes/HGG6d9+/blz9daa61svPHGtX69yxO36QEAAAANql27dtliiy2SJCNGjEhVVVV534QJE/L6668n+f/DqGT+U/KSRdeNWtL1olq3bp3BgwfXeNXTT3/60/LrNYVjF154YebNm5ctt9wy55xzTo3zN2vWLL///e/TqlWrTJkyJXfddddC+ysqKmoMohZ0xhlnpEuXLimVSrnvvvvqHLsiEUYBAAAADa46OJo6dWr+9a9/lV8fNmxYkqRr167lp+glqXXdqOpwqmXLlunXr1+t59tll11qvY1tlVVWKV+F9O677y60b86cOXn44YeTJAcccEAqKipqPUenTp3Sp0+fJPNDtrrMmzcv//73v/PGG2/k5Zdfzssvv5zXXnstPXr0SJK8+OKLdR6/IhFGAQAAAA1uwaueFrza6cvrRX15/PPPP59p06YlSaqqqjJy5MgkSd++fdOmTZtaz7f++uvXWU/1OlRTpkxZ6PVXX30106dPT5KccsopizzB7ssfzz33XJJk/Pjxi5yjVCrllltuyY477pj27dtnrbXWyvrrr58+ffqUP1544YUkycSJE+usd0UijAIAAAAa3Pbbb59mzebHEAte7VRbGLXZZpulffv2qaqqKl91tGAwVdcteknStm3bOvdX1zJ37tyFXv/kk08W96XUqDrAqjZz5swMGjQohx12WJ544onMmDGjzuMXt39FYgFzAAAAoMF16tQpG2+8cUaPHp1hw4alVCpl2rRp5Vv2FrxyKpm/APg222yTIUOGZOjQoRk4cOBCV1QtLoxaVguGUxdffHH+4z/+Y4mOa9eu3UKf/+Y3vynf7jdgwIAcf/zx2WKLLbLGGmukTZs25TCsf//+eeqpp8pP31sZCKMAAACAQvTv3z+jR4/Op59+mldffTUfffRR5s6dm3bt2mXzzTdfZPx2222XIUOGlK+eqv5vZWVleYHz+rbaaquVt+fMmbNMT7ArlUq59tprk8z/Gv7xj3+Uw6cv++yzz5at0OWY2/QAAACAQix4NdPQoUPL4dI222yT5s0XvV6m+ta9Z555JrNmzcrTTz+dJNl0003ToUOHBqlxo402Kj9p7+9///syzTFp0qTyGlIHHXRQrUHU1KlT88Ybb9Q6T12Lpy/PhFEAAABAIRYMo5566qla14uqVh1SzZgxIzfeeGM+/fTTReapb23bts3OO++cJHniiScyatSopZ6jqqqqvP3ltaQWdN1112XOnDm17m/dunV5e9asWUtdR1MljAIAAAAKsfrqq6d3795JFg56vrxeVLV27dpls802S5JceOGF5dcbMoxKktNOO618VdLBBx+cd955p9axc+fOzf/93/9l7Nix5de6du2aTp06JUluv/32zJ49e5Hjnn322Zx++ul11rHaaquVr9Kqq4bljTAKAAAAKEx1kDRu3LjMmDEjzZs3zzbbbFPr+Oqrpt59990k829dqy28qi/bbrttzjjjjCTJmDFjstlmm+Wkk07KQw89lH/9618ZOXJkbr/99px44on52te+lkMOOSSTJ08uH9+sWbMccsghSZIXXngh22+/fW6//fY899xzeeyxx/Lzn/88/fv3T+vWrcvhXE2aN2+eb33rW0mS66+/Prfddltee+21vP3223n77bczadKkhvtDaEAWMAcAAIAl0X6N5GevNnYV9av9GoWfsn///uXFvZNk8803X+RJdAvabrvt8rvf/a78+QYbbJAuXbo0ZIlJkrPOOiudOnXKf//3f2fq1Km5/PLLc/nll9c4tmXLlgvdUpfMf5re008/nRdeeCGjRo3Kd7/73YX2d+7cOX/5y19yxhln5M0336y1jlNOOSV77rlnPv3003zve99baN+ZZ56Zs846a9m+wEYkjAIAAIAl0awy6bBWY1ex3PvyLXa1rRdV7ctXQTX0LXoLOumkk3LggQfmf//3f/Poo4/m7bffzuTJk9OqVaustdZa6dOnT3bZZZfsv//+iwRkHTt2zNNPP51LL700d955Z9566600b948PXv2zKBBg3LiiSemR48ei61h0KBBeeyxx3L55Zfn2WefzYQJE+pcZ2p5UFEqlUqNXQQsjbFjx6Znz55Jkg9fHZUedwxs5IqoN2dMSppV5uNpH2fgXfoKwMppyAFDsnq71Ru7DFihvfXWW6mqqkrz5s3Tq1evxi4HCrO0f/cX+vn7ww+XKDxbEtaMAgAAAKAwwigAAAAACiOMAgAAAKAwwigAAAAACiOMAgAAAKAwwigAAAAACiOMAgAAAKAwwigAAAAACiOMAgAAAKAwwigAAAAACiOMAgAAAKAwwigAAAAACiOMAgAAAKAwwigAAAAACiOMAgAAAKAwwigAAAAACiOMAgAAAKAwwigAAAAACiOMAgAAAKAwwigAAACAWuywww6pqKjIDjvs0NilrDCEUQAAAECDeuKJJ1JRUVHrR/v27dO7d+8cccQReeKJJxq7XBqYMAoAAABoVNOmTctbb72Vm266KTvuuGOOOeaYzJ07t7HLooE0b+wCAAAAYHkwd97cTJwxsbHLqFdd2nRJZbPKQs953HHH5cc//nH581KplEmTJmXEiBG57LLL8sknn+T6669Pjx49cvbZZxdaG8VYKcOoJ554IjvuuOMSjx88eHCOPPLIhitoOfX666/nzjvvzGOPPZYxY8ZkwoQJqayszGqrrZZNNtkkO+64Y773ve9ljTXWaOxSAQAAvrKJMyZm4F0DG7uMejXkgCFZvd3qhZ6zW7du2XjjjRd5fcCAAdlrr73St2/fzJgxI7/73e9y+umnp0WLFoXWR8Nzmx5LbfLkyTnqqKOy8cYb58wzz8zQoUPz4YcfZubMmZk2bVo++OCDPPDAA/n5z3+enj175rjjjsukSZMau2wAAACauA033DCDBg1KknzxxRd57bXXGrkiGsJKH0Ydd9xxeemll+r82GeffRq7zCbjvffeS79+/XLDDTdk7ty56dq1a0488cT89a9/zYgRIzJy5Mj89a9/zU9/+tOstdZaqaqqytVXX52hQ4c2dukAAAAsB9ZZZ53y9syZMxfZ/+677+a3v/1t9txzz6yzzjpp06ZN2rRpk7XXXjv/+Z//mb/97W91zn/DDTeUF05/7733MmvWrPzud7/LNttsky5duqSioiJnnXXWUtf92Wef5X/+53/Sr1+/dOnSJa1atUr37t2z9957569//WuNx1xxxRXlWp555pnFnmP//fdPRUVFOnXqlBkzZix1jU3FSnmb3oJquzyQRc2YMSN77rlnXn/99STJUUcdlcsvvzyrrLLKImP33XffXHzxxbnqqqty2mmnFV0qAAAAy6n33nuvvP21r31toX1jxozJN77xjRqP++CDD/LBBx/kzjvvzKGHHprBgwenefO6Y4+JEydm3333zQsvvPCVan7ooYdyyCGHZPLkyQu9Pm7cuNx333257777MmjQoNx+++1p3759ef9hhx2WX/3qV5k5c2YGDx6crbfeus5aH3jggSTJwQcfnDZt2nylmhvTSn9lFEvu1FNPzcsvv5wkOfLII3P99dfXGERVa9myZU488cQ888wz6dmzZ1FlAgAAsJx6/fXX8+CDDyZJvvWtby2yBvHcuXPTsmXL7LnnnrniiisyZMiQPP/88xkyZEiuvPLKbLTRRkmSW265Jeeee+5iz3fMMcfkxRdfzOGHH54HH3ww//znP3P33XfXGQp92aOPPpq99torkydPzjrrrJMLL7wwTzzxRJ5//vncf//9OfTQQ5MkDz74YI444oiFjl111VWz7777Jkluv/32Oq92uvXWWzN79uwkydFHH73E9TVFwqilMG3atNxxxx059thjs9lmm6Vjx45p0aJFunbtmgEDBuSSSy7J1KlT65yj+vK76kv+/vGPf+TAAw9Mz54906JFi4UuR/zy2Mcffzz77LNPunfvnjZt2mSDDTbIueeem2nTpi10joceeih77LFHedyGG26Y888/v/yXdllMnDgxf/rTn5Ika6yxRq644oolPnajjTbKlltuucznBgAAYMXxySef5OWXXy5/vPTSS3nqqady0UUXZccdd8yMGTPSoUOH/O53v1vk2DXXXDPvvfde7rvvvpxwwgnZeeeds/nmm2fnnXcuL8NT/QCy3/72t/n888/rrGX06NG59tprc+ONN2aPPfbIFltskX322Se77777En0t06ZNy2GHHZa5c+dm1113zSuvvJJf/vKXGTBgQDbffPN85zvfyc0331z+efqvf/1rHnvssYXm+P73v58k+fzzz3P33XfXeq7BgwcnSTbeeONstdVWS1RfUyWMWgqDBg3KwQcfnOuuuy4vvvhivvjii1RVVWXixIkZOnRofvGLX2STTTYp38a2OKeddlp23nnn3HXXXRk7dmyqqqpqHXvBBRdk5513zr333ptx48Zl5syZef3113PGGWdk1113zdSpU1MqlXLSSSdl0KBBefjhh8vjXnvttZx66qnZe++9M3fu3GX62m+//fZMnz49SXLsscfWeUUUAAAA1Oaqq65Knz59yh+bbLJJ+vfvn1/96lf55JNP8sMf/jDPPPNMvv3tby9ybLt27bLmmmvWOndFRUV++9vfprKyMtOmTcuQIUPqrGWnnXb6SlcZDR48OB9//HFat26dm2++OW3btq1x3Pe///1ygFQdKlXbYYcd8s1vfrPGfdWef/75vPjii0mW/6uiEmtGLZWqqqr06dOn/KjJ7t27p1Qq5f3338/dd9+dO++8M2PGjMk+++yTF154Ia1bt651rrvvvjujR49Onz59cvLJJ2fjjTfOjBkzarxP9eGHH86oUaPSr1+/nHDCCendu3cmTpyYyy+/PA8//HCGDx+eCy64IJ07d87ll1+e3XffPccee2zWWWedjB07Nueff35GjhyZv/3tb7nmmmvyox/9aKm/9ieffLK8/Z3vfGepjwcAAIDFmTdvXu688860b98+5513Xlq2bFnn+Dlz5uTjjz/OlClTFrr4YrXVVssnn3ySF198Mfvvv3+txx9yyCFfqd577703STJgwIB069atzrH9+/fPqFGjMmLEiIVer6ioyNFHH51TTz01jz32WD744INF1sqqDqlatGhRvu1vebbSh1HVlwfWplu3buW/UIMHD06vXr0WGbP11lvnoIMOyjHHHJPddtstb7zxRm699dYcc8wxtc47evTo7LzzznnwwQfTqlWr8uv9+/dfZOyoUaOy//7754477khlZWX59YEDB2a77bbLyJEjc8UVV2TOnDk56aSTctlll5XHbLHFFhk4cGA23HDDvP/++7nqqquWKYyqTmCbNWuWzTbbbKmPXxpjx46tc/+4ceMa9PwAAAA0nDPPPHORp9XNmDEjb7/9dm6++eZcdtll+e1vf5vnnnsuDz/88CILdc+ZMyd/+tOfcvPNN+df//pXnUvSTJw4sc5aNtlkk2X+OpLkueeeS5I88sgjqaioWKJjxo8fv8hrRx11VM4444xUVVXlxhtvzK9//evyvlmzZuX//u//kiR77rlnunbt+pVqbgpW+tv0vnx54Jc/rrzyyvLYmoKoBQ0cODB77bVXkuSee+6pc2yzZs1y7bXXLhRE1aZt27b505/+tFAQlSSVlZX54Q9/mCSZMmVKunbtmosuuqjG46sXSRs9evRi75mtSfU3cMeOHZeo5q+iZ8+edX4s7/fGAgAAsLA2bdqkT58+ueiii8o/hz/55JM5//zzFxo3adKk9OvXLz/5yU/yzDPPLHZt5LoWBE/mLyC+rObMmbPI0/OWRPUSOAtaY401MmjQoCTJDTfckFKpVN537733ZtKkSUlS50Uvy5OVPoz6KiZMmJC33nproYXXqhPK6iuJarPtttsutFh5XXbZZZd07ty5xn0Lprj77bdfWrRoUeO4TTfdtLw9ZsyYJTrvgqZMmZJk/v25AAAA0FCOOeaY8s/A11133UL7TjzxxPzzn/9Mkuyzzz6577778t5772X69OmZN29eSqVSSqVS+YnuC4Y6NfnyRR9LY8HbAg866KC89NJLS/xRk2OPPTZJ8u6772bo0KHl16tv0evevXt22223Za63KVnpb9Or6fLAujz99NPlx0dWJ5M1qc9LAXv37l3rvk6dOi31uOpgaWmsssoq+eyzzxZ5cl9D+PDDD+vcP27cOFdHAQAArKCaNWuWXr165Zlnnsm///3vTJo0KZ07d84XX3yRO+64I0nyve99L7feemutc3z22WcNXmfr1q3Ttm3bTJ8+PZMnT87GG2/8lebbfffds9Zaa+Wjjz7K4MGDM2DAgHz00Ud59NFHkyRHHHHEVwrPmhJXRi2Fs846K9ttt13uvPPOOoOopH4vBaxtNf5k/jfp0o5blifqdenSJcn8R03OmjVrqY9fGj169Kjzo64nJwAAALD8W/Bp83PmzEmSvPXWW+Xtgw8+uNZj33jjjUydOrVhC/x/Nt988yTzL1yp6fa7pVFZWZkjjzwySXLXXXdl6tSpufHGG8s/wx911FFfaf6mRBi1hB577LGcffbZSZJ11103V155ZUaPHp3JkyenqqqqfCnggouM1WV5SzOrb/ObN29ejU/8AwAAgPowffr0vPrqq0nmX31UfXHEggFVXcHP1Vdf3bAFLqB63ehp06blj3/841ee75hjjklFRUWmTZuWO+64IzfccEOS+Q87W9w61ssTYdQSuuaaa5LMv91txIgROe6449KnT5907NhxoWCpiEsBG8OAAQPK2w8++GAjVgIAAMCK7MwzzyzfbbTbbruVf+b+5je/WX5i3U033VTjsQ888EB+//vfF1Nokh/96EflsOzXv/51Hn744TrHP/300wutB/VlX//617PzzjsnSU4//fS89dZbSZKjjz66nipuGoRRS+iVV15Jkuy0007p1q1breOqH+u4ojn44IPLj9O89tprC1k7CgAAgBXPJ598stCDwF5++eU899xzue2227L77rvnkksuSTL/qqhzzz23fNxqq62WPfbYI0ny0EMP5T/+4z9y991355///GcefvjhHHvssdlnn32y7rrrlh8u1tA6dOiQ2267Lc2bN8+sWbPyne98JwcddFDuuOOOPPfcc3nuuedy//3356yzzsqmm26a7bbbLqNHj65zzuqFzMePH59k/hrOBx54YIN/LUVa6RcwX1LVlwPWdSngCy+8kJEjRxZVUqG6dOmS73//+7niiisybty4nHTSSeWrxRbnlVdeycyZM7Pllls2cJUAAAANp0ubLhlywJDGLqNedWnTpfBzXnXVVbnqqqvqHNO1a9fccsst6dOnzyLHbrfddvnggw/yyCOP5JFHHllo/9e+9rXcc8895dCqCAMHDswjjzySQw45JOPHj8+f//zn/PnPf651fIcOHeqcb999902XLl3KD0Y7+OCD61wjenkkjFpCvXr1yuuvv55hw4bl3XffzbrrrrvQ/gkTJuTQQw9tpOqKcf7552fIkCF59dVXc+211yZJLrvssrRv377G8XPmzMnVV1+dU089NTfffLMwCgAAWK5VNqvM6u1Wb+wyVkgtW7ZM586ds9FGG2WPPfbIUUcdVeODv3r27Jnnn38+F154Ye699968//77ad26ddZZZ53ss88+OfHEE5fqgWH1Zaeddso777yTwYMH54EHHsiLL76YTz/9NM2aNUvXrl2zwQYbZMCAAdl///2z3nrr1TlXy5Ytc+CBB5YDuxXtFr1EGLXEDj/88Nx///2ZOnVqBgwYkF/96lfZcsstUyqVMnz48Fx66aUZP358+vXrlxEjRjR2uQ2ibdu2uf/++7P77rvnzTffzLXXXpv77rsvhxxySAYMGJA111wzpVIp48aNy9ChQ/OXv/wlH3zwQWOXDQAAQCPbYYcdUiqV6mWu1VZbLRdddFEuuuiiWse89957te478sgjy0+tWxJPPPHEEo1r27Ztjj/++Bx//PFLPHdtXnrppSTJBhtskG222eYrz9fUCKOW0AEHHJCjjjoqgwcPztixY3PCCScstL+ysjKXXXZZPvvssxU2jErmP0lwxIgROemkk3Lrrbfmk08+yWWXXZbLLrusxvEtWrTIcccdlx133LHgSgEAAGD58+abb2bYsGFJ5j9db0VkAfOlcP311+fmm2/O9ttvn1VWWSWtWrXK2muvncMOOyzDhw/PiSee2NglFqJz58656aab8vLLL+fMM8/Mdtttl7XWWiutWrVK27Zt87WvfS177rlnLrvssowdOzaXX355Onbs2NhlAwAAQJO34ALuRxxxRCNX0zAqSvV1nRwUZOzYsenZs2eS5MNXR6XHHQMbuSLqzRmTkmaV+Xjaxxl4l74CsHIacsAQa9JAA3vrrbdSVVWV5s2bp1evXo1dDiu5GTNm5KOPPsr06dNz//3354wzzsi8efPyk5/8JL///e/r9VxL+3d/oZ+/P/wwPXr0qJc63KYHAAAA0EieeeaZRZa26dGjR84+++xGqqjhuU0PAAAAoJFVVFSke/fuOfTQQ/P000+nc+fOjV1Sg3FlFAAAAEAjqc8nDS4vXBkFAAAAQGGEUQAAAAAURhgFAAAAQGGEUQAAAAAURhgFAAAAsBJoKgulC6MAAABYqVRWViZJqqqqMnfu3EauBooxd+7c8t/36u+BxiKMAgAAYKXStm3b8vbkyZMbrxAo0IJ/1xf8HmgMzRv17AAAAFCwTp065bPPPkuSfPLJJ5k7d246dOiQVq1apaKiopGrg/pTKpUya9asfPHFF/n000/Lr6+66qqNWJUwCgAAgJVM69at07Fjx3z++edJkk8//TSffvppKioqGv32JahPc+fOXWSdqI4dO6ZVq1aNVNF8wigAAABWOmuuuWZatmyZCRMmlF8rlUqpqqpqxKqgYXXt2jWrrbZaY5chjAIAAGDlU1FRkS5duqRDhw6ZOnVqpk2bltmzZ2fevHmNXRrUm2bNmqVly5Zp165d2rdvn5YtWzZ2SUmEUQAAAKzEWrZsmc6dO6dz586NXQqsNDxNDwAAAIDCCKMAAAAAKIwwCgAAAIDCCKMAAAAAKIwwCgAAAIDCCKMAAAAAKIwwCgAAAIDCCKMAAAAAKIwwCgAAAIDCCKMAAAAAKIwwCgAAAIDCCKMAAAAAKIwwCgAAAIDCCKMAAAAAKIwwCgAAAIDCCKMAAAAAKIwwCgAAAIDCCKMAAAAAKIwwCgAAAIDCCKMAAAAAKIwwCgAAAIDCCKMAAAAAKIwwCgAAAIDCCKMAAAAAKIwwCgAAAIDCCKMAAAAAKIw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",
                        "text/plain": [
                            "<Figure size 640x480 with 1 Axes>"
                        ]
                    },
                    "metadata": {
                        "image/png": {
                            "height": 413,
                            "width": 593
                        }
                    },
                    "output_type": "display_data"
                }
            ],
            "source": [
                "fig, ax = plt.subplots()\n",
                "df.plot(kind='barh', ax=ax, stacked=True, edgecolor='white')\n",
                "ax.set_xlabel('Tons')"
            ]
        },
        {
            "cell_type": "markdown",
            "id": "45a26b0f",
            "metadata": {},
            "source": [
                "We can also apply styles to the plots as before. Let's apply the 'fivethirtyeight' style here:"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 9,
            "id": "efc0099f",
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": [
                            "Text(0, 0.5, 'Tons')"
                        ]
                    },
                    "execution_count": 9,
                    "metadata": {},
                    "output_type": "execute_result"
                },
                {
                    "data": {
                        "image/png": 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",
                        "text/plain": [
                            "<Figure size 640x480 with 1 Axes>"
                        ]
                    },
                    "metadata": {
                        "image/png": {
                            "height": 432,
                            "width": 677
                        }
                    },
                    "output_type": "display_data"
                }
            ],
            "source": [
                "plt.style.use('fivethirtyeight')\n",
                "fig, ax = plt.subplots()\n",
                "df.plot(kind='barh', ax=ax, stacked=True, edgecolor='white')\n",
                "ax.set_xlabel('Tons')"
            ]
        },
        {
            "cell_type": "markdown",
            "id": "811b5811",
            "metadata": {},
            "source": [
                "These pandas tools can save on coding and enable you to focus more time on what matters: exploring your data!\n",
                "\n",
                "To learn more or to see more examples, check out the panda's [documentation on charting here.](https://pandas.pydata.org/pandas-docs/stable/user_guide/visualization.html#other-plots)"
            ]
        }
    ],
    "metadata": {
        "kernelspec": {
            "display_name": "Python 3.10.6 ('base')",
            "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": "3.12.0"
        },
        "orig_nbformat": 4,
        "vscode": {
            "interpreter": {
                "hash": "718fed28bf9f8c7851519acf2fb923cd655120b36de3b67253eeb0428bd33d2d"
            }
        }
    },
    "nbformat": 4,
    "nbformat_minor": 5
}