diff --git a/module3-make-explanatory-visualizations/LS_DS_123_Make_explanatory_visualizations_LESSON.ipynb b/module3-make-explanatory-visualizations/LS_DS_123_Make_explanatory_visualizations_LESSON.ipynb
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@@ -0,0 +1,839 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "LS_DS_123_Make_explanatory_visualizations_LESSON.ipynb",
+      "version": "0.3.2",
+      "provenance": [],
+      "collapsed_sections": [],
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/DanielMartinAlarcon/DS-Sprint-02-Storytelling-With-Data/blob/master/module3-make-explanatory-visualizations/LS_DS_123_Make_explanatory_visualizations_LESSON.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "-8-trVo__vRE",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "_Lambda School Data Science_\n",
+        "\n",
+        "# Choose appropriate visualizations\n",
+        "\n",
+        "\n",
+        "Recreate this [example by FiveThirtyEight:](https://fivethirtyeight.com/features/al-gores-new-movie-exposes-the-big-flaw-in-online-movie-ratings/)"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "WSHgM3C6fzpg",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## In-class example"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "ya_w5WORGs-n",
+        "colab_type": "code",
+        "outputId": "24ba880f-51b6-4f3c-c4ac-df4ed53e5823",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 355
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "from IPython.display import display, Image\n",
+        "\n",
+        "url = 'https://fivethirtyeight.com/wp-content/uploads/2017/09/mehtahickey-inconvenient-0830-1.png'\n",
+        "example = Image(url=url, width=400)\n",
+        "\n",
+        "display(example)"
+      ],
+      "execution_count": 4,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/html": [
+              "<img src=\"https://fivethirtyeight.com/wp-content/uploads/2017/09/mehtahickey-inconvenient-0830-1.png\" width=\"400\"/>"
+            ],
+            "text/plain": [
+              "<IPython.core.display.Image object>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "HioPkYtUG03B",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Using this data:\n",
+        "\n",
+        "https://github.com/fivethirtyeight/data/tree/master/inconvenient-sequel\n",
+        "\n",
+        "### Stretch goals\n",
+        "\n",
+        "Recreate more examples from [FiveThityEight's shared data repository](https://data.fivethirtyeight.com/).\n",
+        "\n",
+        "For example:\n",
+        "- [thanksgiving-2015](https://fivethirtyeight.com/features/heres-what-your-part-of-america-eats-on-thanksgiving/) ([`altair`](https://altair-viz.github.io/gallery/index.html#maps))\n",
+        "- [candy-power-ranking](https://fivethirtyeight.com/features/the-ultimate-halloween-candy-power-ranking/) ([`statsmodels`](https://www.statsmodels.org/stable/index.html))"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "c03JCR85frjC",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Make the figure look right"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "N89pO3kX_-7p",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 207
+        },
+        "outputId": "b1f8eaab-34f6-4bf1-a53d-0b1531b84c1d"
+      },
+      "cell_type": "code",
+      "source": [
+        "%matplotlib inline\n",
+        "import pandas as pd\n",
+        "import matplotlib.pyplot as plt\n",
+        "import numpy as np\n",
+        "\n",
+        "!pip install --upgrade seaborn\n",
+        "import seaborn as sns\n",
+        "\n",
+        "fake = pd.Series([38,3,1,2,1,4,6,5,5,33], index=range(1,11))"
+      ],
+      "execution_count": 7,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Requirement already up-to-date: seaborn in /usr/local/lib/python3.6/dist-packages (0.9.0)\n",
+            "Requirement already satisfied, skipping upgrade: scipy>=0.14.0 in /usr/local/lib/python3.6/dist-packages (from seaborn) (1.1.0)\n",
+            "Requirement already satisfied, skipping upgrade: pandas>=0.15.2 in /usr/local/lib/python3.6/dist-packages (from seaborn) (0.22.0)\n",
+            "Requirement already satisfied, skipping upgrade: numpy>=1.9.3 in /usr/local/lib/python3.6/dist-packages (from seaborn) (1.14.6)\n",
+            "Requirement already satisfied, skipping upgrade: matplotlib>=1.4.3 in /usr/local/lib/python3.6/dist-packages (from seaborn) (2.1.2)\n",
+            "Requirement already satisfied, skipping upgrade: python-dateutil>=2 in /usr/local/lib/python3.6/dist-packages (from pandas>=0.15.2->seaborn) (2.5.3)\n",
+            "Requirement already satisfied, skipping upgrade: pytz>=2011k in /usr/local/lib/python3.6/dist-packages (from pandas>=0.15.2->seaborn) (2018.7)\n",
+            "Requirement already satisfied, skipping upgrade: cycler>=0.10 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=1.4.3->seaborn) (0.10.0)\n",
+            "Requirement already satisfied, skipping upgrade: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=1.4.3->seaborn) (2.3.0)\n",
+            "Requirement already satisfied, skipping upgrade: six>=1.10 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=1.4.3->seaborn) (1.11.0)\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "lqqPHAyaPDC-",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 391
+        },
+        "outputId": "63fcf7cb-438a-4d47-c9d5-5d01037a2675"
+      },
+      "cell_type": "code",
+      "source": [
+        "plt.style.use('fivethirtyeight');\n",
+        "ax = sns.barplot(x=fake.index, y=fake.values, color='#EC713B');\n",
+        "\n",
+        "ax.set(xlabel='Rating',\n",
+        "      ylabel='Percent of total votes',\n",
+        "      yticks=range(0,50,10));\n",
+        "\n",
+        "ax.tick_params(labelrotation=0);\n",
+        "\n",
+        "ax.text(x=-2, y=50, s=\"'An Inconvenient Sequel: Truth to Power' is divisive'\", \n",
+        "        fontsize=16, fontweight='bold');\n",
+        "\n",
+        "ax.text(x=-2, y=46, s='IMDB ratings for the film as of Aug. 29')"
+      ],
+      "execution_count": 8,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Text(-2,46,'IMDB ratings for the film as of Aug. 29')"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 8
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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wd+9eLF68WJg/ZcoUxMbGYv369dizZw8uX76s1h2YlpaGL774AgMGDMCZM2ew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\niESGxZmIiEhkWJyJiIhE5v/wC3YX8Yhm7QAAAABJRU5ErkJggg==\n",
+            "text/plain": [
+              "<matplotlib.figure.Figure at 0x7fe52be687f0>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "Re84N5aIf_ML",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Populate figure with the real data"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "lhdOLcMcPMCp",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "pd.options.display.max_columns = 50\n",
+        "df = pd.read_csv('https://raw.githubusercontent.com/fivethirtyeight/data/master/inconvenient-sequel/ratings.csv')"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "tqJFHj0FPZ5Q",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "# Turn the timestamps from strings to actual time stamps\n",
+        "df.timestamp = pd.to_datetime(df.timestamp)"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "0AoZ5WX9Pm7x",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "# set the timestamp to be the index.\n",
+        "df.set_index('timestamp', inplace=True)\n"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "7iNgbtAyimH2",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        ""
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "D3FB624cex9H",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 111
+        },
+        "outputId": "413b6294-1446-4702-e82e-6ce616a84d85"
+      },
+      "cell_type": "code",
+      "source": [
+        "columns = ['1_pct','2_pct','3_pct','4_pct','5_pct','6_pct','7_pct','8_pct','9_pct','10_pct']\n",
+        "final = df[columns].tail(1)\n",
+        "final.columns = range(1,11)\n",
+        "final "
+      ],
+      "execution_count": 12,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "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>1</th>\n",
+              "      <th>2</th>\n",
+              "      <th>3</th>\n",
+              "      <th>4</th>\n",
+              "      <th>5</th>\n",
+              "      <th>6</th>\n",
+              "      <th>7</th>\n",
+              "      <th>8</th>\n",
+              "      <th>9</th>\n",
+              "      <th>10</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>timestamp</th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></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>2017-08-29 23:10:06.218251</th>\n",
+              "      <td>38.4</td>\n",
+              "      <td>2.6</td>\n",
+              "      <td>1.4</td>\n",
+              "      <td>0.9</td>\n",
+              "      <td>2.1</td>\n",
+              "      <td>4.1</td>\n",
+              "      <td>5.8</td>\n",
+              "      <td>5.5</td>\n",
+              "      <td>5.5</td>\n",
+              "      <td>33.7</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "                              1    2    3    4    5    6    7    8    9     10\n",
+              "timestamp                                                                     \n",
+              "2017-08-29 23:10:06.218251  38.4  2.6  1.4  0.9  2.1  4.1  5.8  5.5  5.5  33.7"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 12
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "MnioWS0Qj9gX",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "colors = ['#333333']*10\n",
+        "colors[0] = '#EC713B'\n",
+        "colors[-1] = '#EC713B'"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "4lgMFBEIgWfw",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 372
+        },
+        "outputId": "f45f1815-3968-46e0-b489-c71445085d04"
+      },
+      "cell_type": "code",
+      "source": [
+        "plt.style.use('fivethirtyeight');\n",
+        "ax = sns.barplot(x=final.columns, y=final.values[0], color='#EC713B');\n",
+        "\n",
+        "ax.set(xlabel='Rating',\n",
+        "      ylabel='Percent of total votes',\n",
+        "      yticks=range(0,50,10));\n",
+        "\n",
+        "ax.tick_params(labelrotation=0);\n",
+        "\n",
+        "ax.text(x=-2, y=50, s=\"'An Inconvenient Sequel: Truth to Power' is divisive'\", \n",
+        "        fontsize=16, fontweight='bold');\n",
+        "\n",
+        "ax.text(x=-2, y=46, s='IMDB ratings for the film as of Aug. 29');"
+      ],
+      "execution_count": 14,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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NGvTt21dI1JcuXRLGCJQm/oLYC/43MTGBmZmZML8sA0EKvvuFVWQZvm1luRSxuM9aFo6O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\nkwAAAABJRU5ErkJggg==\n",
+            "text/plain": [
+              "<matplotlib.figure.Figure at 0x7fe52c59d0f0>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "AV2mO2XZmmzH",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Next visualization"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "AQTD9GGXhy_f",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 517
+        },
+        "outputId": "23f79f8d-5964-4ccc-a099-4c95c43971aa"
+      },
+      "cell_type": "code",
+      "source": [
+        "# First, let's look at the graph itself\n",
+        "\n",
+        "url = 'https://fivethirtyeight.com/wp-content/uploads/2017/09/methahickey-inconvenient-0830-6.png'\n",
+        "example = Image(url=url, width=600)\n",
+        "\n",
+        "display(example)"
+      ],
+      "execution_count": 15,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/html": [
+              "<img src=\"https://fivethirtyeight.com/wp-content/uploads/2017/09/methahickey-inconvenient-0830-6.png\" width=\"600\"/>"
+            ],
+            "text/plain": [
+              "<IPython.core.display.Image object>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "hs2C2ZEmINHb",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "I don't want to have to generate fake data that fits this pattern, so I'll work instead on extracting the real data first."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "irMf12IqDGll",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 235
+        },
+        "outputId": "719980ef-3a5a-44fa-edbb-5fcf2590d652"
+      },
+      "cell_type": "code",
+      "source": [
+        "# I create new dataframe columns for the number of respondents in several rating ranges\n",
+        "df['1/10'] = df['respondents']*df['1_pct']*0.01\n",
+        "df['10/10'] = df['respondents']*df['10_pct']*0.01\n",
+        "df['2-9/10'] = df['respondents']-df['1/10']-df['10/10']\n",
+        "\n",
+        "# I separate out the rows for Men and Women, and all users.\n",
+        "\n",
+        "males = df[df.category == 'Males']\n",
+        "females = df[df.category == 'Females']\n",
+        "users = df[df.category == 'IMDb users']\n",
+        "\n",
+        "# And combine men, women, and individual scores into a new dataframe\n",
+        "ratings = pd.DataFrame({'males':males.respondents, \n",
+        "                        'females':females.respondents,\n",
+        "                        '1 out of 10 ratings':users['1/10'],\n",
+        "                        '10 out of 10 ratings':users['10/10'],\n",
+        "                        '2-9 out of 10 ratings':users['2-9/10']\n",
+        "                       })\n",
+        "                        \n",
+        "ratings.head(5) "
+      ],
+      "execution_count": 118,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "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>1 out of 10 ratings</th>\n",
+              "      <th>10 out of 10 ratings</th>\n",
+              "      <th>2-9 out of 10 ratings</th>\n",
+              "      <th>females</th>\n",
+              "      <th>males</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>timestamp</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>2017-07-17 12:28:32.785639</th>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>402.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017-07-17 12:28:33.025600</th>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>78.0</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017-07-17 12:28:36.697246</th>\n",
+              "      <td>303.744</td>\n",
+              "      <td>256.284</td>\n",
+              "      <td>117.972</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017-07-17 12:34:17.770310</th>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>402.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017-07-17 12:34:17.963319</th>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>78.0</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "                            1 out of 10 ratings  10 out of 10 ratings  \\\n",
+              "timestamp                                                               \n",
+              "2017-07-17 12:28:32.785639                  NaN                   NaN   \n",
+              "2017-07-17 12:28:33.025600                  NaN                   NaN   \n",
+              "2017-07-17 12:28:36.697246              303.744               256.284   \n",
+              "2017-07-17 12:34:17.770310                  NaN                   NaN   \n",
+              "2017-07-17 12:34:17.963319                  NaN                   NaN   \n",
+              "\n",
+              "                            2-9 out of 10 ratings  females  males  \n",
+              "timestamp                                                          \n",
+              "2017-07-17 12:28:32.785639                    NaN      NaN  402.0  \n",
+              "2017-07-17 12:28:33.025600                    NaN     78.0    NaN  \n",
+              "2017-07-17 12:28:36.697246                117.972      NaN    NaN  \n",
+              "2017-07-17 12:34:17.770310                    NaN      NaN  402.0  \n",
+              "2017-07-17 12:34:17.963319                    NaN     78.0    NaN  "
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 118
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "djmuFOWeFLii",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 235
+        },
+        "outputId": "a79e3583-5531-4d2c-af02-ed27d4c8964d"
+      },
+      "cell_type": "code",
+      "source": [
+        "# Because each entry has a different timestamp, there's a NaN in every single row.\n",
+        "# I interpolate to fill the NaNs, and drop the first and last few rows because \n",
+        "# interpolate doesn't fix them\n",
+        "\n",
+        "ratings.interpolate(method='nearest', inplace=True)\n",
+        "ratings.dropna(inplace=True)\n",
+        "ratings.head()"
+      ],
+      "execution_count": 119,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "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>1 out of 10 ratings</th>\n",
+              "      <th>10 out of 10 ratings</th>\n",
+              "      <th>2-9 out of 10 ratings</th>\n",
+              "      <th>females</th>\n",
+              "      <th>males</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>timestamp</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>2017-07-17 12:28:36.697246</th>\n",
+              "      <td>303.744</td>\n",
+              "      <td>256.284</td>\n",
+              "      <td>117.972</td>\n",
+              "      <td>78.0</td>\n",
+              "      <td>402.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017-07-17 12:34:17.770310</th>\n",
+              "      <td>303.744</td>\n",
+              "      <td>256.284</td>\n",
+              "      <td>117.972</td>\n",
+              "      <td>78.0</td>\n",
+              "      <td>402.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017-07-17 12:34:17.963319</th>\n",
+              "      <td>303.744</td>\n",
+              "      <td>256.284</td>\n",
+              "      <td>117.972</td>\n",
+              "      <td>78.0</td>\n",
+              "      <td>402.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017-07-17 12:34:21.250099</th>\n",
+              "      <td>303.744</td>\n",
+              "      <td>256.284</td>\n",
+              "      <td>117.972</td>\n",
+              "      <td>78.0</td>\n",
+              "      <td>402.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017-07-17 13:00:02.497484</th>\n",
+              "      <td>303.744</td>\n",
+              "      <td>256.284</td>\n",
+              "      <td>117.972</td>\n",
+              "      <td>78.0</td>\n",
+              "      <td>402.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "                            1 out of 10 ratings  10 out of 10 ratings  \\\n",
+              "timestamp                                                               \n",
+              "2017-07-17 12:28:36.697246              303.744               256.284   \n",
+              "2017-07-17 12:34:17.770310              303.744               256.284   \n",
+              "2017-07-17 12:34:17.963319              303.744               256.284   \n",
+              "2017-07-17 12:34:21.250099              303.744               256.284   \n",
+              "2017-07-17 13:00:02.497484              303.744               256.284   \n",
+              "\n",
+              "                            2-9 out of 10 ratings  females  males  \n",
+              "timestamp                                                          \n",
+              "2017-07-17 12:28:36.697246                117.972     78.0  402.0  \n",
+              "2017-07-17 12:34:17.770310                117.972     78.0  402.0  \n",
+              "2017-07-17 12:34:17.963319                117.972     78.0  402.0  \n",
+              "2017-07-17 12:34:21.250099                117.972     78.0  402.0  \n",
+              "2017-07-17 13:00:02.497484                117.972     78.0  402.0  "
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 119
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "H1uijBv9hL6a",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        ""
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "VQbS2rC3J8D6",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 310
+        },
+        "outputId": "da009877-11e0-438c-d565-9ff5f09ccc4a"
+      },
+      "cell_type": "code",
+      "source": [
+        "# And now I plot.\n",
+        "\n",
+        "sns.set()\n",
+        "sns.set_style(\"whitegrid\")\n",
+        "sns.set_context(\"notebook\", font_scale=1.1, rc={\"lines.linewidth\": 2.5})\n",
+        "\n",
+        "ax = sns.lineplot(data=ratings);\n",
+        "ax.set(yticks=range(0,1500,500));\n"
+      ],
+      "execution_count": 135,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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ixAh0Oh233347x48fx9+/4hDozMxM2rQpP8cnNDSUzMzMa67rleV36XJpVQeF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\nQmCC\n",
+            "text/plain": [
+              "<matplotlib.figure.Figure at 0x7fe528bba780>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "IxAWnIK0Yd7Z",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "'''\n",
+        "And those are the only parts that actually worked.  I notice that the way\n",
+        "I calculate the lines for ratings is wrong, clearly because 'IMDB users' is a \n",
+        "category that contains more than just males and females (otherwise those lines\n",
+        "would be squished down further).  I ran out of time to fix that.\n",
+        "\n",
+        "Most of my problems were with @#$%^& Seaborn itself, though, as most of the time \n",
+        "trying to change some of the aesthetic parameters totally backfired for some \n",
+        "reason.  I managed to change the line widths, but not the type of line or their \n",
+        "color, and I didn't even try to change the timestamp labels on the axes.\n",
+        "\n",
+        "'''"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    }
+  ]
+}
\ No newline at end of file