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    "colab": {
      "name": "Untitled104.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyMPZXrya47MhsV8JyFtBZAK",
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      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/nurfnick/Data_Viz/blob/main/Content/Data_Visualization/30_Visualize_Conclusion.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Visualization Conclusion\n"
      ],
      "metadata": {
        "id": "J7t_hIfgAX_V"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "What do you think will be on the exam?"
      ],
      "metadata": {
        "id": "lH6OESsFAdRL"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Why Do We Visualize"
      ],
      "metadata": {
        "id": "qzuunCgfBcD8"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Visualization allows use to see many datapoints and once.  It allows us to compare those points quickly and efficiently.  It allows us to see trends and look for interesting aspects about our data that may be hard to parse out without a graphic.  Graphics we have done in this course.\n",
        "\n",
        "\n",
        "\n",
        "*   Bar Chart\n",
        "*   Histogram\n",
        "*   Violin Plot\n",
        "*   Box and Whisker Plot\n",
        "*   Scatter Plot\n",
        "*   Correlation Heatmap\n",
        "*   Error Bars\n",
        "*   Pie Charts\n",
        "*   Mosaic Plots\n",
        "\n"
      ],
      "metadata": {
        "id": "np9tmbJNBt38"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "Un9js4G_AXbH"
      },
      "outputs": [],
      "source": [
        "import pandas as pa\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "df = pa.read_csv('https://raw.githubusercontent.com/nurfnick/Data_Viz/main/Data_Sets/bank.csv')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 250
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      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   age          job  marital  education default  balance housing loan  \\\n",
              "0   30   unemployed  married    primary      no     1787      no   no   \n",
              "1   33     services  married  secondary      no     4789     yes  yes   \n",
              "2   35   management   single   tertiary      no     1350     yes   no   \n",
              "3   30   management  married   tertiary      no     1476     yes  yes   \n",
              "4   59  blue-collar  married  secondary      no        0     yes   no   \n",
              "\n",
              "    contact  day month  duration  campaign  pdays  previous poutcome   y  \n",
              "0  cellular   19   oct        79         1     -1         0  unknown  no  \n",
              "1  cellular   11   may       220         1    339         4  failure  no  \n",
              "2  cellular   16   apr       185         1    330         1  failure  no  \n",
              "3   unknown    3   jun       199         4     -1         0  unknown  no  \n",
              "4   unknown    5   may       226         1     -1         0  unknown  no  "
            ],
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              "      <td>330</td>\n",
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              "      <td>tertiary</td>\n",
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              "      <td>1476</td>\n",
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              "      <td>yes</td>\n",
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              "      <td>3</td>\n",
              "      <td>jun</td>\n",
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              "      <td>4</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>59</td>\n",
              "      <td>blue-collar</td>\n",
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              "      <td>secondary</td>\n",
              "      <td>no</td>\n",
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              "      <td>5</td>\n",
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            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fc0da308090>"
            ]
          },
          "metadata": {},
          "execution_count": 31
        },
        {
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          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ax = df.groupby('education').duration.agg('mean').plot.barh()\n",
        "ax.set(title = \"Average Duration by education Level\")\n",
        "ax.set(xlabel = 'Average Duration')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "wt9Y_tGyGuHp",
        "outputId": "4dc15e49-87ca-4014-a16e-e7769895fd94",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "What does this graph show?  Everybody has about the same duration..."
      ],
      "metadata": {
        "id": "BfIjFGwFHyXC"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Histogram"
      ],
      "metadata": {
        "id": "94ROYHoQHvfH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ax = df.duration.hist(bins = 25)\n",
        "ax.set(title = \"Histogram of Duration\")\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "PwfqaOExH_8b",
        "outputId": "e0b24796-830c-449c-ef7e-cbe996c5e5f8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        }
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Looks like there are some serious outliers in duration"
      ],
      "metadata": {
        "id": "FanXNnyhIZ3z"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Violin"
      ],
      "metadata": {
        "id": "K-Uqa051IkpR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ax = sns.violinplot(data = df, x = 'duration', y = 'education')\n",
        "ax.set(title = 'Violin of Duration and Education')\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "V034XhnyInED",
        "outputId": "49d821dc-cfcd-4712-907d-4c3dc7c74d9c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Seems like the unknowns dont have the outliers.  Perhaps only un-educated and over-educated have longer waits."
      ],
      "metadata": {
        "id": "R9u6ixjlJYGj"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Box and Whisker"
      ],
      "metadata": {
        "id": "2s-rGw4JJ2bi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.balance.plot.box()"
      ],
      "metadata": {
        "id": "HNv7DC8tJ8rG",
        "outputId": "c7dcbd85-4925-45b0-c3c8-059e8952cfaf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        }
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fc0dffa8910>"
            ]
          },
          "metadata": {},
          "execution_count": 28
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.boxplot(data = df, x = 'balance', y = 'housing')"
      ],
      "metadata": {
        "id": "0DBmseDmKJT1",
        "outputId": "ae5db44b-7b83-4e0c-db7e-de7513cffc24",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        }
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fc0dff2a790>"
            ]
          },
          "metadata": {},
          "execution_count": 29
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "More than $3k is an outlier!"
      ],
      "metadata": {
        "id": "RETWECIJLLSu"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Scatterplot"
      ],
      "metadata": {
        "id": "VypCxKzbLRJe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ax = sns.scatterplot(data = df, x = 'balance', y = 'duration', hue = 'housing', style= 'education')\n",
        "ax.set(title = \"Scatter\")\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "TK1SfRSJLVb2",
        "outputId": "07be5196-af17-4074-881f-a29ecb33c134",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Non-linear trend!  No money: Wait!:: Lot's of money:Close the deal!"
      ],
      "metadata": {
        "id": "VTqVBXubMI5V"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Correlation Heatmap"
      ],
      "metadata": {
        "id": "_dUKhp4XM5gW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ax = sns.heatmap(df.corr(),vmin = -1)\n",
        "ax.set(title = 'Heatmap of Banking')\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "mZRq0c00M9Br",
        "outputId": "d2f8ef4c-208a-4b65-d3d1-cea2dad743b7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 321
        }
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "campagin and day seem to have some correlation."
      ],
      "metadata": {
        "id": "_zy6nBlNNhA7"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Error Bars"
      ],
      "metadata": {
        "id": "KINjgy9BN0Yo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.groupby('education').duration.agg(['mean', 'std', 'count'])"
      ],
      "metadata": {
        "id": "IbE3X91UPlUX",
        "outputId": "eab2bc95-e743-49a4-aece-8383397a4ccf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        }
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 mean         std  count\n",
              "education                               \n",
              "primary    261.709440  271.988443    678\n",
              "secondary  269.863833  260.896979   2306\n",
              "tertiary   256.881481  254.290937   1350\n",
              "unknown    250.449198  241.189640    187"
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>education</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>primary</th>\n",
              "      <td>261.709440</td>\n",
              "      <td>271.988443</td>\n",
              "      <td>678</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>secondary</th>\n",
              "      <td>269.863833</td>\n",
              "      <td>260.896979</td>\n",
              "      <td>2306</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>tertiary</th>\n",
              "      <td>256.881481</td>\n",
              "      <td>254.290937</td>\n",
              "      <td>1350</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>unknown</th>\n",
              "      <td>250.449198</td>\n",
              "      <td>241.189640</td>\n",
              "      <td>187</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-c4da1be6-0c05-4c81-951f-f7865ffa3ad7 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-c4da1be6-0c05-4c81-951f-f7865ffa3ad7');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "\n",
        "df1 = df.groupby('education').duration.agg(['mean', 'std', 'count'])\n",
        "def SE(std,n):\n",
        "  return 2*std/np.sqrt(n)\n",
        "\n",
        "\n",
        "df1['SE'] = df1.apply(lambda x: SE(x['std'],x['count']), axis = 1)\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "FfZD2ofQN2Nn"
      },
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df1.plot.bar(y = 'mean', yerr = 'SE')\n",
        "ax.set(title = \"Average Duration by education Level\")\n",
        "ax.set(xlabel = 'Average Duration')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "BvIWd1u3Pfe-",
        "outputId": "d91ac3e0-b9dd-45fc-afb6-d4df2bad77dd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 325
        }
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "All have the same mean..."
      ],
      "metadata": {
        "id": "CJKNeG_8QED2"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Your Turn\n",
        "\n",
        "Sumbit at least two graphics you made while thinking about what may be on the exam."
      ],
      "metadata": {
        "id": "mIN9m559Aggl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "phtqs6FMAopk"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}