DataWorkshop (3).ipynb

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    "colab": {
      "name": "DataWorkshop",
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      "collapsed_sections": [],
      "toc_visible": true
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7GpE0Qd-gTWk",
        "colab_type": "text"
      },
      "source": [
        "## DataSet Source\n",
        "https://www.glassdoor.com/research/app/uploads/sites/2/2018/01/JMR_data-2020-01.xlsx\n",
        "\n",
        "- Historical information on housing from glassdoor\n",
        "- Metro areas and most common and well paid jobs over time\n",
        "\n",
        "- The folowing code may be a bit hard to follow if you do not look at the structure of the dataset we are working with. Checkout the link above, save as a csv."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U-SjFHBogpqt",
        "colab_type": "text"
      },
      "source": [
        "## Loading in the data set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XXKk6L8nfoHC",
        "colab_type": "code",
        "outputId": "80ed7cee-def9-46d0-ccf4-e895d1035fa1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "# Load in the dataset\n",
        "import pandas as pd\n",
        "raw_data = pd.read_csv(\"glassdoor.csv\")\n",
        "raw_data.head()"
      ],
      "execution_count": 6,
      "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",
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              "    }\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>Metro</th>\n",
              "      <th>Dimension Type</th>\n",
              "      <th>Month</th>\n",
              "      <th>Dimension</th>\n",
              "      <th>Measure</th>\n",
              "      <th>Value</th>\n",
              "      <th>YoY</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>National</td>\n",
              "      <td>Quick Facts</td>\n",
              "      <td>2020-01</td>\n",
              "      <td>U.S. Job Openings</td>\n",
              "      <td>U.S. Job Openings</td>\n",
              "      <td>5,612,853</td>\n",
              "      <td>-3.1%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>National</td>\n",
              "      <td>Quick Facts</td>\n",
              "      <td>2020-01</td>\n",
              "      <td>U.S. Median Pay</td>\n",
              "      <td>U.S. Median Pay</td>\n",
              "      <td>$ 54,430</td>\n",
              "      <td>2.6%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Atlanta</td>\n",
              "      <td>Quick Facts</td>\n",
              "      <td>2020-01</td>\n",
              "      <td>Metro Job Openings</td>\n",
              "      <td>Metro Job Openings</td>\n",
              "      <td>108,545</td>\n",
              "      <td>-12.5%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Atlanta</td>\n",
              "      <td>Quick Facts</td>\n",
              "      <td>2020-01</td>\n",
              "      <td>U.S. Job Openings</td>\n",
              "      <td>U.S. Job Openings</td>\n",
              "      <td>5,612,853</td>\n",
              "      <td>-3.1%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Atlanta</td>\n",
              "      <td>Quick Facts</td>\n",
              "      <td>2020-01</td>\n",
              "      <td>Metro Median Pay</td>\n",
              "      <td>Metro Median Pay</td>\n",
              "      <td>$ 56,521</td>\n",
              "      <td>2.9%</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      Metro Dimension Type    Month  ...             Measure      Value     YoY\n",
              "0  National    Quick Facts  2020-01  ...   U.S. Job Openings  5,612,853   -3.1%\n",
              "1  National    Quick Facts  2020-01  ...     U.S. Median Pay   $ 54,430    2.6%\n",
              "2   Atlanta    Quick Facts  2020-01  ...  Metro Job Openings    108,545  -12.5%\n",
              "3   Atlanta    Quick Facts  2020-01  ...   U.S. Job Openings  5,612,853   -3.1%\n",
              "4   Atlanta    Quick Facts  2020-01  ...    Metro Median Pay   $ 56,521    2.9%\n",
              "\n",
              "[5 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SBSmLAjhiOEe",
        "colab_type": "text"
      },
      "source": [
        "## Now lets look at what we got"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "45RQrpufidEg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Setup charting library\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M7aXLAmJpR6L",
        "colab_type": "text"
      },
      "source": [
        "### Quick Facts"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QLStMO5Ig59t",
        "colab_type": "code",
        "outputId": "05a66664-0245-48df-f4c8-821aeb4a6167",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "# Quick Facts\n",
        "quickFacts_raw = raw_data[raw_data['Dimension Type']=='Quick Facts']\n",
        "\n",
        "# Grab the two types of 'quick facts data'\n",
        "quickFacts_jobOpenings = quickFacts_raw[ quickFacts_raw['Dimension']=='Metro Job Openings']\n",
        "quickFacts_jobPay = quickFacts_raw[ quickFacts_raw['Dimension']=='Metro Median Pay']\n",
        "\n",
        "quickFacts_jobPay.head()\n"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    }\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>Metro</th>\n",
              "      <th>Dimension Type</th>\n",
              "      <th>Month</th>\n",
              "      <th>Dimension</th>\n",
              "      <th>Measure</th>\n",
              "      <th>Value</th>\n",
              "      <th>YoY</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Atlanta</td>\n",
              "      <td>Quick Facts</td>\n",
              "      <td>2020-01</td>\n",
              "      <td>Metro Median Pay</td>\n",
              "      <td>Metro Median Pay</td>\n",
              "      <td>$ 56,521</td>\n",
              "      <td>2.9%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>New York City</td>\n",
              "      <td>Quick Facts</td>\n",
              "      <td>2020-01</td>\n",
              "      <td>Metro Median Pay</td>\n",
              "      <td>Metro Median Pay</td>\n",
              "      <td>$ 65,990</td>\n",
              "      <td>3.6%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Los Angeles</td>\n",
              "      <td>Quick Facts</td>\n",
              "      <td>2020-01</td>\n",
              "      <td>Metro Median Pay</td>\n",
              "      <td>Metro Median Pay</td>\n",
              "      <td>$ 64,277</td>\n",
              "      <td>3.5%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>Philadelphia</td>\n",
              "      <td>Quick Facts</td>\n",
              "      <td>2020-01</td>\n",
              "      <td>Metro Median Pay</td>\n",
              "      <td>Metro Median Pay</td>\n",
              "      <td>$ 57,719</td>\n",
              "      <td>2.0%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>Houston</td>\n",
              "      <td>Quick Facts</td>\n",
              "      <td>2020-01</td>\n",
              "      <td>Metro Median Pay</td>\n",
              "      <td>Metro Median Pay</td>\n",
              "      <td>$ 57,690</td>\n",
              "      <td>3.3%</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            Metro Dimension Type    Month  ...           Measure     Value   YoY\n",
              "4         Atlanta    Quick Facts  2020-01  ...  Metro Median Pay  $ 56,521  2.9%\n",
              "8   New York City    Quick Facts  2020-01  ...  Metro Median Pay  $ 65,990  3.6%\n",
              "12    Los Angeles    Quick Facts  2020-01  ...  Metro Median Pay  $ 64,277  3.5%\n",
              "16   Philadelphia    Quick Facts  2020-01  ...  Metro Median Pay  $ 57,719  2.0%\n",
              "20        Houston    Quick Facts  2020-01  ...  Metro Median Pay  $ 57,690  3.3%\n",
              "\n",
              "[5 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GzD9BjmphDkA",
        "colab_type": "code",
        "outputId": "318c0a4f-107a-4b7b-9c38-d8dfe8294dd3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 153
        }
      },
      "source": [
        "# Lets look at types\n",
        "quickFacts_jobPay.dtypes"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Metro             object\n",
              "Dimension Type    object\n",
              "Month             object\n",
              "Dimension         object\n",
              "Measure           object\n",
              "Value             object\n",
              "YoY               object\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YcJXq5hajNk7",
        "colab_type": "code",
        "outputId": "b1d1b43b-7a2d-4b11-e61f-f508433c1578",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "# Switch them to propper types\n",
        "import re\n",
        "def cleanNumber (num): return re.sub(\"[^0-9\\.-]\", \"\", str(num))\n",
        "\n",
        "valueColCleaned = quickFacts_jobPay['Value'].apply( cleanNumber ).values\n",
        "quickFacts_jobPay.insert(4, \"Average Salary\", valueColCleaned )\n",
        "quickFacts_jobPay = quickFacts_jobPay.astype({'Average Salary' : float})\n",
        "\n",
        "valueColCleaned = quickFacts_jobOpenings['Value'].apply( cleanNumber ).values\n",
        "quickFacts_jobOpenings.insert(4, \"Job Openings\", valueColCleaned )\n",
        "quickFacts_jobOpenings = quickFacts_jobOpenings.astype({'Job Openings' : float})\n",
        "\n",
        "quickFacts_jobPay.head()"
      ],
      "execution_count": 10,
      "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",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\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>Metro</th>\n",
              "      <th>Dimension Type</th>\n",
              "      <th>Month</th>\n",
              "      <th>Dimension</th>\n",
              "      <th>Average Salary</th>\n",
              "      <th>Measure</th>\n",
              "      <th>Value</th>\n",
              "      <th>YoY</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Atlanta</td>\n",
              "      <td>Quick Facts</td>\n",
              "      <td>2020-01</td>\n",
              "      <td>Metro Median Pay</td>\n",
              "      <td>56521.0</td>\n",
              "      <td>Metro Median Pay</td>\n",
              "      <td>$ 56,521</td>\n",
              "      <td>2.9%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>New York City</td>\n",
              "      <td>Quick Facts</td>\n",
              "      <td>2020-01</td>\n",
              "      <td>Metro Median Pay</td>\n",
              "      <td>65990.0</td>\n",
              "      <td>Metro Median Pay</td>\n",
              "      <td>$ 65,990</td>\n",
              "      <td>3.6%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Los Angeles</td>\n",
              "      <td>Quick Facts</td>\n",
              "      <td>2020-01</td>\n",
              "      <td>Metro Median Pay</td>\n",
              "      <td>64277.0</td>\n",
              "      <td>Metro Median Pay</td>\n",
              "      <td>$ 64,277</td>\n",
              "      <td>3.5%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>Philadelphia</td>\n",
              "      <td>Quick Facts</td>\n",
              "      <td>2020-01</td>\n",
              "      <td>Metro Median Pay</td>\n",
              "      <td>57719.0</td>\n",
              "      <td>Metro Median Pay</td>\n",
              "      <td>$ 57,719</td>\n",
              "      <td>2.0%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>Houston</td>\n",
              "      <td>Quick Facts</td>\n",
              "      <td>2020-01</td>\n",
              "      <td>Metro Median Pay</td>\n",
              "      <td>57690.0</td>\n",
              "      <td>Metro Median Pay</td>\n",
              "      <td>$ 57,690</td>\n",
              "      <td>3.3%</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            Metro Dimension Type    Month  ...           Measure     Value   YoY\n",
              "4         Atlanta    Quick Facts  2020-01  ...  Metro Median Pay  $ 56,521  2.9%\n",
              "8   New York City    Quick Facts  2020-01  ...  Metro Median Pay  $ 65,990  3.6%\n",
              "12    Los Angeles    Quick Facts  2020-01  ...  Metro Median Pay  $ 64,277  3.5%\n",
              "16   Philadelphia    Quick Facts  2020-01  ...  Metro Median Pay  $ 57,719  2.0%\n",
              "20        Houston    Quick Facts  2020-01  ...  Metro Median Pay  $ 57,690  3.3%\n",
              "\n",
              "[5 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R-Ita1EMney5",
        "colab_type": "code",
        "outputId": "60aba641-590f-48d0-af3a-45d7e80060a8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        }
      },
      "source": [
        "# Lets look at types again\n",
        "quickFacts_jobOpenings.dtypes"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Metro              object\n",
              "Dimension Type     object\n",
              "Month              object\n",
              "Dimension          object\n",
              "Job Openings      float64\n",
              "Measure            object\n",
              "Value              object\n",
              "YoY                object\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aLQFv9KRkFb0",
        "colab_type": "code",
        "outputId": "46dd7b0c-a3ea-4043-9b52-bf3b7955b398",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 368
        }
      },
      "source": [
        "# Do ploting of Salary information\n",
        "\n",
        "quickFacts_jobPay = quickFacts_jobPay.sort_values(\"Average Salary\", ascending=False)\n",
        "\n",
        "plot = quickFacts_jobPay.plot.bar(x='Metro', y='Average Salary')\n",
        "plot.set_ylim(50000, 80000)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(50000, 80000)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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AdUlnTVNK+FnMBnpVPe4FzC7o2DcB+9Vo35fUHVD0z+IXwE5Vjwfl/6cPA7OK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\nzHAyMDMznAzMzAwnAzMzA/4/9JoITJ/pkBcAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pSSvqiXcnLcL",
        "colab_type": "code",
        "outputId": "d79be287-c00b-47a8-9dc3-5299713d525a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 348
        }
      },
      "source": [
        "# Do ploting of Job Opening information\n",
        "\n",
        "quickFacts_jobOpenings = quickFacts_jobOpenings.sort_values(\"Job Openings\", ascending=False)\n",
        "\n",
        "plot = quickFacts_jobOpenings.plot.bar(x='Metro', y='Job Openings')"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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CzMwqeT+JNuY9LcysJ76TMDOzSk4SZmZWyUnCzMwqOUmYmVklJwkzM6vkJGFmZpWcJMzM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\nSUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oi80sDAopxea",
        "colab_type": "text"
      },
      "source": [
        "### Base Pay over Time"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AQkAuuZ7ouoa",
        "colab_type": "code",
        "outputId": "3b201bad-d074-4f99-d547-5e3633479325",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "# Base Pay\n",
        "timeseries_raw = raw_data[raw_data['Dimension Type']=='Timeseries']\n",
        "medianBasePay_raw = timeseries_raw[ timeseries_raw['Measure'] == 'Median Base Pay']\n",
        "\n",
        "medianBasePay_raw.head()\n"
      ],
      "execution_count": 14,
      "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>Metro</th>\n",
              "      <th>Dimension Type</th>\n",
              "      <th>Month</th>\n",
              "      <th>Dimension</th>\n",
              "      <th>Measure</th>\n",
              "      <th>Value</th>\n",
              "      <th>YoY</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1439</th>\n",
              "      <td>U.S.</td>\n",
              "      <td>Timeseries</td>\n",
              "      <td>2020-01</td>\n",
              "      <td>Metro Median Base Pay</td>\n",
              "      <td>Median Base Pay</td>\n",
              "      <td>54430</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1440</th>\n",
              "      <td>U.S.</td>\n",
              "      <td>Timeseries</td>\n",
              "      <td>2019-12</td>\n",
              "      <td>Metro Median Base Pay</td>\n",
              "      <td>Median Base Pay</td>\n",
              "      <td>54500.33333</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1441</th>\n",
              "      <td>U.S.</td>\n",
              "      <td>Timeseries</td>\n",
              "      <td>2019-11</td>\n",
              "      <td>Metro Median Base Pay</td>\n",
              "      <td>Median Base Pay</td>\n",
              "      <td>54336.66667</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1442</th>\n",
              "      <td>U.S.</td>\n",
              "      <td>Timeseries</td>\n",
              "      <td>2019-10</td>\n",
              "      <td>Metro Median Base Pay</td>\n",
              "      <td>Median Base Pay</td>\n",
              "      <td>54228.66667</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1443</th>\n",
              "      <td>U.S.</td>\n",
              "      <td>Timeseries</td>\n",
              "      <td>2019-09</td>\n",
              "      <td>Metro Median Base Pay</td>\n",
              "      <td>Median Base Pay</td>\n",
              "      <td>54090.33333</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     Metro Dimension Type    Month  ...          Measure        Value  YoY\n",
              "1439  U.S.     Timeseries  2020-01  ...  Median Base Pay        54430  NaN\n",
              "1440  U.S.     Timeseries  2019-12  ...  Median Base Pay  54500.33333  NaN\n",
              "1441  U.S.     Timeseries  2019-11  ...  Median Base Pay  54336.66667  NaN\n",
              "1442  U.S.     Timeseries  2019-10  ...  Median Base Pay  54228.66667  NaN\n",
              "1443  U.S.     Timeseries  2019-09  ...  Median Base Pay  54090.33333  NaN\n",
              "\n",
              "[5 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x5WJ99U4rAIi",
        "colab_type": "code",
        "outputId": "3af52e76-62bc-4bf6-84af-d870c4e78877",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 153
        }
      },
      "source": [
        "# DataTypes\n",
        "medianBasePay_raw = medianBasePay_raw.astype({'Value' : float})\n",
        "medianBasePay_raw.dtypes"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Metro              object\n",
              "Dimension Type     object\n",
              "Month              object\n",
              "Dimension          object\n",
              "Measure            object\n",
              "Value             float64\n",
              "YoY                object\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OcgXNLxbqSZ4",
        "colab_type": "code",
        "outputId": "524e674c-8f8d-42ba-ab6d-0863c3e3b52b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 441
        }
      },
      "source": [
        "# Charting average pay over time\n",
        "\n",
        "basePay_USTotal = medianBasePay_raw[ medianBasePay_raw['Metro'] == 'U.S.' ]\n",
        "basePay_USTotal = basePay_USTotal.sort_values(\"Month\", ascending=True)\n",
        "\n",
        "plot = basePay_USTotal.plot.bar(x='Month', y='Value',figsize=(18,6))\n",
        "plot.set_ylim(40000, 70000)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(40000, 70000)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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AAACAXmhCAAAAAL3QhAAAAAB6oQkBAAAA9EITAgAAAOiFJgQAAADQC00IAAAAoBeaEAAA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\n",
            "text/plain": [
              "<Figure size 1296x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ke8RGLD1q-dO",
        "colab_type": "code",
        "outputId": "c3e434f4-bdec-46ae-bc9b-f01d513d8372",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 441
        }
      },
      "source": [
        "# Charting pay over time for NYC vs USA Average\n",
        "\n",
        "basePay_NYCTotal = medianBasePay_raw[ medianBasePay_raw['Metro'] == 'New York City' ]\n",
        "basePay_NYCTotal = basePay_NYCTotal.sort_values(\"Month\", ascending=True)\n",
        "\n",
        "basePay_NYCTotal.insert(4, \"USAverage\", basePay_USTotal[\"Value\"].values)\n",
        "\n",
        "plot = basePay_NYCTotal.plot(kind= 'bar', x='Month', y=['Value', 'USAverage'], figsize=(18,6))\n",
        "plot.set_ylim(45000, 70000)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(45000, 70000)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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x4k0PSeoqrmiQJElSxfV7TYiI2DQiNltzHzgMuAu4HFjzCRdzgcuK+5cDxxef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\nAi0AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1296x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zj-yU0TitkBx",
        "colab_type": "text"
      },
      "source": [
        "## Lets answer some questions with data!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9_1lc13jtmdc",
        "colab_type": "text"
      },
      "source": [
        "### What is the best city to be a data scientist at?\n",
        "Figure out where data scientists are paid the most and where the profession is growwing the fastest"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BscZr6vQroKS",
        "colab_type": "code",
        "outputId": "c3636a97-ab2c-4761-db36-ea6abd7bc709",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "# Get data scientist job information for current month, lets look at right now first\n",
        "\n",
        "jobTitle_raw = raw_data[raw_data['Dimension Type']=='Job Title']\n",
        "dataScientist_raw = jobTitle_raw[ jobTitle_raw['Dimension'] == 'Data Scientist']\n",
        "\n",
        "# Transform type to be float\n",
        "\n",
        "dataScientist_cleaned = dataScientist_raw['Value'].apply( cleanNumber ).values\n",
        "dataScientist_raw.insert(4, \"Average Salary\", dataScientist_cleaned )\n",
        "dataScientist_raw = dataScientist_raw.astype({'Average Salary' : float})\n",
        "\n",
        "# Check data\n",
        "dataScientist_raw.head()"
      ],
      "execution_count": 18,
      "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>Metro</th>\n",
              "      <th>Dimension Type</th>\n",
              "      <th>Month</th>\n",
              "      <th>Dimension</th>\n",
              "      <th>Average Salary</th>\n",
              "      <th>Measure</th>\n",
              "      <th>Value</th>\n",
              "      <th>YoY</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>61</th>\n",
              "      <td>National</td>\n",
              "      <td>Job Title</td>\n",
              "      <td>2020-01</td>\n",
              "      <td>Data Scientist</td>\n",
              "      <td>97652.0</td>\n",
              "      <td>Median Base Pay</td>\n",
              "      <td>$ 97,652</td>\n",
              "      <td>1.6%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>145</th>\n",
              "      <td>Atlanta</td>\n",
              "      <td>Job Title</td>\n",
              "      <td>2020-01</td>\n",
              "      <td>Data Scientist</td>\n",
              "      <td>105001.0</td>\n",
              "      <td>Median Base Pay</td>\n",
              "      <td>$ 105,001</td>\n",
              "      <td>1.9%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>229</th>\n",
              "      <td>New York City</td>\n",
              "      <td>Job Title</td>\n",
              "      <td>2020-01</td>\n",
              "      <td>Data Scientist</td>\n",
              "      <td>118464.0</td>\n",
              "      <td>Median Base Pay</td>\n",
              "      <td>$ 118,464</td>\n",
              "      <td>2.6%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>313</th>\n",
              "      <td>Los Angeles</td>\n",
              "      <td>Job Title</td>\n",
              "      <td>2020-01</td>\n",
              "      <td>Data Scientist</td>\n",
              "      <td>122011.0</td>\n",
              "      <td>Median Base Pay</td>\n",
              "      <td>$ 122,011</td>\n",
              "      <td>2.6%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>397</th>\n",
              "      <td>Philadelphia</td>\n",
              "      <td>Job Title</td>\n",
              "      <td>2020-01</td>\n",
              "      <td>Data Scientist</td>\n",
              "      <td>104963.0</td>\n",
              "      <td>Median Base Pay</td>\n",
              "      <td>$ 104,963</td>\n",
              "      <td>1.0%</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             Metro Dimension Type    Month  ...          Measure      Value   YoY\n",
              "61        National      Job Title  2020-01  ...  Median Base Pay   $ 97,652  1.6%\n",
              "145        Atlanta      Job Title  2020-01  ...  Median Base Pay  $ 105,001  1.9%\n",
              "229  New York City      Job Title  2020-01  ...  Median Base Pay  $ 118,464  2.6%\n",
              "313    Los Angeles      Job Title  2020-01  ...  Median Base Pay  $ 122,011  2.6%\n",
              "397   Philadelphia      Job Title  2020-01  ...  Median Base Pay  $ 104,963  1.0%\n",
              "\n",
              "[5 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vYYOoM58tgD6",
        "colab_type": "code",
        "outputId": "061b5f75-9f97-425a-89b2-b9e4c9498fe7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 368
        }
      },
      "source": [
        "# Chart data scientist at various locations\n",
        "\n",
        "dataScientist_raw = dataScientist_raw.sort_values(\"Average Salary\", ascending=False)\n",
        "\n",
        "plot = dataScientist_raw.plot.bar(x='Metro', y='Average Salary')\n",
        "plot.set_ylim(90000, 150000)"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(90000, 150000)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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mhZwkzMyskJOEmZkVcpIwM7NCThJmZlbIScLMzAo5SZiZWSEnCTMzK+QkYWZmhZwkzMys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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g8WdCXbPvDFS",
        "colab_type": "code",
        "outputId": "5d4f11b6-6b39-4636-beb1-c3d9d296d025",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "# Where is it growing the fastest?\n",
        "\n",
        "# Transform YOY data to be float\n",
        "\n",
        "dataScientist_cleaned = dataScientist_raw['YoY'].apply( cleanNumber ).values\n",
        "dataScientist_raw.insert(4, \"YoY Growth\", dataScientist_cleaned )\n",
        "dataScientist_raw = dataScientist_raw.astype({'YoY Growth' : float})\n",
        "\n",
        "# Check data\n",
        "dataScientist_raw.head()"
      ],
      "execution_count": 20,
      "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>Metro</th>\n",
              "      <th>Dimension Type</th>\n",
              "      <th>Month</th>\n",
              "      <th>Dimension</th>\n",
              "      <th>YoY Growth</th>\n",
              "      <th>Average Salary</th>\n",
              "      <th>Measure</th>\n",
              "      <th>Value</th>\n",
              "      <th>YoY</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>649</th>\n",
              "      <td>San Francisco</td>\n",
              "      <td>Job Title</td>\n",
              "      <td>2020-01</td>\n",
              "      <td>Data Scientist</td>\n",
              "      <td>2.9</td>\n",
              "      <td>144218.0</td>\n",
              "      <td>Median Base Pay</td>\n",
              "      <td>$ 144,218</td>\n",
              "      <td>2.9%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>565</th>\n",
              "      <td>Seattle</td>\n",
              "      <td>Job Title</td>\n",
              "      <td>2020-01</td>\n",
              "      <td>Data Scientist</td>\n",
              "      <td>1.6</td>\n",
              "      <td>129929.0</td>\n",
              "      <td>Median Base Pay</td>\n",
              "      <td>$ 129,929</td>\n",
              "      <td>1.6%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>313</th>\n",
              "      <td>Los Angeles</td>\n",
              "      <td>Job Title</td>\n",
              "      <td>2020-01</td>\n",
              "      <td>Data Scientist</td>\n",
              "      <td>2.6</td>\n",
              "      <td>122011.0</td>\n",
              "      <td>Median Base Pay</td>\n",
              "      <td>$ 122,011</td>\n",
              "      <td>2.6%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>229</th>\n",
              "      <td>New York City</td>\n",
              "      <td>Job Title</td>\n",
              "      <td>2020-01</td>\n",
              "      <td>Data Scientist</td>\n",
              "      <td>2.6</td>\n",
              "      <td>118464.0</td>\n",
              "      <td>Median Base Pay</td>\n",
              "      <td>$ 118,464</td>\n",
              "      <td>2.6%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>817</th>\n",
              "      <td>Boston</td>\n",
              "      <td>Job Title</td>\n",
              "      <td>2020-01</td>\n",
              "      <td>Data Scientist</td>\n",
              "      <td>2.3</td>\n",
              "      <td>115763.0</td>\n",
              "      <td>Median Base Pay</td>\n",
              "      <td>$ 115,763</td>\n",
              "      <td>2.3%</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             Metro Dimension Type    Month  ...          Measure      Value   YoY\n",
              "649  San Francisco      Job Title  2020-01  ...  Median Base Pay  $ 144,218  2.9%\n",
              "565        Seattle      Job Title  2020-01  ...  Median Base Pay  $ 129,929  1.6%\n",
              "313    Los Angeles      Job Title  2020-01  ...  Median Base Pay  $ 122,011  2.6%\n",
              "229  New York City      Job Title  2020-01  ...  Median Base Pay  $ 118,464  2.6%\n",
              "817         Boston      Job Title  2020-01  ...  Median Base Pay  $ 115,763  2.3%\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OkCsCdcav_OX",
        "colab_type": "code",
        "outputId": "ff382bef-f2dd-4b5d-f0a3-be265950cc9c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 347
        }
      },
      "source": [
        "# Chart Growth \n",
        "\n",
        "dataScientist_raw = dataScientist_raw.sort_values(\"YoY Growth\", ascending=False)\n",
        "\n",
        "plot = dataScientist_raw.plot.bar(x='Metro', y='YoY Growth')"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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MzFZWVxL6RsC8qvvzs2O1DpI0S9K1kjau90KSjpHUKqn1+eefX4XmmplZexo1KPp7YHhE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\ns5JwQjczK4n/DzshNH0noFAHAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rFU3j9x8wWoD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Finaly, lets extrapolate basted on current growth rates across the next few years\n",
        "\n",
        "oneYearSalary = dataScientist_raw['Average Salary'] * ( 1 + (dataScientist_raw['YoY Growth'])/100 )\n",
        "twoYearSalary = dataScientist_raw['Average Salary'] * ( ( 1 + (dataScientist_raw['YoY Growth'])/100 ) ** 2 ) \n",
        "threeYearSalary = dataScientist_raw['Average Salary'] * ( ( 1 + (dataScientist_raw['YoY Growth'])/100 ) ** 3 )\n",
        "\n",
        "dataScientist_raw.insert(4, \"Salary in 1 year\", oneYearSalary )\n",
        "dataScientist_raw.insert(4, \"Salary in 2 years\", twoYearSalary )\n",
        "dataScientist_raw.insert(4, \"Salary in 3 years\", threeYearSalary )"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L7tSXL2Rwvog",
        "colab_type": "code",
        "outputId": "20945807-8ee3-4e1b-d7d3-32899ae7394d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 368
        }
      },
      "source": [
        "# Chart future value ( projected )\n",
        "\n",
        "dataScientist_raw = dataScientist_raw.sort_values(\"Salary in 3 years\", ascending=False)\n",
        "\n",
        "plot = dataScientist_raw.plot.bar(x='Metro', y=['Salary in 3 years','Average Salary'] )\n",
        "plot.set_ylim(90000, 180000)"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(90000, 180000)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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AvwSuNbO9gRXAyFg+ElgRy6+N5znnnMtQY3ame6zut3gzewkgbl2abzAwxcxW\nA/+IW4/mdp1bHHehQ9IUYLCkl4AjgO/GcyYBY4Bx8VpjYvlU4LeS5PtcO+fAp/1mpdhjEnsAS/Ke\n18SypPI2wPtmtqZOea1rxeMr4/nOOecyUhYD15JGSaqWVL1s2bJSV8c558pGsRuJN4EOec/bx7Kk\n8veAnSQ1r1Ne61rxeOt4/gbMbLyZVZlZVbt27Yr0VpxzzhW7kZgGDIszkzoBnYGngblA5ziTqQVh\ncHtaHF94BDghvn4EcHfetUbExycAD/t4hHPOZavBgWtJk4H+QFtJNcBoYDlwA9AOuFfSc2Y20MwW\nSroDeBFYA5xtZmvjdc4BZgDNgIlmtjCGuBiYIuly4FlgQiyfANwWB7+XExoW55xzGWrM7KbhCYfu\nSjj/CuCKAuXTgekFyl9j/Qyo/PJPgRMbqp9zrhHqyxvlOaNcPTwth3PObYRKm3pbFrObnHPOpcMb\nCeecc4m8kXDOOZfIxyScc+nxjZa2eH4n4ZxzLpE3Es455xJ5d5NzrjyVam1ImXWx+Z2Ec865RN5I\nOOecS+SNhHPOuUTeSDjnnEvkjYRzzrlE3kg455xL5FNgnXNuC1GKDLQN3klImihpqaQX8sp2kTRT\n0qL4586xXJKul7RY0nxJB+a9ZkQ8f5GkEXnlvSQtiK+5XpLqi+Gccy47jeluugUYVKfsEuAhM+sM\nPBSfAxxN2LK0MzAKGAfhA5+wo91BhA2GRud96I8Dzsx73aAGYjjnnMtIg42EmT1G2D4032BgUnw8\nCTgur/xWC+YAO0naDRgIzDSz5Wa2ApgJDIrHdjSzOXH/6lvrXKtQDOeccxlp6sD1rmb2Vnz8NrBr\nfLwHsCTvvJpYVl95TYHy+mI455zLyCbPbop3AFaEujQ5hqRRkqolVS9btizNqjjnXEVpaiPxTuwq\nIv65NJa/CXTIO699LKuvvH2B8vpibMDMxptZlZlVtWvXrolvyTnnXF1NbSSmAbkZSiOAu/PKT42z\nnPoCK2OX0QxggKSd44D1AGBGPPaBpL5xVtOpda5VKIZzzrmMNLhOQtJkoD/QVlINYZbSlcAdkkYC\nbwAnxdOnA98EFgMfA6cDmNlySZcBc+N5Y80sNxh+FmEG1bbAffGHemI455zLSIONhJkNTzh0ZIFz\nDTg74ToTgYkFyquB7gXK3ysUwznnXHY8LYdzzrlEnpbDOefKRQq78fmdhHPOuUTeSDjnnEvkjYRz\nzrlE3kg455xL5I2Ec865RN5IOOecS+SNhHPOuUTeSDjnnEvkjYRzzrlE3kg455xL5I2Ec865RN5I\nOOecS+SNhHPOuUTeSDjnnG4/ONMAAB6ASURBVEu0SY2EpPMkvSBpoaTzY9kukmZKWhT/3DmWS9L1\nkhZLmi/pwLzrjIjnL5I0Iq+8l6QF8TXXxy1OnXPOZaTJjYSk7sCZQB9gf+BbkvYGLgEeMrPOwEPx\nOcDRQOf4MwoYF6+zC2FL1IPitUbnGpZ4zpl5rxvU1Po655zbeJtyJ7Ev8JSZfWxma4BHgeOBwcCk\neM4k4Lj4eDBwqwVzgJ0k7QYMBGaa2XIzWwHMBAbFYzua2Zy4LeqteddyzjmXgU1pJF4Avi6pjaTt\ngG8CHYBdzeyteM7bwK7x8R7AkrzX18Sy+sprCpQ755zLSJO3LzWzlyT9EngA+Ah4Dlhb5xyTZJtW\nxYZJGkXowgJYJemVJl0H2gLvJp7w8/SGROqNXaq4KcbebH/XKcbebN9zGf6uG4zt77mQvQoVbtIe\n12Y2AZgAIOm/Cd/235G0m5m9FbuMlsbT3yTcaeS0j2VvAv3rlM+K5e0LnF+oHuOB8ZvyXuJ7qDaz\nqk29zpYUu9LiljK2v+fKiF1u73lTZzf9W/xzT8J4xP8C04DcDKURwN3x8TTg1DjLqS+wMnZLzQAG\nSNo5DlgPAGbEYx9I6htnNZ2ady3nnHMZ2KQ7CeBOSW2Az4Gzzex9SVcCd0gaCbwBnBTPnU4Yt1gM\nfAycDmBmyyVdBsyN5401s+Xx8VnALcC2wH3xxznnXEY2tbvp6wXK3gOOLFBuwNkJ15kITCxQXg10\n35Q6bqRN7rLaAmNXWtxSxvb3XBmxy+o9K3x2O+eccxvytBzOOecSeSPhMiHJ/605twWq6P+4klpL\nulZSdfy5RlLrUtcrTZJa5T6wJXWRdKykrTMIvUjSLyR1ySBWxZL0PUmnFCg/RdJ3S1Ent2Wr6DEJ\nSXcSVo7n0oicAuxvZsdnEHs74EJgTzM7U1JnYB8zuyfluPOArwM7A08SZpV9ZmYnpxx3J2A4YVbb\nZ4SJCneY2ao04+bF3wYYAnQkb8KGmY0tp7iSngKOrPt7ldQKeMzMeqURt0A9jgUOjU8fNbP/yyju\nzoQ8by1zZWb2WEaxuwNd68S+NcV4C4BCH+AKoW2/osSp8EbiOTPr2VBZSrFvB+YBp5pZ99ho/C3t\n2JKeMbMDJZ0LbGtmv8rqPefVoT/wJ2BH4A7gcjP7R8ox7wdWEn7n6zIDmNk15RQ39/ebcGx+sT44\nGqjDLwjJOv8Ui4YDc83sv1KO+33gPMLC2+eAvsBsMzsizbgx9mjCouCuhOn+RwNPmNkJKcYsuEI6\nx8zeKEacTV0nsaX7RNIhZvYEgKR+wCcZxf6KmQ2VNBzAzD7OKBW6JB0MnAyMjGXNMgi6FSGL7+lA\nF+A6wofI14H7gX1SrkJ7MytFFuGs424rqZWZfZRfKGkHoEVGdTgG6GlmX8TYk4BngVQbCUID0RuY\nY2aHS/oq8N8px8w5gZAN+1kzO13SrsAf0wxYrEagIRU9JgH8O/A7Sa9Leh34LfDDjGJ/Jmlb4u2i\npK8AqzOIez5wKXCXmS2U9GXgkQziLgKGAjeY2f5m9isze9PMphBSyqftb5J6ZBCn1HEnAFPzv2VK\n6ghMiceyslPe46zG+T41s08hdPOZ2cuk/+Uj55PYKK6RtCMhHVGHBl5TFDErxVxJqyR9JmmtpA+K\ndf2KvpMws+eA/eNfKmZWtF9sI4wmfIPuIOlPQD/gtLSDmtmjwKOxewszew34j7TjAqfX7RuW1Dem\ngj8rg/iHAKdJ+gehMS5qv+3mEtfMrpa0CnhM0vYx3ofAlWY2Lo2YBfwCeFbSIzH+oazfVyZNNXHs\n66/ATEkrCFkfslAdY99E6FpcBczOKPZvgWHAn4EqQgqjok0QqfQxif8GfmVm78fnOwMXmtlPM4rf\nhtBvKsItcnLmyOLFPJjwjXJ7M9tT0v7AD9L+oC7UVy5pXoYDqQX7b9O+ZS9V3Bh7hxjrw7RjFYi9\nG6HrB+BpM3s74/iHEe5g7jOzzzOO3ZGwF878jOJVm1lV/piTpGfN7ICiXL/CG4kNfpH1DfwVKWa9\n1zazZ9KKHeM/Reg/nZZ775JeMLNU0p9I6gMcDPwYuCrv0I7ASVkMpObVZX/CGAjA42b2fDnGlbQP\nIXX+V2PRS8B4M3s1zbh58Qv9G18JvGFhg7K04t5mZqc0VFbkmF81s5eT/l+n/f851uEx4Cjgfwh7\n+LwFnGZm+xfj+hXd3QQ0i32XqwHiGME2Kcesb1aLAanPxDCzJXXGyNcmnVsErQg57psD7fLKPwRO\nTDFuLZLOI2yF+5dY9EdJ483shnKKG+8U/0LI4TOecJd6ADBL0vEWdoVM2++BA4H5MX53YCHQWtK/\nm9kDKcXtlv9EUjMg7TvVHxEa5EL/rzP5/0yYur8VcA5wAWEsZEjRrm5mFfsDXAw8QZjlMzI+/s+M\nYrdsTFkKcacCXwOeAbYmfMOfkkHcL5f473o+0CrveStgfrnFJWRK7l+g/DBC10sWv+u/AN3ynneN\n/+6+DDyXQrxLCV861gAfxJ8PgfeAX2T576wcfyq6uwlA0iDCrRqEvbZnZBS3UB99ql1dMUZbwvTT\nowjf8h4AzrOQvTeNeNeY2YWS7qLAwh/LYOFirMcCoLetn/3SkjB3P9WZR1nHlfSqmRUctJT0ipml\nPtunUPdlrizNNTmSfmFml6Zx7UbG/xobLppMbTFdXtx+wBjCznL5sb9cjOtXdHdTXIX6gJndH/tx\n95G0taU40CXpS4S9ureVdADhgxpCH/12acXNsTA4nurq6jpuj3/+NsOYhdwMPBUbK4DjKJCevgzi\n1jdI/VE9x4ppoaRxhGm3EKY+v6iw+jy1/1tmdqmkPdjwwzL1FdeSbgO+Qu1tnA1IvZEgTES5gDoL\nNoulou8kVDtFxRNANSmnqJA0gjDVtYqQEiPXSHwATDKzvyS8dFPj3kDhJfwAmFkq02DjnUsbM3ul\nTvk+wLtp3cEk1OVAwpRUCAPIz5ZbXElLWf/hXOsQYaLArmnFzqvDtoQNw3Lv+UnCOMWnwHaWUioW\nhQ3PhgEvkvdBbWbHphGvTuyXgK5Wgg9USU+Z2UGpXb/CG4mSpaiQ9J9m9qs6ZZ0spfQUsXFKZGaT\n6ju+CXH/F7jJzB6pU94f+L6ZfS+NuAXqkfnMl1LELdXf8+ZA0ivAfhYnomQc+8/Af1jYdjnr2FcS\nsib8hbwFuVakmVUV3d1EiVJURMOAX9Upm0pKszHqfjhI2s7MPk4jVh1d6jYQsT6zJGXZBVWKmS+Z\nx90cGgGFZJW/YMNkd0XpI6/Ha4TJGJk1EpL+j3CHvgOhS+1pan9Qp34XA+TuIqryyoo2s6rSG4nM\nU1Qo5JPpRpgOmD9ouyN5/6FSjL9uMR2QxWK6Heo5lnqKckmXEnIGbZuXqkCETLSpbTNZqribiZsJ\nGQWuBQ4n5OvKIgXQx8Bzkh6i9gd1mhkFrk7x2o1iZoenef2K7m4qBUmDCYOXxwLT8g59SJiK+reU\n42e9mG46cF3dWWOSBgA/soyS35Vq5kupZ9yUQm4lvaQFuVlcWayuT+pqy+ruKk5K6UP4Fj/XMlpl\nrrAHzmjyUrMDY81sZTGuX5F3EpJ+Y2bn590q1pLmLaKZ3Q3cLelgM8sqt0vdOmS5mO5HwD2SZhFm\nX0C4LT4M+FaKceu6RzE7qqTvERZ7XWfpp8coVdxSWq2Q9XeRpHOANwl3rqkqZVebQprynwEPE+4Y\nb5A01syymEE3kbAvzknx+SmEu7miTC+vyDsJSb3MbJ5CfpcNWEiCl3YdWhLGQbpRu9/2jJTjTgV+\nTZiSehAhvXKVmQ1LMea2wPcIK28hrL69zcyySsuOpPmEVM77AbcQUhicZGYF/w2UQdx2hJXeHak9\nHTTVf18xdm9CKpCdgMsIXalXWcqrvUs4FpIbNP9abraeQl62v2W0LiXVfXEq8k7CzHLfaKtZn+I3\nN6iYdlqOnNuAl4GBwFjC4PlLGcT9IWEx3R6Eb3gPAGenGTA2BjelGaMR1piZxe6+35rZBEkjG3zV\nlhv3buBx4EHSvVPcgJnNjQ9XEcYjslKqsRAIq7vz16jkVnxnIdV9cSryTiJH0hzgqNy8bYXUyg+Y\n2dcyiP2smR2gmLlRYZ/px82sb9qxK5GkRwmp2c8grI1ZCjyfwYrrXNzTCX3GWcXNdLfBOrFnAida\n7ezKU8xsYMpxSzIWEuPcCvQgNM4GDCakZJkPYGa/TjF2T8IWzK0JXV3LCQn+ipJIsiLvJPK0zF/Y\nY2arFPdZyEBu5en7Cnvjvg38W9pBJV1foHglUB3HS8rVUOC7wBlm9rakPamdlTbtuCMzjnuPpG+a\n2fQMYtXVNtdAAJjZCkmp/9umRGMh0d/jT07u/1J9s/uKwlLeF6fS7ySeBM7NLTqR1IvQJXBwBrG/\nD9xJ6Ku+mfCP+WdmdmPKcccTUkj/ORYNAf4BtAFeM7PzU4rbM/5jzi872szuSyNeQh12pfYeB0uz\nip01SR8Skgl+xvovJGZmO2YQex7wHTP7Z3y+F2Gaedp5yeqOhbQm7BeTRebbzEn6npn9UdKPCh0v\n1t1LpTcSvQkpDP5FuE37EjA0b8yi7MQutn5mtjY+b07ouz4EWGBmXVOK+wzwPTN7MT4/kZBxt3f9\nryxa/JMI3+BnEf6uvw5cZGZTU477Ietn0LUgrA1ZZWZZbemZOYWkmeMJUzFzv+tRdadBl4OkGZI5\nac6UlPQDM/uDpNGFQ9vYosSp5EYCII4F5GYgvGIZ7WIVv9X+N7C7mR0tqStwsJmlug9xnIXRJzeH\nOs6xftrM9lERd7MqEHdv4A7CSvN+wPeBb5nZijTiFYj/PPCN3N1DnP3zoBVpY5ZG1kGEvuq+Zpb6\ndp6SjmX93PlZZnZP2jHzYrcl7LoIKe+6WOIP6npnqWU0U7KfmT3ZUFmTr++NRMnS+95H6Gb6iZnt\nH7/RP5vBgOZI4Kes/0Z9KKGxmgyMMbOLUoz9VUJ+mTeBwZZNWpBc7AX5v9vYd536AHJCXVJrjPNi\nXEnoWvtTLBpOGHdKbWGfSrRL2+bwQR3rsS2wp9VJZplB3FS3HajoRkIJ6X0t3WX8udhzzax3/gdG\nVjNSFPYf7hOfzjWzf6UY61lqf8v7EvA+ISMoafdT59XjKsL4z+RYNJSw+c/FKcfNX9C0FXEhYdrj\nXnF9Rs8607uftRS3i1XYcW+UpEKpbczMUt2lTdJ5ZnZdQ2Upxf42IUVHCzPrFGccjU35LuZgwgZi\n5xOm/ebsSBgT8u1Li6CKEqX3BT6KC24MQFJfwiyjLGwFLCP8/e8taW9LL+f+CSldd6OY2UXxAzuX\nvnq8md1V32uK5Nt5j9cArxO6nLKwE2E6JIRB3FSZ2aj4Z6q5hOoxgrAGKN9pBcrSMIbwxWsWhBlH\nkjqlHLMFYcJLc2rPovqAIv6/q/RG4gXCN9vM0/sS0lVMA74SZ1m1I4MPVEm/JHyLXgh8EYsNSKWR\nMLO/x2+x882sW4MvSJGFvTr+EvvLM1noZGZZLibL9wvg2fitPtetmFkOqSy7cSUNJ0wz7iQpPx/a\njmS3oO1zM1up2uluUv3yGbvRHpV0i6WY5qXSG4m2ZJzeN86oWmJmz8S+1B8QpqE+ANSkFTfPccA+\nlmHOfTNbK+k1SXuY2ZtZxYV1d2hXEr5RX0ZY6d4W2ErSqWZ2f8rx2wM3EAbrIcwkO8/MUv27NrPJ\nCvmycrPHLrbsEs5lvUvb3whf9NoC1+SVG+ELURYWSvou0EwhPch/xHpl4ePYnVo3xU9RuvcqfUwi\n89xNcSroUWa2XNKhhCm45wI9gX3NLNW7iThgfqKltDtYPXEfIeyjMJu8bTQt5T2uJVUTUna3JkzL\nPNrM5sRB9MkZDCDPBP6X0DhByGF1spl9I+W4D5nZkQ2VpRS7lLu0HUC4qziRsP7nTjNLfd+SuAj3\nJ8AAwp3bDOAyi3ubpxz7AcI2wT8mpN0ZASwr1nhbRTcSpSDp+dyAkqTfEf4yx8TnqQ9cS7qTkHAu\ny5z7SCr44WRmD6Ucd93vVNJLZrZv3rEsZhmlmnytQLyWhL3SHwH6Q6091O83s6+mEbdOHTLdpU1S\nF8LsreHAu8QPTDPbK4v4pab16Ujm5yYm5CbGFOP6Fd3dFLsibgD2JQwCNQM+snRXpTaT1NzM1gBH\nAqPyjmXx9zGN2vtYQMp9pxAagzgWkNs9qzrNufN5vsh7XDfpWRbfkN5TSBGem1U1nHT7yX9AmO2y\nO5A/5fQDQubf1Kh0u7S9TOjG+5aZLY51uSClWLWUco1GntzarrckHUNYHLxLsS5e0Y0E4T/NMEKK\niirgVKBLyjEnEwab3iV8aD0O6xabpT67yTbcxrQD4XeQKklDCNP0Hid8u71R0gUZzDDaX2FnOLHh\nLnGp7wRISCh4A+G9G6GfOrXB7Djd8zpJ55rZDWnFSTAN2JX4bzrP10l3csjxhH/Dj0i6n9CFq/pf\nUjS5nemOJ0yC+WN8Phx4J6M6XB4XxV5I+Le2I1C0RrKiu5skVZtZVZ3btCy6IPoCuxEyzn4Uy7oA\n26e14KhO/HaEPtvhhG+cd5nZj1OO+TwwwMzeic93Jbz/zFY8V5I66zNyVhJSr6SSs0rSPcClZrag\nTnkP4L/N7NuFX1m0+K0I04uHE/Z3vpXwb/uBNOPG2NVmVtVQ2Zao0u8kPpbUgrAv7q8I33ZSzz9v\nBRKOmdmracaUtAPh2853CXdLfwE6mVn7NOPm2SrXQERLyS7Xf+Yk3UD93RBpL9gcCRzM+j3b+xN2\nBuyksGPabUkv3AS71m0gAMxsgaSOKcSrG+cjwiSB/1VIT34icDFh5mDaWkn6spm9BhDXSLRKM6Ck\nn9Vz2MzssmLEqfRG4hTCB9U5hNuzDoTpqOVoKfA0ISXHE2Zmkr6TYfwHJN3L+r75YYQZIOWqOu/x\nzwmb4WRpa8Jsufw7t1sJuxE+xvrZVsW0Uz3Htk0hXiILOcHGx58sXADMkvQaoatrL8L4UJo+KlDW\nivAFoQ1hyvcmq9juprjA61YzO7nUdcmCpPMJH8ytCB/UtwMzLYOtHWN8Eb7Z5VY8Pw5MLdFq90xl\n0YVZIOaLlpfRN/7+F5pZ17TqI2ky8LCZ3VSn/PuE5IpZrVkoCUnbENLwA7yc5Vqk2FNwHqGBuAO4\npljdihXbSABIegI4wsw+K3VdsiLpy4TGYjjQmfAN9660urskjSMs5CrqRihbEhUx2dpGxPw9sCe1\n9w2pAS4C7kkjdUa8W7mLsIdFLt1+FWHm4HeyWsxXKgqbh9XdXzvVZKGSdiFkbziZsDvddVbkzMqV\n3kjcSpj+Oo3aC7xS22pwcxL/UQ8n7KGxd0oxLiJM8x1tZv+bRozNXYkaCREahtxK7ycJC8tS/w8v\n6XCge3y60MweTjtmqSns6dCf0EhMB44mdOumtjg2rrI+ntCl9ru0FshWeiNRsJ/YzH6edV3KmaQ9\ngF8T0iaMI2/tQsynVHZUe7Oh7YBcWnSR0Q5xLjuSFhAWqT5rIfX/rsAf01xZL+kLwjqUNdSeJFHU\nf2MVOXCdW8zmjUE2zOzNOGh9BSEran5iwbJsJMws9b2N6xOnwP6SsG+68MYpbZ+Y2ReS1ijsNb2U\nMBEmNWaWyezAimwkCLN8DoQwVdHMzi1xfcqWpG6Eu4d/EXbEK0XG3Ur0K+DbZvZSqStSIaol7QTc\nRBiPWUXIU7bFq8juJtXe6Cfz/uLNRZxL3sHM5qcY4yVC1tMs5qq7SNKTZtav4TNdscU1ITum+f8q\nS5V6J1F5LWOkkD76WMLf/TxgafxA+VFKIXtmORXQrVMt6Xbgr9TOn1SW3XulooStWnPHssigkLZK\nvZP4GFhM6Kf9SnwM6/ttU9visdRyd1Fx7noHMxudn5bElQdJNxcoNjM7I/PKlDEV3qo1xyzlLVuz\nUKl3Evs2fErZaq6wx/VJhPz3rgxZ6XbEqyhprDfZ3FRkI2EpbvW3BRhLSIfxpJnNjYvrFqUdNO4n\n8Tczq5uu2xWRpP80s18l5Y7KIGdURZF0hJk9nJBQsSy69yqykahkZvZn1q/CJSYkyyJf1anAOEnL\nCSk5HiMsNirq6lBHbjZTdb1nuWI5DHiYMLW7rrKY4l2RYxKVTCXaczkv/u7ACYStFnc3M/+i4txm\nzBuJCqPS7bn8PcLmMz0IW0w+ATxuZmUxl3xzE/cn+THQkbweg3IYSN0cxeR+Q9jw9z22VHUqlopu\nJCT1A8YQ0vo2Z/3spkwyo5aCMt5zOS/Gu8DfgRuBR8zs9TTjVbq4ydONhGnOa3PlZjYv8UWuyeKO\neCvZ8Pd9TckqVSSVfqs/gZAHvtZfbJnLes9lAMysbVx9fShwhaTOwCtmdkrasSvUGjMbV+pKVJD2\nZjao1JVIQ9nuDNZIK83sPjNbambv5X5KXamUnUGY/vo2YSe+E4DT0g4a89nsSbhr6wi0Ji/RnysO\nSbvE9NH/J+ksSbvlymK5S8ff4jatZafSu5uuBJoRZiDkr0rd4ldJbgxJ55vZb1KOMZ8wDvEE8FhW\nA+WVRtI/CLNqVOBwWXellkLM/mqEXpnOwGuEz5KyWZhb6Y1EodWSZbFKcmNI+qeZ7ZlRrO3M7OOG\nz3Ru8ydpr/qOl8OarIpuJFwgaYmZpZrWWNLBhDGg7c1sT0n7Az8ws7PSjFupJJ0I3G9mH0r6KSHr\n8WVm9myJq1ZWJLUEfgjsDSwAJpjZmtLWqrgqfeAaSccA3ai95eAWP21tI2XxTeE3wEDCLoCY2fOS\nDs0gbqX6f2b2Z0mHAEcBVxFmOx1U2mqVnUnA54T1RkcTdqY7r6Q1KrKKbiQk3UjYNexw4H8Ig7hP\nl7RSKamzU1qtQ8C2WdTBzJaEXTXXqZQZZaWQ+90eA4w3s3slXV7KCpWprmbWA0DSBMrw86OiGwng\na2a2X8yC+nNJ1wD3lbpSaSj1TmnAEklfA0zS1oRvW74hTnrelPQH4BvAL+Nir0qfzZiGz3MPzGxN\nnS9BZaHS/9Hkks19HNNFfA7sVsL6lLMfAmcDewBvAj3jc5eOkwiJHAea2fvALsBFpa1SWdpf0gfx\n50Ngv9xjSR+UunLFUOl3EvfELQevAp4hdMfcVNoqlSczexc4udT1qBRxBtlfJP2bpNzMtZdLWady\nZGbNSl2HtPnspijejrc0s5Wlrks5kfSzeg6bmV2WWWUqiKRjgWuA3YGlhIWML5tZt5JWzG1xKrK7\nSVJvSV/Ke34qcAdwma9KLbqPCvwAjAQuLlWlKsBlQF/gVTPrRJjhNKe0VXJboopsJIA/AJ8BxGmY\nVwK3EhJ0jS9hvcqOmV2T+yH8brcFTgemAL76Nz2fxxQzW0naysweAapKXSm35anUMYlmZrY8Ph5K\nmCJ4J3CnpOdKWK+yFO/OfkQYk5gEHOibDaXufUnbEzZ3+pOkpay/i3Ou0Sr1TqKZpFwDeSRhZ6mc\nSm04UyHpKmAu8CHQw8zGeAORicGE2XsXAPcT0rQX2j3NuXpV5MC1pJ8A3yRsfrMn4ZutSdobmGRm\n/eq9gGs0SV8QEp6tofZivlwCtB1LUrEyJel84G/AM+WWHsKVRkU2EgCS+hLWRDxgZh/Fsi6E3EIV\nlQXWlQ9JVwNfA75KyCX0JKHR+FteF6tzjVaxjYRz5UxSC8JA9deAg+PP+2bWtaQVc1sc7393rjxt\nC+xI2NypNfAvwp2FcxvF7yScKyOSxhOyGn8IPEVYGzHHJwu4pqrU2U3Olas9gW0I29O+CdQA75e0\nRm6L5ncSzpUZhVSk3QjjEV8DugPLgdlmNrqUdXNbHm8knCtTktoD/QgNxbeANma2U2lr5bY03kg4\nV0Yk/Qfr7yA+J05/jT8LzOyLElbPbYF8dpNz5aUj8GfgAjN7q8R1cWXA7yScc84l8tlNzjnnEnkj\n4ZxzLpE3Es41gSST9Me8580lLZN0TwOv6ynpm+nX0Lni8EbCuab5COguadv4/BuExWsN6UnIQLyB\nvPT1zm02vJFwrummA8fEx8OBybkDklpJmijpaUnPShock+6NBYZKek7SUEljJN0m6UngNkktJd0s\naUF83eHZvy3n1vNGwrmmmwIMk9QS2I+QKynnJ8DDZtYHOBy4Ctga+Blwu5n1NLPb47ldgaPMbDhw\nNmGfjR6EhmdSvL5zJeGNhHNNZGbzCesShhPuKvINAC6J2+HOAloS8ioVMs3MPomPDwH+GK//MvAG\n0KWoFXduI3gfqHObZhpwNdAfaJNXLmCImb2Sf7Kkgwpcw/eedpstv5NwbtNMBH5uZnX3apgBnBuT\n7SHpgFj+IbBDPdd7HDg5vqYL4e7jlXrOdy5V3kg4twnMrMbMri9w6DLCGMR8SQvjc4BHgK65gesC\nr/s9sJWkBcDtwGlmtjqNujvXGJ6WwznnXCK/k3DOOZfIGwnnnHOJvJFwzjmXyBsJ55xzibyRcM45\nl8gbCeecc4m8kXDOOZfIGwnnnHOJ/j8sCZSzSP06BAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hcww9vLstu79",
        "colab_type": "text"
      },
      "source": [
        "### Highest \"real\" wage"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "um_zl1x_tuSs",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "0d26b8ad-43f0-40fb-ae86-a68026e7c821"
      },
      "source": [
        "# Earlier we got average salary from this : quickFacts_jobPay['Average Salary']\n",
        "# Lets combine them into a single chart to show how much data scientists are paid compaired to average saleries\n",
        "\n",
        "dataScientistData = dataScientist_raw[['Metro', 'Average Salary']]\n",
        "metroData = quickFacts_jobPay[['Metro', 'Average Salary']].rename(columns={'Metro': 'Metro', 'Average Salary': 'Metro Salary'} )\n",
        "\n",
        "combinedData = dataScientistData.merge(metroData, how='right')\n",
        "\n",
        "combinedData.head()"
      ],
      "execution_count": 38,
      "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>Metro</th>\n",
              "      <th>Average Salary</th>\n",
              "      <th>Metro Salary</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>San Francisco</td>\n",
              "      <td>144218.0</td>\n",
              "      <td>74854.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Seattle</td>\n",
              "      <td>129929.0</td>\n",
              "      <td>64633.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Los Angeles</td>\n",
              "      <td>122011.0</td>\n",
              "      <td>64277.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>New York City</td>\n",
              "      <td>118464.0</td>\n",
              "      <td>65990.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Boston</td>\n",
              "      <td>115763.0</td>\n",
              "      <td>63160.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           Metro  Average Salary  Metro Salary\n",
              "0  San Francisco        144218.0       74854.0\n",
              "1        Seattle        129929.0       64633.0\n",
              "2    Los Angeles        122011.0       64277.0\n",
              "3  New York City        118464.0       65990.0\n",
              "4         Boston        115763.0       63160.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jF7iOYaGxDHP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "61a45b8b-9bf7-4be1-87b3-683910e71b26"
      },
      "source": [
        "# Getting quick guesdiment of 'real wage'\n",
        "\n",
        "combinedData['RealWage'] = combinedData['Average Salary'] / combinedData['Metro Salary']\n",
        "\n",
        "combinedData.head()"
      ],
      "execution_count": 39,
      "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>Metro</th>\n",
              "      <th>Average Salary</th>\n",
              "      <th>Metro Salary</th>\n",
              "      <th>RealWage</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>San Francisco</td>\n",
              "      <td>144218.0</td>\n",
              "      <td>74854.0</td>\n",
              "      <td>1.926657</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Seattle</td>\n",
              "      <td>129929.0</td>\n",
              "      <td>64633.0</td>\n",
              "      <td>2.010258</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Los Angeles</td>\n",
              "      <td>122011.0</td>\n",
              "      <td>64277.0</td>\n",
              "      <td>1.898206</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>New York City</td>\n",
              "      <td>118464.0</td>\n",
              "      <td>65990.0</td>\n",
              "      <td>1.795181</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Boston</td>\n",
              "      <td>115763.0</td>\n",
              "      <td>63160.0</td>\n",
              "      <td>1.832853</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           Metro  Average Salary  Metro Salary  RealWage\n",
              "0  San Francisco        144218.0       74854.0  1.926657\n",
              "1        Seattle        129929.0       64633.0  2.010258\n",
              "2    Los Angeles        122011.0       64277.0  1.898206\n",
              "3  New York City        118464.0       65990.0  1.795181\n",
              "4         Boston        115763.0       63160.0  1.832853"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mrnB1Qr5xSdo",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 368
        },
        "outputId": "781f8483-cb9f-4b27-9151-a52aa4e7ada5"
      },
      "source": [
        "# Chart it out\n",
        "\n",
        "# Suprisingly this shows a different result, NYC drops way down the list, and sanfrancisco becomes third\n",
        "# This is not a perfect measure, but gets an idea of how it works\n",
        "\n",
        "combinedData = combinedData.sort_values(\"RealWage\", ascending=False)\n",
        "\n",
        "plot = combinedData.plot.bar(x='Metro', y=['RealWage'] )\n",
        "plot.set_ylim(1.5, 2.2)"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1.5, 2.2)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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VRc2AkvQQ8LGIeDy7fRSpEuje3f9k3eM4lLQO4C5W/T0mdZxWWSYtN+SS9YC+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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OhvfclMUyJN9",
        "colab_type": "text"
      },
      "source": [
        "### Most likely city to get a job at\n",
        "\n",
        "Lets see how number of jobs are changin over time in different cities\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dq4R_zHcxYMx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Find YOY growth / loss in jobs across cities\n",
        "\n",
        "jobOpenings = raw_data[raw_data['Dimension Type']=='Quick Facts']\n",
        "jobOpenings = jobOpenings[ jobOpenings['Dimension'] == 'Metro Job Openings']\n",
        "\n",
        "# Transform type to be float\n",
        "\n",
        "jobOpenings_cleanNumber = jobOpenings['Value'].apply( cleanNumber ).values\n",
        "jobOpenings_cleanChange = jobOpenings['YoY'].apply( cleanNumber ).values\n",
        "\n",
        "jobOpenings.insert(4, \"Open Jobs\", jobOpenings_cleanNumber )\n",
        "jobOpenings = jobOpenings.astype({'Open Jobs' : float})\n",
        "\n",
        "jobOpenings.insert(4, \"YoY Change\", jobOpenings_cleanChange )\n",
        "jobOpenings = jobOpenings.astype({'YoY Change' : float})\n",
        "\n",
        "# Check data\n",
        "jobOpenings.head()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_HO2-P2YzA-s",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Lets see how number of open jobs are changing across the US\n",
        "\n",
        "jobOpenings = jobOpenings.sort_values(\"YoY Change\", ascending=False)\n",
        "\n",
        "plot = jobOpenings.plot.bar(x='Metro', y='YoY Change', color = (jobOpenings['YoY Change'] > 0 ).map({True: 'g', False: 'r'}))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZF-NYJfUzf76",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Lets try to extrapolate this out a few years and see opening if things stay at this rate\n",
        "\n",
        "\n",
        "oneYearJobs = jobOpenings['Open Jobs'] * ( 1 + (jobOpenings['YoY Change'])/100 )\n",
        "twoYearJobs = jobOpenings['Open Jobs'] * ( ( 1 + (jobOpenings['YoY Change'])/100 ) ** 2 ) \n",
        "threeYearJobs = jobOpenings['Open Jobs'] * ( ( 1 + (jobOpenings['YoY Change'])/100 ) ** 3 )\n",
        "\n",
        "jobOpenings.insert(4, \"Jobs in 1 year\", oneYearJobs )\n",
        "jobOpenings.insert(4, \"Jobs in 2 years\", twoYearJobs )\n",
        "jobOpenings.insert(4, \"Jobs in 3 years\", threeYearJobs )"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tlsQEobu0job",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# in three years, if the rate stays the exact same\n",
        "\n",
        "jobOpenings = jobOpenings.sort_values(\"Jobs in 3 years\", ascending=False)\n",
        "\n",
        "plot = jobOpenings.plot.bar(x='Metro', y=['Jobs in 3 years','Open Jobs'] )"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RM9ubBWY0xQQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}