Intro to Python for Data Science - 13 & 14

intro-to-python-for-data-science-13-14.ipynb

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      "name": "Intro to Python for Data Science - 13 & 14",
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    {
      "cell_type": "markdown",
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        "<a href=\"https://colab.research.google.com/gist/syamdev/b2e23249d3ee9a6b70de891b90656b65/intro-to-python-for-data-science-13-14.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "markdown",
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        "id": "L1YdsYqWya01",
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      },
      "source": [
        "## Data Analysis"
      ]
    },
    {
      "cell_type": "code",
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      },
      "source": [
        "!pip install pandasql"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: pandasql in /usr/local/lib/python3.6/dist-packages (0.7.3)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.6/dist-packages (from pandasql) (0.25.3)\n",
            "Requirement already satisfied: sqlalchemy in /usr/local/lib/python3.6/dist-packages (from pandasql) (1.3.13)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from pandasql) (1.17.5)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.6/dist-packages (from pandas->pandasql) (2018.9)\n",
            "Requirement already satisfied: python-dateutil>=2.6.1 in /usr/local/lib/python3.6/dist-packages (from pandas->pandasql) (2.6.1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.6/dist-packages (from python-dateutil>=2.6.1->pandas->pandasql) (1.12.0)\n"
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    {
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        "id": "oFGdLCvoyfuY",
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      },
      "source": [
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "import pandasql"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a_eS1b-zyty0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "survey = pd.read_csv('http://bit.ly/dwp-data-survey')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j73j1oYJy37B",
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        "colab": {
          "base_uri": "https://localhost:8080/",
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        }
      },
      "source": [
        "survey.head()"
      ],
      "execution_count": 0,
      "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>Respondent</th>\n",
              "      <th>Age</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Hobbyist</th>\n",
              "      <th>OpenSourcer</th>\n",
              "      <th>Employment</th>\n",
              "      <th>Country</th>\n",
              "      <th>EdLevel</th>\n",
              "      <th>UndergradMajor</th>\n",
              "      <th>OrgSize</th>\n",
              "      <th>YearsCode</th>\n",
              "      <th>JobSat</th>\n",
              "      <th>ConvertedComp</th>\n",
              "      <th>WorkWeekHrs</th>\n",
              "      <th>WorkRemote</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>4</td>\n",
              "      <td>22</td>\n",
              "      <td>Man</td>\n",
              "      <td>No</td>\n",
              "      <td>Never</td>\n",
              "      <td>Employed full-time</td>\n",
              "      <td>United States</td>\n",
              "      <td>Bachelor’s degree (BA, BS, B.Eng., etc.)</td>\n",
              "      <td>Computer science, computer engineering, or sof...</td>\n",
              "      <td>100 to 499 employees</td>\n",
              "      <td>3</td>\n",
              "      <td>Slightly satisfied</td>\n",
              "      <td>61000.0</td>\n",
              "      <td>80.0</td>\n",
              "      <td>Less than once per month / Never</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>9</td>\n",
              "      <td>23</td>\n",
              "      <td>Man</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Once a month or more often</td>\n",
              "      <td>Employed full-time</td>\n",
              "      <td>New Zealand</td>\n",
              "      <td>Some college/university study without earning ...</td>\n",
              "      <td>Computer science, computer engineering, or sof...</td>\n",
              "      <td>10 to 19 employees</td>\n",
              "      <td>12</td>\n",
              "      <td>Slightly satisfied</td>\n",
              "      <td>95179.0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>Less than once per month / Never</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>13</td>\n",
              "      <td>28</td>\n",
              "      <td>Man</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Less than once a month but more than once per ...</td>\n",
              "      <td>Employed full-time</td>\n",
              "      <td>United States</td>\n",
              "      <td>Master’s degree (MA, MS, M.Eng., MBA, etc.)</td>\n",
              "      <td>Computer science, computer engineering, or sof...</td>\n",
              "      <td>10 to 19 employees</td>\n",
              "      <td>17</td>\n",
              "      <td>Very satisfied</td>\n",
              "      <td>90000.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>All or almost all the time (I'm full-time remote)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>14</td>\n",
              "      <td>31</td>\n",
              "      <td>Man</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Less than once per year</td>\n",
              "      <td>Employed full-time</td>\n",
              "      <td>Germany</td>\n",
              "      <td>Other doctoral degree (Ph.D, Ed.D., etc.)</td>\n",
              "      <td>Computer science, computer engineering, or sof...</td>\n",
              "      <td>100 to 499 employees</td>\n",
              "      <td>13</td>\n",
              "      <td>Very satisfied</td>\n",
              "      <td>57060.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>Less than once per month / Never</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>17</td>\n",
              "      <td>29</td>\n",
              "      <td>Man</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Less than once a month but more than once per ...</td>\n",
              "      <td>Employed full-time</td>\n",
              "      <td>Australia</td>\n",
              "      <td>Bachelor’s degree (BA, BS, B.Eng., etc.)</td>\n",
              "      <td>Computer science, computer engineering, or sof...</td>\n",
              "      <td>20 to 99 employees</td>\n",
              "      <td>5</td>\n",
              "      <td>Neither satisfied nor dissatisfied</td>\n",
              "      <td>65277.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>A few days each month</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Respondent  ...                                         WorkRemote\n",
              "0           4  ...                   Less than once per month / Never\n",
              "1           9  ...                   Less than once per month / Never\n",
              "2          13  ...  All or almost all the time (I'm full-time remote)\n",
              "3          14  ...                   Less than once per month / Never\n",
              "4          17  ...                              A few days each month\n",
              "\n",
              "[5 rows x 15 columns]"
            ]
          },
          "metadata": {
            "tags": []
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          "execution_count": 99
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    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2Gn9bz_X0JB1",
        "colab_type": "text"
      },
      "source": [
        "## Questions"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-OPNhuLM2Z21",
        "colab_type": "text"
      },
      "source": [
        "1. Melihat korelasi antara `JobSat` dengan `ConvertedComp`?\n",
        "2. Melihat rata-rata gaji di tiap negara?\n",
        "3. Melihat hubungan antara `Hobbyist` dengan `OpenSourcer`?\n",
        "4. Buat sebuah kategori di `YearsCode`, lalu lihat hub dengan `ConvertedComp`?\n",
        "5. Melihat average `WorkWeekHrs` di tiap kategori `OrgSize`?\n",
        "6. Melihat `JobSat` level di tiap negara (top 10 respondents)?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D4_jdFxX0VoA",
        "colab_type": "text"
      },
      "source": [
        "## Wrangle"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TrG5a27u0Xu3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# 1. Make a new column to map the JobSat into numeric\n",
        "\n",
        "# Very dissatisfied -> 1\n",
        "# Slighty dissatisfied -> 2\n",
        "# Neither satisfied nor dissatisfied -> 3\n",
        "# Slightly satisfied -> 4\n",
        "# Very satisfied -> 5\n",
        "\n",
        "def change_JobSat(value):\n",
        "  if value == 'Very dissatisfied':\n",
        "    return 1\n",
        "  elif value == 'Slightly dissatisfied':\n",
        "    return 2\n",
        "  elif value == 'Neither satisfied nor dissatisfied':\n",
        "    return 3\n",
        "  elif value == 'Slightly satisfied':\n",
        "    return 4\n",
        "  else:\n",
        "    return 5\n",
        "\n",
        "survey['JobSat_Map'] = survey['JobSat'].apply(change_JobSat)\n",
        "\n",
        "survey['JobSat'] = pd.Categorical(survey['JobSat'],\n",
        "                                      ordered=True,\n",
        "                                      categories=['Very dissatisfied',\n",
        "                                                  'Slightly dissatisfied',\n",
        "                                                  'Neither satisfied nor dissatisfied',\n",
        "                                                  'Slightly satisfied',\n",
        "                                                  'Very satisfied'])\n",
        "\n",
        "# 2. Make a new column to map the OpenSourcer into numeric\n",
        "\n",
        "# Never -> 1\n",
        "# Less than once per year -> 2\n",
        "# Less than once a month but more than once per year -> 3\n",
        "# Once a month or more often -> 4\n",
        "\n",
        "def change_OpenSourcer(value):\n",
        "  if value == 'Never':\n",
        "    return 1\n",
        "  elif value == 'Less than once per year':\n",
        "    return 2\n",
        "  elif value == 'Less than once a month but more than once per year':\n",
        "    return 3\n",
        "  else:\n",
        "    return 4\n",
        "\n",
        "survey['OpenSourcer_Map'] = survey['OpenSourcer'].apply(change_OpenSourcer)\n",
        "\n",
        "survey['OpenSourcer'] = pd.Categorical(survey['OpenSourcer'],\n",
        "                                      ordered=True,\n",
        "                                      categories=['Never',\n",
        "                                                  'Less than once per year',\n",
        "                                                  'Less than once a month but more than once per year',\n",
        "                                                  'Once a month or more often'])\n",
        "\n",
        "# 3. Make a new column to map the YearsCode into categorical\n",
        "\n",
        "# 0-10 -> 'beginner'\n",
        "# 11-20 -> 'intermediate'\n",
        "# 21-30 -> 'advance'\n",
        "# > 30 -> 'godlike'\n",
        "\n",
        "def change_YearsCode(value):\n",
        "    if value == 'Less than 1 year':\n",
        "        return 'beginner'\n",
        "    elif value == 'More than 50 years':\n",
        "        return 'godlike'\n",
        "    else:\n",
        "        if int(value) <= 10:\n",
        "            return 'beginner'\n",
        "        elif int(value) <= 20:\n",
        "            return 'intermediate'\n",
        "        elif int(value) <= 30:\n",
        "            return 'advance'\n",
        "        else:\n",
        "            return 'godlike'\n",
        "\n",
        "survey['YearsCode_Map'] = survey['YearsCode'].apply(change_YearsCode)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LhEP6ZQp0YcR",
        "colab_type": "text"
      },
      "source": [
        "## Explore"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q8bK0lGcLXD3",
        "colab_type": "text"
      },
      "source": [
        "### 1. Melihat korelasi antara JobSat dengan ConvertedComp"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dHpHlJ0p0dHu",
        "colab_type": "code",
        "outputId": "1cdf991a-fafd-4360-8ef6-80f4bb057990",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        }
      },
      "source": [
        "# 1. Melihat korelasi antara JobSat dengan ConvertedComp\n",
        "\n",
        "job_salary = survey.groupby('JobSat', as_index=False)['ConvertedComp'].mean()\n",
        "\n",
        "plt.figure(figsize=(8,4))\n",
        "viz = sns.barplot(data=job_salary,\n",
        "                  x='JobSat',\n",
        "                  y='ConvertedComp',\n",
        "                  palette=sns.color_palette('Blues'))\n",
        "viz.set_xticklabels(viz.get_xticklabels(), rotation=20)\n",
        "plt.title('Average Salary per Job Satisfaction Level')\n",
        "plt.xlabel('Job Satisfaction')\n",
        "plt.ylabel('Average Salary')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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kabNmzeqJLJiZmS10ugoCPgo8BFwl6VeStgb0ZjYSEU8AVwHvBgZWBhwaBjxY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\nPpVBQERERJ/KICAiIqJP/T8JOJo1SC13BAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 576x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fDVFv6kVK8Uf",
        "colab_type": "code",
        "outputId": "e8e6410c-805d-410d-e93a-1950618ef638",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 367
        }
      },
      "source": [
        "sns.heatmap(survey.corr(), annot=True, fmt='.2f')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7ff3e5078438>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 103
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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7ISI0uv8+UlMvcebsebZG7KJ1iyaUdXejrLsbrVs0YWvELofn27RZQ34/dpIT\nx2NJS0tj2ZJQ/IM62cX4B3biuwXLAFi5LJy27VsDcOXKVdLT0wEoXdoVYwrnA6r1m9zHqeNxxJ1M\n4FraNVYvX0v7bm3tYhJiEzl68NhNH5o9eSyWU7/HAnA26Rznz17gngrlHJpvw6b1OXH8FKdOxJGW\ndo2flv1Mp4AOdjGdA9qzZNGPAIStXEvrh1retJ/ufbrx47Jwh+Z6KyZD8rzccj/GXAP+BYQDB4Hv\njDEHRGSSiPS0hoUD50QkBlgPjDPGnLvVfguyx9URSDPGfGaT9B5gi4i8LyL7RWSfiIQAiEgHEdkg\nIotF5JCIzBcLfxH5/vo+rHE/Wn/uKiLbRCRKRL4XkTLW9uMi8q6IRAGPAs2B+SKyW0TuFJFmIrLR\n2gsMFxFv6+OaicgeEdkDPGfzXJ4D5hpjttk8l8XGmCQRKS8iy0Rkr/VCYkPrvt4UkbkisllETohI\nHxF5z/qcw0TExSbX6+2RIlKrAH8HmXx8vIiNTchcj4tLxMfH66aYuLjEW8YUFnfPe0iOv3GupiSc\np6xn+RzjW/TvyK8b9gCQePAEtds3xKW0K3fd40bN1vUp613B4TnnJunMObwq3RjO9KxUkaQzZ0k6\ncxavSh432j0s7Y7m5eNJfNyNcyI+LhEvb/u/K729KxFnjUlPTyc1JZXy5S1v9k2bNWTj9pVs+GUF\n48a8mVnIHMnDqyJJ8TcmmSUlnMHD2+MWj8hevcb34eLqQuzxrKNUBcvLuxIJcUmZ64nxSXhmydfT\ny4NEa0x6ejqXUi5xT3n7ghrUqys/LinKwlVgPS6MMaHGmDrWDs1/rG2vG2NWWH82xpgXjTH1jDEN\njDHf5rbPgixc9wPZ/dnYB2gMNAI6A+9fLxxAE+AFoB5QA2gDrAFaicjd1pgQ4FtrF3IC0NkY0xTY\nCbxoc5xzxpimxphvrNsGGmMaA9eAj4B+1l7gl8D1cbM5wPPGmEZ5fC4AE4FoY0xD4DXga5ttNYGH\ngZ7AN8B6Y0wD4AoQZBOXbG3/GJie3UFsZ+qkp1/KIZX/TY2D2+DbsDqbZln+aj2yeR+H1+9m5JI3\nGTDjX5yMOoLJKOYf/S+Bonbtpf0DPejW8RFGvzicO+5wLeqU8qRCpQpM+ujfTBrzdqH1FG9Ho6b3\nc+XKVY4c+q3IcjBG8rwUhcKYnNEWWGiMSTfGJAEbgRbWbZHGmFhjTAawG6hm7VqGAT1EpBSWN/zl\nwANYCtxWEdkNPAnYDnYvyuH492IpRKutj5sA+IlIOaCcMWaTNW5ePp7PPABjzDqggoi4W7etMsak\nAfsAZ+vzwLpezWYfC23+35LWbCEAACAASURBVDq7gxhjZhljmhtjmjs7521cfsSIQURErCIiYhWJ\niafx8/PO3Obr60V8fKJdfHx8Ir6+XreMKSwpSRco63Ojl+TuXZ7kpPM3xdVscz8d/xXM109PJf2v\nGx/b3/DJcj4KfI0vn3gbBM4eK5rnYcvTowKJp2/0pJJOn8XToyKeHhVJPH3mRvsZS7ujJcYn4eN7\n45zw8fUiMSHJLiYh4TS+1hhnZ2fc3N04f95+cteRX4/xxx+XqVvP8ZevzySexdPnxhC3p7cHZxLO\n3OIR9u4ucxfT573LzHdmsz8qJvcH3KbEhNN4+97oxXr5eJKUJd+kxDN4WWOcnZ0p416GCzavcVDv\nrvy4NIyiVJA9LkcoyMJ1AGiWa5S9P21+TufG9Pxvgf5Yei87jTGpgACrjTGNrUs9Y8xQm8f/kcMx\nBDhg87gGxpiuDnguYH0+1kKcZm78eZeB/UcPTA4/35b//vdrWrUKoFWrAFasCGfgwL4AtGzZhOTk\nVBIT7T/Xl5h4mpSUS7Rs2QSAgQP7snLlzwWVTr7E7vmNitW8uMfPA2cXZxr1aM3B1fadXu/6Vek9\nZShfPz2VP86lZLaLk3BXOUtx96pbGa+6VTiyeW+h5p+dDm0fYEXYWowx7Nl/kDJl7sajYnnatGrG\nL5FRJKekkpySyi+RUbRp9XdOt/yJjtpHjZpVqVLVFxcXF4L7BBIeus4uJjx0Hf0fCwagR3A3tmza\nDkCVqr6ZkzH8KvtQq3YNTp2IdXjOMbsPUaW6Hz6VvSnlUoouvTqx6eeteXpsKZdSvP/Ffwj9Ppx1\nP210cKYW+6JjqFa9Mn5VfHBxKUVQcFfWhtkfe23YRvqEdAfAv0cntm/ZkblNRAjo1YWflhbNv8Pr\nMtIlz0tRKMjPca0DpojIcOu8fqzXfy4CISIyFygPtAPGAXVvsa+NWIb0hmEpYgDbgU9EpJYx5qh1\nKNHXGJPdtJtUbkykOAx4iEhrY8w267WmOtYLhBdFpK0xZgtgO8/2YyBSRH4yxkRYn0sfYCuw2Ro7\nWUQ6AGeNMSmWuSl5FgK8Y/3/tlxi/5awsHX4+3ckJmYzly9fYfjwsZnbIiJW0apVAACjR09g9uyp\n3HlnacLD1xMevh6Anj27MW3aJDw8yrN06Rz27o2hR48nHJEqABnpGax4/SuGfD0ecXZi53cbOH0k\njs5j+hG37xgH10QR+OpAXO8qzWMzLbOwLsadY96wqTi7lGL4968D8OelK3w3ZiYZ6Y7/U3DcG++w\nI3ovFy+m0Cn4cZ4d+gTXrll6gSG9g2jXugWbt+0goP8Q7ixdmsmvjQGgrLsbIwY/yoCnRwMw8qnH\nbjnJo6Ckp6fz6tjJfLvkC5ydnVj4zQ8cPnSUl197nj3R+wlftZ4F8xbz8az32B4dzsULyYwYYhmN\nb/lAM54fM4xradfIMBmMf2niTT0xR+X83r+nM2PBBzg7O7Hi21CO/XqcEeOGcHDPYTb9vJV6jery\n3hdv4V7OjbZdHmTE2CGEdHySLj060uSBRpQt7073EMtnWie+8Da/Hjiay1FvL9+Jr77Hl999jLOT\nM4sXLufo4WOMfmUk+3bHsC58E9/PX84HMyezJnIZFy8kM2b4a5mPb9G6KYlxSZw64dhrcbnJbdJF\nUZOCHPO1TkGfjqW3chU4juUa1nAgAEvv4i1jzCLrm/5YY0x362M/xtK7+spmfTBQyRhz2dr2MPAu\ncIf1kBOMMStE5DjQ3Bhz1hrXF5iC5dpSayzDhTOAsliK9XRjzGwRuX7NywA/A4HGmPut+2gNvAdU\nwtJj2gSMAUpbH1MDuAwMN8bsFZE3gUvGmA+sj79kjLk+eSRzmzXXRdbX40/gUWPMLf8llS5dpfgP\nzNsY49U296BiZtLOnKe1F1d+NQOLOoV8qXJ3rregK3YupuU0kFN8HTmz67arzvHGXfL8nlNt9+pC\nr3IFWrhU7rIW2bzQwuV4WrgcTwtX4SiIwvV7o7wXrup7Cr9w6S2flFJK2SnuQ4VauAqZMaZaUeeg\nlFK3UlTT3PNKC5dSSik76UU0WzCvtHAppZSyoz0upZRSJYpe41JKKVWiFPfJ5lq4lFJK2dEel1JK\nqRIlPaN4f8ewFi6llFJ2dKhQKaVUiZKhswqVUkqVJDodXimlVImiQ4XqtpVyci7qFPJl5tmIok4h\n374sYTesBYj9LbSoU8iXO30eKuoU8u2eO/P2Ja7/NDpUqJRSqkTRWYVKKaVKlGI+UqiFSymllD0d\nKlRKKVWi6KxCpZRSJUpGUSeQCy1cSiml7Bi0x6WUUqoEuaZDhUoppUoS7XEppZQqUfQal1JKqRJF\ne1xKKaVKFO1xKaWUKlHStcellFKqJMko3nWL4n0nRfW3de7Sjqjda9mzbz0vvjTypu2urq7M/foj\n9uxbz/qNS6lSxReA8uXLEbpqAYmn9zN12sRCzblT53bsjFpN9J51jHlxRLY5z5k7g+g961i7/ofM\nnDt2bMPGzcv5JSKUjZuX065960LJt2OntmzduYrt0eE8P2ZYNvm6MGvONLZHh7Nq7SIqW/Nt0rQB\nazcvZe3mpazbsoyA7p0LJV+ACVOm0S5oAMGP33xOABhjmPLhpwT0H0LvQc8Qc/ho5rbloasJDBlK\nYMhQloeuLqyUAfhw2iQOxWwhatdqmjS+P9uYpk0aEB21hkMxW/hw2qTM9nffnsD+fRuJ2rWaxd9/\nTtmy7g7N9eFOD7FtZxiR0T8zKofzYvacD4mM/pmwtd/ZnRfrNy+zLFuWE1iI50VWGUiel6JQ6IVL\nRD4UkRds1sNF5HOb9aki8mIe91VNRPZn0x4tIo2tP5cSkUsi8rjN9l0i0vRv5N5BRH7Mpn2wiHyc\npW2DiDTP7zEKgpOTE9M+nESf4ME0b9qVRx7pSd26texinhzcn4sXk2nUoCOffPQFk98aD8DVq38y\nedI0/v3alELPeeq0N+nXZwgtm3ej7yM9uDdLzoOefISLF5Np0uhhZn4yh4mTXwHg3LkLhDwyjAdb\nBTJyxDj+O/uDQsn3namv81i/YTzUsju9+wZR596adjGPDerHxYspPNCkG/+dOZf/m/gSAIcOHqFr\nh350eqg3A/oO44PpE3F2LpyvrgkO7MJn097KcfvmbTs4GRtP6KIvePPlUUz+wHJaJ6ek8umcBSyc\nPZ2Fs6fz6ZwFJKekFkrOAf4PU7tWderWa8szz7zCJx+/nW3cJx+/zciRL1O3Xltq16qOf7eOAKxZ\nu4lGjR+mabMuHDlyjPGv/MthuV4/Lwb0e5o2LYPo3bf7TefFwEGPcPFiCi2bdOWzmV/x+sSxgOW8\n6NyhLx0fCmZA36f5YPqkQjsvsjL5WIpCUfS4tgIPAoiIE1ARqG+z/UHgl9x2IiK3GubMPAbQCPjV\n5ph3AzWBPflNvCCIiMPPxObNG3HstxMcP36KtLQ0Fi9eSVD3LnYxQUFdmP/NDwAsXbqKDh0sL9fl\ny1fYtm0nV6/+6eg07TRr3ohjx27kvGTxjwQF2f/FGRjUmQXzlwCwbOkq2new9Kz27o0hMfE0AAdj\nfuXO0qVxdXV1aL5NmzXk92MnOXE8lrS0NJYtCcU/qJNdjH9gJ75bsAyAlcvCaWvtCV65cpX09HQA\nSpd2xRTit/Y1b9yAsu5uOW5fv2U7Pf07ISI0uv8+UlMvcebsebZG7KJ1iyaUdXejrLsbrVs0YWvE\nrkLJuUePbsybvxiAiMgoypYri5dXJbsYL69KuLm7EREZBcC8+Yvp2dMfgNVrNmW+3tsjovD19XZY\nrk2bNeT4sRM258VPBGQ5LwICH2bRgqWA5bx4KJvz4o7SdxTqeZFVRj6W3IiIv4gcFpGjIjL+FnF9\nRcTk5Q/+oihcvwDXx3LqA/uBVBG5R0TuAO4DokXkfRHZLyL7RCQEMns8m0VkBRBju1MRqWHtabWw\nHuN64XoQ+AxobF1vCewyxqSLyN0i8qWIRFof28u6L2fr8XeIyF4RuWncSkRaWB9TM+u2bGIvWXuS\ne4DWIvKOiMRY913g3QMfHy9i4xIy1+PiEvHx8coS45kZk56eTnJKKhUq3FPQqeSZj48ncbH2OXv7\neNrFePt4Zcakp6eTkpxK+Sw59wr2Z8+eA/z1118OzdfLx5N4m9c4Pi4RL+8s+XpXIs7mNU5NSaV8\n+XKA5Q1u4/aVbPhlBePGvJn5hlXUks6cw6tSxcx1z0oVSTpzlqQzZ/Gq5HGj3cPSXhh8fbyIPRWf\nuR4Xm4BvlvPZ1+bcyCkG4KnBAwgLX++wXL19PImLS8xcj49LwjvLeeHl7Wl3XqSkpFK+vOU8btqs\nIZu3/8imX1YwbswbRXZeZIjkebkV6x/qnwABQD3gURGpl02cGzAayNO30BZ64TLGxAPXRKQKlqKy\nDUuyrYHmwD6gO5ZC0wjoDLwvItf/TGoKjDbG1Lm+TxG5F/gBGGyM2YF9j+tBYBPwp/XFse3R/RtY\nZ4xpCXS0HuduYCiQbIxpAbQAholIdZvjXS+GvYwxv1mbQ0Rk9/XF+lyuuxuIMMY0Ag4CvYH6xpiG\nQLbjNiIyXER2isjOtGuFMyRT0tW9rzYTJ73MC6MmFHUquYratZf2D/SgW8dHGP3icO64w7E9RAWv\njh/FtWvXWLBgSVGnkqOoXXt56IHudOnYj9Evjiiy8yI9H0suWgJHjTHHjDF/Ad8CvbKJmwy8C1zN\nS35FNTnjeo/oeuHaZrO+FWgLLDTGpBtjkoCNWAoIQKQx5nebfXkAy4GBxpg9AMaYE4CriHgBdYHD\nwA6glc0xALoC462FZgNQGqhibR9kbY8AKgC1rY+5D5gF9DDGnLTJY5ExpvH1Bdhpsy0dS2EFSMby\ny/lCRPoAl7N7gYwxs4wxzY0xzV1K5Ty0k534+ET8bIZDfH29iI9PzBKTlBnj7OxMWXc3zp27kK/j\nFKT4+CR8/exzTohPsotJiE/MjHF2dsa9rBvnrTn7+Hgxf8GnjBg+jt9/P4mjJcYn4WPzGvv4epGY\nkCXfhNOZw1LOzs64ubtx/vxFu5gjvx7jjz8uU7deHYoDT48KJJ6+0ZNKOn0WT4+KeHpUJPH0mRvt\nZyztjvLMyCfZueNndu74mYTEJPwq+2Ru8/XzJi7L+Rxnc25kFzPoif4EBXbmiUGOu74FkBCfhK/v\njZ6ej68nCVnOi8SEJLvzwt3djfPn7f/tFfV5kSF5X2z/yLYuw2125QucslmPtbZlss43qGyM+Smv\n+RVV4breI2qAZahwO5YeV16ub/2RZT0ZOIml2Nn6BXgESDCWweLtQBssfwFss8YI0Nem4FQxxhy0\ntj9v017dGPOz9TEJWApPk3w836vGmHQAY8w1aw6LsfQsw/KxnzzZtWsvNWtVo2pVP1xcXOjXrweh\nP62xiwkNXcPAx/sC0Lt3ABs3bstuV4Umatdeata8kXOfft0JDV1rFxMaupbHBvYBILh3AJusOZct\n68Z3P3zOm2+8R8T2wrnuEh21jxo1q1Klqi8uLi4E9wkkPHSdXUx46Dr6PxYMQI/gbmzZtB2AKlV9\nMy+6+1X2oVbtGpw6EVsoeeemQ9sHWBG2FmMMe/YfpEyZu/GoWJ42rZrxS2QUySmpJKek8ktkFG1a\nNXNYHp9+NpfmLbrSvEVXVqwI54mB/QBo1bIpKckpmdc0r0tMPE1qSiqtWlrmXD0xsB8rV4YD0K1r\nB8aOfYbgPoO5ciVPf9D/bdFR+6hesxpVrOdxcJ8gwrKcF2Gh6wh5rDeQ9bzwszsvateuwakTcQ7N\nNyf5mVVo+0e2dZmV1+NY5zlMA17KT35F9TmuX4CxwDHrG/p5ESmH5ZrXMGteI0RkLlAeaAeMw9J7\nyuovLENv4SJyyRizwOYYLwBfWde3Ae8DicaYZGtbOPC8iDxvjDEi0sQYE21tf0ZE1hlj0kSkDnD9\nDLqIZShxtYj8YYzZkJ8nLiJlgLuMMaEishU4lp/H50V6ejovvfgGy1Z8jbOzE/O+/p6DB48w4f/G\nEBW1j9Cf1jD3q0V8/sWH7Nm3ngsXkhk86PnMxx84uBk3tzK4urrQvUcXevUYxKFDR29xxILJeexL\nE1my7CucnZ34Zt5iDh08wmsTXiA6ah+rQtcyb+53zPp8KtF71nHhwkWGDB4NwLARg6hRoyovj3+e\nl8dbnkfvXoM5e+acQ/N9dexkvl3yBc7OTiz85gcOHzrKy689z57o/YSvWs+CeYv5eNZ7bI8O5+KF\nZEYMsUyWbflAM54fM4xradfIMBmMf2niTT0xRxn3xjvsiN7LxYspdAp+nGeHPsG1a9cACOkdRLvW\nLdi8bQcB/YdwZ+nSTH5tDABl3d0YMfhRBjxtec1HPvXYLSd5FKTQVWvx93+Ywwe3cvnKFZ5++sak\n4507fqZ5i64A/Ov51/jiiw+5s3RpwsLXsyrMUjD+3/S3uOOOOwhb9S0AERFRPPevHOcI3BbLeTGJ\n75Z8jpOzc+Z58cpro9gdvZ/wVeuYP28xM2e9T2T0z1y4kMzwIZbXuNUDzRhlc168/NKbN/XECksB\nTguJAyrbrPtx470UwA24H9gglutlXsAKEelpjLEdtbIjRTFzxXrB7gIwwxgzwdr2FdDaGHOvWJ7B\ne1gu6BngLWPMIhHpAIw1xnS3PqYa8KMx5n5r4VsNTDbGrLBO0ogEuhhj1ljjjwPhxpgR1vU7gelY\nenpOwO/GmO7WvwLeAnpg6X2dAYKx9LLGWmOqAKuAIViGD5sbYzLHIURkgzV2p7WglrG2e2MZ2ixt\n3fcHxpi5t3q9ytxVveimF/0Nzk4l7+OBpZ1dijqFfIv9LbSoU8iXO30eKuoU8u2eO8sUdQr5dib5\n8G1/uOpr38fz/J4zKO6bHI9nnf39K9AJS8HaATxmjDmQQ/wGrO+btzpmkRQulT9auBxPC5fjaeEq\nHAVRuL7KR+EafIvCBSAigVg6CM7Al8aY/4jIJGCnMWZFltgN5KFw6S2flFJK2UkvwBtiGGNCgdAs\nba/nENshL/vUwqWUUsqO3h1eKaVUiaKFSymlVIliivnd4bVwKaWUsqM9LqWUUiVK8bhzZs60cCml\nlLJT3L9IUguXUkopOzpUqJRSqkTRwqWUUqpEKe636tHCpZRSyo5e41JKKVWi6KxC9T/Hxcm5qFPI\ntyp3VyrqFPKtpN209kr85qJOId9q1snuy3r/+TKK+WChFi6llFJ2dHKGUkqpEqV497e0cCmllMpC\ne1xKKaVKlGtSvPtcWriUUkrZKd5lSwuXUkqpLHSoUCmlVImi0+GVUkqVKMW7bGnhUkoplYUOFSql\nlCpR0ot5n0sLl1JKKTva41JKKVWiGO1xKaWUKkmKe4/LqagTUI7RuUs7onavZc++9bz40sibtru6\nujL364/Ys2896zcupUoVXwDKly9H6KoFJJ7ez9RpEws154c7P8T2XWFE7l7NqDHDs8nZhc/nTCdy\n92rC131PZWvOTZo1ZP2W5azfspwNW1cQ2L1LoeTbukNLFm/+hiVbF/DkvwbetL1Jq0bMC/+cbSfX\n8XBQ+8z2OvVr8cWKmSxaP5cFa+bQpefDhZLvdR9Om8ShmC1E7VpNk8b3ZxvTtEkDoqPWcChmCx9O\nm5TZ/u7bE9i/byNRu1az+PvPKVvW3aG5TpgyjXZBAwh+/OZzGMAYw5QPPyWg/xB6D3qGmMNHM7ct\nD11NYMhQAkOGsjx0tUPztNW+UxvWR6xg086feHb00Ju2u7q68MkX77Np508sXz0fv8o+APhV9uHX\nuB2s2vg9qzZ+z5Sp/1doOWeVgcnzUhS0cP0DOTk5Me3DSfQJHkzzpl155JGe1K1byy7mycH9uXgx\nmUYNOvLJR18w+a3xAFy9+ieTJ03j369NKfSc3536BiF9h9GmRSB9+nWnzr017WIGDnqEixeTadm4\nC5998hVvTBwHwKGYX+ncvg8d2/YipM9Qpv6/STg7O/arVZycnHh5yhhGDxxH/w6D6NqrE9VrV7WL\nSYxLYuILUwhfusau/eqVq7w5egohHZ9k1MCxvDjxecq4l3FovtcF+D9M7VrVqVuvLc888wqffPx2\ntnGffPw2I0e+TN16baldqzr+3ToCsGbtJho1fpimzbpw5Mgxxr/yL4fmGxzYhc+mvZXj9s3bdnAy\nNp7QRV/w5sujmPzBxwAkp6Ty6ZwFLJw9nYWzp/PpnAUkp6Q6NFewnBdvvfdvnuz/LJ1a96Jn3wBq\n31vDLibk8T4kX0yhXfMgPv90Hq++OSZz24njpwho/wgB7R/htZcmOzzfnJh8LEWhxBQuEbl0i20d\nROTHHLYNEZF9IrJXRPaLyC2/YEdEgkWkXi4xX4nIZRFxs2mbLiJGRCrm9lwcrXnzRhz77QTHj58i\nLS2NxYtXEpSlFxIU1IX53/wAwNKlq+jQ4UEALl++wrZtO7l69c9Czblp84b8fuwEJ6w5L/3hJwKC\nOtvFBAR14tuFSwFYsSyMhzq0BuDKlaukp1u++u6O0ndgjOP/OdVvch+njscRdzKBa2nXWL18Le27\ntbWLSYhN5OjBY5gM+3xOHovl1O+xAJxNOsf5sxe4p0I5h+cM0KNHN+bNXwxARGQUZcuVxcvL/rvI\nvLwq4ebuRkRkFADz5i+mZ09/AFav2ZT5Wm+PiMLX19uh+TZv3ICy7m45bl+/ZTs9/TshIjS6/z5S\nUy9x5ux5tkbsonWLJpR1d6OsuxutWzRha8Quh+YK0LhZA47/fpKTJ2JJS7vGyiWr6BrQ0S6ma2BH\nFn+7AoDQ5atp066Vw/PKr2uYPC9FocQUrr9DRPyAfwNtjTENgQeAvbk8LBi4ZeGyOgr0sh7HCXgY\niPv72RYcHx8vYuMSMtfj4hLx8fHKEuOZGZOenk5ySioVKtxTqHna8vb2JD42MXM9Pj4Rbx/Pm2Li\nYm/knJKSSvnylpybNm/Iloif2LRtJWNfeCPzzdVRPLwqkhR/OnM9KeEMHt4e+d5Pvcb34eLqQuzx\nwjl1fH28iD0Vn7keF5uAb5Zzw9fHK/N1zikG4KnBAwgLX++4ZPMg6cw5vCrd+FvRs1JFks6cJenM\nWbwq3fh9eHpY2h3Ny7sS8XE3zuOE+CQ8vT1zjElPTyc15RL3lLf84VK5ii+hG77ju5VzaPlAU4fn\nmxOTj/+KQokqXGLxvrXntE9EQmw2u4vITyJyWEQ+sxaTSkAqcAnAGHPJGPO7dV/DRGSHiOwRkR9E\n5C4ReRDoCbwvIrtFpGbWHGx8C1w/fgdgK3DNJtdlIrJLRA6IyHCb9ksi8qG1fa2IZPtuJyLDRWSn\niOxMu+b4IY6SLmrnXtq2CqJLh3688NII7rjDtahTylWFShWY9NG/mTTm7ULpJRakV8eP4tq1ayxY\nsKSoU/nHOJ10hgcadiWwQ38mT3ifGbPfpYzb3UWSS0Y+lqJQogoX0AdoDDQCOmMpMNfHKloCz2Pp\nLdW0xu4BkoDfRWSOiPSw2dcSY0wLY0wj4CAw1BjzC7ACGGeMaWyM+e0WufwKeIjIPcCjWAqZrSHG\nmGZAc2CUiFSwtt8N7DTG1Ac2Am9kt3NjzCxjTHNjTHOXUjkPlWQnPj4RP5shHF9fL+LjE7PEJGXG\nODs7U9bdjXPnLuTrOAUpISEJH78bf9X7+HiREJ90U4yv342c3d3dOH/ePucjv/7GH5f+4L56dRya\n75nEs3j63Bhi8/T24EzCmTw//u4ydzF93rvMfGc2+6NiHJFipmdGPsnOHT+zc8fPJCQmZU4GAPD1\n8yYuy7kRF5+Y+TpnFzPoif4EBXbmiUGOvb6VF54eFUg8faMnlXT6LJ4eFfH0qEji6Ru/j6QzlnZH\nS0w4jY/vjfPY28eTpISkHGOcnZ1xcy/DhfMX+euvNC5eSAZg354YTvx+iho17a+bFhbtcRWstsBC\nY0y6MSYJyxt/C+u2SGPMMWNMOrAQy/BgOuAP9MNSaD4UkTet8feLyGYR2QcMBOr/jXyWAAOAVsDm\nLNtGicgeYDtQGahtbc8AFll//sb6nArUrl17qVmrGlWr+uHi4kK/fj0I/cl+gkBo6BoGPt4XgN69\nA9i4cVtBp5Ev0bv2UaNGNapYc+7dN4iw0LV2MWGh6xjwaG8Aegb7s9mac5WqfpmTMfwq+1C7Tg1O\nnnDs0FvM7kNUqe6HT2VvSrmUokuvTmz6eWueHlvKpRTvf/EfQr8PZ91PGx2aJ8Cnn82leYuuNG/R\nlRUrwnliYD8AWrVsSkpyComJp+3iExNPk5qSSquWlqGqJwb2Y+XKcAC6de3A2LHPENxnMFeuXHV4\n7rnp0PYBVoStxRjDnv0HKVPmbjwqlqdNq2b8EhlFckoqySmp/BIZRZtWzRyez56o/VSvUZXKVXxx\ncSlFjz4BrA7bYBezetUG+g3oCUBgry78sjkSgPIV7sHJyfKWXKWqH9VrVOHE8ViH55yd4t7j+id9\njitr6TcAxjIGEwlEishqYA7wJvAVEGyM2SMig7EM9+XXImAXMNcYkyEigGWyCJYeYWtjzGUR2QCU\nzmPety09PZ2XXnyDZSu+xtnZiXlff8/Bg0eY8H9jiIraR+hPa5j71aL/3959x0dVpm0c/11EBFYB\nO9UCFhQ7CIqKDUWQIlYsvHbRFbviWnbtr+trXytrw95QQFQIIFiw0ItIs6N0VJodwv3+8ZyESUjV\nJOec5P7uZz5mzpyZuTibzDNP5/En7mXa9HdYtmwFZ5x2Ud7zZ8waQ926G7PhhjXp2u0Iju52GrNn\nf1HMO5ZP5qv73syAQU9QIyuLF559lTmzv+Dq6y5m6uRPyR42muefGcDDj97J+KkjWb5sBeeeGUZj\n7duuNZdc1pvVq9dga9fS9/Kb1quJVUTeO667j/tfuIusrBoMeWkoX332Def1PYtZ0+bw/ogPabnn\nztzxxK3U26QuBx6xP+ddeRY9Dz2dI7odyt777Un9zerRtWcY9HDTpf/msxkVe40Bhg4bRadOhzFn\n1of88uuvnHPO5XmPDozQWAAAIABJREFUTZwwgn3adATgwouu5Ykn7qVO7dpkD3+HYdmjAfjPfbdS\nq1YtsoeFBoZx4ybT58KrKyxv3xtuZ8KUT1i+fCUdevTigrP/hzVrQot8z2O6cFC7Noz5eAKdTzyL\nOrVrc8u14Xeifr26nHfGyZx0ziUAnH/mKcUO8igvOTk5/Ouq23j21X5kZWXx8vOD+Gz2l1x+TR+m\nT5nByOx3efm5gdzX79+8P/Etli9bwYXnXAXAvvu35opr+rB69RrWrl3LtVfcworlKys8c6H/joQ3\nXSstbevRqMLTgPOAo4DNgImE2s7OwDBCM+Hc6OdHgY+BhmY2OXqNcwiFVVdJ30fnLwOGAvPN7AxJ\nDwCTzax/MVmeAt40s1clnQe8bWZfSvqG0DR4AHCOmXWTtDMwFehkZu9KMuBkM3tJ0j+BBmZ2URFv\nBcDGf2uWjv+TIrU3qBl3hDJrtvH6gw+Sbsr3xbVkJ8+vCwo2SiTf9jsVOwg5kb79cbr+6mucsu0x\npf7MeWHuoGLfT1In4D9AFvC4md1e4PHLgXMIYwSWErpZ5hb3mqloKpS0AfA7MIgwKnAaMBq4ysxy\nG98nAA8S+qu+js6tCdwlabakqYTBFJdE5/8LGEcYVDE74+1eAvpKmlLC4AwAzOy/hfSFZQMbSJoF\n3E5oLsz1M9BW0qeEkYg345xzCVJefVySsoCHgM6EisLJhUw3mgLsE438fhW4o6R8aWkq3BX4Mmr2\n6xvd8pjZu8BBhTxvLqFwWI+ZPQI8UsjxDylhOLyZnVHE8e0y7nYu5vmXF/WYc87FrRz7rtoCX5jZ\nVwCSXiJMI8obkWRmmXMqxgK9SnrRxNe4JJ1PGGzxz7izOOdcdVCWJZ8yp+5Et8z12poA32Xcnxcd\nK8rZhK6eYiW+xmVm/YB+cby3pIcI/VWZ/lNc/1dJzKxy1vZxzrk/qSzD3M3sUcKYgr9EUi/CGIGD\nSzo38QVXnMysT9wZnHOuspXjqML5hOlAuZpSyApDkg4nrHJ0sJmVuN6cF1zOOefyKcdV3ycAO0pq\nRiiwTgJOyTxB0t7Afwkjr5es/xLr84LLOedcPuU1OMPM1ki6EBhOGA7/pJnNkHQzYQWhIcCdwMbA\ngGgu7Ldm1r241/WCyznnXD7luZSTmQ0lzJXNPHZ9xs+Hr/ekEnjB5ZxzLp+4NogsLS+4nHPO5ZP0\nFZW84HLOOZdPjte4nHPOpYk3FTrnnEsVbyp0f9nqtWtKPilBapO+1eGXr/457ghltmmddC3CksaV\n1r/87PW4I8TCa1zOOedSJa6djUvLCy7nnHP5JH0jSS+4nHPO5eNNhc4551LFCy7nnHOp4qMKnXPO\npYrXuJxzzqWKjyp0zjmXKjlWXhubVAwvuJxzzuXjfVzOOedSxfu4nHPOpYr3cTnnnEuVtd5U6Jxz\nLk28xuWccy5Vkj6qsEbcAVzFuefum5g5YwwTJ4xgr712K/ScvffenUkTRzJzxhjuufumvOPHHtuF\nKZPf5tdf5tKq1R6Vkveww9szdlI246eO5OLLeq/3+IYb1uTx/vcxfupIho8ewNbbNAHg4EP3Z9R7\nA3n/4zcY9d5A2h+0X6XkbX9YO4Z//Bpvjx9M74vPKDTvfY/9m7fHD+bV7KdpsnUjALof15kh77yQ\nd5uzeAK77LZTpWQ+rEN7Pp6YzfgpI7j4snMLzfxY/3sZP2UE2aNeybvGe7fanXfGDA63D17nqK6H\nV0pegIM7HMA744bw/sS3uOCSswvN/NATd/L+xLd4feTzNN26MQBNt27MZ/MnMOy9AQx7bwC33f2v\nSsn7z9vu4aAuJ9Gj1/mFPm5m3HbvI3Q+8SyOOe3vzJzzRd5jrw8dyVE9z+aonmfz+tCRlZK3MGvN\nSn2LQ6kKLklNJb0u6XNJX0r6j6QNyzOIpBaS3pU0VdIsSY+W5+uXN0km6bmM+xtIWirpzThz5ep0\n5KHssEMzWu7angv6/IMH7r+t0PMeuP82/n7BVbTctT077NCMIzseAsDMGXPo2bM3Yz4YVyl5a9So\nwf/dfQM9jzuXA9ocxbHHd2WnFtvnO+fU005g+fIVtN3rCPo99BQ33NQXgB9/WMapPc/noHbd6HP+\nP3j40TsrJe+Nt1/NOSddTOcDjqfrMUeyw07N8p1z/Kk9WLl8JYe37UH/fs/T9/qLARjy2jC6H3oK\n3Q89hSsvuJ55cxcw69PPKiXz7Xdfz0nHn8MBbbtwzHFFXeOVtN27I/0eforrb7oSgNmzPufwQ47j\n0PY9OOm4c7jrvpvJysqqlMy33nEdp594AR3aHU334zqzY4vm+c7p2etYVixfyUH7dOHxR57lmhsv\ny3ts7jff0fngE+h88Alce8UtFZ4XoMdRR9DvnluLfHzMxxP4dt4Chr78BDdedTG33PUgACtWruKR\n/i/w4mP38eJj9/FI/xdYsXJVpWQuyMrwvziUWHBJEjAQGGxmOwI7ARsD/1vOWe4H7jWzvcxsF+CB\n8nhRSX/6r0tScU2pPwO7SaoT3T8CmP9n36u8devWkeeefw2A8eOnsMkm9WjYcKt85zRsuBX16m3M\n+PFTAHju+dfo3v1IAGbP+YLPPv+q0vK22mcPvv5qLnO/+Y7Vq1cz6LW36Nwl/7f6zl068NKLgwAY\nMjib9oe0A2D6J7NYtGhJyD3rc2rXqcWGG1bsZpZ7tNqVud98x3dz57N69RreGjyCDp0PyXfO4Z0P\nZuDL4XtM9hujaNe+7Xqv0/XYI3lz8PAKzZqrVes9+Oarucz9Zh6rV69m8MC36NylQ75zOh91GC+/\nEK7xG4OH0/7gcI1//fU3cnJyAKhVu1alzfPZq/XufPP1t3w7dx6rV6/hjYHD6Nj50HzndDzqUF59\naQgAQ18fyQEH7Vsp2Yqyz167U79e3SIff+eDsXTv1AFJ7LnbLqxa9RNLv/+RD8dNol2bvalfry71\n69WlXZu9+XDcpEpMvk5VqHEdBvxmZv0BzCwHuAw4S9IFUU3s3ag2dkPukyT1kjQ+qkH9N7cAkfST\npP+VNE3SWEkNoqc0AublPt/Mpkfn15bUX9J0SVMkHRodP0PSgxnv96akQzLe425J04B2ktpI+ih6\nz/GS6krKknSnpAmSPpF0XvTcQySNkTQEmFnCtRkKdIl+Phl4MSNPW0kfR5k/ktQiI3eh16w8NW7c\nkHnzFuTdnz9/IY0bN1zvnPnzFxZ7TmVp1KgBC+Ytyru/YMEiGjVusN458+eFvDk5OaxcuYrNNts0\n3zndjj6ST6bO5I8/Vldo3oaNtmLh/MV59xctWEyDRlvmO6dBwy1ZFJ2Tk5PDTyt/YtPNNsl3Tpej\nO/LmwMopuBo1bsD8+RnXeP5iGjXKf40bNmqQ9ztR8Bq3ar0HY8a+yfsfDaHvZTfkFWQVqWGjrViQ\nkXnhgsU0WC/zunNycnJYlXGdt96mCUPffYVX3uhP2/1aVXje0li89AcabrVF3v0GW23B4qXfs3jp\n9zTcat3vUIMtw/E4JL3GVZrBGbsC+Yp9M1sp6dvo+W2B3YBfgAmS3iLURnoCB5jZakkPA6cCzwAb\nAWPN7DpJdwDnArcC9wKjJX0EjAD6m9lyoE94S9td0s7ACEkldQhsBIwzsyuiJs3ZQE8zmyCpHvAr\ncDawwszaSKoFfChpRPT8VsBuZvZ1Ce/zEnB91Dy4B/Ak0D56bDbQ3szWSDocuA04LnpsvWtmZhMz\nX1hSb6A3QNYGm5CVla5t2uPQYucduP7mvpzQ48y4o5TKnq1249dff+Pz2V/GHaVUJk/6hPb7dWXH\nnZrzYL//Y9TI9/n99z/ijlWkJYuXst8eHVm+bAW779mSx577D4fv34OfVv0cd7TEy7GK/1LyV5TH\n4IyRZvaDmf1KaFI8EOgAtCZ8KE+N7uc2TP8B5PYDTQK2A4hqdLsAA4BDgLFRgXIg8Fx0zmxgLqG5\nsjg5wGvRzy2AhWY2IXqNlWa2BugInBblGwdsDuwYPWd8KQotzOyTKP/JhNpXpvrAAEmfEgrlXTMe\nK+yaFXztR81sHzPbp7SF1vnnnc74cdmMH5fNwkVLaNq0cd5jTZo0YsGCRfnOX7BgEU2aNCr2nMqy\ncOFiGjddV9tr3LghCxcsXu+cJk1D3qysLOrVq8uPPy4DQm3imRceok/vq/jm6+8qPO+ihUto1GTd\nN/+GjRuweOHSfOcsXrSUhtE5WVlZbFxvY5b9uDzv8S7HdOTNQdkVnjXXwgWLadIk4xo3acDChfmv\n8aKFi/N+Jwpe41yff/YVP//8Czu3rPgBJYsWLqFxRuZGjRuweL3M687JysqibnSd//hjNcuXrQBg\n+rSZzP36O5pvv22FZy5Jgy03Z9GSdTWpxUu+p8GWW9Bgyy1YtGTd79DipeF4HMys1Lc4lKbgmkko\nhPJEtZZtgDWwXl3RAAFPR/1Ve5lZCzO7MXp8ta371+aQUeszswVm9qSZHR29duFD4YI1BfLXzvj5\nt6hJszgCLsrI2MzMcmtcZflKNgS4i4xmwsgtwDtmthvQrUC+wq7ZX9bvv0/Tdt9OtN23E28MGU6v\nU0MFr23bvVmxYlVeP1CuRYuWsHLlT7RtuzcAvU49jjfeGLHe61aGKZOm07z5dmyzbVNq1qzJMcd1\nIXvoqHznZA8dzUknHwNA9x6dGPPexwDUq1+XFwc8xs033M34cZMrJe/0KTPZrtnWNN2mMTVrbkCX\nHh0Zlf1evnNGZb/HsT27AtCpWwfGfjAh7zFJdD76CN4aVHnXe8rk6TTbft017nFsF7KHjs53TvbQ\n0fQ8JVzjbj2O5IP3xwKwzbZN8wZjNN26MTvu2Jzv5lZ8l+60yZ/SrPm2bL1NE2rW3IBux3ZmZPa7\n+c4ZOexdjj+pOwBHHX0EH40ZD8Bmm29KjRo18vI3a74Nc7+ZR9wOOXA/hmSPwsyY9uksNt54I7bc\nYjMO2Lc1H42fzIqVq1ixchUfjZ/MAfu2LvkFK8BarNS3OJSmqXAUcLuk08zsmaiv6m7gKUJT1xGS\nNiM0v/UAzoqOvy7pXjNbEj1e18zmFvUmkjoBo6KmxYaEGtB8YAyhmXF01ES4DTAHqAdcIKkG0ITQ\n/FaYOUAjSW2ipsK6UdbhwN8ljY7ecyf+3OCKJ4HlZjY9t48tUj/j9c4o8JzCrlm5GpY9mk6dDmPW\nzA/45ZdfObf3FXmPjR+XTdt9OwFw8SXX8fhj91CnTm2GD3+H7OHvANC9eyfuvedmttxyMwYPeopP\nPplJ1269yjtmnpycHK7uezMDBj1BjawsXnj2VebM/oKrr7uYqZM/JXvYaJ5/ZgAPP3on46eOZPmy\nFZx7Zhg9dk7vXjRrvg1X/qMPV/6jDwAn9DiT77//sULz3nTNHTz5yoNk1cji1Rdf54s5X3HJP85n\n+tSZjB7+PgOef527Hr6Ft8cPZvmyFVzW+9q857dp14pF8xdXyod/ZuZrrryZVwY+To2sLF587jXm\nzP6Cf1x7MVOnfMrwYaN5/tlXwzWeMoJly1bQ+6xwjffdrzUXX3Yua1avYa2t5aorblyvJlZRmf91\n1W08+2o/srKyePn5QXw2+0suv6YP06fMYGT2u7z83EDu6/dv3p/4FsuXreDCc64KmfdvzRXX9GH1\n6jWsXbuWa6+4hRXLV1Z45r433M6EKZ+wfPlKOvToxQVn/w9r1qwBoOcxXTioXRvGfDyBzieeRZ3a\ntbnl2nCN69ery3lnnMxJ51wCwPlnnlLsII+KlPRFdlWagJK2Bh4GdibUcoYCVxKayHoQPqSbAs+Z\n2U3Rc3oC10Tnrwb6mNlYST+Z2cbROccDXc3sDEn3EAY6/Ba97Z1m9pyk2sAjwD6EWtblZvZONNrx\nOUJtcBawKXCjmb2b+R7R+7QhjFKsQygsDicUrrcSakMClkb/lr2BK82sawnXJN97RMcOyX2upHbA\n04Ta21tALzPbTtIZRV2zotSqvXWyf4sKqLthnZJPSphNa8XzAfFXLP/jp7gjlEmdrHKdQVMpvvzs\n9bgjlFnNLZrrr75Go01alvozZ+HymX/5/cqqVAVXkU8OH8L7mNmF5Zaoivsz18wLrornBVfF84Kr\ncpRHwdVwk11K/ZmzaPmsSi+4fMkn55xz+SR9yae/VHCZ2VOEvq4qSdLmhD6+gjqY2Q9/5jWr+jVz\nzqVf0vu4vMZVjKhw2ivuHM45V5l8WxPnnHOpkvQal68O75xzLp/ynMclqZOkOZK+kHR1IY/XkvRy\n9Pg4SduV9JpecDnnnMunvFbOiOb9PgR0BloCJ0tqWeC0s4FlZrYDYZWh/yspnxdczjnn8smxtaW+\nlaAt8IWZfWVmfxDWdz26wDlHE+a8ArwKdIjm6RbJCy7nnHP5lGVbE0m9JU3MuGXuAtsEyFw8dF50\njMLOidaRXUFYOalIPjjDOedcPmUZnGFmjwKVuvGv17icc87lU477cc0Hts6435T114TNOyfavLc+\nUOw8WS+4nHPO5VOO25pMAHaU1CzaG/Ekwo4amYYAp0c/Hw+MthJe2JsKnXPO5VNeE5CjjXQvJOzG\nkQU8aWYzJN0MTDSzIcATwLOSvgB+JBRuxfpLi+y69JPUO2qjToW05YX0ZU5bXvDM1Y03FbreJZ+S\nKGnLC+nLnLa84JmrFS+4nHPOpYoXXM4551LFCy6Xtjb2tOWF9GVOW17wzNWKD85wzjmXKl7jcs45\nlypecDnnnEsVL7icc86lihdc1ZCkE0pzzLmkkrS5pAckTZY0SdJ/JBW7orirOrzgqp6uKeWx2Elq\nIOkJScOi+y0lnR13ruJIai7pDUnfS1oi6XVJzePOVRxJd0iqJ6mmpFGSlkrqFXeuYrwELAGOI6xv\ntxR4OdZEpSSplaSLJV0kqVXcedLIC65qRFJnSQ8ATSTdn3F7ClgTc7yiPEVY56xxdP8z4NLY0pTO\nC8ArQENC7gHAi7EmKllHM1sJdAW+AXYA+saaqHiNzOwWM/s6ut0KNIg7VEkkXU/YNHFzYAugv6R/\nxpsqfbzgql4WABOB34BJGbchwJEx5irOFmb2CrAW8jaay4k3Uon+ZmbPmtma6PYcUDvuUCWoGf23\nCzDAzFbEGaYURkg6SVKN6HYi4QtO0p0KtDGzG8zsBmA/4H9izpQ6vjp8NWJm04Bpkl4ws9Vx5yml\nn6O+CwOQtB9hh9QkGybpakJzlgE9gaGSNgMwsx/jDFeEIZJ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kOqxbHX57oj6jBNsUmCFp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\nUsULLuecc6niBZdzzrlU+X9BxION7oWVOgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tzHwUxx2To36",
        "colab_type": "text"
      },
      "source": [
        "### 2. Melihat rata-rata gaji di tiap negara"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mxXSdsOpTedx",
        "colab_type": "code",
        "outputId": "8b4ec8e8-bf41-461b-ac86-4eb857955382",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 322
        }
      },
      "source": [
        "# 1. Total respondents per country\n",
        "resp_per_country = survey.groupby('Country', as_index=False)['Respondent'].count()\n",
        "\n",
        "# 2. Filter respondents > 20\n",
        "resp_per_country = resp_per_country[resp_per_country['Respondent'] > 20]\n",
        "\n",
        "# 3. Take the list\n",
        "list_countries = list(resp_per_country['Country'])\n",
        "\n",
        "# 4. Filter data raw\n",
        "filtered_survey = survey[survey['Country'].isin(list_countries)]\n",
        "\n",
        "top10_salary = filtered_survey.groupby('Country', as_index=False)['ConvertedComp'].mean() \\\n",
        "                              .sort_values('ConvertedComp', ascending=False) \\\n",
        "                              .head(10)\n",
        "plt.figure(figsize=(8,4))\n",
        "viz = sns.barplot(data=top10_salary,\n",
        "                  x='Country',\n",
        "                  y='ConvertedComp',\n",
        "                  palette=sns.color_palette('cubehelix', 10))\n",
        "viz.set_xticklabels(viz.get_xticklabels(), rotation=20)\n",
        "plt.title('Average Salary per Country')\n",
        "plt.xlabel('Country')\n",
        "plt.ylabel('Average Salary')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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mZmbWxsGBmZmZtXFwYGZmZm0cHJiZmVkbBwdmZmbWxsGBmZmZtXFwYGZmZm0c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\nAdu0i4I1DpqzAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 576x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QypxOsZUuTzY",
        "colab_type": "text"
      },
      "source": [
        "### 3. Melihat hubungan antara Hobbyist dengan OpenSourcer"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H1g0T78XuISZ",
        "colab_type": "code",
        "outputId": "7a823634-514c-4bf3-ad2c-3f823c489b7f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "hobbyist_open = survey.groupby(['Hobbyist','OpenSourcer'], as_index=False)['Respondent'].count()\n",
        "\n",
        "def convert_percentage(row):\n",
        "    if row['Hobbyist'] == 'Yes':\n",
        "        return row['Respondent'] / hobbyist_open[hobbyist_open['Hobbyist'] == 'Yes' ]['Respondent'].sum()\n",
        "    else:\n",
        "        return row['Respondent'] / hobbyist_open[hobbyist_open['Hobbyist'] == 'No' ]['Respondent'].sum()\n",
        "\n",
        "hobbyist_open['Percentage'] = hobbyist_open.apply(convert_percentage, axis=1)\n",
        "\n",
        "plt.figure(figsize=(10,4))\n",
        "viz = sns.barplot(data=hobbyist_open,\n",
        "                  x='Hobbyist',\n",
        "                  y='Percentage',\n",
        "                  hue='OpenSourcer',\n",
        "                  palette=sns.color_palette(\"ch:2.5,-.2,dark=.3\"))\n",
        "plt.title('Open Source Contribution based on Code as a Hobby')\n",
        "plt.xlabel('Code as a Hobby?')\n",
        "plt.ylabel('Population Percentage')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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gdufcoEgHJyKS5YF3nzmp9d3XOf+PsNKlSwduqxMpEyZMYMCAAZx11lmnrM2i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\n",
            "text/plain": [
              "<Figure size 720x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OiWOjCi87v1t",
        "colab_type": "text"
      },
      "source": [
        "### 4. Average Salary per YearsCode"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3TuUOkrpiRos",
        "colab_type": "code",
        "outputId": "ff2ad15d-3771-4c00-c91b-1c99c32d5641",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "yearscode_salary = survey.groupby('YearsCode_Map', as_index=False)['ConvertedComp'] \\\n",
        "                         .mean() \\\n",
        "                         .sort_values('ConvertedComp')\n",
        "\n",
        "plt.figure(figsize=(6,4))\n",
        "viz = sns.barplot(data=yearscode_salary,\n",
        "                  x='YearsCode_Map',\n",
        "                  y='ConvertedComp',\n",
        "                  palette=sns.color_palette(\"Blues\"))\n",
        "plt.title('Average Salary per Coding Experience')\n",
        "plt.xlabel('Experience')\n",
        "plt.ylabel('Average Salary')\n",
        "plt.show()     "
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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YlpmZNUHvFpS5E/A14DFJD+e07wFnAFdIOgx4HvhyXnY98DlgNvAWcChARCyU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\nmVnpHFzMzKx0Di5mZlY6BxczMyvd/wNhjVs1IcCmrwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cjiyCOieAW8Z",
        "colab_type": "text"
      },
      "source": [
        "### 5. Melihat average WorkWeekHrs di tiap OrgSize"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xrX7QcXG8TCp",
        "colab_type": "code",
        "outputId": "b527acae-ae46-4c82-f499-7f5b29049e82",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 376
        }
      },
      "source": [
        "avg_WorkWeekHrs = survey.groupby('OrgSize', as_index=False)['WorkWeekHrs'].mean() \\\n",
        "                        .sort_values('WorkWeekHrs')\n",
        "\n",
        "plt.figure(figsize=(10,4))\n",
        "viz = sns.barplot(data=avg_WorkWeekHrs,\n",
        "                  x='OrgSize',\n",
        "                  y='WorkWeekHrs',\n",
        "                  palette=sns.color_palette(\"Blues\"))\n",
        "viz.set_xticklabels(viz.get_xticklabels(), rotation=-20)\n",
        "plt.title('Average Work Week Hours per Organization Size')\n",
        "plt.xlabel('Organization Size')\n",
        "plt.ylabel('Work Week Hours')\n",
        "plt.show()   "
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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zM7OmOZg0MzMzs6Y5mDQzMzOzpjmYNDMzM7OmOZg0MzMzs6Y5mDQzMzOzpjmYNDMzM7Om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\nTBqTSZIkSZIkySyTxmSSJEmSJEkyy/x/JVTQLRhGTTsAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 720x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K7YpzY1eAQIX",
        "colab_type": "text"
      },
      "source": [
        "### 6. Melihat JobSat level di tiap negara"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p0opX8bvC_ed",
        "colab_type": "code",
        "outputId": "cb6a898e-697f-4617-abb9-81e437f65886",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "# 1. Total respondents per country\n",
        "respondent_per_country = survey.groupby('Country', as_index=False)['Respondent'] \\\n",
        "                               .count() \\\n",
        "                               .sort_values('Respondent', ascending=False) \\\n",
        "                               .head(10)\n",
        "\n",
        "# 2. Take the list\n",
        "top_countries = list(respondent_per_country['Country'])\n",
        "\n",
        "# 3. Filter data raw\n",
        "filtered_survey_by_country = survey[survey['Country'].isin(top_countries)]\n",
        "\n",
        "# 4. Aggregated data\n",
        "aggregated_data = filtered_survey_by_country.groupby(['Country','JobSat'], as_index=False)['Respondent'].count()\n",
        "series_countries = aggregated_data.groupby('Country')['Respondent'].sum()\n",
        "                                        \n",
        "# 5. Convert to percentages\n",
        "def convert_to_percentage(row):\n",
        "    return row['Respondent'] / series_countries[row['Country']]\n",
        "\n",
        "aggregated_data['Percentage'] = aggregated_data.apply(convert_to_percentage, axis=1)\n",
        "\n",
        "plt.figure(figsize=(15,4))\n",
        "viz = sns.barplot(data=aggregated_data,\n",
        "                  x='Country',\n",
        "                  y='Percentage',\n",
        "                  hue='JobSat',\n",
        "                  palette=sns.color_palette(\"ch:2.5,-.2,dark=.3\"))\n",
        "plt.title('Satisfaction Percentage per Country')\n",
        "plt.xlabel('Country')\n",
        "plt.ylabel('Satisfaction Percentage')\n",
        "plt.legend(loc='upper left', bbox_to_anchor=(1,1.04))\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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aISIiug9xNxYiIiJ60NVmkqQRAPO9oa8A6Fq2kIhMBTATgBWAfmaX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\nH5bhH4phY7vqAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1080x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Gjd1dQyYEC-q",
        "colab_type": "code",
        "outputId": "ed23acac-2fa7-43b3-cb8a-1805c7e21318",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 596
        }
      },
      "source": [
        "from matplotlib.colors import ListedColormap\n",
        "\n",
        "pivoted_data = pd.pivot_table(aggregated_data,\n",
        "                              values='Percentage',\n",
        "                              index='Country',\n",
        "                              columns='JobSat') \\\n",
        "                 .sort_values('Very satisfied', ascending=False)\n",
        "\n",
        "\n",
        "plt.figure(figsize=(15,4))\n",
        "pivoted_data.plot(kind='bar',\n",
        "                  stacked=True,\n",
        "                  figsize=(15,8),\n",
        "                  colormap=ListedColormap(sns.color_palette(\"ch:2.5,-.2,dark=.3\")))\n",
        "plt.legend(loc='upper left', bbox_to_anchor=(1,1.04))\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x288 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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+m8Sf0/F9ku69/CxGiphakJnmoGcOmmI6/aaq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\n",
            "text/plain": [
              "<Figure size 1080x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}