Unicorn_companies_analysisworkshop-2ndAug.ipynb

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              "      Company Valuation ($B) Date Joined        Country           City  \\\n",
              "0   Bytedance          $140     4/7/2017          China        Beijing   \n",
              "1      SpaceX       $100.30    12/1/2012  United States      Hawthorne   \n",
              "2      Stripe           $95    1/23/2014  United States  San Francisco   \n",
              "3      Klarna        $45.60   12/12/2011         Sweden      Stockholm   \n",
              "4  Epic Games           $42   10/26/2018  United States           Cary   \n",
              "\n",
              "                  Industry                                  Select Inverstors  \\\n",
              "0  Artificial intelligence  Sequoia Capital China, SIG Asia Investments, S...   \n",
              "1                    Other  Founders Fund, Draper Fisher Jurvetson, Rothen...   \n",
              "2                  Fintech        Khosla Ventures, LowercaseCapital, capitalG   \n",
              "3                  Fintech  Institutional Venture Partners, Sequoia Capita...   \n",
              "4                    Other              Tencent Holdings, KKR, Smash Ventures   \n",
              "\n",
              "  Founded Year Total Raised Financial Stage Investors Count Deal Terms  \\\n",
              "0         2012       $7.44B             IPO              28          8   \n",
              "1         2002      $6.874B            None              29         12   \n",
              "2         2010      $2.901B           Asset              39         12   \n",
              "3         2005      $3.472B        Acquired              56         13   \n",
              "4         1991      $4.377B        Acquired              25          5   \n",
              "\n",
              "  Portfolio Exits  \n",
              "0               5  \n",
              "1            None  \n",
              "2               1  \n",
              "3               1  \n",
              "4               2  "
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              "      <td>Bytedance</td>\n",
              "      <td>$140</td>\n",
              "      <td>4/7/2017</td>\n",
              "      <td>China</td>\n",
              "      <td>Beijing</td>\n",
              "      <td>Artificial intelligence</td>\n",
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              "      <td>SpaceX</td>\n",
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              "      <td>12/1/2012</td>\n",
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              "      <td>2002</td>\n",
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              "      <td>Stripe</td>\n",
              "      <td>$95</td>\n",
              "      <td>1/23/2014</td>\n",
              "      <td>United States</td>\n",
              "      <td>San Francisco</td>\n",
              "      <td>Fintech</td>\n",
              "      <td>Khosla Ventures, LowercaseCapital, capitalG</td>\n",
              "      <td>2010</td>\n",
              "      <td>$2.901B</td>\n",
              "      <td>Asset</td>\n",
              "      <td>39</td>\n",
              "      <td>12</td>\n",
              "      <td>1</td>\n",
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              "      <th>3</th>\n",
              "      <td>Klarna</td>\n",
              "      <td>$45.60</td>\n",
              "      <td>12/12/2011</td>\n",
              "      <td>Sweden</td>\n",
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              "      <td>Fintech</td>\n",
              "      <td>Institutional Venture Partners, Sequoia Capita...</td>\n",
              "      <td>2005</td>\n",
              "      <td>$3.472B</td>\n",
              "      <td>Acquired</td>\n",
              "      <td>56</td>\n",
              "      <td>13</td>\n",
              "      <td>1</td>\n",
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              "      <td>Epic Games</td>\n",
              "      <td>$42</td>\n",
              "      <td>10/26/2018</td>\n",
              "      <td>United States</td>\n",
              "      <td>Cary</td>\n",
              "      <td>Other</td>\n",
              "      <td>Tencent Holdings, KKR, Smash Ventures</td>\n",
              "      <td>1991</td>\n",
              "      <td>$4.377B</td>\n",
              "      <td>Acquired</td>\n",
              "      <td>25</td>\n",
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      ],
      "source": [
        "import pandas as pd\n",
        "\n",
        "# Load the dataset\n",
        "df = pd.read_csv('Unicorn_Companies.csv')\n",
        "\n",
        "# Display the first few rows of the dataframe\n",
        "df.head()\n"
      ]
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      "source": [
        "df.info()"
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          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1037 entries, 0 to 1036\n",
            "Data columns (total 13 columns):\n",
            " #   Column             Non-Null Count  Dtype \n",
            "---  ------             --------------  ----- \n",
            " 0   Company            1037 non-null   object\n",
            " 1   Valuation ($B)     1037 non-null   object\n",
            " 2   Date Joined        1037 non-null   object\n",
            " 3   Country            1037 non-null   object\n",
            " 4   City               1037 non-null   object\n",
            " 5   Industry           1037 non-null   object\n",
            " 6   Select Inverstors  1037 non-null   object\n",
            " 7   Founded Year       1037 non-null   object\n",
            " 8   Total Raised       1037 non-null   object\n",
            " 9   Financial Stage    1037 non-null   object\n",
            " 10  Investors Count    1037 non-null   object\n",
            " 11  Deal Terms         1037 non-null   object\n",
            " 12  Portfolio Exits    1037 non-null   object\n",
            "dtypes: object(13)\n",
            "memory usage: 105.4+ KB\n"
          ]
        }
      ]
    },
    {
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      "source": [
        "**Dictionary**\n",
        "\n",
        "Company: The name of the company.\n",
        "\n",
        "Valuation ($B): The valuation of the company in billions of dollars.\n",
        "\n",
        "Date Joined: The date when the company joined the unicorn club.\n",
        "\n",
        "Country: The country where the company is based.\n",
        "\n",
        "City: The city where the company is based.\n",
        "\n",
        "Industry: The industry in which the company operates.\n",
        "\n",
        "Select Investors: Key investors in the company.\n",
        "\n",
        "Founded Year: The year the company was founded.\n",
        "\n",
        "Total Raised: The total amount of funding the company has raised.\n",
        "\n",
        "Financial Stage: The current financial stage of the company (e.g., IPO, Acquired).\n",
        "\n",
        "Investors Count: The number of investors in the company.\n",
        "\n",
        "Deal Terms: The number of deal terms the company has had.\n",
        "\n",
        "Portfolio Exits: The number of portfolio exits the company has had."
      ],
      "metadata": {
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    },
    {
      "cell_type": "markdown",
      "source": [
        "Now, let's proceed to the **data cleaning** stage.\n",
        "\n",
        "\n",
        "1. Clean the Valuation field with the $ sign and convert the value to numeric.\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "2. Clean the Total Raised field to remove the '$' sign and 'B' (for billion),Million and Thousands and convert the value to numeric.\n",
        "\n",
        "3. Clean the Industry field to have consistent names.\n",
        "\n",
        "4. Clean the Financial Stage field to have consistent names.\n",
        "5. Missing values"
      ],
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        "id": "riA7NrSAJeXG"
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      "cell_type": "code",
      "source": [
        "df.isnull().sum()"
      ],
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            "text/plain": [
              "Company              0\n",
              "Valuation ($B)       0\n",
              "Date Joined          0\n",
              "Country              0\n",
              "City                 0\n",
              "Industry             0\n",
              "Select Inverstors    0\n",
              "Founded Year         0\n",
              "Total Raised         0\n",
              "Financial Stage      0\n",
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              "dtype: int64"
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      "source": [
        "# Modify the clean_total_raised function to handle 'None' values and cases where the Total Raised field is already in dollars\n",
        "\n",
        "def clean_total_raised(value):\n",
        "    if pd.isnull(value) or value == 'None':\n",
        "        return None\n",
        "    if 'B' in value:\n",
        "        return float(value.replace('$', '').replace('B', '')) * 1e9  # Convert to dollars\n",
        "    elif 'M' in value:\n",
        "        return float(value.replace('$', '').replace('M', '')) * 1e6  # Convert to dollars\n",
        "    elif 'K' in value:\n",
        "        return float(value.replace('$', '').replace('K', '')) * 1e3  # Convert to dollars\n",
        "    else:\n",
        "        return float(value.replace('$', ''))\n",
        "\n",
        "df['Total Raised'] = df['Total Raised'].apply(clean_total_raised)\n",
        "\n",
        "# Display the first few rows of the dataframe\n",
        "df.head()\n"
      ],
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          "data": {
            "text/plain": [
              "      Company Valuation ($B) Date Joined        Country           City  \\\n",
              "0   Bytedance          $140     4/7/2017          China        Beijing   \n",
              "1      SpaceX       $100.30    12/1/2012  United States      Hawthorne   \n",
              "2      Stripe           $95    1/23/2014  United States  San Francisco   \n",
              "3      Klarna        $45.60   12/12/2011         Sweden      Stockholm   \n",
              "4  Epic Games           $42   10/26/2018  United States           Cary   \n",
              "\n",
              "                  Industry                                  Select Inverstors  \\\n",
              "0  Artificial intelligence  Sequoia Capital China, SIG Asia Investments, S...   \n",
              "1                    Other  Founders Fund, Draper Fisher Jurvetson, Rothen...   \n",
              "2                  Fintech        Khosla Ventures, LowercaseCapital, capitalG   \n",
              "3                  Fintech  Institutional Venture Partners, Sequoia Capita...   \n",
              "4                    Other              Tencent Holdings, KKR, Smash Ventures   \n",
              "\n",
              "  Founded Year  Total Raised Financial Stage Investors Count Deal Terms  \\\n",
              "0         2012  7.440000e+09             IPO              28          8   \n",
              "1         2002  6.874000e+09            None              29         12   \n",
              "2         2010  2.901000e+09           Asset              39         12   \n",
              "3         2005  3.472000e+09        Acquired              56         13   \n",
              "4         1991  4.377000e+09        Acquired              25          5   \n",
              "\n",
              "  Portfolio Exits  \n",
              "0               5  \n",
              "1            None  \n",
              "2               1  \n",
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              "      <td>Bytedance</td>\n",
              "      <td>$140</td>\n",
              "      <td>4/7/2017</td>\n",
              "      <td>China</td>\n",
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              "      <td>SpaceX</td>\n",
              "      <td>$100.30</td>\n",
              "      <td>12/1/2012</td>\n",
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              "      <td>Hawthorne</td>\n",
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              "      <td>Fintech</td>\n",
              "      <td>Khosla Ventures, LowercaseCapital, capitalG</td>\n",
              "      <td>2010</td>\n",
              "      <td>2.901000e+09</td>\n",
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              "      <th>3</th>\n",
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              "      <td>$45.60</td>\n",
              "      <td>12/12/2011</td>\n",
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              "      <td>Epic Games</td>\n",
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              "      <td>Tencent Holdings, KKR, Smash Ventures</td>\n",
              "      <td>1991</td>\n",
              "      <td>4.377000e+09</td>\n",
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          "metadata": {},
          "execution_count": 16
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      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TRw5UrKUwnwv",
        "outputId": "4282e61d-3ef7-4bd9-98ec-6bcb4bfe9e7e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1037 entries, 0 to 1036\n",
            "Data columns (total 13 columns):\n",
            " #   Column             Non-Null Count  Dtype  \n",
            "---  ------             --------------  -----  \n",
            " 0   Company            1037 non-null   object \n",
            " 1   Valuation ($B)     1037 non-null   object \n",
            " 2   Date Joined        1037 non-null   object \n",
            " 3   Country            1037 non-null   object \n",
            " 4   City               1037 non-null   object \n",
            " 5   Industry           1037 non-null   object \n",
            " 6   Select Inverstors  1037 non-null   object \n",
            " 7   Founded Year       1037 non-null   object \n",
            " 8   Total Raised       1013 non-null   float64\n",
            " 9   Financial Stage    1037 non-null   object \n",
            " 10  Investors Count    1037 non-null   object \n",
            " 11  Deal Terms         1037 non-null   object \n",
            " 12  Portfolio Exits    1037 non-null   object \n",
            "dtypes: float64(1), object(12)\n",
            "memory usage: 105.4+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.head(100)"
      ],
      "metadata": {
        "id": "PHXlWbSBJvaY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['Industry'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-Ien4CI0xWuR",
        "outputId": "f16ba9f4-99cf-48f5-8f40-3555e6b0ec5d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Artificial intelligence', 'Other', 'Fintech',\n",
              "       'Internet software & services',\n",
              "       'Supply chain, logistics, & delivery',\n",
              "       'Data management & analytics',\n",
              "       'Sequoia Capital, Thoma Bravo, Softbank',\n",
              "       'E-commerce & direct-to-consumer', 'Edtech', 'Hardware',\n",
              "       'Consumer & retail', 'Health', 'Auto & transportation', 'Finttech',\n",
              "       'Cybersecurity', 'Mobile & telecommunications', 'Travel',\n",
              "       'Kuang-Chi',\n",
              "       'Tiger Global Management, Tiger Brokers, DCM Ventures',\n",
              "       'Jungle Ventures, Accel, Venture Highway',\n",
              "       'Artificial Intelligence',\n",
              "       'Vision Plus Capital, GSR Ventures, ZhenFund',\n",
              "       'Hopu Investment Management, Boyu Capital, DC Thomson Ventures',\n",
              "       '500 Global, Rakuten Ventures, Golden Gate Ventures',\n",
              "       'Sequoia Capital China, ING, Alibaba Entrepreneurs Fund',\n",
              "       'Sequoia Capital China, Shunwei Capital Partners, Qualgro',\n",
              "       'Dragonfly Captial, Qiming Venture Partners, DST Global',\n",
              "       'SingTel Innov8, Alpha JWC Ventures, Golden Gate Ventures',\n",
              "       'Mundi Ventures, Doqling Capital Partners, Activant Capital',\n",
              "       'Vertex Ventures SE Asia, Global Founders Capital, Visa Ventures',\n",
              "       'Andreessen Horowitz, DST Global, IDG Capital',\n",
              "       \"B Capital Group, Monk's Hill Ventures, Dynamic Parcel Distribution\",\n",
              "       'Temasek, Guggenheim Investments, Qatar Investment Authority'],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Clean the Financial Stage fields as before\n",
        "df['Financial Stage'] = df['Financial Stage'].replace('Acq', 'Acquired')"
      ],
      "metadata": {
        "id": "CxQe0dSrOW89"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Correct the typo in 'Finttech'\n",
        "df['Industry'] = df['Industry'].replace('Finttech', 'Fintech')"
      ],
      "metadata": {
        "id": "CYxY0ytuzFw_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Let's correct the typo in 'Finttech' and make sure that the Industry column only contains industry names, not investor names.\n",
        "\n",
        "# List of known industries\n",
        "known_industries = ['Artificial intelligence', 'Fintech',\n",
        "                    'Internet software & services',\n",
        "                    'Supply chain, logistics, & delivery',\n",
        "                    'Data management & analytics',\n",
        "                    'E-commerce & direct-to-consumer', 'Edtech', 'Hardware',\n",
        "                    'Consumer & retail', 'Health', 'Auto & transportation',\n",
        "                    'Cybersecurity', 'Mobile & telecommunications', 'Travel']\n",
        "\n",
        "# If the industry is not in the known industries list, categorize it as 'Other'\n",
        "df['Industry'] = df['Industry'].apply(lambda x: x if x in known_industries else 'Other')\n",
        "\n",
        "\n",
        "\n",
        "# Check the unique values of the Industry field\n",
        "industry_values_cleaned = df['Industry'].unique()\n",
        "\n",
        "industry_values_cleaned\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gTp05iiTPQUw",
        "outputId": "00eafb6a-b081-445d-bc89-d9692127eab3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Artificial intelligence', 'Other', 'Fintech',\n",
              "       'Internet software & services',\n",
              "       'Supply chain, logistics, & delivery',\n",
              "       'Data management & analytics', 'E-commerce & direct-to-consumer',\n",
              "       'Edtech', 'Hardware', 'Consumer & retail', 'Health',\n",
              "       'Auto & transportation', 'Cybersecurity',\n",
              "       'Mobile & telecommunications', 'Travel'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Valuation ($B)'] = df['Valuation ($B)'].str.replace('$', '')\n",
        "\n",
        "# Convert the value to numeric\n",
        "df['Valuation ($B)'] = pd.to_numeric(df['Valuation ($B)'], errors='coerce')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oKRXAqUryH1I",
        "outputId": "633ddb9d-6781-49b3-9831-da27bade935d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-21-7cf59079a576>:1: FutureWarning: The default value of regex will change from True to False in a future version. In addition, single character regular expressions will *not* be treated as literal strings when regex=True.\n",
            "  df['Valuation ($B)'] = df['Valuation ($B)'].str.replace('$', '')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DQ2q-HhyyUfW",
        "outputId": "0dd70536-14db-49b3-cc8b-2dd8d89d8e6b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1037 entries, 0 to 1036\n",
            "Data columns (total 13 columns):\n",
            " #   Column             Non-Null Count  Dtype  \n",
            "---  ------             --------------  -----  \n",
            " 0   Company            1037 non-null   object \n",
            " 1   Valuation ($B)     1037 non-null   float64\n",
            " 2   Date Joined        1037 non-null   object \n",
            " 3   Country            1037 non-null   object \n",
            " 4   City               1037 non-null   object \n",
            " 5   Industry           1037 non-null   object \n",
            " 6   Select Inverstors  1037 non-null   object \n",
            " 7   Founded Year       1037 non-null   object \n",
            " 8   Total Raised       1013 non-null   float64\n",
            " 9   Financial Stage    1037 non-null   object \n",
            " 10  Investors Count    1037 non-null   object \n",
            " 11  Deal Terms         1037 non-null   object \n",
            " 12  Portfolio Exits    1037 non-null   object \n",
            "dtypes: float64(2), object(11)\n",
            "memory usage: 105.4+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "\n",
        "# Descriptive Analysis\n",
        "\n",
        "# Measures of central tendency and dispersion for numeric variables\n",
        "numeric_descriptive = df[['Valuation ($B)', 'Total Raised']].describe()\n",
        "\n",
        "# Frequency counts for categorical variables\n",
        "categorical_descriptive = df[['Industry', 'Country', 'Financial Stage']].apply(lambda x: x.value_counts()).T.stack()\n",
        "\n",
        "numeric_descriptive, categorical_descriptive\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I5b_dHP7PP-B",
        "outputId": "3c268a16-f2c9-47cd-d9ea-e6781cca78cd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(       Valuation ($B)  Total Raised\n",
              " count     1037.000000  1.013000e+03\n",
              " mean         3.292083  5.757672e+08\n",
              " std          7.309781  7.360876e+08\n",
              " min          1.000000  1.000000e+04\n",
              " 25%          1.080000  2.273400e+08\n",
              " 50%          1.600000  3.777000e+08\n",
              " 75%          3.100000  6.337800e+08\n",
              " max        140.000000  7.440000e+09,\n",
              " Industry         Artificial intelligence         71.0\n",
              "                  Auto & transportation           29.0\n",
              "                  Consumer & retail               25.0\n",
              "                  Cybersecurity                   49.0\n",
              "                  Data management & analytics     41.0\n",
              "                                                 ...  \n",
              " Financial Stage  IPO                              7.0\n",
              "                  Management                       1.0\n",
              "                  None                           988.0\n",
              "                  Reverse                          1.0\n",
              "                  Take                             1.0\n",
              " Length: 70, dtype: float64)"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wXMcoNDQTG2Y",
        "outputId": "c5ff6a7e-c4cf-47da-f472-f5b9d7ccbc75"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-13-7cf59079a576>:1: FutureWarning: The default value of regex will change from True to False in a future version. In addition, single character regular expressions will *not* be treated as literal strings when regex=True.\n",
            "  df['Valuation ($B)'] = df['Valuation ($B)'].str.replace('$', '')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "id": "SX01p9fCTItY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Rerun the visual analysis with the cleaned Industry field.\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "# Industry Analysis\n",
        "plt.figure(figsize=(10,6))\n",
        "sns.countplot(y=\"Industry\", data=df, order = df['Industry'].value_counts().index)\n",
        "plt.title(\"Distribution of Unicorn Companies by Industry\")\n",
        "plt.xlabel(\"Count\")\n",
        "plt.ylabel(\"Industry\")\n",
        "plt.show()\n",
        "\n",
        "# Country Analysis\n",
        "plt.figure(figsize=(10,6))\n",
        "sns.countplot(y=\"Country\", data=df, order = df['Country'].value_counts().index)\n",
        "plt.title(\"Distribution of Unicorn Companies by Country\")\n",
        "plt.xlabel(\"Count\")\n",
        "plt.ylabel(\"Country\")\n",
        "plt.show()\n",
        "\n",
        "# Valuation Analysis\n",
        "plt.figure(figsize=(10,6))\n",
        "sns.histplot(df['Valuation ($B)'], bins=20, kde=False)\n",
        "plt.title(\"Distribution of Valuations of Unicorn Companies\")\n",
        "plt.xlabel(\"Valuation ($B)\")\n",
        "plt.ylabel(\"Count\")\n",
        "plt.show()\n",
        "\n",
        "# Total Raised Analysis\n",
        "plt.figure(figsize=(10,6))\n",
        "sns.histplot(df['Total Raised'], bins=20, kde=False)\n",
        "plt.title(\"Distribution of Total Raised by Unicorn Companies\")\n",
        "plt.xlabel(\"Total Raised ($)\")\n",
        "plt.ylabel(\"Count\")\n",
        "plt.show()\n",
        "\n",
        "# Financial Stage Analysis\n",
        "plt.figure(figsize=(10,6))\n",
        "sns.countplot(y=\"Financial Stage\", data=df, order = df['Financial Stage'].value_counts().index)\n",
        "plt.title(\"Distribution of Unicorn Companies by Financial Stage\")\n",
        "plt.xlabel(\"Count\")\n",
        "plt.ylabel(\"Financial Stage\")\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "_OuK1-lLRqh5",
        "outputId": "083b8b4a-6933-4b18-f61c-8de0806cfdcf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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3331XPj4++uuvv3KdL0lJSdYg6OnpqYoVK1oDxfW2XZDzd//+/dq1a5c10OU8ateuLenv/mzS38fRwcHBJjxLeR/X6ynI+f3UU0/J1dVVc+fOte7j0qVL1bNnzyL57Z+81nHt+ZzzQfx65/PBgwdVp06d6w5aYPZ1FRISku+6ClOjl5eXJBX45x5u9n0gJ2hUrVo1z/Zraw0MDJSHh4dNW865d/U5MWfOHDVq1Ehubm6qUKGCKlasqO+++y7P878wx2n//v0yDEO1atXK9VrYs2eP9XUQEhKiYcOG6T//+Y98fX0VERGh2NhY+kehVKOPFFAK/fHHHzpz5ozN0NnXcnd31w8//KC1a9fqu+++0/Lly/Xll1+qbdu2WrlypRwdHW+4HTP9mgoqvw+TWVlZBaqpKOS3HeOaDuklwcGDB9WuXTvVrVtXkyZNUtWqVeXi4qJly5Zp8uTJhb5a6evrq/DwcElSRESE6tatq06dOmnq1KkaNmyYpL//pu3bt9epU6f06quvqm7duvLw8NCff/6pvn37XnfbBTl/s7Oz1bBhQ02aNCnPdVz7gfh2KFeunDp16qS5c+dq9OjRWrRokTIyMgo0CqObm5syMjJkGEau14lhGLp06VKefayKy/l8vfeLwtRYs2ZNOTk5WQeAKGr51VSUx/Pzzz9X37591aVLF40YMUJ+fn5ydHTUhAkTcg3MUthtZ2dny2Kx6Pvvv89z+at/m+y9995T37599fXXX2vlypUaPHiwJkyYoM2bN6tKlSqm9w+40xGkgFLos88+k/T3B9jrcXBwULt27dSuXTtNmjRJ48eP1+uvv661a9cqPDy8SL4hv9r+/fttnhuGoQMHDtgM8V2uXLk8fzT2yJEjql69uvW5mdqCg4O1evVqnT171ubb6L1791qnF4Xg4GD9+uuvys7OtrkqVdTbuXp7krRv375c0/bu3StfX1/rN+L5Ha9vv/1WGRkZ+uabb2y+7b521LCbFRkZqQceeEDjx4/X888/Lw8PD+3YsUO//fab5syZoz59+ljnXbVqVYHWeaPzt0aNGvrll1/Url27654vwcHBys7Otl6FyZHXcb2egpzf0t+39z366KPatm2b5s6dqyZNmqhBgwY3XH9wcLCuXLmigwcP5vqS5MCBA8rKyiqyc6xGjRrasmWLLl++nO8gBrfrdZWfMmXKqG3btlqzZo2OHj16w2B8u+s9duyYzp8/b3NV6rfffpMk6y2DixYtUvXq1bV48WKbc/R6vy9nVs7AICEhIdYrYtfTsGFDNWzYUKNGjdLGjRvVsmVLzZgxQ2+99VaR1QTcKbi1Dyhl1qxZozfffFMhISHq2bNnvvOdOnUqV1vOCFE5Q0PnfADIK9gUxqeffmpzG86iRYt0/PhxdezY0dpWo0YNbd68WZmZmda2pUuX5hqm10xtDz/8sLKysvThhx/atE+ePFkWi8Vm+zfj4YcfVnJyss1IY1euXNEHH3wgT0/PIu8DU6lSJYWGhmrOnDk2x2Hnzp1auXKlHn74YWtbfscr5xvqq7/RPnPmjOLi4oq0Vkl69dVXdfLkSX388cf5btswDE2dOvWG6yrI+fvkk0/qzz//tG7vahcvXrTe9pjz93///fdt5pkyZcoN67haQc7vnO35+vrq7bff1vr16wv8m2A567n2PJak2NhYm3luVrdu3ZSWlpbntnL+XrfrdXU9Y8aMkWEY6t27t86dO5drekJCgvXnB253vVeuXNHMmTOtzzMzMzVz5kxVrFhRTZs2lZT3a2DLli3atGlTkdXRtWtXOTo6auzYsbmuXBmGoZMnT0r6u8/ZlStXbKY3bNhQDg4OuX4uACgtuCIFlGDff/+99u7dqytXriglJUVr1qzRqlWrFBwcrG+++ea6QymPGzdOP/zwgyIjIxUcHKzU1FRNmzZNVapUUatWrST9HWp8fHw0Y8YMlS1bVh4eHmrevPl1+zpcT/ny5dWqVSs9/fTTSklJ0ZQpU1SzZk2bIdr79++vRYsW6aGHHtKTTz6pgwcP6vPPP8/Vf8VMbZ07d9aDDz6o119/XYcPH1bjxo21cuVKff311xoyZEiudRfWc889p5kzZ6pv375KSEhQtWrVtGjRIm3YsEFTpkwpcKd4M9555x117NhRYWFh6tevn3X4c29vb8XExFjny/ng9vrrr6t79+5ydnZW586d1aFDB7m4uKhz5856/vnnde7cOX388cfy8/PT8ePHi7TWjh076q677tKkSZMUHR2tunXrqkaNGho+fLj+/PNPeXl56b///e8N+6JJBTt/e/furQULFuiFF17Q2rVr1bJlS2VlZWnv3r1asGCBVqxYoWbNmik0NFQ9evTQtGnTdObMGd13332Kj4/XgQMHTO1fQc5v6e8BSLp3764PP/xQjo6ONoOTXE9oaKj69++vqVOnav/+/Wrfvr2kv6/gLVu2TP3791fjxo1N1ZyfPn366NNPP9WwYcO0detWtW7dWufPn9fq1av14osv6tFHH71tr6vrue+++xQbG6sXX3xRdevWVe/evVWrVi2dPXtW69at0zfffGO9knK76w0MDNTbb7+tw4cPq3bt2vryyy+VmJiojz76yHqVr1OnTlq8eLEee+wxRUZG6tChQ5oxY4bq16+fZzAsjBo1auitt97SyJEjdfjwYXXp0kVly5bVoUOH9NVXX+m5557T8OHDtWbNGg0cOFBPPPGEateurStXruizzz6To6OjunXrViS1AHec2zhCIIDbJGfI4JyHi4uLERAQYLRv396YOnWqzdC8Oa4dEjc+Pt549NFHjcDAQMPFxcUIDAw0evToYfz22282y3399ddG/fr1DScnJ5vhxh944AGjQYMGedaX3/DnX3zxhTFy5EjDz8/PcHd3NyIjI22GuM7x3nvvGZUrVzZcXV2Nli1bGtu3b89z6O38art2+HPDMIyzZ88aQ4cONQIDAw1nZ2ejVq1axjvvvGMz5LJhGHkO0W0Y+Q/Lfq2UlBTj6aefNnx9fQ0XFxejYcOGeQ7RXlTDnxuGYaxevdpo2bKl4e7ubnh5eRmdO3c2du/enWv5N99806hcubLh4OBgM1zzN998YzRq1Mhwc3MzqlWrZrz99tvW4a+vHdK5oMOf57dvs2fPtvlb7d692wgPDzc8PT0NX19f49lnn7UOa331cSvs+ZuZmWm8/fbbRoMGDQxXV1ejXLlyRtOmTY2xY8caZ86csc538eJFY/DgwUaFChUMDw8Po3PnzsbRo0dNDX9e0PPbMAxj69athiSjQ4cO1133tbKysoypU6cajRs3Ntzc3Aw3NzejcePGxvvvv29kZWXZzJvfzyTk1Hv1cOt5/W0vXLhgvP7660ZISIjh7OxsBAQEGI8//rhx8OBB6zw3+7oyU+P1JCQkGP/4xz+sdZQrV85o166dMWfOHJvjcjP15gxB/s477+RZ69XDhue8P27fvt0ICwsz3NzcjODgYOPDDz+0WTY7O9sYP368ERwcbLi6uhpNmjQxli5dmus9LL9t59R69Tl67Wslx3//+1+jVatWhoeHh+Hh4WHUrVvXiI6ONvbt22cYhmH8/vvvxjPPPGPUqFHDcHNzM8qXL288+OCDxurVq/M56kDJZzGMEtg7GgCAO9gvv/yi0NBQffrpp+rdu7e9y0ERa9OmjdLS0rRz5057lwLgJtBHCgCAYubjjz+Wp6enunbtau9SAAD5oI8UAADFxLfffqvdu3fro48+0sCBA3P9zhAAoPggSAEAUEwMGjRIKSkpevjhhzV27Fh7lwMAuA76SAEAAACASfSRAgAAAACTCFIAAAAAYBJ9pCRlZ2fr2LFjKlu2rCwWi73LAQAAAGAnhmHo7NmzCgwMlIND/tedCFKSjh07pqpVq9q7DAAAAADFxNGjR1WlSpV8pxOkJJUtW1bS3wfLy8vLztUAAAAAsJf09HRVrVrVmhHyQ5CSrLfzeXl5EaQAAAAA3LDLD4NNAAAAAIBJBCkAAAAAMIkgBQAAAAAmEaQAAAAAwCSCFAAAAACYRJACAAAAAJMIUgAAAABgEkEKAAAAAEwiSAEAAACASQQpAAAAADCJIAUAAAAAJhGkAAAAAMAkghQAAAAAmESQAgAAAACTCFIAAAAAYJJdg1RMTIwsFovNo27dutbply5dUnR0tCpUqCBPT09169ZNKSkpNutISkpSZGSkypQpIz8/P40YMUJXrly53bsCAAAAoBRxsncBDRo00OrVq63PnZz+r6ShQ4fqu+++08KFC+Xt7a2BAweqa9eu2rBhgyQpKytLkZGRCggI0MaNG3X8+HH16dNHzs7OGj9+/G3fFwAAAAClg92DlJOTkwICAnK1nzlzRrNmzdK8efPUtm1bSVJcXJzq1aunzZs3q0WLFlq5cqV2796t1atXy9/fX6GhoXrzzTf16quvKiYmRi4uLrd7dwAAAACUAnYPUvv371dgYKDc3NwUFhamCRMmKCgoSAkJCbp8+bLCw8Ot89atW1dBQUHatGmTWrRooU2bNqlhw4by9/e3zhMREaEBAwZo165datKkSZ7bzMjIUEZGhvV5enr6rdvBQkhKSlJaWpq9y5Ak+fr6KigoyN5lAAAAAMWKXYNU8+bNNXv2bNWpU0fHjx/X2LFj1bp1a+3cuVPJyclycXGRj4+PzTL+/v5KTk6WJCUnJ9uEqJzpOdPyM2HCBI0dO7Zod6aIJCUlqW7derp48YK9S5EkubuX0d69ewhTAAAAwFXsGqQ6duxo/f9GjRqpefPmCg4O1oIFC+Tu7n7Ltjty5EgNGzbM+jw9PV1Vq1a9ZdszIy0tTRcvXlDzZ8bIq1I1u9aSfvywtnwyVmlpaQQpAAAA4Cp2v7Xvaj4+Pqpdu7YOHDig9u3bKzMzU6dPn7a5KpWSkmLtUxUQEKCtW7farCNnVL+8+l3lcHV1laura9HvQBHyqlRN5YPq2LsMAAAAAHkoVr8jde7cOR08eFCVKlVS06ZN5ezsrPj4eOv0ffv2KSkpSWFhYZKksLAw7dixQ6mpqdZ5Vq1aJS8vL9WvX/+21w8AAACgdLDrFanhw4erc+fOCg4O1rFjxzRmzBg5OjqqR48e8vb2Vr9+/TRs2DCVL19eXl5eGjRokMLCwtSiRQtJUocOHVS/fn317t1bEydOVHJyskaNGqXo6Ohif8UJAAAAwJ3LrkHqjz/+UI8ePXTy5ElVrFhRrVq10ubNm1WxYkVJ0uTJk+Xg4KBu3bopIyNDERERmjZtmnV5R0dHLV26VAMGDFBYWJg8PDwUFRWlcePG2WuXAAAAAJQCdg1S8+fPv+50Nzc3xcbGKjY2Nt95goODtWzZsqIuDQAAAADyVaz6SAEAAADAnYAgBQAAAAAmEaQAAAAAwCSCFAAAAACYRJACAAAAAJMIUgAAAABgEkEKAAAAAEwiSAEAAACASQQpAAAAADCJIAUAAAAAJhGkAAAAAMAkghQAAAAAmESQAgAAAACTCFIAAAAAYBJBCgAAAABMIkgBAAAAgEkEKQAAAAAwiSAFAAAAACYRpAAAAADAJIIUAAAAAJhEkAIAAAAAkwhSAAAAAGASQQoAAAAATCJIAQAAAIBJBCkAAAAAMIkgBQAAAAAmEaQAAAAAwCSCFAAAAACYRJACAAAAAJMIUgAAAABgEkEKAAAAAEwiSAEAAACASQQpAAAAADCJIAUAAAAAJhGkAAAAAMAkghQAAAAAmESQAgAAAACTCFIAAAAAYBJBCgAAAABMIkgBAAAAgEkEKQAAAAAwiSAFAAAAACYRpAAAAADAJIIUAAAAAJhEkAIAAAAAkwhSAAAAAGASQQoAAAAATCJIAQAAAIBJBCkAAAAAMIkgBQAAAAAmEaQAAAAAwCSCFAAAAACYRJACAAAAAJMIUgAAAABgEkEKAAAAAEwiSAEAAACASQQpAAAAADCJIAUAAAAAJhGkAAAAAMAkghQAAAAAmESQAgAAAACTCFIAAAAAYBJBCgAAAABMIkgBAAAAgEkEKQAAAAAwiSAFAAAAACYRpAAAAADAJIIUAAAAAJhEkAIAAAAAkwhSAAAAAGASQQoAAAAATCJIAQAAAIBJBCkAAAAAMIkgBQAAAAAmEaQAAAAAwCSCFAAAAACYRJACAAAAAJOKTZD697//LYvFoiFDhljbLl26pOjoaFWoUEGenp7q1q2bUlJSbJZLSkpSZGSkypQpIz8/P40YMUJXrly5zdUDAAAAKE2KRZDatm2bZs6cqUaNGtm0Dx06VN9++60WLlyo9evX69ixY+ratat1elZWliIjI5WZmamNGzdqzpw5mj17tkaPHn27dwEAAABAKWL3IHXu3Dn17NlTH3/8scqVK2dtP3PmjGbNmqVJkyapbdu2atq0qeLi4rRx40Zt3rxZkrRy5Urt3r1bn3/+uUJDQ9WxY0e9+eabio2NVWZmpr12CQAAAEAJZ/cgFR0drcjISIWHh9u0JyQk6PLlyzbtdevWVVBQkDZt2iRJ2rRpkxo2bCh/f3/rPBEREUpPT9euXbvy3WZGRobS09NtHgAAAABQUE723Pj8+fP1888/a9u2bbmmJScny8XFRT4+Pjbt/v7+Sk5Ots5zdYjKmZ4zLT8TJkzQ2LFjb7J6AAAAAKWV3a5IHT16VC+99JLmzp0rNze327rtkSNH6syZM9bH0aNHb+v2AQAAANzZ7BakEhISlJqaqrvvvltOTk5ycnLS+vXr9f7778vJyUn+/v7KzMzU6dOnbZZLSUlRQECAJCkgICDXKH45z3PmyYurq6u8vLxsHgAAAABQUHYLUu3atdOOHTuUmJhofTRr1kw9e/a0/r+zs7Pi4+Oty+zbt09JSUkKCwuTJIWFhWnHjh1KTU21zrNq1Sp5eXmpfv36t32fAAAAAJQOdusjVbZsWd111102bR4eHqpQoYK1vV+/fho2bJjKly8vLy8vDRo0SGFhYWrRooUkqUOHDqpfv7569+6tiRMnKjk5WaNGjVJ0dLRcXV1v+z4BAAAAKB3sOtjEjUyePFkODg7q1q2bMjIyFBERoWnTplmnOzo6aunSpRowYIDCwsLk4eGhqKgojRs3zo5VAwAAACjpilWQWrdunc1zNzc3xcbGKjY2Nt9lgoODtWzZsltcGQAAAAD8H7v/jhQAAAAA3GkIUgAAAABgEkEKAAAAAEwiSAEAAACASQQpAAAAADCJIAUAAAAAJhGkAAAAAMAkghQAAAAAmESQAgAAAACTCFIAAAAAYBJBCgAAAABMIkgBAAAAgEkEKQAAAAAwiSAFAAAAACYRpAAAAADAJIIUAAAAAJhEkAIAAAAAkwhSAAAAAGASQQoAAAAATCJIAQAAAIBJBCkAAAAAMIkgBQAAAAAmEaQAAAAAwCSCFAAAAACYRJACAAAAAJMIUgAAAABgEkEKAAAAAEwiSAEAAACASQQpAAAAADCJIAUAAAAAJhGkAAAAAMAkghQAAAAAmESQAgAAAACTCFIAAAAAYBJBCgAAAABMIkgBAAAAgEkEKQAAAAAwiSAFAAAAACYRpAAAAADAJIIUAAAAAJhEkAIAAAAAkwhSAAAAAGASQQoAAAAATCJIAQAAAIBJBCkAAAAAMIkgBQAAAAAmEaQAAAAAwCSCFAAAAACYRJACAAAAAJMIUgAAAABgEkEKAAAAAEwiSAEAAACASQQpAAAAADCJIAUAAAAAJhGkAAAAAMAkghQAAAAAmESQAgAAAACTCFIAAAAAYBJBCgAAAABMIkgBAAAAgElO9i4Axd+ePXvsXYIkydfXV0FBQfYuAwAAACBIIX8Xz5yUZFGvXr3sXYokyd29jPbu3UOYAgAAgN0RpJCvyxfOSjIU+o9XVTGkrl1rST9+WFs+Gau0tDSCFAAAAOyOIIUb8vQLUvmgOvYuAwAAACg2GGwCAAAAAEwiSAEAAACASQQpAAAAADCJIAUAAAAAJhGkAAAAAMAkghQAAAAAmESQAgAAAACTCFIAAAAAYBJBCgAAAABMIkgBAAAAgEkEKQAAAAAwiSAFAAAAACYRpAAAAADAJLsGqenTp6tRo0by8vKSl5eXwsLC9P3331unX7p0SdHR0apQoYI8PT3VrVs3paSk2KwjKSlJkZGRKlOmjPz8/DRixAhduXLldu8KAAAAgFLErkGqSpUq+ve//62EhARt375dbdu21aOPPqpdu3ZJkoYOHapvv/1WCxcu1Pr163Xs2DF17drVunxWVpYiIyOVmZmpjRs3as6cOZo9e7ZGjx5tr10CAAAAUAo42XPjnTt3tnn+r3/9S9OnT9fmzZtVpUoVzZo1S/PmzVPbtm0lSXFxcapXr542b96sFi1aaOXKldq9e7dWr14tf39/hYaG6s0339Srr76qmJgYubi42GO3AAAAAJRwxaaPVFZWlubPn6/z588rLCxMCQkJunz5ssLDw63z1K1bV0FBQdq0aZMkadOmTWrYsKH8/f2t80RERCg9Pd16VSsvGRkZSk9Pt3kAAAAAQEHZPUjt2LFDnp6ecnV11QsvvKCvvvpK9evXV3JyslxcXOTj42Mzv7+/v5KTkyVJycnJNiEqZ3rOtPxMmDBB3t7e1kfVqlWLdqcAAAAAlGh2D1J16tRRYmKitmzZogEDBigqKkq7d+++pdscOXKkzpw5Y30cPXr0lm4PAAAAQMli1z5SkuTi4qKaNWtKkpo2bapt27Zp6tSpeuqpp5SZmanTp0/bXJVKSUlRQECAJCkgIEBbt261WV/OqH458+TF1dVVrq6uRbwnAAAAAEoLu1+RulZ2drYyMjLUtGlTOTs7Kz4+3jpt3759SkpKUlhYmCQpLCxMO3bsUGpqqnWeVatWycvLS/Xr17/ttQMAAAAoHex6RWrkyJHq2LGjgoKCdPbsWc2bN0/r1q3TihUr5O3trX79+mnYsGEqX768vLy8NGjQIIWFhalFixaSpA4dOqh+/frq3bu3Jk6cqOTkZI0aNUrR0dFccQIAAABwy9g1SKWmpqpPnz46fvy4vL291ahRI61YsULt27eXJE2ePFkODg7q1q2bMjIyFBERoWnTplmXd3R01NKlSzVgwACFhYXJw8NDUVFRGjdunL12CQAAAEApYNcgNWvWrOtOd3NzU2xsrGJjY/OdJzg4WMuWLSvq0gAAAAAgX8WujxQAAAAAFHcEKQAAAAAwiSAFAAAAACYRpAAAAADAJIIUAAAAAJhEkAIAAAAAkwhSAAAAAGASQQoAAAAATCpUkKpevbpOnjyZq/306dOqXr36TRcFAAAAAMVZoYLU4cOHlZWVlas9IyNDf/75500XBQAAAADFmZOZmb/55hvr/69YsULe3t7W51lZWYqPj1e1atWKrDgAAAAAKI5MBakuXbpIkiwWi6KiomymOTs7q1q1anrvvfeKrDgAAAAAKI5MBans7GxJUkhIiLZt2yZfX99bUhQAAAAAFGemglSOQ4cOFXUdAAAAAHDHKFSQkqT4+HjFx8crNTXVeqUqxyeffHLThQEAAABAcVWoIDV27FiNGzdOzZo1U6VKlWSxWIq6LgAAAAAotgoVpGbMmKHZs2erd+/eRV0PAAAAABR7hfodqczMTN13331FXQsAAAAA3BEKFaT69++vefPmFXUtAAAAAHBHKNStfZcuXdJHH32k1atXq1GjRnJ2draZPmnSpCIpDgAAAACKo0IFqV9//VWhoaGSpJ07d9pMY+AJAAAAACVdoYLU2rVri7oOAAAAALhjFKqPFAAAAACUZoW6IvXggw9e9xa+NWvWFLogAAAAACjuChWkcvpH5bh8+bISExO1c+dORUVFFUVdAAAAAFBsFSpITZ48Oc/2mJgYnTt37qYKAgAAAIDirkj7SPXq1UuffPJJUa4SAAAAAIqdIg1SmzZtkpubW1GuEgAAAACKnULd2te1a1eb54Zh6Pjx49q+fbveeOONIikMAAAAAIqrQgUpb29vm+cODg6qU6eOxo0bpw4dOhRJYQAAAABQXBUqSMXFxRV1HQAAAABwxyhUkMqRkJCgPXv2SJIaNGigJk2aFElRAAAAAFCcFSpIpaamqnv37lq3bp18fHwkSadPn9aDDz6o+fPnq2LFikVZIwAAAAAUK4UatW/QoEE6e/asdu3apVOnTunUqVPauXOn0tPTNXjw4KKuEQAAAACKlUJdkVq+fLlWr16tevXqWdvq16+v2NhYBpsAAAAAUOIV6opUdna2nJ2dc7U7OzsrOzv7posCAAAAgOKsUEGqbdu2eumll3Ts2DFr259//qmhQ4eqXbt2RVYcAAAAABRHhQpSH374odLT01WtWjXVqFFDNWrUUEhIiNLT0/XBBx8UdY0AAAAAUKwUqo9U1apV9fPPP2v16tXau3evJKlevXoKDw8v0uIAAAAAoDgydUVqzZo1ql+/vtLT02WxWNS+fXsNGjRIgwYN0j333KMGDRroxx9/vFW1AgAAAECxYCpITZkyRc8++6y8vLxyTfP29tbzzz+vSZMmFVlxAAAAAFAcmQpSv/zyix566KF8p3fo0EEJCQk3XRQAAAAAFGemglRKSkqew57ncHJy0okTJ266KAAAAAAozkwFqcqVK2vnzp35Tv/1119VqVKlmy4KAAAAAIozU0Hq4Ycf1htvvKFLly7lmnbx4kWNGTNGnTp1KrLiAAAAAKA4MjX8+ahRo7R48WLVrl1bAwcOVJ06dSRJe/fuVWxsrLKysvT666/fkkIBAAAAoLgwFaT8/f21ceNGDRgwQCNHjpRhGJIki8WiiIgIxcbGyt/f/5YUCgAAAADFhekf5A0ODtayZcv0119/6cCBAzIMQ7Vq1VK5cuVuRX0AAAAAUOyYDlI5ypUrp3vuuacoawEAAACAO4KpwSYAAAAAAAQpAAAAADCNIAUAAAAAJhGkAAAAAMAkghQAAAAAmESQAgAAAACTCFIAAAAAYBJBCgAAAABMIkgBAAAAgEkEKQAAAAAwiSAFAAAAACYRpAAAAADAJIIUAAAAAJhEkAIAAAAAkwhSAAAAAGASQQoAAAAATCJIAQAAAIBJBCkAAAAAMIkgBQAAAAAmEaQAAAAAwCSCFAAAAACYRJACAAAAAJMIUgAAAABgEkEKAAAAAEwiSAEAAACASQQpAAAAADCJIAUAAAAAJhGkAAAAAMAkuwapCRMm6J577lHZsmXl5+enLl26aN++fTbzXLp0SdHR0apQoYI8PT3VrVs3paSk2MyTlJSkyMhIlSlTRn5+fhoxYoSuXLlyO3cFAAAAQCli1yC1fv16RUdHa/PmzVq1apUuX76sDh066Pz589Z5hg4dqm+//VYLFy7U+vXrdezYMXXt2tU6PSsrS5GRkcrMzNTGjRs1Z84czZ49W6NHj7bHLgEAAAAoBZzsufHly5fbPJ89e7b8/PyUkJCg+++/X2fOnNGsWbM0b948tW3bVpIUFxenevXqafPmzWrRooVWrlyp3bt3a/Xq1fL391doaKjefPNNvfrqq4qJiZGLi4s9dg0AAABACVas+kidOXNGklS+fHlJUkJCgi5fvqzw8HDrPHXr1lVQUJA2bdokSdq0aZMaNmwof39/6zwRERFKT0/Xrl278txORkaG0tPTbR4AAAAAUFDFJkhlZ2dryJAhatmype666y5JUnJyslxcXOTj42Mzr7+/v5KTk63zXB2icqbnTMvLhAkT5O3tbX1UrVq1iPcGAAAAQElWbIJUdHS0du7cqfnz59/ybY0cOVJnzpyxPo4ePXrLtwkAAACg5LBrH6kcAwcO1NKlS/XDDz+oSpUq1vaAgABlZmbq9OnTNlelUlJSFBAQYJ1n69atNuvLGdUvZ55rubq6ytXVtYj3AgAAAEBpYdcrUoZhaODAgfrqq6+0Zs0ahYSE2Exv2rSpnJ2dFR8fb23bt2+fkpKSFBYWJkkKCwvTjh07lJqaap1n1apV8vLyUv369W/PjgAAAAAoVex6RSo6Olrz5s3T119/rbJly1r7NHl7e8vd3V3e3t7q16+fhg0bpvLly8vLy0uDBg1SWFiYWrRoIUnq0KGD6tevr969e2vixIlKTk7WqFGjFB0dzVUnAAAAALeEXYPU9OnTJUlt2rSxaY+Li1Pfvn0lSZMnT5aDg4O6deumjIwMRUREaNq0adZ5HR0dtXTpUg0YMEBhYWHy8PBQVFSUxo0bd7t2AwAAAEApY9cgZRjGDedxc3NTbGysYmNj850nODhYy5YtK8rSAAAAACBfxWbUPgAAAAC4UxCkAAAAAMAkghQAAAAAmESQAgAAAACTCFIAAAAAYBJBCgAAAABMIkgBAAAAgEkEKQAAAAAwiSAFAAAAACYRpAAAAADAJIIUAAAAAJhEkAIAAAAAkwhSAAAAAGASQQoAAAAATCJIAQAAAIBJBCkAAAAAMIkgBQAAAAAmEaQAAAAAwCSCFAAAAACYRJACAAAAAJMIUgAAAABgEkEKAAAAAEwiSAEAAACASQQpAAAAADCJIAUAAAAAJhGkAAAAAMAkghQAAAAAmESQAgAAAACTCFIAAAAAYBJBCgAAAABMIkgBAAAAgEkEKQAAAAAwiSAFAAAAACYRpAAAAADAJIIUAAAAAJhEkAIAAAAAkwhSAAAAAGASQQoAAAAATCJIAQAAAIBJBCkAAAAAMIkgBQAAAAAmEaQAAAAAwCSCFAAAAACYRJACAAAAAJMIUgAAAABgEkEKAAAAAEwiSAEAAACASQQpAAAAADCJIAUAAAAAJhGkAAAAAMAkghQAAAAAmESQAgAAAACTCFIAAAAAYBJBCgAAAABMIkgBAAAAgEkEKQAAAAAwiSAFAAAAACYRpAAAAADAJIIUAAAAAJhEkAIAAAAAkwhSAAAAAGASQQoAAAAATCJIAQAAAIBJBCkAAAAAMIkgBQAAAAAmEaQAAAAAwCSCFAAAAACYRJACAAAAAJMIUgAAAABgEkEKAAAAAEwiSAEAAACASQQpAAAAADCJIAUAAAAAJhGkAAAAAMAkghQAAAAAmESQAgAAAACTCFIAAAAAYJJdg9QPP/ygzp07KzAwUBaLRUuWLLGZbhiGRo8erUqVKsnd3V3h4eHav3+/zTynTp1Sz5495eXlJR8fH/Xr10/nzp27jXsBAAAAoLSxa5A6f/68GjdurNjY2DynT5w4Ue+//75mzJihLVu2yMPDQxEREbp06ZJ1np49e2rXrl1atWqVli5dqh9++EHPPffc7doFAAAAAKWQkz033rFjR3Xs2DHPaYZhaMqUKRo1apQeffRRSdKnn34qf39/LVmyRN27d9eePXu0fPlybdu2Tc2aNZMkffDBB3r44Yf17rvvKjAw8LbtCwAAAIDSw65B6noOHTqk5ORkhYeHW9u8vb3VvHlzbdq0Sd27d9emTZvk4+NjDVGSFB4eLgcHB23ZskWPPfZYnuvOyMhQRkaG9Xl6evqt2xEUqT179ti7BCtfX18FBQXZuwwAAADYQbENUsnJyZIkf39/m3Z/f3/rtOTkZPn5+dlMd3JyUvny5a3z5GXChAkaO3ZsEVeMW+nimZOSLOrVq5e9S7Fydy+jvXv3EKYAAABKoWIbpG6lkSNHatiwYdbn6enpqlq1qh0rwo1cvnBWkqHQf7yqiiF17V2O0o8f1pZPxiotLY0gBQAAUAoV2yAVEBAgSUpJSVGlSpWs7SkpKQoNDbXOk5qaarPclStXdOrUKevyeXF1dZWrq2vRF41bztMvSOWD6ti7DAAAAJRyxfZ3pEJCQhQQEKD4+HhrW3p6urZs2aKwsDBJUlhYmE6fPq2EhATrPGvWrFF2draaN29+22sGAAAAUDrY9YrUuXPndODAAevzQ4cOKTExUeXLl1dQUJCGDBmit956S7Vq1VJISIjeeOMNBQYGqkuXLpKkevXq6aGHHtKzzz6rGTNm6PLlyxo4cKC6d+/OiH0AAAAAbhm7Bqnt27frwQcftD7P6bcUFRWl2bNn65VXXtH58+f13HPP6fTp02rVqpWWL18uNzc36zJz587VwIED1a5dOzk4OKhbt256//33b/u+AAAAACg97Bqk2rRpI8Mw8p1usVg0btw4jRs3Lt95ypcvr3nz5t2K8gAAAAAgT8W2jxQAAAAAFFcEKQAAAAAwiSAFAAAAACYRpAAAAADAJIIUAAAAAJhEkAIAAAAAkwhSAAAAAGASQQoAAAAATCJIAQAAAIBJBCkAAAAAMIkgBQAAAAAmEaQAAAAAwCSCFAAAAACYRJACAAAAAJMIUgAAAABgEkEKAAAAAEwiSAEAAACASQQpAAAAADCJIAUAAAAAJhGkAAAAAMAkghQAAAAAmESQAgAAAACTCFIAAAAAYBJBCgAAAABMIkgBAAAAgEkEKQAAAAAwiSAFAAAAACYRpAAAAADAJIIUAAAAAJhEkAIAAAAAkwhSAAAAAGASQQoAAAAATCJIAQAAAIBJBCkAAAAAMIkgBQAAAAAmEaQAAAAAwCSCFAAAAACYRJACAAAAAJMIUgAAAABgEkEKAAAAAEwiSAEAAACASQQpAAAAADCJIAUAAAAAJhGkAAAAAMAkghQAAAAAmESQAgAAAACTCFIAAAAAYBJBCgAAAABMcrJ3AcCdbM+ePfYuQZLk6+uroKAge5cBAABQahCkgEK4eOakJIt69epl71IkSe7uZbR37x7CFAAAwG1CkAIK4fKFs5IMhf7jVVUMqWvXWtKPH9aWT8YqLS2NIAUAAHCbEKSAm+DpF6TyQXXsXQYAAABuMwabAAAAAACTCFIAAAAAYBJBCgAAAABMIkgBAAAAgEkEKQAAAAAwiSAFAAAAACYRpAAAAADAJIIUAAAAAJhEkAIAAAAAkwhSAAAAAGASQQoAAAAATCJIAQAAAIBJBCkAAAAAMIkgBQAAAAAmEaQAAAAAwCQnexcAoGjs2bPH3iVIknx9fRUUFGTvMgAAAG4pghRwh7t45qQki3r16mXvUiRJ7u5ltHfvHsIUAAAo0QhSwB3u8oWzkgyF/uNVVQypa9da0o8f1pZPxiotLY0gBQAASjSCFFBCePoFqXxQHXuXAQAAUCoQpAAUOfprAQCAko4gBaDI0F8LAACUFgQpAEWG/loojKSkJKWlpdm7DElcxQQAFBxBCkCRo78WCiopKUl169bTxYsX7F2KJK5iAgAKjiAFALCbtLQ0Xbx4Qc2fGSOvStXsWgtXMQEAZhCkAJRoDHxxZ/CqVI2rmACAO0qJCVKxsbF65513lJycrMaNG+uDDz7Qvffea++yANhJcRv4wtXVTf/97yJVqlTJ3qVIkjIyMuTq6mrvMopN0C2OilPfMYkvA2BecTqHOX9xK5SIIPXll19q2LBhmjFjhpo3b64pU6YoIiJC+/btk5+fn73LA2AHxWngixP7f1Higqnq1KmTXeuwYbFIhmHvKqwuZ2Tau4Ripbj1HZOKV/8xPqAXf8XtHC5O5y9KjhIRpCZNmqRnn31WTz/9tCRpxowZ+u677/TJJ5/otddes3N1AOypOAx8kX78sIpLqJOk4zs2aec3HxWLenJquXLlil3ruFpxuEq2Z8+eYtN3TPq//mM//vij6tWrZ9dajh8/rscff0KXLl20ax05+ICeN/o/5q84fRFQXO5OkO7MLyXu+CCVmZmphIQEjRw50trm4OCg8PBwbdq0Kc9lMjIylJGRYX1+5swZSVJ6evqtLbYAzp07J0k6dWSfrmTY9x+J9ONHJEln/twvZycLtVylONVDLXdOLVmXM+z+uv67jsz//1/715NTS3H4O6Ud3CFJxeZ2UEnKOH/O7n8jSbrwV6qk4nVsarR9Sl4VA+1aw4VTKdq3ap5WrFihOnXs38fPwcFB2dnZ9i5DkrRv3z5J0pVM+7/PXMn8+zNfQkKC9XOWvaSkpKh37z7KyLhk1zqKIzc3d23fvk1Vq1a1dynWTGDc4M4Ni3GjOYq5Y8eOqXLlytq4caPCwsKs7a+88orWr1+vLVu25FomJiZGY8eOvZ1lAgAAALiDHD16VFWqVMl3+h1/RaowRo4cqWHDhlmfZ2dn69SpU6pQoYIsFjt/c52erqpVq+ro0aPy8vKyay2lDcfePjju9sOxtx+OvX1w3O2HY28/HHvzDMPQ2bNnFRh4/aved3yQ8vX1laOjo1JSUmzaU1JSFBAQkOcyrq6uue4H9fHxuVUlFoqXlxcnu51w7O2D424/HHv74djbB8fdfjj29sOxN8fb2/uG8zjchjpuKRcXFzVt2lTx8fHWtuzsbMXHx9vc6gcAAAAAReWOvyIlScOGDVNUVJSaNWume++9V1OmTNH58+eto/gBAAAAQFEqEUHqqaee0okTJzR69GglJycrNDRUy5cvl7+/v71LM83V1VVjxowpNkNRliYce/vguNsPx95+OPb2wXG3H469/XDsb507ftQ+AAAAALjd7vg+UgAAAABwuxGkAAAAAMAkghQAAAAAmESQAgAAAACTCFLFSGxsrKpVqyY3Nzc1b95cW7dutXdJpcIPP/ygzp07KzAwUBaLRUuWLLF3SaXChAkTdM8996hs2bLy8/NTly5dtG/fPnuXVSpMnz5djRo1sv44Y1hYmL7//nt7l1Xq/Pvf/5bFYtGQIUPsXUqJFxMTI4vFYvOoW7euvcsqNf7880/16tVLFSpUkLu7uxo2bKjt27fbu6wSr1q1arnOe4vFoujoaHuXVmIQpIqJL7/8UsOGDdOYMWP0888/q3HjxoqIiFBqaqq9Syvxzp8/r8aNGys2NtbepZQq69evV3R0tDZv3qxVq1bp8uXL6tChg86fP2/v0kq8KlWq6N///rcSEhK0fft2tW3bVo8++qh27dpl79JKjW3btmnmzJlq1KiRvUspNRo0aKDjx49bHz/99JO9SyoV/vrrL7Vs2VLOzs76/vvvtXv3br333nsqV66cvUsr8bZt22Zzzq9atUqS9MQTT9i5spKD4c+LiebNm+uee+7Rhx9+KEnKzs5W1apVNWjQIL322mt2rq70sFgs+uqrr9SlSxd7l1LqnDhxQn5+flq/fr3uv/9+e5dT6pQvX17vvPOO+vXrZ+9SSrxz587p7rvv1rRp0/TWW28pNDRUU6ZMsXdZJVpMTIyWLFmixMREe5dS6rz22mvasGGDfvzxR3uXUuoNGTJES5cu1f79+2WxWOxdTonAFaliIDMzUwkJCQoPD7e2OTg4KDw8XJs2bbJjZcDtc+bMGUl/f6DH7ZOVlaX58+fr/PnzCgsLs3c5pUJ0dLQiIyNt3vNx6+3fv1+BgYGqXr26evbsqaSkJHuXVCp88803atasmZ544gn5+fmpSZMm+vjjj+1dVqmTmZmpzz//XM888wwhqggRpIqBtLQ0ZWVlyd/f36bd399fycnJdqoKuH2ys7M1ZMgQtWzZUnfddZe9yykVduzYIU9PT7m6uuqFF17QV199pfr169u7rBJv/vz5+vnnnzVhwgR7l1KqNG/eXLNnz9by5cs1ffp0HTp0SK1bt9bZs2ftXVqJ9/vvv2v69OmqVauWVqxYoQEDBmjw4MGaM2eOvUsrVZYsWaLTp0+rb9++9i6lRHGydwEAEB0drZ07d9Jn4TaqU6eOEhMTdebMGS1atEhRUVFav349YeoWOnr0qF566SWtWrVKbm5u9i6nVOnYsaP1/xs1aqTmzZsrODhYCxYs4HbWWyw7O1vNmjXT+PHjJUlNmjTRzp07NWPGDEVFRdm5utJj1qxZ6tixowIDA+1dSonCFaliwNfXV46OjkpJSbFpT0lJUUBAgJ2qAm6PgQMHaunSpVq7dq2qVKli73JKDRcXF9WsWVNNmzbVhAkT1LhxY02dOtXeZZVoCQkJSk1N1d133y0nJyc5OTlp/fr1ev/99+Xk5KSsrCx7l1hq+Pj4qHbt2jpw4IC9SynxKlWqlOsLmnr16nFr5W105MgRrV69Wv3797d3KSUOQaoYcHFxUdOmTRUfH29ty87OVnx8PH0WUGIZhqGBAwfqq6++0po1axQSEmLvkkq17OxsZWRk2LuMEq1du3basWOHEhMTrY9mzZqpZ8+eSkxMlKOjo71LLDXOnTungwcPqlKlSvYupcRr2bJlrp+2+O233xQcHGynikqfuLg4+fn5KTIy0t6llDjc2ldMDBs2TFFRUWrWrJnuvfdeTZkyRefPn9fTTz9t79JKvHPnztl8K3no0CElJiaqfPnyCgoKsmNlJVt0dLTmzZunr7/+WmXLlrX2B/T29pa7u7udqyvZRo4cqY4dOyooKEhnz57VvHnztG7dOq1YscLepZVoZcuWzdUH0MPDQxUqVKBv4C02fPhwde7cWcHBwTp27JjGjBkjR0dH9ejRw96llXhDhw7Vfffdp/Hjx+vJJ5/U1q1b9dFHH+mjjz6yd2mlQnZ2tuLi4hQVFSUnJz72FzWOaDHx1FNP6cSJExo9erSSk5MVGhqq5cuX5xqAAkVv+/btevDBB63Phw0bJkmKiorS7Nmz7VRVyTd9+nRJUps2bWza4+Li6Ax7i6WmpqpPnz46fvy4vL291ahRI61YsULt27e3d2nALfHHH3+oR48eOnnypCpWrKhWrVpp8+bNqlixor1LK/HuueceffXVVxo5cqTGjRunkJAQTZkyRT179rR3aaXC6tWrlZSUpGeeecbepZRI/I4UAAAAAJhEHykAAAAAMIkgBQAAAAAmEaQAAAAAwCSCFAAAAACYRJACAAAAAJMIUgAAAABgEkEKAAAAAEwiSAEAAAC4Y/zwww/q3LmzAgMDZbFYtGTJEtPrWLBggUJDQ1WmTBkFBwfrnXfeMb0OghQA4I5R2H8wzWrTpo2GDBlyy9a/bt06WSwWnT59+rrzxcfHq169esrKyso1rW/fvnku89prr2nQoEFFUCUAFE/nz59X48aNFRsbW6jlv//+e/Xs2VMvvPCCdu7cqWnTpmny5Mn68MMPTa2HIAUAMM1isVz3ERMTk++yhw8flsViUWJiYpHX1bdvX2sNzs7OCgkJ0SuvvKJLly6ZWs/ixYv15ptvFnl9Zr3yyisaNWqUHB0dC7zM8OHDNWfOHP3++++3sDIAsJ+OHTvqrbfe0mOPPZbn9IyMDA0fPlyVK1eWh4eHmjdvrnXr1lmnf/bZZ+rSpYteeOEFVa9eXZGRkRo5cqTefvttGYZR4DoIUgAA044fP259TJkyRV5eXjZtw4cPt1ttDz30kI4fP67ff/9dkydP1syZMzVmzBhT6yhfvrzKli17iyosmJ9++kkHDx5Ut27drG2GYSgmJka1a9fWvHnzVLVqVXXo0EG7du2yzuPr66uIiAhNnz7dHmUDgN0NHDhQmzZt0vz58/Xrr7/qiSee0EMPPaT9+/dL+jtoubm52Szj7u6uP/74Q0eOHCnwdghSAADTAgICrA9vb29ZLBbrcz8/P02aNElVqlSRq6urQkNDtXz5cuuyISEhkqQmTZrIYrGoTZs2kqRt27apffv28vX1lbe3tx544AH9/PPPpmtzdXVVQECAqlatqi5duig8PFyrVq2yTj958qR69OihypUrq0yZMmrYsKG++OILm3Vce2vftGnTVKtWLbm5ucnf31+PP/64dVp2drYmTJigkJAQubu7q3Hjxlq0aJHN+pYtW6batWvL3d1dDz74oA4fPnzD/Zg/f77at29v84/9J598ookTJ2rs2LHq3LmzFixYoI4dO+a64ta5c2fNnz+/IIcLAEqUpKQkxcXFaeHChWrdurVq1Kih4cOHq1WrVoqLi5MkRUREaPHixYqPj1d2drZ+++03vffee5L+/qKwoAhSAIAiNXXqVL333nt699139euvvyoiIkKPPPKI9ZvArVu3SpJWr16t48ePa/HixZKks2fPKioqSj/99JM2b96sWrVq6eGHH9bZs2cLXcvOnTu1ceNGubi4WNsuXbqkpk2b6rvvvtPOnTv13HPPqXfv3ta6rrV9+3YNHjxY48aN0759+7R8+XLdf//91ukTJkzQp59+qhkzZmjXrl0aOnSoevXqpfXr10uSjh49qq5du6pz585KTExU//799dprr92w9h9//FHNmjWzafvf//6nli1bqkePHipbtqzCwsI0dOhQNW3a1Ga+e++9V3/88UeBAhsAlCQ7duxQVlaWateuLU9PT+tj/fr1OnjwoCTp2Wef1cCBA9WpUye5uLioRYsW6t69uyTJwaHg8cjpluwBAKDUevfdd/Xqq69a/1F6++23tXbtWk2ZMkWxsbGqWLGiJKlChQoKCAiwLte2bVub9Xz00Ufy8fHR+vXr1alTpwJvf+nSpfL09NSVK1eUkZEhBwcHmw7ElStXtrn1cNCgQVqxYoUWLFige++9N9f6kpKS5OHhoU6dOqls2bIKDg5WkyZNJP19e8j48eO1evVqhYWFSZKqV6+un376STNnztQDDzyg6dOnq0aNGtZvO+vUqaMdO3bo7bffvu5+HDlyRIGBgTZtLVu21KxZs/Txxx/r3Llz+S6bs9yRI0dUrVq1624HAEqSc+fOydHRUQkJCbn6l3p6ekr6u5/v22+/rfHjxys5OVkVK1ZUfHy8pL/fwwuKIAUAKDLp6ek6duyYWrZsadPesmVL/fLLL9ddNiUlRaNGjdK6deuUmpqqrKwsXbhwQUlJSaZqePDBBzV9+nSdP39ekydPlpOTk00/o6ysLI0fP14LFizQn3/+qczMTGVkZKhMmTJ5rq99+/YKDg5W9erV9dBDD+mhhx7SY489pjJlyujAgQO6cOGC2rdvb7NMZmamNWzt2bNHzZs3t5meE7qu5+LFi7nu4e/Ro4cuXLigGTNm6Oeff1a1atX05JNP6o033rDp0+Xu7i5JunDhwg23AwAlSZMmTZSVlaXU1FS1bt36uvM6OjqqcuXKkqQvvvhCYWFh1i/7CoIgBQAoFqKionTy5ElNnTpVwcHBcnV1VVhYmDIzM02tx8PDQzVr1pT0d5+ixo0ba9asWerXr58k6Z133tHUqVM1ZcoUNWzYUB4eHhoyZEi+2ylbtqx+/vlnrVu3TitXrtTo0aMVExOjbdu2Wa8Kfffdd9Z/jHO4urqaPQQ2fH199ddff+Vq79evn/r166devXrpqaee0rBhw/THH39o3rx51nlOnTolSaY+EADAneLcuXM6cOCA9fmhQ4eUmJio8uXLq3bt2urZs6f69Omj9957T02aNNGJEycUHx+vRo0aKTIyUmlpaVq0aJHatGmjS5cuWftU5dySXVD0kQIAFBkvLy8FBgZqw4YNNu0bNmxQ/fr1JcnaX+na30basGGDBg8erIcfflgNGjSQq6ur0tLSbqoeBwcH/fOf/9SoUaN08eJF63YeffRR9erVS40bN1b16tX122+/XXc9Tk5OCg8P18SJE/Xrr7/q8OHDWrNmjerXry9XV1clJSWpZs2aNo+qVatKkurVq5er/9XmzZtvWHuTJk20e/fu69bUuXNnDR48WD/++KPNtJ07d8rZ2VkNGjS44XYA4E6zfft2NWnSxHrlf9iwYWrSpIlGjx4tSYqLi1OfPn308ssvq06dOurSpYu2bdumoKAg6zrmzJmjZs2aqWXLltq1a5fWrVuX5+3d18MVKQBAkRoxYoTGjBmjGjVqKDQ0VHFxcUpMTNTcuXMlSX5+fnJ3d9fy5ctVpUoVubm5ydvbW7Vq1dJnn32mZs2aKT09XSNGjLDeonYznnjiCY0YMUKxsbEaPny4atWqpUWLFmnjxo0qV66cJk2apJSUFGvQu9bSpUv1+++/6/7771e5cuW0bNkyZWdnq06dOipbtqyGDx+uoUOHKjs7W61atdKZM2e0YcMGeXl5KSoqSi+88ILee+89jRgxQv3791dCQoJmz559w7ojIiI0Z84cm7YpU6YoMDDQOtjF3r17NXfu3FyDTfz4449q3bp1kRw/AChu2rRpc93fe3J2dtbYsWM1duzYPKf7+vpq06ZNN10HV6QAAEVq8ODBGjZsmF5++WU1bNhQy5cv1zfffKNatWpJ+vtKyvvvv6+ZM2cqMDBQjz76qCRp1qxZ+uuvv3T33Xerd+/eGjx4sPz8/G66HicnJw0cOFATJ07U+fPnNWrUKN19992KiIhQmzZtFBAQoC5duuS7vI+PjxYvXqy2bduqXr16mjFjhr744gvr1Z4333xTb7zxhiZMmKB69erpoYce0nfffWcd5j0oKEj//e9/tWTJEjVu3FgzZszQ+PHjb1h3z549tWvXLu3bt8/aVrt2bX300Ue666679Omnn6pVq1YKCgrK9ZtR8+fP17PPPluIowUAKCiLYebnewEAwG0zYsQIpaena+bMmbmm9e3bN88rW99//71efvll/frrr3Jy4sYTALhVuCIFAEAx9frrrys4OFjZ2dkFXub8+fOKi4sjRAHALcYVKQAAAAAwiStSAAAAAGASQQoAAAAATCJIAQAAAIBJBCkAAAAAMIkgBQAAAAAmEaQAAAAAwCSCFAAAAACYRJACAAAAAJMIUgAAAABg0v8DJPrx9ovIDbAAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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c/Zs0aaLw8HCn8aZNmyY/Pz+1bNnS6ZisVauWfHx8nI4P4GZEmARwy0hNTb3iP7Y7deqkBg0a6PHHH1fx4sXVuXNnTZ061VKwLFmypKWb7VSoUMHpvc1mU/ny5XP0esGs7N+/X0FBQZk+j7CwMMfyi10apCSpUKFCV72mZ//+/apQoUKmO2pebjs56dJ/pJYqVSpT29X24cI/iKtWrXrZPkeOHNHp06dVqVKlTMvCwsKUkZGR6TrYS+/AeT01SpKvr68kZesRONdS76W/fz8/P0lScHBwpvasar30OJekihUrOh3nx48f17PPPqvixYvL09NTAQEBjs8pOTk50/qX1lSoUCFJuuJntXPnTs2bN08BAQFOr8jISEn/dxOWZ555RhUrVlTr1q1VqlQpPfbYY9m+Rji7+9uyZUuVKFFCkyZNkvRf6Pr222/Vrl27bP2ngPRfSI2MjHR6XYmHh0em08ezOr5+/vln3XXXXfLw8FDhwoUVEBCgjz/+OFu/h0vHPHLkiFJSUq74Z+hy4uPjFRMTo8KFCzuu8WzSpImkrI+J7OjSpYuWLVumEydOaMGCBXr44Ye1ceNGtW3bVmfOnMmRmi58r5UvXz7T+pe27dy5U8nJySpWrFim4zI1NZUbA+GmxzWTAG4Jf//9t5KTk7P8y/0CT09PLV26VIsWLdLs2bM1b948fffdd2revLkWLFiQ5UxSVmPktMv9z316enq2asoJl9uOueRmPTeK3W6/7E04LtyE5dI7Q+anfbjccXKtNVauXFmStHXr1lx5lMHl6sqq/Vo/z44dO2rFihV6/vnnVaNGDfn4+CgjI0NRUVFZ/ofOtXxWGRkZatmypV544YUsl1esWFHSf9fXbdq0SfPnz9fcuXM1d+5cxcbGqmvXrk43j7oerq6uevjhh/X555/ro48+0vLly3Xo0CGnmemclp3vi2XLlum+++5T48aN9dFHH6lEiRIqUKCAYmNjNXny5GyPeb1/rtLT09WyZUsdP35cL774oipXrixvb28dPHhQMTEx1/2oI19fX7Vs2VItW7ZUgQIF9OWXX2r16tWOYHijasrIyFCxYsUc/6lwqZy+dhy40QiTAG4JX3/9tSSpVatWV+zn4uKiFi1aqEWLFnrnnXc0cuRIvfLKK1q0aJEiIyOveErWtdi5c6fTe2OMdu3a5fQ8zEKFCikpKSnTuvv373c6TdFKbSEhIfrll1908uRJp1mQv/76y7E8J4SEhGjLli3KyMhwmp283u2EhIRo+/btWS670J4T+3Dh8730zqMXCwgIkJeXV5b1/PXXX3Jxcck0g5fTWrduLVdXV33zzTdXvQlPXtR76XEuSTt27HDc1fLEiROKi4vT8OHDNWTIkCuudz3KlSun1NTUq87gSf+dYty2bVu1bdtWGRkZeuaZZ/Tpp59q8ODBV/xPKenq+3tB165dNWbMGP3000+aO3euAgICrvodldu+//57eXh4aP78+U6PVomNjb2m8QICAuTr63vFP0NZ2bp1q3bs2KEvv/zS6YZO13rq8ZXUrl1bX375pRISEiRd/rs0uzVd+O7ZtWtXpjEubStXrpx++eUXNWjQIFf+MxLIa5zmCuCm9+uvv+q1115TmTJlFB0dfdl+x48fz9R2YZbn7NmzkuR4HmBW4e5aXLjr4wXTp09XQkKC0507y5Urp1WrVjluoS/9dxrapaciWqntnnvuUXp6uj744AOn9nfffVc2m+2Kdw614p577tHhw4f13XffOdrS0tL0/vvvy8fH54qzAFcbd9WqVVq/fr1Te1JSkiZNmqQaNWooMDDwumqX/vuHcOPGjfXFF18oPj7eadmFmRdXV1fdfffd+vHHH51OY/znn380efJkNWzY0HEaam4JDg5Wz549tWDBAr3//vuZlmdkZGjMmDH6+++/86TemTNnOj3qYs2aNVq9erXjOLswu3XpbNaFu6vmlI4dO2rlypWaP39+pmVJSUlKS0uTpEyPI3FxcXH8B8+F74Irudr+XlCtWjVVq1ZN48eP1/fff6/OnTvn+rNvr8bV1VU2m83pcSH79u3TzJkzr2k8FxcXtW/fXj/99JPWrVuXafnlZjCzOiaMMZYez3Kx06dPa+XKlVkuu3CN+IVTvy/3XZrdmoKCglS1alV99dVXSk1NdbQvWbJEW7duderbsWNHpaen67XXXstUV1paWo79XQPkFWYmAdxU5s6dq7/++ktpaWn6559/9Ouvv2rhwoUKCQnRrFmzrvhQ7BEjRmjp0qVq06aNQkJClJiYqI8++kilSpVSw4YNJf0X7Pz9/fXJJ5+oYMGC8vb2Vt26dS97DdzVFC5cWA0bNlT37t31zz//aOzYsSpfvrzT40sef/xxTZ8+XVFRUerYsaN2796tb775xummF1Zra9u2rZo1a6ZXXnlF+/btU/Xq1bVgwQL9+OOP6tevX6axr9UTTzyhTz/9VDExMVq/fr1CQ0M1ffp0LV++XGPHjs32tWGXeumllzRt2jQ1btxYTz75pCpXrqxDhw5p4sSJSkhIuOZZlKy89957atiwoWrWrKknnnhCZcqU0b59+zR79mxt2rRJkvS///3P8YzSZ555Rm5ubvr000919uzZbD0nMieMGTNGu3fvVt++fTVjxgzde++9KlSokOLj4zVt2jT99ddf6ty5c57UW758eTVs2FBPP/20zp49q7Fjx6pIkSKO0019fX3VuHFjvfXWWzp//rxKliypBQsWaO/evTlax/PPP69Zs2bp3nvvVUxMjGrVqqVTp05p69atmj59uvbt26eiRYvq8ccf1/Hjx9W8eXOVKlVK+/fv1/vvv68aNWo4rve9nv29WNeuXTVw4EBJytVTXLOrTZs2eueddxQVFaWHH35YiYmJ+vDDD1W+fHlt2bLlmsYcOXKkFixYoCZNmuiJJ55QWFiYEhISNG3aNP3222+Om59drHLlyipXrpwGDhyogwcPytfXV99///01P3vx9OnTql+/vu666y5FRUUpODhYSUlJmjlzppYtW6b27ds7HtNyue9SKzWNHDlS7dq1U4MGDdS9e3edOHFCH3zwgapWreoUMJs0aaInn3xSo0aN0qZNm3T33XerQIEC2rlzp6ZNm6Zx48bpwQcfvKZ9BvKFG337WAC4FpfeKt/d3d0EBgaali1bmnHjxjk9guKCS28nHxcXZ9q1a2eCgoKMu7u7CQoKMl26dMn0GIEff/zR3HHHHcbNzc3p9vFNmjQxVapUybK+yz0a5NtvvzWDBg0yxYoVM56enqZNmzaZHkFhjDFjxowxJUuWNHa73TRo0MCsW7cu05hXqu3SR4MYY8zJkyfNc889Z4KCgkyBAgVMhQoVzOjRo50eAWHM5R/DcblHllzqn3/+Md27dzdFixY17u7uJjw8PMtb7lt5NIgxxvz999/m8ccfNyVLljRubm6mcOHC5t577zWrVq3K1Pdyv5tLP5esHqlgjDG///676dChg/H39zceHh6mUqVKZvDgwU59NmzYYFq1amV8fHyMl5eXadasmVmxYoVTnys9wia7NV5JWlqaGT9+vGnUqJHx8/MzBQoUMCEhIaZ79+6ZHhtyPfVe+LNz6WN0unXrZry9vR3vL3yeo0ePNmPGjDHBwcHGbrebRo0amc2bNzut+/fffzs+Yz8/P/PQQw+ZQ4cOGUlm6NChV932hVr37t3raMvqGD158qQZNGiQKV++vHF3dzdFixY19evXN2+//bY5d+6cMcaY6dOnm7vvvtsUK1bMuLu7m9KlS5snn3zSJCQkXPazt7q/FyQkJBhXV1dTsWLFK46d1b5e7lFIl3s0yMW/mwuyeqzGhAkTTIUKFYzdbjeVK1c2sbGxWfaz8t2wf/9+07VrVxMQEGDsdrspW7as6dWrlzl79qwxJutHg/z5558mMjLS+Pj4mKJFi5qePXs6Hj1y8b5l59Eg58+fN59//rlp3769CQkJMXa73Xh5eZmIiAgzevRoRx0XXO67NLs1GWPMlClTTOXKlY3dbjdVq1Y1s2bNMg888ICpXLlypvo+++wzU6tWLePp6WkKFixowsPDzQsvvGAOHTp0xf0C8jubMXl0dwUAAHBT27dvn8qUKaPRo0c7Zt/g7OjRoypRooSGDBmiwYMH53U5yGU1atRQQEBArlz7CeRHXDMJAACQSyZOnKj09PSr3jgJN5fz5887rsG9YPHixdq8ebOaNm2aN0UBeYBrJgEAAHLYr7/+qj///FOvv/662rdvn+lOr7i5HTx4UJGRkXrkkUcUFBSkv/76S5988okCAwP11FNP5XV5wA1DmAQAAMhhI0aM0IoVK9SgQYMs78CLm1uhQoVUq1YtjR8/XkeOHJG3t7fatGmjN954Q0WKFMnr8oAbhmsmAQAAAACWcc0kAAAAAMAywiQAAAAAwDKumYQkKSMjQ4cOHVLBggVls9nyuhwAAAAAecQYo5MnTyooKEguLpeffyRMQpJ06NAhBQcH53UZAAAAAPKJAwcOqFSpUpddTpiEJKlgwYKS/jtgfH1987gaAAAAAHklJSVFwcHBjoxwOYRJSJLj1FZfX1/CJAAAAICrXv7GDXgAAAAAAJYRJgEAAAAAlnGaK5w0fvVbudo987oMAAAA4LaxfnTXvC7hmjAzCQAAAACwjDAJAAAAALCMMAkAAAAAsIwwCQAAAACwjDAJAAAAALCMMAkAAAAAsIwwCQAAAACwjDAJAAAAALCMMAkAAAAAsIwwCQAAAACwjDAJAAAAALCMMAkAAAAAsIwwCQAAAACwjDAJAAAAALCMMAkAAAAAsIwwmUtiYmJks9n0xhtvOLXPnDlTNpstj6oCAAAAgJxBmMxFHh4eevPNN3XixIm8LgUAAAAAchRhMhdFRkYqMDBQo0aNumyf77//XlWqVJHdbldoaKjGjBnjtDw0NFQjR47UY489poIFC6p06dL67LPPnPocOHBAHTt2lL+/vwoXLqx27dpp3759ubFLAAAAACCJMJmrXF1dNXLkSL3//vv6+++/My1fv369OnbsqM6dO2vr1q0aNmyYBg8erIkTJzr1GzNmjGrXrq2NGzfqmWee0dNPP63t27dLks6fP69WrVqpYMGCWrZsmZYvXy4fHx9FRUXp3Llzl63t7NmzSklJcXoBAAAAQHYRJnNZhw4dVKNGDQ0dOjTTsnfeeUctWrTQ4MGDVbFiRcXExKh3794aPXq0U7977rlHzzzzjMqXL68XX3xRRYsW1aJFiyRJ3333nTIyMjR+/HiFh4crLCxMsbGxio+P1+LFiy9b16hRo+Tn5+d4BQcH5+h+AwAAALi1ESZvgDfffFNffvmltm3b5tS+bds2NWjQwKmtQYMG2rlzp9LT0x1t1apVc/xss9kUGBioxMRESdLmzZu1a9cuFSxYUD4+PvLx8VHhwoV15swZ7d69+7I1DRo0SMnJyY7XgQMHcmJXAQAAANwm3PK6gNtB48aN1apVKw0aNEgxMTGW1y9QoIDTe5vNpoyMDElSamqqatWqpUmTJmVaLyAg4LJj2u122e12y7UAAAAAgESYvGHeeOMN1ahRQ5UqVXK0hYWFafny5U79li9frooVK8rV1TVb49asWVPfffedihUrJl9f3xytGQAAAAAuh9Ncb5Dw8HBFR0frvffec7QNGDBAcXFxeu2117Rjxw59+eWX+uCDDzRw4MBsjxsdHa2iRYuqXbt2WrZsmfbu3avFixerb9++Wd70BwAAAAByAmHyBhoxYoTj9FTpv1nFqVOnasqUKapataqGDBmiESNGWDoV1svLS0uXLlXp0qV1//33KywsTD169NCZM2eYqQQAAACQa2zGGJPXRSDvpaSkyM/PT9X7fCJXu2delwMAAADcNtaP7prXJTi5kA2Sk5OvOEHFzCQAAAAAwDLCJAAAAADAMsIkAAAAAMAywiQAAAAAwDLCJAAAAADAMsIkAAAAAMAywiQAAAAAwDLCJAAAAADAMsIkAAAAAMAywiQAAAAAwDLCJAAAAADAMsIkAAAAAMAywiQAAAAAwDLCJAAAAADAMre8LgD5y9L/dZGvr29elwEAAAAgn2NmEgAAAABgGWESAAAAAGAZYRIAAAAAYBlhEgAAAABgGWESAAAAAGAZYRIAAAAAYBlhEgAAAABgGWESAAAAAGAZYRIAAAAAYJlbXheA/OXAG3epoIdrjo5ZesjWHB0PAAAAQN5jZhIAAAAAYBlhEgAAAABgGWESAAAAAGAZYRIAAAAAYBlhEgAAAABgGWESAAAAAGAZYRIAAAAAYBlhEgAAAABgGWESAAAAAGAZYRIAAAAAYBlhEgAAAABgGWESAAAAAGAZYRIAAAAAYBlhEgAAAABgGWESAAAAAGAZYTKPLF68WDabTUlJSTk+9sSJE+Xv75/j4wIAAADABbd1mFy5cqVcXV3Vpk2bG77t+vXrKyEhQX5+fjd82wAAAABwvW7rMDlhwgT16dNHS5cu1aFDh27ott3d3RUYGCibzZbl8vT0dGVkZNzQmgAAAAAgu27bMJmamqrvvvtOTz/9tNq0aaOJEyc6Lf/pp5905513ysPDQ0WLFlWHDh0cyxITE9W2bVt5enqqTJkymjRpkkJDQzV27FhJ0r59+2Sz2bRp0ybHOklJSbLZbFq8eLGkzKe5Xjg1ddasWbrjjjtkt9sVHx+vs2fPauDAgSpZsqS8vb1Vt25dxxgXTJw4UaVLl5aXl5c6dOigY8eO5fCnBQAAAADObtswOXXqVFWuXFmVKlXSI488oi+++ELGGEnS7Nmz1aFDB91zzz3auHGj4uLiVKdOHce6MTExOnDggBYtWqTp06fro48+UmJi4nXXdPr0ab355psaP368/vjjDxUrVky9e/fWypUrNWXKFG3ZskUPPfSQoqKitHPnTknS6tWr1aNHD/Xu3VubNm1Ss2bN9L///e+q2zp79qxSUlKcXgAAAACQXW55XUBemTBhgh555BFJUlRUlJKTk7VkyRI1bdpUr7/+ujp37qzhw4c7+levXl2StGPHDs2dO1dr1qzRnXfe6RgrLCzsums6f/68PvroI8e24uPjFRsbq/j4eAUFBUmSBg4cqHnz5ik2NlYjR47UuHHjFBUVpRdeeEGSVLFiRa1YsULz5s274rZGjRrltH8AAAAAYMVtOTO5fft2rVmzRl26dJEkubm5qVOnTpowYYIkadOmTWrRokWW627btk1ubm6qVauWo61y5co5cvdUd3d3VatWzfF+69atSk9PV8WKFeXj4+N4LVmyRLt373bUU7duXadx6tWrd9VtDRo0SMnJyY7XgQMHrrt+AAAAALeP23JmcsKECUpLS3PM9kmSMUZ2u10ffPCBPD09r2t8FxcXx5gXnD9//qrreXp6Ot2QJzU1Va6urlq/fr1cXV2d+vr4+FxXjXa7XXa7/brGAAAAAHD7uu1mJtPS0vTVV19pzJgx2rRpk+O1efNmBQUF6dtvv1W1atUUFxeX5fqVK1dWWlqa1q9f72jbvn270/MiAwICJEkJCQmOtotvxpNdERERSk9PV2JiosqXL+/0CgwMlCSFhYVp9erVTuutWrXK8rYAAAAAwIrbbmby559/1okTJ9SjR49Mz3h84IEHNGHCBI0ePVotWrRQuXLl1LlzZ6WlpWnOnDl68cUXValSJUVFRenJJ5/Uxx9/LDc3N/Xr189pNtPT01N33XWX3njjDZUpU0aJiYl69dVXLddasWJFRUdHq2vXrhozZowiIiJ05MgRxcXFqVq1amrTpo369u2rBg0a6O2331a7du00f/78q14vCQAAAADX67abmZwwYYIiIyMzBUnpvzC5bt06FS5cWNOmTdOsWbNUo0YNNW/eXGvWrHH0i42NVVBQkJo0aaL7779fTzzxhIoVK+Y01hdffKG0tDTVqlVL/fr1y9YdVrMSGxurrl27asCAAapUqZLat2+vtWvXqnTp0pKku+66S59//rnGjRun6tWra8GCBdcUXAEAAADACpu5+MI+XLPQ0FD169dP/fr1y+tSrklKSor8/Pz0+6AwFfRwvfoKFpQesjVHxwMAAACQey5kg+TkZPn6+l623203MwkAAAAAuH6ESQAAAACAZbfdDXhyy759+/K6BAAAAAC4YZiZBAAAAABYRpgEAAAAAFhGmAQAAAAAWEaYBAAAAABYRpgEAAAAAFhGmAQAAAAAWEaYBAAAAABYRpgEAAAAAFhGmAQAAAAAWEaYBAAAAABY5pbXBSB/CX5plXx9ffO6DAAAAAD5HDOTAAAAAADLCJMAAAAAAMsIkwAAAAAAywiTAAAAAADLCJMAAAAAAMsIkwAAAAAAywiTAAAAAADLCJMAAAAAAMsIkwAAAAAAywiTAAAAAADL3PK6AOQvLT9pKTdP58NieZ/leVQNAAAAgPyKmUkAAAAAgGWESQAAAACAZYRJAAAAAIBlhEkAAAAAgGWESQAAAACAZYRJAAAAAIBlhEkAAAAAgGWESQAAAACAZYRJAAAAAIBlhEkAAAAAgGWESQAAAACAZYRJAAAAAIBlhEkAAAAAgGWESQAAAACAZbd8mLTZbJo5c2ae1tC0aVP169cvT2sAAAAAgJx004bJmJgY2Ww22Ww2FShQQMWLF1fLli31xRdfKCMjw9EvISFBrVu3viE1LV68WDabTUlJSU7tM2bM0GuvveZ4HxoaqrFjx96QmgAAAAAgN9y0YVKSoqKilJCQoH379mnu3Llq1qyZnn32Wd17771KS0uTJAUGBsput+dpnYULF1bBggVzfNxz587l+JgAAAAAkB03dZi02+0KDAxUyZIlVbNmTb388sv68ccfNXfuXE2cOFGS82mu9evX14svvug0xpEjR1SgQAEtXbpUknT27FkNHDhQJUuWlLe3t+rWravFixc7+u/fv19t27ZVoUKF5O3trSpVqmjOnDnat2+fmjVrJkkqVKiQbDabYmJiJDmf5tq0aVPt379fzz33nGNmVZKGDRumGjVqONU2duxYhYaGOt7HxMSoffv2ev311xUUFKRKlSpJkg4cOKCOHTvK399fhQsXVrt27bRv377r+3ABAAAA4Apu6jCZlebNm6t69eqaMWNGpmXR0dGaMmWKjDGOtu+++05BQUFq1KiRJKl3795auXKlpkyZoi1btuihhx5SVFSUdu7cKUnq1auXzp49q6VLl2rr1q1688035ePjo+DgYH3//feSpO3btyshIUHjxo3LVMOMGTNUqlQpjRgxQgkJCUpISLC0f3Fxcdq+fbsWLlyon3/+WefPn1erVq1UsGBBLVu2TMuXL5ePj4+ioqKuOHN59uxZpaSkOL0AAAAAILvc8rqA3FC5cmVt2bIlU3vHjh3Vr18//fbbb47wOHnyZHXp0kU2m03x8fGKjY1VfHy8goKCJEkDBw7UvHnzFBsbq5EjRyo+Pl4PPPCAwsPDJUlly5Z1jF+4cGFJUrFixeTv759lbYULF5arq6sKFiyowMBAy/vm7e2t8ePHy93dXZL0zTffKCMjQ+PHj3fMcsbGxsrf31+LFy/W3XffneU4o0aN0vDhwy1vHwAAAACkW3BmUpKMMY5gdbGAgADdfffdmjRpkiRp7969WrlypaKjoyVJW7duVXp6uipWrCgfHx/Ha8mSJdq9e7ckqW/fvvrf//6nBg0aaOjQoVmG1twUHh7uCJKStHnzZu3atUsFCxZ01Fu4cGGdOXPGUXNWBg0apOTkZMfrwIEDN6J8AAAAALeIW3Jmctu2bSpTpkyWy6Kjo9W3b1+9//77mjx5ssLDwx2zjKmpqXJ1ddX69evl6urqtJ6Pj48k6fHHH1erVq00e/ZsLViwQKNGjdKYMWPUp0+f66rZxcXF6fRbSTp//nymft7e3k7vU1NTVatWLUdAvlhAQMBlt2e32/P8xkQAAAAAbl633Mzkr7/+qq1bt+qBBx7Icnm7du105swZzZs3T5MnT3bMSkpSRESE0tPTlZiYqPLlyzu9Lj4lNTg4WE899ZRmzJihAQMG6PPPP5ckx4xhenr6FWt0d3fP1CcgIECHDx92CpSbNm266v7WrFlTO3fuVLFixTLV7Ofnd9X1AQAAAOBa3NRh8uzZszp8+LAOHjyoDRs2aOTIkWrXrp3uvfdede3aNct1vL291b59ew0ePFjbtm1Tly5dHMsqVqyo6Ohode3aVTNmzNDevXu1Zs0ajRo1SrNnz5Yk9evXT/Pnz9fevXu1YcMGLVq0SGFhYZKkkJAQ2Ww2/fzzzzpy5IhSU1OzrCE0NFRLly7VwYMHdfToUUn/3eX1yJEjeuutt7R79259+OGHmjt37lU/g+joaBUtWlTt2rXTsmXLtHfvXi1evFh9+/bV33//benzBAAAAIDsuqnD5Lx581SiRAmFhoYqKipKixYt0nvvvacff/wx02mqF4uOjtbmzZvVqFEjlS5d2mlZbGysunbtqgEDBqhSpUpq37691q5d6+iXnp6uXr16KSwsTFFRUapYsaI++ugjSVLJkiU1fPhwvfTSSypevLh69+6d5fZHjBihffv2qVy5co5TUcPCwvTRRx/pww8/VPXq1bVmzRoNHDjwqp+Bl5eXli5dqtKlS+v+++9XWFiYevTooTNnzsjX1zdbnyMAAAAAWGUzl16oh9tSSkqK/Pz8VOfNOnLzdL6Udnmf5XlUFQAAAIAb7UI2SE5OvuIE1U09MwkAAAAAyBuESQAAAACAZYRJAAAAAIBlhEkAAAAAgGWESQAAAACAZYRJAAAAAIBlhEkAAAAAgGWESQAAAACAZYRJAAAAAIBlhEkAAAAAgGWESQAAAACAZYRJAAAAAIBlhEkAAAAAgGWESQAAAACAZW55XQDyl4VPLZSvr29elwEAAAAgn2NmEgAAAABgGWESAAAAAGAZYRIAAAAAYBlhEgAAAABgGWESAAAAAGAZYRIAAAAAYBlhEgAAAABgGWESAAAAAGAZYRIAAAAAYJlbXheA/OW3qNbydvu/w6LJ0iV5WA0AAACA/IqZSQAAAACAZYRJAAAAAIBlhEkAAAAAgGWESQAAAACAZYRJAAAAAIBlhEkAAAAAgGWESQAAAACAZYRJAAAAAIBlhEkAAAAAgGWESQAAAACAZYRJAAAAAIBlhEkAAAAAgGWESQAAAACAZYRJAAAAAIBlhEkAAAAAgGWEyXwkJiZG7du3d/xss9lks9nk7u6u8uXLa8SIEUpLS3P0T09P17vvvqvw8HB5eHioUKFCat26tZYvX55HewAAAADgdkGYzMeioqKUkJCgnTt3asCAARo2bJhGjx4tSTLGqHPnzhoxYoSeffZZbdu2TYsXL1ZwcLCaNm2qmTNn5m3xAAAAAG5pbnldAC7PbrcrMDBQkvT000/rhx9+0KxZszRo0CBNnTpV06dP16xZs9S2bVvHOp999pmOHTumxx9/XC1btpS3t3delQ8AAADgFsbM5E3E09NT586dkyRNnjxZFStWdAqSFwwYMEDHjh3TwoULLzvW2bNnlZKS4vQCAAAAgOwiTN4EjDH65ZdfNH/+fDVv3lyStGPHDoWFhWXZ/0L7jh07LjvmqFGj5Ofn53gFBwfnfOEAAAAAblnXFCa//vprNWjQQEFBQdq/f78kaezYsfrxxx9ztLjb3c8//ywfHx95eHiodevW6tSpk4YNG+ZYboy55rEHDRqk5ORkx+vAgQM5UDEAAACA24XlMPnxxx+rf//+uueee5SUlKT09HRJkr+/v8aOHZvT9d3WmjVrpk2bNmnnzp36999/9eWXXzqugaxYsaK2bduW5XoX2itWrHjZse12u3x9fZ1eAAAAAJBdlsPk+++/r88//1yvvPKKXF1dHe21a9fW1q1bc7S42523t7fKly+v0qVLy83N+V5JnTt31s6dO/XTTz9lWm/MmDEqUqSIWrZseaNKBQAAAHCbsRwm9+7dq4iIiEztdrtdp06dypGicHWdO3dWhw4d1K1bN02YMEH79u3Tli1b9OSTT2rWrFkaP348d3IFAAAAkGssh8kyZcpo06ZNmdrnzZt32RvCIOfZbDZNnTpVL7/8st59911VqlRJjRo10v79+7V48WK1b98+r0sEAAAAcAuz/JzJ/v37q1evXjpz5oyMMVqzZo2+/fZbjRo1SuPHj8+NGm8bEydOzPLny3Fzc9PAgQM1cODA3CsKAAAAALJgOUw+/vjj8vT01KuvvqrTp0/r4YcfVlBQkMaNG6fOnTvnRo0AAAAAgHzGcpiUpOjoaEVHR+v06dNKTU1VsWLFcrouAAAAAEA+dk1h8gIvLy95eXnlVC0AAAAAgJuE5TAZEREhm82Wqd1ms8nDw0Ply5dXTEyMmjVrliMFAgAAAADyH8t3c42KitKePXvk7e2tZs2aqVmzZvLx8dHu3bt15513KiEhQZGRkfrxxx9zo14AAAAAQD5geWby6NGjGjBggAYPHuzU/r///U/79+/XggULNHToUL322mtq165djhUKAAAAAMg/LM9MTp06VV26dMnU3rlzZ02dOlWS1KVLF23fvv36qwMAAAAA5EuWw6SHh4dWrFiRqX3FihXy8PCQJGVkZDh+BgAAAADceiyf5tqnTx899dRTWr9+ve68805J0tq1azV+/Hi9/PLLkqT58+erRo0aOVooAAAAACD/sBljjNWVJk2apA8++MBxKmulSpXUp08fPfzww5Kkf//913F3V9wcUlJS5Ofnp9n16svb7f/+j6HJ0iV5WBUAAACAG+1CNkhOTpavr+9l+13Tcyajo6MVHR192eWenp7XMiwAAAAA4CZh+ZpJAAAAAAAsz0ymp6fr3Xff1dSpUxUfH69z5845LT9+/HiOFQcAAAAAyJ8sz0wOHz5c77zzjjp16qTk5GT1799f999/v1xcXDRs2LBcKBEAAAAAkN9YvgFPuXLl9N5776lNmzYqWLCgNm3a5GhbtWqVJk+enFu1Ihdl9yJbAAAAALe27GYDyzOThw8fVnh4uCTJx8dHycnJkqR7771Xs2fPvsZyAQAAAAA3E8thslSpUkpISJD03yzlggULJP33rEm73Z6z1QEAAAAA8iXLYbJDhw6Ki4uTJPXp00eDBw9WhQoV1LVrVz322GM5XiAAAAAAIP+xfM3kpVatWqUVK1aoQoUKatu2bU7VhRuMayYBAAAASNnPBpYfDbJ06VLVr19fbm7/rXrXXXfprrvuUlpampYuXarGjRtfe9UAAAAAgJuC5dNcmzVrluWzJJOTk9WsWbMcKQoAAAAAkL9ZDpPGGNlstkztx44dk7e3d44UBQAAAADI37J9muv9998vSbLZbIqJiXG6c2t6erq2bNmi+vXr53yFAAAAAIB8J9th0s/PT9J/M5MFCxaUp6enY5m7u7vuuusu9ezZM+crBAAAAADkO9kOk7GxsZKk0NBQDRw4kFNaAQAAAOA2dt2PBlmyZIlOnTqlevXqqVChQjlVF24wHg0CAAAAQMqFR4O8+eabSk1N1WuvvSbpv9NdW7durQULFkiSihUrpri4OFWpUuU6SwcAAAAA5HfZvpvrd999p6pVqzreT58+XUuXLtWyZct09OhR1a5dW8OHD8+VIgEAAAAA+Uu2w+TevXtVrVo1x/s5c+bowQcfVIMGDVS4cGG9+uqrWrlyZa4UCQAAAADIX7IdJtPS0pweB7Jy5UqnR4EEBQXp6NGjOVsdAAAAACBfynaYLFeunJYuXSpJio+P144dO9S4cWPH8r///ltFihTJ+QoBAAAAAPlOtm/A06tXL/Xu3VvLli3TqlWrVK9ePd1xxx2O5b/++qsiIiJypUgAAAAAQP6S7TDZs2dPubq66qefflLjxo01dOhQp+WHDh3SY489luMFAgAAAADyn+t+ziRuDTxnEgAAAICU/WyQ7WsmAQAAAAC4gDAJAAAAALCMMAkAAAAAsIwwCQAAAACwjDAJAAAAALAsW48Guf/++7M94IwZM665GAAAAADAzSFbYdLPzy+36wAAAAAA3ESyFSZjY2Nzu45b0sqVK9WwYUNFRUVp9uzZN2Sbw4YN08yZM7Vp06Ybsj0AAAAAtyeumcxFEyZMUJ8+fbR06VIdOnQor8sBAAAAgBxzTWFy+vTp6tixo+666y7VrFnT6YX/pKam6rvvvtPTTz+tNm3aaOLEiY5lJ06cUHR0tAICAuTp6akKFSo4Zn/PnTun3r17q0SJEvLw8FBISIhGjRrlWDcpKUmPP/64AgIC5Ovrq+bNm2vz5s2SpIkTJ2r48OHavHmzbDabbDab03YBAAAAIKdYDpPvvfeeunfvruLFi2vjxo2qU6eOihQpoj179qh169a5UeNNaerUqapcubIqVaqkRx55RF988YWMMZKkwYMH688//9TcuXO1bds2ffzxxypatKik/z7fWbNmaerUqdq+fbsmTZqk0NBQx7gPPfSQEhMTNXfuXK1fv141a9ZUixYtdPz4cXXq1EkDBgxQlSpVlJCQoISEBHXq1CnL+s6ePauUlBSnFwAAAABkV7aumbzYRx99pM8++0xdunTRxIkT9cILL6hs2bIaMmSIjh8/nhs13pQmTJigRx55RJIUFRWl5ORkLVmyRE2bNlV8fLwiIiJUu3ZtSXIKi/Hx8apQoYIaNmwom82mkJAQx7LffvtNa9asUWJioux2uyTp7bff1syZMzV9+nQ98cQT8vHxkZubmwIDA69Y36hRozR8+PAc3msAAAAAtwvLM5Px8fGqX7++JMnT01MnT56UJD366KP69ttvc7a6m9T27du1Zs0adenSRZLk5uamTp06acKECZKkp59+WlOmTFGNGjX0wgsvaMWKFY51Y2JitGnTJlWqVEl9+/bVggULHMs2b96s1NRUFSlSRD4+Po7X3r17tXv3bks1Dho0SMnJyY7XgQMHcmDPAQAAANwuLM9MBgYG6vjx4woJCVHp0qW1atUqVa9eXXv37nWcxnm7mzBhgtLS0hQUFORoM8bIbrfrgw8+UOvWrbV//37NmTNHCxcuVIsWLdSrVy+9/fbbqlmzpvbu3au5c+fql19+UceOHRUZGanp06crNTVVJUqU0OLFizNt09/f31KNdrvdMbsJAAAAAFZZDpPNmzfXrFmzFBERoe7du+u5557T9OnTtW7dOt1///25UeNNJS0tTV999ZXGjBmju+++22lZ+/bt9e233+qpp55SQECAunXrpm7duqlRo0Z6/vnn9fbbb0uSfH191alTJ3Xq1EkPPvigoqKidPz4cdWsWVOHDx+Wm5ub06mxF3N3d1d6enpu7yYAAACA25zlMPnZZ58pIyNDktSrVy8VKVJEK1as0H333acnn3wyxwu82fz88886ceKEevToIT8/P6dlDzzwgCZMmKBDhw6pVq1aqlKlis6ePauff/5ZYWFhkqR33nlHJUqUUEREhFxcXDRt2jQFBgbK399fkZGRqlevntq3b6+33npLFStW1KFDhzR79mx16NBBtWvXVmhoqPbu3atNmzapVKlSKliwIDOQAAAAAHKc5WsmXVxc5Ob2fxm0c+fOeu+999SnTx+5u7vnaHE3owkTJigyMjJTkJT+C5Pr1q2Tm5ubBg0apGrVqqlx48ZydXXVlClTJEkFCxbUW2+9pdq1a+vOO+/Uvn37NGfOHLm4uMhms2nOnDlq3LixunfvrooVK6pz587av3+/ihcv7thGVFSUmjVrpoCAAK5jBQAAAJArbCYbFzpu2bJFVatWlYuLi7Zs2XLFvtWqVcux4nDjpKSkyM/PT8nJyfL19c3rcgAAAADkkexmg2yd5lqjRg0dPnxYxYoVU40aNWSz2bK82Y7NZuN6PQAAAAC4DWQrTO7du1cBAQGOnwEAAAAAt7dshcmQkJAsfwYAAAAA3J4s34Bn1KhR+uKLLzK1f/HFF3rzzTdzpCgAAAAAQP5mOUx++umnqly5cqb2KlWq6JNPPsmRogAAAAAA+ZvlMHn48GGVKFEiU3tAQIASEhJypCgAAAAAQP5mOUwGBwdr+fLlmdqXL1+uoKCgHCkKAAAAAJC/ZesGPBfr2bOn+vXrp/Pnz6t58+aSpLi4OL3wwgsaMGBAjhcIAAAAAMh/LIfJ559/XseOHdMzzzyjc+fOSZI8PDz04osvatCgQTleIAAAAAAg/7EZY8y1rJiamqpt27bJ09NTFSpUkN1uz+nacAOlpKTIz89PycnJ8vX1zetyAAAAAOSR7GYDyzOTF/j4+OjOO++81tUBAAAAADcxy2Hy1KlTeuONNxQXF6fExERlZGQ4Ld+zZ0+OFQcAAAAAyJ8sh8nHH39cS5Ys0aOPPqoSJUrIZrPlRl0AAAAAgHzMcpicO3euZs+erQYNGuRGPQAAAACAm4Dl50wWKlRIhQsXzo1aAAAAAAA3Ccth8rXXXtOQIUN0+vTp3KgHAAAAAHATsHya65gxY7R7924VL15coaGhKlCggNPyDRs25FhxAAAAAID8yXKYbN++fS6UAQAAAAC4mdiMMSavi0Dey+6DSQEAAADc2rKbDSxfMwkAAAAAgOXTXNPT0/Xuu+9q6tSpio+P17lz55yWHz9+PMeKAwAAAADkT5ZnJocPH6533nlHnTp1UnJysvr376/7779fLi4uGjZsWC6UCAAAAADIbyyHyUmTJunzzz/XgAED5Obmpi5dumj8+PEaMmSIVq1alRs1AgAAAADyGcth8vDhwwoPD5ck+fj4KDk5WZJ07733avbs2TlbHQAAAAAgX7IcJkuVKqWEhARJUrly5bRgwQJJ0tq1a2W323O2OgAAAABAvmQ5THbo0EFxcXGSpD59+mjw4MGqUKGCunbtqsceeyzHCwQAAAAA5D/X/ZzJlStXauXKlapQoYLatm2bU3XhBuM5kwAAAACk7GcDy48GuVS9evVUr1696x0GAAAAAHATuaYwuXPnTi1atEiJiYnKyMhwWjZkyJAcKQwAAAAAkH9ZDpOff/65nn76aRUtWlSBgYGy2WyOZTabjTAJAAAAALcBy2Hyf//7n15//XW9+OKLuVEPAAAAAOAmYPluridOnNBDDz2UG7UAAAAAAG4SlsPkQw895Hi2JAAAAADg9mT5NNfy5ctr8ODBWrVqlcLDw1WgQAGn5X379s2x4gAAAAAA+ZPl50yWKVPm8oPZbNqzZ891F4Ubj+dMAgAAAJBy8TmTe/fuva7CAAAAAAA3P8vXTAIAAAAAkK2Zyf79++u1116Tt7e3+vfvf8W+77zzTo4UBgAAAADIv7IVJjdu3Kjz5887fr4cm82WM1UBAAAAAPK1bIXJRYsWac+ePfLz89OiRYtyuyZkITQ0VP369VO/fv3yuhQAAAAAyP41kxUqVNCRI0cc7zt16qR//vknV4q6Fdlstiu+hg0bltclAgAAAEC2ZTtMXvoEkTlz5ujUqVM5XtCtKiEhwfEaO3asfH19ndoGDhyY1yUCAAAAQLZxN9cbJDAw0PHy8/OTzWZzvD916pSio6NVvHhx+fj46M4779Qvv/xyxfHGjx8vf39/xcXFSZJ+//13tW7dWj4+PipevLgeffRRHT169EbsGgAAAIDbULbD5IXTMS9tw/VLTU3VPffco7i4OG3cuFFRUVFq27at4uPjs+z/1ltv6aWXXtKCBQvUokULJSUlqXnz5oqIiNC6des0b948/fPPP+rYseNlt3n27FmlpKQ4vQAAAAAgu7J1Ax7pv9NcY2JiZLfbJUlnzpzRU089JW9vb6d+M2bMyNkKbwPVq1dX9erVHe9fe+01/fDDD5o1a5Z69+7t1PfFF1/U119/rSVLlqhKlSqSpA8++EAREREaOXKko98XX3yh4OBg7dixQxUrVsy0zVGjRmn48OG5tEcAAAAAbnXZDpPdunVzev/II4/keDG3q9TUVA0bNkyzZ89WQkKC0tLS9O+//2aamRwzZoxOnTqldevWqWzZso72zZs3a9GiRfLx8ck09u7du7MMk4MGDXJ6ZmhKSoqCg4NzcK8AAAAA3MqyHSZjY2Nzs47b2sCBA7Vw4UK9/fbbKl++vDw9PfXggw/q3LlzTv0aNWqk2bNna+rUqXrppZcc7ampqWrbtq3efPPNTGOXKFEiy23a7XbHLDMAAAAAWJXtMIncs3z5csXExKhDhw6S/guH+/bty9SvTp066t27t6KiouTm5ua4A2zNmjX1/fffKzQ0VG5u/EoBAAAA5D7u5poPVKhQQTNmzNCmTZu0efNmPfzww8rIyMiyb/369TVnzhwNHz5cY8eOlST16tVLx48fV5cuXbR27Vrt3r1b8+fPV/fu3ZWenn4D9wQAAADA7YIwmQ+88847KlSokOrXr6+2bduqVatWqlmz5mX7N2zYULNnz9arr76q999/X0FBQVq+fLnS09N19913Kzw8XP369ZO/v79cXPgVAwAAAMh5NmOMyesikPdSUlLk5+en5ORk+fr65nU5AAAAAPJIdrMB01YAAAAAAMsIkwAAAAAAywiTAAAAAADLCJMAAAAAAMsIkwAAAAAAywiTAAAAAADLCJMAAAAAAMsIkwAAAAAAywiTAAAAAADLCJMAAAAAAMsIkwAAAAAAywiTAAAAAADLCJMAAAAAAMsIkwAAAAAAywiTAAAAAADLCJMAAAAAAMsIkwAAAAAAywiTAAAAAADLCJMAAAAAAMsIkwAAAAAAywiTAAAAAADLCJMAAAAAAMsIkwAAAAAAywiTAAAAAADLCJMAAAAAAMsIkwAAAAAAywiTAAAAAADLCJMAAAAAAMsIkwAAAAAAywiTAAAAAADLCJMAAAAAAMsIkwAAAAAAywiTAAAAAADLCJMAAAAAAMsIkwAAAAAAywiTAAAAAADLCJMAAAAAAMsIkwAAAAAAywiTAAAAAADLCJMAAAAAAMvyNEzGxMTIZrPpqaeeyrSsV69estlsiomJufGF3cRiYmLUvn37vC4DAAAAwC0uz2cmg4ODNWXKFP3777+OtjNnzmjy5MkqXbp0HlYGAAAAALicPA+TNWvWVHBwsGbMmOFomzFjhkqXLq2IiAhH27x589SwYUP5+/urSJEiuvfee7V7927H8n379slms2nGjBlq1qyZvLy8VL16da1cudLR59ixY+rSpYtKliwpLy8vhYeH69tvv3Wq5+TJk4qOjpa3t7dKlCihd999V02bNlW/fv0cfc6ePauBAweqZMmS8vb2Vt26dbV48WLH8okTJ8rf318///yzKlWqJC8vLz344IM6ffq0vvzyS4WGhqpQoULq27ev0tPTLY87f/58hYWFycfHR1FRUUpISJAkDRs2TF9++aV+/PFH2Ww22Ww2p/UBAAAAIKfkeZiUpMcee0yxsbGO91988YW6d+/u1OfUqVPq37+/1q1bp7i4OLm4uKhDhw7KyMhw6vfKK69o4MCB2rRpkypWrKguXbooLS1N0n8znrVq1dLs2bP1+++/64knntCjjz6qNWvWONbv37+/li9frlmzZmnhwoVatmyZNmzY4LSN3r17a+XKlZoyZYq2bNmihx56SFFRUdq5c6ejz+nTp/Xee+9pypQpmjdvnhYvXqwOHTpozpw5mjNnjr7++mt9+umnmj59uuVx3377bX399ddaunSp4uPjNXDgQEnSwIED1bFjR0fATEhIUP369bP8zM+ePauUlBSnFwAAAABkm8lD3bp1M+3atTOJiYnGbrebffv2mX379hkPDw9z5MgR065dO9OtW7cs1z1y5IiRZLZu3WqMMWbv3r1Gkhk/fryjzx9//GEkmW3btl22hjZt2pgBAwYYY4xJSUkxBQoUMNOmTXMsT0pKMl5eXubZZ581xhizf/9+4+rqag4ePOg0TosWLcygQYOMMcbExsYaSWbXrl2O5U8++aTx8vIyJ0+edLS1atXKPPnkk9c17ocffmiKFy+e6TO9mqFDhxpJmV7JyclXXRcAAADArSs5OTlb2cAtjzKsk4CAALVp00YTJ06UMUZt2rRR0aJFnfrs3LlTQ4YM0erVq3X06FHHjGR8fLyqVq3q6FetWjXHzyVKlJAkJSYmqnLlykpPT9fIkSM1depUHTx4UOfOndPZs2fl5eUlSdqzZ4/Onz+vOnXqOMbw8/NTpUqVHO+3bt2q9PR0VaxY0am+s2fPqkiRIo73Xl5eKleunON98eLFFRoaKh8fH6e2xMTE6xq3RIkSjjGsGDRokPr37+94n5KSouDgYMvjAAAAALg95YswKf13qmvv3r0lSR9++GGm5W3btlVISIg+//xzBQUFKSMjQ1WrVtW5c+ec+hUoUMDxs81mkyRH8Bw9erTGjRunsWPHKjw8XN7e3urXr1+mMa4kNTVVrq6uWr9+vVxdXZ2WXRwUL67jQi1ZtV2o7XrGNcZku/4L7Ha77Ha75fUAAAAAQMpHYTIqKkrnzp2TzWZTq1atnJYdO3ZM27dv1+eff65GjRpJkn777TfL21i+fLnatWunRx55RNJ/IXPHjh264447JElly5ZVgQIFtHbtWsedZJOTk7Vjxw41btxYkhQREaH09HQlJiY6askJOTWuu7u70019AAAAACA35Jsw6erqqm3btjl+vlihQoVUpEgRffbZZypRooTi4+P10ksvWd5GhQoVNH36dK1YsUKFChXSO++8o3/++ccRJgsWLKhu3brp+eefV+HChVWsWDENHTpULi4ujlnOihUrKjo6Wl27dtWYMWMUERGhI0eOKC4uTtWqVVObNm2uaf9zatzQ0FDNnz9f27dvV5EiReTn55dpNhMAAAAArle+uJvrBb6+vvL19c3U7uLioilTpmj9+vWqWrWqnnvuOY0ePdry+K+++qpq1qypVq1aqWnTpgoMDFT79u2d+rzzzjuqV6+e7r33XkVGRqpBgwYKCwuTh4eHo09sbKy6du2qAQMGqFKlSmrfvr3TbOa1yolxe/bsqUqVKql27doKCAjQ8uXLr6smAAAAAMiKzVzLBXe3kVOnTqlkyZIaM2aMevTokdfl5JqUlBT5+fkpOTk5y0APAAAA4PaQ3WyQb05zzS82btyov/76S3Xq1FFycrJGjBghSWrXrl0eVwYAAAAA+QdhMgtvv/22tm/fLnd3d9WqVUvLli3L9KgSAAAAALidESYvERERofXr1+d1GQAAAACQr+WrG/AAAAAAAG4OhEkAAAAAgGWESQAAAACAZYRJAAAAAIBlhEkAAAAAgGWESQAAAACAZYRJAAAAAIBlhEkAAAAAgGWESQAAAACAZYRJAAAAAIBlhEkAAAAAgGWESQAAAACAZYRJAAAAAIBlhEkAAAAAgGWESQAAAACAZYRJAAAAAIBlhEkAAAAAgGWESQAAAACAZYRJAAAAAIBlhEkAAAAAgGWESQAAAACAZYRJAAAAAIBlhEkAAAAAgGWESQAAAACAZYRJAAAAAIBlhEkAAAAAgGWESQAAAACAZYRJAAAAAIBlhEkAAAAAgGWESQAAAACAZYRJAAAAAIBlhEkAAAAAgGWESQAAAACAZYRJAAAAAIBlhEkAAAAAgGWESQAAAACAZYRJAAAAAIBlhEkAAAAAgGWEyYvExMTIZrPJZrOpQIECKlOmjF544QWdOXMmr0sDAAAAgHzFLa8LyG+ioqIUGxur8+fPa/369erWrZtsNpvefPPNPKnn3Llzcnd3z5NtAwAAAMDlMDN5CbvdrsDAQAUHB6t9+/aKjIzUwoULJUkZGRkaNWqUypQpI09PT1WvXl3Tp093LCtVqpQ+/vhjp/E2btwoFxcX7d+/X5KUlJSkxx9/XAEBAfL19VXz5s21efNmR/9hw4apRo0aGj9+vMqUKSMPDw9J0vTp0xUeHi5PT08VKVJEkZGROnXqlGO98ePHKywsTB4eHqpcubI++uijXP2cAAAAANzemJm8gt9//10rVqxQSEiIJGnUqFH65ptv9Mknn6hChQpaunSpHnnkEQUEBKhJkybq0qWLJk+erKefftoxxqRJk9SgQQPHGA899JA8PT01d+5c+fn56dNPP1WLFi20Y8cOFS5cWJK0a9cuff/995oxY4ZcXV2VkJCgLl266K233lKHDh108uRJLVu2TMYYxzaGDBmiDz74QBEREdq4caN69uwpb29vdevWLct9O3v2rM6ePet4n5KSkiufIQAAAIBblIFDt27djKurq/H29jZ2u91IMi4uLmb69OnmzJkzxsvLy6xYscJpnR49epguXboYY4zZuHGjsdlsZv/+/cYYY9LT003JkiXNxx9/bIwxZtmyZcbX19ecOXPGaYxy5cqZTz/91BhjzNChQ02BAgVMYmKiY/n69euNJLNv374s6y5XrpyZPHmyU9trr71m6tWrd9l9HTp0qJGU6ZWcnJydjwoAAADALSo5OTlb2YCZyUs0a9ZMH3/8sU6dOqV3331Xbm5ueuCBB/THH3/o9OnTatmypVP/c+fOKSIiQpJUo0YNhYWFafLkyXrppZe0ZMkSJSYm6qGHHpIkbd68WampqSpSpIjTGP/++692797teB8SEqKAgADH++rVq6tFixYKDw9Xq1atdPfdd+vBBx9UoUKFdOrUKe3evVs9evRQz549HeukpaXJz8/vsvs5aNAg9e/f3/E+JSVFwcHB1/CJAQAAALgdESYv4e3trfLly0uSvvjiC1WvXl0TJkxQ1apVJUmzZ89WyZIlndax2+2On6Ojox1hcvLkyYqKinKEx9TUVJUoUUKLFy/OtF1/f3+nGi7m6uqqhQsXasWKFVqwYIHef/99vfLKK1q9erW8vLwkSZ9//rnq1q2bab3LsdvtTnUDAAAAgBWEyStwcXHRyy+/rP79+2vHjh2y2+2Kj49XkyZNLrvOww8/rFdffVXr16/X9OnT9cknnziW1axZU4cPH5abm5tCQ0Mt1WKz2dSgQQM1aNBAQ4YMUUhIiH744Qf1799fQUFB2rNnj6Kjo691VwEAAADAEsLkVTz00EN6/vnn9emnn2rgwIF67rnnlJGRoYYNGyo5OVnLly+Xr6+v40Y3oaGhql+/vnr06KH09HTdd999jrEiIyNVr149tW/fXm+99ZYqVqyoQ4cOafbs2erQoYNq166dZQ2rV69WXFyc7r77bhUrVkyrV6/WkSNHFBYWJkkaPny4+vbtKz8/P0VFRens2bNat26dTpw44XQqKwAAAADkFMLkVbi5ual379566623tHfvXgUEBGjUqFHas2eP/P39VbNmTb388stO60RHR+uZZ55R165d5enp6Wi32WyaM2eOXnnlFXXv3l1HjhxRYGCgGjdurOLFi1+2Bl9fXy1dulRjx45VSkqKQkJCNGbMGLVu3VqS9Pjjj8vLy0ujR4/W888/L29vb4WHh6tfv3658pkAAAAAgM2Y//98CdzWUlJS5Ofnp+TkZPn6+uZ1OQAAAADySHazgcsNrAkAAAAAcIsgTAIAAAAALCNMAgAAAAAsI0wCAAAAACwjTAIAAAAALCNMAgAAAAAsI0wCAAAAACwjTAIAAAAALCNMAgAAAAAsI0wCAAAAACwjTAIAAAAALCNMAgAAAAAsI0wCAAAAACwjTAIAAAAALCNMAgAAAAAsI0wCAAAAACwjTAIAAAAALCNMAgAAAAAsI0wCAAAAACwjTAIAAAAALCNMAgAAAAAsI0wCAAAAACwjTAIAAAAALCNMAgAAAAAsI0wCAAAAACwjTAIAAAAALCNMAgAAAAAsI0wCAAAAACwjTAIAAAAALCNMAgAAAAAsI0wCAAAAACwjTAIAAAAALCNMAgAAAAAsI0wCAAAAACwjTAIAAAAALCNMAgAAAAAsI0wCAAAAACwjTAIAAAAALCNMAgAAAAAsI0wCAAAAACy7ZcPk4cOH1adPH5UtW1Z2u13BwcFq27at4uLi8rq062Kz2TRz5sy8LgMAAADAbc4trwvIDfv27VODBg3k7++v0aNHKzw8XOfPn9f8+fPVq1cv/fXXX5bHPHfunNzd3XOhWik9PV02m00uLrdstgcAAABwi7kl08szzzwjm82mNWvW6IEHHlDFihVVpUoV9e/fX6tWrZIkxcfHq127dvLx8ZGvr686duyof/75xzHGsGHDVKNGDY0fP15lypSRh4eHJKlp06bq3bu3evfuLT8/PxUtWlSDBw+WMcax7okTJ9S1a1cVKlRIXl5eat26tXbu3OlYPnHiRPn7+2vWrFm64447ZLfbFR8fr7Vr16ply5YqWrSo/Pz81KRJE23YsMGxXmhoqCSpQ4cOstlsjveS9OOPP6pmzZry8PBQ2bJlNXz4cKWlpeXGxwsAAAAAt16YPH78uObNm6devXrJ29s703J/f39lZGSoXbt2On78uJYsWaKFCxdqz5496tSpk1PfXbt26fvvv9eMGTO0adMmR/uXX34pNzc3rVmzRuPGjdM777yj8ePHO5bHxMRo3bp1mjVrllauXCljjO655x6dP3/e0ef06dN68803NX78eP3xxx8qVqyYTp48qW7duum3337TqlWrVKFCBd1zzz06efKkJGnt2rWSpNjYWCUkJDjeL1u2TF27dtWzzz6rP//8U59++qkmTpyo119//bKf09mzZ5WSkuL0AgAAAIBsM7eY1atXG0lmxowZl+2zYMEC4+rqauLj4x1tf/zxh5Fk1qxZY4wxZujQoaZAgQImMTHRad0mTZqYsLAwk5GR4Wh78cUXTVhYmDHGmB07dhhJZvny5Y7lR48eNZ6enmbq1KnGGGNiY2ONJLNp06Yr7kt6eropWLCg+emnnxxtkswPP/zg1K9FixZm5MiRTm1ff/21KVGixGXHHjp0qJGU6ZWcnHzFmgAAAADc2pKTk7OVDW65mUlz0emml7Nt2zYFBwcrODjY0XbHHXfI399f27Ztc7SFhIQoICAg0/p33XWXbDab4329evW0c+dOpaena9u2bXJzc1PdunUdy4sUKaJKlSo5je3u7q5q1ao5jfvPP/+oZ8+eqlChgvz8/OTr66vU1FTFx8dfcX82b96sESNGyMfHx/Hq2bOnEhISdPr06SzXGTRokJKTkx2vAwcOXHEbAAAAAHCxW+4GPBUqVJDNZrumm+xcKqvTZHOKp6enUyCVpG7duunYsWMaN26cQkJCZLfbVa9ePZ07d+6KY6Wmpmr48OG6//77My27cK3npex2u+x2+7XvAAAAAIDb2i03M1m4cGG1atVKH374oU6dOpVpeVJSksLCwnTgwAGn2bg///xTSUlJuuOOO666jdWrVzu9v3B9o6urq8LCwpSWlubU59ixY9q+fftVx16+fLn69u2re+65R1WqVJHdbtfRo0ed+hQoUEDp6elObTVr1tT27dtVvnz5TC/uEAsAAAAgN9ySSePDDz9Uenq66tSpo++//147d+7Utm3b9N5776levXqKjIxUeHi4oqOjtWHDBq1Zs0Zdu3ZVkyZNVLt27auOHx8fr/79+2v79u369ttv9f777+vZZ5+V9N/MaLt27dSzZ0/99ttv2rx5sx555BGVLFlS7dq1u+K4FSpU0Ndff61t27Zp9erVio6Olqenp1Of0NBQxcXF6fDhwzpx4oQkaciQIfrqq680fPhw/fHHH9q2bZumTJmiV1999Ro/QQAAAAC4slsyTJYtW1YbNmxQs2bNNGDAAFWtWlUtW7ZUXFycPv74Y9lsNv34448qVKiQGjdurMjISJUtW1bfffddtsbv2rWr/v33X9WpU0e9evXSs88+qyeeeMKxPDY2VrVq1dK9996revXqyRijOXPmqECBAlccd8KECTpx4oRq1qypRx99VH379lWxYsWc+owZM0YLFy5UcHCwIiIiJEmtWrXSzz//rAULFujOO+/UXXfdpXfffVchISEWPzkAAAAAyB6byc4da+DQtGlT1ahRQ2PHjs3rUnJUSkqK/Pz8lJycLF9f37wuBwAAAEAeyW42uCVnJgEAAAAAuYswCQAAAACw7JZ7NEhuW7x4cV6XAAAAAAB5jplJAAAAAIBlhEkAAAAAgGWESQAAAACAZYRJAAAAAIBlhEkAAAAAgGWESQAAAACAZYRJAAAAAIBlhEkAAAAAgGWESQAAAACAZYRJAAAAAIBlhEkAAAAAgGWESQAAAACAZYRJAAAAAIBlhEkAAAAAgGWESQAAAACAZW55XQDyB2OMJCklJSWPKwEAAACQly5kggsZ4XIIk5AkHTt2TJIUHBycx5UAAAAAyA9OnjwpPz+/yy4nTEKSVLhwYUlSfHz8FQ8YILtSUlIUHBysAwcOyNfXN6/LwS2C4wo5jWMKuYHjCjntRh9TxhidPHlSQUFBV+xHmIQkycXlv8tn/fz8+NJDjvL19eWYQo7juEJO45hCbuC4Qk67kcdUdiaYuAEPAAAAAMAywiQAAAAAwDLCJCRJdrtdQ4cOld1uz+tScIvgmEJu4LhCTuOYQm7guEJOy6/HlM1c7X6vAAAAAABcgplJAAAAAIBlhEkAAAAAgGWESQAAAACAZYRJAAAAAIBlhEnoww8/VGhoqDw8PFS3bl2tWbMmr0tCPjVq1CjdeeedKliwoIoVK6b27dtr+/btTn3OnDmjXr16qUiRIvLx8dEDDzygf/75x6lPfHy82rRpIy8vLxUrVkzPP/+80tLSbuSuIJ964403ZLPZ1K9fP0cbxxSuxcGDB/XII4+oSJEi8vT0VHh4uNatW+dYbozRkCFDVKJECXl6eioyMlI7d+50GuP48eOKjo6Wr6+v/P391aNHD6Wmpt7oXUE+kJ6ersGDB6tMmTLy9PRUuXLl9Nprr+ni+1hyTOFqli5dqrZt2yooKEg2m00zZ850Wp5Tx9CWLVvUqFEjeXh4KDg4WG+99Vbu7ZTBbW3KlCnG3d3dfPHFF+aPP/4wPXv2NP7+/uaff/7J69KQD7Vq1crExsaa33//3WzatMncc889pnTp0iY1NdXR56mnnjLBwcEmLi7OrFu3ztx1112mfv36juVpaWmmatWqJjIy0mzcuNHMmTPHFC1a1AwaNCgvdgn5yJo1a0xoaKipVq2aefbZZx3tHFOw6vjx4yYkJMTExMSY1atXmz179pj58+ebXbt2Ofq88cYbxs/Pz8ycOdNs3rzZ3HfffaZMmTLm33//dfSJiooy1atXN6tWrTLLli0z5cuXN126dMmLXUIee/31102RIkXMzz//bPbu3WumTZtmfHx8zLhx4xx9OKZwNXPmzDGvvPKKmTFjhpFkfvjhB6flOXEMJScnm+LFi5vo6Gjz+++/m2+//dZ4enqaTz/9NFf2iTB5m6tTp47p1auX4316eroJCgoyo0aNysOqcLNITEw0ksySJUuMMcYkJSWZAgUKmGnTpjn6bNu2zUgyK1euNMb890Xq4uJiDh8+7Ojz8ccfG19fX3P27NkbuwPIN06ePGkqVKhgFi5caJo0aeIIkxxTuBYvvviiadiw4WWXZ2RkmMDAQDN69GhHW1JSkrHb7ebbb781xhjz559/Gklm7dq1jj5z5841NpvNHDx4MPeKR77Upk0b89hjjzm13X///SY6OtoYwzEF6y4Nkzl1DH300UemUKFCTn//vfjii6ZSpUq5sh+c5nobO3funNavX6/IyEhHm4uLiyIjI7Vy5co8rAw3i+TkZElS4cKFJUnr16/X+fPnnY6pypUrq3Tp0o5jauXKlQoPD1fx4sUdfVq1aqWUlBT98ccfN7B65Ce9evVSmzZtnI4diWMK12bWrFmqXbu2HnroIRUrVkwRERH6/PPPHcv37t2rw4cPOx1Xfn5+qlu3rtNx5e/vr9q1azv6REZGysXFRatXr75xO4N8oX79+oqLi9OOHTskSZs3b9Zvv/2m1q1bS+KYwvXLqWNo5cqVaty4sdzd3R19WrVqpe3bt+vEiRM5Xrdbjo+Im8bRo0eVnp7u9A8wSSpevLj++uuvPKoKN4uMjAz169dPDRo0UNWqVSVJhw8flru7u/z9/Z36Fi9eXIcPH3b0yeqYu7AMt58pU6Zow4YNWrt2baZlHFO4Fnv27NHHH3+s/v376+WXX9batWvVt29fubu7q1u3bo7jIqvj5uLjqlixYk7L3dzcVLhwYY6r29BLL72klJQUVa5cWa6urkpPT9frr7+u6OhoSeKYwnXLqWPo8OHDKlOmTKYxLiwrVKhQjtZNmARwTXr16qXff/9dv/32W16XgpvYgQMH9Oyzz2rhwoXy8PDI63Jwi8jIyFDt2rU1cuRISVJERIR+//13ffLJJ+rWrVseV4eb0dSpUzVp0iRNnjxZVapU0aZNm9SvXz8FBQVxTOG2xmmut7GiRYvK1dU1010R//nnHwUGBuZRVbgZ9O7dWz///LMWLVqkUqVKOdoDAwN17tw5JSUlOfW/+JgKDAzM8pi7sAy3l/Xr1ysxMVE1a9aUm5ub3NzctGTJEr333ntyc3NT8eLFOaZgWYkSJXTHHXc4tYWFhSk+Pl7S/x0XV/r7LzAwUImJiU7L09LSdPz4cY6r29Dzzz+vl156SZ07d1Z4eLgeffRRPffccxo1apQkjilcv5w6hm7034mEyduYu7u7atWqpbi4OEdbRkaG4uLiVK9evTysDPmVMUa9e/fWDz/8oF9//TXTaRS1atVSgQIFnI6p7du3Kz4+3nFM1atXT1u3bnX6Mly4cKF8fX0z/eMPt74WLVpo69at2rRpk+NVu3ZtRUdHO37mmIJVDRo0yPTYoh07digkJESSVKZMGQUGBjodVykpKVq9erXTcZWUlKT169c7+vz666/KyMhQ3bp1b8BeID85ffq0XFyc/9ns6uqqjIwMSRxTuH45dQzVq1dPS5cu1fnz5x19Fi5cqEqVKuX4Ka6SeDTI7W7KlCnGbrebiRMnmj///NM88cQTxt/f3+muiMAFTz/9tPHz8zOLFy82CQkJjtfp06cdfZ566ilTunRp8+uvv5p169aZevXqmXr16jmWX3iMw9133202bdpk5s2bZwICAniMAxwuvpurMRxTsG7NmjXGzc3NvP7662bnzp1m0qRJxsvLy3zzzTeOPm+88Ybx9/c3P/74o9myZYtp165dlrfgj4iIMKtXrza//fabqVChAo9xuE1169bNlCxZ0vFokBkzZpiiRYuaF154wdGHYwpXc/LkSbNx40azceNGI8m88847ZuPGjWb//v3GmJw5hpKSkkzx4sXNo48+an7//XczZcoU4+XlxaNBkHvef/99U7p0aePu7m7q1KljVq1aldclIZ+SlOUrNjbW0efff/81zzzzjClUqJDx8vIyHTp0MAkJCU7j7Nu3z7Ru3dp4enqaokWLmgEDBpjz58/f4L1BfnVpmOSYwrX46aefTNWqVY3dbjeVK1c2n332mdPyjIwMM3jwYFO8eHFjt9tNixYtzPbt2536HDt2zHTp0sX4+PgYX19f0717d3Py5MkbuRvIJ1JSUsyzzz5rSpcubTw8PEzZsmXNK6+84vT4BY4pXM2iRYuy/HdUt27djDE5dwxt3rzZNGzY0NjtdlOyZEnzxhtv5No+2YwxJufnOwEAAAAAtzKumQQAAAAAWEaYBAAAAABYRpgEAAAAAFhGmAQAAAAAWEaYBAAAAABYRpgEAAAAAFhGmAQAAAAAWEaYBAAAAABYRpgEAAAAAFhGmAQA4BZx+PBh9enTR2XLlpXdbldwcLDatm2ruLi4G1qHzWbTzJkzb+g2AQA3nlteFwAAAK7fvn371KBBA/n7+2v06NEKDw/X+fPnNX/+fPXq1Ut//fVXXpcIALjF2IwxJq+LAAAA1+eee+7Rli1btH37dnl7ezstS0pKkr+/v+Lj49WnTx/FxcXJxcVFUVFRev/991W8eHFJUkxMjJKSkpxmFfv166dNmzZp8eLFkqSmTZuqWrVq8vDw0Pjx4+Xu7q6nnnpKw4YNkySFhoZq//79jvVDQkK0b9++3Nx1AEAe4TRXAABucsePH9e8efPUq1evTEFSkvz9/ZWRkaF27drp+PHjWrJkiRYuXKg9e/aoU6dOlrf35ZdfytvbW6tXr9Zbb72lESNGaOHChZKktWvXSpJiY2OVkJDgeA8AuPVwmisAADe5Xbt2yRijypUrX7ZPXFyctm7dqr179yo4OFiS9NVXX6lKlSpau3at7rzzzmxvr1q1aho6dKgkqUKFCvrggw8UFxenli1bKiAgQNJ/ATYwMPA69goAkN8xMwkAwE0uO1esbNu2TcHBwY4gKUl33HGH/P39tW3bNkvbq1atmtP7EiVKKDEx0dIYAICbH2ESAICbXIUKFWSz2a77JjsuLi6Zgun58+cz9StQoIDTe5vNpoyMjOvaNgDg5kOYBADgJle4cGG1atVKH374oU6dOpVpeVJSksLCwnTgwAEdOHDA0f7nn38qKSlJd9xxhyQpICBACQkJTutu2rTJcj0FChRQenq65fUAADcXwiQAALeADz/8UOnp6apTp46+//577dy5U9u2bdN7772nevXqKTIyUuHh4YqOjtaGDRu0Zs0ade3aVU2aNFHt2rUlSc2bN9e6dev01VdfaefOnRo6dKh+//13y7WEhoYqLi5Ohw8f1okTJ3J6VwEA+QRhEgCAW0DZsmW1YcMGNWvWTAMGDFDVqlXVsmVLxcXF6eOPP5bNZtOPP/6oQoUKqXHjxoqMjFTZsmX13XffOcZo1aqVBg8erBdeeEF33nmnTp48qa5du1quZcyYMVq4cKGCg4MVERGRk7sJAMhHeM4kAAAAAMAyZiYBAAAAAJYRJgEAAAAAlhEmAQAAAACWESYBAAAAAJYRJgEAAAAAlhEmAQAAAACWESYBAAAAAJYRJgEAAAAAlhEmAQAAAACWESYBAAAAAJYRJgEAAAAAlv0/dekjHKckwAIAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 921
        },
        "id": "JJ7-vhz-Siwp",
        "outputId": "cb71aa10-cbb3-4000-ec1b-f6eb036d43ab"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           Company Valuation ($B) Date Joined        Country           City  \\\n",
              "0        Bytedance          $140     4/7/2017          China        Beijing   \n",
              "1           SpaceX       $100.30    12/1/2012  United States      Hawthorne   \n",
              "2           Stripe           $95    1/23/2014  United States  San Francisco   \n",
              "3           Klarna        $45.60   12/12/2011         Sweden      Stockholm   \n",
              "4       Epic Games           $42   10/26/2018  United States           Cary   \n",
              "...            ...            ...         ...            ...            ...   \n",
              "1032     Timescale            $1    2/22/2022  United States       New York   \n",
              "1033      Scalapay            $1    2/23/2022          Italy          Milan   \n",
              "1034  Omada Health            $1    2/23/2022  United States  San Francisco   \n",
              "1035    BlueVoyant            $1    2/23/2022  United States       New York   \n",
              "1036          Veev            $1    2/24/2022  United States      San Mateo   \n",
              "\n",
              "                          Industry  \\\n",
              "0          Artificial intelligence   \n",
              "1                            Other   \n",
              "2                          Fintech   \n",
              "3                          Fintech   \n",
              "4                            Other   \n",
              "...                            ...   \n",
              "1032  Internet software & services   \n",
              "1033                       Fintech   \n",
              "1034                        Health   \n",
              "1035                 Cybersecurity   \n",
              "1036  Internet software & services   \n",
              "\n",
              "                                      Select Inverstors Founded Year  \\\n",
              "0     Sequoia Capital China, SIG Asia Investments, S...         2012   \n",
              "1     Founders Fund, Draper Fisher Jurvetson, Rothen...         2002   \n",
              "2           Khosla Ventures, LowercaseCapital, capitalG         2010   \n",
              "3     Institutional Venture Partners, Sequoia Capita...         2005   \n",
              "4                 Tencent Holdings, KKR, Smash Ventures         1991   \n",
              "...                                                 ...          ...   \n",
              "1032  New Enterprise Associates, Benchmark, Two Sigm...         2015   \n",
              "1033  Fasanara Capital, Tiger Global Management, Bal...         2019   \n",
              "1034  U.S. Venture Partners, dRx Capital, Andreessen...         2011   \n",
              "1035  8VC, Liberty Strategic Capital, Eden Global Pa...         2017   \n",
              "1036           Zeev Ventures, Bond, Fifth Wall Ventures         2008   \n",
              "\n",
              "      Total Raised Financial Stage Investors Count Deal Terms Portfolio Exits  \n",
              "0     7.440000e+09             IPO              28          8               5  \n",
              "1     6.874000e+09            None              29         12            None  \n",
              "2     2.901000e+09           Asset              39         12               1  \n",
              "3     3.472000e+09        Acquired              56         13               1  \n",
              "4     4.377000e+09        Acquired              25          5               2  \n",
              "...            ...             ...             ...        ...             ...  \n",
              "1032  1.810600e+08            None               7          2            None  \n",
              "1033  7.000000e+08            None              10          2            None  \n",
              "1034  4.497200e+08            None              30          6            None  \n",
              "1035  5.255000e+08            None               6          2            None  \n",
              "1036  6.045000e+08            None              11          1            None  \n",
              "\n",
              "[1037 rows x 13 columns]"
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-8a805d86-556a-40a1-845a-42dc6a3e601f\">\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Company</th>\n",
              "      <th>Valuation ($B)</th>\n",
              "      <th>Date Joined</th>\n",
              "      <th>Country</th>\n",
              "      <th>City</th>\n",
              "      <th>Industry</th>\n",
              "      <th>Select Inverstors</th>\n",
              "      <th>Founded Year</th>\n",
              "      <th>Total Raised</th>\n",
              "      <th>Financial Stage</th>\n",
              "      <th>Investors Count</th>\n",
              "      <th>Deal Terms</th>\n",
              "      <th>Portfolio Exits</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Bytedance</td>\n",
              "      <td>$140</td>\n",
              "      <td>4/7/2017</td>\n",
              "      <td>China</td>\n",
              "      <td>Beijing</td>\n",
              "      <td>Artificial intelligence</td>\n",
              "      <td>Sequoia Capital China, SIG Asia Investments, S...</td>\n",
              "      <td>2012</td>\n",
              "      <td>7.440000e+09</td>\n",
              "      <td>IPO</td>\n",
              "      <td>28</td>\n",
              "      <td>8</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>SpaceX</td>\n",
              "      <td>$100.30</td>\n",
              "      <td>12/1/2012</td>\n",
              "      <td>United States</td>\n",
              "      <td>Hawthorne</td>\n",
              "      <td>Other</td>\n",
              "      <td>Founders Fund, Draper Fisher Jurvetson, Rothen...</td>\n",
              "      <td>2002</td>\n",
              "      <td>6.874000e+09</td>\n",
              "      <td>None</td>\n",
              "      <td>29</td>\n",
              "      <td>12</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Stripe</td>\n",
              "      <td>$95</td>\n",
              "      <td>1/23/2014</td>\n",
              "      <td>United States</td>\n",
              "      <td>San Francisco</td>\n",
              "      <td>Fintech</td>\n",
              "      <td>Khosla Ventures, LowercaseCapital, capitalG</td>\n",
              "      <td>2010</td>\n",
              "      <td>2.901000e+09</td>\n",
              "      <td>Asset</td>\n",
              "      <td>39</td>\n",
              "      <td>12</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Klarna</td>\n",
              "      <td>$45.60</td>\n",
              "      <td>12/12/2011</td>\n",
              "      <td>Sweden</td>\n",
              "      <td>Stockholm</td>\n",
              "      <td>Fintech</td>\n",
              "      <td>Institutional Venture Partners, Sequoia Capita...</td>\n",
              "      <td>2005</td>\n",
              "      <td>3.472000e+09</td>\n",
              "      <td>Acquired</td>\n",
              "      <td>56</td>\n",
              "      <td>13</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Epic Games</td>\n",
              "      <td>$42</td>\n",
              "      <td>10/26/2018</td>\n",
              "      <td>United States</td>\n",
              "      <td>Cary</td>\n",
              "      <td>Other</td>\n",
              "      <td>Tencent Holdings, KKR, Smash Ventures</td>\n",
              "      <td>1991</td>\n",
              "      <td>4.377000e+09</td>\n",
              "      <td>Acquired</td>\n",
              "      <td>25</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1032</th>\n",
              "      <td>Timescale</td>\n",
              "      <td>$1</td>\n",
              "      <td>2/22/2022</td>\n",
              "      <td>United States</td>\n",
              "      <td>New York</td>\n",
              "      <td>Internet software &amp; services</td>\n",
              "      <td>New Enterprise Associates, Benchmark, Two Sigm...</td>\n",
              "      <td>2015</td>\n",
              "      <td>1.810600e+08</td>\n",
              "      <td>None</td>\n",
              "      <td>7</td>\n",
              "      <td>2</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1033</th>\n",
              "      <td>Scalapay</td>\n",
              "      <td>$1</td>\n",
              "      <td>2/23/2022</td>\n",
              "      <td>Italy</td>\n",
              "      <td>Milan</td>\n",
              "      <td>Fintech</td>\n",
              "      <td>Fasanara Capital, Tiger Global Management, Bal...</td>\n",
              "      <td>2019</td>\n",
              "      <td>7.000000e+08</td>\n",
              "      <td>None</td>\n",
              "      <td>10</td>\n",
              "      <td>2</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1034</th>\n",
              "      <td>Omada Health</td>\n",
              "      <td>$1</td>\n",
              "      <td>2/23/2022</td>\n",
              "      <td>United States</td>\n",
              "      <td>San Francisco</td>\n",
              "      <td>Health</td>\n",
              "      <td>U.S. Venture Partners, dRx Capital, Andreessen...</td>\n",
              "      <td>2011</td>\n",
              "      <td>4.497200e+08</td>\n",
              "      <td>None</td>\n",
              "      <td>30</td>\n",
              "      <td>6</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1035</th>\n",
              "      <td>BlueVoyant</td>\n",
              "      <td>$1</td>\n",
              "      <td>2/23/2022</td>\n",
              "      <td>United States</td>\n",
              "      <td>New York</td>\n",
              "      <td>Cybersecurity</td>\n",
              "      <td>8VC, Liberty Strategic Capital, Eden Global Pa...</td>\n",
              "      <td>2017</td>\n",
              "      <td>5.255000e+08</td>\n",
              "      <td>None</td>\n",
              "      <td>6</td>\n",
              "      <td>2</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1036</th>\n",
              "      <td>Veev</td>\n",
              "      <td>$1</td>\n",
              "      <td>2/24/2022</td>\n",
              "      <td>United States</td>\n",
              "      <td>San Mateo</td>\n",
              "      <td>Internet software &amp; services</td>\n",
              "      <td>Zeev Ventures, Bond, Fifth Wall Ventures</td>\n",
              "      <td>2008</td>\n",
              "      <td>6.045000e+08</td>\n",
              "      <td>None</td>\n",
              "      <td>11</td>\n",
              "      <td>1</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1037 rows × 13 columns</p>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Correlation Analysis\n",
        "\n",
        "# Calculate the correlation matrix\n",
        "correlation_matrix = df[['Valuation ($B)', 'Total Raised']].corr()\n",
        "\n",
        "# Display the correlation matrix\n",
        "correlation_matrix"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "rkLoTyDASXAw",
        "outputId": "2996ef84-47ad-41df-9204-235602ba7f48"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                Valuation ($B)  Total Raised\n",
              "Valuation ($B)        1.000000      0.616879\n",
              "Total Raised          0.616879      1.000000"
            ],
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              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-bbde2ea8-8c18-4c25-ac32-c40ecf00d54a button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-48a77195-5d38-4c33-bce2-122a39f91c1c button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-48a77195-5d38-4c33-bce2-122a39f91c1c');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The **correlation matrix** shows the correlation between the numeric variables Valuation ($B) and Total Raised. The correlation coefficient is approximately 0.62, indicating a moderate to strong positive correlation. This suggests that companies that have raised more funds tend to have higher valuations, which is a reasonable expectation. However, correlation does not imply causation, and other factors could also influence these variables."
      ],
      "metadata": {
        "id": "RnSjW8iJTyvY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pip install reportlab"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E8t0J_jty9gr",
        "outputId": "6cf97d44-72b8-42fc-838d-cc10a1184f80"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting reportlab\n",
            "  Downloading reportlab-4.0.4-py3-none-any.whl (1.9 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.9/1.9 MB\u001b[0m \u001b[31m11.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: pillow>=9.0.0 in /usr/local/lib/python3.10/dist-packages (from reportlab) (9.4.0)\n",
            "Installing collected packages: reportlab\n",
            "Successfully installed reportlab-4.0.4\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from reportlab.lib.pagesizes import letter\n",
        "from reportlab.pdfgen import canvas\n",
        "from reportlab.lib.utils import ImageReader\n",
        "from reportlab.platypus import SimpleDocTemplate, PageBreak, Paragraph, Image, Table\n",
        "from reportlab.lib.styles import getSampleStyleSheet\n",
        "from reportlab.lib.units import inch\n",
        "\n"
      ],
      "metadata": {
        "id": "wvU5RobcTx8w"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Generate the plots and save them as image files\n",
        "df['Industry'].value_counts().plot(kind='bar')\n",
        "plt.savefig('industry_counts.png')\n",
        "\n",
        "df['Country'].value_counts().plot(kind='bar')\n",
        "plt.savefig('country_counts.png')\n",
        "\n",
        "df['Financial Stage'].value_counts().plot(kind='bar')\n",
        "plt.savefig('financial_stage_counts.png')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 507
        },
        "id": "6EauiPM91M2q",
        "outputId": "31eaacbb-4de9-4252-b42b-eacf75a20eef"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from reportlab.lib.pagesizes import letter\n",
        "from reportlab.lib.utils import ImageReader\n",
        "from reportlab.platypus import SimpleDocTemplate, PageBreak, Paragraph, Image\n",
        "from reportlab.lib.styles import getSampleStyleSheet\n",
        "from reportlab.lib.units import inch\n",
        "\n",
        "# Create a SimpleDocTemplate\n",
        "doc = SimpleDocTemplate(\"unicorn_analysis_report-2ndaug.pdf\", pagesize=letter)\n",
        "\n",
        "# Create a list to hold the report elements\n",
        "elements = []\n",
        "\n",
        "# Add a title, company name, and logo to the report\n",
        "elements.append(Paragraph(\"Company : Decoding Data Science\", getSampleStyleSheet()['Heading1']))\n",
        "logo = \"logo-highres-removebg-preview.png\"\n",
        "im = Image(logo, 2*inch, 2*inch)\n",
        "elements.append(im)\n",
        "\n",
        "# Add the descriptive statistics to the report\n",
        "elements.append(Paragraph(\"Descriptive Statistics:\", getSampleStyleSheet()['Heading2']))\n",
        "elements.append(Paragraph(str(numeric_descriptive)))\n",
        "elements.append(Paragraph(str(categorical_descriptive)))\n",
        "\n",
        "# Add the correlation matrix to the report\n",
        "elements.append(PageBreak())\n",
        "elements.append(Paragraph(\"Correlation Matrix:\", getSampleStyleSheet()['Heading2']))\n",
        "elements.append(Paragraph(str(correlation_matrix)))\n",
        "\n",
        "# Add the data visualizations to the report\n",
        "elements.append(PageBreak())\n",
        "elements.append(Paragraph(\"Data Visualizations:\", getSampleStyleSheet()['Heading2']))\n",
        "im = Image('industry_counts.png', 5*inch, 3*inch)\n",
        "elements.append(im)\n",
        "im = Image('country_counts.png', 5*inch, 3*inch)\n",
        "elements.append(im)\n",
        "im = Image('financial_stage_counts.png', 5*inch, 3*inch)\n",
        "elements.append(im)\n",
        "\n",
        "# Build the PDF\n",
        "doc.build(elements)\n"
      ],
      "metadata": {
        "id": "2Bk180nU1RLo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a SimpleDocTemplate\n",
        "doc = SimpleDocTemplate(\"unicorn_analysis_report.pdf\", pagesize=letter)\n",
        "\n",
        "# Create a list to hold the report elements\n",
        "elements = []\n",
        "\n",
        "# Add a title, company name, and logo to the report\n",
        "elements.append(Paragraph(\"Company Decoding Data Science\", getSampleStyleSheet()['Heading1']))\n",
        "logo = \"logo-highres.jpg\"\n",
        "im = Image(logo, 2*inch, 2*inch)\n",
        "elements.append(im)\n",
        "\n",
        "# Add the descriptive statistics to the report\n",
        "elements.append(PageBreak())\n",
        "elements.append(Paragraph(\"Descriptive Statistics:\", getSampleStyleSheet()['Heading2']))\n",
        "elements.append(Table(numeric_descriptive.reset_index().values.tolist() + categorical_descriptive.reset_index().values.tolist()))\n",
        "\n",
        "# Add the correlation matrix to the report\n",
        "elements.append(PageBreak())\n",
        "elements.append(Paragraph(\"Correlation Matrix:\", getSampleStyleSheet()['Heading2']))\n",
        "elements.append(Table(correlation_matrix.reset_index().values.tolist()))\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "# Add the data visualizations to the report\n",
        "elements.append(PageBreak())\n",
        "elements.append(Paragraph(\"Data Visualizations:\", getSampleStyleSheet()['Heading2']))\n",
        "im = Image('industry_counts.png', 5*inch, 3*inch)\n",
        "elements.append(im)\n",
        "im = Image('country_counts.png', 5*inch, 3*inch)\n",
        "elements.append(im)\n",
        "im = Image('financial_stage_counts.png', 5*inch, 3*inch)\n",
        "elements.append(im)\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "# Build the PDF\n",
        "doc.build(elements)\n"
      ],
      "metadata": {
        "id": "5rxrySZE2faz"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}