backlog/Technology_Trends_Analysis__survey_results_public_2018.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "IZ1irwiFW7cl"
      },
      "outputs": [],
      "source": [
        "#importing the libraries\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Load the dataset\n",
        "df = pd.read_csv('/content/drive/MyDrive/GirlsScriptOpenSource/StockOverflow/survey_results_public_2018.csv')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9SKGuWl1fvsZ",
        "outputId": "c358d73f-195e-45f8-94fb-9ae168d43838"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-13-c242cc7acb8e>:2: DtypeWarning: Columns (8,12,13,14,15,16,50,51,52,53,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128) have mixed types. Specify dtype option on import or set low_memory=False.\n",
            "  df = pd.read_csv('/content/drive/MyDrive/GirlsScriptOpenSource/StockOverflow/survey_results_public_2018.csv')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Display the first few rows of the dataframe\n",
        "print(df.head())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6Sv4xNGyfvvT",
        "outputId": "a5beb9c1-af33-474f-8943-d5576e682d37"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   Respondent Hobby OpenSource         Country         Student  \\\n",
            "0           1   Yes         No           Kenya              No   \n",
            "1           3   Yes        Yes  United Kingdom              No   \n",
            "2           4   Yes        Yes   United States              No   \n",
            "3           5    No         No   United States              No   \n",
            "4           7   Yes         No    South Africa  Yes, part-time   \n",
            "\n",
            "           Employment                                    FormalEducation  \\\n",
            "0  Employed part-time           Bachelor’s degree (BA, BS, B.Eng., etc.)   \n",
            "1  Employed full-time           Bachelor’s degree (BA, BS, B.Eng., etc.)   \n",
            "2  Employed full-time                                   Associate degree   \n",
            "3  Employed full-time           Bachelor’s degree (BA, BS, B.Eng., etc.)   \n",
            "4  Employed full-time  Some college/university study without earning ...   \n",
            "\n",
            "                                      UndergradMajor  \\\n",
            "0                          Mathematics or statistics   \n",
            "1  A natural science (ex. biology, chemistry, phy...   \n",
            "2  Computer science, computer engineering, or sof...   \n",
            "3  Computer science, computer engineering, or sof...   \n",
            "4  Computer science, computer engineering, or sof...   \n",
            "\n",
            "                CompanySize  \\\n",
            "0        20 to 99 employees   \n",
            "1  10,000 or more employees   \n",
            "2        20 to 99 employees   \n",
            "3      100 to 499 employees   \n",
            "4  10,000 or more employees   \n",
            "\n",
            "                                             DevType  ...  \\\n",
            "0                               Full-stack developer  ...   \n",
            "1  Database administrator;DevOps specialist;Full-...  ...   \n",
            "2           Engineering manager;Full-stack developer  ...   \n",
            "3                               Full-stack developer  ...   \n",
            "4  Data or business analyst;Desktop or enterprise...  ...   \n",
            "\n",
            "                     Exercise Gender         SexualOrientation  \\\n",
            "0        3 - 4 times per week   Male  Straight or heterosexual   \n",
            "1   Daily or almost every day   Male  Straight or heterosexual   \n",
            "2                         NaN    NaN                       NaN   \n",
            "3  I don't typically exercise   Male  Straight or heterosexual   \n",
            "4        3 - 4 times per week   Male  Straight or heterosexual   \n",
            "\n",
            "                                    EducationParents  \\\n",
            "0           Bachelor’s degree (BA, BS, B.Eng., etc.)   \n",
            "1           Bachelor’s degree (BA, BS, B.Eng., etc.)   \n",
            "2                                                NaN   \n",
            "3  Some college/university study without earning ...   \n",
            "4  Some college/university study without earning ...   \n",
            "\n",
            "                  RaceEthnicity                Age Dependents  MilitaryUS  \\\n",
            "0   Black or of African descent  25 - 34 years old        Yes         NaN   \n",
            "1  White or of European descent  35 - 44 years old        Yes         NaN   \n",
            "2                           NaN                NaN        NaN         NaN   \n",
            "3  White or of European descent  35 - 44 years old         No          No   \n",
            "4  White or of European descent  18 - 24 years old        Yes         NaN   \n",
            "\n",
            "                          SurveyTooLong     SurveyEasy  \n",
            "0  The survey was an appropriate length      Very easy  \n",
            "1  The survey was an appropriate length  Somewhat easy  \n",
            "2                                   NaN            NaN  \n",
            "3  The survey was an appropriate length  Somewhat easy  \n",
            "4  The survey was an appropriate length  Somewhat easy  \n",
            "\n",
            "[5 rows x 129 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Select relevant columns for technology trends analysis\n",
        "tech_columns = ['LanguageWorkedWith', 'LanguageDesireNextYear', 'DatabaseWorkedWith', 'DatabaseDesireNextYear',\n",
        "                'PlatformWorkedWith', 'PlatformDesireNextYear', 'FrameworkWorkedWith', 'FrameworkDesireNextYear']"
      ],
      "metadata": {
        "id": "JvxYGbhjgheF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Filter the dataset with selected columns\n",
        "tech_df = df[tech_columns]"
      ],
      "metadata": {
        "id": "U4zChoe9gvVl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Display the first few rows of the filtered dataframe\n",
        "print(tech_df.head())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rapkJL4igwzG",
        "outputId": "7a9b4aeb-6a2b-41d7-c44a-06c3c0faf7c7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                                 LanguageWorkedWith  \\\n",
            "0                        JavaScript;Python;HTML;CSS   \n",
            "1                      JavaScript;Python;Bash/Shell   \n",
            "2                                               NaN   \n",
            "3  C#;JavaScript;SQL;TypeScript;HTML;CSS;Bash/Shell   \n",
            "4                C;C++;Java;Matlab;R;SQL;Bash/Shell   \n",
            "\n",
            "                             LanguageDesireNextYear  \\\n",
            "0                        JavaScript;Python;HTML;CSS   \n",
            "1                                         Go;Python   \n",
            "2                                               NaN   \n",
            "3  C#;JavaScript;SQL;TypeScript;HTML;CSS;Bash/Shell   \n",
            "4              Assembly;C;C++;Matlab;SQL;Bash/Shell   \n",
            "\n",
            "                                  DatabaseWorkedWith  \\\n",
            "0  Redis;SQL Server;MySQL;PostgreSQL;Amazon RDS/A...   \n",
            "1                         Redis;PostgreSQL;Memcached   \n",
            "2                                                NaN   \n",
            "3  SQL Server;Microsoft Azure (Tables, CosmosDB, ...   \n",
            "4               SQL Server;PostgreSQL;Oracle;IBM Db2   \n",
            "\n",
            "                              DatabaseDesireNextYear  \\\n",
            "0  Redis;SQL Server;MySQL;PostgreSQL;Amazon RDS/A...   \n",
            "1                                         PostgreSQL   \n",
            "2                                                NaN   \n",
            "3  SQL Server;Microsoft Azure (Tables, CosmosDB, ...   \n",
            "4                          PostgreSQL;Oracle;IBM Db2   \n",
            "\n",
            "                  PlatformWorkedWith             PlatformDesireNextYear  \\\n",
            "0           AWS;Azure;Linux;Firebase           AWS;Azure;Linux;Firebase   \n",
            "1                              Linux                              Linux   \n",
            "2                                NaN                                NaN   \n",
            "3                              Azure                              Azure   \n",
            "4  Arduino;Windows Desktop or Server  Arduino;Windows Desktop or Server   \n",
            "\n",
            "  FrameworkWorkedWith  FrameworkDesireNextYear  \n",
            "0        Django;React             Django;React  \n",
            "1              Django                    React  \n",
            "2                 NaN                      NaN  \n",
            "3                 NaN  Angular;.NET Core;React  \n",
            "4                 NaN                      NaN  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Function to plot technology trends\n",
        "def plot_technology_trends(column, title):\n",
        "    # Split the column values into individual technologies\n",
        "    tech_series = tech_df[column].str.split(';').explode().value_counts()\n",
        "\n",
        "    # Plot the top 10 technologies\n",
        "    plt.figure(figsize=(12, 6))\n",
        "    sns.barplot(x=tech_series.values[:10], y=tech_series.index[:10])\n",
        "    plt.title(title)\n",
        "    plt.xlabel('Count')\n",
        "    plt.ylabel('Technology')\n",
        "    plt.show()"
      ],
      "metadata": {
        "id": "PE8ZcJahgigb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot trends for languages worked with\n",
        "plot_technology_trends('LanguageWorkedWith', 'Top 10 Languages Worked With in 2018')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "Hbj-Y7BFg7Rb",
        "outputId": "cfeaa5eb-d931-4090-e048-ca3790241588"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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t2rWTJJ04cULS5dCkYsWK6tixo8qUKaO+ffs6ApW/c+W8Dampqfrxxx8dcxA0adJEFy9e1ObNm3Xw4EGlpKQ4+mcfR6VKlXKMWaVKFf3+++86e/asU/tfz3e2CxcuqHPnzqpdu7Y++eQTpy/fv/32m1JTU/XOO+/kOA8PPfSQ03k4fPiwKlSokOOxmqvVeDU2m01NmjRxzM2Q/eaN7J/VlWHDXx8puVF/d63e7HjZY+Z1vIEDB2r58uWaOXOmatas6bQuP/7Nb9iwQV26dNGECRP05JNPqkePHnrppZc0atQovfzyy9qzZ0+exwSAwoA5GwAAuIbSpUtf9dWY2W3Xe91dbt1333367rvvNHz4cNWqVUu+vr7KyspShw4dckyUKOmas/Sba8wTcD3XmiTzr3eG/PLLL2rbtq0qV66sl19+WWFhYfLw8NDSpUv1yiuvXLXOv5OVlaU777xTTz/99FXXZ79+slSpUkpMTNSKFSu0bNkyLVu2TLNmzVJcXJzmzJlz3X1c+cpHb29vSZfviJEuP6cfFRWl9evXOyb9u5kv19f6Emq329WpUyd98cUXWr58ubp06eJYl33eHnjgAfXp0+eq22fPoWCFZs2a6csvv9SuXbuc7vKQLocN2fOjrF+/XqGhoYqMjLyp/Vl5rVo13rhx4/TWW29p0qRJevDBB3OsL126tI4ePZqj/Wb+zb/99tsKDg5WvXr1nNq7deumsWPH6rvvvlPVqlXzPC4AFHSEDQCAAiE8PFzffPONTp8+7XR3w08//eRYf6V9+/blGOPnn3+Wt7e3goKCLKmpVq1aWrdunbKyspwmidy0aZO8vb0dX4hv1B9//KGEhASNGzdO//nPfxztVzu23AoPD1dWVpYOHjzodPfH1d4CUKxYMaWmpuZo/+tdJF9++aUyMjK0ePFip78ur1q1Kse+s/d15V/6T548meOvz+XLl9eZM2ccdzJcj4eHh7p27aquXbsqKytLjz/+uN5++22NHj36undslCpVyhEo+Pj4qGrVqgoMDHSsz/5r/v/+9z8VKVLEEURkH8fevXtzjPnTTz+pZMmSuX61pc1m00cffaTu3bvr3nvv1bJlyxwTNAYFBcnPz0+XLl362/MQHh6u3bt3yxjjFBJdrcZruTJ82bBhg2PySUmqW7eu7Ha7Vq9erU2bNqlTp065OrbCZNq0aRo7dqwGDx6sZ5555qp9atWqpVWrVik9Pd1pksjsSS1r1aqV5/0eP378qo92ZWZmSrr82lUAuB3xGAUAoEDo1KmTLl26pDfffNOp/ZVXXpHNZlPHjh2d2jdu3Og0p0FycrK++OILtW/f/pp/Ac2re+65R8ePH9dnn33maPv999+1YMECde3a9arPdudFdp1//cts9psYbkRMTIwk6a233nJq/+uz6dLlL/xpaWnauXOnoy0lJUWLFi362zrT0tI0a9Ysp35t27aVu7t7jldi/vVnKl2+o2Pjxo1asWJFjnWpqamOL2B/fQWom5ub46/9uXntY7NmzZSYmKivv/7a6S/50uWwYePGjVq3bp2io6MdIVfp0qVVq1YtzZkzxymM2b17t77++utcfRG/koeHhz777DPVr19fXbt21ebNmyVdPq933323Pv30U+3evTvHdlc+DtSpUyf9+uuvWrhwoaPt3Llzeuedd3JdR7169eTp6amPPvpIR48edTofdrtdderU0bRp03K8AvRafHx8rvqoT0GU/eaa2NhYvfzyy9fsd8899+jSpUtO5zUjI0OzZs1Sw4YN8/wmCunyXTrHjx93vDY028cffyxJql27dp7HBIDCgDsbAAAFQteuXdW6dWs999xzOnTokGrWrKmvv/5aX3zxhQYPHqzy5cs79a9evbpiYmKcXn0pXb5N+u98+eWX2rFjh6TLf13cuXOnXnjhBUmXb23O/jJ7zz33qFGjRnrooYe0Z88elSxZUm+99ZYuXbqUq/1Il78wZo99pXLlyik2NlYtWrTQ5MmTlZmZqTvuuENff/21Dh48mKuxr6Zu3bq6++679eqrr+rkyZOOV1/+/PPPkpz/Gt2rVy8988wzuuuuuzRo0CCdO3dO06dPV8WKFZ2CnPbt2zvuLnjsscd05swZvfvuuypVqpTTYybBwcGO1/t169ZNHTp00I4dO7Rs2TKVLFnSad/Dhw/X4sWL1aVLF8frC8+ePatdu3Zp4cKFOnTokEqWLKl+/frp1KlTatOmjcqUKaPDhw/rjTfeUK1atRzzeVxPs2bNNGvWLG3ZssXpFZ2SHK98TEtLyzGJ5ZQpU9SxY0c1btxYDz/8sOPVlwEBARo7dmyefibS5ccslixZojZt2qhjx45as2aNqlevrkmTJmnVqlVq2LChHnnkEVWtWlWnTp3S9u3b9c033+jUqVOSpEceeURvvvmm4uLitG3bNpUuXVoffPCB4/GQ3PDw8FD9+vW1bt062e12pwlWs8/HSy+95Dhvf6du3bqaP3++hgwZovr168vX11ddu3bNw1m5NTZv3qy4uDiVKFFCbdu21UcffeS0vkmTJo5HRho2bKh7771XI0eO1IkTJ1ShQgXNmTNHhw4d0v/93/85bbdz504tXrxY0uW7edLS0hz/1mvWrOk4F0888YRmzZqlrl27auDAgQoPD9eaNWv08ccf684771TDhg3z+xQAgGu47D0YAIB/tL+++tIYY06fPm2eeuopExoaaooWLWqioqLMlClTTFZWllM/SWbAgAHmww8/NFFRUcZut5vatWv/7ev/svXp08fpFXRXLrNmzXLqe+rUKfPwww+bEiVKGG9vb9OyZUuzZcuWXO2nZcuW19xP27ZtjTHG/O9//zN33XWXCQwMNAEBAebee+81v/76a47XVGa/YvG3335z2sfVXhN49uxZM2DAAFO8eHHj6+trevToYfbu3WskmUmTJjlt//XXX5vq1asbDw8PU6lSJfPhhx9e9dWXixcvNtHR0cbT09NERESYF1980bz33ns59n3x4kUzevRoExISYry8vEybNm1MUlKSKVGihOnfv7/TmKdPnzYjR440FSpUMB4eHqZkyZKmSZMmZurUqebPP/80xhizcOFC0759e1OqVCnj4eFhypYtax577DGTkpKSq59B9nFLyvGazaysLMfrCOfPn59j22+++cY0bdrUeHl5GX9/f9O1a1ezZ88epz7X+rkY4/zqy2y///67qVq1qgkJCTH79u0zxhhz/PhxM2DAABMWFmaKFi1qQkJCTNu2bc0777zjtO3hw4dNt27djLe3tylZsqR58sknzfLly3P16stsI0eONJJMkyZNcqz77LPPjCTj5+eX41WjV3v15ZkzZ8z999/vOIfZr37M7rtgwQKnMQ4ePHjVf2N/da1XX3bu3DlH35YtW5qWLVvmarzc/ps/f/68GTZsmAkJCTF2u93Ur1//qq+5vd64f32d7k8//WTuuecex884PDzcDBs2zJw9e/a6tQNAYWYz5gZn6QEAwEVsNpsGDBhw1dvzcXWJiYmqXbu2PvzwQ8XGxt7SfaempqpYsWJ64YUX9Nxzz93SfQMAANdgzgYAAG4z58+fz9H26quvys3NTS1atHDJviU5JkYEAAC3P+ZsAADgNjN58mRt27ZNrVu3lru7u+OVkY8++ugNTXCXF/Pnz9fs2bPVqVMn+fr6av369fr444/Vvn17NW3aNF/3DQAACg7CBgAAbjNNmjTRypUr9fzzz+vMmTMqW7asxo4de0seYYiOjpa7u7smT56s9PR0x6SRV5skEwAA3L6YswEAAAAAAFiKORsAAAAAAIClCBsAAAAAAIClmLOhEMvKytKvv/4qPz8/2Ww2V5cDAAAAALjNGWN0+vRphYaGys3t2vcvEDYUYr/++mu+zyoOAAAAAMBfJScnq0yZMtdcT9hQiPn5+Um6/EP29/d3cTUAAAAAgNtdenq6wsLCHN9Hr4WwoRDLfnTC39+fsAEAAAAAcMv83aP8TBAJAAAAAAAsxZ0Nt4EWoz5WEbuXq8sAAAAAAOTRtilxri4hX3BnAwAAAAAAsBRhAwAAAAAAsBRhAwAAAAAAsBRhAwAAAAAAsBRhAwAAAAAAsBRhAwAAAAAAsBRhAwAAAAAAsBRhAwAAAAAAsBRhAwAAAAAAsBRhAwAAAAAAsBRhAwAAAAAAsBRhAwAAAAAAsBRhAwAAAAAAsBRhg4UOHTokm82mxMREV5cCAAAAAIDLFIiwIT4+Xj169Lil+1y0aJEaNWqkgIAA+fn5qVq1aho8ePBNjRkWFqaUlBRVr14919vMnj1bgYGBN7VfAAAAAAAKEndXF+AKCQkJ6tmzpyZMmKBu3brJZrNpz549Wrly5Q2P+eeff8rDw0MhISEWVgoAAAAAQOFTIO5suNLy5cvVrFkzBQYGqkSJEurSpYt++eUXx/omTZromWeecdrmt99+U9GiRbV27VpJ0gcffKB69erJz89PISEhuv/++3XixAlH/y+//FJNmzbV8OHDValSJVWsWFE9evTQtGnTnMb98ssvVb9+fXl6eqpkyZK66667HOsiIiL0/PPPKy4uTv7+/nr00UdzPEaxevVq2Ww2ffXVV4qOjpanp6caNWqk3bt3O9Y/9NBDSktLk81mk81m09ixY608nQAAAAAA3HIFLmw4e/ashgwZoq1btyohIUFubm666667lJWVJUmKjY3VvHnzZIxxbDN//nyFhoaqefPmkqTMzEw9//zz2rFjhz7//HMdOnRI8fHxjv4hISH68ccfHV/6r+arr77SXXfdpU6dOumHH35QQkKCGjRo4NRn6tSpqlmzpn744QeNHj36mmMNHz5cL730krZs2aKgoCB17dpVmZmZatKkiV599VX5+/srJSVFKSkpGjZs2DXHycjIUHp6utMCAAAAAEBBU+Aeo7j77rudPr/33nsKCgrSnj17VL16dd13330aPHiw1q9f7wgX5s6dq969e8tms0mS+vbt69g+MjJSr7/+uur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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot trends for languages desired next year\n",
        "plot_technology_trends('LanguageDesireNextYear', 'Top 10 Languages Desired Next Year (2019)')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "RatzmSsog2wM",
        "outputId": "4f4b9317-d8f1-4927-c403-a8d8f92a65ee"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Combine worked with and desired next year for comparison\n",
        "combined_df = tech_df.melt(value_vars=['LanguageWorkedWith', 'LanguageDesireNextYear'],\n",
        "                           var_name='Category', value_name='Technologies')"
      ],
      "metadata": {
        "id": "SbeEvX6vgmhO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Split the technologies into individual entries\n",
        "combined_df['Technologies'] = combined_df['Technologies'].str.split(';')\n",
        "combined_df = combined_df.explode('Technologies')"
      ],
      "metadata": {
        "id": "9St3jitbgmkY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Group by category and technology to get counts\n",
        "trend_df = combined_df.groupby(['Category', 'Technologies']).size().reset_index(name='Count')"
      ],
      "metadata": {
        "id": "9Icdl05xhRIm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Pivot the data for comparison\n",
        "trend_pivot = trend_df.pivot(index='Technologies', columns='Category', values='Count').fillna(0)"
      ],
      "metadata": {
        "id": "8I_yfm2Zgi38"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate the change in preference\n",
        "trend_pivot['Change'] = trend_pivot['LanguageDesireNextYear'] - trend_pivot['LanguageWorkedWith']"
      ],
      "metadata": {
        "id": "p52QXd_mgjA8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Sort by the change\n",
        "trend_pivot = trend_pivot.sort_values(by='Change', ascending=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FYXaCE-xfvyC",
        "outputId": "d0b4fe1a-a8dd-4d9f-b7ac-98f6ab97d22f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Category      LanguageDesireNextYear  LanguageWorkedWith  Change\n",
            "Technologies                                                    \n",
            "Go                             15529                5532    9997\n",
            "Kotlin                         11992                3508    8484\n",
            "Rust                            7857                1857    6000\n",
            "Swift                           9708                6310    3398\n",
            "TypeScript                     16896               13626    3270\n",
            "Haskell                         5117                1961    3156\n",
            "Scala                           6219                3420    2799\n",
            "F#                              3752                1115    2637\n",
            "Python                         32795               30359    2436\n",
            "R                               7041                4813    2228\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Display the top 10 technologies with the highest increase in preference\n",
        "print(trend_pivot.head(10))"
      ],
      "metadata": {
        "id": "9m_DGc5Xh6at"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot the change in preference for the top 10 technologies\n",
        "plt.figure(figsize=(12, 6))\n",
        "sns.barplot(x=trend_pivot['Change'].head(10), y=trend_pivot.index[:10])\n",
        "plt.title('Top 10 Technologies with Highest Increase in Preference')\n",
        "plt.xlabel('Change in Preference')\n",
        "plt.ylabel('Technology')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "0_IdaPLhfv06",
        "outputId": "03c9c4e5-eebf-4ea4-f566-58e5cfe96c11"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}