pie_charts.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/AshaS591/Data-Analytics/blob/main/pie_charts.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "MoLFBt1z7Fve"
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        },
        "id": "lA9qYBst7O0Z",
        "outputId": "7e0370ee-6931-4db5-ccc3-0e624742353d"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "\n",
        "plt.pie([90,99,95,98],labels=['maths','bio','chemistry','physics'],autopct='%0.0f%%',explode=[0,0.1,0,0],shadow=True)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        },
        "id": "fyHYaCa18Iur",
        "outputId": "c915339f-6df4-44cb-e605-3ec35099688b"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "x=[90,45,67,23,56,76]\n",
        "y=['sec1','sec2','sec3','sec4','sec5','sec6']\n",
        "plt.pie(x,labels=y,autopct='%0.0f%%')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "nV8sjruNIcC0",
        "outputId": "3a529557-f64a-43f4-ae2a-d289a75aa45a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   SNO              Name  Mock1\n",
              "0    1      M Anil kumar      2\n",
              "1    2       Y Narasimha      2\n",
              "2    3    K Dinesh kumar      2\n",
              "3    4  Shraddha M Neeli      2\n",
              "4    5      Rohit P sail      2"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a88c7d51-48a5-4a1b-878f-619580955725\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>SNO</th>\n",
              "      <th>Name</th>\n",
              "      <th>Mock1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>M Anil kumar</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>Y Narasimha</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>K Dinesh kumar</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>Shraddha M Neeli</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>Rohit P sail</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a88c7d51-48a5-4a1b-878f-619580955725')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-a88c7d51-48a5-4a1b-878f-619580955725 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-a88c7d51-48a5-4a1b-878f-619580955725');\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",
              "\n",
              "\n",
              "<div id=\"df-0243d7f6-3925-403b-99b6-855b96976dcf\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-0243d7f6-3925-403b-99b6-855b96976dcf')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <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",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-0243d7f6-3925-403b-99b6-855b96976dcf button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 36,\n  \"fields\": [\n    {\n      \"column\": \"SNO\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10,\n        \"min\": 1,\n        \"max\": 36,\n        \"num_unique_values\": 36,\n        \"samples\": [\n          36,\n          14,\n          27\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 36,\n        \"samples\": [\n          \"N Keerthivasan\",\n          \"C Nagur basha\",\n          \"Atthish P\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Mock1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 2,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "df=pd.read_excel('/content/BTM-M1-DataAnalysis_Mock.xlsx')\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Mock1'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 178
        },
        "id": "R2yUiIQJMNMT",
        "outputId": "68c6de13-18bd-4bb5-de3a-548b152a10e5"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Mock1\n",
              "2    29\n",
              "1     7\n",
              "Name: count, dtype: int64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mock1</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "2gGShvrPIw8k",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        },
        "outputId": "657ab3ed-4ff5-4539-a187-f7cb5071b656"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "\n",
        "plt.pie([29,7],autopct='%0.0f%%',labels=['Two','One'],shadow=True,explode=[0,0.1],colors=['Orange','blue'])\n",
        "plt.title('Data Analysis Mock')\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "karxbsPhJyIB",
        "outputId": "06732dea-6caf-4855-fc7d-eac2dd7e8249"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([2, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "df['Mock1'].unique()"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyNZGWatZnZR2FLKDSht7Y/5",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}