Test_1_(13_march).ipynb

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      "name": "Test 1 (13 march).ipynb",
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      "authorship_tag": "ABX9TyMgO95NmjoyT9+Cj58UNPXP",
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      "name": "python"
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    {
      "cell_type": "markdown",
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        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/Poonguzhali0409/Assignments/blob/main/Test_1_(13_march).ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### **Question 1 [get input - print years]**"
      ],
      "metadata": {
        "id": "4t0Mkuk9m37O"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "name = input('Enter your name : ')\n",
        "age = int(input('Enter your age : '))\n",
        "birthyear = 2022-age\n",
        "result = birthyear+100\n",
        "print('Hi ',name,' You will be 100 years in ',result)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uRA_g4bQnBKr",
        "outputId": "bed6e7a2-2450-451a-cc3f-09665e480f42"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Enter your name : vj\n",
            "Enter your age : 33\n",
            "Hi  vj  You will be 100 years in  2088\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### **Question 2 [Pandas groupby]**"
      ],
      "metadata": {
        "id": "8aUUIN8doHLL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd \n",
        "\n",
        "d1 = {'Subjects':['Science','Maths','Social','English'],'Max Mark':[100,100,100,100]}\n",
        "df2 = pd.DataFrame(d1)\n",
        "df2['Min Mark']= pd.Series([40,40,40,40])\n",
        "df2.groupby('Max Mark').groups\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VuVWhLflncgS",
        "outputId": "b86eb588-6133-4d48-8200-e1f71ceceb28"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{100: [0, 1, 2, 3]}"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### **Question 3 [DataFrame from list]**"
      ],
      "metadata": {
        "id": "mOsg0232pYDX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "list2 = [['carrot',30],['Tomato',20],['Onion',50]]\n",
        "final = pd.DataFrame(list2,columns=['Veggies','Price'])\n",
        "(final)"
      ],
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          "height": 143
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        "id": "tN9sD9yMo6_B",
        "outputId": "97490a48-7900-4c65-9d30-64021295620e"
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      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Veggies  Price\n",
              "0  carrot     30\n",
              "1  Tomato     20\n",
              "2   Onion     50"
            ],
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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          "metadata": {},
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### **Question 4 [combine the dataframe in pandas]**\n",
        "\n"
      ],
      "metadata": {
        "id": "2Zy5y6YhqDUS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# using series\n",
        "\n",
        "d1 = {'Subjects':['Science','Maths','Social','English'],'Max Mark':[100,100,100,100]}\n",
        "df2 = pd.DataFrame(d1)\n",
        "df2['Min Mark']= pd.Series([40,40,40,40])\n",
        "print(df2)\n",
        "\n",
        "#using append\n",
        "\n",
        "dic1 ={'Name':['Poojitha','Pranav','Nithya','Umer'],'Age':[10,13,12,20],'Class':['B.E','B.TECH','B.E','B.E']}\n",
        "list1=pd.DataFrame(dic1)\n",
        "dic2 ={'Name':['Riyas','Sethu','Mukesh','Abarna'],'Age':[11,14,11,10],'Class':['MCA','PHD','BE','B.TECH']}\n",
        "list2=pd.DataFrame(dic2,index=[4,5,6,7])\n",
        "dic3 = list1.append(list2)\n",
        "dic3"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 387
        },
        "id": "p-xfz_37p3Rq",
        "outputId": "de60dd39-4317-4ea0-ac41-932394b59528"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  Subjects  Max Mark  Min Mark\n",
            "0  Science       100        40\n",
            "1    Maths       100        40\n",
            "2   Social       100        40\n",
            "3  English       100        40\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Name  Age   Class\n",
              "0  Poojitha   10     B.E\n",
              "1    Pranav   13  B.TECH\n",
              "2    Nithya   12     B.E\n",
              "3      Umer   20     B.E\n",
              "4     Riyas   11     MCA\n",
              "5     Sethu   14     PHD\n",
              "6    Mukesh   11      BE\n",
              "7    Abarna   10  B.TECH"
            ],
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              "      <td>Riyas</td>\n",
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              "      <td>Mukesh</td>\n",
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              "      <td>Abarna</td>\n",
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              "                                                     [key], {});\n",
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              "\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",
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          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### **Question 5 [difference between list and tuples in Python]**"
      ],
      "metadata": {
        "id": "eacQ6P6rsez7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "list2 = [['mutable','Immutable'],['useful for insertion and deletion','useful for accessing data']]\n",
        "final = pd.DataFrame(list2,columns=['List','Tuple'],index = [1,2])\n",
        "(final)"
      ],
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        "colab": {
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          "height": 112
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        "id": "H5M79MXcsgad",
        "outputId": "6eb710d8-c7cb-4701-85a7-d4771a85b4bc"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "                                List                      Tuple\n",
              "1                            mutable                  Immutable\n",
              "2  useful for insertion and deletion  useful for accessing data"
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              "      <td>useful for accessing data</td>\n",
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              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \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-a5791158-d9cd-442e-bbab-f6b4f9414ad1 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-a5791158-d9cd-442e-bbab-f6b4f9414ad1');\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": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### **Question 6 [mean,median,mode]**"
      ],
      "metadata": {
        "id": "ZWMGqUKmtLrO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "\n",
        "num = np.array([1,5,3,100,4,48])\n",
        "print('median : ',np.median (num))\n",
        "print('mean : ',np.mean (num))\n",
        "print('Standar Deviation : ',np.std (num))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9E4B-PvjsxKS",
        "outputId": "879de160-960d-4414-a4f2-7ee4d15a209f"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "median :  4.5\n",
            "mean :  26.833333333333332\n",
            "Standar Deviation :  36.59424666377065\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### **Question 7 [remove duplicate element from list]** "
      ],
      "metadata": {
        "id": "xDdfliDHud81"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "list1 = [1,2,3,4,'aa','bb',1,'cc','aa']\n",
        "result = list(set(list1))\n",
        "result"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ITJlFaRktYsP",
        "outputId": "e6cf35a4-ef61-4595-f9df-f2330e3ed914"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1, 2, 3, 4, 'cc', 'aa', 'bb']"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### **Question 8 [map () in python]** "
      ],
      "metadata": {
        "id": "3NXaWXvUvNuN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#used to map a specific math fun to the list\n",
        "\n",
        "imlist = [2,6,4,7,5]\n",
        "newlist = list(map(lambda x : x*2,imlist))\n",
        "print(newlist)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KEgLFkmOus-x",
        "outputId": "0ce03f9d-7f1e-4cb6-d6b3-d18b3e590d2a"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[4, 12, 8, 14, 10]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### **Question 9 [pass in python]** "
      ],
      "metadata": {
        "id": "LszUHoQYvtIV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x = [5,7,6]\n",
        "for i in x :\n",
        "  pass"
      ],
      "metadata": {
        "id": "YBDvNTXgvzRI"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### **Question 10 [vstack in python]** "
      ],
      "metadata": {
        "id": "Pm8ukrgEv1MT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from numpy.ma.core import ndim\n",
        "#to get the array veritically\n",
        "\n",
        "a1=np.array([[1,2,3],[4,5,6]])\n",
        "a2=np.array([[7,8,9],[10,11,12]])\n",
        "print (np.stack((a1,a2)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5NzMs-3Uv4gq",
        "outputId": "d54d9f03-d063-41ec-c643-7024d388a00f"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[[ 1  2  3]\n",
            "  [ 4  5  6]]\n",
            "\n",
            " [[ 7  8  9]\n",
            "  [10 11 12]]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "eWITArjfwa3m"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}