machine_learning1.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "machine_learning1.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "authorship_tag": "ABX9TyOGxJ1PNu7wrg5nJdwXXD5b",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/hanyeh00/machine-learning/blob/main/machine_learning1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H_Hdkst1Xi4N"
      },
      "source": [
        "!pip install gutenberg"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "69j-s4upG_9a"
      },
      "source": [
        "# -*- coding: utf-8 -*-\n",
        "\"\"\"\n",
        "Created on Sun Jan 28 20:02:44 2018\n",
        "\n",
        "@author: jpmul\n",
        "\"\"\"\n",
        "import string\n",
        "import markovify\n",
        "\n",
        "from gutenberg.acquire import load_etext\n",
        "from gutenberg.cleanup import strip_headers\n",
        "\n",
        "\n",
        "# This is an unused function to generate the top N words\n",
        "# Could use this to make a nice histogram\n",
        "# Method based on Think Python exercise\n",
        "def top_words(text, n):\n",
        "        \n",
        "    hist = dict()\n",
        "    \n",
        "    for line in text.split('\\n'):\n",
        "        line = line.replace('-', ' ')\n",
        "    \n",
        "        for word in line.split():\n",
        "            word = word.strip(string.punctuation + string.whitespace)\n",
        "            word = word.lower()\n",
        "            hist[word] = hist.get(word, 0) + 1\n",
        "            \n",
        "    print(\"Total words:\\t\", sum(hist.values()))\n",
        "    print(\"Unique words:\\t\", len(hist))\n",
        "    \n",
        "    t = []\n",
        "    for key, value in hist.items():\n",
        "        t.append((value, key))\n",
        "    t.sort(reverse=True)\n",
        "    for word, count in t[:n]:\n",
        "        print(word, count)\n",
        "\n",
        "# Here are some book IDs from Project Gutenberg\n",
        "# Orthodoxy 130\n",
        "# What's Wrong with the World 1717\n",
        "# Heretics 470\n",
        "# All Things Considered 11505\n",
        "# Tremendous Trifles 8092\n",
        "# Pride and Prejudice 1342\n",
        "# Alice's Adventures in Wonderland 11\n",
        "# Dracula 345        \n",
        "# Moby Dick 2701\n",
        "# Kama Sutra 27827        \n",
        "\n",
        "#books = [27827]\n",
        "books = [130, 1717, 470, 11505, 8092]\n",
        "text = ''\n",
        "\n",
        "print(f\"Loading {len(books)} book(s) from Project Gutenberg...\")\n",
        "for book in books:\n",
        "    text = text + strip_headers(load_etext(book)).strip()\n",
        "    print(f\"\\tBook loaded. Total source text length is now {len(text)}\")\n",
        "#top_words(text, 10)\n",
        "\n",
        "print(\"Done.\\n\")\n",
        "print(len(text.split(\"\\n\")))\n",
        "\n",
        "\n",
        "text_model = markovify.Text(text, state_size=3)\n",
        "  \n",
        "\n",
        "#for i in range(2):\n",
        "#    print(text_model.make_sentence())\n",
        "\n",
        "#print('-'*80)    \n",
        "\n",
        "for i in range(10):\n",
        "\n",
        "    tweet = text_model.make_short_sentence(140, max_overlap_ratio=0.70)\n",
        "    if tweet is not None:\n",
        "        print('-'*80)  \n",
        "        print(tweet)\n",
        "\n",
        "print('-'*80)     "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I3Q0y8_pHe85"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ee5lp5nRXktC"
      },
      "source": [
        "# learning algorithm\n",
        "####linear regression\n",
        "##cost function\n",
        "![image.png](data:image/png;base64,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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f2T3CY01Y5mM"
      },
      "source": [
        "# gradient descent\n",
        "The idea is to take repeated steps in the opposite direction of the gradient (or approximate gradient) of the function at the current point.\n",
        "### Gradient descent is an optimization algorithm that minimizes a cost function (J).\n",
        "### the goal of gradient decent is finding parameters(weights).\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3XtN0reForv2",
        "outputId": "8e4152cc-e743-4802-94fb-266c50a8d702"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.linear_model import LinearRegression\n",
        "x = np.array([5, 15, 25, 35, 45, 55]).reshape((-1 ,1)) # one feature 6 samples\n",
        "y = np.array([5, 20, 14, 32, 22, 38])\n",
        "model = LinearRegression()\n",
        "model.fit(x,y)\n",
        "r_sq = model.score(x, y)\n",
        "print(r_sq)\n",
        "pr=model.predict([[5],[6],[7],[8]])\n",
        "print(pr)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.7158756137479542\n",
            "[8.33333333 8.87333333 9.41333333 9.95333333]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5LcH6gb4p2RS",
        "outputId": "1241161e-19a8-484a-f8a5-0a0277a9101c"
      },
      "source": [
        "# manual impliment\n",
        "import numpy as np\n",
        "from sklearn.datasets import load_boston\n",
        "from sklearn import preprocessing\n",
        "x_train,x_test,y_train,y_test=sklearn.model_selection.train_test_split(x,y,test_size=0.1)\n",
        "x_train.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(5, 1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ny4qKYgI4Pu5",
        "outputId": "25d51140-6c43-450b-c259-b36a86d3c752"
      },
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "#cost function\n",
        "def cost_function(x, y, theta,h):\n",
        "    m = len(x) # number of elements\n",
        "    J = (1/(2*m))*np.sum(np.square(h - y))\n",
        "    return J\n",
        "\n",
        "def geradient_decent(lr,theta,x,y):\n",
        "  m = len(y)\n",
        "  jmin2=[]\n",
        "  for i in range(10000000):\n",
        "    h= x.dot(theta)\n",
        "    jmin2.append(cost_function(x, y, theta,h))\n",
        "    \n",
        "    theta = theta - ((lr/m) *((np.dot(x.T, (h-y) )))) #gradient decent\n",
        "  return  min(jmin2)\n",
        "\n",
        "# define min max scaler\n",
        "scaler = MinMaxScaler()\n",
        "theta = np.zeros((1,5))\n",
        "x_train=scaler.fit_transform(x_train)\n",
        "#y_train=scaler.fit_transform(y_train)\n",
        "geradient_decent(0.1,theta,x_train,y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "434.81851851851843"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BHmRgfPbhWN3"
      },
      "source": [
        "input(\"write len message\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jMXyzs-iNa7p"
      },
      "source": [
        "# inverse matrix"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eTvLHE48Qa_P"
      },
      "source": [
        "A=[[3,4],[2,16]]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Eh3ITPtC90My"
      },
      "source": [
        "import numpy as np\n",
        "Ainv=np.linalg.inv(A)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1Oo3WZlrPtBL",
        "outputId": "360659c9-26b9-48e4-9354-c172ad481564"
      },
      "source": [
        "I=[[1,0],[0,1]]\n",
        "np.linalg.solve(A,I) # inverse matrix"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 0.4  , -0.1  ],\n",
              "       [-0.05 ,  0.075]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rIJhZUZM93_P",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8df16c67-ebea-4bd2-fdf9-e72a29dba85b"
      },
      "source": [
        "Ainv"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 0.4  , -0.1  ],\n",
              "       [-0.05 ,  0.075]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O89x2ptgQqUL"
      },
      "source": [
        "# transpose"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HXAxpLYdQ0Sh"
      },
      "source": [
        "At=np.transpose(A)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kSE3uOcz99eD",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3beb0506-335f-44cc-f975-ae8b1ae7b780"
      },
      "source": [
        "At.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2, 2)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wMvZfnPvbIxf"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZTvNNm_xbcv1"
      },
      "source": [
        "# multiple feature"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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",
              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": "OK"
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 93
        },
        "id": "SVe2qUmYbfMo",
        "outputId": "73a1ec52-464b-4167-adde-0a1a8eebee13"
      },
      "source": [
        "from google.colab import files\n",
        "files.upload()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-b1ecd9ec-a1da-4804-b4bc-8890a0f355f9\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-b1ecd9ec-a1da-4804-b4bc-8890a0f355f9\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving kaggle.json to kaggle.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'kaggle.json': b'{\"username\":\"hani00\",\"key\":\"7e3cada1986d0dbb6a488fe267908458\"}'}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6fG4ltdNcoQL",
        "outputId": "76f98ddd-b437-4245-82f9-ce36cfc350ce"
      },
      "source": [
        "!mkdir -p ~/.kaggle\n",
        "!cp kaggle.json ~/.kaggle/\n",
        "!chmod 600 ~/.kaggle/kaggle.json\n",
        "!kaggle datasets download -d shree1992/housedata\n",
        "%cd /content"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading housedata.zip to /content\n",
            "\r  0% 0.00/432k [00:00<?, ?B/s]\n",
            "\r100% 432k/432k [00:00<00:00, 59.7MB/s]\n",
            "/content\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6dXr4y31d42E",
        "outputId": "ced8aae7-39fe-438f-cae0-d4c9b7d86a9b"
      },
      "source": [
        "!unzip \"/content/housedata.zip\" -d \"/content\" "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Archive:  /content/housedata.zip\n",
            "  inflating: /content/data.csv       \n",
            "  inflating: /content/data.dat       \n",
            "  inflating: /content/output.csv     \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b59mLeX7eaPb"
      },
      "source": [
        "import pandas as pd\n",
        "data=pd.read_csv('data.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 348
        },
        "id": "lw4nq9p6fKQF",
        "outputId": "7d042c9e-0d84-473d-db73-c99fd2bf8f49"
      },
      "source": [
        "data.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>price</th>\n",
              "      <th>bedrooms</th>\n",
              "      <th>bathrooms</th>\n",
              "      <th>sqft_living</th>\n",
              "      <th>sqft_lot</th>\n",
              "      <th>floors</th>\n",
              "      <th>waterfront</th>\n",
              "      <th>view</th>\n",
              "      <th>condition</th>\n",
              "      <th>sqft_above</th>\n",
              "      <th>sqft_basement</th>\n",
              "      <th>yr_built</th>\n",
              "      <th>yr_renovated</th>\n",
              "      <th>street</th>\n",
              "      <th>city</th>\n",
              "      <th>statezip</th>\n",
              "      <th>country</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2014-05-02 00:00:00</td>\n",
              "      <td>313000.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.50</td>\n",
              "      <td>1340</td>\n",
              "      <td>7912</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1340</td>\n",
              "      <td>0</td>\n",
              "      <td>1955</td>\n",
              "      <td>2005</td>\n",
              "      <td>18810 Densmore Ave N</td>\n",
              "      <td>Shoreline</td>\n",
              "      <td>WA 98133</td>\n",
              "      <td>USA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2014-05-02 00:00:00</td>\n",
              "      <td>2384000.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2.50</td>\n",
              "      <td>3650</td>\n",
              "      <td>9050</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>3370</td>\n",
              "      <td>280</td>\n",
              "      <td>1921</td>\n",
              "      <td>0</td>\n",
              "      <td>709 W Blaine St</td>\n",
              "      <td>Seattle</td>\n",
              "      <td>WA 98119</td>\n",
              "      <td>USA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2014-05-02 00:00:00</td>\n",
              "      <td>342000.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.00</td>\n",
              "      <td>1930</td>\n",
              "      <td>11947</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>1930</td>\n",
              "      <td>0</td>\n",
              "      <td>1966</td>\n",
              "      <td>0</td>\n",
              "      <td>26206-26214 143rd Ave SE</td>\n",
              "      <td>Kent</td>\n",
              "      <td>WA 98042</td>\n",
              "      <td>USA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2014-05-02 00:00:00</td>\n",
              "      <td>420000.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.25</td>\n",
              "      <td>2000</td>\n",
              "      <td>8030</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>1000</td>\n",
              "      <td>1000</td>\n",
              "      <td>1963</td>\n",
              "      <td>0</td>\n",
              "      <td>857 170th Pl NE</td>\n",
              "      <td>Bellevue</td>\n",
              "      <td>WA 98008</td>\n",
              "      <td>USA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2014-05-02 00:00:00</td>\n",
              "      <td>550000.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2.50</td>\n",
              "      <td>1940</td>\n",
              "      <td>10500</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>1140</td>\n",
              "      <td>800</td>\n",
              "      <td>1976</td>\n",
              "      <td>1992</td>\n",
              "      <td>9105 170th Ave NE</td>\n",
              "      <td>Redmond</td>\n",
              "      <td>WA 98052</td>\n",
              "      <td>USA</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  date      price  bedrooms  ...       city  statezip  country\n",
              "0  2014-05-02 00:00:00   313000.0       3.0  ...  Shoreline  WA 98133      USA\n",
              "1  2014-05-02 00:00:00  2384000.0       5.0  ...    Seattle  WA 98119      USA\n",
              "2  2014-05-02 00:00:00   342000.0       3.0  ...       Kent  WA 98042      USA\n",
              "3  2014-05-02 00:00:00   420000.0       3.0  ...   Bellevue  WA 98008      USA\n",
              "4  2014-05-02 00:00:00   550000.0       4.0  ...    Redmond  WA 98052      USA\n",
              "\n",
              "[5 rows x 18 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "id": "HBbJmkebfkkv",
        "outputId": "2063a09e-a0b0-43e9-91ac-69601e18bc7b"
      },
      "source": [
        "data.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>price</th>\n",
              "      <th>bedrooms</th>\n",
              "      <th>bathrooms</th>\n",
              "      <th>sqft_living</th>\n",
              "      <th>sqft_lot</th>\n",
              "      <th>floors</th>\n",
              "      <th>waterfront</th>\n",
              "      <th>view</th>\n",
              "      <th>condition</th>\n",
              "      <th>sqft_above</th>\n",
              "      <th>sqft_basement</th>\n",
              "      <th>yr_built</th>\n",
              "      <th>yr_renovated</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>4.600000e+03</td>\n",
              "      <td>4600.000000</td>\n",
              "      <td>4600.000000</td>\n",
              "      <td>4600.000000</td>\n",
              "      <td>4.600000e+03</td>\n",
              "      <td>4600.000000</td>\n",
              "      <td>4600.000000</td>\n",
              "      <td>4600.000000</td>\n",
              "      <td>4600.000000</td>\n",
              "      <td>4600.000000</td>\n",
              "      <td>4600.000000</td>\n",
              "      <td>4600.000000</td>\n",
              "      <td>4600.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>5.519630e+05</td>\n",
              "      <td>3.400870</td>\n",
              "      <td>2.160815</td>\n",
              "      <td>2139.346957</td>\n",
              "      <td>1.485252e+04</td>\n",
              "      <td>1.512065</td>\n",
              "      <td>0.007174</td>\n",
              "      <td>0.240652</td>\n",
              "      <td>3.451739</td>\n",
              "      <td>1827.265435</td>\n",
              "      <td>312.081522</td>\n",
              "      <td>1970.786304</td>\n",
              "      <td>808.608261</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>5.638347e+05</td>\n",
              "      <td>0.908848</td>\n",
              "      <td>0.783781</td>\n",
              "      <td>963.206916</td>\n",
              "      <td>3.588444e+04</td>\n",
              "      <td>0.538288</td>\n",
              "      <td>0.084404</td>\n",
              "      <td>0.778405</td>\n",
              "      <td>0.677230</td>\n",
              "      <td>862.168977</td>\n",
              "      <td>464.137228</td>\n",
              "      <td>29.731848</td>\n",
              "      <td>979.414536</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>370.000000</td>\n",
              "      <td>6.380000e+02</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>370.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1900.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>3.228750e+05</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.750000</td>\n",
              "      <td>1460.000000</td>\n",
              "      <td>5.000750e+03</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1190.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1951.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>4.609435e+05</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>2.250000</td>\n",
              "      <td>1980.000000</td>\n",
              "      <td>7.683000e+03</td>\n",
              "      <td>1.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1590.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1976.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>6.549625e+05</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>2.500000</td>\n",
              "      <td>2620.000000</td>\n",
              "      <td>1.100125e+04</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>2300.000000</td>\n",
              "      <td>610.000000</td>\n",
              "      <td>1997.000000</td>\n",
              "      <td>1999.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>2.659000e+07</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>13540.000000</td>\n",
              "      <td>1.074218e+06</td>\n",
              "      <td>3.500000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>9410.000000</td>\n",
              "      <td>4820.000000</td>\n",
              "      <td>2014.000000</td>\n",
              "      <td>2014.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              price     bedrooms  ...     yr_built  yr_renovated\n",
              "count  4.600000e+03  4600.000000  ...  4600.000000   4600.000000\n",
              "mean   5.519630e+05     3.400870  ...  1970.786304    808.608261\n",
              "std    5.638347e+05     0.908848  ...    29.731848    979.414536\n",
              "min    0.000000e+00     0.000000  ...  1900.000000      0.000000\n",
              "25%    3.228750e+05     3.000000  ...  1951.000000      0.000000\n",
              "50%    4.609435e+05     3.000000  ...  1976.000000      0.000000\n",
              "75%    6.549625e+05     4.000000  ...  1997.000000   1999.000000\n",
              "max    2.659000e+07     9.000000  ...  2014.000000   2014.000000\n",
              "\n",
              "[8 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IyBbeevx1Pyq"
      },
      "source": [
        "# feature scaling"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DuFBYCiGf-Em"
      },
      "source": [
        "real_y=[]\n",
        "real_y.append(data[\"price\"])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZmeVb_XhwLF-"
      },
      "source": [
        "from sklearn import preprocessing\n",
        "\"\"\"digits = sklearn.datasets.load_digits()\n",
        "X= digits.data\n",
        "Y= digits.target\n",
        "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.3,train_size=0.7)\"\"\"\n",
        "real_y.append(data[\"price\"])\n",
        "real_y = preprocessing.StandardScaler().fit(real_y)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VemyuU_WgaP-"
      },
      "source": [
        "x.append(data[\"view\"])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8v894nDsiwSP"
      },
      "source": [
        "x.append(data[\"bathrooms\"])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hJMmiMVYjY98"
      },
      "source": [
        "c=np.transpose(x)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bXPppwMIjgM3"
      },
      "source": [
        "tetha=np.random.randn(4600, 2)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4QU8NzFYn3sD"
      },
      "source": [
        "tetha=np.transpose(tetha)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a62Rlwx8oZCn"
      },
      "source": [
        "y_pred=c.dot(tetha)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P9F6P_YFuiWk"
      },
      "source": [
        "# cost function"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QoS0I1jfxnDc"
      },
      "source": [
        "real_y.shape"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9nJTvp5nokxX"
      },
      "source": [
        "cost = -1/4600 * np.sum(real_y * np.log(c) + (1-real_y) * (np.log(1-c)))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SfV5g7t_yz1B"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kjDkzaNAdqCm"
      },
      "source": [
        "# variable"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yZBjjPkDeiPS",
        "outputId": "47c55c99-3377-49a3-a815-a8033c99f4e1"
      },
      "source": [
        "def define_globalvar():\n",
        "  global y\n",
        "  y=7\n",
        "  print(y)\n",
        "#define_globalvar()\n",
        "\n",
        "print(y)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "7\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HzGat7tme5OM",
        "outputId": "86278740-1e67-435d-c7fe-589f34217eaf"
      },
      "source": [
        "x=10\n",
        "y=10\n",
        "z=20\n",
        "x is y"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9DX5-ARbfpK6",
        "outputId": "d5fca8c7-d965-42b0-dd53-b40088450cbd"
      },
      "source": [
        "x is not z"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E0iuXnshfuML"
      },
      "source": [
        "#membership variable"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L2hHD5lofw6u",
        "outputId": "91950409-ba7a-4990-8377-a7ceb2952c73"
      },
      "source": [
        "t=[1,2,3]\n",
        "3 in t"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6KaHvcs3frlc",
        "outputId": "514e438f-d6ae-425c-8d3a-ec821aa1c0f8"
      },
      "source": [
        "4 not in t"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wvp61Kd7f7-f",
        "outputId": "a854c1fc-796f-403e-c63f-a8432a68b056"
      },
      "source": [
        "first=\"marry\"\n",
        "last=\"brown\"\n",
        "print(f\"hello my name is {first} and family is {last}\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "hello my name is marry and family is brown\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LdK6dhRMh-KB",
        "outputId": "7e2352e0-4b03-4c19-a5be-1965b8719c2e"
      },
      "source": [
        "for i in range(10):\n",
        "  if i==5: #skip current iter\n",
        "    continue\n",
        "  print(i, end=\"\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "012346789"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DEh305hfkAHV",
        "outputId": "892cbb70-23fd-422c-ad36-73bb0603427f"
      },
      "source": [
        "l=[\"apple\",\"banana\",\"cherry\",\"peach\"]\n",
        "l.append(\"pineapple\")\n",
        "print(l)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['apple', 'banana', 'cherry', 'peach', 'pineapple']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gIWZhy05k4wI"
      },
      "source": [
        "l.insert(0,\"brinja\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RPyDMh6flGAJ",
        "outputId": "c10d1625-16f0-4a47-b8dc-b2fa384b39ea"
      },
      "source": [
        "l.remove(\"peach\")\n",
        "l.pop()\n",
        "print(l)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['brinja', 'apple', 'banana', 'cherry']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zUvawErVlSI3",
        "outputId": "7b51fb7b-c82e-4b4a-f9e3-c2142c308781"
      },
      "source": [
        "lcopy=[1,2,3,\"pen\"]\n",
        "print(lcopy)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[1, 2, 3, 'pen']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LUkvASrxlhl1",
        "outputId": "095f5799-bcf3-4bd9-fac1-322cbf53bc8a"
      },
      "source": [
        "l.reverse()\n",
        "print(l)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['cherry', 'banana', 'apple', 'brinja']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mTr_ngqplopO"
      },
      "source": [
        "l.sort()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UTdEEhckmhU3"
      },
      "source": [
        "c=[1,20]\n",
        "lcopy.extend(c)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NS5FepbVmrNN",
        "outputId": "d97f9a35-60a4-4f60-924e-c2e0a1ac79e3"
      },
      "source": [
        "print(lcopy)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['pen', 3, 2, 1, 1, 20]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jCXir0wMmtrK",
        "outputId": "7870e941-7a3b-418f-a095-9b09690ac60d"
      },
      "source": [
        "len(lcopy)\n",
        "lcopy.index(1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BzNLN7Nhnu_R"
      },
      "source": [
        "#tuple"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0g3zv-g0nwX2",
        "outputId": "172be0e4-cd0b-4df0-eda2-33f16adf3944"
      },
      "source": [
        "s=(1,2,3,4,5,6,7,8,9)\n",
        "t=(34)\n",
        "s.index(1)\n",
        "len(s)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "9"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ljPYCGT0o3Z4"
      },
      "source": [
        "# dictionary\n",
        "unordered collection of data value"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TmAbRjsLnrPR"
      },
      "source": [
        "d={\"brand\":\"ford\",\"model\":\"Mustang\",\"year\":1997}"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "xR7chWATpYmE",
        "outputId": "a1d8390d-0679-4963-f83e-7be54e5a533e"
      },
      "source": [
        "d.get(\"model\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'Mustang'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "8jW8x1riphVp",
        "outputId": "1b29a27d-0218-4dac-bffd-4582ad3e4248"
      },
      "source": [
        "d[\"model\"]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'Mustang'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l4pahyK0piK_"
      },
      "source": [
        "d[\"year\"]=1964"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qcF7by1dpiiG"
      },
      "source": [
        "d[\"color\"]=red"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nUqxHeLgqXU1"
      },
      "source": [
        "Dict1={'dict1':{},'dict2':{}}\n",
        "Dict = { 'Dict1': {'name': 'Ali', 'age': '30'},\n",
        "         'Dict2': {'name': 'Hanieh', 'age': '32'}}"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Dt1Og1C5qrx9",
        "outputId": "16876be1-a870-4f57-a9bc-3097ddbc4449"
      },
      "source": [
        "Dict['Dict1']"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'age': '30', 'name': 'Ali'}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "Eb1xIRhkq5nJ",
        "outputId": "4d2e09d8-9a12-44cc-e6c3-892bb108866e"
      },
      "source": [
        "Dict[\"Dict1\"][\"name\"]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'Ali'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FnF97eQkrEJa",
        "outputId": "aa15150c-6fa0-423d-a42a-1b622e0203fd"
      },
      "source": [
        "Dict.pop(\"Dict1\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'age': '30', 'name': 'Ali'}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-ObHl3vArIvz",
        "outputId": "553398cb-c4e6-47f4-cb4d-3d95c3211010"
      },
      "source": [
        "Dict"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'Dict2': {'age': '32', 'name': 'Hanieh'}}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3_5vbmxtreCG",
        "outputId": "04cf718b-0f32-42e6-aeb7-4bdfcda76c38"
      },
      "source": [
        "for name in Dict.items():\n",
        "  print (name)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "('Dict1', {'name': 'Ali', 'age': '30'})\n",
            "('Dict2', {'name': 'Hanieh', 'age': '32'})\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L-PTIgx5r3Bq",
        "outputId": "014e5be3-e395-4966-a008-4cc3dbfd955a"
      },
      "source": [
        "for i,j in Dict.items():\n",
        "  for name in j:\n",
        "    print(j[name])\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Ali\n",
            "30\n",
            "Hanieh\n",
            "32\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t_gCBFwpsl95"
      },
      "source": [
        "s={\"apple\",\"pie\",\"eggplant\"}\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ACxXYhtatGdb"
      },
      "source": [
        "s.add(\"tomato\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y1t_STIvtNO2",
        "outputId": "6f88c00e-fe81-4ba9-f3c8-263e26359dfd"
      },
      "source": [
        "s"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'apple', 'eggplant', 'pie', 'tomato'}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a1-eGAzGtOEb"
      },
      "source": [
        "s.update({\"cucamber\",\"orange\"}) # more than 2 items"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YCXywlsmtgrd"
      },
      "source": [
        "#s.discard(\"mango\")\n",
        "s.discard(\"pie\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4UY2v-mytpbD",
        "outputId": "7b41dcb3-1433-41ce-d955-41676680bb7c"
      },
      "source": [
        "s={\"apple\",\"pie\",\"eggplant\",\"pie\"}\n",
        "s.union()\n",
        "s"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'apple', 'eggplant', 'pie'}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ivrTzV7Qwu4u",
        "outputId": "fcb12292-dd06-4887-ca6a-143342e6c0ee"
      },
      "source": [
        "A={1,2,3,4}\n",
        "B={6,2,5}\n",
        "A & B"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{2}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FBCNHSG4xZXs",
        "outputId": "a3866131-b5d9-406a-9436-d1210f8c6acd"
      },
      "source": [
        "A.intersection(B)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{2}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 87
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t32rfqljxhbX",
        "outputId": "8f20843c-9a4a-4524-dc64-21488536b041"
      },
      "source": [
        "A-B"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{1, 3, 4}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4FFTEzpTxrCq",
        "outputId": "04e1bd66-dcc0-40b2-9085-140716102a50"
      },
      "source": [
        "A ^ B"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{1, 3, 4, 5, 6}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 89
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r91ZioVRymOB"
      },
      "source": [
        "# stack LIFO\n",
        "# Fifo"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EKqimVlrx5rC",
        "outputId": "884397e5-16a8-4a36-8126-dcb4828450e4"
      },
      "source": [
        "from collections import deque\n",
        "numbers=deque()\n",
        "numbers.append(1)\n",
        "numbers.append(2)\n",
        "type(numbers)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "collections.deque"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 93
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H4fwoH490zVt"
      },
      "source": [
        "# lambda\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jNz33LFqz83O"
      },
      "source": [
        "# worst casetime complexity O\n",
        "score=[50,55,60,70,75,80,99,100]\n",
        "def score_o(x):\n",
        "  if x>50:\n",
        "    return True\n",
        "  else:\n",
        "    return False\n",
        "passe=filter(score_o,score)\n",
        "for i in passe:\n",
        "  print(i)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VP20T56OF-HG"
      },
      "source": [
        "def percent(x):\n",
        "  return x/5\n",
        "k=map(percent, score)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SLM5z4HyIOUT",
        "outputId": "d668481c-9980-420f-86bd-6f432235fcd7"
      },
      "source": [
        "list(k)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[12.0, 14.0, 15.0, 16.0, 19.8, 20.0]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TlN5zzVxJHaz",
        "outputId": "f13d6766-41f2-4b05-8cd3-8ac20586cee8"
      },
      "source": [
        "l={num:num**2 for num in range(10)}\n",
        "print(l)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{0: 0, 1: 1, 2: 4, 3: 9, 4: 16, 5: 25, 6: 36, 7: 49, 8: 64, 9: 81}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ghontZ92RMTC"
      },
      "source": [
        "p=[i**2 for i in range(10) if i>5]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AFt8N6FQR5G_",
        "outputId": "47916c30-10f8-4ea0-e222-d7183cd1723b"
      },
      "source": [
        "print(p)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[36, 49, 64, 81]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Gck8J-8aS44b"
      },
      "source": [
        "#eval"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YxZjgW5US6MB",
        "outputId": "b36b9e20-74be-4d43-9166-945842dad4a1"
      },
      "source": [
        "x=3\n",
        "eval('x**2+x*3+1')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "19"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 209
        },
        "id": "eHhwsjXFSNen",
        "outputId": "f0ee9e73-2c3b-474e-e8a8-59cf36ed78a8"
      },
      "source": [
        "class Person:\n",
        "  def __init__(self,name,family):\n",
        "    self.name=name\n",
        "    self.family=family\n",
        "\n",
        "p=Person(\"nikita\",\"Brown\")\n",
        "p.name\n",
        "del p\n",
        "p.name"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-26-5ea30bfc917a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0mp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;32mdel\u001b[0m \u001b[0mp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m \u001b[0mp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m: name 'p' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9RR6ONUCTtFR"
      },
      "source": [
        "class Student(Person):\n",
        "  def __init__(self,name,family,age):\n",
        "    super().__init__(name,family)\n",
        "    self.age=age\n",
        "s=Student(\"ali\",\"solhi\",20)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "JELHUj-bUlSE",
        "outputId": "cebe26de-b4a0-4836-bb48-d83bec7ba559"
      },
      "source": [
        "s.name"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'ali'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mQLHA2XcUwDM",
        "outputId": "9e554576-3723-464c-f72e-3ca5b58c680f"
      },
      "source": [
        "import time\n",
        "seconds = time.time()\n",
        "x=12\n",
        "y=16\n",
        "print(x+y)\n",
        "first=time.time()\n",
        "print (first+seconds)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "28\n",
            "3258079820.4889183\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gow4VmirVqaG",
        "outputId": "5d99e445-4eb4-411c-cbaf-60f248dfb33a"
      },
      "source": [
        "from datetime import date\n",
        "date.today().month"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "8"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AA1ZtDPwvnC3"
      },
      "source": [
        "# regular expression\n",
        "meta char:\n",
        "built in block for regular expression"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J0SlH1-XWZaO"
      },
      "source": [
        "import re"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DZ57K5pLAGXe"
      },
      "source": [
        "txt=\"there Hello world\""
      ],
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xouNyjjzvsBr"
      },
      "source": [
        "re.search(\"[There]\",txt) # A set of characters\n",
        "re.search(\"^There\",txt)  # Starts with\n",
        "re.search(\"world$\",txt)  # Ends with"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "05IAsOG6F1YP",
        "outputId": "1273a443-770b-4ef7-a955-26754721b6e2"
      },
      "source": [
        "f=re.findall(\"world\",txt)\n",
        "print(f)    "
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['world', 'world']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uGaKCYwWwUZk",
        "outputId": "ed89b556-0c78-4a52-b254-871f25dc39e6"
      },
      "source": [
        "x = re.split(\"\\s\", txt)\n",
        "print(x) "
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['there', 'Hello', 'world']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PvxV1wkYQA7v",
        "outputId": "8f2fd70c-b95c-4735-d0af-ddfad70e1717"
      },
      "source": [
        "x = re.sub(\"\\s\", \",\", txt)\n",
        "print(x) "
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "there,Hello,world\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KHz8fF0YTgbA",
        "outputId": "6024af7e-e792-4956-f85c-c47225afefc9"
      },
      "source": [
        "x=re.search(\"\\Athere\",txt)\n",
        "print(x)"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<re.Match object; span=(0, 5), match='there'>\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3nDS3NpMUXgN",
        "outputId": "a7300fd2-6049-47fd-fcdb-dcaf4e0188dc"
      },
      "source": [
        "x=re.findall(r\"\\bwo\",txt)\n",
        "print(x)"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['wo']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H41rThSfVOOe",
        "outputId": "b452b34b-397a-47c4-d907-6d19f53ae6ae"
      },
      "source": [
        "txt=\"the 3 third of July\"\n",
        "x=re.sub(\"\\s\",\"\",txt)\n",
        "z=re.findall(\"\\D\",x)\n",
        "y=re.findall(\"\\d\",x)# show digit \n",
        "print(z,y,end=\" \")"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['t', 'h', 'e', 't', 'h', 'i', 'r', 'd', 'o', 'f', 'J', 'u', 'l', 'y'] ['3'] "
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sjU22E4GZyDg"
      },
      "source": [
        "# find most repeated character"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-6kgjCfAYwn9",
        "outputId": "8e67ceff-d85c-4b0f-d6b1-b446dc336777"
      },
      "source": [
        "\n",
        "import re\n",
        "Text=\"\"\"This 10 modules are specified\n",
        "at object creation time by using a type code, which is a single character.\"\"\"\n",
        "Text=re.sub(\"\\s\",\"\",Text) # remove spaces\n",
        "tlist=re.findall(\"\\D\",Text) # remove digits number and convert to list\n",
        "\n",
        "x={} # def dict as I need key and value key is letter value tedad\n",
        "for i in set(tlist): # i is unique\n",
        "    x.update({i:tlist.count(i)}) # update x value in loop\n",
        "v=sorted(x.items(), key=lambda d: d[1],reverse=True)#sort\n",
        "print(v[0]) # print most repeated char"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "('e', 11)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pL_M4KSJRcy_",
        "outputId": "b8ff35a4-5a2a-455a-f4db-872c6644a3b4"
      },
      "source": [
        "\n",
        "txt = \"The rain in Spain\"\n",
        "x = re.search(\"a\", txt)\n",
        "print(x.group()) "
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "a\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-w9nkRruwpgK",
        "outputId": "158de483-c4a1-4a97-e153-601ee3afc9f4"
      },
      "source": [
        "\n",
        "d=re.search(\"^T\",txt) #Starts with\n",
        "print(f\"s start with {d}\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "s start with <re.Match object; span=(0, 1), match='T'>\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fnZoniVgw_Rn",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "98d2ae13-3f4e-49bf-88f8-6a3d0c609854"
      },
      "source": [
        "f=re.search(\"n$\",s)\n",
        "print(f\" f is end with n {f}\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            " f is end with n <re.Match object; span=(5, 6), match='n'>\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D1-xti-GxXFG"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hboOhAKwgUUr"
      },
      "source": [
        "#numpy"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uAlpSSZYgVZZ"
      },
      "source": [
        "import numpy as np\n",
        "ar=np.array([1,2,3,4,5,6])"
      ],
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6htBbHuUgd40"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LvFS3jhbgefK",
        "outputId": "ed57a630-aa28-4914-8d16-d8b47839e49d"
      },
      "source": [
        "np.cos(ar)"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 0.54030231, -0.41614684, -0.9899925 , -0.65364362,  0.28366219,\n",
              "        0.96017029])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I7kIUvFUgiQz",
        "outputId": "26b43d3b-3bac-4d6a-d34b-ccbfd97b81b7"
      },
      "source": [
        "np.log(ar)"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.        , 0.69314718, 1.09861229, 1.38629436, 1.60943791,\n",
              "       1.79175947])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mG2jx6magjBr",
        "outputId": "c325a6a6-7a59-451d-f9b3-53f15717049e"
      },
      "source": [
        "np.median(ar)"
      ],
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3.5"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tKSq-hkPgjED",
        "outputId": "1445f1c5-2b85-448e-fa3d-020e9e4ae71d"
      },
      "source": [
        "np.mean(ar)"
      ],
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3.5"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JZdZnDaFgjXI",
        "outputId": "d107261d-3969-4f30-ffbe-187f85ebf086"
      },
      "source": [
        "np.std(ar)"
      ],
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.707825127659933"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2wd5Uweug2ed",
        "outputId": "b11b8c81-8121-4904-ea32-893a36492234"
      },
      "source": [
        "np.ones((5,2,3))"
      ],
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[[1., 1., 1.],\n",
              "        [1., 1., 1.]],\n",
              "\n",
              "       [[1., 1., 1.],\n",
              "        [1., 1., 1.]],\n",
              "\n",
              "       [[1., 1., 1.],\n",
              "        [1., 1., 1.]],\n",
              "\n",
              "       [[1., 1., 1.],\n",
              "        [1., 1., 1.]],\n",
              "\n",
              "       [[1., 1., 1.],\n",
              "        [1., 1., 1.]]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZbwUNTThg2g4",
        "outputId": "6ae97dd4-623d-4b7f-ac99-b599bf94e16d"
      },
      "source": [
        "a=np.array([1.2,3.2,5.6,9.1])\n",
        "b=np.round(a)\n",
        "b"
      ],
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1., 3., 6., 9.])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lD71Is_1hRkz",
        "outputId": "2b92327f-e2ea-44cc-ea7f-8ccd7d4be2b0"
      },
      "source": [
        "c=np.linspace(0,10,5)\n",
        "c=np.round(c,2)\n"
      ],
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 0. ,  2.5,  5. ,  7.5, 10. ])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-J6VmpJgiIUe"
      },
      "source": [
        "# panda"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9Sdv-X_qiJsh"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn import datasets\n",
        "data= datasets.load_iris()\n",
        "#c=pd.read_csv(data)\n",
        "timeseries=pd.date_range('1/9/2020',periods=30)"
      ],
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nQvwzE4IiJuy"
      },
      "source": [
        "df=pd.DataFrame(np.random.randn(30,5),index=timeseries)"
      ],
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "iN15TxTXhRnu",
        "outputId": "1c7b3326-385c-4b9c-febc-053429520d94"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2020-01-09</th>\n",
              "      <td>-0.204873</td>\n",
              "      <td>0.656293</td>\n",
              "      <td>1.065664</td>\n",
              "      <td>0.582068</td>\n",
              "      <td>-0.356229</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-10</th>\n",
              "      <td>-1.379822</td>\n",
              "      <td>1.623496</td>\n",
              "      <td>1.868442</td>\n",
              "      <td>-0.547354</td>\n",
              "      <td>-0.344235</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-11</th>\n",
              "      <td>1.642781</td>\n",
              "      <td>-0.713615</td>\n",
              "      <td>-0.516867</td>\n",
              "      <td>-1.135334</td>\n",
              "      <td>0.969807</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-12</th>\n",
              "      <td>2.218630</td>\n",
              "      <td>1.178032</td>\n",
              "      <td>0.717944</td>\n",
              "      <td>0.362562</td>\n",
              "      <td>-0.012769</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-13</th>\n",
              "      <td>0.214956</td>\n",
              "      <td>-0.783728</td>\n",
              "      <td>-0.672788</td>\n",
              "      <td>-2.009774</td>\n",
              "      <td>-0.827673</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                   0         1         2         3         4\n",
              "2020-01-09 -0.204873  0.656293  1.065664  0.582068 -0.356229\n",
              "2020-01-10 -1.379822  1.623496  1.868442 -0.547354 -0.344235\n",
              "2020-01-11  1.642781 -0.713615 -0.516867 -1.135334  0.969807\n",
              "2020-01-12  2.218630  1.178032  0.717944  0.362562 -0.012769\n",
              "2020-01-13  0.214956 -0.783728 -0.672788 -2.009774 -0.827673"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "70GYjXsZiHdm",
        "outputId": "0707ec7b-57ee-48d9-c67d-951529a51d25"
      },
      "source": [
        "l=df[0]#colomn\n",
        "k=l[3]# row\n",
        "k"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2.2186299053202654"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "3UnLVrA2iHgD",
        "outputId": "f9de7673-a867-41cf-bdf9-4e16271317ba"
      },
      "source": [
        "df[::3]"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2020-01-09</th>\n",
              "      <td>-0.204873</td>\n",
              "      <td>0.656293</td>\n",
              "      <td>1.065664</td>\n",
              "      <td>0.582068</td>\n",
              "      <td>-0.356229</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-12</th>\n",
              "      <td>2.218630</td>\n",
              "      <td>1.178032</td>\n",
              "      <td>0.717944</td>\n",
              "      <td>0.362562</td>\n",
              "      <td>-0.012769</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-15</th>\n",
              "      <td>-0.015819</td>\n",
              "      <td>-1.429535</td>\n",
              "      <td>1.460178</td>\n",
              "      <td>-0.816152</td>\n",
              "      <td>0.640839</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-18</th>\n",
              "      <td>0.220047</td>\n",
              "      <td>0.258504</td>\n",
              "      <td>-0.078064</td>\n",
              "      <td>-1.303408</td>\n",
              "      <td>0.536591</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-21</th>\n",
              "      <td>1.564967</td>\n",
              "      <td>-1.065176</td>\n",
              "      <td>0.832689</td>\n",
              "      <td>-0.383956</td>\n",
              "      <td>1.049746</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-24</th>\n",
              "      <td>-0.634805</td>\n",
              "      <td>-1.574496</td>\n",
              "      <td>-1.706127</td>\n",
              "      <td>-0.016076</td>\n",
              "      <td>0.780854</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-27</th>\n",
              "      <td>-1.189199</td>\n",
              "      <td>-0.343762</td>\n",
              "      <td>-0.015135</td>\n",
              "      <td>-0.053613</td>\n",
              "      <td>0.234728</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-30</th>\n",
              "      <td>-0.804493</td>\n",
              "      <td>-1.668086</td>\n",
              "      <td>1.616271</td>\n",
              "      <td>0.906198</td>\n",
              "      <td>-0.454494</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-02</th>\n",
              "      <td>0.803970</td>\n",
              "      <td>-1.914004</td>\n",
              "      <td>0.826992</td>\n",
              "      <td>1.352560</td>\n",
              "      <td>1.644904</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-05</th>\n",
              "      <td>-1.512077</td>\n",
              "      <td>-1.619856</td>\n",
              "      <td>-0.358940</td>\n",
              "      <td>0.519907</td>\n",
              "      <td>-1.290761</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                   0         1         2         3         4\n",
              "2020-01-09 -0.204873  0.656293  1.065664  0.582068 -0.356229\n",
              "2020-01-12  2.218630  1.178032  0.717944  0.362562 -0.012769\n",
              "2020-01-15 -0.015819 -1.429535  1.460178 -0.816152  0.640839\n",
              "2020-01-18  0.220047  0.258504 -0.078064 -1.303408  0.536591\n",
              "2020-01-21  1.564967 -1.065176  0.832689 -0.383956  1.049746\n",
              "2020-01-24 -0.634805 -1.574496 -1.706127 -0.016076  0.780854\n",
              "2020-01-27 -1.189199 -0.343762 -0.015135 -0.053613  0.234728\n",
              "2020-01-30 -0.804493 -1.668086  1.616271  0.906198 -0.454494\n",
              "2020-02-02  0.803970 -1.914004  0.826992  1.352560  1.644904\n",
              "2020-02-05 -1.512077 -1.619856 -0.358940  0.519907 -1.290761"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VJi-uHgsiHib"
      },
      "source": [
        "\n",
        "timeseries=pd.date_range('1/9/2020',periods=20)\n",
        "\n",
        "timeseries2=pd.date_range('9/1/2021',periods=20)"
      ],
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RgkXdJfjiH0h"
      },
      "source": [
        "data1=pd.DataFrame(np.random.rand(20,5),index=timeseries,columns=[\"order_id\",\"name\",\n",
        "                                                                  \"profit\",\"shipid\",\"sales\"])"
      ],
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q1eTyQMohRqM"
      },
      "source": [
        "data2=pd.DataFrame(np.random.rand(20,3),index=timeseries2,columns=[\"order_id\",\"order_date\",\"ID\"])"
      ],
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CcLZveRoNmWw"
      },
      "source": [
        "for i in range(20):\n",
        "  data1.loc[\"op_id\"]=i\n"
      ],
      "execution_count": 73,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zAfaZw2YP5_0",
        "outputId": "2e5a13da-3e9e-4d00-a15e-3d87cda82458"
      },
      "source": [
        "data1['op_id'] "
      ],
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2020-01-09 00:00:00    59\n",
              "2020-01-10 00:00:00    59\n",
              "2020-01-11 00:00:00    59\n",
              "2020-01-12 00:00:00    59\n",
              "2020-01-13 00:00:00    59\n",
              "2020-01-14 00:00:00    59\n",
              "2020-01-15 00:00:00    59\n",
              "2020-01-16 00:00:00    59\n",
              "2020-01-17 00:00:00    59\n",
              "2020-01-18 00:00:00    59\n",
              "2020-01-19 00:00:00    59\n",
              "2020-01-20 00:00:00    59\n",
              "2020-01-21 00:00:00    59\n",
              "2020-01-22 00:00:00    59\n",
              "2020-01-23 00:00:00    59\n",
              "2020-01-24 00:00:00    59\n",
              "2020-01-25 00:00:00    59\n",
              "2020-01-26 00:00:00    59\n",
              "2020-01-27 00:00:00    59\n",
              "2020-01-28 00:00:00    59\n",
              "op_id                  19\n",
              "Name: op_id, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zw4oBacIOwHL",
        "outputId": "6b32f484-d6b5-4271-d70f-8684fd5dca7e"
      },
      "source": [
        "d=np.zeros(60)\n",
        "for i in range(60):\n",
        "  d[i]=i\n",
        "d"
      ],
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 0.,  1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10., 11., 12.,\n",
              "       13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24., 25.,\n",
              "       26., 27., 28., 29., 30., 31., 32., 33., 34., 35., 36., 37., 38.,\n",
              "       39., 40., 41., 42., 43., 44., 45., 46., 47., 48., 49., 50., 51.,\n",
              "       52., 53., 54., 55., 56., 57., 58., 59.])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7Yqrh8rQQ-eX"
      },
      "source": [
        "data1['New_ID'] = range(21)\n",
        "data2['New_ID'] = range(20)"
      ],
      "execution_count": 76,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EuHC28FuhRxu"
      },
      "source": [
        "merging=pd.merge(data1,data2, on=\"New_ID\")"
      ],
      "execution_count": 77,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "OuRGDgCmhR0F",
        "outputId": "6df13c0f-4fff-4c43-c2c7-5d31cd7503d6"
      },
      "source": [
        "merging.head()"
      ],
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>order_id_x</th>\n",
              "      <th>name</th>\n",
              "      <th>profit</th>\n",
              "      <th>shipid</th>\n",
              "      <th>sales</th>\n",
              "      <th>op</th>\n",
              "      <th>op_id_x</th>\n",
              "      <th>New_ID</th>\n",
              "      <th>order_id_y</th>\n",
              "      <th>order_date</th>\n",
              "      <th>ID</th>\n",
              "      <th>op_id_y</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.829086</td>\n",
              "      <td>0.414767</td>\n",
              "      <td>0.359064</td>\n",
              "      <td>0.414957</td>\n",
              "      <td>0.091449</td>\n",
              "      <td>59</td>\n",
              "      <td>59</td>\n",
              "      <td>0</td>\n",
              "      <td>0.501408</td>\n",
              "      <td>0.311616</td>\n",
              "      <td>0.350900</td>\n",
              "      <td>59</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.608561</td>\n",
              "      <td>0.500733</td>\n",
              "      <td>0.136809</td>\n",
              "      <td>0.696423</td>\n",
              "      <td>0.579013</td>\n",
              "      <td>59</td>\n",
              "      <td>59</td>\n",
              "      <td>1</td>\n",
              "      <td>0.447630</td>\n",
              "      <td>0.226996</td>\n",
              "      <td>0.206651</td>\n",
              "      <td>59</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.706676</td>\n",
              "      <td>0.436974</td>\n",
              "      <td>0.280071</td>\n",
              "      <td>0.156248</td>\n",
              "      <td>0.360004</td>\n",
              "      <td>59</td>\n",
              "      <td>59</td>\n",
              "      <td>2</td>\n",
              "      <td>0.490223</td>\n",
              "      <td>0.990357</td>\n",
              "      <td>0.435717</td>\n",
              "      <td>59</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.907964</td>\n",
              "      <td>0.878812</td>\n",
              "      <td>0.603285</td>\n",
              "      <td>0.696813</td>\n",
              "      <td>0.427666</td>\n",
              "      <td>59</td>\n",
              "      <td>59</td>\n",
              "      <td>3</td>\n",
              "      <td>0.944644</td>\n",
              "      <td>0.036474</td>\n",
              "      <td>0.284522</td>\n",
              "      <td>59</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.967215</td>\n",
              "      <td>0.401988</td>\n",
              "      <td>0.440622</td>\n",
              "      <td>0.892037</td>\n",
              "      <td>0.243800</td>\n",
              "      <td>59</td>\n",
              "      <td>59</td>\n",
              "      <td>4</td>\n",
              "      <td>0.682363</td>\n",
              "      <td>0.353533</td>\n",
              "      <td>0.102613</td>\n",
              "      <td>59</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   order_id_x      name    profit  ...  order_date        ID  op_id_y\n",
              "0    0.829086  0.414767  0.359064  ...    0.311616  0.350900       59\n",
              "1    0.608561  0.500733  0.136809  ...    0.226996  0.206651       59\n",
              "2    0.706676  0.436974  0.280071  ...    0.990357  0.435717       59\n",
              "3    0.907964  0.878812  0.603285  ...    0.036474  0.284522       59\n",
              "4    0.967215  0.401988  0.440622  ...    0.353533  0.102613       59\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fHzaFsLYgjZa",
        "outputId": "bf6ae7d6-2c37-440f-fd94-799acfa7f5b0"
      },
      "source": [
        "merging.iloc[1]"
      ],
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "order_id_x     0.608561\n",
              "name           0.500733\n",
              "profit         0.136809\n",
              "shipid         0.696423\n",
              "sales          0.579013\n",
              "op            59.000000\n",
              "op_id_x       59.000000\n",
              "New_ID         1.000000\n",
              "order_id_y     0.447630\n",
              "order_date     0.226996\n",
              "ID             0.206651\n",
              "op_id_y       59.000000\n",
              "Name: 1, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AwIipS-yRZX5",
        "outputId": "f1e646db-7930-451a-b08f-f43632bdad05"
      },
      "source": [
        "merging.loc[1]"
      ],
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "order_id_x     0.608561\n",
              "name           0.500733\n",
              "profit         0.136809\n",
              "shipid         0.696423\n",
              "sales          0.579013\n",
              "op            59.000000\n",
              "op_id_x       59.000000\n",
              "New_ID         1.000000\n",
              "order_id_y     0.447630\n",
              "order_date     0.226996\n",
              "ID             0.206651\n",
              "op_id_y       59.000000\n",
              "Name: 1, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EOKIEHoSRrFP"
      },
      "source": [
        "def func1(x):\n",
        "  return x/10\n",
        "merging[\"profit\"]=merging[\"profit\"].map(func1)\n"
      ],
      "execution_count": 87,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A0w4ya4fTF9V"
      },
      "source": [
        "def percent(x):\n",
        "  return x/100\n",
        "merging[\"op_id_x\"]=merging[\"op_id_x\"].map(percent)\n"
      ],
      "execution_count": 89,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HstmldjcSvWw",
        "outputId": "8ab5612a-f9fd-4cd9-ec34-19a8dce3db03",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 677
        }
      },
      "source": [
        "merging[[\"op_id_x\",'profit']].apply(percent)"
      ],
      "execution_count": 93,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>op_id_x</th>\n",
              "      <th>profit</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.0059</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.0059</td>\n",
              "      <td>0.059</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.0059</td>\n",
              "      <td>0.059</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.0059</td>\n",
              "      <td>0.059</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.0059</td>\n",
              "      <td>0.059</td>\n",
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              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.0059</td>\n",
              "      <td>0.059</td>\n",
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              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0.0059</td>\n",
              "      <td>0.059</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0.0059</td>\n",
              "      <td>0.059</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0.0059</td>\n",
              "      <td>0.059</td>\n",
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              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>0.0059</td>\n",
              "      <td>0.059</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>0.0059</td>\n",
              "      <td>0.059</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>0.0059</td>\n",
              "      <td>0.059</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>0.0059</td>\n",
              "      <td>0.059</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>0.0059</td>\n",
              "      <td>0.059</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>0.0059</td>\n",
              "      <td>0.059</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>0.0059</td>\n",
              "      <td>0.059</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>0.0059</td>\n",
              "      <td>0.059</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>0.0059</td>\n",
              "      <td>0.059</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>0.0059</td>\n",
              "      <td>0.059</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>0.0059</td>\n",
              "      <td>0.059</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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            ],
            "text/plain": [
              "    op_id_x  profit\n",
              "0    0.0059   0.059\n",
              "1    0.0059   0.059\n",
              "2    0.0059   0.059\n",
              "3    0.0059   0.059\n",
              "4    0.0059   0.059\n",
              "5    0.0059   0.059\n",
              "6    0.0059   0.059\n",
              "7    0.0059   0.059\n",
              "8    0.0059   0.059\n",
              "9    0.0059   0.059\n",
              "10   0.0059   0.059\n",
              "11   0.0059   0.059\n",
              "12   0.0059   0.059\n",
              "13   0.0059   0.059\n",
              "14   0.0059   0.059\n",
              "15   0.0059   0.059\n",
              "16   0.0059   0.059\n",
              "17   0.0059   0.059\n",
              "18   0.0059   0.059\n",
              "19   0.0059   0.059"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 93
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GEEnA8k9SysB",
        "outputId": "69b56908-c36e-44dc-add7-629e0dbc71c7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        }
      },
      "source": [
        "merging.head()"
      ],
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>order_id_x</th>\n",
              "      <th>name</th>\n",
              "      <th>profit</th>\n",
              "      <th>shipid</th>\n",
              "      <th>sales</th>\n",
              "      <th>op</th>\n",
              "      <th>op_id_x</th>\n",
              "      <th>New_ID</th>\n",
              "      <th>order_id_y</th>\n",
              "      <th>order_date</th>\n",
              "      <th>ID</th>\n",
              "      <th>op_id_y</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.829086</td>\n",
              "      <td>0.414767</td>\n",
              "      <td>5.9</td>\n",
              "      <td>0.414957</td>\n",
              "      <td>0.091449</td>\n",
              "      <td>59</td>\n",
              "      <td>0.59</td>\n",
              "      <td>0</td>\n",
              "      <td>0.501408</td>\n",
              "      <td>0.311616</td>\n",
              "      <td>0.350900</td>\n",
              "      <td>59</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.608561</td>\n",
              "      <td>0.500733</td>\n",
              "      <td>5.9</td>\n",
              "      <td>0.696423</td>\n",
              "      <td>0.579013</td>\n",
              "      <td>59</td>\n",
              "      <td>0.59</td>\n",
              "      <td>1</td>\n",
              "      <td>0.447630</td>\n",
              "      <td>0.226996</td>\n",
              "      <td>0.206651</td>\n",
              "      <td>59</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.706676</td>\n",
              "      <td>0.436974</td>\n",
              "      <td>5.9</td>\n",
              "      <td>0.156248</td>\n",
              "      <td>0.360004</td>\n",
              "      <td>59</td>\n",
              "      <td>0.59</td>\n",
              "      <td>2</td>\n",
              "      <td>0.490223</td>\n",
              "      <td>0.990357</td>\n",
              "      <td>0.435717</td>\n",
              "      <td>59</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.907964</td>\n",
              "      <td>0.878812</td>\n",
              "      <td>5.9</td>\n",
              "      <td>0.696813</td>\n",
              "      <td>0.427666</td>\n",
              "      <td>59</td>\n",
              "      <td>0.59</td>\n",
              "      <td>3</td>\n",
              "      <td>0.944644</td>\n",
              "      <td>0.036474</td>\n",
              "      <td>0.284522</td>\n",
              "      <td>59</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
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              "      <td>0.401988</td>\n",
              "      <td>5.9</td>\n",
              "      <td>0.892037</td>\n",
              "      <td>0.243800</td>\n",
              "      <td>59</td>\n",
              "      <td>0.59</td>\n",
              "      <td>4</td>\n",
              "      <td>0.682363</td>\n",
              "      <td>0.353533</td>\n",
              "      <td>0.102613</td>\n",
              "      <td>59</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   order_id_x      name  profit  ...  order_date        ID  op_id_y\n",
              "0    0.829086  0.414767     5.9  ...    0.311616  0.350900       59\n",
              "1    0.608561  0.500733     5.9  ...    0.226996  0.206651       59\n",
              "2    0.706676  0.436974     5.9  ...    0.990357  0.435717       59\n",
              "3    0.907964  0.878812     5.9  ...    0.036474  0.284522       59\n",
              "4    0.967215  0.401988     5.9  ...    0.353533  0.102613       59\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 94
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Gbfc6SDZT9Xa"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UxcdibFzUAVw"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7pzojNKMUAYZ"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TRb64gx1UAav"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XrSSE6xhUAmS"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u9wgVAZGUAo6"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uM9Y3vwJUArJ"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}