Exercise_2.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Nawel-Bellil/Data-analysis/blob/main/Exercise_2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1z31C-nAoQVr"
      },
      "source": [
        "# Ex2 - Getting and Knowing your Data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vcOCMP2_oQVw"
      },
      "source": [
        "This time we are going to pull data directly from the internet.\n",
        "Special thanks to: https://github.com/justmarkham for sharing the dataset and materials.\n",
        "\n",
        "### Step 1. Import the necessary libraries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "F49ELV9GoQVx"
      },
      "outputs": [],
      "source": [
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EFsDyAeooQVy"
      },
      "source": [
        "### Step 2. Import the dataset from this [address](https://raw.githubusercontent.com/justmarkham/DAT8/master/data/chipotle.tsv)."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "url = 'https://raw.githubusercontent.com/justmarkham/DAT8/master/data/chipotle.tsv'"
      ],
      "metadata": {
        "id": "l3NWiDzsuhQD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N050Jk46oQVy"
      },
      "source": [
        "### Step 3. Assign it to a variable called chipo."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "yuxf7zPooQVz"
      },
      "outputs": [],
      "source": [
        "chipo = pd.read_csv(url, sep = '\\t')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_jZmXfyzoQVz"
      },
      "source": [
        "### Step 4. See the first 10 entries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "scrolled": false,
        "id": "S_MMrV95oQVz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "926b3f7e-82b2-4cf3-f025-2b1d1a26704b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "20"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ctMTmPBroQV0"
      },
      "source": [
        "### Step 5. What is the number of observations in the dataset?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "JjuSOQcuoQV0"
      },
      "outputs": [],
      "source": [
        "# Solution 1\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Hl4TIeCjoQV1"
      },
      "outputs": [],
      "source": [
        "# Solution 2\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uWTB4ciHoQV2"
      },
      "source": [
        "### Step 6. What is the number of columns in the dataset?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7Ys3lOz4oQV2"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wHRozTDooQV2"
      },
      "source": [
        "### Step 7. Print the name of all the columns."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OJEHmt5DoQV3"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CNEGN-TroQV3"
      },
      "source": [
        "### Step 8. How is the dataset indexed?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "El0NcndFoQV3"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hg6wP9jwoQV3"
      },
      "source": [
        "### Step 9. Which was the most-ordered item?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pQ4eqk2YoQV3"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DBlu7PIGoQV3"
      },
      "source": [
        "### Step 10. For the most-ordered item, how many items were ordered?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "b5gptXMJoQV4"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6HQRWH3loQV4"
      },
      "source": [
        "### Step 11. What was the most ordered item in the choice_description column?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6icj2Q8SoQV4"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lgCOttQ9oQV4"
      },
      "source": [
        "### Step 12. How many items were orderd in total?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SFw7sHgioQV4"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KgrVkL9NoQV4"
      },
      "source": [
        "### Step 13. Turn the item price into a float"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C7TZvpjAoQV4"
      },
      "source": [
        "#### Step 13.a. Check the item price type"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "sKAIh2iToQV4"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FbGWiyqUoQV5"
      },
      "source": [
        "#### Step 13.b. Create a lambda function and change the type of item price"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": true,
        "id": "ALeDjUizoQV5"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ciEghzA8oQV5"
      },
      "source": [
        "#### Step 13.c. Check the item price type"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Lizb0lCjoQV5"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bXgkeZztoQV5"
      },
      "source": [
        "### Step 14. How much was the revenue for the period in the dataset?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BWippYyDoQV5"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dh3sKjXjoQV5"
      },
      "source": [
        "### Step 15. How many orders were made in the period?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ybTwgpInoQV5"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BVrOro-6oQV5"
      },
      "source": [
        "### Step 16. What is the average revenue amount per order?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6_Ei6g2voQV5"
      },
      "outputs": [],
      "source": [
        "# Solution 1\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fd5IRggKoQV6"
      },
      "outputs": [],
      "source": [
        "# Solution 2\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4iP-4klPoQV-"
      },
      "source": [
        "### Step 17. How many different items are sold?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "E-mwweGCoQV-"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "anaconda-cloud": {},
    "kernelspec": {
      "display_name": "Python [default]",
      "language": "python",
      "name": "python2"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 2
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython2",
      "version": "2.7.12"
    },
    "colab": {
      "provenance": [],
      "include_colab_link": true
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}