{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Visualizing Chipotle's Data" ] }, { "cell_type": "markdown", "metadata": {}, "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": 132, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import pandas as pd\n", "import collections\n", "import matplotlib.pyplot as plt \n", "\n", "# set this so the \n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Step 2. Import the dataset from this [address](https://raw.githubusercontent.com/justmarkham/DAT8/master/data/chipotle.tsv). " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Step 3. Assign it to a variable called chipo." ] }, { "cell_type": "code", "execution_count": 133, "metadata": { "collapsed": false }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Step 4. See the first 10 entries" ] }, { "cell_type": "code", "execution_count": 134, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "data": { "text/html": [ "
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order_idquantityitem_namechoice_descriptionitem_price
011Chips and Fresh Tomato SalsaNaN$2.39
111Izze[Clementine]$3.39
211Nantucket Nectar[Apple]$3.39
311Chips and Tomatillo-Green Chili SalsaNaN$2.39
422Chicken Bowl[Tomatillo-Red Chili Salsa (Hot), [Black Beans...$16.98
531Chicken Bowl[Fresh Tomato Salsa (Mild), [Rice, Cheese, Sou...$10.98
631Side of ChipsNaN$1.69
741Steak Burrito[Tomatillo Red Chili Salsa, [Fajita Vegetables...$11.75
841Steak Soft Tacos[Tomatillo Green Chili Salsa, [Pinto Beans, Ch...$9.25
951Steak Burrito[Fresh Tomato Salsa, [Rice, Black Beans, Pinto...$9.25
\n", "
" ], "text/plain": [ " order_id quantity item_name \\\n", "0 1 1 Chips and Fresh Tomato Salsa \n", "1 1 1 Izze \n", "2 1 1 Nantucket Nectar \n", "3 1 1 Chips and Tomatillo-Green Chili Salsa \n", "4 2 2 Chicken Bowl \n", "5 3 1 Chicken Bowl \n", "6 3 1 Side of Chips \n", "7 4 1 Steak Burrito \n", "8 4 1 Steak Soft Tacos \n", "9 5 1 Steak Burrito \n", "\n", " choice_description item_price \n", "0 NaN $2.39 \n", "1 [Clementine] $3.39 \n", "2 [Apple] $3.39 \n", "3 NaN $2.39 \n", "4 [Tomatillo-Red Chili Salsa (Hot), [Black Beans... $16.98 \n", "5 [Fresh Tomato Salsa (Mild), [Rice, Cheese, Sou... $10.98 \n", "6 NaN $1.69 \n", "7 [Tomatillo Red Chili Salsa, [Fajita Vegetables... $11.75 \n", "8 [Tomatillo Green Chili Salsa, [Pinto Beans, Ch... $9.25 \n", "9 [Fresh Tomato Salsa, [Rice, Black Beans, Pinto... $9.25 " ] }, "execution_count": 134, "metadata": {}, "output_type": "execute_result" } ], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Step 5. Create a histogram of the top 5 items bought" ] }, { "cell_type": "code", "execution_count": 135, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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z6vR/LU/FN3v27Oce9/X10dfXtyYxRkRMOP39/fT39zd1bkensJZ0KvAb4F1U7QjLJE0D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\ntv9G0mWUrpSNbM+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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Step 6. Create a scatterplot with the number of items orderered per order price\n", "#### Hint: Price should be in the X-axis and Items ordered in the Y-axis" ] }, { "cell_type": "code", "execution_count": 136, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(0, 40.0)" ] }, "execution_count": 136, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Step 7. BONUS: Create a question and a graph to answer your own question." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "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": "2.7.11" } }, "nbformat": 4, "nbformat_minor": 0 }