{ "cells": [ { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# Problem 1: Healthy Breakfast revisited\n", "In this exercise we will do some simple analyses on a data set that contains information about 77 types of breakfast cereal. You can read about the data set at [CMU's website](http://lib.stat.cmu.edu/DASL/Stories/HealthyBreakfast.html). Download the files [cereal.csv](https://ucl-cs-grad.github.io/scipython/notebooks/day4/cereal.txt) from the course website. \n", "\n", "Today you will be using pandas to read the data and do the analysis!" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import numpy as np" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1.1 Import the data using pandas" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "col_labels = [\"name\", \"mfr\", \"type\", \"calories (number)\", \"protein (g)\", \"fat (g)\", \"sodium (mg)\", \n", " \"dietary fiber (g)\", \"complex carbohydrates (g)\", \"sugars (g)\", \n", " \"shelf\", \"potassium (mg)\", \n", " \"vitamins and minerals\", \n", " \"weight (in ounces) of one serving (serving size)\", \"cups per serving\"]\n", "\n", "df = pd.read_csv('cereal.txt', sep=' ', header=None, names=col_labels)" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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namemfrtypecalories (number)protein (g)fat (g)sodium (mg)dietary fiber (g)complex carbohydrates (g)sugars (g)shelfpotassium (mg)vitamins and mineralsweight (in ounces) of one serving (serving size)cups per serving
0100%_BranNC704113010.05.063280251.000.33
1100%_Natural_BranQC12035152.08.08313501.00-1.00
2All-BranKC70412609.07.053320251.000.33
3All-Bran_with_Extra_FiberKC504014014.08.003330251.000.50
4Almond_DelightRC110222001.014.083-1251.000.75
5Apple_Cinnamon_CheeriosGC110221801.510.510170251.000.75
6Apple_JacksKC110201251.011.014230251.001.00
7Basic_4GC130322102.018.083100251.330.75
8Bran_ChexRC90212004.015.061125251.000.67
9Bran_FlakesPC90302105.013.053190251.000.67
10Cap'n'CrunchQC120122200.012.012235251.000.75
11CheeriosGC110622902.017.011105251.001.25
12Cinnamon_Toast_CrunchGC120132100.013.09245251.000.75
13ClustersGC110321402.013.073105251.000.50
14Cocoa_PuffsGC110111800.012.013255251.001.00
15Corn_ChexRC110202800.022.03125251.001.00
16Corn_FlakesKC100202901.021.02135251.001.00
17Corn_PopsKC11010901.013.012220251.001.00
18Count_ChoculaGC110111800.012.013265251.001.00
19Cracklin'_Oat_BranKC110331404.010.073160251.000.50
20Cream_of_Wheat_(Quick)NH10030801.021.002-101.001.00
21CrispixKC110202201.021.03330251.001.00
22Crispy_Wheat_&_RaisinsGC100211402.011.0103120251.000.75
23Double_ChexRC100201901.018.05380251.000.75
24Froot_LoopsKC110211251.011.013230251.001.00
25Frosted_FlakesKC110102001.014.011125251.000.75
26Frosted_Mini-WheatsKC1003003.014.072100251.000.80
27Fruit_&_Fibre_Dates,_Walnuts,_and_OatsPC120321605.012.0103200251.250.67
28Fruitful_BranKC120302405.014.0123190251.330.67
29Fruity_PebblesPC110111350.013.012225251.000.75
................................................
47Multi-Grain_CheeriosGC100212202.015.06190251.001.00
48Nut&Honey_CrunchKC120211900.015.09240251.000.67
49Nutri-Grain_Almond-RaisinKC140322203.021.073130251.330.67
50Nutri-grain_WheatKC90301703.018.02390251.00-1.00
51Oatmeal_Raisin_CrispGC130321701.513.5103120251.250.50
52Post_Nat._Raisin_BranPC120312006.011.0143260251.330.67
53Product_19KC100303201.020.033451001.001.00
54Puffed_RiceQC501000.013.0031500.501.00
55Puffed_WheatQC502001.010.0035000.50-1.00
56Quaker_Oat_SquaresQC100411352.014.063110251.000.50
57Quaker_OatmealQH1005202.7-1.0-1111001.000.67
58Raisin_BranKC120312105.014.0122240251.330.75
59Raisin_Nut_BranGC100321402.510.583140251.000.50
60Raisin_SquaresKC902002.015.063110251.000.50
61Rice_ChexRC110102400.023.02130251.001.13
62Rice_KrispiesKC110202900.022.03135251.001.00
63Shredded_WheatNC802003.016.0019500.83-1.00
64Shredded_Wheat_'n'BranNC903004.019.00114001.000.67
65Shredded_Wheat_spoon_sizeNC903003.020.00112001.000.67
66SmacksKC11021701.09.015240251.000.75
67Special_KKC110602301.016.03155251.001.00
68Strawberry_Fruit_WheatsNC9020153.015.05290251.00-1.00
69Total_Corn_FlakesGC110212000.021.033351001.001.00
70Total_Raisin_BranGC140311904.015.01432301001.501.00
71Total_Whole_GrainGC100312003.016.0331101001.001.00
72TriplesGC110212500.021.03360251.000.75
73TrixGC110111400.013.012225251.001.00
74Wheat_ChexRC100312303.017.031115251.000.67
75WheatiesGC100312003.017.031110251.001.00
76Wheaties_Honey_GoldGC110212001.016.08160251.000.75
\n", "

77 rows × 15 columns

\n", "
" ], "text/plain": [ " name mfr type calories (number) \\\n", "0 100%_Bran N C 70 \n", "1 100%_Natural_Bran Q C 120 \n", "2 All-Bran K C 70 \n", "3 All-Bran_with_Extra_Fiber K C 50 \n", "4 Almond_Delight R C 110 \n", "5 Apple_Cinnamon_Cheerios G C 110 \n", "6 Apple_Jacks K C 110 \n", "7 Basic_4 G C 130 \n", "8 Bran_Chex R C 90 \n", "9 Bran_Flakes P C 90 \n", "10 Cap'n'Crunch Q C 120 \n", "11 Cheerios G C 110 \n", "12 Cinnamon_Toast_Crunch G C 120 \n", "13 Clusters G C 110 \n", "14 Cocoa_Puffs G C 110 \n", "15 Corn_Chex R C 110 \n", "16 Corn_Flakes K C 100 \n", "17 Corn_Pops K C 110 \n", "18 Count_Chocula G C 110 \n", "19 Cracklin'_Oat_Bran K C 110 \n", "20 Cream_of_Wheat_(Quick) N H 100 \n", "21 Crispix K C 110 \n", "22 Crispy_Wheat_&_Raisins G C 100 \n", "23 Double_Chex R C 100 \n", "24 Froot_Loops K C 110 \n", "25 Frosted_Flakes K C 110 \n", "26 Frosted_Mini-Wheats K C 100 \n", "27 Fruit_&_Fibre_Dates,_Walnuts,_and_Oats P C 120 \n", "28 Fruitful_Bran K C 120 \n", "29 Fruity_Pebbles P C 110 \n", ".. ... .. ... ... \n", "47 Multi-Grain_Cheerios G C 100 \n", "48 Nut&Honey_Crunch K C 120 \n", "49 Nutri-Grain_Almond-Raisin K C 140 \n", "50 Nutri-grain_Wheat K C 90 \n", "51 Oatmeal_Raisin_Crisp G C 130 \n", "52 Post_Nat._Raisin_Bran P C 120 \n", "53 Product_19 K C 100 \n", "54 Puffed_Rice Q C 50 \n", "55 Puffed_Wheat Q C 50 \n", "56 Quaker_Oat_Squares Q C 100 \n", "57 Quaker_Oatmeal Q H 100 \n", "58 Raisin_Bran K C 120 \n", "59 Raisin_Nut_Bran G C 100 \n", "60 Raisin_Squares K C 90 \n", "61 Rice_Chex R C 110 \n", "62 Rice_Krispies K C 110 \n", "63 Shredded_Wheat N C 80 \n", "64 Shredded_Wheat_'n'Bran N C 90 \n", "65 Shredded_Wheat_spoon_size N C 90 \n", "66 Smacks K C 110 \n", "67 Special_K K C 110 \n", "68 Strawberry_Fruit_Wheats N C 90 \n", "69 Total_Corn_Flakes G C 110 \n", "70 Total_Raisin_Bran G C 140 \n", "71 Total_Whole_Grain G C 100 \n", "72 Triples G C 110 \n", "73 Trix G C 110 \n", "74 Wheat_Chex R C 100 \n", "75 Wheaties G C 100 \n", "76 Wheaties_Honey_Gold G C 110 \n", "\n", " protein (g) fat (g) sodium (mg) dietary fiber (g) \\\n", "0 4 1 130 10.0 \n", "1 3 5 15 2.0 \n", "2 4 1 260 9.0 \n", "3 4 0 140 14.0 \n", "4 2 2 200 1.0 \n", "5 2 2 180 1.5 \n", "6 2 0 125 1.0 \n", "7 3 2 210 2.0 \n", "8 2 1 200 4.0 \n", "9 3 0 210 5.0 \n", "10 1 2 220 0.0 \n", "11 6 2 290 2.0 \n", "12 1 3 210 0.0 \n", "13 3 2 140 2.0 \n", "14 1 1 180 0.0 \n", "15 2 0 280 0.0 \n", "16 2 0 290 1.0 \n", "17 1 0 90 1.0 \n", "18 1 1 180 0.0 \n", "19 3 3 140 4.0 \n", "20 3 0 80 1.0 \n", "21 2 0 220 1.0 \n", "22 2 1 140 2.0 \n", "23 2 0 190 1.0 \n", "24 2 1 125 1.0 \n", "25 1 0 200 1.0 \n", "26 3 0 0 3.0 \n", "27 3 2 160 5.0 \n", "28 3 0 240 5.0 \n", "29 1 1 135 0.0 \n", ".. ... ... ... ... \n", "47 2 1 220 2.0 \n", "48 2 1 190 0.0 \n", "49 3 2 220 3.0 \n", "50 3 0 170 3.0 \n", "51 3 2 170 1.5 \n", "52 3 1 200 6.0 \n", "53 3 0 320 1.0 \n", "54 1 0 0 0.0 \n", "55 2 0 0 1.0 \n", "56 4 1 135 2.0 \n", "57 5 2 0 2.7 \n", "58 3 1 210 5.0 \n", "59 3 2 140 2.5 \n", "60 2 0 0 2.0 \n", "61 1 0 240 0.0 \n", "62 2 0 290 0.0 \n", "63 2 0 0 3.0 \n", "64 3 0 0 4.0 \n", "65 3 0 0 3.0 \n", "66 2 1 70 1.0 \n", "67 6 0 230 1.0 \n", "68 2 0 15 3.0 \n", "69 2 1 200 0.0 \n", "70 3 1 190 4.0 \n", "71 3 1 200 3.0 \n", "72 2 1 250 0.0 \n", "73 1 1 140 0.0 \n", "74 3 1 230 3.0 \n", "75 3 1 200 3.0 \n", "76 2 1 200 1.0 \n", "\n", " complex carbohydrates (g) sugars (g) shelf potassium (mg) \\\n", "0 5.0 6 3 280 \n", "1 8.0 8 3 135 \n", "2 7.0 5 3 320 \n", "3 8.0 0 3 330 \n", "4 14.0 8 3 -1 \n", "5 10.5 10 1 70 \n", "6 11.0 14 2 30 \n", "7 18.0 8 3 100 \n", "8 15.0 6 1 125 \n", "9 13.0 5 3 190 \n", "10 12.0 12 2 35 \n", "11 17.0 1 1 105 \n", "12 13.0 9 2 45 \n", "13 13.0 7 3 105 \n", "14 12.0 13 2 55 \n", "15 22.0 3 1 25 \n", "16 21.0 2 1 35 \n", "17 13.0 12 2 20 \n", "18 12.0 13 2 65 \n", "19 10.0 7 3 160 \n", "20 21.0 0 2 -1 \n", "21 21.0 3 3 30 \n", "22 11.0 10 3 120 \n", "23 18.0 5 3 80 \n", "24 11.0 13 2 30 \n", "25 14.0 11 1 25 \n", "26 14.0 7 2 100 \n", "27 12.0 10 3 200 \n", "28 14.0 12 3 190 \n", "29 13.0 12 2 25 \n", ".. ... ... ... ... \n", "47 15.0 6 1 90 \n", "48 15.0 9 2 40 \n", "49 21.0 7 3 130 \n", "50 18.0 2 3 90 \n", "51 13.5 10 3 120 \n", "52 11.0 14 3 260 \n", "53 20.0 3 3 45 \n", "54 13.0 0 3 15 \n", "55 10.0 0 3 50 \n", "56 14.0 6 3 110 \n", "57 -1.0 -1 1 110 \n", "58 14.0 12 2 240 \n", "59 10.5 8 3 140 \n", "60 15.0 6 3 110 \n", "61 23.0 2 1 30 \n", "62 22.0 3 1 35 \n", "63 16.0 0 1 95 \n", "64 19.0 0 1 140 \n", "65 20.0 0 1 120 \n", "66 9.0 15 2 40 \n", "67 16.0 3 1 55 \n", "68 15.0 5 2 90 \n", "69 21.0 3 3 35 \n", "70 15.0 14 3 230 \n", "71 16.0 3 3 110 \n", "72 21.0 3 3 60 \n", "73 13.0 12 2 25 \n", "74 17.0 3 1 115 \n", "75 17.0 3 1 110 \n", "76 16.0 8 1 60 \n", "\n", " vitamins and minerals weight (in ounces) of one serving (serving size) \\\n", "0 25 1.00 \n", "1 0 1.00 \n", "2 25 1.00 \n", "3 25 1.00 \n", "4 25 1.00 \n", "5 25 1.00 \n", "6 25 1.00 \n", "7 25 1.33 \n", "8 25 1.00 \n", "9 25 1.00 \n", "10 25 1.00 \n", "11 25 1.00 \n", "12 25 1.00 \n", "13 25 1.00 \n", "14 25 1.00 \n", "15 25 1.00 \n", "16 25 1.00 \n", "17 25 1.00 \n", "18 25 1.00 \n", "19 25 1.00 \n", "20 0 1.00 \n", "21 25 1.00 \n", "22 25 1.00 \n", "23 25 1.00 \n", "24 25 1.00 \n", "25 25 1.00 \n", "26 25 1.00 \n", "27 25 1.25 \n", "28 25 1.33 \n", "29 25 1.00 \n", ".. ... ... \n", "47 25 1.00 \n", "48 25 1.00 \n", "49 25 1.33 \n", "50 25 1.00 \n", "51 25 1.25 \n", "52 25 1.33 \n", "53 100 1.00 \n", "54 0 0.50 \n", "55 0 0.50 \n", "56 25 1.00 \n", "57 0 1.00 \n", "58 25 1.33 \n", "59 25 1.00 \n", "60 25 1.00 \n", "61 25 1.00 \n", "62 25 1.00 \n", "63 0 0.83 \n", "64 0 1.00 \n", "65 0 1.00 \n", "66 25 1.00 \n", "67 25 1.00 \n", "68 25 1.00 \n", "69 100 1.00 \n", "70 100 1.50 \n", "71 100 1.00 \n", "72 25 1.00 \n", "73 25 1.00 \n", "74 25 1.00 \n", "75 25 1.00 \n", "76 25 1.00 \n", "\n", " cups per serving \n", "0 0.33 \n", "1 -1.00 \n", "2 0.33 \n", "3 0.50 \n", "4 0.75 \n", "5 0.75 \n", "6 1.00 \n", "7 0.75 \n", "8 0.67 \n", "9 0.67 \n", "10 0.75 \n", "11 1.25 \n", "12 0.75 \n", "13 0.50 \n", "14 1.00 \n", "15 1.00 \n", "16 1.00 \n", "17 1.00 \n", "18 1.00 \n", "19 0.50 \n", "20 1.00 \n", "21 1.00 \n", "22 0.75 \n", "23 0.75 \n", "24 1.00 \n", "25 0.75 \n", "26 0.80 \n", "27 0.67 \n", "28 0.67 \n", "29 0.75 \n", ".. ... \n", "47 1.00 \n", "48 0.67 \n", "49 0.67 \n", "50 -1.00 \n", "51 0.50 \n", "52 0.67 \n", "53 1.00 \n", "54 1.00 \n", "55 -1.00 \n", "56 0.50 \n", "57 0.67 \n", "58 0.75 \n", "59 0.50 \n", "60 0.50 \n", "61 1.13 \n", "62 1.00 \n", "63 -1.00 \n", "64 0.67 \n", "65 0.67 \n", "66 0.75 \n", "67 1.00 \n", "68 -1.00 \n", "69 1.00 \n", "70 1.00 \n", "71 1.00 \n", "72 0.75 \n", "73 1.00 \n", "74 0.67 \n", "75 1.00 \n", "76 0.75 \n", "\n", "[77 rows x 15 columns]" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1.2 Plot FatVsCalories" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df.plot(x='fat (g)', y='calories (number)', kind='scatter')" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## 1.3 Compute the average number of calories, as well as the average amount of complex carbohydrates and sugars in the data set." ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "#Start your answers here - add more cells as necessary!\n", "avg_calories = df['calories (number)'].mean()\n", "avg_cplx_carbs = df['complex carbohydrates (g)'].mean()\n", "avg_sugars = df['sugars (g)'].mean()" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(106.88311688311688, 14.597402597402597, 6.922077922077922)" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "avg_calories, avg_cplx_carbs, avg_sugars" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## 1.4 What is the name of the cereal that has the highest sugar content?" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "15" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['sugars (g)'].max()" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "30 Golden_Crisp\n", "66 Smacks\n", "Name: name, dtype: object" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[df['sugars (g)'] == df['sugars (g)'].max()].name" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## 1.5 Calorie content by shelf\n", "The 8th column of the data set contains the shelf at which the cereal is displayed in the supermarket (either 1, 2, or 3). Compute the mean number of calories of the cereals displayed in each shelf. If you do this correctly you should find the the cereals displayed on the middle shelf have a higher calorie content on average. " ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "0 3\n", "1 3\n", "2 3\n", "3 3\n", "4 3\n", "5 1\n", "6 2\n", "7 3\n", "8 1\n", "9 3\n", "10 2\n", "11 1\n", "12 2\n", "13 3\n", "14 2\n", "15 1\n", "16 1\n", "17 2\n", "18 2\n", "19 3\n", "20 2\n", "21 3\n", "22 3\n", "23 3\n", "24 2\n", "25 1\n", "26 2\n", "27 3\n", "28 3\n", "29 2\n", " ..\n", "47 1\n", "48 2\n", "49 3\n", "50 3\n", "51 3\n", "52 3\n", "53 3\n", "54 3\n", "55 3\n", "56 3\n", "57 1\n", "58 2\n", "59 3\n", "60 3\n", "61 1\n", "62 1\n", "63 1\n", "64 1\n", "65 1\n", "66 2\n", "67 1\n", "68 2\n", "69 3\n", "70 3\n", "71 3\n", "72 3\n", "73 2\n", "74 1\n", "75 1\n", "76 1\n", "Name: shelf, dtype: int64" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.shelf" ] }, { "cell_type": "markdown", "metadata": { "collapsed": false, "deletable": true, "editable": true }, "source": [ "Hint: remember `.grouby` ? " ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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calories (number)protein (g)fat (g)sodium (mg)dietary fiber (g)complex carbohydrates (g)sugars (g)potassium (mg)vitamins and mineralsweight (in ounces) of one serving (serving size)cups per serving
shelf
1102.5000002.6500000.60176.2500001.68500015.8000004.80000075.50000020.0000000.9915000.797000
2109.5238101.9047621.00145.7142860.90476213.6190489.61904857.80952423.8095241.0157140.720952
3107.7777782.8611111.25158.6111113.13888914.5000006.527778129.83333335.4166670.9477780.392778
\n", "
" ], "text/plain": [ " calories (number) protein (g) fat (g) sodium (mg) \\\n", "shelf \n", "1 102.500000 2.650000 0.60 176.250000 \n", "2 109.523810 1.904762 1.00 145.714286 \n", "3 107.777778 2.861111 1.25 158.611111 \n", "\n", " dietary fiber (g) complex carbohydrates (g) sugars (g) \\\n", "shelf \n", "1 1.685000 15.800000 4.800000 \n", "2 0.904762 13.619048 9.619048 \n", "3 3.138889 14.500000 6.527778 \n", "\n", " potassium (mg) vitamins and minerals \\\n", "shelf \n", "1 75.500000 20.000000 \n", "2 57.809524 23.809524 \n", "3 129.833333 35.416667 \n", "\n", " weight (in ounces) of one serving (serving size) cups per serving \n", "shelf \n", "1 0.991500 0.797000 \n", "2 1.015714 0.720952 \n", "3 0.947778 0.392778 " ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.groupby('shelf').mean()" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## 1.6 Make a box plot showing the sugar content of cereals grouped by shelf (recreating the plot that you'll find on the homepage for this data set). " ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df.boxplot(by='shelf', column='sugars (g)');" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## 1.7 Is there a relationship between sugar content and fat content? \n", "Make a scatter plot to explore." ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df.plot(kind='scatter', x='sugars (g)', y=\"fat (g)\");" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1.8 Create a pivot table to analyse contents by manufacturer and type of cereal (hot/cold)\n", "Let's focus on protein and sodium contents." ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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protein (g)sodium (mg)
mfrtype
AH4.0000000.000000
GC2.318182200.454545
KC2.652174174.782609
NC2.80000029.000000
H3.00000080.000000
PC2.444444146.111111
QC2.285714105.714286
H5.0000000.000000
RC2.500000198.125000
All2.545455159.675325
\n", "
" ], "text/plain": [ " protein (g) sodium (mg)\n", "mfr type \n", "A H 4.000000 0.000000\n", "G C 2.318182 200.454545\n", "K C 2.652174 174.782609\n", "N C 2.800000 29.000000\n", " H 3.000000 80.000000\n", "P C 2.444444 146.111111\n", "Q C 2.285714 105.714286\n", " H 5.000000 0.000000\n", "R C 2.500000 198.125000\n", "All 2.545455 159.675325" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.pivot_table(values=[\"protein (g)\", \"sodium (mg)\"], index=['mfr', 'type'], margins=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1.9 Which cereal has the heighest protein per cup ratio?" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "collapsed": false }, "outputs": [], "source": [ "proteinPerCup = df['protein (g)'] / df['cups per serving']" ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "12.121212121212121" ] }, "execution_count": 51, "metadata": {}, "output_type": "execute_result" } ], "source": [ "proteinPerCup.max()" ] }, { "cell_type": "code", "execution_count": 50, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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namemfrtypecalories (number)protein (g)fat (g)sodium (mg)dietary fiber (g)complex carbohydrates (g)sugars (g)shelfpotassium (mg)vitamins and mineralsweight (in ounces) of one serving (serving size)cups per serving
0100%_BranNC704113010.05.063280251.00.33
2All-BranKC70412609.07.053320251.00.33
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" ], "text/plain": [ " name mfr type calories (number) protein (g) fat (g) sodium (mg) \\\n", "0 100%_Bran N C 70 4 1 130 \n", "2 All-Bran K C 70 4 1 260 \n", "\n", " dietary fiber (g) complex carbohydrates (g) sugars (g) shelf \\\n", "0 10.0 5.0 6 3 \n", "2 9.0 7.0 5 3 \n", "\n", " potassium (mg) vitamins and minerals \\\n", "0 280 25 \n", "2 320 25 \n", "\n", " weight (in ounces) of one serving (serving size) cups per serving \n", "0 1.0 0.33 \n", "2 1.0 0.33 " ] }, "execution_count": 50, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[proteinPerCup == proteinPerCup.max()]" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# Problem 2: Chocolate makes you faster?\n", "Your colleague (a psychologist) has conducted an experiment where he measured subjects' performance on a memory and a reaction time task. He claims to have found a correlation between the two, but you are sceptical, because you know your colleague is not very good at clicking buttons in SPSS. Luckily, you can convince your colleague to give you his data file so you can assess his claims. \n", " \n", "> **1.** He gives you the file [experiment.csv](https://ucl-cs-grad.github.io/scipython/notebooks/day4/experiment.csv). It contains one line per subject, and each line contains the values for the following variables that he measured: `height`, `weight`, `amount-chocolate`, `task-one-score`, `task-two-score`. (He allowed the participants to eat chocolate during the experiment, and the third column shows the amount of chocolate they ate in grams.) Load the data into an array." ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "col_names = [\"height\", \"weight\", \"amount-chocolate\", \"task-one-score\", \"task-two-score\"]\n", "df = pd.read_csv(\"experiment.csv\", header=None, sep=' ', names=col_names)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "> **2.** As a sanity check, create a scatter plot of height vs. weight. These two should clearly be correlated and you should see a straight line relationship." ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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P+G4LDX0xcH9GnibMfUcTJbQX2EGmQGCrnZT0lsQH/9WemwVsqowyYts8CmFT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\nSEEIIYSIIgUhhBAiihSEEEKIKP8fryeLwFtm/SkAAAAASUVORK5CYII=\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df.plot(kind='scatter', x='height', y='weight')" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "> **3.** To assess your colleague's claim, first create a box plot of the scores in task 1 and task 2. Also, compute the correlation between the two (using Pearson's $r$) and check whether it is significant." ] }, { "cell_type": "code", "execution_count": 53, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Pearson's r is 0.04231920574803165\n" ] }, { "data": { "image/png": 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wo7ZvZmb18Zm2ZmaJcOCbmSXCgW9mlggHvplZIhz4ZmaJcOCbmSXCgW9mlggHvplZIhz4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\nAdPTuwV2VfeUAAAAAElFTkSuQmCC\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "print(\"Pearson's r is {}\".format(df['task-one-score'].corr(df['task-two-score'])))\n", "\n", "df.boxplot(column=['task-one-score', 'task-two-score'],return_type='axes');" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "> **4.** When your colleague hears about your results, he is a bit disappointed. However, he already has a new hypothesis: maybe chocolate is acting as a promoter somehow and the amount of chocolate consumed has some effect? Create some plots and hypothesis tests to assess this new hypothesis. " ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Pearson's r for task one is 0.0116431816265\n", "Pearson's r for task two is -0.00551700962886\n" ] }, { "data": { "image/png": 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IUZKub5/hgRvc/R3uflE/FYPoPe0Yk+WU044hW1eNl+oGZ2v2BoOzyTxPaDebTS2EYDen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\nB2P+W4Ffyyl/C3WHTT9O2HJ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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df.plot(kind='scatter', x='task-one-score', y='amount-chocolate');\n", "df.plot(kind='scatter', x='task-two-score', y='amount-chocolate');\n", "\n", "\n", "\n", "print(\"Pearson's r for task one is {}\".format(df['task-one-score'].corr( \n", " df['amount-chocolate'])))\n", "\n", "print(\"Pearson's r for task two is {}\".format(df['task-two-score'].corr( \n", " df['amount-chocolate'])))" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "> **5.** After you come back with your results, your colleague has one last idea: Maybe you need to eat more than some critical amount of chocolate (which depends on your body mass index) in order for there to be a correlation between the two tasks? He guesses that you need to eat at least six times your BMI in chocolate in order for the correlation to show up. You think your colleague is crazy, but go ahead an test his claim. What do you find? Again, produce some figures, compute correlations and test for significance." ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Pearson's r for task one is -0.167591510532\n", "Pearson's r for task two is -0.207677642024\n" ] }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "bmi = df['weight'] / (df['height'] ** 2) \n", "filtered_group = df.ix[df['amount-chocolate'] > bmi * 6]\n", "\n", "filtered_group.plot(kind='scatter', x='task-one-score', y='amount-chocolate');\n", "filtered_group.plot(kind='scatter', x='task-two-score',y='amount-chocolate');\n", "\n", "print(\"Pearson's r for task one is {}\".format(filtered_group['task-one-score'].corr( \n", " filtered_group['amount-chocolate'])))\n", "\n", "print(\"Pearson's r for task two is {}\".format(filtered_group['task-two-score'].corr(\n", " filtered_group['amount-chocolate'])))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.0" } }, "nbformat": 4, "nbformat_minor": 0 }