girlvsdata/Kaggle_5DayDataChallenge_Day2_Cereal.ipynb

## Kaggle_5DayDataChallenge_Day2_Cereal.ipynb
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Kaggle 5 Day Data Challenge - Day 2 - Cereal\n",
    "## Plotting a numeric value using a histogram\n",
    "\n",
    "Welcome to day 2 of Kaggle's 5 Day Data Challenge! \n",
    "\n",
    "Today we're going to look at this cereal database from Kaggle: ('https://www.kaggle.com/crawford/80-cereals/data') and choose a numeric value from it to plot on a histogram.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 1 - Import the libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 2 - Read your data into a dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cereal = pd.read_csv('cereal.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 3 - Pick a column with a numeric value for our histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                        name mfr type  calories  protein  fat  sodium  fiber  \\\n",
      "0                  100% Bran   N    C        70        4    1     130   10.0   \n",
      "1          100% Natural Bran   Q    C       120        3    5      15    2.0   \n",
      "2                   All-Bran   K    C        70        4    1     260    9.0   \n",
      "3  All-Bran with Extra Fiber   K    C        50        4    0     140   14.0   \n",
      "4             Almond Delight   R    C       110        2    2     200    1.0   \n",
      "\n",
      "   carbo  sugars  potass  vitamins  shelf  weight  cups     rating  \n",
      "0    5.0       6     280        25      3     1.0  0.33  68.402973  \n",
      "1    8.0       8     135         0      3     1.0  1.00  33.983679  \n",
      "2    7.0       5     320        25      3     1.0  0.33  59.425505  \n",
      "3    8.0       0     330        25      3     1.0  0.50  93.704912  \n",
      "4   14.0       8      -1        25      3     1.0  0.75  34.384843  \n"
     ]
    }
   ],
   "source": [
    "print(cereal[0:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "name        0\n",
       "mfr         0\n",
       "type        0\n",
       "calories    0\n",
       "protein     0\n",
       "fat         0\n",
       "sodium      0\n",
       "fiber       0\n",
       "carbo       0\n",
       "sugars      0\n",
       "potass      0\n",
       "vitamins    0\n",
       "shelf       0\n",
       "weight      0\n",
       "cups        0\n",
       "rating      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cereal.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I'm going to go with the 'rating' column, I wonder if there's some big outliers!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "Rating = cereal['rating']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 4 - Plot a histogram of the column"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Just for fun, we'll change the colour palette to make for a prettier histogram\n",
    "\n",
    "Check out this matplotlib documentation to see the full range of colour palettes you can choose:\n",
    "\n",
    "('https://matplotlib.org/users/colormaps.html')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sns.set_palette(\"spring\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Distribution of ratings for cereal')"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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C7SCh+w7l3PGTwj0oLtmi+zDLmKaAUIjuaIUe4N1qLhoTzOBrtVBsmhFVxjQF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\nbkvG3v7tXsahyt2+DW9s65x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      "text/plain": [
       "<matplotlib.figure.Figure at 0xfd5706a550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(Rating).set_title('Distribution of ratings for cereal')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### All done! \n",
    "We've successfully read in the .csv, summarized it, chose a numeric value and plotted it on a (pink) histogram.\n",
    "Check back here tomorrow for Day 3 of the Kaggle 5 Day Data Challenge!"
   ]
  }
 ],
 "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": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}