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    "# 01: Building a pandas Cheat Sheet, Part 1: Animals\n",
    "\n",
    "* Use the csv I've attached to answer the following questions\n",
    "* Import pandas with the right name\n",
    "* Set all graphics from matplotlib to display inline\n",
    "* Read the csv in (it should be UTF-8 already so you don't have to worry about encoding), save it with the proper boring name\n",
    "* Display the names of the columns in the csv\n",
    "* Display the first 3 animals.\n",
    "* Sort the animals to see the 3 longest animals.\n",
    "* What are the counts of the different values of the \"animal\" column? a.k.a. how many cats and how many dogs.\n",
    "* Only select the dogs.\n",
    "* Display all of the animals that are greater than 40 cm.\n",
    "'length' is the animal's length in cm. Create a new column called inches that is the length in inches.\n",
    "* Save the cats to a separate variable called \"cats.\" Save the dogs to a separate variable called \"dogs.\"\n",
    "* Display all of the animals that are cats and above 12 inches long. First do it using the \"cats\" variable, then do it using your normal dataframe.\n",
    "* What's the mean length of a cat?\n",
    "* What's the mean length of a dog?\n",
    "* Use groupby to accomplish both of the above tasks at once.\n",
    "* Make a histogram of the length of dogs. I apologize that it is so boring.\n",
    "* Change your graphing style to be something else (anything else!)\n",
    "* Make a horizontal bar graph of the length of the animals, with their name as the label (look at the billionaires notebook I put on Slack!)\n",
    "* Make a sorted horizontal bar graph of the cats, with the larger cats on top.\n"
   ]
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   "cell_type": "code",
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   "source": [
    "# Import pandas with the right name\n",
    "# Set all graphics from matplotlib to display inline\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "# don't do this, but it means the thing above\n",
    "#from matplotlib import pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
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    "* Read the csv in (it should be UTF-8 already so you don't have to worry about encoding), save it with the proper boring name\n",
    "* Display the names of the columns in the csv\n",
    "* Display the first 3 animals."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
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   "outputs": [],
   "source": [
    "df = pd.read_csv(\"07-hw-animals.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
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    {
     "data": {
      "text/plain": [
       "Index(['animal', 'name', 'length'], dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
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       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>animal</th>\n",
       "      <th>name</th>\n",
       "      <th>length</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>cat</td>\n",
       "      <td>Anne</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>cat</td>\n",
       "      <td>Bob</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>dog</td>\n",
       "      <td>Egglesburg</td>\n",
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       "  animal        name  length\n",
       "0    cat        Anne      35\n",
       "1    cat         Bob      45\n",
       "2    dog  Egglesburg      65"
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   "source": [
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   "source": [
    "# * Sort the animals to see the 3 longest animals."
   ]
  },
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   "cell_type": "code",
   "execution_count": 10,
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       "  animal        name  length\n",
       "2    dog  Egglesburg      65\n",
       "3    dog       Devon      50\n",
       "1    cat         Bob      45"
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    "df.sort_values(by='length', ascending=False).head(3)"
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   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
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    {
     "data": {
      "text/plain": [
       "cat    3\n",
       "dog    3\n",
       "Name: animal, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# * What are the counts of the different values of the \"animal\" column? a.k.a. how many cats and how many dogs.\n",
    "df['animal'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
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    {
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       "0    False\n",
       "1    False\n",
       "2     True\n",
       "3     True\n",
       "4    False\n",
       "5     True\n",
       "Name: animal, dtype: bool"
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     "execution_count": 14,
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    "# * Only select the dogs.\n",
    "df['animal'] == 'dog'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
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    "# If you want the rows back, you have to put a df[ ] on the outside\n",
    "df[df['animal'] == 'dog']"
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    "is_dog = df['animal'] == 'dog'\n",
    "is_dog"
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    "dogs = df[is_dog]\n",
    "dogs"
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      "/Users/soma/.virtualenvs/data-analysis/lib/python3.4/site-packages/ipykernel/__main__.py:1: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  if __name__ == '__main__':\n"
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    "#* Display all of the animals that are greater than 40 cm.\n",
    "# 'length' is the animal's length in cm. Create a new column called inches that is the length in inches.\n",
    "#* Save the cats to a separate variable called \"cats.\" Save the dogs to a separate variable called \"dogs.\"\n",
    "#* Display all of the animals that are cats and above 12 inches long. First do it using the \"cats\" variable, then do it using your normal dataframe."
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   "execution_count": 26,
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    "# save a new column using a calculation on an existing column\n",
    "df['inches'] = df['length'] / 2.54"
   ]
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       "      <td>cat</td>\n",
       "      <td>Charlie</td>\n",
       "      <td>32</td>\n",
       "      <td>12.598425</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  animal     name  length     inches\n",
       "0    cat     Anne      35  13.779528\n",
       "1    cat      Bob      45  17.716535\n",
       "4    cat  Charlie      32  12.598425"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#* Display all of the animals that are cats and above 12 inches long.\n",
    "# First do it using the \"cats\" variable, then do it using your normal dataframe.\n",
    "cats = df[df['animal'] == 'cat']\n",
    "cats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>animal</th>\n",
       "      <th>name</th>\n",
       "      <th>length</th>\n",
       "      <th>inches</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>cat</td>\n",
       "      <td>Anne</td>\n",
       "      <td>35</td>\n",
       "      <td>13.779528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>cat</td>\n",
       "      <td>Bob</td>\n",
       "      <td>45</td>\n",
       "      <td>17.716535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>cat</td>\n",
       "      <td>Charlie</td>\n",
       "      <td>32</td>\n",
       "      <td>12.598425</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  animal     name  length     inches\n",
       "0    cat     Anne      35  13.779528\n",
       "1    cat      Bob      45  17.716535\n",
       "4    cat  Charlie      32  12.598425"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cats[cats['inches'] > 12]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>animal</th>\n",
       "      <th>name</th>\n",
       "      <th>length</th>\n",
       "      <th>inches</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>cat</td>\n",
       "      <td>Anne</td>\n",
       "      <td>35</td>\n",
       "      <td>13.779528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>cat</td>\n",
       "      <td>Bob</td>\n",
       "      <td>45</td>\n",
       "      <td>17.716535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>cat</td>\n",
       "      <td>Charlie</td>\n",
       "      <td>32</td>\n",
       "      <td>12.598425</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  animal     name  length     inches\n",
       "0    cat     Anne      35  13.779528\n",
       "1    cat      Bob      45  17.716535\n",
       "4    cat  Charlie      32  12.598425"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#df[df['animal'] == 'cat' & df['inches'] > 12]\n",
    "big_cats = df[(df['animal'] == 'cat') & (df['inches'] > 12)]\n",
    "big_cats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>animal</th>\n",
       "      <th>name</th>\n",
       "      <th>length</th>\n",
       "      <th>inches</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>cat</td>\n",
       "      <td>Anne</td>\n",
       "      <td>35</td>\n",
       "      <td>13.779528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>cat</td>\n",
       "      <td>Bob</td>\n",
       "      <td>45</td>\n",
       "      <td>17.716535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>cat</td>\n",
       "      <td>Charlie</td>\n",
       "      <td>32</td>\n",
       "      <td>12.598425</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  animal     name  length     inches\n",
       "0    cat     Anne      35  13.779528\n",
       "1    cat      Bob      45  17.716535\n",
       "4    cat  Charlie      32  12.598425"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "is_cat = df['animal'] == 'cat'\n",
    "is_over_twelve_inches = df['inches'] > 12\n",
    "df[is_cat & is_over_twelve_inches]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#* What's the mean length of a cat?\n",
    "#* What's the mean length of a dog?\n",
    "#* Use groupby to accomplish both of the above tasks at once.\n",
    "#* Make a histogram of the length of dogs. I apologize that it is so boring.\n",
    "#* Change your graphing style to be something else (anything else!)\n",
    "#* Make a horizontal bar graph of the length of the animals, with their name as the label (look at the billionaires notebook I put on Slack!)\n",
    "#* Make a sorted horizontal bar graph of the cats, with the larger cats on top."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "37.333333333333336"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cats['length'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     3.000000\n",
       "mean     37.333333\n",
       "std       6.806859\n",
       "min      32.000000\n",
       "25%      33.500000\n",
       "50%      35.000000\n",
       "75%      40.000000\n",
       "max      45.000000\n",
       "Name: length, dtype: float64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cats['length'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "50.0"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dogs = df[df['animal'] == 'dog']\n",
    "dogs['length'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     3.0\n",
       "mean     50.0\n",
       "std      15.0\n",
       "min      35.0\n",
       "25%      42.5\n",
       "50%      50.0\n",
       "75%      57.5\n",
       "max      65.0\n",
       "Name: length, dtype: float64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dogs['length'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>inches</th>\n",
       "      <th>length</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>animal</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">cat</th>\n",
       "      <th>count</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>14.698163</td>\n",
       "      <td>37.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.679866</td>\n",
       "      <td>6.806859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>12.598425</td>\n",
       "      <td>32.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>13.188976</td>\n",
       "      <td>33.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>13.779528</td>\n",
       "      <td>35.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>15.748031</td>\n",
       "      <td>40.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>17.716535</td>\n",
       "      <td>45.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">dog</th>\n",
       "      <th>count</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>19.685039</td>\n",
       "      <td>50.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>5.905512</td>\n",
       "      <td>15.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>13.779528</td>\n",
       "      <td>35.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>16.732283</td>\n",
       "      <td>42.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>19.685039</td>\n",
       "      <td>50.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>22.637795</td>\n",
       "      <td>57.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>25.590551</td>\n",
       "      <td>65.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 inches     length\n",
       "animal                            \n",
       "cat    count   3.000000   3.000000\n",
       "       mean   14.698163  37.333333\n",
       "       std     2.679866   6.806859\n",
       "       min    12.598425  32.000000\n",
       "       25%    13.188976  33.500000\n",
       "       50%    13.779528  35.000000\n",
       "       75%    15.748031  40.000000\n",
       "       max    17.716535  45.000000\n",
       "dog    count   3.000000   3.000000\n",
       "       mean   19.685039  50.000000\n",
       "       std     5.905512  15.000000\n",
       "       min    13.779528  35.000000\n",
       "       25%    16.732283  42.500000\n",
       "       50%    19.685039  50.000000\n",
       "       75%    22.637795  57.500000\n",
       "       max    25.590551  65.000000"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#* Use groupby to accomplish both of the above tasks at once.\n",
    "df.groupby('animal').describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "animal\n",
       "cat    112\n",
       "dog    150\n",
       "Name: length, dtype: int64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('animal')['length'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x108c64358>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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rUF9D58LXlpGkRedwH1H13K96f5VV37vq/Q3hcJekgszcpU5D2erENaivoXNh5i5Ji87h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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x108c58d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dogs['length'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "plt.style.use(\"ggplot\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x108d21ac8>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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JWZY1ac3IyEjmeGRkZNIaSYrH44rH45nj1tZWrdjwwIwKXyhCodB8l+AqT/e3cqX02on5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\nPQAYgLAHAAMQ9gBgAMIeAAzw/22Ag7FyQfe3AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x108c58780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dogs['length'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#* Make a horizontal bar graph of the length of the animals, with their name as the label (look at the billionaires notebook I put on Slack!)\n",
    "#* Make a sorted horizontal bar graph of the cats, with the larger cats on top."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x108ed2a58>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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27Gk0davIPv/8c6SmpmL69OmYOXMmTp8+jQ8//FDVmjQX4gMGDMDx48exa9cu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\nYAxxIiKBMcSJiATGECciEtj/A4l1OBZRiXcrAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x108f1df60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(kind='bar', x='name', y='length')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1090b78d0>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Xrl275bXNmzff82BERETe8niK9fHHH0Ov1+Pdd99FUFAQrFYrUlJS8Nprr8mZj4iIyGMe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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1090cb390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(kind='barh', x='name', y='length', legend=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#* Make a sorted horizontal bar graph of the cats,\n",
    "# with the larger cats on top."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x109be75c0>"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x109bee518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[df['animal'] == 'cat'].sort_values(by='length').plot(kind='barh', x='name', y='length', legend=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part 02: Doing some research (billionaires)\n",
    "\n",
    "Answer your own selection out of the following questions, or any other questions you might be able to think of.  Write the question down first in a markdown cell (use a # to make the question a nice header), THEN try to get an answer to it. A lot of these are remarkably similar, and some you'll need to do manual work for - the GDP ones, for example.\n",
    "\n",
    "If you are trying to figure out some other question that we didn't cover in class and it does not have to do with joining to another data set, we're happy to help you figure it out during lab!\n",
    "\n",
    "* Take a peek at the billionaires notebook I uploaded into Slack, it should be helpful for the graphs (I added a few other styles and options, too). You'll probably also want to look at the \"sum()\" line I added.\n",
    "* What country are most billionaires from? For the top ones, how many billionaires per billion people?\n",
    "* Who are the top 10 richest billionaires?\n",
    "* What's the average wealth of a billionaire? Male? Female?\n",
    "* Who is the poorest billionaire? Who are the top 10 poorest billionaires?\n",
    "* 'What is relationship to company'? And what are the most common relationships?\n",
    "* Most common source of wealth? Male vs. female?\n",
    "* Given the richest person in a country, what % of the GDP is their wealth?\n",
    "* Add up the wealth of all of the billionaires in a given country (or a few countries) and then compare it to the GDP of the country, or other billionaires, so like pit the US vs India\n",
    "* What are the most common industries for billionaires to come from? What's the total amount of billionaire money from each industry?\n",
    "* How many self made billionaires vs. others?\n",
    "* How old are billionaires? How old are billionaires self made vs. non self made? or different industries?\n",
    "* Who are the youngest billionaires? The oldest? Age distribution - maybe make a graph about it?\n",
    "* Maybe just made a graph about how wealthy they are in general?\n",
    "* Maybe plot their net worth vs age (scatterplot)\n",
    "* Make a bar graph of the top 10 or 20 richest\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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 },
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