01-Animals.ipynb

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    "# Homework 5, Part 1: Building a pandas cheat sheet\n",
    "\n",
    "**Use `animals.csv` to answer the following questions.** The data is small and the questions are pretty simple, so hopefully you can use this for pandas reference in the future."
   ]
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    "## First: things I want you to know first\n",
    "\n",
    "### Counting things\n",
    "\n",
    "If during class we had wanted to know how many countries were on each continent, I would use `df.continent.value_counts()`.\n",
    "\n",
    "Lots of people like to try `groupby` when you're counting things, but in pandas there is only one rule: **every time you want to count things and think you should use groupby.... don't use `groupby`!** Instead use `value_counts()`.\n",
    "\n",
    "### Data types\n",
    "\n",
    "Just like you can have strings and integers in Python, each column can have a different data type. You can run `df.dtypes` or `df.info()` to get the data types of each column.\n",
    "\n",
    "* `int64` is an integer\n",
    "* `float64` is a float/decimal\n",
    "* `object` is *anything else*. This normally means a string.\n",
    "\n",
    "You can also (try to) convert between types! In normal Python it might be `int(age)`, but with pandas you'll use `df.age.astype(int)`\n",
    "\n",
    "\n",
    "### Graphing things\n",
    "\n",
    "Just put `.plot()` on the end of whatever you're looking at. It works like 75% of the time!\n",
    "\n",
    "```python\n",
    "df.groupby('continent').population.sum().plot(kind='barh')\n",
    "```\n",
    "\n",
    "The code above will give me a horizontal bar graph of the sum of each continent's population. Technically speaking it works because the result of the `groupby` and `sum` is a Series, matplotlib plots the index compared to the values. \n",
    "\n",
    "If you have a full dataframe, though, you usually need to give it the `x` and `y`.\n",
    "\n",
    "```python\n",
    "df.plot(x='life_expectancy', y='per_capita_gdp', kind='scatter')\n",
    "```\n",
    "\n",
    "This will give you a scatterplot of each country's life expectancy vs. its per-capita GDP.\n",
    "\n",
    "If you want to use Altair, though, you can find details [on its website](https://altair-viz.github.io/). We'll cover it a lot more in the future!"
   ]
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   "source": [
    "## 0) Setup\n",
    "\n",
    "Import pandas **with the correct name**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
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   "source": [
    "## 1) Reading in a csv file\n",
    "\n",
    "Use pandas to read in the animals CSV file, saving it as a variable with the normal name for a dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.read_csv('animals.csv')\n",
    "df = pd.read_csv('animals.csv')"
   ]
  },
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   "source": [
    "## 3) Display the first 3 animals\n",
    "\n",
    "Hmmm, we know how to take the first 5, but we really just want the first 3. Maybe there is an option to change how many you get?\n",
    "\n",
    "> **Tip:** Use `shift+tab` or `?` to check the documentation on commands"
   ]
  },
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       "<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",
       "    </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",
       "      <td>65</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "  animal        name  length\n",
       "0    cat        Anne      35\n",
       "1    cat         Bob      45\n",
       "2    dog  Egglesburg      65"
      ]
     },
     "execution_count": 224,
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   "source": [
    "df.head(3)"
   ]
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   "source": [
    "## 4) Sort the animals to show me the 3 longest animals\n",
    "\n",
    "> **TIP:** You can use `.head()` after you sort things!"
   ]
  },
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   "execution_count": 225,
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       "<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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>dog</td>\n",
       "      <td>Egglesburg</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>dog</td>\n",
       "      <td>Devon</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>cat</td>\n",
       "      <td>Bob</td>\n",
       "      <td>45</td>\n",
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      "text/plain": [
       "  animal        name  length\n",
       "2    dog  Egglesburg      65\n",
       "3    dog       Devon      50\n",
       "1    cat         Bob      45"
      ]
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   "source": [
    "df.sort_values(by='length', ascending=False).head(3)\n",
    "#df.sort_values(by='length', ascending).head(3)\n"
   ]
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   "source": [
    "## 5) Get the mean and standard deviation of animal lengths\n",
    "\n",
    "You can do this with separate commands or with a single command.\n",
    "\n",
    "> **Tip:** You don't know how to do standard deviation, but remember when we did `df.so` and hit tab and it suggested some options for sorting? I'm assuming the standard deviation method starts with `s`...."
   ]
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   "execution_count": 226,
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       "17.101935284528196"
      ]
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     "execution_count": 226,
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   "source": [
    "df.length.mean()\n",
    "df.length.std()\n",
    "#df.length.median()"
   ]
  },
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   "source": [
    "## 6) How many cats do we have and how many dogs?"
   ]
  },
  {
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   "execution_count": 227,
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    {
     "data": {
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       "4"
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     "execution_count": 227,
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   ],
   "source": [
    "df[df.animal == 'cat']['animal'].count()\n",
    "#[\"animal\"]=column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
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       "3"
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     "execution_count": 228,
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   "source": [
    "df[df.animal == 'dog']['animal'].count()"
   ]
  },
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   "source": [
    "## 7) Only display the dogs\n",
    "\n",
    "> **TIP:** It's probably easiest to make it display the list of `True`/`False` first, then wrap the `df[]` around it."
   ]
  },
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   "execution_count": 229,
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       "      <th></th>\n",
       "      <th>animal</th>\n",
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       "  animal        name  length\n",
       "2    dog  Egglesburg      65\n",
       "3    dog       Devon      50\n",
       "5    dog    Fontaine      35"
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    "df[df.animal == 'dog']"
   ]
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   "source": [
    "## 8) Only display the animals that are longer than 40cm\n",
    "\n",
    "Do this in **a different way than you did the dogs display**\n",
    "\n",
    "> **TIP:** While the `df[...]` technique is by far the popular approach, `df.query` is (I think) more readable."
   ]
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       "  animal        name  length\n",
       "1    cat         Bob      45\n",
       "2    dog  Egglesburg      65\n",
       "3    dog       Devon      50"
      ]
     },
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    "df[df.length > 40]"
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   "source": [
    "## 9) `length` is the animal's length in centimeters. Create a new column called `inches` that is the length in inches.\n",
    "\n",
    "> **TIP:** If your first try fails, remember there are multiple ways to talk about columns, but only one way works to create new ones."
   ]
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   "execution_count": 231,
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       "      <th>animal</th>\n",
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       "      <th>inches</th>\n",
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       "      <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>2</th>\n",
       "      <td>dog</td>\n",
       "      <td>Egglesburg</td>\n",
       "      <td>65</td>\n",
       "      <td>25.590551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>dog</td>\n",
       "      <td>Devon</td>\n",
       "      <td>50</td>\n",
       "      <td>19.685039</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",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>dog</td>\n",
       "      <td>Fontaine</td>\n",
       "      <td>35</td>\n",
       "      <td>13.779528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>cat</td>\n",
       "      <td>Xypher</td>\n",
       "      <td>10</td>\n",
       "      <td>3.937008</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "  animal        name  length     inches\n",
       "0    cat        Anne      35  13.779528\n",
       "1    cat         Bob      45  17.716535\n",
       "2    dog  Egglesburg      65  25.590551\n",
       "3    dog       Devon      50  19.685039\n",
       "4    cat     Charlie      32  12.598425\n",
       "5    dog    Fontaine      35  13.779528\n",
       "6    cat      Xypher      10   3.937008"
      ]
     },
     "execution_count": 231,
     "metadata": {},
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   ],
   "source": [
    "#df\n",
    "\n",
    "df['inches'] = df.length / 2.54\n",
    "df\n"
   ]
  },
  {
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   "execution_count": null,
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    "## 10) Save just the cats to a separate variable called `cats`. Save just the dogs to a separate variable called `dogs`.\n",
    "\n",
    "This is the same as listing them, but you just save the result to a variable instead of looking at it. Be sure to use `.head()` to make sure your data looks right.\n",
    "\n",
    "Once you do this, every time you use `cats` you'll only be talking about the cats, and same for the dogs."
   ]
  },
  {
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       "      <th>4</th>\n",
       "      <td>cat</td>\n",
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      "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\n",
       "6    cat   Xypher      10   3.937008"
      ]
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   "source": [
    "cats = df[df.animal == 'cat']\n",
    "\n",
    "cats"
   ]
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       "    }\n",
       "</style>\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>2</th>\n",
       "      <td>dog</td>\n",
       "      <td>Egglesburg</td>\n",
       "      <td>65</td>\n",
       "      <td>25.590551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>dog</td>\n",
       "      <td>Devon</td>\n",
       "      <td>50</td>\n",
       "      <td>19.685039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>dog</td>\n",
       "      <td>Fontaine</td>\n",
       "      <td>35</td>\n",
       "      <td>13.779528</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  animal        name  length     inches\n",
       "2    dog  Egglesburg      65  25.590551\n",
       "3    dog       Devon      50  19.685039\n",
       "5    dog    Fontaine      35  13.779528"
      ]
     },
     "execution_count": 233,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dogs = df[df.animal == 'dog']\n",
    "dogs\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 11) Display all of the animals that are cats and above 12 inches long.\n",
    "\n",
    "First do it using the `cats` variable, then also do it using your `df` dataframe.\n",
    "\n",
    "> **TIP:** For multiple conditions, you use `df[(one condition) & (another condition)]`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\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": 234,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cats_above1 = cats[cats.inches > 12]\n",
    "cats_above1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [],
   "source": [
    "# new_df = old_df[condition]\n",
    "\n",
    "# old_df.column == "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\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": 236,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cats_above2 = df[(df.animal == 'cat') & (df.inches > 12)]\n",
    "cats_above2"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 12) What's the mean length of a cat? What's the mean length of a dog?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30.5"
      ]
     },
     "execution_count": 237,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cats_mean = cats.length.mean()\n",
    "cats_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "50.0"
      ]
     },
     "execution_count": 238,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dogs_mean = dogs.length.mean()\n",
    "dogs_mean"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 13) If you didn't already, use `groupby` to do #12 all at once"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\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>2</th>\n",
       "      <td>dog</td>\n",
       "      <td>Egglesburg</td>\n",
       "      <td>65</td>\n",
       "      <td>25.590551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>dog</td>\n",
       "      <td>Devon</td>\n",
       "      <td>50</td>\n",
       "      <td>19.685039</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",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>dog</td>\n",
       "      <td>Fontaine</td>\n",
       "      <td>35</td>\n",
       "      <td>13.779528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>cat</td>\n",
       "      <td>Xypher</td>\n",
       "      <td>10</td>\n",
       "      <td>3.937008</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",
       "2    dog  Egglesburg      65  25.590551\n",
       "3    dog       Devon      50  19.685039\n",
       "4    cat     Charlie      32  12.598425\n",
       "5    dog    Fontaine      35  13.779528\n",
       "6    cat      Xypher      10   3.937008"
      ]
     },
     "execution_count": 239,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "animal\n",
       "cat    30.5\n",
       "dog    50.0\n",
       "Name: length, dtype: float64"
      ]
     },
     "execution_count": 240,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(\"animal\").length.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\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>2</th>\n",
       "      <td>dog</td>\n",
       "      <td>Egglesburg</td>\n",
       "      <td>65</td>\n",
       "      <td>25.590551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>dog</td>\n",
       "      <td>Devon</td>\n",
       "      <td>50</td>\n",
       "      <td>19.685039</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",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>dog</td>\n",
       "      <td>Fontaine</td>\n",
       "      <td>35</td>\n",
       "      <td>13.779528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>cat</td>\n",
       "      <td>Xypher</td>\n",
       "      <td>10</td>\n",
       "      <td>3.937008</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",
       "2    dog  Egglesburg      65  25.590551\n",
       "3    dog       Devon      50  19.685039\n",
       "4    cat     Charlie      32  12.598425\n",
       "5    dog    Fontaine      35  13.779528\n",
       "6    cat      Xypher      10   3.937008"
      ]
     },
     "execution_count": 241,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 14) Make a [histogram](https://www.tibco.com/reference-center/what-is-a-histogram-chart) of the length of dogs.\n",
    "\n",
    "This is the worst histogram ever.\n",
    "\n",
    "> **TIP:** It **does not** use `plot()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 242,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dogs.length.hist()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 15) Make a horizontal bar graph of the length of the animals, with the animal's name as the label\n",
    "\n",
    "> **TIP:** It isn't `df['length'].plot()`, because it needs *both* columns. Think about how we did the scatterplot in class.\n",
    ">\n",
    "> **TIP:** Which is the `x` axis and which is the `y` axis? You'll notice pandas is kind of weird and wrong.\n",
    ">\n",
    "> **TIP:** Make sure you specify the `kind` of graph or else it will be a weird line thing\n",
    ">\n",
    "> **TIP:** If you want, you can set a custom size for your plot by sending it something like `figsize=(15,2)`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='name'>"
      ]
     },
     "execution_count": 243,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.barh(x='name', y='length')\n",
    "#(x='lentgh', y='name')\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 16) Make a sorted horizontal bar graph of the cats, with the larger cats on top\n",
    "\n",
    "> **TIP:** Think in steps, even though it's all on one line - first make sure you can sort it, then try to graph it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {},
   "outputs": [],
   "source": [
    "cats_sorted = cats.sort_values(by=\"length\", ascending=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\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",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>cat</td>\n",
       "      <td>Xypher</td>\n",
       "      <td>10</td>\n",
       "      <td>3.937008</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\n",
       "6    cat   Xypher      10   3.937008"
      ]
     },
     "execution_count": 245,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='name'>"
      ]
     },
     "execution_count": 246,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#cats.plot.barh(x='name', y='length')\n",
    "cats_sorted.plot.barh(x='name', y='length')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 17) As a reward (...is it a reward?) for this being the last question: run the following code, then plot the number of dogs vs. the number of cats\n",
    "\n",
    "When you are done, be sure to tell Dhrumil that FiveThirtyEight has been lonely without him.\n",
    "\n",
    "> **TIP:** Counting the number of dogs and number of cats does NOT use `.groupby`! That's only for calculations.\n",
    ">\n",
    "> **TIP:** You can set a title with `title=\"Number of animals\"`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('fivethirtyeight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='animal'>"
      ]
     },
     "execution_count": 287,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.animal.value_counts().plot.barh(x='dog', y='cat')\n",
    "\n",
    "# \"Number of animals\"\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}