EDA_mini_project.ipynb

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   "source": [
    "# Welcome to your first EDA \\(Exploratory Data Analysis\\)\n",
    "\n"
   ]
  },
  {
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    "### Our task at hand \\-\\- I hope you like penguins!\n",
    "\n",
    "For our mini project, we are going to use Pandas to conduct a basic Exploratory Data Analysis on a penguins dataset. Specifically, we are looking at the <u>Palmer Archipelago \\(Antarctica\\) Penguins</u> dataset, which you can find here:  [https://raw.githubusercontent.com/mcnakhaee/palmerpenguins/master/palmerpenguins/data/penguins\\-raw.csv](https://raw.githubusercontent.com/mcnakhaee/palmerpenguins/master/palmerpenguins/data/penguins-raw.csv)\n",
    "\n",
    "Not sure what an exploratory data analysis is? You can google it! The best definition I found is below:\n",
    "\n",
    "_**Exploratory Data Analysis**_ **refers to the critical process of performing initial investigations on data so as to discover patterns, to spot anomalies, to test hypotheses and to check assumptions with the help of summary statistics and graphical representations.**\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check out this video about data preparation: https://youtu.be/7EA5Dk2LWXk"
   ]
  },
  {
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   "execution_count": 8,
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   "source": [
    "#Before anything else let's import our library for data analysis, pandas.\n",
    "import pandas as pd"
   ]
  },
  {
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   "source": [
    "### Step 1: Loading in and understanding our data\n",
    "\n",
    "We need to read in our data. It is currently a .csv \\(comma\\-separated value\\) file, and we generally use the pd.read\\_csv\\(\\) function from pandas to read our data into a dataframe. \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
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   },
   "outputs": [],
   "source": [
    "penguins_dataframe = pd.read_csv(\"https://raw.githubusercontent.com/mcnakhaee/palmerpenguins/master/palmerpenguins/data/penguins-raw.csv\")"
   ]
  },
  {
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   "source": [
    "**TODO:** Can you run the .head\\(\\) and .tail\\(\\) function on our new dataframe? Can you print the name of all the columns in our dataframe? How about the number of rows?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
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   },
   "outputs": [],
   "source": [
    "# Run head() on penguins_dataframe\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Run tail() on penguins_dataframe\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Print the name of all the columns in our dataframe\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Use google to find out how to print the number of rows in this dataset.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# use describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# use info()"
   ]
  },
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   "source": [
    "### Step 2: Do your research\n",
    "\n",
    "What do the column names mean? At this point you should look up the meanings of any columns you don't know about and make some comments below. \n",
    "\n",
    "Typically at this point we would read up on the domain we are performing data analysis on. To do research on penguins you should probably get to know something about penguins!\n"
   ]
  },
  {
   "cell_type": "raw",
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   },
   "source": [
    "What do these columns mean?\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
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   "source": [
    "### Step 3: Cleaning up our Data\n",
    "\n",
    "This data is a lot to take in. How do we know if we can even use this data? I see some weird columns and lots of N/A values.\n",
    "\n",
    "Let's talk through some functions to clean our data and see if it is salvagable. To start, we cannot use all of these columns. I'm thinking I want to get rid of \"studyName\" and \"Comments\" since they are not super useful for understanding the data relationships. I'll do that below. <br>\n",
    "Check out this video on cleaning steps: https://youtu.be/mwEPXevpqls\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
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   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sample Number</th>\n",
       "      <th>Species</th>\n",
       "      <th>Region</th>\n",
       "      <th>Island</th>\n",
       "      <th>Stage</th>\n",
       "      <th>Individual ID</th>\n",
       "      <th>Clutch Completion</th>\n",
       "      <th>Date Egg</th>\n",
       "      <th>Culmen Length (mm)</th>\n",
       "      <th>Culmen Depth (mm)</th>\n",
       "      <th>Flipper Length (mm)</th>\n",
       "      <th>Body Mass (g)</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Delta 15 N (o/oo)</th>\n",
       "      <th>Delta 13 C (o/oo)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Adelie Penguin (Pygoscelis adeliae)</td>\n",
       "      <td>Anvers</td>\n",
       "      <td>Torgersen</td>\n",
       "      <td>Adult, 1 Egg Stage</td>\n",
       "      <td>N1A1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2007-11-11</td>\n",
       "      <td>39.1</td>\n",
       "      <td>18.7</td>\n",
       "      <td>181.0</td>\n",
       "      <td>3750.0</td>\n",
       "      <td>MALE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "   Sample Number                              Species  Region     Island  \\\n",
       "0              1  Adelie Penguin (Pygoscelis adeliae)  Anvers  Torgersen   \n",
       "\n",
       "                Stage Individual ID Clutch Completion    Date Egg  \\\n",
       "0  Adult, 1 Egg Stage          N1A1               Yes  2007-11-11   \n",
       "\n",
       "   Culmen Length (mm)  Culmen Depth (mm)  Flipper Length (mm)  Body Mass (g)  \\\n",
       "0                39.1               18.7                181.0         3750.0   \n",
       "\n",
       "    Sex  Delta 15 N (o/oo)  Delta 13 C (o/oo)  \n",
       "0  MALE                NaN                NaN  "
      ]
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   "source": [
    "#Make a list of what you want to drop\n",
    "columns_to_drop = ['studyName', 'Comments']\n",
    "\n",
    "#Drop the columns using drop()\n",
    "penguins_dataframe.drop(columns_to_drop, axis=1, inplace = True) #axis = 1 lets pandas know we are dropping columns, not rows.\n",
    "\n",
    "#Check that they are dropped\n",
    "penguins_dataframe.head(1)"
   ]
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   "source": [
    "**What does inplace mean?** This argument in the `drop()` function means we are changing the dataframe in place! If we had `inplace = False`, we would need to create a new dataframe like so: \n",
    "\n",
    "`new_penguins_dataframe = penguins_dataframe.drop(columns_to_drop, axis=1, inplace = False)`\n",
    "\n",
    "**TODO:** Can you drop the Sample Number, Individual ID, Delta 15 N \\(o/oo\\), and Delta 13 C \\(o/oo\\) columns using the example above?\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
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   "source": [
    "# Drop the Sample Number, Individual ID, Delta 15 N (o/oo), and Delta 13 C (o/oo) columns using the example above.\n",
    "\n",
    "\n",
    "#Drop the columns using drop()\n",
    "\n",
    "\n",
    "#Check that they are dropped\n"
   ]
  },
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   "source": [
    "#### Getting Rid of N/A's\n",
    "\n",
    "I see lots of N/A's, which we typically cannot visualize or use in machine learning.\n",
    "Now, I want to get rid of all the rows that have NA's in them. I'll show you how. **TODO:** Can you print the number of rows our dataframe has after I drop the NAs?\n"
   ]
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   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
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   "source": [
    "# This is a very convenient function to drop all rows that have N/A values!\n",
    "penguins_dataframe.dropna(inplace=True)\n",
    "penguins_dataframe.reset_index(drop=True, inplace=True) #Very good practice to reset how your rows are counted when you drop rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
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   "outputs": [],
   "source": [
    "#Print the number of rows our new dataframe has.\n"
   ]
  },
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   "source": [
    "### Exploring the Data\n",
    "\n",
    "Pandas has some amazing tools for exploring your data. Since this is a **mini** project, we will walk through only a few key features of pandas exploration. You can expect to do lots of research on your own to best explore, clean, and visualize your data for your project. \n",
    "\n",
    "First we will learn how to access certain columns of data and use conditions to get subsets of data.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    Adelie Penguin (Pygoscelis adeliae)\n",
       "1    Adelie Penguin (Pygoscelis adeliae)\n",
       "2    Adelie Penguin (Pygoscelis adeliae)\n",
       "3    Adelie Penguin (Pygoscelis adeliae)\n",
       "4    Adelie Penguin (Pygoscelis adeliae)\n",
       "Name: Species, dtype: object"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# access a column using df[\"<COLUMN_NAME\"] or df.COLUMN_NAME\n",
    "penguins_dataframe[\"Species\"].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
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    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Species</th>\n",
       "      <th>Region</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Adelie Penguin (Pygoscelis adeliae)</td>\n",
       "      <td>Anvers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Adelie Penguin (Pygoscelis adeliae)</td>\n",
       "      <td>Anvers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Adelie Penguin (Pygoscelis adeliae)</td>\n",
       "      <td>Anvers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Adelie Penguin (Pygoscelis adeliae)</td>\n",
       "      <td>Anvers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Adelie Penguin (Pygoscelis adeliae)</td>\n",
       "      <td>Anvers</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "                               Species  Region\n",
       "0  Adelie Penguin (Pygoscelis adeliae)  Anvers\n",
       "1  Adelie Penguin (Pygoscelis adeliae)  Anvers\n",
       "2  Adelie Penguin (Pygoscelis adeliae)  Anvers\n",
       "3  Adelie Penguin (Pygoscelis adeliae)  Anvers\n",
       "4  Adelie Penguin (Pygoscelis adeliae)  Anvers"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#You can also access multiple columns using double brackets (printing a \"list\" of columns)\n",
    "penguins_dataframe[[\"Species\",\"Region\"]].head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "**TODO: Can you print the last five rows of the Island and Region columns?** \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Print the last five rows of the Island and Region columns.\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "<u>**Conditions in Pandas**</u>\n",
    "\n",
    "Pandas is also incredible for selecting subsets of columns that fit a certain condition. Check out the code below to get only the Adelie penguins.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
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   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sample Number</th>\n",
       "      <th>Species</th>\n",
       "      <th>Region</th>\n",
       "      <th>Island</th>\n",
       "      <th>Stage</th>\n",
       "      <th>Individual ID</th>\n",
       "      <th>Clutch Completion</th>\n",
       "      <th>Date Egg</th>\n",
       "      <th>Culmen Length (mm)</th>\n",
       "      <th>Culmen Depth (mm)</th>\n",
       "      <th>Flipper Length (mm)</th>\n",
       "      <th>Body Mass (g)</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Delta 15 N (o/oo)</th>\n",
       "      <th>Delta 13 C (o/oo)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>Adelie Penguin (Pygoscelis adeliae)</td>\n",
       "      <td>Anvers</td>\n",
       "      <td>Torgersen</td>\n",
       "      <td>Adult, 1 Egg Stage</td>\n",
       "      <td>N1A2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2007-11-11</td>\n",
       "      <td>39.5</td>\n",
       "      <td>17.4</td>\n",
       "      <td>186.0</td>\n",
       "      <td>3800.0</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>8.94956</td>\n",
       "      <td>-24.69454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>Adelie Penguin (Pygoscelis adeliae)</td>\n",
       "      <td>Anvers</td>\n",
       "      <td>Torgersen</td>\n",
       "      <td>Adult, 1 Egg Stage</td>\n",
       "      <td>N2A1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2007-11-16</td>\n",
       "      <td>40.3</td>\n",
       "      <td>18.0</td>\n",
       "      <td>195.0</td>\n",
       "      <td>3250.0</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>8.36821</td>\n",
       "      <td>-25.33302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>Adelie Penguin (Pygoscelis adeliae)</td>\n",
       "      <td>Anvers</td>\n",
       "      <td>Torgersen</td>\n",
       "      <td>Adult, 1 Egg Stage</td>\n",
       "      <td>N3A1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2007-11-16</td>\n",
       "      <td>36.7</td>\n",
       "      <td>19.3</td>\n",
       "      <td>193.0</td>\n",
       "      <td>3450.0</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>8.76651</td>\n",
       "      <td>-25.32426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>Adelie Penguin (Pygoscelis adeliae)</td>\n",
       "      <td>Anvers</td>\n",
       "      <td>Torgersen</td>\n",
       "      <td>Adult, 1 Egg Stage</td>\n",
       "      <td>N3A2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2007-11-16</td>\n",
       "      <td>39.3</td>\n",
       "      <td>20.6</td>\n",
       "      <td>190.0</td>\n",
       "      <td>3650.0</td>\n",
       "      <td>MALE</td>\n",
       "      <td>8.66496</td>\n",
       "      <td>-25.29805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7</td>\n",
       "      <td>Adelie Penguin (Pygoscelis adeliae)</td>\n",
       "      <td>Anvers</td>\n",
       "      <td>Torgersen</td>\n",
       "      <td>Adult, 1 Egg Stage</td>\n",
       "      <td>N4A1</td>\n",
       "      <td>No</td>\n",
       "      <td>2007-11-15</td>\n",
       "      <td>38.9</td>\n",
       "      <td>17.8</td>\n",
       "      <td>181.0</td>\n",
       "      <td>3625.0</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>9.18718</td>\n",
       "      <td>-25.21799</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   Sample Number                              Species  Region     Island  \\\n",
       "0              2  Adelie Penguin (Pygoscelis adeliae)  Anvers  Torgersen   \n",
       "1              3  Adelie Penguin (Pygoscelis adeliae)  Anvers  Torgersen   \n",
       "2              5  Adelie Penguin (Pygoscelis adeliae)  Anvers  Torgersen   \n",
       "3              6  Adelie Penguin (Pygoscelis adeliae)  Anvers  Torgersen   \n",
       "4              7  Adelie Penguin (Pygoscelis adeliae)  Anvers  Torgersen   \n",
       "\n",
       "                Stage Individual ID Clutch Completion    Date Egg  \\\n",
       "0  Adult, 1 Egg Stage          N1A2               Yes  2007-11-11   \n",
       "1  Adult, 1 Egg Stage          N2A1               Yes  2007-11-16   \n",
       "2  Adult, 1 Egg Stage          N3A1               Yes  2007-11-16   \n",
       "3  Adult, 1 Egg Stage          N3A2               Yes  2007-11-16   \n",
       "4  Adult, 1 Egg Stage          N4A1                No  2007-11-15   \n",
       "\n",
       "   Culmen Length (mm)  Culmen Depth (mm)  Flipper Length (mm)  Body Mass (g)  \\\n",
       "0                39.5               17.4                186.0         3800.0   \n",
       "1                40.3               18.0                195.0         3250.0   \n",
       "2                36.7               19.3                193.0         3450.0   \n",
       "3                39.3               20.6                190.0         3650.0   \n",
       "4                38.9               17.8                181.0         3625.0   \n",
       "\n",
       "      Sex  Delta 15 N (o/oo)  Delta 13 C (o/oo)  \n",
       "0  FEMALE            8.94956          -24.69454  \n",
       "1  FEMALE            8.36821          -25.33302  \n",
       "2  FEMALE            8.76651          -25.32426  \n",
       "3    MALE            8.66496          -25.29805  \n",
       "4  FEMALE            9.18718          -25.21799  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "penguins_dataframe[penguins_dataframe[\"Species\"] == \"Adelie Penguin (Pygoscelis adeliae)\"].head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "Conditions in pandas work just like `if` statements in python. \n",
    "\n",
    "**TODO: Can you tell me which species of penguin can have a flipper length greater than or equal to 230 by using conditions in pandas?**\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Use pandas to find out which species of penguin can have a flipper length greater than or equal to 230\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "**TODO:** Take 5 minutes to explore the dataset on your own. There's some space for you to code below.\n",
    "\n",
    "Think of what you want to learn. Maybe what the smallest flipper size penguin is? How about the smallest culmen Length? What is the largest penguin by mass?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualizations\n",
    "\n",
    "Check out this video on data visualizatiom: https://youtu.be/EC7EBJ1Bn-c"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "### Creating Beautiful Visualizations with Plotly\n",
    "\n",
    "So far, it looks like a scatter plot might be our best bet! Now we want to make a beautiful scatter plot using `plotly.`Just like pandas and numpy, plotly is a module that lets us create beautiful visualizations in just a few lines of code. \n",
    "\n",
    "I'm only going to help with the import, so I need you to spend time on plotly's documentation in order to build the same scatter plot you did in the last section, but nicer. Maybe google \"Scatter plots in plotly python\" to start!\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import plotly.express as px #importing plotly"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "**TODO:** Create a scatter plot using plotly. Please add a title here!\n",
    "\n",
    "Maybe google \"Scatter plots in plotly python\" to start!\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Create a scatter plot\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "**TODO:** Let's make it look even better. Copy your function from above but add another argument to the function by setting the `color=\"Species\"`. Not sure what this means? Get to the scatter plots page on plotly and scroll down to the \"Setting size and color with column names\" section.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Create the same scatter plot with color=\"Species\". Feeling crazy? Set a column name to the \"size\" argument as well!\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "**Scatter Matrix:** I wanted to create a scatter plot for every possible numerical combination of 'Culmen Length \\(mm\\)', 'Culmen Depth \\(mm\\)', 'Flipper Length \\(mm\\)', and 'Body Mass \\(g\\)'. This could help me understand what is correlated and could mean a relationship between each other. The code to do so is below; are there any findings you can take away from this data?\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
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    "# My scatter matrix!\n",
    "numerical_penguins_df = penguins_dataframe[['Culmen Length (mm)', 'Culmen Depth (mm)', 'Flipper Length (mm)',\n",
    "       'Body Mass (g)']]\n",
    "fig = px.scatter_matrix(numerical_penguins_df)\n",
    "fig.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "### Putting the DA in EDA\n",
    "\n",
    "We now have explored our data some, and have built some beautiful visualizations to showcase patterns our data. But having a plot doesn't mean anything to someone who can't understand how to read plots.\n",
    "\n",
    "Please write up some descriptions for your findings on this dataset. One question we answered earlier was _which species has the largest flipper length?_ You can start with you\n"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "Write up some descriptions for your findings here....\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Extra Graphs using Plotly!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let's look at histogram!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
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            "linecolor": "white",
            "ticks": ""
           },
           "baxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "caxis": {
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            "linecolor": "white",
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          },
          "title": {
           "x": 0.05
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          "xaxis": {
           "automargin": true,
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           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
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          "yaxis": {
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           "linecolor": "white",
           "ticks": "",
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           "zerolinewidth": 2
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        },
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        "yaxis": {
         "anchor": "x",
         "domain": [
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         "title": {
          "text": "count"
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = px.histogram(penguins_dataframe, x='Flipper Length (mm)', color='Sex')\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Heatmap using plotly figure factory!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
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            "gridwidth": 2,
            "linecolor": "#506784",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "#C8D4E3"
           },
           "yaxis": {
            "backgroundcolor": "rgb(17,17,17)",
            "gridcolor": "#506784",
            "gridwidth": 2,
            "linecolor": "#506784",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "#C8D4E3"
           },
           "zaxis": {
            "backgroundcolor": "rgb(17,17,17)",
            "gridcolor": "#506784",
            "gridwidth": 2,
            "linecolor": "#506784",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "#C8D4E3"
           }
          },
          "shapedefaults": {
           "line": {
            "color": "#f2f5fa"
           }
          },
          "sliderdefaults": {
           "bgcolor": "#C8D4E3",
           "bordercolor": "rgb(17,17,17)",
           "borderwidth": 1,
           "tickwidth": 0
          },
          "ternary": {
           "aaxis": {
            "gridcolor": "#506784",
            "linecolor": "#506784",
            "ticks": ""
           },
           "baxis": {
            "gridcolor": "#506784",
            "linecolor": "#506784",
            "ticks": ""
           },
           "bgcolor": "rgb(17,17,17)",
           "caxis": {
            "gridcolor": "#506784",
            "linecolor": "#506784",
            "ticks": ""
           }
          },
          "title": {
           "x": 0.05
          },
          "updatemenudefaults": {
           "bgcolor": "#506784",
           "borderwidth": 0
          },
          "xaxis": {
           "automargin": true,
           "gridcolor": "#283442",
           "linecolor": "#506784",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "#283442",
           "zerolinewidth": 2
          },
          "yaxis": {
           "automargin": true,
           "gridcolor": "#283442",
           "linecolor": "#506784",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "#283442",
           "zerolinewidth": 2
          }
         }
        },
        "title": {
         "text": "Correlation Heatmap",
         "x": 0.5
        },
        "width": 500,
        "xaxis": {
         "dtick": 1,
         "gridcolor": "rgb(0, 0, 0)",
         "side": "bottom",
         "ticks": ""
        },
        "yaxis": {
         "autorange": "reversed",
         "dtick": 1,
         "ticks": "",
         "ticksuffix": "  "
        }
       }
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import plotly.figure_factory as ff\n",
    "import numpy as np\n",
    "\n",
    "numerical_penguins_df = penguins_dataframe[['Culmen Length (mm)', 'Culmen Depth (mm)', 'Flipper Length (mm)',\n",
    "       'Body Mass (g)']]\n",
    "penguins_corr = numerical_penguins_df.corr() # Generate correlation matrix\n",
    "x = list(penguins_corr.columns)\n",
    "y = list(penguins_corr.index)\n",
    "z = np.array(penguins_corr)\n",
    "\n",
    "figSix = ff.create_annotated_heatmap(\n",
    "    z,\n",
    "    x = x,\n",
    "    y = y ,\n",
    "    annotation_text = np.around(z, decimals=2),\n",
    "    hoverinfo='z',\n",
    "    colorscale='Blues',\n",
    "    showscale=True,\n",
    "    )\n",
    "figSix.update_xaxes(side=\"bottom\")\n",
    "figSix.update_layout(\n",
    "    title_text='Correlation Heatmap', \n",
    "    title_x=0.5, \n",
    "    width=500, \n",
    "    height=500,\n",
    "    yaxis_autorange='reversed',\n",
    "    template='plotly_dark'\n",
    ")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Extra Libraries\n",
    "\n",
    "We have used plotly which is a fairly simple library for data visualization. There exists other libraries that are much more powerful. We are going to explore two libraries: Seaborn and Matplotlib! Seaborn and Matplotlib are built on top of each other.\n",
    "\n",
    "Seaborn is strong for statistical data and complex data-set.\n",
    "MatplotLib is very good for customizing the visualization such as titles, adjusting scales, etc.\n",
    "When in doubt, google!\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Seaborn\n",
    "\n",
    "With seaborn, we will make 3 graphs: a scatterplot, a overlaid histogram, and a line graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Culmen Length (mm)', ylabel='Culmen Depth (mm)'>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### SCATTERPLOT ###\n",
    "# Import seaborn\n",
    "import seaborn as sns\n",
    "\n",
    "# Apply the default theme\n",
    "sns.set_theme()\n",
    "\n",
    "# Using the same penguins dataframe\n",
    "penguins_dataframe = pd.read_csv(\"https://raw.githubusercontent.com/mcnakhaee/palmerpenguins/master/palmerpenguins/data/penguins-raw.csv\")\n",
    "\n",
    "# Create a visualization\n",
    "sns.scatterplot(\n",
    "    data=penguins_dataframe,\n",
    "    x=\"Culmen Length (mm)\", y=\"Culmen Depth (mm)\",\n",
    "    hue=\"Sex\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Flipper Length (mm)', ylabel='Count'>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### HISTOGRAM ###\n",
    "sns.histplot(penguins_dataframe, x = 'Flipper Length (mm)', hue = 'Sex')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Body Mass (g)', ylabel='Flipper Length (mm)'>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### LINE GRAPH ###\n",
    "sns.lineplot(\n",
    "    data=penguins_dataframe,\n",
    "    x = 'Body Mass (g)', y = 'Flipper Length (mm)',\n",
    "    hue = 'Sex'\n",
    ")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Matplotlib\n",
    "Matplotlib is another powerful Python visualization library for data plotting and explorations. We will use it today to easily add and change titles and labels on our plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fb75395a160>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Import Matplotlib\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Plot our seaborn scatterplot from the previous step\n",
    "sns.scatterplot(\n",
    "    data=penguins_dataframe,\n",
    "    x=\"Culmen Length (mm)\", y=\"Culmen Depth (mm)\",\n",
    "    hue=\"Sex\",\n",
    ")\n",
    "\n",
    "# Add a title to our graph and change the legend title!\n",
    "plt.title(\"Culmen Length vs Culmen Depth\")\n",
    "plt.legend(title = 'Gender')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is another example of editing the titles and axis labels. This is a very important step in data visualization as it provides clarity and interpretation to our viewers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Flipper Length in millimeters')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot our seaborn histogram from an earlier step\n",
    "sns.histplot(penguins_dataframe, x = 'Flipper Length (mm)', hue = 'Sex')\n",
    "\n",
    "# Add a descriptive title to our histogram!\n",
    "plt.title(\"Distribution of Flipper Lengths Based on Gender\")\n",
    "\n",
    "# Change x-axis label\n",
    "plt.xlabel('Flipper Length in millimeters')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Thank you!\n",
    "\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.9.7 ('base')",
   "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": "3.9.7"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "42221d9da9d164c35186927567fbb78de8c8290438567e84586721ce63a852ac"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}