part_2_mars_weather.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Module 12 Challenge\n",
    "## Deliverable 2: Scrape and Analyze Mars Weather Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import relevant libraries\n",
    "from splinter import Browser\n",
    "from bs4 import BeautifulSoup as soup\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "browser = Browser('chrome')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 1: Visit the Website\n",
    "\n",
    "Use automated browsing to visit the [Mars Temperature Data Site](https://static.bc-edx.com/data/web/mars_facts/temperature.html). Inspect the page to identify which elements to scrape.\n",
    "\n",
    "   > **Hint** To identify which elements to scrape, you might want to inspect the page by using Chrome DevTools to discover whether the table contains usable classes.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Visit the website\n",
    "# https://static.bc-edx.com/data/web/mars_facts/temperature.html\n",
    "url = \"https://static.bc-edx.com/data/web/mars_facts/temperature.html\"\n",
    "browser.visit(url)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2: Scrape the Table\n",
    "\n",
    "Create a Beautiful Soup object and use it to scrape the data in the HTML table.\n",
    "\n",
    "Note that this can also be achieved by using the Pandas `read_html` function. However, use Beautiful Soup here to continue sharpening your web scraping skills."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a Beautiful Soup Object\n",
    "html = soup(browser.html, 'html.parser')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extract all rows of data\n",
    "all_text = html.select('div.list_text')\n",
    "all_text"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3: Store the Data\n",
    "\n",
    "Assemble the scraped data into a Pandas DataFrame. The columns should have the same headings as the table on the website. Here’s an explanation of the column headings:\n",
    "\n",
    "* `id`: the identification number of a single transmission from the Curiosity rover\n",
    "* `terrestrial_date`: the date on Earth\n",
    "* `sol`: the number of elapsed sols (Martian days) since Curiosity landed on Mars\n",
    "* `ls`: the solar longitude\n",
    "* `month`: the Martian month\n",
    "* `min_temp`: the minimum temperature, in Celsius, of a single Martian day (sol)\n",
    "* `pressure`: The atmospheric pressure at Curiosity's location"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create an empty list\n",
    "mars = []\n",
    "# Loop through the scraped data to create a list of rows\n",
    "rows = table.find_all('tr')\n",
    "for row in rows\n",
    "    table_data=row.find_all('td')\n",
    "    data= [data.text for data in table_data]\n",
    "    mars.append(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a Pandas DataFrame by using the list of rows and a list of the column names\n",
    "mars_df = pd.DataFrame(mars, columns =[\"id\",\"terrestrial_date\",\"sol\",\"ls\",\"month\",\"min_temp\",\"pressure\"])\n",
    "mars_df = df.drop([0])\n",
    "mars_df = df.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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>id</th>\n",
       "      <th>terrestrial_date</th>\n",
       "      <th>sol</th>\n",
       "      <th>ls</th>\n",
       "      <th>month</th>\n",
       "      <th>min_temp</th>\n",
       "      <th>pressure</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>2012-08-16</td>\n",
       "      <td>10</td>\n",
       "      <td>155</td>\n",
       "      <td>6</td>\n",
       "      <td>-75.0</td>\n",
       "      <td>739.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13</td>\n",
       "      <td>2012-08-17</td>\n",
       "      <td>11</td>\n",
       "      <td>156</td>\n",
       "      <td>6</td>\n",
       "      <td>-76.0</td>\n",
       "      <td>740.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>24</td>\n",
       "      <td>2012-08-18</td>\n",
       "      <td>12</td>\n",
       "      <td>156</td>\n",
       "      <td>6</td>\n",
       "      <td>-76.0</td>\n",
       "      <td>741.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>35</td>\n",
       "      <td>2012-08-19</td>\n",
       "      <td>13</td>\n",
       "      <td>157</td>\n",
       "      <td>6</td>\n",
       "      <td>-74.0</td>\n",
       "      <td>732.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>46</td>\n",
       "      <td>2012-08-20</td>\n",
       "      <td>14</td>\n",
       "      <td>157</td>\n",
       "      <td>6</td>\n",
       "      <td>-74.0</td>\n",
       "      <td>740.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id terrestrial_date sol   ls month min_temp pressure\n",
       "0   2       2012-08-16  10  155     6    -75.0    739.0\n",
       "1  13       2012-08-17  11  156     6    -76.0    740.0\n",
       "2  24       2012-08-18  12  156     6    -76.0    741.0\n",
       "3  35       2012-08-19  13  157     6    -74.0    732.0\n",
       "4  46       2012-08-20  14  157     6    -74.0    740.0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Confirm DataFrame was created successfully\n",
    "mars_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 4: Prepare Data for Analysis\n",
    "\n",
    "Examine the data types that are currently associated with each column. If necessary, cast (or convert) the data to the appropriate `datetime`, `int`, or `float` data types.\n",
    "\n",
    "  > **Hint** You can use the Pandas `astype` and `to_datetime` methods to accomplish this task.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                  object\n",
       "terrestrial_date    object\n",
       "sol                 object\n",
       "ls                  object\n",
       "month               object\n",
       "min_temp            object\n",
       "pressure            object\n",
       "dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Examine data type of each column\n",
    "mars_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Change data types for data analysis\n",
    "mars_df['id']=mars_df['id'].astype(int)\n",
    "mars_df['terrestrial_date']=pd.to_datetime(mars_df['terrestrial_date'])\n",
    "mars_df['sol']=mars_df['sol'].astype(int)\n",
    "mars_df['ls']=mars_df['ls'].astype(int)\n",
    "mars_df['month']=mars_df['month'].astype(int)\n",
    "mars_df['min_temp']=mars_df['min_temp'].astype(float)\n",
    "mars_df['pressure']=mars_df['pressure'].astype(float)\n",
    "mars_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                          object\n",
       "terrestrial_date    datetime64[ns]\n",
       "sol                          int64\n",
       "ls                           int64\n",
       "month                        int64\n",
       "min_temp                   float64\n",
       "pressure                   float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Confirm type changes were successful by examining data types again\n",
    "mars_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 5: Analyze the Data\n",
    "\n",
    "Analyze your dataset by using Pandas functions to answer the following questions:\n",
    "\n",
    "1. How many months exist on Mars?\n",
    "2. How many Martian (and not Earth) days worth of data exist in the scraped dataset?\n",
    "3. What are the coldest and the warmest months on Mars (at the location of Curiosity)? To answer this question:\n",
    "    * Find the average the minimum daily temperature for all of the months.\n",
    "    * Plot the results as a bar chart.\n",
    "4. Which months have the lowest and the highest atmospheric pressure on Mars? To answer this question:\n",
    "    * Find the average the daily atmospheric pressure of all the months.\n",
    "    * Plot the results as a bar chart.\n",
    "5. About how many terrestrial (Earth) days exist in a Martian year? To answer this question:\n",
    "    * Consider how many days elapse on Earth in the time that Mars circles the Sun once.\n",
    "    * Visually estimate the result by plotting the daily minimum temperature.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1     174\n",
       "2     178\n",
       "3     192\n",
       "4     194\n",
       "5     149\n",
       "6     147\n",
       "7     142\n",
       "8     141\n",
       "9     134\n",
       "10    112\n",
       "11    138\n",
       "12    166\n",
       "Name: month, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1. How many months are there on Mars?\n",
    "months=mars_df.month.value_counts().sort_index()\n",
    "months"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1867"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2. How many Martian days' worth of data are there?\n",
    "martian_days = mars_df['terrestrial_date'].count()\n",
    "martian_days"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "month\n",
      "1    -77.160920\n",
      "2    -79.932584\n",
      "3    -83.307292\n",
      "4    -82.747423\n",
      "5    -79.308725\n",
      "6    -75.299320\n",
      "7    -72.281690\n",
      "8    -68.382979\n",
      "9    -69.171642\n",
      "10   -71.982143\n",
      "11   -71.985507\n",
      "12   -74.451807\n",
      "Name: min_temp, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# 3. What is the average low temperature by month?\n",
    "avg_low_temp = mars_df.groupby(\"month\")[\"min_temp\"].mean().sort_values()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the average temperature by month\n",
    "av_low_temp.plot.bar()\n",
    "plt.xlabel('month')\n",
    "plt.ylabel('temperature (C)')\n",
    "plt.title('average temperature by month')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Identify the coldest and hottest months in Curiosity's location\n",
    "order_temp = av_low_temp.sort_values(by=['min_temp'], ascending=False)\n",
    "order_temp.plot.bar()\n",
    "plt.xlabel('month')\n",
    "plt.ylabel('temperature (C)')\n",
    "plt.title('coldest and hottest months in location')\n",
    "plt.savefig('cold_hottest_temp.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "month\n",
      "1     862.488506\n",
      "2     889.455056\n",
      "3     877.322917\n",
      "4     806.329897\n",
      "5     748.557047\n",
      "6     745.054422\n",
      "7     795.105634\n",
      "8     873.829787\n",
      "9     913.305970\n",
      "10    887.312500\n",
      "11    857.014493\n",
      "12    842.156627\n",
      "Name: pressure, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# 4. Average pressure by Martian month\n",
    "avg_pressure = mars_df.groupby(\"month\")[\"pressure\"].mean().sort_values()\n",
    "avg_pressure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AAEC5eFx2hg4dquuuu04ZGRmKjY2Vj8/Jy36uuOIKj6/ZAQAAqGgelx1Jatu2rVq3bq309HQ1adJEfn5+6t69u93ZAAAAys3jd2MVFBRo8ODBCgoK0tVXX+26J058fLwmT55se0AAAIDy8LjsjBkzRtu2bdOnn37qdkFy586dtXTpUlvDAQAAlJfHL2MtW7ZMS5cu1fXXX+/2cQwtWrTQ3r17bQ0HAABQXh6v7Pz8889q0KBBqfGjR4/+6edVAQAAXGgel5127drp/fffd+2fKjizZ892fQI6AABAZeHxy1iTJk3Sbbfdpl27dunEiRN65ZVXtHPnTm3YsEFr166tiIwAAABl5vHKTseOHbV+/XoVFBSoSZMmWr16tUJDQ7VhwwZFRUVVREYAAIAy82hl5/jx43r44Yf13HPPad68eRWVCQAAwDYerez4+/srJSWlorIAAADYzuOXsfr06aNly5ZVQBQAAAD7eXyBctOmTfXCCy9o/fr1ioqKUo0aNdyOx8fH2xYOAACgvDwuO2+88YZq166ttLQ0paWluR2zLIuyAwAAKhWPy056enpF5AAAAKgQHpWdL7/8UsuXL9eJEycUExOjLl26VFQuAAAAW5x32UlJSVG/fv1UvXp1+fn56R//+IemTp2qhISECowHAABQPuf9bqykpCQ9+OCDOnz4sA4fPqzx48dr4sSJFZkNAACg3M677OzevVujR4+Wn9/JxaAnn3xShw8f1i+//FJh4QAAAMrrvMvOkSNHVLt2bdd+QECAAgMDlZeXVxG5AAAAbOHRBcoffvihQkJCXPslJSVas2aNduzY4RqLi4uzLx0AAEA5eVR2Bg4cWGpsyJAhrl9blqXi4uLypwIAALDJeZedkpKSiswBAABQITz+bCwAAICqhLIDAAAcjbIDAAAcjbIDAAAcjbIDAAAczeOys2nTJn355Zelxr/88ktt3rzZllAAAAB28bjsDBs2TBkZGaXGDx48qGHDhtkSCgAAwC4el51du3bp2muvLTXepk0b7dq1y5ZQAAAAdvG47AQEBOi///1vqfHMzEzXh4QCAABUFh6XndjYWI0ZM0a5ubmuscOHD+uZZ55RbGysreEAAADKy+OlmKlTp+qmm25So0aN1KZNG0nS1q1bFRoaqgULFtgeEAAAoDw8LjuXXnqptm/frn/961/atm2bAgMD9dBDD+mee+6Rv79/RWQEAAAoszJdZFOjRg09/PDDdmcBAACw3XmVneXLl+v222+Xv7+/li9ffs65cXFxtgQDAACww3mVnd69eysrK0sNGjRQ7969zzrPsiwVFxfblQ0AAKDczqvslJSUnPHXAAAAlZ1Hbz0/fvy4brnlFu3Zs6ei8gAAANjKo7Lj7++vHTt2yLKsisoDAABgK49vKjhgwADNmTOnIrIAAADYzuO3nhcVFemNN95Qamqq2rZtqxo1argdnzZtmm3hAAAAysvjsrNjxw7XB4Gefu0OL28BAIDKxuOy88knn1REDgAAgArh8TU7p/zwww/68MMPdezYMUmSMca2UAAAAHbxuOz8+uuviomJ0ZVXXqlu3bopMzNTkvS3v/1No0aNsj0gAABAeXhcdh5//HH5+/tr//79CgoKco3fddddWrVqla3hAAAAysvja3ZWr16tDz/8UJdddpnbeLNmzbRv3z7bggEAANjB45Wdo0ePuq3onPLLL78oICDAllAAAAB28bjs3HTTTZo/f75r37IslZSU6OWXX9Ytt9xiazgAAIDy8vhlrJdfflnR0dHavHmzioqKNHr0aO3cuVO//fabvvjii4rICAAAUGYer+y0aNFC27dv13XXXafY2FgdPXpUffv21ZYtW9SkSZOKyAgAAFBmHq/sSFJYWJjGjx9vdxYAAADblansHD58WF999ZWys7NVUlLidmzAgAG2BAMAALCDx2VnxYoVuu+++3T06FEFBwe7fR6WZVmUHQAAUKl4fM3OqFGjNGjQIOXn5+vw4cPKyclxbb/99ltFZAQAACgzj8vOwYMHFR8ff8Z77QAAAFQ2Hpedrl27avPmzRWRBQAAwHbndc3O8uXLXb/u3r27nnzySe3atUutWrWSv7+/29y4uDh7EwIAAJTDeZWd3r17lxqbMGFCqTHLslRcXFzuUAAAAHY5r7Jz+tvLAQAAqgqPr9kBAACoSspUdtasWaMePXqoSZMmatq0qXr06KGPPvrI7mwAAADl5nHZmTlzpm677TYFBwdrxIgRio+PV61atdStWzfNnDmzIjICAACUmcdlZ9KkSZo+fboWL16s+Ph4xcfHa9GiRZo+fbqSkpLKHGTSpEmyLEsJCQmuMWOMEhMTFR4ersDAQEVHR2vnzp1ujyssLNTw4cNVv3591ahRQ3FxcTpw4ECZcwAAAGfxuOzk5eXptttuKzXepUsX5eXllSnEpk2b9Prrr6t169Zu41OmTNG0adM0c+ZMbdq0SWFhYYqNjVV+fr5rTkJCglJSUrRkyRKtW7dOR44cUY8ePXhXGAAAkFSGshMXF6eUlJRS4++995569uzpcYAjR47ovvvu0+zZs1WnTh3XuDFGM2bM0NixY9W3b1+1bNlS8+bNU0FBgRYtWiRJys3N1Zw5czR16lR17txZbdq00cKFC/XNN99wDREAAJBUhg8Cbd68uV588UV9+umn6tChgyRp48aN+uKLLzRq1Ci9+uqrrrnx8fF/er5hw4ape/fu6ty5syZOnOgaT09PV1ZWlrp06eIaCwgI0M0336z169dryJAhSktL0/Hjx93mhIeHq2XLllq/fr26du16xq9ZWFiowsJC135ZV6QAAEDl53HZmTNnjurUqaNdu3Zp165drvHatWtrzpw5rn3Lsv607CxZskRff/21Nm3aVOpYVlaWJCk0NNRtPDQ0VPv27XPNqVatmtuK0Kk5px5/JpMmTdL48ePPmQ0AADiDx2UnPT3dli+ckZGhESNGaPXq1apevfpZ51mW5bZvjCk1dro/mzNmzBiNHDnStZ+Xl6eIiIjzTA4AAKqSct9UsLi4WFu3blVOTo5Hj0tLS1N2draioqLk5+cnPz8/rV27Vq+++qr8/PxcKzqnr9BkZ2e7joWFhamoqKjU1/7jnDMJCAhQrVq13DYAAOBMHpedhIQE18tVxcXFuummm3TttdcqIiJCn3766XmfJyYmRt988422bt3q2tq2bav77rtPW7du1RVXXKGwsDClpqa6HlNUVKS1a9eqY8eOkqSoqCj5+/u7zcnMzNSOHTtccwAAwMXN45ex3nnnHd1///2SpBUrVuinn37Sd999p/nz52vs2LH64osvzus8wcHBatmypdtYjRo1VK9ePdd4QkKCkpKS1KxZMzVr1kxJSUkKCgrSvffeK0kKCQnR4MGDNWrUKNWrV09169bVE088oVatWqlz586ePjUAAOBAHpedX375RWFhYZKkDz74QP369dOVV16pwYMHu70Tyw6jR4/WsWPHNHToUOXk5Kh9+/ZavXq1goODXXOmT58uPz8/9e/fX8eOHVNMTIzmzp0rX19fW7MAAICqyeOyExoaql27dqlhw4ZatWqVXnvtNUlSQUFBuQvG6S+DWZalxMREJSYmnvUx1atXV3JyspKTk8v1tQEAgDN5XHYeeugh9e/fXw0bNpRlWYqNjZUkffnll7rqqqtsDwgAAFAeHpedxMREtWzZUhkZGerXr58CAgIkSb6+vnr66adtDwgAAFAeHpcdSbrzzjtLjQ0cOLDcYQAAAOxWpvvsrF27Vj179lTTpk3VrFkzxcXF6fPPP7c7GwAAQLl5XHYWLlyozp07KygoSPHx8XrssccUGBiomJgY1wd0AgAAVBYev4z14osvasqUKXr88cddYyNGjNC0adP0wgsvuO6BAwAAUBl4vLLz448/qmfPnqXG4+LibPvcLAAAALt4XHYiIiK0Zs2aUuNr1qzhwzQBAECl4/HLWKNGjVJ8fLy2bt2qjh07yrIsrVu3TnPnztUrr7xSERkBAADKzOOy8+ijjyosLExTp07V22+/LUlq3ry5li5dql69etkeEAAAoDzKdJ+dPn36qE+fPnZnAQAAsF2Zys4pR44cUUlJidtYrVq1yhUIAADATh5foJyenq7u3burRo0aCgkJUZ06dVSnTh3Vrl1bderUqYiMAAAAZebxys59990nSXrzzTcVGhoqy7JsDwUAAGAXj8vO9u3blZaWpr/85S8VkQcAAMBWHr+M1a5dO2VkZFREFgAAANt5vLLzxhtv6JFHHtHBgwfVsmVL+fv7ux1v3bq1beEAAADKy+Oy8/PPP2vv3r166KGHXGOWZckYI8uyVFxcbGtAAACA8vC47AwaNEht2rTR4sWLuUAZAABUeh6XnX379mn58uVq2rRpReQBAACwlccXKN96663atm1bRWQBAACwnccrOz179tTjjz+ub775Rq1atSp1gXJcXJxt4QAAAMrL47LzyCOPSJImTJhQ6hgXKAMAgMrG47Jz+mdhAQAAVGYeX7Mzf/58FRYWlhovKirS/PnzbQkFAABgF4/LzkMPPaTc3NxS4/n5+W733gEAAKgMPC47p24eeLoDBw4oJCTEllAAAAB2Oe9rdtq0aSPLsmRZlmJiYuTn97+HFhcXKz09XbfddluFhAQAACir8y47vXv3liRt3bpVXbt2Vc2aNV3HqlWrpsaNG+uOO+6wPSAAAEB5nHfZGTdunCSpcePGuuuuu1S9evVSc7Zu3aprrrnGtnAAAADl5fE1OwMHDnQrOrm5uXrttdd07bXXKioqytZwAAAA5eVx2Tnl448/1v3336+GDRsqOTlZ3bp10+bNm+3MBgAAUG4e3VTwwIEDmjt3rt58800dPXpU/fv31/Hjx/Xuu++qRYsWFZURAACgzM57Zadbt25q0aKFdu3apeTkZB06dEjJyckVmQ0AAKDczntlZ/Xq1YqPj9ejjz6qZs2aVWQmAAAA25z3ys7nn3+u/Px8tW3bVu3bt9fMmTP1888/V2Q2AACAcjvvstOhQwfNnj1bmZmZGjJkiJYsWaJLL71UJSUlSk1NVX5+fkXmBAAAKBOP340VFBSkQYMGad26dfrmm280atQoTZ48WQ0aNFBcXFxFZAQAACizMr/1XJL+8pe/aMqUKTpw4IAWL15sVyYAAADblKvsnOLr66vevXtr+fLldpwOAADANraUHQAAgMqKsgMAAByNsgMAAByNsgMAAByNsgMAAByNsgMAAByNsgMAAByNsgMAAByNsgMAAByNsgMAAByNsgMAAByNsgMAAByNsgMAAByNsgMAAByNsgMAAByNsgMAAByNsgMAAByNsgMAAByNsgMAAByNsgMAAByNsgMAAByNsgMAAByNsgMAAByNsgMAAByNsgMAAByNsgMAABzNq2Vn0qRJateunYKDg9WgQQP17t1bu3fvdptjjFFiYqLCw8MVGBio6Oho7dy5021OYWGhhg8frvr166tGjRqKi4vTgQMHLuRTAQAAlZRXy87atWs1bNgwbdy4UampqTpx4oS6dOmio0ePuuZMmTJF06ZN08yZM7Vp0yaFhYUpNjZW+fn5rjkJCQlKSUnRkiVLtG7dOh05ckQ9evRQcXGxN54WAACoRPy8+cVXrVrltv/WW2+pQYMGSktL00033SRjjGbMmKGxY8eqb9++kqR58+YpNDRUixYt0pAhQ5Sbm6s5c+ZowYIF6ty5syRp4cKFioiI0EcffaSuXbte8OcFAAAqj0p1zU5ubq4kqW7dupKk9PR0ZWVlqUuXLq45AQEBuvnmm7V+/XpJUlpamo4fP+42Jzw8XC1btnTNOV1hYaHy8vLcNgAA4EyVpuwYYzRy5EjdcMMNatmypSQpKytLkhQaGuo2NzQ01HUsKytL1apVU506dc4653STJk1SSEiIa4uIiLD76QAAgEqi0pSdxx57TNu3b9fixYtLHbMsy23fGFNq7HTnmjNmzBjl5ua6toyMjLIHBwAAlVqlKDvDhw/X8uXL9cknn+iyyy5zjYeFhUlSqRWa7Oxs12pPWFiYioqKlJOTc9Y5pwsICFCtWrXcNgAA4ExeLTvGGD322GP697//rY8//liRkZFuxyMjIxUWFqbU1FTXWFFRkdauXauOHTtKkqKiouTv7+82JzMzUzt27HDNAQAAFy+vvhtr2LBhWrRokd577z0FBwe7VnBCQkIUGBgoy7KUkJCgpKQkNWvWTM2aNVNSUpKCgoJ07733uuYOHjxYo0aNUr169VS3bl098cQTatWqlevdWQAA4OLl1bIza9YsSVJ0dLTb+FtvvaUHH3xQkjR69GgdO3ZMQ4cOVU5Ojtq3b6/Vq1crODjYNX/69Ony8/NT//79dezYMcXExGju3Lny9fW9UE8FAABUUl4tO8aYP51jWZYSExOVmJh41jnVq1dXcnKykpOTbUwHAACcoFJcoAwAAFBRKDsAAMDRKDsAAMDRKDsAAMDRKDsAAMDRKDsAAMDRKDsAAMDRKDsAAMDRKDsAAMDRKDsAAMDRKDsAAMDRKDsAAMDRKDsAAMDRKDsAAMDRKDsAAMDRKDsAAMDRKDsAAMDRKDsAAMDRKDsAAMDRKDsAAMDRKDsAAMDRKDsAAMDRKDsAAMDRKDsAAMDRKDsAAMDRKDsAAMDRKDsAAMDRKDsAAMDRKDsAAMDRKDsAAMDRKDsAAMDRKDsAAMDRKDsAAMDRKDsAAMDRKDsAAMDRKDsAAMDRKDsAAMDRKDsAAMDRKDsAAMDRKDsAAMDRKDsAAMDRKDsAAMDRKDsAAMDRKDsAAMDRKDsAAMDRKDsAAMDRKDsAAMDRKDsAAMDRKDsAAMDRKDsAAMDRKDsAAMDRKDsAAMDRKDsAAMDRKDsAAMDRKDsAAMDRKDsAAMDRKDsAAMDRKDsAAMDRKDsAAMDRKDsAAMDRKDsAAMDRKDsAAMDRKDsAAMDRKDsAAMDRKDsAAMDRKDsAAMDRHFN2XnvtNUVGRqp69eqKiorS559/7u1IAACgEnBE2Vm6dKkSEhI0duxYbdmyRTfeeKNuv/127d+/39vRAACAlzmi7EybNk2DBw/W3/72NzVv3lwzZsxQRESEZs2a5e1oAADAy/y8HaC8ioqKlJaWpqefftptvEuXLlq/fv0ZH1NYWKjCwkLXfm5uriQpLy/vT79eSWFBOdKe2fl8XU/ZnbMqZJTsz1kVMkr8edulKmSU+PO2S1XIKPHnfT5zjDHnnmiquIMHDxpJ5osvvnAbf/HFF82VV155xseMGzfOSGJjY2NjY2NzwJaRkXHOrlDlV3ZOsSzLbd8YU2rslDFjxmjkyJGu/ZKSEv3222+qV6/eWR/jqby8PEVERCgjI0O1atWy5Zx2I6N9qkJOMtqnKuQko32qQs6LNaMxRvn5+QoPDz/nvCpfdurXry9fX19lZWW5jWdnZys0NPSMjwkICFBAQIDbWO3atSskX61atSrtN94pZLRPVchJRvtUhZxktE9VyHkxZgwJCfnTOVX+AuVq1aopKipKqampbuOpqanq2LGjl1IBAIDKosqv7EjSyJEj9cADD6ht27bq0KGDXn/9de3fv1+PPPKIt6MBAAAvc0TZueuuu/Trr79qwoQJyszMVMuWLfXBBx+oUaNGXssUEBCgcePGlXq5rDIho32qQk4y2qcq5CSjfapCTjKem2XMn71fCwAAoOqq8tfsAAAAnAtlBwAAOBplBwAAOBplBwAAOBplB7AB1/kDQOXliLeeA94WEBCgbdu2qXnz5t6OAgCVQmZmpmbNmqV169YpMzNTvr6+ioyMVO/evfXggw/K19f3gmVhZccGW7ZsUXp6umt/4cKF6tSpkyIiInTDDTdoyZIlXkx3Zjk5OZoxY4aGDRumiRMnKiMjw9uRNHz4cH3++efejnFOI0eOPONWXFysyZMnu/Zx8cjIyNCgQYO8HUPJyckaOHCg3n77bUnSggUL1KJFC1111VV65plndOLECS8nlL799lu99dZb+u677yRJ3333nR599FENGjRIH3/8sZfTVS3Hjh3TunXrtGvXrlLHfv/9d82fP98Lqf5n8+bNat68uVasWKHff/9de/bs0bXXXqsaNWroiSee0I033qj8/PwLF8iOTx6/2LVp08Z8/PHHxhhjZs+ebQIDA018fLyZNWuWSUhIMDVr1jRz5szxasaGDRuaX375xRhjzI8//mjCwsJMWFiYiY2NNZdddpkJCQkx3377rVczWpZlfHx8TLNmzczkyZNNZmamV/OciWVZ5pprrjHR0dFum2VZpl27diY6Otrccsst3o7pkpGRYfLz80uNFxUVmbVr13ohkWeysrLM+PHjvR3jnLZu3Wp8fHy8mmHChAkmODjY3HHHHSYsLMxMnjzZ1KtXz0ycONEkJSWZSy65xDz//PNezbhy5UpTrVo1U7duXVO9enWzcuVKc8kll5jOnTubmJgY4+fnZ9asWePVjKccOXLEvP766+bBBx80t912m7n99tvNgw8+aGbPnm2OHDni7Xhm9+7dplGjRq6fmTfffLM5dOiQ63hWVpbXvyc7depkEhMTXfsLFiww7du3N8YY89tvv5lrrrnGxMfHX7A8lB0bBAUFmX379hljThaf//u//3M7/q9//cu0aNHCG9FcLMsy//3vf40xxtx9990mOjraHD161BhjzO+//2569Ohh7rzzTm9GNJZlmY8++siMGDHC1K9f3/j7+5u4uDizYsUKU1xc7NVspyQlJZnIyMhSP5T9/PzMzp07vZSqtEOHDpl27doZHx8f4+vrawYMGOBWeirDD8PzURmKxHvvvXfObfr06V7PeMUVV5h3333XGHPy98zX19csXLjQdfzf//63adq0qbfiGWOM6dChgxk7dqwxxpjFixebOnXqmGeeecZ1/JlnnjGxsbHeiueyc+dOEx4ebmrXrm169eplHn74YfP3v//d9OrVy9SuXdtceumlXv+73rt3b9OjRw/z888/m++//9707NnTREZGuv4dqgx/vwMDA83evXtd+8XFxcbf399kZWUZY4xZvXq1CQ8Pv2B5KDs2qFevntm8ebMxxpgGDRqYrVu3uh3/4YcfTGBgoDeiufyx7JzpH+uNGzeayy67zBvRXP6YsaioyCxdutR07drV+Pr6mvDwcPPMM8+Y77//3qsZjTHmq6++MldeeaUZNWqUKSoqMsZUvrIzYMAAc/3115tNmzaZ1NRU07ZtWxMVFWV+++03Y8zJH4aWZXk5pTHbtm0757Z06VKv/9A+9b9ny7LOunk7Y2BgoOsfOmOM8ff3Nzt27HDt//TTTyYoKMgb0Vxq1arl+vtbXFxs/Pz8TFpamuv4N998Y0JDQ70VzyU6OtrcfffdprCwsNSxwsJCc88995jo6GgvJPufBg0amO3bt7uNDR061Fx++eVm7969laLsNGrUyKxbt861f+jQIWNZlikoKDDGGJOenm6qV69+wfJQdmxw//33m8GDBxtjjOnXr5959tln3Y4nJSWZVq1aeSOai2VZJjs72xhjTHh4uNsPQmNOfuMFBAR4I5rLH8vOH+3bt8+MGzfONGrUyOt/gU/Jz883AwYMMK1btzbbt283/v7+larshIeHmy+//NK1//vvv5tevXqZa665xvz666+V4oehMecuEqfGvZ0zPDzcpKSknPX4li1bvJ4xMjLSrFy50hhjzJ49e4yPj495++23Xcfff/9907hxY2/FM8a4lx1jjKlZs6bb//x/+umnC/qP39kEBgae8+/yN9984/X/vAYHB5tdu3aVGn/sscfMZZddZj777DOvf0+OGDHCtGzZ0qxcudJ8/PHH5pZbbnEriatWrTJNmjS5YHl4N5YNXnrpJXXq1Ek333yz2rZtq6lTp+rTTz9V8+bNtXv3bm3cuFEpKSnejqmYmBj5+fkpLy9Pe/bs0dVXX+06tn//ftWvX9+L6c7u8ssvV2JiosaNG6ePPvrI23EkSTVr1tS8efO0ZMkSxcbGqri42NuR3OTm5qpOnTqu/YCAAL3zzjvq16+fbrnlFi1cuNCL6f6nXr16eumllxQTE3PG4zt37lTPnj0vcCp3UVFR+vrrr9W7d+8zHrcsy+u3Hrj33ns1YMAA9erVS2vWrNFTTz2lJ554Qr/++qssy9KLL76oO++806sZGzdurB9++EFNmzaVJG3YsEGXX36563hGRoYaNmzorXguderU0ffff68WLVqc8fgPP/zg9nfLG6666irXBcB/lJycLGOM4uLivJTsfyZOnKjMzEz17NlTxcXF6tChg9vPHcuyNGnSpAuWh7Jjg/DwcG3ZskWTJ0/WihUrZIzRV199pYyMDHXq1ElffPGF2rZt69WM48aNc9sPCgpy21+xYoVuvPHGCxmplEaNGp3zrYiWZSk2NvYCJvpzd999t2644QalpaWpUaNG3o7jcsUVV2j79u1q1qyZa8zPz0//7//9P/Xr1089evTwYrr/iYqK0qFDh876e3f48GGvF4knn3xSR48ePevxpk2b6pNPPrmAiUobP368AgMDtXHjRg0ZMkRPPfWUWrdurdGjR6ugoEA9e/bUCy+84NWMjz76qNt/Clq2bOl2fOXKlbr11lsvdKxS/v73v2vgwIF69tlnFRsbq9DQUFmWpaysLKWmpiopKUkJCQlezdinTx8tXrxYDzzwQKljM2fOVElJif75z396Idn/1KxZU0uXLtXvv/+uEydOqGbNmm7Hu3TpckHz8KnngAM99dRT2rp1qz788MNSx06cOKE77rhDK1asUElJiRfS/U9KSoqOHj2q+++//4zHc3JytHz5cg0cOPACJ8PF7KWXXtIrr7yirKwsWZYl6eSNQ8PCwpSQkKDRo0d7OSE8RdkBHOjEiRMqKChQrVq1zni8uLhYBw4cqFSrUUBlk56erqysLElSWFiYIiMjvZwIZcVNBQEH8vPzO2vRkaRDhw5p/PjxFzBR2VSWG/bh4hQZGakOHTqoQ4cOrqLD92TVxMoOcBHatm2brr322kp3YfXpqkpOXDz4nqyauEAZcKDly5ef8/iPP/54gZKcW1XJiYsH35POxMoO4EA+Pj5/+pZoy7K8/r/TqpITFw++J52Ja3YAB2rYsKHeffddlZSUnHH7+uuvvR1RUtXJiYsH35PORNkBHOjUjfDOpjLcCE+qOjlx8eB70pm4ZgdwoKpwIzyp6uTExYPvSWfimh0AAOBovIwFAAAcjbIDAAAcjbIDAAAcjbIDAAAcjbIDAGeRmJioa665xtsxAJQTZQcAdPL+KcuWLfN2DAAVgLIDAAAcjbIDoFKJjo7W8OHDlZCQoDp16ig0NFSvv/66jh49qoceekjBwcFq0qSJVq5c6XrM2rVrdd111ykgIEANGzbU008/rRMnTridMz4+XqNHj1bdunUVFhamxMRE1/HGjRtLkvr06SPLslz7pyxYsECNGzdWSEiI7r77buXn51fkbwEAm1F2AFQ68+bNU/369fXVV19p+PDhevTRR9WvXz917NhRX3/9tbp27aoHHnhABQUFOnjwoLp166Z27dpp27ZtmjVrlubMmaOJEyeWOmeNGjX05ZdfasqUKZowYYJSU1MlSZs2bZIkvfXWW8rMzHTtS9LevXu1bNky/ec//9F//vMfrV27VpMnT75wvxkAyo07KAOoVKKjo1VcXKzPP/9cklRcXKyQkBD17dtX8+fPlyRlZWWpYcOG2rBhg1asWKF3331X3377rSzLkiS99tpreuqpp5SbmysfH59S55Sk6667TrfeequruFiWpZSUFPXu3ds1JzExUS+//LKysrIUHBwsSRo9erQ+++wzbdy48UL8dgCwASs7ACqd1q1bu37t6+urevXqqVWrVq6x0NBQSVJ2dra+/fZbdejQwVV0JKlTp046cuSIDhw4cMZzSic/3To7O/tPszRu3NhVdDx5HIDKg7IDoNLx9/d327csy23sVLEpKSmRMcat6EhyfSr1H8fPdM6SkpIyZTmfxwGoPCg7AKq0Fi1aaP369frjK/Lr169XcHCwLr300vM+j7+/v4qLiysiIgAvo+wAqNKGDh2qjIwMDR8+XN99953ee+89jRs3TiNHjpSPz/n/iGvcuLHWrFmjrKws5eTkVGBiABcaZQdAlXbppZfqgw8+0FdffaW//vWveuSRRzR48GA9++yzHp1n6tSpSk1NVUREhNq0aVNBaQF4A+/GAgAAjsbKDgAAcDTKDgAAcDTKDgAAcDTKDgAAcDTKDgAAcDTKDgAAcDTKDgAAcDTKDgAAcDTKDgAAcDTKDgAAcDTKDgAAcLT/D+bxiyW0X9Q4AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the average pressure by month\n",
    "ascend_avg_pressure = avg_pres.sort_values()\n",
    "ascend_avg_pressure.plot.bar()\n",
    "plt.xlabel('month')\n",
    "plt.ylabel('pressure')\n",
    "plt.title('Average pressure by Martian month')\n",
    "plt.savefig('av_pressure.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 5. How many terrestrial (earth) days are there in a Martian year?\n",
    "plt.plot(mars_df['sol'], mars_df['min_temp'])\n",
    "plt.xlabel('Number of Terrestrial Days')\n",
    "plt.ylabel\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On average, the third month has the coldest minimum temperature on Mars, and the eighth month is the warmest. But it is always very cold there in human terms!\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Atmospheric pressure is, on average, lowest in the sixth month and highest in the ninth."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The distance from peak to peak is roughly 1425-750, or 675 days. A year on Mars appears to be about 675 days from the plot. Internet search confirms that a Mars year is equivalent to 687 earth days."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 6: Save the Data\n",
    "\n",
    "Export the DataFrame to a CSV file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Write the data to a CSV\n",
    "file_name = 'mars_data_collection.csv'\n",
    "mars_df.to_csv(file_name, index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "browser.quit()"
   ]
  }
 ],
 "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": "3.9.6"
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  "vscode": {
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 },
 "nbformat": 4,
 "nbformat_minor": 4
}