mars_data_challenge_part_2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1862</th>\n",
       "      <td>1889</td>\n",
       "      <td>2018-02-23</td>\n",
       "      <td>1973</td>\n",
       "      <td>133</td>\n",
       "      <td>5</td>\n",
       "      <td>-78.0</td>\n",
       "      <td>730.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1863</th>\n",
       "      <td>1892</td>\n",
       "      <td>2018-02-24</td>\n",
       "      <td>1974</td>\n",
       "      <td>134</td>\n",
       "      <td>5</td>\n",
       "      <td>-77.0</td>\n",
       "      <td>729.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1864</th>\n",
       "      <td>1894</td>\n",
       "      <td>2018-02-25</td>\n",
       "      <td>1975</td>\n",
       "      <td>134</td>\n",
       "      <td>5</td>\n",
       "      <td>-76.0</td>\n",
       "      <td>729.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1865</th>\n",
       "      <td>1893</td>\n",
       "      <td>2018-02-26</td>\n",
       "      <td>1976</td>\n",
       "      <td>135</td>\n",
       "      <td>5</td>\n",
       "      <td>-77.0</td>\n",
       "      <td>728.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1866</th>\n",
       "      <td>1895</td>\n",
       "      <td>2018-02-27</td>\n",
       "      <td>1977</td>\n",
       "      <td>135</td>\n",
       "      <td>5</td>\n",
       "      <td>-77.0</td>\n",
       "      <td>727.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1867 rows × 7 columns</p>\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\n",
       "...    ...              ...   ...  ...    ...       ...       ...\n",
       "1862  1889       2018-02-23  1973  133      5     -78.0     730.0\n",
       "1863  1892       2018-02-24  1974  134      5     -77.0     729.0\n",
       "1864  1894       2018-02-25  1975  134      5     -76.0     729.0\n",
       "1865  1893       2018-02-26  1976  135      5     -77.0     728.0\n",
       "1866  1895       2018-02-27  1977  135      5     -77.0     727.0\n",
       "\n",
       "[1867 rows x 7 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url = 'https://data-class-mars-challenge.s3.amazonaws.com/Mars/index.html'\n",
    "# retrieve the table from the website and convert it into a dataframe\n",
    "mars_df = pd.DataFrame(pd.read_html(url)[0])\n",
    "mars_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                           int64\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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mars_df['terrestrial_date'] = pd.to_datetime(mars_df['terrestrial_date'])\n",
    "mars_df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# How many months exist on Mars?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unique_mon = len(mars_df['month'].unique())\n",
    "unique_mon"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# How many Martian (and not Earth) days worth of data exist in the scraped dataset?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1867"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "martian_days = len(mars_df['sol'].unique())\n",
    "martian_days"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# What are the coldest and the warmest months on Mars (at the location of Curiosity)?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# group the dataframe by month\n",
    "mars_by_month = mars_df.groupby('month')\n",
    "# find the average temperature of each month\n",
    "monthly_temp = mars_by_month[['min_temp']].mean().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The coldest month on mars is month #3 with the average temperature -83.30729166666667\n",
      "The warmest month on mars is month #8 with the average temperature -68.38297872340425\n"
     ]
    }
   ],
   "source": [
    "# find the coldest month \n",
    "coldest_month = monthly_temp[monthly_temp['min_temp'] == monthly_temp['min_temp'].min()]\n",
    "print(f\"The coldest month on mars is month #{coldest_month['month'].values[0]} with the average temperature {coldest_month['min_temp'].values[0]}\" )\n",
    "\n",
    "# find the warmest month\n",
    "warmest_month = monthly_temp[monthly_temp['min_temp'] == monthly_temp['min_temp'].max()]\n",
    "print(f\"The warmest month on mars is month #{warmest_month['month'].values[0]} with the average temperature {warmest_month['min_temp'].values[0]}\" )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='month'>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the monthly temperature as a bar chart\n",
    "monthly_temp.plot(kind='bar', x='month', y='min_temp')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Which months have the lowest and highest atmospheric pressure on Mars?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The month of lowest pressure is month #6 with an average pressure 745.0544217687075\n",
      "The month of highest pressure is month #9 with an average pressure 913.3059701492538\n"
     ]
    }
   ],
   "source": [
    "# find the average pressure of each month\n",
    "monthly_pressure = mars_by_month[['pressure']].mean().reset_index()\n",
    "\n",
    "# find the month of lowest pressure\n",
    "low_pressure = monthly_pressure[monthly_pressure['pressure'] == monthly_pressure['pressure'].min()]\n",
    "print(f\"The month of lowest pressure is month #{low_pressure['month'].values[0]} with an average pressure {low_pressure['pressure'].values[0]}\")\n",
    "\n",
    "# find the month of highest pressure\n",
    "high_pressure = monthly_pressure[monthly_pressure['pressure'] == monthly_pressure['pressure'].max()]\n",
    "print(f\"The month of highest pressure is month #{high_pressure['month'].values[0]} with an average pressure {high_pressure['pressure'].values[0]}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='month'>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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16sh9NLA2M3+ZmW8Bc4BJLaolSdpFZGbzdxpxJXBxZl5Xzf8JMCYzb6hbZyowtZr9A2B1N0qdAPy6h921nvWsd3DXsl7XTs3MgZ0t6NWz/nQpOml7z6tIZt4K3NqjIhFLMnNUT/ZhPetZ7+CuZb3uadWwTAcwpG5+MPBSi2pJknbRqnB/EhgWEUMj4n3AZGB+i2pJknbRkmGZzHwnIm4AHgDagB9n5soWlOrRsI71rGe9Q6KW9bqhJR+oSpIOLL+hKkkFMtwlqUCGuyQVyHDfg4g4PSImRkTfXdovblG90RHx4Wp6eET8x4i4pBW1Oqn9t/ujTl29CdXzu7BF+x8TEf2r6aMi4i8j4r6I+GZEvL/JtT4XEUP2vmbT6r0vIv5NRJxfzX8yIr4fEdMioneLan4gIr4YEd+JiP8WEf+u2T9HNVcRH6hGxJ9m5m1N3ufngGnAKmAk8PnMvLda9nRmntPkel+ndi2eXsBDwBhgIXA+8EBmzmxirV1PSw3gXwGPAGTmx5pVq67m4swcXU3/W2o/258CFwL3ZebNTa63Eji7OnPrVmALcDcwsWr/eBNr/TPwBvA8cBcwLzM3NWv/ndS7k9rfydHAb4C+wD3Unltk5pQm1/sc8EfAPwKXAMuAfwL+GPhsZi5sZj01SWYe8v+AdS3Y5zNA32q6HVhCLeABlraoXhu1/7C/BfpX7UcBy5tc62ngDuA84A+rxw3V9B+26He0tG76SWBgNX0M8EwL6q2qf767LFvW7OdG7V3whcCPgE3APwBTgH4teG7Lq8dewEagrZqPZv+tVPt9pq7G0cDCavp3W/R/4f3AzcBzwObq36qq7dhm19tLX/6+BfvsD3wD+O/AJ3dZNqtZdVp1+YGmi4jlXS0CTmxBybbMfB0gM1+MiPOAuyPiVDq/vEJPvZOZ24EtEfF8Zv62qv1mROxocq1RwOeBrwBfysxlEfFmZv5jk+vUOyIijqMWgpHVkW1mvhER77Sg3oq6d3S/iIhRmbkkIn4feLvJtTIzdwAPAg9WQyMfBa4B/gro9NofPXBE9eXAY6iF7fuBV4EjgZYMy1B7Idle1egHkJnrWjQMNJfau8jzMvNlgIj4HWovlvOAC5pZLCK6ehce1N61N9ttwBrg74A/i4grqIX8NmBss4ocMuFOLcAvovZ2sF4A/7sF9V6OiJGZuQwgM1+PiMuAHwMfbEG9tyLi6MzcAvyLnY3VuGZTw70Kom9HxLzqcSOt/1t4P/AUtd9XRsTvZObL1ecZrXixvA74TkR8ldoFmR6PiPXA+mpZM72n/5n5NrVvZM+PiKOaXAtq7w6eo/ZO7yvAvIj4JbVgmNOCej8EnoyIJ4BzgW8CRMRAai8qzdaemd+sb6hC/psR8WctqPcktSGnzv4Oj21BvQ9k5hXV9M8i4ivAIxHR1OHQQ2bMPSJ+BNyWmY92sux/ZOYnm1xvMLWj6Zc7WTY+Mx9rcr0jq1fuXdtPAE7KzGeaWW+XGpcC4zPzy62qsYfaRwMnZuYLLdp/P+D3qL14dWTmxhbU+P3M/D/N3u9eap4MkJkvRcSx1D6bWZeZi1tU70zgDGBFZj7Xihp1tR4EHgZm7/x9RcSJwKeACzLz/CbXWwH8cWau6WTZ+sxs6oflEbEKOLM6yNrZNgX4c2pDwac2pc6hEu6SDg/V8N10aveAGFQ1b6T2bujmzNz13XtP611J7XOf3S47HhGXZ+bPmlzvvwAPZubDu7RfDHwvM4c1pY7hLulQ0Yoz40qtZ7hLOmRExLrM/F3r7d2h9IGqpMPA/j4zrtR6hrukg83+PjOuyHqGu6SDzc+pnTWybNcFEbHQeo1xzF2SCuSFwySpQIa7JBXIcJe6ISKOjYjP1s2fFxE/P5B9kuoZ7lL3HAt8dm8rSQeK4a7iRUR7RDwXET+MiBURcWdEnB8Rj0XEmqjdJOX4iPhZRCyPiCci4qxq27+IiB9HxMKI+GV1bXOoXX72AxGxLCL+a9XWNyLurmrdGRGtuCCa1BBPhdTh4jTgKmAqtasAfhKYAHwM+DK1q0UuzczLI+IjwN/y/y/3ejq1m5n0A1ZHxN9Qu/bJiMwcCbVhGeBDwJnAS8BjwHhgtwvdSfuDR+46XLyQmc9UV+JbCSzI2nnAz1C7GcsEajdPIDMfAQbU3Ubu/szclpm/Bl6h628RLs7MjqrGsmq/0gFhuOtwUX855R118zuovYPtbAhl55dA6rfdTtfveBtdT2o5w12q+V/Av4Z3h1h+vfNuWF14jeqORNLByCMLqeYvgNuqizptoXZLty5l5ubqA9kVwN8D97e+i1LjvPyAJBXIYRlJKpDhLkkFMtwlqUCGuyQVyHCXpAIZ7pJUIMNdkgpkuEtSgf4fwpuogTJnU2sAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the monthly atmospheric pressure as a bar chart\n",
    "monthly_pressure.plot(kind='bar', x='month', y='pressure')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# About how many terrestrial (Earth) days exist in a Martian year?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='terrestrial_date'>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mars_df.plot(x='terrestrial_date', y='min_temp')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the plot graph, we observe that Mars reaches its lowest temperature at the mark of 2014, and the second time it reches the lowest\n",
    "temperature was slightly before 2016. Therefore, we can roughly estimate that a Mars year is around 700 Earth days."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Export the data as a csv\n",
    "mars_df.to_csv(\"mars_data.csv\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.9.12 ('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": "3.9.12"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "fb833273add3e7c60eb33c0608260b79a61e072ade6f02cc8d07b0a26eef8ab8"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}