free-code-camp-data-analysis-with-python.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2> Use Pandas to import the data from `epa-sea-level.csv`</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.stats import linregress"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": []
   },
   "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>Year</th>\n",
       "      <th>CSIRO Adjusted Sea Level</th>\n",
       "      <th>Lower Error Bound</th>\n",
       "      <th>Upper Error Bound</th>\n",
       "      <th>NOAA Adjusted Sea Level</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1880</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.952756</td>\n",
       "      <td>0.952756</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1881</td>\n",
       "      <td>0.220472</td>\n",
       "      <td>-0.732283</td>\n",
       "      <td>1.173228</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1882</td>\n",
       "      <td>-0.440945</td>\n",
       "      <td>-1.346457</td>\n",
       "      <td>0.464567</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1883</td>\n",
       "      <td>-0.232283</td>\n",
       "      <td>-1.129921</td>\n",
       "      <td>0.665354</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1884</td>\n",
       "      <td>0.590551</td>\n",
       "      <td>-0.283465</td>\n",
       "      <td>1.464567</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>2009</td>\n",
       "      <td>8.586614</td>\n",
       "      <td>8.311024</td>\n",
       "      <td>8.862205</td>\n",
       "      <td>8.046354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>2010</td>\n",
       "      <td>8.901575</td>\n",
       "      <td>8.618110</td>\n",
       "      <td>9.185039</td>\n",
       "      <td>8.122973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>2011</td>\n",
       "      <td>8.964567</td>\n",
       "      <td>8.661417</td>\n",
       "      <td>9.267717</td>\n",
       "      <td>8.053065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>2012</td>\n",
       "      <td>9.326772</td>\n",
       "      <td>8.992126</td>\n",
       "      <td>9.661417</td>\n",
       "      <td>8.457058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>2013</td>\n",
       "      <td>8.980315</td>\n",
       "      <td>8.622047</td>\n",
       "      <td>9.338583</td>\n",
       "      <td>8.546648</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>134 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Year  CSIRO Adjusted Sea Level  Lower Error Bound  Upper Error Bound  \\\n",
       "0    1880                  0.000000          -0.952756           0.952756   \n",
       "1    1881                  0.220472          -0.732283           1.173228   \n",
       "2    1882                 -0.440945          -1.346457           0.464567   \n",
       "3    1883                 -0.232283          -1.129921           0.665354   \n",
       "4    1884                  0.590551          -0.283465           1.464567   \n",
       "..    ...                       ...                ...                ...   \n",
       "129  2009                  8.586614           8.311024           8.862205   \n",
       "130  2010                  8.901575           8.618110           9.185039   \n",
       "131  2011                  8.964567           8.661417           9.267717   \n",
       "132  2012                  9.326772           8.992126           9.661417   \n",
       "133  2013                  8.980315           8.622047           9.338583   \n",
       "\n",
       "     NOAA Adjusted Sea Level  \n",
       "0                        NaN  \n",
       "1                        NaN  \n",
       "2                        NaN  \n",
       "3                        NaN  \n",
       "4                        NaN  \n",
       "..                       ...  \n",
       "129                 8.046354  \n",
       "130                 8.122973  \n",
       "131                 8.053065  \n",
       "132                 8.457058  \n",
       "133                 8.546648  \n",
       "\n",
       "[134 rows x 5 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_1880_to_2013 = pd.read_csv('epa-sea-level.csv',float_precision=\"legacy\")\n",
    "df_1880_to_2013"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2> Use matplotlib to create a scatter plot using the \"Year\" column as the x-axis and the \"CSIRO Adjusted Sea Level\" column as the y-axis. </h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 0, 'Years'), Text(0, 0.5, 'CSIRO Adjusted Sea Level')]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig ,ax = plt.subplots(figsize = (15,5))\n",
    "x = df_1880_to_2013['Year']\n",
    "y = df_1880_to_2013['CSIRO Adjusted Sea Level']\n",
    "ax.scatter(x,y)\n",
    "ax.set(xlabel = \"Years\", ylabel = \"CSIRO Adjusted Sea Level\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Use the `linregress` function from `scipy.stats` to get the slope and y-intercept of the line of best fit.</h2>\n",
    "<h2>Plot the line of best fit over the top of the scatter plot. Make the line go through the year 2013 to predict the sea level rise in 2013.</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinregressResult(slope=0.0630445840121348, intercept=-119.06594196773978, rvalue=0.984757131182585, pvalue=3.7886969791131554e-102, stderr=0.000969211871328715)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result = linregress(df_1880_to_2013['Year'], df_1880_to_2013['CSIRO Adjusted Sea Level'])\n",
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f68264fbeb0>]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig ,ax = plt.subplots(figsize = (15,5))\n",
    "x = df_1880_to_2013['Year']\n",
    "y = df_1880_to_2013['CSIRO Adjusted Sea Level']\n",
    "ax.scatter(x,y)\n",
    "ax.set(xlabel = \"Years\", ylabel = \"CSIRO Adjusted Sea Level\")\n",
    "ax.plot(df_1880_to_2013['Year'],result.slope*df_1880_to_2013['Year']+ result.intercept,'r')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Plot a new line of best fit just using the data from year 2000 through the most recent year in the dataset.</h2> \n",
    "<h2>Make the line also go through the year 2013 to predict the sea level rise in 2013 if the rate of rise continues as it has since the year 2000.</h2>\n",
    "<h2> \n",
    "The x label should be \"Year\", the y label should be \"Sea Level (inches)\", and the title should be \"Rise in Sea Level\"</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>Year</th>\n",
       "      <th>CSIRO Adjusted Sea Level</th>\n",
       "      <th>Lower Error Bound</th>\n",
       "      <th>Upper Error Bound</th>\n",
       "      <th>NOAA Adjusted Sea Level</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>2000</td>\n",
       "      <td>7.062992</td>\n",
       "      <td>6.791339</td>\n",
       "      <td>7.334646</td>\n",
       "      <td>6.920582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>2001</td>\n",
       "      <td>7.287402</td>\n",
       "      <td>7.019685</td>\n",
       "      <td>7.555118</td>\n",
       "      <td>7.087460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>2002</td>\n",
       "      <td>7.381890</td>\n",
       "      <td>7.110236</td>\n",
       "      <td>7.653543</td>\n",
       "      <td>7.319698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>2003</td>\n",
       "      <td>7.759843</td>\n",
       "      <td>7.484252</td>\n",
       "      <td>8.035433</td>\n",
       "      <td>7.488353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>2004</td>\n",
       "      <td>7.740157</td>\n",
       "      <td>7.464567</td>\n",
       "      <td>8.015748</td>\n",
       "      <td>7.507933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>2005</td>\n",
       "      <td>7.744094</td>\n",
       "      <td>7.472441</td>\n",
       "      <td>8.015748</td>\n",
       "      <td>7.644563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>2006</td>\n",
       "      <td>7.917323</td>\n",
       "      <td>7.645669</td>\n",
       "      <td>8.188976</td>\n",
       "      <td>7.697420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>2007</td>\n",
       "      <td>7.996063</td>\n",
       "      <td>7.712598</td>\n",
       "      <td>8.279528</td>\n",
       "      <td>7.702147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>2008</td>\n",
       "      <td>8.350394</td>\n",
       "      <td>8.078740</td>\n",
       "      <td>8.622047</td>\n",
       "      <td>7.907365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>2009</td>\n",
       "      <td>8.586614</td>\n",
       "      <td>8.311024</td>\n",
       "      <td>8.862205</td>\n",
       "      <td>8.046354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>2010</td>\n",
       "      <td>8.901575</td>\n",
       "      <td>8.618110</td>\n",
       "      <td>9.185039</td>\n",
       "      <td>8.122973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>2011</td>\n",
       "      <td>8.964567</td>\n",
       "      <td>8.661417</td>\n",
       "      <td>9.267717</td>\n",
       "      <td>8.053065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>2012</td>\n",
       "      <td>9.326772</td>\n",
       "      <td>8.992126</td>\n",
       "      <td>9.661417</td>\n",
       "      <td>8.457058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>2013</td>\n",
       "      <td>8.980315</td>\n",
       "      <td>8.622047</td>\n",
       "      <td>9.338583</td>\n",
       "      <td>8.546648</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Year  CSIRO Adjusted Sea Level  Lower Error Bound  Upper Error Bound  \\\n",
       "120  2000                  7.062992           6.791339           7.334646   \n",
       "121  2001                  7.287402           7.019685           7.555118   \n",
       "122  2002                  7.381890           7.110236           7.653543   \n",
       "123  2003                  7.759843           7.484252           8.035433   \n",
       "124  2004                  7.740157           7.464567           8.015748   \n",
       "125  2005                  7.744094           7.472441           8.015748   \n",
       "126  2006                  7.917323           7.645669           8.188976   \n",
       "127  2007                  7.996063           7.712598           8.279528   \n",
       "128  2008                  8.350394           8.078740           8.622047   \n",
       "129  2009                  8.586614           8.311024           8.862205   \n",
       "130  2010                  8.901575           8.618110           9.185039   \n",
       "131  2011                  8.964567           8.661417           9.267717   \n",
       "132  2012                  9.326772           8.992126           9.661417   \n",
       "133  2013                  8.980315           8.622047           9.338583   \n",
       "\n",
       "     NOAA Adjusted Sea Level  \n",
       "120                 6.920582  \n",
       "121                 7.087460  \n",
       "122                 7.319698  \n",
       "123                 7.488353  \n",
       "124                 7.507933  \n",
       "125                 7.644563  \n",
       "126                 7.697420  \n",
       "127                 7.702147  \n",
       "128                 7.907365  \n",
       "129                 8.046354  \n",
       "130                 8.122973  \n",
       "131                 8.053065  \n",
       "132                 8.457058  \n",
       "133                 8.546648  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2000_to_2013 = df_1880_to_2013[df_1880_to_2013['Year'] >= 2000]\n",
    "df_2000_to_2013"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinregressResult(slope=0.16642727333186808, intercept=-325.79346680596467, rvalue=0.976287571614062, pvalue=2.4388064141616947e-09, stderr=0.010652933111541106)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_1 = linregress(df_2000_to_2013['Year'], df_2000_to_2013['CSIRO Adjusted Sea Level'])\n",
    "result_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f6826441100>]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig ,ax = plt.subplots(figsize = (15,5))\n",
    "x = df_1880_to_2013['Year']\n",
    "y = df_1880_to_2013['CSIRO Adjusted Sea Level']\n",
    "ax.scatter(x,y)\n",
    "ax.set(xlabel = \"Year\", ylabel = \"Sea Level (inches)\", title =\"Rise in Sea Level\")\n",
    "ax.plot(df_1880_to_2013['Year'],result.slope*df_1880_to_2013['Year']+ result.intercept,'g')\n",
    "ax.plot(df_2000_to_2013['Year'],result_1.slope*df_2000_to_2013['Year']+ result_1.intercept,'red')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2> For predictions till 2050 </h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.read_csv(\"epa-sea-level.csv\", float_precision=\"legacy\")\n",
    "plt.figure(1, figsize=(16, 9))\n",
    "plt.scatter(df[\"Year\"], df[\"CSIRO Adjusted Sea Level\"], c= \"r\")\n",
    "regress = linregress(df[\"Year\"], df[\"CSIRO Adjusted Sea Level\"])\n",
    "last_year = df[\"Year\"].max()\n",
    "df = df.append([{\"Year\": y} for y in range(last_year + 1, 2050)])\n",
    "plt.plot(df[\"Year\"],regress.intercept + regress.slope * df[\"Year\"],c=\"r\",label=\"fit all\")\n",
    "df_recent = df.loc[(df[\"Year\"] >= 2000) & (df[\"Year\"] <= last_year)]\n",
    "bestfit = linregress(df_recent[\"Year\"], df_recent[\"CSIRO Adjusted Sea Level\"])\n",
    "df_recent = df_recent.append([{\"Year\": y} for y in range(last_year + 1, 2050)])\n",
    "plt.plot(df_recent[\"Year\"],bestfit.intercept + bestfit.slope * df_recent[\"Year\"],c=\"b\",label=\"fit recent\")\n",
    "plt.xlabel(\"Year\")\n",
    "plt.ylabel(\"Sea Level (inches)\")\n",
    "plt.title(\"Rise in Sea Level\")\n",
    "plt.gca()\n",
    "plt.savefig('sea_level_plot.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def draw_plot():\n",
    "    # Read data from file\n",
    "    df = pd.read_csv(\"epa-sea-level.csv\", float_precision=\"legacy\")\n",
    "\n",
    "    # Create scatter plot\n",
    "    plt.figure(1, figsize=(16, 9))\n",
    "    plt.scatter(df[\"Year\"], df[\"CSIRO Adjusted Sea Level\"], c= \"r\")\n",
    "\n",
    "    # Create first line of best fit\n",
    "    regress = linregress(df[\"Year\"], df[\"CSIRO Adjusted Sea Level\"])\n",
    "    last_year = df[\"Year\"].max()\n",
    "    df = df.append([{\"Year\": y} for y in range(last_year + 1, 2050)])\n",
    "    plt.plot(df[\"Year\"],regress.intercept + regress.slope * df[\"Year\"],c=\"r\",label=\"fit all\")\n",
    "\n",
    "    # Create second line of best fit\n",
    "    df_recent = df.loc[(df[\"Year\"] >= 2000) & (df[\"Year\"] <= last_year)]\n",
    "    bestfit = linregress(df_recent[\"Year\"], df_recent[\"CSIRO Adjusted Sea Level\"])\n",
    "    df_recent = df_recent.append([{\"Year\": y} for y in range(last_year + 1, 2050)])\n",
    "    plt.plot(df_recent[\"Year\"],bestfit.intercept + bestfit.slope * df_recent[\"Year\"],c=\"b\",label=\"fit recent\")\n",
    "\n",
    "    # Add labels and title\n",
    "    plt.xlabel(\"Year\")\n",
    "    plt.ylabel(\"Sea Level (inches)\")\n",
    "    plt.title(\"Rise in Sea Level\")\n",
    "    \n",
    "    # Save plot and return data for testing (DO NOT MODIFY)\n",
    "    plt.savefig('sea_level_plot.png')\n",
    "    return plt.gca()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}