Temp_Scrub-AllYears-Min-Max.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Team Green Project:  Scrub Global Temperature File\n",
    "### Note:  Temperatures are in Celsius"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dependencies and Setup\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "pd.set_option('display.max_rows', 500)\n",
    "pd.set_option('display.max_columns', 500)\n",
    "pd.set_option('display.width', 1000)\n",
    "\n",
    "# File to Load\n",
    "temp_data_to_load = \"data/GlobalTemperatures.csv\"\n",
    "\n",
    "# Read Temperature Data\n",
    "temp_data = pd.read_csv(temp_data_to_load)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dt</th>\n",
       "      <th>LandAverageTemperature</th>\n",
       "      <th>LandAverageTemperatureUncertainty</th>\n",
       "      <th>LandMaxTemperature</th>\n",
       "      <th>LandMaxTemperatureUncertainty</th>\n",
       "      <th>LandMinTemperature</th>\n",
       "      <th>LandMinTemperatureUncertainty</th>\n",
       "      <th>LandAndOceanAverageTemperature</th>\n",
       "      <th>LandAndOceanAverageTemperatureUncertainty</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1750-01-01</td>\n",
       "      <td>3.034</td>\n",
       "      <td>3.574</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1750-02-01</td>\n",
       "      <td>3.083</td>\n",
       "      <td>3.702</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1750-03-01</td>\n",
       "      <td>5.626</td>\n",
       "      <td>3.076</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1750-04-01</td>\n",
       "      <td>8.490</td>\n",
       "      <td>2.451</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1750-05-01</td>\n",
       "      <td>11.573</td>\n",
       "      <td>2.072</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           dt  LandAverageTemperature  LandAverageTemperatureUncertainty  LandMaxTemperature  LandMaxTemperatureUncertainty  LandMinTemperature  LandMinTemperatureUncertainty  LandAndOceanAverageTemperature  LandAndOceanAverageTemperatureUncertainty\n",
       "0  1750-01-01                   3.034                              3.574                 NaN                            NaN                 NaN                            NaN                             NaN                                        NaN\n",
       "1  1750-02-01                   3.083                              3.702                 NaN                            NaN                 NaN                            NaN                             NaN                                        NaN\n",
       "2  1750-03-01                   5.626                              3.076                 NaN                            NaN                 NaN                            NaN                             NaN                                        NaN\n",
       "3  1750-04-01                   8.490                              2.451                 NaN                            NaN                 NaN                            NaN                             NaN                                        NaN\n",
       "4  1750-05-01                  11.573                              2.072                 NaN                            NaN                 NaN                            NaN                             NaN                                        NaN"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dt</th>\n",
       "      <th>LandAndOceanAverageTemperature</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1750-01-01</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1750-02-01</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1750-03-01</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1750-04-01</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1750-05-01</td>\n",
       "      <td>NaN</td>\n",
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      ],
      "text/plain": [
       "           dt  LandAndOceanAverageTemperature\n",
       "0  1750-01-01                             NaN\n",
       "1  1750-02-01                             NaN\n",
       "2  1750-03-01                             NaN\n",
       "3  1750-04-01                             NaN\n",
       "4  1750-05-01                             NaN"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a new dataset with only dt, LandAndOceanAverageTemperature, and LandAndOceanAverageTemperatureUncertainty\n",
    "temp_data_1 = temp_data[['dt', 'LandAndOceanAverageTemperature']]\n",
    "temp_data_1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dt</th>\n",
       "      <th>LandAndOceanAverageTemperature</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1200</th>\n",
       "      <td>1850-01-01</td>\n",
       "      <td>12.833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1201</th>\n",
       "      <td>1850-02-01</td>\n",
       "      <td>13.588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1202</th>\n",
       "      <td>1850-03-01</td>\n",
       "      <td>14.043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1203</th>\n",
       "      <td>1850-04-01</td>\n",
       "      <td>14.667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1204</th>\n",
       "      <td>1850-05-01</td>\n",
       "      <td>15.507</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "              dt  LandAndOceanAverageTemperature\n",
       "1200  1850-01-01                          12.833\n",
       "1201  1850-02-01                          13.588\n",
       "1202  1850-03-01                          14.043\n",
       "1203  1850-04-01                          14.667\n",
       "1204  1850-05-01                          15.507"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drop rows with NaN\n",
    "temp_data_2 = temp_data_1.dropna()\n",
    "temp_data_2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dt</th>\n",
       "      <th>LandAndOceanAverageTemperature</th>\n",
       "      <th>Year</th>\n",
       "      <th>Min Avg Temp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1200</th>\n",
       "      <td>1850-01-01</td>\n",
       "      <td>12.833</td>\n",
       "      <td>1850</td>\n",
       "      <td>12.833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1201</th>\n",
       "      <td>1850-02-01</td>\n",
       "      <td>13.588</td>\n",
       "      <td>1850</td>\n",
       "      <td>13.588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1202</th>\n",
       "      <td>1850-03-01</td>\n",
       "      <td>14.043</td>\n",
       "      <td>1850</td>\n",
       "      <td>14.043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1203</th>\n",
       "      <td>1850-04-01</td>\n",
       "      <td>14.667</td>\n",
       "      <td>1850</td>\n",
       "      <td>14.667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1204</th>\n",
       "      <td>1850-05-01</td>\n",
       "      <td>15.507</td>\n",
       "      <td>1850</td>\n",
       "      <td>15.507</td>\n",
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      ],
      "text/plain": [
       "             dt  LandAndOceanAverageTemperature  Year  Min Avg Temp\n",
       "1200 1850-01-01                          12.833  1850        12.833\n",
       "1201 1850-02-01                          13.588  1850        13.588\n",
       "1202 1850-03-01                          14.043  1850        14.043\n",
       "1203 1850-04-01                          14.667  1850        14.667\n",
       "1204 1850-05-01                          15.507  1850        15.507"
      ]
     },
     "execution_count": 7,
     "metadata": {},
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    }
   ],
   "source": [
    "# Disable the SettingWithCopyWarning warning\n",
    "pd.options.mode.chained_assignment = None\n",
    "# Create a Year Column\n",
    "temp_data_2['Year'] = temp_data_2.dt.str[:4]\n",
    "\n",
    "# Create another temp. column for min or max number\n",
    "temp_data_2['Min Avg Temp'] = temp_data_2['LandAndOceanAverageTemperature']\n",
    "\n",
    "# Convert dt to datetime format\n",
    "temp_data_2['dt'] = pd.to_datetime(temp_data_2['dt'])\n",
    "temp_data_2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Min Avg Temp</th>\n",
       "      <th>Max Avg Temp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1850</td>\n",
       "      <td>12.833</td>\n",
       "      <td>16.783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1851</td>\n",
       "      <td>13.245</td>\n",
       "      <td>16.831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1852</td>\n",
       "      <td>13.231</td>\n",
       "      <td>16.984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1853</td>\n",
       "      <td>13.143</td>\n",
       "      <td>16.999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1854</td>\n",
       "      <td>12.983</td>\n",
       "      <td>16.775</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   Year  Min Avg Temp  Max Avg Temp\n",
       "0  1850        12.833        16.783\n",
       "1  1851        13.245        16.831\n",
       "2  1852        13.231        16.984\n",
       "3  1853        13.143        16.999\n",
       "4  1854        12.983        16.775"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Group by Year and get average of both numerical columns\n",
    "#temp_data_3 = temp_data_2.groupby('Year').min('Min Avg Temp').max('LandAndOceanAverageTemperature').reset_index()\n",
    "temp_data_3 = temp_data_2.groupby('Year').agg({'Min Avg Temp':'min', 'LandAndOceanAverageTemperature':'max'})[['Min Avg Temp','LandAndOceanAverageTemperature']].reset_index()\n",
    "temp_data_3.rename(columns={'LandAndOceanAverageTemperature':'Max Avg Temp'}, \n",
    "                 inplace=True)\n",
    "temp_data_3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save Data to .csv\n",
    "# Save scrubbed file to .csv\n",
    "temp_data_3.to_csv(r'Data/temp_data-AllYears-Min-Max.csv', index = False, header=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt  # To visualize\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "#Data\n",
    "data = temp_data_3  # load data set\n",
    "X = data.iloc[:, 1].values.reshape(-1, 1)  # values converts it into a numpy array\n",
    "Y = data.iloc[:, 2].values.reshape(-1, 1)  # -1 means that calculate the dimension of rows, but have 1 column\n",
    "linear_regressor = LinearRegression()  # create object for the class\n",
    "linear_regressor.fit(X, Y)  # perform linear regression\n",
    "Y_pred = linear_regressor.predict(X)  # make predictions\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot \n",
    "plt.scatter(X, Y)\n",
    "plt.plot(X, Y_pred, color='red')\n",
    "plt.xlabel('Min Average Annual Temperature')\n",
    "plt.ylabel('Max Average Annual Temperature')\n",
    "plt.title('Scatter Plot of Global Temperature Min vs Max - Celsius')\n",
    "fig1 = plt.gcf()\n",
    "fig1.savefig('images/Min_Max_Temp_scatter.png', bbox_inches='tight')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}