temp_analysis_bonus_1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "molecular-struggle",
   "metadata": {},
   "source": [
    "# Temperature Analysis 1: Bonus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "cutting-official",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import dependencies\n",
    "import pandas as pd\n",
    "from datetime import datetime as dt\n",
    "from scipy import stats as sts\n",
    "\n",
    "# Import matplotlib dependencies\n",
    "%matplotlib inline\n",
    "from matplotlib import style\n",
    "style.use('fivethirtyeight')\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "executive-teaching",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>station</th>\n",
       "      <th>date</th>\n",
       "      <th>prcp</th>\n",
       "      <th>tobs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>USC00519397</td>\n",
       "      <td>2010-01-01</td>\n",
       "      <td>0.08</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>USC00519397</td>\n",
       "      <td>2010-01-02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>USC00519397</td>\n",
       "      <td>2010-01-03</td>\n",
       "      <td>0.00</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>USC00519397</td>\n",
       "      <td>2010-01-04</td>\n",
       "      <td>0.00</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>USC00519397</td>\n",
       "      <td>2010-01-06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       station        date  prcp  tobs\n",
       "0  USC00519397  2010-01-01  0.08    65\n",
       "1  USC00519397  2010-01-02  0.00    63\n",
       "2  USC00519397  2010-01-03  0.00    74\n",
       "3  USC00519397  2010-01-04  0.00    76\n",
       "4  USC00519397  2010-01-06   NaN    73"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import csv file into dataframe\n",
    "measurements_df = pd.read_csv('Resources/hawaii_measurements.csv')\n",
    "measurements_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "destroyed-attendance",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert the date column format from string to datetime\n",
    "measurements_df[\"date\"] = pd.to_datetime(measurements_df[\"date\"], format=\"%Y-%m-%d\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "opponent-cycling",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 19550 entries, 0 to 19549\n",
      "Data columns (total 4 columns):\n",
      " #   Column   Non-Null Count  Dtype         \n",
      "---  ------   --------------  -----         \n",
      " 0   station  19550 non-null  object        \n",
      " 1   date     19550 non-null  datetime64[ns]\n",
      " 2   prcp     18103 non-null  float64       \n",
      " 3   tobs     19550 non-null  int64         \n",
      "dtypes: datetime64[ns](1), float64(1), int64(1), object(1)\n",
      "memory usage: 611.1+ KB\n"
     ]
    }
   ],
   "source": [
    "# Check data type of dates\n",
    "measurements_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "earlier-commodity",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>prcp</th>\n",
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       "    </tr>\n",
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       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-01-01</th>\n",
       "      <td>USC00519397</td>\n",
       "      <td>0.08</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-02</th>\n",
       "      <td>USC00519397</td>\n",
       "      <td>0.00</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-03</th>\n",
       "      <td>USC00519397</td>\n",
       "      <td>0.00</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-04</th>\n",
       "      <td>USC00519397</td>\n",
       "      <td>0.00</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-06</th>\n",
       "      <td>USC00519397</td>\n",
       "      <td>NaN</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-19</th>\n",
       "      <td>USC00516128</td>\n",
       "      <td>0.09</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-20</th>\n",
       "      <td>USC00516128</td>\n",
       "      <td>NaN</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-21</th>\n",
       "      <td>USC00516128</td>\n",
       "      <td>0.56</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-22</th>\n",
       "      <td>USC00516128</td>\n",
       "      <td>0.50</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-23</th>\n",
       "      <td>USC00516128</td>\n",
       "      <td>0.45</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>19550 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                station  prcp  tobs\n",
       "date                               \n",
       "2010-01-01  USC00519397  0.08    65\n",
       "2010-01-02  USC00519397  0.00    63\n",
       "2010-01-03  USC00519397  0.00    74\n",
       "2010-01-04  USC00519397  0.00    76\n",
       "2010-01-06  USC00519397   NaN    73\n",
       "...                 ...   ...   ...\n",
       "2017-08-19  USC00516128  0.09    71\n",
       "2017-08-20  USC00516128   NaN    78\n",
       "2017-08-21  USC00516128  0.56    76\n",
       "2017-08-22  USC00516128  0.50    76\n",
       "2017-08-23  USC00516128  0.45    76\n",
       "\n",
       "[19550 rows x 3 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Set the date column as the DataFrame index\n",
    "measurements_df.set_index(\"date\", drop=True, inplace=True)\n",
    "measurements_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "auburn-nightmare",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
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       "      <th>2010-06-01</th>\n",
       "      <td>USC00519397</td>\n",
       "      <td>0.00</td>\n",
       "      <td>78</td>\n",
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       "    <tr>\n",
       "      <th>2010-06-02</th>\n",
       "      <td>USC00519397</td>\n",
       "      <td>0.01</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-06-03</th>\n",
       "      <td>USC00519397</td>\n",
       "      <td>0.00</td>\n",
       "      <td>78</td>\n",
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       "    <tr>\n",
       "      <th>2010-06-04</th>\n",
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       "    <tr>\n",
       "      <th>2010-06-05</th>\n",
       "      <td>USC00519397</td>\n",
       "      <td>0.00</td>\n",
       "      <td>77</td>\n",
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       "    <tr>\n",
       "      <th>2017-06-26</th>\n",
       "      <td>USC00516128</td>\n",
       "      <td>0.02</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-27</th>\n",
       "      <td>USC00516128</td>\n",
       "      <td>0.10</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-28</th>\n",
       "      <td>USC00516128</td>\n",
       "      <td>0.02</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-29</th>\n",
       "      <td>USC00516128</td>\n",
       "      <td>0.04</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-30</th>\n",
       "      <td>USC00516128</td>\n",
       "      <td>0.20</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1700 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                station  prcp  tobs\n",
       "date                               \n",
       "2010-06-01  USC00519397  0.00    78\n",
       "2010-06-02  USC00519397  0.01    76\n",
       "2010-06-03  USC00519397  0.00    78\n",
       "2010-06-04  USC00519397  0.00    76\n",
       "2010-06-05  USC00519397  0.00    77\n",
       "...                 ...   ...   ...\n",
       "2017-06-26  USC00516128  0.02    79\n",
       "2017-06-27  USC00516128  0.10    74\n",
       "2017-06-28  USC00516128  0.02    74\n",
       "2017-06-29  USC00516128  0.04    76\n",
       "2017-06-30  USC00516128  0.20    75\n",
       "\n",
       "[1700 rows x 3 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find the June temperatures for all stations\n",
    "df = measurements_df.reset_index()\n",
    "\n",
    "june = df.loc[df[\"date\"].dt.month == 6]\n",
    "june.set_index(\"date\", inplace=True)\n",
    "june"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "isolated-vertical",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Find the average temperature in June\n",
    "avg_june = june.tobs.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "aerial-painting",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make into a list\n",
    "june_temperatures = june[\"tobs\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "selective-virtue",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>station</th>\n",
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       "      <th>2010-12-01</th>\n",
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       "      <td>0.04</td>\n",
       "      <td>76</td>\n",
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       "      <th>2010-12-03</th>\n",
       "      <td>USC00519397</td>\n",
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       "      <th>2010-12-04</th>\n",
       "      <td>USC00519397</td>\n",
       "      <td>0.00</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-12-06</th>\n",
       "      <td>USC00519397</td>\n",
       "      <td>0.00</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-12-07</th>\n",
       "      <td>USC00519397</td>\n",
       "      <td>0.00</td>\n",
       "      <td>64</td>\n",
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       "      <th>2016-12-27</th>\n",
       "      <td>USC00516128</td>\n",
       "      <td>0.14</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-28</th>\n",
       "      <td>USC00516128</td>\n",
       "      <td>0.14</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-29</th>\n",
       "      <td>USC00516128</td>\n",
       "      <td>1.03</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30</th>\n",
       "      <td>USC00516128</td>\n",
       "      <td>2.37</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31</th>\n",
       "      <td>USC00516128</td>\n",
       "      <td>0.90</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1517 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                station  prcp  tobs\n",
       "date                               \n",
       "2010-12-01  USC00519397  0.04    76\n",
       "2010-12-03  USC00519397  0.00    74\n",
       "2010-12-04  USC00519397  0.00    74\n",
       "2010-12-06  USC00519397  0.00    64\n",
       "2010-12-07  USC00519397  0.00    64\n",
       "...                 ...   ...   ...\n",
       "2016-12-27  USC00516128  0.14    71\n",
       "2016-12-28  USC00516128  0.14    71\n",
       "2016-12-29  USC00516128  1.03    69\n",
       "2016-12-30  USC00516128  2.37    65\n",
       "2016-12-31  USC00516128  0.90    65\n",
       "\n",
       "[1517 rows x 3 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find the December temperatures for all stations\n",
    "dec = df.loc[df[\"date\"].dt.month == 12]\n",
    "dec.set_index(\"date\", inplace=True)\n",
    "dec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "signal-utilization",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Find the average temperature in December\n",
    "avg_dec = dec.tobs.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "broad-individual",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make into a list\n",
    "dec_temperatures = dec[\"tobs\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "tough-filing",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "USC00519281    236\n",
       "USC00519397    236\n",
       "USC00513117    236\n",
       "USC00519523    232\n",
       "USC00516128    223\n",
       "USC00514830    186\n",
       "USC00511918    165\n",
       "USC00517948    148\n",
       "USC00518838     38\n",
       "Name: station, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the value counts of station for June\n",
    "june[\"station\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "voluntary-testament",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "USC00519281    217\n",
       "USC00513117    216\n",
       "USC00519523    210\n",
       "USC00519397    210\n",
       "USC00516128    206\n",
       "USC00514830    178\n",
       "USC00511918    152\n",
       "USC00517948    109\n",
       "USC00518838     19\n",
       "Name: station, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the value counts of station for December\n",
    "dec[\"station\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "charged-dayton",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The average temperature in December is 71.04\n",
      "The average temperature in June is 74.94\n"
     ]
    }
   ],
   "source": [
    "print(f\"The average temperature in December is {avg_dec:.2f}\")\n",
    "print(f\"The average temperature in June is {avg_june:.2f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "improved-condition",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=31.355036920962423, pvalue=4.193529835915755e-187)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# t-test: Unpaired (because lists were different sizes)\n",
    "sts.ttest_ind(june_temperatures, dec_temperatures, equal_var=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "continental-sauce",
   "metadata": {},
   "source": [
    "## Analysis:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "strange-decade",
   "metadata": {},
   "source": [
    "### The pvalue is extremely low: 4.19e-187, so there is statistical significance between the difference in the mean temperature in Hawaii in June and December."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "existing-february",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a y axis list for June\n",
    "y_j = []\n",
    "for temp in range(len(june_temperatures)):\n",
    "    y_j.append(temp)\n",
    "    \n",
    "# Create a y axis list for December\n",
    "y_d = []\n",
    "for temp in range(len(dec_temperatures)):\n",
    "    y_d.append(temp)\n",
    "\n",
    "# Create a scatter plot for June and December temperatures\n",
    "plt.figure(figsize=(7,5))\n",
    "plt.scatter(june_temperatures, y_j, color='pink', alpha=1)\n",
    "plt.scatter(dec_temperatures, y_d, color = \"blue\", alpha=0.5)\n",
    "plt.ylim(-100,2000)\n",
    "plt.title(\"Temperatures in Hawaii\")\n",
    "plt.legend(labels=[\"June\", \"December\"])\n",
    "plt.xlabel(\"Temperatures (F)\")\n",
    "plt.ylabel(\"Observation\")\n",
    "plt.savefig(\"Images/bonus_1_temp_dec_june.png\", bbox_inches=\"tight\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "abstract-upgrade",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:PythonData] *",
   "language": "python",
   "name": "conda-env-PythonData-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}