temp_analysis_bonus_1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Bonus: Temperature Analysis I"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from datetime import datetime as dt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "<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",
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      ],
      "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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# \"tobs\" is \"temperature observations\"\n",
    "df = pd.read_csv('Resources/hawaii_measurements.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert the date column format from string to datetime\n",
    "df['date']=pd.to_datetime(df['date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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       "  <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",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-01-01</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>2010-01-02</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>2010-01-03</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>2010-01-04</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>2010-01-06</th>\n",
       "      <td>USC00519397</td>\n",
       "      <td>2010-01-06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "                station       date  prcp  tobs\n",
       "date                                          \n",
       "2010-01-01  USC00519397 2010-01-01  0.08    65\n",
       "2010-01-02  USC00519397 2010-01-02  0.00    63\n",
       "2010-01-03  USC00519397 2010-01-03  0.00    74\n",
       "2010-01-04  USC00519397 2010-01-04  0.00    76\n",
       "2010-01-06  USC00519397 2010-01-06   NaN    73"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Set the date column as the DataFrame index\n",
    "df.set_index(df['date'], inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>station</th>\n",
       "      <th>prcp</th>\n",
       "      <th>tobs</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <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",
       "  </tbody>\n",
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      "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"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drop the date column\n",
    "del df['date']\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compare June and December data across all years "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy import stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Filter data for desired months\n",
    "june_df = df[(df.index).month == 6]\n",
    "dec_df = df[(df.index).month == 12]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The average temperature for June is 74.94\n"
     ]
    }
   ],
   "source": [
    "# Identify the average temperature for June\n",
    "avg_june = np.mean(june_df['tobs'])\n",
    "print(f'The average temperature for June is {round(avg_june,2)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The average temperature for December is 71.04\n"
     ]
    }
   ],
   "source": [
    "# Identify the average temperature for December\n",
    "avg_dec = np.mean(dec_df['tobs'])\n",
    "print(f'The average temperature for December is {round(avg_dec,2)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create collections of temperature data\n",
    "dec_temps = dec_df['tobs']\n",
    "june_temps = june_df['tobs']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=31.60372399000329, pvalue=3.9025129038616655e-191)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Run t-test\n",
    "stats.ttest_ind(june_temps, dec_temps)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The p-value is 3.9e-191 which is approaching zero which means there is evidence that there is a significant difference between the average temperatures in June and December.\n",
    "\n",
    "We used an unpaired t-test because the temperatures in June and December are independent of each other. Additionally, in order to perform a paired t-test, the two data inputs must be the same length which isn't possible with our dataframe because (a) there is data for June 2017 but not data for Dec 2017 and (b) June is 30 days long and December is 31 days long so it is not possible to pair the two."
   ]
  }
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