Weather Analysis v1.ipynb

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    "# Exploring Weather Trends<img src=\"data/udacity.png\" align=\"right\" width=200>\n",
    "\n",
    "\n",
    "The below analysis was created to compare local and global temperature data sets and the temperature trends between Melbournne Australia to the overall global temperature trends.\n",
    "\n",
    "### Approach\n",
    "Two data sets (global monthly temperatures and local yearly temperatures) were imported via Python and transfered into a local SQL database[<sup>1</sup>](#fn1). SQL queries were created to extract the temperatures related to the city of Melbourne and globally between the years of 1820 - 2013.\n",
    "\n",
    "The following queries were created to extract the global and local data from the SQL databases respecitvly:\n",
    "\n",
    "    SELECT * \n",
    "    FROM global_data \n",
    "    WHERE dt BETWEEN \"1820-01-01\" AND \"2013-12-01\";\n",
    "\n",
    "    SELECT * \n",
    "    FROM city_data \n",
    "    WHERE city = \"Melbourne\" AND year BETWEEN \"1850\" AND \"2013\"\n",
    "\n",
    "With the data extracted it was imported into a Python dataframe and filtered to begin on the first year both land and ocean average temperatures were avaialble[<sup>2</sup>](#fn2). A ten year moving average was applied to both data frames (with the following code) to ensure the charts were smoothed to make for a result which was visually easy to compare abnd intepret.\n",
    "\n",
    "    df['avg_temp'].rolling(window=10).mean() \n",
    "\n",
    "Finally the results were charted into a plotly line chart within Python. The predominant selection creiteria was based on the fact of having the option of dynamic feedback / filterting. After the preliminary analysis of the results it was clear there was an increasing trend in the last years. Enabling a dynamic chart would allow users to investigate further in a simple manner."
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   "source": [
    "import pandas as pd\n",
    "import sqlite3\n",
    "import plotly.plotly as py\n",
    "import cufflinks as cf"
   ]
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   "cell_type": "code",
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   "source": [
    "# Set closest city variable\n",
    "city_var = 'Melbourne'"
   ]
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   "cell_type": "code",
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   "source": [
    "# Import datasets\n",
    "city = pd.read_csv('data/city_data.csv')\n",
    "world = pd.read_csv('data/global_data.csv')\n",
    "\n",
    "# Create SQL database\n",
    "conn = sqlite3.connect('data/weather.db')\n",
    "\n",
    "# Transfer CSV to SQL\n",
    "#city.to_sql('city_data', conn)\n",
    "#world.to_sql('global_data', conn)"
   ]
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   "source": [
    "# SQL queries from global database into dataframes\n",
    "sql_global_string = 'SELECT * FROM global_data WHERE dt BETWEEN \"1820-01-01\" AND \"2013-12-01\";'\n",
    "world = pd.read_sql(sql_global_string, conn)\n",
    "\n",
    "# Checking first year land & ocean data was available\n",
    "year_filter = world[\"LandAndOceanAverageTemperature\"].first_valid_index()\n",
    "\n",
    "# Filter dataset accordingly\n",
    "world = world.iloc[year_filter:]\n",
    "\n",
    "# Rename ugly column title\n",
    "world = world.rename(columns={'LandAndOceanAverageTemperature':'avg_global_temp'})\n",
    "\n",
    "# Filtered and avereaged to yearly values to compare with city data set\n",
    "world['year'] = pd.DatetimeIndex(world['dt']).year\n",
    "world = world.groupby('year')['avg_global_temp'].agg('mean')\n",
    "world = world.reset_index()\n",
    "world['rolling_global_temp'] = world['avg_global_temp'].rolling(window=10).mean()"
   ]
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   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# SQL queries from city database into dataframes\n",
    "sql_city_string = 'SELECT * FROM city_data WHERE city = \"%s\" AND year BETWEEN \"1850\" AND \"2013\"' % city_var\n",
    "city = pd.read_sql(sql_city_string, conn)\n",
    "city['rolling_temp'] = city['avg_temp'].rolling(window=10).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
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   "source": [
    "# Merge datasets into one for charts\n",
    "temps = pd.merge(world, city, on='year')\n",
    "temps = temps.rename(columns={'rolling_global_temp':'Global','rolling_temp':city_var})"
   ]
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     },
     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "# Plotting global warming charts\n",
    "cf.go_offline()\n",
    "temps.iplot(kind=\"line\", theme=\"white\",\n",
    "            x ='year', y =[city_var, 'Global'], \n",
    "            title=\"Global Warming Trends\", xTitle='Year', yTitle='Average Temperature')"
   ]
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   "cell_type": "code",
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    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Global</th>\n",
       "      <th>Melbourne</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1960</th>\n",
       "      <td>15.3</td>\n",
       "      <td>13.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>15.8</td>\n",
       "      <td>14.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Delta</th>\n",
       "      <td>3.3%</td>\n",
       "      <td>7.6%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Global Melbourne\n",
       "year                  \n",
       "1960    15.3      13.2\n",
       "2010    15.8      14.2\n",
       "Delta   3.3%      7.6%"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Last 50 years summary\n",
    "summary = temps.loc[temps['year'].isin(['1960','2010'])]\n",
    "summary = summary.set_index('year')\n",
    "summary = (summary[['Global',city_var]]).round(1)\n",
    "summary.loc['Delta'] = (((summary.iloc[1] / summary.iloc[0])-1)*100)\n",
    "summary.loc['Delta'] = summary.loc['Delta'].map('{:,.1f}%'.format)\n",
    "summary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Observations / Findings\n",
    "Over the last 50 years of the dataset (1960 - 2010) we have seen globally there was a **3.3% increase**, this compared to 2.7% the 50 before that (1910 - 1960). Interestingly enough Melbourne faced a **7.6% increase** compared to 0.0% the 50 before.\n",
    "\n",
    "Although Melbourne is approximatley 4.3% higher then the global average in the same time span its growing at a significantly higher rate (7%) then the world average when measured over the past 100 years.\n",
    "\n",
    "Its clear that the data inidcates the **world is getting significantly hotter**, of significant concern though is that the fastest growing city in Australia (the dryest continent on earth[<sup>3</sup>](#fn3)) is increasing in temperature well above and beyond the global norm."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Footnotes\n",
    "<span id=\"fn1\"> [1] The conversion of the tables into a SQL database and queried back into a dataframe was simply to keep consistent with the goal of the project. This was obviously not the most efficient solution.</span>\n",
    "\n",
    "<span id=\"fn2\"> [2] Average land and ocean temperatures for the analysis was selected simply as its been shown this is a more accurate global measure.</span>\n",
    "\n",
    "<span id=\"fn3\"> [3] [GeoScience - Australian government data source](https://www.ga.gov.au/scientific-topics/national-location-information/landforms/deserts)</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "source": []
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