Class3_dataIO_plot.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "4a821b0a",
   "metadata": {},
   "source": [
    "# Practical math and computing workshop day 3\n",
    "File IO and Plotting using numpy, matplotlib.pyplot and pandas\n",
    "This exercise includes several mini projects. All of these requires reading in data, selecting a portion of the data to be plotted. Then plotting it in different ways. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76bf2750",
   "metadata": {},
   "source": [
    "## Project 1. Download the excel sheet containing the historical weather station data from Atlanta GA from https://o2.eas.gatech.edu/data/ATL_MonMeanTemp_1894_2020.xls\n",
    "### Select the mean Februrary temperature from 1900 to 2020 and make a line plot. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5b31cdaa",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import libraries\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45e90853",
   "metadata": {},
   "source": [
    "### Reading in Excel file using pd.read_excel function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ffb2af57",
   "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>JAN</th>\n",
       "      <th>FEB</th>\n",
       "      <th>MAR</th>\n",
       "      <th>APR</th>\n",
       "      <th>MAY</th>\n",
       "      <th>JUN</th>\n",
       "      <th>JUL</th>\n",
       "      <th>AUG</th>\n",
       "      <th>SEP</th>\n",
       "      <th>OCT</th>\n",
       "      <th>NOV</th>\n",
       "      <th>DEC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1879</td>\n",
       "      <td>44.3</td>\n",
       "      <td>43.7</td>\n",
       "      <td>57.6</td>\n",
       "      <td>58.9</td>\n",
       "      <td>69.8</td>\n",
       "      <td>74.9</td>\n",
       "      <td>79.6</td>\n",
       "      <td>73.9</td>\n",
       "      <td>68.7</td>\n",
       "      <td>64.3</td>\n",
       "      <td>53.9</td>\n",
       "      <td>51.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1880</td>\n",
       "      <td>54.3</td>\n",
       "      <td>51.4</td>\n",
       "      <td>55.5</td>\n",
       "      <td>63.4</td>\n",
       "      <td>71.0</td>\n",
       "      <td>76.5</td>\n",
       "      <td>79.2</td>\n",
       "      <td>76.8</td>\n",
       "      <td>69.4</td>\n",
       "      <td>60.8</td>\n",
       "      <td>47.2</td>\n",
       "      <td>42.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1881</td>\n",
       "      <td>40.1</td>\n",
       "      <td>46.6</td>\n",
       "      <td>49.1</td>\n",
       "      <td>59.1</td>\n",
       "      <td>70.8</td>\n",
       "      <td>77.8</td>\n",
       "      <td>81.1</td>\n",
       "      <td>78.8</td>\n",
       "      <td>75.5</td>\n",
       "      <td>67.2</td>\n",
       "      <td>52.6</td>\n",
       "      <td>49.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1882</td>\n",
       "      <td>48.8</td>\n",
       "      <td>52.4</td>\n",
       "      <td>57.4</td>\n",
       "      <td>64.8</td>\n",
       "      <td>66.1</td>\n",
       "      <td>76.6</td>\n",
       "      <td>75.9</td>\n",
       "      <td>75.8</td>\n",
       "      <td>71.7</td>\n",
       "      <td>65.8</td>\n",
       "      <td>51.1</td>\n",
       "      <td>41.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1883</td>\n",
       "      <td>43.0</td>\n",
       "      <td>50.2</td>\n",
       "      <td>50.2</td>\n",
       "      <td>61.6</td>\n",
       "      <td>67.4</td>\n",
       "      <td>76.4</td>\n",
       "      <td>80.1</td>\n",
       "      <td>76.2</td>\n",
       "      <td>71.3</td>\n",
       "      <td>66.0</td>\n",
       "      <td>53.7</td>\n",
       "      <td>48.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>2016</td>\n",
       "      <td>42.4</td>\n",
       "      <td>48.5</td>\n",
       "      <td>60.5</td>\n",
       "      <td>64.1</td>\n",
       "      <td>71.1</td>\n",
       "      <td>81.0</td>\n",
       "      <td>83.4</td>\n",
       "      <td>82.7</td>\n",
       "      <td>78.8</td>\n",
       "      <td>69.6</td>\n",
       "      <td>58.9</td>\n",
       "      <td>48.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>2017</td>\n",
       "      <td>52.0</td>\n",
       "      <td>56.1</td>\n",
       "      <td>57.7</td>\n",
       "      <td>67.8</td>\n",
       "      <td>71.1</td>\n",
       "      <td>76.6</td>\n",
       "      <td>81.2</td>\n",
       "      <td>79.2</td>\n",
       "      <td>73.8</td>\n",
       "      <td>65.7</td>\n",
       "      <td>56.2</td>\n",
       "      <td>46.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>2018</td>\n",
       "      <td>40.4</td>\n",
       "      <td>57.3</td>\n",
       "      <td>53.5</td>\n",
       "      <td>59.8</td>\n",
       "      <td>74.8</td>\n",
       "      <td>79.8</td>\n",
       "      <td>80.8</td>\n",
       "      <td>79.7</td>\n",
       "      <td>81.0</td>\n",
       "      <td>67.4</td>\n",
       "      <td>50.3</td>\n",
       "      <td>48.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>2019</td>\n",
       "      <td>45.9</td>\n",
       "      <td>53.8</td>\n",
       "      <td>55.4</td>\n",
       "      <td>65.3</td>\n",
       "      <td>76.4</td>\n",
       "      <td>77.7</td>\n",
       "      <td>82.2</td>\n",
       "      <td>82.5</td>\n",
       "      <td>82.4</td>\n",
       "      <td>68.4</td>\n",
       "      <td>52.5</td>\n",
       "      <td>51.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>2020</td>\n",
       "      <td>49.2</td>\n",
       "      <td>49.9</td>\n",
       "      <td>61.6</td>\n",
       "      <td>61.6</td>\n",
       "      <td>68.9</td>\n",
       "      <td>76.8</td>\n",
       "      <td>82.1</td>\n",
       "      <td>80.9</td>\n",
       "      <td>73.8</td>\n",
       "      <td>67.3</td>\n",
       "      <td>58.9</td>\n",
       "      <td>46.1</td>\n",
       "    </tr>\n",
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       "<p>142 rows × 13 columns</p>\n",
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      "text/plain": [
       "     Year   JAN   FEB   MAR   APR   MAY   JUN   JUL   AUG   SEP   OCT   NOV  \\\n",
       "0    1879  44.3  43.7  57.6  58.9  69.8  74.9  79.6  73.9  68.7  64.3  53.9   \n",
       "1    1880  54.3  51.4  55.5  63.4  71.0  76.5  79.2  76.8  69.4  60.8  47.2   \n",
       "2    1881  40.1  46.6  49.1  59.1  70.8  77.8  81.1  78.8  75.5  67.2  52.6   \n",
       "3    1882  48.8  52.4  57.4  64.8  66.1  76.6  75.9  75.8  71.7  65.8  51.1   \n",
       "4    1883  43.0  50.2  50.2  61.6  67.4  76.4  80.1  76.2  71.3  66.0  53.7   \n",
       "..    ...   ...   ...   ...   ...   ...   ...   ...   ...   ...   ...   ...   \n",
       "137  2016  42.4  48.5  60.5  64.1  71.1  81.0  83.4  82.7  78.8  69.6  58.9   \n",
       "138  2017  52.0  56.1  57.7  67.8  71.1  76.6  81.2  79.2  73.8  65.7  56.2   \n",
       "139  2018  40.4  57.3  53.5  59.8  74.8  79.8  80.8  79.7  81.0  67.4  50.3   \n",
       "140  2019  45.9  53.8  55.4  65.3  76.4  77.7  82.2  82.5  82.4  68.4  52.5   \n",
       "141  2020  49.2  49.9  61.6  61.6  68.9  76.8  82.1  80.9  73.8  67.3  58.9   \n",
       "\n",
       "      DEC  \n",
       "0    51.5  \n",
       "1    42.1  \n",
       "2    49.6  \n",
       "3    41.4  \n",
       "4    48.0  \n",
       "..    ...  \n",
       "137  48.9  \n",
       "138  46.8  \n",
       "139  48.6  \n",
       "140  51.2  \n",
       "141  46.1  \n",
       "\n",
       "[142 rows x 13 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# read in data file as DataFrame using pandas\n",
    "df=pd.read_excel(\"https://o2.eas.gatech.edu/data/ATL_MonMeanTemp_1879_2020.xls\",skiprows=1)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "cf18ecba",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extract Feb temperature\n",
    "atl=df.to_numpy()\n",
    "year=atl[:,0]\n",
    "feb=atl[:,2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "32bdf1fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "# select the data range to be only within 1900 to 2020\n",
    "index = (year >= 1900) & (year <= 2020)\n",
    "year1 = year[index]\n",
    "feb1  = feb[index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7575d81a",
   "metadata": {},
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the result using matplotlib\n",
    "plt.plot(year1,feb1,'r-')\n",
    "plt.xlabel('year')\n",
    "plt.ylabel('Atlanta Feb temperature, deg F')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac33935c",
   "metadata": {},
   "source": [
    "## Project 2. Download the excel sheet containing the historical weather station data from Seattle WA from https://o2.eas.gatech.edu/data/SEA_MonMeanTemp_1894_2020.xls\n",
    "\n",
    "### This data contains missing values as -999. Avoid plotting the missing data.  Select the mean Februrary temperature from 1900 to 2020 and make a line plot. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3a05595d",
   "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>JAN</th>\n",
       "      <th>FEB</th>\n",
       "      <th>MAR</th>\n",
       "      <th>APR</th>\n",
       "      <th>MAY</th>\n",
       "      <th>JUN</th>\n",
       "      <th>JUL</th>\n",
       "      <th>AUG</th>\n",
       "      <th>SEP</th>\n",
       "      <th>OCT</th>\n",
       "      <th>NOV</th>\n",
       "      <th>DEC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1894</td>\n",
       "      <td>38.8</td>\n",
       "      <td>38.2</td>\n",
       "      <td>42.7</td>\n",
       "      <td>48.0</td>\n",
       "      <td>53.4</td>\n",
       "      <td>58.7</td>\n",
       "      <td>64.3</td>\n",
       "      <td>65.9</td>\n",
       "      <td>57.6</td>\n",
       "      <td>50.2</td>\n",
       "      <td>47.4</td>\n",
       "      <td>39.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1895</td>\n",
       "      <td>38.7</td>\n",
       "      <td>44.7</td>\n",
       "      <td>44.1</td>\n",
       "      <td>50.1</td>\n",
       "      <td>55.2</td>\n",
       "      <td>60.9</td>\n",
       "      <td>63.6</td>\n",
       "      <td>62.0</td>\n",
       "      <td>55.8</td>\n",
       "      <td>51.7</td>\n",
       "      <td>44.2</td>\n",
       "      <td>41.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1896</td>\n",
       "      <td>42.4</td>\n",
       "      <td>44.9</td>\n",
       "      <td>42.7</td>\n",
       "      <td>46.9</td>\n",
       "      <td>52.4</td>\n",
       "      <td>60.2</td>\n",
       "      <td>67.2</td>\n",
       "      <td>63.9</td>\n",
       "      <td>56.7</td>\n",
       "      <td>52.8</td>\n",
       "      <td>38.4</td>\n",
       "      <td>44.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1897</td>\n",
       "      <td>40.0</td>\n",
       "      <td>42.2</td>\n",
       "      <td>40.2</td>\n",
       "      <td>52.5</td>\n",
       "      <td>58.4</td>\n",
       "      <td>60.2</td>\n",
       "      <td>62.7</td>\n",
       "      <td>67.8</td>\n",
       "      <td>56.9</td>\n",
       "      <td>51.0</td>\n",
       "      <td>43.6</td>\n",
       "      <td>42.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1898</td>\n",
       "      <td>39.9</td>\n",
       "      <td>45.6</td>\n",
       "      <td>43.4</td>\n",
       "      <td>50.1</td>\n",
       "      <td>57.1</td>\n",
       "      <td>61.4</td>\n",
       "      <td>64.6</td>\n",
       "      <td>66.3</td>\n",
       "      <td>60.8</td>\n",
       "      <td>52.2</td>\n",
       "      <td>45.0</td>\n",
       "      <td>40.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>2016</td>\n",
       "      <td>43.5</td>\n",
       "      <td>47.7</td>\n",
       "      <td>49.4</td>\n",
       "      <td>56.3</td>\n",
       "      <td>59.2</td>\n",
       "      <td>63.6</td>\n",
       "      <td>67.0</td>\n",
       "      <td>68.6</td>\n",
       "      <td>61.4</td>\n",
       "      <td>55.4</td>\n",
       "      <td>51.3</td>\n",
       "      <td>38.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>2017</td>\n",
       "      <td>38.7</td>\n",
       "      <td>41.6</td>\n",
       "      <td>46.5</td>\n",
       "      <td>50.8</td>\n",
       "      <td>58.1</td>\n",
       "      <td>62.6</td>\n",
       "      <td>67.0</td>\n",
       "      <td>69.7</td>\n",
       "      <td>64.9</td>\n",
       "      <td>53.2</td>\n",
       "      <td>46.6</td>\n",
       "      <td>40.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>2018</td>\n",
       "      <td>44.9</td>\n",
       "      <td>41.4</td>\n",
       "      <td>46.2</td>\n",
       "      <td>50.6</td>\n",
       "      <td>60.8</td>\n",
       "      <td>62.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>68.5</td>\n",
       "      <td>62.3</td>\n",
       "      <td>53.4</td>\n",
       "      <td>48.2</td>\n",
       "      <td>43.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>2019</td>\n",
       "      <td>44.0</td>\n",
       "      <td>36.6</td>\n",
       "      <td>47.4</td>\n",
       "      <td>52.0</td>\n",
       "      <td>59.5</td>\n",
       "      <td>62.0</td>\n",
       "      <td>65.5</td>\n",
       "      <td>68.5</td>\n",
       "      <td>62.3</td>\n",
       "      <td>50.9</td>\n",
       "      <td>46.4</td>\n",
       "      <td>44.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>2020</td>\n",
       "      <td>44.2</td>\n",
       "      <td>43.6</td>\n",
       "      <td>44.6</td>\n",
       "      <td>51.8</td>\n",
       "      <td>58.7</td>\n",
       "      <td>61.5</td>\n",
       "      <td>65.8</td>\n",
       "      <td>67.2</td>\n",
       "      <td>65.4</td>\n",
       "      <td>54.1</td>\n",
       "      <td>46.6</td>\n",
       "      <td>44.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>127 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Year   JAN   FEB   MAR   APR   MAY   JUN   JUL   AUG   SEP   OCT   NOV  \\\n",
       "0    1894  38.8  38.2  42.7  48.0  53.4  58.7  64.3  65.9  57.6  50.2  47.4   \n",
       "1    1895  38.7  44.7  44.1  50.1  55.2  60.9  63.6  62.0  55.8  51.7  44.2   \n",
       "2    1896  42.4  44.9  42.7  46.9  52.4  60.2  67.2  63.9  56.7  52.8  38.4   \n",
       "3    1897  40.0  42.2  40.2  52.5  58.4  60.2  62.7  67.8  56.9  51.0  43.6   \n",
       "4    1898  39.9  45.6  43.4  50.1  57.1  61.4  64.6  66.3  60.8  52.2  45.0   \n",
       "..    ...   ...   ...   ...   ...   ...   ...   ...   ...   ...   ...   ...   \n",
       "122  2016  43.5  47.7  49.4  56.3  59.2  63.6  67.0  68.6  61.4  55.4  51.3   \n",
       "123  2017  38.7  41.6  46.5  50.8  58.1  62.6  67.0  69.7  64.9  53.2  46.6   \n",
       "124  2018  44.9  41.4  46.2  50.6  60.8  62.0  70.0  68.5  62.3  53.4  48.2   \n",
       "125  2019  44.0  36.6  47.4  52.0  59.5  62.0  65.5  68.5  62.3  50.9  46.4   \n",
       "126  2020  44.2  43.6  44.6  51.8  58.7  61.5  65.8  67.2  65.4  54.1  46.6   \n",
       "\n",
       "      DEC  \n",
       "0    39.3  \n",
       "1    41.9  \n",
       "2    44.5  \n",
       "3    42.6  \n",
       "4    40.4  \n",
       "..    ...  \n",
       "122  38.5  \n",
       "123  40.5  \n",
       "124  43.5  \n",
       "125  44.2  \n",
       "126  44.1  \n",
       "\n",
       "[127 rows x 13 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# read in data file as DataFrame using pandas\n",
    "df2=pd.read_excel(\"https://o2.eas.gatech.edu/data/SEA_MonMeanTemp_1894_2020.xls\",skiprows=1)\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "591bfa8f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extract Feb temperature\n",
    "sea=df2.to_numpy()\n",
    "year_sea=sea[:,0]\n",
    "feb_sea=sea[:,2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9d9dcd8e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# select the data range to be only within 1900 to 2020\n",
    "index = (year_sea >= 1900) & (year_sea <= 2020)\n",
    "year2 = year_sea[index]\n",
    "feb2  = feb_sea[index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "84ee430c",
   "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 the result using matplotlib\n",
    "plt.plot(year2,feb2,'b-')\n",
    "plt.xlabel('year')\n",
    "plt.ylabel('Seattle Feb temperature, deg F')\n",
    "plt.show()\n",
    "# Oh no! the plot is ruined because there is a missing data (-999)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1c83abe8",
   "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": [
    "# first replace the missing data (-999) with np.nan\n",
    "missing_index= (feb2 == -999) # first identify the missing data\n",
    "feb2[missing_index]=np.nan\n",
    "\n",
    "# then plot the result using matplotlib\n",
    "plt.plot(year2,feb2,'b-')\n",
    "plt.xlabel('year')\n",
    "plt.ylabel('temperature, deg F')\n",
    "plt.show()\n",
    "# now we can see the stuff! "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc33cf76",
   "metadata": {},
   "source": [
    "## Project 3. Plot all 12 months of Atlanta data as a contour plot\n",
    "### use x-axis for year (1900-2020), y-axis for month (1-12), and color-filled contour for temperature. \n",
    "### Do the same for Seattle. You will have to remove the missing data\n",
    "### For different choices of colormaps, please look at https://matplotlib.org/stable/tutorials/colors/colormaps.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3b106849",
   "metadata": {},
   "outputs": [],
   "source": [
    "# read in data file as DataFrame using pandas\n",
    "df=pd.read_excel(\"https://o2.eas.gatech.edu/data/ATL_MonMeanTemp_1879_2020.xls\",skiprows=1)\n",
    "\n",
    "# Extract temperature for all 12 months\n",
    "atl=df.to_numpy()\n",
    "year=atl[:,0]   # first column\n",
    "temp=atl[:,1:]  # 2nd column and all the rest\n",
    "\n",
    "# select the years from 1900 to 2020\n",
    "index = (year >= 1900) & (year <= 2020)\n",
    "year1 = year[index]\n",
    "temp1 = temp[index,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "2a5735ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## contourf from matplotlib\n",
    "mon=np.arange(1,13,1)\n",
    "plt.contourf(year1,mon,temp1.T) # Try pcolor too\n",
    "plt.xlabel('year')\n",
    "plt.ylabel('month')\n",
    "plt.colorbar()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ae410f68",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## contourf from matplotlib, with a different color scale\n",
    "mon=np.arange(1,13,1)\n",
    "plt.contourf(year1,mon,temp1.T,cmap='inferno') # check out the cmap option\n",
    "plt.xlabel('year')\n",
    "plt.ylabel('month')\n",
    "plt.colorbar()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "1af5cdb6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's try seattle now\n",
    "df2=pd.read_excel(\"https://o2.eas.gatech.edu/data/SEA_MonMeanTemp_1894_2020.xls\",skiprows=1)\n",
    "\n",
    "# Extract temperature for all 12 months\n",
    "sea=df2.to_numpy()\n",
    "year=sea[:,0]   # first column\n",
    "temp=sea[:,1:]  # 2nd column and all the rest\n",
    "\n",
    "# select the years from 1900 to 2020\n",
    "index = (year >= 1900) & (year <= 2020)\n",
    "year2 = year[index]\n",
    "temp2 = temp[index,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ce4a8fcf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## contourf from matplotlib\n",
    "mon=np.arange(1,13,1)\n",
    "plt.contourf(year2,mon,temp2.T)\n",
    "plt.xlabel('year')\n",
    "plt.ylabel('month')\n",
    "plt.colorbar()\n",
    "plt.show()\n",
    "# Oh no! I fornot to replace the missing data (-999) again! "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "44d1f447",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## replace missing data with np.nan\n",
    "temp2[temp2 == -999] = np.nan  # directly insert the conditional statement as index\n",
    "\n",
    "## contourf from matplotlib\n",
    "mon=np.arange(1,13,1)\n",
    "plt.contourf(year2,mon,temp2.T)\n",
    "plt.xlabel('year')\n",
    "plt.ylabel('month')\n",
    "plt.colorbar()\n",
    "plt.show()\n",
    "# Yes! now I can see the data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6354548c",
   "metadata": {},
   "source": [
    "## Project 4. Display the monthly statistics using boxplot\n",
    "\n",
    "### Extract mean March data from Atlanta data. Select 1950 to 2020. Generate a box plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f4a17b15",
   "metadata": {},
   "outputs": [],
   "source": [
    "# read in data file as DataFrame using pandas\n",
    "df=pd.read_excel(\"https://o2.eas.gatech.edu/data/ATL_MonMeanTemp_1879_2020.xls\",skiprows=1)\n",
    "\n",
    "# Extract temperature for March months (4th column)\n",
    "atl=df.to_numpy()\n",
    "year=atl[:,0]   # first column\n",
    "mar=atl[:,3]   # 4th column\n",
    "\n",
    "# select the years from 1950 to 2020\n",
    "index = (year >= 1950) & (year <= 2020)\n",
    "mar1 = mar[index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "5d525d03",
   "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": [
    "# generate a boxplot\n",
    "plt.boxplot(mar1)\n",
    "plt.ylabel('temperature, deg F')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ccafe70",
   "metadata": {},
   "source": [
    "### Extract all 12 months from the Atlanta data. Select 1950 to 2020. Generate a box plot for all 12 months and plot them side by side"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "c6f5c397",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extract temperature for all 12 months\n",
    "year=atl[:,0]    # first column\n",
    "temp=atl[:,1:]   # 2nd column and on\n",
    "\n",
    "# select the years from 1950 to 2020\n",
    "index = (year >= 1950) & (year <= 2020)\n",
    "temp1 = temp[index,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "6988b25a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX4AAAEGCAYAAABiq/5QAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAAcSUlEQVR4nO3df3Acd5nn8ffnFBERkx8acHKGYLzsmmTYORJAm2M3qlQUky1gc0mglgUfXPlgNr4slPh1dTgwVyT5Q9ncLkttynW1JkSAd8nOhh+BpNhbjqxRjhuuQpAhBIPgcpAYQkxsYkGCsw6K89wf3TKybEmj0XSPZvrzqprSTEvdzyNbetTzfL/9bUUEZmZWHP+q0wmYmVm+XPjNzArGhd/MrGBc+M3MCsaF38ysYE7qdALNeN7znhcbNmzodBpmZl1l9+7dP4+ItfO3d0Xh37BhA5OTk51Ow8ysq0jae6LtbvWYmRWMC7+ZWcG48JuZFYwLv5lZwbjwm5kVjAu/WQ+r1+tUKhX6+vqoVCrU6/VOp2SrQFdM5zSz5avX69RqNcbHxxkeHqbRaFCtVgHYvHlzh7OzTlI3LMs8NDQUnsdvtjyVSoXt27czMjJydNvExASjo6Ps2bOng5lZXiTtjoih47a78Jv1pr6+Pg4fPkx/f//RbTMzMwwMDHDkyJEOZmZ5Wajwu8dvlhNJiz7arVwu02g0jtnWaDQol8ttj2XdxYXfLCcRcfQx/3UW77xrtRrVapWJiQlmZmaYmJigWq1Sq9XaHsu6iwd3zXrU7ADu6OgoU1NTlMtlxsbGPLBr7vGbdYKkTM7yzeZyj9/MzAAXfjOzwnGP36xHLTVTyK2m4vIZv1mPynsWEXiJiG7hM34zawsvEdE9fMZvZm0xNjbG+Pg4IyMj9Pf3MzIywvj4OGNjY51OzebxdE6zDsh7Omce8bxExOrj6Zxm8+S9hEKv8xIR3cM9fiusuWfAvqBq5Wq1Gm9605tYs2YNe/fu5UUvehGHDh3ipptu6nRqNo/P+M2s7fyOaXVz4TezthgbG+O2227jwQcf5MiRIzz44IPcdtttHtxdhTy4a0Y2rZ5SqcT09PSy9xscHOTgwYNtzcWDu8XUkcFdSe+V9F1JeyTVJQ1IKkm6S9ID6cfBLHMw65Tp6enjLppq5tHKH4vVwIO73SOzwi/pBcC7gKGIqAB9wJuBa4BdEbER2JW+NrMu5/X/u0fWs3pOAp4taQY4BXgE+ABwcfr5ncDdwLaM8zDreUu1lhYacG1Xa8nr/3ePTHv8kt4NjAH/Anw5It4i6RcRccacr5mOiOPaPZK2AlsB1q9f/8q9e/dmlqdZFj3wVo/ZLfvZ6pd7jz/t3V8B/BbwfGCNpLc2u39E3BwRQxExtHbt2qzSNLM2Gh0dZWBgAEkMDAwwOjra6ZTsBLIc3H018GBEHIiIGeB24A+ARyWtA0g/7s8wBzPLyejoKDt27OCGG27g0KFD3HDDDezYscPFfxXKrNUj6d8CHwd+j6TV80lgElgPPBYRN0q6BihFxPsXO5anc1rWMml3XHf6Cvb95eqPN8/AwAA33HAD73vf+45u+8hHPsIHP/hBDh8+vOLj2/It1OrJusd/PfAm4GngW8CfAs8BPk3yB+DHwBsjYtGRJRd+y5p7/Cv/3iVx6NAhTjnllKPbnnzySdasWeMxhA7pyDz+iLg2Is6NiEpE/IeIeCoiHouITRGxMf3Y3itVzBZQKpUWXZBtoc+VSqUOZ94dTj75ZHbs2HHMth07dnDyySd3KCNbiBdps8KYvaBquVay7kwr+w4Oduc1jVdddRXbtiUzs6+++mp27NjBtm3buPrqqzucmc3nJRusMDrdClmtx2xnq2cx3VBres1CrR6f8ZtZW3iZ6+7hwm/WQ4rUWrLWufBbYcS1p7U05TGuPS2DbNpvsTNsn4HbXC78Vhi6/vHWe+DXtT8fs07xjVjMzArGZ/y2anhWiFk+XPht1Zhf2N2XNsuGWz1mZgXjM34rFE93NHPhtwLxdEezhFs9ZmYF48JvZi3xaqfdy60eM2tJJ1Y7tfZw4TfLyfyCN/91Fqt15hnPuocLv1lO8i60Luy2EBd+M2tJry9618tc+M2sJV70rnt5Vo+ZWcG48JuZFYwLv3VUK3PBPQ/cbGXc47eOamUuuOeBrx5e+6g7ufCbWUu89lH3cuG3wvIFTlZULvxWWC7sVlSZDe5KOkfSfXMej0t6j6SSpLskPZB+dMPPzCxHmRX+iPhBRJwfEecDrwSeBD4PXAPsioiNwK70tZmZ5SSv6ZybgB9GxF7gCmBnun0ncGVOOZiZGfn1+N8M1NPnZ0XEPoCI2CfpzBPtIGkrsBVg/fr1uSRp+WtlvRev9WK2Msp6gEvSs4BHgN+NiEcl/SIizpjz+emIWLTPPzQ0FJOTk5nmaZ3RyrQ/TxVcnZaa05/1stNZxupWknZHxND87Xmc8b8W+GZEPJq+flTSuvRsfx2wP4cczCxjnVx22icDy5NHj38zv2nzANwJbEmfbwHuyCEHMzNLZVr4JZ0CXArcPmfzjcClkh5IP3djljmYmdmxMm31RMSTwHPnbXuMZJaPmZl1gK/ctY5b7kJfXuTLbGVc+K2jvNCXWf5c+G1BeU/PM7N8+EYstqCIOOYxf5vZfPV6nUqlQl9fH5VKhXq9vvROljuf8ZtZW9TrdWq1GuPj4wwPD9NoNKhWqwBs3ry5w9nZXD7jN7O2GBsbY3x8nJGREfr7+xkZGWF8fJyxsbFOp2bzZL5kQzt4yYbVIe/BVg/udpe+vj4OHz5Mf3//0W0zMzMMDAxw5MiRTGP7Z+XEFlqywWf8ZtYW5XKZRqNxzLZGo0G5XO5QRrYQF34za4tarUa1WmViYoKZmRkmJiaoVqvUarVOp2bzeHDXzNpidgB3dHSUqakpyuUyY2NjHthdhZY845f0Ekm7JO1JX79M0n/NPjVbiKfM2Wq1efNm9uzZw5EjR9izZ4+L/irVTKvnY8AHgBmAiLif5MYq1gGzU+a2b9/O4cOH2b59O7VazcXfel6pVELSCR/Agp8rlUodznz1aabwnxIR987b9nQWydjSPGXOimp6evq4iwqbeUxPT3c69VWnmcL/c0m/DQSApD8G9mWalS1oamqK4eHhY7YNDw8zNTXVoYzMrNs0U/jfCXwUOFfST4H3AFdnmZQtLOspc518O71UPDNrj0ULv6Q+4M8i4tXAWuDciBiOiL25ZGfHyXrKXCffTi8Vw8zaY9HpnBFxRNIr0+eH8knJFuMpc2a2Uksu2SDpr4CNwGeAo8U/Im5fcKc285IN+Wn10ndfMm+Zu+70Fez7y/bl0UUWWrKhmQu4SsBjwCVztgXH3kfXzCxTuv7x1k9Krmt/Pt1sycIfEW/LIxEzM8vHkoVf0idIp3LOFRFvzyQjMzPLVDOtni/OeT4AvB54JJt0zMwsa820ej4397WkOvDPmWVkHRXXntbSIFpce1oG2ZitDr12/+lWVufcCKxvdyK2OngAzVazVi7kGxwcXHHc+b8T3T6LrZke/xMc2+P/GbAts4xsQb121mG2HIv9fHd7Ic5bM62eU/NIxJbWa2cdZtYZzazHv6uZbQvse4akz0r6vqQpSb8vqSTpLkkPpB9X/j7MzMyatmDhlzQgqQQ8T9JgWrBLkjYAz2/y+DcBX4qIc4HzgCngGmBXRGwEdqWvbRVZaCG2xR7t6KOaWT4Wa/X8J5KVOJ8P7AZmG8yPA/99qQNLOg24CPiPABHxa+DXkq4ALk6/bCdwNx4zWDXcRzXrfQsW/oi4CbhJ0mhEbG/h2C8GDgCfkHQeyR+PdwNnRcS+NMY+SWeeaGdJW4GtAOvXexKRmVm7NDO4u11SBXgpyQVcs9v/toljvwIYjYivS7qJZbR1IuJm4GZIFmlrdj8zM1tcM4O71wLb08cI8BfA5U0c+2Hg4Yj4evr6syR/CB6VtC499jpgfwt5m5lZi5q5A9cfA5uAn6ULtp0HnLzUThHxM+Anks5JN20CvgfcCWxJt20B7lhu0mZm1rpmrtz9l4h4RtLT6YDtfpL+fTNGgVslPQv4EfA2kj82n5ZUBX4MvLGFvM3MrEXNFP5JSWcAHyMZoP0VcG8zB4+I+4DjbgJAcvZvZmYdsGjhV7JGwJ9HxC+AHZK+BJwWEffnkZyZ2ULmL2Ey97WnHS9uqXvuhqQvALP33X0oh5zMzJbk4t66ZgZ375H0e5lnYmZmuWimxz8CXC3pIZKbrYvkzcDLskzMzKyosl6Jt5kz/teSzOK5BPh3wGXpx8Jbav0aM8tWvV6nUqnQ19dHpVKhXq93OqW2iIhjHvO3rVQzV+7ulTQMbIyIT0haCzxnxZF7gJdJNuucer1OrVZjfHyc4eFhGo0G1WoVgM2bN3c4u9VNSxWq9MrdIeCciHiJpOcDn4mIC/NIEJIlGyYnJ/MK17K8C3+vxzNbTKVSYfv27YyMjBzdNjExwejoKHv27Mk0drf87knaHRHHTalvptXzepIlGg4BRMQjgG/OkpNSqbRoK2mhz5VKpRXHXiqeWSdNTU0xPDx8zLbh4WGmpqY6lFH3aKbw/zqSPzUBIGlNtinZXNPT08f1+5p5TE9Przj2UjHMOqlcLtNoNI7Z1mg0KJfLHcqoezRT+D8t6aPAGZKuAv6Z5CpeM7OOqdVqVKtVJiYmmJmZYWJigmq1Sq1W63Rqq14zg7sflnQpyQ1YXgJ8KCLuyjyzFvhm5GbFMTuAOzo6ytTUFOVymbGxsbYN7JZKpUXfOS9UbwYHBzl48GBbcsjKkoO7AJL+NXABSbvnG+nKm7lpdXC3WwZgsjimB2LNVmY1/e7lPrgr6U9JFmV7A8kSzfdIevuyMzAzs1WhmSt3/wvw8oh4DEDSc4H/A3w8y8TMzCwbzQzuPgw8Mef1E8BPsknHzMyy1swZ/0+Br0u6g6THfwVwr6T3AUTERzLMr/Di2tPgutNb28/M7ASaKfw/TB+zZm+V6Iu4cqDrH299gOm69udjZt2vmemc1+eRiJmZ5WPJwi9pCKgBL5r79V6W2cysOzXT6rmVZGbPd4Bnsk3HzGx16OXxtWYK/4GIuDPzTMzMVpFeHl9rpvBfK+kWYBfw1OzGiLg9s6zMzCwzzRT+twHnAv38ptUTgAu/mVkXaqbwnxcR/ybzTMzMLBfNXLl7j6SXZp6JLWipe/ue6DE4ONjptM1slWrmjH8Y2CLpQZIev4DwdM58LDa45BU4zawVzRT+17R6cEkPkaztcwR4OiKGJJWA24ANwEPAn0TEym8XZWZmTVmy1RMRe4EXApekz59sZr85RiLi/DlrQl8D7IqIjSQzha5ZZs5mZrno1TZrM1fuXgsMAecAnyCZ3fMp4MIWY14BXJw+3wncDWxr8VhmZpno5TZrM2furwcuBw4BRMQjNL9AWwBflrRb0tZ021kRsS891j7gzBPtKGmrpElJkwcOHGgynJmZLaWZwv/rSP60BYCkNcs4/oUR8QrgtcA7JV3U7I4RcXNEDEXE0Nq1a5cRMlulUmnBt3iw8FvDUqnU4czNzBLNDO5+WtJHgTMkXQW8HfhYMwdP3x0QEfslfZ7kvr2PSloXEfskrQP2t5h7R0xPT7d8GbeZ2WrQzBn/WuCzwOdI+vwfAs5eaidJaySdOvsc+ENgD3AnsCX9si38Zn1/MzPLQTNn/JdGxDbgrtkNkv6KpQdkzwI+n57pngT8fUR8SdI3SN5FVIEfA29sKXMzsx5SKpWYnl54ZvtCXYPBwUEOHjy4rFgLFn5Jfwa8A3ixpPvnfOpU4GtLHTgifgScd4LtjwGblpWlmVmPy7ONvNgZ/98D/wT8OcfOtX8iIpb358XMzFaNBQt/RPwS+CWwOb90zMwsa8u5AtfMzHqAC7+ZWcE0M6vH5ujl+3CaWTG48C9TJ+/DeaLR+7nbunntEDPLjwt/F3FhN7N26Poev9fOMTNbnq4/4/faOWZmy9P1Z/xmZrY8LvxmZgXT9a0eM7NekOdUcRd+M7NVIM+p4m71mJkVjAu/mVnBuNVjZraEXrtq3oW/Ba1cAzA4OJhBJmaWh24r7Etx4V+mxX4AJPXcD4iZ9Z6uL/xeLdPMbHm6vvB3crVMM7N2yquN7Fk9Xaher1OpVOjr66NSqVCv1zudkpmtUEQs+Fjs8wcPLv8W6F1/xl809XqdWq3G+Pg4w8PDNBoNqtUqAJs3+/bIZrY0n/F3mbGxMcbHxxkZGaG/v5+RkRHGx8cZGxvrdGpm1iXUDbNQhoaGYnJy8oSfa3UmTRYzcPKY1dPX18fhw4fp7+8/um1mZoaBgQGOHDmSaWwz64wV1LndETE0f7vP+LtMuVym0Wgcs63RaFAulzuUkZl1Gxf+LlOr1ahWq0xMTDAzM8PExATVapVardbp1MysS2Q+uCupD5gEfhoRl0kqAbcBG4CHgD+JiOms8+gVswO4o6OjTE1NUS6XGRsb88CumTUt8x6/pPcBQ8BpaeH/C+BgRNwo6RpgMCK2LXYM9/jNrMi6qscv6Wzgj4Bb5my+AtiZPt8JXJllDmZmdqysWz1/DbwfOHXOtrMiYh9AROyTdOaJdpS0FdgKsH79+kWDeNE0M7PmZXbGL+kyYH9E7G5l/4i4OSKGImJo7dq1i31dble7mZn1gizP+C8ELpf0OmAAOE3Sp4BHJa1Lz/bXAfszzMHMzObJ7Iw/Ij4QEWdHxAbgzcBXIuKtwJ3AlvTLtgB3ZJWDmZkdrxPz+G8ELpX0AHBp+trMzHKSyyJtEXE3cHf6/DFgUx5xzczseL5y18ysYFz4zcwKxuvxr8CJrh+Yu81X8ZrZauTCvwIu7GbWjdzqMTMrGBd+M7OCceE3MysYF34zs4Jx4TczKxgXfjOzgvF0TjOzVSbra4Rc+M3MVpmsrxFyq8fMrGBc+M3MCsaF38ysYFz4zcwKxoXfzKxgempWj5dJNjNbWk8Vfhd2M7OludVjZlYwLvxmZgXjwm9mVjAu/GZmBePCb2ZWMC78ZmYF48JvZlYwmRV+SQOS7pX0bUnflXR9ur0k6S5JD6QfB7PKwczMjpflGf9TwCURcR5wPvAaSa8CrgF2RcRGYFf62szMcpJZ4Y/Er9KX/ekjgCuAnen2ncCVWeVgZmbHy7THL6lP0n3AfuCuiPg6cFZE7ANIP565wL5bJU1Kmjxw4MCy4tbrdSqVCn19fVQqFer1+sq+ETOzHpJp4Y+IIxFxPnA2cIGkyjL2vTkihiJiaO3atU3HrNfr1Go1tm/fzuHDh9m+fTu1Ws3F38wslcusnoj4BXA38BrgUUnrANKP+9sZa2xsjPHxcUZGRujv72dkZITx8XHGxsbaGcbMrGtlOatnraQz0ufPBl4NfB+4E9iSftkW4I52xp2ammJ4ePiYbcPDw0xNTbUzjJlZ18ryjH8dMCHpfuAbJD3+LwI3ApdKegC4NH3dNuVymUajccy2RqNBuVxuZxgzs66V2Xr8EXE/8PITbH8M2JRV3FqtRrVaZXx8nOHhYRqNBtVq1a0eM7NUT92IBWDz5s0AjI6OMjU1RblcZmxs7Oh2M7OiUzfctWpoaCgmJyc7nYaZWVeRtDsihuZv91o9ZmYF48JvZlYwLvxmZgXjwm9mVjAu/GZmBdMVs3okHQD2trDr84Cftzkdx+vNeL38vTleceO9KCKOW+ysKwp/qyRNnmgqk+M5XidjOZ7jdTqeWz1mZgXjwm9mVjC9XvhvdjzHW4WxHM/xOhqvp3v8ZmZ2vF4/4zczs3lc+M3MCqYnC7+kj0vaL2lPDrFeKGlC0pSk70p6d8bxBiTdK+nbabzrs4w3J26fpG9J+mIOsR6S9B1J90nKfFlWSWdI+qyk76f/j7+fYaxz0u9r9vG4pPdkFS+N+d70Z2WPpLqkgYzjvTuN9d0svrcT/X5LKkm6S9ID6cfBjOO9Mf3+npHU1mmdC8T7y/Tn835Jn5+9u2GrerLwA58kub9vHp4G/nNElIFXAe+U9NIM4z0FXBIR5wHnA6+R9KoM4816N5Dn/StHIuL8nOZK3wR8KSLOBc4jw+8zIn6Qfl/nA68EngQ+n1U8SS8A3gUMRUQF6APenGG8CnAVcAHJv+Vlkja2OcwnOf73+xpgV0RsBHalr7OMtwd4A/DVNsZZLN5dQCUiXgb8X+ADKwnQk4U/Ir4KHMwp1r6I+Gb6/AmSovGCDONFRPwqfdmfPjIdoZd0NvBHwC1ZxukESacBFwHjABHx64j4RU7hNwE/jIhWrkpfjpOAZ0s6CTgFeCTDWGXgnoh4MiKeBv4X8Pp2Bljg9/sKYGf6fCdwZZbxImIqIn7QrhhNxPty+u8JcA9w9kpi9GTh7xRJG0huN/n1jOP0SboP2E9yL+NM4wF/DbwfeCbjOLMC+LKk3ZK2ZhzrxcAB4BNpK+sWSWsyjjnrzUA9ywAR8VPgw8CPgX3ALyPiyxmG3ANcJOm5kk4BXge8MMN4s86KiH2QnIwBZ+YQs1PeDvzTSg7gwt8mkp4DfA54T0Q8nmWsiDiStgrOBi5I315nQtJlwP6I2J1VjBO4MCJeAbyWpHV2UYaxTgJeAfxNRLwcOER72wQnJOlZwOXAZzKOM0hyNvxbwPOBNZLemlW8iJgC/htJa+JLwLdJ2qHWBpJqJP+et67kOC78bSCpn6To3xoRt+cVN21J3E224xkXApdLegj4B+ASSZ/KMB4R8Uj6cT9J//uCDMM9DDw8513TZ0n+EGTttcA3I+LRjOO8GngwIg5ExAxwO/AHWQaMiPGIeEVEXETSsnggy3ipRyWtA0g/7s8hZq4kbQEuA94SK7wAy4V/hSSJpD88FREfySHe2tkRfUnPJvnF/n5W8SLiAxFxdkRsIGlNfCUiMjtjlLRG0qmzz4E/JGkfZCIifgb8RNI56aZNwPeyijfHZjJu86R+DLxK0inpz+omMh6kl3Rm+nE9yQBoHt/nncCW9PkW4I4cYuZG0muAbcDlEfHkig8YET33IPlB2wfMkJzRVTOMNUzSk74fuC99vC7DeC8DvpXG2wN8KMd/14uBL2Yc48Uk7YFvA98Fajl8X+cDk+m/6ReAwYzjnQI8Bpye0//b9SQnB3uAvwNOzjje/yb54/ltYFMGxz/u9xt4LslsngfSj6WM470+ff4U8CjwPzOO9/+An8ypMTtWEsNLNpiZFYxbPWZmBePCb2ZWMC78ZmYF48JvZlYwLvxmZgXjwm/WJukqn++Y8/riPFYzNVsuF36z9jkDeMdSX2TWaS78VkiSNqTrm9+Srh1/q6RXS/pauqb7Beka719I10C/R9LL0n2vS9dMv1vSjyS9Kz3sjcBvp+vs/2W67Tlz1vq/Nb16Fkk3SvpeeuwPd+CfwArspE4nYNZBvwO8EdgKfAP49yRXYl8OfJDkSslvRcSVki4B/pbkKl+Ac4ER4FTgB5L+hmRxt0okC+gh6WKS1Vp/l2Qp5K8BF0r6HsmVn+dGRKz0phpmy+UzfiuyByPiOxHxDMnyELsiuZT9O8AGkj8CfwcQEV8Bnivp9HTff4yIpyLi5yQLgp21QIx7I+LhNMZ96XEfBw4Dt0h6A8nNWMxy48JvRfbUnOfPzHn9DMm7YZ1gn9k1Tubue4SF3z0f93WR3FDjApIVXa8kWb7YLDcu/GYL+yrwFjjatvl5LH6vhSdIWj+LSu/dcHpE/A/gPfymfWSWC/f4zRZ2Hcmdue4nacdsWeyLI+KxdHB4D8kdkv5xgS89Fbgjvem5gPe2L2WzpXl1TjOzgnGrx8ysYFz4zcwKxoXfzKxgXPjNzArGhd/MrGBc+M3MCsaF38ysYP4/yUt9xioCb/EAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot boxplot all together\n",
    "plt.boxplot(temp1)\n",
    "plt.xlabel('months')\n",
    "plt.ylabel('temperature')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1142d736",
   "metadata": {},
   "source": [
    "## Project 5. Display histogram\n",
    "### Display the histogram of mean July temperature of Seattle from 1900 to 2020, and overlay Gaussian function\n",
    "Gaussian ($f(x)$, defined below) should be plotted scaled by $N \\Delta x$.  \n",
    "This is because the area of histogram is equal to sample size times bin width. \n",
    "\n",
    "$ f(x) = \\frac{1}{\\sqrt{2 \\pi}\\sigma} exp \\left\\{ -\\frac{1}{2} \\left( \\frac{x-\\mu}{\\sigma} \\right)^2\\right\\}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "634b69bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# read in data file as DataFrame using pandas\n",
    "df=pd.read_excel(\"https://o2.eas.gatech.edu/data/SEA_MonMeanTemp_1894_2020.xls\",skiprows=1)\n",
    "\n",
    "# Extract temperature for July months (8th column)\n",
    "sea=df.to_numpy()\n",
    "year=sea[:,0]   # first column\n",
    "jul=sea[:,7]   # 13th column\n",
    "\n",
    "# select the years from 1900 to 2020\n",
    "index = (year >= 1900) & (year <= 2020)\n",
    "jul1 = jul[index]\n",
    "\n",
    "# in this case, remove the missing data \n",
    "jul1[jul1 == -999] = np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "3bbff0a5",
   "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": [
    "# define bins\n",
    "dx=1\n",
    "bins=np.arange(np.nanmin(jul1)-2,np.nanmax(jul1)+2,dx)\n",
    "plt.hist(jul1,bins)\n",
    "plt.xlabel('temperature deg F')\n",
    "plt.ylabel('data count, frequency')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "b62f6eec",
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculate sample size\n",
    "N=np.size(jul1)\n",
    "\n",
    "# calculate mean and stdev\n",
    "mu=np.nanmean(jul1)\n",
    "sig=np.nanstd(jul1)\n",
    "\n",
    "# calculate Gaussian overlay\n",
    "x=np.arange(np.nanmin(jul1)-2,np.nanmax(jul1)+2,.1)\n",
    "f=N*dx/(np.sqrt(2*np.pi)*sig)*np.exp(-.5*((x-mu)/sig)**2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "8399b744",
   "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": [
    "# replot the histogram\n",
    "plt.hist(jul1,bins)\n",
    "plt.plot(x,f,'r-') # plot the Gaussian\n",
    "plt.xlabel('temperature, deg F')\n",
    "plt.ylabel('data count, frequency')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "620809ee",
   "metadata": {},
   "source": [
    "## Project 6. Create a scatter plot comparing January temperature of Atlanta and Seattle between 1900 and 2020"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "c78f17f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# read in data file as DataFrame using pandas\n",
    "df=pd.read_excel(\"https://o2.eas.gatech.edu/data/ATL_MonMeanTemp_1879_2020.xls\",skiprows=1)\n",
    "\n",
    "# Extract temperature for Jan\n",
    "atl=df.to_numpy()\n",
    "year=atl[:,0]   # first column\n",
    "jan=atl[:,1]   \n",
    "\n",
    "# select the years from 1900 to 2020\n",
    "index = (year >= 1900) & (year <= 2020)\n",
    "janatl = jan[index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "5334b76f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# read in data file as DataFrame using pandas\n",
    "df=pd.read_excel(\"https://o2.eas.gatech.edu/data/SEA_MonMeanTemp_1894_2020.xls\",skiprows=1)\n",
    "\n",
    "# Extract temperature \n",
    "sea=df.to_numpy()\n",
    "year=sea[:,0]   # first column\n",
    "jan=sea[:,1]   \n",
    "\n",
    "# select the years from 1900 to 2020\n",
    "index = (year >= 1900) & (year <= 2020)\n",
    "jansea= jan[index]\n",
    "\n",
    "jansea[ jansea == -999 ] = np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "39737512",
   "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": [
    "plt.plot(janatl,jansea,'o')\n",
    "plt.xlabel('Atlanta mean Jan temperature, deg F')\n",
    "plt.ylabel('Seattle mean Jan temperature, deg F')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9c98345",
   "metadata": {},
   "source": [
    "## Project 7. Save your result in a file\n",
    "### case 1. create a dataframe from numpy array, selecting only June, July, August months and save it as a csv file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "2a2cda22",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Take the Seattle data file from the previous project\n",
    "# JJA will be 7th through 9th columns\n",
    "# including all years\n",
    "df3=pd.DataFrame(sea[:,[0,6,7,8]],columns=['Year','Jun','Jul','Aug'])\n",
    "df3.to_csv('Seattle_JJA_1894_2020.csv') # this writes the data into a CSV file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "31a2e083",
   "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>Jun</th>\n",
       "      <th>Jul</th>\n",
       "      <th>Aug</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1894.0</td>\n",
       "      <td>58.7</td>\n",
       "      <td>64.3</td>\n",
       "      <td>65.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1895.0</td>\n",
       "      <td>60.9</td>\n",
       "      <td>63.6</td>\n",
       "      <td>62.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1896.0</td>\n",
       "      <td>60.2</td>\n",
       "      <td>67.2</td>\n",
       "      <td>63.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1897.0</td>\n",
       "      <td>60.2</td>\n",
       "      <td>62.7</td>\n",
       "      <td>67.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1898.0</td>\n",
       "      <td>61.4</td>\n",
       "      <td>64.6</td>\n",
       "      <td>66.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>2016.0</td>\n",
       "      <td>63.6</td>\n",
       "      <td>67.0</td>\n",
       "      <td>68.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>2017.0</td>\n",
       "      <td>62.6</td>\n",
       "      <td>67.0</td>\n",
       "      <td>69.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>2018.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>68.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>2019.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>65.5</td>\n",
       "      <td>68.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>2020.0</td>\n",
       "      <td>61.5</td>\n",
       "      <td>65.8</td>\n",
       "      <td>67.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>127 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Year   Jun   Jul   Aug\n",
       "0    1894.0  58.7  64.3  65.9\n",
       "1    1895.0  60.9  63.6  62.0\n",
       "2    1896.0  60.2  67.2  63.9\n",
       "3    1897.0  60.2  62.7  67.8\n",
       "4    1898.0  61.4  64.6  66.3\n",
       "..      ...   ...   ...   ...\n",
       "122  2016.0  63.6  67.0  68.6\n",
       "123  2017.0  62.6  67.0  69.7\n",
       "124  2018.0  62.0  70.0  68.5\n",
       "125  2019.0  62.0  65.5  68.5\n",
       "126  2020.0  61.5  65.8  67.2\n",
       "\n",
       "[127 rows x 4 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# have a look at the DataFrame\n",
    "df3"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0049dbe6",
   "metadata": {},
   "source": [
    "### case 2. save a single numpy array as .npy file\n",
    "single array can be saved in a .npy file. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "1bd95bdc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the difference is  [0. 0. 0.]\n"
     ]
    }
   ],
   "source": [
    "np.save('Seattle_JJA.npy',sea[:,6:9]) # save the JJA data \n",
    "\n",
    "# check if it was saved correctly\n",
    "jjadata=np.load('Seattle_JJA.npy')   # load it as jjadata as numpy array\n",
    "diffdata=jjadata-sea[:,6:9]   # check to make sure they are the same\n",
    "print('the difference is ',str(np.sum(diffdata,0)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e16720d",
   "metadata": {},
   "source": [
    "### case 3. save multiple numpy arrays as .npz file\n",
    "save the year and the temperature separately as different array into a single file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "375fd7bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "year=sea[:,0]    # extract year \n",
    "temp=sea[:,1:13] # extract temperature from all months\n",
    "np.savez('Seattle_MomMeanTemp.npz',year=year,temp=temp) # save the two arrays as a single file\n",
    "\n",
    "# to read the data, use np.load\n",
    "tempdata=np.load('Seattle_MomMeanTemp.npz') # returns a dictonary-like object\n",
    "year1=tempdata['year']          # extract year\n",
    "temp1=tempdata['temp']          # extract temperature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "903b542a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3f5ef50a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
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