notebooks/02_pandas.ipynb

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       "\"\\nCLASS: Pandas for Data Exploration, Analysis, and Visualization\\n\\nWHO alcohol consumption data:\\n    article: http://fivethirtyeight.com/datalab/dear-mona-followup-where-do-people-drink-the-most-beer-wine-and-spirits/    \\n    original data: https://github.com/fivethirtyeight/data/tree/master/alcohol-consumption\\n    files: drinks.csv (with additional 'continent' column)\\n\""
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    "'''\n",
    "CLASS: Pandas for Data Exploration, Analysis, and Visualization\n",
    "\n",
    "WHO alcohol consumption data:\n",
    "    article: http://fivethirtyeight.com/datalab/dear-mona-followup-where-do-people-drink-the-most-beer-wine-and-spirits/    \n",
    "    original data: https://github.com/fivethirtyeight/data/tree/master/alcohol-consumption\n",
    "    files: drinks.csv (with additional 'continent' column)\n",
    "'''"
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      "/Users/sinanozdemir/anaconda/envs/sfdat26-env/lib/python2.7/site-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.\n",
      "  warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')\n"
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   "source": [
    "# imports\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
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       "pandas.core.frame.DataFrame"
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   "source": [
    "'''\n",
    "Pandas Basics: Reading Files, Summarizing, Handling Missing Values, Filtering, Sorting\n",
    "'''\n",
    "\n",
    "# read in the CSV file from a URL\n",
    "drinks = pd.read_csv('../data/drinks.csv')\n",
    "\n",
    "type(drinks)            # Use the type method to check python type\n"
   ]
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>beer_servings</th>\n",
       "      <th>spirit_servings</th>\n",
       "      <th>wine_servings</th>\n",
       "      <th>total_litres_of_pure_alcohol</th>\n",
       "      <th>continent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>AS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>89</td>\n",
       "      <td>132</td>\n",
       "      <td>54</td>\n",
       "      <td>4.9</td>\n",
       "      <td>EU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>0.7</td>\n",
       "      <td>AF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Andorra</td>\n",
       "      <td>245</td>\n",
       "      <td>138</td>\n",
       "      <td>312</td>\n",
       "      <td>12.4</td>\n",
       "      <td>EU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Angola</td>\n",
       "      <td>217</td>\n",
       "      <td>57</td>\n",
       "      <td>45</td>\n",
       "      <td>5.9</td>\n",
       "      <td>AF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Antigua &amp; Barbuda</td>\n",
       "      <td>102</td>\n",
       "      <td>128</td>\n",
       "      <td>45</td>\n",
       "      <td>4.9</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>193</td>\n",
       "      <td>25</td>\n",
       "      <td>221</td>\n",
       "      <td>8.3</td>\n",
       "      <td>SA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Armenia</td>\n",
       "      <td>21</td>\n",
       "      <td>179</td>\n",
       "      <td>11</td>\n",
       "      <td>3.8</td>\n",
       "      <td>EU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Australia</td>\n",
       "      <td>261</td>\n",
       "      <td>72</td>\n",
       "      <td>212</td>\n",
       "      <td>10.4</td>\n",
       "      <td>OC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Austria</td>\n",
       "      <td>279</td>\n",
       "      <td>75</td>\n",
       "      <td>191</td>\n",
       "      <td>9.7</td>\n",
       "      <td>EU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Azerbaijan</td>\n",
       "      <td>21</td>\n",
       "      <td>46</td>\n",
       "      <td>5</td>\n",
       "      <td>1.3</td>\n",
       "      <td>EU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Bahamas</td>\n",
       "      <td>122</td>\n",
       "      <td>176</td>\n",
       "      <td>51</td>\n",
       "      <td>6.3</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Bahrain</td>\n",
       "      <td>42</td>\n",
       "      <td>63</td>\n",
       "      <td>7</td>\n",
       "      <td>2.0</td>\n",
       "      <td>AS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Bangladesh</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>AS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Barbados</td>\n",
       "      <td>143</td>\n",
       "      <td>173</td>\n",
       "      <td>36</td>\n",
       "      <td>6.3</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Belarus</td>\n",
       "      <td>142</td>\n",
       "      <td>373</td>\n",
       "      <td>42</td>\n",
       "      <td>14.4</td>\n",
       "      <td>EU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Belgium</td>\n",
       "      <td>295</td>\n",
       "      <td>84</td>\n",
       "      <td>212</td>\n",
       "      <td>10.5</td>\n",
       "      <td>EU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Belize</td>\n",
       "      <td>263</td>\n",
       "      <td>114</td>\n",
       "      <td>8</td>\n",
       "      <td>6.8</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Benin</td>\n",
       "      <td>34</td>\n",
       "      <td>4</td>\n",
       "      <td>13</td>\n",
       "      <td>1.1</td>\n",
       "      <td>AF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Bhutan</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>AS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Bolivia</td>\n",
       "      <td>167</td>\n",
       "      <td>41</td>\n",
       "      <td>8</td>\n",
       "      <td>3.8</td>\n",
       "      <td>SA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Bosnia-Herzegovina</td>\n",
       "      <td>76</td>\n",
       "      <td>173</td>\n",
       "      <td>8</td>\n",
       "      <td>4.6</td>\n",
       "      <td>EU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Botswana</td>\n",
       "      <td>173</td>\n",
       "      <td>35</td>\n",
       "      <td>35</td>\n",
       "      <td>5.4</td>\n",
       "      <td>AF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>245</td>\n",
       "      <td>145</td>\n",
       "      <td>16</td>\n",
       "      <td>7.2</td>\n",
       "      <td>SA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Brunei</td>\n",
       "      <td>31</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>AS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Bulgaria</td>\n",
       "      <td>231</td>\n",
       "      <td>252</td>\n",
       "      <td>94</td>\n",
       "      <td>10.3</td>\n",
       "      <td>EU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Burkina Faso</td>\n",
       "      <td>25</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>4.3</td>\n",
       "      <td>AF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Burundi</td>\n",
       "      <td>88</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6.3</td>\n",
       "      <td>AF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Cote d'Ivoire</td>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>4.0</td>\n",
       "      <td>AF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Cabo Verde</td>\n",
       "      <td>144</td>\n",
       "      <td>56</td>\n",
       "      <td>16</td>\n",
       "      <td>4.0</td>\n",
       "      <td>AF</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>163</th>\n",
       "      <td>Suriname</td>\n",
       "      <td>128</td>\n",
       "      <td>178</td>\n",
       "      <td>7</td>\n",
       "      <td>5.6</td>\n",
       "      <td>SA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>Swaziland</td>\n",
       "      <td>90</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4.7</td>\n",
       "      <td>AF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>Sweden</td>\n",
       "      <td>152</td>\n",
       "      <td>60</td>\n",
       "      <td>186</td>\n",
       "      <td>7.2</td>\n",
       "      <td>EU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>Switzerland</td>\n",
       "      <td>185</td>\n",
       "      <td>100</td>\n",
       "      <td>280</td>\n",
       "      <td>10.2</td>\n",
       "      <td>EU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>Syria</td>\n",
       "      <td>5</td>\n",
       "      <td>35</td>\n",
       "      <td>16</td>\n",
       "      <td>1.0</td>\n",
       "      <td>AS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>Tajikistan</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>AS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169</th>\n",
       "      <td>Thailand</td>\n",
       "      <td>99</td>\n",
       "      <td>258</td>\n",
       "      <td>1</td>\n",
       "      <td>6.4</td>\n",
       "      <td>AS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>Macedonia</td>\n",
       "      <td>106</td>\n",
       "      <td>27</td>\n",
       "      <td>86</td>\n",
       "      <td>3.9</td>\n",
       "      <td>EU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>Timor-Leste</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0.1</td>\n",
       "      <td>AS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>Togo</td>\n",
       "      <td>36</td>\n",
       "      <td>2</td>\n",
       "      <td>19</td>\n",
       "      <td>1.3</td>\n",
       "      <td>AF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>Tonga</td>\n",
       "      <td>36</td>\n",
       "      <td>21</td>\n",
       "      <td>5</td>\n",
       "      <td>1.1</td>\n",
       "      <td>OC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>Trinidad &amp; Tobago</td>\n",
       "      <td>197</td>\n",
       "      <td>156</td>\n",
       "      <td>7</td>\n",
       "      <td>6.4</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>Tunisia</td>\n",
       "      <td>51</td>\n",
       "      <td>3</td>\n",
       "      <td>20</td>\n",
       "      <td>1.3</td>\n",
       "      <td>AF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>Turkey</td>\n",
       "      <td>51</td>\n",
       "      <td>22</td>\n",
       "      <td>7</td>\n",
       "      <td>1.4</td>\n",
       "      <td>AS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>Turkmenistan</td>\n",
       "      <td>19</td>\n",
       "      <td>71</td>\n",
       "      <td>32</td>\n",
       "      <td>2.2</td>\n",
       "      <td>AS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>Tuvalu</td>\n",
       "      <td>6</td>\n",
       "      <td>41</td>\n",
       "      <td>9</td>\n",
       "      <td>1.0</td>\n",
       "      <td>OC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>Uganda</td>\n",
       "      <td>45</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>8.3</td>\n",
       "      <td>AF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>Ukraine</td>\n",
       "      <td>206</td>\n",
       "      <td>237</td>\n",
       "      <td>45</td>\n",
       "      <td>8.9</td>\n",
       "      <td>EU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>16</td>\n",
       "      <td>135</td>\n",
       "      <td>5</td>\n",
       "      <td>2.8</td>\n",
       "      <td>AS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>219</td>\n",
       "      <td>126</td>\n",
       "      <td>195</td>\n",
       "      <td>10.4</td>\n",
       "      <td>EU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>Tanzania</td>\n",
       "      <td>36</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>5.7</td>\n",
       "      <td>AF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>USA</td>\n",
       "      <td>249</td>\n",
       "      <td>158</td>\n",
       "      <td>84</td>\n",
       "      <td>8.7</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>Uruguay</td>\n",
       "      <td>115</td>\n",
       "      <td>35</td>\n",
       "      <td>220</td>\n",
       "      <td>6.6</td>\n",
       "      <td>SA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>Uzbekistan</td>\n",
       "      <td>25</td>\n",
       "      <td>101</td>\n",
       "      <td>8</td>\n",
       "      <td>2.4</td>\n",
       "      <td>AS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>Vanuatu</td>\n",
       "      <td>21</td>\n",
       "      <td>18</td>\n",
       "      <td>11</td>\n",
       "      <td>0.9</td>\n",
       "      <td>OC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>Venezuela</td>\n",
       "      <td>333</td>\n",
       "      <td>100</td>\n",
       "      <td>3</td>\n",
       "      <td>7.7</td>\n",
       "      <td>SA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>Vietnam</td>\n",
       "      <td>111</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>AS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>Yemen</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>AS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>32</td>\n",
       "      <td>19</td>\n",
       "      <td>4</td>\n",
       "      <td>2.5</td>\n",
       "      <td>AF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>64</td>\n",
       "      <td>18</td>\n",
       "      <td>4</td>\n",
       "      <td>4.7</td>\n",
       "      <td>AF</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>193 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  country  beer_servings  spirit_servings  wine_servings  \\\n",
       "0             Afghanistan              0                0              0   \n",
       "1                 Albania             89              132             54   \n",
       "2                 Algeria             25                0             14   \n",
       "3                 Andorra            245              138            312   \n",
       "4                  Angola            217               57             45   \n",
       "5       Antigua & Barbuda            102              128             45   \n",
       "6               Argentina            193               25            221   \n",
       "7                 Armenia             21              179             11   \n",
       "8               Australia            261               72            212   \n",
       "9                 Austria            279               75            191   \n",
       "10             Azerbaijan             21               46              5   \n",
       "11                Bahamas            122              176             51   \n",
       "12                Bahrain             42               63              7   \n",
       "13             Bangladesh              0                0              0   \n",
       "14               Barbados            143              173             36   \n",
       "15                Belarus            142              373             42   \n",
       "16                Belgium            295               84            212   \n",
       "17                 Belize            263              114              8   \n",
       "18                  Benin             34                4             13   \n",
       "19                 Bhutan             23                0              0   \n",
       "20                Bolivia            167               41              8   \n",
       "21     Bosnia-Herzegovina             76              173              8   \n",
       "22               Botswana            173               35             35   \n",
       "23                 Brazil            245              145             16   \n",
       "24                 Brunei             31                2              1   \n",
       "25               Bulgaria            231              252             94   \n",
       "26           Burkina Faso             25                7              7   \n",
       "27                Burundi             88                0              0   \n",
       "28          Cote d'Ivoire             37                1              7   \n",
       "29             Cabo Verde            144               56             16   \n",
       "..                    ...            ...              ...            ...   \n",
       "163              Suriname            128              178              7   \n",
       "164             Swaziland             90                2              2   \n",
       "165                Sweden            152               60            186   \n",
       "166           Switzerland            185              100            280   \n",
       "167                 Syria              5               35             16   \n",
       "168            Tajikistan              2               15              0   \n",
       "169              Thailand             99              258              1   \n",
       "170             Macedonia            106               27             86   \n",
       "171           Timor-Leste              1                1              4   \n",
       "172                  Togo             36                2             19   \n",
       "173                 Tonga             36               21              5   \n",
       "174     Trinidad & Tobago            197              156              7   \n",
       "175               Tunisia             51                3             20   \n",
       "176                Turkey             51               22              7   \n",
       "177          Turkmenistan             19               71             32   \n",
       "178                Tuvalu              6               41              9   \n",
       "179                Uganda             45                9              0   \n",
       "180               Ukraine            206              237             45   \n",
       "181  United Arab Emirates             16              135              5   \n",
       "182        United Kingdom            219              126            195   \n",
       "183              Tanzania             36                6              1   \n",
       "184                   USA            249              158             84   \n",
       "185               Uruguay            115               35            220   \n",
       "186            Uzbekistan             25              101              8   \n",
       "187               Vanuatu             21               18             11   \n",
       "188             Venezuela            333              100              3   \n",
       "189               Vietnam            111                2              1   \n",
       "190                 Yemen              6                0              0   \n",
       "191                Zambia             32               19              4   \n",
       "192              Zimbabwe             64               18              4   \n",
       "\n",
       "     total_litres_of_pure_alcohol continent  \n",
       "0                             0.0        AS  \n",
       "1                             4.9        EU  \n",
       "2                             0.7        AF  \n",
       "3                            12.4        EU  \n",
       "4                             5.9        AF  \n",
       "5                             4.9       NaN  \n",
       "6                             8.3        SA  \n",
       "7                             3.8        EU  \n",
       "8                            10.4        OC  \n",
       "9                             9.7        EU  \n",
       "10                            1.3        EU  \n",
       "11                            6.3       NaN  \n",
       "12                            2.0        AS  \n",
       "13                            0.0        AS  \n",
       "14                            6.3       NaN  \n",
       "15                           14.4        EU  \n",
       "16                           10.5        EU  \n",
       "17                            6.8       NaN  \n",
       "18                            1.1        AF  \n",
       "19                            0.4        AS  \n",
       "20                            3.8        SA  \n",
       "21                            4.6        EU  \n",
       "22                            5.4        AF  \n",
       "23                            7.2        SA  \n",
       "24                            0.6        AS  \n",
       "25                           10.3        EU  \n",
       "26                            4.3        AF  \n",
       "27                            6.3        AF  \n",
       "28                            4.0        AF  \n",
       "29                            4.0        AF  \n",
       "..                            ...       ...  \n",
       "163                           5.6        SA  \n",
       "164                           4.7        AF  \n",
       "165                           7.2        EU  \n",
       "166                          10.2        EU  \n",
       "167                           1.0        AS  \n",
       "168                           0.3        AS  \n",
       "169                           6.4        AS  \n",
       "170                           3.9        EU  \n",
       "171                           0.1        AS  \n",
       "172                           1.3        AF  \n",
       "173                           1.1        OC  \n",
       "174                           6.4       NaN  \n",
       "175                           1.3        AF  \n",
       "176                           1.4        AS  \n",
       "177                           2.2        AS  \n",
       "178                           1.0        OC  \n",
       "179                           8.3        AF  \n",
       "180                           8.9        EU  \n",
       "181                           2.8        AS  \n",
       "182                          10.4        EU  \n",
       "183                           5.7        AF  \n",
       "184                           8.7       NaN  \n",
       "185                           6.6        SA  \n",
       "186                           2.4        AS  \n",
       "187                           0.9        OC  \n",
       "188                           7.7        SA  \n",
       "189                           2.0        AS  \n",
       "190                           0.1        AS  \n",
       "191                           2.5        AF  \n",
       "192                           4.7        AF  \n",
       "\n",
       "[193 rows x 6 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# examine the data\n",
    "drinks                  # print the first 30 and last 30 rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>beer_servings</th>\n",
       "      <th>spirit_servings</th>\n",
       "      <th>wine_servings</th>\n",
       "      <th>total_litres_of_pure_alcohol</th>\n",
       "      <th>continent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>AS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>89</td>\n",
       "      <td>132</td>\n",
       "      <td>54</td>\n",
       "      <td>4.9</td>\n",
       "      <td>EU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>0.7</td>\n",
       "      <td>AF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Andorra</td>\n",
       "      <td>245</td>\n",
       "      <td>138</td>\n",
       "      <td>312</td>\n",
       "      <td>12.4</td>\n",
       "      <td>EU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Angola</td>\n",
       "      <td>217</td>\n",
       "      <td>57</td>\n",
       "      <td>45</td>\n",
       "      <td>5.9</td>\n",
       "      <td>AF</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       country  beer_servings  spirit_servings  wine_servings  \\\n",
       "0  Afghanistan              0                0              0   \n",
       "1      Albania             89              132             54   \n",
       "2      Algeria             25                0             14   \n",
       "3      Andorra            245              138            312   \n",
       "4       Angola            217               57             45   \n",
       "\n",
       "   total_litres_of_pure_alcohol continent  \n",
       "0                           0.0        AS  \n",
       "1                           4.9        EU  \n",
       "2                           0.7        AF  \n",
       "3                          12.4        EU  \n",
       "4                           5.9        AF  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks.head()           # print the first 5 rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>beer_servings</th>\n",
       "      <th>spirit_servings</th>\n",
       "      <th>wine_servings</th>\n",
       "      <th>total_litres_of_pure_alcohol</th>\n",
       "      <th>continent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>Venezuela</td>\n",
       "      <td>333</td>\n",
       "      <td>100</td>\n",
       "      <td>3</td>\n",
       "      <td>7.7</td>\n",
       "      <td>SA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>Vietnam</td>\n",
       "      <td>111</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>AS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>Yemen</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>AS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>32</td>\n",
       "      <td>19</td>\n",
       "      <td>4</td>\n",
       "      <td>2.5</td>\n",
       "      <td>AF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>64</td>\n",
       "      <td>18</td>\n",
       "      <td>4</td>\n",
       "      <td>4.7</td>\n",
       "      <td>AF</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       country  beer_servings  spirit_servings  wine_servings  \\\n",
       "188  Venezuela            333              100              3   \n",
       "189    Vietnam            111                2              1   \n",
       "190      Yemen              6                0              0   \n",
       "191     Zambia             32               19              4   \n",
       "192   Zimbabwe             64               18              4   \n",
       "\n",
       "     total_litres_of_pure_alcohol continent  \n",
       "188                           7.7        SA  \n",
       "189                           2.0        AS  \n",
       "190                           0.1        AS  \n",
       "191                           2.5        AF  \n",
       "192                           4.7        AF  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks.tail()           # print the last 5 rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>beer_servings</th>\n",
       "      <th>spirit_servings</th>\n",
       "      <th>wine_servings</th>\n",
       "      <th>total_litres_of_pure_alcohol</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>193.000000</td>\n",
       "      <td>193.000000</td>\n",
       "      <td>193.000000</td>\n",
       "      <td>193.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>106.160622</td>\n",
       "      <td>80.994819</td>\n",
       "      <td>49.450777</td>\n",
       "      <td>4.717098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>101.143103</td>\n",
       "      <td>88.284312</td>\n",
       "      <td>79.697598</td>\n",
       "      <td>3.773298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>20.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>76.000000</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>4.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>188.000000</td>\n",
       "      <td>128.000000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>7.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>376.000000</td>\n",
       "      <td>438.000000</td>\n",
       "      <td>370.000000</td>\n",
       "      <td>14.400000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       beer_servings  spirit_servings  wine_servings  \\\n",
       "count     193.000000       193.000000     193.000000   \n",
       "mean      106.160622        80.994819      49.450777   \n",
       "std       101.143103        88.284312      79.697598   \n",
       "min         0.000000         0.000000       0.000000   \n",
       "25%        20.000000         4.000000       1.000000   \n",
       "50%        76.000000        56.000000       8.000000   \n",
       "75%       188.000000       128.000000      59.000000   \n",
       "max       376.000000       438.000000     370.000000   \n",
       "\n",
       "       total_litres_of_pure_alcohol  \n",
       "count                    193.000000  \n",
       "mean                       4.717098  \n",
       "std                        3.773298  \n",
       "min                        0.000000  \n",
       "25%                        1.300000  \n",
       "50%                        4.200000  \n",
       "75%                        7.200000  \n",
       "max                       14.400000  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks.describe()       # describe any numeric columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([u'country', u'beer_servings', u'spirit_servings', u'wine_servings',\n",
       "       u'total_litres_of_pure_alcohol', u'continent'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks.columns          # get series of column names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   a  b\n",
       "0  1  3\n",
       "1  2  4"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Note: the dataframe is like a more complicated dictionary where the \n",
    "# column names are the keys and the column itself is the value\n",
    "\n",
    "d = {'a':[1,2], 'b':[3,4]}\n",
    "pd.DataFrame(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(193, 6)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks.shape            # tuple of (#rows, #cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "country                          0\n",
       "beer_servings                    0\n",
       "spirit_servings                  0\n",
       "wine_servings                    0\n",
       "total_litres_of_pure_alcohol     0\n",
       "continent                       23\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# find missing values in a DataFrame\n",
    "drinks.isnull()         # DataFrame of booleans\n",
    "drinks.isnull().sum()   # convert booleans to integers and add"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>beer_servings</th>\n",
       "      <th>spirit_servings</th>\n",
       "      <th>wine_servings</th>\n",
       "      <th>total_litres_of_pure_alcohol</th>\n",
       "      <th>continent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Antigua &amp; Barbuda</td>\n",
       "      <td>102</td>\n",
       "      <td>128</td>\n",
       "      <td>45</td>\n",
       "      <td>4.9</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Bahamas</td>\n",
       "      <td>122</td>\n",
       "      <td>176</td>\n",
       "      <td>51</td>\n",
       "      <td>6.3</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Barbados</td>\n",
       "      <td>143</td>\n",
       "      <td>173</td>\n",
       "      <td>36</td>\n",
       "      <td>6.3</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Belize</td>\n",
       "      <td>263</td>\n",
       "      <td>114</td>\n",
       "      <td>8</td>\n",
       "      <td>6.8</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Canada</td>\n",
       "      <td>240</td>\n",
       "      <td>122</td>\n",
       "      <td>100</td>\n",
       "      <td>8.2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>Costa Rica</td>\n",
       "      <td>149</td>\n",
       "      <td>87</td>\n",
       "      <td>11</td>\n",
       "      <td>4.4</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>Cuba</td>\n",
       "      <td>93</td>\n",
       "      <td>137</td>\n",
       "      <td>5</td>\n",
       "      <td>4.2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>Dominica</td>\n",
       "      <td>52</td>\n",
       "      <td>286</td>\n",
       "      <td>26</td>\n",
       "      <td>6.6</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>Dominican Republic</td>\n",
       "      <td>193</td>\n",
       "      <td>147</td>\n",
       "      <td>9</td>\n",
       "      <td>6.2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>El Salvador</td>\n",
       "      <td>52</td>\n",
       "      <td>69</td>\n",
       "      <td>2</td>\n",
       "      <td>2.2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>Grenada</td>\n",
       "      <td>199</td>\n",
       "      <td>438</td>\n",
       "      <td>28</td>\n",
       "      <td>11.9</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>Guatemala</td>\n",
       "      <td>53</td>\n",
       "      <td>69</td>\n",
       "      <td>2</td>\n",
       "      <td>2.2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>Haiti</td>\n",
       "      <td>1</td>\n",
       "      <td>326</td>\n",
       "      <td>1</td>\n",
       "      <td>5.9</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>Honduras</td>\n",
       "      <td>69</td>\n",
       "      <td>98</td>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>Jamaica</td>\n",
       "      <td>82</td>\n",
       "      <td>97</td>\n",
       "      <td>9</td>\n",
       "      <td>3.4</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>Mexico</td>\n",
       "      <td>238</td>\n",
       "      <td>68</td>\n",
       "      <td>5</td>\n",
       "      <td>5.5</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>Nicaragua</td>\n",
       "      <td>78</td>\n",
       "      <td>118</td>\n",
       "      <td>1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>Panama</td>\n",
       "      <td>285</td>\n",
       "      <td>104</td>\n",
       "      <td>18</td>\n",
       "      <td>7.2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>St. Kitts &amp; Nevis</td>\n",
       "      <td>194</td>\n",
       "      <td>205</td>\n",
       "      <td>32</td>\n",
       "      <td>7.7</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>St. Lucia</td>\n",
       "      <td>171</td>\n",
       "      <td>315</td>\n",
       "      <td>71</td>\n",
       "      <td>10.1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>St. Vincent &amp; the Grenadines</td>\n",
       "      <td>120</td>\n",
       "      <td>221</td>\n",
       "      <td>11</td>\n",
       "      <td>6.3</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>Trinidad &amp; Tobago</td>\n",
       "      <td>197</td>\n",
       "      <td>156</td>\n",
       "      <td>7</td>\n",
       "      <td>6.4</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>USA</td>\n",
       "      <td>249</td>\n",
       "      <td>158</td>\n",
       "      <td>84</td>\n",
       "      <td>8.7</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          country  beer_servings  spirit_servings  \\\n",
       "5               Antigua & Barbuda            102              128   \n",
       "11                        Bahamas            122              176   \n",
       "14                       Barbados            143              173   \n",
       "17                         Belize            263              114   \n",
       "32                         Canada            240              122   \n",
       "41                     Costa Rica            149               87   \n",
       "43                           Cuba             93              137   \n",
       "50                       Dominica             52              286   \n",
       "51             Dominican Republic            193              147   \n",
       "54                    El Salvador             52               69   \n",
       "68                        Grenada            199              438   \n",
       "69                      Guatemala             53               69   \n",
       "73                          Haiti              1              326   \n",
       "74                       Honduras             69               98   \n",
       "84                        Jamaica             82               97   \n",
       "109                        Mexico            238               68   \n",
       "122                     Nicaragua             78              118   \n",
       "130                        Panama            285              104   \n",
       "143             St. Kitts & Nevis            194              205   \n",
       "144                     St. Lucia            171              315   \n",
       "145  St. Vincent & the Grenadines            120              221   \n",
       "174             Trinidad & Tobago            197              156   \n",
       "184                           USA            249              158   \n",
       "\n",
       "     wine_servings  total_litres_of_pure_alcohol continent  \n",
       "5               45                           4.9       NaN  \n",
       "11              51                           6.3       NaN  \n",
       "14              36                           6.3       NaN  \n",
       "17               8                           6.8       NaN  \n",
       "32             100                           8.2       NaN  \n",
       "41              11                           4.4       NaN  \n",
       "43               5                           4.2       NaN  \n",
       "50              26                           6.6       NaN  \n",
       "51               9                           6.2       NaN  \n",
       "54               2                           2.2       NaN  \n",
       "68              28                          11.9       NaN  \n",
       "69               2                           2.2       NaN  \n",
       "73               1                           5.9       NaN  \n",
       "74               2                           3.0       NaN  \n",
       "84               9                           3.4       NaN  \n",
       "109              5                           5.5       NaN  \n",
       "122              1                           3.5       NaN  \n",
       "130             18                           7.2       NaN  \n",
       "143             32                           7.7       NaN  \n",
       "144             71                          10.1       NaN  \n",
       "145             11                           6.3       NaN  \n",
       "174              7                           6.4       NaN  \n",
       "184             84                           8.7       NaN  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# So we have 23 missing continent values, that's a no bueno situation\n",
    "\n",
    "''' SELECT * from drinks where continent is NULL '''\n",
    "drinks[drinks['continent'].isnull()]         # DataFrame of values with null continent value\n",
    "\n",
    "# Anyone have any thoughts?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>beer_servings</th>\n",
       "      <th>spirit_servings</th>\n",
       "      <th>wine_servings</th>\n",
       "      <th>total_litres_of_pure_alcohol</th>\n",
       "      <th>continent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>AS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>89</td>\n",
       "      <td>132</td>\n",
       "      <td>54</td>\n",
       "      <td>4.9</td>\n",
       "      <td>EU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>0.7</td>\n",
       "      <td>AF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Andorra</td>\n",
       "      <td>245</td>\n",
       "      <td>138</td>\n",
       "      <td>312</td>\n",
       "      <td>12.4</td>\n",
       "      <td>EU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Angola</td>\n",
       "      <td>217</td>\n",
       "      <td>57</td>\n",
       "      <td>45</td>\n",
       "      <td>5.9</td>\n",
       "      <td>AF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Antigua &amp; Barbuda</td>\n",
       "      <td>102</td>\n",
       "      <td>128</td>\n",
       "      <td>45</td>\n",
       "      <td>4.9</td>\n",
       "      <td>NA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>193</td>\n",
       "      <td>25</td>\n",
       "      <td>221</td>\n",
       "      <td>8.3</td>\n",
       "      <td>SA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Armenia</td>\n",
       "      <td>21</td>\n",
       "      <td>179</td>\n",
       "      <td>11</td>\n",
       "      <td>3.8</td>\n",
       "      <td>EU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Australia</td>\n",
       "      <td>261</td>\n",
       "      <td>72</td>\n",
       "      <td>212</td>\n",
       "      <td>10.4</td>\n",
       "      <td>OC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Austria</td>\n",
       "      <td>279</td>\n",
       "      <td>75</td>\n",
       "      <td>191</td>\n",
       "      <td>9.7</td>\n",
       "      <td>EU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Azerbaijan</td>\n",
       "      <td>21</td>\n",
       "      <td>46</td>\n",
       "      <td>5</td>\n",
       "      <td>1.3</td>\n",
       "      <td>EU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Bahamas</td>\n",
       "      <td>122</td>\n",
       "      <td>176</td>\n",
       "      <td>51</td>\n",
       "      <td>6.3</td>\n",
       "      <td>NA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Bahrain</td>\n",
       "      <td>42</td>\n",
       "      <td>63</td>\n",
       "      <td>7</td>\n",
       "      <td>2.0</td>\n",
       "      <td>AS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Bangladesh</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>AS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Barbados</td>\n",
       "      <td>143</td>\n",
       "      <td>173</td>\n",
       "      <td>36</td>\n",
       "      <td>6.3</td>\n",
       "      <td>NA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Belarus</td>\n",
       "      <td>142</td>\n",
       "      <td>373</td>\n",
       "      <td>42</td>\n",
       "      <td>14.4</td>\n",
       "      <td>EU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Belgium</td>\n",
       "      <td>295</td>\n",
       "      <td>84</td>\n",
       "      <td>212</td>\n",
       "      <td>10.5</td>\n",
       "      <td>EU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Belize</td>\n",
       "      <td>263</td>\n",
       "      <td>114</td>\n",
       "      <td>8</td>\n",
       "      <td>6.8</td>\n",
       "      <td>NA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Benin</td>\n",
       "      <td>34</td>\n",
       "      <td>4</td>\n",
       "      <td>13</td>\n",
       "      <td>1.1</td>\n",
       "      <td>AF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Bhutan</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>AS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Bolivia</td>\n",
       "      <td>167</td>\n",
       "      <td>41</td>\n",
       "      <td>8</td>\n",
       "      <td>3.8</td>\n",
       "      <td>SA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Bosnia-Herzegovina</td>\n",
       "      <td>76</td>\n",
       "      <td>173</td>\n",
       "      <td>8</td>\n",
       "      <td>4.6</td>\n",
       "      <td>EU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Botswana</td>\n",
       "      <td>173</td>\n",
       "      <td>35</td>\n",
       "      <td>35</td>\n",
       "      <td>5.4</td>\n",
       "      <td>AF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>245</td>\n",
       "      <td>145</td>\n",
       "      <td>16</td>\n",
       "      <td>7.2</td>\n",
       "      <td>SA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Brunei</td>\n",
       "      <td>31</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>AS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Bulgaria</td>\n",
       "      <td>231</td>\n",
       "      <td>252</td>\n",
       "      <td>94</td>\n",
       "      <td>10.3</td>\n",
       "      <td>EU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Burkina Faso</td>\n",
       "      <td>25</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>4.3</td>\n",
       "      <td>AF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Burundi</td>\n",
       "      <td>88</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6.3</td>\n",
       "      <td>AF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Cote d'Ivoire</td>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>4.0</td>\n",
       "      <td>AF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Cabo Verde</td>\n",
       "      <td>144</td>\n",
       "      <td>56</td>\n",
       "      <td>16</td>\n",
       "      <td>4.0</td>\n",
       "      <td>AF</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>163</th>\n",
       "      <td>Suriname</td>\n",
       "      <td>128</td>\n",
       "      <td>178</td>\n",
       "      <td>7</td>\n",
       "      <td>5.6</td>\n",
       "      <td>SA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>Swaziland</td>\n",
       "      <td>90</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4.7</td>\n",
       "      <td>AF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>Sweden</td>\n",
       "      <td>152</td>\n",
       "      <td>60</td>\n",
       "      <td>186</td>\n",
       "      <td>7.2</td>\n",
       "      <td>EU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>Switzerland</td>\n",
       "      <td>185</td>\n",
       "      <td>100</td>\n",
       "      <td>280</td>\n",
       "      <td>10.2</td>\n",
       "      <td>EU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>Syria</td>\n",
       "      <td>5</td>\n",
       "      <td>35</td>\n",
       "      <td>16</td>\n",
       "      <td>1.0</td>\n",
       "      <td>AS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>Tajikistan</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>AS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169</th>\n",
       "      <td>Thailand</td>\n",
       "      <td>99</td>\n",
       "      <td>258</td>\n",
       "      <td>1</td>\n",
       "      <td>6.4</td>\n",
       "      <td>AS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>Macedonia</td>\n",
       "      <td>106</td>\n",
       "      <td>27</td>\n",
       "      <td>86</td>\n",
       "      <td>3.9</td>\n",
       "      <td>EU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>Timor-Leste</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0.1</td>\n",
       "      <td>AS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>Togo</td>\n",
       "      <td>36</td>\n",
       "      <td>2</td>\n",
       "      <td>19</td>\n",
       "      <td>1.3</td>\n",
       "      <td>AF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>Tonga</td>\n",
       "      <td>36</td>\n",
       "      <td>21</td>\n",
       "      <td>5</td>\n",
       "      <td>1.1</td>\n",
       "      <td>OC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>Trinidad &amp; Tobago</td>\n",
       "      <td>197</td>\n",
       "      <td>156</td>\n",
       "      <td>7</td>\n",
       "      <td>6.4</td>\n",
       "      <td>NA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>Tunisia</td>\n",
       "      <td>51</td>\n",
       "      <td>3</td>\n",
       "      <td>20</td>\n",
       "      <td>1.3</td>\n",
       "      <td>AF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>Turkey</td>\n",
       "      <td>51</td>\n",
       "      <td>22</td>\n",
       "      <td>7</td>\n",
       "      <td>1.4</td>\n",
       "      <td>AS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>Turkmenistan</td>\n",
       "      <td>19</td>\n",
       "      <td>71</td>\n",
       "      <td>32</td>\n",
       "      <td>2.2</td>\n",
       "      <td>AS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>Tuvalu</td>\n",
       "      <td>6</td>\n",
       "      <td>41</td>\n",
       "      <td>9</td>\n",
       "      <td>1.0</td>\n",
       "      <td>OC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>Uganda</td>\n",
       "      <td>45</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>8.3</td>\n",
       "      <td>AF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>Ukraine</td>\n",
       "      <td>206</td>\n",
       "      <td>237</td>\n",
       "      <td>45</td>\n",
       "      <td>8.9</td>\n",
       "      <td>EU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>16</td>\n",
       "      <td>135</td>\n",
       "      <td>5</td>\n",
       "      <td>2.8</td>\n",
       "      <td>AS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>219</td>\n",
       "      <td>126</td>\n",
       "      <td>195</td>\n",
       "      <td>10.4</td>\n",
       "      <td>EU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>Tanzania</td>\n",
       "      <td>36</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>5.7</td>\n",
       "      <td>AF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>USA</td>\n",
       "      <td>249</td>\n",
       "      <td>158</td>\n",
       "      <td>84</td>\n",
       "      <td>8.7</td>\n",
       "      <td>NA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>Uruguay</td>\n",
       "      <td>115</td>\n",
       "      <td>35</td>\n",
       "      <td>220</td>\n",
       "      <td>6.6</td>\n",
       "      <td>SA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>Uzbekistan</td>\n",
       "      <td>25</td>\n",
       "      <td>101</td>\n",
       "      <td>8</td>\n",
       "      <td>2.4</td>\n",
       "      <td>AS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>Vanuatu</td>\n",
       "      <td>21</td>\n",
       "      <td>18</td>\n",
       "      <td>11</td>\n",
       "      <td>0.9</td>\n",
       "      <td>OC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>Venezuela</td>\n",
       "      <td>333</td>\n",
       "      <td>100</td>\n",
       "      <td>3</td>\n",
       "      <td>7.7</td>\n",
       "      <td>SA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>Vietnam</td>\n",
       "      <td>111</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>AS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>Yemen</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>AS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>32</td>\n",
       "      <td>19</td>\n",
       "      <td>4</td>\n",
       "      <td>2.5</td>\n",
       "      <td>AF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>64</td>\n",
       "      <td>18</td>\n",
       "      <td>4</td>\n",
       "      <td>4.7</td>\n",
       "      <td>AF</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>193 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  country  beer_servings  spirit_servings  wine_servings  \\\n",
       "0             Afghanistan              0                0              0   \n",
       "1                 Albania             89              132             54   \n",
       "2                 Algeria             25                0             14   \n",
       "3                 Andorra            245              138            312   \n",
       "4                  Angola            217               57             45   \n",
       "5       Antigua & Barbuda            102              128             45   \n",
       "6               Argentina            193               25            221   \n",
       "7                 Armenia             21              179             11   \n",
       "8               Australia            261               72            212   \n",
       "9                 Austria            279               75            191   \n",
       "10             Azerbaijan             21               46              5   \n",
       "11                Bahamas            122              176             51   \n",
       "12                Bahrain             42               63              7   \n",
       "13             Bangladesh              0                0              0   \n",
       "14               Barbados            143              173             36   \n",
       "15                Belarus            142              373             42   \n",
       "16                Belgium            295               84            212   \n",
       "17                 Belize            263              114              8   \n",
       "18                  Benin             34                4             13   \n",
       "19                 Bhutan             23                0              0   \n",
       "20                Bolivia            167               41              8   \n",
       "21     Bosnia-Herzegovina             76              173              8   \n",
       "22               Botswana            173               35             35   \n",
       "23                 Brazil            245              145             16   \n",
       "24                 Brunei             31                2              1   \n",
       "25               Bulgaria            231              252             94   \n",
       "26           Burkina Faso             25                7              7   \n",
       "27                Burundi             88                0              0   \n",
       "28          Cote d'Ivoire             37                1              7   \n",
       "29             Cabo Verde            144               56             16   \n",
       "..                    ...            ...              ...            ...   \n",
       "163              Suriname            128              178              7   \n",
       "164             Swaziland             90                2              2   \n",
       "165                Sweden            152               60            186   \n",
       "166           Switzerland            185              100            280   \n",
       "167                 Syria              5               35             16   \n",
       "168            Tajikistan              2               15              0   \n",
       "169              Thailand             99              258              1   \n",
       "170             Macedonia            106               27             86   \n",
       "171           Timor-Leste              1                1              4   \n",
       "172                  Togo             36                2             19   \n",
       "173                 Tonga             36               21              5   \n",
       "174     Trinidad & Tobago            197              156              7   \n",
       "175               Tunisia             51                3             20   \n",
       "176                Turkey             51               22              7   \n",
       "177          Turkmenistan             19               71             32   \n",
       "178                Tuvalu              6               41              9   \n",
       "179                Uganda             45                9              0   \n",
       "180               Ukraine            206              237             45   \n",
       "181  United Arab Emirates             16              135              5   \n",
       "182        United Kingdom            219              126            195   \n",
       "183              Tanzania             36                6              1   \n",
       "184                   USA            249              158             84   \n",
       "185               Uruguay            115               35            220   \n",
       "186            Uzbekistan             25              101              8   \n",
       "187               Vanuatu             21               18             11   \n",
       "188             Venezuela            333              100              3   \n",
       "189               Vietnam            111                2              1   \n",
       "190                 Yemen              6                0              0   \n",
       "191                Zambia             32               19              4   \n",
       "192              Zimbabwe             64               18              4   \n",
       "\n",
       "     total_litres_of_pure_alcohol continent  \n",
       "0                             0.0        AS  \n",
       "1                             4.9        EU  \n",
       "2                             0.7        AF  \n",
       "3                            12.4        EU  \n",
       "4                             5.9        AF  \n",
       "5                             4.9        NA  \n",
       "6                             8.3        SA  \n",
       "7                             3.8        EU  \n",
       "8                            10.4        OC  \n",
       "9                             9.7        EU  \n",
       "10                            1.3        EU  \n",
       "11                            6.3        NA  \n",
       "12                            2.0        AS  \n",
       "13                            0.0        AS  \n",
       "14                            6.3        NA  \n",
       "15                           14.4        EU  \n",
       "16                           10.5        EU  \n",
       "17                            6.8        NA  \n",
       "18                            1.1        AF  \n",
       "19                            0.4        AS  \n",
       "20                            3.8        SA  \n",
       "21                            4.6        EU  \n",
       "22                            5.4        AF  \n",
       "23                            7.2        SA  \n",
       "24                            0.6        AS  \n",
       "25                           10.3        EU  \n",
       "26                            4.3        AF  \n",
       "27                            6.3        AF  \n",
       "28                            4.0        AF  \n",
       "29                            4.0        AF  \n",
       "..                            ...       ...  \n",
       "163                           5.6        SA  \n",
       "164                           4.7        AF  \n",
       "165                           7.2        EU  \n",
       "166                          10.2        EU  \n",
       "167                           1.0        AS  \n",
       "168                           0.3        AS  \n",
       "169                           6.4        AS  \n",
       "170                           3.9        EU  \n",
       "171                           0.1        AS  \n",
       "172                           1.3        AF  \n",
       "173                           1.1        OC  \n",
       "174                           6.4        NA  \n",
       "175                           1.3        AF  \n",
       "176                           1.4        AS  \n",
       "177                           2.2        AS  \n",
       "178                           1.0        OC  \n",
       "179                           8.3        AF  \n",
       "180                           8.9        EU  \n",
       "181                           2.8        AS  \n",
       "182                          10.4        EU  \n",
       "183                           5.7        AF  \n",
       "184                           8.7        NA  \n",
       "185                           6.6        SA  \n",
       "186                           2.4        AS  \n",
       "187                           0.9        OC  \n",
       "188                           7.7        SA  \n",
       "189                           2.0        AS  \n",
       "190                           0.1        AS  \n",
       "191                           2.5        AF  \n",
       "192                           4.7        AF  \n",
       "\n",
       "[193 rows x 6 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Pandas isn't brilliant, it saw the \"NA\" string and assumed it meant a null value\n",
    "\n",
    "# handling missing values\n",
    "drinks.dropna()             # drop a row if ANY values are missing\n",
    "drinks.fillna(value='NA')   # fill in missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "country                         0\n",
       "beer_servings                   0\n",
       "spirit_servings                 0\n",
       "wine_servings                   0\n",
       "total_litres_of_pure_alcohol    0\n",
       "continent                       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fix the original import, this time using a live url!\n",
    "drinks = pd.read_csv('https://raw.githubusercontent.com/sinanuozdemir/sfdat28/master/data/drinks.csv', na_filter=False)\n",
    "drinks.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      AS\n",
       "1      EU\n",
       "2      AF\n",
       "3      EU\n",
       "4      AF\n",
       "5      NA\n",
       "6      SA\n",
       "7      EU\n",
       "8      OC\n",
       "9      EU\n",
       "10     EU\n",
       "11     NA\n",
       "12     AS\n",
       "13     AS\n",
       "14     NA\n",
       "15     EU\n",
       "16     EU\n",
       "17     NA\n",
       "18     AF\n",
       "19     AS\n",
       "20     SA\n",
       "21     EU\n",
       "22     AF\n",
       "23     SA\n",
       "24     AS\n",
       "25     EU\n",
       "26     AF\n",
       "27     AF\n",
       "28     AF\n",
       "29     AF\n",
       "       ..\n",
       "163    SA\n",
       "164    AF\n",
       "165    EU\n",
       "166    EU\n",
       "167    AS\n",
       "168    AS\n",
       "169    AS\n",
       "170    EU\n",
       "171    AS\n",
       "172    AF\n",
       "173    OC\n",
       "174    NA\n",
       "175    AF\n",
       "176    AS\n",
       "177    AS\n",
       "178    OC\n",
       "179    AF\n",
       "180    EU\n",
       "181    AS\n",
       "182    EU\n",
       "183    AF\n",
       "184    NA\n",
       "185    SA\n",
       "186    AS\n",
       "187    OC\n",
       "188    SA\n",
       "189    AS\n",
       "190    AS\n",
       "191    AF\n",
       "192    AF\n",
       "Name: continent, dtype: object"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# selecting a column ('Series')\n",
    "drinks['continent']\n",
    "drinks.continent            # equivalent\n",
    "\n",
    "# Note the dictionary like selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(drinks.continent)      # Series if pandas equivalent to list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     193\n",
       "unique      6\n",
       "top        AF\n",
       "freq       53\n",
       "Name: continent, dtype: object"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# summarizing a non-numeric column\n",
    "drinks.continent.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AF    53\n",
       "EU    45\n",
       "AS    44\n",
       "NA    23\n",
       "OC    16\n",
       "SA    12\n",
       "Name: continent, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks.continent.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>beer_servings</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Andorra</td>\n",
       "      <td>245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Angola</td>\n",
       "      <td>217</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       country  beer_servings\n",
       "0  Afghanistan              0\n",
       "1      Albania             89\n",
       "2      Algeria             25\n",
       "3      Andorra            245\n",
       "4       Angola            217"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "'''\n",
    "note the double square bracket\n",
    "the outer pair is used like in a python dictionary\n",
    "    to select\n",
    "the inner pair is a list!\n",
    "\n",
    "so in all, the double use of square brackets is telling\n",
    "the dataframe to select a list!\n",
    "'''\n",
    "# selecting multiple columns\n",
    "''' SELECT country,beer_servings from drinks '''\n",
    "drinks[['country', 'beer_servings']].head()\n",
    "\n",
    "\n",
    "# remember the .head() just shows us the first 5 rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>beer_servings</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Andorra</td>\n",
       "      <td>245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Angola</td>\n",
       "      <td>217</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       country  beer_servings\n",
       "0  Afghanistan              0\n",
       "1      Albania             89\n",
       "2      Algeria             25\n",
       "3      Andorra            245\n",
       "4       Angola            217"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_cols = ['country', 'beer_servings']\n",
    "drinks[my_cols].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>beer_servings</th>\n",
       "      <th>spirit_servings</th>\n",
       "      <th>wine_servings</th>\n",
       "      <th>total_litres_of_pure_alcohol</th>\n",
       "      <th>continent</th>\n",
       "      <th>total_servings</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>AS</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>89</td>\n",
       "      <td>132</td>\n",
       "      <td>54</td>\n",
       "      <td>4.9</td>\n",
       "      <td>EU</td>\n",
       "      <td>275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>0.7</td>\n",
       "      <td>AF</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Andorra</td>\n",
       "      <td>245</td>\n",
       "      <td>138</td>\n",
       "      <td>312</td>\n",
       "      <td>12.4</td>\n",
       "      <td>EU</td>\n",
       "      <td>695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Angola</td>\n",
       "      <td>217</td>\n",
       "      <td>57</td>\n",
       "      <td>45</td>\n",
       "      <td>5.9</td>\n",
       "      <td>AF</td>\n",
       "      <td>319</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       country  beer_servings  spirit_servings  wine_servings  \\\n",
       "0  Afghanistan              0                0              0   \n",
       "1      Albania             89              132             54   \n",
       "2      Algeria             25                0             14   \n",
       "3      Andorra            245              138            312   \n",
       "4       Angola            217               57             45   \n",
       "\n",
       "   total_litres_of_pure_alcohol continent  total_servings  \n",
       "0                           0.0        AS               0  \n",
       "1                           4.9        EU             275  \n",
       "2                           0.7        AF              39  \n",
       "3                          12.4        EU             695  \n",
       "4                           5.9        AF             319  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# add a new column as a function of existing columns\n",
    "drinks['total_servings'] = drinks.beer_servings + drinks.spirit_servings + drinks.wine_servings\n",
    "drinks.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>beer_servings</th>\n",
       "      <th>spirit_servings</th>\n",
       "      <th>wine_servings</th>\n",
       "      <th>total_litres_of_pure_alcohol</th>\n",
       "      <th>continent</th>\n",
       "      <th>total_servings</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>89</td>\n",
       "      <td>132</td>\n",
       "      <td>54</td>\n",
       "      <td>4.9</td>\n",
       "      <td>EU</td>\n",
       "      <td>275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Andorra</td>\n",
       "      <td>245</td>\n",
       "      <td>138</td>\n",
       "      <td>312</td>\n",
       "      <td>12.4</td>\n",
       "      <td>EU</td>\n",
       "      <td>695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Armenia</td>\n",
       "      <td>21</td>\n",
       "      <td>179</td>\n",
       "      <td>11</td>\n",
       "      <td>3.8</td>\n",
       "      <td>EU</td>\n",
       "      <td>211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Austria</td>\n",
       "      <td>279</td>\n",
       "      <td>75</td>\n",
       "      <td>191</td>\n",
       "      <td>9.7</td>\n",
       "      <td>EU</td>\n",
       "      <td>545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Azerbaijan</td>\n",
       "      <td>21</td>\n",
       "      <td>46</td>\n",
       "      <td>5</td>\n",
       "      <td>1.3</td>\n",
       "      <td>EU</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       country  beer_servings  spirit_servings  wine_servings  \\\n",
       "1      Albania             89              132             54   \n",
       "3      Andorra            245              138            312   \n",
       "7      Armenia             21              179             11   \n",
       "9      Austria            279               75            191   \n",
       "10  Azerbaijan             21               46              5   \n",
       "\n",
       "    total_litres_of_pure_alcohol continent  total_servings  \n",
       "1                            4.9        EU             275  \n",
       "3                           12.4        EU             695  \n",
       "7                            3.8        EU             211  \n",
       "9                            9.7        EU             545  \n",
       "10                           1.3        EU              72  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# logical filtering and sorting\n",
    "\n",
    "'''\n",
    "How it works:\n",
    "    drinks.continent=='EU' by itself returns a bunch\n",
    "        of Trues and Falses\n",
    "        \n",
    "drinks.continent=='EU'\n",
    "\n",
    "See?\n",
    "\n",
    "\n",
    "when you wrap drinks around it with square brackets\n",
    "you're telling the drinks dataframe to select\n",
    "only those that are True, and not the False ones\n",
    "\n",
    "drinks[drinks.continent=='EU']\n",
    "'''\n",
    "drinks.continent=='EU' # this is a series of T and F\n",
    "\n",
    "# we put tht series of Trues and Falses directly into the square brackets of justice\n",
    "drinks[drinks.continent=='EU'] .head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>total_servings</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Antigua &amp; Barbuda</td>\n",
       "      <td>275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Bahamas</td>\n",
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       "    <tr>\n",
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       "      <td>Barbados</td>\n",
       "      <td>352</td>\n",
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       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Belize</td>\n",
       "      <td>385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Canada</td>\n",
       "      <td>462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>Costa Rica</td>\n",
       "      <td>247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>Cuba</td>\n",
       "      <td>235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>Dominica</td>\n",
       "      <td>364</td>\n",
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       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>Dominican Republic</td>\n",
       "      <td>349</td>\n",
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       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>El Salvador</td>\n",
       "      <td>123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>Grenada</td>\n",
       "      <td>665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>Guatemala</td>\n",
       "      <td>124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>Haiti</td>\n",
       "      <td>328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>Honduras</td>\n",
       "      <td>169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>Jamaica</td>\n",
       "      <td>188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>Mexico</td>\n",
       "      <td>311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>Nicaragua</td>\n",
       "      <td>197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>Panama</td>\n",
       "      <td>407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>St. Kitts &amp; Nevis</td>\n",
       "      <td>431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>St. Lucia</td>\n",
       "      <td>557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>St. Vincent &amp; the Grenadines</td>\n",
       "      <td>352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>Trinidad &amp; Tobago</td>\n",
       "      <td>360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>USA</td>\n",
       "      <td>491</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          country  total_servings\n",
       "5               Antigua & Barbuda             275\n",
       "11                        Bahamas             349\n",
       "14                       Barbados             352\n",
       "17                         Belize             385\n",
       "32                         Canada             462\n",
       "41                     Costa Rica             247\n",
       "43                           Cuba             235\n",
       "50                       Dominica             364\n",
       "51             Dominican Republic             349\n",
       "54                    El Salvador             123\n",
       "68                        Grenada             665\n",
       "69                      Guatemala             124\n",
       "73                          Haiti             328\n",
       "74                       Honduras             169\n",
       "84                        Jamaica             188\n",
       "109                        Mexico             311\n",
       "122                     Nicaragua             197\n",
       "130                        Panama             407\n",
       "143             St. Kitts & Nevis             431\n",
       "144                     St. Lucia             557\n",
       "145  St. Vincent & the Grenadines             352\n",
       "174             Trinidad & Tobago             360\n",
       "184                           USA             491"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# North American countries with total servings\n",
    "# drinks[['country', 'total_servings']][drinks.continent=='NA']\n",
    "\n",
    "new_df = drinks[['country', 'total_servings']] # selection of two columns\n",
    "new_df[drinks.continent=='NA']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/sinanozdemir/anaconda/envs/sfdat26-env/lib/python2.7/site-packages/ipykernel/__main__.py:2: FutureWarning: by argument to sort_index is deprecated, pls use .sort_values(by=...)\n",
      "  from ipykernel import kernelapp as app\n"
     ]
    },
    {
     "data": {
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "  </thead>\n",
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       "      <td>St. Vincent &amp; the Grenadines</td>\n",
       "      <td>352</td>\n",
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       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>Trinidad &amp; Tobago</td>\n",
       "      <td>360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>Dominica</td>\n",
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       "      <th>17</th>\n",
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       "      <th>130</th>\n",
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       "      <td>St. Kitts &amp; Nevis</td>\n",
       "      <td>431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
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       "      <td>462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
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       "      <td>491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>St. Lucia</td>\n",
       "      <td>557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>Grenada</td>\n",
       "      <td>665</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          country  total_servings\n",
       "54                    El Salvador             123\n",
       "69                      Guatemala             124\n",
       "74                       Honduras             169\n",
       "84                        Jamaica             188\n",
       "122                     Nicaragua             197\n",
       "43                           Cuba             235\n",
       "41                     Costa Rica             247\n",
       "5               Antigua & Barbuda             275\n",
       "109                        Mexico             311\n",
       "73                          Haiti             328\n",
       "51             Dominican Republic             349\n",
       "11                        Bahamas             349\n",
       "14                       Barbados             352\n",
       "145  St. Vincent & the Grenadines             352\n",
       "174             Trinidad & Tobago             360\n",
       "50                       Dominica             364\n",
       "17                         Belize             385\n",
       "130                        Panama             407\n",
       "143             St. Kitts & Nevis             431\n",
       "32                         Canada             462\n",
       "184                           USA             491\n",
       "144                     St. Lucia             557\n",
       "68                        Grenada             665"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# same thing, sorted by total_servings\n",
    "drinks[['country', 'total_servings']][drinks.continent=='NA'].sort_index(by='total_servings')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>total_servings</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>68 </th>\n",
       "      <td>                      Grenada</td>\n",
       "      <td> 665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>                    St. Lucia</td>\n",
       "      <td> 557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>                          USA</td>\n",
       "      <td> 491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32 </th>\n",
       "      <td>                       Canada</td>\n",
       "      <td> 462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>            St. Kitts &amp; Nevis</td>\n",
       "      <td> 431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>                       Panama</td>\n",
       "      <td> 407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17 </th>\n",
       "      <td>                       Belize</td>\n",
       "      <td> 385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50 </th>\n",
       "      <td>                     Dominica</td>\n",
       "      <td> 364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>            Trinidad &amp; Tobago</td>\n",
       "      <td> 360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14 </th>\n",
       "      <td>                     Barbados</td>\n",
       "      <td> 352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td> St. Vincent &amp; the Grenadines</td>\n",
       "      <td> 352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51 </th>\n",
       "      <td>           Dominican Republic</td>\n",
       "      <td> 349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11 </th>\n",
       "      <td>                      Bahamas</td>\n",
       "      <td> 349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73 </th>\n",
       "      <td>                        Haiti</td>\n",
       "      <td> 328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>                       Mexico</td>\n",
       "      <td> 311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5  </th>\n",
       "      <td>            Antigua &amp; Barbuda</td>\n",
       "      <td> 275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41 </th>\n",
       "      <td>                   Costa Rica</td>\n",
       "      <td> 247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43 </th>\n",
       "      <td>                         Cuba</td>\n",
       "      <td> 235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>                    Nicaragua</td>\n",
       "      <td> 197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84 </th>\n",
       "      <td>                      Jamaica</td>\n",
       "      <td> 188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74 </th>\n",
       "      <td>                     Honduras</td>\n",
       "      <td> 169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69 </th>\n",
       "      <td>                    Guatemala</td>\n",
       "      <td> 124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54 </th>\n",
       "      <td>                  El Salvador</td>\n",
       "      <td> 123</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          country  total_servings\n",
       "68                        Grenada             665\n",
       "144                     St. Lucia             557\n",
       "184                           USA             491\n",
       "32                         Canada             462\n",
       "143             St. Kitts & Nevis             431\n",
       "130                        Panama             407\n",
       "17                         Belize             385\n",
       "50                       Dominica             364\n",
       "174             Trinidad & Tobago             360\n",
       "14                       Barbados             352\n",
       "145  St. Vincent & the Grenadines             352\n",
       "51             Dominican Republic             349\n",
       "11                        Bahamas             349\n",
       "73                          Haiti             328\n",
       "109                        Mexico             311\n",
       "5               Antigua & Barbuda             275\n",
       "41                     Costa Rica             247\n",
       "43                           Cuba             235\n",
       "122                     Nicaragua             197\n",
       "84                        Jamaica             188\n",
       "74                       Honduras             169\n",
       "69                      Guatemala             124\n",
       "54                    El Salvador             123"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#sorted in reverse order\n",
    "drinks[['country', 'total_servings']][drinks.continent=='NA'].sort_index(by='total_servings', ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/sinanozdemir/anaconda/envs/sfdat26-env/lib/python2.7/site-packages/ipykernel/__main__.py:2: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  from ipykernel import kernelapp as app\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>beer_servings</th>\n",
       "      <th>spirit_servings</th>\n",
       "      <th>wine_servings</th>\n",
       "      <th>total_litres_of_pure_alcohol</th>\n",
       "      <th>continent</th>\n",
       "      <th>total_servings</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Andorra</td>\n",
       "      <td>245</td>\n",
       "      <td>138</td>\n",
       "      <td>312</td>\n",
       "      <td>12.4</td>\n",
       "      <td>EU</td>\n",
       "      <td>695</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   country  beer_servings  spirit_servings  wine_servings  \\\n",
       "3  Andorra            245              138            312   \n",
       "\n",
       "   total_litres_of_pure_alcohol continent  total_servings  \n",
       "3                          12.4        EU             695  "
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# contries with wine servings over 300 and total liters over 12\n",
    "drinks[drinks.wine_servings > 300][drinks.total_litres_of_pure_alcohol > 12]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>beer_servings</th>\n",
       "      <th>spirit_servings</th>\n",
       "      <th>wine_servings</th>\n",
       "      <th>total_litres_of_pure_alcohol</th>\n",
       "      <th>continent</th>\n",
       "      <th>total_servings</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Andorra</td>\n",
       "      <td>245</td>\n",
       "      <td>138</td>\n",
       "      <td>312</td>\n",
       "      <td>12.4</td>\n",
       "      <td>EU</td>\n",
       "      <td>695</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   country  beer_servings  spirit_servings  wine_servings  \\\n",
       "3  Andorra            245              138            312   \n",
       "\n",
       "   total_litres_of_pure_alcohol continent  total_servings  \n",
       "3                          12.4        EU             695  "
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Same result but using python logic operators\n",
    "# Note the paranthesis around each filter when using logical operators\n",
    "drinks[(drinks.wine_servings > 300) & (drinks.total_litres_of_pure_alcohol > 12)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>beer_servings</th>\n",
       "      <th>spirit_servings</th>\n",
       "      <th>wine_servings</th>\n",
       "      <th>total_litres_of_pure_alcohol</th>\n",
       "      <th>continent</th>\n",
       "      <th>total_servings</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Andorra</td>\n",
       "      <td>245</td>\n",
       "      <td>138</td>\n",
       "      <td>312</td>\n",
       "      <td>12.4</td>\n",
       "      <td>EU</td>\n",
       "      <td>695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>193</td>\n",
       "      <td>25</td>\n",
       "      <td>221</td>\n",
       "      <td>8.3</td>\n",
       "      <td>SA</td>\n",
       "      <td>439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Chile</td>\n",
       "      <td>130</td>\n",
       "      <td>124</td>\n",
       "      <td>172</td>\n",
       "      <td>7.6</td>\n",
       "      <td>SA</td>\n",
       "      <td>426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>Cook Islands</td>\n",
       "      <td>0</td>\n",
       "      <td>254</td>\n",
       "      <td>74</td>\n",
       "      <td>5.9</td>\n",
       "      <td>OC</td>\n",
       "      <td>328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>Croatia</td>\n",
       "      <td>230</td>\n",
       "      <td>87</td>\n",
       "      <td>254</td>\n",
       "      <td>10.2</td>\n",
       "      <td>EU</td>\n",
       "      <td>571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>Denmark</td>\n",
       "      <td>224</td>\n",
       "      <td>81</td>\n",
       "      <td>278</td>\n",
       "      <td>10.4</td>\n",
       "      <td>EU</td>\n",
       "      <td>583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>Equatorial Guinea</td>\n",
       "      <td>92</td>\n",
       "      <td>0</td>\n",
       "      <td>233</td>\n",
       "      <td>5.8</td>\n",
       "      <td>AF</td>\n",
       "      <td>325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>France</td>\n",
       "      <td>127</td>\n",
       "      <td>151</td>\n",
       "      <td>370</td>\n",
       "      <td>11.8</td>\n",
       "      <td>EU</td>\n",
       "      <td>648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>Georgia</td>\n",
       "      <td>52</td>\n",
       "      <td>100</td>\n",
       "      <td>149</td>\n",
       "      <td>5.4</td>\n",
       "      <td>EU</td>\n",
       "      <td>301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>Greece</td>\n",
       "      <td>133</td>\n",
       "      <td>112</td>\n",
       "      <td>218</td>\n",
       "      <td>8.3</td>\n",
       "      <td>EU</td>\n",
       "      <td>463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>Italy</td>\n",
       "      <td>85</td>\n",
       "      <td>42</td>\n",
       "      <td>237</td>\n",
       "      <td>6.5</td>\n",
       "      <td>EU</td>\n",
       "      <td>364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>Laos</td>\n",
       "      <td>62</td>\n",
       "      <td>0</td>\n",
       "      <td>123</td>\n",
       "      <td>6.2</td>\n",
       "      <td>AS</td>\n",
       "      <td>185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>Lebanon</td>\n",
       "      <td>20</td>\n",
       "      <td>55</td>\n",
       "      <td>31</td>\n",
       "      <td>1.9</td>\n",
       "      <td>AS</td>\n",
       "      <td>106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>Luxembourg</td>\n",
       "      <td>236</td>\n",
       "      <td>133</td>\n",
       "      <td>271</td>\n",
       "      <td>11.4</td>\n",
       "      <td>EU</td>\n",
       "      <td>640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>Montenegro</td>\n",
       "      <td>31</td>\n",
       "      <td>114</td>\n",
       "      <td>128</td>\n",
       "      <td>4.9</td>\n",
       "      <td>EU</td>\n",
       "      <td>273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>Portugal</td>\n",
       "      <td>194</td>\n",
       "      <td>67</td>\n",
       "      <td>339</td>\n",
       "      <td>11.0</td>\n",
       "      <td>EU</td>\n",
       "      <td>600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>Qatar</td>\n",
       "      <td>1</td>\n",
       "      <td>42</td>\n",
       "      <td>7</td>\n",
       "      <td>0.9</td>\n",
       "      <td>AS</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>Sao Tome &amp; Principe</td>\n",
       "      <td>56</td>\n",
       "      <td>38</td>\n",
       "      <td>140</td>\n",
       "      <td>4.2</td>\n",
       "      <td>AF</td>\n",
       "      <td>234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>Slovenia</td>\n",
       "      <td>270</td>\n",
       "      <td>51</td>\n",
       "      <td>276</td>\n",
       "      <td>10.6</td>\n",
       "      <td>EU</td>\n",
       "      <td>597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>Sweden</td>\n",
       "      <td>152</td>\n",
       "      <td>60</td>\n",
       "      <td>186</td>\n",
       "      <td>7.2</td>\n",
       "      <td>EU</td>\n",
       "      <td>398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>Switzerland</td>\n",
       "      <td>185</td>\n",
       "      <td>100</td>\n",
       "      <td>280</td>\n",
       "      <td>10.2</td>\n",
       "      <td>EU</td>\n",
       "      <td>565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>Syria</td>\n",
       "      <td>5</td>\n",
       "      <td>35</td>\n",
       "      <td>16</td>\n",
       "      <td>1.0</td>\n",
       "      <td>AS</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>Timor-Leste</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0.1</td>\n",
       "      <td>AS</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>Turkmenistan</td>\n",
       "      <td>19</td>\n",
       "      <td>71</td>\n",
       "      <td>32</td>\n",
       "      <td>2.2</td>\n",
       "      <td>AS</td>\n",
       "      <td>122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>Tuvalu</td>\n",
       "      <td>6</td>\n",
       "      <td>41</td>\n",
       "      <td>9</td>\n",
       "      <td>1.0</td>\n",
       "      <td>OC</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>Uruguay</td>\n",
       "      <td>115</td>\n",
       "      <td>35</td>\n",
       "      <td>220</td>\n",
       "      <td>6.6</td>\n",
       "      <td>SA</td>\n",
       "      <td>370</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 country  beer_servings  spirit_servings  wine_servings  \\\n",
       "3                Andorra            245              138            312   \n",
       "6              Argentina            193               25            221   \n",
       "35                 Chile            130              124            172   \n",
       "40          Cook Islands              0              254             74   \n",
       "42               Croatia            230               87            254   \n",
       "48               Denmark            224               81            278   \n",
       "55     Equatorial Guinea             92                0            233   \n",
       "61                France            127              151            370   \n",
       "64               Georgia             52              100            149   \n",
       "67                Greece            133              112            218   \n",
       "83                 Italy             85               42            237   \n",
       "92                  Laos             62                0            123   \n",
       "94               Lebanon             20               55             31   \n",
       "99            Luxembourg            236              133            271   \n",
       "113           Montenegro             31              114            128   \n",
       "136             Portugal            194               67            339   \n",
       "137                Qatar              1               42              7   \n",
       "148  Sao Tome & Principe             56               38            140   \n",
       "156             Slovenia            270               51            276   \n",
       "165               Sweden            152               60            186   \n",
       "166          Switzerland            185              100            280   \n",
       "167                Syria              5               35             16   \n",
       "171          Timor-Leste              1                1              4   \n",
       "177         Turkmenistan             19               71             32   \n",
       "178               Tuvalu              6               41              9   \n",
       "185              Uruguay            115               35            220   \n",
       "\n",
       "     total_litres_of_pure_alcohol continent  total_servings  \n",
       "3                            12.4        EU             695  \n",
       "6                             8.3        SA             439  \n",
       "35                            7.6        SA             426  \n",
       "40                            5.9        OC             328  \n",
       "42                           10.2        EU             571  \n",
       "48                           10.4        EU             583  \n",
       "55                            5.8        AF             325  \n",
       "61                           11.8        EU             648  \n",
       "64                            5.4        EU             301  \n",
       "67                            8.3        EU             463  \n",
       "83                            6.5        EU             364  \n",
       "92                            6.2        AS             185  \n",
       "94                            1.9        AS             106  \n",
       "99                           11.4        EU             640  \n",
       "113                           4.9        EU             273  \n",
       "136                          11.0        EU             600  \n",
       "137                           0.9        AS              50  \n",
       "148                           4.2        AF             234  \n",
       "156                          10.6        EU             597  \n",
       "165                           7.2        EU             398  \n",
       "166                          10.2        EU             565  \n",
       "167                           1.0        AS              56  \n",
       "171                           0.1        AS               6  \n",
       "177                           2.2        AS             122  \n",
       "178                           1.0        OC              56  \n",
       "185                           6.6        SA             370  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# contries with more wine servings than beer servings\n",
    "drinks[drinks.wine_servings > drinks.beer_servings]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      False\n",
       "1      False\n",
       "2      False\n",
       "3       True\n",
       "4      False\n",
       "5      False\n",
       "6       True\n",
       "7      False\n",
       "8      False\n",
       "9      False\n",
       "10     False\n",
       "11     False\n",
       "12     False\n",
       "13     False\n",
       "14     False\n",
       "15     False\n",
       "16     False\n",
       "17     False\n",
       "18     False\n",
       "19     False\n",
       "20     False\n",
       "21     False\n",
       "22     False\n",
       "23     False\n",
       "24     False\n",
       "25     False\n",
       "26     False\n",
       "27     False\n",
       "28     False\n",
       "29     False\n",
       "       ...  \n",
       "163    False\n",
       "164    False\n",
       "165     True\n",
       "166     True\n",
       "167     True\n",
       "168    False\n",
       "169    False\n",
       "170    False\n",
       "171     True\n",
       "172    False\n",
       "173    False\n",
       "174    False\n",
       "175    False\n",
       "176    False\n",
       "177     True\n",
       "178     True\n",
       "179    False\n",
       "180    False\n",
       "181    False\n",
       "182    False\n",
       "183    False\n",
       "184    False\n",
       "185     True\n",
       "186    False\n",
       "187    False\n",
       "188    False\n",
       "189    False\n",
       "190    False\n",
       "191    False\n",
       "192    False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# to reiterate how the filter works:\n",
    "drinks.wine_servings > drinks.beer_servings # is a series of T and F"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>beer_servings</th>\n",
       "      <th>spirit_servings</th>\n",
       "      <th>wine_servings</th>\n",
       "      <th>total_litres_of_pure_alcohol</th>\n",
       "      <th>continent</th>\n",
       "      <th>total_servings</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>98 </th>\n",
       "      <td>      Lithuania</td>\n",
       "      <td> 343</td>\n",
       "      <td> 244</td>\n",
       "      <td>  56</td>\n",
       "      <td> 12.9</td>\n",
       "      <td> EU</td>\n",
       "      <td> 643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>         Poland</td>\n",
       "      <td> 343</td>\n",
       "      <td> 215</td>\n",
       "      <td>  56</td>\n",
       "      <td> 10.9</td>\n",
       "      <td> EU</td>\n",
       "      <td> 614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65 </th>\n",
       "      <td>        Germany</td>\n",
       "      <td> 346</td>\n",
       "      <td> 117</td>\n",
       "      <td> 175</td>\n",
       "      <td> 11.3</td>\n",
       "      <td> EU</td>\n",
       "      <td> 638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62 </th>\n",
       "      <td>          Gabon</td>\n",
       "      <td> 347</td>\n",
       "      <td>  98</td>\n",
       "      <td>  59</td>\n",
       "      <td>  8.9</td>\n",
       "      <td> AF</td>\n",
       "      <td> 504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45 </th>\n",
       "      <td> Czech Republic</td>\n",
       "      <td> 361</td>\n",
       "      <td> 170</td>\n",
       "      <td> 134</td>\n",
       "      <td> 11.8</td>\n",
       "      <td> EU</td>\n",
       "      <td> 665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>        Namibia</td>\n",
       "      <td> 376</td>\n",
       "      <td>   3</td>\n",
       "      <td>   1</td>\n",
       "      <td>  6.8</td>\n",
       "      <td> AF</td>\n",
       "      <td> 380</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            country  beer_servings  spirit_servings  wine_servings  \\\n",
       "98        Lithuania            343              244             56   \n",
       "135          Poland            343              215             56   \n",
       "65          Germany            346              117            175   \n",
       "62            Gabon            347               98             59   \n",
       "45   Czech Republic            361              170            134   \n",
       "117         Namibia            376                3              1   \n",
       "\n",
       "     total_litres_of_pure_alcohol continent  total_servings  \n",
       "98                           12.9        EU             643  \n",
       "135                          10.9        EU             614  \n",
       "65                           11.3        EU             638  \n",
       "62                            8.9        AF             504  \n",
       "45                           11.8        EU             665  \n",
       "117                           6.8        AF             380  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# last 5 elements of the dataframe sorted by beer servings\n",
    "drinks.sort_index(by='beer_servings').tail(6)\n",
    "# note tail took in an optional input of 6, showing us the last 6 rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Quiz\n",
    "\n",
    "# 1. List countries that drink more spirits than beer on average\n",
    "\n",
    "# 2. What are the top three beer drinking countries?\n",
    "\n",
    "# 3. What are the top three beer drinking countries in Europe?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Answers below don't peak!\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1                           Albania\n",
       "5                 Antigua & Barbuda\n",
       "7                           Armenia\n",
       "10                       Azerbaijan\n",
       "11                          Bahamas\n",
       "12                          Bahrain\n",
       "14                         Barbados\n",
       "15                          Belarus\n",
       "21               Bosnia-Herzegovina\n",
       "25                         Bulgaria\n",
       "30                         Cambodia\n",
       "36                            China\n",
       "38                          Comoros\n",
       "40                     Cook Islands\n",
       "43                             Cuba\n",
       "49                         Djibouti\n",
       "50                         Dominica\n",
       "54                      El Salvador\n",
       "61                           France\n",
       "64                          Georgia\n",
       "68                          Grenada\n",
       "69                        Guatemala\n",
       "71                    Guinea-Bissau\n",
       "72                           Guyana\n",
       "73                            Haiti\n",
       "74                         Honduras\n",
       "77                            India\n",
       "82                           Israel\n",
       "84                          Jamaica\n",
       "85                            Japan\n",
       "                   ...             \n",
       "89                         Kiribati\n",
       "91                       Kyrgyzstan\n",
       "94                          Lebanon\n",
       "96                          Liberia\n",
       "101                          Malawi\n",
       "112                        Mongolia\n",
       "113                      Montenegro\n",
       "119                           Nepal\n",
       "122                       Nicaragua\n",
       "125                            Niue\n",
       "134                     Philippines\n",
       "137                           Qatar\n",
       "139                         Moldova\n",
       "141              Russian Federation\n",
       "143               St. Kitts & Nevis\n",
       "144                       St. Lucia\n",
       "145    St. Vincent & the Grenadines\n",
       "149                    Saudi Arabia\n",
       "155                        Slovakia\n",
       "161                       Sri Lanka\n",
       "162                           Sudan\n",
       "163                        Suriname\n",
       "167                           Syria\n",
       "168                      Tajikistan\n",
       "169                        Thailand\n",
       "177                    Turkmenistan\n",
       "178                          Tuvalu\n",
       "180                         Ukraine\n",
       "181            United Arab Emirates\n",
       "186                      Uzbekistan\n",
       "Name: country, dtype: object"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1. \n",
    "drinks['country'][drinks.spirit_servings>drinks.beer_servings]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/sinanozdemir/anaconda/envs/sfdat26-env/lib/python2.7/site-packages/ipykernel/__main__.py:2: FutureWarning: by argument to sort_index is deprecated, pls use .sort_values(by=...)\n",
      "  from ipykernel import kernelapp as app\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>beer_servings</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>Gabon</td>\n",
       "      <td>347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>Czech Republic</td>\n",
       "      <td>361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>Namibia</td>\n",
       "      <td>376</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            country  beer_servings\n",
       "62            Gabon            347\n",
       "45   Czech Republic            361\n",
       "117         Namibia            376"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2. \n",
    "drinks[['country', 'beer_servings']].sort_index(by='beer_servings').tail(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/sinanozdemir/anaconda/envs/sfdat26-env/lib/python2.7/site-packages/ipykernel/__main__.py:2: FutureWarning: by argument to sort_index is deprecated, pls use .sort_values(by=...)\n",
      "  from ipykernel import kernelapp as app\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>beer_servings</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>Czech Republic</td>\n",
       "      <td>361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>Germany</td>\n",
       "      <td>346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>Poland</td>\n",
       "      <td>343</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            country  beer_servings\n",
       "45   Czech Republic            361\n",
       "65          Germany            346\n",
       "135          Poland            343"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 3. \n",
    "drinks[drinks.continent=='EU'][['country', 'beer_servings']].sort_index(by='beer_servings', ascending=False).head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "145.434782609\n"
     ]
    }
   ],
   "source": [
    "# average North American beer consumption\n",
    "'''\n",
    "Note the procedure:\n",
    "drinks                                          Dataframe\n",
    "drinks.beer_servings                            one column (Series)\n",
    "drinks.beer_servings[drinks.continent=='NA']    logical filtering\n",
    "drinks.beer_servings[drinks.continent=='NA'].mean() mean of that filtered column\n",
    "'''\n",
    "\n",
    "print drinks.beer_servings[drinks.continent=='NA'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# average European beer consumption\n",
    "drinks.beer_servings[drinks.continent=='EU'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "**Split Apply Combine**\n",
    "\n",
    "<img src=\"https://www.safaribooksonline.com/library/view/learning-pandas/9781783985128/graphics/5128OS_09_01.jpg\">\n",
    "\n",
    "Pandas uses the \"groupby\" command to split apply and combine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "continent\n",
       "AF     61.471698\n",
       "AS     37.045455\n",
       "EU    193.777778\n",
       "NA    145.434783\n",
       "OC     89.687500\n",
       "SA    175.083333\n",
       "Name: beer_servings, dtype: float64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# for each continent, calculate mean beer servings\n",
    "drinks.groupby('continent').beer_servings.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AF    53\n",
       "EU    45\n",
       "AS    44\n",
       "NA    23\n",
       "OC    16\n",
       "SA    12\n",
       "Name: continent, dtype: int64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# for each continent, count number of occurrences\n",
    "drinks.groupby('continent').continent.count()\n",
    "drinks.continent.value_counts() #same thing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "continent\n",
       "AF     94.075472\n",
       "AS    106.954545\n",
       "EU    468.555556\n",
       "NA    335.695652\n",
       "OC    183.750000\n",
       "SA    352.250000\n",
       "Name: total_servings, dtype: float64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We can apply any function using .apply\n",
    "drinks.groupby('continent').total_servings.apply(lambda x: x.mean())    # mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# on lambda functions:\n",
    "\n",
    "'''\n",
    "Lamda Functions are ananonymous functions\n",
    "\n",
    "f = lambda x: x + 1   IS THE SAME AS \n",
    "def f(x): return x + 1\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "continent\n",
       "AF    114.040622\n",
       "AS    132.630446\n",
       "EU    176.921857\n",
       "NA    134.437696\n",
       "OC    175.450848\n",
       "SA     81.923606\n",
       "Name: total_servings, dtype: float64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# note x here is an entire series\n",
    "drinks.groupby('continent').total_servings.apply(lambda x: x.std())     # standard deviation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "continent\n",
      "AF      0\n",
      "AS      0\n",
      "EU      0\n",
      "NA    123\n",
      "OC      0\n",
      "SA    216\n",
      "Name: total_servings, dtype: int64\n",
      "continent\n",
      "AF    504\n",
      "AS    646\n",
      "EU    695\n",
      "NA    665\n",
      "OC    545\n",
      "SA    439\n",
      "Name: total_servings, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# for each continent, calculate the min, max, and range for total servings\n",
    "print drinks.groupby('continent').total_servings.min()\n",
    "print drinks.groupby('continent').total_servings.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "continent\n",
       "AF    504\n",
       "AS    646\n",
       "EU    695\n",
       "NA    542\n",
       "OC    545\n",
       "SA    223\n",
       "Name: total_servings, dtype: int64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# What does this do?\n",
    "drinks.groupby('continent').total_servings.apply(lambda x: x.max() - x.min())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x11296bc50>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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bP220uAzpTN5a+tfP1jKkE0On15DOdIH/ZeAE29dNWL4f8H7bh/e90t9tJ4E/WWvpXz9b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\nEAn8iIhC/H/nV+EatLvFiAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112053610>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "'''\n",
    "Plotting\n",
    "'''\n",
    "\n",
    "# bar plot of number of countries in each continent\n",
    "drinks.continent.value_counts().plot(kind='bar', title='Countries per Continent')\n",
    "plt.xlabel('Continent')\n",
    "plt.ylabel('Count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x112053a10>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x112118c90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# bar plot of average number of beer servings by continent\n",
    "drinks.groupby('continent').beer_servings.mean().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1120acf50>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1120be110>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# histogram of beer servings\n",
    "drinks.beer_servings.hist(bins=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x117fe4650>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1183a9050>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x118427750>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x118496a10>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x118647d90>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x11844c450>]], dtype=object)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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0SqASbOF7hMglSfgMIRJEL7hBVlGnu2yW6rLpccadCcAg8GpJv5R0TtOxG4CP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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1180c1510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# grouped histogram of beer servings\n",
    "drinks.beer_servings.hist(by=drinks.continent)\n",
    "\n",
    "# stop and think, does this make sense\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x118992310>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x118b0fc50>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x118b4e8d0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x118bb4b90>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x118c378d0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x118be26d0>]], dtype=object)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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cSdcCNWAfSU8CFwPLJS0FdpDNuHlG29mamVnPFBneeQGYCzwyeWUsSTcB1wFL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\ngMiiD48AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1189a3b50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# same charts with the same scale for x and y axis\n",
    "drinks.beer_servings.hist(by=drinks.continent, sharex=True, sharey=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x118f50fd0>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ZSTX+VcuFdyqNPHGj6AD6Io19mUo9G0UHsJOkGv8q5cKdc/WyZEnREewsqZx/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\nziXIG3/nnEuQN/7OOZeg/w9o59RUDHomSgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x118d7c210>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# boxplot of beer servings by continent\n",
    "drinks.boxplot(column='beer_servings', by='continent')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11998c310>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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jTJ8+3XcJJNuFUjAYZMqUKSxZckXRz9cY62cyHy9I8o0ljyxz8+EfesU60hfg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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x119ef4850>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# scatterplot of beer servings versus wine servings\n",
    "drinks.plot(x='beer_servings', y='wine_servings', kind='scatter', alpha=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['b', 'r', 'b', 'r', 'b', 'b', 'b', 'r', 'b', 'r', 'r', 'b', 'b',\n",
       "       'b', 'b', 'r', 'r', 'b', 'b', 'b', 'b', 'r', 'b', 'b', 'b', 'r',\n",
       "       'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b',\n",
       "       'b', 'b', 'b', 'r', 'b', 'r', 'r', 'b', 'b', 'r', 'b', 'b', 'b',\n",
       "       'b', 'b', 'b', 'b', 'b', 'r', 'b', 'b', 'r', 'r', 'b', 'b', 'r',\n",
       "       'r', 'b', 'r', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'r', 'r', 'b',\n",
       "       'b', 'b', 'b', 'r', 'b', 'r', 'b', 'b', 'b', 'b', 'b', 'b', 'b',\n",
       "       'b', 'b', 'r', 'b', 'b', 'b', 'b', 'r', 'r', 'b', 'b', 'b', 'b',\n",
       "       'b', 'r', 'b', 'b', 'b', 'b', 'b', 'r', 'b', 'r', 'b', 'b', 'b',\n",
       "       'b', 'b', 'b', 'r', 'b', 'b', 'b', 'b', 'b', 'r', 'b', 'b', 'b',\n",
       "       'b', 'b', 'b', 'b', 'b', 'r', 'r', 'b', 'b', 'r', 'r', 'b', 'b',\n",
       "       'b', 'b', 'b', 'b', 'r', 'b', 'b', 'b', 'r', 'b', 'b', 'b', 'r',\n",
       "       'r', 'b', 'b', 'b', 'r', 'b', 'b', 'b', 'b', 'r', 'r', 'b', 'b',\n",
       "       'b', 'r', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'r', 'b',\n",
       "       'r', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b'], \n",
       "      dtype='|S1')"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# same scatterplot, except all European countries are colored red\n",
    "colors = np.where(drinks.continent=='EU', 'r', 'b')\n",
    "colors      # is a series of 'r' and 'b' that \n",
    "            # correspond to countries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['a', 'b', 'b'], \n",
       "      dtype='|S1')"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "np.where is like a condensed if statement\n",
    "it's like a list comprehension for pandas!\n",
    "\n",
    "it will loop through drinks.continent which is a series\n",
    "for each element:\n",
    "    if it is \"EU\":\n",
    "        make it 'r'\n",
    "    else:\n",
    "        make it 'b'\n",
    "\n",
    "More in depth:\n",
    "    drinks.continent=='EU' is a logical statement\n",
    "        It will return a bunch of Trues and Falses\n",
    "        and np.where makes the True ones 'r' and\n",
    "        the False ones 'b'\n",
    "        \n",
    "        Recall logical filtering!\n",
    "\n",
    "'''\n",
    "\n",
    "# Side quest\n",
    "np.where([True, False, False], 'a', 'b')\n",
    "\n",
    "# 10 gold coins earned"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11a96e210>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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QlDrjBXK01ftAM/6++rgW2AVUAnaKyIiiBlEcmjxKt8zMTNavX4/dbufss8/GYtH1x04m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\nJYEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a97b1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "drinks.plot(x='beer_servings', y='wine_servings', kind='scatter', c=colors)\n",
    "# passing colors into the chart makes the european dots, red!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\n----UFO data----\\nScraped from: http://www.nuforc.org/webreports.html\\n'"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "----UFO data----\n",
    "Scraped from: http://www.nuforc.org/webreports.html\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ufo = pd.read_csv('../data/ufo.csv')   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>City</th>\n",
       "      <th>Colors Reported</th>\n",
       "      <th>Shape Reported</th>\n",
       "      <th>State</th>\n",
       "      <th>Time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ithaca</td>\n",
       "      <td>NaN</td>\n",
       "      <td>TRIANGLE</td>\n",
       "      <td>NY</td>\n",
       "      <td>6/1/1930 22:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Willingboro</td>\n",
       "      <td>NaN</td>\n",
       "      <td>OTHER</td>\n",
       "      <td>NJ</td>\n",
       "      <td>6/30/1930 20:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Holyoke</td>\n",
       "      <td>NaN</td>\n",
       "      <td>OVAL</td>\n",
       "      <td>CO</td>\n",
       "      <td>2/15/1931 14:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Abilene</td>\n",
       "      <td>NaN</td>\n",
       "      <td>DISK</td>\n",
       "      <td>KS</td>\n",
       "      <td>6/1/1931 13:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>New York Worlds Fair</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LIGHT</td>\n",
       "      <td>NY</td>\n",
       "      <td>4/18/1933 19:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   City Colors Reported Shape Reported State             Time\n",
       "0                Ithaca             NaN       TRIANGLE    NY   6/1/1930 22:00\n",
       "1           Willingboro             NaN          OTHER    NJ  6/30/1930 20:00\n",
       "2               Holyoke             NaN           OVAL    CO  2/15/1931 14:00\n",
       "3               Abilene             NaN           DISK    KS   6/1/1931 13:00\n",
       "4  New York Worlds Fair             NaN          LIGHT    NY  4/18/1933 19:00"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ufo.head()              # Look at the top 5 observations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>City</th>\n",
       "      <th>Colors Reported</th>\n",
       "      <th>Shape Reported</th>\n",
       "      <th>State</th>\n",
       "      <th>Time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>80538</th>\n",
       "      <td>Neligh</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CIRCLE</td>\n",
       "      <td>NE</td>\n",
       "      <td>9/4/2014 23:20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80539</th>\n",
       "      <td>Uhrichsville</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LIGHT</td>\n",
       "      <td>OH</td>\n",
       "      <td>9/5/2014 1:14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80540</th>\n",
       "      <td>Tucson</td>\n",
       "      <td>RED BLUE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>AZ</td>\n",
       "      <td>9/5/2014 2:40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80541</th>\n",
       "      <td>Orland park</td>\n",
       "      <td>RED</td>\n",
       "      <td>LIGHT</td>\n",
       "      <td>IL</td>\n",
       "      <td>9/5/2014 3:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80542</th>\n",
       "      <td>Loughman</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LIGHT</td>\n",
       "      <td>FL</td>\n",
       "      <td>9/5/2014 5:30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               City Colors Reported Shape Reported State            Time\n",
       "80538        Neligh             NaN         CIRCLE    NE  9/4/2014 23:20\n",
       "80539  Uhrichsville             NaN          LIGHT    OH   9/5/2014 1:14\n",
       "80540        Tucson        RED BLUE            NaN    AZ   9/5/2014 2:40\n",
       "80541   Orland park             RED          LIGHT    IL   9/5/2014 3:43\n",
       "80542      Loughman             NaN          LIGHT    FL   9/5/2014 5:30"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ufo.tail()              # Look at the bottom 5 observations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ufo['Location'] = ufo['City'] + ', ' + ufo['State']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>City</th>\n",
       "      <th>Colors Reported</th>\n",
       "      <th>Shape Reported</th>\n",
       "      <th>State</th>\n",
       "      <th>Time</th>\n",
       "      <th>Location</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ithaca</td>\n",
       "      <td>NaN</td>\n",
       "      <td>TRIANGLE</td>\n",
       "      <td>NY</td>\n",
       "      <td>6/1/1930 22:00</td>\n",
       "      <td>Ithaca, NY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Willingboro</td>\n",
       "      <td>NaN</td>\n",
       "      <td>OTHER</td>\n",
       "      <td>NJ</td>\n",
       "      <td>6/30/1930 20:00</td>\n",
       "      <td>Willingboro, NJ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Holyoke</td>\n",
       "      <td>NaN</td>\n",
       "      <td>OVAL</td>\n",
       "      <td>CO</td>\n",
       "      <td>2/15/1931 14:00</td>\n",
       "      <td>Holyoke, CO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Abilene</td>\n",
       "      <td>NaN</td>\n",
       "      <td>DISK</td>\n",
       "      <td>KS</td>\n",
       "      <td>6/1/1931 13:00</td>\n",
       "      <td>Abilene, KS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>New York Worlds Fair</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LIGHT</td>\n",
       "      <td>NY</td>\n",
       "      <td>4/18/1933 19:00</td>\n",
       "      <td>New York Worlds Fair, NY</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   City Colors Reported Shape Reported State             Time  \\\n",
       "0                Ithaca             NaN       TRIANGLE    NY   6/1/1930 22:00   \n",
       "1           Willingboro             NaN          OTHER    NJ  6/30/1930 20:00   \n",
       "2               Holyoke             NaN           OVAL    CO  2/15/1931 14:00   \n",
       "3               Abilene             NaN           DISK    KS   6/1/1931 13:00   \n",
       "4  New York Worlds Fair             NaN          LIGHT    NY  4/18/1933 19:00   \n",
       "\n",
       "                   Location  \n",
       "0                Ithaca, NY  \n",
       "1           Willingboro, NJ  \n",
       "2               Holyoke, CO  \n",
       "3               Abilene, KS  \n",
       "4  New York Worlds Fair, NY  "
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ufo.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# rename some columns\n",
    "ufo.rename(columns={'Colors Reported':'Colors', 'Shape Reported':'Shape'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>City</th>\n",
       "      <th>Colors</th>\n",
       "      <th>Shape</th>\n",
       "      <th>State</th>\n",
       "      <th>Time</th>\n",
       "      <th>Location</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ithaca</td>\n",
       "      <td>NaN</td>\n",
       "      <td>TRIANGLE</td>\n",
       "      <td>NY</td>\n",
       "      <td>6/1/1930 22:00</td>\n",
       "      <td>Ithaca, NY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Willingboro</td>\n",
       "      <td>NaN</td>\n",
       "      <td>OTHER</td>\n",
       "      <td>NJ</td>\n",
       "      <td>6/30/1930 20:00</td>\n",
       "      <td>Willingboro, NJ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Holyoke</td>\n",
       "      <td>NaN</td>\n",
       "      <td>OVAL</td>\n",
       "      <td>CO</td>\n",
       "      <td>2/15/1931 14:00</td>\n",
       "      <td>Holyoke, CO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Abilene</td>\n",
       "      <td>NaN</td>\n",
       "      <td>DISK</td>\n",
       "      <td>KS</td>\n",
       "      <td>6/1/1931 13:00</td>\n",
       "      <td>Abilene, KS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>New York Worlds Fair</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LIGHT</td>\n",
       "      <td>NY</td>\n",
       "      <td>4/18/1933 19:00</td>\n",
       "      <td>New York Worlds Fair, NY</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   City Colors     Shape State             Time  \\\n",
       "0                Ithaca    NaN  TRIANGLE    NY   6/1/1930 22:00   \n",
       "1           Willingboro    NaN     OTHER    NJ  6/30/1930 20:00   \n",
       "2               Holyoke    NaN      OVAL    CO  2/15/1931 14:00   \n",
       "3               Abilene    NaN      DISK    KS   6/1/1931 13:00   \n",
       "4  New York Worlds Fair    NaN     LIGHT    NY  4/18/1933 19:00   \n",
       "\n",
       "                   Location  \n",
       "0                Ithaca, NY  \n",
       "1           Willingboro, NJ  \n",
       "2               Holyoke, CO  \n",
       "3               Abilene, KS  \n",
       "4  New York Worlds Fair, NY  "
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ufo.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "del ufo['City']                  # delete a column (permanently)\n",
    "del ufo['State']                 # delete a column (permanently)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LIGHT        16332\n",
       "TRIANGLE      7816\n",
       "CIRCLE        7725\n",
       "FIREBALL      6249\n",
       "OTHER         5506\n",
       "SPHERE        5231\n",
       "DISK          5226\n",
       "OVAL          3721\n",
       "FORMATION     2405\n",
       "CIGAR         1983\n",
       "VARIOUS       1957\n",
       "FLASH         1329\n",
       "RECTANGLE     1295\n",
       "CYLINDER      1252\n",
       "DIAMOND       1152\n",
       "CHEVRON        940\n",
       "EGG            733\n",
       "TEARDROP       723\n",
       "CONE           310\n",
       "CROSS          241\n",
       "DELTA            7\n",
       "CRESCENT         2\n",
       "ROUND            2\n",
       "HEXAGON          1\n",
       "FLARE            1\n",
       "PYRAMID          1\n",
       "DOME             1\n",
       "Name: Shape, dtype: int64"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ufo.Shape.value_counts()                # excludes missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LIGHT        16332\n",
       "NaN           8402\n",
       "TRIANGLE      7816\n",
       "CIRCLE        7725\n",
       "FIREBALL      6249\n",
       "OTHER         5506\n",
       "SPHERE        5231\n",
       "DISK          5226\n",
       "OVAL          3721\n",
       "FORMATION     2405\n",
       "CIGAR         1983\n",
       "VARIOUS       1957\n",
       "FLASH         1329\n",
       "RECTANGLE     1295\n",
       "CYLINDER      1252\n",
       "DIAMOND       1152\n",
       "CHEVRON        940\n",
       "EGG            733\n",
       "TEARDROP       723\n",
       "CONE           310\n",
       "CROSS          241\n",
       "DELTA            7\n",
       "CRESCENT         2\n",
       "ROUND            2\n",
       "HEXAGON          1\n",
       "PYRAMID          1\n",
       "DOME             1\n",
       "FLARE            1\n",
       "Name: Shape, dtype: int64"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ufo.Shape.value_counts(dropna=False)    # includes missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8402"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ufo.Shape.isnull().sum() # count the missing values in the shape column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Colors      63509\n",
       "Shape        8402\n",
       "Time            0\n",
       "Location       47\n",
       "dtype: int64"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ufo.isnull().sum()       # returns a count of missing values in all columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Colors</th>\n",
       "      <th>Shape</th>\n",
       "      <th>Time</th>\n",
       "      <th>Location</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>RED</td>\n",
       "      <td>SPHERE</td>\n",
       "      <td>6/30/1939 20:00</td>\n",
       "      <td>Belton, SC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>RED</td>\n",
       "      <td>OTHER</td>\n",
       "      <td>4/30/1943 23:00</td>\n",
       "      <td>Bering Sea, AK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>RED</td>\n",
       "      <td>FORMATION</td>\n",
       "      <td>7/10/1945 1:30</td>\n",
       "      <td>Portsmouth, VA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>GREEN</td>\n",
       "      <td>SPHERE</td>\n",
       "      <td>6/30/1946 19:00</td>\n",
       "      <td>Blairsden, CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>BLUE</td>\n",
       "      <td>CHEVRON</td>\n",
       "      <td>7/15/1947 21:00</td>\n",
       "      <td>San Jose, CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>BLUE</td>\n",
       "      <td>DISK</td>\n",
       "      <td>8/8/1947 22:00</td>\n",
       "      <td>Modesto, CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>RED</td>\n",
       "      <td>SPHERE</td>\n",
       "      <td>5/10/1948 19:00</td>\n",
       "      <td>Scipio, IN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>ORANGE</td>\n",
       "      <td>CIRCLE</td>\n",
       "      <td>8/15/1949 22:00</td>\n",
       "      <td>Tarrant City, AL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>GREEN</td>\n",
       "      <td>DISK</td>\n",
       "      <td>6/10/1950 0:00</td>\n",
       "      <td>Napa, CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>ORANGE</td>\n",
       "      <td>CIGAR</td>\n",
       "      <td>7/2/1950 13:00</td>\n",
       "      <td>Coeur d'Alene, ID</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>BLUE</td>\n",
       "      <td>DISK</td>\n",
       "      <td>4/15/1951 0:30</td>\n",
       "      <td>Irving, KS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>GREEN</td>\n",
       "      <td>DISK</td>\n",
       "      <td>6/15/1951 20:30</td>\n",
       "      <td>Greenville, MS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>163</th>\n",
       "      <td>GREEN</td>\n",
       "      <td>SPHERE</td>\n",
       "      <td>7/3/1951 12:00</td>\n",
       "      <td>Green River, WY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>BLUE</td>\n",
       "      <td>DISK</td>\n",
       "      <td>7/10/1951 23:30</td>\n",
       "      <td>Provo, UT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>ORANGE</td>\n",
       "      <td>TRIANGLE</td>\n",
       "      <td>4/15/1952 16:00</td>\n",
       "      <td>Greenville, TX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>RED</td>\n",
       "      <td>FIREBALL</td>\n",
       "      <td>6/1/1952 22:00</td>\n",
       "      <td>Norfolk, VA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>GREEN</td>\n",
       "      <td>OVAL</td>\n",
       "      <td>7/13/1952 21:00</td>\n",
       "      <td>Arlington, VA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>226</th>\n",
       "      <td>RED</td>\n",
       "      <td>SPHERE</td>\n",
       "      <td>4/1/1953 18:00</td>\n",
       "      <td>Cambridge, MA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>229</th>\n",
       "      <td>YELLOW</td>\n",
       "      <td>FIREBALL</td>\n",
       "      <td>4/15/1953 16:00</td>\n",
       "      <td>Midwest City, OK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>238</th>\n",
       "      <td>RED</td>\n",
       "      <td>FIREBALL</td>\n",
       "      <td>6/30/1953 0:00</td>\n",
       "      <td>Cleveland, OH</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>241</th>\n",
       "      <td>BLUE</td>\n",
       "      <td>DISK</td>\n",
       "      <td>7/4/1953 14:00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>ORANGE</td>\n",
       "      <td>OTHER</td>\n",
       "      <td>8/15/1953 19:00</td>\n",
       "      <td>Artesia, NM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>256</th>\n",
       "      <td>GREEN</td>\n",
       "      <td>DISK</td>\n",
       "      <td>11/21/1953 22:30</td>\n",
       "      <td>Pendleton, IN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>288</th>\n",
       "      <td>RED</td>\n",
       "      <td>OVAL</td>\n",
       "      <td>7/1/1954 21:00</td>\n",
       "      <td>St. Louis Airport, MO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>289</th>\n",
       "      <td>RED</td>\n",
       "      <td>CIRCLE</td>\n",
       "      <td>7/1/1954 22:00</td>\n",
       "      <td>Los Angeles, CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>304</th>\n",
       "      <td>RED</td>\n",
       "      <td>DISK</td>\n",
       "      <td>9/9/1954 12:30</td>\n",
       "      <td>Beaumont, TX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>311</th>\n",
       "      <td>ORANGE</td>\n",
       "      <td>CIRCLE</td>\n",
       "      <td>12/15/1954 23:10</td>\n",
       "      <td>Red Bank, NJ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>314</th>\n",
       "      <td>YELLOW</td>\n",
       "      <td>EGG</td>\n",
       "      <td>5/1/1955 15:00</td>\n",
       "      <td>Holbrook, MA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>323</th>\n",
       "      <td>ORANGE</td>\n",
       "      <td>CIRCLE</td>\n",
       "      <td>6/15/1955 0:00</td>\n",
       "      <td>Terre Haute, IN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>354</th>\n",
       "      <td>ORANGE</td>\n",
       "      <td>CYLINDER</td>\n",
       "      <td>6/1/1956 20:00</td>\n",
       "      <td>Memphis, TN</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>80429</th>\n",
       "      <td>RED</td>\n",
       "      <td>CIRCLE</td>\n",
       "      <td>8/31/2014 20:50</td>\n",
       "      <td>Bettendorf, IA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80430</th>\n",
       "      <td>RED</td>\n",
       "      <td>SPHERE</td>\n",
       "      <td>8/31/2014 21:00</td>\n",
       "      <td>Pompton Lakes, NJ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80436</th>\n",
       "      <td>ORANGE</td>\n",
       "      <td>SPHERE</td>\n",
       "      <td>8/31/2014 21:45</td>\n",
       "      <td>Montrose, MI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80438</th>\n",
       "      <td>ORANGE</td>\n",
       "      <td>CYLINDER</td>\n",
       "      <td>8/31/2014 22:00</td>\n",
       "      <td>Brunswick, OH</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80441</th>\n",
       "      <td>RED</td>\n",
       "      <td>TEARDROP</td>\n",
       "      <td>8/31/2014 22:30</td>\n",
       "      <td>Scribner, NE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80453</th>\n",
       "      <td>RED ORANGE GREEN BLUE</td>\n",
       "      <td>TRIANGLE</td>\n",
       "      <td>9/1/2014 11:56</td>\n",
       "      <td>Minersville, PA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80457</th>\n",
       "      <td>ORANGE</td>\n",
       "      <td>CIRCLE</td>\n",
       "      <td>9/1/2014 16:05</td>\n",
       "      <td>Syracuse, NY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80458</th>\n",
       "      <td>ORANGE</td>\n",
       "      <td>FIREBALL</td>\n",
       "      <td>9/1/2014 20:00</td>\n",
       "      <td>Parma, OH</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80459</th>\n",
       "      <td>ORANGE</td>\n",
       "      <td>CIRCLE</td>\n",
       "      <td>9/1/2014 20:00</td>\n",
       "      <td>Roswell, NM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80465</th>\n",
       "      <td>GREEN</td>\n",
       "      <td>VARIOUS</td>\n",
       "      <td>9/1/2014 21:30</td>\n",
       "      <td>South Londonderry, VT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80467</th>\n",
       "      <td>ORANGE</td>\n",
       "      <td>LIGHT</td>\n",
       "      <td>9/1/2014 22:00</td>\n",
       "      <td>Gold Hill, NC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80469</th>\n",
       "      <td>ORANGE</td>\n",
       "      <td>CIRCLE</td>\n",
       "      <td>9/1/2014 22:19</td>\n",
       "      <td>Milford, DE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80473</th>\n",
       "      <td>BLUE</td>\n",
       "      <td>TRIANGLE</td>\n",
       "      <td>9/2/2014 5:50</td>\n",
       "      <td>Trevose, PA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80482</th>\n",
       "      <td>ORANGE</td>\n",
       "      <td>FIREBALL</td>\n",
       "      <td>9/2/2014 22:30</td>\n",
       "      <td>Spearfish, SD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80489</th>\n",
       "      <td>ORANGE</td>\n",
       "      <td>SPHERE</td>\n",
       "      <td>9/3/2014 5:00</td>\n",
       "      <td>Pleasure Island, NC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80494</th>\n",
       "      <td>RED GREEN</td>\n",
       "      <td>CIRCLE</td>\n",
       "      <td>9/3/2014 19:00</td>\n",
       "      <td>Boca Raton, FL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80495</th>\n",
       "      <td>YELLOW</td>\n",
       "      <td>FLASH</td>\n",
       "      <td>9/3/2014 19:00</td>\n",
       "      <td>Kingston, NH</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80496</th>\n",
       "      <td>RED</td>\n",
       "      <td>FLASH</td>\n",
       "      <td>9/3/2014 19:00</td>\n",
       "      <td>New York City, NY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80500</th>\n",
       "      <td>RED BLUE</td>\n",
       "      <td>LIGHT</td>\n",
       "      <td>9/3/2014 21:00</td>\n",
       "      <td>Johnsburg, IL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80503</th>\n",
       "      <td>ORANGE</td>\n",
       "      <td>VARIOUS</td>\n",
       "      <td>9/3/2014 21:15</td>\n",
       "      <td>Bethel Park, PA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80505</th>\n",
       "      <td>YELLOW</td>\n",
       "      <td>TRIANGLE</td>\n",
       "      <td>9/3/2014 21:30</td>\n",
       "      <td>Clarence, NY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80509</th>\n",
       "      <td>RED BLUE</td>\n",
       "      <td>CIRCLE</td>\n",
       "      <td>9/3/2014 23:30</td>\n",
       "      <td>Chesapeake Beach, MD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80510</th>\n",
       "      <td>RED</td>\n",
       "      <td>OVAL</td>\n",
       "      <td>9/4/2014 0:00</td>\n",
       "      <td>Rogersville, TN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80519</th>\n",
       "      <td>RED</td>\n",
       "      <td>CIRCLE</td>\n",
       "      <td>9/4/2014 20:20</td>\n",
       "      <td>Glen Ellyn, IL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80522</th>\n",
       "      <td>ORANGE</td>\n",
       "      <td>LIGHT</td>\n",
       "      <td>9/4/2014 20:30</td>\n",
       "      <td>Lawrence, MA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80524</th>\n",
       "      <td>RED</td>\n",
       "      <td>LIGHT</td>\n",
       "      <td>9/4/2014 21:10</td>\n",
       "      <td>Olympia, WA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80525</th>\n",
       "      <td>BLUE</td>\n",
       "      <td>LIGHT</td>\n",
       "      <td>9/4/2014 21:11</td>\n",
       "      <td>Iowa City, IA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80528</th>\n",
       "      <td>RED</td>\n",
       "      <td>TRIANGLE</td>\n",
       "      <td>9/4/2014 21:30</td>\n",
       "      <td>North Royalton, OH</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80536</th>\n",
       "      <td>RED</td>\n",
       "      <td>DISK</td>\n",
       "      <td>9/4/2014 23:00</td>\n",
       "      <td>Wyoming, PA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80541</th>\n",
       "      <td>RED</td>\n",
       "      <td>LIGHT</td>\n",
       "      <td>9/5/2014 3:43</td>\n",
       "      <td>Orland park, IL</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>15516 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Colors      Shape              Time  \\\n",
       "12                       RED     SPHERE   6/30/1939 20:00   \n",
       "19                       RED      OTHER   4/30/1943 23:00   \n",
       "36                       RED  FORMATION    7/10/1945 1:30   \n",
       "44                     GREEN     SPHERE   6/30/1946 19:00   \n",
       "82                      BLUE    CHEVRON   7/15/1947 21:00   \n",
       "84                      BLUE       DISK    8/8/1947 22:00   \n",
       "91                       RED     SPHERE   5/10/1948 19:00   \n",
       "111                   ORANGE     CIRCLE   8/15/1949 22:00   \n",
       "129                    GREEN       DISK    6/10/1950 0:00   \n",
       "138                   ORANGE      CIGAR    7/2/1950 13:00   \n",
       "152                     BLUE       DISK    4/15/1951 0:30   \n",
       "157                    GREEN       DISK   6/15/1951 20:30   \n",
       "163                    GREEN     SPHERE    7/3/1951 12:00   \n",
       "164                     BLUE       DISK   7/10/1951 23:30   \n",
       "174                   ORANGE   TRIANGLE   4/15/1952 16:00   \n",
       "178                      RED   FIREBALL    6/1/1952 22:00   \n",
       "202                    GREEN       OVAL   7/13/1952 21:00   \n",
       "226                      RED     SPHERE    4/1/1953 18:00   \n",
       "229                   YELLOW   FIREBALL   4/15/1953 16:00   \n",
       "238                      RED   FIREBALL    6/30/1953 0:00   \n",
       "241                     BLUE       DISK    7/4/1953 14:00   \n",
       "249                   ORANGE      OTHER   8/15/1953 19:00   \n",
       "256                    GREEN       DISK  11/21/1953 22:30   \n",
       "288                      RED       OVAL    7/1/1954 21:00   \n",
       "289                      RED     CIRCLE    7/1/1954 22:00   \n",
       "304                      RED       DISK    9/9/1954 12:30   \n",
       "311                   ORANGE     CIRCLE  12/15/1954 23:10   \n",
       "314                   YELLOW        EGG    5/1/1955 15:00   \n",
       "323                   ORANGE     CIRCLE    6/15/1955 0:00   \n",
       "354                   ORANGE   CYLINDER    6/1/1956 20:00   \n",
       "...                      ...        ...               ...   \n",
       "80429                    RED     CIRCLE   8/31/2014 20:50   \n",
       "80430                    RED     SPHERE   8/31/2014 21:00   \n",
       "80436                 ORANGE     SPHERE   8/31/2014 21:45   \n",
       "80438                 ORANGE   CYLINDER   8/31/2014 22:00   \n",
       "80441                    RED   TEARDROP   8/31/2014 22:30   \n",
       "80453  RED ORANGE GREEN BLUE   TRIANGLE    9/1/2014 11:56   \n",
       "80457                 ORANGE     CIRCLE    9/1/2014 16:05   \n",
       "80458                 ORANGE   FIREBALL    9/1/2014 20:00   \n",
       "80459                 ORANGE     CIRCLE    9/1/2014 20:00   \n",
       "80465                  GREEN    VARIOUS    9/1/2014 21:30   \n",
       "80467                 ORANGE      LIGHT    9/1/2014 22:00   \n",
       "80469                 ORANGE     CIRCLE    9/1/2014 22:19   \n",
       "80473                   BLUE   TRIANGLE     9/2/2014 5:50   \n",
       "80482                 ORANGE   FIREBALL    9/2/2014 22:30   \n",
       "80489                 ORANGE     SPHERE     9/3/2014 5:00   \n",
       "80494              RED GREEN     CIRCLE    9/3/2014 19:00   \n",
       "80495                 YELLOW      FLASH    9/3/2014 19:00   \n",
       "80496                    RED      FLASH    9/3/2014 19:00   \n",
       "80500               RED BLUE      LIGHT    9/3/2014 21:00   \n",
       "80503                 ORANGE    VARIOUS    9/3/2014 21:15   \n",
       "80505                 YELLOW   TRIANGLE    9/3/2014 21:30   \n",
       "80509               RED BLUE     CIRCLE    9/3/2014 23:30   \n",
       "80510                    RED       OVAL     9/4/2014 0:00   \n",
       "80519                    RED     CIRCLE    9/4/2014 20:20   \n",
       "80522                 ORANGE      LIGHT    9/4/2014 20:30   \n",
       "80524                    RED      LIGHT    9/4/2014 21:10   \n",
       "80525                   BLUE      LIGHT    9/4/2014 21:11   \n",
       "80528                    RED   TRIANGLE    9/4/2014 21:30   \n",
       "80536                    RED       DISK    9/4/2014 23:00   \n",
       "80541                    RED      LIGHT     9/5/2014 3:43   \n",
       "\n",
       "                    Location  \n",
       "12                Belton, SC  \n",
       "19            Bering Sea, AK  \n",
       "36            Portsmouth, VA  \n",
       "44             Blairsden, CA  \n",
       "82              San Jose, CA  \n",
       "84               Modesto, CA  \n",
       "91                Scipio, IN  \n",
       "111         Tarrant City, AL  \n",
       "129                 Napa, CA  \n",
       "138        Coeur d'Alene, ID  \n",
       "152               Irving, KS  \n",
       "157           Greenville, MS  \n",
       "163          Green River, WY  \n",
       "164                Provo, UT  \n",
       "174           Greenville, TX  \n",
       "178              Norfolk, VA  \n",
       "202            Arlington, VA  \n",
       "226            Cambridge, MA  \n",
       "229         Midwest City, OK  \n",
       "238            Cleveland, OH  \n",
       "241                      NaN  \n",
       "249              Artesia, NM  \n",
       "256            Pendleton, IN  \n",
       "288    St. Louis Airport, MO  \n",
       "289          Los Angeles, CA  \n",
       "304             Beaumont, TX  \n",
       "311             Red Bank, NJ  \n",
       "314             Holbrook, MA  \n",
       "323          Terre Haute, IN  \n",
       "354              Memphis, TN  \n",
       "...                      ...  \n",
       "80429         Bettendorf, IA  \n",
       "80430      Pompton Lakes, NJ  \n",
       "80436           Montrose, MI  \n",
       "80438          Brunswick, OH  \n",
       "80441           Scribner, NE  \n",
       "80453        Minersville, PA  \n",
       "80457           Syracuse, NY  \n",
       "80458              Parma, OH  \n",
       "80459            Roswell, NM  \n",
       "80465  South Londonderry, VT  \n",
       "80467          Gold Hill, NC  \n",
       "80469            Milford, DE  \n",
       "80473            Trevose, PA  \n",
       "80482          Spearfish, SD  \n",
       "80489    Pleasure Island, NC  \n",
       "80494         Boca Raton, FL  \n",
       "80495           Kingston, NH  \n",
       "80496      New York City, NY  \n",
       "80500          Johnsburg, IL  \n",
       "80503        Bethel Park, PA  \n",
       "80505           Clarence, NY  \n",
       "80509   Chesapeake Beach, MD  \n",
       "80510        Rogersville, TN  \n",
       "80519         Glen Ellyn, IL  \n",
       "80522           Lawrence, MA  \n",
       "80524            Olympia, WA  \n",
       "80525          Iowa City, IA  \n",
       "80528     North Royalton, OH  \n",
       "80536            Wyoming, PA  \n",
       "80541        Orland park, IL  \n",
       "\n",
       "[15516 rows x 4 columns]"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Shows how many rows has a not null shape AND a not null color\n",
    "ufo[(ufo.Shape.notnull()) & (ufo.Colors.notnull())]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(15510, 4)\n",
      "(80543, 4)\n"
     ]
    }
   ],
   "source": [
    "ufo.dropna()             # drop a row if ANY values are missing\n",
    "print ufo.dropna().shape\n",
    "ufo.dropna(how='all')    # drop a row only if ALL values are missing\n",
    "print ufo.dropna(how='all').shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Colors</th>\n",
       "      <th>Shape</th>\n",
       "      <th>Time</th>\n",
       "      <th>Location</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>TRIANGLE</td>\n",
       "      <td>6/1/1930 22:00</td>\n",
       "      <td>Ithaca, NY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>OTHER</td>\n",
       "      <td>6/30/1930 20:00</td>\n",
       "      <td>Willingboro, NJ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>OVAL</td>\n",
       "      <td>2/15/1931 14:00</td>\n",
       "      <td>Holyoke, CO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>DISK</td>\n",
       "      <td>6/1/1931 13:00</td>\n",
       "      <td>Abilene, KS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>LIGHT</td>\n",
       "      <td>4/18/1933 19:00</td>\n",
       "      <td>New York Worlds Fair, NY</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Colors     Shape             Time                  Location\n",
       "0    NaN  TRIANGLE   6/1/1930 22:00                Ithaca, NY\n",
       "1    NaN     OTHER  6/30/1930 20:00           Willingboro, NJ\n",
       "2    NaN      OVAL  2/15/1931 14:00               Holyoke, CO\n",
       "3    NaN      DISK   6/1/1931 13:00               Abilene, KS\n",
       "4    NaN     LIGHT  4/18/1933 19:00  New York Worlds Fair, NY"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ufo.head()                      # Without an inplace=True, the dataframe is unaffected!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ufo.Colors.fillna(value='Unknown', inplace=True) # Permanent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Colors</th>\n",
       "      <th>Shape</th>\n",
       "      <th>Time</th>\n",
       "      <th>Location</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Unknown</td>\n",
       "      <td>TRIANGLE</td>\n",
       "      <td>6/1/1930 22:00</td>\n",
       "      <td>Ithaca, NY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Unknown</td>\n",
       "      <td>OTHER</td>\n",
       "      <td>6/30/1930 20:00</td>\n",
       "      <td>Willingboro, NJ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Unknown</td>\n",
       "      <td>OVAL</td>\n",
       "      <td>2/15/1931 14:00</td>\n",
       "      <td>Holyoke, CO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Unknown</td>\n",
       "      <td>DISK</td>\n",
       "      <td>6/1/1931 13:00</td>\n",
       "      <td>Abilene, KS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Unknown</td>\n",
       "      <td>LIGHT</td>\n",
       "      <td>4/18/1933 19:00</td>\n",
       "      <td>New York Worlds Fair, NY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Unknown</td>\n",
       "      <td>DISK</td>\n",
       "      <td>9/15/1934 15:30</td>\n",
       "      <td>Valley City, ND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Unknown</td>\n",
       "      <td>CIRCLE</td>\n",
       "      <td>6/15/1935 0:00</td>\n",
       "      <td>Crater Lake, CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Unknown</td>\n",
       "      <td>DISK</td>\n",
       "      <td>7/15/1936 0:00</td>\n",
       "      <td>Alma, MI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Unknown</td>\n",
       "      <td>CIGAR</td>\n",
       "      <td>10/15/1936 17:00</td>\n",
       "      <td>Eklutna, AK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Unknown</td>\n",
       "      <td>CYLINDER</td>\n",
       "      <td>6/15/1937 0:00</td>\n",
       "      <td>Hubbard, OR</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Colors     Shape              Time                  Location\n",
       "0  Unknown  TRIANGLE    6/1/1930 22:00                Ithaca, NY\n",
       "1  Unknown     OTHER   6/30/1930 20:00           Willingboro, NJ\n",
       "2  Unknown      OVAL   2/15/1931 14:00               Holyoke, CO\n",
       "3  Unknown      DISK    6/1/1931 13:00               Abilene, KS\n",
       "4  Unknown     LIGHT   4/18/1933 19:00  New York Worlds Fair, NY\n",
       "5  Unknown      DISK   9/15/1934 15:30           Valley City, ND\n",
       "6  Unknown    CIRCLE    6/15/1935 0:00           Crater Lake, CA\n",
       "7  Unknown      DISK    7/15/1936 0:00                  Alma, MI\n",
       "8  Unknown     CIGAR  10/15/1936 17:00               Eklutna, AK\n",
       "9  Unknown  CYLINDER    6/15/1937 0:00               Hubbard, OR"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ufo.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ufo.fillna(value = 'Unknown', inplace = True)   # Permanent for the entire dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>City</th>\n",
       "      <th>Colors Reported</th>\n",
       "      <th>Shape Reported</th>\n",
       "      <th>State</th>\n",
       "      <th>Time</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ithaca</td>\n",
       "      <td>NaN</td>\n",
       "      <td>TRIANGLE</td>\n",
       "      <td>NY</td>\n",
       "      <td>6/1/1930 22:00</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Willingboro</td>\n",
       "      <td>NaN</td>\n",
       "      <td>OTHER</td>\n",
       "      <td>NJ</td>\n",
       "      <td>6/30/1930 20:00</td>\n",
       "      <td>6</td>\n",
       "      <td>30</td>\n",
       "      <td>1930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Holyoke</td>\n",
       "      <td>NaN</td>\n",
       "      <td>OVAL</td>\n",
       "      <td>CO</td>\n",
       "      <td>2/15/1931 14:00</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>1931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Abilene</td>\n",
       "      <td>NaN</td>\n",
       "      <td>DISK</td>\n",
       "      <td>KS</td>\n",
       "      <td>6/1/1931 13:00</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>New York Worlds Fair</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LIGHT</td>\n",
       "      <td>NY</td>\n",
       "      <td>4/18/1933 19:00</td>\n",
       "      <td>4</td>\n",
       "      <td>18</td>\n",
       "      <td>1933</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   City Colors Reported Shape Reported State             Time  \\\n",
       "0                Ithaca             NaN       TRIANGLE    NY   6/1/1930 22:00   \n",
       "1           Willingboro             NaN          OTHER    NJ  6/30/1930 20:00   \n",
       "2               Holyoke             NaN           OVAL    CO  2/15/1931 14:00   \n",
       "3               Abilene             NaN           DISK    KS   6/1/1931 13:00   \n",
       "4  New York Worlds Fair             NaN          LIGHT    NY  4/18/1933 19:00   \n",
       "\n",
       "   Month  Day  Year  \n",
       "0      6    1  1930  \n",
       "1      6   30  1930  \n",
       "2      2   15  1931  \n",
       "3      6    1  1931  \n",
       "4      4   18  1933  "
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "''' Fun Stuff '''\n",
    "\n",
    "ufo = pd.read_csv('../data/ufo.csv')\n",
    "\n",
    "# Make a new month column\n",
    "ufo['Month'] = ufo['Time'].apply(lambda x:int(x.split('/')[0]))\n",
    "\n",
    "'''\n",
    "the apply function applys the lambda funciton to every element in the Series\n",
    "lambda x:x.split('/')[0] will take in x and split it by '/' and return the first element\n",
    "so if we pass in say 9/3/2014 01:22 into the function we would get:\n",
    "9                i.e. the month\n",
    "'''\n",
    "\n",
    "# similar for day\n",
    "ufo['Day'] = ufo['Time'].apply(lambda x:int(x.split('/')[1]))\n",
    "\n",
    "# for year, I need the [:4] at the end to remove the time\n",
    "ufo['Year'] = ufo['Time'].apply(lambda x:int(x.split('/')[2][:4]))\n",
    "ufo.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11e73b910>"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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nVO5ALLhHb2bWKbU0deMevZlZJ1POWbEF/fvDyy83utqB3sysErWkR9+zJzz5ZKOrHejN\nzCpRSwI9wHHHNbrKgd7MrBK1ZDC2GQ70ZmaVqKU9+iY40JuZVSIHejOzKteSWTfNKCvQS1ol6UlJ\nj0t6JJf1lTRX0jJJcyQdUlR/sqQVkpZKOruofJSkhZKWS7quVV6BmVk16oAefT1QExEnRcToXHYF\nMC8iRgD3ApMBJB0HjAdGAmOBG6SdV+W5Ebg0IoYDwyWNaZVXYWZWbTog0KtE3XHAtLw8DTg/L58H\nzIiI7RGxClgBjJY0COgTEfNzvelF25iZWcGGDbBjR/umboAA7pY0X9LnctnAiKgDiIgXgQG5fDCw\npmjbdblsMLC2qHxtLjMzs2LLlsGIEeVdorgM3cusd1pEvCCpPzBX0jJS8C/W8Pk+mTp16s7lmpoa\nampqWnP3ZmaVqxDom1FbW0ttbW2z9coK9BHxQv75sqRfAaOBOkkDI6Iup2UKl05bBwwp2vzIXNZY\neUnFgd7MrEspM9A37ARfddVVJes1m7qRdKCkg/Jyb+BsYBFwF3BJrnYxcGdevguYIKmnpKHAscAj\nOb2zUdLoPDh7UdE2ZmZWUGagL1c5PfqBwB2SIte/JSLmSloAzJQ0EVhNmmlDRCyRNBNYAmwDLouI\nQlpnEnAT0AuYFRGzW+2VmJlVi6eegne+s9V2p10xuHJIikpsl5lZm9uxI90Q/NVX4cADW7SpJCJi\njxFcnxlrZlZJVq9OtwZsYZBvigO9mVklaeX8PDjQm5lVFgd6M7Mq99RTDvRmZp3Sb38L27c3X2/Z\nsladcQMO9GZmbW/DBvjEJ2DOnObrOnVjZtYJPfggdOsGt9zSdL1Nm9JjcOteBsyB3sysrd1/P3zh\nCzBrFmze3Hi95cvhHe9IHwqtyIHezKyt3Xcf/NVfwWmnwa9+1Xi9NhiIBQd6M7O2tWULPPkkvO99\ncOGFTadv2mAgFhzozcza1p/+BO9+N/TuDePGwcMPw0svla7bBgOx4EBvZta27r8fPvShtNy7N3z8\n43D77aXrOtCbmXVC990Hp5++63lj6Zv6elixAoYPb/UmONCbmbWVbdtS6ua003aVffSj8Oyz8PTT\nu9d97jno1y9dubKVOdCbmbWVxx6DYcOgb99dZd27w/jxcNttu9dto4FYcKA3M2s799+/e9qmoJC+\nqa/fVdZG+XlwoDczazv33bdrILbYqaems1/PPBOeeSaVOdCbmXUy9fXwwAOlA70Ec+emGTijR8ON\nN8LSpW0W6H0rQTOztrBoUTobdsWKpustXQqXXAKPPJIGaY85Zq8Puc+3EpTUTdJjku7Kz/tKmitp\nmaQ5kg4pqjtZ0gpJSyWdXVQ+StJCScslXbfXr8bMrNI1nFbZmJEj00XPZs+Go49uk6a0JHVzObCk\n6PkVwLyIGAHcC0wGkHQcMB4YCYwFbpBU+IS5Ebg0IoYDwyWN2cf2m5lVpsYGYkvp3h3GjEkpnTZQ\nVqCXdCTwMeAnRcXjgGl5eRpwfl4+D5gREdsjYhWwAhgtaRDQJyLm53rTi7YxM6seEbufEdvByu3R\nfw/4GlCcOB8YEXUAEfEiMCCXDwbWFNVbl8sGA2uLytfmMjOz6rJqVQr2Q4d2dEsA6N5cBUnnAnUR\n8YSkmiaqturo6dSpU3cu19TUUFPT1KHNzCrIgw+ms2HbKBVTUFtbS21tbbP1mp11I+lq4DPAduAA\noA9wB3AKUBMRdTkt8/uIGCnpCiAi4tq8/WxgCrC6UCeXTwDOiIgvlTimZ92YWef1xS+mQdbLL2/X\nw+71rJuIuDIijoqIYcAE4N6I+B/Ar4FLcrWLgTvz8l3ABEk9JQ0FjgUeyemdjZJG58HZi4q2MTOr\nHoUefYVoNnXThG8CMyVNJPXWxwNExBJJM0kzdLYBlxV1zycBNwG9gFkRMXsfjm9m1n527ID99mu+\n3vr1KUf/nve0eZPK5ROmzMyas3UrHHtsup/r3/89nHtu4/d1nTULvvMduOee9m0jrXDClJlZl3XH\nHWkGzWc/C9/4Rrpm/A9+sPtFyQoqLG0DDvRmZs37f/8P/u7v4DOfSZcqmD49XZ/mN7/Zs+4DD1Rc\noHfqxsysKYsWwTnnpLx7jx67ym++OV1qeHbRUOPWrenmIc8/Dwcf3O5NderGzGxv3HgjfP7zuwd5\ngAsuSDcWKb5o2eOPp1x+BwT5pjjQm5k1ZtOmdCeo//k/91zXqxdMnAg//OGusgpM24ADvZlZ426+\nGc46C444ovT6L3wBpk2DLVvS8wcfhA9+sP3aVyYHejOzUiLghhtg0qTG6wwdCu97H8yYkepX4Iwb\ncKA3MyvtvvtS8D7jjKbrTZqUZuU8/TT07AlHHdU+7WsBB3ozs1JuuAEuu6z5C5ONGZPOhv3udysy\nbQMO9GZme9q6NZ3h+ulPN1+3Wzf40pfSoGwFpm3Agd7MbE8LFqTLHfTrV179iRPTLJwK7dHvy0XN\nzMyq0x/+AC25B8Zhh8HKlY3Pzulg7tGbmTVUW9v8IGxDFRrkwZdAMDPb3bZtqYe+alX5qZsK4Usg\nmJmVY8ECePvbO12Qb4oDvZlZsZbm5zsBB3ozs2K1tVUX6J2jNzMr6MT5eXCO3syseY8+CsOGdcog\n35RmA72k/SX9SdLjkhZJmpLL+0qaK2mZpDmSDinaZrKkFZKWSjq7qHyUpIWSlku6rm1ekpnZXqrC\ntA2UEegj4i3gwxFxEnAiMFbSaOAKYF5EjADuBSYDSDoOGA+MBMYCN0g7LxZxI3BpRAwHhksa09ov\nyMxsr1XhQCyUmbqJiHyxZfYnnU0bwDhgWi6fBpyfl88DZkTE9ohYBawARksaBPSJiPm53vSibczM\nOta2bfDQQ3D66R3dklZXVqCX1E3S48CLwN05WA+MiDqAiHgRGJCrDwbWFG2+LpcNBtYWla/NZWZm\nHe+xx9L15assPw9lXusmIuqBkyQdDNwh6XhSr363aq3ZsKlTp+5crqmpoaYKv06ZWQXZm8sedLDa\n2lpqa2ubrdfi6ZWS/gXYAnwOqImIupyW+X1EjJR0BRARcW2uPxuYAqwu1MnlE4AzIuJLJY7h6ZVm\n1rZWroQlS9J9YTduhB//GKZMgU9+sqNbttf2enqlpMMLM2okHQB8FFgK3AVckqtdDNyZl+8CJkjq\nKWkocCzwSE7vbJQ0Og/OXlS0jZlZ+9m4EU49FX70o3Td+UWL4Pzz001EqlA5qZu3AdMkdSN9MNwe\nEbMk/RGYKWkiqbc+HiAilkiaCSwBtgGXFXXPJwE3Ab2AWRExu1VfjZlZOX7wAzj33HRj7y7AZ8aa\nWdfy+uvppKgHHoARIzq6Na3KZ8aaWdeyfXvp8htugLPOqrog3xT36M2sOn3wg+nkp3/91103+H7j\njXQJ4nvugeOP79DmtYXGevS+laCZVZ8IWLgQXnklBflvfCP9/PGP0wdAFQb5pjjQm1n1eeUV6NED\n7r8fzjwzlV15JXzrW2mWTRfjQG9m1efpp1OKpn9/uPfeFOzvvhve+1448cSObl2782CsmVWflSvh\n2GPTciHYH3QQFJ1x35W4R29m1afQoy/o3x/mzeu49nQw9+jNrPqsXLl7oO/iHOjNrPoUp27Mgd7M\nqlDD1E0X50BvZtVl0ybYsgUGDerollQMB3ozqy4rV6Zr2WiPE0S7LAd6M6suHojdgwO9mVUXD8Tu\nwYHezKqLB2L34EBvZtXFPfo9ONCbWXVxj34Pvh69mVWPt96CQw6BzZuhe9e7wovvMGVm1e/ZZ+Go\no7pkkG9Ks4Fe0pGS7pW0WNIiSV/O5X0lzZW0TNIcSYcUbTNZ0gpJSyWdXVQ+StJCScslXdc2L8nM\nuiynbUoqp0e/HfhqRBwPvB+YJOmdwBXAvIgYAdwLTAaQdBwwHhgJjAVukHaeuXAjcGlEDAeGSxrT\nqq/GzLo2D8SW1Gygj4gXI+KJvLwZWAocCYwDpuVq04Dz8/J5wIyI2B4Rq4AVwGhJg4A+ETE/15te\ntI2Z2b5zj76kFuXoJR0DnAj8ERgYEXWQPgyAAbnaYGBN0WbrctlgYG1R+dpcZmbWOnxWbEllj1hI\nOgj4BXB5RGyW1HBaTKtOk5ladCeYmpoaampqWnP3ZlaNuljqpra2ltra2mbrlTW9UlJ34DfA7yLi\n+ly2FKiJiLqclvl9RIyUdAUQEXFtrjcbmAKsLtTJ5ROAMyLiSyWO5+mVZtYyO3ZA796wYQP06tXR\nrekQ+zq98qfAkkKQz+4CLsnLFwN3FpVPkNRT0lDgWOCRnN7ZKGl0Hpy9qGgbM7N9s2YNDBjQZYN8\nU5pN3Ug6DbgQWCTpcVKK5krgWmCmpImk3vp4gIhYImkmsATYBlxW1D2fBNwE9AJmRcTs1n05ZtZl\nOT/fKJ8Za2bV4Uc/gvnz4Sc/6eiWdBifGWtm1a2LDcS2hAO9mVUHz6FvlAO9mXV+EfD443D88R3d\nkorkQG9mnd/y5bB9O4wc2dEtqUgO9GbW+f3ud3DOOb4heCMc6M2s85s9G8aO7ehWVCxPrzSzzm3L\nFhg4ENauTTcd6cI8vdLMqtMf/gAnndTlg3xTHOjNrHP73e+ctmmGA72ZdW6zZ6eBWGuUA72ZdV4r\nV8Lrr8OJJ3Z0SyqaA72ZdV6zZ8OYMZ5W2QwHejOrTJs3pzNem+L8fFkc6M2sMo0bB7/6VePr33wT\n7rsPPvrR9mtTJ+VAb2aVadEi+PWvG1//wAPwrndBv37t16ZOyoHezCrP+vXp8bvfQX196TqFyx5Y\nsxzozazyLFsG73lPOgnqiSf2XB+R0jrnntv+beuEmr2VoJlZu1u2DEaMSJc2mDULRo3aff1DD8H+\n++9ZbiU126OX9J+S6iQtLCrrK2mupGWS5kg6pGjdZEkrJC2VdHZR+ShJCyUtl3Rd678UM6sahUD/\nsY+lQN/Q9Olw0UWeVlmmclI3/wWMaVB2BTAvIkYA9wKTASQdR7pJ+EhgLHCDtPM3cSNwaUQMB4ZL\narhPM7OkEOg/9CFYvBheeWXXujffhF/8Ai68sOPa18k0G+gj4gFgfYPiccC0vDwNOD8vnwfMiIjt\nEbEKWAGMljQI6BMR83O96UXbmJntrhDo998fPvxhmDNn17q77koXMRsypOPa18ns7WDsgIioA4iI\nF4EBuXwwsKao3rpcNhhYW1S+NpeZme1ux450aYN3vCM9b5i+mT4dLr64Y9rWSbXWrBtfPN7MWsfq\n1dC/P/TunZ6PHZt69Dt2QF1dmj//yU92bBs7mb2ddVMnaWBE1OW0zEu5fB1Q/H3qyFzWWHmjpk6d\nunO5pqaGmpqavWyqmXUqhbRNwZAhMHgwPPII/OlP6YzZgw7quPZVkNraWmpra5utV9YdpiQdA/w6\nIk7Iz68FXouIayX9E9A3Iq7Ig7G3AKeSUjN3A++IiJD0R+DLwHzgt8D3I2J2I8fzHabMuqrrroOn\nn4Z///ddZZMnQ/fu8JvfwLe/DR/5SMe1r4I1doepZnv0km4FaoDDJD0HTAG+Cfxc0kRgNWmmDRGx\nRNJMYAmwDbisKGJPAm4CegGzGgvyZtbFLVsGxx+/e9nHPgYXXAA9e6bBWWsR3zPWzCrLhz8MV165\n+8XKtm+Hww+Hyy6Dq6/uuLZVuL3u0ZuZtauGOXpIaZvrr4ezzuqYNnVy7tGbWeXYtAne9rZ016hu\nvhRXSzXWo/c7aWaVY/nyNH/eQb5V+d00s8pRKm1j+8yB3swqhwN9m3CgN7PK4UDfJhzozaxyONC3\nCc+6MbPKUF8PffrACy/AwQd3dGs6Jc+6MbPKtnZtunWgg3yrc6A3s8rgtE2bcaA3s8rgQN9mHOjN\nrDI40LcZB3oz63hPPgl33AEnn9zRLalKDvRm1rFuvz1drOzb34bTT+/o1lQlX73SrC1EwPz58PLL\ncMopMHBg0/VXrIDvfAd69YKrrkqzT6rdjh3wz/8MM2bA3XfDiSd2dIuqlgO9WWtasgRuvRVuuy1d\nWveoo2DBghS43/teOOkkGDkyPd7+9lT/mmvgnnvStdZffBGOOw6++10YPx60x5Tozu/55+G//xtu\nvjndF3b+/HSteWszPmHKbG9FpBtZ/+EPcN996eebb8KECfDpT6egLqUTgVauTPc7XbgQli5NAX7d\nOujXD76LRUaaAAALvklEQVT6VfjCF9LJQgAPPghf/CIceSRcdBG88QZs3pwu3XvCCXDeee1/dcf6\nenjuufR6161Lj+efT+3o12/X44gj0ofb4MHpg66+HlatgsWL4c9/hlmz0s9PfAL++q/h3HNTPWsV\njZ0w5UBvndeOHSnlsWFDuo75pk2wdWu6cXSfPunn1q1pNsdTT6WfBxwAl18O73//3h932zb45S9T\nqmXNGjjjjJRbPuOM1BsvNwj/5S+w337p9niljnH99emG2H36pEfv3jB3btruyitTj39vguSWLWm/\nDz2U2nr88elxzDHpTk4rVqTAvHhxet+eeirdw7Vv31Rn8OD0OOKItL/XXkuPV19NwX/1anjppZSu\nWr8+bVc4xplnpnz8/vu3vN3WLAd6qy5PPQUTJ6ae5aBB6WzKgw+GHj1SD/j119OjRw8YPhze+c40\nde/55+F730s3t/ja11rWO371VbjpphSAhw6Ff/gH+PjH27d3HZGC/b/9W0rznHZaCqaFYDtoUAqm\nZ56Zxga6dUvXeF+wID0efjgF8BNOSNtKu4L6q6+mHvhRR+0KzCNHpvdt+PBd3zjKsW1b+t0cemh6\nWLuomEAv6RzgOtKMn/+MiGtL1HGgt9K2b0896W99Kw1afulLLQ+027enqXzf+lbq5R91FAwZklIl\nRx8Nw4btemzcCHfdBXfeCU88kQL7V76SgmhHu//+FMQPOyylTfr2Tb3pe+9NOf9Vq9IHQ//+qb2n\nnAKjR6fHAQfsub9Nm9K3i1692v2lWOuoiEAvqRuwHPgI8DwwH5gQEU81qFfRgb62tpaampqObkZJ\nldw2aGH7tm2Durp0DZQ1a9LPW29NPcT/+I+URtgXEakXXNj/mjXU3n8/Ndu3wzPPpMf++6d88rhx\nqZdcKkC2oxa9f6+8siuH3k6q6u+vA+xr+yrl5uCjgRURsTo3agYwDniqya0qTCX/sTTZtvr6lDs9\n/PA9c7s7dqSA9/LLqSfbWHB4/fWU33344fRYtSp9tT/hhPQYOjT1mN96Kw1M/uUvqVe8fj1s2EDt\nPfdQ86lPpR70kCEpcK5YsSsX/MwzKbi/+GLa7vDDd9U98sg0cDlhQuvMRpFSb/iww+A970nvX10d\nNVOn7qoTUVEzX1r0t9cBM1kq+X8Dum772jvQDwbWFD1fSwr+VlBfn/KlDz4IDzyQZmiccEIaPPzA\nB9Jg3/btuw+AvfRSCtAvvwxz5uw+E2K//eDxx1NwfvTRlLPetCl9nR8yJPWOV6+GZ59NZYcdloJt\nr14pgA8enI5RCL5vvJFmk7z//fC5z6XAvnw5LFoEP/tZ2lePHqknvP/+KZD37bsrV9utW0qB/PrX\nqRe9ZUu6R+iIETBqFFxwQcozDxqU2rLffh37+6igIG+2typ3XtMZZ+z+XNr1T1dYLn40JiI96uvT\no2FKqLC+sUfD40spQM2bt+t58f7r63fff3196tkWerdvvrl73UKPsVu39Ni6Nc1mOO00qKlJc6sX\nLUq95+9+NwVhafcpbf37w4AB6XHQQSnlsXRp6kW/9VbqrX796ylHe/jhaf0LL6Qe/Pr1KQUybNiu\ntEREWr9sWRq8PPzwNINi0KDS3wZOPDHNACnH1KnpYWbtpr1z9O8DpkbEOfn5FUA0HJCVVLkJejOz\nClYJg7H7ActIg7EvAI8An4qIpe3WCDOzLqZdUzcRsUPS/wLmsmt6pYO8mVkbqsgTpszMrPW0yyl9\nkv5TUp2khUVl75b0kKQnJd0p6aBc/l5Jjxc9zi/aZpSkhZKWS7quI9pXtP4oSa9L+moltU/S0ZK2\nSHosP26opPY1WPfnvL5npbRP0qfz391j+ecOSe/O605u7fa1sG3dJd2U27A4j3EVtqmE966HpJ/m\ndjwu6YyibdqqfUdKuje/H4skfTmX95U0V9IySXMkHVK0zWRJKyQtlXR2W7axpe2T1C/Xf13S9xvs\na+/bFxFt/gA+CJwILCwqewT4YF6+BPhGXu4FdMvLg4C6oud/At6bl2cBY9q7fUXrfw7cDny1qKzD\n2wccXVyvwX4qoX37AU8C78rP+7Lrm2WHt6/Bdu8inffRZu9fC9+7TwG35uUDgGeBoyrlvQMuI6Vj\nAfoDC9rhb28QcGJePog0BvhO4Frg67n8n4Bv5uXjgMdJaetjgKfb8u9vL9p3IPAB4PPA9xvsa6/b\n1y49+oh4AFjfoPgduRxgHvDXue6bEVGYo3gAUA8gaRDQJyLm53XTgfNpBS1pX27LOOAZYHFRWcW0\nD9hz1L1y2nc28GRE/Dlvuz4iooLaV+xTwAxou/evhW0LoLfSpIYDgbeATRXw3v1VXj4OuDdv9zKw\nQdIpbdy+FyPiiby8GVgKHEk6EXNarjat6HjnATMiYntErAJWAKPb8PfbovZFxJaIeIj0u91pX9vX\nkXeYWizpvLw8nvTiAZA0WtKfST2/L+bAP5h0glXB2lzWru3LX1O/DlzF7gG1ItqXHZNTD7+X9MEK\na99wAEmzJS2Q9LUKa1+xvwVuy8vt2b7G2vYLYAtpxtoq4NsRsaGd21aqfUPy8pPAeZL2kzQUODmv\na5f2STqG9O3jj8DAiKiDFGyBAblaw5M21+WyNm9jme1rzD61ryMD/URgkqT5QG9ga2FFRDwSEe8C\n3gtcWcjhVkj7pgDfi4gtHdCmYo217wXS1/lRwD8At6rB+EIHt687cBqpt/wh4JOSPlxB7QNSZwN4\nIyKWVFDbTgW2k9IBw4B/zMGjUtr3U1LgnA98F3gQ2NEeDcp/478ALs8954azTDp01klHt6/DzoyN\niOXAGABJ7wDOLVFnmaTNpFzpOnb1HCD1ctZ1QPtOBf5a0v8l5Zd3SHoT+O9KaF9EbCX/40XEY5JW\nknrRlfL+rQXui4j1ed0sYBRwS4W0r2ACu3rz0I7vXxNt+xQwO3/DfVnSg8ApwAPt1bam2hcRO4Di\nyQkPki5iuKEt2yepOymI3hwRd+biOkkDI6Iupz1eyuWN/R7b7PfbwvY1Zp/a1549elGU6pDUP//s\nBvwz8MP8/Jicg0TS0cAIYFX+erMxp3UEXATcSespq30RcXpEDIuIYaTLLV8dETdUSvskHZ7LkDQM\nOBZ4plLaB8wBTpDUK/8DnAEsrqD2kY8/npyfh51fr9uqfc217ca86jngzLyuN/A+YGmlvHeSDpB0\nYF7+KLAtIp5qh/b9FFgSEdcXld1FGigGuLjoeHcBEyT1zOmlY4FH2riNLWlfsZ3v+T63b19Hlcsc\neb6VdFnit0h/rJ8FvkwagX6KFCwLdT8D/Bl4DFgAfKJo3cnAItIAyvUd0b4G201h91k3Hd4+0sBY\n8fv3sUpqX67/6dzGhcA1Fdi+M4CHSuyn1dvXwt9tb2Bmfu/+XIF/e0fnssWkkyKHtEP7TiOlh54g\nzaZ5DDgH6EcaKF6W23Jo0TaTSbNtlgJnt/Hvd2/a9yzwCrApv+fv3Nf2+YQpM7Mq15GDsWZm1g4c\n6M3MqpwDvZlZlXOgNzOrcg70ZmZVzoHezKzKOdCbAZLul3RO0fML8lm7Zp2e59GbAZKOJ116+kSg\nJ+nElrMjXeFwb/e5X6TLAph1KAd6s0zSN0lXh+wNbIqI/yPpImAS0IN0tuz/ynV/BJxEupT27RHx\nb7l8DfAz0uWYr46IX7b/KzHbXYdd1MysAn2D1JN/Czgl9/I/Cbw/Iuol/UjShIiYAfxTRGzI12X6\nvaRfRMRTeT91EXFyx7wEsz050JtlEbFF0u3A6xGxTdJZpKtDLsgXkupFuvYIwIWSJpL+h95GuulG\nIdDf3s5NN2uSA73Z7urzA9LVA38aEVOKK0g6lnRhr1Mi4nVJN5M+BAreaJeWmpXJs27MGjcPGC/p\nMNh54+YhwMGkKwtulvQ28rXZzSqVe/RmjYiIP0u6CpiXr72+lXRry0clLSVd5nY16cYfOzfrgKaa\nNcmzbszMqpxTN2ZmVc6B3sysyjnQm5lVOQd6M7Mq50BvZlblHOjNzKqcA72ZWZVzoDczq3L/H+Og\nUSnRC3mjAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11e7508d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sightings_per_year = ufo.groupby('Year').City.count()\n",
    "\n",
    "sightings_per_year.plot(kind='line', \n",
    "                        color='r', \n",
    "                        linewidth=1, \n",
    "                        title='UFO Sightings by year')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11ac11890>"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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CuPsc4J+Ef4knpkbgHeDFqHvoE8L4S5/m1JilOmsN4YutgiR9lqcAT7t7TdQF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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a8cfcd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# -----Analysis-----\n",
    "# Clearly, Aliens love the X-Files (which came out in 1993).\n",
    "# Aliens are a natural extension of the target demographic so it makes sense.\n",
    "\n",
    "# Well hold on Sinan, the US population is always increasing\n",
    "# So maybe there's a jump in population which would make sense!\n",
    "# US Population data from 1930 as taken from the Census\n",
    "\n",
    "us_population = pd.read_csv('../data/us_population.csv')\n",
    "us_population.plot(x = 'Date', y = 'Population', legend = False)\n",
    "# Seems like a steady increase to me..\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11f0e2d50>"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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A4gtHFJfh9hrXWd2uvKc8J2peadRUbL/rn4L7VeuKr7pxUy223/WPIbbWVXd1\n47pN/vlcM7NMTNrfcjEzs944oZuZZcIJ3cwsE77KxTY5kp6l+LG1LSl+a+c84MvhE0o2xTmh26bo\nyYh4LUD6be1vUvz2zlA/G2U2Xh5ysU1aRDxE8ZstHweQtJukayXdkqY/SeWLJR06ul76I4S/6k+r\nzVrzZYu2yZH0WERsWyl7GNiT4rfm10XEHyS9AvhmRBwg6c+AT0TEO1T8ld9PKX55sv6XPswa5iEX\ns8LoV7a3BE6XNIfi99/3AIiIa5X+do3iVycvdjK3ycYJ3TZ5kl5G8VsaD6r4T9q1EbGvpM2B35VC\nzwXeDxxB8dvuZpOKE7ptitb/gJKKP+Y+E/jXVDSd4i/jAI6i+AW8UYspfrd8TUTctRHaadYTJ3Tb\nFL1A0q08d9niuRHx5bTsDOBiSUdR/N3dk6MrRcQDklYA/7mxG2xWh0+KmtUkaWuK69dfGxGP97s9\nZlW+bNGsBkkHAcuBrziZ22TlHrqZWSbcQzczy4QTuplZJpzQzcwy4YRuZpYJJ3Qzs0w4oZuZZeL/\nA29USNyQpbAIAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11fdddc90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the sightings in in July \n",
    "ufo[(ufo.Year==2014) & (ufo.Month == 7)].groupby('Day').City.count().plot(  kind='bar',\n",
    "                                                        color='b', \n",
    "                                                        title='UFO Sightings in July 2014')\n",
    "                                                        \n",
    "\n",
    "# -----Analysis-----\n",
    "# Aliens are love the 4th of July. The White House is still standing. Therefore\n",
    "# it follows that Aliens are just here for the party.\n",
    "\n",
    "# Well maybe it's just 2014?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x11fe25a50>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x1208b9390>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x12093a950>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x12099f8d0>], dtype=object)"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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HlbgOWD8QrBIrgMXJ8/OA28psw2qglENW9Xoep1Gu2avXv+9IaJT3sBTNvO9DlTNL8K3A\nT4FHefWv+CngQeBm4HCgF1gYEb/PUd8jrDrWSPs+Ev3y/63C5RsxQP5Rw0h83kvp10io1/e9Wn/f\n4UZYvnC4pDYq269i1fYQF1Rq30fqC8IBq7400ud9JNTyOwUq9yOi0L+vA1aJder1S6W2beRvp16/\nIOrxPanX/1ulKPYHVCN93kdCs33eq3kOy6zheMp5cXyOxUaKA5bZEPm+gNP4JVzsRA0H6+LU60SY\neu1XuXxIsKQ2KtevkToBXax6OkRQrnp835v5/3wz73spGmXf6/qQoKSTJT0u6UllcgpaDiP1a75R\nf3GZpYlHsOWpSsCSNAr4V+AkoAM4R9IxhW+hu8gWi12/XtsopU5h6+8dGO+muKBYbJ+qV6f8wFts\nv4pdf6TqNEobpdRJbxv7/kCtx8/iSLRRWp1qjbDmABsjojcidgM3kUmKW6DuIpsrdv16baOUOo3S\nRmF19v7AL6X40Wix/Sp2/ZGq0yhtlFKnUdoopU6jtFFanWoFrMOAzVmvn0nKCtRT2d6kSk+tO1BD\nPbXuQA311LoDNdRT6w7UUE+tO1BDPUXXqNNZgj217kAN9dS6AzXUU+sO1FBPrTtQQz217kAN9dS6\nAzXUU3SNqswSlHQi0BURJyevlwAREVdkrVO7Kx3NzKxujWimC0mjgSeABcCvyeQXPCciNlS8MTMz\nawpl3w8rl4h4RdJHgVVkDjte62BlZmblqNmFw2ZmZsWo00kXZmZme3PAMjOzVHDAMjOzVHDAMjOz\nVHDAMqsQSWMlXSOpR9I2Sb+UdHLW8gWSNkjaLmmNpGlZyzol3SXp95KeHrLd10m6UdKzkn4n6R5J\nc0Zy38zqgQOWWeWMATYBb4+IycBngZslTZN0MHAL8GngtcBDwHez6u4ArgX+Lsd2J5C5lvHYpO43\ngTsktVRrR8zqkae1m1WRpF8BXcAhwHkR8bakvAXYCsyOiCez1l8AfD0ijtzPdrcBnRHxcLX6blZv\nPMIyqxJJrcDRwDoyt9n51cCyiHgJ+O+kvNjtzgYOSOqbNQ0HLLMqkDQGuAH4RjKCmgBsG7LaC8DE\nIrc7icwhwa6IeLESfTVLCwcsswpT5l7gNwA7gY8lxduBSUNWnQwUHHQkvQZYAfwsIr5Uga6apYoD\nllnlXUvmnNVZEfFKUrYOmD2wgqTxwOuT8v2SNBb4D2BTRFxc2e6apYMDllkFSboaOAY4LSJ2ZS26\nFeiQdKakcWRul7x2YMKFMsYBY4FRksZJOiBZNobMDMOXgMUjtzdm9cWzBM0qJLmuqgd4GRgYWQXw\n4Yj4jqT5wL8B04AHgMURsSmpOxe4O1l/wE8iYr6kP02W/W/W8gDeFRH/Vd29Mqsf+w1YkqaSOcnb\nCuwhM+X2XyRNIXMdyXQyH9KFEbEtqXMZcD7QD1waEauqtgdmZtYUCglYbUBbRKyVNIHMBY+nAx8C\nfhsRX5L0SWBKRCyR9Ebg28DxwFRgNXB0eChnZmZl2O85rIjYEhFrk+fbgQ1kAtHpwPJkteXAGcnz\n04CbIqI/InqAjYDTyJiZWVmKmnQhqZ3MTKf7gdaI6INMUAMOTVY7DNicVe3ZpMzMzKxkBQes5HDg\n98mck9rO3ieHyfHazMysYsYUslIyrfb7wLci4rakuE9Sa0T0Jee5nkvKnwUOz6o+NSkbuk0HODMz\n20dEKFd5oSOs64D1EfHVrLIVvHpNyHnAbVnl70tutXAEcBSZTNO5OpXzMXfu3LzLcj2WLl1a1Pql\n1BmJNrzv3vd66pf33ftei30fzn5HWJLeCrwfeFTSw2QO/X0KuILMrRPOB3qBhUkQWi/pZmA9sBu4\nJPbXiyHa29uLWb2heN+bk/e9OXnfi7PfgBWZCxNH51n8jjx1vgB8oejeJPwmNifve3PyvjenUvZ9\ndFdXV8U7Uohly5Z1Ddd2sTtTys7XYxul1GmUNkqp0yhtlFKnUdoopU6jtFFKnUZpI1+dZcuW0dXV\ntSzX+jVLzSSp2COFZmbW4CQRZU66aEptU9uQlPPRNrWt1t0zszrU3t6e93vDj1cfpYzIPMIahqTM\nzc1z6WK/M1rMrPkkI4Rad6Pu5fs7eYRlZmap54BlZmap4IBlZmap4IBlZmap4IBlZlZlw804rsSj\nmFnLu3bt4sILL6S9vZ3Jkydz3HHHsXLlysHla9asYebMmUyYMIEFCxawadOmwWXd3d3Mnz+fgw46\niCOPPHKv7f7mN7/h3HPP5bDDDmPKlCm8/e1v58EHc2blK1lByW/NzKx0fc/25Z9xXIntd/UVvG5/\nfz/Tpk3jnnvu4fDDD+eOO+5g4cKFPPbYY4wfP56zzz6b6667jlNPPZXPfOYzvPe97+W+++4DYPz4\n8VxwwQWce+65fP7zn99ru9u3b2fOnDn88z//M6973eu45pprOOWUU+jt7aWlpaUi++lp7cPwtHYz\nK1au6drDfpdUQld530ezZs2iq6uLrVu3snz5cu69914AXnrpJQ455BDWrl3LjBkzBtdfs2YNF110\nEU8//fSw2508eTLd3d0ce+yx+yzztHYzMytKX18fGzdupKOjg3Xr1jFr1qzBZS0tLRx11FGsW7eu\n6O2uXbuW3bt3c9RRR1Wsrw5YZmZNqr+/n0WLFrF48WJmzJjB9u3bmTx58l7rTJo0iRdffLGo7b7w\nwgt88IMfpKuri4kTJ1asvw5YZmZNKCJYtGgR48aN48orrwRgwoQJvPDCC3utt23btqKCzssvv8xp\np53Gn/zJn/D3f//3Fe2zA5aZWRO64IIL2Lp1Kz/4wQ8YPTpzB6mOjg7Wrl07uM6OHTt46qmn6Ojo\nKGibu3bt4owzzmDatGlcffXVFe+zA5aZWZO5+OKLefzxx1mxYgVjx44dLD/zzDNZt24dt956Kzt3\n7mTZsmXMnj17cMJFRLBz50527drFnj172LlzJ7t37wYyhxfPPvtsWlpa+MY3vlGVfjtgmZk1kU2b\nNvG1r32NtWvX0traysSJE5k0aRLf+c53OOSQQ7jlllv41Kc+xWtf+1p+8YtfcNNNNw3W/elPf8qB\nBx7IqaeeyubNm2lpaeGkk04C4Gc/+xk/+tGPWLVqFZMnTx7c7n/9139VrO+e1j4MT2s3s2Llmq7d\nNrUtcy1WlbQe1sqWZ7ZUbfvVUMq0dl84bGZWZWkLJvVqv4cEJV0rqU/SI1llSyU9I+mXyePkrGWX\nSdooaYOkd1ar42Zm1lwKOYd1PXBSjvJ/iojjksdKAEkzgYXATOBdwFWScg7tzMzMirHfgBUR9wK/\ny7EoVyA6HbgpIvojogfYCMwpq4dmZmaUN0vwo5LWSrpG0sCl0YcBm7PWeTYpMzMzK0upky6uAj4X\nESHpH4AvAxcWu5Gurq7B552dnXR2dpbYHTMzS6Pu7m66u7sLWregae2SpgO3R8RbhlsmaQkQEXFF\nsmwlsDQiHshRz9PazazhtLe309vbW+tu1L3p06fT09OzT3klprWLrHNWktoiYmCe5lnAY8nzFcC3\nJX2FzKHAo4DK3sHLzKyO5foStsrYb8CSdCPQCRwsaROwFJgnaTawB+gBPgwQEesl3QysB3YDl9T9\nMMrMzFLBmS6G4UOCZmYjyzdwNDOz1HPAMjOzVHDAMjOzVHDAMjOzVHDAMjOzVHDAMjOzVHDAMjOz\nVHDAMjOzVHDAMjOzVHDAMjOzVHDAMjOzVHDAMjOzVHDAMjOzVHDAMjOzVHDAMjOzVHDAMjOzVHDA\nMjOzVHDAMjOzVNhvwJJ0raQ+SY9klU2RtErSE5LulDQ5a9llkjZK2iDpndXquJmZNZdCRljXAycN\nKVsCrI6INwB3AZcBSHojsBCYCbwLuEqSKtddMzNrVvsNWBFxL/C7IcWnA8uT58uBM5LnpwE3RUR/\nRPQAG4E5lemqmZk1s1LPYR0aEX0AEbEFODQpPwzYnLXes0mZmZlZWcZUaDtRSqWurq7B552dnXR2\ndlaoO2Zmlgbd3d10d3cXtK4i9h9rJE0Hbo+ItySvNwCdEdEnqQ24OyJmSloCRERckay3ElgaEQ/k\n2GYU0nYtSYKuPAu7oN77b2aWNpKIiJxzHwo9JKjkMWAFsDh5fh5wW1b5+ySNlXQEcBTwYNE9NjMz\nG2K/hwQl3Qh0AgdL2gQsBb4IfE/S+UAvmZmBRMR6STcD64HdwCV1P4wyM7NUKOiQYFUa9iFBMzMb\nohKHBM3MzGrKAcvMzFLBAcvMzFLBAcvMzFLBAcvMzFLBAcvMzFLBAcvMzFLBAcvMzFLBAcvMzFLB\nAcvMzFLBAcvMzFLBAcvMzFLBAcvMzFLBAcvMzFLBAcvMzFLBAcvMzFLBAcvMzFLBAcvMzFJhTDmV\nJfUA24A9wO6ImCNpCvBdYDrQAyyMiG1l9tPMzJpcuSOsPUBnRBwbEXOSsiXA6oh4A3AXcFmZbZiZ\nmZUdsJRjG6cDy5Pny4EzymzDzMys7IAVwI8l/VzShUlZa0T0AUTEFuDQMtswMzMr7xwW8NaI+LWk\n1wGrJD1BJohlG/razMysaGUFrIj4dfLvbyT9BzAH6JPUGhF9ktqA5/LV7+rqGnze2dlJZ2dnOd0x\nM7OU6e7upru7u6B1FVHaAEhSCzAqIrZLGg+sApYBC4DnI+IKSZ8EpkTEkhz1o9S2R4ok6MqzsAvq\nvf9mZmkjiYhQrmXljLBagVslRbKdb0fEKkm/AG6WdD7QCywsow0zMzOgjIAVEf8DzM5R/jzwjnI6\nZWZmNpQzXaRQ29Q2JO3zaJvaVuuumZlVTbmzBK0G+p7ty3lura+rb8T7Ylastqltmf/DObQe1sqW\nZ7aMcI8sLRywzGxE5fvBBf7RZcPzIUEzM0sFByyzIfKdI/R5QkuLRj3P7UOCZkP4kJWlXaOe526q\nEVaj/uowM2sGTTXCatRfHWZmzaCpRlhmzcZHFayRNNUIqx75mhSrJh9VsEZSFyOsZp6VNfiFkuOR\nL5CNhEZ6TxpllNEo+9FI/J4UrhLfKXUxwmqkWVn5Rky1Hi0V269Gek8aZZTRKPvRSBrlPRmJIz2V\n+E6pi4DVSOr1P3C99svqSylfXI3yI61ejcR+pOUHqgOWVUyjfEE0s1K+uOr1x1C99qtYjbIflZDa\ngOUvx/pT7AerkX7Nm1n1pTZg+VdH+jXSr3kH0ury39cgxQHLrJ7UayBtFI3y922ky1hq8SPCAcvM\nbISkZXJDIWrxI6Jq12FJOlnS45KelPTJarVjZlYJjXTtYaOqSsCSNAr4V+AkoAM4R9IxBW/gf4ps\nsNj167WNUuo0Shul1ElxG2V/OaZ438uuU6U29rqI/zyKv4g/xfs+4m2UWKdaI6w5wMaI6I2I3cBN\nwOkF1+4psrVi16/XNkqp0yhtlFInxW3sk+FkLsV9OVapXyPeRil1GqWNUuo0Shsl1qlWwDoM2Jz1\n+pmkrDC/r3R3UsT73py8783J+16UusgluA+/ic3J+96cvO/NqYR9V0RUvB+STgS6IuLk5PUSICLi\niqx1Kt+wmZmlXkQoV3m1AtZo4AlgAfBr4EHgnIjYUPHGzMysKVTlOqyIeEXSR4FVZA47XutgZWZm\n5ajKCMvMzKzS6nPShZmZ2RAOWGZmlgoOWGZmlgoOWGZmlgoOWGYVImmspGsk9UjaJumXkk7OWr5A\n0gZJ2yWtkTQta1mnpLsk/V7S0zm2fZek55LlD0s6baT2y6xeOGCZVc4YYBPw9oiYDHwWuFnSNEkH\nA7cAnwZeCzwEfDer7g7gWuDv8mz7UuCwiDgI+DBwg6TW6uyGWX3ytHazKpL0KzJpbQ8BzouItyXl\nLcBWYHZEPJm1/gLg6xFx5DDbnAN0A38aEb+oXu/N6otHWGZVkoyAjgbWkbnNzq8GlkXES8B/J+WF\nbu92Sf8L3A/c7WBlzcZ3HDarAkljgBuAb0TEk5ImAM8NWe0FYGKh24yI9yRpz94BzKxYZ81SwiMs\nswqTJDLBaifwsaR4OzBpyKqTgReL2XZEvBIRdwInSTq13L6apYkDllnlXUvmnNVZEfFKUrYOmD2w\ngqTxwOuT8lKMSeqbNQ0HLLMKknQ1cAxwWkTsylp0K9Ah6UxJ44ClwNqBCRfKGAeMBUZJGifpgGTZ\nGySdLOmtya9RAAAgAElEQVQ1ksZIWgS8HfjJSO6bWa15lqBZhSTXVfUALwMDI6sAPhwR35E0H/g3\nYBrwALA4IjYldecCdyfrD/hJRMyXdAzwDTLnrV4BNgL/b0SsqPpOmdWR/QYsSdcCpwJ9EfGWpGwW\ncDXwGmA3cMnAjCVJlwHnA/3ApRGxqnrdNzOzZlHIIcHrgZOGlH0JWBoRx5I5tPGPAJLeCCwk80vw\nXcBVyQloMzOzsuw3YEXEvcDvhhTvITPDCeAg4Nnk+WnATRHRHxE9ZA5dzKlMV83MrJmVeh3WXwN3\nSvoyIOBPkvLDgPuy1ns2KTMzMytLqbMEP0Lm/NQ0MsHrusp1yczMbF+ljrDOi4hLASLi+5KuScqf\nBQ7PWm8qrx4u3IskT080M7N9RETOuQ+FjrCUPAY8m0zDHUjWuTEpXwG8L7nNwhHAUcCDw3Qq52Pu\n3Ll5l+V6LF26tKj1S6kzEm14373v9dQv77v3vRb7Ppz9jrAk3Qh0AgdL2kRmVuBFwL8kec1eBv4i\nCUDrJd0MrOfV6e5Fj6Ta29uLrVIV7W1t9Pb1Db5etmzZ4PPpra30bNlS+TbrZN9rwfvenLzvzamU\nfd9vwIqIc/Ms+uM8638B+ELRPclSL29ib1/f4FWcXcljgLICWSXVy77Xgve9OXnfm1Mp+16XqZk6\nOzurun5JbRTdwgj1q0HaKKVOo7RRSp1GaaOUOo3SRil1GqWNUuvULDWTpFKOFo4oSeTroWC/x1vN\nzKw4kog8ky58Pywzswpqb2+nt7e31t2oe9OnT6enp6eoOiXlEkzKPwZcQiZn4B0RsSQpLyiXoEdY\nZtaIkhFCrbtR9/L9ncodYV0PXAl8M2uDncB7gDdHRL+kQ5LymbyaS3AqsFrS0XUfmczMrO6Vmkvw\nI8AXI6I/WWdrUn46ziVoZmZVUOoswRnAn0q6X9Ldkv4oKT8M2Jy1nnMJmplZRZQ66WIMMCUiTpR0\nPPA94MjKdcvMzGxvpY6wNgM/AIiInwOvSDqYzIhqWtZ6eXMJAnR1dQ0+uru7S+yKmVl9a29rQ1LV\nHu1tbQX3ZdeuXVx44YW0t7czefJkjjvuOFauXDm4fM2aNcycOZMJEyawYMECNm3aNLisu7ub+fPn\nc9BBB3HkkfuOUebPn8+hhx7KQQcdxLHHHsuKFfu/KXZ3d/desWBYheR8AtqBR7Ne/wWwLHk+A+hN\nnr8ReBgYCxwB/DfJTMQc24x6B0TkeaSh/2Y28nJ9Nwz3XVKJRzHfRzt27Ihly5bFpk2bIiLihz/8\nYUycODF6e3tj69atMXny5Ljlllti586d8YlPfCJOPPHEwboPPvhg3HDDDfH1r389jjjiiH22/cgj\nj8SuXbsiIuKBBx6IiRMnxpYtWwr+O2WV54xFpeYSvA64XtKjwE7gg0kEqkguQTMzq46WlhYuv/zy\nwdennHIKRxxxBA899BBbt27lTW96E2eddRaQOQp2yCGH8OSTTzJjxgyOP/54jj/+eNasWZNz229+\n85v3et3f38/mzZtpbW2tSN/LySX4gTzrl51L0MzMRkZfXx8bN26ko6ODq666ilmzZg0ua2lp4aij\njmLdunXMmDGjoO295z3vYfXq1ezcuZN3vetd/PEf50w7WxJnujAza1L9/f0sWrSIxYsXM2PGDLZv\n386hhx661zqTJk3ixRdfLHibt99+O6+88gqrV69mw4YNFe1vXSa/NTOz6ooIFi1axLhx47jyyisB\nmDBhAi+88MJe623bto2JEycWte3Ro0dz0kknceedd/LDH/6wYn12wDIza0IXXHABW7du5Qc/+AGj\nR48GoKOjg7Vr1w6us2PHDp566ik6OjpKaqO/v5+nnnqqIv2FAgKWpGsl9Ul6JMeyv5W0R9Jrs8ou\nk7RR0gZJ76xYT83MrCIuvvhiHn/8cVasWMHYsWMHy88880zWrVvHrbfeys6dO1m2bBmzZ88ePH8V\nEezcuZNdu3axZ88edu7cye7duwF44oknWLlyJS+//DL9/f3ccMMN3HPPPcydO7dyHc83fXDgAbwN\nmA08MqR8KrAS+B/gtUnZTDLT2seQmQrvae1m1lRyfTcM910y0tPae3t7Q1IceOCBMWHChJgwYUJM\nnDgxbrzxxoiIWLNmTRxzzDHR0tIS8+bNi97e3sG63d3dISlGjRo1+Jg3b15ERGzYsCFOOOGEmDRp\nUkyZMiXmzJkTt912W1F/p6zynPGooPthSZoO3B57Z2v/HvA5YAXwRxHxvKQlSWNXJOv8J9AVEQ/k\n2GYU0nYtOVu7mRUrVxby9rY2eqt0l3KA6a2t9GzZUrXtV0O1srXn2uBpwOaIeFTaa7uHAfdlvXYu\nQTNremkLJvWq6IAl6UDgU8CfVb47ZmZmuZUywno9mfNTv1JmeDUV+KWkOZSQS3BAZ2cnnZ2dJXTH\nzMzSqru7u+BcsoWew2oncw7rzTmW/Q9wXET8TtIbgW8DJ5A5FPhjIOcNHH0Oy8wake84XJhSzmEV\nMq39RuBnwAxJmyR9aMgqQeb7m4hYDwzkEvwRziVoZmYVUtAIqyoNe4RlZg3II6zCjNgsQTMzy236\n9OkMmT1tOUyfPr3oOh5hDcMjLDOzkVXWOSwzM7N64IBlZmapUFLyW0lfSpLbrpV0i6RJWcuc/NbM\nzCqukBHW9cBJQ8pWAR0RMRvYCFwGkFyHtZBMEtx3AVfJZx/NzKwC9huwIuJe4HdDylZHxJ7k5f1k\nMloAnAbcFBH9EdFDJpjNqVx3zcysWVXiHNb5ZC4Shkx2i81Zy5z81szMKqKs67AkfRrYHRHfKaW+\ncwmamTW3auQSzHU/rMXARcD8iNiZlA29H9ZKYKnvh2VmZoWoxHVYSh4DGzwZ+ARw2kCwSqwA3idp\nrKQjgKOAB0vrtpmZ2av2e0gwSX7bCRwsaROwlMz9sMYCP04mAd4fEZdExHpJA8lvd+Pkt2ZmViFO\nzTQMHxI0MxtZTs1kZmap54BlZmap4IBlZmapUGouwSmSVkl6QtKdkiZnLXMuQTMzq7hScwkuAVZH\nxBuAu3AuQTMzq7KScgkCpwPLk+fLgTOS584laGZmVVHqOaxDI6IPICK2AIcm5c4laGZmVVFWLsEs\nJV2Q5FyCZmbNreq5BCVtADojok9SG3B3RMx0LkEzMytHxXMJkskZuDh5fh5wW1a5cwmamVnFlZpL\n8IvA9ySdD/SSmRmIcwmamVm1OJfgMHxI0MxsZDmXoJmZpZ4DlpmZpYIDlpmZpUJZAUvSX0t6TNIj\nkr6dzA7Mm2fQzMysVCUHLEl/CHwMOC65PmsMcA558gyamZmVo9xDgqOB8ZLGAAeSScWUL8+gmZlZ\nyUoOWBHxf4AvA5vIBKptEbEaaM2TZ9DMzKxk5RwSPIjMaGo68IdkRlrvZ9+8gr5YyczMylZO8tt3\nAE9HxPMAkm4F/gTok9SalWfwuXwbcPJbaG9ro7evL+ey6a2t9GzZMsI9MjMbORVPfpuzojQHuBY4\nHthJ5kaPPwemAc9HxBWSPglMiYglOeo708UItWFmlhbDZbooeYQVEQ9K+j7wMJm8gQ8DXwMmAjcP\nzTNoZmZWDucSHIZHWGZmI8u5BM3MLPUcsMzMLBUcsMzMLBXKzSU4WdL3JG2QtE7SCc4laGZm1VDu\nCOurwI8iYiYwC3gc5xI0M7MqKOc6rEnAwxHx+iHljwNzsy4c7o6IY3LU9yzBEWrDzCwtqjVL8Ahg\nq6TrJf1S0tckteBcgmZmVgXlBKwxwHHAv0XEccAOMocDnUvQzMwqrpxcgs8AmyPiF8nrW8gELOcS\nNDOzgoxILkEAST8BLoqIJyUtBVqSRc4lWEdtmJmlxXDnsMoNWLOAa4ADgKeBD5G5qePNwOEkuQQj\n4vc56jpgjVAbZmZpUbWAVQ4HrJFrw8wsLZxL0MzMUs8Byxpee1sbkvZ5tLe11bprZlYEB6wK85dj\n/ent6yNgn0e+Oz2bWX0q+xyWpFHAL4BnIuI0SVOA7wLTgR4yky625ajXkOew8tUpdv3h6lhxin1P\nzKx2qn0O61JgfdZr5xI0M7OKKzdb+1Tg3WSmtg84HViePF8OnFFOG5Xkw3VmZulV7gjrK8An2Dv9\nUt3mEvS5DDNLG//QflXJAUvSKUBfRKwlczogH58kMDMrkX9ov6qcXIJvBU6T9G7gQGCipG8BW5xL\n0MzMCjFiuQQHNyLNBf42mSX4JeC39ZhLcCRm8HmWYP3xLEFLs2b7/zvSmS6+CPyZpCeABclrMzOz\nsjRVLkGPsJpTs/1CtcbSbP9/nUvQzMxSzwHLrIF5SrQ1Egcss5QoJfh4SrQVIt//rXr7cVPOdVhT\nJd0laZ2kRyV9PCmfImmVpCck3SlpcuW6a9Dcv5qbed8dfKxa8v3fqrf/XyVPukiusWqLiLWSJgAP\nkUnL9CEy09q/5Gnt9dFGIyll34ut097WlvdDOr21lZ4tWwrubyWNxL5b/RmJ97CeJn9VZdJFRGxJ\nslwQEduBDcBU6jiXoFkh0vJr09InLYfe6lVFzmFJagdmA/dTx7kEzczyGYlg4h9D5SknNRMAyeHA\n7wOXRsR2SUPHjj7uYGZ1byCY5CIHk7pQVsCSNIZMsPpWRNyWFPc5l6CZNYN85ztrea4zbUYsl6Ck\nbwJbI+JvssquAJ53LsH6aaORjMTEg1Lek5H44vKki+pq5s97Pf2fr8qkC0lvBd4PzJf0sKRfSjoZ\nuIIicwn6RGT11eN08EZ634udct5I+16P/7ea3Ui8J7W4zKIucgmO1JTKevw1NFK/uOrxl3a97rvf\n9+LU4/+tUjTz+15PI7+qjLBqzb/q6o/fk/RrpJGfNZ7UjrAa5VdHve57KRpl35v5fa+nX9ojze97\ndftV6AX5DTnCMrPmUcro3SP++lKJa9CqFrAknSzpcUlPKjNb0FLEh4asnpRygt+5FxtPVQKWpFHA\nvwInAR3AOZKOKbR+d5HtFbt+vbZRSp1qtZH9Yb+b4j/w1epXI7ZRSp00t1Huj6Fq9Wuk2yilTqO0\nUWqdao2w5gAbI6I3InYDN5HJMViQ7iIbK3b9em2jlDqN0kYpdRqljVLqpLmNoSOfpTTnj6FS6jRK\nG6XWqVbAOgzYnPX6maSsID2V7k2K9NS6AzXUU+sO1FBPrTtQQz217kAN9dS6AzXUU0Kdupx00VPr\nDtRQT607UEM9te5ADfXUugM11FPrDtRQT607UEM9JdSpyrR2SScCXRFxcvJ6CRARcUXWOumZ72pm\nZiMm37T2agWs0cBAaqZfAw8C50TEhoo3ZmZmTaHs24vkEhGvSPoosIrMYcdrHazMzKwcNct0YWZm\nVoy6nHRhZmY2lAOWmZmlggOWmZmlggOWmZmlggOWWYVIGivpGkk9krbp1btwDyxfIGmDpO2S1kia\nlrWsU9Jdkn4v6elh2pgraY+kz1V7f8zqjQOWWeWMATYBb4+IycBngZslTZN0MHAL8GngtcBDwHez\n6u4ArgX+Lt/GJY0B/hm4vzrdN6tvntZuVkWSfgV0AYcA50XE25LyFmArMDsinsxafwHw9Yg4Mse2\nPglMAQ4FnomIy6u/B2b1wyMssyqR1AocDawjc5udXw0si4iXgP9OygvZ1nTgQ8DnyNzU1azpOGCZ\nVUFy+O4G4BvJCGoCsG3Iai8AEwvc5FeBzySBzqwpOWCZVZgkkQlWO4GPJcXbgUlDVp0MvFjA9t4D\nTIyI71eyn2ZpU5VcgmZN7loy56zeHRGvJGXrgPMGVpA0Hnh9Ur4/84E/kvTr5PVkoF/SmyPizMp1\n26y+eYRlVkGSrgaOAU6LiF1Zi24FOiSdKWkcmRvtrh2YcKGMccBYYJSkcZIOSOp+BpgBzEoeK4Cv\nkzmnZdY0HLDMKiS5ruovgNlAn6QXJb0g6ZyI2AqcDXweeB74Y+B9WdX/FPhf4IfA4cBLwJ0AEbEj\nIp4beCTr7YiI34/UvpnVg/1Oa09+9f2UzC+/McD3I2KZpClkriOZTubmkQsjYltS5zLgfKAfuDQi\nVlVtD8zMrCkUdB2WpJaIeCm5MeN/AR8n82vxtxHxpYHrQyJiiaQ3At8GjgemAquBo8MXfJmZWRkK\nOiSYNZV2HJlRVgCnA8uT8uXAGcnz04CbIqI/InqAjcCcSnXYzMyaU0EBS9IoSQ8DW4AfR8TPgdaI\n6AOIiC1krr4HOAzYnFX92aTMzMysZIWOsPZExLFkDvHNkdRBZpS112qV7pyZmdmAoq7DiogXJHUD\nJ5OZBdUaEX2S2oDnktWeJTPLacDUpGwvkhzgzMxsHxGRM/3YfkdYkg6RNDl5fiDwZ8AGMteCLE5W\nOw+4LXm+AnhfcquFI4CjgAfzdCrnY+7cuXmX5XosXbq0qPVLqTMSbXjfve/11C/vu/e9Fvs+nEJG\nWH8ALJc0ikyA+25E/EjS/WRunXA+0AssTILQekk3A+uB3cAlsb9eDNHe3l7M6g3F+96cvO/Nyfte\nnP0GrIh4FDguR/nzwDvy1PkC8IWie5Pwm9icvO/NyfvenErZ99FdXV0V70ghli1b1jVc28XuTCk7\nX49tlFKnUdoopU6jtFFKnUZpo5Q6jdJGKXUapY18dZYtW0ZXV9eyXOvX7AaOkoo9UmhmZg1OEpFn\n0oWztZuZVVB7ezu9vb217kbdmz59Oj09PUXV8QjLzKyCkhFCrbtR9/L9nYYbYTlbu5mZpYIDlpmZ\npYIDlpmZpYIDlpmZpYIDlplZlbVNm4akqj3apk0ruC+7du3iwgsvpL29ncmTJ3PcccexcuXKweVr\n1qxh5syZTJgwgQULFrBp06bBZd3d3cyfP5+DDjqII488Mm8bP/nJTxg1ahSXX355aX+wPPY7rV3S\nVOCbQCuwB/haRFwpaSlwEa8mvf1URKxM6viOw2Zmib7Nm+Huu6u3/XnzCl63v7+fadOmcc8993D4\n4Ydzxx13sHDhQh577DHGjx/P2WefzXXXXcepp57KZz7zGd773vdy3333ATB+/HguuOACzj33XD7/\n+c/n3f5f/dVfceKJJ1Zk37IVch1WP/A3EbFW0gTgIUk/Tpb9U0T8U/bKkmaSySs4k+SOw5J8x2Ez\nszrQ0tKy18jnlFNO4YgjjuChhx5i69atvOlNb+Kss84CoKuri0MOOYQnn3ySGTNmcPzxx3P88cez\nZs2avNv/8pe/zEknncRzzz2Xd51S7feQYERsiYi1yfPtZDK1D9yQMddc+dNpkDsODzeML2YIbmZW\nr/r6+ti4cSMdHR2sW7eOWbNmDS5raWnhqKOOYt26dQVtq7e3l+uvv57LL7+8KteiFZXpQlI7MBt4\nAHgb8FFJHwB+AfxtRGwjE8zuy6qW2jsODzeML2YIbmZWj/r7+1m0aBGLFy9mxowZbN++nUMPPXSv\ndSZNmsSLL75Y0PYuvfRS/uEf/oGWlpZqdLfwSRfJ4cDvkzkntR24CjgyImYDW4AvV6WHZmZWcRHB\nokWLGDduHFdeeSUAEyZM4IUXXthrvW3btjFx4sT9bu/222/nxRdf5M///M+r0l8ocIQlaQyZYPWt\niLgNICJ+k7XK14Hbk+cF3XEYMsdHB3R2dtLZ2Vlgt83MrBwXXHABW7du5Uc/+hGjR48GoKOjg+XL\nlw+us2PHDp566ik6Ojr2u7277rqLhx56iD/4gz8AMoFuzJgxPProo9x6661563V3d9Pd3V1QnwvK\nJSjpm8DWiPibrLK2iNiSPP9r4PiIOFfSG4FvAyeQORT4Y2CfSRdpyCUoKf/MnnnznC/MzPaRK0fe\nsN8llVDk99HFF1/MI488wurVq/c6fLd161aOPvporrvuOt797nfz2c9+lnvvvZef/exnQGZUtmvX\nLu666y4+8pGP8MQTTzBq1CgOOOAAduzYwY4dOwa39fGPf5zDDjuMz372sxx00EH79KGUXIKFTGt/\nK/B+4FFJDwMBfAo4V9JsMlPde4APJztU9h2HzcysOjZt2sTXvvY1XvOa19Da2gpkgsS///u/c845\n53DLLbfwl3/5lyxatIgTTjiBm266abDuT3/6U+bNm5cJwGQmZcydO5e77rqL8ePHM378+MF1Dzzw\nQMaPH58zWJXK2dqH4RGWmRUr18ihbdq0zCSuKmk9/HC2ZF3gmwZVGWGZmVl50hZM6pVTM5mZWSo4\nYJmZWSo4YJmZWSo4YJmZWSo4YJmZWSp4lqCZWQVNnz598Doly2/69OlF13HAMjOroJ6enlp3oWH5\nkKCZmaWCA5aZmaXCfgOWpKmS7pK0TtKjkj6elE+RtErSE5LulDQ5q85lkjZK2iDpndXcATMzaw6F\njLD6gb+JiA7g/wL+UtIxwBJgdUS8AbgLuAwgyda+EJgJvAu4Sj4DaWZmZdpvwIqILRGxNnm+HdhA\n5h5XpwMDN05ZDpyRPD8NuCki+iOiB9gIzKlwv83MrMkUdQ5LUjswG7gfaI2IPsgENWDgvsqHAdlp\niZ9NyszMzEpW8LR2SRPI3HX40ojYLmloXvii77XhOw6bmTW3atxxeAzwQ+A/I+KrSdkGoDMi+iS1\nAXdHxExJS4CIiCuS9VYCSyPigSHb9P2wzMxsL8PdD6vQQ4LXAesHglViBbA4eX4ecFtW+fskjZV0\nBHAU8GDRvTYzM8uy30OCkt4KvB94VNLDZA79fQq4ArhZ0vlAL5mZgUTEekk3A+uB3cAldT+UMjOz\nulfQIcGqNOxDgmZmNkQlDgmamZnVlAOWmZmlggOWmZmlggOWmZmlggOWmZmlggOWmZmlggOWmZml\nggOWmZmlggOWmZmlQiF3HL5WUp+kR7LKlkp6RtIvk8fJWct8t2EzM6u4QkZY1wMn5Sj/p4g4Lnms\nBJA0E99t2MzMqqCQOw7fC/wux6Jcgeh0fLdhMzOrgnLOYX1U0lpJ10ianJT5bsNmZlYVBd9xeIir\ngM9FREj6B+DLwIXFbsR3HDYza27VuOPwdOD2iHjLcMsKvdtwssy3FzEzs71U4vYiIuuclaS2rGVn\nAY8lz323YTMzq4pC7jh8I9AJHCxpE7AUmCdpNrAH6AE+DL7bsJmZVY/vODwMHxI0MxtZvuOwmZml\nngOWmZmlggOWmZmlggOWmZmlggOWmZmlggOWmZmlggOWmZmlggOWmZmlggOWmZmlQql3HJ4iaZWk\nJyTdmXV7Ed9x2MzMqqLUOw4vAVZHxBuAu4DLACS9Ed9x2MzMqqDUOw6fDixPni8Hzkien4bvOGxm\nZlVQ6jmsQyOiDyAitgCHJuW+47CZmVVFpSZdOG25mZlV1X7vh5VHn6TWiOhLbub4XFL+LHB41npT\nk7Kcurq6Bp93dnbS2dlZYnfMzCyNuru76e7uLmjdgu6HJakduD0i3py8vgJ4PiKukPRJYEpELEkm\nXXwbOIHMocAfA0fnuvFVo94Pq23aNPo2b96nvPXww9myaVOlu2hm1lCGux9WqXcc/iLwPUnnA71k\nZgb6jsOQCVY5glzfvHk16I2ZWePYb8CKiHPzLHpHnvW/AHyhnE6ZmZkN5UwXZmaWCg5YZmaWCg5Y\nZmaWCg5YZmaWCg5Ylipt06YhaZ9H27Rpte6amVVZqRcOm9WELxswa14eYZmZWSo4YJmZWSo4YJmZ\nWSqUdQ5LUg+wDdgD7I6IOZKmAN8FpgM9wMKI2FZmP83MrMmVO8LaA3RGxLERMXCjxpx3IzYzMytH\nuQFLObaR727EZmZmJSs3YAXwY0k/l3RhUtaa527EZmZmJSv3Oqy3RsSvJb0OWCXpCfa9+3BT3V7E\nzMyqo6yAFRG/Tv79jaT/AOaQ/27E+/Adh83MmlvF7zics6LUAoyKiO2SxgOrgGXAAnLcjThH/bq/\nt2MpdxzOWyfP+lYc/33NGltZdxweRitwq6RItvPtiFgl6RfAzUPvRmxmZlaOkgNWRPwPMDtH+fPk\nuRuxmZlZqZzpwszMUqGpApZvTWE2vHyfEX9OKsN/3/I01e1FfGsKs+Hl+4yAPyeV4L9veZpqhFWP\n/IvLzKwwTTXCqkf+xWVmVhiPsKxmPLosTinnYH3etrr89y1cJT7vHmFZzYzU6LJt2rRMW0O0Hn44\nWzZtqlg7xcjXJ8jfr1LOwfq8bXX571u4Snze6yJglfLhrccvoUZSyntSr+rxS8WHgq1QpXzXFVsn\nLZ/3ughYpXx46/FLqJE08xdqWj68aVWvP1Dr9X0fiZF1Wj7vVQtYkk4G/pnMebJrI+KKarXVbEbi\nF9dI9asepeXDWy+Kfd9H6gfqSPSrmdXi816VSReSRgH/CpwEdADnSDqm4A2sXVtcg8WuX69tFFhn\n8IN1993wla8MPs/367DUOsX0qew2imin5PWr2EbZJ99TvO/1+r6PxP/5Ea9TR22M+PtO9WYJzgE2\nRkRvROwGbiJzJ+LCpPhNHPE6jdJGKXXqqI29PrznnVc3X9p130YpdRqljVLqNEobJdapVsA6DMj+\npD6TlBVmy5ZK9yc9vO/NyfvenLzvRanP67D8JjYn73tz8r43pxL2veQbOA67UelEoCsiTk5eLwEi\ne+JFch8tMzOzveS7gWO1AtZo4Akydx/+NfAgcE5EbKh4Y2Zm1hSqMq09Il6R9FFgFa9Oa3ewMjOz\nklVlhGVmZlZp9TnpwszMbAgHLDMzS4VUBixJx0haIGnCkPKTh6kzR9LxyfM3SvobSe8uos1vFtnH\ntyVtvDPP8hMkTUqeHyhpmaTbJV0haXKeOh+XdHgRfRgr6YOS3pG8PlfSv0r6S0kHDFPvSEl/J+mr\nkv5J0sUDfTVrBpIOHYE2Dq52G42mrgOWpA/lKPs4cBvwMeAxSdkZND6fZztLgX8B/j9JXyCTNmo8\nsETSp3Osv2LI43bgrIHXedp4MOv5RUkbE4GlybT+oa4DXkqefxWYDFyRlF2fqw3g/wEekHSPpEsk\nvS7PegOuB04BLpX0LeD/Bh4AjgeuybMfHweuBl6TrDcO/v/2zjXWiquK479/oVSveC/PAE2R2hZK\nrXsQBtAAAAibSURBVC0UaWO0CpFGqQ/EBBNtYsUv/SQ02ERMWgOSxqIxbdQWPlRSgQa0lrT1g1Br\nQYlaBEIpF7w0sTyqkVYsWvogtsL2w15HJtPZ59y53DP3nMv6JTtnn7XXmrVnz561Z/a8mAjskDS7\ngb9BiQevYiR1SVop6aCkE5JekdRjshEll7U5Ie+UdI+k9ZJuyZWtStiMl7Ra0gOSRktaLqlb0iOS\nJhToj8ql0cBOSSMljUr4mJvJd0laI2mfpA2SxhXor5Q0xvIzJR0i7stHJc1K+Ngj6S5JlxeVJ2xm\nStom6WFJEyU9JelVSbskXVegP1zSCkkHTO+4pB2SFtbx0fTtniSE0LIJeLFA1g0Mt/ylwG7gdvv/\nbGI53cAQoAM4CXSa/N3AvgL9PcDDwGxglv0es/yshI9nM/ldwFjLvwfoLtDvyfrLle1N+SAeZHwS\nWAMcB7YAXwXeW6C/z36HAi8DQ+y/itY721aW7wB+a/n31WnfLmAlcBA4AbwC9JhsRB+2++YCWSdw\nD7AeuCVXtiqxnPHAauABYDSw3NbvEWBCwmZULo0GjgAjgVEF+nNz7bAG2AdsAMYlfKwExlh+JnAI\n+AtwtKh/WX+8C7i8RBvOBLZZP54IPAW8an3zuoTNcGAFcMB0jwM7gIUJ/SeBpcD4XJsvBX5doD8j\nkT4EHEv42GTtNR/4pf2/qGi/ydhsIR7Qfsu2xVJrg0XAEwX6Z4DDufS2/R5K+NiTyf8EuBuYBCwB\nHi/arzL5bcD1lp8C7E74OAz8AHiR+GjQEuDiBtt9J3Az8GXi24YWmHwO8EyB/hPAQuAS4BvAt4HJ\nwFrguwO13ZPrV0a5Gck6VFHqBv5ToH+gYCfbAtxLnUBflLf/77AhDgpLiDv5dJMVdtyMzXPEoDY6\nvyPlfZrsF8DXLP8QMDPTgXc12kns/4XAPGAjcLxAfz8wzOr1GhZwiWdPPQkf3ZwNCCOzOxOwvz86\ncF86MRUELrMpFbwoGbhqbZzJNwxeVBC4rKxU8AKer+P/HWXAaWCrrXM+nUosZ2/u/53AHyjYz4r2\nN3IHvfnlmewO6yvXZNu8QfvuqVPHIh89wFDL70j1hzo+PgasAl6y9rqtD+teFIeey/3fZb8XAAd7\nu237e7snl19GuRmJeOQ/3XbybLoU+HuB/lZsEMnIhgLrgNMJH38COmobIiPvSnV6K7+EOLDcn9/4\nBbpHiEfKh+13gsmHJzpwF/BT4AWr39tm9ztgWqPOWFDWUSBbYss8CiwGngYeJA5KyxLLuZ0Y3B8k\nnjHVBtWxwPb+6MB96cT5NqQJgcvkpYIXJQOXyUsFLyoIXCYvFbyIz1l+k8yZJDCOeGDwmwL9/cDk\nhO+/1mmrC3KyhcSzwKON1gO4u1H7mry2r99LnMpvdID6N+Kgfgdxn1emrGjWZpG11yeIZ/o/JM7Y\nfAdY36hvZWRDgLnAQwmbZ4gzMF8k7vfzTT6L4oOhPwI3Wn4e8GSmLLXvNn27J9u9jHIzEnEK5cZE\n2YZExxqf0P9oQn5RQj6GTGCqU8fPkDg97oVtB/D+OuWdwDTimUXhFFJGd0of/F+MHY0DI4AFwA0N\nbK42vam99FGqA1t5qU5cVeDK9LFeBa+ygcvkpYJXFYHLykoFL+IZ+PeIBzb/Ik4H95isaPp0AXBl\nwvf8hPz7wE0F8rnEL0IU2azALhvk5FcAjzboy/OI06AvNdBblku1SwDjgXUJm9nAz4lT+93Ar4Db\ngAsT+j+rV4eEzTTijMdmYKr1rX/bfvKRhP5O236/r20f4gHq4oSPpm/35PqVbRBPnvIp14FP5Drw\nyIRNqU5cdeAyvYbBqy+By8pTwWtogW7TA5fZXJsLXlNMXi94TQVuyrczmWt7BfpzeqvfwObmPtg0\nrBfx2vYHz6FeVax7PZurStbrqjLb0Mpu4OxU9tXEA7ZP91L/A8SDvKR+cjllDTx5KpOwKcVm2jTT\nRy54tUy9qvSRsiFOMz8PPE6cEv98pqzorLCUvskXNduminpVuO6LiQeOZerVa32TLyMeyO0m3gj1\nNPF653bgzl7ob62nX7cflu24njyVSTS49tcfNlX4aNV6DeS6U/KO3bL6VdkMFh8V16vMXdel9Oul\nprz81jm/kLQvVUS8lnXONlX4aNV6teq6E68pvg4QQjhiz+k9KmmS2ZyrflU2g8VHVfX6bwjhNPCm\npBdCCCfN/pSkM/2gn8QHLKc/GAd8injtI4uIF/L7w6YKH61ar1Zd95clTQ8h7AUIIbwu6bPEh+Kv\n6Qf9qmwGi4+q6vWWpI4QwpvEm8WA+EAx8bGQc9VPU+Z0zJOnokTJOz37YlOFj1atVwuve6k7dsvq\nV2UzWHxUWK9Sd12X1a+X/PMijuM4TlvQ0u8SdBzHcZwaPmA5juM4bYEPWI7jOE5b4HcJOk6TkXSa\n+HLkYcR3Rq4H7gt+AdlxSuEDluM0nzdCCDMA7JtIG4nvkFw+kJVynHbDpwQdp0JCCP8kvjPw6wCS\nJknaLmm3pQ+bfK2keTU7+yDf5wam1o7TGvht7Y7TZCSdDCF05mQngCuJ3yk7E0J4S9IVwMYQwvWS\nPg4sCSF8QVIn8SW5k0MI5R60dJxBhE8JOs7AUHvtzTDgfknTid8ImwwQQtgu+8Q78c32m3ywcs53\nfMBynIqRdBnx/WrHJS0jfr7kWklDgFMZ1XXAV4AvEb/95TjnNT5gOU7z+f9LRCWNBVYDPzZRF/ET\n9gC3Et9qXWMt8ftUx0IIByuop+O0ND5gOU7zeZekPZy9rX1dCOE+K1sFbJJ0K7AFeKNmFEL4h6Qe\n4LGqK+w4rYjfdOE4LYqkDuLzWzNCCK8NdH0cZ6Dx29odpwWRNAf4M/AjH6wcJ+JnWI7jOE5b4GdY\njuM4TlvgA5bjOI7TFviA5TiO47QFPmA5juM4bYEPWI7jOE5b4AOW4ziO0xb8Dyfj9m4VOlaBAAAA\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1204cb690>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot multiple plots on the same plot (plots neeed to be in column format)\n",
    "ufo_fourth = ufo[(ufo.Year.isin([2011, 2012, 2013, 2014])) & (ufo.Month == 7)]\n",
    "\n",
    "\n",
    "# unstack will take a groupby of multiple indices and split it by column (mainly great for sub plotting)\n",
    "\n",
    "# Hmm let's make that prettier by making it 4 seperate charts\n",
    "ufo_fourth.groupby(['Year', 'Day']).City.count().unstack(0).plot(\n",
    "                                        kind = 'bar',\n",
    "                                        subplots=True, \n",
    "                                        figsize = (7,9))\n"
   ]
  },
  {
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  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
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   "source": [
    "\n",
    "'''\n",
    "Further Exploration\n",
    "'''\n",
    "\n",
    "'''\n",
    "Joining Data\n",
    "\n",
    "MovieLens 100k data:\n",
    "    main page: http://grouplens.org/datasets/movielens/\n",
    "    data dictionary: http://files.grouplens.org/datasets/movielens/ml-100k-README.txt\n",
    "    files: u.user, u.data, u.item\n",
    "'''\n",
    "\n",
    "# read 'u.data' into 'ratings'\n",
    "r_cols = ['user_id', 'movie_id', 'rating', 'unix_timestamp']\n",
    "ratings = pd.read_table('https://raw.githubusercontent.com/sinanuozdemir/sfdat26/master/data/u.data', header=None, names=r_cols, sep='\\t')\n",
    "\n",
    "# read 'u.item' into 'movies'\n",
    "m_cols = ['movie_id', 'title']\n",
    "movies = pd.read_table('https://raw.githubusercontent.com/sinanuozdemir/sfdat26/master/data/u.item', header=None, names=m_cols, sep='|', usecols=[0,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movie_id</th>\n",
       "      <th>title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>GoldenEye (1995)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Four Rooms (1995)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Get Shorty (1995)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Copycat (1995)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   movie_id              title\n",
       "0         1   Toy Story (1995)\n",
       "1         2   GoldenEye (1995)\n",
       "2         3  Four Rooms (1995)\n",
       "3         4  Get Shorty (1995)\n",
       "4         5     Copycat (1995)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>movie_id</th>\n",
       "      <th>rating</th>\n",
       "      <th>unix_timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>196</td>\n",
       "      <td>242</td>\n",
       "      <td>3</td>\n",
       "      <td>881250949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>186</td>\n",
       "      <td>302</td>\n",
       "      <td>3</td>\n",
       "      <td>891717742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>22</td>\n",
       "      <td>377</td>\n",
       "      <td>1</td>\n",
       "      <td>878887116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>244</td>\n",
       "      <td>51</td>\n",
       "      <td>2</td>\n",
       "      <td>880606923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>166</td>\n",
       "      <td>346</td>\n",
       "      <td>1</td>\n",
       "      <td>886397596</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id  movie_id  rating  unix_timestamp\n",
       "0      196       242       3       881250949\n",
       "1      186       302       3       891717742\n",
       "2       22       377       1       878887116\n",
       "3      244        51       2       880606923\n",
       "4      166       346       1       886397596"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
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   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movie_id</th>\n",
       "      <th>title</th>\n",
       "      <th>user_id</th>\n",
       "      <th>rating</th>\n",
       "      <th>unix_timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>308</td>\n",
       "      <td>4</td>\n",
       "      <td>887736532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>287</td>\n",
       "      <td>5</td>\n",
       "      <td>875334088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>148</td>\n",
       "      <td>4</td>\n",
       "      <td>877019411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>280</td>\n",
       "      <td>4</td>\n",
       "      <td>891700426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>66</td>\n",
       "      <td>3</td>\n",
       "      <td>883601324</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   movie_id             title  user_id  rating  unix_timestamp\n",
       "0         1  Toy Story (1995)      308       4       887736532\n",
       "1         1  Toy Story (1995)      287       5       875334088\n",
       "2         1  Toy Story (1995)      148       4       877019411\n",
       "3         1  Toy Story (1995)      280       4       891700426\n",
       "4         1  Toy Story (1995)       66       3       883601324"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# merge 'movies' and 'ratings' (inner join on 'movie_id')\n",
    "movie_ratings = pd.merge(movies, ratings)\n",
    "movie_ratings.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Star Wars (1977)                                      583\n",
       "Contact (1997)                                        509\n",
       "Fargo (1996)                                          508\n",
       "Return of the Jedi (1983)                             507\n",
       "Liar Liar (1997)                                      485\n",
       "English Patient, The (1996)                           481\n",
       "Scream (1996)                                         478\n",
       "Toy Story (1995)                                      452\n",
       "Air Force One (1997)                                  431\n",
       "Independence Day (ID4) (1996)                         429\n",
       "Raiders of the Lost Ark (1981)                        420\n",
       "Godfather, The (1972)                                 413\n",
       "Pulp Fiction (1994)                                   394\n",
       "Twelve Monkeys (1995)                                 392\n",
       "Silence of the Lambs, The (1991)                      390\n",
       "Jerry Maguire (1996)                                  384\n",
       "Chasing Amy (1997)                                    379\n",
       "Rock, The (1996)                                      378\n",
       "Empire Strikes Back, The (1980)                       367\n",
       "Star Trek: First Contact (1996)                       365\n",
       "Back to the Future (1985)                             350\n",
       "Titanic (1997)                                        350\n",
       "Mission: Impossible (1996)                            344\n",
       "Fugitive, The (1993)                                  336\n",
       "Indiana Jones and the Last Crusade (1989)             331\n",
       "Willy Wonka and the Chocolate Factory (1971)          326\n",
       "Princess Bride, The (1987)                            324\n",
       "Forrest Gump (1994)                                   321\n",
       "Monty Python and the Holy Grail (1974)                316\n",
       "Saint, The (1997)                                     316\n",
       "                                                     ... \n",
       "Brother's Kiss, A (1997)                                1\n",
       "Coldblooded (1995)                                      1\n",
       "Silence of the Palace, The (Saimt el Qusur) (1994)      1\n",
       "Century (1993)                                          1\n",
       "Homage (1995)                                           1\n",
       "Hana-bi (1997)                                          1\n",
       "Symphonie pastorale, La (1946)                          1\n",
       "They Made Me a Criminal (1939)                          1\n",
       "Brothers in Trouble (1995)                              1\n",
       "Girls Town (1996)                                       1\n",
       "Touki Bouki (Journey of the Hyena) (1973)               1\n",
       "Marlene Dietrich: Shadow and Light (1996)               1\n",
       "Sleepover (1995)                                        1\n",
       "Sunchaser, The (1996)                                   1\n",
       "Nothing Personal (1995)                                 1\n",
       "Niagara, Niagara (1997)                                 1\n",
       "Further Gesture, A (1996)                               1\n",
       "Great Day in Harlem, A (1994)                           1\n",
       "Eye of Vichy, The (Oeil de Vichy, L') (1993)            1\n",
       "Daens (1992)                                            1\n",
       "Woman in Question, The (1950)                           1\n",
       "Someone Else's America (1995)                           1\n",
       "Man from Down Under, The (1943)                         1\n",
       "Jupiter's Wife (1994)                                   1\n",
       "Spanish Prisoner, The (1997)                            1\n",
       "Next Step, The (1995)                                   1\n",
       "I, Worst of All (Yo, la peor de todas) (1990)           1\n",
       "Two Friends (1986)                                      1\n",
       "Gate of Heavenly Peace, The (1995)                      1\n",
       "Crude Oasis, The (1995)                                 1\n",
       "Name: title, dtype: int64"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# for each movie, count number of ratings\n",
    "movie_ratings.title.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/sinanozdemir/anaconda/envs/sfdat26-env/lib/python2.7/site-packages/ipykernel/__main__.py:1: FutureWarning: order is deprecated, use sort_values(...)\n",
      "  if __name__ == '__main__':\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "title\n",
       "Marlene Dietrich: Shadow and Light (1996)                 5.000000\n",
       "Prefontaine (1997)                                        5.000000\n",
       "Santa with Muscles (1996)                                 5.000000\n",
       "Star Kid (1997)                                           5.000000\n",
       "Someone Else's America (1995)                             5.000000\n",
       "Entertaining Angels: The Dorothy Day Story (1996)         5.000000\n",
       "Saint of Fort Washington, The (1993)                      5.000000\n",
       "Great Day in Harlem, A (1994)                             5.000000\n",
       "They Made Me a Criminal (1939)                            5.000000\n",
       "Aiqing wansui (1994)                                      5.000000\n",
       "Pather Panchali (1955)                                    4.625000\n",
       "Anna (1996)                                               4.500000\n",
       "Everest (1998)                                            4.500000\n",
       "Maya Lin: A Strong Clear Vision (1994)                    4.500000\n",
       "Some Mother's Son (1996)                                  4.500000\n",
       "Close Shave, A (1995)                                     4.491071\n",
       "Schindler's List (1993)                                   4.466443\n",
       "Wrong Trousers, The (1993)                                4.466102\n",
       "Casablanca (1942)                                         4.456790\n",
       "Wallace & Gromit: The Best of Aardman Animation (1996)    4.447761\n",
       "Shawshank Redemption, The (1994)                          4.445230\n",
       "Rear Window (1954)                                        4.387560\n",
       "Usual Suspects, The (1995)                                4.385768\n",
       "Star Wars (1977)                                          4.358491\n",
       "12 Angry Men (1957)                                       4.344000\n",
       "Third Man, The (1949)                                     4.333333\n",
       "Letter From Death Row, A (1998)                           4.333333\n",
       "Bitter Sugar (Azucar Amargo) (1996)                       4.333333\n",
       "Citizen Kane (1941)                                       4.292929\n",
       "Some Folks Call It a Sling Blade (1993)                   4.292683\n",
       "                                                            ...   \n",
       "Symphonie pastorale, La (1946)                            1.000000\n",
       "Baton Rouge (1988)                                        1.000000\n",
       "Boys in Venice (1996)                                     1.000000\n",
       "Tigrero: A Film That Was Never Made (1994)                1.000000\n",
       "Office Killer (1997)                                      1.000000\n",
       "T-Men (1947)                                              1.000000\n",
       "Terror in a Texas Town (1958)                             1.000000\n",
       "Nobody Loves Me (Keiner liebt mich) (1994)                1.000000\n",
       "The Courtyard (1995)                                      1.000000\n",
       "King of New York (1990)                                   1.000000\n",
       "Liebelei (1933)                                           1.000000\n",
       "Stefano Quantestorie (1993)                               1.000000\n",
       "Hostile Intentions (1994)                                 1.000000\n",
       "Pharaoh's Army (1995)                                     1.000000\n",
       "New Age, The (1994)                                       1.000000\n",
       "Hungarian Fairy Tale, A (1987)                            1.000000\n",
       "Death in the Garden (Mort en ce jardin, La) (1956)        1.000000\n",
       "Promise, The (Versprechen, Das) (1994)                    1.000000\n",
       "I, Worst of All (Yo, la peor de todas) (1990)             1.000000\n",
       "Power 98 (1995)                                           1.000000\n",
       "Police Story 4: Project S (Chao ji ji hua) (1993)         1.000000\n",
       "Low Life, The (1994)                                      1.000000\n",
       "Somebody to Love (1994)                                   1.000000\n",
       "Invitation, The (Zaproszenie) (1986)                      1.000000\n",
       "Lotto Land (1995)                                         1.000000\n",
       "Touki Bouki (Journey of the Hyena) (1973)                 1.000000\n",
       "JLG/JLG - autoportrait de d�cembre (1994)                 1.000000\n",
       "Daens (1992)                                              1.000000\n",
       "Butterfly Kiss (1995)                                     1.000000\n",
       "Eye of Vichy, The (Oeil de Vichy, L') (1993)              1.000000\n",
       "Name: rating, dtype: float64"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_ratings.groupby('title').rating.mean().order(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
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  }
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