├── Assignment2.ipynb
├── Assignment3.ipynb
├── Assignment4.ipynb
├── Dejunkifying a Plot.ipynb
├── LICENSE
├── README.md
├── Rain_1961_1990.xls
├── Temp_1961_1990.xls
├── UNdata_Export_20180725_144821933.csv
├── UnderstandingDistributionsThroughSampling.ipynb
├── Week2.ipynb
├── Week3.ipynb
├── Week4.ipynb
├── heroes_information.csv
└── iris.csv


/Assignment4.ipynb:
--------------------------------------------------------------------------------
   1 | {
   2 |  "cells": [
   3 |   {
   4 |    "cell_type": "markdown",
   5 |    "metadata": {},
   6 |    "source": [
   7 |     "# Did Snowfall and Rainfall Effect Temperature of United States of America (1961 - 1990)"
   8 |    ]
   9 |   },
  10 |   {
  11 |    "cell_type": "markdown",
  12 |    "metadata": {},
  13 |    "source": [
  14 |     "## Assignment 4\n",
  15 |     "\n",
  16 |     "Before working on this assignment please read these instructions fully. In the submission area, you will notice that you can click the link to **Preview the Grading** for each step of the assignment. This is the criteria that will be used for peer grading. Please familiarize yourself with the criteria before beginning the assignment.\n",
  17 |     "\n",
  18 |     "This assignment requires that you to find **at least** two datasets on the web which are related, and that you visualize these datasets to answer a question with the broad topic of **weather phenomena** (see below) for the region of **Ann Arbor, Michigan, United States**, or **United States** more broadly.\n",
  19 |     "\n",
  20 |     "You can merge these datasets with data from different regions if you like! For instance, you might want to compare **Ann Arbor, Michigan, United States** to Ann Arbor, USA. In that case at least one source file must be about **Ann Arbor, Michigan, United States**.\n",
  21 |     "\n",
  22 |     "You are welcome to choose datasets at your discretion, but keep in mind **they will be shared with your peers**, so choose appropriate datasets. Sensitive, confidential, illicit, and proprietary materials are not good choices for datasets for this assignment. You are welcome to upload datasets of your own as well, and link to them using a third party repository such as github, bitbucket, pastebin, etc. Please be aware of the Coursera terms of service with respect to intellectual property.\n",
  23 |     "\n",
  24 |     "Also, you are welcome to preserve data in its original language, but for the purposes of grading you should provide english translations. You are welcome to provide multiple visuals in different languages if you would like!\n",
  25 |     "\n",
  26 |     "As this assignment is for the whole course, you must incorporate principles discussed in the first week, such as having as high data-ink ratio (Tufte) and aligning with Cairo’s principles of truth, beauty, function, and insight.\n",
  27 |     "\n",
  28 |     "Here are the assignment instructions:\n",
  29 |     "\n",
  30 |     " * State the region and the domain category that your data sets are about (e.g., **Ann Arbor, Michigan, United States** and **weather phenomena**).\n",
  31 |     " * You must state a question about the domain category and region that you identified as being interesting.\n",
  32 |     " * You must provide at least two links to available datasets. These could be links to files such as CSV or Excel files, or links to websites which might have data in tabular form, such as Wikipedia pages.\n",
  33 |     " * You must upload an image which addresses the research question you stated. In addition to addressing the question, this visual should follow Cairo's principles of truthfulness, functionality, beauty, and insightfulness.\n",
  34 |     " * You must contribute a short (1-2 paragraph) written justification of how your visualization addresses your stated research question.\n",
  35 |     "\n",
  36 |     "What do we mean by **weather phenomena**?  For this category you might want to consider seasonal changes, natural disasters, or historical trends.\n",
  37 |     "\n",
  38 |     "## Tips\n",
  39 |     "* Wikipedia is an excellent source of data, and I strongly encourage you to explore it for new data sources.\n",
  40 |     "* Many governments run open data initiatives at the city, region, and country levels, and these are wonderful resources for localized data sources.\n",
  41 |     "* Several international agencies, such as the [United Nations](http://data.un.org/), the [World Bank](http://data.worldbank.org/), the [Global Open Data Index](http://index.okfn.org/place/) are other great places to look for data.\n",
  42 |     "* This assignment requires you to convert and clean datafiles. Check out the discussion forums for tips on how to do this from various sources, and share your successes with your fellow students!\n",
  43 |     "\n",
  44 |     "## Example\n",
  45 |     "Looking for an example? Here's what our course assistant put together for the **Ann Arbor, MI, USA** area using **sports and athletics** as the topic. [Example Solution File](./readonly/Assignment4_example.pdf)"
  46 |    ]
  47 |   },
  48 |   {
  49 |    "cell_type": "code",
  50 |    "execution_count": 1,
  51 |    "metadata": {},
  52 |    "outputs": [],
  53 |    "source": [
  54 |     "import pandas as pd \n",
  55 |     "import numpy as np\n",
  56 |     "import matplotlib.pyplot as plt\n",
  57 |     "\n",
  58 |     "%matplotlib notebook"
  59 |    ]
  60 |   },
  61 |   {
  62 |    "cell_type": "code",
  63 |    "execution_count": 2,
  64 |    "metadata": {},
  65 |    "outputs": [],
  66 |    "source": [
  67 |     "plt.style.use('seaborn-colorblind')"
  68 |    ]
  69 |   },
  70 |   {
  71 |    "cell_type": "code",
  72 |    "execution_count": 3,
  73 |    "metadata": {
  74 |     "scrolled": false
  75 |    },
  76 |    "outputs": [
  77 |     {
  78 |      "data": {
  79 |       "text/html": [
  80 |        "<div>\n",
  81 |        "<style scoped>\n",
  82 |        "    .dataframe tbody tr th:only-of-type {\n",
  83 |        "        vertical-align: middle;\n",
  84 |        "    }\n",
  85 |        "\n",
  86 |        "    .dataframe tbody tr th {\n",
  87 |        "        vertical-align: top;\n",
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  89 |        "\n",
  90 |        "    .dataframe thead th {\n",
  91 |        "        text-align: right;\n",
  92 |        "    }\n",
  93 |        "</style>\n",
  94 |        "<table border=\"1\" class=\"dataframe\">\n",
  95 |        "  <thead>\n",
  96 |        "    <tr style=\"text-align: right;\">\n",
  97 |        "      <th></th>\n",
  98 |        "      <th>Country or Territory</th>\n",
  99 |        "      <th>Station Name</th>\n",
 100 |        "      <th>WMO Station Number</th>\n",
 101 |        "      <th>National Station Id Number</th>\n",
 102 |        "      <th>Period</th>\n",
 103 |        "      <th>Element-Statistic Qualifier Code</th>\n",
 104 |        "      <th>Statistic Description</th>\n",
 105 |        "      <th>Unit</th>\n",
 106 |        "      <th>Jan</th>\n",
 107 |        "      <th>Jan Footnotes</th>\n",
 108 |        "      <th>...</th>\n",
 109 |        "      <th>Oct</th>\n",
 110 |        "      <th>Oct Footnotes</th>\n",
 111 |        "      <th>Nov</th>\n",
 112 |        "      <th>Nov Footnotes</th>\n",
 113 |        "      <th>Dec</th>\n",
 114 |        "      <th>Dec Footnotes</th>\n",
 115 |        "      <th>Annual</th>\n",
 116 |        "      <th>Annual Footnotes</th>\n",
 117 |        "      <th>Annual NCDC Computed Value</th>\n",
 118 |        "      <th>Annual NCDC Computed Value Footnotes</th>\n",
 119 |        "    </tr>\n",
 120 |        "  </thead>\n",
 121 |        "  <tbody>\n",
 122 |        "    <tr>\n",
 123 |        "      <th>0</th>\n",
 124 |        "      <td>UNITED STATES OF AMERICA</td>\n",
 125 |        "      <td>BARROW/W. POST W. ROGERS, AK</td>\n",
 126 |        "      <td>70026.0</td>\n",
 127 |        "      <td>500546.0</td>\n",
 128 |        "      <td>1961-1990</td>\n",
 129 |        "      <td>NaN</td>\n",
 130 |        "      <td>Median Value</td>\n",
 131 |        "      <td>cm</td>\n",
 132 |        "      <td>4.3</td>\n",
 133 |        "      <td>NaN</td>\n",
 134 |        "      <td>...</td>\n",
 135 |        "      <td>14.0</td>\n",
 136 |        "      <td>NaN</td>\n",
 137 |        "      <td>6.6</td>\n",
 138 |        "      <td>NaN</td>\n",
 139 |        "      <td>5.1</td>\n",
 140 |        "      <td>NaN</td>\n",
 141 |        "      <td>-9999.9</td>\n",
 142 |        "      <td>2.0</td>\n",
 143 |        "      <td>-9999.9</td>\n",
 144 |        "      <td>2.0</td>\n",
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 146 |        "    <tr>\n",
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 148 |        "      <td>UNITED STATES OF AMERICA</td>\n",
 149 |        "      <td>KOTZEBUE/RALPH WIEN, AK</td>\n",
 150 |        "      <td>70133.0</td>\n",
 151 |        "      <td>505076.0</td>\n",
 152 |        "      <td>1961-1990</td>\n",
 153 |        "      <td>NaN</td>\n",
 154 |        "      <td>Median Value</td>\n",
 155 |        "      <td>cm</td>\n",
 156 |        "      <td>14.2</td>\n",
 157 |        "      <td>NaN</td>\n",
 158 |        "      <td>...</td>\n",
 159 |        "      <td>15.2</td>\n",
 160 |        "      <td>NaN</td>\n",
 161 |        "      <td>19.0</td>\n",
 162 |        "      <td>NaN</td>\n",
 163 |        "      <td>16.5</td>\n",
 164 |        "      <td>NaN</td>\n",
 165 |        "      <td>-9999.9</td>\n",
 166 |        "      <td>2.0</td>\n",
 167 |        "      <td>-9999.9</td>\n",
 168 |        "      <td>2.0</td>\n",
 169 |        "    </tr>\n",
 170 |        "    <tr>\n",
 171 |        "      <th>2</th>\n",
 172 |        "      <td>UNITED STATES OF AMERICA</td>\n",
 173 |        "      <td>BETTLES/FIELD AK</td>\n",
 174 |        "      <td>70174.0</td>\n",
 175 |        "      <td>500761.0</td>\n",
 176 |        "      <td>1961-1990</td>\n",
 177 |        "      <td>NaN</td>\n",
 178 |        "      <td>Median Value</td>\n",
 179 |        "      <td>cm</td>\n",
 180 |        "      <td>26.4</td>\n",
 181 |        "      <td>NaN</td>\n",
 182 |        "      <td>...</td>\n",
 183 |        "      <td>28.7</td>\n",
 184 |        "      <td>NaN</td>\n",
 185 |        "      <td>27.4</td>\n",
 186 |        "      <td>NaN</td>\n",
 187 |        "      <td>36.3</td>\n",
 188 |        "      <td>NaN</td>\n",
 189 |        "      <td>-9999.9</td>\n",
 190 |        "      <td>2.0</td>\n",
 191 |        "      <td>-9999.9</td>\n",
 192 |        "      <td>2.0</td>\n",
 193 |        "    </tr>\n",
 194 |        "    <tr>\n",
 195 |        "      <th>3</th>\n",
 196 |        "      <td>UNITED STATES OF AMERICA</td>\n",
 197 |        "      <td>NOME, AK</td>\n",
 198 |        "      <td>70200.0</td>\n",
 199 |        "      <td>506496.0</td>\n",
 200 |        "      <td>1961-1990</td>\n",
 201 |        "      <td>NaN</td>\n",
 202 |        "      <td>Median Value</td>\n",
 203 |        "      <td>cm</td>\n",
 204 |        "      <td>17.3</td>\n",
 205 |        "      <td>NaN</td>\n",
 206 |        "      <td>...</td>\n",
 207 |        "      <td>10.4</td>\n",
 208 |        "      <td>NaN</td>\n",
 209 |        "      <td>27.4</td>\n",
 210 |        "      <td>NaN</td>\n",
 211 |        "      <td>22.9</td>\n",
 212 |        "      <td>NaN</td>\n",
 213 |        "      <td>-9999.9</td>\n",
 214 |        "      <td>2.0</td>\n",
 215 |        "      <td>-9999.9</td>\n",
 216 |        "      <td>2.0</td>\n",
 217 |        "    </tr>\n",
 218 |        "    <tr>\n",
 219 |        "      <th>4</th>\n",
 220 |        "      <td>UNITED STATES OF AMERICA</td>\n",
 221 |        "      <td>BETHEL/BETHEL AIRPORT, AK</td>\n",
 222 |        "      <td>70219.0</td>\n",
 223 |        "      <td>500754.0</td>\n",
 224 |        "      <td>1961-1990</td>\n",
 225 |        "      <td>NaN</td>\n",
 226 |        "      <td>Median Value</td>\n",
 227 |        "      <td>cm</td>\n",
 228 |        "      <td>14.2</td>\n",
 229 |        "      <td>NaN</td>\n",
 230 |        "      <td>...</td>\n",
 231 |        "      <td>9.4</td>\n",
 232 |        "      <td>NaN</td>\n",
 233 |        "      <td>18.8</td>\n",
 234 |        "      <td>NaN</td>\n",
 235 |        "      <td>17.3</td>\n",
 236 |        "      <td>NaN</td>\n",
 237 |        "      <td>-9999.9</td>\n",
 238 |        "      <td>2.0</td>\n",
 239 |        "      <td>-9999.9</td>\n",
 240 |        "      <td>2.0</td>\n",
 241 |        "    </tr>\n",
 242 |        "  </tbody>\n",
 243 |        "</table>\n",
 244 |        "<p>5 rows × 36 columns</p>\n",
 245 |        "</div>"
 246 |       ],
 247 |       "text/plain": [
 248 |        "       Country or Territory                  Station Name  WMO Station Number  \\\n",
 249 |        "0  UNITED STATES OF AMERICA  BARROW/W. POST W. ROGERS, AK             70026.0   \n",
 250 |        "1  UNITED STATES OF AMERICA       KOTZEBUE/RALPH WIEN, AK             70133.0   \n",
 251 |        "2  UNITED STATES OF AMERICA              BETTLES/FIELD AK             70174.0   \n",
 252 |        "3  UNITED STATES OF AMERICA                      NOME, AK             70200.0   \n",
 253 |        "4  UNITED STATES OF AMERICA     BETHEL/BETHEL AIRPORT, AK             70219.0   \n",
 254 |        "\n",
 255 |        "   National Station Id Number     Period  Element-Statistic Qualifier Code  \\\n",
 256 |        "0                    500546.0  1961-1990                               NaN   \n",
 257 |        "1                    505076.0  1961-1990                               NaN   \n",
 258 |        "2                    500761.0  1961-1990                               NaN   \n",
 259 |        "3                    506496.0  1961-1990                               NaN   \n",
 260 |        "4                    500754.0  1961-1990                               NaN   \n",
 261 |        "\n",
 262 |        "  Statistic Description Unit   Jan  Jan Footnotes  \\\n",
 263 |        "0          Median Value   cm   4.3            NaN   \n",
 264 |        "1          Median Value   cm  14.2            NaN   \n",
 265 |        "2          Median Value   cm  26.4            NaN   \n",
 266 |        "3          Median Value   cm  17.3            NaN   \n",
 267 |        "4          Median Value   cm  14.2            NaN   \n",
 268 |        "\n",
 269 |        "                   ...                    Oct  Oct Footnotes   Nov  \\\n",
 270 |        "0                  ...                   14.0            NaN   6.6   \n",
 271 |        "1                  ...                   15.2            NaN  19.0   \n",
 272 |        "2                  ...                   28.7            NaN  27.4   \n",
 273 |        "3                  ...                   10.4            NaN  27.4   \n",
 274 |        "4                  ...                    9.4            NaN  18.8   \n",
 275 |        "\n",
 276 |        "   Nov Footnotes   Dec  Dec Footnotes  Annual  Annual Footnotes  \\\n",
 277 |        "0            NaN   5.1            NaN -9999.9               2.0   \n",
 278 |        "1            NaN  16.5            NaN -9999.9               2.0   \n",
 279 |        "2            NaN  36.3            NaN -9999.9               2.0   \n",
 280 |        "3            NaN  22.9            NaN -9999.9               2.0   \n",
 281 |        "4            NaN  17.3            NaN -9999.9               2.0   \n",
 282 |        "\n",
 283 |        "   Annual NCDC Computed Value  Annual NCDC Computed Value Footnotes  \n",
 284 |        "0                     -9999.9                                   2.0  \n",
 285 |        "1                     -9999.9                                   2.0  \n",
 286 |        "2                     -9999.9                                   2.0  \n",
 287 |        "3                     -9999.9                                   2.0  \n",
 288 |        "4                     -9999.9                                   2.0  \n",
 289 |        "\n",
 290 |        "[5 rows x 36 columns]"
 291 |       ]
 292 |      },
 293 |      "execution_count": 3,
 294 |      "metadata": {},
 295 |      "output_type": "execute_result"
 296 |     }
 297 |    ],
 298 |    "source": [
 299 |     "Snowfall = pd.read_csv('UNdata_Snowfall.csv')\n",
 300 |     "Snowfall.head()"
 301 |    ]
 302 |   },
 303 |   {
 304 |    "cell_type": "code",
 305 |    "execution_count": 4,
 306 |    "metadata": {},
 307 |    "outputs": [
 308 |     {
 309 |      "data": {
 310 |       "text/html": [
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 324 |        "</style>\n",
 325 |        "<table border=\"1\" class=\"dataframe\">\n",
 326 |        "  <thead>\n",
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 329 |        "      <th>tas</th>\n",
 330 |        "      <th>Year</th>\n",
 331 |        "      <th>Month</th>\n",
 332 |        "      <th>Country</th>\n",
 333 |        "      <th>ISO3</th>\n",
 334 |        "      <th>ISO2</th>\n",
 335 |        "    </tr>\n",
 336 |        "  </thead>\n",
 337 |        "  <tbody>\n",
 338 |        "    <tr>\n",
 339 |        "      <th>0</th>\n",
 340 |        "      <td>-5.21460</td>\n",
 341 |        "      <td>1961</td>\n",
 342 |        "      <td>1</td>\n",
 343 |        "      <td>USA</td>\n",
 344 |        "      <td>NaN</td>\n",
 345 |        "      <td>NaN</td>\n",
 346 |        "    </tr>\n",
 347 |        "    <tr>\n",
 348 |        "      <th>1</th>\n",
 349 |        "      <td>-3.21400</td>\n",
 350 |        "      <td>1961</td>\n",
 351 |        "      <td>2</td>\n",
 352 |        "      <td>USA</td>\n",
 353 |        "      <td>NaN</td>\n",
 354 |        "      <td>NaN</td>\n",
 355 |        "    </tr>\n",
 356 |        "    <tr>\n",
 357 |        "      <th>2</th>\n",
 358 |        "      <td>-0.15080</td>\n",
 359 |        "      <td>1961</td>\n",
 360 |        "      <td>3</td>\n",
 361 |        "      <td>USA</td>\n",
 362 |        "      <td>NaN</td>\n",
 363 |        "      <td>NaN</td>\n",
 364 |        "    </tr>\n",
 365 |        "    <tr>\n",
 366 |        "      <th>3</th>\n",
 367 |        "      <td>4.52072</td>\n",
 368 |        "      <td>1961</td>\n",
 369 |        "      <td>4</td>\n",
 370 |        "      <td>USA</td>\n",
 371 |        "      <td>NaN</td>\n",
 372 |        "      <td>NaN</td>\n",
 373 |        "    </tr>\n",
 374 |        "    <tr>\n",
 375 |        "      <th>4</th>\n",
 376 |        "      <td>11.86120</td>\n",
 377 |        "      <td>1961</td>\n",
 378 |        "      <td>5</td>\n",
 379 |        "      <td>USA</td>\n",
 380 |        "      <td>NaN</td>\n",
 381 |        "      <td>NaN</td>\n",
 382 |        "    </tr>\n",
 383 |        "  </tbody>\n",
 384 |        "</table>\n",
 385 |        "</div>"
 386 |       ],
 387 |       "text/plain": [
 388 |        "        tas  \\tYear   Month  Country   ISO3   ISO2\n",
 389 |        "0  -5.21460    1961       1      USA    NaN    NaN\n",
 390 |        "1  -3.21400    1961       2      USA    NaN    NaN\n",
 391 |        "2  -0.15080    1961       3      USA    NaN    NaN\n",
 392 |        "3   4.52072    1961       4      USA    NaN    NaN\n",
 393 |        "4  11.86120    1961       5      USA    NaN    NaN"
 394 |       ]
 395 |      },
 396 |      "execution_count": 4,
 397 |      "metadata": {},
 398 |      "output_type": "execute_result"
 399 |     }
 400 |    ],
 401 |    "source": [
 402 |     "Temperature = pd.read_excel('Temp_1961_1990.xls')\n",
 403 |     "Temperature.head()"
 404 |    ]
 405 |   },
 406 |   {
 407 |    "cell_type": "code",
 408 |    "execution_count": 5,
 409 |    "metadata": {},
 410 |    "outputs": [
 411 |     {
 412 |      "data": {
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 423 |        "\n",
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 426 |        "    }\n",
 427 |        "</style>\n",
 428 |        "<table border=\"1\" class=\"dataframe\">\n",
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 430 |        "    <tr style=\"text-align: right;\">\n",
 431 |        "      <th></th>\n",
 432 |        "      <th>pr</th>\n",
 433 |        "      <th>Year</th>\n",
 434 |        "      <th>Month</th>\n",
 435 |        "      <th>Country</th>\n",
 436 |        "      <th>ISO3</th>\n",
 437 |        "      <th>ISO2</th>\n",
 438 |        "    </tr>\n",
 439 |        "  </thead>\n",
 440 |        "  <tbody>\n",
 441 |        "    <tr>\n",
 442 |        "      <th>0</th>\n",
 443 |        "      <td>29.8744</td>\n",
 444 |        "      <td>1961</td>\n",
 445 |        "      <td>1</td>\n",
 446 |        "      <td>USA</td>\n",
 447 |        "      <td>NaN</td>\n",
 448 |        "      <td>NaN</td>\n",
 449 |        "    </tr>\n",
 450 |        "    <tr>\n",
 451 |        "      <th>1</th>\n",
 452 |        "      <td>52.7065</td>\n",
 453 |        "      <td>1961</td>\n",
 454 |        "      <td>2</td>\n",
 455 |        "      <td>USA</td>\n",
 456 |        "      <td>NaN</td>\n",
 457 |        "      <td>NaN</td>\n",
 458 |        "    </tr>\n",
 459 |        "    <tr>\n",
 460 |        "      <th>2</th>\n",
 461 |        "      <td>59.5436</td>\n",
 462 |        "      <td>1961</td>\n",
 463 |        "      <td>3</td>\n",
 464 |        "      <td>USA</td>\n",
 465 |        "      <td>NaN</td>\n",
 466 |        "      <td>NaN</td>\n",
 467 |        "    </tr>\n",
 468 |        "    <tr>\n",
 469 |        "      <th>3</th>\n",
 470 |        "      <td>48.6108</td>\n",
 471 |        "      <td>1961</td>\n",
 472 |        "      <td>4</td>\n",
 473 |        "      <td>USA</td>\n",
 474 |        "      <td>NaN</td>\n",
 475 |        "      <td>NaN</td>\n",
 476 |        "    </tr>\n",
 477 |        "    <tr>\n",
 478 |        "      <th>4</th>\n",
 479 |        "      <td>57.4379</td>\n",
 480 |        "      <td>1961</td>\n",
 481 |        "      <td>5</td>\n",
 482 |        "      <td>USA</td>\n",
 483 |        "      <td>NaN</td>\n",
 484 |        "      <td>NaN</td>\n",
 485 |        "    </tr>\n",
 486 |        "  </tbody>\n",
 487 |        "</table>\n",
 488 |        "</div>"
 489 |       ],
 490 |       "text/plain": [
 491 |        "        pr  \\tYear   Month  Country   ISO3   ISO2\n",
 492 |        "0  29.8744    1961       1      USA    NaN    NaN\n",
 493 |        "1  52.7065    1961       2      USA    NaN    NaN\n",
 494 |        "2  59.5436    1961       3      USA    NaN    NaN\n",
 495 |        "3  48.6108    1961       4      USA    NaN    NaN\n",
 496 |        "4  57.4379    1961       5      USA    NaN    NaN"
 497 |       ]
 498 |      },
 499 |      "execution_count": 5,
 500 |      "metadata": {},
 501 |      "output_type": "execute_result"
 502 |     }
 503 |    ],
 504 |    "source": [
 505 |     "Rainfall = pd.read_excel('Rain_1961_1990.xls')\n",
 506 |     "Rainfall.head()"
 507 |    ]
 508 |   },
 509 |   {
 510 |    "cell_type": "code",
 511 |    "execution_count": 6,
 512 |    "metadata": {},
 513 |    "outputs": [],
 514 |    "source": [
 515 |     "Jan = Snowfall['Jan'].mean()\n",
 516 |     "Feb = Snowfall['Feb'].mean()\n",
 517 |     "Mar = Snowfall['Mar'].mean()\n",
 518 |     "Apr = Snowfall['Apr'].mean()\n",
 519 |     "May = Snowfall['May'].mean()\n",
 520 |     "Jun = Snowfall['Jun'].mean()\n",
 521 |     "Jul = Snowfall['Jul'].mean()\n",
 522 |     "Aug = Snowfall['Aug'].mean()\n",
 523 |     "Sep = Snowfall['Sep'].mean()\n",
 524 |     "Oct = Snowfall['Oct'].mean()\n",
 525 |     "Nov = Snowfall['Nov'].mean()\n",
 526 |     "Dec = Snowfall['Dec'].mean()"
 527 |    ]
 528 |   },
 529 |   {
 530 |    "cell_type": "code",
 531 |    "execution_count": 7,
 532 |    "metadata": {},
 533 |    "outputs": [
 534 |     {
 535 |      "data": {
 536 |       "text/html": [
 537 |        "<div>\n",
 538 |        "<style scoped>\n",
 539 |        "    .dataframe tbody tr th:only-of-type {\n",
 540 |        "        vertical-align: middle;\n",
 541 |        "    }\n",
 542 |        "\n",
 543 |        "    .dataframe tbody tr th {\n",
 544 |        "        vertical-align: top;\n",
 545 |        "    }\n",
 546 |        "\n",
 547 |        "    .dataframe thead th {\n",
 548 |        "        text-align: right;\n",
 549 |        "    }\n",
 550 |        "</style>\n",
 551 |        "<table border=\"1\" class=\"dataframe\">\n",
 552 |        "  <thead>\n",
 553 |        "    <tr style=\"text-align: right;\">\n",
 554 |        "      <th></th>\n",
 555 |        "      <th>Snowfall</th>\n",
 556 |        "    </tr>\n",
 557 |        "  </thead>\n",
 558 |        "  <tbody>\n",
 559 |        "    <tr>\n",
 560 |        "      <th>0</th>\n",
 561 |        "      <td>32910.929764</td>\n",
 562 |        "    </tr>\n",
 563 |        "    <tr>\n",
 564 |        "      <th>1</th>\n",
 565 |        "      <td>32099.560367</td>\n",
 566 |        "    </tr>\n",
 567 |        "    <tr>\n",
 568 |        "      <th>2</th>\n",
 569 |        "      <td>32814.367087</td>\n",
 570 |        "    </tr>\n",
 571 |        "    <tr>\n",
 572 |        "      <th>3</th>\n",
 573 |        "      <td>35263.127507</td>\n",
 574 |        "    </tr>\n",
 575 |        "    <tr>\n",
 576 |        "      <th>4</th>\n",
 577 |        "      <td>35805.531129</td>\n",
 578 |        "    </tr>\n",
 579 |        "    <tr>\n",
 580 |        "      <th>5</th>\n",
 581 |        "      <td>30106.764987</td>\n",
 582 |        "    </tr>\n",
 583 |        "    <tr>\n",
 584 |        "      <th>6</th>\n",
 585 |        "      <td>29014.876535</td>\n",
 586 |        "    </tr>\n",
 587 |        "    <tr>\n",
 588 |        "      <th>7</th>\n",
 589 |        "      <td>28906.219423</td>\n",
 590 |        "    </tr>\n",
 591 |        "    <tr>\n",
 592 |        "      <th>8</th>\n",
 593 |        "      <td>32462.127717</td>\n",
 594 |        "    </tr>\n",
 595 |        "    <tr>\n",
 596 |        "      <th>9</th>\n",
 597 |        "      <td>35576.560892</td>\n",
 598 |        "    </tr>\n",
 599 |        "    <tr>\n",
 600 |        "      <th>10</th>\n",
 601 |        "      <td>34939.955171</td>\n",
 602 |        "    </tr>\n",
 603 |        "    <tr>\n",
 604 |        "      <th>11</th>\n",
 605 |        "      <td>33215.316745</td>\n",
 606 |        "    </tr>\n",
 607 |        "  </tbody>\n",
 608 |        "</table>\n",
 609 |        "</div>"
 610 |       ],
 611 |       "text/plain": [
 612 |        "        Snowfall\n",
 613 |        "0   32910.929764\n",
 614 |        "1   32099.560367\n",
 615 |        "2   32814.367087\n",
 616 |        "3   35263.127507\n",
 617 |        "4   35805.531129\n",
 618 |        "5   30106.764987\n",
 619 |        "6   29014.876535\n",
 620 |        "7   28906.219423\n",
 621 |        "8   32462.127717\n",
 622 |        "9   35576.560892\n",
 623 |        "10  34939.955171\n",
 624 |        "11  33215.316745"
 625 |       ]
 626 |      },
 627 |      "execution_count": 7,
 628 |      "metadata": {},
 629 |      "output_type": "execute_result"
 630 |     }
 631 |    ],
 632 |    "source": [
 633 |     "Weather = pd.DataFrame({'Snowfall': [Jan, Feb, Mar, Apr, May, Jun, Jul, Aug, Sep, Oct, Nov, Dec]})\n",
 634 |     "Weather"
 635 |    ]
 636 |   },
 637 |   {
 638 |    "cell_type": "code",
 639 |    "execution_count": 8,
 640 |    "metadata": {},
 641 |    "outputs": [],
 642 |    "source": [
 643 |     "Monthly_Temp = Temperature.groupby(' Month').mean()['tas']\n",
 644 |     "Jan = Monthly_Temp.iloc[0]\n",
 645 |     "Feb = Monthly_Temp.iloc[1]\n",
 646 |     "Mar = Monthly_Temp.iloc[2]\n",
 647 |     "Apr = Monthly_Temp.iloc[3]\n",
 648 |     "May = Monthly_Temp.iloc[4]\n",
 649 |     "Jun = Monthly_Temp.iloc[5]\n",
 650 |     "Jul = Monthly_Temp.iloc[6]\n",
 651 |     "Aug = Monthly_Temp.iloc[7]\n",
 652 |     "Sep = Monthly_Temp.iloc[8]\n",
 653 |     "Oct = Monthly_Temp.iloc[9]\n",
 654 |     "Nov = Monthly_Temp.iloc[10]\n",
 655 |     "Dec = Monthly_Temp.iloc[11]"
 656 |    ]
 657 |   },
 658 |   {
 659 |    "cell_type": "code",
 660 |    "execution_count": 9,
 661 |    "metadata": {},
 662 |    "outputs": [
 663 |     {
 664 |      "data": {
 665 |       "text/html": [
 666 |        "<div>\n",
 667 |        "<style scoped>\n",
 668 |        "    .dataframe tbody tr th:only-of-type {\n",
 669 |        "        vertical-align: middle;\n",
 670 |        "    }\n",
 671 |        "\n",
 672 |        "    .dataframe tbody tr th {\n",
 673 |        "        vertical-align: top;\n",
 674 |        "    }\n",
 675 |        "\n",
 676 |        "    .dataframe thead th {\n",
 677 |        "        text-align: right;\n",
 678 |        "    }\n",
 679 |        "</style>\n",
 680 |        "<table border=\"1\" class=\"dataframe\">\n",
 681 |        "  <thead>\n",
 682 |        "    <tr style=\"text-align: right;\">\n",
 683 |        "      <th></th>\n",
 684 |        "      <th>Snowfall</th>\n",
 685 |        "      <th>Temperature</th>\n",
 686 |        "    </tr>\n",
 687 |        "  </thead>\n",
 688 |        "  <tbody>\n",
 689 |        "    <tr>\n",
 690 |        "      <th>0</th>\n",
 691 |        "      <td>32910.929764</td>\n",
 692 |        "      <td>-6.266277</td>\n",
 693 |        "    </tr>\n",
 694 |        "    <tr>\n",
 695 |        "      <th>1</th>\n",
 696 |        "      <td>32099.560367</td>\n",
 697 |        "      <td>-4.228557</td>\n",
 698 |        "    </tr>\n",
 699 |        "    <tr>\n",
 700 |        "      <th>2</th>\n",
 701 |        "      <td>32814.367087</td>\n",
 702 |        "      <td>0.161376</td>\n",
 703 |        "    </tr>\n",
 704 |        "    <tr>\n",
 705 |        "      <th>3</th>\n",
 706 |        "      <td>35263.127507</td>\n",
 707 |        "      <td>5.987872</td>\n",
 708 |        "    </tr>\n",
 709 |        "    <tr>\n",
 710 |        "      <th>4</th>\n",
 711 |        "      <td>35805.531129</td>\n",
 712 |        "      <td>12.186650</td>\n",
 713 |        "    </tr>\n",
 714 |        "    <tr>\n",
 715 |        "      <th>5</th>\n",
 716 |        "      <td>30106.764987</td>\n",
 717 |        "      <td>17.210970</td>\n",
 718 |        "    </tr>\n",
 719 |        "    <tr>\n",
 720 |        "      <th>6</th>\n",
 721 |        "      <td>29014.876535</td>\n",
 722 |        "      <td>20.030330</td>\n",
 723 |        "    </tr>\n",
 724 |        "    <tr>\n",
 725 |        "      <th>7</th>\n",
 726 |        "      <td>28906.219423</td>\n",
 727 |        "      <td>18.935290</td>\n",
 728 |        "    </tr>\n",
 729 |        "    <tr>\n",
 730 |        "      <th>8</th>\n",
 731 |        "      <td>32462.127717</td>\n",
 732 |        "      <td>14.488357</td>\n",
 733 |        "    </tr>\n",
 734 |        "    <tr>\n",
 735 |        "      <th>9</th>\n",
 736 |        "      <td>35576.560892</td>\n",
 737 |        "      <td>7.789156</td>\n",
 738 |        "    </tr>\n",
 739 |        "    <tr>\n",
 740 |        "      <th>10</th>\n",
 741 |        "      <td>34939.955171</td>\n",
 742 |        "      <td>0.468967</td>\n",
 743 |        "    </tr>\n",
 744 |        "    <tr>\n",
 745 |        "      <th>11</th>\n",
 746 |        "      <td>33215.316745</td>\n",
 747 |        "      <td>-4.694447</td>\n",
 748 |        "    </tr>\n",
 749 |        "  </tbody>\n",
 750 |        "</table>\n",
 751 |        "</div>"
 752 |       ],
 753 |       "text/plain": [
 754 |        "        Snowfall  Temperature\n",
 755 |        "0   32910.929764    -6.266277\n",
 756 |        "1   32099.560367    -4.228557\n",
 757 |        "2   32814.367087     0.161376\n",
 758 |        "3   35263.127507     5.987872\n",
 759 |        "4   35805.531129    12.186650\n",
 760 |        "5   30106.764987    17.210970\n",
 761 |        "6   29014.876535    20.030330\n",
 762 |        "7   28906.219423    18.935290\n",
 763 |        "8   32462.127717    14.488357\n",
 764 |        "9   35576.560892     7.789156\n",
 765 |        "10  34939.955171     0.468967\n",
 766 |        "11  33215.316745    -4.694447"
 767 |       ]
 768 |      },
 769 |      "execution_count": 9,
 770 |      "metadata": {},
 771 |      "output_type": "execute_result"
 772 |     }
 773 |    ],
 774 |    "source": [
 775 |     "Weather['Temperature'] = [Jan, Feb, Mar, Apr, May, Jun, Jul, Aug, Sep, Oct, Nov, Dec]\n",
 776 |     "Weather"
 777 |    ]
 778 |   },
 779 |   {
 780 |    "cell_type": "code",
 781 |    "execution_count": 10,
 782 |    "metadata": {},
 783 |    "outputs": [],
 784 |    "source": [
 785 |     "Monthly_Rain = Rainfall.groupby(' Month').mean()['pr']\n",
 786 |     "Monthly_Rain.iloc[0]\n",
 787 |     "Jan = Monthly_Rain.iloc[0]\n",
 788 |     "Feb = Monthly_Rain.iloc[1]\n",
 789 |     "Mar = Monthly_Rain.iloc[2]\n",
 790 |     "Apr = Monthly_Rain.iloc[3]\n",
 791 |     "May = Monthly_Rain.iloc[4]\n",
 792 |     "Jun = Monthly_Rain.iloc[5]\n",
 793 |     "Jul = Monthly_Rain.iloc[6]\n",
 794 |     "Aug = Monthly_Rain.iloc[7]\n",
 795 |     "Sep = Monthly_Rain.iloc[8]\n",
 796 |     "Oct = Monthly_Rain.iloc[9]\n",
 797 |     "Nov = Monthly_Rain.iloc[10]\n",
 798 |     "Dec = Monthly_Rain.iloc[11]"
 799 |    ]
 800 |   },
 801 |   {
 802 |    "cell_type": "code",
 803 |    "execution_count": 11,
 804 |    "metadata": {},
 805 |    "outputs": [
 806 |     {
 807 |      "data": {
 808 |       "text/html": [
 809 |        "<div>\n",
 810 |        "<style scoped>\n",
 811 |        "    .dataframe tbody tr th:only-of-type {\n",
 812 |        "        vertical-align: middle;\n",
 813 |        "    }\n",
 814 |        "\n",
 815 |        "    .dataframe tbody tr th {\n",
 816 |        "        vertical-align: top;\n",
 817 |        "    }\n",
 818 |        "\n",
 819 |        "    .dataframe thead th {\n",
 820 |        "        text-align: right;\n",
 821 |        "    }\n",
 822 |        "</style>\n",
 823 |        "<table border=\"1\" class=\"dataframe\">\n",
 824 |        "  <thead>\n",
 825 |        "    <tr style=\"text-align: right;\">\n",
 826 |        "      <th></th>\n",
 827 |        "      <th>Snowfall</th>\n",
 828 |        "      <th>Temperature</th>\n",
 829 |        "      <th>Rainfall</th>\n",
 830 |        "    </tr>\n",
 831 |        "  </thead>\n",
 832 |        "  <tbody>\n",
 833 |        "    <tr>\n",
 834 |        "      <th>0</th>\n",
 835 |        "      <td>32910.929764</td>\n",
 836 |        "      <td>-6.266277</td>\n",
 837 |        "      <td>44.567710</td>\n",
 838 |        "    </tr>\n",
 839 |        "    <tr>\n",
 840 |        "      <th>1</th>\n",
 841 |        "      <td>32099.560367</td>\n",
 842 |        "      <td>-4.228557</td>\n",
 843 |        "      <td>41.915983</td>\n",
 844 |        "    </tr>\n",
 845 |        "    <tr>\n",
 846 |        "      <th>2</th>\n",
 847 |        "      <td>32814.367087</td>\n",
 848 |        "      <td>0.161376</td>\n",
 849 |        "      <td>51.627430</td>\n",
 850 |        "    </tr>\n",
 851 |        "    <tr>\n",
 852 |        "      <th>3</th>\n",
 853 |        "      <td>35263.127507</td>\n",
 854 |        "      <td>5.987872</td>\n",
 855 |        "      <td>49.433157</td>\n",
 856 |        "    </tr>\n",
 857 |        "    <tr>\n",
 858 |        "      <th>4</th>\n",
 859 |        "      <td>35805.531129</td>\n",
 860 |        "      <td>12.186650</td>\n",
 861 |        "      <td>60.396267</td>\n",
 862 |        "    </tr>\n",
 863 |        "    <tr>\n",
 864 |        "      <th>5</th>\n",
 865 |        "      <td>30106.764987</td>\n",
 866 |        "      <td>17.210970</td>\n",
 867 |        "      <td>64.473557</td>\n",
 868 |        "    </tr>\n",
 869 |        "    <tr>\n",
 870 |        "      <th>6</th>\n",
 871 |        "      <td>29014.876535</td>\n",
 872 |        "      <td>20.030330</td>\n",
 873 |        "      <td>65.537783</td>\n",
 874 |        "    </tr>\n",
 875 |        "    <tr>\n",
 876 |        "      <th>7</th>\n",
 877 |        "      <td>28906.219423</td>\n",
 878 |        "      <td>18.935290</td>\n",
 879 |        "      <td>65.038193</td>\n",
 880 |        "    </tr>\n",
 881 |        "    <tr>\n",
 882 |        "      <th>8</th>\n",
 883 |        "      <td>32462.127717</td>\n",
 884 |        "      <td>14.488357</td>\n",
 885 |        "      <td>61.432697</td>\n",
 886 |        "    </tr>\n",
 887 |        "    <tr>\n",
 888 |        "      <th>9</th>\n",
 889 |        "      <td>35576.560892</td>\n",
 890 |        "      <td>7.789156</td>\n",
 891 |        "      <td>49.504067</td>\n",
 892 |        "    </tr>\n",
 893 |        "    <tr>\n",
 894 |        "      <th>10</th>\n",
 895 |        "      <td>34939.955171</td>\n",
 896 |        "      <td>0.468967</td>\n",
 897 |        "      <td>50.642860</td>\n",
 898 |        "    </tr>\n",
 899 |        "    <tr>\n",
 900 |        "      <th>11</th>\n",
 901 |        "      <td>33215.316745</td>\n",
 902 |        "      <td>-4.694447</td>\n",
 903 |        "      <td>50.472827</td>\n",
 904 |        "    </tr>\n",
 905 |        "  </tbody>\n",
 906 |        "</table>\n",
 907 |        "</div>"
 908 |       ],
 909 |       "text/plain": [
 910 |        "        Snowfall  Temperature   Rainfall\n",
 911 |        "0   32910.929764    -6.266277  44.567710\n",
 912 |        "1   32099.560367    -4.228557  41.915983\n",
 913 |        "2   32814.367087     0.161376  51.627430\n",
 914 |        "3   35263.127507     5.987872  49.433157\n",
 915 |        "4   35805.531129    12.186650  60.396267\n",
 916 |        "5   30106.764987    17.210970  64.473557\n",
 917 |        "6   29014.876535    20.030330  65.537783\n",
 918 |        "7   28906.219423    18.935290  65.038193\n",
 919 |        "8   32462.127717    14.488357  61.432697\n",
 920 |        "9   35576.560892     7.789156  49.504067\n",
 921 |        "10  34939.955171     0.468967  50.642860\n",
 922 |        "11  33215.316745    -4.694447  50.472827"
 923 |       ]
 924 |      },
 925 |      "execution_count": 11,
 926 |      "metadata": {},
 927 |      "output_type": "execute_result"
 928 |     }
 929 |    ],
 930 |    "source": [
 931 |     "Weather['Rainfall'] = [Jan, Feb, Mar, Apr, May, Jun, Jul, Aug, Sep, Oct, Nov, Dec]\n",
 932 |     "Weather"
 933 |    ]
 934 |   },
 935 |   {
 936 |    "cell_type": "code",
 937 |    "execution_count": 12,
 938 |    "metadata": {},
 939 |    "outputs": [
 940 |     {
 941 |      "data": {
 942 |       "text/html": [
 943 |        "<div>\n",
 944 |        "<style scoped>\n",
 945 |        "    .dataframe tbody tr th:only-of-type {\n",
 946 |        "        vertical-align: middle;\n",
 947 |        "    }\n",
 948 |        "\n",
 949 |        "    .dataframe tbody tr th {\n",
 950 |        "        vertical-align: top;\n",
 951 |        "    }\n",
 952 |        "\n",
 953 |        "    .dataframe thead th {\n",
 954 |        "        text-align: right;\n",
 955 |        "    }\n",
 956 |        "</style>\n",
 957 |        "<table border=\"1\" class=\"dataframe\">\n",
 958 |        "  <thead>\n",
 959 |        "    <tr style=\"text-align: right;\">\n",
 960 |        "      <th></th>\n",
 961 |        "      <th>Snowfall</th>\n",
 962 |        "      <th>Temperature</th>\n",
 963 |        "      <th>Rainfall</th>\n",
 964 |        "    </tr>\n",
 965 |        "  </thead>\n",
 966 |        "  <tbody>\n",
 967 |        "    <tr>\n",
 968 |        "      <th>0</th>\n",
 969 |        "      <td>32.910930</td>\n",
 970 |        "      <td>-6.266277</td>\n",
 971 |        "      <td>44.567710</td>\n",
 972 |        "    </tr>\n",
 973 |        "    <tr>\n",
 974 |        "      <th>1</th>\n",
 975 |        "      <td>32.099560</td>\n",
 976 |        "      <td>-4.228557</td>\n",
 977 |        "      <td>41.915983</td>\n",
 978 |        "    </tr>\n",
 979 |        "    <tr>\n",
 980 |        "      <th>2</th>\n",
 981 |        "      <td>32.814367</td>\n",
 982 |        "      <td>0.161376</td>\n",
 983 |        "      <td>51.627430</td>\n",
 984 |        "    </tr>\n",
 985 |        "    <tr>\n",
 986 |        "      <th>3</th>\n",
 987 |        "      <td>35.263128</td>\n",
 988 |        "      <td>5.987872</td>\n",
 989 |        "      <td>49.433157</td>\n",
 990 |        "    </tr>\n",
 991 |        "    <tr>\n",
 992 |        "      <th>4</th>\n",
 993 |        "      <td>35.805531</td>\n",
 994 |        "      <td>12.186650</td>\n",
 995 |        "      <td>60.396267</td>\n",
 996 |        "    </tr>\n",
 997 |        "    <tr>\n",
 998 |        "      <th>5</th>\n",
 999 |        "      <td>30.106765</td>\n",
1000 |        "      <td>17.210970</td>\n",
1001 |        "      <td>64.473557</td>\n",
1002 |        "    </tr>\n",
1003 |        "    <tr>\n",
1004 |        "      <th>6</th>\n",
1005 |        "      <td>29.014877</td>\n",
1006 |        "      <td>20.030330</td>\n",
1007 |        "      <td>65.537783</td>\n",
1008 |        "    </tr>\n",
1009 |        "    <tr>\n",
1010 |        "      <th>7</th>\n",
1011 |        "      <td>28.906219</td>\n",
1012 |        "      <td>18.935290</td>\n",
1013 |        "      <td>65.038193</td>\n",
1014 |        "    </tr>\n",
1015 |        "    <tr>\n",
1016 |        "      <th>8</th>\n",
1017 |        "      <td>32.462128</td>\n",
1018 |        "      <td>14.488357</td>\n",
1019 |        "      <td>61.432697</td>\n",
1020 |        "    </tr>\n",
1021 |        "    <tr>\n",
1022 |        "      <th>9</th>\n",
1023 |        "      <td>35.576561</td>\n",
1024 |        "      <td>7.789156</td>\n",
1025 |        "      <td>49.504067</td>\n",
1026 |        "    </tr>\n",
1027 |        "    <tr>\n",
1028 |        "      <th>10</th>\n",
1029 |        "      <td>34.939955</td>\n",
1030 |        "      <td>0.468967</td>\n",
1031 |        "      <td>50.642860</td>\n",
1032 |        "    </tr>\n",
1033 |        "    <tr>\n",
1034 |        "      <th>11</th>\n",
1035 |        "      <td>33.215317</td>\n",
1036 |        "      <td>-4.694447</td>\n",
1037 |        "      <td>50.472827</td>\n",
1038 |        "    </tr>\n",
1039 |        "  </tbody>\n",
1040 |        "</table>\n",
1041 |        "</div>"
1042 |       ],
1043 |       "text/plain": [
1044 |        "     Snowfall  Temperature   Rainfall\n",
1045 |        "0   32.910930    -6.266277  44.567710\n",
1046 |        "1   32.099560    -4.228557  41.915983\n",
1047 |        "2   32.814367     0.161376  51.627430\n",
1048 |        "3   35.263128     5.987872  49.433157\n",
1049 |        "4   35.805531    12.186650  60.396267\n",
1050 |        "5   30.106765    17.210970  64.473557\n",
1051 |        "6   29.014877    20.030330  65.537783\n",
1052 |        "7   28.906219    18.935290  65.038193\n",
1053 |        "8   32.462128    14.488357  61.432697\n",
1054 |        "9   35.576561     7.789156  49.504067\n",
1055 |        "10  34.939955     0.468967  50.642860\n",
1056 |        "11  33.215317    -4.694447  50.472827"
1057 |       ]
1058 |      },
1059 |      "execution_count": 12,
1060 |      "metadata": {},
1061 |      "output_type": "execute_result"
1062 |     }
1063 |    ],
1064 |    "source": [
1065 |     "Weather['Snowfall'] = Weather['Snowfall']/1000\n",
1066 |     "Weather"
1067 |    ]
1068 |   },
1069 |   {
1070 |    "cell_type": "code",
1071 |    "execution_count": 13,
1072 |    "metadata": {},
1073 |    "outputs": [
1074 |     {
1075 |      "data": {
1076 |       "text/html": [
1077 |        "<div>\n",
1078 |        "<style scoped>\n",
1079 |        "    .dataframe tbody tr th:only-of-type {\n",
1080 |        "        vertical-align: middle;\n",
1081 |        "    }\n",
1082 |        "\n",
1083 |        "    .dataframe tbody tr th {\n",
1084 |        "        vertical-align: top;\n",
1085 |        "    }\n",
1086 |        "\n",
1087 |        "    .dataframe thead th {\n",
1088 |        "        text-align: right;\n",
1089 |        "    }\n",
1090 |        "</style>\n",
1091 |        "<table border=\"1\" class=\"dataframe\">\n",
1092 |        "  <thead>\n",
1093 |        "    <tr style=\"text-align: right;\">\n",
1094 |        "      <th></th>\n",
1095 |        "      <th>Snowfall</th>\n",
1096 |        "      <th>Temperature</th>\n",
1097 |        "      <th>Rainfall</th>\n",
1098 |        "    </tr>\n",
1099 |        "  </thead>\n",
1100 |        "  <tbody>\n",
1101 |        "    <tr>\n",
1102 |        "      <th>Jan</th>\n",
1103 |        "      <td>32.910930</td>\n",
1104 |        "      <td>-6.266277</td>\n",
1105 |        "      <td>44.567710</td>\n",
1106 |        "    </tr>\n",
1107 |        "    <tr>\n",
1108 |        "      <th>Feb</th>\n",
1109 |        "      <td>32.099560</td>\n",
1110 |        "      <td>-4.228557</td>\n",
1111 |        "      <td>41.915983</td>\n",
1112 |        "    </tr>\n",
1113 |        "    <tr>\n",
1114 |        "      <th>Mar</th>\n",
1115 |        "      <td>32.814367</td>\n",
1116 |        "      <td>0.161376</td>\n",
1117 |        "      <td>51.627430</td>\n",
1118 |        "    </tr>\n",
1119 |        "    <tr>\n",
1120 |        "      <th>Apr</th>\n",
1121 |        "      <td>35.263128</td>\n",
1122 |        "      <td>5.987872</td>\n",
1123 |        "      <td>49.433157</td>\n",
1124 |        "    </tr>\n",
1125 |        "    <tr>\n",
1126 |        "      <th>May</th>\n",
1127 |        "      <td>35.805531</td>\n",
1128 |        "      <td>12.186650</td>\n",
1129 |        "      <td>60.396267</td>\n",
1130 |        "    </tr>\n",
1131 |        "    <tr>\n",
1132 |        "      <th>Jun</th>\n",
1133 |        "      <td>30.106765</td>\n",
1134 |        "      <td>17.210970</td>\n",
1135 |        "      <td>64.473557</td>\n",
1136 |        "    </tr>\n",
1137 |        "    <tr>\n",
1138 |        "      <th>Jul</th>\n",
1139 |        "      <td>29.014877</td>\n",
1140 |        "      <td>20.030330</td>\n",
1141 |        "      <td>65.537783</td>\n",
1142 |        "    </tr>\n",
1143 |        "    <tr>\n",
1144 |        "      <th>Aug</th>\n",
1145 |        "      <td>28.906219</td>\n",
1146 |        "      <td>18.935290</td>\n",
1147 |        "      <td>65.038193</td>\n",
1148 |        "    </tr>\n",
1149 |        "    <tr>\n",
1150 |        "      <th>Sep</th>\n",
1151 |        "      <td>32.462128</td>\n",
1152 |        "      <td>14.488357</td>\n",
1153 |        "      <td>61.432697</td>\n",
1154 |        "    </tr>\n",
1155 |        "    <tr>\n",
1156 |        "      <th>Oct</th>\n",
1157 |        "      <td>35.576561</td>\n",
1158 |        "      <td>7.789156</td>\n",
1159 |        "      <td>49.504067</td>\n",
1160 |        "    </tr>\n",
1161 |        "    <tr>\n",
1162 |        "      <th>Nov</th>\n",
1163 |        "      <td>34.939955</td>\n",
1164 |        "      <td>0.468967</td>\n",
1165 |        "      <td>50.642860</td>\n",
1166 |        "    </tr>\n",
1167 |        "    <tr>\n",
1168 |        "      <th>Dec</th>\n",
1169 |        "      <td>33.215317</td>\n",
1170 |        "      <td>-4.694447</td>\n",
1171 |        "      <td>50.472827</td>\n",
1172 |        "    </tr>\n",
1173 |        "  </tbody>\n",
1174 |        "</table>\n",
1175 |        "</div>"
1176 |       ],
1177 |       "text/plain": [
1178 |        "      Snowfall  Temperature   Rainfall\n",
1179 |        "Jan  32.910930    -6.266277  44.567710\n",
1180 |        "Feb  32.099560    -4.228557  41.915983\n",
1181 |        "Mar  32.814367     0.161376  51.627430\n",
1182 |        "Apr  35.263128     5.987872  49.433157\n",
1183 |        "May  35.805531    12.186650  60.396267\n",
1184 |        "Jun  30.106765    17.210970  64.473557\n",
1185 |        "Jul  29.014877    20.030330  65.537783\n",
1186 |        "Aug  28.906219    18.935290  65.038193\n",
1187 |        "Sep  32.462128    14.488357  61.432697\n",
1188 |        "Oct  35.576561     7.789156  49.504067\n",
1189 |        "Nov  34.939955     0.468967  50.642860\n",
1190 |        "Dec  33.215317    -4.694447  50.472827"
1191 |       ]
1192 |      },
1193 |      "execution_count": 13,
1194 |      "metadata": {},
1195 |      "output_type": "execute_result"
1196 |     }
1197 |    ],
1198 |    "source": [
1199 |     "Weather.index = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']\n",
1200 |     "Weather"
1201 |    ]
1202 |   },
1203 |   {
1204 |    "cell_type": "code",
1205 |    "execution_count": 14,
1206 |    "metadata": {},
1207 |    "outputs": [
1208 |     {
1209 |      "data": {
1210 |       "image/png": 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\n",
1211 |       "text/plain": [
1212 |        "<Figure size 432x288 with 1 Axes>"
1213 |       ]
1214 |      },
1215 |      "metadata": {},
1216 |      "output_type": "display_data"
1217 |     }
1218 |    ],
1219 |    "source": [
1220 |     "x = Weather.index.tolist()\n",
1221 |     "y1 = Weather['Snowfall'].tolist()\n",
1222 |     "y2 = Weather['Temperature'].tolist()\n",
1223 |     "y3 = Weather['Rainfall'].tolist()\n",
1224 |     "\n",
1225 |     "plt.plot(x, y1, '-s', label = \"Snowfall (in 0.1*m)\")\n",
1226 |     "plt.plot(x, y2, '-*', label = \"Temperature (in $\\,^{\\circ}\\mathrm{C}$)\")\n",
1227 |     "plt.plot(x, y3, '-o', label = \"Rainfall (in 0.1*cm)\")\n",
1228 |     "\n",
1229 |     "plt.title('Average Temperature, Snowfall and Rainfall in United States(1961-1990)')\n",
1230 |     " \n",
1231 |     "plt.legend(frameon=False)\n",
1232 |     "[plt.gca().spines[loc].set_visible(False) for loc in ['top', 'right']]\n",
1233 |     "\n",
1234 |     "plt.show()"
1235 |    ]
1236 |   }
1237 |  ],
1238 |  "metadata": {
1239 |   "kernelspec": {
1240 |    "display_name": "Python 3",
1241 |    "language": "python",
1242 |    "name": "python3"
1243 |   },
1244 |   "language_info": {
1245 |    "codemirror_mode": {
1246 |     "name": "ipython",
1247 |     "version": 3
1248 |    },
1249 |    "file_extension": ".py",
1250 |    "mimetype": "text/x-python",
1251 |    "name": "python",
1252 |    "nbconvert_exporter": "python",
1253 |    "pygments_lexer": "ipython3",
1254 |    "version": "3.6.5"
1255 |   }
1256 |  },
1257 |  "nbformat": 4,
1258 |  "nbformat_minor": 2
1259 | }
1260 | 


--------------------------------------------------------------------------------
/Dejunkifying a Plot.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 3,
  6 |    "metadata": {},
  7 |    "outputs": [
  8 |     {
  9 |      "data": {
 10 |       "image/png": 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\n",
 11 |       "text/plain": [
 12 |        "<Figure size 432x288 with 1 Axes>"
 13 |       ]
 14 |      },
 15 |      "metadata": {},
 16 |      "output_type": "display_data"
 17 |     }
 18 |    ],
 19 |    "source": [
 20 |     "import matplotlib.pyplot as plt\n",
 21 |     "import numpy as np\n",
 22 |     "\n",
 23 |     "plt.figure()\n",
 24 |     "\n",
 25 |     "languages =['Python', 'SQL', 'Java', 'C++', 'JavaScript']\n",
 26 |     "pos = np.arange(len(languages))\n",
 27 |     "popularity = [56, 39, 34, 34, 29]\n",
 28 |     "\n",
 29 |     "plt.bar(pos, popularity, align='center')\n",
 30 |     "plt.xticks(pos, languages)\n",
 31 |     "plt.ylabel('% Popularity')\n",
 32 |     "plt.title('Top 5 Languages for Math & Data \\nby % popularity on Stack Overflow', alpha=0.8)\n",
 33 |     "\n",
 34 |     "plt.show()"
 35 |    ]
 36 |   },
 37 |   {
 38 |    "cell_type": "code",
 39 |    "execution_count": 7,
 40 |    "metadata": {},
 41 |    "outputs": [
 42 |     {
 43 |      "name": "stderr",
 44 |      "output_type": "stream",
 45 |      "text": [
 46 |       "c:\\users\\5559\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\matplotlib\\cbook\\deprecation.py:107: MatplotlibDeprecationWarning: Passing one of 'on', 'true', 'off', 'false' as a boolean is deprecated; use an actual boolean (True/False) instead.\n",
 47 |       "  warnings.warn(message, mplDeprecation, stacklevel=1)\n"
 48 |      ]
 49 |     },
 50 |     {
 51 |      "data": {
 52 |       "image/png": 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\n",
 53 |       "text/plain": [
 54 |        "<Figure size 432x288 with 1 Axes>"
 55 |       ]
 56 |      },
 57 |      "metadata": {},
 58 |      "output_type": "display_data"
 59 |     }
 60 |    ],
 61 |    "source": [
 62 |     "import matplotlib.pyplot as plt\n",
 63 |     "import numpy as np\n",
 64 |     "\n",
 65 |     "plt.figure()\n",
 66 |     "\n",
 67 |     "languages =['Python', 'SQL', 'Java', 'C++', 'JavaScript']\n",
 68 |     "pos = np.arange(len(languages))\n",
 69 |     "popularity = [56, 39, 34, 34, 29]\n",
 70 |     "\n",
 71 |     "plt.bar(pos, popularity, align='center')\n",
 72 |     "plt.xticks(pos, languages)\n",
 73 |     "plt.ylabel('% Popularity')\n",
 74 |     "plt.title('Top 5 Languages for Math & Data \\nby % popularity on Stack Overflow', alpha=0.8)\n",
 75 |     "\n",
 76 |     "# remove all the ticks (both axes), and tick labels on the Y axis\n",
 77 |     "plt.tick_params(top='off', bottom='off', left='off', right='off', labelleft='off', labelbottom='on')\n",
 78 |     "\n",
 79 |     "plt.show()"
 80 |    ]
 81 |   },
 82 |   {
 83 |    "cell_type": "code",
 84 |    "execution_count": 9,
 85 |    "metadata": {},
 86 |    "outputs": [
 87 |     {
 88 |      "name": "stderr",
 89 |      "output_type": "stream",
 90 |      "text": [
 91 |       "c:\\users\\5559\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\matplotlib\\cbook\\deprecation.py:107: MatplotlibDeprecationWarning: Passing one of 'on', 'true', 'off', 'false' as a boolean is deprecated; use an actual boolean (True/False) instead.\n",
 92 |       "  warnings.warn(message, mplDeprecation, stacklevel=1)\n"
 93 |      ]
 94 |     },
 95 |     {
 96 |      "data": {
 97 |       "image/png": 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\n",
 98 |       "text/plain": [
 99 |        "<Figure size 432x288 with 1 Axes>"
100 |       ]
101 |      },
102 |      "metadata": {},
103 |      "output_type": "display_data"
104 |     }
105 |    ],
106 |    "source": [
107 |     "import matplotlib.pyplot as plt\n",
108 |     "import numpy as np\n",
109 |     "\n",
110 |     "plt.figure()\n",
111 |     "\n",
112 |     "languages =['Python', 'SQL', 'Java', 'C++', 'JavaScript']\n",
113 |     "pos = np.arange(len(languages))\n",
114 |     "popularity = [56, 39, 34, 34, 29]\n",
115 |     "\n",
116 |     "plt.bar(pos, popularity, align='center')\n",
117 |     "plt.xticks(pos, languages)\n",
118 |     "plt.ylabel('% Popularity')\n",
119 |     "plt.title('Top 5 Languages for Math & Data \\nby % popularity on Stack Overflow', alpha=0.8)\n",
120 |     "\n",
121 |     "# remove all the ticks (both axes), and tick labels on the Y axis\n",
122 |     "plt.tick_params(top='off', bottom='off', left='off', right='off', labelleft='off', labelbottom='on')\n",
123 |     "\n",
124 |     "# remove the frame of the chart\n",
125 |     "for spine in plt.gca().spines.values():\n",
126 |     "    spine.set_visible(False)\n",
127 |     "\n",
128 |     "plt.show()"
129 |    ]
130 |   },
131 |   {
132 |    "cell_type": "code",
133 |    "execution_count": 10,
134 |    "metadata": {},
135 |    "outputs": [
136 |     {
137 |      "name": "stderr",
138 |      "output_type": "stream",
139 |      "text": [
140 |       "c:\\users\\5559\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\matplotlib\\cbook\\deprecation.py:107: MatplotlibDeprecationWarning: Passing one of 'on', 'true', 'off', 'false' as a boolean is deprecated; use an actual boolean (True/False) instead.\n",
141 |       "  warnings.warn(message, mplDeprecation, stacklevel=1)\n"
142 |      ]
143 |     },
144 |     {
145 |      "data": {
146 |       "image/png": 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\n",
147 |       "text/plain": [
148 |        "<Figure size 432x288 with 1 Axes>"
149 |       ]
150 |      },
151 |      "metadata": {},
152 |      "output_type": "display_data"
153 |     }
154 |    ],
155 |    "source": [
156 |     "import matplotlib.pyplot as plt\n",
157 |     "import numpy as np\n",
158 |     "\n",
159 |     "plt.figure()\n",
160 |     "\n",
161 |     "languages =['Python', 'SQL', 'Java', 'C++', 'JavaScript']\n",
162 |     "pos = np.arange(len(languages))\n",
163 |     "popularity = [56, 39, 34, 34, 29]\n",
164 |     "\n",
165 |     "# change the bar colors to be less bright blue\n",
166 |     "bars = plt.bar(pos, popularity, align='center', linewidth=0, color='lightslategrey')\n",
167 |     "# make one bar, the python bar, a contrasting color\n",
168 |     "bars[0].set_color('#1F77B4')\n",
169 |     "\n",
170 |     "# soften all labels by turning grey\n",
171 |     "plt.xticks(pos, languages, alpha=0.8)\n",
172 |     "plt.ylabel('% Popularity', alpha=0.8)\n",
173 |     "plt.title('Top 5 Languages for Math & Data \\nby % popularity on Stack Overflow', alpha=0.8)\n",
174 |     "\n",
175 |     "# remove all the ticks (both axes), and tick labels on the Y axis\n",
176 |     "plt.tick_params(top='off', bottom='off', left='off', right='off', labelleft='off', labelbottom='on')\n",
177 |     "\n",
178 |     "# remove the frame of the chart\n",
179 |     "for spine in plt.gca().spines.values():\n",
180 |     "    spine.set_visible(False)\n",
181 |     "plt.show()"
182 |    ]
183 |   },
184 |   {
185 |    "cell_type": "code",
186 |    "execution_count": 12,
187 |    "metadata": {},
188 |    "outputs": [
189 |     {
190 |      "name": "stderr",
191 |      "output_type": "stream",
192 |      "text": [
193 |       "c:\\users\\5559\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\matplotlib\\cbook\\deprecation.py:107: MatplotlibDeprecationWarning: Passing one of 'on', 'true', 'off', 'false' as a boolean is deprecated; use an actual boolean (True/False) instead.\n",
194 |       "  warnings.warn(message, mplDeprecation, stacklevel=1)\n"
195 |      ]
196 |     },
197 |     {
198 |      "data": {
199 |       "image/png": 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\n",
200 |       "text/plain": [
201 |        "<Figure size 432x288 with 1 Axes>"
202 |       ]
203 |      },
204 |      "metadata": {},
205 |      "output_type": "display_data"
206 |     }
207 |    ],
208 |    "source": [
209 |     "import matplotlib.pyplot as plt\n",
210 |     "import numpy as np\n",
211 |     "\n",
212 |     "plt.figure()\n",
213 |     "\n",
214 |     "languages =['Python', 'SQL', 'Java', 'C++', 'JavaScript']\n",
215 |     "pos = np.arange(len(languages))\n",
216 |     "popularity = [56, 39, 34, 34, 29]\n",
217 |     "\n",
218 |     "# change the bar colors to be less bright blue\n",
219 |     "bars = plt.bar(pos, popularity, align='center', linewidth=0, color='lightslategrey')\n",
220 |     "# make one bar, the python bar, a contrasting color\n",
221 |     "bars[0].set_color('#1F77B4')\n",
222 |     "\n",
223 |     "# soften all labels by turning grey\n",
224 |     "plt.xticks(pos, languages, alpha=0.8)\n",
225 |     "\n",
226 |     "# remove the Y label since bars are directly labeled\n",
227 |     "# plt.ylabel('% Popularity', alpha=0.8)\n",
228 |     "\n",
229 |     "plt.title('Top 5 Languages for Math & Data \\nby % popularity on Stack Overflow', alpha=0.8)\n",
230 |     "\n",
231 |     "# remove all the ticks (both axes), and tick labels on the Y axis\n",
232 |     "plt.tick_params(top='off', bottom='off', left='off', right='off', labelleft='off', labelbottom='on')\n",
233 |     "\n",
234 |     "# remove the frame of the chart\n",
235 |     "for spine in plt.gca().spines.values():\n",
236 |     "    spine.set_visible(False)\n",
237 |     "\n",
238 |     "# direct label each bar with Y axis values\n",
239 |     "for bar in bars:\n",
240 |     "    plt.gca().text(bar.get_x() + bar.get_width()/2, bar.get_height() - 5, str(int(bar.get_height())) + '%', \n",
241 |     "                 ha='center', color='w', fontsize=11)\n",
242 |     "plt.show()"
243 |    ]
244 |   }
245 |  ],
246 |  "metadata": {
247 |   "kernelspec": {
248 |    "display_name": "Python 3",
249 |    "language": "python",
250 |    "name": "python3"
251 |   },
252 |   "language_info": {
253 |    "codemirror_mode": {
254 |     "name": "ipython",
255 |     "version": 3
256 |    },
257 |    "file_extension": ".py",
258 |    "mimetype": "text/x-python",
259 |    "name": "python",
260 |    "nbconvert_exporter": "python",
261 |    "pygments_lexer": "ipython3",
262 |    "version": "3.6.5"
263 |   }
264 |  },
265 |  "nbformat": 4,
266 |  "nbformat_minor": 2
267 | }
268 | 


--------------------------------------------------------------------------------
/LICENSE:
--------------------------------------------------------------------------------
 1 | MIT License
 2 | 
 3 | Copyright (c) 2018 VaaibhaviSingh
 4 | 
 5 | Permission is hereby granted, free of charge, to any person obtaining a copy
 6 | of this software and associated documentation files (the "Software"), to deal
 7 | in the Software without restriction, including without limitation the rights
 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
 9 | copies of the Software, and to permit persons to whom the Software is
10 | furnished to do so, subject to the following conditions:
11 | 
12 | The above copyright notice and this permission notice shall be included in all
13 | copies or substantial portions of the Software.
14 | 
15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
21 | SOFTWARE.
22 | 


--------------------------------------------------------------------------------
/README.md:
--------------------------------------------------------------------------------
1 | # Applied-Plotting-Charting-and-Data-Representation-in-Python
2 | Coursera
3 | 


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/Rain_1961_1990.xls:
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https://raw.githubusercontent.com/VaaibhaviSingh/Applied-Plotting-Charting-and-Data-Representation-in-Python/438fc7bc403c25bc57a5929e3ae1358baff62b2b/Rain_1961_1990.xls


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/Temp_1961_1990.xls:
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https://raw.githubusercontent.com/VaaibhaviSingh/Applied-Plotting-Charting-and-Data-Representation-in-Python/438fc7bc403c25bc57a5929e3ae1358baff62b2b/Temp_1961_1990.xls


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/heroes_information.csv:
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  1 | ,name,Gender,Eye color,Race,Hair color,Height,Publisher,Skin color,Alignment,Weight
  2 | 0,A-Bomb,Male,yellow,Human,No Hair,203.0,Marvel Comics,-,good,441.0
  3 | 1,Abe Sapien,Male,blue,Icthyo Sapien,No Hair,191.0,Dark Horse Comics,blue,good,65.0
  4 | 2,Abin Sur,Male,blue,Ungaran,No Hair,185.0,DC Comics,red,good,90.0
  5 | 3,Abomination,Male,green,Human / Radiation,No Hair,203.0,Marvel Comics,-,bad,441.0
  6 | 4,Abraxas,Male,blue,Cosmic Entity,Black,-99.0,Marvel Comics,-,bad,-99.0
  7 | 5,Absorbing Man,Male,blue,Human,No Hair,193.0,Marvel Comics,-,bad,122.0
  8 | 6,Adam Monroe,Male,blue,-,Blond,-99.0,NBC - Heroes,-,good,-99.0
  9 | 7,Adam Strange,Male,blue,Human,Blond,185.0,DC Comics,-,good,88.0
 10 | 8,Agent 13,Female,blue,-,Blond,173.0,Marvel Comics,-,good,61.0
 11 | 9,Agent Bob,Male,brown,Human,Brown,178.0,Marvel Comics,-,good,81.0
 12 | 10,Agent Zero,Male,-,-,-,191.0,Marvel Comics,-,good,104.0
 13 | 11,Air-Walker,Male,blue,-,White,188.0,Marvel Comics,-,bad,108.0
 14 | 12,Ajax,Male,brown,Cyborg,Black,193.0,Marvel Comics,-,bad,90.0
 15 | 13,Alan Scott,Male,blue,-,Blond,180.0,DC Comics,-,good,90.0
 16 | 14,Alex Mercer,Male,-,Human,-,-99.0,Wildstorm,-,bad,-99.0
 17 | 15,Alex Woolsly,Male,-,-,-,-99.0,NBC - Heroes,-,good,-99.0
 18 | 16,Alfred Pennyworth,Male,blue,Human,Black,178.0,DC Comics,-,good,72.0
 19 | 17,Alien,Male,-,Xenomorph XX121,No Hair,244.0,Dark Horse Comics,black,bad,169.0
 20 | 18,Allan Quatermain,Male,-,-,-,-99.0,Wildstorm,-,good,-99.0
 21 | 19,Amazo,Male,red,Android,-,257.0,DC Comics,-,bad,173.0
 22 | 20,Ammo,Male,brown,Human,Black,188.0,Marvel Comics,-,bad,101.0
 23 | 21,Ando Masahashi,Male,-,-,-,-99.0,NBC - Heroes,-,good,-99.0
 24 | 22,Angel,Male,blue,-,Blond,183.0,Marvel Comics,-,good,68.0
 25 | 23,Angel,Male,-,Vampire,-,-99.0,Dark Horse Comics,-,good,-99.0
 26 | 24,Angel Dust,Female,yellow,Mutant,Black,165.0,Marvel Comics,-,good,57.0
 27 | 25,Angel Salvadore,Female,brown,-,Black,163.0,Marvel Comics,-,good,54.0
 28 | 26,Angela,Female,-,-,-,-99.0,Image Comics,-,bad,-99.0
 29 | 27,Animal Man,Male,blue,Human,Blond,183.0,DC Comics,-,good,83.0
 30 | 28,Annihilus,Male,green,-,No Hair,180.0,Marvel Comics,-,bad,90.0
 31 | 29,Ant-Man,Male,blue,Human,Blond,211.0,Marvel Comics,-,good,122.0
 32 | 30,Ant-Man II,Male,blue,Human,Blond,183.0,Marvel Comics,-,good,86.0
 33 | 31,Anti-Monitor,Male,yellow,God / Eternal,No Hair,61.0,DC Comics,-,bad,-99.0
 34 | 32,Anti-Spawn,Male,-,-,-,-99.0,Image Comics,-,bad,-99.0
 35 | 33,Anti-Venom,Male,blue,Symbiote,Blond,229.0,Marvel Comics,-,-,358.0
 36 | 34,Apocalypse,Male,red,Mutant,Black,213.0,Marvel Comics,grey,bad,135.0
 37 | 35,Aquababy,Male,blue,-,Blond,-99.0,DC Comics,-,good,-99.0
 38 | 36,Aqualad,Male,blue,Atlantean,Black,178.0,DC Comics,-,good,106.0
 39 | 37,Aquaman,Male,blue,Atlantean,Blond,185.0,DC Comics,-,good,146.0
 40 | 38,Arachne,Female,blue,Human,Blond,175.0,Marvel Comics,-,good,63.0
 41 | 39,Archangel,Male,blue,Mutant,Blond,183.0,Marvel Comics,blue,good,68.0
 42 | 40,Arclight,Female,violet,-,Purple,173.0,Marvel Comics,-,bad,57.0
 43 | 41,Ardina,Female,white,Alien,Orange,193.0,Marvel Comics,gold,good,98.0
 44 | 42,Ares,Male,brown,-,Brown,185.0,Marvel Comics,-,good,270.0
 45 | 43,Ariel,Female,purple,-,Pink,165.0,Marvel Comics,-,good,59.0
 46 | 44,Armor,Female,black,-,Black,163.0,Marvel Comics,-,good,50.0
 47 | 45,Arsenal,Male,-,Human,-,-99.0,DC Comics,-,good,-99.0
 48 | 46,Astro Boy,Male,brown,-,Black,-99.0,,-,good,-99.0
 49 | 47,Atlas,Male,brown,Mutant,Red,183.0,Marvel Comics,-,good,101.0
 50 | 48,Atlas,Male,blue,God / Eternal,Brown,198.0,DC Comics,-,bad,126.0
 51 | 49,Atom,Male,blue,-,Red,178.0,DC Comics,-,good,68.0
 52 | 50,Atom,Male,-,-,-,-99.0,DC Comics,-,good,-99.0
 53 | 51,Atom Girl,Female,black,-,Black,168.0,DC Comics,-,good,54.0
 54 | 52,Atom II,Male,brown,Human,Auburn,183.0,DC Comics,-,good,81.0
 55 | 53,Atom III,Male,-,-,Red,-99.0,DC Comics,-,good,-99.0
 56 | 54,Atom IV,Male,brown,-,Black,-99.0,DC Comics,-,good,72.0
 57 | 55,Aurora,Female,blue,Mutant,Black,180.0,Marvel Comics,-,good,63.0
 58 | 56,Azazel,Male,yellow,Neyaphem,Black,183.0,Marvel Comics,red,bad,67.0
 59 | 57,Azrael,Male,brown,Human,Black,-99.0,DC Comics,-,good,-99.0
 60 | 58,Aztar,Male,-,-,-,-99.0,DC Comics,-,good,-99.0
 61 | 59,Bane,Male,-,Human,-,203.0,DC Comics,-,bad,180.0
 62 | 60,Banshee,Male,green,Human,Strawberry Blond,183.0,Marvel Comics,-,good,77.0
 63 | 61,Bantam,Male,brown,-,Black,165.0,Marvel Comics,-,good,54.0
 64 | 62,Batgirl,Female,-,-,-,-99.0,DC Comics,-,good,-99.0
 65 | 63,Batgirl,Female,green,Human,Red,170.0,DC Comics,-,good,57.0
 66 | 64,Batgirl III,Female,-,-,-,-99.0,DC Comics,-,good,-99.0
 67 | 65,Batgirl IV,Female,green,Human,Black,165.0,DC Comics,-,good,52.0
 68 | 66,Batgirl V,Female,-,-,-,-99.0,DC Comics,-,good,-99.0
 69 | 67,Batgirl VI,Female,blue,-,Blond,168.0,DC Comics,-,good,61.0
 70 | 68,Batman,Male,blue,Human,black,188.0,DC Comics,-,good,95.0
 71 | 69,Batman,Male,blue,Human,Black,178.0,DC Comics,-,good,77.0
 72 | 70,Batman II,Male,blue,Human,Black,178.0,DC Comics,-,good,79.0
 73 | 71,Battlestar,Male,brown,-,Black,198.0,Marvel Comics,-,good,133.0
 74 | 72,Batwoman V,Female,green,Human,Red,178.0,DC Comics,-,good,-99.0
 75 | 73,Beak,Male,black,-,White,175.0,Marvel Comics,-,good,63.0
 76 | 74,Beast,Male,blue,Mutant,Blue,180.0,Marvel Comics,blue,good,181.0
 77 | 75,Beast Boy,Male,green,Human,Green,173.0,DC Comics,green,good,68.0
 78 | 76,Beetle,Male,-,-,-,-99.0,Marvel Comics,-,bad,-99.0
 79 | 77,Ben 10,Male,-,-,-,-99.0,DC Comics,-,good,-99.0
 80 | 78,Beta Ray Bill,Male,-,-,No Hair,201.0,Marvel Comics,-,good,216.0
 81 | 79,Beyonder,Male,-,God / Eternal,-,-99.0,Marvel Comics,-,good,-99.0
 82 | 80,Big Barda,Female,blue,New God,Black,188.0,DC Comics,-,bad,135.0
 83 | 81,Big Daddy,Male,-,-,-,-99.0,Icon Comics,-,good,-99.0
 84 | 82,Big Man,Male,blue,-,Brown,165.0,Marvel Comics,-,bad,71.0
 85 | 83,Bill Harken,Male,-,Alpha,-,-99.0,SyFy,-,good,-99.0
 86 | 84,Billy Kincaid,Male,-,-,-,-99.0,Image Comics,-,bad,-99.0
 87 | 85,Binary,Female,blue,-,Blond,180.0,Marvel Comics,-,good,54.0
 88 | 86,Bionic Woman,Female,blue,Cyborg,Black,-99.0,,-,good,-99.0
 89 | 87,Bird-Brain,-,-,-,-,-99.0,Marvel Comics,-,good,-99.0
 90 | 88,Bird-Man,Male,-,Human,-,-99.0,Marvel Comics,-,bad,-99.0
 91 | 89,Bird-Man II,Male,-,Human,-,-99.0,Marvel Comics,-,bad,-99.0
 92 | 90,Birdman,Male,-,God / Eternal,-,-99.0,Hanna-Barbera,-,good,-99.0
 93 | 91,Bishop,Male,brown,Mutant,No Hair,198.0,Marvel Comics,-,good,124.0
 94 | 92,Bizarro,Male,black,Bizarro,Black,191.0,DC Comics,white,neutral,155.0
 95 | 93,Black Abbott,Male,red,-,Black,-99.0,Marvel Comics,-,bad,-99.0
 96 | 94,Black Adam,Male,brown,-,Black,191.0,DC Comics,-,bad,113.0
 97 | 95,Black Bolt,Male,blue,Inhuman,Black,188.0,Marvel Comics,-,good,95.0
 98 | 96,Black Canary,Female,blue,Human,Blond,165.0,DC Comics,-,good,58.0
 99 | 97,Black Canary,Female,blue,Metahuman,Blond,170.0,DC Comics,-,good,59.0
100 | 98,Black Cat,Female,green,Human,Blond,178.0,Marvel Comics,-,good,54.0
101 | 99,Black Flash,Male,-,God / Eternal,-,-99.0,DC Comics,-,neutral,-99.0
102 | 100,Black Goliath,Male,-,-,-,-99.0,Marvel Comics,-,good,-99.0
103 | 101,Black Knight III,Male,brown,Human,Brown,183.0,Marvel Comics,-,good,86.0
104 | 102,Black Lightning,Male,brown,-,No Hair,185.0,DC Comics,-,good,90.0
105 | 103,Black Mamba,Female,green,-,Black,170.0,Marvel Comics,-,bad,52.0
106 | 104,Black Manta,Male,black,Human,No Hair,188.0,DC Comics,-,bad,92.0
107 | 105,Black Panther,Male,brown,Human,Black,183.0,Marvel Comics,-,good,90.0
108 | 106,Black Widow,Female,green,Human,Auburn,170.0,Marvel Comics,-,good,59.0
109 | 107,Black Widow II,Female,blue,-,Blond,170.0,Marvel Comics,-,good,61.0
110 | 108,Blackout,Male,red,Demon,White,191.0,Marvel Comics,white,bad,104.0
111 | 109,Blackwing,Male,blue,-,Black,185.0,Marvel Comics,-,bad,86.0
112 | 110,Blackwulf,Male,red,Alien,White,188.0,Marvel Comics,-,-,88.0
113 | 111,Blade,Male,brown,Vampire,Black,188.0,Marvel Comics,-,good,97.0
114 | 112,Blaquesmith,-,black,-,No Hair,-99.0,Marvel Comics,-,good,-99.0
115 | 113,Bling!,Female,-,-,-,168.0,Marvel Comics,-,good,68.0
116 | 114,Blink,Female,green,Mutant,Magenta,165.0,Marvel Comics,pink,good,56.0
117 | 115,Blizzard,Male,-,-,-,-99.0,Marvel Comics,-,bad,-99.0
118 | 116,Blizzard,Male,-,-,Brown,-99.0,Marvel Comics,-,bad,-99.0
119 | 117,Blizzard II,Male,brown,-,Brown,175.0,Marvel Comics,-,bad,77.0
120 | 118,Blob,Male,brown,-,Brown,178.0,Marvel Comics,-,bad,230.0
121 | 119,Bloodaxe,Female,blue,Human,Brown,218.0,Marvel Comics,-,bad,495.0
122 | 120,Bloodhawk,Male,black,Mutant,No Hair,-99.0,Marvel Comics,-,good,-99.0
123 | 121,Bloodwraith,Male,white,-,No Hair,30.5,Marvel Comics,-,bad,-99.0
124 | 122,Blue Beetle,Male,blue,-,Brown,-99.0,DC Comics,-,good,-99.0
125 | 123,Blue Beetle,Male,-,-,-,-99.0,DC Comics,-,good,-99.0
126 | 124,Blue Beetle II,Male,blue,-,Brown,183.0,DC Comics,-,good,86.0
127 | 125,Blue Beetle III,Male,brown,Human,Black,-99.0,DC Comics,-,good,-99.0
128 | 126,Boba Fett,Male,brown,Human / Clone,Black,183.0,George Lucas,-,bad,-99.0
129 | 127,Bolt,Male,-,-,-,-99.0,Marvel Comics,-,good,-99.0
130 | 128,Bomb Queen,Female,-,-,-,-99.0,Image Comics,-,bad,-99.0
131 | 129,Boom-Boom,Female,blue,Mutant,Blond,165.0,Marvel Comics,-,good,55.0
132 | 130,Boomer,Female,-,-,-,-99.0,Marvel Comics,-,good,-99.0
133 | 131,Booster Gold,Male,blue,Human,Blond,196.0,DC Comics,-,good,97.0
134 | 132,Box,Male,-,-,-,-99.0,Marvel Comics,-,good,-99.0
135 | 133,Box III,-,blue,-,Blond,193.0,Marvel Comics,-,good,110.0
136 | 134,Box IV,-,brown,-,Brown / Black,-99.0,Marvel Comics,-,good,-99.0
137 | 135,Brainiac,Male,green,Android,No Hair,198.0,DC Comics,green,bad,135.0
138 | 136,Brainiac 5,Male,green,-,Blond,170.0,DC Comics,-,good,61.0
139 | 137,Brother Voodoo,Male,brown,Human,Brown / White,183.0,Marvel Comics,-,good,99.0
140 | 138,Brundlefly,Male,-,Mutant,-,193.0,,-,-,-99.0
141 | 139,Buffy,Female,green,Human,Blond,157.0,Dark Horse Comics,-,good,52.0
142 | 140,Bullseye,Male,blue,Human,blond,183.0,Marvel Comics,-,bad,90.0
143 | 141,Bumblebee,Female,brown,Human,Black,170.0,DC Comics,-,good,59.0
144 | 142,Bumbleboy,Male,-,-,-,-99.0,Marvel Comics,-,good,-99.0
145 | 143,Bushido,Male,-,Human,-,-99.0,DC Comics,-,good,-99.0
146 | 144,Cable,Male,blue,Mutant,White,203.0,Marvel Comics,-,good,158.0
147 | 145,Callisto,Female,blue,-,Black,175.0,Marvel Comics,-,bad,74.0
148 | 146,Cameron Hicks,Male,-,Alpha,-,-99.0,SyFy,-,good,-99.0
149 | 147,Cannonball,Male,blue,-,Blond,183.0,Marvel Comics,-,good,81.0
150 | 148,Captain America,Male,blue,Human,blond,188.0,Marvel Comics,-,good,108.0
151 | 149,Captain Atom,Male,blue,Human / Radiation,Silver,193.0,DC Comics,silver,good,90.0
152 | 150,Captain Britain,Male,blue,Human,Blond,198.0,Marvel Comics,-,good,116.0
153 | 151,Captain Cold,Male,brown,Human,Brown,-99.0,DC Comics,-,neutral,-99.0
154 | 152,Captain Epic,Male,blue,-,Brown,188.0,Team Epic TV,-,good,-99.0
155 | 153,Captain Hindsight,Male,-,Human,Black,-99.0,South Park,-,good,-99.0
156 | 154,Captain Mar-vell,Male,blue,-,Blond,188.0,Marvel Comics,-,good,108.0
157 | 155,Captain Marvel,Female,blue,Human-Kree,Blond,180.0,Marvel Comics,-,good,74.0
158 | 156,Captain Marvel,Male,blue,Human,Black,193.0,DC Comics,-,good,101.0
159 | 157,Captain Marvel II,Male,blue,Human,Black,175.0,DC Comics,-,good,74.0
160 | 158,Captain Midnight,Male,-,Human,-,-99.0,Dark Horse Comics,-,good,-99.0
161 | 159,Captain Planet,Male,red,God / Eternal,Green,-99.0,Marvel Comics,-,good,-99.0
162 | 160,Captain Universe,-,-,God / Eternal,-,-99.0,Marvel Comics,-,good,-99.0
163 | 161,Carnage,Male,green,Symbiote,Red,185.0,Marvel Comics,-,bad,86.0
164 | 162,Cat,Female,blue,-,Blond,173.0,Marvel Comics,-,good,61.0
165 | 163,Cat II,Female,-,-,-,-99.0,Marvel Comics,-,good,-99.0
166 | 164,Catwoman,Female,green,Human,Black,175.0,DC Comics,-,good,61.0
167 | 165,Cecilia Reyes,-,brown,-,Brown,170.0,Marvel Comics,-,good,62.0
168 | 166,Century,Male,white,Alien,White,201.0,Marvel Comics,grey,good,97.0
169 | 167,Cerebra,Female,-,Mutant,-,-99.0,Marvel Comics,-,good,-99.0
170 | 168,Chamber,Male,brown,Mutant,Brown,175.0,Marvel Comics,-,good,63.0
171 | 169,Chameleon,Male,-,-,-,-99.0,DC Comics,-,bad,-99.0
172 | 170,Changeling,Male,brown,-,Black,180.0,Marvel Comics,-,bad,81.0
173 | 171,Cheetah,Female,green,Human,Blond,163.0,DC Comics,-,bad,50.0
174 | 172,Cheetah II,Female,green,Human,Brown,170.0,DC Comics,-,bad,55.0
175 | 173,Cheetah III,Female,brown,Human,Brown,175.0,DC Comics,-,bad,54.0
176 | 174,Chromos,Male,brown,-,Red / Grey,185.0,Team Epic TV,-,bad,86.0
177 | 175,Chuck Norris,Male,-,-,-,178.0,,-,good,-99.0
178 | 176,Citizen Steel,Male,green,Human,Red,183.0,DC Comics,-,good,170.0
179 | 177,Claire Bennet,Female,blue,-,Blond,-99.0,NBC - Heroes,-,good,-99.0
180 | 178,Clea,-,-,-,White,-99.0,Marvel Comics,-,good,-99.0
181 | 179,Cloak,Male,brown,-,black,226.0,Marvel Comics,-,good,70.0
182 | 180,Clock King,Male,blue,Human,Black,178.0,DC Comics,-,bad,78.0
183 | 181,Cogliostro,Male,-,-,-,-99.0,Image Comics,-,bad,-99.0
184 | 182,Colin Wagner,Male,grey,-,Brown,-99.0,HarperCollins,-,good,-99.0
185 | 183,Colossal Boy,Male,-,-,-,-99.0,DC Comics,-,good,-99.0
186 | 184,Colossus,Male,silver,Mutant,Black,226.0,Marvel Comics,-,good,225.0
187 | 185,Copycat,Female,red,Mutant,White,183.0,Marvel Comics,blue,neutral,67.0
188 | 186,Corsair,Male,brown,-,Brown,191.0,Marvel Comics,-,good,79.0
189 | 187,Cottonmouth,Male,brown,Human,Black,183.0,Marvel Comics,-,bad,99.0
190 | 188,Crimson Crusader,Male,blue,-,Strawberry Blond,-99.0,Marvel Comics,-,good,-99.0
191 | 189,Crimson Dynamo,Male,brown,-,No Hair,180.0,Marvel Comics,-,good,104.0
192 | 190,Crystal,Female,green,Inhuman,Red,168.0,Marvel Comics,-,good,50.0
193 | 191,Curse,Male,-,-,-,-99.0,Image Comics,-,bad,-99.0
194 | 192,Cy-Gor,Male,-,-,-,-99.0,Image Comics,-,bad,-99.0
195 | 193,Cyborg,Male,brown,Cyborg,Black,198.0,DC Comics,-,good,173.0
196 | 194,Cyborg Superman,Male,blue,Cyborg,Black,-99.0,DC Comics,-,bad,-99.0
197 | 195,Cyclops,Male,brown,Mutant,Brown,191.0,Marvel Comics,-,good,88.0
198 | 196,Cypher,-,blue,-,Blond,175.0,Marvel Comics,-,good,68.0
199 | 197,Dagger,Female,blue,-,Blond,165.0,Marvel Comics,-,good,52.0
200 | 198,Danny Cooper,Male,brown,-,Blond,-99.0,HarperCollins,-,good,-99.0
201 | 199,Daphne Powell,Female,-,-,-,-99.0,ABC Studios,-,good,-99.0
202 | 200,Daredevil,Male,blue,Human,Red,183.0,Marvel Comics,-,good,90.0
203 | 201,Darkhawk,Male,brown,Human,Brown,185.0,Marvel Comics,-,good,81.0
204 | 202,Darkman,Male,-,-,-,-99.0,Universal Studios,-,good,-99.0
205 | 203,Darkseid,Male,red,New God,No Hair,267.0,DC Comics,grey,bad,817.0
206 | 204,Darkside,-,-,-,-,-99.0,,-,bad,-99.0
207 | 205,Darkstar,Female,brown,Mutant,Blond,168.0,Marvel Comics,-,good,56.0
208 | 206,Darth Maul,Male,yellow / red,Dathomirian Zabrak,-,170.0,George Lucas,red / black,bad,-99.0
209 | 207,Darth Vader,Male,yellow,Cyborg,No Hair,198.0,George Lucas,-,bad,135.0
210 | 208,Dash,Male,blue,Human,Blond,122.0,Dark Horse Comics,-,good,27.0
211 | 209,Data,Male,yellow,Android,Brown,-99.0,Star Trek,-,good,-99.0
212 | 210,Dazzler,Female,blue,Mutant,Blond,173.0,Marvel Comics,-,good,52.0
213 | 211,Deadman,Male,blue,Human,Black,183.0,DC Comics,-,good,90.0
214 | 212,Deadpool,Male,brown,Mutant,No Hair,188.0,Marvel Comics,-,neutral,95.0
215 | 213,Deadshot,Male,brown,Human,Brown,185.0,DC Comics,-,bad,91.0
216 | 214,Deathlok,Male,brown,Cyborg,Grey,193.0,Marvel Comics,-,good,178.0
217 | 215,Deathstroke,Male,blue,Human,White,193.0,DC Comics,-,neutral,101.0
218 | 216,Demogoblin,Male,red,Demon,No Hair,185.0,Marvel Comics,-,bad,95.0
219 | 217,Destroyer,Male,-,-,-,188.0,Marvel Comics,-,bad,383.0
220 | 218,Diamondback,Male,brown,Human,Black,193.0,Marvel Comics,-,bad,90.0
221 | 219,DL Hawkins,Male,-,-,-,-99.0,NBC - Heroes,-,good,-99.0
222 | 220,Doc Samson,Male,blue,Human / Radiation,Green,198.0,Marvel Comics,-,good,171.0
223 | 221,Doctor Doom,Male,brown,Human,Brown,201.0,Marvel Comics,-,bad,187.0
224 | 222,Doctor Doom II,Male,brown,-,Brown,201.0,Marvel Comics,-,bad,132.0
225 | 223,Doctor Fate,Male,blue,Human,Blond,188.0,DC Comics,-,good,89.0
226 | 224,Doctor Octopus,Male,brown,Human,Brown,175.0,Marvel Comics,-,bad,110.0
227 | 225,Doctor Strange,Male,grey,Human,Black,188.0,Marvel Comics,-,good,81.0
228 | 226,Domino,Female,blue,Human,Black,173.0,Marvel Comics,white,good,54.0
229 | 227,Donatello,Male,green,Mutant,No Hair,-99.0,IDW Publishing,green,good,-99.0
230 | 228,Donna Troy,Female,blue,Amazon,Black,175.0,DC Comics,-,good,63.0
231 | 229,Doomsday,Male,red,Alien,White,244.0,DC Comics,-,bad,412.0
232 | 230,Doppelganger,Male,white,-,No Hair,196.0,Marvel Comics,-,bad,104.0
233 | 231,Dormammu,Male,yellow,-,No Hair,185.0,Marvel Comics,-,bad,-99.0
234 | 232,Dr Manhattan,Male,white,Human / Cosmic,No Hair,-99.0,DC Comics,blue,good,-99.0
235 | 233,Drax the Destroyer,Male,red,Human / Altered,No Hair,193.0,Marvel Comics,green,good,306.0
236 | 234,Ego,-,-,-,-,-99.0,Marvel Comics,-,bad,-99.0
237 | 235,Elastigirl,Female,brown,Human,Brown,168.0,Dark Horse Comics,-,good,56.0
238 | 236,Electro,Male,blue,Human,Auburn,180.0,Marvel Comics,-,bad,74.0
239 | 237,Elektra,Female,blue,Human,Black,175.0,Marvel Comics,-,good,59.0
240 | 238,Elle Bishop,Female,blue,-,Blond,-99.0,NBC - Heroes,-,bad,-99.0
241 | 239,Elongated Man,Male,blue,-,Red,185.0,DC Comics,-,good,80.0
242 | 240,Emma Frost,Female,blue,-,Blond,178.0,Marvel Comics,-,good,65.0
243 | 241,Enchantress,Female,blue,Human,Blond,168.0,DC Comics,-,good,57.0
244 | 242,Energy,Female,-,-,-,-99.0,HarperCollins,-,good,-99.0
245 | 243,ERG-1,Male,-,-,-,-99.0,DC Comics,-,good,-99.0
246 | 244,Ethan Hunt,Male,brown,Human,Brown,168.0,,-,good,-99.0
247 | 245,Etrigan,Male,red,Demon,No Hair,193.0,DC Comics,yellow,neutral,203.0
248 | 246,Evil Deadpool,Male,white,Mutant,Red,188.0,Marvel Comics,-,bad,95.0
249 | 247,Evilhawk,Male,red,Alien,Black,191.0,Marvel Comics,green,bad,106.0
250 | 248,Exodus,Male,blue,Mutant,Black,183.0,Marvel Comics,red,bad,88.0
251 | 249,Fabian Cortez,-,blue,-,Brown,196.0,Marvel Comics,-,bad,96.0
252 | 250,Falcon,Male,brown,Human,Black,188.0,Marvel Comics,-,good,108.0
253 | 251,Fallen One II,Male,black,-,Blue,-99.0,Marvel Comics,-,bad,-99.0
254 | 252,Faora,Female,-,Kryptonian,-,-99.0,DC Comics,-,bad,-99.0
255 | 253,Feral,-,yellow (without irises),-,Orange / White,175.0,Marvel Comics,-,good,50.0
256 | 254,Fighting Spirit,Female,-,-,Red,-99.0,DC Comics,-,good,-99.0
257 | 255,Fin Fang Foom,Male,red,Kakarantharaian,No Hair,975.0,Marvel Comics,green,good,18.0
258 | 256,Firebird,Female,brown,-,Black,165.0,Marvel Comics,-,good,56.0
259 | 257,Firelord,-,white,-,Yellow,193.0,Marvel Comics,-,good,99.0
260 | 258,Firestar,Female,green,Mutant,Red,173.0,Marvel Comics,-,good,56.0
261 | 259,Firestorm,Male,brown,-,Black,-99.0,DC Comics,-,good,-99.0
262 | 260,Firestorm,Male,blue,Human,Auburn,188.0,DC Comics,-,good,91.0
263 | 261,Fixer,-,red,-,No Hair,-99.0,Marvel Comics,-,bad,-99.0
264 | 262,Flash,Male,blue,Human,Brown / White,180.0,DC Comics,-,good,81.0
265 | 263,Flash Gordon,Male,-,-,-,-99.0,,-,good,-99.0
266 | 264,Flash II,Male,blue,Human,Blond,183.0,DC Comics,-,good,88.0
267 | 265,Flash III,Male,-,Human,-,183.0,DC Comics,-,good,86.0
268 | 266,Flash IV,Male,yellow,Human,Auburn,157.0,DC Comics,-,good,52.0
269 | 267,Forge,-,brown,-,Black,183.0,Marvel Comics,-,good,81.0
270 | 268,Franklin Richards,Male,blue,Mutant,Blond,142.0,Marvel Comics,-,good,45.0
271 | 269,Franklin Storm,-,blue,-,Grey,188.0,Marvel Comics,-,good,92.0
272 | 270,Frenzy,Female,brown,-,Black,211.0,Marvel Comics,-,bad,104.0
273 | 271,Frigga,Female,blue,-,White,180.0,Marvel Comics,-,good,167.0
274 | 272,Galactus,Male,black,Cosmic Entity,Black,876.0,Marvel Comics,-,neutral,16.0
275 | 273,Gambit,Male,red,Mutant,Brown,185.0,Marvel Comics,-,good,81.0
276 | 274,Gamora,Female,yellow,Zen-Whoberian,Black,183.0,Marvel Comics,green,good,77.0
277 | 275,Garbage Man,Male,-,Mutant,-,-99.0,DC Comics,-,good,-99.0
278 | 276,Gary Bell,Male,-,Alpha,-,-99.0,SyFy,-,good,-99.0
279 | 277,General Zod,Male,black,Kryptonian,Black,-99.0,DC Comics,-,bad,-99.0
280 | 278,Genesis,Male,blue,-,Blond,185.0,Marvel Comics,-,good,86.0
281 | 279,Ghost Rider,Male,red,Demon,No Hair,188.0,Marvel Comics,-,good,99.0
282 | 280,Ghost Rider II,-,-,-,-,-99.0,Marvel Comics,-,good,-99.0
283 | 281,Giant-Man,Male,-,Human,-,-99.0,Marvel Comics,-,good,-99.0
284 | 282,Giant-Man II,Male,-,-,-,-99.0,Marvel Comics,-,good,-99.0
285 | 283,Giganta,Female,green,-,Red,62.5,DC Comics,-,bad,630.0
286 | 284,Gladiator,Male,blue,Strontian,Blue,198.0,Marvel Comics,purple,neutral,268.0
287 | 285,Goblin Queen,Female,green,-,Red,168.0,Marvel Comics,-,bad,50.0
288 | 286,Godzilla,-,-,Kaiju,-,108.0,,grey,bad,
289 | 287,Gog,Male,-,-,-,-99.0,DC Comics,-,bad,-99.0
290 | 288,Goku,Male,-,Saiyan,-,175.0,Shueisha,-,good,62.0
291 | 289,Goliath,Male,-,-,-,-99.0,Marvel Comics,-,good,-99.0
292 | 290,Goliath,Male,-,Human,-,-99.0,Marvel Comics,-,good,-99.0
293 | 291,Goliath,Male,-,Human,-,-99.0,Marvel Comics,-,good,-99.0
294 | 292,Goliath IV,Male,brown,-,Black,183.0,Marvel Comics,-,good,90.0
295 | 293,Gorilla Grodd,Male,yellow,Gorilla,Black,198.0,DC Comics,-,bad,270.0
296 | 294,Granny Goodness,Female,blue,-,White,178.0,DC Comics,-,bad,115.0
297 | 295,Gravity,Male,blue,Human,Brown,178.0,Marvel Comics,-,good,79.0
298 | 296,Greedo,Male,purple,Rodian,-,170.0,George Lucas,green,bad,-99.0
299 | 297,Green Arrow,Male,green,Human,Blond,188.0,DC Comics,-,good,88.0
300 | 298,Green Goblin,Male,blue,Human,Auburn,180.0,Marvel Comics,-,bad,83.0
301 | 299,Green Goblin II,Male,blue,-,Auburn,178.0,Marvel Comics,-,bad,77.0
302 | 300,Green Goblin III,Male,-,-,-,183.0,Marvel Comics,-,good,88.0
303 | 301,Green Goblin IV,Male,green,-,Brown,178.0,Marvel Comics,-,good,79.0
304 | 302,Groot,Male,yellow,Flora Colossus,-,701.0,Marvel Comics,-,good,4.0
305 | 303,Guardian,Male,brown,Human,Black,-99.0,Marvel Comics,-,good,-99.0
306 | 304,Guy Gardner,Male,blue,Human-Vuldarian,Red,188.0,DC Comics,-,good,95.0
307 | 305,Hal Jordan,Male,brown,Human,Brown,188.0,DC Comics,-,good,90.0
308 | 306,Han Solo,Male,brown,Human,Brown,183.0,George Lucas,-,good,79.0
309 | 307,Hancock,Male,brown,Human,Black,188.0,Sony Pictures,-,good,-99.0
310 | 308,Harley Quinn,Female,blue,Human,Blond,170.0,DC Comics,-,bad,63.0
311 | 309,Harry Potter,Male,green,Human,Black,-99.0,J. K. Rowling,-,good,-99.0
312 | 310,Havok,Male,blue,Mutant,Blond,183.0,Marvel Comics,-,good,79.0
313 | 311,Hawk,Male,red,-,Brown,185.0,DC Comics,-,good,89.0
314 | 312,Hawkeye,Male,blue,Human,Blond,191.0,Marvel Comics,-,good,104.0
315 | 313,Hawkeye II,Female,blue,Human,Black,165.0,Marvel Comics,-,good,57.0
316 | 314,Hawkgirl,Female,green,-,Red,175.0,DC Comics,-,good,61.0
317 | 315,Hawkman,Male,blue,-,Brown,185.0,DC Comics,-,good,88.0
318 | 316,Hawkwoman,Female,green,-,Red,175.0,DC Comics,-,good,54.0
319 | 317,Hawkwoman II,Female,-,-,-,-99.0,DC Comics,-,good,-99.0
320 | 318,Hawkwoman III,Female,blue,-,Red,170.0,DC Comics,-,good,65.0
321 | 319,Heat Wave,Male,blue,Human,No Hair,180.0,DC Comics,-,bad,81.0
322 | 320,Hela,Female,green,Asgardian,Black,213.0,Marvel Comics,-,bad,225.0
323 | 321,Hellboy,Male,gold,Demon,Black,259.0,Dark Horse Comics,-,good,158.0
324 | 322,Hellcat,Female,blue,Human,Red,173.0,Marvel Comics,-,good,61.0
325 | 323,Hellstorm,Male,red,-,Red,185.0,Marvel Comics,-,good,81.0
326 | 324,Hercules,Male,blue,Demi-God,Brown,196.0,Marvel Comics,-,good,146.0
327 | 325,Hiro Nakamura,Male,-,-,-,-99.0,NBC - Heroes,-,good,-99.0
328 | 326,Hit-Girl,Female,-,Human,-,-99.0,Icon Comics,-,good,-99.0
329 | 327,Hobgoblin,Male,blue,-,Grey,180.0,Marvel Comics,-,bad,83.0
330 | 328,Hollow,Female,blue,-,Red,170.0,Marvel Comics,-,good,-99.0
331 | 329,Hope Summers,Female,green,-,Red,168.0,Marvel Comics,-,good,48.0
332 | 330,Howard the Duck,Male,brown,-,Yellow,79.0,Marvel Comics,-,good,18.0
333 | 331,Hulk,Male,green,Human / Radiation,Green,244.0,Marvel Comics,green,good,630.0
334 | 332,Human Torch,Male,blue,Human / Radiation,Blond,178.0,Marvel Comics,-,good,77.0
335 | 333,Huntress,Female,blue,-,Black,180.0,DC Comics,-,good,59.0
336 | 334,Husk,Female,blue,Mutant,Blond,170.0,Marvel Comics,-,good,58.0
337 | 335,Hybrid,Male,brown,Symbiote,Black,175.0,Marvel Comics,-,good,77.0
338 | 336,Hydro-Man,Male,brown,-,Brown,188.0,Marvel Comics,-,bad,119.0
339 | 337,Hyperion,Male,blue,Eternal,Red,183.0,Marvel Comics,-,good,207.0
340 | 338,Iceman,Male,brown,Mutant,Brown,173.0,Marvel Comics,-,good,65.0
341 | 339,Impulse,Male,yellow,Human,Auburn,170.0,DC Comics,-,good,65.0
342 | 340,Indiana Jones,Male,-,Human,-,183.0,George Lucas,-,good,79.0
343 | 341,Indigo,Female,-,Alien,Purple,-99.0,DC Comics,-,neutral,-99.0
344 | 342,Ink,Male,blue,Mutant,No Hair,180.0,Marvel Comics,-,good,81.0
345 | 343,Invisible Woman,Female,blue,Human / Radiation,Blond,168.0,Marvel Comics,-,good,54.0
346 | 344,Iron Fist,Male,blue,Human,Blond,180.0,Marvel Comics,-,good,79.0
347 | 345,Iron Man,Male,blue,Human,Black,198.0,Marvel Comics,-,good,191.0
348 | 346,Iron Monger,Male,blue,-,No Hair,-99.0,Marvel Comics,-,bad,2.0
349 | 347,Isis,Female,-,-,-,-99.0,DC Comics,-,good,-99.0
350 | 348,Jack Bauer,Male,-,-,-,-99.0,,-,good,-99.0
351 | 349,Jack of Hearts,Male,blue / white,Human,Brown,155.0,Marvel Comics,-,good,79.0
352 | 350,Jack-Jack,Male,blue,Human,Brown,71.0,Dark Horse Comics,-,good,14.0
353 | 351,James Bond,Male,blue,Human,Blond,183.0,Titan Books,-,good,-99.0
354 | 352,James T. Kirk,Male,hazel,Human,Brown,178.0,Star Trek,-,good,77.0
355 | 353,Jar Jar Binks,Male,yellow,Gungan,-,193.0,George Lucas,orange / white,good,-99.0
356 | 354,Jason Bourne,Male,-,Human,-,-99.0,,-,good,-99.0
357 | 355,Jean Grey,Female,green,Mutant,Red,168.0,Marvel Comics,-,good,52.0
358 | 356,Jean-Luc Picard,Male,-,Human,-,-99.0,Star Trek,-,good,-99.0
359 | 357,Jennifer Kale,Female,blue,-,Blond,168.0,Marvel Comics,-,good,55.0
360 | 358,Jesse Quick,Female,-,Human,-,-99.0,DC Comics,-,good,-99.0
361 | 359,Jessica Cruz,Female,green,Human,Brown,-99.0,DC Comics,-,good,-99.0
362 | 360,Jessica Jones,Female,brown,Human,Brown,170.0,Marvel Comics,-,good,56.0
363 | 361,Jessica Sanders,Female,-,-,-,-99.0,NBC - Heroes,-,good,-99.0
364 | 362,Jigsaw,Male,blue,-,Black,188.0,Marvel Comics,-,bad,113.0
365 | 363,Jim Powell,Male,-,-,-,-99.0,ABC Studios,-,good,-99.0
366 | 364,JJ Powell,Male,-,-,-,-99.0,ABC Studios,-,good,-99.0
367 | 365,Johann Krauss,Male,-,-,-,-99.0,Dark Horse Comics,-,good,-99.0
368 | 366,John Constantine,Male,blue,Human,Blond,183.0,DC Comics,-,good,-99.0
369 | 367,John Stewart,Male,green,Human,Black,185.0,DC Comics,-,good,90.0
370 | 368,John Wraith,Male,brown,-,Black,183.0,Marvel Comics,-,good,88.0
371 | 369,Joker,Male,green,Human,Green,196.0,DC Comics,white,bad,86.0
372 | 370,Jolt,Female,blue,-,Black,165.0,Marvel Comics,-,good,49.0
373 | 371,Jubilee,Female,red,Mutant,Black,165.0,Marvel Comics,-,good,52.0
374 | 372,Judge Dredd,Male,-,Human,-,188.0,Rebellion,-,good,-99.0
375 | 373,Juggernaut,Male,blue,Human,Red,287.0,Marvel Comics,-,neutral,855.0
376 | 374,Junkpile,Male,-,Mutant,-,-99.0,Marvel Comics,-,bad,-99.0
377 | 375,Justice,Male,hazel,Human,Brown,178.0,Marvel Comics,-,good,81.0
378 | 376,Jyn Erso,Female,green,Human,Brown,-99.0,George Lucas,-,good,-99.0
379 | 377,K-2SO,Male,white,Android,No Hair,213.0,George Lucas,gray,good,-99.0
380 | 378,Kang,Male,brown,-,Brown,191.0,Marvel Comics,-,bad,104.0
381 | 379,Karate Kid,Male,brown,Human,Brown,173.0,DC Comics,-,good,72.0
382 | 380,Kathryn Janeway,Female,-,Human,-,-99.0,Star Trek,-,good,-99.0
383 | 381,Katniss Everdeen,Female,-,Human,-,-99.0,,-,good,-99.0
384 | 382,Kevin 11,Male,-,Human,Black,-99.0,DC Comics,-,good,-99.0
385 | 383,Kick-Ass,Male,blue,Human,Blond,-99.0,Icon Comics,-,good,-99.0
386 | 384,Kid Flash,Male,green,Human,Red,-99.0,DC Comics,-,good,-99.0
387 | 385,Kid Flash II,Male,-,-,-,-99.0,DC Comics,-,good,-99.0
388 | 386,Killer Croc,Male,red,Metahuman,No Hair,244.0,DC Comics,green,bad,356.0
389 | 387,Killer Frost,Female,blue,Human,Blond,-99.0,DC Comics,blue,bad,-99.0
390 | 388,Kilowog,Male,red,Bolovaxian,No Hair,234.0,DC Comics,pink,good,324.0
391 | 389,King Kong,Male,yellow,Animal,Black,30.5,,-,good,
392 | 390,King Shark,Male,black,Animal,No Hair,-99.0,DC Comics,-,bad,-99.0
393 | 391,Kingpin,Male,blue,Human,No Hair,201.0,Marvel Comics,-,bad,203.0
394 | 392,Klaw,Male,red,Human,No Hair,188.0,Marvel Comics,red,bad,97.0
395 | 393,Kool-Aid Man,Male,black,-,No Hair,-99.0,,red,good,-99.0
396 | 394,Kraven II,Male,brown,Human,Black,191.0,Marvel Comics,-,bad,99.0
397 | 395,Kraven the Hunter,Male,brown,Human,Black,183.0,Marvel Comics,-,bad,106.0
398 | 396,Krypto,Male,blue,Kryptonian,White,64.0,DC Comics,-,good,18.0
399 | 397,Kyle Rayner,Male,green,Human,Black,180.0,DC Comics,-,good,79.0
400 | 398,Kylo Ren,Male,-,Human,-,-99.0,George Lucas,-,bad,-99.0
401 | 399,Lady Bullseye,Female,-,-,Black,-99.0,Marvel Comics,-,bad,-99.0
402 | 400,Lady Deathstrike,Female,brown,Cyborg,Black,175.0,Marvel Comics,-,bad,58.0
403 | 401,Leader,Male,green,-,No Hair,178.0,Marvel Comics,-,bad,63.0
404 | 402,Leech,Male,-,-,-,-99.0,Marvel Comics,-,good,-99.0
405 | 403,Legion,Male,green / blue,Mutant,Black,175.0,Marvel Comics,-,good,59.0
406 | 404,Leonardo,Male,blue,Mutant,No Hair,-99.0,IDW Publishing,green,good,-99.0
407 | 405,Lex Luthor,Male,green,Human,No Hair,188.0,DC Comics,-,bad,95.0
408 | 406,Light Lass,Female,blue,-,Red,165.0,DC Comics,-,good,54.0
409 | 407,Lightning Lad,Male,blue,-,Red,155.0,DC Comics,-,good,65.0
410 | 408,Lightning Lord,Male,blue,-,Red,191.0,DC Comics,-,bad,95.0
411 | 409,Living Brain,-,yellow,-,-,198.0,Marvel Comics,-,bad,360.0
412 | 410,Living Tribunal,-,blue,Cosmic Entity,No Hair,-99.0,Marvel Comics,gold,neutral,-99.0
413 | 411,Liz Sherman,Female,-,-,-,-99.0,Dark Horse Comics,-,good,-99.0
414 | 412,Lizard,Male,red,Human,No Hair,203.0,Marvel Comics,-,bad,230.0
415 | 413,Lobo,Male,red,Czarnian,Black,229.0,DC Comics,blue-white,neutral,288.0
416 | 414,Loki,Male,green,Asgardian,Black,193.0,Marvel Comics,-,bad,236.0
417 | 415,Longshot,Male,blue,Human,Blond,188.0,Marvel Comics,-,good,36.0
418 | 416,Luke Cage,Male,brown,Human,Black,198.0,Marvel Comics,-,good,191.0
419 | 417,Luke Campbell,Male,-,-,-,-99.0,NBC - Heroes,-,bad,-99.0
420 | 418,Luke Skywalker,Male,blue,Human,Blond,168.0,George Lucas,-,good,77.0
421 | 419,Luna,Female,-,Human,-,-99.0,Marvel Comics,-,good,-99.0
422 | 420,Lyja,Female,green,-,Green,-99.0,Marvel Comics,-,good,-99.0
423 | 421,Mach-IV,Male,brown,-,Brown,180.0,Marvel Comics,-,bad,79.0
424 | 422,Machine Man,-,red,-,Black,183.0,Marvel Comics,-,good,383.0
425 | 423,Magneto,Male,grey,Mutant,White,188.0,Marvel Comics,-,bad,86.0
426 | 424,Magog,Male,blue,-,Blond,-99.0,DC Comics,-,good,-99.0
427 | 425,Magus,Male,black,-,-,183.0,Marvel Comics,-,bad,-99.0
428 | 426,Man of Miracles,-,blue,God / Eternal,Silver,-99.0,Image Comics,-,-,-99.0
429 | 427,Man-Bat,Male,brown,Human,Brown,-99.0,DC Comics,-,neutral,-99.0
430 | 428,Man-Thing,Male,red,-,No Hair,213.0,Marvel Comics,green,good,225.0
431 | 429,Man-Wolf,Male,brown,-,Auburn,188.0,Marvel Comics,-,good,90.0
432 | 430,Mandarin,Male,blue,Human,White,188.0,Marvel Comics,-,bad,97.0
433 | 431,Mantis,Female,green,Human-Kree,Black,168.0,Marvel Comics,green,good,52.0
434 | 432,Martian Manhunter,Male,red,Martian,No Hair,201.0,DC Comics,green,good,135.0
435 | 433,Marvel Girl,Female,green,-,Red,170.0,Marvel Comics,-,good,56.0
436 | 434,Master Brood,Male,blue,-,Black,183.0,Team Epic TV,-,good,81.0
437 | 435,Master Chief,Male,brown,Human / Altered,Brown,213.0,Microsoft,-,good,-99.0
438 | 436,Match,Male,black,-,Black,-99.0,DC Comics,-,bad,-99.0
439 | 437,Matt Parkman,Male,-,-,-,-99.0,NBC - Heroes,-,good,-99.0
440 | 438,Maverick,Male,blue,-,Black,193.0,Marvel Comics,-,good,110.0
441 | 439,Maxima,Female,brown,-,Red,180.0,DC Comics,-,bad,72.0
442 | 440,Maya Herrera,Female,-,-,-,-99.0,NBC - Heroes,-,good,-99.0
443 | 441,Medusa,Female,green,Inhuman,Red,180.0,Marvel Comics,-,good,59.0
444 | 442,Meltdown,Female,blue,-,Blond,165.0,Marvel Comics,-,good,54.0
445 | 443,Mephisto,Male,white,-,Black,198.0,Marvel Comics,-,bad,140.0
446 | 444,Mera,Female,blue,Atlantean,Red,175.0,DC Comics,-,good,72.0
447 | 445,Metallo,Male,green,Android,Brown,196.0,DC Comics,-,bad,90.0
448 | 446,Metamorpho,Male,black,-,No Hair,185.0,DC Comics,-,good,90.0
449 | 447,Meteorite,Female,-,-,-,-99.0,Marvel Comics,-,good,-99.0
450 | 448,Metron,Male,blue,-,Black,185.0,DC Comics,-,good,86.0
451 | 449,Micah Sanders,Male,brown,-,Black,-99.0,NBC - Heroes,-,good,-99.0
452 | 450,Michelangelo,Male,blue,Mutant,-,-99.0,IDW Publishing,green,good,-99.0
453 | 451,Micro Lad,Male,grey,-,Brown,183.0,DC Comics,-,good,77.0
454 | 452,Mimic,Male,brown,-,Brown,188.0,Marvel Comics,-,good,101.0
455 | 453,Minna Murray,Female,-,-,-,-99.0,Wildstorm,-,good,-99.0
456 | 454,Misfit,Female,blue,-,Red,-99.0,DC Comics,-,good,-99.0
457 | 455,Miss Martian,Female,red,-,Red,178.0,DC Comics,-,good,61.0
458 | 456,Mister Fantastic,Male,brown,Human / Radiation,Brown,185.0,Marvel Comics,-,good,81.0
459 | 457,Mister Freeze,Male,-,Human,-,183.0,DC Comics,-,bad,86.0
460 | 458,Mister Knife,Male,blue,Spartoi,Brown,-99.0,Marvel Comics,-,bad,-99.0
461 | 459,Mister Mxyzptlk,Male,-,God / Eternal,-,-99.0,DC Comics,-,bad,-99.0
462 | 460,Mister Sinister,Male,red,Human / Altered,Black,196.0,Marvel Comics,-,bad,128.0
463 | 461,Mister Zsasz,Male,blue,Human,Blond,-99.0,DC Comics,-,bad,-99.0
464 | 462,Mockingbird,Female,blue,Human,Blond,175.0,Marvel Comics,-,good,61.0
465 | 463,MODOK,Male,white,Cyborg,Brownn,366.0,Marvel Comics,-,bad,338.0
466 | 464,Mogo,Male,-,Planet,-,-99.0,DC Comics,-,good,-99.0
467 | 465,Mohinder Suresh,Male,-,-,-,-99.0,NBC - Heroes,-,good,-99.0
468 | 466,Moloch,Male,-,-,-,-99.0,DC Comics,-,bad,-99.0
469 | 467,Molten Man,Male,gold,-,Gold,196.0,Marvel Comics,-,bad,248.0
470 | 468,Monarch,Male,blue,-,White,193.0,DC Comics,-,good,90.0
471 | 469,Monica Dawson,Female,-,-,-,-99.0,NBC - Heroes,-,good,-99.0
472 | 470,Moon Knight,Male,brown,Human,Brown,188.0,Marvel Comics,-,good,101.0
473 | 471,Moonstone,Female,blue,-,Blond,180.0,Marvel Comics,-,bad,59.0
474 | 472,Morlun,Male,white / red,-,Black,188.0,Marvel Comics,-,bad,79.0
475 | 473,Morph,Male,white,-,No Hair,178.0,Marvel Comics,-,good,79.0
476 | 474,Moses Magnum,Male,brown,-,Black,175.0,Marvel Comics,-,bad,72.0
477 | 475,Mr Immortal,Male,blue,Mutant,Blond,188.0,Marvel Comics,-,good,70.0
478 | 476,Mr Incredible,Male,blue,Human,Blond,201.0,Dark Horse Comics,-,good,158.0
479 | 477,Ms Marvel II,Female,blue,-,Red,173.0,Marvel Comics,-,good,61.0
480 | 478,Multiple Man,Male,blue,-,Brown,180.0,Marvel Comics,-,good,70.0
481 | 479,Mysterio,Male,brown,Human,No Hair,180.0,Marvel Comics,-,bad,79.0
482 | 480,Mystique,Female,yellow (without irises),Mutant,Red / Orange,178.0,Marvel Comics,blue,bad,54.0
483 | 481,Namor,Male,-,-,-,-99.0,Marvel Comics,-,good,-99.0
484 | 482,Namor,Male,grey,Atlantean,Black,188.0,Marvel Comics,-,good,125.0
485 | 483,Namora,Female,blue,-,Blond,180.0,Marvel Comics,-,good,85.0
486 | 484,Namorita,Female,blue,-,Blond,168.0,Marvel Comics,-,good,101.0
487 | 485,Naruto Uzumaki,Male,-,Human,-,168.0,Shueisha,-,good,54.0
488 | 486,Nathan Petrelli,Male,brown,-,-,-99.0,NBC - Heroes,-,good,-99.0
489 | 487,Nebula,Female,blue,Luphomoid,No Hair,185.0,Marvel Comics,blue,bad,83.0
490 | 488,Negasonic Teenage Warhead,Female,black,Mutant,Black,-99.0,Marvel Comics,-,good,-99.0
491 | 489,Nick Fury,Male,brown,Human,Brown / White,185.0,Marvel Comics,-,good,99.0
492 | 490,Nightcrawler,Male,yellow,-,Indigo,175.0,Marvel Comics,-,good,88.0
493 | 491,Nightwing,Male,blue,Human,Black,178.0,DC Comics,-,good,79.0
494 | 492,Niki Sanders,Female,blue,-,Blond,-99.0,NBC - Heroes,-,good,-99.0
495 | 493,Nina Theroux,Female,-,Alpha,-,-99.0,SyFy,-,good,-99.0
496 | 494,Nite Owl II,Male,-,-,-,-99.0,DC Comics,-,good,-99.0
497 | 495,Northstar,Male,blue,-,Black,180.0,Marvel Comics,-,good,83.0
498 | 496,Nova,Male,brown,Human,Brown,185.0,Marvel Comics,-,good,86.0
499 | 497,Nova,Female,white,Human / Cosmic,Red,163.0,Marvel Comics,gold,good,59.0
500 | 498,Odin,Male,blue,God / Eternal,White,206.0,Marvel Comics,-,good,293.0
501 | 499,Offspring,Male,-,-,-,-99.0,DC Comics,-,good,-99.0
502 | 500,Omega Red,Male,red,-,Blond,211.0,Marvel Comics,-,bad,191.0
503 | 501,Omniscient,Male,brown,-,Black,180.0,Team Epic TV,-,good,65.0
504 | 502,One Punch Man,Male,-,Human,No Hair,175.0,Shueisha,-,good,69.0
505 | 503,One-Above-All,-,-,Cosmic Entity,-,-99.0,Marvel Comics,-,neutral,-99.0
506 | 504,Onslaught,Male,red,Mutant,No Hair,305.0,Marvel Comics,-,bad,405.0
507 | 505,Oracle,Female,blue,Human,Red,178.0,DC Comics,-,good,59.0
508 | 506,Osiris,Male,brown,-,Brown,-99.0,DC Comics,-,good,-99.0
509 | 507,Overtkill,Male,-,-,-,-99.0,Image Comics,-,bad,-99.0
510 | 508,Ozymandias,Male,blue,Human,Blond,-99.0,DC Comics,-,bad,-99.0
511 | 509,Parademon,-,-,Parademon,-,-99.0,DC Comics,-,bad,-99.0
512 | 510,Paul Blart,Male,-,Human,-,170.0,Sony Pictures,-,good,117.0
513 | 511,Penance,-,-,-,-,-99.0,Marvel Comics,-,good,-99.0
514 | 512,Penance I,Female,-,-,-,-99.0,Marvel Comics,-,good,-99.0
515 | 513,Penance II,Male,blue,-,Blond,183.0,Marvel Comics,-,good,89.0
516 | 514,Penguin,Male,blue,Human,Black,157.0,DC Comics,-,bad,79.0
517 | 515,Peter Petrelli,Male,-,-,-,-99.0,NBC - Heroes,-,good,-99.0
518 | 516,Phantom,Male,-,-,-,-99.0,DC Comics,-,good,-99.0
519 | 517,Phantom Girl,Female,blue,-,Black,168.0,DC Comics,-,good,54.0
520 | 518,Phoenix,Female,green,Mutant,Red,168.0,Marvel Comics,-,good,52.0
521 | 519,Plantman,Male,green,Mutant,Grey,183.0,Marvel Comics,-,bad,87.0
522 | 520,Plastic Lad,Male,-,-,-,-99.0,DC Comics,-,good,-99.0
523 | 521,Plastic Man,Male,blue,Human,Black,185.0,DC Comics,-,good,80.0
524 | 522,Plastique,Female,blue,-,Red,168.0,DC Comics,-,bad,55.0
525 | 523,Poison Ivy,Female,green,Human,Red,168.0,DC Comics,green,bad,50.0
526 | 524,Polaris,Female,green,Mutant,Green,170.0,Marvel Comics,-,good,52.0
527 | 525,Power Girl,Female,blue,Kryptonian,blond,180.0,DC Comics,-,good,81.0
528 | 526,Power Man,Male,-,Mutant,-,-99.0,Marvel Comics,-,good,-99.0
529 | 527,Predator,Male,-,Yautja,-,213.0,Dark Horse Comics,-,bad,234.0
530 | 528,Professor X,Male,blue,Mutant,No Hair,183.0,Marvel Comics,-,good,86.0
531 | 529,Professor Zoom,Male,blue,Human,Strawberry Blond,180.0,DC Comics,-,bad,81.0
532 | 530,Proto-Goblin,Male,green,-,Blond,-99.0,Marvel Comics,-,bad,-99.0
533 | 531,Psylocke,Female,blue,Mutant,Purple,180.0,Marvel Comics,-,good,70.0
534 | 532,Punisher,Male,blue,Human,Black,183.0,Marvel Comics,-,good,90.0
535 | 533,Purple Man,Male,purple,Human,Purple,180.0,Marvel Comics,purple,bad,74.0
536 | 534,Pyro,Male,blue,-,Blond,178.0,Marvel Comics,-,bad,68.0
537 | 535,Q,Male,-,God / Eternal,-,-99.0,Star Trek,-,-,-99.0
538 | 536,Quantum,Male,-,-,-,-99.0,HarperCollins,-,good,-99.0
539 | 537,Question,Male,blue,Human,Blond,188.0,DC Comics,-,good,83.0
540 | 538,Quicksilver,Male,blue,Mutant,Silver,183.0,Marvel Comics,-,good,79.0
541 | 539,Quill,Male,brown,-,Brown,163.0,Marvel Comics,-,good,56.0
542 | 540,Ra's Al Ghul,Male,green,Human,Grey,193.0,DC Comics,-,bad,97.0
543 | 541,Rachel Pirzad,Female,-,Alpha,-,-99.0,SyFy,-,good,-99.0
544 | 542,Rambo,Male,brown,Human,Black,178.0,,-,good,83.0
545 | 543,Raphael,Male,-,Mutant,No Hair,-99.0,IDW Publishing,green,good,-99.0
546 | 544,Raven,Female,indigo,Human,Black,165.0,DC Comics,-,neutral,50.0
547 | 545,Ray,Male,green,Human,Red,178.0,DC Comics,-,good,70.0
548 | 546,Razor-Fist II,Male,blue,-,No Hair,191.0,Marvel Comics,-,bad,117.0
549 | 547,Red Arrow,Male,green,Human,Red,180.0,DC Comics,-,good,83.0
550 | 548,Red Hood,Male,blue,Human,Black,183.0,DC Comics,-,neutral,81.0
551 | 549,Red Hulk,Male,yellow,Human / Radiation,Black,213.0,Marvel Comics,red,neutral,630.0
552 | 550,Red Mist,Male,-,-,-,-99.0,Icon Comics,-,bad,-99.0
553 | 551,Red Robin,Male,blue,Human,Black,165.0,DC Comics,-,good,56.0
554 | 552,Red Skull,Male,blue,-,No Hair,188.0,Marvel Comics,-,bad,108.0
555 | 553,Red Tornado,Male,green,Android,No Hair,185.0,DC Comics,-,good,146.0
556 | 554,Redeemer II,Male,-,-,-,-99.0,Image Comics,-,bad,-99.0
557 | 555,Redeemer III,Male,-,-,-,-99.0,Image Comics,-,bad,-99.0
558 | 556,Renata Soliz,Female,-,-,-,-99.0,HarperCollins,-,good,-99.0
559 | 557,Rey,Female,hazel,Human,Brown,297.0,George Lucas,-,good,-99.0
560 | 558,Rhino,Male,brown,Human / Radiation,Brown,196.0,Marvel Comics,-,bad,320.0
561 | 559,Rick Flag,Male,blue,-,Brown,185.0,DC Comics,-,bad,85.0
562 | 560,Riddler,Male,-,-,-,-99.0,DC Comics,-,bad,-99.0
563 | 561,Rip Hunter,Male,blue,Human,Blond,-99.0,DC Comics,-,good,-99.0
564 | 562,Ripcord,Female,green,-,Black,180.0,Marvel Comics,-,good,72.0
565 | 563,Robin,Male,blue,Human,Black,178.0,DC Comics,-,good,79.0
566 | 564,Robin II,Male,blue,Human,Red,183.0,DC Comics,-,good,101.0
567 | 565,Robin III,Male,blue,Human,Black,165.0,DC Comics,-,good,56.0
568 | 566,Robin V,Male,blue,Human,Black,137.0,DC Comics,-,good,38.0
569 | 567,Robin VI,Female,green,Human,Red,-99.0,DC Comics,-,neutral,-99.0
570 | 568,Rocket Raccoon,Male,brown,Animal,Brown,122.0,Marvel Comics,-,good,25.0
571 | 569,Rogue,Female,green,-,Brown / White,173.0,Marvel Comics,-,good,54.0
572 | 570,Ronin,Male,blue,Human,Blond,191.0,Marvel Comics,-,good,104.0
573 | 571,Rorschach,Male,blue,Human,Red,168.0,DC Comics,-,good,63.0
574 | 572,Sabretooth,Male,amber,Mutant,Blond,198.0,Marvel Comics,-,bad,171.0
575 | 573,Sage,Female,blue,-,Black,170.0,Marvel Comics,-,good,61.0
576 | 574,Sandman,Male,brown,Human,Brown,185.0,Marvel Comics,-,neutral,203.0
577 | 575,Sasquatch,Male,red,-,Orange,305.0,Marvel Comics,-,good,900.0
578 | 576,Sauron,Male,-,Maiar,-,279.0,J. R. R. Tolkien,-,bad,-99.0
579 | 577,Savage Dragon,Male,-,-,-,-99.0,Image Comics,-,good,-99.0
580 | 578,Scarecrow,Male,blue,Human,Brown,183.0,DC Comics,-,bad,63.0
581 | 579,Scarlet Spider,Male,blue,Human,Blond,178.0,Marvel Comics,-,good,74.0
582 | 580,Scarlet Spider II,Male,brown,Clone,Brown,193.0,Marvel Comics,-,good,113.0
583 | 581,Scarlet Witch,Female,blue,Mutant,Brown,170.0,Marvel Comics,-,bad,59.0
584 | 582,Scorpia,Female,green,-,Red,-99.0,Marvel Comics,-,bad,-99.0
585 | 583,Scorpion,Male,brown,Human,Brown,211.0,Marvel Comics,-,bad,310.0
586 | 584,Sebastian Shaw,Male,-,Mutant,-,-99.0,Marvel Comics,-,bad,-99.0
587 | 585,Sentry,Male,blue,Mutant,Blond,188.0,Marvel Comics,-,neutral,87.0
588 | 586,Shadow King,-,red,-,-,185.0,Marvel Comics,-,good,149.0
589 | 587,Shadow Lass,Female,black,Talokite,Black,173.0,DC Comics,blue,good,54.0
590 | 588,Shadowcat,Female,hazel,Mutant,Brown,168.0,Marvel Comics,-,good,50.0
591 | 589,Shang-Chi,Male,brown,Human,Black,178.0,Marvel Comics,-,good,79.0
592 | 590,Shatterstar,Male,brown,-,Red,191.0,Marvel Comics,-,good,88.0
593 | 591,She-Hulk,Female,green,Human,Green,201.0,Marvel Comics,-,good,315.0
594 | 592,She-Thing,Female,blue,Human / Radiation,No Hair,183.0,Marvel Comics,-,good,153.0
595 | 593,Shocker,Male,brown,Human,Brown,175.0,Marvel Comics,-,bad,79.0
596 | 594,Shriek,Female,yellow / blue,-,Black,173.0,Marvel Comics,-,good,52.0
597 | 595,Shrinking Violet,Female,-,-,-,-99.0,DC Comics,-,good,-99.0
598 | 596,Sif,Female,blue,Asgardian,Black,188.0,Marvel Comics,-,good,191.0
599 | 597,Silk,Female,brown,Human,Black,-99.0,Marvel Comics,-,good,-99.0
600 | 598,Silk Spectre,Female,-,-,-,-99.0,DC Comics,-,good,-99.0
601 | 599,Silk Spectre II,Female,-,-,-,-99.0,DC Comics,-,good,-99.0
602 | 600,Silver Surfer,Male,white,Alien,No Hair,193.0,Marvel Comics,silver,good,101.0
603 | 601,Silverclaw,Female,brown,-,Black,157.0,Marvel Comics,-,good,50.0
604 | 602,Simon Baz,Male,bown,Human,Black,-99.0,DC Comics,-,good,-99.0
605 | 603,Sinestro,Male,black,Korugaran,Black,201.0,DC Comics,red,neutral,92.0
606 | 604,Siren,Female,blue,Atlantean,Purple,175.0,DC Comics,-,bad,72.0
607 | 605,Siren II,Female,black,-,-,-99.0,DC Comics,-,bad,-99.0
608 | 606,Siryn,Female,blue,-,Strawberry Blond,168.0,Marvel Comics,-,bad,52.0
609 | 607,Skaar,Male,green,-,Black,198.0,Marvel Comics,-,good,180.0
610 | 608,Snake-Eyes,Male,-,Animal,-,-99.0,Marvel Comics,-,bad,-99.0
611 | 609,Snowbird,Female,white,-,Blond,178.0,Marvel Comics,-,good,49.0
612 | 610,Sobek,Male,white,-,No Hair,-99.0,DC Comics,-,good,-99.0
613 | 611,Solomon Grundy,Male,black,Zombie,White,279.0,DC Comics,-,bad,437.0
614 | 612,Songbird,Female,green,-,Red / White,165.0,Marvel Comics,-,good,65.0
615 | 613,Space Ghost,Male,-,Human,-,188.0,DC Comics,-,good,113.0
616 | 614,Spawn,Male,brown,Demon,Black,211.0,Image Comics,-,good,405.0
617 | 615,Spectre,Male,white,God / Eternal,No Hair,-99.0,DC Comics,white,good,-99.0
618 | 616,Speedball,Male,-,-,-,-99.0,Marvel Comics,-,good,-99.0
619 | 617,Speedy,Male,-,Human,-,-99.0,DC Comics,-,good,-99.0
620 | 618,Speedy,Female,green,Human,Brown,-99.0,DC Comics,-,good,-99.0
621 | 619,Spider-Carnage,Male,-,Symbiote,-,-99.0,Marvel Comics,-,bad,-99.0
622 | 620,Spider-Girl,Female,blue,Human,Brown,170.0,Marvel Comics,-,good,54.0
623 | 621,Spider-Gwen,Female,blue,Human,Blond,165.0,Marvel Comics,-,good,56.0
624 | 622,Spider-Man,Male,hazel,Human,Brown,178.0,Marvel Comics,-,good,74.0
625 | 623,Spider-Man,-,red,Human,Brown,178.0,Marvel Comics,-,good,77.0
626 | 624,Spider-Man,Male,brown,Human,Black,157.0,Marvel Comics,-,good,56.0
627 | 625,Spider-Woman,Female,green,Human,Black,178.0,Marvel Comics,-,good,59.0
628 | 626,Spider-Woman II,Female,-,-,-,-99.0,Marvel Comics,-,good,-99.0
629 | 627,Spider-Woman III,Female,brown,-,Brown,173.0,Marvel Comics,-,good,55.0
630 | 628,Spider-Woman IV,Female,red,-,White,178.0,Marvel Comics,-,bad,58.0
631 | 629,Spock,Male,brown,Human-Vulcan,Black,185.0,Star Trek,-,good,81.0
632 | 630,Spyke,Male,brown,Mutant,Blond,183.0,Marvel Comics,-,good,83.0
633 | 631,Stacy X,Female,-,-,-,-99.0,Marvel Comics,-,good,-99.0
634 | 632,Star-Lord,Male,blue,Human-Spartoi,Blond,188.0,Marvel Comics,-,good,79.0
635 | 633,Stardust,Male,-,-,-,-99.0,Marvel Comics,-,good,-99.0
636 | 634,Starfire,Female,green,Tamaranean,Auburn,193.0,DC Comics,orange,good,71.0
637 | 635,Stargirl,Female,blue,Human,Blond,165.0,DC Comics,-,good,62.0
638 | 636,Static,Male,brown,Mutant,Black,170.0,DC Comics,-,good,63.0
639 | 637,Steel,Male,brown,-,No Hair,201.0,DC Comics,-,good,131.0
640 | 638,Stephanie Powell,Female,-,-,Blond,-99.0,ABC Studios,-,good,-99.0
641 | 639,Steppenwolf,Male,red,New God,Black,183.0,DC Comics,white,bad,91.0
642 | 640,Storm,Female,blue,Mutant,White,180.0,Marvel Comics,-,good,57.0
643 | 641,Stormtrooper,Male,-,Human,-,183.0,George Lucas,-,bad,-99.0
644 | 642,Sunspot,Male,brown,Mutant,black,173.0,Marvel Comics,-,good,77.0
645 | 643,Superboy,Male,blue,-,Black,170.0,DC Comics,-,good,68.0
646 | 644,Superboy-Prime,Male,blue,Kryptonian,Black / Blue,180.0,DC Comics,-,bad,77.0
647 | 645,Supergirl,Female,blue,Kryptonian,Blond,165.0,DC Comics,-,good,54.0
648 | 646,Superman,Male,blue,Kryptonian,Black,191.0,DC Comics,-,good,101.0
649 | 647,Swamp Thing,Male,red,God / Eternal,No Hair,-99.0,DC Comics,green,bad,-99.0
650 | 648,Swarm,Male,yellow,Mutant,No Hair,196.0,Marvel Comics,yellow,bad,47.0
651 | 649,Sylar,Male,-,-,-,-99.0,NBC - Heroes,-,bad,-99.0
652 | 650,Synch,Male,brown,-,Black,180.0,Marvel Comics,-,good,74.0
653 | 651,T-1000,Male,-,Android,-,183.0,Dark Horse Comics,silver,bad,146.0
654 | 652,T-800,Male,red,Cyborg,-,-99.0,Dark Horse Comics,-,bad,176.0
655 | 653,T-850,Male,red,Cyborg,-,-99.0,Dark Horse Comics,-,bad,198.0
656 | 654,T-X,Female,-,Cyborg,-,-99.0,Dark Horse Comics,silver,bad,149.0
657 | 655,Taskmaster,Male,brown,Human,Brown,188.0,Marvel Comics,-,bad,99.0
658 | 656,Tempest,Female,brown,-,Black,163.0,Marvel Comics,-,good,54.0
659 | 657,Thanos,Male,red,Eternal,No Hair,201.0,Marvel Comics,purple,bad,443.0
660 | 658,The Cape,Male,-,-,-,-99.0,,-,good,-99.0
661 | 659,The Comedian,Male,brown,Human,Black,188.0,DC Comics,-,neutral,101.0
662 | 660,Thing,Male,blue,Human / Radiation,No Hair,183.0,Marvel Comics,-,good,225.0
663 | 661,Thor,Male,blue,Asgardian,Blond,198.0,Marvel Comics,-,good,288.0
664 | 662,Thor Girl,Female,blue,Asgardian,Blond,175.0,Marvel Comics,-,good,143.0
665 | 663,Thunderbird,Male,brown,-,Black,185.0,Marvel Comics,-,good,101.0
666 | 664,Thunderbird II,Male,-,-,-,-99.0,Marvel Comics,-,good,-99.0
667 | 665,Thunderbird III,Male,brown,-,Black,175.0,Marvel Comics,-,good,74.0
668 | 666,Thunderstrike,Male,blue,-,Blond,198.0,Marvel Comics,-,good,288.0
669 | 667,Thundra,Female,green,-,Red,218.0,Marvel Comics,-,good,158.0
670 | 668,Tiger Shark,Male,grey,Human,No Hair,185.0,Marvel Comics,grey,bad,203.0
671 | 669,Tigra,Female,green,-,Auburn,178.0,Marvel Comics,-,good,81.0
672 | 670,Tinkerer,Male,brown,-,White,163.0,Marvel Comics,-,bad,54.0
673 | 671,Titan,Male,-,-,-,-99.0,HarperCollins,-,good,-99.0
674 | 672,Toad,Male,black,Mutant,Brown,175.0,Marvel Comics,green,neutral,76.0
675 | 673,Toxin,Male,blue,Symbiote,Brown,188.0,Marvel Comics,-,good,97.0
676 | 674,Toxin,Male,black,Symbiote,Blond,191.0,Marvel Comics,-,good,117.0
677 | 675,Tracy Strauss,Female,-,-,-,-99.0,NBC - Heroes,-,good,-99.0
678 | 676,Trickster,Male,blue,Human,Blond,183.0,DC Comics,-,-,81.0
679 | 677,Trigon,Male,yellow,God / Eternal,Black,-99.0,DC Comics,red,bad,-99.0
680 | 678,Triplicate Girl,Female,purple,-,Brown,168.0,DC Comics,-,good,59.0
681 | 679,Triton,Male,green,Inhuman,No Hair,188.0,Marvel Comics,green,good,86.0
682 | 680,Two-Face,Male,-,-,-,183.0,DC Comics,-,bad,82.0
683 | 681,Ultragirl,Female,blue,-,Blond,168.0,Marvel Comics,-,good,105.0
684 | 682,Ultron,Male,red,Android,-,206.0,Marvel Comics,silver,bad,331.0
685 | 683,Utgard-Loki,Male,blue,Frost Giant,White,15.2,Marvel Comics,-,bad,58.0
686 | 684,Vagabond,Female,blue,-,Strawberry Blond,168.0,Marvel Comics,-,good,54.0
687 | 685,Valerie Hart,Female,hazel,-,Black,175.0,Team Epic TV,-,good,56.0
688 | 686,Valkyrie,Female,blue,-,Blond,191.0,Marvel Comics,-,good,214.0
689 | 687,Vanisher,Male,green,-,No Hair,165.0,Marvel Comics,-,bad,79.0
690 | 688,Vegeta,Male,-,Saiyan,Black,168.0,Shueisha,-,bad,73.0
691 | 689,Venom,Male,blue,Symbiote,Strawberry Blond,191.0,Marvel Comics,-,bad,117.0
692 | 690,Venom II,Male,brown,-,Black,175.0,Marvel Comics,-,bad,50.0
693 | 691,Venom III,Male,brown,Symbiote,Brown,229.0,Marvel Comics,-,bad,334.0
694 | 692,Venompool,Male,-,Symbiote,-,226.0,Marvel Comics,-,-,-99.0
695 | 693,Vertigo II,Female,blue,-,Silver,168.0,Marvel Comics,-,good,52.0
696 | 694,Vibe,Male,brown,Human,Black,178.0,DC Comics,-,good,71.0
697 | 695,Vindicator,Female,green,Human,Red,165.0,Marvel Comics,-,good,54.0
698 | 696,Vindicator,Male,-,-,-,-99.0,Marvel Comics,-,good,-99.0
699 | 697,Violator,Male,-,-,-,-99.0,Image Comics,-,bad,-99.0
700 | 698,Violet Parr,Female,violet,Human,Black,137.0,Dark Horse Comics,-,good,41.0
701 | 699,Vision,Male,gold,Android,No Hair,191.0,Marvel Comics,red,good,135.0
702 | 700,Vision II,-,red,-,No Hair,191.0,Marvel Comics,-,good,135.0
703 | 701,Vixen,Female,amber,Human,Black,175.0,DC Comics,-,good,63.0
704 | 702,Vulcan,Male,black,-,Black,-99.0,Marvel Comics,-,good,-99.0
705 | 703,Vulture,Male,brown,Human,No Hair,180.0,Marvel Comics,-,bad,79.0
706 | 704,Walrus,Male,blue,Human,Black,183.0,Marvel Comics,-,bad,162.0
707 | 705,War Machine,Male,brown,Human,Brown,185.0,Marvel Comics,-,good,95.0
708 | 706,Warbird,Female,blue,-,Blond,180.0,Marvel Comics,-,good,54.0
709 | 707,Warlock,Male,red,-,Blond,188.0,Marvel Comics,-,good,108.0
710 | 708,Warp,Male,brown,-,Black,173.0,DC Comics,-,bad,67.0
711 | 709,Warpath,Male,brown,Mutant,Black,218.0,Marvel Comics,-,good,158.0
712 | 710,Wasp,Female,blue,Human,Auburn,163.0,Marvel Comics,-,good,50.0
713 | 711,Watcher,Male,-,-,-,-99.0,Marvel Comics,-,good,-99.0
714 | 712,Weapon XI,Male,-,-,-,-99.0,Marvel Comics,-,bad,-99.0
715 | 713,White Canary,Female,brown,Human,Black,-99.0,DC Comics,-,bad,-99.0
716 | 714,White Queen,Female,blue,-,Blond,178.0,Marvel Comics,-,good,65.0
717 | 715,Wildfire,Male,-,-,-,-99.0,DC Comics,-,good,-99.0
718 | 716,Winter Soldier,Male,brown,Human,Brown,175.0,Marvel Comics,-,good,117.0
719 | 717,Wiz Kid,-,brown,-,Black,140.0,Marvel Comics,-,good,39.0
720 | 718,Wolfsbane,Female,green,-,Auburn,366.0,Marvel Comics,-,good,473.0
721 | 719,Wolverine,Male,blue,Mutant,Black,160.0,Marvel Comics,-,good,135.0
722 | 720,Wonder Girl,Female,blue,Demi-God,Blond,165.0,DC Comics,-,good,51.0
723 | 721,Wonder Man,Male,red,-,Black,188.0,Marvel Comics,-,good,171.0
724 | 722,Wonder Woman,Female,blue,Amazon,Black,183.0,DC Comics,-,good,74.0
725 | 723,Wondra,Female,-,-,-,-99.0,Marvel Comics,-,good,-99.0
726 | 724,Wyatt Wingfoot,Male,brown,-,Black,196.0,Marvel Comics,-,good,117.0
727 | 725,X-23,Female,green,Mutant / Clone,Black,155.0,Marvel Comics,-,good,50.0
728 | 726,X-Man,Male,blue,-,Brown,175.0,Marvel Comics,-,good,61.0
729 | 727,Yellow Claw,Male,blue,-,No Hair,188.0,Marvel Comics,-,bad,95.0
730 | 728,Yellowjacket,Male,blue,Human,Blond,183.0,Marvel Comics,-,good,83.0
731 | 729,Yellowjacket II,Female,blue,Human,Strawberry Blond,165.0,Marvel Comics,-,good,52.0
732 | 730,Ymir,Male,white,Frost Giant,No Hair,304.8,Marvel Comics,white,good,-99.0
733 | 731,Yoda,Male,brown,Yoda's species,White,66.0,George Lucas,green,good,17.0
734 | 732,Zatanna,Female,blue,Human,Black,170.0,DC Comics,-,good,57.0
735 | 733,Zoom,Male,red,-,Brown,185.0,DC Comics,-,bad,81.0
736 | 


--------------------------------------------------------------------------------
/iris.csv:
--------------------------------------------------------------------------------
  1 | SepalLength,SepalWidth,PetalLength,PetalWidth,Name
  2 | 5.1,3.5,1.4,0.2,Iris-setosa
  3 | 4.9,3.0,1.4,0.2,Iris-setosa
  4 | 4.7,3.2,1.3,0.2,Iris-setosa
  5 | 4.6,3.1,1.5,0.2,Iris-setosa
  6 | 5.0,3.6,1.4,0.2,Iris-setosa
  7 | 5.4,3.9,1.7,0.4,Iris-setosa
  8 | 4.6,3.4,1.4,0.3,Iris-setosa
  9 | 5.0,3.4,1.5,0.2,Iris-setosa
 10 | 4.4,2.9,1.4,0.2,Iris-setosa
 11 | 4.9,3.1,1.5,0.1,Iris-setosa
 12 | 5.4,3.7,1.5,0.2,Iris-setosa
 13 | 4.8,3.4,1.6,0.2,Iris-setosa
 14 | 4.8,3.0,1.4,0.1,Iris-setosa
 15 | 4.3,3.0,1.1,0.1,Iris-setosa
 16 | 5.8,4.0,1.2,0.2,Iris-setosa
 17 | 5.7,4.4,1.5,0.4,Iris-setosa
 18 | 5.4,3.9,1.3,0.4,Iris-setosa
 19 | 5.1,3.5,1.4,0.3,Iris-setosa
 20 | 5.7,3.8,1.7,0.3,Iris-setosa
 21 | 5.1,3.8,1.5,0.3,Iris-setosa
 22 | 5.4,3.4,1.7,0.2,Iris-setosa
 23 | 5.1,3.7,1.5,0.4,Iris-setosa
 24 | 4.6,3.6,1.0,0.2,Iris-setosa
 25 | 5.1,3.3,1.7,0.5,Iris-setosa
 26 | 4.8,3.4,1.9,0.2,Iris-setosa
 27 | 5.0,3.0,1.6,0.2,Iris-setosa
 28 | 5.0,3.4,1.6,0.4,Iris-setosa
 29 | 5.2,3.5,1.5,0.2,Iris-setosa
 30 | 5.2,3.4,1.4,0.2,Iris-setosa
 31 | 4.7,3.2,1.6,0.2,Iris-setosa
 32 | 4.8,3.1,1.6,0.2,Iris-setosa
 33 | 5.4,3.4,1.5,0.4,Iris-setosa
 34 | 5.2,4.1,1.5,0.1,Iris-setosa
 35 | 5.5,4.2,1.4,0.2,Iris-setosa
 36 | 4.9,3.1,1.5,0.1,Iris-setosa
 37 | 5.0,3.2,1.2,0.2,Iris-setosa
 38 | 5.5,3.5,1.3,0.2,Iris-setosa
 39 | 4.9,3.1,1.5,0.1,Iris-setosa
 40 | 4.4,3.0,1.3,0.2,Iris-setosa
 41 | 5.1,3.4,1.5,0.2,Iris-setosa
 42 | 5.0,3.5,1.3,0.3,Iris-setosa
 43 | 4.5,2.3,1.3,0.3,Iris-setosa
 44 | 4.4,3.2,1.3,0.2,Iris-setosa
 45 | 5.0,3.5,1.6,0.6,Iris-setosa
 46 | 5.1,3.8,1.9,0.4,Iris-setosa
 47 | 4.8,3.0,1.4,0.3,Iris-setosa
 48 | 5.1,3.8,1.6,0.2,Iris-setosa
 49 | 4.6,3.2,1.4,0.2,Iris-setosa
 50 | 5.3,3.7,1.5,0.2,Iris-setosa
 51 | 5.0,3.3,1.4,0.2,Iris-setosa
 52 | 7.0,3.2,4.7,1.4,Iris-versicolor
 53 | 6.4,3.2,4.5,1.5,Iris-versicolor
 54 | 6.9,3.1,4.9,1.5,Iris-versicolor
 55 | 5.5,2.3,4.0,1.3,Iris-versicolor
 56 | 6.5,2.8,4.6,1.5,Iris-versicolor
 57 | 5.7,2.8,4.5,1.3,Iris-versicolor
 58 | 6.3,3.3,4.7,1.6,Iris-versicolor
 59 | 4.9,2.4,3.3,1.0,Iris-versicolor
 60 | 6.6,2.9,4.6,1.3,Iris-versicolor
 61 | 5.2,2.7,3.9,1.4,Iris-versicolor
 62 | 5.0,2.0,3.5,1.0,Iris-versicolor
 63 | 5.9,3.0,4.2,1.5,Iris-versicolor
 64 | 6.0,2.2,4.0,1.0,Iris-versicolor
 65 | 6.1,2.9,4.7,1.4,Iris-versicolor
 66 | 5.6,2.9,3.6,1.3,Iris-versicolor
 67 | 6.7,3.1,4.4,1.4,Iris-versicolor
 68 | 5.6,3.0,4.5,1.5,Iris-versicolor
 69 | 5.8,2.7,4.1,1.0,Iris-versicolor
 70 | 6.2,2.2,4.5,1.5,Iris-versicolor
 71 | 5.6,2.5,3.9,1.1,Iris-versicolor
 72 | 5.9,3.2,4.8,1.8,Iris-versicolor
 73 | 6.1,2.8,4.0,1.3,Iris-versicolor
 74 | 6.3,2.5,4.9,1.5,Iris-versicolor
 75 | 6.1,2.8,4.7,1.2,Iris-versicolor
 76 | 6.4,2.9,4.3,1.3,Iris-versicolor
 77 | 6.6,3.0,4.4,1.4,Iris-versicolor
 78 | 6.8,2.8,4.8,1.4,Iris-versicolor
 79 | 6.7,3.0,5.0,1.7,Iris-versicolor
 80 | 6.0,2.9,4.5,1.5,Iris-versicolor
 81 | 5.7,2.6,3.5,1.0,Iris-versicolor
 82 | 5.5,2.4,3.8,1.1,Iris-versicolor
 83 | 5.5,2.4,3.7,1.0,Iris-versicolor
 84 | 5.8,2.7,3.9,1.2,Iris-versicolor
 85 | 6.0,2.7,5.1,1.6,Iris-versicolor
 86 | 5.4,3.0,4.5,1.5,Iris-versicolor
 87 | 6.0,3.4,4.5,1.6,Iris-versicolor
 88 | 6.7,3.1,4.7,1.5,Iris-versicolor
 89 | 6.3,2.3,4.4,1.3,Iris-versicolor
 90 | 5.6,3.0,4.1,1.3,Iris-versicolor
 91 | 5.5,2.5,4.0,1.3,Iris-versicolor
 92 | 5.5,2.6,4.4,1.2,Iris-versicolor
 93 | 6.1,3.0,4.6,1.4,Iris-versicolor
 94 | 5.8,2.6,4.0,1.2,Iris-versicolor
 95 | 5.0,2.3,3.3,1.0,Iris-versicolor
 96 | 5.6,2.7,4.2,1.3,Iris-versicolor
 97 | 5.7,3.0,4.2,1.2,Iris-versicolor
 98 | 5.7,2.9,4.2,1.3,Iris-versicolor
 99 | 6.2,2.9,4.3,1.3,Iris-versicolor
100 | 5.1,2.5,3.0,1.1,Iris-versicolor
101 | 5.7,2.8,4.1,1.3,Iris-versicolor
102 | 6.3,3.3,6.0,2.5,Iris-virginica
103 | 5.8,2.7,5.1,1.9,Iris-virginica
104 | 7.1,3.0,5.9,2.1,Iris-virginica
105 | 6.3,2.9,5.6,1.8,Iris-virginica
106 | 6.5,3.0,5.8,2.2,Iris-virginica
107 | 7.6,3.0,6.6,2.1,Iris-virginica
108 | 4.9,2.5,4.5,1.7,Iris-virginica
109 | 7.3,2.9,6.3,1.8,Iris-virginica
110 | 6.7,2.5,5.8,1.8,Iris-virginica
111 | 7.2,3.6,6.1,2.5,Iris-virginica
112 | 6.5,3.2,5.1,2.0,Iris-virginica
113 | 6.4,2.7,5.3,1.9,Iris-virginica
114 | 6.8,3.0,5.5,2.1,Iris-virginica
115 | 5.7,2.5,5.0,2.0,Iris-virginica
116 | 5.8,2.8,5.1,2.4,Iris-virginica
117 | 6.4,3.2,5.3,2.3,Iris-virginica
118 | 6.5,3.0,5.5,1.8,Iris-virginica
119 | 7.7,3.8,6.7,2.2,Iris-virginica
120 | 7.7,2.6,6.9,2.3,Iris-virginica
121 | 6.0,2.2,5.0,1.5,Iris-virginica
122 | 6.9,3.2,5.7,2.3,Iris-virginica
123 | 5.6,2.8,4.9,2.0,Iris-virginica
124 | 7.7,2.8,6.7,2.0,Iris-virginica
125 | 6.3,2.7,4.9,1.8,Iris-virginica
126 | 6.7,3.3,5.7,2.1,Iris-virginica
127 | 7.2,3.2,6.0,1.8,Iris-virginica
128 | 6.2,2.8,4.8,1.8,Iris-virginica
129 | 6.1,3.0,4.9,1.8,Iris-virginica
130 | 6.4,2.8,5.6,2.1,Iris-virginica
131 | 7.2,3.0,5.8,1.6,Iris-virginica
132 | 7.4,2.8,6.1,1.9,Iris-virginica
133 | 7.9,3.8,6.4,2.0,Iris-virginica
134 | 6.4,2.8,5.6,2.2,Iris-virginica
135 | 6.3,2.8,5.1,1.5,Iris-virginica
136 | 6.1,2.6,5.6,1.4,Iris-virginica
137 | 7.7,3.0,6.1,2.3,Iris-virginica
138 | 6.3,3.4,5.6,2.4,Iris-virginica
139 | 6.4,3.1,5.5,1.8,Iris-virginica
140 | 6.0,3.0,4.8,1.8,Iris-virginica
141 | 6.9,3.1,5.4,2.1,Iris-virginica
142 | 6.7,3.1,5.6,2.4,Iris-virginica
143 | 6.9,3.1,5.1,2.3,Iris-virginica
144 | 5.8,2.7,5.1,1.9,Iris-virginica
145 | 6.8,3.2,5.9,2.3,Iris-virginica
146 | 6.7,3.3,5.7,2.5,Iris-virginica
147 | 6.7,3.0,5.2,2.3,Iris-virginica
148 | 6.3,2.5,5.0,1.9,Iris-virginica
149 | 6.5,3.0,5.2,2.0,Iris-virginica
150 | 6.2,3.4,5.4,2.3,Iris-virginica
151 | 5.9,3.0,5.1,1.8,Iris-virginica


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