04_OSEMN.ipynb

{
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  {
   "cell_type": "markdown",
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   "source": [
    "# Data science is OSEMN\n",
    "\n",
    "According to a popular model, the elements of data science are\n",
    "\n",
    "* Obtaining data\n",
    "* Scrubbing data\n",
    "* Exploring data\n",
    "* Modeling data\n",
    "* iNterpreting data\n",
    "\n",
    "and hence the acronym OSEMN, pronounced as “Awesome”.\n",
    "\n",
    "We will start with the **O**, moving towards the rest later, but first let's have a quick look at what it all boils down to:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "data = np.loadtxt('populations.txt')\n",
    "year, hares, lynxes, carrots = data.T # trick: columns to variables\n",
    "\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "plt.axes([0.2, 0.1, 0.5, 0.8]) \n",
    "plt.plot(year, hares, year, lynxes, year, carrots) \n",
    "plt.legend(('Hare', 'Lynx', 'Carrot'), loc=(1.05, 0.5)) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By plotting the data a clear (and reasonable) correlations between pray and predator becomes evident. How can it be quantified? Is that statistical significant? What about the correlation between carrots and hares? Is that evident? Is that significant?\n",
    "\n",
    "Finding correlations in data is the main goal of data science, though that is not the end of the story: as this precious [site](http://tylervigen.com/spurious-correlations) demonstrates, **correlations is not causation**. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Exercise*: write an algorithm that determins and quantifies a correlation between two time series. Use as an example the hare-lynx-carrot dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Obtaining and processing (remote) data\n",
    "\n",
    "Accessing data is a really serious business. Data can sit on public or on remote machines. In the case of the former, things may be straightforward, whereas in the latter case you need to worry about a few things.\n",
    "\n",
    "In both cases, depending on the size of the dataset, the managment of the dataset can become extremely complicated. We won't deal here with large datasets, which require a whole course per se.., but still care should be put. In particular, it is not wise to keep (and even worse commit) data into a git repository!\n",
    "\n",
    "The suggestion is then to create a directory somewhere and copy the example datasets there. From a terminal:\n",
    "\n",
    "```bash\n",
    "\n",
    "# create a data directory in your home directory\n",
    "mkdir ~/data/\n",
    "\n",
    "# check the content (it's empty now of course)\n",
    "ls -ltr ~/data/\n",
    "\n",
    "# in the case you need to move there:\n",
    "cd ~/data/\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Download data from a server\n",
    "\n",
    "A nice set of interesting datasets can be found on this [server](https://archive.ics.uci.edu/ml/datasets.html?sort=nameUp&view=list) that collects training/test data for machine learning developments. Several of those pertein physical sciences, it is worth browsing through those.\n",
    "\n",
    "You can download any of those, in the following we will consider a dataset from the MAGIC experiment. For that we will the `wget` command"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get the dataset and its description on the proper data directory\n",
    "!wget https://archive.ics.uci.edu/ml/machine-learning-databases/magic/magic04.data -P ~/data/\n",
    "!wget https://archive.ics.uci.edu/ml/machine-learning-databases/magic/magic04.names -P ~/data/    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# print the description. This can (and better) be done from a terminal\n",
    "!cat ~/data/magic04.names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is possible to download and load remote files via their url's directly from within python (and thus on a jupyter session). This is a rather powerful tool as it allows http communications, IO streaming and so on.\n",
    "\n",
    "Care should be put as the dataset is stored in memory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import urllib.request\n",
    "url ='https://archive.ics.uci.edu/ml/machine-learning-databases/magic/magic04.names'\n",
    "with urllib.request.urlopen(url) as data_file:\n",
    "    #print (data_file.read(300))\n",
    "    for line in data_file:\n",
    "        print (line)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Copy data from a remote machine\n",
    "\n",
    "Often datasets are not available on websites but rather they are sitting on some remote machine. Several tools are there that can allow you to get hold off remote data, even from within python (e.g. [paramiko](https://www.paramiko.org/)), but best in this case is to get a local copy. E.g. from a terminal:\n",
    "\n",
    "```bash\n",
    "scp lemma@lxplus.cern.ch:/eos/project/l/lemma/data/2018/raw/Run000333/data_000637.* ~/data/\n",
    "```\n",
    "\n",
    "by issuing that command you are immediately exposed to the most relevant problem in obtaining the data: permissions/authorization.\n",
    "\n",
    "Secondily (essentially a further consequence of the same issue), the remote machine itself may have accessibility restrictions, e.g. being behind a firewall. In that case you may need to use a tunnel:\n",
    "\n",
    "``` bash\n",
    "ssh -L 1234:<address of R known to G>:22 <user at G>@<address of G> \n",
    "\n",
    "scp -P 1234 <user at R>@127.0.0.1:/path/to/file file-name-to-be-copied\n",
    "```\n",
    "\n",
    "In summary, just getting the data is a complicated business."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Formats\n",
    "\n",
    "datasets can be stored in a gazillion different ways, often they have formats which are application dependent, even though more and more standards are being established. Python have \"readers\" for most of the formats, another reason for being the optimal programming language for data analysis."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Text files \n",
    "\n",
    "Plain text files are commonly used for \"readibility\", at the price of a very poor storing efficiency due to their low entropy. [UTF-8](https://en.wikipedia.org/wiki/UTF-8) is the most common encoding.\n",
    "\n",
    "Reading (and writing) text files in python is straightforward:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "file_name = \"~/data/magic04.data\"\n",
    "\n",
    "# mode can be specified for writing, reading or both\n",
    "with open(file_name, mode='r') as f:\n",
    "    # print-out the whole file\n",
    "    # print (f.read()) \n",
    "    for line in f:\n",
    "        ## print line by line\n",
    "        print (line)\n",
    "        ## each line is a string, you need to split it yourself\n",
    "        # for c in line.split(): print(c) # check the functionalities of the split() method "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CSV files\n",
    "\n",
    "If you are lucky text files are already framed into a defined structured, in a \"table-like\" manner. These files are colled \"comma separated values\" (csv), even though the separator may well not be the \",\" symbol.\n",
    "Python have package to deal with that:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "\n",
    "with open('magic04.data') as data_file:\n",
    "    for line in csv.reader(data_file, delimiter=','): # the delimiter is often guessed by the reader\n",
    "        # again note that elements of each line are treated as strings\n",
    "        # if you need to convert them into numbers, you need to to that yourself\n",
    "        fLength,fWidth,fSize,\\\n",
    "        fConc,fConc1,fAsym,\\\n",
    "        fM3Long,fM3Trans,fAlpha,fDist = map(float,line[:-1])\n",
    "        category = line[-1]\n",
    "        print (fLength,fWidth,fSize,fConc,fConc1,fAsym,fM3Long,fM3Trans,fAlpha,fDist)\n",
    "        print (category)\n",
    "        break"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "More often than not, csv files have comments (e.g. starting with '#'), which cannot be interpreted by the reader. Tricks like:\n",
    "\n",
    "```python\n",
    "csv.reader(row for row in f if not row.startswith('#'))\n",
    "```\n",
    "\n",
    "may be useful"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Binary (hexadecimal) files\n",
    "\n",
    "The output of sensors often is stored as hexadecimal files. Information is packed in a well defined format (similarly to how floating point numbers are formatted).\n",
    "To read and process hexadecimal files in python you need to use the \"b\" option of `open` and progress along the file at step of defined lenght (depending on the size of the words information is packed into)\n",
    "\n",
    "The following is an example from data collected from an FPGA implementing a TDC. Relevant infomation are the coordinates of the TDC channels and their time measurements."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import struct, time\n",
    "\n",
    "with  open('~/data/data_000636.dat','rb') as file:\n",
    "    file_content=file.read()\n",
    "    word_counter=0\n",
    "    word_size = 8 # size of the word in bytes\n",
    "    for i in range(0, len(file_content), word_size):\n",
    "        word_counter+=1\n",
    "        if word_counter>100: break\n",
    "        time.sleep(0.1)\n",
    "        thisInt = struct.unpack('<q', file_content[i:i+word_size])[0]\n",
    "        head = (thisInt >> 62) & 0x3\n",
    "        if head == 1:\n",
    "            fpga     = (thisInt >> 58) & 0xF\n",
    "            tdc_chan = (thisInt >> 49) & 0x1FF\n",
    "            orb_cnt  = (thisInt >> 17) & 0xFFFFFFFF\n",
    "            bx       = (thisInt >> 5 ) & 0xFFF\n",
    "            tdc_meas = (thisInt >> 0 ) & 0x1F\n",
    "            if i==0 : print ('{0},{1},{2},{3},{4},{5}'.format('HEAD', 'FPGA', 'TDC_CHANNEL', 'ORB_CNT', 'BX', 'TDC_MEAS'))\n",
    "            print ('{0},{1},{2},{3},{4},{5}'.format(head, fpga, tdc_chan, orb_cnt, bx, tdc_meas))\n",
    "        else:\n",
    "            print ('ERROR! head =', head)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### JSON files\n",
    "\n",
    "JSON is JavaScript Object Notation - a format used widely for web-based resource sharing. It is very similar in structure to a Python nested dictionary. Here is an example from http://json.org/example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%file example.json\n",
    "{\n",
    "    \"glossary\": {\n",
    "        \"title\": \"example glossary\",\n",
    "            \"GlossDiv\": {\n",
    "            \"title\": \"S\",\n",
    "                    \"GlossList\": {\n",
    "                \"GlossEntry\": {\n",
    "                    \"ID\": \"SGML\",\n",
    "                                    \"SortAs\": \"SGML\",\n",
    "                                    \"GlossTerm\": \"Standard Generalized Markup Language\",\n",
    "                                    \"Acronym\": \"SGML\",\n",
    "                                    \"Abbrev\": \"ISO 8879:1986\",\n",
    "                                    \"GlossDef\": {\n",
    "                        \"para\": \"A meta-markup language, used to create markup languages such as DocBook.\",\n",
    "                                            \"GlossSeeAlso\": [\"GML\", \"XML\"]\n",
    "                    },\n",
    "                                    \"GlossSee\": \"markup\"\n",
    "                }\n",
    "            }\n",
    "        }\n",
    "    }\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!cat example.json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "data = json.load(open('example.json'))\n",
    "print (data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# and can be parsed using standard key lookups\n",
    "data['glossary']['GlossDiv']['GlossList']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### HDF5\n",
    "\n",
    "HDF5 is a hierarchical format often used to store complex scientific data. For instance, Matlab now saves its data to HDF5. It is particularly useful to store complex hierarchical data sets with associated metadata, for example, the results of a computer simulation experiment.\n",
    "\n",
    "The main concepts associated with HDF5 are\n",
    "\n",
    "* file: container for hierachical data - serves as ‘root’ for tree\n",
    "* group: a node for a tree\n",
    "* dataset: array for numeric data - can be huge\n",
    "* attribute: small pieces of metadata that provide additional context\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import h5py\n",
    "import numpy as np\n",
    "\n",
    "# creating a HDF5 file\n",
    "import datetime\n",
    "\n",
    "if not os.path.exists('example.hdf5'):\n",
    "\n",
    "    with h5py.File('example.hdf5') as f:\n",
    "        project = f.create_group('project')\n",
    "        project.attrs.create('name', 'My project')\n",
    "        project.attrs.create('date', str(datetime.date.today()))\n",
    "\n",
    "        expt1 = project.create_group('expt1')\n",
    "        expt2 = project.create_group('expt2')\n",
    "        expt1.create_dataset('counts', (100,), dtype='i')\n",
    "        expt2.create_dataset('values', (1000,), dtype='f')\n",
    "\n",
    "        expt1['counts'][:] = range(100)\n",
    "        expt2['values'][:] = np.random.random(1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "with h5py.File('example.hdf5') as f:\n",
    "    project = f['project']\n",
    "    print project.attrs['name']\n",
    "    print project.attrs['date']\n",
    "    print project['expt1']['counts'][:10]\n",
    "    print project['expt2']['values'][:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pandas\n",
    "\n",
    "the most convenient tool to read and process formatted dataset is however Pandas. In the following a couple of examples. Pandas will be the main subject of one of the next classes\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "file_name=\"/Users/mzanetti/data/LEMMA2018/DT/raw/data_000636.txt\"\n",
    "data=pd.read_csv(file_name,nrows=10,skiprows=range(1,1))\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
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       "      <td>0.0187</td>\n",
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       "      <td>-64.8580</td>\n",
       "      <td>-45.2160</td>\n",
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       "      <td>2.9085</td>\n",
       "      <td>0.2420</td>\n",
       "      <td>0.1340</td>\n",
       "      <td>50.8761</td>\n",
       "      <td>43.1887</td>\n",
       "      <td>9.8145</td>\n",
       "      <td>3.6130</td>\n",
       "      <td>238.0980</td>\n",
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       "      <td>48.2468</td>\n",
       "      <td>17.3565</td>\n",
       "      <td>3.0332</td>\n",
       "      <td>0.2529</td>\n",
       "      <td>0.1515</td>\n",
       "      <td>8.5730</td>\n",
       "      <td>38.0957</td>\n",
       "      <td>10.5868</td>\n",
       "      <td>4.7920</td>\n",
       "      <td>219.0870</td>\n",
       "      <td>g</td>\n",
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       "      <th>6</th>\n",
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       "      <td>13.7595</td>\n",
       "      <td>2.5521</td>\n",
       "      <td>0.4236</td>\n",
       "      <td>0.2174</td>\n",
       "      <td>29.6339</td>\n",
       "      <td>20.4560</td>\n",
       "      <td>-2.9292</td>\n",
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       "      <th>7</th>\n",
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       "      <td>4.1540</td>\n",
       "      <td>0.0779</td>\n",
       "      <td>0.0390</td>\n",
       "      <td>110.3550</td>\n",
       "      <td>85.0486</td>\n",
       "      <td>43.1844</td>\n",
       "      <td>4.8540</td>\n",
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       "      <th>8</th>\n",
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       "      <td>15.1993</td>\n",
       "      <td>2.5786</td>\n",
       "      <td>0.3377</td>\n",
       "      <td>0.1913</td>\n",
       "      <td>24.7548</td>\n",
       "      <td>43.8771</td>\n",
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       "      <td>7.8750</td>\n",
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       "      <th>9</th>\n",
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       "      <td>3.3331</td>\n",
       "      <td>0.2475</td>\n",
       "      <td>0.1261</td>\n",
       "      <td>-33.9065</td>\n",
       "      <td>57.5848</td>\n",
       "      <td>23.7710</td>\n",
       "      <td>9.9144</td>\n",
       "      <td>323.0940</td>\n",
       "      <td>g</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>18.8562</td>\n",
       "      <td>16.4600</td>\n",
       "      <td>2.4385</td>\n",
       "      <td>0.5282</td>\n",
       "      <td>0.2933</td>\n",
       "      <td>25.1269</td>\n",
       "      <td>-6.5401</td>\n",
       "      <td>-16.9327</td>\n",
       "      <td>11.4610</td>\n",
       "      <td>162.8480</td>\n",
       "      <td>g</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>45.6321</td>\n",
       "      <td>22.7100</td>\n",
       "      <td>3.0441</td>\n",
       "      <td>0.2213</td>\n",
       "      <td>0.1215</td>\n",
       "      <td>-18.3986</td>\n",
       "      <td>-20.6427</td>\n",
       "      <td>-14.3164</td>\n",
       "      <td>0.3822</td>\n",
       "      <td>178.2550</td>\n",
       "      <td>g</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>33.1818</td>\n",
       "      <td>12.4655</td>\n",
       "      <td>2.4955</td>\n",
       "      <td>0.4696</td>\n",
       "      <td>0.2412</td>\n",
       "      <td>-17.0341</td>\n",
       "      <td>-22.0762</td>\n",
       "      <td>-8.1803</td>\n",
       "      <td>41.9290</td>\n",
       "      <td>41.3816</td>\n",
       "      <td>g</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>60.4580</td>\n",
       "      <td>33.1061</td>\n",
       "      <td>3.1944</td>\n",
       "      <td>0.4679</td>\n",
       "      <td>0.2464</td>\n",
       "      <td>14.3000</td>\n",
       "      <td>-33.8765</td>\n",
       "      <td>28.8315</td>\n",
       "      <td>8.2920</td>\n",
       "      <td>372.5680</td>\n",
       "      <td>g</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>29.4741</td>\n",
       "      <td>22.4581</td>\n",
       "      <td>2.6258</td>\n",
       "      <td>0.3077</td>\n",
       "      <td>0.1740</td>\n",
       "      <td>18.1242</td>\n",
       "      <td>22.7035</td>\n",
       "      <td>13.1673</td>\n",
       "      <td>48.0364</td>\n",
       "      <td>214.1260</td>\n",
       "      <td>g</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>36.1741</td>\n",
       "      <td>17.6865</td>\n",
       "      <td>2.9460</td>\n",
       "      <td>0.2865</td>\n",
       "      <td>0.1591</td>\n",
       "      <td>-4.7746</td>\n",
       "      <td>-18.9697</td>\n",
       "      <td>11.3256</td>\n",
       "      <td>0.2540</td>\n",
       "      <td>191.4550</td>\n",
       "      <td>g</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>46.2915</td>\n",
       "      <td>16.0328</td>\n",
       "      <td>2.7756</td>\n",
       "      <td>0.3403</td>\n",
       "      <td>0.1920</td>\n",
       "      <td>49.9706</td>\n",
       "      <td>33.3653</td>\n",
       "      <td>-12.7979</td>\n",
       "      <td>38.7070</td>\n",
       "      <td>75.5234</td>\n",
       "      <td>g</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>46.0588</td>\n",
       "      <td>18.7870</td>\n",
       "      <td>3.2217</td>\n",
       "      <td>0.2431</td>\n",
       "      <td>0.1504</td>\n",
       "      <td>49.2704</td>\n",
       "      <td>34.1066</td>\n",
       "      <td>10.7955</td>\n",
       "      <td>8.6860</td>\n",
       "      <td>180.7830</td>\n",
       "      <td>g</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>44.7394</td>\n",
       "      <td>15.6364</td>\n",
       "      <td>2.6380</td>\n",
       "      <td>0.3107</td>\n",
       "      <td>0.1715</td>\n",
       "      <td>14.0430</td>\n",
       "      <td>37.2246</td>\n",
       "      <td>12.4183</td>\n",
       "      <td>3.2510</td>\n",
       "      <td>221.8060</td>\n",
       "      <td>g</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>91.6423</td>\n",
       "      <td>71.8818</td>\n",
       "      <td>3.8484</td>\n",
       "      <td>0.0780</td>\n",
       "      <td>0.0430</td>\n",
       "      <td>-56.7107</td>\n",
       "      <td>72.3016</td>\n",
       "      <td>60.0190</td>\n",
       "      <td>16.6220</td>\n",
       "      <td>285.4140</td>\n",
       "      <td>g</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>86.0486</td>\n",
       "      <td>21.8287</td>\n",
       "      <td>3.3587</td>\n",
       "      <td>0.2369</td>\n",
       "      <td>0.1662</td>\n",
       "      <td>-58.7854</td>\n",
       "      <td>57.8212</td>\n",
       "      <td>-18.8093</td>\n",
       "      <td>2.0050</td>\n",
       "      <td>302.5530</td>\n",
       "      <td>g</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>90.5299</td>\n",
       "      <td>17.8721</td>\n",
       "      <td>3.0330</td>\n",
       "      <td>0.2122</td>\n",
       "      <td>0.1080</td>\n",
       "      <td>-57.4170</td>\n",
       "      <td>84.6189</td>\n",
       "      <td>14.9662</td>\n",
       "      <td>2.0390</td>\n",
       "      <td>272.0380</td>\n",
       "      <td>g</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>20.3836</td>\n",
       "      <td>9.0462</td>\n",
       "      <td>2.3365</td>\n",
       "      <td>0.6359</td>\n",
       "      <td>0.3894</td>\n",
       "      <td>22.2700</td>\n",
       "      <td>12.9487</td>\n",
       "      <td>-2.0403</td>\n",
       "      <td>43.0032</td>\n",
       "      <td>66.1667</td>\n",
       "      <td>g</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>87.2384</td>\n",
       "      <td>27.9350</td>\n",
       "      <td>2.8848</td>\n",
       "      <td>0.3155</td>\n",
       "      <td>0.1806</td>\n",
       "      <td>-18.2102</td>\n",
       "      <td>66.6867</td>\n",
       "      <td>-11.3438</td>\n",
       "      <td>4.4265</td>\n",
       "      <td>342.5040</td>\n",
       "      <td>g</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>107.3380</td>\n",
       "      <td>29.1356</td>\n",
       "      <td>3.5265</td>\n",
       "      <td>0.1315</td>\n",
       "      <td>0.0683</td>\n",
       "      <td>-47.1175</td>\n",
       "      <td>89.5136</td>\n",
       "      <td>-20.0479</td>\n",
       "      <td>3.3120</td>\n",
       "      <td>293.7810</td>\n",
       "      <td>g</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>27.2304</td>\n",
       "      <td>19.2817</td>\n",
       "      <td>2.6375</td>\n",
       "      <td>0.3710</td>\n",
       "      <td>0.2039</td>\n",
       "      <td>23.0406</td>\n",
       "      <td>23.3428</td>\n",
       "      <td>9.7216</td>\n",
       "      <td>77.5379</td>\n",
       "      <td>104.2830</td>\n",
       "      <td>g</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>26.7065</td>\n",
       "      <td>18.9070</td>\n",
       "      <td>2.6781</td>\n",
       "      <td>0.4281</td>\n",
       "      <td>0.2214</td>\n",
       "      <td>9.1616</td>\n",
       "      <td>18.6858</td>\n",
       "      <td>-11.6888</td>\n",
       "      <td>26.8810</td>\n",
       "      <td>63.7637</td>\n",
       "      <td>g</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>23.5647</td>\n",
       "      <td>9.9926</td>\n",
       "      <td>2.2095</td>\n",
       "      <td>0.5679</td>\n",
       "      <td>0.3364</td>\n",
       "      <td>30.0056</td>\n",
       "      <td>-13.8665</td>\n",
       "      <td>-8.2600</td>\n",
       "      <td>26.5328</td>\n",
       "      <td>185.1890</td>\n",
       "      <td>g</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>34.7486</td>\n",
       "      <td>19.2286</td>\n",
       "      <td>2.5629</td>\n",
       "      <td>0.3666</td>\n",
       "      <td>0.1984</td>\n",
       "      <td>30.3637</td>\n",
       "      <td>-16.3092</td>\n",
       "      <td>14.3186</td>\n",
       "      <td>1.5210</td>\n",
       "      <td>109.4450</td>\n",
       "      <td>g</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>50.4829</td>\n",
       "      <td>25.9699</td>\n",
       "      <td>3.3194</td>\n",
       "      <td>0.1941</td>\n",
       "      <td>0.1133</td>\n",
       "      <td>32.1193</td>\n",
       "      <td>39.0162</td>\n",
       "      <td>-18.1839</td>\n",
       "      <td>22.4710</td>\n",
       "      <td>141.6010</td>\n",
       "      <td>g</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>970</th>\n",
       "      <td>27.1111</td>\n",
       "      <td>14.7768</td>\n",
       "      <td>2.5826</td>\n",
       "      <td>0.3817</td>\n",
       "      <td>0.2288</td>\n",
       "      <td>15.8706</td>\n",
       "      <td>-12.3721</td>\n",
       "      <td>11.6509</td>\n",
       "      <td>12.4588</td>\n",
       "      <td>225.2810</td>\n",
       "      <td>g</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>971</th>\n",
       "      <td>67.4091</td>\n",
       "      <td>22.8023</td>\n",
       "      <td>3.3188</td>\n",
       "      <td>0.3283</td>\n",
       "      <td>0.2455</td>\n",
       "      <td>-26.2686</td>\n",
       "      <td>39.4368</td>\n",
       "      <td>18.2520</td>\n",
       "      <td>6.1180</td>\n",
       "      <td>334.5970</td>\n",
       "      <td>g</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>972</th>\n",
       "      <td>21.2163</td>\n",
       "      <td>10.8702</td>\n",
       "      <td>2.1732</td>\n",
       "      <td>0.5705</td>\n",
       "      <td>0.2852</td>\n",
       "      <td>6.1402</td>\n",
       "      <td>-17.9415</td>\n",
       "      <td>7.7792</td>\n",
       "      <td>27.5830</td>\n",
       "      <td>189.7830</td>\n",
       "      <td>g</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>973</th>\n",
       "      <td>18.4901</td>\n",
       "      <td>14.4091</td>\n",
       "      <td>2.5017</td>\n",
       "      <td>0.5795</td>\n",
       "      <td>0.3543</td>\n",
       "      <td>17.8822</td>\n",
       "      <td>18.6212</td>\n",
       "      <td>2.3384</td>\n",
       "      <td>5.5150</td>\n",
       "      <td>184.5650</td>\n",
       "      <td>g</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>974</th>\n",
       "      <td>55.0826</td>\n",
       "      <td>20.1824</td>\n",
       "      <td>3.0308</td>\n",
       "      <td>0.1956</td>\n",
       "      <td>0.1020</td>\n",
       "      <td>0.4276</td>\n",
       "      <td>42.2168</td>\n",
       "      <td>15.1545</td>\n",
       "      <td>3.9190</td>\n",
       "      <td>227.3320</td>\n",
       "      <td>g</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>975</th>\n",
       "      <td>24.7179</td>\n",
       "      <td>14.9382</td>\n",
       "      <td>2.4541</td>\n",
       "      <td>0.4323</td>\n",
       "      <td>0.2478</td>\n",
       "      <td>10.1766</td>\n",
       "      <td>15.6319</td>\n",
       "      <td>7.7679</td>\n",
       "      <td>8.9760</td>\n",
       "      <td>219.2840</td>\n",
       "      <td>g</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>976</th>\n",
       "      <td>33.3944</td>\n",
       "      <td>16.7128</td>\n",
       "      <td>2.5384</td>\n",
       "      <td>0.3792</td>\n",
       "      <td>0.2098</td>\n",
       "      <td>9.1264</td>\n",
       "      <td>-24.9142</td>\n",
       "      <td>11.4659</td>\n",
       "      <td>2.3935</td>\n",
       "      <td>88.3223</td>\n",
       "      <td>g</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>977</th>\n",
       "      <td>51.6406</td>\n",
       "      <td>22.7532</td>\n",
       "      <td>2.7139</td>\n",
       "      <td>0.3227</td>\n",
       "      <td>0.1652</td>\n",
       "      <td>23.5217</td>\n",
       "      <td>40.3722</td>\n",
       "      <td>8.5649</td>\n",
       "      <td>15.3487</td>\n",
       "      <td>279.0690</td>\n",
       "      <td>g</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>978</th>\n",
       "      <td>30.9955</td>\n",
       "      <td>22.3031</td>\n",
       "      <td>2.8591</td>\n",
       "      <td>0.2725</td>\n",
       "      <td>0.1404</td>\n",
       "      <td>34.0777</td>\n",
       "      <td>17.6726</td>\n",
       "      <td>-16.2964</td>\n",
       "      <td>32.5233</td>\n",
       "      <td>121.9150</td>\n",
       "      <td>g</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>979</th>\n",
       "      <td>19.7725</td>\n",
       "      <td>12.5631</td>\n",
       "      <td>2.4108</td>\n",
       "      <td>0.4971</td>\n",
       "      <td>0.2777</td>\n",
       "      <td>-2.7050</td>\n",
       "      <td>11.2371</td>\n",
       "      <td>-10.1412</td>\n",
       "      <td>43.5610</td>\n",
       "      <td>99.4016</td>\n",
       "      <td>g</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>980</th>\n",
       "      <td>48.1057</td>\n",
       "      <td>28.4766</td>\n",
       "      <td>3.1635</td>\n",
       "      <td>0.2533</td>\n",
       "      <td>0.1294</td>\n",
       "      <td>52.6424</td>\n",
       "      <td>29.7204</td>\n",
       "      <td>19.7497</td>\n",
       "      <td>13.0285</td>\n",
       "      <td>265.3340</td>\n",
       "      <td>g</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>981</th>\n",
       "      <td>20.1024</td>\n",
       "      <td>12.5616</td>\n",
       "      <td>2.5045</td>\n",
       "      <td>0.4789</td>\n",
       "      <td>0.2613</td>\n",
       "      <td>2.3241</td>\n",
       "      <td>-10.1706</td>\n",
       "      <td>-9.9981</td>\n",
       "      <td>16.4200</td>\n",
       "      <td>209.1940</td>\n",
       "      <td>g</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>982</th>\n",
       "      <td>27.5928</td>\n",
       "      <td>12.1125</td>\n",
       "      <td>2.4786</td>\n",
       "      <td>0.4850</td>\n",
       "      <td>0.2807</td>\n",
       "      <td>-20.2481</td>\n",
       "      <td>13.3007</td>\n",
       "      <td>-8.2076</td>\n",
       "      <td>8.9470</td>\n",
       "      <td>129.5180</td>\n",
       "      <td>g</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>983</th>\n",
       "      <td>19.0792</td>\n",
       "      <td>9.9980</td>\n",
       "      <td>2.2553</td>\n",
       "      <td>0.5889</td>\n",
       "      <td>0.3083</td>\n",
       "      <td>24.1207</td>\n",
       "      <td>9.1640</td>\n",
       "      <td>5.0535</td>\n",
       "      <td>75.0613</td>\n",
       "      <td>202.8630</td>\n",
       "      <td>g</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>984</th>\n",
       "      <td>29.6570</td>\n",
       "      <td>14.4583</td>\n",
       "      <td>2.6955</td>\n",
       "      <td>0.4012</td>\n",
       "      <td>0.2026</td>\n",
       "      <td>2.5911</td>\n",
       "      <td>-13.3073</td>\n",
       "      <td>-10.0069</td>\n",
       "      <td>19.4898</td>\n",
       "      <td>121.7980</td>\n",
       "      <td>g</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>985</th>\n",
       "      <td>24.4701</td>\n",
       "      <td>23.4296</td>\n",
       "      <td>2.5664</td>\n",
       "      <td>0.2877</td>\n",
       "      <td>0.1506</td>\n",
       "      <td>30.1628</td>\n",
       "      <td>18.0136</td>\n",
       "      <td>-14.6589</td>\n",
       "      <td>35.9900</td>\n",
       "      <td>134.7780</td>\n",
       "      <td>g</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>986</th>\n",
       "      <td>73.1549</td>\n",
       "      <td>24.0524</td>\n",
       "      <td>3.5849</td>\n",
       "      <td>0.2034</td>\n",
       "      <td>0.1140</td>\n",
       "      <td>-75.8154</td>\n",
       "      <td>54.5200</td>\n",
       "      <td>12.0307</td>\n",
       "      <td>0.3485</td>\n",
       "      <td>254.4550</td>\n",
       "      <td>g</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>987</th>\n",
       "      <td>40.8148</td>\n",
       "      <td>19.8202</td>\n",
       "      <td>2.4676</td>\n",
       "      <td>0.3407</td>\n",
       "      <td>0.1789</td>\n",
       "      <td>57.6349</td>\n",
       "      <td>7.8058</td>\n",
       "      <td>-16.1105</td>\n",
       "      <td>25.5050</td>\n",
       "      <td>285.6960</td>\n",
       "      <td>g</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>988</th>\n",
       "      <td>17.1544</td>\n",
       "      <td>11.2490</td>\n",
       "      <td>2.2405</td>\n",
       "      <td>0.5632</td>\n",
       "      <td>0.2960</td>\n",
       "      <td>0.7747</td>\n",
       "      <td>-5.3697</td>\n",
       "      <td>-4.0671</td>\n",
       "      <td>48.5893</td>\n",
       "      <td>119.2250</td>\n",
       "      <td>g</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>989</th>\n",
       "      <td>138.8380</td>\n",
       "      <td>32.8249</td>\n",
       "      <td>3.2464</td>\n",
       "      <td>0.1713</td>\n",
       "      <td>0.1035</td>\n",
       "      <td>-122.6340</td>\n",
       "      <td>95.0450</td>\n",
       "      <td>-27.8132</td>\n",
       "      <td>3.4990</td>\n",
       "      <td>297.6140</td>\n",
       "      <td>g</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>990</th>\n",
       "      <td>109.5810</td>\n",
       "      <td>38.4424</td>\n",
       "      <td>3.1392</td>\n",
       "      <td>0.2823</td>\n",
       "      <td>0.1890</td>\n",
       "      <td>-95.6673</td>\n",
       "      <td>-47.4613</td>\n",
       "      <td>28.5505</td>\n",
       "      <td>0.8340</td>\n",
       "      <td>244.8190</td>\n",
       "      <td>g</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>991</th>\n",
       "      <td>64.8397</td>\n",
       "      <td>22.7859</td>\n",
       "      <td>3.4214</td>\n",
       "      <td>0.1804</td>\n",
       "      <td>0.0904</td>\n",
       "      <td>2.5880</td>\n",
       "      <td>51.7055</td>\n",
       "      <td>13.6157</td>\n",
       "      <td>0.1090</td>\n",
       "      <td>229.0450</td>\n",
       "      <td>g</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>992</th>\n",
       "      <td>22.4688</td>\n",
       "      <td>14.9097</td>\n",
       "      <td>2.4232</td>\n",
       "      <td>0.4642</td>\n",
       "      <td>0.2887</td>\n",
       "      <td>12.0427</td>\n",
       "      <td>12.2064</td>\n",
       "      <td>12.1709</td>\n",
       "      <td>59.4506</td>\n",
       "      <td>158.9470</td>\n",
       "      <td>g</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>993</th>\n",
       "      <td>57.0078</td>\n",
       "      <td>21.2557</td>\n",
       "      <td>3.0330</td>\n",
       "      <td>0.2215</td>\n",
       "      <td>0.1126</td>\n",
       "      <td>30.4996</td>\n",
       "      <td>38.3893</td>\n",
       "      <td>15.1188</td>\n",
       "      <td>7.7050</td>\n",
       "      <td>268.7330</td>\n",
       "      <td>g</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>994</th>\n",
       "      <td>20.0489</td>\n",
       "      <td>9.2926</td>\n",
       "      <td>2.4014</td>\n",
       "      <td>0.6429</td>\n",
       "      <td>0.4226</td>\n",
       "      <td>4.1464</td>\n",
       "      <td>-14.5626</td>\n",
       "      <td>10.2506</td>\n",
       "      <td>25.8830</td>\n",
       "      <td>187.8470</td>\n",
       "      <td>g</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>13.3396</td>\n",
       "      <td>10.5628</td>\n",
       "      <td>2.1917</td>\n",
       "      <td>0.7910</td>\n",
       "      <td>0.4019</td>\n",
       "      <td>-14.8910</td>\n",
       "      <td>-2.3901</td>\n",
       "      <td>11.9001</td>\n",
       "      <td>10.7959</td>\n",
       "      <td>182.9100</td>\n",
       "      <td>g</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>49.8521</td>\n",
       "      <td>11.4525</td>\n",
       "      <td>2.3971</td>\n",
       "      <td>0.4529</td>\n",
       "      <td>0.2305</td>\n",
       "      <td>52.6071</td>\n",
       "      <td>39.3622</td>\n",
       "      <td>-8.6905</td>\n",
       "      <td>6.0457</td>\n",
       "      <td>183.4950</td>\n",
       "      <td>g</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>29.2688</td>\n",
       "      <td>14.2779</td>\n",
       "      <td>2.5723</td>\n",
       "      <td>0.4016</td>\n",
       "      <td>0.2021</td>\n",
       "      <td>-21.2132</td>\n",
       "      <td>-23.9344</td>\n",
       "      <td>-8.2073</td>\n",
       "      <td>47.5590</td>\n",
       "      <td>102.7790</td>\n",
       "      <td>g</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>21.9470</td>\n",
       "      <td>20.3281</td>\n",
       "      <td>2.4864</td>\n",
       "      <td>0.4078</td>\n",
       "      <td>0.2072</td>\n",
       "      <td>12.8338</td>\n",
       "      <td>10.4007</td>\n",
       "      <td>14.8348</td>\n",
       "      <td>40.4341</td>\n",
       "      <td>137.8100</td>\n",
       "      <td>g</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>61.2167</td>\n",
       "      <td>21.0987</td>\n",
       "      <td>2.8993</td>\n",
       "      <td>0.2875</td>\n",
       "      <td>0.1482</td>\n",
       "      <td>-75.2965</td>\n",
       "      <td>27.4636</td>\n",
       "      <td>-18.4181</td>\n",
       "      <td>5.8060</td>\n",
       "      <td>285.1400</td>\n",
       "      <td>g</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      fLength    fWidth   fSize   fConc  fConc1     fAsym  fM3Long  fM3Trans  \\\n",
       "0     31.6036   11.7235  2.5185  0.5303  0.3773   26.2722  23.8238   -9.9574   \n",
       "1    162.0520  136.0310  4.0612  0.0374  0.0187  116.7410 -64.8580  -45.2160   \n",
       "2     23.8172    9.5728  2.3385  0.6147  0.3922   27.2107  -6.4633   -7.1513   \n",
       "3     75.1362   30.9205  3.1611  0.3168  0.1832   -5.5277  28.5525   21.8393   \n",
       "4     51.6240   21.1502  2.9085  0.2420  0.1340   50.8761  43.1887    9.8145   \n",
       "5     48.2468   17.3565  3.0332  0.2529  0.1515    8.5730  38.0957   10.5868   \n",
       "6     26.7897   13.7595  2.5521  0.4236  0.2174   29.6339  20.4560   -2.9292   \n",
       "7     96.2327   46.5165  4.1540  0.0779  0.0390  110.3550  85.0486   43.1844   \n",
       "8     46.7619   15.1993  2.5786  0.3377  0.1913   24.7548  43.8771   -6.6812   \n",
       "9     62.7766   29.9104  3.3331  0.2475  0.1261  -33.9065  57.5848   23.7710   \n",
       "10    18.8562   16.4600  2.4385  0.5282  0.2933   25.1269  -6.5401  -16.9327   \n",
       "11    45.6321   22.7100  3.0441  0.2213  0.1215  -18.3986 -20.6427  -14.3164   \n",
       "12    33.1818   12.4655  2.4955  0.4696  0.2412  -17.0341 -22.0762   -8.1803   \n",
       "13    60.4580   33.1061  3.1944  0.4679  0.2464   14.3000 -33.8765   28.8315   \n",
       "14    29.4741   22.4581  2.6258  0.3077  0.1740   18.1242  22.7035   13.1673   \n",
       "15    36.1741   17.6865  2.9460  0.2865  0.1591   -4.7746 -18.9697   11.3256   \n",
       "16    46.2915   16.0328  2.7756  0.3403  0.1920   49.9706  33.3653  -12.7979   \n",
       "17    46.0588   18.7870  3.2217  0.2431  0.1504   49.2704  34.1066   10.7955   \n",
       "18    44.7394   15.6364  2.6380  0.3107  0.1715   14.0430  37.2246   12.4183   \n",
       "19    91.6423   71.8818  3.8484  0.0780  0.0430  -56.7107  72.3016   60.0190   \n",
       "20    86.0486   21.8287  3.3587  0.2369  0.1662  -58.7854  57.8212  -18.8093   \n",
       "21    90.5299   17.8721  3.0330  0.2122  0.1080  -57.4170  84.6189   14.9662   \n",
       "22    20.3836    9.0462  2.3365  0.6359  0.3894   22.2700  12.9487   -2.0403   \n",
       "23    87.2384   27.9350  2.8848  0.3155  0.1806  -18.2102  66.6867  -11.3438   \n",
       "24   107.3380   29.1356  3.5265  0.1315  0.0683  -47.1175  89.5136  -20.0479   \n",
       "25    27.2304   19.2817  2.6375  0.3710  0.2039   23.0406  23.3428    9.7216   \n",
       "26    26.7065   18.9070  2.6781  0.4281  0.2214    9.1616  18.6858  -11.6888   \n",
       "27    23.5647    9.9926  2.2095  0.5679  0.3364   30.0056 -13.8665   -8.2600   \n",
       "28    34.7486   19.2286  2.5629  0.3666  0.1984   30.3637 -16.3092   14.3186   \n",
       "29    50.4829   25.9699  3.3194  0.1941  0.1133   32.1193  39.0162  -18.1839   \n",
       "..        ...       ...     ...     ...     ...       ...      ...       ...   \n",
       "970   27.1111   14.7768  2.5826  0.3817  0.2288   15.8706 -12.3721   11.6509   \n",
       "971   67.4091   22.8023  3.3188  0.3283  0.2455  -26.2686  39.4368   18.2520   \n",
       "972   21.2163   10.8702  2.1732  0.5705  0.2852    6.1402 -17.9415    7.7792   \n",
       "973   18.4901   14.4091  2.5017  0.5795  0.3543   17.8822  18.6212    2.3384   \n",
       "974   55.0826   20.1824  3.0308  0.1956  0.1020    0.4276  42.2168   15.1545   \n",
       "975   24.7179   14.9382  2.4541  0.4323  0.2478   10.1766  15.6319    7.7679   \n",
       "976   33.3944   16.7128  2.5384  0.3792  0.2098    9.1264 -24.9142   11.4659   \n",
       "977   51.6406   22.7532  2.7139  0.3227  0.1652   23.5217  40.3722    8.5649   \n",
       "978   30.9955   22.3031  2.8591  0.2725  0.1404   34.0777  17.6726  -16.2964   \n",
       "979   19.7725   12.5631  2.4108  0.4971  0.2777   -2.7050  11.2371  -10.1412   \n",
       "980   48.1057   28.4766  3.1635  0.2533  0.1294   52.6424  29.7204   19.7497   \n",
       "981   20.1024   12.5616  2.5045  0.4789  0.2613    2.3241 -10.1706   -9.9981   \n",
       "982   27.5928   12.1125  2.4786  0.4850  0.2807  -20.2481  13.3007   -8.2076   \n",
       "983   19.0792    9.9980  2.2553  0.5889  0.3083   24.1207   9.1640    5.0535   \n",
       "984   29.6570   14.4583  2.6955  0.4012  0.2026    2.5911 -13.3073  -10.0069   \n",
       "985   24.4701   23.4296  2.5664  0.2877  0.1506   30.1628  18.0136  -14.6589   \n",
       "986   73.1549   24.0524  3.5849  0.2034  0.1140  -75.8154  54.5200   12.0307   \n",
       "987   40.8148   19.8202  2.4676  0.3407  0.1789   57.6349   7.8058  -16.1105   \n",
       "988   17.1544   11.2490  2.2405  0.5632  0.2960    0.7747  -5.3697   -4.0671   \n",
       "989  138.8380   32.8249  3.2464  0.1713  0.1035 -122.6340  95.0450  -27.8132   \n",
       "990  109.5810   38.4424  3.1392  0.2823  0.1890  -95.6673 -47.4613   28.5505   \n",
       "991   64.8397   22.7859  3.4214  0.1804  0.0904    2.5880  51.7055   13.6157   \n",
       "992   22.4688   14.9097  2.4232  0.4642  0.2887   12.0427  12.2064   12.1709   \n",
       "993   57.0078   21.2557  3.0330  0.2215  0.1126   30.4996  38.3893   15.1188   \n",
       "994   20.0489    9.2926  2.4014  0.6429  0.4226    4.1464 -14.5626   10.2506   \n",
       "995   13.3396   10.5628  2.1917  0.7910  0.4019  -14.8910  -2.3901   11.9001   \n",
       "996   49.8521   11.4525  2.3971  0.4529  0.2305   52.6071  39.3622   -8.6905   \n",
       "997   29.2688   14.2779  2.5723  0.4016  0.2021  -21.2132 -23.9344   -8.2073   \n",
       "998   21.9470   20.3281  2.4864  0.4078  0.2072   12.8338  10.4007   14.8348   \n",
       "999   61.2167   21.0987  2.8993  0.2875  0.1482  -75.2965  27.4636  -18.4181   \n",
       "\n",
       "      fAlpha     fDist category  \n",
       "0     6.3609  205.2610        g  \n",
       "1    76.9600  256.7880        g  \n",
       "2    10.4490  116.7370        g  \n",
       "3     4.6480  356.4620        g  \n",
       "4     3.6130  238.0980        g  \n",
       "5     4.7920  219.0870        g  \n",
       "6     0.8120  237.1340        g  \n",
       "7     4.8540  248.2260        g  \n",
       "8     7.8750  102.2510        g  \n",
       "9     9.9144  323.0940        g  \n",
       "10   11.4610  162.8480        g  \n",
       "11    0.3822  178.2550        g  \n",
       "12   41.9290   41.3816        g  \n",
       "13    8.2920  372.5680        g  \n",
       "14   48.0364  214.1260        g  \n",
       "15    0.2540  191.4550        g  \n",
       "16   38.7070   75.5234        g  \n",
       "17    8.6860  180.7830        g  \n",
       "18    3.2510  221.8060        g  \n",
       "19   16.6220  285.4140        g  \n",
       "20    2.0050  302.5530        g  \n",
       "21    2.0390  272.0380        g  \n",
       "22   43.0032   66.1667        g  \n",
       "23    4.4265  342.5040        g  \n",
       "24    3.3120  293.7810        g  \n",
       "25   77.5379  104.2830        g  \n",
       "26   26.8810   63.7637        g  \n",
       "27   26.5328  185.1890        g  \n",
       "28    1.5210  109.4450        g  \n",
       "29   22.4710  141.6010        g  \n",
       "..       ...       ...      ...  \n",
       "970  12.4588  225.2810        g  \n",
       "971   6.1180  334.5970        g  \n",
       "972  27.5830  189.7830        g  \n",
       "973   5.5150  184.5650        g  \n",
       "974   3.9190  227.3320        g  \n",
       "975   8.9760  219.2840        g  \n",
       "976   2.3935   88.3223        g  \n",
       "977  15.3487  279.0690        g  \n",
       "978  32.5233  121.9150        g  \n",
       "979  43.5610   99.4016        g  \n",
       "980  13.0285  265.3340        g  \n",
       "981  16.4200  209.1940        g  \n",
       "982   8.9470  129.5180        g  \n",
       "983  75.0613  202.8630        g  \n",
       "984  19.4898  121.7980        g  \n",
       "985  35.9900  134.7780        g  \n",
       "986   0.3485  254.4550        g  \n",
       "987  25.5050  285.6960        g  \n",
       "988  48.5893  119.2250        g  \n",
       "989   3.4990  297.6140        g  \n",
       "990   0.8340  244.8190        g  \n",
       "991   0.1090  229.0450        g  \n",
       "992  59.4506  158.9470        g  \n",
       "993   7.7050  268.7330        g  \n",
       "994  25.8830  187.8470        g  \n",
       "995  10.7959  182.9100        g  \n",
       "996   6.0457  183.4950        g  \n",
       "997  47.5590  102.7790        g  \n",
       "998  40.4341  137.8100        g  \n",
       "999   5.8060  285.1400        g  \n",
       "\n",
       "[1000 rows x 11 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "file_name=\"/Users/mzanetti/data/magic04.data\"\n",
    "data=pd.read_csv(file_name,nrows=1000)\n",
    "data.columns=['fLength','fWidth','fSize',\n",
    "        'fConc','fConc1','fAsym',\n",
    "        'fM3Long','fM3Trans','fAlpha','fDist','category']\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x110d4ed30>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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BVGM4zPWQ6pjZo+4etWlb/nkKECKNsW1nN+u3vAY4c6aNr+koJpFc1QYI9UHIgA31lmcjyl9uWGlhP8FQvKYyPChAyIAM9fHzjSh/LYaVDvUgLc2pYZ3UZhY3sz+Y2V3h40PMbJWZbTSzn5hZslFlk+oM9cXP+lv+amYaV3NONbOa+2P56s0suPZ+PvS9VSy49n5WrN68V69TreE+41r2aOQdxKeAJ4B9w8fXAl9399vN7LvAx4CbGlU4qWyoj5/vT/mrudOo9m6kbeJoelKDM6y01HyKWk1yG+p3jNI/DbmDMLM24N3A98LHBrwDWBqe8kPgPY0om1RvqI+fr7b81dxp9OduZGXny6Ry3rclbmWHlZZrsUfdjXT3pfnRqucqX4B+Gup3jNJ/jUoxfQO4BLJNt8nAK+7eFz7uAtQsaXJDffx8teWvJiVUbdooU8n25cSlmMGCWVMiy1gpfRR1NwJw4wMb+11xV0odDXZqTJpf3VNMZrYQeMndHzWzEzOHI06NHH9rZouBxQAzZ86sSRmlekNp8bOojtxqyl/NnUa1dyNRaa1kPJ6X1sqUc2wyHpE+WpOXPpo8rpULTprF1+59Ou99Cl+zkmpSR0P9jlH6rxF3EAuARWb2J+B2gtTSN4AJZpYJWG3Alqgvdveb3b3d3dunTp1aj/JKBZPHtTJvxoSmDg5RLfFMixkoW/7MnUZrIsaYZJzWiPWYqjkHqltVNVPO07+9Ek/nt5O6+7woffTBY2bSmshvY/Wn4q42dTTU7xil/+p+B+HulwOXA4R3EBe5+3lmdgdwNkHQOB9YXu+yyfBRONmssCX+mSWricdiJOPVdbZ65n83cm9uc9+n1Dm5+ruqapQbH+jktLkH8HpPKnvXc/3Z8yJfsxr96axvhjtGDemtn2aaB3EpcLuZfRn4A/D9BpdHhqjCdMknTpxFIpbfwu5LQ186TXdf5XkImYq7u8/JLKR3ybK17NjdxzV3b6AlFqMnlSLt0JvKPyfqNfuzqmoUd+f0b6+ktSC47W3F3d/UUSOXy9Yoqvpq6GJ97v4bd18Yfv6Mu7/V3We5+/vcXUMjpKyoTtWodMk379vIzu7oFVIzyu2u1rV9V1GqJ512rr5zffZ9uvs8DA57xM144MmXIjt9o9JyURV1lJ6U09NXnA7am1Rf5vu7cuHspk8daRRV/TXTHYRI1Uq1JKNa4X3pyuuNZVrM23Z2c9uq5/jOA53Z9NNnTjmc7oLKvyfljGmJRW4olPF6T4ov3rmezy9fV1VLNzf9FI8ZPeHdTWHgaU3Esnc+sPdzTwqv4ZXvns3c6eObNnUz1OfdDEVa7luGhNwWfbmWZKlhn5Vc+e7ZrOx8meO/ch833Ps03Tkt9K/+x1O0JvL/VFoTMXoL3icRC46PTcazx3Z2p7Ll63xxR+Qw0tzvbdH86Vz57tn09qVJxmO4Oy1xY2xrnGQixudOPwIrGPO3NyOJoq7hNXdvaNrgABpF1Qi6g5CqVNMxWO6cSnsvl6uUqulTyLQk582YEDnss5yxrXFmTBrN4n97NOxnyNcSj9FbcBeSSqcpvDE57k2T+ca5R/G1/3iaHz1SPFHt9G/9ltZEnJ5UigtOOowPHjOTlZ0v57fiF87mmrs3BGmkVJAWa03ATecdnV3x9YB9R+11h3RGrVrjtexALtfB32xlHS4UIIa5wfgjGOgyE5X2Xi7X2Rg1sufr9z5d1I2b25L84DEzufGBjUWV/dhknL6wYs9N26TSDljJDuKevhSL5k3jzrXPZyv4VBrSBUvl/7ZzG/euf4E7OoqDw+7e4HV7UsFc0K/d+zQ3PtBJKp2mL032fa++cwMtBcEvGY8zfnSyX3M3KqlFa7weHciDNYpKnd3V0X4Qw9hg/BFs29nNgmvvz1ZwAKNaYtx1wQnZYZZA5DkPXfqOyOeCdI3nVeCZ8wv/4NdseoUPfW9Vdl/mUv7hvXM575iDso8LN+O5cuFs5k4L8uu/XP9CUBHHjVTaue6sI1kwawrHf+X+vNx+lHfPPYD3v3UGi299lN0R5yZiFtnn0RKzoruQKGNb4/T2pfP6NpJx454L38bEsclBbfFGbViU+/vRn8ZFqd+TqJ9pow2lstaK9oMY4QZrSemoVISn84dZfuLEWSXTFZnPc5+LmRE0XvdUgpkRPycdsV/2fccm47y6q4fuvvLBYWwyztxp47Pfd9f2XSyYNYWHLn1HUQW3fPVmvnTnBuIGPX1pvnjGHBbNn87y1Zvz1kcq5e51L/A3Cw4uMcshukO8JQaxGFQYvQoEdzNXnTGHa+7egKed7pQTixmnfvNBzIxRifigtXjLtcZzGxc9qTQXnDSLDx4zs+TvzlDqQB5KZW00BYgmt7cposH6I4hKRQQjejw7yubGBzZSuFpKbrqi8Ot39RYPOc2M+Ln8Z4+TTjuJmNGdcka1xCgzUAiAlDttE0dz28PPcvVdG0jGg1Z8VIv4ojvW5KWXvnjnet68/zguvmMNFW4esv607Q2uP/tIPr1kTZieKu/Ckw/noMljuHjpmsg+jlyfOeVwTp17AONHt/DpJasBclq6Tm+Yohro/hEZUXMaohoXQUpsI9efPa/kCrVDpQN5KJW10TSKqYkNZJ3/sck43X35FXHuH0G1a/oXLq+QjBujWvJ/bZLxOBecNCtyHP3kca20HzSxqjLv7E7Rm3JSTnZY6e7eNKUGJY1NxrPv9ct1L3DFz9fR05fOGzmU+/2t3/Jq0ZDR3pRz7v9bVXa4aqH5MyawaP50HvncyXzlvXOJRa0kFopb0CeyaP50fnfZyXz2nYfTmogVjYrK+M1TW1lw7f1csmxtUVlzZYJ9qbkgA9mvIWpRPgiW+Sg172AoLcMxlMraaLqDaFL9TRHl3mlkRsbEYgYppzVuWGzPktL56YM9I2ogev/j3FTE2GSchTeuzHvv3nSaDx4zkw8eM7Po6ztf3MHKzm0lv8/WRIyYwa7e8s33ZNzAjJa40ZtyLnrn4RzzpsnZgHfcP91X9DXuzp1rtnDGvGlheaJr8mrmSWSc097GxLFJ1mx6hbaJo/mLaeMZk4yXnIgXy4kek8e18smTg2v97fs28oPfP1t0/u+eKX2tcvWm06zb/Crvv/n3eX0IO3b3cfWd62mJx0h58V1URrk703IT9srdhTbDMhxR9naRRlGAaFp7u5lN/pIPATfj7gtOYNb++5RMH3zr/o24O6NbEkWTpjLlyfwhlRtqWFi21eFieKW4p0lXcSPbk3IWHbk/v1z/Ii3xGDf8+mmuO+tI5s2YwJpNr9ASj2WHhWZ09zlfvHMDX757AzecM58Fs6aQiFF1KqnQR447iKMOmsiCa+/P6/wud/eRTET/zG7v2FT1+7bEIDd+tsSNK98dDIfN/Tl+Nid9lrkWUY2KSoMXMj/ji5euLeq0r5SKaeQyHFH6s/e3FFOAaFLBhK/iFNHYZDzbeq12gbfWeIzXe4LXKrXeT6ZiyYwWuuLn6xjXGqe7L42705qI05tKc9UZczjv2IOqbn3NnzGh7Pf5sRPexJhkghsf6Kw4gmjF2heAPZXfRUvXMvvAfWmbOLoonZarLx0sk/27y07mhnPmc/HSNcQtRl86hWN5wTQeMzztRVeyNWF86NiDWHjjyrxrfc1dG7joXYfzj/c8Gfner3enWLf5VeblXIdq11wK3jcWDqfdU8aYwYxJxZV0VEoqbla0lHg1d6aZFvaPVj3HjQ9sJBmPFzUGml0t9v4eaRQgmtTKzpfzJmIlYnDOW9pYeOPKvNbQQZPHVqxsclt91a73A+SlTfrCAHPFz9eBwXnHHFTVH9ms/ffhzPkHsnz180XPJWLGLQ/9kWQ8Djin/4/9ue+Jl0jEYuzqSWFBhqyknr40p3/rt1x1xpyiSWtF3Fi/5bWi1MJDnS9zybK1xMzo7k1Hdjq3Jozrz57H6z2pyLu6Yw6ZzD+8Zy5X37kexynsg7/6zvUcc8gkZu2/D1D9z6Alblxw0ixufvCZbOc0QCIe47VdvXnDNEvpTeW3+PtzZ5qbEhuKqRiNVho4dVI3oUzLJ7dFaMBPOrrylka4aOlaevtSRZVN3MIlH8LlGa5cODsvBRTsW1CmZ7WCL65Yn9dR2fniDpZ2bKLzxR2R38tBk8ZGvk4qHcyFyCx2d/+TW7n7k2/j6JkTSFM+OGT0pJyrlq+rGCC6U2n+9tYOVqzenLeo3YJZUzj+0Cm80ZMiFTEnqDVufO19wcidcqNfzjv2IH5/+cn803uPpKXg0vakgmHBmUEGmZ9BMl7+ZxAzOG3uAUXv+Xp3ivVbXqO1wtcDXHXGnIoLAlaTNmr2/T6iaLTSwClANKGoUSS9ETN3e/rSnPv/HuZds/ejJW7ZGbiJmNGXSrO7N0VLLJide9uqPR2ihSNqRrfEiMeCwJK7jlApvSnnI//6CJ0v7uALP3+cU77+IBctXcspX3+QS5auyZ5328PPctw/3cf3fvvfka9TGKNaYjE+//PH+W2ZTu0oFUaOZnX35Y9sWr56M8f9033c/+RLpb8m5Xz2jrXZwFJu9Mvkca3MnzGB3ojy9BS896L50/nR/z6mbHmT8Tiv96S4cuHsoudueeiPFC3KFGpNxEgmYsHkwWMPyntuJI3gGUnfa60oxdQglUaRFPY/QHSOuS8NK9a8kHcsu/Kow+s9QQvqip+tAyevwnCgty+dvQFPxIyP/9WhTBqX5Jq7NhCPGa+XGJ3z+ObXOOXrDxYdX9LRxaz9xjE2mQjSUUBPie6Bwor09e4+Hv7j9uiTB0nuBL5Ll62tanhrd1+ai+5Yw+wD9604+uX1nhSjWmKR6Z/C9EZLIl6207wnleLVXT3MmDiGscl4th8JguCx+O1vilxSxN2558K3ZVNahUbSCJ6R9L3WggJEA5QaZprpdO7avosPvHUmP/hd8TDIeIyS8wIqufLn65gxaTSb/ryLL921oahTuC/tfOv+jTx8+cmcOucAHnjyJa5asT6vYqrGdb98qlTjtkjcYEwyQU8qRV/KS23ENmh6UsHyIP3pKA6+LkgTffXsYBRMqYqmXPqiML3x8DPbIoPDmGSMnr5gzahP3PYHelLpolnemaHF82aM5+P//hhv5PyMWhPxij+z4TiCp1Sjazh+r/WiAFEjpX5Zt+3s5pJwRm3uMNNv39/Je46axvLVm4lbLHK2McCXz5zL55ev26sgkQYW39rB7jI5md6Us37Lq7z98P046Yj9+Pzydf1+n0QMzKzsRK/sufEY3znvaMD5+L89yhtVdLwOxLy28azf8hpjWmJlRz5FyaSJyo2CyaQ1PrNkdV7lHzfy0hvbdnaXXHH2tLkHctfa5+nuS2c7p1viRmuCotFEc6aNL0o9jsQ8uxbfqw0FiBoo98t626rnIpdb6EmlWdLRFT4qXXE99+c3eN/RbdyePbd/ygWHPYLm/57x8JWXiMjlGL1VTjZIxoOF+yA6hTbYHvnTK3z4lkeAoNLtr1KjYHIbBAtmTSEei9GX0+pPOazs3Jr9Pejavotk3OiJWGbqzjVbaInHyJ2vPCoR5zvnHcX40cm8RsdgL4FdrWZaKlvDWWtHAWKQlftlBfjOA50Dev2b/vMZ9qJe65dp40cBwfcyYUyStx82lXufKN2Rm9ESg3g8GDX1xRXrSVVR4b/Rm+Jvb+0I7jj6MaN5MOxNQMqkqHJlGgSJmNGTcj74lzMiU2xLOjaz+G2HMmv/fWibOLrkDO6WRKwowPam09n9IArVO8/ebK11DWetHQWIQbRtZzcPPPlS5GY267e8yuNdrxbtbbw3at3QXvaHLo44YF8+W5AmqcQhuwx4tcPjUmkP02WNW3Y+GY9VvQtd2uGhzpezFWJugyAjagmNjNWbXmHW/vtkW/6fLVg8EIJr8p6jprGkY8/aW+e0t5Wt7OqVZ2/G1rqGs9ZO3QOEmc0AbgUOIEiL3+zu3zSzScBPgIOBPwHnuHtth7QMkj37GG8kblaUR9/dl+L8W/6rgVVg/3z3N88QqzBJLUo8Zrzek2LVM9uK9nCutxYrHiVVivfjJ9Ob8rwKsWv7rqIGQTm5M8sXzJoSudDfZ045nBt+nd8/saSji0+dfHjDW8TN2FpvVJptJGjEHUSwU7MOAAATKElEQVQf8Fl3f8zM9gEeNbN7gY8A97n7V8zsMuAy4NIGlK9flq/ezCV5a9bsqWwyyyTUI7c+mJy9u0vp7nNe3rGb634VvexEPfV66c17MjIT1Qo7eSvJrRCDIcnVff057W15Q0+Dfoh43n4XY5NxJo1NNl0lnNGsrXUNZ62Nuk+Uc/fn3f2x8PMdwBPAdOBM4IfhaT8E3lPvsvXHtp3dPPj01oLgkK+nLz3kgsNAxA0+fttjVaWlCpcMLxQj2L1tbzqSMyqt0hqPGWDECzoMWhMxknFjn9YErQkrKkNuhTh5XCvvmT+tYlnGJGN5O95BdGWbcmf+jAns6s3vvd7V29fwShiae/LZUJ3x3cwa2gdhZgcDRwGrgP3d/XkIgoiZ7dfAopW0J53USdys7AJzIyc0BFJOVR3TBvRVyPm3JGI8uPFlYgYfOf4gErEYP37k2ezEv8FQaonxtDu/uPBt2S1VM+s1RaUvtu3sZsWaLRXfK+3FcyRKpUYmjk1iZuT+Blm1E0vqQK31kaNhAcLMxgHLgL9399eq/QMws8XAYoCZM2fWroA5tu3sZv2WV7n/iZf491XP7vVy0RJwyi+53RIPAm8m+P7gd8+GM4nrc+HdnYljk8zav3jvgMyM5m07u7N9EOUm3I1NxrP7MuROhMxUrIWVLcADT75EayJGb85s+lGJeFOkmDI0+WxkaEiAMLMWguBwm7v/NDz8opkdGN49HAhEjqt095uBmwHa29tr3khfvnpz0TaVEmiJG8mcpcQHS9S1Huz3KGd0S6KoMp48rjW7EVNua3/BrCmRK7O2JmJ84YzZzJ02PhsMSg0PzVS2mefjZkXfbzPk+WXkqXsfhAW3Ct8HnnD3G3KeWgGcH35+PrC83mUrFLWHsQQSMfjiGXPyJoMNBfEYfO60I7I59NZEjMLdP6Mq49zhnZnVdC9ZthagaEvW8946k7s/eQLnHXNQNide6uszi/flPp8bHMa2xivm+Qe6xahIKY24g1gA/C/gcTNbHR77HPAVYImZfQx4DnhfA8qWJ2oPYwl84/3zWThvOg91buWedS82ujgljWqJkUp7sAVn2rk+XEvprLe0Fe0JUW6IZLnhnZk0UWao84o1W1j2h668CWSVhodGPT82GefqM+Zw0hH7lQwOzTZpTYaXugcId19Jqc2B4eR6lqWSzeGqn5IvEYPjDp3Ctp3d/MeG5g0OGbkdzrlLVFSzP3Gmz2BsMl5xeOc//6aT7j7PDlvNnS9RaXhoqRFN5YJDM05ak+FF+0GUsHz1Zr6wYn2ji9F04gY3nDOfyeNaWb/l1UHpsLfwdbPvEbO8FkTcgtRQJck4nHX0NJJxskMwr1w4uyg4RIkaIrl89WYWXHs/H/reKhbeuJJz2ttKDu+M2sMjd2nx3I2axrTEaU1Y0V4S/R0+Wuk9RQZKS21EiNrRTQKJeCy7rlTpG8H+vqbxiwvfxpZXdwPOtPGjefe3V2ZHMaU82FyoNRHDDHb3pmmNG2mCEUejWxJ56ZXPnT6bru27WLf5Va65a0NRp3I1wzOjWudLOrqyS4kUfn01E8g8c80M8OJr19/ho2OTcbpTzTdpTYYPBYgI/d0rYCRJxC2bN9/05zcqnj8qEWN3hduMUeH+BW8/fCoAaza9QjIey5tjMrolwXfOO5rxo1uyQ00zFWFhhZr5+P6bf59XwX9myWrisRjJeOV8/fotrxErCIAtsWDE1rxwuYzCIavllnvofHEHF9+xJm/WdVQ6qNrho5m+BwtngWcmHjbLpDUZHkZkgMjMawBjzrR9I3d0q2ZT+ZGoN+W0TRxN54s7uPquDWXPbU3EuPnD7Wza/ka2JZ/Z/CY3ZhS2eku1xqN+VkDksa7tu4pmSPeloS+9Z35FqXx98fIpxeUs1Tm8YNYU1m95DXDmTBufPffipcW71+3t8hlRCwSm0+V3kRPZGyMuQETNa/jWufOzLcnOF3fwsz90ceT0fXns2VdIO8P+PqIlFux5XY2/+6s3sbLz5aLWMIRrG5nRmtNCz9wVnDrngGxQvmvt5rIrlQ7G4mvrNr9ace5EVAWdqXwLg0Nun0G5zuHCuRJXLpzNNXdtoCfiLmpv00FRd7jV7CIn0l8jKkAEu7kV9y1cePtqFsyawjd//TS3Pvxcg0rXGJ86eRbf/c9noMo7ppt+89+k3SM7p2MxK5mjz1SciZixs2Cf66iVSgeynMO2nd1cc3fx3U3h/s9RFXRU5TsmGee7Hzqatx++X8lzgiXdXysKHFffuYGWiCVbk/Eg4ECQUuvP99isC+bJ8DOiAkTX9l0l1wB6y5d/XefSNIeNL+0o2u+4nFIrlyYTwaibqBRHVEokV6lUy94u51Cqkv+7vzqU7/yms+xdSVTlm/Y96aJS5wSPvThwxIt310smYtzzyRNY//xrLLj2/n7PYdDy1lIvIypAtE0cXfPNdoaaex4f+DyGZNy455MnlMx/V+r0H+zWb1QF/kZPikljkzx06TvK3pVUU/mWOmfOtPHFcxnSzlVnzOGau/NHU00cmxzQHAYtmCf1MKICxORxrfz1X+zHr6rYPnMoG52IkcZJ+95tqzkmGaenL1VyjkNL3IgZJOPxbIVXrnO0VKd/4UJ2g2XyuFauXDibK362Lu/4NXdv4NS5B2RHIZVSTeVb6pyowLFo/nROnXtA3rlrNr0y4D0ftGCe1NqIChAAF596xLAOEK2JGP/y4XbmTNs3u4QEBHMHqumMznTGfvaO1UX7KWTmIfRnPgFEt7ivXJi/kN1gmzttfHY4bEZ/KuBqKt+oc0oFjsJz1Y8gQ8GICxArO18u+VwMWPJ/juVfHnyGe+sURPJX/R+YRAyuP3vPyKHcyurhZ7bxtXufpjUG3X3BRDOLGee0t7GkoyuvxTtj0piiQrUY/MN75uYt/dCfir3eKZEgnZj/TdSrAq42uDRbP0LhvA6RERcgppT5xf/0Ow+n/ZDJHDJ1HIf+9plgdE+NDWaXSDyWO8s5kPlD//qvn84baulm3H1B0G/wqZMPz6sYOl/cUbSndK8H+ykPpOKoZ0qkGSvgQs3Uj6BF/yTKiAsQxx06mZgFO3zlak3E+OAxM0tOkmqUlrhx8bvezA2/fjr7x/uXB03kt53bis5NxqNTKJHj5nP2cSisuF/vSTGqJZY36mhUy+Dv+1BrzVQBl9IM/Qha9E9KGXEBYvK4Vr7x/vl8+iersyOaWuLG9WcHY9KjJkk10oXvOIzFf3Vo3vLUk8e10vHHbXzge6vyOqFLpVD6m+/u7/Fm1gwVcLOrtBS5jFwjcjXXRfOn88gVp3DrR9/KrR/9Sx6+/GQWzZ8euTpmf41pifHFM2YHs4qrVOrU1oTxwWOCbVULVxttP2QyX3vfvKpW/+zvSqF7s7KoDF3qMJdSRtwdRMbkca3ZztyMwViDKQ2cMW8ak8YmuWTZWjztRfn8Qt889yiOOGAffrHuBW58YGPe8NFylXJ/Uij9TbcMhfSMDI6h0F8jjWHuQ3fmWHt7u3d0dAzqa65YvTn7h9KTSvPRBQdz3KGTmTZ+NE++8Bo/WvUsv3tme9HXxQ1awtnEmc693M1mgsq/k7R7Xlrow8fN5Etn/o/sY40kkUbR797IYWaPunt7xfMUIIpV+kO57eFn+cLydWQu3YUnz+KkI/avao+Bru276O1L8adtbzB/xgStvikidacAUWNqbYnIUFVtgBixfRADpdExIjLcNd0oJjM71cyeMrNOM7us0eURERmpmuoOwsziwHeAdwJdwH+Z2Qp3L791WR0VppZy+xXWbXmNLa/sYtP213nLzEmc+Ob9IvdGEBEZCpoqQABvBTrd/RkAM7sdOBNoigBRuBzBOW9pY8mjXfSl0kUrn/5i3Ut8+Z4ns2seaekCERlqmi3FNB3YlPO4KzzWcLnLEezo7mN3b5pbH36O3b3FwSFXd8rZ3ZvmkmVr2bazu34FFhEZoGYLEFFzivOGWZnZYjPrMLOOrVu31qlYDHiWdWbpAhGRoaLZAkQXMCPncRuwJfcEd7/Z3dvdvX3q1PyZ0LU00FnWWrpARIaaZgsQ/wUcZmaHmFkSOBdY0eAyAdHrE334uJmMaomRKHMVW+OmtYxEZEhqqk5qd+8zswuAXwFx4BZ3X9/gYmVFrU+U2UtBo5hEZLhpqgAB4O73APc0uhylFE6Qy33cfsjkRhVLRGTQNVuKSUREmoQChIiIRFKAEBGRSAoQIiISSQFCREQiDen9IMxsK/Bsg95+CvByg967Gs1ePlAZB0uzl7HZywcjr4wHuXvFmcZDOkA0kpl1VLPhRqM0e/lAZRwszV7GZi8fqIylKMUkIiKRFCBERCSSAsTeu7nRBaig2csHKuNgafYyNnv5QGWMpD4IERGJpDsIERGJpABRgZnNMLMHzOwJM1tvZp8Kj08ys3vNbGP4cWITlDVuZn8ws7vCx4eY2aqwjD8Jl1BvZPkmmNlSM3syvJ7HNdN1NLNPhz/jdWb2YzMb1ehraGa3mNlLZrYu51jkNbPAt8ys08zWmtnRDSzj9eHPea2Z/czMJuQ8d3lYxqfM7K8bVcac5y4yMzezKeHjul/HUuUzs0+G12m9mV2Xc7wu11ABorI+4LPu/hfAscAnzGw2cBlwn7sfBtwXPm60TwFP5Dy+Fvh6WMbtwMcaUqo9vgn80t2PAOYRlLUprqOZTQcuBNrdfS7BcvPn0vhr+APg1IJjpa7ZacBh4b/FwE0NLOO9wFx3PxJ4GrgcIPzbOReYE37NP5tZvEFlxMxmAO8Enss53IjrWFQ+MzsJOBM40t3nAF8Nj9fvGrq7/vXjH7Cc4BfqKeDA8NiBwFMNLlcbQWXxDuAugu1bXwYS4fPHAb9qYPn2Bf5I2O+Vc7wpriN79kOfRLAM/l3AXzfDNQQOBtZVumbAvwAfiDqv3mUseO69wG3h55cDl+c89yvguEaVEVhK0Fj5EzClkdcx4ue8BDgl4ry6XUPdQfSDmR0MHAWsAvZ39+cBwo/7Na5kAHwDuATI7Is6GXjF3fvCx10ElWCjvAnYCvxrmAb7npmNpUmuo7tvJmihPQc8D7wKPEpzXcOMUtcsE+QymqW8HwV+EX7eNGU0s0XAZndfU/BUs5TxcOBtYYrzP83sL8PjdSufAkSVzGwcsAz4e3d/rdHlyWVmC4GX3P3R3MMRpzZyyFoCOBq4yd2PAl6nOdJyAIR5/DOBQ4BpwFiCVEOhZh7212w/c8zsCoI07W2ZQxGn1b2MZjYGuAL4QtTTEccacR0TwESC1PbFwBIzM+pYPgWIKphZC0FwuM3dfxoeftHMDgyfPxB4qVHlAxYAi8zsT8DtBGmmbwATzCyza2AbsKUxxQOCVk6Xu68KHy8lCBjNch1PAf7o7lvdvRf4KXA8zXUNM0pdsy5gRs55DS2vmZ0PLATO8zAXQvOU8VCCxsCa8O+mDXjMzA6gecrYBfzUA48QZAem1LN8ChAVhBH7+8AT7n5DzlMrgPPDz88n6JtoCHe/3N3b3P1ggs6r+939POAB4OzwtEaX8QVgk5m9OTx0MrCB5rmOzwHHmtmY8GeeKV/TXMMcpa7ZCuDD4SicY4FXM6moejOzU4FLgUXu/kbOUyuAc82s1cwOIegIfqTe5XP3x919P3c/OPy76QKODn9Pm+U6/pygsYeZHQ4kCfrE6ncN69E5NJT/AScQ3L6tBVaH/04nyPHfB2wMP05qdFnD8p4I3BV+/qbwF6cTuANobXDZ5gMd4bX8OcHtc9NcR+Bq4ElgHfBvQGujryHwY4I+kV6CSuxjpa4ZQerhO8B/A48TjMhqVBk7CfLkmb+Z7+acf0VYxqeA0xpVxoLn/8SeTuq6X8cS1zAJ/Hv4+/gY8I56X0PNpBYRkUhKMYmISCQFCBERiaQAISIikRQgREQkkgKEiIhEUoAQKWBmF1qw2uxmM7uxhu8zwcz+b87jEy1ciVekGShAiBT7vwRzXa6o8ftMCN9LpCkpQIjkMLPvEkyOW0EwkS/qnHeZ2e/N7DEzuyNcpwsz+5OZXR0ef9zMjgiPTw33bXjMzP7FzJ4N9x74CnComa02s+vDlx9ne/bMuC2c1S3SEAoQIjnc/eME69qcRLD/Q56wYv88wTLMRxPMDP9Mzikvh8dvAi4Kj11FsPzJ0cDPgJnh8cuA/3b3+e5+cXjsKODvgdkEgWrBIH57Iv2SqHyKiOQ4lqDyfihs3CeB3+c8n1nM8VHgf4afn0CwJwLu/kszKwo8OR5x9y4AM1tNsEfAysEqvEh/KECI9I8B97r7B0o83x1+TLHn76s/aaLunM9zX0Ok7pRiEumfh4EFZjYLgn0FwpU2y1kJnBOe/y729G3sAPapVUFFBkoBQqS8j5hZV+YfwQqvHwF+bGZrCQLGERVe42rgXWb2GMEmRM8DO9x9G0Gqal1OJ7VI09BqriI1ZmatQMrd+8zsOIJd9eY3ulwilSi/KVJ7Mwm2i4wBPcDfNrg8IlXRHYSIiERSH4SIiERSgBARkUgKECIiEkkBQkREIilAiIhIJAUIERGJ9P8BWrw/BVSQ/soAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110d4e668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "data.plot.scatter(\"fLength\",\"fWidth\",)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x110dc4668>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x107495fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.hist(\"fAlpha\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}