twp_202_Reading_csv_files.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline\n",
    "%run common.py\n",
    "import tradingWithPython as twp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Working with csv files\n",
    "\n",
    "## Reading csv\n",
    "\n",
    "[ref]: # (csv files - reading)\n",
    "[ref]: # (data io - csv files)\n",
    "\n",
    "[Csv files](http://en.wikipedia.org/wiki/Comma-separated_values) are frequently used for price data storage. This a very  popular method because:\n",
    "\n",
    "* Files are plain text, human readable\n",
    "* Supported by most systems\n",
    "\n",
    "Let's take a look at working with this type of data. \n",
    "\n",
    "To start we need an example csv file. The code below creates a `tmp` subdirectory and writes `test.csv` into it. \n",
    "\n",
    "By the way, we use a *multiline* string notation below. By using triple quotes, the newline characters are included in the string."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "if not os.path.exists('tmp'): \n",
    "    os.mkdir('tmp') # create tmp subdirectory if it does not exist yet\n",
    "\n",
    "#------write data to test.csv     \n",
    "txt = \"\"\"date, colB, colC\n",
    "01-01-2013,2,a\n",
    "02-01-2013,4,b\n",
    "03-01-2013,6,c\n",
    "\"\"\"\n",
    "\n",
    "with open('tmp/test.csv','w') as f: # with ... open ... as  is a robust way to open a file and read/write to it\n",
    "    f.write(txt)\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The easy way\n",
    "\n",
    "[ref]: # (csv files - reading with pandas)\n",
    "\n",
    "The easiest way to load a csv file is to use [`DataFrame.from_csv()`](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.from_csv.html) or [`read_csv()`](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_csv.html) functions from pandas. When it parses data correctly, it is the best way to read and write csv files. However, there are some situations where the result is not exactly what you would expect. \n",
    "Let's read our `test.csv`  , the *date* column should be used as index and converted to `datetime` type."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data types:\n",
      "--------------\n",
      " date     object\n",
      " colB     int64\n",
      " colC    object\n",
      "dtype: object\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>colB</th>\n",
       "      <th>colC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>01-01-2013</td>\n",
       "      <td>2</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>02-01-2013</td>\n",
       "      <td>4</td>\n",
       "      <td>b</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>03-01-2013</td>\n",
       "      <td>6</td>\n",
       "      <td>c</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date   colB  colC\n",
       "0  01-01-2013      2     a\n",
       "1  02-01-2013      4     b\n",
       "2  03-01-2013      6     c"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv('tmp/test.csv') # load DataFrame from csv\n",
    "print('Data types:\\n--------------\\n', df.dtypes) # show column types\n",
    "df # output dataframe in pretty formatting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this case the columns are read correctly, but the date, which we actually want to be the index column is not parsed (it is still a string). An index has also been automatically added. This can be fixed by specifying some parameters (see the [documentation](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_csv.html) for all options):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data types:\n",
      "--------------\n",
      "  colB     int64\n",
      " colC    object\n",
      "dtype: object\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>colB</th>\n",
       "      <th>colC</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>2</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>4</td>\n",
       "      <td>b</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>6</td>\n",
       "      <td>c</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             colB  colC\n",
       "date                   \n",
       "2013-01-01      2     a\n",
       "2013-01-02      4     b\n",
       "2013-01-03      6     c"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('tmp/test.csv',index_col=0, header=0,dayfirst=True,parse_dates=True) # load DataFrame from csv\n",
    "print('Data types:\\n--------------\\n', df.dtypes) # show column types\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2013-01-01 00:00:00')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This looks better! \n",
    "For the (rare) cases where `read_csv()` does not work as you would like there is always a manual way of parsing file line-by-line. \n",
    "The code below is provided mainly for illustrative purposes, showing how to manually parse a text file.\n",
    "\n",
    "### The hard way\n",
    "\n",
    "[ref]: # (csv files - parse manually)\n",
    "\n",
    "The advantage of doing this manually is that you can parse any type of csv file exactly to your liking.This is the method I use for parsing most of the csv data coming from the net (see `yahooFinance.py` in the TWP library).\n",
    "The code below parses a csv in several steps:\n",
    "\n",
    "1. read text lines from file\n",
    "2. parse header data\n",
    "3. fill `data` array with values, converting strings to the right data type\n",
    "4. create a dictionary of header-data pairs\n",
    "5. create a DataFrame\n",
    "\n",
    "\n",
    "First a simple example of parsing a single line:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# example of parsing a single line\n",
    "s = 'foo,bar,24'  # create a string consisting of two substrings and a number\n",
    "l = s.split(',') # split string to a list, using ',' as a separator\n",
    "int(l[2]) # convert third element to an integer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "...and now parsing the csv file we saved earlier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lines: ['date, colB, colC\\n', '01-01-2013,2,a\\n', '02-01-2013,4,b\\n', '03-01-2013,6,c\\n']\n",
      "header: ['date', ' colB', ' colC']\n",
      "data: [[datetime.date(2013, 1, 1), datetime.date(2013, 1, 2), datetime.date(2013, 1, 3)], [2, 4, 6], ['a', 'b', 'c']]\n",
      "dataDict: {' colB': [2, 4, 6], ' colC': ['a', 'b', 'c']}\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>colB</th>\n",
       "      <th>colC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>2</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>4</td>\n",
       "      <td>b</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>6</td>\n",
       "      <td>c</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             colB  colC\n",
       "2013-01-01      2     a\n",
       "2013-01-02      4     b\n",
       "2013-01-03      6     c"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import datetime as dt # shorthand notation for datetime module\n",
    "\n",
    "#---------- read lines from file\n",
    "with open('tmp/test.csv','r') as f: # open file\n",
    "    lines = f.readlines() # read all lines to a list\n",
    "    \n",
    "print('lines:',lines)\n",
    "\n",
    "#----------- parse header\n",
    "header = lines[0].strip().split(',') # strip whitespace, split on ','. \n",
    "print('header:', header)\n",
    "\n",
    "#------------parse data\n",
    "data = [[],[],[]] # create an empty array for each column data\n",
    "\n",
    "for line in lines[1:]:  # start at second line, because first line is header\n",
    "    values = line.strip().split(',') # get the values from a line \n",
    "    data[0].append(dt.datetime.strptime(values[0],\"%d-%m-%Y\").date()) # parse date and append to column 0\n",
    "    data[1].append(int(values[1])) # conert to int and append to column 1\n",
    "    data[2].append(values[2]) # append to column 2, no conversion needed\n",
    "    \n",
    "print('data:', data)\n",
    "\n",
    "#----------create a dictionary with column-data . Column 0 is discarded as it is an index and not data\n",
    "dataDict = dict(list(zip(header[1:],data[1:])))\n",
    "print('dataDict:',dataDict)\n",
    "\n",
    "#-----------finally create the dataframe\n",
    "df =pd.DataFrame(data=dataDict, index=data[0]) # use standard constructor to create a dataframe\n",
    "\n",
    "df\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "### Advanced example\n",
    "We'll use a Put/Call datafrom CBOE as an example. This file can be parsed automatically, but we'll be doing it manually first to learn how to do it.\n",
    "\n",
    "The following piece of code downloads the data using `urllib2` module to a `lines` list. Each element of the list now contains a single line from the online url. Saving to disk is not neccesary. To take a look at the file, just click [this link](http://www.cboe.com/publish/ScheduledTask/MktData/datahouse/totalpc.csv) to download it through your browser.\n",
    "Steps taken to parse this file are similar to the steps taken in the simple example above, but while previous code was written for easy reading, the following code uses a couple of tricks to automate iterating through the coulumns.\n",
    "\n",
    "[ref]: # (getting data - get put/call data from CBOE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import urllib.request, urllib.error, urllib.parse\n",
    "\n",
    "urlStr = 'http://www.cboe.com/publish/ScheduledTask/MktData/datahouse/totalpc.csv'\n",
    "lines = urllib.request.urlopen(urlStr).readlines() # get all data to lines list\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's print several lines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[lineNr 0]b'Volume and Put/Call Ratio data is compiled for the convenience of site visitors and is furnished without responsibility for accuracy and is accepted by the site visitor on the condition that transmission or omissions shall not be made the basis for any claim demand or cause for action.  Historical index volume from November 1 2006 through May 31 2012 is based on cleared volume as reported by the OCC. The volume data reflected after May 31 2012 below is based upon preliminary reported volume rather than cleared volume. The information and data was obtained from sources believed to be reliable but accuracy is not guaranteed. Your use of Cboe Volume and Put/Call Ratio data is subject to the Terms and Conditions of Cboe Websites.,,,,\\r\\n' \n",
      "[lineNr 1]b', PRODUCT: TOTAL,,EXCHANGE: Cboe,\\r\\n' \n",
      "[lineNr 2]b'DATE,CALLS,PUTS,TOTAL,P/C Ratio\\r\\n' \n",
      "[lineNr 3]b'11/1/2006,1401036,1271445,2672481,0.91\\r\\n' \n",
      "[lineNr 4]b'11/2/2006,1348240,1218592,2566832,0.9\\r\\n' \n",
      "[lineNr 5]b'11/3/2006,1317371,1197794,2515165,0.91\\r\\n' \n",
      "[lineNr 6]b'11/6/2006,1260832,960752,2221584,0.76\\r\\n' \n",
      "[lineNr 7]b'11/7/2006,1463685,1158790,2622475,0.79\\r\\n' \n",
      "[lineNr 8]b'11/8/2006,1587664,1232542,2820206,0.78\\r\\n' \n",
      "[lineNr 9]b'11/9/2006,1539157,1288147,2827304,0.84\\r\\n' \n"
     ]
    }
   ],
   "source": [
    "for lineNr, line in enumerate(lines[:10]):\n",
    "    print('[lineNr %i]%s ' % (lineNr,line))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Line 2 contains column names, which now goes to the `header` list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['DATE', 'CALLS', 'PUTS', 'TOTAL', 'P/C Ratio']\n"
     ]
    }
   ],
   "source": [
    "headerLine = 2 # line number\n",
    "header = lines[headerLine].decode().strip().split(',') # strip whitespace from line, and split it on ','\n",
    "\n",
    "print(header)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now create a list that will hold the actual data. It is a list of lists, one for each column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[], [], [], [], []]\n"
     ]
    }
   ],
   "source": [
    "columnData =   [[] for i in range(len(header))] # create empty list for each data column\n",
    "\n",
    "print(columnData)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now the data can be filled by iterating through each line after the header. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CALLS</th>\n",
       "      <th>P/C Ratio</th>\n",
       "      <th>PUTS</th>\n",
       "      <th>TOTAL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-02-06</th>\n",
       "      <td>5.74e+06</td>\n",
       "      <td>0.99</td>\n",
       "      <td>5.67e+06</td>\n",
       "      <td>1.14e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-07</th>\n",
       "      <td>4.06e+06</td>\n",
       "      <td>0.91</td>\n",
       "      <td>3.67e+06</td>\n",
       "      <td>7.73e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-08</th>\n",
       "      <td>3.92e+06</td>\n",
       "      <td>1.14</td>\n",
       "      <td>4.45e+06</td>\n",
       "      <td>8.37e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-09</th>\n",
       "      <td>3.99e+06</td>\n",
       "      <td>1.31</td>\n",
       "      <td>5.24e+06</td>\n",
       "      <td>9.23e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-12</th>\n",
       "      <td>3.57e+06</td>\n",
       "      <td>0.94</td>\n",
       "      <td>3.37e+06</td>\n",
       "      <td>6.94e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-13</th>\n",
       "      <td>3.04e+06</td>\n",
       "      <td>0.96</td>\n",
       "      <td>2.90e+06</td>\n",
       "      <td>5.94e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-14</th>\n",
       "      <td>2.98e+06</td>\n",
       "      <td>1.17</td>\n",
       "      <td>3.48e+06</td>\n",
       "      <td>6.46e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-15</th>\n",
       "      <td>3.18e+06</td>\n",
       "      <td>0.92</td>\n",
       "      <td>2.93e+06</td>\n",
       "      <td>6.11e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-16</th>\n",
       "      <td>3.31e+06</td>\n",
       "      <td>0.93</td>\n",
       "      <td>3.07e+06</td>\n",
       "      <td>6.38e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-20</th>\n",
       "      <td>2.40e+06</td>\n",
       "      <td>0.86</td>\n",
       "      <td>2.06e+06</td>\n",
       "      <td>4.45e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               CALLS  P/C Ratio      PUTS     TOTAL\n",
       "2018-02-06  5.74e+06       0.99  5.67e+06  1.14e+07\n",
       "2018-02-07  4.06e+06       0.91  3.67e+06  7.73e+06\n",
       "2018-02-08  3.92e+06       1.14  4.45e+06  8.37e+06\n",
       "2018-02-09  3.99e+06       1.31  5.24e+06  9.23e+06\n",
       "2018-02-12  3.57e+06       0.94  3.37e+06  6.94e+06\n",
       "2018-02-13  3.04e+06       0.96  2.90e+06  5.94e+06\n",
       "2018-02-14  2.98e+06       1.17  3.48e+06  6.46e+06\n",
       "2018-02-15  3.18e+06       0.92  2.93e+06  6.11e+06\n",
       "2018-02-16  3.31e+06       0.93  3.07e+06  6.38e+06\n",
       "2018-02-20  2.40e+06       0.86  2.06e+06  4.45e+06"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import datetime as dt # shorthand notation for datetime module\n",
    "import pandas as pd\n",
    "\n",
    "for line in lines[(headerLine+1):]: # for each line starting after header line\n",
    "        fields = line.decode().rstrip().split(',') # split line to data fields. Each field is a string\n",
    "        # first field is the date, it is treated separately because it must be converted to\n",
    "        #datetime format. String from first field is appended to data[0]\n",
    "        columnData[0].append(dt.datetime.strptime(fields[0],'%m/%d/%Y').date()) \n",
    "        \n",
    "        # the rest of the fields are converted to floats in a loop\n",
    "        for i,field  in enumerate(fields[1:]): # for all fields from 1. i is the field number-1\n",
    "            columnData[i+1].append(float(field)) # convert data to float and append it to a column data\n",
    "\n",
    "d =   dict(list(zip(header[1:],columnData[1:]))) # create a dictionary with header-columnData pairs, exclude column 0       \n",
    "            \n",
    "df = pd.DataFrame(d, index = pd.Index(columnData[0])) #create DataFrame with index from column 0\n",
    "\n",
    "df.tail(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### And the very easy way again\n",
    "`read_csv` can accept a `buffer` as input and parse it automatically. So getting the data from the net and parsing it can be done on one single line!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CALLS</th>\n",
       "      <th>PUTS</th>\n",
       "      <th>TOTAL</th>\n",
       "      <th>P/C Ratio</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DATE</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2006-11-01</th>\n",
       "      <td>1401036</td>\n",
       "      <td>1271445</td>\n",
       "      <td>2672481</td>\n",
       "      <td>0.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-11-02</th>\n",
       "      <td>1348240</td>\n",
       "      <td>1218592</td>\n",
       "      <td>2566832</td>\n",
       "      <td>0.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-11-03</th>\n",
       "      <td>1317371</td>\n",
       "      <td>1197794</td>\n",
       "      <td>2515165</td>\n",
       "      <td>0.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-11-06</th>\n",
       "      <td>1260832</td>\n",
       "      <td>960752</td>\n",
       "      <td>2221584</td>\n",
       "      <td>0.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-11-07</th>\n",
       "      <td>1463685</td>\n",
       "      <td>1158790</td>\n",
       "      <td>2622475</td>\n",
       "      <td>0.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-13</th>\n",
       "      <td>3039673</td>\n",
       "      <td>2903417</td>\n",
       "      <td>5943090</td>\n",
       "      <td>0.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-14</th>\n",
       "      <td>2981929</td>\n",
       "      <td>3481044</td>\n",
       "      <td>6462973</td>\n",
       "      <td>1.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-15</th>\n",
       "      <td>3179289</td>\n",
       "      <td>2929984</td>\n",
       "      <td>6109273</td>\n",
       "      <td>0.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-16</th>\n",
       "      <td>3307999</td>\n",
       "      <td>3067685</td>\n",
       "      <td>6375684</td>\n",
       "      <td>0.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-20</th>\n",
       "      <td>2396334</td>\n",
       "      <td>2055649</td>\n",
       "      <td>4451983</td>\n",
       "      <td>0.86</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2844 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              CALLS     PUTS    TOTAL  P/C Ratio\n",
       "DATE                                            \n",
       "2006-11-01  1401036  1271445  2672481       0.91\n",
       "2006-11-02  1348240  1218592  2566832       0.90\n",
       "2006-11-03  1317371  1197794  2515165       0.91\n",
       "2006-11-06  1260832   960752  2221584       0.76\n",
       "2006-11-07  1463685  1158790  2622475       0.79\n",
       "...             ...      ...      ...        ...\n",
       "2018-02-13  3039673  2903417  5943090       0.96\n",
       "2018-02-14  2981929  3481044  6462973       1.17\n",
       "2018-02-15  3179289  2929984  6109273       0.92\n",
       "2018-02-16  3307999  3067685  6375684       0.93\n",
       "2018-02-20  2396334  2055649  4451983       0.86\n",
       "\n",
       "[2844 rows x 4 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(urllib.request.urlopen(urlStr), header = 2, index_col=0,parse_dates=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CALLS</th>\n",
       "      <th>PUTS</th>\n",
       "      <th>TOTAL</th>\n",
       "      <th>P/C Ratio</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DATE</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-02-13</th>\n",
       "      <td>3039673</td>\n",
       "      <td>2903417</td>\n",
       "      <td>5943090</td>\n",
       "      <td>0.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-14</th>\n",
       "      <td>2981929</td>\n",
       "      <td>3481044</td>\n",
       "      <td>6462973</td>\n",
       "      <td>1.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-15</th>\n",
       "      <td>3179289</td>\n",
       "      <td>2929984</td>\n",
       "      <td>6109273</td>\n",
       "      <td>0.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-16</th>\n",
       "      <td>3307999</td>\n",
       "      <td>3067685</td>\n",
       "      <td>6375684</td>\n",
       "      <td>0.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-20</th>\n",
       "      <td>2396334</td>\n",
       "      <td>2055649</td>\n",
       "      <td>4451983</td>\n",
       "      <td>0.86</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              CALLS     PUTS    TOTAL  P/C Ratio\n",
       "DATE                                            \n",
       "2018-02-13  3039673  2903417  5943090       0.96\n",
       "2018-02-14  2981929  3481044  6462973       1.17\n",
       "2018-02-15  3179289  2929984  6109273       0.92\n",
       "2018-02-16  3307999  3067685  6375684       0.93\n",
       "2018-02-20  2396334  2055649  4451983       0.86"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAecAAAFlCAYAAADRdSCHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nac5CCCFEhPkPc3TZi9RDnZAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9f4ad653c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['P/C Ratio'].plot()\n",
    "df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Writing csv files\n",
    "\n",
    "[ref]: # (csv files - writing)\n",
    "\n",
    "Normally you would not need to write csv files manually.  `DataFrame.to_csv()` takes care of this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CALLS</th>\n",
       "      <th>PUTS</th>\n",
       "      <th>TOTAL</th>\n",
       "      <th>P/C Ratio</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DATE</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2006-11-01</th>\n",
       "      <td>1401036</td>\n",
       "      <td>1271445</td>\n",
       "      <td>2672481</td>\n",
       "      <td>0.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-11-02</th>\n",
       "      <td>1348240</td>\n",
       "      <td>1218592</td>\n",
       "      <td>2566832</td>\n",
       "      <td>0.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-11-03</th>\n",
       "      <td>1317371</td>\n",
       "      <td>1197794</td>\n",
       "      <td>2515165</td>\n",
       "      <td>0.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-11-06</th>\n",
       "      <td>1260832</td>\n",
       "      <td>960752</td>\n",
       "      <td>2221584</td>\n",
       "      <td>0.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-11-07</th>\n",
       "      <td>1463685</td>\n",
       "      <td>1158790</td>\n",
       "      <td>2622475</td>\n",
       "      <td>0.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-13</th>\n",
       "      <td>3039673</td>\n",
       "      <td>2903417</td>\n",
       "      <td>5943090</td>\n",
       "      <td>0.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-14</th>\n",
       "      <td>2981929</td>\n",
       "      <td>3481044</td>\n",
       "      <td>6462973</td>\n",
       "      <td>1.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-15</th>\n",
       "      <td>3179289</td>\n",
       "      <td>2929984</td>\n",
       "      <td>6109273</td>\n",
       "      <td>0.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-16</th>\n",
       "      <td>3307999</td>\n",
       "      <td>3067685</td>\n",
       "      <td>6375684</td>\n",
       "      <td>0.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-20</th>\n",
       "      <td>2396334</td>\n",
       "      <td>2055649</td>\n",
       "      <td>4451983</td>\n",
       "      <td>0.86</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2844 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              CALLS     PUTS    TOTAL  P/C Ratio\n",
       "DATE                                            \n",
       "2006-11-01  1401036  1271445  2672481       0.91\n",
       "2006-11-02  1348240  1218592  2566832       0.90\n",
       "2006-11-03  1317371  1197794  2515165       0.91\n",
       "2006-11-06  1260832   960752  2221584       0.76\n",
       "2006-11-07  1463685  1158790  2622475       0.79\n",
       "...             ...      ...      ...        ...\n",
       "2018-02-13  3039673  2903417  5943090       0.96\n",
       "2018-02-14  2981929  3481044  6462973       1.17\n",
       "2018-02-15  3179289  2929984  6109273       0.92\n",
       "2018-02-16  3307999  3067685  6375684       0.93\n",
       "2018-02-20  2396334  2055649  4451983       0.86\n",
       "\n",
       "[2844 rows x 4 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.to_csv('tmp/pc_ratio.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A big advantage is that the files written by `DataFrame.to_csv()`, will also be readable by `DataFrame.read_csv()` and `read_csv()` functions\n",
    "\n",
    "<div class=\"alert alert-info\">\n",
    "**note:**\n",
    "\n",
    "  `DataFrame.read_csv()` will be depreciated in the future. It is better to use `read_csv()`.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CALLS</th>\n",
       "      <th>PUTS</th>\n",
       "      <th>TOTAL</th>\n",
       "      <th>P/C Ratio</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DATE</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-02-13</th>\n",
       "      <td>3039673</td>\n",
       "      <td>2903417</td>\n",
       "      <td>5943090</td>\n",
       "      <td>0.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-14</th>\n",
       "      <td>2981929</td>\n",
       "      <td>3481044</td>\n",
       "      <td>6462973</td>\n",
       "      <td>1.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-15</th>\n",
       "      <td>3179289</td>\n",
       "      <td>2929984</td>\n",
       "      <td>6109273</td>\n",
       "      <td>0.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-16</th>\n",
       "      <td>3307999</td>\n",
       "      <td>3067685</td>\n",
       "      <td>6375684</td>\n",
       "      <td>0.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-20</th>\n",
       "      <td>2396334</td>\n",
       "      <td>2055649</td>\n",
       "      <td>4451983</td>\n",
       "      <td>0.86</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              CALLS     PUTS    TOTAL  P/C Ratio\n",
       "DATE                                            \n",
       "2018-02-13  3039673  2903417  5943090       0.96\n",
       "2018-02-14  2981929  3481044  6462973       1.17\n",
       "2018-02-15  3179289  2929984  6109273       0.92\n",
       "2018-02-16  3307999  3067685  6375684       0.93\n",
       "2018-02-20  2396334  2055649  4451983       0.86"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = pd.read_csv('tmp/pc_ratio.csv', index_col=0,parse_dates=True)\n",
    "df2.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2006-11-01', '2006-11-02', '2006-11-03', '2006-11-06',\n",
       "               '2006-11-07', '2006-11-08', '2006-11-09', '2006-11-10',\n",
       "               '2006-11-13', '2006-11-14',\n",
       "               ...\n",
       "               '2018-02-06', '2018-02-07', '2018-02-08', '2018-02-09',\n",
       "               '2018-02-12', '2018-02-13', '2018-02-14', '2018-02-15',\n",
       "               '2018-02-16', '2018-02-20'],\n",
       "              dtype='datetime64[ns]', name='DATE', length=2844, freq=None)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jev/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:1: FutureWarning: from_csv is deprecated. Please use read_csv(...) instead. Note that some of the default arguments are different, so please refer to the documentation for from_csv when changing your function calls\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CALLS</th>\n",
       "      <th>PUTS</th>\n",
       "      <th>TOTAL</th>\n",
       "      <th>P/C Ratio</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DATE</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2006-11-01</th>\n",
       "      <td>1401036</td>\n",
       "      <td>1271445</td>\n",
       "      <td>2672481</td>\n",
       "      <td>0.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-11-02</th>\n",
       "      <td>1348240</td>\n",
       "      <td>1218592</td>\n",
       "      <td>2566832</td>\n",
       "      <td>0.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-11-03</th>\n",
       "      <td>1317371</td>\n",
       "      <td>1197794</td>\n",
       "      <td>2515165</td>\n",
       "      <td>0.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-11-06</th>\n",
       "      <td>1260832</td>\n",
       "      <td>960752</td>\n",
       "      <td>2221584</td>\n",
       "      <td>0.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-11-07</th>\n",
       "      <td>1463685</td>\n",
       "      <td>1158790</td>\n",
       "      <td>2622475</td>\n",
       "      <td>0.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-13</th>\n",
       "      <td>3039673</td>\n",
       "      <td>2903417</td>\n",
       "      <td>5943090</td>\n",
       "      <td>0.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-14</th>\n",
       "      <td>2981929</td>\n",
       "      <td>3481044</td>\n",
       "      <td>6462973</td>\n",
       "      <td>1.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-15</th>\n",
       "      <td>3179289</td>\n",
       "      <td>2929984</td>\n",
       "      <td>6109273</td>\n",
       "      <td>0.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-16</th>\n",
       "      <td>3307999</td>\n",
       "      <td>3067685</td>\n",
       "      <td>6375684</td>\n",
       "      <td>0.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-20</th>\n",
       "      <td>2396334</td>\n",
       "      <td>2055649</td>\n",
       "      <td>4451983</td>\n",
       "      <td>0.86</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2844 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              CALLS     PUTS    TOTAL  P/C Ratio\n",
       "DATE                                            \n",
       "2006-11-01  1401036  1271445  2672481       0.91\n",
       "2006-11-02  1348240  1218592  2566832       0.90\n",
       "2006-11-03  1317371  1197794  2515165       0.91\n",
       "2006-11-06  1260832   960752  2221584       0.76\n",
       "2006-11-07  1463685  1158790  2622475       0.79\n",
       "...             ...      ...      ...        ...\n",
       "2018-02-13  3039673  2903417  5943090       0.96\n",
       "2018-02-14  2981929  3481044  6462973       1.17\n",
       "2018-02-15  3179289  2929984  6109273       0.92\n",
       "2018-02-16  3307999  3067685  6375684       0.93\n",
       "2018-02-20  2396334  2055649  4451983       0.86\n",
       "\n",
       "[2844 rows x 4 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3 = pd.DataFrame.from_csv('tmp/pc_ratio.csv') # this works, but produces a depreciation warning. Not recommended.\n",
    "df3"
   ]
  }
 ],
 "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": "3.6.2"
  },
  "toc": {
   "nav_menu": {},
   "number_sections": false,
   "sideBar": true,
   "skip_h1_title": false,
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": "block",
   "toc_window_display": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}