diff --git a/Exercise6.ipynb b/Exercise6.ipynb
new file mode 100644
index 0000000..a15cb4d
--- /dev/null
+++ b/Exercise6.ipynb
@@ -0,0 +1,846 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "AttributeError",
+     "evalue": "type object 'file' has no attribute 'txt'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-3-22314214f613>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfile\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfile\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtxt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mfirstLine\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfile\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreadline\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mfile\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclose\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mAttributeError\u001b[0m: type object 'file' has no attribute 'txt'"
+     ]
+    }
+   ],
+   "source": [
+    "file=open(file.txt)\n",
+    "firstLine=file.readline()\n",
+    "file=(close)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "AttributeError",
+     "evalue": "type object 'file' has no attribute 'txt'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-4-336be0464280>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfile\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtxt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mAttributeError\u001b[0m: type object 'file' has no attribute 'txt'"
+     ]
+    }
+   ],
+   "source": [
+    "print(file.txt)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# %load file.txt\n",
+    "one\n",
+    "two\n",
+    "three\n",
+    "four\n",
+    "five\n",
+    "six\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "AttributeError",
+     "evalue": "type object 'file' has no attribute 'txt'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-6-336be0464280>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfile\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtxt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mAttributeError\u001b[0m: type object 'file' has no attribute 'txt'"
+     ]
+    }
+   ],
+   "source": [
+    "print(file.txt)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# %load file.txt\n",
+    "one\n",
+    "two\n",
+    "three\n",
+    "four\n",
+    "five\n",
+    "six\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "None\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "SyntaxError",
+     "evalue": "invalid syntax (<ipython-input-9-8dd276e2b8bc>, line 1)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-9-8dd276e2b8bc>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    x=[%load file.txt]\u001b[0m\n\u001b[0m       ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
+     ]
+    }
+   ],
+   "source": [
+    "x=[%load file.txt]\n",
+    "firstLine=x.readline(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "AttributeError",
+     "evalue": "type object 'file' has no attribute 'txt'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-10-ee8fd9cf0762>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mfile\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtxt\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mfirstLine\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreadline\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mAttributeError\u001b[0m: type object 'file' has no attribute 'txt'"
+     ]
+    }
+   ],
+   "source": [
+    "x=[file.txt]\n",
+    "firstLine=x.readline(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "IOError",
+     "evalue": "File iris.csv does not exist",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mIOError\u001b[0m                                   Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-12-763dc876adf7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'iris.csv'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mline\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/anaconda2/lib/python2.7/site-packages/pandas/io/parsers.pyc\u001b[0m in \u001b[0;36mparser_f\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, escapechar, comment, encoding, dialect, tupleize_cols, error_bad_lines, warn_bad_lines, skipfooter, doublequote, delim_whitespace, low_memory, memory_map, float_precision)\u001b[0m\n\u001b[1;32m    676\u001b[0m                     skip_blank_lines=skip_blank_lines)\n\u001b[1;32m    677\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 678\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    679\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    680\u001b[0m     \u001b[0mparser_f\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/anaconda2/lib/python2.7/site-packages/pandas/io/parsers.pyc\u001b[0m in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m    438\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    439\u001b[0m     \u001b[0;31m# Create the parser.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 440\u001b[0;31m     \u001b[0mparser\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    441\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    442\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/anaconda2/lib/python2.7/site-packages/pandas/io/parsers.pyc\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m    785\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'has_index_names'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'has_index_names'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    786\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 787\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    788\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    789\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/anaconda2/lib/python2.7/site-packages/pandas/io/parsers.pyc\u001b[0m in \u001b[0;36m_make_engine\u001b[0;34m(self, engine)\u001b[0m\n\u001b[1;32m   1012\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mengine\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'c'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1013\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'c'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1014\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mCParserWrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1015\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1016\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'python'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/anaconda2/lib/python2.7/site-packages/pandas/io/parsers.pyc\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, src, **kwds)\u001b[0m\n\u001b[1;32m   1706\u001b[0m         \u001b[0mkwds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'usecols'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0musecols\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1707\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1708\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reader\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mparsers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTextReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1709\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1710\u001b[0m         \u001b[0mpassed_names\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnames\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader.__cinit__\u001b[0;34m()\u001b[0m\n",
+      "\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader._setup_parser_source\u001b[0;34m()\u001b[0m\n",
+      "\u001b[0;31mIOError\u001b[0m: File iris.csv does not exist"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas\n",
+    "df = pandas.read_csv('iris.csv')\n",
+    "line = 3\n",
+    "df.head(line)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "IOError",
+     "evalue": "File iris.csv does not exist",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mIOError\u001b[0m                                   Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-13-4ee4677351e2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'iris.csv'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0;32mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/anaconda2/lib/python2.7/site-packages/pandas/io/parsers.pyc\u001b[0m in \u001b[0;36mparser_f\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, escapechar, comment, encoding, dialect, tupleize_cols, error_bad_lines, warn_bad_lines, skipfooter, doublequote, delim_whitespace, low_memory, memory_map, float_precision)\u001b[0m\n\u001b[1;32m    676\u001b[0m                     skip_blank_lines=skip_blank_lines)\n\u001b[1;32m    677\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 678\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    679\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    680\u001b[0m     \u001b[0mparser_f\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/anaconda2/lib/python2.7/site-packages/pandas/io/parsers.pyc\u001b[0m in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m    438\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    439\u001b[0m     \u001b[0;31m# Create the parser.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 440\u001b[0;31m     \u001b[0mparser\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    441\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    442\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/anaconda2/lib/python2.7/site-packages/pandas/io/parsers.pyc\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m    785\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'has_index_names'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'has_index_names'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    786\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 787\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    788\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    789\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/anaconda2/lib/python2.7/site-packages/pandas/io/parsers.pyc\u001b[0m in \u001b[0;36m_make_engine\u001b[0;34m(self, engine)\u001b[0m\n\u001b[1;32m   1012\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mengine\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'c'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1013\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'c'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1014\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mCParserWrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1015\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1016\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'python'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/anaconda2/lib/python2.7/site-packages/pandas/io/parsers.pyc\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, src, **kwds)\u001b[0m\n\u001b[1;32m   1706\u001b[0m         \u001b[0mkwds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'usecols'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0musecols\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1707\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1708\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reader\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mparsers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTextReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1709\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1710\u001b[0m         \u001b[0mpassed_names\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnames\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader.__cinit__\u001b[0;34m()\u001b[0m\n",
+      "\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader._setup_parser_source\u001b[0;34m()\u001b[0m\n",
+      "\u001b[0;31mIOError\u001b[0m: File iris.csv does not exist"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas\n",
+    "df = pandas.read_csv('iris.csv')\n",
+    "print(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>Sepal.Length</th>\n",
+       "      <th>Sepal.Width</th>\n",
+       "      <th>Petal.Length</th>\n",
+       "      <th>Petal.Width</th>\n",
+       "      <th>Species</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>5.1</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>setosa</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4.9</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>setosa</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>4.7</td>\n",
+       "      <td>3.2</td>\n",
+       "      <td>1.3</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>setosa</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Sepal.Length  Sepal.Width  Petal.Length  Petal.Width Species\n",
+       "0           5.1          3.5           1.4          0.2  setosa\n",
+       "1           4.9          3.0           1.4          0.2  setosa\n",
+       "2           4.7          3.2           1.3          0.2  setosa"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas\n",
+    "df = pandas.read_csv('iris.csv')\n",
+    "line = 3\n",
+    "df.head(line)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'irish' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-19-cafbe128b8c2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'iris.csv'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0;32mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mirish\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcsv\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m149\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m150\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m5\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m6\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m: name 'irish' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas\n",
+    "df = pandas.read_csv('iris.csv')\n",
+    "print(irish.csv[149:150, 5:6])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'iris' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-21-aacb3071f43e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miris\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcsv\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m149\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m150\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m5\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m6\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m: name 'iris' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "print(iris.csv[149:150, 5:6])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "unhashable type",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-23-49b89a87e51f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'iris.csv'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0;32mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m149.\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m150.\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m5.\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m6.\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m/anaconda2/lib/python2.7/site-packages/pandas/core/frame.pyc\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   2683\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2684\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2685\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_column\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2686\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2687\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_getitem_column\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/anaconda2/lib/python2.7/site-packages/pandas/core/frame.pyc\u001b[0m in \u001b[0;36m_getitem_column\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   2690\u001b[0m         \u001b[0;31m# get column\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2691\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_unique\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2692\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_item_cache\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2693\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2694\u001b[0m         \u001b[0;31m# duplicate columns & possible reduce dimensionality\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/anaconda2/lib/python2.7/site-packages/pandas/core/generic.pyc\u001b[0m in \u001b[0;36m_get_item_cache\u001b[0;34m(self, item)\u001b[0m\n\u001b[1;32m   2482\u001b[0m         \u001b[0;34m\"\"\"Return the cached item, item represents a label indexer.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2483\u001b[0m         \u001b[0mcache\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_item_cache\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2484\u001b[0;31m         \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcache\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2485\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mres\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2486\u001b[0m             \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mTypeError\u001b[0m: unhashable type"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas\n",
+    "df = pandas.read_csv('iris.csv')\n",
+    "print(df[149.:150., 5.:6.])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "unhashable type",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-25-057d3ec8b218>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'iris.csv'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0;32mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m148\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m149\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m4\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m/anaconda2/lib/python2.7/site-packages/pandas/core/frame.pyc\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   2683\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2684\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2685\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_column\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2686\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2687\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_getitem_column\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/anaconda2/lib/python2.7/site-packages/pandas/core/frame.pyc\u001b[0m in \u001b[0;36m_getitem_column\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   2690\u001b[0m         \u001b[0;31m# get column\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2691\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_unique\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2692\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_item_cache\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2693\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2694\u001b[0m         \u001b[0;31m# duplicate columns & possible reduce dimensionality\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/anaconda2/lib/python2.7/site-packages/pandas/core/generic.pyc\u001b[0m in \u001b[0;36m_get_item_cache\u001b[0;34m(self, item)\u001b[0m\n\u001b[1;32m   2482\u001b[0m         \u001b[0;34m\"\"\"Return the cached item, item represents a label indexer.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2483\u001b[0m         \u001b[0mcache\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_item_cache\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2484\u001b[0;31m         \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcache\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2485\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mres\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2486\u001b[0m             \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mTypeError\u001b[0m: unhashable type"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas\n",
+    "df = pandas.read_csv('iris.csv')\n",
+    "print(df[148:151, 4:6])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "No axis named setosa for object type <class 'pandas.core.frame.DataFrame'>",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-28-58c79c3c2f4b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'iris.csv'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0;32mprint\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcount\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'setosa'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m/anaconda2/lib/python2.7/site-packages/pandas/core/frame.pyc\u001b[0m in \u001b[0;36mcount\u001b[0;34m(self, axis, level, numeric_only)\u001b[0m\n\u001b[1;32m   6766\u001b[0m         \u001b[0mMyla\u001b[0m      \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   6767\u001b[0m         \"\"\"\n\u001b[0;32m-> 6768\u001b[0;31m         \u001b[0maxis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_axis_number\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   6769\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mlevel\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   6770\u001b[0m             return self._count_level(level, axis=axis,\n",
+      "\u001b[0;32m/anaconda2/lib/python2.7/site-packages/pandas/core/generic.pyc\u001b[0m in \u001b[0;36m_get_axis_number\u001b[0;34m(self, axis)\u001b[0m\n\u001b[1;32m    372\u001b[0m                 \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    373\u001b[0m         raise ValueError('No axis named {0} for object type {1}'\n\u001b[0;32m--> 374\u001b[0;31m                          .format(axis, type(self)))\n\u001b[0m\u001b[1;32m    375\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    376\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_get_axis_name\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mValueError\u001b[0m: No axis named setosa for object type <class 'pandas.core.frame.DataFrame'>"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas\n",
+    "df = pandas.read_csv('iris.csv')\n",
+    "print[df.count('setosa')]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "setosa        50\n",
+      "versicolor    50\n",
+      "virginica     50\n",
+      "Name: Species, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas\n",
+    "df = pandas.read_csv('iris.csv')\n",
+    "counts = df['Species'].value_counts()\n",
+    "print counts"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas\n",
+    "df = pandas.read_csv('iris.csv')\n",
+    "maximum = df['Petal.Length'].max()\n",
+    "minimum = df['Petal.Length'].min()\n",
+    "mean = df['Petal.Length'].mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "       Species\n",
+      "148  virginica\n",
+      "149  virginica\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas\n",
+    "df = pandas.read_csv('iris.csv')\n",
+    "print(df.iloc[148:151, 4:6])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas\n",
+    "df = pandas.read_csv('iris.csv')\n",
+    "virginica = df.iloc[100:151, ]\n",
+    "maximum = virginica['Petal.Length'].max()\n",
+    "minimum = virginica['Petal.Length'].min()\n",
+    "mean = virginica['Petal.Length'].mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "     Sepal.Length  Sepal.Width  Petal.Length  Petal.Width    Species\n",
+      "100           6.3          3.3           6.0          2.5  virginica\n",
+      "101           5.8          2.7           5.1          1.9  virginica\n",
+      "102           7.1          3.0           5.9          2.1  virginica\n",
+      "103           6.3          2.9           5.6          1.8  virginica\n",
+      "104           6.5          3.0           5.8          2.2  virginica\n",
+      "105           7.6          3.0           6.6          2.1  virginica\n",
+      "106           4.9          2.5           4.5          1.7  virginica\n",
+      "107           7.3          2.9           6.3          1.8  virginica\n",
+      "108           6.7          2.5           5.8          1.8  virginica\n",
+      "109           7.2          3.6           6.1          2.5  virginica\n",
+      "110           6.5          3.2           5.1          2.0  virginica\n",
+      "111           6.4          2.7           5.3          1.9  virginica\n",
+      "112           6.8          3.0           5.5          2.1  virginica\n",
+      "113           5.7          2.5           5.0          2.0  virginica\n",
+      "114           5.8          2.8           5.1          2.4  virginica\n",
+      "115           6.4          3.2           5.3          2.3  virginica\n",
+      "116           6.5          3.0           5.5          1.8  virginica\n",
+      "117           7.7          3.8           6.7          2.2  virginica\n",
+      "118           7.7          2.6           6.9          2.3  virginica\n",
+      "119           6.0          2.2           5.0          1.5  virginica\n",
+      "120           6.9          3.2           5.7          2.3  virginica\n",
+      "121           5.6          2.8           4.9          2.0  virginica\n",
+      "122           7.7          2.8           6.7          2.0  virginica\n",
+      "123           6.3          2.7           4.9          1.8  virginica\n",
+      "124           6.7          3.3           5.7          2.1  virginica\n",
+      "125           7.2          3.2           6.0          1.8  virginica\n",
+      "126           6.2          2.8           4.8          1.8  virginica\n",
+      "127           6.1          3.0           4.9          1.8  virginica\n",
+      "128           6.4          2.8           5.6          2.1  virginica\n",
+      "129           7.2          3.0           5.8          1.6  virginica\n",
+      "130           7.4          2.8           6.1          1.9  virginica\n",
+      "131           7.9          3.8           6.4          2.0  virginica\n",
+      "132           6.4          2.8           5.6          2.2  virginica\n",
+      "133           6.3          2.8           5.1          1.5  virginica\n",
+      "134           6.1          2.6           5.6          1.4  virginica\n",
+      "135           7.7          3.0           6.1          2.3  virginica\n",
+      "136           6.3          3.4           5.6          2.4  virginica\n",
+      "137           6.4          3.1           5.5          1.8  virginica\n",
+      "138           6.0          3.0           4.8          1.8  virginica\n",
+      "139           6.9          3.1           5.4          2.1  virginica\n",
+      "140           6.7          3.1           5.6          2.4  virginica\n",
+      "141           6.9          3.1           5.1          2.3  virginica\n",
+      "142           5.8          2.7           5.1          1.9  virginica\n",
+      "143           6.8          3.2           5.9          2.3  virginica\n",
+      "144           6.7          3.3           5.7          2.5  virginica\n",
+      "145           6.7          3.0           5.2          2.3  virginica\n",
+      "146           6.3          2.5           5.0          1.9  virginica\n",
+      "147           6.5          3.0           5.2          2.0  virginica\n",
+      "148           6.2          3.4           5.4          2.3  virginica\n",
+      "149           5.9          3.0           5.1          1.8  virginica\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas\n",
+    "df = pandas.read_csv('iris.csv')\n",
+    "virginica = df.iloc[100:151, ]\n",
+    "print(virginica)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(6.9, 4.5, 5.552)\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas\n",
+    "df = pandas.read_csv('iris.csv')\n",
+    "virginica = df.iloc[100:151, ]\n",
+    "maximum = virginica['Petal.Length'].max()\n",
+    "minimum = virginica['Petal.Length'].min()\n",
+    "mean = virginica['Petal.Length'].mean()\n",
+    "print(maximum, minimum, mean)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0      5.1\n",
+      "1      4.9\n",
+      "2      4.7\n",
+      "3      4.6\n",
+      "4      5.0\n",
+      "5      5.4\n",
+      "6      4.6\n",
+      "7      5.0\n",
+      "8      4.4\n",
+      "9      4.9\n",
+      "10     5.4\n",
+      "11     4.8\n",
+      "12     4.8\n",
+      "13     4.3\n",
+      "14     5.8\n",
+      "15     5.7\n",
+      "16     5.4\n",
+      "17     5.1\n",
+      "18     5.7\n",
+      "19     5.1\n",
+      "20     5.4\n",
+      "21     5.1\n",
+      "22     4.6\n",
+      "23     5.1\n",
+      "24     4.8\n",
+      "25     5.0\n",
+      "26     5.0\n",
+      "27     5.2\n",
+      "28     5.2\n",
+      "29     4.7\n",
+      "      ... \n",
+      "120    6.9\n",
+      "121    5.6\n",
+      "122    7.7\n",
+      "123    6.3\n",
+      "124    6.7\n",
+      "125    7.2\n",
+      "126    6.2\n",
+      "127    6.1\n",
+      "128    6.4\n",
+      "129    7.2\n",
+      "130    7.4\n",
+      "131    7.9\n",
+      "132    6.4\n",
+      "133    6.3\n",
+      "134    6.1\n",
+      "135    7.7\n",
+      "136    6.3\n",
+      "137    6.4\n",
+      "138    6.0\n",
+      "139    6.9\n",
+      "140    6.7\n",
+      "141    6.9\n",
+      "142    5.8\n",
+      "143    6.8\n",
+      "144    6.7\n",
+      "145    6.7\n",
+      "146    6.3\n",
+      "147    6.5\n",
+      "148    6.2\n",
+      "149    5.9\n",
+      "Name: Sepal.Length, Length: 150, dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas\n",
+    "df = pandas.read_csv('iris.csv')\n",
+    "#want the whole row for values greater than 3.5 in column 2\n",
+    "print(Width)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "     Sepal.Length  Sepal.Width  Petal.Length  Petal.Width    Species\n",
+      "4             5.0          3.6           1.4          0.2     setosa\n",
+      "5             5.4          3.9           1.7          0.4     setosa\n",
+      "10            5.4          3.7           1.5          0.2     setosa\n",
+      "14            5.8          4.0           1.2          0.2     setosa\n",
+      "15            5.7          4.4           1.5          0.4     setosa\n",
+      "16            5.4          3.9           1.3          0.4     setosa\n",
+      "18            5.7          3.8           1.7          0.3     setosa\n",
+      "19            5.1          3.8           1.5          0.3     setosa\n",
+      "21            5.1          3.7           1.5          0.4     setosa\n",
+      "22            4.6          3.6           1.0          0.2     setosa\n",
+      "32            5.2          4.1           1.5          0.1     setosa\n",
+      "33            5.5          4.2           1.4          0.2     setosa\n",
+      "37            4.9          3.6           1.4          0.1     setosa\n",
+      "44            5.1          3.8           1.9          0.4     setosa\n",
+      "46            5.1          3.8           1.6          0.2     setosa\n",
+      "48            5.3          3.7           1.5          0.2     setosa\n",
+      "109           7.2          3.6           6.1          2.5  virginica\n",
+      "117           7.7          3.8           6.7          2.2  virginica\n",
+      "131           7.9          3.8           6.4          2.0  virginica\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas\n",
+    "df = pandas.read_csv('iris.csv')\n",
+    "length = df[df['Sepal.Width'] > 3.5]\n",
+    "print(length)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas\n",
+    "df = pandas.read_csv('iris.csv')\n",
+    "sentosa = df.iloc[1:50, ]\n",
+    "sentosa.to_csv(path_or_buf='SentosaOut.csv', sep=',')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'SentosaOut' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-51-b5442defabe5>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mSentosaOut\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcsv\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m: name 'SentosaOut' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "print(SentosaOut.csv)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "       Species\n",
+      "148  virginica\n",
+      "149  virginica\n",
+      "setosa        50\n",
+      "versicolor    50\n",
+      "virginica     50\n",
+      "Name: Species, dtype: int64\n",
+      "     Sepal.Length  Sepal.Width  Petal.Length  Petal.Width    Species\n",
+      "4             5.0          3.6           1.4          0.2     setosa\n",
+      "5             5.4          3.9           1.7          0.4     setosa\n",
+      "10            5.4          3.7           1.5          0.2     setosa\n",
+      "14            5.8          4.0           1.2          0.2     setosa\n",
+      "15            5.7          4.4           1.5          0.4     setosa\n",
+      "16            5.4          3.9           1.3          0.4     setosa\n",
+      "18            5.7          3.8           1.7          0.3     setosa\n",
+      "19            5.1          3.8           1.5          0.3     setosa\n",
+      "21            5.1          3.7           1.5          0.4     setosa\n",
+      "22            4.6          3.6           1.0          0.2     setosa\n",
+      "32            5.2          4.1           1.5          0.1     setosa\n",
+      "33            5.5          4.2           1.4          0.2     setosa\n",
+      "37            4.9          3.6           1.4          0.1     setosa\n",
+      "44            5.1          3.8           1.9          0.4     setosa\n",
+      "46            5.1          3.8           1.6          0.2     setosa\n",
+      "48            5.3          3.7           1.5          0.2     setosa\n",
+      "109           7.2          3.6           6.1          2.5  virginica\n",
+      "117           7.7          3.8           6.7          2.2  virginica\n",
+      "131           7.9          3.8           6.4          2.0  virginica\n",
+      "(6.9, 4.5, 5.552)\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Part 1\n",
+    "import pandas\n",
+    "df = pandas.read_csv('iris.csv')\n",
+    "line = 3\n",
+    "df.head(line)\n",
+    "\n",
+    "#Part2\n",
+    "#print the last 2 rows in the last two columns to the Python terminal\n",
+    "import pandas\n",
+    "df = pandas.read_csv('iris.csv')\n",
+    "print(df.iloc[148:151, 4:6])\n",
+    "\n",
+    "#get the number of observations for each species included in the data set\n",
+    "import pandas\n",
+    "df = pandas.read_csv('iris.csv')\n",
+    "counts = df['Species'].value_counts()\n",
+    "print counts\n",
+    "\n",
+    "#get rows with Sepal.Width > 3.5\n",
+    "import pandas\n",
+    "df = pandas.read_csv('iris.csv')\n",
+    "length = df[df['Sepal.Width'] > 3.5]\n",
+    "print(length)\n",
+    "\n",
+    "#write the data for the species setosa to a comma-delimited file named ‘setosa.csv’\n",
+    "import pandas\n",
+    "df = pandas.read_csv('iris.csv')\n",
+    "sentosa = df.iloc[1:50, ]\n",
+    "sentosa.to_csv(path_or_buf='SentosaOut.csv', sep=',')\n",
+    "\n",
+    "#calculate the mean, minimum, and maximum of Petal.Length for observations from virginica\n",
+    "import pandas\n",
+    "df = pandas.read_csv('iris.csv')\n",
+    "virginica = df.iloc[100:151, ]\n",
+    "maximum = virginica['Petal.Length'].max()\n",
+    "minimum = virginica['Petal.Length'].min()\n",
+    "mean = virginica['Petal.Length'].mean()\n",
+    "print(maximum, minimum, mean)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.15"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/SentosaOut.csv b/SentosaOut.csv
new file mode 100644
index 0000000..5b2e5f6
--- /dev/null
+++ b/SentosaOut.csv
@@ -0,0 +1,50 @@
+,Sepal.Length,Sepal.Width,Petal.Length,Petal.Width,Species
+1,4.9,3.0,1.4,0.2,setosa
+2,4.7,3.2,1.3,0.2,setosa
+3,4.6,3.1,1.5,0.2,setosa
+4,5.0,3.6,1.4,0.2,setosa
+5,5.4,3.9,1.7,0.4,setosa
+6,4.6,3.4,1.4,0.3,setosa
+7,5.0,3.4,1.5,0.2,setosa
+8,4.4,2.9,1.4,0.2,setosa
+9,4.9,3.1,1.5,0.1,setosa
+10,5.4,3.7,1.5,0.2,setosa
+11,4.8,3.4,1.6,0.2,setosa
+12,4.8,3.0,1.4,0.1,setosa
+13,4.3,3.0,1.1,0.1,setosa
+14,5.8,4.0,1.2,0.2,setosa
+15,5.7,4.4,1.5,0.4,setosa
+16,5.4,3.9,1.3,0.4,setosa
+17,5.1,3.5,1.4,0.3,setosa
+18,5.7,3.8,1.7,0.3,setosa
+19,5.1,3.8,1.5,0.3,setosa
+20,5.4,3.4,1.7,0.2,setosa
+21,5.1,3.7,1.5,0.4,setosa
+22,4.6,3.6,1.0,0.2,setosa
+23,5.1,3.3,1.7,0.5,setosa
+24,4.8,3.4,1.9,0.2,setosa
+25,5.0,3.0,1.6,0.2,setosa
+26,5.0,3.4,1.6,0.4,setosa
+27,5.2,3.5,1.5,0.2,setosa
+28,5.2,3.4,1.4,0.2,setosa
+29,4.7,3.2,1.6,0.2,setosa
+30,4.8,3.1,1.6,0.2,setosa
+31,5.4,3.4,1.5,0.4,setosa
+32,5.2,4.1,1.5,0.1,setosa
+33,5.5,4.2,1.4,0.2,setosa
+34,4.9,3.1,1.5,0.2,setosa
+35,5.0,3.2,1.2,0.2,setosa
+36,5.5,3.5,1.3,0.2,setosa
+37,4.9,3.6,1.4,0.1,setosa
+38,4.4,3.0,1.3,0.2,setosa
+39,5.1,3.4,1.5,0.2,setosa
+40,5.0,3.5,1.3,0.3,setosa
+41,4.5,2.3,1.3,0.3,setosa
+42,4.4,3.2,1.3,0.2,setosa
+43,5.0,3.5,1.6,0.6,setosa
+44,5.1,3.8,1.9,0.4,setosa
+45,4.8,3.0,1.4,0.3,setosa
+46,5.1,3.8,1.6,0.2,setosa
+47,4.6,3.2,1.4,0.2,setosa
+48,5.3,3.7,1.5,0.2,setosa
+49,5.0,3.3,1.4,0.2,setosa