Testing pandas issue #2697

Issue_2697.ipynb

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   "source": [
    "from StringIO import StringIO\n",
    "import pandas as pd"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test 1"
   ]
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     "text": [
      "True\n",
      "True\n"
     ]
    }
   ],
   "source": [
    "f=StringIO(\"\"\"id, text\n",
    "135217135789158401, 'testing lost precision from csv'\n",
    "1352171357E+5, 'any item scientific format loses the precision on all other entries'\"\"\")\n",
    "\n",
    "test = pd.read_csv(f)\n",
    "print test['id'][0] == 135217135789158401\n",
    "print test['id'][1] == 1352171357E+5"
   ]
  },
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   "source": [
    "f2=StringIO(\"\"\"id,A,B,C\n",
    "1, 99999999999, 'a', 99999999999\n",
    "2, 123456789012345, 'b', 123456789012345\n",
    "3, 1234E+0, 'c', 1234\"\"\")\n",
    "df = pd.read_csv(f2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test 2"
   ]
  },
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   "execution_count": 4,
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   "outputs": [
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1.000000e+11</td>\n",
       "      <td>'a'</td>\n",
       "      <td>99999999999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1.234568e+14</td>\n",
       "      <td>'b'</td>\n",
       "      <td>123456789012345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1.234000e+03</td>\n",
       "      <td>'c'</td>\n",
       "      <td>1234</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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      "text/plain": [
       "   id             A     B                C\n",
       "0   1  1.000000e+11   'a'      99999999999\n",
       "1   2  1.234568e+14   'b'  123456789012345\n",
       "2   3  1.234000e+03   'c'             1234"
      ]
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     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df"
   ]
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   "execution_count": 5,
   "metadata": {
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    True\n",
       "1    True\n",
       "2    True\n",
       "dtype: bool"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df['A'] == df['C']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test 3\n",
    "> Well the problem here is that if you have an integer above 2^53 and some other number in the column causes the entire column to be interpreted as float64, say, you are going to lose precision. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
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   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
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   "execution_count": 7,
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   "outputs": [
    {
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      "text/plain": [
       "True"
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     "execution_count": 7,
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   "source": [
    "np.log(1234567890123333345)/np.log(2) > 53"
   ]
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   "execution_count": 8,
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1.000000e+11</td>\n",
       "      <td>'a'</td>\n",
       "      <td>99999999999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1.234568e+18</td>\n",
       "      <td>'b'</td>\n",
       "      <td>1234567890123333345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1.234000e+03</td>\n",
       "      <td>'c'</td>\n",
       "      <td>1234</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "   id             A     B                    C\n",
       "0   1  1.000000e+11   'a'          99999999999\n",
       "1   2  1.234568e+18   'b'  1234567890123333345\n",
       "2   3  1.234000e+03   'c'                 1234"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "f2=StringIO(\"\"\"id,A,B,C\n",
    "1, 99999999999, 'a', 99999999999\n",
    "2, 1234567890123333345, 'b', 1234567890123333345\n",
    "3, 1234E+0, 'c', 1234\"\"\")\n",
    "df = pd.read_csv(f2)\n",
    "df"
   ]
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   "cell_type": "code",
   "execution_count": 9,
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      "text/plain": [
       "0     True\n",
       "1    False\n",
       "2     True\n",
       "dtype: bool"
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     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df['A'] == df['C']"
   ]
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   "cell_type": "code",
   "execution_count": null,
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