Example of doing Pandas date conversion using a converter function

pandas_date_conversion.ipynb

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   "source": [
    "Examples of using a conversion function to convert some coded integers to a datetime while reading a file."
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   "execution_count": 2,
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   "source": [
    "import datetime as dt\n",
    "import pandas as pd"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This function takes an integer, 0-pads it to a six digit string, and tries to convert it to a datetime. If there's any error during the conversion it returns `None`."
   ]
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   "cell_type": "code",
   "execution_count": 11,
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   "source": [
    "def date_converter(date_num):\n",
    "    date_str = '{:06}'.format(date_num)\n",
    "\n",
    "    try:\n",
    "        return dt.datetime.strptime(date_str, '%m%d%y')\n",
    "    except:\n",
    "        return None"
   ]
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   "execution_count": 12,
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2012, 6, 4, 0, 0)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
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   "source": [
    "date_converter(60412)"
   ]
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   "execution_count": 13,
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2012, 10, 4, 0, 0)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
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   ],
   "source": [
    "date_converter(100412)"
   ]
  },
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   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
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   "outputs": [],
   "source": [
    "date_converter(0)"
   ]
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   "cell_type": "markdown",
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   "source": [
    "If the column names are in the Excel file you can specify the column name and a conversion function while reading the file:"
   ]
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   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "pd.read_excel(f, header=None, sheetname='Sheet1', converters={'Date Paid/Coded': date_converter})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If there are no column names you can still specify the converter based on an integer column number (the numbering starts from 0):"
   ]
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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   "outputs": [],
   "source": [
    "pd.read_excel(f, header=None, sheetname='Sheet1', converters={9: date_converter})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you want to do the conversion after reading the DataFrame out of the Excel file you can use the Series.map method to apply a function to the values in a column:"
   ]
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   "execution_count": 16,
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date Paid/Coded</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2012-06-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2012-10-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Date Paid/Coded\n",
       "0      2012-06-04\n",
       "1      2012-10-04\n",
       "2             NaT"
      ]
     },
     "execution_count": 16,
     "metadata": {},
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   ],
   "source": [
    "df = pd.DataFrame({'Date Paid/Coded': [60412, 100412, 0]})\n",
    "df['Date Paid/Coded'] = df['Date Paid/Coded'].map(date_converter)\n",
    "df"
   ]
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   "execution_count": null,
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