{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# datetime-series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-05-21T07:48:21.670643",
     "start_time": "2016-05-21T07:48:20.908200"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-05-21T07:48:21.716199",
     "start_time": "2016-05-21T07:48:21.672770"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date_range</th>\n",
       "      <th>date_range_str</th>\n",
       "      <th>date_range_str_nan</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-05-01</td>\n",
       "      <td>2016-05-01</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-05-02</td>\n",
       "      <td>2016-05-02</td>\n",
       "      <td>2016-05-02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  date_range date_range_str date_range_str_nan\n",
       "0 2016-05-01     2016-05-01                NaN\n",
       "1 2016-05-02     2016-05-02         2016-05-02"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({\"date_range\": pd.date_range(\"2016-05-01\", \"2016-05-02\")})\n",
    "df[\"date_range_str\"] = df.date_range.copy().dt.strftime(\"%Y-%m-%d\")\n",
    "df[\"date_range_str_nan\"] = df.date_range_str.copy()\n",
    "df.loc[0, \"date_range_str_nan\"] = np.nan\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-05-21T07:48:21.732704",
     "start_time": "2016-05-21T07:48:21.718390"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2 entries, 0 to 1\n",
      "Data columns (total 3 columns):\n",
      "date_range            2 non-null datetime64[ns]\n",
      "date_range_str        2 non-null object\n",
      "date_range_str_nan    1 non-null object\n",
      "dtypes: datetime64[ns](1), object(2)\n",
      "memory usage: 128.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-05-21T07:48:21.748455",
     "start_time": "2016-05-21T07:48:21.736184"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[True, False, False, True]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[\n",
    "    hasattr(df.date_range, \"dt\"),\n",
    "    hasattr(df.date_range, \"str\"),\n",
    "    hasattr(df.date_range_str, \"dt\"),\n",
    "    hasattr(df.date_range_str, \"str\"),\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-05-21T07:48:21.762710",
     "start_time": "2016-05-21T07:48:21.751672"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   2016-05-01\n",
       "1   2016-05-02\n",
       "Name: date_range_str, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.to_datetime(df.date_range_str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-05-21T07:48:21.774643",
     "start_time": "2016-05-21T07:48:21.766153"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0          NaT\n",
       "1   2016-05-02\n",
       "Name: date_range_str_nan, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.to_datetime(df.date_range_str_nan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-05-21T07:48:21.789518",
     "start_time": "2016-05-21T07:48:21.777187"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   2016-05-01\n",
       "1   2016-05-02\n",
       "Name: date_range_str, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.date_range_str.astype(\"datetime64\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-05-21T07:48:21.809077",
     "start_time": "2016-05-21T07:48:21.799185"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Could not convert object to NumPy datetime\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    df.date_range_str_nan.astype(\"datetime64\")\n",
    "except Exception as e:\n",
    "    print(e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-05-21T07:48:21.846817",
     "start_time": "2016-05-21T07:48:21.813764"
    },
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date_range</th>\n",
       "      <th>date_range_str</th>\n",
       "      <th>date_range_str_nan</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-05-01</td>\n",
       "      <td>2016-05-01</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-05-02</td>\n",
       "      <td>2016-05-02</td>\n",
       "      <td>2016-05-02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  date_range date_range_str date_range_str_nan\n",
       "0 2016-05-01     2016-05-01                NaT\n",
       "1 2016-05-02     2016-05-02         2016-05-02"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import tempfile\n",
    "f = tempfile.NamedTemporaryFile()\n",
    "df.to_csv(f.name, index=False)\n",
    "df_temp = pd.read_csv(f.name, parse_dates=list(df.columns))\n",
    "df_temp\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-05-21T07:48:21.858135",
     "start_time": "2016-05-21T07:48:21.848864"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2 entries, 0 to 1\n",
      "Data columns (total 3 columns):\n",
      "date_range            2 non-null datetime64[ns]\n",
      "date_range_str        2 non-null datetime64[ns]\n",
      "date_range_str_nan    1 non-null datetime64[ns]\n",
      "dtypes: datetime64[ns](3)\n",
      "memory usage: 128.0 bytes\n"
     ]
    }
   ],
   "source": [
    "df_temp.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-05-21T07:48:21.865902",
     "start_time": "2016-05-21T07:48:21.860326"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# https://twitter.com/ksomemo/status/730296410868985856"
   ]
  }
 ],
 "metadata": {
  "_draft": {
   "nbviewer_url": "https://gist.github.com/94460950b46662ca6a460424132658be"
  },
  "gist": {
   "data": {
    "description": "datetime-series.ipynb",
    "public": true
   },
   "id": "94460950b46662ca6a460424132658be"
  },
  "hide_input": false,
  "kernelspec": {
   "display_name": "Python [default]",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  },
  "toc": {
   "toc_cell": false,
   "toc_number_sections": true,
   "toc_threshold": 6,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}