solution-chapter-4-v1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 305,
   "metadata": {},
   "outputs": [],
   "source": [
    "import datetime as dt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2021, 3, 18, 3, 0, 17, 834224)"
      ]
     },
     "execution_count": 316,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cur_dt= dt.datetime.now()\n",
    "cur_dt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 314,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2019, 7, 4, 2, 54, 8, 110714)"
      ]
     },
     "execution_count": 314,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "date= cur_dt - dt.timedelta(weeks=89)\n",
    "date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 315,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'04/07/2019'"
      ]
     },
     "execution_count": 315,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "date.strftime('%d/%m/%Y')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "metadata": {},
   "outputs": [],
   "source": [
    "def autocorrect(text):\n",
    "    s= text.split()\n",
    "    cur_dt= dt.datetime.now()\n",
    "    if 'day' in text:\n",
    "        date= cur_dt - dt.timedelta(days=int(s[0]))\n",
    "    elif 'hour' in text:\n",
    "        date= cur_dt - dt.timedelta(hours=int(s[0]))\n",
    "    elif 'minute' in text:\n",
    "        date= cur_dt - dt.timedelta(minutes=int(s[0]))\n",
    "    elif 'week' in text:\n",
    "        date= cur_dt - dt.timedelta(weeks=int(s[0]))\n",
    "    elif 'months' in text:\n",
    "        date= cur_dt - dt.timedelta(months=int(s[0]))\n",
    "    elif 'year' in text:\n",
    "        date= cur_dt - dt.timedelta(years=int(s[0]))\n",
    "    else:\n",
    "        pass\n",
    "    return date.strftime('%d/%m/%Y')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'17/03/2021'"
      ]
     },
     "execution_count": 303,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "autocorrect('1 day ago')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'18/03/2021'"
      ]
     },
     "execution_count": 304,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "autocorrect('1 minute ago')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 301,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'18/03/2021'"
      ]
     },
     "execution_count": 301,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "autocorrect('3 minutes ago')"
   ]
  }
 ],
 "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}