Chapter 7 - Handling Dates and Times.ipynb

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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7.1 Converting Strings to Dates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Timestamp('2005-04-03 23:35:00'),\n",
       " Timestamp('2010-05-23 00:01:00'),\n",
       " Timestamp('2009-09-04 21:09:00')]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "date_strings = np.array([\n",
    "    '03-04-2005 11:35 PM',\n",
    "    '23-05-2010 12:01 AM',\n",
    "    '04-09-2009 09:09 PM'\n",
    "])\n",
    "\n",
    "# convert to datetimes\n",
    "[pd.to_datetime(date, format='%d-%m-%Y %I:%M %p') for date in date_strings]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Timestamp('2005-04-03 23:35:00'),\n",
       " Timestamp('2010-05-23 00:01:00'),\n",
       " Timestamp('2009-09-04 21:09:00')]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[pd.to_datetime(date, format='%d-%m-%Y %I:%M %p', errors='coerce') for date in date_strings]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### See Also\n",
    "* http://strftime.org/\n",
    "\n",
    "## 7.2 Handling Time Zones"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2017-05-01 06:00:00+0100', tz='Europe/London')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "pd.Timestamp('2017-05-01 06:00:00', tz='Europe/London')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2017-05-01 06:00:00+0100', tz='Europe/London')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "date = pd.Timestamp('2017-05-01 06:00:00')\n",
    "\n",
    "date_in_london = date.tz_localize('Europe/London')\n",
    "\n",
    "date_in_london"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2017-05-01 05:00:00+0000', tz='Africa/Abidjan')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "date_in_london.tz_convert('Africa/Abidjan')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   2002-02-28 00:00:00+00:00\n",
       "1   2002-03-31 00:00:00+00:00\n",
       "2   2002-04-30 00:00:00+00:00\n",
       "dtype: datetime64[ns, Africa/Abidjan]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dates = pd.Series(pd.date_range('2/2/2002', periods=3, freq='M'))\n",
    "\n",
    "dates.dt.tz_localize('Africa/Abidjan')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7.3 Selecting Dates and Times\n",
    "## 7.4 Breaking Up Date Data into Multiple Features\n",
    "## 7.5 Calculating the Difference Between Dates\n",
    "## 7.6 Encoding Days of the Week\n",
    "## 7.7 Creating Lagged Feature\n",
    "## 7.8 Using Rolling Time Windows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Stock_Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-01-31</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-02-28</th>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-03-31</th>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-04-30</th>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-05-31</th>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Stock_Price\n",
       "2010-01-31          NaN\n",
       "2010-02-28          1.5\n",
       "2010-03-31          2.5\n",
       "2010-04-30          3.5\n",
       "2010-05-31          4.5"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "time_index = pd.date_range('01/01/2010', periods=5, freq='M')\n",
    "df = pd.DataFrame(index=time_index)\n",
    "df['Stock_Price'] = [1,2,3,4,5]\n",
    "df.rolling(window=2).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### See Also\n",
    "* pandas documentation: Rolling Windows (https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.rolling.html)\n",
    "* What are Moving Average or Smoothing Techniques (https://www.itl.nist.gov/div898/handbook/pmc/section4/pmc42.htm)\n",
    "\n",
    "## 7.9 Handling Missing Data in Time Series"
   ]
  },
  {
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   "execution_count": 11,
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   "outputs": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-01-31</th>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>2010-02-28</th>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-03-31</th>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-04-30</th>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-05-31</th>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "            Sales\n",
       "2010-01-31    1.0\n",
       "2010-02-28    2.0\n",
       "2010-03-31    3.0\n",
       "2010-04-30    4.0\n",
       "2010-05-31    5.0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
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   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "time_index = pd.date_range('01/01/2010', periods=5, freq='M')\n",
    "\n",
    "df = pd.DataFrame(index=time_index)\n",
    "\n",
    "df[\"Sales\"] = [1.0, 2.0, np.nan, np.nan, 5.0]\n",
    "\n",
    "df.interpolate()"
   ]
  },
  {
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   "execution_count": 12,
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       "      <th>Sales</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-01-31</th>\n",
       "      <td>1.0</td>\n",
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       "      <th>2010-02-28</th>\n",
       "      <td>2.0</td>\n",
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       "      <th>2010-03-31</th>\n",
       "      <td>2.0</td>\n",
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       "      <th>2010-04-30</th>\n",
       "      <td>2.0</td>\n",
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       "    <tr>\n",
       "      <th>2010-05-31</th>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "            Sales\n",
       "2010-01-31    1.0\n",
       "2010-02-28    2.0\n",
       "2010-03-31    2.0\n",
       "2010-04-30    2.0\n",
       "2010-05-31    5.0"
      ]
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     "execution_count": 12,
     "metadata": {},
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   ],
   "source": [
    "df.ffill()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>2010-01-31</th>\n",
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       "      <th>2010-02-28</th>\n",
       "      <td>2.0</td>\n",
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       "      <th>2010-03-31</th>\n",
       "      <td>5.0</td>\n",
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       "      <th>2010-04-30</th>\n",
       "      <td>5.0</td>\n",
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       "      <th>2010-05-31</th>\n",
       "      <td>5.0</td>\n",
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      "text/plain": [
       "            Sales\n",
       "2010-01-31    1.0\n",
       "2010-02-28    2.0\n",
       "2010-03-31    5.0\n",
       "2010-04-30    5.0\n",
       "2010-05-31    5.0"
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    "df.bfill()"
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   "execution_count": 14,
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-01-31</th>\n",
       "      <td>1.000000</td>\n",
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       "    <tr>\n",
       "      <th>2010-02-28</th>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-03-31</th>\n",
       "      <td>3.059808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-04-30</th>\n",
       "      <td>4.038069</td>\n",
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       "      <th>2010-05-31</th>\n",
       "      <td>5.000000</td>\n",
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       "               Sales\n",
       "2010-01-31  1.000000\n",
       "2010-02-28  2.000000\n",
       "2010-03-31  3.059808\n",
       "2010-04-30  4.038069\n",
       "2010-05-31  5.000000"
      ]
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     "execution_count": 14,
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   "source": [
    "df.interpolate(method=\"quadratic\")"
   ]
  },
  {
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   "execution_count": 15,
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   "outputs": [
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       "      <th>Sales</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-01-31</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-02-28</th>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-03-31</th>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-04-30</th>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2010-05-31</th>\n",
       "      <td>5.0</td>\n",
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      "text/plain": [
       "            Sales\n",
       "2010-01-31    1.0\n",
       "2010-02-28    2.0\n",
       "2010-03-31    3.0\n",
       "2010-04-30    NaN\n",
       "2010-05-31    5.0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.interpolate(limit=1, limit_direction=\"forward\")"
   ]
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   "execution_count": null,
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