Data School's Tuesday Tip #7

pandas_timezones.ipynb

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   "source": [
    "## [Tuesday Tip #7](https://tuesday.tips/): Time zones and Daylight Savings Time in pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "869919ba",
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   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd80c5b3",
   "metadata": {},
   "source": [
    "[List of offset aliases](https://pandas.pydata.org/docs/user_guide/timeseries.html#timeseries-offset-aliases) (such as \"H\")"
   ]
  },
  {
   "cell_type": "code",
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   2023-03-12 04:00:00\n",
       "1   2023-03-12 05:00:00\n",
       "2   2023-03-12 06:00:00\n",
       "3   2023-03-12 07:00:00\n",
       "4   2023-03-12 08:00:00\n",
       "5   2023-03-12 09:00:00\n",
       "dtype: datetime64[ns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "times = pd.Series(pd.date_range(start='2023-03-12 4:00', periods=6, freq='H'))\n",
    "times"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "33f98c8e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   2023-03-12 04:00:00+00:00\n",
       "1   2023-03-12 05:00:00+00:00\n",
       "2   2023-03-12 06:00:00+00:00\n",
       "3   2023-03-12 07:00:00+00:00\n",
       "4   2023-03-12 08:00:00+00:00\n",
       "5   2023-03-12 09:00:00+00:00\n",
       "dtype: datetime64[ns, UTC]"
      ]
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     "execution_count": 3,
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    "UTC_times = times.dt.tz_localize('UTC')\n",
    "UTC_times"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e221ee76",
   "metadata": {},
   "source": [
    "[List of time zones](https://en.wikipedia.org/wiki/List_of_tz_database_time_zones) (such as \"America/New_York\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "107d60e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   2023-03-11 23:00:00-05:00\n",
       "1   2023-03-12 00:00:00-05:00\n",
       "2   2023-03-12 01:00:00-05:00\n",
       "3   2023-03-12 03:00:00-04:00\n",
       "4   2023-03-12 04:00:00-04:00\n",
       "5   2023-03-12 05:00:00-04:00\n",
       "dtype: datetime64[ns, America/New_York]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "UTC_times.dt.tz_convert('America/New_York')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "003d4346",
   "metadata": {},
   "outputs": [],
   "source": [
    "times = pd.Series(pd.date_range(start='2023-11-05 3:00', periods=6, freq='H'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "34163f28",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   2023-11-04 23:00:00-04:00\n",
       "1   2023-11-05 00:00:00-04:00\n",
       "2   2023-11-05 01:00:00-04:00\n",
       "3   2023-11-05 01:00:00-05:00\n",
       "4   2023-11-05 02:00:00-05:00\n",
       "5   2023-11-05 03:00:00-05:00\n",
       "dtype: datetime64[ns, America/New_York]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "times.dt.tz_localize('UTC').dt.tz_convert('America/New_York')"
   ]
  }
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