25-Working with Methods in Pandas - Part 2 .ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Working with Methods in Pandas - Part 2 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd  \n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## to_datetime method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\lenovo\\AppData\\Local\\Temp\\ipykernel_15352\\2752892349.py:1: UserWarning: Parsing '15/08/2019' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  pd.to_datetime(\"15/08/2019\")\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Timestamp('2019-08-15 00:00:00')"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.to_datetime(\"15/08/2019\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "date=[\"2019-01-05\",\"jan 6, 2019\",\n",
    "      \"7/05/2019\",\"2019/01/9\",\"20190110\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2019-01-05', '2019-01-06', '2019-07-05', '2019-01-09',\n",
       "               '2019-01-10'],\n",
       "              dtype='datetime64[ns]', freq=None)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.to_datetime(date)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2019-03-05 00:00:00')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.to_datetime(\"03/05/2019\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2019-03-05 00:00:00')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.to_datetime(\"05/03/2019\", dayfirst=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2019-03-05 00:00:00')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.to_datetime(\"05*03*2019\", \n",
    "               format=\"%d*%m*%Y\" )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2019-03-05 00:00:00')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.to_datetime(\"05$03$2019\", \n",
    "               format=\"%d$%m$%Y\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "date=[\"2019-01-05\",\n",
    "     \"jan 6, 2019\",\n",
    "     \"7/05/2019\",\n",
    "     \"2019/01/9\",\n",
    "     \"20190110\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2019-01-05', '2019-01-06', '2019-07-05', '2019-01-09',\n",
       "               '2019-01-10'],\n",
       "              dtype='datetime64[ns]', freq=None)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.to_datetime(date)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2019-01-05', '2019-01-06', '2019-07-05', '2019-01-09',\n",
       "               '2019-01-10'],\n",
       "              dtype='datetime64[ns]', freq=None)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.to_datetime(date, errors='coerce')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "t=1000000000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2001-09-09 01:46:40')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.to_datetime(t, unit=\"s\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Frequency and Date Offsets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2010-01-01 00:00:00', '2010-01-01 04:00:00',\n",
       "               '2010-01-01 08:00:00', '2010-01-01 12:00:00',\n",
       "               '2010-01-01 16:00:00', '2010-01-01 20:00:00',\n",
       "               '2010-01-02 00:00:00', '2010-01-02 04:00:00',\n",
       "               '2010-01-02 08:00:00', '2010-01-02 12:00:00',\n",
       "               '2010-01-02 16:00:00', '2010-01-02 20:00:00',\n",
       "               '2010-01-03 00:00:00'],\n",
       "              dtype='datetime64[ns]', freq='4H')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.date_range(\n",
    "    \"2010-01-01\",\"2010-01-03\", freq=\"4h\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2010-01-24', '2010-02-28', '2010-03-28', '2010-04-25',\n",
       "               '2010-05-23', '2010-06-27', '2010-07-25', '2010-08-22'],\n",
       "              dtype='datetime64[ns]', freq='WOM-4SUN')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.date_range(\n",
    "    \"2010-01-01\",\"2010-09-03\", \n",
    "    freq=\"WOM-4SUN\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Period and PeriodIndex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Period('2020', 'A-DEC')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p=pd.Period(2020)\n",
    "p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['__add__',\n",
       " '__array_priority__',\n",
       " '__class__',\n",
       " '__delattr__',\n",
       " '__dict__',\n",
       " '__dir__',\n",
       " '__doc__',\n",
       " '__eq__',\n",
       " '__format__',\n",
       " '__ge__',\n",
       " '__getattribute__',\n",
       " '__gt__',\n",
       " '__hash__',\n",
       " '__init__',\n",
       " '__init_subclass__',\n",
       " '__le__',\n",
       " '__lt__',\n",
       " '__module__',\n",
       " '__ne__',\n",
       " '__new__',\n",
       " '__pyx_vtable__',\n",
       " '__radd__',\n",
       " '__reduce__',\n",
       " '__reduce_ex__',\n",
       " '__repr__',\n",
       " '__rsub__',\n",
       " '__setattr__',\n",
       " '__setstate__',\n",
       " '__sizeof__',\n",
       " '__str__',\n",
       " '__sub__',\n",
       " '__subclasshook__',\n",
       " '__weakref__',\n",
       " '_add_offset',\n",
       " '_add_timedeltalike_scalar',\n",
       " '_dtype',\n",
       " '_from_ordinal',\n",
       " '_get_to_timestamp_base',\n",
       " '_maybe_convert_freq',\n",
       " '_require_matching_freq',\n",
       " 'asfreq',\n",
       " 'day',\n",
       " 'day_of_week',\n",
       " 'day_of_year',\n",
       " 'dayofweek',\n",
       " 'dayofyear',\n",
       " 'days_in_month',\n",
       " 'daysinmonth',\n",
       " 'end_time',\n",
       " 'freq',\n",
       " 'freqstr',\n",
       " 'hour',\n",
       " 'is_leap_year',\n",
       " 'minute',\n",
       " 'month',\n",
       " 'now',\n",
       " 'ordinal',\n",
       " 'quarter',\n",
       " 'qyear',\n",
       " 'second',\n",
       " 'start_time',\n",
       " 'strftime',\n",
       " 'to_timestamp',\n",
       " 'week',\n",
       " 'weekday',\n",
       " 'weekofyear',\n",
       " 'year']"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dir(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2020-01-01 00:00:00')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p.start_time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2020-12-31 23:59:59.999999999')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p.end_time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Period('2020-01', 'M')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a=pd.Period(\"2020-01\", freq=\"M\")\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Period('2020-06', 'M')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a+5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Period('2019-10', 'M')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a-3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<5 * YearEnds: month=12>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p-pd.Period(\"2015\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PeriodIndex(['2019-01', '2019-02', '2019-03', '2019-04', '2019-05', '2019-06',\n",
       "             '2019-07', '2019-08'],\n",
       "            dtype='period[M]')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rng=pd.period_range(\n",
    "    \"2019-01-01\",\"2019-08-30\",freq=\"M\")\n",
    "rng"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2019-01    0\n",
       "2019-02    1\n",
       "2019-03    2\n",
       "2019-04    3\n",
       "2019-05    4\n",
       "2019-06    5\n",
       "2019-07    6\n",
       "2019-08    7\n",
       "Freq: M, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(range(8),index=rng)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Period('2019', 'A-DEC')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p=pd.Period(\"2019\",freq=\"A-DEC\")\n",
    "p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Period('2019-01', 'M')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p.asfreq('M', how='start')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Period('2019-12', 'M')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p.asfreq(\"M\",how=\"end\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "p=pd.Period(\"2019Q4\", freq=\"Q-DEC\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Period('2019Q4', 'Q-FEB')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p=pd.Period(\"2019Q4\", freq=\"Q-FEB\")\n",
    "p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2019-02-28 23:59:59.999999999')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p.end_time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Period('2018-12-01', 'D')"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p.asfreq('D', 'start')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PeriodIndex(['2019Q3', '2019Q4', '2020Q1', '2020Q2', '2020Q3', '2020Q4'], dtype='period[Q-JAN]')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rng=pd.period_range(\n",
    "    \"2019Q3\",\"2020Q4\", freq=\"Q-JAN\")\n",
    "rng"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2019Q3    0\n",
       "2019Q4    1\n",
       "2020Q1    2\n",
       "2020Q2    3\n",
       "2020Q3    4\n",
       "2020Q4    5\n",
       "Freq: Q-JAN, dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts=pd.Series(range(len(rng)),index=rng)\n",
    "ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "rng=pd.date_range(\n",
    "    \"2020-01-01\", periods=5, freq=\"M\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2020-01-31    0\n",
       "2020-02-29    1\n",
       "2020-03-31    2\n",
       "2020-04-30    3\n",
       "2020-05-31    4\n",
       "Freq: M, dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts=pd.Series(range(len(rng)),index=rng)\n",
    "ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PeriodIndex(['2020-01', '2020-02', '2020-03', '2020-04', '2020-05'], dtype='period[M]')"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pts=ts.to_period()\n",
    "pts.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PeriodIndex(['2020-01', '2020-02', '2020-03', '2020-04', '2020-05'], dtype='period[M]')"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pts.index"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}