006 - Time Series and DateTimeIndex (Session 6).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a1d8212d-1553-4c3c-9bb9-3ba62eda3100",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Bismillah"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "1e10a20b-ea03-4b15-ad40-470c878da1d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import datetime\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "pd.set_option('precision', 7)\n",
    "from pandas_datareader import data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "54f691f5-be4f-411d-be19-e9913eab5752",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "int"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(2014-8-1)\n",
    "# it looks date to us, but for pandas, it is just an int. So, the operations permitted on date cannot be performed\n",
    "# on this int.\n",
    "# If we enter date in this format in datetime function, shown below, then we will get error since pandas does not\n",
    "# recognize date in this format as a date."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e6434fdd-2452-4564-bf76-fd244a4467e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2014, 8, 1, 9, 30, 45, 100000)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datetime.datetime(2014, 8, 1, 9, 30, 45, 100000)\n",
    "# datetime object created. So, relevant operations can be performed on it now.\n",
    "# year, month, day, hours, minutes, seconds, and microseconds must be seperated by a comma."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "263fc29a-4b30-42db-b19e-0ae2996f846a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(datetime.datetime(2014, 8, 1, 9, 30, 45, 100000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7136a38e-fd8f-4163-957d-84a0ff30992c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[datetime.datetime(2014, 8, 1, 0, 0), datetime.datetime(2014, 8, 2, 0, 0)]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dates = [datetime.datetime(2014, 8, 1), datetime.datetime(2014, 8, 2)]\n",
    "dates\n",
    "# a list of datetime objects created."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "31a8d4d9-f1a1-44e5-b269-f97c4013c36c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "list"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(dates)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "dc015455-941d-4aa5-b9b1-0fc6b4182616",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(dates[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b528a867-9408-49cb-b650-e7701860bd76",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2014-08-01', '2014-08-02'], dtype='datetime64[ns]', freq=None)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dti = pd.DatetimeIndex(dates)\n",
    "dti\n",
    "# datetime index created from a list of datetime objects. Each item in DatetimeIndex is an object of Timestamp class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2f9a4eda-0cae-42b2-93cf-0946e5eb0eaf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.indexes.datetimes.DatetimeIndex"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(dti)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5c67c91b-873e-4412-bf0a-551228e83150",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas._libs.tslibs.timestamps.Timestamp"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(dti[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7a8783eb-ba75-45a6-8419-b0cdc6073ff1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.7155494\n",
       "1    0.3843026\n",
       "dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts = pd.Series(np.random.randn(2))\n",
    "ts\n",
    "# the index is zero-based integer index."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "85c0cc02-51c1-4622-8916-109b1d87f937",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(ts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a5829c5f-1bea-4371-877d-b43df1c5b509",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2014-08-01   -0.2817949\n",
       "2014-08-02    1.4247490\n",
       "dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts1 = pd.Series(np.random.randn(2), dates)\n",
    "ts1\n",
    "# the index is datetime index rather than zero-based integer index.\n",
    "# a series will automatically construct a DatetimeIndex as its index when passing a list of datetime objects (dates)\n",
    "# as the index parameter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a69f02db-6635-48c8-a5a1-b539c00c7c04",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(ts1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "01ede082-2bca-47df-9e7c-73655a53e816",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2014-08-01    1.3427996\n",
       "2014-08-02    0.4560590\n",
       "dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts2 = pd.Series(np.random.randn(2), dti)\n",
    "ts2\n",
    "# here we have directly passed the DatetimeIndex as the index parameter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "0c119c87-d974-4440-bf60-bdec1705dfb2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(ts2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ba11874a-3a92-4130-9a60-2943c27151a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2014-08-01', '2014-08-02'], dtype='datetime64[ns]', freq=None)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts2.index\n",
    "# the index of the series is DatetimeIndex as can be confirmed from the following type command."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "378245bf-4465-4c10-b5c2-b763e46302c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.indexes.datetimes.DatetimeIndex"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(ts2.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "cf80214f-f083-4958-9250-073f98c456f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Important: The series object has taken the datetime objects (contained in the variable, dates) and constructed a \n",
    "# DatetimeIndex from the date values contained in the datetime objects. Each value of the DatetimeIndex is the \n",
    "# Timestamp object and each element (Timestamp) of the DatetimeIndex can be used to access the corresponding value in\n",
    "# the Series object. This is demonstrated in the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "e000d83a-a8aa-4c39-8c85-a50ad85c4d5b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2014-08-01    1.3427996\n",
       "2014-08-02    0.4560590\n",
       "dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "4b20a8e5-ddeb-4441-844c-a18ef7281d78",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.3427995967335267"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts2['2014-08-01']\n",
    "# first row retreived using [] without slicing, loc or iloc. It is possible because there is only one column since\n",
    "# this is a series. So, all we can give is an argument for a row or rows so pandas doesn't get confused."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "ba2142ec-9f3c-4787-845e-5e0ec29b7dad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2014-08-01    1.3427996\n",
       "2014-08-02    0.4560590\n",
       "dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts2['2014-08-01':'2014-08-02']\n",
    "# the end date is inclusive."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "924366b7-9ece-4cf7-9023-6941966c6b68",
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'module' object is not callable",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32mC:\\Users\\AITZAZ~1\\AppData\\Local\\Temp/ipykernel_10732/1977750479.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mts2\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mdatetime\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m2014\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m8\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[1;31m# instead of using the date in the string with - seperator, we can use the date with , seperator without '' to produce\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;31m# the same output.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: 'module' object is not callable"
     ]
    }
   ],
   "source": [
    "ts2[datetime(2014, 8, 1)]\n",
    "# instead of using the date in the string with - seperator, we can use the date with , seperator without '' to produce\n",
    "# the same output."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "7482613f-f6e0-4366-8162-e41e184f8153",
   "metadata": {},
   "outputs": [],
   "source": [
    "# The next method to create DatetimeIndex by passing a list of dates in string as shown below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "eddcb5c9-b319-4dcb-8b82-673128f34c66",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['2014-08-01', '2014-08-02']"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dates = ['2014-08-01', '2014-08-02']\n",
    "dates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "56e3d474-a7f1-46b3-8bce-03e93a6387d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "list"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(dates)\n",
    "# list of strings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "de4323c9-272a-4b67-9739-7a1a9dc08bfd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "str"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(dates[0])\n",
    "# the first element (date) in the list is of type string and the same is the second element."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "b52d6d88-f734-4a94-9651-32b58982cf29",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2014-08-01   -0.4337860\n",
       "2014-08-02   -1.0260749\n",
       "dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts3 = pd.Series(np.random.randn(2), dates)\n",
    "ts3\n",
    "# here dates as a string have been passed as an index argument. But the pandas itself figured out that these are dates\n",
    "# and converted them into DatetimeIndex as shown below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "189143dd-6f7d-4234-85b7-15ce8ba83982",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.indexes.base.Index"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(ts3.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "564d662a-d99c-4adc-9737-44fece7fea35",
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert a list of mixed type items/elements into DatetimeIndex using pandas' function, to_datetime()."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "817c81f1-a7e8-4a37-8a28-caec69a6fbc1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2014-08-01', '2014-08-02', '2014-08-03', 'NaT'], dtype='datetime64[ns]', freq=None)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dti2 = pd.to_datetime(['Aug 1, 2014', '2014-08-02', '2014.8.3', None])\n",
    "dti2\n",
    "# note that all are strings in the list except the keyword, None.\n",
    "# all the strings have been converted into DatetimeIndex (pandas recognized dates as shown below:)\n",
    "# None has been converted into NaT (not a time-value), which means that the source data could not be converted into\n",
    "# datetime."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "d2f68f0e-43ea-4e58-b547-f35c14761bb5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.indexes.datetimes.DatetimeIndex"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(dti2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "68f43b44-e24b-454d-b42e-17dbd0034f7b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#dti3 = pd.to_datetime(['Aug 1, 2014', '2014-08-02', '2014.8.3', 'foo'], coerce = True)\n",
    "# gives error. pandas cannot parse 'foo' since it does not seem like a date. Previously, pandas recognized the keyword\n",
    "# None, so didn't give the error and produced NaT. Also, the coerce parameter is not working here unlike in the book."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "c96d50b8-59a2-4c46-a868-0a4a718f793d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Changing the date order: The default order is "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "3a0b100d-3b9b-4a55-9028-3210fdcee4a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2014-08-01'], dtype='datetime64[ns]', freq=None)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dti = pd.to_datetime(['2014, 8, 1'])\n",
    "dti\n",
    "# 8 is taken as a month and 1 as a date."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "506a70bc-6ecf-4175-822a-8ffc8e37ca59",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2014-08-01'], dtype='datetime64[ns]', freq=None)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dti = pd.to_datetime(['8, 1, 2014'])\n",
    "dti\n",
    "# same result produced, 8 is the month and 1 is the date."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "b6e17dbc-a0da-4bfd-812f-95d4f3e9bed7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2014-01-08'], dtype='datetime64[ns]', freq=None)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dti = pd.to_datetime(['8, 1, 2014'], dayfirst = True)\n",
    "dti\n",
    "# if we mean to say that the date is 8th of January, then we have to tell pandas that the first argument is the day or\n",
    "# date by typing 'dayfirst = True' since by default pandas will treat the first value as the month and the second as\n",
    "# a day or date.\n",
    "# now the date is 8th of January as we desired."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "fe1c8fd4-9bea-4e19-8f5f-abcada9b64bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2014-01-08 00:00:00')"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dti[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "2d7f491e-65e7-402b-bf75-91f834f7e71c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Using date_range function to create DatetineIndex (a series of TimeStamps):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "09d44b18-69ac-44c9-b682-7c435a6e5e9a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2014-08-01', '2014-08-02', '2014-08-03', '2014-08-04',\n",
       "               '2014-08-05', '2014-08-06', '2014-08-07', '2014-08-08',\n",
       "               '2014-08-09', '2014-08-10'],\n",
       "              dtype='datetime64[ns]', freq='D')"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dates = pd.date_range('8, 1, 2014', periods = 10)\n",
    "dates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "d7e0a619-2b9a-42e0-986e-adc364af049d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2014-08-01    0.4982921\n",
       "2014-08-02    0.8954158\n",
       "2014-08-03   -0.2979222\n",
       "2014-08-04   -0.5524807\n",
       "2014-08-05   -0.7184446\n",
       "2014-08-06   -0.3337892\n",
       "2014-08-07    0.7901778\n",
       "2014-08-08   -0.6462600\n",
       "2014-08-09   -0.9974112\n",
       "2014-08-10    2.9479791\n",
       "Freq: D, dtype: float64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1 = pd.Series(np.random.randn(10), dates)\n",
    "s1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "8e481936-569d-4218-9cfe-2fb25b8c936c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2014-08-01', '2014-08-02', '2014-08-03', '2014-08-04',\n",
       "               '2014-08-05', '2014-08-06', '2014-08-07', '2014-08-08',\n",
       "               '2014-08-09', '2014-08-10'],\n",
       "              dtype='datetime64[ns]', freq='D')"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "4e0287a1-caed-4bc6-9d5f-9c389fe97f5b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.indexes.datetimes.DatetimeIndex"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(s1.index)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39bb6256-9079-4eb4-b480-20bbbb859b24",
   "metadata": {},
   "source": [
    "# S&P 500 Example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "d1b05e87-a2a0-4523-afc9-f284ce442d4c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Open</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Adj Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-01-03</th>\n",
       "      <td>26.9599991</td>\n",
       "      <td>26.3899994</td>\n",
       "      <td>26.5499992</td>\n",
       "      <td>26.7700005</td>\n",
       "      <td>64731500.0</td>\n",
       "      <td>21.6231365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-04</th>\n",
       "      <td>27.4699993</td>\n",
       "      <td>26.7800007</td>\n",
       "      <td>26.8199997</td>\n",
       "      <td>27.3999996</td>\n",
       "      <td>80516100.0</td>\n",
       "      <td>22.1320114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-05</th>\n",
       "      <td>27.7299995</td>\n",
       "      <td>27.2900009</td>\n",
       "      <td>27.3799992</td>\n",
       "      <td>27.6800003</td>\n",
       "      <td>56081400.0</td>\n",
       "      <td>22.3581772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-06</th>\n",
       "      <td>28.1900005</td>\n",
       "      <td>27.5300007</td>\n",
       "      <td>27.5300007</td>\n",
       "      <td>28.1100006</td>\n",
       "      <td>99455500.0</td>\n",
       "      <td>22.7055035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-09</th>\n",
       "      <td>28.1000004</td>\n",
       "      <td>27.7199993</td>\n",
       "      <td>28.0499992</td>\n",
       "      <td>27.7399998</td>\n",
       "      <td>59706800.0</td>\n",
       "      <td>22.4066391</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  High         Low        Open       Close      Volume  \\\n",
       "Date                                                                     \n",
       "2012-01-03  26.9599991  26.3899994  26.5499992  26.7700005  64731500.0   \n",
       "2012-01-04  27.4699993  26.7800007  26.8199997  27.3999996  80516100.0   \n",
       "2012-01-05  27.7299995  27.2900009  27.3799992  27.6800003  56081400.0   \n",
       "2012-01-06  28.1900005  27.5300007  27.5300007  28.1100006  99455500.0   \n",
       "2012-01-09  28.1000004  27.7199993  28.0499992  27.7399998  59706800.0   \n",
       "\n",
       "             Adj Close  \n",
       "Date                    \n",
       "2012-01-03  21.6231365  \n",
       "2012-01-04  22.1320114  \n",
       "2012-01-05  22.3581772  \n",
       "2012-01-06  22.7055035  \n",
       "2012-01-09  22.4066391  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "msft = data.DataReader('MSFT', 'yahoo', '2012-1-1', '2013-12-30')\n",
    "msft[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "2528e749-c52e-4ec6-9e2a-59b3420eb274",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "High         2.6959999e+01\n",
       "Low          2.6389999e+01\n",
       "Open         2.6549999e+01\n",
       "Close        2.6770000e+01\n",
       "Volume       6.4731500e+07\n",
       "Adj Close    2.1623137e+01\n",
       "Name: 2012-01-03 00:00:00, dtype: float64"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "msft.loc['2012-01-03']\n",
    "# Thus, using DatetimeIndex, any desirable part of the dataframe can be accessed for any period."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "9d90d661-196b-4134-aba6-0719a8053787",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "2012-01-03    21.6231365\n",
       "2012-01-04    22.1320114\n",
       "2012-01-05    22.3581772\n",
       "2012-01-06    22.7055035\n",
       "2012-01-09    22.4066391\n",
       "Name: Adj Close, dtype: float64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "msftAC = msft['Adj Close']\n",
    "msftAC.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "108bdc83-16d0-4c25-b8cb-a5ec42d4abaf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21.623136520385742"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "msftAC['2012-01-03']\n",
    "# Nothe that msftAC is a series, therefore, a row index (which is DatetimeIndex) can be used with [] without slicing, \n",
    "# loc or iloc. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "7c03121a-9f49-4e81-b4f9-9d2885506b8e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "2012-01-03    21.6231365\n",
       "2012-01-04    22.1320114\n",
       "2012-01-05    22.3581772\n",
       "Name: Adj Close, dtype: float64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "msftAC['2012-01-01':'2012-01-05']\n",
    "# DatetimeIndex can easily allow access to any chunk of dataframe for any desirable period."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "6180b06d-f740-4278-8d88-48051dedb382",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\AITZAZ~1\\AppData\\Local\\Temp/ipykernel_10732/3994141690.py:1: FutureWarning: Indexing a DataFrame with a datetimelike index using a single string to slice the rows, like `frame[string]`, is deprecated and will be removed in a future version. Use `frame.loc[string]` instead.\n",
      "  msft['2012-02']\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Open</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Adj Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-02-01</th>\n",
       "      <td>30.0499992</td>\n",
       "      <td>29.7600002</td>\n",
       "      <td>29.7900009</td>\n",
       "      <td>29.8899994</td>\n",
       "      <td>67409900.0</td>\n",
       "      <td>24.1432762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-02-02</th>\n",
       "      <td>30.1700001</td>\n",
       "      <td>29.7099991</td>\n",
       "      <td>29.8999996</td>\n",
       "      <td>29.9500008</td>\n",
       "      <td>52223300.0</td>\n",
       "      <td>24.1917381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-02-03</th>\n",
       "      <td>30.3999996</td>\n",
       "      <td>30.0900002</td>\n",
       "      <td>30.1399994</td>\n",
       "      <td>30.2399998</td>\n",
       "      <td>41838500.0</td>\n",
       "      <td>24.4259853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-02-06</th>\n",
       "      <td>30.2199993</td>\n",
       "      <td>29.9699993</td>\n",
       "      <td>30.0400009</td>\n",
       "      <td>30.2000008</td>\n",
       "      <td>28039700.0</td>\n",
       "      <td>24.3936729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-02-07</th>\n",
       "      <td>30.4899998</td>\n",
       "      <td>30.0499992</td>\n",
       "      <td>30.1499996</td>\n",
       "      <td>30.3500004</td>\n",
       "      <td>39242400.0</td>\n",
       "      <td>24.5148354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-02-08</th>\n",
       "      <td>30.6700001</td>\n",
       "      <td>30.2199993</td>\n",
       "      <td>30.2600002</td>\n",
       "      <td>30.6599998</td>\n",
       "      <td>49659100.0</td>\n",
       "      <td>24.7652302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-02-09</th>\n",
       "      <td>30.7999992</td>\n",
       "      <td>30.4799995</td>\n",
       "      <td>30.6800003</td>\n",
       "      <td>30.7700005</td>\n",
       "      <td>50481600.0</td>\n",
       "      <td>24.8540802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-02-10</th>\n",
       "      <td>30.7999992</td>\n",
       "      <td>30.3600006</td>\n",
       "      <td>30.6399994</td>\n",
       "      <td>30.5000000</td>\n",
       "      <td>44605300.0</td>\n",
       "      <td>24.6359997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-02-13</th>\n",
       "      <td>30.7700005</td>\n",
       "      <td>30.4300003</td>\n",
       "      <td>30.6299992</td>\n",
       "      <td>30.5799999</td>\n",
       "      <td>33319800.0</td>\n",
       "      <td>24.7006092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-02-14</th>\n",
       "      <td>30.4599991</td>\n",
       "      <td>29.8500004</td>\n",
       "      <td>30.3299999</td>\n",
       "      <td>30.2500000</td>\n",
       "      <td>59644000.0</td>\n",
       "      <td>24.5949192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-02-15</th>\n",
       "      <td>30.3899994</td>\n",
       "      <td>30.0300007</td>\n",
       "      <td>30.3299999</td>\n",
       "      <td>30.0499992</td>\n",
       "      <td>43311300.0</td>\n",
       "      <td>24.4323063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-02-16</th>\n",
       "      <td>31.5499992</td>\n",
       "      <td>30.2999992</td>\n",
       "      <td>30.3099995</td>\n",
       "      <td>31.2900009</td>\n",
       "      <td>94705100.0</td>\n",
       "      <td>25.4405003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-02-17</th>\n",
       "      <td>31.3199997</td>\n",
       "      <td>30.9500008</td>\n",
       "      <td>31.2000008</td>\n",
       "      <td>31.2500000</td>\n",
       "      <td>70036500.0</td>\n",
       "      <td>25.4079723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-02-21</th>\n",
       "      <td>31.6100006</td>\n",
       "      <td>31.1499996</td>\n",
       "      <td>31.1800003</td>\n",
       "      <td>31.4400005</td>\n",
       "      <td>50829900.0</td>\n",
       "      <td>25.5624523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-02-22</th>\n",
       "      <td>31.6800003</td>\n",
       "      <td>31.1800003</td>\n",
       "      <td>31.4500008</td>\n",
       "      <td>31.2700005</td>\n",
       "      <td>49253200.0</td>\n",
       "      <td>25.4242325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-02-23</th>\n",
       "      <td>31.5900002</td>\n",
       "      <td>31.0000000</td>\n",
       "      <td>31.2000008</td>\n",
       "      <td>31.3700008</td>\n",
       "      <td>35034700.0</td>\n",
       "      <td>25.5055466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-02-24</th>\n",
       "      <td>31.5000000</td>\n",
       "      <td>31.2399998</td>\n",
       "      <td>31.4799995</td>\n",
       "      <td>31.4799995</td>\n",
       "      <td>35575400.0</td>\n",
       "      <td>25.5949726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-02-27</th>\n",
       "      <td>31.5000000</td>\n",
       "      <td>31.1000004</td>\n",
       "      <td>31.2399998</td>\n",
       "      <td>31.3500004</td>\n",
       "      <td>34568400.0</td>\n",
       "      <td>25.4892769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-02-28</th>\n",
       "      <td>31.9300003</td>\n",
       "      <td>31.3799992</td>\n",
       "      <td>31.4099998</td>\n",
       "      <td>31.8700008</td>\n",
       "      <td>45230600.0</td>\n",
       "      <td>25.9120712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-02-29</th>\n",
       "      <td>32.0000000</td>\n",
       "      <td>31.6100006</td>\n",
       "      <td>31.8899994</td>\n",
       "      <td>31.7399998</td>\n",
       "      <td>59323600.0</td>\n",
       "      <td>25.8063736</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  High         Low        Open       Close      Volume  \\\n",
       "Date                                                                     \n",
       "2012-02-01  30.0499992  29.7600002  29.7900009  29.8899994  67409900.0   \n",
       "2012-02-02  30.1700001  29.7099991  29.8999996  29.9500008  52223300.0   \n",
       "2012-02-03  30.3999996  30.0900002  30.1399994  30.2399998  41838500.0   \n",
       "2012-02-06  30.2199993  29.9699993  30.0400009  30.2000008  28039700.0   \n",
       "2012-02-07  30.4899998  30.0499992  30.1499996  30.3500004  39242400.0   \n",
       "2012-02-08  30.6700001  30.2199993  30.2600002  30.6599998  49659100.0   \n",
       "2012-02-09  30.7999992  30.4799995  30.6800003  30.7700005  50481600.0   \n",
       "2012-02-10  30.7999992  30.3600006  30.6399994  30.5000000  44605300.0   \n",
       "2012-02-13  30.7700005  30.4300003  30.6299992  30.5799999  33319800.0   \n",
       "2012-02-14  30.4599991  29.8500004  30.3299999  30.2500000  59644000.0   \n",
       "2012-02-15  30.3899994  30.0300007  30.3299999  30.0499992  43311300.0   \n",
       "2012-02-16  31.5499992  30.2999992  30.3099995  31.2900009  94705100.0   \n",
       "2012-02-17  31.3199997  30.9500008  31.2000008  31.2500000  70036500.0   \n",
       "2012-02-21  31.6100006  31.1499996  31.1800003  31.4400005  50829900.0   \n",
       "2012-02-22  31.6800003  31.1800003  31.4500008  31.2700005  49253200.0   \n",
       "2012-02-23  31.5900002  31.0000000  31.2000008  31.3700008  35034700.0   \n",
       "2012-02-24  31.5000000  31.2399998  31.4799995  31.4799995  35575400.0   \n",
       "2012-02-27  31.5000000  31.1000004  31.2399998  31.3500004  34568400.0   \n",
       "2012-02-28  31.9300003  31.3799992  31.4099998  31.8700008  45230600.0   \n",
       "2012-02-29  32.0000000  31.6100006  31.8899994  31.7399998  59323600.0   \n",
       "\n",
       "             Adj Close  \n",
       "Date                    \n",
       "2012-02-01  24.1432762  \n",
       "2012-02-02  24.1917381  \n",
       "2012-02-03  24.4259853  \n",
       "2012-02-06  24.3936729  \n",
       "2012-02-07  24.5148354  \n",
       "2012-02-08  24.7652302  \n",
       "2012-02-09  24.8540802  \n",
       "2012-02-10  24.6359997  \n",
       "2012-02-13  24.7006092  \n",
       "2012-02-14  24.5949192  \n",
       "2012-02-15  24.4323063  \n",
       "2012-02-16  25.4405003  \n",
       "2012-02-17  25.4079723  \n",
       "2012-02-21  25.5624523  \n",
       "2012-02-22  25.4242325  \n",
       "2012-02-23  25.5055466  \n",
       "2012-02-24  25.5949726  \n",
       "2012-02-27  25.4892769  \n",
       "2012-02-28  25.9120712  \n",
       "2012-02-29  25.8063736  "
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "msft['2012-02']\n",
    "# Note that this didn't require the use of the .loc method, as pandas first identifies this as a partial date and then\n",
    "# looks along the index of the dataframe instead of column (although .loc can be used to perform the same operation)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "387d8f7e-bdc8-4db2-8cf3-a22a734881b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Open</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Adj Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-02-01</th>\n",
       "      <td>30.0499992</td>\n",
       "      <td>29.7600002</td>\n",
       "      <td>29.7900009</td>\n",
       "      <td>29.8899994</td>\n",
       "      <td>67409900.0</td>\n",
       "      <td>24.1432762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-02-02</th>\n",
       "      <td>30.1700001</td>\n",
       "      <td>29.7099991</td>\n",
       "      <td>29.8999996</td>\n",
       "      <td>29.9500008</td>\n",
       "      <td>52223300.0</td>\n",
       "      <td>24.1917381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-02-03</th>\n",
       "      <td>30.3999996</td>\n",
       "      <td>30.0900002</td>\n",
       "      <td>30.1399994</td>\n",
       "      <td>30.2399998</td>\n",
       "      <td>41838500.0</td>\n",
       "      <td>24.4259853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-02-06</th>\n",
       "      <td>30.2199993</td>\n",
       "      <td>29.9699993</td>\n",
       "      <td>30.0400009</td>\n",
       "      <td>30.2000008</td>\n",
       "      <td>28039700.0</td>\n",
       "      <td>24.3936729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-02-07</th>\n",
       "      <td>30.4899998</td>\n",
       "      <td>30.0499992</td>\n",
       "      <td>30.1499996</td>\n",
       "      <td>30.3500004</td>\n",
       "      <td>39242400.0</td>\n",
       "      <td>24.5148354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-02-08</th>\n",
       "      <td>30.6700001</td>\n",
       "      <td>30.2199993</td>\n",
       "      <td>30.2600002</td>\n",
       "      <td>30.6599998</td>\n",
       "      <td>49659100.0</td>\n",
       "      <td>24.7652302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-02-09</th>\n",
       "      <td>30.7999992</td>\n",
       "      <td>30.4799995</td>\n",
       "      <td>30.6800003</td>\n",
       "      <td>30.7700005</td>\n",
       "      <td>50481600.0</td>\n",
       "      <td>24.8540802</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  High         Low        Open       Close      Volume  \\\n",
       "Date                                                                     \n",
       "2012-02-01  30.0499992  29.7600002  29.7900009  29.8899994  67409900.0   \n",
       "2012-02-02  30.1700001  29.7099991  29.8999996  29.9500008  52223300.0   \n",
       "2012-02-03  30.3999996  30.0900002  30.1399994  30.2399998  41838500.0   \n",
       "2012-02-06  30.2199993  29.9699993  30.0400009  30.2000008  28039700.0   \n",
       "2012-02-07  30.4899998  30.0499992  30.1499996  30.3500004  39242400.0   \n",
       "2012-02-08  30.6700001  30.2199993  30.2600002  30.6599998  49659100.0   \n",
       "2012-02-09  30.7999992  30.4799995  30.6800003  30.7700005  50481600.0   \n",
       "\n",
       "             Adj Close  \n",
       "Date                    \n",
       "2012-02-01  24.1432762  \n",
       "2012-02-02  24.1917381  \n",
       "2012-02-03  24.4259853  \n",
       "2012-02-06  24.3936729  \n",
       "2012-02-07  24.5148354  \n",
       "2012-02-08  24.7652302  \n",
       "2012-02-09  24.8540802  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "msft['2012-02':'2012-02-09']\n",
    "# the slice will start at the beginning of the month."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "156c61ac-c524-46b5-89fc-ad7293da2021",
   "metadata": {},
   "source": [
    "# Creating time-series with specific frequencies:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "437890a8-1a20-4e26-9b88-3de01652d293",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2014-08-01 00:00:00', '2014-08-01 00:01:00',\n",
       "               '2014-08-01 00:02:00', '2014-08-01 00:03:00',\n",
       "               '2014-08-01 00:04:00', '2014-08-01 00:05:00',\n",
       "               '2014-08-01 00:06:00', '2014-08-01 00:07:00',\n",
       "               '2014-08-01 00:08:00', '2014-08-01 00:09:00',\n",
       "               ...\n",
       "               '2014-10-29 23:50:00', '2014-10-29 23:51:00',\n",
       "               '2014-10-29 23:52:00', '2014-10-29 23:53:00',\n",
       "               '2014-10-29 23:54:00', '2014-10-29 23:55:00',\n",
       "               '2014-10-29 23:56:00', '2014-10-29 23:57:00',\n",
       "               '2014-10-29 23:58:00', '2014-10-29 23:59:00'],\n",
       "              dtype='datetime64[ns]', length=129600, freq='T')"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dates = pd.date_range('2014-08-01', '2014-10-29 23:59:00', freq = 'T')\n",
    "dates\n",
    "# create DatetimeIndex with hourly frequency. The default is 'D' for daily. Weekly, monthly etc is also possible."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "2fcf534a-dd47-4460-83ec-449b864b69ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([     0,      1,      2, ..., 129597, 129598, 129599])"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np_array = np.arange(0, 90 * 60 * 24)\n",
    "np_array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "d381d596-060e-4e2c-911f-a52b2d36257f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2014-08-01 00:00:00         0\n",
       "2014-08-01 00:01:00         1\n",
       "2014-08-01 00:02:00         2\n",
       "2014-08-01 00:03:00         3\n",
       "2014-08-01 00:04:00         4\n",
       "                        ...  \n",
       "2014-10-29 23:55:00    129595\n",
       "2014-10-29 23:56:00    129596\n",
       "2014-10-29 23:57:00    129597\n",
       "2014-10-29 23:58:00    129598\n",
       "2014-10-29 23:59:00    129599\n",
       "Freq: T, Length: 129600, dtype: int32"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bymin = pd.Series(np_array, dates)\n",
    "bymin\n",
    "# the first argument for the values of the series and the second one is for the index of the series."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "b1790be9-d92a-4ed6-9f36-1243402e86ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2014-08-01 12:30:00    750\n",
       "2014-08-01 12:31:00    751\n",
       "2014-08-01 12:32:00    752\n",
       "2014-08-01 12:33:00    753\n",
       "2014-08-01 12:34:00    754\n",
       "2014-08-01 12:35:00    755\n",
       "2014-08-01 12:36:00    756\n",
       "2014-08-01 12:37:00    757\n",
       "2014-08-01 12:38:00    758\n",
       "2014-08-01 12:39:00    759\n",
       "2014-08-01 12:40:00    760\n",
       "2014-08-01 12:41:00    761\n",
       "2014-08-01 12:42:00    762\n",
       "2014-08-01 12:43:00    763\n",
       "2014-08-01 12:44:00    764\n",
       "2014-08-01 12:45:00    765\n",
       "2014-08-01 12:46:00    766\n",
       "2014-08-01 12:47:00    767\n",
       "2014-08-01 12:48:00    768\n",
       "2014-08-01 12:49:00    769\n",
       "2014-08-01 12:50:00    770\n",
       "2014-08-01 12:51:00    771\n",
       "2014-08-01 12:52:00    772\n",
       "2014-08-01 12:53:00    773\n",
       "2014-08-01 12:54:00    774\n",
       "2014-08-01 12:55:00    775\n",
       "2014-08-01 12:56:00    776\n",
       "2014-08-01 12:57:00    777\n",
       "2014-08-01 12:58:00    778\n",
       "2014-08-01 12:59:00    779\n",
       "Freq: T, dtype: int32"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bymin['2014-08-01 12:30:00':'2014-08-01 12:59']\n",
    "# the data can be extracted to the minute details."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b03dd06-b49a-4e49-be50-63a55749a552",
   "metadata": {},
   "source": [
    "### Representing intervals of time using periods:\n",
    "#### Period () method is used to determine a period starting and ending at the desired dates with the specified frequencies. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "c74cec8f-fdb6-4eba-9039-72f9332bd480",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Period('2014-08', 'M')"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aug2014 = pd.Period('2014-08', freq = 'M')\n",
    "aug2014\n",
    "# period('2014-08', 'M') contains only two dates, 1st and 31 of Aug, 2014 since the frequency is monthly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "a83fd45e-cf26-4d00-9d05-e5f778a61aa9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Timestamp('2014-08-01 00:00:00'), Timestamp('2014-08-31 23:59:59.999999999'))"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aug2014.start_time, aug2014.end_time\n",
    "# pandas assumes the start date to be 1st of Aug, when only month (partial date) is mentioned. Then using the one \n",
    "# month frequency, it figures the end date to be 31st of Aug."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "858a9db1-c819-42be-8325-95a2a3deabe1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Period('2014-09', 'M')"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sep2014 = aug2014 + 1\n",
    "sep2014\n",
    "# operators overloading when plus sign in this carries a different meaning and operation that it's usual behaviour.\n",
    "# In this case, addition of 1 to aug2014 will create the next month (sep2014) since the existing period (aug2014) is \n",
    "# based on a monthly frequency. If it were based on a quarterly frequency, then +1 will create next quarter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "8e7ed349-0a65-466d-8ea3-419ff3dbf4b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Timestamp('2014-09-01 00:00:00'), Timestamp('2014-09-30 23:59:59.999999999'))"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sep2014.start_time, sep2014.end_time\n",
    "# note that the pandas itself has figured out that September has 30 days unlike Aug that ended with the date 31."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "bf842fe2-e72a-4542-bedf-d8718ba78bd0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Period Objects: can be combined to form a PeriodIndex as shown below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "779c1cd8-95c5-4910-906b-2bc27ed15fd4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PeriodIndex(['2013-01', '2013-02', '2013-03', '2013-04', '2013-05', '2013-06',\n",
       "             '2013-07', '2013-08', '2013-09', '2013-10', '2013-11', '2013-12'],\n",
       "            dtype='period[M]')"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mp2013 = pd.period_range('2013-01-01', '2013-12-31', freq = 'M')\n",
    "mp2013\n",
    "# PeriodIndex has been created as shown below and confirmed in the type command.\n",
    "# In PeriodIndex, the index labels are period objects whereas in DatetimeIndex the index labels are TimeStamps."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "84c6b03f-a39f-460b-8f9b-ec85839b7fde",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.indexes.period.PeriodIndex"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(mp2013)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "96203ab7-89cb-432a-b204-671cf27eca8e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas._libs.tslibs.period.Period"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(mp2013[0])\n",
    "# PeriodIndex contains period objects."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "3b1040f6-4931-4e0c-85c7-ca23a5a2fd74",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2013-01 <MonthEnd> 2013-01-01 00:00:00 2013-01-31 23:59:59.999999999\n",
      "2013-02 <MonthEnd> 2013-02-01 00:00:00 2013-02-28 23:59:59.999999999\n",
      "2013-03 <MonthEnd> 2013-03-01 00:00:00 2013-03-31 23:59:59.999999999\n",
      "2013-04 <MonthEnd> 2013-04-01 00:00:00 2013-04-30 23:59:59.999999999\n",
      "2013-05 <MonthEnd> 2013-05-01 00:00:00 2013-05-31 23:59:59.999999999\n",
      "2013-06 <MonthEnd> 2013-06-01 00:00:00 2013-06-30 23:59:59.999999999\n",
      "2013-07 <MonthEnd> 2013-07-01 00:00:00 2013-07-31 23:59:59.999999999\n",
      "2013-08 <MonthEnd> 2013-08-01 00:00:00 2013-08-31 23:59:59.999999999\n",
      "2013-09 <MonthEnd> 2013-09-01 00:00:00 2013-09-30 23:59:59.999999999\n",
      "2013-10 <MonthEnd> 2013-10-01 00:00:00 2013-10-31 23:59:59.999999999\n",
      "2013-11 <MonthEnd> 2013-11-01 00:00:00 2013-11-30 23:59:59.999999999\n",
      "2013-12 <MonthEnd> 2013-12-01 00:00:00 2013-12-31 23:59:59.999999999\n"
     ]
    }
   ],
   "source": [
    "for p in mp2013:\n",
    "    print (\"{0} {1} {2} {3}\".format(p, p.freq, p.start_time, p.end_time))\n",
    "# first '2013-01' is taken from 'mp2013' and put in p, then print is executed. In print, the {} represents placeholders\n",
    "# for what comes in () after format. For instance,p relates with {0}, p.freq relates with {1} and so on. Note that the\n",
    "# PeriodIndex has Period objects as can be seen immediately above. Therefore, they have the property of .start_time and\n",
    "# .end_time.    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "68a3d5e2-d108-4c78-bca6-1ad48c563185",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.31781645,  0.0779821 ,  1.06029028,  1.32375514,  1.15061768,\n",
       "        0.53569604, -0.50476028, -1.10768209, -0.29793797, -0.71732139,\n",
       "        0.79841159,  0.94840427])"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = np.random.randn(12)\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "374640e0-0698-4d65-a7cd-0304c9885b2d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2013-01    0.3178164\n",
       "2013-02    0.0779821\n",
       "2013-03    1.0602903\n",
       "2013-04    1.3237551\n",
       "2013-05    1.1506177\n",
       "2013-06    0.5356960\n",
       "2013-07   -0.5047603\n",
       "2013-08   -1.1076821\n",
       "2013-09   -0.2979380\n",
       "2013-10   -0.7173214\n",
       "2013-11    0.7984116\n",
       "2013-12    0.9484043\n",
       "Freq: M, dtype: float64"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pandas_series = pd.Series(s, mp2013)\n",
    "pandas_series\n",
    "# this series has index labels containing Period Objects so the index is PeriodIndex rather than DatetimeIndex.\n",
    "# this type of indexing is especially helpfull when we are interested in finding average prices each month rather than\n",
    "# prices at specific dates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "80d2c990",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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