python-pour-finance/06-Pandas-avec-Time-Series/Datetime Index.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction aux Séries temporelles avec Pandas\n",
    "\n",
    "Beaucoup de nos données financières auront un index datatime, alors apprenons comment traiter ce genre de données avec pandas !"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pour illustrer l'ordre des arguments\n",
    "my_year = 2017\n",
    "my_month = 1\n",
    "my_day = 2\n",
    "my_hour = 13\n",
    "my_minute = 30\n",
    "my_second = 15"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# January 2nd, 2017\n",
    "my_date = datetime(my_year,my_month,my_day)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "datetime.datetime(2017, 1, 2, 0, 0)"
      ]
     },
     "metadata": {},
     "execution_count": 5
    }
   ],
   "source": [
    "# Par défaut 0:00\n",
    "my_date "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# January 2nd, 2017 à 13:30:15\n",
    "my_date_time = datetime(my_year,my_month,my_day,my_hour,my_minute,my_second)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "datetime.datetime(2017, 1, 2, 13, 30, 15)"
      ]
     },
     "metadata": {},
     "execution_count": 7
    }
   ],
   "source": [
    "my_date_time"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vous pouvez saisir n'importe quelle partie de l'objet datetime:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "metadata": {},
     "execution_count": 8
    }
   ],
   "source": [
    "my_date.day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "13"
      ]
     },
     "metadata": {},
     "execution_count": 9
    }
   ],
   "source": [
    "my_date_time.hour"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Index dataetime avec Pandas\n",
    "\n",
    "Vous traiterez généralement les séries temporelles comme un index lorsque vous travaillez avec des dataframes pandas obtenues à partir d'APIs financières. Heureusement, pandas a beaucoup de fonctions et de méthodes pour travailler avec des séries temporelles !"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "[datetime.datetime(2016, 1, 1, 0, 0), datetime.datetime(2016, 1, 2, 0, 0)]"
      ]
     },
     "metadata": {},
     "execution_count": 10
    }
   ],
   "source": [
    "# Créer un exemple de liste/tableau datetime\n",
    "first_two = [datetime(2016, 1, 1), datetime(2016, 1, 2)]\n",
    "first_two"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "DatetimeIndex(['2016-01-01', '2016-01-02'], dtype='datetime64[ns]', freq=None)"
      ]
     },
     "metadata": {},
     "execution_count": 11
    }
   ],
   "source": [
    "# Converti en un index\n",
    "dt_ind = pd.DatetimeIndex(first_two)\n",
    "dt_ind"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "[[-1.05763777 -0.15818632]\n [-0.79985649 -0.93157171]]\n"
     ]
    }
   ],
   "source": [
    "# Quelques données alétaoires\n",
    "data = np.random.randn(2,2)\n",
    "print(data)\n",
    "cols = ['A','B']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(data,dt_ind,cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "                   A         B\n",
       "2016-01-01 -1.057638 -0.158186\n",
       "2016-01-02 -0.799856 -0.931572"
      ],
      "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>A</th>\n      <th>B</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2016-01-01</th>\n      <td>-1.057638</td>\n      <td>-0.158186</td>\n    </tr>\n    <tr>\n      <th>2016-01-02</th>\n      <td>-0.799856</td>\n      <td>-0.931572</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 14
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "DatetimeIndex(['2016-01-01', '2016-01-02'], dtype='datetime64[ns]', freq=None)"
      ]
     },
     "metadata": {},
     "execution_count": 15
    }
   ],
   "source": [
    "df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "metadata": {},
     "execution_count": 16
    }
   ],
   "source": [
    "# Emplacement de la date la plus récente\n",
    "df.index.argmax()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "Timestamp('2016-01-02 00:00:00')"
      ]
     },
     "metadata": {},
     "execution_count": 17
    }
   ],
   "source": [
    "df.index.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "metadata": {},
     "execution_count": 18
    }
   ],
   "source": [
    "# Emplacement de la date la plus ancienne\n",
    "df.index.argmin()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "Timestamp('2016-01-01 00:00:00')"
      ]
     },
     "metadata": {},
     "execution_count": 19
    }
   ],
   "source": [
    "df.index.min()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Top, passons à la suite!"
   ]
  }
 ],
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