Stock Dynamics Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "791c8347",
   "metadata": {},
   "source": [
    "# Stock Dynamics Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c75a62e4",
   "metadata": {},
   "source": [
    "A stock market is where buyers and sellers trade shares of a company, and is one of the most popular ways for individuals and companies to invest money. The size of the world stock market  is now estimated to be in the trillions. The largest stock market in the world is the New York Stock Exchange (NYSE), located in New York City. About 2,800 companies were listed on the NSYE at the time time of this data xtarcted. In this problem, we'll look at the monthly stock prices of five of these companies: IBM, General Electric (GE), Procter and Gamble, Coca Cola, and Boeinga and create someisngiths and studies out of it with analysis and visulization. The data used in this problem comes from Infochimps.\n",
    "\n",
    "All data set will have same fillowing variable to keep the parity amoing all data sets for clear comparison.\n",
    "\n",
    "- Date: the date of the stock price, always given as the first of the month.\n",
    "- StockPrice: the average stock price of the company in the given month.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38577b94",
   "metadata": {},
   "source": [
    "### Agenda:\n",
    "    \n",
    "- Load, observe, summarise the data sets\n",
    "- Changing date formats compatible to pandas, and extract Date and years columns\n",
    "- Analyse price of each Company on plot chart.\n",
    "- Observe trends of stiock prices  from 1970 tO 201o.\n",
    "- Comparison of one company's stock price to the other.\n",
    "- Impact of 2000 Global Technogy buuuble in the stock price of each of five company\n",
    "- Impact of 1997 Asian Economy Mini Crash Ton the stock price of each of five company"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05476501",
   "metadata": {},
   "source": [
    "### Libraries \n",
    "Installing all required libraries to be used in our analysis and visulization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "86c74403",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7eb4a53f",
   "metadata": {},
   "source": [
    "### Loading data sets.\n",
    "Laoding data set of each comapny to start our Analaysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "726b1dcb",
   "metadata": {},
   "outputs": [],
   "source": [
    "IBM = pd.read_csv(\"IBMStock.csv\")\n",
    "GE = pd.read_csv(\"GEStock.csv\")\n",
    "PG = pd.read_csv(\"ProcterGambleStock.csv\")\n",
    "CocaCola = pd.read_csv(\"CocaColaStock.csv\")\n",
    "Boeing = pd.read_csv(\"BoeingStock.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c83a933",
   "metadata": {},
   "source": [
    "### Summary \n",
    "Visulizing summary of one random data set, data frame of second, and length of any other random dataset to get an gist of our data sets before going ahead."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "187beb5b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "480"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(CocaCola)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "873f81ef",
   "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>Date</th>\n",
       "      <th>StockPrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1/1/1970</td>\n",
       "      <td>360.319048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2/1/1970</td>\n",
       "      <td>346.723684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3/1/1970</td>\n",
       "      <td>327.345714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4/1/1970</td>\n",
       "      <td>319.852727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5/1/1970</td>\n",
       "      <td>270.375238</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Date  StockPrice\n",
       "0  1/1/1970  360.319048\n",
       "1  2/1/1970  346.723684\n",
       "2  3/1/1970  327.345714\n",
       "3  4/1/1970  319.852727\n",
       "4  5/1/1970  270.375238"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IBM.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "08312c2e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(480, 2)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "PG.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91ab1ce4",
   "metadata": {},
   "source": [
    "### Changing Data format into Pandas\n",
    "Date column in each data set has combined month, date  and years written in one single column. Lets change the date format to datetime format compatible to pandas to extract Years and month into differents columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "9b6b5f0c",
   "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>Date</th>\n",
       "      <th>StockPrice</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1970-01-01</td>\n",
       "      <td>360.319048</td>\n",
       "      <td>1970</td>\n",
       "      <td>January</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1970-02-01</td>\n",
       "      <td>346.723684</td>\n",
       "      <td>1970</td>\n",
       "      <td>February</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1970-03-01</td>\n",
       "      <td>327.345714</td>\n",
       "      <td>1970</td>\n",
       "      <td>March</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1970-04-01</td>\n",
       "      <td>319.852727</td>\n",
       "      <td>1970</td>\n",
       "      <td>April</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1970-05-01</td>\n",
       "      <td>270.375238</td>\n",
       "      <td>1970</td>\n",
       "      <td>May</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date  StockPrice  Year     Month\n",
       "0 1970-01-01  360.319048  1970   January\n",
       "1 1970-02-01  346.723684  1970  February\n",
       "2 1970-03-01  327.345714  1970     March\n",
       "3 1970-04-01  319.852727  1970     April\n",
       "4 1970-05-01  270.375238  1970       May"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IBM[\"Date\"] = pd.to_datetime(IBM[\"Date\"])\n",
    "IBM[\"Year\"] = IBM[\"Date\"].dt.year\n",
    "IBM[\"Month\"] = IBM[\"Date\"].dt.month_name(locale='English')\n",
    "\n",
    "PG[\"Date\"] = pd.to_datetime(PG[\"Date\"])\n",
    "PG[\"Year\"] = PG[\"Date\"].dt.year\n",
    "PG[\"Month\"] = PG[\"Date\"].dt.month_name(locale='English')\n",
    "\n",
    "Boeing[\"Date\"] = pd.to_datetime(Boeing[\"Date\"])\n",
    "Boeing[\"Year\"] = Boeing[\"Date\"].dt.year\n",
    "Boeing[\"Month\"] = Boeing[\"Date\"].dt.month_name(locale='English')\n",
    "\n",
    "GE[\"Date\"] = pd.to_datetime(GE[\"Date\"])\n",
    "GE[\"Year\"] = GE[\"Date\"].dt.year\n",
    "GE[\"Month\"] = GE[\"Date\"].dt.month_name(locale='English')\n",
    "\n",
    "CocaCola[\"Date\"] = pd.to_datetime(CocaCola[\"Date\"])\n",
    "CocaCola[\"Year\"] = CocaCola[\"Date\"].dt.year\n",
    "CocaCola[\"Month\"] = CocaCola[\"Date\"].dt.month_name(locale='English')\n",
    "\n",
    "IBM.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45e93a5c",
   "metadata": {},
   "source": [
    "### Earliest and Latest Data\n",
    "Lets find the the the earliest date and Latest date in our data set for our understanding "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "bd0c75a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('1970-01-01 00:00:00')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IBM.Date.iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "d8281ef9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2009-12-01 00:00:00')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IBM.Date.iloc[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "9bcdc366",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1970"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IBM.Year.iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "b0a3d070",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2009"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IBM.Year.iloc[-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1d0037c",
   "metadata": {},
   "source": [
    "### Max, Min, Mean, Median and SD \n",
    "Lets find the the the maximun, minimum, average and standard deviation of different data set for our understanding."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "72623cf7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "146.5842857"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CocaCola.StockPrice.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "d5dadb1e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "146.58"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#To write it better, rounding up the value upto two decimal points.\n",
    "round(float(CocaCola.StockPrice.max()),2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "56abbaae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "46.88"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Minmum stock price of PG\n",
    "round(float(PG.StockPrice.min()),2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "4a4c13d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "77.7"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Mean Stock Price of Boeing\n",
    "round(float(Boeing.StockPrice.mean()),2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "5fdf02be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "112.11459525000001"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Median Stock Price of IBM\n",
    "IBM.StockPrice.median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "e237b706",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19.89"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Standard deviation of stock price of Boeing\n",
    "round(float(Boeing.StockPrice.std()),2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c817e6fc",
   "metadata": {},
   "source": [
    "### Ploting stock Price of CocaCola\n",
    "Lets put stock price of CocaCola on the plot chart to observe the trend of its Stock proce over the from 1970 to 2010"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "893fe7d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(CocaCola.Year, CocaCola.StockPrice,\"o\")\n",
    "plt.xlabel(\"Year\")\n",
    "plt.ylabel(\"Stock Price\")\n",
    "plt.title (\"Stock Price of CocaCola\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7d875cc",
   "metadata": {},
   "source": [
    "##### We can observe the overall decreasing trend in the stock price of CocaCola from 1970 to 2010. 1972 witnessed the highest stock price whereas 1980 the lowest"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ac47801",
   "metadata": {},
   "source": [
    "### Ploting stock Price of  Procter and Gamble\n",
    "Lets out one more Plot chart to observe one more trend, and this time we will keep the chart in lines to for easy observations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "705c3ad4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(PG.Year, PG.StockPrice,\"-\")\n",
    "plt.xlabel(\"Year\")\n",
    "plt.ylabel(\"Stock Price\")\n",
    "plt.title (\"Stock Price of PG\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3dcfd168",
   "metadata": {},
   "source": [
    "We can observe the overall Constant Performance in the stock price of PG from 1970 to 2010. 1996 witnessed the highest stock price whereas 2009 the lowest"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5d6800b",
   "metadata": {},
   "source": [
    "### Comparison of Stock Prices of Procter and Gamble and Coca Cola"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "ae6bebf7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "PG[\"Date\"] = pd.to_datetime(PG[\"Date\"])\n",
    "PG[\"Year\"] = PG[\"Date\"].dt.year\n",
    "\n",
    "Line1, = plt.plot(PG.Year, PG.StockPrice)\n",
    "Line2, = plt.plot(CocaCola.Year, CocaCola.StockPrice)\n",
    "plt.xlabel(\"Year\")\n",
    "plt.ylabel(\"Stock Price\")\n",
    "\n",
    "\n",
    "plt.title (\"Stock Price of CocaCola and PG\")\n",
    "plt.legend([Line1,Line2],[\"PG\",\"CocaCola\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1925fd88",
   "metadata": {},
   "source": [
    "###### Two lines, Each for different company can be seen on created plot. In March of 2000, the technology bubble burst worldwide, and a stock market crash occurred. According to our plot, The bubble impacted Coca Cola in a drastic way. It can be seen that after 2000, Stock prices of Procater and Gamble started to rise where as for Coca Cola, it started to fall."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "233a80c4",
   "metadata": {},
   "source": [
    "### Impact of 2000 Technology bubble\n",
    "\n",
    "In Technogly bubble of 2000, Mnay comapny Stock prices started to crashed and Maany started to rise. We Will see this Impact all five companies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "ee2a6837",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(CocaCola.Year[301:432], CocaCola.StockPrice[301:432],label = \"CocaCola\")\n",
    "plt.plot(PG.Year[301:432], PG.StockPrice[301:432],label = \"PG\")\n",
    "plt.plot(Boeing.Year[301:432], Boeing.StockPrice[301:432],label = \"Boeing\")\n",
    "plt.plot(GE.Year[301:432], GE.StockPrice[301:432],label = \"GE\")\n",
    "plt.plot(IBM.Year[301:432], IBM.StockPrice[301:432],label = \"IBM\")\n",
    "\n",
    "plt.title (\"Stock Price of all comanies during Mini Asian Crash from Sep to Nov in 1997\")\n",
    "plt.xlabel(\"Month\")\n",
    "plt.ylabel(\"Stock Price\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86acb7c4",
   "metadata": {},
   "source": [
    "###### It can be observed that General Electric stock Price was worst hit by the impact of 2005 Technogy buuble. There is a huge difference in the trends of General Electric stock price before and after 2000 Year"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49d12e04",
   "metadata": {},
   "source": [
    "### Impact of 1997 Asin Economy MiniCrash\n",
    "\n",
    "In October of 1997, there was a global stock market crash that was caused by an economic crisis in Asia. Comparing September 1997 to November 1997, Lets observe which companies saw a decreasing trend in their stock price?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "111587bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(CocaCola.Month[332:335], CocaCola.StockPrice[332:335],label = \"CocaCola\")\n",
    "plt.plot(PG.Month[332:335], PG.StockPrice[332:335],label = \"PG\")\n",
    "plt.plot(Boeing.Month[332:335], Boeing.StockPrice[332:335],label = \"Boeing\")\n",
    "plt.plot(GE.Month[332:335], GE.StockPrice[332:335],label = \"GE\")\n",
    "plt.plot(IBM.Month[332:335], IBM.StockPrice[332:335],label = \"IBM\")\n",
    "\n",
    "plt.title (\"Stock Price of all comanies during Mini Asian Crash from Sep to Nov in 1997\")\n",
    "plt.xlabel(\"Month\")\n",
    "plt.ylabel(\"Stock Price\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a050507",
   "metadata": {},
   "source": [
    "###### AS we Asian Mini crash of economy inpacted the stock Proce of beoing the most."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}