netflix_visualizations.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction\n",
    "\n",
    "In this project, you will act as a data visualization developer at Yahoo Finance! You will be helping the \"Netflix Stock Profile\" team visualize the Netflix stock data. In finance, a _stock profile_ is a series of studies, visualizations, and analyses that dive into different aspects a publicly traded company's data. \n",
    "\n",
    "For the purposes of the project, you will only visualize data for the year of 2017. Specifically, you will be in charge of creating the following visualizations:\n",
    "+ The distribution of the stock prices for the past year\n",
    "+ Netflix's earnings and revenue in the last four quarters\n",
    "+ The actual vs. estimated earnings per share for the four quarters in 2017\n",
    "+ A comparison of the Netflix Stock price vs the Dow Jones Industrial Average price in 2017 \n",
    "\n",
    "Note: We are using the Dow Jones Industrial Average to compare the Netflix stock to the larter stock market. Learn more about why the Dow Jones Industrial Average is a general reflection of the larger stock market [here](https://www.investopedia.com/terms/d/djia.asp).\n",
    "\n",
    "During this project, you will analyze, prepare, and plot data. Your visualizations will help the financial analysts asses the risk of the Netflix stock.\n",
    "\n",
    "After you complete your visualizations, you'll be creating a presentation to share the images with the rest of the Netflix Stock Profile team. Your slides should include:\n",
    "\n",
    "- A title slide\n",
    "- A list of your visualizations and your role in their creation for the \"Stock Profile\" team\n",
    "- A visualization of the distribution of the stock prices for Netflix in 2017\n",
    "- A visualization and a summary of Netflix stock and revenue for the past four quarters and a summary\n",
    "- A visualization and a brief summary of their earned versus actual earnings per share\n",
    "- A visualization of Netflix stock against the Dow Jones stock (to get a sense of the market) in 2017\n",
    "\n",
    "Financial Data Source: [Yahoo Finance](https://finance.yahoo.com/quote/DATA/)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 1\n",
    "\n",
    "Let's get our notebook ready for visualizing! Import the modules that you'll be using in this project:\n",
    "- `from matplotlib import pyplot as plt`\n",
    "- `import pandas as pd`\n",
    "- `import seaborn as sns`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's load the datasets and inspect them."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load **NFLX.csv** into a DataFrame called `netflix_stocks`. Then, quickly inspect the DataFrame using `print()`.\n",
    "\n",
    "Hint: Use the `pd.read_csv()`function).\n",
    "\n",
    "Note: In the Yahoo Data, `Adj Close` represents the adjusted close price adjusted for both dividends and splits. This means this is the true closing stock price for a given business day."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          Date        Open        High         Low       Close   Adj Close  \\\n",
      "0   2017-01-01  124.959999  143.460007  124.309998  140.710007  140.710007   \n",
      "1   2017-02-01  141.199997  145.949997  139.050003  142.130005  142.130005   \n",
      "2   2017-03-01  142.839996  148.289993  138.259995  147.809998  147.809998   \n",
      "3   2017-04-01  146.699997  153.520004  138.660004  152.199997  152.199997   \n",
      "4   2017-05-01  151.910004  164.750000  151.610001  163.070007  163.070007   \n",
      "5   2017-06-01  163.520004  166.869995  147.300003  149.410004  149.410004   \n",
      "6   2017-07-01  149.800003  191.500000  144.250000  181.660004  181.660004   \n",
      "7   2017-08-01  182.490005  184.619995  164.229996  174.710007  174.710007   \n",
      "8   2017-09-01  175.550003  189.949997  172.440002  181.350006  181.350006   \n",
      "9   2017-10-01  182.110001  204.380005  176.580002  196.429993  196.429993   \n",
      "10  2017-11-01  197.240005  202.479996  184.320007  195.509995  195.509995   \n",
      "11  2017-12-01  186.990005  194.490005  178.380005  191.960007  191.960007   \n",
      "\n",
      "       Volume  \n",
      "0   181772200  \n",
      "1    91432000  \n",
      "2   110692700  \n",
      "3   149769200  \n",
      "4   116795800  \n",
      "5   135675800  \n",
      "6   185144700  \n",
      "7   136523100  \n",
      "8   111427900  \n",
      "9   208657800  \n",
      "10  161719700  \n",
      "11  115103700  \n"
     ]
    }
   ],
   "source": [
    "netflix_stocks = pd.read_csv('NFLX.csv')\n",
    "print(netflix_stocks)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load **DJI.csv** into a DataFrame called `dowjones_stocks`. Then, quickly inspect the DataFrame using `print()`.\n",
    "\n",
    "Note: You can learn more about why the Dow Jones Industrial Average is a industry reflection of the larger stock market [here](https://www.investopedia.com/terms/d/djia.asp). \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          Date          Open          High           Low         Close  \\\n",
      "0   2017-01-01  19872.859375  20125.580078  19677.939453  19864.089844   \n",
      "1   2017-02-01  19923.810547  20851.330078  19831.089844  20812.240234   \n",
      "2   2017-03-01  20957.289063  21169.109375  20412.800781  20663.220703   \n",
      "3   2017-04-01  20665.169922  21070.900391  20379.550781  20940.509766   \n",
      "4   2017-05-01  20962.730469  21112.320313  20553.449219  21008.650391   \n",
      "5   2017-06-01  21030.550781  21535.029297  20994.220703  21349.630859   \n",
      "6   2017-07-01  21392.300781  21929.800781  21279.300781  21891.119141   \n",
      "7   2017-08-01  21961.419922  22179.109375  21600.339844  21948.099609   \n",
      "8   2017-09-01  21981.769531  22419.509766  21709.630859  22405.089844   \n",
      "9   2017-10-01  22423.470703  23485.250000  22416.000000  23377.240234   \n",
      "10  2017-11-01  23442.900391  24327.820313  23242.750000  24272.349609   \n",
      "11  2017-12-01  24305.400391  24876.070313  23921.900391  24719.220703   \n",
      "\n",
      "       Adj Close      Volume  \n",
      "0   19864.089844  6482450000  \n",
      "1   20812.240234  6185580000  \n",
      "2   20663.220703  6941970000  \n",
      "3   20940.509766  5392630000  \n",
      "4   21008.650391  6613570000  \n",
      "5   21349.630859  7214590000  \n",
      "6   21891.119141  5569720000  \n",
      "7   21948.099609  6150060000  \n",
      "8   22405.089844  6342130000  \n",
      "9   23377.240234  7302910000  \n",
      "10  24272.349609  7335640000  \n",
      "11  24719.220703  6589890000  \n"
     ]
    }
   ],
   "source": [
    "dowjones_stocks = pd.read_csv('DJI.csv')\n",
    "print(dowjones_stocks)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load **NFLX_daily_by_quarter.csv** into a DataFrame called `netflix_stocks_quarterly`. Then, quickly inspect the DataFrame using `print()`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          Date          Open          High           Low         Close  \\\n",
      "0   2017-01-01  19872.859375  20125.580078  19677.939453  19864.089844   \n",
      "1   2017-02-01  19923.810547  20851.330078  19831.089844  20812.240234   \n",
      "2   2017-03-01  20957.289063  21169.109375  20412.800781  20663.220703   \n",
      "3   2017-04-01  20665.169922  21070.900391  20379.550781  20940.509766   \n",
      "4   2017-05-01  20962.730469  21112.320313  20553.449219  21008.650391   \n",
      "5   2017-06-01  21030.550781  21535.029297  20994.220703  21349.630859   \n",
      "6   2017-07-01  21392.300781  21929.800781  21279.300781  21891.119141   \n",
      "7   2017-08-01  21961.419922  22179.109375  21600.339844  21948.099609   \n",
      "8   2017-09-01  21981.769531  22419.509766  21709.630859  22405.089844   \n",
      "9   2017-10-01  22423.470703  23485.250000  22416.000000  23377.240234   \n",
      "10  2017-11-01  23442.900391  24327.820313  23242.750000  24272.349609   \n",
      "11  2017-12-01  24305.400391  24876.070313  23921.900391  24719.220703   \n",
      "\n",
      "       Adj Close      Volume  \n",
      "0   19864.089844  6482450000  \n",
      "1   20812.240234  6185580000  \n",
      "2   20663.220703  6941970000  \n",
      "3   20940.509766  5392630000  \n",
      "4   21008.650391  6613570000  \n",
      "5   21349.630859  7214590000  \n",
      "6   21891.119141  5569720000  \n",
      "7   21948.099609  6150060000  \n",
      "8   22405.089844  6342130000  \n",
      "9   23377.240234  7302910000  \n",
      "10  24272.349609  7335640000  \n",
      "11  24719.220703  6589890000  \n"
     ]
    }
   ],
   "source": [
    "netflix_stocks_quarterly = pd.read_csv('NFLX_daily_by_quarter.csv')\n",
    "print(dowjones_stocks)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's learn more about our data. The datasets are large and it may be easier to view the entire dataset locally on your computer. Open the CSV files directly from the folder you downloaded for this project.\n",
    " - `NFLX` is the stock ticker symbol for Netflix and `^DJI` is the stock ticker symbol for the Dow Jones industrial Average, which is why the CSV files are named accordingly\n",
    " - In the Yahoo Data, `Adj Close` is documented as adjusted close price adjusted for both dividends and splits.\n",
    " - You can learn more about why the Dow Jones Industrial Average is a industry reflection of the larger stock market [here](https://www.investopedia.com/terms/d/djia.asp). \n",
    " \n",
    "Answer the following questions by inspecting the data in the **NFLX.csv**,**DJI.csv**, and **NFLX_daily_by_quarter.csv** in your computer."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What year is represented in the data? Look out for the latest and earliest date."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2017"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "2017"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "+ Is the data represented by days, weeks, or months? \n",
    "+ In which ways are the files different? \n",
    "+ What's different about the columns for `netflix_stocks` versus `netflix_stocks_quarterly`?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*day-month-year(all)\n",
    "*Data representation; netflix_stocks_quarterly shows daily price whereas others shows the data for the first day of each month\n",
    "*netflix_stocks_quarterly reports the quarterly volume"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 4\n",
    "\n",
    "Great! Now that we have spent sometime looking at the data, let's look at the column names of the DataFrame `netflix_stocks` using `.head()`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-01-01</td>\n",
       "      <td>124.959999</td>\n",
       "      <td>143.460007</td>\n",
       "      <td>124.309998</td>\n",
       "      <td>140.710007</td>\n",
       "      <td>140.710007</td>\n",
       "      <td>181772200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-02-01</td>\n",
       "      <td>141.199997</td>\n",
       "      <td>145.949997</td>\n",
       "      <td>139.050003</td>\n",
       "      <td>142.130005</td>\n",
       "      <td>142.130005</td>\n",
       "      <td>91432000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-03-01</td>\n",
       "      <td>142.839996</td>\n",
       "      <td>148.289993</td>\n",
       "      <td>138.259995</td>\n",
       "      <td>147.809998</td>\n",
       "      <td>147.809998</td>\n",
       "      <td>110692700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-04-01</td>\n",
       "      <td>146.699997</td>\n",
       "      <td>153.520004</td>\n",
       "      <td>138.660004</td>\n",
       "      <td>152.199997</td>\n",
       "      <td>152.199997</td>\n",
       "      <td>149769200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-05-01</td>\n",
       "      <td>151.910004</td>\n",
       "      <td>164.750000</td>\n",
       "      <td>151.610001</td>\n",
       "      <td>163.070007</td>\n",
       "      <td>163.070007</td>\n",
       "      <td>116795800</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date        Open        High         Low       Close   Adj Close  \\\n",
       "0  2017-01-01  124.959999  143.460007  124.309998  140.710007  140.710007   \n",
       "1  2017-02-01  141.199997  145.949997  139.050003  142.130005  142.130005   \n",
       "2  2017-03-01  142.839996  148.289993  138.259995  147.809998  147.809998   \n",
       "3  2017-04-01  146.699997  153.520004  138.660004  152.199997  152.199997   \n",
       "4  2017-05-01  151.910004  164.750000  151.610001  163.070007  163.070007   \n",
       "\n",
       "      Volume  \n",
       "0  181772200  \n",
       "1   91432000  \n",
       "2  110692700  \n",
       "3  149769200  \n",
       "4  116795800  "
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "netflix_stocks.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What do you notice? The first two column names are one word each, and the only one that is not is `Adj Close`! \n",
    "\n",
    "The term `Adj Close` is a confusing term if you don't read the Yahoo Documentation. In Yahoo, `Adj Close` is documented as adjusted close price adjusted for both dividends and splits.\n",
    "\n",
    "This means this is the column with the true closing price, so these data are very important.\n",
    "\n",
    "Use Pandas to change the name of of the column to `Adj Close` to `Price` so that it is easier to work with the data. Remember to use `inplace=True`.\n",
    "\n",
    "Do this for the Dow Jones and Netflix Quarterly pandas dataframes as well.\n",
    "Hint: Use [`.rename()`](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.rename.html)).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "netflix_stocks = netflix_stocks.rename(columns={'Adj Close': 'Price'})\n",
    "dowjones_stocks = dowjones_stocks.rename(columns={'Adj Close': 'Price'})\n",
    "netflix_stocks_quarterly = netflix_stocks_quarterly.rename(columns={'Adj Close': 'Price'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run `netflix_stocks.head()` again to check your column name has changed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Price</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-01-01</td>\n",
       "      <td>124.959999</td>\n",
       "      <td>143.460007</td>\n",
       "      <td>124.309998</td>\n",
       "      <td>140.710007</td>\n",
       "      <td>140.710007</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-02-01</td>\n",
       "      <td>141.199997</td>\n",
       "      <td>145.949997</td>\n",
       "      <td>139.050003</td>\n",
       "      <td>142.130005</td>\n",
       "      <td>142.130005</td>\n",
       "      <td>91432000</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-03-01</td>\n",
       "      <td>142.839996</td>\n",
       "      <td>148.289993</td>\n",
       "      <td>138.259995</td>\n",
       "      <td>147.809998</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-04-01</td>\n",
       "      <td>146.699997</td>\n",
       "      <td>153.520004</td>\n",
       "      <td>138.660004</td>\n",
       "      <td>152.199997</td>\n",
       "      <td>152.199997</td>\n",
       "      <td>149769200</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-05-01</td>\n",
       "      <td>151.910004</td>\n",
       "      <td>164.750000</td>\n",
       "      <td>151.610001</td>\n",
       "      <td>163.070007</td>\n",
       "      <td>163.070007</td>\n",
       "      <td>116795800</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "         Date        Open        High         Low       Close       Price  \\\n",
       "0  2017-01-01  124.959999  143.460007  124.309998  140.710007  140.710007   \n",
       "1  2017-02-01  141.199997  145.949997  139.050003  142.130005  142.130005   \n",
       "2  2017-03-01  142.839996  148.289993  138.259995  147.809998  147.809998   \n",
       "3  2017-04-01  146.699997  153.520004  138.660004  152.199997  152.199997   \n",
       "4  2017-05-01  151.910004  164.750000  151.610001  163.070007  163.070007   \n",
       "\n",
       "      Volume  \n",
       "0  181772200  \n",
       "1   91432000  \n",
       "2  110692700  \n",
       "3  149769200  \n",
       "4  116795800  "
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "netflix_stocks.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Call `.head()` on the DataFrame `dowjones_stocks` and `netflix_stocks_quarterly`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Price</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-01-01</td>\n",
       "      <td>19872.859375</td>\n",
       "      <td>20125.580078</td>\n",
       "      <td>19677.939453</td>\n",
       "      <td>19864.089844</td>\n",
       "      <td>19864.089844</td>\n",
       "      <td>6482450000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-02-01</td>\n",
       "      <td>19923.810547</td>\n",
       "      <td>20851.330078</td>\n",
       "      <td>19831.089844</td>\n",
       "      <td>20812.240234</td>\n",
       "      <td>20812.240234</td>\n",
       "      <td>6185580000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-03-01</td>\n",
       "      <td>20957.289063</td>\n",
       "      <td>21169.109375</td>\n",
       "      <td>20412.800781</td>\n",
       "      <td>20663.220703</td>\n",
       "      <td>20663.220703</td>\n",
       "      <td>6941970000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-04-01</td>\n",
       "      <td>20665.169922</td>\n",
       "      <td>21070.900391</td>\n",
       "      <td>20379.550781</td>\n",
       "      <td>20940.509766</td>\n",
       "      <td>20940.509766</td>\n",
       "      <td>5392630000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-05-01</td>\n",
       "      <td>20962.730469</td>\n",
       "      <td>21112.320313</td>\n",
       "      <td>20553.449219</td>\n",
       "      <td>21008.650391</td>\n",
       "      <td>21008.650391</td>\n",
       "      <td>6613570000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date          Open          High           Low         Close  \\\n",
       "0  2017-01-01  19872.859375  20125.580078  19677.939453  19864.089844   \n",
       "1  2017-02-01  19923.810547  20851.330078  19831.089844  20812.240234   \n",
       "2  2017-03-01  20957.289063  21169.109375  20412.800781  20663.220703   \n",
       "3  2017-04-01  20665.169922  21070.900391  20379.550781  20940.509766   \n",
       "4  2017-05-01  20962.730469  21112.320313  20553.449219  21008.650391   \n",
       "\n",
       "          Price      Volume  \n",
       "0  19864.089844  6482450000  \n",
       "1  20812.240234  6185580000  \n",
       "2  20663.220703  6941970000  \n",
       "3  20940.509766  5392630000  \n",
       "4  21008.650391  6613570000  "
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dowjones_stocks.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Price</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Quarter</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-01-03</td>\n",
       "      <td>124.959999</td>\n",
       "      <td>128.190002</td>\n",
       "      <td>124.309998</td>\n",
       "      <td>127.489998</td>\n",
       "      <td>127.489998</td>\n",
       "      <td>9437900</td>\n",
       "      <td>Q1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-01-04</td>\n",
       "      <td>127.489998</td>\n",
       "      <td>130.169998</td>\n",
       "      <td>126.550003</td>\n",
       "      <td>129.410004</td>\n",
       "      <td>129.410004</td>\n",
       "      <td>7843600</td>\n",
       "      <td>Q1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-01-05</td>\n",
       "      <td>129.220001</td>\n",
       "      <td>132.750000</td>\n",
       "      <td>128.899994</td>\n",
       "      <td>131.809998</td>\n",
       "      <td>131.809998</td>\n",
       "      <td>10185500</td>\n",
       "      <td>Q1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-01-06</td>\n",
       "      <td>132.080002</td>\n",
       "      <td>133.880005</td>\n",
       "      <td>129.809998</td>\n",
       "      <td>131.070007</td>\n",
       "      <td>131.070007</td>\n",
       "      <td>10657900</td>\n",
       "      <td>Q1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-01-09</td>\n",
       "      <td>131.479996</td>\n",
       "      <td>131.990005</td>\n",
       "      <td>129.889999</td>\n",
       "      <td>130.949997</td>\n",
       "      <td>130.949997</td>\n",
       "      <td>5766900</td>\n",
       "      <td>Q1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date        Open        High         Low       Close       Price  \\\n",
       "0  2017-01-03  124.959999  128.190002  124.309998  127.489998  127.489998   \n",
       "1  2017-01-04  127.489998  130.169998  126.550003  129.410004  129.410004   \n",
       "2  2017-01-05  129.220001  132.750000  128.899994  131.809998  131.809998   \n",
       "3  2017-01-06  132.080002  133.880005  129.809998  131.070007  131.070007   \n",
       "4  2017-01-09  131.479996  131.990005  129.889999  130.949997  130.949997   \n",
       "\n",
       "     Volume Quarter  \n",
       "0   9437900      Q1  \n",
       "1   7843600      Q1  \n",
       "2  10185500      Q1  \n",
       "3  10657900      Q1  \n",
       "4   5766900      Q1  "
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "netflix_stocks_quarterly.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 5\n",
    "\n",
    "In this step, we will be visualizing the Netflix quarterly data! \n",
    "\n",
    "We want to get an understanding of the distribution of the Netflix quarterly stock prices for 2017. Specifically, we want to see in which quarter stock prices flucutated the most. We can accomplish this using a violin plot with four violins, one for each business quarter!\n",
    "\n",
    "\n",
    "1. Start by creating a variable `ax` and setting it equal to `sns.violinplot()`. This will instantiate a figure and give us access to the axes through the variable name `ax`.\n",
    "2. Use `sns.violinplot()` and pass in the following arguments:\n",
    "+ The `Quarter` column as the `x` values\n",
    "+ The `Price` column as your `y` values\n",
    "+ The `netflix_stocks_quarterly` dataframe as your `data`\n",
    "3. Improve the readability of the chart by adding a title of the plot. Add `\"Distribution of 2017 Netflix Stock Prices by Quarter\"` by using `ax.set_title()`\n",
    "4. Change your `ylabel` to \"Closing Stock Price\"\n",
    "5. Change your `xlabel` to \"Business Quarters in 2017\"\n",
    "6. Be sure to show your plot!\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 6))\n",
    "ax = sns.violinplot(data=netflix_stocks_quarterly, x='Quarter', y='Price')\n",
    "ax.set_title(\"Distribution of 2017 Netflix Stock Prices by Quarter\")\n",
    "plt.xlabel(\"Business Quarters in 2017\")\n",
    "plt.ylabel(\"Closing Stock Price\")\n",
    "plt.show()\n",
    "plt.savefig(\"distribution_of_2017_netflix_stock_price_per_quarter.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Graph Literacy\n",
    "- What are your first impressions looking at the visualized data?\n",
    "\n",
    "- In what range(s) did most of the prices fall throughout the year?\n",
    "\n",
    "- What were the highest and lowest prices? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Price increases with each close of the quarter\n",
    "140 - 185\n",
    "124.31 to 132.75"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 6\n",
    "\n",
    "Next, we will chart the performance of the earnings per share (EPS) by graphing the estimate Yahoo projected for the Quarter compared to the actual earnings for that quarters. We will accomplish this using a scatter chart. \n",
    "\n",
    "1. Plot the actual EPS by using `x_positions` and `earnings_actual` with the `plt.scatter()` function. Assign `red` as the color.\n",
    "2. Plot the actual EPS by using `x_positions` and `earnings_estimate` with the `plt.scatter()` function. Assign `blue` as the color\n",
    "\n",
    "3. Often, estimates and actual EPS are the same. To account for this, be sure to set your transparency  `alpha=0.5` to allow for visibility pf overlapping datapoint.\n",
    "4. Add a legend by using `plt.legend()` and passing in a list with two strings `[\"Actual\", \"Estimate\"]`\n",
    "\n",
    "5. Change the `x_ticks` label to reflect each quarter by using `plt.xticks(x_positions, chart_labels)`\n",
    "6. Assing \"`\"Earnings Per Share in Cents\"` as the title of your plot.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_positions = [1, 2, 3, 4]\n",
    "chart_labels = [\"1Q2017\",\"2Q2017\",\"3Q2017\",\"4Q2017\"]\n",
    "earnings_actual =[.4, .15,.29,.41]\n",
    "earnings_estimate = [.37,.15,.32,.41 ]\n",
    "\n",
    "plt.figure(figsize=(12, 6))\n",
    "plt.scatter(x_positions, earnings_actual, color='red', alpha=0.5)\n",
    "plt.scatter(x_positions, earnings_estimate, color='blue', alpha=0.5)\n",
    "\n",
    "plt.legend([\"Actual\", \"Estimate\"])\n",
    "plt.title(\"Earnings Per Share in Cents\")\n",
    "plt.xticks(x_positions, chart_labels)\n",
    "plt.show()\n",
    "plt.savefig(\"earning_per_cent_in_cents.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Graph Literacy\n",
    "\n",
    "+ What do the purple dots tell us about the actual and estimate earnings per share in this graph? Hint: In color theory red and blue mix to make purple.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "The actual earning and the estimated earning are the same at the 2nd and fourth point"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 7"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we will visualize the earnings and revenue reported by Netflix by mapping two bars side-by-side. We have visualized a similar chart in the second Matplotlib lesson [Exercise 4](https://www.codecademy.com/courses/learn-matplotlib/lessons/matplotlib-ii/exercises/side-by-side-bars).\n",
    "\n",
    "As you may recall, plotting side-by-side bars in Matplotlib requires computing the width of each bar before hand. We have pasted the starter code for that exercise below. \n",
    "\n",
    "1. Fill in the `n`, `t`, `d`, `w` values for the revenue bars\n",
    "2. Plot the revenue bars by calling `plt.bar()` with the newly computed `x_values` and the `revenue_by_quarter` data\n",
    "3. Fill in the `n`, `t`, `d`, `w` values for the earnings bars\n",
    "4. Plot the revenue bars by calling `plt.bar()` with the newly computed `x_values` and the `earnings_by_quarter` data\n",
    "5. Create a legend for your bar chart with the `labels` provided\n",
    "6. Add a descriptive title for your chart with `plt.title()`\n",
    "7. Add labels to each quarter by assigning the position of the ticks through the code provided. Hint:  `plt.xticks(middle_x, quarter_labels)`\n",
    "8. Be sure to show your plot!\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# The metrics below are in billions of dollars\n",
    "revenue_by_quarter = [2.79, 2.98,3.29,3.7]\n",
    "earnings_by_quarter = [.0656,.12959,.18552,.29012]\n",
    "quarter_labels = [\"2Q2017\",\"3Q2017\",\"4Q2017\", \"1Q2018\"]\n",
    "\n",
    "plt.figure(figsize=(12, 6))\n",
    "\n",
    "# Revenue\n",
    "n = 1  # This is our first dataset (out of 2)\n",
    "t = 2 # Number of dataset\n",
    "d = 4 # Number of sets of bars\n",
    "w = 0.8 # Width of each bar\n",
    "bars1_x = [t*element + w*n for element\n",
    "             in range(d)]\n",
    "\n",
    "plt.bar(bars1_x, revenue_by_quarter)\n",
    "\n",
    "# Earnings\n",
    "n = 2  # This is our second dataset (out of 2)\n",
    "t = 2 # Number of dataset\n",
    "d = 4 # Number of sets of bars\n",
    "w = 0.8 # Width of each bar\n",
    "bars2_x = [t*element + w*n for element\n",
    "             in range(d)]\n",
    "\n",
    "plt.bar(bars2_x, earnings_by_quarter)\n",
    "\n",
    "\n",
    "\n",
    "middle_x = [ (a + b) / 2.0 for a, b in zip(bars1_x, bars2_x)]\n",
    "labels = [\"Revenue\", \"Earnings\"]\n",
    "\n",
    "\n",
    "plt.legend(labels)\n",
    "plt.title('Revenue Chart wrt Earning')\n",
    "plt.xticks(middle_x, quarter_labels)\n",
    "plt.show()\n",
    "plt.savefig(\"earning_vs_revenue_per_quarter.png\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Graph Literacy\n",
    "What are your first impressions looking at the visualized data?\n",
    "\n",
    "- Does Revenue follow a trend?\n",
    "- Do Earnings follow a trend?\n",
    "- Roughly, what percentage of the revenue constitutes earnings?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Revenue tends to increase through the quarter\n",
    "Yes it follows similar trend\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 8\n",
    "\n",
    "In this last step, we will compare Netflix stock to the Dow Jones Industrial Average in 2017. We will accomplish this by plotting two line charts side by side in one figure. \n",
    "\n",
    "Since `Price` which is the most relevant data is in the Y axis, let's map our subplots to align vertically side by side.\n",
    "- We have set up the code for you on line 1 in the cell below. Complete the figure by passing the following arguments to `plt.subplots()` for the first plot, and tweaking the third argument for the second plot\n",
    "    - `1`-- the number of rows for the subplots\n",
    "    - `2` -- the number of columns for the subplots\n",
    "    - `1` -- the subplot you are modifying\n",
    "\n",
    "- Chart the Netflix Stock Prices in the left-hand subplot. Using your data frame, access the `Date` and `Price` charts as the x and y axes respectively. Hint: (`netflix_stocks['Date'], netflix_stocks['Price']`)\n",
    "- Assign \"Netflix\" as a title to this subplot. Hint: `ax1.set_title()`\n",
    "- For each subplot, `set_xlabel` to `\"Date\"` and `set_ylabel` to `\"Stock Price\"`\n",
    "- Chart the Dow Jones Stock Prices in the left-hand subplot. Using your data frame, access the `Date` and `Price` charts as the x and y axes respectively. Hint: (`dowjones_stocks['Date'], dowjones_stocks['Price']`)\n",
    "- Assign \"Dow Jones\" as a title to this subplot. Hint: `plt.set_title()`\n",
    "- There is some crowding in the Y axis labels, add some space by calling `plt.subplots_adjust(wspace=.5)`\n",
    "- Be sure to `.show()` your plots.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1584x792 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Left plot Netflix\n",
    "# ax1 = plt.subplot(total number rows, total number columns, index of subplot to modify)\n",
    "fig = plt.subplots(figsize=(22, 11))\n",
    "\n",
    "ax1 = plt.subplot(1, 2, 1)\n",
    "plt.plot(netflix_stocks['Date'], netflix_stocks['Price'], color='darkblue')\n",
    "ax1.set_title('Netflix')\n",
    "ax1.set_xlabel('Date')\n",
    "ax1.set_ylabel('Stock Price')\n",
    "ax1.set_xticklabels(netflix_stocks['Date'], rotation=45)\n",
    "\n",
    "# Right plot Dow Jones\n",
    "# ax2 = plt.subplot(total number rows, total number columns, index of subplot to modify)\n",
    "ax2 = plt.subplot(1, 2, 2)\n",
    "plt.plot(dowjones_stocks['Date'], dowjones_stocks['Price'], color='red')\n",
    "ax2.set_title('Dow Jones')\n",
    "ax2.set_xlabel('Date')\n",
    "ax2.set_ylabel('Stock Price')\n",
    "ax2.set_xticklabels(netflix_stocks['Date'], rotation=45)\n",
    "\n",
    "plt.subplots_adjust(wspace=.15)\n",
    "plt.show()\n",
    "plt.savefig(\"stock_price_of_netflix_vs_dow.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- How did Netflix perform relative to Dow Jones Industrial Average in 2017?\n",
    "- Which was more volatile?\n",
    "- How do the prices of the stocks compare?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    " *Similar trend\n",
    " *Netflix\n",
    " *Dow jones is averagely 100 times of Netflix value"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 9\n",
    "\n",
    "It's time to make your presentation! Save each of your visualizations as a png file with `plt.savefig(\"filename.png\")`.\n",
    "\n",
    "As you prepare your slides, think about the answers to the graph literacy questions. Embed your observations in the narrative of your slideshow!\n",
    "\n",
    "Remember that your slideshow must include:\n",
    "- A title slide\n",
    "- A list of your visualizations and your role in their creation for the \"Stock Profile\" team\n",
    "- A visualization of the distribution of the stock prices for Netflix in 2017\n",
    "- A visualization and a summary of Netflix stock and revenue for the past four quarters and a summary\n",
    "- A visualization and a brief summary of their earned versus actual earnings per share\n",
    "- A visualization of Netflix stock against the Dow Jones stock (to get a sense of the market) in 2017\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
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