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 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since you launched the notebook from within `venv`, you can access the dependencies installed in that virtual environment by using an `import` statement in the notebook. If you install more dependencies later on, remember to relaunch the notebook from the terminal virtual environment before running the `import` statement in the notebook.\n",
    "\n",
    "In the project directory, create a new file called `.env` (notice the dot in front of this filename) formatted like `.env.example` to store your credentials from the [Twilio Console](https://www.twilio.com/console).\n",
    "\n",
    " This code imports the Python dependencies and loads the environment variables stored in `.env` so you can access the Twilio REST API with your credentials."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "import pandas as pd\n",
    "import os\n",
    "from dotenv import load_dotenv\n",
    "load_dotenv()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This makes an HTTP `GET` request to the Twilio API to retrieve all of your SMS messages. The data is saved to a variable called `messages` that we can use while running subsequent cells. The resulting output is a log statement with a count of your SMS messages and a JSON object of those messages."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "accountSID = os.getenv('ACCOUNT_SID')\n",
    "authToken = os.getenv('AUTH_TOKEN')\n",
    "url = f'https://api.twilio.com/2010-04-01/Accounts/{accountSID}/Messages.json'\n",
    "response = requests.get(url, auth=(accountSID, authToken))\n",
    "messages = response.json()['messages']\n",
    "print('Number of messages: ', len(messages))\n",
    "# json is a built-in module for Python - no install required\n",
    "import json\n",
    "print(json.dumps(messages, indent=2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that you know how the response from the Twilio API is formatted, you can handle the data like a SQL table using the primary pandas data structure called a [DataFrame](https://pandas.pydata.org/pandas-docs/stable/user_guide/dsintro.html#dataframe). Run this code in the next cell of the notebook to see the first five rows of SMS messages."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(messages)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tidy up the data by displaying only the `Date`, `Price`, and `Direction` of your messages, renaming the columns and formatting the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.rename(columns={'price': 'Price', 'direction': 'Direction', 'date_sent': 'Date'})\n",
    "df['Date'] = pd.to_datetime(df.Date).dt.strftime('%m/%d/%Y')\n",
    "print(df[['Date','Price', 'Direction']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ax = df.plot(kind='scatter',x='Date',y='Price')\n",
    "df[['Date','Price','Direction']].apply(lambda row: ax.text(*row),axis=1);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Show this data in a bar graph, adding up the prices and displaying this information over time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Price'] = df['Price'].astype(float)\n",
    "df['Date'] = pd.to_datetime(df['Date'])\n",
    "df['Price'].groupby(df['Date'].dt.to_period('D')).sum().plot(kind='bar', color='red')"
   ]
  }
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