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api_data_wrangling_mini_project.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This exercise will require you to pull some data from the Qunadl API. Qaundl is currently the most widely used aggregator of financial market data."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As a first step, you will need to register a free account on the http://www.quandl.com website."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "After you register, you will be provided with a unique API key, that you should store:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Store the API key as a string - according to PEP8, constants are always named in all upper case\n",
+    "API_KEY = ''"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Qaundl has a large number of data sources, but, unfortunately, most of them require a Premium subscription. Still, there are also a good number of free datasets."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For this mini project, we will focus on equities data from the Frankfurt Stock Exhange (FSE), which is available for free. We'll try and analyze the stock prices of a company called Carl Zeiss Meditec, which manufactures tools for eye examinations, as well as medical lasers for laser eye surgery: https://www.zeiss.com/meditec/int/home.html. The company is listed under the stock ticker AFX_X."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can find the detailed Quandl API instructions here: https://docs.quandl.com/docs/time-series"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "While there is a dedicated Python package for connecting to the Quandl API, we would prefer that you use the *requests* package, which can be easily downloaded using *pip* or *conda*. You can find the documentation for the package here: http://docs.python-requests.org/en/master/ "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Finally, apart from the *requests* package, you are encouraged to not use any third party Python packages, such as *pandas*, and instead focus on what's available in the Python Standard Library (the *collections* module might come in handy: https://pymotw.com/3/collections/ ).\n",
+    "Also, since you won't have access to DataFrames, you are encouraged to us Python's native data structures - preferably dictionaries, though some questions can also be answered using lists.\n",
+    "You can read more on these data structures here: https://docs.python.org/3/tutorial/datastructures.html"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Keep in mind that the JSON responses you will be getting from the API map almost one-to-one to Python's dictionaries. Unfortunately, they can be very nested, so make sure you read up on indexing dictionaries in the documentation provided above."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# First, import the relevant modules\n",
+    "import requests\n",
+    "import collections\n",
+    "import json\n",
+    "from statistics import mean\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "200"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Now, call the Quandl API and pull out a small sample of the data (only one day) to get a glimpse\n",
+    "# into the JSON structure that will be returned\n",
+    "dcollect= requests.get('https://www.quandl.com/api/v3/datasets/FSE/AFX_X.json?start_date=2020-01-01&end_date=2020-01-02&api_key=')\n",
+    "\n",
+    "dcollect.status_code\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "dataset:  {'id': 10095370, 'dataset_code': 'AFX_X', 'database_code': 'FSE', 'name': 'Carl Zeiss Meditec (AFX_X)', 'description': 'Stock Prices for Carl Zeiss Meditec (2020-11-02) from the Frankfurt Stock Exchange.<br><br>Trading System: Xetra<br><br>ISIN: DE0005313704', 'refreshed_at': '2020-12-01T14:48:09.907Z', 'newest_available_date': '2020-12-01', 'oldest_available_date': '2000-06-07', 'column_names': ['Date', 'Open', 'High', 'Low', 'Close', 'Change', 'Traded Volume', 'Turnover', 'Last Price of the Day', 'Daily Traded Units', 'Daily Turnover'], 'frequency': 'daily', 'type': 'Time Series', 'premium': False, 'limit': None, 'transform': None, 'column_index': None, 'start_date': '2020-01-01', 'end_date': '2020-01-02', 'data': [['2020-01-02', 114.0, 114.9, 112.7, 113.8, None, 143464.0, 16291666.0, None, None, None]], 'collapse': None, 'order': None, 'database_id': 6129}\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Inspect the JSON structure of the object you created, and take note of how nested it is,\n",
+    "# as well as the overall structure\n",
+    "\n",
+    "\n",
+    "dcol_dict=dcollect.json()\n",
+    "\n",
+    "for k in dcol_dict.keys():\n",
+    "    print(k + ': ', dcol_dict[k])\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'The highest opening price for 2017 was: 53.11 and The lowest opening price for 2017 was: 34.0'"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Collect data from the Franfurt Stock Exchange, for the ticker AFX_X, for the whole year 2017\n",
+    "\n",
+    "dyear=requests.get('https://www.quandl.com/api/v3/datasets/FSE/AFX_X.json?start_date=2017-01-01&end_date=2017-12-31&api_key=')\n",
+    "\n",
+    "dyear.status_code\n",
+    "\n",
+    "#Convert the returned JSON object into a Python dictionary.\n",
+    "dyeardict=dyear.json()\n",
+    "\n",
+    "\n",
+    "\n",
+    "# highest and lowest opening prices were for the stock in this period\n",
+    "\n",
+    "openlist = []\n",
+    "\n",
+    "for item in dyeardict['dataset']['data']:\n",
+    "    \n",
+    "    if item[1] != None:\n",
+    "        \n",
+    "        openlist.append((item[1]))\n",
+    "        \n",
+    "\n",
+    "openlistso = sorted(openlist, key = float) \n",
+    "\n",
+    "\"The highest opening price for 2017 was: \" + str(max(openlistso)) + \" and\" + \" The lowest opening price for 2017 was: \" + str(min(openlistso))\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "            \n",
+    "\n",
+    "    \n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'The largest change in any one day was: 2.8100000000000023'"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#What was the largest change in any one day (based on High and Low price)?\n",
+    "\n",
+    "changelist=[]\n",
+    "\n",
+    "for item in dyeardict['dataset']['data']:\n",
+    "    \n",
+    "    change=item[2]-item[3]\n",
+    "    changelist.append(change)\n",
+    "\n",
+    "changeso= sorted(changelist, key = float)\n",
+    "\n",
+    "\n",
+    "\"The largest change in any one day was: \" + str(max(changeso))\n",
+    "    \n",
+    "    \n",
+    "        \n",
+    "       \n",
+    "\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'The largest change between any two days (based on Closing Price) was: 19.03'"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#What was the largest change between any two days (based on Closing Price)?\n",
+    "\n",
+    "listforchange=[]\n",
+    "for item in dyeardict['dataset']['data']:\n",
+    "    \n",
+    "    listforchange.append(item[4])\n",
+    "    \n",
+    "    \n",
+    "listforchangeso = (sorted(listforchange, key=float, reverse=True))\n",
+    "\n",
+    "\n",
+    "change= (max(listforchangeso) - min(listforchangeso))\n",
+    "\n",
+    "\"The largest change between any two days (based on Closing Price) was: \" + str(change)\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'The average daily trading volume during this year was: 89124.33725490196'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#What was the average daily trading volume during this year?\n",
+    "\n",
+    "traded=[]\n",
+    "\n",
+    "for item in dyeardict['dataset']['data']:\n",
+    "    \n",
+    "    traded.append(item[6])\n",
+    "    \n",
+    "\"The average daily trading volume during this year was: \" + str(mean(traded))\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The median trading volume during this year was: 76286.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "#What was the median trading volume during this year?\n",
+    "\n",
+    "tradedso=sorted(traded, key=float)\n",
+    "#print(tradedso)\n",
+    "\n",
+    "#print(len(tradedso))\n",
+    "\n",
+    "mid=int(len(tradedso)/2)\n",
+    "\n",
+    "print(\"The median trading volume during this year was: \" + str(tradedso[mid]))\n",
+    "\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "These are your tasks for this mini project:\n",
+    "\n",
+    "1. Collect data from the Franfurt Stock Exchange, for the ticker AFX_X, for the whole year 2017 (keep in mind that the date format is YYYY-MM-DD).\n",
+    "2. Convert the returned JSON object into a Python dictionary.\n",
+    "3. Calculate what the highest and lowest opening prices were for the stock in this period.\n",
+    "4. What was the largest change in any one day (based on High and Low price)?\n",
+    "5. What was the largest change between any two days (based on Closing Price)?\n",
+    "6. What was the average daily trading volume during this year?\n",
+    "7. (Optional) What was the median trading volume during this year. (Note: you may need to implement your own function for calculating the median.)"
+   ]
+  }
+ ],
+ "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": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}