ADVDMInClass1_2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   ID  Sales\n",
      "0   1   7641\n",
      "1   7   6621\n",
      "2   6   8089\n",
      "3   9   4719\n",
      "4   7   2162\n",
      "   ID  Sales\n",
      "0   2   7668\n",
      "1   7   7365\n",
      "2   7   1837\n",
      "3   5   1320\n",
      "4   4   3674\n"
     ]
    }
   ],
   "source": [
    "# Section 1\n",
    "# Q1\n",
    "import pandas as pd\n",
    "from io import StringIO\n",
    "\n",
    "data1 = \"\"\"\n",
    "ID\tSales\n",
    "1\t7641\n",
    "7\t6621\n",
    "6\t8089\n",
    "9\t4719\n",
    "7\t2162\n",
    "4\t5235\n",
    "2\t2082\n",
    "9\t3887\n",
    "5\t4782\n",
    "3\t2576\n",
    "8\t6701\n",
    "9\t2154\n",
    "8\t3546\n",
    "7\t8648\n",
    "5\t9777\n",
    "7\t3143\n",
    "7\t2768\n",
    "5\t8518\n",
    "1\t8063\n",
    "9\t9086\n",
    "5\t8942\"\"\"\n",
    "Qtr1 = pd.read_table(StringIO(data1), sep=\"\\s+\")\n",
    "print(Qtr1.head(5))\n",
    "\n",
    "data2 = \"\"\"\n",
    "ID\tSales\n",
    "2\t7668\n",
    "7\t7365\n",
    "7\t1837\n",
    "5\t1320\n",
    "4\t3674\n",
    "2\t9952\n",
    "4\t7874\n",
    "9\t4598\n",
    "4\t7393\n",
    "5\t5986\n",
    "7\t1120\n",
    "2\t2323\n",
    "5\t9756\n",
    "3\t7382\n",
    "2\t8156\n",
    "1\t8620\n",
    "4\t5689\n",
    "5\t6785\n",
    "7\t8425\n",
    "1\t1976\n",
    "7\t1858\"\"\"\n",
    "Qtr2 = pd.read_table(StringIO(data2), sep=\"\\s+\")\n",
    "print(Qtr2.head(5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Person</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>Label</th>\n",
       "      <th>Data</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>W</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>W</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>X</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>W</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>CE</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Person  Quantity Label  Data\n",
       "0       2         3     W     5\n",
       "1       3         2     W     7\n",
       "2       4         4     X     7\n",
       "3       5         3     W     5\n",
       "4       6         5    CE     6"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Q2\n",
    "# part a\n",
    "sheet1 = \"\"\"\n",
    "1\t4\tX\t9\n",
    "2\t3\tW\t5\n",
    "3\t2\tW\t7\n",
    "4\t4\tX\t7\n",
    "5\t3\tW\t5\n",
    "6\t5\tCE\t6\n",
    "1\t6\tE\t5\n",
    "6\t4\tW\t7\n",
    "1\t3\tV\t5\n",
    "2\t4\tS\t7\n",
    "2\t7\tW\t3\n",
    "4\t9\tW\t9\n",
    "2\t7\tX\t9\n",
    "2\t5\tV\t2\n",
    "4\t7\tS\t7\n",
    "5\t6\tV\t6\n",
    "5\t1\tS\t5\n",
    "1\t7\tV\t3\n",
    "1\t7\tS\t9\n",
    "3\t7\tW\t2\n",
    "6\t8\tX\t9\n",
    "5\t5\tCE\t8\n",
    "6\t5\tCE\t7\n",
    "4\t5\tW\t2\n",
    "2\t7\tX\t8\n",
    "5\t3\tCE\t3\n",
    "2\t2\tS\t5\n",
    "1\t4\tV\t2\n",
    "2\t1\tCE\t9\n",
    "1\t3\tW\t3\n",
    "2\t3\tS\t8\n",
    "2\t5\tV\t4\"\"\"\n",
    "Sheet1 = pd.read_table(StringIO(sheet1), sep=\"\\s+\")\n",
    "Sheet1 = pd.DataFrame(Sheet1)\n",
    "Sheet1.columns = [\"Person\", \"Quantity\", \"Label\", \"Data\"]\n",
    "Sheet1.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>W</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>W</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>X</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>CE</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "   ID  Quantity Total\n",
       "0   2         3     W\n",
       "1   3         2     W\n",
       "2   4         4     X\n",
       "3   5         3     W\n",
       "4   6         5    CE"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# part b\n",
    "# Dont' quite sure about this one\n",
    "# Do you mean read from original file again?\n",
    "# if so, what's the point to repeat same process as part a?\n",
    "col3 = \"\"\"\n",
    "1\t4\tX\n",
    "2\t3\tW\n",
    "3\t2\tW\n",
    "4\t4\tX\n",
    "5\t3\tW\n",
    "6\t5\tCE\n",
    "1\t6\tE\n",
    "6\t4\tW\n",
    "1\t3\tV\n",
    "2\t4\tS\n",
    "2\t7\tW\n",
    "4\t9\tW\n",
    "2\t7\tX\n",
    "2\t5\tV\n",
    "4\t7\tS\n",
    "5\t6\tV\n",
    "5\t1\tS\n",
    "1\t7\tV\n",
    "1\t7\tS\n",
    "3\t7\tW\n",
    "6\t8\tX\n",
    "5\t5\tCE\n",
    "6\t5\tCE\n",
    "4\t5\tW\n",
    "2\t7\tX\n",
    "5\t3\tCE\n",
    "2\t2\tS\n",
    "1\t4\tV\n",
    "2\t1\tCE\n",
    "1\t3\tW\n",
    "2\t3\tS\n",
    "2\t5\tV\"\"\"\n",
    "Col3 = pd.read_table(StringIO(col3), sep=\"\\s+\")\n",
    "Col3 = pd.DataFrame(Col3)\n",
    "Col3.columns = [\"ID\", \"Quantity\", \"Total\"]\n",
    "Col3.head(5)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Flavor  Data  Total\n",
      "0   Choc    23   46.0\n",
      "1    Van    12   18.0\n",
      "2    Van    43   64.5\n",
      "3   Choc    32   64.0\n",
      "4    Van    43   64.5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\fengh\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  app.launch_new_instance()\n",
      "C:\\Users\\fengh\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:17: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
     ]
    }
   ],
   "source": [
    "# part c\n",
    "sheet2 = \"\"\"\n",
    "Flavor\tData\n",
    "Choc\t23\n",
    "Van\t12\n",
    "Van\t43\n",
    "Choc\t32\n",
    "Van\t43\n",
    "Choc\t91\n",
    "Van\t29\n",
    "Choc\t31\n",
    "Choc\t75\n",
    "Choc\t34\"\"\"\n",
    "Sheet2 = pd.read_table(StringIO(sheet2), sep=\"\\s+\")\n",
    "Sheet2['Price'] = 0\n",
    "Sheet2.Price[Sheet2.Flavor == 'Choc'] = 2.00\n",
    "Sheet2.Price[Sheet2.Flavor == 'Van'] = 1.50\n",
    "Sheet2['Total'] = Sheet2['Price'] * Sheet2['Data']\n",
    "Sheet2 = Sheet2.drop(columns='Price')\n",
    "print(Sheet2.head(5))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C:\\\\Users\\\\fengh\\\\Desktop\\\\CurrentData'"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Q3\n",
    "# part a\n",
    "import os\n",
    "\n",
    "os.chdir(\"C:\\\\Users\\\\fengh\\\\Desktop\\\\CurrentData\")\n",
    "os.getcwd()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['ADVDML1TestCurrentPath.tsv']"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# part b\n",
    "os.listdir(\"C:\\\\Users\\\\fengh\\\\Desktop\\\\CurrentData\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\fengh\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:8: ParserWarning: Falling back to the 'python' engine because the 'c' engine does not support regex separators (separators > 1 char and different from '\\s+' are interpreted as regex); you can avoid this warning by specifying engine='python'.\n",
      "  \n"
     ]
    },
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     "data": {
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Code</th>\n",
       "      <th>CurrentData</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>36.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>41.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>42.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>30.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>27.45</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Code  CurrentData\n",
       "0     2        36.64\n",
       "1     1        41.76\n",
       "2     3        42.52\n",
       "3     2        30.61\n",
       "4     2        27.45"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# part c\n",
    "# first I don't understand what do you mean by upload the file to python environment\n",
    "# second, what do you mean by not use full path\n",
    "# since I have the file under my current folder\n",
    "# there is no reason to use full path then\n",
    "# I will just read this .tsv file and make it a Dataframe for this part\n",
    "# Let me know if I were wrong\n",
    "TestPath = pd.read_csv('ADVDML1TestCurrentPath.tsv', sep=\"\\t+\")\n",
    "TestPath.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Q4\n",
    "# part a\n",
    "import os\n",
    "if not os.path.exists('SubData'):\n",
    "    os.makedirs('SubData')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\fengh\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:2: ParserWarning: Falling back to the 'python' engine because the 'c' engine does not support regex separators (separators > 1 char and different from '\\s+' are interpreted as regex); you can avoid this warning by specifying engine='python'.\n",
      "  \n"
     ]
    },
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Code</th>\n",
       "      <th>SubData</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>41.87</td>\n",
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       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>45.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>48.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>36.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>32.71</td>\n",
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      ],
      "text/plain": [
       "   Code  SubData\n",
       "0     2    41.87\n",
       "1     3    45.55\n",
       "2     3    48.54\n",
       "3     2    36.73\n",
       "4     2    32.71"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# part b\n",
    "TestPath = pd.read_csv(\".\\\\SubData\\\\ADVDML1TestDataPath.tsv\", sep=\"\\t+\")\n",
    "TestPath.head(5)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['ADVDML1TestParentPath.tsv', 'CurrentData']"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Q5\n",
    "# part a\n",
    "import os\n",
    "\n",
    "os.listdir(\"C:\\\\Users\\\\fengh\\\\Desktop\\\\ParentData\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\fengh\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:5: ParserWarning: Falling back to the 'python' engine because the 'c' engine does not support regex separators (separators > 1 char and different from '\\s+' are interpreted as regex); you can avoid this warning by specifying engine='python'.\n",
      "  \"\"\"\n"
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Code</th>\n",
       "      <th>ParentData</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>30.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>32.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>34.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>20.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>19.87</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Code  ParentData\n",
       "0     2       30.53\n",
       "1     1       32.06\n",
       "2     3       34.19\n",
       "3     2       20.30\n",
       "4     2       19.87"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# part b\n",
    "# I assume you want us to create parent folder outside\n",
    "# then read file from subfolder \n",
    "os.chdir(\"C:\\\\Users\\\\fengh\\\\Desktop\\\\ParentData\\\\CurrentData\")\n",
    "TestPath = pd.read_csv(\"..\\\\ADVDML1TestParentPath.tsv\", sep=\"\\t+\")\n",
    "TestPath.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      Sales  Flavor\n",
      "Time               \n",
      "2     10.04       2\n",
      "2     11.79       2\n",
      "2     10.99       1\n",
      "6      8.37       1\n",
      "5     10.58       3\n",
      "      Sales  Flavor\n",
      "Time               \n",
      "2     13.98       2\n",
      "3     15.90       1\n",
      "3      6.35       1\n",
      "4      8.43       1\n",
      "5     18.76       3\n"
     ]
    }
   ],
   "source": [
    "# Section 2\n",
    "# Q6\n",
    "# part a\n",
    "import urllib.request\n",
    "from urllib.request import Request, urlopen\n",
    "# here is another way may not use urllib\n",
    "# and it works\n",
    "'''\n",
    "url = 'https://github.com/kkParker/ClassData/raw/master/Python/ADVDML2Mike.csv'\n",
    "df = pd.read_csv(url, index_col=0, parse_dates=[0])\n",
    "print(df)\n",
    "'''\n",
    "# something missing on the slides when use .read_csv\n",
    "# if not add inedx and parse thing, it never works\n",
    "response = urllib.request.urlopen(\"https://github.com/kkParker/ClassData/raw/master/Python/ADVDML2Sue.csv\")\n",
    "dfSue = pd.read_csv(response, index_col=0, parse_dates=[0])\n",
    "print(dfSue.head(5))\n",
    "\n",
    "response = urllib.request.urlopen(\"https://github.com/kkParker/ClassData/raw/master/Python/ADVDML2Mike.csv\")\n",
    "dfMike = pd.read_csv(response, index_col=0, parse_dates=[0])\n",
    "print(dfMike.head(5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      Sales     Flavor\n",
      "Time                  \n",
      "2     10.04    Vanilla\n",
      "2     11.79    Vanilla\n",
      "2     10.99  Chocolate\n",
      "6      8.37  Chocolate\n",
      "5     10.58      Mixed\n",
      "      Sales     Flavor\n",
      "Time                  \n",
      "2     13.98    Vanilla\n",
      "3     15.90  Chocolate\n",
      "3      6.35  Chocolate\n",
      "4      8.43  Chocolate\n",
      "5     18.76      Mixed\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\fengh\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n",
      "C:\\Users\\fengh\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "# part b\n",
    "dfSue.Flavor[dfSue.Flavor==1] = \"Chocolate\"\n",
    "dfSue.Flavor[dfSue.Flavor==2] = \"Vanilla\"\n",
    "dfSue.Flavor[dfSue.Flavor==3] = \"Mixed\"\n",
    "print(dfSue.head(5))\n",
    "dfMike.Flavor[dfMike.Flavor==1] = \"Chocolate\"\n",
    "dfMike.Flavor[dfMike.Flavor==2] = \"Vanilla\"\n",
    "dfMike.Flavor[dfMike.Flavor==3] = \"Mixed\"\n",
    "print(dfMike.head(5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1e1e4610ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Q7\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "counts = pd.value_counts(dfSue.Flavor)\n",
    "counts = pd.DataFrame(counts)\n",
    "plt.grid(True)\n",
    "objects = tuple(counts.index)\n",
    "plt.bar(np.arange(len(counts.index)), counts.Flavor)\n",
    "plt.xticks(np.arange(3),objects)\n",
    "plt.title(\"Barchart of Sue's Flavor\")\n",
    "plt.xlabel('Flavors')\n",
    "plt.ylabel('Counts')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1e1e72bd630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Q8\n",
    "# part a\n",
    "counts = pd.value_counts(dfMike.Flavor)\n",
    "counts = pd.DataFrame(counts)\n",
    "plt.grid(True)\n",
    "objects = tuple(counts.index)\n",
    "plt.bar(np.arange(len(counts.index)), counts.Flavor)\n",
    "plt.xticks(np.arange(3),objects)\n",
    "plt.title(\"Barchart of Mike's Flavor\")\n",
    "plt.xlabel('Flavors')\n",
    "plt.ylabel('Counts')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1e1e8d8c198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# part b\n",
    "def barchart(x, y, xlabel, ylabel, xticksnames, title):\n",
    "    plt.bar(x,y,width=.5)\n",
    "    plt.xlabel(xlabel)\n",
    "    plt.ylabel(ylabel)\n",
    "    plt.xticks(x,xticksnames)\n",
    "    plt.title(title)\n",
    "    plt.show()\n",
    "    \n",
    "y = counts.Flavor\n",
    "x = np.arange(len(counts.index))\n",
    "xlabel = 'Flavors'\n",
    "ylabel = 'Counts'\n",
    "xticksnames = objects\n",
    "title = \"Barchart of Mike's Flavor\" \n",
    "barchart(x,y,xlabel,ylabel,xticksnames,title)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "<title>ClassData/Python at master · kkParker/ClassData · GitHub</title>\n",
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      "</body>\n",
      "</html>\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# part c\n",
    "from bs4 import BeautifulSoup\n",
    "\n",
    "url = \"https://github.com/kkParker/ClassData/tree/master/Python\"\n",
    "response = urllib.request.urlopen(url)\n",
    "html = response.read()\n",
    "soup = BeautifulSoup(html, \"lxml\")\n",
    "print(soup)"
   ]
  },
  {
   "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",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.5"
  },
  "widgets": {
   "state": {},
   "version": "1.1.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}